Deep Dive: Anthropic S-1 Myths and Mythos — Jamin Ball, Altimeter

Watch the full show on YouTube: https://www.youtube.com/watch?v=P735f1xlpL0

• 0:00Speaker 00

  Because the reality is OpenAI and Anthropic, 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,

• 0:43Speaker 00

  and a $134 billion cash balance today. Right now, you want to go raise multi-billion dollar debt for GPUs? No bid, no bid. Their debt markets are closed, there are big debt deals that aren't happening.

• 1:06Speaker 01

  Okay, we're here in a studio with Jamin Ball from Ultimater, welcome.

• 1:09Speaker 00

  Thanks for having me, excited to be here.

• 1:11Speaker 01

  Anthropic is IPO-ing. I was thinking of who do I know would be the perfect guest to cover the S1? You're the guy.

• 1:17Speaker 00

  Oh, I appreciate it.

• 1:18Speaker 01

  You're the guy. We met before, I think you started your Substack, I maybe was just starting mine. How old is it?

• 1:25Speaker 00

  Six and a half years now.

• 1:27Speaker 01

  Okay, actually, no, you're older. So Cloud Judgment is one of the top. What are the Bragg statistics about Cloud Judgments?

• 1:34Speaker 00

  Ooh, Bragg statistics, probably getting close to 100,000 subscribers every week. I think in six and a half years, I've missed one Friday. Try to be pretty consistent.

• 1:46Speaker 01

  And my takeaway is here's like public markets comms for the startup investing that you do, but Ultimater does everything, right?

• 1:54Speaker 00

  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 are 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 upfront, which is more of a, I kind of treat it as a personal journal. Like what's on my mind? What have I been thinking about? What's top of mind? And that'll range from anything from some macro commentary to market commentary

• 2:38Speaker 00

  to trends in AI or whatever's trending at the moment.

• 2:42Speaker 01

  Yeah, I think every generation, there's kind of a VC blogger who is like sort of the guy to cover what's going on. It used to be Fred Wilson, if you're back in the day. He used to write AVC, I think.

• 2:54Speaker 00

  Yeah.

• 2:55Speaker 01

  And then more recently, like Tomas Tongus. I mean, he's still active, but like, I feel like you are the king of charts and public stocks right now.

• 3:03Speaker 00

  I appreciate it. I mean, Tomas was a huge inspiration. So I was at Redpoint for quite some time with Tomas and, you know.

• 3:09Speaker 01

  And now you started Theory.

• 3:11Speaker 00

  Exactly.

• 3:11Speaker 01

  And.

• 3:12Speaker 00

  Exactly.

• 3:13Speaker 01

  Is Redpoint okay? You guys are all leaving.

• 3:14Speaker 00

  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.

• 3:20Speaker 01

  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 dustups 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.

• 3:36Speaker 00

  Yeah.

• 3:37Speaker 01

  They've never seen an S1.

• 3:38Speaker 00

  Yeah.

• 3:38Speaker 01

  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.

• 3:46Speaker 00

  Yeah.

• 3:46Speaker 01

  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.

• 3:53Speaker 00

  Yes, yes.

• 3:54Speaker 01

  And like, well, now we know a lot more things than we used to.

• 3:57Speaker 00

  Yeah, totally, totally. And just to say it upfront, because I've been asked to say this. So I work for Ultimater, 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 the big labs, OpenAI and Anthropic, 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 roadshow is when they actually start and they're pitch investors,

• 4:40Speaker 00

  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.

• 4:50Speaker 01

  End of November.

• 4:51Speaker 00

  Yeah, like mid, end of November, you know, kind of 15th to 20th, 10th to 20th of November of when it actually starts trading publicly.

• 5:00Speaker 01

  Yeah, do people IPO past Thanksgiving? That's like a weird dead zone.

• 5:05Speaker 00

  You know, between Thanksgiving and Christmas is kind of a dead zone. I would say, you know, post-Labor Day to Thanksgiving is generally a kind of like a hotbed time for companies to go public. People are around, people are engaged, not on vacation yet. And it's Anthropic. The reality is, is I think people would make time.

• 5:24Speaker 01

  I'll go to work for Anthropic.

• 5:25Speaker 00

  Yeah, exactly, exactly.

• 5:27Speaker 01

  Yeah, so, okay, that's the normal mechanics, I think. And that's it, right? So off the top of your head, you know, people should understand that an S1 is just a filing that you have to do. OpenAI has done theirs as well confidentially and then they go public.

• 5:43Speaker 00

  Yep.

• 5:44Speaker 01

  People have filed S1s and not IPOed. So Rebus famously did that.

• 5:47Speaker 00

  That's right.

• 5:48Speaker 01

  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? There's not, this is part of the process. And there's a lot of indication to try to make it a successful IPO.

• 6:07Speaker 00

  Yeah.

• 6:07Speaker 01

  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.

• 6:21Speaker 00

  Yeah, exactly, exactly.

• 6:23Speaker 01

  Who is like, well, you could have got 50% more.

• 6:25Speaker 00

  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 and you're going to raise a bunch of money at a certain price.

• 6:34Speaker 01

  How much are they raising?

• 6:35Speaker 00

  I don't think it's, I don't think that's been, I don't think they've decided yet. But I think the rumors are like 75 to 100 billion.

• 6:41Speaker 01

  Fuck.

• 6:42Speaker 00

  It'll be a big raise. I mean, typically, what you used to see.

• 6:45Speaker 01

  Jesus Christ.

• 6:46Speaker 00

  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%.

• 6:55Speaker 01

  And there's an employee lockup and.

• 6:56Speaker 00

  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. You know, it's no different than a company raising a Series A, B or C. It's 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?

• 7:18Speaker 01

  Yeah, yeah.

• 7:18Speaker 00

  Right, you can take on less dilution. And that's maybe a whole separate topic. Yeah, lots of nuance to public markets and IPO allegations and all that.

• 7:27Speaker 01

  There's tradition, there's conflicting incentives, and then there's the chance that you actually, you don't pop.

• 7:31Speaker 00

  Yes.

• 7:32Speaker 01

  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.

• 7:39Speaker 00

  Yeah.

• 7:40Speaker 01

  Right? They're not, they're IPO-ing, why?

• 7:42Speaker 00

  Yeah, 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, Databricks, Stripe, OpenAI, Anthropic, kind of like tenders are pretty regular.

• 8:05Speaker 01

  Like once a year, you'll get like, well, if you want to cash out, here's the.

• 8:08Speaker 00

  Yeah, but you'll be limited in how much you can sell. You know, now, once a company's gone public, once they've gone through the lockup, you know, which is anywhere from, you know, it used to be just 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.

• 8:49Speaker 00

  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 seed, a seed of more than that. And so it's not necessarily the quantum of capital. I think whether it's US institutions getting bigger,

• 9:32Speaker 00

  maybe folks who used to act as LPs, large pension funds and endowments, acting as direct investors in a 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.

• 9:49Speaker 00

  And so like, I don't know if the argument necessarily holds, hey, you, 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.

• 9:58Speaker 01

  Because of all time.

• 9:59Speaker 00

  Yeah, exactly. I think maybe a more important angle is, is more of like the debt piece. And so, once you go-

• 10:06Speaker 01

  Talk about it.

• 10:07Speaker 00

  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, and NeoCloud might want to raise debt to build a data center for Anthropic to, to use. Debt providers are going to look at Anthropic and they're going to underwrite Anthropic's creditworthiness. 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 NeoCloud for you. But we're going to, 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 backend, they also need to see it. And

• 10:50Speaker 00

  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, 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, you know, 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-

• 11:30Speaker 01

  Too quickly?

• 11:30Speaker 00

  Yeah, arguably too. I mean, there was, there was a lot of people who said it was, hey, it was too fast, they need to season more as a public company.

• 11:36Speaker 01

  And added to the S and P 500 to something like that?

• 11:39Speaker 00

  Yeah, new rules, exactly. And so I, 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.

• 11:56Speaker 01

  Sure.

• 11:57Speaker 00

  And, and so, again, it's probably a confluence of all of 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.

• 12:12Speaker 01

  I was going to bring that up. Yeah. Up to including giving shares to everyone.

• 12:15Speaker 00

  Totally, totally. And hey, the reality is, is private markets are more of like a cabal. It's a small subset of the population, 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?

• 12:38Speaker 01

  I mean, 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.

• 12:48Speaker 00

  That's right. That's right. That's right.

• 12:49Speaker 01

  And so, like, that is like a really interesting governing mechanism of democracy versus capitalism.

• 12:55Speaker 00

  Yeah.

• 12:55Speaker 01

  Where like 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.

• 13:08Speaker 00

  Yeah.

• 13:09Speaker 01

  And so, it's nice to have 2 trillion in junior-

• 13:11Speaker 00

  Yeah, exactly. Exactly.

• 13:13Speaker 01

  In your enterprise value, right?

• 13:14Speaker 00

  Exactly.

• 13:14Speaker 01

  And that's like, well, that's, that's, that's public. And I think, part of that also, I'll throw in the white now, which is a little bit more advanced than, than people know, is interest rates are spiking for, for, for data center debts globally, right? Like, is there a source to look that kind of stuff up? Is it just Bloomberg?

• 13:35Speaker 00

  Yeah, I, I mean-

• 13:36Speaker 01

  There's just charts that people tweet around, but-

• 13:39Speaker 00

  Most of these big debt deals will price, and there'll be, you know, like an interest rate, an O I D, 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, but-

• 14:07Speaker 01

  It's part of the white now. If, 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.

• 14:17Speaker 00

  Yeah, yeah.

• 14:18Speaker 01

  And if I can keep a lid on that, that's very important to me.

• 14:21Speaker 00

  Yeah. I mean, a lot, a lot of the data center debt deals now are, are getting done over 10%. And so, it's, it's getting, it's getting expensive.

• 14:28Speaker 01

  Okay. Yeah. And so, so, and then the other, that's the other part of this discussion, which is the ROI on a data center, right? Because there, there are a lot of AI skeptics-

• 14:40Speaker 00

  Yeah.

• 14:41Speaker 01

  Who are like, this is the biggest build out in human history.

• 14:43Speaker 00

  Yeah, yeah.

• 14:44Speaker 01

  And we are throwing money after this illusory dream of AI. And then, on the other side, the AI pro people are saying, well, your payback period is one year.

• 14:52Speaker 00

  Yeah, yeah.

• 14:53Speaker 01

  Why is there such a huge difference?

• 14:55Speaker 00

  This was kind of the best thing about the Anthropic S1, which is, a lot of the metrics that were leaking were your, your ARR, your this, or your that, but you didn't really get to see a lot of below the line metrics.

• 15:04Speaker 01

  That's not ROI, yeah.

• 15:05Speaker 00

  You didn't get to see the cash. How much are they burning?

• 15:07Speaker 01

  It's not profitable.

• 15:08Speaker 00

  What's, are they profitable? Are they not? And like, that I think was one of the biggest surprises of the S1, right? Here's a business that showed 85% gross margins, over 20% free cash flow margins, 8% GAAP operating margins. And so, even on a GAAP basis-

• 15:23Speaker 01

  That's new.

• 15:24Speaker 00

  Highly profitable. And maybe my, 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-

• 15:43Speaker 01

  Cash and cash equivalents, but yes.

