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

Dylan Patel is founder and CEO of SemiAnalysis, Jordan Nanos is a member of its technical staff. Patel and Nanos join Big Technology to discuss whether AI’s massive infrastructure buildout can justify the trillions of dollars being spent on compute. Tune in to hear how big the boom could get, where the revenue needed to support it will come from, and what it could mean for knowledge work and the broader economy. We also cover Nvidia’s role in financing AI infrastructure, the economics and security of neoclouds, Anthropic’s potential IPO, Oracle’s AI bet, and whether frontier labs are keeping their best models to themselves. Hit play for a deep look at the financial and technical machinery powering the AI boom, and what could happen if it keeps accelerating. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  How big does AI have to get to justify the trillions in infrastructure spending? Let's talk about it with the leading authorities from semi analysis right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience? And what companies need to get right in the AI era? Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial to go inside a large scale agentic CX transformation, including the business decisions, trade offs and lessons along the way. You can watch both conversations now on my YouTube channel today.

• 0:45Speaker 00

  It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility and peace of mind. That's how companies like Razor were able to cut their inference cost by 15 times. Visit akash.network slash Alex for free consultation. That's akash.network slash Alex for free consultation. Welcome to 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 build out looks like and whether it can continue to sustain itself

• 1:30Speaker 00

  without not maybe not destroying the world, but destroying the economy. Cause we have some very interesting guests to speak with us about it today. We're joined today by Dylan Patel, the CEO and founder of Semi Analysis and Jordan Nanos, member of the technical staff at Semi Analysis to talk all about everything going on within the infrastructure build out. Dylan, Jordan, welcome to the show.

• 1:54Speaker 01

  Thanks. Great to be here.

• 1:55Speaker 00

  Let's just start by talking about the scale of what we're seeing in this AI infrastructure build out, if we can. Dylan, there's like a chart going around X right now that compares the AI infrastructure build out to previous build outs in the past, like railroads,

• 2:12Speaker 00

  like highways. And the chart is showing that the percent of GDP for the AI build out today is about 3.6%. Oh, 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 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.

• 2:50Speaker 02

  Yeah, I mean, 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.

• 2:57Speaker 00

  Really? Okay. So share the real numbers.

• 2:59Speaker 02

  Yeah. Yeah. So like, I mean, it's, it's, 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.

• 3:18Speaker 02

  And then you stack on top of that, GDP is like close to 30, 30 and some change trillion. So you're at like, you know, five or six percent, right? Not, not 3% or 2%. So, 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, and, and North America and the world.

• 3:44Speaker 02

  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 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. And similarly, if you look at a lot of Asian countries. And so there is a bit of this, like this, 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.

• 4:29Speaker 02

  And so ultimately you're sort of at this stage where capacity is much, the economic impact of it is much larger

• 4:38Speaker 02

  than any other sort of physical infrastructure investment cycle ever before.

• 4:43Speaker 00

  All right, Jordan, and you are the author of ClusterMax 3.0. This is semi analysis rankings of the GPU cloud systems. Basically putting a lot of the hyperscalers and the Neo clouds 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, the hyperscaler quarterly year on year capex growth is going. And in the third quarter of 2026, the growth is at 116% year over year. Yeah. Which is astonishing.

• 5:26Speaker 00

  The consensus estimate, so what Wall Street is expecting to come after that, you know, they dropped down in the first quarter, well, in the fourth quarter to below 100% year over year growth. And then the first quarter next year in the 70% 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, we're all on a bigger base, so it can't possibly exceed expectations again. It 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 going 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,

• 6:07Speaker 00

  all right, you've, you're basically at the biggest numbers you could possibly imagine, but you still need to think bigger.

• 6:14Speaker 01

  Yeah, I think we're definitely going to hit limits towards the end of next year and into the

• 6:17Speaker 02

  Wait, Jordan, before you answer, seriously, I got, I got, 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 1,100, right? And now, and then it's like, no, no, no, no, we're at a trillion, babe.

• 6:31Speaker 01

  Yeah. Yeah. Four commas, club.

• 6:34Speaker 00

  So wait, Dylan, in this example, the kid is Wall Street.

