Latent Space: The AI Engineer Podcast
Deep Dive: Anthropic S-1 Myths and Mythos — Jamin Ball, Altimeter
Watch the full show on YouTube: https://www.youtube.com/watch?v=P735f1xlpL0
7 Nov 2026 · 1:42:28 · transcript: transcribed · original
462claims extracted
1speakers named
68%of speech attributed
1858transcript segments
Claims
In the order they were said
“Because the reality is, is OpenAI 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 two point three X.”
Claim: OpenAI and Anthropic have developed more powerful models that they have not yet released.
“Because the reality is, is OpenAI 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 two point three X.”
“Their revenue over the same period of time has gone up fourteen X. So embedded in that is basically a seven X efficiency gain in serving inference, which-- I mean, this is a business that I think is almost accidentally profitable, right?”
Claim: The revenue and compute-spending figures imply a sevenfold efficiency gain in serving inference.
“Their revenue over the same period of time has gone up fourteen X. So embedded in that is basically a seven X efficiency gain in serving inference, which-- I mean, this is a business that I think is almost accidentally profitable, right?”
Claim: The speaker thinks the business is almost accidentally profitable.
“Their revenue over the same period of time has gone up fourteen X. So embedded in that is basically a seven X efficiency gain in serving inference, which-- I mean, this is a business that I think is almost accidentally profitable, right?”
“Maybe my, my favorite stat of the whole S-1 was a hundred and forty-one billion dollars of capital raised to date. That's inclusive of equity, converts, and safes, and a one hundred and thirty-four billion dollar-”
“Maybe my, my favorite stat of the whole S-1 was a hundred and forty-one billion dollars of capital raised to date. That's inclusive of equity, converts, and safes, and a one hundred and thirty-four billion dollar- ... cash balance today. Like, right now, you wanna go raise multi-billion dollar debt for GPUs? No bid. No bid.”
Claim: Anthropic has a cash balance of $134 billion today.
“... cash balance today. Like, right now, you wanna go raise multi-billion dollar debt for GPUs? No bid. No bid.”
“Their, like, debt markets are closed. Their, you know, big debt deals, they aren't happening.”
Claim: The speaker says Anthropic's debt markets are closed and its large debt deals are not taking place.
“C- probably getting close to a hundred thousand subscribers-”
Claim: Clouded Judgment is probably approaching 100,000 subscribers.
“... you know, every week. I, I think in six and a half years I've, I've missed one Friday.”
“Yeah. The, the idea was, "Hey, how can I provide some commentary on metrics around software companies?" And, you know, the, the universe of companies that, that typically has public metrics are public companies.”
Claim: Companies with publicly available metrics are typically public companies.
“Yeah. The, the idea was, "Hey, how can I provide some commentary on metrics around software companies?" And, you know, the, the universe of companies that, that typically has public metrics are public companies.”
Claim: The speaker started his commentary to provide analysis of metrics for software companies.
“Um, as a, as a way to kinda g- not guide, but, uh, provide a blueprint to early stage companies and founders of like, "Hey, here are some of the metrics required to be public one day."”
“Um, and so there's lots of tables and charts and graphs on public metrics and valuation multiples. And maybe three, four years ago, I, I kinda started writing a little ramble up front, which is more of a...”
“I kinda treat it as a personal journal. Like, what's on my mind? What, what have I been thinking about? What's, what, what, what's top of mind? Um, and, you know, that'll range from anything from, you know, some macro commentary to, uh, market commentary to, you know, trends in AI or what- you know, what- whatever's trending at the moment.”
“I kinda treat it as a personal journal. Like, what's on my mind? What, what have I been thinking about? What's, what, what, what's top of mind? Um, and, you know, that'll range from anything from, you know, some macro commentary to, uh, market commentary to, you know, trends in AI or what- you know, what- whatever's trending at the moment.”
“... I was at Redpoint for, for quite some time, uh-”
Claim: The speaker worked at Redpoint for a substantial period.
“No, Redpoint's great. They're, they're, they're great. Uh, I mean, I owe everything I learned to, to those guys, and I, I have a ton of respect for them.”
“Yeah, totally. Totally. And just, just to say up, say it upfront, um, 'cause I've been asked to say this, so these are... I work for Altimeter. We have a big position in Anthropic. Everything I say is...”
Claim: The speaker works for Altimeter, which holds a large position in Anthropic.
“You know, these are all my personal views versus, versus, uh, versus my firm views. Uh, but yeah, it's a big deal. I think, you know, the big labs, OpenAI and Anthropic, seems like every quarter their numbers leak.”
Claim: The speaker says his comments represent his personal views rather than Altimeter's views.
“You know, these are all my personal views versus, versus, uh, versus my firm views. Uh, but yeah, it's a big deal. I think, you know, the big labs, OpenAI and Anthropic, seems like every quarter their numbers leak.”
Claim: The speaker says financial figures for OpenAI and Anthropic seem to leak every quarter.
“Uh, but it's a subset of the numbers. It's not, it's not everything. And now we kinda 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.”
Claim: The figures that leak about the major AI labs are only a subset of their financial numbers.
“Uh, but it's a subset of the numbers. It's not, it's not everything. And now we kinda 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, a quick process, uh, process refresher. They'll file an S-1, uh, probably wait about 15 days, there's usually a minimum, until they'll start a roadshow.”
“The roadshow's when they actually start and they pitch investors, IPO investors that, you know, 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.”
“Yeah, like mid, end of November. Um, you know, kinda 15th to 20th, 10th to 20th of November of, of when it actually starts trading publicly.”
Claim: The speaker expects Anthropic to begin public trading around November 10–20.
“Yeah, it's... You know, between Thanksgiving and Christmas is, is kind of a dead zone.”
“Um, I would say, you know, post-Labor Day to Thanksgiving is generally a, kind of like a hotbed- ... uh, time for companies to go public. People are around, people are engaged, um, not on- ... not on vacation yet.”
“And it's Anthropic. The reality is, is- ... I think people would, people would make time for-”
Claim: The speaker thinks people would make time to participate in Anthropic's IPO process.
“Uh, yeah, so okay. That, that's the normal mechanics. I, I think, um, and, and that's it, right? So, so off the top of your head, uh, you know, people should understand that an S-1 is just a, a filing that you have to do.”
Claim: An S-1 is a filing that a company is required to make as part of the IPO process.
“Um, OpenAI has done theirs as well, confidentially- ... and then, and then they go public.”
Claim: OpenAI has filed its S-1 confidentially and will later make the filing public.
“Um, people have filed S-1s and not IPO'd. Cerebras famously did that.”
Claim: Some companies file S-1s but do not complete an IPO; Cerebras is a notable example.
“Uh, and then they did it again. Um, and, uh, it, it's just part of the routine process. And you have investor day, and you have whisper- ... rumors and all these things, right?”
Claim: The IPO process routinely includes filing an S-1, an investor day, and whisper rumors.
“So- Uh, as someone who is-- unless you're literally a banker or, or fund manager, that's all you need to know basically.”
“There's not, there's no... This is part of the process, um, and there's a lot of indication to try to make it a successful IPO.”
Claim: The IPO process includes various signals and activities intended to support a successful offering.
“Successful is a mixed or loaded word this, these days because, um, typically you'll hear, hear things like, "IPO was so successful, the stock popped 50%-" "... on the first day." That's actually potentially a failure if you're, um, Bill Gurley. Who is like, "Well, you could've got 50% more."”
“Yeah, exactly. And you know, and just, just for the audience, what, what that mechanism is, is in an IPO it's like a fundraise on steroids. You're gonna go out, you're gonna raise a bunch of money at a certain price.”
“Uh, I don't think it's- I don't think that's been- I don't think they've decided yet.”
Claim: At the time of the conversation, OpenAI had not yet decided how much it would raise in its IPO.