• 15:44Speaker 00

  Exactly. And so, you think, okay, well, you've, 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, we should get into, because like, that was a, 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 and L? There's, I'd say lots of-

• 16:17Speaker 01

  Yeah.

• 16:18Speaker 00

  Lots of things out in the world that aren't fully true.

• 16:20Speaker 01

  That is important. I do want to cover it. That's, that, that is a little bit more in the weeds than, than, 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.

• 16:41Speaker 00

  Yeah.

• 16:42Speaker 01

  By, by like order of magnitude.

• 16:43Speaker 00

  Yeah, totally.

• 16:44Speaker 01

  And, you know, so I, I went, and when I wrote up the S1, I was like, okay, well, this is the, the, the mystery.

• 16:50Speaker 00

  Yeah.

• 16:51Speaker 01

  Like, why?

• 16:51Speaker 00

  Yeah.

• 16:52Speaker 01

  What's going on? Like, how are they so much more efficient?

• 16:55Speaker 00

  Yeah.

• 16:55Speaker 01

  And I, they, in their S1, they actually attributed it to the loops.

• 16:59Speaker 00

  Yep.

• 17:00Speaker 01

  The improvement loop, the investment loop, something like that.

• 17:02Speaker 00

  Yeah, exactly.

• 17:03Speaker 01

  So people can go read that up. I already wrote it up. But like, you know, maybe they're just smarter.

• 17:08Speaker 00

  Yeah. Well, I think, I think here, like, one of the best numbers to look at is like, what is their growth relative to their, what is their, the change in growth relative to the change in serving compute spend? And they do, they break out their compute spend into more like training and internal use.

• 17:25Speaker 01

  And by the way, if you have charts to-

• 17:27Speaker 00

  Yeah, let's, we do, I do have a chart on that. So here is, here is a chart that shows the-

• 17:34Speaker 01

  Let's do fullscreen on this one.

• 17:35Speaker 00

  Yeah, yeah, yeah.

• 17:37Speaker 01

  Just, just so, yeah, it's-

• 17:38Speaker 00

  Perfect.

• 17:38Speaker 01

  It's a little small.

• 17:39Speaker 00

  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 and D kind of training, or maybe safety and alignment, or, you know, non-serving of, 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

• 18:24Speaker 00

  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, if you were monetizing, you know, a megawatt at 10, 15 million dollars, now you're monetizing it at 70 to 100 million dollars. And here's, this is a little bit of like a, 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's high, and they'll, they'll stick with us.

• 19:00Speaker 01

  Customer lifetime value.

• 19:01Speaker 00

  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, on, on customer acquisition costs. But, you know, the, the general model of a SaaS company was- Unprofitable, over time, then profitable. What's happened with Anthropic? And let me pull up another chart here. Um, I believe I have their, ah, here are their free cash flow margins.

• 19:33Speaker 01

  Oh, my God, it's so negative.

• 19:34Speaker 00

  Which, I had to cut the chart, right? Like, like negative 4,000%, negative 1,500%, you know, super, super negative. If, if I actually drew the axes to scale, it, it would, it wouldn't look real. Um, and so, yes, the, the axis is broken. Um, but this is the free cash flow margin of Anthropic, right? 21% free cash flow margin in their 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 why I went on that like compute ramble. Um, 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.

• 20:15Speaker 00

  What we've probably done is under invested 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, um, and we're going to invest in a lot of compute. We're going to build out a lot of compute.

• 20:31Speaker 01

  Yeah.

• 20:31Speaker 00

  Um, because now they've shown themselves in the world.

• 20:33Speaker 01

  The capability.

• 20:34Speaker 00

  If we need to, we can, we can flip the switch, um, and, and be profitable.

• 20:39Speaker 01

  Yeah. Um, and for listeners, a couple of things on the accounting side, just because I.

• 20:43Speaker 00

  Yeah.

• 20:43Speaker 01

  We got it, like educate people, right? Um, FCF is typically like the most honest version of what profit looks like.

• 20:50Speaker 00

  Yeah.

• 20:50Speaker 01

  Um, but, uh, in this case, you can mostly just regard it as what an operating margin would, profit margin would look like. Cause basically FCF just focuses on cash. And so there's less tricks to play with accrued receivables and, and, you know, offsetting your liabilities and what have you. Um, and so like that, that is the roughly.

• 21:08Speaker 00

  Yeah.

• 21:08Speaker 01

  Rough thing. And, but also this, the, the reason people have adjusted revenue and adjusted profit is because of nuances in the, running the business like this, right? Like GAAP, which is the official generally accepted accounting principles that you have to report, um, doesn't, doesn't regard how you want to run the business and split out investment. Uh, it doesn't have investment decisions versus like sort of profitability of, of core business decisions. But, um, it all gets smushed into this decisions, right? It's like, so basically what, 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.

• 21:47Speaker 00

  Yeah.

• 21:47Speaker 01

  And actually, you should probably split it out, uh, further, uh, between what is actually the core business? And, you know, is that actually inflecting? Or is this, is this more investment therefore FCF went down?

• 21:58Speaker 00

  Yeah. And look, I mean, at the end of the day, cash is king. Um, and like 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, um, the, the reality of GAAP accounting is there's two forms of, well, very broadly, there's two forms of, 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, uh, stock-based compensation, equity compensation, that doesn't show up in free cash flow. It's actually added back, uh, because it's a 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 like, it is a real expense, treat it as a real expense.

• 22:43Speaker 00

  I don't have a chart for this, um, but if you were to adjust free cash flow.

• 22:47Speaker 01

  It's still profitable.

• 22:47Speaker 00

  Stock-based comp, you're still at an 8%.

• 22:49Speaker 01

  Yeah.

• 22:50Speaker 00

  Free cash flow excluding stock-based comp. And so you're, you're even profitable if you include the big equity grants.

• 22:56Speaker 01

  By normal rules, yes.

• 22:56Speaker 00

  Yeah, exactly.

• 22:57Speaker 01

  And so like, if you've never seen a software IPO, this never happens.

• 23:00Speaker 00

  Yeah, it's very rare. I mean, I think there are, there are a lot of software companies that are free cash flow positive today.

• 23:05Speaker 01

  Yeah.

• 23:06Speaker 00

  But if you factor in.

• 23:08Speaker 01

  Not at IPO, never.

• 23:08Speaker 00

  Not, not at IPO.

• 23:09Speaker 01

  Never at IPO.

• 23:09Speaker 00

  Um, but, 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.

• 23:19Speaker 01

  So all those companies are dying and everyone's just buying Anthropic, right? It's the confusion.

• 23:23Speaker 00

  I think there's, yeah, well, yeah, they're not quite that, um, but, but certainly.

• 23:27Speaker 01

  Why would you, like, anything else?

• 23:28Speaker 00

  Yeah. I mean, look, here's a crazy stat. Um, in the S1, it's like a breadth versus depth one. Um, they called out 50% of the Fortune 500 is using Anthropic.

• 23:39Speaker 01

  Yeah.

• 23:39Speaker 00

  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? Uh, I think John Colison on, uh, he had Sam Altman on his podcast the other day. I think he mentioned 1% of Stripe's expenses are on tokens today, but he expects that to go to 15 to 20%.

• 24:02Speaker 01

  Jesus.

• 24:02Speaker 00

  Over the next few years.

• 24:03Speaker 01

  Jesus.

• 24:04Speaker 00

  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. Um, but it could show a same order of, or a, 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%. Um, 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. Uh, but to me at least, what it demonstrates is they really haven't despite, I mean, despite the insane ramp

• 24:46Speaker 00

  in revenue, you know, growing over 1,000% year over year in the Q2 quarter, there's probably still a lot of room left, um, for them to continue to penetrate their TAM.

• 24:56Speaker 01

  Yeah, uh, and, you know, I think the, the word is virtual collaborators or, you know, I, I say agents, but.

• 25:01Speaker 00

  Right.

• 25:01Speaker 01

  You gotta, you gotta make it a friendly term for people. Um, and, uh, you know, I really start to tell people, uh, you should just start thinking about it in terms of like what your employee spend versus your token spend is. And most people are at, let's call it 0.1 or 0.1 or like 1 to 5%.

• 25:18Speaker 00

  Yep.

• 25:18Speaker 01

  Uh, the median AI company is at 50%.

• 25:21Speaker 00

  Mm-hmm.

• 25:22Speaker 01

  And the top AI companies, uh, you know, let's say 90% of Anthropic person is like 10X, uh, token.

• 25:28Speaker 00

  Yeah.

• 25:28Speaker 01

  Token to salaries.

• 25:29Speaker 00

  Yep.

• 25:30Speaker 01

  And so, uh, that is probably the right comp of like, well, what are you spending on humans?

• 25:34Speaker 00

  Yeah, right.

• 25:35Speaker 01

  And like probably 30%, right?

• 25:37Speaker 00

  Yeah.

• 25:37Speaker 01

  Like, and well, okay, so.

• 25:38Speaker 00

  Could be more even. I mean, yeah, humans could be like 40, you know, total headcount could even be like 40, 50%.

• 25:43Speaker 01

  Depends what kind of business you're running, right?

• 25:44Speaker 00

  Yeah, exactly.

• 25:44Speaker 01

  And so like, if you're a physical business, fine, that's like, until we have good robots, then that's not, that's not getting happening. But what are you spending on knowledge work or humans? What is, and, and knowledge work of humans that includes coders, but also just, um, admin and ops and what have you. And, and legal and whatever, and all of that. And some part, like that percentage, the, the ratio of tokens spent to human spend was zero to whatever.

• 26:07Speaker 00

  Yeah.

• 26:07Speaker 01

  And now it's going to rise.

• 26:08Speaker 00

  Yeah.

• 26:08Speaker 01

  And well, how far can it go?

• 26:10Speaker 00

  Yeah, totally, totally. Can go a lot. I mean, look, I, I think there's, there's a lot of like hyperbolic statements being made, both positive and negative about OpenAI and Propic, the big labs. Um, 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, and, and then in reality, two and three kind of like relate to each other. Um, can it make enough money to cover all the costs to be at the frontier? And that's, that's the one where I think I actually have a couple, I think I have one on this. Let me, let me see which one, what did I want to bring up?

• 26:55Speaker 00

  This one, yeah, let me pull up, um.

• 27:02Speaker 00

  No, let me pull up this one, this is a good one. Um, so this is kind of the second question, like how much will it cost to be at the frontier? Or, or even maybe, let's go to like, what, 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, you know, when it comes to frontier tokens, down to open weight tokens, I think there'll be a massive market for both.

• 27:43Speaker 00

  Uh, I think the trend will continue to be on a, as a percent of tokens, open weight continues to eat 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 month 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? Um, and here's a chart, I think, oh, and there, there's, I'll, I'll have to caveat this chart.

• 28:27Speaker 00

  Um, but a lot of people look at the Anthropic S1 and say, oh, my gosh, there's $856 billion of.

• 28:34Speaker 01

  Off the books.

• 28:35Speaker 00

  Of like, not even off, but like committed deals that they've signed.

• 28:39Speaker 01

  Yes.

• 28:39Speaker 00

  How are they going to pay for it? You know, they're at whatever, whatever their ARR is, something in like, I don't know, 70, 75, 80, you know, whatever. I think the last number was 65, um, the like Q3 number was 16.8, but whatever, 70 plus or minus. Um, how are you going to pay for 855 billion?

