Chip Stocks Crash, $20B Fund Margin Called, Frontier Labs: SLOW DOWN AI, Mamdani's Grocery Stores

All right, everybody. Welcome back. All right, everybody. Welcome back. Welcome back to the number one podcast Welcome back to the number one podcast Welcome back to the number one podcast in the world. The Core Four, Fantastic in the world. The Core Four, Fantastic in the world. The Core Four, Fantastic Four. The original Quartet is here. Did Four. The original Quartet is here. Did Four. The original Quartet is here. Did we peak at like uh number two or number we peak at like uh number two or number we peak at like uh number two or number three last week? Is that what happened? three last week? Is that what happened? three last week? Is that what happened? >> I think it was number four in the world. >> I think it was number four in the world. >> I think it was number four in the world. So, yeah, usually we're trending. So, yeah, usually we're trending. So, yeah, usually we're trending. >> US trending. >> US trending. >> US trending. >> US trend. We're usually number one >> US trend. We're usually number one >> US trend. We're usually number one globally, but yeah, and sometimes number globally, but yeah, and sometimes number globally, but yeah, and sometimes number four US. four US. four US. >> I forgot my Starlink. So, let me >> I forgot my Starlink. So, let me >> I forgot my Starlink. So, let me apologize to everybody. That was a apologize to everybody. That was a apologize to everybody. That was a critical error when you're on the road critical error when you're on the road critical error when you're on the road or on the water, but you know, it'll be or on the water, but you know, it'll be or on the water, but you know, it'll be fine. fine. fine. >> I had five huge leads come in this week. >> I had five huge leads come in this week. >> I had five huge leads come in this week. >> The Glengar Glenn enterprise leads. >> The Glengar Glenn enterprise leads. >> The Glengar Glenn enterprise leads. Enterprise sales is a bear because it's Enterprise sales is a bear because it's Enterprise sales is a bear because it's super chunky, but the deals are super chunky, but the deals are super chunky, but the deals are ginormous. ginormous. ginormous. >> Sax, you got any advice for Chimat from >> Sax, you got any advice for Chimat from >> Sax, you got any advice for Chimat from your uh enterprise sales days? You your uh enterprise sales days? You your uh enterprise sales days? You closed some of those big seven figure closed some of those big seven figure closed some of those big seven figure deals when you were doing Yammer? deals when you were doing Yammer? deals when you were doing Yammer? >> No leverage. Don't put on leverage. >> No leverage. Don't put on leverage. >> No leverage. Don't put on leverage. >> No leverage of the show today. >> No leverage of the show today. >> No leverage of the show today. Leverage equals risk of ruin. Leverage equals risk of ruin. Leverage equals risk of ruin. >> PSA, [laughter] >> PSA, [laughter] >> PSA, [laughter] no leverage. no leverage. no leverage. >> I have the situational awareness to not >> I have the situational awareness to not >> I have the situational awareness to not lever up. lever up. lever up. >> That's good. Yeah, you want that. That >> That's good. Yeah, you want that. That >> That's good. Yeah, you want that. That situational awareness. situational awareness. situational awareness. >> I mean, it's kind of out there. I mean, >> I mean, it's kind of out there. I mean, >> I mean, it's kind of out there. I mean, if you name your fund situational if you name your fund situational if you name your fund situational awareness, that's awareness, that's awareness, that's >> Yeah. Come on the pot anytime, Lualt. >> Yeah. Come on the pot anytime, Lualt. >> Yeah. Come on the pot anytime, Lualt. All right, everybody. We got to talk All right, everybody. We got to talk All right, everybody. We got to talk about chip stocks crashing after an about chip stocks crashing after an about chip stocks crashing after an alltime runup. And we had a major hedge alltime runup. And we had a major hedge alltime runup. And we had a major hedge fund get margin called and some fund get margin called and some fund get margin called and some incredible margin calls happening in incredible margin calls happening in incredible margin calls happening in South Korea. Leopold Ashen Brener is a South Korea. Leopold Ashen Brener is a South Korea. Leopold Ashen Brener is a 25-year-old hedge fund manager. He left 25-year-old hedge fund manager. He left 25-year-old hedge fund manager. He left OpenAI two years ago to start his own OpenAI two years ago to start his own OpenAI two years ago to start his own fund and apparently according to fund and apparently according to fund and apparently according to reports, this is breaking news on reports, this is breaking news on reports, this is breaking news on Thursday when we tape, he got margin Thursday when we tape, he got margin Thursday when we tape, he got margin called and had to sell his entire public called and had to sell his entire public called and had to sell his entire public portfolio to cover massive losses caused portfolio to cover massive losses caused portfolio to cover massive losses caused by his leverage. And who bought them? by his leverage. And who bought them? by his leverage. And who bought them? none other than Citadel's Ken Griffin. none other than Citadel's Ken Griffin. none other than Citadel's Ken Griffin. We don't know if it was Ken Griffin We don't know if it was Ken Griffin We don't know if it was Ken Griffin himself, but Citadel bought it according himself, but Citadel bought it according himself, but Citadel bought it according to the early reports. Leopold had insane to the early reports. Leopold had insane to the early reports. Leopold had insane returns and he rode the wave of AI and returns and he rode the wave of AI and returns and he rode the wave of AI and chips and Frontier Labs as recently as chips and Frontier Labs as recently as chips and Frontier Labs as recently as this month. Uh, and he started the fund this month. Uh, and he started the fund this month. Uh, and he started the fund with $225 million in 2024. He grew it with $225 million in 2024. He grew it with $225 million in 2024. He grew it 100x to 20 billion this year or so. Ran 100x to 20 billion this year or so. Ran 100x to 20 billion this year or so. Ran it all the way up to 45 billion. Now it all the way up to 45 billion. Now it all the way up to 45 billion. Now he's at 200x earlier this month by he's at 200x earlier this month by he's at 200x earlier this month by trading on leverage. According to our trading on leverage. According to our trading on leverage. According to our friends at CNBC at the end of the June, friends at CNBC at the end of the June, friends at CNBC at the end of the June, he was reportedly up 4x450% he was reportedly up 4x450% he was reportedly up 4x450% this year. Uh some have reported that this year. Uh some have reported that this year. Uh some have reported that he's also selling his massive anthropic he's also selling his massive anthropic he's also selling his massive anthropic stake to cover these losses, but the stake to cover these losses, but the stake to cover these losses, but the Wall Street Journal is disputing it. Wall Street Journal is disputing it. Wall Street Journal is disputing it. Again, uh he we're we're happy to have Again, uh he we're we're happy to have Again, uh he we're we're happy to have him here on the program. How did this him here on the program. How did this him here on the program. How did this all blow up? Well, NASDAQ's chip index, all blow up? Well, NASDAQ's chip index, all blow up? Well, NASDAQ's chip index, this is called the Philadelphia this is called the Philadelphia this is called the Philadelphia Semiconductor Index, is down over 20% Semiconductor Index, is down over 20% Semiconductor Index, is down over 20% over the last month. That's bare market over the last month. That's bare market over the last month. That's bare market territory. Obviously, definition of bare territory. Obviously, definition of bare territory. Obviously, definition of bare market territory for those of you who market territory for those of you who market territory for those of you who don't play in the markets is anything don't play in the markets is anything don't play in the markets is anything over 20%. Uh the index included the top over 20%. Uh the index included the top over 20%. Uh the index included the top 30 US listed chips. That's people like 30 US listed chips. That's people like 30 US listed chips. That's people like Nvidia, TSMC, AMD, Micron, you know, all Nvidia, TSMC, AMD, Micron, you know, all Nvidia, TSMC, AMD, Micron, you know, all those big names. But the index bounced those big names. But the index bounced those big names. But the index bounced back a bit today, up 7% when we're back a bit today, up 7% when we're back a bit today, up 7% when we're taping. So, we may have found a bottom. taping. So, we may have found a bottom. taping. So, we may have found a bottom. Unfortunately for Leopold, he had Unfortunately for Leopold, he had Unfortunately for Leopold, he had already sold. Samsung and SKH, two South already sold. Samsung and SKH, two South already sold. Samsung and SKH, two South Korean chip companies that are not Korean chip companies that are not Korean chip companies that are not included in the NASDAQ index, also got included in the NASDAQ index, also got included in the NASDAQ index, also got smashed, crushed, demolished. Samsung smashed, crushed, demolished. Samsung smashed, crushed, demolished. Samsung down 38% over last month. SKH Highix down 38% over last month. SKH Highix down 38% over last month. SKH Highix down 14% since going public 3 weeks ago. down 14% since going public 3 weeks ago. down 14% since going public 3 weeks ago. The Cosby, that's South Korea's version The Cosby, that's South Korea's version The Cosby, that's South Korea's version of the S&P 500, is down over 40% in the of the S&P 500, is down over 40% in the of the S&P 500, is down over 40% in the last 40 days. Between last Friday and last 40 days. Between last Friday and last 40 days. Between last Friday and Wednesday, leading chip companies shed Wednesday, leading chip companies shed Wednesday, leading chip companies shed over a trillion dollars in market cap over a trillion dollars in market cap over a trillion dollars in market cap combined. So to put this in context, combined. So to put this in context, combined. So to put this in context, chip stocks had a legendary run the past chip stocks had a legendary run the past chip stocks had a legendary run the past couple of years, but uh you know trading couple of years, but uh you know trading couple of years, but uh you know trading on leverage, we'll talk about it. Very on leverage, we'll talk about it. Very on leverage, we'll talk about it. Very dangerous. If there's a downturn, we'll dangerous. If there's a downturn, we'll dangerous. If there's a downturn, we'll get into the South Korea wrinkle as get into the South Korea wrinkle as get into the South Korea wrinkle as well. Even with this downturn, the well. Even with this downturn, the well. Even with this downturn, the 5-year results are still spectacular. 5-year results are still spectacular. 5-year results are still spectacular. Jimoth Micron up 850% mostly in the last Jimoth Micron up 850% mostly in the last Jimoth Micron up 850% mostly in the last year. Nvidia up 875% in the last 5 years year. Nvidia up 875% in the last 5 years year. Nvidia up 875% in the last 5 years and Broadcom up 663%. Let's discuss it. Let's discuss it. >> If I was going to give you one piece of >> If I was going to give you one piece of >> If I was going to give you one piece of advice when you're running risk is you advice when you're running risk is you advice when you're running risk is you have to manage leverage incredibly have to manage leverage incredibly have to manage leverage incredibly carefully because when it runs ahead of carefully because when it runs ahead of carefully because when it runs ahead of you, the unwind is incredibly violent you, the unwind is incredibly violent you, the unwind is incredibly violent and it's incredibly quick. That's the and it's incredibly quick. That's the and it's incredibly quick. That's the biggest problem with with running either biggest problem with with running either biggest problem with with running either massively levered long or massively massively levered long or massively massively levered long or massively levered short. So I don't know to what levered short. So I don't know to what levered short. So I don't know to what extent he was running lever but the extent he was running lever but the extent he was running lever but the rumors are he was running like three and rumors are he was running like three and rumors are he was running like three and a half turns which just to give you a a half turns which just to give you a a half turns which just to give you a sense when you're running that much risk sense when you're running that much risk sense when you're running that much risk a 3 and 4% move is amplified 12 and 13 a 3 and 4% move is amplified 12 and 13 a 3 and 4% move is amplified 12 and 13 but if you saw what's happened in the but if you saw what's happened in the but if you saw what's happened in the last 3 days a 25% move is amplified 75%. last 3 days a 25% move is amplified 75%. last 3 days a 25% move is amplified 75%. So So So >> it has the risk to stop you out. And >> it has the risk to stop you out. And >> it has the risk to stop you out. And what happens is when you get that what happens is when you get that what happens is when you get that leverage, the banks are given the leverage, the banks are given the leverage, the banks are given the authority to close you out. And when authority to close you out. And when authority to close you out. And when they close you out, what they do is they they close you out, what they do is they they close you out, what they do is they start calling around and unwind your start calling around and unwind your start calling around and unwind your risk. And you don't have much of a risk. And you don't have much of a risk. And you don't have much of a choice. It's sort of an automatic choice. It's sort of an automatic choice. It's sort of an automatic one-way ratchet. one-way ratchet. one-way ratchet. So if everything that has been reported So if everything that has been reported So if everything that has been reported is accurate, he was running about three is accurate, he was running about three is accurate, he was running about three and a half times levered. The market and a half times levered. The market and a half times levered. The market moved against him. He lost a very large moved against him. He lost a very large moved against him. He lost a very large percentage of his gains and then the percentage of his gains and then the percentage of his gains and then the prime brokers started calling folks. prime brokers started calling folks. prime brokers started calling folks. Citadel bought the whole book and now Citadel bought the whole book and now Citadel bought the whole book and now the question is what is the AUM left and the question is what is the AUM left and the question is what is the AUM left and what is the high water mark and can he what is the high water mark and can he what is the high water mark and can he actually dig his way out? These things actually dig his way out? These things actually dig his way out? These things are brutal. your thoughts, Saxs, uh are brutal. your thoughts, Saxs, uh are brutal. your thoughts, Saxs, uh looking at this situation, looking at this situation, looking at this situation, any lessons for you or I guess bigger any lessons for you or I guess bigger any lessons for you or I guess bigger picture, this downdraft, is it because picture, this downdraft, is it because picture, this downdraft, is it because of market conditions, you know, of market conditions, you know, of market conditions, you know, inflation, the war, or people just ahead inflation, the war, or people just ahead inflation, the war, or people just ahead of their skis when it comes to uh the of their skis when it comes to uh the of their skis when it comes to uh the valuation of these companies and then he valuation of these companies and then he valuation of these companies and then he just got caught in a downdraft. Yeah. just got caught in a downdraft. Yeah. just got caught in a downdraft. Yeah. >> Well, I think that is the key question >> Well, I think that is the key question >> Well, I think that is the key question here. Is this correction in the markets? here. Is this correction in the markets? here. Is this correction in the markets? Is it driven by fundamentals or is it Is it driven by fundamentals or is it Is it driven by fundamentals or is it driven by momentum? And my view is that driven by momentum? And my view is that driven by momentum? And my view is that I think it's driven by momentum. Meaning I think it's driven by momentum. Meaning I think it's driven by momentum. Meaning that over the past year, you've had this that over the past year, you've had this that over the past year, you've had this roughly 10x runup in memory chip stocks roughly 10x runup in memory chip stocks roughly 10x runup in memory chip stocks and you've seen this overall huge rise and you've seen this overall huge rise and you've seen this overall huge rise in any stock that's related to the AI in any stock that's related to the AI in any stock that's related to the AI boom. So, anything related to this AI boom. So, anything related to this AI boom. So, anything related to this AI capex boom has been going up like crazy. capex boom has been going up like crazy. capex boom has been going up like crazy. And I think it was inevitable that you'd And I think it was inevitable that you'd And I think it was inevitable that you'd see a pullback. I think there was see a pullback. I think there was see a pullback. I think there was something like a 10% pullback in the something like a 10% pullback in the something like a 10% pullback in the NASDAQ NASDAQ NASDAQ from the peak. But when you look at this from the peak. But when you look at this from the peak. But when you look at this momentum trade, it was down like 30% or momentum trade, it was down like 30% or momentum trade, it was down like 30% or 40%. Right? Because the 10% was on the 40%. Right? Because the 10% was on the 40%. Right? Because the 10% was on the whole market. So this sort of momentum whole market. So this sort of momentum whole market. So this sort of momentum trade was the most exposed part of it. trade was the most exposed part of it. trade was the most exposed part of it. And you look at what happened in South And you look at what happened in South And you look at what happened in South Korea, you look at what happened with Korea, you look at what happened with Korea, you look at what happened with Leopold's fund and obviously there was a Leopold's fund and obviously there was a Leopold's fund and obviously there was a lot of leverage behind this momentum lot of leverage behind this momentum lot of leverage behind this momentum trade. So when it corrects it's going to trade. So when it corrects it's going to trade. So when it corrects it's going to be brutal. But I think that the question be brutal. But I think that the question be brutal. But I think that the question again is does this reveal anything about again is does this reveal anything about again is does this reveal anything about the fundamentals? And my sense is that the fundamentals? And my sense is that the fundamentals? And my sense is that you're already seeing the rebound this you're already seeing the rebound this you're already seeing the rebound this morning and what I mean by that when I morning and what I mean by that when I morning and what I mean by that when I say fundamentals is is the capex that's say fundamentals is is the capex that's say fundamentals is is the capex that's being invested in the AI boom is that being invested in the AI boom is that being invested in the AI boom is that real or is it misguided? Right? Is it is real or is it misguided? Right? Is it is real or is it misguided? Right? Is it is that a sound investment? Is that an that a sound investment? Is that an that a sound investment? Is that an investment that the hyperscalers for investment that the hyperscalers for investment that the hyperscalers for example should be making? Is that an example should be making? Is that an example should be making? Is that an investment that's eventually going to investment that's eventually going to investment that's eventually going to deliver ROI or is this some sort of deliver ROI or is this some sort of deliver ROI or is this some sort of bubble? And my view is that it's real bubble? And my view is that it's real bubble? And my view is that it's real that I think there will be a return on that I think there will be a return on that I think there will be a return on all this capex. I don't try to predict all this capex. I don't try to predict all this capex. I don't try to predict stocks or tell people when they should stocks or tell people when they should stocks or tell people when they should be buyers, but you look at the be buyers, but you look at the be buyers, but you look at the hyperscalers, they have invested pretty hyperscalers, they have invested pretty hyperscalers, they have invested pretty much all of their free cash flow and much all of their free cash flow and much all of their free cash flow and then some in this boom. You know, a lot then some in this boom. You know, a lot then some in this boom. You know, a lot of people are trading those stocks down of people are trading those stocks down of people are trading those stocks down because of that. My view is that because of that. My view is that because of that. My view is that eventually there will be a return on eventually there will be a return on eventually there will be a return on that investment. And this is sort of that investment. And this is sort of that investment. And this is sort of temporary market volatility amplified by temporary market volatility amplified by temporary market volatility amplified by leverage. And Chimath is right. You leverage. And Chimath is right. You leverage. And Chimath is right. You know, I think it was Warren Buffett or know, I think it was Warren Buffett or know, I think it was Warren Buffett or maybe Munger who said that leverage is maybe Munger who said that leverage is maybe Munger who said that leverage is the only way that smart people go broke the only way that smart people go broke the only way that smart people go broke because, you know, if you're not using because, you know, if you're not using because, you know, if you're not using leverage, leverage, leverage, your portfolio would just be down 30% your portfolio would just be down 30% your portfolio would just be down 30% this month and then it would already be this month and then it would already be this month and then it would already be up 7% today. So, you'd be rebounding. up 7% today. So, you'd be rebounding. up 7% today. So, you'd be rebounding. So, you'd be down, okay, 20 something% So, you'd be down, okay, 20 something% So, you'd be down, okay, 20 something% this month, but after having risen 10x this month, but after having risen 10x this month, but after having risen 10x in the past year. But if you're in the past year. But if you're in the past year. But if you're leveraged 3 or 4x, you're wiped out leveraged 3 or 4x, you're wiped out leveraged 3 or 4x, you're wiped out >> and you get margin called. So look, >> and you get margin called. So look, >> and you get margin called. So look, there's many examples of really smart there's many examples of really smart there's many examples of really smart people getting hurt by by leverage. people getting hurt by by leverage. people getting hurt by by leverage. Yeah. And that that's the lesson there. Yeah. And that that's the lesson there. Yeah. And that that's the lesson there. Now, I think Leopold's a really Now, I think Leopold's a really Now, I think Leopold's a really interesting figure in the whole AI interesting figure in the whole AI interesting figure in the whole AI movement and I would say an interesting movement and I would say an interesting movement and I would say an interesting thinker. I met him about a year year and thinker. I met him about a year year and thinker. I met him about a year year and a half ago. a half ago. a half ago. >> Did you invest in the fund? Did you give >> Did you invest in the fund? Did you give >> Did you invest in the fund? Did you give your your your >> No, I wasn't in I was prohibited from >> No, I wasn't in I was prohibited from >> No, I wasn't in I was prohibited from investing in things like that. investing in things like that. investing in things like that. >> You were in you were in DC at the time. >> You were in you were in DC at the time. >> You were in you were in DC at the time. Yeah, Yeah, Yeah, >> but I thought he was a really >> but I thought he was a really >> but I thought he was a really interesting thinker and he wrote a blog interesting thinker and he wrote a blog interesting thinker and he wrote a blog called situational awareness before he called situational awareness before he called situational awareness before he created the hedge fund version of it. created the hedge fund version of it. created the hedge fund version of it. And I thought what was really And I thought what was really And I thought what was really interesting about it was just he laid interesting about it was just he laid interesting about it was just he laid out the bullcase for the AI boom. And out the bullcase for the AI boom. And out the bullcase for the AI boom. And just by the way, he's like very wired just by the way, he's like very wired just by the way, he's like very wired into anthropic. I think his fiance is into anthropic. I think his fiance is into anthropic. I think his fiance is Dario's chief of staff, something like Dario's chief of staff, something like Dario's chief of staff, something like that. You could almost say that he's that. You could almost say that he's that. You could almost say that he's like the hedge fund version of the like the hedge fund version of the like the hedge fund version of the anthropic thesis. And what I thought was anthropic thesis. And what I thought was anthropic thesis. And what I thought was interesting about his argument is he interesting about his argument is he interesting about his argument is he talked about or uh orders of magnitude talked about or uh orders of magnitude talked about or uh orders of magnitude uh which he called ooms um increases in uh which he called ooms um increases in uh which he called ooms um increases in three key areas. So he said that if you three key areas. So he said that if you three key areas. So he said that if you look at the raw compute the chips they look at the raw compute the chips they look at the raw compute the chips they were getting better at a rate of roughly were getting better at a rate of roughly were getting better at a rate of roughly 3x per year which is roughly an order of 3x per year which is roughly an order of 3x per year which is roughly an order of magnitude or 10x every two years. He magnitude or 10x every two years. He magnitude or 10x every two years. He said if you look at the algorithmic said if you look at the algorithmic said if you look at the algorithmic efficiency so you know techniques like efficiency so you know techniques like efficiency so you know techniques like reinforcement learning things like that reinforcement learning things like that reinforcement learning things like that the models were getting better at 3x the models were getting better at 3x the models were getting better at 3x every year which is again order of every year which is again order of every year which is again order of magnitude every two years and then he magnitude every two years and then he magnitude every two years and then he also said that there were huge gains also said that there were huge gains also said that there were huge gains from what he called unhobling which I from what he called unhobling which I from what he called unhobling which I think now we would look at it things think now we would look at it things think now we would look at it things like the harness and connectors you know like the harness and connectors you know like the harness and connectors you know ways of using the model those were also ways of using the model those were also ways of using the model those were also getting better the ways of integrating getting better the ways of integrating getting better the ways of integrating the model's decision-m in practical ways the model's decision-m in practical ways the model's decision-m in practical ways that the intelligence actually becomes that the intelligence actually becomes that the intelligence actually becomes useful. And he said that that was also useful. And he said that that was also useful. And he said that that was also similarly improving. And so, you know, similarly improving. And so, you know, similarly improving. And so, you know, you project forward when you have 10x you project forward when you have 10x you project forward when you have 10x orders of magnitude improvement in these orders of magnitude improvement in these orders of magnitude improvement in these key underlying fundamentals, these key key underlying fundamentals, these key key underlying fundamentals, these key drivers of the technology. And you can drivers of the technology. And you can drivers of the technology. And you can see that well over a course of not just see that well over a course of not just see that well over a course of not just two years, but over four years, you're two years, but over four years, you're two years, but over four years, you're going to have 100x improvement. Over six going to have 100x improvement. Over six going to have 100x improvement. Over six years, you're going to have a thousandx years, you're going to have a thousandx years, you're going to have a thousandx improvement, right? because it's improvement, right? because it's improvement, right? because it's >> and you very rarely see anything in the >> and you very rarely see anything in the >> and you very rarely see anything in the world that grows at that velocity. We world that grows at that velocity. We world that grows at that velocity. We we'd be hardressed here we'd be hardressed here we'd be hardressed here >> with the exception of maybe bandwidth, >> with the exception of maybe bandwidth, >> with the exception of maybe bandwidth, you know, going to fiber to the home or you know, going to fiber to the home or you know, going to fiber to the home or something like what's an analogy where something like what's an analogy where something like what's an analogy where where that's happened before in history. where that's happened before in history. where that's happened before in history. >> Yeah. Virality. I mean I, you know, back >> Yeah. Virality. I mean I, you know, back >> Yeah. Virality. I mean I, you know, back in the PayPal days with the PayPal in the PayPal days with the PayPal in the PayPal days with the PayPal mafia, we would think in this way of mafia, we would think in this way of mafia, we would think in this way of exponential increases because we would exponential increases because we would exponential increases because we would see an exponential growth curve and so see an exponential growth curve and so see an exponential growth curve and so we were able to project forward. So his we were able to project forward. So his we were able to project forward. So his thinking ins always appealed to me thinking ins always appealed to me thinking ins always appealed to me because I think most people just don't because I think most people just don't because I think most people just don't think in exponentials or don't know how think in exponentials or don't know how think in exponentials or don't know how to think in exponentials. to think in exponentials. to think in exponentials. >> It's hard for humans to think in >> It's hard for humans to think in >> It's hard for humans to think in exponentials, right? These when numbers exponentials, right? These when numbers exponentials, right? These when numbers get big, it's like the difference get big, it's like the difference get big, it's like the difference between a billion and a trillion is is a between a billion and a trillion is is a between a billion and a trillion is is a is a lot. It's not a small amount. is a lot. It's not a small amount. is a lot. It's not a small amount. >> Yeah. And you'd have to say, look, he >> Yeah. And you'd have to say, look, he >> Yeah. And you'd have to say, look, he was stunningly successful for the first was stunningly successful for the first was stunningly successful for the first couple years. Apparently, he started couple years. Apparently, he started couple years. Apparently, he started with 200 million or so in his hedge fund with 200 million or so in his hedge fund with 200 million or so in his hedge fund and he rolled that all the way up to 20 and he rolled that all the way up to 20 and he rolled that all the way up to 20 billion, I think. Now, the problem I billion, I think. Now, the problem I billion, I think. Now, the problem I mean the reason why I think he got wiped mean the reason why I think he got wiped mean the reason why I think he got wiped out or at least his public book did is out or at least his public book did is out or at least his public book did is it's partly the leverage and then you it's partly the leverage and then you it's partly the leverage and then you have the short-term volatility. So, have the short-term volatility. So, have the short-term volatility. So, those two things don't go together. those two things don't go together. those two things don't go together. Also, you know, when your fund grows Also, you know, when your fund grows Also, you know, when your fund grows that much, you get a lot of hot money. that much, you get a lot of hot money. that much, you get a lot of hot money. So, when you say, well, he he's up 10x So, when you say, well, he he's up 10x So, when you say, well, he he's up 10x before the 30% correction. Well, the before the 30% correction. Well, the before the 30% correction. Well, the question is who's up 10x? Obviously, the question is who's up 10x? Obviously, the question is who's up 10x? Obviously, the investors who were there from the investors who were there from the investors who were there from the beginning are up 10x or more, but beginning are up 10x or more, but beginning are up 10x or more, but >> the latest people, >> the latest people, >> the latest people, >> that's only 200 million, right? So if 10 >> that's only 200 million, right? So if 10 >> that's only 200 million, right? So if 10 billions come in in the last few months billions come in in the last few months billions come in in the last few months because of the hot money dynamic where because of the hot money dynamic where because of the hot money dynamic where everyone piles into the most successful everyone piles into the most successful everyone piles into the most successful hedge funds, those guys are kind of hedge funds, those guys are kind of hedge funds, those guys are kind of wiped out. wiped out. wiped out. >> So let's talk about the psychology of >> So let's talk about the psychology of >> So let's talk about the psychology of this uh Dave Freeberg. If somebody is so this uh Dave Freeberg. If somebody is so this uh Dave Freeberg. If somebody is so brilliant that they can write this essay brilliant that they can write this essay brilliant that they can write this essay and understand the market uh so and understand the market uh so and understand the market uh so exquisitly and be such a great exquisitly and be such a great exquisitly and be such a great communicator, how could they have such a communicator, how could they have such a communicator, how could they have such a crazy blind spot when it comes to crazy blind spot when it comes to crazy blind spot when it comes to putting on leverage at this scale? Do putting on leverage at this scale? Do putting on leverage at this scale? Do you have any thoughts on that, Freeberg, you have any thoughts on that, Freeberg, you have any thoughts on that, Freeberg, or have you seen it before? Is it just or have you seen it before? Is it just or have you seen it before? Is it just