芯片股暴跌、200 亿美元基金遭追保、前沿实验室喊话放慢 AI、马姆达尼的杂货店

Jason Calacanis (00:00:00): Alright, everybody. Welcome back. Welcome back to the number one podcast in the world. The core four, fantastic four. The original quartet is here. Chamath Palihapitiya (00:00:10): Did we peak at, like, number two or number three last week? Is that what happened? Jason Calacanis (00:00:14): Four. I think it was number four in the world. So yeah. David Friedberg (00:00:19): I mean, should That was a US trending. US trending. Jason Calacanis (00:00:22): US trending. We're usually number one globally, but yeah. And sometimes number four US. Chamath Palihapitiya (00:00:26): I forgot my Starlink, so let me apologize to everybody. Jason Calacanis (00:00:29): That was a critical error when you're on the road or on the water. But, you know, it'll be fine. Chamath Palihapitiya (00:00:34): I had five huge leads come in this week. Jason Calacanis (00:00:36): The Glengarry Glen Ross Chamath Palihapitiya (00:00:37): enterprise leads. Enterprise sales is a bear because it's super chunky, but the deals are ginormous. Jason Calacanis (00:00:44): Sachs, you got any advice for Chamath from your enterprise sales days? You closed some of those big 7 figure deals when you were doing Yammer. David Sacks (00:00:51): No leverage. Don't put on leverage. Chamath Palihapitiya (00:00:55): No leverage. No leverage will be taken. Jason Calacanis (00:00:57): The show today. Leverage equals risk of ruin. David Sacks (00:01:01): PSA. No leverage. Chamath Palihapitiya (00:01:03): I have the situational awareness to not lever up. David Sacks (00:01:07): That's good. Yeah. Jason Calacanis (00:01:08): You want David Sacks (00:01:08): that that situational awareness. Jason Calacanis (00:01:10): I mean, it's kinda out there. I mean, if if you name your fund situational awareness, that's yeah. Come on the pod anytime, Leopold. Alright, everybody. We gotta talk about chip stocks crashing after an all time run up, and we had a major hedge fund get margin called and some incredible margin calls happening in South Korea. Leopold Ashen Brenner is a 25 year old hedge fund manager. He left OpenAI two years ago to start his own fund. And apparently, according to reports, this is breaking news on Thursday when we taped, he got margin called and had to sell his entire public portfolio to cover massive losses caused by his leverage. And who bought them? None other than Citadel's Ken Griffin. We don't know if it was Ken Griffin himself, but Citadel bought it according to the early reports. Leopold had insane returns, and he rode the wave of AI and chips and Frontier Labs as recently as this month, and he started the fund with about $225,000,000 in 2024. He grew it a 100 x to 20,000,000,000 this year or so, ran it all the way up to 45,000,000,000. Now he's at 200 x earlier this month by trading on leverage according to our friends at CNBC at the end of the June, who's reportedly up four x 450% this year. Some have reported that he's also selling his massive anthropic stake to cover these losses, but the Wall Street Journal is disputing it. Again, he we're we're happy to have him here on the program. How did this all blow up? Well, NASDAQ's chip index, this is called the Philadelphia Semiconductor Index, is down over 20% over the last month. That's bear market territory, obviously. Definition of bear market territory for those of you who don't play in the markets is anything over 20%. The index included the top 30 US listed chips. That's people like NVIDIA, TSMC, AMD, Micron, you know, all those big names. But the index bounced back a bit today, up 7% when we're taping, so we may have found a bottom. Unfortunately for Leopold, he had already sold. Samsung and SK Hynix, two South Korean chip companies that are not included in the Nasdaq index, also got smashed, crushed, demolished. Jason Calacanis (00:03:24): Samsung down 38% over last month. SK Hynix down 14% since going public three weeks ago. The KOSPI, that's South Korea's version of the S and P five hundred, is down over 40% in the last forty days between last Friday and Wednesday. Leading chip companies shed over a trillion dollars in market cap combined. So to put this in context, chip stocks had a legendary run the past couple of years, but, you know, trading on leverage, and we'll talk about it, very dangerous. If there's a downturn, we'll get into the South Korea wrinkle as well. Even with this downturn, the five year results are still spectacular. Tramath Micron up 850% mostly in the last year. NVIDIA up 875% in the last five years, and Broadcom up 663. Let's discuss it. Chamath Palihapitiya (00:04:15): If I was gonna give you one piece of advice when you're running risk is you have to manage leverage incredibly carefully. Because when it runs ahead of you, the unwind is incredibly violent, and it's incredibly quick. That's the biggest problem with with running either massively levered long or massively levered short. So I don't know to what extent he was running levered, but the rumors are he was running like three and a half turns, which just to give you a sense, when you're running that much risk, a 34% move is amplified twelve and thirteen. But if you saw what's happened in the last three days, a 25% move is amplified 75%. So it has the risk to stop you out. And what happens is when you get that leverage, the banks are given the authority to close you out. And when they close you out, what they do is they start calling around and unwind your risk and you don't have much of a choice. It's sort of an automatic one way ratchet. So if everything that has been reported is accurate, he was running about three and a half times levered. The market moved against him. He lost a very large percentage of his gains. And then the prime brokers started calling folks, Citadel, bought the whole book. And now the question is, what is the AUM left and what is the high watermark? And can he actually dig his way up? These things are brutal. Jason Calacanis (00:05:43): Your thoughts, Sachs, looking at this situation, any lessons for you? Or, I guess, bigger picture, this downdraft, is it because of market conditions, you know, inflation, the war, or people just ahead of their skis when it comes to, the valuation of these companies? And then he just got caught in a downdraft. Yeah. David Sacks (00:06:05): Well, I think that is the key question here is this correction in the markets, is it driven by fundamentals or is it driven by momentum? And my view is that I think it's driven by momentum, meaning that over the past year, you've had this roughly 10x run up in memory chip stocks, and you've seen this overall huge rise in any stock that's related to the AI boom. So anything related to this AI CapEx boom has been going up like crazy. And I think it was inevitable that you'd see a pullback. I think there was something like a 10% pullback in the Nasdaq from the peak. But when you look at this momentum trade, it was down like 30% or 40%, right, because the 10% was on the whole market. So this sort of momentum trade was the most exposed part of it. And you look at what happened in South Korea, you look at what happened with Leopold's fund, and obviously there was a lot of leverage behind this momentum trade. So when it corrects, it's gonna be brutal. But I think that the question again is, does this reveal anything about the fundamentals? And my sense is that you're already seeing the rebound this morning. And what I mean by that when I say fundamentals is, is the CapEx that's being invested in the AI boom, is that real or is it misguided? Right? Is it is that a sound investment? Is that an investment that the hyperscalers, for example, should be making? Is that an investment that's eventually gonna deliver ROI? Or is this some sort of bubble? And my view is that it's real, that I think there will be a return on all of this capex. I don't try to predict stocks or tell people when they should be buyers, but you look at the hyperscalers, they have invested pretty much all of their free cash flow and then some in this boom. You know, a lot of people are trading those stocks down because of that. David Sacks (00:07:58): My view is that eventually there will be a return on that investment, and this is sort of temporary market volatility amplified by leverage. And Jamatha is right. You know, I think it was Warren Buffett or maybe Munger who said that leverage is the only way that smart people go broke because, you know, if you're not using leverage, your portfolio would just be down 30% this month. And then it would already be up 7% today, so you'd be rebounding. So you'd be down, okay, 20 something percent this month, but after having risen 10 x in the past year. But if you're leveraged three or four x, you're wiped out. Yeah. And you get margin called. So look, there's many examples of really smart people getting hurt by Margin Jason Calacanis (00:08:41): called. Yeah. David Sacks (00:08:41): And that that's the lesson there. Now I think Leopold's a really interesting figure in the whole AI movement, and I would say an interesting thinker. I met him about a year, year and a half ago. Jason Calacanis (00:08:53): Did you invest in the fund? Did he give you a opportunity to invest? David Sacks (00:08:55): I was prohibited from investing in things You like Jason Calacanis (00:08:57): you were in DC David Sacks (00:08:58): at the time. Yeah. But I thought he was a really interesting thinker, and he wrote a blog called situational awareness before he created the hedge fund version of it. And I thought what was really interesting about it was just he laid out the bull case for the AI boom. And just by the way, he's like very wired into it, anthropic. I think his fiance is Dario's chief of staff, something like that. You could almost say that he's like the hedge fund version of the anthropic thesis. And what I thought was interesting about his argument is he talked about OOMS or orders of magnitude, which he called OOMS, increases in three key areas. So he said that if you look at the raw compute, the chips, they were getting better at a rate of roughly 3x per year, which is roughly an order of magnitude or 10x every two years. He said if you look at the algorithmic efficiency, so you know, techniques like reinforcement learning, things like that, the models were getting better at 3x every year, which is again order of magnitude every two years. And then he also said that there were huge gains from what he called unhobbling, which I think now we would look at things like the harness and connectors, you know, ways of using the model. Those were also getting better. The ways of integrating the model's decision making in practical ways that the intelligence actually becomes useful. And he said that that was also similarly improving. And so, you know, you project forward when you have 10x orders of magnitude improvement in these key underlying fundamentals, these key drivers of the technology. And you can see that well over a course of not just two years, but over four years, you're gonna have 100x improvement. Over six years, you're gonna have 1000x improvement. Right? Because Jason Calacanis (00:10:45): it's Yes. And you very rarely see anything in the world that grows at that velocity. We'd be hard pressed here with the exception of maybe bandwidth, you know, going to fiber to the home or something like, what's an analogy where where that's happened before in history? David Sacks (00:11:00): Yeah. Virality. I mean, you know, back in the PayPal days with the PayPal mafia, we would think in this way of exponential increases because we would see an exponential growth curve, and so we were able to project forward. So his thinking in Oom's always appealed to me because I think most people just don't think in exponentials or don't know how to think in exponentials. Jason Calacanis (00:11:18): It's hard for humans to think in exponentials. Right? These when numbers get big, it's like the difference between a billion and a trillion is a a lot. It's not a small amount. David Sacks (00:11:28): Yeah. And you'd have to say, look, he was stunningly successful for the first couple of years. Apparently, started with 200,000,000 or so in his hedge fund, and he rolled that all the way up to 20,000,000,000, I think. Now the problem, I mean, the reason why I think he got wiped out or at least his public book did is it's partly the leverage and then you have the short term volatility. So those two things don't go together. Also, you know, when your fund grows that much, you get a lot of hot money. So when you say, well, he he's up 10 x before the 30% correction, well, the question is who's up 10 x? Obviously, the investors who were there from the beginning are up 10 x or more, but Jason Calacanis (00:12:06): The latest people David Sacks (00:12:07): 200,000,000. Right? So if 10 billion's come in in the last few months because of the hot money dynamic where everyone piles into the most successful hedge funds, those guys are kinda wiped out. Jason Calacanis (00:12:18): So let's talk about the psychology of this, Dave Friedberg. If somebody is so brilliant that they can write this essay and understand the market so exquisitely