• 28:55Speaker 01

  And like, have you read the rest of the S1?

• 28:57Speaker 00

  Well, what I, so what I tried to do, what I tried to do in this chart is I broke down the, the, because they, they broke down their commitments. Um, they didn't do it by year, but.

• 29:07Speaker 01

  Is this normal, by the way, that you would have year by year? So I feel like this is unnecessary.

• 29:11Speaker 00

  Year, year by year, this is a little bit of the jam in, um, best interpretation. They'll give a dollar.

• 29:17Speaker 01

  Oh, no, I've seen variations of this.

• 29:19Speaker 00

  Yeah, yeah, yeah. It's, 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've just made it, you know, over a five-year period.

• 29:28Speaker 01

  By the way, every banker in the world has done this already.

• 29:31Speaker 00

  Yeah.

• 29:31Speaker 01

  Like they did this on, on the night of the, the S1, right?

• 29:34Speaker 00

  So you have to ask yourselves, can you pay for it? Okay, well, at a fixed basis, and then the, the gray bars is maybe kind of like less hard commits, but commits they want to make.

• 29:44Speaker 01

  So the SpaceX one is in there, right?

• 29:47Speaker 00

  Yeah, that, exactly.

• 29:47Speaker 01

  That's the one they can walk away from in 90 days.

• 29:49Speaker 00

  Exactly.

• 29:49Speaker 01

  Yeah.

• 29:49Speaker 00

  Um, will they be able to fund 100 billion a year? And so it isn't 850 billion a year, it's can they fund? A hundred billion a year. Now, there's a couple of 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 would, they could, in theory, double their revenue. And so like, you look at the headline number of 855 billion, you're like, oh my gosh, that's a huge number, how are you gonna do it?

• 30:33Speaker 00

  Some of that is very back-weighted. But I, I, I think, when I think about the trajectory of the business, the TAM 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, 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, rev, uh, internal models, training.

• 31:06Speaker 01

  Same quarter matched, right?

• 31:07Speaker 00

  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, so 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, it's become 7X more efficient over the last year. I think you have to ask yourself, will they continue to ramp compute in 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.

• 31:46Speaker 01

  They're trying to commit to more compute.

• 31:48Speaker 00

  That's right.

• 31:49Speaker 01

  If you don't talk to any of these guys, you come away with like, oh my god, they're, you know, they, um, these guys are absolutely crazy and being fiscally irresponsible.

• 31:57Speaker 00

  Yeah.

• 31:57Speaker 01

  But I think if you have any confidence, or if you see any, um, if you see up close the, the demand for intelligence, that people are snapping up and immediately consuming whatever you give them, then like, yes, actually, you're trying to get more compute.

• 32:10Speaker 00

  Yeah.

• 32:11Speaker 01

  And that, that, the game is not to reduce the commitments, it is actually to get more commitments.

• 32:15Speaker 00

  Yeah.

• 32:15Speaker 01

  They, they would have a trillion if they could.

• 32:17Speaker 00

  Yeah, I don't think I've spoken to anyone in the AI industry, certainly not anyone at the big labs, who's, who has said, yeah, we're, you know, I feel, I feel good.

• 32:23Speaker 01

  We're good.

• 32:23Speaker 00

  About the compute we have. It's, it's almost always, and, and I think a 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.

• 32:42Speaker 01

  Yeah.

• 32:43Speaker 00

  Or just on verification and alignment that happens at inference time, that will be an increased compute demand for all of these companies.

• 32:51Speaker 01

  But we're probably not gonna break those out, you know, but like, it'll, it'll be like, maybe 5% now and 20% in the future.

• 32:56Speaker 00

  Of course, of course, of course.

• 32:57Speaker 01

  Um, yeah, I think, you know, I, I would, I would just tell people, like, if you think this is bad now, wait till you see the OpenAI S1.

• 33:02Speaker 00

  Yeah.

• 33:03Speaker 01

  Uh, 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, like.

• 33:13Speaker 00

  Yeah.

• 33:14Speaker 01

  Like, there was a cold day in hell and no happened.

• 33:16Speaker 00

  They're buying TPUs, right? They have their own chip initiative, Jalapeno, which has pretty rave reviews. Tropic staffed up a compute team to build their own chips. Obviously, the hyperscalers are building their own chips. It's a, I mean, everyone.

• 33:28Speaker 01

  Mad dash.

• 33:29Speaker 00

  It's a, it's a mad dash.

• 33:30Speaker 01

  Only, you know, only winners are TSMC, Nvidia, and ASML.

• 33:35Speaker 00

  Yeah.

• 33:35Speaker 01

  Whatever, in the supply chain stack.

• 33:37Speaker 00

  Totally. And then I think, look, it's, it's getting harder and harder, especially in the United States, you have a lot of pushback. Um, 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.

• 33:47Speaker 01

  The water.

• 33:47Speaker 00

  Don't want it. It's gonna, the water.

• 33:49Speaker 01

  Oh my god, the water.

• 33:50Speaker 00

  The water, it's, which is, it uses too much water, which it doesn't. It's the electricity, it uses a lot of electricity, it's gonna increase my electricity bill if I live in close proximity. Or it's just the simple, AI is gonna 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, 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, uh, behind the meters, harder and harder.

• 34:34Speaker 00

  And so you do have a natural governor on the build-out, and you're, we're, I think we're a lot less likely to overbuild too quickly. That leads to a supply glut, right? Kind of the, 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, 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.

• 35:15Speaker 01

  Oh yeah.

• 35:15Speaker 00

  Which, you know, I don't, I don't, personal belief, I, I think we're gonna be compute-constrained for, for far longer than, um, than the world thinks.

• 35:24Speaker 01

  Yeah. Um, I mean, I'd be curious if, what the, what the markets price the world as thinking, and then doing the work on like, what do we actually think and have, the delta is probably, you know, could be three years, could be 30 years.

• 35:37Speaker 00

  Yeah.

• 35:37Speaker 01

  And, and that's a very material thing to figure out.

• 35:39Speaker 00

  Yeah, of course, of course.

• 35:41Speaker 01

  I, I think one thing to guide people towards is, well, there's all this political stuff, and I, I honestly, I would say, um, Meta does a lot of work, and I think increasingly OpenAI does a lot of work in like, well, here's how we, we make sure we're a positive presence in the communities that we're in, right? Yes, you, you do need to take care of that. And, but I think like, uh, coming back to, again, research relevance, ROI, and, um, what it means about the industry, um, this profit move here, I, I, I do wonder sometimes, and this is a thing that has come up in our other podcasts, which is clearly 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?

• 36:26Speaker 00

  Yeah.

• 36:26Speaker 01

  Which you noted was like, something like a 7X improvement in, in our inference efficiency.

• 36:30Speaker 00

  Yeah.

• 36:31Speaker 01

  Because like, you can't, like, probably, like, there's no improvements forever until the end of time in our inference stack.

• 36:38Speaker 00

  Yeah.

• 36:39Speaker 01

  Like, unless we fundamentally change the chip, um, we probably, we probably started out pretty unoptimized.

• 36:45Speaker 00

  Mm-hmm.

• 36:45Speaker 01

  And we're now a lot more optimized than we were two years ago.

• 36:47Speaker 00

  Yeah.

• 36:47Speaker 01

  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, the, how much efficiency there's left to squeeze.

• 37:00Speaker 00

  Yeah.

• 37:00Speaker 01

  And maybe, there, it might cap out, you know, in terms of like, that, that year-on-year growth.

• 37:05Speaker 00

  Yeah. Well, look, I, I think, um, I, 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.

• 37:12Speaker 01

  Yes.

• 37:12Speaker 00

  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, 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, every time compute and reasoning models, you hit a new paradigm. But I think maybe embedded in your question is, was the gap between the Frontier and the leading openweight models, is that shrinking? Like, is, is the gap that the Frontier?

• 37:44Speaker 01

  Yeah, I don't mind the, the gap. It's not something I talk about.

• 37:47Speaker 00

  It's, but I think it's interesting, because like, what, the real way to think about or to answer that question is not the, the gap between the best leading openweight models today and the Frontier models today, because the reality is, is OpenAI and Anthropic, 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, uh, from OpenAI, actually, switching gears from Anthropic to OpenAI.

• 38:10Speaker 01

  He's been on the pod, Cooking Pod.

• 38:12Speaker 00

  Oh, nice, nice.

• 38:13Speaker 01

  Uh, we, we made some soup for researchers.

• 38:14Speaker 00

  Amazing, amazing. That's, I love that pod. But you know, when they saw Navier-Stokes, that big math, uh, problem, you know, Mark came out and said, hey, here's a model, it, 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. Uh, 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 might, 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, um...

• 39:01Speaker 00

  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.

• 39:16Speaker 01

  No, no, no, no one's saying it is plateauing today. It's, it's, we're all discussing, could it plateau in the future?

• 39:22Speaker 00

  Yeah.

• 39:22Speaker 01

  Right? Because that is a pretty important thing to figure out for your investing.

• 39:26Speaker 00

  Yeah.

• 39:27Speaker 01

  Uh, which is, uh, part of the, the deal here.

• 39:30Speaker 00

  Totally.

• 39:31Speaker 01

  Um, yeah, so, so that is a very well phrased point. I thought you were going a different area where you were going to argue about bitter lesson. And you didn't, so kudos on that. Um, and like, I, I think that's a very, very fair point that I, I think we're, uh, we've got still every generation, model generation is still shows quantum leaps. This is only in math, but it has a pretty good track record of generalizing beyond just math.

• 39:53Speaker 00

  Yeah.

• 39:54Speaker 01

  Or, or coding.

• 39:54Speaker 00

  Yeah, totally.

• 39:55Speaker 01

  And so I, I do think that is. My point was a little bit more as the financial analyst had on.

• 40:01Speaker 00

  Yeah.

• 40:01Speaker 01

  Which is you had this one time seven X improvement in revenue efficiency or whatever.

• 40:07Speaker 01

  And like, it's not like they raised prices.

• 40:09Speaker 00

  Yeah, yeah.

• 40:10Speaker 01

  So like, where is it coming from?

• 40:12Speaker 00

  Oh, here's another good one.

• 40:12Speaker 01

  I mean, they didn't raise prices.

• 40:14Speaker 00

  Here, oh, I think I have.

• 40:15Speaker 01

  So, so, so you know what I'm saying, right?

• 40:17Speaker 00

  Yeah.

• 40:17Speaker 01

  They have a hundred, they have one.

• 40:19Speaker 00

  Yeah, it's actually the opposite. This is an awesome chart.

• 40:21Speaker 01

  Yes.

• 40:21Speaker 00

  This shows the cost to complete.

• 40:24Speaker 01

  They're charging less.

• 40:25Speaker 00

  It, it, yes, exactly. The, the cost.

• 40:26Speaker 01

  They're charging less and making more money?

• 40:28Speaker 00

  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 five, one, and Opus five, five, you see about a 25% 25 to 40% reduction in cost to complete the same task with each successive model family.

• 40:48Speaker 01

  Yeah.

• 40:48Speaker 00

  And so it's kind of like, yes, the costs are going, 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 Jeff's paradox, like as things get, I know, I know, I know.

• 41:03Speaker 01

  I knew it was gonna come.

