Dylan Patel – Anthropic & OpenAI will have most of the world’s compute by 2028

Okay, I'm back with Dylan Patel, founder of Semi Analysis. Our version of Family Thanksgiving Dinner is a regular yearly podcast, but you're not actually related. I won't tell the people this. We'll destroy the myth. Basically where the world economy is headed is more and more becoming a function of where like lab economics are headed, where like the compute market is headed, etc. So I want to understand where the crazy future ends up within a few years, but let's start with just where we are today. So walk me through lab compute and lab revenue right now and be protecting out a year or two. Yeah, so when we go back to last year, even at the end of the year, most of GDP growth in America was just AI infrastructure. And as we look towards this year, about a third of the compute coming online is for the labs, for open-air and theropic. Now it may be built by others and then rented to them, but it's at the end customer it's them. As we go forward into the future, the numbers for computer ballooning were at a little bit over a trillion dollars of CapEx this year. As we got into 28, it's going to be more than $2 trillion. The labs are also taking an increasing percentage of this. And so ultimately, you've got a very interesting situation where the labs are going from companies that spend tens of billions of dollars a year to hundreds of billions of dollars a year to forecasting to spend trillions of dollars a year even towards the end of the decade. And this is at least some of the contracts they've begun signing with their partners. And so this requires a big reshaping of what happens with their economics. So up until now, there have been companies that mostly lost money and theropic started turning a profit in Q2. It's believed at some point in Q3 opening I could potentially start turning a profit even with the big rise of codex and 5.6 and all this. But if we go back a year ago, everything that all the money they had was venture-funded losses. Right? If we go back to even the beginning of this year, those venture-funded losses, they've now turned the corner and are actually starting to profit. Now, that doesn't mean they're not taking a new capital. The new capital is still coming in to accelerate the growth further. But ultimately, there's more and more of their business as being funded off of their own revenue rather than capital injections into them. Over the last year and a half, their margins have really skyrocketed. The base cost of compute tends to be around $10 or $13 or $15 million per megawatt. The most interesting aspect about what's happening now is, before, again, they were generating, if they served a model, GPK4 being served on Nvidia Hopper GPUs, was generating negative gross margin for OpenAI. But now, when OpenAI serves GPT 5.6 or Anthropic serves, Opus 5 or Mythos Fable 5, their revenue generation has passed well beyond the incremental $10, $15 million per megawatt. In the case of Anthropic, the revenue has gone as high as $50 million per megawatt. And what that now enables them to do is, hey, if I spend $10 on inference capacity, actually, $150 of revenue, and then I can turn around and incrementally spend all of that profit on training. One thing I'm very interested in understanding is how you see the centralization of compute happening in the labs, or the relative ratio of compute that goes to the world versus goes to the labs, where if you say, right now, a third of marginal compute is going to the labs. By one, is it over half of the incremental compute in the world is going to the labs. And by what point do the labs have basically vast majority of the world's compute? Yeah. So earlier this year, the beginning of this year, Anthropic Company, I started at two for OpenAI and less than two for Anthropic. End of this year, they're both above five. So they've 3, 4x compute as a whole. When you look at the incremental compute added, that's about 30% of the compute added this year. And as we step forward to next year, given what's already been signed and penned and inked, you've got something even more dramatic, right? You've got Anthropic OpenAI or taking as much as 40 to 50% of compute next year. And this centralization doesn't look like it's slowing down or stopping. In fact, it looks like it's only accelerating. Right. Now, who's building that compute for them will change. Next year, big at Neon Train is, for example, SpaceX is building a ton of compute. And they're actively going to lease quite a bit of it. Two Anthropic and OpenAI most likely because they're the ones who have the marginal capability to pay the highest price. In addition, OpenAI and Anthropic are also starting to build their own compute, OpenAI with their own chips, Anthropic with TPUs that they're purchasing from Google and deploying with FluidStack. And so when you ask, "Hey, when does half of the world's incremental new compute go to just OpenAI and Anthropic?" I mean, it's really, by the end of next year, it's already half of the incremental compute is going to Anthropic and OpenAI. And because compute is growing so fast, incremental compute is going to be basically most of compute. So it's very soon, you're saying maybe within a year and a half or two years, and most of the world's compute is owned by two labs, or at least it's serving the demand from two labs. How long do you think, "So there's this trend where maybe a world compute in gigawatts doubles every year, but the compute at the frontier labs triples every single year?" But if you keep the current trend going, it goes from, like, two at the beginning of this year to close to, like, six at the end of this year, just multiplying up by three, 18 by the end of 2027, 54 by the end of 2028. Are you like, "Okay, at that point, they simply can't continue tripling given the amount of world's compute or how, why do you see the world's compute situation over the next few years?" Yeah, so if the incremental compute adds this year, 30 gigawatts next year, 50 gigawatts in the year after that 70, roughly, you end up with this really interesting phenomenon, which is, okay, well, a new watt deployed this year is significantly more efficient than the watts deployed two years ago. So actually, you know, a humongous percentage of the world's compute was deployed this year. Even though it didn't double the number of watts deployed, I'm deploying GB300s and TPUV7s and Tranium-3s, which are way, way, way more efficient, you know, 3X, 5X, more performance per watt than the prior generation chips. And so ultimately, you've got a huge ladder here. So if anthropic and open AI take on, you know, 45% of compute next year, you've got them in let's say December 27, they have taken on half of the world's incremental compute. But that half of the world's new incremental compute is actually at a higher performance than everything else before it. So you've got another multiplier on that. So by the time you're in, like, towards the end of 2028, if this trend continues, which I see nothing that's stopping it, you've got them just controlling most of the usable, you know, flops in the world on their own. The thing I'm confused about is why you think we only add 80 gigawatts in 2028, if we enter in a world in which the value of compute increases so much. That's the upper bound, by the way, that's the, that's the like, I'm so fucking bullish. Right. Okay. So let's, let's, let's do some chain of thought here. So when I interviewed a few months ago, you said, in order to make a gigawatt of, I think Vera Rubens, you need, what's it? You need 55,000 and three vapors, 6K and five vapors and 170K DRAM vapors. I don't know those numbers, but I'm gonna troll you, but the way you said, wafers was so fucking bad. When we first, when we first moved to the US, I had the, the VW thing pretty bad. And I was a vegetarian. Vegetarian. I remember you told me about this. Is that inert Dakota? I was in elementary school. I'd have to be like, could I get a wedgie? Could I get a wedgie? Anyways, so that's where one gigawatt, right? Yeah. Now, I had an LLM run your way for Fab Equipment Model and figure out how much tooling, how much the tooling cost to produce a gigawatt of compute basically every single year. And it was like three to four billion dollars. Now suppose you add in, you know, clean rooms and a shell and everything else of the Fab. So six billion dollars of like Fab CapEx produces every single year, a gigawatt and a gigawatt produces right now a hundred billion dollars of revenue. But also that six billion in CapEx is producing a gigawatt every single year. And that gigawatt is producing a hundred billion dollars every single year. So even over the course of five years. So you know, the first gigawatt has generated five years of profits. And the second gigawatt that the Fab is produced is generated four years of profits and so on. Six billion of CapEx at the Fab level will have generated over a trillion dollars of end revenue. Yeah. There's a lot of OpEx along the way. Of course it does. There's a lot of other CapEx like the data center, the power and you had to pay like, you know, the opening eye for the organization, there's a lot of different people who need money here. But yeah, it's huge. But take away half of it for all this, all these middlemen, there's a hundred X discrepancy between Fab CapEx and end revenue generated more than that actually really, but we're just being very conservative. And as a result, this is capitalism, right? Like you would imagine that people are going to figure out like we're going to be, you have this huge discrepancy where you can turn one dollar into a hundred dollars. And they're not going to figure out a way to make more mirrors. I mean, they are. Right. These mirrors take some time to bake. Right. But the emergency are so big. We're like, I thought I was going to be like, we could make a trillion dollars right now, but we're just bottling that down the mirrors that go into the ASM on the machines. They've spent, okay, how can we make more mirrors if we spend a hundred billion dollars on this, right? That's the situation we're going to be in pretty soon. and I'm just like, we're not. You're not going to be able to solve that supply constraint that just