The Man Who Calls BS On AI: They’re LYING About AI, 2027 Is When It All Breaks! | Ed Zitron

I think generative AI is at its heart I think generative AI is at its heart con and seeing these ultra rich ultra con and seeing these ultra rich ultra con and seeing these ultra rich ultra powerful people lie through their teeth powerful people lie through their teeth powerful people lie through their teeth turns my stomach. The word con is a turns my stomach. The word con is a turns my stomach. The word con is a strong word. strong word. strong word. >> Well, what do you call something where >> Well, what do you call something where >> Well, what do you call something where from the very beginning they've sold from the very beginning they've sold from the very beginning they've sold [music] it in the terms of magic but [music] it in the terms of magic but [music] it in the terms of magic but it's just a halfass arcery machine. They it's just a halfass arcery machine. They it's just a halfass arcery machine. They are misleading the entire world. are misleading the entire world. are misleading the entire world. >> You are the first person that I've >> You are the first person that I've >> You are the first person that I've spoken to that has that opinion. spoken to that has that opinion. spoken to that has that opinion. >> Well, the fact that this is happening is >> Well, the fact that this is happening is >> Well, the fact that this is happening is insane and the fact it's not a scandal insane and the fact it's not a scandal insane and the fact it's not a scandal is insane. And I've been in the tech is insane. And I've been in the tech is insane. And I've been in the tech industry for 16 years now and I love industry for 16 years now and I love industry for 16 years now and I love technology and I'm enthusiastic about technology and I'm enthusiastic about technology and I'm enthusiastic about it, but I don't like being misled. And it, but I don't like being misled. And it, but I don't like being misled. And this is the largest non-consensual push this is the largest non-consensual push this is the largest non-consensual push of technology in history. of technology in history. of technology in history. >> So, we're going to play a game, Ed. I >> So, we're going to play a game, Ed. I >> So, we're going to play a game, Ed. I have the things that you consider to be have the things that you consider to be have the things that you consider to be myths about the AI industry. myths about the AI industry. myths about the AI industry. >> Let's play it. The AI industry is >> Let's play it. The AI industry is >> Let's play it. The AI industry is creating enormous economic growth. No, creating enormous economic growth. No, creating enormous economic growth. No, it's not. All of these companies run at it's not. All of these companies run at it's not. All of these companies run at a horrifying loss. Open AI lost $20.9 a horrifying loss. Open AI lost $20.9 a horrifying loss. Open AI lost $20.9 billion last year. None of these people billion last year. None of these people billion last year. None of these people can just say, "Yeah, we're on the path can just say, "Yeah, we're on the path can just say, "Yeah, we're on the path to making this profitable." because they to making this profitable." because they to making this profitable." because they can't. can't. can't. >> Next one. >> Next one. >> Next one. >> AI will replace all human jobs. That >> AI will replace all human jobs. That >> AI will replace all human jobs. That just isn't happening and there's no just isn't happening and there's no just isn't happening and there's no economic data to support it. Next, the economic data to support it. Next, the economic data to support it. Next, the United States need to spend trillions to United States need to spend trillions to United States need to spend trillions to beat China in the AI race. What's the beat China in the AI race. What's the beat China in the AI race. What's the race to do for us to constantly piss our race to do for us to constantly piss our race to do for us to constantly piss our pants worrying about China? But people pants worrying about China? But people pants worrying about China? But people keep saying, "What if these models fall keep saying, "What if these models fall keep saying, "What if these models fall into the wrong hands? They're already in into the wrong hands? They're already in into the wrong hands? They're already in the wrong hands." Mark Zuckerberg, Sam the wrong hands." Mark Zuckerberg, Sam the wrong hands." Mark Zuckerberg, Sam Olman, Dario Amade.

Olman, Dario Amade. Olman, Dario Amade. >> Mark Zuckerberg says, "We'll continue to >> Mark Zuckerberg says, "We'll continue to >> Mark Zuckerberg says, "We'll continue to invest aggressively in infrastructure to invest aggressively in infrastructure to invest aggressively in infrastructure to meet the demand." God met as a meet the demand." God met as a meet the demand." God met as a monstrosity. Makes me think of Shrek monstrosity. Makes me think of Shrek monstrosity. Makes me think of Shrek with L fogquad. Some of you may die, but with L fogquad. Some of you may die, but with L fogquad. Some of you may die, but that's a risk I'm willing to accept. If that's a risk I'm willing to accept. If that's a risk I'm willing to accept. If only these people gave a about only these people gave a about only these people gave a about poverty or actual problems in the world poverty or actual problems in the world poverty or actual problems in the world versus are we buying enough GPUs. If versus are we buying enough GPUs. If versus are we buying enough GPUs. If this continues, [music] what does the this continues, [music] what does the this continues, [music] what does the future look like? This is super interesting to me. My team This is super interesting to me. My team given me this report to show me how many given me this report to show me how many given me this report to show me how many of you that watch this show subscribe. of you that watch this show subscribe. of you that watch this show subscribe. And some of you have told us according And some of you have told us according And some of you have told us according to this that you are unsubscribed from to this that you are unsubscribed from to this that you are unsubscribed from the channel randomly. So, favor to ask the channel randomly. So, favor to ask the channel randomly. So, favor to ask all of you. Please could you check right all of you. Please could you check right all of you. Please could you check right now if you've hit the subscribe button now if you've hit the subscribe button now if you've hit the subscribe button if you are a regular viewer of the show if you are a regular viewer of the show if you are a regular viewer of the show and you like what we do here. We're and you like what we do here. We're and you like what we do here. We're approaching quite a significant landmark approaching quite a significant landmark approaching quite a significant landmark on this show in terms of a subscriber on this show in terms of a subscriber on this show in terms of a subscriber number. So, if there was one simple free number. So, if there was one simple free number. So, if there was one simple free thing that you could do to help us, my thing that you could do to help us, my thing that you could do to help us, my team, everyone here to keep this show team, everyone here to keep this show team, everyone here to keep this show free, to keep it improving year over free, to keep it improving year over free, to keep it improving year over year and week over week, it is just to year and week over week, it is just to year and week over week, it is just to hit that subscribe button and to double hit that subscribe button and to double hit that subscribe button and to double check if you've hit it. Only thing I'll check if you've hit it. Only thing I'll check if you've hit it. Only thing I'll ever ask of you, do we have a deal? If ever ask of you, do we have a deal? If ever ask of you, do we have a deal? If you do it, I'll tell you what I'll do.

you do it, I'll tell you what I'll do. you do it, I'll tell you what I'll do. I'll make sure every single week, every I'll make sure every single week, every I'll make sure every single week, every single month, we fight harder and harder single month, we fight harder and harder single month, we fight harder and harder and harder and harder to bring you the and harder and harder to bring you the and harder and harder to bring you the guests and conversations that you want guests and conversations that you want guests and conversations that you want to hear. I've stayed true to that to hear. I've stayed true to that to hear. I've stayed true to that promise since the very beginning of the promise since the very beginning of the promise since the very beginning of the Dire of Sio and I will not let you down. Dire of Sio and I will not let you down. Dire of Sio and I will not let you down. Please help us. Really appreciate it. Please help us. Really appreciate it. Please help us. Really appreciate it. Let's get on with the show. Let's get on with the show. Let's get on with the show. [music] >> Ed Zitron, >> Ed Zitron, there are a number of things that you there are a number of things that you there are a number of things that you believe that a lot of other people don't believe that a lot of other people don't believe that a lot of other people don't believe, right? You have, I think, a believe, right? You have, I think, a believe, right? You have, I think, a couple of controversial opinions and couple of controversial opinions and couple of controversial opinions and opinions that are in contrast to the opinions that are in contrast to the opinions that are in contrast to the other guests that I've sat here with. other guests that I've sat here with. other guests that I've sat here with. What exactly are those opinions, Ed? I What exactly are those opinions, Ed? I What exactly are those opinions, Ed? I think generative AI is at its heart con. think generative AI is at its heart con. think generative AI is at its heart con. I don't think it is sold as honest I don't think it is sold as honest I don't think it is sold as honest software. I think that they overstate software. I think that they overstate software. I think that they overstate both what it can do, what it will do, both what it can do, what it will do, both what it can do, what it will do, and the underlying financials to the and the underlying financials to the and the underlying financials to the point that they are misleading the point that they are misleading the point that they are misleading the entire world. And they're actively entire world. And they're actively entire world. And they're actively exploiting the weaknesses in journalism, exploiting the weaknesses in journalism, exploiting the weaknesses in journalism, in our economies, and indeed within the in our economies, and indeed within the in our economies, and indeed within the responsible parties with sellside responsible parties with sellside responsible parties with sellside analysts, governments, and all over the analysts, governments, and all over the analysts, governments, and all over the shop.

shop. shop. >> The word con is a strong word. >> The word con is a strong word. >> The word con is a strong word. >> Yeah. I mean, what do you call something >> Yeah. I mean, what do you call something >> Yeah. I mean, what do you call something where from the very beginning they've where from the very beginning they've where from the very beginning they've sold it in the terms of magic as this sold it in the terms of magic as this sold it in the terms of magic as this thing that will replace all jobs, that thing that will replace all jobs, that thing that will replace all jobs, that will cure cancer, and all of these will cure cancer, and all of these will cure cancer, and all of these things? And when you look at it, it's things? And when you look at it, it's things? And when you look at it, it's boring cloud software that's extremely boring cloud software that's extremely boring cloud software that's extremely expensive and unprofitable and also expensive and unprofitable and also expensive and unprofitable and also unreliable at its core. unreliable at its core. unreliable at its core. >> People will be asking where are you >> People will be asking where are you >> People will be asking where are you drawing from in terms of your drawing from in terms of your drawing from in terms of your references, your personal experiences? references, your personal experiences? references, your personal experiences? Where were you educate? What you study? Where were you educate? What you study? Where were you educate? What you study? What you write about? What do you do Ed? What you write about? What do you do Ed? What you write about? What do you do Ed? >> So that's the funny thing is people say >> So that's the funny thing is people say >> So that's the funny thing is people say he's not got a finance experience. He's he's not got a finance experience. He's he's not got a finance experience. He's not going to take. I've been in the tech not going to take. I've been in the tech not going to take. I've been in the tech industry 15 16 years now in PR but still industry 15 16 years now in PR but still industry 15 16 years now in PR but still had practical experience and I love had practical experience and I love had practical experience and I love technology and I'm enthusiastic about technology and I'm enthusiastic about technology and I'm enthusiastic about it. And this thing just comes along that it. And this thing just comes along that it. And this thing just comes along that everyone is telling me is the best thing everyone is telling me is the best thing everyone is telling me is the best thing since sliced bread. And it can't even do since sliced bread. And it can't even do since sliced bread. And it can't even do the basics. It can't even do search. the basics. It can't even do search. the basics. It can't even do search. Well, whenever you ask an AI person, Well, whenever you ask an AI person, Well, whenever you ask an AI person, well, what's your setup? They describe well, what's your setup? They describe well, what's your setup? They describe this PeeWee's Playhouse thing of like, this PeeWee's Playhouse thing of like, this PeeWee's Playhouse thing of like, well, you got to harness here and you well, you got to harness here and you well, you got to harness here and you got to use the right prompt. Well, you got to use the right prompt. Well, you got to use the right prompt. Well, you don't want to use that prompt. You want don't want to use that prompt. You want don't want to use that prompt. You want to use this prompt here with this model, to use this prompt here with this model, to use this prompt here with this model, but don't use this model for the but don't use this model for the but don't use this model for the beginning, but at the end, you're going beginning, but at the end, you're going beginning, but at the end, you're going to want to use this model. And this is to want to use this model. And this is to want to use this model. And this is meant to be artificial intelligence.

meant to be artificial intelligence. meant to be artificial intelligence. It's meant to be smart. It's meant to be It's meant to be smart. It's meant to be It's meant to be smart. It's meant to be autonomous. It's meant to be something autonomous. It's meant to be something autonomous. It's meant to be something that you set and forget. that you set and forget. that you set and forget. >> We have the sort of six leading AI >> We have the sort of six leading AI >> We have the sort of six leading AI companies on the table here. Anthropic companies on the table here. Anthropic companies on the table here. Anthropic Amazon, Nvidia, Microsoft, OpenAI, Amazon, Nvidia, Microsoft, OpenAI, Amazon, Nvidia, Microsoft, OpenAI, Google. You're saying that their Google. You're saying that their Google. You're saying that their fundamental business model is a con. fundamental business model is a con. fundamental business model is a con. >> Well, their revenues are not really >> Well, their revenues are not really >> Well, their revenues are not really coming from AI. Up until fairly coming from AI. Up until fairly coming from AI. Up until fairly recently, none of their revenues were recently, none of their revenues were recently, none of their revenues were coming from AI. Like dribbles a bit. coming from AI. Like dribbles a bit. coming from AI. Like dribbles a bit. Right now, 70% of all AI revenues across Right now, 70% of all AI revenues across Right now, 70% of all AI revenues across those three companies are from OpenAI those three companies are from OpenAI those three companies are from OpenAI and Anthropic to unprofitable, and Anthropic to unprofitable, and Anthropic to unprofitable, unsustainable companies that literally unsustainable companies that literally unsustainable companies that literally cannot afford to exist without these cannot afford to exist without these cannot afford to exist without these very same companies giving them money. very same companies giving them money. very same companies giving them money. Amazon sent $50 billion to OpenAI this Amazon sent $50 billion to OpenAI this Amazon sent $50 billion to OpenAI this year. They sent $5 billion to Anthropic. year. They sent $5 billion to Anthropic. year. They sent $5 billion to Anthropic. Google sent $10 billion to Anthropic. Google sent $10 billion to Anthropic. Google sent $10 billion to Anthropic. And in the next three and a half years, And in the next three and a half years, And in the next three and a half years, OpenAI and Anthropic based on actual OpenAI and Anthropic based on actual OpenAI and Anthropic based on actual sellside analyst evaluations, their sellside analyst evaluations, their sellside analyst evaluations, their estimates that inform whether stock is estimates that inform whether stock is estimates that inform whether stock is going to go up or down after earnings, going to go up or down after earnings, going to go up or down after earnings, they are expecting 400 or more billion they are expecting 400 or more billion they are expecting 400 or more billion dollar of revenue, 30 or something% of dollar of revenue, 30 or something% of dollar of revenue, 30 or something% of cloud growth just from these two cloud growth just from these two cloud growth just from these two unprofitable companies that will need to unprofitable companies that will need to unprofitable companies that will need to be given the money from somewhere. And be given the money from somewhere. And be given the money from somewhere. And on top of that, these companies have on top of that, these companies have on top of that, these companies have such low respect for the average such low respect for the average such low respect for the average investor, for the analyst, for everyone investor, for the analyst, for everyone investor, for the analyst, for everyone really that they don't even disclose really that they don't even disclose really that they don't even disclose their AI revenues. The few times they their AI revenues. The few times they their AI revenues. The few times they dain us worthy, they use something dain us worthy, they use something dain us worthy, they use something called a run rate, an annualized run called a run rate, an annualized run called a run rate, an annualized run rate, which means well, nothing. They rate, which means well, nothing. They rate, which means well, nothing. They never define it. It can mean months 12.

never define it. It can mean months 12. never define it. It can mean months 12. It can mean month 13. It can mean last 4 It can mean month 13. It can mean last 4 It can mean month 13. It can mean last 4 weeks time 13. It's different every weeks time 13. It's different every weeks time 13. It's different every time, and they never define it. And then time, and they never define it. And then time, and they never define it. And then they sometimes just don't mention it. they sometimes just don't mention it. they sometimes just don't mention it. So, you've got this big thing that is So, you've got this big thing that is So, you've got this big thing that is meant to be the biggest, most meant to be the biggest, most meant to be the biggest, most influential change to software ever. And influential change to software ever. And influential change to software ever. And whenever you ask them about it, when you whenever you ask them about it, when you whenever you ask them about it, when you say, "What? How much you making from say, "What? How much you making from say, "What? How much you making from this?" They go, "Oh, I couldn't possibly this?" They go, "Oh, I couldn't possibly this?" They go, "Oh, I couldn't possibly say. I'm too shy." These are public say. I'm too shy." These are public say. I'm too shy." These are public companies, or at least the ones that companies, or at least the ones that companies, or at least the ones that aren't anthropic and open AI. When they aren't anthropic and open AI. When they aren't anthropic and open AI. When they have good news, they'll tell you. And have good news, they'll tell you. And have good news, they'll tell you. And when they don't tell you something, when they don't tell you something, when they don't tell you something, well, that actually speaks volumes. well, that actually speaks volumes. well, that actually speaks volumes. >> Have you you used these tools, the AI >> Have you you used these tools, the AI >> Have you you used these tools, the AI tools, Gemini, Anthropic, Chat, GBT, tools, Gemini, Anthropic, Chat, GBT, tools, Gemini, Anthropic, Chat, GBT, etc., and you found no value in them? etc., and you found no value in them? etc., and you found no value in them? There's some value, but it's not there's There's some value, but it's not there's There's some value, but it's not there's they have spent over a trillion dollars they have spent over a trillion dollars they have spent over a trillion dollars in capex. What in capex. What in capex. What >> does capex mean for you? >> does capex mean for you? >> does capex mean for you? >> Capital expenditures. So, when you are a >> Capital expenditures. So, when you are a >> Capital expenditures. So, when you are a business and you have operating expenses business and you have operating expenses business and you have operating expenses like electricity, for example, those like electricity, for example, those like electricity, for example, those come right off immediately. Capital come right off immediately. Capital come right off immediately. Capital expenditures are long-term investments expenditures are long-term investments expenditures are long-term investments that are theoretically one-off. So, a that are theoretically one-off. So, a that are theoretically one-off. So, a data center or indeed the GPUs you put data center or indeed the GPUs you put data center or indeed the GPUs you put inside an AI data center. inside an AI data center. inside an AI data center. >> Okay? So, you've got a data center >> Okay? So, you've got a data center >> Okay? So, you've got a data center >> and then you have these GPUs which are >> and then you have these GPUs which are >> and then you have these GPUs which are like computer chips. So AI GPUs are much like computer chips. So AI GPUs are much like computer chips. So AI GPUs are much bigger, much more power intensive. They bigger, much more power intensive. They bigger, much more power intensive. They take a bunch of high bandwidth memory take a bunch of high bandwidth memory take a bunch of high bandwidth memory and they because of how many of them you and they because of how many of them you and they because of how many of them you need. You need thousands of them, tens need. You need thousands of them, tens need. You need thousands of them, tens of thousands, hundreds of thousands in of thousands, hundreds of thousands in of thousands, hundreds of thousands in some case. You need a bunch of power. So some case. You need a bunch of power. So some case. You need a bunch of power. So an example, OpenAI and Oracle are an example, OpenAI and Oracle are an example, OpenAI and Oracle are building a data center in Texas in building a data center in Texas in building a data center in Texas in Abalene, Texas. 1.2 GW called Stargate Abalene, Texas. 1.2 GW called Stargate Abalene, Texas. 1.2 GW called Stargate Abene. Within that, with each one of the Abene. Within that, with each one of the Abene. Within that, with each one of the eight buildings, there'll be 50,000 eight buildings, there'll be 50,000 eight buildings, there'll be 50,000 Nvidia GB200 GPUs. So, city of Bristol

Nvidia GB200 GPUs. So, city of Bristol Nvidia GB200 GPUs. So, city of Bristol takes about 7800 megawatt of power a takes about 7800 megawatt of power a takes about 7800 megawatt of power a year, right? Well, Stargate Abene is year, right? Well, Stargate Abene is year, right? Well, Stargate Abene is condensing more power than that, 1.2 condensing more power than that, 1.2 condensing more power than that, 1.2 gawatt into a space around 1,172 gawatt into a space around 1,172 gawatt into a space around 1,172 times smaller. City of Bristol is about times smaller. City of Bristol is about times smaller. City of Bristol is about 1.2 billion square ft. Star Evelyn is 1.2 billion square ft. Star Evelyn is 1.2 billion square ft. Star Evelyn is about 998,000. about 998,000. about 998,000. So, you're condensing all of this power, So, you're condensing all of this power, So, you're condensing all of this power, all of this money, all of this labor all of this money, all of this labor all of this money, all of this labor into this one spot. And all of these into this one spot. And all of these into this one spot. And all of these data centers cost billions of dollars. data centers cost billions of dollars. data centers cost billions of dollars. All of these companies other than All of these companies other than All of these companies other than Microsoft are now to take out debt. And Microsoft are now to take out debt. And Microsoft are now to take out debt. And the thing is they've spent over a the thing is they've spent over a the thing is they've spent over a trillion dollars so far and they want to trillion dollars so far and they want to trillion dollars so far and they want to spend another trillion dollars next spend another trillion dollars next spend another trillion dollars next year. And for what? To make tens of year. And for what? To make tens of year. And for what? To make tens of billions of dollars, most of which comes billions of dollars, most of which comes billions of dollars, most of which comes from two unprofitable companies, from two unprofitable companies, from two unprofitable companies, Anthropic and Open AI. One of the Anthropic and Open AI. One of the Anthropic and Open AI. One of the rebuttals to that would be that the rebuttals to that would be that the rebuttals to that would be that the adoption, the customer adoption of adoption, the customer adoption of adoption, the customer adoption of people using Open AI and Enthropic has people using Open AI and Enthropic has people using Open AI and Enthropic has been absolutely insane. These are the been absolutely insane. These are the been absolutely insane. These are the fastest growing products in all of fastest growing products in all of fastest growing products in all of history, especially as it relates to history, especially as it relates to history, especially as it relates to sort of technology. If we just focus in sort of technology. If we just focus in sort of technology. If we just focus in on technology, they are, you know, on technology, they are, you know, on technology, they are, you know, hundreds and hundreds of millions of hundreds and hundreds of millions of hundreds and hundreds of millions of people, billions of people are using people, billions of people are using people, billions of people are using these tools every single day for things these tools every single day for things these tools every single day for things that they have subjectively decided are that they have subjectively decided are that they have subjectively decided are problems they need solving. So, you problems they need solving. So, you problems they need solving. So, you know, money is a lagging indicator of know, money is a lagging indicator of know, money is a lagging indicator of value. So, one would argue that they're value. So, one would argue that they're value. So, one would argue that they're just investing ahead of the monetization just investing ahead of the monetization just investing ahead of the monetization options.

options. options. >> The first let's start with this >> The first let's start with this >> The first let's start with this adoption. Is it honest adoption when you adoption. Is it honest adoption when you adoption. Is it honest adoption when you are forced to use generative AI when you are forced to use generative AI when you are forced to use generative AI when you load Google? When you load Google Docs, load Google? When you load Google Docs, load Google? When you load Google Docs, Gemini screams in your ear. When you Gemini screams in your ear. When you Gemini screams in your ear. When you load Word, co-pilot's bugging you. When load Word, co-pilot's bugging you. When load Word, co-pilot's bugging you. When you use Amazon, whatever rofus AI is you use Amazon, whatever rofus AI is you use Amazon, whatever rofus AI is wants has opinions on what socks you're wants has opinions on what socks you're wants has opinions on what socks you're buying. This is the largest buying. This is the largest buying. This is the largest non-consensual push of technology in non-consensual push of technology in non-consensual push of technology in history. Chat GPD for example, every history. Chat GPD for example, every history. Chat GPD for example, every single media outlet has been screaming single media outlet has been screaming single media outlet has been screaming about this for 3 years. They've been about this for 3 years. They've been about this for 3 years. They've been saying, "This will take your job. You saying, "This will take your job. You saying, "This will take your job. You must use this. If you don't use this, must use this. If you don't use this, must use this. If you don't use this, you're going to be falling behind." So you're going to be falling behind." So you're going to be falling behind." So people are using it because they've been people are using it because they've been people are using it because they've been told to use it constantly and they're told to use it constantly and they're told to use it constantly and they're using it like search predominantly and using it like search predominantly and using it like search predominantly and that's partly because Google fell behind that's partly because Google fell behind that's partly because Google fell behind search and also because it's better at search and also because it's better at search and also because it's better at ingesting queries sometimes. Sometimes ingesting queries sometimes. Sometimes ingesting queries sometimes. Sometimes if you use a generative search it's like if you use a generative search it's like if you use a generative search it's like a trolling vessel. It's not very good at a trolling vessel. It's not very good at a trolling vessel. It's not very good at specifics but if you're like does this specifics but if you're like does this specifics but if you're like does this thing exist? Has this person ever said thing exist? Has this person ever said thing exist? Has this person ever said anything like this? It'll still probably anything like this? It'll still probably anything like this? It'll still probably get it wrong but it'll scour the ocean get it wrong but it'll scour the ocean get it wrong but it'll scour the ocean for you. Nevertheless, that's not worth for you. Nevertheless, that's not worth for you. Nevertheless, that's not worth a trillion dollars. None of it is. The a trillion dollars. None of it is. The a trillion dollars. None of it is. The amount of money being sunk into this is amount of money being sunk into this is amount of money being sunk into this is just incomparable to anything. Railways, just incomparable to anything. Railways, just incomparable to anything. Railways, it blows everything out of the water it blows everything out of the water it blows everything out of the water because there is no postbubble story because there is no postbubble story because there is no postbubble story even for this. AIG GPU is not useful for even for this. AIG GPU is not useful for even for this. AIG GPU is not useful for other things either. There's it's a other things either. There's it's a other things either. There's it's a directionless egregor of capitalism.

directionless egregor of capitalism. directionless egregor of capitalism. this headless beast that lumbers around this headless beast that lumbers around this headless beast that lumbers around desperate to seek out growth everywhere desperate to seek out growth everywhere desperate to seek out growth everywhere in the hopes that if it harasses people in the hopes that if it harasses people in the hopes that if it harasses people and scares people and demonizes labor and scares people and demonizes labor and scares people and demonizes labor enough, people will be forced to use it. enough, people will be forced to use it. enough, people will be forced to use it. >> The the reason I I pause is because I >> The the reason I I pause is because I >> The the reason I I pause is because I just I think about my own company. just I think about my own company. just I think about my own company. Obviously, everybody thinks about their Obviously, everybody thinks about their Obviously, everybody thinks about their own personal situation. So, you have own personal situation. So, you have own personal situation. So, you have people listening now that don't use any people listening now that don't use any people listening now that don't use any AI tools. Then you'll have people that AI tools. Then you'll have people that AI tools. Then you'll have people that are using it for everything from coding are using it for everything from coding are using it for everything from coding new software tools to everything they new software tools to everything they new software tools to everything they write to, you know, images, whatever. write to, you know, images, whatever. write to, you know, images, whatever. And when you look at the the stats And when you look at the the stats And when you look at the the stats around enterprise adoption, it says 88% around enterprise adoption, it says 88% around enterprise adoption, it says 88% of organizations regularly use AI at of organizations regularly use AI at of organizations regularly use AI at least once for one particular business least once for one particular business least once for one particular business function. And I'd say in our company, function. And I'd say in our company, function. And I'd say in our company, 95% of people use a one of these AI 95% of people use a one of these AI 95% of people use a one of these AI tools like anthropical chatbt or Gemini tools like anthropical chatbt or Gemini tools like anthropical chatbt or Gemini every day, every day, every day, >> right? And that exists on some kind of >> right? And that exists on some kind of >> right? And that exists on some kind of spectrum of like the super users that spectrum of like the super users that spectrum of like the super users that are using it probably, you know, every are using it probably, you know, every are using it probably, you know, every hour of every day for almost everything hour of every day for almost everything hour of every day for almost everything to, you know, someone maybe hiring the to, you know, someone maybe hiring the to, you know, someone maybe hiring the executive team that's using it less executive team that's using it less executive team that's using it less because their job doesn't require of it because their job doesn't require of it because their job doesn't require of it as much, as much, as much, >> right?

>> right? >> right? >> And when you look out into the world, >> And when you look out into the world, >> And when you look out into the world, you know, at how the world is changing you know, at how the world is changing you know, at how the world is changing from a content perspective, if we're from a content perspective, if we're from a content perspective, if we're looking at generative AI, it is obvious looking at generative AI, it is obvious looking at generative AI, it is obvious that these tools are being widely that these tools are being widely that these tools are being widely adopted. Part of the symptom is the AI adopted. Part of the symptom is the AI adopted. Part of the symptom is the AI slop you see all over the internet, slop you see all over the internet, slop you see all over the internet, >> right? >> right? >> right? So, I I don't know this this this idea So, I I don't know this this this idea So, I I don't know this this this idea that it's not being used. I struggle that it's not being used. I struggle that it's not being used. I struggle with with with >> it's being used. Here's the thing with >> it's being used. Here's the thing with >> it's being used. Here's the thing with the slop. Before we had AI slop, we had the slop. Before we had AI slop, we had the slop. Before we had AI slop, we had SEO slop because Google incentivized SEO slop because Google incentivized SEO slop because Google incentivized doing the lowest common denominator that doing the lowest common denominator that doing the lowest common denominator that would rank well in search. There's a would rank well in search. There's a would rank well in search. There's a whole story about how they pulled back whole story about how they pulled back whole story about how they pulled back spam guards thanks to Bravagar Ragavan, spam guards thanks to Bravagar Ragavan, spam guards thanks to Bravagar Ragavan, which we can get into, which we can get into, which we can get into, >> where they made the internet worse by >> where they made the internet worse by >> where they made the internet worse by allowing worse content to rank higher. allowing worse content to rank higher. allowing worse content to rank higher. It's why we have when you used to It's why we have when you used to It's why we have when you used to Google, oh, best washing machine, Google, oh, best washing machine, Google, oh, best washing machine, there's 11 different horrible blogs that there's 11 different horrible blogs that there's 11 different horrible blogs that read like somebody got a concussion. read like somebody got a concussion. read like somebody got a concussion. They are built to rank rather than be They are built to rank rather than be They are built to rank rather than be read by humans or built to be good made read by humans or built to be good made read by humans or built to be good made good. So AI helps weaponize that at good. So AI helps weaponize that at good. So AI helps weaponize that at scale. Yeah, you can make a bunch of scale. Yeah, you can make a bunch of scale. Yeah, you can make a bunch of generic slop. We've had slop for years.

generic slop. We've had slop for years. generic slop. We've had slop for years. We've just found a slop machine. But We've just found a slop machine. But We've just found a slop machine. But then also there's the problem of cost. then also there's the problem of cost. then also there's the problem of cost. So when you use AI services, you burn So when you use AI services, you burn So when you use AI services, you burn tokens and it's per million tokens. So tokens and it's per million tokens. So tokens and it's per million tokens. So >> what's a token? So it's around 3/4 of a >> what's a token? So it's around 3/4 of a >> what's a token? So it's around 3/4 of a word. So it's characters. word. So it's characters. word. So it's characters. >> So the AI companies have a currency in >> So the AI companies have a currency in >> So the AI companies have a currency in which they charge you. Like a taxi in which they charge you. Like a taxi in which they charge you. Like a taxi in New York has a meter. New York has a meter. New York has a meter. >> Yeah. >> Yeah. >> Yeah. >> And they call it tokens. >> And they call it tokens. >> And they call it tokens. >> Yeah. >> Yeah. >> Yeah. >> And every word, let's just say for ease >> And every word, let's just say for ease >> And every word, let's just say for ease it's a word. You're paying per word. it's a word. You're paying per word. it's a word. You're paying per word. >> About a word. Yeah. And it's per million >> About a word. Yeah. And it's per million >> About a word. Yeah. And it's per million tokens. So you'll be charged per million tokens. So you'll be charged per million tokens. So you'll be charged per million input tokens. The stuff you feed into it input tokens. The stuff you feed into it input tokens. The stuff you feed into it like a document or a bunch a code base. like a document or a bunch a code base. like a document or a bunch a code base. And the output tokens are both the stuff And the output tokens are both the stuff And the output tokens are both the stuff it spits out at the end but also when it it spits out at the end but also when it it spits out at the end but also when it thinks. So, okay, you've asked me to thinks. So, okay, you've asked me to thinks. So, okay, you've asked me to give you the best restaurants in this give you the best restaurants in this give you the best restaurants in this area of New York. I should find the best area of New York. I should find the best area of New York. I should find the best restaurants in New York. All of that's restaurants in New York. All of that's restaurants in New York. All of that's output tokens as well. output tokens as well. output tokens as well. >> However, when you're paying for a >> However, when you're paying for a >> However, when you're paying for a monthly service, you don't see any of monthly service, you don't see any of monthly service, you don't see any of that. Put all that crap to the side. that. Put all that crap to the side. that. Put all that crap to the side. They just have rate limits. So, you can They just have rate limits. So, you can They just have rate limits. So, you can use them a certain amount and then when use them a certain amount and then when use them a certain amount and then when you run out, but they kind of offiscate you run out, but they kind of offiscate you run out, but they kind of offiscate what that was. Now, someone recently what that was. Now, someone recently what that was. Now, someone recently found, semi analysis actually found found, semi analysis actually found found, semi analysis actually found this, a big analyst group. They found this, a big analyst group. They found this, a big analyst group. They found that on a $200 a month chat GPD that on a $200 a month chat GPD that on a $200 a month chat GPD subscription, you can burn $14,000 subscription, you can burn $14,000 subscription, you can burn $14,000 worth of tokens and on anthropics you worth of tokens and on anthropics you worth of tokens and on anthropics you can burn $8,000 for 200 bucks. That is can burn $8,000 for 200 bucks. That is can burn $8,000 for 200 bucks. That is how most and even on the 20 buck a month how most and even on the 20 buck a month how most and even on the 20 buck a month service you can burn $400.

service you can burn $400. service you can burn $400. Now most people don't realize that. Most Now most people don't realize that. Most Now most people don't realize that. Most people have no idea what AI costs. Most people have no idea what AI costs. Most people have no idea what AI costs. Most people just think, "Oh, it's 20 bucks a people just think, "Oh, it's 20 bucks a people just think, "Oh, it's 20 bucks a month." No. All of these companies run month." No. All of these companies run month." No. All of these companies run at a horrifying loss. OpenAI lost $20.9 at a horrifying loss. OpenAI lost $20.9 at a horrifying loss. OpenAI lost $20.9 billion last year because people can billion last year because people can billion last year because people can burn as many tokens as they want. And burn as many tokens as they want. And burn as many tokens as they want. And when they tried to move everybody on the when they tried to move everybody on the when they tried to move everybody on the enterprise side, so companies bigger enterprise side, so companies bigger enterprise side, so companies bigger than 150 onto actually paying the cost than 150 onto actually paying the cost than 150 onto actually paying the cost of AI in around March of 2026, to quote of AI in around March of 2026, to quote of AI in around March of 2026, to quote Sam Orman, they said, uh, people have a Sam Orman, they said, uh, people have a Sam Orman, they said, uh, people have a big problem with it. I think it's a huge big problem with it. I think it's a huge big problem with it. I think it's a huge issue, which is not really what the air issue, which is not really what the air issue, which is not really what the air apparent text history is meant to be apparent text history is meant to be apparent text history is meant to be saying, but the point is enterprises saying, but the point is enterprises saying, but the point is enterprises immediately started freaking out. Uber immediately started freaking out. Uber immediately started freaking out. Uber burned through their entire annual token burned through their entire annual token burned through their entire annual token budget in three months. So suddenly budget in three months. So suddenly budget in three months. So suddenly after everyone saying AI is the most after everyone saying AI is the most after everyone saying AI is the most productive thing ever. It's amazing. productive thing ever. It's amazing. productive thing ever. It's amazing. It's changing everything. The moment It's changing everything. The moment It's changing everything. The moment people actually had to pay for it, they people actually had to pay for it, they people actually had to pay for it, they go, [snorts] go, [snorts] go, [snorts] I don't know actually. Um maybe it's I don't know actually. Um maybe it's I don't know actually. Um maybe it's obviously we all love it. It's all obviously we all love it. It's all obviously we all love it. It's all great, right? But it's costing too much. great, right? But it's costing too much. great, right? But it's costing too much. So we need to reduce the cost because So we need to reduce the cost because So we need to reduce the cost because people are just dumping stuff into it people are just dumping stuff into it people are just dumping stuff into it being like what do I do here and getting being like what do I do here and getting being like what do I do here and getting whatever the median is out because whatever the median is out because whatever the median is out because that's what these things do. they that's what these things do. they that's what these things do. they provide the median answer.

provide the median answer. provide the median answer. >> So essentially, someone like me who's a >> So essentially, someone like me who's a >> So essentially, someone like me who's a power user of these tools, power user of these tools, power user of these tools, >> I could be costing Anthropic or OpenAI >> I could be costing Anthropic or OpenAI >> I could be costing Anthropic or OpenAI $1,000, but they're only charging me $1,000, but they're only charging me $1,000, but they're only charging me $100, let's say. So they are having to $100, let's say. So they are having to $100, let's say. So they are having to subsidize $900 of my usage because of subsidize $900 of my usage because of subsidize $900 of my usage because of the electricity costs and the costs at the electricity costs and the costs at the electricity costs and the costs at their data centers. And so your their data centers. And so your their data centers. And so your assertion here is that that is assertion here is that that is assertion here is that that is unsustainable. unsustainable. unsustainable. >> Yes. And just to be clear, they're >> Yes. And just to be clear, they're >> Yes. And just to be clear, they're probably not one for$1. It might be 30 probably not one for$1. It might be 30 probably not one for$1. It might be 30 for. We don't we don't know. I think for. We don't we don't know. I think for. We don't we don't know. I think it's unprofitable. These companies don't it's unprofitable. These companies don't it's unprofitable. These companies don't disclose them even in their auditive disclose them even in their auditive disclose them even in their auditive financials. They play funny games with financials. They play funny games with financials. They play funny games with how they categorize things. But how they categorize things. But how they categorize things. But nevertheless, yes. And on top of that, nevertheless, yes. And on top of that, nevertheless, yes. And on top of that, the way that you stand up inference, the way that you stand up inference, the way that you stand up inference, which is the thing that creates the which is the thing that creates the which is the thing that creates the output within these data centers, you're output within these data centers, you're output within these data centers, you're not just saying, "Okay, turn the not just saying, "Okay, turn the not just saying, "Okay, turn the inference machine on. Let's go." You are inference machine on. Let's go." You are inference machine on. Let's go." You are standing up the GPUs necessary to take standing up the GPUs necessary to take standing up the GPUs necessary to take in the demand, and if you buy too much, in the demand, and if you buy too much, in the demand, and if you buy too much, you've wasted the money. You You have to you've wasted the money. You You have to you've wasted the money. You You have to pay for the hourly GPU use regardless. pay for the hourly GPU use regardless. pay for the hourly GPU use regardless. If you buy too few, your customers can't If you buy too few, your customers can't If you buy too few, your customers can't use it. They get pissed off at you. They use it. They get pissed off at you. They use it. They get pissed off at you. They cancel. They go with someone else. But cancel. They go with someone else. But cancel. They go with someone else. But nevertheless, yeah, they would get nevertheless, yeah, they would get nevertheless, yeah, they would get demand selling $20 or $40 for a dollar.

demand selling $20 or $40 for a dollar. demand selling $20 or $40 for a dollar. And that's what these services do. And And that's what these services do. And And that's what these services do. And really, the simplest way to explain it really, the simplest way to explain it really, the simplest way to explain it is they were actually profitable if they is they were actually profitable if they is they were actually profitable if they were actually just they believed that were actually just they believed that were actually just they believed that these services were worthwhile and that these services were worthwhile and that these services were worthwhile and that they were worthy of the cost, they'd they were worthy of the cost, they'd they were worthy of the cost, they'd charge it. Regular people wouldn't be charge it. Regular people wouldn't be charge it. Regular people wouldn't be able to get a monthly subscription. able to get a monthly subscription. able to get a monthly subscription. They'd just be paying what it's worth, They'd just be paying what it's worth, They'd just be paying what it's worth, unless, of course, there was an economic unless, of course, there was an economic unless, of course, there was an economic problem. And it's very simple. You pay problem. And it's very simple. You pay problem. And it's very simple. You pay when you use an LLM regardless of when you use an LLM regardless of when you use an LLM regardless of whether you get what you want. When whether you get what you want. When whether you get what you want. When these things hallucinate, say you're these things hallucinate, say you're these things hallucinate, say you're doing something, you're coding something doing something, you're coding something doing something, you're coding something and they go through a code base and they and they go through a code base and they and they go through a code base and they up a bunch of stuff, they break a up a bunch of stuff, they break a up a bunch of stuff, they break a bunch of stuff, you're paying for that. bunch of stuff, you're paying for that. bunch of stuff, you're paying for that. You're paying for it whether it works or You're paying for it whether it works or You're paying for it whether it works or not, unless of course you're using one not, unless of course you're using one not, unless of course you're using one of these subscriptions. I think the the of these subscriptions. I think the the of these subscriptions. I think the the really interesting point is are they really interesting point is are they really interesting point is are they spending ahead of the value showing up spending ahead of the value showing up spending ahead of the value showing up which is I imagine what they would argue which is I imagine what they would argue which is I imagine what they would argue or are they spending all of this money or are they spending all of this money or are they spending all of this money and subsidizing all of their users in a and subsidizing all of their users in a and subsidizing all of their users in a way that's unsustainable and that will way that's unsustainable and that will way that's unsustainable and that will never be justified like does it you know never be justified like does it you know never be justified like does it you know because you think back through the because you think back through the because you think back through the history of technology you often get history of technology you often get history of technology you often get people people people losing money to grab market share losing money to grab market share losing money to grab market share >> right >> right >> right >> and they're also focusing on bringing >> and they're also focusing on bringing >> and they're also focusing on bringing the costs down and making it more the costs down and making it more the costs down and making it more profitable for them as well. But they profitable for them as well. But they profitable for them as well. But they can't afford to underinvest.

can't afford to underinvest. can't afford to underinvest. >> If they were bringing the cost down, >> If they were bringing the cost down, >> If they were bringing the cost down, they would have brought the cost down, they would have brought the cost down, they would have brought the cost down, which they have not. It seems to be which they have not. It seems to be which they have not. It seems to be getting more expensive. In fact, getting more expensive. In fact, getting more expensive. In fact, everyone inference providers don't seem everyone inference providers don't seem everyone inference providers don't seem to be profitable. Even the companies to be profitable. Even the companies to be profitable. Even the companies renting out GPUs don't seem to be renting out GPUs don't seem to be renting out GPUs don't seem to be profitable. I imagine that it wasn't profitable. I imagine that it wasn't profitable. I imagine that it wasn't like they started out and they were like they started out and they were like they started out and they were like, "Shit, this is unprofitable at the like, "Shit, this is unprofitable at the like, "Shit, this is unprofitable at the beginning. We know it. Screw it. We'll beginning. We know it. Screw it. We'll beginning. We know it. Screw it. We'll keep doing it any screw." I don't think keep doing it any screw." I don't think keep doing it any screw." I don't think it's some big conspiracy. They probably it's some big conspiracy. They probably it's some big conspiracy. They probably thought at some point, yeah, this will thought at some point, yeah, this will thought at some point, yeah, this will go profitable. The chips will catch up. go profitable. The chips will catch up. go profitable. The chips will catch up. Customers will pay for the overwhelming Customers will pay for the overwhelming Customers will pay for the overwhelming value because you don't know in 2023 value because you don't know in 2023 value because you don't know in 2023 where it's going to be in 2026. You where it's going to be in 2026. You where it's going to be in 2026. You assume it's going to go up. That's the assume it's going to go up. That's the assume it's going to go up. That's the nature of venture capital. They should nature of venture capital. They should nature of venture capital. They should have stopped in like 2024 when OpenAI have stopped in like 2024 when OpenAI have stopped in like 2024 when OpenAI lost over $5 billion. They should have lost over $5 billion. They should have lost over $5 billion. They should have been like, "Yep, this is not going to been like, "Yep, this is not going to been like, "Yep, this is not going to work." But they kept going because it work." But they kept going because it work." But they kept going because it helped number go up so much. It helped helped number go up so much. It helped helped number go up so much. It helped stock values pump. It helped everyone stock values pump. It helped everyone stock values pump. It helped everyone pump. It helped Nvidia pump, Microsoft, pump. It helped Nvidia pump, Microsoft, pump. It helped Nvidia pump, Microsoft, everyone. and not from the revenues. everyone. and not from the revenues. everyone. and not from the revenues. Because here's the funny thing about Because here's the funny thing about Because here's the funny thing about Google, Microsoft, and Amazon. People Google, Microsoft, and Amazon. People Google, Microsoft, and Amazon. People for years have been saying their AI bets for years have been saying their AI bets for years have been saying their AI bets have paid off. Wow, their AI bets have have paid off. Wow, their AI bets have have paid off. Wow, their AI bets have paid off. As these companies refused to paid off. As these companies refused to paid off. As these companies refused to say how much they're making from AI, but say how much they're making from AI, but say how much they're making from AI, but because their existing businesses because their existing businesses because their existing businesses continued to grow and did so, by the continued to grow and did so, by the continued to grow and did so, by the way, through price increases, changes to way, through price increases, changes to way, through price increases, changes to how Google and Meta uh did advertising.

how Google and Meta uh did advertising. how Google and Meta uh did advertising. Amazon bumped up prices and changed how Amazon bumped up prices and changed how Amazon bumped up prices and changed how they did actually Amazon started a they did actually Amazon started a they did actually Amazon started a remarkable ad business during this whole remarkable ad business during this whole remarkable ad business during this whole time as well. and the selling through time as well. and the selling through time as well. and the selling through Amazon platform anyway nothing to do Amazon platform anyway nothing to do Amazon platform anyway nothing to do with AI but because number go up because with AI but because number go up because with AI but because number go up because revenue go up everyone went it's AI revenue go up everyone went it's AI revenue go up everyone went it's AI because these companies wouldn't spend a because these companies wouldn't spend a because these companies wouldn't spend a trillion dollars for for no reason right trillion dollars for for no reason right trillion dollars for for no reason right except in fiscal year 2026 which just except in fiscal year 2026 which just except in fiscal year 2026 which just ended for Microsoft annoying I know they ended for Microsoft annoying I know they ended for Microsoft annoying I know they made total according to Bloomberg about made total according to Bloomberg about made total according to Bloomberg about $34.33 billion $24.1 billion of that was $34.33 billion $24.1 billion of that was $34.33 billion $24.1 billion of that was from OpenAI so that leaves them with from OpenAI so that leaves them with from OpenAI so that leaves them with about $10 billion in a year when they about $10 billion in a year when they about $10 billion in a year when they spent 115 billion on capital spent 115 billion on capital spent 115 billion on capital expenditures just intend to spend 175 expenditures just intend to spend 175 expenditures just intend to spend 175 billion next year. The math does not billion next year. The math does not billion next year. The math does not make sense. I imagine their plan was make sense. I imagine their plan was make sense. I imagine their plan was okay, this is just going to get okay, this is just going to get okay, this is just going to get exponentially more valuable and at some exponentially more valuable and at some exponentially more valuable and at some point the costs will be outpaced by the point the costs will be outpaced by the point the costs will be outpaced by the return. Problem is that large language return. Problem is that large language return. Problem is that large language models need a bunch of money to train models need a bunch of money to train models need a bunch of money to train them. They need constant data flow. They them. They need constant data flow. They them. They need constant data flow. They need customized data. It's just this big need customized data. It's just this big need customized data. It's just this big expensive monster. And when you try and expensive monster. And when you try and expensive monster. And when you try and talk to people about it and you try and talk to people about it and you try and talk to people about it and you try and say, "Hey, look, this is really bad.

