The Truth About The AI Stock Market Crash — Gavin Baker Interview

[00:00:00] Speaker 1: I want to be scared. You know, I don't want to feel like a lunatic watching these stocks get more cheaper, thinking the expected forward returns are going up. My main kind of mission out here this week is like pressure test. Yeah. Yeah. Fine. Tell me something negative. Yeah. But I haven't been able to find one that is like a quantitative metric. The underlying fundamentals are improving and stocks. NVIDIA is actually, as we record this, at its lowest forward P.E. of the last 10 years. The market 100% thinks they are significantly over it. [00:00:48] Speaker 2: Gavin, it's only been two months. Like the model release cycles, the gap between our podcast episodes are shortening. We're basically, you and I are basically on a model release cadence [00:00:58] Speaker 1: at this point. Well, I was sensitive to criticism that I think somebody pointed out that our podcasts were coincident with like local market peaks. And nobody can say that after this. What's on your mind? It's been a crazy, crazy month. Yeah. I would describe July as 2022 in a month. Yeah. There are some fundamental negatives, which we should talk. But like on the whole, the balance of fundamentals, I think is improving significantly. Loads of AI names are down 50, 60% from their highs. We'll call it 40 to 60% in a month in a straight line. And I asked you before we started, you've, you've been out here for the summer. Have you heard a single negative quantitative metric about AI? Yeah. A single instance of deceleration? Nothing. Nothing. In fact, [00:02:00] Speaker 2: every metric is accelerating. And to your point, not just blind optimism from people excited about AI. Yeah. Here's some data that they can show you and from their different vantage points. [00:02:09] Speaker 1: Absolutely. I mean, however you cut it, whether you cut GPU availability, whether you cut GPU rental pricing, I mean, whether you cut like the spot price of DRAM this month, token growth, everything is actually accelerated. And I do think a big part of the problem is, one, the market does not have visibility into anthropic open AI. And then I would say these open source inference clouds that monetize inference here in America, fireworks based to modal together. And the picture looks very different when you see that. Because open source has accelerated massively because GLM 5.2, KBK3. And then, you know, Nematron continues to kind of chug along. We had a great, you know, a very small American open source model release. Open AI has accelerated. And Anthropik continues to grow really strongly, and is almost certainly pumping out significant amounts of free cash flow. And I just think if, you know, there's this chart that everybody looks at of semiconductor cash flow going like this, and hyperscale cash, free cash flow going like that. And you're missing these private companies. But I also think that that chart misses something very important, which is just that you have everyone in 24 and 25 thought, even if you were really bullish, you thought that they would price to rent a GPU would, you know, decline slowly. You know, if you're bearish, you thought it would decline precipitously. I don't think anyone in 24 or 25 thought that the prices of old GPUs would still be would be going vertical in 2026. Yeah. And so everybody thought, hey, we're going to be smart. We're going to sign these long term contracts. And to some degree, like a lot of the neoclouds had to do that because they needed an offtake agreement to finance the GPUs. And so essentially, you have the contracted base of installed compute trading at a massive discount to the current spot market. And as those contracts roll off, and compute gets repriced higher, and spot can decline and compute will still get repriced higher. You know, I think you're going to see a lot of acceleration that's going to answer these ROI questions. You've started to see that this quarter. If we look at operating cash flow, not free cash flow, operating cash flow from Microsoft, Meta and Amazon has reported accelerated from 28 to 32. There are some actually pretty big unusual items now, like these hyperscalers, they always seem to have like billions of dollars of legal expenses that are unusual, mostly fines to the EU. But there is an unusual amount of one timers this quarter. And if you adjust for that, we went from 28 to 35. And that's a material acceleration at this scale. And that's really before, like they start to light up the Rubens, which will come at a meaningful premium before these contracts reprice. It's been a challenging month that it's almost, you know, like, is it helpful to kind of like walk through the month, how we got here? Yeah. Yeah. You know, so first, there's Meta is going to rent out compute. And this is seen as like very bearish. They have excess capacity. They're going to cut CapEx. This is a disaster. This is not at all what it was. They just reported, they didn't cut CapEx. What it was is they saw SpaceX have a big installed base of compute and sell some big trading optimized clusters into the market at a truly massive premium to these contracted rates. And you know, at least analysts like that, they saw