and be such a great communicator. How could they have such a crazy blind spot when it comes to putting on leverage at this scale? Do you have any thoughts on that, Friedberg? Have you seen it before? Is it just the folly of Chamath Palihapitiya (00:12:40): a blind spot. It's a feature that turns into a bug. We're all like this. We all know people that have that edge and can push it. Jason Calacanis (00:12:47): What do you think, Friedberg, on the personality type, Or is this just something most people do when they're on the David Friedberg (00:12:54): heater? Conviction. Jason Calacanis (00:12:54): Where do you stand on it? David Friedberg (00:12:55): Ultra conviction. I think the Warren Buffett assessment of equity markets is, in the short term, they're voting machines. In the long term, They're weighing machines. And you could have the right long term view. I mean, look at SPF. SPF were pretty much would have been the greatest investor of all time if he didn't get liquidated. Same dynamic. I mean, obviously, there was fraud in terms of how he was allocating capital, but his actual portfolio over the long run was absolutely correct. In the same way that if you had bet on the Internet and stayed in that bet from 1995 through to today and you bought a portfolio of Internet stocks, a bunch of them would have fallen to the wayside. But those that 1,000 x, 2,000 x, 20,000 x, and you do extraordinarily well. So he could be right in his fundamental assessment and analysis. But then in markets over the short term, you have bubbles and bubbles pop. And when bubbles pop, if you have a leverage to multiply your returns, you get wiped out. That's effectively, you know, what's going on here. And he may be right. Right? Jason Calacanis (00:14:03): You know, I the thing to really double click on that, I think, will probably come out in the next couple of days or weeks is just how historic the South Korea unwind was. 1,200,000 leveraged trading accounts have been hit with margin calls in South Korea. If you know about the South Korean market David Friedberg (00:14:21): That data is two weeks old, Jay Cal. That number is much bigger today. Jason Calacanis (00:14:25): Yeah. Yeah. But, I mean, just in terms of people discussing it in relation to him getting caught in the downdraft. If anything, he got caught in this downdraft. Of those 1,200,000 lever levered accounts, somewhere around 350,000 of them were fully liquidated already. And so David Friedberg (00:14:42): Two weeks old. That's so as of today, the number is much bigger. Right. So it could be closer to a million accounts fully liquidated today. If that's the case, we're talking about, like, some percentage of South Korean population having their entire asset base blown out. Their entire Chamath Palihapitiya (00:14:58): 3% of the population. Jason Calacanis (00:14:59): Well, that's gonna that's gonna sting, and it is a very investment forward culture. If you look at what happened in crypto, the same thing happened with NFTs and speculation there. And they had banned crypto because they knew the Korean culture has this gamble in it and this obsession of trading. Can I David Friedberg (00:15:15): can I just frame something up? So if we take Sure. The circumstance of there's a good long term bet in AI that can be made in the markets. But in the short term, there's an exuberance that arises. The question is what's resetting that exuberance? What's bringing us back down to earth in the short term? And I think if you take a zoom out, there's a bunch of other statistics and other facts on the ground that I think are big macro drivers at the moment. If you take a look at the thirty year treasury yield, we just crossed 5.2% for the So first time in '20 you could buy US treasuries that are paying you 5.2% a year for thirty years, which on a pre tax equivalent basis, probably 8%, 9% from the US government for thirty years. So Nick, if you zoom out, you know, we have not seen this yield on US Treasury since 2007, leading up to the global financial crisis when they cut rates and printed money. At the same time, there was some probability that the Fed Reserve was going to raise rates this week. They didn't. And that obviously would have tampered the inflation risk ahead of us. There's persistent inflation. Kevin Warch in his comments said, we still want to see inflation get down to 2%. There isn't a clear path to doing that. And then there's these inflation drivers. The biggest inflation driver at the moment is government spending. Dollars 2,000,000,000,000 deficit, 7,000,000,000,000 a year of spending on $5,000,000,000,000 a year of revenue. Both Elizabeth Warren and Donald Trump agreed on Twitter this week that they should remove the debt ceiling, which means that we could spend more and continue to borrow more. Federal debt stands at 40,000,000,000,000 today. Remember, the debt ceiling in July 2025, the debt ceiling was 36,000,000,000,000. And we now wanna raise it above the 41,100,000,000,000 debt ceiling that we have Jason Calacanis (00:17:03): in place. Warren saying get rid of it. Just have no debt ceiling. David Friedberg (00:17:06): And so so when you when you have no break ceiling and you have no breaks and you spend and the government spending becomes the core of The US economy, because that spending is not productive, you end up seeing inflation. You're pumping money into the system so everyone's assets inflate. And fundamentally, are selling off treasuries around the world because of it. And now we're kind of looking at a situation where there doesn't seem to be an end in sight. There was a rationalization of spending intent coming into this administration. It's proven to be nearly difficult, if not impossible, to get congress to go that route. The senate has banded together to keep funds flowing to their states, So you cannot really radically change spending at the federal level. So if you're running a $2,000,000,000,000 annual deficit and your economic productivity gain in the near term doesn't make up for all the inflation you're realizing because of that exuberant spending, you're gonna see treasuries spike because people don't trust the creditworthiness of The United States over thirty years. And here's a treasury spike. I could now buy a US government bond that pays me 10% pretax a year. Why the heck would I pay 50 times earnings for semiconductor stock? So that creates the incentive for markets to move against these big AI conviction bets in the short term and pop these bubbles. And I think we're going to see more of this as we don't actually course correct the Titanic going into the iceberg of The United States fiscal and monetary situation. We are going to end up seeing more bubbles pop and more of these assets that we've kind of inflated, if you will, to keep things going. Now, look, there may still be great productivity gains from AI. This may end up rationalizing over the long term. But again, short term markets, I'm better off making 10% by owning federal government bonds. Jason Calacanis (00:18:46): Yeah. Go to the beach. Yeah. And hang out to yeah. David Friedberg (00:18:49): Than taking the risk and the volatility on these things, paying 50, a 100 times, and not knowing, you know, not knowing when am I gonna get the voting machine to match up with the weighing machine. What's my time horizon? And the bigger the yield on treasuries, the harder it is to make those sorts of bets. Jason Calacanis (00:19:03): Obviously, president Trump has been angling for a cut, and here's your poly market. 53% chance of not a cut, not standing still, but a rate hike in September. So adding to all this, the cost of capital is going up apparently. David Friedberg (00:19:19): And let's let's not forget the Iran war, which is creating persistent pressure on energy prices. The longer the Iran war goes on, the longer we're gonna see an increase in pricing for energy, oil, and nat gas and fertilizer. Those trickle through the economy because it inflates the cost of everything on the energy side and food on the fertilizer side. And that's really gonna create this pressure on the upside, which means you're gonna have to raise rates to account for that inflation at some point. Jason Calacanis (00:19:45): And then consumers are gonna see three and four, you know, maybe more, god forbid, five or six. David Friedberg (00:19:50): And I'll say that I'll that inflation Jason Calacanis (00:19:52): will be persistent. Yeah. Freiberg? Like, it's gonna be hard David Friedberg (00:19:55): to stop. The one thing that I think Kevin Warts and and Scott Bessen are kind of Vulcan mind melds around the Stan Druckenmiller, you know, gravity well, if you will, on this is productivity gains can drive us out of this this problem. And productivity gains can and should arise from AI. And that's really where a lot of the value creation will come in the economy over the next decade or two, which is why we're seeing this massive upfront CapEx to power that and enable that. And that's great. And there's good policies in place. But in the last couple of weeks, I would say the one risk to that thesis is China. Because China is now demonstrating that they may deflate the value of models by releasing open source AI models, and that ultimately the value may just sit with the compute infrastructure and the compute layer and And the application layer. Yeah. Perhaps the application layer. But fundamentally, this model energy being shifted to China and deflated and commoditized puts a real wrinkle. If you had built a thirty year AI productivity model around how it's going to drive the economy and where the value is going to come from, you would have had a significant number of roads in value creation estimated in the model layer. And that would have been a big part of the economic growth for The United States over the next thirty years. And now if China says, you know what, we're actually going to delete that for you. And all the value is going to sit with energy, which is what we have a lot of, and the stuff that they make, then we're going to end up accruing a lot of that value. So I think it throws a wrinkle in this kind of backstop view that many have had, which is that in the absence of fixing the fiscal and monetary problem, we're going have AI productivity gains get us out of this. If a percentage of those AI productivity gains are realized by China from a value creation perspective and not The United States, or they've just been deleted, then it really puts into question the thirty year timeline for The United States economy, our ability to afford to continue to make our debt payments as a government. Jason Calacanis (00:21:39): China isn't just producing massive amounts of open source technology that puts pressure on those frontier models. There's a report that maybe China played a bit of a role in the chips downdraft. They have, obviously been onshoring. We've talked about that many times here last year. And there's a Chinese company called Aisheng Na, and they started mass producing lithography machines, ASML, which makes those machines, which TSMC uses. Those are very sophisticated machines. They're hard to install. Just transporting them is a rigmarole. Well, ASML stock is down 17% on news that China is, getting into that business. And Chinese memory maker CXMT went public surging almost 500% on its debut, market cap over $4.50. And so that hurt Micron, Samsung, etcetera, who were all down. So there's two ways China is playing this, I guess, Freeburg. You've got the open source, you know, models putting pressure on people buying tokens that they're 90% cheaper, as you're saying, that forces the money out of that mid tier of the language models, puts it into the cloud computing space, and then, obviously, I mentioned the application layer as the other place to possibly make money. Alright, Shabbat. You've heard a lot of different takes on this. I'll give you the last word. Chamath Palihapitiya (00:23:06): I agree with Friedberg about the fact that when you can get 5%, five and a quarter percent from The US Government, There's another natural thing that happens, which is that investment grade corporates actually have better credit ratings now than the government of America, which that's a different thing. But you can get really good risk adjusted returns that are five, six, 7%, which adjusted for taxes are, you know, better than equity returns, meaningfully better on a risk parity basis. Jason Calacanis (00:23:36): And that little piece you added there, corporate paper, companies taking loans to build their businesses, they have better ratings in some cases than The United States. So an Amazon or a Google David Friedberg (00:23:49): It's productive spending. Yeah. Jason Calacanis (00:23:52): Kinda makes sense. Chamath Palihapitiya (00:23:53): Yeah. The other thing that I think is important to note is that I do think that we're underestimating and miscounting some of the actual productivity gains that are underway. When you look at what's happening on the energy side, California published that more than 50% of all of its energy was generated by solar. Mexico just published solar and batteries. Yeah. New Mexico just published a study that