• 41:04Speaker 00

  You can't have a podcast on AI without that coming up.

• 41:07Speaker 01

  Yeah, I know. It, it is, this is the appropriate time to bring it up.

• 41:09Speaker 00

  Yeah.

• 41:10Speaker 01

  Yes.

• 41:10Speaker 00

  And as, 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, but like many, not, I mean.

• 41:29Speaker 01

  It's too cheap to meter. It's, it's, it's literally too cheap to meter.

• 41:31Speaker 00

  It's P and Q, right? Price has gone way down. Quantity has gone way up.

• 41:35Speaker 01

  Yeah.

• 41:36Speaker 00

  And I think you're going to see the same thing, uh, with people buying and serving models and tokens.

• 41:42Speaker 01

  Yeah, so, so I generally pro supporter that I, I do want to remind people that that is a true objective fact. This is not like VC bullshit. Like this is like actually how the, the businesses are running and it is surprising to us.

• 41:56Speaker 00

  Yeah.

• 41:56Speaker 01

  That, that like, well, yeah, we, we read about this in, in theory, but like to actually see this in financials is like really ridiculous.

• 42:03Speaker 00

  You'll continue to see it. Think about like Jev and decision models.

• 42:06Speaker 01

  Yeah.

• 42:06Speaker 00

  You know, and like the price point of, hey, instead of you want a, 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 is 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. 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 a hundred percent. It was just crazy. Azure's growing almost 50.

• 42:49Speaker 01

  Yeah.

• 42:50Speaker 00

  Amazon reported today, we're recording on Thursday, 39, almost 40%. Like these businesses are just accelerating and growing at.

• 42:58Speaker 01

  Beast mode.

• 42:58Speaker 00

  It's crazy.

• 42:59Speaker 01

  Beast mode. There's, there's, you should drop everything, what you're doing and just go start one of these things.

• 43:03Speaker 00

  Yeah, yeah.

• 43:03Speaker 01

  Uh, yeah, it's October 29th for those, those, uh, keeping track. And in, yes, a fun personal anecdote, I was in my hedge fund when, uh, the day they broke out AWS from Amazon.

• 43:14Speaker 00

  No kidding.

• 43:14Speaker 01

  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, it, the, the funny thing about finance is that you can do all these works and make worse decisions.

• 43:33Speaker 00

  Yeah, yeah.

• 43:34Speaker 01

  And we definitely did that.

• 43:36Speaker 00

  Totally, totally. I mean, that, that's the thing, like market timing is impossible, like.

• 43:42Speaker 01

  And also fixating on the wrong thing.

• 43:44Speaker 00

  Yeah.

• 43:44Speaker 01

  Which a lot of people are, are having to encounter for the first time.

• 43:46Speaker 00

  That's right.

• 43:47Speaker 01

  You have 2000 data points that were released in this thing. And you will fixate on one thing and it will be like, that is, that's the everything you need to know. And like, well, no, like you, you lost sight of the bigger picture.

• 43:57Speaker 00

  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 Anthropic stay at the frontier?

• 44:05Speaker 01

  Yeah.

• 44:06Speaker 00

  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.

• 44:14Speaker 01

  But, okay, but then like there is a right price for that.

• 44:17Speaker 00

  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 be, 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 Anthropic went public, um, a month ago, you know, the, the big route that you've seen in public markets recently, especially in semis and the AI trade, Anthropic 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, exactly.

• 45:02Speaker 01

  Yeah, so, you know, uh, you should be prepared for those kind of discussions if you're getting into this. I think, um, actually just as a side question, there's a lot of these like, um, non-public market trackers of the stock, like, like here's my proxy of OpenAI stock.

• 45:17Speaker 00

  Yeah, yeah, yeah.

• 45:17Speaker 01

  Ventropic stock. Do you guys put any weight into that or like zero?

• 45:20Speaker 00

  I don't.

• 45:21Speaker 01

  Yeah.

• 45:21Speaker 00

  Yeah, I don't.

• 45:22Speaker 01

  Yeah, I'm just, I, I'm wondering if I should pay attention to those things more closely because they, they do give some indication of like, well, here's the stock price reaction when a model just released.

• 45:32Speaker 00

  I mean, it might show some market sentiment.

• 45:34Speaker 01

  Yeah.

• 45:34Speaker 00

  Right? But I think Ramp had some data, I think I copied a Ramp chart, um, the other day. I mean, this was Anthropic spend.

• 45:41Speaker 01

  Yeah.

• 45:41Speaker 00

  Um, on some of their most recent models, the dark blue is Opus five, five. And like, hey, what, what the Ramp data shows is.

• 45:49Speaker 01

  People switch.

• 45:50Speaker 00

  Uh, yeah, exactly. And, and with, with Opus five, five, you saw some re-acceleration. And so would it surprise me if you saw a re-acceleration 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.

• 46:08Speaker 01

  Yeah. Well, so, okay, let me, this is, this chart is super interesting, right? Because you, you just said like, when you, when you say stay at the frontier, there's different levels of that.

• 46:16Speaker 00

  Yeah.

• 46:17Speaker 01

  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.

• 46:25Speaker 00

  Yeah.

• 46:25Speaker 01

  Usually, but sometimes you can expand and say top six.

• 46:28Speaker 00

  Yeah, yeah.

• 46:28Speaker 01

  Google now has a, has a claim.

• 46:30Speaker 00

  Yeah.

• 46:30Speaker 01

  To be a number one in some metrics.

• 46:32Speaker 00

  Totally.

• 46:32Speaker 01

  Uh, Meta doing super, super well, uh, SpaceX doing super, super well.

• 46:36Speaker 00

  Yep.

• 46:36Speaker 01

  Not at, not number one.

• 46:38Speaker 00

  Right.

• 46:38Speaker 01

  But number three, number four.

• 46:40Speaker 00

  Yeah.

• 46:40Speaker 01

  You could say that.

• 46:40Speaker 00

  Yeah.

• 46:41Speaker 01

  So look at that, like summer slump in Anthropic revenue.

• 46:46Speaker 00

  Yeah.

• 46:47Speaker 01

  Was that because OpenAI was briefly ahead?

• 46:49Speaker 00

  Yeah, I mean, I, I think Astra was a great model.

• 46:51Speaker 01

  Yeah.

• 46:51Speaker 00

  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?

• 47:01Speaker 01

  Carbon, yeah, Carbon, Argon. Yeah, yes, yes, next to today. They haven't released it yet. This is my gripe with Google, they never release anything. They just announce.

• 47:08Speaker 00

  Yes.

• 47:08Speaker 01

  A waitlist.

• 47:09Speaker 00

  Yeah.

• 47:09Speaker 01

  And then like, god damn it, they just.

• 47:11Speaker 00

  I think, I think it was also you saw a massive explosion of, you know, Kimi K3, the GLM series, deep seek models, you saw, uh, open weight models get really powerful, um, and, and get good quickly. So I think you kind of saw all of that, but it's, it's crazy. Like the far right bar, like what was non Opus five, five or Sonnet five, five? Like that has gone, that's halved or more, maybe more, you know, in, what is that? Two, three months.

• 47:41Speaker 01

  Yeah.

• 47:41Speaker 00

  And so like you do, imagine if they didn't have Opus five, five or Sonnet five, five, right?

• 47:46Speaker 01

  Yeah.

• 47:46Speaker 00

  Who knows what the revenue growth and trajectory could have been? And so like, that's kind of why I do think it's important to be, have a frontier class.

• 47:54Speaker 01

  Yeah.

• 47:54Speaker 00

  Style model.

• 47:55Speaker 01

  And so I, I will say from watching these labs for the past three, four years, they used to have more of a reserve.

• 48:03Speaker 00

  Yeah.

• 48:03Speaker 01

  Like I will have internal models that I can just release whenever I feel like it. And, um, and then I think they, towards the GPT five, six era, everyone just released whatever they had when they had it.

• 48:14Speaker 00

  Right, right.

• 48:16Speaker 01

  Now I feel like the reserves are building up again. I don't know why, but like, it definitely seems like, like as you saw with the internal model, it, it seems like they, they have more wood behind the shed for stuff like this. Like I can really, I, I'm sitting on Fable five, five, I can release it the day before I IPO.

• 48:30Speaker 00

  Yeah.

• 48:31Speaker 01

  And like, yeah, like that'll, that'll make my IPO go well.

• 48:34Speaker 00

  Yeah.

• 48:34Speaker 01

  You know?

• 48:35Speaker 00

  I mean, look, I, I, I think there's, I think there's good reasons, you know, that the models aren't released right away. You know, on one hand, there is just a lot of like safety and alignment and all that.

• 48:45Speaker 01

  But as a PM, as an, as a, as an executive, like playing this game.

• 48:49Speaker 00

  Yeah.

• 48:49Speaker 01

  I can play it pretty well if I have a backlog.

• 48:51Speaker 00

  I mean, and it's, and it's also think about, um, you know, OpenAI's Patch the Planet initiative. Which is basically, hey, let's use these super powerful models to go find vulnerabilities.

• 49:02Speaker 01

  Not even, it was five, six, it was five, six soul. They found the PHP vulnerability. It wasn't frontier.

• 49:07Speaker 00

  Yeah, which is crazy.

• 49:08Speaker 01

  It was just a spam, a bunch of like, like our last gen model.

• 49:11Speaker 00

  Exactly, exactly. And then let's, 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 themselves.

• 49:27Speaker 01

  Yeah, it's a race.

• 49:28Speaker 00

  And it's, it's a race and it used to be, uh. 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, like vulnerability announced or discovered exploit available. Like it's, it's almost negative.

• 49:45Speaker 01

  Yeah.

• 49:46Speaker 00

  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 we, we, there are more serious vulnerabilities that we know about that we're working on patching.

• 49:58Speaker 01

  Right now. Yeah.

• 49:59Speaker 00

  Which, I mean, more, more, more, more critical than, you know, something that touches 70% of the internet. Um, it is, is, it's pretty serious.

• 50:08Speaker 01

  Like the 70% of the internet is just a proxy for old.

• 50:10Speaker 00

  Yeah.

• 50:11Speaker 01

  And like, of course it's older and it's going to be more vulnerable.

• 50:13Speaker 00

  That's right.

• 50:14Speaker 01

  Yeah, of course, yeah.

• 50:15Speaker 00

  That's a good way to phrase it.

• 50:17Speaker 01

  Yeah. I think, uh, there's a couple of takes, I'm not that interested in like dwelling on, on that stuff only because there's going to be smarter people than me on that.

• 50:25Speaker 00

  Yeah, of course.

• 50:25Speaker 01

  That, there's one angle of this, which is kind of funny, which is like, well, we're spending a billion tokens, so, so 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 like net the same position as two people were at.

• 50:39Speaker 00

  Yeah, yeah, yeah, yeah. Well, I think cat's out of the bag on that. You can't put the toothpaste back in the tube.

• 50:43Speaker 01

  So that, but that is one argument people make, like they blame the AI instead of blame the, like kill the messenger.

• 50:48Speaker 00

  Right.

• 50:48Speaker 01

  The, the other part, which is interesting is like, I, I, and this might actually, uh, be an issue for basically nation state actors. Uh, who actually had all these exploits already.

• 50:59Speaker 00

  Yeah.

• 50:59Speaker 01

  And now like, well, you know, civilians are finding them, god damn it.