• 6:39Speaker 02

  Not, not making any, any statements that people are children, just, yeah, go ahead. Sorry.

• 6:46Speaker 01

  Dylan may have a habit of referring to his employees and his customers as kids sometimes, but that aside.

• 6:53Speaker 02

  I think, I think I use a much more mean word.

• 6:59Speaker 00

  Okay. Yeah. I like where this is going already.

• 7:01Speaker 01

  We're, we're running out of big numbers. There are, there are things to 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 for chemical construction, to chips, to cooling systems, to, you know, all the components that go outside of that and, and IT, you know, estate. Basically, there's a lot of work being done to expand the supply chain in order to be able to accommodate for all of this capex to, to go into it. And it's a lot of it's working. And so we're going to see massive growth, but yeah, you, you can't just have triple digit growth and accelerate the second derivative.

• 7:44Speaker 01

  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 Neo clouds 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 a, you know, a difficult thing to do when you're, when you're talking about numbers in the trillions of dollars.

• 8:23Speaker 02

  I would, I would say the other thing is like, by definition, the numbers have to shrink. You know, this year we have 10x growth, Anthropic going from sub $10 billion of revenue to north of 10, 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 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 definitely decelerating.

• 9:04Speaker 00

  Okay, so the growth on spending is gonna need to decelerate at some point. But the growth on revenue and profit, for that matter, is gonna have to accelerate, you know, beyond, uh, it's basically gonna have to trail this big infrastructure build out and accelerate probably beyond where we've seen, uh, the, the dollars being spent. I mean, you would, you would imagine, uh, to facilitate all the AI action. And there's been, uh, further, 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, uh, report, um, and sorry, so Goldman Sachs, they say Meta, this is a tweet, right? Meta's committed a corporate suicide after taking

• 9:45Speaker 00

  on insurmountable debt that the company will struggle to pay back. There's Bain. Bain says hyperscalers will need six trillion in revenue, not ever. This is per year by 2020, by 2031, uh, to break even. And there's a Columbia professor, uh, Stin van Neuberg, 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. Um, 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?

• 10:27Speaker 02

  Yes, I mean, I think, I think everyone's numbers for revenue requirements are quite low, um, given where this build out is gonna go. I think, I think it's gonna go like, I think revenue will be much higher than that, uh, by 2031, for example. Um, I mean, there's a couple of points here, right? One is like, you know, how much is people, are people spending on CapEx and then revenue? And the revenue always trails to 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 gonna 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,

• 11:11Speaker 02

  um, 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 revenue a year for that trillion dollars of CapEx, or it's gonna 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 kinda 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 $100 billion of data centers next year. They could pull the plug whenever they feel like it. Um, there's obviously contracts that they've committed to, but there's also a lot of stuff they haven't committed to yet. Um, and so as far as like

• 11:56Speaker 02

  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 $3 trillion in 2031, then yeah, 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 OpenAI and Anthropic alone by the, by December of next year. Um, I'm in 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 clear, right? Um, you know, where they're

• 12:39Speaker 02

  getting that from. Um, ultimately, um, the, the capacity that people are building out is matched to what they think business is coming to. Um, and so there's always gonna be a mismatch if we're in the building phase of how much infrastructure are you spending on versus what's the AI revenue? Cause 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 at 10x a 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.

• 13:21Speaker 02

  Um, and OpenAI gets there, you know, within the next couple of 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 profitable today. Um, now, you know, the customers that for them could go insolvent, those customers being OpenAI, Anthropic, et cetera. Um, but right now they are 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

• 14:06Speaker 02

  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 ultimately.

• 14:18Speaker 01

  Yeah, I think we need to stretch it a little bit in our minds to understand what we are gonna 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 of these industries and say, if they wanna 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

• 14:59Speaker 01

  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.

• 15:22Speaker 00

  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 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 gonna lap or it'll pass food in 2026. So guys, I, 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 year. 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,

• 16:05Speaker 00

  the customers of these companies, uh, where's that money coming from that they can funnel it into the AI companies?

