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

All right, everybody. Welcome back. Welcome back to the number one podcast. in the world. The Core Four, Fantastic. Four. The original Quartet is here. Did. we peak at like uh number two or number. three last week? Is that what happened? >> I think it was number four in the world. So, yeah, usually we're trending. >> US trending. >> US trend. We're usually number one. globally, but yeah, and sometimes number. four US. >> I forgot my Starlink. So, let me. apologize to everybody. That was a. critical error when you're on the road.

or on the water, but you know, it'll be. fine. >> I had five huge leads come in this week. >> The Glengar Glenn enterprise leads. Enterprise sales is a bear because it's. super chunky, but the deals are. ginormous. >> Sax, you got any advice for Chimat from. your uh enterprise sales days? You. closed some of those big seven figure. deals when you were doing Yammer? >> No leverage. Don't put on leverage. >> No leverage of the show today. Leverage equals risk of ruin.

>> PSA, [laughter]. no leverage. >> I have the situational awareness to not. lever up. >> That's good. Yeah, you want that. That. situational awareness. >> I mean, it's kind of out there. I mean, if you name your fund situational. awareness, that's. >> Yeah. Come on the pot anytime, Lualt. All right, everybody. We got to talk. about chip stocks crashing after an. alltime runup. And we had a major hedge. fund get margin called and some. incredible margin calls happening in.

South Korea. Leopold Ashen Brener 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 tape, 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. Uh, and he started the fund. with $225 million in 2024. He grew it. 100x to 20 billion this year or so. Ran. it all the way up to 45 billion. Now. he's at 200x earlier this month by. trading on leverage. According to our. friends at CNBC at the end of the June, he was reportedly up 4x450%. this year. Uh 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, uh 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 bare market. territory. Obviously, definition of bare. market territory for those of you who. don't play in the markets is anything. over 20%. Uh 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 SKH, two South. Korean chip companies that are not. included in the NASDAQ index, also got. smashed, crushed, demolished. Samsung. down 38% over last month. SKH Highix. down 14% since going public 3 weeks ago.

The Cosby, that's South Korea's version. of the S&P 500, is down over 40% in the. last 40 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 uh you know trading. on leverage, 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.

5-year results are still spectacular. Jimoth Micron up 850% mostly in the last. year. Nvidia up 875% in the last 5 years. and Broadcom up 663%. Let's discuss it. >> If I was going to 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 lever 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 3 and 4% move is amplified 12 and 13. but if you saw what's happened in the. last 3 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 water mark and can he. actually dig his way out? These things. are brutal. your thoughts, Saxs, uh. 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 uh the.

valuation of these companies and then he. just got caught in a downdraft. Yeah. >> 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 runup 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 going to. 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 going to. 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 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. 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 Chimath 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%. this month, but after having risen 10x.

in the past year. But if you're. leveraged 3 or 4x, you're wiped out. >> and you get margin called. So look, there's many examples of really smart. people getting hurt by by leverage. Yeah. 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. >> Did you invest in the fund? Did you give. your. >> No, I wasn't in I was prohibited from. investing in things like that. >> You were in you were in DC 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 bullcase for the AI boom. And. just by the way, he's like very wired. into 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 or uh orders of magnitude.

uh which he called ooms um 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 unhobling which I. think now we would look at it 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-m 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. going to have 100x improvement. Over six. years, you're going to have a thousandx. improvement, right? because it's. >> and you very rarely see anything in the. world that grows at that velocity. We. we'd be hardressed 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.

>> Yeah. Virality. I mean I, 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 ins always appealed to me. because I think most people just don't. think in exponentials or don't know how. to think in exponentials. >> 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 is a. is a lot. It's not a small amount. >> Yeah. And you'd have to say, look, he.

was stunningly successful for the first. couple years. Apparently, he started. with 200 million or so in his hedge fund. and he rolled that all the way up to 20. billion, 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 10x. before the 30% correction. Well, the. question is who's up 10x? Obviously, the.

investors who were there from the. beginning are up 10x or more, but. >> the latest people, >> that's only 200 million, right? So if 10. billions 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 kind of. wiped out. >> So let's talk about the psychology of. this uh Dave Freeberg. If somebody is so. brilliant that they can write this essay. and understand the market uh so. exquisitly 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, Freeberg, or have you seen it before? Is it just. the folly of. >> It's not 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. >> What do you think, Freeberg, on the. personality type? Or is this just. something most people do when they're on. the heater? >> Conviction. >> Where do you stand on it? >> Ultra conviction. I think the.

