How big does AI have to get to justify the trillions in infrastructure spending? Let's talk about it with the leading authorities from SemiAnalysis right after this. Welcome to Big Technology Podcast, a show for coolheaded and nuanced conversation of the tech world and beyond. The AI infrastructure numbers just seem to be getting bigger with no end in sight. So, we're going to talk about what the physics of this entire buildout looks like and whether it can continue to sustain itself without, maybe not destroying the world, but destroying the economy. Because we have some very interesting guests to speak with us about it. Today, we're joined by Dylan Patel, the CEO and founder of SemiAnalysis, and Jordan Anos, member of the technical staff at SemiAnalysis, to talk all about everything going on within the infrastructure buildout. Dylan, Jordan, welcome to the show.
Thanks. Great to be here.
Let's just start by talking about the scale of what we're seeing in this AI infrastructure buildout, if we can. Dylan, there's like a chart going around X right now that compares the AI infrastructure buildout to previous buildouts in the past, like railroads, like highways. And the chart is showing that the percent of GDP for the AI buildout today is about 3.6% — or sorry, the median is about 3.6% the average year — compared to 2.2% for the railroads. And basically the commentary is that this is far bigger, right? That would put it basically 50% bigger than railroads even in terms of its share of GDP. And the commentary is this is bigger than anything we've ever seen before on a scale that is, you know, effectively unprecedented. I'd love to hear your perspective on just how big this is, whether that's the right way to look at it, and then what it means if we're building out something this big.
Yeah, I mean, the first thing — when that chart was going around, I was like, well, people's numbers are too low. It's actually much higher than that already.
Really? Okay. So, share the real numbers.
Yeah. Yeah. So, like, I mean, if you look at like next year, capex across the US will be on the order of $2 trillion, not just data centers and chips, but also all the rest of the supply chain that people are investing in. So you're close to $2 trillion of capex. And then you stack on top of that, GDP is like close to 30 and some change trillion. So you're at like, you know, five or 6%, right? Not 3% or 2%. So sort of my thought process is actually like, oh wow, these numbers are lower than the actual because people are understating the amount and size of capex that's happening in the US and North America and the world. And I think like part of this is because, you know, with railroads, your capex is in the country that you're deploying it in. With fiber, your capex is in the country you're deploying it in. But for AI infrastructure, your capex in America is actually a lot of it's being used to serve outside of America, right? So, if you look at, for example, Europe, most of Europe's inference for Anthropic is in America and most of OpenAI's inference in Europe is in America. And similarly if you look at a lot of Asian countries. And so there is a bit of this, like, this, you know, capex in America is actually like much higher as a result, because one, you're doing all the training here, and then two, you're actually serving other countries from data centers in America. And so ultimately you're sort of at this stage where capacity is much — the economic impact of it is much larger than any other sort of physical infrastructure investment cycle ever before.
All right, Jordan. And you are the author of ClusterMax 3.0. This is SemiAnalysis rankings of the GPU cloud systems, basically putting a lot of the hyperscalers and the neoclouds in tiers, talking a little bit about their performance, and we're going to get to that in a bit. But I, you know, you do look across the board in your report, and this is something that I'd like you to comment on. Basically we're looking at where the hyperscaler quarterly year-over-year capex growth is going, and in the third quarter of 2026, the growth is at 116% year-over-year, which is astonishing. The consensus estimates — so what Wall Street is expecting to come after that — you know, they drop down in the fourth quarter to below 100% year-over-year growth, and then the first quarter next year in the 70s, and it continues to drop from there. And I think this is a cycle that we're used to, right? Where it's like, well, you know, we're on a bigger base, so it can't possibly exceed expectations again, and it can't possibly double again. So I'm curious to hear from your perspective in your research: do you think that we're actually going to see that trend, like the growth is going to decelerate, or are we again falling into this trap, or is Wall Street again falling into this trap where it's like, all right, you're basically at the biggest numbers you could possibly imagine, but you still need to think bigger?
Yeah, I think we're definitely going to hit limits towards the end of next year and into...
