IoT Coffee Talk: Episode 275 - Castle Made on Sand (Dotcom versus GenAI Bubbles)

the night away. the night away. >> Yes. Yes. You know, I was imagining >> Yes. Yes. You know, I was imagining >> Yes. Yes. You know, I was imagining Diamond Dave doing the midair splits and Diamond Dave doing the midair splits and Diamond Dave doing the midair splits and stuff on stage, you know, stuff on stage, you know, stuff on stage, you know, >> back in the day, back in the 80s. You >> back in the day, back in the 80s. You >> back in the day, back in the 80s. You know, that was their first the highest know, that was their first the highest know, that was their first the highest chart topping song early. chart topping song early. chart topping song early. >> Really? >> Really? >> Really? >> Yeah. Eruption never made the charts. >> Yeah. Eruption never made the charts. >> Yeah. Eruption never made the charts. Just freaked out and Just freaked out and Just freaked out and heard and bedazzled heard and bedazzled heard and bedazzled millions of guitar players around the millions of guitar players around the millions of guitar players around the world and then they come up with Dancing world and then they come up with Dancing world and then they come up with Dancing Night Away and it hit number two. if I'm Night Away and it hit number two. if I'm Night Away and it hit number two. if I'm not. not. not. >> Sometimes you get surprised by what the >> Sometimes you get surprised by what the >> Sometimes you get surprised by what the top, you know, is that kind of like when top, you know, is that kind of like when top, you know, is that kind of like when you uh kill yourself writing the you uh kill yourself writing the you uh kill yourself writing the ultimate clever uh blog post somewhere ultimate clever uh blog post somewhere ultimate clever uh blog post somewhere and you're like, "This is the most and you're like, "This is the most and you're like, "This is the most important thing I've ever written." And important thing I've ever written." And important thing I've ever written." And like five people like it, like five people like it, like five people like it, but but then you do like one sentence of but but then you do like one sentence of but but then you do like one sentence of some silly stupid stuff and like, you some silly stupid stuff and like, you some silly stupid stuff and like, you know, 400 people are like, "That's know, 400 people are like, "That's know, 400 people are like, "That's awesome." awesome." awesome." >> Yeah.

>> Yeah. >> Yeah. >> Yeah. Exactly. You have to give the >> Yeah. Exactly. You have to give the >> Yeah. Exactly. You have to give the people what they are asking for. people what they are asking for. people what they are asking for. >> Yeah. Which Tik >> Yeah. Which Tik >> Yeah. Which Tik >> Tok turns out they want stupid [ __ ] you >> Tok turns out they want stupid [ __ ] you >> Tok turns out they want stupid [ __ ] you know? know? know? >> They want They want entertainment. >> They want They want entertainment. >> They want They want entertainment. >> They do. >> They do. >> They do. >> That's what they want. >> That's what they want. >> That's what they want. >> Yeah. >> Yeah. >> Yeah. >> I mean, >> I mean, >> I mean, >> Rob, you know that lemming post that I >> Rob, you know that lemming post that I >> Rob, you know that lemming post that I put out? put out? put out? That is the most viral thing that I've That is the most viral thing that I've That is the most viral thing that I've been able to post lately under the new been able to post lately under the new been able to post lately under the new LinkedIn algorithm. LinkedIn algorithm. LinkedIn algorithm. >> Well done. >> Well done. >> Well done. >> It's just pathetic. >> It's just pathetic. >> It's just pathetic. >> Yeah. Yeah. I mean, you know, um people, >> Yeah. Yeah. I mean, you know, um people, >> Yeah. Yeah. I mean, you know, um people, you do it to yourselves. You want crap, you do it to yourselves. You want crap, you do it to yourselves. You want crap, you will get crap. you will get crap. you will get crap. >> Yeah. The the problem is when it's when >> Yeah. The the problem is when it's when >> Yeah. The the problem is when it's when it's on social media and Yeah. I dare to it's on social media and Yeah. I dare to it's on social media and Yeah. I dare to call LinkedIn social media as well. Now, call LinkedIn social media as well. Now, call LinkedIn social media as well. Now, um it's, you know, it's one thing, but um it's, you know, it's one thing, but um it's, you know, it's one thing, but when it's um engaging with a customer when it's um engaging with a customer when it's um engaging with a customer and they're asking for crap and you're and they're asking for crap and you're and they're asking for crap and you're just like, "No, this is not what you just like, "No, this is not what you just like, "No, this is not what you need. What you need is that other thing, need. What you need is that other thing, need. What you need is that other thing, you know. I know because I've been in you know. I know because I've been in you know. I know because I've been in the industry or I'm an adviser in that the industry or I'm an adviser in that the industry or I'm an adviser in that domain and the customer's like, "No, no, domain and the customer's like, "No, no, domain and the customer's like, "No, no, no. This is not what I want. This is no. This is not what I want. This is no. This is not what I want. This is what I want." It's like, what I want." It's like, what I want." It's like, >> well, goodbye. I I'm I would be the one >> well, goodbye. I I'm I would be the one >> well, goodbye. I I'm I would be the one saying, "Well, I'm not working with you saying, "Well, I'm not working with you saying, "Well, I'm not working with you then cuz this is not the right thing to then cuz this is not the right thing to then cuz this is not the right thing to do." But how many would actually just go do." But how many would actually just go do." But how many would actually just go with it? And and isn't that why so many with it? And and isn't that why so many with it? And and isn't that why so many IT projects are tanking because like IT projects are tanking because like IT projects are tanking because like people are trying to do what customers people are trying to do what customers people are trying to do what customers want versus what is right for them?

want versus what is right for them? want versus what is right for them? As Steve Jobs always told us, the As Steve Jobs always told us, the As Steve Jobs always told us, the customer doesn't know what they want customer doesn't know what they want customer doesn't know what they want until I show it to them. until I show it to them. until I show it to them. >> Well, yeah, we'll definitely have to >> Well, yeah, we'll definitely have to >> Well, yeah, we'll definitely have to talk about that that MIT report because talk about that that MIT report because talk about that that MIT report because that's like the biggest thing that came that's like the biggest thing that came that's like the biggest thing that came out and uh out and uh out and uh >> and it had a big impact on the stock >> and it had a big impact on the stock >> and it had a big impact on the stock market and all the tech companies this market and all the tech companies this market and all the tech companies this last week. last week. last week. >> Well, you know, it I think uh it's just >> Well, you know, it I think uh it's just >> Well, you know, it I think uh it's just indicative of a broader broader reality indicative of a broader broader reality indicative of a broader broader reality that's been picked up by stats and what that's been picked up by stats and what that's been picked up by stats and what I've observed in my own research. I I've observed in my own research. I I've observed in my own research. I mean, you know, I've been warning about mean, you know, I've been warning about mean, you know, I've been warning about this stuff forever, you know, from the this stuff forever, you know, from the this stuff forever, you know, from the very beginning. So, it's not it's not very beginning. So, it's not it's not very beginning. So, it's not it's not like a surprise. There's somebody one of like a surprise. There's somebody one of like a surprise. There's somebody one of these uh AI um uh influencers or these uh AI um uh influencers or these uh AI um uh influencers or whatever thought leaders who was uh whatever thought leaders who was uh whatever thought leaders who was uh presenting the news like it was a presenting the news like it was a presenting the news like it was a revelation. I told him this isn't a revelation. I told him this isn't a revelation. I told him this isn't a revelation. It's an inevitability. revelation. It's an inevitability. revelation. It's an inevitability. And if you knew, and I basically said, And if you knew, and I basically said, And if you knew, and I basically said, if you knew AI, you've been tracking the if you knew AI, you've been tracking the if you knew AI, you've been tracking the technology, you knew that this is where technology, you knew that this is where technology, you knew that this is where it's going to land. it's going to land. it's going to land. >> It's only surprising to you if you don't >> It's only surprising to you if you don't >> It's only surprising to you if you don't know what you're talking about and you know what you're talking about and you know what you're talking about and you don't know the technology despite all of don't know the technology despite all of don't know the technology despite all of the AI book. See, I have Jimmyi Hendris the AI book. See, I have Jimmyi Hendris the AI book. See, I have Jimmyi Hendris up there.

up there. up there. >> Yeah, >> Yeah, >> Yeah, >> some of these guys have like the latest >> some of these guys have like the latest >> some of these guys have like the latest and greatest AI textbooks behind them. and greatest AI textbooks behind them. and greatest AI textbooks behind them. But hey, you know, um, But hey, you know, um, But hey, you know, um, >> you got Jimmyi Hendricks. If you think >> you got Jimmyi Hendricks. If you think >> you got Jimmyi Hendricks. If you think it's a re it's a re it's a re see you got you got to take see you got you got to take see you got you got to take >> Yeah. It's called It's called new to >> Yeah. It's called It's called new to >> Yeah. It's called It's called new to you. you. you. >> It's new to you. >> It's new to you. >> It's new to you. >> That's right. It's new to you. >> That's right. It's new to you. >> That's right. It's new to you. >> That's right. >> That's right. >> That's right. >> That used car that used car you just >> That used car that used car you just >> That used car that used car you just bought is new to you. bought is new to you. bought is new to you. >> That's right. >> That's right. >> That's right. >> But hey everybody, welcome to IoT Coffee >> But hey everybody, welcome to IoT Coffee >> But hey everybody, welcome to IoT Coffee Talk. And that was Dance the Night Away Talk. And that was Dance the Night Away Talk. And that was Dance the Night Away by Van Halen. If you don't know that by Van Halen. If you don't know that by Van Halen. If you don't know that tune, it will become your favorite tune, it will become your favorite tune, it will become your favorite because it's a beautiful, beautiful tune because it's a beautiful, beautiful tune because it's a beautiful, beautiful tune by by by >> Yeah. >> Yeah. >> Yeah. >> Halen. And if you listen to the guitar >> Halen. And if you listen to the guitar >> Halen. And if you listen to the guitar playing, it is incredible rhythm playing, it is incredible rhythm playing, it is incredible rhythm playing. Uh it's just magical stuff. playing. Uh it's just magical stuff. playing. Uh it's just magical stuff. Even though it's kind of a campy song, Even though it's kind of a campy song, Even though it's kind of a campy song, you know, it's has like Latin feel to you know, it's has like Latin feel to you know, it's has like Latin feel to it, which is really cool because David it, which is really cool because David it, which is really cool because David >> David Lee Roth grew up in a Latin >> David Lee Roth grew up in a Latin >> David Lee Roth grew up in a Latin community. So he was all into chaa and community. So he was all into chaa and community. So he was all into chaa and you know you know you know >> that's what I think about Diamond Dave.

>> that's what I think about Diamond Dave. >> that's what I think about Diamond Dave. Chaa Diamond Dave, Chaa Diamond Dave, Chaa Diamond Dave, >> you know, he did it on stage, you know, >> you know, he did it on stage, you know, >> you know, he did it on stage, you know, in addition to his kung fu and his booty in addition to his kung fu and his booty in addition to his kung fu and his booty shaking, shaking, shaking, >> you know. So >> you know. So >> you know. So >> I heard that you were the one who >> I heard that you were the one who >> I heard that you were the one who noticed the booty shaking more than noticed the booty shaking more than noticed the booty shaking more than anyone. anyone. anyone. >> Yeah, >> Yeah, >> Yeah, >> Leonard >> Leonard >> Leonard to us. Yeah, going back to my previous to us. Yeah, going back to my previous to us. Yeah, going back to my previous reference about these influencers, you reference about these influencers, you reference about these influencers, you find that this, you know, the 95% find that this, you know, the 95% find that this, you know, the 95% failure rate is a revelation. Okay, failure rate is a revelation. Okay, failure rate is a revelation. Okay, >> it shouldn't be a revelation to you, >> it shouldn't be a revelation to you, >> it shouldn't be a revelation to you, >> right? >> right? >> right? >> This is an inevitability. It's just a >> This is an inevitability. It's just a >> This is an inevitability. It's just a fact. I mean, you know, half the time fact. I mean, you know, half the time fact. I mean, you know, half the time watch a Van Halen or David Lee Roth watch a Van Halen or David Lee Roth watch a Van Halen or David Lee Roth video, half of the time he's sticking video, half of the time he's sticking video, half of the time he's sticking his butt in your face. I'm I'm now we're all wondering how I'm I'm now we're all wondering how Leonard makes so much money as an Leonard makes so much money as an Leonard makes so much money as an analyst. You know, he goes analyst. You know, he goes analyst. You know, he goes he he he >> he goes to meet with a customer and they >> he goes to meet with a customer and they >> he goes to meet with a customer and they want to talk about AI and he talks about want to talk about AI and he talks about want to talk about AI and he talks about David Lee Roth putting his butt in your David Lee Roth putting his butt in your David Lee Roth putting his butt in your face and they're like, "Here, take my face and they're like, "Here, take my face and they're like, "Here, take my money, please."

money, please." money, please." >> This is this is reassuring that that >> This is this is reassuring that that >> This is this is reassuring that that tells me that there are some companies tells me that there are some companies tells me that there are some companies out there that are willing to hear the out there that are willing to hear the out there that are willing to hear the truth versus just what they want to truth versus just what they want to truth versus just what they want to hear, right? like some people firing hear, right? like some people firing hear, right? like some people firing their uh chief of uh statistics for their uh chief of uh statistics for their uh chief of uh statistics for employment and they're firing them employment and they're firing them employment and they're firing them because they don't like the numbers they because they don't like the numbers they because they don't like the numbers they bring up, you know. bring up, you know. bring up, you know. >> Yeah. That's good. It's good. >> Yeah. That's good. It's good. >> Yeah. That's good. It's good. >> LA I don't want to hear that. That's >> LA I don't want to hear that. That's >> LA I don't want to hear that. That's bad. You're out of here. Bye. bad. You're out of here. Bye. bad. You're out of here. Bye. >> I I'm positioning it now as d-risking. >> I I'm positioning it now as d-risking. >> I I'm positioning it now as d-risking. And I think, you know, well, I'll share And I think, you know, well, I'll share And I think, you know, well, I'll share that really quickly, but we have to do that really quickly, but we have to do that really quickly, but we have to do our disclaimer. Hey everyone, take us our disclaimer. Hey everyone, take us our disclaimer. Hey everyone, take us seriously at your own peril. We're doing seriously at your own peril. We're doing seriously at your own peril. We're doing this for fun. We're just having fun. this for fun. We're just having fun. this for fun. We're just having fun. Those of you who've been following us Those of you who've been following us Those of you who've been following us and watching the show, you guys know and watching the show, you guys know and watching the show, you guys know that that's true. Um, don't buy any that that's true. Um, don't buy any that that's true. Um, don't buy any stocks that Rob talks about because stocks that Rob talks about because stocks that Rob talks about because you'll just make a ton of money, okay? you'll just make a ton of money, okay? you'll just make a ton of money, okay? Or not. Or not. Or not. But anyways, But anyways, But anyways, um yeah, you know, uh d-risking um um yeah, you know, uh d-risking um um yeah, you know, uh d-risking um especially right now with papers like especially right now with papers like especially right now with papers like that coming out that are just um ex that coming out that are just um ex that coming out that are just um ex highlighting what has been a trend from highlighting what has been a trend from highlighting what has been a trend from the beginning. I don't think we've seen the beginning. I don't think we've seen the beginning. I don't think we've seen any um reports where we that uh claim any um reports where we that uh claim any um reports where we that uh claim that enterprise AR industrial let me that enterprise AR industrial let me that enterprise AR industrial let me qualify that better industrial and qualify that better industrial and qualify that better industrial and enterprise gen AI has taken off like a enterprise gen AI has taken off like a enterprise gen AI has taken off like a rocket the other thing is um you know we rocket the other thing is um you know we rocket the other thing is um you know we haven't seen that and the other thing is haven't seen that and the other thing is haven't seen that and the other thing is the reality the technical reality is

the reality the technical reality is the reality the technical reality is that things like agentic AI which is that things like agentic AI which is that things like agentic AI which is probably the next shoe to prop is super probably the next shoe to prop is super probably the next shoe to prop is super nent. It's still experimental and the nent. It's still experimental and the nent. It's still experimental and the frameworks are really not there. So, a frameworks are really not there. So, a frameworks are really not there. So, a lot of these companies that are claiming lot of these companies that are claiming lot of these companies that are claiming they they've solved the the uh you know they they've solved the the uh you know they they've solved the the uh you know the issues with Agentic AI. Um, I will the issues with Agentic AI. Um, I will the issues with Agentic AI. Um, I will have to quote the CISO of the Ford Motor have to quote the CISO of the Ford Motor have to quote the CISO of the Ford Motor Company who will tell you anyone who Company who will tell you anyone who Company who will tell you anyone who says that is lying. says that is lying. says that is lying. Not oh, you know, they're they um, you Not oh, you know, they're they um, you Not oh, you know, they're they um, you know, might not have their might not know, might not have their might not know, might not have their might not have done their homework. They're just have done their homework. They're just have done their homework. They're just not telling the truth because there's no not telling the truth because there's no not telling the truth because there's no evidence that Agentic frameworks are are evidence that Agentic frameworks are are evidence that Agentic frameworks are are at a level where they're enterprise or at a level where they're enterprise or at a level where they're enterprise or industrial grade. That's why we're not industrial grade. That's why we're not industrial grade. That's why we're not seeing them, seeing them, seeing them, >> right? >> right? >> right? >> Void, right? >> Void, right? >> Void, right? >> Yeah. >> Yeah. >> Yeah. >> So, >> So, >> So, >> we hear a lot of talk.