• 16:12Speaker 02

  It's a few factors, right? First off is, um, obviously companies are going to be able to accelerate what they're building, um, and so they can invest more. Um, increasing share of R and 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, 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, labor sort of stays, you know, flattish. Um, and, and so like, um, you know, in the past, we've had different cycles across the economy, but one longstanding one has been labor share of GDP has fallen over the decades. And it's

• 16:56Speaker 02

  been a steady slow line since the seventies. And so a lot of the like strife that people say, oh, America's economy sucks, and yet all of the economy is bigger is actually well, like labor share of economy has shrank and that's why. Um, in reality, a lot more of the labor, the value is accruing to capital. What AI does is it's gonna supercharge that trend, long-term trend of labor share versus capital share because AI tokens are generally gen, basically just capital, um, you know, in the form of chips and data centers that are sitting there and creating labor. Um, and so that labor share is gonna fall even faster. Um, and, and, and so, you know, I, I would hope and for society, that, that's my belief, you know, and maybe it's like a irrational belief, but I believe that we

• 17:40Speaker 02

  will just grow the economy so fast that labor 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, uh, offer various services, their services are gonna get better, they're gonna be able to charge more, they're gonna be able to sell more, um, and they're gonna be able to do it with fewer people. Um, or another way to look at it is they're gonna have the same number of people and their business is just gonna get better. Um, and that's, that's, and, and there's a number of industries where this is possible, right? Um, 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

• 18:24Speaker 02

  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 chip design is a great example, right? Chips, the number of American R and 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 of 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 that's accrued has exploded. And why is that? There

• 19:08Speaker 02

  have been a lot of tools. Now, what is, what does AI stitched to chip design now do? Well, now all of the chips that are across the economy get better and better. Your phones are gonna get better, your, you know, AirPods are gonna get better, they're chipped in them, chips for robots are gonna get better, chips for, obviously AI, um, are gonna get better, chips for all sorts of things, Bluetooth and GPS and all these other things, sensors, um, for gas leaks or whatever that Bosch makes. These are all just gonna 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.

• 19:45Speaker 00

  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?

• 19:53Speaker 02

  Um, I think most knowledge work, okay, like, let's separate like my belief in like Looney Tunes 2035 versus like, you know, 2028.

• 20:06Speaker 02

  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.

• 20:23Speaker 02

  Now, you know, in like, the case where AI is just better than everyone,

• 20:29Speaker 02

  that, that could change drastically, right? 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.

• 20:50Speaker 00

  We won't go there today. Jordan, 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 the story. And, you know, we'd already talked about the fact that there are some contracts that a company like Meta, you know, may not have to go through with. You're obviously looking a lot at the businesses of the Neo clouds, which are dependent on companies like Meta actually making good on the, on the promises that they've, 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 Neo clouds, because they'll tell you that they have the best customers in the world.

• 21:34Speaker 00

  They have the Metas and Meta will make good on the, on the, 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 Neo clouds tell us they are.

• 21:53Speaker 01

  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 Neo clouds, which is that generally speaking, these hyperscalers or, you know, large frontier labs who are 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. Generally speaking, they're signing five year, six 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 Neo 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

• 22:38Speaker 01

  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 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, 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 portion of the bill.

• 23:24Speaker 01

  And so, 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 who have demand on their consumer facing applications like a ChatGPT or Claude Code or Codex or even Muse 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

• 24:06Speaker 01

  and hundreds of millions for Claude Code and things like that. So anyway, 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 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?

• 24:50Speaker 01

  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 answered, 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.

• 25:16Speaker 00

  Yeah, but so far, I guess those limits haven't been found. So, all right. Before we get into Cluster Max, one last topic I want to cover, a big IPO on the way, which is of course the Anthropic IPO. 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 fucked, or it could be 20 trillion, but at 20 trillion, we're all fucked." Can you explain that and, and tell us a little bit about what you mean by that?

• 25:45Speaker 02

  To be clear, it's, it's entirely a shitpost. I was, I was like.

• 25:50Speaker 00

  But you know what? I, I get it, but I think there's some truth to, to.

• 25:54Speaker 02

  Yeah, yeah.

• 25:55Speaker 00

  So talk to us about it.