Wait, Jordan, before you answer seriously, I got — it's like when you ask a kid what's the biggest number they can think of and they say like, 100. Right? And now it's like, no, no, no, we're at a trillion, babe.
Yeah. Four commas club. So wait, Dylan, in this example, the kid is Wall Street. Not making any statements that people are children. Just, yeah, go ahead. Sorry.
Dylan may have a habit of referring to his employees and his customers as kids sometimes, but that aside...
I think I use a much more mean word.
Okay. Yeah, I like where this is going already.
We're running out of big numbers. There are things to look at when it comes to the buildout. This could be everything from like industrial equipment to firms that are prepared to do EPC work, like engineering, procurement, construction, to chips, to cooling systems, to, you know, all the components that go outside of that and an IT estate, basically. There's a lot of work being done to expand this supply chain in order to be able to accommodate for all of this capex to go into it. And a lot of it's working, and so we're going to see massive growth. But yeah, you can't just have triple-digit growth and accelerate the second derivative, you know, even further from where it is right now. You run out of things, and I think the biggest thing right now is twofold. One is like it becomes really hard for the biggest neoclouds and hyperscalers to just organize and marshal the resources required to build another gigawatt on top of what they're already trying to build, for example. And it becomes pretty hard for them to raise the capital. Even though there's incredible returns on a lot of these investments already, you still just need to find the capital to pour into these projects, and that's, you know, a difficult thing to do when we're talking about numbers in the trillions of dollars.
I would say the other thing is like by definition the numbers have to shrink. You know, this year we have 10x growth in Anthropic going from sub-10 billion of revenue to north of 100 billion. So they had 10x growth. Definitionally, they're just not going to have a trillion dollars of revenue by the end of next year. So yes, there's some decelerating growth there. You know, Nvidia's revenue growth is still 2x a year, but it's, you know, it's not as much as it was at an earlier point, just because it is definitionally harder to grow 2x off the same base, or 3x off the same base. So it's sort of like, same applies to like other firms. So it is definitionally like definitely decelerating.
Okay. So the growth on spending is going to need to decelerate at some point. But the growth on revenue and profit, for that matter, is going to have to accelerate, you know, beyond — it's basically going to have to trail this big infrastructure buildout and accelerate probably beyond where we've seen the dollars being spent. I mean, you would imagine to facilitate all the AI action. And there's been further conversations that have circulated about this. And Dylan, let me just throw these numbers out to you. Right. So this is a Goldman Sachs report. And sorry, so Goldman Sachs, they say Meta — this is a tweet, right? — 'Meta's committed corporate suicide after taking on insurmountable debt that the company will struggle to pay back.' There's Bane. Bane says hyperscalers will need $6 trillion in revenue — not ever, this is per year by 2031 — to break even. And there's a Columbia professor, Steven N. Goodman, who said that America will need to spend roughly $3.5 trillion annually on AI services by 2032, 8.8% of GDP, to justify today's massive data center investment. What do you think of these sort of assessments of where the spending and where the revenue needs to be to pay off the investments, and do they have a chance at making good on those investments?
