Can AI Keep Growing Exponentially? Let’s Ask SemiAnalysis — With Dylan Patel and Jordan Nanos

0:00How 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 [music] after this. Welcome to the Big Technology podcast, a show for cool-headed 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 to sustain itself without not maybe not destroying the world, but destroying the economy. Uh cuz we have some very interesting guests to speak with us about it today. We're joined today by

0:35Dylan Patel, CEO and founder of SemiAnalysis, [music] and Jordan Nanos, member of the technical staff at SemiAnalysis, to talk all about everything uh going on within [music] the infrastructure buildout. Uh 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 uh in this AI infrastructure buildout if we can. Uh Dylan, there's like a chart going around X right now that compares uh the AI infrastructure buildout to previous buildouts in the past like railroads, um like highways. And the chart is showing that the percent of GDP for the AI buildout today is about 3.6% or sorry,

1:17the the median is about 3.6% uh 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. Um I'd love to hear your perspective on just how big this is, whether that's the right way to look at it, um and then what it means if we're building out

1:45something this big. Yeah, I mean uh the first thing I when that chart was going around, I was like, "Whoa, people's numbers are too low. It's actually much higher than that already." Um Really? Okay, so share the real numbers. Yeah, yeah. So, so like I mean it's it's if you look at like next year, um CapEx across the US will be on the order of $2 trillion uh include not just data centers and chips, but also all the rest of the supply chain that people are investing in. Um so, you're close to $2 trillion

2:12of CapEx. Um and then you stack on top of that uh GEP is like close to 30 30 and some change trillion. So, you're at like you know, 5 or 6% right? Not not 3% or 2%. Um so, so sort of my thought process is actually like, "Oh, wow, these numbers are lower than the actual cuz people are understating the amount and size of CapEx that's happening in the US and and

2:37and North America and 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

2:56serve outside of America uses, 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. Um and and similarly, if you look at a lot of Asian countries. Um and so, there is a bit of this like this this you know, CapEx in America is

3:17actually like much higher as a result. Um because one, you're doing all the training here, and then two, you're actually serving other countries from data centers in America. Um and so, ultimately, you're sort of at this stage where capacity is much um the economic impact of it is much larger um than any other sort of physical infrastructure investment cycle ever

3:39before. All right, Jordan, and you are the author of Cluster Max 3.0. This is SemiAnalysis rankings of the GPU uh cloud systems. Uh basically, putting a lot of the hyperscalers and the neo-clouds uh 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 like you to comment on. Basically we're looking at where the the hyperscaler quarterly year-on-year capex growth is going and in the third quarter of 2026 the growth is at 116%

4:19year-over-year. Yeah, which is astonishing. The consensus estimate so what Wall Street is expecting to come after that, you know, they drop down in the first quarter well in the fourth quarter to below 100% year-over-year growth and in the first quarter next year in the 70% and it continues to drop from there. I I think this is a cycle that we're used to right where it's like well you know we're we're all in a bigger base so it can't possibly exceed expectations again and can't possibly double again. So I'm curious to hear from your perspective and your research do you think that we're actually going to see that trend like the growth is

4:56going to decelerate or are you know are we again falling into this trap or is Wall Street again falling into this trap where it's like all right you've you're basically at the biggest numbers you could possibly imagine but you still need to think bigger. [sighs] Yeah, I think we're definitely going to hit limits towards the end of next year and into the Wait Jordan before you answer seriously I got I got to I got you know it's it's like when you ask a kid what's the biggest number they can think of and they say like 1100 [laughter] right and and now and it's like no no no

5:25no we're at a trillion babe. Yeah. [laughter] Yeah, four commas club. So wait in in Dylan in in this example the kid is Wall Street. [laughter] Not not making any any statements that people are children just uh I got it. Yeah go ahead sorry. [laughter] Dylan may have an a habit of referring to his employees and his customers as kids sometimes, but that aside, um [laughter] I think I think I use a much more mean

5:52word. Okay, yeah. I like where this is going already. We're We're running out of big numbers. There are There are things to uh look at when it comes to the build-out. 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 um and IT, you know, estate. Basically, uh there's a lot of work being done to expand the supply chain in order to be able to accommodate for all of this

6:30CapEx to to go into it. Um and it's A lot of it's working, and so we're going to see massive growth, but yeah, you you you can't just have triple-digit growth and accelerate the second derivative, you know, even further from where it is right now. Um you run out of things. And I think the biggest thing right now is uh twofold. One is like it it becomes really hard for the biggest neo clouds and hyperscalers to just organize and marshal the resources required to build another gigawatt on top of what they're

6:59already 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 a you know, a difficult thing to do when you're when you're talking about numbers

7:18in the trillions of dollars. I would I would say the other thing is like by definition the numbers have to shrink. Um you know, this year we have 10x growth in profit going from sub $10 billion of revenue to north of 10 north of 100 billion, so they had 10x growth. Um definitionally, they're just not going to have a trillion dollars of revenue by the end of next year. Um so yes, there's some decelerating growth

7:38there. Um you you Nvidia's revenue uh growth is still 2x year on 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, so 3x off the same base. So, it's sort of like same applies to like other firms. So, it's it is it is definitionally like

