Dwarkesh Podcast - Dylan Patel _ Anthropic _ OpenAI will have most of the world_s compute by 2028

0:00Dwarkesh PatelOkay, I'm back with Dylan Patel, founder of Simi Analysis. Our version of a family Thanksgiving dinner is a regular yearly podcast, but you're not actually related. I will tell the people this. It will 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.

0:30Dylan PatelSo 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.

0:51Dylan Patelfor open iron and thropic. Now it may be built by others and then rented to them, but at the end customer it's them. As we go forward into the future, the numbers for computer ballooning, we're at a little bit over a trillion dollars of CapEx this year. As we got into 28, it's going to be more than two trillion dollars. The labs are also taking an increasing percentage of this.

1:15Dylan PatelAnd 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, they have been companies that mostly lost money.

1:45Dylan PatelAnthropics started turning a profit in Q2. It's believed at some point in Q3, OpenAI 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. If we go back to even the beginning of this year, it was 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.

2:14Dylan Patelthe 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 year and a half, their margins have really skyrocketed. The base cost of compute tends to be around 10 or 13 or 15 million dollars per megawatt.

2:36Dylan Patelthe most interesting aspect about what's happening now is before, again, they were generating, if they served a model, right, GPT-4 being served on, you know, Nvidia Hopper GPUs was generating negative gross margin for OpenAI. But now when OpenAI serves GPT-5.6 or Anthropics serves Opus 5 or Mythos, Fable 5, their revenue generation has passed well beyond the sort of incremental 10, 15 million dollars per megawatt. In the case of Anthropic, the revenue has gone as high as 50 million dollars per megawatt. 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

3:22Dwarkesh Patelall 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 when 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 a vast majority of the world's compute?

3:51Dylan PatelYeah, so earlier this year, the beginning of this year, Anthropic OpenAI started at two for OpenAI and less than two for Anthropic. End of this year, they're both above five. So they've three, four X 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 are 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. Now, who's building that compute for them will change. Next year, big at new entrances, for example, SpaceX is building a ton of compute. And they're actively going to lease quite a bit of it. Too anthropic and open AI, most likely, because they're the ones who can...

4:44Dylan Patelwho have the marginal capability to pay the highest price. In addition, Open Ion Anthropic are also starting to build their own compute. Open Ion 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 Open Ion 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 Open Ion. Because compute is growing so fast.

5:13Dwarkesh Patelincremental 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 that most of the world's compute is owned by two labs or at least is serving the demand from two labs. 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 labs triples every single year. But if you keep the current trend going, it goes from like two at the beginning of this year or to close to like six at the end of this year, just multiplying out by three. 18 by the end of 2027.

5:42Dylan Patel54 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, why do you see the world 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.

6:02Dylan PatelOkay, well a new watt deployed this year 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 GB 300s and TP v7s 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 anthropic and open AI take on 45% of compute next year. You've got them in let's say December 27, they have taken on half of the world's increment when you 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 flops in the world.

7:00Dwarkesh PatelThe thing I'm confused about is why you think we only add 80 gigawatts in 2028 if we enter a world in which the price, the value of computing increases so much. That's the upper bound, by the way. That's the like, I'm so fucking bullish. Right. Okay, so let's do some chain 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, let's see.

7:26Dwarkesh PatelYou need 55,000 N3 wafers, 6K N5 wafers, and 170K DRAM wafers. I don't know those numbers. I'm going to troll you, but the way you said wafers was so fucking Indian wafers. By the way, when we first moved to the US, I had the VW thing pretty bad, and I was a vegetarian. A vegetarian, I remember you told me about this. Is that in North Dakota? I was in elementary school, and I'd be like... Can I get a wedgie? Can I get some wedgies?

7:55Dwarkesh PatelAnyways, so that's for one gigawatt, right? Yeah. Now, I had an LLM run your wafer 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 at like three to four billion dollars. Now, suppose you add in, you know, clean rooms and shell and everything else at the fab. So six billion dollars of like fab capex.

8:24Dwarkesh Patelproduces every single year a gigawatt. And a gigawatt produces right now $100 billion of revenue. But also that $6 billion in CapEx is producing a gigawatt every single year. And that gigawatt is producing $100 billion every single year. So even over the course of five years, so you know, the first gigawatt is generated five years of profits, the second gigawatt that the fab is produced is generated four years of profits and so on.

8:53Dwarkesh Patel6 billion of CapEx at the fab level will have generated over a trillion dollars of end AI revenue. There's a lot of OpEx along the way. There's a lot of other CapEx, like the data center, the power. And you had to pay like, you know, the open AI for the R&D. There's a lot of different people who need money here. But yeah, it's a huge... Take away half of it for all these middlemen. That still means there's a hundred X discrepancy between fab CapEx.