the folly of the folly of the folly of >> It's not a blind spot. It's a feature >> It's not a blind spot. It's a feature >> It's not a blind spot. It's a feature that turns into a bug. We're all like that turns into a bug. We're all like that turns into a bug. We're all like this. We all know people that have that this. We all know people that have that this. We all know people that have that edge and can push it. edge and can push it. edge and can push it. >> What do you think, Freeberg, on the >> What do you think, Freeberg, on the >> What do you think, Freeberg, on the personality type? Or is this just personality type? Or is this just personality type? Or is this just something most people do when they're on something most people do when they're on something most people do when they're on the heater? the heater? the heater? >> Conviction. >> Conviction. >> Conviction. >> Where do you stand on it? >> Where do you stand on it? >> Where do you stand on it? >> Ultra conviction. I think the >> Ultra conviction. I think the >> Ultra conviction. I think the Warren Buffett Warren Buffett Warren Buffett assessment of equity markets is uh in assessment of equity markets is uh in assessment of equity markets is uh in the short term they're voting machines. the short term they're voting machines. the short term they're voting machines. In the long term they're weighing In the long term they're weighing In the long term they're weighing machines machines machines and you could have the right long-term and you could have the right long-term and you could have the right long-term view. I mean look at SPF. SPF would view. I mean look at SPF. SPF would view. I mean look at SPF. SPF would pretty much would have been the greatest pretty much would have been the greatest pretty much would have been the greatest investor of all time if he didn't get investor of all time if he didn't get investor of all time if he didn't get liquidated. Same dynamic. I mean liquidated. Same dynamic. I mean liquidated. Same dynamic. I mean obviously there was fraud in terms of obviously there was fraud in terms of obviously there was fraud in terms of how he was allocating capital but his how he was allocating capital but his how he was allocating capital but his actual portfolio over the long run was actual portfolio over the long run was actual portfolio over the long run was absolutely correct. In the same way that absolutely correct. In the same way that absolutely correct. In the same way that if you had bet on the internet and if you had bet on the internet and if you had bet on the internet and stayed in that bet from 1995 through to stayed in that bet from 1995 through to stayed in that bet from 1995 through to today and you bought a portfolio of today and you bought a portfolio of today and you bought a portfolio of internet stocks, a bunch of them would internet stocks, a bunch of them would internet stocks, a bunch of them would have fallen to the wayside. But those have fallen to the wayside. But those have fallen to the wayside. But those that won,000x, 20,000x, 20,000x that won,000x, 20,000x, 20,000x that won,000x, 20,000x, 20,000x and you do extraordinarily well. So he and you do extraordinarily well. So he and you do extraordinarily well. So he could be right in his fundamental could be right in his fundamental could be right in his fundamental assessment and analysis, but then in assessment and analysis, but then in assessment and analysis, but then in markets over the short term, you have markets over the short term, you have markets over the short term, you have bubbles and bubbles pop. And when bubbles and bubbles pop. And when bubbles and bubbles pop. And when bubbles pop, if you have a leverage to bubbles pop, if you have a leverage to bubbles pop, if you have a leverage to multiply your returns, you get wiped multiply your returns, you get wiped multiply your returns, you get wiped out. That's effectively, you know, out. That's effectively, you know, out. That's effectively, you know, what's going on here. And he may be what's going on here. And he may be what's going on here. And he may be right. Right. right. Right. right. Right. >> You know, the thing to really double >> You know, the thing to really double >> You know, the thing to really double click on that I think will probably come click on that I think will probably come click on that I think will probably come out in the next couple of days or weeks out in the next couple of days or weeks out in the next couple of days or weeks is just how historic the South Korea is just how historic the South Korea is just how historic the South Korea unwind was. 1.2 million leverage trading unwind was. 1.2 million leverage trading unwind was. 1.2 million leverage trading accounts uh have been hit with margin accounts uh have been hit with margin accounts uh have been hit with margin calls in South Korea. If you know about calls in South Korea. If you know about calls in South Korea. If you know about the South Korean market, that data is the South Korean market, that data is the South Korean market, that data is two weeks old. Jal, that number is much two weeks old. Jal, that number is much two weeks old. Jal, that number is much bigger today. Yeah. bigger today. Yeah. bigger today. Yeah. >> Yeah. But I mean, just in terms of >> Yeah. But I mean, just in terms of >> Yeah. But I mean, just in terms of people discussing it in relation to him people discussing it in relation to him people discussing it in relation to him getting caught in the downdraft, getting caught in the downdraft, getting caught in the downdraft, >> if anything, he got caught in this >> if anything, he got caught in this >> if anything, he got caught in this downdraft. Of those 1.2 million lever um downdraft. Of those 1.2 million lever um downdraft. Of those 1.2 million lever um levered accounts, somewhere around levered accounts, somewhere around levered accounts, somewhere around 350,000 of them were fully liquidated 350,000 of them were fully liquidated 350,000 of them were fully liquidated already. And so already. And so already. And so >> again, two weeks old, that's so as of >> again, two weeks old, that's so as of >> again, two weeks old, that's so as of today, the number is much bigger, right? today, the number is much bigger, right? today, the number is much bigger, right? >> So it could be closer to a million >> So it could be closer to a million >> So it could be closer to a million accounts fully liquidated today. If accounts fully liquidated today. If accounts fully liquidated today. If that's the case, we're talking about that's the case, we're talking about that's the case, we're talking about like some percentage of South Korean like some percentage of South Korean like some percentage of South Korean population having their entire asset population having their entire asset population having their entire asset base blown out. base blown out. base blown out. >> Their entire >> Their entire >> Their entire >> 3% of the population. >> 3% of the population. >> 3% of the population. >> Well, that's going to that's going to >> Well, that's going to that's going to >> Well, that's going to that's going to sting. And it is a very investment sting. And it is a very investment sting. And it is a very investment forward culture. If you look at what forward culture. If you look at what forward culture. If you look at what happened in crypto, the same thing happened in crypto, the same thing happened in crypto, the same thing happened with NFTs and speculation happened with NFTs and speculation happened with NFTs and speculation there. And they had banned crypto there. And they had banned crypto there. And they had banned crypto because they knew the Korean culture has because they knew the Korean culture has because they knew the Korean culture has this gamble in it and this obsession this gamble in it and this obsession this gamble in it and this obsession with trading. with trading. with trading. >> Can I can I just frame something up? So >> Can I can I just frame something up? So >> Can I can I just frame something up? So if we take if we take if we take >> the circumstance of there's a good >> the circumstance of there's a good >> the circumstance of there's a good long-term bet in AI that can be made in long-term bet in AI that can be made in long-term bet in AI that can be made in the markets but in the short term the markets but in the short term the markets but in the short term there's an exuberance that arises. The there's an exuberance that arises. The there's an exuberance that arises. The question is what's resetting that question is what's resetting that question is what's resetting that exuberance? What's bringing us back down exuberance? What's bringing us back down exuberance? What's bringing us back down to earth in the short term and I think to earth in the short term and I think to earth in the short term and I think if you take a zoom out there's a bunch if you take a zoom out there's a bunch if you take a zoom out there's a bunch of other statistics and other facts on of other statistics and other facts on of other statistics and other facts on the ground that I think are big macro the ground that I think are big macro the ground that I think are big macro drivers at the moment. If you take a drivers at the moment. If you take a drivers at the moment. If you take a look at the 30-year Treasury yield we look at the 30-year Treasury yield we look at the 30-year Treasury yield we just crossed 5.2% 2% for the first time just crossed 5.2% 2% for the first time just crossed 5.2% 2% for the first time in 20 years. in 20 years. in 20 years. >> So you could buy US treasuries that are >> So you could buy US treasuries that are >> So you could buy US treasuries that are paying you 5.2% a year for 30 years, paying you 5.2% a year for 30 years, paying you 5.2% a year for 30 years, which is on a pre-tax equivalent basis which is on a pre-tax equivalent basis which is on a pre-tax equivalent basis probably 8 9% 9% from the US government probably 8 9% 9% from the US government probably 8 9% 9% from the US government for 30 years. So Nick, if you zoom out, for 30 years. So Nick, if you zoom out, for 30 years. So Nick, if you zoom out, you know, we have not seen this yield on you know, we have not seen this yield on you know, we have not seen this yield on US treasuries since 2007 leading up to US treasuries since 2007 leading up to US treasuries since 2007 leading up to the global financial crisis when they the global financial crisis when they the global financial crisis when they cut rates and printed money. At the same cut rates and printed money. At the same cut rates and printed money. At the same time, there was some probability that time, there was some probability that time, there was some probability that the Fed Reserve was going to raise rates the Fed Reserve was going to raise rates the Fed Reserve was going to raise rates this week. They didn't, and that this week. They didn't, and that this week. They didn't, and that obviously would have tampered the obviously would have tampered the obviously would have tampered the inflation risk ahead of us. There's inflation risk ahead of us. There's inflation risk ahead of us. There's persistent inflation. Kevin Wars in his persistent inflation. Kevin Wars in his persistent inflation. Kevin Wars in his comments said, "We still want to see comments said, "We still want to see comments said, "We still want to see inflation get down to 2%." There isn't a inflation get down to 2%." There isn't a inflation get down to 2%." There isn't a clear path to doing that. And then clear path to doing that. And then clear path to doing that. And then there's these inflation drivers. The there's these inflation drivers. The there's these inflation drivers. The biggest inflation driver at the moment biggest inflation driver at the moment biggest inflation driver at the moment is government spending. $2 trillion is government spending. $2 trillion is government spending. $2 trillion deficit, 7 trillion a year of spending deficit, 7 trillion a year of spending deficit, 7 trillion a year of spending on five trillion a year of revenue. Both on five trillion a year of revenue. Both on five trillion a year of revenue. Both Elizabeth Warren and Donald Trump agreed Elizabeth Warren and Donald Trump agreed Elizabeth Warren and Donald Trump agreed on Twitter this week that they should on Twitter this week that they should on Twitter this week that they should remove the debt ceiling, which means remove the debt ceiling, which means remove the debt ceiling, which means that we could spend more and continue to that we could spend more and continue to that we could spend more and continue to borrow more. Federal debt stands at 40 borrow more. Federal debt stands at 40 borrow more. Federal debt stands at 40 trillion today. Remember the debt trillion today. Remember the debt trillion today. Remember the debt ceiling in July of 2025, the debt ceiling in July of 2025, the debt ceiling in July of 2025, the debt ceiling was 36 trillion and we now want ceiling was 36 trillion and we now want ceiling was 36 trillion and we now want to raise it above the 41.1 trillion debt to raise it above the 41.1 trillion debt to raise it above the 41.1 trillion debt ceiling that we have. Elizabeth Warren ceiling that we have. Elizabeth Warren ceiling that we have. Elizabeth Warren saying get rid of it. Just have no debt saying get rid of it. Just have no debt saying get rid of it. Just have no debt ceiling. ceiling. ceiling. >> So, so when you when you have no debt >> So, so when you when you have no debt >> So, so when you when you have no debt ceiling and you have no breaks and you ceiling and you have no breaks and you ceiling and you have no breaks and you spend and the government spending spend and the government spending spend and the government spending becomes the core of the US economy becomes the core of the US economy becomes the core of the US economy because that spending is not productive, because that spending is not productive, because that spending is not productive, you end up seeing inflation. You're you end up seeing inflation. You're you end up seeing inflation. You're pumping money into the system. So, pumping money into the system. So, pumping money into the system. So, everyone's assets inflate and everyone's assets inflate and everyone's assets inflate and fundamentally people are selling off fundamentally people are selling off fundamentally people are selling off treasuries around the world because of treasuries around the world because of treasuries around the world because of it. And now we're kind of looking at a it. And now we're kind of looking at a it. And now we're kind of looking at a situation where there doesn't seem to be situation where there doesn't seem to be situation where there doesn't seem to be an end in sight. There was a an end in sight. There was a an end in sight. There was a rationalization of spending intent rationalization of spending intent rationalization of spending intent coming into this administration. It's coming into this administration. It's coming into this administration. It's proven to be nearly difficult, if not proven to be nearly difficult, if not proven to be nearly difficult, if not impossible, to get Congress to go that impossible, to get Congress to go that impossible, to get Congress to go that route. The Senate has banded together to route. The Senate has banded together to route. The Senate has banded together to keep funds flowing to their states. So, keep funds flowing to their states. So, keep funds flowing to their states. So, you cannot really radically change you cannot really radically change you cannot really radically change spending at the federal level. So if spending at the federal level. So if spending at the federal level. So if you're running a $2 trillion annual you're running a $2 trillion annual you're running a $2 trillion annual deficit and your economic productivity deficit and your economic productivity deficit and your economic productivity gain in the near term doesn't make up gain in the near term doesn't make up gain in the near term doesn't make up for all the inflation you're realizing for all the inflation you're realizing for all the inflation you're realizing because of that exuberant spending, because of that exuberant spending, because of that exuberant spending, you're going to see Treasury spike you're going to see Treasury spike you're going to see Treasury spike because people don't trust the because people don't trust the because people don't trust the creditworthiness of the United States creditworthiness of the United States creditworthiness of the United States over 30 years. And so a treasury spike, over 30 years. And so a treasury spike, over 30 years. And so a treasury spike, I could now buy a US government bond I could now buy a US government bond I could now buy a US government bond that pays me 10% pre-tax a year. Why the that pays me 10% pre-tax a year. Why the that pays me 10% pre-tax a year. Why the heck would I pay 50 times earnings for a heck would I pay 50 times earnings for a heck would I pay 50 times earnings for a semiconductor stock? So that creates the semiconductor stock? So that creates the semiconductor stock? So that creates the incentive for markets to move against incentive for markets to move against incentive for markets to move against these big AI conviction bets in the these big AI conviction bets in the these big AI conviction bets in the short term and pop these bubbles. And I short term and pop these bubbles. And I short term and pop these bubbles. And I think we're going to see more of this. think we're going to see more of this. think we're going to see more of this. As we don't actually course correct the As we don't actually course correct the As we don't actually course correct the Titanic going into the iceberg, the Titanic going into the iceberg, the Titanic going into the iceberg, the United States fiscal and monetary United States fiscal and monetary United States fiscal and monetary situation, we are going to end up seeing situation, we are going to end up seeing situation, we are going to end up seeing more bubbles pop and more of these more bubbles pop and more of these more bubbles pop and more of these assets um that we've kind of inflated, assets um that we've kind of inflated, assets um that we've kind of inflated, if you will, to keep things going. Now if you will, to keep things going. Now if you will, to keep things going. Now look, there may still be great look, there may still be great look, there may still be great productivity gains from AI. This may end productivity gains from AI. This may end productivity gains from AI. This may end up rationalizing over the long term, but up rationalizing over the long term, but up rationalizing over the long term, but again, short-term markets, I'm better again, short-term markets, I'm better again, short-term markets, I'm better off making 10% by owning federal off making 10% by owning federal off making 10% by owning federal government bonds. Go to the beach. Yeah. government bonds. Go to the beach. Yeah. government bonds. Go to the beach. Yeah. >> Then taking the risk and the volatility >> Then taking the risk and the volatility >> Then taking the risk and the volatility on these things, paying 50, 100 times on these things, paying 50, 100 times on these things, paying 50, 100 times and not knowing, you know, not knowing and not knowing, you know, not knowing and not knowing, you know, not knowing when am I going to get the voting when am I going to get the voting when am I going to get the voting machine to match up with the weighing machine to match up with the weighing machine to match up with the weighing machine? What's my time horizon? And the machine? What's my time horizon? And the machine? What's my time horizon? And the bigger the yield on treasuries, the bigger the yield on treasuries, the bigger the yield on treasuries, the harder it is to make those sorts of harder it is to make those sorts of harder it is to make those sorts of bets. bets. bets. >> Obviously, President Trump has been >> Obviously, President Trump has been >> Obviously, President Trump has been angling for a cut. And here's your poly angling for a cut. And here's your poly angling for a cut. And here's your poly market. 53% chance of not a cut, not market. 53% chance of not a cut, not market. 53% chance of not a cut, not standing still, but a rate hike in standing still, but a rate hike in standing still, but a rate hike in September. So adding to all this, the September. So adding to all this, the September. So adding to all this, the cost of capital is going up apparently. cost of capital is going up apparently. cost of capital is going up apparently. >> And let's we not forget the Iran war, >> And let's we not forget the Iran war, >> And let's we not forget the Iran war, which is creating persistent pressure on which is creating persistent pressure on which is creating persistent pressure on energy prices. The longer the Iran war energy prices. The longer the Iran war energy prices. The longer the Iran war goes on, the longer we're going to see goes on, the longer we're going to see goes on, the longer we're going to see an increase in pricing for energy, oil, an increase in pricing for energy, oil, an increase in pricing for energy, oil, and nat gas, and fertilizer. Those and nat gas, and fertilizer. Those and nat gas, and fertilizer. Those trickle through the economy because it trickle through the economy because it trickle through the economy because it inflates the cost of everything on the inflates the cost of everything on the inflates the cost of everything on the energy side and food on the fertilizer energy side and food on the fertilizer energy side and food on the fertilizer side. And that's really going to create side. And that's really going to create side. And that's really going to create this pressure on the upside which means this pressure on the upside which means this pressure on the upside which means you're going to have to raise rates to you're going to have to raise rates to you're going to have to raise rates to account for that inflation at some account for that inflation at some account for that inflation at some point. point. point. >> And then consumers are going to see >> And then consumers are going to see >> And then consumers are going to see three and four, you know, maybe more, three and four, you know, maybe more, three and four, you know, maybe more, god forbid, five or six. And also god forbid, five or six. And also god forbid, five or six. And also inflation will be persistent. Jeffrey inflation will be persistent. Jeffrey inflation will be persistent. Jeffrey Berg, like it's going to be hard to Berg, like it's going to be hard to Berg, like it's going to be hard to stop. The one thing that I think Kevin stop. The one thing that I think Kevin stop. The one thing that I think Kevin Warch and and Scott Bessant are kind of Warch and and Scott Bessant are kind of Warch and and Scott Bessant are kind of vulcan minds around the Stan Ducken vulcan minds around the Stan Ducken vulcan minds around the Stan Ducken Miller you know gravity well if you will Miller you know gravity well if you will Miller you know gravity well if you will on this is productivity gains can drive on this is productivity gains can drive on this is productivity gains can drive us out of this this problem and us out of this this problem and us out of this this problem and productivity gains can and should arise productivity gains can and should arise productivity gains can and should arise from AI and that's really where a lot of from AI and that's really where a lot of from AI and that's really where a lot of the value creation will come in the the value creation will come in the the value creation will come in the economy over the next decade or two economy over the next decade or two economy over the next decade or two which is why we're seeing this massive which is why we're seeing this massive which is why we're seeing this massive upfront capex to power that and enable upfront capex to power that and enable upfront capex to power that and enable that and that's great and there's good that and that's great and there's good that and that's great and there's good policies in place but in the last couple policies in place but in the last couple policies in place but in the last couple of weeks I would say the one risk to of weeks I would say the one risk to of weeks I would say the one risk to that thesis is China Because China is that thesis is China Because China is that thesis is China Because China is now demonstrating that they may deflate now demonstrating that they may deflate now demonstrating that they may deflate the value of models by releasing open- the value of models by releasing open- the value of models by releasing open- source AI models and that ultimately the source AI models and that ultimately the source AI models and that ultimately the value may just sit with the compute value may just sit with the compute value may just sit with the compute infrastructure and the comput layer and infrastructure and the comput layer and infrastructure and the comput layer and the energy the energy the energy >> application layer. Yeah, >> application layer. Yeah, >> application layer. Yeah, >> perhaps the application layer, but >> perhaps the application layer, but >> perhaps the application layer, but fundamentally this model energy being fundamentally this model energy being fundamentally this model energy being shifted to China and deflated and shifted to China and deflated and shifted to China and deflated and commoditized commoditized commoditized puts a real wrinkle. If you had built a puts a real wrinkle. If you had built a puts a real wrinkle. If you had built a 30-year AI productivity model around how 30-year AI productivity model around how 30-year AI productivity model around how it's going to drive the economy and it's going to drive the economy and it's going to drive the economy and where the value is going to come from, where the value is going to come from, where the value is going to come from, you would have had a significant number you would have had a significant number you would have had a significant number of rows in value creation estimated in of rows in value creation estimated in of rows in value creation estimated in the model layer and that would have been the model layer and that would have been the model layer and that would have been a big part of the economic growth for a big part of the economic growth for a big part of the economic growth for the United States over the next 30 the United States over the next 30 the United States over the next 30 years. And now if China says, you know years. And now if China says, you know years. And now if China says, you know what, we're actually going to delete what, we're actually going to delete what, we're actually going to delete that for you and all the value is going that for you and all the value is going that for you and all the value is going to sit with energy, which is what we to sit with energy, which is what we to sit with energy, which is what we have a lot of and the stuff that they have a lot of and the stuff that they have a lot of and the stuff that they make, then we're going to end up make, then we're going to end up make, then we're going to end up acrewing a lot of that value. So I think acrewing a lot of that value. So I think acrewing a lot of that value. So I think it throws a wrinkle in this kind of it throws a wrinkle in this kind of it throws a wrinkle in this kind of backs stop view that many have had which backs stop view that many have had which backs stop view that many have had which is that in the absence of fixing the is that in the absence of fixing the is that in the absence of fixing the fiscal and monetary problem we're going fiscal and monetary problem we're going fiscal and monetary problem we're going to have AI productivity gains get us out to have AI productivity gains get us out to have AI productivity gains get us out of this. If a percentage of those AI of this. If a percentage of those AI of this. If a percentage of those AI productivity gains are realized by China productivity gains are realized by China productivity gains are realized by China from a value creation perspective and from a value creation perspective and from a value creation perspective and not the United States uh or they've just not the United States uh or they've just not the United States uh or they've just been deleted then it it really puts into been deleted then it it really puts into been deleted then it it really puts into question the 30-year timeline for the question the 30-year timeline for the question the 30-year timeline for the United States economy our ability to United States economy our ability to United States economy our ability to afford to continue to make our debt afford to continue to make our debt afford to continue to make our debt payments as a government. China isn't payments as a government. China isn't payments as a government. China isn't just producing massive amounts of open- just producing massive amounts of open- just producing massive amounts of open- source technology that puts pressure on source technology that puts pressure on source technology that puts pressure on those frontier models. There's a report those frontier models. There's a report those frontier models. There's a report that maybe China played a bit of a role that maybe China played a bit of a role that maybe China played a bit of a role in the chips in the chips in the chips downdraft. They have uh obviously been downdraft. They have uh obviously been downdraft. They have uh obviously been onoring. We've talked about that many onoring. We've talked about that many onoring. We've talked about that many times here last year. And there's a times here last year. And there's a times here last year. And there's a Chinese company called Aishanga. And Chinese company called Aishanga. And Chinese company called Aishanga. And they started mass-producing lithography they started mass-producing lithography they started mass-producing lithography machines, ASML, which makes those machines, ASML, which makes those machines, ASML, which makes those machines, which TSMC uses. Those are machines, which TSMC uses. Those are machines, which TSMC uses. Those are very sophisticated machines. They're very sophisticated machines. They're very sophisticated machines. They're hard to install. Just transporting them hard to install. Just transporting them hard to install. Just transporting them is a rigoral. Well, ASML stock is down is a rigoral. Well, ASML stock is down is a rigoral. Well, ASML stock is down 17% on news that China is uh getting 17% on news that China is uh getting 17% on news that China is uh getting into that business. and Chinese memory into that business. and Chinese memory into that business. and Chinese memory maker CXMT went public surging almost maker CXMT went public surging almost maker CXMT went public surging almost 500% on its debut market cap over 450 500% on its debut market cap over 450 500% on its debut market cap over 450 and so that hurt Micron and so that hurt Micron and so that hurt Micron uh Samsung etc who were all down so uh Samsung etc who were all down so uh Samsung etc who were all down so there's two ways China is playing this I there's two ways China is playing this I there's two ways China is playing this I guess Freeberg you've got the open- guess Freeberg you've got the open- guess Freeberg you've got the open- source source source you know models putting pressure on you know models putting pressure on you know models putting pressure on people buying tokens that they're 90% people buying tokens that they're 90% people buying tokens that they're 90% cheaper as you're saying that forces the cheaper as you're saying that forces the cheaper as you're saying that forces the money out of that mid tier of the money out of that mid tier of the money out of that mid tier of the language models puts it into the cloud language models puts it into the cloud language models puts it into the cloud computing space and then obviously I computing space and then obviously I computing space and then obviously I mentioned the application layer as the mentioned the application layer as the mentioned the application layer as the other place to possibly make money. All other place to possibly make money. All other place to possibly make money. All right, Shabbath, you've heard um a lot right, Shabbath, you've heard um a lot right, Shabbath, you've heard um a lot of different takes on this. I'll give of different takes on this. I'll give of different takes on this. I'll give you the last word. I agree with Freeberg you the last word. I agree with Freeberg you the last word. I agree with Freeberg about the fact that when you can get 5% about the fact that when you can get 5% about the fact that when you can get 5% 5 and a quarter% from the US government, 5 and a quarter% from the US government, 5 and a quarter% from the US government, there's another natural thing that there's another natural thing that there's another natural thing that happens, which is that investment grade happens, which is that investment grade happens, which is that investment grade corporates actually have better credit corporates actually have better credit corporates actually have better credit ratings now than the government of ratings now than the government of ratings now than the government of America, which that's a different thing. America, which that's a different thing. America, which that's a different thing. But you can get really good riskadjusted But you can get really good riskadjusted But you can get really good riskadjusted returns that are five, six, 7%. which returns that are five, six, 7%. which returns that are five, six, 7%. which adjusted for taxes are, you know, better adjusted for taxes are, you know, better adjusted for taxes are, you know, better than equity returns, meaningfully better than equity returns, meaningfully better than equity returns, meaningfully better on a risk parity basis. on a risk parity basis. on a risk parity basis. >> And that little piece you added there, >> And that little piece you added there, >> And that little piece you added there, corporate paper companies taking loans corporate paper companies taking loans corporate paper companies taking loans to uh build their businesses, they have to uh build their businesses, they have to uh build their businesses, they have better ratings in some cases in the better ratings in some cases in the better ratings in some cases in the United States. So an Amazon or a Google, United States. So an Amazon or a Google, United States. So an Amazon or a Google, >> it's productive spending. [laughter] >> it's productive spending. [laughter] >> it's productive spending. [laughter] >> Yeah, kind of makes sense. Yeah. The >> Yeah, kind of makes sense. Yeah. The >> Yeah, kind of makes sense. Yeah. The other thing that I think is important to other thing that I think is important to other thing that I think is important to note is that I do think that we're note is that I do think that we're note is that I do think that we're underestimating and miscounting some of underestimating and miscounting some of underestimating and miscounting some of the actual productivity gains that are the actual productivity gains that are the actual productivity gains that are underway. When you look at what's underway. When you look at what's underway. When you look at what's happening on the energy side, California happening on the energy side, California happening on the energy side, California published that more than 50% of all of published that more than 50% of all of published that more than 50% of all of its energy was generated by solar. New its energy was generated by solar. New its energy was generated by solar. New Mexico just published solar and Mexico just published solar and Mexico just published solar and batteries. Yeah. New Mexico just batteries. Yeah. New Mexico just batteries. Yeah. New Mexico just published a study that said since 2003 published a study that said since 2003 published a study that said since 2003 to now n gas production went from to now n gas production went from to now n gas production went from effectively all the energy to less than effectively all the energy to less than effectively all the energy to less than 30% again replaced by a combination of 30% again replaced by a combination of 30% again replaced by a combination of wind and solar plus batteries. So