IoTCT Webcast Episode 288 - "Round & Round" (Circular logic for circular AI investment)

Heat Heat >> [music] [music] [music] >> up here. Round and round everyone. here. Round and round everyone. The theme song for The theme song for The theme song for the last I I would say month. the last I I would say month. the last I I would say month. >> Yeah, it's been rank ramping up ramping >> Yeah, it's been rank ramping up ramping >> Yeah, it's been rank ramping up ramping up. Uh, [clears throat] up. Uh, [clears throat] up. Uh, [clears throat] ever since every everyone started ever since every everyone started ever since every everyone started getting uh uh, you know, a sniff of all of the uh, you know, a sniff of all of the circular stuff circular stuff circular stuff round and round, round and round, round and round, right? Hey everyone, welcome to IoT right? Hey everyone, welcome to IoT right? Hey everyone, welcome to IoT Coffee Talk. Uh, Coffee Talk. Uh, Coffee Talk. Uh, yeah, let me see if I can get this to yeah, let me see if I can get this to yeah, let me see if I can get this to regular sound mode here on Zoom because regular sound mode here on Zoom because regular sound mode here on Zoom because Zoom really Zoom really Zoom really >> Oh my gosh.

>> Oh my gosh. >> Oh my gosh. >> Yeah. And uh fair warning, I am really >> Yeah. And uh fair warning, I am really >> Yeah. And uh fair warning, I am really really tired. I got stuck in Dallas really tired. I got stuck in Dallas really tired. I got stuck in Dallas yesterday. Almost didn't get home. Like yesterday. Almost didn't get home. Like yesterday. Almost didn't get home. Like they canled probably about 80% of the they canled probably about 80% of the they canled probably about 80% of the flights yesterday. So there's a lot of flights yesterday. So there's a lot of flights yesterday. So there's a lot of folks who are stranded in Dallas at the folks who are stranded in Dallas at the folks who are stranded in Dallas at the moment and today they're frantically moment and today they're frantically moment and today they're frantically trying to get home. trying to get home. trying to get home. And a shout out to all the air traffic And a shout out to all the air traffic And a shout out to all the air traffic controllers and all of the airport and controllers and all of the airport and controllers and all of the airport and airline staff who uh I'm sure were not airline staff who uh I'm sure were not airline staff who uh I'm sure were not having a good day. And um you know, having a good day. And um you know, having a good day. And um you know, shame on shame on shame on you know the government for shutting you know the government for shutting you know the government for shutting down the down the down the uh you know shutting down the government uh you know shutting down the government uh you know shutting down the government and putting all these people in that and putting all these people in that and putting all these people in that situation. You know, everyone situation. You know, everyone situation. You know, everyone >> difficult situation. Yeah, for sure. >> difficult situation. Yeah, for sure. >> difficult situation. Yeah, for sure. >> Wow. >> Wow. >> Wow. >> Yeah. So, you know, um that was a >> Yeah. So, you know, um that was a >> Yeah. So, you know, um that was a nonpartisan statement.

nonpartisan statement. nonpartisan statement. Obviously, we will get censored on Obviously, we will get censored on Obviously, we will get censored on YouTube for making it especially YouTube for making it especially YouTube for making it especially >> No, you you get blocked uh I if um >> No, you you get blocked uh I if um >> No, you you get blocked uh I if um there's any kind of political especially there's any kind of political especially there's any kind of political especially in Europe. So, yeah, there's some in Europe. So, yeah, there's some in Europe. So, yeah, there's some >> Well, but the thing is the following. I >> Well, but the thing is the following. I >> Well, but the thing is the following. I mean it is a this is not the way I mean it is a this is not the way I mean it is a this is not the way I position it. I mean the the issue is position it. I mean the the issue is position it. I mean the the issue is that if there are internal politic that if there are internal politic that if there are internal politic issues no internal politic issue should issues no internal politic issue should issues no internal politic issue should block essential services is my point. block essential services is my point. block essential services is my point. Whether you have ideas on the right on Whether you have ideas on the right on Whether you have ideas on the right on the left I mean okay fine it's a fair the left I mean okay fine it's a fair the left I mean okay fine it's a fair debate. This is what the democracy is. debate. This is what the democracy is. debate. This is what the democracy is. >> Yeah. >> Yeah. >> Yeah. >> Democracy should have a sort of standard >> Democracy should have a sort of standard >> Democracy should have a sort of standard level of services level of services level of services public services. public services. public services. >> Yeah. >> Yeah. >> Yeah. >> Yeah. Sorry. that actually operates >> Yeah. Sorry. that actually operates >> Yeah. Sorry. that actually operates because it's beneficial for everybody because it's beneficial for everybody because it's beneficial for everybody for the business for the people whether for the business for the people whether for the business for the people whether you're a rightist or a leftist and if you're a rightist or a leftist and if you're a rightist or a leftist and if you have to move you need plane you need you have to move you need plane you need you have to move you need plane you need air traffic controllers so it just air traffic controllers so it just air traffic controllers so it just >> yeah exactly and but that we're about >> yeah exactly and but that we're about >> yeah exactly and but that we're about technology we're not about that stuff technology we're not about that stuff technology we're not about that stuff and the things that are around the and the things that are around the and the things that are around the periphery of technology and we can talk periphery of technology and we can talk periphery of technology and we can talk about you know not [clears throat] about you know not [clears throat] about you know not [clears throat] politics but government right and their politics but government right and their politics but government right and their impact on technology and policy and impact on technology and policy and impact on technology and policy and stuff like that but anyways Um, you stuff like that but anyways Um, you stuff like that but anyways Um, you know, I think, um, it's pretty obvious know, I think, um, it's pretty obvious know, I think, um, it's pretty obvious that all of our stuff gets scanned.

that all of our stuff gets scanned. that all of our stuff gets scanned. [laughter] [laughter] [laughter] >> We live in a surveillance >> We live in a surveillance >> We live in a surveillance >> society. >> society. >> society. >> Society. Yeah. So, it's pretty much >> Society. Yeah. So, it's pretty much >> Society. Yeah. So, it's pretty much a done deal, sadly. Right. But, uh, a done deal, sadly. Right. But, uh, a done deal, sadly. Right. But, uh, yeah, welcome everyone. And remember to yeah, welcome everyone. And remember to yeah, welcome everyone. And remember to take us seriously at your own peril. take us seriously at your own peril. take us seriously at your own peril. We're here just to have fun and uh don't We're here just to have fun and uh don't We're here just to have fun and uh don't you know don't make any kind of serious you know don't make any kind of serious you know don't make any kind of serious decisions based on anything that we say decisions based on anything that we say decisions based on anything that we say and uh we hope you enjoy the banter. Uh and uh we hope you enjoy the banter. Uh and uh we hope you enjoy the banter. Uh it goes on for a long time. But guess it goes on for a long time. But guess it goes on for a long time. But guess what? You know what? If you listen to what? You know what? If you listen to what? You know what? If you listen to IoT Coffee Talk on a weekly basis, it's IoT Coffee Talk on a weekly basis, it's IoT Coffee Talk on a weekly basis, it's a wonderful dose of reality, especially a wonderful dose of reality, especially a wonderful dose of reality, especially to cap off the week, that is typically to cap off the week, that is typically to cap off the week, that is typically filled with a lot of nonsensical, filled with a lot of nonsensical, filled with a lot of nonsensical, ridiculous hype. And so, you can come ridiculous hype. And so, you can come ridiculous hype. And so, you can come here and we will distill everything down here and we will distill everything down here and we will distill everything down to something a hell of a lot more to something a hell of a lot more to something a hell of a lot more reasonable reasonable reasonable than anything that you're hearing or than anything that you're hearing or than anything that you're hearing or listening to out there. So, um, anyways, listening to out there. So, um, anyways, listening to out there. So, um, anyways, uh, we're glad that you're here.

uh, we're glad that you're here. uh, we're glad that you're here. >> We're here to, uh, encourage your >> We're here to, uh, encourage your >> We're here to, uh, encourage your critical thinking. critical thinking. critical thinking. >> Yeah, exactly. >> Yeah, exactly. >> Yeah, exactly. >> Yeah. >> Yeah. >> Yeah. >> We can >> We can >> We can >> in an age where critical thinking is >> in an age where critical thinking is >> in an age where critical thinking is diminishing and there's more and more diminishing and there's more and more diminishing and there's more and more studies coming out about that, right? studies coming out about that, right? studies coming out about that, right? That uh, That uh, That uh, uh, you know, students uh, you know, uh, you know, students uh, you know, uh, you know, students uh, you know, students are basically succumbing to the students are basically succumbing to the students are basically succumbing to the the convenience. And it's not that the convenience. And it's not that the convenience. And it's not that they're cheating. Um, cheating is they're cheating. Um, cheating is they're cheating. Um, cheating is different from different from different from deprivation, deprivation, deprivation, right? It's one thing to cheat. It and right? It's one thing to cheat. It and right? It's one thing to cheat. It and and cheating has its its own um, you and cheating has its its own um, you and cheating has its its own um, you know, unique issues, right? I mean, know, unique issues, right? I mean, know, unique issues, right? I mean, that's like basically gaming your own uh that's like basically gaming your own uh that's like basically gaming your own uh gaming education, right? Uh, in a bad gaming education, right? Uh, in a bad gaming education, right? Uh, in a bad way. Uh but the use of LLMs, way. Uh but the use of LLMs, way. Uh but the use of LLMs, look at more and more of these papers. look at more and more of these papers. look at more and more of these papers. It's it's about cognitive deprivation. It's it's about cognitive deprivation. It's it's about cognitive deprivation. Yeah. It's like you're depriving Yeah. It's like you're depriving Yeah. It's like you're depriving yourself of the exercise of developing yourself of the exercise of developing yourself of the exercise of developing your critical thinking. And so your critical thinking. And so your critical thinking. And so >> yeah, but >> yeah, but >> yeah, but and I've seen a number of these research and I've seen a number of these research and I've seen a number of these research and I think we should always take those and I think we should always take those and I think we should always take those kind of studies with a with grain of kind of studies with a with grain of kind of studies with a with grain of salt as well because the thing is the salt as well because the thing is the salt as well because the thing is the following. People always go the path the following. People always go the path the following. People always go the path the the path of least resistance. So if you the path of least resistance. So if you the path of least resistance. So if you freeze an existing system and say oh now freeze an existing system and say oh now freeze an existing system and say oh now there's these and people go around the there's these and people go around the there's these and people go around the existing system with yeah change the existing system with yeah change the existing system with yeah change the system system system change the system so that you drive the

change the system so that you drive the change the system so that you drive the good behavior. So and that's one thing good behavior. So and that's one thing good behavior. So and that's one thing that you know you you know me for my that you know you you know me for my that you know you you know me for my passion and my kind of opinions here but passion and my kind of opinions here but passion and my kind of opinions here but I I I get very very quickly upset I I I get very very quickly upset I I I get very very quickly upset especially when it comes to education especially when it comes to education especially when it comes to education because it is 100% the responsibility to because it is 100% the responsibility to because it is 100% the responsibility to adapt and adjust education to the tools adapt and adjust education to the tools adapt and adjust education to the tools and to the systems and to the innovation and to the systems and to the innovation and to the systems and to the innovation that exists. that exists. that exists. But I think it it's even with enterprise But I think it it's even with enterprise But I think it it's even with enterprise adoption of generative AI which has been adoption of generative AI which has been adoption of generative AI which has been I mean now it's being very openly I mean now it's being very openly I mean now it's being very openly uh you know talked about as being uh you know talked about as being uh you know talked about as being disappointingly [clears throat] disappointingly [clears throat] disappointingly [clears throat] lackluster in terms of adoption and lackluster in terms of adoption and lackluster in terms of adoption and value. That's exactly the problem. It's, value. That's exactly the problem. It's, value. That's exactly the problem. It's, you know, and so as we look at what's you know, and so as we look at what's you know, and so as we look at what's happening right now where people are happening right now where people are happening right now where people are starting to open their eyes about starting to open their eyes about starting to open their eyes about generative AI in a broad sense, generative AI in a broad sense, generative AI in a broad sense, including with the AI infrastructure including with the AI infrastructure including with the AI infrastructure stuff and all these so-called deals, stuff and all these so-called deals, stuff and all these so-called deals, they're more like announcements. They're they're more like announcements. They're they're more like announcements. They're not deals, right? Um the, not deals, right? Um the, not deals, right? Um the, you know, you know, you know, everyone started off with the wrong everyone started off with the wrong everyone started off with the wrong assumption set for generative AI. It assumption set for generative AI. It assumption set for generative AI. It [clears throat] was completely wrong.

[clears throat] was completely wrong. [clears throat] was completely wrong. And so this is why we end up in these And so this is why we end up in these And so this is why we end up in these places where expectations early on um places where expectations early on um places where expectations early on um eventually just become massive eventually just become massive eventually just become massive disappointments and then eventually disappointments and then eventually disappointments and then eventually people will disregard technology and a people will disregard technology and a people will disregard technology and a great example I just got back from great example I just got back from great example I just got back from fortunately got back from Dallas. Jesus fortunately got back from Dallas. Jesus fortunately got back from Dallas. Jesus Christ. Uh we had a chat about how oh Christ. Uh we had a chat about how oh Christ. Uh we had a chat about how oh this sounds a lot like 5G because we this sounds a lot like 5G because we this sounds a lot like 5G because we still have a lot of these taco tech still have a lot of these taco tech still have a lot of these taco tech companies that you know try to smoke the companies that you know try to smoke the companies that you know try to smoke the next crackpipe. You know what I'm next crackpipe. You know what I'm next crackpipe. You know what I'm saying? And it's like you know okay AI saying? And it's like you know okay AI saying? And it's like you know okay AI all this stuff all this stuff all this stuff you know now you're going to bank you know now you're going to bank you know now you're going to bank [clears throat] on this to float your [clears throat] on this to float your [clears throat] on this to float your company or do whatever the hell you company or do whatever the hell you company or do whatever the hell you know. um you're going to run this race know. um you're going to run this race know. um you're going to run this race instead of looking at what's right in instead of looking at what's right in instead of looking at what's right in front of you, right? And um front of you, right? And um front of you, right? And um 5G is a great example. It's a great 5G is a great example. It's a great 5G is a great example. It's a great there's all this great technology.

there's all this great technology. there's all this great technology. Everyone created the worst expectations Everyone created the worst expectations Everyone created the worst expectations for these things. never did any of the for these things. never did any of the for these things. never did any of the critical thinking to figure out how critical thinking to figure out how critical thinking to figure out how would you actually apply these things in would you actually apply these things in would you actually apply these things in markets and how would you deal with the markets and how would you deal with the markets and how would you deal with the brownfield the stuff that already exists brownfield the stuff that already exists brownfield the stuff that already exists there whether it's Wi-Fi or heart or there whether it's Wi-Fi or heart or there whether it's Wi-Fi or heart or other connectivity protocols out there other connectivity protocols out there other connectivity protocols out there and technologies how how do you approach and technologies how how do you approach and technologies how how do you approach those things but instead everybody just those things but instead everybody just those things but instead everybody just went bonkers with the 5G narrative went bonkers with the 5G narrative went bonkers with the 5G narrative thinking that it's going to solve thinking that it's going to solve thinking that it's going to solve everything I mean remember everything I mean remember everything I mean remember same with open rand and you know um just same with open rand and you know um just same with open rand and you know um just happened. Yeah. Oh no, I had I had I had happened. Yeah. Oh no, I had I had I had happened. Yeah. Oh no, I had I had I had some interesting conversations and I did some interesting conversations and I did some interesting conversations and I did bring up Open Rand Squid Game. bring up Open Rand Squid Game. bring up Open Rand Squid Game. >> Oh. Oh >> Oh. Oh >> Oh. Oh >> yeah. Well, >> yeah. Well, >> yeah. Well, >> red light green light for Mavanir. >> red light green light for Mavanir. >> red light green light for Mavanir. >> Yeah. Well, you know, there there's a >> Yeah. Well, you know, there there's a >> Yeah. Well, you know, there there's a bunch of Yeah, exactly. There there are bunch of Yeah, exactly. There there are bunch of Yeah, exactly. There there are what what am I famous for in the telco what what am I famous for in the telco what what am I famous for in the telco industry? Open rand Squid Game. And look industry? Open rand Squid Game. And look industry? Open rand Squid Game. And look at what happened to them.

