Assessment note: [[2026-08-25-dwarkesh-dylan-patel-two-labs-workforce]].
Okay, I'm back with Dylan Patel, founder of semi analysis. Our version of a family Thanksgiving dinner is a regular yearly podcast, but you're not actually related. We'll tell the people this. It'll destroy the myth. Um, walk me through. So, basically where the world economy is headed is more and more becoming a function of where like lab economics are headed, where like the comput is headed, etc. So, I want to understand where the crazy future ends up within a few years. But uh let's start with just where we are today. So walk me through lab compute and lab revenue right now and then be projecting 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 eye. Now it may be built by others and then rented to them but it's in at the end customer it's them. Um as we go forward into the future the the numbers
[00:01:02] for computer ballooning right we're at you know you know a little bit over a trillion dollars of capex this year as we go out into 2018 it's going to be more than $2 trillion. Um 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, you know, 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 um at 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 um economics, right? you know, so up until now, there have been companies that mostly lost money. Um, Anthropic started turning a profit in Q2. Um, it's believed at some point in Q3, OpenAI could potentially start turning a profit even. Um, with the big rise of Codeex and and 5.6 and all this. Um, but if we go back a year ago, everything that they
[00:02:00] all the money they had was venture funded losses, right? If we go back to even the beginning of this year was venture funded losses. um they they've now turned the corner and are actually starting to profit. Now, that doesn't mean they're not taking in new capital. The new capital is still coming in to accelerate the growth further, but ultimately there's more and more of their business is being funded off of their own revenue rather than capital injections into them. Over the last, you know, year and a half, their margins have really skyrocketed. You know, the 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 right GPT4 being served on um you know Nvidia hopper GPUs was generating negative gross margin for open AAI but now when openai serves GPD 5.6 or anthropic serves, meet Opus 5 or Mythos uh Fable 5, their revenue
[00:03:00] generation has passed well beyond the sort of incremental 10 $15 million per megawatt. In the case of Anthropic, the the revenue has gone as high as 50 uh million per megawatt. Um and and what that now enables them to do is hey if I spend 10 bucks on inference capacity actually generate 50 bucks 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 at the labs or the relative ratio of compute that goes to the world versus goes to the labs. um where if you say right now a third of marginal compute is going to the labs by when is it over half of the incremental comput in the world is going to the labs and by what point do the labs have basically a vast majority of the world's compute >> yeah so so earlier this year you know the beginning of this year anthropic openi started at two for openai and less than two for anthropic um end of this year they're both above five um so
[00:04:01] they've three 4x compute as a Um when you 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 open AI are taking uh as much as 40 to 50% of compute uh next year and the centralization doesn't look like it's slowing down or stopping in fact it looks like it's only accelerating now who's building that compute for them will change. Um, you know, next year a big new entrance is for example, SpaceX is building a ton of compute and they're actively going to lease quite a bit of it to Anthropic and OpenAI most likely because they're the ones who can who have the marginal capability to pay the highest price. In addition, OpenA and Enthropic are also starting to build their own compute. OpenAI with their own chips, Anthropic with TPUs that they're purchasing with from Google and deploying with Fluid Stack. And so when you ask, hey, when does half of the world's incremental new compute go to
[00:05:01] just open anthropic? >> I mean, it's it's really by the end of next year, >> it's already half of the incremental compute is going to entropic and open >> because uh comput 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 is serving the demand from two labs. Um, how long do you think? So, there's this trend where maybe world compute in gigawatts doubles every year, but the compute at the frontier laps 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 to just multiplying out 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 compute or how how do you see the world compute situation over the next few years? Yeah. So if the incremental compute adds this year 30 gawatt, next year 50 gawatts and the year after that 70 roughly, you end up with this really interesting phenomenon
[00:06:00] which is okay well a new watt deployed this year significantly more efficient than the watts deployed 2 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 um ladder here. So if Enthropic and OpenAI take on, you know, 45% of compute next year, you've you've got them in, let's say, December 27, they have taken on half of the world's incremental new 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, um you you've got them just controlling most of the usable, you know, flops in the world on their own.
[00:07:01] >> The thing I'm confused about is why you think we only add 80 gawatt in 2028 if we entered a world in which the price the value of comput increases so much. >> That's the upper bound, by the way. That's the that's the like I'm so bullish, >> right? Okay. So le let's let's do some train of thought here. So when I interviewed you a few months ago, you said in order to make a gigawatt of I think Vera Rubins, you need one sec. You need 55,000 N3 wafers, 6K N5 wafers, and 170K DRAM wafers. Um I don't know those numbers. >> I'm going to troll you, but the way you said wafers was so wafers. By the way, when we first uh 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 >> in North Dakota. I was in elementary school and I'd be like, >> "Can I get a wedgie? >> Can I get some wedges?" >> Anyways, so that's for one gigawatt,
[00:08:00] right? Yeah. Yeah. Now I had an LLM run your wafer fab equipment model and figure out how much um tooling how much the tooling cost to produce a gigawatt of compute basically every single year and we said had like three to four billion. Now suppose you add in you know clean rooms and uh shell and everything else at the fab. So $6 billion 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 6 billion in capex is producing a gigawatt every single year and that gigawatt is producing hundred billion every single year. So even over the course of five years so you know the first gigawatt has generated five years of profits the second gigawatt that the fab has produces generated four years of profits and so on. Um 6 billion of capex at the fab level will have generated over a trillion dollars of end
[00:09:00] re end AI revenue. >> Yeah there's a lot of opex along the way. There's a lot of um other capbacks like the data center, the power >> and you have to pay like you know the open for there's a lot of different people who need money here. >> So but take away half of it for all this all these middlemen. The thing is there's a 100x discrepancy between fab capex and end revenue generated. More than that actually really but uh 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're you have this huge discrepancy where you can turn $1 into $100 and you're they're not going to figure out a way to make more mirrors. >> I mean, they are. It's just these mirrors take some time to bake, right? >> But the emergency are so big. were like anthropic and open air like we could make a trillion dollars right now but we're just bottlenecked on the mirrors that go into the ASML machines like they'd spend okay how can we make more mirrors if we spend hundred billion dollars on this right that's a situation we're going to be in pretty soon and I'm just like we're not going to be able to