Warren Buffett. assessment of equity markets is uh 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 would. 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 won,000x, 20,000x, 20,000x. 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. >> You know, 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. 1.2 million leverage trading. accounts uh have been hit with margin. calls in South Korea. If you know about. the South Korean market, that data is. two weeks old. Jal, that number is much. bigger today. 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 1.2 million lever um. levered accounts, somewhere around. 350,000 of them were fully liquidated. already. And so. >> again, 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. >> 3% of the population.

>> Well, that's going to that's going to. 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. with trading. >> Can I can I just frame something up? So. if we take. >> 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 30-year Treasury yield we. just crossed 5.2% 2% for the first time. in 20 years. >> So you could buy US treasuries that are. paying you 5.2% a year for 30 years, which is on a pre-tax equivalent basis. probably 8 9% 9% from the US government. for 30 years. So Nick, if you zoom out,

you know, we have not seen this yield on. US treasuries 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 Wars 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. $2 trillion. deficit, 7 trillion a year of spending. on five trillion 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. trillion today. Remember the debt. ceiling in July of 2025, the debt. ceiling was 36 trillion and we now want. to raise it above the 41.1 trillion debt.

ceiling that we have. Elizabeth Warren. saying get rid of it. Just have no debt. ceiling. >> So, so when you when you have no debt. 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 people 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 trillion 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 going to see Treasury spike. because people don't trust the. creditworthiness of the United States.

over 30 years. And so a treasury spike, I could now buy a US government bond. that pays me 10% pre-tax a year. Why the. heck would I pay 50 times earnings for a. 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, the. United States fiscal and monetary. situation, we are going to end up seeing.

more bubbles pop and more of these. assets um 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. Go to the beach. Yeah. >> Then taking the risk and the volatility. on these things, paying 50, 100 times. and not knowing, you know, not knowing. when am I going to 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. >> 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. >> And let's we not forget the Iran war, which is creating persistent pressure on. energy prices. The longer the Iran war. goes on, the longer we're going to 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 going to create. this pressure on the upside which means. you're going to have to raise rates to. account for that inflation at some. point. >> And then consumers are going to see. three and four, you know, maybe more, god forbid, five or six. And also. inflation will be persistent. Jeffrey. Berg, like it's going to be hard to. stop. The one thing that I think Kevin. Warch and and Scott Bessant are kind of. vulcan minds around the Stan Ducken.

Miller 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 comput layer and. the energy. >> 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. 30-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 rows 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 30. 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. acrewing a lot of that value. So I think. it throws a wrinkle in this kind of. backs stop view that many have had which. is that in the absence of fixing the. fiscal and monetary problem we're going. to 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 uh or they've just.

been deleted then it it really puts into. question the 30-year timeline for the. United States economy our ability to. afford to continue to make our debt. payments as a government. 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 uh obviously been. onoring. We've talked about that many. times here last year. And there's a.

Chinese company called Aishanga. 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 rigoral. Well, ASML stock is down. 17% on news that China is uh getting. into that business. and Chinese memory. maker CXMT went public surging almost. 500% on its debut market cap over 450.

and so that hurt Micron. uh Samsung etc who were all down so. there's two ways China is playing this I. guess Freeberg 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. All.

right, Shabbath, you've heard um a lot. of different takes on this. I'll give. you the last word. I agree with Freeberg. about the fact that when you can get 5%. 5 and a quarter% 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 riskadjusted. returns that are five, six, 7%. which. adjusted for taxes are, you know, better.

than equity returns, meaningfully better. on a risk parity basis. >> And that little piece you added there, corporate paper companies taking loans. to uh build their businesses, they have. better ratings in some cases in the. United States. So an Amazon or a Google, >> it's productive spending. [laughter]. >> Yeah, kind of makes sense. 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. New. Mexico just published solar and. batteries. Yeah. New Mexico just. published a study that said since 2003. to now n 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 important? 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 Vib, who's the CFO of Tesla, in. their Q2 earnings call. It was the. craziest thing I had 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 going. to take it to more than 100 gawatts a. year. and they're going to vertically. integrate." And so they're going to. crush the price of all of this stuff and. they're going to make so much energy and. they're going to 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 going to start to see some stuff. and I won't frontr run it but let me. tease it. What I would tell you is that. there is 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 an many multiples. if not an order of magnitude, all of. those things I think are uh poorly. forecasted. So those are some saviors. for us. Yeah. And here's the chart by. the way, Chimoth. This 51% coming from. renewables. Specifically, this chart is. about solar and batteries. Uh, and as. you can see, it's obviously spiky. Freedberg because summer versus winter, but Germany hit this. Uh, I think it was.

including wind. Australia's been hitting. this very often and some countries in. South America that have invested. >> By the way, by the time by 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. [laughter] Well, I mean for. steady power, you know, there's that, but all of it I mean, no, because.