“But I think the rumors are, like, 75 to 100 billion.”
Claim: Rumors at the time suggested that the IPO raise could be between $75 billion and $100 billion.
“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%.”
“Ex- ex- exactly. Uh, now it's, it's maybe as, as the companies get bigger, and bigger, and bigger, uh, they might float less of the company because, hey, you can take less dilution.”
“Uh, you know, it's no different than a company raising a series A, B, or C. It's you're gonna raise money at a certain dilution. Um, and yeah, the Bill Gurley thing is, well, if the stock pops 50% on day one, why wouldn't you have just raised money-”
“Uh, you know, it's no different than a company raising a series A, B, or C. It's you're gonna raise money at a certain dilution. Um, 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? Right, you can take on, take on less dilution.”
“There's tradition, there's conflicting incentives- ... and then there's the, there's the chance that you actually, you don't pop.”
Claim: An IPO may fail to produce a first-day increase in the stock price.
“Yeah. I think, I mean, I think there's a couple 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. Um, there's kind of the obvious let's provide liquidity to our employees and our investors in, like, an unrestricted way.”
“Yeah. I think, I mean, I think there's a couple 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. Um, there's kind of the obvious let's provide liquidity to our employees and our investors in, like, an unrestricted way.”
“Um, 'cause of course, you know, now big companies, Databricks, Stripe, OpenAI, Anthropic, kind of like tenders are, are pretty regular, uh, thing that they'll do.”
“But you know, you'll have a, you'll be limited in how much you can sell. Um, you know, now once a company's gone public, once they've gone through the lockup, you know, which is anywhere from, you know... So it used to be h- just 90 day, or 180 days, six months, and now it's kinda like every company has a bespoke lockup that's, like, dribbled out. Some are shorter, some are longer. Uh, but it's kinda like unrestricted access, and you can generate liquidity for your employees, um, help morale, whatever.”
“So it used to be h- just 90 day, or 180 days, six months, and now it's kinda like every company has a bespoke lockup that's, like, dribbled out. Some are shorter, some are longer. Uh, but it's kinda like unrestricted access, and you can generate liquidity for your employees, um, help morale, whatever.”
“So it used to be h- just 90 day, or 180 days, six months, and now it's kinda like every company has a bespoke lockup that's, like, dribbled out. Some are shorter, some are longer. Uh, but it's kinda like unrestricted access, and you can generate liquidity for your employees, um, help morale, whatever.”
Claim: Going public can provide employee liquidity and improve morale.
“Or, um, you need a liquid currency for acquisitions.”
Claim: A company may go public to obtain liquid shares that it can use as acquisition currency.
“And hey, a company might be more willing to take an acquisition from a public company because the currency you're getting back is, is liquid, versus in a private company, maybe it isn't.”
“You don't really know the value. Yeah, it's monopoly money.”
Claim: The value of shares in a private company may be difficult to determine.
“Um, 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.”
“Um, that effect probably means less. I would say a, a bigger reason to go public, um, because...”
“And, and, and it used to be companies went public because now you could access much larger quantums of capital. You know, you, if you wanted to raise $100 million, you know, it used to be the only way to raise $100 million is to, to go public.”
“... and raise a series A or a seed, a seed of, of more than that. Um, and so it's not necessarily the quantum of capital. I think whether it's US institutions getting bigger, um, 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.”
“H- and, and so, like, I don't know if the argument necessarily holds, hey, you, if you wanna raise a lot of money, um, go public. I think OpenAI raised a round of, like, 120, 130 billion. You know, some ridiculous amount.”
“Um, I think maybe a more important angle is, is more of, like, the debt piece. Um, and so once you go-”
“A NeoCloud might wanna raise debt to build a data center for Anthropic to, to use. Debt providers are gonna look at Anthropic, and they're gonna underwrite Anthropic's credit worthiness.”
“And how it works is they'll go, they'll say, "Hey, we're gonna syndicate however many billions of dollars of debt for you, Anthropic, or via a NeoCloud for you. But we're gonna need, we're gonna need, like, full open kimono on your financials. We're gonna need to know exactly how this business is doing, uh, so that we can underwrite you." And then everyone we syndicate the debt to on the back end, they also need to see it.”
“And I'd say historically, companies have just been hesitant to go full open kimono with their financials to the debt and credit markets because they don't want them to be fully public.”
“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.”
Claim: A company with a higher credit rating can generally raise debt at a lower interest rate.
“So going public, um, is a great way for companies to basically, you're gonna get rated. I think SpaceX very famously got rated very quickly after going public.”
“Yeah, arguably too... I mean, there, there was a lot of people who said it was, hey, it was too fast. They need to season more as a public company. Um-”
“Uh, 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 wanna tap into the credit markets.”
“And I think if you wanna tap into the credit and debt markets, you can do that much more efficiently as a public company.”
“And, and so again, it, it's probably a confluence of all those things adding 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.”
“And, and so again, it, it's probably a confluence of all those things adding 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.”
“Totally. And hey, the reality is, is private markets are more of like a cabal. It's a small subset of the population, um, either qualified purchasers or accredited investors or, or, you know, big funds and pensions and endowments get to participate in the equity appreciation of these businesses.”
“But, you know, your doctors and dentists and your everyday American doesn't, and there is just something that doesn't, doesn't feel right about that, right? Like everyone's-”
“But, you know, your doctors and dentists and your everyday American doesn't, and there is just something that doesn't, doesn't feel right about that, right? Like everyone's-”
“Um, and so like th- that is like a, a really interesting governing mechanism of democracy versus capitalism- ... where like capitalism encourages centralization- ... and democracy encourages, um, decentralization somewhat.”
“Um, anyway, so there, there is all that. I think it's very, very valid. Uh, I'll point out for people who don't know about capital structure that, uh, equity is m- is junior to debt.”
Claim: In a company’s capital structure, equity ranks below debt.
“Most of these big debt deals will price, um, and there'll be, you know, like an interest rate, an OID. You know, there'll be a bunch of things associated like with the price, and like oftentimes those will be public once they've priced.”
“Um, but yeah, rates are going up. Um, risk-free rates are going up. The Fed paused here, decided not to hike yesterday or earlier this week.”
“Um, I don't know if that's necessarily a reason to go public, um, but-”
Claim: The speaker is uncertain whether rising interest rates are a reason for a company to go public.
“If, if it does help with that, that if my debt went from like, I don't know, uh, 3% to 5%, that is billions of dollars.”
“Um, and if I can keep a lid on that, that's very important to me.”
Claim: The speaker considers preventing a large increase in debt costs very important.
“Yeah. I mean, a lo- a lot of the data center debt deals now are, are getting done over 10%. Um-”
Claim: At the time of the conversation, many data-center debt deals carried interest rates above 10%.
“... and so it's, it's getting, it's getting expensive.”
Claim: The speaker says data-center debt financing is becoming expensive.
“It's-- This, this was kinda the best thing about the Anthropic S-1, 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.”
“Um, you didn't get to see the cash. How much are they burning?”
“... what's-- Are they profitable? Are they not? And like that I think was one of the biggest surprises of the S-1, right? Here's a business that showed 85% gross margins, over 20% free cash flow margins, um, 8% GAAP operating margins.”
“... um, highly profitable. And maybe my, my favorite stat of the whole S-1 was $141 billion of capital raised to date. That's inclusive of equity, converts, and SAFEs, and a $134 billion-”
“That's inclusive of equity, converts, and SAFEs, and a $134 billion- ... cash balance today. And so-”
Claim: Anthropic's cash balance was about $134 billion at the time discussed.
“Ex- ex- 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 S-1 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.”
Claim: Anthropic's broadly reported Q3 quarterly revenue was $16.8 billion.
“Ex- ex- 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 S-1 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.”