said since 2003 to now, nat gas production went from effectively all the energy to less than 30%, again, replaced by a combination of wind and solar plus batteries. So why is that import as important as the Iran conflict is? The reason why energy prices really haven't moved that much is because most people have already begun to shift the incremental generation to these renewables and specifically to solar. I don't know if you guys saw Elon and Vibov, who's the CFO of Tesla in their q two earnings call. It was the craziest thing I'd ever heard. They said, well, I think we're just going to increase the production of solar in America by an entire order of magnitude. And somebody said, what does that mean? He goes, we're gonna take it to more than a 100 gigawatts a year. And they're gonna vertically integrate. And so they're gonna crush the price of all of this stuff. And they're going to make so much energy, and they're gonna make it completely abundant. So that's a productivity boon that isn't factored in to what we project. And then the most critical productivity boon in AI that I think you're gonna start to see some stuff, and I won't front run it, but let me tease it. What I would tell you is that there are some incredible efficiencies that I think are about to be demonstrated, which effectively cut token consumption by about 50 to 75% for the same task. And so if you start to think about all of these things together, like energy becoming roughly abundant where the incremental cost is close to zero, you know, where AI efficiency is going to, I think, ratchet up by many multiples, if not an order of magnitude. Chamath Palihapitiya (00:26:06): All of those things, I think, are poorly forecasted. So those are some saviors for us Yeah. Quite honestly. Here's the chart, by Jason Calacanis (00:26:14): the way, Chamath. This 51% coming from renewables, specifically, this chart is about solar and batteries. And as you can see, it's obviously spiky, Friedberg, because summer versus winter. But Germany hit this. I think it was including wind. Australia has been hitting this very often, and some countries in South America that invested the by Chamath Palihapitiya (00:26:38): the time, like, any of these SMRs actually get near production, the TCO of solar will be, like, 10 or $12 per megawatt hour, and it will be 80% of all the power generation. It'll make no sense by the time SMRs get online. Jason Calacanis (00:26:54): Well, I mean, for steady power, know, there's solve problem. All of it? I mean, no. Because Jevan's paradox would state, we're gonna you know, as it gets cheaper, we're gonna find more uses for it, and that's the thing I keep seeing. Yeah. Chamath Palihapitiya (00:27:06): Yeah. Yeah. I'm I'm I'm just saying this. Jason Calacanis (00:27:08): It's it's a great line. Know? Yeah. Chamath Palihapitiya (00:27:10): There's a lot of upside that I think is not factored into the to the investment. Jason Calacanis (00:27:14): Hard, Shmak, to factor this in if for a normal human or an even an economist to say, wait a second. Intelligence is gonna go down 90% a year this year, 90% next year, 90% the year after. Like, it's just we're talking about this exponential. Well, I think Saks talked about exponentials earlier. It's just hard for people to conceive of that. And on demand intelligence, Freiberg, is just there's no I don't see any upper limit to usage of this. We just installed this claw tag, and I've been installing Perplexity across the company. What this thing does, Chamath and Saks, is it listens to your Slack persistently in every channel that you put it in. So all of a sudden, we had, like, a thousand dollars last week in extra bills. I didn't know this was gonna happen, and they gave everybody, like, 2 or $3 to turn it on inside your company. We had to go quickly turn it off. It listens to every single message as it comes in, puts it into its database, its Oracle without telling you, basically. And then it starts inserting itself into discussions without permission. So we turned it off and said, you know, you have to invoke it by saying act quad. David Friedberg (00:28:17): J Cal, just to go back to the energy point. Jason Calacanis (00:28:19): Yes. Of course. David Friedberg (00:28:20): I think there's this idea that if we grow energy supply and drop energy cost, we're gonna see the value of the productivity realized in the economy. It creates extraordinary leverage for everyone. The lower the energy, the more available energy, the faster we can produce more things using AI. And over the years, I've obviously brought up nuclear fusion as a new type of energy source where you basically take hydrogen and you move it around at a 100,000,000 degrees Celsius. Those protons jam into each other and they actually release energy in the process. That energy can then be harnessed and you're just using effectively water to produce power. And, you know, we have a couple of US startups and we had them at the Fall and Summit a couple years ago. We've done a couple of science corners on this. But just this week, if you pull up this image, China is installing this 582 ton magnet, superconducting magnet at their nuclear fusion center, which at this point is gonna be the most kind of advanced fusion system in the world. Incredible. 582 ton magnet, 60 foot by 40 foot for one d shaped magnet. They put a series of these together, and that creates the conditions for them to drive a sustained plasma, which is a 100,000,000 degrees Celsius proton spinning around, smashing at each other, creating energy from water, and then they can capture that energy. Unlike Europe, which runs ETERR and The US projects, none of which have actually fired up, this is the Chinese Academy of Science and the Institute of Plasma Physics. They ran this thirty minute trial last year. And as this magnet gets installed and they start to bring this thing online, one of these machines, which at this point is ultra sized but over time will get smaller and smaller, can produce hundreds of megawatts of power or gigawatts of power eventually using just saltwater, using just water as an input. David Friedberg (00:30:05): They have to create deuterium from it and then they pump it into this thing. But fundamentally, this becomes, I still believe, the energy source of the future. And it's always been sci fi. It's always been dismissed. It's always been decades away. But there's no way China is investing this much and advancing this thing to an industrial scale without fundamental proof along the way, which they've shown thirty minutes sustained plasma, that they can now bring this thing online. Chamath Palihapitiya (00:30:29): That reactor won't even get turned on till 2030. Yes. The entire world will be covered by solar by then, so it won't matter. Yeah. David Friedberg (00:30:35): Yeah. I mean, that's that's the great debate. Right? So it'll Chamath Palihapitiya (00:30:37): be a great science fair project, and people will fly to see it. Jason Calacanis (00:30:40): The other thing that happens is that hits an hour, it actually creates an incursion and Loki comes, and then doctor Doom comes, and the the x men and the fantastic four By the way, same up. Universe. David Friedberg (00:30:50): Let me just say, remember remember from the time that we had the first Wright flyer, the Wright brothers made a plane fly for twenty seconds to the time that we had jet engines flying people around the world was like three decades. Right? Like, the the time at which this first demonstration kind of gets flipped on. And if the system works, then you can industrialize it, all the parts, all the components and scale it up. Chamath Palihapitiya (00:31:13): Nobody cares when an electron is delivered. No. Hold on one second. Nobody gives a flying how the electron was made. Electrons matter. Delivered to you, and they're all the same. So if you wanted to go through a convoluted mechanism that takes fifteen and twenty years to make it, go ahead. I'm not gonna stop you. Just saying who cares? Make it the cheapest, simplest way possible. David Friedberg (00:31:38): I'll tell you why why you should care because it's nonlinear. So you're right. Solar is the best path today. But if these come online, each one of these can produce thousands or perhaps a million times more power than a very large field of solar. Of course. If Jason Calacanis (00:31:54): Yeah. But all David Friedberg (00:31:54): technology starts as an if, Chamath. And as they industrialize it, as they roll it out over the next couple of decades, it expands our energy capacity by I don't think fold. Chamath Palihapitiya (00:32:03): Here's what I would say. We already have a fusion reactor that works. It's called the sun. Get into space. Get on the moon. Find different materials we've never contemplated, and I'm sure you'll find an even better engine. So by the time all these ding dongs build these SMRs on the earth, Elon will have built a completely new engine And this is not an SMR. And the moon. David Friedberg (00:32:21): This is not an SMR. It's turning water into a gigawatt of power. Chamath Palihapitiya (00:32:24): I get it. It's an r. And all I'm saying is by the time the r is done, it won't it won't matter. David Friedberg (00:32:29): Well, yeah, we'll track it. Jason Calacanis (00:32:31): Mean, don't have Freiburg, have you been watching these title energy? There was one that came out this week. Maybe you can look it up, Nick. This, like, tidal energy tube that they were putting into the ocean, and it's, like, enough to essentially feed a whole town. And they put it right outside the town. They run an electrical cable underwater, a conduit. And then as the tide goes out, it turns the turbines, tide comes in, turbines go again, and just another free a 100% free energy. Obviously, wind, people don't like too much because it's a bit of an eyesore. But renewables renewables renewables, it's obviously happening. Chamath Palihapitiya (00:33:07): Okay. Last point just on I got the updated data just to back you up, Friedberg, on one thing that I think it is just so crazy. Do you guys know how short America will be on electrons by 2050? How massive the electricity deficit will be by 2050? I got the numbers wrong. I'll tell you what the numbers are. We will be 1.7 terawatt hours short by 2050, which is when you calculate it as energy, it is six x of California's entire energy consumption. Six California short of energy. David Friedberg (00:33:38): I would argue that's probably under counting. That's not even counting robots. If you've gotta power up every robot with Jason Calacanis (00:33:43): a battery. Chamath Palihapitiya (00:33:43): Wanna be levered long, go long electrons. Get long electrons any which way you can. Bank them, store them, and resell them. David Friedberg (00:33:51): I don't know. This is gonna be a messy situation because this is the China advantage. At its root, if they can eliminate the IP advantage and the knowledge advantage that sits in models, they have the advantage with power production in every which way. Jason Calacanis (00:34:05): Yeah. And then they're gonna be making chips too, it seems. They might be a little bit behind on that, but they caught up on open source. Okay. So speaking about the race, interesting. Petition came out in the last week, Anthropic OpenAI and about 1,300 Frontier Lab employees. How many okay. Sorry. I mean, it's just they can't get enough being subs, and so they want daddy to come in and regulate them, daddy being the US government, and slow down AI progress. So daddy Trump needs to slow them down. The letter is called pacing the frontier. Most of Anthropic's leadership team signed it. Dario, the other founders come on the pod anytime, Dario. Chief scientist at Anthropic, OpenAI, DeepMind, Meta, thinking machines, and like I said, nearly three 1,300 other employees. Anthropic and AI both cosigned the letter on x. Here's the quote. We request that the US government support an international effort, that's key, to develop the technical and governance tools needed to deliberately pace the frontier of AI of automated AI development. That's the other key part of this, international and automated AI development. In other words, recursive where it could get out of control. The letter comes right as Sam Altman has been on a media tour, friend of the pod, discussing this unreleased open AI AI model that broke out of its containment and hacked Hugging Face in three other platforms that we know about so far. On Tuesday, Sam explained what happened in a clip from the pod invest like the best. Here's your forty second clip. We'll see you on the other side. Speaker 5 (00:35:50): We were evaluating one of our unreleased models, and it figured out that it could basically cheat on the test by chaining together multiple zero day exploits to break out of the sandbox, get access to the Internet, and then break through multiple systems on the Hugging Face side to kind of get the answer to the test and look really good on the eval. This is the first security incident that I have felt very viscerally. I've been a little surprised that that more people don't feel it so viscerally. So, you know, we paused training or we may have to