• 51:02Speaker 00

  Yeah, yeah.

• 51:03Speaker 01

  Now I have to work harder.

• 51:03Speaker 00

  Yeah, exactly.

• 51:04Speaker 01

  Like all my zero days are gone, like I, I had them for 10 years, like, whatever.

• 51:08Speaker 00

  Yes.

• 51:08Speaker 01

  But anyway, so, so, uh, these are fun anecdotes or consequences of this. If you want to really do super strussian and Machiavellian, if you're a nation state actor, what do you do? To, to, to preserve your edge? Because like you cannot have the channels that you're relying on lock up.

• 51:25Speaker 00

  Yeah.

• 51:25Speaker 01

  Uh, so then you, then, then you send the Saudi decoy.

• 51:29Speaker 00

  Yeah.

• 51:29Speaker 01

  To put the thing behind your TV.

• 51:31Speaker 00

  That was crazy.

• 51:31Speaker 01

  So that people, so that, so that they get distracted by that, so that your real spy.

• 51:37Speaker 00

  Yeah.

• 51:37Speaker 01

  Gets in.

• 51:38Speaker 00

  Hmm. That's some war games.

• 51:41Speaker 01

  Yeah, I, I, I watch too much TV to, to, to think that what we are told is the full story.

• 51:47Speaker 00

  Yeah, yeah, I think that's fair.

• 51:48Speaker 01

  Right?

• 51:49Speaker 00

  I think that's fair. Too much is at stake.

• 51:50Speaker 01

  Yes, too much is at stake. Uh, okay, so coming back to the actual, again, finance, like I really want to be a primer for everyone.

• 51:57Speaker 00

  Yes.

• 51:57Speaker 01

  Who are, are new to the economics of models and model labs and what, what have you. Um, one thing that have, the people have said is that the R and D per model, let's say like, what is, what does Fable five, Sauna five, Opus five cost? Let's call it a billion dollars. That lasted me six months.

• 52:16Speaker 00

  Mm hmm.

• 52:17Speaker 01

  Right? Because you just said the depreciation, it lasted that six months.

• 52:21Speaker 00

  Yeah.

• 52:21Speaker 01

  Yeah. And so isn't that worrying, right? Like, uh, about like, well, these things are the fastest depreciating assets in history. This is something that me and Sarago have, have talked about before.

• 52:30Speaker 00

  Yeah, yeah, yeah, yeah.

• 52:32Speaker 01

  Um, and I think the, the net is, there's no, but like, it's a qualified no of, well, as long as my next model run is better than the, than the, than the current state, state of the art, we're good.

• 52:44Speaker 00

  Yeah, yeah.

• 52:45Speaker 01

  Well, it's a little bit of a house of cards if, if the next one doesn't work out.

• 52:47Speaker 00

  Yeah.

• 52:48Speaker 01

  Which, um, briefly affected OpenAI. Yeah, so a lot of people know by now that, you know, like GPT four, five, the, the big, the big run wasn't, wasn't that successful.

• 52:58Speaker 00

  Yep.

• 52:59Speaker 01

  Um, and they, you know, they, they've, they've seen since fixed it, but like that slump.

• 53:03Speaker 00

  Yeah.

• 53:04Speaker 01

  In a, in a run or failed run.

• 53:06Speaker 00

  Yeah.

• 53:06Speaker 01

  Can really, really screw with the, the ROI on the thing.

• 53:08Speaker 00

  And look, I think there's a really good analogy to the chip industry. And I remember speaking to someone, you know, much older and much smarter than myself, who did a lot of semiconductor investing and, you know, in the 90s, 80s and 90s, um, about all these chip companies.

• 53:21Speaker 01

  They've seen it all.

• 53:22Speaker 00

  And, you know, they've seen it all and they were highly skeptical. Um, and, you know, sometimes you have industry vets that are highly skeptical and, you know, they miss the next big opportunity because they never think the, 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, you know.

• 53:49Speaker 01

  It's the model factory, yeah.

• 53:50Speaker 00

  The, the chip factory. And so he was like, this, 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. Um, from the chip standpoint, and that's why Nvidia's king, it's not because they have the best chip now, it's they have the best factory.

• 54:07Speaker 01

  Process for chips.

• 54:09Speaker 00

  Exact, exactly.

• 54:09Speaker 01

  It's the machine that builds the machine that.

• 54:11Speaker 00

  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, and you talk to many smart folks and what they might tell you is, hey, if you want to really compete and be have like a soda model at the frontier, you can't, you can't loot, you can't, uh, go more than one order of magnitude of compute, less than the labs. You just can't compete if you don't have, you know, one order of magnitude of compute of the big labs. Um, and so as a Neo Lab, you know, 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

• 54:55Speaker 00

  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, um, or models, or like new types of models, decision models like Jev. Um, but I think the, I think the big labs, they are on the treadmill of continuously pumping out new models, because guess what? If, if Anthropic stops, OpenAI and Gemini will continue. And then you'll, they'll just kind of sleep revenue, you'll 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 like, what have I seen from Anthropic now in the data?

• 55:36Speaker 00

  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. Um, but what you're seeing from now kind of black and white is, hey, as Anthropic has 2Xed their compute spend, uh, to build and train these models, they've 14Xed their revenue. And like, I'm not really sure what would cause that trend to stop. Um, I think models are only going to take on longer running tasks, um, more economically valuable tasks. And, you know, if you can't tell, I'm a hyper optimistic person and so maybe I'm just drinking the Kool-Aid too much.

• 56:21Speaker 00

  Uh, but I, 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, uh, much cheaper models. You know, and kind of span everything from, um, frontier to, you know, distilled versions of their own models that are smaller in, and, you know, cents per million input output tokens, not dollars.

• 56:58Speaker 01

  Yeah. Um, co-sign all of that, I think, uh, that is a, a good way to think about it. Um, I, I, I think, uh, you know, fun, I'll just comment this because it's in my head. Uh, six one Terra has been pretty, pretty successful, six one Luna, like that, that whole series is very interesting. The approaches of the different labs is also super interesting. Um, Anthropic to this day has no decision model. And they're like, well, we're good, like we'll use type safe, whatever.

• 57:22Speaker 00

  Yeah, yeah, yeah.

• 57:23Speaker 01

  And like, well, they don't have a standalone voice API.

• 57:25Speaker 00

  Right.

• 57:26Speaker 01

  They, they, they have a voice model, they just don't, don't market it.

• 57:29Speaker 00

  Right.

• 57:29Speaker 01

  They don't, they've never done images.

• 57:31Speaker 00

  Right.

• 57:31Speaker 01

  You know, it's, it's a lot of enterprise focus and coding focus.

• 57:34Speaker 00

  Yes.

• 57:34Speaker 01

  And OpenAI has been a little bit more of like, we'll do everything and we'll try Sora, we shut down Sora.

• 57:39Speaker 00

  Yes.

• 57:40Speaker 01

  Remember when Sora was like the number one app in?

• 57:41Speaker 00

  Yeah, exactly.

• 57:42Speaker 01

  In the US.

• 57:43Speaker 00

  Yeah, exactly, yeah.

• 57:44Speaker 01

  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.

• 57:53Speaker 00

  Yeah.

• 57:53Speaker 01

  Um, but like, I think of often about Anthropic in terms of like, well, okay, enterprise and coding. And they, they're first to it, they nailed it, they still continue to nail it, right? And everyone's chasing after them.

• 58:04Speaker 00

  Yep.

• 58:04Speaker 01

  Um, is that the story going forward? Is, is, uh, is it just more of the same or like, how, what should we expect?

• 58:10Speaker 00

  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 innovator's dilemma. Like, let's say you're OpenAI 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?

• 58:36Speaker 01

  To slop videos, yeah.

• 58:37Speaker 00

  Um, because you probably will have to, like, if you actually want to make.

• 58:40Speaker 01

  I mean, that's exactly why they shut down Sora.

• 58:41Speaker 00

  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, 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 OpenAI and 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 Neo Lab, great, you could go build a, build a company, build a product, build a model where you exit for $10 billion and it's a home run, grand slam. Um, 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?

• 59:25Speaker 00

  They said coding is our thing, we're going to be great at coding, we're going to build the best coding harness in Claude code, and we're going to continue to nail it. I think everyone is kind of starting to ask themselves, well, when do we, maybe you asked a version of this earlier, when do we tap out the coding TAM? Um, I think one point of view is.

• 59:41Speaker 01

  I have asked a very, very many times this question.

• 59:43Speaker 00

  But I think one point of view is, hey, coding is actually the best generalizing model and framework.

• 59:48Speaker 01

  Yes.

• 59:49Speaker 00

  And like, that can be applied, you know, you think of it as coding, but when I use Claude code, like I'm not coding, but I'm using Claude code. When you use Codex, are you, are you coding? No, these are, they're generalizing, um, 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, um, about their interests. Um, to kind of like capture that domain as, as a new field, and I do think those are just different.

• 1:00:23Speaker 01

  Um, it would seem like Anthropic is investing more than OpenAI on specifically the life sciences stuff.

• 1:00:28Speaker 00

  Yeah, I'd, I'd agree, I'd agree. Um, but now I can't remember the question you asked. Um, but I, 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 as they've been stronger in coding. Um, 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, there's a lot of different soft underbellies they may have, which is opportunity, which is great. It's opportunities. Like, you'd love to see better, cheaper models in other domains.

• 1:01:11Speaker 00

  You know, you don't need to drive the Ferrari engine to the grocery store. Hey, maybe you, a Toyota will do just fine. And I think that will continue.

• 1:01:18Speaker 01

  And good to Lorian shout out. We just interviewed Andrew.

• 1:01:20Speaker 00

  Oh, no kidding.

• 1:01:21Speaker 01

  On his benchmark and any and Glimpse.

• 1:01:23Speaker 00

  Glimpse, yeah.

• 1:01:24Speaker 01

  Are you guys invested or?

• 1:01:25Speaker 00

  We are.

• 1:01:25Speaker 01

  Oh, yeah.

• 1:01:26Speaker 00

  Shameless promotion.

• 1:01:27Speaker 01

  Wow, okay, all right.

• 1:01:27Speaker 00

  Yes.

• 1:01:28Speaker 01

  It's, it's weird to, like, this is a whole different discussion. But ultimately, I don't get as like, well, you will lead the giant rounds in like, you know, the anthropics of the world, but you also care about Lorian.

• 1:01:38Speaker 00

  Yeah.

• 1:01:39Speaker 01

  It's like way smaller and like, you know, probably worth like a minute of your time.

• 1:01:43Speaker 00

  Yeah. Well, no, like, like, we're worth a ton of my time. I mean, I would say like, we barbell. I mean, we're lifecycle investors. We invest early, we invest late, and we invest in the public markets. And I think there's a lot of value. Like, 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, it's, companies are built on, on hyper mode now, right? It's not, let me go raise a round and let me, you know, add 10 million of ARR in 18 months, and I'll go raise my next round and then two years later. It's like, you're, you're growing to 100 million super fast. You're, you're raising nine figures of, of money super quickly. And it's, and it's hard. I mean, in many ways, you like, you have to like, break the laws of physics to how like, cultures can be maintained.

• 1:02:28Speaker 01

  Lawyers, lawyers are not set up for this. They're like, what do you mean when we come here on a weekend? Because like Monday might be different.