say, "Hey, look, this is really bad. say, "Hey, look, this is really bad. Nvidia has sold it was $215.9 billion in Nvidia has sold it was $215.9 billion in Nvidia has sold it was $215.9 billion in the last fiscal year worth of GPUs the last fiscal year worth of GPUs the last fiscal year worth of GPUs mostly. And you try and go, yeah, that's mostly. And you try and go, yeah, that's mostly. And you try and go, yeah, that's to support like $22 billion of revenue to support like $22 billion of revenue to support like $22 billion of revenue total in the entire world outside of total in the entire world outside of total in the entire world outside of these two companies that literally these two companies that literally these two companies that literally require money being fed into them require money being fed into them require money being fed into them sometimes by Nvidia to keep alive. When sometimes by Nvidia to keep alive. When sometimes by Nvidia to keep alive. When you tell people that, they go, "Well, you tell people that, they go, "Well, you tell people that, they go, "Well, companies just lose money, right? companies just lose money, right? companies just lose money, right? Companies because we have this quote Companies because we have this quote Companies because we have this quote Edson from Prophy Markets. We have this Edson from Prophy Markets. We have this Edson from Prophy Markets. We have this cult-like worship of the wealthy where cult-like worship of the wealthy where cult-like worship of the wealthy where we think that someone wouldn't spend all we think that someone wouldn't spend all we think that someone wouldn't spend all this money for no reason. Right? Because this money for no reason. Right? Because this money for no reason. Right? Because reconciling with that with this idea reconciling with that with this idea reconciling with that with this idea that the ultra wealthy, the ultra that the ultra wealthy, the ultra that the ultra wealthy, the ultra powerful didn't get there through big powerful didn't get there through big powerful didn't get there through big brains. They didn't get there through brains. They didn't get there through brains. They didn't get there through anything other than luck and opportunism anything other than luck and opportunism anything other than luck and opportunism and getting an MBA perhaps with the and getting an MBA perhaps with the and getting an MBA perhaps with the right people. That they just got there right people. That they just got there right people. That they just got there because they're regular people and they because they're regular people and they because they're regular people and they just happen to be in the right place at just happen to be in the right place at just happen to be in the right place at the right time. reconciling with that the right time. reconciling with that the right time. reconciling with that and realizing that the world is not and realizing that the world is not and realizing that the world is not controlled by people like a meritocracy controlled by people like a meritocracy controlled by people like a meritocracy is kind of grim. So it's easy to be like is kind of grim. So it's easy to be like is kind of grim. So it's easy to be like no they're not making a mistake I must no they're not making a mistake I must no they're not making a mistake I must be missing something and that's what be missing something and that's what be missing something and that's what they want. So you know I think back they want. So you know I think back they want. So you know I think back through the history of technological through the history of technological through the history of technological breakthroughs and I think about I mean breakthroughs and I think about I mean breakthroughs and I think about I mean you can look at different industries and you can look at different industries and you can look at different industries and one of my favorite books on this subject one of my favorite books on this subject one of my favorite books on this subject is the innovator's dilemma. not read it.

is the innovator's dilemma. not read it. is the innovator's dilemma. not read it. >> And one of the things it talks about is >> And one of the things it talks about is >> And one of the things it talks about is how the the innovation that ends up how the the innovation that ends up how the the innovation that ends up taking out or transforming an industry taking out or transforming an industry taking out or transforming an industry often starts worse, doesn't make often starts worse, doesn't make often starts worse, doesn't make economic sense, none of your customers economic sense, none of your customers economic sense, none of your customers are asking for it. And this is typically are asking for it. And this is typically are asking for it. And this is typically why we end up ignoring it. So like why we end up ignoring it. So like why we end up ignoring it. So like you've got horse and carriages in the you've got horse and carriages in the you've got horse and carriages in the 1800s. 1800s. 1800s. >> Amazing form of transport according to >> Amazing form of transport according to >> Amazing form of transport according to the 1800s, you know, people of the the 1800s, you know, people of the the 1800s, you know, people of the 1800s. And then you have this thing 1800s. And then you have this thing 1800s. And then you have this thing called cars come along. Now the problem called cars come along. Now the problem called cars come along. Now the problem with cars is they broke down all the with cars is they broke down all the with cars is they broke down all the time. It's kind of like AI hallucinates time. It's kind of like AI hallucinates time. It's kind of like AI hallucinates now. um they were more expensive and the now. um they were more expensive and the now. um they were more expensive and the the economics of it didn't make sense. the economics of it didn't make sense. the economics of it didn't make sense. You might as well walk than buy a car. You might as well walk than buy a car. You might as well walk than buy a car. There was a law at the time that meant There was a law at the time that meant There was a law at the time that meant you had to walk in front of it with a you had to walk in front of it with a you had to walk in front of it with a red flag and wave and someone had you red flag and wave and someone had you red flag and wave and someone had you had to employ someone to walk in front had to employ someone to walk in front had to employ someone to walk in front of it waving a red flag. Obviously, it's of it waving a red flag. Obviously, it's of it waving a red flag. Obviously, it's worse. It's like a worse solution. worse. It's like a worse solution. worse. It's like a worse solution. However, these things that are However, these things that are However, these things that are disruptive innovations, they have a disruptive innovations, they have a disruptive innovations, they have a higher ceiling of growth and so they higher ceiling of growth and so they higher ceiling of growth and so they eventually overtake the horse. And I eventually overtake the horse. And I eventually overtake the horse. And I when I think about that analogy in the when I think about that analogy in the when I think about that analogy in the context of all of this, I go, okay, it's context of all of this, I go, okay, it's context of all of this, I go, okay, it's imperfect at the at the moment. the imperfect at the at the moment. the imperfect at the at the moment. the economic models aren't perfectly ironed economic models aren't perfectly ironed economic models aren't perfectly ironed out. They're still figuring out how to out. They're still figuring out how to out. They're still figuring out how to make it cheaper, the infrastructure, make it cheaper, the infrastructure, make it cheaper, the infrastructure, etc. But as if you think about the rate etc. But as if you think about the rate etc. But as if you think about the rate of improvement versus other you know of improvement versus other you know of improvement versus other you know let's say coding how much could I train let's say coding how much could I train let's say coding how much could I train a human coder to improve and to increase a human coder to improve and to increase a human coder to improve and to increase their output versus an AI agent one their output versus an AI agent one their output versus an AI agent one would go if you just imagine any rate of would go if you just imagine any rate of would go if you just imagine any rate of improvement in these AI tools at some improvement in these AI tools at some improvement in these AI tools at some point if you just imagine a 5% rate of point if you just imagine a 5% rate of point if you just imagine a 5% rate of improvement per month at some point it's improvement per month at some point it's improvement per month at some point it's you know and then you imagine a 5% you know and then you imagine a 5% you know and then you imagine a 5% reduction in cost which is what we did reduction in cost which is what we did reduction in cost which is what we did with the internet what we did with cars

with the internet what we did with cars with the internet what we did with cars but Mo's law but Mo's law but Mo's law >> mos law is a mos law is not with GPUs. >> mos law is a mos law is not with GPUs. >> mos law is a mos law is not with GPUs. So let me let me actually explain. So So let me let me actually explain. So So let me let me actually explain. So Nvidia Nvidia invented I think it was in Nvidia Nvidia invented I think it was in Nvidia Nvidia invented I think it was in the 2000s they put out something called the 2000s they put out something called the 2000s they put out something called CUDA which is the underlying software CUDA which is the underlying software CUDA which is the underlying software library and the way to run software on library and the way to run software on library and the way to run software on GPUs. took them solid decade or more to GPUs. took them solid decade or more to GPUs. took them solid decade or more to make it something where they could do make it something where they could do make it something where they could do data analytics, one of the early things, data analytics, one of the early things, data analytics, one of the early things, mapper and such. And then when AI came mapper and such. And then when AI came mapper and such. And then when AI came along, they'd had lots of experience along, they'd had lots of experience along, they'd had lots of experience with it. But nevertheless, this company with it. But nevertheless, this company with it. But nevertheless, this company has got more money, more attention, more has got more money, more attention, more has got more money, more attention, more geniuses behind them, more people geniuses behind them, more people geniuses behind them, more people focused on making their things more focused on making their things more focused on making their things more efficient than anyone could ever ask efficient than anyone could ever ask efficient than anyone could ever ask for. for. for. >> And Nvidia, for anyone that doesn't >> And Nvidia, for anyone that doesn't >> And Nvidia, for anyone that doesn't know, makes the chips. know, makes the chips. know, makes the chips. >> They So, and that CUDA thing I >> They So, and that CUDA thing I >> They So, and that CUDA thing I mentioned, they were the ones with CUDA mentioned, they were the ones with CUDA mentioned, they were the ones with CUDA and CUDA allowed generative AI to grow. and CUDA allowed generative AI to grow. and CUDA allowed generative AI to grow. Okay, so they're chips. Okay, so they're chips. Okay, so they're chips. >> Chips and chips are needed. Those are >> Chips and chips are needed. Those are >> Chips and chips are needed. Those are the things that go into the data the things that go into the data the things that go into the data centers. centers. centers. >> And there specific chips are the ones >> And there specific chips are the ones >> And there specific chips are the ones where you can run AI software on it. So where you can run AI software on it. So where you can run AI software on it. So the training runs and also the the training runs and also the the training runs and also the inference. Now, here's the thing. The inference. Now, here's the thing. The inference. Now, here's the thing. The the car example back then you didn't the car example back then you didn't the car example back then you didn't have pretty much every mathematician and have pretty much every mathematician and have pretty much every mathematician and scientist going into the car industry.

scientist going into the car industry. scientist going into the car industry. You didn't have the combined world's You didn't have the combined world's You didn't have the combined world's governments never shutting up about governments never shutting up about governments never shutting up about this. And by the way, giving them credit this. And by the way, giving them credit this. And by the way, giving them credit early since 2023, they've been saying early since 2023, they've been saying early since 2023, they've been saying this is inevitable. Even in what you this is inevitable. Even in what you this is inevitable. Even in what you said, 5% improvement. I don't even know said, 5% improvement. I don't even know said, 5% improvement. I don't even know how you'd measure that because a junior how you'd measure that because a junior how you'd measure that because a junior software engineer can still experience software engineer can still experience software engineer can still experience things and learn things from context, things and learn things from context, things and learn things from context, from how people deal with problems. And from how people deal with problems. And from how people deal with problems. And the way that people deal with problems the way that people deal with problems the way that people deal with problems is not as simple as looking at the code is not as simple as looking at the code is not as simple as looking at the code or reading some emails. It's context or reading some emails. It's context or reading some emails. It's context cues from speaking to a person. It's cues from speaking to a person. It's cues from speaking to a person. It's being in different environments. And being in different environments. And being in different environments. And there may there are uses for LLM's there may there are uses for LLM's there may there are uses for LLM's encoding. I don't dispute that. But even encoding. I don't dispute that. But even encoding. I don't dispute that. But even saying 5% uh what does that mean? Is it saying 5% uh what does that mean? Is it saying 5% uh what does that mean? Is it better at Rust? Is it better at C++? better at Rust? Is it better at C++? better at Rust? Is it better at C++? >> I'd say productivity just like yeah >> I'd say productivity just like yeah >> I'd say productivity just like yeah shipped. If we did it in the context of shipped. If we did it in the context of shipped. If we did it in the context of coding, it would be like shipped code. coding, it would be like shipped code. coding, it would be like shipped code. >> That's the thing that would be like he's >> That's the thing that would be like he's >> That's the thing that would be like he's the best writer in the world cuz his the best writer in the world cuz his the best writer in the world cuz his newsletter's really long. That's an newsletter's really long. That's an newsletter's really long. That's an insane way of evaluing it. With coding, insane way of evaluing it. With coding, insane way of evaluing it. With coding, it would be I mean it's even difficult it would be I mean it's even difficult it would be I mean it's even difficult to evaluate because it's is the software to evaluate because it's is the software to evaluate because it's is the software out there better is actually a great way out there better is actually a great way out there better is actually a great way of evaluating it. And I would say of evaluating it. And I would say of evaluating it. And I would say uniformly not. I would say the standard uniformly not. I would say the standard uniformly not. I would say the standard of software across Google, Microsoft, of software across Google, Microsoft, of software across Google, Microsoft, Amazon, Meta, especially God, Meta is a Amazon, Meta, especially God, Meta is a Amazon, Meta, especially God, Meta is a monstrosity, is worse. GitHub, GitHub, monstrosity, is worse. GitHub, GitHub, monstrosity, is worse. GitHub, GitHub, someone posted on Twitter earlier today, someone posted on Twitter earlier today, someone posted on Twitter earlier today, we should get a notification when GitHub we should get a notification when GitHub we should get a notification when GitHub is up rather than when it's down because is up rather than when it's down because is up rather than when it's down because that would be more reliable. Microsoft's that would be more reliable. Microsoft's that would be more reliable. Microsoft's one of the largest companies in the one of the largest companies in the one of the largest companies in the world, and they can barely wipe their world, and they can barely wipe their world, and they can barely wipe their own ass when it comes to GitHub. The own ass when it comes to GitHub. The own ass when it comes to GitHub. The quality of software is going down quality of software is going down quality of software is going down weirdly enough as more people use LLMs weirdly enough as more people use LLMs weirdly enough as more people use LLMs and more businesses demand and I really and more businesses demand and I really and more businesses demand and I really do mean demand that people use these

do mean demand that people use these do mean demand that people use these services. So on this point of if we go services. So on this point of if we go services. So on this point of if we go back to this horse and carriage and car back to this horse and carriage and car back to this horse and carriage and car analogy say that we're at whatever point analogy say that we're at whatever point analogy say that we're at whatever point today if you imagine any rate of today if you imagine any rate of today if you imagine any rate of improvement in the technology which we improvement in the technology which we improvement in the technology which we have seen since tragedy came out have seen since tragedy came out have seen since tragedy came out >> I remember when tragy came out and I was >> I remember when tragy came out and I was >> I remember when tragy came out and I was in Asia and I was there showing it to my in Asia and I was there showing it to my in Asia and I was there showing it to my fiance I was like look it can do this fiance I was like look it can do this fiance I was like look it can do this and it was hallucinating once in a while and it was hallucinating once in a while and it was hallucinating once in a while and getting things wrong. I actually and getting things wrong. I actually and getting things wrong. I actually don't have that experience anymore. I don't have that experience anymore. I don't have that experience anymore. I have moments where I believe it's have moments where I believe it's have moments where I believe it's reasoning is weak, but I don't have reasoning is weak, but I don't have reasoning is weak, but I don't have outright hallucinations anymore. See outright hallucinations anymore. See outright hallucinations anymore. See that? I I disagree. So, that? I I disagree. So, that? I I disagree. So, >> give me an example of what you define as >> give me an example of what you define as >> give me an example of what you define as a hallucination. a hallucination. a hallucination. >> Okay, great one. So, I have a Bloomberg >> Okay, great one. So, I have a Bloomberg >> Okay, great one. So, I have a Bloomberg terminal. Yeah. The very useful thing terminal. Yeah. The very useful thing terminal. Yeah. The very useful thing they have on there is ask B. So, when they have on there is ask B. So, when they have on there is ask B. So, when you do a Bloomberg inquiry to like look you do a Bloomberg inquiry to like look you do a Bloomberg inquiry to like look up what we think Nvidia's revenue is up what we think Nvidia's revenue is up what we think Nvidia's revenue is going to be next quarter, it runs going to be next quarter, it runs going to be next quarter, it runs something called BQL, which is its own something called BQL, which is its own something called BQL, which is its own programming language. Now, instead of programming language. Now, instead of programming language. Now, instead of having to learn that, you can just type having to learn that, you can just type having to learn that, you can just type into RSB and it will generate it and run into RSB and it will generate it and run into RSB and it will generate it and run it for you. And so, you get it pulled up it for you. And so, you get it pulled up it for you. And so, you get it pulled up and you know where the data is coming and you know where the data is coming and you know where the data is coming from. It deals with hallucinations real from. It deals with hallucinations real from. It deals with hallucinations real well. The other day, I was like, you well. The other day, I was like, you well. The other day, I was like, you know what, get a little spicy. I'm going know what, get a little spicy. I'm going know what, get a little spicy. I'm going to look up the growth rate of stocks of to look up the growth rate of stocks of to look up the growth rate of stocks of Microsoft, Google, Meta, and Amazon over Microsoft, Google, Meta, and Amazon over Microsoft, Google, Meta, and Amazon over the course of 5 years, I think it was.

the course of 5 years, I think it was. the course of 5 years, I think it was. >> And I was about to I was copy pasted it >> And I was about to I was copy pasted it >> And I was about to I was copy pasted it over to something looked at in Excel. I over to something looked at in Excel. I over to something looked at in Excel. I was about to was writing the newsletter. was about to was writing the newsletter. was about to was writing the newsletter. I went, Microsoft stocks never been $575 I went, Microsoft stocks never been $575 I went, Microsoft stocks never been $575 a stock. a stock. a stock. You know what? When it's a cute little You know what? When it's a cute little You know what? When it's a cute little thing like, oh, it's a stock price and I thing like, oh, it's a stock price and I thing like, oh, it's a stock price and I kind of call it was no harm, no foul. kind of call it was no harm, no foul. kind of call it was no harm, no foul. That's fine. But when you're talking That's fine. But when you're talking That's fine. But when you're talking about, I don't know, like a transcribing about, I don't know, like a transcribing about, I don't know, like a transcribing tool for a doctor or a financial model tool for a doctor or a financial model tool for a doctor or a financial model that a hedge fund is dependent on, at that a hedge fund is dependent on, at that a hedge fund is dependent on, at that point it becomes a little more that point it becomes a little more that point it becomes a little more dangerous. And the thing is a dangerous. And the thing is a dangerous. And the thing is a hallucination with a software package. hallucination with a software package. hallucination with a software package. For example, you're refactoring a code For example, you're refactoring a code For example, you're refactoring a code base and it leaves a door open base and it leaves a door open base and it leaves a door open security-wise or it just breaks security-wise or it just breaks security-wise or it just breaks something and you I don't know maybe something and you I don't know maybe something and you I don't know maybe you've been vibe coding for 6 months. you've been vibe coding for 6 months. you've been vibe coding for 6 months. You haven't really been coding with your You haven't really been coding with your You haven't really been coding with your own hands for a while. Maybe you've own hands for a while. Maybe you've own hands for a while. Maybe you've forgotten a few things. You had this forgotten a few things. You had this forgotten a few things. You had this slop to look for. I'm not doing slop to look for. I'm not doing slop to look for. I'm not doing it. And so the problems become it. And so the problems become it. And so the problems become multiplicative. And I don't really know multiplicative. And I don't really know multiplicative. And I don't really know how you train them out of that. And how you train them out of that. And how you train them out of that. And they've certainly not succeeded. So on they've certainly not succeeded. So on they've certainly not succeeded. So on one hand they have got better but one of one hand they have got better but one of one hand they have got better but one of the main ways they evaluate them getting the main ways they evaluate them getting the main ways they evaluate them getting better are benchmarks that are adjusted better are benchmarks that are adjusted better are benchmarks that are adjusted specifically for large language models specifically for large language models specifically for large language models because you can't just have them do because you can't just have them do because you can't just have them do tasks. They've got better at that. They tasks. They've got better at that. They tasks. They've got better at that. They found some tasks they can have them do found some tasks they can have them do found some tasks they can have them do on them like meter me they have this on them like meter me they have this on them like meter me they have this thing where it's like check out this thing where it's like check out this thing where it's like check out this chart look how much better it's getting chart look how much better it's getting chart look how much better it's getting at running tasks. Wow it can go for an at running tasks. Wow it can go for an at running tasks. Wow it can go for an hour and then you look it's like yeah hour and then you look it's like yeah hour and then you look it's like yeah and successfully completing them 50% of and successfully completing them 50% of and successfully completing them 50% of the time. They they have a hallucination the time. They they have a hallucination the time. They they have a hallucination leaderboard and it really focuses on leaderboard and it really focuses on leaderboard and it really focuses on basic tasks and it shows that the basic tasks and it shows that the basic tasks and it shows that the four-year trend according to historical

four-year trend according to historical four-year trend according to historical data from the Victaria hallucination data from the Victaria hallucination data from the Victaria hallucination leaderboard shows that hallucination leaderboard shows that hallucination leaderboard shows that hallucination rates on simple summarization tasks have rates on simple summarization tasks have rates on simple summarization tasks have plummeted from around 21% 21.8% 4 years plummeted from around 21% 21.8% 4 years plummeted from around 21% 21.8% 4 years ago down to 0.7% ago down to 0.7% ago down to 0.7% roughly on today's top frontier models roughly on today's top frontier models roughly on today's top frontier models like Gemini and Chat GPT. Again the like Gemini and Chat GPT. Again the like Gemini and Chat GPT. Again the point of nuance here is that these are point of nuance here is that these are point of nuance here is that these are on simple tasks which is kind of what on simple tasks which is kind of what on simple tasks which is kind of what I've experienced. I've experienced that I've experienced. I've experienced that I've experienced. I've experienced that on day-to-day things that hallucinates on day-to-day things that hallucinates on day-to-day things that hallucinates less again rate of improvement thinking. less again rate of improvement thinking. less again rate of improvement thinking. So if I just imagine the trajectory to So if I just imagine the trajectory to So if I just imagine the trajectory to continue there is going to become a time continue there is going to become a time continue there is going to become a time where hallucinations become rarer than where hallucinations become rarer than where hallucinations become rarer than they are today increasingly and also they are today increasingly and also they are today increasingly and also what I would say is when I think about what I would say is when I think about what I would say is when I think about other technologies there's two more other technologies there's two more other technologies there's two more points other technologies at their points other technologies at their points other technologies at their inception when they first came to the inception when they first came to the inception when they first came to the world like the internet also had world like the internet also had world like the internet also had technical difficulties. I remember technical difficulties. I remember technical difficulties. I remember growing up with dialup modems and I growing up with dialup modems and I growing up with dialup modems and I couldn't go on the phone at the same couldn't go on the phone at the same couldn't go on the phone at the same time as going on the internet. I'd have time as going on the internet. I'd have time as going on the internet. I'd have to stop Runescape upstairs to go on the to stop Runescape upstairs to go on the to stop Runescape upstairs to go on the phone. And you thought this is crap. phone. And you thought this is crap. phone. And you thought this is crap. This is technology crap. All the This is technology crap. All the This is technology crap. All the >> I I don't know, mate. I loved it. >> I I don't know, mate. I loved it. >> I I don't know, mate. I loved it. >> Yeah, I know. You It felt like magic.

>> Yeah, I know. You It felt like magic. >> Yeah, I know. You It felt like magic. And then in hindsight, you go, "Wow, I And then in hindsight, you go, "Wow, I And then in hindsight, you go, "Wow, I now have Starink and 5G internet from my now have Starink and 5G internet from my now have Starink and 5G internet from my phone. It's unbelievable." You couldn't phone. It's unbelievable." You couldn't phone. It's unbelievable." You couldn't leave the house with internet before. leave the house with internet before. leave the house with internet before. And that's what I mean by the rate of And that's what I mean by the rate of And that's what I mean by the rate of improvement thinking. I'd say the last improvement thinking. I'd say the last improvement thinking. I'd say the last point is we often compare AI to point is we often compare AI to point is we often compare AI to perfection, perfection, perfection, >> right? >> right? >> right? >> Whereas that's not actually the >> Whereas that's not actually the >> Whereas that's not actually the alternative in the working world. Like alternative in the working world. Like alternative in the working world. Like if I wanted to do let's say a simple if I wanted to do let's say a simple if I wanted to do let's say a simple writing task, I should compare AI to my writing task, I should compare AI to my writing task, I should compare AI to my alternative alternative way of doing alternative alternative way of doing alternative alternative way of doing that simple writing task which is both that simple writing task which is both that simple writing task which is both measured in my time right and my ability measured in my time right and my ability measured in my time right and my ability to hallucinate as a person who doesn't to hallucinate as a person who doesn't to hallucinate as a person who doesn't know everything know everything know everything or if I'm hiring someone an intern who or if I'm hiring someone an intern who or if I'm hiring someone an intern who might also be prone to hallucination or might also be prone to hallucination or might also be prone to hallucination or have gaps in their knowledge. have gaps in their knowledge. have gaps in their knowledge. >> So it's not actually like we're >> So it's not actually like we're >> So it's not actually like we're comparing we should compare AI to comparing we should compare AI to comparing we should compare AI to perfection. It's AI to the other perfection. It's AI to the other perfection. It's AI to the other alternatives. And if someone alternatives. And if someone alternatives. And if someone hallucinates 0.7% of the time, but knows hallucinates 0.7% of the time, but knows hallucinates 0.7% of the time, but knows way more and is faster, maybe on a net way more and is faster, maybe on a net way more and is faster, maybe on a net basis, that's a good trade. Maybe I basis, that's a good trade. Maybe I basis, that's a good trade. Maybe I should use AI. So, let's start with an should use AI. So, let's start with an should use AI. So, let's start with an example. Someone I love dearly, Matt example. Someone I love dearly, Matt example. Someone I love dearly, Matt Hughes, my editor, lives out of Hughes, my editor, lives out of Hughes, my editor, lives out of Liverpool. Wonderful guy. I don't pay Liverpool. Wonderful guy. I don't pay Liverpool. Wonderful guy. I don't pay Matt Hughes because he knows everything.

Matt Hughes because he knows everything. Matt Hughes because he knows everything. I pay him because he has incredible I pay him because he has incredible I pay him because he has incredible context and a ton of knowledge and he's context and a ton of knowledge and he's context and a ton of knowledge and he's willing to expand it and work with me willing to expand it and work with me willing to expand it and work with me and moral sport and he's a great editor, and moral sport and he's a great editor, and moral sport and he's a great editor, but he's also someone who gets into the but he's also someone who gets into the but he's also someone who gets into the guts of it and has the experiences of guts of it and has the experiences of guts of it and has the experiences of it. He's a decorated tech journalist and it. He's a decorated tech journalist and it. He's a decorated tech journalist and on top of that a wonderful loving being on top of that a wonderful loving being on top of that a wonderful loving being with empathy and joy in his heart for with empathy and joy in his heart for with empathy and joy in his heart for the stuff he loves and absolute the stuff he loves and absolute the stuff he loves and absolute venom for the people he hates. That's I venom for the people he hates. That's I venom for the people he hates. That's I can't get that from a large language can't get that from a large language can't get that from a large language model. But on top of that, I don't I model. But on top of that, I don't I model. But on top of that, I don't I push back on just the assumption there. push back on just the assumption there. push back on just the assumption there. >> When you say knows everything, what good >> When you say knows everything, what good >> When you say knows everything, what good is something that knows everything when is something that knows everything when is something that knows everything when it sometimes doesn't know anything when it sometimes doesn't know anything when it sometimes doesn't know anything when it's sometimes? And on the thing is, are it's sometimes? And on the thing is, are it's sometimes? And on the thing is, are you really paying an intern for you really paying an intern for you really paying an intern for something basic? Are you really going to something basic? Are you really going to something basic? Are you really going to them and saying, "Yeah, can you look up them and saying, "Yeah, can you look up them and saying, "Yeah, can you look up what the date is?" No, you're doing that what the date is?" No, you're doing that what the date is?" No, you're doing that on Google. Whatever the task is, you are on Google. Whatever the task is, you are on Google. Whatever the task is, you are trying to also train an intern. The trying to also train an intern. The trying to also train an intern. The point of an intern is to train them and point of an intern is to train them and point of an intern is to train them and turn them in, take them out of Pinocchio turn them in, take them out of Pinocchio turn them in, take them out of Pinocchio status, status, status, >> but it's also an intern learns. And in >> but it's also an intern learns. And in >> but it's also an intern learns. And in turn gets context and in turn learns turn gets context and in turn learns turn gets context and in turn learns your habits. Learns your habits. Learns your habits. Learns >> AI gets context and learns. >> AI gets context and learns. >> AI gets context and learns. >> No, it doesn't. It >> No, it doesn't. It >> No, it doesn't. It >> doesn't learn.

>> doesn't learn. >> doesn't learn. >> I mean, it doesn't. The way it learns is >> I mean, it doesn't. The way it learns is >> I mean, it doesn't. The way it learns is you create a giant claw. MD file that it you create a giant claw. MD file that it you create a giant claw. MD file that it sometimes doesn't read, sometimes does sometimes doesn't read, sometimes does sometimes doesn't read, sometimes does read. You create a harness. You put it's read. You create a harness. You put it's read. You create a harness. You put it's like it's Pee-Wee's breakfast machine like it's Pee-Wee's breakfast machine like it's Pee-Wee's breakfast machine from PeeWee's Playhouse. You have to do from PeeWee's Playhouse. You have to do from PeeWee's Playhouse. You have to do all these controversies to mitigate the all these controversies to mitigate the all these controversies to mitigate the hallucinations. And even then at the hallucinations. And even then at the hallucinations. And even then at the end, how much effort have you put in? end, how much effort have you put in? end, how much effort have you put in? >> But so, okay, this is an extreme >> But so, okay, this is an extreme >> But so, okay, this is an extreme simplified example. If I went on my simplified example. If I went on my simplified example. If I went on my Claude now and said, "What's my dog? my Claude now and said, "What's my dog? my Claude now and said, "What's my dog? my dog's name. dog's name. dog's name. >> Uhhuh. >> Uhhuh. >> Uhhuh. >> It would know my dog's name. >> It would know my dog's name. >> It would know my dog's name. >> Jesus Christ. This this company raised >> Jesus Christ. This this company raised >> Jesus Christ. This this company raised 95 billion. 95 billion. 95 billion. >> I'm saying I'm I'm using an extreme >> I'm saying I'm I'm using an extreme >> I'm saying I'm I'm using an extreme simplified example to show that it can simplified example to show that it can simplified example to show that it can remember things from the past. remember things from the past. remember things from the past. Obviously, it knows much more complex Obviously, it knows much more complex Obviously, it knows much more complex things as well, but I just use that as things as well, but I just use that as things as well, but I just use that as an example. So, we we we accept the fact an example. So, we we we accept the fact an example. So, we we we accept the fact that it can it does have memory of the that it can it does have memory of the that it can it does have memory of the past. past. past. >> It has files it can access that have >> It has files it can access that have >> It has files it can access that have stuff on it, but that's not the same as stuff on it, but that's not the same as stuff on it, but that's not the same as memory. And it's also just okay. So, it memory. And it's also just okay. So, it memory. And it's also just okay. So, it remembers your dog's name. It might remembers your dog's name. It might remembers your dog's name. It might remember your habits. It might be able remember your habits. It might be able remember your habits. It might be able to read things you've said before. to read things you've said before. to read things you've said before. >> Does it know your moods? Does it know >> Does it know your moods? Does it know >> Does it know your moods? Does it know what's going on in the world around it? what's going on in the world around it? what's going on in the world around it? Does it have good days and bad days? Is Does it have good days and bad days? Is Does it have good days and bad days? Is it there for you? Because it's just a it there for you? Because it's just a it there for you? Because it's just a text machine. And the thing is text machine. And the thing is text machine. And the thing is the intern example. An intern is the intern example. An intern is the intern example. An intern is something that can grow. It's something something that can grow. It's something something that can grow. It's something that you invest in. That's not something that you invest in. That's not something that you invest in. That's not something you do through feeding files and text to you do through feeding files and text to you do through feeding files and text to it. The way that we store memories it. The way that we store memories it. The way that we store memories ourselves, the way in which we acrue ourselves, the way in which we acrue ourselves, the way in which we acrue experiences is a a milerum of emotion experiences is a a milerum of emotion experiences is a a milerum of emotion and feelings and facts and feelings and facts and feelings and facts >> completely different. So I think there's >> completely different. So I think there's >> completely different. So I think there's two things here. There's the process in two things here. There's the process in two things here. There's the process in which something happens and then there's which something happens and then there's which something happens and then there's the output.

the output. the output. >> So the process you're describing the >> So the process you're describing the >> So the process you're describing the process of how a human does memory, process of how a human does memory, process of how a human does memory, >> right? >> right? >> right? >> The way that an AI does memory is >> The way that an AI does memory is >> The way that an AI does memory is different. But the thing that people different. But the thing that people different. But the thing that people care about is there value in the output. care about is there value in the output. care about is there value in the output. I.e. You know, if I dump all of my files I.e. You know, if I dump all of my files I.e. You know, if I dump all of my files into Claude, I don't really care how it into Claude, I don't really care how it into Claude, I don't really care how it processes it as long as when I ask it, processes it as long as when I ask it, processes it as long as when I ask it, what's my revenue? It has the number. what's my revenue? It has the number. what's my revenue? It has the number. And one could say the same thing about And one could say the same thing about And one could say the same thing about training someone. You could say, you training someone. You could say, you training someone. You could say, you teach them, you put lots of effort into teach them, you put lots of effort into teach them, you put lots of effort into them. You give them lots of context. You them. You give them lots of context. You them. You give them lots of context. You you educate them and give them you educate them and give them you educate them and give them experiences. And then you might come and experiences. And then you might come and experiences. And then you might come and say to them, by the way, what's my say to them, by the way, what's my say to them, by the way, what's my revenue? Now, the processes are entirely revenue? Now, the processes are entirely revenue? Now, the processes are entirely different, but the outcome is what I different, but the outcome is what I different, but the outcome is what I care about. Do they know the revenue care about. Do they know the revenue care about. Do they know the revenue number when I ask them? And so, I think number when I ask them? And so, I think number when I ask them? And so, I think that's the part that we sometimes get that's the part that we sometimes get that's the part that we sometimes get lost. we get, you know, cuz I have I've lost. we get, you know, cuz I have I've lost. we get, you know, cuz I have I've heard this debate about like can AI be heard this debate about like can AI be heard this debate about like can AI be creative, creative, creative, >> right? >> right? >> right? >> I think like the way to answer that >> I think like the way to answer that >> I think like the way to answer that question is like it's about the output question is like it's about the output question is like it's about the output when I ask it to do a creative thing when I ask it to do a creative thing when I ask it to do a creative thing does it give me the answer not is the does it give me the answer not is the does it give me the answer not is the process the same as a human process cuz process the same as a human process cuz process the same as a human process cuz actually no who cares what the people actually no who cares what the people actually no who cares what the people care about they pay for the outcome the care about they pay for the outcome the care about they pay for the outcome the product. product. product. >> I actually disagree about the process >> I actually disagree about the process >> I actually disagree about the process because Matt Hughes for example because Matt Hughes for example because Matt Hughes for example >> your editor >> your editor >> your editor >> Yeah.

>> Yeah. >> Yeah. >> Yeah. watching him go down a rabbit hole >> Yeah. watching him go down a rabbit hole >> Yeah. watching him go down a rabbit hole and being there with him and actually and being there with him and actually and being there with him and actually vice versa him doing the same thing. We vice versa him doing the same thing. We vice versa him doing the same thing. We wrote these well I mean we were working wrote these well I mean we were working wrote these well I mean we were working on the research I ended up sitting there on the research I ended up sitting there on the research I ended up sitting there for like the dayong session of writing for like the dayong session of writing for like the dayong session of writing 11,000 words and he he had given me a 11,000 words and he he had given me a 11,000 words and he he had given me a bunch of notes. It was actually just bunch of notes. It was actually just bunch of notes. It was actually just even describing that process, I feel so even describing that process, I feel so even describing that process, I feel so happy cuz it was like us being like I happy cuz it was like us being like I happy cuz it was like us being like I can't believe how these Jesus can't believe how these Jesus can't believe how these Jesus Christ they can't do like just like the Christ they can't do like just like the Christ they can't do like just like the misanthropy of just the horrible cynical misanthropy of just the horrible cynical misanthropy of just the horrible cynical people of asset managers like Blackstone people of asset managers like Blackstone people of asset managers like Blackstone just learning about them and being like just learning about them and being like just learning about them and being like it can't be this and having a back and it can't be this and having a back and it can't be this and having a back and forth with him that is fundamentally forth with him that is fundamentally forth with him that is fundamentally different because we were both learning different because we were both learning different because we were both learning together and the learning process was as together and the learning process was as together and the learning process was as much about creating the output as the much about creating the output as the much about creating the output as the output itself. When you learn something, output itself. When you learn something, output itself. When you learn something, you're not creating the average, which you're not creating the average, which you're not creating the average, which really is what these things do, of the really is what these things do, of the really is what these things do, of the documents it could find. You're not documents it could find. You're not documents it could find. You're not getting particularly novel outputs. If I getting particularly novel outputs. If I getting particularly novel outputs. If I needed a generic slop output, sure, but needed a generic slop output, sure, but needed a generic slop output, sure, but I've I've used some of the higherend LLM I've I've used some of the higherend LLM I've I've used some of the higherend LLM harness machines that the hedge funds harness machines that the hedge funds harness machines that the hedge funds use, and they all give the same shite.

use, and they all give the same shite. use, and they all give the same shite. It's all the same the same generic It's all the same the same generic It's all the same the same generic reports, the same, oh, we noticed this reports, the same, oh, we noticed this reports, the same, oh, we noticed this analysis, things that you can find on analysis, things that you can find on analysis, things that you can find on any kind of AI slop out there. what you any kind of AI slop out there. what you any kind of AI slop out there. what you described to me there, what I heard described to me there, what I heard described to me there, what I heard anyway is there's two points of value anyway is there's two points of value anyway is there's two points of value you're getting from your time with that. you're getting from your time with that. you're getting from your time with that. I mean, I mean, there's many more, but I mean, I mean, there's many more, but I mean, I mean, there's many more, but you said you're you're learning and then you said you're you're learning and then you said you're you're learning and then you're getting this book edited blog you're getting this book edited blog you're getting this book edited blog blog. You're getting a blog edited, blog. You're getting a blog edited, blog. You're getting a blog edited, which is the output, and you're getting which is the output, and you're getting which is the output, and you're getting learning and you're also really getting learning and you're also really getting learning and you're also really getting connection and all these other things. connection and all these other things. connection and all these other things. But when I come to when people sort of But when I come to when people sort of But when I come to when people sort of think about the value of AI, of course, think about the value of AI, of course, think about the value of AI, of course, they could use it to learn. But in the they could use it to learn. But in the they could use it to learn. But in the example I gave of like repeat my revenue example I gave of like repeat my revenue example I gave of like repeat my revenue number back to me or do this number, I I number back to me or do this number, I I number back to me or do this number, I I just care about the output. I could use just care about the output. I could use just care about the output. I could use it to learn. I could say what if the it to learn. I could say what if the it to learn. I could say what if the revenue number was wrong once you should revenue number was wrong once you should revenue number was wrong once you should have defined deterministic ways of have defined deterministic ways of have defined deterministic ways of knowing those numbers you should not knowing those numbers you should not knowing those numbers you should not rely on them even with the terminal rely on them even with the terminal rely on them even with the terminal running BQL which I trust I will double running BQL which I trust I will double running BQL which I trust I will double triple treble check everything just to triple treble check everything just to triple treble check everything just to be sure partly because also the process be sure partly because also the process be sure partly because also the process of learning for me I don't want just a of learning for me I don't want just a of learning for me I don't want just a report I go like that I want something report I go like that I want something report I go like that I want something that I fully understand and also that I fully understand and also that I fully understand and also understand the context around it I don't understand the context around it I don't understand the context around it I don't think that LLM do that and I just don't think that LLM do that and I just don't think that LLM do that and I just don't see them getting see them getting see them getting in a way that does that because it's in a way that does that because it's in a way that does that because it's it's just not what they do. And also it's just not what they do. And also it's just not what they do. And also there's the other problem of the more there's the other problem of the more there's the other problem of the more detailed the report, the more likely detailed the report, the more likely detailed the report, the more likely there are things to be wrong with it. If there are things to be wrong with it. If there are things to be wrong with it. If you are with Matt Hughes, for example, I you are with Matt Hughes, for example, I you are with Matt Hughes, for example, I can trust he's got it right. I can trust can trust he's got it right. I can trust can trust he's got it right. I can trust he understood and I can trust that I can he understood and I can trust that I can he understood and I can trust that I can have a back and forth with him that will have a back and forth with him that will have a back and forth with him that will inform me if I've missed something. I inform me if I've missed something. I inform me if I've missed something. I can read the stuff that he's read and can read the stuff that he's read and can read the stuff that he's read and actually trust him because there's a big actually trust him because there's a big actually trust him because there's a big trust part as well. What is the basis of trust part as well. What is the basis of trust part as well. What is the basis of your trust in Matt? Could it be his

your trust in Matt? Could it be his your trust in Matt? Could it be his historical performance? historical performance? historical performance? >> I mean, yes. >> I mean, yes. >> I mean, yes. >> Okay. >> Okay. >> Okay. >> And also the fact we've learned half of >> And also the fact we've learned half of >> And also the fact we've learned half of this stuff together, this stuff together, this stuff together, >> but but tenure tenure doesn't >> but but tenure tenure doesn't >> but but tenure tenure doesn't necessarily There's probably people, you necessarily There's probably people, you necessarily There's probably people, you know, for 15 years who you also don't know, for 15 years who you also don't know, for 15 years who you also don't trust. Yes. trust. Yes. trust. Yes. >> So, I think I was trying to figure out >> So, I think I was trying to figure out >> So, I think I was trying to figure out like what is the what is the thing like what is the what is the thing like what is the what is the thing that's causing humans to trust another that's causing humans to trust another that's causing humans to trust another thing. And I guess it would be continual thing. And I guess it would be continual thing. And I guess it would be continual delivery of a commitment made of sorts. delivery of a commitment made of sorts. delivery of a commitment made of sorts. And so with Claude for example on simple And so with Claude for example on simple And so with Claude for example on simple tasks as we've seen from this tasks as we've seen from this tasks as we've seen from this hallucination leaderboard it continually hallucination leaderboard it continually hallucination leaderboard it continually delivers for people and that's why we've delivers for people and that's why we've delivers for people and that's why we've seen the fast seen the fast seen the fast >> I mean is that what that board says >> I mean is that what that board says >> I mean is that what that board says >> well it's it's saying like is it getting >> well it's it's saying like is it getting >> well it's it's saying like is it getting it wrong is it hallucinating it wrong is it hallucinating it wrong is it hallucinating >> simple task how are those defined >> simple task how are those defined >> simple task how are those defined >> I I don't know >> I I don't know >> I I don't know >> that's the thing though because this is >> that's the thing though because this is >> that's the thing though because this is actually a very very illustrative thing actually a very very illustrative thing actually a very very illustrative thing of the AI industry they are the what of the AI industry they are the what of the AI industry they are the what aboutist masters they have like well aboutist masters they have like well aboutist masters they have like well look we got this we got this benchmark look we got this we got this benchmark look we got this we got this benchmark that says we're good at this and look that says we're good at this and look that says we're good at this and look the numbers higher What's the number the numbers higher What's the number the numbers higher What's the number mean? No. What does that mean? And I'm mean? No. What does that mean? And I'm mean? No. What does that mean? And I'm not using this as a critic against you. not using this as a critic against you. not using this as a critic against you. It's It's It's >> when you can't give a direct answer, you >> when you can't give a direct answer, you >> when you can't give a direct answer, you give a side answer. When you as the LLM give a side answer. When you as the LLM give a side answer. When you as the LLM industry want to prove your worth, you industry want to prove your worth, you industry want to prove your worth, you can't just be like just use the product.

can't just be like just use the product. can't just be like just use the product. When the first iPhone came out, go was When the first iPhone came out, go was When the first iPhone came out, go was Penn State at the time. Oh, I felt like Penn State at the time. Oh, I felt like Penn State at the time. Oh, I felt like the uh apes at the beginning of 2001. the uh apes at the beginning of 2001. the uh apes at the beginning of 2001. official voicemail. It was official voicemail. It was official voicemail. It was immediate. And I showed it to tech immediate. And I showed it to tech immediate. And I showed it to tech friends. I showed it to the most normal friends. I showed it to the most normal friends. I showed it to the most normal people in the world. And everyone was people in the world. And everyone was people in the world. And everyone was like, "Holy this is They were on like, "Holy this is They were on like, "Holy this is They were on razors. They were on Nokia 3210s. It was razors. They were on Nokia 3210s. It was razors. They were on Nokia 3210s. It was obvious the value." Amazon Web Services, obvious the value." Amazon Web Services, obvious the value." Amazon Web Services, same deal. same deal. same deal. >> It wasn't obvious though. >> It wasn't obvious though. >> It wasn't obvious though. >> Yes, it was. I mean, I bought it >> Yes, it was. I mean, I bought it >> Yes, it was. I mean, I bought it >> to you. To you, it was. >> to you. To you, it was. >> to you. To you, it was. >> It was. And I also showed it to a bunch >> It was. And I also showed it to a bunch >> It was. And I also showed it to a bunch of people because I'm aware that I had of people because I'm aware that I had of people because I'm aware that I had bias when I just love gadgets. bias when I just love gadgets. bias when I just love gadgets. >> But but I remember the famous Steve >> But but I remember the famous Steve >> But but I remember the famous Steve Balmer who was the CEO of Microsoft Balmer who was the CEO of Microsoft Balmer who was the CEO of Microsoft interview where he was told about the interview where he was told about the interview where he was told about the iPhone and he bursts out laughing. iPhone and he bursts out laughing. iPhone and he bursts out laughing. [laughter] [laughter] [laughter] $500 fully subsidized with a plan. I $500 fully subsidized with a plan. I $500 fully subsidized with a plan. I said that is the most expensive phone in said that is the most expensive phone in said that is the most expensive phone in the world and it doesn't appeal to the world and it doesn't appeal to the world and it doesn't appeal to business customers because it doesn't business customers because it doesn't business customers because it doesn't have a keyboard which makes it not a have a keyboard which makes it not a have a keyboard which makes it not a very good email machine. You can get a very good email machine. You can get a very good email machine. You can get a Motorola Q phone now for $99. It's a Motorola Q phone now for $99. It's a Motorola Q phone now for $99. It's a very capable machine. It'll do music.

