Inside Anthropic’s trillion-dollar AI gamble

Today, I’m joined by Joey Brookhart, an analyst at the chip and AI research firm SemiAnalysis, to help me dig into the story inside the massive Anthropic IPO filing. This isn’t just about a single company’s financials, or its lofty ambitions or frankly ludicrous risk factors, which include maybe automating every job or, you know, killing everyone. It’s also the clearest look yet at how the business of frontier AI actually works and who’s really paying for it. Read the full interview transcript on The Verge. Links: Anthropic files for its IPO | The Verge Five weird details from Anthropic’s IPO filing | The Verge Anthropic’s elaborate plan to keep Wall Street at bay | The Verge Anthropic revenue jumps to $16.8B in quarter before IPO | Bloomberg Anthropic raises concerns | Matt Levine Anthropic reveals skyrocketing growth in IPO filing | NYT The Anthropic IPO financials sneak peak | SemiAnalysis Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder’s producers are Greg Ott, Kate Cox, and Nick Statt. This episode was edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nilay Patel?

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• 0:54Nilay Patel?

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• 1:32Speaker 01

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• 2:09Nilay Patel?

  Hello, and welcome to Decoder. I'm Neelai Patel, editor in chief of The Verge, and Decoder is my show about big ideas and other problems. Today, I'm joined by Joey Brookhart, an analyst at chip and AI research firm SemiAnalysis, to help me dig into the story inside the numbers of the Anthropic IPO filing, a document known as an S1. Anthropic just filed its S1 to go public on the Nasdaq Stock Exchange, and it could start trading as soon as next month, at a valuation of anywhere between $1.5 to $2 trillion, immediately making it one of the most valuable companies on the planet. But the price tag really isn't the whole story here. The S1, published earlier this week, is hundreds upon hundreds of pages filled with details that finally paint a picture of the economics of a frontier AI lab. And, as you might imagine, the picture is both really complicated and built on a series

• 2:51Nilay Patel?

  of very, very ambitious assumptions about the future and what AI will do to it. So I wanted to have Joey on the show to help break down what this document really tells us, both about Anthropic's finances and the broader AI economy. Joey and his colleagues at SemiAnalysis actually built a model of Anthropic's finances from the outside back in July, so he's been waiting for the S1 about as eagerly as anyone. And I was excited to hear his take on what we've learned, and importantly, what we still don't know. Before we get going, do me a favor and subscribe to Decoder on YouTube, where we put out new episodes every Monday and Thursday. Okay, SemiAnalysis, Joey Brookhart, and the Anthropic S1, and the costs and risks of building frontier AI. Here we go. Joey Brookhart, you're an analyst at SemiAnalysis. You're also co-lead the Tokenomics model, which is gonna come up a lot in this episode. Welcome to Decoder.

• 3:32Speaker 02

  Yeah, thanks so much, Neelai.

• 3:33Nilay Patel?

  I am very excited to talk to you. We're talking just a couple of days after the big Anthropic S1 was filed. It's our first look inside of the finances of one of the leading frontier models. What's your take? What's your read into it?

• 3:44Speaker 02

  It was an awesome S1. I think, you know, it's been a rough three weeks since the Investor Day. I think, you know, some people were somewhat disappointed in the September ARR numbers, but what we saw here was a company that has capitally efficiently scaled to an amazing level: trillion-plus valuation, you know, $70 billion-plus of ARR in very short amount of time. And gross margins, compute margins, free cash flow, everything is what you'd want to see, so I don't think there was much to nitpick in this filing. And it's the reason you've seen the last two days, I think, a lot of the semiconductor complex and Neo clouds and compute stocks really rally again.

• 4:19Nilay Patel?

  I'm actually, it's funny, I don't think I can nitpick the S1 itself. I want to get into some of the details, because there's a lot of numbers in there that are worth thinking about. I actually just want to start at really big picture. I look at Anthropic and where it could go. I look at Dario saying that he can see his way to $1 trillion in revenue. Where does that money come from? Because so much of these businesses are about eating every other business, and it feels like somewhere, someone else has to make some money to pay Anthropic to generate these revenues, and I haven't actually seen that laid out very clearly.

• 4:51Speaker 02

  It's so interesting, I think, today, because even though the scale are at, you know, $120 billion of ARR between the two leading labs, it's still an extremely narrow market. The top 1% of customers are the one that are spending, you know, $100,000, $50,000 per employee. Your broader Fortune 500, Global 2000, who typically is a lot of IT spend, isn't spending much. And then, too, a lot of the spending, especially at Anthropic, is coding-specific. So we're kind of in, you know, it's almost a single vertical company. And then we look in the future in a 27, you know, you think of just this broader adoption and white-collar work. This, you know, typical enterprise Q4 budgeting season. We actually haven't gone through with, you know, one of these labs yet in any meaningful way, because ARR did really start to accelerate till the beginning of the year.

• 5:33Speaker 02

  You know, we think of other cases like Cyber, where, you know, that can be similar to coding, where, you know, these are eight-figure-plus contracts with leading enterprises that can scale very quickly due to all the cybersecurity incidents we're seeing. And then I think you get to other verticals like Bio, and then, you know, to get to a trillion, you know, I think we do have to move some of that white-collar work, you know, and where it's more of AI's augmentation to, you know, labor-saving or replacement in some areas. And I think you look at things like Microsoft's offerings to employees as, you know, maybe down the line at some point, but, you know, there's no wealth of other verticals that they can get into and really start to move the needle.

