AnthropicのS-1を読み解く：神話と実像 — Jamin Ball（Altimeter）

Because the reality is that OpenAnthorpic, they have far more powerful models that they've developed that they just haven't released yet. If you compare the dollar spend in compute a year ago versus today, their compute that went towards serving models has gone up by 2.3x. Their revenue over the same period of time has gone up 14x. So embedded in that is basically a 7x efficiency gain in serving inference. which, I mean, this is a business that I think is almost accidentally profitable, right? Maybe my favorite stat of the whole S1 was $141 billion of capital raised to date. That's inclusive of equity, converts, and safes, and a $134 billion cash balance today. Right now, you want to go raise multi-billion dollar debt for GPUs? No bid. No bid. They're like, debt markets are close. They're big debt deals that aren't happening.

Okay, we're here in the studio with Jammin Ball from Ultimate Air. Welcome. Thanks for having me. Excited to be here. And so, because I'm IPO-ing, I was thinking of who do I know would be the perfect guest to cover the S1. You're the guy. Oh, I appreciate it. You're the guy. We met before. I think you started your Substack. I maybe was just starting mine. How old is it? six and a half years now. Okay, actually, no, you're older. So clouded judgment is one of the top. What are the brag statistics about clouded judgment? Ooh, brag statistics. Probably getting close to 100,000 subscribers every week. I think in six and a half years, I've missed one Friday. I try to be pretty consistent. And my takeaway is here's public markets comms for the startup investing that you do, but Altimeter does everything, right? Yeah, the idea was, hey, how can I provide some commentary on metrics around software companies and the universe of companies that typically has public metrics or public companies as a way to kind of not guide but provide a blueprint to early stage companies and founders of like, hey, here are some of the metrics required to be public one day.

And so there's lots of tables and charts and graphs on public metrics and valuation multiples. And maybe three, four years ago, I kind of started writing a little ramble up front, which is more of a kind of treated as a personal journal. Like, what's on my mind? What have I been thinking about? What's top of mind? And that will range from anything from some macro commentary to market commentary to trends in AI or whatever's trending at the moment. Yeah, I think every generation there's kind of a VC blogger who is sort of the guy to cover what's going on. It used to be Fred Wilson back in the day. He used to write ABC, I think. Yeah. And then more recently, like Tomas Tongas. I mean, he's still active, but I feel like you are the king of charts and public stocks right now. I appreciate it. I mean, Tomas was a huge inspiration. So I was at Red Point for quite some time with Tomas. And now he's started Fury. Exactly. Exactly. Exactly. Is Redpoint okay? You guys are all leaving.

No, Redpoint's great. They're great. I mean, I owe everything I learned to those guys, and I have a ton of respect for them. Yeah, yeah, amazing. Okay, so we're going to get right into Anthropic, and you have a sort of some spiel. You've had some public dust-ups with people that we won't name. People are just generally very confused because a lot of them have never – a lot of our audience are engineers and researchers. Yeah. They've never seen an S1. Yeah. They've never been through an IPO process. They don't know what a roadshow is. They don't know how this is done. But more importantly than that, that's the mechanics. More importantly than that, what does it mean about our general state of AI, right? This is the most visibility we've ever gotten into Anthropic. Yes. Yes. And, like, well, now we know a lot more things than we used to. Yeah, totally. Totally. And just to say it up front, because I've been asked to say this, so these are, I work for Altimeter. We have a big position in Anthropic. Everything I say is, you know, these are all my personal views versus my firm views. But, yeah, it's a big deal. I think, you know, the big labs, OpenAI and Anthropics, seems like every quarter their numbers leak.

But it's a subset of the numbers. It's not everything. And now we kind of get a full open kimono view of the financial profile of, you know, one of the most important and iconic companies maybe of our lifetime. And just kind of a quick process refresher. They'll file an S1, probably wait about 15 days. There's usually a minimum until they'll start a roadshow. The road shows when they actually start and they're pitch investors, IPO investors that travel across the country, across the world to pitch the IPO. That'll usually last a week and a half, and then it will start trading publicly. So we're probably looking at – End of November. Yeah, like mid-end of November, kind of 15th to 20th, 10th to 20th of November of when it actually starts trading publicly. Yeah. Do people IPO past Thanksgiving? That's like a weird dead zone. Yeah, it's, you know, between Thanksgiving and Christmas is kind of a dead zone. I would say, you know, post-Labor Day to Thanksgiving is generally kind of like a hotbed time for companies to go public.

People are around. People are engaged. And it's on vacation. And it's not. The reality is I think people would make time. I'll go to work for a topic. Yeah, exactly. Exactly. Yeah, so, okay, that's the normal mechanics. I think, and that's it, right? So off the top of your head, people should understand that an S1 is just a filing that you have to do. OpenIA has done theirs as well, confidentially and in the legal public. People have filed S1s and not IPO'd. That's right. The Rebus famously did that. That's right. And then they did it again. And it's just part of the routine process. And you have investor day and you have whisper rumors and all these things, right? So as someone who is, unless you're literally a banker or fund manager, that's all you need to know, basically, right? This is part of the process, and there's a lot of indication to try to make it a successful IPO. Successful is a mixed or loaded word these days because typically you'll hear things like, IPO was so successful, the stock popped 50% on the first day.

That's actually potentially a failure if you're Bill Gurley. Yeah, exactly. Who is like, well, you could have got 50% more. Yeah, exactly. And just for the audience, what that mechanism is, is in an IPO, it's like a fundraise on steroids. You're going to go out, you're going to raise a bunch of money at a certain price. How much are they raising? I don't think that's been, I don't think they've decided yet. I think the rumors are like 75 to 100 billion. It'll be a big raise. I mean, typically, what you used to see. I know. What you used to see in normal IPOs was companies would float about 10 to 15% of their company, which means like what you would sell into the IPO would be 10 to 15%. And there's an employee lockup. Exactly. Now it's maybe as the companies get bigger and bigger and bigger, they might float less of the company because, hey, you can take less dilution. It's no different than a company raising a Series A, B, or C. You're going to raise money at a certain dilution. And, yeah, the Bill Gurley thing is, well, if the stock pops 50% on day one, why wouldn't you have just raised money at a higher price?

Yeah, yeah, yeah. Right, you can take on less dilution. And we don't have to get into that. Yeah, yeah, yeah. You've got lots of nuance to public markets and IPO allocations and all that. There's conflicting incentives, and then there's the chance that you actually don't pop. Yes. So all that, let's put it at a high level. Why now? Why IPO now, right? They could do a Stripe. They could do a Databricks. Yeah. Right? They're not. They're IPOing. Why? Yeah. I mean, I think there's a couple of reasons. Well, one, it's all speculation. It's hard to know. But I think there's a number of reasons why companies have historically chosen to go public. there's kind of the obvious let's provide liquidity to our employees and our investors in like an unrestricted way because of course you know now big companies data bricks stripe open ianthropic kind of like tenders are are pretty regular uh like once a year you'll you'll get like well if you want to cash out here's who's yeah but you know you'll have a you'll be limited in how much you can sell um you know now once the company's gone public once they've gone through the lockup you know, which is anywhere from, you know, so it used to be just 90 days, 180 days, six months.

And now it's kind of like every company has a bespoke lockup that's like dribbled out. Some are shorter, some are longer, but it's kind of like unrestricted access and you can generate liquidity for your employees, help morale, whatever. Or you need a liquid currency for acquisitions. And hey, a company might be more willing to take an acquisition from a public company because the currency you're getting back is liquid. versus in a private company, maybe it isn't. You don't really know the value. Yeah, it's monopoly money. I would say these quasi-public companies, you know, OpenAI, Anthropic, Databricks, I think their currency has almost started to be viewed as cash, not monopoly money, because they're very high quality, they're very late stage. That effect probably means less. I would say a bigger reason to go public, because it used to be companies went public, because now you could access much larger quantums of capital. You know, if you wanted to raise $100 million, you know, it used to be the only way to raise $100 million is to go public. You know, now you seize and raise a series A or a seed of more than that.

And so it's not necessarily the quantum of capital. I think whether it's U.S. institutions getting bigger, maybe folks who used to act as LPs, large pension funds and endowments, acting as direct investors in the company themselves, or kind of the emergence of big sovereign international funds, investing direct in companies. There's a huge capital base available to private companies. And so, like, I don't know if the argument necessarily holds, hey, if you want to raise a lot of money, go public. I think OpenAI raised a round of, like, $120, $130 billion, you know, some ridiculous amount. Biggest of all time. Yeah, exactly. I think maybe a more important angle is more of, like, the debt piece. And so once you go public, Actually, let me take a step back. If you want to raise debt or if you want someone else to raise debt, not on your behalf, but like as a neocloud, a neocloud might want to raise debt to build a data center for Anthropic to use. Debt providers are going to look at Anthropic and they're going to underwrite Anthropic's credit worthiness.

And how it works is they'll go, they'll say, hey, we're going to syndicate how many billions of dollars of debt for you, Anthropic or via a NeoCloud for you. But we're going to need like full open kimono on your financials. We're going to need to know exactly how this business is doing so that we can underwrite you. And then everyone we syndicate the debt to on the back end, they also need to see it. And I'd say historically, companies have just been hesitant to go full open kimono with their financials to the debt and credit markets because they don't want them to be fully public. But if you want to be rated by the rating agencies who basically they'll give you a credit rating, investment grade is the highest, your credit rating will kind of impact the rate with which you can raise debt at. And the higher your credit rating, the lower the rate that you'll receive. But you have to be open kimono with your financials. So going public is a great way for companies to basically, you're going to get rated. I think SpaceX very famously got rated very quickly after going public. Now you can tap into, yeah, arguably two. I mean, there was a lot

of people who said it was, hey, it was too fast. They need to season more as a public company. And add it to the S&P 500. Yeah, new rules. Exactly. And so I think, hey, a lot of these companies, they will be capital intensive, whether it's for training or building out compute clusters. And you want to tap into the credit markets. And I think if you want to tap into the credit and debt markets, you can do that much more efficiently as a public company. And so, again, it's probably a confluence of all those things added up to, Hey, why, why go public? I think there's also just, if you ask, if you ask these executive teams and management teams, Hey, they want the everyday American to be able to participate in the equity upside of these companies. I was going to bring that up. Yeah. Up to including giving shares to everyone. Totally. Totally. And Hey, the reality is, is private markets are more of like a cabal. It's a small subset of the population, um, either qualified purchasers or accredited investors or, or, you know, big funds and pensions and endowments get to participate in the equity appreciation of these businesses. But,

you know, your doctors and dentists and your everyday American doesn't. And there is just something that doesn't, doesn't feel right about that. Right. Like straight up, you'll get social unrest, right. And straight up you'll get, you get voted regulation because like, well, those guys have their AI and I'm going to vote against it. I have my vote. That's right. That's right. That's right. And so like that is like a really interesting governing mechanism of democracy versus capitalism. Yeah. where capitalism encourages centralization and democracy encourages decentralization somewhat. Anyway, so there's all that. I think it's very, very valid. I'll point out for people who don't know about capital structure that equity is junior to debt. Yeah. And so it's nice to have two children in junior. Yeah, exactly. Exactly. In your enterprise value. Exactly. And that's like, well, that's public. And I think part of that also, I'll throw in the way now, which is a little bit more advanced than people know, is interest rates are spiking for data center debts globally, right? Like, is there a source to look that kind of stuff up?