7:59definitely 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 build out and accelerate probably beyond where we've seen the the dollars being spent. I mean, you would you would imagine to facilitate all of the AI action. And there's been further further conversations that have circulated about this. And Tim, let me just throw these numbers out to you, right? So, this is a

8:31Goldman Sachs report. Sorry, so Goldman Sachs, they say meta, this is a tweet, right? Meta's committed a corporate suicide after taking on insurmountable debt that the company will struggle to pay back. There's Bain. Bain says hyperscalers will need 6 trillion in revenue, not ever. This is per year by 2020 by 2031 to break even. And there's a Columbia professor Sten Van Nieuwenhuizen 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

9:09massive data center investment. What do you think of these 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 I think everyone's numbers for revenue requirements are quite low. Um given the

9:29Okay. where this build out is going to go. I think I think it's going to go like I think revenue will be much higher than that by 2031 for example. Um, mean, there's a couple points here, right? One is like you know, how much is are people spending on capex and then revenue? And

9:43the revenue always trails the capex. These infrastructure investments are six years on an accounting basis at least appreciable assets. Although, there's quite a bit of evidence that they're actually going to last longer than that, but you know, six years is let's just continue to use six years. So, if I spend a trillion dollars today on, you know, AI data center and and GPUs, um, the GPUs are lasting six years, the data centers are lasting 15. Um, but that means definitionally in terms of revenue, um, either I could assume it's a straight line, right? So, then over the six years I need call it 1.5 trillion of revenue. So, $250 billion of

10:17revenue 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, um, to get the payback. Um, as far as like, you know, like statements on meta, it's like this is this is just kind of silly because meta 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. Um, there's

10:48obviously contracts that they've a committed to, but there's also a lot of stuff they haven't committed to yet. Um, and so as far as like the solvency of meta, like they're completely solvent, they're completely fine. Um, and then the question is like what revenue do they make on top of that? Um, and as far as like AI's impact in the in the US economy, um, yeah, I mean, if if AI revenue is only three trillion dollars in 2031, then,

11:11yeah, we'll have big problems. Um, but you know, I I'm I'm like of the belief that we can get to like 600, 700 million across open AI and Anthropic alone uh, by the by December of next year. Um, I'm of the opinion that you can uh, get to, you know, one and a half trillion maybe even by 2020 end of 2027 uh, with just those two companies. Um, so that's end of 27. So then the payback is clearer, right? Um, you know, where they're getting that from. Um, ultimately, um, the the capacity that people are building out is matched to what they

11:44think business is coming to. Um, and so there's always going to be a mismatch if we're in the building phase of how much infrastructure you're spending on versus what's the AI revenue. Cuz that AI revenue for today is really based on the infrastructure you spent in prior years. Um, and right now CAPEX is not going up that 10x year, but inference just revenue just went up 10x year on year um, for the AI labs. And so, um, we've seen Anthropic is now, you know, profitable in terms of, um, compute cost minus revenue or revenue minus compute cost, um, both training and inference combined. Um, and OpenAI gets there, you

12:20know, within the next couple quarters, uh, maybe two or three quarters. Um, so based on at least what like we expect in the tokenomics model. So so I think, you know, those companies are already uh, breaking even. If you look at like Amazon, for example, the biggest builder of AI infrastructure, they all of their AI infrastructure investments are

12:38profitable today. Um, now, you know, the customers that for them could go insolvent, those customers being OpenAI, Anthropic, etc. Um, but right now they're solvent on those, um, you know, making very strong gross margins. In fact, AI has made their gross margin go up in the most recent quarter, um, from Bedrock and AI revenue from Bedrock. Um, so so yes, AI revenue needs to grow a lot, um, 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, um, is going to generate meaningful revenue and surplus for the consumer

13:14ultimately. 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 to the future. If these companies really do have the best models in in 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 investment

13:33similar 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. We can take this across all of 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

14:05really 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 cloud code subscription and per token pricing regime of today. Yeah, there's there's this chart that looks at how much we're spending as a share of GDP. This is just 2025. So, I

14:25share of GDP, eight 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 it you know, let's say OpenAI and Anthropic combined get to $700 billion in revenue next

14:50year. Um is the money coming from these companies labor pools? Is it just additive because they can sell more? Uh where is the money that these companies are the customers of these companies? Uh where's that money coming from that they can funnel it into the AI companies? It It's a few factors, right? Well, first off is obviously companies are going to be able

15:14to accelerate what they're building. Um and so, they can invest more. Um increasing share of R&D dollars will go towards AI. Increasing share of cost of goods sold dollars will go towards AI. Um but also you'll have a significant amount of money sort of going coming from um you know, labor as well, right? Labor

15:33and 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 labor sort of stays, you know, flatish. Um and and so like um 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 slow line since the '70s and so a lot of the like strife that people say America's economy sucks and yet oh look the economy's bigger is actually well like labor share of economy has shrunk and

16:04that's why. In reality a lot more of the labor the value is getting accruing 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 general basically just capital. You know, in the form of chips and data centers that are sitting there and creating labor. Um and so that labor share is going to fall even faster. Um and and and so, you know, I I would hope and and for society that that that's my belief, you know, and maybe it's like a irrational belief but I believe that we will just grow the economy so fast that

16:39labor in terms of dollars will not shrink um and it might even grow slowly. It's just that the rest of the economy grows so fast. Um and that's where that's sort of where it comes from, right? So, all these companies that do 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. Um or another way to look at it is they're going to have the same number of people and their business is going to get better. Um and that's