very capable machine. It'll do music. very capable machine. It'll do music. It'll do internet. It'll do email. It'll It'll do internet. It'll do email. It'll It'll do internet. It'll do email. It'll do instant messaging. So, I I kind of do instant messaging. So, I I kind of do instant messaging. So, I I kind of look at that and I say, "Well, I like look at that and I say, "Well, I like look at that and I say, "Well, I like our strategy. I like it a lot. our strategy. I like it a lot. our strategy. I like it a lot. >> He burst out laughing, mocking it >> He burst out laughing, mocking it >> He burst out laughing, mocking it because it was so disruptive. It was way because it was so disruptive. It was way because it was so disruptive. It was way more expensive more expensive more expensive >> and it was way different. No keyboard. >> and it was way different. No keyboard. >> and it was way different. No keyboard. >> Well, phones used to be insanely >> Well, phones used to be insanely >> Well, phones used to be insanely expensive and the carriers would cover expensive and the carriers would cover expensive and the carriers would cover them, but you had to sign a long them, but you had to sign a long them, but you had to sign a long contract. You were still spending 500 contract. You were still spending 500 contract. You were still spending 500 bucks. But the thing I'm getting at is bucks. But the thing I'm getting at is bucks. But the thing I'm getting at is you didn't have to explain to someone you didn't have to explain to someone you didn't have to explain to someone why perhaps you'd have to get past the why perhaps you'd have to get past the why perhaps you'd have to get past the cost part, but you could just be like, cost part, but you could just be like, cost part, but you could just be like, "Look how good this is." And then once "Look how good this is." And then once "Look how good this is." And then once the app was the iPhone 3G with the App the app was the iPhone 3G with the App the app was the iPhone 3G with the App Store, people were like, "Oh this Store, people were like, "Oh this Store, people were like, "Oh this could actually change things." mobile could actually change things." mobile could actually change things." mobile web. Even though it was a monstrosity, web. Even though it was a monstrosity, web. Even though it was a monstrosity, it was so bad at first. Even then, you it was so bad at first. Even then, you it was so bad at first. Even then, you could get your emails and you could just could get your emails and you could just could get your emails and you could just look at them. Point is, Blackberries look at them. Point is, Blackberries look at them. Point is, Blackberries were also expensive and were still were also expensive and were still were also expensive and were still actually kind of cool, but the way they actually kind of cool, but the way they actually kind of cool, but the way they worked was not like consumer software. worked was not like consumer software. worked was not like consumer software. They didn't have the classic GUI. They didn't have the classic GUI. They didn't have the classic GUI. iPhones felt like that. It felt like an iPhones felt like that. It felt like an iPhones felt like that. It felt like an a cell phone designed even like a a cell phone designed even like a a cell phone designed even like a computer. It was obvious. It was obvious computer. It was obvious. It was obvious computer. It was obvious. It was obvious from the beginning. Everyone I was I was from the beginning. Everyone I was I was from the beginning. Everyone I was I was dating a girl in the center of dating a girl in the center of dating a girl in the center of Pennsylvania at the time and everyone I Pennsylvania at the time and everyone I Pennsylvania at the time and everyone I showed it to was like, "Wow, this is showed it to was like, "Wow, this is showed it to was like, "Wow, this is incredible." That to me is the obvious incredible." That to me is the obvious incredible." That to me is the obvious thing with AI to this day when you're thing with AI to this day when you're thing with AI to this day when you're like, "Okay, why is it so amazing?"

like, "Okay, why is it so amazing?" like, "Okay, why is it so amazing?" People still dither. People are still People still dither. People are still People still dither. People are still like, "Yeah, you can't run a business like, "Yeah, you can't run a business like, "Yeah, you can't run a business fully with it without this weird system fully with it without this weird system fully with it without this weird system of pulleys and levers and such." of pulleys and levers and such." of pulleys and levers and such." >> But how come then when you look at the >> But how come then when you look at the >> But how come then when you look at the stats around ChachiBT's growth, stats around ChachiBT's growth, stats around ChachiBT's growth, >> 100 million active users in just the >> 100 million active users in just the >> 100 million active users in just the first 60 days after launching? For first 60 days after launching? For first 60 days after launching? For comparison, Tik Tok took 9 months. comparison, Tik Tok took 9 months. comparison, Tik Tok took 9 months. Instagram took 2.5 years. And the Instagram took 2.5 years. And the Instagram took 2.5 years. And the internet itself for the worldwide web internet itself for the worldwide web internet itself for the worldwide web took roughly 7 years to reach that took roughly 7 years to reach that took roughly 7 years to reach that scale. Over 60% of the US adults are scale. Over 60% of the US adults are scale. Over 60% of the US adults are integrated into AI tools in their daily integrated into AI tools in their daily integrated into AI tools in their daily and regular routines within 3 years of and regular routines within 3 years of and regular routines within 3 years of the launch, reaching a 40% of the the launch, reaching a 40% of the the launch, reaching a 40% of the population. And that same milestone took population. And that same milestone took population. And that same milestone took the internet 5 years and personal the internet 5 years and personal the internet 5 years and personal computers nearly 12. computers nearly 12. computers nearly 12. >> Okay. So like this is the I think this >> Okay. So like this is the I think this >> Okay. So like this is the I think this is the part that's giving me dissonance is the part that's giving me dissonance is the part that's giving me dissonance is like when I showed my fiance chachi is like when I showed my fiance chachi is like when I showed my fiance chachi okay it was didn't [clears throat] okay it was didn't [clears throat] okay it was didn't [clears throat] really work really work really work >> but as a sole entrepreneur who English >> but as a sole entrepreneur who English >> but as a sole entrepreneur who English isn't her first language isn't her first language isn't her first language >> who has to write lots of text lots of >> who has to write lots of text lots of >> who has to write lots of text lots of copy and generate lots of images and was copy and generate lots of images and was copy and generate lots of images and was paying a graphic designer to help her paying a graphic designer to help her paying a graphic designer to help her make um certain images that she you know make um certain images that she you know make um certain images that she you know couldn't make herself because she couldn't make herself because she couldn't make herself because she doesn't have the skills.

doesn't have the skills. doesn't have the skills. >> She would describe it as being >> She would describe it as being >> She would describe it as being transformative for her business. What transformative for her business. What transformative for her business. What I'm hearing from you is that it's not I'm hearing from you is that it's not I'm hearing from you is that it's not transformative and there's no value in transformative and there's no value in transformative and there's no value in it for people. But she if she was sat it for people. But she if she was sat it for people. But she if she was sat here transformative, here transformative, here transformative, would she pay the per million token would she pay the per million token would she pay the per million token rate? Would she pay the actual rate? Cuz rate? Would she pay the actual rate? Cuz rate? Would she pay the actual rate? Cuz that's the thing. If this was sold at that's the thing. If this was sold at that's the thing. If this was sold at its honest cost. Yeah. its honest cost. Yeah. its honest cost. Yeah. >> I would actually if and people were >> I would actually if and people were >> I would actually if and people were reacting like that and they were paying reacting like that and they were paying reacting like that and they were paying 23 $4 every time they did something and 23 $4 every time they did something and 23 $4 every time they did something and they were genuinely happy. That might be they were genuinely happy. That might be they were genuinely happy. That might be an argument. an argument. an argument. >> What is the what would be the honest >> What is the what would be the honest >> What is the what would be the honest cost if they weren't sub cost if they weren't sub cost if they weren't sub >> the actual per million token cost? The >> the actual per million token cost? The >> the actual per million token cost? The actual API cost they should char. actual API cost they should char. actual API cost they should char. >> Do you know how much that is relative to >> Do you know how much that is relative to >> Do you know how much that is relative to God? Depends on it depends on the model. God? Depends on it depends on the model. God? Depends on it depends on the model. But there's actually kind of a point I But there's actually kind of a point I But there's actually kind of a point I want to make about the thing you said want to make about the thing you said want to make about the thing you said with the internet earlier. So when I with the internet earlier. So when I with the internet earlier. So when I first got on the internet 33.4 kilobits first got on the internet 33.4 kilobits first got on the internet 33.4 kilobits a second modem even back then I was like a second modem even back then I was like a second modem even back then I was like if this was faster and that was if this was faster and that was if this was faster and that was like immediate just like if this was like immediate just like if this was like immediate just like if this was faster cuz it was slow. You go on like faster cuz it was slow. You go on like faster cuz it was slow. You go on like happy puppy or something download take happy puppy or something download take happy puppy or something download take all bloody day waiting for share word to all bloody day waiting for share word to all bloody day waiting for share word to download immediately like if I could do download immediately like if I could do download immediately like if I could do this faster it would be better. And even this faster it would be better. And even this faster it would be better. And even back then I'm like, man, you could back then I'm like, man, you could back then I'm like, man, you could probably do video camera stuff with this probably do video camera stuff with this probably do video camera stuff with this stuff that eventually happened. And stuff that eventually happened. And stuff that eventually happened. And actually, there's this guy called Jim actually, there's this guy called Jim actually, there's this guy called Jim Cavell from Goldman Sachs in a report he Cavell from Goldman Sachs in a report he Cavell from Goldman Sachs in a report he did in 2024 that was geni too much spend did in 2024 that was geni too much spend did in 2024 that was geni too much spend for not enough return. Paraphrasing for not enough return. Paraphrasing for not enough return. Paraphrasing there. And he made the point that in the there. And he made the point that in the there. And he made the point that in the run-up to the iPhone, there was run-up to the iPhone, there was run-up to the iPhone, there was thousands of presentations that when GSM thousands of presentations that when GSM thousands of presentations that when GSM radios get smaller, when Bluetooth radios get smaller, when Bluetooth radios get smaller, when Bluetooth radios get smaller, when Wi-Fi radios radios get smaller, when Wi-Fi radios radios get smaller, when Wi-Fi radios get smaller, it is inevitable that we get smaller, it is inevitable that we get smaller, it is inevitable that we will get something like this. And then will get something like this. And then will get something like this. And then he said that there is no such path for he said that there is no such path for he said that there is no such path for AI. There was no road map to AI becoming

AI. There was no road map to AI becoming AI. There was no road map to AI becoming this thing that they promised. And I this thing that they promised. And I this thing that they promised. And I must be clear, if these companies had must be clear, if these companies had must be clear, if these companies had gone out there and are like, "Yeah, this gone out there and are like, "Yeah, this gone out there and are like, "Yeah, this is interesting cloud software. It's is interesting cloud software. It's is interesting cloud software. It's generative. It's really expensive. We're generative. It's really expensive. We're generative. It's really expensive. We're not sure if we can fully not trust it. not sure if we can fully not trust it. not sure if we can fully not trust it. Not in the I'm scared way. I mean, just Not in the I'm scared way. I mean, just Not in the I'm scared way. I mean, just like we're not sure that this is going like we're not sure that this is going like we're not sure that this is going to be a disruptive world changing thing. to be a disruptive world changing thing. to be a disruptive world changing thing. It has potential, but we're going to go It has potential, but we're going to go It has potential, but we're going to go slow. It's really expensive. This is an slow. It's really expensive. This is an slow. It's really expensive. This is an R&D effort. We're not going to expose R&D effort. We're not going to expose R&D effort. We're not going to expose consumers to it." and actually being consumers to it." and actually being consumers to it." and actually being like called them like I don't know like called them like I don't know like called them like I don't know language models and no no generative AI language models and no no generative AI language models and no no generative AI stuff just being not even call it stuff just being not even call it stuff just being not even call it because it isn't AI it's not autonomous because it isn't AI it's not autonomous because it isn't AI it's not autonomous it's not smart I actually might respect it's not smart I actually might respect it's not smart I actually might respect it but this is not they've gone out it but this is not they've gone out it but this is not they've gone out there since 2023 and said it was 2022 there since 2023 and said it was 2022 there since 2023 and said it was 2022 this is the best thing since sliced this is the best thing since sliced this is the best thing since sliced bread this is changing everything this bread this is changing everything this bread this is changing everything this is going to do all your work this is is going to do all your work this is is going to do all your work this is going to take your job you're going to going to take your job you're going to going to take your job you're going to talk to Bing and it's going to tell you talk to Bing and it's going to tell you talk to Bing and it's going to tell you to leave your wife all of these crazy to leave your wife all of these crazy to leave your wife all of these crazy things and what's funny is when the things and what's funny is when the things and what's funny is when the writer uh Kevin Roose I think it was writer uh Kevin Roose I think it was writer uh Kevin Roose I think it was He was speaking to Kevin Scott, the CTO He was speaking to Kevin Scott, the CTO He was speaking to Kevin Scott, the CTO of Microsoft, about it. And Kevin Scott of Microsoft, about it. And Kevin Scott of Microsoft, about it. And Kevin Scott goes, you know, I'm just glad we're goes, you know, I'm just glad we're goes, you know, I'm just glad we're having this conversation. Instead of having this conversation. Instead of having this conversation. Instead of being like, "Settle down, Beas. It's a being like, "Settle down, Beas. It's a being like, "Settle down, Beas. It's a website. The website told you something.

website. The website told you something. website. The website told you something. It's just LLM." They talked it up. And It's just LLM." They talked it up. And It's just LLM." They talked it up. And that's because everyone is talking about that's because everyone is talking about that's because everyone is talking about what they wish this was. Rather than what they wish this was. Rather than what they wish this was. Rather than talking about what it can actually do. talking about what it can actually do. talking about what it can actually do. This makes it scary to people This makes it scary to people This makes it scary to people deliberately. So, it makes it deliberately. So, it makes it deliberately. So, it makes it environmentally destructive. Look at the environmentally destructive. Look at the environmentally destructive. Look at the gas turbines poisoning black gas turbines poisoning black gas turbines poisoning black neighborhoods. I think it's in neighborhoods. I think it's in neighborhoods. I think it's in Louisiana. It's one of Musk's data Louisiana. It's one of Musk's data Louisiana. It's one of Musk's data centers. Look at the incredible energy centers. Look at the incredible energy centers. Look at the incredible energy draws. It is raising power bills and draws. It is raising power bills and draws. It is raising power bills and also it is creating inflation across all also it is creating inflation across all also it is creating inflation across all consumer electronics because of the consumer electronics because of the consumer electronics because of the massive RAM. massive RAM. massive RAM. >> You know what's interesting? I almost >> You know what's interesting? I almost >> You know what's interesting? I almost feel like so much of what you're saying feel like so much of what you're saying feel like so much of what you're saying is true and also it can be true that is true and also it can be true that is true and also it can be true that this technology is going to profoundly this technology is going to profoundly this technology is going to profoundly change the world. And I think like you change the world. And I think like you change the world. And I think like you know I think back to the early days of know I think back to the early days of know I think back to the early days of the internet is maybe the closest the internet is maybe the closest the internet is maybe the closest analogy we have of you know in the com analogy we have of you know in the com analogy we have of you know in the com bubble. you know, you wrote this great bubble. you know, you wrote this great bubble. you know, you wrote this great essay. essay. essay. >> Yes. Yes. >> Yes. Yes. >> Yes. Yes. >> Which I found really funny um especially >> Which I found really funny um especially >> Which I found really funny um especially the name the rot economy and you talked the name the rot economy and you talked the name the rot economy and you talked about the rotcom bubble. about the rotcom bubble. about the rotcom bubble. >> Yes. >> Yes. >> Yes. >> Talking about how AI is of less value >> Talking about how AI is of less value >> Talking about how AI is of less value than people think.

than people think. than people think. >> And in that in the sort of com bubble, >> And in that in the sort of com bubble, >> And in that in the sort of com bubble, what you saw is huge hype, people what you saw is huge hype, people what you saw is huge hype, people overselling the capabilities of their overselling the capabilities of their overselling the capabilities of their websites and what they were building. websites and what they were building. websites and what they were building. But in the wake of the dotcom bubble, But in the wake of the dotcom bubble, But in the wake of the dotcom bubble, yes, 90% of stuff went to zero, yes, 90% of stuff went to zero, yes, 90% of stuff went to zero, >> but you had generational companies born >> but you had generational companies born >> but you had generational companies born that changed the world, that changed the world, that changed the world, >> right? >> right? >> right? >> And so I I do I kind of and that's what >> And so I I do I kind of and that's what >> And so I I do I kind of and that's what bubbles do, right? Huge hype, bubbles do, right? Huge hype, bubbles do, right? Huge hype, overinvestment, investors get crazy, overinvestment, investors get crazy, overinvestment, investors get crazy, delusional. They think it's everything's delusional. They think it's everything's delusional. They think it's everything's going to change. At the same time, you going to change. At the same time, you going to change. At the same time, you do have skeptics do have skeptics do have skeptics >> in these moments. The the dot bubble had >> in these moments. The the dot bubble had >> in these moments. The the dot bubble had I mean the internet itself had the I mean the internet itself had the I mean the internet itself had the biggest skeptics in 1998. Nobel Prize biggest skeptics in 1998. Nobel Prize biggest skeptics in 1998. Nobel Prize winning economist Paul Krugman said by winning economist Paul Krugman said by winning economist Paul Krugman said by 2005 or so it will become clear that the 2005 or so it will become clear that the 2005 or so it will become clear that the internet's impact on the economy has internet's impact on the economy has internet's impact on the economy has been no greater than the fax machine. In been no greater than the fax machine. In been no greater than the fax machine. In 1995 astrophysicist Clifford stool 1995 astrophysicist Clifford stool 1995 astrophysicist Clifford stool famously I wrote about this in my book famously I wrote about this in my book famously I wrote about this in my book wrote famously in Newsweek. Do our wrote famously in Newsweek. Do our wrote famously in Newsweek. Do our computer pundits lack all common sense? computer pundits lack all common sense? computer pundits lack all common sense? The truth is no online database will The truth is no online database will The truth is no online database will replace your daily newspaper. No CDROM replace your daily newspaper. No CDROM replace your daily newspaper. No CDROM can take the place of a competent can take the place of a competent can take the place of a competent teacher. Commerce and businesses will teacher. Commerce and businesses will teacher. Commerce and businesses will shift from offices and malls to networks shift from offices and malls to networks shift from offices and malls to networks and modems. Bologoney. So, how come my and modems. Bologoney. So, how come my and modems. Bologoney. So, how come my local mall does a roaring business and local mall does a roaring business and local mall does a roaring business and the cyber mall gets zero business? And the cyber mall gets zero business? And the cyber mall gets zero business? And then I'll give you one more from then I'll give you one more from then I'll give you one more from Krueger, who was the award-winning Krueger, who was the award-winning Krueger, who was the award-winning economist. He said, "The growth of the economist. He said, "The growth of the economist. He said, "The growth of the internet will slow drastically as it internet will slow drastically as it internet will slow drastically as it becomes apparent most people have becomes apparent most people have becomes apparent most people have nothing to say to each other."

nothing to say to each other." nothing to say to each other." That's that that that may actually be That's that that that may actually be That's that that that may actually be the worst one of those predict like hang the worst one of those predict like hang the worst one of those predict like hang around any bar in middle America. around any bar in middle America. around any bar in middle America. Honestly, the best conversation, Honestly, the best conversation, Honestly, the best conversation, >> but it's just all the same thing. >> but it's just all the same thing. >> but it's just all the same thing. >> I actually So, Clifford Stall actually >> I actually So, Clifford Stall actually >> I actually So, Clifford Stall actually his piece was interesting cuz that there his piece was interesting cuz that there his piece was interesting cuz that there were some boner points in it, but he were some boner points in it, but he were some boner points in it, but he made points about how like an made points about how like an made points about how like an overwhelming amount of bad information overwhelming amount of bad information overwhelming amount of bad information out there is bad for society. He's out there is bad for society. He's out there is bad for society. He's completely right saying how online completely right saying how online completely right saying how online education would not be a great education would not be a great education would not be a great replacement for regular education. I replacement for regular education. I replacement for regular education. I think we've seen that. But there is an think we've seen that. But there is an think we've seen that. But there is an economic difference that's vastly it's economic difference that's vastly it's economic difference that's vastly it's just completely different. So.com bubble just completely different. So.com bubble just completely different. So.com bubble was actually two bubbles. There was the was actually two bubbles. There was the was actually two bubbles. There was the website bubble which was just trash on website bubble which was just trash on website bubble which was just trash on trash on trash. It was just like I think trash on trash. It was just like I think trash on trash. It was just like I think what was it? Excite at home bought a what was it? Excite at home bought a what was it? Excite at home bought a eury incard company for like a billion eury incard company for like a billion eury incard company for like a billion dollars. It was insane crap happening dollars. It was insane crap happening dollars. It was insane crap happening that was so small. The big thing that that was so small. The big thing that that was so small. The big thing that people are thinking about is the dark people are thinking about is the dark people are thinking about is the dark fiber. fiber. fiber. >> Dark fiber. dark fiber was all of the >> Dark fiber. dark fiber was all of the >> Dark fiber. dark fiber was all of the wires that put in the ground thinking wires that put in the ground thinking wires that put in the ground thinking we're going to have all this demand for we're going to have all this demand for we're going to have all this demand for internet and it turned out that demand internet and it turned out that demand internet and it turned out that demand for internet I think the analyst for internet I think the analyst for internet I think the analyst estimate was it was doubling every 90 estimate was it was doubling every 90 estimate was it was doubling every 90 days when it was doing that every 6 to days when it was doing that every 6 to days when it was doing that every 6 to 12 months maybe maybe longer and just 12 months maybe maybe longer and just 12 months maybe maybe longer and just thus there was a massive overbuild of thus there was a massive overbuild of thus there was a massive overbuild of fiber optic cable and indeed the fiber optic cable and indeed the fiber optic cable and indeed the transmission stations and such just transmission stations and such just transmission stations and such just simplifying to bring that to people's simplifying to bring that to people's simplifying to bring that to people's houses and there was the assumption that houses and there was the assumption that houses and there was the assumption that well that would all get lit up and well that would all get lit up and well that would all get lit up and people would want it immediately didn't people would want it immediately didn't people would want it immediately didn't really really really Now the post.com bubble thing people say Now the post.com bubble thing people say Now the post.com bubble thing people say is well but after that there was demand is well but after that there was demand is well but after that there was demand from the internet. That's the thing from the internet. That's the thing from the internet. That's the thing though that's very different to demand though that's very different to demand though that's very different to demand for generative AI. Right now the demand

for generative AI. Right now the demand for generative AI. Right now the demand we have for generative AI is we have for generative AI is we have for generative AI is predominantly subsidized. Just let's predominantly subsidized. Just let's predominantly subsidized. Just let's start there. start there. start there. >> Yeah >> Yeah >> Yeah >> predominantly subsidized and most people >> predominantly subsidized and most people >> predominantly subsidized and most people experience it are not paying the real experience it are not paying the real experience it are not paying the real cost. cost. cost. >> I agree. >> I agree. >> I agree. >> On top of that we already have all of >> On top of that we already have all of >> On top of that we already have all of the possible marketing in the world. We the possible marketing in the world. We the possible marketing in the world. We have the largest, most disingenuous have the largest, most disingenuous have the largest, most disingenuous marketing campaign in the history of marketing campaign in the history of marketing campaign in the history of man, pushing this up the hill. We have man, pushing this up the hill. We have man, pushing this up the hill. We have the apex predator of cloud software, the apex predator of cloud software, the apex predator of cloud software, Microsoft. They can only get singledigit Microsoft. They can only get singledigit Microsoft. They can only get singledigit billions from selling AI software. And billions from selling AI software. And billions from selling AI software. And Christ almighty, outside of OpenAI and Christ almighty, outside of OpenAI and Christ almighty, outside of OpenAI and Anthropic, we barely get $22 billion. Anthropic, we barely get $22 billion. Anthropic, we barely get $22 billion. And the thing is, $22 billion is a large And the thing is, $22 billion is a large And the thing is, $22 billion is a large amount to you and me. It's not a large amount to you and me. It's not a large amount to you and me. It's not a large amount of money when you spent a amount of money when you spent a amount of money when you spent a trillion plus dollars. When you have trillion plus dollars. When you have trillion plus dollars. When you have anthropic and open AI with $1.1 trillion anthropic and open AI with $1.1 trillion anthropic and open AI with $1.1 trillion worth of cloud commitments and on top of worth of cloud commitments and on top of worth of cloud commitments and on top of that, how does this turn into a post.com that, how does this turn into a post.com that, how does this turn into a post.com bubble thing? A data center built today bubble thing? A data center built today bubble thing? A data center built today is going to be as expensive to run in is going to be as expensive to run in is going to be as expensive to run in 2050 as it is today unless there's some 2050 as it is today unless there's some 2050 as it is today unless there's some breakthrough in electricity. But again, breakthrough in electricity. But again, breakthrough in electricity. But again, that's not happening with AI. AI is not that's not happening with AI. AI is not that's not happening with AI. AI is not doing that unless there's some doing that unless there's some doing that unless there's some breakthrough in GPU technology. But we breakthrough in GPU technology. But we breakthrough in GPU technology. But we already have Broadcom, Nvidia, etched.

already have Broadcom, Nvidia, etched. already have Broadcom, Nvidia, etched. We have every major chip company ARM We have every major chip company ARM We have every major chip company ARM trying to do something about this. And trying to do something about this. And trying to do something about this. And no one seems to magically be able to no one seems to magically be able to no one seems to magically be able to make this profitable or indeed even less make this profitable or indeed even less make this profitable or indeed even less costly. Even Nvidia with Vera Rubin, costly. Even Nvidia with Vera Rubin, costly. Even Nvidia with Vera Rubin, their more expensive new GPU system. their more expensive new GPU system. their more expensive new GPU system. Even then, they're like, "Yeah, 10x more Even then, they're like, "Yeah, 10x more Even then, they're like, "Yeah, 10x more efficient. It's uh more dollars per efficient. It's uh more dollars per efficient. It's uh more dollars per megawatt." They're all koi about it. megawatt." They're all koi about it. megawatt." They're all koi about it. They don't just say, "Yeah, we worked They don't just say, "Yeah, we worked They don't just say, "Yeah, we worked with OpenAI and Anthropic and we found with OpenAI and Anthropic and we found with OpenAI and Anthropic and we found it reduced our cost by 50%." Easiest it reduced our cost by 50%." Easiest it reduced our cost by 50%." Easiest thing in the world if it was true. And thing in the world if it was true. And thing in the world if it was true. And that's because it's not happening. And that's because it's not happening. And that's because it's not happening. And this isn't a case where this isn't a case where this isn't a case where >> So are you saying there's not going to >> So are you saying there's not going to >> So are you saying there's not going to be the demand for let's say let's you be the demand for let's say let's you be the demand for let's say let's you know there's different types of AI know there's different types of AI know there's different types of AI generative AI we generative AI we generative AI we >> Yeah. And actually that's a good point >> Yeah. And actually that's a good point >> Yeah. And actually that's a good point to make. The reason they use the term to make. The reason they use the term to make. The reason they use the term artificial intelligence is so everyone artificial intelligence is so everyone artificial intelligence is so everyone would lump everything into it. would lump everything into it. would lump everything into it. >> They [clears throat] would lump uh >> They [clears throat] would lump uh >> They [clears throat] would lump uh protein folding nothing to do with LLMs. protein folding nothing to do with LLMs. protein folding nothing to do with LLMs. Robotics not LLM. Robotics not LLM. Robotics not LLM. >> Autonomous weapons even horrible as they >> Autonomous weapons even horrible as they >> Autonomous weapons even horrible as they are not LLMs because you couldn't trust are not LLMs because you couldn't trust are not LLMs because you couldn't trust them. But they've mushed everything into them. But they've mushed everything into them. But they've mushed everything into AI so that when you say, "Well, AI AI so that when you say, "Well, AI AI so that when you say, "Well, AI can't," they'll go, "Um, um, sir, you can't," they'll go, "Um, um, sir, you can't," they'll go, "Um, um, sir, you forgot to give us homework and also AI forgot to give us homework and also AI forgot to give us homework and also AI it's working on curing cancer." When it's working on curing cancer." When it's working on curing cancer." When it's just like, "No, that's not LLM.

it's just like, "No, that's not LLM. it's just like, "No, that's not LLM. Stop giving them credit." Stop giving them credit." Stop giving them credit." >> The similarity though is they all need >> The similarity though is they all need >> The similarity though is they all need GPUs, all these. GPUs, all these. GPUs, all these. >> And that's the funny thing. All those >> And that's the funny thing. All those >> And that's the funny thing. All those data centers that we're building, all of data centers that we're building, all of data centers that we're building, all of them are for just generative AI. They're them are for just generative AI. They're them are for just generative AI. They're not for all of the other stuff. They're not for all of the other stuff. They're not for all of the other stuff. They're not for the cool AI has been not for the cool AI has been not for the cool AI has been around for a long time. Google. A lot of around for a long time. Google. A lot of around for a long time. Google. A lot of the good stuff that comes out of Google the good stuff that comes out of Google the good stuff that comes out of Google from the search side is AI but from the search side is AI but from the search side is AI but pre-generative. pre-generative. pre-generative. >> How would you run the the type of AI >> How would you run the the type of AI >> How would you run the the type of AI that sits in a robot? Let's say one of that sits in a robot? Let's say one of that sits in a robot? Let's say one of the Optimus robots if you didn't have a the Optimus robots if you didn't have a the Optimus robots if you didn't have a GPU. GPU. GPU. >> So Matic Matic has this cleaning robot >> So Matic Matic has this cleaning robot >> So Matic Matic has this cleaning robot for example. That thing is not got a for example. That thing is not got a for example. That thing is not got a little GPU in it. What it has and may little GPU in it. What it has and may little GPU in it. What it has and may indeed have used some GPUs but no year indeed have used some GPUs but no year indeed have used some GPUs but no year as many as they need for generative AI as many as they need for generative AI as many as they need for generative AI to run the data feed training data into to run the data feed training data into to run the data feed training data into it so it's able to clean a house. But it so it's able to clean a house. But it so it's able to clean a house. But when the little buggers going around when the little buggers going around when the little buggers going around cleaning my floor, turdsly I call him, cleaning my floor, turdsly I call him, cleaning my floor, turdsly I call him, it goes around mopping my floor, it's it goes around mopping my floor, it's it goes around mopping my floor, it's not like burning money the whole time. not like burning money the whole time. not like burning money the whole time. But when it comes to these massive But when it comes to these massive But when it comes to these massive amount of data center, sighteline amount of data center, sighteline amount of data center, sighteline climate said in February there's 190 climate said in February there's 190 climate said in February there's 190 gawatts of data centers under in gawatts of data centers under in gawatts of data centers under in planning. Don't know about under planning. Don't know about under planning. Don't know about under construction that works out if about 12 construction that works out if about 12 construction that works out if about 12 million megawatt that's what like $1.6 million megawatt that's what like $1.6 million megawatt that's what like $1.6 trillion to3 trillion a year in annual trillion to3 trillion a year in annual trillion to3 trillion a year in annual demand you'd need for that. We don't demand you'd need for that. We don't demand you'd need for that. We don't even have $130 billion worth of annual even have $130 billion worth of annual even have $130 billion worth of annual demand. And people say, well, it will demand. And people say, well, it will demand. And people say, well, it will grow. how when most of the demand is grow. how when most of the demand is grow. how when most of the demand is coming from Amazon feeding money to open coming from Amazon feeding money to open coming from Amazon feeding money to open AAI or anthropic, Microsoft feeding AAI or anthropic, Microsoft feeding AAI or anthropic, Microsoft feeding money to OpenAI and Anthropic, Google money to OpenAI and Anthropic, Google money to OpenAI and Anthropic, Google feeding money to Open AI and anthrop feeding money to Open AI and anthrop feeding money to Open AI and anthrop well hasn't fed it to Open AI yet, but well hasn't fed it to Open AI yet, but well hasn't fed it to Open AI yet, but they're a pretty big customer, billions they're a pretty big customer, billions they're a pretty big customer, billions of dollars. The conside is that we are

of dollars. The conside is that we are of dollars. The conside is that we are building these effiges to capitalism, building these effiges to capitalism, building these effiges to capitalism, these giant GPU data centers, and people these giant GPU data centers, and people these giant GPU data centers, and people are being told, well, it's for AI, you are being told, well, it's for AI, you are being told, well, it's for AI, you know, the thing that's done all this know, the thing that's done all this know, the thing that's done all this other stuff that's unrelated. Or the other stuff that's unrelated. Or the other stuff that's unrelated. Or the worst thing I've seen is like, oh, you worst thing I've seen is like, oh, you worst thing I've seen is like, oh, you don't like you like online banking. don't like you like online banking. don't like you like online banking. Well, you do like data centers. There's Well, you do like data centers. There's Well, you do like data centers. There's a big difference between a data center a big difference between a data center a big difference between a data center for regular nonGPU compute for standing for regular nonGPU compute for standing for regular nonGPU compute for standing up a server, a content delivery system up a server, a content delivery system up a server, a content delivery system like Akami or something that brings the like Akami or something that brings the like Akami or something that brings the website to you or how Meta runs website to you or how Meta runs website to you or how Meta runs Facebook. That is not the same. It takes Facebook. That is not the same. It takes Facebook. That is not the same. It takes way less power, mostly CPUdriven way less power, mostly CPUdriven way less power, mostly CPUdriven compared to these giant GPU data centers compared to these giant GPU data centers compared to these giant GPU data centers that offer one thing, one thing only. that offer one thing, one thing only. that offer one thing, one thing only. >> But I was doing the the research and >> But I was doing the the research and >> But I was doing the the research and looking at some of these notes here. It looking at some of these notes here. It looking at some of these notes here. It does say that for tougher types of AI does say that for tougher types of AI does say that for tougher types of AI systems designed to solve concrete systems designed to solve concrete systems designed to solve concrete physics, biology, and spatial problems, physics, biology, and spatial problems, physics, biology, and spatial problems, they require some of the most intense they require some of the most intense they require some of the most intense data center infrastructure on the data center infrastructure on the data center infrastructure on the planet. planet. planet. >> Yeah. >> Yeah. >> Yeah. >> AI systems like Deep Mind's AlphaFold, >> AI systems like Deep Mind's AlphaFold, >> AI systems like Deep Mind's AlphaFold, the protein folding company the protein folding company the protein folding company >> used for genomic sequencing and climate >> used for genomic sequencing and climate >> used for genomic sequencing and climate forecasting, etc. run on high forecasting, etc. run on high forecasting, etc. run on high performance computing clusters. These performance computing clusters. These performance computing clusters. These require immense precision and continuous require immense precision and continuous require immense precision and continuous heavy computing data centers.

heavy computing data centers. heavy computing data centers. >> Yeah. Training the brains for >> Yeah. Training the brains for >> Yeah. Training the brains for self-driving cars requires billions of self-driving cars requires billions of self-driving cars requires billions of miles of simulated physics environments. miles of simulated physics environments. miles of simulated physics environments. The AI isn't generating text. It's The AI isn't generating text. It's The AI isn't generating text. It's learning to navigate 3D spaces and learning to navigate 3D spaces and learning to navigate 3D spaces and gravity and relies on data centers, gravity and relies on data centers, gravity and relies on data centers, >> right? And the thing is those data >> right? And the thing is those data >> right? And the thing is those data centers, they might have GPUs in them. centers, they might have GPUs in them. centers, they might have GPUs in them. We had GPUs used for this HPC, the high We had GPUs used for this HPC, the high We had GPUs used for this HPC, the high performance computing before generative performance computing before generative performance computing before generative AI. And yeah, that's how AI has been AI. And yeah, that's how AI has been AI. And yeah, that's how AI has been trained before. That's how Tesla did. trained before. That's how Tesla did. trained before. That's how Tesla did. believe they've had their own data believe they've had their own data believe they've had their own data centers when it comes to training the centers when it comes to training the centers when it comes to training the autopilot system for better or for autopilot system for better or for autopilot system for better or for worse. That's how we've done it before. worse. That's how we've done it before. worse. That's how we've done it before. Again, that is not why we're building Again, that is not why we're building Again, that is not why we're building these data centers. These data centers these data centers. These data centers these data centers. These data centers are being built to sell to AI generative are being built to sell to AI generative are being built to sell to AI generative AI companies to either train systems or AI companies to either train systems or AI companies to either train systems or run inference. These things are being run inference. These things are being run inference. These things are being built in this brainless way where it's built in this brainless way where it's built in this brainless way where it's just well actually maybe this is a good just well actually maybe this is a good just well actually maybe this is a good way of illustrating the con because way of illustrating the con because way of illustrating the con because everyone saw Google, Microsoft, Amazon everyone saw Google, Microsoft, Amazon everyone saw Google, Microsoft, Amazon and Meta give Nvidia over call it 800 and Meta give Nvidia over call it 800 and Meta give Nvidia over call it 800 something billion dollars something billion dollars something billion dollars because everyone saw that they went well because everyone saw that they went well because everyone saw that they went well they wouldn't do that for no reason.

they wouldn't do that for no reason. they wouldn't do that for no reason. They went we got to build more of these They went we got to build more of these They went we got to build more of these things. There must be all this demand. things. There must be all this demand. things. There must be all this demand. Even though the demand 70% or more of Even though the demand 70% or more of Even though the demand 70% or more of all that demand comes from these two all that demand comes from these two all that demand comes from these two companies who were funded by these three companies who were funded by these three companies who were funded by these three companies and that's the funny thing. companies and that's the funny thing. companies and that's the funny thing. The reason that they don't want to break The reason that they don't want to break The reason that they don't want to break out their AI revenues is because it will out their AI revenues is because it will out their AI revenues is because it will become alarmingly obvious that this was become alarmingly obvious that this was become alarmingly obvious that this was the case. It turns out that the only the case. It turns out that the only the case. It turns out that the only real big customers cuz it's not like real big customers cuz it's not like real big customers cuz it's not like they're building a few data centers. they're building a few data centers. they're building a few data centers. They're building trillion plus revenue They're building trillion plus revenue They're building trillion plus revenue potential. They believe they'll get potential. They believe they'll get potential. They believe they'll get speculative. It's entirely speculative. speculative. It's entirely speculative. speculative. It's entirely speculative. They're building it because they saw the They're building it because they saw the They're building it because they saw the biggest companies in the world buy a biggest companies in the world buy a biggest companies in the world buy a bunch of GPUs and they said, "I want in bunch of GPUs and they said, "I want in bunch of GPUs and they said, "I want in on that." They must have diverse on that." They must have diverse on that." They must have diverse customers, right? They wouldn't just customers, right? They wouldn't just customers, right? They wouldn't just have two unprofitable fail sons that have two unprofitable fail sons that have two unprofitable fail sons that they're propping up with. Christ, they're propping up with. Christ, they're propping up with. Christ, they've raised $217 billion just in they've raised $217 billion just in they've raised $217 billion just in 2026. 2026. 2026. >> So, we know that some of the biggest >> So, we know that some of the biggest >> So, we know that some of the biggest companies in the world are using AI, companies in the world are using AI, companies in the world are using AI, generative AI to write a lot of their generative AI to write a lot of their generative AI to write a lot of their code. code. code. >> Mhm. >> Mhm. >> Mhm. >> That is a great productivity gain for >> That is a great productivity gain for >> That is a great productivity gain for those companies, right? I mean, have you those companies, right? I mean, have you those companies, right? I mean, have you used Google or Facebook or Instagram or used Google or Facebook or Instagram or used Google or Facebook or Instagram or GitHub recently because they are GitHub recently because they are GitHub recently because they are catastrophically worse? Amazon Web catastrophically worse? Amazon Web catastrophically worse? Amazon Web Services went down multiple times Services went down multiple times Services went down multiple times because of their AI coding tool. How because of their AI coding tool. How because of their AI coding tool. How >> how is how is Google worse?