• 6:11Nilay Patel?

  This, to me, is the single biggest question I have, not just about the S1, but the entire industry. I would understand that if there are one or two leading labs, right? There's two big companies, they've got a technology that can automate a whole bunch of things. We will see how well that automation goes, sector by sector, but there's not just two leading labs. There's quite a few of them, there's open weight models. I've heard from a lot of CEOs who are very nervous about giving all their data to an OpenAI or Anthropic, and they would like to run some things on. There's like law firms running on-prem AI now, which is the funniest turn in maybe all of history. How do you see that playing out? Because so much of the pressure on the S1 was the numbers inside themselves, which I want to talk about, but then there's the external pressure of, well, you've got to

• 6:54Nilay Patel?

  grow, and it, growth is not assured. So how do you see all of that playing out?

• 6:58Speaker 02

  When we look at like coding today, for example, and I think we saw this in Q3 with OpenAI kind of catching up, there's areas of coding where there's very low switching costs, and, you know, I can just move between that. I think, you know, what we hear from enterprises, and maybe, too, on the open weight side, like, obviously, I think they somewhat botched the Fable release, and why we saw some slowdown because of the zero data retention or lack of zero data retention, and is that something they can fix with Fable 5.5 and maybe drive more, you know, enterprise adoption there? I think that's a huge thing for enterprises when we talk with them. We definitely see, you know, enterprises now, and more so to a lot of the AI natives, you know, custom training, you know, post-training on open weight models, and then running either their inference off of that, or even their, you know, their products for internal or external use cases. So there's that.

• 7:43Speaker 02

  And then, obviously, you know, DeepMind at Google and MSL, you know, with Meta, are very well-funded labs that have pretty similar training compute, you know, capacity, and they're bringing on a ton of, you know, and not even to mention SpaceX as well, with what Elon said on how much, you know, 10 net new gigawatts next year, and half of that kind of is the long-term internal use case. I think in the first half of the year, Anthropic ran away with it. They were 90% of the industry's net new ARR, and then we saw in Q3, you know, OpenAI was probably actually the majority of industry net new ARR, and then open source, you know, has grown from probably a billion-dollar ARR in Q1 to $10 billion-plus here in Q3. So yes, the industry's gotten much more competitive, but I think all these new use cases, too, and some of these verticalized use cases are really where we're going to

• 8:28Speaker 02

  see the growth, you know, going forward, because it's obvious coding isn't going to power us to, you know, even triple from here.

• 8:34Nilay Patel?

  Essentially, the concept is called Tokenomics. You run that practice alongside a co-lead at SemiAnalysis. Explain what it is, because to me, it's such a smart way of thinking about, okay, we're gonna put some money in, and maybe we'll get some money out.

• 8:47Speaker 02

  I think of it as like the unit economics, or even just the broader, everything from the macro to microeconomics of running an AI business, or even looking at AI demand. So we cover the hyperscalers, you know, now some of the Neo clouds as well, in kind of a separate product, but then, too, just the AI labs. And I think for, you know, investors or just people that are interested in the business, it's, you know, Anthropic had negative 94% gross margins on inference two years ago. Now they have positive 85, and people didn't understand kind of, you know, how do they allocate compute? What are actually, you know, how do we think about the inference business model? I think, you know, part of that gross margin conversation is the revenue per megawatt in inference, and why are all companies that are running inference? Seeing massive gains in inference and compute efficiency.

• 9:30Speaker 02

  So we try to model out these businesses. And obviously, when Anthropic was private, we're dealing with information we can get from the press and things like that. But now that we have numbers, concrete audited numbers, it's easy to explain the unit economics of this. And two, how the businesses differ. OpenAI is a much different business than Anthropic, just in both their consumption versus subscription and then historical weight versus consumer versus enterprise. So trying to understand across the demand level,

• 10:02Speaker 02

  the economics of these businesses.

• 10:04Nilay Patel?

  Several years ago, when I talked to Sundar Bhatia about the cost of inference, I said, how are you ever going to make money? He was just very clear. He's like, if there's one thing Google's good at, it's bringing this stuff down the cost curve. And it was a very blunt answer. And I understand how Google can do it. They make their own chips. Anthropic is running on everyone else's clouds. This is like the core part of the S1 is how much they've committed to spend on other people's clouds, SpaceX, Google, Amazon. Where does their efficiency come from?

• 10:33Speaker 02

  Really, token throughput, and I think they have a very diverse chip fleet. So between Traniums, TPUs and GPUs, across all of those, we hear a ton of token efficiencies. So I think like token throughput, for example, on Tranium chips are up five to 10x year over year right now. So dealing with both the hardware level efficiencies and the software, how the model interacts with the hardware. There's a lot of token throughput gains where, if you're doing X tokens per second, now you're doing 5x, 10x just a year later. And I think what they'll tell you is they see continued compute efficiencies as well going forward. So we've even published some research. So Max Huco leads Tokenomics Model. Right now, if Anthropic's at 65 million of revenue per inference megawatt, this was mid-teens last year at this time.