an opportunity. There's a lot of speculation they're going to raise capital. So like, you know, maybe what they're thinking is like, hey, we will show on a small chunk of capacity that we could generate really strong IRRs, then we're going to raise equity capital and we'll be off to the races and probably raise CapEx. It doesn't look like that's what they're doing. But nonetheless, the market sold off because it interpreted this very negatively. And I was really sure it wasn't negative. You know, there's a lot of telemetry into Meta's CapEx plans. None of that telemetry had shifted at all. If anything, it was, you know, continuing to, they're continuing to get more aggressive. And then shortly after that, they released their best model in a long time, Muse 1.1, which is actually really a very good model. I mean, it was overshadowed by Grok 4.5, but it was a good model. Way better than anything in two years. So just no chance they're taking their foot off the gas. Then Kimi comes out. And then there's this huge freak out about open source. And at the same time, this silicon data token index kind of dips and flattens. And the two are connected. What the silicon data token index captures is mix. And they don't see all the tokens, but because of GLM 5.2 and then Kimi, although it took a while to layer in, there's kind of a mix shift in this data from more expensive frontier tokens, which probably have an inference margin. We can make whether it's 80, 90, or 95, but super high towards open source tokens. And for whatever reason, the market thought this was negative. But the reality is a token is a token. And you need the exact same amount of compute to make a token all else equal. It takes the same amount of flops, the same amount of memory, the same amount of watts. Now, tokens are not equal, but broadly speaking, all open source taking share does is kind of take margin dollars out of the frontier model layer. And effectively, there is elasticity, thereby driving token demand. You need more demand for compute. And the margins, Anthropic and open source, they all run on the same underlying cloud providers who charge the same amount of compute. So you're literally just taking margin from frontier models and essentially driving more margin dollars into the AI infrastructure layer. And like, I think that's, that was the catalyst. Well, this combination of things. Well, yeah, then it kept, it's like, Jensen is the world's largest supporter of open source. Do we really, and he's like a super idealistic guy. He's a patriotic American. I think he always does what's right. But is it, does it really stand to reason that Jensen would be the world's biggest supporter of open source if it was bad for his business? He'd still support if it was the right thing for the world, but maybe it wouldn't be his signature issue. Yeah. And by the way, I think open source is really important to worlds where there's just one or two dominant frontier models that charge like 90% margins. It's not good for humans. It might not be good for society. And I think we want a lot of models as we've discussed before. So then it's like, okay, the market digests that comes from it. Then China has a DUV machine. And this causes, you know, everybody's in these baskets. This causes a huge sell-off in semi-cap equipment. And then we get to what I think is, in a lot of ways, like the real concern, which is real yields have gone up, which makes sense. You know, we're investing a lot to fund this investment. And for sure, credit is an increasing part of it, even if the majority is still funded, overwhelming majority still funded out of operating cash flows. And so real yields go up and spreads widened. Meta-priced a bond last week. And, you know, it did not price where you would think a meta bond would price. And this just shows that the credit market... [00:10:41] Speaker 2: And maybe a CDS was still blowing out. [00:10:44] Speaker 1: All of these CDS... CDS for everybody is blowing out. And, you know, very smart private capital people just like, hey, this is just exactly what you'd expect. These are just banks, you know, kind of hedging. Hedging their commitments. But nonetheless, it doesn't look good. And these are undeniable facts. CDS is up, spreads widened, real yields are up. And that is, that would be really, really scary if we needed debt to finance this build-out. And that's where I think it's this differential between spot and contract pricing for the installed base of compute is so important. [00:11:27] Speaker 2: Ramp is the only platform built to make your finance team leaner, faster, and better. Saving businesses 5% annually on average, so you can stay focused on growth. Ramp customers grew revenue 3.2 times faster than the average American business. Visa, Vercel, Cursor, Stripe, Notion, 11lab, Shopify, and 70,000 other businesses all run on Ramp. Mine does too, and so should yours. Learn more at ramp.com/invest. The best AI and software companies from OpenAI to Cursor to Perplexity use WorkOS to become enterprise-ready overnight, not in months. Visit WorkOS.com to skip the unglamorous infrastructure work and focus on your product. Felix by Rogo is a personal finance agent that turns a single prompt into finished, client-ready work using your firm's own templates, context, and standards. Send Felix an email like, take these comments and turn them for me, or update my tracker with the context of these emails. Or run the ability to pay math on this buyer, and Felix sends back finished PowerPoint decks, Excel models, and sourced research. Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.ai/felix. It's so important to understand what the financing will be like for the next six months or something. The degree to which this build-out is going to require credit. Right. Which would be the classic, like, capital cycle. Absolutely. We start to overextend ourselves with debt, and that's where things get started. [00:12:49] Speaker 1: A hundred percent. And then, you know, debt-fueled build-outs, you know, they demand immediate repayment. Yeah. So if supply and demand get a little bit out of whack, things can unwind very, very, very quickly. That's what happened in the internet. And so if one believes, as I do, rightly or wrongly, and I'm like, after this month, I'm super open. You know, I'm looking like I've been pressure testing all of these. And like, I really went deep on credit because, hey, this is real. It's undeniable. And if we need credit to fund this build-out, this is like a significant negative. And if you model it out, has, if you look at the amount of gigawatts that are supposed to come on, and consensus estimates for hyperscalers, they're effectively modeled. And these are gigawatts of Blackwell and Rubin. Rubin being NVIDIA's next chip, Blackwell being the current chip. They are essentially modeled to monetize roughly at the rate of Ampere, which is two generations behind, not at Hopper, but Ampere. So there's 1.3 trillion and 1.3 to 1.4 trillion in hyperscale operating cash flow. If you just assume that they, they're not, I think it's very unlikely they monetize at the rate of Ampere. And we could, we could go into why. And some of it comes from just, you know, seeing what is happening on the ground with demand here from real quantitative metrics. But like, let's just say they monetize at a discount to current Blackwell's. Then it's more like 2 trillion of operating cash flow. And that kind of takes 700 billion of credit demand out. And, you know, and then obviously these, you know, ironically, has, you know, that improves all the credit ratios, has these installed bases of compute, reprice. We're going to continue accelerating because since this is modeling at a deceleration, which I think is unlikely, then the credit metrics look better. And then all of a sudden, it gets easier to finance with credit. Now, whether they, whether they, they choose to do that or not, we'll see. But this, this is all a little bit, you know, I think we spoke. Two months ago. No, but the time before that about kind of the risks of a Blackwell air pocket, where you're spending hundreds of billions of dollars on Blackwell's. They're mostly being used for trading initially. Trading does not generate, you know, a return. And that this could be a risk. And you actually really saw that kind of in, you know, in the first quarter. And I think one reason, you know, like to the podcast two months ago, I got comfortable with that risk was just that you were seeing such incredible things out of Anthropic. And then it's like, okay, well, the market's kind of going to look past this. And it did look past it in April, in May, in June. And then in July, because of this kind of confluence of things, stopped looking past it. Just has the operating cash flow started to really accelerate. And this is just a fact. It is accelerating at big scale. And, you know, like Microsoft, they brought on a huge slug of capacity in the month of June that didn't even show up in the second quarter. So essentially, what this all comes down to is, do you believe that the kind of quantitative demand signals seeing on the ground here in Silicon Valley from private companies are going to continue such that the installed base of compute reprices higher as contracts roll off. Operating cash flows go up. Yeah, operating cash flows go up. And you can fund this out of most of this out of operating cash flows, maybe all of it. Like if it reprices at current rates, you can probably fund all of it for the next several years. And so it's been a very unusual episode in the market. And, you know, in some ways, the fact that, and we should talk about what the fundamentals are that are getting better that I'm talking about, you know, technicians would say it's actually in 22, okay, the market is worried about a recession, rates going up, you know, inflation. That's what the market was worried about in 22. You knew exactly what it was. Okay, deep seek, you know what it's worried about. Liberation day, you know what it's worried about. There's something very clear. And in a weird way, that's, that is comforting. Sure. And here, you know, we talked about a lot of specific things, but it just feels all those specific things with the exception of credit, like are just kind of ridiculous. And so the fact that it is still going down, you know, a technician would say, "Hey, that's, that's a little scary." You know, it's definitely the bullet you don't see that gets you. You