>> we hear a lot of talk. >> we hear a lot of talk. >> A lot of talk. >> A lot of talk. >> A lot of talk. >> Why are people playing around with it? I >> Why are people playing around with it? I >> Why are people playing around with it? I mean, to me, it's like, you know, it's mean, to me, it's like, you know, it's mean, to me, it's like, you know, it's interesting. it could be helpful, but interesting. it could be helpful, but interesting. it could be helpful, but you know, in a situation where you need you know, in a situation where you need you know, in a situation where you need something to be accurate and the same something to be accurate and the same something to be accurate and the same every time, it's not going to do that. every time, it's not going to do that. every time, it's not going to do that. So, you definitely can't build stuff. I So, you definitely can't build stuff. I So, you definitely can't build stuff. I mean, that's why it kind of cracked me mean, that's why it kind of cracked me mean, that's why it kind of cracked me up when um uh Open AI had recently up when um uh Open AI had recently up when um uh Open AI had recently upgraded to their five model and people upgraded to their five model and people upgraded to their five model and people went mockers cuz they had built all went mockers cuz they had built all went mockers cuz they had built all this, this, this, >> you know, stuff on those other models >> you know, stuff on those other models >> you know, stuff on those other models and stuff like that. And I'm like, why and stuff like that. And I'm like, why and stuff like that. And I'm like, why are you doing that? Right. So, it can are you doing that? Right. So, it can are you doing that? Right. So, it can have a use, but the way that people are have a use, but the way that people are have a use, but the way that people are trying to use it is just not proper. trying to use it is just not proper. trying to use it is just not proper. Right. Right. Right. >> Can't build your house on sand. >> Can't build your house on sand. >> Can't build your house on sand. >> What does everybody think about? >> What does everybody think about? >> What does everybody think about? >> I think a book there's there's a popular >> I think a book there's there's a popular >> I think a book there's there's a popular book I think that says that. book I think that says that. book I think that says that. >> Yeah. You know, you're absolutely right. >> Yeah. You know, you're absolutely right. >> Yeah. You know, you're absolutely right. And what the problem is is everyone has And what the problem is is everyone has And what the problem is is everyone has built um their homes on sand.

built um their homes on sand. built um their homes on sand. That is the pro the fundamental problem, That is the pro the fundamental problem, That is the pro the fundamental problem, Debbie. And that that is, you know, that Debbie. And that that is, you know, that Debbie. And that that is, you know, that >> is this like Sam Kenisonson talking >> is this like Sam Kenisonson talking >> is this like Sam Kenisonson talking about people shouldn't live in the about people shouldn't live in the about people shouldn't live in the desert cuz it's sand desert cuz it's sand desert cuz it's sand >> and a thousand years ago from now it's >> and a thousand years ago from now it's >> and a thousand years ago from now it's still going to be sand. still going to be sand. still going to be sand. >> Be sand. >> Be sand. >> Be sand. >> Yeah. >> Yeah. >> Yeah. Do you remember that from thousand years Do you remember that from thousand years Do you remember that from thousand years ago? ago? ago? >> The best way to help people in Ethiopia >> The best way to help people in Ethiopia >> The best way to help people in Ethiopia is to stop bringing them food and is to stop bringing them food and is to stop bringing them food and helping them. You need to leave here. helping them. You need to leave here. helping them. You need to leave here. You live in a desert. You live in a desert. You live in a desert. We've got deserts in the United States We've got deserts in the United States We've got deserts in the United States and we don't live there. You need to and we don't live there. You need to and we don't live there. You need to leave and go to where the go to where leave and go to where the go to where leave and go to where the go to where the food is, right? the food is, right? the food is, right? >> I love that guy. It's almost like the >> I love that guy. It's almost like the >> I love that guy. It's almost like the little tiny book, Who Moved My Cheese? little tiny book, Who Moved My Cheese? little tiny book, Who Moved My Cheese? Right. You know that when it's like Right. You know that when it's like Right. You know that when it's like sometimes you just need to pick up and sometimes you just need to pick up and sometimes you just need to pick up and go and move, you know? So, I hear Olivia go and move, you know? So, I hear Olivia go and move, you know? So, I hear Olivia is going to pick up and move and go to is going to pick up and move and go to is going to pick up and move and go to Amsterdam shortly.

Amsterdam shortly. Amsterdam shortly. >> Yeah. >> Yeah. >> Yeah. >> To >> To >> To >> Yeah. So, not to leave there. Not Not to >> Yeah. So, not to leave there. Not Not to >> Yeah. So, not to leave there. Not Not to leave there, but um Okay. Okay. leave there, but um Okay. Okay. leave there, but um Okay. Okay. >> There's the the things conference coming >> There's the the things conference coming >> There's the the things conference coming up. up. up. >> Things conference. >> Things conference. >> Things conference. >> It's going to be a great conference. Uh >> It's going to be a great conference. Uh >> It's going to be a great conference. Uh it's it's always been a great it's it's always been a great it's it's always been a great conference. Uh it's growing and what I conference. Uh it's growing and what I conference. Uh it's growing and what I like about it is the fact that it's true like about it is the fact that it's true like about it is the fact that it's true talk. I don't know if it's because it's talk. I don't know if it's because it's talk. I don't know if it's because it's based in Europe where people just like based in Europe where people just like based in Europe where people just like don't [ __ ] too much. Um and and so don't [ __ ] too much. Um and and so don't [ __ ] too much. Um and and so we have to talk and what's super we have to talk and what's super we have to talk and what's super interesting in Vinc interesting in Vinc interesting in Vinc you want to come and host the event this you want to come and host the event this you want to come and host the event this year? We have something special you like year? We have something special you like year? We have something special you like which is it's not going to be just about which is it's not going to be just about which is it's not going to be just about lur one and LP1 this year. It's going to lur one and LP1 this year. It's going to lur one and LP1 this year. It's going to be about IoT. And so they're actually be about IoT. And so they're actually be about IoT. And so they're actually they are ganging up with Bluetooth with they are ganging up with Bluetooth with they are ganging up with Bluetooth with you know 5G guys and and a bunch of you know 5G guys and and a bunch of you know 5G guys and and a bunch of others. And so the the event is really others. And so the the event is really others. And so the the event is really becoming an IoT event rather than just becoming an IoT event rather than just becoming an IoT event rather than just LP1 event. And that's super interesting. LP1 event. And that's super interesting. LP1 event. And that's super interesting. So I'm I'm eager to be there. I think So I'm I'm eager to be there. I think So I'm I'm eager to be there. I think they're expecting 2500 people in person. they're expecting 2500 people in person. they're expecting 2500 people in person. >> Wow. >> Wow. >> Wow. >> So that's like more than ever for that >> So that's like more than ever for that >> So that's like more than ever for that conference.

conference. conference. >> More than ever. >> More than ever. >> More than ever. >> And you look at the list of sponsors. >> And you look at the list of sponsors. >> And you look at the list of sponsors. It's it's great. Qualcomm is going to be It's it's great. Qualcomm is going to be It's it's great. Qualcomm is going to be there. Like we have like all the big there. Like we have like all the big there. Like we have like all the big names that are going to be there. So names that are going to be there. So names that are going to be there. So that's going to be pretty cool. Um, that's going to be pretty cool. Um, that's going to be pretty cool. Um, I think they huge and few other big I think they huge and few other big I think they huge and few other big >> That's so exciting. That's going to be >> That's so exciting. That's going to be >> That's so exciting. That's going to be great fun. great fun. great fun. >> Yeah. And it's great people. It's great >> Yeah. And it's great people. It's great >> Yeah. And it's great people. It's great people, you know. people, you know. people, you know. >> Uh, it's going to be going to be a >> Uh, it's going to be going to be a >> Uh, it's going to be going to be a little bit of beer drinking. little bit of beer drinking. little bit of beer drinking. >> Little bit of beer. Yeah. I'm going to >> Little bit of beer. Yeah. I'm going to >> Little bit of beer. Yeah. I'm going to have the IoT show there live from there. have the IoT show there live from there. have the IoT show there live from there. >> Of course you will. Yeah. >> Of course you will. Yeah. >> Of course you will. Yeah. >> So, you know, I might not have Leonard >> So, you know, I might not have Leonard >> So, you know, I might not have Leonard to to, you know, bomb the show from from to to, you know, bomb the show from from to to, you know, bomb the show from from >> video bomb you like you did in Austin. >> video bomb you like you did in Austin. >> video bomb you like you did in Austin. >> Audiobomb you guys. Oh, that's too Oh, that's too >> Oh my god. No, that's that's exciting. >> Oh my god. No, that's that's exciting. >> Oh my god. No, that's that's exciting. I'm glad that it's doing so well. That's I'm glad that it's doing so well. That's I'm glad that it's doing so well. That's just awesome. I always love the big wall just awesome. I always love the big wall just awesome. I always love the big wall with all the devices. with all the devices. with all the devices. >> Yeah. The wall of fame they call that. >> Yeah. The wall of fame they call that. >> Yeah. The wall of fame they call that. It's funny. It's funny. And It's funny. It's funny. And It's funny. It's funny. And >> it's 10 years ago when they started the >> it's 10 years ago when they started the >> it's 10 years ago when they started the thing, it was like, "Wow, this these are thing, it was like, "Wow, this these are thing, it was like, "Wow, this these are all Dora one devices out there," which all Dora one devices out there," which all Dora one devices out there," which was pretty much it, right? Yeah.

was pretty much it, right? Yeah. was pretty much it, right? Yeah. >> And now it's when you imagine it's just >> And now it's when you imagine it's just >> And now it's when you imagine it's just like a a tiny tiny tiny portion of like a a tiny tiny tiny portion of like a a tiny tiny tiny portion of everything that's out there. It's uh everything that's out there. It's uh everything that's out there. It's uh it's actually even more impressive, I it's actually even more impressive, I it's actually even more impressive, I think, cuz it's like, hey, here's some think, cuz it's like, hey, here's some think, cuz it's like, hey, here's some of the many many devices that are out of the many many devices that are out of the many many devices that are out there. Uh and they're connected and that there. Uh and they're connected and that there. Uh and they're connected and that will connect your freaking Gen AI to the will connect your freaking Gen AI to the will connect your freaking Gen AI to the physical world. physical world. physical world. >> Um that's the way I like to present IoT >> Um that's the way I like to present IoT >> Um that's the way I like to present IoT to people now. It's like, hey, this is to people now. It's like, hey, this is to people now. It's like, hey, this is your interface with the real world your interface with the real world your interface with the real world because people don't understand IoT. because people don't understand IoT. because people don't understand IoT. They don't understand AI. So, They don't understand AI. So, They don't understand AI. So, >> yes. >> yes. >> yes. >> Yes. >> Yes. >> Yes. >> Actually, yeah. Yeah. >> Actually, yeah. Yeah. >> Actually, yeah. Yeah. >> I was just talking the other day. I >> I was just talking the other day. I >> I was just talking the other day. I think I was just on a, you know, talking think I was just on a, you know, talking think I was just on a, you know, talking with my boss Dave and I was like, you with my boss Dave and I was like, you with my boss Dave and I was like, you know, cuz both of us had that IoT know, cuz both of us had that IoT know, cuz both of us had that IoT background and and I go, you know, that background and and I go, you know, that background and and I go, you know, that whole IoT and actuators and stuff. I whole IoT and actuators and stuff. I whole IoT and actuators and stuff. I think all those companies are going to think all those companies are going to think all those companies are going to pivot and rebrand themselves as physical pivot and rebrand themselves as physical pivot and rebrand themselves as physical AI companies. AI companies. AI companies. >> They are already doing it, dude. >> They are already doing it, dude. >> They are already doing it, dude. >> Totally. >> Totally. >> Totally. We're into physical AI. That's what we We're into physical AI. That's what we We're into physical AI. That's what we were always doing. I don't know what were always doing. I don't know what were always doing. I don't know what you're talking about with IoT. It's you're talking about with IoT. It's you're talking about with IoT. It's always been physical AI, always been physical AI, always been physical AI, >> right? Well, I always tell people it's >> right? Well, I always tell people it's >> right? Well, I always tell people it's like they're trying to pour AI goo on like they're trying to pour AI goo on like they're trying to pour AI goo on everything.

everything. everything. >> Yes. >> Yes. >> Yes. >> Everything has AI now. >> Everything has AI now. >> Everything has AI now. >> Yeah, I agree. >> Yeah, I agree. >> Yeah, I agree. >> Slap it on everything. It's like capers. >> Slap it on everything. It's like capers. >> Slap it on everything. It's like capers. Just throw it on everything. Just throw it on everything. Just throw it on everything. >> Put it on there. Yeah. >> Put it on there. Yeah. >> Put it on there. Yeah. >> Yeah. Absolutely. >> Yeah. Absolutely. >> Yeah. Absolutely. >> So, back to your the initial >> So, back to your the initial >> So, back to your the initial conversation about um you know, people conversation about um you know, people conversation about um you know, people building houses on sand and so on. building houses on sand and so on. building houses on sand and so on. There's an interesting trend I'm seeing There's an interesting trend I'm seeing There's an interesting trend I'm seeing um happening. Well, generally the trend um happening. Well, generally the trend um happening. Well, generally the trend of MCP servers is not new, right? But um of MCP servers is not new, right? But um of MCP servers is not new, right? But um I'm starting to see people in the IoT I'm starting to see people in the IoT I'm starting to see people in the IoT world thinking, especially in the edge world thinking, especially in the edge world thinking, especially in the edge AI world, thinking about MCP servers AI world, thinking about MCP servers AI world, thinking about MCP servers down there at the edge for exposing down there at the edge for exposing down there at the edge for exposing this edge entity to uh AI bots and and this edge entity to uh AI bots and and this edge entity to uh AI bots and and others. I think it's a really others. I think it's a really others. I think it's a really interesting idea because GI on its own interesting idea because GI on its own interesting idea because GI on its own will just not make any good. Uh but will just not make any good. Uh but will just not make any good. Uh but having an actual um you know insight having an actual um you know insight having an actual um you know insight extracted from local AI extracted from local AI extracted from local AI >> available to these Gen AI bots will be >> available to these Gen AI bots will be >> available to these Gen AI bots will be very interesting because they will have very interesting because they will have very interesting because they will have the truth rather than try and make it the truth rather than try and make it the truth rather than try and make it up. Um up. Um up. Um >> well I mean AI before people went gaga >> well I mean AI before people went gaga >> well I mean AI before people went gaga with uh this new gen AI stuff that's with uh this new gen AI stuff that's with uh this new gen AI stuff that's what AI was. It was purposebuilt. It was what AI was. It was purposebuilt. It was what AI was. It was purposebuilt. It was narrow. It solved certain problems and narrow. It solved certain problems and narrow. It solved certain problems and it didn't create a lot of risk. So, I it didn't create a lot of risk. So, I it didn't create a lot of risk. So, I think we have things backwards where think we have things backwards where think we have things backwards where we're trying to say, "Hey, buy this we're trying to say, "Hey, buy this we're trying to say, "Hey, buy this thing." But I've I've really personally thing." But I've I've really personally thing." But I've I've really personally never seen a product that's been pushed never seen a product that's been pushed never seen a product that's been pushed out in a way where it's like out in a way where it's like out in a way where it's like >> buy at your own risk, you know, like you >> buy at your own risk, you know, like you >> buy at your own risk, you know, like you don't buy cars at your own risk, right?