[00:10:00] solve that supply constraint that just seems quite hard to imagine >> no there's definitely um you you you've seen people do funny arbitragees here where they buy like turbines and then they try and resell them because the value of a turbine is way more because it's the thing bottlenecking $400 million conceion dollars, right? But but ultimately like yes, capitalism will cause these things to expand, but it's it's a whip, right? It takes a long time for the whip signal to get to the tail end um of of that. And so the supply chain doesn't react immediately. In fact, you go to talk to to someone at Carl's Ice, they're like, "Yeah, yeah, yeah. We need to make a hundred 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 EUV tools a year. Um, and and so now they're they've like sort of they're
[00:11:00] like, "Okay, we need to do that." But in reality, you know, because of all the economics of what's going on, it it should be even more. Um, but it takes so long to to >> suppose they every single company in the firm, sorry, in the stack got private equityed >> like somebody came in who was super hi pill and was like we're going to maximize production. How fast what do you think the physical constraints on making more things would be >> because we're the reason I ask is we're phys pretty soon going to be in a world where the lab revenue or just AI cash flows because obviously the accelerators also have these huge cash flows will be so big that you can just fund extreme expansion of all this production from cash flows themselves. >> Yeah, I I I do agree generally there's obviously some physical constraints. Um, the way the supply chain is expanding currently, the 100 is roughly still the right number >> for 2030. >> 100 ASML tools >> for 2030, but you know, if you said, Carl's ice, here's $10 billion, please just expand production, that
[00:12:00] 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 next year. I don't have I don't think it'll happen the year after because the world is capital constrained. But in a 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 >> I don't think they're going to do that but >> yeah or hundreds of billions at least right it seems like and they realize where the world is headed >> I feel like they could just make >> so so the thing is the labs can spend hundreds of billion they're going to 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 um you know the the wafer fabrication equipment supply chain will do, you know, two, you know, 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 a number. You know, you you sum all this up, it's going to be, you know, 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. Yeah. Yeah. I mean,
[00:13:00] obviously, they will like never get to that point, right? because they want to keep reinst um yeah if the current catch continues would be like north of 50 gigawatts per lab by the end of 2028. So between them they'd have 100 gigawatts. Um those gigawatts as you're saying drive manyfold more throughput or more performance by 2028 than they are now right because the hardware has gotten better. So not only have like flops for watt increase but also the hardware gets better at um working with AI workloads. Okay. So 100 gawatt for the labs end of 2028. How much is like 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 Amazon willing to sell even that much um also
[00:14:03] one caveat when we're sort of talking about these gigawatt numbers is you know when Amazon is serving bedrock anthropic models that counts as enthropic compute in sort of our worldview because it is effectively at the end of the day counted as revenue for Enthropic. >> Um even though like there's a revenue share and credit back and all that. Um but ultimately >> in 2028 it's 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 or SGlang. 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. Um, and and you'll start generating more revenue than you're paying for the compute. Um, and so this is this has sort of already led
[00:15:02] to this compute pricing 10 to$15 million per megawatt start to inflect up. Um, and to get to that 100 gawatts 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, does compute now get to $25 million a megawatt? Does it get to $40 million a >> But as you're saying, it's already the case that the labs are generating way more revenue per megawatt than everybody else. If they stay as far ahead as they are currently, you'd expect that to be the continue the case. If there's like some kind of recursive self-improvement, where the AI labs are like relatively uplifted, where they have models internally they're not releasing externally that are helping them make the next model better, you'd expect that to be even more the case. And aren't you already seeing this where like SpaceX or whoever's like slightly further behind, we'll just sell compute to the highest bidder. If they can't internally monetize it as well as the labs, so I feel like it's continue expecting them to be able to gobble up like bid for larger and larger shares of the comput. 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 at current pricing or anywhere close
[00:16:01] to it. >> Sure. Sure. >> They they do have to start paying 25 30 50 million a megawatt to really gobble up 70% of the world's compute in 2028 to get to that 100 gawatts by 2028, which is a very sort of 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 this regulation that they advocate for is actually slowing down the labs a lot more than it slows down you know sort of the open source Chinese language models. Um you know OpenAI not releasing uh Astra OpenAI stopping training for two weeks. Anthropic not releasing what their safety assessment said is model 2 which is widely believed to be the next version of Mythos. they're clearly not releasing their best models and in which case their revenue per megawatt stalls or even can start to decline again because other models are competitive again. Um 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
[00:17:00] 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 is sort of in in a world where safety doesn't matter. I 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 a megawatt and no one else has any logical reason to do anything with their compute besides say please Daario take everything off of my hands. Um but there are you know forces at play uh that which we cannot describe u that that would potentially slow this down. >> Yeah. Yeah. Yeah. I mean I think an good intuition pump is just how 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, you know, six figures or north of that a year. Um and if you have a gigawatt that can sustain a population of like say a million of
[00:18:02] white collar workers just let's say roughly right. Um that's like you could then off the back of that that would be 100 billion. That's actually surprisingly low. >> Yeah. 100k per person million population. Yeah. >> Yeah. Yeah. Um >> I don't know but it' be many hundreds of billions of dollars if you get like full AGI uh per >> 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 anthropic u thankfully so far it is mostly just being given to the users right Jane Street with their exclusive contract with openai for GP5.6 six ultra fast mode or Jane Street where they're like one of Anthropic's biggest customers is generating way way way way more value out of the tokens they're paying for than Anthropic is uh generating in terms of profit, right? Because they get to, you know, >> make money off of the >> market. Um or Meta, who at one point