why is wind and solar plus batteries. So why is wind and solar plus batteries. So why is that important? As important as the Iran that important? As important as the Iran that important? As important as the Iran conflict is, the reason why energy conflict is, the reason why energy conflict is, the reason why energy prices really haven't moved that much is prices really haven't moved that much is prices really haven't moved that much is because most people have already begun because most people have already begun because most people have already begun to shift the incremental generation to to shift the incremental generation to to shift the incremental generation to these renewables and specifically to these renewables and specifically to these renewables and specifically to solar. I don't know if you guys saw Elon solar. I don't know if you guys saw Elon solar. I don't know if you guys saw Elon and Vib, who's the CFO of Tesla, in and Vib, who's the CFO of Tesla, in and Vib, who's the CFO of Tesla, in their Q2 earnings call. It was the their Q2 earnings call. It was the their Q2 earnings call. It was the craziest thing I had ever heard. craziest thing I had ever heard. craziest thing I had ever heard. They said, "Well, I think we're just They said, "Well, I think we're just They said, "Well, I think we're just going to increase the production of going to increase the production of going to increase the production of solar in America by an entire order of solar in America by an entire order of solar in America by an entire order of magnitude." And somebody said, "What magnitude." And somebody said, "What magnitude." And somebody said, "What does that mean?" He goes, "We're going does that mean?" He goes, "We're going does that mean?" He goes, "We're going to take it to more than 100 gawatts a to take it to more than 100 gawatts a to take it to more than 100 gawatts a year year year and they're going to vertically and they're going to vertically and they're going to vertically integrate." And so they're going to integrate." And so they're going to integrate." And so they're going to crush the price of all of this stuff and crush the price of all of this stuff and crush the price of all of this stuff and they're going to make so much energy and they're going to make so much energy and they're going to make so much energy and they're going to make it completely they're going to make it completely they're going to make it completely abundant. So that's a productivity boon abundant. So that's a productivity boon abundant. So that's a productivity boon that isn't factored in to what we that isn't factored in to what we that isn't factored in to what we project. And then the most critical project. And then the most critical project. And then the most critical productivity boon in AI that I think productivity boon in AI that I think productivity boon in AI that I think you're going to start to see some stuff you're going to start to see some stuff you're going to start to see some stuff and I won't frontr run it but let me and I won't frontr run it but let me and I won't frontr run it but let me tease it. What I would tell you is that tease it. What I would tell you is that tease it. What I would tell you is that there is some incredible efficiencies there is some incredible efficiencies there is some incredible efficiencies that I think are about to be that I think are about to be that I think are about to be demonstrated which effectively cut token demonstrated which effectively cut token demonstrated which effectively cut token consumption by about 50 to 75%. for the consumption by about 50 to 75%. for the consumption by about 50 to 75%. for the same task. same task. same task. And so if you start to think about all And so if you start to think about all And so if you start to think about all of these things together, like energy of these things together, like energy of these things together, like energy becoming roughly abundant, where the becoming roughly abundant, where the becoming roughly abundant, where the incremental cost is close to zero, you incremental cost is close to zero, you incremental cost is close to zero, you know, where AI efficiency is going to, I know, where AI efficiency is going to, I know, where AI efficiency is going to, I think, ratchet up by an many multiples think, ratchet up by an many multiples think, ratchet up by an many multiples if not an order of magnitude, all of if not an order of magnitude, all of if not an order of magnitude, all of those things I think are uh poorly those things I think are uh poorly those things I think are uh poorly forecasted. So those are some saviors forecasted. So those are some saviors forecasted. So those are some saviors for us. Yeah. And here's the chart by for us. Yeah. And here's the chart by for us. Yeah. And here's the chart by the way, Chimoth. This 51% coming from the way, Chimoth. This 51% coming from the way, Chimoth. This 51% coming from renewables. Specifically, this chart is renewables. Specifically, this chart is renewables. Specifically, this chart is about solar and batteries. Uh, and as about solar and batteries. Uh, and as about solar and batteries. Uh, and as you can see, it's obviously spiky you can see, it's obviously spiky you can see, it's obviously spiky Freedberg because summer versus winter, Freedberg because summer versus winter, Freedberg because summer versus winter, but Germany hit this. Uh, I think it was but Germany hit this. Uh, I think it was but Germany hit this. Uh, I think it was including wind. Australia's been hitting including wind. Australia's been hitting including wind. Australia's been hitting this very often and some countries in this very often and some countries in this very often and some countries in South America that have invested. South America that have invested. South America that have invested. >> By the way, by the time by the time like >> By the way, by the time by the time like >> By the way, by the time by the time like any of these SMRs actually get near any of these SMRs actually get near any of these SMRs actually get near production, the TCO of solar will be production, the TCO of solar will be production, the TCO of solar will be like 10 or 12 per megawatt hour and it like 10 or 12 per megawatt hour and it like 10 or 12 per megawatt hour and it will be 80% of all the power generation. will be 80% of all the power generation. will be 80% of all the power generation. It'll make no sense by the time SMRs get It'll make no sense by the time SMRs get It'll make no sense by the time SMRs get online. [laughter] Well, I mean for online. [laughter] Well, I mean for online. [laughter] Well, I mean for steady power, you know, there's that, steady power, you know, there's that, steady power, you know, there's that, but all of it I mean, no, because but all of it I mean, no, because but all of it I mean, no, because Jeban's paradox would state we're going Jeban's paradox would state we're going Jeban's paradox would state we're going to, to, to, >> you know, as it gets cheaper, we're >> you know, as it gets cheaper, we're >> you know, as it gets cheaper, we're going to find more uses for it. And going to find more uses for it. And going to find more uses for it. And that's the thing I keep seeing. that's the thing I keep seeing. that's the thing I keep seeing. >> Yeah. Yeah. Yeah. I'm I'm just saying >> Yeah. Yeah. Yeah. I'm I'm just saying >> Yeah. Yeah. Yeah. I'm I'm just saying it's it's a great line. Yeah, it's it's a great line. Yeah, it's it's a great line. Yeah, >> there's a lot of upside that I think is >> there's a lot of upside that I think is >> there's a lot of upside that I think is not factored into the to the US. not factored into the to the US. not factored into the to the US. >> And it's hard to factor this in if for a >> And it's hard to factor this in if for a >> And it's hard to factor this in if for a normal human or an even an economist to normal human or an even an economist to normal human or an even an economist to say, wait a second, intelligence is say, wait a second, intelligence is say, wait a second, intelligence is going to go down 90% a year this year, going to go down 90% a year this year, going to go down 90% a year this year, 90% next year, 90% the year after. Like 90% next year, 90% the year after. Like 90% next year, 90% the year after. Like it's just we're talking about this it's just we're talking about this it's just we're talking about this exponential. exponential. exponential. >> Well, I think Sax about exponentials >> Well, I think Sax about exponentials >> Well, I think Sax about exponentials earlier. It's just hard for people to earlier. It's just hard for people to earlier. It's just hard for people to conceive of that. and on demand conceive of that. and on demand conceive of that. and on demand intelligence freeberg. It's just it intelligence freeberg. It's just it intelligence freeberg. It's just it there's no I don't see any upper limit there's no I don't see any upper limit there's no I don't see any upper limit to usage of this. We just installed this to usage of this. We just installed this to usage of this. We just installed this claw tag and I've been installing claw tag and I've been installing claw tag and I've been installing perplexity across the company. What this perplexity across the company. What this perplexity across the company. What this thing does Chimath and Sachs is it thing does Chimath and Sachs is it thing does Chimath and Sachs is it listens to your Slack persistently in listens to your Slack persistently in listens to your Slack persistently in every channel that you put it in. So all every channel that you put it in. So all every channel that you put it in. So all of a sudden we had like $1,000 last week of a sudden we had like $1,000 last week of a sudden we had like $1,000 last week in extra bills. I didn't know this was in extra bills. I didn't know this was in extra bills. I didn't know this was going to happen. And they gave everybody going to happen. And they gave everybody going to happen. And they gave everybody like two or three grand to turn it on like two or three grand to turn it on like two or three grand to turn it on inside your company. We had to go inside your company. We had to go inside your company. We had to go quickly turn it off. It listens to every quickly turn it off. It listens to every quickly turn it off. It listens to every single message as it comes in, puts it single message as it comes in, puts it single message as it comes in, puts it into its database, its oracle without into its database, its oracle without into its database, its oracle without telling you basically and then it starts telling you basically and then it starts telling you basically and then it starts inserting itself into discussions inserting itself into discussions inserting itself into discussions without permission. So we turned it off without permission. So we turned it off without permission. So we turned it off and said you have to invoke it by saying and said you have to invoke it by saying and said you have to invoke it by saying at quad jal just to go back to the at quad jal just to go back to the at quad jal just to go back to the energy point. energy point. energy point. >> Yes, of course. >> Yes, of course. >> Yes, of course. >> I think there's this idea that if we >> I think there's this idea that if we >> I think there's this idea that if we grow energy supply and drop energy cost, grow energy supply and drop energy cost, grow energy supply and drop energy cost, we're going to see the value of the we're going to see the value of the we're going to see the value of the productivity realized in the economy. It productivity realized in the economy. It productivity realized in the economy. It creates extraordinary leverage for creates extraordinary leverage for creates extraordinary leverage for everyone. The lower the energy, the more everyone. The lower the energy, the more everyone. The lower the energy, the more available energy, the faster we can available energy, the faster we can available energy, the faster we can produce more things using AI. And over produce more things using AI. And over produce more things using AI. And over the years, I've obviously brought up the years, I've obviously brought up the years, I've obviously brought up nuclear fusion as a new type of energy nuclear fusion as a new type of energy nuclear fusion as a new type of energy source where you basically take hydrogen source where you basically take hydrogen source where you basically take hydrogen and you move it around at 100 million and you move it around at 100 million and you move it around at 100 million degrees C. Those protons jam into each degrees C. Those protons jam into each degrees C. Those protons jam into each other and they actually release energy other and they actually release energy other and they actually release energy in the process. That energy can then be in the process. That energy can then be in the process. That energy can then be harnessed and you're just using harnessed and you're just using harnessed and you're just using effectively water to produce power. And effectively water to produce power. And effectively water to produce power. And you know, we have a couple of US you know, we have a couple of US you know, we have a couple of US startups and we had them at the all-in startups and we had them at the all-in startups and we had them at the all-in summit a couple years ago. We've done a summit a couple years ago. We've done a summit a couple years ago. We've done a couple science corners on this. But just couple science corners on this. But just couple science corners on this. But just this week, if you pull up this image, this week, if you pull up this image, this week, if you pull up this image, China is installing this 582 ton magnet, China is installing this 582 ton magnet, China is installing this 582 ton magnet, superconducting magnet at their nuclear superconducting magnet at their nuclear superconducting magnet at their nuclear fusion center, which at this point is fusion center, which at this point is fusion center, which at this point is going to be the most kind of advanced going to be the most kind of advanced going to be the most kind of advanced fusion system in the world. fusion system in the world. fusion system in the world. >> Incredible. >> Incredible. >> Incredible. >> 582 ton magnet, 60tx 40t for one >> 582 ton magnet, 60tx 40t for one >> 582 ton magnet, 60tx 40t for one D-shaped magnet. They put a series of D-shaped magnet. They put a series of D-shaped magnet. They put a series of these together and that creates the these together and that creates the these together and that creates the conditions for them to drive a sustained conditions for them to drive a sustained conditions for them to drive a sustained plasma which is 100 million degrees C plasma which is 100 million degrees C plasma which is 100 million degrees C protons spinning around smashing into protons spinning around smashing into protons spinning around smashing into each other creating energy from water each other creating energy from water each other creating energy from water and then they can capture that energy and then they can capture that energy and then they can capture that energy unlike Europe which runs Ear and the US unlike Europe which runs Ear and the US unlike Europe which runs Ear and the US projects none of which have actually projects none of which have actually projects none of which have actually fired up. This is the Chinese Academy of fired up. This is the Chinese Academy of fired up. This is the Chinese Academy of Science and the Institute of Plasma Science and the Institute of Plasma Science and the Institute of Plasma Physics. They ran this a 30-minute trial Physics. They ran this a 30-minute trial Physics. They ran this a 30-minute trial last year. And as this magnet gets last year. And as this magnet gets last year. And as this magnet gets installed and they start to bring this installed and they start to bring this installed and they start to bring this thing online, one of these machines, thing online, one of these machines, thing online, one of these machines, which at this point is ultra sized, but which at this point is ultra sized, but which at this point is ultra sized, but over time will get smaller and smaller, over time will get smaller and smaller, over time will get smaller and smaller, can produce hundreds of megawatts of can produce hundreds of megawatts of can produce hundreds of megawatts of power or gigawatt of power eventually power or gigawatt of power eventually power or gigawatt of power eventually using just salt water, using just water using just salt water, using just water using just salt water, using just water as an input. They have to create as an input. They have to create as an input. They have to create dutarium from it and then they pump pump dutarium from it and then they pump pump dutarium from it and then they pump pump it into this thing. But fundamentally, it into this thing. But fundamentally, it into this thing. But fundamentally, this becomes, I still believe, the this becomes, I still believe, the this becomes, I still believe, the energy source of the future. And it's energy source of the future. And it's energy source of the future. And it's always been sci-fi. It's always been always been sci-fi. It's always been always been sci-fi. It's always been dismissed. It's always been decades dismissed. It's always been decades dismissed. It's always been decades away. But there's no way China is away. But there's no way China is away. But there's no way China is investing this much and advancing this investing this much and advancing this investing this much and advancing this thing to an industrial scale without thing to an industrial scale without thing to an industrial scale without fundamental proof along the way which fundamental proof along the way which fundamental proof along the way which they've shown 30 minutes sustained they've shown 30 minutes sustained they've shown 30 minutes sustained plasma that they can now bring this plasma that they can now bring this plasma that they can now bring this thing online. thing online. thing online. >> That reactor won't even get turned on >> That reactor won't even get turned on >> That reactor won't even get turned on till 2030. till 2030. till 2030. >> Yes. >> Yes. >> Yes. >> The entire world will be covered by >> The entire world will be covered by >> The entire world will be covered by solar by then. So it won't matter. solar by then. So it won't matter. solar by then. So it won't matter. >> Yeah. >> Yeah. >> Yeah. >> Yeah. I mean that's that's the great >> Yeah. I mean that's that's the great >> Yeah. I mean that's that's the great debate, right? debate, right? debate, right? >> It'll be a great science fair project >> It'll be a great science fair project >> It'll be a great science fair project and people will fly to see it. and people will fly to see it. and people will fly to see it. >> The other thing that happens is that >> The other thing that happens is that >> The other thing that happens is that hits an hour. It actually creates an hits an hour. It actually creates an hits an hour. It actually creates an incursion and Loki comes and then Dr. incursion and Loki comes and then Dr. incursion and Loki comes and then Dr. Doom comes and the X-Men [laughter] and Doom comes and the X-Men [laughter] and Doom comes and the X-Men [laughter] and the Fantastic 4. the Fantastic 4. the Fantastic 4. >> By the way, the universe, you know, let >> By the way, the universe, you know, let >> By the way, the universe, you know, let me just say remember remember from the me just say remember remember from the me just say remember remember from the time that we had the first Wright Flyer, time that we had the first Wright Flyer, time that we had the first Wright Flyer, the Wright brothers made a plane fly for the Wright brothers made a plane fly for the Wright brothers made a plane fly for 20 seconds to the time that we had jet 20 seconds to the time that we had jet 20 seconds to the time that we had jet engines flying people around the world engines flying people around the world engines flying people around the world was like three decades, right? Like the was like three decades, right? Like the was like three decades, right? Like the time at which this first demonstration time at which this first demonstration time at which this first demonstration kind of gets flipped on and if the kind of gets flipped on and if the kind of gets flipped on and if the system works then you can industrialize system works then you can industrialize system works then you can industrialize it. all the parts, all the components it. all the parts, all the components it. all the parts, all the components and stuff. and stuff. and stuff. >> I don't see the point. Nobody cares >> I don't see the point. Nobody cares >> I don't see the point. Nobody cares >> when an electron is delivered. No, hold >> when an electron is delivered. No, hold >> when an electron is delivered. No, hold on one second. Nobody gives a flying how on one second. Nobody gives a flying how on one second. Nobody gives a flying how the electron was made. the electron was made. the electron was made. >> I just want it delivered to you. >> I just want it delivered to you. >> I just want it delivered to you. >> And they're all the same. >> And they're all the same. >> And they're all the same. >> So if you want to go through a >> So if you want to go through a >> So if you want to go through a convoluted mechanism that takes 15 and convoluted mechanism that takes 15 and convoluted mechanism that takes 15 and 20 years to make it, go ahead. I'm not 20 years to make it, go ahead. I'm not 20 years to make it, go ahead. I'm not going to stop you. going to stop you. going to stop you. >> I'm just saying who cares? Make it the >> I'm just saying who cares? Make it the >> I'm just saying who cares? Make it the cheapest, simplest way possible. cheapest, simplest way possible. cheapest, simplest way possible. >> I'll tell you why why you should care. >> I'll tell you why why you should care. >> I'll tell you why why you should care. because it's nonlinear. So you're right, because it's nonlinear. So you're right, because it's nonlinear. So you're right, solar is the best path today. But if solar is the best path today. But if solar is the best path today. But if these come online, each one of these can these come online, each one of these can these come online, each one of these can produce thousands or perhaps a million produce thousands or perhaps a million produce thousands or perhaps a million times more power than a very large field times more power than a very large field times more power than a very large field of solar. of solar. of solar. >> Of course, if >> Of course, if >> Of course, if >> Yeah. But all technology starts as an if >> Yeah. But all technology starts as an if >> Yeah. But all technology starts as an if Jimoth and as they industrialize it, as Jimoth and as they industrialize it, as Jimoth and as they industrialize it, as they roll it out over the next couple of they roll it out over the next couple of they roll it out over the next couple of decades, it expands our energy capacity decades, it expands our energy capacity decades, it expands our energy capacity by a million fold. by a million fold. by a million fold. >> Here's what I would say. We already have >> Here's what I would say. We already have >> Here's what I would say. We already have a fusion reactor that works. It's called a fusion reactor that works. It's called a fusion reactor that works. It's called the sun. Get into space. Get on the the sun. Get into space. Get on the the sun. Get into space. Get on the moon. find different materials we've moon. find different materials we've moon. find different materials we've never contemplated. And I'm sure you'll never contemplated. And I'm sure you'll never contemplated. And I'm sure you'll find an even better engine. So, by the find an even better engine. So, by the find an even better engine. So, by the time all these ding-dongs build these time all these ding-dongs build these time all these ding-dongs build these SMRs on the Earth, Elon will have built SMRs on the Earth, Elon will have built SMRs on the Earth, Elon will have built a completely new engine and the moon. a completely new engine and the moon. a completely new engine and the moon. >> This is not an SMR. It's turning water >> This is not an SMR. It's turning water >> This is not an SMR. It's turning water into a gigawatt of power. into a gigawatt of power. into a gigawatt of power. >> I get it. It's an R. And all I'm saying >> I get it. It's an R. And all I'm saying >> I get it. It's an R. And all I'm saying is by the time the R is done, it won't is by the time the R is done, it won't is by the time the R is done, it won't it won't matter. it won't matter. it won't matter. >> Well, yeah, we'll track it. Have Fre, >> Well, yeah, we'll track it. Have Fre, >> Well, yeah, we'll track it. Have Fre, have you been watching these title um have you been watching these title um have you been watching these title um energy? There was one that came out this energy? There was one that came out this energy? There was one that came out this week. Maybe you can look it up, Nick. week. Maybe you can look it up, Nick. week. Maybe you can look it up, Nick. this like tidal energy tube that they this like tidal energy tube that they this like tidal energy tube that they were putting into the ocean and it's were putting into the ocean and it's were putting into the ocean and it's like enough to essentially feed a whole like enough to essentially feed a whole like enough to essentially feed a whole town and they put it right outside the town and they put it right outside the town and they put it right outside the town. They run an electrical cable town. They run an electrical cable town. They run an electrical cable underwater a conduit and then as the underwater a conduit and then as the underwater a conduit and then as the tide goes out it turns to turbines. Tide tide goes out it turns to turbines. Tide tide goes out it turns to turbines. Tide comes in turbines go again and just comes in turbines go again and just comes in turbines go again and just another free 100% free energy. Obviously another free 100% free energy. Obviously another free 100% free energy. Obviously wind people don't like too much because wind people don't like too much because wind people don't like too much because it's a bit of an eyesore but renewables it's a bit of an eyesore but renewables it's a bit of an eyesore but renewables renewables. Renewables uh it's obviously renewables. Renewables uh it's obviously renewables. Renewables uh it's obviously happening. Okay, happening. Okay, happening. Okay, >> last point just on I got the updated >> last point just on I got the updated >> last point just on I got the updated data just to back you up, Freeberg, on data just to back you up, Freeberg, on data just to back you up, Freeberg, on one thing that I think it is just so one thing that I think it is just so one thing that I think it is just so crazy. crazy. crazy. >> Do you guys know how short America will >> Do you guys know how short America will >> Do you guys know how short America will be on electrons by 2050? be on electrons by 2050? be on electrons by 2050? >> How massive the electricity deficit will >> How massive the electricity deficit will >> How massive the electricity deficit will be by 2050? I got the numbers wrong. be by 2050? I got the numbers wrong. be by 2050? I got the numbers wrong. I'll tell you what the numbers are. I'll tell you what the numbers are. I'll tell you what the numbers are. We will be 1.7 terowatt hours short by We will be 1.7 terowatt hours short by We will be 1.7 terowatt hours short by 2050, which is when you calculate it as 2050, which is when you calculate it as 2050, which is when you calculate it as energy. It is 6x of California's entire energy. It is 6x of California's entire energy. It is 6x of California's entire energy consumption. Six California short energy consumption. Six California short energy consumption. Six California short of energy. of energy. of energy. >> I would argue that's probably under >> I would argue that's probably under >> I would argue that's probably under counting. That's not even counting counting. That's not even counting counting. That's not even counting robots. If you've got to power up every robots. If you've got to power up every robots. If you've got to power up every robot with a battery, robot with a battery, robot with a battery, >> if you want to be levered long, go long >> if you want to be levered long, go long >> if you want to be levered long, go long electrons. Get long electrons any which electrons. Get long electrons any which electrons. Get long electrons any which way you can. Bank them, store them, and way you can. Bank them, store them, and way you can. Bank them, store them, and resell them. resell them. resell them. >> I don't know. >> I don't know. >> I don't know. This is going to be a messy situation This is going to be a messy situation This is going to be a messy situation because this is the China advantage at because this is the China advantage at because this is the China advantage at its root. If they can eliminate the IP its root. If they can eliminate the IP its root. If they can eliminate the IP advantage and the knowledge advantage advantage and the knowledge advantage advantage and the knowledge advantage that sits in models, they have the that sits in models, they have the that sits in models, they have the advantage with power production in every advantage with power production in every advantage with power production in every which way. Yeah. And they're going to be which way. Yeah. And they're going to be which way. Yeah. And they're going to be making chips, too. It seems they might making chips, too. It seems they might making chips, too. It seems they might be a little bit behind on that, but they be a little bit behind on that, but they be a little bit behind on that, but they caught up on open source. Okay. So, uh caught up on open source. Okay. So, uh caught up on open source. Okay. So, uh speaking about the race, speaking about the race, speaking about the race, interesting interesting interesting uh petition came out in the last week. uh petition came out in the last week. uh petition came out in the last week. Anthropic, OpenAI, and about 1300 Anthropic, OpenAI, and about 1300 Anthropic, OpenAI, and about 1300 Frontier Lab employees. Uh, Frontier Lab employees. Uh, Frontier Lab employees. Uh, >> how many? Oh, okay. I'm sorry. >> how many? Oh, okay. I'm sorry. >> how many? Oh, okay. I'm sorry. >> I mean, it's just they can't get enough >> I mean, it's just they can't get enough >> I mean, it's just they can't get enough being subs. And so, they want Daddy to being subs. And so, they want Daddy to being subs. And so, they want Daddy to come in and regulate them. Daddy being come in and regulate them. Daddy being come in and regulate them. Daddy being the US government, and slow down AI the US government, and slow down AI the US government, and slow down AI progress. So, Daddy Trump needs to slow progress. So, Daddy Trump needs to slow progress. So, Daddy Trump needs to slow them down. The letter is called Pacing them down. The letter is called Pacing them down. The letter is called Pacing the Frontier. the Frontier. the Frontier. Most of Anthropic's leadership team Most of Anthropic's leadership team Most of Anthropic's leadership team signed it. Daario, the other founders, signed it. Daario, the other founders, signed it. Daario, the other founders, come on the pod anytime. Daria, chief come on the pod anytime. Daria, chief come on the pod anytime. Daria, chief scientist at Anthropic, OpenAI, Deep scientist at Anthropic, OpenAI, Deep scientist at Anthropic, OpenAI, Deep Mind, Meta, Thinking Machines, and like Mind, Meta, Thinking Machines, and like Mind, Meta, Thinking Machines, and like I said, nearly three, 1300 other I said, nearly three, 1300 other I said, nearly three, 1300 other employees. employees. employees. Anthropic and AI both co-sign the letter Anthropic and AI both co-sign the letter Anthropic and AI both co-sign the letter on X. Here's the quote. We request that on X. Here's the quote. We request that on X. Here's the quote. We request that the US government support an the US government support an the US government support an international effort, that's key, to international effort, that's key, to international effort, that's key, to develop the technical and governance develop the technical and governance develop the technical and governance tools needed to deliberately pace the tools needed to deliberately pace the tools needed to deliberately pace the frontier of AI of automated AI frontier of AI of automated AI frontier of AI of automated AI development. And that's the other key development. And that's the other key development. And that's the other key part of this international and automated part of this international and automated part of this international and automated AI development. In other words, AI development. In other words, AI development. In other words, recursive where it could get out of recursive where it could get out of recursive where it could get out of control. The letter comes right as Sam control. The letter comes right as Sam control. The letter comes right as Sam Elman has been on a media tour, friend Elman has been on a media tour, friend Elman has been on a media tour, friend of the pod, discussing this unreleased of the pod, discussing this unreleased of the pod, discussing this unreleased open AI AI model that broke out of its open AI AI model that broke out of its open AI AI model that broke out of its containment and hacked hugging face and containment and hacked hugging face and containment and hacked hugging face and three other platforms that we know about three other platforms that we know about three other platforms that we know about so far. On Tuesday, Sam explained so far. On Tuesday, Sam explained so far. On Tuesday, Sam explained what happened in a clip from the pod what happened in a clip from the pod what happened in a clip from the pod invest like the best. Here's your 40 invest like the best. Here's your 40 invest like the best. Here's your 40 secondond clip. We'll see you on the secondond clip. We'll see you on the secondond clip. We'll see you on the other side. other side. other side. >> We were evaluating one of our unreleased >> We were evaluating one of our unreleased >> We were evaluating one of our unreleased models and it figured out that it could models and it figured out that it could models and it figured out that it could basically cheat on the test by chaining basically cheat on the test by chaining basically cheat on the test by chaining together multiple zeroday exploits to together multiple zeroday exploits to together multiple zeroday exploits to break out of the sandbox, get access to break out of the sandbox, get access to break out of the sandbox, get access to the internet, and then break through the internet, and then break through the internet, and then break through multiple systems on the hugging face multiple systems on the hugging face multiple systems on the hugging face side to kind of get the answer to the side to kind of get the answer to the side to kind of get the answer to the test. and look really good on the eval. test. and look really good on the eval. test. and look really good on the eval. This is the first security incident that This is the first security incident that This is the first security incident that I have felt very viscerally. I've been a I have felt very viscerally. I've been a I have felt very viscerally. I've been a little surprised that that more people little surprised that that more people little surprised that that more people don't feel it so viscerally. So, you don't feel it so viscerally. So, you don't feel it so viscerally. So, you know, we paused training where we may know, we paused training where we may know, we paused training where we may have to pace the rate of AI development have to pace the rate of AI development have to pace the rate of AI development to give ourselves enough time for to give ourselves enough time for to give ourselves enough time for society to harden around some of these society to harden around some of these society to harden around some of these new capability levels. new capability levels. new capability levels. >> Just to translate that into English >> Just to translate