Claim: Anthropic's implied lifetime cash burn was about $7 billion by the time of the reported Q3 figures.
“Which-- and the burn we should, we should get into 'cause like that was a, a huge debate on like, well, have-- well, it looks like they did raise debt, or are they really profitable? What is this net loss on the P&L? There's, I'd say, lots of- ... lots of things out in the world that aren't fully true.”
Claim: Many public claims about Anthropic's cash burn and profitability are not fully accurate.
“But, like, that overall efficiency is by far, uh, if you've been following the relative fates of, let's call it SpaceX, uh, Anthropic, OpenAI- ... uh, the, the ones that are going public, um, they are the most efficient lab in the world- ... by, by like order of magnitude.”
Claim: Anthropic is roughly an order of magnitude more efficient than the other labs being discussed.
“And, uh, I ac- they, they, in their S-1, they attributed it to the loops. Um, the improvement loop, the investment loop, something like that.”
Claim: Anthropic's S-1 attributed its efficiency to its improvement or investment loops.
“Um, so people can go read that up. I, I already wrote it up. Um, but like, uh, you know, maybe they're just smarter.”
Claim: Anthropic may be more capable or smarter than its competitors.
“Yeah. Well, I th- I think here, like one, one of the best numbers to look at is, like what is their growth relative to their-- what is the, the, 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, um, internal use-”
Claim: Anthropic's S-1 separates compute spending into categories including training and internal use.
“Yeah, exactly. All right, here's a chart, and this is broken down in their S-1, that shows the percent of their compute that goes towards basically serving models, so inference revenue generating, or internal R&D kind of training or maybe safety and alignment or, you know, non-serving of, uh, of, of tokens.”
“And what's interesting is you can see now that, I mean, they're at 27%. Only 27% of their compute today is going towards serving models.”
Claim: At the time discussed, 27% of Anthropic's compute was used for model serving.
“Um, 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 fourteen X.”
“So embedded in that is basically a seven X efficiency gain in serving inference, which... I mean, this is a business that I think is almost accidentally profitable, right?”
“Where, I mean, a seven X increase in efficiency of serving tokens, if you were, if you were monetizing, you know, a megawatt at $10, $15 million, now you're monetizing it at $70 to $100 million.”
“And here's... This is a little bit of like a, a, a random rant. Um, 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, they'll, they'll stick with us."”
“Cu- Ex- exactly. "And we're gonna be burning a lot of money early on, um, 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, um, 'cause we're not spending a lot of money on, on, um, 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.”
“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.”
Claim: Anthropic's free-cash-flow margin was 21% in its most recent quarter, according to the speaker.
“And so what you're seeing is a business that all of a sudden, because they were able to drive such efficiency in their compute, that's kind of when, 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. What we've probably done is underinvested in compute, in compute build-out, and we need to be more aggressive on it."”
“And so my guess is that you're actually gonna see this level off and maybe go negative again as they say, "Hey, we're gonna reinvest, um, and we're gonna invest in a lot of compute. We're gonna build out a lot of compute."”
“Um, because now they've shown themselves and the world- ..."Hey, if we need to, we can, we can flip the switch, um, and, and be profitable."”
Claim: Anthropic has demonstrated that it can become profitable if necessary.
“... we gotta like educate people, right? Um, FCF is typically, like, the most honest version of what profit looks like.”
Claim: Free cash flow is typically the most honest representation of a company's profit.
“Um, but, uh, in this case, you can mostly just regard it as what an operating margin would, profit margin would look, look like. 'Cause basically FCF just focuses on cash- ... and so there's less tricks to play with accrued receivables and lia- and, you know, offsetting of liabilities and what have you. Um, and so, like, that, that is the roughly-”
“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.”
Claim: A change in a company's margin profile reflects a conscious business choice.
“... you should probably split it out, uh, further, uh, between what is actually the core business and, you know, is that actually infecting- ... or is this, is this more investment, therefore FCF went down?”
“Yeah. And look, I mean, at, 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...”
“... and then 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. I don't have a chart for this, um, but if you were to adjust free cash flow-”
Claim: Stock-based compensation is a real economic expense and should be treated as such.
“... for stock-based comp- ... you're still at an 8%- ... free cash flow excluding stock-based comp. And so you're, you're even profitable if you include the big equity grants.”
“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.”
Claim: Software companies are very rarely free-cash-flow positive at the time of their IPO.
“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.”
“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.”
Claim: Anthropic's S-1 reports that half of Fortune 500 companies use Anthropic.
“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 Collison on, uh, he had Sam Altman on his podcast the other day.”
Claim: Anthropic spending currently represents 0.1% of Fortune 500 companies' expenses.
“I think he mentioned 1% of Stripe's expenses are on tokens today, but he expects that to go to 15% to 20%- ... over the next few years.”
“And so obviously Stripe is a very forward-leaning tech company. Your average business in the Fortune 500 isn't gonna show that level. Um, but it could s- show a same order of, or a, a, a same magnitude of growth.”
“So if Stripe is saying we're gonna 15 to 20x- The spend on tokens relative to other expenses in the org, maybe you see something similar in the Fortune One- 500 go from 0.1% to 1%.”
“Uh, but to me at least, what it demonstrates is they really haven't despite... I mean, despite the insane ramp in revenue, you know, growing over 1000% year over year in the Q2 quarter, there's probably still a, a lot of room left, um, for them to kind of continue to penetrate their TAM.”
“... you know, 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.”
Claim: Businesses should assess AI token spending in relation to their spending on employees.
“And most people are at, let's call it 0.1 or- ... or 0 point... or like 1 to 5%.”
Claim: For most businesses, token spending is currently roughly 0.1% to 1–5% of employee spending.
“Uh, the median AI company is at 50%.”
Claim: The median AI company has token spending equal to about 50% of its salary spending.
“And the top AI companies, uh, you know, let's say 90th percentile, anthropic person is like 10X, uh, token- ... token to salaries.”
“... uh, that is probably the right comp of like- ... well, what are you spending on humans?”
Claim: The appropriate benchmark for token spending is likely a company's spending on human workers.
“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 gonna happen. But what are you spending on knowledge worker humans?”
“What is... And, and knowledge worker humans, that includes coders, but also just, um, admin and ops and what have you- ... and, and, and legal and whatever you want-”
Claim: Knowledge workers include coders, administrative and operations staff, and legal workers.
“... and all that. And some part, like that percentage, the, the to- the ratio of tokens spent to human spend was zero to whatever. And now it's gonna rise.”
“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.”
Claim: The speaker expects a massive market for both frontier-model tokens and open-weight-model tokens.
“Uh, I think the trend will continue to be on a, as a percent of tokens, open weight kinda continues to eat share, but as a percent of wallet share in dollars, frontier continues to take the lion 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 kinda this stat on 80%, which is much bigger than I would've expected just in models six month old or less, kind of demonstrates the value of being on the frontier.”
“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 100 billion a year?”
“Um, or you go back to the, well, only 25% of their compute today is allocated towards, um, 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 eight hundred and fifty-five billion, you're like, "Oh my gosh, that's a huge number.”
“How are you gonna do it?" Some of that is very back weighted. Um, but I, I, I think when I think about the trajectory of the business, um, 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, um, the expense."”
“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 fifty percent.”
“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 seven X more efficient over the last year.”
“I think you have to ask yourself, will they continue to ramp compute and capacity? I think the answer is yes. I think if you ask anyone at Anthropic, again, this is all speculation, they'd probably say, "We wish we started doing it sooner.”
Claim: The speaker expects Anthropic to continue increasing its compute and capacity.
“Uh, but I think if you have any confidence or if you see any, um... If you see up close the, 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. And-”
“...that, that, the game is not to reduce the commitments, it is actually to get more commitments.”