9:22Dwarkesh Pateland 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 gonna figure out, like we're gonna be, you have this huge discrepancy where you can turn $1 into $100, and they're not gonna figure out a way to make more mirrors. I mean they are, which is these mirrors taking some time to bake, right? But the emergency are so big we're like, and they're probably gonna be like, we could make a trillion dollars right now.

9:50Dylan Patelbut we're just bottlenecked on the mirrors that go into the ASML machines. How can we make more mirrors if we spend $100 billion on this? That's the situation we're going to be in pretty soon. I'm just like, we'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 arbitrageers 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 and the ability to convince ASML to sell them any UV tool, they should totally just go buy one and wait and then sell it for north of a billion dollars, right? But ultimately, yes, capitalism will 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,

10:44Dylan Patelyou go to talk to someone at Carl Zeiss, they're like, yeah, 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 EUV 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.

11:08Dwarkesh PatelBut it takes so long to pill. Suppose every single company in the firm, sorry, in the stack, got private equities. Like somebody came in who was super HEI Pilled 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 the reason I asked is we're 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.

11:36Dylan Patelextreme expansion of all this 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 100 is roughly still the right number. For 2030? 100 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.

12:01Dwarkesh PatelAnd 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 think it'll happen the year after, because the world is capital constrained. 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 of that. I don't think they're going to do that, but. Yeah, or hundreds of billions at least, right? Yeah. It seems like 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 going to generate hundreds of billions of revenue next year.

12:31Dylan PatelBut ultimately capex next year is like two trillion dollars. So you've got this big mismatch, right? You know the the wafer fabrication equipment supply chain will do, you know, something on the order of two hundred billion dollars. The data center market supply chain will do even more. The accelerator supply chain will do even more. The energy supply chain will do a number. You know, you sum all this up. It's going to be, you know, well north of two trillion dollars of capex. So the labs have not yet

12:57Dwarkesh PatelGotten to the point where their cash flows can fund this stuff. Of course. Yeah, I mean obviously they will like never get to that point right because they want to keep Yeah, you reinvest you want to keep maker catbacks higher than your returns but the key question I really want to understand is if 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 a hundred 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 AI workloads. Okay, so 100 gigawatts for the lab's end of 2028. How much is like world compute? I think that may be a little difficult given 2028 you start to have.

13:44Dylan Patelthey've taken 70, 80% of incremental compute, and I'm not sure what happens to markets then, right? How much does the price of compute skyrocket for them to actually be able to buy 70, 80% of compute? Is Google or Meta or my Amazon willing to sell even that much? Also one caveat when we're sort of talking about these gigawatt numbers is when Amazon is serving bedrock and thropic models, that counts as anthropic compute in sort of our worldview because it is effectively at the end of the day counted as revenue for anthropic, even though there's a revenue share and credit back, all that. But ultimately, in 2028, 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 a kid, you're not, it's not that hard. Go get a GB 300 rack.

14:39Dylan Patelgo download the Kimi weights, go download VLM or SG Lang, set it up. Codex and Fable can actually help you do this. It's pretty simple. It's not trivial, but it's not like rocket science, and go put it on open router. It's very simple. You'll start generating more revenue than you're paying for the compute. This has already led to this compute pricing $10 to $15 million per megawatt start to inflect up.

15:07Dylan PatelAnd 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. Does compute now get to $25 million a megawatt? Does it get to $40 million a megawatt? But as you're saying, it's already the case that the labs are generating way more revenue per megawatt.

15:26Dwarkesh Patelthan everybody else. If they stay as far ahead as they are currently, you'd expect that to be the continuity case. If there's like some kind of recursive cell improvement where the AI labs are like relatively uplifted or they have models internally, they're not releasing externally, they're helping them make the next model better, you'd expect that to be even more of the case. And aren't you already seeing this where like SpaceX or whoever is like slightly further behind will just all compute to the highest bidder 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

15:53Dylan Patellarger and larger shares of the computer. 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 to it. Sure. They do have to start paying 25, 30, 50 million dollars a 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 sort of aggressive goal. The other aspect of this that's really challenging is we've already seen a huge slow down for the AI labs, right? 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. You know, OpenAI not releasing Astra. OpenAI stopping training for two weeks, Anthropic not releasing what their safety assessment set is Model 2, which is widely believed to be the next version of Methos. They're clearly not releasing their best models, and in which case the revenue per megawatt stalls.

16:48Dylan Patelor 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...