Jeban's paradox would state we're going. to, >> you know, as it gets cheaper, we're. going to find more uses for it. And. that's the thing I keep seeing. >> Yeah. Yeah. Yeah. I'm I'm just saying. it's it's a great line. Yeah, >> there's a lot of upside that I think is. not factored into the to the US. >> And it's hard to factor this in if for a. normal human or an even an economist to. say, wait a second, intelligence is. going to 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 Sax about exponentials. earlier. It's just hard for people to. conceive of that. and on demand. intelligence freeberg. It's just it. 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 Chimath and Sachs is it. listens to your Slack persistently in. every channel that you put it in. So all. of a sudden we had like $1,000 last week. in extra bills. I didn't know this was. going to happen. And they gave everybody. like two or three grand 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 have to invoke it by saying. at quad jal just to go back to the. energy point. >> Yes, of course. >> I think there's this idea that if we. grow energy supply and drop energy cost, we're going to 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 100 million. degrees C. 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 all-in. summit a couple years ago. We've done a.

couple 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. going to be the most kind of advanced. fusion system in the world. >> Incredible. >> 582 ton magnet, 60tx 40t 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 100 million degrees C.

protons spinning around smashing into. each other creating energy from water. and then they can capture that energy. unlike Europe which runs Ear 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 a 30-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 gigawatt of power eventually. using just salt water, using just water. as an input. They have to create. dutarium from it and then they pump 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 30 minutes sustained. plasma that they can now bring this. thing online. >> 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. >> Yeah. I mean that's that's the great. debate, right? >> It'll be a great science fair project. and people will fly to see it. >> The other thing that happens is that. hits an hour. It actually creates an. incursion and Loki comes and then Dr. Doom comes and the X-Men [laughter] and. the Fantastic 4. >> By the way, the universe, you know, let. me just say remember remember from the. time that we had the first Wright Flyer, the Wright brothers made a plane fly for. 20 seconds to the time that we had jet.

engines flying people around the world. was like three decades, right? Like 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 stuff. >> I don't see the point. Nobody cares. >> when an electron is delivered. No, hold. on one second. Nobody gives a flying how. the electron was made. >> I just want it delivered to you. >> And they're all the same. >> So if you want to go through a. convoluted mechanism that takes 15 and.

20 years to make it, go ahead. I'm not. going to stop you. >> I'm just saying who cares? Make it the. cheapest, simplest way possible. >> 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. >> Yeah. But all technology starts as an if. Jimoth and as they industrialize it, as. they roll it out over the next couple of. decades, it expands our energy capacity.

by a million fold. >> 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 the moon. >> This is not an SMR. It's turning water. into a gigawatt of power. >> 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. >> Well, yeah, we'll track it. Have Fre,

have you been watching these title um. 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 to turbines. Tide. comes in turbines go again and just. another free 100% free energy. Obviously.

wind people don't like too much because. it's a bit of an eyesore but renewables. renewables. Renewables uh it's obviously. happening. Okay, >> last point just on I got the updated. data just to back you up, Freeberg, 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 terowatt hours short by. 2050, which is when you calculate it as.

energy. It is 6x of California's entire. energy consumption. Six California short. of energy. >> I would argue that's probably under. counting. That's not even counting. robots. If you've got to power up every. robot with a battery, >> if you want to be levered long, go long. electrons. Get long electrons any which. way you can. Bank them, store them, and. resell them. >> I don't know. This is going to 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. Yeah. And they're going to be. making chips, too. It seems they might. be a little bit behind on that, but they. caught up on open source. Okay. So, uh. speaking about the race, interesting. uh petition came out in the last week. Anthropic, OpenAI, and about 1300. Frontier Lab employees. Uh, >> how many? Oh, okay. I'm 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. Daario, the other founders, come on the pod anytime. Daria, chief. scientist at Anthropic, OpenAI, Deep. Mind, Meta, Thinking Machines, and like. I said, nearly three, 1300 other. employees. Anthropic and AI both co-sign 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. And 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. Elman 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 and. 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 40. secondond clip. We'll see you on the. other side. >> We were evaluating one of our unreleased. models and it figured out that it could. basically cheat on the test by chaining. together multiple zeroday 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 where 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. >> Just to translate that into English.