know, I think we've talked before about how, like, I think the three most important words in investing aren't margin of safety, but I don't know. But just, you know, I've, you've, you've been out here for two months. I've been out here, you know, I literally spoke to a company this morning who rented a cluster of several, and this is one of, you know, kind of sexiest startups that people want to be in business with. And they had rented a cluster of several thousand black wells and we'll just call it, you know, somewhere in the mid $2 per GPU hour. They're renting the exact same cluster, exact same size cluster, essentially identical in every way, B200, no differences. And they're hoping seven months later to pay just under $4. Like, you know, just, you hear this today. And that's like, that's pretty crazy because again, you would just, you would expect a really, like a gentle decline in prices would be bullish. Instead, you know, we're up, you know, depending on the starting point, 50 to 60% in six or seven months. And it just, there've been so many anecdotes like that. Like, I think one of the inference clouds, I think it was based in, I'm not sure, they went on a podcast and they essentially said, we are planning to pay 100% more for Blackwell's when our contract expires. And that just means that essentially all the hyperscalers are under earning. And I haven't like, my main kind of mission out here this week... Is like pressure test? [00:19:43] Speaker 2: Pressure test? [00:19:44] Speaker 1: Yeah. Yeah. Yeah. [00:19:45] Speaker 3: Fine. Tell me something negative. Yeah. Yeah. [00:19:47] Speaker ?: Yeah. [00:19:47] Speaker 3: You know, like, you know, the question I asked you, have you, is there one negative [00:19:52] Speaker 2: quantitative metric you've, you've heard? Has been what I've been asking everyone. Yeah. The main thing people are saying is the anthropic, like the third party data suggests that the anthropic like curve started to go off of its trajectory a little bit. That's like the only thing that I think, I think that's, I think that may very well be true. But then you have [00:20:12] Speaker 1: open AI and open source massively accelerating. And if you look at the sub, it is net accelerating. I don't know that it looks the same. I think it may have accelerated. Like I think open source is a little bit of a, you know, they talk about dark matter in the universe, like open source is kind of dark matter to the public markets. You know, it's hard for public markets to measure it. But like, if you just track what these inference clouds are saying, you know, these are people saying things on podcasts or people saying things in meetings, they're not, you know, audited financials, but like demand is clearly accelerating, which makes sense because you had this huge capability leap with GLM 5.2 and Kimi K3, which I think we're going to see continue. I think you're going to see NVIDIA bring Nebotron steadily closer to the frontier. It has been a very, like it's been a humbling, challenging month. And, but just, it's also like, wow, I've kind of pressure tested every assumption. The underlying fundamentals are improving. And stocks, NVIDIA is actually, as we record this, at its lowest forward PE of the last 10 years. Crazy. The only time the SIMIs have been cheaper were Liberation Day and Deep Seek. And that was, those were kind of V bottoms. [00:21:37] Speaker 2: And that means to you just that the market thinks they're significantly over earning? [00:21:41] Speaker 1: Yeah, the market 100% thinks they're significantly over earning. And we need to be humble. Maybe they are. Maybe they are. Yeah. But like, my kind of mission out here this week was to look for negative data points, as hard as I could. And normally you come to Silicon Valley, and you know, there's a mixture of like, okay, here's something negative, here's something positive. On balance, it's positive. You know, tech, it creates value over time. But I haven't been able to find one that is like a quantitative metric. Other, like that, that anthropic third-party data, I would say that seems to be hotly contested by the, [00:22:23] Speaker 3: by the anthropic shareholders who are like, who are bound, or kind of like, chomping at the bit to tell you what they know. We're also very scared they're not going to get an IPO allocation if it gets back to the company that they're the ones who said, actually, things are great. You know, you can just see anthropic shareholders, like they want to be like, it's not true. [00:22:47] Speaker 1: I mean, it's hard for me to believe that open source and open AI have accelerated to the extent they did. And, but yeah, anthropic is clearly, you know, kind of in the, in the pole position. And oh, by the way, you know, Grock and cursor have also, you can see from third-party data, like July was a pretty transformational month with Grock 4.5, Grock Builds coming out. So it has been a tricky month. And I have a friend, I have a friend of Fidelity, who just says the way to have navigated, [00:23:25] Speaker 3: like the last three years, is just do the dumbest, most superficial thing, as quickly as possible, and just cycle between them. What is that? What is that now? [00:23:38] Speaker 1: Well, that's just, that has been to cut risk. Yeah. All month in response to these kind of narratives that just like factually, except for credit, are not true. And the work we've done makes me think that credit just isn't going to matter, has this repriced. Let's just say you do need credit to like, build the flops we need. Well, if credit's not there, it just means the flops that are there are going to be even more valuable because there is an interesting like essay that got sent to me. You know, I think we've talked before about Mike Bobison's theory that like a breakdown of diversity is kind of what leads, you know, bubbles and crashes. Yeah. And essentially everyone I know in the public equity investment business, whether retail or institutional, everything immediately, every piece of news gets fed into Claude. And Claude, Claude code, sometimes, you know, a Claude agent and, you know, Claude, it's probabilistic. There's probably not that much variation in the way it's interpreting this news. And so it's almost like we're back to, you know, in stock market terms, like there's never really been this way in the stock market before, but people talk about the fragmentation of media and how it used to be like Walter Cronkite was the only voice of truth. And now we don't have that anymore. It's like, [00:25:04] Speaker 3: Claude is kind of Walter Cronkite for the stock market and everybody just believes whatever it says. [00:25:13] Speaker 1: And this is leading to like really, and by the way, it's really smart, but it's not always right. It's not, um, it's interpretation isn't always correct. And with the stock market, you are fundamentally dealing about, you know, a probabilistic Bayesian interpretation of the future. And so it just, it feels like in the market, there is this, here's this piece of news. It gets fed through Claude, Claude interpreted this way, 90, a huge chunk of people trade on Claude's view. Um, and so you've seen stuff. There's this guy, uh, TBU, TBU. He's like, uh, part of like the anonymous semiconductor mafia, but he posted this amazing chart of Japanese capacitor stocks. And he said, we've had a capacitor, an entire capacitor cycle in six weeks. And it's true. You know, the stocks, like whether they double, triple or quadruple, I don't know, but like vertical and then whoosh, you know what I mean? Like the actual fundamentals haven't even hit. And yet you've already had what probably would have normally been a three year cycle in like six weeks. [00:26:27] Speaker 2: What's your sense of being out here, especially, it makes me especially curious about this, the innovation that is going on here to improve the efficiency and every aspect of serving inference, of training models, et cetera, and how that will affect like public markets over time. Like, have you learned anything interesting about like the long lead time innovation type stuff that has you especially excited or, or curious? Yeah, I am very curious. [00:26:53] Speaker 1: It was like, Oh, there seemed to be like a lot of people seem to feel like they are very close to solving continual learning and sample efficient learning, which we've talked about before. And it is possible that if those are solved that, you know, could that be like a temporary, like kind of like discontinuity, you know, in demand, if instead of, you know, having to, like, I think somebody told me that the, like, I was trained on effectively 20 billion tokens, and that it's like these models are trained on 300 trillion tokens. And if, you know, you can train something on 10 trillion tokens, then let it out into the world and learn sample efficiently. You know, that, that doesn't sound good for trading demand, but like trading has a percentage of semiconductor demand to compute is going to asymptote to something not approaching zero, but very small. But I would say that is the most kind of interesting and, you know, who knows if it's long horizon or short horizon, you know, SSI says that they're going to come out, you know, with their, their model in August, you know, there's this whole generation of new labs that are focused on this. And this would be good for the world. This would be amazing. To be clear. Yeah. This would be awesome for the world. Yeah. We all want, we want this. Yeah, we want this. It would be amazing for the world. And it's just, it's hard for me to believe that that would actually be negative for AI infrastructure demand. But again, trying to be really, really open-minded. I would say that was probably like the biggest, like whether we call it scientific or technical takeaway, but it's just, you know, it's also like. We just don't know. Well, yeah. And also like Nvidia is heavily involved with [00:28:40] Speaker 2: all of these startups. Yeah. So what would, like, if I was forced to, if you were just forced to come up with, uh, the set of circumstances that would really switch you around and get you really scared, is it, would it just be, uh, that this operating cashflow thing doesn't play out and therefore we [00:28:57] Speaker 1: just need to debt finance this whole thing? Yeah. If the operating cashflow does not continue to accelerate, that, that would be negative. Um, and that to some degree is going to be a function