17:11Dylan PatelIn 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 a 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, which we cannot describe, that

17:36Dwarkesh Patelthat would potentially slow this down. I think an 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? I think they're far from just being able to fully automate the job of 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, say, a million, of white collar workers. Let's say roughly, right? 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, yeah. I don't know. But it would be many hundreds of billions of dollars if you get like full AGI per gigabyte. I think the other aspect of this is, and we've continued to see this, the most of the value capture.

18:28Dylan Patelis not happening, right? Like most of the value that these models generate does not get given to OpenAI Anthropic. Thankfully, so far it is mostly just being given to the users, right? Jane Street with their exclusive contract with OpenAI for GPT 5.6 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 generating in terms of profit, right? Because they get to, you know, make money off of the market. Or Metta, 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 and getting engagement time 5% longer and all these things. They're making way more money off of using these models.

19:20Dwarkesh Patelthan anthropic. And so ultimately, 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 going to use about is, does the market come in equilibrium? And if it comes in equilibrium, would you just expect the price of compute to equal whatever Anthropic and OpenEI can generate from it or be very close to it with like a small amount of markup for Anthropic and OpenEI. 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 Anthropic's ability to monetize a gigawatt doubles or triples or something, it'd be weird if then the gap continued to increase. And so Anthropic just by like having some software.

20:08Dylan Patelhaving some weights can take something that costs them $10 and then turn it into $100. Yeah, so there's always a fun question, right? Which is, where does the value go in AI as generating all this value? You've got...

20:20Dylan Patelyou know, 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, you know, the app layer. Well, so far, the app layer has 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 it looks like it's on the path to generating, you know, $100 million per megawatt. So, turning, you know, $10, $15 into $100, as you said.

20:49Dylan PatelBut 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. Open Enthropic were just plowing VC money in and as were many other startups and many of these hyperscalers are building infrastructure without knowing if there was going to be a payoff. So ultimately, you had this 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 infraside and all the values being created used at the chip.

21:18Dylan Patelthe 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, QSMC 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 Street as an example. It's not that hard.

21:53Dylan Patelyou know, what happens, you know, going forward? Does Anthropica and OpenAI, you know, they've slowly started a balloon in value capture. Do they balloon and take all the value capture? Well, that was a thought. And then Elon showed, actually, no, I can sell my compute for $25 million a megawatt or $40 million a megawatt to Anthropica and 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 Toronto compute, like B300s or whatever, that's sold for 40B a gigawatt. The SpaceX sold for 40B a gigawatt to Google. What does that sell for at the end of next year? I think most compute will still continue to transact at sub $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, fuck it, I'm going to build this compute.

22:49Dylan Pateland 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. 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 plus billion dollars per gig a lot. Why don't you just buy my stuff for a crazy amount of money? And obviously it's not like Elon decided this or Anthropic decided this, the sort of markets figured itself out. Other people, you go to a random cloud, they're like, okay, I'm going to build a gigawatt 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 on 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. There's this completely different power structure where Meta, who is effectively hoarding compute,

23:45Dylan Patelthem and 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 gigawatt, 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? 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

24:41Dwarkesh Pateldon't fit the profile that I'm looking for. So I created a recruiter in Grogbot to see if it would help. I gave it a huge context 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 X feed and DMs. One went 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.

25:06Dwarkesh PatelGrockbot then took all of these different candidates that the 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.

25:28Dwarkesh PatelSo 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 slash bot. What do you think their revenue per gigawatt is by the end of 2027? Like for an anthropocore open AI by end of 2027? 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 the end of 2027. Oh, by the end of 2027. Yeah.

25:55Dylan PatelThat's where it gets more challenging, but I think I think it could get to, you know, higher than that's like 70, 80 million dollars a megawatt blended across the company. It's not higher. Yeah. 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 it. Maybe I spend 40 million dollars a megawatt on SpaceX compute. And if I'm SpaceX, you know, 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.

26:24Dylan PatelAnd, you know, Elon's saying they're exclusive to Nvidia, but why doesn't Jensen raise his prices? And then, you know, SK Hynex and Micron and Samsung looked at Nvidia like, well, why don't they raise their price? So I think the value capture, there's a bull whip 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.

26:51Dylan Patelyou know, to get that incremental capacity 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, different parts of supply chains raise, 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'm sort of surprised you think like revenue per gigawatt doesn't increase way more than even like 100 per.

27:14Dwarkesh PatelThey go off by the end of next year. When does RSI happen? When does take off, right? 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 won't be able to release the best model. Like, and Opinii says they're not training models for two weeks, man. What the hell? Yeah, yeah.