>> how is how is Google worse? >> how is how is Google worse? >> Well, I'll tell the story of a real >> Well, I'll tell the story of a real >> Well, I'll tell the story of a real guy called Preaggo Ragavan. guy called Preaggo Ragavan. guy called Preaggo Ragavan. Previously, one of the heads of ads at Previously, one of the heads of ads at Previously, one of the heads of ads at Google in 2019, Google called something Google in 2019, Google called something Google in 2019, Google called something called a code yellow, which is when they called a code yellow, which is when they called a code yellow, which is when they said, "We've got a problem." And it was said, "We've got a problem." And it was said, "We've got a problem." And it was material weakness in query numbers which material weakness in query numbers which material weakness in query numbers which means the amount of times that people means the amount of times that people means the amount of times that people were searching on Google search. Guy were searching on Google search. Guy were searching on Google search. Guy called Ben Gomes internal at Google then called Ben Gomes internal at Google then called Ben Gomes internal at Google then the head of Google search says wait a the head of Google search says wait a the head of Google search says wait a minute to increase this number of using minute to increase this number of using minute to increase this number of using Google more. Google more. Google more. >> Mhm. We're going to have to I mean you >> Mhm. We're going to have to I mean you >> Mhm. We're going to have to I mean you what you're suggesting would mean we what you're suggesting would mean we what you're suggesting would mean we give worse answers because if someone give worse answers because if someone give worse answers because if someone got the answer quickly that would reduce got the answer quickly that would reduce got the answer quickly that would reduce the amount of queries right and people the amount of queries right and people the amount of queries right and people at Google Shashi Tako was another at Google Shashi Tako was another at Google Shashi Tako was another engineer was saying yeah can we please engineer was saying yeah can we please engineer was saying yeah can we please tell Sunda this because this doesn't tell Sunda this because this doesn't tell Sunda this because this doesn't seem good. We can't just increase the seem good. We can't just increase the seem good. We can't just increase the amount of queries. That would just mean amount of queries. That would just mean amount of queries. That would just mean that people would have to search more that people would have to search more that people would have to search more which would make the product worse. which would make the product worse. which would make the product worse. >> But but it would make them more money. >> But but it would make them more money. >> But but it would make them more money. You saying you'd show them more ads. So You saying you'd show them more ads. So You saying you'd show them more ads. So if you're spending more time on Google if you're spending more time on Google if you're spending more time on Google because Google's work, because Google's work, because Google's work, >> but is this linked to AI doing code? >> but is this linked to AI doing code? >> but is this linked to AI doing code? >> Oh, I'll get there. So >> Oh, I'll get there. So >> Oh, I'll get there. So >> this is the problem is is that this guy >> this is the problem is is that this guy >> this is the problem is is that this guy called Pragar Ragavan who's the head of called Pragar Ragavan who's the head of called Pragar Ragavan who's the head of ads at the time was pushing pushing and ads at the time was pushing pushing and ads at the time was pushing pushing and saying, "No, we need to make more saying, "No, we need to make more saying, "No, we need to make more queries happen. Got to make it happen."

queries happen. Got to make it happen." queries happen. Got to make it happen." and Nick Fox who was there as well I and Nick Fox who was there as well I and Nick Fox who was there as well I believe was actually taking over Google believe was actually taking over Google believe was actually taking over Google search got to make them go up this is search got to make them go up this is search got to make them go up this is our new reality sometime in early 2020 our new reality sometime in early 2020 our new reality sometime in early 2020 propagar ragavan takes over Google propagar ragavan takes over Google propagar ragavan takes over Google search from then and this is this is search from then and this is this is search from then and this is this is what I believe can't prove it if you go what I believe can't prove it if you go what I believe can't prove it if you go and look around the various SEO sites and look around the various SEO sites and look around the various SEO sites such journal and the various forums such journal and the various forums such journal and the various forums Google stripped back a lot of the Google stripped back a lot of the Google stripped back a lot of the suppression of spammy sites so that suppression of spammy sites so that suppression of spammy sites so that people would be on Google more and then people would be on Google more and then people would be on Google more and then over the course of time Google wanted to over the course of time Google wanted to over the course of time Google wanted to create more queries and Google search create more queries and Google search create more queries and Google search became much worse. It's why people became much worse. It's why people became much worse. It's why people always do like plus Reddit or from always do like plus Reddit or from always do like plus Reddit or from Reddit or what have you. It's because Reddit or what have you. It's because Reddit or what have you. It's because the actual underlying search results of the actual underlying search results of the actual underlying search results of Google had got worse. And then Google had got worse. And then Google had got worse. And then Generative AI came along and Praagar, Generative AI came along and Praagar, Generative AI came along and Praagar, wouldn't you know, it gets put to run wouldn't you know, it gets put to run wouldn't you know, it gets put to run part of Gemini. And Google also was part of Gemini. And Google also was part of Gemini. And Google also was having trouble getting people back on having trouble getting people back on having trouble getting people back on Google. And what did they think they'd Google. And what did they think they'd Google. And what did they think they'd do? Well, everyone's talking about do? Well, everyone's talking about do? Well, everyone's talking about this AI thing. We'll just put it right this AI thing. We'll just put it right this AI thing. We'll just put it right at the top so people have to stay at at the top so people have to stay at at the top so people have to stay at Google. And actually, they'll use it Google. And actually, they'll use it Google. And actually, they'll use it more because instead of searching more because instead of searching more because instead of searching websites and doing that annoying thing websites and doing that annoying thing websites and doing that annoying thing where they click away from Google, where they click away from Google, where they click away from Google, they'll just only use Google. Instead of they'll just only use Google. Instead of they'll just only use Google. Instead of generating answers, by which I mean generating answers, by which I mean generating answers, by which I mean giving you search results you click giving you search results you click giving you search results you click through, now Google is the answer. Is it through, now Google is the answer. Is it through, now Google is the answer. Is it right? God know. It might tell you to right? God know. It might tell you to right? God know. It might tell you to eat rocks, might eat poisonous eat rocks, might eat poisonous eat rocks, might eat poisonous mushrooms. Maybe it'll give you a little mushrooms. Maybe it'll give you a little mushrooms. Maybe it'll give you a little few links you could click through. But few links you could click through. But few links you could click through. But the ideal situation was that AI was the the ideal situation was that AI was the the ideal situation was that AI was the ultimate form of Google's evil which was ultimate form of Google's evil which was ultimate form of Google's evil which was >> But I'm saying here I'm saying here but >> But I'm saying here I'm saying here but >> But I'm saying here I'm saying here but that's not the fact that coders could that's not the fact that coders could that's not the fact that coders could code on Google that's made Google worse.

code on Google that's made Google worse. code on Google that's made Google worse. That's human decisions have made it That's human decisions have made it That's human decisions have made it worse. worse. worse. >> Yes. And then there's the instability of >> Yes. And then there's the instability of >> Yes. And then there's the instability of Google's platform which is actually I Google's platform which is actually I Google's platform which is actually I should have probably led with that a should have probably led with that a should have probably led with that a problem across the whole tech industry. problem across the whole tech industry. problem across the whole tech industry. >> Okay. So you're saying that you're >> Okay. So you're saying that you're >> Okay. So you're saying that you're saying Google is going down more. saying Google is going down more. saying Google is going down more. >> Yes. Google is less stable. Google Docs >> Yes. Google is less stable. Google Docs >> Yes. Google is less stable. Google Docs is a bugfest right now and has been for is a bugfest right now and has been for is a bugfest right now and has been for a while. Google Sheets, same deal. And a while. Google Sheets, same deal. And a while. Google Sheets, same deal. And the thing is, you're right, I'm being a the thing is, you're right, I'm being a the thing is, you're right, I'm being a little unfair. This is everyone. It's little unfair. This is everyone. It's little unfair. This is everyone. It's the same with Microsoft. It's the same the same with Microsoft. It's the same the same with Microsoft. It's the same with Amazon. It's the same across. with Amazon. It's the same across. with Amazon. It's the same across. >> How do we quantify that outside of >> How do we quantify that outside of >> How do we quantify that outside of anecdotes? Like, is there a way to anecdotes? Like, is there a way to anecdotes? Like, is there a way to >> You're right. I mean, GitHub downtime is >> You're right. I mean, GitHub downtime is >> You're right. I mean, GitHub downtime is the best example. Amazon Web Services the best example. Amazon Web Services the best example. Amazon Web Services went down two or three times this year went down two or three times this year went down two or three times this year because of AI tools. And honestly, because of AI tools. And honestly, because of AI tools. And honestly, you're right. It is kind of hard to you're right. It is kind of hard to you're right. It is kind of hard to quantify outside of anecdotes. But I quantify outside of anecdotes. But I quantify outside of anecdotes. But I challenge anyone listening to this. Go challenge anyone listening to this. Go challenge anyone listening to this. Go and use a website these days and tell me and use a website these days and tell me and use a website these days and tell me how well it works. Tell me how buggy it how well it works. Tell me how buggy it how well it works. Tell me how buggy it is. Tell me how many problems even with is. Tell me how many problems even with is. Tell me how many problems even with my iPhone. The supposed best UX in town. my iPhone. The supposed best UX in town. my iPhone. The supposed best UX in town. Even the iPhone is a flipping mess these Even the iPhone is a flipping mess these Even the iPhone is a flipping mess these days. days. days. >> Okay, so the research says the short >> Okay, so the research says the short >> Okay, so the research says the short answer is yes. Tech downtime and answer is yes. Tech downtime and answer is yes. Tech downtime and software outages have demonstrabably software outages have demonstrabably software outages have demonstrabably increased over the last few years and increased over the last few years and increased over the last few years and industry data points directly to the industry data points directly to the industry data points directly to the explosion of AI assisted coding as a explosion of AI assisted coding as a explosion of AI assisted coding as a primary culprit. The problem is hitting primary culprit. The problem is hitting primary culprit. The problem is hitting the tech industry from two entirely the tech industry from two entirely the tech industry from two entirely different directions. The code itself is different directions. The code itself is different directions. The code itself is getting buggier and the sheer volume of getting buggier and the sheer volume of getting buggier and the sheer volume of AI activity is literally crashing the AI activity is literally crashing the AI activity is literally crashing the underlying infrastructure. Interesting.

underlying infrastructure. Interesting. underlying infrastructure. Interesting. >> Yeah, that's because GitHub people are >> Yeah, that's because GitHub people are >> Yeah, that's because GitHub people are just writing a bunch of code, pushing just writing a bunch of code, pushing just writing a bunch of code, pushing it, and thus there's just more code on it, and thus there's just more code on it, and thus there's just more code on there. there. there. >> That's interesting. >> That's interesting. >> That's interesting. >> Yeah, it's it's a real mess as well >> Yeah, it's it's a real mess as well >> Yeah, it's it's a real mess as well because because because open source has had this problem as well open source has had this problem as well open source has had this problem as well because it's well-meaning people. because it's well-meaning people. because it's well-meaning people. They're like, I learned a bit of code They're like, I learned a bit of code They're like, I learned a bit of code with an LLM. I'm going to go out and do with an LLM. I'm going to go out and do with an LLM. I'm going to go out and do some stuff. I'm going to make this some stuff. I'm going to make this some stuff. I'm going to make this project better. And these people barely project better. And these people barely project better. And these people barely understand what they're shipping. Or understand what they're shipping. Or understand what they're shipping. Or maybe they understand a bit of code and maybe they understand a bit of code and maybe they understand a bit of code and they say, "Oh, Dunning Krueger, this they say, "Oh, Dunning Krueger, this they say, "Oh, Dunning Krueger, this I'm going to I'm just I'm going to I'm just I'm going to I'm just like, I can understand some of this." like, I can understand some of this." like, I can understand some of this." And now the code's all written and just And now the code's all written and just And now the code's all written and just push it right now. So GitHub is flooded push it right now. So GitHub is flooded push it right now. So GitHub is flooded with AI code. with AI code. with AI code. >> This sounds like it's making humans >> This sounds like it's making humans >> This sounds like it's making humans complacent. complacent. complacent. >> It is >> It is >> It is >> because we're going, "Okay, look, I let >> because we're going, "Okay, look, I let >> because we're going, "Okay, look, I let it write the the code for the last 100 it write the the code for the last 100 it write the the code for the last 100 lines and it was broadly right. So the lines and it was broadly right. So the lines and it was broadly right. So the next 100 lines, I won't check them as next 100 lines, I won't check them as next 100 lines, I won't check them as much." much." much." >> Yeah. Yeah. And that's human nature is >> Yeah. Yeah. And that's human nature is >> Yeah. Yeah. And that's human nature is to get sort of to take shortcuts to to get sort of to take shortcuts to to get sort of to take shortcuts to spend less energy on an activity if you spend less energy on an activity if you spend less energy on an activity if you can right but the AI's still making the can right but the AI's still making the can right but the AI's still making the mistake and we're still making all the mistake and we're still making all the mistake and we're still making all the promises of AI that's the thing this promises of AI that's the thing this promises of AI that's the thing this thing is meant to be this autonomous per thing is meant to be this autonomous per thing is meant to be this autonomous per you say it can't be perfect I don't know you say it can't be perfect I don't know you say it can't be perfect I don't know based on what Samman has been saying for based on what Samman has been saying for based on what Samman has been saying for the last few years clammy Sammy has been the last few years clammy Sammy has been the last few years clammy Sammy has been promising the world saying this will promising the world saying this will promising the world saying this will replace software engineers Dario replace software engineers Dario replace software engineers Dario Ammedday Wario himself has been saying Ammedday Wario himself has been saying Ammedday Wario himself has been saying oh yeah 50% of white collar labor is oh yeah 50% of white collar labor is oh yeah 50% of white collar labor is going to go away in the next few years.

going to go away in the next few years. going to go away in the next few years. These people are promising the world. These people are promising the world. These people are promising the world. Again, if they were saying it would be Again, if they were saying it would be Again, if they were saying it would be smaller and they were like, yeah, it smaller and they were like, yeah, it smaller and they were like, yeah, it does have issues and we must be none of does have issues and we must be none of does have issues and we must be none of this, oh, what if it wakes up and it's this, oh, what if it wakes up and it's this, oh, what if it wakes up and it's super powerful. Just like, yeah, it's super powerful. Just like, yeah, it's super powerful. Just like, yeah, it's probabilistic. It's going to make probabilistic. It's going to make probabilistic. It's going to make mistakes and if you don't know what mistakes and if you don't know what mistakes and if you don't know what you're doing, you don't really know what you're doing, you don't really know what you're doing, you don't really know what you're looking at, you're going to miss you're looking at, you're going to miss you're looking at, you're going to miss those mistakes and it's going to get those mistakes and it's going to get those mistakes and it's going to get multiplicatively worse as you go when multiplicatively worse as you go when multiplicatively worse as you go when you don't know what you're doing. So you don't know what you're doing. So you don't know what you're doing. So yeah, human nature is part of it, but so yeah, human nature is part of it, but so yeah, human nature is part of it, but so is the marketing. So are the promises. is the marketing. So are the promises. is the marketing. So are the promises. 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technology. technology. >> Yes. >> Yes. >> Yes. >> I sat here with Dra from Uber and he was >> I sat here with Dra from Uber and he was >> I sat here with Dra from Uber and he was saying that I think in a couple of years saying that I think in a couple of years saying that I think in a couple of years time time time we won't need drivers um for Uber we won't need drivers um for Uber we won't need drivers um for Uber because the cars will drive themselves because the cars will drive themselves because the cars will drive themselves like they'll be fully autonomous. like they'll be fully autonomous. like they'll be fully autonomous. >> And I think if I'm not mistaken >> And I think if I'm not mistaken >> And I think if I'm not mistaken driving is one of the biggest driving is one of the biggest driving is one of the biggest professions on planet earth. So when you professions on planet earth. So when you professions on planet earth. So when you hear people when you hear these CEOs hear people when you hear these CEOs hear people when you hear these CEOs saying that there will be job disruption saying that there will be job disruption saying that there will be job disruption >> you say that they are not telling the >> you say that they are not telling the >> you say that they are not telling the truth. truth. truth. >> Yes. Or they're guessing in a way that's >> Yes. Or they're guessing in a way that's >> Yes. Or they're guessing in a way that's very good for them. Think about it from very good for them. Think about it from very good for them. Think about it from perspective of Microsoft Sachin Nadella. perspective of Microsoft Sachin Nadella. perspective of Microsoft Sachin Nadella. He's not going to be like yeah we don't He's not going to be like yeah we don't He's not going to be like yeah we don't know if this is going to work mate. Of know if this is going to work mate. Of know if this is going to work mate. Of course he's going to talk his book and course he's going to talk his book and course he's going to talk his book and he's going to say yeah this is going to he's going to say yeah this is going to he's going to say yeah this is going to replace all workers. It's going to be replace all workers. It's going to be replace all workers. It's going to be amazing. He it's going to be so amazing. He it's going to be so amazing. He it's going to be so powerful. And then he'll change his tune powerful. And then he'll change his tune powerful. And then he'll change his tune and say actually it's not going to and say actually it's not going to and say actually it's not going to replace workers. that make him more replace workers. that make him more replace workers. that make him more powerful because the things aren't powerful because the things aren't powerful because the things aren't catching up. Dor from Uber for example, catching up. Dor from Uber for example, catching up. Dor from Uber for example, of course he's going to say if this of course he's going to say if this of course he's going to say if this happens then that would be good for Uber happens then that would be good for Uber happens then that would be good for Uber because Uber would just become an because Uber would just become an because Uber would just become an autonomous taxi service. There's a autonomous taxi service. There's a autonomous taxi service. There's a reason that Whimo's taken I I find Whimo reason that Whimo's taken I I find Whimo reason that Whimo's taken I I find Whimo fascinating. I think that it's fascinating. I think that it's fascinating. I think that it's really cool. I think there are really cool. I think there are really cool. I think there are socioeconomic problems that will come socioeconomic problems that will come socioeconomic problems that will come from it. I think there are actual real from it. I think there are actual real from it. I think there are actual real problems that will emerge and also problems that will emerge and also problems that will emerge and also >> what kind of problems?

>> what kind of problems? >> what kind of problems? >> Well, I mean socioeconomically there are >> Well, I mean socioeconomically there are >> Well, I mean socioeconomically there are like you said one of the largest like you said one of the largest like you said one of the largest employment centers in the world. I mean employment centers in the world. I mean employment centers in the world. I mean just the economics of cabs will fall just the economics of cabs will fall just the economics of cabs will fall apart but again we are nowhere nowhere apart but again we are nowhere nowhere apart but again we are nowhere nowhere nowhere near that. We're not even close. nowhere near that. We're not even close. nowhere near that. We're not even close. Whimo has had to do the smallest Whimo has had to do the smallest Whimo has had to do the smallest rollouts and the most control things rollouts and the most control things rollouts and the most control things because the problem with pretty much because the problem with pretty much because the problem with pretty much every AI system but especially driving every AI system but especially driving every AI system but especially driving is not the getting 95% of the way. It's is not the getting 95% of the way. It's is not the getting 95% of the way. It's those edge cases. It's raining which is those edge cases. It's raining which is those edge cases. It's raining which is a big problem for them in San Francisco. a big problem for them in San Francisco. a big problem for them in San Francisco. It's a kid runs across the road but It's a kid runs across the road but It's a kid runs across the road but they're wearing a high viz thing. Does they're wearing a high viz thing. Does they're wearing a high viz thing. Does it even notice it's a child? Again, this it even notice it's a child? Again, this it even notice it's a child? Again, this is a really interesting but very very is a really interesting but very very is a really interesting but very very applicable example of uh the right applicable example of uh the right applicable example of uh the right comparison to be made shouldn't be comparison to be made shouldn't be comparison to be made shouldn't be autonomous vehicles versus perfection. autonomous vehicles versus perfection. autonomous vehicles versus perfection. It should be autonomous vehicles versus It should be autonomous vehicles versus It should be autonomous vehicles versus human drivers. I mean, I don't know if I human drivers. I mean, I don't know if I human drivers. I mean, I don't know if I agree because a human driver might make agree because a human driver might make agree because a human driver might make mistakes, sure, but again, not an expert mistakes, sure, but again, not an expert mistakes, sure, but again, not an expert in autonomous cars. Just want to be in autonomous cars. Just want to be in autonomous cars. Just want to be clear. But if we're pushing autonomous clear. But if we're pushing autonomous clear. But if we're pushing autonomous cars out there willy-nilly and we're not cars out there willy-nilly and we're not cars out there willy-nilly and we're not doing so in extremely controlled doing so in extremely controlled doing so in extremely controlled environments, those edge cases will environments, those edge cases will environments, those edge cases will multiply and be dangerous. Yeah, they multiply and be dangerous. Yeah, they multiply and be dangerous. Yeah, they might be better at human drivers in some might be better at human drivers in some might be better at human drivers in some ways, but they might also I was in Vegas ways, but they might also I was in Vegas ways, but they might also I was in Vegas the other day and I was in a hotel and I the other day and I was in a hotel and I the other day and I was in a hotel and I watched a bunch of Zuk's cars just get watched a bunch of Zuk's cars just get watched a bunch of Zuk's cars just get stuck.

stuck. stuck. >> They're autonomous cars. >> They're autonomous cars. >> They're autonomous cars. >> Yeah, they these weird boxy things. They >> Yeah, they these weird boxy things. They >> Yeah, they these weird boxy things. They just blocked the exit. They just all just blocked the exit. They just all just blocked the exit. They just all kind of lined up and just fell asleep. I kind of lined up and just fell asleep. I kind of lined up and just fell asleep. I saw the same thing actually happen saw the same thing actually happen saw the same thing actually happen outside of a hotel when I got out of a outside of a hotel when I got out of a outside of a hotel when I got out of a Whimo in San Francisco. Just stopped at Whimo in San Francisco. Just stopped at Whimo in San Francisco. Just stopped at the and then a bunch of cars and another the and then a bunch of cars and another the and then a bunch of cars and another Whimo got stuck behind it. And these are Whimo got stuck behind it. And these are Whimo got stuck behind it. And these are kind of kind of kind of >> I've seen some human bad drivers as >> I've seen some human bad drivers as >> I've seen some human bad drivers as well. I I agree, but it's just we have well. I I agree, but it's just we have well. I I agree, but it's just we have control over deploying these bad or good control over deploying these bad or good control over deploying these bad or good drivers. We have an ability to roll them drivers. We have an ability to roll them drivers. We have an ability to roll them out slowly, which is exactly what we out slowly, which is exactly what we out slowly, which is exactly what we should do. I'm not saying autonomous should do. I'm not saying autonomous should do. I'm not saying autonomous cars are bad. I'm saying we need to be cars are bad. I'm saying we need to be cars are bad. I'm saying we need to be so so so careful and treat them as so so so careful and treat them as so so so careful and treat them as guilty and pro till proven innocent guilty and pro till proven innocent guilty and pro till proven innocent because we can prove and also they have because we can prove and also they have because we can prove and also they have people overlooking them. They actually people overlooking them. They actually people overlooking them. They actually have people monitoring the roots. It is have people monitoring the roots. It is have people monitoring the roots. It is something they cannot rush out and it something they cannot rush out and it something they cannot rush out and it doesn't seem like they're rushing it, doesn't seem like they're rushing it, doesn't seem like they're rushing it, which is good. and they're not promising which is good. and they're not promising which is good. and they're not promising the world. the world. the world. >> I do agree. Listen, I I'm a big fan of a >> I do agree. Listen, I I'm a big fan of a >> I do agree. Listen, I I'm a big fan of a big fan of taxi drivers generally in big fan of taxi drivers generally in big fan of taxi drivers generally in part because I spend a lot of time in part because I spend a lot of time in part because I spend a lot of time in taxis and I think I'm not just getting taxis and I think I'm not just getting taxis and I think I'm not just getting in there because I want to get to from A in there because I want to get to from A in there because I want to get to from A to B. I'm getting in there for lots of to B. I'm getting in there for lots of to B. I'm getting in there for lots of other reasons. other reasons. other reasons. >> Yeah. >> Yeah. >> Yeah. >> However, when I look at the stats >> However, when I look at the stats >> However, when I look at the stats >> around what is more dangerous >> around what is more dangerous >> around what is more dangerous >> driving myself or having an autonomous >> driving myself or having an autonomous >> driving myself or having an autonomous vehicle drive me, there's an 68% lower vehicle drive me, there's an 68% lower vehicle drive me, there's an 68% lower overall crash involvement rate when overall crash involvement rate when overall crash involvement rate when you're in an an autonomous vehicle. Mhm.

you're in an an autonomous vehicle. Mhm. you're in an an autonomous vehicle. Mhm. >> Autonomous vehicles experience roughly >> Autonomous vehicles experience roughly >> Autonomous vehicles experience roughly 2.1 police reported crashes per million 2.1 police reported crashes per million 2.1 police reported crashes per million miles compared to humans that are at miles compared to humans that are at miles compared to humans that are at roughly 4.68 per million miles. So, a roughly 4.68 per million miles. So, a roughly 4.68 per million miles. So, a 55% reduction when you get in an 55% reduction when you get in an 55% reduction when you get in an autonomous vehicle. And autonomous autonomous vehicle. And autonomous autonomous vehicle. And autonomous vehicles show an 80 to 81% reduction in vehicles show an 80 to 81% reduction in vehicles show an 80 to 81% reduction in crashes resulting in injuries versus crashes resulting in injuries versus crashes resulting in injuries versus human drivers. human drivers. human drivers. >> Uhhuh. >> Uhhuh. >> Uhhuh. >> So, you're 85% less likely to be >> So, you're 85% less likely to be >> So, you're 85% less likely to be involved in a single vehicle crash like involved in a single vehicle crash like involved in a single vehicle crash like hitting a wall or a tree if you're an hitting a wall or a tree if you're an hitting a wall or a tree if you're an autonomous vehicle autonomous vehicle autonomous vehicle >> versus being driven by I agree. But >> versus being driven by I agree. But >> versus being driven by I agree. But >> so it's safer >> so it's safer >> so it's safer >> in also that data is what's the sample >> in also that data is what's the sample >> in also that data is what's the sample size of human drivers? I mean we've got size of human drivers? I mean we've got size of human drivers? I mean we've got many many many many many many more years many many many many many many more years many many many many many many more years of drivers and many many many more years of drivers and many many many more years of drivers and many many many more years of accidents and also man does that not of accidents and also man does that not of accidents and also man does that not have anything to do with generative AI. have anything to do with generative AI. have anything to do with generative AI. If we were just talking about that be If we were just talking about that be If we were just talking about that be having a different conversation. having a different conversation. having a different conversation. >> I guess the question here was really >> I guess the question here was really >> I guess the question here was really around job disruption. Like you know we around job disruption. Like you know we around job disruption. Like you know we we look across industries and we go we look across industries and we go we look across industries and we go driving is a massive profession. Is driving is a massive profession. Is driving is a massive profession. Is there going to be job disruption because there going to be job disruption because there going to be job disruption because cars can now drive themselves? If we cars can now drive themselves? If we cars can now drive themselves? If we think about white collar, you know, think about white collar, you know, think about white collar, you know, jobs, you know, lawyers and accountants, jobs, you know, lawyers and accountants, jobs, you know, lawyers and accountants, people sit here and they tell me that people sit here and they tell me that people sit here and they tell me that lawyers and accountants would the lawyers and accountants would the lawyers and accountants would the profession, right? I should say some of profession, right? I should say some of profession, right? I should say some of the skills within the profession will be the skills within the profession will be the skills within the profession will be relegated to AIS to do.

relegated to AIS to do. relegated to AIS to do. >> Here's the thing. Lawyers, for example, >> Here's the thing. Lawyers, for example, >> Here's the thing. Lawyers, for example, great example. Always hearing great example. Always hearing great example. Always hearing legal partners talking about AI. Never legal partners talking about AI. Never legal partners talking about AI. Never the associates. The associates are the the associates. The associates are the the associates. The associates are the ones that go out and find the president. ones that go out and find the president. ones that go out and find the president. They're the ones that go and do the They're the ones that go and do the They're the ones that go and do the grunt work. They're the ones who are grunt work. They're the ones who are grunt work. They're the ones who are pulling motions half the time. The pulling motions half the time. The pulling motions half the time. The partner is the one that might be the partner is the one that might be the partner is the one that might be the litigant. It may be the client facing, litigant. It may be the client facing, litigant. It may be the client facing, but the ones that are actually doing the but the ones that are actually doing the but the ones that are actually doing the day-to-day work. I'm not hearing from day-to-day work. I'm not hearing from day-to-day work. I'm not hearing from them. I'm not hearing associates being them. I'm not hearing associates being them. I'm not hearing associates being like, "This is awesome." I'm like, "This is awesome." I'm like, "This is awesome." I'm hearing a bunch of well- paid people hearing a bunch of well- paid people hearing a bunch of well- paid people that have sat on Chat GPT and gone, that have sat on Chat GPT and gone, that have sat on Chat GPT and gone, "Yeah, yeah, I'm the greatest lawyer "Yeah, yeah, I'm the greatest lawyer "Yeah, yeah, I'm the greatest lawyer ever." They're not the ones that I want ever." They're not the ones that I want ever." They're not the ones that I want to hear from the actual workers. White to hear from the actual workers. White to hear from the actual workers. White collar labor disruption is not collar labor disruption is not collar labor disruption is not happening. Open AAI had a study that happening. Open AAI had a study that happening. Open AAI had a study that came out I think like a week ago that came out I think like a week ago that came out I think like a week ago that said there was no corre connection said there was no corre connection said there was no corre connection between spending on AI tokens and between spending on AI tokens and between spending on AI tokens and revenue per employee. Like this is open revenue per employee. Like this is open revenue per employee. Like this is open and that's and that's and that's >> what does that mean? Could you explain >> what does that mean? Could you explain >> what does that mean? Could you explain that to me? that to me? that to me? >> As in the more tokens you spend has no >> As in the more tokens you spend has no >> As in the more tokens you spend has no no correlation at all with the amount of no correlation at all with the amount of no correlation at all with the amount of money you make. It's the second report money you make. It's the second report money you make. It's the second report they've put out. The other one was like they've put out. The other one was like they've put out. The other one was like hallucinations are mathematically hallucinations are mathematically hallucinations are mathematically guaranteed kind of almost the one thing guaranteed kind of almost the one thing guaranteed kind of almost the one thing I respect about that company that I respect about that company that I respect about that company that occasion they just put out a study. It's occasion they just put out a study. It's occasion they just put out a study. It's like, yeah, kind of sucks.

like, yeah, kind of sucks. like, yeah, kind of sucks. [clears throat] But the people that are [clears throat] But the people that are [clears throat] But the people that are having their lives disrupted work-wise having their lives disrupted work-wise having their lives disrupted work-wise are art directors. It's people, art are art directors. It's people, art are art directors. It's people, art directors, transcribers, translators, directors, transcribers, translators, directors, transcribers, translators, who have bosses that don't care about who have bosses that don't care about who have bosses that don't care about the output. It's what they consider the output. It's what they consider the output. It's what they consider cheap work. And the problem is is those cheap work. And the problem is is those cheap work. And the problem is is those people would have automated your work people would have automated your work people would have automated your work away anyway. They would have sold it. away anyway. They would have sold it. away anyway. They would have sold it. They would have taken the cheapest for They would have taken the cheapest for They would have taken the cheapest for they would have sold it to the global they would have sold it to the global they would have sold it to the global self. They would have taken the self. They would have taken the self. They would have taken the shittiest option they could. That is shittiest option they could. That is shittiest option they could. That is something that AI is doing. And again, something that AI is doing. And again, something that AI is doing. And again, those people are not paying the actual those people are not paying the actual those people are not paying the actual cost of AI. They're using a cost of AI. They're using a cost of AI. They're using a subscription. The actual white collar subscription. The actual white collar subscription. The actual white collar labor force might have some things that labor force might have some things that labor force might have some things that are slightly changing, but there is no are slightly changing, but there is no are slightly changing, but there is no evidence of like productivity gains. In evidence of like productivity gains. In evidence of like productivity gains. In fact, if there were, they would be fact, if there were, they would be fact, if there were, they would be screaming it from the rooftops. There screaming it from the rooftops. There screaming it from the rooftops. There was an Oxford economics study last year was an Oxford economics study last year was an Oxford economics study last year where it's like, oh, young people are where it's like, oh, young people are where it's like, oh, young people are finding less jobs because of AI. We finding less jobs because of AI. We finding less jobs because of AI. We actually read the study, which multiple actually read the study, which multiple actually read the study, which multiple journalists did not. It was a single journalists did not. It was a single journalists did not. It was a single line that said, "Yeah, we saw some line that said, "Yeah, we saw some line that said, "Yeah, we saw some correlation." Didn't give a number. correlation." Didn't give a number. correlation." Didn't give a number. Didn't actually say what the correlation Didn't actually say what the correlation Didn't actually say what the correlation was. We are so conditioned to believe was. We are so conditioned to believe was. We are so conditioned to believe that the rich and powerful know what that the rich and powerful know what that the rich and powerful know what they're doing that we internalize these they're doing that we internalize these they're doing that we internalize these narratives about like, well, previous narratives about like, well, previous narratives about like, well, previous booms lost a lot of money. Well, booms lost a lot of money. Well, booms lost a lot of money. Well, technology takes time to do stuff. And technology takes time to do stuff. And technology takes time to do stuff. And they are intentionally playing on those they are intentionally playing on those they are intentionally playing on those mythologies. They are playing on these mythologies. They are playing on these mythologies. They are playing on these knowing that journalists, analysts, knowing that journalists, analysts, knowing that journalists, analysts, investors will believe them. And this is investors will believe them. And this is investors will believe them. And this is partly because our our realities are partly because our our realities are partly because our our realities are defined by stock prices. Because the defined by stock prices. Because the defined by stock prices. Because the stock prices of these companies went up, stock prices of these companies went up, stock prices of these companies went up, we're like, "Oh, look, it must be we're like, "Oh, look, it must be we're like, "Oh, look, it must be working, right?"

working, right?" working, right?" >> Both of those things you said were true, >> Both of those things you said were true, >> Both of those things you said were true, though, right? Like that previous though, right? Like that previous though, right? Like that previous technologies didn't make money at the technologies didn't make money at the technologies didn't make money at the start and you The other one you said was start and you The other one you said was start and you The other one you said was um they'll get better. um they'll get better. um they'll get better. >> But that's the thing. Okay. Because >> But that's the thing. Okay. Because >> But that's the thing. Okay. Because another thing got better, this will get another thing got better, this will get another thing got better, this will get better. better. better. >> No, but there's there's got to be >> No, but there's there's got to be >> No, but there's there's got to be something that they're saying that is something that they're saying that is something that they're saying that is fundamentally not true because those are fundamentally not true because those are fundamentally not true because those are two true statements that okay, two true statements that okay, two true statements that okay, technology often starts technology often starts technology often starts >> I know. I get what you mean. What they >> I know. I get what you mean. What they >> I know. I get what you mean. What they are fundamentally misleading people are fundamentally misleading people are fundamentally misleading people about is how possible it is. How many about is how possible it is. How many about is how possible it is. How many actual signs they have because they actual signs they have because they actual signs they have because they don't have the signs. If they had the don't have the signs. If they had the don't have the signs. If they had the signs as in the signs of this getting signs as in the signs of this getting signs as in the signs of this getting cheaper as in the signs of this being cheaper as in the signs of this being cheaper as in the signs of this being able to autonomously do work without the able to autonomously do work without the able to autonomously do work without the Rub Goldberg machine and even then in a Rub Goldberg machine and even then in a Rub Goldberg machine and even then in a reliable way that was making the reliable way that was making the reliable way that was making the customer more money being productive in customer more money being productive in customer more money being productive in a way you can say with your whole chest a way you can say with your whole chest a way you can say with your whole chest without a series of asterisks and that's without a series of asterisks and that's without a series of asterisks and that's how it is across the board. The people how it is across the board. The people how it is across the board. The people that are most excited about this, that are most excited about this, that are most excited about this, psychopaths on Twitter in many cases are psychopaths on Twitter in many cases are psychopaths on Twitter in many cases are people that I believe there really are people that I believe there really are people that I believe there really are some I'm sorry, there are some people on some I'm sorry, there are some people on some I'm sorry, there are some people on Twitter because the other thing about Twitter because the other thing about Twitter because the other thing about this is this is really unique to the AI this is this is really unique to the AI this is this is really unique to the AI industry. I've never seen it any other industry. I've never seen it any other industry. I've never seen it any other industry outside of maybe like sports industry outside of maybe like sports industry outside of maybe like sports teams. The attachment that some people teams. The attachment that some people teams. The attachment that some people online have to these companies. If you online have to these companies. If you online have to these companies. If you dare dare to criticize anthropic, it's dare dare to criticize anthropic, it's dare dare to criticize anthropic, it's almost this religious attachment. Good almost this religious attachment. Good almost this religious attachment. Good example was this week Bloomberg reported example was this week Bloomberg reported example was this week Bloomberg reported that OpenAI was on track to hit $40 that OpenAI was on track to hit $40 that OpenAI was on track to hit $40 billion in annualized revenue. Month billion in annualized revenue. Month billion in annualized revenue. Month times 12, four weeks times 13, we don't times 12, four weeks times 13, we don't times 12, four weeks times 13, we don't know. They don't define it. I saw know. They don't define it. I saw know. They don't define it. I saw multiple people and I going actually multiple people and I going actually multiple people and I going actually it's 60 billion. It's actually 60 it's 60 billion. It's actually 60 it's 60 billion. It's actually 60 billion. I heard from someone it is like billion. I heard from someone it is like billion. I heard from someone it is like a cult and it's a cult of software

a cult and it's a cult of software a cult and it's a cult of software driven around growth and this idea that driven around growth and this idea that driven around growth and this idea that by backing the right horse you will have by backing the right horse you will have by backing the right horse you will have some grand thing and open AI in some grand thing and open AI in some grand thing and open AI in particular in particular Mr. Baltman particular in particular Mr. Baltman particular in particular Mr. Baltman they have been fermenting this that Tibo they have been fermenting this that Tibo they have been fermenting this that Tibo as well the Tibbo the one of the guys at as well the Tibbo the one of the guys at as well the Tibbo the one of the guys at uh OpenAI they ferment this thing online uh OpenAI they ferment this thing online uh OpenAI they ferment this thing online they build this kind of parasocial they build this kind of parasocial they build this kind of parasocial relationship with both the large relationship with both the large relationship with both the large language model themselves and the language model themselves and the language model themselves and the companies and one's allegiance to the companies and one's allegiance to the companies and one's allegiance to the companies is so important it's truly companies is so important it's truly companies is so important it's truly vile if only these people gave a vile if only these people gave a vile if only these people gave a about I don't know Medicare for all or about I don't know Medicare for all or about I don't know Medicare for all or poverty or thing like actual problems in poverty or thing like actual problems in poverty or thing like actual problems in the world versus are we buying enough the world versus are we buying enough the world versus are we buying enough GPUs Do you know what's interesting is GPUs Do you know what's interesting is GPUs Do you know what's interesting is some of what your narrative some of what your narrative some of what your narrative one would argue actually helps them. one would argue actually helps them. one would argue actually helps them. How? Because you know the AI doomers How? Because you know the AI doomers How? Because you know the AI doomers that have come here and told you know that have come here and told you know that have come here and told you know some of the original founding fathers of some of the original founding fathers of some of the original founding fathers of AI like Jeffrey Hinton have told me that AI like Jeffrey Hinton have told me that AI like Jeffrey Hinton have told me that what they're building is highly highly what they're building is highly highly what they're building is highly highly dangerous and that it will be dangerous and that it will be dangerous and that it will be fundamentally disruptive to society. And fundamentally disruptive to society. And fundamentally disruptive to society. And it's interesting because some of the it's interesting because some of the it's interesting because some of the CEOs who you've mentioned, their CEOs who you've mentioned, their CEOs who you've mentioned, their historical narrative was also, by the historical narrative was also, by the historical narrative was also, by the way, this is really dangerous way, this is really dangerous way, this is really dangerous and there is a significant chance it and there is a significant chance it and there is a significant chance it could f we could up the planet.

could f we could up the planet. could f we could up the planet. >> And what we've seen is this slow pivot >> And what we've seen is this slow pivot >> And what we've seen is this slow pivot away from it because now they're getting away from it because now they're getting away from it because now they're getting booed and they're being attacked. booed and they're being attacked. booed and they're being attacked. There've been this slow pivot away from There've been this slow pivot away from There've been this slow pivot away from it. And the pivot almost sounds a little it. And the pivot almost sounds a little it. And the pivot almost sounds a little bit like your narrative. bit like your narrative. bit like your narrative. >> It now sounds like actually no, it's not >> It now sounds like actually no, it's not >> It now sounds like actually no, it's not going to change anything and you're all going to change anything and you're all going to change anything and you're all going to be fine. And it's now there's going to be fine. And it's now there's going to be fine. And it's now there's just not it's nah it's not dangerous at just not it's nah it's not dangerous at just not it's nah it's not dangerous at all. all. all. >> But that's the funny thing >> But that's the funny thing >> But that's the funny thing >> and that's why I'm saying like you're >> and that's why I'm saying like you're >> and that's why I'm saying like you're you're not they I actually think there you're not they I actually think there you're not they I actually think there might be a couple PR people at these big might be a couple PR people at these big might be a couple PR people at these big AI companies thinking thank god for Ed AI companies thinking thank god for Ed AI companies thinking thank god for Ed some [laughter] of it because you're some [laughter] of it because you're some [laughter] of it because you're like you're saying actually don't worry like you're saying actually don't worry like you're saying actually don't worry everything's going to be fine. It's not everything's going to be fine. It's not everything's going to be fine. It's not going to take your job. It's not going going to take your job. It's not going going to take your job. It's not going to disrupt the economy. It's just a fad. to disrupt the economy. It's just a fad. to disrupt the economy. It's just a fad. There's no technology. And I think they There's no technology. And I think they There's no technology. And I think they don't think that. don't think that. don't think that. >> Here's the thing. I think Alman and >> Here's the thing. I think Alman and >> Here's the thing. I think Alman and Amday are some of the most deeply Amday are some of the most deeply Amday are some of the most deeply corrupt and cynical people in the world. corrupt and cynical people in the world. corrupt and cynical people in the world. I don't think of course they were going I don't think of course they were going I don't think of course they were going to say from the it was early 2023 or man to say from the it was early 2023 or man to say from the it was early 2023 or man said we're a little bit scared about said we're a little bit scared about said we're a little bit scared about what we're creating. Oh, shut up. I'm what we're creating. Oh, shut up. I'm what we're creating. Oh, shut up. I'm just I hear that and I feel so just I hear that and I feel so just I hear that and I feel so frustrated because I've met so many of frustrated because I've met so many of frustrated because I've met so many of these rich liars, these people. these rich liars, these people. these rich liars, these people. And you know why he wants to say that? And you know why he wants to say that? And you know why he wants to say that? So you'll invest in his company and buy So you'll invest in his company and buy So you'll invest in his company and buy the software. So you'll be scared that the software. So you'll be scared that the software. So you'll be scared that if you don't use AI today, you'll be if you don't use AI today, you'll be if you don't use AI today, you'll be left behind in the future, which is left behind in the future, which is left behind in the future, which is their continual narrative that if you their continual narrative that if you their continual narrative that if you don't get on the train today, don't get on the train today, don't get on the train today, then you'll be left behind. By the way, then you'll be left behind. By the way, then you'll be left behind. By the way, every single scam and con starts with every single scam and con starts with every single scam and con starts with rushing you. Every single trick in rushing you. Every single trick in rushing you. Every single trick in history begins with saying you must do history begins with saying you must do history begins with saying you must do this now. And best piece of advice I this now. And best piece of advice I this now. And best piece of advice I ever got was if anyone tries to rush you ever got was if anyone tries to rush you ever got was if anyone tries to rush you and it's not literally a mortal thing and it's not literally a mortal thing and it's not literally a mortal thing like you are bleeding or on fire or the like you are bleeding or on fire or the like you are bleeding or on fire or the house is on fire, slow down. And yet all house is on fire, slow down. And yet all house is on fire, slow down. And yet all of these companies saying it's so scary.

of these companies saying it's so scary. of these companies saying it's so scary. And now they're talking about slowdowns. And now they're talking about slowdowns. And now they're talking about slowdowns. But you ever noticed that Amade and But you ever noticed that Amade and But you ever noticed that Amade and Ortman, they say, "Oh, maybe we should Ortman, they say, "Oh, maybe we should Ortman, they say, "Oh, maybe we should slow down progress." And then they slow down progress." And then they slow down progress." And then they don't. Right now, Orman's saying, "Oh, don't. Right now, Orman's saying, "Oh, don't. Right now, Orman's saying, "Oh, we slow down progress because we're so we slow down progress because we're so we slow down progress because we're so delayed." No, they're out of compute. delayed." No, they're out of compute. delayed." No, they're out of compute. Now, they're doing it. I can guarantee Now, they're doing it. I can guarantee Now, they're doing it. I can guarantee you, by the way, their PR people do not you, by the way, their PR people do not you, by the way, their PR people do not like me. I know for I know I don't think like me. I know for I know I don't think like me. I know for I know I don't think OpenAI's PR people are super fond of me. OpenAI's PR people are super fond of me. OpenAI's PR people are super fond of me. >> But I bet there's elements of what >> But I bet there's elements of what >> But I bet there's elements of what you're saying because you're calming you're saying because you're calming you're saying because you're calming people. You You are theoretically people. You You are theoretically people. You You are theoretically calming down the general public. calming down the general public. calming down the general public. >> And you know what? I hope I am because >> And you know what? I hope I am because >> And you know what? I hope I am because >> the fear based tactics is horrible. >> the fear based tactics is horrible. >> the fear based tactics is horrible. These companies don't want that. These These companies don't want that. These These companies don't want that. These companies want people scared. I'm 100% companies want people scared. I'm 100% companies want people scared. I'm 100% sure. sure. sure. >> Uh I don't I just fundamentally >> Uh I don't I just fundamentally >> Uh I don't I just fundamentally disagree. I think it disagree. I think it disagree. I think it >> can I so the timelines there and I sit >> can I so the timelines there and I sit >> can I so the timelines there and I sit here and what I do is I log their quotes here and what I do is I log their quotes here and what I do is I log their quotes over time over time over time >> and I read them out from 2015 >> and I read them out from 2015 >> and I read them out from 2015 >> to 2026 and the change you see is them >> to 2026 and the change you see is them >> to 2026 and the change you see is them going from there could be extinction going from there could be extinction going from there could be extinction that's the narrative the early narrative that's the narrative the early narrative that's the narrative the early narrative Elon said it himself he says it's the Elon said it himself he says it's the Elon said it himself he says it's the single most dangerous thing in single most dangerous thing in single most dangerous thing in >> Elon and then you track it over time and >> Elon and then you track it over time and >> Elon and then you track it over time and it evolves to this age of abundance it evolves to this age of abundance it evolves to this age of abundance we're all going to have unlimited stuff we're all going to have unlimited stuff we're all going to have unlimited stuff and then um the the new slogan at and then um the the new slogan at and then um the the new slogan at trackbt is intelligence for everyone.

trackbt is intelligence for everyone. trackbt is intelligence for everyone. It's suddenly and all the and and It's suddenly and all the and and It's suddenly and all the and and whenever Daario comes out and says, "By whenever Daario comes out and says, "By whenever Daario comes out and says, "By the way, it's really dangerous." the way, it's really dangerous." the way, it's really dangerous." They attack Daario. Yeah. They hate him. They attack Daario. Yeah. They hate him. They attack Daario. Yeah. They hate him. >> That man [laughter] Daario is >> That man [laughter] Daario is >> That man [laughter] Daario is >> They're like, "Dario, shut the up." >> They're like, "Dario, shut the up." >> They're like, "Dario, shut the up." >> Honestly, I I've been saying Dario, shut >> Honestly, I I've been saying Dario, shut >> Honestly, I I've been saying Dario, shut the up for years. But it's But the the up for years. But it's But the the up for years. But it's But the thing is, I get your point where it's thing is, I get your point where it's thing is, I get your point where it's like I don't think they've changed to like I don't think they've changed to like I don't think they've changed to calm the public down so much as they're calm the public down so much as they're calm the public down so much as they're desperate to not get regulated, which is desperate to not get regulated, which is desperate to not get regulated, which is laughable. We don't regulate tech. We laughable. We don't regulate tech. We laughable. We don't regulate tech. We don't regulate America doesn't don't regulate America doesn't don't regulate America doesn't regulate We are in the We are regulate We are in the We are regulate We are in the We are still trapped in the hands of Milton still trapped in the hands of Milton still trapped in the hands of Milton Freriedman, Margaret Thatcher, and Freriedman, Margaret Thatcher, and Freriedman, Margaret Thatcher, and Ronald Reagan. We're still stuck Ronald Reagan. We're still stuck Ronald Reagan. We're still stuck in the neoliberalistic hellscape, which in the neoliberalistic hellscape, which in the neoliberalistic hellscape, which is growth at all cost, free market is growth at all cost, free market is growth at all cost, free market capitalism. So, no, no one's regulating capitalism. So, no, no one's regulating capitalism. So, no, no one's regulating the regulation of these companies should the regulation of these companies should the regulation of these companies should have been, I don't know, breaking up. have been, I don't know, breaking up. have been, I don't know, breaking up. Put these bastards to the side. Break up Put these bastards to the side. Break up Put these bastards to the side. Break up these for sure. We shouldn't these for sure. We shouldn't these for sure. We shouldn't have companies this big. It makes things have companies this big. It makes things have companies this big. It makes things worse. worse. worse. >> But these technologies are dangerous. >> But these technologies are dangerous. >> But these technologies are dangerous. >> I mean, they're dangerous, but not in >> I mean, they're dangerous, but not in >> I mean, they're dangerous, but not in the ways they've been warning about. the ways they've been warning about. the ways they've been warning about. Let's if we think about cyber hacking, Let's if we think about cyber hacking, Let's if we think about cyber hacking, >> right? And just to be clear, those cyber >> right? And just to be clear, those cyber >> right? And just to be clear, those cyber hacking things that happened were not a hacking things that happened were not a hacking things that happened were not a result of they were like break out of result of they were like break out of result of they were like break out of the sandbox and then they set the the sandbox and then they set the the sandbox and then they set the sandbox up wrong. They set up the server sandbox up wrong. They set up the server sandbox up wrong. They set up the server they were on wrong. But I mean, you they were on wrong. But I mean, you they were on wrong. But I mean, you know, advanced AI models could very know, advanced AI models could very know, advanced AI models could very easily cuz they can go out onto the open easily cuz they can go out onto the open easily cuz they can go out onto the open internet as agents. They could very internet as agents. They could very internet as agents. They could very easily go and look at code bases of easily go and look at code bases of easily go and look at code bases of different websites, find vulnerabilities different websites, find vulnerabilities different websites, find vulnerabilities and exploit those vulnerabilities.