• 11:15Speaker 02

  And I think what we've said on maybe Opus 5.5 on a Blackwell, you can get to 100, 120 million of revenue per megawatt. But the big thing here is they're paying on average now just a little over 10 per megawatt in COGS or just in compute costs. So what you can do with those sort of margins and then reinvest in training. People don't realize this, but only about 30% of the compute is actually in inference, serving revenue. The rest is being for experimentation and research, training models. So there's a lot of compute that is building the next model. And that's more of like the R and D side and where you actually get your returns and defensibility in this business.

• 11:51Nilay Patel?

  Actually, what I was expecting to see in the S1 was that dynamic play out much more strongly. That Anthropic makes a bunch of money selling the models they have now. And there are more customers in the future for the models they have now. That's going to throw off a whole bunch of margin. And then because they're Anthropic and they think they're building Digital God, they're going to train the next models as much as they can. And that is like a very particular Anthropic story. They are chasing AGI. They will happily tell you that in the S1, that Claude might be conscious and this could all go to hell. Like Anthropic is a very unique company in its conception itself. But you don't really see that dynamic playing out in the S1, right? They're managing their training costs against their inference margin pretty well.

• 12:34Nilay Patel?

  Do you see that going up or changing in any way?

• 12:37Speaker 02

  I agree. I think long term, and we've even said this internally, they want to increase actually the percentage of training. If you believe you're building Digital God or going towards HEI. And as long as you have the margins, gross margins, 85% plus on inference to do so, you can. I think they've grown faster, obviously a lot faster than they thought. And they didn't expect to be at 11% non-GAAP EBIT margins. I think they'd love to be closer to zero or even below that if they could get that compute and allocate it towards training and continue to see these compute efficiencies. And I think they've messaged that too to investors in the roadshow that, hey, you can look at this S1 and just the curve of profitability over the last few years and how little cash they burned to get to this point. And you can say, hey, this is a good business model,

• 13:21Speaker 02

  but we're going towards HEI. Don't expect the margin leverage we're seeing to continue to this level, even though we can, long-term margins can be quite a bit higher.

• 13:32Nilay Patel?

  There's some real shades of Amazon in there. Jeff Bezos famously telling investors he wasn't going to make money until they could make a lot of money. Obviously, Amazon and Anthropic have a lot of back and forth. Maybe there's some family resemblance there. But I'm curious, there's a point of model capability that would allow you to go into more verticals than just coding, right? We might be at it now, right? The idea of capability overhang exists. The idea that the models we have today could go and take over bio research. They're taking over mathematics today. Like, there's a point in which the smartest business decision is to stop training and to just sell your profitable model into more and more companies. Anthropic is a public company will face that pressure. Do you think they're organized or structured to fend it off?

• 14:17Speaker 02

  Yeah, I think they're structured well. I think with some like the voting stock and areas like that, they definitely are. We've seen this in tech before and even publicly, when Zuckerberg has made these decisions before to invest heavily.

• 14:30Nilay Patel?

  I just want to point out, didn't always pay off.

• 14:32Speaker 02

  Yeah, did not always pay off.

• 14:34Nilay Patel?

  Although you and I technically are in the metaverse right now.

• 14:38Speaker 02

  Yeah.

• 14:38Nilay Patel?

  By some definition.

• 14:40Speaker 02

  Right. But I think that's something, and there's an interesting number in the S1 that 80% of revenue in the last six or last three months comes from models released in the last six months. So then you say, okay, 20% of the models are like Opus 4.5 in earlier versions. I think one thing going forward is you always need to be, if you're a frontier model company, you always need to be at the frontier.

• 15:02Nilay Patel?

  Wait, can I ask a really dumb question about that?

• 15:03Speaker 02

  Yeah.

• 15:05Nilay Patel?

  Why?

• 15:07Nilay Patel?

  Like, this is, I think, the heart of my sort of curiosity about the industry right now is if you enabled some bleeding edge technology that could revolutionize the entire world, at some point, the most profitable thing to do is to just go play it out in all the different verticals, right? And it's not to go make the next one. And it's to actually run an efficient model in legal services and medicine and wherever else that there hasn't been diffusion of AI yet. And so this idea that you always have to be at the frontier, that Anthropic always has to be competing with Google and OpenAI and whoever else. I get it right from their self-conception. But from a business point of view, an AI-enabled business is going to throw out more money and spend more money with me, the AI provider.

• 15:52Nilay Patel?

  And that isn't necessarily related to being at the frontier.

• 15:55Speaker 02

  Yeah, I think there's a few portions of that. You know, four new verticals, and this is like very cyber-specific. If an adversary is using a frontier model, you need to be defending with a frontier model. You know, things like, when we talk to like some enterprises, it's not like email summary where I can run that on open source or Sonnet. Like, there are just certain verticals where having, I mean, if you were in a legal case as a defendant, would you want your lawyer using a non-frontier model if the prosecution was using?

• 16:24Nilay Patel?

  I got bad news for you about what's going on in the legal market right now.