Claim: The speaker says AI companies’ objective is to secure more compute commitments, not reduce them.
“...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 and alignment and verification.”
“Like I think more and more compute is going to go towards those functions, whether that's on research- ...or just on verification and alignment that happens at inference time.”
“That will be an increased compute demand for all these companies.”
“...it'll be like maybe five percent now and- ...twenty percent in the future.”
“Uh, and then like it's not bad. It's like that's the whole point is like they're actually really trying to get compute from every source they can get.”
Claim: The speaker says OpenAI is trying to secure compute from every available source.
“And like, uh, OpenAI's buying TPUs like -”
Claim: The speaker says OpenAI is buying TPUs.
“Tropic staffed up a compute team to build their own chips. Obviously, the hyperscalers are building their own chips. It's, uh, I mean every-”
Claim: The speaker says Tropic expanded its compute team to develop its own chips.
“Tropic staffed up a compute team to build their own chips. Obviously, the hyperscalers are building their own chips. It's, uh, I mean every-”
Claim: The speaker says hyperscalers are developing their own chips.
“...only, you know, only winners are TSMC, NVID- uh, NVIDIA, and ASML- ...uh, whatever in the supply chain stack.”
“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 'em in my neighborhood.”
Claim: The speaker says opposition to data centers is growing, particularly in the United States.
“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 'em in my neighborhood. Uh, they add a lot of air pollution. ...the water. It's-- which is it uses too much water, which it doesn't. Um, 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.”
“...the water. It's-- which is it uses too much water, which it doesn't. Um, 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.”
Claim: The speaker says data centers do not use too much water.
“You do see a lot more pushback. Um, midterms are coming up, right? Who, who kind of like takes control in like the House and the Senate? Like w- 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 meter is harder and harder. 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.”
“And the good thing is that it's getting harder and harder to build these data centers. Uh, behind the meter is harder and harder. 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. Um, and so it's, it's probably a good thing that there's some rate limiter.”
Claim: The speaker thinks some constraint on the pace of data-center construction is probably beneficial.
“There was a lot of dark fiber, right? Fiber that had no bits and bytes traveling over it. We have no dark GPUs today. Um, 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. Um, especially as these models get more and more powerful, you know, the security concerns go through the roof.”
“It will stop us from entering a huge bubble, but it's also bad because we need... You know, the industry needs more compute. Um, especially as these models get more and more powerful, you know, the security concerns go through the roof.”
Claim: The speaker says security concerns increase substantially as AI models become more powerful.
“You also need a lot more-- like the defenders need more compute. The defenders need more capabilities.”
Claim: The speaker says defenders need increased compute and capabilities.
“Which, you know, I don't, I don't know. Personal belief, I, I think we're gonna be compute constrained for, for far longer than, um, than the world thinks.”
“And, and that's a very material thing to figure out.”
“Um, I, I think one thing to guide people towards is... Well, there's all this political stuff, and 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, um, presence in the communities that we're in, right?”
“Yes, you, you do need to take care of that. Um, and, uh... But I think like, uh, coming back to, again, research relevance, ROI, and, um, what it means about the industry, um, this profit, um, move here, I, I, I do wonder sometimes, and this is a thing that has come up in our other podcasts, specifically tying in the research side-”
“...is how much left is there to improve, right?”
Claim: The speaker questions how much further AI systems can improve.
“...w-which you noted was like something like a seven X improvement in, in our inference efficiency.”
“Because like you can't like- Probably, like, there's no improvements forever until the end of time- ... in our inference stack. Like, unless we fundamentally change the chip, um, we probably-- Like, we probably started out pretty unoptimized.”
“Like, unless we fundamentally change the chip, um, we probably-- Like, we probably started out pretty unoptimized.”
Claim: The speaker thinks the inference stack probably began in a relatively unoptimized state.
“And we are now a lot more optimized than we were two years ago.”
Claim: The speaker says the inference stack is substantially more optimized now than it was two years ago.
“Okay. Well, there's a limit to how optimized you can get. Just, like, physical, like, joules 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 is left-”
“And maybe there-- it might cap out- ... you know, in terms of, like, that, that year-on-year growth.”
“... because, like, what-- the real way to think about or to answer that question is not the, the gap between the best leading open weight 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.”
Claim: OpenAI and Anthropic have developed more powerful models that they have not yet released publicly.
“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, like, could scare you a little bit, right?”
“Like, hey, these labs have even more powerful models. So if you think about what's the gap between open weight 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, some combination of everything, you know, unclear.”
“But I do think you're still seeing the frontier accelerate, um, even if it may appear that, um, what's released is plateauing.”
“... 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, um, and Opus five five, you see about a twenty-five percent, twenty-five to forty percent reduction in cost to complete the same task with each successive model family.”
“And so it's kind of like, yes, the costs are going, 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 gonna sound a little like VC cliché.”
“And it's just 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 by, like, many-- Not, I mean, many orders of magnitude. Price has gone way down- ... quantity has gone way up.”
“And I think you're gonna see the same thing, uh, with people buying and serving models and tokens.”
“And you'll continue to see it. Think about, like, Jevon decision models.”
Claim: The speaker expects the described cost-and-usage pattern to continue.
“You know, and, like, the price point of, hey, instead of you want a, a binary classifier, uh, you don't need a language model to do that. Just go use a decision model.”
Claim: For a binary classification task, a decision model can be used instead of a language model.
“Super cheap, super efficient. I think you're gonna continue to see efficiencies like that. You know, I go back to S3 and EC2, AWS's big initial products, storage and compute.”
Claim: The speaker expects cloud services such as storage and compute to continue becoming more efficient.
“You've seen, you know, ninety-nine percent 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?”
Claim: AWS's S3 and EC2 services have each had prices fall by 99% since launch.
“You've seen, you know, ninety-nine percent price reductions in both of those services from the day they were launched. But, like, what's happened to the EC2 and S3 business or just the broader storage and compute, you know, more CPU compute? Uh, it's, it's gone up exponentially. I mean, all the hyperscalers reported today, they're all over a hundred-billion-dollar run rate businesses today if they were individual companies. Google Cloud's grown over a hundred percent- It was just crazy.”
Claim: Despite major price reductions, the broader storage and compute businesses have grown exponentially.
“Uh, it's, it's gone up exponentially. I mean, all the hyperscalers reported today, they're all over a hundred-billion-dollar run rate businesses today if they were individual companies. Google Cloud's grown over a hundred percent- It was just crazy.”
“Uh, it's, it's gone up exponentially. I mean, all the hyperscalers reported today, they're all over a hundred-billion-dollar run rate businesses today if they were individual companies. Google Cloud's grown over a hundred percent- It was just crazy.”
Claim: Google Cloud had grown by more than 100% at the time being discussed.
“Azure's growing almost 50.”
Claim: Azure was growing at almost 50% at the time being discussed.
“It was unreported today, we're recording on Thursday, uh, 39, almost 40%. Like, these businesses are just accelerating and growing at- ... it's, it's crazy.”
Claim: The speaker characterized the cloud businesses as accelerating and growing rapidly.
“It was unreported today, we're recording on Thursday, uh, 39, almost 40%. Like, these businesses are just accelerating and growing at-”
Claim: The speaker said Azure's then-unreported growth was 39%, nearly 40%.
“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, 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.”
“We definitely did that.”
Claim: The speaker's team had made worse decisions after doing extensive analysis.
“Total- totally. Totally. I mean, that, that's the thing, like market timing's impossible. Like-”
Claim: The speaker believes market timing is impossible.
“You have 2,000 data points- ... that were released- ... in this thing. And you'll fixate on one thing, and it'll be like that is- ... that everything that you need to know, and like, well, no. Like as you, you lost sight of the bigger picture.”
Claim: Investors can fixate on one data point among many and lose sight of the bigger picture.