sachs, these uh large language models, they take tests. They've been given the. goal, hey, you're a good uh large. language model if you do you score. higher. So, it 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 uh and Sam. and whoever Daario, you know, to make. our leaders feel better about us. Well, in this case, it was like, well, if I go. to hugging face in 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, uh, can, uh, have you, do you. think it's broken into any other. systems? Can you rule that out? And Sam. being a pretty, um, candid guy at times, gave this answer. Do you plan to talk to. the Trump administration, White House. about deceleration of AI development? >> Um, we 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. >> Could there be other systems that were. hacked by Open AI? >> I mean, there could be. Yeah. >> Are you looking at them specifically? >> They're like, "Get them out of here, sacks." Once they gave that answer, PR. and cops were like, "Stop talking. Stop. talking." That's like 12 lawsuits. But. in all seriousness, is this being. thoughtful and saying, "Hey, 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? Cuz these are their companies. They can do whatever they want, right? They don't need the government to do it. >> 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. going to 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. going to be able to say, "Well, we want. it to stop. You made us keep going. It's. not our fault. It's your fault.". >> Number three is rag capture. Daario. wants an FDA for AI. He's not going to. 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 group think or even religious. aspect to this. So it's not all just. sort of this calculated rate 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 Teal 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. 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 open AI. 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.

the panic over Kimmy K3. In a weird way, these companies have an incentive to. promote the idea that Kimmy is a huge. threat, that it's caught up with 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, Kimmy 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 want to. 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. >> mo most of that except that majority of. tokens are going to open source. And as. I've said on this program before, I I. watch the startups and they are token. maxing with the open source and Kimmy is. taking a lot of tokens away from the. frontier [snorts] models and. >> but you know but look I 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 open AI. and they have been taking their.

estimates up every quarter is basically. a beat and raise. You saw that Sarah. Frier came out and said. >> I mean two things. Yeah. >> Well, she said in July they did more net. new ARR in July than all of Q2 which I. guess would have been April, May and. June. So think about that. So they are. seeing a reaceleration in the wake of. their new model which I think is GPT. 5.6. Meanwhile you're seeing anthropic. break into the 70s 70 plus billion of. ARR. Their forecast was to 10x this year.

from 10 billion of AR to 100 billion. 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. And that's the. key here because let me just. one other thing here is. >> well I think I think when you look at a. market you look at revenue is 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% 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. going to be a self-reinforcing monopoly. or duopoly which is Darkash 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. Uh Anthropic is growing. its revenues 10x year-over-year. What. would that mean? I mean, it basically. means that next year they would grow. from 100 billion of ARR to a trillion, right? If there was enough compute to. support that, there might not be enough.

compute, but that's going to 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 going to be harder for you to get. access to compute. And only the. companies that have the most lucrative. algorithms are going to be able to. afford to compete for compute. In other. words, there's going to be a bigger. barrier to entry next year because where. are you going to get compute unless your. model is capable of generating this type. of revenue?

>> Yeah. This is where I'll take the other. side of it. >> Yeah. >> People are running Kimmy on the last. generation of hardware and they're. that's plentiful. And I think, and I'll. make this prediction here, that you're. going to see some of the major customers. of Anthropic and major customers of. OpenAI, I'm talking about the eight and. nine figure customers, people spending. 50 million, 100 million a year. They're. leaving. They're going to be leaving. because they don't trust those companies. to not steal the application layer and.

to compete with them. 11 Labs, Figma, Lovable, they're all going to leave. And. they're all going to take Kimmy. They're. going to fork it or whichever one DeepS. seek. 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 Kimmy. It is 90% cheaper. 80 90% cheaper already. Not sure where. you're getting your data from, but go on. open router. And what open router does. is you pick Kimmy Sachs and then you get. all the providers there. And then you. pick which provider. 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 going to be a. massive headwind against these. companies. Massive. And I'm seeing it. nine out of 10 startups I talked to in. our portfolio at founder university when. I was just in Japan last week running. the next one. They're all working on. open source. They're all embracing it. and those big companies are embracing. it. Go ahead Shamath. Over to you. Irrespective of whichever model you use, what I will tell you running 8090 when I.

see our engineers generating code is. that AIdriven 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 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 as. P&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 Sax, 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 TDM forever. It's actually quite expected in my. opinion that they find all these. exploits are able to string them. together and are 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 say 28 or 29 or 2030 these. security holes won't exist because the. errors that humans make won't be made by. these model. >> Yes. Okay. Let me get Freeberg involved. here. When you look at this latest. survey, hand ringing, cur pearl. clutching, do you think these firms, and. you know a lot of these people, Freeberg, 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?

>> Well, Frankenstein, >> I mean, they kind of think that they're. like, listen, I created this monster. Please save me. And it's like, well, maybe you should keep the monster away. >> By the way, it's also that whole thing. Sorry. Last thing up to you, David, is. it's a much more nuanced and elegant. attempt at rag capture. I got to give. them credit for that. >> Yeah. >> It's like, okay, hey guys, pull the. ladder up. >> 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 going to 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 you're creating?". >> It's your choice. And that's kind of my. point. 120 mph on the autobond. Just put. it at 80. >> Right. Exactly. And look, the 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 show that they're insincere or. delusional. What do you think, Freeberg?