seems quite hard to imagine. No, there's definitely, you've seen people do funny arbitrages here where they buy turbines and then they try and resell them. Because the value of a turbine is way more because it's the thing bottlenecking a data center. I think if anyone had $400 million in the ability to convince ASL to sell them in a U of E tool, they should totally just go buy one and wait and sell it for North of a billion dollars. But ultimately, yes, capitalism looks cause these things to expand. But it's a whip, right? It takes a long time for the whip signal to get to the tail end of that. And so the supply chain doesn't react immediately. In fact, you go to talk to someone at Carl's Ice, they're like, yeah, yeah, we need to make 100 EUV tools by the end of the decade. I think when we first had our, when we had our episode earlier this year, they didn't even think they needed to make that many enough mirrors to make 100 UV tools a year. And so now they've like sort of, they're like, okay, we need to do that. But in reality, you know, because of all the economics of what's going on, it should be even more. But it takes so long to, to, to, to, to, to, to, to, to, to, to, to, to, to, to - But it takes so long to, to, to, to, to, to, to, to, to, to, to, to, to, to, to, to production from cash flows themselves. - Yeah, I do agree generally, there's obviously some physical constraints, the way the supply chain is expanding currently, the hundred is roughly still the right number. - We're 2030. - A hundred ASML tools for 2030. But, you know, if you said Carl Zeiss, here's $10 billion, please fucking just expand production. That would change things. And you would have to do this with every company in the supply chain. I don't think it'll happen this year. I don't think it'll happen this year after, because the world is capital constraint. - But in the world where, say, the top labs are generating, let's say, even combined, a trillion dollars in revenue next year. They're not able to say 10 that. - I don't think they're gonna do that, but. - Yeah, or hundreds of billions at least, right? - Yeah. - They realize where the world is headed. I feel like they could just make. - So the thing is the labs can spend hundreds of billions, they're gonna generate hundreds of billions of revenue next year, but ultimately, CapEx next year is like $2 trillion. So you've got this big mismatch, right? You know, the wafer fabrication equipment supply chain will do something on the order of $200 billion. The data center market supply chain will do even more. The accelerator supply chain will do even more. You know, the energy supply chain will do the number. You know, you sum all this up. It's gonna be well north of $2 trillion of CapEx. So the labs have not yet gotten to the point where their cash flows can fund this stuff. - Of course, yeah. I mean, obviously they will never get to that point, right? Because they want to keep-- - Yeah, you want to make your CapEx higher than your returns. But the key question I really want to understand is if, yeah, if the current cash flows would be like north of 50 gigawatts per lab by the end of 2028. So between them, they'd have 100 gigawatts. Those gigawatts as you're saying drive many fold more throughput or more performance by 2028 than they are now, right? Because the hardware's gotten better. So not only have like flops for a wide increase, but also the hardware gets better at working with the AI workloads. Okay, so 100 gigawatts for the labs end of 2028. How much is that world compute? - I think that may be a little difficult. Given 2028, you start to have, they've taken 70, 80% of incremental compute. And I'm not sure what happens to markets then, right? You know, how much does the price of compute skyrocket for them to actually be able to buy? 70, 80% of compute is, you know, Google or Meta or my Amazon willing to sell even that much. Also one caveat when we're sort of talking off these gigawatt numbers is, you know, when Amazon is serving bedrock and throttpick models, that counts as a throttpick compute in sort of our whole view because it is effectively at the end of the day counted as revenue for a throttpick, even though like there's a revenue share and credit back all that. But ultimately, in 2028, it's, you know, if they get to 100 gigawatts combined, they have done really disruptive things to the market because anyone can make money off of 10 to $15 million per megawatt compute today. You literally, like, I kid you not, it's not that hard. Go get a GB300 rack. Go download the Kimmy weights. Go download VLM restyling. Set it up, you know, codex and fable can actually help you do this. It's pretty simple. I mean, it's not like it's, you know, it's not trivial but it's not like rocket science and go put it on open router. It's very simple. And you'll start generating more revenue than you're paying for the compute. And so this is sort of already led to this compute pricing 10 to $15 million per megawatt, start to inflect up. And to get to that 100 gigawatts in 2028, you have to believe that the labs can outpay for compute because anyone can make money at 10 to 15. You, you know, it does compute now get to $25 million per megawatt. Does it get to $40 million per megawatt? - But as you're saying, it's already the case that the labs are generating with more revenue per megawatt than everybody else. If they stay as far ahead as they are currently, you'd expect that to be the continuing case. If there's like some kind of recursive self improvement where the AI labs are like relatively uplifted or they have models internally, they're not releasing externally that are helping them make their next model better. You'd expect that to be even more of the case. And you're already seeing this or like SpaceX or whoever's like slightly further behind will just sell compute to the high spitter. If they can't internally monetize it as well as the labs, I feel like it's continue expecting them to be able to gobble up like bid for larger and larger shares of the company. - I think that is my worldview that they will continue to gobble up more of the compute. But ultimately, they can't do it at current pricing or anywhere close to it. - Sure, sure. - They do have to start paying $25, 30, 50 million in megawatt to really gobble up 70% of the world's compute in 2028 to get to that 100 gigawatts by 2028, which is a very aggressive goal. The other aspect of this that's really challenging is we've already seen a huge slowdown for the AI labs, right? This regulation that they advocate for is actually slowing down the labs a lot more than it slows down sort of the open source Chinese language models. Open AI not releasing Astra. Open AI stopping training for two weeks and Thropic not releasing with their safety assessment set as Model 2, which is widely believed to be the next version of Meet Us. They're clearly not releasing their best models and in which case, the revenue per megawatt stalls or even can start to decline again because other models are competitive again. So it's not that they're falling behind, it's just that they're not releasing their best stuff. What if there is some regulatory impact that prevents them from releasing their best models? Now their revenue per megawatt does not climb as fast, then their ability to buy that incremental compute for a higher price than everyone else starts to diminish and then maybe they can't get to that 100 gigawatts is sort of in a world where safety doesn't matter. I do believe that's exactly what happens, right? They can start generating $100 million per megawatt or more and they can pay $50 million per megawatt and no one else has any logical reason to do anything with their compute. Besides say please Dario, take everything off of my hands. But there are forces at play that which we cannot describe that would potentially slow this down. - Yeah, yeah, yeah. I think an good intuition pump is just, what if the AI models were literally as good as a fully automated software engineer? They're not currently there yet, right? Like I think they're far from just being able to fully automate the job of like a full white collar worker. But white collar workers earn six figures or north of that a year. And if you have a gigawatt that can sustain a population of like say a million white collar workers, it's just like, let's say roughly, right? That's like, you could then off the back of that. That would be a hundred billion. That's actually surprisingly low. (laughing) - Yeah, a hundred K per person, million population, yeah? - Yeah, yeah. - I don't know. But maybe many hundreds of billions of dollars if you get like full AGI, per big loss. - I think the other aspect of this is, and we've continued to see this, the most of the value capture is not happening, right? Like most of the value that these models generate does not get given to open-ended anthropic. Thankfully so far, it is mostly just being given to the users. - Yeah, yeah. - Right? Change street with their exclusive contract with open AI for GPT 5.6 ultra fast mode or change street where they're like one of anthropics biggest customers is generating way, way, way, way more value out of the tokens they're paying for than anthropic is generating in terms of profit, right? 