Is it just Bloomberg? Yeah. I mean, there's just parts that people tweet around, but... Most of these big debt deals will price, and there'll be, you know, like an interest rate, an OID. You know, there'll be a bunch of things associated, like, with the price, and, like, oftentimes those will be public once they've priced. But, yeah, rates are going up. Risk-free rates are going up. The Fed paused or decided not to hike yesterday or earlier this week. I don't know if that's necessarily a reason to go public. It's part of the way now. Yeah. If it does help with that, that if my debt went from, like, I don't know, 3% to 5%, that is billions of dollars. Yeah. Yeah. And if I can keep a lid on that, that's very important to me. Yeah. I mean, a lot of the data center debt deals now are getting done over 10%. And so it's getting expensive. Okay. Yeah. And so that's the other part of this discussion, which is the ROI on a data center, right?

Because there are a lot of AI skeptics who are like, this is the biggest build out in human history. Yeah. And we are throwing money after this illusory dream of AI. And on the other side, the AI pro people are saying, well, you'll pay that period one year. Yeah. Yeah. Why is there such a huge difference? This was kind of the best thing about the Anthropic S1, which is a lot of the metrics that were leaking were your ARR, your this or your that, but you didn't really get to see a lot of below the line metrics. That's not ROI. You didn't get to see the cash. How much are they burning? It's not profitable. Are they profitable or are they not? And that, I think, was one of the biggest surprises of the S1. Here's a business that showed 85% gross margins, over 20% free cash flow margins, 8% gap operating margins. And so even on a gap basis, highly profitable. And maybe my favorite stat of the whole S1 was $141 billion of capital raised to date. That's inclusive of equity, converts, and safes, and a $134 billion cash balance today.

And so... Cash and cash equivalents, but yes. Exactly. And so you think, okay, well, you've raised this much. You have this much in the bank. That means you've burned $7 billion to get to, you know, the Q3 numbers that aren't in the S1, but that were kind of, like, broadly reported a few weeks ago. $16.8 billion of quarterly revenue off of $7 billion of lifetime burn, which... And the burn we should get into, because, like, that was a huge debate on, like, well, it looks like they did raise debt, or are they really profitable? What is this net loss on the P&L? There's, I'd say, lots of things out in the world that aren't fully true. That is important. I do want to cover it. There's a little bit more in the weeds than at the start of this conversation. But, like, that overall efficiency is, by far, if you've been following the relative fates of, let's call it SpaceX, Anthropic, OpenAI, the ones that are going public, they are the most efficient lab in the world by like order of magnitude.

Yeah, totally. And, you know, so when I wrote up the S1, I was like, okay, well, this is the mystery. Yeah. Like, why? Yeah. What's going on? Yeah. How are they so much more efficient? Yeah. And in their S1, they actually built it to the loops. Yep. The improvement loop, the investment loop, something like that. Yeah, exactly. So people can go read that up. I already wrote it up. But like, you know, maybe they're just smarter. Yeah. Well, I think here, like, one of the best numbers to look at is, like, what is their growth relative to their, what is the change in growth relative to the change in serving compute spend? And they break out their compute spend into more, like, training and internal use. And, by the way, if you have charts to. Yeah, I do have a chart on that. So here is a chart that shows the. Let's do full screen on this one. Yeah, yeah, yeah. Yeah, it's a little small. Yeah, exactly. All right, here's a chart, and this is broken down in the S1, that shows the percent of their compute that goes towards basically serving models,

so inference revenue generating or internal R&D kind of training or maybe safety alignment or non-serving of tokens. And what's interesting is you can see now, I mean, they're at 27%. Only 27% of their compute today is going towards serving models. Here's why this is so interesting. If you compare the dollar spend in compute a year ago versus today, their compute that went towards serving models has gone up by 2.3x. Their revenue over the same period of time has gone up 14x. So embedded in that is basically a 7x efficiency gain in serving inference, which, I mean, this is a business that I think is almost accidentally profitable, right? Where, I mean, a 7x increase in efficiency of serving tokens, if you were monetizing, you know, a megawatt at $10, $15 million, now you're monetizing it at $70 to $100 million.

And here is a little bit of like a random rant. But software companies have historically told the world and told investors, hey, we're an annuity of cash flows. Once we acquire a customer, our net retention is high, and they'll stick with us. Customer lifetime value. Exactly. And we're going to be burning a lot of money early on, but we're just in growth mode. We're in customer acquisition mode. Once we exit growth mode, we'll be more in harvest mode. And that's when we'll start to become, we'll flip profitable. because we're not spending a lot of money on customer acquisition costs. But, you know, the general model of a SaaS company was unprofitable, overtime, then profitable. What's happened with Anthropic, and let me pull up another chart here. I believe I have their, here are their free cash flow margins. Oh, my God, it's so negative. Which I had to cut the chart, right? Like negative 4,000%, negative 1,500%, you know, super, super negative. If I actually drew the axis to scale, it wouldn't look real. And so, yes, the axis is broken.

But this is the free cash flow margin of Anthropic, right? 21% free cash flow margin in the most recent quarter. And so what you're seeing is a business that all of a sudden, because they were able to drive such efficiency in their compute, that's kind of why I went on that like compute ramble, they've been able to monetize at much higher rates and actually deliver positive free cash flow margins pretty early on. And so this is a business that says, hey, we've proven that we can operate highly profitably. What we've probably done is underinvested in compute and compute build out, and we need to be more aggressive on it. And so my guess is that you're actually going to see this level off and maybe go negative again as they say, hey, we're going to reinvest and we're going to invest in a lot of compute. We're going to build out a lot of compute. Because now they've shown themselves in the world the capability. Hey, if we need to, we can flip the switch and be profitable. And for listeners, a couple of things on the accounting side, just because we got to educate people, right? FCF is typically the most honest version of what profit looks like.

But in this case, you can mostly just regard it as what an operating margin, profit margin would look like. Because basically, FCF just focuses on cash. And so there's less tricks to play with accrued receivables and offsetting your liabilities and what have you. And so that is the roughly rough thing. But also, the reason people have adjusted revenue and adjusted profit is because of nuances in running a business like this, right? Like GAAP, which is the official generally accepted accounting principles that you have to report, doesn't regard how you want to run the business and split out investment. it doesn't have investment decisions versus like sort of profitability of core business decisions, but it all gets smushed into this decision, right? So basically what I'm trying to sort of guide people towards, if you do see a change in this margin profile, it is a conscious choice. And actually you should probably split it out further between what is actually the core business

and is that actually infecting or is this more investment therefore the SCF went down? Yeah, and look, at the end of the day, cash is king. And your measure of profitability should be, is your cash balance going up or is your cash balance going down? Now, if you wanted to poke holes at this, the reality of gap accounting is there's two forms of, well, very broadly, there's two forms of expense. There's the cash and the salary that you pay people. And then there's also the equity and the non-cash that you pay people. So non-cash, stock-based compensation, equity compensation, that doesn't show up in free cash flow. It's actually added back because of the non-cash expense. So there are a lot of people who look at the free cash flow metric and say, well, it actually overstates your profitability. Because the reality is, is if you didn't grant equity to your hires, they wouldn't join you. And so it is a real expense. Treat it as a real expense. I don't have a chart for this. But if you were to adjust free cash flow for stock-based comp, you're still at an 8% free cash flow excluding stock-based comp. And so you're even profitable if you include the big equity brands.

By normal rules. Yeah, exactly. And so if you've never seen a software IPO, this never happens. Yeah, it's very rare. I mean, I think there are a lot of software companies that are free cash flow positive today. But if you factor in... Not at IPO. Never. Not at IPO. Never at IPO. But even today, the mature ones, even if you factor in stock-based comp, most of them are free cash flow neutral or negative if you treat stock-based comp as a cash expense. So all those companies are dying and everyone's just buying in stock-based, right? I think there's a... Well, you're not quite that, but certainly... Why would you? Exactly. Anything else, yeah. I mean, look, here's a crazy stat. In the S1, it's like a breadth versus depth one. They called out 50% of the Fortune 500 is using Anthropic. Yeah. However, if you look at what that cohort, the Fortune 500, is spending on Anthropic as a percent of their expenses, it's 0.1%. And so you ask yourself, where's that headed? I think John Collison on, he had Sam Altman on his podcast the other day. I think he mentioned 1% of Stripe's expenses on tokens today, but he expects that to go to 15% to 20% over the next few years.

And so obviously Stripe is a very forward-leaning tech company. Your average business in the Fortune 500 isn't going to show that level, but it could show a same magnitude of growth. So if Stripe is saying we're going to 15% to 20x the spend on tokens relative to other expenses in the org, Maybe you see something similar and the Fortune 500 go from 0.1% to 1%. But I do think it's, you know, ultimately, where do you think that settles out? How deep can they penetrate their TAM? It's kind of like an important question that, you know, folks should think about. But to me, at least, what it demonstrates is they really haven't, I mean, despite the insane ramp in revenue, you know, growing over 1,000% year over year in the Q2 quarter, there's probably still a lot of room left for them to kind of continue to penetrate their damn. Yeah, and I think the word is virtual collaborators. I say agents, but you got to make it a friendly term for people.