and exploit those vulnerabilities. and exploit those vulnerabilities. >> Yeah. in at scale and arguably um at a >> Yeah. in at scale and arguably um at a >> Yeah. in at scale and arguably um at a higher intelligence and faster and wider higher intelligence and faster and wider higher intelligence and faster and wider than humans a human hacker could than humans a human hacker could than humans a human hacker could theoretically. So that's dangerous. theoretically. So that's dangerous. theoretically. So that's dangerous. >> Well, here's the funny thing. We don't >> Well, here's the funny thing. We don't >> Well, here's the funny thing. We don't know how much compute was spent to do know how much compute was spent to do know how much compute was spent to do the hugging face attack, the open AI the hugging face attack, the open AI the hugging face attack, the open AI one. We also do know that they one. We also do know that they one. We also do know that they improperly set up the server to keep it improperly set up the server to keep it improperly set up the server to keep it in. They thought they'd turn the in. They thought they'd turn the in. They thought they'd turn the internet off and they didn't. That's internet off and they didn't. That's internet off and they didn't. That's human error. And that's human error in a human error. And that's human error in a human error. And that's human error in a sense that yeah, they threw about an sense that yeah, they threw about an sense that yeah, they threw about an indeterminately large amount of compute. indeterminately large amount of compute. indeterminately large amount of compute. This is dangerous, but people keep This is dangerous, but people keep This is dangerous, but people keep saying we can't let the the Chinese get saying we can't let the the Chinese get saying we can't let the the Chinese get a hold of these models. We couldn't a hold of these models. We couldn't a hold of these models. We couldn't possibly because what if these models possibly because what if these models possibly because what if these models fall into the wrong hands? They're fall into the wrong hands? They're fall into the wrong hands? They're already in the wrong hands. Mark already in the wrong hands. Mark already in the wrong hands. Mark Zuckerberg, Sam Olman, Dario Amade. The Zuckerberg, Sam Olman, Dario Amade. The Zuckerberg, Sam Olman, Dario Amade. The wrong hands are the hands of those who wrong hands are the hands of those who wrong hands are the hands of those who are running these companies. We should are running these companies. We should are running these companies. We should not be training these models to do these not be training these models to do these not be training these models to do these things. I don't know why the we're things. I don't know why the we're things. I don't know why the we're doing it other than they've run out of doing it other than they've run out of doing it other than they've run out of other things they can train on. There's other things they can train on. There's other things they can train on. There's a ton. And the fact that they can do it, a ton. And the fact that they can do it, a ton. And the fact that they can do it, it's kind of interesting. But you do it's kind of interesting. But you do it's kind of interesting. But you do would you agree that it's an would you agree that it's an would you agree that it's an intelligence and I'll call it that you intelligence and I'll call it that you intelligence and I'll call it that you know you might disagree with that know you might disagree with that know you might disagree with that terminology but an intelligence that can terminology but an intelligence that can terminology but an intelligence that can go out onto the internet and click go out onto the internet and click go out onto the internet and click around and take actions is inherently around and take actions is inherently around and take actions is inherently there's risks associated with that. Well there's risks associated with that. Well there's risks associated with that. Well the second part I agree with the risks the second part I agree with the risks the second part I agree with the risks we've had people running automated we've had people running automated we've had people running automated scripts hacking scripts for a while scripts hacking scripts for a while scripts hacking scripts for a while we've had hackers doing that for years we've had hackers doing that for years we've had hackers doing that for years and years and years. This is brute and years and years. This is brute and years and years. This is brute forcing it with a bunch of compute and forcing it with a bunch of compute and forcing it with a bunch of compute and yet it is dangerous. These companies are yet it is dangerous. These companies are yet it is dangerous. These companies are doing something dangerous. That is not doing something dangerous. That is not doing something dangerous. That is not what Jeffrey Hinton at have been warning

what Jeffrey Hinton at have been warning what Jeffrey Hinton at have been warning about. They've been saying, "Oh, these about. They've been saying, "Oh, these about. They've been saying, "Oh, these things could destroy society. They could things could destroy society. They could things could destroy society. They could manipulate people." When you actually manipulate people." When you actually manipulate people." When you actually look at the underlying things, not so look at the underlying things, not so look at the underlying things, not so much. Jeffrey Hinton as well talking his much. Jeffrey Hinton as well talking his much. Jeffrey Hinton as well talking his book still got his Google stock, I book still got his Google stock, I book still got his Google stock, I think. And weirdly enough, he left think. And weirdly enough, he left think. And weirdly enough, he left Google because he was worried about the Google because he was worried about the Google because he was worried about the AI there, but then immediately made a AI there, but then immediately made a AI there, but then immediately made a comment being like, "Yeah, actually comment being like, "Yeah, actually comment being like, "Yeah, actually though, Google's very responsible." though, Google's very responsible." though, Google's very responsible." Strange thing. But let's get back to the Strange thing. But let's get back to the Strange thing. But let's get back to the the cyber security side. I agree this is the cyber security side. I agree this is the cyber security side. I agree this is dangerous. These people should not have dangerous. These people should not have dangerous. These people should not have access to so much comput. They clearly access to so much comput. They clearly access to so much comput. They clearly don't know what to do with it. There's a don't know what to do with it. There's a don't know what to do with it. There's a really easy way of dealing with this. really easy way of dealing with this. really easy way of dealing with this. It's not letting them use so much It's not letting them use so much It's not letting them use so much compute. It's regulating that part out compute. It's regulating that part out compute. It's regulating that part out of existence. What if the Chinese do it? of existence. What if the Chinese do it? of existence. What if the Chinese do it? The Chinese were able to distill the The Chinese were able to distill the The Chinese were able to distill the models. And also, models. And also, models. And also, I don't know, regulate it and stop I I I don't know, regulate it and stop I I I don't know, regulate it and stop I I feel like with this particular thing as feel like with this particular thing as feel like with this particular thing as well, we got to this point and let the well, we got to this point and let the well, we got to this point and let the genie out of the bottle to use an genie out of the bottle to use an genie out of the bottle to use an annoying Samman term. We let this happen annoying Samman term. We let this happen annoying Samman term. We let this happen because we let these companies be because we let these companies be because we let these companies be unregulated and use as much computers we unregulated and use as much computers we unregulated and use as much computers we want. We had these enablers want. We had these enablers want. We had these enablers allowing them to burn as much computers allowing them to burn as much computers allowing them to burn as much computers as they want. And also we for all of as they want. And also we for all of as they want. And also we for all of these dire warnings about AI dangers, no these dire warnings about AI dangers, no these dire warnings about AI dangers, no one seems to have done anything.

one seems to have done anything. one seems to have done anything. >> Okay, we're going to play a game, Ed. >> Okay, we're going to play a game, Ed. >> Okay, we're going to play a game, Ed. >> Let's play it. >> Let's play it. >> Let's play it. >> On these cards here, >> On these cards here, >> On these cards here, >> I have the things that you consider to >> I have the things that you consider to >> I have the things that you consider to be myths about the AI industry. be myths about the AI industry. be myths about the AI industry. >> The challenge is I want you to give me >> The challenge is I want you to give me >> The challenge is I want you to give me one sentence. one sentence. one sentence. on each myth. on each myth. on each myth. >> Oh, Christ. >> Oh, Christ. >> Oh, Christ. >> So, just your first reaction. You're >> So, just your first reaction. You're >> So, just your first reaction. You're going to pick it up, you're going to going to pick it up, you're going to going to pick it up, you're going to read it, read it, read it, >> and then you're going to give me one >> and then you're going to give me one >> and then you're going to give me one sentence on your opinion of that sentence on your opinion of that sentence on your opinion of that >> um belief. >> um belief. >> um belief. >> Okay, let's go. >> Okay, let's go. >> Okay, let's go. >> So, let's do this. >> What does it say in your says the the AI >> What does it say in your says the the AI industry is creating enormous economic industry is creating enormous economic industry is creating enormous economic growth? growth? growth? >> No, it's not. It's nowhere in the data. >> No, it's not. It's nowhere in the data. >> No, it's not. It's nowhere in the data. >> Okay. [laughter] Like, it's just May I >> Okay. [laughter] Like, it's just May I >> Okay. [laughter] Like, it's just May I do a second sentence? do a second sentence? do a second sentence? >> Go ahead. pretty much all of the >> Go ahead. pretty much all of the >> Go ahead. pretty much all of the economics is either Nvidia feeding money economics is either Nvidia feeding money economics is either Nvidia feeding money to it companies like Corewave or these to it companies like Corewave or these to it companies like Corewave or these three companies feeding money to these three companies feeding money to these three companies feeding money to these ones to spend it with the them. ones to spend it with the them. ones to spend it with the them. >> Okay. And what evidence do you have that >> Okay. And what evidence do you have that >> Okay. And what evidence do you have that there's it's not causing economic there's it's not causing economic there's it's not causing economic growth? growth? growth? >> Just to be clear, other than the spend >> Just to be clear, other than the spend >> Just to be clear, other than the spend on semiconductors, so the speculative on semiconductors, so the speculative on semiconductors, so the speculative investment in GPUs and data center investment in GPUs and data center investment in GPUs and data center infrastructure that's happening, but as infrastructure that's happening, but as infrastructure that's happening, but as far as like spend on AI goes, barely far as like spend on AI goes, barely far as like spend on AI goes, barely cracking hundred billion. And most of cracking hundred billion. And most of cracking hundred billion. And most of that is just these two running their that is just these two running their that is just these two running their services and paying these three services and paying these three services and paying these three companies, Oracle, Core, and others.

companies, Oracle, Core, and others. companies, Oracle, Core, and others. >> But a hundred billion is a lot of money >> But a hundred billion is a lot of money >> But a hundred billion is a lot of money for a relatively new technology. for a relatively new technology. for a relatively new technology. >> Not when you've spent $300 billion in >> Not when you've spent $300 billion in >> Not when you've spent $300 billion in equity funding. And it if we're going equity funding. And it if we're going equity funding. And it if we're going with just these three, I think $600 with just these three, I think $600 with just these three, I think $600 billion in capital expenditures. billion in capital expenditures. billion in capital expenditures. >> Yeah, I get that. That means it's not >> Yeah, I get that. That means it's not >> Yeah, I get that. That means it's not profitable. But the hundred billion is profitable. But the hundred billion is profitable. But the hundred billion is an expression of consumer demand an expression of consumer demand an expression of consumer demand >> when the compute is mostly driven by >> when the compute is mostly driven by >> when the compute is mostly driven by subscriptions that subsidized. No, it's subscriptions that subsidized. No, it's subscriptions that subsidized. No, it's not. When you're giving someone $20 or not. When you're giving someone $20 or not. When you're giving someone $20 or $40 for a dollar, they're going to use $40 for a dollar, they're going to use $40 for a dollar, they're going to use it more. If this was all on a per it more. If this was all on a per it more. If this was all on a per million token basis, we'd be having a million token basis, we'd be having a million token basis, we'd be having a different conversation. different conversation. different conversation. >> Okay, fair. Fine. Cool. Next one. >> Okay, fair. Fine. Cool. Next one. >> Okay, fair. Fine. Cool. Next one. >> The United States need to spend >> The United States need to spend >> The United States need to spend trillions to beat China in the AI race. trillions to beat China in the AI race. trillions to beat China in the AI race. Let's see. Let's see. Let's see. What AI race? What AI race? What AI race? That's actually That's actually my That's actually That's actually my That's actually That's actually my point. It's what AI race is there. Is it point. It's what AI race is there. Is it point. It's what AI race is there. Is it to make big scary LLMs? They they did to make big scary LLMs? They they did to make big scary LLMs? They they did that already without the Nvidia GPUs. By that already without the Nvidia GPUs. By that already without the Nvidia GPUs. By the way, they've got Blackwell GPUs. the way, they've got Blackwell GPUs. the way, they've got Blackwell GPUs. Kakashi and Jastario, two amazing Kakashi and Jastario, two amazing Kakashi and Jastario, two amazing analysts I love. They've been on this analysts I love. They've been on this analysts I love. They've been on this for years. It's like China's already had for years. It's like China's already had for years. It's like China's already had Nvidia GPUs that they're not meant to Nvidia GPUs that they're not meant to Nvidia GPUs that they're not meant to have for years. But also to do what?

have for years. But also to do what? have for years. But also to do what? They already got the LMS. What What's They already got the LMS. What What's They already got the LMS. What What's the race to do? To make us spend more the race to do? To make us spend more the race to do? To make us spend more money than them? For us to constantly money than them? For us to constantly money than them? For us to constantly piss our pants worrying about China? piss our pants worrying about China? piss our pants worrying about China? Because u they won if that's the case. Because u they won if that's the case. Because u they won if that's the case. Myth number three, AI will replace all Myth number three, AI will replace all Myth number three, AI will replace all human jobs. human jobs. human jobs. that just isn't happening and there's no that just isn't happening and there's no that just isn't happening and there's no economic data to support it. economic data to support it. economic data to support it. >> Will it replace some jobs? >> Will it replace some jobs? >> Will it replace some jobs? >> I mean, it's replaced some contract >> I mean, it's replaced some contract >> I mean, it's replaced some contract labor that would otherwise be replaced labor that would otherwise be replaced labor that would otherwise be replaced with cheap labor out in the global with cheap labor out in the global with cheap labor out in the global south. It's a digital globalization in south. It's a digital globalization in south. It's a digital globalization in that sense, but all jobs, most jobs, a that sense, but all jobs, most jobs, a that sense, but all jobs, most jobs, a lot of jobs. No. lot of jobs. No. lot of jobs. No. >> What about robotics? >> What about robotics? >> What about robotics? >> Robotics is not what we're talking >> Robotics is not what we're talking >> Robotics is not what we're talking about. Robotics is a very different about. Robotics is a very different about. Robotics is a very different thing. And even then, thing. And even then, thing. And even then, >> robotics will be powered by AI. >> robotics will be powered by AI. >> robotics will be powered by AI. >> I mean, yes, but there are tons of >> I mean, yes, but there are tons of >> I mean, yes, but there are tons of different kinds of AI. We're talking different kinds of AI. We're talking different kinds of AI. We're talking explicitly about generative AI. And explicitly about generative AI. And explicitly about generative AI. And that's what I this mythbusters piece that's what I this mythbusters piece that's what I this mythbusters piece that was definitely about generative AI. that was definitely about generative AI. that was definitely about generative AI. >> Okay. But what about robotics? Like the >> Okay. But what about robotics? Like the >> Okay. But what about robotics? Like the thing is the Optimus robot that Elon's thing is the Optimus robot that Elon's thing is the Optimus robot that Elon's working on at Tesla. working on at Tesla. working on at Tesla. >> The one where even in the demo of the >> The one where even in the demo of the >> The one where even in the demo of the hand he like they had to have a guy hand he like they had to have a guy hand he like they had to have a guy controlling it. Wasn't doing it controlling it. Wasn't doing it controlling it. Wasn't doing it autonomously. Here's the thing. If they autonomously. Here's the thing. If they autonomously. Here's the thing. If they can beat all these challenges, yeah, can beat all these challenges, yeah, can beat all these challenges, yeah, robotics would be really cool. I don't robotics would be really cool. I don't robotics would be really cool. I don't know how long that's that's one I'd know how long that's that's one I'd know how long that's that's one I'd actually be willing to believe in a actually be willing to believe in a actually be willing to believe in a couple decades.

couple decades. couple decades. >> Have you seen them ch them Chinese >> Have you seen them ch them Chinese >> Have you seen them ch them Chinese robots? I know you've seen them. the robots? I know you've seen them. the robots? I know you've seen them. the uni, what's it called? The one that can uni, what's it called? The one that can uni, what's it called? The one that can dance and that, but they can't really do dance and that, but they can't really do dance and that, but they can't really do human things. human things. human things. >> Well, it's just it is pretty >> Well, it's just it is pretty >> Well, it's just it is pretty mindblowing. mindblowing. mindblowing. >> Robotics are cool. I like I'm >> Robotics are cool. I like I'm >> Robotics are cool. I like I'm not going to pretend. I don't think not going to pretend. I don't think not going to pretend. I don't think robots are cool. I wish they were robots are cool. I wish they were robots are cool. I wish they were building robots and actually doing cool building robots and actually doing cool building robots and actually doing cool I wish the tech industry still I wish the tech industry still I wish the tech industry still made fun stuff and interesting stuff. made fun stuff and interesting stuff. made fun stuff and interesting stuff. Instead, we get these large Instead, we get these large Instead, we get these large language models. But with AI plus language models. But with AI plus language models. But with AI plus robotics is, you know, I was in San robotics is, you know, I was in San robotics is, you know, I was in San Francisco and I went to this massive um Francisco and I went to this massive um Francisco and I went to this massive um incubator there. And when I'd gone there incubator there. And when I'd gone there incubator there. And when I'd gone there three years earlier, it was all software three years earlier, it was all software three years earlier, it was all software startups, right? And when I went back startups, right? And when I went back startups, right? And when I went back three years later, it was all these three years later, it was all these three years later, it was all these robot startups. And I remember saying to robot startups. And I remember saying to robot startups. And I remember saying to the founder of the incubator, I was the founder of the incubator, I was the founder of the incubator, I was like, "Why is everything robots now?" like, "Why is everything robots now?" like, "Why is everything robots now?" There was this one robot where it was There was this one robot where it was There was this one robot where it was just the arm and it had a frying pan on just the arm and it had a frying pan on just the arm and it had a frying pan on it. Yeah. it. Yeah. it. Yeah. >> And it whole thing is it cooks for you. >> And it whole thing is it cooks for you. >> And it whole thing is it cooks for you. >> Yeah. >> Yeah. >> Yeah. >> So it was he was showing me it cooking >> So it was he was showing me it cooking >> So it was he was showing me it cooking whatever. And he goes, "Well, you know whatever. And he goes, "Well, you know whatever. And he goes, "Well, you know the arm." He goes, "The the hardware the arm." He goes, "The the hardware the arm." He goes, "The the hardware part, the physical parts, part, the physical parts, part, the physical parts, >> that's always been fairly cheap." Yeah. >> that's always been fairly cheap." Yeah. >> that's always been fairly cheap." Yeah. >> He goes, "The expensive part was the >> He goes, "The expensive part was the >> He goes, "The expensive part was the intelligence. And now that's come down intelligence. And now that's come down intelligence. And now that's come down to pennies." So what you're seeing is to pennies." So what you're seeing is to pennies." So what you're seeing is this explosion in the robotics industry this explosion in the robotics industry this explosion in the robotics industry because robotics is a function of because robotics is a function of because robotics is a function of intelligence plus hardware. We've always intelligence plus hardware. We've always intelligence plus hardware. We've always had the had the had the >> and a ton of data though as well and the >> and a ton of data though as well and the >> and a ton of data though as well and the data is very expensive.

data is very expensive. data is very expensive. >> Yeah. >> Yeah. >> Yeah. >> The thing is cyber cabs rolled out real >> The thing is cyber cabs rolled out real >> The thing is cyber cabs rolled out real slow. It's going to take a long time. It slow. It's going to take a long time. It slow. It's going to take a long time. It could be a threat if they do a robot could be a threat if they do a robot could be a threat if they do a robot that could replace a human job. Sure it that could replace a human job. Sure it that could replace a human job. Sure it could. But that human jobs are could. But that human jobs are could. But that human jobs are multifaceted. Human jobs change with multifaceted. Human jobs change with multifaceted. Human jobs change with environments. And also a lot of human environments. And also a lot of human environments. And also a lot of human jobs that you might think of like I jobs that you might think of like I jobs that you might think of like I don't know dishwashing robot for don't know dishwashing robot for don't know dishwashing robot for example. example. example. >> Yeah. >> Yeah. >> Yeah. some guy at a restaurant isn't paying 10 some guy at a restaurant isn't paying 10 some guy at a restaurant isn't paying 10 20 grand for a robot to replace the job 20 grand for a robot to replace the job 20 grand for a robot to replace the job that they're already not paying enough that they're already not paying enough that they're already not paying enough for. The point is, yeah, it could if you for. The point is, yeah, it could if you for. The point is, yeah, it could if you can replace the jobs. That is not what can replace the jobs. That is not what can replace the jobs. That is not what we're talking about with this. we're talking about with this. we're talking about with this. >> Yeah. I I just I just I ask these >> Yeah. I I just I just I ask these >> Yeah. I I just I just I ask these questions not because I'm trying to be questions not because I'm trying to be questions not because I'm trying to be like I actually I'm trying to form my like I actually I'm trying to form my like I actually I'm trying to form my own opinion on these things and own opinion on these things and own opinion on these things and >> I I do think, you know, as it's written >> I I do think, you know, as it's written >> I I do think, you know, as it's written there, it says AI will replace all human there, it says AI will replace all human there, it says AI will replace all human jobs. Obviously not. Obviously, that's jobs. Obviously not. Obviously, that's jobs. Obviously not. Obviously, that's Yeah. Yeah. Yeah. >> But um I'm trying to figure out if the >> But um I'm trying to figure out if the >> But um I'm trying to figure out if the truth is somewhere in the middle that truth is somewhere in the middle that truth is somewhere in the middle that there's a certain type of job which there's a certain type of job which there's a certain type of job which actually humans probably shouldn't have actually humans probably shouldn't have actually humans probably shouldn't have ever been doing really. ever been doing really. ever been doing really. >> Um if you think back through history, >> Um if you think back through history, >> Um if you think back through history, there was someone's job just to sit in there was someone's job just to sit in there was someone's job just to sit in an elevator and press the buttons.

an elevator and press the buttons. an elevator and press the buttons. >> That's an example of a job that humans >> That's an example of a job that humans >> That's an example of a job that humans probably shouldn't have been doing. And probably shouldn't have been doing. And probably shouldn't have been doing. And as technology gets more advanced, it as technology gets more advanced, it as technology gets more advanced, it takes on a lot of that takes on a lot of that takes on a lot of that >> sort of automated monotonous stuff. >> sort of automated monotonous stuff. >> sort of automated monotonous stuff. >> Right? The thing is with this particular >> Right? The thing is with this particular >> Right? The thing is with this particular thing that I know that this is from, thing that I know that this is from, thing that I know that this is from, it's a specific blog I wrote. I was it's a specific blog I wrote. I was it's a specific blog I wrote. I was explicitly talking about generative AI explicitly talking about generative AI explicitly talking about generative AI though. I was explicitly [clears throat] though. I was explicitly [clears throat] though. I was explicitly [clears throat] talking about people when they say this talking about people when they say this talking about people when they say this they are referring to that. they are referring to that. they are referring to that. >> So you're not talking about agentic AI >> So you're not talking about agentic AI >> So you're not talking about agentic AI which is which is which is >> agentic AI is LLMs. Agentic AI is just a >> agentic AI is LLMs. Agentic AI is just a >> agentic AI is LLMs. Agentic AI is just a fancy way of saying an LLM talking to fancy way of saying an LLM talking to fancy way of saying an LLM talking to another LLM with a harness on top. That another LLM with a harness on top. That another LLM with a harness on top. That is still LLM. Agentic AI is one of the is still LLM. Agentic AI is one of the is still LLM. Agentic AI is one of the big the bigger lies they to tell. It's big the bigger lies they to tell. It's big the bigger lies they to tell. It's like when you hear agent you're meant to like when you hear agent you're meant to like when you hear agent you're meant to think autonomous AI can do what you think autonomous AI can do what you think autonomous AI can do what you want. It's still LLMs. It's still LM want. It's still LLMs. It's still LM want. It's still LLMs. It's still LM talking to other LMLs talking to other LMLs talking to other LMLs >> taking screenshots and putting them in >> taking screenshots and putting them in >> taking screenshots and putting them in LLM and stuff. LLM and stuff. LLM and stuff. >> Oh god. Yeah. >> Oh god. Yeah. >> Oh god. Yeah. >> Okay. But but you know I could I could >> Okay. But but you know I could I could >> Okay. But but you know I could I could make the case that make the case that make the case that I'm just thinking about my personal I'm just thinking about my personal I'm just thinking about my personal usage. I definitely use agents to do usage. I definitely use agents to do usage. I definitely use agents to do things that I would have previously things that I would have previously things that I would have previously asked people to do. It's not to say that asked people to do. It's not to say that asked people to do. It's not to say that I didn't I still don't hire cuz we're I didn't I still don't hire cuz we're I didn't I still don't hire cuz we're hiring like crazy. hiring like crazy. hiring like crazy. >> Yeah. >> Yeah. >> Yeah. >> And I still in that particular function. >> And I still in that particular function. >> And I still in that particular function. I'm thinking about like the chief of I'm thinking about like the chief of I'm thinking about like the chief of staff role. So my chief of staff would staff role. So my chief of staff would staff role. So my chief of staff would have triaged all of my inboxes have triaged all of my inboxes have triaged all of my inboxes previously and put them somewhere and previously and put them somewhere and previously and put them somewhere and told me about them or maybe once upon a told me about them or maybe once upon a told me about them or maybe once upon a time shown me a piece of paper back in time shown me a piece of paper back in time shown me a piece of paper back in the day. I guess now my chief of staff the day. I guess now my chief of staff the day. I guess now my chief of staff is no longer doing that job. You still is no longer doing that job. You still is no longer doing that job. You still have a chief of staff though.

have a chief of staff though. have a chief of staff though. >> This is what I'm saying. They're doing >> This is what I'm saying. They're doing >> This is what I'm saying. They're doing other things, other things, other things, >> right? But the thing is again what you >> right? But the thing is again what you >> right? But the thing is again what you were describing is were describing is were describing is fairly basic automation. I don't know fairly basic automation. I don't know fairly basic automation. I don't know what the tasks are triaging. what the tasks are triaging. what the tasks are triaging. >> Basic spend a trillion dollars on >> Basic spend a trillion dollars on >> Basic spend a trillion dollars on triaging email. Like that's the the triaging email. Like that's the the triaging email. Like that's the the promise. If they'd spent $10 billion and promise. If they'd spent $10 billion and promise. If they'd spent $10 billion and this was much smaller and you I go cool this was much smaller and you I go cool this was much smaller and you I go cool software. Yay. A lot of the things that software. Yay. A lot of the things that software. Yay. A lot of the things that people are impressed with like script people are impressed with like script people are impressed with like script stuff as well. It's just LM's doing stuff as well. It's just LM's doing stuff as well. It's just LM's doing Python. You should be impressed by Python. You should be impressed by Python. You should be impressed by Python code. Python's incredible. You Python code. Python's incredible. You Python code. Python's incredible. You can scrape websites. You can download can scrape websites. You can download can scrape websites. You can download It's awesome. But the point I'm It's awesome. But the point I'm It's awesome. But the point I'm making is none of this would be anywhere making is none of this would be anywhere making is none of this would be anywhere near as much of a problem if they didn't near as much of a problem if they didn't near as much of a problem if they didn't ask for all of the attention, all of the ask for all of the attention, all of the ask for all of the attention, all of the money, and promise the world. It's their money, and promise the world. It's their money, and promise the world. It's their promises that are the problem. And the promises that are the problem. And the promises that are the problem. And the journalists who went along with it, and journalists who went along with it, and journalists who went along with it, and the analysts and the Twitter people who the analysts and the Twitter people who the analysts and the Twitter people who went along with this, saying that this went along with this, saying that this went along with this, saying that this would change everything and replace would change everything and replace would change everything and replace everything and leaving the realm of everything and leaving the realm of everything and leaving the realm of reality. Is there any technological reality. Is there any technological reality. Is there any technological innovation through history that was innovation through history that was innovation through history that was really, really game-changing where that really, really game-changing where that really, really game-changing where that didn't happen? didn't happen? didn't happen? I mean I mean I mean the internet the internet the internet >> I mean people overpromised that >> I mean people overpromised that >> I mean people overpromised that >> I mean they overpromised on the >> I mean they overpromised on the >> I mean they overpromised on the businesses but I've read through a great businesses but I've read through a great businesses but I've read through a great many pieces about the early internet a many pieces about the early internet a many pieces about the early internet a lot of people were excited but hesitant lot of people were excited but hesitant lot of people were excited but hesitant they were worried that there was not they were worried that there was not they were worried that there was not enough demand but they were still like enough demand but they were still like enough demand but they were still like oh yeah this could have potential oh yeah this could have potential oh yeah this could have potential ramifications if it happened. People ramifications if it happened. People ramifications if it happened. People were not super negative about the were not super negative about the were not super negative about the internet. A lot of the skeptics were internet. A lot of the skeptics were internet. A lot of the skeptics were saying we're worried about an overload saying we're worried about an overload saying we're worried about an overload of bad information. Look at where we of bad information. Look at where we of bad information. Look at where we are. A lot of people were worried about are. A lot of people were worried about are. A lot of people were worried about the social consequences of everyone the social consequences of everyone the social consequences of everyone talking online, which they were correct talking online, which they were correct talking online, which they were correct about. With the economic things, they about. With the economic things, they about. With the economic things, they were specifically talking about like the

were specifically talking about like the were specifically talking about like the globe, which I think made hundreds of globe, which I think made hundreds of globe, which I think made hundreds of thousands of dollars and had like a I thousands of dollars and had like a I thousands of dollars and had like a I think a billion dollar market cap, but think a billion dollar market cap, but think a billion dollar market cap, but they were talking. they were talking. they were talking. >> Yeah, there was massive hype in the com >> Yeah, there was massive hype in the com >> Yeah, there was massive hype in the com era. era. era. >> I read a lot of those stories. The hype >> I read a lot of those stories. The hype >> I read a lot of those stories. The hype was nowhere in it. You didn't have was nowhere in it. You didn't have was nowhere in it. You didn't have articles everywhere that were saying if articles everywhere that were saying if articles everywhere that were saying if you don't get online, you'll be left you don't get online, you'll be left you don't get online, you'll be left behind. You didn't have professional behind. You didn't have professional behind. You didn't have professional consequences. Nick Sesh mentioned his consequences. Nick Sesh mentioned his consequences. Nick Sesh mentioned his blog earlier. He described this thing blog earlier. He described this thing blog earlier. He described this thing global uh AI sisterating global global uh AI sisterating global global uh AI sisterating global decision-m where he said that you have decision-m where he said that you have decision-m where he said that you have businesses you work at where if you businesses you work at where if you businesses you work at where if you don't say that you're more productive don't say that you're more productive don't say that you're more productive with AI whether or not it's true is with AI whether or not it's true is with AI whether or not it's true is irrelevant you have professional irrelevant you have professional irrelevant you have professional consequences you can get fired there are consequences you can get fired there are consequences you can get fired there are people having to AI wash their jobs by people having to AI wash their jobs by people having to AI wash their jobs by saying AI did it otherwise their bosses saying AI did it otherwise their bosses saying AI did it otherwise their bosses who don't do will get mad at them who don't do will get mad at them who don't do will get mad at them this did not happen with the internet it this did not happen with the internet it this did not happen with the internet it was not present and part of the thing is was not present and part of the thing is was not present and part of the thing is social media was not like it is today social media was not like it is today social media was not like it is today the kind of uh was it decentralization the kind of uh was it decentralization the kind of uh was it decentralization of media in general has caused this as of media in general has caused this as of media in general has caused this as well and also the fact of day trading well and also the fact of day trading well and also the fact of day trading there's so many different things that there's so many different things that there's so many different things that are different it's crazy are different it's crazy are different it's crazy >> I I do think AI is different from the >> I I do think AI is different from the >> I I do think AI is different from the internet in part if you just measured it internet in part if you just measured it internet in part if you just measured it on the speed of adoption especially if on the speed of adoption especially if on the speed of adoption especially if we just think about generative AI AI we just think about generative AI AI we just think about generative AI AI >> but the this adoption of the internet >> but the this adoption of the internet >> but the this adoption of the internet required physical connections to your required physical connections to your required physical connections to your house the adoption of generative AI house the adoption of generative AI house the adoption of generative AI involves having a web browser it took a involves having a web browser it took a involves having a web browser it took a vast amount of effort to bring internet vast amount of effort to bring internet vast amount of effort to bring internet to people Even with dialup connections, to people Even with dialup connections, to people Even with dialup connections, it still required the distribution it still required the distribution it still required the distribution >> and that's why it was so slow and there >> and that's why it was so slow and there >> and that's why it was so slow and there was less, you know, there was less hype was less, you know, there was less hype was less, you know, there was less hype than AI. I do agree that there's way than AI. I do agree that there's way than AI. I do agree that there's way more hype and we again going back to more hype and we again going back to more hype and we again going back to this point that we're clustering AI in

this point that we're clustering AI in this point that we're clustering AI in this big category of lots of different this big category of lots of different this big category of lots of different things. things. things. >> There's generative AI. >> There's generative AI. >> There's generative AI. >> There's generative AI. There's like real >> There's generative AI. There's like real >> There's generative AI. There's like real world AI. world AI. world AI. >> Generative AI is explicitly what I'm >> Generative AI is explicitly what I'm >> Generative AI is explicitly what I'm talking about here. When bosses are talking about here. When bosses are talking about here. When bosses are saying you need to use AI, they're not saying you need to use AI, they're not saying you need to use AI, they're not saying I need you to go and buy a saying I need you to go and buy a saying I need you to go and buy a Unibeam robot. They're saying use LLM so Unibeam robot. They're saying use LLM so Unibeam robot. They're saying use LLM so that I and that's the thing. They have that I and that's the thing. They have that I and that's the thing. They have this theory, the era of the business this theory, the era of the business this theory, the era of the business idiot where it's like we are ruled by idiot where it's like we are ruled by idiot where it's like we are ruled by people that don't do work because nobody people that don't do work because nobody people that don't do work because nobody who actually does a bunch of work who who actually does a bunch of work who who actually does a bunch of work who really is productive is harassing really is productive is harassing really is productive is harassing someone who works for them for not being someone who works for them for not being someone who works for them for not being productive enough. productive enough. productive enough. >> They're not they don't have the time. >> They're not they don't have the time. >> They're not they don't have the time. They're doing work. Someone who is They're doing work. Someone who is They're doing work. Someone who is sitting there with the ingratiation sitting there with the ingratiation sitting there with the ingratiation machine that's telling them that every machine that's telling them that every machine that's telling them that every beautiful idea out of their messy little beautiful idea out of their messy little beautiful idea out of their messy little skull is amazing. Yeah. They're going, skull is amazing. Yeah. They're going, skull is amazing. Yeah. They're going, "Damn, this thing says I'm a genius. Why "Damn, this thing says I'm a genius. Why "Damn, this thing says I'm a genius. Why are you not using the genius machine to are you not using the genius machine to are you not using the genius machine to do more work?" And yeah, if you're a do more work?" And yeah, if you're a do more work?" And yeah, if you're a boss that goes to lunch, leaves lunch, boss that goes to lunch, leaves lunch, boss that goes to lunch, leaves lunch, and sometimes reads your emails, LM are and sometimes reads your emails, LM are and sometimes reads your emails, LM are magic. magic. magic. >> I, you know, one of the most compelling >> I, you know, one of the most compelling >> I, you know, one of the most compelling arguments I have for the overhype of AI arguments I have for the overhype of AI arguments I have for the overhype of AI >> in a world where everybody has access to >> in a world where everybody has access to >> in a world where everybody has access to these tools, whatever the these tools, whatever the these tools, whatever the [clears throat] tools can do, would [clears throat] tools can do, would [clears throat] tools can do, would largely be commoditized. What the tools largely be commoditized. What the tools largely be commoditized. What the tools can't do, which one could say is the can't do, which one could say is the can't do, which one could say is the human taste, judgment, you could say human taste, judgment, you could say human taste, judgment, you could say it's people, skills, whatever you want it's people, skills, whatever you want it's people, skills, whatever you want to say, is now going to be the valuable to say, is now going to be the valuable to say, is now going to be the valuable thing because the scarce and the hard thing because the scarce and the hard thing because the scarce and the hard becomes the most valuable through becomes the most valuable through becomes the most valuable through history and the commoditized becomes the history and the commoditized becomes the history and the commoditized becomes the least valuable. So the very nature that least valuable. So the very nature that least valuable. So the very nature that we're commoditizing, the generation of we're commoditizing, the generation of we're commoditizing, the generation of content or whatever you want to call it, content or whatever you want to call it, content or whatever you want to call it, code means that's actually not where the code means that's actually not where the code means that's actually not where the value will acrue as for the user. And value will acrue as for the user. And value will acrue as for the user. And actually if you think about what it actually if you think about what it actually if you think about what it takes to now make something that is

takes to now make something that is takes to now make something that is objectively great if an AI can do it objectively great if an AI can do it objectively great if an AI can do it then it's not the the great thing is not then it's not the the great thing is not then it's not the the great thing is not of value. of value. of value. >> So so I think a lot I've been thinking a >> So so I think a lot I've been thinking a >> So so I think a lot I've been thinking a lot actually about how lot actually about how lot actually about how >> how do you um avoid the temptation of >> how do you um avoid the temptation of >> how do you um avoid the temptation of sloppification of the things you make sloppification of the things you make sloppification of the things you make the value you put into the world. It's the value you put into the world. It's the value you put into the world. It's very simple example that people will be very simple example that people will be very simple example that people will be able to relate to. If you use chat GBT able to relate to. If you use chat GBT able to relate to. If you use chat GBT or anthropic, you know, Claude to make or anthropic, you know, Claude to make or anthropic, you know, Claude to make your LinkedIn posts, let's say, your LinkedIn posts, let's say, your LinkedIn posts, let's say, >> they will be LinkedIn posts because >> they will be LinkedIn posts because >> they will be LinkedIn posts because everybody else is using them. And everybody else is using them. And everybody else is using them. And actually, a great LinkedIn post now is actually, a great LinkedIn post now is actually, a great LinkedIn post now is someone who doesn't use them and makes someone who doesn't use them and makes someone who doesn't use them and makes something that's like irreplaceably something that's like irreplaceably something that's like irreplaceably human, human, human, >> right? >> right? >> right? >> And deeper and more personal N of one >> And deeper and more personal N of one >> And deeper and more personal N of one lived experience. lived experience. lived experience. >> Yeah. >> Yeah. >> Yeah. >> All these things that AI can't do. And I >> All these things that AI can't do. And I >> All these things that AI can't do. And I think that's a compelling argument that think that's a compelling argument that think that's a compelling argument that actually the commodity tools produce actually the commodity tools produce actually the commodity tools produce commodity outcomes. So everyone has commodity outcomes. So everyone has commodity outcomes. So everyone has access to these things and what's access to these things and what's access to these things and what's changed? Like really like what changed? Like really like what changed? Like really like what >> the slopification we've we've got a >> the slopification we've we've got a >> the slopification we've we've got a bunch of slop but these people were bunch of slop but these people were bunch of slop but these people were halfassing their jobs before. It's just halfassing their jobs before. It's just halfassing their jobs before. It's just a halfass arcery machine and it's just a halfass arcery machine and it's just a halfass arcery machine and it's just it's it's the thing. It's what I'm it's it's the thing. It's what I'm it's it's the thing. It's what I'm talking about with the slot blogs. It's talking about with the slot blogs. It's talking about with the slot blogs. It's like it's it yeah people that gave you like it's it yeah people that gave you like it's it yeah people that gave you dog before have now got the dog dog before have now got the dog dog before have now got the dog machine to pump out dog It's machine to pump out dog It's machine to pump out dog It's so there's a guy called Carl Brown uh so there's a guy called Carl Brown uh so there's a guy called Carl Brown uh internet bucks. Awesome guy. Great internet bucks. Awesome guy. Great internet bucks. Awesome guy. Great software engineer. He he said I might software engineer. He he said I might software engineer. He he said I might have said this earlier. So, it makes the have said this earlier. So, it makes the have said this earlier. So, it makes the easy things easy, the hard things easy things easy, the hard things easy things easy, the hard things harder. When you know you're doing a harder. When you know you're doing a harder. When you know you're doing a really distinct small script for really distinct small script for really distinct small script for something and it can plop that out. It's something and it can plop that out. It's something and it can plop that out. It's awesome. I used Claude the other day for awesome. I used Claude the other day for awesome. I used Claude the other day for something useful. My kid loves something useful. My kid loves something useful. My kid loves Minecraft. I was trying to fix a Minecraft. I was trying to fix a Minecraft. I was trying to fix a broken mod cuz he loves his wither broken mod cuz he loves his wither broken mod cuz he loves his wither storm. It's awesome.

storm. It's awesome. storm. It's awesome. >> And it still took me half an hour and >> And it still took me half an hour and >> And it still took me half an hour and kept getting things wrong. What do you kept getting things wrong. What do you kept getting things wrong. What do you use AI for? Generative. use AI for? Generative. use AI for? Generative. >> I really don't. I don't use it >> I really don't. I don't use it >> I really don't. I don't use it >> with Bloomberg terminal. I use AskB, >> with Bloomberg terminal. I use AskB, >> with Bloomberg terminal. I use AskB, which is just when it's like requesting which is just when it's like requesting which is just when it's like requesting the consensus analyst estimates for the consensus analyst estimates for the consensus analyst estimates for Nvidia, Nvidia, Nvidia, >> but otherwise you don't use it. >> but otherwise you don't use it. >> but otherwise you don't use it. >> No. So, how do you know it's bad? I've >> No. So, how do you know it's bad? I've >> No. So, how do you know it's bad? I've used it. I've put it through its paces. used it. I've put it through its paces. used it. I've put it through its paces. I've used it to try and do financial I've used it to try and do financial I've used it to try and do financial models and found one error and models and found one error and models and found one error and immediately be like, "Ah, I've never immediately be like, "Ah, I've never immediately be like, "Ah, I've never been particularly impressed." The one been particularly impressed." The one been particularly impressed." The one thing I will defend it on is it's really thing I will defend it on is it's really thing I will defend it on is it's really good for like tech support. Like I have good for like tech support. Like I have good for like tech support. Like I have this thing called Synergy in my New York this thing called Synergy in my New York this thing called Synergy in my New York New York place I go to. I have this New York place I go to. I have this New York place I go to. I have this monitor where I have a MacBook and a PC monitor where I have a MacBook and a PC monitor where I have a MacBook and a PC laptop and this thing Synergy for using laptop and this thing Synergy for using laptop and this thing Synergy for using the same mouse and keyboard. the same mouse and keyboard. the same mouse and keyboard. >> Dropping a giant >> Dropping a giant >> Dropping a giant troubleshooting log into this thing and troubleshooting log into this thing and troubleshooting log into this thing and going, "What's wrong?" And it going, going, "What's wrong?" And it going, going, "What's wrong?" And it going, "This is wrong." Yeah, super useful. Is "This is wrong." Yeah, super useful. Is "This is wrong." Yeah, super useful. Is that trillion dollars? No. Is that a $2 that trillion dollars? No. Is that a $2 that trillion dollars? No. Is that a $2 trillion company? No. Pretty use. trillion company? No. Pretty use. trillion company? No. Pretty use. >> Better than Google though, right? Better >> Better than Google though, right? Better >> Better than Google though, right? Better than Google search. than Google search. than Google search. >> I know. I mean, yeah. Remember, >> I know. I mean, yeah. Remember, >> I know. I mean, yeah. Remember, >> do you use Google search still? >> do you use Google search still? >> do you use Google search still? >> I try. I have to push the crap >> I try. I have to push the crap >> I try. I have to push the crap out of the way. And out of the way. And out of the way. And >> I can't remember the last time I did a >> I can't remember the last time I did a >> I can't remember the last time I did a Google search.

Google search. Google search. >> Christ, I find myself using Bing >> Christ, I find myself using Bing >> Christ, I find myself using Bing sometimes. I know. I hate saying it, sometimes. I know. I hate saying it, sometimes. I know. I hate saying it, too. But I have to scroll past the AI too. But I have to scroll past the AI too. But I have to scroll past the AI crap cuz I want the good stuff. I want crap cuz I want the good stuff. I want crap cuz I want the good stuff. I want the I want the actual links to stuff so the I want the actual links to stuff so the I want the actual links to stuff so that I can read the thing and go. But that I can read the thing and go. But that I can read the thing and go. But you can ask the AI to give you the you can ask the AI to give you the you can ask the AI to give you the links. links. links. >> Yeah. And it doesn't do a particularly >> Yeah. And it doesn't do a particularly >> Yeah. And it doesn't do a particularly good job. Like my good job. Like my good job. Like my >> So say that the other day my iPad wasn't >> So say that the other day my iPad wasn't >> So say that the other day my iPad wasn't turning on and it was doing this funny turning on and it was doing this funny turning on and it was doing this funny little thing on the screen. You think little thing on the screen. You think little thing on the screen. You think that it's better to type that into that it's better to type that into that it's better to type that into Google than Google than Google than >> Oh, no. I must be clear that may be the >> Oh, no. I must be clear that may be the >> Oh, no. I must be clear that may be the only LLM use case I defend. The only LLM use case I defend. The only LLM use case I defend. The troubleshooting thing is awesome for it. troubleshooting thing is awesome for it. troubleshooting thing is awesome for it. I It's the the one weakness I have. It's I It's the the one weakness I have. It's I It's the the one weakness I have. It's like genuinely being able to drop a log like genuinely being able to drop a log like genuinely being able to drop a log into it. That's awesome. Again, that is into it. That's awesome. Again, that is into it. That's awesome. Again, that is not what they're selling it as. They're not what they're selling it as. They're not what they're selling it as. They're not selling it as a useful little tool. not selling it as a useful little tool. not selling it as a useful little tool. They're selling it as the uh software as They're selling it as the uh software as They're selling it as the uh software as the thing that will change everything the thing that will change everything the thing that will change everything that will replace all jobs that will do that will replace all jobs that will do that will replace all jobs that will do this and that. It's not like they sold this and that. It's not like they sold this and that. It's not like they sold it as a quirky bit of software. it as a quirky bit of software. it as a quirky bit of software. >> No, you are right. They are, you know, >> No, you are right. They are, you know, >> No, you are right. They are, you know, telling us that it is going to replace telling us that it is going to replace telling us that it is going to replace everything. But funnily enough, the everything. But funnily enough, the everything. But funnily enough, the critics are saying that as well. critics are saying that as well. critics are saying that as well. >> Which one I mean I mean >> Which one I mean I mean >> Which one I mean I mean >> they are like the Jeffrey Hintons of the >> they are like the Jeffrey Hintons of the >> they are like the Jeffrey Hintons of the world. you know, even people that have world. you know, even people that have world. you know, even people that have left the safety team in chat who who left the safety team in chat who who left the safety team in chat who who I've sat here with the these are critics I've sat here with the these are critics I've sat here with the these are critics that are that are warning of the impacts that are that are warning of the impacts that are that are warning of the impacts it's going to have on the world. It's it's going to have on the world. It's it's going to have on the world. It's weird how all these critics also have weird how all these critics also have weird how all these critics also have vested interest in AI doing well though.

vested interest in AI doing well though. vested interest in AI doing well though. Daniel, former open AI guy, AI 2027 Daniel, former open AI guy, AI 2027 Daniel, former open AI guy, AI 2027 written with the Star Codeex guy that written with the Star Codeex guy that written with the Star Codeex guy that was nothing more than badly written was nothing more than badly written was nothing more than badly written science fiction that he's already had to science fiction that he's already had to science fiction that he's already had to walk back. walk back. walk back. >> You know, he could have made more money >> You know, he could have made more money >> You know, he could have made more money by staying at chat. by staying at chat. by staying at chat. >> Could he? >> Could he? >> Could he? >> I mean, looks like he lost >> I mean, looks like he lost >> I mean, looks like he lost >> if he had options early. it sticking >> if he had options early. it sticking >> if he had options early. it sticking around. around. around. >> Did he lose the options? How much do >> Did he lose the options? How much do >> Did he lose the options? How much do they they they >> You're not saying that they're they're >> You're not saying that they're they're >> You're not saying that they're they're being critical. They're not critical of being critical. They're not critical of being critical. They're not critical of the companies themselves. They're not the companies themselves. They're not the companies themselves. They're not critical of the stealing. They're not critical of the stealing. They're not critical of the stealing. They're not critical of the environmental damage. critical of the environmental damage. critical of the environmental damage. They're not critical of the fact that They're not critical of the fact that They're not critical of the fact that you cannot rely on the answers. They're you cannot rely on the answers. They're you cannot rely on the answers. They're critical of this big scary boogeyman out critical of this big scary boogeyman out critical of this big scary boogeyman out in the future where it's like, "Oh, I'm in the future where it's like, "Oh, I'm in the future where it's like, "Oh, I'm scared of when this becomes so powerful scared of when this becomes so powerful scared of when this becomes so powerful and everyone should talk to me about how and everyone should talk to me about how and everyone should talk to me about how scary and powerful it is." They're not scary and powerful it is." They're not scary and powerful it is." They're not saying, "Hey, here are the harms today. saying, "Hey, here are the harms today. saying, "Hey, here are the harms today. Here are the things we're actually Here are the things we're actually Here are the things we're actually looking at today. Here are the social looking at today. Here are the social looking at today. Here are the social problems of having this automated way of problems of having this automated way of problems of having this automated way of spewing out slop, of filling our feeds spewing out slop, of filling our feeds spewing out slop, of filling our feeds with crap, of having information that with crap, of having information that with crap, of having information that will pop up that is presented even with will pop up that is presented even with will pop up that is presented even with the little disclaimer thing of saying, the little disclaimer thing of saying, the little disclaimer thing of saying, "Yeah, sometimes this gets wrong." "Yeah, sometimes this gets wrong." "Yeah, sometimes this gets wrong." So, in the tiniest words possible, they So, in the tiniest words possible, they So, in the tiniest words possible, they don't talk about the fact that these don't talk about the fact that these don't talk about the fact that these things are trained on stealing millions things are trained on stealing millions things are trained on stealing millions of people's work. But on that last point of people's work. But on that last point of people's work. But on that last point where you say that it's going to get where you say that it's going to get where you say that it's going to get progressively more intelligent and when progressively more intelligent and when progressively more intelligent and when it does, it will be a danger.

it does, it will be a danger. it does, it will be a danger. >> Yeah. Would you agree with the statement >> Yeah. Would you agree with the statement >> Yeah. Would you agree with the statement that artificial intelligence has gotten that artificial intelligence has gotten that artificial intelligence has gotten more intelligent more intelligent more intelligent if you measure it based on any sort of if you measure it based on any sort of if you measure it based on any sort of measure of intelligence one might use? measure of intelligence one might use? measure of intelligence one might use? >> It's got better on the tests that are >> It's got better on the tests that are >> It's got better on the tests that are rigged for the models. It's got better rigged for the models. It's got better rigged for the models. It's got better at tests where you can train for the at tests where you can train for the at tests where you can train for the test. test. test. >> Okay, so it's got better at >> Okay, so it's got better at >> Okay, so it's got better at >> it's got better at tests that they're >> it's got better at tests that they're >> it's got better at tests that they're intentionally trained for. intentionally trained for. intentionally trained for. >> So if you logged the rate of improvement >> So if you logged the rate of improvement >> So if you logged the rate of improvement on a graph, it would look something like on a graph, it would look something like on a graph, it would look something like this, this, this, >> right? >> right? >> right? >> You agree? in terms of what it's capable >> You agree? in terms of what it's capable >> You agree? in terms of what it's capable of doing. of doing. of doing. There we go. Yeah, There we go. Yeah, There we go. Yeah, >> cuz it's not it's not got new features. >> cuz it's not it's not got new features. >> cuz it's not it's not got new features. You'll notice that outside of OpenAI and You'll notice that outside of OpenAI and You'll notice that outside of OpenAI and Anthropic the VA when you remove the Anthropic the VA when you remove the Anthropic the VA when you remove the coding startups, there's basically no coding startups, there's basically no coding startups, there's basically no successful AI startup company. successful AI startup company. successful AI startup company. >> So, we agree that it's got better. It's >> So, we agree that it's got better. It's >> So, we agree that it's got better. It's got more capable got more capable got more capable at doing things. at doing things. at doing things. >> Yeah. Okay. Over time, AI's got more >> Yeah. Okay. Over time, AI's got more >> Yeah. Okay. Over time, AI's got more capable. If we imagine that trajectory capable. If we imagine that trajectory capable. If we imagine that trajectory will continue, it will get more capable. will continue, it will get more capable. will continue, it will get more capable. Then at some point it does cross you Then at some point it does cross you Then at some point it does cross you know this is what they say to me it know this is what they say to me it know this is what they say to me it crosses human intelligence and at such crosses human intelligence and at such crosses human intelligence and at such time time time >> will it not start to do some of the jobs >> will it not start to do some of the jobs >> will it not start to do some of the jobs that people are doing today that people are doing today that people are doing today >> outside of software engineering remove >> outside of software engineering remove >> outside of software engineering remove software because I will concede software software because I will concede software software because I will concede software engineering it's got better at that engineering it's got better at that engineering it's got better at that outside of software engineering where outside of software engineering where outside of software engineering where >> so the chief of staff things that admin >> so the chief of staff things that admin >> so the chief of staff things that admin >> okay so it's got better admin video >> okay so it's got better admin video >> okay so it's got better admin video generation photo generation generation photo generation generation photo generation >> text generation theoretically coding >> text generation theoretically coding >> text generation theoretically coding >> right >> right >> right >> and then I'd say agentic workflows. So >> and then I'd say agentic workflows. So >> and then I'd say agentic workflows. So >> what is an agentic workflow?