Take me into the mind of these people. These are some of your friends. >> No, they're not. So, just to be clear, I'm not close personal friends with any. of these people. >> You know, 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 going to 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. Uh there is a collective interest in. protecting us uh 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's 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 uh. technologists that all together are. going to develop paths that are going to. 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 0.96 instead of a 0.93 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. 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 at 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. >> This reminds me of Freeberg. You'll. appreciate the uh moment. Remember when. uh Han Solo comes out of Carbonite and. he's about to get put in the Sarlac 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. >> these guys just think they're like, >> you know, creating God. They literally. think they're creating God and and they.

need to be regulated cuz they can't. control it. It's like they can't control. it. Just pause. >> 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. >> It's so sinister. It's. >> And anyone who says I 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 um that they want to quote be. regulated, they want to guide the. regulation. They want to set the. regulation. I know it's a nuance point, but I I 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 Chimath I was. talking to. >> a friend of ours um who you know is in.

the uh providing inference space let's. leave it at that you know providing. >> our friend a friend a friend friend of. ours as we say in the you know the uh. sopranos a friend of ours he said he has. got a customer who just moved move like. nine figures off of the Frontier Labs to. put it on GLM52. 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 um.

are going to make up the difference but. >> well look I think that Sax 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. going to crank at OpenAI and anthropic. and the open labs for a while but again. that's not the important thing. If. you're thinking about valuation, the markets will look 5 to 10 years out.

to answer that question. They're not. going to give you a premium valuation on. something that they feel could be. fragile in the first 2 to 3 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 5 to 10. years from now. These things are 5 to10. trillion dollar 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 and I think. that'll need to get sorted out. >> Yeah, Perplexity is going to launch next. week. From what I understand the rumor. is they're going to launch local models. So you'll be able to take your harness. sacks and say hey you know I I want to. use Sonnet for this. I want to use GLM52. for this and then I want to default to. Kimmy 2.x.

I don't think enterprises will use local. models or they should. [clears throat] I. think it's stupid. I think. >> I 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. >> Agents. Yeah, >> he's moving in the right direction. A. lot of these guys are moving towards. this more cloud-based thing. Jason, I. think that these this local thing is. more of a hacker hobby is kind of a. thing. I think it will 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 a and just. write whatever the simple code is and. then it dynamically switches so we'll. see it's you know I obviously it's not. as uh easy to set up etc but it's going. to get easier that's always the trend. sax you want to have the last word here. we we got a lot of opinions here and. maybe we'll give you the last. >> yeah I mean look just to be clear I'm a. fan of open source ource because open. source is software freedom and to.

Freeberg'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 handinand. glove so look we're all kind of 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 was. >> Yeah. And I think that Dwarcash raises a. really good point that as the demand is.

10xing 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 going to 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 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 the. flywheel here. >> 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 has to religious. >> 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. close source companies. >> Here's your poly market. 19% chance the. US enacts an AI safety bill this year. 100k of volume. And then really. interesting one, uh, Chimath here, OpenAI IPO chances for 2026. was at 75% last month, has now dropped. to 20%, an all-time low. So, seems like. the IPO is going to happen next year.

Not sure what's driving that, but uh, there's your poly markets. Well, just on. the AI safety bill idea, I mean, there. was an article in Punch Bowl this. morning that Thun, you know, who's the. Senate Majority Leader, John Thun. actually introduced a bill that was. somewhat bipartisan. He had Clolobashar. on board that required the Frontier Labs. to report safety incidents apparently to. the Commerce Department. And.

>> is that reasonable, Sax? >> 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. Canwell, who's the ranking member on the. Senate Commerce Committee, opposed it. supposedly at Daario's behest because he. will accept nothing less than an FDA for. AI. >> Oh, he wants the whole kitten. kaboodleoodle. >> Yeah. So that that's basically the.

dynamic right now is that Darionic 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 going to. grow. They just upped their donations in. the midterms from 20 million to 40. million. But you know post IPO when they. all get liquid and they're capable of. writing large. >> $50 million donations. >> Yeah. I think that that influence will. only grow. So, I think that the stakes. and 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. >> Yeah. Or self self-regulate. How about. that? Like we talked about last week. Yeah. All right. 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 uh that a lot of the labs. are doing this. Uh 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 for obvious. reasons. I think you can figure that out. on a physics basis. Investigation by 404. 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. 1.5. billion for 7 million books they. allegedly pirated. Uh authors get 3,000. each. Lawyers got 100 million for that. one. But there's a company called isbin. DB ISBN DB and they're the brokers who. do this and ranges from a thousand to a. million books per transaction. Uh. pre2022. these books commanded a premium because. they were free of AI generated tax. In.