And I really start to tell people, you should just start thinking about it in terms of what your employee spend versus your token spend is. And most people are at, let's call it 0.1 or like 1 to 5%. Yep. The median AI company is at 50%. And the top AI companies, you know, let's say 90th percentile in the top of a percentile is like 10x token to salaries. Yep. And so that is probably the right comp of like well what are you spending on humans Yeah Right And like probably 30 right Yeah Yeah And well okay so Could be more even I mean humans could be like 40 you know total headcount could even be like 45 Yeah, it depends what kind of business you're running, right? Yeah, exactly. And so like if your physical business is fine, that's like until we have good robots, then that's not getting happening. But what are you spending on knowledge worker humans? What is a knowledge worker humans that includes coders, but also just admin and ops and what have you and legal and whatever you call it and all of that. And that percentage, the ratio of tokens spent to human spend was zero to whatever, and now it's going to rise.

And how far can it go? Totally, totally. It can go a lot. I mean, look, I think there's a lot of hyperbolic statements being made, both positive and negative, about OpenAI, Anthropic, the big labs. I think it really boils down to three main questions. Can Anthropic stay on the frontier? Is there a business and a durable business to be built at the frontier? And if so, how much is it going to cost? And then in reality, two and three kind of like relate to each other. Can it make enough money to cover all the costs to be at the frontier? And that's the one where I think I actually have a couple. I think I have one on this. Let me see which one. What did I want to bring up? So this one, yeah, let me pull up. No, let me pull up this one. This is a good one. So this is kind of the second question, like how much will it cost to be at the frontier?

Or even maybe let's go to like what can you build a business at the frontier? The other interesting stat that Anthropic had in their S1 was for the three months ending September, 80% of their revenue was driven by models that were six months old or younger. And so what does that tell you? The vast majority of their revenue is coming from their most recent, most powerful models. And that's something that I kind of expect to continue. I think when it comes to frontier tokens down to open weight tokens, I think there'll be a massive market for both. I think the trend will continue to be as a percent of tokens, open weight kind of continues to each share, but as a percent of wallet share in dollars, frontier continues to take the lion's share. So I expect frontier tokens to be the most economically valuable to capture the most market share. I expect open weight and cheap tokens to capture the most market share in terms of total tokens. And I think kind of this stat on 80%, which is much bigger than I would have expected just in models six months old or less,

kind of demonstrates the value of being on the frontier. Okay, so then let's go next go to the second question, which is, well, what is it going to, or the third, like, what is it going to cost? And here's a chart, I think, and I'll have to caveat this chart, but a lot of people look at the Anthropic S1 and say, oh, my gosh, there's $856 billion on the books of, like, not even not, but, like, committed deals that they've signed. How are they going to pay for it? You know, they're at whatever, whatever their ARR is, something in, like, 70, 75, 80, you know, whatever. I think the last number was 65. The, like, Q3 number was 16.8, but whatever. or 70 plus or minus, how are you going to pay for $855 billion? Have you read the rest of the S1? Well, what I tried to do in this chart is I broke down the – because they broke down their commitments. They didn't do it by year. Is this normal, by the way, that you would have year by year? No. I feel like this is unnecessary. Year by year, this is a little bit of the jamming best interpretation.

Don't give a dollar. Oh, no, I've seen variations of this. Yeah, yeah, yeah. And so I think what you have to ask yourself is in the commitments that they've made, and I've tried to annualize the commitments. If they said this is a five-year deal, I just made it over a five-year deal. By the way, every banker in the world has done this already. Yeah. Like they did this on the night of the S1, right? So you have to ask yourselves, can you pay for it? Okay, well, at a fixed basis, and then the gray bars is maybe kind of like less hard commits, but commits they want to make. So the SpaceX one is in there, right? Yeah, exactly. That's the one you can walk away from in 90 days. Exactly. will they be able to fund $100 billion a year? And so it isn't $850 billion a year. It's can they fund $100 billion a year? Now, there's a couple ways they could do that. Well, one, do you expect their growth to continue, which is a question of, like, will there continue to be a business at the frontier? Or you go back to the, well, only 25% of their compute today is allocated towards revenue generating

or kind of inference serving use cases. If they were to just double that and add zero compute, they could, in theory, double their revenue. And so you look at the headline number of $855 billion, you're like, oh my gosh, that's a huge number. How are you going to do it? Some of that is very back-weighted. But when I think about the trajectory of the business, the tan that's been penetrated today, I don't think it's far-fetched at all to say, hey, like the business trajectory will more than cover the revenue will more than cover the expense. And I think I have another chart in here. This is another great one. This is the compute margin. So this looks at if you were to take their revenue and subtract all compute internal models training. Same quarter matched, right? Exactly. Exactly. So even as what you're seeing is even as they've shifted more of their compute to non-revenue generating use cases, they've still scaled their compute margin to well over 50%, which what you're seeing is they're

getting hyper-efficient at the compute they bring online. The compute they decide to use for revenue generating use cases is becoming 7x more efficient over the last year. I think you have to ask yourself, will they continue to ramp compute and capacity? I think the answer is yes. I think if you ask anyone at Anthropic, again, this is all speculation, they'd probably say, we wish we started doing it sooner. We wish we were more. They're trying to commit to more compute. That's right. If you don't talk to any of these guys, you come away with like, oh, my God, these guys are absolutely crazy and being fiscally irresponsible. But I think if you have any confidence or if you see up close the demand for intelligence that people are snapping up and immediately consuming whatever you give them, then yes, actually, you're trying to get more compute. And the game is not to reduce the commitments. It is actually to get more commitments. They would have a trillion if they could. I don't think I've spoken to anyone in the AI industry, certainly not anyone at the big labs, who has said, yeah, we're good about the compute we have.

It's almost always... And I think an underrated factor on compute demands in the future will be compute that's used for safety alignment and verification. Like, I think more and more compute is going to go towards those functions, whether that's on research or just on verification and alignment that happens at inference time. That will be an increased compute demand for all these. Yeah, but we're probably not going to break those out, you know, but, like, it will be, like, maybe 5% now and 20% in future. Yeah, of course, of course. Yeah, I think, you know, I would just tell people, like, if you think this is bad now, wait till you see the OpenAI as one. Yeah. And then, like, it's not bad. It's, like, that's the whole point. It's, like, they're actually really trying to get compute from every source they can get. And, like, OpenAI is buying TPUs. Yeah. Like, there was a cold day in hell. They're buying TPUs, right? They have their own chip initiative. Jalapeno, which had pretty rave reviews. Thropic staffed up a compute team to build their own chips. Obviously, the hyperscalers are building their own chips.

It's a mad dash. It's a mad dash. Only, you know, only when there's a TSMC, NVIDIA, and ASML, whatever, in the supply chain stack. Totally. And I think, look, it's getting harder and harder, especially in the United States. You have a lot of pushback. I mean, you have all kinds of pushback. Data centers are too noisy. Don't want them in my neighborhood. They add a lot of air pollution. The water. Don't want it. Oh, my God, the water. The water, which uses too much water, which it doesn't. It's the electricity. It uses a lot of electricity. It's going to increase my electricity bill if I live in close proximity. or it's just the simple AI is going to kill us. And I don't want to enable that, which again, you have all kinds of pushback. It's become hyper, hyper political. But I think the unfortunate thing is it has become a lot harder. You do see a lot more pushback. Midterms are coming up, right? Who kind of like takes control in like the House and the Senate, like could have a big impact on regulation or lack thereof around data center and intelligence center build outs.

And the good thing is that it's getting harder and harder to build these data centers. Behind the meter is harder and harder. And so you do have a natural governor on the build-out. And I think we're a lot less likely to overbuild too quickly. That leads to a supply glut, right? Kind of the Internet bubble analogy of the early 2000s where we built way too much fiber. There wasn't as much demand for the Internet. There was a lot of dark fiber, right? Fiber that had no bits and bytes traveling over it. We have no dark GPUs today. and so it's probably a good thing that there's some rate limiter it will stop us from entering a huge bubble but it's also bad because we need you know the industry needs more compute especially as these models get more and more powerful you know the security concerns go through the roof you also need a lot more like the defenders need more compute the defenders need more capabilities which you know I don't personal belief I think we're going to be compute constrained for far longer than the world thinks Yeah. I mean, I'd be curious if what the markets price the world as thinking and then doing the work on like, what do we actually think?

And the Delta is probably, you know, could be three years, could be 30 years. Yeah. And that's a very material thing to figure out. Yeah, of course. Of course. I think one thing to guide people towards is, well, there's all this political stuff. And honestly, I would say Meta does a lot of work. And I think increasingly, OpenAI does a lot of work in, like, well, here's how we make sure we're a positive presence in the community that we're in, right? Yes, you do need to take care of that. But I think, like, coming back to, again, research, relevance, ROI, and what it means about the industry, this profit move here, I do wonder sometimes, and this is a thing that has come up in our other podcast, specifically tying in the research side is how much left is there to improve right so was this kind of like a one-time upgrade of our inference stack yeah which you know there was like something like a 7x improvement in you know inference efficiency yeah because like you can't like probably like there's no improvements for ever until the end of time in our inference stack

yeah like unless we fundamentally change the chip um we probably we probably started out pretty unoptimized and we are now a lot more optimized than we were two years ago yeah okay well there's a limit to how optimized you can get just like physical like jewels moving around and bits and bytes moving around so uh that that is my question in terms of like the how much efficiency there's left to squeeze yeah and maybe they're in my cap out you know in terms of like that that year-on-year growth yeah well look i think um i have a chart i'll get to in a second but I think this is a debate that's existed for quite some time. Yes. Right? You had, you know, the, I don't remember when this was, two years ago? You know, like the Ilya moment of like, hey, the data and the internet is a fossil fuel. We've exhausted it. There's no more data left to train on and like we're hitting a plateau. Yeah, that was years ago. Then we hit inference time reasoning, inference time compute and reasoning models. We hit a new paradigm. But I think maybe embedded in your question is, what is the gap between the Frontier and the leading open-weight models?

Is that shrinking? Like, is the gap that the Frontier... Yeah, I don't mind the gap. It's not something I talked about. But I think it's interesting because the real way to think about or to answer that question is not the gap between the best leading open-weight models today and the Frontier models today, because the reality is that open and anentropic, they have far more powerful models that they've developed that they just haven't released yet. Like, this is a chart that Mark Chen published from OpenAI, actually, switching gears from Anthropik to OpenAI. Yeah, he's been on the cooking pod. Oh, nice. We made some soup for researchers. Amazing. Amazing. I love that pod. But, you know, when they saw Navier-Stokes, that big math problem, you know, Mark came out and said, hey, here's a model. It may be dark blue, light blue is harder to see, but like the top bar is an internal model we used to solve that problem. The blue bar is Astra. Hey, look at how much more powerful the internal model is to what's publicly out there, which on some ways that like could scare you a little bit, right?