[00:19:01] was, you know, rumored to be, you know, as much as 10% of Anthropic's business. um you know, they're generating way more efficiencies by optimizing their ad algorithms or what have you and and getting engagement time 5% longer and you know, all these things. They're they're making way more money off of using these models than than anthropic. And so the ultimately, you know, and that's that's what's required. So sure, if you had a million new software engineers, the cost per software engineer would also fall. >> One thing I'm confused 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 enthropic and OpenAI can generate from it or be very close to it with like a small amount of markup for Anthropic and Open AAI? Like right now, it's really weird that there's a 4x or more difference between what compute sells for and how much money anthropic can make from it. And in a world where the revenue per gigawatt continues to increase if Anthropic's ability to monetize a gigawatt doubles or triples
[00:20:01] or something, it'd be weird if then the gap continued to increase. And so Enthropic just by like having some software having some weights can take something that cost them $10 and then turn it turn into $100. >> Yeah. So there's there's a bit of um this is always a fun question, right? Which is where does the value go in AI? AI is generating all this value. You've got, you know, the end user, which we 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, you know, the app layer. Well, so far the app layer has generated very little value. Um, then 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. Um, and looks like it's on the path to generating, you know, hund00 million per megawatt. Um, so turning, you know, $105 into $100, as you said. Um but if we go back again a year ago, the hardware supply chain was generating all this gross margin while literally everyone else was losing money on it. Um open anthropic were just plowing VC money in. Um and and as were
[00:21:00] many other startups and and many of these hyperscalers are building infrastructure without knowing if there was going to be a payoff. Um so ultimately you had this like you know negative value being created on the model layer almost if you will um because they were selling the tokens for less than it cost them on the infra side 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 HPM or memory for AI even though theoretically their value they were delivering was humongous. Now you've got well actually KSMC makes way less value than the memory guys. um is that actually how much you know they're capturing less value even though so so the the value capture is shifted around a lot which is very fun for um people tracking the market or participating in the market like like Jane Street as an example >> you got to plug them that hard >> um so you know what happens you know going forward does anthropic and openi you know they've they've they've slowly started a balloon in value capture do
[00:22:00] they balloon and take all the value capture well that was the thought and then and then Elon showed actually no, I can sell my compute for $25 million a megawatt or $40 million a megawatt to Enthropic and Google. Um, 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 Toronto compute like B300s or whatever that sold for 40b a gigawatt the SpaceX sold for 40b a gawatt to Google? What does that sell for at the end of next year? I think most compute will still continue to transact at sub20 $20 billion a gigawatt >> even at the end of next year >> because all of it has to be financed for compute that you can build without financing right if if Meta can build compute Microsoft Amazon SpaceX can build compute without finding a customer just saying it 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 so most compute is contracted well before it's built >> and so this is sort of what Elon took
[00:23:00] advantage of in market is. He actually had all this compute and he was like, "Hey, Anthropic, I know you're making like 60 plus billion dollars per gigawatt. Why don't you just buy my stuff for a crazy amount of money?" And obviously, you know, it's not like Elon decided this or Anthropic decided this. It sort of markets figured itself out. Um, other people, you know, you go to a random cloud, they're like, "Okay, I'm going to build a gigawatt 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 and SpaceX are plausibly the like 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 an end customer that's monetizing it at a huge degree. and they have an actual balance sheet. So they can go to the credit market and being like hey guys I have you know you you
[00:24:02] build a gigawatt you can make you know 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 AAI at crazy margins. Yeah. So now we've sort of entered a regime where um 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 timeconuming because the vast majority of candidates don't fit the profile that I'm looking for. So I created a recruiter in Grockbot to see if it would help. I gave it a huge contact dump where I monologued basically everything that I wanted and then it spun up 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 ex feed and DMs. One went
[00:25:00] through the end credits on various documentaries I like. And the last one looked for editors who work for some of the YouTubers that I follow. Grogbot then took all these different candidates that these sub agents had found. It filtered them against my criteria and then delivered for me a final short list 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 Grockbot some more feedback about what it was missing, it came back with a new list of candidates that I'm actually extremely excited about. I ended up saving this whole workflow as a routine. So, every week now, Grockbot checks my inbound email and XDMs for promising new candidates to potentially interview. If you want to try Grockbot yourself, go to x.ai/bot. What do you think their revenue per gigawatt is by the end of 2027? like for anthropic or open AI by end. >> I think I think it's highly dependent on who has 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 a megawatt >> by end of 27. >> Oh, by the end of 27. >> Um that's where it gets more challenging. But I think I think it could get to you know higher than that. It's like 70 80 million a megawatt uh
[00:26:00] blended across the company >> if not higher. >> Yeah. And so I think >> if if that's the case, right, then what happens to the price of compute? Well, if I'm anthropic, incremental comput is worth it. Maybe I spend $40 million a megawatt on SpaceX compute. Um, and if I'm SpaceX, you know, I I look to the supply chain. I'm like, well, you know, I've struck this deal with Jensen where he's now all of a sudden using Twitter. Um, and and you know, there's Elon saying they're exclusive to Nvidia, but why doesn't Jensen raise his prices? And then, you know, SKH and Micron and Samsung looked at Nvidia like well, why don't they raise their price? So I think I think the value capture there's a bull whip effect here right where uh 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 you know to get that incremental capacity you sort of have to right so TSMC raising prices very slowly but memory companies raising prices very quickly you know substrate companies raising prices very quickly
[00:27:00] different parts of the supply chain raised you know 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 I'm sort of surprised you think like revenue per gigawatt doesn't increase way more than even like 100 per >> by the end of next year. >> When when does RSI happen? When does take off, right? I think 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 a model from a year and a half ago? Let's say like >> my problem with this is the best model that exists in the world was trained in February. >> Okay. You're saying maybe we just won't be able to release the >> open says they're not training models for two weeks, man. What the hell? >> Yeah. Yeah. I mean there's another there's one thing like internally are they getting enough use for it to so they'll like bid up the price of computer or another is like does AI progress as a whole slow down because of regulation? >> Yeah. But they're not even allowed to use this like new model intern like Astro is not widely deployed internally even. >> Yeah. Yeah. But still I don't know just like you go if you have like a model that is what was a model released like let's say the beginning of last year like GPT >> 40. Is that 40?