“But I believe there's one question that matters, and that's can Anthropic stay at the frontier?”
Claim: The speaker believes the central question about Anthropic is whether it can remain at the frontier.
“And to me, if I believe the answer to that question is yes, and I think everything they've done historically has demonstrated that, I think they'll build a great business and will continue to.”
“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 gonna be yet.”
“Um, 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.”
“I don't. Yeah, I don't.”
“No, I mean, it might, it might show some market sentiment.”
Claim: The speaker thinks non-public-market stock trackers might provide an indication of market sentiment.
“... um, on some of their most recent models. The dark blue is Opus 5.5. And like, hey, what, what the Ramp data shows is- And w- and with, with Opus 5.5, you saw some re-acceleration. And so would it surprise me if you saw a re-acceleration of revenue and run rate in October?”
Claim: The speaker said Ramp data showed some re-acceleration associated with Opus 5.5.
“And w- and with, with Opus 5.5, 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. 'Cause I do think a lot of the third-party data is showing and suggesting that.”
“Stay at the frontier, meaning we're at one of the top two, one of the top six- Um, usually people mean top two. Usually, but sometimes you can expand and say top six.”
“Google now has a, has a claim- ... to be a number one- ... in some metrics.”
Claim: The speaker said Google could claim to rank number one on some metrics.
“Uh, Meta doing super, super well. Uh, SpaceX doing super, super well. Not at, not number one- ... but number three, number four.”
“Yeah, I mean, I, I think Astra was a great model. And I think a lot of people started using Astra.”
Claim: The speaker considered Astra a great model and believed many people began using it.
“Yeah. Yes, yes, next yesterday. Uh, they haven't released it yet. This is my gripe with Google. They never release anything. They just announce- ... a wait list.”
“I think, I think, I think it was also you saw a massive explosion of, you know, Kimi K3, the GLM series, DeepSeek models. You saw, uh, open weight models get really powerful, um, and y- and get good quickly.”
“So I think you kind of saw all that, but like it's, it's crazy, like the far right bar, like what was non-Opus 5.5 or Sonnet 5.5, like that has gone-- that's halved- ... or more. Maybe more. You know, in, what is that? Two, three months.”
“And so like you do-- Im- imagine if they didn't have Opus 5.5 or Sonnet 5.5, right? Like who knows what the revenue growth and trajectory could have been.”
“And so like that's kinda why I do think it's important to be, have a frontier class- ... style model.”
Claim: The speaker believes it is important for a company to have a frontier-class model.
“And so I, I will say from watching these labs for the past three, four years, they used to have more of a reserve. 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-5, 6 era, everyone just released whatever they had when they had it.”
“Now I feel like the reserves are building up again. I don't know why, but like I... it definitely seems like, like as you saw with the internal model, uh, it, it seems like they, they have more wood behind the shed- ... for stuff like this. Like I can re- I, I'm w-- sitting on Fable 5.5-”
“... for stuff like this. Like I can re- I, I'm w-- sitting on Fable 5.5- ... I can release it the day before our IPO. ... like that'll, that'll make my IPO go well.”
“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. You know-”
“Totally. I mean, but 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."”
“It was 5/6. It was 5/6 Sol. They found the PHP vulnerability. It wasn't Frontier.”
Claim: The speaker says GPT-5/6 Sol found the PHP vulnerability and that it was not a frontier model.
“It was just, it was just let's spam a bunch of like, like a last gen model.”
“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 while 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.”
“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 avail- like it's, it's almost negative.”
“Um, 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- ... right now."”
“Which I mean, more, more, more, more critical than, you know, something that touches 70% of the internet, um- ... is, is, is pretty serious.”
“Like the 70% of the internet is just a proxy for old. And like of course it's older- ... and it's gonna be more vulnerable. Yeah.”
“Um, 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 gonna be smarter people than me on that.”
“The-- 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 gonna spend a billion more- ... to fix the vulnerabilities. What if we just didn't do either? And it-- which would like net the same position- ... as to where we're at.”
“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. And now like, well, you know, civilians are finding them. Goddammit. Like-”
“... like now I have to work harder. Like all my zero days are gone. Like I, I had them for 10 years. Like, ugh-”
“This is-- yeah, I, I watch too much m- tv to, to, to think that what we are told is the full story.”
“I think that's fair. Too much is at stake.”
Claim: The speaker believes the stakes are high.
“That lasted me six months. Right? Because you just said the dep- the depreciation, it lasts, lasts six months.”
“Um, and I think that the net is, is no- ... but like it's a qualified no- ... of, well, as long as my next model run is better than the f- than the, than the current st- state-of-the-art, we're good.”
“But it's a little bit of a house of cards if, if the next one doesn't work out.”
Claim: The speaker says the model-development strategy is somewhat fragile if the next model run fails.
“Um, and they, they, you know, they, they've, they've since, since fixed it, but like that, a slump- ... in a, in a run- ... or a failed run- ... uh, can really, really screw with the, the ROI on the thing.”
“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 'cause 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- ... the Varro gens.”
“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 gonna 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.”
“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 wanna really compete and be- have like a soda model at the frontier, you can't, you can't lo- 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- 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?”
Claim: The speaker says major AI labs must continually release new models to remain competitive.
“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 just kinda see 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? 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 n- now kind of black and white is, hey, as Anthropic has 2x'd their compute spend, uh, to build and train these models, they've 14x'd their revenue.”
“And like I'm not really sure what would cause that trend to stop. Um, I think models are only gonna take on longer-running tasks, um, more economically valuable tasks.”
Claim: The speaker expects AI models to take on tasks that run longer and have greater economic value.
“And like I'm not really sure what would cause that trend to stop. Um, I think models are only gonna 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 may- maybe I'm just drinking the Kool-Aid too much. 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 and, and, you know, cents per million input-output tokens, not dollars.”
“Um, Anthropic to this day has no decision model.”
Claim: The speaker states that Anthropic does not have a decision model as of the conversation.
“Um, and like well, they don't have a standalone voice API. They, they, they have a voice model. They just don't, don't pub- market it.”
“They don't-- They've never done images.”
Claim: The speaker says Anthropic has never developed image capabilities or image products.
“You know, it's like the-- There's a lot of enterprise focus- ... coding focus.”
“And OpenAI has been a little bit more of like, "We'll do everything- ... and we'll try Sora. We shut down Sora."”
“Um, and then Gemini has been the most like- ... we will explore everything. Um, and so like we appreciate them because if they didn't try it, no one else would. Like they have the funds for it.”
“Um, but like I think, uh, often about Anthropic in terms of like, well, okay, enterprise and coding, and they, they're first to it. They nailed it. They st- continue to nail it, right? And everyone's chasing after them.”
“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 gonna allocate a lot of your Codex compute to Sora?”
“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? They said, "Coding is our thing. We're gonna be great at coding. We're gonna build the best coding harness in Claude Code, and we're gonna continue to nail it." I think everyone is kind of starting to ask themselves, "Well, when do we..."”
“... but I think one point of view is, hey, coding is actually the best generalizing- ... model and framework.”
“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.”
Claim: Jamin Ball says users can use Claude Code for tasks without themselves coding.
“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.”
“Uh, totally I'd agree.”
Claim: Jamin Ball agrees that Anthropic appears to be investing more than OpenAI in life sciences.
“Yeah. I'd, I'd agree. I'd agree. Um, but, uh, 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 wanna 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 Ellorian. Maybe it's decision models like Jev and TypeSafe, right? Like there's, there's a lot of different, uh- Soft underbellies they may have, which is opport- which is great.”
“You know, you wanna 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.”
Claim: Jamin Ball says models have generalized best as they have become stronger at coding.