• 16:29Speaker 02

  So, you know, there are certain areas, yes, that are more,

• 16:34Speaker 02

  the competitive dynamics do dictate that. And I think from a go-forward perspective, we've seen this with like open source too catching up. The demand will be there. I think Dylan says this a lot, like the demand will always be there for frontier intelligence, especially in competitive industries. And there are certain tasks, I think, as you go towards more labor replacement versus labor augmentation, where that also is the case. At the same time, they are getting more vertical. If you go look at like job postings or what they're hiring for,

• 17:02Speaker 02

  putting out more of these SKUs and areas. And there's a lot of beta testing going on, like Claude for financial services, Claude for legal, their cyber program, they're giving away 250, or sorry, 25K of credits per month for people in their cyber program. So that'll be a huge SKU for them in 27. Those verticalized areas and then ones that are specifically where you need to be, it's more adversarial, I should say, or competitive, are kind of the main reasons why. But I think we can both agree, there's areas like email summary and work that definitely doesn't need to happen.

• 17:39Speaker 03

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• 18:50Speaker 01

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• 20:32Nilay Patel?

  I'll just make a dumb comparison to the iPhone. It's iPhone season. I just reviewed the iPhone Duo. Apple keeps the iPhone looking the same for several years now. Right? They build all of the tooling for the new iPhone and then they just reuse it for two or three or four models to get all the value out of that investment back before they move on to the next thing. Apple puts out a new iPhone. They keep selling the old iPhone for a while to get more value out of that investment and that development program. The AI industry does not seem to be doing that. Right? All of their emphasis is on developing the next new thing, deprecating the old thing pretty fast and moving you on to the, to the new expensive product, which at the same time they're building the next new expensive product. At some point, it feels like that will rationalize, right?

• 21:13Nilay Patel?

  That there will be something that is worth just collecting margin from while they go invest in the new thing. And I just can't figure out where Anthropic sits in that cycle.

• 21:22Speaker 02

  I mean, we do see this. You know, open source token volumes are picking up and there's a lot of areas, yes, where the open source models have become good enough. And I think on a go forward basis, you're like, okay, was that because of distillation? And the frontier labs have some defensibility actually going forward. Or, you know, I think when we talk to clients too, it's, you know, how do we actually think about frontier versus open source, you know, on this debate or even non frontier? So something like a sonnet type model, if you want to do the iPhone 18 versus, you know, the SE type, type, you know, analogy. We do see that dynamic and obviously open source is, it's a question of how much can that take? And then two, because we're so early in these verticals, we don't know what's going to be like fully agentic. There's no human in the loop for actually human in the loop where yes, you can do, you

• 22:07Speaker 02

  know, where the harness actually has, I should say, some value and defensibility to it. The kind of, you know, big area I'd look at here, especially when we talk to enterprises today is yeah, only the coders are allowed to use the frontier models at a lot of these enterprises. If you're a non coder, you are, some of these companies just limit you to sonnet if you're a, you know, Claude, an anthropic company. And I think that's reflective of how, you know, management views and wants to budget for this, like what frontier intelligence is actually worth to them in certain areas. Like in my financial analyst brain, you know, Claude could not build a good financial model, you know, six months ago. It's getting better if you connect it to the right areas. But we've gone from something where, okay, I needed a junior analyst six months ago. Maybe I don't as much anymore.

• 22:51Speaker 02

  And then will I have a full junior analyst at my disposal anytime or multiple pretty quickly if I have unlimited budget or, you know, token budget. And I think we just look at that across, you know, every vertical and like major job category.

• 23:04Nilay Patel?

  And the time horizon in each of those categories is going to be different. And I think that's just one of the, one of the bigger questions I have is on how quickly do they get to where they think they can go? I want to dig in on some of the numbers here that the one that jumped out in all of the comment sections on all of the forums and all of ours articles yesterday was $80 billion. Anthropic lost $80 billion. That is related to a technical charge. Can you just explain the $80 billion? Cause it doesn't seem, it doesn't seem particularly relevant to the revenue and profits that they're talking about.

• 23:34Speaker 02

  Yeah, it's a, it's a non cash charge because their valuation has gone up so much and they've issued, you know, warrants or convertible notes. You have to account for that in your balance sheet. So it's just a non cash charge because their valuation has gone up so much since they issued, you know, these warrants and notes. So yeah, that's something you would always back out. You know, you can argue on stuff like stock based compensation. You know, should we really be thinking about that on a non gap versus, you know, a cash, you know, versus because it's a non cash, if I just hand employee shares, you know, I have to account for that on my income statement, but I'm not actually handing them pure cash out of my, you know, bank balance. So it's an area like that. It's, you know, it gets the accounting, you know, the high level. If you don't know accounting, it looks bad, but it's because they've grown so much and their valuation has gone up so much.

• 24:16Nilay Patel?

  Some of the argument here about that charge is obviously that's, that's, Amazon was the other party there. Obviously Amazon's very happy because Anthropic is buying a ton of AWS. I'm sure they're happy the valuation has gone up, but over time, Amazon's going to get diluted this way. Right? That's, that's part of the dynamic there. They're going to obviously IPO, they're going to put a bunch of shares on the market. It's very clear that over time they will issue more shares to raise more money. It doesn't seem like they're going to stop wanting to raise money anytime soon. How would, how are you thinking about that in terms of just investment into a company like Anthropic that you, your valuation might be going up, but your actual ownership stake might be getting diluted over time?