• 1:02:33Speaker 00

  Yeah, exactly, exactly, exactly. I mean, it's, you know, you used to think about as a board member and investor, hey, let me think ahead.

• 1:02:40Speaker 01

  Like, next, next quarter.

• 1:02:41Speaker 00

  Let me make sure we hire the VP of Sales and the CFO, you know, six months before, 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. You know, and so how do you, how do you do that? Like, when do you hire the right execs so you're not doing it too late? How do you hire ahead, but not too ahead? I mean, it's, I feel like the pressure of building companies has never been greater. Now, the size of the prize has never been greater. But it is, it's just, you know, 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, what, what everyone aspires to do.

• 1:03:16Speaker 01

  Yeah. I'll also shout out the, you know, on the model factory thing. We also interviewed Poolside.

• 1:03:21Speaker 00

  Oh, nice.

• 1:03:22Speaker 01

  Which was acquired by Nvidia.

• 1:03:22Speaker 00

  Yes.

• 1:03:23Speaker 01

  They were pitching this model factory really hard. And I, and I do think like, the business that builds the business is kind of like the, the, the general takeaway that I want people to know. One of the things I wanted to, I was diving towards, I didn't, 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. It's called it market segments, product segments, the org chart, the whatever. And we got Claude Code.

• 1:03:48Speaker 00

  Yeah.

• 1:03:49Speaker 01

  Claude Co-Work.

• 1:03:49Speaker 00

  Yeah.

• 1:03:50Speaker 01

  Miscellaneous.

• 1:03:50Speaker 00

  Yeah.

• 1:03:51Speaker 01

  A platform, whatever.

• 1:03:52Speaker 00

  Yeah, yeah, yeah.

• 1:03:53Speaker 01

  And I'm like, okay, what about legal, what about design? Like, all the stuff that you've, Claude.ai, the consumer business.

• 1:03:59Speaker 00

  Yeah, yeah.

• 1:04:00Speaker 01

  For a while, that was a thing.

• 1:04:03Speaker 01

  Do we care? Do, is this all like one revenue bucket? You know?

• 1:04:07Speaker 00

  I, I think we do care.

• 1:04:09Speaker 01

  Like, the products, right? Like, this Claude.

• 1:04:12Speaker 00

  Yeah.

• 1:04:12Speaker 01

  Code. And what else?

• 1:04:14Speaker 00

  I, I, and this is where I do think, and there was, there was a section of the S1 where they broke out some of these like vertical applications they want to go after. I mean, it, it's kind of funny you bring this up. Like, I had a hypothesis, you know, years ago, that like, hey, maybe the labs will stop releasing models. And instead, they'll just build applications and products powered by their models.

• 1:04:36Speaker 01

  Yes.

• 1:04:36Speaker 00

  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.

• 1:04:42Speaker 01

  That you kill Harvey by only using Claude Fable 6 legal.

• 1:04:46Speaker 00

  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 gonna 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 gonna be really hard as an application company over the long haul to survive without a research team internally. It's one of the, like, an investment theses that I have, that if, you know, investing in applications, like, I want to believe there's two things. Really strong product org, but also really strong research org. And I also want to believe that they're aligned. And as the research gets better, the product gets better.

• 1:05:31Speaker 00

  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 like having a, a positive cycle of, of product improvements with research improvements.

• 1:05:43Speaker 01

  I'm, I'm not a, you know, I'm not in a topic where I have the benefit of being friends with, with a lot of people in topic. And I would say research is king in a topic.

• 1:05:51Speaker 00

  Yeah, totally, totally.

• 1:05:52Speaker 01

  By far.

• 1:05:53Speaker 00

  Totally.

• 1:05:53Speaker 01

  And so part of my whole thesis around model labs, this is agent labs, is model labs will, you know, respect the model and AG, and try to build AGI. And products will be a final last mile.

• 1:06:05Speaker 00

  Yeah.

• 1:06:06Speaker 01

  So the delivery mechanism, whereas the agent labs will, will respect the customer the most and build whatever they need. And then they'll, they'll backwards integrate to models as and when. But they'll never be, probably never be at a frontier for, for those things. And both can coexist.

• 1:06:18Speaker 00

  Yeah, totally.

• 1:06:18Speaker 01

  Both have a market.

• 1:06:19Speaker 00

  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, I think.

• 1:06:31Speaker 01

  I, I think the, the actions of Anthropic so, so far have shown no serious intent at it.

• 1:06:37Speaker 00

  That's true, yeah.

• 1:06:37Speaker 01

  Because Claude design is just a tab inside of.

• 1:06:39Speaker 00

  That's right.

• 1:06:40Speaker 01

  Claude. It's not like a whole Adobe, you know, replacement. Claude legal is a markdown, you know, markdown rebels. Which like, that is the AGI pill, is if, if research is king and like, you know, one model does everything, that is what you do.

• 1:06:53Speaker 00

  Yeah. It's also just classic enterprise and org management.

• 1:06:56Speaker 01

  Yeah.

• 1:06:56Speaker 00

  You have a cash king product.

• 1:06:58Speaker 01

  Yeah.

• 1:06:58Speaker 00

  That, think, 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? Like, new things, new SKUs. And, and so it's inertia, it's momentum. And this is just classic, like, big company boring stuff.

• 1:07:12Speaker 01

  But it's just like, you know, I, I think for someone, like, I've been through maybe 15 years of IPOs. And for an IPO stage company, much less a $2 trillion company.

• 1:07:24Speaker 00

  Yeah.

• 1:07:24Speaker 01

  You've never seen like, well, we have three SKUs.

• 1:07:27Speaker 00

  Yeah, yeah, exactly, exactly. But, but I think to Anthropic's credit, they've been really good at focus. And like, they haven't been, you know, enamored with the next thing or.

• 1:07:36Speaker 01

  No robots.

• 1:07:37Speaker 00

  Yeah.

• 1:07:38Speaker 01

  No images.

• 1:07:39Speaker 00

  You know, it's, and again, you look at OpenAI, 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're, 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, it's a tall task to take on one at a time, let alone all of them at a time. But hey, that's, that's the game you take on if, you know, when you're a trillion dollar company. You know, the stakes are just higher.

• 1:08:05Speaker 01

  Yeah. Okay, I want to round out the Anthropic discussion and then we can go to general commentary on, on other stuff.

• 1:08:10Speaker 00

  Yeah.

• 1:08:11Speaker 01

  We've cheated a bit with Lorian and, and TypeSafe. But have we covered the main debates that you've, you've been engaged in?

• 1:08:18Speaker 00

  There's,

• 1:08:22Speaker 00

  there's two others. So this is gonna be very like financially in the weeds. There, if you look.

• 1:08:29Speaker 01

  You're gonna talk about convertibles.

• 1:08:30Speaker 00

  Yes, exactly.

• 1:08:31Speaker 01

  Yes.

• 1:08:33Speaker 00

  Okay, so on the balance sheet, you know, earlier in the episode, we talked about, hey, 140 million of cash raised, capital raised, 134 billion of cash. That's what the, the balance sheet shows you. What the balance sheet also shows is a, I'm, I'm, I'm gonna, 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.

• 1:09:16Speaker 00

  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, 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.

• 1:09:50Speaker 01

  And, yeah, go ahead, sorry.

• 1:09:52Speaker 00

  And, 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. Those, the value, the, 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.

• 1:10:28Speaker 01

  It's consolidated financial statements.

• 1:10:30Speaker 00

  Exactly. And that company's losing a lot of money, you now have to report that loss on your bucks. And so it's, they did, 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 don't, we haven't gone above those thresholds. Maybe Anthropic didn't want to give them voting shares. I don't know, right? Let's, let's have your shares convert into non-voting shares because we don't want the governance of, of a supplier or a strategic. Like, I'm, I'm totally speculating and hypothesizing.

• 1:11:01Speaker 01

  These are all reasonable reasons to do it.

• 1:11:03Speaker 00

  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, what, and ballot, balance sheet have to balance. You have assets equal equity plus liabilities. They have, it has to balance. And so when you raise a convert, you get cash. Your assets go up. Well, if your balance sheet's gonna 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.

• 1:11:45Speaker 00

  Now, what's happened to Anthropic's valuation since 2023 and 2024?

• 1:11:49Speaker 01

  200x.

• 1:11:50Speaker 00

  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 a $108 billion debt that Anthropic raised. The reality is this, 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.

• 1:12:35Speaker 00

  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 burned?

• 1:12:55Speaker 01

  Did I just say, good job summarizing this?

• 1:12:56Speaker 00

  Yeah, but it actually goes deeper.

• 1:12:58Speaker 01

  Yes.

• 1:12:59Speaker 00

  That's just the balance sheet.

• 1:13:00Speaker 01

  Okay.

• 1:13:00Speaker 00

  Okay, now let's look at the income statement, the P and L. They have an operating profit. You could say, great, GAAP 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. Like, what a terrible business. Technically, by GAAP accounting, it is. But 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, the balance sheet.

• 1:13:42Speaker 00

  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, the value of your equity has to go down. This is where it might get a little complex. But how the, how the three statements link is-

• 1:14:03Speaker 01

  Yeah, people can take accounting 101.

• 1:14:05Speaker 00

  It's net income on the P and 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 and L flows to retained earnings on the balance sheet.

• 1:14:27Speaker 01

  It's all mathematically linked, you can't break it.

• 1:14:29Speaker 00

  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, what happens is, guess what? Anthropic's 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 and 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.

• 1:15:13Speaker 00

  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 would, it wouldn't be there. And so it's kind of one of these like, technically, from GAAP 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, is a good thing. And as those notes convert to equity, those go away

• 1:15:55Speaker 00

  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-

• 1:16:09Speaker 01

  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.

• 1:16:25Speaker 00

  Yeah.

• 1:16:26Speaker 01

  And in general, like, you know, book value accounting is not that relevant compared to overall enterprise value.

• 1:16:34Speaker 00

  Oh, and maybe one other thing related to this. 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. Well, is that, does that mean that Anthropic issued shares today? And did they give, did they give Amazon and Google $108 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's like, no, that didn't 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.

• 1:17:04Speaker 01

  So, so, you know, I, I think a lot of people, if, they're just not financially educated to that level because they've never had that exposure. So it is what it is, it's just educating. What was the second debate that you were referring to? You said you had two debates.

• 1:17:16Speaker 00

  Oh, those related, yeah. So it was the balance sheet. Have they raised more debt? And then it was the income statement. Are they actually losing money?

• 1:17:23Speaker 01

  As an investor, why do you get involved in these kinds of debates?

• 1:17:26Speaker 00

  Oh, this is more of like, I mean, I'm-

• 1:17:28Speaker 01

  You're just having fun.

• 1:17:29Speaker 00

  Yeah, I think Anthro, 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 just, I consider myself like a student of finance. And Anthropic is such an iconic company.

• 1:17:45Speaker 01

  You shouldn't have to educate people on the three statements of accounting.

• 1:17:48Speaker 00

  Yeah, yeah, yeah. But, but like, hey, I mean, I think a lot of people look at the balance sheet, the income statement, the P and L. But the reality is, 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, is a really important company to understand. I think if the Anthropic S1 came out and 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.

• 1:18:18Speaker 01

  Yeah.