Yes. I mean, I think everyone's numbers for revenue requirements are quite low given the pace of where this buildout is going to go. I think revenue will be much higher than that by 2031, for example. I mean, there's a couple points here, right? One is like, you know, how much are people spending on capex and then revenue, and the revenue always trails the capex. These infrastructure investments are six years on an accounting basis at least depreciable assets, although there's quite a bit of evidence that they're actually going to last longer than that, but you know, six years, let's just continue to use six years. So if I spend a trillion dollars today on, you know, AI data center and GPUs, the GPUs are lasting six years, the data centers are lasting 15. But that means definitionally in terms of revenue, either I could assume it's a straight line, right? So then over the six years I need to call it 1.5 trillion of revenue, so $250 billion of revenue a year for that trillion dollars of capex, or it's going to be something sloped where the first year is much less and the last year is much higher to get the payback. As far as like, you know, statements on Meta, it's like this is just kind of silly because Meta at any point can just stop buying new stuff and their cash flow can easily pay for all the stuff that they've signed, right? They just don't have to, you know, they don't have to build a hundred billion dollars of data centers next year. They could pull the plug whenever they feel like it. There's obviously contracts that they've committed to, but there's also a lot of stuff they haven't committed to yet. And so as far as like the solvency of Meta, like they're completely solvent. They're completely fine. And then the question is like what revenue do they make on top of that? And as far as like AI's impact in the US economy, yeah, I mean if AI revenue is only $3 trillion in 2031, then yeah, we'll have big problems. But you know, I'm of the belief that we can get to like 600, 700 billion across OpenAI and Anthropic alone by December of next year. I'm of the opinion that you can get to, you know, 1.5 trillion maybe even by end of 2027 with just those two companies. So that's end of '27. So then the payback is clear, right? You know where they're getting that from. Ultimately, the capacity that people are building out is matched to what they think business is coming to. And so there's always going to be a mismatch if we're in the building phase of how much infrastructure are you spending on versus what's the AI revenue, because that AI revenue for today is really based on the infrastructure you spent in prior years. And right now capex is not going up at 10x a year, but inference revenue just went up 10x year on year for the AI labs. And so we've seen Anthropic is now, you know, profitable in terms of compute cost minus revenue, or revenue minus compute cost, both training and inference combined. And OpenAI gets there, you know, within the next couple quarters, maybe two or three quarters, so based on at least what like we expect in the tokonomics model. So I think, you know, those companies are already breaking even. If you look at like Amazon, for example, the biggest builder of AI infrastructure, all of their AI infrastructure investments are profitable today. Now, you know, the customers for them could go insolvent, those customers being OpenAI, Anthropic, etc. But right now they are solvent on those, you know, making very strong gross margins. In fact, AI has made their gross margin go up in the most recent quarter from Bedrock and AI revenue from Bedrock. So yes, AI revenue needs to grow a lot. But I would posit that like being able to automate all the stuff that we've been able to and being able to build all the stuff that the world has been able to is going to generate meaningful revenue and surplus for the consumer ultimately.
Yeah, I think we need to stretch it a little bit in our minds to understand what we are going to measure as AI revenue going into the future. If these companies really do have the best models in the world that are capable of effectively all knowledge work, you can imagine them going into a whole bunch of different industries beyond coding and having returns on their investments similar to what they've seen in coding. So you can look at things like robotics and self-driving vehicles. You can look at drug discovery. You can look at just health and life sciences generally. And we can take this across all these industries and say if they want to bring a product to market, like a cancer drug or like a self-driving vehicle, you can just imagine how much revenue they can potentially return based on the investments they have today. And if they have the AI, the system to be able to produce the knowledge work required to build that product incredibly well, I think they're the only ones that can really compete on the frontier of being able to do these things. And so I think people lack the creativity to understand how returns can be viewed beyond the like Claude Code subscription and per-token pricing regime of today.
Yeah, there's this chart that looks at how much we're spending as a share of GDP. This is just 2025. So a share of GDP, health is 18%, that's the top category. Food is 9.1%. AI services is 8.8%. It's the third category in 2025. I imagine it's going to lap or it will pass food in 2026. So guys, I'm just curious, where's this money coming from? Like, you know, let's say OpenAI and Anthropic combined get to $700 billion in revenue next year. Is the money coming from these companies' labor pools? Is it just additive because they can sell more? Where is the money that these companies — the customers of these companies — where's that money coming from that they can funnel it into the AI companies?