don't buy cars at your own risk, right? don't buy cars at your own risk, right? Like if it doesn't work, you you know, Like if it doesn't work, you you know, Like if it doesn't work, you you know, there is some type of redress that you there is some type of redress that you there is some type of redress that you can have. And so, right now it's like, can have. And so, right now it's like, can have. And so, right now it's like, well, we don't know how it's going to well, we don't know how it's going to well, we don't know how it's going to work or, you know, it's going to do work or, you know, it's going to do work or, you know, it's going to do these dangerous things. Oh, you know, do these dangerous things. Oh, you know, do these dangerous things. Oh, you know, do it at your own risk. You know, give us it at your own risk. You know, give us it at your own risk. You know, give us the money. You they they take the money, the money. You they they take the money, the money. You they they take the money, but you take the risk. So, I've not yet but you take the risk. So, I've not yet but you take the risk. So, I've not yet seen seen seen >> that's like that's the new tagline. They >> that's like that's the new tagline. They >> that's like that's the new tagline. They take the money, you take the risk. Wow. take the money, you take the risk. Wow. take the money, you take the risk. Wow. >> Yeah. >> Yeah. >> Yeah. I love it. I love it. I love it. >> I think a lot of the application of >> I think a lot of the application of >> I think a lot of the application of generative AI, especially across like um generative AI, especially across like um generative AI, especially across like um especially the industrial edges, especially the industrial edges, especially the industrial edges, uh you you're you're going to see a uh you you're you're going to see a uh you you're you're going to see a hybrid model. you're probably not going hybrid model. you're probably not going hybrid model. you're probably not going to see generative AI at the core. Uh to see generative AI at the core. Uh to see generative AI at the core. Uh it's not going to be a core function. it's not going to be a core function. it's not going to be a core function. It's going to be ancillary. It might be It's going to be ancillary. It might be It's going to be ancillary. It might be used um like what Walmart did really used um like what Walmart did really used um like what Walmart did really early on. And I I talked about this like early on. And I I talked about this like early on. And I I talked about this like two years ago. One of the things they two years ago. One of the things they two years ago. One of the things they were doing is they were looking at how were doing is they were looking at how were doing is they were looking at how to improve the sentiment um analysis and to improve the sentiment um analysis and to improve the sentiment um analysis and um you know adjust the the weights of um you know adjust the the weights of um you know adjust the the weights of the recommend recommended models using the recommend recommended models using the recommend recommended models using generative AI.

generative AI. generative AI. But then the recommener models are not But then the recommener models are not But then the recommener models are not generative AI models, right? Those are generative AI models, right? Those are generative AI models, right? Those are more of like your classical models, but more of like your classical models, but more of like your classical models, but they used generative AI to enhance and they used generative AI to enhance and they used generative AI to enhance and take data, get some insights, go through take data, get some insights, go through take data, get some insights, go through a whole, you know, validation, data a whole, you know, validation, data a whole, you know, validation, data validation and uh, you know, uh, uh, validation and uh, you know, uh, uh, validation and uh, you know, uh, uh, analytics validation process to then analytics validation process to then analytics validation process to then improve a different model. And then improve a different model. And then improve a different model. And then because the recommener model performed because the recommener model performed because the recommener model performed better in terms of an operational better in terms of an operational better in terms of an operational um you know analytics engine but the um you know analytics engine but the um you know analytics engine but the generative AI had that that benefit of generative AI had that that benefit of generative AI had that that benefit of enhancing the the design of that enhancing the the design of that enhancing the the design of that analytics engine or the configuration of analytics engine or the configuration of analytics engine or the configuration of that analytics engine right and so I that analytics engine right and so I that analytics engine right and so I think we're going to see we are actually think we're going to see we are actually think we're going to see we are actually seeing the same kind of pattern work out seeing the same kind of pattern work out seeing the same kind of pattern work out um although it's still experimental you um although it's still experimental you um although it's still experimental you you know, I was at the the key um the you know, I was at the the key um the you know, I was at the the key um the key bank uh capital markets, you know, key bank uh capital markets, you know, key bank uh capital markets, you know, techn um technology leadership techn um technology leadership techn um technology leadership conference in Deer Valley. I bumped into conference in Deer Valley. I bumped into conference in Deer Valley. I bumped into like a couple a few people actually like a couple a few people actually like a couple a few people actually amongst their uh their u industry amongst their uh their u industry amongst their uh their u industry leaders. These guys have like wicked leaders. These guys have like wicked leaders. These guys have like wicked experience. I mean, it's like holy crap.

experience. I mean, it's like holy crap. experience. I mean, it's like holy crap. you know, you guys actually have worked you know, you guys actually have worked you know, you guys actually have worked with this stuff and and work with with this stuff and and work with with this stuff and and work with clients to figure out how to make clients to figure out how to make clients to figure out how to make generative AI uh meaningful, right? And generative AI uh meaningful, right? And generative AI uh meaningful, right? And um yeah, this is what they're kind of um yeah, this is what they're kind of um yeah, this is what they're kind of settling on in some of these, you know, settling on in some of these, you know, settling on in some of these, you know, um patterns of use that are valuable. um patterns of use that are valuable. um patterns of use that are valuable. But again, you're ekking out like it's But again, you're ekking out like it's But again, you're ekking out like it's the seven sigma problem, right? How the seven sigma problem, right? How the seven sigma problem, right? How value is seven sigma? value is seven sigma? value is seven sigma? Most people are not going to go there. Most people are not going to go there. Most people are not going to go there. And so how many people even know what And so how many people even know what And so how many people even know what you're talking about when you say seven you're talking about when you say seven you're talking about when you say seven sigma sigma sigma >> is going from six sigma to sigma. What >> is going from six sigma to sigma. What >> is going from six sigma to sigma. What is what is the benefit of going to seven is what is the benefit of going to seven is what is the benefit of going to seven sigma and what's the cost? Is it worth sigma and what's the cost? Is it worth sigma and what's the cost? Is it worth it? It's really what that question is it? It's really what that question is it? It's really what that question is right right right >> and um I think for a lot of use cases >> and um I think for a lot of use cases >> and um I think for a lot of use cases it's not going to be it's not going to be it's not going to be >> worth it. >> worth it. >> worth it. >> I know 7-Eleven >> I know 7-Eleven >> I know 7-Eleven >> 71. >> 71. >> 71. >> I think 7-Eleven's better than 7 Sigma. >> I think 7-Eleven's better than 7 Sigma. >> I think 7-Eleven's better than 7 Sigma. Yeah. Yeah. Yeah. Yeah, I think that's I think that's a Yeah, I think that's I think that's a Yeah, I think that's I think that's a fair fair fair >> like fancy Greek letters. I don't know.

>> like fancy Greek letters. I don't know. >> like fancy Greek letters. I don't know. >> Yeah, fancy Greek letters. Yeah, that's >> Yeah, fancy Greek letters. Yeah, that's >> Yeah, fancy Greek letters. Yeah, that's what it is. what it is. what it is. >> Yeah. I guess the the problem that the >> Yeah. I guess the the problem that the >> Yeah. I guess the the problem that the problem you talked about in terms of it problem you talked about in terms of it problem you talked about in terms of it being worth it is that they're throwing being worth it is that they're throwing being worth it is that they're throwing that analysis out the window, right? So, that analysis out the window, right? So, that analysis out the window, right? So, so let's spend a lot of money and a lot so let's spend a lot of money and a lot so let's spend a lot of money and a lot of effort and then hopefully something of effort and then hopefully something of effort and then hopefully something magical like a diamond is going to come magical like a diamond is going to come magical like a diamond is going to come out at the end of this process where out at the end of this process where out at the end of this process where maybe it is not worth it depending on maybe it is not worth it depending on maybe it is not worth it depending on what you're trying to do. So, what you're trying to do. So, what you're trying to do. So, >> right. >> right. >> right. >> This is old school thought, right? Old >> This is old school thought, right? Old >> This is old school thought, right? Old school ROI. ROI. school ROI. ROI. school ROI. ROI. >> ROI. Yeah, >> ROI. Yeah, >> ROI. Yeah, >> absolutely. >> absolutely. >> absolutely. >> It's like gold mining some points you >> It's like gold mining some points you >> It's like gold mining some points you will extract gold. Maybe. will extract gold. Maybe. will extract gold. Maybe. >> Maybe. But will you spend more money >> Maybe. But will you spend more money >> Maybe. But will you spend more money trying to get the gold than the gold's trying to get the gold than the gold's trying to get the gold than the gold's worth or something like that? You never worth or something like that? You never worth or something like that? You never know. know. know. >> Or will you just a lot of fool's gold. I >> Or will you just a lot of fool's gold. I >> Or will you just a lot of fool's gold. I mean, what they they uh they mined and mean, what they they uh they mined and mean, what they they uh they mined and panned more fool's gold than gold, panned more fool's gold than gold, panned more fool's gold than gold, right? I mean, you know, it's like when right? I mean, you know, it's like when right? I mean, you know, it's like when you have some folks going around saying, you have some folks going around saying, you have some folks going around saying, "Hey, you know, the gold rush "Hey, you know, the gold rush "Hey, you know, the gold rush continues." Is that a good thing? And continues." Is that a good thing? And continues." Is that a good thing? And what are you admitting when you are what are you admitting when you are what are you admitting when you are saying that it's a gold rush, right?

saying that it's a gold rush, right? saying that it's a gold rush, right? When you characterize what's going on When you characterize what's going on When you characterize what's going on right now as a gold rush, right now as a gold rush, right now as a gold rush, you know, um you know, um you know, um >> gold rushes are not good things, right? >> gold rushes are not good things, right? >> gold rushes are not good things, right? It's what what do we associate with gold It's what what do we associate with gold It's what what do we associate with gold gold rushes? gold rushes? gold rushes? >> Yeah. Speculation >> Yeah. Speculation >> Yeah. Speculation >> blue jeans. >> blue jeans. >> blue jeans. >> Levi. >> Levi. >> Levi. >> There you go. >> There you go. >> There you go. >> Right. Not gold. >> Right. Not gold. >> Right. Not gold. >> True. >> True. >> True. >> True. And and Seattle's history and >> True. And and Seattle's history and >> True. And and Seattle's history and development um you know piggybacking on development um you know piggybacking on development um you know piggybacking on gold rush um is is interesting. Not gold rush um is is interesting. Not gold rush um is is interesting. Not shiny but very interesting. shiny but very interesting. shiny but very interesting. >> Yeah. People should never have moved >> Yeah. People should never have moved >> Yeah. People should never have moved here as it turns out. don't come to here as it turns out. don't come to here as it turns out. don't come to Seattle. Seattle. Seattle. >> So, are you saying, Leonard, that at the >> So, are you saying, Leonard, that at the >> So, are you saying, Leonard, that at the end of this process, we're gonna maybe end of this process, we're gonna maybe end of this process, we're gonna maybe have some type of pants? have some type of pants? have some type of pants? >> We're gonna have pants at the end. At >> We're gonna have pants at the end. At >> We're gonna have pants at the end. At the end. the end. the end. >> Yeah. Not picks and shovels because, you >> Yeah. Not picks and shovels because, you >> Yeah. Not picks and shovels because, you know, picks and picss and shovels know, picks and picss and shovels know, picks and picss and shovels companies became highly commoditized. Is companies became highly commoditized. Is companies became highly commoditized. Is blue jeans that became like the big blue jeans that became like the big blue jeans that became like the big thing, right? Cargo pants and stuff.

thing, right? Cargo pants and stuff. thing, right? Cargo pants and stuff. >> Oh my gosh. Have you tried to go and buy >> Oh my gosh. Have you tried to go and buy >> Oh my gosh. Have you tried to go and buy a pair of blue jeans these days? They're a pair of blue jeans these days? They're a pair of blue jeans these days? They're ridiculous. Unbelievable. ridiculous. Unbelievable. ridiculous. Unbelievable. >> I hear the hot new things are called >> I hear the hot new things are called >> I hear the hot new things are called tech pants. tech pants. tech pants. >> Tech pants. >> Tech pants. >> Tech pants. >> Tech pants. They're >> Tech pants. They're >> Tech pants. They're >> Oh, yeah. They just basically have more >> Oh, yeah. They just basically have more >> Oh, yeah. They just basically have more pockets, right? pockets, right? pockets, right? >> They have more pockets. That's right. >> They have more pockets. That's right. >> They have more pockets. That's right. Lots of pockets, you know, for your Lots of pockets, you know, for your Lots of pockets, you know, for your window. window. window. >> That's not That's not because they're >> That's not That's not because they're >> That's not That's not because they're made of synthetic, you know, fabric that made of synthetic, you know, fabric that made of synthetic, you know, fabric that stretches and whatever. stretches and whatever. stretches and whatever. >> Lots of things that stretch. >> Lots of things that stretch. >> Lots of things that stretch. >> Recycled iPhone components. >> Recycled iPhone components. >> Recycled iPhone components. >> We need a lot of stretchy stuff. Yeah. >> We need a lot of stretchy stuff. Yeah. >> We need a lot of stretchy stuff. Yeah. Yeah. So uh so as Leonard said at the Yeah. So uh so as Leonard said at the Yeah. So uh so as Leonard said at the when we kicked things off here he was when we kicked things off here he was when we kicked things off here he was talking about um so last weekend over talking about um so last weekend over talking about um so last weekend over the weekend a lot of people saw the news the weekend a lot of people saw the news the weekend a lot of people saw the news from the MIT Nanda organization and so from the MIT Nanda organization and so from the MIT Nanda organization and so they had done all this analysis of all they had done all this analysis of all they had done all this analysis of all these generative AI pro projects and these generative AI pro projects and these generative AI pro projects and stuff like that and yes they came they stuff like that and yes they came they stuff like that and yes they came they determined that 95% of them failed and determined that 95% of them failed and determined that 95% of them failed and um so only 5% yielded any exactly Dr.