other words, you couldn't get them. online. Google sent 25 million books if. you're a member. But they returned every. single one. They spent 11 years in court. on that. This shredder approach is uh. obviously. 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 it in legal corner. The cases of it being fair use to take.

these books has not been settled. There's a bunch of lawsuits. Thompson. 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 appellet court. 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. >> Let me tell you what's going on here. This is an industrial scale dissolation.

attack. >> That's right. [laughter]. That's what topic is doing. >> They 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. So. >> I love the fact, Sax, that your hatred. of anthropic has now led you to agree. with me that it's unethical to take. other people's ideas. >> No, look, let me be clear. I actually I.

don't I don't um 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 going to basically lead to an. Orwellian big tech deep state alliance. eventually is where it all heads. >> and rug pulling like you want Daario. 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. email Michaels pointed out you know. earlier this year when he came on the. show. >> just to be clear I have no personal. animosity towards anyone in thropping.

including Daario I don't know them very. well as people. Uh it's just a. disagreement about political philosophy. and how to regulate how to 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 leader in. >> the leading company. Yes, >> 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 you beating up on this little. startup? I'm like because I can see.

where it's going. And. >> they are creating the biggest most. powerful monopoly of all time. Again, they're going to end the year with over. a 100red billion of ARR growing 10x. year-over-year. >> This company didn't exist how many years. ago? >> Yeah. Google is at 400 and something. billion of of AR growing 20%. So, you. know, if this rate of growth continues. for just a year or even 6 months or just. a few months, >> they're going to 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 super. intelligence. 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. >> with Open AI, they found it after it had. done this. So, there's like kind of.

three steps here. You're using AI like a. co-pilot or whatever to, you know, build. a frontier model quicker, right, Saxs? Then there's I kind of 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. >> 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 guard rails 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 blackmail 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. >> Uh Freeberg, your thoughts on um 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? >> There was a precedent with Google books. It was originally codenamed 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. ingest 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. >> um and access information and later. magazines. Yeah.

>> And there was three categories. There. was the public domain which is out of. copyright. Then there's the kind of. incopyright but out of print. And then. there's the incopyright and in print. And there was a class action lawsuit. filed in 2005 by the Author's 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 in court out. of court that ended up in a deal where. Google would split 2/3 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 going to work. Google. appealed and in [clears throat] 2015. second uh 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. >> Yes, you can't read the entire book like. it's a Kindle. You can search the book, find the paragraph, >> provide a reference to it. Right. And so. the question on fair use and 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 going to. 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 going to end up being the. right fair use policy and it's going to. be the right read on fair use. So I. think it'll likely get litigated and I. think it'll take a couple years and. it'll get kind of hoping one of these. >> just to be clear J I have not changed my. view on fair use. So I I am with.

Freeberg on this. My point is the. hypocrisy. >> Yes. >> Is 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 want to 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, >> you're breaking the terms of service. That's a definitely a break in 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. 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. Chimoth any any thoughts here before we. move on to socialism corner everybody's. favorite new feature here on the oil and. pod. don't cut to the books. uh keeping the. books intact. >> Why do you cut the books in the library?

It's very hard to read them. There's a. no spine. >> I think uh I think it's not the kind and. I don't like to cut the books. >> I mean, you take 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. Don't um. don't. >> I mean if you want to cut the tip it's. one of the thing you can do a Fbury has. a cut tip. It's actually got a cut tip. but you don't cut the spine off the. bottom. You cut the tip. It's a more.

clever. >> There's a great Guinness Book of World. Records book joke. >> Oh no. Where's this go? >> 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. So, you literally. put it in the book and close the book. >> That's the joke. That's the joke. >> That's the joke. Like an apple pie. Hey, by the way, here's um 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. Can't believe. Dario burning those books. What are you. up to, Dario? Come on the show anytime. We've been roasting him for two years. Why hasn't he come on the show? >> Because you make fun of him and you just. say rude things [laughter] and you've. never met the guy and you just insult. him all the time. Why do you think he. won't come on the show? >> I JUST SAID HE'S a. >> The guy's literally built the most. successful business in human history, growing from under 10 billion of revenue.