Like, hey, these labs have even more powerful models. So if you think about what's the gap between Openweight and Frontier today, I think it's actually far wider than people expect because there are just far better models at the Frontier that haven't been publicly released yet, either whether that's safety, testing, alignment, some combination of everything, you know, unclear. But I do think you're still seeing the frontier accelerate, even if it may appear that what's released is plateauing. I'm not even sure if it does look like what's released has been plateauing. No one's saying it is plateauing today. We're all discussing could it plateau in the future. Yeah. Because that is a pretty important thing to figure out for your investing, which is part of the deal here. Totally. Yeah, so that is a very well phrased point. I thought you were going a different area where you were going to argue about better lesson, and you didn't. So kudos on that. And I think that's a very fair point.

I think we've got still every model generation still shows quantum leads. this is only in math but opening has a pretty good track record of generalizing beyond just math yeah or coding yeah right and so i i do think that is my point was a little bit more as the financial analyst had on yeah which is you had this one time 7x improvement in revenue efficiency or whatever um and like it's not like they raised prices yeah yeah right so like where is it coming from oh here's another good i mean they didn't raise prices here oh i think so so so you know i'm seeing right yeah they have a hundred they have one yeah it's actually opposite this is an awesome chart yes this shows the cost to come they're charging less yes exactly the cost charging less and making more money the cost to complete a task with each model family and so what you see is with whether it's fable or opus and then you have fable 5-1 um and opus 5-5 you see about a 25% 25 to 40% reduction in cost to complete

the same task with each successive model family. And so it's kind of like yes, the costs are going way down. Their compute efficiency is going way up. And I think a lot of that just demonstrates I mean, again, it's going to sound a little like VC cliche, but like Jevin's Paradox. I know, I know. You can't have a podcast on AI without that coming up. Yeah, no. This is the appropriate time to bring it up. Yeah. Yes. And as things get, you know, you go back to, like, the early days of the Internet. Like, how many bytes were traveling across the Internet in 2000? How many bytes are traveling across the Internet today? Like, many, many, many, many orders of magnitude more. And guess what? The price to transfer bytes over the network has, like, dropped, like, many. I mean, many. It's switching to meter. Exactly. It's P and Q, right? Price has gone way down. Quantity has gone way up. Yeah. And I think you're going to see the same thing with people buying and serving models and tokens. Yeah. So generally pro-support of that, I do want to remind people that that is a true objective fact.

This is not like VC bullshit. This is actually how the businesses are running. And it is surprising to us that we read about this in theory, but to actually see this in financials is really ridiculous. You'll continue to see it. Think about like Jev and decision models. You know, and like the price point of, hey, instead of you want a binary classifier, you don't need a language model to do that. Just go use a decision model. Super cheap, super efficient. I think you're going to continue to see efficiencies like that. You know, I go back to S3 and EC2, AWS's big initial product, storage and compute. You've seen, you know, 99% price reductions in both of those services from the day they were launched. But like what's happened to the EC2 and S3 business or just the broader storage and compute, you know more cpu compute uh it's it's gone up exponentially i mean all the hyperscalers reported today they're all over a hundred billion dollar run rate businesses today if they were individual companies google cloud's grown over 100 it was just crazy azure's growing almost 50 yeah it was

unreported today we're recording on thursday 39 almost 40 like these businesses are just accelerating and growing at us it's crazy you should drop everything what you're doing and just go start one of these things yeah yeah uh yeah it's october tonight for those those uh keeping track and in yes uh a fun personal anecdote i was in my hedge fund when uh the day they broke out aws from amazon no kidding and at the time we were not long amazon because we were worried about uh aws margins and also like oh is it just startups and what if like you know we go through uh a startup winter and like all that yeah it was so nonsensical so like it the the funny thing about finance is that you can do all these works and make worse decisions yeah yeah yeah we definitely did that totally totally i mean that that's the thing like market timing is impossible like and also fixating on the wrong thing yeah which a lot of people are having to encounter for the first time that's right you have 2 000 data points that were released in this thing and you'll fixate

on one thing and it'll be like that is that everything you need to know and like well no like you you lost sight of the bigger picture yeah i mean that's that's for me i mean again there's a lot of things that I believe that others would disagree with me on, but I believe there's one question that matters and that's can Anthropics stay at the frontier? And to me, if I believe the answer to that question is yes, and I think everything they've done historically has demonstrated that, I think they'll build a great business and will continue to. But okay, but then like there's a right price for that. Of course, of course. And this is, this is not like, will it be priced right or this or that? That's more of, Hey, if you're able to take a longer term view on, will the beat, will the business be successful over time? I think to me, that's the most important question. Now, whether you think the IPO price is cheap, expensive, just right. I mean, we don't even know what that's going to be yet. Prices can change in public companies for idiosyncratic reasons, reasons based on individual company performance, or they could change for macro correlated risk. Like the reality is, is if Anthropoc went public a month ago, you know, the big route that you've seen in public markets recently, especially in semis and the AI trade,

Anthropoc would also be down even if the business was doing well. So stocks can go up and down based on individual company performance. as well as just market beta. Exactly. Yeah, so you should be prepared for those kind of discussions if you're getting into this. I think, actually, just as a side question, there's a lot of these non-public market trackers of the stock, like here's my proxy of OpenAI stock, here's my proxy of Endopix stock. Do you guys put any weight into that or like zero? I don't. Yeah, I don't. Yeah, I'm wondering if I should pay attention to those things more closely because they do give some indication of like, well, here's the stock price reaction when a model just released. I mean, it might show some market sentiment, right? But I think Ramp had some data. I think I copied a Ramp chart the other day. I mean, this was anthropic spend on some of the most recent models. The dark blue is Opus 5.5. And like, hey, what the Ramp data shows is... People switch. Yeah, exactly. And with Opus 5.5, you saw some reacceleration.

And so would it surprise me if you saw a reacceleration of revenue and run rate in October? And October won't be in Q3. That's the first month of Q4. No, it wouldn't surprise me because I do think a lot of the third-party data is showing and suggesting that. Yeah. Well, so, okay, let me – this chart is super interesting, right? Because you just said, like, when you say stay at the frontier, there's different levels of that. Yeah. Stay at the frontier, meaning we're one of the top two, one of the top six, one of the top 20, whatever. usually people mean top two yeah usually but sometimes you can expand and say top six yeah yeah google now has a has a claim yeah to be a number one in some metrics totally meta doing super super well uh spacex doing super super well yep not at not number one right but number three number four yeah you could say that yeah so look at that like summer slump in and topic revenue yeah um was that because opening i was briefly ahead yeah i mean i i think astro was a great model And I think a lot of people started using Astra.

And it wasn't just Astra, right? It was, what was Google's models? What was the one they just released? Carbon or Argon? Carbon, yeah. Carbon and Argon. Yes, yes, next yesterday. They haven't released it yet. This is my gripe with Google. They never release anything. They just announced a wait list. And then, like, God damn it. I think it was also you saw a massive explosion of, you know, Kimi K3, the GLM series, DeepSeek models. You saw open weight models get really powerful and get good quickly. So I think you kind of saw all of that. But it's crazy. Like, the far right bar, like, what was non-Opus 5.5 or Sonnet 5.5, like, that has gone, that's halved or more, maybe more, you know, in, what is that, two, three months. Yeah, yeah. And so, like, you do, imagine if they didn't have Opus 5.5 or Sonnet 5.5, right? Like, who knows what the revenue growth and trajectory could have been. So, like, that's kind of why I do think it's important to have a frontier class style model. And so I will say from watching these labs for the past three, four years, they used to have more reserve.

Yeah. Like, I will have internal models that I can just release whenever I feel like it. And then I think they, towards the GPT-5, 6th era, everyone just released whatever they had when they had it. Right. Right. now I feel like the reserves are building up again. I don't know why, but it definitely seems like, as you saw with the internal model, it seems like they have more wood behind the shed for stuff like this. Like, I can, sitting on Fable 5.5, I can release it the day before I IPO. That'll make my IPO go well. Yeah. I mean, look, I think there's good reasons that the models aren't released right away. On one hand, there is just a lot of safety and alignment and all that. But as a PM, as an executive playing this game, I can play it pretty well if I have a backlog. And it's also, think about OpenAI's Patch the Planet initiative, which is basically, hey, let's use these super powerful models to go find vulnerabilities. Not even. It was 5-6. It was 5-6-Soul.

They found the PHP vulnerability. It wasn't Frontier. Yeah. Which is crazy. It was just a spam a bunch of, like, a last-gen model. Exactly. Exactly. And then let's find all these vulnerabilities like the PHP one. Let's go create the patches so that we can instantaneously release the patch by announcing the vulnerability. The challenge is attackers are using these models, are using really powerful open-weight models, and they're finding these vulnerabilities. Yeah, yeah, it's a race. And it's a race. And it used to be, you know, you had weeks, months from like vulnerability discovered to like exploit. I mean, now it's collapsed and it's basically zero. You know, it's zero days. It's like vulnerability announced or discovered, exploit available. Like it's almost negative. And I think in, you know, OpenAI's announcement of the PHP thing, I think hidden in there was, I think they basically said like, oh, and there are more serious vulnerabilities that we know about that we're working on patching right now. Yeah. Which, I mean, more critical than, you know, something that touches 70% of the Internet is pretty serious.

Like the 70% of the Internet is just a proxy for old. Yeah. Of course it's older and it's going to be more vulnerable. That's right. Yeah. That's a good way to phrase it. Yeah. I think there's a couple of takes. I'm not that interested in dwelling on that stuff only because there's going to be smarter people than me on that. Yeah, of course. There's one angle of this which is kind of funny, which is like, well, we're spending a billion tokens finding all the vulnerability. And we're going to spend a billion more to fix the vulnerabilities. What if we just didn't do either? And it would be like net the same position. Yeah, yeah, yeah. Well, I think Kat's out of the background. Yeah, you can't put the toothpaste back at the tube. But that is one argument people make, like, they blame the AI instead of killing the messenger. Right. The other part, which is interesting, is, like, and this might actually be an issue for basically nation-state actors who actually had all these exploits already. Yeah. And now, like, well, you know, civilians are finding them. God damn it. Yeah, yeah. And now I have to work harder. Yeah, exactly. Like, all my zero days are gone. Like, I had them for 10 years. Like, whatever. Yes.