at what happened to them. at what happened to them. >> And everybody else had these massive >> And everybody else had these massive >> And everybody else had these massive forecasts about how open Rand's going to forecasts about how open Rand's going to forecasts about how open Rand's going to take over everything. And now everyone's take over everything. And now everyone's take over everything. And now everyone's wondering if it's dead, you know, it's wondering if it's dead, you know, it's wondering if it's dead, you know, it's like is it dead? So I mean, you know, like is it dead? So I mean, you know, like is it dead? So I mean, you know, but the I think you and the thing is you but the I think you and the thing is you but the I think you and the thing is you do believe that and and I, you know, I do believe that and and I, you know, I do believe that and and I, you know, I think we are in general totally aligned think we are in general totally aligned think we are in general totally aligned around over expectation. What is I around over expectation. What is I around over expectation. What is I believe different in this instance is believe different in this instance is believe different in this instance is that this is a technology. No, let me that this is a technology. No, let me that this is a technology. No, let me finish please. This is a techn it is finish please. This is a techn it is finish please. This is a techn it is different in what I'm trying to say that different in what I'm trying to say that different in what I'm trying to say that it is different in the sense that it's it is different in the sense that it's it is different in the sense that it's going to be much more longlasting in going to be much more longlasting in going to be much more longlasting in terms of damage. And the reason I'm terms of damage. And the reason I'm terms of damage. And the reason I'm saying that is because it is not a an saying that is because it is not a an saying that is because it is not a an exotic or nerdy or niche specialist exotic or nerdy or niche specialist exotic or nerdy or niche specialist thing like open rad or 5G. It is and I thing like open rad or 5G. It is and I thing like open rad or 5G. It is and I keep on repeating that every single time keep on repeating that every single time keep on repeating that every single time here. It is about language. So people here. It is about language. So people here. It is about language. So people get because you you if you want to say get because you you if you want to say get because you you if you want to say oh I'm going to save money with 5G oh I'm going to save money with 5G oh I'm going to save money with 5G because it's going to replace that and because it's going to replace that and because it's going to replace that and I'm going to do this and I'm going to go I'm going to do this and I'm going to go I'm going to do this and I'm going to go faster my transaction 2 milliseconds and faster my transaction 2 milliseconds and faster my transaction 2 milliseconds and 5 millcond this is an engineering thing 5 millcond this is an engineering thing 5 millcond this is an engineering thing [snorts] here the perception is I talk [snorts] here the perception is I talk [snorts] here the perception is I talk to that thing that thing replies to me to that thing that thing replies to me to that thing that thing replies to me and does something and oh it's much and does something and oh it's much and does something and oh it's much cheaper than a human. No, but cheaper than a human. No, but cheaper than a human. No, but >> no no but the perception again I'm >> no no but the perception again I'm >> no no but the perception again I'm talking about the perception you're talking about the perception you're talking about the perception you're talking you're talking to the thing it's talking you're talking to the thing it's talking you're talking to the thing it's the first time you have a technology the first time you have a technology the first time you have a technology where you're talking to the technology where you're talking to the technology where you're talking to the technology and the technology replies to you in and the technology replies to you in and the technology replies to you in language it has a very very profound language it has a very very profound language it has a very very profound impact

impact impact >> but it all boils down to trust how many >> but it all boils down to trust how many >> but it all boils down to trust how many times are you going to go back to that times are you going to go back to that times are you going to go back to that same thing when it lies ultimately lies same thing when it lies ultimately lies same thing when it lies ultimately lies to you because it's hallucinating it to you because it's hallucinating it to you because it's hallucinating it doesn't know doesn't know doesn't know >> but Leonard the whole world the whole >> but Leonard the whole world the whole >> but Leonard the whole world the whole world lies to you 100% % of the time. world lies to you 100% % of the time. world lies to you 100% % of the time. >> Not if you have deterministic >> Not if you have deterministic >> Not if you have deterministic expectations of technology. No, they use expectations of technology. No, they use expectations of technology. No, they use it for non-deterministic it for non-deterministic it for non-deterministic applications like adult entertainment applications like adult entertainment applications like adult entertainment and all this other crap that you know and all this other crap that you know and all this other crap that you know doesn't really doesn't really doesn't really your productivity. your productivity. your productivity. >> Wait a second, Dimmitri. Are you saying >> Wait a second, Dimmitri. Are you saying >> Wait a second, Dimmitri. Are you saying that my new AI girlfriend is going to be that my new AI girlfriend is going to be that my new AI girlfriend is going to be lying to me? lying to me? lying to me? >> No, no, no. >> No, no, no. >> No, no, no. I say, I say, I say, >> "No, >> "No, >> "No, >> I'm saying that your existing human >> I'm saying that your existing human >> I'm saying that your existing human girlfriends already lies to you." girlfriends already lies to you." girlfriends already lies to you." >> No, they don't. [laughter] >> No, they don't. [laughter] >> No, they don't. [laughter] >> No, but that's the but that's the point, >> No, but that's the but that's the point, >> No, but that's the but that's the point, guys. The the first of all, the the the guys. The the first of all, the the the guys. The the first of all, the the the if if you still I mean, if you have a a if if you still I mean, if you have a a if if you still I mean, if you have a a an informed conversation amongst an informed conversation amongst an informed conversation amongst friends, yeah, you can probably decipher friends, yeah, you can probably decipher friends, yeah, you can probably decipher what is right and wrong. Okay.

what is right and wrong. Okay. what is right and wrong. Okay. But if you listen to everything around But if you listen to everything around But if you listen to everything around you in the world right now from all the you in the world right now from all the you in the world right now from all the sources, there's how much lies from sources, there's how much lies from sources, there's how much lies from human that there's lies from AI. Come human that there's lies from AI. Come human that there's lies from AI. Come on. on. on. >> No. You know what? Look, even humans, if >> No. You know what? Look, even humans, if >> No. You know what? Look, even humans, if they keep lying to you, you don't trust they keep lying to you, you don't trust they keep lying to you, you don't trust them anymore. You know, it's like going them anymore. You know, it's like going them anymore. You know, it's like going back to the girlfriend, like if you back to the girlfriend, like if you back to the girlfriend, like if you smell, you know, your girlfriend or your smell, you know, your girlfriend or your smell, you know, your girlfriend or your wife is going to, "Oh my god, Rob, what wife is going to, "Oh my god, Rob, what wife is going to, "Oh my god, Rob, what was that?" was that?" was that?" >> Rob's going to lie. He's gonna say, "Oh, >> Rob's going to lie. He's gonna say, "Oh, >> Rob's going to lie. He's gonna say, "Oh, honey, that wasn't me. [laughter] honey, that wasn't me. [laughter] honey, that wasn't me. [laughter] >> It's that guy. >> It's that guy. >> It's that guy. >> It's that guy." [laughter] >> It's that guy." [laughter] >> It's that guy." [laughter] >> Again, I I >> Again, I I >> Again, I I [laughter] [laughter] [laughter] >> Yeah. Come on. No, it boils down to >> Yeah. Come on. No, it boils down to >> Yeah. Come on. No, it boils down to trust. And you know, you trust tools trust. And you know, you trust tools trust. And you know, you trust tools that you uh you can I mean, you trust that you uh you can I mean, you trust that you uh you can I mean, you trust tools that are reliable and and meet the tools that are reliable and and meet the tools that are reliable and and meet the expectations. And so if your whole use expectations. And so if your whole use expectations. And so if your whole use case and your requirement is fantasy and case and your requirement is fantasy and case and your requirement is fantasy and it can fulfill it, yeah, sure. It's it can fulfill it, yeah, sure. It's it can fulfill it, yeah, sure. It's going to freaking lie. It's going to going to freaking lie. It's going to going to freaking lie. It's going to say, "Oh my god, Leonard, you're so say, "Oh my god, Leonard, you're so say, "Oh my god, Leonard, you're so sexy. You're like more handsome than sexy. You're like more handsome than sexy. You're like more handsome than like freaking, you know, some K-pop."

like freaking, you know, some K-pop." like freaking, you know, some K-pop." >> But no, no, we we live we live in the >> But no, no, we we live we live in the >> But no, no, we we live we live in the world of the few% that actually think world of the few% that actually think world of the few% that actually think they have at least the critical thinking they have at least the critical thinking they have at least the critical thinking to be able to assess trust and and to be able to assess trust and and to be able to assess trust and and decicate whenever something or somebody decicate whenever something or somebody decicate whenever something or somebody talks to you. The vast majority does not talks to you. The vast majority does not talks to you. The vast majority does not do that. This is why advertising works. do that. This is why advertising works. do that. This is why advertising works. This is society where you bombard people This is society where you bombard people This is society where you bombard people with stuff and they believe it. But what with stuff and they believe it. But what with stuff and they believe it. But what I'm saying is that now it's becoming I'm saying is that now it's becoming I'm saying is that now it's becoming artificial and it's going to be at a artificial and it's going to be at a artificial and it's going to be at a scale that we don't imagine. And I'm scale that we don't imagine. And I'm scale that we don't imagine. And I'm actually I'm saying it's going to get actually I'm saying it's going to get actually I'm saying it's going to get worse and worse and worse. Well, yeah, worse and worse and worse. Well, yeah, worse and worse and worse. Well, yeah, it will it will get worse because people it will it will get worse because people it will it will get worse because people will apply generative AI for the the, will apply generative AI for the the, will apply generative AI for the the, you know, for the intent of deceiving you know, for the intent of deceiving you know, for the intent of deceiving and doing and and, you know, stealing. I and doing and and, you know, stealing. I and doing and and, you know, stealing. I I I totally think that that is going to I I totally think that that is going to I I totally think that that is going to be the big frontier market. You know, be the big frontier market. You know, be the big frontier market. You know, cyber cyber cyber >> what we've talked about, right? Maybe we >> what we've talked about, right? Maybe we >> what we've talked about, right? Maybe we should lower the bar instead of the should lower the bar instead of the should lower the bar instead of the you're right the future fantasy thing you're right the future fantasy thing you're right the future fantasy thing about generative AI maybe we just say about generative AI maybe we just say about generative AI maybe we just say what do we agree on that it actually what do we agree on that it actually what do we agree on that it actually does well and just have that and say does well and just have that and say does well and just have that and say okay we're okay like we know that Claude okay we're okay like we know that Claude okay we're okay like we know that Claude is really good at writing code is really good at writing code is really good at writing code >> okay good check >> okay good check >> okay good check >> um it's good at summarizing documents >> um it's good at summarizing documents >> um it's good at summarizing documents >> if I upload a PDF I can ask questions >> if I upload a PDF I can ask questions >> if I upload a PDF I can ask questions about it it's pretty good at summarizing about it it's pretty good at summarizing about it it's pretty good at summarizing documents documents documents >> how that had had had that >> how that had had had that >> how that had had had that [clears throat] conversation. So for [clears throat] conversation. So for [clears throat] conversation. So for like remember last week we had a you like remember last week we had a you like remember last week we had a you know chat about the vibe coding right?

know chat about the vibe coding right? know chat about the vibe coding right? >> Mhm. >> Mhm. >> Mhm. >> But when you use it for like let's say a >> But when you use it for like let's say a >> But when you use it for like let's say a product manager or product uh you know product manager or product uh you know product manager or product uh you know yeah product u lead uh it wants to yeah product u lead uh it wants to yeah product u lead uh it wants to ideulate it has an idea and they want to ideulate it has an idea and they want to ideulate it has an idea and they want to ideulate. It's a great way of just ideulate. It's a great way of just ideulate. It's a great way of just framing, get some working code out framing, get some working code out framing, get some working code out there. They can test it, but it's ne, there. They can test it, but it's ne, there. They can test it, but it's ne, you know, it's never the final code, you know, it's never the final code, you know, it's never the final code, right? You're, and we've already said right? You're, and we've already said right? You're, and we've already said this before, it you're still going to go this before, it you're still going to go this before, it you're still going to go through an SDLC. It may be adapted um through an SDLC. It may be adapted um through an SDLC. It may be adapted um for this, let's say, um prototype a for this, let's say, um prototype a for this, let's say, um prototype a prototyping type um um process, but prototyping type um um process, but prototyping type um um process, but ideation, it's great for that. And you ideation, it's great for that. And you ideation, it's great for that. And you know we see that even in um a lot of the know we see that even in um a lot of the know we see that even in um a lot of the creative use cases uh in art in music it creative use cases uh in art in music it creative use cases uh in art in music it can be used as a um prototyping and can be used as a um prototyping and can be used as a um prototyping and ideiation tool right and that can ideiation tool right and that can ideiation tool right and that can entirely be a uh you know a different entirely be a uh you know a different entirely be a uh you know a different flow from let's say a more deterministic flow from let's say a more deterministic flow from let's say a more deterministic production or design flow. Those things production or design flow. Those things production or design flow. Those things don't go away. you still need those don't go away. you still need those don't go away. you still need those tools but it's you know I that's where I tools but it's you know I that's where I tools but it's you know I that's where I see a lot of the benefit happening but see a lot of the benefit happening but see a lot of the benefit happening but then also uh in analytics in enhancing then also uh in analytics in enhancing then also uh in analytics in enhancing the data sets that are coming in so the data sets that are coming in so the data sets that are coming in so contextualizing it right contextualizing it right contextualizing it right um and then uh also with the models

um and then uh also with the models um and then uh also with the models enhancing models or whatever algorithm enhancing models or whatever algorithm enhancing models or whatever algorithm but the thing is you're not going to in but the thing is you're not going to in but the thing is you're not going to in most cases because of economics you're most cases because of economics you're most cases because of economics you're not going to run it uh on GPUs or not going to run it uh on GPUs or not going to run it uh on GPUs or anything like that, you're going to be anything like that, you're going to be anything like that, you're going to be running it on traditional stuff because running it on traditional stuff because running it on traditional stuff because it's cheaper, right? So, you might take it's cheaper, right? So, you might take it's cheaper, right? So, you might take something like an agentic flow and something like an agentic flow and something like an agentic flow and reduce it to a uh RPA workflow with an reduce it to a uh RPA workflow with an reduce it to a uh RPA workflow with an algorithm instead of, you know, having, algorithm instead of, you know, having, algorithm instead of, you know, having, you know, [clears throat] an LLM at the you know, [clears throat] an LLM at the you know, [clears throat] an LLM at the end of all the tooling. you're going to end of all the tooling. you're going to end of all the tooling. you're going to try to reduce the cost and actually try to reduce the cost and actually try to reduce the cost and actually codify [clears throat] whatever codify [clears throat] whatever codify [clears throat] whatever intelligence that you want to scale out intelligence that you want to scale out intelligence that you want to scale out or whatever automation you want to scale or whatever automation you want to scale or whatever automation you want to scale out. And so, you know, I think again I'm out. And so, you know, I think again I'm out. And so, you know, I think again I'm outlining something that actually is outlining something that actually is outlining something that actually is going to be boring as hell for most going to be boring as hell for most going to be boring as hell for most people. But worst of all, for all the AI people. But worst of all, for all the AI people. But worst of all, for all the AI [clears throat] gear heads out there [clears throat] gear heads out there [clears throat] gear heads out there that have taken things over overboard, that have taken things over overboard, that have taken things over overboard, it it's not going to or these it it's not going to or these it it's not going to or these infrastructure guys, it's not going to infrastructure guys, it's not going to infrastructure guys, it's not going to require a lot of uh GPUs. You're going require a lot of uh GPUs. You're going require a lot of uh GPUs. You're going to resort to other cheaper forms of to resort to other cheaper forms of to resort to other cheaper forms of comput. But I think Rob, I would I would comput. But I think Rob, I would I would comput. But I think Rob, I would I would actually take the counter point because actually take the counter point because actually take the counter point because I think that the approach I think that the approach I think that the approach using the same my same line of thinking, using the same my same line of thinking, using the same my same line of thinking, the approach of saying, "Oh, let's the approach of saying, "Oh, let's the approach of saying, "Oh, let's figure out what it's good at."

figure out what it's good at." figure out what it's good at." It's not going to cut it. Because if you It's not going to cut it. Because if you It's not going to cut it. Because if you ask me what is it good at, my answer, my ask me what is it good at, my answer, my ask me what is it good at, my answer, my answer is going to be it's good at answer is going to be it's good at answer is going to be it's good at talking to you. It speaks the same talking to you. It speaks the same talking to you. It speaks the same language as you. It communicates with language as you. It communicates with language as you. It communicates with you with language. Again, I'm going to you with language. Again, I'm going to you with language. Again, I'm going to go back to that. So you can very well go back to that. So you can very well go back to that. So you can very well said you it's like a human. You're said you it's like a human. You're said you it's like a human. You're talking to it, but you don't know if you talking to it, but you don't know if you talking to it, but you don't know if you can trust it. You don't know if he's can trust it. You don't know if he's can trust it. You don't know if he's right. You don't know if he's wrong. You right. You don't know if he's wrong. You right. You don't know if he's wrong. You don't know if he's smoking something. don't know if he's smoking something. don't know if he's smoking something. You don't know if he has weird ideas. You don't know if he has weird ideas. You don't know if he has weird ideas. You don't know that. So now we're going You don't know that. So now we're going You don't know that. So now we're going to have to live in a world where you to have to live in a world where you to have to live in a world where you have those digital entities that have those digital entities that have those digital entities that actually communicates with you the same actually communicates with you the same actually communicates with you the same way you communicate to humans. They're way you communicate to humans. They're way you communicate to humans. They're going to scale in numbers, but you have going to scale in numbers, but you have going to scale in numbers, but you have no idea what's behind. And it's your job no idea what's behind. And it's your job no idea what's behind. And it's your job to investigate, to interrogate it, to to investigate, to interrogate it, to to investigate, to interrogate it, to understand what it's good at. But understand what it's good at. But understand what it's good at. But there's no silver bullet. It's good at there's no silver bullet. It's good at there's no silver bullet. It's good at that, it's bad at that. And the trouble that, it's bad at that. And the trouble that, it's bad at that. And the trouble again is it talks to you. That I think again is it talks to you. That I think again is it talks to you. That I think that's people don't realize how profound that's people don't realize how profound that's people don't realize how profound this is from an interface. This is the this is from an interface. This is the this is from an interface. This is the first technology we have where the first technology we have where the first technology we have where the interface is your natural language. This interface is your natural language. This interface is your natural language. This is why there's so much buzz to me around is why there's so much buzz to me around is why there's so much buzz to me around it.