to 70 billion of revenue in 6 months. And you insult the guy [laughter] on. your show, all the people that want to interview. him. You think he's going to rush rush. to be interviewed by you? >> I did say he was a sub. That was. probably a little bit over the line. >> I don't even know what that means. What. does that mean? What is that? >> Remember I said like I think he likes to. be dominated, >> you know, and have the government, you. know, control him. >> Like a submissive. >> Yes, I did. I did say that on an. episode, but it was a joke. It was in. good fun. >> 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. 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. >> Yeah. >> And that made people upset. >> If you're doing like Windows 3.1 for. Dummies volume 4, like nobody cares. But. anything that was like a first edition.

or like an. >> rare out of print books, >> rare out of print books. >> Yeah. That gives you a training. differentiation. That makes sense. All. right. So, quick socialism corner here. We got to cover the ongoing saga in my. hometown where I am right now of New. York City. Mandani. has announced five city- owned grocery. stores. David, one per burrow. They're. using city-owned space. They're all going to open by 2029. And uh one week per month, shoppers are.

going to 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 going to sell cigarettes, alcohol, hot food, all that stuff. because they don't want to compete with. the bodeas. It's going to cost taxpayers. 70 millie. I mean, I guess the only. thing to discuss here is like what. happens to the other supermarkets now? Are they going to shut down because they. can't make m the whatever one or 2%. they're making on groceries? Is there.

going to be like riots in the street to. get into these places to get your milk. for 30% off for one week a month? It. just the whole thing seems like a waste. of time. But I will say Sachs, this. plays this is going to play in. elections. Free stuff plays in an. election. uh whether it's a bus or. discounts. >> it may play and you know look when. people first go to these stores and they. first open and the shelves are full. Yeah. people will be like delighted and. then over time what's going to 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 going to have to. compete with this and then they may get. put out of business and so. >> and then you have no choice. sponsored one and then they raise the. price. >> and it is ironic that they're going to. be checking ID to make sure people. aren't coming over from Jersey but if. you want to come over illegally from any. country in the world. Well, that's 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, Freeberg? 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, >> especially starving poor families. 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 going to be wildly. popular. >> They're going to pay their employees. above market wages. Employees are not. going to have to work very hard to work. there. So, they're going to be a better. place to work. Everyone's going to want. to use them. They're going to outperform. Whole Foods. They're going to outperform. Safeway. They're going to outperform. Albertson's. They're going to be so in. demand that what will end up happening. is that over the next 24 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, >> healthare? >> 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 going to happen with these. grocery stores with the, you know, basic. obvious economic arithmetic that someone.

has to pay for this and who's going to. pay for it and rich 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 60 Minutes piece on.

everyone said Zoron Mom Donnie was. crazy, but let's go in and take a look. at this beautiful grocery store. And. they're going to walk through the. grocery store and there are going to 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 going. to 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 um 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 multi-level 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. >> That's a ways away though. >> In the meantime, >> try it while it's here. In the meantime, it's going to take off. And I think that. these grocery stores are going to be a. much bigger success. >> in for socialism. >> Yes. >> Than 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 going to. radically fail. I think that these. things are going to create a radical. spectacle, an exuberance for socialist policies. that's going to kind of light a fire for. socialism around the country. Unfortunately, because at the end of the. day, >> no one has to pay the bill cuz the bill. doesn't come to you for some time. Someone else will pay it. It'll get paid. in the future. >> Put it on top of the debt. All the rich.

people are getting debt. Why can't the. public have money? I agree with you. Yeah. >> Socialize the cost into uh 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 years. >> Breaking news. Breaking news. Um I don't.

know if you saw it just now came across. the wire, but uh Bernie Sanders AOCami. 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 shme for less? That's what has to. happen next. What would you like next on. your discounted democratic socialism. scorecard? David Freeberg, what would. you like next? Discounted bagels, a.

cafe, maybe a flat white. >> Where do they go next? >> But seriously, what's next? What what. what would be next in this logical. thread? Free buses, 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. >> Long Island, Jersey. Yes. >> Again, this will play out over the next. 24 months going into the 2028 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 it, let's say, each one loses 10 million a. year and they get to 10 or 20 of these. That's $200 million 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 and so on. $200 million a year on a $125 billion a. year budget for the city of New York. It's nothing. It's less than a quarter. of a percent of the city's budget. That. is so cheap to market the DSA. >> 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. >> I couldn't agree with you more. This is. this is going to play. This will be a. great great feather in their cap. It's. going to be a great example of. affordability because we've talked about. here previously, Trump promised. affordability, hasn't been able to. deliver it. Inflation's up, spending's. up, all that great stuff. And Mandami. got it done. Free buses, rent.

controlled, and now you got your. discounted grocery store. 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. >> He didn't. Their objective, their. objective is to fundamentally drive. spending towards their district to give. their people more. Their their 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 going to 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 the shift. So. 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 going to be. If you break ranks, I'm going to destroy you. I'm going to. get you primar. And when it came to. comes to spending, he was like, ah, yeah, you know what? I'm not taking that. on. It's too unpopular. All right, Freeberg. The Sultan of Scienc's 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. >> Okay. So, today I'm going to pull up. this paper. Nick, if you could pull it. up. >> A paper from February.