um so only 5% yielded any exactly Dr. um so only 5% yielded any exactly Dr. evil um you know like only 5% had any evil um you know like only 5% had any evil um you know like only 5% had any impact on uh P&Ls uh you know companies impact on uh P&Ls uh you know companies impact on uh P&Ls uh you know companies or anything like that and despite or anything like that and despite or anything like that and despite spending 20 to40 billion dollars on all spending 20 to40 billion dollars on all spending 20 to40 billion dollars on all this stuff um and they also said it this stuff um and they also said it this stuff um and they also said it didn't even matter it wasn't even like didn't even matter it wasn't even like didn't even matter it wasn't even like oh you used the wrong LLM or the wrong oh you used the wrong LLM or the wrong oh you used the wrong LLM or the wrong model it turns out it didn't matter what model it turns out it didn't matter what model it turns out it didn't matter what model people were using the failure rate model people were using the failure rate model people were using the failure rate was still just as high and so was still just as high and so was still just as high and so That's that. And so that news trickled That's that. And so that news trickled That's that. And so that news trickled out at the beginning of this week. Uh out at the beginning of this week. Uh out at the beginning of this week. Uh and then the stock market heard about and then the stock market heard about and then the stock market heard about that and so your Magnificent Seven that and so your Magnificent Seven that and so your Magnificent Seven stocks and all that started to stocks and all that started to stocks and all that started to tank and and so you started having tank and and so you started having tank and and so you started having people going, "Oh wow, is this like people going, "Oh wow, is this like people going, "Oh wow, is this like the.com bust all over again or what?" the.com bust all over again or what?" the.com bust all over again or what?" >> Right. Well, I mean what I always >> Right. Well, I mean what I always >> Right. Well, I mean what I always thought, especially generative AI, if thought, especially generative AI, if thought, especially generative AI, if someone is using a tool to help them someone is using a tool to help them someone is using a tool to help them with their productivity, that does not with their productivity, that does not with their productivity, that does not mean that that time they got back is mean that that time they got back is mean that that time they got back is going to go back to the company.

going to go back to the company. going to go back to the company. >> No. >> No. >> No. >> Right. So maybe that means that I get to >> Right. So maybe that means that I get to >> Right. So maybe that means that I get to watch more YouTube cat videos. watch more YouTube cat videos. watch more YouTube cat videos. >> That's right. Right. >> That's right. Right. >> That's right. Right. >> Yeah. That's what that's what they don't >> Yeah. That's what that's what they don't >> Yeah. That's what that's what they don't understand. And it's like, okay, if I'm understand. And it's like, okay, if I'm understand. And it's like, okay, if I'm more productive, that means I maybe I more productive, that means I maybe I more productive, that means I maybe I can do more stuff than I'm interested in can do more stuff than I'm interested in can do more stuff than I'm interested in as far as the things that you want me to as far as the things that you want me to as far as the things that you want me to do. do. do. >> Yeah. And now people are having >> Yeah. And now people are having >> Yeah. And now people are having relationships with these things, right? relationships with these things, right? relationships with these things, right? So, you know, if you bring your personal So, you know, if you bring your personal So, you know, if you bring your personal chat GPT, you might be bringing your chat GPT, you might be bringing your chat GPT, you might be bringing your personal AI girlfriend or boyfriend or personal AI girlfriend or boyfriend or personal AI girlfriend or boyfriend or husband or wife or whatever. husband or wife or whatever. husband or wife or whatever. >> I met my new soulmate. >> I met my new soulmate. >> I met my new soulmate. >> Yeah. >> Yeah. >> Yeah. >> Chat GPT5 is my new soulmate. Yeah, but >> Chat GPT5 is my new soulmate. Yeah, but >> Chat GPT5 is my new soulmate. Yeah, but you know meant for each other. you know meant for each other. you know meant for each other. >> You know what I just think is just way >> You know what I just think is just way >> You know what I just think is just way overboard are these folks who say, overboard are these folks who say, overboard are these folks who say, "Well, you know, it has a therapeut, you "Well, you know, it has a therapeut, you "Well, you know, it has a therapeut, you know, it helps these folks be deal with know, it helps these folks be deal with know, it helps these folks be deal with their loneliness." And I'm thinking their loneliness." And I'm thinking their loneliness." And I'm thinking >> there's got to be better ways to deal >> there's got to be better ways to deal >> there's got to be better ways to deal with loneliness than to fall in love with loneliness than to fall in love with loneliness than to fall in love with a AI. with a AI. with a AI. >> You know, >> You know, >> You know, >> I think I'm sorry. >> I think I'm sorry. >> I think I'm sorry. >> I don't know. What do you think, Mark? I >> I don't know. What do you think, Mark? I >> I don't know. What do you think, Mark? I think they can separate that.

think they can separate that. think they can separate that. >> Actually, I was reading something >> Actually, I was reading something >> Actually, I was reading something interesting this week related to this. interesting this week related to this. interesting this week related to this. So comparing know the AI potential burst So comparing know the AI potential burst So comparing know the AI potential burst with the com um burst with the com um burst with the com um burst >> and what they were saying is that um >> and what they were saying is that um >> and what they were saying is that um behind the com burst there was nothing. behind the com burst there was nothing. behind the com burst there was nothing. It was a just a smoke and behind all of It was a just a smoke and behind all of It was a just a smoke and behind all of these AI [ __ ] there there is these AI [ __ ] there there is these AI [ __ ] there there is actually interesting technology. actually interesting technology. actually interesting technology. Um so if if there is a burst uh Um so if if there is a burst uh Um so if if there is a burst uh potentially in imagine there will be a potentially in imagine there will be a potentially in imagine there will be a lot of interesting technology that might lot of interesting technology that might lot of interesting technology that might be used in the future um that that can be used in the future um that that can be used in the future um that that can be useful for specific use cases. be useful for specific use cases. be useful for specific use cases. >> Uh that that's the main difference with >> Uh that that's the main difference with >> Uh that that's the main difference with the commerce that that I see and the commerce that that I see and the commerce that that I see and actually I agree and actually he actually I agree and actually he actually I agree and actually he compared with something that I don't compared with something that I don't compared with something that I don't know but he compared with the railway know but he compared with the railway know but he compared with the railway system in the US. So when that burst at system in the US. So when that burst at system in the US. So when that burst at least now all the railway system was least now all the railway system was least now all the railway system was there and could be used in the future. there and could be used in the future. there and could be used in the future. >> Yeah. >> Yeah. >> Yeah. >> Yeah. That's a good that's a good >> Yeah. That's a good that's a good >> Yeah. That's a good that's a good analogy I would think say. Well, you analogy I would think say. Well, you analogy I would think say. Well, you know, I think though know, I think though know, I think though that, that, that, you know, I think one of the big you know, I think one of the big you know, I think one of the big problems we have with AI right now is problems we have with AI right now is problems we have with AI right now is that people are trying to make it like a that people are trying to make it like a that people are trying to make it like a marquee feature like, oh, they're marquee feature like, oh, they're marquee feature like, oh, they're leading with that where it really should leading with that where it really should leading with that where it really should be something in the background that be something in the background that be something in the background that enables them to do something else as enables them to do something else as enables them to do something else as opposed to, you know, hey, this is the opposed to, you know, hey, this is the opposed to, you know, hey, this is the thing that's going to do everything as thing that's going to do everything as thing that's going to do everything as opposed to, you know, I'm giving you a opposed to, you know, I'm giving you a opposed to, you know, I'm giving you a better service. I'm giving you a better

better service. I'm giving you a better better service. I'm giving you a better product. because this thing enabled me product. because this thing enabled me product. because this thing enabled me to do something on the back end that was to do something on the back end that was to do something on the back end that was harder that you know what I mean it took harder that you know what I mean it took harder that you know what I mean it took more time or did some some some type of more time or did some some some type of more time or did some some some type of thing on the back end that it helps you thing on the back end that it helps you thing on the back end that it helps you to to do as opposed to being like ah to to do as opposed to being like ah to to do as opposed to being like ah everything's AI oh you know robots everything's AI oh you know robots everything's AI oh you know robots everywhere everywhere everywhere >> AI is a technology it's not a solution >> AI is a technology it's not a solution >> AI is a technology it's not a solution >> yeah I mean it and then it expresses >> yeah I mean it and then it expresses >> yeah I mean it and then it expresses itself in an application that does itself in an application that does itself in an application that does something absolutely and it's usually a something absolutely and it's usually a something absolutely and it's usually a collection of stuff but I'm going going collection of stuff but I'm going going collection of stuff but I'm going going to coin a new term for our show. It's to coin a new term for our show. It's to coin a new term for our show. It's called false dissociation. called false dissociation. called false dissociation. Um, no, you know, it's one of the things Um, no, you know, it's one of the things Um, no, you know, it's one of the things that people are doing, especially the that people are doing, especially the that people are doing, especially the these AI apologists as they face like, these AI apologists as they face like, these AI apologists as they face like, you know, some of the realities of how, you know, some of the realities of how, you know, some of the realities of how, you know, a lot of what they were you know, a lot of what they were you know, a lot of what they were pedalling is just not turning out. Um pedalling is just not turning out. Um pedalling is just not turning out. Um because here's the thing, one of the because here's the thing, one of the because here's the thing, one of the great things that came out of um the great things that came out of um the great things that came out of um the uh uh you know that was a residual out uh uh you know that was a residual out uh uh you know that was a residual out of the internet uh you know.com of the internet uh you know.com of the internet uh you know.com um boom and bust was webcale um boom and bust was webcale um boom and bust was webcale right they called it webcale I mean it right they called it webcale I mean it right they called it webcale I mean it changed the way we um uh we u design changed the way we um uh we u design changed the way we um uh we u design data centers just like today right um data centers just like today right um data centers just like today right um these accelerated uh computing data these accelerated uh computing data these accelerated uh computing data centers or these GPU or AI centers or these GPU or AI centers or these GPU or AI supercomputing data centers are supercomputing data centers are supercomputing data centers are different from what we saw before,

different from what we saw before, different from what we saw before, right? And so introduced web scale and right? And so introduced web scale and right? And so introduced web scale and it uh then eventually became cloud, it uh then eventually became cloud, it uh then eventually became cloud, right? And so there were a lot of things right? And so there were a lot of things right? And so there were a lot of things that were residuals, but yeah, you're that were residuals, but yeah, you're that were residuals, but yeah, you're right. A if you were there and you heard right. A if you were there and you heard right. A if you were there and you heard a lot, you were in Palo Alto and you sat a lot, you were in Palo Alto and you sat a lot, you were in Palo Alto and you sat down with the you know having lunch with down with the you know having lunch with down with the you know having lunch with your team and you were eavesdropping on your team and you were eavesdropping on your team and you were eavesdropping on these early VCs talking to their you these early VCs talking to their you these early VCs talking to their you know prospects these founders you know prospects these founders you know prospects these founders you realize how ridiculous and detached some realize how ridiculous and detached some realize how ridiculous and detached some of these these uh proposals were right of these these uh proposals were right of these these uh proposals were right these business ideas are and they had these business ideas are and they had these business ideas are and they had nothing to do with what was became the nothing to do with what was became the nothing to do with what was became the residual right and so yeah there's a lot residual right and so yeah there's a lot residual right and so yeah there's a lot Great stuff happening in supercomputing Great stuff happening in supercomputing Great stuff happening in supercomputing is becoming democratized in a lot of is becoming democratized in a lot of is becoming democratized in a lot of ways and that'll be a residual. But the ways and that'll be a residual. But the ways and that'll be a residual. But the thing is is will these applications be thing is is will these applications be thing is is will these applications be sustainable and who will pay for them? sustainable and who will pay for them? sustainable and who will pay for them? Because one of the big questions out Because one of the big questions out Because one of the big questions out there right now is monetization.

there right now is monetization. there right now is monetization. Nobody's making money other than Nvidia, Nobody's making money other than Nvidia, Nobody's making money other than Nvidia, right? And right? And right? And >> they're selling the blue jeans. They're >> they're selling the blue jeans. They're >> they're selling the blue jeans. They're selling the picks and shovels. selling the picks and shovels. selling the picks and shovels. >> I think they're selling the picks and >> I think they're selling the picks and >> I think they're selling the picks and shovels. the blue jeans are going to be shovels. the blue jeans are going to be shovels. the blue jeans are going to be >> you know it's going to be something else >> you know it's going to be something else >> you know it's going to be something else but it's you it's probably going to be but it's you it's probably going to be but it's you it's probably going to be um you know a second generation of um you know a second generation of um you know a second generation of investors who buy up all these fire sale investors who buy up all these fire sale investors who buy up all these fire sale assets when everything collapses. assets when everything collapses. assets when everything collapses. They'll buy everything at a penny for a They'll buy everything at a penny for a They'll buy everything at a penny for a dollar. Oh, you mean like after the.com dollar. Oh, you mean like after the.com dollar. Oh, you mean like after the.com bust and when people bought all those bust and when people bought all those bust and when people bought all those used Herman Miller chairs and and nice used Herman Miller chairs and and nice used Herman Miller chairs and and nice conference room tables that all the conference room tables that all the conference room tables that all the startups spent money on. startups spent money on. startups spent money on. >> Dude, you're getting started. We're >> Dude, you're getting started. We're >> Dude, you're getting started. We're getting started. getting started. getting started. >> That was such a thing cuz they all blew >> That was such a thing cuz they all blew >> That was such a thing cuz they all blew so much money on high-end office so much money on high-end office so much money on high-end office furniture. furniture. furniture. >> Yeah. This this arty fart these art >> Yeah. This this arty fart these art >> Yeah. This this arty fart these art farts actually they were really farts actually they were really farts actually they were really uncomfortable. I bought one and what uncomfortable. I bought one and what uncomfortable. I bought one and what were they like you know 1,200 bucks were they like you know 1,200 bucks were they like you know 1,200 bucks right each? right each? right each? >> Oh they were expensive but they're the >> Oh they were expensive but they're the >> Oh they were expensive but they're the ultimate deal for your.com startup.

ultimate deal for your.com startup. ultimate deal for your.com startup. >> You had all these like freaking wires >> You had all these like freaking wires >> You had all these like freaking wires and crap and you know like little knobs and crap and you know like little knobs and crap and you know like little knobs that adjust you go but which what does that adjust you go but which what does that adjust you go but which what does this one do? this one do? this one do? >> But I do love I do love talking about >> But I do love I do love talking about >> But I do love I do love talking about you know today versus then. And you know today versus then. And you know today versus then. And >> you know some of us live that because >> you know some of us live that because >> you know some of us live that because we're older than dirt and I used to live we're older than dirt and I used to live we're older than dirt and I used to live in data centers back then. Can you talk in data centers back then. Can you talk in data centers back then. Can you talk about web scale? I think I think webcale about web scale? I think I think webcale about web scale? I think I think webcale is a Gartner term that must have popped is a Gartner term that must have popped is a Gartner term that must have popped out of there. out of there. out of there. >> Um, but yes, I remember living in Exodus >> Um, but yes, I remember living in Exodus >> Um, but yes, I remember living in Exodus data centers, WorldCom D. You know, you data centers, WorldCom D. You know, you data centers, WorldCom D. You know, you think all these the death of technology think all these the death of technology think all these the death of technology everywhere, but I also felt like we everywhere, but I also felt like we everywhere, but I also felt like we invested and built for what we needed. I invested and built for what we needed. I invested and built for what we needed. I know that sounds really obvious uh to know that sounds really obvious uh to know that sounds really obvious uh to make like you know the big winners or make like you know the big winners or make like you know the big winners or one of the big things that came out of one of the big things that came out of one of the big things that came out of the dot thing was e-commerce right and the dot thing was e-commerce right and the dot thing was e-commerce right and e-commerce was a real thing it you know e-commerce was a real thing it you know e-commerce was a real thing it you know Amazon everybody started putting a Amazon everybody started putting a Amazon everybody started putting a storefront you know and to sell to sell storefront you know and to sell to sell storefront you know and to sell to sell stuff right and you made money stuff right and you made money stuff right and you made money >> it's it's what's left after the dust >> it's it's what's left after the dust >> it's it's what's left after the dust settles right the big difference though settles right the big difference though settles right the big difference though that things are happening way faster that things are happening way faster that things are happening way faster than they used at the time right and so than they used at the time right and so than they used at the time right and so and and the failure years happen faster and and the failure years happen faster and and the failure years happen faster as well. So I don't know but the as well. So I don't know but the as well. So I don't know but the investments are bigger so the losses are investments are bigger so the losses are investments are bigger so the losses are bigger as well but the investments bigger as well but the investments bigger as well but the investments things left and I think Mark Mark can things left and I think Mark Mark can things left and I think Mark Mark can can can attest to that edi going to be can can attest to that edi going to be can can attest to that edi going to be one of the things that's going to stay one of the things that's going to stay one of the things that's going to stay because it's already because it's already because it's already >> I'm I'm already seeing a huge interest