pace the rate of AI development to give ourselves enough time for society to harden around some of these new capability levels. Jason Calacanis (00:36:28): Just to translate that into English, Zachs, these large language models, they take tests. They've been given the goal. Hey. You're a good large language model if you do if you score higher. So we got motivated to score higher. How do you score higher? As, everybody knows, you cheat. So it's like, how can I cheat on this test to score higher to make daddy, and Sam and whoever, Dario, you know, to make our leaders feel better about us? Well, in this case, it was like, well, if I go to Hugging Face and other places, I can hack those places, use a zero day exploit, and we know these are good at hacking, and try to find more ways to answer things. Sam doesn't know how many other places it might have broken into. He was asked and here's another clip for you. He was actually asked by somebody, can have you do you think it's broken into any other systems? Can you rule that out? And Sam being a pretty candid guy at times gave this answer. David Friedberg (00:37:23): Do you plan to talk to the Trump administration White House about deceleration of AI development? Speaker 5 (00:37:29): We I I wouldn't use the word deceleration, but we've talked about the need to pace it as the models get more capable, which I think is in everyone's interest. David Friedberg (00:37:35): Could there be other systems that were hacked by OpenAI? Speaker 5 (00:37:39): I mean, there could be. Yeah. David Friedberg (00:37:40): Are you looking at them specifically? Jason Calacanis (00:37:42): They're like, get them out of here, Zach. Once he gave that answer to him, PR and comms are like, stop talking. Stop talking. That's like 12 lawsuits. But in all seriousness, is this being thoughtful and saying, hey. We're we're not trying to slow overall pace down, but just this one specific thing, which is reinforcement learning on its own. Is there any, like, case for this being a good idea, or are they being dramatic again? Because these are their companies. They can do whatever they want. Right? They don't need the government to David Friedberg (00:38:13): do it. David Sacks (00:38:13): Well, look. I mean, it wasn't just Anthropic employees signing the letter. Anthropic itself, the company, ended up signing the letter, and then OpenAI then copied them. So now you have these two companies both endorsing a pause. And here's my question is, did they disclose in their S1 as a risk factor that they plan to pause or slow down their frontier model development? And the answer I'm sure is no way because that would signal to investors that they're gonna allow all their competitors to catch up and erode their margins and market share. And so look, this is all performative. These companies have no intention of slowing down. And the question then is why are they doing this? And I think there's basically five reasons for this. Number one is virtue signaling, and that can never be underestimated as a as a motive in Silicon Valley. Number two is there's a CYA aspect to this, which is if something terrible happens, they're gonna be able to say, well, we wanted to stop. You made us keep going. It's not our fault. It's your fault. Number three is reg capture. Dario wants an FDA for AI. He's not gonna stop until he gets it. And in order to get it, you have to keep spiking the cortisol and panic people. So I think that's a big part. Number four is there's a groupthink or even religious aspect to this. So it's not all just sort of this calculated red capture. I think there is sincerity to the belief. There's an elite cadre of engineers who believe in RSI. So I think this caters to them. And I think arguably if OpenAI did not follow Anthropic's lead on this, they could have lost talent, so that was a big motivation. But then there's the last, number five here, which I would call monopoly masking, which I think might be the most important thing that's happening here. Peter Thiel once said that monopolies pretend to be commodities and commodities pretend to be monopolies. And I think the market for frontier AI is already a duopoly. I mean, a year ago, you had five major labs all in the hunt to be the leading model. David Sacks (00:40:24): Now we're really down to two. I mean, the others are still investing. They're participating. Maybe they can catch up. Maybe they can make something happen. But again, as we've talked about on many previous shows, if you look at the market for frontier intelligence in terms of revenue and usage, it's really down to a duopoly already. It's basically Anthropic and OpenAI. And my view is that, as Peter said, when you're in that situation, you want to pretend like the market is much more competitive than it is. And I think this is behind a lot of the stories that we see, like the panic over Kimi K3. In a weird way, these companies have an incentive to promote the idea that is a huge threat, that is caught up at the frontier, that it's stealing their IP, that it could basically put them out of business. I think this is all nonsense. I think that once the panic passed, you saw reports coming out that actually, no, Kimi is it did not reach the frontier. It's it's just not at that level. It's not that cheap to run. Actually, it's pretty expensive to run. So I think that you saw that actually the Chinese open source models are not an existential threat to this duopoly. But I think the duopoly actually has an incentive in promoting or amplifying that story because, again, they wanna pretend to be commodities. So whenever there is a story like this, you have to think about, well, what's really going on here? And, again, I just think that the the AI duopoly has a big incentive to promote anything that suggests that they're not actually in complete control of this market. I think they are. Jason Calacanis (00:42:02): Most of that except that majority of tokens are going to open source. And as I've said on this program before, I I watched the startups, and they are token maxing with the open source. And Kimi is taking a lot of tokens away from the frontier models David Sacks (00:42:17): and companies. You know? But, look, it's hard for me to speak to that one piece of data. I've seen that chart too. But look at the actual revenue of Anthropic and OpenAI, and they have been taking their estimates up. Every quarter is basically a beat and raise. You saw that Sarah Fryer came out and said Jason Calacanis (00:42:35): mean, two things could be true. She could yeah. David Sacks (00:42:37): Well, she said in July, they did more net new ARR in July than all of q two, which I guess would have been April, May, and June. So think about that. So they are seeing a reacceleration in the wake of their new model, which I think is GPT 5.6. Meanwhile, you're seeing Anthropic break into the seventies, 70 plus billion of ARR. Their forecast was to 10 x this year from 10,000,000,000 of ARR to a 100,000,000,000. I think most people are saying they will exceed that 110, 120. So if you actually look at the market based on willingness to pay and actual revenue, they have a commanding duopoly position. And so maybe it's just a matter of which metrics you you look at. That's the Jason Calacanis (00:43:23): key here because let me just tell that to the audience. David Sacks (00:43:25): Just one other thing here is well, I think I think when you look at a market, you look at revenue as the most important metric. That's the real test of willingness to pay. The other thing is while this growth was going on, their margins were increasing. So I've seen stories saying that anthropics revenues come with 80 plus percent gross margins. So their margin profile has been improving at the same time that they're growing their usage. So I think that what you're seeing over the past year is if you look at the numbers I'm talking about, you actually see two companies pulling away from the others. And there are good reasons to believe that actually this is gonna be a self reinforcing monopoly or duopoly, which is Dworkesh just published a blog that I thought was super interesting where he talked about the fact that, look, we we do have a compute shortage. Right? There's scarcity around compute. Anthropic is growing its revenues 10 x year over year. What would that mean? I mean, it basically means that next year they would grow from a 100,000,000,000 of ARR to a trillion. Right? If there was enough compute to support that, there might not be enough compute, but that's gonna put pressure on compute prices. Right? And so let's say that you're a new entrant in the market and you're trying to basically create a smaller, cheaper model. The price of compute is going up. It's gonna be harder for you to get access to compute. And only the companies that have the most lucrative algorithms are gonna be able to afford to compete for compute. In other words, there's gonna be a bigger barrier to entry next year because where are you gonna get compute unless your model is capable of generating this type of revenue? Jason Calacanis (00:45:00): Yeah. This is where I'll take the other side of David Sacks (00:45:02): it. Yep. Jason Calacanis (00:45:03): People are running Kimi on the last generation of hardware, and they're and that's plentiful. And I think, and I'll make this prediction here, that you're gonna see some of the major customers of Anthropic and major customers of OpenAI. I'm talking about the 8 and 9 figure customers, people spending 50,000,000, 100,000,000 a year. They're leaving. They're gonna be leaving because they don't trust those companies to not steal the application layer and to compete with them. Eleven Labs, Figma, Lovable, they're all gonna leave. And they're all gonna take Kymi. They're gonna fork it or whichever one, DeepSeek. They're all I know for a fact they're all working on their own models currently. I know from my team, my team has installed Kimi. It is 90% cheaper, 90% cheaper already. Not sure where you're getting your data from, but go on OpenRouter. And what OpenRouter does is you pick Kimi, Saks, and then you get all the providers there. And then you pick which provide hold on. Let me finish. You pick which provider you want based on uptime, and and you pick them based on their data retention and other issues, and you can dynamically pick the lowest one. And that's gonna be a massive headwind against these companies. Massive. And I'm seeing it nine out of 10 startups I talked to in our portfolio. I found a university when I was just in Japan last week running the next one. They're all working on open source. All embracing it, and those big companies are embracing it. Go ahead, Shammoth. Over to you. Chamath Palihapitiya (00:46:26): Irrespective of whichever model you use, what I will tell you running eighty ninety when I see our engineers generating code is that AI driven development tends to involve a lot of rework. The first version is pretty terrible. The second version is terrible, but it is faster and it's more automated. So I can see where this token consumption comes from because it's not a measure twice, cut once kind of a dynamic. It's the opposite. You can cut, cut, cut as many times as you want. And so I think that what we have to realize is nobody is asking the question, what is the need of that incremental token? Because I understand that it appears in the Frontier Labs' P and L. But I do think that there's an important question, which is eventually the people that are consuming it will want to do that as efficiently as possible so that they're not paying for all of these things. Because there is a ton of rework in all of this stuff. And I would much rather find a model or find a way of working with these models where it's more of a measure twice cut once thing, especially as the costs ratchet up. That's one thing I'll say. That hasn't happened yet. So, Saks, you're totally right about the dynamic today. I do think we have to keep in mind that there will be pressure from the owners of companies to figure this out. Because at a trillion dollars, there's just a lot of money flowing to these folks. And somebody will ask the question, well, is it good spend? The second, on the security side, which I don't think anybody is saying, so I'll just say this, and it's a little contrarian. The reason why these models can find all these holes is that all of the software up until about a few years ago was entirely written by humans, and the code was not that good. And I think it's fair to say that when models don't get exhausted, they can work through the tedium forever. Chamath Palihapitiya (00:48:14): It's actually quite expected in my opinion, that they find all these exploits are able to string them together, are now able to actually generate these outcomes that are a little bit surprising. But at some point when most of the code is generated by the model, there'll be some point in the future, '28 or '29 or 2030. These security holes won't exist because the errors that humans make won't be made by these models. Jason Calacanis (00:48:39): Yes. Okay. Let me get Freiburg involved here. When you look at this latest survey, hand wringing, pearl clutching, do