“... you'd love to see better, cheaper models in other domains. You know, you don't need to drive the Ferrari engine to the grocery store. Hey, maybe you-- a Toyota will do just fine.”
“And I think that will continue.”
Claim: Jamin Ball expects the development of better, cheaper models for other domains to continue.
“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 ten million of ARR in 18 months, and I'll go raise my next round- ... in a few years later." It's like you're, you're growing to a hundred 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-”
“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.”
Claim: The pressure of building companies is greater than it has ever been.
“Now, the size of the prize has never been greater. Um, but it is. It's this-- 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.”
“I mean, it, 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.”
“And if they're powered by the best models that are never released publicly, hey, maybe th- they have the right to build all the best applications.”
“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 wanna 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. Um, it's one of the, like an investment theses that I have that if, you know, investing in applications, like, I wanna believe there's two things: really strong product org, but also a really strong research org.”
“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. Um, it's one of the, like an investment theses that I have that if, you know, investing in applications, like, I wanna believe there's two things: really strong product org, but also a really strong research org.”
“And I also wanna believe that they're aligned, and as the research gets better, the product gets better, which sounds counterintuitive, but there are a lot of research orgs that are just doing research for the sake of research versus- ... closing the loop and actually like having a, a, a positive cycle of, of product improvements with research improvements.”
“But I mean, maybe to your question, I do expect to see a much bigger push by both labs, Anthropic and OpenAI, into vertical applications and kinda like diversifying out of just delivering models.”
“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-”
“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.”
Claim: OpenAI competes with NVIDIA, Google, Meta, and 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 stake, the stakes are just higher.”
Claim: The stakes are higher for a trillion-dollar company.
“That's what the s- the balance sheet shows you. What the balance sheet also shows is a... I'm, I'm, I've, I'm gonna-- I might mess up the exact numbers 'cause 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, well, is that debt? Did they raise debt? Um, 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. Um, so any company and any startup founder listening to this would understand and appreciate this, like there's different ways to raise capital. You can raise preferred equity, you can raise converts, you can raise SAFes. There's a whole different host of options to raise money. Anthropic, just like every other startup, did everything.”
Claim: Startups can raise capital through preferred equity, convertible securities, and SAFEs.
“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, um, they raised some SAFes. And along the way, there's different reasons that certain investors or the company might prefer one or the other.”
Claim: Anthropic raised capital through preferred equity, convertible securities, and SAFEs.
“They raised some preferred equity, they raised some converts, um, they raised some SAFes. And along the way, there's different reasons that certain investors or 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, right?”
Claim: Investors or a company may prefer one type of financing over another for different reasons.
“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, right?”
Claim: Amazon and Google were large strategic investors and major owners of Anthropic early on.
“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.”
“Um, those, the value, the, 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, uh, Google and Amazon. If they have an ownership in a third party that exceeds a certain limit, they now have to start reporting more on that look-through ownership, right?”
“If you own 20% of another company, you almost have to- It can-- Exactly. And that company's losing a lot of money, you now have to report that loss on your books.”
Claim: An investor owning 20% of another company may have to report that company's losses on its own books.
“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 no- 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 wanna give them voting shares. I don't know, right? "Let's, let's have your shares convert into non-voting shares 'cause we don't want the governance of a s- of a supplier or a strategic." Like, I'm, I'm totally speculating and hypothesizing. Uh-”
“... reason- reasonable reasons why, hey, they were converts. But the conversion price was at a much lower valuation relative to what it is today, okay?”
Claim: The convertible securities' conversion price was much lower than Anthropic's current valuation.
“And so when you look at the balance sheet, what... And balance, balance sheet have to balance. You have assets equal equity plus liabilities.”
Claim: A balance sheet must satisfy the accounting equation: 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.”
“Now, what's happened to Anthropic's valuation since 2023 and 2024? It's, it's gone way up.”
Claim: Anthropic's valuation has risen substantially since 2023 and 2024.
“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.”
“The reality is, is they didn't raise $108 billion of debt. There was about $11 billion in those combined convertible notes. About $6 billion has already been-- or 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.”
“There's 6 billion outstanding. The market value of that 6 billion is 108 billion because the company's value has appreciated meaningfully. And so there's this debate of like, well, they have 108 billion of debt on the balance sheet.”
“They haven't raised 141. They've raised 141 plus 108. Um, that debate is just misguided. Um, there is no debt on the balance sheet, and the 11 billion of converts is inclusive in the 141 billion of total capital raised.”
“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-”
“Technically, by GAAP accounting, it is. But okay, let's go back, and I'm gonna go back to the balance sheet. The liability has gone way up. That liability is now 108 billion.”
Claim: Anthropic's liabilities have risen to $108 billion.
“Okay, let's think about the, the balance sheet. Assets equals liabilities plus equity. As liabilities have gone up, two things have to happen: either your assets have to go down or your equity has to go down. Well, your assets haven't gone down. That means your equity, the, 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-”
“... on the P&L flows to retained earnings on the equity value of your balance sheet. And so what has to happen, as that liability has gone up, retained earnings has to go down by the same amount, which basically means net income has to go down by the same amount because net income on the P&L flows to retained earnings on the balance sheet.”
Claim: Net income reported on the P&L flows into retained earnings on the balance sheet.
“... on the P&L flows to retained earnings on the equity value of your balance sheet. And so what has to happen, as that liability has gone up, retained earnings has to go down by the same amount, which basically means net income has to go down by the same amount because net income on the P&L flows to retained earnings on the balance sheet.”
“... it's all mathematically linked. You can't break it. 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 ha- what happens is, guess what? Anthropic's value has just skyrocketed recently. Um, they've raised big rounds over the last nine months.”
“Um, and so as those converts have appreciated in value, the value that those notes have appreciated shows up as a loss on the P&L.”
Claim: Appreciation in the value of Anthropic's convertible notes is recorded as a loss on the P&L.
“And of the $81 billion of loss in net income on the first six months of this year, 72 billion is from a series of converts for Anthropic, uh, for AWS and Google, and 9 billion is for a separate set of safes and notes.”
“And so 100% of the loss in the first six months is just the value appreciation of their converts. If Anthropic never issued converts to Amazon and Google, just did straight equity, that loss would p- it wouldn't be there.”
“And so it's kinda 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 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.”
“Um, and uh, in general, like, you know, book value accounting is not that relevant- ... co- compared to overall enterprise value.”
“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.”
“I think Anthropic-- Look, Altimeter is a firm. We do venture investing, growth investing, and public investing.”
Claim: Altimeter invests in venture, growth, and public markets.
“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.”
Claim: The speaker describes Anthropic as an iconic company.
“Yeah, yeah. But, but like, hey, I mean, I think a lot of people look at the balance sheet, uh, the income statement, the P&L. But the reality is, is there's a lot to learn from the balance sheet and the cash flow statement too. Um, and so this was more of a, hey, as, as just someone who's intellectually curious, I mean, Anthropic is, is a really important company to understand.”
“But the reality is, is there's a lot to learn from the balance sheet and the cash flow statement too. Um, and so this was more of a, hey, as, as just someone who's intellectually curious, I mean, Anthropic is, is a really important company to understand.”
Claim: The speaker considers Anthropic an 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.”
“Um, because if they're not delivering profits and high ROI, what does that mean for everyone downstream of them? Like, that's, it's... I think it's really important to understand the Anthropic P&L.”
“But what I understood from the Anthropic P&L, we-- I, you... Let me just rip through these really quickly. Um, here's what you're seeing about their gross margins. Lots of leverage in the gross margins. They're improving rapidly.”
Claim: The speaker says Anthropic's gross margins show substantial leverage and are improving rapidly.
“Exa- Yeah, uh, this, this... No, this is just the GAAP gross margin.”