'Cause they get to make money off of the market. Or meta, who at one point was rumored to be, as much as 10% of anthropics business, they're generating way more efficiencies by optimizing their ad algorithms or what have you been getting engagement time, 5% longer, and all these things, they're making way more money off of using these models than anthropic. And so ultimately, and that's what's required. So sure, if you had a million new software engineers, the cost for software engineer would also fall. - What I think I'm going to use about is, does the market come into equilibrium? And if it comes into equilibrium, would you just expect the price of compute to equal whatever anthropic and open AI can generate from it? Or be very close to it with a small amount of markup for anthropic and open AI? Like right now, it's really weird that there is a 4x or more difference between what compute sells for and how much money anthropic can make from it. And in the world where the revenue per gigawatt continues to increase, if anthropics I'm really going to monetize a giga-wise. doubles or triples or something. It'd be weird if then the gap continued to increase. And so inthropic, just having some software, having some weights, can take something that costs $10 and then turn into $100. >>Yeah, so there's always a fun question, right? Which is, where does the value go in AI? AI's general is value. You've got the end user, which I think we all agree, is generating more value than anyone else. Hence, they're paying a lot for these models. But then you have the app layer, well, so far the app layer is generated very little value. And you've got the model layer, which again, up until a year ago was generating negative gross margins and is now generating massive positive gross margins. And looks like it's on the path to generating, you know, $100 million per megawatt. So turning $10, $15 into $100 as you said. But if we go back again a year ago, the hardware supply chain was generating all those gross margins, while literally everyone else was losing money on it. Opinion and thropic were just plowing VC money in. And as were many other startups and many of these hyper-scales, they were building infrastructure without knowing if there was going to be a payoff. So ultimately, you had this like, you know, negative value being created on the model layer almost, if you will, because they were selling the tokens for less than it cost them on the inside. And all the values being created used at the chip, the fab, initially in 2023, the memory guys were making no money off of, you know, HBM or memory for AI, even though theoretically their value they were delivering was humongous. Now you've got, well, actually, KISSMC makes way less value than the memory guys, is that actually how much, you know, they're capturing less value, you know. So the value capture shifted around a lot, which is very fun for people tracking the market or participating in the market, like Jane Scheer does, I think, example. So, you know, what happens, you know, going forward does anthropic and open AI, you know, they've slowly started a balloon and value capture. Do they balloon and take all the value capture? Well, that was the thought. And then Elon showed, actually, no, I can sell my compute for $25 million of megawatt or $40 million of megawatt to anthropic in Google. Even if it's a short-term thing, I've sold it for this price and I'll recoup my entire capex in a year. So what's your prediction of how much the relevant tranche of compute, like B-300s or whatever, that sold for $40, B-A-G-Go-A, that's basically sold for $40, B-A-G-Go-A to Google, what is that sell for at the end of next year? I think most compute will still continue to transact at sub $20 billion of G-Go-A-Go-A. Even at the end of next year? Because all of it has to be financed. For a compute that you can build without financing, right? If Medicare can build compute, Microsoft, Amazon, SpaceX can build compute without finding a customer just saying, "Fuck, and I'm going to build this compute." And then turn around and wait till it's already built, they now control what's going on. So most compute is contracted well before it's built. Yeah, yeah. And so this is sort of what Elon took advantage of in the market is he actually had all this compute and he was like, "Hey, anthropic, I know you're making like $60 billion per gigalot. Why don't you just buy my stuff for a crazy amount of money?" Obviously, it's not like Elon decided this or anthropic decided this sort of market figured itself out. Other people, you go to a random cloud, they're like, "Okay, I'm going to build a gigalot of compute or 100 megawatts of compute. I'm going to spend the CapEx. I need to turn around and find a customer. If I want to find a customer, I need to find the capital. Who's going to give me the capital and the customer? The customer has to sign a deal and then I take the customer's commitment to the credit markets and I raise the capital. And so there's this sort of like completely different power structure where meta, who is effectively hoarding compute, them in SpaceX are plausibly the number three and the only plausible number three is because they're hoarding all this compute. They're using their balance sheets and capabilities to build compute, to build compute without end customer that's monetizing at a huge degree. And they have an actual balance sheet so they can go to the credit market and being like, "Hey, guys, you build a gigalot, you can make your margin, not a crazy margin, but you can make a good margin." And I now have all this compute. And now meta and SpaceX have this optionality of looking around and being like, "Is my internal use case going to make me more money or should I go out there and sell it to Anthropic Open AI at crazy margins?" Yeah. So now we've sort of entered a regime where SpaceX and Meta are saying, "Actually, I'm going to build the compute and I can start to rent it out for not 13. I can sell it for 25, 50, and more." So as I've been doing video essays and other formats, I've been looking to hire a new editor for the podcast. But actively searching for editors has been quite time consuming because the vast majority of candidates don't fit the profile that I'm looking for. So I created a recruiter in Grogbot to see if it would help. I give it a huge context dump where I monologue to basically everything that I wanted and then it's fun to have four other bots to narrow in on different parts of the search. One went through the last year of my email for relevant inbound. One searched my xfeed and DMs. One went through the end credits on various documentaries I like. And the last one looked for editors who worked for some of the YouTubers that I followed. Grogbot then took all of these different candidates that the sub agents I found. It filtered them against my criteria and then delivered for me a final shortlist to review. To be honest, the first batch had a few good candidates I wanted to see, but mainly a bunch of duds. But after I gave Grogbot some more feedback about what it was missing, you came back with a new list of candidates that I'm actually extremely excited about. I ended up stating this whole workflow as a routine. So every week now, Grogbot checks my inbound email and xdms for promising new candidates to potentially interview. If you want to try Grogbot yourself, go to x.ai/bot. What do you think their revenue per gigawatt is by the end of 2027? Like for an anthropical open AI by the end of 2027. I think it's highly dependent on who is the best model if they're allowed to keep releasing their best models, but I don't see why it wouldn't be 50 plus million dollars in megawatt. By the end of 2027. By the end of 2027. That's where it gets more challenging, but I think I think it could get to higher than that's like 7080 million dollars in megawatt blended across the company. If not higher. And so I think if that's the case, right, then what happens to the price of compute? Well, if I'm anthropic, incremental compute is worth that maybe I spend 40 million dollars in megawatt on SpaceX compute. And if I'm SpaceX, I look to the supply chain and I'm like, well, I've struck this deal with Jensen where he's now all of a sudden using Twitter. And Elon's saying they're exclusive to Nvidia, but why doesn't Jensen raise his prices? And then SK high nakes and my crown and Samsung looked at it and they're like, well, why don't they raise their price? So I think the value capture, there's a bowl with effect here, right, where just because someone has risen the prices, doesn't mean the entire supply chain rebalances immediately. But over time, the supply chain will rebalance and things will cost more and more. And to get that incremental capacity, you sort of have to, right? So TSMC raising prices very slowly, but memory companies raising prices very quickly, substrate companies raising prices very quickly, different parts of supply chain raise, Elon wouldn't have sold if it was 15, but he's selling because it's 25 plus. So obviously he rose his prices really quickly. Yeah. I'm sort of surprised you think like revenue per gigawatt doesn't increase way more than even like a hundred per gigawatt at the end of next year. When does RSI happen? When does take off, right? I think even if RSI doesn't happen, the current rate of progress continues. If you just look at how much progress have you made in, let's say the last year and a half, like what was the model from a year and a half ago? I mean, my problem with this is the best model that exists in the world was trained in February. Okay. You're saying maybe we just want to be able to rely to release the last time. Like, it's obvious as they're not training models for two weeks, man, what the hell? Yeah. I mean, there's one thing like internally are they getting enough use for it so they'll like bit up the price of computer or another is like does AI progress as a whole slowdown makes a regulation? Yeah, but they're not even allowed to use this like new model and like Astro is not widely deployed internally even. But still, I don't know, just like the, yeah, if you go, if you have like a model that is, what was the model released? Like, let's say the beginning of last year, like GPT 40, is that 40? Yeah. That's like you're talking about a 40 to fable size or mythos two size leap by this point. Again, by the end of 2027. Yeah, but mythos two is not out. They are like even mythos, right? Like that leaf again. Even mythos is not allowed to be out, right? They've neutered it. Yeah. Like we can't, we can't use it to optimize inference performance. We can't use it to optimize all sorts of things. Right. Yeah. Maybe there's like some slowdown in AI progress or the deployment of AI. That means that the revenue per gigawatt can be lower. But that's the only way I could see that being only 100 per megawatt by the end of next year. Yeah. I mean, as long as the model gets better, the value generated out of it gets better. Obviously, who captures the value is still up for debate. But ultimately, everyone's going to raise their prices because they can. And it's super inflationary, especially if the method of regulation is right now so far, it's just don't release the models. But more and more, the method of