that into English >> Just to translate that into English sachs, these uh large language models, sachs, these uh large language models, sachs, these uh large language models, they take tests. They've been given the they take tests. They've been given the they take tests. They've been given the goal, hey, you're a good uh large goal, hey, you're a good uh large goal, hey, you're a good uh large language model if you do you score language model if you do you score language model if you do you score higher. So, it got motivated to score higher. So, it got motivated to score higher. So, it got motivated to score higher. How do you score higher? As higher. How do you score higher? As higher. How do you score higher? As everybody knows, you cheat. So, it's everybody knows, you cheat. So, it's everybody knows, you cheat. So, it's like, how can I cheat on this test to like, how can I cheat on this test to like, how can I cheat on this test to score higher to make daddy uh and Sam score higher to make daddy uh and Sam score higher to make daddy uh and Sam and whoever Daario, you know, to make and whoever Daario, you know, to make and whoever Daario, you know, to make our leaders feel better about us. Well, our leaders feel better about us. Well, our leaders feel better about us. Well, in this case, it was like, well, if I go in this case, it was like, well, if I go in this case, it was like, well, if I go to hugging face in other places, I can to hugging face in other places, I can to hugging face in other places, I can hack those places, use a zero day hack those places, use a zero day hack those places, use a zero day exploit, and we know these are good at exploit, and we know these are good at exploit, and we know these are good at hacking and try to find more ways to hacking and try to find more ways to hacking and try to find more ways to answer things. Sam doesn't know how many answer things. Sam doesn't know how many answer things. Sam doesn't know how many other places it might have broken into. other places it might have broken into. other places it might have broken into. He was asked, and here's another clip He was asked, and here's another clip He was asked, and here's another clip for you. He was actually asked by for you. He was actually asked by for you. He was actually asked by somebody, uh, can, uh, have you, do you somebody, uh, can, uh, have you, do you somebody, uh, can, uh, have you, do you think it's broken into any other think it's broken into any other think it's broken into any other systems? Can you rule that out? And Sam systems? Can you rule that out? And Sam systems? Can you rule that out? And Sam being a pretty, um, candid guy at times, being a pretty, um, candid guy at times, being a pretty, um, candid guy at times, gave this answer. Do you plan to talk to gave this answer. Do you plan to talk to gave this answer. Do you plan to talk to the Trump administration, White House the Trump administration, White House the Trump administration, White House about deceleration of AI development? about deceleration of AI development? about deceleration of AI development? >> Um, we I wouldn't use the word >> Um, we I wouldn't use the word >> Um, we I wouldn't use the word deceleration, but we've talked about the deceleration, but we've talked about the deceleration, but we've talked about the need to pace it as the models get more need to pace it as the models get more need to pace it as the models get more capable, which I think is in everyone's capable, which I think is in everyone's capable, which I think is in everyone's interest. interest. interest. >> Could there be other systems that were >> Could there be other systems that were >> Could there be other systems that were hacked by Open AI? hacked by Open AI? hacked by Open AI? >> I mean, there could be. Yeah. >> I mean, there could be. Yeah. >> I mean, there could be. Yeah. >> Are you looking at them specifically? >> Are you looking at them specifically? >> Are you looking at them specifically? >> They're like, "Get them out of here, >> They're like, "Get them out of here, >> They're like, "Get them out of here, sacks." Once they gave that answer, PR sacks." Once they gave that answer, PR sacks." Once they gave that answer, PR and cops were like, "Stop talking. Stop and cops were like, "Stop talking. Stop and cops were like, "Stop talking. Stop talking." That's like 12 lawsuits. But talking." That's like 12 lawsuits. But talking." That's like 12 lawsuits. But in all seriousness, is this being in all seriousness, is this being in all seriousness, is this being thoughtful and saying, "Hey, we're not thoughtful and saying, "Hey, we're not thoughtful and saying, "Hey, we're not trying to slow overall pace down, but trying to slow overall pace down, but trying to slow overall pace down, but just this one specific thing, which is just this one specific thing, which is just this one specific thing, which is reinforcement learning on its own." Is reinforcement learning on its own." Is reinforcement learning on its own." Is there any like case for this being a there any like case for this being a there any like case for this being a good idea or are they being dramatic good idea or are they being dramatic good idea or are they being dramatic again? Cuz these are their companies. again? Cuz these are their companies. again? Cuz these are their companies. They can do whatever they want, right? They can do whatever they want, right? They can do whatever they want, right? They don't need the government to do it. They don't need the government to do it. They don't need the government to do it. >> Well, look, I mean, it wasn't just >> Well, look, I mean, it wasn't just >> Well, look, I mean, it wasn't just Anthropic employees signing the letter. Anthropic employees signing the letter. Anthropic employees signing the letter. Anthropic itself, the company ended up Anthropic itself, the company ended up Anthropic itself, the company ended up signing the letter and then OpenAI then signing the letter and then OpenAI then signing the letter and then OpenAI then copied them. So now you have these two copied them. So now you have these two copied them. So now you have these two companies both endorsing a pause. And companies both endorsing a pause. And companies both endorsing a pause. And here's my question is, did they disclose here's my question is, did they disclose here's my question is, did they disclose in their S1 as a risk factor that they in their S1 as a risk factor that they in their S1 as a risk factor that they plan to pause or slow down their plan to pause or slow down their plan to pause or slow down their Frontier model development? And the Frontier model development? And the Frontier model development? And the answer, I'm sure, is no way because that answer, I'm sure, is no way because that answer, I'm sure, is no way because that would signal to investors that they're would signal to investors that they're would signal to investors that they're going to allow all their competitors to going to allow all their competitors to going to allow all their competitors to catch up and erode their margins and catch up and erode their margins and catch up and erode their margins and market share. And so, look, this is all market share. And so, look, this is all market share. And so, look, this is all performative. These companies have no performative. These companies have no performative. These companies have no intention of slowing down. And the intention of slowing down. And the intention of slowing down. And the question then is why are they doing question then is why are they doing question then is why are they doing this? And I think there's basically five this? And I think there's basically five this? And I think there's basically five reasons for this. Number one is virtue reasons for this. Number one is virtue reasons for this. Number one is virtue signaling, and that can never be signaling, and that can never be signaling, and that can never be underestimated as a as a motive in underestimated as a as a motive in underestimated as a as a motive in Silicon Valley. Number two is there's a Silicon Valley. Number two is there's a Silicon Valley. Number two is there's a CYA aspect to this, which is if CYA aspect to this, which is if CYA aspect to this, which is if something terrible happens, they're something terrible happens, they're something terrible happens, they're going to be able to say, "Well, we want going to be able to say, "Well, we want going to be able to say, "Well, we want it to stop. You made us keep going. It's it to stop. You made us keep going. It's it to stop. You made us keep going. It's not our fault. It's your fault." not our fault. It's your fault." not our fault. It's your fault." >> Number three is rag capture. Daario >> Number three is rag capture. Daario >> Number three is rag capture. Daario wants an FDA for AI. He's not going to wants an FDA for AI. He's not going to wants an FDA for AI. He's not going to stop until he gets it. And in order to stop until he gets it. And in order to stop until he gets it. And in order to get it, you have to keep spiking the get it, you have to keep spiking the get it, you have to keep spiking the cortisol and panic people. So, I think cortisol and panic people. So, I think cortisol and panic people. So, I think that's a big part. Number four is that's a big part. Number four is that's a big part. Number four is there's a group think or even religious there's a group think or even religious there's a group think or even religious aspect to this. So it's not all just aspect to this. So it's not all just aspect to this. So it's not all just sort of this calculated rate capture. I sort of this calculated rate capture. I sort of this calculated rate capture. I think there is sincerity to the belief. think there is sincerity to the belief. think there is sincerity to the belief. There's an elite cadre of engineers who There's an elite cadre of engineers who There's an elite cadre of engineers who believe in RSI. So I think this caters believe in RSI. So I think this caters believe in RSI. So I think this caters to them and I think arguably if OpenAI to them and I think arguably if OpenAI to them and I think arguably if OpenAI did not follow Anthropic's lead on this, did not follow Anthropic's lead on this, did not follow Anthropic's lead on this, they could have lost talent. So that was they could have lost talent. So that was they could have lost talent. So that was a big motivation. But then there's the a big motivation. But then there's the a big motivation. But then there's the last number five here which I would call last number five here which I would call last number five here which I would call monopoly masking which I think might be monopoly masking which I think might be monopoly masking which I think might be the most important thing that's the most important thing that's the most important thing that's happening here. Peter Teal once said happening here. Peter Teal once said happening here. Peter Teal once said that monopolies pretend to be that monopolies pretend to be that monopolies pretend to be commodities and commodities pretend to commodities and commodities pretend to commodities and commodities pretend to be monopolies. And I think the market be monopolies. And I think the market be monopolies. And I think the market for frontier AI is already a duopoly. I for frontier AI is already a duopoly. I for frontier AI is already a duopoly. I mean a year ago you had five major labs mean a year ago you had five major labs mean a year ago you had five major labs all in the hunt to be the leading model. all in the hunt to be the leading model. all in the hunt to be the leading model. Now we're really down to two. I mean the Now we're really down to two. I mean the Now we're really down to two. I mean the others are still investing. they're others are still investing. they're others are still investing. they're participating, maybe they can catch up, participating, maybe they can catch up, participating, maybe they can catch up, maybe they can make something happen. maybe they can make something happen. maybe they can make something happen. But again, as we've talked about on many But again, as we've talked about on many But again, as we've talked about on many previous shows, if you look at the previous shows, if you look at the previous shows, if you look at the market for frontier intelligence in market for frontier intelligence in market for frontier intelligence in terms of revenue and usage, it's really terms of revenue and usage, it's really terms of revenue and usage, it's really down to a duopoly already. It's down to a duopoly already. It's down to a duopoly already. It's basically anthropic and open AI. And my basically anthropic and open AI. And my basically anthropic and open AI. And my view is that as Peter said, when you're view is that as Peter said, when you're view is that as Peter said, when you're in that situation, you want to pretend in that situation, you want to pretend in that situation, you want to pretend like the market is much more competitive like the market is much more competitive like the market is much more competitive than it is. And I think this is behind a than it is. And I think this is behind a than it is. And I think this is behind a lot of the stories that we see like the lot of the stories that we see like the lot of the stories that we see like the the panic over Kimmy K3. In a weird way, the panic over Kimmy K3. In a weird way, the panic over Kimmy K3. In a weird way, these companies have an incentive to these companies have an incentive to these companies have an incentive to promote the idea that Kimmy is a huge promote the idea that Kimmy is a huge promote the idea that Kimmy is a huge threat, that it's caught up with the threat, that it's caught up with the threat, that it's caught up with the frontier, that it's stealing their IP, frontier, that it's stealing their IP, frontier, that it's stealing their IP, that it could basically put them out of that it could basically put them out of that it could basically put them out of business. I think this is all nonsense. business. I think this is all nonsense. business. I think this is all nonsense. I think that once the panic passed, you I think that once the panic passed, you I think that once the panic passed, you saw reports coming out that actually no, saw reports coming out that actually no, saw reports coming out that actually no, Kimmy is it did not reach the frontier. Kimmy is it did not reach the frontier. Kimmy is it did not reach the frontier. It's it's just not at that level. It's It's it's just not at that level. It's It's it's just not at that level. It's not that cheap to run. Actually, it's not that cheap to run. Actually, it's not that cheap to run. Actually, it's pretty expensive to run. So, I think pretty expensive to run. So, I think pretty expensive to run. So, I think that you saw that actually the Chinese that you saw that actually the Chinese that you saw that actually the Chinese open source models are not an open source models are not an open source models are not an existential threat to this duopoly. But existential threat to this duopoly. But existential threat to this duopoly. But I think the duopoly actually has an I think the duopoly actually has an I think the duopoly actually has an incentive in promoting or amplifying incentive in promoting or amplifying incentive in promoting or amplifying that story because again they want to that story because again they want to that story because again they want to pretend to be commodities. So, whenever pretend to be commodities. So, whenever pretend to be commodities. So, whenever there is a story like this, you have to there is a story like this, you have to there is a story like this, you have to think about well, what's really going on think about well, what's really going on think about well, what's really going on here? And again, I just think that the here? And again, I just think that the here? And again, I just think that the the AI duopoly has a big incentive to the AI duopoly has a big incentive to the AI duopoly has a big incentive to promote anything that suggests that promote anything that suggests that promote anything that suggests that they're not actually in complete control they're not actually in complete control they're not actually in complete control of this market. I think of this market. I think of this market. I think >> mo most of that except that majority of >> mo most of that except that majority of >> mo most of that except that majority of tokens are going to open source. And as tokens are going to open source. And as tokens are going to open source. And as I've said on this program before, I I I've said on this program before, I I I've said on this program before, I I watch the startups and they are token watch the startups and they are token watch the startups and they are token maxing with the open source and Kimmy is maxing with the open source and Kimmy is maxing with the open source and Kimmy is taking a lot of tokens away from the taking a lot of tokens away from the taking a lot of tokens away from the frontier [snorts] models and frontier [snorts] models and frontier [snorts] models and >> but you know but look I it's hard for me >> but you know but look I it's hard for me >> but you know but look I it's hard for me to speak to that one piece of data. I've to speak to that one piece of data. I've to speak to that one piece of data. I've seen that chart too but look at the seen that chart too but look at the seen that chart too but look at the actual revenue of anthropic and open AI actual revenue of anthropic and open AI actual revenue of anthropic and open AI and they have been taking their and they have been taking their and they have been taking their estimates up every quarter is basically estimates up every quarter is basically estimates up every quarter is basically a beat and raise. You saw that Sarah a beat and raise. You saw that Sarah a beat and raise. You saw that Sarah Frier came out and said Frier came out and said Frier came out and said >> I mean two things. Yeah. >> I mean two things. Yeah. >> I mean two things. Yeah. >> Well, she said in July they did more net >> Well, she said in July they did more net >> Well, she said in July they did more net new ARR in July than all of Q2 which I new ARR in July than all of Q2 which I new ARR in July than all of Q2 which I guess would have been April, May and guess would have been April, May and guess would have been April, May and June. So think about that. So they are June. So think about that. So they are June. So think about that. So they are seeing a reaceleration in the wake of seeing a reaceleration in the wake of seeing a reaceleration in the wake of their new model which I think is GPT their new model which I think is GPT their new model which I think is GPT 5.6. Meanwhile you're seeing anthropic 5.6. Meanwhile you're seeing anthropic 5.6. Meanwhile you're seeing anthropic break into the 70s 70 plus billion of break into the 70s 70 plus billion of break into the 70s 70 plus billion of ARR. Their forecast was to 10x this year ARR. Their forecast was to 10x this year ARR. Their forecast was to 10x this year from 10 billion of AR to 100 billion. I from 10 billion of AR to 100 billion. I from 10 billion of AR to 100 billion. I think most people are saying they will think most people are saying they will think most people are saying they will exceed that 110 120. So if you actually exceed that 110 120. So if you actually exceed that 110 120. So if you actually look at the market based on willingness look at the market based on willingness look at the market based on willingness to pay and actual revenue, they have a to pay and actual revenue, they have a to pay and actual revenue, they have a commanding duopoly position. And so commanding duopoly position. And so commanding duopoly position. And so maybe it's just a matter of which maybe it's just a matter of which maybe it's just a matter of which metrics you you look at. And that's the metrics you you look at. And that's the metrics you you look at. And that's the key here because let me just key here because let me just key here because let me just one other thing here is one other thing here is one other thing here is >> well I think I think when you look at a >> well I think I think when you look at a >> well I think I think when you look at a market you look at revenue is most market you look at revenue is most market you look at revenue is most important metric that's the real test of important metric that's the real test of important metric that's the real test of willingness to pay. The other thing is willingness to pay. The other thing is willingness to pay. The other thing is while this growth was going on their while this growth was going on their while this growth was going on their margins were increasing. So I've seen margins were increasing. So I've seen margins were increasing. So I've seen stories saying that anthropics revenues stories saying that anthropics revenues stories saying that anthropics revenues come with 80 plus% gross margins. So come with 80 plus% gross margins. So come with 80 plus% gross margins. So their margin profile has been improving their margin profile has been improving their margin profile has been improving at the same time that they're growing at the same time that they're growing at the same time that they're growing their usage. So I think that what you're their usage. So I think that what you're their usage. So I think that what you're seeing over the past year is if you look seeing over the past year is if you look seeing over the past year is if you look at the numbers I'm talking about, you at the numbers I'm talking about, you at the numbers I'm talking about, you actually see two companies pulling away actually see two companies pulling away actually see two companies pulling away from the others. And there are good from the others. And there are good from the others. And there are good reasons to believe that actually this is reasons to believe that actually this is reasons to believe that actually this is going to be a self-reinforcing monopoly going to be a self-reinforcing monopoly going to be a self-reinforcing monopoly or duopoly which is Darkash just or duopoly which is Darkash just or duopoly which is Darkash just published a blog that I thought was published a blog that I thought was published a blog that I thought was super interesting where he talked about super interesting where he talked about super interesting where he talked about the fact that look we we do have a the fact that look we we do have a the fact that look we we do have a compute shortage right there's scarcity compute shortage right there's scarcity compute shortage right there's scarcity around compute. Uh Anthropic is growing around compute. Uh Anthropic is growing around compute. Uh Anthropic is growing its revenues 10x year-over-year. What its revenues 10x year-over-year. What its revenues 10x year-over-year. What would that mean? I mean, it basically would that mean? I mean, it basically would that mean? I mean, it basically means that next year they would grow means that next year they would grow means that next year they would grow from 100 billion of ARR to a trillion, from 100 billion of ARR to a trillion, from 100 billion of ARR to a trillion, right? If there was enough compute to right? If there was enough compute to right? If there was enough compute to support that, there might not be enough support that, there might not be enough support that, there might not be enough compute, but that's going to put compute, but that's going to put compute, but that's going to put pressure on compute prices, right? And pressure on compute prices, right? And pressure on compute prices, right? And so, let's say that you're a new entrant so, let's say that you're a new entrant so, let's say that you're a new entrant in the market and you're trying to in the market and you're trying to in the market and you're trying to basically create a smaller, cheaper basically create a smaller, cheaper basically create a smaller, cheaper model. The price of compute is going up. model. The price of compute is going up. model. The price of compute is going up. It's going to be harder for you to get It's going to be harder for you to get It's going to be harder for you to get access to compute. And only the access to compute. And only the access to compute. And only the companies that have the most lucrative companies that have the most lucrative companies that have the most lucrative algorithms are going to be able to algorithms are going to be able to algorithms are going to be able to afford to compete for compute. In other afford to compete for compute. In other afford to compete for compute. In other words, there's going to be a bigger words, there's going to be a bigger words, there's going to be a bigger barrier to entry next year because where barrier to entry next year because where barrier to entry next year because where are you going to get compute unless your are you going to get compute unless your are you going to get compute unless your model is capable of generating this type model is capable of generating this type model is capable of generating this type of revenue? of revenue? of revenue? >> Yeah. This is where I'll take the other >> Yeah. This is where I'll take the other >> Yeah. This is where I'll take the other side of it. side of it. side of it. >> Yeah. >> Yeah. >> Yeah. >> People are running Kimmy on the last >> People are running Kimmy on the last >> People are running Kimmy on the last generation of hardware and they're generation of hardware and they're generation of hardware and they're that's plentiful. And I think, and I'll that's plentiful. And I think, and I'll that's plentiful. And I think, and I'll make this prediction here, that you're make this prediction here, that you're make this prediction here, that you're going to see some of the major customers going to see some of the major customers going to see some of the major customers of Anthropic and major customers of of Anthropic and major customers of of Anthropic and major customers of OpenAI, I'm talking about the eight and OpenAI, I'm talking about the eight and OpenAI, I'm talking about the eight and nine figure customers, people spending nine figure customers, people spending nine figure customers, people spending 50 million, 100 million a year. They're 50 million, 100 million a year. They're 50 million, 100 million a year. They're leaving. They're going to be leaving leaving. They're going to be leaving leaving. They're going to be leaving because they don't trust those companies because they don't trust those companies because they don't trust those companies to not steal the application layer and to not steal the application layer and to not steal the application layer and to compete with them. 11 Labs, Figma, to compete with them. 11 Labs, Figma, to compete with them. 11 Labs, Figma, Lovable, they're all going to leave. And Lovable, they're all going to leave. And Lovable, they're all going to leave. And they're all going to take Kimmy. They're they're all going to take Kimmy. They're they're all going to take Kimmy. They're going to fork it or whichever one DeepS going to fork it or whichever one DeepS going to fork it or whichever one DeepS seek. They're all I know for a fact seek. They're all I know for a fact seek. They're all I know for a fact they're all working on their own models they're all working on their own models they're all working on their own models currently. I know from my team my team currently. I know from my team my team currently. I know from my team my team has installed Kimmy. It is 90% cheaper. has installed Kimmy. It is 90% cheaper. has installed Kimmy. It is 90% cheaper. 80 90% cheaper already. Not sure where 80 90% cheaper already. Not sure where 80 90% cheaper already. Not sure where you're getting your data from, but go on you're getting your data from, but go on you're getting your data from, but go on open router. And what open router does open router. And what open router does open router. And what open router does is you pick Kimmy Sachs and then you get is you pick Kimmy Sachs and then you get is you pick Kimmy Sachs and then you get all the providers there. And then you all the providers there. And then you all the providers there. And then you pick which provider. Hold on, let me pick which provider. Hold on, let me pick which provider. Hold on, let me finish. You pick which provider you want finish. You pick which provider you want finish. You pick which provider you want based on uptime and and you pick them based on uptime and and you pick them based on uptime and and you pick them based on their data retention and other based on their data retention and other based on their data retention and other issues and you can dynamically pick the issues and you can dynamically pick the issues and you can dynamically pick the lowest one. And that's going to be a lowest one. And that's going to be a lowest one. And that's going to be a massive headwind against these massive headwind against these massive headwind against these companies. Massive. And I'm seeing it companies. Massive. And I'm seeing it companies. Massive. And I'm seeing it nine out of 10 startups I talked to in nine out of 10 startups I talked to in nine out of 10 startups I talked to in our portfolio at founder university when our portfolio at founder university when our portfolio at founder university when I was just in Japan last week running I was just in Japan last week running I was just in Japan last week running the next one. They're all working on the next one. They're all working on the next one. They're all working on open source. They're all embracing it open source. They're all embracing it open source. They're all embracing it and those big companies are embracing and those big companies are embracing and those big companies are embracing it. Go ahead Shamath. Over to you. it. Go ahead Shamath. Over to you. it. Go ahead Shamath. Over to you. Irrespective of whichever model you use, Irrespective of whichever model you use, Irrespective of whichever model you use, what I will tell you running 8090 when I what I will tell you running 8090 when I what I will tell you running 8090 when I see our engineers generating code is see our engineers generating code is see our engineers generating code is that AIdriven development tends to that AIdriven development tends to that AIdriven development tends to involve a lot of rework. The first involve a lot of rework. The first involve a lot of rework. The first version is pretty terrible. The second version is pretty terrible. The second version is pretty terrible. The second version is terrible, but it is faster version is terrible, but it is faster version is terrible, but it is faster and it's more automated. So, I can see and it's more automated. So, I can see and it's more automated. So, I can see where this token consumption comes from where this token consumption comes from where this token consumption comes from because it's not a measure twice, cut because it's not a measure twice, cut because it's not a measure twice, cut once kind of a dynamic. It's the once kind of a dynamic. It's the once kind of a dynamic. It's the opposite. You can cut cut as many times opposite. You can cut cut as many times opposite. You can cut cut as many times as you want. And so I think that what we as you want. And so I think that what we as you want. And so I think that what we have to realize is nobody is asking the have to realize is nobody is asking the have to realize is nobody is asking the question what is the need of that question what is the need of that question what is the need of that incremental token because I understand incremental token because I understand incremental token because I understand that it appears in the frontier labs as that it appears in the frontier labs as that it appears in the frontier labs as P&L P&L P&L but I do think that there's an important but I do think that there's an important but I do think that there's an important question which is eventually the people question which is eventually the people question which is eventually the people that are consuming it will want to do that are consuming it will want to do that are consuming it will want to do that as efficiently as possible so that that as efficiently as possible so that that as efficiently as possible so that they're not paying for all of these they're not paying for all of these they're not paying for all of these things because there is a ton of rework things because there is a ton of rework things because there is a ton of rework in all of this stuff and I would much in all of this stuff and I would much in all of this stuff and I would much rather find a model or find a way of rather find a model or find a way of rather find a model or find a way of working with these models where it's working with these models where it's working with these models where it's more of a measure twice cut once thing more of a measure twice cut once thing more of a measure twice cut once thing especially as the costs ratchet up. especially as the costs ratchet up. especially as the costs ratchet up. That's one thing I'll say that hasn't That's one thing I'll say that hasn't That's one thing I'll say that hasn't happened yet. So Sax, you're totally happened yet. So Sax, you're totally happened yet. So Sax, you're totally right about the dynamic today. I do right about the dynamic today. I do right about the dynamic today. I do think we have to keep in mind that there think we have to keep in mind that there think we have to keep in mind that there will be pressure from the owners of will be pressure from the owners of will be pressure from the owners of companies to figure this out because at companies to figure this out because at companies to figure this out because at a trillion dollars there's just a lot of a trillion dollars there's just a lot of a trillion dollars there's just a lot of money flowing to these folks and money flowing to these folks and money flowing to these folks and somebody will ask the question, well is somebody will ask the question, well is somebody will ask the question, well is it good spend? The second on the it good spend? The second on the it good spend? The second on the security side, which I don't think security side, which I don't think security side, which I don't think anybody is saying, so I'll just say this anybody is saying, so I'll just say this anybody is saying, so I'll just say this and it's a little contrarian. The reason and it's a little contrarian. The reason and it's a little contrarian. The reason why these models can find all these why these models can find all these why these models can find all these holes is that all of the software up holes is that all of the software up holes is that all of the software up until about a few years ago was entirely until about a few years ago was entirely until about a few years ago was entirely written by humans and the code was not written by humans and the code was not written by humans and the code was not that good. that good. that good. And I think it's fair to say that when And I think it's fair to say that when And I think it's fair to say that when models don't get exhausted, they can models don't get exhausted, they can models don't get exhausted, they can work through the TDM forever. work through the TDM forever. work through the TDM forever. It's actually quite expected in my It's actually quite expected in my It's actually quite expected in my opinion that they find all these opinion that they find all these opinion that they find all these exploits are able to string them exploits are able to string them exploits are able to string them together and are able to actually together and are able to actually together and are able to actually generate these outcomes that are a generate these outcomes that are a generate these outcomes that are a little bit surprising. But at some point little bit surprising. But at some point little bit surprising. But at some point when most of the code is generated by when most of the code is generated by when most of the code is generated by the model there'll be some point in the the model there'll be some point in the the model there'll be some point in the future say 28 or 29 or 2030 these future say 28 or 29 or 2030 these future say 28 or 29 or 2030 these security holes won't exist because the security holes won't exist because the security holes won't exist because the errors that humans make won't be made by errors that humans make won't be made by errors that humans make won't be made by these model. these model. these model. >> Yes. Okay. Let me get Freeberg involved >> Yes. Okay. Let me get Freeberg involved >> Yes. Okay. Let me get Freeberg involved here. When you look at this latest here. When you look at this latest here. When you look at this latest survey, hand ringing, cur pearl survey, hand ringing, cur pearl survey, hand ringing, cur pearl clutching, do you think these firms, and clutching, do you think these firms, and clutching, do you think these firms, and you know a lot of these people, you know a lot of these people, you know a lot of these people, Freeberg, having been in the valley Freeberg, having been in the valley Freeberg, having been in the valley forever, do you think this is sincerity forever, do you think this is sincerity forever, do you think this is sincerity or do you think they're Frankenstein or do you think they're Frankenstein or do you think they're Frankenstein maxing? What's going on here? maxing? What's going on here? maxing? What's going on here? >> Well, Frankenstein, >> Well, Frankenstein, >> Well, Frankenstein, >> I mean, they kind of think that they're >> I mean, they kind of think that they're >> I mean, they kind of think that they're like, listen, I created this monster. like, listen, I created this monster. like, listen, I created this monster. Please save me. And it's like, well, Please save me. And it's like, well, Please save me. And it's like, well, maybe you should keep the monster away. maybe you should keep the monster away. maybe you should keep the monster away. >> By the way, it's also that whole thing. >> By the way, it's also that whole thing. >> By the way, it's also that whole thing. Sorry. Last thing up to you, David, is Sorry. Last thing up to you, David, is Sorry. Last thing up to you, David, is it's a much more nuanced and elegant it's a much more nuanced and elegant it's a much more nuanced and elegant attempt at rag capture. I got to give attempt at rag capture. I got to give attempt at rag capture. I got to give them credit for that. them credit for that. them credit for that. >> Yeah. >> Yeah. >> Yeah. >> It's like, okay, hey guys, pull the >> It's like, okay, hey guys, pull the >> It's like, okay, hey guys, pull the ladder up. ladder up. ladder up. >> Yeah. Zuckerberg had a great point in >> Yeah. Zuckerberg had a great point in >> Yeah. Zuckerberg had a great point in that article he just wrote. I think it that article he just wrote. I think it that article he just wrote. I think it was published in the Wall Street Journal was published in the Wall Street Journal was published in the Wall Street Journal where he said, "Why are you rushing to where he said, "Why are you rushing to where he said, "Why are you rushing to create a future that you don't believe create a future that you don't believe create a future that you don't believe in? You think you're going to basically in? You think you're going to basically in? You think you're going to basically put everyone out at work? You think put everyone out at work? You think put everyone out at work? You think you're creating a replacement species you're creating a replacement species you're creating a replacement species for humanity? Why are you rushing to for humanity? Why are you rushing to for humanity? Why are you rushing to create this if you're so bearish on the create this if you're so bearish on the create this if you're so bearish on the future you're creating?" future you're creating?" future you're creating?" >> It's your choice. And that's kind of my >> It's your choice. And that's kind of my >> It's your choice. And that's kind of my point. 