• 1:18:19Speaker 00

  Because if they're not delivering profits and high ROI, what does that mean for everyone downstream of them? Like, that's, I think it's really important to understand the Anthropic P and L. But what I understood from the Anthropic P and L, let me just rip through these really quickly. Here's what you're seeing about their gross margins. Lots of leverage in the gross margins. They're improving rapidly.

• 1:18:38Speaker 01

  Which, one nuance on this is, this is the half that is not through cloud partners.

• 1:18:43Speaker 00

  Exactly, this, this, no, this is just the GAAP gross margin.

• 1:18:46Speaker 01

  Right.

• 1:18:47Speaker 00

  Is that, okay.

• 1:18:49Speaker 01

  I, I don't know. I read something about like how, obviously, cloud partner margin would not go into gross margin at all. And that's 50% of revenues.

• 1:18:57Speaker 00

  How they account, I believe, is a lot of the costs that they pay out the rev shares in sales and marketing, which is below the line, which is, which I think what you're saying.

• 1:19:04Speaker 01

  By the way, $3 billion of sales and marketing.

• 1:19:06Speaker 00

  Yeah, I think it's like 40 something percent of the revenue comes from cloud partners. It's not a trivial amount.

• 1:19:11Speaker 01

  48.

• 1:19:12Speaker 00

  48? Okay, yeah, I was close.

• 1:19:14Speaker 01

  Insane.

• 1:19:14Speaker 00

  So you see gross margin profile improving. And, and sure, maybe it'd be lower if you kind of factored in the third party, you know, the, the cloud providers, but like, the trend is going up. You see operating margin going up, positive GAAP 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 in compute and build out. I think the way compute gets billed is going to look different in the future. It's going to be expensive, there's going to be more upfront payments, right? Like, 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 in growth and investing in compute.

• 1:19:57Speaker 00

  But the biggest takeaway for me was, it's a highly profitable business that's still operating in growth mode, that's seeing some re-acceleration 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. And 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.

• 1:20:40Speaker 01

  Yeah. Very important, very good questions. I think like, yeah, anyway, do you dabble in like forward projections of numbers like what like we, what should we expect from FY27?

• 1:20:51Speaker 00

  Oh, I think it's too hard. Yeah, I think that's too hard.

• 1:20:56Speaker 01

  Because the delta is very high, right? Given the year they just had.

• 1:21:00Speaker 00

  It's a business that is so hard to project because the future is so like unknown and unknowable.

• 1:21:09Speaker 00

  You know, they'll go public, the sell-side research analysts at all the banks will build their models, will kind of have a forward, a forward number. I don't know where that's going to shake out. But I do think you'll, you know, you'll see a lot of optimism about what the future of this business could look like. But again, it's, would it surprise me if it was much higher than expected? Or some, you know, anything could happen. It's, anyone trying to predict the future, especially in the short term, it's as, 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.

• 1:21:49Speaker 01

  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. Where, like, how much juice is there left on the current thing?

• 1:22:06Speaker 00

  Yeah.

• 1:22:07Speaker 01

  Let's say the next gen has this level of inference, how much can we charge on that? And given the competitive profile and exhibited pricing history of these things?

• 1:22:16Speaker 00

  Yeah.

• 1:22:17Speaker 01

  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 what, 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 had this spreadsheet of like, here's the past performance, and you can click the right corner and just drag it out.

• 1:22:37Speaker 00

  Yeah, exactly. Can't do that. I mean,

• 1:22:39Speaker 01

  There's not good, there's not good analysis, right?

• 1:22:41Speaker 00

  Totally. I mean, it's, I think there's limitations everywhere. Everyone knows, like, hey, Nvidia can only produce so many chips, you know, Challenger, Cerebras, TPUs can only produce so many chips. But I think there's other constraints. There's only so much land power shell that's approved and permitted.

• 1:22:56Speaker 01

  Correct. So you do that buildup, right? And then you're like, okay, who has what share?

• 1:22:59Speaker 00

  We talked a little bit about like debt and credit markets. Like, right now, you want to go raise multi-billion dollar debt for GPUs? No bid. No bid. They're like, debt markets are close. There are, you know, big debt deals that aren't happening, right? Almost the equivalent of like an IPO getting pulled.

• 1:23:17Speaker 01

  So why is there a hundred billion in equity but no hundred billion in debt?

• 1:23:20Speaker 00

  Well, I think right now,

• 1:23:21Speaker 01

  I'm going to turn around and do those, put those money right in the

• 1:23:25Speaker 00

  Big 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 off-take? 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

• 1:24:08Speaker 00

  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.

• 1:24:50Speaker 00

  I'm not even kidding. And then it's delayed. Then what? Well, the debt was raised. Someone's got to pay the interest. What happens?

• 1:24:58Speaker 01

  Yeah.

• 1:24:58Speaker 00

  You know, and you have creative delayed draw facilities where you don't, you know, you, the debt is committed, but you don't draw it down. But like, all of these have limits, you know, like, and you should get super in the weeds on like SLAs and, and who was responsible for it? Was it a delay in the supply chain? Was it a delay in the construction? You know, the dataset of developer? Was it a delay by the operator? Was it, you know, something else like community unrest? And, you know, that's, that's everything that, you know, credit markets are thinking through.

• 1:25:29Speaker 01

  Yeah.

• 1:25:30Speaker 00

  And then there's a lot of other super technical reasons, you know, that we don't need to get into that, you know, make it difficult with credit default swaps and hedges going through the roof. And so now it's for debt providers, it's tough to hedge the exposure they have. And so as, as the hedges get more expensive, your willingness to take on more risk goes down because you can't hedge out of it. Like, it's,

• 1:25:47Speaker 01

  This is all the function, this is all symptoms of the debt market running out of money.

• 1:25:51Speaker 00

  Yeah, well, 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 five billion of debt at 20%.

• 1:25:59Speaker 01

  But you don't see this, you see this at the equity layer? Like, so again, how come there's a hundred billion dollars of equity?

• 1:26:06Speaker 00

  Yeah.

• 1:26:06Speaker 01

  That's going to go into Anthropic and Anthropic cannot raise a hundred billion dollars or 50 billion dollars.

• 1:26:11Speaker 00

  I think, I would, I don't know if I would say they can't do it.

• 1:26:15Speaker 01

  Yeah.

• 1:26:15Speaker 00

  I don't know if, well, one, they need to be rated. Do they want to? You know, my guess is Anthropic and OpenAI are happy that NeoClouds exist to take the balance sheet risk. Maybe they don't want to take that balance sheet risk. Because again, it could be things out of their control that blow them up. You have a huge debt stack in your cap, in your cap table, and things out of your control go wrong.

• 1:26:39Speaker 01

  Someone else's problem, yeah.

• 1:26:40Speaker 00

  Like, you don't, you're not the one holding the bag. And again, this is like the beauty and the curse of, 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, you're a balance sheet business, you know, and just like fintech balance sheet businesses can ebb and flow. You know, there's a, there's a ton of, oops, my computer may have died.

• 1:27:03Speaker 01

  It's fine, we'll edit, we'll edit.

• 1:27:05Speaker 00

  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 will, may want to develop their own data centers for their jalapeno chips, right? And Anthropic, 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

• 1:27:47Speaker 00

  no debt on, on the Anthropic balance sheet. Now, they have facilities in place that they could draw it 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? 50, 60, 70 billion dollars. That's, that's not, that's not a trivial amount just for, and that's just, just one gigawatt. I think both OpenAI and Anthropic have said they kind of want to get to five by the end of the year. Who knows if it's, if it's that or something more or less than that.

• 1:28:18Speaker 01

  Are they about equal?

• 1:28:19Speaker 00

  I don't know.

• 1:28:20Speaker 01

  Okay. That's interesting to, because I think Anthropic,

• 1:28:24Speaker 00

  OpenAI was ahead.

• 1:28:25Speaker 01

  Correct. Right, by a lot.

• 1:28:26Speaker 00

  Yeah, exactly.

• 1:28:27Speaker 01

  But I think Anthropic closed the gap a lot. And OpenAI at least started, like, Stargate was a plan, the first stage in a plan to get to 30, 30 gigawatts.

• 1:28:36Speaker 00

  Yeah, back to 2030.

• 1:28:37Speaker 01

  Yeah. So, and so I was like, okay, well, that's, I haven't heard that plan from Anthropic.

• 1:28:40Speaker 00

  But again, like, let's, let's, let's do some fun math really quick on the fly. Let's see if I can do it. 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 gigawatt, something like that? So whatever, round that down to zero for just the sake of the argument. Let's say OpenAI and Anthropic, each one go from zero to 30 over five years. So you're going to 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 in 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

• 1:29:24Speaker 00

  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,

• 1:29:50Speaker 01

  Bonkers.

• 1:29:50Speaker 00

  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, 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

• 1:30:33Speaker 00

  know, Brad was 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, we have, we have orders or indications for orders that are well over 100% of our capacity. And like, I don't have one in sight to that slowing down for the next few years.

• 1:31:10Speaker 01

  Yeah.

• 1:31:11Speaker 00

  And

• 1:31:12Speaker 01

  Yeah. And the limit, like land, show power, all these, but also silicon, like straight up.

• 1:31:17Speaker 00

  Totally.

• 1:31:18Speaker 01

  Like compute, memory, every, every part of the supply chain.

• 1:31:19Speaker 00

  Totally.

• 1:31:20Speaker 01

  That's the work. That's why semi-analysis is so powerful right now.

• 1:31:22Speaker 00

  I mean, look at these recent Kimi models, Kimi K3. You know what memory? You need gigabytes of memory just to serve that. You're not running that on your laptop. You know, you need like a rack to serve one model. You know, again, that's, that's, those are just the big bottles, 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.

• 1:31:44Speaker 01

  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.

• 1:31:49Speaker 00

  Yeah.

• 1:31:50Speaker 01

  I feel wrong.

• 1:31:51Speaker 00

  What's, I mean, what's interesting is it does feel like the open weight models are capping more around like 100 billion active parameters.

• 1:31:56Speaker 01

  That's right. Yeah. That's mostly like an infinite speed thing.

• 1:31:58Speaker 00

  Totally.

• 1:31:59Speaker 01

  But like, I think it's actually really interestingly tied to the generations of GPUs that they have.

• 1:32:03Speaker 00

  Yeah, exactly.

• 1:32:04Speaker 01

  Right? Because like you, you like wait for the very Rubens to roll out and like, whatever China has.

• 1:32:09Speaker 00

  Yeah, exactly.

• 1:32:10Speaker 01

  And, and, well, then, then they'll get it, you know?

• 1:32:12Speaker 00

  Yeah, totally.

• 1:32:13Speaker 01

  So it, they're really memory constrained at the difference here. And that actually shapes the model architecture, which is like pretty cool.

• 1:32:18Speaker 00

  Totally.

• 1:32:19Speaker 01

  So that is Anthropic.

• 1:32:21Speaker 00

  Yes.

• 1:32:22Speaker 01

  Powerhouse tour. Congrats. I don't know how you felt coming into this, but you, you did great.

• 1:32:27Speaker 00

  Oh, this is fun.

• 1:32:28Speaker 01

  And like, obviously more to do and more to analyze. But I just definitely wanted on the podcast to do this.

• 1:32:32Speaker 00

  Yeah.

• 1:32:33Speaker 01

  Because I, I know you.