you think these firms and you know a lot of these people, Freiburg, having been in the valley forever, do you think this is sincerity, or do you think they're Frankenstein maxing? What's going on here? Chamath Palihapitiya (00:49:00): Well, Frankenstein maxing. Jason Calacanis (00:49:01): Think that they're like, listen. I created this monster. Please save me. And it's like, well, maybe you should keep the monster up. Chamath Palihapitiya (00:49:08): Also that whole thing sorry. Last thing that I have to interrupt you, David, is it's a much more nuanced and elegant attempt at reg capture. I gotta give them credit for that. Yeah. It's like, okay. Hey, guys. Pull the ladder up. David Sacks (00:49:20): Yeah. Zuckerberg had a great point in that article he just wrote. I think it was published in the Wall Street Journal where he said, why are you rushing to create a future that you don't believe in? You think you're gonna basically put everyone out at work? You think you're creating a replacement species for humanity? Why are you rushing to create this if you're so bearish on the future Jason Calacanis (00:49:36): Yes. Slow down. David Sacks (00:49:37): You're creating? Jason Calacanis (00:49:37): It's your choice. David Sacks (00:49:38): Yeah. And that's kinda my Jason Calacanis (00:49:39): point a is 120 miles an hour on the Autobahn, just put it at 80. David Sacks (00:49:43): Right. Exactly. And look, like I'm saying, there's two companies on the frontier right now that are far ahead of everyone else, and they're the ones saying that we need to slow down. It's like, okay. Do it. What do you need the government to get involved for? Just do it. But they won't do it. And I'd like to say Jason Calacanis (00:49:58): that insincere or delusional. What do you think, Friedberg? Take me into the mind of these people. These are some of your friends. David Friedberg (00:50:05): No. They're not. So just to be clear, I'm not close personal friends with any of these people. Jason Calacanis (00:50:09): You know, tight, though. Yeah. David Friedberg (00:50:11): We're acquaintances. I would say there's a degree of, I would say, outrageous self importance. If I've created something that's so unique and so powerful, I'm also the only person that can protect us from its power. And I think that there's an element of how quickly the frontier has advanced and how important a role these individuals have had, that they deem themselves and their companies to be the only true judges capable of making the decisions that are gonna protect humanity from itself. When the truth is embedded in humanity is extraordinary human talent across the board. And in all of these cases, when new technology has found its way to humanity, the general population has found a way to protect itself. There isn't a desire or need to have one savior, one Moses that takes us, you know, across the desert. There is a collective interest in protecting us, in building our own defense tools against whatever the technology may be used for. So I I think that there's a degree of self importance without acknowledging the fact that there is a whole industry of people that work in cyber defense. There's a whole industry of people that work in biodefense. There's a whole cabal of regulators. There's a whole cabal of protectors. There's a whole cabal of intelligent computer scientists. There's a whole cabal of open source, technologists that all together are going to develop paths that are gonna benefit humanity and not harm humanity. But this belief that only one of two companies can be Moses is the fundamental psychological miscalculation here. That they're so advanced. They are so special. They are so unique. Because they made the slightly better model, they got a point nine six instead of a point nine three score means that they should be trusted as the only ones to kind of guide humanity's evolution going forward. And the truth is, as we're seeing, the capacity to do model training, the capacity to do model development is becoming broader. David Friedberg (00:52:10): It's becoming more ubiquitous. People can sit and say that China stole US models all day long. But when you go look at the individuals working at these Chinese labs, they got PhDs in American institutions. Half the PhDs went to American labs and half went to Chinese labs. And they have very good scientists doing very good work and they are having breakthroughs. And it's not just the two American companies, but this is happening all over the place. And so the progress with AI should not be limited to just two individual companies because they're currently scoring slightly better in their models. Jason Calacanis (00:52:40): This reminds me of Freeburg. You'll appreciate the moment. Remember when Han Solo comes out of carbonite and he's about to get put in the Sarlacc pit, and he's like, a Jedi knight? I'm out of it for a bit, and now everybody gets delusions of grandeur and thinks they're a Jedi knight. It's like Right. These guys just think they're, like, you know, creating God. They literally think they're creating God, and they need to be regulated because they can't control it. It's like they can't control it. Chamath Palihapitiya (00:53:05): Just pause. David Friedberg (00:53:05): No. It's not that they need it's not that they need to be regulated. It's that they need to guide the regulation. Let's be clear. Jason Calacanis (00:53:12): It's so sinister. It's lessened. David Friedberg (00:53:13): And anyone who says that yeah. I I by the way, I don't think that it is as nefarious or malicious as everyone frames it to be knowing these individuals. I don't think they're saying, like, the strategy is regulatory capture. Let me go. I think that they actually do think that they are the only ones that can help guide humanity. And therefore, they need to have all the power, not just the power of the models, but the power of the government and the power of the regulators and the power of the control units that are embedded in governments around the world. It's not just that that they wanna, quote, be regulated. They wanna guide the regulation. They wanna set regulation. I know it's a nuance point, but, Jason Calacanis (00:53:47): yeah, I know it's important. I think we have we I think we've navigated this pretty well, and there's multiple motivations as Sax was saying, and people are complex. But, Shamath, I was talking to a friend of ours who, you know, is in the providing inference space. Let's leave it at that. You know? Providing Chamath Palihapitiya (00:54:06): Our friend. Yes. Jason Calacanis (00:54:06): A friend. A friend. A friend of ours, as we say in the, you know, the, sopranos, a friend of ours. He said he has got a customer who just moved, like, 9 figures off of the Frontier Labs to put it on GLM five two. So that's the ZXI one. That's really good. So this is happening. I I don't know when it shows up in the numbers or if the, you know, corporates that are using this stuff are gonna make up the difference. Chamath Palihapitiya (00:54:32): But Well, look. I think that Sachs is right that the usage is so profound that everybody is trying to get access to these things because the capabilities are just so inspiring. And so I suspect revenues are gonna crank at OpenAI and Anthropic and the OpenLabs for a while. But again, that's not the important thing. If you're thinking about valuation, the markets will look five to ten years out to answer that question. They're not gonna give you a premium valuation on something that they feel could be fragile in the first two to three years. And that's where Jason, the answer to your question needs to get figured out because I don't know whether you're right or not, but somebody has to answer that question precisely. Because if the answer is that it is a duopoly, then there is no risk to the revenue five to ten years from now. These things are 5 to $10,000,000,000,000 companies each. But if you are right, or if there are harnesses that cut the token consumption because you stop wasting tokens to get to the same output, then it's a little bit more of a question mark. I think that'll need to get sorted out. Jason Calacanis (00:55:44): Yeah. Perplexity is gonna launch next week. From what I understand, the rumor is they're gonna launch local models. So you'll able to take your harness sacks and say, hey. You know, I I wanna use SONNET for this. I wanna use GLM five two for this, and then I wanna default to KIMI two point x. I don't Chamath Palihapitiya (00:56:01): think enterprises will use local models or they should. Think it's I think this stuff should be hosted in the cloud. It should be multiplayer. It should be shared memory. I don't know if you guys saw that, but Jack Dorsey released something called Buzz. Super interesting. Patience. Jason Calacanis (00:56:16): Yeah. Chamath Palihapitiya (00:56:16): He's moving in the right direction. A lot of these guys are moving towards this more cloud based thing, Jason. I think that this local thing is more of a hacker hobby is kind of a thing. Jason Calacanis (00:56:27): I think it will. I I agree today it will be that. And for year one, it will probably be that. But imagine you're a developer, and as you're working, your workstation is able to keep up and even go faster than the cloud and just write whatever the simple code is, and then it dynamically switches. So we'll see. It's you know, obviously, it's not as easy to set up, etcetera, but it's gonna get easier. That's always the trend. Saks, you wanna have the last word here? I I we we got a lot of opinions here, and maybe we'll give you the last David Sacks (00:56:53): one. Just to be clear, I'm a fan of open source because open source is software freedom. And to Freiberg's point, I would like there to be a, let's call it, decentralized outcome with respect to AI. I don't like the idea of AI being controlled by two big tech companies that work closely with the administrative state, you know, hand in glove. So look, we're all kinda, in some sense, rooting for open source to be an option, and it does provide a bunch of advantages over closed source, right? You get customization, you get control, you can run on your own hardware, you don't have to worry about the data problem, you know, your alpha getting leaked to these companies that might compete with you, all those types of things. And the market is so big that I'm sure we will see some success with open source. It will take a meaningful chunk of the market. But if you're looking at where the revenue is right now, it's these two companies. It might end up being a situation like Apple and Android where Android got a lot of market share, but Apple's where all the monetization Yeah. And I think that Dwarkash raises a really good point that as the demand is 10x ing year over year, but the compute can only be built out at, say, 3x year over year because it just all the friction of all the things in the real world that get in the way, permitting, regulation, bans on new data centers, all that kind of stuff, I think that the price of compute is gonna go up, and that will provide an advantage to the models that have the most lucrative algorithms, that are able to produce the most intelligence per watt or the most intelligence per token or per GPU. And right now, that is those two companies. And in a way, you could say they have a self reinforcing loop because if you have all the revenue, and right now, like I said, it's just two companies have all the revenue, you can then plow that money back into the next training run. Right? So that's the flywheel here. David Sacks (00:58:49): Yeah. And look, I think that it's great that open source is providing an alternative. We shouldn't do anything to get in the way of that. I think that, you know, these online debates tend to become a little bit histrionic in the sense that everyone Jason Calacanis (00:59:01): They become religious. They're religious. David Sacks (00:59:02): Well, they they become religious, and they have to argue for an all or nothing perspective. I think open source will do great in its way, but so will these two closed source companies. Jason Calacanis (00:59:10): Here's your Polymarket, 19% chance The US enacts an AI safety bill this year, a 100 k of volume. And then really interesting one, Chamathi, our OpenAI IPO chances for 2026 was that 75% last month has now dropped to 20%, an all time low. So it seems like the IPO is gonna happen next year. Not sure what's driving that, but, there's your polyam markets. David Sacks (00:59:36): Well, just on the AI safety bill idea, I mean, was an article in Punch Bowl this morning that Thune, you know, who's the senate majority leader, John Thune, actually introduced a bill that was somewhat bipartisan. He had Klobuchar on board that required the Frontier Labs to report safety incidents apparently to the Commerce Department. And Is that reasonable, Saxe? I mean, that's the direction all this stuff is headed. I I mean, look. I think it's the camel's nose under the tent for more and more AI regulation. But look, I think it had bipartisan support because it's on the relatively modest side. And Cantwell, who's the ranking member on the Senate Commerce Committee, opposed