regulation is New York's banning data centers. Texas is holding memoratoriums. Ohio saying you have to, or at least trying to say you have to like pay everyone's property tax in a certain radius. These sorts of things are going to decrease supply and increase cost. And that's going to get passed on as well. So you start to end up in a spot where progress does slow at least in the external sense. Yeah. Even if the models internally keep getting better and better, I see no reason why like, you know, again, like in a takeoff scenario, why would it inthropic not have their best model six months ahead of what is externally available because of safety and regulation, but also, you know, the competitive advantage. And then that six month difference if progress accelerates is actually a bigger differential. So that's the thing that would cap revenue per megawatt gains to a much lower growth than we've seen in the first half this year. Here's something I'm very interested in as these companies go public and they're accountable to investors. And they let's say at the end of next year, they have, I don't know, close to 20 gigawatts. So like 10% of the compute to gigawatts, let's say they want to go from 60% compute to training to 70% compute to training. And their investors are like, well, if you're able to generate $100 billion per gigawatt, you're basically saying no to like $200 billion of revenue in order to increase your training compute. As investors are like, what the fuck? You're already spending so much on training, why are you spending even more on training? As a public company, do you think, yeah, what do you think would happen if they're just like, no, we will keep increasing the share of compute we spent on training to offset the increase in revenue that each gigawatt of compute is giving us? Yeah, so this is sort of what I personally believe that the labs are going to allocate less and less compute to inference over time, which I think is very non-consensus, right? Everyone's sort of the standard belief of most people's, oh, most compute will go to inference. Most of it will go to forward passes for training, not maybe necessarily revenue generating inference. Ultimately, you end up with, if they're generating $30, $40 million per megawatt today, you allocate 40 percent to inference. If you now get to generating $60, $70 million per megawatt, do you still allocate 40 percent to inference and generate all this profit and then do dividends and share buybacks? Or do you go build a GI? And I think the obvious answer from Anthropical Open AI, and not just at the executive level, but also their board is go build a GI because it's way more profitable. And so ultimately, you're going to see them ratchet up their percentage of compute dedicated to training while each increment of compute is getting more and more profit generating if they had dedicated to inference. Right. And so the whole point is, well, okay, if I'm selling tokens is Anthrop is Open AI releasing an ultra-fast mode for just external or they're doing it internally, too. And it turns out, no, actually, I'm going to allocate it to internal and external because my internal value that I'm generating from super fast AI or the best AI model is way more than what someone externally is. And so ultimately, sure, I could generate $100 million per megawatt, but if I turn that towards AI research, what is the incremental progress that I get and then what does that do towards my future earnings potential, the discounted cash flows of whatever the hell I've done, right? And so they're not going through that calculation, but ultimately, it makes more sense to dedicate more and more compute internally. And the only reason to have inference compute be so large is so you can grow your training fleet. Right. I think this is an interesting economic question that I feel like we can have the models digest of what would have to be true about a world where they reduce fraction of compute spent on inference? I think they have been over the last three months already. Interesting. I think parts of this year, they were increasing fraction of compute. So let's just take a month by month. You would agree that every month anthropic is added more compute than the prior month. There might be some noise when they like sinus-based X-deal or whatever, but in general, the amount of compute is a curve up. And so in January, they added less compute than December. And yet, their revenue adds skyrocketed and then they've flat, sort of plateaued. They're only adding, you know, they're not adding $25 billion or ARR every month now. And so that means the marginal megawatt they're getting is going higher percentage to R&D than it is inference. Interesting. Yeah. And so they are factually increasing their compute towards R&D today. Yeah, yeah. Yeah, I think this is like self-evident if you look at what they're doing enough. Yeah. So if I look at the numbers, you said of how fast world compute grows, here's some things I want to understand. So it seems like if I add of the numbers, you just said it would be over 200 gigawatts of world compute by the end of 2028, right? Yeah, globally. Yeah. And how fast can that continue growing? Like globally, globally I compute after 2028. Yeah. So 30 this year, 50 next year, 70 and 28, 29 should be like on the order of 90 to 100? Like then just 100 more every single year or something. I think the slope can continue to go upwards and it's hard to predict anything more than four years out, given who knows what's, you know, R&R SI regime or, you know, when is the world economy growing at 10% a year? Yeah. If you've got 100 plus gigawatts a year, you're at absurd revenue GDP growth. Right. If you think there's 200 gigawatts globally in 2028, how much is in China by that point? And how does Chinese compute continuing increasing through this whole trend? Because if the RSI stuff kicks off in the West before China has a large amount of compute, maybe we're living in a different world than when it doesn't. Yes, a China today, so if we sort of level set back to 2022, the US was adding about 45 to 50% of the world's compute. China was adding about 30 to 35% of the world's compute and the rest being taken up by the rest of the world. Since 2022, we've had big regulations against China and the dramatic increase in America. Go today, 70% of Watts are being deployed in America and China is really a very small number. It's sub 10% of Watts being deployed for data center AI compute is in China. And as we step forward, there's still a very small number. Their domestic production is quite small. They're purchasing from Nvidia is still quite small. And a lot of that ends up in other places as well, Malaysia or what have you. So ultimately, China domestically still continues to have 10% of incremental new compute. So in 2028, it might start to inflect up, I think. But it's pretty easy to say China will have like 30 gigawatts of AI compute or less. By 2028? Yeah, in 2028. And then how fast does their hockey stick go up? I do think in 2028, they have a big uplift in what compute they're able to deploy. In 2026, they're still mostly relying on a lot of the smuggled chips, you know, a lot of the chips that TSMC made for companies that they thought weren't Huawei, but ended up being Huawei or a lot of HBM that Samsung is shipping, you know, sort of. But in 27, fab start to go up in 28, especially fab start to go up from SMIC and CXMT and such, where domestic production is actually reaching many millions of units a year. And now they're incrementally adding, you know, five, 10 gigawatts in just 2028 of domestically produced chips. Right. Those chips are definitely worse than the chips that Nvidia will have in 28 or Google will have in 28 or opening I will have in 2028. So even the gigawatt number overstates things are saying, it's like 30 gigawatts, but it's really much worse chips. But then how, yeah, how does it, if you think the world is going to add 100 gigawatts in the following year or something, you know, I know you said you can't really say that if I were out, how much is China able to have the subsequent year? Basically, I want to know, did they just hockey stick at the point at which they are able to start shipping large amounts of compute or is it still going to be less than US plus allies? Um, there's a lot left to, you know, whether or not the US passes the match act, whether or not X tools continue to get export controlled, how fast China can build their new equipment that they're starting to be able to domestically, but ultimately, you know, China is definitely going to hockey stick. If there's anything China's really good at is scaling manufacturing really, really quickly. And you know, I imagine, you know, China's China will start to be able to extract more and more purchasing of even foreign chips into domestic China or at least close the gap in what the US is allowing, you know, Nvidia to sell them or have you, but doing China could do adding 50 gigawatts by 2029, marginal, incremental gigawatts in 2029. I think that's, I think that's completely reasonable. Yeah. Um, and part of that could also be purchased from foreign, um, but yeah, I think it's completely reasonable that China in 2029 can do 50 gigs, um, but if most of those are domestic chips, there is some factor there where that 50 gigawatts is really worth as much as 20 gigawatts in America or in, in, from, from American chips. Right. Right. So yeah, you're actually projecting a world where maybe the leading lab in 2028 has more compute that China will have and like, all of China will have in 29 or even 30. If you, if you, if you weighed gigawatts by their quality, implying that there's nothing to done to slow down the US labs, yeah, that's right. Clearly, the government is starting to, politicians are starting to do that. Yeah. Whereas China is not going to slow down AI. In fact, the only thing they're going to do is accelerate it. So honestly, I, I'm an interviewer at Jensen and asked about expert controls. I, I'm a libertarian person and I'm like, I wasn't like genuinely sure what I thought about this issue. I was still manning what I, what is like the opposite view that he has and it, because I think it's important to hash out ideas. Um, but I'm like, yeah, maybe there's a