27:36Dwarkesh PatelI mean there's another, 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 because of regulation? Yeah, but they're not even allowed to use this like new model and turn like astros not widely deployed internally. But still I don't know just like 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? 4.0? Is that 4.0? Yeah, that's like you're talking about a 4.0 to fable size or mythos to size leap by this point.

28:06Dwarkesh PatelAgain by the end of 2027. Yeah, but Mythos 2 is not out. Yeah, or like even Mythos, right? Like that leaf again. Even Mythos is not allowed to be out, right? They've neutered it. Like 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 being only 100 per megawatt.

28:31Dylan Patelby 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.

28:43Dylan Pateland 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 is saying you have to, or at least trying to say you have to 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, even if the model internally keep getting better and better. I see no reason why like, you know, again, like 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. And then that six month difference, if progress accelerates, is actually a bigger...

29:34Dwarkesh Pateldifferential. 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 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, 2 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 were 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?

30:30Dylan PatelYeah, 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? The standard belief of most people is, oh, most compute will go to inference. 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 $30, $40 million per megawatt today, you allocate 40% to inference. If you now get to generating $60, $70 million per megawatt, 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 Anthropica and OpenAI, and not just at the executive level, but also their board, is go build AGI because it's way more profitable.

31:23Dylan PatelAnd 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 OpenAI releasing ultra-fast mode for just external, or are they doing it internally too? And it turns out, no, actually, I'm going to allocate it to internal and external.

31:52Dylan Patelbecause my internal value that I'm generating from superfast AI or the best AI model is way more than what someone externally is. 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. So they're not going through that calculation, but ultimately it's

32:19Dwarkesh Patelit 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, right, right. I think this is an interesting economics question that I feel like we can have the models digest. 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 month by month.

32:48Dylan PatelYou would agree that every month enthropic has added more compute than the prior month There might be some noise when they like sinus face x 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 their revenue adds, you know skyrocketed and then they've flat you know sort of plateaued They're only adding, you know, they're not adding 25 billion dollars 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. Yeah

33:16Dwarkesh PatelAnd 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 added the numbers you just said, it would be over 200 gigawatts of world compute by the end of 2028, right? Yeah, globally. OK. And how fast can that continue growing? Like globally, I compute after 2028.

33:45Dylan PatelYeah, so 30 this year, 50 next year, 70 and 28. 29 should be like on the order of 90 to 100. Maybe 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, our RSI regime or, you know, when is the world economy growing at 10% a year? Because if you're at 100 plus gigawatts a year, you're at...

34:13Dwarkesh Patelabsurd 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 continue 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. Yeah, so China today, so if we sort of level set back to 2022,

34:41Dylan Patelthe 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 a dramatic increase in America. So 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.

35:11Dylan PatelAnd as we step forward, they're still at 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, right? Malaysia or what have you. So ultimately China domestically still continues to have sub 10% of incremental new compute. So in 2028 might start to inflect up, I think. But it's pretty easy to say China will have like 30 gigawatts of AI compute.

35:41Dylan Patelor less. By 2028? Yeah, in 2028. And then how fast does their hockey set go up? 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.

35:56Dylan Patelyou 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, 5, 10 gigawatts in just 2028 of domestically produced chips. Those chips are definitely

36:25Dwarkesh Pateldefinitely worse than the chips that Nvidia will have in 28, or Google will have in 28, or OpenAI will have in 2028. So even the gigawatt number overstates things, you're 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 gonna add 100 gigawatts the following year, or something, you know, I know you said you can't really say that far out. How much is China able to have the subsequent year? Basically, I wanna know,

36:52Dylan PatelDid 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? There's a lot left to whether or not the US passes the match act, whether or not tools continue to get export controlled, how fast China can build their new equipment that they're starting to be able to produce domestically.

37:17Dylan PatelBut ultimately, you know China 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 ships Into into domestic China or at least close the gap in what the US is allowing the you know, Nvidia to sell them or what have you do think China could do adding 50 gigawatts by 2029 Marginal incremental gigawatts in 2029. I think that's I think that's completely reasonable Yeah, and part of that could also be purchased from foreign. Yeah But yeah, I think it's completely reasonable that China in 2029 can do 50 gigs But if most of those are the domestic chips there is some factor there where that 50 gigawatts is really worth as much as 20 gigawatts in America or And from American chips, right? So yeah, you're actually projecting a world where maybe the leading lab in 2028

38:16Dylan PatelHas more compute that China will have and like all the China will have in 29 or even 30 if we leave you weighed gigawatts by their quality Implying that there's nothing to do and to slow down the US labs. Yeah, that's right Clearly the government is starting input politicians are starting to do that. Yeah Whereas China is not gonna slow down AI. In fact, the only thing they're gonna do is accelerate it. So honestly, I When I renewed Jensen and asked about expert controls, I am a libertarian person and I'm like, I wasn't like genuinely sure

38:45Dwarkesh Patelwhat I thought about this issue, I was steelmanning what is like the opposite view that he has, because I think it's important to hash out ideas. But I'm like, yeah, maybe there's a world where if you 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 and other things. But I didn't realize the compute situation was as fucked as you're saying. Like actually, the extra controls do seem to have like really, if they ship the amount that you're saying, That's a huge difference. By the time we have automated coder and even getting into automated research, China is way far behind on the compute stock. And so if that ends up being the case, that would have worked. I think that's actually a notable success. I would say the only caveat there is some of it is export controls. But some of it is also just...