• 25:57Speaker 02

  I, I'm generally extremely bullish Anthropic. I think Anthropic will make you, make, you know, 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 like that fucking bullish Anthropic, but at the same time, for them to be a 10 or 20 trillion dollar 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, you know, they used to raise at 30X and now their, you know, 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. You know, if they get to $10 trillion, I bet you they would need to be at like a trillion dollars of revenue or something crazy right like that, right?

• 26:42Speaker 02

  Which could happen at some point in 28 or 29.

• 26:48Speaker 02

  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 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, they're at, are they at like 2, 3, 4 trillion dollars, a $20 trillion valuation, 2, 3, 4 trillion dollars revenue? And at that point, you know, what is the state of AI? If on 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. In which case, like, you know, all of this worry of like,

• 27:33Speaker 02

  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 distrust 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 fucked.

• 28:07Speaker 01

  Jordan, you have similar worries? I think, yeah, I think, I think there's a lot to do to get to that point. Like, I, I actually don't think I'm necessarily as worried as Dylan.

• 28:20Speaker 02

  I'm optimistic, by the way. I just painted a pessimistic picture. I'll go optimistic.

• 28:24Speaker 01

  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 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, have had massive, you know, returns

• 29:05Speaker 01

  on, 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 a lot of, 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, you know, 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

• 29:49Speaker 01

  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-clouts on that road from a $2 trillion IPO to a $20 trillion valuation in the public markets.

• 30:04Speaker 00

  Yeah, Anthropic, kind of an interesting business case, right? They're spending all this money on infrastructure. Even in the S1, 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.

• 30:38Speaker 02

  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, like, 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, 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

• 31:21Speaker 02

  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, like, scalability is the other thing, right? Like, the beautiful thing about

• 32:06Speaker 02

  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 okay, 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

• 32:49Speaker 02

  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 an AI lab.

• 32:58Speaker 00

  But how much is the risk, the fact that, like, there, this pacing the frontier movement is coming out?

• 33:06Speaker 02

  I think that's just signaling. I think it's completely-

• 33:09Speaker 00

  Talk about that. You don't think that's gonna, that's true at all?

• 33:12Speaker 02

  Um, I think pacing the frontier is a responsible thing to do if you believe that we could kill ourselves. I don't necessarily think that 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. And people have made skits about this, but like, you know, Dario can't slow down because he doesn't trust Sam, and Sam's not going to slow down. And Sam can't slow down much, because same with Dario. Obviously, they're going to slow down a little bit when they have these massive security incidents. And neither of them can slow down, because China's not too far behind. So it's, like, sort of, like, very difficult.

• 33:53Speaker 00

  That's reassuring. All right. Look, we're gonna take a quick break, and then when we come back, we're gonna talk a little bit about the state of the AI clouds, the ClusterMax, of course, 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. This episode is brought to you by AppPoint. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data, and most teams have no single way to see it, secure it, and prove it's under control. That's exactly what AppPoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended

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• 36:42Speaker 00

  And we're back here on Big Technology Podcast with Dylan Patel and Jordan Nanos from Semi Analysis. Semi Analysis, I'm sure you're familiar. It's the bible for anything AI infrastructure. These guys are the best. Very grateful to have your time, both of you, and, you know, definitely recommend you at least check out the blog. You can also get Semi Analysis on Substack. It's newsletter.semianalysis.com. It's the number-one technology newsletter, and well-deserved. So congratulations, guys. In the newsletter, you can find the ClusterMax 3.0. 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, and

• 37:27Speaker 00

  the subhead tells you, you know, it's already kicked off in the right way, and the gory detail, the reliability, performance, support, and pricing and security of the GPU cloud providers. There's a lot in there. There's a lot of areas we can go in this, Jordan and Dylan, but let's begin with the role of Nvidia here. The report, you know, though it spends most of the time on the technical side of things, does go into the fact that Nvidia is playing a major role in keeping these neo-clouts, so the company's providing infrastructure, AI infrastructure, maybe to some of the frontier labs, but really across the board, and making this whole thing work. And Nvidia has been backstopping a lot of

• 38:08Speaker 00

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

• 38:48Speaker 00

  make Nvidia look a bit smaller, and make these neo-clouts, you know, non-viable. What's your thought on that, after going so deep into this?