it. it. >> Yeah. But you know like um if I go >> Yeah. But you know like um if I go >> Yeah. But you know like um if I go beyond the summarizing but like the beyond the summarizing but like the beyond the summarizing but like the reason I've been pushing this whole reason I've been pushing this whole reason I've been pushing this whole enterprise brain thing and it is back to enterprise brain thing and it is back to enterprise brain thing and it is back to hey I kind of know what I you know it hey I kind of know what I you know it hey I kind of know what I you know it can it do more than what I'm saying? can it do more than what I'm saying? can it do more than what I'm saying? Sure. I think of it like a database and Sure. I think of it like a database and Sure. I think of it like a database and it is it is a kind of a database. It's it is it is a kind of a database. It's it is it is a kind of a database. It's this vector thing. Um, if I when I talk this vector thing. Um, if I when I talk this vector thing. Um, if I when I talk about the enterprise brain and I'm about the enterprise brain and I'm about the enterprise brain and I'm putting all the data from a company, all putting all the data from a company, all putting all the data from a company, all its documents, all its PDFs, its documents, all its PDFs, its documents, all its PDFs, spreadsheets, databases, tables, and spreadsheets, databases, tables, and spreadsheets, databases, tables, and suck it in there. All of a sudden, it suck it in there. All of a sudden, it suck it in there. All of a sudden, it does a great job of now I I can it's does a great job of now I I can it's does a great job of now I I can it's like a select star from vector database like a select star from vector database like a select star from vector database where blah blah blah blah blah except it where blah blah blah blah blah except it where blah blah blah blah blah except it seems more natural and like you say, it seems more natural and like you say, it seems more natural and like you say, it talks to you and everything like that. I talks to you and everything like that. I talks to you and everything like that. I think it's I think it's good at that and think it's I think it's good at that and think it's I think it's good at that and I think there's value for organizations I think there's value for organizations I think there's value for organizations for something because we do it today for something because we do it today for something because we do it today like we we do queries on relational like we we do queries on relational like we we do queries on relational databases to get an answer to a databases to get an answer to a databases to get an answer to a question. And so when I think of it like question. And so when I think of it like question. And so when I think of it like that way and and I'll probably get fired that way and and I'll probably get fired that way and and I'll probably get fired for you know lowering the bar so much on for you know lowering the bar so much on for you know lowering the bar so much on what I think AI is about because what I think AI is about because what I think AI is about because everybody else is like oh it kicked ass everybody else is like oh it kicked ass everybody else is like oh it kicked ass on humanity's last exam and it's PhD on humanity's last exam and it's PhD on humanity's last exam and it's PhD level and all these things and it's level and all these things and it's level and all these things and it's going to do this.

going to do this. going to do this. >> This is [laughter] All the >> This is [laughter] All the >> This is [laughter] All the really smart people think it's not really smart people think it's not really smart people think it's not but it doesn't mean they're but it doesn't mean they're but it doesn't mean they're right. right. right. >> What do you find smart? >> What do you find smart? >> What do you find smart? >> I know PhDs from MIT and you know that's >> I know PhDs from MIT and you know that's >> I know PhDs from MIT and you know that's smart. Those are smart guys and they smart. Those are smart guys and they smart. Those are smart guys and they live it dayto-day and so and when I hear live it dayto-day and so and when I hear live it dayto-day and so and when I hear them and they talk deeply about it, I them and they talk deeply about it, I them and they talk deeply about it, I don't think they're making stuff up. don't think they're making stuff up. don't think they're making stuff up. there see but in my mind I was like there see but in my mind I was like there see but in my mind I was like here's how my simple brain works and here's how my simple brain works and here's how my simple brain works and it's just about a store of data and it's just about a store of data and it's just about a store of data and easily retrieving it maybe more easily easily retrieving it maybe more easily easily retrieving it maybe more easily than a SQL statement when they say that than a SQL statement when they say that than a SQL statement when they say that AI like the latest chatbt 5.1 or AI like the latest chatbt 5.1 or AI like the latest chatbt 5.1 or whatever let's just say it passes the whatever let's just say it passes the whatever let's just say it passes the MCAT so that it can go to med school or MCAT so that it can go to med school or MCAT so that it can go to med school or it passes whatever medical exams and so it passes whatever medical exams and so it passes whatever medical exams and so now it's as good as any doctor now it's as good as any doctor now it's as good as any doctor people get excited about that but at the people get excited about that but at the people get excited about that but at the same time I go Well, wait a second. The same time I go Well, wait a second. The same time I go Well, wait a second. The reason it can pass the exam is because reason it can pass the exam is because reason it can pass the exam is because the new updated version of the models, the new updated version of the models, the new updated version of the models, you've pumped in all the questions and you've pumped in all the questions and you've pumped in all the questions and answers to every medical question ever answers to every medical question ever answers to every medical question ever made in history and it's in the made in history and it's in the made in history and it's in the database. And so when it's looking at database. And so when it's looking at database. And so when it's looking at the test and having to answer the the test and having to answer the the test and having to answer the questions, probably with the help of a questions, probably with the help of a questions, probably with the help of a person, um, of course it gets 100% on person, um, of course it gets 100% on person, um, of course it gets 100% on the exam because the answers are already the exam because the answers are already the exam because the answers are already in its vector database. That's why in its vector database. That's why in its vector database. That's why people are like, it's amazing. But it's people are like, it's amazing. But it's people are like, it's amazing. But it's like, well, no, it scraped everything.

like, well, no, it scraped everything. like, well, no, it scraped everything. That's why it seems so smart. That's why it seems so smart. That's why it seems so smart. >> Rob, you you exactly would explain what >> Rob, you you exactly would explain what >> Rob, you you exactly would explain what I meant by And that's why I I meant by And that's why I I meant by And that's why I said you the people that say that are said you the people that say that are said you the people that say that are saying By the way, I would saying By the way, I would saying By the way, I would oppose if you seen the meta guys that oppose if you seen the meta guys that oppose if you seen the meta guys that actually left Meta to go work on another actually left Meta to go work on another actually left Meta to go work on another company who is really seeking Lan what company who is really seeking Lan what company who is really seeking Lan what is real intelligence. That's a smart is real intelligence. That's a smart is real intelligence. That's a smart move and and it's a logical move. But move and and it's a logical move. But move and and it's a logical move. But what I've also because I've commented what I've also because I've commented what I've also because I've commented actually on that on on LinkedIn. I think actually on that on on LinkedIn. I think actually on that on on LinkedIn. I think for a company like Meta what those model for a company like Meta what those model for a company like Meta what those model do is fantastic. do is fantastic. do is fantastic. >> No. Yeah. You know why >> No. Yeah. You know why >> No. Yeah. You know why >> does it influence people to buy some >> does it influence people to buy some >> does it influence people to buy some stuff but is it really intentious? No, stuff but is it really intentious? No, stuff but is it really intentious? No, it is. Now I agree with you. If you it is. Now I agree with you. If you it is. Now I agree with you. If you point those train model to knowledge point those train model to knowledge point those train model to knowledge that is creating of good value it is that is creating of good value it is that is creating of good value it is extremely effective and fantastic at extremely effective and fantastic at extremely effective and fantastic at distilling it and also finding distilling it and also finding distilling it and also finding misalignment in pattern. I use it for misalignment in pattern. I use it for misalignment in pattern. I use it for that myself in my everyday job. I have that myself in my everyday job. I have that myself in my everyday job. I have reference stuff messaging documents and reference stuff messaging documents and reference stuff messaging documents and every time I build marketing assets say every time I build marketing assets say every time I build marketing assets say hey check that across the messaging and hey check that across the messaging and hey check that across the messaging and it's fantastic at that. So if you point it's fantastic at that. So if you point it's fantastic at that. So if you point I think that the way it is what is great I think that the way it is what is great I think that the way it is what is great at is actually distilling and giving you at is actually distilling and giving you at is actually distilling and giving you information about knowledge assuming information about knowledge assuming information about knowledge assuming that knowledge is the good thing. Now if that knowledge is the good thing. Now if that knowledge is the good thing. Now if you put bad knowledge it will give you you put bad knowledge it will give you you put bad knowledge it will give you bad bad sting but it is consistent bad bad sting but it is consistent bad bad sting but it is consistent because it's by nature it finds because it's by nature it finds because it's by nature it finds patterns. So it is consistent. It give patterns. So it is consistent. It give patterns. So it is consistent. It give you consistency. You give him crap, it you consistency. You give him crap, it you consistency. You give him crap, it will give you crap. You give him not will give you crap. You give him not will give you crap. You give him not enough, it will hallucinate.

enough, it will hallucinate. enough, it will hallucinate. >> You give him great, it will reive you >> You give him great, it will reive you >> You give him great, it will reive you great. So So it is a great tool. But great. So So it is a great tool. But great. So So it is a great tool. But again, you have to understand, again, you have to understand, again, you have to understand, you know, the way to interact with it. you know, the way to interact with it. you know, the way to interact with it. And you said the words the first step of And you said the words the first step of And you said the words the first step of AI is pattern matching. AI is pattern matching. AI is pattern matching. >> Um, you know, machine learning, we're >> Um, you know, machine learning, we're >> Um, you know, machine learning, we're matching and and we did that with all matching and and we did that with all matching and and we did that with all our analytics actually. And so I our analytics actually. And so I our analytics actually. And so I remember thinking a great early use back remember thinking a great early use back remember thinking a great early use back to summarization and stuff what like to to summarization and stuff what like to to summarization and stuff what like to tie it into IoT is I might use analytics tie it into IoT is I might use analytics tie it into IoT is I might use analytics or if this then that or maybe machine or if this then that or maybe machine or if this then that or maybe machine learning on a machine and the machine I learning on a machine and the machine I learning on a machine and the machine I it says it's going to fail next Tuesday. it says it's going to fail next Tuesday. it says it's going to fail next Tuesday. I want to fix it before it fails and I want to fix it before it fails and I want to fix it before it fails and here's what's wrong and I'm a journeyman here's what's wrong and I'm a journeyman here's what's wrong and I'm a journeyman mechanic and I don't know how to fix it. mechanic and I don't know how to fix it. mechanic and I don't know how to fix it. But if I take for that particular But if I take for that particular But if I take for that particular machine, if I've uploaded the user machine, if I've uploaded the user machine, if I've uploaded the user manual and everything about that machine manual and everything about that machine manual and everything about that machine to AI and it's already got distilled to AI and it's already got distilled to AI and it's already got distilled now, I can take the information I got now, I can take the information I got now, I can take the information I got from my IoT system and plug it in there. from my IoT system and plug it in there. from my IoT system and plug it in there. And I've seen people do this and they And I've seen people do this and they And I've seen people do this and they go, "Oh yes, when you see this problem go, "Oh yes, when you see this problem go, "Oh yes, when you see this problem pattern matching again, right, do you pattern matching again, right, do you pattern matching again, right, do you need to go take a screwdriver and do need to go take a screwdriver and do need to go take a screwdriver and do this and do that and replace this?" And this and do that and replace this?" And this and do that and replace this?" And so, and so kind of completing the so, and so kind of completing the so, and so kind of completing the problem, you know, we s we found there's problem, you know, we s we found there's problem, you know, we s we found there's a problem, now we got to solve it, and a problem, now we got to solve it, and a problem, now we got to solve it, and I'm an idiot, but AI can help me. Well, I'm an idiot, but AI can help me. Well, I'm an idiot, but AI can help me. Well, it's because it's it's been trained on it's because it's it's been trained on it's because it's it's been trained on the information about that machine. And the information about that machine. And the information about that machine. And so, it helps you complete the task. And so, it helps you complete the task. And so, it helps you complete the task. And so, uh, I I think it's I think it's good so, uh, I I think it's I think it's good so, uh, I I think it's I think it's good for that stuff. I just don't know. We're for that stuff. I just don't know. We're for that stuff. I just don't know. We're still waiting for it to still waiting for it to still waiting for it to >> come up with new things. People are >> come up with new things. People are >> come up with new things. People are waiting for it to be brilliant on stuff

waiting for it to be brilliant on stuff waiting for it to be brilliant on stuff we don't know yet. When I when I hear we don't know yet. When I when I hear we don't know yet. When I when I hear the guys on Moonshot talking about we're the guys on Moonshot talking about we're the guys on Moonshot talking about we're going to solve maths, going to solve maths, going to solve maths, >> we're going to solve physics, >> we're going to solve physics, >> we're going to solve physics, >> we're going to solve everything, and >> we're going to solve everything, and >> we're going to solve everything, and then all of a sudden all the secrets of then all of a sudden all the secrets of then all of a sudden all the secrets of the universe are be like bing. the universe are be like bing. the universe are be like bing. >> Yeah. Not not with the contact, not with >> Yeah. Not not with the contact, not with >> Yeah. Not not with the contact, not with the contact. I I totally agree with the contact. I I totally agree with the contact. I I totally agree with that. I think it's a it's a great source that. I think it's a it's a great source that. I think it's a it's a great source of knowledge and again great at of knowledge and again great at of knowledge and again great at distilling stuff. So, it's a fantastic distilling stuff. So, it's a fantastic distilling stuff. So, it's a fantastic tool and this is I'm I'm with you on tool and this is I'm I'm with you on tool and this is I'm I'm with you on that. We had this conversation I think that. We had this conversation I think that. We had this conversation I think last week or the week before. What I'm last week or the week before. What I'm last week or the week before. What I'm personally fascinated is all those personally fascinated is all those personally fascinated is all those people you mentioned say, "Oh, it's people you mentioned say, "Oh, it's people you mentioned say, "Oh, it's going to solve all this problem we going to solve all this problem we going to solve all this problem we haven't solved as human." I'm not haven't solved as human." I'm not haven't solved as human." I'm not interesting in that. I'm interested in interesting in that. I'm interested in interesting in that. I'm interested in something that actually give me access something that actually give me access something that actually give me access to knowledge I don't have very quickly. to knowledge I don't have very quickly. to knowledge I don't have very quickly. I want to fix a problem. Something is I want to fix a problem. Something is I want to fix a problem. Something is very knowledgeable. I go to it, it helps very knowledgeable. I go to it, it helps very knowledgeable. I go to it, it helps me fix the problem. That's great. me fix the problem. That's great. me fix the problem. That's great. >> But but that the problem is generalizing >> But but that the problem is generalizing >> But but that the problem is generalizing that because it takes a lot of work. that because it takes a lot of work. that because it takes a lot of work. what we're what I'm seeing in all the what we're what I'm seeing in all the what we're what I'm seeing in all the research I've done in three years on research I've done in three years on research I've done in three years on this the generative AI genie this the generative AI genie this the generative AI genie you know release is it's a hell of a lot you know release is it's a hell of a lot you know release is it's a hell of a lot harder than that to get to good and harder than that to get to good and harder than that to get to good and again it boils down to trust and again it boils down to trust and again it boils down to trust and trustworthiness and then underneath that trustworthiness and then underneath that trustworthiness and then underneath that you have to have reliability you have to have reliability you have to have reliability it has to be safe and it has to be it has to be safe and it has to be it has to be safe and it has to be accurate right and sound those are accurate right and sound those are accurate right and sound those are really really difficult to achieve for really really difficult to achieve for really really difficult to achieve for almost any application. So when you say almost any application. So when you say almost any application. So when you say knowledge, yeah, if it's just a guess, I knowledge, yeah, if it's just a guess, I knowledge, yeah, if it's just a guess, I wouldn't call it a knowledge it a base,