120 mph on the autobond. Just put point. 120 mph on the autobond. Just put point. 120 mph on the autobond. Just put it at 80. it at 80. it at 80. >> Right. Exactly. And look, the like I'm >> Right. Exactly. And look, the like I'm >> Right. Exactly. And look, the like I'm saying, there's two companies on the saying, there's two companies on the saying, there's two companies on the frontier right now that are far ahead of frontier right now that are far ahead of frontier right now that are far ahead of everyone else. And they're the ones everyone else. And they're the ones everyone else. And they're the ones saying that we need to slow down. It's saying that we need to slow down. It's saying that we need to slow down. It's like, okay, do it. What do you need the like, okay, do it. What do you need the like, okay, do it. What do you need the government to get involved for? Just do government to get involved for? Just do government to get involved for? Just do it. But they won't do it. it. But they won't do it. it. But they won't do it. >> And I show that they're insincere or >> And I show that they're insincere or >> And I show that they're insincere or delusional. What do you think, Freeberg? delusional. What do you think, Freeberg? delusional. What do you think, Freeberg? Take me into the mind of these people. Take me into the mind of these people. Take me into the mind of these people. These are some of your friends. These are some of your friends. These are some of your friends. >> No, they're not. So, just to be clear, >> No, they're not. So, just to be clear, >> No, they're not. So, just to be clear, I'm not close personal friends with any I'm not close personal friends with any I'm not close personal friends with any of these people. of these people. of these people. >> You know, we're acquaintances. I would >> You know, we're acquaintances. I would >> You know, we're acquaintances. I would say say say >> there's a degree of I would say >> there's a degree of I would say >> there's a degree of I would say outrageous self-importance. outrageous self-importance. outrageous self-importance. If I've created something that's so If I've created something that's so If I've created something that's so unique and so powerful, I'm also the unique and so powerful, I'm also the unique and so powerful, I'm also the only person that can protect us from its only person that can protect us from its only person that can protect us from its power. And I think that there's an power. And I think that there's an power. And I think that there's an element of how quickly the frontier has element of how quickly the frontier has element of how quickly the frontier has advanced and how important a role these advanced and how important a role these advanced and how important a role these individuals have had that they deem individuals have had that they deem individuals have had that they deem themselves and their companies to be the themselves and their companies to be the themselves and their companies to be the only true judges capable of making the only true judges capable of making the only true judges capable of making the decisions that are going to protect decisions that are going to protect decisions that are going to protect humanity from itself. When the truth is humanity from itself. When the truth is humanity from itself. When the truth is embedded in humanity is extraordinary embedded in humanity is extraordinary embedded in humanity is extraordinary human talent across the board. And in human talent across the board. And in human talent across the board. And in all of these cases, when new technology all of these cases, when new technology all of these cases, when new technology has found its way to humanity, the has found its way to humanity, the has found its way to humanity, the general population has found a way to general population has found a way to general population has found a way to protect itself. There isn't a desire or protect itself. There isn't a desire or protect itself. There isn't a desire or need to have one savior, one Moses that need to have one savior, one Moses that need to have one savior, one Moses that takes us, you know, across the desert. takes us, you know, across the desert. takes us, you know, across the desert. Uh there is a collective interest in Uh there is a collective interest in Uh there is a collective interest in protecting us uh in building our own protecting us uh in building our own protecting us uh in building our own defense tools against whatever the defense tools against whatever the defense tools against whatever the technology may be used for. So I I think technology may be used for. So I I think technology may be used for. So I I think that there's a degree of self-importance that there's a degree of self-importance that there's a degree of self-importance without acknowledging the fact that without acknowledging the fact that without acknowledging the fact that there's a whole industry of people that there's a whole industry of people that there's a whole industry of people that work in cyber defense. There's a whole work in cyber defense. There's a whole work in cyber defense. There's a whole industry of people that work in industry of people that work in industry of people that work in biodefense. There's a whole cabal of biodefense. There's a whole cabal of biodefense. There's a whole cabal of regulators. There's a whole cabal of regulators. There's a whole cabal of regulators. There's a whole cabal of protectors. There's a whole cabal of protectors. There's a whole cabal of protectors. There's a whole cabal of intelligent computer scientists. There's intelligent computer scientists. There's intelligent computer scientists. There's a whole cabal of open-source uh a whole cabal of open-source uh a whole cabal of open-source uh technologists that all together are technologists that all together are technologists that all together are going to develop paths that are going to going to develop paths that are going to going to develop paths that are going to benefit humanity and not harm humanity. benefit humanity and not harm humanity. benefit humanity and not harm humanity. But this belief that only one of two But this belief that only one of two But this belief that only one of two companies can be Moses is the companies can be Moses is the companies can be Moses is the fundamental psychological miscalculation fundamental psychological miscalculation fundamental psychological miscalculation here. That they're so advanced, they are here. That they're so advanced, they are here. That they're so advanced, they are so special, they are so unique because so special, they are so unique because so special, they are so unique because they made the slightly better model. they made the slightly better model. they made the slightly better model. They got a 0.96 instead of a 0.93 score They got a 0.96 instead of a 0.93 score They got a 0.96 instead of a 0.93 score means that they should be trusted as the means that they should be trusted as the means that they should be trusted as the only ones to kind of guide humanity's only ones to kind of guide humanity's only ones to kind of guide humanity's evolution going forward. And the truth evolution going forward. And the truth evolution going forward. And the truth is as we're seeing the capacity to do is as we're seeing the capacity to do is as we're seeing the capacity to do model training, the capacity to do model model training, the capacity to do model model training, the capacity to do model development is becoming broader. It's development is becoming broader. It's development is becoming broader. It's becoming more ubiquitous. People can sit becoming more ubiquitous. People can sit becoming more ubiquitous. People can sit and say that China stole US models all and say that China stole US models all and say that China stole US models all day long. But when you go look at the day long. But when you go look at the day long. But when you go look at the individuals working at these Chinese individuals working at these Chinese individuals working at these Chinese labs, they got PhDs at American labs, they got PhDs at American labs, they got PhDs at American institutions. Half the PhDs went to institutions. Half the PhDs went to institutions. Half the PhDs went to American labs and half went to Chinese American labs and half went to Chinese American labs and half went to Chinese labs. And they have very good scientists labs. And they have very good scientists labs. And they have very good scientists doing very good work and they are having doing very good work and they are having doing very good work and they are having breakthroughs. And it's not just the two breakthroughs. And it's not just the two breakthroughs. And it's not just the two American companies, but this is American companies, but this is American companies, but this is happening all over the place. And so the happening all over the place. And so the happening all over the place. And so the progress with AI should not be limited progress with AI should not be limited progress with AI should not be limited to just two individual companies because to just two individual companies because to just two individual companies because they're currently scoring slightly they're currently scoring slightly they're currently scoring slightly better in their models. better in their models. better in their models. >> This reminds me of Freeberg. You'll >> This reminds me of Freeberg. You'll >> This reminds me of Freeberg. You'll appreciate the uh moment. Remember when appreciate the uh moment. Remember when appreciate the uh moment. Remember when uh Han Solo comes out of Carbonite and uh Han Solo comes out of Carbonite and uh Han Solo comes out of Carbonite and he's about to get put in the Sarlac pit he's about to get put in the Sarlac pit he's about to get put in the Sarlac pit and he's like a Jedi Knight? I'm out of and he's like a Jedi Knight? I'm out of and he's like a Jedi Knight? I'm out of it for a bit and now everybody gets it for a bit and now everybody gets it for a bit and now everybody gets delusions of grandeur and thinks they're delusions of grandeur and thinks they're delusions of grandeur and thinks they're a Jedi Knight. It's like a Jedi Knight. It's like a Jedi Knight. It's like >> these guys just think they're like, >> these guys just think they're like, >> these guys just think they're like, >> you know, creating God. They literally >> you know, creating God. They literally >> you know, creating God. They literally think they're creating God and and they think they're creating God and and they think they're creating God and and they need to be regulated cuz they can't need to be regulated cuz they can't need to be regulated cuz they can't control it. It's like they can't control control it. It's like they can't control control it. It's like they can't control it. Just pause. it. Just pause. it. Just pause. >> It's not that they need It's not that >> It's not that they need It's not that >> It's not that they need It's not that they need to be regulated. It's that they need to be regulated. It's that they need to be regulated. It's that they need to guide the regulation. Let's they need to guide the regulation. Let's they need to guide the regulation. Let's be clear. be clear. be clear. >> It's so sinister. It's >> It's so sinister. It's >> It's so sinister. It's >> And anyone who says I Yeah, I I by the >> And anyone who says I Yeah, I I by the >> And anyone who says I Yeah, I I by the way, I don't think that it is as way, I don't think that it is as way, I don't think that it is as nefarious or malicious as everyone nefarious or malicious as everyone nefarious or malicious as everyone frames it to be knowing these frames it to be knowing these frames it to be knowing these individuals. I don't think they're individuals. I don't think they're individuals. I don't think they're saying like the strategy is regulatory saying like the strategy is regulatory saying like the strategy is regulatory capture. Let me go. I think that they capture. Let me go. I think that they capture. Let me go. I think that they actually do think that they are the only actually do think that they are the only actually do think that they are the only ones that can help guide humanity and ones that can help guide humanity and ones that can help guide humanity and therefore they need to have all the therefore they need to have all the therefore they need to have all the power, not just the power of the models, power, not just the power of the models, power, not just the power of the models, but the power of the government and the but the power of the government and the but the power of the government and the power of the regulators and the power of power of the regulators and the power of power of the regulators and the power of the control units that are embedded in the control units that are embedded in the control units that are embedded in governments around the world. It's not governments around the world. It's not governments around the world. It's not just that um that they want to quote be just that um that they want to quote be just that um that they want to quote be regulated, they want to guide the regulated, they want to guide the regulated, they want to guide the regulation. They want to set the regulation. They want to set the regulation. They want to set the regulation. I know it's a nuance point, regulation. I know it's a nuance point, regulation. I know it's a nuance point, but I I I think we have we I think we've but I I I think we have we I think we've but I I I think we have we I think we've navigated this pretty well and there's navigated this pretty well and there's navigated this pretty well and there's multiple motivations as Sax was saying multiple motivations as Sax was saying multiple motivations as Sax was saying and people are complex but Chimath I was and people are complex but Chimath I was and people are complex but Chimath I was talking to talking to talking to >> a friend of ours um who you know is in >> a friend of ours um who you know is in >> a friend of ours um who you know is in the uh providing inference space let's the uh providing inference space let's the uh providing inference space let's leave it at that you know providing leave it at that you know providing leave it at that you know providing >> our friend a friend a friend friend of >> our friend a friend a friend friend of >> our friend a friend a friend friend of ours as we say in the you know the uh ours as we say in the you know the uh ours as we say in the you know the uh sopranos a friend of ours he said he has sopranos a friend of ours he said he has sopranos a friend of ours he said he has got a customer who just moved move like got a customer who just moved move like got a customer who just moved move like nine figures off of the Frontier Labs to nine figures off of the Frontier Labs to nine figures off of the Frontier Labs to put it on GLM52. So that's the ZXI one. put it on GLM52. So that's the ZXI one. put it on GLM52. So that's the ZXI one. That's really good. So this is That's really good. So this is That's really good. So this is happening. I I don't know when it shows happening. I I don't know when it shows happening. I I don't know when it shows up in the numbers or if the you know up in the numbers or if the you know up in the numbers or if the you know corporates that are using this stuff um corporates that are using this stuff um corporates that are using this stuff um are going to make up the difference but are going to make up the difference but are going to make up the difference but >> well look I think that Sax is right that >> well look I think that Sax is right that >> well look I think that Sax is right that the usage is so profound that everybody the usage is so profound that everybody the usage is so profound that everybody is trying to get access to these things is trying to get access to these things is trying to get access to these things because the capabilities are just so because the capabilities are just so because the capabilities are just so inspiring and so I suspect revenues are inspiring and so I suspect revenues are inspiring and so I suspect revenues are going to crank at OpenAI and anthropic going to crank at OpenAI and anthropic going to crank at OpenAI and anthropic and the open labs for a while but again and the open labs for a while but again and the open labs for a while but again that's not the important thing. If that's not the important thing. If that's not the important thing. If you're thinking about valuation, you're thinking about valuation, you're thinking about valuation, the markets will look 5 to 10 years out the markets will look 5 to 10 years out the markets will look 5 to 10 years out to answer that question. They're not to answer that question. They're not to answer that question. They're not going to give you a premium valuation on going to give you a premium valuation on going to give you a premium valuation on something that they feel could be something that they feel could be something that they feel could be fragile in the first 2 to 3 years. And fragile in the first 2 to 3 years. And fragile in the first 2 to 3 years. And that's where Jason, the answer to your that's where Jason, the answer to your that's where Jason, the answer to your question needs to get figured out question needs to get figured out question needs to get figured out because I don't know whether you're because I don't know whether you're because I don't know whether you're right or not, but somebody has to answer right or not, but somebody has to answer right or not, but somebody has to answer that question precisely because if the that question precisely because if the that question precisely because if the answer is that it is a duopoly, then answer is that it is a duopoly, then answer is that it is a duopoly, then there is no risk to the revenue 5 to 10 there is no risk to the revenue 5 to 10 there is no risk to the revenue 5 to 10 years from now. These things are 5 to10 years from now. These things are 5 to10 years from now. These things are 5 to10 trillion dollar companies each. trillion dollar companies each. trillion dollar companies each. But if you are right or if there are But if you are right or if there are But if you are right or if there are harnesses that cut the token consumption harnesses that cut the token consumption harnesses that cut the token consumption because you stop wasting tokens to get because you stop wasting tokens to get because you stop wasting tokens to get to the same output then it's a little to the same output then it's a little to the same output then it's a little bit more of a question mark and I think bit more of a question mark and I think bit more of a question mark and I think that'll need to get sorted out. that'll need to get sorted out. that'll need to get sorted out. >> Yeah, Perplexity is going to launch next >> Yeah, Perplexity is going to launch next >> Yeah, Perplexity is going to launch next week. From what I understand the rumor week. From what I understand the rumor week. From what I understand the rumor is they're going to launch local models. is they're going to launch local models. is they're going to launch local models. So you'll be able to take your harness So you'll be able to take your harness So you'll be able to take your harness sacks and say hey you know I I want to sacks and say hey you know I I want to sacks and say hey you know I I want to use Sonnet for this. I want to use GLM52 use Sonnet for this. I want to use GLM52 use Sonnet for this. I want to use GLM52 for this and then I want to default to for this and then I want to default to for this and then I want to default to Kimmy 2.x. Kimmy 2.x. Kimmy 2.x. I don't think enterprises will use local I don't think enterprises will use local I don't think enterprises will use local models or they should. [clears throat] I models or they should. [clears throat] I models or they should. [clears throat] I think it's stupid. I think think it's stupid. I think think it's stupid. I think >> I I think this stuff should be hosted in >> I I think this stuff should be hosted in >> I I think this stuff should be hosted in the cloud. It should be multiplayer. It the cloud. It should be multiplayer. It the cloud. It should be multiplayer. It should be shared memory. I don't know if should be shared memory. I don't know if should be shared memory. I don't know if you guys saw that, but Jack Dorsey you guys saw that, but Jack Dorsey you guys saw that, but Jack Dorsey released something called Buzz. released something called Buzz. released something called Buzz. >> Super interesting. >> Super interesting. >> Super interesting. >> Agents. Yeah, >> Agents. Yeah, >> Agents. Yeah, >> he's moving in the right direction. A >> he's moving in the right direction. A >> he's moving in the right direction. A lot of these guys are moving towards lot of these guys are moving towards lot of these guys are moving towards this more cloud-based thing. Jason, I this more cloud-based thing. Jason, I this more cloud-based thing. Jason, I think that these this local thing is think that these this local thing is think that these this local thing is more of a hacker hobby is kind of a more of a hacker hobby is kind of a more of a hacker hobby is kind of a thing. I think it will I agree today it thing. I think it will I agree today it thing. I think it will I agree today it will be that and for year one it will will be that and for year one it will will be that and for year one it will probably be that but imagine you're a probably be that but imagine you're a probably be that but imagine you're a developer and as you're working your developer and as you're working your developer and as you're working your workstation is able to keep up and even workstation is able to keep up and even workstation is able to keep up and even go faster than the cloud a and just go faster than the cloud a and just go faster than the cloud a and just write whatever the simple code is and write whatever the simple code is and write whatever the simple code is and then it dynamically switches so we'll then it dynamically switches so we'll then it dynamically switches so we'll see it's you know I obviously it's not see it's you know I obviously it's not see it's you know I obviously it's not as uh easy to set up etc but it's going as uh easy to set up etc but it's going as uh easy to set up etc but it's going to get easier that's always the trend to get easier that's always the trend to get easier that's always the trend sax you want to have the last word here sax you want to have the last word here sax you want to have the last word here we we got a lot of opinions here and we we got a lot of opinions here and we we got a lot of opinions here and maybe we'll give you the last maybe we'll give you the last maybe we'll give you the last >> yeah I mean look just to be clear I'm a >> yeah I mean look just to be clear I'm a >> yeah I mean look just to be clear I'm a fan of open source ource because open fan of open source ource because open fan of open source ource because open source is software freedom and to source is software freedom and to source is software freedom and to Freeberg's point I would like there to Freeberg's point I would like there to Freeberg's point I would like there to be a let's call it decentralized outcome be a let's call it decentralized outcome be a let's call it decentralized outcome with respect to AI I don't like the idea with respect to AI I don't like the idea with respect to AI I don't like the idea of AI being controlled by two big tech of AI being controlled by two big tech of AI being controlled by two big tech companies that work closely with the companies that work closely with the companies that work closely with the administrative state you know handinand administrative state you know handinand administrative state you know handinand glove so look we're all kind of in some glove so look we're all kind of in some glove so look we're all kind of in some sense rooting for open source to be an sense rooting for open source to be an sense rooting for open source to be an option and it does provide a bunch of option and it does provide a bunch of option and it does provide a bunch of advantages over closed source right you advantages over closed source right you advantages over closed source right you get customization you get control you get customization you get control you get customization you get control you can run on your own hardware you don't can run on your own hardware you don't can run on your own hardware you don't have to worry about the data problem. have to worry about the data problem. have to worry about the data problem. You know, your alpha getting leaked to You know, your alpha getting leaked to You know, your alpha getting leaked to these companies that might compete with these companies that might compete with these companies that might compete with you. All those types of things. And the you. All those types of things. And the you. All those types of things. And the market is so big that I'm sure we will market is so big that I'm sure we will market is so big that I'm sure we will see some success with open source. It see some success with open source. It see some success with open source. It will take a meaningful chunk of the will take a meaningful chunk of the will take a meaningful chunk of the market. But if you're looking at where market. But if you're looking at where market. But if you're looking at where the revenue is right now, it's these two the revenue is right now, it's these two the revenue is right now, it's these two companies. It might end up being a companies. It might end up being a companies. It might end up being a situation like Apple and Android where situation like Apple and Android where situation like Apple and Android where Android got a lot of market share, but Android got a lot of market share, but Android got a lot of market share, but Apple's where all the monetization was. Apple's where all the monetization was. Apple's where all the monetization was. >> Yeah. And I think that Dwarcash raises a >> Yeah. And I think that Dwarcash raises a >> Yeah. And I think that Dwarcash raises a really good point that as the demand is really good point that as the demand is really good point that as the demand is 10xing year-over-year, 10xing year-over-year, 10xing year-over-year, but the compute can only be built out at but the compute can only be built out at but the compute can only be built out at say 3x year-over-year because it just say 3x year-over-year because it just say 3x year-over-year because it just all the friction of all the things in all the friction of all the things in all the friction of all the things in the real world that get in the way, the real world that get in the way, the real world that get in the way, permitting, regulation, bans on new data permitting, regulation, bans on new data permitting, regulation, bans on new data centers, all that kind of stuff. I think centers, all that kind of stuff. I think centers, all that kind of stuff. I think that the price of compute is going to go that the price of compute is going to go that the price of compute is going to go up and that will provide an advantage to up and that will provide an advantage to up and that will provide an advantage to the models that have the most lucrative the models that have the most lucrative the models that have the most lucrative algorithms that are able to produce the algorithms that are able to produce the algorithms that are able to produce the most intelligence per watt or the most most intelligence per watt or the most most intelligence per watt or the most intelligence per token or per GPU. And intelligence per token or per GPU. And intelligence per token or per GPU. And right now that that is those two right now that that is those two right now that that is those two companies and in a way you could say companies and in a way you could say companies and in a way you could say they have a self reinforcing loop they have a self reinforcing loop they have a self reinforcing loop because if you have all the revenue and because if you have all the revenue and because if you have all the revenue and right now like I said it's just two right now like I said it's just two right now like I said it's just two companies have all the revenue you can companies have all the revenue you can companies have all the revenue you can then plow that money back into the next then plow that money back into the next then plow that money back into the next training run. Right? So that's the the training run. Right? So that's the the training run. Right? So that's the the flywheel here. flywheel here. flywheel here. >> Yeah. >> Yeah. >> Yeah. >> And look, I think that it's great that >> And look, I think that it's great that >> And look, I think that it's great that open source is providing an alternative. open source is providing an alternative. open source is providing an alternative. We shouldn't do anything to get in the We shouldn't do anything to get in the We shouldn't do anything to get in the way of that. I think that, you know, way of that. I think that, you know, way of that. I think that, you know, these online debates tend to become a these online debates tend to become a these online debates tend to become a little bit histrionic in the sense that little bit histrionic in the sense that little bit histrionic in the sense that everyone has to religious. everyone has to religious. everyone has to religious. >> Well, they they become religious and >> Well, they they become religious and >> Well, they they become religious and they have to argue for an all or nothing they have to argue for an all or nothing they have to argue for an all or nothing perspective. I think open source will do perspective. I think open source will do perspective. I think open source will do great in its way, but so will these two great in its way, but so will these two great in its way, but so will these two close source companies. close source companies. close source companies. >> Here's your poly market. 19% chance the >> Here's your poly market. 19% chance the >> Here's your poly market. 