• 24:53Speaker 02

  Yeah. I mean, I think the deals have always been very creatively structured and I think the numbers they've actually only burned 10 billion of cash. You know, they have 120 billion plus of cash and marketable securities on the balance sheet today. You know, post IPO, probably another 100 billion. You know, it's a great deal for both parties. They only really rent compute. It's not like they have these massive capital outlays or even rent to own. So yeah, and you can make some arguments about like certain accounting and okay, there's these massive commute commitment obligations. That's technically, you know, future debt. From a dilution perspective, all, all tech companies dilute. You know, do they, are they going to need to raise another 100 million of debt or equity at some point? You know, possibly can they continue to creatively structure and finance, you know, future compute build outs? You'll probably as well.

• 25:36Speaker 02

  I know there's been a lot of, in the last few weeks, a lot of angst about the future of, you know, compute financing and data center financing. But if you're Anthropic, you know, if you're a major lab, a hyperscaler or, you know, Broadcom and Nvidia that are backstopping a lot of these deals, you know, that's probably not going to change anytime soon. As an equity investor, would I be worried about getting super diluted? I don't think so. If you believe in this long-term AGI bull case and that Anthropic is going to be, you know, a leader in this, you're probably looking at a 10, you know, multi, you know, 10 trillion plus dollar business. You know, I think they can outrun dilution at that point.

• 26:09Nilay Patel?

  Let's talk about the commute, compute commitments. I saw at least one analysis saying over the next 10 years, it's 800 billion plus in commitments to the various hyperscalers and Neo clouds. That's a lot. You're saying that they don't have that much money. They haven't even spent that much money yet. Do you see them generating the revenues to pay off those commitments?

• 26:28Speaker 02

  Yeah, I think when, when people look at the headline commitments, you know, the five, 600 billion, you know, number over seven to 10 years, let's call it. You know, it actually at what they're paying for compute today actually isn't that many gigawatts. You know, if you think about them spending, you know, right now, you know, I think on average they're close to 11 million per megawatt in, in spend or 11 billion per gigawatt. You know, if you kind of take that back to the annual level and say, okay, you know, the 520 numbers over, you know, seven years, for example, that's really not that many gigawatts and they're going to be well beyond, you know, when they get to something like 20 gigawatts, that future compute commitments that they're, they're running, you know, essentially is going to be well over, you know, well into the trillions. So yes, they are going to have the revenue to do so.

• 27:13Speaker 02

  I think it's, you know, more of how much revenue are they going to have relative to how much compute? And then two, the compute efficiency equation. If they keep getting, you know, a lot more revenue out of every inference megawatt, you know, how much do you really want to invest in training and take margins to zero from like a rental perspective? Cause you can get to something where they get to even 80, 85% of their fleet is just on training and research. The revenue, yes, it's going to scale pretty well. They're not in any dire, you know, unless, you know, unless we realize like all this revenue disappears or people churn, you know, we're really not in a huge like, it's like they have to hit, you know, a trillion dollars of revenue extremely, extremely quickly, you know, to achieve all this and pay for this.

• 27:52Nilay Patel?

  Unless you're trying to build digital god and unless it doesn't diffuse across the economy as fast as everyone wants it to. Those are bets, right?

• 27:59Speaker 02

  They're huge bets. And I think like the numbers you look at, if you're really bullish on this ramp of like into the digital economy is, you know, they say within their Fortune 500 customers, they have 50% of the Fortune 500. They are 0.1% of their total customers operating expenses. I think when you look at that on like a per user basis, it's like $8 a month. If you believe OpEx is, you know, doesn't include cost of goods sold. And if you believe it caught, you know, if they include a cost of goods sold, it, you know, it's sub $50 per month. And they said the same thing of their top 5% of Claude code users, they're spending $18,000 a year on average, the top 5% of users. Their non Claude code users spend under 75 a month, you know, well under 75.

• 28:42Speaker 02

  So there's this huge, especially when we talk to, to enterprises and these traditional buyers of software, of IT, penetration is extremely low in like traditional white collar work. And I think that's the big point is two, there is a large ramp period here of both seats, adoption and usage. You know, you can make arguments of who's going to win in this, but it's obvious, you know, the frontier labs are going to get some portion of this. It's going to be pretty material to net new ARR next year in 2027.

• 29:10Nilay Patel?

  One of the things that's really interesting about that is we talk about the frontier labs, we talk about the hyperscalers. Anthropic has a lot of wild risk factors in this S1, including that Claude might be conscious and kill us all. But one of the most direct and maybe easiest to understand is that AWS, Azure and Google Cloud are both suppliers and competitors in some real way, particularly Google. They say, well, these relationships could turn, right? They have a big relationship with SpaceX AI. Elon is going to train Grok. They're going to compete with SpaceX AI at some point. That relationship might become precarious. As you say, they're only renting their compute. At some point, the market might force them to own more of their compute because their own competitors

• 29:52Nilay Patel?

  don't want to lease their capacity to the leading company. How do you see that playing out?

• 29:57Speaker 02

  We definitely see both OpenAI and Anthropic going more direct and owning, not owning the compute, but more rent to own or going through an SPV, you know, that some financial sponsors does.