[00:28:00] >> Yeah. That that's like you're talking about a 40 to Fable size or Mythos 2 size leap by this point uh again by the end of 2027. >> Yeah, but Mythos 2 is not out. >> Yeah. Or like even Mythos, right? Like that leap 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 or 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 I that that's the only way I could see it being only 100 per megawatt by the end of next year. >> Yeah. I mean, as as long as the 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 me moratoriums. Ohio's saying you have to or at least trying to say you have to like pay everyone's property tax in a certain radius. Um these sorts
[00:29:00] 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 um progress does slow at least in the external sense. Um even if the models internally keep getting better and better. I I I see no reason why like you know again like in a in a takeoff scenario why would Anthropic 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. Um and then that six-month difference if progress in accelerates is actually a bigger um differential. So 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 end of next year they have I don't know close to 20 gawatt. So like 10% of their compute 2 gawatt let's say they want to go from 60% compute to um training to 70% compute to training. And their investors are like
[00:30:01] well if you're be able to generate a hundred billion dollars per gigawatt. you're basically saying no to like $200 billion dollars of revenue in order to increase your training compute. As investors are like, "What the 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 comput is giving us." >> Yeah. So, so this is sort of what I I personally believe that the labs are going to allocate less and less compute to inference over time, which I think is very non-conensus, right? Everyone's sort of >> the standard belief of most people is, oh, most compute will go to inference. Um, most of it will go to forward passes for training, not maybe necessarily revenue generating inference, but ultimately you end up with if they're generating, you know, 30 $40 million per megawatt today, you allocate 40% to inference. If you now get to jo generating 60 $70 million per megawatt,
[00:31:02] do you still allocate 40% to inference and generate all this profit and then do dividends and share buybacks or do you go build AGI? And I think the obvious answer from Anthropic and OpenAI and not just at the executive level but also their board is go build AGI because it's way more profitable, right? Um and 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 and so the the whole point is well okay if I'm gener if I'm selling tokens is enthrop is openai releasing ultra fast mode for just external or they doing it internally too and it turns out no actually I'm going to allocate it to internal and external because my internal, you know, value that I'm generating from super fast AI or the best AI model is way more than what someone externally is.
[00:32:01] >> 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? Um and so you know they're not going through that calculation but ultimately it's it it makes more sense to dedicate more and more compute internally and the only reason to in you know have inference compute be so large is so you can grow your training fleet. >> Right. Right. Right. I think this is an interesting economics question that I feel like we can have the models digest of what is the at what what would have to be true about a world where they reduce fraction of comput spend on inference. >> I think they have been over the last three months already. >> Interesting. I think I think at parts of this year they were increasing fraction of compute. So let's let's just take month by month. You would agree that every month Anthropic has added more compute than the prior month. There might be some noise when they like sign a SpaceX deal or whatever, but in general the amount of compute is is a curve up and so in January they added less compute than December. And yet
[00:33:01] their revenue ads, you know, skyrocketed and then they've plat, you know, sort of plateaued. They're only adding, you know, they're not adding $25 billion of 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. >> Yeah. >> And so they are factually increasing their compute towards R&D today. >> Yeah. Yeah. Yeah. >> Yeah. I think this this is like self-evident um if you if you like look at what they're doing enough. >> Yeah. So I if I look at the numbers you said of like how fast world compute grows here here are some things I want to understand. So it seems like if I add up the numbers you just said it would be over 200 gawatts of world compute by the end of 2028, right? >> Yeah, globally. >> Okay. And uh how fast can that continue growing like global global AI compute after 2028? >> Yeah. So 30 this year, 50 next year, 70 and 28. Um 29 should be like on the order of 90 to 100 >> like then just 100 more every single year or something. >> I think I think the the slope could continue to go upwards. I mean it's hard to predict anything more than four years
[00:34:00] out given uh >> who knows what you know are we an RSI regime or you know when is the world economy growing at 10% a year uh because if you're at 100 plus gigawatts a year you're you're at absurd revenue uh uh GDP growth >> right if you think there's 200 gawatts globally in 2028 how much is in China by that point and how does like yeah how does Chinese compute continue increasing through this whole trend because if if the RSI stuff kicks off in the west before China has a large amount of compute and maybe we're living in a different world than when it doesn't. >> Yeah. So, China today um so if we 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. um and a dramatic increase in America. So today 70% of watts are being deployed in
[00:35:01] America and and and you know China is is really uh a very small number. It's sub 10% of watts being deployed for data center AI compute is in China. Um and as we step forward they're still at a very small number. Um their domestic production is quite small. Their purchasing from Nvidia is still quite small. Um and a lot of that ends up in in other places as well, right? You know, Malaysia or what have you. Um so ultimately, China domestically still continues to have 10 sub 10% of incremental new compute. So in 2028 might start to inflect up, I think. Um but it's pretty easy to say China will have like 30 gawatts of AI compute or less >> by 2028. >> Yeah, in 2028. >> Okay. And then how fast does their hockey stick go up? >> I I do think in 2028 they have a big uplift in what compute they're able to deploy. 2026, they're still mostly relying on a lot of the smuggled chips. Um, you know, you know, a lot of the chips that uh TSMC made uh for companies
[00:36:02] 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, fabs start to go up in 28 especially. Fabs 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, 510 gigawatts in just 2028 of domestically produced chips. Those chips are definitely worse than the chips that Nvidia will have in 28 or Google will have in 28 or OpenAI will have in 2028. >> So even even the gigawatt number overstates things you're saying like it's like 30 gawatt, 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 the following year or some you know I know you said you can't really say that far out how much is China able to add 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
[00:37:01] >> um there's a lot left to you know whether or not the US passes the match act um whether or not ex tools continue to get export controlled how fast China can build their new equipment that they're starting to be able to do produce domestically. Um, but ultimately, you know, China China is definitely going to hockey stick if there's anything uh China's really good at is is scaling manufacturing really really quickly. Um 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 um into into domestic China or at least uh close the gap in what the US is allowing the uh you know Nvidia to sell them or what have you. >> But do you think China could do adding 50 gawatts 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 could do 50 gigs. Um, but if most of those are