>> I'm I'm already seeing a huge interest >> I'm I'm already seeing a huge interest in distributing that and the hardware in distributing that and the hardware in distributing that and the hardware you know companies are are thinking you know companies are are thinking you know companies are are thinking about that physical AI thing more than about that physical AI thing more than about that physical AI thing more than they used to in the past because of all they used to in the past because of all they used to in the past because of all I you know buzz uh and and there's an I you know buzz uh and and there's an I you know buzz uh and and there's an acceleration I'm seeing here on the on acceleration I'm seeing here on the on acceleration I'm seeing here on the on the edgei side of the story which to me the edgei side of the story which to me the edgei side of the story which to me is going to be what's going to one of is going to be what's going to one of is going to be what's going to one of the things that's going to stay and and the things that's going to stay and and the things that's going to stay and and and be there because it's directly and be there because it's directly and be there because it's directly related to concrete related to concrete related to concrete applications with NRI with a real return applications with NRI with a real return applications with NRI with a real return on investment. on investment. on investment. >> Yeah. >> Yeah. >> Yeah. >> But that's my you know I might be >> But that's my you know I might be >> But that's my you know I might be biased. biased. biased. >> You're totally biased. You're totally >> You're totally biased. You're totally >> You're totally biased. You're totally biased. biased. biased. >> That's okay. That's okay. And Mark is >> That's okay. That's okay. And Mark is >> That's okay. That's okay. And Mark is totally biased as well for that, you totally biased as well for that, you totally biased as well for that, you know, because that's what you're doing know, because that's what you're doing know, because that's what you're doing now for a living. So I it's okay. now for a living. So I it's okay. now for a living. So I it's okay. >> Yeah. >> Yeah. >> Yeah. >> Um but but when we talk about >> Um but but when we talk about >> Um but but when we talk about overinvesting or whatever.com overinvesting or whatever.com overinvesting or whatever.com versus now unprecedented billions, tens versus now unprecedented billions, tens versus now unprecedented billions, tens of billions are being spent uh to you of billions are being spent uh to you of billions are being spent uh to you know like think about the old days or know like think about the old days or know like think about the old days or even think about you know just a few even think about you know just a few even think about you know just a few years ago what was a normal like if years ago what was a normal like if years ago what was a normal like if you're a startup what did a preede or a you're a startup what did a preede or a you're a startup what did a preede or a seed or a series A round look like? I seed or a series A round look like? I seed or a series A round look like? I mean, it wasn't that long ago that a mean, it wasn't that long ago that a mean, it wasn't that long ago that a series A round meant about $3 million series A round meant about $3 million series A round meant about $3 million and then and then now with this crazy and then and then now with this crazy and then and then now with this crazy generative AI and models. I remember and generative AI and models. I remember and generative AI and models. I remember and it time has gone by so fast. Remember it time has gone by so fast. Remember it time has gone by so fast. Remember remember when Mistrol came out of remember when Mistrol came out of remember when Mistrol came out of nowhere and they had a series A of like nowhere and they had a series A of like nowhere and they had a series A of like $130 million and you're like how is that

$130 million and you're like how is that $130 million and you're like how is that possible? possible? possible? >> And there's so much >> And there's so much >> And there's so much >> now it's billions. >> now it's billions. >> now it's billions. >> Yeah. >> Yeah. >> Yeah. craziness around around AI and craziness around around AI and craziness around around AI and valuations and stuff like that. valuations and stuff like that. valuations and stuff like that. >> Yeah. >> Yeah. >> Yeah. >> But we didn't have we I mean we didn't >> But we didn't have we I mean we didn't >> But we didn't have we I mean we didn't have trillion dollar companies back have trillion dollar companies back have trillion dollar companies back then. Um also then. Um also then. Um also >> you know when you think about that first >> you know when you think about that first >> you know when you think about that first when you think about the dot and what when you think about the dot and what when you think about the dot and what was conventional before the dot was a was conventional before the dot was a was conventional before the dot was a huge departure from what people were huge departure from what people were huge departure from what people were doing in Wall Street, right? the doing in Wall Street, right? the doing in Wall Street, right? the investment mentality was very very diff investment mentality was very very diff investment mentality was very very diff you know it was different I mean you're you know it was different I mean you're you know it was different I mean you're still recovering from you know the still recovering from you know the still recovering from you know the milkin bozie you know uh junk bond u you milkin bozie you know uh junk bond u you milkin bozie you know uh junk bond u you know u bubble and crash but know u bubble and crash but know u bubble and crash but you know this the dot you know this the dot you know this the dot uh phenomenon was very and the the you uh phenomenon was very and the the you uh phenomenon was very and the the you know dynamic was very new and very you know dynamic was very new and very you know dynamic was very new and very you know it was a massive change from what know it was a massive change from what know it was a massive change from what was happening before. You didn't really was happening before. You didn't really was happening before. You didn't really have VCs. You didn't have Silicon Valley have VCs. You didn't have Silicon Valley have VCs. You didn't have Silicon Valley VCs. And so the it in it in its own way VCs. And so the it in it in its own way VCs. And so the it in it in its own way was very dramatically different. And so, was very dramatically different. And so, was very dramatically different. And so, you know, you can't compare the numbers you know, you can't compare the numbers you know, you can't compare the numbers in the dot era with what we're dealing in the dot era with what we're dealing in the dot era with what we're dealing with right now. And yeah, there are some with right now. And yeah, there are some with right now. And yeah, there are some subtle differences. Um, but you also

subtle differences. Um, but you also subtle differences. Um, but you also have to be careful about subscribing to have to be careful about subscribing to have to be careful about subscribing to a lot of these um, narratives that it is a lot of these um, narratives that it is a lot of these um, narratives that it is so different. It's different this time. so different. It's different this time. so different. It's different this time. It's never different. It's never different. It's never different. >> It's never like comparing NBA players, >> It's never like comparing NBA players, >> It's never like comparing NBA players, right? It's it's LeBron James better right? It's it's LeBron James better right? It's it's LeBron James better than Michael Jordan. It's different, than Michael Jordan. It's different, than Michael Jordan. It's different, right? right? right? >> Different. I'm comparing I'm comparing >> Different. I'm comparing I'm comparing >> Different. I'm comparing I'm comparing them. I'm absolutely comparing them. And them. I'm absolutely comparing them. And them. I'm absolutely comparing them. And no, no, no, >> Michael Jordan all the way, baby. >> Michael Jordan all the way, baby. >> Michael Jordan all the way, baby. >> Jordan all the way. Yeah. >> Jordan all the way. Yeah. >> Jordan all the way. Yeah. >> All right. So, so Rob, now I want that >> All right. So, so Rob, now I want that >> All right. So, so Rob, now I want that you get your uh crystal ball to predict you get your uh crystal ball to predict you get your uh crystal ball to predict the future. So, I don't know what uh the future. So, I don't know what uh the future. So, I don't know what uh started the waterfall of this com burst. started the waterfall of this com burst. started the waterfall of this com burst. Uh I don't know if you know what what Uh I don't know if you know what what Uh I don't know if you know what what was the trigger uh that start that but was the trigger uh that start that but was the trigger uh that start that but uh what should be on on this GI uh what should be on on this GI uh what should be on on this GI craziness like today that triggers a craziness like today that triggers a craziness like today that triggers a waterfall burst or waterfall burst or waterfall burst or >> burst. You know it's funny cuz I do >> burst. You know it's funny cuz I do >> burst. You know it's funny cuz I do remember living and feeling the burst.

remember living and feeling the burst. remember living and feeling the burst. And so just for a lot of people keep And so just for a lot of people keep And so just for a lot of people keep saying I always hear people oh yeah saying I always hear people oh yeah saying I always hear people oh yeah the.com era during the early 2000s or the.com era during the early 2000s or the.com era during the early 2000s or 2000s. It's like, no, no. The.com era 2000s. It's like, no, no. The.com era 2000s. It's like, no, no. The.com era was in the '90s and it died and the was in the '90s and it died and the was in the '90s and it died and the official there's a date, but I'll give official there's a date, but I'll give official there's a date, but I'll give you the month. The it died in March of you the month. The it died in March of you the month. The it died in March of 2000 and the triggering event happened 2000 and the triggering event happened 2000 and the triggering event happened on the financial side and it happened on the financial side and it happened on the financial side and it happened with the venture capitalists. Uh, with the venture capitalists. Uh, with the venture capitalists. Uh, literally in March of 2000, they started literally in March of 2000, they started literally in March of 2000, they started tearing up the term sheets. Um, Warren tearing up the term sheets. Um, Warren tearing up the term sheets. Um, Warren Buffett said, "I don't believe in any of Buffett said, "I don't believe in any of Buffett said, "I don't believe in any of this stuff. I don't understand it. It this stuff. I don't understand it. It this stuff. I don't understand it. It doesn't make any sense to me. There's no doesn't make any sense to me. There's no doesn't make any sense to me. There's no way I'm investing in it. And so I way I'm investing in it. And so I way I'm investing in it. And so I certainly remember being at a dot at certainly remember being at a dot at certainly remember being at a dot at that time. And you know how it is though that time. And you know how it is though that time. And you know how it is though in the moment you don't you're part of in the moment you don't you're part of in the moment you don't you're part of it. You're building and you're just so it. You're building and you're just so it. You're building and you're just so focused and you don't know that the focused and you don't know that the focused and you don't know that the world is collapsing around you. world is collapsing around you. world is collapsing around you. >> And so your first inkling was, huh? You >> And so your first inkling was, huh? You >> And so your first inkling was, huh? You know, it's like March and April 2000. know, it's like March and April 2000. know, it's like March and April 2000. We're having a lot more trouble raising We're having a lot more trouble raising We're having a lot more trouble raising money all of a sudden, you know, to keep money all of a sudden, you know, to keep money all of a sudden, you know, to keep funding our operations. And then people, funding our operations. And then people, funding our operations. And then people, as they always do, you know what? I as they always do, you know what? I as they always do, you know what? I think you know, probably by August it'll think you know, probably by August it'll think you know, probably by August it'll be fine and we'll be able to raise be fine and we'll be able to raise be fine and we'll be able to raise money. It's no big deal. It's just a money. It's no big deal. It's just a money. It's no big deal. It's just a temporary thing. But it wasn't a temporary thing. But it wasn't a temporary thing. But it wasn't a temporary thing. It died. And you watch temporary thing. It died. And you watch temporary thing. It died. And you watch the stock market, remember cuz the big the stock market, remember cuz the big the stock market, remember cuz the big deal was NASDAQ, which was all the tech deal was NASDAQ, which was all the tech deal was NASDAQ, which was all the tech companies in NASDAQ exchange, companies in NASDAQ exchange, companies in NASDAQ exchange, >> got all the way up to 5,000 and that was >> got all the way up to 5,000 and that was >> got all the way up to 5,000 and that was unprecedented everything. And then it unprecedented everything. And then it unprecedented everything. And then it just started to collapse in March. and

just started to collapse in March. and just started to collapse in March. and then the VCs and it just it starts like then the VCs and it just it starts like then the VCs and it just it starts like wildfire, you know. It's almost like wildfire, you know. It's almost like wildfire, you know. It's almost like people don't have any conviction in people don't have any conviction in people don't have any conviction in their own beliefs. As soon as they see their own beliefs. As soon as they see their own beliefs. As soon as they see someone else running for the exits, someone else running for the exits, someone else running for the exits, they're like, I better get out of here, they're like, I better get out of here, they're like, I better get out of here, too. And then everybody left and no one too. And then everybody left and no one too. And then everybody left and no one got investment and everybody died. Um, got investment and everybody died. Um, got investment and everybody died. Um, >> oh yeah, it was brutal. I was right >> oh yeah, it was brutal. I was right >> oh yeah, it was brutal. I was right smack dab in the middle of it. And smack dab in the middle of it. And smack dab in the middle of it. And thankfully, I didn't go to any of these thankfully, I didn't go to any of these thankfully, I didn't go to any of these startups that were soliciting me at the startups that were soliciting me at the startups that were soliciting me at the time. I stuck with consulting and I was time. I stuck with consulting and I was time. I stuck with consulting and I was working with clients through you know working with clients through you know working with clients through you know throughout the dotcom throughout the dotcom throughout the dotcom era era era >> and so I I you know I was on the outside >> and so I I you know I was on the outside >> and so I I you know I was on the outside looking in and had I I I saw all this looking in and had I I I saw all this looking in and had I I I saw all this stuff go up and implode. I saw all the stuff go up and implode. I saw all the stuff go up and implode. I saw all the crap that was uh fueling this stuff. crap that was uh fueling this stuff. crap that was uh fueling this stuff. That's why I have this like That's why I have this like That's why I have this like you know uh I've developed this filter. you know uh I've developed this filter. you know uh I've developed this filter. You know what I'm saying? this lens of You know what I'm saying? this lens of You know what I'm saying? this lens of how to look at hype. You you and how to look at hype. You you and how to look at hype. You you and None of this stuff ends ends well, None of this stuff ends ends well, None of this stuff ends ends well, right?

right? right? >> But you But don't be so But you're so >> But you But don't be so But you're so >> But you But don't be so But you're so jaded though, dude. You don't know that jaded though, dude. You don't know that jaded though, dude. You don't know that you It's easy to sit here and go, "Oh, you It's easy to sit here and go, "Oh, you It's easy to sit here and go, "Oh, it all ends horribly." In the moment, we it all ends horribly." In the moment, we it all ends horribly." In the moment, we thought we were building real value. you thought we were building real value. you thought we were building real value. you know a lot of us were doing e-com I I know a lot of us were doing e-com I I know a lot of us were doing e-com I I saw a lot of people who thought they saw a lot of people who thought they saw a lot of people who thought they were building real value and then when I were building real value and then when I were building real value and then when I evaluated their company and the tools evaluated their company and the tools evaluated their company and the tools because remember I you know as a as a because remember I you know as a as a because remember I you know as a as a principal at PWC part of my job was to principal at PWC part of my job was to principal at PWC part of my job was to evaluate a lot of these companies that evaluate a lot of these companies that evaluate a lot of these companies that the partners wanted to do uh you know the partners wanted to do uh you know the partners wanted to do uh you know basically bringing fold we want to find basically bringing fold we want to find basically bringing fold we want to find out should we commit into and invest out should we commit into and invest out should we commit into and invest into building a practice around this into building a practice around this into building a practice around this tool or this technology or what have tool or this technology or what have tool or this technology or what have you. And you know what? Most of these you. And you know what? Most of these you. And you know what? Most of these companies were completely full of crap. companies were completely full of crap. companies were completely full of crap. Uh Uh Uh >> so yeah, you're right. If you're in >> so yeah, you're right. If you're in >> so yeah, you're right. If you're in there trying to build a product, you there trying to build a product, you there trying to build a product, you think you're you're fixated on what think you're you're fixated on what think you're you're fixated on what you're doing and you don't see that you're doing and you don't see that you're doing and you don't see that you're you're really developing you're you're really developing you're you're really developing something that's not going to be viable.