• 38:57Speaker 01

  Yeah, well, I mean, both in ClusterMax and in 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 NeoClouds raise the money they need to deploy the GPUs, which then customers are renting pretty strongly at this point. And 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 yet another unbelievable number in the list that we've been going through on this podcast so far. I mean, but Nvidia's revenue is approaching $400, about to cross $500 billion. So we're talking about really big numbers there. For context, that's like 2.5% of the M2 money supply in the US.

• 39:42Speaker 01

  So obviously, there's massive, massive amounts of money going on in this universe, as we refer to it. And I mean, the only thing I can say about this is that it just seems really pragmatic from Nvidia. They really want a wide diversity of customers. They don't want to be dependent on the hyperscalers for all of their revenue. 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 nice, healthy market with a diversity of options and lots of selection for all of the customers to go work with.

• 40:22Speaker 01

  And in order to deliver on that, people have more problems than just 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 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 they sell them to those guys. They don't take them for their internal research, and they roll on to the next one.

• 41:04Speaker 01

  And for everybody who says that Nvidia, you know, is doing this for some nefarious purpose or 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 support on their GPUs. And they train models like Nematron. We're not the biggest fan of these models or this software. You know, we think there should be a diverse ecosystem. But the idea 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 a very small percentage of Nvidia's total revenue that is, you know, going back for their own internal research workloads.

• 41:49Speaker 01

  So what we're seeing is just that they are supporting NeoCloud's build-out the capacity for the demand from the customers, which is so incredibly strong right now and hard to serve. And then maybe the last thing to say about this is that it's not just the chips. Like, 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, to people building fabs, to 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 project.

• 42:27Speaker 00

  Yep. Let's talk about security, because, you know, the first image in this report is a quote tweet of an Ilya Sutskever post, which was made right after the Hugging Face incident. Ilya says, "NeoClouds have limited cybersecurity. Next time agents successfully go rogue, they'll try taking over a NeoCloud to run more copies. This is bad. Thus, NeoCloud should greatly strengthen their cybersecurity, and every company with strong cyber models should help with that." This is a rare tweet from Ilya. Dylan, what do you make of this? Is this, it sounds to me like if he's out saying this, right? Remember, he's building this stuff himself. I'd say of superintelligence, that we might have a problem here.

• 43:10Speaker 02

  Yeah, so we posted an article titled "Most NeoClouds Suck at Security."

• 43:15Speaker 00

  By the way, the art for that is incredible. We'll put it on the screen for those who are watching. It's like this guy getting, yeah, exactly, getting stabbed by everybody. And it's like, it's S O C to compliant.

• 43:30Speaker 02

  But yeah, so we posted that report. And then that, you know, like maybe eight or eight hours later or whatever, Ilya posted his tweet, because they are very much, you know, using various clouds. They're obviously super secretive and care a lot about the secrecy of like what they're working on. But also, they are big believers in superintelligence. You know, I guess, safe superintelligence, SSI, they were the first. And Trump actually copied Ilya. Ilya's first with another one, right? First to reasoning, first to LLM scaling, and now first to superintelligence branding. But Ilya sort of tweeted that out, and it sort of echoed what we wrote, right?

• 44:15Speaker 02

  Which is like, look, most of these clouds are terrible at security. You can get open-source models to just hack NeoClouds or get access to stuff within NeoClouds. You know, GLM 5.3 especially is like really good at this. So open-source Chinese models can just hack dozens of the NeoClouds that exist out there, and you can go look at what other people are doing. And so we've seen stuff like this at other clouds. There was a cloud that, you know, we didn't even intend to, 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 SLARM, et cetera. And there were like intelligence agencies, right? Like on this, so we immediately closed out, like reported it to them, we're like, yo.

• 44:59Speaker 00

  Which intelligence agencies?

• 45:01Speaker 02

  We cannot say. I don't think that's a valid thing to say.

• 45:04Speaker 00

  National, like, like federal, well, security agencies of governments.