wouldn't call it a knowledge it a base, wouldn't call it a knowledge it a base, it's a guess base. U the the thing is is it's a guess base. U the the thing is is it's a guess base. U the the thing is is it is it it time and time again the it is it it time and time again the it is it it time and time again the experts have been wrong. Okay, it experts have been wrong. Okay, it experts have been wrong. Okay, it doesn't matter whether or not they have doesn't matter whether or not they have doesn't matter whether or not they have a degree from MIT, there's exaggerations a degree from MIT, there's exaggerations a degree from MIT, there's exaggerations that have colored all the narratives. that have colored all the narratives. that have colored all the narratives. You know, LLMs have fallen flat on their You know, LLMs have fallen flat on their You know, LLMs have fallen flat on their faces on their promises and the faces on their promises and the faces on their promises and the expectations. The scaling laws didn't expectations. The scaling laws didn't expectations. The scaling laws didn't work out. So, what do they have to do? work out. So, what do they have to do? work out. So, what do they have to do? They had to move toe. Then next, in They had to move toe. Then next, in They had to move toe. Then next, in order to improve the um you know the order to improve the um you know the order to improve the um you know the accuracy, they had to move to to um you accuracy, they had to move to to um you accuracy, they had to move to to um you know this long these long thinking know this long these long thinking know this long these long thinking reasoning models, right? Which actually reasoning models, right? Which actually reasoning models, right? Which actually weren't improving on top of the LLMs. weren't improving on top of the LLMs. weren't improving on top of the LLMs. And then we realize that oh wow this is And then we realize that oh wow this is And then we realize that oh wow this is like these things can not only like these things can not only like these things can not only hallucinate they can get into chains of hallucinate they can get into chains of hallucinate they can get into chains of confusion. And by the way that chain of confusion. And by the way that chain of confusion. And by the way that chain of confusion thing that I observed early on confusion thing that I observed early on confusion thing that I observed early on is a real real thing and it impacts the is a real real thing and it impacts the is a real real thing and it impacts the the economics of anything that anybody the economics of anything that anybody the economics of anything that anybody is imagining they can do with this is imagining they can do with this is imagining they can do with this stuff. The one thing that people haven't stuff. The one thing that people haven't stuff. The one thing that people haven't haven't realized yet and it definitely haven't realized yet and it definitely haven't realized yet and it definitely hasn't trickled up to Jensen Huang is hasn't trickled up to Jensen Huang is hasn't trickled up to Jensen Huang is how expensive it is to do anything with how expensive it is to do anything with how expensive it is to do anything with this stuff. And so you have a lot of this stuff. And so you have a lot of this stuff. And so you have a lot of developers who are saying this is not developers who are saying this is not developers who are saying this is not viable. Uh and so but and the illusion viable. Uh and so but and the illusion viable. Uh and so but and the illusion there's this constant illusion and there's this constant illusion and there's this constant illusion and disconnect from reality grounding which disconnect from reality grounding which disconnect from reality grounding which always brings you back to the question always brings you back to the question always brings you back to the question of can I trust this stuff right and this

of can I trust this stuff right and this of can I trust this stuff right and this you know the people who think about this you know the people who think about this you know the people who think about this very sha in a very shallow way very sha in a very shallow way very sha in a very shallow way non-technical way have [clears throat] non-technical way have [clears throat] non-technical way have [clears throat] not yet not yet not yet arrived at these questions or um arrived at these questions or um arrived at these questions or um grounding grounding realities you know grounding grounding realities you know grounding grounding realities you know and so these are that's why everyone is and so these are that's why everyone is and so these are that's why everyone is so behind on IoT coffee talk quite so behind on IoT coffee talk quite so behind on IoT coffee talk quite frankly cuz a lot of the conversations frankly cuz a lot of the conversations frankly cuz a lot of the conversations we've had on the show have already we've had on the show have already we've had on the show have already addressed a lot of these things for the addressed a lot of these things for the addressed a lot of these things for the last two years since Jack 3 years since last two years since Jack 3 years since last two years since Jack 3 years since chat GPT has come out and you know um chat GPT has come out and you know um chat GPT has come out and you know um and these are not learning systems and and these are not learning systems and and these are not learning systems and this is a huge misconception that I this is a huge misconception that I this is a huge misconception that I still hear a lot of people um you know still hear a lot of people um you know still hear a lot of people um you know make is that they they say these things make is that they they say these things make is that they they say these things are learning no they're trained it's are learning no they're trained it's are learning no they're trained it's different the cost of actually learning different the cost of actually learning different the cost of actually learning Having these things learn is Having these things learn is Having these things learn is tremendously expensive. So these aren't tremendously expensive. So these aren't tremendously expensive. So these aren't learning systems. They don't learn learning systems. They don't learn learning systems. They don't learn merely merely merely remotely as quickly as humans do. Humans remotely as quickly as humans do. Humans remotely as quickly as humans do. Humans learn like that. these things. You gotta learn like that. these things. You gotta learn like that. these things. You gotta freaking send a data set, make sure that freaking send a data set, make sure that freaking send a data set, make sure that data set is kosher, and then run it data set is kosher, and then run it data set is kosher, and then run it through like millions of dollars of uh through like millions of dollars of uh through like millions of dollars of uh GPU hours in order to retrain the model.

GPU hours in order to retrain the model. GPU hours in order to retrain the model. Then it learns. Then it learns. Then it learns. Think about how crappy that is, Think about how crappy that is, Think about how crappy that is, [clears throat] right? And then rag and [clears throat] right? And then rag and [clears throat] right? And then rag and all this other stuff are proxies for all this other stuff are proxies for all this other stuff are proxies for learning. So it creates not only a learning. So it creates not only a learning. So it creates not only a illusion of thinking, illusion of illusion of thinking, illusion of illusion of thinking, illusion of creativity also creates these additional creativity also creates these additional creativity also creates these additional constructs create an illusion of constructs create an illusion of constructs create an illusion of learning and but they don't learn. The learning and but they don't learn. The learning and but they don't learn. The LLM does not learn and that's the big LLM does not learn and that's the big LLM does not learn and that's the big problem they or all kinds of constructs problem they or all kinds of constructs problem they or all kinds of constructs and all of them are falling short. and all of them are falling short. and all of them are falling short. >> Well, it it it it does not learn the >> Well, it it it it does not learn the >> Well, it it it it does not learn the same way humans learn. It learns between same way humans learn. It learns between same way humans learn. It learns between versions of models. But you covered on versions of models. But you covered on versions of models. But you covered on on different things. on different things. on different things. >> Huh. >> Huh. >> Huh. >> It's what makes humans awesome. >> It's what makes humans awesome. >> It's what makes humans awesome. >> Yeah. I don't think >> Yeah. I don't think >> Yeah. I don't think >> Yeah. Let's not get into that because >> Yeah. Let's not get into that because >> Yeah. Let's not get into that because because I mean the the the the because I mean the the the the because I mean the the the the fundamental problem you have. I didn't fundamental problem you have. I didn't fundamental problem you have. I didn't say humanity. I said that's what makes say humanity. I said that's what makes say humanity. I said that's what makes humans as machines. humans as machines. humans as machines. >> So specific specific humans might be >> So specific specific humans might be >> So specific specific humans might be functional. No, but what I'm saying the functional. No, but what I'm saying the functional. No, but what I'm saying the problem with the trust the problem with problem with the trust the problem with problem with the trust the problem with the trust argument Leonard is you can the trust argument Leonard is you can the trust argument Leonard is you can replace AI by human and you have the replace AI by human and you have the replace AI by human and you have the same question. So that's why I'm saying same question. So that's why I'm saying same question. So that's why I'm saying it doesn't resonate psychologically to it doesn't resonate psychologically to it doesn't resonate psychologically to people. You have you have the same people. You have you have the same people. You have you have the same problem with humans. How do I trust you?

problem with humans. How do I trust you? problem with humans. How do I trust you? I trust you because you and I be on this I trust you because you and I be on this I trust you because you and I be on this for many many different shows. But the for many many different shows. But the for many many different shows. But the random person in the street is the same random person in the street is the same random person in the street is the same thing with AI. So that argument yes it thing with AI. So that argument yes it thing with AI. So that argument yes it is important for people to understand. I is important for people to understand. I is important for people to understand. I there's no disagreement on that. But he there's no disagreement on that. But he there's no disagreement on that. But he will not fly in convincing people that will not fly in convincing people that will not fly in convincing people that oh you have to trust AI. Yeah, you have oh you have to trust AI. Yeah, you have oh you have to trust AI. Yeah, you have to trust people too. So yeah. to trust people too. So yeah. to trust people too. So yeah. >> Okay. So then you have the economic >> Okay. So then you have the economic >> Okay. So then you have the economic problem which I'm totally with you. problem which I'm totally with you. problem which I'm totally with you. People don't realize how much it costs, People don't realize how much it costs, People don't realize how much it costs, money it costs and how expensive those money it costs and how expensive those money it costs and how expensive those things are. And finally the learning things are. And finally the learning things are. And finally the learning aspect is something probably we don't aspect is something probably we don't aspect is something probably we don't insist enough. Yes, it doesn't learn. insist enough. Yes, it doesn't learn. insist enough. Yes, it doesn't learn. It's trained and it's statically trained It's trained and it's statically trained It's trained and it's statically trained between different versions. No, but between different versions. No, but between different versions. No, but that's exactly the reason why you don't that's exactly the reason why you don't that's exactly the reason why you don't you you're going to have trouble using you you're going to have trouble using you you're going to have trouble using uh probabilistic uh probabilistic uh probabilistic systems um to improve deterministic systems um to improve deterministic systems um to improve deterministic processes. The reason why you come up processes. The reason why you come up processes. The reason why you come up with [clears throat] those those with [clears throat] those those with [clears throat] those those deterministic processes is create deterministic processes is create deterministic processes is create consistency in execution and quality. consistency in execution and quality. consistency in execution and quality. Right? We strip out human as well as any Right? We strip out human as well as any Right? We strip out human as well as any other deter uh you know deterministic other deter uh you know deterministic other deter uh you know deterministic element especially for any kind of element especially for any kind of element especially for any kind of scaleout production. Right. And you know scaleout production. Right. And you know scaleout production. Right. And you know the dilemma is how do you how do you the dilemma is how do you how do you the dilemma is how do you how do you take a probabilistic [clears throat] take a probabilistic [clears throat] take a probabilistic [clears throat] um you know uh element and introduce it um you know uh element and introduce it um you know uh element and introduce it into a six sigma process and expect six into a six sigma process and expect six into a six sigma process and expect six sigma at the end of that.

sigma at the end of that. sigma at the end of that. >> Well I know the answer to that. >> Well I know the answer to that. >> Well I know the answer to that. >> I I know the I know the answer to that. >> I I know the I know the answer to that. >> I I know the I know the answer to that. You don't and you're right on that. You don't and you're right on that. You don't and you're right on that. people. That's what people are doing. people. That's what people are doing. people. That's what people are doing. >> No, but yeah over the head trying to >> No, but yeah over the head trying to >> No, but yeah over the head trying to make it work and wasting everyone's make it work and wasting everyone's make it work and wasting everyone's time. That's what I'm saying. time. That's what I'm saying. time. That's what I'm saying. >> No disagree. No disagreement with that, >> No disagree. No disagreement with that, >> No disagree. No disagreement with that, >> man. That's the argument. >> man. That's the argument. >> man. That's the argument. >> But but but my what I'm trying to say >> But but but my what I'm trying to say >> But but but my what I'm trying to say and my message is that we should we and my message is that we should we and my message is that we should we should spend more time explaining to should spend more time explaining to should spend more time explaining to people that those system are not people that those system are not people that those system are not deterministic rather than having deterministic rather than having deterministic rather than having philosophical discussion whether we philosophical discussion whether we philosophical discussion whether we could trust them or not because yes we could trust them or not because yes we could trust them or not because yes we know we cannot trust them the same way know we cannot trust them the same way know we cannot trust them the same way that we cannot trust humans. [snorts] that we cannot trust humans. [snorts] that we cannot trust humans. [snorts] Now what are they useful for is and Now what are they useful for is and Now what are they useful for is and maybe B Rob I'm going back to your to maybe B Rob I'm going back to your to maybe B Rob I'm going back to your to your argument and your what are they your argument and your what are they your argument and your what are they good at? So are they good at that good at? So are they good at that good at? So are they good at that deteministic? No, not at all. Forget deteministic? No, not at all. Forget deteministic? No, not at all. Forget that. that. that. >> No, that's that that is why you know but >> No, that's that that is why you know but >> No, that's that that is why you know but Rob wanted to take it down a level so Rob wanted to take it down a level so Rob wanted to take it down a level so that we can actually share with the that we can actually share with the that we can actually share with the audience what are the some of the things audience what are the some of the things audience what are the some of the things that it's good at. that it's good at. that it's good at. >> What they're not good at. >> What they're not good at. >> What they're not good at. >> Summarization. Well, we talk a lot about >> Summarization. Well, we talk a lot about >> Summarization. Well, we talk a lot about what they're not good at, but you know what they're not good at, but you know what they're not good at, but you know to Rob's point now let's flip it on its to Rob's point now let's flip it on its to Rob's point now let's flip it on its head. So what are we learning right head. So what are we learning right head. So what are we learning right >> like if a like for instance let's say >> like if a like for instance let's say >> like if a like for instance let's say that all new training and advancements that all new training and advancements that all new training and advancements and money being thrown at creating new and money being thrown at creating new and money being thrown at creating new models and new versions of anthropic and models and new versions of anthropic and models and new versions of anthropic and everything else what if it just stopped everything else what if it just stopped everything else what if it just stopped right now today and so what we got is right now today and so what we got is right now today and so what we got is what we got right now and then you'd what we got right now and then you'd what we got right now and then you'd step back and say was this worth it is step back and say was this worth it is step back and say was this worth it is it valuable um if we get nothing more if

it valuable um if we get nothing more if it valuable um if we get nothing more if we don't hit super intelligence we don't we don't hit super intelligence we don't we don't hit super intelligence we don't hit AGI could you still say though that hit AGI could you still say though that hit AGI could you still say though that there was some value and what we have there was some value and what we have there was some value and what we have today. And yeah, there is value. It can today. And yeah, there is value. It can today. And yeah, there is value. It can write code. It can summarize. It's a write code. It can summarize. It's a write code. It can summarize. It's a great store of information, uh, like for great store of information, uh, like for great store of information, uh, like for reference and stuff like that. Um, you reference and stuff like that. Um, you reference and stuff like that. Um, you know, and maybe a few other things. Um, know, and maybe a few other things. Um, know, and maybe a few other things. Um, cuz you're right. We we do talk about we cuz you're right. We we do talk about we cuz you're right. We we do talk about we like we spend so much time beating up on like we spend so much time beating up on like we spend so much time beating up on it, you know, or trying to debunk other it, you know, or trying to debunk other it, you know, or trying to debunk other people who are saying it's the future people who are saying it's the future people who are saying it's the future and, you know, and, you know, and, you know, >> well, there's a lot of hype out there. >> well, there's a lot of hype out there. >> well, there's a lot of hype out there. >> You know, it's PhD level at every one of >> You know, it's PhD level at every one of >> You know, it's PhD level at every one of these domains, you know, that kind of these domains, you know, that kind of these domains, you know, that kind of thing. And it's like, well, maybe it's thing. And it's like, well, maybe it's thing. And it's like, well, maybe it's PhD level because you trained it on PhD level because you trained it on PhD level because you trained it on every little all the data and all the every little all the data and all the every little all the data and all the books and everything that a professor books and everything that a professor books and everything that a professor would teach you in your doctorate class. would teach you in your doctorate class. would teach you in your doctorate class. And of course, it's PhD level. And of course, it's PhD level. And of course, it's PhD level. >> All the data got stuck in there. And so >> All the data got stuck in there. And so >> All the data got stuck in there. And so it knows the answers and the questions it knows the answers and the questions it knows the answers and the questions to every test. That's why it passed the to every test. That's why it passed the to every test. That's why it passed the test. test. test. >> The calculator is the smartest thing in >> The calculator is the smartest thing in >> The calculator is the smartest thing in the world if you think. the world if you think. the world if you think. >> Absolutely. I'll take my TI 55 >> Absolutely. I'll take my TI 55 >> Absolutely. I'll take my TI 55 calculator any day over that or my Casio calculator any day over that or my Casio calculator any day over that or my Casio with the solar panel on it. H that's with the solar panel on it. H that's with the solar panel on it. H that's true.

true. true. >> But that's that's the root cause to me. >> But that's that's the root cause to me. >> But that's that's the root cause to me. The root cause is that the expectation The root cause is that the expectation The root cause is that the expectation is that it will totally replace humans. is that it will totally replace humans. is that it will totally replace humans. This is the first time we have a tech This is the first time we have a tech This is the first time we have a tech that does that. that does that. that does that. >> In the past, >> In the past, >> In the past, >> I don't know about that. >> I don't know about that. >> I don't know about that. >> Some humans. It will replace some >> Some humans. It will replace some >> Some humans. It will replace some humans. I mean, humans. I mean, humans. I mean, >> oh guys. Okay. Now, now let's cut the >> oh guys. Okay. Now, now let's cut the >> oh guys. Okay. Now, now let's cut the Okay. Let's look at the board Okay. Let's look at the board Okay. Let's look at the board of director MBA in the US. of director MBA in the US. of director MBA in the US. Increase revenue, reduce cost. Cut the Increase revenue, reduce cost. Cut the Increase revenue, reduce cost. Cut the people, reduce cost. Sure. And there'll people, reduce cost. Sure. And there'll people, reduce cost. Sure. And there'll be some some jobs that get eliminated be some some jobs that get eliminated be some some jobs that get eliminated because because because >> what CEOs have been doing in the past 20 >> what CEOs have been doing in the past 20 >> what CEOs have been doing in the past 20 years is using the human workforce as a years is using the human workforce as a years is using the human workforce as a variable for that. Now they just want to variable for that. Now they just want to variable for that. Now they just want to use human because they can animate even use human because they can animate even use human because they can animate even more jobs. This is the real this is a more jobs. This is the real this is a more jobs. This is the real this is a perception perception. perception perception. perception perception. >> Yeah. >> Yeah. >> Yeah. >> Because because there is this illusion >> Because because there is this illusion >> Because because there is this illusion and I and I attribute that predominantly and I and I attribute that predominantly and I and I attribute that predominantly because of its language interface that because of its language interface that because of its language interface that it can really totally replace human. it can really totally replace human. it can really totally replace human. This is the first time as opposed to hey This is the first time as opposed to hey This is the first time as opposed to hey calculators we never say calculators calculators we never say calculators calculators we never say calculators going to replace you. Well this only going to replace you. Well this only going to replace you. Well this only flight for like a year or two. No but flight for like a year or two. No but flight for like a year or two. No but >> there were people that were calculators >> there were people that were calculators >> there were people that were calculators they they they >> but it last but it last No but the point >> but it last but it last No but the point >> but it last but it last No but the point is that it will not replace completely a is that it will not replace completely a is that it will not replace completely a human. It replaces tasks a human were human. It replaces tasks a human were human. It replaces tasks a human were doing. That's my point. So doing. That's my point. So doing. That's my point. So >> yeah the early part of the space program >> yeah the early part of the space program >> yeah the early part of the space program right we had that was the what was that right we had that was the what was that right we had that was the what was that movie?