world where we, if you just cooperated with China, it would be better for us, especially since they control so much of the supply trading, the other things that we needed for robotics and other things. Um, but I didn't realize the compute situation was as fucked as you were saying. Like actually, the expert control is the do seem to have like really, if, if they should the amount that you're saying, that's a huge difference. By the time we have automated coder and even getting into like automated research or China is like way far behind on the compute stock. And so if, if that ends up being the case, that would have worked. I think that's actually a notable success. Um, I would say the only caveat there is some of it is expert controls, um, but some of it is also just, uh, financial systems, right? American financial systems are more willing to yolo into startups than Chinese financial systems. But once Chinese financial systems choose an industry to focus on, they'll subsidize it a hell of a lot more. Yeah. Um, and so the Chinese semiconductor industry has significantly more subsidies than the rest of the world's semiconductor industries combined, um, which, which points to like, you know, if takeoff is not as fast as sort of you're implying, but actually it takes longer than ultimately China will be. will catch up drastically on the semiconductor side, which then is compute at some point. The other aspect of this that I would think is like, I think is like noteworthy is Chinese companies today are not that far behind in AI models, at least perceivably by the public relative to the amount of compute they have, right? The leading Chinese labs have 100, 200 megawatts total of compute at most, bite dance seed being the one outlier where they have significantly more than that, but, you know, Kimi is not running, you know, a gigawatt or anywhere close to it, whereas inthropic is, you know, nearly five gigawatts by the end of the year, right, or more, sorry. And so, you know, the question is sort of, well, does it matter? And I think right now, it doesn't matter that much, this difference in compute, because, you know, when we break down the compute ratio or budget of a lab, historically, it's been, you know, let's say six or so far, it's been like 60% training, 40% inference, but that training gets broken down further. And this is actually like 50% of the compute is research, like 10% of the compute is development, and then 40% is inference. And what I mean by research and development is, you know, researchers are generating ideas, testing new architectures, testing new data mixes, testing new hyper parameters, whatever it is they're doing, new attention techniques, blah, blah, blah, but ultimately, when they do the training run, when inthropic trains meet those, it's sub 200 megawatts, right? The pre-training of the whole thing. The pre-training, it's sub 200 megawatts for call it two months, and then the RL is even less. But you think the RL was less the compute than the pre-training? At least in terms of single-side inference, I mean, single-side of pre-training at, but total compute was really high, right? Total compute, but it's like sequential, right? Yeah. And most they ever used at one point in time was maybe 200 megawatts. And then in reality, they had multiple gigawatts. So most of their compute was going to the research, not the development of a model. And there's reasons for this, right? It's hard to coordinate all these clusters. It's hard to co-locate all them. It's hard to do multi-site training. It's hard to do RL, you know, generating even more rollouts during RL does not necessarily make it better. That's the sort of reasons why you may not be able to leverage, you know, all two gigawatts that you have for training onto training, right? Actually, I can only leverage 200 megawatts. As we get further and further down, implement automated coding, automated researcher, I actually expect the percentage of the compute budget that goes to research versus training to start to like become a lot more fuzzy or even higher for training. Also things like continual learning, right? All of these things start to mean that more and more is actually going to training the model. So if you end up in a world where you're doing 100 gigawatts a year, at current prices, that would be 5 trillion of cat-backs every single year. And then stack on the fact that you have to build the power plants way before then, slash it's a 30-year asset. You stack on the fact that the data centers are a 15, 20-year asset and you have to build that then too. So the 5 trillion, you know, you have to account for future year's growth. It's actually going to be more like 7 or 10 trillion of cat-backs. I understand. Because you're not including the fact that like that doesn't include the fact that there's not the infrastructure for the power generation or whatever in the data center itself. Right. Yeah, yeah. And the data center itself is when you talk about AI cat-backs, people are saying $40, $50 billion, but that's really just the critical IT, right? The servers, the networking, the fiber, the transceivers, optical communications, all this sort of stuff. It doesn't account for the data center itself or the power plants themselves, which are being built ahead of time. If I'm building 100 gigawatts this year and 150 gigawatts next year, well then all of the buildings for that 150 gigawatts need to be built and cat-backs this year. And if I'm building 200 gigawatts a year after that, all those power plants need to be spent. You have to buy the turbines this year, right? And so you've got this much bigger than even $5 trillion if you're building 100 gigawatts. Right. So very plausibly incremental cat-backs every year is getting close to $10 trillion. By the end of the decade, right, which is going to be close to a tenth of the world economy. And if all of it's going up in the US, it's like, well, the US economy will have grown as well, but still in the current size of the US economy, it'll be like a third to a quarter of the US economy will just be going towards data centers. And as I say that out loud, I'm like, maybe you're right, and we just won't allow it. And that's the reason this doesn't happen, right? Because like, for this exponential, continue, just like a quarter of the world, a quarter of America's economy is just building data centers. Yeah, I mean, I believe in capitalism and reallocation of resources towards the most profitable thing, but at the same time, politics exist. And credit markets exist and capital markets exist. So to enable, let's say that 100 gigawatts by 2030, or let's even like, let's even like pair it down to 2028, where it's like three or four trillion dollars of catbacks across all of these items, a couple, you know, over two and a half towards IT catbacks and then another one to two on data center and energy, and all the supply chain downstream, like semiconductors and all that stuff. So if you're at three or four trillion dollars of catbacks, where does all this cash come from? No one is generating that much cash from the business yet, right? Hyper-scalers, they funded all of the growth up until now, Google, Microsoft, Amazon, meta, they funded a huge percentage of it. They were more than half of compute, but they now don't generate cash. They actually spend everything on catbacks, and in addition, they raise debt and spend everything on catbacks, right? You've seen meta do it, even Amazon, even Google, you know, Microsoft will be there soon. One is raising debt to pay for their catbacks. So now who is the incremental person to pay for this? That was not doing it before. In the case of like Google, it's pretty simple for them to stop doing buybacks or meta, stop doing buybacks and turn around and buy computer infrastructure, and that doesn't have a huge effect on the market, but it does have some effect. But as you, as you step forward to 2028, where the hyper-scalers are now raising hundreds of billions of dollars of debt, and then all of their supply chain is raising hundreds of billions of dollars of debt, who pays for this? And so there's a few different ways. You know, there's the semiconductor companies like Nvidia and Broadcom and the memory companies turning around and deciding to fund some of this catbacks. There's the traditional infrastructure investors who are turning around and gathering capital and investing in infrastructure and instead of bridges its data centers. And then lastly, there's everyone in the economy who's realizing maybe I shouldn't buy a home or maybe I shouldn't invest in credit for a home that's helping people buy homes or maybe I shouldn't buy government debt. I should just buy hyper-scaler debt or I should just buy this data centers debt or I should buy inthropic debt because inthropic's willing to pay 20% rates for the incremental billion dollars to build their capacity because they know their revenue from it's going to be huge and they're going to pay 20% because it's still better than renting it from SpaceX for $50 billion. So you've got all of this contention but if you now do this, the whole world economy is like really shifted around. Antithesis is a deterministic software testing platform that enables perfect reproducibility. It also unlocks some pretty insane approaches to debugging, like time travel. With Antithesis, you can jump to any point in the trajectory and start from there. So when there's a crash, you can rewind to the exact moment that something went wrong and freeze the entire system. The application, the database, even the environment itself. This lets you do something that would otherwise be impossible, which is to observe every part of a distributed system at the exact same instant. Time travel also allows you to add telemetry and logging to an event that has already happened. For example, you can rewind to five seconds before a crash and decide to capture all the network traffic. Most powerfully, Antithesis gives you a live terminal interior system that you can use to perturb anything you wish. Fill a node or disable feature and then hit play and see what happens. Then go back and try something else. In production, you often only get one shot on goal with this sort of destructive analysis. If you restart a deadlock