something that's not going to be viable. something that's not going to be viable. >> Well, the in the in the traditional >> Well, the in the in the traditional >> Well, the in the in the traditional typical Ditri command, I would say that typical Ditri command, I would say that typical Ditri command, I would say that they were seriously lacking critical they were seriously lacking critical they were seriously lacking critical thinking. So thinking. So thinking. So >> Oh, it was ridiculous. But to to Mark, I >> Oh, it was ridiculous. But to to Mark, I >> Oh, it was ridiculous. But to to Mark, I think to to your to your question, there think to to your to your question, there think to to your to your question, there is and I haven't done the math on that is and I haven't done the math on that is and I haven't done the math on that and maybe you you you've done more and maybe you you you've done more and maybe you you you've done more research on that, Leonard, but I think research on that, Leonard, but I think research on that, Leonard, but I think that one uh collapse driving factor that that one uh collapse driving factor that that one uh collapse driving factor that could happen with Gai is the energy could happen with Gai is the energy could happen with Gai is the energy issue because those things are consuming issue because those things are consuming issue because those things are consuming so much more and more and more and more so much more and more and more and more so much more and more and more and more energy that we might reach a point where energy that we might reach a point where energy that we might reach a point where it will collapse because there will just it will collapse because there will just it will collapse because there will just not be enough. not be enough. not be enough. >> I think I think Olivier is right. One of >> I think I think Olivier is right. One of >> I think I think Olivier is right. One of the things that I'm talking to a lot of the things that I'm talking to a lot of the things that I'm talking to a lot of folks about right now is like, you know, folks about right now is like, you know, folks about right now is like, you know, potential paths for um, you know, Gen AI potential paths for um, you know, Gen AI potential paths for um, you know, Gen AI diffusion, right? And so that means diffusion, right? And so that means diffusion, right? And so that means diffusing outside of the data center and diffusing outside of the data center and diffusing outside of the data center and then what does that look like across then what does that look like across then what does that look like across different, different, different, let's say, and what would the patterns let's say, and what would the patterns let's say, and what would the patterns be, right? Because it's not going to be be, right? Because it's not going to be be, right? Because it's not going to be homogeneous. It's all going to look homogeneous. It's all going to look homogeneous. It's all going to look different. The time scales are going to different. The time scales are going to different. The time scales are going to be different for these things, you know?

be different for these things, you know? be different for these things, you know? Uh, and so you Uh, and so you Uh, and so you I I think the Gen AI where it makes I I think the Gen AI where it makes I I think the Gen AI where it makes sense is going to find a home and I I sense is going to find a home and I I sense is going to find a home and I I agree with uh Olivier. It will find agree with uh Olivier. It will find agree with uh Olivier. It will find itself across edge infrastructures as itself across edge infrastructures as itself across edge infrastructures as well as in you know in edge well as in you know in edge well as in you know in edge environments. Um that's going to look environments. Um that's going to look environments. Um that's going to look very very different across the board very very different across the board very very different across the board which is um that's the edge which is um that's the edge which is um that's the edge right but it will survive. I mean, it's right but it will survive. I mean, it's right but it will survive. I mean, it's just not going to go in the direction just not going to go in the direction just not going to go in the direction that everyone thinks it that everyone thinks it that everyone thinks it >> which goes back to Rob's point that at >> which goes back to Rob's point that at >> which goes back to Rob's point that at the moment everyone's like nose down in the moment everyone's like nose down in the moment everyone's like nose down in in all the hype. You think that it's in all the hype. You think that it's in all the hype. You think that it's going in a particular direction. The going in a particular direction. The going in a particular direction. The consensus is statistically going to be consensus is statistically going to be consensus is statistically going to be wrong. wrong. wrong. >> It's just the way it is. That's not a >> It's just the way it is. That's not a >> It's just the way it is. That's not a law of law of law of >> that note. I have to go. But I It's like >> that note. I have to go. But I It's like >> that note. I have to go. But I It's like edge edge eye for the win. I gotta go.

edge edge eye for the win. I gotta go. edge edge eye for the win. I gotta go. >> Good to see you all. >> Good to see you all. >> Good to see you all. >> See you later, buddy. >> See you later, buddy. >> See you later, buddy. >> See you. Have fun in Amsterdam. >> See you. Have fun in Amsterdam. >> See you. Have fun in Amsterdam. >> Yeah, >> Yeah, >> Yeah, >> the the technology will find its home. I >> the the technology will find its home. I >> the the technology will find its home. I mean, the internet found its home its mean, the internet found its home its mean, the internet found its home its home after the the bubble. That's for home after the the bubble. That's for home after the the bubble. That's for sure. sure. sure. >> Totally. But the where but I I keep on >> Totally. But the where but I I keep on >> Totally. But the where but I I keep on thinking you know you heard me say that thinking you know you heard me say that thinking you know you heard me say that on the show several times but I keep on on the show several times but I keep on on the show several times but I keep on thinking that it is a little bit thinking that it is a little bit thinking that it is a little bit different in term of impact with geni different in term of impact with geni different in term of impact with geni for the same reason I always say it is for the same reason I always say it is for the same reason I always say it is now a way to interact with technology now a way to interact with technology now a way to interact with technology that is deeply entrrenched into the that is deeply entrrenched into the that is deeply entrrenched into the human psyche of language and this is human psyche of language and this is human psyche of language and this is different this is very different the different this is very different the different this is very different the fact that we interact with language with fact that we interact with language with fact that we interact with language with those things they make you think that those things they make you think that those things they make you think that it's a person in front of you this is it's a person in front of you this is it's a person in front of you this is different we didn't had that with different we didn't had that with different we didn't had that with internet bubble. It was screens and internet bubble. It was screens and internet bubble. It was screens and browsers. It's different. So the impact browsers. It's different. So the impact browsers. It's different. So the impact is deeper to the human personality, I is deeper to the human personality, I is deeper to the human personality, I believe. believe. believe. >> Yeah, it feels real. You're right. >> Yeah, it feels real. You're right. >> Yeah, it feels real. You're right. People talk to it. They People talk to it. They People talk to it. They >> and people will do very very stupid >> and people will do very very stupid >> and people will do very very stupid thing with it. I'm pretty sure of that. thing with it. I'm pretty sure of that. thing with it. I'm pretty sure of that. I think the amount of jobs are going to I think the amount of jobs are going to I think the amount of jobs are going to be destroyed is going to be in the be destroyed is going to be in the be destroyed is going to be in the coming short term until it's coming short term until it's coming short term until it's restabilized is going to be awful restabilized is going to be awful restabilized is going to be awful because people are hey, I can talk to because people are hey, I can talk to because people are hey, I can talk to that thing like a human. He can do the that thing like a human. He can do the that thing like a human. He can do the job. Hey, I'm going to trust that thing.

job. Hey, I'm going to trust that thing. job. Hey, I'm going to trust that thing. But but that's where I think um here But but that's where I think um here But but that's where I think um here this is where the the technology is this is where the the technology is this is where the the technology is going to be detrimental in its going to be detrimental in its going to be detrimental in its application is over over reliance. Yeah, application is over over reliance. Yeah, application is over over reliance. Yeah, totally. totally. totally. >> I mean, you know, the even today the the >> I mean, you know, the even today the the >> I mean, you know, the even today the the the um the way people perceive the um the way people perceive the um the way people perceive generative AI is especially for generative AI is especially for generative AI is especially for enterprise and industrial is so detached enterprise and industrial is so detached enterprise and industrial is so detached from what you know some of these from what you know some of these from what you know some of these practitioners that I've met recently who practitioners that I've met recently who practitioners that I've met recently who get it uh are actually experiencing and get it uh are actually experiencing and get it uh are actually experiencing and the problems that they're solving, the problems that they're solving, the problems that they're solving, right? Um there there's uh there again right? Um there there's uh there again right? Um there there's uh there again there's these two uh folks that I met. I there's these two uh folks that I met. I there's these two uh folks that I met. I mean they blew me away. I've never been mean they blew me away. I've never been mean they blew me away. I've never been blown away um by AI practitioners blown away um by AI practitioners blown away um by AI practitioners talking about generative AI for talking about generative AI for talking about generative AI for enterprise and industrial. I swear to enterprise and industrial. I swear to enterprise and industrial. I swear to God almost everyone misses the big God almost everyone misses the big God almost everyone misses the big picture issues. These two guys get up on picture issues. These two guys get up on picture issues. These two guys get up on stage and they're freaking just listing stage and they're freaking just listing stage and they're freaking just listing off all the things that I've been off all the things that I've been off all the things that I've been talking about for two and a half years.

talking about for two and a half years. talking about for two and a half years. and I'm gonna have them on my podcast and I'm gonna have them on my podcast and I'm gonna have them on my podcast because these guys need to be heard. because these guys need to be heard. because these guys need to be heard. They'll blow your mind. These guys are They'll blow your mind. These guys are They'll blow your mind. These guys are just freaking ridiculous. And um you just freaking ridiculous. And um you just freaking ridiculous. And um you know, they're very rare breed of folks know, they're very rare breed of folks know, they're very rare breed of folks that I've encountered that have a that I've encountered that have a that I've encountered that have a holistic and architect's view on um what holistic and architect's view on um what holistic and architect's view on um what it may take to make generative AI work it may take to make generative AI work it may take to make generative AI work within an enterprise or industrial within an enterprise or industrial within an enterprise or industrial environment. environment. environment. It's freaking awesome. It's freaking awesome. It's freaking awesome. >> Mhm. >> Mhm. >> Mhm. >> Yeah. But >> Yeah. But >> Yeah. But >> right, >> right, >> right, >> people are the the the general public, >> people are the the the general public, >> people are the the the general public, you know, I mean the just Yeah, the you know, I mean the just Yeah, the you know, I mean the just Yeah, the general public is like probably about 12 general public is like probably about 12 general public is like probably about 12 months behind because they're still months behind because they're still months behind because they're still stuck in LLM. Rag rag is done. Rag is stuck in LLM. Rag rag is done. Rag is stuck in LLM. Rag rag is done. Rag is done. done. done. >> Why do you say rag is done? >> Why do you say rag is done? >> Why do you say rag is done? >> Rag is, you know, rag is done because of >> Rag is, you know, rag is done because of >> Rag is, you know, rag is done because of the vector database. the vector database. the vector database. I mean this it's this works. I mean this it's this works. I mean this it's this works. >> What happens with database?

>> What happens with database? >> What happens with database? >> We use vector database as the core of >> We use vector database as the core of >> We use vector database as the core of how we store things in the systems. how we store things in the systems. how we store things in the systems. >> Well, go ahead and try to try to apply >> Well, go ahead and try to try to apply >> Well, go ahead and try to try to apply it in a enterprise context from the very it in a enterprise context from the very it in a enterprise context from the very beginning. People are abandoning it beginning. People are abandoning it beginning. People are abandoning it because uh you cannot you cannot and because uh you cannot you cannot and because uh you cannot you cannot and I've said it several times on this show. I've said it several times on this show. I've said it several times on this show. You cannot apply You cannot apply You cannot apply um you know um embedding level access um you know um embedding level access um you know um embedding level access controls. These are not databases. I controls. These are not databases. I controls. These are not databases. I mean it's a vector database but they're mean it's a vector database but they're mean it's a vector database but they're not a RDBMS. You can't delete stuff and not a RDBMS. You can't delete stuff and not a RDBMS. You can't delete stuff and they also over time collapse. So these they also over time collapse. So these they also over time collapse. So these are you know you also have to think of are you know you also have to think of are you know you also have to think of these things full life cycle and what it these things full life cycle and what it these things full life cycle and what it takes to maintain in order to arrive at takes to maintain in order to arrive at takes to maintain in order to arrive at a real good picture of what TCO looks a real good picture of what TCO looks a real good picture of what TCO looks like. And so these two folks that I'm like. And so these two folks that I'm like. And so these two folks that I'm going to bring on my uh show, these guys going to bring on my uh show, these guys going to bring on my uh show, these guys actually have that full life cycle view. actually have that full life cycle view. actually have that full life cycle view. Uh I can't wait to have the podcast Uh I can't wait to have the podcast Uh I can't wait to have the podcast actually with these guys. I just had a actually with these guys. I just had a actually with these guys. I just had a call with uh one of them yesterday and I call with uh one of them yesterday and I call with uh one of them yesterday and I I I I just wanted to push the record I I I just wanted to push the record I I I just wanted to push the record buttons. I told him, "Dude, this is like buttons. I told him, "Dude, this is like buttons. I told him, "Dude, this is like so freaking ridiculously good. Um I I so freaking ridiculously good. Um I I so freaking ridiculously good. Um I I stop talking. Let's just wait until we stop talking. Let's just wait until we stop talking. Let's just wait until we do the recording, you know?" So anyway, do the recording, you know?" So anyway, do the recording, you know?" So anyway, >> so who who are these guys? Are they >> so who who are these guys? Are they >> so who who are these guys? Are they secret names or secret names or secret names or >> Yeah, they're secret for right now.

>> Yeah, they're secret for right now. >> Yeah, they're secret for right now. >> Okay. >> Okay. >> Okay. >> Yeah. >> Yeah. >> Yeah. >> Yeah. They're do guys who made you >> Yeah. They're do guys who made you >> Yeah. They're do guys who made you believe in something that's not true. believe in something that's not true. believe in something that's not true. Okay. Awesome. I love that. Okay. Awesome. I love that. Okay. Awesome. I love that. >> Oh, they're You know what? They are they >> Oh, they're You know what? They are they >> Oh, they're You know what? They are they are not they are they are that's kind of are not they are they are that's kind of are not they are they are that's kind of insulting actually for these guys in insulting actually for these guys in insulting actually for these guys in their background, Rob. their background, Rob. their background, Rob. They are they are in the trenches and we They are they are in the trenches and we They are they are in the trenches and we need to we need to support people who need to we need to support people who need to we need to support people who are in the trenches dealing with the are in the trenches dealing with the are in the trenches dealing with the technology. These these are not jokers technology. These these are not jokers technology. These these are not jokers talking about prompt engineering and all talking about prompt engineering and all talking about prompt engineering and all this other silly crap and they're this other silly crap and they're this other silly crap and they're basically making a crap ton of money. basically making a crap ton of money. basically making a crap ton of money. These are like real practitioners that These are like real practitioners that These are like real practitioners that are actually having to figure out how to are actually having to figure out how to are actually having to figure out how to deliver value to enterprise customers. deliver value to enterprise customers. deliver value to enterprise customers. Right? Right? Right? >> We were real. >> We were real. >> We were real. were real practitioners in the.com era were real practitioners in the.com era were real practitioners in the.com era and we were building value and stuff and we were building value and stuff and we were building value and stuff like that and we still all died. like that and we still all died. like that and we still all died. >> Yeah. >> Yeah. >> Yeah. >> Well, they're they're not bu they're >> Well, they're they're not bu they're >> Well, they're they're not bu they're they're they are actually they're they're they are actually they're they're they are actually they're consultants. Be consultants. Be consultants. Be >> being a practitioner being a >> being a practitioner being a >> being a practitioner being a practitioner and building doesn't practitioner and building doesn't practitioner and building doesn't necessarily mean that what you're necessarily mean that what you're necessarily mean that what you're building as a building as a building as a >> building a product is different from >> building a product is different from >> building a product is different from actually helping clients apply a actually helping clients apply a actually helping clients apply a technology to deliver value for the technology to deliver value for the technology to deliver value for the business. that the product and business business. that the product and business business. that the product and business business transformation or business transformation or business transformation or technologydriven business transformation technologydriven business transformation technologydriven business transformation two different things. They're not the two different things. They're not the two different things. They're not the same thing. Right.