movie? movie? >> Computers >> Computers >> Computers >> hidden figures. They were they were >> hidden figures. They were they were >> hidden figures. They were they were calculators. They were computers. Yeah. calculators. They were computers. Yeah. calculators. They were computers. Yeah. And they just did the math. And they just did the math. And they just did the math. >> Yeah. They were they were just >> Yeah. They were they were just >> Yeah. They were they were just African-Amean and and [laughter] African-Amean and and [laughter] African-Amean and and [laughter] >> the women that did all the calculations. >> the women that did all the calculations. >> the women that did all the calculations. >> That's what they didn't want to >> That's what they didn't want to >> That's what they didn't want to >> they all got replaced with the >> they all got replaced with the >> they all got replaced with the computers, right? computers, right? computers, right? >> Yeah. >> Yeah. >> Yeah. >> Yeah. But my point is the Yes. There was >> Yeah. But my point is the Yes. There was >> Yeah. But my point is the Yes. There was this initial expectation, but we quickly this initial expectation, but we quickly this initial expectation, but we quickly realized with these technologies, the realized with these technologies, the realized with these technologies, the calculators that it will accelerate calculators that it will accelerate calculators that it will accelerate tasks. tasks. tasks. >> Accelerate. >> Accelerate. >> Accelerate. >> They were good computers. Those those >> They were good computers. Those those >> They were good computers. Those those women in the space program were great women in the space program were great women in the space program were great computers. computers. computers. >> And as Steve Jobs says, what's a >> And as Steve Jobs says, what's a >> And as Steve Jobs says, what's a computer? You know, a Mac? It's a computer? You know, a Mac? It's a computer? You know, a Mac? It's a bicycle for your mind. And so the word bicycle for your mind. And so the word bicycle for your mind. And so the word accelerated comes on. It does what you accelerated comes on. It does what you accelerated comes on. It does what you already know how to do, just faster. already know how to do, just faster. already know how to do, just faster. >> Right. >> Right. >> Right. >> To your point, excellent point, Rob. But >> To your point, excellent point, Rob. But >> To your point, excellent point, Rob. But today, nobody is thinking AI is a is a today, nobody is thinking AI is a is a today, nobody is thinking AI is a is a bicycle is a is a motorbike for your bicycle is a is a motorbike for your bicycle is a is a motorbike for your mind. Everybody is thinking AI will mind. Everybody is thinking AI will mind. Everybody is thinking AI will replace your mind. replace your mind. replace your mind. >> And this is the flow. This is the flow. >> And this is the flow. This is the flow. >> And this is the flow. This is the flow. But that's where we, you know, we're But that's where we, you know, we're But that's where we, you know, we're going back to trust again. You know, going back to trust again. You know, going back to trust again. You know, trust is proven over time. And you can trust is proven over time. And you can trust is proven over time. And you can have misconceptions about a a have misconceptions about a a have misconceptions about a a technology. At some point, you're going technology. At some point, you're going technology. At some point, you're going to get bitten in the ass, right? Like to get bitten in the ass, right? Like to get bitten in the ass, right? Like the 5G guys, like the open rand guys, the 5G guys, like the open rand guys, the 5G guys, like the open rand guys, all of them had some sort of, you know, all of them had some sort of, you know, all of them had some sort of, you know, um, expectation, okay, and illusion um, expectation, okay, and illusion um, expectation, okay, and illusion about the technology. And it all, you about the technology. And it all, you about the technology. And it all, you know, largely fell flat on his face, know, largely fell flat on his face, know, largely fell flat on his face, right? to the point where now uh 5G was right? to the point where now uh 5G was right? to the point where now uh 5G was a strategic critical technology that all

a strategic critical technology that all a strategic critical technology that all governments needed to fund and then all governments needed to fund and then all governments needed to fund and then all of a sudden nobody gives a about of a sudden nobody gives a about of a sudden nobody gives a about it. I mean you know how hard it is to it. I mean you know how hard it is to it. I mean you know how hard it is to talk about 5G people laugh in your face. talk about 5G people laugh in your face. talk about 5G people laugh in your face. It's like crazy. It's like crazy. It's like crazy. >> Even the host they don't they don't give >> Even the host they don't they don't give >> Even the host they don't they don't give a about it. So, you know, there's a about it. So, you know, there's a about it. So, you know, there's always a grounding. There's a, you know, always a grounding. There's a, you know, always a grounding. There's a, you know, slope of grounding that happens as the slope of grounding that happens as the slope of grounding that happens as the >> I keep hearing the term grounding. And >> I keep hearing the term grounding. And >> I keep hearing the term grounding. And now people tell me I'm supposed to take now people tell me I'm supposed to take now people tell me I'm supposed to take off my shoes and walk on grass. And off my shoes and walk on grass. And off my shoes and walk on grass. And apparently that's grounding as well. I apparently that's grounding as well. I apparently that's grounding as well. I don't know. I've heard that recently. don't know. I've heard that recently. don't know. I've heard that recently. >> Well, for electrical reasons, that's a >> Well, for electrical reasons, that's a >> Well, for electrical reasons, that's a good idea. good idea. good idea. >> Okay. >> Okay. >> Okay. >> You can hold >> You can hold >> You can hold on. on. on. >> Yeah. So, you know, so we talked about >> Yeah. So, you know, so we talked about >> Yeah. So, you know, so we talked about so it's going to replace some people or so it's going to replace some people or so it's going to replace some people or some jobs, right? like IoT, you know, some jobs, right? like IoT, you know, some jobs, right? like IoT, you know, instead of people going and taking instead of people going and taking instead of people going and taking readings everywhere, now IoT is doing readings everywhere, now IoT is doing readings everywhere, now IoT is doing taking the readings and sending it back taking the readings and sending it back taking the readings and sending it back to the source of truth instead of a to the source of truth instead of a to the source of truth instead of a person going and looking with their eyes person going and looking with their eyes person going and looking with their eyes and writing it down and going back and and writing it down and going back and and writing it down and going back and typing it in. And so it replaced some of typing it in. And so it replaced some of typing it in. And so it replaced some of those people. It depends on, you know, I those people. It depends on, you know, I those people. It depends on, you know, I always think about the whole, you know, always think about the whole, you know, always think about the whole, you know, the whole the Dutch people throwing the whole the Dutch people throwing the whole the Dutch people throwing their sabot their shoes, you know, to their sabot their shoes, you know, to their sabot their shoes, you know, to sabotage right into the gears of the sabotage right into the gears of the sabotage right into the gears of the machine because when are we going to see machine because when are we going to see machine because when are we going to see people start sabotaging people start sabotaging people start sabotaging things related to AI taking their jobs?

things related to AI taking their jobs? things related to AI taking their jobs? You'll have to hit some kind of You'll have to hit some kind of You'll have to hit some kind of threshold. Right now, we're still too threshold. Right now, we're still too threshold. Right now, we're still too low. People are like, "Oh, well, too low. People are like, "Oh, well, too low. People are like, "Oh, well, too bad, you know, or I'm getting bad, you know, or I'm getting bad, you know, or I'm getting unemployment." But when will we hit the unemployment." But when will we hit the unemployment." But when will we hit the threshold of enough people losing their threshold of enough people losing their threshold of enough people losing their jobs that because jobs that because jobs that because >> oh my god >> oh my god >> oh my god >> my personal experience and everybody's >> my personal experience and everybody's >> my personal experience and everybody's seen this how how many times do you show seen this how how many times do you show seen this how how many times do you show up with just any kind of technology in up with just any kind of technology in up with just any kind of technology in your whole career to a customer and the your whole career to a customer and the your whole career to a customer and the CEO loves it and all the executives love CEO loves it and all the executives love CEO loves it and all the executives love it but guess who doesn't love it when it but guess who doesn't love it when it but guess who doesn't love it when you go talk to the boots on the ground you go talk to the boots on the ground you go talk to the boots on the ground who actually are going to work with it who actually are going to work with it who actually are going to work with it and they go instead of being excited and they go instead of being excited and they go instead of being excited they go huh cuz they immediately see they go huh cuz they immediately see they go huh cuz they immediately see that it's actually going to eliminate that it's actually going to eliminate that it's actually going to eliminate their job And what do they do? They try their job And what do they do? They try their job And what do they do? They try to kill the project or they sabotage the to kill the project or they sabotage the to kill the project or they sabotage the project or they slow it down or they go, project or they slow it down or they go, project or they slow it down or they go, "Well, you know, this would we're too "Well, you know, this would we're too "Well, you know, this would we're too unique. It's not going to work for us." unique. It's not going to work for us." unique. It's not going to work for us." This a thousand times. When we were This a thousand times. When we were This a thousand times. When we were doing the vending machines at Realtime doing the vending machines at Realtime doing the vending machines at Realtime Data, the executives were loving it. You Data, the executives were loving it. You Data, the executives were loving it. You know who sabotaged it? The route drivers know who sabotaged it? The route drivers know who sabotaged it? The route drivers who we were making more efficient who we were making more efficient who we were making more efficient because it made it too efficient. because it made it too efficient. because it made it too efficient. [laughter] And so the road drivers were [laughter] And so the road drivers were [laughter] And so the road drivers were pulling plugs out and and sabotaging our pulling plugs out and and sabotaging our pulling plugs out and and sabotaging our our our [clears throat] our our [clears throat] our our [clears throat] gear, our devices in the vending gear, our devices in the vending gear, our devices in the vending machines. And it's cuz they they saw machines. And it's cuz they they saw machines. And it's cuz they they saw what was going to happen. We need fewer what was going to happen. We need fewer what was going to happen. We need fewer route drivers to replace candy and route drivers to replace candy and route drivers to replace candy and machines.

machines. machines. >> This is it's all the same thing. >> This is it's all the same thing. >> This is it's all the same thing. >> Worried about though, but worried about >> Worried about though, but worried about >> Worried about though, but worried about AI is going to be more exponential, AI is going to be more exponential, AI is going to be more exponential, right? That's the the thinking or the right? That's the the thinking or the right? That's the the thinking or the fear potentially exponential. I don't fear potentially exponential. I don't fear potentially exponential. I don't know. [clears throat] know. [clears throat] know. [clears throat] >> It's it's going to be much easier to >> It's it's going to be much easier to >> It's it's going to be much easier to sabotage. sabotage. sabotage. Oh yeah, that's what AI is for. Oh yeah, that's what AI is for. Oh yeah, that's what AI is for. >> I mean, prompt injections super easy. >> I mean, prompt injections super easy. >> I mean, prompt injections super easy. There's already I mean, I haven't done There's already I mean, I haven't done There's already I mean, I haven't done the test myself, but I've seen people the test myself, but I've seen people the test myself, but I've seen people that, you know, put some specific that, you know, put some specific that, you know, put some specific language in their LinkedIn profile language in their LinkedIn profile language in their LinkedIn profile >> to hack the AI agent that scan for jobs. >> to hack the AI agent that scan for jobs. >> to hack the AI agent that scan for jobs. >> I like that. >> I like that. >> I like that. >> Yeah, it's projections. I mean, >> Yeah, it's projections. I mean, >> Yeah, it's projections. I mean, >> yeah. >> yeah. >> yeah. >> Well, there's also clothing you can wear >> Well, there's also clothing you can wear >> Well, there's also clothing you can wear so that you're not visible by like AI so that you're not visible by like AI so that you're not visible by like AI vision cameras and stuff. vision cameras and stuff. vision cameras and stuff. special patterns that uh will disrupt special patterns that uh will disrupt special patterns that uh will disrupt the algorithm. the algorithm. the algorithm. >> Did you just did you just start a new >> Did you just did you just start a new >> Did you just did you just start a new textile company? textile company? textile company? >> Yeah, that's right. Yeah, my new >> Yeah, that's right. Yeah, my new >> Yeah, that's right. Yeah, my new clothing edge AI clothing line which you clothing edge AI clothing line which you clothing edge AI clothing line which you will have will have will have >> Oh, really? >> Oh, really? >> Oh, really? >> I think it's I think it's AI vision. >> I think it's I think it's AI vision. >> I think it's I think it's AI vision. >> Going to be a big hit in the next 5 >> Going to be a big hit in the next 5 >> Going to be a big hit in the next 5 years. years. years. >> Yeah, >> Yeah, >> Yeah, >> I won in on the IPO. >> I won in on the IPO. >> I won in on the IPO. >> New type of camo. New type of camo.

>> New type of camo. New type of camo. >> New type of camo. New type of camo. [laughter] Yeah, camo. [laughter] Yeah, camo. [laughter] Yeah, camo. >> Yeah. How was Taiwan, man? How was that? >> Yeah. How was Taiwan, man? How was that? >> Yeah. How was Taiwan, man? How was that? >> Oh, it was great. Yeah, it was like I >> Oh, it was great. Yeah, it was like I >> Oh, it was great. Yeah, it was like I spent a week in Korea and a week in spent a week in Korea and a week in spent a week in Korea and a week in Taiwan. Taiwan. Taiwan. >> Oh my god. [laughter] >> Oh my god. [laughter] >> Oh my god. [laughter] >> It was awesome. >> It was awesome. >> It was awesome. >> Got back last Saturday morning at 5:30 >> Got back last Saturday morning at 5:30 >> Got back last Saturday morning at 5:30 in the morning. I landed in in the morning. I landed in in the morning. I landed in >> So beautiful. >> So beautiful. >> So beautiful. >> But uh it was great. It's my third time >> But uh it was great. It's my third time >> But uh it was great. It's my third time in Taiwan this past year. So we we're in Taiwan this past year. So we we're in Taiwan this past year. So we we're building a lot of community there and building a lot of community there and building a lot of community there and it's it's really a global hub for what's it's it's really a global hub for what's it's it's really a global hub for what's happening in tech over there. So it was happening in tech over there. So it was happening in tech over there. So it was it was good to be there and uh and we it was good to be there and uh and we it was good to be there and uh and we have just missed a typhoon. We almost we have just missed a typhoon. We almost we have just missed a typhoon. We almost we had a two-day event there, the Agi had a two-day event there, the Agi had a two-day event there, the Agi Taipei event, and uh I found this the Taipei event, and uh I found this the Taipei event, and uh I found this the typhoon was coming up from the typhoon was coming up from the typhoon was coming up from the Philippines. Everyone's like, "Oh, we Philippines. Everyone's like, "Oh, we Philippines. Everyone's like, "Oh, we might have to close down the building or might have to close down the building or might have to close down the building or whatever." whatever." whatever." >> Oh, you you mean your edge stuff >> Oh, you you mean your edge stuff >> Oh, you you mean your edge stuff couldn't predict that. couldn't predict that. couldn't predict that. >> Yeah, that's one thing. That job will >> Yeah, that's one thing. That job will >> Yeah, that's one thing. That job will never get replaced. Weather weather never get replaced. Weather weather never get replaced. Weather weather predictor apparently, no matter how many predictor apparently, no matter how many predictor apparently, no matter how many supercomputers. supercomputers. supercomputers. >> Well, talking about trust, your >> Well, talking about trust, your >> Well, talking about trust, your weatherman, talking about trust, weatherman, talking about trust, weatherman, talking about trust, >> the day before they're like, "Well, it >> the day before they're like, "Well, it >> the day before they're like, "Well, it might be a typhoon." your your master might be a typhoon." your your master might be a typhoon." your your master argument about trust, you know, or not.