service, for example, the exact deadlock you need to study disappears. But with Antithesis, the original timeline is always reproducible, so you can test as many hypotheses as you need. And if you don't want to do all this time traveling yourself, you can just have your agents do it for you via the Antithesis API. Go to Antithesis.com/thwarkash to learn more. So you and I have been debating off-air for the last few days, whether there will be a sovereign dead crisis as a result of AI. And the logic is this, AI is, you have a situation where as we were mentioning, very little investment turns into a lot of money, right? So the rate of return. What a fucking problem, dude. Can't believe it. No, it isn't. Here's probably everybody else who can't turn a little money into a lot of money, right? So the rate of return is incredibly high. Even at the data center level, if you build a data center and you're getting rented out to a therapeutic or an open AI, if you're like 10X, what it costs to you in a depreciated basis to build it, it's fucking crazy. And so you turn $1 into $2 or $10 or something at the end of the year. That raises the rate of interest higher. Now if the rate of interest goes higher, and if it does that for the entire economy, and people are borrowing more and more money, they're competing against the other lending that the government would have done or the other companies would have done or that you as a consumer or a mortgage buyer would have done, then that's just making it, basically, more expensive for everybody else to borrow. There's this huge implications for tons and tons of people. Sorry, I'm gonna go on a bit of a monologue here, but we've been thinking about this together. So I think the US will be fine at the end of the day because if the data centers are built in America, we can fundamentally just like tax the data centers. But the way the current tax system is set up, you know, corporate income is like less than 10% of federal revenues. And 80% plus is payroll taxes and income taxes, which as more and more automation happens, will shrink. At the same time on the spending side, currently 20% of tax revenue spending goes towards paying, servicing the debt basically, paying interest payments on the debt. Now, a lot of the debt is short duration, so it refurbishes every five years it rolls over. What are you fucking laughing? - 'Cause you know, it's like things we've learned you've learned in the last, what? (laughing) - Yeah, like it's any different for you. (laughing) - Then you got into green by financial economics. (laughing) The internet thinks I'm a VQ person. (laughing) - A few months, few months, for you. - Good. (laughing) - This is our business, Dylan. - I know, I know, sorry, sorry. (laughing) - And so I'm self-conscious, fuck. - No, it's good, you're doing good, I just think it's funny. (laughing) Million people, listen to this guy, you just heard about that this month. (laughing) So you go from 20% of, is the supposed interest rates rise 1%, then over a five-year basis, the amount of the fraction of tax revenue that goes towards servicing the debt basically, goes from 20% to 25%, if there are five percentages, that would go towards like north of 40%, but if you take into account the fact the government is borrowing $2 trillion every single year, then that goes from like 40% to like north of 60%. So 60% of tax revenue basically just goes towards paying interest payments on the debt. Now I think the US is gonna be fine, because also the tax-based will increase if we let data centers get built in America. Other countries are absolutely fucked, in my opinion. I was just looking at which countries have a lot of debt, have very little tax revenue, and also a lot of their debt is serviced quite often. And those countries, like Pakistan or Nigeria or something, I think they're just gonna be very fucked in this new interest rate regime. So this, this, this crowding out effect is actually like the thing that I've like, is the reason it's not like yellow 1 billion gigawatts. - Yeah, yeah. - Right, you've got, you've got all these industries and countries that use a lot of debt, whether it's, you know, all these impoverished countries that you mentioned earlier that are just gonna default, you've got like consumer packaged goods, right? Like all of these like companies that make things you see at trader Joe's or Reverend use a lot of debt. All these telecom companies use a lot of debt, and banks use a lot of debt. And so if interest rates go up in the market, not necessarily the government set interest rate, but in the market, the spread of interest rate between what the government says their federal rate is versus what everyone else is charging because Amazon wants to raise $100 billion of debt next year or whatever the hell the number is. You know, probably less, but you end up with this like really challenging problem of where does the cash come from? There is some level that is funded by cash flows and cash flows keep going up, but the logical thing to do is to invest way more than your cash flows because then the returns of the future years will be amazing. So you have this delta, and then what's pushing down on the delta is all of these other things, right? There's regulations against data centers, regulations, you know, consumers getting mad, politicians getting mad, regulations against AI, the AI labs not releasing their latest models because of safety reasons, all of these things and interest rates going up are an influence on all of these things. So all of these things bend the curve from what does capitalism want in terms of just pure simple economics to what is the complex system that we have want and bends it lower and lower and lower to where not as many gigawatts as should be built will be built. Well, the interest rate is part of capitalism, right? Yeah, but like, you know, like in the simple economic model versus like the more complex what we have. Yeah. What is the rate at which you think Amazon or anthropical or whatever we've releasing bonds for debt next year? They do hundreds of billions of dollars of debt. What is the rate at? What is the average rate? I don't think Amazon will do hundreds of billions of dollars of debt. To total, let's say the big target. The hyperscalers in total will rate and all the clouds in the modeling that we do. We have about $11 trillion of CapEx from 2024 to 2029, total. And if you do, you know, if you fund a lot of this with cash flows and as much as you can, you still end up with North of $5 trillion of credit that need to be issued for this $11 trillion plus bill. You don't think the A-Broad and your continues even three X in a year over here? A-Broad and you does go up. I don't think it can go up forever. I don't, you know, like just like without like certain constraints being hit, I think labs will have certain incentives. And labs are not the ones building all the compute in many cases, even though they're increasingly trying to go. They'll have all these cash flow. Like if their revenue keeps increasing, whatever, that's fine. If you, how much should you say their revenue will be? You think they'll not have that much revenue? - No, I'm just saying till 2029, there's, you know, something on the order of $11 trillion of CapEx and six of that is funded with cash and five of that is funded with debt. And if that's the case, $5 trillion of debt being raised across the whole ecosystem does make interest rates go up. And then what prevents that, you know, there's a couple of things, one, do labs increase their revenue per megawatt more and keep inference allocations large in which case they're taking all this profit. They're accumulating all the profit across the S&P 500 'cause everyone's paying to, you know, reduce their costs. Of course, their profits will also go up, but, you know, cash has to come from somewhere. So there's an upper limit on how fast their revenue can grow versus the value they deliver into the world. And there's a diffusion aspect of the technology. But ultimately, labs revenue keep going up. They can't cash flow fund everything. The optimal scenarios you actually use credit as much as you can to fund because even if cash flows from the labs fund a lot of stuff, you want to build more than theirs. And so there is some amount of credit that gets built. Our current modeling has $5 trillion of credit and $6 trillion of cash funded infrastructure investments through 29. And when you take that, you've sort of got, you know, this is not enough compute relative to what this demand growth is from the AI models. And so you've got the obvious answer, which is revenue from megawatt keeps growing up. >> Yeah, that makes sense. So how much do you think interest rates will increase by 2029, is there a little flow of this? >> Dude, you know, all right, this is just vibing a number. But if you're vibing a number out, you know, growth in the world and economy is growing up a lot. So why wouldn't interest rates for Amazon go from, you know, from where they are today. I think meta pay, okay, let's say what, so this is going to be extremely vibed out. But recently meta's raised at like five to six percent. I don't see why they wouldn't pay eight percent. Because they would happily pay eight percent. Because the return from the compute that they're going to build is humongous. >> Right. >> And the market won't want them to. But if they, they'll want to pay eight percent. The flip side is if they pay eight percent versus the five they do, five and a half, six they do today, you know, two 50 beps increase. That makes everyone else in the economy also pay two 50 beps more. >> Yeah, yeah. >> Which then causes a lot of things, right? Banks will scream because if their credit spread goes up, then their assets don't re, their debt themselves reprises faster than their assets reprise. And you ultimately end up with, they're losing tons of money if their credit spread blows up. >> The other consequences of this are, this is the point you made. But the, if interest rates rise, the discount rate increases, which means that the discount cash flows of all equities. >> Yeah. >> Crater, which means that even though the stock market as a whole might be doing fine, like S&P 500, we'll be fine. >> Yeah. >> Any individual