• 30:08Speaker 02

  And kind of doing the compute more directly versus renting. So there's two points of this, too. Google, from one perspective, is also selling their external chips to customers and saying, hey, we don't care to monetize this within Google Cloud or even allocate the share of, you know, if you think we're very chip constrained, we don't even care that these are going to DeepMind or internal GCP compute for us to monetize. We're just willing to become a chip seller. And Anthropic loves TPUs and they have, you know, there's, you know, three and a half, four gigawatts plus and probably more in the pipeline in terms of orders there of Anthropic buying these chips directly to then rent. So that's, yes.

• 30:44Nilay Patel?

  Buying them directly to rent. So they're buying them into a NeoCloud?

• 30:47Speaker 02

  Yeah, they buy them through like an SPV or the SPV buys them and then they rent it from the SPV. Even within, you know, Google Cloud or AWS or Foundry, Anthropic is massive. Forty-eight percent of their business comes through like the Bedrock, Foundry and Vertex where people are paying, you know, through their existing cloud relationship to access these models. They're not even talking to, you know, they are, but you know, you don't have to interact with the Anthropic sales force.

• 31:13Nilay Patel?

  You mentioned this earlier. This is the marketing channel where Anthropic gets rev share.

• 31:16Speaker 02

  Right. And if you're AWS and you're saying, oh, I'm renting Anthropic compute for, you know, let's say 10, you know, 10 million per megawatt, but now I'm getting 16% of 65 a megawatt plus the compute cost as well. You know, we're seeing margins at all the clouds go up right now. Part of it is CPU utilization is going up because of all the coding and then some of the RL side, but these token as a service businesses, as we call them, so AWS, Bedrock, Microsoft Foundry, you know, Google, Gemini, Agent Enterprise Platform, formerly Vertex, are actually massively increasing kind of the margins on AI at these clouds. So they're benefiting as well and shareholders are, you know, giving them credit for this recently. I think in Q2, both Azure and AWS had,

• 31:59Speaker 02

  you know, extremely, extremely strong quarters on the back, AWS specifically on the back of Anthropic's growth on Bedrock.

• 32:06Nilay Patel?

  Do you think that will keep Amazon from ever wanting to train its own frontier models? They're doing a good job of letting AWS be the infrastructure for everybody else, but they're one of the only big tech companies that hasn't felt the insecurity of not having their own models.

• 32:19Speaker 02

  It's interesting. I think they're very happy to serve a wide variety of customers and they have very little. And we look at like our estimates of training compute, they have the least of any hyperscaler, you know, if you don't, you know, ex Oracle maybe. It doesn't seem like there's much strategy there. I know both, you know, Microsoft in the last two quarters has been talking about a little more, you know, AWS on the call has been talking about a little more, but it's not a huge, huge, you know, they're not doing it to the same extent that Meta is or, you know, trying to be competitive here or even Google with Gemini. And I think, you know, especially someone like Dylan will tell you, if you're not trying to compete at the frontier, you know, there's really not a place for you, especially if you're only dedicating a few hundred megawatts to internal, you know, one, internal use cases like serving Amazon retail, but then two, training. There's a lot of commitments,

• 33:04Speaker 02

  yeah, to make to be good at this.

• 33:06Nilay Patel?

  It's a conversation for a different day, but I look at these Microsoft job cuts and I see it as the glimmer of evidence that they're going to try to compete in the future. They got burned, they obviously got burned pretty bad by OpenAI.

• 33:17Speaker 02

  Yeah. I mean, they have in the new agreement that they signed, they have the right to monetize OpenAI models for essentially not paying OpenAI anything. They're going to have, you know, some interesting things ahead where they monetize, you know, OpenAI models or even through like Microsoft 365 cowork and copilot applications where they're not paying OpenAI anything for a frontier model.

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• 36:39Nilay Patel?

  The other dynamic here, and you brought it up earlier, is over the last few weeks, there's just been a lot of stress about NeoClouds, whether they will succeed, whether they will fail. I think Elon Musk said there will be a guppy-like extinction event, which is just a brutal metaphor to use to describe a bunch of data centers. But these, this is a bunch of circular financing and Nvidia stood a bunch of them up and maybe the hyperscalers are just going to run away with it. Or maybe Anthropic's demands, maybe they will want to own more of their own compute because renting it is precarious in all these ways. What do you make of the NeoClouds? And do you think they're going to survive the market actually shaking itself, shaking itself out as these companies go public?

• 37:18Speaker 02

  Yeah, look, Elon is, obviously has some incentive to say NeoClouds are going to be extinct. You know, there's a lot of, and ClusterMax and somebody else just does amazing ratings of the NeoClouds. There's a lot of like NeoClouds or like ex crypto miners that are not good businesses and are not good at operating.

• 37:35Nilay Patel?

  CoreWeave is an ex crypto mining company, right?

• 37:38Speaker 02

  Yeah. And I don't mean CoreWeave, but there's others further down the list. I think CoreWeave, Nebius, anyone that has backing or backstops from, you know, one of the major labs, hyperscalers or Nvidia or Broadcom, you know, is going to be okay. It's that longer tail. I think, you know, hyperscalers have been 65% of CapEx and net new, you know, compute brought online. So then there's 35% of the market that's non, you know, doesn't have the balance sheets that the hyperscalers have. Both, you know, Google and Meta have recently, you know, raised or announced they're going to raise. You know, when we look at the big four hyperscalers, they don't actually need to raise that money over the next two years, even though CapEx is growing from, you know, 1.2 to 2 trillion pretty quickly. But a lot of these other NeoClouds, if you don't have that sort of backing, I think it gets, you

• 38:23Speaker 02

  know, it is very hard, even right now, and becoming extremely difficult if you don't have the backing or backstops from those players to keep building compute. It's a very, very tough financing market.