of how Anthropic, OpenAI, GrokCursor, which we call Grok, and open source do. You know, if like all, if there was a pretty dramatic, like contraction in GPU prices that was kind of sustained, I mean, the market would react to that instantly. That would be worrisome if it started to get to be really easy to get GPUs. I mean, have you heard anyone say they have too many GPUs? No. Like not, not a single person. And it's not like it's the opposite. It sounds like a drug market or something. Yeah, it really does. It's just wild. But yeah, I mean, I think there's a long list of pretty obvious things, you know, if like Anthropic OpenAI, if the sub of these labs plateaus or, you know, starts to decline, that's really negative, unless it's just because open source tokens are net growing the pie and taking share. And I do really think the future is like multi-model. I think particularly for the AI natives, they're going to want to take an open source model. It's got, you know, all these inference clouds have gotten really good at, you know, supervised fine tuning and reinforcement learning. So you can take your data, customize an open source model, and then get something that you can put behind a router, and the router routes it to often first your model, and then Claude, a frontier model, whatever Claude, Grock, checks it. And you can, in a lot of cases, get slightly better outcomes at half the cost. But again, that half the cost, I think a lot of people hear that. They're like, "That's bad for AI demand." It's actually not at all, because the cost the user pays has, you know, it's just a function of the margin on the tokens. And you're literally just shifting tokens from really expensive tokens with like 90% gross margins to tokens with maybe, let's call it a 30% gross margin. That's where the savings are coming from, but the tokens cost the same amount of compute to produce. And then also, all these things are kind of happening at kind of different cycle times. You know, all these, you know, big public companies are like, "Oh my God, my AI spend is 20x. I've burned my budget in three months." So they set up a router, and that actually cuts their AI spend, but it doesn't really impact. It may actually increase the amount of tokens that they are generating just by shifting them to these cheaper open source tokens, and that's just more compute. So, you know, a company getting smarter about which model to use for which task, that may lead to a stabilization of their spend or even a decline, but it actually has nothing to do with the amount of, you know, GPU compute hours they're effectively consuming behind, you know, these model layers of this router. The GPU compute hours probably are going up as you, you know, shift to these cheaper tokens you can use more of. So, and then, you know, that's happening to like a cutting edge of public companies, and then you have this whole wave of AI natives, and like they're leaning into this so hard, and they're not hiring humans. They're just really putting it mostly into tokens. And so they're not slowing down. And then you have companies on the east coast of America who have like barely adopted AI companies, you know, broadly speaking on other, you know, not in the coast who maybe are as cutting, and then Europe who's like just trying to figure out how to regulate AI, you know. Before using it. Yeah. So just like there's kind of these differential, differential kind of waves of adoption all happening at the same time. But the thought I can't get out of my mind is like, I think I said it maybe last time, but just yeah, I don't know, 500,000 people in the world, 250,000 maybe are using agentic AI. And we're in an acute compute shortage. That's, you know, there's seven or 8 billion people on the planet. What happens when we go from 500,000 to 100 million, you know, to 500 million. And then I do think it's, it is interesting. You know, a lot of people are just like, okay, well, you know, I do think it's like helpful to post on X to see the pushback. And a lot of people are saying, well, you know, where fundamentally is the, okay, we accept your argument that hyperscalers are under earning and it's compute reprices. Their operating cash flow is going to accelerate and maybe we could fund this, but like who, where's that operating cash flow going to come from? Where is the customer? And kind of definitionally, it has to either come from, you know, faster economic growth through productivity, kind of Satya's comments, like either we're going to start growing 10% or we're not, or labor substitution. And for sure, I think in a lot of these AI natives, you're seeing labor substitution, but not because they're firing people, they're just not hiring nearly as many humans, you know, the gross profit dollars per FTE and, you know, A16Z, Iconic, a bunch of companies that have done this work, you know, they're, you know, they're, they're vertical, particularly relative to past generations of startups. And then it is interesting, you know, like, are you kind of doing any surveys of your companies and their token spend relative to labor spend? Oh, yeah. I mean, it's always reported as a [00:34:58] Speaker 2: percent of percent, tokens as a percent of like, total comp spend or