stock will probably have just like created in value, especially the, the Buffett like Berkshire type, you know, pay good cash flows for 30 year type stocks. >> Yeah, that's like, why would I pay this much for, you know, you know, Johnson and Johnson? >> Right. >> You know, like they're seen as a stable stock, good cash flows, they'll return their cash flows over time, or a railway company, like why the fuck would I invest that much if my discount rate isn't three percent, or five percent, it's now eight percent or 10 percent. >> And for developing countries, what's, the, the Basil Hopperan, who's a good friend and he's an economist, he made this point that we'll see a second bokeh or shock. So in the 80s, to fight inflation, Fad chair poll worker raised interest rates like more than five percent, or it's like something like eight percent, real interest rates eight percent. And that caused some 40 different countries, mostly in Latin America, to default in that decade. And I think that would probably happen again. In fact, okay, now we're getting into like singularity talk. So we've been talking about happens if interest rates- >> I think this all happens before singularity, but- >> Yeah, that's what I was saying, that's what I was saying. So we were talking about like, you know, before singularity interest rates rise two percent, three percent, et cetera. At some point, I think it's very likely that the world economy will be doubling every single year. Okay, this is not happening in five years, but it'll happen eventually. It's just like, there will be, there's just, there's a researcher, David Binder, who's done great work on this. But basically, if you look at like input output tables in a fully automated economy, you're just like, what would it take to like, double the entire stock of things in the economy? Ever so- >> Yeah, if economy grows at three percent a year, then it's like, you know, rule of 70, it's like 20 something years. >> Right, but then, but he was like, okay, well, right now we're bottlenecked by the fact that there's people. You can't like double. people every single year. But in a world where like you can also double labor force every single year. How fast can be economy grow? And I think it could double every single year and the very least would be like tens of percent every single year. Okay. The rate of interest should be pretty close to the growth rate. It won't be exactly that because of consumption, but it should it should be pretty similar. So then we'll go into a world I think in the 2030s where the rate of interest is like tens of percent. And like I don't know, part of my brain is like it might be hundreds of percent. But like, okay, let's say it's at least tens of percent. I'm just like, okay, every country that is not involved in the production of AI defaults. Every stock that is not an AI stock is like worth basically zero because discounted cash laws are worth nothing. If the federal government can't figure out a way to tax AI, you know, the servicing the debt is more than the current tax revenue. All of these other effects that I'm sure we're not even pricing in, like you can't get a mortgage, et cetera, et cetera, because fundamentally what is happening in this world? This is all nerds speak, right? But like, let's step back. What's happening? Just now it started the nerds speech. We'd be entering a regime where just we're in a totally different growth regime basically. And the economy is basically saying, hey, you like paying people, the government borrowing money to pay people pensions, the opportunity cost of that is extremely high now because that money could be spent building a robot factory that builds a robot factory that builds a robot factory. And so the opportunity cost of capital is going to increase a ton. I mean, that's fundamentally what the cause of all of these things we're talking about. Yeah, so as interest rates go up, equity markets get pummeled. And even AI companies, right, people are like, you know, some people who really believe an AI are like, why does micron or high necks or kioksia trade it two or three times earnings? And it's like, well, if you're really AI-pilled, everything in the economy should trade it like two or three times earnings. And if you're not AI-pilled, then sure, they're over-earning. An argument for why I think memory is going to do great, but memory stocks shouldn't ten acts or whatever, again, because if we're in the market where there's that much demand for memory, which means AI's causes drastic change in the economy, then everything should trade it like two or three X multiples and the stock market should fucking crash, right? And so in a sense like meta trading, I don't know, I think meta trades are like 1.5 trillion dollar company. It's like, what? Silly. There were way more than that, at least in like a logical sense, you just look at their cash flows and like all the infrastructure they're hoarding and all the compute that they're going to be able to sell for crazy amounts of dollars per watt, either as tokens because their lab works or just to anthropic and open AI, ultimately becomes a question of like, you have to reallocate all the capital to the AGI and you do that by pricing everyone else out. And so the limiter on AGI is not how fast can the research engineers like our roommate Shultok and crank the gears, it's actually just like, how much does the rest of the world let that happen? Right. Because they're going to regulate. They're going to obviously increase interest rates. They're going to say, no data centers are going to say, stop building fabs, they're going to say, oh shit, every company's equity value is tanking. So how can I pay for AI to increase my business? Well then, like, okay, then anthropic and open AI have to start like building their own stuff. And obviously they're going to eventually focus on, you know, they're building their own chips already or at least designing their own chips and it will expand out their, you know, they're contracting their own data centers and building their own, uh, infra in the next couple of years, um, you know, they're, they're sort of like, how does this reallocation of the economy happen? But there's a lot of downward pressure on it not being, you know, just straight take off. Um, even if the models were capable of it, uh, which, which I think you and I believe we're in a world where models are capable of that. But slow take off is, is, you know, at least my hope possible because everything in the economy and, and regulatory world, like governments saying, don't release your models, government saying, actually, you can't even use your models internally that much because that's going to happen soon. Um, they're already saying you can't release your models, which is actually, the thing I'm most worried about is, you know, a singularity, which external deployment is, is actually helping, right? So the fact that we're preventing external deployment. Well, does that prevent singularity? I mean, right now we're at least more revenue because the models are on capable of our side. Yeah. But I'm worried about a world where it's 2030 and the government's like, we're going to wait six months before you can release your new model to public six months, hundred x. Let's go. Yeah. And that's six months. And just like they do like recursive software and internally, they just have all kinds of crazy shit happening in the company, we know all the rest of us are stuck with models that are like, I current pace yours behind. Yeah. So here's my thought. I suppose that the whole world gets in on this conspiracy to, like, try to slow down AI. I don't think it's a conspiracy. It's like outwardly written, you know, from like every politician, suppose they basically prevent an entire, they slow down AI by a year. If computers increasing two to three x every single year, they prevent a whole year of AI deployments, such that you're a year behind where you would otherwise been. Having RSI, you're getting three to six years of AI progress in a single year. But they can't, they don't just limit compute, right? They also limit the lab's ability to release the model internally, right? We saw that. We thought they kind of stopped giving me those two foreign employees for a bit. I didn't, I didn't know that was true. They get internally as well. I mean, that's what they claimed. I thought that was just a different checkpoint. That was not mythos, but it was basically mythos. Yeah. But I mean, like stuff like that is not going to be allowed either, right? Like the government is dumb, but they're not that dumb. Right? Like, you know, I would hope at least. You know, governments are going to not want companies, at least the US government has the cards here. There's not going to want anthropic to use mythos for internally. They would be like, hold the fuck on, right? Like slow down, you know, because all of these regulatory reasons, everyone who's elected is going to hate AI. Even the people who are elected already hate AI, all the constituents, you're going to literally have like, I bet you at some point, your parents are going to call you and be like, do you want to keep it that you're doing a terrible job? You're making every AI progress happen faster than I like it. It's like, it's going to happen. It's going to happen. I mean, maybe you educate people, right? And maybe if they're smarter, they're progressing AI faster. But anyways, like, you're going to have real world constraints on the progress and development and employment of AI, even though, you know, it will happen eventually. It's like where we could tear ourselves apart before we get there. I think this domain is fundamentally understudied. Often we have unanswered questions within our deep learning research team where we don't understand some, say, some market participants' behaviors or certain dynamics of how trading happens. And it's sort of questions make for really good intern projects because they are ultimately topics that we care about and just haven't gotten around to figuring out yet. So even as an intern, you'll be contributing to real research, not working on some sort of contrived exercise. The Jane Street team follows frontier LLM research closely. A relatively common intern project is adapting