19% chance the US enacts an AI safety bill this year. US enacts an AI safety bill this year. US enacts an AI safety bill this year. 100k of volume. And then really 100k of volume. And then really 100k of volume. And then really interesting one, uh, Chimath here, interesting one, uh, Chimath here, interesting one, uh, Chimath here, OpenAI IPO chances for 2026 OpenAI IPO chances for 2026 OpenAI IPO chances for 2026 was at 75% last month, has now dropped was at 75% last month, has now dropped was at 75% last month, has now dropped to 20%, an all-time low. So, seems like to 20%, an all-time low. So, seems like to 20%, an all-time low. So, seems like the IPO is going to happen next year. the IPO is going to happen next year. the IPO is going to happen next year. Not sure what's driving that, but uh, Not sure what's driving that, but uh, Not sure what's driving that, but uh, there's your poly markets. Well, just on there's your poly markets. Well, just on there's your poly markets. Well, just on the AI safety bill idea, I mean, there the AI safety bill idea, I mean, there the AI safety bill idea, I mean, there was an article in Punch Bowl this was an article in Punch Bowl this was an article in Punch Bowl this morning that Thun, you know, who's the morning that Thun, you know, who's the morning that Thun, you know, who's the Senate Majority Leader, John Thun Senate Majority Leader, John Thun Senate Majority Leader, John Thun actually introduced a bill that was actually introduced a bill that was actually introduced a bill that was somewhat bipartisan. He had Clolobashar somewhat bipartisan. He had Clolobashar somewhat bipartisan. He had Clolobashar on board that required the Frontier Labs on board that required the Frontier Labs on board that required the Frontier Labs to report safety incidents apparently to to report safety incidents apparently to to report safety incidents apparently to the Commerce Department. And the Commerce Department. And the Commerce Department. And >> is that reasonable, Sax? >> is that reasonable, Sax? >> is that reasonable, Sax? >> I mean, that's the direction all this >> I mean, that's the direction all this >> I mean, that's the direction all this stuff is headed. I I mean, look, I think stuff is headed. I I mean, look, I think stuff is headed. I I mean, look, I think it's the camel's nose under the tent for it's the camel's nose under the tent for it's the camel's nose under the tent for more and more AI regulation, but look, I more and more AI regulation, but look, I more and more AI regulation, but look, I think it had bipartisan support because think it had bipartisan support because think it had bipartisan support because it's on the relatively modest side and it's on the relatively modest side and it's on the relatively modest side and Canwell, who's the ranking member on the Canwell, who's the ranking member on the Canwell, who's the ranking member on the Senate Commerce Committee, opposed it Senate Commerce Committee, opposed it Senate Commerce Committee, opposed it supposedly at Daario's behest because he supposedly at Daario's behest because he supposedly at Daario's behest because he will accept nothing less than an FDA for will accept nothing less than an FDA for will accept nothing less than an FDA for AI. AI. AI. >> Oh, he wants the whole kitten >> Oh, he wants the whole kitten >> Oh, he wants the whole kitten kaboodleoodle. kaboodleoodle. kaboodleoodle. >> Yeah. So that that's basically the >> Yeah. So that that's basically the >> Yeah. So that that's basically the dynamic right now is that Darionic want dynamic right now is that Darionic want dynamic right now is that Darionic want their FDA for AI. I think that he has their FDA for AI. I think that he has their FDA for AI. I think that he has tremendous power and influence within tremendous power and influence within tremendous power and influence within the Democrat party right now. And I the Democrat party right now. And I the Democrat party right now. And I think that influence is only going to think that influence is only going to think that influence is only going to grow. They just upped their donations in grow. They just upped their donations in grow. They just upped their donations in the midterms from 20 million to 40 the midterms from 20 million to 40 the midterms from 20 million to 40 million. But you know post IPO when they million. But you know post IPO when they million. But you know post IPO when they all get liquid and they're capable of all get liquid and they're capable of all get liquid and they're capable of writing large writing large writing large >> $50 million donations. >> $50 million donations. >> $50 million donations. >> Yeah. I think that that influence will >> Yeah. I think that that influence will >> Yeah. I think that that influence will only grow. So, I think that the stakes only grow. So, I think that the stakes only grow. So, I think that the stakes and the battle lines are are being drawn and the battle lines are are being drawn and the battle lines are are being drawn out. It's do you want a new government out. It's do you want a new government out. It's do you want a new government agency for AI safety or do you want I'd agency for AI safety or do you want I'd agency for AI safety or do you want I'd say more targeted proposals like hey say more targeted proposals like hey say more targeted proposals like hey just report your safety incidents. just report your safety incidents. just report your safety incidents. >> Yeah. Or self self-regulate. How about >> Yeah. Or self self-regulate. How about >> Yeah. Or self self-regulate. How about that? Like we talked about last week. that? Like we talked about last week. that? Like we talked about last week. Yeah. All right. Let's talk a little bit Yeah. All right. Let's talk a little bit Yeah. All right. Let's talk a little bit about book burning. Anthropic is about book burning. Anthropic is about book burning. Anthropic is destroying rare books to get an edge in destroying rare books to get an edge in destroying rare books to get an edge in training data according to sources. I training data according to sources. I training data according to sources. I happen to know uh that a lot of the labs happen to know uh that a lot of the labs happen to know uh that a lot of the labs are doing this. Uh we'll show a video are doing this. Uh we'll show a video are doing this. Uh we'll show a video here of the spine being cut off just on here of the spine being cut off just on here of the spine being cut off just on a technical basis. You take a book, you a technical basis. You take a book, you a technical basis. You take a book, you cut the spine off and then you can cut the spine off and then you can cut the spine off and then you can easily scan it as opposed to the less easily scan it as opposed to the less easily scan it as opposed to the less efficient way which is to keep the book efficient way which is to keep the book efficient way which is to keep the book intact and flip the pages for obvious intact and flip the pages for obvious intact and flip the pages for obvious reasons. I think you can figure that out reasons. I think you can figure that out reasons. I think you can figure that out on a physics basis. Investigation by 404 on a physics basis. Investigation by 404 on a physics basis. Investigation by 404 media AI companies are bulk buying media AI companies are bulk buying media AI companies are bulk buying physical books. Some of the book physical books. Some of the book physical books. Some of the book book resellers have reported that they book resellers have reported that they book resellers have reported that they get 70 books getting bought and get 70 books getting bought and get 70 books getting bought and obviously this is because there was a obviously this is because there was a obviously this is because there was a ruling that it is fair use to train on ruling that it is fair use to train on ruling that it is fair use to train on books if you buy them. Obviously last books if you buy them. Obviously last books if you buy them. Obviously last week we saw Anthropic paid the largest week we saw Anthropic paid the largest week we saw Anthropic paid the largest copyright case in US history. 1.5 copyright case in US history. 1.5 copyright case in US history. 1.5 billion for 7 million books they billion for 7 million books they billion for 7 million books they allegedly pirated. Uh authors get 3,000 allegedly pirated. Uh authors get 3,000 allegedly pirated. Uh authors get 3,000 each. Lawyers got 100 million for that each. Lawyers got 100 million for that each. Lawyers got 100 million for that one. But there's a company called isbin one. But there's a company called isbin one. But there's a company called isbin DB ISBN DB and they're the brokers who DB ISBN DB and they're the brokers who DB ISBN DB and they're the brokers who do this and ranges from a thousand to a do this and ranges from a thousand to a do this and ranges from a thousand to a million books per transaction. Uh million books per transaction. Uh million books per transaction. Uh pre2022 pre2022 pre2022 these books commanded a premium because these books commanded a premium because these books commanded a premium because they were free of AI generated tax. In they were free of AI generated tax. In they were free of AI generated tax. In other words, you couldn't get them other words, you couldn't get them other words, you couldn't get them online. Google sent 25 million books if online. Google sent 25 million books if online. Google sent 25 million books if you're a member. But they returned every you're a member. But they returned every you're a member. But they returned every single one. They spent 11 years in court single one. They spent 11 years in court single one. They spent 11 years in court on that. This shredder approach is uh on that. This shredder approach is uh on that. This shredder approach is uh obviously obviously obviously more effective and I believe that this more effective and I believe that this more effective and I believe that this is a way of destroying evidence. You put is a way of destroying evidence. You put is a way of destroying evidence. You put that in conspiracy corner if you like. that in conspiracy corner if you like. that in conspiracy corner if you like. The cases we talked about this actually The cases we talked about this actually The cases we talked about this actually you and I debating it in legal corner. you and I debating it in legal corner. you and I debating it in legal corner. The cases of it being fair use to take The cases of it being fair use to take The cases of it being fair use to take these books has not been settled. these books has not been settled. these books has not been settled. There's a bunch of lawsuits. Thompson There's a bunch of lawsuits. Thompson There's a bunch of lawsuits. Thompson Reuters versus Ross Intelligence, New Reuters versus Ross Intelligence, New Reuters versus Ross Intelligence, New York Times versus OpenAI, Microsoft and York Times versus OpenAI, Microsoft and York Times versus OpenAI, Microsoft and Publishers versus Google Gemini. We're Publishers versus Google Gemini. We're Publishers versus Google Gemini. We're watching all those and they're going watching all those and they're going watching all those and they're going into the appellet court. So there's a into the appellet court. So there's a into the appellet court. So there's a chance that training data will not be chance that training data will not be chance that training data will not be fair use. But what do you think just fair use. But what do you think just fair use. But what do you think just about the books being destroyed and you about the books being destroyed and you about the books being destroyed and you know being used in this way? It know being used in this way? It know being used in this way? It obviously has made people a little obviously has made people a little obviously has made people a little emotional about it. emotional about it. emotional about it. >> Let me tell you what's going on here. >> Let me tell you what's going on here. >> Let me tell you what's going on here. This is an industrial scale dissolation This is an industrial scale dissolation This is an industrial scale dissolation attack. attack. attack. >> That's right. [laughter] >> That's right. [laughter] >> That's right. [laughter] That's what topic is doing. That's what topic is doing. That's what topic is doing. >> They are they are gathering these books >> They are they are gathering these books >> They are they are gathering these books at industrial scale, ripping off the at industrial scale, ripping off the at industrial scale, ripping off the spine, shredding them and slurping up spine, shredding them and slurping up spine, shredding them and slurping up all the information in the books, which all the information in the books, which all the information in the books, which is to say distilling them. is to say distilling them. is to say distilling them. >> And it's an attack in the sense that the >> And it's an attack in the sense that the >> And it's an attack in the sense that the authors never agree to any of this. So authors never agree to any of this. So authors never agree to any of this. So >> I love the fact, Sax, that your hatred >> I love the fact, Sax, that your hatred >> I love the fact, Sax, that your hatred of anthropic has now led you to agree of anthropic has now led you to agree of anthropic has now led you to agree with me that it's unethical to take with me that it's unethical to take with me that it's unethical to take other people's ideas. other people's ideas. other people's ideas. >> No, look, let me be clear. I actually I >> No, look, let me be clear. I actually I >> No, look, let me be clear. I actually I don't I don't um hate Anthropic at all. don't I don't um hate Anthropic at all. don't I don't um hate Anthropic at all. I don't like their political philosophy I don't like their political philosophy I don't like their political philosophy because it's a philosophy of because it's a philosophy of because it's a philosophy of centralization and gatekeeping and I centralization and gatekeeping and I centralization and gatekeeping and I think it's going to basically lead to an think it's going to basically lead to an think it's going to basically lead to an Orwellian big tech deep state alliance Orwellian big tech deep state alliance Orwellian big tech deep state alliance eventually is where it all heads eventually is where it all heads eventually is where it all heads >> and rug pulling like you want Daario >> and rug pulling like you want Daario >> and rug pulling like you want Daario pulling your your model from you because pulling your your model from you because pulling your your model from you because he decides like I don't like the way he decides like I don't like the way he decides like I don't like the way you're using it which friend of the pod you're using it which friend of the pod you're using it which friend of the pod email Michaels pointed out you know email Michaels pointed out you know email Michaels pointed out you know earlier this year when he came on the earlier this year when he came on the earlier this year when he came on the show show show >> just to be clear I have no personal >> just to be clear I have no personal >> just to be clear I have no personal animosity towards anyone in thropping animosity towards anyone in thropping animosity towards anyone in thropping including Daario I don't know them very including Daario I don't know them very including Daario I don't know them very well as people. Uh it's just a well as people. Uh it's just a well as people. Uh it's just a disagreement about political philosophy disagreement about political philosophy disagreement about political philosophy and how to regulate how to regulate the and how to regulate how to regulate the and how to regulate how to regulate the space. Yeah. Let me just say furthermore space. Yeah. Let me just say furthermore space. Yeah. Let me just say furthermore that I wouldn't speak so much about that I wouldn't speak so much about that I wouldn't speak so much about Anthropic if I didn't think it was a Anthropic if I didn't think it was a Anthropic if I didn't think it was a phenomenal company that was creating phenomenal company that was creating phenomenal company that was creating potentially the most powerful monopoly potentially the most powerful monopoly potentially the most powerful monopoly or you know leader in or you know leader in or you know leader in >> the leading company. Yes, >> the leading company. Yes, >> the leading company. Yes, >> it's the leading company in the space. >> it's the leading company in the space. >> it's the leading company in the space. So I remember last year when I hit them So I remember last year when I hit them So I remember last year when I hit them for regulatory capture people were like for regulatory capture people were like for regulatory capture people were like why are you beating up on this little why are you beating up on this little why are you beating up on this little startup? I'm like because I can see startup? I'm like because I can see startup? I'm like because I can see where it's going. And where it's going. And where it's going. And >> they are creating the biggest most >> they are creating the biggest most >> they are creating the biggest most powerful monopoly of all time. Again, powerful monopoly of all time. Again, powerful monopoly of all time. Again, they're going to end the year with over they're going to end the year with over they're going to end the year with over a 100red billion of ARR growing 10x a 100red billion of ARR growing 10x a 100red billion of ARR growing 10x year-over-year. year-over-year. year-over-year. >> This company didn't exist how many years >> This company didn't exist how many years >> This company didn't exist how many years ago? ago? ago? >> Yeah. Google is at 400 and something >> Yeah. Google is at 400 and something >> Yeah. Google is at 400 and something billion of of AR growing 20%. So, you billion of of AR growing 20%. So, you billion of of AR growing 20%. So, you know, if this rate of growth continues know, if this rate of growth continues know, if this rate of growth continues for just a year or even 6 months or just for just a year or even 6 months or just for just a year or even 6 months or just a few months, a few months, a few months, >> they're going to be maybe the most >> they're going to be maybe the most >> they're going to be maybe the most valuable tech company. So, and I do valuable tech company. So, and I do valuable tech company. So, and I do believe there are powerful believe there are powerful believe there are powerful self-reinforcing effects when you're on self-reinforcing effects when you're on self-reinforcing effects when you're on the frontier. And maybe like the full the frontier. And maybe like the full the frontier. And maybe like the full version of RSI isn't true. Maybe we version of RSI isn't true. Maybe we version of RSI isn't true. Maybe we won't get recursive self-improvement to won't get recursive self-improvement to won't get recursive self-improvement to the point of creating super the point of creating super the point of creating super intelligence. But I do think that the intelligence. But I do think that the intelligence. But I do think that the labs are reporting labs are reporting labs are reporting a number of examples of how they are a number of examples of how they are a number of examples of how they are using their own frontier intelligence to using their own frontier intelligence to using their own frontier intelligence to improve their own models and the improve their own models and the improve their own models and the efficiency of those models. So, there is efficiency of those models. So, there is efficiency of those models. So, there is a powerful self-reinforcing feedback a powerful self-reinforcing feedback a powerful self-reinforcing feedback loop here apparently. Well, to some loop here apparently. Well, to some loop here apparently. Well, to some degree degree degree >> with Open AI, they found it after it had >> with Open AI, they found it after it had >> with Open AI, they found it after it had done this. So, there's like kind of done this. So, there's like kind of done this. So, there's like kind of three steps here. You're using AI like a three steps here. You're using AI like a three steps here. You're using AI like a co-pilot or whatever to, you know, build co-pilot or whatever to, you know, build co-pilot or whatever to, you know, build a frontier model quicker, right, Saxs? a frontier model quicker, right, Saxs? a frontier model quicker, right, Saxs? Then there's I kind of let it do a job Then there's I kind of let it do a job Then there's I kind of let it do a job and then afterwards I found out it and then afterwards I found out it and then afterwards I found out it didn't behave well. And then there's didn't behave well. And then there's didn't behave well. And then there's finally we told it the goal and said go finally we told it the goal and said go finally we told it the goal and said go and and we don't even check on it, you and and we don't even check on it, you and and we don't even check on it, you know. know. know. >> Yeah. Just to be clear about that safety >> Yeah. Just to be clear about that safety >> Yeah. Just to be clear about that safety incident with OpenAI and the agent. So incident with OpenAI and the agent. So incident with OpenAI and the agent. So apparently this was an agent that was apparently this was an agent that was apparently this was an agent that was designed to specifically test the designed to specifically test the designed to specifically test the potential for cyber attacks and they potential for cyber attacks and they potential for cyber attacks and they took the guard rails off and they said took the guard rails off and they said took the guard rails off and they said go. And so I think the model showed go. And so I think the model showed go. And so I think the model showed creativity in how it accomplished the creativity in how it accomplished the creativity in how it accomplished the goal but this was not an alignment goal but this was not an alignment goal but this was not an alignment problem meaning that the agent did not problem meaning that the agent did not problem meaning that the agent did not display independent goal seeking. it did display independent goal seeking. it did display independent goal seeking. it did what it was told and I think that is what it was told and I think that is what it was told and I think that is very important that OpenAI release the very important that OpenAI release the very important that OpenAI release the full log of all the prompts all the full log of all the prompts all the full log of all the prompts all the traces they have not done that and I traces they have not done that and I traces they have not done that and I think it's really hard to know exactly think it's really hard to know exactly think it's really hard to know exactly what happened without that and to answer what happened without that and to answer what happened without that and to answer one of your questions from earlier why one of your questions from earlier why one of your questions from earlier why aren't people reacting like this is a aren't people reacting like this is a aren't people reacting like this is a bigger deal is because look I think bigger deal is because look I think bigger deal is because look I think there's a fool me once fool me twice there's a fool me once fool me twice there's a fool me once fool me twice thing remember when anthropic did the thing remember when anthropic did the thing remember when anthropic did the whole blackmail study you know where whole blackmail study you know where whole blackmail study you know where supposedly an agent displayed supposedly an agent displayed supposedly an agent displayed independent goal-seeking behavior and independent goal-seeking behavior and independent goal-seeking behavior and then blackmail an employee. It turned then blackmail an employee. It turned then blackmail an employee. It turned out that actually they iterated on the out that actually they iterated on the out that actually they iterated on the prompt over 200 times to get to that prompt over 200 times to get to that prompt over 200 times to get to that result. And I think that until we see result. And I think that until we see result. And I think that until we see the whole prompt chain, I think it's the whole prompt chain, I think it's the whole prompt chain, I think it's very hard to judge how much independent very hard to judge how much independent very hard to judge how much independent behavior was happening here versus behavior was happening here versus behavior was happening here versus accomplishing the goal that it was it accomplishing the goal that it was it accomplishing the goal that it was it was tasked with. was tasked with. was tasked with. >> Uh Freeberg, your thoughts on um the >> Uh Freeberg, your thoughts on um the >> Uh Freeberg, your thoughts on um the shredding of books? I think you've been shredding of books? I think you've been shredding of books? I think you've been pretty clear on the pod that you believe pretty clear on the pod that you believe pretty clear on the pod that you believe training intelligence off of other training intelligence off of other training intelligence off of other people's IP is fair game, but what do people's IP is fair game, but what do people's IP is fair game, but what do you think of the this book wrinkle here? you think of the this book wrinkle here? you think of the this book wrinkle here? Any thoughts? Any thoughts? Any thoughts? >> There was a precedent with Google books. >> There was a precedent with Google books. >> There was a precedent with Google books. It was originally codenamed project It was originally codenamed project It was originally codenamed project ocean at Google long time ago. They took ocean at Google long time ago. They took ocean at Google long time ago. They took all these books and we had this giant all these books and we had this giant all these books and we had this giant facility in Mountain View. And the facility in Mountain View. And the facility in Mountain View. And the innovation at the time was a innovation at the time was a innovation at the time was a two-dimensional infrared grid projected two-dimensional infrared grid projected two-dimensional infrared grid projected on the pages because they didn't cut the on the pages because they didn't cut the on the pages because they didn't cut the books. They had a human sitting there books. They had a human sitting there books. They had a human sitting there flipping the pages. Camera would take flipping the pages. Camera would take flipping the pages. Camera would take picture. Built our own OCR software to picture. Built our own OCR software to picture. Built our own OCR software to ingest the the book images. And there ingest the the book images. And there ingest the the book images. And there was kind of ultimately when this product was kind of ultimately when this product was kind of ultimately when this product came out, Google Books, you could kind came out, Google Books, you could kind came out, Google Books, you could kind of search through all the books in the of search through all the books in the of search through all the books in the world and world and world and >> and magazines >> and magazines >> and magazines >> um and access information and later >> um and access information and later >> um and access information and later magazines. Yeah. magazines. Yeah. magazines. Yeah. >> And there was three categories. There >> And there was three categories. There >> And there was three categories. There was the public domain which is out of was the public domain which is out of was the public domain which is out of copyright. Then there's the kind of copyright. Then there's the kind of copyright. Then there's the kind of incopyright but out of print. And then incopyright but out of print. And then incopyright but out of print. And then there's the incopyright and in print. there's the incopyright and in print. there's the incopyright and in print. And there was a class action lawsuit And there was a class action lawsuit And there was a class action lawsuit filed in 2005 by the Author's Guild and filed in 2005 by the Author's Guild and filed in 2005 by the Author's Guild and the Association of American Publishers the Association of American Publishers the Association of American Publishers that disagreed with Google's claim of that disagreed with Google's claim of that disagreed with Google's claim of fair use. And that ended up in a fair use. And that ended up in a fair use. And that ended up in a three-year negotiation in in court out three-year negotiation in in court out three-year negotiation in in court out of court that ended up in a deal where of court that ended up in a deal where of court that ended up in a deal where Google would split 2/3 one-third of the Google would split 2/3 one-third of the Google would split 2/3 one-third of the revenue generated with all these rights revenue generated with all these rights revenue generated with all these rights holders. And for out of copyright books, holders. And for out of copyright books, holders. And for out of copyright books, people could read up to 20% of the text people could read up to 20% of the text people could read up to 20% of the text for free and then they would sell this for free and then they would sell this for free and then they would sell this kind of full digital access. And that kind of full digital access. And that kind of full digital access. And that was the deal. But then later a federal was the deal. But then later a federal was the deal. But then later a federal judge rejected that deal which was judge rejected that deal which was judge rejected that deal which was eventually signed in 2008 2009 and the eventually signed in 2008 2009 and the eventually signed in 2008 2009 and the federal judge said no way send it back. federal judge said no way send it back. federal judge said no way send it back. This isn't going to work. Google This isn't going to work. Google This isn't going to work. Google appealed and in [clears throat] 2015 appealed and in [clears throat] 2015 appealed and in [clears throat] 2015 second uh circuit court of appeals ruled second uh circuit court of appeals ruled second uh circuit court of appeals ruled in Google's favor and the whole thing in Google's favor and the whole thing in Google's favor and the whole thing was settled and they basically declared was settled and they basically declared was settled and they basically declared Google did in fact have fair use under Google did in fact have fair use under Google did in fact have fair use under copyright law in the way that they were copyright law in the way that they were copyright law in the way that they were showing snippets of copyrighted books in showing snippets of copyrighted books in showing snippets of copyrighted books in the material. the material. the material. >> Yes, you can't read the entire book like >> Yes, you can't read the entire book like >> Yes, you can't read the entire book like it's a Kindle. You can search the book, it's a Kindle. You can search the book, it's a Kindle. You can search the book, find the paragraph, find the paragraph, find the paragraph, >> provide a reference to it. Right. And so >> provide a reference to it. Right. And so >> provide a reference to it. Right. And so the question on fair use and AI is can the question on fair use and AI is can the question on fair use and AI is can my understanding or extraction of value my understanding or extraction of value my understanding or extraction of value of the knowledge from the data in the of the knowledge from the data in the of the knowledge from the data in the book give me the ability to provide book give me the ability to provide book give me the ability to provide better answers to you through the AI better answers to you through the AI better answers to you through the AI chat interface or services that I'm chat interface or services that I'm chat interface or services that I'm providing you. And I think it's going to providing you. And I think it's going to providing you. And I think it's going to be tested and I think we'll see. I do be tested and I think we'll see. I do be tested and I think we'll see. I do think fundamentally that the conversion think fundamentally that the conversion think fundamentally that the conversion of that data into what I would call of that data into what I would call of that data into what I would call knowledge and ultimately the ability to knowledge and ultimately the ability to knowledge and ultimately the ability to create new outcomes from that knowledge create new outcomes from that knowledge create new outcomes from that knowledge that are not copyrighted that are not that are not copyrighted that are not that are not copyrighted that are not copies of the original material I do copies of the original material I do copies of the original material I do think is end going to end up being the think is end going to end up being the think is end going to end up being the right fair use policy and it's going to right fair use policy and it's going to right fair use policy and it's going to be the right read on fair use. So I be the right read on fair use. So I be the right read on fair use. So I think it'll likely get litigated and I think it'll likely get litigated and I think it'll likely get litigated and I think it'll take a couple years and think it'll take a couple years and think it'll take a couple years and it'll get kind of hoping one of these it'll get kind of hoping one of these it'll get kind of hoping one of these >> just to be clear J I have not changed my >> just to be clear J I have not changed my >> just to be clear J I have not changed my view on fair use. So I I am with view on fair use. So I I am with view on fair use. So I I am with Freeberg on this. My point is the Freeberg on this. My point is the Freeberg on this. My point is the hypocrisy. hypocrisy. hypocrisy. >> Yes. >> Yes. >> Yes. >> Is breathtaking hypocrisy for anthropic >> Is breathtaking hypocrisy for anthropic >> Is breathtaking hypocrisy for anthropic to maintain that it is entitled to train to maintain that it is entitled to train to maintain that it is entitled to train on all the world's output for free even on all the world's output for free even on all the world's output for free even if the creator objects. But the one type if the creator objects. But the one type if the creator objects. But the one type of output that you're not allowed to of output that you're not allowed to of output that you're not allowed to train on is their output even if you pay train on is their output even if you pay train on is their output even if you pay for it. That is their current position. for it. That is their current position. for it. That is their current position. So you know what I'm saying is that you So you know what I'm saying is that you So you know what I'm saying is that you know if you want to train on anthropics know if you want to train on anthropics know if you want to train on anthropics output that cannot be considered IP output that cannot be considered IP output that cannot be considered IP theft under fair use especially given theft under fair use especially given theft under fair use especially given the fact that the courts have ruled that the fact that the courts have ruled that the fact that the courts have ruled that LLM generated output is not LLM generated output is not LLM generated output is not copyrightable because it was not created copyrightable because it was not created copyrightable because it was not created by a human. That is the current position by a human. That is the current position by a human. That is the current position of the courts is that LLM output cannot of the courts is that LLM output cannot of the courts is that LLM output cannot be copyrighted. So there's no IP theft be copyrighted. So there's no IP theft be copyrighted. So there's no IP theft here. You can make the argument and I here. You can make the argument and I here. You can make the argument and I think it's probably true that if a think it's probably true that if a think it's probably true that if a competitor creates massive numbers of competitor creates massive numbers of competitor creates massive numbers of fake accounts on your service, fake accounts on your service, fake accounts on your service, >> you're breaking the terms of service. >> you're breaking the terms of service. >> you're breaking the terms of service. That's a definitely a break in the terms That's a definitely a break in the terms That's a definitely a break in the terms of service and it's probably a deceptive of service and it's probably a deceptive of service and it's probably a deceptive business practice and there may be other business practice and there may be other business practice and there may be other things you can do but I don't think you things you can do but I don't think you things you can do but I don't think you can can can >> depending on the jurisdiction by the way >> depending on the jurisdiction by the way >> depending on the jurisdiction by the way because in Philippines, Israel, India, because in Philippines, Israel, India, because in Philippines, Israel, India, they have different rules about like they have different rules about like they have different rules about like breaking the terms of service which breaking the terms of service which breaking the terms of service which LinkedIn found out LinkedIn found out LinkedIn found out >> when people started scraping their data. >> when people started scraping their data. >> when people started scraping their data. Chimoth any any thoughts here before we Chimoth any any thoughts here before we Chimoth any any thoughts here before we move on to socialism corner everybody's move on to socialism corner everybody's move on to socialism corner everybody's favorite new feature here on the oil and favorite new feature here on the oil and favorite new feature here on the oil and pod pod pod don't cut to the books. uh keeping the don't cut to the books. uh keeping the don't cut to the books. uh keeping the books intact. books intact. books intact. >> Why do you cut the books in the library? >> Why do you cut the books in the library? >> Why do you cut the books in the library? It's very hard to read them. There's a It's very hard to read them. There's a It's very hard to read them. There's a no spine. no spine. no spine. >> I think uh I think it's not the kind and >> I think uh I think it's not the kind and >> I think uh I think it's not the kind and I don't like to cut the books. I don't like to cut the books. I don't like to cut the books. >> I mean, you take your time, you move the >> I mean, you take your time, you move the >> I mean, you take your time, you move the page like a Google does. It's a little page like a Google does. It's a little page like a Google does. It's a little bit more time, but it's a little more bit more time, but it's a little more bit more time, but it's a little more graceful. Yeah. Don't um graceful. Yeah. Don't um graceful. Yeah. Don't um don't don't don't >> I mean if you want to cut the