17:06that's and 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. Um 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. Um, but there's a lot of industries like pharma and uh trading um and chip design and such, right? Like

17:30chip 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. Um, but the designers are like, you know, two the most valuable companies in the world, right? Nvidia, Broadcom, um 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

18:02that's accrued has exploded. And why is that? There've been a lot of tools. Now what is 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 chip in them, chips for robots are going to get better, chips for obviously AI um are going to get better, chips for all sorts of things, Bluetooth and GPS and all these other things, sensors um for gas

18:26leaks or whatever that Bosch makes. These are all just going to get better because AI can help you design chips. You do it with the same number of people, revenue grows, margins grow, um everything looks fantastic, right? And that's sort of like There's many industries where this can get replicated um in my view. Dylan, you've said that uh I think in the past you've said something to the extent of knowledge work is effed. So do

18:47you no longer believe that? Um, I think most knowledge work Okay, like let's separate like my belief in like Looney Tunes 2035 versus like, [laughter] you know, 2028. Um, I think in 20 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 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. Um, now, you know, in in like the case where AI is just better than everyone, um, that that could change drastically,

19:27right? Um, you know, then 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, [snorts]

19:46We We won't go there today. Um, Jordan, I you know, this is kind of in your your area of expertise. I want to get started talking a little bit about the risks to like what might derail this story. Um, and you know, we'd already talked about the fact that uh there are some contracts that a company like Meta uh could you know, may not have to go

20:05through with. Um, you're obviously looking a lot at the businesses of the neo clouds which are dependent on uh companies like Meta actually making good uh on the on the promises that they've they've put forth to build with these companies. Um, is this sort of an under appreciated risk that like everything looks like fairly [snorts] fine right now if you speak to one of these neo clouds because they'll tell you that they have the best customers in the world. Uh, they have the Metas and Meta will make good on the on the um on the promise and they're raising debt based off of this. Uh, but in reality

20:39the 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 uh you know, the neo clouds tell us they are. Okay, so I 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 neo clouds which is that generally speaking these hyperscalers or you know, large frontier labs or signing up with contracts for the neo clouds do not have arbitrary cancellation rights on like a monthly basis where they can just pull the plug and the contract's done. Uh generally

21:11speaking, they're signing 5-year, 6-year term lengths and they're off-taking the full capacity and they have a set, you know, hourly rate and total contract value that's very very attractive to the new cloud providers because those guys have gone out and secured land and power and built a site and even in some cases purchased the chips prospectively before they've actually gone and done that contract. What Dylan's referring to with the the potential to slow down or pause or cancel projects is 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 base load of

21:44capacity 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 uh project. Like, in other words, the concrete and the steel and the walls is quite, you know, it's important and big and 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 the bigger uh portion

22:18of the bill. Um And so, and you know, what it's 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 have demand on their consumer-facing applications like a ChatGPT or Claude Code or Codex or even

22:42Muse 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 uh different, you know, uh surfaces that they serve and approaching a billion of monthly active users for chat GPT and hundreds of millions for cloud code and things like that. So, anyway, um with that distinction in mind, what's the the risk with project cancellation or a downturn or things like that? I mean, in order to for for us to forecast some sort of change in the

23:22demand supply curve there, you'd really need to see demand slow. Um 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 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

23:58answered, if we find limits to the demand for knowledge work, to the demand for intelligence from the economy, then we'll we'll be able to figure out who needs to slow at what time, where, and how that plays out. Yeah, but so far, I guess those limits haven't been found. So, um all right. Uh before we get into cluster max, one last topic I want to cover, uh big IPO on the way, uh which

24:22is of course the Anthropic IPO. Uh Dylan, you had a very interesting tweet about it. I'm just going to read it. You said, "You can't invest in the Anthropic IPO at $2 trillion because it could be zero and I'm or it could be 20 trillion, but at 20 trillion, we're all fucked." Uh can you explain that and and tell us a little

24:40bit about what you mean by that? Um to be clear, it's it's entirely a post. Um I was I was like listening know what? I I get it, but I think there's some truth to to what you're saying at the time. So, talk to us about it. I I'm I'm generally extremely bullish Anthropic. I think Anthropic will make you all make, you know, be a very

25:01successful company in the world. Anthropic could even be the first company to be a $10 trillion valuation company in the world. Like I'm like that bullish Anthropic, but at the same time um for them to be a 10 or 20 trillion dollar valuation company, especially 20, um their first of all, their multiples on revenue are going to have to collapse

25:19quite a bit. Um you know, so they used to you know, they used to raise at 30 X and now they're, you know, IPO is going to be, you know, let's call it they're at a $100 billion of revenue and they're going to maybe be at 20 X. Um you know, if they get to $10 trillion, I bet you they would need to be at like a trillion dollars of revenue um or something crazy right like that, right? Um which could happen in at some

25:40point in '28 um or '29. Um you know, it depends of course like the stock market can also go crazy and like make multiples go crazy, but you know, in the world where they're doing, you know, where they're worth 20 trillion, you know, I don't think their revenue, I don't think they're even at a 10 X sales multiple now. They're probably at like a 5 X or even less. Um in which case they're, you know, if they're at $20 trillion of revenue, they're at are they at like two, three, four trillion dollars of a 20 trillion dollars valuation, two, three, four trillion dollars revenue? And at that point, you