39:34Dylan Patelfinancial 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.

39:46Dylan PatelAnd so the Chinese semiconductor industry has significantly more subsidies than the rest of the world's semiconductor industries combined, which points to like, you know, if takeoff is not as fast as sort of you're implying, but actually it takes longer, then ultimately China 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.

40:15Dylan PatelRelative to the amount of compute they have right the leading Chinese labs have a hundred 200 megawatts total of compute at most Byte dense seed seed being the one outlier where they have significantly more than that But you know Kimmy is not running, you know a gigawatt or anywhere close to it Yeah, whereas anthropic 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 when we break down the compute ratio or budget of a lab, historically it's been, let's say, so far it's been like 60% training, 40% inference, but that training gets broken down further. And that'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

41:13Dylan Patelyou know, 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 Anthropic trains Mithos, it's sub 200 megawatts, right? Like the pre-trained or the whole thing? The pre-trained. Yeah. It's sub 200 megawatts for, call it, two months, and then the RL is even less. But you think the RL is less to compute than the pre-trained?

41:39Dylan PatelAt least in terms of single-site inference. I mean single-site training at the total computer was really higher, right? Total computer, but it's like sequential, right? Yeah, so at most the 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 of them. It's hard to do multi-site training. It's hard to do RL. 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 all two gigawatts that you have for training onto training.

42:23Dylan PatelActually, 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 become a lot more fuzzy or even higher for training, also things like continual learning. All of these things start to mean that more and more is actually going to training the model. 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 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 five trillion, you have to account for future year's growth, so it's actually gonna be more like seven or 10 trillion 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.

43:22Dylan Patelgeneration or whatever in the data center itself. Right, exactly. And the data center itself is, when you talk about AI CapEx, people are saying $40-50 billion, but that's really just a 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.

43:50Dylan Patelif 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 in CapEx this year. And if I'm building 200 gigawatts the 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 like, this like, actually it's much bigger than even $5 trillion if you're building 100 gigawatts. Right, so very plausibly incremental CapEx every year is getting close to $10 trillion.

44:20Dwarkesh Patelby 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 you're right and we just won't allow it. And that's the reason this doesn't happen, right? Because like for the exponential continue, just like a quarter of the world, a quarter of America's economy is just building data centers.

44:48Dylan PatelYeah, 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 pare it down to 2028 where it's like three or four trillion dollars of capex across all of these items you know a couple you know over you know two and a half towards compute it capex and then another one to two one data center and energy 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

45:31Dylan PatelThey 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 CapEx. And in addition, they raise debt and spend everything on CapEx, right? You've seen Meta do it. Even Amazon, even Google, 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?

46:01Dylan Patelthat 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 infrastructure. And that doesn't have a huge effect on the market, but it does have some effect. But as you step forward to 2028, where the hyperscalers 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.

46:27Dylan PatelThere's the semiconductor companies like NVIDIA 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. 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.

46:54Dylan Patelfor a home that's helping people buy homes, or maybe I shouldn't buy government debt, I should just buy hyperscalar debt, or I should just buy this data center's debt, or I should buy anthropics debt, because anthropics willing to pay 20% rates for the incremental billion dollars to build their capacity, because they know their revenue from it's gonna be huge.

47:14Dwarkesh Pateland they're going to pay 20% because it's still better than renting it from SpaceX for $50 billion a gigabyte. 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.

48:10Dwarkesh PatelMost 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.

48:37Dwarkesh PatelAnd 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 slash thwartcash to learn more. So you and I have been debating 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. AI is you have a situation where, as we were mentioning, very little investment turns into a lot of money.

49:06Dwarkesh PatelRight? So the rate of return- What a fucking 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? So the rate of return is incredibly high. Even at the data center level, you know, if you build a data center and you're like trying to get rendered out to anthropic or an open AI for like 10x what it costs you on a depreciated basis to build it. It's fucking crazy.