something like this. And what, what are the ranges you've seen? I mean, like in the really pilled companies, like it gets really high, [00:35:08] Speaker 1: 20%, 25%, something like that. Well, our, our, our, our, our friend Dylan Patel at his company, he's an ASI maxi, but he's at 30%. Yeah. He probably, that's probably the highest one I've heard. I've actually heard of 50 and there's $25 trillion in knowledge work. And so let's, you know, let's say that that's, you know, let's take your 20% number. That's 5 trillion. And that either comes out of labor substitution or faster economic growth. And we really, really, really want as, you know, humans to come [00:35:44] Speaker 2: from faster economic growth. One interesting thing I heard this morning from one of the great, like leading technology CEOs that's founded several companies that if you look at the founder-led and controlled companies and adjust for some of the like COVID era, you know, overhiring, like nobody's really laying people off. Like these are the people that would probably be most quick to adopt AI to, you know, become more efficient or whatever. Like they're not really doing jack aside, like huge scale layoffs, which probably tells you something about where they think there will be lots of opportunity to still have people plus token spend. A hundred percent. Well, the bull case- So growth, not labor replacement. Yeah, yeah, growth. And the bull case, [00:36:20] Speaker 1: and you know, you've seen church from Cognition, Ramp, and Stripe, that the companies that are spending the most on AI are growing meaningfully faster. Yeah, I love that Cognition Index. Yeah, the Cognition Index is wild. Now, all the skeptics will point out rightfully, it's not really controlling for industry. But then if like you dig down into it, you know, I think one of them gave an example of, I forget if it was a plumber or an HVAC contractor, but like, you know, everybody who's a blue collar worker is doing great because of AI. By the way, something that I think we should touch on, and we could do it now or later, is just everybody is citing these LTAs. So we're, everything's at a shortage. Everything's at a shortage right now. You know, if there's weakness, it's just because we can't energize the gigawatts fast enough. The gigawatts are going to get energized. Like it, you know, regulatory policies moving in a good way. The turbine manufacturers, the diesel jet manufacturers, you know, they're ramping up. You're, you know, you're ripping turbines off old airplanes and, you know, reconditioning them and then repurposing them. There's crazy things happening. Capitalism is very, very good at this. But I do think one of the most important questions in the market and like a transition of the market that like I got wrong is we are shifting particularly for memory more than anything else from, you know, crushing numbers in the short-term to their trading short-term upside for these, you know, what they call supply chain agreements, long-term agreements, LTAs, where they essentially, you know, agree there's, there's many flavors, but customer prepays and it's, you know, there's a floor and a ceiling. And this comes back to the point about labor because, you know, a lot of people after, you know, after kind of like firing, you know, too many people were, you know, during, during COVID, we're really reluctant to lay people off in that, you know, they talked about labor hoarding. If you remember a few years ago, you remember this? I'm just, let's just think about the game theory of breaking an LTA. So there's four companies that like matter at scale. There's Amazon with their tradiums, there's Google with their TPUs, there's AMD, and then there's Nvidia who's like much bigger than everybody else combined. You know, let's just say it's 2027. It's very important to realize memory is the more memory you put with flop for a given unit of compute, the more tokens you get out. It's the single most important thing you could do to increase kind of token output per unit of compute. And then that obviously, definitionally, actually lowers costs, which is why the demand hasn't responded at all negatively. There's been no elasticity, just because it's like, kind of the only, it's the axis that is dominating all others. And this is like, at some level, like a giant Game of Thrones or IMPERS between these companies. And okay, it's 2027. You're like, or 28, you're vaguely tempted to break one of these LTAs, try and get a lower price. But to a large degree, market shares are, I think for the next several years, are going to be determined by supply chain allocations and kind of what you have kind of pre-purchased. So if you break the LTA, and this is assuming we're not in a severe oversupply situation. But the logic almost, the game theory even holds in a severe oversupply situation. If you break your LTA, and then in the next two or three years, for any reason, leverage shifts back to the memory guys, you're out of business. It's over. Let's just say Google breaks an LTA. There's an oversupply, I'm making this up, and 28, 29, they break their LTAs. Well, if they're breaking their LTAs, it probably means your oversupply, prices are coming down, and then capacity naturally