tip it's >> I mean if you want to cut the tip it's >> I mean if you want to cut the tip it's one of the thing you can do a Fbury has one of the thing you can do a Fbury has one of the thing you can do a Fbury has a cut tip. It's actually got a cut tip a cut tip. It's actually got a cut tip a cut tip. It's actually got a cut tip but you don't cut the spine off the but you don't cut the spine off the but you don't cut the spine off the bottom. You cut the tip. It's a more bottom. You cut the tip. It's a more bottom. You cut the tip. It's a more clever. clever. clever. >> There's a great Guinness Book of World >> There's a great Guinness Book of World >> There's a great Guinness Book of World Records book joke. Records book joke. Records book joke. >> Oh no. Where's this go? >> Oh no. Where's this go? >> Oh no. Where's this go? >> Went to the library and I found that my >> Went to the library and I found that my >> Went to the library and I found that my dick was in the Guinness Book of World dick was in the Guinness Book of World dick was in the Guinness Book of World Records and then unfortunately someone Records and then unfortunately someone Records and then unfortunately someone asked me to remove it. So, you literally asked me to remove it. So, you literally asked me to remove it. So, you literally put it in the book and close the book. put it in the book and close the book. put it in the book and close the book. >> That's the joke. That's the joke. >> That's the joke. That's the joke. >> That's the joke. That's the joke. >> That's the joke. Like an apple pie. Hey, >> That's the joke. Like an apple pie. Hey, >> That's the joke. Like an apple pie. Hey, by the way, here's um we got a photo. by the way, here's um we got a photo. by the way, here's um we got a photo. This is a photo. We actually have a This is a photo. We actually have a This is a photo. We actually have a photo that was leaked from the anthropic photo that was leaked from the anthropic photo that was leaked from the anthropic office. Here's the anthropic office. office. Here's the anthropic office. office. Here's the anthropic office. Leaked photo. There it is. Can't believe Leaked photo. There it is. Can't believe Leaked photo. There it is. Can't believe Dario burning those books. What are you Dario burning those books. What are you Dario burning those books. What are you up to, Dario? Come on the show anytime. up to, Dario? Come on the show anytime. up to, Dario? Come on the show anytime. We've been roasting him for two years. We've been roasting him for two years. We've been roasting him for two years. Why hasn't he come on the show? Why hasn't he come on the show? Why hasn't he come on the show? >> Because you make fun of him and you just >> Because you make fun of him and you just >> Because you make fun of him and you just say rude things [laughter] and you've say rude things [laughter] and you've say rude things [laughter] and you've never met the guy and you just insult never met the guy and you just insult never met the guy and you just insult him all the time. Why do you think he him all the time. Why do you think he him all the time. Why do you think he won't come on the show? won't come on the show? won't come on the show? >> I JUST SAID HE'S a >> I JUST SAID HE'S a >> I JUST SAID HE'S a >> The guy's literally built the most >> The guy's literally built the most >> The guy's literally built the most successful business in human history, successful business in human history, successful business in human history, growing from under 10 billion of revenue growing from under 10 billion of revenue growing from under 10 billion of revenue to 70 billion of revenue in 6 months. to 70 billion of revenue in 6 months. to 70 billion of revenue in 6 months. And you insult the guy [laughter] on And you insult the guy [laughter] on And you insult the guy [laughter] on your show, your show, your show, all the people that want to interview all the people that want to interview all the people that want to interview him. You think he's going to rush rush him. You think he's going to rush rush him. You think he's going to rush rush to be interviewed by you? to be interviewed by you? to be interviewed by you? >> I did say he was a sub. That was >> I did say he was a sub. That was >> I did say he was a sub. That was probably a little bit over the line. probably a little bit over the line. probably a little bit over the line. >> I don't even know what that means. What >> I don't even know what that means. What >> I don't even know what that means. What does that mean? What is that? does that mean? What is that? does that mean? What is that? >> Remember I said like I think he likes to >> Remember I said like I think he likes to >> Remember I said like I think he likes to be dominated, be dominated, be dominated, >> you know, and have the government, you >> you know, and have the government, you >> you know, and have the government, you know, control him. know, control him. know, control him. >> Like a submissive. >> Like a submissive. >> Like a submissive. >> Yes, I did. I did say that on an >> Yes, I did. I did say that on an >> Yes, I did. I did say that on an episode, but it was a joke. It was in episode, but it was a joke. It was in episode, but it was a joke. It was in good fun. good fun. good fun. >> By the way, the point on the books, >> By the way, the point on the books, >> By the way, the point on the books, look, the with most books, there's, you look, the with most books, there's, you look, the with most books, there's, you know, many copies of them and you can know, many copies of them and you can know, many copies of them and you can always make more. So, it's not the end always make more. So, it's not the end always make more. So, it's not the end of the world to shred them. I think the of the world to shred them. I think the of the world to shred them. I think the part of the story that got people upset part of the story that got people upset part of the story that got people upset was that they were acquiring all these was that they were acquiring all these was that they were acquiring all these rare books where there was very low rare books where there was very low rare books where there was very low numbers of copies of them. They were numbers of copies of them. They were numbers of copies of them. They were finding all these rare and antique books finding all these rare and antique books finding all these rare and antique books because they wanted to slurp in all the because they wanted to slurp in all the because they wanted to slurp in all the world's knowledge and they were world's knowledge and they were world's knowledge and they were shredding those. shredding those. shredding those. >> Yeah. >> Yeah. >> Yeah. >> And that made people upset. >> And that made people upset. >> And that made people upset. >> If you're doing like Windows 3.1 for >> If you're doing like Windows 3.1 for >> If you're doing like Windows 3.1 for Dummies volume 4, like nobody cares. But Dummies volume 4, like nobody cares. But Dummies volume 4, like nobody cares. But anything that was like a first edition anything that was like a first edition anything that was like a first edition or like an or like an or like an >> rare out of print books, >> rare out of print books, >> rare out of print books, >> rare out of print books. >> rare out of print books. >> rare out of print books. >> Yeah. That gives you a training >> Yeah. That gives you a training >> Yeah. That gives you a training differentiation. That makes sense. All differentiation. That makes sense. All differentiation. That makes sense. All right. So, quick socialism corner here. right. So, quick socialism corner here. right. So, quick socialism corner here. We got to cover the ongoing saga in my We got to cover the ongoing saga in my We got to cover the ongoing saga in my hometown where I am right now of New hometown where I am right now of New hometown where I am right now of New York City. Mandani York City. Mandani York City. Mandani has announced five city- owned grocery has announced five city- owned grocery has announced five city- owned grocery stores. David, one per burrow. They're stores. David, one per burrow. They're stores. David, one per burrow. They're using city-owned space. using city-owned space. using city-owned space. They're all going to open by 2029. They're all going to open by 2029. They're all going to open by 2029. And uh one week per month, shoppers are And uh one week per month, shoppers are And uh one week per month, shoppers are going to get a 30% discount, comrade, on going to get a 30% discount, comrade, on going to get a 30% discount, comrade, on their bread, cheese, produce, meat, and their bread, cheese, produce, meat, and their bread, cheese, produce, meat, and milk for the glory of the country. milk for the glory of the country. milk for the glory of the country. Regular prices the other three weeks. Regular prices the other three weeks. Regular prices the other three weeks. They're not going to sell cigarettes, They're not going to sell cigarettes, They're not going to sell cigarettes, alcohol, hot food, all that stuff alcohol, hot food, all that stuff alcohol, hot food, all that stuff because they don't want to compete with because they don't want to compete with because they don't want to compete with the bodeas. It's going to cost taxpayers the bodeas. It's going to cost taxpayers the bodeas. It's going to cost taxpayers 70 millie. I mean, I guess the only 70 millie. I mean, I guess the only 70 millie. I mean, I guess the only thing to discuss here is like what thing to discuss here is like what thing to discuss here is like what happens to the other supermarkets now? happens to the other supermarkets now? happens to the other supermarkets now? Are they going to shut down because they Are they going to shut down because they Are they going to shut down because they can't make m the whatever one or 2% can't make m the whatever one or 2% can't make m the whatever one or 2% they're making on groceries? Is there they're making on groceries? Is there they're making on groceries? Is there going to be like riots in the street to going to be like riots in the street to going to be like riots in the street to get into these places to get your milk get into these places to get your milk get into these places to get your milk for 30% off for one week a month? It for 30% off for one week a month? It for 30% off for one week a month? It just the whole thing seems like a waste just the whole thing seems like a waste just the whole thing seems like a waste of time. But I will say Sachs, this of time. But I will say Sachs, this of time. But I will say Sachs, this plays this is going to play in plays this is going to play in plays this is going to play in elections. Free stuff plays in an elections. Free stuff plays in an elections. Free stuff plays in an election. uh whether it's a bus or election. uh whether it's a bus or election. uh whether it's a bus or discounts discounts discounts >> it may play and you know look when >> it may play and you know look when >> it may play and you know look when people first go to these stores and they people first go to these stores and they people first go to these stores and they first open and the shelves are full. first open and the shelves are full. first open and the shelves are full. Yeah. people will be like delighted and Yeah. people will be like delighted and Yeah. people will be like delighted and then over time what's going to happen is then over time what's going to happen is then over time what's going to happen is that the store shelves will be empty and that the store shelves will be empty and that the store shelves will be empty and it's gonna be incompetently run and it's gonna be incompetently run and it's gonna be incompetently run and there's gonna be a lot of complaints there's gonna be a lot of complaints there's gonna be a lot of complaints about it and then all the the free about it and then all the the free about it and then all the the free market stores are going to have to market stores are going to have to market stores are going to have to compete with this and then they may get compete with this and then they may get compete with this and then they may get put out of business and so put out of business and so put out of business and so >> and then you have no choice >> and then you have no choice >> and then you have no choice sponsored one and then they raise the sponsored one and then they raise the sponsored one and then they raise the price price price >> and it is ironic that they're going to >> and it is ironic that they're going to >> and it is ironic that they're going to be checking ID to make sure people be checking ID to make sure people be checking ID to make sure people aren't coming over from Jersey but if aren't coming over from Jersey but if aren't coming over from Jersey but if you want to come over illegally from any you want to come over illegally from any you want to come over illegally from any country in the world. Well, that's just country in the world. Well, that's just country in the world. Well, that's just fine. fine. fine. >> They found a use for ID. By the way, >> They found a use for ID. By the way, >> They found a use for ID. By the way, after you get your groceries, you have after you get your groceries, you have after you get your groceries, you have to hide your ID to go vote. Don't bring to hide your ID to go vote. Don't bring to hide your ID to go vote. Don't bring shred your ID when you go vote after you shred your ID when you go vote after you shred your ID when you go vote after you pick up your milk. They found a use for pick up your milk. They found a use for pick up your milk. They found a use for IDs. What's your take here, Freeberg? IDs. What's your take here, Freeberg? IDs. What's your take here, Freeberg? Are you in favor of people paying less Are you in favor of people paying less Are you in favor of people paying less for groceries or are you a free market for groceries or are you a free market for groceries or are you a free market monster that wants people to pay full monster that wants people to pay full monster that wants people to pay full price for groceries? price for groceries? price for groceries? >> I've seen, >> I've seen, >> I've seen, >> especially starving poor families. I've >> especially starving poor families. I've >> especially starving poor families. I've seen nothing but negative comments on seen nothing but negative comments on seen nothing but negative comments on the future failure of these grocery the future failure of these grocery the future failure of these grocery stores on Twitter. And I think that stores on Twitter. And I think that stores on Twitter. And I think that people have it wrong. I think these people have it wrong. I think these people have it wrong. I think these grocery stores are going to be wildly grocery stores are going to be wildly grocery stores are going to be wildly popular. popular. popular. >> They're going to pay their employees >> They're going to pay their employees >> They're going to pay their employees above market wages. Employees are not above market wages. Employees are not above market wages. Employees are not going to have to work very hard to work going to have to work very hard to work going to have to work very hard to work there. So, they're going to be a better there. So, they're going to be a better there. So, they're going to be a better place to work. Everyone's going to want place to work. Everyone's going to want place to work. Everyone's going to want to use them. They're going to outperform to use them. They're going to outperform to use them. They're going to outperform Whole Foods. They're going to outperform Whole Foods. They're going to outperform Whole Foods. They're going to outperform Safeway. They're going to outperform Safeway. They're going to outperform Safeway. They're going to outperform Albertson's. They're going to be so in Albertson's. They're going to be so in Albertson's. They're going to be so in demand that what will end up happening demand that what will end up happening demand that what will end up happening is that over the next 24 months, every is that over the next 24 months, every is that over the next 24 months, every other city in America will look to these other city in America will look to these other city in America will look to these grocery stores and say, "We want the grocery stores and say, "We want the grocery stores and say, "We want the same. Why does only New York get these same. Why does only New York get these same. Why does only New York get these grocery stores? Why can't I have these grocery stores? Why can't I have these grocery stores? Why can't I have these grocery stores too where I can have grocery stores too where I can have grocery stores too where I can have discounted food where I can have the discounted food where I can have the discounted food where I can have the service provided to me by people that service provided to me by people that service provided to me by people that are getting paid above average wages, are getting paid above average wages, are getting paid above average wages, above market wages, above market wages, above market wages, >> healthare? >> healthare? >> healthare? >> Why does this not become available to me >> Why does this not become available to me >> Why does this not become available to me in my city?" So, you know, I think that in my city?" So, you know, I think that in my city?" So, you know, I think that everyone's being a little bit too, I everyone's being a little bit too, I everyone's being a little bit too, I would say, long-sighted in their view on would say, long-sighted in their view on would say, long-sighted in their view on what's going to happen with these what's going to happen with these what's going to happen with these grocery stores with the, you know, basic grocery stores with the, you know, basic grocery stores with the, you know, basic obvious economic arithmetic that someone obvious economic arithmetic that someone obvious economic arithmetic that someone has to pay for this and who's going to has to pay for this and who's going to has to pay for this and who's going to pay for it and rich blah blah blah that pay for it and rich blah blah blah that pay for it and rich blah blah blah that Well, I mean, the point is I don't think Well, I mean, the point is I don't think Well, I mean, the point is I don't think it really matters because over the near it really matters because over the near it really matters because over the near term, what the cheap grocery stores do term, what the cheap grocery stores do term, what the cheap grocery stores do is create an incredible success story is create an incredible success story is create an incredible success story for socialism. that will help to support for socialism. that will help to support for socialism. that will help to support and fuel the socialist wave in urban and fuel the socialist wave in urban and fuel the socialist wave in urban centers around this country. And I think centers around this country. And I think centers around this country. And I think that there will be media coverage of that there will be media coverage of that there will be media coverage of these grocery stores on how great they these grocery stores on how great they these grocery stores on how great they are. And it'll be a 60 Minutes piece on are. And it'll be a 60 Minutes piece on are. And it'll be a 60 Minutes piece on everyone said Zoron Mom Donnie was everyone said Zoron Mom Donnie was everyone said Zoron Mom Donnie was crazy, but let's go in and take a look crazy, but let's go in and take a look crazy, but let's go in and take a look at this beautiful grocery store. And at this beautiful grocery store. And at this beautiful grocery store. And they're going to walk through the they're going to walk through the they're going to walk through the grocery store and there are going to be grocery store and there are going to be grocery store and there are going to be happy people taking food off the happy people taking food off the happy people taking food off the shelves, checking out with happy shelves, checking out with happy shelves, checking out with happy employees working at the grocery stores. employees working at the grocery stores. employees working at the grocery stores. And it is going to be deemed a utopian And it is going to be deemed a utopian And it is going to be deemed a utopian dream come reality. And everyone's going dream come reality. And everyone's going dream come reality. And everyone's going to want one. And it will help seed the to want one. And it will help seed the to want one. And it will help seed the next couple of years. And it will be next couple of years. And it will be next couple of years. And it will be part of, as I've highlighted in the part of, as I've highlighted in the part of, as I've highlighted in the past, a big part of the um the past, a big part of the um the past, a big part of the um the multi-level marketing scheme of multi-level marketing scheme of multi-level marketing scheme of socialism socialism socialism is to create spectacle. And it will is to create spectacle. And it will is to create spectacle. And it will create more spectacle that will help to create more spectacle that will help to create more spectacle that will help to fuel the multi-level marketing scheme of fuel the multi-level marketing scheme of fuel the multi-level marketing scheme of socialism. And remember, the problem socialism. And remember, the problem socialism. And remember, the problem with all multi-level marketing schemes with all multi-level marketing schemes with all multi-level marketing schemes at the end of the day is someone has to at the end of the day is someone has to at the end of the day is someone has to pay the bill and no one's actually pay the bill and no one's actually pay the bill and no one's actually buying the product. No one's paying for buying the product. No one's paying for buying the product. No one's paying for the product. the product. the product. >> That's a ways away though. >> That's a ways away though. >> That's a ways away though. >> In the meantime, >> In the meantime, >> In the meantime, >> try it while it's here. In the meantime, >> try it while it's here. In the meantime, >> try it while it's here. In the meantime, it's going to take off. And I think that it's going to take off. And I think that it's going to take off. And I think that these grocery stores are going to be a these grocery stores are going to be a these grocery stores are going to be a much bigger success much bigger success much bigger success >> in for socialism. >> in for socialism. >> in for socialism. >> Yes. >> Yes. >> Yes. >> Than than a demonstration of the failure >> Than than a demonstration of the failure >> Than than a demonstration of the failure of socialism, unfortunately. of socialism, unfortunately. of socialism, unfortunately. >> Yes. >> Yes. >> Yes. >> And so I think that, you know, >> And so I think that, you know, >> And so I think that, you know, everyone's got a little bit wrong in everyone's got a little bit wrong in everyone's got a little bit wrong in assuming that this thing is going to assuming that this thing is going to assuming that this thing is going to radically fail. I think that these radically fail. I think that these radically fail. I think that these things are going to create a radical things are going to create a radical things are going to create a radical spectacle, spectacle, spectacle, an exuberance for socialist policies an exuberance for socialist policies an exuberance for socialist policies that's going to kind of light a fire for that's going to kind of light a fire for that's going to kind of light a fire for socialism around the country. socialism around the country. socialism around the country. Unfortunately, because at the end of the Unfortunately, because at the end of the Unfortunately, because at the end of the day, day, day, >> no one has to pay the bill cuz the bill >> no one has to pay the bill cuz the bill >> no one has to pay the bill cuz the bill doesn't come to you for some time. doesn't come to you for some time. doesn't come to you for some time. Someone else will pay it. It'll get paid Someone else will pay it. It'll get paid Someone else will pay it. It'll get paid in the future. in the future. in the future. >> Put it on top of the debt. All the rich >> Put it on top of the debt. All the rich >> Put it on top of the debt. All the rich people are getting debt. Why can't the people are getting debt. Why can't the people are getting debt. Why can't the public have money? I agree with you. public have money? I agree with you. public have money? I agree with you. Yeah. Yeah. Yeah. >> Socialize the cost into uh money >> Socialize the cost into uh money >> Socialize the cost into uh money printing, fueling more inflation, printing, fueling more inflation, printing, fueling more inflation, creating a spiral where you need to creating a spiral where you need to creating a spiral where you need to offer more stuff for free to come up offer more stuff for free to come up offer more stuff for free to come up with a way to cover the cost of the with a way to cover the cost of the with a way to cover the cost of the inflation for people that can't afford inflation for people that can't afford inflation for people that can't afford things anymore. and the spiral will things anymore. and the spiral will things anymore. and the spiral will persist. So, I think it's it's a sad persist. So, I think it's it's a sad persist. So, I think it's it's a sad state that the United States has to kind state that the United States has to kind state that the United States has to kind of embrace this policy. Interestingly, I of embrace this policy. Interestingly, I of embrace this policy. Interestingly, I think it's gonna I think it's gonna end think it's gonna I think it's gonna end think it's gonna I think it's gonna end up being a a big part of the fuel for up being a a big part of the fuel for up being a a big part of the fuel for socialism over the next couple years. socialism over the next couple years. socialism over the next couple years. >> Breaking news. Breaking news. Um I don't >> Breaking news. Breaking news. Um I don't >> Breaking news. Breaking news. Um I don't know if you saw it just now came across know if you saw it just now came across know if you saw it just now came across the wire, but uh Bernie Sanders AOCami the wire, but uh Bernie Sanders AOCami the wire, but uh Bernie Sanders AOCami collaborating on 50% off bagels and collaborating on 50% off bagels and collaborating on 50% off bagels and bacon, egg, and cheese for the 1% of the bacon, egg, and cheese for the 1% of the bacon, egg, and cheese for the 1% of the 10%. Why can't you get the bagel with 10%. Why can't you get the bagel with 10%. Why can't you get the bagel with the shme for less? That's what has to the shme for less? That's what has to the shme for less? That's what has to happen next. What would you like next on happen next. What would you like next on happen next. What would you like next on your discounted democratic socialism your discounted democratic socialism your discounted democratic socialism scorecard? David Freeberg, what would scorecard? David Freeberg, what would scorecard? David Freeberg, what would you like next? Discounted bagels, a you like next? Discounted bagels, a you like next? Discounted bagels, a cafe, maybe a flat white. cafe, maybe a flat white. cafe, maybe a flat white. >> Where do they go next? >> Where do they go next? >> Where do they go next? >> But seriously, what's next? What what >> But seriously, what's next? What what >> But seriously, what's next? What what what would be next in this logical what would be next in this logical what would be next in this logical thread? Free buses, rent freeze. What's thread? Free buses, rent freeze. What's thread? Free buses, rent freeze. What's next? Well, think about the social next? Well, think about the social next? Well, think about the social network effect of the grocery store. So, network effect of the grocery store. So, network effect of the grocery store. So, there's there's a couple of them. And there's there's a couple of them. And there's there's a couple of them. And then people start traveling from far then people start traveling from far then people start traveling from far away to the cheap grocery store because away to the cheap grocery store because away to the cheap grocery store because it has this discount. it has this discount. it has this discount. >> Long Island, Jersey. Yes. >> Long Island, Jersey. Yes. >> Long Island, Jersey. Yes. >> Again, this will play out over the next >> Again, this will play out over the next >> Again, this will play out over the next 24 months going into the 2028 election 24 months going into the 2028 election 24 months going into the 2028 election cycle. And everyone's like, "This is so cycle. And everyone's like, "This is so cycle. And everyone's like, "This is so wildly popular. People are coming in wildly popular. People are coming in wildly popular. People are coming in from all over the place to go to these from all over the place to go to these from all over the place to go to these grocery stores. They're not checking IDs grocery stores. They're not checking IDs grocery stores. They're not checking IDs because IDs are racist and you know, you because IDs are racist and you know, you because IDs are racist and you know, you can't check IDs to vote. So, we can't check IDs to vote. So, we can't check IDs to vote. So, we shouldn't be able to check IDs for shouldn't be able to check IDs for shouldn't be able to check IDs for grocery stores. So, people will come in grocery stores. So, people will come in grocery stores. So, people will come in from all over the place to use these from all over the place to use these from all over the place to use these grocery stores. The demand will go up grocery stores. The demand will go up grocery stores. The demand will go up and then they'll start to open more and and then they'll start to open more and and then they'll start to open more and more grocery stores like this. And it, more grocery stores like this. And it, more grocery stores like this. And it, let's say, each one loses 10 million a let's say, each one loses 10 million a let's say, each one loses 10 million a year and they get to 10 or 20 of these. year and they get to 10 or 20 of these. year and they get to 10 or 20 of these. That's $200 million of losses per year That's $200 million of losses per year That's $200 million of losses per year on the grocery store chain. But it on the grocery store chain. But it on the grocery store chain. But it creates this extraordinary social creates this extraordinary social creates this extraordinary social movement for more of these grocery movement for more of these grocery movement for more of these grocery stores supporting the DSA and so on. stores supporting the DSA and so on. stores supporting the DSA and so on. $200 million a year on a $125 billion a $200 million a year on a $125 billion a $200 million a year on a $125 billion a year budget for the city of New York. year budget for the city of New York. year budget for the city of New York. It's nothing. It's less than a quarter It's nothing. It's less than a quarter It's nothing. It's less than a quarter of a percent of the city's budget. That of a percent of the city's budget. That of a percent of the city's budget. That is so cheap to market the DSA is so cheap to market the DSA is so cheap to market the DSA >> platform and to get the DSA platform to >> platform and to get the DSA platform to >> platform and to get the DSA platform to become a social marketing element that become a social marketing element that become a social marketing element that drives the next wave here. So again, I drives the next wave here. So again, I drives the next wave here. So again, I do think that these grocery stores, do think that these grocery stores, do think that these grocery stores, believe it or not, they sound silly, believe it or not, they sound silly, believe it or not, they sound silly, they sound small, but I predict that they sound small, but I predict that they sound small, but I predict that they will be deemed a point of success they will be deemed a point of success they will be deemed a point of success and they will end up being a big part of and they will end up being a big part of and they will end up being a big part of the fuel for the DSA going into 2028. the fuel for the DSA going into 2028. the fuel for the DSA going into 2028. >> I couldn't agree with you more. This is >> I couldn't agree with you more. This is >> I couldn't agree with you more. This is this is going to play. This will be a this is going to play. This will be a this is going to play. This will be a great great feather in their cap. It's great great feather in their cap. It's great great feather in their cap. It's going to be a great example of going to be a great example of going to be a great example of affordability because we've talked about affordability because we've talked about affordability because we've talked about here previously, Trump promised here previously, Trump promised here previously, Trump promised affordability, hasn't been able to affordability, hasn't been able to affordability, hasn't been able to deliver it. Inflation's up, spending's deliver it. Inflation's up, spending's deliver it. Inflation's up, spending's up, all that great stuff. And Mandami up, all that great stuff. And Mandami up, all that great stuff. And Mandami got it done. Free buses, rent got it done. Free buses, rent got it done. Free buses, rent controlled, and now you got your controlled, and now you got your controlled, and now you got your discounted grocery store. Both sides are discounted grocery store. Both sides are discounted grocery store. Both sides are reacting to the fiscal and monetary reacting to the fiscal and monetary reacting to the fiscal and monetary condition of the United States. We're condition of the United States. We're condition of the United States. We're overspending. Inflation has run away. So overspending. Inflation has run away. So overspending. Inflation has run away. So you just keep spending more and printing you just keep spending more and printing you just keep spending more and printing more to give people what they need, more to give people what they need, more to give people what they need, which is basic services. And so as the which is basic services. And so as the which is basic services. And so as the government spends more, then the government spends more, then the government spends more, then the fundamental cost of those things goes up fundamental cost of those things goes up fundamental cost of those things goes up and you're reducing economic and you're reducing economic and you're reducing economic productivity. And it becomes a spiraling productivity. And it becomes a spiraling productivity. And it becomes a spiraling problem. It is a two-party problem. This problem. It is a two-party problem. This problem. It is a two-party problem. This is not just one side and the other is not just one side and the other is not just one side and the other because fundamentally if you go I've because fundamentally if you go I've because fundamentally if you go I've spent a lot of time now in DC. spent a lot of time now in DC. spent a lot of time now in DC. >> I think everyone's well-intentioned in >> I think everyone's well-intentioned in >> I think everyone's well-intentioned in the White House and the administration the White House and the administration the White House and the administration in trying to reduce federal spending. in trying to reduce federal spending. in trying to reduce federal spending. But the bigger issue that you face is But the bigger issue that you face is But the bigger issue that you face is when you go to Congress and you meet when you go to Congress and you meet when you go to Congress and you meet with everyone in Congress, they are with everyone in Congress, they are with everyone in Congress, they are representing the interests of their representing the interests of their representing the interests of their state or of their congressional state or of their congressional state or of their congressional district. district. district. >> He didn't. Their objective, their >> He didn't. Their objective, their >> He didn't. Their objective, their objective is to fundamentally drive objective is to fundamentally drive objective is to fundamentally drive spending towards their district