26:09know, what is the state of AI? If on [snorts] a $150 billion global economy or trillion dollar economy, they're generating multiple trillion dollars of revenue and by far the largest company in the world, like AI's capabilities are freaking insane. Um 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 um constituent supply chains of it and not distributed well, then people are going to have dis- disrest and like overthrow governments

26:49because 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, you have similar worries? I think Yeah, I think I think uh there's a lot to do to get to that point. Like um I I actually don't think I'm necessarily as worried uh as Dylan on the by the way. I just painted a pessimistic picture. I'll I'll go optimistic

27:20instead. Yeah, I mean like the the rosy optimistic view is kind of what I described earlier, which is like if they're getting to 20 trillion dollars 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 a 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

27:48something 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 have had massive uh you know, returns on on that incredible research that led to those uh incredible drugs that help people. And so, I think um I would be more optimistic for the future where theoretically, [laughter] Anthropic sat at a 20 trillion-dollar

28:16valuation. Um but I do think that there are a lot of uh going to be a lot of bumps along the road along the way. Um which is to say, going back to your to your question previously, like how can they possibly serve all of this demand and train these models and uh you know, have intelligence diffused to all of these different parts of the economy so

28:39that 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 Neo clouds on that road from a $2 trillion IPO to a $20 trillion

28:58valuation in the public markets. Yeah, Anthropic kind of an interesting business case, right? They're spending all this money on infrastructure. Uh even in the S1, you saw big losses accompanying their revenue. Although um 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 uh right now than they're spending

29:18on compute. Um Talk a little bit cuz I think the the um the belief here is that a company like Anthropic is a low-margin business or at least much less than uh 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

29:40there. Um you know, I mean I think like at least like consistently grown to become one of the biggest SaaS companies. Um their gross margins are like 75% and Anthropic on their inference is doing 75% gross margins, a little bit higher even. Um you know, especially on the frontier models. So it's sort of like well, and it took it took Salesforce quite some time to get there, right? They were not

30:03at 75% when they IPO'd. Um you know, they they they took quite a bit of time. Actually, they were at but then they like, you know, in 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

30:18to that strong levels of gross margin. Um and if you look at many other SaaS companies that are not even that high. Um and then the other thing is like their customer acquisition cost is insane. Right? If you look at like a a SaaS company, it's like, you know, find my uh my COGS is really low, but then my customer acquisition cost can be as high as like 30%. Um and Anthropic has near or customer acquisition cost. Right now, obviously, they have a humongous R&D spend, um but their R&D spend has grown slower

30:48than their revenue. Um and so now they have reached profitability. And there's no reason why that trend doesn't continue, right? And they continue to grow more profitable. Um so at least operating margin-wise. And then and then like scalability is the other thing, right? Like the the beautiful thing about a lot of SaaS companies is 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

31:18messaging 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 uh switching cost, even when, you know, some other Chinese model comes out or OpenAI comes out with a better model, doesn't really shift the spend share that much um for existing customers. Um 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

31:53AI lab. If you can successfully But how much is the risk the fact that like they're at this pacing the frontier um move- movement movement is coming out. Um I think that's just signaling. I think it's completely Talk about that. You don't think that's going to that's true at all. Um uh I think pacing the frontier is a responsible thing to do if you believe

32:13that we could kill ourselves. Um I don't necessarily think that um they will pace the frontier too much, though. I think like there's there's like very little pacing they can do given the race that they're in. Um and people have made skits about this, but like, you know, you know, Dario can't slow down because he doesn't trust

32:35Sam and Sam's not going to slow down. And Sam can't slow down much because same with Darya. Obviously, they're going to slow down a little bit when they have these massive security incidents. Um and neither of them can slow down because China's not too far behind. So, it's like sort of like very difficult.

32:49That's reassuring. [laughter] All right. Um look, we're going to take a quick break and then when we come back, we're going to talk a little bit about um the state of the AI clouds, uh the ClusterMax of course, uh and then we'll continue on with some more questions about the technology and the business. So, we'll do that when we're back right after this. And we're back here on Big Technology Podcast with Dylan Patel and Jordan Nanos from SemiAnalysis. SemiAnalysis, I'm sure you're familiar. It's the Bible for

33:17anything AI infrastructure. Uh these guys are the best. We're very grateful to have your time, both of you and um you know, I definitely recommend you at least uh check out the blog. You can also get uh SemiAnalysis uh on Substack. It's newsletter.semianalysis.com. It's the number one technology newsletter, uh and well deserved. So,

33:38congratulations, guys. Um in the newsletter, you can uh find the ClusterMax 3.0. Uh it's it's actually even if you're not technical, I was talking with Jordan about this, it's a fun read. It goes into the state of the build out um and the subhead tells you, you know, it already kicks off in the right way in the gory detail, the reliability, performance, support, and pricing, and

34:00security uh the GPU cloud providers. There's a lot in there. Um There's a lot of areas we can go in this, uh Jordan and Dylan, but let's begin with the role of Nvidia here. Um the report, you know, though it spends most of the time on the technical side of things, does go into the fact that uh Nvidia is playing a major role in keeping these these um these neo clouds, so the companies providing uh infrastructure AI infrastructure, maybe to some of the frontier labs, but really across the board uh in making this whole thing work and Nvidia has been backstopping uh a lot of their um