[00:38:01] domestic chips, there is some factor there where that 50 gawatts is really worth as much as 20 gawatts in America or in from from American chips. >> Right. Right. Right. So yeah, you're you're actually projecting a world where maybe the leading lab in 2028 has more compute than China will have in like all of China will have in 29 or even 30. If you if you weighed gigawatts by their quality, >> implying that there's nothing done to slow down the US labs, right? Clearly the government is starting and politicians are starting to do that. >> Yeah. Yeah. 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 when I interviewed Jensen and asked about expert controls I am a libertarian person and I'm like I wasn't like genuinely sure what I thought about this issue. I was steel manning what I what is like the opposite view that he has and because I think it's important to hash out ideas. Um but I'm like yeah maybe there's a world where if we just cooperated with China it would be better for us especially since they control so much of the supply chain and the other things that will be needed for robotics
[00:39:01] and other things. Um, but I didn't realize the comput situation was as as you're saying. Like actually the export controls do seem to have like really if if they ship the amount that you're saying, that's a huge difference. By the time we have automated coder and getting into like automated researcher, 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 export 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. >> 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 takes longer then ultimately China will catch up
[00:40:00] 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 um Chinese companies today are not that far behind in AI models at least perceivably by the public uh relative to the amount of compute they have right the leading Chinese labs have 100 200 megawatts total of compute at most um bite dance uh seed seed being the one outlier where they have significantly more than that um but you know Kimmy is not running you know a gigawatt or anywhere close to it. Yeah. >> Um whereas anthropic is you know nearly 5 gawatt by the end of the year right or more sorry. Um and so you know the question is sort of well does it matter? And I think right now it doesn't matter that much the this difference in compute because you know when we break down the compute ratio or budget of a lab historically it's um been you know let's say 60 or so far it's been like 60% training 40% inference but that training
[00:41:01] gets broken down further and it's 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 research Researchers are generating ideas, testing new architectures, testing new data mixes, testing new hyperparameters, whatever it is they're doing, new attention techniques, blah blah blah. But ultimately when they do the training run, when Enthropic trains mythos, it's sub 200 megawatts, right? >> The pre-train or the whole thing? >> The pre-train. >> Um, it's sub 200 megawatts for call it two months. Um, and then the RL is even less. >> But you think the RL was less to compute than the pre-train? >> Uh, at least in terms of uh single sight inference, I mean single sight of pre-training. Yeah, >> but total computer was probably higher, right? >> Total, but it's like sequential, right? >> Yeah. >> So, at most the most they ever used at one point in time was maybe 200 megawatt. >> Mhm. >> 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
[00:42:00] reasons for this, right? You can't it's hard to coordinate all these clusters. It's hard to um colllocate all of them. It's hard to do multi-sight training. it's hard to do um RL, you know, generating even more rollouts during RL does not necessarily make it better. There's all sorts of reasons why you may not be able to leverage, you know, all 2 gigawatts that you have for training onto training, right? Actually, I can only leverage 200 megawatts. As we get closer and 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 and uh to start to like become a lot more fuzzy or even higher for training. Um 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 you have if you end up in a world where you're doing 100 gigawatts a year at current prices that would be five trillion of capex every single year >> and then stack on the fact that you have to build the power plants way before
[00:43:00] then slash it's a 30-year asset. You stack on the fact that the data centers are you know 15 20 year asset and you have to build that then too. So the 5 trillion you know you have to account for future years growth so it's actually going to be more like seven or 10 trillion >> huh of capex. I don't 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 uh generation or whatever in the data center itself, >> right? Exactly. Um and and the data center itself is um when when you talk about AI capex, people are saying 4050 billion, but that's really just the critical IT. Yeah. >> Right. The servers, the networking, the fiber, the transceivers, optical communications, all this sort of stuff. um it doesn't account for the data center itself >> or the power plants themselves which are being built ahead of time. >> Yeah. >> Um >> if I'm building 100 gawatts this year and 150 gawatts next year, well then all of the buildings for that 150 gawatts need to be built and capex this year. And if I'm building 200 gawatts the year
[00:44:00] 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 like l this like actually it's much bigger than even $5 trillion if you're building uh 100 gigawatts, >> right? So very plausibly incremental capex every year is getting close to 10 trillion dollars >> by the end of the decade >> right which is going to be like close to a tenth of the world economy and like a third of 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 would just be going towards data centers and as I say that out loud I'm like maybe maybe you're right and we just won't allow it and that's the reason this doesn't happen right cuz like for this exponential to 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 >> um and credit markets exist and capital markets exist so to enable let's say
[00:45:02] that 100 gawatts by 2030 or let's even like let's even like pare it down to 2028 where it's like three or four trillion dollars of capex across all of these items uh you know a couple you know over you know two and a half towards comput uh IT capex and then another one to two on data center and energy um and all the supply chain downstream like semiconductors and all that stuff so if you're at if you're at three or four trillion dollars of capex where does all this cash come from no one is generating that much cash from the business yet right um hyperscalers they funded all of the growth up until now Google Microsoft Amazon uh 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 capex and in addition they raise debt and spend everything on capex right you've seen meta do it even Amazon even Google um you know Microsoft will be there soon everyone is raising debt to pay for their capex so now who is the incremental person to pay for this um
[00:46:00] that was not doing it before in the case of like Google it was pretty simple for them to stop doing buybacks or meta stop doing buybacks and turn around and buy computer infrastruure structure and that doesn't have a huge effect on the market um but it does have some effect. But as you as you step forward to 2028 where the hyperscalers are now raising hundreds of billions of dollars of debt um 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 um you know there's the semiconductor companies like Nvidia uh and Broadcom and the memory companies turning around and deciding to fund some of this capex. There's the traditional infrastructure investors who are turning around and gathering capital and investing in infrastructure and instead of bridges, it's data centers. Um, 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 hyperscaler debt or I