>> what is an agentic workflow? >> what is an agentic workflow? >> So automated workflows where you're >> So automated workflows where you're >> So automated workflows where you're doing the same I mean a good example is doing the same I mean a good example is doing the same I mean a good example is looking at the backend data of the dire looking at the backend data of the dire looking at the backend data of the dire of a CEO of a CEO of a CEO >> summarizing >> summarizing >> summarizing >> looking at all of the data ingesting all >> looking at all of the data ingesting all >> looking at all of the data ingesting all of it going out into the internet and of it going out into the internet and of it going out into the internet and searching who Ed is searching who Ed is searching who Ed is >> looking at every interview you've ever >> looking at every interview you've ever >> looking at every interview you've ever done ever. done ever. done ever. >> Uhhuh. >> Uhhuh. >> Uhhuh. >> This is summarizing and generating >> This is summarizing and generating >> This is summarizing and generating >> making a little model on you know the >> making a little model on you know the >> making a little model on you know the things people want to know from Ed. things people want to know from Ed. things people want to know from Ed. >> Producing a report sending that to my >> Producing a report sending that to my >> Producing a report sending that to my inbox. inbox. inbox. >> Me getting a 20 30 40 50page report on >> Me getting a 20 30 40 50page report on >> Me getting a 20 30 40 50page report on Ed before he arrives. Ed before he arrives. Ed before he arrives. >> This is all basically the same thing. I >> This is all basically the same thing. I >> This is all basically the same thing. I think it's been doing for years though. think it's been doing for years though. think it's been doing for years though. It's It's not really new capabilities. It's It's not really new capabilities. It's It's not really new capabilities. >> Research. It's It's >> Research. It's It's >> Research. It's It's >> still the same things. They've had web >> still the same things. They've had web >> still the same things. They've had web search for years. They've had report search for years. They've had report search for years. They've had report generation for years. generation for years. generation for years. >> Well, we couldn't generate >> Well, we couldn't generate >> Well, we couldn't generate highquality videos that are like highquality videos that are like highquality videos that are like indistinguishable from cameras. Seed indistinguishable from cameras. Seed indistinguishable from cameras. Seed dance and these ones that look like dance and these ones that look like dance and these ones that look like movies. movies. movies. >> I mean, they >> I mean, they >> I mean, they >> are incredible. >> are incredible. >> are incredible. >> So, I'm saying the point I'm trying to >> So, I'm saying the point I'm trying to >> So, I'm saying the point I'm trying to make is that if we imagine that over the make is that if we imagine that over the make is that if we imagine that over the last 10 years there has been a rate of last 10 years there has been a rate of last 10 years there has been a rate of improvement in terms of capabilities and improvement in terms of capabilities and improvement in terms of capabilities and output and quality. We've seen output and quality. We've seen output and quality. We've seen hallucinations drop. We've seen the hallucinations drop. We've seen the hallucinations drop. We've seen the models get more quote unquote models get more quote unquote models get more quote unquote intelligent, get better at, you know, if intelligent, get better at, you know, if intelligent, get better at, you know, if you did give it an IQ test, it's getting you did give it an IQ test, it's getting you did give it an IQ test, it's getting higher scores than it was 10 years ago.

higher scores than it was 10 years ago. higher scores than it was 10 years ago. We agree that there's been a upward We agree that there's been a upward We agree that there's been a upward motion of improvement. motion of improvement. motion of improvement. >> This is pretty much how machine learning >> This is pretty much how machine learning >> This is pretty much how machine learning goes when you feed it more data. goes when you feed it more data. goes when you feed it more data. >> Exactly. And you put more compute behind >> Exactly. And you put more compute behind >> Exactly. And you put more compute behind it. So if this continues, it. So if this continues, it. So if this continues, what does the future look like? So the what does the future look like? So the what does the future look like? So the rebuttal I was expecting to hear is that rebuttal I was expecting to hear is that rebuttal I was expecting to hear is that it won't continue. And actually, it won't continue. And actually, it won't continue. And actually, >> I actually don't think it I think that >> I actually don't think it I think that >> I actually don't think it I think that there are hard limits that we're going there are hard limits that we're going there are hard limits that we're going to hit. So you do believe in that to hit. So you do believe in that to hit. So you do believe in that there's a hard limit somewhere. there's a hard limit somewhere. there's a hard limit somewhere. >> We've kind of already hit the >> We've kind of already hit the >> We've kind of already hit the diminishing returns level because for diminishing returns level because for diminishing returns level because for example video generation which is by the example video generation which is by the example video generation which is by the way far less an American concern way far less an American concern way far less an American concern anymore. OpenAI shut down Sora. I think anymore. OpenAI shut down Sora. I think anymore. OpenAI shut down Sora. I think you can still use the API but you can still use the API but you can still use the API but nevertheless look at the look around you nevertheless look at the look around you nevertheless look at the look around you with the amount of stuff in the crew you with the amount of stuff in the crew you with the amount of stuff in the crew you need to get a shot. People think the need to get a shot. People think the need to get a shot. People think the movies are just shot by shot by shot and movies are just shot by shot by shot and movies are just shot by shot by shot and they just magically happen. When you've they just magically happen. When you've they just magically happen. When you've got my my wonderful girlfriend of first got my my wonderful girlfriend of first got my my wonderful girlfriend of first ads, assistant directors, you've got ads, assistant directors, you've got ads, assistant directors, you've got gaffers, you've got lighters, and also gaffers, you've got lighters, and also gaffers, you've got lighters, and also simulating light is insanely difficult. simulating light is insanely difficult. simulating light is insanely difficult. There are so many magical things that There are so many magical things that There are so many magical things that happen in creating visual images that happen in creating visual images that happen in creating visual images that yeah, you could create a one minute long yeah, you could create a one minute long yeah, you could create a one minute long thing that might fool someone. How do thing that might fool someone. How do thing that might fool someone. How do you practically turn that into a movie?

you practically turn that into a movie? you practically turn that into a movie? Because that movie, I forget what the Because that movie, I forget what the Because that movie, I forget what the name is. There was a movie that claimed name is. There was a movie that claimed name is. There was a movie that claimed it aired at Can. It didn't. No one. It it aired at Can. It didn't. No one. It it aired at Can. It didn't. No one. It aired in the city of Can during the Can aired in the city of Can during the Can aired in the city of Can during the Can Film Festival. It was not at the film Film Festival. It was not at the film Film Festival. It was not at the film festival. When it comes to the practical festival. When it comes to the practical festival. When it comes to the practical creation of actual things at the end of creation of actual things at the end of creation of actual things at the end of it versus magic tricks, the actual it versus magic tricks, the actual it versus magic tricks, the actual practical outcomes are not there. The practical outcomes are not there. The practical outcomes are not there. The reason I keep coming back to the reason I keep coming back to the reason I keep coming back to the capabilities thing for the example is capabilities thing for the example is capabilities thing for the example is yeah, they can do better at tests, do yeah, they can do better at tests, do yeah, they can do better at tests, do better number go up. When it comes to better number go up. When it comes to better number go up. When it comes to can this actually do distinct tasks you can this actually do distinct tasks you can this actually do distinct tasks you can rely on it, you can rely on it for can rely on it, you can rely on it for can rely on it, you can rely on it for summaries. You can rely on it for summaries. You can rely on it for summaries. You can rely on it for generations. The things it was doing, generations. The things it was doing, generations. The things it was doing, it's getting linearlyish better at. But it's getting linearlyish better at. But it's getting linearlyish better at. But again, there's a ceiling to that. Like, again, there's a ceiling to that. Like, again, there's a ceiling to that. Like, okay, so it gets really good at okay, so it gets really good at okay, so it gets really good at research. What does that actually mean? research. What does that actually mean? research. What does that actually mean? you've already kind of got the you've already kind of got the you've already kind of got the automation there. What is the next step automation there. What is the next step automation there. What is the next step of that? Because training it to be more of that? Because training it to be more of that? Because training it to be more autonomous for example, that's not autonomous for example, that's not autonomous for example, that's not something that comes from training data. something that comes from training data. something that comes from training data. That is actually a new Gary Marcus a That is actually a new Gary Marcus a That is actually a new Gary Marcus a neuros symbolic. You actually need to neuros symbolic. You actually need to neuros symbolic. You actually need to build a structure around the AI to make build a structure around the AI to make build a structure around the AI to make it work. And even then, it doesn't fix it work. And even then, it doesn't fix it work. And even then, it doesn't fix the the the >> So you're saying that there will become >> So you're saying that there will become >> So you're saying that there will become a point where the rate of improvement a point where the rate of improvement a point where the rate of improvement will plateau.

will plateau. will plateau. >> We're already there and stop. >> We're already there and stop. >> We're already there and stop. >> We've already hit that diminishing. Gary >> We've already hit that diminishing. Gary >> We've already hit that diminishing. Gary Marcus said this in 2022 as well. Do you Marcus said this in 2022 as well. Do you Marcus said this in 2022 as well. Do you know there's lots of people listening know there's lots of people listening know there's lots of people listening now that like they've had their now that like they've had their now that like they've had their workflows completely transformed by workflows completely transformed by workflows completely transformed by these tools? Have they? these tools? Have they? these tools? Have they? >> There'll be people. Yeah, there are. >> There'll be people. Yeah, there are. >> There'll be people. Yeah, there are. Yeah. The thing is, first of all, every Yeah. The thing is, first of all, every Yeah. The thing is, first of all, every single one of them, did you pay for the single one of them, did you pay for the single one of them, did you pay for the tokens? That's the thing. Did you pay tokens? That's the thing. Did you pay tokens? That's the thing. Did you pay for the tokens? And also, how many for the tokens? And also, how many for the tokens? And also, how many tokens did you burn? But putting all tokens did you burn? But putting all tokens did you burn? But putting all that aside, what workflows? Because if that aside, what workflows? Because if that aside, what workflows? Because if it's, yeah, I did a bunch of web it's, yeah, I did a bunch of web it's, yeah, I did a bunch of web scraping or web searches. I'm just not scraping or web searches. I'm just not scraping or web searches. I'm just not impressed. Did you make an entire impressed. Did you make an entire impressed. Did you make an entire movie? No, you didn't. Is it movie? No, you didn't. Is it movie? No, you didn't. Is it speeding up your coding? Yeah, I believe speeding up your coding? Yeah, I believe speeding up your coding? Yeah, I believe that. I've heard that from multiple that. I've heard that from multiple that. I've heard that from multiple people. But again, how much can you people. But again, how much can you people. But again, how much can you trust this? trust this? trust this? >> I think I'm I was getting at is, you >> I think I'm I was getting at is, you >> I think I'm I was getting at is, you know, when in the moment of any know, when in the moment of any know, when in the moment of any technological innovation, people they technological innovation, people they technological innovation, people they extrapolate linearly or they view it as extrapolate linearly or they view it as extrapolate linearly or they view it as a static state, i.e. they think today is a static state, i.e. they think today is a static state, i.e. they think today is going to look like tomorrow or they going to look like tomorrow or they going to look like tomorrow or they think it's going to get better in this think it's going to get better in this think it's going to get better in this sort of straight line. But what we end sort of straight line. But what we end sort of straight line. But what we end up seeing a lot of the time is this up seeing a lot of the time is this up seeing a lot of the time is this exponential improvement. All of the exponential improvement. All of the exponential improvement. All of the innovations we're talking about with you innovations we're talking about with you innovations we're talking about with you with like with compute and all that with with like with compute and all that with with like with compute and all that with fast processes, those are hardware fast processes, those are hardware fast processes, those are hardware breakthroughs. The hardware breakthrough breakthroughs. The hardware breakthrough breakthroughs. The hardware breakthrough companies don't seem to be fixing the companies don't seem to be fixing the companies don't seem to be fixing the LLM problems despite the all the king's LLM problems despite the all the king's LLM problems despite the all the king's horses, all the king's men with what horses, all the king's men with what horses, all the king's men with what nine 10 generations of TPUs from Google nine 10 generations of TPUs from Google nine 10 generations of TPUs from Google now. Broadcoms building stuff with open now. Broadcoms building stuff with open now. Broadcoms building stuff with open AI, their halapeno chip. And yet none of AI, their halapeno chip. And yet none of AI, their halapeno chip. And yet none of these people can just say, "Yeah, we're these people can just say, "Yeah, we're these people can just say, "Yeah, we're on the path to making this profitable."

on the path to making this profitable." on the path to making this profitable." Because they can't. If we fix the Because they can't. If we fix the Because they can't. If we fix the environmental problems and the environmental problems and the environmental problems and the profitability situation, maybe I'd be profitability situation, maybe I'd be profitability situation, maybe I'd be more generous with this stuff. But they more generous with this stuff. But they more generous with this stuff. But they don't seem to be able to. And you talk don't seem to be able to. And you talk don't seem to be able to. And you talk about these improvements and about these improvements and about these improvements and capabilities. There's a certain point at capabilities. There's a certain point at capabilities. There's a certain point at which I'm saying, "Okay, can it do even which I'm saying, "Okay, can it do even which I'm saying, "Okay, can it do even a tenth of the stuff they're promising?" a tenth of the stuff they're promising?" a tenth of the stuff they're promising?" Sam the other week was saying it Sam the other week was saying it Sam the other week was saying it was going to be in like 6 months will be was going to be in like 6 months will be was going to be in like 6 months will be like a genie that you can ask wishes for like a genie that you can ask wishes for like a genie that you can ask wishes for from like never watched from like never watched from like never watched Aladdin. What's he talking about? Like Aladdin. What's he talking about? Like Aladdin. What's he talking about? Like also the the genie was charming. Anyway, also the the genie was charming. Anyway, also the the genie was charming. Anyway, long story short, the promises do not long story short, the promises do not long story short, the promises do not line up with the capabilities or the line up with the capabilities or the line up with the capabilities or the capability improvements. An exponential capability improvements. An exponential capability improvements. An exponential improvement improvement improvement in software and software performance is in software and software performance is in software and software performance is always a result of direct hardware always a result of direct hardware always a result of direct hardware improvement. We have all the gifted improvement. We have all the gifted improvement. We have all the gifted mathematicians, all the gifted software mathematicians, all the gifted software mathematicians, all the gifted software engineers, all the gifted hardware engineers, all the gifted hardware engineers, all the gifted hardware engineers. And where are we? Trillion engineers. And where are we? Trillion engineers. And where are we? Trillion plus dollars in with the future great plus dollars in with the future great plus dollars in with the future great financial crisis and the world's financial crisis and the world's financial crisis and the world's greatest marketing scop. greatest marketing scop. greatest marketing scop. >> I just think in the future I do think >> I just think in the future I do think >> I just think in the future I do think that all of the devices and the that all of the devices and the that all of the devices and the computers we use and the physical items computers we use and the physical items computers we use and the physical items in our world will be more intelligent. I in our world will be more intelligent. I in our world will be more intelligent. I mean sure but is that LLMs mean sure but is that LLMs mean sure but is that LLMs >> and that will be powered by the >> and that will be powered by the >> and that will be powered by the underlying AI infrastructure. It will be underlying AI infrastructure. It will be underlying AI infrastructure. It will be the more data data centers. It will be the more data data centers. It will be the more data data centers. It will be energy coming down.

energy coming down. energy coming down. >> How does a GPU full data center >> How does a GPU full data center >> How does a GPU full data center translate to a Nikon camera that can I translate to a Nikon camera that can I translate to a Nikon camera that can I don't know even what you'd think think don't know even what you'd think think don't know even what you'd think think like because what is the thing we're like because what is the thing we're like because what is the thing we're talking about here? Because the idea talking about here? Because the idea talking about here? Because the idea that devices will get smarter. Sure, I that devices will get smarter. Sure, I that devices will get smarter. Sure, I can see that. It's a very broad can see that. It's a very broad can see that. It's a very broad statement. I could see it happening. statement. I could see it happening. statement. I could see it happening. It's really kind of happening. What does It's really kind of happening. What does It's really kind of happening. What does that have to do with the data centers? that have to do with the data centers? that have to do with the data centers? Cuz these data centers again are not Cuz these data centers again are not Cuz these data centers again are not being built to make your consumer being built to make your consumer being built to make your consumer electronics smarter. They're not being electronics smarter. They're not being electronics smarter. They're not being built for anything other than built for anything other than built for anything other than speculating on the ability to capture speculating on the ability to capture speculating on the ability to capture demand for generative AI services. demand for generative AI services. demand for generative AI services. >> But it's not just generative AI. We went >> But it's not just generative AI. We went >> But it's not just generative AI. We went through that earlier. through that earlier. through that earlier. >> Yes. No, but those data centers, they >> Yes. No, but those data centers, they >> Yes. No, but those data centers, they are being built for generative AI. They are being built for generative AI. They are being built for generative AI. They are not being built for anything else. are not being built for anything else. are not being built for anything else. Would you consider generative AI to be Would you consider generative AI to be Would you consider generative AI to be the fact that on Meta's earnings call the fact that on Meta's earnings call the fact that on Meta's earnings call like a couple of weeks ago, Mark like a couple of weeks ago, Mark like a couple of weeks ago, Mark Zuckerberg said, "The big breakthrough Zuckerberg said, "The big breakthrough Zuckerberg said, "The big breakthrough we've had, which has resulted in 15 we've had, which has resulted in 15 we've had, which has resulted in 15 basis points of increased retention, I basis points of increased retention, I basis points of increased retention, I believe he was referring to Instagram, believe he was referring to Instagram, believe he was referring to Instagram, is that we now take anything you post on is that we now take anything you post on is that we now take anything you post on social media and we run it through an AI social media and we run it through an AI social media and we run it through an AI to get full context of what it is." And to get full context of what it is." And to get full context of what it is." And because we can see guy sat in front of because we can see guy sat in front of because we can see guy sat in front of me called Ed with blue shirt and coffee, me called Ed with blue shirt and coffee, me called Ed with blue shirt and coffee, we now can train the AI to serve whoever we now can train the AI to serve whoever we now can train the AI to serve whoever wants blue shirt, Ed, and with coffee to wants blue shirt, Ed, and with coffee to wants blue shirt, Ed, and with coffee to the right user, which means people are the right user, which means people are the right user, which means people are retained longer because retained longer because retained longer because >> it'sn't 15 basis points, like 0.15%.

>> it'sn't 15 basis points, like 0.15%. >> it'sn't 15 basis points, like 0.15%. >> Yeah, it's cool. But it makes a >> Yeah, it's cool. But it makes a >> Yeah, it's cool. But it makes a difference at scale. It makes a big difference at scale. It makes a big difference at scale. It makes a big difference at scale. difference at scale. difference at scale. >> Yeah. But 10 and something billion >> Yeah. But 10 and something billion >> Yeah. But 10 and something billion dollars in and the best you've got is dollars in and the best you've got is dollars in and the best you've got is 0.15%. If if he could be fight I mean 0.15%. If if he could be fight I mean 0.15%. If if he could be fight I mean how much of a difference because how much of a difference because how much of a difference because >> there's a reason he's saying basis >> there's a reason he's saying basis >> there's a reason he's saying basis points versus dollars points versus dollars points versus dollars >> because think about it like this if Mark >> because think about it like this if Mark >> because think about it like this if Mark Zuckerberg was Zuckerberg was Zuckerberg was >> I take your point about scale. No, I'm >> I take your point about scale. No, I'm >> I take your point about scale. No, I'm saying the point I was making was that saying the point I was making was that saying the point I was making was that that is another application of these that is another application of these that is another application of these data centers because it needs a data data centers because it needs a data data centers because it needs a data center that is driving revenues, but center that is driving revenues, but center that is driving revenues, but also that's not out that's outside of us also that's not out that's outside of us also that's not out that's outside of us thinking about just generating thinking about just generating thinking about just generating >> and that's generative >> and that's generative >> and that's generative model. Muse was it? Oh, Muse Spark is model. Muse was it? Oh, Muse Spark is model. Muse was it? Oh, Muse Spark is their LLM. Gem is their generative ad their LLM. Gem is their generative ad their LLM. Gem is their generative ad model. Well, Muse then then that's them model. Well, Muse then then that's them model. Well, Muse then then that's them doing the weird thing where it's like on doing the weird thing where it's like on doing the weird thing where it's like on Instagram and it's like Dave the cat. Instagram and it's like Dave the cat. Instagram and it's like Dave the cat. Why is Dave the cat suffering? Like it's Why is Dave the cat suffering? Like it's Why is Dave the cat suffering? Like it's the weird popup things. Meta is the weird popup things. Meta is the weird popup things. Meta is god damn that company sucks. Like every god damn that company sucks. Like every god damn that company sucks. Like every time I think about how they've ruined time I think about how they've ruined time I think about how they've ruined that product. But that's the thing that product. But that's the thing that product. But that's the thing though, again, why can't he just say though, again, why can't he just say though, again, why can't he just say with his whole chest, we've made a with his whole chest, we've made a with his whole chest, we've made a couple billion. Why can't he say that?

couple billion. Why can't he say that? couple billion. Why can't he say that? Because he isn't. Because there's not Because he isn't. Because there's not Because he isn't. Because there's not actually a way of going, I spent all actually a way of going, I spent all actually a way of going, I spent all this money. I spent 14 billion goddamn this money. I spent 14 billion goddamn this money. I spent 14 billion goddamn dollars on scale Alexander Wong and I dollars on scale Alexander Wong and I dollars on scale Alexander Wong and I made this much. They can't. It gets back made this much. They can't. It gets back made this much. They can't. It gets back to a very simple point of, hey, if it to a very simple point of, hey, if it to a very simple point of, hey, if it was going well, you'd tell me how well was going well, you'd tell me how well was going well, you'd tell me how well it was going rather than, I don't know, it was going rather than, I don't know, it was going rather than, I don't know, doing this weird rain dance thing where doing this weird rain dance thing where doing this weird rain dance thing where you're like, well, if we move all the you're like, well, if we move all the you're like, well, if we move all the pieces around in 3 years, theoretically, pieces around in 3 years, theoretically, pieces around in 3 years, theoretically, this will happen. this will happen. this will happen. I've done almost 700 interviews with I've done almost 700 interviews with I've done almost 700 interviews with some of the most interesting people in some of the most interesting people in some of the most interesting people in the world. And one of the things you the world. And one of the things you the world. And one of the things you learn, which is unexpected, is that learn, which is unexpected, is that learn, which is unexpected, is that vulnerability is the doorway to vulnerability is the doorway to vulnerability is the doorway to connection. And after sitting here for 2 connection. And after sitting here for 2 connection. And after sitting here for 2 three hours with a guest, I feel a deep three hours with a guest, I feel a deep three hours with a guest, I feel a deep sense of connection to them. And as they sense of connection to them. And as they sense of connection to them. And as they leave, what I get them to do is to write leave, what I get them to do is to write leave, what I get them to do is to write a question in the diary of a CEO. We've a question in the diary of a CEO. We've a question in the diary of a CEO. We've taken all of the questions from the taken all of the questions from the taken all of the questions from the diary of a CEO. We have put the question diary of a CEO. We have put the question diary of a CEO. We have put the question here on this card with the name of the here on this card with the name of the here on this card with the name of the person that wrote it. So you can sit at person that wrote it. So you can sit at person that wrote it. So you can sit at home as I do with my fiance and my home as I do with my fiance and my home as I do with my fiance and my colleagues at work and other people in colleagues at work and other people in colleagues at work and other people in my life. Whenever we get a minute, we my life. Whenever we get a minute, we my life. Whenever we get a minute, we play the diio conversation cards and it play the diio conversation cards and it play the diio conversation cards and it is incredible what happens. These are is incredible what happens. These are is incredible what happens. These are great if you're in a romantic great if you're in a romantic great if you're in a romantic relationship and you want to connect relationship and you want to connect relationship and you want to connect your partner more. These are also great your partner more. These are also great your partner more. These are also great if you're in a team and you want to bond if you're in a team and you want to bond if you're in a team and you want to bond your team together. And I have to say your team together. And I have to say your team together. And I have to say they're also great for families that they're also great for families that they're also great for families that want to learn more about each other and want to learn more about each other and want to learn more about each other and that need a good excuse to spend some that need a good excuse to spend some that need a good excuse to spend some time in a digital world in the analog time in a digital world in the analog time in a digital world in the analog environment connecting human to human.

environment connecting human to human. environment connecting human to human. It is remarkable what the right question It is remarkable what the right question It is remarkable what the right question at the right time can do. Go to the at the right time can do. Go to the at the right time can do. Go to the diary.com diary.com diary.com and you can get these conversation cards and you can get these conversation cards and you can get these conversation cards right now. There should be a button just right now. There should be a button just right now. There should be a button just down below here. And if it says down below here. And if it says down below here. And if it says subscribed, you're already subscribed. subscribed, you're already subscribed. subscribed, you're already subscribed. If it says subscriber, that means you're If it says subscriber, that means you're If it says subscriber, that means you're not yet. And if you're not subscribed, not yet. And if you're not subscribed, not yet. And if you're not subscribed, please could you do us a favor and hit please could you do us a favor and hit please could you do us a favor and hit that button? It helps the show more than that button? It helps the show more than that button? It helps the show more than you know. And according to the you know. And according to the you know. And according to the algorithm, you're someone that watches algorithm, you're someone that watches algorithm, you're someone that watches our show, but you haven't yet hit that our show, but you haven't yet hit that our show, but you haven't yet hit that button. Thank you so much. I do think button. Thank you so much. I do think button. Thank you so much. I do think you're accurate and right when you talk you're accurate and right when you talk you're accurate and right when you talk about the fact that there's a lot of about the fact that there's a lot of about the fact that there's a lot of like is the word for gazy? like is the word for gazy? like is the word for gazy? >> Yeah. >> Yeah. >> Yeah. >> Where like there's a lot of people that >> Where like there's a lot of people that >> Where like there's a lot of people that have spent a lot of money and they kind have spent a lot of money and they kind have spent a lot of money and they kind of shouldn't have spent it and they of shouldn't have spent it and they of shouldn't have spent it and they up and now they're thinking up and now they're thinking up and now they're thinking like we've spent all this invested money like we've spent all this invested money like we've spent all this invested money kind of like the metaverse was a bit of kind of like the metaverse was a bit of kind of like the metaverse was a bit of a a a >> oh my god that was a bit of a joke. >> oh my god that was a bit of a joke. >> oh my god that was a bit of a joke. >> That's so weird. >> That's so weird. >> That's so weird. >> A lot of money spent. We kind of thought >> A lot of money spent. We kind of thought >> A lot of money spent. We kind of thought this dream was coming of this well I this dream was coming of this well I this dream was coming of this well I shouldn't say dream cuz it's not a dream shouldn't say dream cuz it's not a dream shouldn't say dream cuz it's not a dream I've had but I've had but I've had but >> dream that they had. >> dream that they had. >> dream that they had. >> Yeah. This sort of virtual world and >> Yeah. This sort of virtual world and >> Yeah. This sort of virtual world and actually it never transpired and there's actually it never transpired and there's actually it never transpired and there's no sign that it will in the near term. no sign that it will in the near term. no sign that it will in the near term. AI and the dotcom boom in this regard AI and the dotcom boom in this regard AI and the dotcom boom in this regard are the same. NFTTS were the same, are the same. NFTTS were the same, are the same. NFTTS were the same, >> you know. So crypto, one could argue >> you know. So crypto, one could argue >> you know. So crypto, one could argue that a lot of the crypto industry was that a lot of the crypto industry was that a lot of the crypto industry was the same. It's weighing that is inflated the same. It's weighing that is inflated the same. It's weighing that is inflated by the media. The difference is the by the media. The difference is the by the media. The difference is the reason the metaverse and NFTs didn't reason the metaverse and NFTs didn't reason the metaverse and NFTs didn't escape this was there weren't stocks to escape this was there weren't stocks to escape this was there weren't stocks to speculate on. There weren't big speculate on. There weren't big speculate on. There weren't big companies that you could invest in. They companies that you could invest in. They companies that you could invest in. They had re record earnings in 2021. There's had re record earnings in 2021. There's had re record earnings in 2021. There's a bunch of money floating in the system a bunch of money floating in the system a bunch of money floating in the system thanks to postcoid uh the PDC that thanks to postcoid uh the PDC that thanks to postcoid uh the PDC that basically government federal money basically government federal money basically government federal money flowed in to the banks. There was a flowed in to the banks. There was a flowed in to the banks. There was a bunch of easy money zero interest free bunch of easy money zero interest free bunch of easy money zero interest free era money was easy to find. Then after

era money was easy to find. Then after era money was easy to find. Then after that there was the hangover. Growth that there was the hangover. Growth that there was the hangover. Growth started to slow down dramatically. This started to slow down dramatically. This started to slow down dramatically. This is actually my rockcom bubble theory is actually my rockcom bubble theory is actually my rockcom bubble theory which is they don't have any hyperrowth which is they don't have any hyperrowth which is they don't have any hyperrowth ideas anymore. So suddenly they started ideas anymore. So suddenly they started ideas anymore. So suddenly they started buying GPUs. And when they bought GPUs buying GPUs. And when they bought GPUs buying GPUs. And when they bought GPUs people went they're doing AI. Oh we people went they're doing AI. Oh we people went they're doing AI. Oh we better buy the stock. And the stocks better buy the stock. And the stocks better buy the stock. And the stocks went on an incredible run. may like went on an incredible run. may like went on an incredible run. may like several hundred percent grow in the last several hundred percent grow in the last several hundred percent grow in the last few years. the stock has grown by few years. the stock has grown by few years. the stock has grown by hundreds of percent. Despite zero proof hundreds of percent. Despite zero proof hundreds of percent. Despite zero proof and because the media was just saying, and because the media was just saying, and because the media was just saying, "Yeah, Meta's revenues growing because "Yeah, Meta's revenues growing because "Yeah, Meta's revenues growing because of AI, right? Microsoft's revenue is of AI, right? Microsoft's revenue is of AI, right? Microsoft's revenue is grown because of AI, right? The fugazi grown because of AI, right? The fugazi grown because of AI, right? The fugazi you're talking about was the fact that you're talking about was the fact that you're talking about was the fact that everyone just gave them credit in everyone just gave them credit in everyone just gave them credit in advance and now we're kind of getting to advance and now we're kind of getting to advance and now we're kind of getting to the point where it's like, hey, you the point where it's like, hey, you the point where it's like, hey, you didn't spend that trillion dollars for didn't spend that trillion dollars for didn't spend that trillion dollars for no reason, did you? Satcha Amy Amy Hood no reason, did you? Satcha Amy Amy Hood no reason, did you? Satcha Amy Amy Hood just going to take him out back, send just going to take him out back, send just going to take him out back, send him to the glue factory or something?" him to the glue factory or something?" him to the glue factory or something?" Like, Like, Like, >> I do think there's overspending. I I >> I do think there's overspending. I I >> I do think there's overspending. I I want to concede that but I doic want to concede that but I doic want to concede that but I doic >> yeah no I do think there is and I think >> yeah no I do think there is and I think >> yeah no I do think there is and I think the reason why there's overspending Ed the reason why there's overspending Ed the reason why there's overspending Ed is I think there is something here is I think there is something here is I think there is something here >> and what >> and what >> and what >> in terms of like I think there is pra p p p p p p p p p p p p p p p p p p p p practical uses for this technology and I practical uses for this technology and I practical uses for this technology and I think when people realize that through think when people realize that through think when people realize that through history they go crazy because they want history they go crazy because they want history they go crazy because they want to be the person that owns the to be the person that owns the to be the person that owns the opportunity.

opportunity. opportunity. >> I'm going to be honest I just I >> I'm going to be honest I just I >> I'm going to be honest I just I fundamentally don't agree. fundamentally don't agree. fundamentally don't agree. >> You don't agree with which part you >> You don't agree with which part you >> You don't agree with which part you >> I don't agree that this that the >> I don't agree that this that the >> I don't agree that this that the speculation is a result of actual speculation is a result of actual speculation is a result of actual demand. I don't believe it's suspect. I demand. I don't believe it's suspect. I demand. I don't believe it's suspect. I don't think private credit is sinking don't think private credit is sinking don't think private credit is sinking hundreds of billions of dollars into AI hundreds of billions of dollars into AI hundreds of billions of dollars into AI because of actual demand. They are doing because of actual demand. They are doing because of actual demand. They are doing it because they saw the biggest it because they saw the biggest it because they saw the biggest companies in the world building data companies in the world building data companies in the world building data centers making a ton of money from two centers making a ton of money from two centers making a ton of money from two companies they feed money and went I companies they feed money and went I companies they feed money and went I want some of that money. want some of that money. want some of that money. >> I am saying that I do think there is >> I am saying that I do think there is >> I am saying that I do think there is value in the underlying technology. I value in the underlying technology. I value in the underlying technology. I think that and so I think I'm not saying think that and so I think I'm not saying think that and so I think I'm not saying how much value how much value how much value >> right okay I actually I get your meaning >> right okay I actually I get your meaning >> right okay I actually I get your meaning that's fair. that's fair. that's fair. >> I'm not saying it's proportionate to the >> I'm not saying it's proportionate to the >> I'm not saying it's proportionate to the investment. All I'm saying is that do investment. All I'm saying is that do investment. All I'm saying is that do you know what it's like? It's like if I you know what it's like? It's like if I you know what it's like? It's like if I take your example, the rot economy essay take your example, the rot economy essay take your example, the rot economy essay that you wrote. that you wrote. that you wrote. >> Yeah. >> Yeah. >> Yeah. >> Say that you're on a desert island and >> Say that you're on a desert island and >> Say that you're on a desert island and then someone says they found a banana then someone says they found a banana then someone says they found a banana tree, tree, tree, >> right? >> right? >> right? >> And there's there's 10,000 people on the >> And there's there's 10,000 people on the >> And there's there's 10,000 people on the island. island. island. >> Okay. >> Okay. >> Okay. >> They are going to stam peed >> They are going to stam peed >> They are going to stam peed towards where they think the banana tree towards where they think the banana tree towards where they think the banana tree is. They are going to claw each is. They are going to claw each is. They are going to claw each other to pieces. And if if your essay other to pieces. And if if your essay other to pieces. And if if your essay here is right that there was desperation here is right that there was desperation here is right that there was desperation cuz they hadn't found an innovation in a cuz they hadn't found an innovation in a cuz they hadn't found an innovation in a while, while, while, >> maybe that explains it. Maybe there is a >> maybe that explains it. Maybe there is a >> maybe that explains it. Maybe there is a bit of value here, bit of value here, bit of value here, >> right?

>> right? >> right? >> And they're stam peeding and >> And they're stam peeding and >> And they're stam peeding and killing each other and making irrational killing each other and making irrational killing each other and making irrational decisions like hungry people would. decisions like hungry people would. decisions like hungry people would. >> I actually think we're then we actually >> I actually think we're then we actually >> I actually think we're then we actually agree. That is actually my point, which agree. That is actually my point, which agree. That is actually my point, which is these three companies in Meta, their is these three companies in Meta, their is these three companies in Meta, their main business lines are running out of main business lines are running out of main business lines are running out of growth. There's only so much they can growth. There's only so much they can growth. There's only so much they can grow. And indeed, in the next three and grow. And indeed, in the next three and grow. And indeed, in the next three and a half years, analysts think that these a half years, analysts think that these a half years, analysts think that these two bastards, these two, OpenAI and two bastards, these two, OpenAI and two bastards, these two, OpenAI and Anthropic are going to spend over $400 Anthropic are going to spend over $400 Anthropic are going to spend over $400 billion on these people alone, billion on these people alone, billion on these people alone, Microsoft, Google, and Amazon. And the Microsoft, Google, and Amazon. And the Microsoft, Google, and Amazon. And the crazy thing is is that's a large part of crazy thing is is that's a large part of crazy thing is is that's a large part of their future growth. And if this money their future growth. And if this money their future growth. And if this money isn't spent, their growth slows down. isn't spent, their growth slows down. isn't spent, their growth slows down. Okay, Okay, Okay, >> so your point about a bananas, I >> so your point about a bananas, I >> so your point about a bananas, I actually agree. That is the rockcom actually agree. That is the rockcom actually agree. That is the rockcom bubble, it's they don't have a new thing bubble, it's they don't have a new thing bubble, it's they don't have a new thing and they're desperate. And indeed, they and they're desperate. And indeed, they and they're desperate. And indeed, they got rewarded for buying the GPUs. They got rewarded for buying the GPUs. They got rewarded for buying the GPUs. They got when they bought these goddamn GPUs got when they bought these goddamn GPUs got when they bought these goddamn GPUs from Nvidia, all the markets went from Nvidia, all the markets went from Nvidia, all the markets went rockard overnight. They loved it. There rockard overnight. They loved it. There rockard overnight. They loved it. There were stories about how they were sending were stories about how they were sending were stories about how they were sending armored cars with the GPUs to Microsoft armored cars with the GPUs to Microsoft armored cars with the GPUs to Microsoft to make sure Microsoft got the GPUs. And to make sure Microsoft got the GPUs. And to make sure Microsoft got the GPUs. And so everyone saw all that money flowing so everyone saw all that money flowing so everyone saw all that money flowing in. Even though they never disclosed AI in. Even though they never disclosed AI in. Even though they never disclosed AI revenues, they saw the expenditures and revenues, they saw the expenditures and revenues, they saw the expenditures and they went, "Well, I want to do what they went, "Well, I want to do what they went, "Well, I want to do what these people are doing. I want to get a these people are doing. I want to get a these people are doing. I want to get a little of that money, don't I?"

little of that money, don't I?" little of that money, don't I?" >> I think the area where we have a slight >> I think the area where we have a slight >> I think the area where we have a slight disagreement is that I think the disagreement is that I think the disagreement is that I think the underlying technology has a lot more underlying technology has a lot more underlying technology has a lot more promise over the long term than you do. promise over the long term than you do. promise over the long term than you do. So the thing I want to push back on So the thing I want to push back on So the thing I want to push back on there is there is there is to have progress with AI just on a to have progress with AI just on a to have progress with AI just on a taking it in a vacuum to have progress taking it in a vacuum to have progress taking it in a vacuum to have progress for these two companies to keep going for these two companies to keep going for these two companies to keep going and to keep progressing they need to and to keep progressing they need to and to keep progressing they need to spend tens of billions of dollars a year spend tens of billions of dollars a year spend tens of billions of dollars a year on training. on training. on training. >> The only way that that can happen is if >> The only way that that can happen is if >> The only way that that can happen is if these companies and venture capitalists these companies and venture capitalists these companies and venture capitalists and private credit firms and Nvidia and private credit firms and Nvidia and private credit firms and Nvidia >> keep circulating money to them. So the >> keep circulating money to them. So the >> keep circulating money to them. So the progress progress progress >> that we've got so far is entirely a >> that we've got so far is entirely a >> that we've got so far is entirely a result of this circular system. So it result of this circular system. So it result of this circular system. So it means that means that means that >> circular you talked about VCs there >> circular you talked about VCs there >> circular you talked about VCs there >> venture capitalists who are by the way >> venture capitalists who are by the way >> venture capitalists who are by the way the majority of the funding that open the majority of the funding that open the majority of the funding that open AAI got in the last 6 months came from AAI got in the last 6 months came from AAI got in the last 6 months came from SoftBank Nvidia and Amazon SoftBank Nvidia and Amazon SoftBank Nvidia and Amazon >> okay yeah >> okay yeah >> okay yeah >> so just the point is is you're talking >> so just the point is is you're talking >> so just the point is is you're talking about progress continuing progress in about progress continuing progress in about progress continuing progress in LLM can only continue as long as the LLM can only continue as long as the LLM can only continue as long as the money keeps flowing once the money keep money keeps flowing once the money keep money keeps flowing once the money keep once the money stops flowing the once the money stops flowing the once the money stops flowing the progress stops which progress stops which progress stops which >> but isn't that most like early like >> but isn't that most like early like >> but isn't that most like early like Spotify didn't make money for 20 years Spotify didn't make money for 20 years Spotify didn't make money for 20 years >> Spotify didn't lose 20.9 9 billion in >> Spotify didn't lose 20.9 9 billion in >> Spotify didn't lose 20.9 9 billion in one year. They didn't need to raise $217 one year. They didn't need to raise $217 one year. They didn't need to raise $217 billion in the space of 6 months.

billion in the space of 6 months. billion in the space of 6 months. >> Yeah. And Uber is another example. >> Yeah. And Uber is another example. >> Yeah. And Uber is another example. >> $33 billion since inception before it >> $33 billion since inception before it >> $33 billion since inception before it became a messy kind of profitable. became a messy kind of profitable. became a messy kind of profitable. Amazon Web Services between 2003 and Amazon Web Services between 2003 and Amazon Web Services between 2003 and 2015 when it became profitable. $29.7 2015 when it became profitable. $29.7 2015 when it became profitable. $29.7 billion the scale. Yeah. That's the billion the scale. Yeah. That's the billion the scale. Yeah. That's the total capital expenditures and that's total capital expenditures and that's total capital expenditures and that's not just Amazon Web Services. That's the not just Amazon Web Services. That's the not just Amazon Web Services. That's the entire logistics operation normalized entire logistics operation normalized entire logistics operation normalized for inflation. for inflation. for inflation. >> So they all lost money for a long period >> So they all lost money for a long period >> So they all lost money for a long period of time is the TLDDR. of time is the TLDDR. of time is the TLDDR. >> Yes. But the amount of money they lost >> Yes. But the amount of money they lost >> Yes. But the amount of money they lost is is is completely completely completely just magnitudes different on a level just magnitudes different on a level just magnitudes different on a level where these three where these three where these three >> Can I argue then that the that's because >> Can I argue then that the that's because >> Can I argue then that the that's because the potential of intelligence permeates the potential of intelligence permeates the potential of intelligence permeates everything whereas Amazon at the time everything whereas Amazon at the time everything whereas Amazon at the time was like selling books was like selling books was like selling books >> no >> no >> no >> that was that was bringing retail online >> that was that was bringing retail online >> that was that was bringing retail online >> when Amazon web services grew it was >> when Amazon web services grew it was >> when Amazon web services grew it was >> oh so cloud with Amazon web services the >> oh so cloud with Amazon web services the >> oh so cloud with Amazon web services the reason I bring that up going to repeat reason I bring that up going to repeat reason I bring that up going to repeat something but it's really important 2003 something but it's really important 2003 something but it's really important 2003 it was founded it was founded it was founded >> and it was founded mostly because Amazon >> and it was founded mostly because Amazon >> and it was founded mostly because Amazon as a growing online store needed as a growing online store needed as a growing online store needed hardcore infrastructure. 2006, I think, hardcore infrastructure. 2006, I think, hardcore infrastructure. 2006, I think, is when they turned it client-f facing. is when they turned it client-f facing. is when they turned it client-f facing. I may be wrong on the dates there, but I may be wrong on the dates there, but I may be wrong on the dates there, but 2015 was the year it became profitable.