But anyway so these are fun anecdotes or consequences of this If you want to really do super Straussian and Machiavellian if you a nation actor what do you do to preserve your edge Because you cannot have the channels that you're relying on lock up. Yeah. So then you send a Saudi decoy to put the thing behind your TV. That was crazy. So that they get distracted by that, so that your real spy gets in. that's the war game yeah i i watch too much tv to to to think that what we are told is this will yeah yeah i think that's fair right i think that's fair too much is a stake yes too much is a stake um okay so coming back to the actual again finance like i really want to be a primer for everyone yes who are new to the economics of models and model labs and what what have you one thing that people have said is that the R&D per model let's say like what does

Fable 5 Sonic 5 Opus 5 cost let's call it a billion dollars that lasted me six months because you just said the depreciation lasted six months so isn't that worrying about like well these things are the fastest depreciating assets in history this is something that me and Sargo have talked about before Yeah, yeah, yeah, yeah. And I think the net is no, but, like, it's a qualified no of, well, as long as my next model run is better than the current state-of-the-art, we're good. Yeah, yeah. But it's a little bit of a house of cards if the next one doesn't work out. Yeah. Which briefly affected OpenAI. So a lot of people know by now that, you know, like, GipT 4.5, the big run wasn't that successful. Yep. and they've since fixed it. But a slump in a run, a failed run, can really, really screw with the ROI on the thing.

And look, I think there's a really good analogy to the chip industry. And I remember speaking to someone much older and much smarter than myself who did a lot of semiconductor investing in the 80s and 90s about all these chip companies. They've seen it all. And they've seen it all, and they were highly skeptical. And sometimes you have industry vets that are highly skeptical, and they miss the next big opportunity because they never think the incumbent can be disrupted. But the point of view they had was it's very different to tape out a chip that is competitive with the leading chips at the time from building a machine that can continuously tape out chips that compete with the model factory, the chip factory. And so he was like, this individual's point of view was maybe a chip startup can build one chip, But guess what? They're not going to have the machine to constantly pump out a new chip every year and stay at the frontier from the chip standpoint. And that's why NVIDIA is king. It's not because they have the best chip now. They have the best process for chips.

Exactly. It's the machine that builds the machine. And so it's kind of one of these. I mean, this is the bear case on all these NEO labs, which is, you know, and you talk to many smart folks and what they might tell you is, hey, if you want to really compete and have like a soda model at the frontier, You can't go more than one order of magnitude of compute less than the labs. You just can't compete if you don't have one order of magnitude of compute of the big labs. And so as a Neolab, the question isn't, can you build one model? It's, can you attract compute and talent required to continuously pump out new generations of models every year to ultimately compete in your domain? And so I think for the labs, the reality is, is I think it's very hard for anyone, unknown players to catch up to kind of like the generalized frontier big models. I think you'll continue to see domain specific models or models or like new types of models, decision models like Jev.

but I think the big labs, they are on the treadmill of continuously pumping out new models because guess what? If Anthropic stops, OpenAI and Gemini will continue and then you'll just kind of see revenue fall off, which is why I do think it's so important to stay at the frontier. Now, what do I expect and what have I seen from Anthropic now in the data? it's as their model families get better and better, they get more efficient, but they also capture more revenue. That could be because they're doing more economically valuable work or they're doing the work faster. But what you're seeing from now kind of black and white is, hey, as Anthropic has 2x their compute spend to build and train these models, they've 14x their revenue. And like, I'm not really sure what would cause that trend to stop. I think models are only going to take on longer running tasks, more economically valuable tasks.

And if you can't tell, I'm a hyper optimistic person. And so maybe I'm just drinking the Kool-Aid too much. But I do think we're nowhere close to hitting a limit of the dollar kind of like economic value that leading models can capture. I think what's maybe more interesting is, you know, OpenAI, you know, Luna class models kind of also trying to capture and compete at the low end of the market with much smaller, much cheaper models, you know, and kind of span everything from frontier to, you know, distilled versions of their own models that are smaller in and, you know, cents per million in put output tokens, not dollars. Yeah. Cosign all of that. I think that is a good way to think about it. I think, you know, I'll just comment this because it's in my head. 6 on Terra has been pretty successful, 6 on Luna. Like, that whole series is really interesting. The approaches of the different labs is also super interesting.

And probably to this day has no decision model. And they're like, well, we're good. Like, please, type safe, whatever. And, like, well, they don't have a standalone voice API. They have a voice model. they just don't don't market it right they don't they've never done images right there's a lot of enterprise focus yes coding focus yes and open ai has been a little bit more of like we'll do everything and we'll try sora we shut down sora yes remember when sora was like the number one app in exactly in the u.s yeah exactly um and then gemini has been the most like we will explore everything um and so like we appreciate them because if they didn't try it no one else would right like they have the funds for it yeah um but like i think of often about anthropic in terms of like well okay enterprise encoding and they they're first to it they nailed it they continue to nail it right and everyone's chasing after them yeah um is that the story going forward is is uh is it just more of the same or like how what should we expect i think it's i think a lot of this boils down to like a new type of innovator's dilemma you know which i almost call like the compute innovators dilemma. Like let's say you're opening up and you have Sora, you have many different

model families, you have image models, you have everything. Well, the reality is, is how do you choose what to serve? Maybe Sora demand explodes. Are you going to allocate a lot of your codex compute to small videos? Because you probably will have to, like if you actually want to make, I mean, that's exactly why they shut down. Yeah. Experience good. You need to. And at the end of the day, what a lot of people have decided is, well, guess what? A lot of the highest ROI, most economically valuable work has been in coding. So it's hard to prioritize anything else other than that. It's why there's probably opportunities for other companies and startups to build models and build products in those domains. Because guess what? What is open ad anthropic chasing? They're not chasing the incremental $10 billion market cap. They're chasing the next trillion. And so the reality is, is for a startup or a neolab, great, You could go build a company, build a product, build a model where you exit for $10 billion and it's a home run, grand slam. That's awesome. And simultaneously, it's too small for OpenAI and Anthropic to matter.

And so like, what has Anthropic done just a bang up job at? They said coding is our thing. We're going to be great at coding. We're going to build the best coding harness in Cloud Code. And we're going to continue to nail it. I think everyone is kind of starting to ask themselves, well, when do we, and maybe you asked a version of this earlier, when do we tap out the coding, Tim? I think one point of view is... I have asked very many times this question. But I think one point of view is, hey, coding is actually the best generalizing knowledge framework. And that can be applied. You think of it as coding, but when I use Cloud Code, like I'm not coding, but I'm using Cloud Code. When you use Codex, are you coding? No, they're generalizing and coding is a good framework for generalizing knowledge work. I do think you're seeing a big push by both OpenAI and Anthropic into life sciences and drug discovery. I think they've both been quite public about their interests to kind of like capture that domain as a new field. And I do think those are just different.

And it would seem like Anthropic is investing more than OpenAI. I agree. Yeah, I'd agree. I'd agree. But now I can't remember the question you asked. But I do think you're starting to see these companies evolve the scope of what they want to go after. and it has been very targeted on coding, given it's been the best generalizing, you know, you want to generalize knowledge work. These models have generalized the best that they've been stronger in coding. But it also can give it some, a soft underbelly to other areas. Maybe it's visual thinking and reasoning. You're seeing great models there like a Lorian. Maybe it's decision models like Jev and TypeSafe, right? Like there's a lot of different soft underbellies they may have, which is great. It's opportunities. Like, you'd love to see better, cheaper models in other domains. You know, you don't need to drive the Ferrari engine to the grocery store. Hey, maybe a Toyota will do just fine. And I think that will continue. And good Elorian shout-out. We just interviewed Andrew on his benchmark. And Glimpse, I think. Glimpse, yeah.

Are you guys invested? We are. Shameless promotion. Wow, okay, all right. Yes. It's weird to, like, this is a whole different discussion. The altimeter I don't get is, like, well, you will lead the giant rounds in, like, you all the anthropics of the world, but you also care about Lurian. Yeah. It's way smaller and probably worth a minute of your time. Yeah. Well, no. We're the ton of my time. I mean, I would say we've our belt. I mean, we're lifecycle investors. We invest early. We invest late. We invest in the public markets. And I think there's a lot of value. The way we think about it is some of the best founders today, they need late-stage capabilities early on. And late-stage capabilities isn't just capital. It's relationships with execs. It's relationships with public companies. I mean, companies are built on hyper mode now, right? It's not, let me go raise a round and let me add $10 million of ARR in 18 months and I'll go raise my next round a few years later. It's like you're growing to $100 million super fast. You're raising nine figures of money super quickly, and it's hard. I mean, in many ways, you have to break the laws of physics to how cultures can be maintained.

Lawyers are not set up for this. They're like, what do you mean when we come in on a weekend? Because Monday might be different. Yeah, exactly. I mean, you used to think about as a board member and investor, hey, let me think ahead. Let me make sure we hire the VP of sales and the CFO six months before we need to so we're not behind. Now it's like, hey, six months before you need to may have been before the company was even incorporated. So how do you do that? When do you hire the right exec so you're not doing it too late? How do you hire ahead but not too ahead? I mean, I feel like the pressure of building companies has never been greater. Now, the size of the price has never been greater. But it might appear as easy as ever to build a company, but I actually think it's the opposite. It's almost as hard as ever to build a good company, and maybe more importantly, to build a durable company, which is, I think, what everyone aspires to do. Yeah. I'll also shout out on the Model Factory thing. We also interviewed Pulsite, which was acquired by NVIDIA. Yeah, they were pitching this Model Factory really hard. And I do think the business that builds a business is kind of the general takeaway that I want people to know.

One of the things I was diving towards, I didn't quite start it yet, but I'll start it now, which is typically when you have an S1, you have a clear sense of the business. Let's call it market segments, product segments, the org chart, whatever. And we got cloud code, cloud co-work, miscellaneous, or platform, whatever. And I'm like, okay, what about legal, what about design, all the stuff that you've got. Cloud.ai, the consumer business. Yeah. For a while, that was a thing. do we care? Is this all like one revenue bucket? I think we do care. The products, right? Like this cloud, code, and what else? And this is where I do think, and there was a section of the S1 where they broke out some of these vertical applications they want to go after. I mean, it's kind of funny you bring this up. I had a hypothesis years ago that, hey, maybe the labs will stop releasing models. and instead they'll just build applications and products powered by their models.