34:36their businesses with uh as you write um revenue floors, landlord guarantees, and leases that it plans to transfer to third parties. Uh helping the neo clouds and AI labs in obtain ingre- investment grade financing without a hyperscaler being involved. Um Jordan, to you on this one, uh you know, there's the the criticism, sort of like the Ed Zitron criticism, of this uh of this industry is that Nvidia is basically funding uh the demand for all these neo clouds and without that uh backstop, um you know, this whole thing would basically evaporate and make Nvidia look a bit smaller and make these neo clouds

35:18um you know, non-nonviable. What's your thought on that after going so deep into this? Uh yeah, well, I mean, both in ClusterTrucks and another article we did recently called Nvidia's backstop universe, we go into a little bit of the dynamics of what backstops are and how they help the neo clouds raise the money they need and to deploy the GPUs, which then customers are renting pretty strongly at this point. And uh the number that we have is that currently we've got tracking of $588 billion of off-balance sheet backstops that Nvidia is supporting right now, which is one of yet another unbelievable number in the list that we've been going through on

35:55this podcast so far. Um I mean, but Nvidia's revenue is approaching 400 of those across $500 so we're talking about really big numbers there. For context, that's like 2 and 1/2% of the M2 money supply in the US. So, obviously there's massive massive amounts of money going on uh in this um universe as we refer to it. And uh I mean, the only thing I can say about this is that it just seems really pragmatic from Nvidia. They really

36:25uh want a wide diversity of customers. They won't don't want to be dependent on the hyperscalers for all of their revenue. Um, they also don't want their customers to be dependent on getting their chips from a select set of three or four different hyperscalers. And so, they have every incentive to have a a nice healthy market with a diversity of options and uh, lots of selection for all of the customers to go

36:47uh, work with. And in order to deliver on that, people have more problems than just uh, raising the money required to buy the GPUs. Well, first of all, they need the guarantee that if they raise the money, those creditors are going to see returns and that requires, you know, some sort of investment grade off-taker, which Nvidia can serve in these cases in

37:08many in many of these cases. But, the way it's worked so far is that Nvidia signs up as that off-taker and then when it comes time to actually, you know, take those GPUs for themselves for their own internal research work, somebody else comes along and wants them more at a higher price. And so, then

37:25they sell them to their to those guys. They don't take them for their internal research and they roll on to the next one. And for everybody who says that Nvidia, you know, is doing this for some nefarious purpose or to to keep some bubble going, I'd encourage you to look at the quality of Nvidia's research. They publish more models on hugging face than anybody else. They have incredible CI across the suite of software that they they support on their GPUs and they train models like NeMo Triton. We're not the biggest fan of these models or this software, you know, we think there should be a diverse ecosystem. But, the

37:55idea that Nvidia does not use their own GPUs for research that serves the purposes of their company is, you know, itself wrong. And then the dynamic that we've seen is that this amount of compute is actually not a meaningful portion of Nvidia's revenue. It's it's a very small percentage of Nvidia's total revenue that is, you know, going back for their own internal research workloads. So, what we're seeing is just that they are supporting the old cloud's build up the capacity for the demand from the customers, which is so incredibly strong right now and hard to

38:24serve. Um and then maybe the the last thing to say about this is that it's not just the chips. Like we we we track a whole wide range of things that they're backstopping. This is everything from like, you know, the construction costs, the memory suppliers, the people building fabs, the people building industrial equipment. I mean, it's not just like I need help raising money for my GPUs. It's I need help raising money for all of the other things in the supply chain, which is just the long pole in the tent for that specific

38:52project. Yep. Let's talk about security because uh you know, the first image in this report uh is a quote tweet of an Ilya Sutskever post, which was made right after uh the Hugging Face incident. Uh Ilya says, "Neo clouds have limited cybersecurity. Next time agents successfully go rogue, uh they'll try taking over a Neo cloud to run more copies. This is bad. Thus, Neo clouds should greatly strengthen their cybersecurity and every company with strong cyber models uh should help with that." This is a rare tweet uh from

39:25Ilya. Dylan, what do you make of this? Uh is this It sounds to me like if he's out saying this, right? Remember, he's building this stuff himself. Uh I'd say if superintelligence that we might have a problem here. Yeah, so we we posted an article titled most Neo clouds suck at security. Yeah. documenting way, the art for that uh is is incredible. We'll put it on the screen

39:46uh for those who are watching. The it's I'll tell you what, that guy is getting [laughter] Yeah, exactly. Getting stabbed by everybody and it's like it's SOC 2 compliant. [laughter] Um but yeah, so we we posted that report and then uh that, you know, like maybe eight or eight hours later or whatever, Ilya uh posted his tweet um because they are very much um

40:10you know, [snorts] using various clouds. Um they're obviously super secretive um and care a lot about the secrecy of like what they're working on, but also they're big believers in super intelligence. Um, you know, I guess safe super intelligence SSI, they were the first to and Trump actually copied Ilya. Ilya's first with another one, right? Um, first to reasoning, first to uh, LLM scaling, and now first to uh, super intelligence

40:35branding. Um, but Ilya sort of tweeted that out. Um, and and it sort of echoed what we wrote, right? Which is like, look, most of these clouds are terrible security. You can get open source models to just hack neo clouds or get access to stuff within other within neo clouds. Um, you know, GLM 5.3 especially is like really good at this. So, open source Chinese models can just hack dozens of the neo clouds that exist out there and you can go look at what other people are doing. And so, we've seen stuff like this um, at other clouds. There was a cloud that you know, we we didn't even intend

41:08to, but we just accidentally ended up like seeing all of the other clients on that cluster, seeing what stuff they had in storage, see what stuff they had in Slurm, etc. And they were like intelligence agencies, right? Like on this So, we immediately posted out like reported it to them, we're like, yo. agency?