[00:47:00] should just buy this data centers debt or I should buy enthropics debt because Enthropic is willing to pay 20% uh you know rates for you know 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 a gigawatt. Um so you 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
[00:48:00] allows you to add telemetry and logging to an event that has already happened. For example, you can rewind to 5 seconds before a crash and decide to capture all the network traffic. Most powerfully, antithesis gives you a live terminal into your system that you can use to perturb anything you wish. Kill a node or disable a 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 needed 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/warcash to learn more. So you and I have been debating uh off air for the last few days whether there will be a sovereign debt crisis as a result of AI. And the logic is this. Um AI is you you have a situation where as
[00:49:01] we were mentioning very little investment turns into a lot of money right so the rate of return >> what a problem dude oh my god can't believe it. No, it is a huge problem for everybody else who can't turn a little money into a lot of money, right? Um, so the rate of return is incredibly high. Even at the data center level, you know, if you build if you like build a data center and you're like give rent out to anthropic or urban AI for like 10x what it cost you on a depreciated basis to build it, it's crazy. Um, and so you turn $1 into like $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 that other companies would have done or that you as a consumer or a mortgage buyer would have done. Uh then that's just making it basically more expensive for everybody else to borrow. This has huge implications for tons and
[00:50:02] tons of people. Sorry, I'm going to go on a bit of a monologue here. Um but we we we've been thinking about this together. So I think the US will be fine at the end of the day because they can 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. Um 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. Um now a lot of the debt is short duration so it refurbishes every 5 years it rolls over. Why why are you laughing? >> Cuz you know it's like things we've learn you've learned in the last >> Yeah. like it's any different for you. >> Like you got a degree in
[00:51:00] financial economics. >> I didn't I didn't >> The internet thinks I'm a beekeeper. >> Few months. Few months. Few months. >> Um this is our business. Dylan, sorry. Sorry. Um and uh so now I'm self-conscious. >> No, it's good. You're doing good. I just think it's funny. million people listen to this guy who just learned about death this month. >> Um so you go from 20% of is suppose interest rates rise uh 1%. Then the um over a 5year basis the amount of the fraction of tax revenue that goes towards servicing the debt basically goes from 20% to 25%. If they rise five percentages that would go towards like north of 40%. But if you take into account the fact that 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
[00:52:00] basically just goes towards paying interest payments on the debt. Now I think the US is going to be fine because also the tax base will increase if we let data centers get built in America. Um other countries are absolutely 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 are just going to be very in this new interest rate regime. >> So, so, so this this this crowding out effect is actually like the thing that I've like is the reason it's not like yolo 1 billion gigawatts. Yeah. >> Right. Um, 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 going to 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 wherever 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 um in the market, not
[00:53:01] necessarily the government set interest rate, but in the market, the spread of interest rate uh between what the government says their federal rate is versus what everyone else is charging because Amazon wants to raise a hundred billion dollars of debt next year or whatever the hell the number is. Um you know, probably less, but um you end up with this like really challenging problem of where does the cash come from? um 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 in the future years will be amazing. So, you have this delta. Um, and then what's pushing down on the delta is all of these other things, right? There's regulations against data centers, regulations, uh, you know, consumers getting mad, uh, politicians getting mad, regulations against AI, um, the AI labs not releasing their latest models because of safety reasons. Um, all of these things and and and interest rates going up um are are 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
[00:54:01] what does 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. Um what is the rate at which you think Amazon or Anthropic or whatever will be releasing bonds for debt next year? They do hundreds of billions of dollars of debt. What is the rate? What is the average rate? >> I I don't think Amazon will do hundreds of billions of dollars of debt >> to total. Let's say the big >> the hyperscalers in total will raise and and all the clouds in in the modeling that we do, we have about 11 trillion of capex from 2024 to 2029 >> total >> 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. >> You don't think the AI revenue continues even 3xing year over year? >> AI revenue does go up. I don't think it
[00:55:01] can go up forever. I don't you know like just like w without like certain constraints being hit. I think c labs will have certain incentives. Um and and labs are not the ones building all the compute in many cases even though they're increasingly trying to go that way. >> They'll have all these cash flow like if their revenue keeps incre whatever that's fine but if you how much would you say the 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. Um, 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 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 because everyone's paying to, you know, reduce their costs. Um, of course, their profits will also go up, but um, you know, cash has to come from somewhere. Um, so there's an upper limit on how
[00:56:00] fast their revenue can grow versus the value they deliver into the world. And there's a diffusion aspect of the technology. Um, but ultimately labs revenue keep going up. They can't cash flow fund everything. The optimal scenario is 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 that. >> Um, 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. Um, and when you take that, you 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 per megawatt keeps going up. >> Yeah, that that makes sense. So, how much do you think interest rates will increase by 2029 as a result of all this? Dude, you know, it's 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 y >> for Amazon go from, you know, from where
[00:57:02] they are today? Um I think Meta pay Okay, let's like So this is going to be extremely vibed out, but recently Meta's raised at like 5 to 6%. I don't see why they wouldn't pay 8%. because they would happily pay 8% because the return from the compute that they're going to build is humongous, right? Um and the market won't want them to, but if they they they'll want to pay 8%. The flip side is if they pay 8%. >> versus the five they do, five and a half, six they do today, you know, 250 bips increase, >> that makes everyone else in the economy also pay 250 bips 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 rep their their uh their debt themselves reprices faster than their assets repric. Um 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 a point you made but the if interest rates rise the discount rate