Yes. And if they're powered by the best models that are never released publicly, hey, maybe they have the right to build all the best applications. Yeah, you kill Harvey by only using Claude Fable 6 legal. Right. Or it's, hey, you know what? Cursor, guess what? We have, well, you know, years ago, we got the best model and we're not releasing it. So guess what? We're going to have the best coding agent because our model that's the most powerful isn't released publicly. And I do think it's why some of the leading application companies today are moving towards in-housing their models. They want to control their own destiny. You need to. I think it's going to be really hard as an application company over the long haul to survive without a research team internally. It's one of the investment theses that I have that if we're, you know, investing in applications, like I want to believe there's two things, really strong product org, but also a really strong research org. And I also want to believe that they're aligned. and as the research gets better, the product gets better, which sounds counterintuitive, but there are a lot of research orgs that are just doing research for the sake of research versus closing the loop and actually having a positive cycle of product improvements

with research improvements. I'm not in a topic where I have the benefit of being friends with a lot of people in a topic, and I would say research is king in a topic. Yeah, totally, totally. By far. And so part of my whole thesis around model labs versus agent labs is model labs will respect the model and try to build AGI, and products will be a final last mile to the delivery mechanism, whereas the agent labs will respect the customer the most and build whatever they need, and they'll backwards integrate to models as and when, but they'll probably never be at a frontier for those things. And both can coexist. Yeah, totally. Both have a market. Totally. But maybe to your question, I do expect to see a much bigger push by both labs, Anthropic and OpenAI into vertical applications and kind of like diversifying out of just delivering models. I think the actions of Anthropic so far have shown no serious intent at it. Yeah. Because cloud design is just a tab inside of cloud. That's right.

It's not like a whole Adobe, you know, replacement. Cloud legal is a markdown, you know, markdown rebounds. Mm-hmm. Which, like, that is the AGI pill. If your research is getting in, like, you know, one model does everything, that is what you do. Yeah. Right. This is classic enterprise and org management. You have a cash king product. Your sales reps, what do they want to sell? The thing they know how to sell that they've been selling, that is crushing it. What's harder to sell? New things. New things, new SKUs. So it's inertia. It's momentum. It's just classic big company boring stuff. I think for someone, I've been through maybe 15 years of IPOs. and for an IPO stage company, much less a $2 trillion company, you've never seen like, well, we have three shoes. Yeah, yeah, exactly, exactly. But I think, to Anthropics' credit, they've been really good at focus and like they haven't been, you know, enamored with the next thing or... No robots. Yeah. No images. You know, it's... And again, you look at OpenEye, it's tough.

Like, who are they competing with? They're competing with NVIDIA. They're competing with Google. They're competing with Meta. They're competing with Anthropic. You know, like they have different lines of business that are all competing with giants, like the biggest tech company in the world. And it's like, it's a tall task to take on one at a time, let alone all of them at a time. But hey, that's the game you take on when you're a trillion dollar company. The stakes are just higher. Yeah. Okay. I want to round out the Anthropic discussion and then we can go to general commentary on other stuff. Yeah. We've cheated a bit with Elorian and TypeSafe. But have we covered the main debates that you've been engaged in? There's two others. So this is going to be very, like, financially in the weeds. You're going to talk about convertibles. Yes, exactly. Yes! Okay. So on the balance sheet, you know, earlier in the episode, we talked about, hey, $140 million of capital raised, $134 billion of cash.

That's what the balance sheet shows you. What the balance sheet also shows is a, I'm going to, I might mess up the exact numbers because I don't have them in front of me, but I think about $108 billion of convertible notes payable as a liability. And so the question is, was that debt? Did they raise debt? And what you also see is a huge portion below the operating income line of like other income and expenses that makes it look like they have a huge net loss. They're both related. And let me explain. so any company and any startup founder listening to this would understand and appreciate this like there's different ways to raise capital you can raise preferred equity you can raise converts you can raise safes there's a whole different host of options to raise money anthropic just like every other startup did everything they raised some preferred equity they raised some converts and they raised some safes and along the way there's different reasons that certain investors of the company might prefer one or the other. When it comes to strategic investors, there's even more reasons why they might prefer one or the other. And I think it's very well known that some of the

strategic investors, Amazon and Google in particular, were very large investors and very big owners of Anthropic early on. And so early on, Anthropic and Google, 23, 24, 25, a lot of the dollars that they provided the company were in the form of converts and safes and notes. The value that those converted at at the time was pegged more towards the valuation those companies were raising at at the time. Why might they prefer a convert? Well, they're public companies, Google and Amazon. If they have an ownership in a third party that exceeds a certain limit, they now have to start reporting more on that, look through ownership, right? If you own 20% of another company, you almost have to... It's consolidated financial statements. Exactly. And that company is losing a lot of money. You now have to report that loss on your bucks. So, you know, strategics don't want to hit certain ownership thresholds. So, hey, give us a convertible. Give us a note. We'll give you cash. You haven't given us shares yet.

And so we don't have that equity ownership. We haven't gone above those thresholds. Maybe Anthropic didn't want to give them voting shares. I don't know, right? Let's have your shares convert into non-voting shares because we don't want the governance of a supplier or a strategic. Like I'm totally speculating and hypothesizing. These are all reasonable reasons to do it. Reasonable reasons why, hey, they were converts. But the conversion price was at a much lower valuation relative to what it is today. Okay? And so when you look at the balance sheet, and balance sheet has to balance. You have assets, equal, equity, plus liabilities. it has to balance. And so when you raise a convert, you get cash. Your assets go up. Well, if your balance sheet is going to have to balance, your liabilities also have to go up. And it makes sense. A convert is a promise for shares in exchange for cash. And so Anthropic took on that liability. And at the time, the liability matched the cash raised.

Now, what's happened to Anthropic's valuation since 2023 and 2024? It's gone way up. And so now what happens is the dollar value of your liability, because you promised shares at a price, the dollar value of that liability has skyrocketed because the valuation of the business has gone up. What does that mean? Okay, now the liability looks big. It looks like $108 billion debt that Anthropic raised. The reality is they didn't raise $108 billion of debt. There was about $11 billion in those combined convertible notes. about $6 billion has already been, no, $5 billion has been converted to equity. There's $6 billion outstanding. The market value of that $6 billion is $108 billion because the company's value has appreciated meaningfully. And so there's this debate of like, well, they have $108 billion of debt on the balance sheet. They haven't raised $141. They've raised $141 plus $108. That debate is just misguided. There is no debt on the balance sheet. and the $11 billion of converts is inclusive in the $141 billion of total capital raised.

And so debate one of... Well, have they really only earned... Good job summarizing this. Yeah, but it actually goes deeper. Yes. That's just the balance sheet. Okay. Okay. Now let's look at the income statement, the P&L. They have an operating profit. You could say, great, gap operating profit. But what comes below operating profit? Net income. Someone could say, well, in the first six months of 2026, they had negative $80 billion of net income. So you could say, and this is a debate, this is what people are saying. Again, it's very misguided. They're losing $80 billion. It's true. What a terrible business. Technically, by gap accounting, it is. Okay, let's go back. And I'm going to go back to the balance sheet. The liability has gone way up. That liability is now $108 billion. Okay, let's think about the balance sheet. Assets equals liabilities plus equity. As liabilities have gone up, two things have to happen. Either your assets have to go down or your equity has to go down.

Well, your assets haven't gone down. That means your equity, the value of your equity has to go down. This is where it might get a little complex. But how the three statements link is... Yeah, people can take a Connie 101. Net income on the P&L flows to retained earnings on the equity value of your balance sheet. And so what has to happen as that liability has gone up, retained earnings has to go down by the same amount, which basically means net income has to go down by the same amount because net income on the P&L flows to retained earnings on the balance sheet. It's all mathematically linked. You can't break it. It's all mathematically linked. You can't break it. But what's happened is in order for those statements to link and for those statements to tie, you need a huge decrease in value on your income statement. And what happens is, guess what? Anthropics value has just skyrocketed recently. They've raised big rounds over the last nine months. And so as those converts have appreciated in value, the value that those notes have

appreciated shows up as a loss on the P&L. and of the $81 billion of loss in net income on the first six months of this year, $72 billion is from a series of converts for Anthropic, for AWS and Google and $9 billion is for a separate set of safes and notes. And so 100% of the loss in the first six months is just the value appreciation of their converts. If Anthropic never issued converts to Amazon and Google, just did straight equity, that loss wouldn't be there. And so it's kind of one of these like, technically, from Gap Accounting, they do show an $80 billion net loss. Do I view that as an accurate representation of the business? No, because the reality is, is 100% of that loss is just the appreciation of their equity, which everyone would say is a good thing. And as those notes convert to equity, those go away because you no longer have this dynamic of your liability going up as your stock

price goes up, which means your net income has to go down over here. So, you know, all the financial statements tick and tie. And I, yeah, sorry if that got too like financial. No, that was a really good recap of the discussion. That should be a definitive answer for anyone who has come across that topic. There's another discussion about the circular financing of Google and Amazon. We don't have to go into that i mean i think that is small in comparison to the numbers we're talking about today um and uh in general like you know book value accounting is not that relevant compared to overall enterprise value um oh maybe one other thing related to this yeah i think there are a lot of people that say okay well the only reason the liability has gone up is because the value has gone up was that did that mean that anthropic issued shares today and did they give did they they give Amazon and Google billion worth of value but did they give them those shares at the price from four years ago Did they just give them shares at a huge discount It like no that didn happen What happened is they raised that money years ago in normal

financings like a normal startup would. And over time, it's just appreciated in value. So I think a lot of people, they're just not financially educated to that level because they've never had that exposure. So it is what it is. You're just educating. What was the second in the debate that you were referring to? You said you had two debates. Oh, those related. Yeah, so it was the balance sheet. Oh, wow. Have they raised more debt? And then it was the income statement. Are they actually losing money? As an investor, why do you get involved in these kinds of debates? Oh, this is more of like, I mean, I'm... You're just having fun. Yeah, I think, look, Altimeter is a firm. We do venture investing, growth investing, and public investing. And I spend a lot of my time on the venture investing the early stage. However, I consider myself like a student of finance. and Anthropic is such an iconic company. You shouldn't have to educate people on the three statements of accounting. Yeah, yeah, yeah. But like, hey, I mean, I think a lot of people look at the balance sheet, the income statement, the P&L, but the reality is there's a lot to learn from the balance sheet and the cash flow statement too. And so this was more of a, hey, as just someone who's intellectually curious,

I mean, Anthropic is a really important company to understand. I think if the Anthropic S1 came out in the financial profile that was pictured was doomy, and it didn't look like a good business, I think we have a lot to be worried about. Yeah. Because if they're not delivering profits and high ROI, what does that mean for everyone downstream of them? I think it's really important to understand the Anthropic P&L. But what I understood from the Anthropic P&L, let me just rip through these really quickly. Here's what you're seeing about the gross margins. Lots of leverage in the gross margins. They're improving rapidly. Which one nuance on this is this is the half that is not through cloud partners. Exactly. This, this, no, this is just the gap gross margin. Right. Okay. Is that, okay. I don't know. I read something about like how obviously cloud partner margin would not go into gross margin at all. Yeah. And that's 50% of revenue. How they account, I believe, is a lot of the cost that they pay out the rev shares in sales and marketing. Yeah, yeah. Which is below the line.