• 45:09Speaker 02

  They were not American.

• 45:10Speaker 01

  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 country.

• 45:18Speaker 00

  I'm losing my mind here that that was possible.

• 45:20Speaker 02

  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 themselves, presumably. Because this is a GPU infrastructure. So what else would you do with a bunch of blackwell GPUs?

• 45:39Speaker 01

  Yeah, we throw around this word sovereign AI, and it gets entangled with the concept of a NeoCloud many times. 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 an asset that the country would like to control and have sovereignty over. And they believe that, similar to what you're saying at the beginning of the show, 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 federal government research

• 46:24Speaker 01

  and intelligence agencies are on some of these clusters, sharing infrastructure with others. You would like them to have an airgap data center. But if there's exactly one facility in the entire country with GB300, NVL72, I mean, it's a seller's market.

• 46:44Speaker 00

  Goodness. All right, well, there goes my sleep tonight. So, you know, you do in this report also rank, you know, a lot of these hyperscalers into tiers, and NeoClouds into tiers. And at the top tier is CoreWeave and Nebius. I'm more interested in the second tier, and that is Oracle and Google Cloud. Mostly, Jordan, because, 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 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. Because that's kind of how I feel about that company. I don't know how to feel about them.

• 47:27Speaker 00

  Obviously, their debt has been rated, you know, not well by the rating agencies. They're making this all in bet. They rate pretty high, and they actually, in your report, have been credited with pioneering some techniques. But I would love to get your readout on Oracle and, you know, whether this has been a smart bet for them and whether, you know, they're going to rise in glory or sort of fall in flames like a lot of people anticipate.

• 47:54Speaker 02

  I think the key thing about ClusterMax is it is not stock recommendations, right? It is not anything to do with, hey, this NeoCloud is going to build a gigawatt next year or 200 megawatts, which is far more relevant for the stock than, you know, what, how good are they at operating a cluster, right? Has nothing to do with, hey, this data center that they said they're going to deliver with this much capacity next year got delayed by three months. That's not what ClusterMax measures. ClusterMax is explicitly measuring sort of how good is the cluster for someone to use? If you rent the GPUs, how reliable is it? How secure is it? How's the storage? How's the network? How's the lifecycle? How's the SLARM on Kubernetes? How are all these things?

• 48:37Speaker 02

  In which case, you know, it's just sort of, we try to be more objective. Obviously, making a rating pyramid is like, you know, it is something you kind of have to do. It would 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 and set certain boundaries. And you can always litigate the hell out of why and what is in what tier. But like, it is an objective fact that we think Oracle is one of the top NeoClouds in terms of quality of service. Now, that doesn't mean, on the other hand, hey, at 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 OpenAI, because OpenAI has contracted a huge data center with them in New Mexico.

• 49:22Speaker 02

  But if these pipelines don't get built, this data center can't go up. And then later, you know, we said it again in May, in June. And then most recently, Oracle announced a force measure on it, and their stock tanked because of it, right? So we're disentangling two things, right? Yes, Oracle is an amazing cloud at managed, for their managed cluster, one of the top NeoClouds. 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 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 GPUs can use.

• 50:05Speaker 02

  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 NeoCloud. If they're Neo, if they, if they upgrade rankings from bronze to silver, their rate will actually go down. Um, you know, things like this, or like, hey, I'm going to invest in stocks that are at the top of ClusterMax.

• 50:23Speaker 00

  Yeah, it's just good context.

• 50:25Speaker 02

  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, you 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.

• 50:51Speaker 00

  Yeah.

• 50:51Speaker 02

  Um, I think it might've been exactly a year ago. Um, but the interesting thing is they sign these massive deals, um, and there's a number of issu- risks embedded with them. One is they signed them 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 hammed in there. 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, with the main one being this, you know, such as in, in Wisconsin, uh, but the main one being this New Mexico

• 51:33Speaker 02

  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 have the shortest average contract length of the top clouds in ClusterMax. 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 long-term contracts primarily.