41:26Which intelligence agencies? Uh, we we cannot say. I don't think that's a valid thing to say. Uh, national like like federal well, security agencies of governments. They were not American. They were not American, but we said it was for a country that was in a has a top 10 GDP. It's not like some tiny

41:43country. I'm I'm losing my mind here that that was possible. Yeah, and it's like it's like if we were malicious, and by the way, there were probably a dozen other customers, and maybe one of them was malicious, we could have just like seen what they were training. Right? And they were training models and running models for their themselves,

42:00presumably. Um, cuz this was a GPU infrastructure. So, what else would you do with a bunch of Blackwell GPUs? Yeah, we we throw around this this word sovereign AI, and it it gets entangled with the concept of a neo cloud many times and and people even when they're raising money they use this word a lot because they view compute as infrastructure. And I think that's exactly right. This is just like the telecommunications build-out where this is this infrastructure is viewed by the sovereign nations that are deploying it and are involved in this as a asset that the country would like to control and have sovereignty over and they believe

42:34that that similar to what you're saying at the beginning of the show uh they want to be a net token importer or exporter and that is driven by the infrastructure they build which means data centers with GPUs inside of it. And so I think it's actually no surprise that uh federal government research and intelligence agencies are on some of these clusters sharing infrastructure with others. You would like them to have an air-gapped data center but if there's exactly one facility in the entire country with GB200 NVL72

43:05I mean it's a seller's market. Goodness. Uh all right. Well, there goes my sleep tonight. So uh what's you know you you you do in this report also rank uh all you know a lot of these hyperscalers um into tiers right and and the neo-clouds into tiers and the at the top tier is CoreWeave and Nebius. Um I'm more interested in the second tier and that is Oracle and Google Cloud. Um mostly Jordan because you know I I I you know I have like in the document that I put together for you know our conversation today I have like a lot of like topics and sentences

43:42um that I've like pulled out of the report with questions and then with Oracle I just have Oracle and then a bunch of question marks cuz that's kind of kind of how I feel about that company. I don't know how to feel about about them. They're obviously their debt has been rated um you know not well by the rating agencies. They're making this all-in bet. Um they're they rate pretty high and they actually in your report have been credited with pioneering some

44:06techniques. Um but I would love to get your read out on on Oracle and, you know, whether whether this has been a smart bet for them and whether, you know, they're they're going to to rise in glory or or sort of fall in flames like a lot of people anticipate. I think the key thing about ClusterMax is it is not stock recommendations, right? Um, it is not anything to do with, "Hey, this neo cloud is going to build a gigawatt next year or 200 megawatts." Um, which is far more relevant for the stock than, you know, what how good are they at operating a cluster, right? Um, has nothing to do

44:41with, "Hey, this data center that they said they're going to deliver with this much capacity next year got delayed by 3 months." That's not what ClusterMax measures. ClusterMax is explicitly measuring sort of how how good is the cluster for someone to use if you rent the GPUs, um, how reliable is it, how secure is it, how's the storage, how's the network, how's the life cycle, how's the Slurm on Kubernetes, how are all these things? Um, in which case, you know, it's just sort of I we try to be more objective. Obviously, making a rating pyramid is like, you know, it is it is something you kind of have to do. It

45:12would be nice if you could just make a long report and no one cared if there were no rankings. I mean, that's what would happen. So, you kind of have to do the rankings, um, and and set certain boundaries and you can always litigate the hell out of why and what is in what tier. Um, but like, it is an objective fact that we think Oracle is one of the top neo

45:30clouds in terms of quality of service. Now, that doesn't mean on the other hand, "Hey, it's semi analysis in April we published to our clients that their New Mexico data center was going to be delayed because they weren't going to get the pipelines." And this was a big issue for Open AI because Open AI has contracted a huge data center with them in New Mexico. But if these pipelines don't get built, this data center can't go up. Um, and then later, you know, we said it again in, um, May, um, in June, and then most recently Oracle announced a force majeure on it and their stock

46:02tanked because of it. Right? So, so we're disentangling two things, right? Yes, Oracle was an amazing cloud at managed for their managed cluster, one of the top neo clouds, but they also at the same time may have execution issues, whether it's regulatory in the case of this New Mexico data center where they can't get the pipeline there, or other issues, you know, on their balance sheet, right? And so that is we're disentangling like what is good or bad for the stock versus what are we trying to test here. The goal in first ClusterMax was create something that people renting

46:30GPUs can use. Um and it turns out now like there's a lot of other uses for it that people try to ham-fist in, whether it's like, you know, um hey, we're going to write debt for this neo cloud. If their neo if they if they upgrade rankings from bronze to silver, their rate will actually go