49:35Dwarkesh PatelAnd so you turn $1 into like $2 or $10 or something at the end of the year. The reason is 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. This is 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 they can, if the data centers are built in America, you can fundamentally just like tax the data centers. But the way the current tax system is set up, 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.

50:32Dwarkesh PatelAt the same time on the spending side, currently, 20% of tax revenue spending goes towards servicing the debt, basically, paying interest payments on the debt. Now, a lot of debt is short duration, so it refurbishes every five years it rolls over. Why are you fucking laughing? Because, you know, it's like things you've learned in the last...

50:57Dwarkesh PatelYeah, like it's any different for you. Like you got a degree in financial economics. I did. I did. The internet thinks I'm a beekeeper. A few months, a few months, a few months. This is our business, Dylan. I know, I know, sorry, sorry. And so, I'm self-conscious, fuck. No, it's good. You're doing good. I just think it's funny.

51:24Dwarkesh PatelA million people, listen to this guy who just learned about debt this month. So you go from 20% of, suppose interest rates rise 1%, then the 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 it writes 5% 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 going to be fine because also the tax base 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.

52:19Dylan PatelAnd those countries, like Pakistan or Nigeria or something, I think are just going to 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 YOLO 1 billion gigawatts. Yeah. Right. You've got you've got all these industries and countries that use a lot of debt, whether it's, you know.

52:40Dylan Patelall 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 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 a hundred billion dollars 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 in the future years will be amazing. So you have this delta.

53:33Dylan PatelAnd then what's pushing down on the Delta is all of these other things, right? There's regulations against data centers, regulations, consumers getting mad, politicians getting mad, regulations against AI. The AI lab's 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?

53:59Dylan Patelin 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. The interest rate is part of capitalism, right? Yeah, but in the simple economic model versus the more complex what we have. What is the rate at which you think Amazon or Anthropica or whatever will be releasing bonds for debt next year? They do hundreds of billions of dollars of debt. What is the average rate?

54:28Dylan Pateldon't think Amazon will do hundreds of billions of dollars of debt? To total, let's say the big type. The hyperscalers in total will rate and all the clouds. Yeah, yeah. 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 build up. You don't think the Abram and your continuous, even 3x thing you're over here.

54:58Dylan PatelYeah, revenue does go up. I don't think it can go up forever. Without 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 that way. They'll have all these cash flow. If their revenue keeps increasing, whatever, that's fine. But how much did you say their revenue will be? You think they'll not have that much revenue? No, I'm just saying till 2029, there's something on the order of $11 trillion of cat-backs.

55:26Dylan PatelAnd six of that is funded with cash. And five of that is funded with debt. And if that's the case, $500,000 of debt being raised across the whole ecosystem does make interest rates go up. And then what prevents that? 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 because everyone's paying to reduce their costs. Of course, their profits will also go up, but cash has to come from somewhere. So there's an upper limit on how fast the 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 cashflow fund everything. The optimal scenarios, you actually use credit as much as you can.

56:14Dylan Patelto fund because even if cash flows from the labs fund a lot of stuff, you want to build more than this. 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.

56:43Dylan PatelYeah, 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 for Amazon go from, you know, from where they are today? I think meta, okay, let's like, so this is going to be extremely libed out, but recently Meta's raised at like five to 6%.

57:12Dylan PatelI 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. And the market won't want them to, but if they don't 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, 250 BEPS increase, that makes everyone else in the economy also pay 250 BEPS more, which then causes a lot of things. Banks will scream because if their credit spread goes up, their debt themselves reprices faster than their assets reprice, 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 if interest rates rise, the discount rate increases, which means that the discount cash flows of all equities crater, which means that

58:09Dwarkesh PatelEven 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 Buffett, like Berkshire type, you know, pay good cash flows for 30 Euro type stocks. Yeah, it's like, why would I pay this much for, you know, Johnson & Johnson?

58:29Dylan PatelRight. They're seen as a stable stock, good cash flows, they'll return their cash flows over time, or a railway company. Why the fuck would I invest that much if my discount rate isn't 3% or 5%? It's now 8% or 10%. And for developing countries, Basil Hopper, who's a good friend and he's an economist, he made this point that we'll see a second Volker shock. So in the 80s, to fight inflation,

58:57Dwarkesh Patela fact-chair poll worker raised interest rates like more than 5%, or it's like something like 8%, 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 singularity talk. So we've been talking about what happens if interest rates- I think this all happens before singularity. Yeah, that's what I'm saying. So we were talking about before singularity, interest rates rise 2%, 3%, 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. There will be, there's a researcher, David Binder, who's done great work on this. But basically, if you look at input output tables in a fully automated economy, what would it take to double the entire stock of things in the economy? If economy grows at 3% a year, then it's rule of 70, it's 20-something years.