“... how they account, I believe, is a lot of the costs that they pay out the rev share is in sales and marketing-”
“It's, it's... Yeah, I think it's, like, 40-something percent of the revenue comes from cloud partners.”
Claim: The business receives roughly 40% of its revenue from cloud partners.
“Yeah. So you see gross margin profile Improving. And, and sure, maybe it, it'd be lower if, uh, you kinda 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 fri- 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.”
Claim: The business's operating margin is increasing and has become positive under GAAP.
“You see operating margin going up, positive GAAP operating margin. You see fri- 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.”
“You see operating margin going up, positive GAAP operating margin. You see fri- 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.”
Claim: The business's free cash flow margin is increasing.
“I think the way compute gets build, um, is gonna look different in the future. It's gonna be expensive. There's gonna be more upfront payments, right? Like, I do think you should expect to see the profitability profile of these labs, um, kind of go the other way in the short term as they shift to investing in growth and investing in compute.”
“I think the way compute gets build, um, is gonna look different in the future. It's gonna be expensive. There's gonna be more upfront payments, right? Like, I do think you should expect to see the profitability profile of these labs, um, kind of go the other way in the short term as they shift to investing in growth and investing in compute.”
“Uh, 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.”
“Uh, 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, um, 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 gonna stop growing because open weight models are gonna eat their lunch, like this, this, and that.”
“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 gonna stop growing because open weight models are gonna eat their lunch, like this, this, and that.”
“Um, but I think those are the important kind of questions.”
“Oh, I think it's too hard Uh, yeah, and you-- if-- Yeah, I, I think that's, that's too hard.”
Claim: The speaker thinks it is too difficult to project the business's FY 2027 financial results.
“It's, it's a business that is so hard to project because the future is so, like, unknown and unknowable. Um, you know, they'll go public.”
“The sell side research analysts at all the banks will, will build their models, will kinda have a forward, a forward number. I don't know where that's gonna shake out. Um, but I, I, I do think you'll, you know, you'll, you'll see, um, a lot of optimism, um, about what the future of this business could look like.”
“The sell side research analysts at all the banks will, will build their models, will kinda have a forward, a forward number. I don't know where that's gonna shake out. Um, but I, I, I do think you'll, you know, you'll, you'll see, um, a lot of optimism, um, about what the future of this business could look like.”
Claim: The speaker expects substantial optimism about the business's future prospects.
“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, at le- especially in the short term-”
“... it's, 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, um, I don't know, we li- we live in a highly uncertain time.”
“... it's, 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, um, I don't know, we li- we live in a highly uncertain time.”
Claim: The speaker characterizes the current period as highly uncertain.
“Totally. I mean, it's-- I think there's limitations everywhere.”
“Everyone knows, like, "Hey, NVIDIA can only produce so many chips," you know?”
Claim: NVIDIA's chip production capacity is limited.
“Challengers, Cerebras TPUs can only produce so many chips. But I think there's other constraints. There's only so much land power shell, um, that's approved and permitted.”
Claim: Chip production capacity is limited for challengers and for Cerebras TPUs.
“Challengers, Cerebras TPUs can only produce so many chips. But I think there's other constraints. There's only so much land power shell, um, that's approved and permitted.”
Claim: The supply of approved and permitted land, power, and data-center shell is limited.
“When we were-- W-we talked a little bit about, like, debt and credit markets. Like, right now, you wanna go raise multi-billion dollar debt for GPUs? No bid. No bid. They're-- Like, debt markets are closed. There are, you know, big debt deals that aren't happening, right? Almost the equivalent of, like, an IPO getting pulled, big debt deals aren't happening.”
“... the 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 gonna say is, "Well, who's the offtake?"”
“Because the reality is, is that's actually who I'm underwriting-”
Claim: The speaker says that when underwriting financing, he is actually underwriting the offtake customer.
“And right now, the end payer of the bills are Anthropic and OpenAI. So many of the data centers and the, the, the, 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.”
“And right now, the end payer of the bills are Anthropic and OpenAI. So many of the data centers and the, the, the, 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, um, for either one of these." And didn't matter if it was CoreWeave or Nebius or an upstart like Volta or someone else.”
“It's like, "Hey, I'm feeling full." And I think debt markets just are gonna 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, um, because there are just a thousand reasons why, you know, a project could get delayed, right?”
“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, um, because there are just a thousand reasons why, you know, a project could get delayed, right?”
“And then you know what? There could be local community uprising- ... and picketing. R-I'm lit-- I'm not even being kidding. And then it's delayed. Then what? Well, the debt was raised. Someone's gotta pay the interest.”
“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?”
Claim: Delayed-draw debt facilities, in which committed debt is not drawn immediately, have limits.
“Like, and you get, 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 data center developer? Was it a delay by the operator? Was it You know, something else like- ... community unrest and, you know, that's all-- that's everything that, you know, credit markets are, are thinking through.”
“Um, 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.”
“Yeah. Well, I don't, I don't know if... Debt markets are super deep. I think it's more of like an appetite.”
“Um, and of course, there's a price for everything. Hey, great. You know what? Go take out $5 billion of debt at 20%.”
“I, I think, um, I would-- I don't know if I would say they can't do it.”
“Um, I don't know if-- Well, one, they, they need to be rated. Um, do they want to?”
Claim: Anthropic would need a credit rating to raise debt.
“You know, my g- my guess is Anthropic and OpenAI are happy that Neo Clouds exist to take the balance sheet risk. Maybe they don't want to take that balance sheet risk, 'cause again, it could be things- ... out of their control-”
“... that blow them up. You have a huge debt stack in your cap t- in your cap table, and things out of your control go wrong- ... 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 Neo Clouds can be great businesses or they could be brutal businesses if the contracts aren't airtight, if things aren't structured well, your balance sheet business, you know.”
“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.”
“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 wanna own a balance sheet business when a market cycle tips over.”
“So, hey, on, at on some level, I'm sure, you know, especially for internal chip efforts, right? Like the OpenAI team will-- might- may wanna develop their own data centers for their Jalapeno chips, right? And Anthropic, same thing for their chips. Um, but I'm sure they're also very happy for other people to take on that risk, um, because it's someone else's core competency and, you know, a lot of debt could add a lot of blow-up risk.”
“And Anthropic, same thing for their chips. Um, but I'm sure they're also very happy for other people to take on that risk, um, because it's someone else's core competency and, you know, a lot of debt could add a lot of blow-up risk.”
“Um, and there was no debt on, on the A- Anthropic balance sheet. Now, they have facilities in place that they could draw down, and so would it surprise me if you start to see debt on their balance sheet in the future?”
Claim: The speaker would not be surprised if Anthropic took on balance-sheet debt in the future.
“Um, and there was no debt on, on the A- Anthropic balance sheet. Now, they have facilities in place that they could draw down, and so would it surprise me if you start to see debt on their balance sheet in the future?”
“No. Um, I mean, but again, it's like, do you wanna build a gigawatt, right? $50 billion, $60 billion, $70 billion dollars. That's, that's not a that's not a trivial amount just for... And that's just, just one gigawatt. Um, I think both OpenAI and Anthropic have said they kinda wanna 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.”
“And that's just, just one gigawatt. Um, I think both OpenAI and Anthropic have said they kinda wanna 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.”
“I don't know, 40%, 50%? Um, let's say it's 40%. And 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 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, 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- ... massive build-out. You know, rivals the railroad build-out. You know, I, I, I think I've seen some charts somewhere that kind of compare those expected numbers, but like fi...”
“You know, when you're measuring things in like whole number percents of GDP- ... uh, you're, that's, that's huge. And so like the credit markets, debt markets need to be super, super, super deep and super, super open for that to happen.”
“But I think it's like a real debate in the world, you know? And then you could see a pretty wide range of outcomes. You know, do we really add 50 gigawatts of capacity next year as an industry, um- ... 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."”