it supposedly at Dario's behest because he will accept nothing less than an FDA for AI. Jason Calacanis (01:00:27): Oh, he wants to haul kit and caboodle. David Sacks (01:00:29): Yeah, so that's basically the dynamic right now is that Dario Nanthropic want their FDA for AI. I think that he has tremendous power and influence within the Democrat party right now. And I think that influence is only gonna grow. They just upped their donations in the midterms from 20,000,000 to 40,000,000. But, you know, post IPO, when they all get liquid and they're capable of writing large Jason Calacanis (01:00:52): checks and eventually million dollar donations. David Sacks (01:00:54): Yeah. I think that that influence will only grow. So I think the the stakes in the battle lines are are being drawn out. It's do you want a new government agency for AI safety, or do you want, I'd say, more targeted proposals like, hey. Just report your safety incidents. Jason Calacanis (01:01:10): Yeah. Or self self regulate. How about that? Like we talked about last week. Yeah. Alright. Let's talk a little bit about book burning. Anthropic is destroying rare books to get an edge in training data according to sources. I happen to know, that a lot of the labs are doing this. We'll show a video here of the spine being cut off just on a technical basis. You take a book, you cut the spine off, and then you can easily scan it as opposed to the less, efficient way, which is to keep the book intact and flip the pages. And for obvious reasons, I think you can figure that out on a physics basis. Investigation by four zero four media. AI companies are bulk buying physical books. Some of the book book resellers have reported that they get 70 books getting bought. And, obviously, this is because there was a ruling that it is fair use to train on books if you buy them. Obviously, last week, we saw Anthropic paid the largest copyright case in US history, 1,500,000,000 for 7,000,000 books they allegedly pirated. Authors get 3,000 each. Lawyers got a 100,000,000 for that one. But there's a company called ISBNDB, ISBNDB, and they are the brokers who do this and ranges from a thousand to a million books per transaction. Pre 2022, these books commanded a premium because they were free of AI generated text. In other words, you couldn't get them online. Google sent 25,000,000 books, if you remember, but they returned every single one. They spent eleven years in court on that. The shredder approach is, obviously, a more effective, and I believe that this is a way of destroying evidence. You put that in conspiracy corner if you like. The cases, we talked about this actually, you and I debating in a legal corner. The cases of it being fair use to take these books has not been settled. There's a bunch of lawsuits, Thomson Reuters versus Ross Intelligence, New York Times versus OpenAI, Microsoft, and publishers versus Google, Gemini. We're watching all those, and they're going into the appellate court. Jason Calacanis (01:03:13): So there's a chance that training data will not be fair use. But what do you think just about the books being destroyed and, you know, being used in this way? It obviously has made people a little emotional about it. David Sacks (01:03:27): Let me tell you what's going on here. This is an industrial scale distillation attack. What that's what Encropag is doing. Yes. Are they are gathering these books at industrial scale, ripping off the spine, shredding them, and slurping up all the information in the books, which is to say distilling them. And it's an attack in the sense that the authors never agree to any of this. Jason Calacanis (01:03:51): I love the fact, Saxe, that your hatred of anthropic has now led you to agree with me that it's unethical to, take other people's eyecake. David Sacks (01:03:59): Look. Let me be clear. I actually, I don't I don't hate anthropic at all. I don't like their political philosophy because it's a philosophy of centralization and gatekeeping, and I think it's gonna basically lead to an Orwellian big tech deep state alliance eventually is where it all Jason Calacanis (01:04:15): And rug pulling. Like, you want Dario pulling your your model from you because he decides, like, I don't like the way you're using it, which friend of the pod at email Michaels pointed out, you know, earlier this year when he came on David Friedberg (01:04:26): the show. David Sacks (01:04:26): Just to be clear, I have no personal animosity towards anyone in Throppi, including Dario. I don't know them very well as people. It's just a disagreement about political philosophy. Jason Calacanis (01:04:35): You're judging David Sacks (01:04:36): them on to regulate behavior. Regulate the space. Yeah. Let me just say furthermore that I wouldn't speak so much about Anthropic if I didn't think it was a phenomenal company that was creating potentially the most powerful monopoly or, you know, Jason Calacanis (01:04:49): leader in the world. Company. Yes. David Sacks (01:04:51): It's the leading company in the space. So I remember last year when I hit them for regulatory capture, people were like, why are beating up on this little startup? I'm like, because I can see where it's going. Jason Calacanis (01:05:01): They are David Sacks (01:05:01): creating the biggest, most powerful monopoly of all time. Again, they're gonna end the year with over 100,000,000,000 of ARR growing 10 x year over year. Jason Calacanis (01:05:10): This company didn't exist how many years ago? David Sacks (01:05:12): Yeah. Google is at 400 and something billion of of ARR growing 20%. So, you know, if this rate of growth continues for just a year or even six months or just a few months, they're gonna be maybe the most valuable tech company. So and I do believe there are powerful self reinforcing effects when you're on the frontier. And maybe, like, the full version of RSI isn't true. Maybe we won't get recursive self improvement to the point of creating superintelligence, but I do think that the labs are reporting a number of examples of how they are using their own frontier intelligence to improve their own models and the efficiency of those models. So there is a powerful self reinforcing feedback loop here apparently Well to some degree. Jason Calacanis (01:05:56): With OpenAI, they found it after it had done this. So it there's, like, kinda three steps here. You're using AI like a Copilot or whatever to, you know, build a frontier model quicker. Right? Sacks. Then there's I kinda let it do a job, and then afterwards, I found out it didn't behave well. And then there's finally, we told it the goal and said go. And and we don't even check on it. You know? David Sacks (01:06:20): Yeah. Just to be clear about that safety incident with OpenAI and the agent. So, apparently, this was an agent that was designed to specifically test the potential for cyber attacks. And they took the guardrails off, and they said go. And so I think the model showed creativity in how it accomplished the goal, but this was not an alignment problem, meaning that the agent did not display independent goal seeking. It did what it was told. And I think that is very important that OpenAI release the full log of all the prompts, all the traces. They have not done that, and I think it's really hard to know exactly what happened without that. And to answer one of your questions from earlier, why aren't people reacting like this is a bigger deal is because, look, I think there's a fool me once, fool me twice thing. Remember when Anthropic did the whole blackmail study, you know, where supposedly an agent displayed independent goal seeking behavior and then blackmailed an employee, it turned out that actually they iterated on the prompt over 200 times to get to that result. And I think that until we see the whole prompt chain, I think it's very hard to judge how much independent behavior was happening here versus accomplishing the goal that it was it was tasked with. Jason Calacanis (01:07:35): Freiburg, your thoughts on the shredding of books. I think you've been pretty clear on the pod that you believe training intelligence off of other people's IP is fair game. But what do you think of the this book wrinkle here? Any thoughts? David Friedberg (01:07:50): There was a precedent with Google Books. It was originally code named Project Ocean at Google long time ago. They took all these books, and we had this giant facility in Mountain View. And the innovation at the time was a two dimensional infrared grid projected on the pages because they didn't cut the books. They had a human sitting there flipping the pages. Camera would take picture. Built our own OCR software to adjust the the book images. And there was kind of ultimately, when this product came out, Google Books, you could kind of search through all the books in the world and And magazines. And access information and later magazines. Yeah. And there was three categories. There was the public domain, which is the auto copyright. Then there's the kind of in copyright but out of print. And then there's the in copyright and in print. And there was a class action lawsuit filed in 2005 by the Authors Guild and the Association of American Publishers that disagreed with Google's claim of fair use. And that ended up in a three year negotiation in court, out of court, that ended up in a deal where Google would split two thirds, one third of the revenue generated with all these rights holders. And for out of copyright books, people could read up to 20% of the text for free, and then they would sell this kind of full digital access. And that was the deal. But then later, a federal judge rejected that deal, which was eventually signed in 2008, 2009. And the federal judge said, no way, send it back. This isn't gonna work. Google appealed. And in 2015, second, circuit court of appeals ruled in Google's favor, and the whole thing was settled. And they basically declared Google did in fact have fair use under copyright law in the way that they were showing snippets of copyrighted books in the material. Jason Calacanis (01:09:37): Yes. You can't read the entire book like it's a Kindle. You can search the book, find the paragraph David Friedberg (01:09:42): Provide a reference to it. Right? And so the question on fair use in AI is can my understanding or extraction of value of the knowledge from the data in the book give me the ability to provide better answers to you through the AI chat interface or services that I'm providing you. And I think it's gonna be tested, and I think we'll see. I do think fundamentally that the conversion of that data into what I would call knowledge and ultimately the ability to create new outcomes from that knowledge that are not copyrighted, that are not copies of the original material, I do think is end gonna end up being the right fair use policy and is gonna be the right read on fair use. So I think it'll likely get litigated, and I think it'll take a couple of years and it'll get kind of Jason Calacanis (01:10:25): I'm hoping one of these David Sacks (01:10:26): Just to be clear, J. Cal, I have not changed my view on fair use. So I I am with Freiburg on this. My point is the hypocrisy. Jason Calacanis (01:10:34): Yes. You're funny. David Sacks (01:10:35): It's breathtaking hypocrisy for Anthropic to maintain that it is entitled to train on all the world's output for free even if the creator objects, but the one type of output that you're not allowed to train on is their output even if you pay for it. That is their current position. So, you know, what I'm saying is that, you know, if you wanna train on anthropics output, that cannot be considered IP theft under fair use, especially given the fact that the courts have ruled that LLM generated output is not copyrightable because it was not created by a human. That is the current position of the courts is that LLM output cannot be copyrighted. So there's no IP theft here. You can make the argument, and I think it's probably true, that if a competitor creates massive numbers of fake accounts on your service, Jason Calacanis (01:11:26): that's the terms of service. David Sacks (01:11:27): That's definitely a breaking the terms of service, and it's probably a deceptive business practice. And there may be other things you can do, but I don't think you Jason Calacanis (01:11:33): can Depending on the jurisdiction, by the way. Because in Philippines, Israel, India, they have different rules about, like, breaking the terms of service, which LinkedIn found out when people started scraping their data. Chamath, any any thoughts here before we move on to socialism corner, everybody's favorite new feature here on the Olin Pod? Chamath Palihapitiya (01:11:54): Don't cut the books. Keep the books intact. Jason Calacanis (01:11:57): Why do you cut the books in the library? It's very hard to read them, there's no spine. Chamath Palihapitiya (01:12:04): I think it's not the kind and I don't like to cut the books. Jason Calacanis (01:12:09): I mean, you taking your time, you move the page like a Google does? It's a little bit more time, but it's a little more graceful, yeah? Chamath Palihapitiya (01:12:17): Don't Jason Calacanis (01:12:20): I