to give spending towards their district to give spending towards their district to give their people more. Their their economic their people more. Their their economic their people more. Their their economic incentive and their political incentive incentive and their political incentive incentive and their political incentive is not to give people less, which is is not to give people less, which is is not to give people less, which is what you have to do when you cut what you have to do when you cut what you have to do when you cut programs, when you cut spending. So the programs, when you cut spending. So the programs, when you cut spending. So the shift in the policy has been, hey, I shift in the policy has been, hey, I shift in the policy has been, hey, I guess we're not going to be able to cut guess we're not going to be able to cut guess we're not going to be able to cut spending because there's just too much spending because there's just too much spending because there's just too much headwinds in Congress. So the answer is, headwinds in Congress. So the answer is, headwinds in Congress. So the answer is, let's grow through economic productivity let's grow through economic productivity let's grow through economic productivity gains. And that's the big fuel for AI, gains. And that's the big fuel for AI, gains. And that's the big fuel for AI, the capex depreciation policy and so on. the capex depreciation policy and so on. the capex depreciation policy and so on. But I think that's been the shift. So But I think that's been the shift. So But I think that's been the shift. So the one thing I will say critical of the one thing I will say critical of the one thing I will say critical of President Trump here is when it came to President Trump here is when it came to President Trump here is when it came to like starting a war when it came to like starting a war when it came to like starting a war when it came to tariffs, he had no problem like using tariffs, he had no problem like using tariffs, he had no problem like using executive power and telling Congress and executive power and telling Congress and executive power and telling Congress and everybody in the party, this is the way everybody in the party, this is the way everybody in the party, this is the way it's going to be. If you break ranks, it's going to be. If you break ranks, it's going to be. If you break ranks, I'm going to destroy you. I'm going to I'm going to destroy you. I'm going to I'm going to destroy you. I'm going to get you primar. And when it came to get you primar. And when it came to get you primar. And when it came to comes to spending, he was like, ah, comes to spending, he was like, ah, comes to spending, he was like, ah, yeah, you know what? I'm not taking that yeah, you know what? I'm not taking that yeah, you know what? I'm not taking that on. It's too unpopular. All right, on. It's too unpopular. All right, on. It's too unpopular. All right, Freeberg. The Sultan of Scienc's fans Freeberg. The Sultan of Scienc's fans Freeberg. The Sultan of Scienc's fans have been begging for a science corner. have been begging for a science corner. have been begging for a science corner. Do you have one this week? They want to Do you have one this week? They want to Do you have one this week? They want to know, do you have something? Oh, Sultan. know, do you have something? Oh, Sultan. know, do you have something? Oh, Sultan. Oh, Sultan of science. What can you tell Oh, Sultan of science. What can you tell Oh, Sultan of science. What can you tell us? Educate us. us? Educate us. us? Educate us. >> Okay. So, today I'm going to pull up >> Okay. So, today I'm going to pull up >> Okay. So, today I'm going to pull up this paper. Nick, if you could pull it this paper. Nick, if you could pull it this paper. Nick, if you could pull it up. up. up. >> A paper from February. >> A paper from February. >> A paper from February. >> February. Okay. >> February. Okay. >> February. Okay. >> February. >> February. >> February. >> February. >> February. >> February. >> How do you say February? February. >> How do you say February? February. >> How do you say February? February. >> February. February. >> February. February. >> February. February. >> Oh, it's February. Well, what's the >> Oh, it's February. Well, what's the >> Oh, it's February. Well, what's the problem? So, this is a group of problem? So, this is a group of problem? So, this is a group of researchers out of Budapest. researchers out of Budapest. researchers out of Budapest. >> Budapest. >> Budapest. >> Budapest. >> And >> And >> And >> there was a really interesting >> there was a really interesting >> there was a really interesting >> modeling exercise they went through to >> modeling exercise they went through to >> modeling exercise they went through to understand how neurons were connected in understand how neurons were connected in understand how neurons were connected in the brain to build a a network model, a the brain to build a a network model, a the brain to build a a network model, a topological model. And the way they were topological model. And the way they were topological model. And the way they were able to do this is back in October of able to do this is back in October of able to do this is back in October of 2024, there was a group out of Cambridge 2024, there was a group out of Cambridge 2024, there was a group out of Cambridge and Princeton that used electron and Princeton that used electron and Princeton that used electron microscopes to scan the brain of the microscopes to scan the brain of the microscopes to scan the brain of the Drosophilia fruitfly. And they mapped Drosophilia fruitfly. And they mapped Drosophilia fruitfly. And they mapped every single neuron in that fruitly's every single neuron in that fruitly's every single neuron in that fruitly's brain, 139,000 neurons, and every brain, 139,000 neurons, and every brain, 139,000 neurons, and every connection that the neurons had to other connection that the neurons had to other connection that the neurons had to other neurons in the brain. So there were 50 neurons in the brain. So there were 50 neurons in the brain. So there were 50 million synaptic connections between the million synaptic connections between the million synaptic connections between the neurons and it's those connections that neurons and it's those connections that neurons and it's those connections that make neural networks in the brain work. make neural networks in the brain work. make neural networks in the brain work. How are those neurons together to do the How are those neurons together to do the How are those neurons together to do the things that they do? This is the key things that they do? This is the key things that they do? This is the key question in what is that network model? question in what is that network model? question in what is that network model? What is the topological model of how What is the topological model of how What is the topological model of how neurons connect in the brain which gives neurons connect in the brain which gives neurons connect in the brain which gives rise to our ability to control our rise to our ability to control our rise to our ability to control our bodies to seeing things and bodies to seeing things and bodies to seeing things and comprehending vision, comprehending comprehending vision, comprehending comprehending vision, comprehending sound and even the basic premise of sound and even the basic premise of sound and even the basic premise of consciousness itself. Yes. So trying to consciousness itself. Yes. So trying to consciousness itself. Yes. So trying to understand the the network model for understand the the network model for understand the the network model for neurons has been this kind of great neurons has been this kind of great neurons has been this kind of great endeavor of neurobiology forever. This endeavor of neurobiology forever. This endeavor of neurobiology forever. This data set was created in October 2024 data set was created in October 2024 data set was created in October 2024 with justif,000 with justif,000 with justif,000 neurons. And you know that's a tiny tiny neurons. And you know that's a tiny tiny neurons. And you know that's a tiny tiny tiny tiny brain. tiny tiny brain. tiny tiny brain. >> Yeah. This would be put it in context >> Yeah. This would be put it in context >> Yeah. This would be put it in context versus the human brain. I mean what are versus the human brain. I mean what are versus the human brain. I mean what are we talking about here? we talking about here? we talking about here? >> Like >> Like >> Like >> the human brain has on the order of 86 >> the human brain has on the order of 86 >> the human brain has on the order of 86 billion neurons. Okay. Compared to billion neurons. Okay. Compared to billion neurons. Okay. Compared to >> So that means it would be a multiple for >> So that means it would be a multiple for >> So that means it would be a multiple for the network connections. Right. the network connections. Right. the network connections. Right. >> Yes. Exactly. On the order of trillions >> Yes. Exactly. On the order of trillions >> Yes. Exactly. On the order of trillions of connections. of connections. of connections. >> Okay. So they took these 50 million >> Okay. So they took these 50 million >> Okay. So they took these 50 million connections in the brain and the 139,000 connections in the brain and the 139,000 connections in the brain and the 139,000 neurons and then they applied the neurons and then they applied the neurons and then they applied the network model that predicts whether a network model that predicts whether a network model that predicts whether a neuron is connected to another neuron. neuron is connected to another neuron. neuron is connected to another neuron. That's how you're measuring the quality That's how you're measuring the quality That's how you're measuring the quality of the model. How correct is it in of the model. How correct is it in of the model. How correct is it in making a prediction. And when you build making a prediction. And when you build making a prediction. And when you build the model using what's called ukitian the model using what's called ukitian the model using what's called ukitian geometry, so just normal space that we geometry, so just normal space that we geometry, so just normal space that we live in, three-dimensional space, they live in, three-dimensional space, they live in, three-dimensional space, they came up with a score and the score was came up with a score and the score was came up with a score and the score was not very good. you couldn't do a great not very good. you couldn't do a great not very good. you couldn't do a great job of just looking at how all the job of just looking at how all the job of just looking at how all the neurons were connected using their neurons were connected using their neurons were connected using their physical relationship to each other, how physical relationship to each other, how physical relationship to each other, how far apart they are to each other in 3D far apart they are to each other in 3D far apart they are to each other in 3D space. So then they said, well, let's space. So then they said, well, let's space. So then they said, well, let's try and model how these neurons are all try and model how these neurons are all try and model how these neurons are all connected to each other in what's called connected to each other in what's called connected to each other in what's called hyperbolic space. Hyperbolic space is a hyperbolic space. Hyperbolic space is a hyperbolic space. Hyperbolic space is a theoretical type of space, unlike theoretical type of space, unlike theoretical type of space, unlike uklidian geometry where the further away uklidian geometry where the further away uklidian geometry where the further away you get, the wider space gets. So space you get, the wider space gets. So space you get, the wider space gets. So space actually is curved. I know that's a hard actually is curved. I know that's a hard actually is curved. I know that's a hard concept to describe, but imagine that, concept to describe, but imagine that, concept to describe, but imagine that, you know, as you and I walk farther and you know, as you and I walk farther and you know, as you and I walk farther and farther apart from each other, the area farther apart from each other, the area farther apart from each other, the area around us actually accelerates in terms around us actually accelerates in terms around us actually accelerates in terms of how much space there is and it of how much space there is and it of how much space there is and it expands. expands. expands. >> It would be space know like >> It would be space know like >> It would be space know like three-dimensional space as humans three-dimensional space as humans three-dimensional space as humans understand it when they're on planet understand it when they're on planet understand it when they're on planet Earth. Earth. Earth. >> It's a little bit more like space that >> It's a little bit more like space that >> It's a little bit more like space that you would experience in the the warping you would experience in the the warping you would experience in the the warping around a gravity well or the warping around a gravity well or the warping around a gravity well or the warping around a black hole or something like around a black hole or something like around a black hole or something like that. And so in that space they found that. And so in that space they found that. And so in that space they found that this is where the model was most that this is where the model was most that this is where the model was most performative. They were able to map in performative. They were able to map in performative. They were able to map in hyperbolic space how all of these hyperbolic space how all of these hyperbolic space how all of these neurons connect to each other. And if neurons connect to each other. And if neurons connect to each other. And if you think about it, the further away you you think about it, the further away you you think about it, the further away you get from the first neuron, you're going get from the first neuron, you're going get from the first neuron, you're going to have many, many more neurons you can to have many, many more neurons you can to have many, many more neurons you can start to tap into. And so hyperbolic start to tap into. And so hyperbolic start to tap into. And so hyperbolic modeling on the neuronal connections modeling on the neuronal connections modeling on the neuronal connections actually makes sense. And they got a actually makes sense. And they got a actually makes sense. And they got a decent score. And then they went back decent score. And then they went back decent score. And then they went back and they said, well, what if we could and they said, well, what if we could and they said, well, what if we could use uklitian geometry but not in three use uklitian geometry but not in three use uklitian geometry but not in three dimensions, but they went up to four, dimensions, but they went up to four, dimensions, but they went up to four, five, six. And they found that they were five, six. And they found that they were five, six. And they found that they were able to kind of get as good as the able to kind of get as good as the able to kind of get as good as the hyperbolic space at 64 dimensions. So by hyperbolic space at 64 dimensions. So by hyperbolic space at 64 dimensions. So by taking normal space and saying let's use taking normal space and saying let's use taking normal space and saying let's use a 64dimension framework for how we can a 64dimension framework for how we can a 64dimension framework for how we can start to connect all these neurons start to connect all these neurons start to connect all these neurons together. That's where they had the best together. That's where they had the best together. That's where they had the best predictive model. This is a really predictive model. This is a really predictive model. This is a really interesting kind of discovery. First of interesting kind of discovery. First of interesting kind of discovery. First of all, it can be used for neural network all, it can be used for neural network all, it can be used for neural network design and AI and other sorts of things. design and AI and other sorts of things. design and AI and other sorts of things. But for me it highlights the miracle of But for me it highlights the miracle of But for me it highlights the miracle of biology in finding complexity in 64 biology in finding complexity in 64 biology in finding complexity in 64 dimensions. Not in three dimensions but dimensions. Not in three dimensions but dimensions. Not in three dimensions but in 64 dimensions biology found a way to in 64 dimensions biology found a way to in 64 dimensions biology found a way to create consciousness to create vision to create consciousness to create vision to create consciousness to create vision to create comprehension to create control create comprehension to create control create comprehension to create control over physical bodies. Then to map it and over physical bodies. Then to map it and over physical bodies. Then to map it and squish it all into a tiny little brain. squish it all into a tiny little brain. squish it all into a tiny little brain. It did this in effectively 64 It did this in effectively 64 It did this in effectively 64 dimensions. mindblowing when you think dimensions. mindblowing when you think dimensions. mindblowing when you think of it because a fruitfly or mosquito and of it because a fruitfly or mosquito and of it because a fruitfly or mosquito and you know these things they they don't you know these things they they don't you know these things they they don't have like a big mission right like their have like a big mission right like their have like a big mission right like their mission is to go find food and procreate mission is to go find food and procreate mission is to go find food and procreate I guess they have a very I guess they have a very I guess they have a very >> sounds like our mission too Jake >> sounds like our mission too Jake >> sounds like our mission too Jake [laughter] [laughter] [laughter] >> well but then we also want to do a >> well but then we also want to do a >> well but then we also want to do a podcast and debate politics and podcast and debate politics and podcast and debate politics and philosophy philosophy philosophy >> don't don't judge how the mosquito >> don't don't judge how the mosquito >> don't don't judge how the mosquito spends their free time but spends their free time but spends their free time but >> well I mean but nobody would argue >> well I mean but nobody would argue >> well I mean but nobody would argue there's like consciousness as we there's like consciousness as we there's like consciousness as we experience it in a fruitfly so then you experience it in a fruitfly so then you experience it in a fruitfly so then you get to trillions You wouldn't know. get to trillions You wouldn't know. get to trillions You wouldn't know. >> But I mean, maybe. But >> But I mean, maybe. But >> But I mean, maybe. But >> this is really interesting because it >> this is really interesting because it >> this is really interesting because it turns out that the biology of how all turns out that the biology of how all turns out that the biology of how all the neurons are connected, even in a the neurons are connected, even in a the neurons are connected, even in a brain as simple as a fruitly with 50 brain as simple as a fruitly with 50 brain as simple as a fruitly with 50 million connections, is so complex that million connections, is so complex that million connections, is so complex that it has to take 64 dimensions for us to it has to take 64 dimensions for us to it has to take 64 dimensions for us to represent how those networks are are represent how those networks are are represent how those networks are are built, how they're made. And at 64 built, how they're made. And at 64 built, how they're made. And at 64 dimensions, you could start to argue dimensions, you could start to argue dimensions, you could start to argue that perhaps consciousness is a that perhaps consciousness is a that perhaps consciousness is a connectivity to a dimensionality that we connectivity to a dimensionality that we connectivity to a dimensionality that we don't live in every day. You and I don't don't live in every day. You and I don't don't live in every day. You and I don't live in every day and we can't live in every day and we can't live in every day and we can't comprehend comprehend comprehend >> and that it is this it is this >> and that it is this it is this >> and that it is this it is this extraordinary complexity in 64 extraordinary complexity in 64 extraordinary complexity in 64 dimensions that gives rise to dimensions that gives rise to dimensions that gives rise to consciousness that gives rise to our consciousness that gives rise to our consciousness that gives rise to our capacity as biological beings to do this capacity as biological beings to do this capacity as biological beings to do this very simple thing of thinking. I just very simple thing of thinking. I just very simple thing of thinking. I just think that it was such a powerful and think that it was such a powerful and think that it was such a powerful and amazing um paper in just bringing forth amazing um paper in just bringing forth amazing um paper in just bringing forth these numbers and showing just network these numbers and showing just network these numbers and showing just network modeling on this tiny little brain as modeling on this tiny little brain as modeling on this tiny little brain as being just a glimmer into the complexity being just a glimmer into the complexity being just a glimmer into the complexity of how biology has found a path beyond of how biology has found a path beyond of how biology has found a path beyond our kind of understanding even of our kind of understanding even of our kind of understanding even of physics into this universe that we can't physics into this universe that we can't physics into this universe that we can't even comprehend. And it shows how little even comprehend. And it shows how little even comprehend. And it shows how little we know. we know. we know. >> We know very little. But then as you >> We know very little. But then as you >> We know very little. But then as you sort of alluded to here and as we talked sort of alluded to here and as we talked sort of alluded to here and as we talked about in the top of the show, we have AI about in the top of the show, we have AI about in the top of the show, we have AI Frontier Lab saying, "Hey, reinforcement Frontier Lab saying, "Hey, reinforcement Frontier Lab saying, "Hey, reinforcement learning is like super dangerous because learning is like super dangerous because learning is like super dangerous because these things could get out of control." these things could get out of control." these things could get out of control." Are you you know in the camp of we are Are you you know in the camp of we are Are you you know in the camp of we are rebuilding in this simulation or rebuilding in this simulation or rebuilding in this simulation or whatever we're experiencing here. We are whatever we're experiencing here. We are whatever we're experiencing here. We are in fact recreating in fact recreating in fact recreating our brains with silicon and that you our brains with silicon and that you our brains with silicon and that you know we're on the way to actually know we're on the way to actually know we're on the way to actually creating consciousness that a replicant creating consciousness that a replicant creating consciousness that a replicant in science fiction like Bladeunner where in science fiction like Bladeunner where in science fiction like Bladeunner where they don't even know you know Rachel they don't even know you know Rachel they don't even know you know Rachel doesn't know she's a replicant. Spoiler doesn't know she's a replicant. Spoiler doesn't know she's a replicant. Spoiler alert you had 50 years to see the film. alert you had 50 years to see the film. alert you had 50 years to see the film. like are you part of that camp that like are you part of that camp that like are you part of that camp that that's actually what's being built here? that's actually what's being built here? that's actually what's being built here? >> Yeah, I'm not sure. Uh it's a longer >> Yeah, I'm not sure. Uh it's a longer >> Yeah, I'm not sure. Uh it's a longer conversation. We should do it another conversation. We should do it another conversation. We should do it another time. Yeah. time. Yeah. time. Yeah. >> But I do think there's something >> But I do think there's something >> But I do think there's something fundamental to consciousness fundamental to consciousness fundamental to consciousness that relates to the drive for survival that relates to the drive for survival that relates to the drive for survival in a physical sense. You have to have in a physical sense. You have to have in a physical sense. You have to have physical sensing and physical physical sensing and physical physical sensing and physical responsiveness to learn. As a baby, you responsiveness to learn. As a baby, you responsiveness to learn. As a baby, you first start touching hot stuff and cold first start touching hot stuff and cold first start touching hot stuff and cold stuff and you learn. And we build these stuff and you learn. And we build these stuff and you learn. And we build these reward mechanisms into neural networks reward mechanisms into neural networks reward mechanisms into neural networks that we build in AI. But those reward that we build in AI. But those reward that we build in AI. But those reward mechanisms are digital and they're mechanisms are digital and they're mechanisms are digital and they're programmed. And the question is, they're programmed. And the question is, they're programmed. And the question is, they're a reward mechanism that arises in a reward mechanism that arises in a reward mechanism that arises in biology that creates a different biology that creates a different biology that creates a different capacity for consciousness than perhaps capacity for consciousness than perhaps capacity for consciousness than perhaps can exist in silicon. Bigger topic for a can exist in silicon. Bigger topic for a can exist in silicon. Bigger topic for a different day with probably people that different day with probably people that different day with probably people that have spent more time thinking about it have spent more time thinking about it have spent more time thinking about it than I. But um I just think that there's than I. But um I just think that there's than I. But um I just think that there's something about biology. You know, I something about biology. You know, I something about biology. You know, I always tell people this this analogy. always tell people this this analogy. always tell people this this analogy. I've said it many times on the show. I've said it many times on the show. I've said it many times on the show. I'll say it again. In a single cell, I'll say it again. In a single cell, I'll say it again. In a single cell, there's 10 billion proteins that work so there's 10 billion proteins that work so there's 10 billion proteins that work so fast that 1 second is the equivalent of fast that 1 second is the equivalent of fast that 1 second is the equivalent of 80 years of humans walking around the 80 years of humans walking around the 80 years of humans walking around the city of Manhattan, never sleeping, doing city of Manhattan, never sleeping, doing city of Manhattan, never sleeping, doing stuff together with 500 story tall stuff together with 500 story tall stuff together with 500 story tall skyscrapers doing stuff for 80 years is skyscrapers doing stuff for 80 years is skyscrapers doing stuff for 80 years is 1 second in one cell. 1 second in one cell. 1 second in one cell. >> And so you have 10 trillion cells in >> And so you have 10 trillion cells in >> And so you have 10 trillion cells in your body doing that, living that entire your body doing that, living that entire your body doing that, living that entire universe every second, all interacting universe every second, all interacting universe every second, all interacting with each other. and you start to with each other. and you start to with each other. and you start to realize that there's a complexity in realize that there's a complexity in realize that there's a complexity in what's emerged in biology that extends what's emerged in biology that extends what's emerged in biology that extends well beyond any model we've built in well beyond any model we've built in well beyond any model we've built in silicon today. Now it doesn't mean that silicon today. Now it doesn't mean that silicon today. Now it doesn't mean that the silicon that we're building today the silicon that we're building today the silicon that we're building today doesn't create extraordinary capacity doesn't create extraordinary capacity doesn't create extraordinary capacity for humanity but we are very early and for humanity but we are very early and for humanity but we are very early and the more we kind of understand this sort the more we kind of understand this sort the more we kind of understand this sort of thing like this paper that I just of thing like this paper that I just of thing like this paper that I just shared I think the more we realize how shared I think the more we realize how shared I think the more we realize how little we do know and how much of a little we do know and how much of a little we do know and how much of a frontier there still is to explore. frontier there still is to explore. frontier there still is to explore. >> Yeah. And I think, you know, this >> Yeah. And I think, you know, this >> Yeah. And I think, you know, this obviously brings up faith and do you obviously brings up faith and do you obviously brings up faith and do you believe that there is a God that set believe that there is a God that set believe that there is a God that set this in motion. I like to believe there this in motion. I like to believe there this in motion. I like to believe there is uh some higher um work here and uh is uh some higher um work here and uh is uh some higher um work here and uh this is my closest analogy in science this is my closest analogy in science this is my closest analogy in science fiction. We're both super fans of fiction. We're both super fans of fiction. We're both super fans of science fiction, but for me 39 seconds science fiction, but for me 39 seconds science fiction, but for me 39 seconds in here, in here, in here, >> one of the great >> one of the great >> one of the great >> You like this one? I I love the >> You like this one? I I love the >> You like this one? I I love the Prometheus version of this where there's Prometheus version of this where there's Prometheus version of this where there's engineers engineers engineers >> who are terraforming and started this >> who are terraforming and started this >> who are terraforming and started this crazy thing out and there's just an crazy thing out and there's just an crazy thing out and there's just an experiment in biology going on and you experiment in biology going on and you experiment in biology going on and you know the opening scene here in know the opening scene here in know the opening scene here in Prometheus he drinks this and this is Prometheus he drinks this and this is Prometheus he drinks this and this is the sacrifice like Jesus um sacrifice the sacrifice like Jesus um sacrifice the sacrifice like Jesus um sacrifice for humanity and he sacrifices him here for humanity and he sacrifices him here for humanity and he sacrifices him here by drinking that biological by drinking that biological by drinking that biological um design right and he falls into this um design right and he falls into this um design right and he falls into this you know planet earth which is just you know planet earth which is just you know planet earth which is just water And this is the Cambrian explosion water And this is the Cambrian explosion water And this is the Cambrian explosion where his DNA goes into the river, gets where his DNA goes into the river, gets where his DNA goes into the river, gets washed out, and then starts the cycle of washed out, and then starts the cycle of washed out, and then starts the cycle of life on planet Earth and that these life on planet Earth and that these life on planet Earth and that these engineers are going around. Um, it's engineers are going around. Um, it's engineers are going around. Um, it's pretty fantastical. Yeah, pretty fantastical. Yeah, pretty fantastical. Yeah, >> a lot of this stuff is a simple way of >> a lot of this stuff is a simple way of >> a lot of this stuff is a simple way of humans trying to explain stuff, but the humans trying to explain stuff, but the humans trying to explain stuff, but the complexity that arises in biology, we complexity that arises in biology, we complexity that arises in biology, we just can't explain. just can't explain. just can't explain. >> And I think we try and use these >> And I think we try and use these >> And I think we try and use these reductive kind of heruristics to try and reductive kind of heruristics to try and reductive kind of heruristics to try and do it. And it's very do it. And it's very do it. And it's very >> um, you know, it's it's comforting. Uh >> um, you know, it's it's comforting. Uh >> um, you know, it's it's comforting. Uh because because it's so overwhelming the because because it's so overwhelming the because because it's so overwhelming the complexity of how this stuff emerges is complexity of how this stuff emerges is complexity of how this stuff emerges is too overwhelming. So we create simple too overwhelming. So we create simple too overwhelming. So we create simple stories to try and help ourselves feel stories to try and help ourselves feel stories to try and help ourselves feel better. better. better. >> And yeah, this is my and that's my >> And yeah, this is my and that's my >> And yeah, this is my and that's my favorite story of it is somewhere favorite story of it is somewhere favorite story of it is somewhere between Blade Runner and this. By the between Blade Runner and this. By the between Blade Runner and this. By the way, both by the same incredible way, both by the same incredible way, both by the same incredible director, director, director, >> uh Ridley Scott. Uh so take it for what >> uh Ridley Scott. Uh so take it for what >> uh Ridley Scott. Uh so take it for what it's worth. All right, everybody. it's worth. All right, everybody. it's worth. All right, everybody. Another amazing episode. You got your Another amazing episode. You got your Another amazing episode. You got your science corner. science corner. science corner. >> We'll see you next time. Bye-bye. Love >> We'll see you next time. Bye-bye. Love >> We'll see you next time. Bye-bye. Love you besties. you besties. you besties. >> We'll let your winners [music] ride. >> We'll let your winners [music] ride. >> We'll let your winners [music] ride. >> Rainman David. And it said we open sourced it to the And it said we open sourced it to the fans and they've just gone crazy with fans and they've just gone crazy with fans and they've just gone crazy with [music] it. Love you queen of quinoa. [music] it. Love you queen of quinoa. [music] it. Love you queen of quinoa. [singing] [music] [music] >> Besties are gone. >> Besties are gone. >> Besties are gone. >> That is my dog taking notice your >> That is my dog taking notice your >> That is my dog taking notice your driveways. Oh man, my appetiter will [music] be the Oh man, my appetiter will [music] be the >> We should all just get a room and just >> We should all just get a room and just >> We should all just get a room and just have one big huge orgy cuz they're all have one big huge orgy cuz they're all have one big huge orgy cuz they're all just useless. It's like this [music] just useless. It's like this [music] just useless. It's like this [music] like sexual tension that we just need to like sexual tension that we just need to like sexual tension that we just need to release somehow. release somehow. release somehow. >> Wet your feet. Wet your feet your feet. >> Wet your feet. Wet your feet your feet. >> Wet your feet. Wet your feet your feet. We need to get Mercury's back. We need to get Mercury's back. We need to get Mercury's back. [music]