“... 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."”
“... line of sight to that slowing down for the next few years.”
Claim: The speaker does not see supply-chain demand for AI infrastructure slowing over the next few years.
“To-- I mean, look at these recent Kimmy models. Kimmy K3. You know how much 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.”
“Um, you know, again, that's, that's, those are just the big models, right? Like you're also gonna have tons of small models, um- But I know the big models are getting bigger, which only require more memory, um, and more compute to run.”
“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.”
“That's mostly like an inference speed thing. ... uh, it's actually really interestingly tied to the generations of GPUs that they have.”
“Like, uh, then so it, it... They're really memory constrained at the interface layer, and that actually shapes the model architecture-”
“Ooh, man. It's, um, again, in a world that's highly unknown and unknowable and uncertain, I kind of fall back to what are my core beliefs that I do have conviction in, and that, that is the world's gonna consume more models, world's gonna consume more power, world's gonna consume more compute, networking, and storage, and we're gonna need to build a lot more data centers to serve the intelligence we're building.”
“Those are kinda 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.”
“Um, there's, you know, a power company, American Terawatt. They wanna build a new grid, an industrial grid to serve industrial offtake. Big, huge, bold vision.”
Claim: American Terawatt aims to build a new grid designed to supply industrial offtake.
“... and SF Compute and all those, those guys. Uh, in, in New York, we launched a compute trading track where- ... where, where, like mostly it's just to increase utilization- ... is the broader goal.”
Claim: The New York compute-trading track was launched mainly to increase compute utilization.
“But that's how you can magically create a whole bunch more compute, which is better use the ones- ... we already have.”
Claim: Improving utilization of existing compute can substantially increase effective compute capacity.
“Uh, and so if, you know, if the MFU, whatever choice of utilization is at 30 to 40% right now, taking it up to 60%, you've doubled- ... the number of GPUs you have.”
“I mean, I think there's gonna 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 of these startups 'cause 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.”
“... creative destruction everywhere.”
Claim: The speaker characterizes the current period as involving creative destruction across many areas.
“... than necessarily on the, the Altimeter behalf. Um, wanna believe there's a strong product research org combo. Um, there's certainly a lot of great teams, and we think, you know, early on in market cycles, you know, we kinda use these like triangle analogies where you have, um, compute or infra like, like hardware infra, um, 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. Um, early on in cycles, it's inverted and, you know, the biggest markets are at the, at the, at the both top or bottom of the triangle, more of the, the physical hardware and then the infrastructure and then the apps.”
“And in a mature market, the biggest, the biggest TAM is the apps, and you know, the smallest TAM is the, the hardware. Um, early on in cycles, it's inverted and, you know, the biggest markets are at the, at the, at the both 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-”
“Um, anyway, so, um, they'll, uh, they're... Yeah, I, 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.”
“They- they're just like the biggest outcome here is Cursor- ... right now. Uh, and that's 60 billion. We're here talking about 2 trillion dollar idea.”
“Yeah, totally. Totally. I think I also just like skew a little bit more infra than, yeah, you know, just personal bias.”
Claim: The speaker says his personal investment or interest bias leans more toward infrastructure.
“Six years.”
Claim: The speaker reports having worked with Brad for six years.
“No, I think we met earlier 'cause I, I was, I would think I was still at Redpoint when we first met. That's when we met.”
“Um, hon- honestly, I would say favorite Brad story, just how much he's worked at Invest America. Hey, e- exactly. And so I, I think just seeing him work on that behind the scenes for years, um, 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 grind he puts in.”
“I 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've participated in the equity upside of America and have kind of compounded, um, into capitalism.”
“But most people don't have that luxury. And so how can we educate people with firsthand experience about the benefits of capitalism by helping them participate early on?”
“So the whole idea is an investment account funded by the US government at birth that can be funded by anyone along the way. That could be philanthropists, like, like Michael Dell, um, who made a $6 billion, uh, donation.”
“So 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, um, who made a $6 billion, uh, donation.”
Claim: The speaker says Michael Dell donated $6 billion to the Invest America initiative.
“I think Elon Musk has pledged one share to every kid in America, which is- ... yeah, I think $10 billion. Um-”
“So it's passed. Yeah, it's passed into law.”
Claim: The speaker says the Invest America initiative has passed into law.
“Um, any new kid born gets $1,000 at birth.”
Claim: Under the described account program, each newly born child receives $1,000 at birth.
“Um, parents can contribute to it. Employers can contribute to it. Um, friends can contribute to it.”
Claim: Parents, employers, and friends are allowed to contribute to the child investment account.
“Exactly. Exactly. And, you know, you have people like Michael Dell who say, "Hey, here's my donation, and I want it to be gifted to..." I can't remember the exact specifics, but I think it was like, "Hey, bottom 75%, uh, income by ZIP Code."”
“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.”
“Uh, what he wanted to do was, "Hey, let me create investment accounts for the people who actually need them." Um, and I think it's, you know, it's an awesome initiative that's just getting started.”
Claim: The speaker considers the initiative excellent and says it is only just getting started.
“Uh, what he wanted to do was, "Hey, let me create investment accounts for the people who actually need them." Um, and I think it's, you know, it's an awesome initiative that's just getting started.”
Claim: The person being discussed wanted to create investment accounts for people who need them.
“I think you're gonna see a lot more direct philanthropy. What's a big issue people have with philanthropy is, well, you're gonna donate a bunch of money, and then middlemen are gonna take half of it.”
Claim: The speaker expects direct philanthropy to increase substantially.
“I think you're gonna see a lot more direct philanthropy. What's a big issue people have with philanthropy is, well, you're gonna donate a bunch of money, and then middlemen are gonna take half of it.”
Claim: One concern about philanthropy is that intermediaries may take half of the donated money.
“Ex- 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.”
Claim: Direct contributions into the bank accounts of people most in need can avoid intermediaries.
“Ex- 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.”
Claim: Direct accounts let people observe and value their account balances over time.
“Can't touch it till you're 18 years old. Um, 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.”
Claim: The money in the accounts cannot be accessed until the account holder turns 18.
“Can't touch it till you're 18 years old. Um, 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.”
“Um, and I think it's, it's, it's really, like, it, it's caught on, and just seeing how passionate he is about it, um, has been kind of inspiring to see.”
Claim: The speaker finds the initiative's proponent's passion inspiring.
“So the... Yeah. The u- Well, it's, it's made, it's made more for kids now.”
Claim: The initiative is now designed more for children.
“And so hopefully you're not... Well, maybe there, there are plenty of homeless kids.”
Claim: There are many children who are homeless.
“So that, that's the biggest thing now, which is, um, people need to claim their accounts. The government opened up accounts for every kid in America.”
“And parents need to claim them.”
Claim: Parents need to claim the accounts opened for their children.
“It, it is sitting there. Now, the money's sitting there for newborns.”
Claim: Money has been set aside in the accounts for newborns.
“Um, 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.”
“Um, 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.”
Claim: Converting created accounts into claimed accounts is a grassroots, word-of-mouth effort.
“Because there are people whose accounts are sitting there with money in it- ... and it just needs to be claimed.”
Claim: Some people have accounts containing money that remain unclaimed.
“Yeah. And by, by the way, most people have money sitting with the government that has not been unclaimed. You can look it up.”
Claim: The speaker says most people have unclaimed money held by the government that they can search for.
“Have a, have an agent go look up your name.”
Claim: An AI agent can search for unclaimed property using a person's name.
“Uh, you probably have a few thousand dollars sitting somewhere that someone returned to you, and it's just sitting there.”
“You just need to know your address. Your prior addresses.”
Claim: Finding unclaimed money may require knowing one's prior addresses.
“Yes. Highest ROI on this podcast is that. Not, not the Anthropic investment stuff.”