[00:58:02] 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 will be fine >> any individual stock will probably have just like cratered in value especially the um the Buffett like Berkshire type you know pay good cash flows for 30 year type >> yeah it's like it's like why would I pay this much for you know um you know Johnson and Johnson >> right >> who you know like they're 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 would I invest that much if my discount rate isn't 3% or 5% it's now 8% or 10% >> and and and for developing countries what's there Basil Hopper who's a good friend and he's an economist he um made this point that we'll see a second vulker shock so in the 80s to fight inflation uh Fed chair Paul Vulker raised interest rates like more than 5%
[00:59:00] um or it's like something like 8% uh real interest rates 8% and that caused some 40 different countries, mostly Latin America, to default in that decade. And I think that will probably happen again. In fact, okay, now we're getting into like singularity talk. So, we're even talking about what happens if interest >> I think this all happens before singularity. >> Yeah, that's what I'm saying. That's what I'm saying. So, we were talking about like, you know, before singularity, interest rates rise 2%, 3%, etc. 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 just like there will be there's this uh there's a researcher Damon Binder who's done great work on this, but basically if you look at like input output tables in a fully automated economy, just like what would it take to like double the entire stock of things in the economy ever? >> If economy grows at 3% 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 and you can't like double people every single year but
[01:00:01] in a world where like you can also double labor force every single year how fast can the economy grow and I think it could double every single year at 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. Um, and like I 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. Um, every stock that is not an AI stock is like worth basically zero because discounted cash flows are worth nothing. If the federal government can't figure out a way to tax AI, you know, the the servicing the debt is more than the current uh tax revenue. um all these other effects that I'm sure we're not even pricing in like you can't get a mortgage etc etc off because fundamentally what is happening in this world like this is all nerd speak right but like let's step back what's h happening >> just now it started the nerd speech
[01:01:00] >> we'd be entering a regime we're just we're in a totally different growth regime basically and the economy is basically saying hey you like paying people you bar 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 and that just like that's fundamentally what the cause of all of these things we're talking about. >> Yeah. So, so as interest rates go up, equity markets get pummeled. Yeah. >> Um and even AI companies, right? People are like, you know, some people who really believe in AI are like, why does Micron or Heinex or Kyia trade at two or three times earnings? And it's like, well, if you're really AIP, everything in the economy should trade at like two or three times earnings. >> Um, and if you're not AID, then sure, they're they're over earning. Um, so it's sort of like a an argument for why like I think memory is going to do great, but you know, memory memory stocks shouldn't, you know, 10x or
[01:02:01] whatever again because if they were if we're in the market where there's that much demand for memory, which means AI's caused this drastic change in the economy, then everything should trade at like two or 3x multiples and the stock market should crash. Right. >> Right. And so in a sense, like Meta trading at I don't know, I think Meta trades at like some they're like $1.5 trillion company. It's like what silly? Um, they're worth way more than that, at least in like a logical sense. you just look at their cash flows and 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 uh dollars per watt either as tokens because their lab works or just anthropic and open AI um 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 and and so the limiter on AGI is not how fast can the research engineers like you know our roommate Shoto can crank the gear It's actually just like how much does the rest of the world let that happen, right? Because they're
[01:03:01] going to regulate. They're going to obviously increase interest rates. They're going to say no data centers. They're going to say stop building fabs. They're going to say, oh every company's equity value is tanking, so how can I pay for AI, you know, to increase my business? Well, then, you know, like, okay, then open have to start like building their own stuff. And obviously they're going to eventually focus, you know, they're building their own chips already or at least designing their own chips and it'll expand out. They're, you know, they're contracting their own data centers and building their own uh infra in the next couple years. Um, you know, there there's 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 takeoff. 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 takeoff is is at least my hope possible because everything in the economy and and regulatory world like government saying don't release your models government saying actually you can't even use your models internally that
[01:04:00] much because that's going to happen soon 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 does that prevent singularity? >> I mean, right now it would lead to more revenue because the models are on capable of RSI, but I'm worried about a world where it's 2030 and the government's like, "We're going to wait 6 months before you can release your model to the public." >> 6 months, 100x, let's go. >> Yeah. And in that six months, they're just like they do like recursive self- improvement internally. They just have all kinds of crazy happening in the company. Meanwhile, the rest of us are stuck with models that are like at current pace years behind. Yeah. >> So, here's my thought. Okay. 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 it's outwardly written, you know, from like every politician. >> Suppose they basically prevent an entire they slow down AI by a year. They if comput is increasing two to 3x every
[01:05:00] single year. They prevent a whole year of AI deployment such that you're a year behind where you would otherwise been. During um RSI, you're getting 3 to six years of AI progress in a single year. >> But 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 had to stop giving Mythos to foreign employees for a bit. >> I didn't I didn't know that was true. Like internally as well. >> I mean that's what they claimed. And >> I thought that was just a different checkpoint that was not mythos, but it was basically Mythos. >> Yeah. 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 I would hope at least. Um, you know, governments are going to not want companies, at least the US government has the cards here, is not going to want Anthropic to use methos for internally. They're going to be like, hold the on, right? Like, slow down, you know, cuz cuz all of these regulatory reasons, everyone who's elected is going to hate AI. Even the people who are elected already hate AI. um all the constituents
[01:06:00] you're going to literally have like I bet you at some point your parents are going to call you and be like Daresha you're doing a terrible job you're making every AI progress happen faster like it's my podcast >> it's going to happen it's going to happen my podcast I'm accelerating progress >> I mean maybe you educate people right and and maybe if they're smarter they're progressing AI faster but anyways like you you're going to have real world constraints on the progress and development and deployment of AI even though you It will happen eventually. It's like we're we could tear ourselves apart before we get there. >> Jane Street is hiring for two separate ML internships right now. One focused on ML engineering and the other focused on ML research. I sat down with Aloque who helps run the research track to learn more about that program. I think this domain is fundamentally underststudied. 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. These unanswered questions make for really