2015 was the year it became profitable. 2015 was the year it became profitable. >> Yeah. >> Yeah. >> Yeah. >> The total capital expenditures >> The total capital expenditures >> The total capital expenditures normalized for inflation with $29.7 normalized for inflation with $29.7 normalized for inflation with $29.7 billion across that 12-year period. billion across that 12-year period. billion across that 12-year period. >> Yeah. >> Yeah. >> Yeah. >> And yeah, it lost money, but >> And yeah, it lost money, but >> And yeah, it lost money, but >> if we speak cold economics here, Amazon >> if we speak cold economics here, Amazon >> if we speak cold economics here, Amazon didn't have to go into the they were didn't have to go into the they were didn't have to go into the they were unprofitable in in a way, but their unprofitable in in a way, but their unprofitable in in a way, but their margins actually started improving margins actually started improving margins actually started improving because AWS was a very margin heavy because AWS was a very margin heavy because AWS was a very margin heavy business. It was great. business. It was great. business. It was great. >> Yeah, >> Yeah, >> Yeah, >> these these two Google cash flow >> these these two Google cash flow >> these these two Google cash flow negative, Amazon cash flow negative. negative, Amazon cash flow negative. negative, Amazon cash flow negative. These businesses, the reason you liked These businesses, the reason you liked These businesses, the reason you liked software businesses was they are meant software businesses was they are meant software businesses was they are meant to be cash heavy asset light. These to be cash heavy asset light. These to be cash heavy asset light. These companies along with Meta have added companies along with Meta have added companies along with Meta have added more than $700 billion of new property, more than $700 billion of new property, more than $700 billion of new property, plants and equipment. So assets, data plants and equipment. So assets, data plants and equipment. So assets, data centers, GPUs in the last four years. centers, GPUs in the last four years. centers, GPUs in the last four years. They have gone from being these cash They have gone from being these cash They have gone from being these cash machines to these cash furnaces. machines to these cash furnaces. machines to these cash furnaces. >> You said a second ago, this can only >> You said a second ago, this can only >> You said a second ago, this can only continue if if investors continue to continue if if investors continue to continue if if investors continue to invest. invest. invest. >> Yes. >> Yes. >> Yes. >> And I was saying I I think that >> And I was saying I I think that >> And I was saying I I think that investors are used to pumping money into investors are used to pumping money into investors are used to pumping money into things that are burning cash. Your things that are burning cash. Your things that are burning cash. Your rebuttal to me sounds like well this is rebuttal to me sounds like well this is rebuttal to me sounds like well this is burning more cash than ever. And then so burning more cash than ever. And then so burning more cash than ever. And then so I would say well is the opportunity I would say well is the opportunity I would say well is the opportunity bigger than those other case studies you bigger than those other case studies you bigger than those other case studies you referenced like AWS? And one would say referenced like AWS? And one would say referenced like AWS? And one would say that the opportunity of intelligence that the opportunity of intelligence that the opportunity of intelligence permeates everything. So the TAM the permeates everything. So the TAM the permeates everything. So the TAM the total addressable market is enormous.

total addressable market is enormous. total addressable market is enormous. Maybe the revival back to me is about Maybe the revival back to me is about Maybe the revival back to me is about open source and all these kind of open source and all these kind of open source and all these kind of >> No, no, no. I I actually know what >> No, no, no. I I actually know what >> No, no, no. I I actually know what you're getting at. So what you were you're getting at. So what you were you're getting at. So what you were describing there is the argument that describing there is the argument that describing there is the argument that Sachinadella or Sam would make that the Sachinadella or Sam would make that the Sachinadella or Sam would make that the theoretical opportunity of large theoretical opportunity of large theoretical opportunity of large language models and I could have bought language models and I could have bought language models and I could have bought that into any 24 from them when that into any 24 from them when that into any 24 from them when they were like, "Oh, we see the they were like, "Oh, we see the they were like, "Oh, we see the opportunity. We've gone way past the opportunity. We've gone way past the opportunity. We've gone way past the point at which you can rationally argue point at which you can rationally argue point at which you can rationally argue that LLMs need this much money. And when that LLMs need this much money. And when that LLMs need this much money. And when I say the money needs to keep flowing, I I say the money needs to keep flowing, I I say the money needs to keep flowing, I am talking these two compan Open AI just am talking these two compan Open AI just am talking these two compan Open AI just open AI Clammy Sam has said Wall Street open AI Clammy Sam has said Wall Street open AI Clammy Sam has said Wall Street Journal and Isaagi reported a few weeks Journal and Isaagi reported a few weeks Journal and Isaagi reported a few weeks ago they plan to spend $750 billion on ago they plan to spend $750 billion on ago they plan to spend $750 billion on compute through 2030. I think they're compute through 2030. I think they're compute through 2030. I think they're going to be dead before then, but $750 going to be dead before then, but $750 going to be dead before then, but $750 billion. billion. billion. That is an insane amount of money. That That is an insane amount of money. That That is an insane amount of money. That is crazy is crazy is crazy >> and [laughter] >> and [laughter] >> and [laughter] a large chunk of that is training. So a large chunk of that is training. So a large chunk of that is training. So when I say progress, I mean literally to when I say progress, I mean literally to when I say progress, I mean literally to make the models better at stuff requires make the models better at stuff requires make the models better at stuff requires billions of dollars invested just in billions of dollars invested just in billions of dollars invested just in data data data and also tens of billions of dollars of and also tens of billions of dollars of and also tens of billions of dollars of taking that data. And so training taking that data. And so training taking that data. And so training training is actually a really training is actually a really training is actually a really interesting thing because when you think interesting thing because when you think interesting thing because when you think of like for Jake and Troy my trainers of like for Jake and Troy my trainers of like for Jake and Troy my trainers when I train with them when I lift with when I train with them when I lift with when I train with them when I lift with them I have a defined thing and when I them I have a defined thing and when I them I have a defined thing and when I do it and I eat right muscles get bigger do it and I eat right muscles get bigger do it and I eat right muscles get bigger they would. And here's the thing. When they would. And here's the thing. When they would. And here's the thing. When you train with an LLM, you're you train with an LLM, you're you train with an LLM, you're experimenting each and this is not experimenting each and this is not experimenting each and this is not actually a hit on the companies because actually a hit on the companies because actually a hit on the companies because they're still trying to work out how to they're still trying to work out how to they're still trying to work out how to do the thing because putting aside how I do the thing because putting aside how I do the thing because putting aside how I feel like they're trying to innovate. I feel like they're trying to innovate. I feel like they're trying to innovate. I think there are people at these think there are people at these think there are people at these companies that actually want to do companies that actually want to do companies that actually want to do something interesting. It's costing too something interesting. It's costing too something interesting. It's costing too much money. So once the money tap turns

much money. So once the money tap turns much money. So once the money tap turns off, the money won't be there to buy the off, the money won't be there to buy the off, the money won't be there to buy the data or feed the data into the GPUs. Put data or feed the data into the GPUs. Put data or feed the data into the GPUs. Put aside all the thoughts I have, just the aside all the thoughts I have, just the aside all the thoughts I have, just the raw capital to get them this far has raw capital to get them this far has raw capital to get them this far has cost increasingly larger amounts of cost increasingly larger amounts of cost increasingly larger amounts of money and increasingly larger amounts of money and increasingly larger amounts of money and increasingly larger amounts of training money for training runs that training money for training runs that training money for training runs that sometimes can fail. GPT5 was meant to be sometimes can fail. GPT5 was meant to be sometimes can fail. GPT5 was meant to be this panacea for the AI industry. They this panacea for the AI industry. They this panacea for the AI industry. They had at least one training run that cost had at least one training run that cost had at least one training run that cost half a billion dollars and did nothing. half a billion dollars and did nothing. half a billion dollars and did nothing. And that's the thing. If we are thinking And that's the thing. If we are thinking And that's the thing. If we are thinking about progress in a in a vacuum, they about progress in a in a vacuum, they about progress in a in a vacuum, they need so much more money just to maybe need so much more money just to maybe need so much more money just to maybe get somewhere. There's no guarantee. get somewhere. There's no guarantee. get somewhere. There's no guarantee. There's never any guarantee, but there's There's never any guarantee, but there's There's never any guarantee, but there's a reason that Google and Amazon are cash a reason that Google and Amazon are cash a reason that Google and Amazon are cash flow negative now. There's a reason why flow negative now. There's a reason why flow negative now. There's a reason why Oracle's probably going to die as a Oracle's probably going to die as a Oracle's probably going to die as a result of OpenAI because Oracle's future result of OpenAI because Oracle's future result of OpenAI because Oracle's future depends on OpenAI spending $300 billion depends on OpenAI spending $300 billion depends on OpenAI spending $300 billion over 5 years. over 5 years. over 5 years. >> It's absolutely fascinating because I >> It's absolutely fascinating because I >> It's absolutely fascinating because I was just reading through a list of was just reading through a list of was just reading through a list of quotes from the big CEOs of AI companies quotes from the big CEOs of AI companies quotes from the big CEOs of AI companies to see what they would rebuttle you. to see what they would rebuttle you. to see what they would rebuttle you. >> Yeah. >> Yeah. >> Yeah. >> And they're all basically saying the >> And they're all basically saying the >> And they're all basically saying the same thing. They're all saying, this is same thing. They're all saying, this is same thing. They're all saying, this is actual an exact quote from Sundar who is actual an exact quote from Sundar who is actual an exact quote from Sundar who is the CEO of Google. He says the risk of the CEO of Google. He says the risk of the CEO of Google. He says the risk of underinvesting is dramatically greater underinvesting is dramatically greater underinvesting is dramatically greater than the risk of overinvesting.

than the risk of overinvesting. than the risk of overinvesting. And you go down, you go through this, And you go down, you go through this, And you go down, you go through this, you know, Andy Jasse, CEO of Amazon, you know, Andy Jasse, CEO of Amazon, you know, Andy Jasse, CEO of Amazon, we're not investing approximately 200 we're not investing approximately 200 we're not investing approximately 200 billion in capex in 2026 on a hunch. billion in capex in 2026 on a hunch. billion in capex in 2026 on a hunch. We're not going to be conservative in We're not going to be conservative in We're not going to be conservative in how we play this. We're investing to be how we play this. We're investing to be how we play this. We're investing to be the meaningful leader and our future the meaningful leader and our future the meaningful leader and our future business operating income and free cash business operating income and free cash business operating income and free cash flow will be much larger because of this flow will be much larger because of this flow will be much larger because of this investment. Then Mark Zuckerberg, CE of investment. Then Mark Zuckerberg, CE of investment. Then Mark Zuckerberg, CE of Meta, says we'll continue to invest Meta, says we'll continue to invest Meta, says we'll continue to invest aggressively in infrastructure to meet aggressively in infrastructure to meet aggressively in infrastructure to meet the demand. I'd rather risk building the demand. I'd rather risk building the demand. I'd rather risk building capacity before it's needed than being capacity before it's needed than being capacity before it's needed than being late. Makes me think of Shrek with L late. Makes me think of Shrek with L late. Makes me think of Shrek with L Farquad. Some of you may die, but that's Farquad. Some of you may die, but that's Farquad. Some of you may die, but that's a risk I'm willing to accept. It's like, a risk I'm willing to accept. It's like, a risk I'm willing to accept. It's like, you know, I'm just going to spend all you know, I'm just going to spend all you know, I'm just going to spend all this money. You can't fire me cuz Mark this money. You can't fire me cuz Mark this money. You can't fire me cuz Mark Zuckerberg can't be fired due to the Zuckerberg can't be fired due to the Zuckerberg can't be fired due to the unique board situation he's got going. unique board situation he's got going. unique board situation he's got going. So yeah, he's just going to piss the So yeah, he's just going to piss the So yeah, he's just going to piss the money away and hope he's right. And I money away and hope he's right. And I money away and hope he's right. And I know from the people who know it matter, know from the people who know it matter, know from the people who know it matter, he's not right. The thing is, why might he's not right. The thing is, why might he's not right. The thing is, why might you be wrong? you be wrong? you be wrong? >> I mean, this is the thing. The AI people >> I mean, this is the thing. The AI people >> I mean, this is the thing. The AI people who claim this is going to be the who claim this is going to be the who claim this is going to be the biggest, strongest thing in the world, biggest, strongest thing in the world, biggest, strongest thing in the world, did they ever get that? I I mean this did they ever get that? I I mean this did they ever get that? I I mean this like like like >> it's a good question because it's like >> it's a good question because it's like >> it's a good question because it's like they don't. And the thing is, what would they don't. And the thing is, what would they don't. And the thing is, what would it take for me to be wrong? A bunch of it take for me to be wrong? A bunch of it take for me to be wrong? A bunch of hardware breakthroughs to make this hardware breakthroughs to make this hardware breakthroughs to make this profitable. A bunch of profitable. A bunch of profitable. A bunch of >> question new mathemat because the thing >> question new mathemat because the thing >> question new mathemat because the thing is is is >> when it comes to being a critic or a >> when it comes to being a critic or a >> when it comes to being a critic or a skeptic, skeptic, skeptic, >> you are put on the hot seat. Not the >> you are put on the hot seat. Not the >> you are put on the hot seat. Not the people spending a trillion dollars, not people spending a trillion dollars, not people spending a trillion dollars, not the people promising the world. The the people promising the world. The the people promising the world. The person the the with a blog is person the the with a blog is person the the with a blog is the one who's like me. Trust me. If they the one who's like me. Trust me. If they the one who's like me. Trust me. If they came here, they'd be on the hot seat, came here, they'd be on the hot seat, came here, they'd be on the hot seat, too. Trust me.

too. Trust me. too. Trust me. >> Oh, I Oh, they they won't talk to me. >> Oh, I Oh, they they won't talk to me. >> Oh, I Oh, they they won't talk to me. Don't know why, Steve. They don't know. Don't know why, Steve. They don't know. Don't know why, Steve. They don't know. It's cuz I call him Clammy Sammy. Um It's cuz I call him Clammy Sammy. Um It's cuz I call him Clammy Sammy. Um >> I think it's cuz my guests are quite >> I think it's cuz my guests are quite >> I think it's cuz my guests are quite quite critical that I don't think Solman quite critical that I don't think Solman quite critical that I don't think Solman wants to come here. wants to come here. wants to come here. >> Mr. Orman, go on Steve show. Do it. But >> Mr. Orman, go on Steve show. Do it. But >> Mr. Orman, go on Steve show. Do it. But this is the thing like of course they're this is the thing like of course they're this is the thing like of course they're going to say that. And also, if they going to say that. And also, if they going to say that. And also, if they thought they were right, I don't think thought they were right, I don't think thought they were right, I don't think they do anymore. If I was in their shoes they do anymore. If I was in their shoes they do anymore. If I was in their shoes and I thought that this was an and I thought that this was an and I thought that this was an existential thing, sure. But it gets existential thing, sure. But it gets existential thing, sure. But it gets back to the rocom bubble which is yeah back to the rocom bubble which is yeah back to the rocom bubble which is yeah this is the last thing they've got. this is the last thing they've got. this is the last thing they've got. >> But I really want to know that question. >> But I really want to know that question. >> But I really want to know that question. It was one of the questions I was really It was one of the questions I was really It was one of the questions I was really excited to ask you which is you have a excited to ask you which is you have a excited to ask you which is you have a different opinion. We said this at the different opinion. We said this at the different opinion. We said this at the top. You have a very different opinion top. You have a very different opinion top. You have a very different opinion from a lot of people. I would categorize from a lot of people. I would categorize from a lot of people. I would categorize the the two most popular opinions as the the two most popular opinions as the the two most popular opinions as >> uh AI is going to hurt everybody and >> uh AI is going to hurt everybody and >> uh AI is going to hurt everybody and it's going to be catastrophic and we it's going to be catastrophic and we it's going to be catastrophic and we need to stop. need to stop. need to stop. >> Yeah. >> Yeah. >> Yeah. >> The other opinion is age of abundance is >> The other opinion is age of abundance is >> The other opinion is age of abundance is going to be amazing. Let us crack on. going to be amazing. Let us crack on. going to be amazing. Let us crack on. yours is different from both of those yours is different from both of those yours is different from both of those which is as you said in your words it's which is as you said in your words it's which is as you said in your words it's a con and it's and there's no real a con and it's and there's no real a con and it's and there's no real underlying value in the technology and underlying value in the technology and underlying value in the technology and it's overhyped. it's overhyped. it's overhyped. >> Yes. >> Yes. >> Yes. >> And there's way too much spending. I >> And there's way too much spending. I >> And there's way too much spending. I mean a few people agree on the spending mean a few people agree on the spending mean a few people agree on the spending part but the other part. So with you part but the other part. So with you part but the other part. So with you it's one of probably the first person it's one of probably the first person it's one of probably the first person that I've spoken to that's had this that I've spoken to that's had this that I've spoken to that's had this opinion.

opinion. opinion. >> So how what would it take for you to >> So how what would it take for you to >> So how what would it take for you to change your mind about what you believe change your mind about what you believe change your mind about what you believe here? There would need to be a hardware here? There would need to be a hardware here? There would need to be a hardware breakthrough that reduced the cost by breakthrough that reduced the cost by breakthrough that reduced the cost by like a thousand but it would have to be like a thousand but it would have to be like a thousand but it would have to be just a dramatic breakthrough that is not just a dramatic breakthrough that is not just a dramatic breakthrough that is not happening just to be clear because happening just to be clear because happening just to be clear because they've all been trying. So it's the they've all been trying. So it's the they've all been trying. So it's the cost for you that would have to change. cost for you that would have to change. cost for you that would have to change. >> It's the cost and it's also the data >> It's the cost and it's also the data >> It's the cost and it's also the data centers. I think the way they're centers. I think the way they're centers. I think the way they're building the data centers is reckless building the data centers is reckless building the data centers is reckless and damaging to communities. The fact and damaging to communities. The fact and damaging to communities. The fact that you have communities like in that you have communities like in that you have communities like in violent New Jersey where the residents violent New Jersey where the residents violent New Jersey where the residents like I don't want this but the planning like I don't want this but the planning like I don't want this but the planning boards vote for it because they're all I boards vote for it because they're all I boards vote for it because they're all I assume having chummy lunches with the assume having chummy lunches with the assume having chummy lunches with the people doing it. I think the use of gas people doing it. I think the use of gas people doing it. I think the use of gas turbines is disgraceful. I the turbines is disgraceful. I the turbines is disgraceful. I the water situation I'm not super well read water situation I'm not super well read water situation I'm not super well read on, so I'm not going to wait into it, on, so I'm not going to wait into it, on, so I'm not going to wait into it, but the use of gas turbines and behind but the use of gas turbines and behind but the use of gas turbines and behind the meter power is reckless and damaging the meter power is reckless and damaging the meter power is reckless and damaging to communities. The noise that these to communities. The noise that these to communities. The noise that these things make and also generative AI is things make and also generative AI is things make and also generative AI is this egregious pornographic this egregious pornographic this egregious pornographic demonstration of how unfair the world demonstration of how unfair the world demonstration of how unfair the world is. Regular people try and get a loan is. Regular people try and get a loan is. Regular people try and get a loan for a business, a random business. They for a business, a random business. They for a business, a random business. They want I have a good idea. They go to a want I have a good idea. They go to a want I have a good idea. They go to a bank, a bank of town, go bank, a bank of town, go bank, a bank of town, go themselves. They'll say, "I'm not g you themselves. They'll say, "I'm not g you themselves. They'll say, "I'm not g you going to make a store that sells stuff.

going to make a store that sells stuff. going to make a store that sells stuff. Screw you. You want to build a data Screw you. You want to build a data Screw you. You want to build a data center? You Jensen Hang will back you. center? You Jensen Hang will back you. center? You Jensen Hang will back you. Jensen Hong will give you 25% residual Jensen Hong will give you 25% residual Jensen Hong will give you 25% residual value. You want to build a regular value. You want to build a regular value. You want to build a regular business that's even profitable? business that's even profitable? business that's even profitable? you. No, a venture capitalist won't give you. No, a venture capitalist won't give you. No, a venture capitalist won't give you the money. Something that's just you the money. Something that's just you the money. Something that's just growing steadily, but it's profitable. growing steadily, but it's profitable. growing steadily, but it's profitable. Screw that. No, I need 10 100x return. Screw that. No, I need 10 100x return. Screw that. No, I need 10 100x return. Try and get a mortgage. You have to give Try and get a mortgage. You have to give Try and get a mortgage. You have to give the bank a full colonic. But you want to the bank a full colonic. But you want to the bank a full colonic. But you want to get money for Jensen Hong to buy some get money for Jensen Hong to buy some get money for Jensen Hong to buy some GPUs? He'll give you a contract. GPUs? He'll give you a contract. GPUs? He'll give you a contract. Corewave is a great example. C Neocloud, Corewave is a great example. C Neocloud, Corewave is a great example. C Neocloud, which is just a company that builds data which is just a company that builds data which is just a company that builds data centers and puts GPUs and rent them to centers and puts GPUs and rent them to centers and puts GPUs and rent them to people. Nvidia, one of their first people. Nvidia, one of their first people. Nvidia, one of their first investors in 2023, signed a $1.3 billion investors in 2023, signed a $1.3 billion investors in 2023, signed a $1.3 billion contract to rent back their GPUs from contract to rent back their GPUs from contract to rent back their GPUs from Core. So that Core go to a bank and go, Core. So that Core go to a bank and go, Core. So that Core go to a bank and go, I got a customer. Yeah, it's the guy I'm I got a customer. Yeah, it's the guy I'm I got a customer. Yeah, it's the guy I'm buying the GPUs from with the debt I'm buying the GPUs from with the debt I'm buying the GPUs from with the debt I'm getting from you. If you want to buy getting from you. If you want to buy getting from you. If you want to buy GPUs, it's open season. If you want to GPUs, it's open season. If you want to GPUs, it's open season. If you want to live a regular life where you build a live a regular life where you build a live a regular life where you build a regular business or buy a house, highest regular business or buy a house, highest regular business or buy a house, highest interest rates ever. Screw you. Up interest rates ever. Screw you. Up interest rates ever. Screw you. Up yours. Yeah, you need to show us way yours. Yeah, you need to show us way yours. Yeah, you need to show us way more than that. I don't trust you more than that. I don't trust you more than that. I don't trust you regular folks. But if you're an regular folks. But if you're an regular folks. But if you're an unprofitable Neocloud, you get billions unprofitable Neocloud, you get billions unprofitable Neocloud, you get billions from Jensen. It doesn't matter.

from Jensen. It doesn't matter. from Jensen. It doesn't matter. >> It's so interesting. You It's >> It's so interesting. You It's >> It's so interesting. You It's interesting because you are the first interesting because you are the first interesting because you are the first person that I've spoken to that has that person that I've spoken to that has that person that I've spoken to that has that opinion. opinion. opinion. >> I am prouser. Let's take another myth. >> I am prouser. Let's take another myth. >> I am prouser. Let's take another myth. AI will be conscious. Mhm. So AI will be conscious. Mhm. So AI will be conscious. Mhm. So super intelligence, artificial general super intelligence, artificial general super intelligence, artificial general intelligence, these are theories. Anyone intelligence, these are theories. Anyone intelligence, these are theories. Anyone saying this stuff will become this is saying this stuff will become this is saying this stuff will become this is just guessing and does not have proof. just guessing and does not have proof. just guessing and does not have proof. >> Okay. >> Okay. >> Okay. >> And like that's really it. >> And like that's really it. >> And like that's really it. >> Okay. >> Okay. >> Okay. >> Okay. Let's take another myth. >> Okay. Let's take another myth. >> Okay. Let's take another myth. AI systems are already blackmailing and AI systems are already blackmailing and AI systems are already blackmailing and escaping control. So this is a really escaping control. So this is a really escaping control. So this is a really specific one. Anthropic. There's specific one. Anthropic. There's specific one. Anthropic. There's actually two. Open AAI's GPT 3.5. I actually two. Open AAI's GPT 3.5. I actually two. Open AAI's GPT 3.5. I realize this is more than the sentence. realize this is more than the sentence. realize this is more than the sentence. I apologize. I apologize. I apologize. In their system card, and a bunch of In their system card, and a bunch of In their system card, and a bunch of media outlets covered this, saying that media outlets covered this, saying that media outlets covered this, saying that OpenAI's model blackmailed a task rabbit OpenAI's model blackmailed a task rabbit OpenAI's model blackmailed a task rabbit into solving a capture. What actually into solving a capture. What actually into solving a capture. What actually happened was a user of GPT doing the happened was a user of GPT doing the happened was a user of GPT doing the experiment experiment experiment got it to generate things to say to a got it to generate things to say to a got it to generate things to say to a task rabbit to make a task rabbit do task rabbit to make a task rabbit do task rabbit to make a task rabbit do stuff.

stuff. stuff. >> A task rabbit >> A task rabbit >> A task rabbit >> as in a person that you rent, not even >> as in a person that you rent, not even >> as in a person that you rent, not even to do a capture. It's something you rent to do a capture. It's something you rent to do a capture. It's something you rent to like nail a picture up in your to like nail a picture up in your to like nail a picture up in your apartment. It's an insane example. This apartment. It's an insane example. This apartment. It's an insane example. This was covered as if these things was covered as if these things was covered as if these things blackmailed someone and and it and they blackmailed someone and and it and they blackmailed someone and and it and they specifically said, "Yeah, we prompted it specifically said, "Yeah, we prompted it specifically said, "Yeah, we prompted it to do this." And also the other note was to do this." And also the other note was to do this." And also the other note was that yeah, AI systems can't do that yeah, AI systems can't do that yeah, AI systems can't do autonomous stuff like this. Then there autonomous stuff like this. Then there autonomous stuff like this. Then there was this other one where Anthropic said, was this other one where Anthropic said, was this other one where Anthropic said, "Oh yeah, a model was blackmailing "Oh yeah, a model was blackmailing "Oh yeah, a model was blackmailing someone saying that if you don't do someone saying that if you don't do someone saying that if you don't do this, I'll email proof that you slept this, I'll email proof that you slept this, I'll email proof that you slept with someone else other than your wife." with someone else other than your wife." with someone else other than your wife." I think it was what actually happened I think it was what actually happened I think it was what actually happened was Anthropic explicitly trained a model was Anthropic explicitly trained a model was Anthropic explicitly trained a model to do this and then prompted it to to do this and then prompted it to to do this and then prompted it to blackmail. blackmail. blackmail. This keeps happening and the media just This keeps happening and the media just This keeps happening and the media just slop slot me up. I don't need no slop slot me up. I don't need no slop slot me up. I don't need no thoughts. Put the story in the bag. And thoughts. Put the story in the bag. And thoughts. Put the story in the bag. And it's frustrating because it scares it's frustrating because it scares it's frustrating because it scares people. Put aside the fact it's wrong. people. Put aside the fact it's wrong. people. Put aside the fact it's wrong. It's scary. It's scary to people. people It's scary. It's scary to people. people It's scary. It's scary to people. people living their lives who have to work living their lives who have to work living their lives who have to work longer hours to make less money and longer hours to make less money and longer hours to make less money and their money doesn't go far and they turn their money doesn't go far and they turn their money doesn't go far and they turn on the news and there's some on the news and there's some on the news and there's some being like, "Yeah, you should be being like, "Yeah, you should be being like, "Yeah, you should be terrified it blackmailed someone."

terrified it blackmailed someone." terrified it blackmailed someone." >> But this is this is so counterintuitive >> But this is this is so counterintuitive >> But this is this is so counterintuitive of their interest to some degree and of their interest to some degree and of their interest to some degree and they've experienced it backfire. they've experienced it backfire. they've experienced it backfire. >> Well, they have now like it's it's >> Well, they have now like it's it's >> Well, they have now like it's it's literally backfired. literally backfired. literally backfired. >> It's backfired. Eric Schmidt getting >> It's backfired. Eric Schmidt getting >> It's backfired. Eric Schmidt getting booed at a commencement speech by 8,000 booed at a commencement speech by 8,000 booed at a commencement speech by 8,000 people every time he said the word AI. people every time he said the word AI. people every time he said the word AI. But I mean this is this is I mean these But I mean this is this is I mean these But I mean this is this is I mean these serious are being attacked at home. serious are being attacked at home. serious are being attacked at home. >> Yeah. Which sucks. Which is >> Yeah. Which sucks. Which is >> Yeah. Which sucks. Which is >> terrible. I must be clear like you >> terrible. I must be clear like you >> terrible. I must be clear like you dislike the don't hurt people. dislike the don't hurt people. dislike the don't hurt people. >> Yeah. Don't don't attack people at home. >> Yeah. Don't don't attack people at home. >> Yeah. Don't don't attack people at home. But but the point here is that that But but the point here is that that But but the point here is that that narrative is backfiring in a big big way narrative is backfiring in a big big way narrative is backfiring in a big big way for them. I don't think they saw it for them. I don't think they saw it for them. I don't think they saw it coming because you have to remember you coming because you have to remember you coming because you have to remember you mentioned regulation earlier. These tech mentioned regulation earlier. These tech mentioned regulation earlier. These tech companies have been glazed for their companies have been glazed for their companies have been glazed for their entire existence. Travis Kick's like oh entire existence. Travis Kick's like oh entire existence. Travis Kick's like oh what? People don't like me now. And it's what? People don't like me now. And it's what? People don't like me now. And it's because Uber was a horribly run place because Uber was a horribly run place because Uber was a horribly run place and he was kind of a monster. Also tons and he was kind of a monster. Also tons and he was kind of a monster. Also tons of articles about how great Uber was at of articles about how great Uber was at of articles about how great Uber was at the time. The point I'm making is these the time. The point I'm making is these the time. The point I'm making is these companies are not used to push back. companies are not used to push back. companies are not used to push back. They thought what would happen I believe They thought what would happen I believe They thought what would happen I believe just guessing. They thought they do this just guessing. They thought they do this just guessing. They thought they do this scary stuff and they would just get scary stuff and they would just get scary stuff and they would just get floods of money and everyone would just floods of money and everyone would just floods of money and everyone would just be like I kneel before you. I'll do be like I kneel before you. I'll do be like I kneel before you. I'll do whatever you want. They didn't expect I whatever you want. They didn't expect I whatever you want. They didn't expect I think what has I I agree this has think what has I I agree this has think what has I I agree this has backfired on them because they were in backfired on them because they were in backfired on them because they were in articulate. They're disconnected from articulate. They're disconnected from articulate. They're disconnected from regular people. Samman drives a $5 regular people. Samman drives a $5 regular people. Samman drives a $5 million car around San Francisco. So million car around San Francisco. So million car around San Francisco. So that that man's doing it like 9 miles an that that man's doing it like 9 miles an that that man's doing it like 9 miles an hour. It's hilarious. But these people hour. It's hilarious. But these people hour. It's hilarious. But these people are disconnected from everyone else. So are disconnected from everyone else. So are disconnected from everyone else. So they don't they don't experience real they don't they don't experience real they don't they don't experience real problems, so they can't build the problems, so they can't build the problems, so they can't build the solutions for them. And they think, solutions for them. And they think, solutions for them. And they think, well, if we scare people into doing what well, if we scare people into doing what well, if we scare people into doing what we want, that'll work, right? It didn't.

we want, that'll work, right? It didn't. we want, that'll work, right? It didn't. They was all of this blackmail stuff was They was all of this blackmail stuff was They was all of this blackmail stuff was an attempt to make it mystic. It was a an attempt to make it mystic. It was a an attempt to make it mystic. It was a mysticism attempt. It was to make it mysticism attempt. It was to make it mysticism attempt. It was to make it seem like this unknowable, impossible to seem like this unknowable, impossible to seem like this unknowable, impossible to control, just this powerful thing. But control, just this powerful thing. But control, just this powerful thing. But we're the only ones. We are the o only we're the only ones. We are the o only we're the only ones. We are the o only us only these two angels could possibly us only these two angels could possibly us only these two angels could possibly control the beast we've created. control the beast we've created. control the beast we've created. >> This is this is quite a controversial >> This is this is quite a controversial >> This is this is quite a controversial statement but I think that for some statement but I think that for some statement but I think that for some reason I trust Dario a little bit more reason I trust Dario a little bit more reason I trust Dario a little bit more because I think he's been the most because I think he's been the most because I think he's been the most balanced in his writing about the risk balanced in his writing about the risk balanced in his writing about the risk profile. profile. profile. >> I >> I >> I >> whereas the others they they seem to >> whereas the others they they seem to >> whereas the others they they seem to kind of move with the wind. kind of move with the wind. kind of move with the wind. >> I I do you know >> I I do you know >> I I do you know >> I get what you mean. The reason I don't >> I get what you mean. The reason I don't >> I get what you mean. The reason I don't like Dario is Daario was doing the scare like Dario is Daario was doing the scare like Dario is Daario was doing the scare tactics thing when he worked at OpenAI tactics thing when he worked at OpenAI tactics thing when he worked at OpenAI when GPT2 came out say it's too scary to when GPT2 came out say it's too scary to when GPT2 came out say it's too scary to release. He's also gone on television release. He's also gone on television release. He's also gone on television and given AI psychosis to Axios being and given AI psychosis to Axios being and given AI psychosis to Axios being like 50% of jobs are going to go away like 50% of jobs are going to go away like 50% of jobs are going to go away because of AI. because of AI. because of AI. >> What I respect is the consistency. He's >> What I respect is the consistency. He's >> What I respect is the consistency. He's now being attacked by them. now being attacked by them. now being attacked by them. >> Good. >> Good. >> Good. >> Um but the thing is sorry I mean let me >> Um but the thing is sorry I mean let me >> Um but the thing is sorry I mean let me clarify the word attack. Darian is being clarify the word attack. Darian is being clarify the word attack. Darian is being verbally attacked by Silicon Valley and verbally attacked by Silicon Valley and verbally attacked by Silicon Valley and you know if Silicon Valley if powerful you know if Silicon Valley if powerful you know if Silicon Valley if powerful people in Silicon Valley are attacking people in Silicon Valley are attacking people in Silicon Valley are attacking someone.

someone. someone. >> Four months ago he wasn't though. They >> Four months ago he wasn't though. They >> Four months ago he wasn't though. They were all saying he was the smartest boy were all saying he was the smartest boy were all saying he was the smartest boy ever. ever. ever. >> The point I want to make there as well >> The point I want to make there as well >> The point I want to make there as well is again wow you're so scared of how is again wow you're so scared of how is again wow you're so scared of how powerful this is. You're so scared of powerful this is. You're so scared of powerful this is. You're so scared of it. It's so scary. What are you doing it. It's so scary. What are you doing it. It's so scary. What are you doing about it? Oh nothing. Like it's just about it? Oh nothing. Like it's just about it? Oh nothing. Like it's just like what are you doing? Well we have an like what are you doing? Well we have an like what are you doing? Well we have an alignment team. So does every AI lab. alignment team. So does every AI lab. alignment team. So does every AI lab. Well I guess open AI cycles through Well I guess open AI cycles through Well I guess open AI cycles through those really quickly. Here's the thing. those really quickly. Here's the thing. those really quickly. Here's the thing. If I'm Dario Amade, I'm sitting there If I'm Dario Amade, I'm sitting there If I'm Dario Amade, I'm sitting there going, I'm scared of all things changing going, I'm scared of all things changing going, I'm scared of all things changing and I thought I had made a thing that and I thought I had made a thing that and I thought I had made a thing that would eliminate all jobs, I'd be would eliminate all jobs, I'd be would eliminate all jobs, I'd be terrified. I'd be walking around with terrified. I'd be walking around with terrified. I'd be walking around with like like a 10 ton weight on my back. like like a 10 ton weight on my back. like like a 10 ton weight on my back. The show, the responsibility, the fact The show, the responsibility, the fact The show, the responsibility, the fact he doesn't, the fact he wants to be this he doesn't, the fact he wants to be this he doesn't, the fact he wants to be this weird elder statesman that's too scared weird elder statesman that's too scared weird elder statesman that's too scared to hold Sam Orman's hand at an event to hold Sam Orman's hand at an event to hold Sam Orman's hand at an event just makes me believe that he's just just makes me believe that he's just just makes me believe that he's just saying it because it's convenient and saying it because it's convenient and saying it because it's convenient and he'll wind that back as he kind of he'll wind that back as he kind of he'll wind that back as he kind of already has whenever it's convenient for already has whenever it's convenient for already has whenever it's convenient for him. I think Open AAI and Anthropic are him. I think Open AAI and Anthropic are him. I think Open AAI and Anthropic are basically the same level of Bad Company. basically the same level of Bad Company. basically the same level of Bad Company. I think Anthropic is more cultlike. I I think Anthropic is more cultlike. I I think Anthropic is more cultlike. I think it's so weird like Jack Clark over think it's so weird like Jack Clark over think it's so weird like Jack Clark over there, one of the co-founders. That fell there, one of the co-founders. That fell there, one of the co-founders. That fell used to be at the register. He used to used to be at the register. He used to used to be at the register. He used to be one of the most critical journalists be one of the most critical journalists be one of the most critical journalists ever. Now he's it's like like something ever. Now he's it's like like something ever. Now he's it's like like something took over him because they talk of these took over him because they talk of these took over him because they talk of these things in these high fluent terms. But things in these high fluent terms. But things in these high fluent terms. But then again, maybe the people at then again, maybe the people at then again, maybe the people at anthropic buy their Maybe some of anthropic buy their Maybe some of anthropic buy their Maybe some of the people at OpenAI buy their I the people at OpenAI buy their I the people at OpenAI buy their I don't know. So going back to the central don't know. So going back to the central don't know. So going back to the central question we asked at the top here was question we asked at the top here was question we asked at the top here was what would have to be the case for you what would have to be the case for you what would have to be the case for you to look back and say do you know what I to look back and say do you know what I to look back and say do you know what I was wrong in 2026 and you said to me it was wrong in 2026 and you said to me it was wrong in 2026 and you said to me it would be mainly that the cost of would be mainly that the cost of would be mainly that the cost of production around AI drops dramatically production around AI drops dramatically production around AI drops dramatically >> and it would have to also do insane >> and it would have to also do insane >> and it would have to also do insane amounts of stuff it does it would have amounts of stuff it does it would have amounts of stuff it does it would have to be a truly autonomous to be a truly autonomous to be a truly autonomous >> it would have to continue its >> it would have to continue its >> it would have to continue its improvement in terms of capability.

improvement in terms of capability. improvement in terms of capability. >> It would have to be a different product. >> It would have to be a different product. >> It would have to be a different product. It would have to be it would have to be It would have to be it would have to be It would have to be it would have to be indistinguishable from magic. And the indistinguishable from magic. And the indistinguishable from magic. And the reason they have these high standards is reason they have these high standards is reason they have these high standards is they set them. they set them. they set them. >> Okay. Fair. It's interesting as well >> Okay. Fair. It's interesting as well >> Okay. Fair. It's interesting as well because all these myths and all these because all these myths and all these because all these myths and all these conversations, it's about technology, conversations, it's about technology, conversations, it's about technology, but it's also it's an information war. but it's also it's an information war. but it's also it's an information war. It's literally It's literally It's literally narrative versus narrative. Everyone narrative versus narrative. Everyone narrative versus narrative. Everyone trying to escape the financials, trying to escape the financials, trying to escape the financials, everyone trying to actually escape what everyone trying to actually escape what everyone trying to actually escape what the models can do. And the big thing I the models can do. And the big thing I the models can do. And the big thing I always say about AI boosters is if I always say about AI boosters is if I always say about AI boosters is if I could regulate them, I'd regulate them. could regulate them, I'd regulate them. could regulate them, I'd regulate them. They can't speak in the future tense They can't speak in the future tense They can't speak in the future tense anymore. Just you got to talk about anymore. Just you got to talk about anymore. Just you got to talk about today, mate. You get two weeks in the today, mate. You get two weeks in the today, mate. You get two weeks in the future, Max. Because if they were future, Max. Because if they were future, Max. Because if they were constrained to what was happening today, constrained to what was happening today, constrained to what was happening today, it they would sound like insane people. it they would sound like insane people. it they would sound like insane people. >> Yeah. No, I think yeah, most I guess >> Yeah. No, I think yeah, most I guess >> Yeah. No, I think yeah, most I guess most technology companies would at the most technology companies would at the most technology companies would at the time. Like Uber would sound insane. time. Like Uber would sound insane. time. Like Uber would sound insane. Amazon was Amazon was Amazon was >> Uber was basically the difference. >> Uber was basically the difference. >> Uber was basically the difference. >> They were pissing money though, weren't >> They were pissing money though, weren't >> They were pissing money though, weren't they? they? they? >> They were pissing money away, but the >> They were pissing money away, but the >> They were pissing money away, but the unit economics were the same just unit economics were the same just unit economics were the same just subsidized. So you were still getting a subsidized. So you were still getting a subsidized. So you were still getting a service from A to B and paying a much service from A to B and paying a much service from A to B and paying a much lower cost. It wasn't like you paid Uber lower cost. It wasn't like you paid Uber lower cost. It wasn't like you paid Uber 200 sorry 20 bucks a month and you could 200 sorry 20 bucks a month and you could 200 sorry 20 bucks a month and you could get 500 miles of Uber and then one day get 500 miles of Uber and then one day get 500 miles of Uber and then one day you started paying by the mile cuz you started paying by the mile cuz you started paying by the mile cuz that's what's happening with this.

that's what's happening with this. that's what's happening with this. >> Have they they've changed their business >> Have they they've changed their business >> Have they they've changed their business model for customers like me now so that model for customers like me now so that model for customers like me now so that I have to buy credits. I have to buy credits. I have to buy credits. >> No. So you well kind of with >> No. So you well kind of with >> No. So you well kind of with >> they asked me the other day. So with the >> they asked me the other day. So with the >> they asked me the other day. So with the anthropics fable model with some anthropics fable model with some anthropics fable model with some accounts you have to pay for usage and accounts you have to pay for usage and accounts you have to pay for usage and also adoption of fable has been pretty also adoption of fable has been pretty also adoption of fable has been pretty low because of this because of the cost low because of this because of the cost low because of this because of the cost but with enterprises so companies over but with enterprises so companies over but with enterprises so companies over 150 people you have to pay by the token 150 people you have to pay by the token 150 people you have to pay by the token now or per million token. now or per million token. now or per million token. >> Oh so they are moving to a token. >> Oh so they are moving to a token. >> Oh so they are moving to a token. >> Yeah. But when they did that everyone >> Yeah. But when they did that everyone >> Yeah. But when they did that everyone went from being like this is the most went from being like this is the most went from being like this is the most impressive thing ever to being like impressive thing ever to being like impressive thing ever to being like >> it's always we got to control these >> it's always we got to control these >> it's always we got to control these costs. Uber's COO said as Andrew costs. Uber's COO said as Andrew costs. Uber's COO said as Andrew McDonald I think he said that it's McDonald I think he said that it's McDonald I think he said that it's getting hard to justify cuz it's hard to getting hard to justify cuz it's hard to getting hard to justify cuz it's hard to connect spending money on tokens to connect spending money on tokens to connect spending money on tokens to actual useful outcomes. actual useful outcomes. actual useful outcomes. >> He said the thing like he said the >> He said the thing like he said the >> He said the thing like he said the actual thing I've been saying and it's actual thing I've been saying and it's actual thing I've been saying and it's so we're in an AI bubble. so we're in an AI bubble. so we're in an AI bubble. >> Yes. >> Yes. >> Yes. >> And when will when this AI bubble >> And when will when this AI bubble >> And when will when this AI bubble collapses so much of the economy is collapses so much of the economy is collapses so much of the economy is resting upon it. resting upon it. resting upon it. >> Yeah. >> Yeah. >> Yeah. >> It's going to have downstream >> It's going to have downstream >> It's going to have downstream consequences. So I got two questions for consequences. So I got two questions for consequences. So I got two questions for you. I guess the first question is are you. I guess the first question is are you. I guess the first question is are we in an AI bubble and what happens when we in an AI bubble and what happens when we in an AI bubble and what happens when the bubble pops?

the bubble pops? the bubble pops? >> Yes. And it's it depends. So the big >> Yes. And it's it depends. So the big >> Yes. And it's it depends. So the big thing that people say is, "Oh, we'll get thing that people say is, "Oh, we'll get thing that people say is, "Oh, we'll get bailed out. Donald Trump scared of bailed out. Donald Trump scared of bailed out. Donald Trump scared of Donald Trump." Here's the problem with Donald Trump." Here's the problem with Donald Trump." Here's the problem with this. this. this. It isn't just an AI bubble. It's the It isn't just an AI bubble. It's the It isn't just an AI bubble. It's the rockcom bubble. So the AI bubble rockcom bubble. So the AI bubble rockcom bubble. So the AI bubble collapsing will probably be this company collapsing will probably be this company collapsing will probably be this company running out of money. Open AI. running out of money. Open AI. running out of money. Open AI. >> And the thing is with Open AI is they >> And the thing is with Open AI is they >> And the thing is with Open AI is they were meant to go public this year and were meant to go public this year and were meant to go public this year and now it's been pushed to next year a week now it's been pushed to next year a week now it's been pushed to next year a week and a half after I released their and a half after I released their and a half after I released their auditive financials. Wonder where that auditive financials. Wonder where that auditive financials. Wonder where that was. Um, but they've delayed to next was. Um, but they've delayed to next was. Um, but they've delayed to next year. Sarah Frier, the CFO, has now year. Sarah Frier, the CFO, has now year. Sarah Frier, the CFO, has now said, "Well, they'll do it earlier than said, "Well, they'll do it earlier than said, "Well, they'll do it earlier than 2027 or 2027." Great answer there. 2027 or 2027." Great answer there. 2027 or 2027." Great answer there. >> For anyone that doesn't understand what >> For anyone that doesn't understand what >> For anyone that doesn't understand what going public means, that means joining going public means, that means joining going public means, that means joining the stock market. And at such a time the stock market. And at such a time the stock market. And at such a time when you join the stock market, your when you join the stock market, your when you join the stock market, your investors can finally sell their equity investors can finally sell their equity investors can finally sell their equity that they got for investing in the that they got for investing in the that they got for investing in the company when it was private. So often company when it was private. So often company when it was private. So often times companies will flirt with the idea times companies will flirt with the idea times companies will flirt with the idea of we'll go public someday soon because of we'll go public someday soon because of we'll go public someday soon because investors will have a moment in their investors will have a moment in their investors will have a moment in their head where they'll get their money back head where they'll get their money back head where they'll get their money back at a return. So you kind of need to if at a return. So you kind of need to if at a return. So you kind of need to if you're in these guys shoes, you kind of you're in these guys shoes, you kind of you're in these guys shoes, you kind of need to be flirting with going public or need to be flirting with going public or need to be flirting with going public or investors won't want to invest.