It's a few factors, right? First off is obviously companies are going to be able to accelerate what they're building and so they can invest more. Increasing share of R&D dollars will go towards AI. Increasing share of cost of goods sold dollars will go towards AI. But also you'll have a significant amount of money sort of coming from, you know, labor as well, right? Labor, and I don't think labor shrinks per se. I think the rest of the economy grows so fast, at least in the short term, that labor sort of stays, you know, flattish. And so like, you know, in the past, we've had different cycles across the economy, but one long-standing one has been labor share of GDP has fallen over the decades, and it's been a steady slope since the '70s. And so a lot of the like strife that people say, 'Oh, America's economy sucks,' and yet, oh look, the economy is bigger, is actually well like labor share of the economy has shrunk, and that's why in reality a lot more of the labor — the value is getting accrued to capital. What AI does is it's going to supercharge that trend, long-term trend of labor share versus capital share, because AI tokens are basically just capital, you know, in the form of chips and data centers that are sitting there and creating labor. And so that labor share is going to fall even faster. And so, you know, I would hope, and for society, that that's my belief, you know, and maybe it's like an irrational belief, but I believe that we will just grow the economy so fast that labor in terms of dollars will not shrink. And it might even grow slowly. It's just that the rest of the economy grows so fast. And that's where — that's sort of where it comes from, right? So all these companies that offer various services, their services are going to get better. They're going to be able to charge more. They're going to be able to sell more. And they're going to be able to do it with fewer people. Or another way to look at it is they're going to have the same number of people and their business just going to get better. And that's — and there's a number of industries where this is possible, right? You know, obviously coding is an area where people have done it quite a bit. Obviously general white collar work is getting rapidly automated, but also companies are just able to do a shitload more than they were able to do in the past. But there's a lot of industries like pharma and trading and chip design and such, right? Like chip design is a great example, right? Chips. The number of American R&D engineers working in chip design has been basically flat for 20 years. And yet the economy for it has exploded. Now, obviously manufacturing isn't — we're not counting manufacturing. We're just counting the designers. But the designers are like, you know, two of the most valuable companies in the world, right? Nvidia, Broadcom, as well as like Google and all these other companies that are making their own chips and many others, right? These designers have not really grown that much in terms of number of people. It's been flat as an industry for us, and yet the value that's accrued has exploded. And why is that? There have been a lot of tools. Now what does AI-assisted chip design now do? Well, now all of the chips that are across the economy get better and better. Your phones are going to get better. Your, you know, AirPods are going to get better, the chips in them. Chips for robots are going to get better. Chips for obviously AI are going to get better. Chips for all sorts of things. Bluetooth and GPS and all these other things. Sensors for gas leaks or whatever that Bosch makes. These are all just going to get better because AI can help you design chips. You do the same number of people, revenue grows, margins grow, everything looks fantastic, right? And that's sort of like — there's many industries where this can get replicated, in my view.
Dylan, you've said that — I think in the past you've said something to the extent of knowledge work is effed. So do you no longer believe that?
I think most knowledge work — okay, like let's separate like my belief in like Looney Tunes 2035 versus like, you know, 2028. I think for the next, you know, X number of years, as long as AI is very powerful but it is not better than humans in most every way, the most high-agency people are going to be able to do the coolest stuff. They're going to be able to make way bigger impacts on the world. Now, you know, in like the case where AI is just better than everyone, that could change drastically, right? You know, then like knowledge work is screwed and actually like, you know, what matters is like people being able to like play games and have fun, people being able to act, people being able to like, you know, do the things that humans do that humans only appreciate in each other, if the AI doesn't kill us all, right? But that's sort of like, you know...
We won't go there today. Jordan, you know, this is kind of in your area of expertise. I want to get started talking a little bit about the risks to like what might derail this story. And you know, we'd already talked about the fact that there are some contracts that a company like Meta could, you know, may not have to go through with. You're obviously looking a lot at the businesses of the neoclouds, which are dependent on companies like Meta actually making good on the promises that they've put forth to build with these companies. Is this sort of an underappreciated risk that like everything looks like fairly fine right now if you speak to one of these neoclouds because they'll tell you that they have the best customers in the world, they have the Metas, and Meta will make good on the promise, and they're raising debt based off of this. But in reality the risk might be that a company like Meta might just decide, yeah, we're not going to do that, and that the contracts might not be as binding as, you know, the neoclouds tell us they are.