• 52:11Speaker 00

  Oh, that is fascinating. All right, Jordan, let me ask you a technology question then. Um, you know, as Dylan put out, uh, put out there, it is a pyramid. Uh, 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 section, there's a lot of logos. Uh, there's underperforming and unavailable. Uh, 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 you?

• 52:57Speaker 01

  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, 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 NeoCloud at Semi Analysis, 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 talk to the customers of these NeoClouds about what they want. 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?

• 53:43Speaker 01

  Rather than working on the true 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 copies them. And when you have a market that, by our tracking, is actually 323 providers as of this morning, uh, which we track in our AI 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

• 54:25Speaker 01

  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, 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 to security and reliability, it 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.

• 54:57Speaker 00

  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, uh, Argon coming out, Gemini four Argon. Um, is it your view that, that, you know, there've been some people who've called upon Google to just be a, you know, a, basically a NeoCloud and rent out, uh, the TPUs from Google Cloud and the computing. Uh, do you see them going that way? Or do you think that they're still going to use like a large percentage of their compute for model training and inference?

• 55:29Speaker 02

  I mean, definitionally, they have allocated less compute to, uh, model training and deployment, um, in the last six months than they had two or three 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 Thomas 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, now it still has a third of Google's compute. Um, it's actually falling as a percentage, uh, which is really interesting

• 56:12Speaker 02

  because now if I look at just DeepMind versus Anthropic and OpenAI, DeepMind has the least compute of the three, um, uh, uh, by the end of this year. So like, it's, it's a really, it's really a big deal. Um, and so Google, you know, whether or not there's a NeoCloud, what the word NeoCloud even means, um, you know, does selling tokens of other models count as NeoCloud? 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 NeoCloud, right? Um, the definition is quite.

• 56:44Speaker 01

  Using Blackstone and going and doing new construction projects to build your GPUs to deploy directly for customers outside of your core platform?

• 56:51Speaker 02

  Or selling chips, right? Is that NeoCloud? 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 DeepMind's business. Um, and I think that's.

• 57:15Speaker 01

  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 right now, it's, I mean, even with this Gemini four Argon release, it's just not there on coding compared to the top two or three.

• 57:38Speaker 02

  And, and, and the risk here is what happens next year when OpenAI and Anthropic both attempt to add five 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's 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 they, 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's, it's meta.

• 58:22Speaker 00

  Discovery loop.

• 58:24Speaker 02

  Discovery loop, yeah.

• 58:25Speaker 00

  Right. Okay, I, I want to ask one more before we go, if that's okay. Um, 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 clods, 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 numbers that we talked about in the past, um, 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,

• 59:09Speaker 00

  and CoWork, 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 bought and built?

• 59:17Speaker 02

  Yeah. So I mean, I think, I think we've already seen this, right? Um, Anthropic had Mythos ready in February. Uh, they've, their model safety cards noted, noted, uh, 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, it took many, many months before Fable even came out. They had the Glasswing, 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 has not released new models. OpenAI likewise, Astra was actually done a couple months ago, and it only came out, like, you know, it took a couple of months before they released Astra. Um, and their

• 59:59Speaker 02

  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, the gap between them and Chinese open source is still six months. Um, they're kind of keeping that. But in reality, they 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 OpenAI will likely have to release a new

• 1:00:43Speaker 02

  model in the coming weeks as well, just because it's such a competitive environment. But at the same time, they're like keeping a lot of their stuff internal only.

• 1:00:51Speaker 01

  Yeah, we were discussing internally some of the capitalistic incentives. 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 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 the frontier for yourself, right?

• 1:01:26Speaker 00

  And what's your gut on where it's going to go? Which one of those scenarios you think plays out?

• 1:01:31Speaker 01

  I think Anthropic has publicly stated that they are pacing the frontier by keeping themselves 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 happening. And I think the incentives 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.

• 1:02:10Speaker 01

  So 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 source ecosystem.

• 1:02:22Speaker 00

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

• 1:02:44Speaker 01

  Thanks for having us, Alex.

• 1:02:45Speaker 02

  Thanks, Alex.

• 1:02:46Speaker 00

  All right. Good stuff. Thanks, everybody, for listening and watching, and we'll see you next time on Big Technology Podcast.

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