argument about trust, you know, or not. argument about trust, you know, or not. Can you Can you Can you >> thing the sun came out the day of the >> thing the sun came out the day of the >> thing the sun came out the day of the thing that when the typhoon was supposed thing that when the typhoon was supposed thing that when the typhoon was supposed to hit, I'm like, is that weather to hit, I'm like, is that weather to hit, I'm like, is that weather forecasting? What's happening there? So, forecasting? What's happening there? So, forecasting? What's happening there? So, we we missed it. we we missed it. we we missed it. >> Maybe not everybody's going to get fired >> Maybe not everybody's going to get fired >> Maybe not everybody's going to get fired because of AI, too. That the weatherman because of AI, too. That the weatherman because of AI, too. That the weatherman doesn't get fired. doesn't get fired. doesn't get fired. >> No, the weatherman never gets fired. >> No, the weatherman never gets fired. >> No, the weatherman never gets fired. >> Well, well, but I'm telling you that if >> Well, well, but I'm telling you that if >> Well, well, but I'm telling you that if you can't trust the weatherman human or you can't trust the weatherman human or you can't trust the weatherman human or you can't trust the weatherman AI, some you can't trust the weatherman AI, some you can't trust the weatherman AI, some CEO is going to think that the CEO is going to think that the CEO is going to think that the weatherman is cheaper. weatherman is cheaper. weatherman is cheaper. >> You know what Bob Dylan once said? You >> You know what Bob Dylan once said? You >> You know what Bob Dylan once said? You don't need the weather man to know which don't need the weather man to know which don't need the weather man to know which way the wind blows, right? So, way the wind blows, right? So, way the wind blows, right? So, >> yeah. >> yeah. >> yeah. >> Genius. >> Genius. >> Genius. >> That's genius. >> That's genius. >> That's genius. >> I was, you know, Speaking of >> I was, you know, Speaking of >> I was, you know, Speaking of >> Pretty obvious, >> Pretty obvious, >> Pretty obvious, >> speaking of weather, I found something >> speaking of weather, I found something >> speaking of weather, I found something disconcerting. Um, Ginger Z, who is a disconcerting. Um, Ginger Z, who is a disconcerting. Um, Ginger Z, who is a weather woman on, I don't know, ABC or weather woman on, I don't know, ABC or weather woman on, I don't know, ABC or one of them. Um, she did a little video one of them. Um, she did a little video one of them. Um, she did a little video that I just happened to see and she's that I just happened to see and she's that I just happened to see and she's like, "Hey people in Houston, just so like, "Hey people in Houston, just so like, "Hey people in Houston, just so you know, you've only had 79 days below you know, you've only had 79 days below you know, you've only had 79 days below 80° this year. 80° this year. 80° this year. And I was like, And I was like, And I was like, [clears throat] [clears throat] [clears throat] that doesn't sound too good. Really?

that doesn't sound too good. Really? that doesn't sound too good. Really? >> If I was in Phoenix, maybe that would >> If I was in Phoenix, maybe that would >> If I was in Phoenix, maybe that would seem normal to me. seem normal to me. seem normal to me. >> Well, welcome Texas. Welcome to Texas. >> Well, welcome Texas. Welcome to Texas. >> Well, welcome Texas. Welcome to Texas. >> I know, but just 79 days where the high >> I know, but just 79 days where the high >> I know, but just 79 days where the high was under 80. That's That's kind of was under 80. That's That's kind of was under 80. That's That's kind of scary. scary. scary. >> I'm waiting for some local news outlet >> I'm waiting for some local news outlet >> I'm waiting for some local news outlet to create a fully synthetic AI generated to create a fully synthetic AI generated to create a fully synthetic AI generated weather person to do the weather. I weather person to do the weather. I weather person to do the weather. I think that would be an interesting think that would be an interesting think that would be an interesting experiment. But experiment. But experiment. But >> yeah, I know what they'll make. It'll be >> yeah, I know what they'll make. It'll be >> yeah, I know what they'll make. It'll be like like like >> Yeah. Unfortunately, I think we know how >> Yeah. Unfortunately, I think we know how >> Yeah. Unfortunately, I think we know how that's going to turn out. that's going to turn out. that's going to turn out. >> If you watch If you watch it with a >> If you watch If you watch it with a >> If you watch If you watch it with a woman on the Mexican news channels, woman on the Mexican news channels, woman on the Mexican news channels, you'll see what you'll end up getting you'll see what you'll end up getting you'll see what you'll end up getting from >> YouTube. >> YouTube. >> Holy crap. Oh, by the way, um uh Bitcoin >> Holy crap. Oh, by the way, um uh Bitcoin >> Holy crap. Oh, by the way, um uh Bitcoin hit 80,000. hit 80,000. hit 80,000. >> Yeah. >> Yeah. >> Yeah. >> Sell seller >> Sell seller >> Sell seller stock market. stock market. stock market. Selling Selling Selling my my my >> no comment. >> no comment. >> no comment. >> Losing my ass over here. >> Losing my ass over here. >> Losing my ass over here. >> Holy It went down 25% in in uh >> Holy It went down 25% in in uh >> Holy It went down 25% in in uh >> I mean how high did it get? 110 115 119 >> I mean how high did it get? 110 115 119 >> I mean how high did it get? 110 115 119 >> way north >> way north >> way north then. No a lot. Yeah.

then. No a lot. Yeah. then. No a lot. Yeah. >> It's just like I know I was >> It's just like I know I was >> It's just like I know I was >> four and now it's 85. It went down to 80 >> four and now it's 85. It went down to 80 >> four and now it's 85. It went down to 80 >> and uh it's on a downward trajectory. I >> and uh it's on a downward trajectory. I >> and uh it's on a downward trajectory. I was watching one of my financial shows was watching one of my financial shows was watching one of my financial shows this morning and someone said, "I wonder this morning and someone said, "I wonder this morning and someone said, "I wonder if Bitcoin is a leading indicator for if Bitcoin is a leading indicator for if Bitcoin is a leading indicator for the stock market." the stock market." the stock market." >> That those guys are idiot. >> That those guys are idiot. >> That those guys are idiot. >> Listening to one um I think he's Korean. >> Listening to one um I think he's Korean. >> Listening to one um I think he's Korean. >> Oh, but guys, it's Wall Street. >> Oh, but guys, it's Wall Street. >> Oh, but guys, it's Wall Street. >> It's too big to fail. It's too big to >> It's too big to fail. It's too big to >> It's too big to fail. It's too big to fail. fail. fail. >> Yeah. Yeah. He um [clears throat] >> Yeah. Yeah. He um [clears throat] >> Yeah. Yeah. He um [clears throat] he was on and man, he was spouting out he was on and man, he was spouting out he was on and man, he was spouting out some serious BS. And then you have these some serious BS. And then you have these some serious BS. And then you have these guys trying to associate guys trying to associate guys trying to associate Bitcoin price fluctuations and movements Bitcoin price fluctuations and movements Bitcoin price fluctuations and movements with the AI trade. It's how are they with the AI trade. It's how are they with the AI trade. It's how are they coming up with this crap? coming up with this crap? coming up with this crap? >> You know, what I love is in on Coindesk. >> You know, what I love is in on Coindesk. >> You know, what I love is in on Coindesk. So, I'm looking at Coindesk right now So, I'm looking at Coindesk right now So, I'm looking at Coindesk right now and some of these other sites, they have and some of these other sites, they have and some of these other sites, they have this button said create your own this button said create your own this button said create your own narrative. narrative. narrative. >> Yeah, >> Yeah, >> Yeah, I'm going to create my own narrative.

I'm going to create my own narrative. I'm going to create my own narrative. Boy, I think it's everybody does in Boy, I think it's everybody does in Boy, I think it's everybody does in their own mind. their own mind. their own mind. >> I got an easy button for you. I It's >> I got an easy button for you. I It's >> I got an easy button for you. I It's called the button. [laughter] called the button. [laughter] called the button. [laughter] >> You know, speaking of crypto, I was >> You know, speaking of crypto, I was >> You know, speaking of crypto, I was listening to I didn't I didn't know as listening to I didn't I didn't know as listening to I didn't I didn't know as much about um as I didn't know as much much about um as I didn't know as much much about um as I didn't know as much about stable coins as I thought I did. about stable coins as I thought I did. about stable coins as I thought I did. Um but I was listening to a recent Um but I was listening to a recent Um but I was listening to a recent Moonshots podcast and they had Jeremy Moonshots podcast and they had Jeremy Moonshots podcast and they had Jeremy Aair on there who's the CEO of Circle. Aair on there who's the CEO of Circle. Aair on there who's the CEO of Circle. >> And so that's [clears throat] a a a US >> And so that's [clears throat] a a a US >> And so that's [clears throat] a a a US dollar stable coin. I don't think USDC dollar stable coin. I don't think USDC dollar stable coin. I don't think USDC or whatever like that. And just you know or whatever like that. And just you know or whatever like that. And just you know the discussion because when we talk the discussion because when we talk the discussion because when we talk about especially everybody and their dog about especially everybody and their dog about especially everybody and their dog talking about agentic AI what is the talking about agentic AI what is the talking about agentic AI what is the wallet that that agent is going to use wallet that that agent is going to use wallet that that agent is going to use to make purchases to do stuff over the to make purchases to do stuff over the to make purchases to do stuff over the MCP protocols like that and so that that MCP protocols like that and so that that MCP protocols like that and so that that that's kind of like their thinking is that's kind of like their thinking is that's kind of like their thinking is you know a stable coin like USDC or or you know a stable coin like USDC or or you know a stable coin like USDC or or there's another competitor to them there's another competitor to them there's another competitor to them called Tether um that's trying to do the called Tether um that's trying to do the called Tether um that's trying to do the same thing. And so it's like you know same thing. And so it's like you know same thing. And so it's like you know true digital currency not having to go true digital currency not having to go true digital currency not having to go through clearing houses blah blah blah. through clearing houses blah blah blah. through clearing houses blah blah blah. And do you know what? Do you know what And do you know what? Do you know what And do you know what? Do you know what Rob which is interesting? Do you know Rob which is interesting? Do you know Rob which is interesting? Do you know which one which cryptocurrency the which one which cryptocurrency the which one which cryptocurrency the criminal uses?

criminal uses? criminal uses? >> The majority is the stable coins. >> The majority is the stable coins. >> The majority is the stable coins. >> Well, the stable coin is supposed to be >> Well, the stable coin is supposed to be >> Well, the stable coin is supposed to be a transactional thing instead of store a transactional thing instead of store a transactional thing instead of store value. value. value. >> Yeah, but it's I mean it's now the one >> Yeah, but it's I mean it's now the one >> Yeah, but it's I mean it's now the one that is backed to the USD. You can argue that is backed to the USD. You can argue that is backed to the USD. You can argue that the USD can fluctuate. that the USD can fluctuate. that the USD can fluctuate. >> Interesting. The idea of stable coin is >> Interesting. The idea of stable coin is >> Interesting. The idea of stable coin is actually the true vision of digital actually the true vision of digital actually the true vision of digital currency because it's backed up by a currency because it's backed up by a currency because it's backed up by a more stable asset. So the criminals more stable asset. So the criminals more stable asset. So the criminals apparently are smarter than all these apparently are smarter than all these apparently are smarter than all these investors because they take money they investors because they take money they investors because they take money they don't take it in bitcoin they take it in don't take it in bitcoin they take it in don't take it in bitcoin they take it in stable coins only. And that's that's a stable coins only. And that's that's a stable coins only. And that's that's a true fact. I mean I've seen that from true fact. I mean I've seen that from true fact. I mean I've seen that from from a reliable source I trust. And it's from a reliable source I trust. And it's from a reliable source I trust. And it's interesting, you know, trust us. interesting, you know, trust us. interesting, you know, trust us. >> You know what's interesting about that? >> You know what's interesting about that? >> You know what's interesting about that? Because the stable coins are much closer Because the stable coins are much closer Because the stable coins are much closer to fiat. In [clears throat] fact, yeah. to fiat. In [clears throat] fact, yeah. to fiat. In [clears throat] fact, yeah. And so, you know, all these folks that And so, you know, all these folks that And so, you know, all these folks that try to argue that Bitcoin is the actual try to argue that Bitcoin is the actual try to argue that Bitcoin is the actual currency are wrong because if it was, currency are wrong because if it was, currency are wrong because if it was, you wouldn't have this movement toward you wouldn't have this movement toward you wouldn't have this movement toward stable coins, right? Um fiatbacked. stable coins, right? Um fiatbacked. stable coins, right? Um fiatbacked. Basically, it's fiatbacked Basically, it's fiatbacked Basically, it's fiatbacked crypto, right? Even though they hate crypto, right? Even though they hate crypto, right? Even though they hate Even though they hate fiat currencies Even though they hate fiat currencies Even though they hate fiat currencies and they're railing it's the rage and they're railing it's the rage and they're railing it's the rage against the machine and we're just against the machine and we're just against the machine and we're just >> we're the we're the woman in the Apple >> we're the we're the woman in the Apple >> we're the we're the woman in the Apple commercial swinging like you know you commercial swinging like you know you commercial swinging like you know you know smashing the it's it's always a know smashing the it's it's always a know smashing the it's it's always a consequence of simplification. The thing consequence of simplification. The thing consequence of simplification. The thing that people hated in fiat is the fact that people hated in fiat is the fact that people hated in fiat is the fact that it's centrally controlled not the that it's centrally controlled not the that it's centrally controlled not the fact that it's stable because it's back fact that it's stable because it's back fact that it's stable because it's back up by an asset. So that's why you need up by an asset. So that's why you need up by an asset. So that's why you need to decorticate. We always go back to to decorticate. We always go back to to decorticate. We always go back to critical thinking. We have to

critical thinking. We have to critical thinking. We have to decorticate what is it really what's in decorticate what is it really what's in decorticate what is it really what's in fiat. Yeah. The great thing about fiat fiat. Yeah. The great thing about fiat fiat. Yeah. The great thing about fiat is that it's backed by a tangible asset. is that it's backed by a tangible asset. is that it's backed by a tangible asset. The bad thing is that it's central The bad thing is that it's central The bad thing is that it's central control. I want to eliminate the central control. I want to eliminate the central control. I want to eliminate the central control but I don't eliminate control but I don't eliminate control but I don't eliminate everything. everything. everything. >> But that's why if you critically think >> But that's why if you critically think >> But that's why if you critically think about this stuff, you realize that fi about this stuff, you realize that fi about this stuff, you realize that fi fiat is what has the real value. Of fiat is what has the real value. Of fiat is what has the real value. Of these crypto things are just constructs. these crypto things are just constructs. these crypto things are just constructs. They're just instruments. They're not They're just instruments. They're not They're just instruments. They're not the actual thing that's valuable. And so the actual thing that's valuable. And so the actual thing that's valuable. And so they're like derivatives, right? And so they're like derivatives, right? And so they're like derivatives, right? And so when you early on when you looked at when you early on when you looked at when you early on when you looked at what the crypto community was doing to what the crypto community was doing to what the crypto community was doing to pivot out of all these failures and con pivot out of all these failures and con pivot out of all these failures and con jobs, you realize that they're trying jobs, you realize that they're trying jobs, you realize that they're trying to, you know, you heard about the layer to, you know, you heard about the layer to, you know, you heard about the layer 2 and then they probably like have layer 2 and then they probably like have layer 2 and then they probably like have layer five now. They're just layering on more five now. They're just layering on more five now. They're just layering on more abstractions to not only scale this abstractions to not only scale this abstractions to not only scale this thing out, but to uh make this thing thing out, but to uh make this thing thing out, but to uh make this thing look like something that it isn't and look like something that it isn't and look like something that it isn't and allow people to actually leverage this allow people to actually leverage this allow people to actually leverage this thing uh in a way that makes it look thing uh in a way that makes it look thing uh in a way that makes it look more like a security, right?

more like a security, right? more like a security, right? >> You know, this heard problem. I heard >> You know, this heard problem. I heard >> You know, this heard problem. I heard that as things are getting weird that at that as things are getting weird that at that as things are getting weird that at least Tether which is a bigger one that least Tether which is a bigger one that least Tether which is a bigger one that backs you that's a stable coin is like backs you that's a stable coin is like backs you that's a stable coin is like stocking up on bars of gold or silver or stocking up on bars of gold or silver or stocking up on bars of gold or silver or something right now you know cuz you something right now you know cuz you something right now you know cuz you know the whole talk track was you know know the whole talk track was you know know the whole talk track was you know and and Jeremy it it's funny my tiein to and and Jeremy it it's funny my tiein to and and Jeremy it it's funny my tiein to this guy who's the CEO of a circle is this guy who's the CEO of a circle is this guy who's the CEO of a circle is because back in the '90s when I was because back in the '90s when I was because back in the '90s when I was developing when the web took off right developing when the web took off right developing when the web took off right and we were doing web pages and the and we were doing web pages and the and we were doing web pages and the early tech that Microsoft had was active early tech that Microsoft had was active early tech that Microsoft had was active server pages server pages server pages >> pages and VBScript and stuff like that. >> pages and VBScript and stuff like that. >> pages and VBScript and stuff like that. [clears throat] Yeah, I know. I did it. [clears throat] Yeah, I know. I did it. [clears throat] Yeah, I know. I did it. I did it. But Jeremy Aair actually I did it. But Jeremy Aair actually I did it. But Jeremy Aair actually created a a development tool called created a a development tool called created a a development tool called Homesite. And so that's how I knew his Homesite. And so that's how I knew his Homesite. And so that's how I knew his name. name. name. >> Yeah. So I And me and a lot of people >> Yeah. So I And me and a lot of people >> Yeah. So I And me and a lot of people use Homesite to develop ASP websites and use Homesite to develop ASP websites and use Homesite to develop ASP websites and stuff like that. So uh anyway, yes, stuff like that. So uh anyway, yes, stuff like that. So uh anyway, yes, shame on me to being such a Microsoft shame on me to being such a Microsoft shame on me to being such a Microsoft guy my whole career. I apologize in guy my whole career. I apologize in guy my whole career. I apologize in advance or retroactively, whatever. Um I advance or retroactively, whatever. Um I advance or retroactively, whatever. Um I baby. Yeah. Um but yeah, you know, he baby. Yeah. Um but yeah, you know, he baby. Yeah. Um but yeah, you know, he was talking he was like, "Hey, you know, was talking he was like, "Hey, you know, was talking he was like, "Hey, you know, you're right. is we all know the story.