Which I think what you're saying. By the way, $3 billion is sales and marketing. I think it's like 40-something percent of the revenue comes from cloud partners. It's not a trivially amount. 48. 48? Okay, yeah, I was close. Insane. So you see gross margin profile improving. And sure, maybe it'd be lower if you kind of factored in the third party, you know, the cloud providers. But, like, the trend is going up. You see operating margin going up, positive gap operating margin. You see free cash flow margin going up. And so to me, what I see is a highly profitable business that is probably going to flip to reinvest and compute and build out. I think the way compute gets built is going to look different in the future. It's going to be expensive. There's going to be more upfront payments. Right. I do think you should expect to see the profitability profile of these labs kind of go the other way in the short term as they shift to investing and growth and investing in compute. But the biggest takeaway for me was it's a highly profitable business that's still operating in growth mode, that's seeing some reacceleration probably, you know, in the third-party data from the new models,

which gave me a lot more confidence for the rest of the industry. So long as the revenues show up, the compute obligations will be hit, the supply chains will, you know, continue to be saturated and constrained, and companies will continue to perform. And OpenAI and Anthropic, the reality is, is they're a huge percentage of the overall AI revenues. So everyone looks to them as a proxy for the rest of the industry. So if they keep growing, which, again, I think a lot of the bears would say they're going to stop growing because open-weight models are going to eat their lunch, like this, this, and that. But I think those are the important kind of questions. Yeah. Very important. Very good questions. I think, like, yeah, anyway, do you dabble in, like, forward projections of numbers? Like, what, like, what should we expect from FY27? What is the... Oh, I think it's too hard. Yeah, I think that's too hard. Because the delta is very high, right? And given the year they just had. It's a business that is so hard to project because the future is so, like, unknown and unknowable.

They'll go public. The sell-side research analysts at all the banks will build their models. We'll kind of have a forward number. I don't know where that's going to shake out. But I do think you'll see a lot of optimism about what the future of this business could look like. But again, would it surprise me if it was much higher than expected? Anything could happen. Anyone trying to predict the future, especially in the short term, it's as hard as ever. I think businesses at the frontier are the most predictable because they're the least likely to be disrupted. but I don't know, we live in a highly uncertain time. Yeah. The kind of A-plus financial analyst as a PM in my hedge fund I would have wanted is to do physical limits calculations on like, okay, you have all these inference optimizations. How much juice is there left on the current thing? Let's say the next gen has this level of inference.

How much can we charge on that? and given the competitor profile and exhibited pricing history of these things. Yeah. And then finally, how much GPU supply has been locked in and secured so that that translates to this number of tokens and that's what we can sell for. Because that's like the physical limits of where these things can go. Because I think a lot of the financial analysts, and I used to do this, you would just kind of have this spreadsheet of like, here's the past performance, and you can click the right corner and just drag it out. Yeah, exactly. That's not how it works. Can't do that. There's not good analysis, right? Totally. I mean, I think there's limitations everywhere. Everyone knows, like, hey, NVIDIA can only produce so many chips. You know, Challenger, Cerebris, TPUs can only produce so many chips. But I think there's other constraints. There's only so much LAN power shell that's approved and permitted. Correct. So you do that buildup, right? And then you're like, okay, who has what share? We talked a little bit about, like, debt and credit markets. Like, right now, you want to go raise multibillion-dollar debt for GPUs? No bid. No bid.

They're like, debt markets are close. They're, you know, big debt deals that aren't happening, right? Almost the equivalent of like an IPO getting pulled. So why is there $100 billion in equity but no $100 billion in debt? Well, I think right now... I'm going to turn around and do those, put those money right in the data. Credit funds, they're all looking at the look through exposure and any neocloud or a hyperscaler that's raising debt is the biggest question the credit providers are going to say is, well, who's the offtake? Because the reality is that that's actually who I'm underwriting. Because that's the customer. That's the one paying. That's the end payer of the bill. And if the end payer of the bill does poorly, the bills don't get paid. And right now, the end payer of the bills are Anthropic and OpenAI. So many of the data centers and the being built by the neoclouds are for OpenAI and Anthropic. And a lot of the credit providers are saying, I'm feeling kind of full. You know, I just did a ton. I just did a ton of debt for either one of these. and it didn't matter if it was CoreWeaver or Nebius or an upstart like a Volta or someone else. It's like, hey, I'm feeling full.

And I think debt markets just are going to go through a little bit of indigestion. They need to figure out, well, do I feel good? Do I feel great? Hey, some of these projects that were promised, are they being built on time? Is the revenue flowing back to me? And I do think the credit and debt markets will ebb and flow because there are just a thousand reasons why, you know, a project could get delayed, right? And NeoCloud could go sign a contract with OpenAI. They could sign with NVIDIA. They could go have Dell package them up. They could raise the debt. And then, you know what? There could be local community uprising and picketing. I'm not even being kidding. And then it's delayed. Then what? Well, the debt was raised. Someone's got to pay the interest. What happens? You know, and you have creative delay-draw facilities where you don't, you know, the debt is committed, but you don't draw it down. But, like, all of these have limits, you know, like, and you just get super in the weeds on, like, SLAs and who was responsible for it.

Was it a delay in the supply chain? Was it a delay in the construction, you know, the data center developer? Was it a delay by the operator? Was it, you know, something else like community unrest? And, you know, that's everything that, you know, credit markets are thinking through. and then there's a lot of other super technical reasons that we don't need to get into that make it difficult with credit default swaps and hedges going through the roof and so now for debt providers it's tough to hedge the exposure they have and so as the hedges get more expensive your willingness to take on more risk goes down because you can't hedge out of it this is all the function this is all symptoms of the debt market running out of money I don't know if debt markets are super deep I think it's more of like an appetite and of course there's a price for everything hey, great, you know what, go take out $5 billion of debt at 20%. But you don't see this? You see this at equity layer? Again, how come there's $100 billion of equity that's going to go into Anthropic and Anthropic cannot raise $100 billion or $50 billion? I think I don't know if I would say they can't

do it. I don't know if one, they need to be rated. Do they want to? My guess is Anthropic and OpenAI are happy that neoclubs exist to take the balance sheet risk. Maybe they don't want to take that balance sheet risk because again, it can be things out of their control that blow them up. You have a huge debt stack in your cap table and things out of your control go wrong. Someone else's problem, yeah. Like, you're not the one holding the bag. And again, this is like the beauty and the curse of why neoclouds can be great businesses or they could be brutal businesses if the contracts aren't airtight, if things aren't structured well. Your balance sheet business, you know, and just like FinTech balance sheet businesses can ebb and flow. You know, there's a ton of, oops, my computer may have died. It's fine. We'll edit. Okay. You know, balance sheet businesses, right? Like there were a ton of FinTech companies in 2020 that did super well when interest rates were low, that got blown up when interest rates were high. And balance sheet businesses can be really tricky and very cyclical.

And you don't want to own a balance sheet business when a market cycle tips over. So, hey, on some level, I'm sure, you know, especially for internal chip efforts, right, like the OpenAI team may want to develop their own data centers for their jalapeno chips, right? And Anthropics, same thing for their chips. But I'm sure they're also very happy for other people to take on that risk because it's someone else's core competency. And, you know, a lot of debt could add a lot of blow up risk. There was no debt on the Anthropics balance sheet. Now, they have facilities in place that they can draw down, and so would it surprise me if you start to see debt on their balance sheet in the future? No. I mean, but again, it's like you want to build a gigawatt, right? Fifty, sixty, seventy billion dollars. That's not a trivially amount, and that's just one gigawatt. I think both Open Ananthropic have said they kind of want to get to five by the end of the year. Who knows if it's that or something more or less than that. Are they about equal?

I don't know. Okay. That's interesting. Because I think Anthropic... OpenAI was ahead. Correct. Yeah. Right? By a lot. Yeah. But I think Anthropic is supposed to get up a lot. And OpenAI at least started, like, Stargate was a plan, the first stage in a plan to get to 30 gigawatts. Yeah, by 2030. Yeah. And so I was like, okay, well, that's, I haven't heard that plan from Anthropic. But again, like, let's do some fun math really quick on the fly. Let's see if I can do it. Ooh. I'm getting tired. OpenAI, let's say, I think when they came out with that, it was maybe a year ago, and they were what, like one and a half gigawatts, something like that. So whatever, round that down to zero for just the sake of the argument. Let's say open an anthropic, each one go from zero to 30 over five years. So you're gonna add 60 gigawatts of AI capacity, just the two labs. What's a good assumption for their total capacity? I don't know, 40, 50%. Let's say it's 40%. And well, I don't know, now we're getting too tough and math in my head. But you know, let's say that gets you to, I don't know, 110, 120 gigawatts of total capacity brought online over the next

five years. Let's say 100, just so the math is easier. And let's say it costs $50 billion to build out a gigawatt. That's $5 trillion. $5 trillion over the next five years, right? And so call it a trillion a year. You're talking about a non-trivial percent of GDP, you know, real GDP, that's like 5% plus or minus of GDP. Like that is a massive bonkers, massive build out, you know, rivals, the real world build out, you know, I think I've seen some charts somewhere that kind of compare those expected numbers, but like, you know, when you're measuring things in like whole number percents of GDP, uh, you're that's, That's huge. And so, like, the credit markets, debt markets need to be super, super, super deep and super, super open for that to happen. You need global build-out. You know, it's kind of funny.