46:45down. Um you know, things like this, or like, hey, I'm going to invest in stocks that are at the top of ClusterMax. Um so obviously these are like these are not the decision these are not what ClusterMax is designed for. Um it's a data point that you should use if you're doing anything with these companies, but um as far as Oracle as a company, um there's sort of like,

47:06you know, two things to go here, right? Like one is they signed a ton of AI data center deals um with OpenAI. Um was it last year? Yeah, it was last year. Holy crap, it was only last year. It feels like eons ago. Yep. I think it might have been exactly a year ago. Um but the interesting thing is they signed these massive deals, um and there's a number of issue risks embedded with them. One is they signed

47:30them with a fixed margin, essentially. Um so now that the price of GPUs has gone up a lot for clouds, they can't take advantage of this higher margin. So, they're kind of hemmed in there. [clears throat] Number two, um they raised a bunch of debt, and then the market started getting freaked out about how much debt companies can have, and so they spooked out, even though Oracle's completely solvent. And then number three, they have some execution issues, you know, I mentioned the regulatory one. There's some other data centers that have some delays and such there as well, but the main one being this new you know, such as in in Wisconsin, uh

47:58but the main one being this New Mexico site that is delayed potentially multiple years because of this pipeline issue. Um and so, you know, these things can have very negative impacts on the stock, but I think the company is still fine. Now, you know, they're not Nebius. Nebius gets to turn around and they had the they had the shortest average contract length of the top clouds in

48:17ClusterMax. Um and so, Nebius has been able to turn around and really jack up pricing and take advantage of this. Right? Whereas CoreWeave and Oracle, despite having also good cluster um, you know, management and cluster and reliability and security and all this stuff, were not able to take as much advantage because they locked themselves into

48:36long-term contracts primarily. Oh, that is fascinating. All right, Jordan, let me ask you a technology question then. Um you know, as Dylan put out uh put it out there, it is a pyramid. Um and there's like these top-tier uh groups like we talked about CoreWeave and Nebius, Oracle, Google Cloud is up there. But in the not recommended

48:59section, there's a lot of logos. Uh there's underperforming and unavailable. Um I think there's more, something like more logos in the unavailable than, you know, the top four tiers combined. What do you think that says about the fact that, I mean, what do you think that says about this technology that there's so many that are out there uh that just aren't meeting the bar for

49:20you? Look, as people who are trying to represent the interests of buyers, I think it just makes us sad. It's sad to see the uh Look, it's sad to see that like a year and a half, almost, you know, since um the first version of ClusterMax was published, actually approaching two years now. Um and since we coined the term neo cloud at SemiAnalysis, that a lot of people have just not made progress towards the things that we were writing about. And the things we were writing about is just exactly what we talked to the customers of these neo clouds about what they

49:56want. Um and so I I think it's just like the dynamic of it being a seller's market means that the priority for these companies is how can they raise as much money as they can, how can they build data centers as fast as they can, and how can they deploy chips as fast as they can, rather than working on the

50:10true quality of the underlying service. And that's that's a little bit of the dynamic that you see represented there where people can have these great businesses without actually worrying too much about delivering a great service that their customers really enjoy and recommend to other people. The other thing is that I think there's like this constant dynamic that we'll continue to see over time, which is that there can only be so many leaders because there's only so many people who truly innovate and then everybody else

50:34copies them. And when you have a market that for by our tracking is actually 323 providers as of this morning, uh which we track in our cloud TCO model for people who are subscribers to semi analysis, they have this full list. There's only a certain amount of them that are even relevant, like large enough to put on the list, and then a certain number of those that can pass the bar for what we believe is what we need to see to recommend them. And you know, in some ways we think that the bar for being recommended is quite low. Like the um the basic things that fail with respect

51:10to security and reliability as being the two top ones are just um in our view dead simple. And it's it's just sad to see that others aren't paying attention enough or caring enough to actually implement this stuff. Okay, two quick ones before we go. Um just a more broad question. Uh you know, Google obviously they're not they've announced they have this Gemini Argon coming out, Gemini for Argon. Um do is it your view that that there you know, there've been some people who've called upon Google to just be you know, an basically a neo cloud and rent out Uh, TPUs from Google Cloud and the computing. Uh, do you see them going

51:48that way, or do you think that they're still going to use like a large percentage of their compute for model training and inference? I mean, definitionally they have allocated less compute to uh, model training and deployment um, in the last 6 months than they had 2 3 years ago. Um, and that that percentage allocation has fallen over time. Um, this is sort of, you know, one of the things that we made a joke about like uh, maybe not even joke, but just a statement that

52:14Thomas Kurian won and Demis lost, right? Um, now I guess Karai. Um, Google is producing significantly more compute um, you know, year on year, but the amount that's going to DeepMind is not rising as fast on a, you know, it's not like, it's not like, oh, DeepMind had a third of Google's compute and now it still has a third of Google's compute. Um, it's actually falling as a percentage, uh, which is really interesting because now if I look at just DeepMind versus Anthropic and OpenAI, DeepMind has the least compute

52:45of the three. Um, and by the end of this year. So, like it's it's a really it's really uh, big deal. Um, and so Google, you know, whether or not there's a neo cloud, what the word neo cloud even means, um, you know, does selling tokens of other models count as neo cloud? Um, you know, does does, you know, in a sense that's what the new cloud business is is selling AI model tokens, right? So, maybe that's a neo cloud, right? Um, the definition is