investors won't want to invest. investors won't want to invest. >> Open AAI up until this point has been a >> Open AAI up until this point has been a >> Open AAI up until this point has been a private company and their last funding private company and their last funding private company and their last funding round they were valued at $865 billion. round they were valued at $865 billion. round they were valued at $865 billion. Now when they tried to go public, New Now when they tried to go public, New Now when they tried to go public, New York Times Mike Isaac reported this. York Times Mike Isaac reported this. York Times Mike Isaac reported this. They tried to list well they wanted to They tried to list well they wanted to They tried to list well they wanted to go at a set a 1 trillion valuation. go at a set a 1 trillion valuation. go at a set a 1 trillion valuation. Apparently their advisor said no don't Apparently their advisor said no don't Apparently their advisor said no don't do that. That is very bad for a number do that. That is very bad for a number do that. That is very bad for a number of reasons. One open AI needs perpetual of reasons. One open AI needs perpetual of reasons. One open AI needs perpetual amounts of money. They raised $122 amounts of money. They raised $122 amounts of money. They raised $122 billion this year. Most of it's crossed. billion this year. Most of it's crossed. billion this year. Most of it's crossed. There's some left but they are going to There's some left but they are going to There's some left but they are going to need to raise at least hundred billion a need to raise at least hundred billion a need to raise at least hundred billion a year just to survive. If they can't go year just to survive. If they can't go year just to survive. If they can't go public they will have to raise another public they will have to raise another public they will have to raise another funding round. The problem is it's going funding round. The problem is it's going funding round. The problem is it's going to be difficult to raise at even the to be difficult to raise at even the to be difficult to raise at even the same one they raise that. They're same one they raise that. They're same one they raise that. They're probably going to have to take a flat. probably going to have to take a flat. probably going to have to take a flat. So the same amount. Exactly. But they So the same amount. Exactly. But they So the same amount. Exactly. But they need money. They need money so bad. need money. They need money so bad. need money. They need money so bad. Amazon sent them $35 billion that was Amazon sent them $35 billion that was Amazon sent them $35 billion that was meant to be contingent on them going meant to be contingent on them going meant to be contingent on them going public early. public early. public early. >> They did that because they need the >> They did that because they need the >> They did that because they need the money. Now, OpenAI is the kind of money. Now, OpenAI is the kind of money. Now, OpenAI is the kind of catastrophe center here because catastrophe center here because catastrophe center here because Anthropic is likely going to beat it to Anthropic is likely going to beat it to Anthropic is likely going to beat it to go public. And once Anthropic goes go public. And once Anthropic goes go public. And once Anthropic goes public, it'll be borderline impossible public, it'll be borderline impossible public, it'll be borderline impossible for Open AI to do so because Anthropic, for Open AI to do so because Anthropic, for Open AI to do so because Anthropic, an unprofitable, unsustainable AI lab, an unprofitable, unsustainable AI lab, an unprofitable, unsustainable AI lab, but a better business that's growing but a better business that's growing but a better business that's growing faster than Open AI's. I believe they faster than Open AI's. I believe they faster than Open AI's. I believe they have a ceiling. They're eventually going have a ceiling. They're eventually going have a ceiling. They're eventually going to face predition, too. I think sometime to face predition, too. I think sometime to face predition, too. I think sometime in 2027, things are going to start in 2027, things are going to start in 2027, things are going to start running out of steam. Because the thing running out of steam. Because the thing running out of steam. Because the thing I said earlier, the only way these I said earlier, the only way these I said earlier, the only way these models get better is if you feed more models get better is if you feed more models get better is if you feed more money, tens of billions of dollars into money, tens of billions of dollars into money, tens of billions of dollars into them.

them. them. >> So, you think OpenAI runs out of steam >> So, you think OpenAI runs out of steam >> So, you think OpenAI runs out of steam in 2027? in 2027? in 2027? >> I think they're already running out of >> I think they're already running out of >> I think they're already running out of steam. Yeah. But I think they run out of steam. Yeah. But I think they run out of steam. Yeah. But I think they run out of cash. You think they run out of cash? cash. You think they run out of cash? cash. You think they run out of cash? Yes. And the sequence of events here Yes. And the sequence of events here Yes. And the sequence of events here will be they they go out and try and will be they they go out and try and will be they they go out and try and raise raise raise >> and they have trouble raising another >> and they have trouble raising another >> and they have trouble raising another round. I think maybe Invidia props them round. I think maybe Invidia props them round. I think maybe Invidia props them up a little. Maybe Private Credit, up a little. Maybe Private Credit, up a little. Maybe Private Credit, Blackstone, Black Rockck and the like Blackstone, Black Rockck and the like Blackstone, Black Rockck and the like the ones and the reason that Private the ones and the reason that Private the ones and the reason that Private Credit is getting involved. So asset Credit is getting involved. So asset Credit is getting involved. So asset managers is because they're investing in managers is because they're investing in managers is because they're investing in the data centers and they know this the data centers and they know this the data centers and they know this company's most of the data center company's most of the data center company's most of the data center demand. demand. demand. >> Okay. So they run out of steam in 2027 >> Okay. So they run out of steam in 2027 >> Okay. So they run out of steam in 2027 according to you. according to you. according to you. >> Yep. And maybe they try if they bum rush >> Yep. And maybe they try if they bum rush >> Yep. And maybe they try if they bum rush to go public they're going to have worse to go public they're going to have worse to go public they're going to have worse economics than anthropic. They're going economics than anthropic. They're going economics than anthropic. They're going to get savage. it. We work was a great to get savage. it. We work was a great to get savage. it. We work was a great example. Another SoftBank classic. Now, example. Another SoftBank classic. Now, example. Another SoftBank classic. Now, I think Open AI collapses, there are I think Open AI collapses, there are I think Open AI collapses, there are many different ways it could happen. many different ways it could happen. many different ways it could happen. There are many different ways it could There are many different ways it could There are many different ways it could end. But the crucial thing is is that end. But the crucial thing is is that end. But the crucial thing is is that there are multiple companies that are there are multiple companies that are there are multiple companies that are existentially tied to OpenAI. SoftBank, existentially tied to OpenAI. SoftBank, existentially tied to OpenAI. SoftBank, one of the largest companies in the one of the largest companies in the one of the largest companies in the Japanese stock market, a holding company Japanese stock market, a holding company Japanese stock market, a holding company with lots of investments. They have on with lots of investments. They have on with lots of investments. They have on paper about hundred billion worth of paper about hundred billion worth of paper about hundred billion worth of OpenAI stock. If they can't go public, OpenAI stock. If they can't go public, OpenAI stock. If they can't go public, they can't do diddly squat with that.

they can't do diddly squat with that. they can't do diddly squat with that. And so Soft Bank's future, their ability And so Soft Bank's future, their ability And so Soft Bank's future, their ability to continue paying the people around to continue paying the people around to continue paying the people around them and existing as a business relies them and existing as a business relies them and existing as a business relies on their ability to continually on their ability to continually on their ability to continually liquidate funds to be to take the things liquidate funds to be to take the things liquidate funds to be to take the things they've invested in and have value from they've invested in and have value from they've invested in and have value from them either by selling the stock or them either by selling the stock or them either by selling the stock or taking loans out on the stock. If OpenAI taking loans out on the stock. If OpenAI taking loans out on the stock. If OpenAI can't go public, SoftBank can't do that. can't go public, SoftBank can't do that. can't go public, SoftBank can't do that. SoftBank probably won't run out of SoftBank probably won't run out of SoftBank probably won't run out of money, but we're going to see one of the money, but we're going to see one of the money, but we're going to see one of the largest holding companies in the world largest holding companies in the world largest holding companies in the world become much smaller. We will also see become much smaller. We will also see become much smaller. We will also see Amazon, Google, and Microsoft have to Amazon, Google, and Microsoft have to Amazon, Google, and Microsoft have to restate guidance. they will have to say restate guidance. they will have to say restate guidance. they will have to say actually we don't think we're going to actually we don't think we're going to actually we don't think we're going to grow as fast grow as fast grow as fast >> and what happens then >> and what happens then >> and what happens then >> well I think we enter a tech depression >> well I think we enter a tech depression >> well I think we enter a tech depression because the rockcom bubble the core of because the rockcom bubble the core of because the rockcom bubble the core of my theory is that they're out of my theory is that they're out of my theory is that they're out of hyperrowth ideas but the market doesn't hyperrowth ideas but the market doesn't hyperrowth ideas but the market doesn't think so the reason they're so think so the reason they're so think so the reason they're so maniacally spending is because buying AI maniacally spending is because buying AI maniacally spending is because buying AI GPUs allows them to kick the can further GPUs allows them to kick the can further GPUs allows them to kick the can further allows them to say we're still doing allows them to say we're still doing allows them to say we're still doing something we're working on AI don't something we're working on AI don't something we're working on AI don't think too hard and also their current think too hard and also their current think too hard and also their current businesses are still growing their businesses are still growing their businesses are still growing their current businesses will eventually slow current businesses will eventually slow current businesses will eventually slow there's only so many price increases.

there's only so many price increases. there's only so many price increases. There's only so many tweaks to ads. Only There's only so many tweaks to ads. Only There's only so many tweaks to ads. Only so many tweaks to Google search. Only so so many tweaks to Google search. Only so so many tweaks to Google search. Only so only so many ways that Amazon can screw only so many ways that Amazon can screw only so many ways that Amazon can screw merchants. So in that tech depression, merchants. So in that tech depression, merchants. So in that tech depression, which you think it might be triggered in which you think it might be triggered in which you think it might be triggered in 2027, is that a cascading downstream 2027, is that a cascading downstream 2027, is that a cascading downstream economic depression? Because the stock economic depression? Because the stock economic depression? Because the stock market is heavily dependent on these market is heavily dependent on these market is heavily dependent on these companies. The stock market sees a companies. The stock market sees a companies. The stock market sees a pullback, investors stop investing, they pullback, investors stop investing, they pullback, investors stop investing, they get panicked. get panicked. get panicked. >> Yes. I think that because >> Yes. I think that because >> Yes. I think that because >> what's the sort of downstream >> what's the sort of downstream >> what's the sort of downstream consequence the sort of domino effect consequence the sort of domino effect consequence the sort of domino effect >> there's so much to imagine that it's >> there's so much to imagine that it's >> there's so much to imagine that it's difficult to capture everything but difficult to capture everything but difficult to capture everything but there are a few things that worry me there are a few things that worry me there are a few things that worry me first of all a ton of American money first of all a ton of American money first of all a ton of American money just regular people's money retail just regular people's money retail just regular people's money retail investors are in these companies and investors are in these companies and investors are in these companies and they bought into the magnificent 7 they bought into the magnificent 7 they bought into the magnificent 7 thinking the number go up forever is the thinking the number go up forever is the thinking the number go up forever is the largest company on the Fortune 500 and largest company on the Fortune 500 and largest company on the Fortune 500 and NASDAQ as well and like 7 to 8% of the NASDAQ as well and like 7 to 8% of the NASDAQ as well and like 7 to 8% of the S&P 500 that company when in when the S&P 500 that company when in when the S&P 500 that company when in when the bottom falls out from Nvidia and we bottom falls out from Nvidia and we bottom falls out from Nvidia and we haven't really got into it but Nvidia is haven't really got into it but Nvidia is haven't really got into it but Nvidia is doing the most circular of financing, doing the most circular of financing, doing the most circular of financing, feeding companies money so that they can feeding companies money so that they can feeding companies money so that they can raise debt to buy more GPUs. I think raise debt to buy more GPUs. I think raise debt to buy more GPUs. I think Nvidia's revenue could go 50 to 70% Nvidia's revenue could go 50 to 70% Nvidia's revenue could go 50 to 70% down. I think that Nvidia could put down. I think that Nvidia could put down. I think that Nvidia could put Nvidia back in 2022 was making Nvidia back in 2022 was making Nvidia back in 2022 was making singledigit billion dollars.

singledigit billion dollars. singledigit billion dollars. >> And what happens though, I'm thinking >> And what happens though, I'm thinking >> And what happens though, I'm thinking about like Jenny and Dave that are about like Jenny and Dave that are about like Jenny and Dave that are watching this right now and they are watching this right now and they are watching this right now and they are just normal people just normal people just normal people >> with normal jobs. >> with normal jobs. >> with normal jobs. >> People's retirements are going to >> People's retirements are going to >> People's retirements are going to contract severely and I don't believe contract severely and I don't believe contract severely and I don't believe they're going to return to those values. they're going to return to those values. they're going to return to those values. And I think that because so much of the And I think that because so much of the And I think that because so much of the value of the S&P 500 and Russell 1000 value of the S&P 500 and Russell 1000 value of the S&P 500 and Russell 1000 index comes from these four companies index comes from these four companies index comes from these four companies and the rest of the magnificent 7. So and the rest of the magnificent 7. So and the rest of the magnificent 7. So Apple, Tesla, Meta as well. And the Apple, Tesla, Meta as well. And the Apple, Tesla, Meta as well. And the thing is I don't know what happens after thing is I don't know what happens after thing is I don't know what happens after that because venture capital has also that because venture capital has also that because venture capital has also more than half of venture capital last more than half of venture capital last more than half of venture capital last year went into AI. I think most venture year went into AI. I think most venture year went into AI. I think most venture capital investments in AI are going to capital investments in AI are going to capital investments in AI are going to zero because when it comes to building a zero because when it comes to building a zero because when it comes to building a company on top of an LLM, all of those company on top of an LLM, all of those company on top of an LLM, all of those are unprofitable too. And the thing is are unprofitable too. And the thing is are unprofitable too. And the thing is LLM companies have not really been LLM companies have not really been LLM companies have not really been acquired. The exception being Cursible acquired. The exception being Cursible acquired. The exception being Cursible by Elon Musk for the coding side, but by Elon Musk for the coding side, but by Elon Musk for the coding side, but you have Cognition, which is just you have Cognition, which is just you have Cognition, which is just another LLM company raising a $26 another LLM company raising a $26 another LLM company raising a $26 billion valuation. That means that billion valuation. That means that billion valuation. That means that company has to go public cuz who's company has to go public cuz who's company has to go public cuz who's buying a company at $26 billion other buying a company at $26 billion other buying a company at $26 billion other than Elon Musk. And there were rumors than Elon Musk. And there were rumors than Elon Musk. And there were rumors that Elon Musk was trying to buy them as that Elon Musk was trying to buy them as that Elon Musk was trying to buy them as well. Is Elon Musk just going to pick well. Is Elon Musk just going to pick well. Is Elon Musk just going to pick off every like LLM company like going to off every like LLM company like going to off every like LLM company like going to TJ Maxx for AI? Like Jesus TJ Maxx for AI? Like Jesus TJ Maxx for AI? Like Jesus Christ.

Christ. Christ. >> So is that a recession you're >> So is that a recession you're >> So is that a recession you're describing? It is a recession, but it's describing? It is a recession, but it's describing? It is a recession, but it's also a depression within people's also a depression within people's also a depression within people's retirements. Like I'm talking about 20, retirements. Like I'm talking about 20, retirements. Like I'm talking about 20, 30, 40% off the top of these companies 30, 40% off the top of these companies 30, 40% off the top of these companies stock value. stock value. stock value. >> Economic contractions, recessions >> Economic contractions, recessions >> Economic contractions, recessions consistently lead to job losses and consistently lead to job losses and consistently lead to job losses and rising unemployment. When an economy rising unemployment. When an economy rising unemployment. When an economy contracts, the mechanism driving job contracts, the mechanism driving job contracts, the mechanism driving job losses typically follows a predictable losses typically follows a predictable losses typically follows a predictable sequence. Falling demand, consumers and sequence. Falling demand, consumers and sequence. Falling demand, consumers and businesses spend less money, causing businesses spend less money, causing businesses spend less money, causing revenues across most industries to drop. revenues across most industries to drop. revenues across most industries to drop. margin compression. With lower revenue margin compression. With lower revenue margin compression. With lower revenue and often fixed overhead costs like rent and often fixed overhead costs like rent and often fixed overhead costs like rent or debt, corporate profit shrink, and or debt, corporate profit shrink, and or debt, corporate profit shrink, and lastly, cost cutting measures to survive lastly, cost cutting measures to survive lastly, cost cutting measures to survive or protect profit margins, businesses or protect profit margins, businesses or protect profit margins, businesses freeze hiring, reduce hours, and resort freeze hiring, reduce hours, and resort freeze hiring, reduce hours, and resort to layoffs. Yes, that's that would all to layoffs. Yes, that's that would all to layoffs. Yes, that's that would all happen. But the thing is, we're talking happen. But the thing is, we're talking happen. But the thing is, we're talking about equity values dropping and we're about equity values dropping and we're about equity values dropping and we're talking about there not really being a talking about there not really being a talking about there not really being a home for that value or that money. home for that value or that money. home for that value or that money. [snorts] So much is riding on these [snorts] So much is riding on these [snorts] So much is riding on these companies, but you can't bail it out. companies, but you can't bail it out. companies, but you can't bail it out. You can theoretically bail out OpenAI. I You can theoretically bail out OpenAI. I You can theoretically bail out OpenAI. I don't think it happens. You could pump don't think it happens. You could pump don't think it happens. You could pump these dogs full of money and keep them these dogs full of money and keep them these dogs full of money and keep them alive for a bit, but at some point alive for a bit, but at some point alive for a bit, but at some point they're going to have to start. They they're going to have to start. They they're going to have to start. They have between these two companies, have between these two companies, have between these two companies, Anthropic and Open AI, you have $1.1 Anthropic and Open AI, you have $1.1 Anthropic and Open AI, you have $1.1 trillion of commitments.

trillion of commitments. trillion of commitments. >> Just OpenAI. >> Just OpenAI. >> Just OpenAI. >> Oracle is building 7.1 gawatt of data >> Oracle is building 7.1 gawatt of data >> Oracle is building 7.1 gawatt of data centers. So over $400 billion worth just centers. So over $400 billion worth just centers. So over $400 billion worth just for OpenAI. There is not a customer on for OpenAI. There is not a customer on for OpenAI. There is not a customer on Earth. And Oracle's revenue has been Earth. And Oracle's revenue has been Earth. And Oracle's revenue has been flat the last 15 years when you adjust flat the last 15 years when you adjust flat the last 15 years when you adjust for inflation. Without Open AI, Oracle for inflation. Without Open AI, Oracle for inflation. Without Open AI, Oracle dies. So you think open AAI is going to dies. So you think open AAI is going to dies. So you think open AAI is going to crash and run out of money and that's crash and run out of money and that's crash and run out of money and that's going to cause this domino effect across going to cause this domino effect across going to cause this domino effect across these other big tech companies which is these other big tech companies which is these other big tech companies which is going to impact the stock market and going to impact the stock market and going to impact the stock market and impact the broader economy. impact the broader economy. impact the broader economy. >> Yes. And also the tens of thousands of >> Yes. And also the tens of thousands of >> Yes. And also the tens of thousands of people that will be laid off from the people that will be laid off from the people that will be laid off from the tech sector. But also the venture tech sector. But also the venture tech sector. But also the venture capital thing is significant because capital thing is significant because capital thing is significant because venture capital has been having one of venture capital has been having one of venture capital has been having one of the most historic the most historic the most historic bad runs in history since 2018. The bad runs in history since 2018. The bad runs in history since 2018. The average return from venture capital average return from venture capital average return from venture capital total value put in. So the amount of total value put in. So the amount of total value put in. So the amount of money you get back for your dollar is money you get back for your dollar is money you get back for your dollar is between8 and 1.21 meaning for every between8 and 1.21 meaning for every between8 and 1.21 meaning for every dollar you invest you get 80 cents to dollar you invest you get 80 cents to dollar you invest you get 80 cents to $120 $120 $120 >> paper gains. >> paper gains. >> paper gains. >> Well no that's just actual g like actual >> Well no that's just actual g like actual >> Well no that's just actual g like actual returns. Paper gains they'll give you returns. Paper gains they'll give you returns. Paper gains they'll give you but even then internal rate return which but even then internal rate return which but even then internal rate return which is a whole separate thing even that's is a whole separate thing even that's is a whole separate thing even that's not very happy. But long story short not very happy. But long story short not very happy. But long story short very simple venture capital is not very simple venture capital is not very simple venture capital is not making money come out. Venture capital making money come out. Venture capital making money come out. Venture capital is not actually providing returns.

is not actually providing returns. is not actually providing returns. >> They're celebrating paper gains. >> They're celebrating paper gains. >> They're celebrating paper gains. >> They're celebrating paper gains >> They're celebrating paper gains >> They're celebrating paper gains >> and they're raising off paper gains. >> and they're raising off paper gains. >> and they're raising off paper gains. >> Mhm. And actually paper gains I mean >> Mhm. And actually paper gains I mean >> Mhm. And actually paper gains I mean just being able to say oh look the just being able to say oh look the just being able to say oh look the valuation of anthropic went up. So valuation of anthropic went up. So valuation of anthropic went up. So that's that's that's >> but that's that's what Google and Amazon >> but that's that's what Google and Amazon >> but that's that's what Google and Amazon were doing. Google's last quarter they were doing. Google's last quarter they were doing. Google's last quarter they boosted their net profits profits on boosted their net profits profits on boosted their net profits profits on paper by $99 billion because of the paper by $99 billion because of the paper by $99 billion because of the increased value of their SpaceX holding increased value of their SpaceX holding increased value of their SpaceX holding and their anthropic holding. And again and their anthropic holding. And again and their anthropic holding. And again the fact that this is happening is the fact that this is happening is the fact that this is happening is insane and the fact it's not a scandal insane and the fact it's not a scandal insane and the fact it's not a scandal is insane but we live in this culture I is insane but we live in this culture I is insane but we live in this culture I guess. But everyone is really benefiting guess. But everyone is really benefiting guess. But everyone is really benefiting right now. Oh, it's really that it's right now. Oh, it's really that it's right now. Oh, it's really that it's that great tweet. It's like when you're that great tweet. It's like when you're that great tweet. It's like when you're reaping, it's like, "Yeah, yeah, reaping, it's like, "Yeah, yeah, reaping, it's like, "Yeah, yeah, this rocks." Sewing. Ah, This this rocks." Sewing. Ah, This this rocks." Sewing. Ah, This sucks. Because right now, they're all sucks. Because right now, they're all sucks. Because right now, they're all like, "Yeah, all the speculative gains like, "Yeah, all the speculative gains like, "Yeah, all the speculative gains are awesome. The paper gains are are awesome. The paper gains are are awesome. The paper gains are awesome. The theoreticals of anthropic awesome. The theoreticals of anthropic awesome. The theoreticals of anthropic being worth $2 trillion. Wow. The being worth $2 trillion. Wow. The being worth $2 trillion. Wow. The articles we can write, the promises we articles we can write, the promises we articles we can write, the promises we can make. Then when the rubber meets the can make. Then when the rubber meets the can make. Then when the rubber meets the road, it's going to be pretty rough on road, it's going to be pretty rough on road, it's going to be pretty rough on them because the valuation of Amazon, them because the valuation of Amazon, them because the valuation of Amazon, Google, Microsoft, and Meta is based on Google, Microsoft, and Meta is based on Google, Microsoft, and Meta is based on this idea that they will grow eternally, this idea that they will grow eternally, this idea that they will grow eternally, that they will grow forever. If that that they will grow forever. If that that they will grow forever. If that changes, to quote Ed Elson from ProfitG changes, to quote Ed Elson from ProfitG changes, to quote Ed Elson from ProfitG Markets again, it's this. They're all Markets again, it's this. They're all Markets again, it's this. They're all doing Botox right now. They're sinking doing Botox right now. They're sinking doing Botox right now. They're sinking money into it to make themselves feel money into it to make themselves feel money into it to make themselves feel young again and the market believes young again and the market believes young again and the market believes them. When the market doesn't, we're not them. When the market doesn't, we're not them. When the market doesn't, we're not just talking about a depression. I'm just talking about a depression. I'm just talking about a depression. I'm talking about the market valuing them talking about the market valuing them talking about the market valuing them like airlines and saying, "Yeah, you're like airlines and saying, "Yeah, you're like airlines and saying, "Yeah, you're real big and you make money off your real big and you make money off your real big and you make money off your existing products, but guess what? You existing products, but guess what? You existing products, but guess what? You don't have new You're just going don't have new You're just going don't have new You're just going to be doing this forever and we're going to be doing this forever and we're going to be doing this forever and we're going to value you as such."

to value you as such." to value you as such." >> So, if it's Jenny and Dave, should they >> So, if it's Jenny and Dave, should they >> So, if it's Jenny and Dave, should they do anything differently? Should they be do anything differently? Should they be do anything differently? Should they be conserving money? If there's a recession conserving money? If there's a recession conserving money? If there's a recession or depression coming, should they be a or depression coming, should they be a or depression coming, should they be a little bit more conservative? Should little bit more conservative? Should little bit more conservative? Should they they they >> I Yes. I actually I actually think it's >> I Yes. I actually I actually think it's >> I Yes. I actually I actually think it's I don't know. I don't have money in the I don't know. I don't have money in the I don't know. I don't have money in the market. I think it's a casino. Casino market. I think it's a casino. Casino market. I think it's a casino. Casino pumped up by the media. pumped up by the media. pumped up by the media. >> Should they invest in the S&P 500? >> Should they invest in the S&P 500? >> Should they invest in the S&P 500? Should they invest in Open AI? Should they invest in Open AI? Should they invest in Open AI? Unfortunately, Unfortunately, Unfortunately, >> oh god, no. I honestly I live in cash >> oh god, no. I honestly I live in cash >> oh god, no. I honestly I live in cash right now. I live in cash. Yeah. I don't right now. I live in cash. Yeah. I don't right now. I live in cash. Yeah. I don't trust the market, man. Try and trust the market, man. Try and trust the market, man. Try and get some gains here. I'm like I'm not get some gains here. I'm like I'm not get some gains here. I'm like I'm not comfortable giving financial comfortable giving financial comfortable giving financial >> advice, but it's like if you like it's >> advice, but it's like if you like it's >> advice, but it's like if you like it's like you're gambling. like you're gambling. like you're gambling. >> Okay. be conservative. Things might get >> Okay. be conservative. Things might get >> Okay. be conservative. Things might get volatile. volatile. volatile. >> Yeah, it really is. It's going to be act >> Yeah, it really is. It's going to be act >> Yeah, it really is. It's going to be act as you would with volatility. Take the as you would with volatility. Take the as you would with volatility. Take the gains when you've got them. gains when you've got them. gains when you've got them. >> Don't sell everything, but be suspicious >> Don't sell everything, but be suspicious >> Don't sell everything, but be suspicious of tech. Like, that's actually the of tech. Like, that's actually the of tech. Like, that's actually the biggest thing. It's like be suspicious biggest thing. It's like be suspicious biggest thing. It's like be suspicious of what they're promising. If you're of what they're promising. If you're of what they're promising. If you're acting based on their promises, don't acting based on their promises, don't acting based on their promises, don't trust the promises. Trust that they are trust the promises. Trust that they are trust the promises. Trust that they are going to say what will make the stock going to say what will make the stock going to say what will make the stock run rather than what's actually run rather than what's actually run rather than what's actually happening. and that they will find every happening. and that they will find every happening. and that they will find every dodgy way to make you think something is dodgy way to make you think something is dodgy way to make you think something is happening rather than it's actually happening rather than it's actually happening rather than it's actually happening. Annualized run rate, great happening. Annualized run rate, great happening. Annualized run rate, great example. Microsoft said that they had 38 example. Microsoft said that they had 38 example. Microsoft said that they had 38 $37 billion of annualized run rate in $37 billion of annualized run rate in $37 billion of annualized run rate in AI. You hear that, you go, they made 38 AI. You hear that, you go, they made 38 AI. You hear that, you go, they made 38 $37 billion, right? Wow, that's so much $37 billion, right? Wow, that's so much $37 billion, right? Wow, that's so much run rate maybe month times 12. They run rate maybe month times 12. They run rate maybe month times 12. They don't even define it, but it's built to don't even define it, but it's built to don't even define it, but it's built to manipulate. And they do that because we manipulate. And they do that because we manipulate. And they do that because we don't have a functional SEC and we don't don't have a functional SEC and we don't don't have a functional SEC and we don't have a media environment that actually have a media environment that actually have a media environment that actually where skepticism is the priority and where skepticism is the priority and where skepticism is the priority and where protecting the readers is

where protecting the readers is where protecting the readers is necessary. necessary. necessary. >> What would they say? They would say Ed >> What would they say? They would say Ed >> What would they say? They would say Ed this technology is going to be so great this technology is going to be so great this technology is going to be so great and so transformative that we are and so transformative that we are and so transformative that we are investing a ton of money investing a ton of money investing a ton of money >> um in advance of the value and utility >> um in advance of the value and utility >> um in advance of the value and utility showing up. That's what they would say, showing up. That's what they would say, showing up. That's what they would say, >> right? >> right? >> right? >> And I've heard your rebuttal, but I just >> And I've heard your rebuttal, but I just >> And I've heard your rebuttal, but I just wanted to express I think that's their wanted to express I think that's their wanted to express I think that's their sentiment. I'm not defending them or sentiment. I'm not defending them or sentiment. I'm not defending them or anything. I'm just I'm trying to provide anything. I'm just I'm trying to provide anything. I'm just I'm trying to provide enough like balance to we see if we can enough like balance to we see if we can enough like balance to we see if we can dance between these these two dance between these these two dance between these these two perspectives. perspectives. perspectives. >> And a lot of people would say that >> And a lot of people would say that >> And a lot of people would say that there's going to be a blood bath because there's going to be a blood bath because there's going to be a blood bath because they can't all win big in the way that they can't all win big in the way that they can't all win big in the way that they're kind of describing. So, they're kind of describing. So, they're kind of describing. So, someone's going to have to lose. And someone's going to have to lose. And someone's going to have to lose. And >> when one of these players starts to lose >> when one of these players starts to lose >> when one of these players starts to lose big, I think it could, as you say, there big, I think it could, as you say, there big, I think it could, as you say, there could be some kind of domino effect or could be some kind of domino effect or could be some kind of domino effect or contraction. contraction. contraction. >> Yeah. And I think the thing that people >> Yeah. And I think the thing that people >> Yeah. And I think the thing that people want to believe is they the com bubble want to believe is they the com bubble want to believe is they the com bubble thing. It's like it worked out thing. It's like it worked out thing. It's like it worked out afterwards because Amazon, Oracle, they afterwards because Amazon, Oracle, they afterwards because Amazon, Oracle, they didn't die after the com bubble. They're didn't die after the com bubble. They're didn't die after the com bubble. They're actually fine. This isn't like that. actually fine. This isn't like that. actually fine. This isn't like that. They're bigger companies. They're have They're bigger companies. They're have They're bigger companies. They're have bigger promises. And even I'm not like bigger promises. And even I'm not like bigger promises. And even I'm not like Oracle I actually think could die. I RIP Oracle I actually think could die. I RIP Oracle I actually think could die. I RIP Larry. What couldn't happen to a nastier Larry. What couldn't happen to a nastier Larry. What couldn't happen to a nastier man? They'll probably man? They'll probably man? They'll probably >> You don't like these people, do you?

>> You don't like these people, do you? >> You don't like these people, do you? >> No, I No. Again, I asked this question >> No, I No. Again, I asked this question >> No, I No. Again, I asked this question purely because I want an answer, not purely because I want an answer, not purely because I want an answer, not because I agree or disagree. But um why because I agree or disagree. But um why because I agree or disagree. But um why don't you like these these people? I don't you like these these people? I don't you like these these people? I don't like being misled and I don't don't like being misled and I don't don't like being misled and I don't think regular people like being misled think regular people like being misled think regular people like being misled either. And I really don't think that either. And I really don't think that either. And I really don't think that the average person can get away with the average person can get away with the average person can get away with bullshitting as much these companies do. bullshitting as much these companies do. bullshitting as much these companies do. And I don't think the average person And I don't think the average person And I don't think the average person gets anywhere near the level of gets anywhere near the level of gets anywhere near the level of affordance for failure and lying as affordance for failure and lying as affordance for failure and lying as these companies do. And I think there is these companies do. And I think there is these companies do. And I think there is a real economic and human cost to a real economic and human cost to a real economic and human cost to allowing these companies to run rampant allowing these companies to run rampant allowing these companies to run rampant and promise the world and never really and promise the world and never really and promise the world and never really get called up on it. The tepid nature of get called up on it. The tepid nature of get called up on it. The tepid nature of criticism these days is so frustrating. criticism these days is so frustrating. criticism these days is so frustrating. There are some really great critics out There are some really great critics out There are some really great critics out there that really great people, but it's there that really great people, but it's there that really great people, but it's like like like seeing these ultra rich, ultra wealthy, seeing these ultra rich, ultra wealthy, seeing these ultra rich, ultra wealthy, ultra powerful people lie through their ultra powerful people lie through their ultra powerful people lie through their teeth or misstate or whatever teeth or misstate or whatever teeth or misstate or whatever people want to call it, it turns my people want to call it, it turns my people want to call it, it turns my stomach. And I hate seeing people being stomach. And I hate seeing people being stomach. And I hate seeing people being misled. And I feel like I write at such misled. And I feel like I write at such misled. And I feel like I write at such length because I really want people to length because I really want people to length because I really want people to see why I've come to a conclusion. Am I see why I've come to a conclusion. Am I see why I've come to a conclusion. Am I right? Am I wrong? I think I am. Of right? Am I wrong? I think I am. Of right? Am I wrong? I think I am. Of course I do. But I also course I do. But I also course I do. But I also I just find it loathome. I find these I just find it loathome. I find these I just find it loathome. I find these companies don't make good products companies don't make good products companies don't make good products anymore. They don't care about their anymore. They don't care about their anymore. They don't care about their customers and and they treat their customers and and they treat their customers and and they treat their customers with contempt.

customers with contempt. customers with contempt. >> If people want to go read more about >> If people want to go read more about >> If people want to go read more about your work, um you have a great Substack your work, um you have a great Substack your work, um you have a great Substack >> Ghost actually. It looks exactly like I >> Ghost actually. It looks exactly like I >> Ghost actually. It looks exactly like I moved off of Substack in 2024. moved off of Substack in 2024. moved off of Substack in 2024. >> Oh, okay. And you also have a podcast >> Oh, okay. And you also have a podcast >> Oh, okay. And you also have a podcast you do. you do. you do. >> Yeah, Better of Flame. >> Yeah, Better of Flame. >> Yeah, Better of Flame. >> Um I'm going to link both of them below. >> Um I'm going to link both of them below. >> Um I'm going to link both of them below. So, if anyone wants to read more, get So, if anyone wants to read more, get So, if anyone wants to read more, get more detail and and follow Ed. I think more detail and and follow Ed. I think more detail and and follow Ed. I think it's it's it's >> I would highly recommend. It's it is >> I would highly recommend. It's it is >> I would highly recommend. It's it is fascinating. And you know what? One of fascinating. And you know what? One of fascinating. And you know what? One of the things people um sometimes struggle the things people um sometimes struggle the things people um sometimes struggle with when they listen to podcasts is you with when they listen to podcasts is you with when they listen to podcasts is you get lots of different opinions. And get lots of different opinions. And get lots of different opinions. And weirdly, I think they think of some weirdly, I think they think of some weirdly, I think they think of some people assume podcasts are going to be people assume podcasts are going to be people assume podcasts are going to be like one person saying the same thing as like one person saying the same thing as like one person saying the same thing as the next person and then the next the next person and then the next the next person and then the next person. That is just not the nature of person. That is just not the nature of person. That is just not the nature of information in the world and opinions information in the world and opinions information in the world and opinions and progress and discussion. What what and progress and discussion. What what and progress and discussion. What what happens is people have different happens is people have different happens is people have different opinions. And I think my job, but also opinions. And I think my job, but also opinions. And I think my job, but also the listener's job is to try and pass the listener's job is to try and pass the listener's job is to try and pass through it and over time collect more of through it and over time collect more of through it and over time collect more of these reference points from different these reference points from different these reference points from different people and and do your own research. people and and do your own research. people and and do your own research. >> Yeah. whether it's on your health or >> Yeah. whether it's on your health or >> Yeah. whether it's on your health or whether it's on something like this is whether it's on something like this is whether it's on something like this is to watch endear and research and to to watch endear and research and to to watch endear and research and to learn and I would say also never believe learn and I would say also never believe learn and I would say also never believe one person never believe one particular one person never believe one particular one person never believe one particular perspective religiously you know collect perspective religiously you know collect perspective religiously you know collect a body of evidence and follow follow the a body of evidence and follow follow the a body of evidence and follow follow the evidence yourself but I love watching evidence yourself but I love watching evidence yourself but I love watching your YouTube um because it provides a your YouTube um because it provides a your YouTube um because it provides a different opinion and that challenges me different opinion and that challenges me different opinion and that challenges me to think beyond my current opinion about to think beyond my current opinion about to think beyond my current opinion about what might be possible so when I've what might be possible so when I've what might be possible so when I've heard you talking about how this is an heard you talking about how this is an heard you talking about how this is an economic bubble and I've heard you talk economic bubble and I've heard you talk economic bubble and I've heard you talk about the capex spend on with these big about the capex spend on with these big about the capex spend on with these big sort of frontier AI labs. It really did sort of frontier AI labs. It really did sort of frontier AI labs. It really did make me pause for a second and it really make me pause for a second and it really make me pause for a second and it really did make me consider did make me consider did make me consider that there could be a bit of fazy going

that there could be a bit of fazy going that there could be a bit of fazy going on here. on here. on here. >> Yeah. >> Yeah. >> Yeah. >> And then it made me reflect on history >> And then it made me reflect on history >> And then it made me reflect on history and go, you know, through history and go, you know, through history and go, you know, through history there's always a bit of fazy in these there's always a bit of fazy in these there's always a bit of fazy in these moments and oh that's an interesting moments and oh that's an interesting moments and oh that's an interesting take on what's going to happen in 2027 take on what's going to happen in 2027 take on what's going to happen in 2027 2028 when there's a bit of a market 2028 when there's a bit of a market 2028 when there's a bit of a market pullback and so I highly recommend pullback and so I highly recommend pullback and so I highly recommend people go watch because you do you people go watch because you do you people go watch because you do you challenge me to think differently. Um, challenge me to think differently. Um, challenge me to think differently. Um, >> yeah. >> yeah. >> yeah. >> And we need some of those contrarian >> And we need some of those contrarian >> And we need some of those contrarian voices to to have honest discussions. voices to to have honest discussions. voices to to have honest discussions. So, thank you for doing what you do. So, thank you for doing what you do. So, thank you for doing what you do. Really appreciate it. And I find you to Really appreciate it. And I find you to Really appreciate it. And I find you to be a very compelling, captivating be a very compelling, captivating be a very compelling, captivating communicator. And I've I feel like I've communicator. And I've I feel like I've communicator. And I've I feel like I've learned a lot today. So, I appreciate learned a lot today. So, I appreciate learned a lot today. So, I appreciate that. We have a closing tradition. that. We have a closing tradition. that. We have a closing tradition. >> Yeah. >> Yeah. >> Yeah. >> Where the last guest leaves a question >> Where the last guest leaves a question >> Where the last guest leaves a question for the next guest not knowing who for the next guest not knowing who for the next guest not knowing who they're leaving it for. And the question they're leaving it for. And the question they're leaving it for. And the question left for you is given that high quality left for you is given that high quality left for you is given that high quality relationships are important for health relationships are important for health relationships are important for health and longevity, what should we be doing and longevity, what should we be doing and longevity, what should we be doing to improve our relationships and social to improve our relationships and social to improve our relationships and social connection? So this is actually connection? So this is actually connection? So this is actually connected to the AI bubble. So I am a connected to the AI bubble. So I am a connected to the AI bubble. So I am a critic. I'm a skeptic. What quote I have critic. I'm a skeptic. What quote I have critic. I'm a skeptic. What quote I have found that showing and appreciating and found that showing and appreciating and found that showing and appreciating and loving the people around you and loving the people around you and loving the people around you and uplifting them and me and and raising uplifting them and me and and raising uplifting them and me and and raising them up as you succeed is the way we do them up as you succeed is the way we do them up as you succeed is the way we do that. Your success should be everyone that. Your success should be everyone that. Your success should be everyone around you. It's not economic. It's around you. It's not economic. It's around you. It's not economic. It's talking about Matt Hughes for a while talking about Matt Hughes for a while talking about Matt Hughes for a while made me really happy. This whole thing made me really happy. This whole thing made me really happy. This whole thing has been at times quite grueling and has been at times quite grueling and has been at times quite grueling and quite negative and quite brutal. But the quite negative and quite brutal. But the quite negative and quite brutal. But the love I found and the joy I found from love I found and the joy I found from love I found and the joy I found from community and the people around because community and the people around because community and the people around because even in the in the small groups of even in the in the small groups of even in the in the small groups of haters even like Gary Marcus and sort of haters even like Gary Marcus and sort of haters even like Gary Marcus and sort of the people I talked to Edward on Grao the people I talked to Edward on Grao the people I talked to Edward on Grao Jr. Molly White, Brian Merchant, there Jr. Molly White, Brian Merchant, there Jr. Molly White, Brian Merchant, there are so many people who have been loving are so many people who have been loving are so many people who have been loving and caring. And I think within and caring. And I think within and caring. And I think within especially these very critical moments

especially these very critical moments especially these very critical moments when you're like very much dialing in on when you're like very much dialing in on when you're like very much dialing in on how negative things are, how bad things how negative things are, how bad things how negative things are, how bad things are, finding the people who maybe find are, finding the people who maybe find are, finding the people who maybe find it repulsive, too. Finding the people, it repulsive, too. Finding the people, it repulsive, too. Finding the people, >> finding your people who can be and the >> finding your people who can be and the >> finding your people who can be and the people who will talk to you about it. people who will talk to you about it. people who will talk to you about it. Even like Troy and Jake, my my trainers Even like Troy and Jake, my my trainers Even like Troy and Jake, my my trainers who's so excited about this. um even who's so excited about this. um even who's so excited about this. um even talking to them about the as normal talking to them about the as normal talking to them about the as normal people knowing that there are people people knowing that there are people people knowing that there are people there going through their own struggles there going through their own struggles there going through their own struggles but also to just give you the but also to just give you the but also to just give you the perspective and also remind you that you perspective and also remind you that you perspective and also remind you that you are human to and focus I know this is are human to and focus I know this is are human to and focus I know this is kind of a all over the place point but kind of a all over the place point but kind of a all over the place point but it's just it's really easy to get hard it's just it's really easy to get hard it's just it's really easy to get hard locked on everything in life and to locked on everything in life and to locked on everything in life and to >> kind of get away from why you do things >> kind of get away from why you do things >> kind of get away from why you do things and focus too much on the work when the and focus too much on the work when the and focus too much on the work when the most important thing at times is just to most important thing at times is just to most important thing at times is just to know there are other people feeling the know there are other people feeling the know there are other people feeling the way you do and when I hear from my way you do and when I hear from my way you do and when I hear from my listeners and my readers a lot the most listeners and my readers a lot the most listeners and my readers a lot the most common thing they feel is they feel like common thing they feel is they feel like common thing they feel is they feel like they have a voice and they feel like they have a voice and they feel like they have a voice and they feel like someone is there for you. someone is there for you. someone is there for you. >> And I don't think it can be understated >> And I don't think it can be understated >> And I don't think it can be understated how much it means when you just reach how much it means when you just reach how much it means when you just reach out to someone you love and tell them out to someone you love and tell them out to someone you love and tell them you love them. Tell them their you love them. Tell them their you love them. Tell them their rocks. Say that their bangs. Tell rocks. Say that their bangs. Tell rocks. Say that their bangs. Tell everyone you when you like an artist or everyone you when you like an artist or everyone you when you like an artist or a writer they were a podcast like this.

a writer they were a podcast like this. a writer they were a podcast like this. Tell them you love it. We don't Tell them you love it. We don't Tell them you love it. We don't do this enough and we need to do it do this enough and we need to do it do this enough and we need to do it more. Well, that's a good closing more. Well, that's a good closing more. Well, that's a good closing message. So, if you do have you have message. So, if you do have you have message. So, if you do have you have enjoyed the conversation today with Ed, enjoyed the conversation today with Ed, enjoyed the conversation today with Ed, please do let Ed know that you love it please do let Ed know that you love it please do let Ed know that you love it down below. Um, but please do leave your down below. Um, but please do leave your down below. Um, but please do leave your opinions down below and I shall read all opinions down below and I shall read all opinions down below and I shall read all of them. Ed, thank you so much. I'll of them. Ed, thank you so much. I'll of them. Ed, thank you so much. I'll link to your website, but also to your link to your website, but also to your link to your website, but also to your YouTube channel where people can learn YouTube channel where people can learn YouTube channel where people can learn more and I would highly recommend you do more and I would highly recommend you do more and I would highly recommend you do because it is truly fascinating and I because it is truly fascinating and I because it is truly fascinating and I think we need more voices that are think we need more voices that are think we need more voices that are demystifying a lot of the fugazi and the demystifying a lot of the fugazi and the demystifying a lot of the fugazi and the narrative in this moment in time and you narrative in this moment in time and you narrative in this moment in time and you are certainly one of them. I really are certainly one of them. I really are certainly one of them. I really enjoyed the conversation. Thank you so enjoyed the conversation. Thank you so enjoyed the conversation. Thank you so much. much. much. >> YouTube have this new crazy algorithm >> YouTube have this new crazy algorithm >> YouTube have this new crazy algorithm where they know exactly what video you where they know exactly what video you where they know exactly what video you would like to watch next based on AI and would like to watch next based on AI and would like to watch next based on AI and all of your viewing behavior. And the all of your viewing behavior. And the all of your viewing behavior. And the algorithm says that this video is the algorithm says that this video is the algorithm says that this video is the perfect video for you. It's different perfect video for you. It's different perfect video for you. It's different for everybody looking right now. Check for everybody looking right now. Check for everybody looking right now. Check this video out and I bet you you might this video out and I bet you you might this video out and I bet you you might love it.