59:56Dwarkesh PatelBut he was like, well, right now we're bottlenecked by the fact that there's people, and you can't double people every single year. But in a world where you can also double labor force every single year, how fast can the economy grow? And I think it could double every single year, or at the very least it would be tens of percent every single year. The rate of interest should be pretty close to the growth rate. It won't be exactly that because of consumption, but it should be pretty similar. So then we'll go into a world, I think in the 2030s, where the rate of interest is tens of percent.

1:00:22Dwarkesh PatelAnd like, I don't know, part of my brain is like, it might be hundreds of percent. 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 flows are worth nothing. If the federal government can't figure out a way to tax AI, you know, servicing the debt is more than the current tax revenue.

1:00:48Dwarkesh PatelAll these other effects that I'm sure we're not even pricing in, like you can't get a mortgage, et cetera, et cetera off. Because fundamentally, what is happening in this world? Like this is all nerd speak, right? But like, let's step back. What's happening? Just now it started the nerd speak. 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, the government borrowing money to pay people pensions.

1:01:12Dwarkesh PatelThe 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's fundamentally with the cause of all of these things we're talking about. Yeah, so as interest rates go up, equity markets get pummeled. Yeah. And even AI companies, right? Some people who really believe in AI are like, why does Micron or Hynex or Kyoksia trade it?

1:01:41Dylan Pateltwo or three times earnings. And it's like, well, if you're really AI-pilled, everything in the economy should trade at two or three times earnings. And if you're not AI-pilled, then sure, they're over-earning. So it's sort of like an argument for why I think memory is gonna do great, but memory stocks shouldn't 10X or whatever again. Because if they were, if we're in the market where there's that much demand for memory, which means AI's cause this drastic change in the economy, Then everything should trade at like two or three X multiples and the stock market should fucking crash, right? And so in a sense like meta trading at I don't know I think meta trades at like something they're like 1.5 trillion dollar company. It's like what silly There were way more than that at least in a 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 gonna be able to sell for crazy amounts of Dollars per watt either as tokens because their lab works or just anthropic and open AI

1:02:39Dylan Patelultimately 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 Sholto can crank the gears. It's actually just like, how much does the rest of the world let that happen? Because they're 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, shit, every company's equity value is tanking. So how can I pay for AI to increase my business? Well, then, okay, then in Throbbing Open, I have to start building their own stuff. And obviously, they're going to eventually focus on their building their own chips already, or at least designing their own chips. And it'll expand out their contracting their own data centers and building their own infra in the next couple of years.

1:03:32Dylan PatelThere's sort of like, how does this reallocation of the economy happen? But there's a lot of downward pressure on it not being just straight takeoff. Even if the models were capable of it, which I think you and I believe we're in a world where models are capable of that.

1:03:49Dylan PatelSlow takeoff is as you know at least my hope possible because everything in the economy and regulatory world like government saying don't release your models government saying actually you can't even use your models internally that much because that's gonna 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 that fact that we're from ethnic external deployment. Well does that prevent singularity?

1:04:16Dwarkesh PatelI mean, right now, it would lease to more revenue because models are incapable of our side. 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 model to the public. Six months, 100X, let's go. Yeah, and in that six months, they do like recursive self-affirming internally. They just have all kinds of crazy shit happening in the company. Meanwhile, the rest of us are stuck with models that are like at current pace, yours behind. Yeah. So here's my thought. Okay, suppose that the whole world gets in on this conspiracy to try to slow down AI. I don't think it's a conspiracy. It's outwardly written from every politician. Suppose they slow down AI by year. If computer is increasing two to three X every single year, they prevent a whole year of AI deployment such that you're a year behind where you would otherwise been. During RSI, you're getting

1:05:10Dwarkesh PatelThree 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 Stop giving me those two 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 they claim I thought it was like just a different checkpoint. That was not mythos, but it was basically yeah But I mean like stuff like that is not gonna be allowed either right like the government is dumb But they're not that dumb right like you know, I would hope at least

1:05:39Dylan PatelYou know governments are gonna not want companies at least the US government has the cards here There's not gonna want Anthropic to use meet those four internally. They're gonna be like hold the fuck on right like slow down You know cuz cuz all of these regulatory reasons everyone who's elected is gonna hate AI even the people who are elected already hate AI All the constituents you're gonna literally have like I bet you at some point Your parents are gonna call you and be like do I catch that you're doing a terrible job? You're making every AI progress happen faster than I like it's like it's gonna happen It's not a pretty good progress. 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

1:06:27Dwarkesh Pateleven though it will happen eventually, it's like 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 Alok, who helps run the research track, to learn more about that program.