You know, semi-analysis number for next year is like 50. And, you know, I think Brad at the All-In Summit said, oh, maybe it's lower. And I think there was some trolling going on back and forth. And I think the end result of that was like, hey, maybe the 50 gigawatt number, I don't want to speak for anyone, was more of like what's constructed. You know, Brad is more of what's constructed and live generating tokens because there can be a gap in what's constructed and ready to go versus what's actually live and generating tokens. But I think it's like a real debate in the world, you know, and you could see a pretty wide range of outcomes. You know, do we really add 50 gigawatts of capacity next year as an industry or is it 20 or 30? You know, like, who knows? I just know everyone you talk to in the supply chain is saying, you know, we have orders or indications for orders that are well over 100% of our capacity. And, like, I don't have line of sight to that slowing down for the next few years. Yeah. And. Yeah. And the limit, like, LAN, shell power, all these, but also the silicon, like, just straight up. Totally. Like, compute memory. Totally. Every part of the supply chain. Totally.

That's the work. That's why Semi-Loss is so powerful right now. I mean, look at these recent Kimi models. Kimi K3. You know, with memory, you need gigabytes of memory just to serve that. You're not running out on your laptop. You know, you need, like, a rack to serve one model. Again, those are just the big models, right? Like, you're also going to have tons of small models. But, I don't know, the big models are getting bigger, which only require more memory and more compute to run. I used to say they were capping out. I used to say, like, the max size is one to two, and then, like, we would just optimize from there. Yeah. I've been wrong. What's interesting is it does feel like the open-weight models are capping more around, like, 100 billion active perimeters. That's right, yeah. It's mostly, like, an infant speed thing. Totally. But, like, I think it's actually really interestingly tied to the generations of GPUs that they have. Yeah, exactly. It's just, like, you wait for the Garibans to roll out in whatever China has. Yeah, exactly. And, well, then they'll get it, you know? Yeah, totally. So they're really memory-constrained at the University of the Year, and that actually shapes the model architecture, which is, like, kind of cool. Totally. so that is

entropic yes powerhouse tour congrats I don't know how you felt coming into this but you did great oh this is fun yeah and like obviously more to do and more to analyze but I definitely wanted on the podcast to do this yeah because I know you yeah of course you know it's been fun we've been meaning to do this for a while when else we're going to get finally make it happen yeah exactly exactly any other commentary when you don't spend time on you know the labs what are you spending time on oh man it's um again in a world that's highly unknown and unknowable and uncertain, I kind of fall back to what are my core beliefs that I do have conviction in. And that is the world's going to consume more models. World's going to consume more power. World's going to consume more compute, networking and storage. And we're going to need to build a lot more data centers to serve the intelligence we're building. Those are kind of like my six core truths. And so like a lot of what I'm looking for are what are businesses that are a direct derivative of one of those core beliefs that I have. Yeah. And any other expressions of those core beliefs that you've done recently? Done recently? Yeah, certainly.

There's a chip company, still in stealth, that's super exciting. Maybe we can talk about later. There's a power company, American Terawatt. They want to build a new grid, an industrial grid to serve industrial offtake. Big, huge, bold vision. Yeah, we talked about Alorian and Glimpse and kind of the visual thinking. Anything on the compute market side? So effectively slicing out compute in order to increase the efficiency? trading futures trading futures on compute oh I haven't I haven't spent as much time there yeah I haven't spent as much time there you know we spent some time with Anj who's doing national compute and AMP grid and SF compute and all those those guys in New York we launched a compute trading track where we're like mostly it's just the increased utilization the broader goal yeah but that's how you can magically create a whole bunch more compute which is better use the ones we already have totally and so you know if the MFU whatever choice of utilization is at 30 to 40% right now, taking up to 60%,

you've doubled the number of views you have. Yeah, totally. I mean, I think there's going to be so much cool innovation. It's why, like, this is the most exciting time to be in tech, to be investing, to be around all these startups. Because, like, you're just, you're seeing innovation at the silicon layer. You're seeing innovation at the capital markets layer. You're seeing innovation at the product layer. I mean, like, it's like every, creative destruction everywhere. I notice you're not doing much on the application layer. You know, look, I would say. The agent layer. No, certainly are. Again, like, I mean, I'm speaking more personally than necessarily on the altimeter behalf. I want to believe there's a strong product research or combo. There's certainly a lot of great teams. And we think, you know, early on in market cycles, you know, we kind of use these, like, triangle analogies where you have compute or infra, like hardware infra, software infra software apps. And in a mature market, the biggest TAM is the apps.

And, you know, the smallest TAM is the hardware. Early on in cycles, it's inverted. And, you know, the biggest markets are at the top or bottom of the triangle, more of the physical hardware and then the infrastructure and then the apps. and eventually the triangles invert. And so, hey, at some point in the future, I think the apps and the agents will be the biggest part of the TAM. We're just early on. And there's lots of opportunity in the other parts. Objectively, that is correct. Yeah. I do feel like that. And by the way, you can see various versions of this in all of computer history. Yeah. Right down to like the Western micro, like when hard disks were getting built out or whatever. Anyway, so I don't think those are the best examples. Western Digital? Western Digital. Yeah, yeah, yeah. Anyway, so, yeah, I think that is true. But, like, to me, domain specificity is very interesting. And to me, the app layer, the agent layer is supposed to capture that. Yeah, totally. They're just, like, the biggest outcome here is Cursor right now.

Yeah, yeah, yeah. And that's $60 billion. We're here talking about $2 trillion. Yeah, exactly. Very, very different. Yeah, totally. I think I also just, like, skew a little bit more infra than, yeah, you know, just personal bias. Yeah, yeah, we're infra guys at heart. Yeah, exactly. Okay, great. My closing question was going to be favorite Brad story. I mean, Brad's a character. Yeah. And we've worked with him for, I don't know, three, four years now. Six years. Six years. Yeah. Okay, so it's a probably just man. Okay, all right. Right around. Yeah, yeah, yeah. I feel like I'm getting my time. Either my timeline is wrong or I met you way earlier than I thought I met you. No, I think we met earlier because I would think I was still at Red Point. Oh, my God. Okay, all right. Where were you? Netlify? No. Airbite. No, no, before that. Before that was Temporal. Before that, Netlify. That's when we met. Really? Yeah. Jesus, that's ancient history. Yeah. Anyway, Brad's a character. I just think he's interesting. Yeah. What's a good story you have? Oh, man, you put me on the spot. Honestly, I would say favorite Brad story, just how much he's worked at Invest America.

And I don't know if people know this initiative. It is the idea of how can we get people invested in capitalism early on in their life. I think right now in the world, you survey America, most people have a negative view of capitalism because they haven't participated in it. A lot of people view capitalism as a great thing, and it's the people who have participated in the equity upside of America and have kind of compounded into capitalism. But most people don't have that luxury. And so how can we educate people with firsthand experience about the benefits of capitalism by helping them participate early on? So the whole idea is an investment account funded by the U.S. government at birth that can be funded by anyone along the way. That could be philanthropists like Michael Dell, who made a $6 billion donation. I think Elon Musk has pledged one share to every kid in America, which is, I think, $10 billion. And so I think there's some version of the public benefit fund or something. Exactly. And so I think just seeing him work on that behind the scenes for years

and kind of seeing it come to fruition all very quickly, the classic overnight success. And so there's not like one specific story. It's more of just seeing the grinding that goes on. Yeah, exactly. Is it done, by the way? I actually don't know. Yeah, it's passed in the law. So it's like if I have a kid now, they get it. You open an account. Yeah, you open an account. Any new kid born gets $1,000 of birth. Parents can contribute to it. Employers can contribute to it. friends can contribute to it. It's like a baby 401k. Exactly. Exactly. And you know, you have people like Michael Dell who say, Hey, here's my donation. And I want it to be gifted to, I can't remember the exact specifics, but I think it was like, Hey, bottom 75%, uh, income by zip code. And the money that's better. Yeah. Exactly. The money just, as Brad would say, look, what's what 529 plans, which are kind of education plans. Like, Hey, I can fund money that can be used for my kids' education one day. Like, the reality is the people using 529 plans are generally, like, the wealthiest part of the country.

Like, 529 plans and the tax advantage wasn't made for the wealthy part of the country, but that's who uses it the most. What he wanted to do was, hey, let me create investment accounts for the people who actually need them. And I think it's, you know, it's an awesome initiative that's just getting started. I think you're going to see a lot more direct philanthropy. What's a big issue people have with philanthropy is, well, you're going to donate a bunch of money, and then middlemen are going to take half of it. I have seen literally directly this. Tons of loss. It's used for corruption. Exactly. But what's better? Direct contributions. Direct to bank account of people who need it most. No middleman. Direct accounts that then people can see appreciate and value over time. Can't touch it until you're 18 years old. And so you get to see the appreciation happen over time, maybe learn and participate in the upside of America along the way. And I think it's really like it's caught on and just seeing how passionate he is about it has been kind of inspiring to see. Okay.

My one pushback is like I'm, you know, homeless on the streets. Yeah. Food stamps, everything. Yeah. I have $1,000 sitting there. Yeah. Yeah. Then like, well, give it to me. Yeah. Yeah. Well, it's made more for kids now. Yeah. So hopefully you're not. Well, maybe they're playing homeless kids. Yeah, exactly. Right. So that's the thing. It's like, well, did you know you have a bank account and you're participating in the growth of capitalism? That is the biggest thing. I don't care. So that's the biggest thing now, which is people need to claim their accounts. The government opened up accounts for every kid in America, and parents need to claim them. It is sitting there. Now, the money is sitting there for newborns. If you're 15 years old, money doesn't show up in your account. I think donations will. And so you could be a 15-year-old that does have a $250 donation from Michael Dell sitting in your account. But they need to be claimed. And so, like, now I would say the focus has gone from let's get this passed into law. Let's get it up and running. Let's get the infrastructure set up so people can donate.

Now it's let's go convert those created accounts to claimed accounts. And that's just a – that's like a grassroots word of mouth game because there are people whose accounts are sitting there with money in it. And it just needs to be claimed. Yeah. By the way, most people have money sitting in the government that has not been unclaimed. You can look it up. Unclaimed property. It's like a great use case for like a muse. Have an agent. Go look up your name. Exactly. You probably have a few thousand dollars sitting somewhere that someone returned to you. You just need to know your address. Your prior addresses and then go have instinct or muse. Highest ROI on this podcast. Exactly. Not the anthropic. Exactly. Exactly. Cool. Thank you so much for spending your time with us. Yep. This is great. We'll have you back. We'll see how the IPO goes. This was fun. Thanks for doing it. Thank you.