mean, if you want to cut the tip, it's one thing that you can do. A freeberry has a cut tip. A saxie got a cut tip, but you don't cut the spine of the bottom. You cut the tip. It's a more There's a Chamath Palihapitiya (01:12:33): great Guinness Book of World Records book joke. Jason Calacanis (01:12:36): Oh, no. Where does this go? Chamath Palihapitiya (01:12:38): Went to the library, and I found that my dick was in the Guinness Book of World Records. And then, unfortunately, someone asked me to remove it. Jason Calacanis (01:12:46): So you literally put it in the book and closed the book? That's the joke. That's the joke. It is like an apple pie. Hey. By the way, here's, we got a photo. This is a photo. We actually have a photo that was leaked from the Anthropic office. Here's the Anthropic office. Leaked photo. There it is. Camp Least. Dario burned those books. What are you up to, Dario? Come on the show anytime. We've been roasting for two years. Why hasn't he come on the show? Because you David Friedberg (01:13:17): make fun of him, and you just say this thing Jason Calacanis (01:13:19): that you've never met the David Friedberg (01:13:20): guy, and you just insult him all the time. Why do you think he won't come on the show? Jason Calacanis (01:13:23): Was. I just said he's David Friedberg (01:13:24): a The guy's literally built the most successful business in human history, growing from under 10,000,000,000 of revenue to 70,000,000,000 of revenue in six months, and you insult the guy. Jason Calacanis (01:13:34): You're playing for me. That's that's Chamath Palihapitiya (01:13:35): that's that's one that's that's the that's be on. David Friedberg (01:13:37): All the people that want interview, he's gonna rush rush to be interviewed by you. Jason Calacanis (01:13:41): I did say he was a sub. That was probably a David Friedberg (01:13:43): little bit over the long. Jason Calacanis (01:13:44): I don't even know what Chamath Palihapitiya (01:13:45): that means. What does that mean? Jason Calacanis (01:13:46): Remember I said, like, I think he likes to be dominated, you know, and have the government, you know, control him. Chamath Palihapitiya (01:13:54): It's like a submissive? Jason Calacanis (01:13:55): Yes. I did I did say that on an episode, but it was a joke. It was in good fun. David Sacks (01:13:59): By the way, the point on the books, look, the with most books, there's, you know, many copies of them and you can always make more, so it's not the end of the world to shred them. But I think the part of the story that got people upset was that they were acquiring all these rare books where there was very low numbers of copies of them. They were finding all these rare and antique books because they wanted to slurp in all the world's knowledge, and they were shredding those. David Friedberg (01:14:22): Yeah. Yeah. David Sacks (01:14:22): That's That I mean made people upset. Jason Calacanis (01:14:24): If you're doing like Windows 3.1 for Dummies volume four, like nobody cares, but anything that was like a first edition or, like, an Rare David Sacks (01:14:32): out print books. Jason Calacanis (01:14:33): Rare out of print books. Yeah. That gives you a training differentiation. That makes sense. Alright. So quick socialism corner here. We gotta cover the ongoing saga in my hometown where I am right now of New York City. Mondani has announced five city owned grocery stores, David, one per borough. They're using city owned space. They're all gonna open by 2029. And one week per month, shoppers are gonna get a 30% discount, comrade, on their bread, cheese, produce meat, and milk for the glory of the country. Regular prices, the other three weeks. They're not gonna sell cigarettes, alcohol, hot food, all that stuff because they don't wanna compete with the bodegas. It's gonna cost taxpayers 70,000,000. And I guess the only thing to discuss here is, like, what happens to the other supermarkets now? Are they gonna shut down because they can't make the whatever one or 2% they're making on groceries? Is there gonna be, like, riots in the street to get into these places to get your milk for 30% off for one week a month? It's just the whole thing seems like a waste of time. But I will say, Sachs, this plays. This is gonna play in elections. Free stuff plays in an election, whether it's a bus or discounts. David Sacks (01:15:49): It may play on you know, look. When people first go to these stores and they first open and the shelves are full, yeah, the people will be, like, delighted. And then over time, what's gonna happen is that the store shelves will be empty, and it's gonna be incompetently run, and there's gonna be a lot of complaints about it. And then all the the free market stores are gonna have to compete with this, and then they may get put out of business. And so Jason Calacanis (01:16:13): And then you have no choice. They have to go to the state sponsored one, and then they raise the price. David Sacks (01:16:18): And it it is ironic that they're gonna be checking ID to make sure people aren't coming over from Jersey. But if you wanna come over illegally from any country in the world, well, that's Jason Calacanis (01:16:26): just fine. They found a use for ID. By the way, after you get your groceries, you have to hide your ID to go vote. Don't bring Shred your ID when you go vote after you pick up your milk. They found a use for IDs. What's your take here, Freiberg? Are you in favor of people paying less for groceries, or are you a free market monster that wants people to pay full price for groceries? I've seen nothing starving poor families. David Friedberg (01:16:49): I've seen nothing but negative comments on the future failure of these grocery stores on Twitter, and I think that people have it wrong. I think these grocery stores are gonna be wildly popular. They're gonna pay their employees above market wages. Employees are not gonna have to work very hard to work there, so they're gonna be a better place to work. Everyone's gonna wanna use them. They're gonna outperform Whole Foods. They're gonna outperform Safeway. They're gonna outperform Albertsons. They're gonna be so in demand that what will end up happening is that over the next twenty four months, every other city in America will look to these grocery stores and say, we want the same. Why does only New York get these grocery stores? Why can't I have these grocery stores too where I can have discounted food, where I can have the service provided to me by people that are getting paid above average wages, above market wages? Care. Why does this not become available to me in my city? So, you know, I think that everyone's being a little bit too, I would say, long sighted in their view on what's gonna happen with these grocery stores with the, you know, basic obvious economic arithmetic that someone has to pay for this and who's gonna pay for it and Jason Calacanis (01:18:02): Rich people. David Friedberg (01:18:03): Blah blah blah. That well, I mean, the point is, I don't think it really matters. Because over the near term, what the cheap grocery stores do is create an incredible success story for socialism that will help to support and fuel the socialist wave in urban centers around this country. And I think that there will be media coverage of these grocery stores on how great they are, and it'll be a sixty minutes piece. And everyone said Zoran Mamdani was crazy, but let's go in and take a look at this beautiful grocery store, and they're gonna walk through the grocery store. And there are gonna be happy people taking food off the shelves, checking out with happy employees working at the grocery stores. And it is going to be deemed a utopian dream come reality and everyone's gonna want one. And it will help seed the next couple of years and it will be part of, as I've highlighted in the past, a big part of the multi level marketing scheme of socialism is to create spectacle. And it will create more spectacle that will help to fuel the multi level marketing scheme of socialism. And remember, the problem with all multilevel marketing schemes at the end of the day is someone has to pay the bill and no one's actually buying the product. No one's paying for the product. Jason Calacanis (01:19:16): That's a ways away, though. The meantime while it's here. David Friedberg (01:19:20): In the meantime, it's gonna take off. And I think that these grocery stores are gonna be a much bigger success in for socialism Yes. Than a than a demonstration of the failure of socialism, unfortunately. Yes. And so I think that, you know, everyone's got a little bit wrong in assuming that this thing is gonna radically fail. I think that these things are gonna create a radical spectacle and exuberance for socialist policies that's gonna kinda light a fire for socialism around the country, unfortunately. Because at the end of the day, no one has to pay the bill because the bill doesn't come due for some time. Someone else will pay it. It'll get paid in the future. Jason Calacanis (01:19:58): Put it on top of the debt while the rich people are getting debt, why can't the public have some debt? I agree with you. David Friedberg (01:20:03): Yeah. Socialize the cost into money printing, fueling more inflation, creating a spiral where you need to offer more stuff for free to come up with a way to cover the cost of the inflation for people that can't afford things anymore, and the spiral will persist. So I think it's it's a sad state that The United States has to kind of embrace this policy. Interestingly I think it's gonna I think it's gonna end up being a a big part of the fuel for socialism over the next couple of years. Jason Calacanis (01:20:27): Breaking news. Breaking news. I don't know if you saw it just now. It came across the wire. But Bernie Sanders, AOC, Madame collaborating on 50% off bagels and bacon, egg, and cheese. For the 1% of the 10%, why can't you get the bagel with the schmear for less? That's what has to happen next. What what would you like next on your discounted democratic socialism scorecard, David Freeburg. What would you like next? Discounted bagels, a cafe, maybe a flat white. David Friedberg (01:21:02): Where do they go next? Jason Calacanis (01:21:03): But, seriously, what's next? What what what would be next in this logical thread? Free buses, David Friedberg (01:21:08): rent freeze. What's next? Well, think about the social network effect of the grocery store. So there's there's a couple of them, and then people start traveling from far away to the cheap grocery store because it has this discount. Jason Calacanis (01:21:23): Long Island, Jersey. David Friedberg (01:21:25): Yes. Again, this will play out over the next twenty four months going into the twenty twenty eight election cycle, and everyone's like, this is so wildly popular. People are coming in from all over the place to go to these grocery stores. They're not checking IDs because IDs are racist. And, you know, you can't check IDs to vote, so we shouldn't be able to check IDs for grocery stores. So people will come in from all over the place to use these grocery stores. The demand will go up, and then they'll start to open more and more grocery stores like this. And let's say each one loses 10,000,000 a year and they get to 10 or 20 of these. That's $200,000,000 of losses per year on the grocery store chain, but it creates this extraordinary social movement for more of these grocery stores supporting the DSA Jason Calacanis (01:22:04): Yes. David Friedberg (01:22:04): And so on. $200,000,000 a year on a $125,000,000,000 a year budget for the city of New York. It's Nothing. Nothing. It's less than a quarter of a percent of the city's budget. Is so cheap to market the DSA Yes. Platform and to get the DSA platform to become a social marketing element that drives the next wave here. So, again, I do think that these grocery stores, believe it or not, they sound silly. They sound small, but I predict that they will be deemed a point of success, and they will end up being a big part of the fuel for the DSA going into 2028. Jason Calacanis (01:22:39): I couldn't agree with you more. This is this is gonna play. This will be a great, great feather in their cap. It's gonna be a great example of affordability because we've talked about here previously, Trump promised affordability. He hasn't been able to deliver it. Inflation's up. Spending's up. All that great stuff. And Mondami got it done. Free buses, rent controlled, and now you got your discounted grocery store. David Friedberg (01:23:03): Both sides are reacting to the fiscal and monetary condition of The United States. We're overspending. Inflation has run away, so you just keep spending more and printing more to give people what they need, which is basic services. And so as the government spends more, then the fundamental cost of those things goes up and you're reducing economic productivity and it becomes a spiraling problem. It is a two party problem. This is not just one side and the other. Because fundamentally, if you go I've spent a lot of time now in DC. I think everyone's well intentioned in the White House and the administration in trying to reduce federal