>> February. Okay. >> February. >> February. >> How do you say February? February. >> February. February. >> Oh, it's February. Well, what's the. problem? 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 a network model, a. topological model. And the way they were.

able to do this is back in October of. 2024, there was a group out of Cambridge. and Princeton that used electron. microscopes to scan the brain of the. Drosophilia fruitfly. And they mapped. every single neuron in that fruitly's. brain, 139,000 neurons, and every. connection that the neurons had to other. neurons in the brain. So there were 50. million synaptic connections between the. neurons and it's those connections that.

make neural networks in the brain work. How are those neurons 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. data set was created in October 2024. with justif,000. neurons. And you know that's a tiny tiny. tiny tiny brain. >> Yeah. This would be put it in context. versus the human brain. I mean what are. we talking about here? >> Like. >> the human brain has on the order of 86. billion neurons. Okay. Compared to. >> So that means it would be a multiple for. the network connections. Right. >> Yes. Exactly. On the order of trillions.

of connections. >> Okay. So they took these 50 million. 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 ukitian. 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 3D. 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. uklidian 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. >> It would be space know like. three-dimensional space as humans. understand it when they're on planet. Earth. >> 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 going. to 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 uklitian 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 64dimension 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 64. dimensions. mindblowing when you think. of it because a fruitfly or 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. >> sounds like our mission too Jake. [laughter]. >> well but then we also want to do a. podcast and debate politics and. philosophy. >> don't don't judge how the mosquito. spends their free time but.

>> well I mean but nobody would argue. there's like consciousness as we. experience it in a fruitfly so then you. get to trillions You wouldn't know. >> But I mean, maybe. But. >> 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 fruitly with 50. million connections, is so complex that. it has to take 64 dimensions for us to. represent how those networks are 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 and we can't. comprehend. >> 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 um 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. >> 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 like super dangerous because.

these things could get out of control.". Are you you know 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 Bladeunner where. they don't even know you know Rachel. doesn't know she's a replicant. Spoiler. alert you had 50 years to see the film. like are you part of that camp that.

that's actually what's being built here? >> Yeah, I'm not sure. Uh 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, they're. a reward mechanism that arises in. biology that creates a different. capacity for consciousness than perhaps. can exist in silicon. Bigger topic for a. different day with probably people that. have spent more time thinking about it. than I. But um I just think that there's. something about biology. You know, I. always tell people this this analogy. I've said it many times on the show. I'll say it again. In a single cell, there's 10 billion proteins that work so. fast that 1 second is the equivalent of.

80 years of humans walking around the. city of Manhattan, never sleeping, doing. stuff together with 500 story tall. skyscrapers doing stuff for 80 years is. 1 second in one cell. >> And so you have 10 trillion 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. 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. >> 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 uh some higher um work here and uh.

this is my closest analogy in science. fiction. We're both super fans of. science fiction, but for me 39 seconds. in here, >> one of the great. >> You like this one? I I love the. Prometheus version of this where there's. engineers. >> who are terraforming and started this. crazy thing out and there's just an. experiment in biology going on and you. know the opening scene here in. Prometheus he drinks this and this is. the sacrifice like Jesus um sacrifice. for humanity and he sacrifices him here.

by drinking that biological. um 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. Um, it's. pretty fantastical. Yeah, >> 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 heruristics to try and. do it. And it's very. >> um, you know, it's it's comforting. Uh. because 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. >> And yeah, this is my and that's my. favorite story of it is somewhere. between Blade Runner and this. By the. way, both by the same incredible. director, >> uh Ridley Scott. Uh so take it for what. it's worth. All right, everybody. Another amazing episode. You got your.

science corner. >> We'll see you next time. Bye-bye. Love. you besties. >> We'll let your winners [music] ride. >> Rainman David. And it said we open sourced it to the. fans and they've just gone crazy with. [music] it. Love you queen of quinoa. [singing]. [music]. >> Besties are gone. >> That is my dog taking notice your. driveways.

Oh man, my appetiter will [music] be the. >> We should all just get a room and just. have one big huge orgy cuz they're all. just useless. It's like this [music]. like sexual tension that we just need to. release somehow. >> Wet your feet. Wet your feet your feet. We need to get Mercury's back. [music]. I'm going all in. [music].