you're right. is we all know the story. you're right. is we all know the story. The US currency was always backed by The US currency was always backed by The US currency was always backed by gold in Fort Knox and in New York. Uh gold in Fort Knox and in New York. Uh gold in Fort Knox and in New York. Uh and then after spending so much money uh and then after spending so much money uh and then after spending so much money uh during the 60s on uh the Vietnam War and during the 60s on uh the Vietnam War and during the 60s on uh the Vietnam War and a bunch of other things, you know, a bunch of other things, you know, a bunch of other things, you know, President Nixon's like, "We don't have President Nixon's like, "We don't have President Nixon's like, "We don't have enough gold to back our currency enough gold to back our currency enough gold to back our currency anymore." A lot of people think it was anymore." A lot of people think it was anymore." A lot of people think it was an arbitrary thing to get off the gold an arbitrary thing to get off the gold an arbitrary thing to get off the gold standard, but it was like it was weird. standard, but it was like it was weird. standard, but it was like it was weird. It was almost like rational economics It was almost like rational economics It was almost like rational economics for a change, which we don't have today. for a change, which we don't have today. for a change, which we don't have today. And it was like we don't have enough And it was like we don't have enough And it was like we don't have enough gold to back the dollar anymore. And so gold to back the dollar anymore. And so gold to back the dollar anymore. And so we're going to create fiat currency we're going to create fiat currency we're going to create fiat currency where we're just going to now say the where we're just going to now say the where we're just going to now say the dollar is backed by the full faith and dollar is backed by the full faith and dollar is backed by the full faith and credit of the United States. What I credit of the United States. What I credit of the United States. What I often tell you, and I've heard other often tell you, and I've heard other often tell you, and I've heard other people say similar things, it's backed people say similar things, it's backed people say similar things, it's backed by ICBMs. by ICBMs. by ICBMs. Other people will say it's backed by the Other people will say it's backed by the Other people will say it's backed by the fact that the US military can just take fact that the US military can just take fact that the US military can just take out anybody. And so there's no such out anybody. And so there's no such out anybody. And so there's no such thing as America defaulting and oh no, thing as America defaulting and oh no, thing as America defaulting and oh no, it's over for us. It's like suck it, you it's over for us. It's like suck it, you it's over for us. It's like suck it, you know? That's that's the difference. It's know? That's that's the difference. It's know? That's that's the difference. It's not like wheelbarls full of cash to buy not like wheelbarls full of cash to buy not like wheelbarls full of cash to buy a hamburger. a hamburger. a hamburger. >> And so, uh, yeah. And so, you know, here >> And so, uh, yeah. And so, you know, here >> And so, uh, yeah. And so, you know, here we go. And so, it's funny to seeing we go. And so, it's funny to seeing we go. And so, it's funny to seeing these crypto guys stocking up on gold.

these crypto guys stocking up on gold. these crypto guys stocking up on gold. >> Yeah. >> Yeah. >> Yeah. >> To try to create the illusion that >> To try to create the illusion that >> To try to create the illusion that there's something backing their crypto. there's something backing their crypto. there's something backing their crypto. >> Well, in the case of stable coins, it is >> Well, in the case of stable coins, it is >> Well, in the case of stable coins, it is a viable model if you back it up with a viable model if you back it up with a viable model if you back it up with assets. It actually, as I said earlier, assets. It actually, as I said earlier, assets. It actually, as I said earlier, it actually realize the idea of a it actually realize the idea of a it actually realize the idea of a digital currency. digital currency. digital currency. >> Yeah. But you don't need it. Well, not >> Yeah. But you don't need it. Well, not >> Yeah. But you don't need it. Well, not really. Um, it's a digital it's a form really. Um, it's a digital it's a form really. Um, it's a digital it's a form of digital payment. It of digital payment. It of digital payment. It >> is currency as a way of payment. Let me >> is currency as a way of payment. Let me >> is currency as a way of payment. Let me rephrase. rephrase. rephrase. >> No, there you have digital the wallet >> No, there you have digital the wallet >> No, there you have digital the wallet that that is one of the elements of the that that is one of the elements of the that that is one of the elements of the pay payment mechanism, right? And so pay payment mechanism, right? And so pay payment mechanism, right? And so this is where people get payments this is where people get payments this is where people get payments confused with um currency. And it confused with um currency. And it confused with um currency. And it doesn't necessarily have to be a doesn't necessarily have to be a doesn't necessarily have to be a currency that you're dealing with. It currency that you're dealing with. It currency that you're dealing with. It could be just the, you know, an could be just the, you know, an could be just the, you know, an instrument you they're not making a instrument you they're not making a instrument you they're not making a payment with, but at the end of the day, payment with, but at the end of the day, payment with, but at the end of the day, because one of the guy, somebody on because one of the guy, somebody on because one of the guy, somebody on LinkedIn challenged me on this. I I LinkedIn challenged me on this. I I LinkedIn challenged me on this. I I asked him, "Well, can you buy a In-N-Out asked him, "Well, can you buy a In-N-Out asked him, "Well, can you buy a In-N-Out burger, a Double Double with extra burger, a Double Double with extra burger, a Double Double with extra veggies and fresh onions uh with veggies and fresh onions uh with veggies and fresh onions uh with Bitcoin?" He goes, "Of course you can."

Bitcoin?" He goes, "Of course you can." Bitcoin?" He goes, "Of course you can." He sends me this wallet thing. But the He sends me this wallet thing. But the He sends me this wallet thing. But the funny thing is you in order to buy um funny thing is you in order to buy um funny thing is you in order to buy um anything, you still have to translate it anything, you still have to translate it anything, you still have to translate it into fiat. You pay in fiat. into fiat. You pay in fiat. into fiat. You pay in fiat. >> Yes. Right. >> Yes. Right. >> Yes. Right. >> So, what I'm curious about is I would >> So, what I'm curious about is I would >> So, what I'm curious about is I would love to try an experiment. My wife will love to try an experiment. My wife will love to try an experiment. My wife will not like this idea, but if everything not like this idea, but if everything not like this idea, but if everything goes well, we're supposed to close on a goes well, we're supposed to close on a goes well, we're supposed to close on a house next week, right? And so, that's house next week, right? And so, that's house next week, right? And so, that's exciting. But I was like, huh, I wonder exciting. But I was like, huh, I wonder exciting. But I was like, huh, I wonder if I could show up at the closing and if I could show up at the closing and if I could show up at the closing and say the the closing cost, the down say the the closing cost, the down say the the closing cost, the down payment, all that stuff. I wonder if I payment, all that stuff. I wonder if I payment, all that stuff. I wonder if I should show up and say, I'm going to pay should show up and say, I'm going to pay should show up and say, I'm going to pay for that with a stable coin. And for that with a stable coin. And for that with a stable coin. And [laughter] I'm like, I'm going to use [laughter] I'm like, I'm going to use [laughter] I'm like, I'm going to use crypto to pay for it cuz you know, all crypto to pay for it cuz you know, all crypto to pay for it cuz you know, all these guys are all talking a good game. these guys are all talking a good game. these guys are all talking a good game. They're raising all this money. It's They're raising all this money. It's They're raising all this money. It's supposed to be the new way, you know. supposed to be the new way, you know. supposed to be the new way, you know. So, so if I believed them, it might take So, so if I believed them, it might take So, so if I believed them, it might take some research. I'm not going to try it. some research. I'm not going to try it. some research. I'm not going to try it. Obviously, Obviously, Obviously, >> we have a bunch of tulip bulbs in the >> we have a bunch of tulip bulbs in the >> we have a bunch of tulip bulbs in the back of our pickup truck here. We could back of our pickup truck here. We could back of our pickup truck here. We could pay with those, too. You know, pay with those, too. You know, pay with those, too. You know, >> bulbs. >> bulbs. >> bulbs. >> They used to be worth a lot. No. No. >> They used to be worth a lot. No. No. >> They used to be worth a lot. No. No. >> Yes. Yes, they did. So, it would be >> Yes. Yes, they did. So, it would be >> Yes. Yes, they did. So, it would be interesting.

interesting. interesting. >> You should maybe set the price in >> You should maybe set the price in >> You should maybe set the price in Bitcoin. Wait a little and at the last Bitcoin. Wait a little and at the last Bitcoin. Wait a little and at the last moment. moment. moment. >> Yeah. Oh, they think it's going >> Yeah. Oh, they think it's going >> Yeah. Oh, they think it's going [laughter] down. [laughter] down. [laughter] down. >> Oh, I got a great deal on my house, sir. >> Oh, I got a great deal on my house, sir. >> Oh, I got a great deal on my house, sir. I got a bad deal. I got a bad deal. I got a bad deal. >> Put an offer. Put an offer in Bitcoin. >> Put an offer. Put an offer in Bitcoin. >> Put an offer. Put an offer in Bitcoin. >> Yes. >> Yes. >> Yes. >> And then say, "Oh, wait, wait, wait, >> And then say, "Oh, wait, wait, wait, >> And then say, "Oh, wait, wait, wait, wait." wait." wait." >> Yeah. >> Yeah. >> Yeah. >> I mean, we always flash back when we >> I mean, we always flash back when we >> I mean, we always flash back when we thought Bitcoin was going to be a thought Bitcoin was going to be a thought Bitcoin was going to be a transactional money when people bought transactional money when people bought transactional money when people bought pizza with it. Little did they know that pizza with it. Little did they know that pizza with it. Little did they know that it's actually like gold bars instead. it's actually like gold bars instead. it's actually like gold bars instead. >> Yeah. >> Yeah. >> Yeah. >> Or tulip bulbs. Or probably more like >> Or tulip bulbs. Or probably more like >> Or tulip bulbs. Or probably more like tulip bulbs than gold bars. Yeah, tulip bulbs than gold bars. Yeah, tulip bulbs than gold bars. Yeah, >> more tulip bulbs. Gold bars. Yeah. >> more tulip bulbs. Gold bars. Yeah. >> more tulip bulbs. Gold bars. Yeah. >> Can you buy tulip bulbs at Costco? >> Can you buy tulip bulbs at Costco? >> Can you buy tulip bulbs at Costco? Because I hear you can buy gold bars at Because I hear you can buy gold bars at Because I hear you can buy gold bars at Costco, which I Costco, which I Costco, which I >> You can buy tulip bulbs at Costco. I'm >> You can buy tulip bulbs at Costco. I'm >> You can buy tulip bulbs at Costco. I'm sure there's lots of sure there's lots of sure there's lots of >> Can you imagine? So, you know, >> Can you imagine? So, you know, >> Can you imagine? So, you know, [laughter] everybody has the experience. [laughter] everybody has the experience. [laughter] everybody has the experience. >> I think they're right next to each other >> I think they're right next to each other >> I think they're right next to each other on the shelves. on the shelves. on the shelves. >> Right next to each other on the shelves. >> Right next to each other on the shelves. >> Right next to each other on the shelves. You know the experience when you're You know the experience when you're You know the experience when you're walking out of Costco and you got your walking out of Costco and you got your walking out of Costco and you got your receipt and the guy looks at it and receipt and the guy looks at it and receipt and the guy looks at it and shines off of it. Can you imagine shines off of it. Can you imagine shines off of it. Can you imagine walking out with your bars of gold in walking out with your bars of gold in walking out with your bars of gold in your hand? And he's like, "Yeah."

your hand? And he's like, "Yeah." your hand? And he's like, "Yeah." [laughter] And then, you know, the the [laughter] And then, you know, the the [laughter] And then, you know, the the minute you walk out in the parking lot, minute you walk out in the parking lot, minute you walk out in the parking lot, a bunch of guys just jump all over you a bunch of guys just jump all over you a bunch of guys just jump all over you and run away with your gold bars. and run away with your gold bars. and run away with your gold bars. [laughter] [laughter] [laughter] >> Well, Costco on a Saturday at the >> Well, Costco on a Saturday at the >> Well, Costco on a Saturday at the original Kirkland store is quite the uh original Kirkland store is quite the uh original Kirkland store is quite the uh adventure. So, adventure. So, adventure. So, >> it is an adventure. >> it is an adventure. >> it is an adventure. >> Original Kirkland. Actually, when I was >> Original Kirkland. Actually, when I was >> Original Kirkland. Actually, when I was in Taiwan, I was in line at the airport in Taiwan, I was in line at the airport in Taiwan, I was in line at the airport and there was a guy there um from Taiwan and there was a guy there um from Taiwan and there was a guy there um from Taiwan and he had an older gentleman and he had and he had an older gentleman and he had and he had an older gentleman and he had a Kirkland hat on, Kirkland signature. a Kirkland hat on, Kirkland signature. a Kirkland hat on, Kirkland signature. >> Oh, I love it. >> Oh, I love it. >> Oh, I love it. >> And he asked, "Where are you from?" I >> And he asked, "Where are you from?" I >> And he asked, "Where are you from?" I said, "Actually, I'm from Belleview, said, "Actually, I'm from Belleview, said, "Actually, I'm from Belleview, Washington. I actually shop at the Washington. I actually shop at the Washington. I actually shop at the original Costco in Kirkland." And he was original Costco in Kirkland." And he was original Costco in Kirkland." And he was like, "This like, "This like, "This magic magic magic >> like going to Disneyland." I'm like, >> like going to Disneyland." I'm like, >> like going to Disneyland." I'm like, "Not really." "Not really." "Not really." >> No, but he was said that I think that >> No, but he was said that I think that >> No, but he was said that I think that made his day that he met someone that made his day that he met someone that made his day that he met someone that went to the original Kirkland. You went to the original Kirkland. You went to the original Kirkland. You should have given an autograph and should have given an autograph and should have given an autograph and created an NFT for it. created an NFT for it. created an NFT for it. >> Yeah, >> Yeah, >> Yeah, >> there you go. >> there you go. >> there you go. >> Yeah. You know, >> Yeah. You know, >> Yeah. You know, >> retirement plan. >> retirement plan. >> retirement plan. >> I've been using Costco as a gauge of how >> I've been using Costco as a gauge of how >> I've been using Costco as a gauge of how well the wine industry is doing. And I well the wine industry is doing. And I well the wine industry is doing. And I think I've talked about this on the show think I've talked about this on the show think I've talked about this on the show before. Costco is the largest seller of before. Costco is the largest seller of before. Costco is the largest seller of spirits and wine in the United States by spirits and wine in the United States by spirits and wine in the United States by a wide margin. as it turns out. But when a wide margin. as it turns out. But when a wide margin. as it turns out. But when I started noticing at the Kirkland, the I started noticing at the Kirkland, the I started noticing at the Kirkland, the Isakqua, the Woodenville, Costos that we Isakqua, the Woodenville, Costos that we Isakqua, the Woodenville, Costos that we used to have like three big sections of used to have like three big sections of used to have like three big sections of wine and now it's getting smaller.

wine and now it's getting smaller. wine and now it's getting smaller. >> Is it? >> Is it? >> Is it? >> And yeah, and so it's you can tell >> And yeah, and so it's you can tell >> And yeah, and so it's you can tell there's there's there's >> maybe this is an offline, but I should >> maybe this is an offline, but I should >> maybe this is an offline, but I should get your take on some of the uh Kirkland get your take on some of the uh Kirkland get your take on some of the uh Kirkland Malbeck and some of the other uh Costco Malbeck and some of the other uh Costco Malbeck and some of the other uh Costco wines there cuz I wines there cuz I wines there cuz I >> are pretty good. >> are pretty good. >> are pretty good. >> The champagne >> The champagne >> The champagne the Kirkland champagne. Yeah, the Kirkland champagne. Yeah, the Kirkland champagne. Yeah, >> I pick champagne over a lot of $60 >> I pick champagne over a lot of $60 >> I pick champagne over a lot of $60 bottles of any other champagne you can bottles of any other champagne you can bottles of any other champagne you can find. find. find. >> Exactly. If you want to trigger >> Exactly. If you want to trigger >> Exactly. If you want to trigger Dimmitri, just say Vlo. Dimmitri, just say Vlo. Dimmitri, just say Vlo. >> Oh yeah. [laughter] >> Oh yeah. [laughter] >> Oh yeah. [laughter] >> My least favorite. >> My least favorite. >> My least favorite. Yeah. You guys Americans have an Yeah. You guys Americans have an Yeah. You guys Americans have an obsession for Ver Kiko. It's not a bad obsession for Ver Kiko. It's not a bad obsession for Ver Kiko. It's not a bad champagne, but it's very average. champagne, but it's very average. champagne, but it's very average. >> It's It's because it's been marketed to >> It's It's because it's been marketed to >> It's It's because it's been marketed to them and they think there's something them and they think there's something them and they think there's something special about it and there's not. Having special about it and there's not. Having special about it and there's not. Having said that, the high-end edition Gondam said that, the high-end edition Gondam said that, the high-end edition Gondam is really great. is really great. is really great. >> Okay. >> Okay. >> Okay. >> That's twice the price or time, >> That's twice the price or time, >> That's twice the price or time, >> right? You'd rather just stick with >> right? You'd rather just stick with >> right? You'd rather just stick with Tader or something else, right? Tader or something else, right? Tader or something else, right? >> Yeah. In the US. >> Yeah. In the US. >> Yeah. In the US. >> Yeah. >> Yeah. >> Yeah. >> Kirkland. Kirkland Champagne. All right.