[01:07:01] good intern projects cuz 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 highdimensional problem that we were 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/bourash. One thing I find crazy about these scenarios is just how much of the world's future labor supply ends up in
[01:08:03] uh very few companies and also how fast the labor supply grows yearover-year. So if like computer at the frontier you know in flop terms is growing four or 5x a year and further the comput required to achability is like decreasing 3x a year. So the computer at the frontier or the basically the effective AI population size at the frontier labs is increasing 10x year over year. And so that doesn't really matter that much right now because AIs are not good enough 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 the next year to a billion the year after that. And then pretty soon, even if comput scaling slows down, it doesn't take many more years before each company individually has more labor
[01:09:00] equivalence than there are people on Earth. Um, and I think that's like a thing that is very plausible by the end of this decade that there's more AI labor, more effective population within a single lab um, 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 most people like in terms of like the a work output or something is just like concentrated within two labs who are consuming more and more of the world's compute and so if if these AIs are misaligned then most of the world is misaligned basically because like most of the world's minds are there um but even if they're not it just very few companies have like a lot of influence or a lot Yeah, it's it's sort of um there was the whole spat recently where it's like um I think Gavin Baker was like and Daario believes that there's only be one company in the world and then you know Shelto and Daario came out and were like no no no we didn't say that. Um but ultimately you know if you
[01:10:01] believe in RSI you believe in the labs are the most effective uh user of compute and can generate the most um value from the compute then the only thing that's going to happen is centralization of compute and if you believe in you know sort of AI researchers RSI AGI then all of this exists all of this is the base this is even true there's no RSI the current effective like >> effective population of the frontier is currently increasing 10x year over year for a given level of capabilities Right. So if you get to the level of capabilities which is a human um a very competent remote worker or like a very competent software engineer or very competent researcher that population of those would like 10x year over year at the current rate of cap current rate of capabilities >> and without RSI then once you have RSI it's >> like maybe growing like 100x a year or thousandx a year or they're like intelligence is increasing but the population isn't increasing or some mixture of the two right. Um, yeah. I mean, I guess I guess like what world do you see, Dwarash, where everything is not centralized? Um, because it seems to
[01:11:00] me that every force is screeching towards centralization. And that's scary as hell. >> Yeah. >> Um, I don't I don't, you know, I would love for it not to be centralized completely. Um, but maybe that's that's the whole point of a m a machine that loves grace, right? Is is it is everything and it makes our lives great. >> Yeah. It's so hard to think about the future. Um 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 an AI for a specific skill or specific set of knowledge gets amortized across billions of sessions or billions of users. Um the further so that's like one effect. The other effect is if you're slightly ahead in the AI race and comput is in shortage, you can charge a much higher markoff 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 a models are learning from deployment and one model
[01:12:01] is like deployed much more widely than another one. It's getting much more like real world data. Yeah, your point your point is taken that like whether it's user deployment and continual learning um whether it's uh training having these economies of scales um whether it's the incremental progress that the best AI model helps you to make the next AI model um 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 um that yeah we should spend some time thinking about uh async or at least I'll spend some time thinking about is what is a vision of like a decentral centralized, broadly empowered future after AGI 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 a private corporation. >> I don't trust the government and I don't trust Daario and I don't trust Z. >> Yeah. Yeah. That's the problem, right? Um but there's no at least obviously obviously it's like very easy to be wrong about the future and you don't anticipate a key effect or something that changes everything. But Xanti, it's
[01:13:02] very hard to see a reason why there or like how we avoid a scenario where we have to choose one. >> Why 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 rule of law and all this, >> but then AI flips all this on its head, right? And ultimately you're like actually private ownership is probably not the most efficient economy and therefore it grows slower than an AI economy which is centralized. It's still private ownership, but it's like how many firms are really involved in this this like the share of the economy that's not what like 5% of the economy or something like that in the US sorry 1 trillion divided by 30 less than that sorry but um yeah maybe two 2% of the economy right now it's like Nvidia is a huge share of it and anthropic and open AI and these hyperscalers and obviously there's other firms involved but like a large share of the EI stuff is just happening from very few companies so it's like it could be private property but like very few companies are involved >> I mean this is what the structure of the
[01:14:00] market is doing so you What what can prevent it? I don't know. I don't unless AI progress slows down >> unless governments regulate the 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 and you have a slowdown of progress somehow hopefully >> and and there is a more of a balance of power and even as we go towards an AGI, ASI, RSI, >> um >> everything along the way will still lead to someone's going to allocate going to capture more resources. So, so it's kind of hard for a framework in which AI doesn't lead to super concentration. >> Yeah. >> Now, the one positive thing here is that today Anthropic 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 to $100 million a megawatt, but they're still paying 13 um for a lot of the compute they're buying. But at the end of the day, the reason
[01:15:01] they've gone to $100 million per megawatt is because Jane Street is capturing $300 million per megawatt or $500 million per megawatt. Um where Dwares 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. >> Um but you know, I think I think that's the like one saving grace is that the rest of the economy maybe profits so much more from anthropic. No, but the whole logic you're laying out earlier of them reallocating inference to AI R&D, the whole logic of that is that the uh returns to labor inside AI labs is much higher. >> This is my cope. >> Returns outside. Yeah, >> this is my cope. I agree. >> In in all scenarios of the world, you know, there's 80,000 worlds and only one of them anthropic doesn't own the whole world. Is is that 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 Jane Street, you know, make all this money off of these degenerate
[01:16:00] options traders? >> Hey, they're they're a sponsor. Come on. >> 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, Jane 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 compete to that if enth if if if the end, you know, monetization that Jane 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 that's what's happening. >> Yeah. Well, on that somber note, um I guess I guess uh I guess we'll meet again when the RSI is officially kicked off. You're not You're not going to have me on your podcast again for like two months. >> All right, cool. Thanks, dude.