a recent paper to financial markets, which come with their own set of gnarly problems. Ultimately, we're trying to model thousands of interconnected, irregular time series. The signal to noise ratios are extremely low because we have a lot of competitors trying to do the same. So we have this like adversarial, non-stationary, extremely high dimensional problem that we are trying to solve. To be clear, you don't need to know anything about finance in order to be a good fit. As long as you have a background in ML research, Jane Street can teach you the rest. Their 2027 internship applications are open now. Apply at JaneStreet.com/thorkesh. Something I find crazy about these scenarios is just how much of the world's future labor supply ends up in very few companies, and also how fast the labor supply grows here over here. So if like, compute of the frontier, you know, in flop terms, it's growing for 5x a year. And further, the compute required to achieve the liquidity is like decreasing 3x a year. So the basically the effective AI population size at the frontier lab is increasing 10x a year over here. And so that doesn't really matter that much right now because AI's are not going to have to do full jobs or be like as autonomous as people in their capacity to do work or pull off schemes or whatever. But if the current trend continues, you have a world where open AI goes from having, say, 10 million basically AI laborers this year to 100 million in next year to a billion a year after that. And then pretty soon, even if compute scaling slows down, it doesn't take up many more years before each company individually has more labor equivalence than there are people on earth. And I think that's like a thing that is very plausible by the end of the decade. There's more AI labor, more effective population within a single lab than there are people on earth. So we talk often about centralization of power because of nationalization or whatever. But we don't think enough about the fact that we're actually moving very fast into a regime where most AI labor, or sorry, most people, like in terms of like the work output or something, is just like concentrated within two labs, we're consuming more and more of the world's compute. And so if these AI's are misaligned, then most of the world is misaligned basically because they're most of the world's minds out there. But even if they're not, it just very few companies have like a lot of influence or a lot of. Yeah, it's sort of. There's the whole spat recently where it's like I think Gavin Baker was like in Dario believes that there's only one company in the world. And then, you know, Shulto and Dario came out and were like, "No, no, no, we didn't say that." But ultimately, if you believe in RSI, you believe in the labs are the most effective user of compute and can generate the most value from the compute, then the only thing that's going to happen is centralization of compute. And if you believe in AI researchers, RSI, AGI, then all of this exists. All of this is the base. This is even true there's no RSI. Effective population of the frontier is currently increasing 10 next year over year. For a given level of capabilities, right? So if you get to the level of capabilities, which is a human, a very competent remote worker, or like a very competent software engineer or a very competent researcher, that population of those would like 10 next year over year at the current rate of, current rate of capability. - I see, and without RSI, then once you have RSI, it's even bigger. - Maybe growing like 100 next year or 1000 next year. Or they're like, intelligence is increasing, but the population isn't increasing, or some mixture of the two, right? - Yeah, I mean, I guess, I guess like, what world do you see, Dr. Cash, where everything is not centralized? Because it seems to me that every force is screeching toward centralization. And that's scary as hell. I don't, I would love for it not to be centralized completely, but maybe that's the whole point of a machine that loves grace, right? Is everything and it makes our lives great. - Yeah, it's so hard to think about the future, but I agree with you that I think the fundamental problem is that lab AI training has huge economies of scale because any effort you spend into training in AI for a specific skill or a specific set of knowledge, gets amortized across billions of sessions or billions of users. So that's like one effect. The other effect is if you're slightly ahead in the AI rate and compute is in shortage, you can charge a much higher markup because you can better economize the scarce resource. So there's like two effects, which are give more and more to the person who's like ahead in the AI race. There may be more, right? So if there's models that are learning from deployment and one model is like deployed much more widely than another one. It's getting much more like real world data. - Yeah, your point is taken that like, whether it's user deployment and continual learning, whether it's training, having these economies of scales, whether it's the incremental progress that the best AI model helps you to make the next AI model, RSI, all of these things. - I didn't mention RSI. All of these things point to centralization. - So I think one of the big intellectual projects honestly, that yeah, we should spend some time thinking about A-Sync or at least I'll spend some time thinking about is, what is a vision of like a decentralized, broadly empowered future after a AI that takes these economies of scale seriously? The alternative vision is that the government controls it. And maybe you think that you can trust the government more because it's not private corporation. - I don't trust the government and I don't trust Dario and I don't trust Sam. - Yeah, yeah, yeah, that's a problem, right? But there's no, at least obviously, obviously it's very easy to be wrong about the future and you don't anticipate a key effect or something that changes everything. But X-Anti, it's very hard to see a reason why they're, or like, how we avoid a scenario where we have to choose one source as a vision. - Capitalism worked, right? It's the decentralized decision making and decentralized power. And why super centralized capitalistic economies actually grew slower than super decentralized capitalist economies to some extent. You have to have a little blow on all this. But then AI flips all this on its head. And ultimately you're actually a private ownership is probably not the most efficient economy and they're for it grows slower than an AI economy. Which is centralized. - It's no private ownership but it's like, how many firms are really involved in this? - Well, this share of the economy, that's not like 5% of the economy or something like that in the US. I was right, one trillion divided by 30, less than that, sorry. But yeah, maybe 2% of the economy right now. It's like, in video is a huge share of it and then throw up like an opening eye and these hyper scalers. And obviously there's other firms involved that have large share of the AI. So I was just having from very few companies. So it's like, it could be private property but very few companies that are involved. - I mean, this is what the structure of the market is doing, so what can prevent it? I don't know, unless AI progress slows down, unless governments regulate the fuck out of it. This is all that happens in which case, you know, we're headed for a world where either we have super concentration of resources and we pray that that one company gets everything right or we have governments slow everything down and people slow everything down and you have a slow down of progress somehow, hopefully. And there is a more of a balance of power. And even as we go towards an AGI, ASI, RSI, everything along the way will still lead to someone's gonna allocate, gonna capture more resources. So it's kind of hard for a framework in which AI doesn't lead to super concentration. Now, the one positive thing here is that today and Thropic does not capture most of the value. So we can talk all we want about, oh, you know, they went from $20 million per megawatt, $200 million per megawatt, but they're still paying 13 for a lot of the compute they're buying. But at the end of the day, the reason they've gone to $100 million per megawatt is because James Street is capturing $300 million per megawatt or $500 million per megawatt, or Dwork Esch from researching his podcast and learning about credit is capturing, you know, how many dollars per megawatt? Now, how much can you use? Tough. - Yeah, yeah, yeah. - But, you know, I think that's the like one saving grace is that the rest of the economy may be profits so much more from enthrough it. - No, but the whole logic we were laying out earlier of them reallocating inference to AIR and D. The whole logic of that is that the returns to labor inside AI labs is much higher in the returns. - This is my cup, return to the outside, yeah. - This is my cup, I agree. In all scenarios of the world, you know, there's 80,000 worlds and only one of them in Therophic doesn't own the whole world is, is that, you know, again, power concentrates because I don't want to send the tokens outside, they're more valuable inside. And so it's the same thing, right? Why would I let James Street, you know, make all this money off of these degenerate options traders? Hey, there's some off there, come on. (laughing) - I'm sorry, I'm sorry. - Jesus Christ. - No, I think it's great. I think it's great, it's a good value for the world to make it an efficient market. - Yeah, yeah, yeah. - You know, James Street making all this money off of getting the world view correctly, making money off of degenerate options traders, whatever it is, you know, why would Anthropic allocate compute to that? If the end, you know, monetization that James Street has per megawatt is 200, so they're willing to pay Anthropic 100, well, what if Anthropic can just generate hundreds of millions of dollars per megawatt by using that compute internally? And that's what's happening. - There you go. Well, on that summer note, I guess, I guess we'll meet again when the RSI is officially kicked off. - You're not gonna hit me on your podcast again for like, two months. - All right, cool, thanks dude.