>> Kirkland. Kirkland Champagne. All right. >> Kirkland. Kirkland Champagne. All right. You heard it here. You heard it here. You heard it here. >> Really? >> Really? >> Really? >> Connection holidays. >> Connection holidays. >> Connection holidays. Some of the and I think I've said the Some of the and I think I've said the Some of the and I think I've said the story I may not say the story on the story I may not say the story on the story I may not say the story on the show but some of the Napa Valley ones show but some of the Napa Valley ones show but some of the Napa Valley ones they actually when I moved to the US they actually when I moved to the US they actually when I moved to the US 2007 they were not recognized the mum 2007 they were not recognized the mum 2007 they were not recognized the mum napa the titan the orderers they were napa the titan the orderers they were napa the titan the orderers they were like 20 bucks nobody would buy them like 20 bucks nobody would buy them like 20 bucks nobody would buy them because everybody was buying both you because everybody was buying both you because everybody was buying both you know now they are higher in price but know now they are higher in price but know now they are higher in price but they are very good in quality I mean I they are very good in quality I mean I they are very good in quality I mean I like the the titan the napa tang like the the titan the napa tang like the the titan the napa tang >> I am totally I'm totally going to Costco >> I am totally I'm totally going to Costco >> I am totally I'm totally going to Costco today and I'm going to get some Kirkland today and I'm going to get some Kirkland today and I'm going to get some Kirkland champagne I'm excited about this good to champagne I'm excited about this good to champagne I'm excited about this good to try out over the weekend. Yes, try out over the weekend. Yes, try out over the weekend. Yes, >> it's it's not it's not equivalent to a >> it's it's not it's not equivalent to a >> it's it's not it's not equivalent to a to a crystal orderer or to a to a to a crystal orderer or to a to a to a crystal orderer or to a to a high-end, but I mean at $29. high-end, but I mean at $29. high-end, but I mean at $29. >> Yeah, >> Yeah, >> Yeah, >> it's really a great deal. I think my >> it's really a great deal. I think my >> it's really a great deal. I think my coolest, most bizarre Champagne coolest, most bizarre Champagne coolest, most bizarre Champagne experience when my wife and I were experience when my wife and I were experience when my wife and I were driving through Champagne and we pulled driving through Champagne and we pulled driving through Champagne and we pulled over to get gas and it was literally over to get gas and it was literally over to get gas and it was literally like a truck stop in France and you go like a truck stop in France and you go like a truck stop in France and you go into and I know what I expected a truck into and I know what I expected a truck into and I know what I expected a truck stop interior to look like and there's stop interior to look like and there's stop interior to look like and there's just this whole wall of the most just this whole wall of the most just this whole wall of the most high-end champagne on the planet earth high-end champagne on the planet earth high-end champagne on the planet earth in this truck stop and like near Epne or in this truck stop and like near Epne or in this truck stop and like near Epne or whatever and I was just like wow we're whatever and I was just like wow we're whatever and I was just like wow we're in a different world this is amazing. It in a different world this is amazing. It in a different world this is amazing. It is it is actually fairly common on is it is actually fairly common on is it is actually fairly common on highways when you stop for gas, they highways when you stop for gas, they highways when you stop for gas, they always usually have a regional shop always usually have a regional shop always usually have a regional shop >> where you will find local produce >> where you will find local produce >> where you will find local produce >> and and because it's not touristic, >> and and because it's not touristic, >> and and because it's not touristic, usually the deals are good because the usually the deals are good because the usually the deals are good because the normal people that stop there and they normal people that stop there and they normal people that stop there and they buy stuff. Now, some of them are a

buy stuff. Now, some of them are a buy stuff. Now, some of them are a little bit fancy. If you if you go to little bit fancy. If you if you go to little bit fancy. If you if you go to near chart or near near touristic near chart or near near touristic near chart or near near touristic places, it's crap. But in the middle of places, it's crap. But in the middle of places, it's crap. But in the middle of nowhere, you always have a small little nowhere, you always have a small little nowhere, you always have a small little shop and there will be some good shop and there will be some good shop and there will be some good products over there, right? products over there, right? products over there, right? >> Absolutely. It's all good. Yeehaw! >> Absolutely. It's all good. Yeehaw! >> Absolutely. It's all good. Yeehaw! Woohoo! [clears throat] Woohoo! [clears throat] Woohoo! [clears throat] >> Yep. >> Yep. >> Yep. >> Wow. It feels like we've run out of >> Wow. It feels like we've run out of >> Wow. It feels like we've run out of things to say. [laughter] It's like a things to say. [laughter] It's like a things to say. [laughter] It's like a bad relationship with your AI girlfriend bad relationship with your AI girlfriend bad relationship with your AI girlfriend when she stopped talking, you know? when she stopped talking, you know? when she stopped talking, you know? >> So, well, you go to you go to Costco and >> So, well, you go to you go to Costco and >> So, well, you go to you go to Costco and you buy some coke champagne that you you buy some coke champagne that you you buy some coke champagne that you drink because Oh, no. We need to create drink because Oh, no. We need to create drink because Oh, no. We need to create that. We need to create the AI that. We need to create the AI that. We need to create the AI girlfriend that can actually drink girlfriend that can actually drink girlfriend that can actually drink champagne, champagne, champagne, >> huh? >> huh? >> huh? >> Like an IoT. That's an IoT device. You >> Like an IoT. That's an IoT device. You >> Like an IoT. That's an IoT device. You an IoT device. So, so when you're an IoT device. So, so when you're an IoT device. So, so when you're talking to your AI girlfriend, you have talking to your AI girlfriend, you have talking to your AI girlfriend, you have actually IoT things. actually IoT things. actually IoT things. >> That's right. >> That's right. >> That's right. Physical AI. Physical AI. Physical AI. >> That's what Pete's working on, >> That's what Pete's working on, >> That's what Pete's working on, >> you know, physical AI. >> Not Olivia Newton John physical. >> Not Olivia Newton John physical. >> There you go. >> There you go. >> There you go. >> Um, [laughter and clears throat] by the >> Um, [laughter and clears throat] by the >> Um, [laughter and clears throat] by the way, by the way, way, by the way, way, by the way, >> the music >> the music >> the music >> by the way, congratulations because uh >> by the way, congratulations because uh >> by the way, congratulations because uh Edge AI Foundation is like and Edge AI Foundation is like and Edge AI Foundation is like and everyone's talking about it. So, everyone's talking about it. So, everyone's talking about it. So, >> oh, what happened? All right, you guys.

>> oh, what happened? All right, you guys. >> oh, what happened? All right, you guys. No, no, you're doing a wonderful job, No, no, you're doing a wonderful job, No, no, you're doing a wonderful job, man. You and the team. It's It's really man. You and the team. It's It's really man. You and the team. It's It's really awesome. Everyone now wants to attach awesome. Everyone now wants to attach awesome. Everyone now wants to attach themselves to Edge AI Foundation. So, themselves to Edge AI Foundation. So, themselves to Edge AI Foundation. So, congratulations, dude. I mean, congratulations, dude. I mean, congratulations, dude. I mean, seriously, it's [laughter] seriously, it's [laughter] seriously, it's [laughter] pretty extraordinary what you've done. pretty extraordinary what you've done. pretty extraordinary what you've done. >> Yeah. >> Yeah. >> Yeah. >> Yeah, it is. >> Yeah, it is. >> Yeah, it is. >> I remember of you for being the CEO of a >> I remember of you for being the CEO of a >> I remember of you for being the CEO of a company of like what, two people? company of like what, two people? company of like what, two people? [laughter] Well, according to some of those new AI Well, according to some of those new AI predictors, this is the future with AI. predictors, this is the future with AI. predictors, this is the future with AI. Now, you can have companies of, you Now, you can have companies of, you Now, you can have companies of, you know, one person. know, one person. know, one person. >> By the way, >> By the way, >> By the way, >> one person unicorn, >> one person unicorn, >> one person unicorn, >> one person unicorn, billion dollar. >> one person unicorn, billion dollar. >> one person unicorn, billion dollar. >> I have this secret sarcastic thinking >> I have this secret sarcastic thinking >> I have this secret sarcastic thinking that the easiest job to replace with AI that the easiest job to replace with AI that the easiest job to replace with AI is the CEO job. is the CEO job. is the CEO job. >> You're probably right. Actually, >> You're probably right. Actually, >> You're probably right. Actually, >> this is the thing. >> this is the thing. >> this is the thing. >> Yeah, >> Yeah, >> Yeah, >> I think so. Seriously. Seriously. >> I think so. Seriously. Seriously. >> I think so. Seriously. Seriously. problem is it's also a democratizing problem is it's also a democratizing problem is it's also a democratizing technology just like um Bitcoin and so technology just like um Bitcoin and so technology just like um Bitcoin and so it it should allow uh a company of one it it should allow uh a company of one it it should allow uh a company of one anyone to become a scaled out company of anyone to become a scaled out company of anyone to become a scaled out company of one but at some point everyone's just one but at some point everyone's just one but at some point everyone's just going to be noise and there's going to going to be noise and there's going to going to be noise and there's going to be a few be a few be a few >> companies that deliver old school stuff >> companies that deliver old school stuff >> companies that deliver old school stuff like quality you know um value that like quality you know um value that like quality you know um value that become the signal and everyone else is become the signal and everyone else is become the signal and everyone else is just creat creating noise, right? Junk just creat creating noise, right? Junk just creat creating noise, right? Junk products, junk services, like junk products, junk services, like junk products, junk services, like junk content, right? Look at think about all

content, right? Look at think about all content, right? Look at think about all the content that gets produced that the content that gets produced that the content that gets produced that nobody watches or consumes that doesn't nobody watches or consumes that doesn't nobody watches or consumes that doesn't uh deliver any kind of end market value. uh deliver any kind of end market value. uh deliver any kind of end market value. In fact, it's fabricated and In fact, it's fabricated and In fact, it's fabricated and manufactured so that some companies can manufactured so that some companies can manufactured so that some companies can make ad revenue, more ad revenue. make ad revenue, more ad revenue. make ad revenue, more ad revenue. >> Right. Right. >> Right. Right. >> Right. Right. >> Yeah. It creates the illusion of >> Yeah. It creates the illusion of >> Yeah. It creates the illusion of advertisement. advertisement. advertisement. >> Right. So, you know, it's just going to >> Right. So, you know, it's just going to >> Right. So, you know, it's just going to be a whole bunch of crap. Scaled out be a whole bunch of crap. Scaled out be a whole bunch of crap. Scaled out crap. Um, that's going to create slop. crap. Um, that's going to create slop. crap. Um, that's going to create slop. >> Is that the proper word now? Slop. >> Is that the proper word now? Slop. >> Is that the proper word now? Slop. >> AI slop. >> AI slop. >> AI slop. >> Not AI It's or is that like >> Not AI It's or is that like >> Not AI It's or is that like >> you say slop is a little better. >> you say slop is a little better. >> you say slop is a little better. >> Well, yeah. It's not as offensive. It's >> Well, yeah. It's not as offensive. It's >> Well, yeah. It's not as offensive. It's like saying the network is the computer like saying the network is the computer like saying the network is the computer or or the journey is the destination or or or the journey is the destination or or or the journey is the destination or whatever. whatever. whatever. >> What? >> What? >> What? >> Yeah. >> Yeah. >> Yeah. >> Well, it's probably big enough that it >> Well, it's probably big enough that it >> Well, it's probably big enough that it needs needs needs It's all slop. It's all slop. It's all slop. >> It needs its own word. We should create >> It needs its own word. We should create >> It needs its own word. We should create a new word for a new word for a new word for >> Well, we had email spam and now we have >> Well, we had email spam and now we have >> Well, we had email spam and now we have AI slump. So then AI slump. So then AI slump. So then >> AI slop like email is the metaphor. >> AI slop like email is the metaphor. >> AI slop like email is the metaphor. >> Yeah, you're right.

>> Yeah, you're right. >> Yeah, you're right. >> Yeah. I mean, I think that's becoming >> Yeah. I mean, I think that's becoming >> Yeah. I mean, I think that's becoming the standard for it, right? But the standard for it, right? But the standard for it, right? But >> you know, >> you know, >> you know, >> but just like companies came out with >> but just like companies came out with >> but just like companies came out with spam filters, maybe we should come up spam filters, maybe we should come up spam filters, maybe we should come up with our own filter. Yeah, with our own filter. Yeah, with our own filter. Yeah, [clears throat] [clears throat] [clears throat] >> startup filter. >> startup filter. >> startup filter. >> Lop AI. >> Lop AI. >> Lop AI. >> That's that's already happening. So, >> That's that's already happening. So, >> That's that's already happening. So, think about all, you know, the think about all, you know, the think about all, you know, the >> Yeah. Multiply the self-inflicted energy >> Yeah. Multiply the self-inflicted energy >> Yeah. Multiply the self-inflicted energy crisis with another one to filter out crisis with another one to filter out crisis with another one to filter out all the slop so that people actually get all the slop so that people actually get all the slop so that people actually get valuable content instead of all the valuable content instead of all the valuable content instead of all the slop, right? It's >> domain is for sale. >> domain is for sale. >> Yeah. But it's already happening, you >> Yeah. But it's already happening, you >> Yeah. But it's already happening, you know. Tell it billion dollars. Million know. Tell it billion dollars. Million know. Tell it billion dollars. Million dollars. dollars. dollars. >> Yeah. Send some send some Bitcoin. Use >> Yeah. Send some send some Bitcoin. Use >> Yeah. Send some send some Bitcoin. Use this to buy it with a stable coin. this to buy it with a stable coin. this to buy it with a stable coin. You'll be fine. You'll be fine. You'll be fine. >> Noisy lives. >> Noisy lives. >> Noisy lives. >> Yes. >> Yes. >> Yes. >> All right. All right. >> All right. All right. >> All right. All right. >> Rob, you want to take us out? >> Rob, you want to take us out? >> Rob, you want to take us out? >> It's been so lovely spending time with >> It's been so lovely spending time with >> It's been so lovely spending time with all you gentlemen today. Um, we've all you gentlemen today. Um, we've all you gentlemen today. Um, we've learned so much. We've really raised and learned so much. We've really raised and learned so much. We've really raised and lowered the bar around AI. Uh, dragged lowered the bar around AI. Uh, dragged lowered the bar around AI. Uh, dragged it through the mud and it's kind of it through the mud and it's kind of it through the mud and it's kind of sloppy now. Um, but it's all good stuff, sloppy now. Um, but it's all good stuff, sloppy now. Um, but it's all good stuff, you know, and this show wouldn't be you know, and this show wouldn't be you know, and this show wouldn't be nearly as interesting without Dimmitri nearly as interesting without Dimmitri nearly as interesting without Dimmitri just [clears throat] just [clears throat] just [clears throat] stirring the pot and just railing stirring the pot and just railing stirring the pot and just railing against the machine. And that machine against the machine. And that machine against the machine. And that machine would be Leonard Lee who's railing would be Leonard Lee who's railing would be Leonard Lee who's railing against that's usually his combatant against that's usually his combatant against that's usually his combatant partner, you know. But luckily Pete partner, you know. But luckily Pete partner, you know. But luckily Pete showed up to calm chill everybody out showed up to calm chill everybody out showed up to calm chill everybody out cuz obviously Pete's just cuz obviously Pete's just cuz obviously Pete's just >> knocking it out of the park with with

>> knocking it out of the park with with >> knocking it out of the park with with Edji and so it's all good. But it's good Edji and so it's all good. But it's good Edji and so it's all good. But it's good spending time with you. I'm sorry that spending time with you. I'm sorry that spending time with you. I'm sorry that Bitcoin is collapsing and the stock Bitcoin is collapsing and the stock Bitcoin is collapsing and the stock market maybe people are buying into oh market maybe people are buying into oh market maybe people are buying into oh maybe it's a bubble thing. I don't know. maybe it's a bubble thing. I don't know. maybe it's a bubble thing. I don't know. Jensen's like it's not a bubble. Jensen's like it's not a bubble. Jensen's like it's not a bubble. [laughter] [laughter] [laughter] >> Go buy some champagne. >> Go buy some champagne. >> Go buy some champagne. >> Go buy some champagne. Yeah, absolutely. >> Go buy some champagne. Yeah, absolutely. >> Go buy some champagne. Yeah, absolutely. And that's what we recommend to all of And that's what we recommend to all of And that's what we recommend to all of you for the weekend. Go to Costco, try you for the weekend. Go to Costco, try you for the weekend. Go to Costco, try out Kirkland Champagne, and give us your out Kirkland Champagne, and give us your out Kirkland Champagne, and give us your insights. We'd like some good reviews. insights. We'd like some good reviews. insights. We'd like some good reviews. And on that note, And on that note, And on that note, and we'll see you on the other side. and we'll see you on the other side. and we'll see you on the other side. Have a great weekend, everyone. Bye. Okay, cool. All right. All right. Oh my Okay, cool. All right. All right. Oh my god. [music]