1:06:46Speaker 3I 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. These unanswered 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.

1:07:15Speaker 3A 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 adversarial, non-stationary, extremely high-dimensional 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.

1:07:45Dwarkesh PatelThe 2027 internship applications are open now. Apply at JaneStreet.com slash Dworkesh. One thing 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 year over year. So if like, compute at the frontier, you know, in flop terms is growing four or five X a year.

1:08:15Dwarkesh PatelAnd further, the compute required to achieve the likability is decreasing 3x a year. So the compute of the frontier, basically the effective AI population size at the frontier lab is increasing 10x a year over a year. And so that doesn't really matter that much right now because AIs are not good enough to do full jobs or be 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 compute scaling slows down, it doesn't take many more years before each company individually has more labor equivalents than there are people on earth. And I think that's like a thing that is very plausible by the end of this decade. There's more.

1:09:10Dwarkesh PatelAI labor, more effective population within a single lab, then 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 a work output or something is just like concentrated within two labs who are consuming more and more of the world's compute.

1:09:36Dylan PatelAnd so if these guys are misaligned, then most of the world is misaligned, basically, because most of the world's minds are there. But even if they're not, it's just very few companies have a lot of influence or a lot of control. Yeah, it's sort of, there's the whole spat recently where it's like, I think Gavin Baker was like, Dario believes that there's only one company in the world. And then Sholto 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 if there's no RSI. The current effective population of the frontier is currently increasing 10x year over year for given level of capabilities. So if you get to the level of capabilities, which is a human

1:10:34Dylan Patela very competent remote worker, or like a very competent software engineer, or a very competent researcher, that population of those would like 10 XUR over the current rate of capability. Without RSI, then once you have RSI, it's even crazier. Then it's maybe growing like 100 XUR, or 1000 XUR, 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 like, what world do you see, Dworkash, where everything is not centralized? Because it seems to me that every force is screeching towards centralization. And that's scary as hell. I would love for it not to be centralized completely. But maybe that's the whole point of a machine that loves grace, right? It 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.

1:11:31Dwarkesh Patelbecause 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. So that's like one effect. The other effect is if you're slightly ahead in the AI race and computers 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 ahead in the AI race. There may be more, right? So there's models that are learning from deployment. And one model is deployed much more widely than another one. It's getting much more real world data. Yeah, your point has taken that like.

1:12:08Dylan Patelwhether 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. Oh, I didn't even 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.

1:12:31Dwarkesh Patelat least I'll spend some time thinking about, is what is a vision of like a decentralized 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 private corporation. I don't trust the government and I don't trust Dario and I don't trust him. Yeah, that's a problem, right? But there's no, at least.

1:12:55Dylan PatelObviously, it's very easy to be wrong about the future, and you don't anticipate a key effect or something that changes everything. But ex ante, it's very hard to see a reason why they're, or like, how we avoid a scenario where we have to choose one sort of centralization. I mean, it's 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.

1:13:23Dwarkesh PatelAnd ultimately, you're like, actually, private ownership is probably not the most efficient economy and therefore 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 share of the economy that's not like 5% of the economy or something like that in the US? Sorry, 1 trillion divided by 30, less than that, sorry. But yeah, maybe 2% of the economy right now.

1:13:48Dylan PatelNvidia is a huge share of it and then Thropic and OpenAI and these hyperscalers and obviously there's other firms involved but like a large share of the AI so I was just having 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 market is doing so you know 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 government 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 Everything along the way will still lead to someone's gonna allocate you're gonna get more resources So so it's kind of hard for a framework in which AI doesn't lead to super concentration. Yeah now

1:14:43Dylan PatelThe one positive thing here is that today, Anthropic does not capture most of the value. So we can talk all we want about, oh, they went from $20 million per megawatt to $100 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 Jane Street is capturing $300 million per megawatt or $500 million per megawatt.

1:15:09Dwarkesh Patelor Dworkesh 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 maybe profits so much more from anthropic. No, but the whole logic you're laying out earlier of them reallocating inference to AR and D. The whole logic of that is that the returns to labor inside

1:15:37Dylan PatelAI Live is much higher in the returns. This is my Cope. Returns outside, yeah. This is my Cope. I agree. In all scenarios of the world, there's 80,000 worlds and only one of them, and Throphic doesn't own the whole world, is that, 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, make all this money off of these degenerate options traders. Hey, there's 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 compute to that?

1:16:20Dylan Patelif the end 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 what's happening. Well, on that somber note, I guess we'll meet again when the RSI is officially kicked off. You're not gonna have me on your podcast again for like two months?

1:16:51Dwarkesh PatelAll right, cool. Thanks, dude.