Okay. So I want to be clear about one thing Dylan was talking about there in terms of termination versus what's going on with Meta and the neoclouds, which is that generally speaking these hyperscalers or, you know, large frontier labs who are signing up with contracts for the neoclouds do not have arbitrary cancellation rights on like a monthly basis where they can just pull the plug and the contract's done. Generally speaking, they're signing five-year, six-year term lengths and they're offtaking the full capacity and they have a set, you know, hourly rate and total contract value that's very, very attractive to the neocloud providers because those guys have gone out and secured land and power and built a site and even in some cases purchased the chips respectively before they've actually gone and done that contract. What Dylan's referring to with the potential to slow down or pause or cancel projects, it's more with respect to the self-build sites, which is to say like Meta has massive campuses that are going to serve as more of a baseload of capacity going into the future, which they can either choose to buy chips for or just leave as an empty shell for years if they want to slow down and not go as hard as they can. And generally speaking, construction costs while very expensive are a small percentage of the total cost of a new chip project. Like in other words, the concrete and the steel and the walls is quite, you know, it's important and big and, you know, very a real challenge to get it done, but on a dollar basis like the HBM and the optics and the GPUs themselves are the bigger portion of the bill. And so, you know, what it's looking like right now is that all of these big companies are really needing to contend with the massive growth that they're expected to pursue by their demand from their customer base. Either that's Meta or OpenAI or Anthropic who have demand on their consumer-facing applications like ChatGPT or Claude Code or Codex or even Muses from Meta, things like that. In order to serve that demand they need GPUs and it's profitable to serve the demand, so they need to ramp up and be prepared to roll this thing out to the billions of monthly active users on the Meta different, you know, surfaces that they serve and approaching a billion of monthly active users for ChatGPT and hundreds of millions for Claude Code and things like that. So anyway, with that distinction in mind, what's the risk with project cancellation or a downturn or things like that? I mean, in order for us to forecast some sort of change in the demand-supply curve there, you'd really need to see demand slow. There are hard constraints on how many chips you can produce, how many data centers you can build, and how fast this stuff is going to come online. And as of right now, all of the modeling we can do basically shows that demand outstrips supply. And so, is demand 10% more than supply right now? Is it 50%? Is it infinite? I mean, we don't get to answer that question because we are not able to catch up to demand with the supply of GPUs that you can bring online right now. And so when that question gets answered, if it gets answered, if we find limits to the demand for knowledge work, to the demand for intelligence from the economy, then we'll be able to figure out who needs to slow at what time, where, and how that plays out. But so far, I guess those limits haven't been found. So, all right.
Before we get into ClusterMax, one last topic I want to cover: big IPO on its way, which is, of course, the Anthropic IPO. Dylan, you had a very interesting tweet about it. I'm just gonna read it. You said, 'You can't invest in the Anthropic IPO at $2 trillion because it could be zero, or it could be $20 trillion, but at $20 trillion, we're all fucked.' Can you explain that and tell us a little bit about what you mean by that?
To be clear, it's entirely a post. I was like...
You know what? I get it, but I think there's some truth to what you're saying. So talk to us about it.
I'm generally extremely bullish on Anthropic. I think Anthropic will be a very successful company in the world. Anthropic could even be the first company to be a $10 trillion valuation company in the world. Like, I'm that bullish on Anthropic. But at the same time, for them to be a $10 or $20 trillion valuation company, especially $20, they're first of all, their multiples on revenue are going to have to collapse quite a bit. You know, so they used to raise at 30x and now their IPO is going to be, you know, let's call it they're at $100 billion of revenue and they're going to maybe be at 20x. If they get to $10 trillion, I bet you they would need to be at like a trillion dollars of revenue or something crazy like that, right? Which could happen at some point in '28 or '29. You know, it depends, of course, like the stock market can also go crazy and make multiples go crazy, but you know, in the world where they're worth $20 trillion, I don't think their revenue — I don't think they're even at a 10x sales multiple now. They're probably at like a 5x or even less, in which case they're, you know, if they're at $20 trillion of revenue, are they at like 2, 3, 4 trillion? $20 trillion valuation, 2, 3, 4 trillion revenue. And at that point, you know, what is the state of AI if on a $150 trillion global economy, they're generating multiple trillions of dollars of revenue and by far the largest company in the world? Like, AI's capabilities are freaking insane. In which case, like, you know, all of this worry of like whether it's AI kill us all or AI so good that everyone loses their jobs, and because distribution of that earnings is going to be heavily leveraged to the people who own Anthropic stock or constituent supply chains of it and not distributed well, then people are going to have distress and like overthrow governments because all of the growth is accumulating to a very small number of people. So like these things could like tear the fabric of society apart, and therefore like if you believe Anthropic will get to $20 trillion, you know, society as a whole could be...