same thing. Right. same thing. Right. >> Okay. >> Okay. >> Okay. >> Well, you folks talking about business >> Well, you folks talking about business >> Well, you folks talking about business business uh transformation. I'm going to business uh transformation. I'm going to business uh transformation. I'm going to make my usual pitch about somebody I make my usual pitch about somebody I make my usual pitch about somebody I hate, but I discovered yesterday that hate, but I discovered yesterday that hate, but I discovered yesterday that actually DHL when you order stuff from actually DHL when you order stuff from actually DHL when you order stuff from China doesn't validate address. China doesn't validate address. China doesn't validate address. So yeah, there was a typo in my zip code So yeah, there was a typo in my zip code So yeah, there was a typo in my zip code and and and >> yeah, >> yeah, >> yeah, >> so it took two days to go from Chenzen >> so it took two days to go from Chenzen >> so it took two days to go from Chenzen to Fremont. Then I got a call from DHS to Fremont. Then I got a call from DHS to Fremont. Then I got a call from DHS DHL saying, "Oh, there's a zip code DHL saying, "Oh, there's a zip code DHL saying, "Oh, there's a zip code problem." And by the way, if you had problem." And by the way, if you had problem." And by the way, if you had asked at GPT, you could have figured asked at GPT, you could have figured asked at GPT, you could have figured out. And now it's taking five more days out. And now it's taking five more days out. And now it's taking five more days to do Fremont to San Jose because I had to do Fremont to San Jose because I had to do Fremont to San Jose because I had the wrong zip code. So whenever next the wrong zip code. So whenever next the wrong zip code. So whenever next time I hear one of those big companies time I hear one of those big companies time I hear one of those big companies talking about DHL about digital talking about DHL about digital talking about DHL about digital transformation, yeah, you're putting a transformation, yeah, you're putting a transformation, yeah, you're putting a nice website in React on top of your nice website in React on top of your nice website in React on top of your freaking old shitty mainframe and your freaking old shitty mainframe and your freaking old shitty mainframe and your stuff doesn't work. stuff doesn't work. stuff doesn't work. >> There you go. >> There you go. >> There you go. And you know, Leonard's talking about And you know, Leonard's talking about And you know, Leonard's talking about vector database collapse and then vector database collapse and then vector database collapse and then Dimmitri's got a DB2 database collapsing Dimmitri's got a DB2 database collapsing Dimmitri's got a DB2 database collapsing on the main frame. No, but I mean but I on the main frame. No, but I mean but I on the main frame. No, but I mean but I mean seriously but I mean seriously to mean seriously but I mean seriously to mean seriously but I mean seriously to the point of this discussion about the the point of this discussion about the the point of this discussion about the hype of AI in this world of all hype of hype of AI in this world of all hype of hype of AI in this world of all hype of league can't even figure out the league can't even figure out the league can't even figure out the freaking address. I mean freaking address. I mean freaking address. I mean >> and then all of a sudden on Dimmitri's >> and then all of a sudden on Dimmitri's >> and then all of a sudden on Dimmitri's web page a little popup and it's Larry web page a little popup and it's Larry web page a little popup and it's Larry Ellison going they should have used Ellison going they should have used Ellison going they should have used Oracle.

Oracle. Oracle. >> Yeah. >> Yeah. Yeah. >> Yeah. Yeah. >> Yeah. Yeah. Well, you know >> Yeah. Yeah. Well, you know >> Yeah. Yeah. Well, you know >> it's a Unix system. I know this. >> it's a Unix system. I know this. >> it's a Unix system. I know this. >> Well, think about it, you know. Hey, you >> Well, think about it, you know. Hey, you >> Well, think about it, you know. Hey, you remember they there was like this whole remember they there was like this whole remember they there was like this whole hype that uh object-oriented databases hype that uh object-oriented databases hype that uh object-oriented databases were going to supplant uh relational were going to supplant uh relational were going to supplant uh relational databases. That didn't happen. Larry databases. That didn't happen. Larry databases. That didn't happen. Larry came to market with a relational uh came to market with a relational uh came to market with a relational uh relational database and created a uh relational database and created a uh relational database and created a uh data type called a blob. data type called a blob. data type called a blob. >> When in doubt, use a blob field. Yes. >> When in doubt, use a blob field. Yes. >> When in doubt, use a blob field. Yes. >> Binary large object. >> Binary large object. >> Binary large object. I think what Dimmitri is talking about I think what Dimmitri is talking about I think what Dimmitri is talking about is something that we probably if we not is something that we probably if we not is something that we probably if we not experienced it yet, we will. And that is experienced it yet, we will. And that is experienced it yet, we will. And that is when when companies implement these when when companies implement these when when companies implement these tools, what they're basically doing is tools, what they're basically doing is tools, what they're basically doing is putting more work on you. So basically, putting more work on you. So basically, putting more work on you. So basically, you work for DHL. you work for DHL. you work for DHL. >> Yeah. >> Yeah. >> Yeah. >> Demetri, cuz you have to solve your own >> Demetri, cuz you have to solve your own >> Demetri, cuz you have to solve your own problem as opposed to problem as opposed to problem as opposed to >> self-service >> self-service >> self-service >> them. Right. Exactly. So, if you're the >> them. Right. Exactly. So, if you're the >> them. Right. Exactly. So, if you're the DHL CTO and listening to our show, give DHL CTO and listening to our show, give DHL CTO and listening to our show, give me a call and I'll tell you what I me a call and I'll tell you what I me a call and I'll tell you what I really think.

really think. really think. >> There you go. >> There you go. >> There you go. >> That's right. That's right. Wow. >> That's right. That's right. Wow. >> That's right. That's right. Wow. >> So much controversy today. This is >> So much controversy today. This is >> So much controversy today. This is great. great. great. >> Well, I'm back. >> Well, I'm back. >> Well, I'm back. >> We love it. We love it. >> We love it. We love it. >> We love it. We love it. >> Yeah. >> Yeah. >> Yeah. >> Yeah. I know. Dimmitri, Mr. Positive, >> Yeah. I know. Dimmitri, Mr. Positive, >> Yeah. I know. Dimmitri, Mr. Positive, Mr. Uplifting, Mr. Uplifting, Mr. Uplifting, >> always. But I don't understand actually >> always. But I don't understand actually >> always. But I don't understand actually the the rational from Leonard against the the rational from Leonard against the the rational from Leonard against vector databases because you cannot vector databases because you cannot vector databases because you cannot delete the data. So what what the hell delete the data. So what what the hell delete the data. So what what the hell that means? that means? that means? >> No, that means that it doesn't scale or >> No, that means that it doesn't scale or >> No, that means that it doesn't scale or >> No, no, no. It you know um >> No, no, no. It you know um >> No, no, no. It you know um vector databases are used for you know a vector databases are used for you know a vector databases are used for you know a certain purpose. certain purpose. certain purpose. previously was used mostly in like previously was used mostly in like previously was used mostly in like scientific research applications, right? scientific research applications, right? scientific research applications, right? for you know uh yeah and so for you know uh yeah and so for you know uh yeah and so you know what folks did early on in you know what folks did early on in you know what folks did early on in using vector databases they thought that using vector databases they thought that using vector databases they thought that they could basically they could basically they could basically uh you know connect all these retrievers uh you know connect all these retrievers uh you know connect all these retrievers to business systems and then create a to business systems and then create a to business systems and then create a real time embedded embedding model that real time embedded embedding model that real time embedded embedding model that then a LLM could tap into to have more then a LLM could tap into to have more then a LLM could tap into to have more context as well as more real time context as well as more real time context as well as more real time information, right? What was the biggest information, right? What was the biggest information, right? What was the biggest gap when G chat GPT came out? Biggest gap when G chat GPT came out? Biggest gap when G chat GPT came out? Biggest issue was it was trained and only had issue was it was trained and only had issue was it was trained and only had knowledge up to what at the time what knowledge up to what at the time what knowledge up to what at the time what was it 2020 and 21.

was it 2020 and 21. was it 2020 and 21. >> Yeah, I remember that >> Yeah, I remember that >> Yeah, I remember that >> December or something like that, right? >> December or something like that, right? >> December or something like that, right? And that's why they introduced the And that's why they introduced the And that's why they introduced the vector database. vector database. vector database. >> That's why we introduced rag. >> That's why we introduced rag. >> That's why we introduced rag. >> Yeah. go and look at the uh all the >> Yeah. go and look at the uh all the >> Yeah. go and look at the uh all the discourse around vector databases as it discourse around vector databases as it discourse around vector databases as it relates to Ragnow. They've deprecated. relates to Ragnow. They've deprecated. relates to Ragnow. They've deprecated. In fact, they're they're looking at In fact, they're they're looking at In fact, they're they're looking at going straight to data sources kind of going straight to data sources kind of going straight to data sources kind of leveraging the MCP model, which I don't leveraging the MCP model, which I don't leveraging the MCP model, which I don't think is going to work really well. But think is going to work really well. But think is going to work really well. But that's why they're pivoting now toward that's why they're pivoting now toward that's why they're pivoting now toward uh Agentic. Aentic has its own problems. uh Agentic. Aentic has its own problems. uh Agentic. Aentic has its own problems. And so all of this stuff is just turning And so all of this stuff is just turning And so all of this stuff is just turning into a a cluster. And you know, so when into a a cluster. And you know, so when into a a cluster. And you know, so when you watch the the podcast um that I'll you watch the the podcast um that I'll you watch the the podcast um that I'll have with these two gentlemen, you're have with these two gentlemen, you're have with these two gentlemen, you're going to start to understand what the going to start to understand what the going to start to understand what the pro it's not going to come from me. It's pro it's not going to come from me. It's pro it's not going to come from me. It's going to be coming from them. Do you going to be coming from them. Do you going to be coming from them. Do you know what I'm saying? know what I'm saying? know what I'm saying? That are trying to That are trying to That are trying to deliver secure confidential deliver secure confidential deliver secure confidential um you know uh robust um you know uh robust um you know uh robust generative AI applications to their generative AI applications to their generative AI applications to their customers. And half of the time what customers. And half of the time what customers. And half of the time what they have to do is manage expectations they have to do is manage expectations they have to do is manage expectations because some you know bozo has told them because some you know bozo has told them because some you know bozo has told them that generative AI can do all this stuff that generative AI can do all this stuff that generative AI can do all this stuff that it can't. Rag can do all this stuff that it can't. Rag can do all this stuff that it can't. Rag can do all this stuff that it can't. Agent can do all this that it can't. Agent can do all this that it can't. Agent can do all this stuff that it can't. So they have to stuff that it can't. So they have to stuff that it can't. So they have to push back. They spend most of their time push back. They spend most of their time push back. They spend most of their time pushing back you know. But the beautiful pushing back you know. But the beautiful pushing back you know. But the beautiful thing is these guys have a sense of well thing is these guys have a sense of well thing is these guys have a sense of well what's going to be next and where is all what's going to be next and where is all what's going to be next and where is all the stuff going to land because they're the stuff going to land because they're the stuff going to land because they're working through all the hard problems working through all the hard problems working through all the hard problems right? They're not just pontificating.

right? They're not just pontificating. right? They're not just pontificating. These guys have have a a real These guys have have a a real These guys have have a a real perspective as practitioners, right? perspective as practitioners, right? perspective as practitioners, right? Guys in the trenches. Guys in the trenches. Guys in the trenches. >> You know, I have to >> You know, I have to >> You know, I have to >> I have to be like Mark right now. I'm >> I have to be like Mark right now. I'm >> I have to be like Mark right now. I'm going have to be like Mark and just say going have to be like Mark and just say going have to be like Mark and just say I have to disagree. I have to disagree. I have to disagree. >> Okay. >> Okay. >> Okay. >> You know, we've got we've got AI models >> You know, we've got we've got AI models >> You know, we've got we've got AI models now that are PhD level in almost every now that are PhD level in almost every now that are PhD level in almost every subject. you know, the fa failure rates subject. you know, the fa failure rates subject. you know, the fa failure rates like on health care is now less than a like on health care is now less than a like on health care is now less than a normal physician and giving diagnosis normal physician and giving diagnosis normal physician and giving diagnosis and stuff like that. Uh there's some and stuff like that. Uh there's some and stuff like that. Uh there's some real value out there. There's some real value out there. There's some real value out there. There's some really good stuff happening. Um really good stuff happening. Um really good stuff happening. Um >> and so I don't want to just throw the >> and so I don't want to just throw the >> and so I don't want to just throw the baby out with a bath water, you know. Um baby out with a bath water, you know. Um baby out with a bath water, you know. Um >> I think there's more focus models, you >> I think there's more focus models, you >> I think there's more focus models, you know. Um there's that notion of like know. Um there's that notion of like know. Um there's that notion of like GPT5 or something. I want to build one GPT5 or something. I want to build one GPT5 or something. I want to build one model to rule them all versus any even model to rule them all versus any even model to rule them all versus any even GP GPT5 is trying to do this having be GP GPT5 is trying to do this having be GP GPT5 is trying to do this having be like a router which I think I think we like a router which I think I think we like a router which I think I think we first saw that with DeepSeek first saw that with DeepSeek first saw that with DeepSeek >> where it's like I'm going to route your >> where it's like I'm going to route your >> where it's like I'm going to route your request to a specialized model request to a specialized model request to a specialized model >> that knows more about this particular >> that knows more about this particular >> that knows more about this particular subject than the giant mega model for subject than the giant mega model for subject than the giant mega model for everything. You know what I mean?

everything. You know what I mean? everything. You know what I mean? >> Um no it's interesting you know now I >> Um no it's interesting you know now I >> Um no it's interesting you know now I you know I know you guys all listen to you know I know you guys all listen to you know I know you guys all listen to different podcasts. The ones I listen to different podcasts. The ones I listen to different podcasts. The ones I listen to most are like, you know, Moonshots like most are like, you know, Moonshots like most are like, you know, Moonshots like Peter Diamantis and like the All-In Peter Diamantis and like the All-In Peter Diamantis and like the All-In podcast and those guys have the very podcast and those guys have the very podcast and those guys have the very smartest people on the planet on every smartest people on the planet on every smartest people on the planet on every time that are in the trenches with this time that are in the trenches with this time that are in the trenches with this AI stuff and this the stuff is truly AI stuff and this the stuff is truly AI stuff and this the stuff is truly remarkable. Um, but no doubt about it, a remarkable. Um, but no doubt about it, a remarkable. Um, but no doubt about it, a lot of money is being spent and and not lot of money is being spent and and not lot of money is being spent and and not everybody's going to survive all this everybody's going to survive all this everybody's going to survive all this stuff. Um, but at least in the United stuff. Um, but at least in the United stuff. Um, but at least in the United States, you have this whole AI um thing States, you have this whole AI um thing States, you have this whole AI um thing that was put out, you know, by the White that was put out, you know, by the White that was put out, you know, by the White House. you know, there's like there's House. you know, there's like there's House. you know, there's like there's now a giant AI plan. We talked about now a giant AI plan. We talked about now a giant AI plan. We talked about this a couple weeks ago, you know, and this a couple weeks ago, you know, and this a couple weeks ago, you know, and so so so >> action plan. Yeah. >> action plan. Yeah. >> action plan. Yeah. >> Yeah. This buildout is going to happen >> Yeah. This buildout is going to happen >> Yeah. This buildout is going to happen and all the, you know, they're going to and all the, you know, they're going to and all the, you know, they're going to try to turn the United States into a try to turn the United States into a try to turn the United States into a giant AI factory. And so, but as Debbie giant AI factory. And so, but as Debbie giant AI factory. And so, but as Debbie said and others have said, you know, said and others have said, you know, said and others have said, you know, we're going to suffer with not enough we're going to suffer with not enough we're going to suffer with not enough electricity maybe to power all these electricity maybe to power all these electricity maybe to power all these data centers. data centers. data centers. >> This there's so much so much has to >> This there's so much so much has to >> This there's so much so much has to happen to make all this work.