• 38:34Nilay Patel?

  One of the things I think is really interesting about Elon in particular, talking about NeoClouds dying and obviously hyping up his own data center business, is he's trying to put them in space and to get away from the backlash in the United States around data centers in particular. We have a lot of coverage about data center backlash in the United States. It is slowing down a lot of projects, but at the rate we're going. If you think the neoclasts are shaky, you might actually end up abandoning a bunch of GPUs and half-built data centers, or have them tied up in financing agreements where they can't be lit up. What do you make of that? Do you think we actually just need to go to space? Or do you think the market is going to rationalize and actually take advantage of all the compute that is currently allocated but not being used?

• 39:15Speaker 02

  The market's very good at finding places to put chips. Here, you know, on the ground, I should say. There will be, politically, you know, we'll see what happens next week with midterms, and kind of the fallout from that. And obviously, there's some very large sites, you know, still that have massive permitting problems. And then there's the entire power equation as we look towards next year. The data centers will, they'll be built. There will be a method to get these built. I think the biggest thing, and like the hyperscale CFOs have said this, from like an overbuilt perspective, you know, it's on a relative basis, it's pretty cheap to build the data center shell. What they can do if they overbuild is they're going to say, okay, we're just not going to take delivery of the chip. We're not going to buy chips. We're not going to fill them up. And these,

• 39:58Speaker 02

  you know, they'll just sit empty and chipless. But that's where a lot of the, you know, that's the majority of the spend is on the chip side. So if we do end up in a place where we're actually oversupplied from a data center perspective, I know that's a little bit different. It's the exact opposite of what the question was. They kind of have said that from like an ROI perspective. I think this comes back to that larger question. Are we still going to be super, you know, supply constrained? A lot of people now, especially in the last three weeks, are talking about, are we in this massive glut of compute as we look towards 2028, actually? Because the same thing we talked about from Anthropic going from negative 94% margins to positive 85, and kind of compute efficiencies, you know, means as you bring on more inference megawatts that are making a lot more revenue per megawatt, you know, all of SQL, you need less compute. So it's a question of, you

• 40:42Speaker 02

  know, are we going to have enough revenue in 2028 to justify the ROI, you know, on 2 trillion plus of CapEx?

• 40:49Nilay Patel?

  This is why I spent so much time asking about capability overhang and how good the models are today. Because you can see some competitor that is less idealistic about AGI, saying, actually, we're going to do a bunch of forward deployed engineers in all of these verticals where they haven't taken advantage of AI, and sell them high margin inference, because we'll automate big chunks of their business and they'll see the return. And right now, a bunch of the Fortune 500 has not seen return on AI spending. So if you're actually just better at customer service and deployment and productizing the model, you might get way ahead of where Anthropic needs to be, because they're still thinking about AGI every single minute of every single day. And I don't know, man, I look at Dario

• 41:34Nilay Patel?

  standing next to Marc Benioff at Dreamforce, and like, that dude does not want to be there right now. Right? He wants to be thinking about, like, the machine god. There's something there where you can just see that competitors coming up with an open source model and be like, we're actually going to prioritize this. And we will actually take the revenue that everyone else is depending on.

• 41:50Speaker 02

  And too, like, in the interim, that's why it is getting, you know, more verticalized. Because you need something to bridge, you know, AGI where it just does, fully does the job. You need something to bridge, you know, the actual model today.

• 42:01Nilay Patel?

  We'll just take that for granted.

• 42:02Speaker 02

  Yeah, to get there, you need some bridges of where it actually sits in with the actual, you know, end user at some point. And so I think everything is going to come down to this, you know, is there going to be some timing or adoption mismatch, where we just don't get the revenue at a certain point in time to justify that chase of, you know, of training compute towards AGI. And I think that's, you know, a risk, you know, the other risk I think we look at is, do we get more models at the frontier, even at the open source, that are as competitive in coding, where token pricing across the frontier needs to be taken down? Because, you know, like I mentioned earlier, like, you know, lower switching costs. Even at semi-analysis, we went from, you know, 95%, 90% plus of our

• 42:44Speaker 02

  usage at the end of Q2 was on Anthropic models after release of 5.6 in Astra. That went down to 70%. And then with Opus 5.5, it's gone back to 90% plus, you know, roughly. So it does come back, even like the base of revenue today, and they've said, you know, that we have net revenue retention of 900%, seeing this massive, massive growth. This isn't a traditional software business like ServiceNow or Workday, where you have like, just incredible, like, logo or gross, you know, retention of 95, 98%. And customers, you know, sign these nice three-year contracts, they never churn, you know, they add some seats, maybe they adopt another, you know, product that is being upsold. This is purely consumption. I think the other portion was the 20% drop in the two weeks, you know,

• 43:27Speaker 02

  last two weeks of the year versus the prior two weeks, you know, last year, in token, or, you know, in token spend, because the holiday season. You know, people aren't, you know, you're not going to do work code stuff. You're not going to be coding for work during the holidays. So this consumption business, I mean, this is extremely, extremely pure consumption business, even different than how we think about like Snowflake or Datadog today in public markets.