Jordan, do you have similar worries?
I think — yeah, I think there's a lot to do to get to that point. Like, I actually don't think I'm necessarily as worried as Dylan.
I'm optimistic, by the way. I just painted the pessimistic picture. I'll go optimistic.
Yeah. I mean, like the rosy optimistic view is kind of what I described earlier, which is like if they're getting to $20 trillion of revenue, this is not done on a per-token metered basis for people using Claude Code as a product. And I don't think, you know, you go and see Claude Code go 20x better from where it is right now in terms of like quality and that drives the revenue. I think that they get into other areas of business, and I think I would trust Anthropic quite a bit to bring, like I said earlier, a cancer drug to market or something like that. And we've seen similar stuff happen recently with GLP-1s just be such a meaningful thing for so many people in the economy, and as a result, you know, companies like Lilly have had massive returns on that incredible research that led to those incredible drugs that help people. And so I think I would be more optimistic for the future where theoretically Anthropic's at a $20 trillion valuation. But I do think that there are going to be a lot of bumps along the road along the way. Which is to say, going back to your question previously, like how can they possibly serve all of this demand and train these models and have intelligence diffused to all of these different parts of the economy so that people can realize the benefits? And I think the way you do that is to build compute, and it so depends on, you know, the nitty-gritty details of construction and installing GPUs and running them reliably and all of that. Like to me, the center of the world is the neoclouds on that road from a $2 trillion IPO to a $20 trillion valuation in the public markets.
Yeah, Anthropic, kind of an interesting business case, right? They're spending all this money on infrastructure. Even in the S-1, you saw big losses accompanying their revenue. Although, Dylan, as you mentioned, it looks like what they've done is they've turned the tide a bit and they're making more money right now than they're spending on compute. Talk a little bit, because I think the belief here is that a company like Anthropic is a low-margin business, or at least much less than software. But that doesn't actually seem to be the case, and you guys have run the numbers.
I would say like if you look at like Salesforce, which is probably like one of the greatest SaaS companies out there, you know, I mean I think like at least consistently grown to become one of the biggest SaaS companies, their gross margins are like 75%. And Anthropic on their inference is doing 75% gross margins, a little bit higher even, you know, especially on the frontier models. So it's sort of like, well, and it took Salesforce quite some time to get...
There, right? They were not at 75% when they IPOed. You know, they took quite a bit of time actually. They were, but then they like, you know, the mid in the late 2000s, like they were, you know, I think like it took, they were like not quite there. It took, you know, Anthropic quite a bit less time to get to that strong levels of gross margin. And if you look at many other SaaS companies, they're not even that high. And then the other thing is like their customer acquisition cost is insane, right? If you look at like a SaaS company, it's like, you know, fine, my COGS is really low, but then my customer acquisition cost can be as high as like 30%. And Anthropic has near no customer acquisition cost. Right now, obviously, they have a humongous R&D spend, but their R&D spend has grown slower than their revenue. And so now they've reached profitability and there's no reason why that trend doesn't continue, right? And they continue to grow more profitable. So at least operating margin wise, and then, and then like scalability is the other thing, right? Like the beautiful thing about a lot of SaaS companies, even if your customer acquisition cost is so high, you know, as long as your margins are high enough to pay for it initially, eventually you don't have to reacquire that customer. Now you've got this super profitable business. But with like the AI labs, their business seems somewhat sticky. Yes, there's all this like messaging of like, oh, you know, Meta's cutting spend or Microsoft cutting spend or what have you, but ultimately the revenue has only been a straight line up on the chart and almost every customer's only grown on revenue for them. And switching cost, even when you know some other Chinese model comes out or comes out with a better model, doesn't really shift the spend share that much for existing customers. You end up with like this like pretty sticky customer potentially who is growing revenue at some pace and there's no customer acquisition cost and the margins are high, so it's arguably a better business than software is, is an AI lab.
If you can successfully... But how much is the risk the fact that like there this pacing the frontier movement is coming out?
I think that's just signaling. I think it's completely...