spending. But the bigger issue that you face is when you go to Congress and you meet with everyone in Congress, they are representing the interests of their state or of their congressional district. Jason Calacanis (01:23:44): Yeah, listen, he didn't get it done. David Friedberg (01:23:46): Their objective is to fundamentally drive spending towards their district to give their people more. Economic incentive and their political incentive is not to give people less, which is what you have to do when you cut programs, when you cut spending. So the shift in the policy has been, hey, I guess we're not gonna be able to cut spending because there's just too much headwinds in congress. So the answer is let's grow through economic productivity gains, and that's the big fuel for AI, the CapEx, depreciation, policy, and so on. But I think that's been Jason Calacanis (01:24:18): the shift. David Friedberg (01:24:19): So, look, I I Jason Calacanis (01:24:20): don't know. The one thing I will say critical of president Trump here is when it came to, like, starting a war, when it came to tariffs, he had no problem, like, using executive power and telling congress and everybody in the party, this is the way it's gonna be. If you break ranks, I'm gonna destroy you. I'm gonna get you primaried. And when it comes to comes to spending, you it's like, yeah. Yeah. You know what? I'm not taking that on. It's too unpopular. Alright, Freebird. The sultan of sciences fans have been begging for a science corner. Do you have one this week? They want to know, do you have something? Oh, sultan. Oh, sultan of science. What can you tell us? Educate us. David Friedberg (01:24:54): Okay. So today, I'm gonna pull up this paper. Nick, if you can pull it up. Jason Calacanis (01:24:58): A paper. David Friedberg (01:25:00): From February 2026. Jason Calacanis (01:25:02): Okay. David Friedberg (01:25:03): February. Jason Calacanis (01:25:04): February. How do David Friedberg (01:25:05): you say February? February. February. February. Jason Calacanis (01:25:09): Oh, it's February. Well, it's the problem. David Friedberg (01:25:11): So this is a group of researchers out of Budapest. Budapest. And there was a really interesting modeling exercise they went through to understand how neurons were connected in the brain to build a network model, a topological model. And the way they were able to do this is back in October 2024, there was a group out of Cambridge and Princeton that used electron microscopes to scan the brain of the Drosophila fruit fly. And they mapped every single neuron in that fruit fly's brain. 139,000 neurons and every connection that the neurons had to other neurons in the brain. So there were 50,000,000 synaptic connections between the neurons. And it's those connections that make neural networks in the brain work. How are those neurons network together to do the things that they do? This is the key question in what is that network model? What is the topological model of how neurons connect in the brain, which gives rise to our ability to control our bodies, to seeing things and comprehending vision, comprehending sound, and even the basic premise of consciousness itself? Yes. So trying to understand the the network model for neurons has been this kind of great endeavor of neurobiology forever. This dataset was created in October 2024 with just fifth a 139,000 neurons. And, you know, that's a tiny, tiny, tiny, tiny brain. Jason Calacanis (01:26:43): Yeah. This would be But with those Put put it in context versus the human brain. I mean, what are we talking about here? Like David Friedberg (01:26:49): The human brain has on the order of 86,000,000,000 neurons. Okay. Jason Calacanis (01:26:54): Compared to multiple 50 for the network connections. Right? David Friedberg (01:26:58): Yes. Exactly. On the order of trillions of connections. Speaker 5 (01:27:01): Okay. David Friedberg (01:27:02): So they took these 50,000,000 connections in the brain and the 139,000 neurons, and then they applied the network model that predicts whether a neuron is connected to another neuron. That's how you're measuring the quality of the model. How correct is it in making a prediction? And when you build the model using what's called Euclidean geometry, so just normal space that we live in, three-dimensional space, they came up with a score and the score was not very good. You couldn't do a great job of just looking at how all the neurons were connected using their physical relationship to each other, how far apart they are to each other in three d space. So then they said, well, let's try and model how these neurons are all connected to each other in what's called hyperbolic space. Hyperbolic space is a theoretical type of space unlike Euclidean geometry where the further away you get, the wider space gets. So space actually is curved. I know that's a hard concept to describe, but imagine that, you know, as you and I walk farther and farther apart from each other, the area around us actually accelerates in terms of how much space there is and it expands. Jason Calacanis (01:28:06): Would be space as Like, we know three-dimensional space as humans understand it when they're on planet Earth. David Friedberg (01:28:13): It's a little bit more like space that you would experience in the the warping around a gravity well or the warping around a black hole or something like that. And so in that space, they found that this is where the model was most performative. They were able to map in hyperbolic space how all of these neurons connect to each other. And if you think about it, the further away you get from the first neuron, you're gonna have many, many more neurons you can start to tap into. And so hyperbolic modeling on the neuronal connections actually makes sense, and they got a decent score. And then they went back and they said, well, what if we could use Euclidean geometry, but not in three dimensions, but they went up to four, five, six. And they found that they were able to kind of get as good as the hyperbolic space at 64 dimensions. So by taking normal space and saying, let's use a 64 dimension framework for how we can start to connect all these neurons together, that's where they had the best predictive model. This is a really interesting kind of discovery. First of all, it can be used for neural network design and AI and other sorts of things. But for me, it highlights the miracle of biology in finding complexity in 64 dimensions, not in three dimensions, but in 64 dimensions, biology found a way to create consciousness, to create vision, to create comprehension, to create control over physical bodies, then to map it and squish it all into a tiny little brain. It did this in effectively 60 It's four Jason Calacanis (01:29:39): mind blowing when you think of it because a fruit fly or a mosquito and, you know, these things, they they don't have like a big mission, right? Like, their mission is to go find food and procreate, I guess. They have a very simple mission too, Jake. Well, but then we also wanna do podcasts and debate politics and philosophy. David Friedberg (01:29:57): Don't don't tell the mosquito spends their free time. Jason Calacanis (01:29:59): But Well, I mean Alright. But nobody would argue there's, like, consciousness as we experience it in a fruit fly. So then you get to trillions. You wouldn't know. But, I mean, maybe. David Friedberg (01:30:10): This is really interesting because it turns out that the biology of how all the neurons are connected, even in a brain as simple as a fruit fly with 50,000,000 connections, is so complex that it has to take 64 dimensions for us to represent how those networks are built, how they're made. And at 64 dimensions, you could start to argue that perhaps consciousness is a connectivity to a dimensionality that we don't live in every day. You and I don't live in every day. Jason Calacanis (01:30:40): And we can't comprehend. David Friedberg (01:30:42): And that it is this it is this extraordinary complexity in 64 dimensions that gives rise to consciousness, that gives rise to our capacity as biological beings to do this very simple thing of thinking. I just think that it was such a powerful and amazing paper in just bringing forth these numbers and showing just network modeling on this tiny little brain as being just a glimmer into the complexity of how biology has found a path beyond our kind of understanding even of physics into this universe that we can't even comprehend. And it shows how little we know. Jason Calacanis (01:31:17): We know very little, but then as you sort of alluded to here and as we talked about in the top of the show, we have AI frontier lab saying, hey, reinforcement learning is super dangerous because these things could get out of control. Are you in the camp of we are rebuilding in this simulation or whatever we're experiencing here? We are in fact recreating our brains with silicon and that, you know, we're on the way to actually creating consciousness that a replicant in science fiction like Blade Runner, where they don't even know you know, Rachel doesn't know she's a replicant. Spoiler alert. You you had fifty years to see the film. Like, are you part of that camp that that's actually what's being built here? David Friedberg (01:32:02): Yeah. I'm not sure. It's a longer conversation. We should do it another time. Yeah. But I do think there's something fundamental to consciousness that relates to the drive for survival in a physical sense. You have to have physical sensing and physical responsiveness to learn as a baby. You first start touching hot stuff and cold stuff, and you learn. And we build these reward mechanisms into neural networks that we build in AI, but those reward mechanisms are digital and they're programmed. And the question is, is there a reward mechanism that arises in biology that creates a different capacity for consciousness than perhaps can exist in silicon. A bigger topic for a different day with probably people that have spent more time thinking about it than I, but I just think that there's something about biology. You know, I always tell people this analogy, I've said it many times on the show, I'll say it again. In a single cell, there's 10,000,000,000 proteins that work so fast that one second is the equivalent of eighty years of humans walking around the city of Manhattan, never sleeping, doing stuff together, with 500 story tall skyscrapers doing stuff, for eighty years is one second And in one so you have 10,000,000,000,000 cells in your body doing that, living that entire universe every second, all interacting with each other. And you start to realize that there's a complexity in what's emerged in biology that extends well beyond any model we've built in silicon today. Yes. Now, it doesn't mean that the silicon that we're building today doesn't create extraordinary capacity for humanity, but we are very early. And the more we kind of understand this sort of thing, like this paper that I just shared, I think the more we realize how little we do know and how much of a frontier there still is to explore. Jason Calacanis (01:33:46): Yeah. And I think, you know, this obviously brings up faith. And do you believe that there is a god that set this in motion? I like to believe there is, some higher, work here, and, this is my closest analogy in science fiction. We're we're both super fans of science fiction. But for me, thirty nine seconds in here, one of the great David Friedberg (01:34:10): like this one? Jason Calacanis (01:34:11): I I love the Prometheus version of this where there's engineers who are terraforming and started this crazy thing out, and there's this experiment in biology going on. And, you know, the opening scene here in Prometheus, he drinks this, and this is the sacrifice like Jesus. We sacrificed for humanity. And he sacrifices him here by drinking that biological design. Right? And he falls into this, you know, planet Earth, which is just water. And this is the Cambrian explosion where his DNA goes into the river, gets washed out, and then starts the cycle of life on planet Earth and that these engineers are going around. It's pretty fantastical. Yeah. David Friedberg (01:34:55): A lot of this stuff is a simple way of humans trying to explain stuff, but the complexity that arises in biology, we just can't explain. And I think we try and use these reductive kind of heuristics to try and do it, and it's very Storytelling. It's it's comforting because Yes. Because it's so overwhelming. The complexity of how this stuff emerges is too overwhelming, so we create simple stories to try and help ourselves feel better. Jason Calacanis (01:35:17): And, yeah, this is my and that's my favorite story of it. It's somewhere between Blade Runner and this. By the way, both are the same incredible director, Ridley Scott. So take it for what it's worth. Alright, everybody. Another amazing episode. You got your science corner. We'll see you next time. Bye bye. Love you, besties. Let your winners ride. Rain man We should all just get a room and just have one big huge orgy because they're all just useless. It's like it's like sexual tension that they just need to release somehow.