• 43:49Nilay Patel?

  Obviously, Anthropic is not the only company that's going to IPO. OpenAI is right next to them. They've delayed theirs. You mentioned earlier, Anthropic is much more enterprise focused. I think OpenAI, for a long time, thought that Google wouldn't react to them, and they would just take the search business away and everything would be fine. Google obviously reacted, for better or worse, searches now, AI overviews. Looking at the Anthropic S1, what are you looking ahead to in OpenAI's? Because they are a more consumer-facing company.

• 44:19Speaker 02

  Yeah. So OpenAI was 60, 40 consumer, you know, 60% consumer, 40% enterprise at the end of Q1. You know, that didn't flip for the first time until January or August to be enterprise focused. And what they'll tell you is, you know, enterprise business, the consumption business, because it was so consumer and subscription focused, consumption, you know, this consumption business that's 80% of Anthropic's revenue, just the token business, that didn't take off till the release of 5.6, you know, and then with Astra. And those are really great models. OpenAI took, you know, the majority of actual net new ARR in Q3. But it's going to take some time. Because I think what's been publicly, you know, what's been in the Wall Street Journal, like, negative 100% EBIT margins versus, we're

• 45:01Speaker 02

  seeing positive 11 here. You know, there's some differences, some, you know, pretty big differences in the business model where they just haven't, they just don't monetize at the same rate that, you know, Anthropic does. And that's a little bit because the business makeup, you know, the verticals, you know, like, that consumer versus enterprise split, you know, that's changing. And I think what they'll tell you is, you know, they see a really good Q4. They have a much stronger enterprise sales, you know, function than Anthropic has. So I'd expect them to come up, but I don't think the financials is going to look as good as this, even when they, S1, maybe, you know, in early 27.

• 45:33Nilay Patel?

  Do you think that's why they delayed it? To get their financials looking better?

• 45:36Speaker 02

  Yeah, I think they're, you know, they had a really strong Q3 enterprise, you know, grew 100% quarter-over-quarter versus the total business was 77%. So I think, you know, you get another strong quarter of enterprise here. You know, if Opus 5.5 didn't take a ton of, you know, share, that, yeah, it'll put them in a much better position to kind of tell the story.

• 45:56Nilay Patel?

  There are some other turns here between now and when Anthropic actually goes public. They're going to do a roadshow. There'll be some pricing events. What are you looking for from Anthropic?

• 46:03Speaker 02

  They did an investor day two or three weeks ago, and where they did give some more color. You know, I think they'll kind of walk through the why Q3 was so weak. And there were, you know, Meta was a very, very large customer, you know, probably over 10%. I think what's been publicly reported is they went from 60,000 cloud code seats down to 30,000. These token volumes didn't disappear, they just shifted to Meta's internal models. You know, they started cracking down on anti-distillation, Anthropic did, and that was, you know, 5% of revenue, you know, prior. And I think they said 5% to 7% was of revenue in the S1 and stuff against their Terms of Service. What's here on the roadshow is a lot of this October acceleration after Opus 5.5. I think we believe, you know, kind of they're back

• 46:45Speaker 02

  on track. If they were doing, you know, 10 billion of net new ARR per month in Q2, and that fell down to 4 billion in Q3, you know, kind of that they're getting back on that 8 to 10, you know, pace here in Q4. And then too, I think people want to hear about these new verticals, about, you know, specifically cyber. I think they're doing some patching, you know, until they have that full cyber skew rolled out. But they're definitely monetizing cyber today for, you know, vulnerability, code scanning. They're not small contracts, I should say, with existing customers. So I think they'll start to message that in what kind of the 2027, you know, ARR growth path kind of looks like. The other thing too is expectations management. Keeping numbers, you know, low enough, you know, not talking about AGI as much.

• 47:26Nilay Patel?

  It's hard to do expectation management when your risk factors are like, we could pose an existential risk to all of humanity.

• 47:32Speaker 02

  Right. And so saying you do that, you know, you're going to achieve AGI at some point. We're going to replace everyone's jobs. But then too, trying to keep numbers low enough for Q4 and to beat, you know, have this massive beat the first quarter you become public and have a very nice, you know, a decent guide for 27, it'll be a nice mix of a seesawing between, you know, that long-term vision and short-term, you know, expectations management.

• 47:56Nilay Patel?

  There's a lot going on. Joey, this has been great. We're going to have to have you back as these companies barrel towards actually being public and their economics become much more visible. Thank you so much for being on Decoder.

• 48:06Speaker 02

  Yeah, thank you so much.

• 48:09Nilay Patel?

  Hello, I'm Joey Brookhart for joining me, and thank you for checking out this episode of Decoder. To get new episodes every Monday and Thursday, subscribe to our YouTube channel at DecoderPod and find us on TikTok and Instagram under at DecoderPod for more fun stuff we put out almost every day. If you enjoyed this episode, please send a link to it to someone you think you might like it and really helps us grow the show. You can also subscribe wherever you get your podcasts. Decoder is Praxion Verge and part of the Vox Media Podcast Network. The show's producers are Greg Ott, Kate Cox, and Nick Stat. This episode was edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. We'll see you next time.

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