53:10quite bizarre. Blackstone and going and doing new construction projects to build your GPUs to deploy directly for customers outside of your core platform. Or selling chips, right? Is that neo cloud? No, I mean, they're selling a lot of chips to Anthropic. So, it's, you know, physically selling the chips rather than like just renting them out. So, I would say like Google's business model's evolving and they want to make money at every layer of the stack, um, but by definition, you know, helping Anthropic this much with TPUs, um, and helping them this much with these other things is hurting Google

53:39DeepMind's business. Okay. I think that's they they need to find a niche, man. Like for Gemini, I mean, nobody wants the third or fourth or fifth best coding model. So, they need to find a niche that drives Gemini token sales if they want DeepMind to continue competing on the frontier of AI research and because it right now it's I mean, even with this Gemini 4 Argon release, it's just not there on coding compared to the top two

54:04or three. And and and the risk here is what happens next year when OpenAI and Anthropic both attempt to add 5 to 10 gigawatts of additional compute for their labs. And you know, for OpenAI and Anthropic each, and then um, Google only adds a few gigawatts. Now, their compute deficit it it was Google was way ahead in compute and they were kind of behind {slash} equivalent. Um, mostly just a little bit behind. Um, and now they've fallen further behind, but their compute is the same. So, what happens when they have less compute? Are

54:35they Do they somehow magically catch up? It's possible, right? They have the talent, they have the people, they need to reorganize and really focus up. Um, but also like more and more of the best people just keep getting poached to Anthropic, to OpenAI, and it it's it it meta. Discovery loop. Discovery loop, yeah. Discovery loop, right. Okay, I I want to ask one more before we go if that's

54:55okay. You know, the I think Dylan, you said in the past that um, the the people who are getting most of the value out of the AI models that are created are the end users, right? The people that are using the Claudes, for instance, at the you know, at the base. Um, and of course, Anthropic makes that model available via API and to go into all these various uses. You know, it's it's going to have to do more licensing and and who knows if they'll be able to do products for all this. I'm curious if you think if you see a scenario where given the big

55:23numbers that we talked out about in the past, where do they keep their frontier models at home for longer, much longer, and use them to build their own products? Like we know they have the wet lab, we know they have Claude code uh, and co-work. Um, and then they don't make them available because they want to get more margin out of the infrastructure that they've they've

55:43bought and built. Yeah, so I mean I think I think we've already seen this, right? Anthropic had Mythos ready in February. Uh they've their model safety cards noted noted noted that they have two other models since Mythos that have released and publicly we still don't have full Mythos. We have a neutered version of it called Fable. Um and it and it took many many months before Fable even came out. They had the glass wing which was like Mythos but for only some subsectors and it was neutered in some areas, right? Not all areas. And so it's clear that Anthropic is not released new models. Open AI likewise,

56:18Astra was actually done a couple months ago and it only came out like, you know, it took a couple months before they released Astra. Um and their new model Bell, um they even showed a blog where they showed the performance of it in math um and how it, you know, solved all this stuff. Um and they have not released it. So clearly they are just using it internally, not externally. And and so, you know, the labs have already moved along this line, right? Where it's like, oh yeah, yeah, the gap between them and and Chinese open source is still 6

56:49months. Um they're kind of keeping that but in reality they've kept, you know, months of progress internal only um because the moment you make it external people can distill it um you know, and and all these other things. So I think, you know, Anthropic will likely release a new model in the coming weeks, you know, for their IPO. Um I think Open AI will likely have to release a new model in the coming weeks as well just because it's such a competitive environment. But at the same time they are like keeping a lot of

57:15their stuff internal only. Yeah, we were discussing internally some of the capitalistic incentives if if the incentives are actually going to motivate people to release more models and get them in the hands of consumers because of the incredible gross margins on the per token API pricing or if the incentive is to keep it internally so that you can pursue the end-user applications which are even more uh high value than selling the access to the model per token. It's going to be interesting to see how it plays out, but in one of those scenarios the capitalist approach will be to pace

57:51the frontier for yourself, right? And where which what's your gut on where it's going to go? Which one of those scenarios you think plays out? I think Anthropic has publicly stated that they are pacing the frontier by keeping themselves uh restricted, and that's the only way that their blog posts have manifest in reality. Nobody else is slowing down. They're only slowing themselves down. I think that's true. I think that's what's

58:13happening. And uh I think the incentive's to both improve the status of AI by solving things that are you know, like cancer that are just unequivocally good that everybody can agree is a good thing would be, you know, a positive incentive. And then I think there's also plenty of money to be made if you do something like that. So I my my bet is that we continue to see the minimum viable models released by both OpenAI and Anthropic to keep a lead over Chinese and the rest of the open

58:47source ecosystem. The website semianalysis.com, uh sign up for the blog as Dylan says, you know, sign up for the newsletter as I have. Uh it's a wealth of insight and analysis, and I think you'll love it if you're not there already. Uh and this conversation has just, you know, been the manifestation of that. So uh Dylan, great to see you again. Jordan, really nice to meet you. You guys are welcome anytime, and we appreciate your time

59:10today. Thanks for having us, Alex. Thanks, Alex. All right, good stuff. Thanks everybody for listening and watching, and we'll see you next time on Big Technology Podcast.

59:34Mhm.