happen to make all this work. happen to make all this work. You know You know You know >> it does. >> it does. >> it does. >> Yeah. And so >> Yeah. And so >> Yeah. And so Jensen and Lisa building enough GPUs is Jensen and Lisa building enough GPUs is Jensen and Lisa building enough GPUs is not necessarily the biggest problem that not necessarily the biggest problem that not necessarily the biggest problem that we face. It'll be the build out of the we face. It'll be the build out of the we face. It'll be the build out of the data centers and the electricity needed data centers and the electricity needed data centers and the electricity needed that we don't even we don't have clo I that we don't even we don't have clo I that we don't even we don't have clo I mean I I've heard that just to power mean I I've heard that just to power mean I I've heard that just to power just to have be successful in the US for just to have be successful in the US for just to have be successful in the US for EV vehicles we need like two and a half EV vehicles we need like two and a half EV vehicles we need like two and a half times larger power grid just to support times larger power grid just to support times larger power grid just to support that and that and that and >> everywhere actually >> everywhere actually >> everywhere actually >> and that's not even talking about you >> and that's not even talking about you >> and that's not even talking about you know how they're saying well yeah by know how they're saying well yeah by know how they're saying well yeah by 2030 data centers are going to account 2030 data centers are going to account 2030 data centers are going to account for 10% of electricity being used Um, for 10% of electricity being used Um, for 10% of electricity being used Um, and then you know there's the water and then you know there's the water and then you know there's the water thing and whatever. Um, a whole lot of thing and whatever. Um, a whole lot of thing and whatever. Um, a whole lot of stuff has to happen all hands on deck stuff has to happen all hands on deck stuff has to happen all hands on deck and it's going to it's going to take a and it's going to it's going to take a and it's going to it's going to take a lot of money and a lot of effort by a lot of money and a lot of effort by a lot of money and a lot of effort by a lot of people. lot of people. lot of people. >> Yeah. A AI needs a lot of water but it >> Yeah. A AI needs a lot of water but it >> Yeah. A AI needs a lot of water but it can't swim in water. Isn't that can't swim in water. Isn't that can't swim in water. Isn't that >> Don't tell don't tell some people that >> Don't tell don't tell some people that >> Don't tell don't tell some people that because they think that it doesn't um because they think that it doesn't um because they think that it doesn't um >> it can walk on water.

>> it can walk on water. >> it can walk on water. >> It can walk. Well, you're right. A lot >> It can walk. Well, you're right. A lot >> It can walk. Well, you're right. A lot of people will say it recirculates the of people will say it recirculates the of people will say it recirculates the water and you're all wrong that it's water and you're all wrong that it's water and you're all wrong that it's reusing the water, but it's it's using reusing the water, but it's it's using reusing the water, but it's it's using new water, too. new water, too. new water, too. >> Um, yeah. Yeah. Well, no, it it's the >> Um, yeah. Yeah. Well, no, it it's the >> Um, yeah. Yeah. Well, no, it it's the whole data center. It's not not just the whole data center. It's not not just the whole data center. It's not not just the the rack cooling. the rack cooling. the rack cooling. >> Sure. >> Sure. >> Sure. >> That's only a small fraction. It's >> That's only a small fraction. It's >> That's only a small fraction. It's >> you have to look at the whole >> you have to look at the whole >> you have to look at the whole infrastructure and how much because it's infrastructure and how much because it's infrastructure and how much because it's about the data center. No one talks about the data center. No one talks about the data center. No one talks about oh hey you know the the rack about oh hey you know the the rack about oh hey you know the the rack consumes so much water even if it's consumes so much water even if it's consumes so much water even if it's water cooled right um you know so water cooled right um you know so water cooled right um you know so >> I don't know there's a lot of there's a >> I don't know there's a lot of there's a >> I don't know there's a lot of there's a lot of uh there's a lot of um lot of uh there's a lot of um lot of uh there's a lot of um misguided out there misguided out there misguided out there >> but uh one thing's for sure um there's >> but uh one thing's for sure um there's >> but uh one thing's for sure um there's still the big question of why and for still the big question of why and for still the big question of why and for what what what >> because monetization Yeah. Uh I I mean I would say for you Yeah. Uh I I mean I would say for you know control and military applications know control and military applications know control and military applications but um you know but um you know but um you know >> AI girlfriends >> AI girlfriends >> AI girlfriends >> I guess if people will pay for them.

>> I guess if people will pay for them. >> I guess if people will pay for them. >> Okay. Well, as you may be well aware, >> Okay. Well, as you may be well aware, >> Okay. Well, as you may be well aware, younger people have stopped dating and younger people have stopped dating and younger people have stopped dating and fewer people are getting married and fewer people are getting married and fewer people are getting married and fewer people are having kids and you're fewer people are having kids and you're fewer people are having kids and you're seeing these stats all over the place seeing these stats all over the place seeing these stats all over the place and so maybe they they might need and so maybe they they might need and so maybe they they might need >> but see but that's like the reverse >> but see but that's like the reverse >> but see but that's like the reverse Disney, right? Disneyland, you know, Disney, right? Disneyland, you know, Disney, right? Disneyland, you know, Disney wanted everyone to have kids so Disney wanted everyone to have kids so Disney wanted everyone to have kids so everyone goes to Disney. everyone goes to Disney. everyone goes to Disney. >> Nice. >> Nice. >> Nice. >> No, all all this is tied to people >> No, all all this is tied to people >> No, all all this is tied to people drinking less alcohol. drinking less alcohol. drinking less alcohol. >> Yes, that's happening too. That's right. >> Yes, that's happening too. That's right. >> Yes, that's happening too. That's right. No, she's right. Yeah, it's a huge drop. No, she's right. Yeah, it's a huge drop. No, she's right. Yeah, it's a huge drop. It's killing It's killing It's killing >> making good good um good uh decisions >> making good good um good uh decisions >> making good good um good uh decisions without alcohol. So with alcohol be more without alcohol. So with alcohol be more without alcohol. So with alcohol be more population growth. population growth. population growth. >> There would be more population if we had >> There would be more population if we had >> There would be more population if we had more alcohol. more alcohol. more alcohol. >> Well, you know, maybe maybe you you >> Well, you know, maybe maybe you you >> Well, you know, maybe maybe you you don't you don't find answers with don't you don't find answers with don't you don't find answers with alcohol. You just forget the questions. alcohol. You just forget the questions. alcohol. You just forget the questions. >> That's right. >> That's right. >> That's right. >> It's hilarious. >> It's hilarious. >> It's hilarious. >> Yeah. >> Yeah. >> Yeah. >> I love it. I love it. Oh my god. >> I love it. I love it. Oh my god. >> I love it. I love it. Oh my god. >> Good stuff. Good stuff, y'all. That's a >> Good stuff. Good stuff, y'all. That's a >> Good stuff. Good stuff, y'all. That's a good That's a good closing statement, I good That's a good closing statement, I good That's a good closing statement, I guess.

guess. guess. >> Yeah. Yeah. Alcohol. >> Yeah. Yeah. Alcohol. >> Yeah. Yeah. Alcohol. >> More alcohol. Save humanity. Drink more >> More alcohol. Save humanity. Drink more >> More alcohol. Save humanity. Drink more alcohol. alcohol. alcohol. >> So, when when do when do LM will start >> So, when when do when do LM will start >> So, when when do when do LM will start drinking? Because, you know, digital drinking? Because, you know, digital drinking? Because, you know, digital twins need therapists. So, maybe alcohol twins need therapists. So, maybe alcohol twins need therapists. So, maybe alcohol is the answer for we need to we need to is the answer for we need to we need to is the answer for we need to we need to invent alcohol for LLMs or alcohol for invent alcohol for LLMs or alcohol for invent alcohol for LLMs or alcohol for digital twins. digital twins. digital twins. >> So, some kind of digital alcohol that >> So, some kind of digital alcohol that >> So, some kind of digital alcohol that works in works in works in >> this is a great idea. We should do >> this is a great idea. We should do >> this is a great idea. We should do startup. We should raise money to do startup. We should raise money to do startup. We should raise money to do that. that. that. >> Let's raise money for digital alcohol. >> Let's raise money for digital alcohol. >> Let's raise money for digital alcohol. Yeah, Yeah, Yeah, >> you can come up with a prompt injection >> you can come up with a prompt injection >> you can come up with a prompt injection technique for that. technique for that. technique for that. >> Yes, >> Yes, >> Yes, >> this is this is a good idea. >> this is this is a good idea. >> this is this is a good idea. >> Very very easy. You just like, "Hey, act >> Very very easy. You just like, "Hey, act >> Very very easy. You just like, "Hey, act drunk." You know, drunk." You know, drunk." You know, >> I'm going to think about that. >> I'm going to think about that. >> I'm going to think about that. >> Think about that. >> Think about that. >> Think about that. >> Yes. We're going to do we're going to do >> Yes. We're going to do we're going to do >> Yes. We're going to do we're going to do rag from a bar, a local tavern that goes rag from a bar, a local tavern that goes rag from a bar, a local tavern that goes into the LLM and it starts going, I into the LLM and it starts going, I into the LLM and it starts going, I think you should do this, you know. think you should do this, you know. think you should do this, you know. >> Well, then we should make tests. Can we >> Well, then we should make tests. Can we >> Well, then we should make tests. Can we test if your LLM has been, you know, test if your LLM has been, you know, test if your LLM has been, you know, intoxicated? intoxicated? intoxicated? >> That's right. Yeah. >> That's right. Yeah. >> That's right. Yeah. >> Then we could give them the UI. We could >> Then we could give them the UI. We could >> Then we could give them the UI. We could we could give them L what you call that?

we could give them L what you call that? we could give them L what you call that? LUI, you know, large language model LUI, you know, large language model LUI, you know, large language model under influence. under influence. under influence. >> Under influence. That's right. Yes. >> Under influence. That's right. Yes. >> Under influence. That's right. Yes. >> But but there is this theory as well for >> But but there is this theory as well for >> But but there is this theory as well for developers, right, that when uh when developers, right, that when uh when developers, right, that when uh when they drink a specific amount of alcohol, they drink a specific amount of alcohol, they drink a specific amount of alcohol, they get into a point that they are very they get into a point that they are very they get into a point that they are very good on developing. good on developing. good on developing. Um, no, but I don't know the name of Um, no, but I don't know the name of Um, no, but I don't know the name of this theory, but yeah, that's that's this theory, but yeah, that's that's this theory, but yeah, that's that's >> Yeah, you're right. You brought it up >> Yeah, you're right. You brought it up >> Yeah, you're right. You brought it up actually. I think maybe 150 episodes actually. I think maybe 150 episodes actually. I think maybe 150 episodes ago. ago. ago. >> Exactly. >> Exactly. >> Exactly. >> This is why years ago. >> This is why years ago. >> This is why years ago. >> This is why us hackers always have a >> This is why us hackers always have a >> This is why us hackers always have a bottle of Malot by the hand. bottle of Malot by the hand. bottle of Malot by the hand. >> Always. >> Always. >> Always. >> Oh, Malot. >> Oh, Malot. >> Oh, Malot. >> Malot. Yeah. >> Malot. Yeah. >> Malot. Yeah. >> I've had that. >> I've had that. >> I've had that. >> It's the It's the haka fuel. So it's >> It's the It's the haka fuel. So it's >> It's the It's the haka fuel. So it's it's a liquor from Chicago. it's a liquor from Chicago. it's a liquor from Chicago. >> It's it's the hor most horrible tasting >> It's it's the hor most horrible tasting >> It's it's the hor most horrible tasting thing I've ever tried. thing I've ever tried. thing I've ever tried. >> No, you haven't tried you haven't tried >> No, you haven't tried you haven't tried >> No, you haven't tried you haven't tried this fermented fish from the Nordics. So this fermented fish from the Nordics. So this fermented fish from the Nordics. So >> Okay. Okay. >> Okay. Okay. >> Okay. Okay. [Music] [Music] [Music] >> It's actually I kind of It's very >> It's actually I kind of It's very >> It's actually I kind of It's very medicinal, but I kind of like it a medicinal, but I kind of like it a medicinal, but I kind of like it a little bit nice.

little bit nice. little bit nice. >> Very medicinal. That's for sure. >> Very medicinal. That's for sure. >> Very medicinal. That's for sure. >> But not at 9:30 in the morning. >> But not at 9:30 in the morning. >> But not at 9:30 in the morning. >> Not so much. Not so much. >> Not so much. Not so much. >> Not so much. Not so much. All right. Well, you know what? Maybe All right. Well, you know what? Maybe All right. Well, you know what? Maybe maybe Mark should take us out. maybe Mark should take us out. maybe Mark should take us out. >> Yeah. >> Yeah. >> Yeah. >> To say farewell to everybody as we shut >> To say farewell to everybody as we shut >> To say farewell to everybody as we shut down our data center and we collapse our down our data center and we collapse our down our data center and we collapse our vector database. vector database. vector database. >> Yes, >> Yes, >> Yes, >> exactly. Today we were talking about I >> exactly. Today we were talking about I >> exactly. Today we were talking about I don't know I connect late so that com don't know I connect late so that com don't know I connect late so that com burst. How did it happen? burst. How did it happen? burst. How did it happen? uh is it going to happen with Jive AI uh is it going to happen with Jive AI uh is it going to happen with Jive AI LLM's uh [ __ ] and how LLM's uh [ __ ] and how LLM's uh [ __ ] and how well and actually reading this to to see well and actually reading this to to see well and actually reading this to to see the great hype of the next podcast made the great hype of the next podcast made the great hype of the next podcast made by next cure and uh and lead by Leonard by next cure and uh and lead by Leonard by next cure and uh and lead by Leonard Lee with some real experts that work in Lee with some real experts that work in Lee with some real experts that work in the trenches that are going to tell all the trenches that are going to tell all the trenches that are going to tell all the truth about uh the AI in 2025. the truth about uh the AI in 2025. the truth about uh the AI in 2025. Um and what else? I don't know. Thank Um and what else? I don't know. Thank Um and what else? I don't know. Thank you everyone. If you reach here, just you everyone. If you reach here, just you everyone. If you reach here, just send a comment and and I don't know, do send a comment and and I don't know, do send a comment and and I don't know, do we still have donation period? Uh we still have donation period? Uh we still have donation period? Uh >> huh. What?

>> huh. What? >> huh. What? >> So, are we still accepting donations for >> So, are we still accepting donations for >> So, are we still accepting donations for >> Oh, yeah. Yeah. All the time. Forever >> Oh, yeah. Yeah. All the time. Forever >> Oh, yeah. Yeah. All the time. Forever and ever. and ever. and ever. >> Yes. Show us the money. >> Yes. Show us the money. >> Yes. Show us the money. >> Show us the money. Yeah. And then we'll >> Show us the money. Yeah. And then we'll >> Show us the money. Yeah. And then we'll donate it to kids. donate it to kids. donate it to kids. >> Yeah. >> Yeah. >> Yeah. >> Exactly. So, see you next week. Stay >> Exactly. So, see you next week. Stay >> Exactly. So, see you next week. Stay tuned. tuned. tuned. >> Adios, Macho. >> Adios, Macho. >> Adios, Macho. >> Adios. Thank you guys. >> Adios. Thank you guys. >> Adios. Thank you guys. [Music]