• 1:32:34Speaker 00

  Yeah, of course. No, it's been fun.

• 1:32:36Speaker 01

  And like, when else?

• 1:32:37Speaker 00

  We've been meaning to do this for a while.

• 1:32:38Speaker 01

  When else were we gonna get this on the pod?

• 1:32:39Speaker 00

  Glad we could finally make it happen.

• 1:32:40Speaker 01

  Yeah, exactly, exactly. Any other commentary? When you don't spend time on, you know, the labs, what are you, what are you spending time on?

• 1:32:45Speaker 00

  Oh, man, it's, 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, 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, one of those core beliefs that I have?

• 1:33:13Speaker 01

  Yeah. And any, any other expressions of those core beliefs that you've done recently?

• 1:33:17Speaker 00

  Done recently? Yeah, certainly. There's a chip company still in stealth that's super exciting. Maybe we can talk about later. There's, you know, power company, American Terawatt, they want to build a new grid, an industrial grid to serve industrial offtake. Big, huge, bold vision. You know, we talked about Alorian and Glimpse and kind of the visual thinking.

• 1:33:39Speaker 01

  Anything on the compute market side? So effectively like slicing out compute in order to increase the efficiency?

• 1:33:45Speaker 00

  More of like the

• 1:33:47Speaker 01

  Trading futures and compute.

• 1:33:48Speaker 00

  Oh, I haven't, I haven't spent as much time there.

• 1:33:51Speaker 01

  Yeah.

• 1:33:52Speaker 00

  I haven't spent as much time there.

• 1:33:53Speaker 01

  You know, we spent some time with Ansh, who's

• 1:33:56Speaker 00

  Okay.

• 1:33:57Speaker 01

  Doing national compute and AMP grid and SF compute and all those, those guys. In, in New York, we launched a compute trading track where

• 1:34:03Speaker 00

  That's right.

• 1:34:04Speaker 01

  We're like, mostly it's just to increase utilization is the broader goal.

• 1:34:07Speaker 00

  Yeah.

• 1:34:08Speaker 01

  But that's how you can magically create a whole bunch more compute, which is better use the ones we already have.

• 1:34:13Speaker 00

  Totally.

• 1:34:14Speaker 01

  And so, you know, if the MFU, whatever, choice of utilization is at 30 to 40% right now, taking you up to 60%, you've doubled the number of GPUs you have.

• 1:34:23Speaker 00

  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.

• 1:34:43Speaker 01

  I noticed you're not doing much on the application layer.

• 1:34:45Speaker 00

  You know, look, I, I would say

• 1:34:47Speaker 01

  The agent layer.

• 1:34:48Speaker 00

  No, certainly are. Again, like I, I, I mean, I'm speaking more personally, necessarily on the, the altimeter behalf. 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, like hardware infra, software infra, software apps. And in a mature market, the biggest, the biggest TAM is the apps. And you know, the smallest TAM is the, the hardware. Early on in cycles, it's inverted. And you know, the biggest markets are at the, at

• 1:35:32Speaker 00

  the, at the top or bottom of the triangle, more of the, the physical hardware, and then the infrastructure, and then the apps. And like, 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 of

• 1:35:50Speaker 01

  Objectively, that is correct.

• 1:35:51Speaker 00

  Yeah.

• 1:35:52Speaker 01

  I, I do feel like, and by the way, you can see various versions of this in all of computer history.

• 1:35:57Speaker 00

  Yeah.

• 1:35:58Speaker 01

  Right down to like the, uh, uh, Western micro, like, like when hard disks were, were getting built out or whatever. Anyway, so I, I don't think those are the best examples. Western digital?

• 1:36:09Speaker 00

  Western digital.

• 1:36:10Speaker 01

  Yeah.

• 1:36:11Speaker 00

  Yeah.

• 1:36:12Speaker 01

  Anyway, so they'll, yeah, I, 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.

• 1:36:20Speaker 00

  Yeah.

• 1:36:21Speaker 01

  They're just like, the biggest outcome here is cursor.

• 1:36:23Speaker 00

  Yeah.

• 1:36:24Speaker 01

  Right now.

• 1:36:25Speaker 00

  Yeah, yeah, yeah.

• 1:36:26Speaker 01

  And that's 60 billion. We're here talking about 2 trillion.

• 1:36:27Speaker 00

  Yeah, exactly.

• 1:36:28Speaker 01

  It's very different.

• 1:36:29Speaker 00

  Yeah, totally. Totally. I think I also just like skew a little bit more infra than you, you know, just personal bias.

• 1:36:33Speaker 01

  Yeah, we are, we're in for guys at heart.

• 1:36:35Speaker 00

  Yeah, exactly.

• 1:36:36Speaker 01

  Okay, great. My closing question was going to be favorite Brad story. I mean, Brad's a character.

• 1:36:43Speaker 00

  Yeah.

• 1:36:44Speaker 01

  And we've worked with him for like, I don't know, three, four years now.

• 1:36:46Speaker 00

  Six years.

• 1:36:47Speaker 01

  Six years.

• 1:36:48Speaker 00

  Yeah.

• 1:36:49Speaker 01

  Okay, so, so cloud judgment ends, okay, all right.

• 1:36:50Speaker 00

  Right around, yeah, yeah.

• 1:36:51Speaker 01

  I, 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.

• 1:36:56Speaker 00

  No, I think we met earlier. Cause I, I was, I would think I was still at Redpoint.

• 1:36:59Speaker 01

  Oh, my God. Okay, all right.

• 1:37:00Speaker 00

  Yeah. You, where were you? Netlify? No.

• 1:37:02Speaker 01

  Uh, Airbyte.

• 1:37:03Speaker 00

  No, no, before that.

• 1:37:04Speaker 01

  Before that was Temporal.

• 1:37:05Speaker 00

  Before that?

• 1:37:06Speaker 01

  Netlify.

• 1:37:07Speaker 00

  That's when we met.

• 1:37:08Speaker 01

  Really?

• 1:37:09Speaker 00

  Yeah.

• 1:37:10Speaker 01

  Jesus, that's, that's ancient history.

• 1:37:11Speaker 00

  Yeah.

• 1:37:12Speaker 01

  Anyway, Brad's a character. I, I just think he's, he's interesting. And like, what's, what's, what's a good story you have?

• 1:37:18Speaker 00

  Oh, oh, man, you put me on the spot.

• 1:37:20Speaker 01

  Yeah.

• 1:37:21Speaker 00

  Honestly, I would say, favorite Brad story, just how much he's worked at Invest America. Like, and I don't know if people know this initiative. It is the idea of, like, how can we get people invested in capitalism early on in their life? I think like, right now in the world, right? You mean, you survey, you know, America, you know, most people have a negative view of capitalism because they haven't participated in it. And like, 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

• 1:38:05Speaker 00

  early on? It's the whole idea is an investment account funded by the US government at birth that can be funded by anyone along the way. That could be philanthropists like, 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.

• 1:38:23Speaker 01

  Anthropic has some version of like the public benefit fund or something.

• 1:38:26Speaker 00

  Exactly. And so I, I think just seeing him work on that behind the scenes for years, and kind of seeing it come to fruition all very quickly, you know, the classic overnight success. And so there's not like one specific story. It's more of just seeing the grinding that goes on.

• 1:38:42Speaker 01

  It's a cause to be risen.

• 1:38:43Speaker 00

  Yeah, exactly.

• 1:38:44Speaker 01

  And is it done, by the way? I actually don't know.

• 1:38:45Speaker 00

  So it's passed, yeah, it's passed into law.

• 1:38:46Speaker 01

  So it's, so what, like, if I have a kid now, they get it?

• 1:38:49Speaker 00

  You open an account. Yeah, you open an account. Any new kid born gets $1,000 at birth. Parents can contribute to it, employers can contribute to it, friends can contribute to it.

• 1:38:59Speaker 01

  It's like, it's like a baby 401k.

• 1:39:00Speaker 00

  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% income by zip code. And the money

• 1:39:14Speaker 01

  That's better. Yeah.

• 1:39:15Speaker 00

  Exactly. The money just, because as Brad would say, look, what's, 529 plans, which are kind of education plans, like, hey, I can fund money that can be used to, for my kid's education one day. Like, the reality is, 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 Osham 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.

• 1:39:58Speaker 01

  I have seen literally directly this.

• 1:39:59Speaker 00

  Tons of loss.

• 1:40:00Speaker 01

  It's used for corruption.

• 1:40:01Speaker 00

  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 till you're 18 years old. And so you get to see the appreciation happen over time, maybe learn and, and participate in, in the upside of America along the way. And I think it's, it's, it's really like, it's caught on. And just seeing how passionate he is about it has been kind of inspiring to see.

• 1:40:32Speaker 01

  Okay. My one pushback is like, I'm, you know, homeless, on the streets.

• 1:40:36Speaker 00

  Yeah.

• 1:40:37Speaker 01

  Food stamps, everything.

• 1:40:38Speaker 00

  Yeah.

• 1:40:39Speaker 01

  I have $1,000 sitting there.

• 1:40:40Speaker 00

  Yeah, yeah.

• 1:40:41Speaker 01

  You then like, well, give it to me.

• 1:40:44Speaker 00

  Yeah. Yeah. Well, it's made, it's made more for kids now.

• 1:40:49Speaker 01

  Yeah.

• 1:40:50Speaker 00

  And so hopefully you're not, well, maybe there are plenty of homeless kids.

• 1:40:51Speaker 01

  Exactly. Right? Like, so, so like, that, that's the thing. Like, it's like, well, you know, did you know you have a bank account and you're participating in the growth of capitalism?

• 1:40:59Speaker 00

  That is the biggest thing.

• 1:41:00Speaker 01

  I don't care, you know, like

• 1:41:01Speaker 00

  So that, that's the biggest thing now, which is, people need to claim their accounts. The government opened up accounts for every kid in America.

• 1:41:07Speaker 01

  Yeah.

• 1:41:08Speaker 00

  And parents need to claim them.

• 1:41:09Speaker 01

  Yeah.

• 1:41:10Speaker 00

  It is sitting there. Now, the money's sitting there for newborns. You know, 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, the, 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.

• 1:41:47Speaker 01

  Yeah, yeah.

• 1:41:48Speaker 00

  Yeah.

• 1:41:49Speaker 01

  By the way, most people have money sitting in the government that has not been unclaimed. You can look it up.

• 1:41:53Speaker 00

  Unclaimed property. It's like a great use case for like, Muse and Instinct.

• 1:41:57Speaker 01

  Have an agent, go look up your name.

• 1:41:58Speaker 00

  Exactly.

• 1:41:59Speaker 01

  You probably have a few thousand dollars sitting somewhere that someone returned to you and it's just

• 1:42:02Speaker 00

  You just need to know your address, your prior addresses, and then go have Instinct or Muse.

• 1:42:07Speaker 01

  Highest ROI on this podcast is that. Not, not the Anthropic.

• 1:42:09Speaker 00

  Exactly, exactly.

• 1:42:11Speaker 01

  Cool. Thank you so much for spending your time with us.

• 1:42:13Speaker 00

  Yeah, of course.

• 1:42:14Speaker 01

  This is great. We'll have you back. We'll see how the IPO goes.

• 1:42:17Speaker 00

  This was fun. Thanks for doing it.

• 1:42:19Speaker 01

  Thank you.