contracts. Well, what do you think is going to happen to Google's allocations? And then this is a cyclical industry and oversupply is followed by undersupply. What do you think they think is going to happen to their allocations next time? So I just think given that this is like the axis around which kind of everything is revolving, man, like you might blow up your entire business and your franchise by breaking an LTA. And that was never the case before. You know, Apple, who cares? You know, they're buying, they don't have a competitor. They're overwhelmingly the largest purchaser. They know they can do whatever. This is going back three, four, five years. They know they can do whatever they want with no consequences because their volume is so big that even if they like super screw Hydex, Micron will of course take them. This is just different. You have at least four players. Did you have all the startups? You're an investor and etched. And if you break an LTA and that they just say, okay, fine. You know what? Great. You broke the price agreement. We're going to break the volume agreement. And, you know, screw you. We're going to give the volume to your competitor. You just lost share, you know? So, I think the, you know, like I think, you know, NVIDIA's dominance I think is like I think the current environment the extent to which it favors NVIDIA, like it is a little hard for me to understand why it's trading at such a low multiple. You know, in other words, like if you need to be able to finance the chips and you do, nothing's more financeable than an NVIDIA GPU, nothing. If you need to get, you know, land and power, well, they're doing a very good job of playing that chess game and matchmaking. And then they've kind of rolled out this really clever, you know, new business model, which I would describe as kind of like a credit wrapper with a revenue share if GPU prices are above a floor. Yeah. And this could lead to them like having a really giant cloud business effectively through royalties really quickly. And it is another way of kind of alleviating this, you know, cash flow mismatch. Like, hey, we're making all the cash. Yeah. And like, this isn't really vendor financing because they're not loading them the money. Somebody else is loading the GPU buyer the money. So it's not quite vendor. It's not vendor financing. It's, you know, they're still making equity investments, but it's not like you're just putting money into someone in return for the, you know, and then some of that money, you know, is used to buy your chips, even though, you know, NVIDIA has said that they write into all their, you know, equity investments that, you know, the money can't be used to buy NVIDIA chips, but obviously money is fungible and... Funny thing. What's that? It's just like a funny little thing. Yes. Makes the sense. Yeah. But, you know, I think at some level it probably makes everybody feel better. [00:43:46] Speaker 2: Sure. What would you do if you were the member, like if you were the CEO of Hynix? [00:43:50] Speaker 1: I'd do the exact same thing NVIDIA is doing right now. Which is? I would be going, I would be going to the buyers of GPUs, Tradiums and whoever and say, I'll participate in the NVIDIA credit wrapper. Now their business is just inherently less stable and predictable, but in some way, and maybe they just put up some cash upfront. So it's like, they're not on the hook. They're not, I mean, I'm just making this up, but like, like do something like you can, because you have money now and credit markets are revolting. There are many, you know, like, you know, the, the people I'm sure the, you know, our friends that, you know, Blackstone and Apollo are suggesting some variant of this to the memory companies, but Hey, we will like put up some amount of money from our cashflow today. And then it's gone. It's, you know, surety, um, that, you know, makes the, the person who's extending the debt feel better, but we want a, some sort of a cut of the ongoing revenues as well. Right. Like that is like 100% what I would do. And it's almost like a logical extension of, you know, the LTAs where they're kind of trading upside for durability here, you know, you could, you know, you can effectively get a royalty on recurring revenues. And that is, that is what Nvidia is doing. And I do think that is very misunderstood. And I think it would serve Nvidia well to really explain this one. They're really bullish on AI. Um, essentially every time they haven't taken an equity stake in something, it's been a mistake, you know, I mean, they've taken equity stake in everything, essentially, except the memory companies that for a long while, Anthropic, that they took an equity stake in Anthropic. But like, why not if you have cashflow and you're bullish on AI and Jensen, because he sees every lab, he knows all the advances, you know, like all these continual learning labs, you know, safe super intelligence is now working with them. You know, he sees everything and like what he sees makes him bullish. Um, so one, have some equity upside, and then two, have a revenue share, and you're generating hundreds of billions of dollars of, um, of free cashflow, um, and helping to kind of bridge, you know, what, what is clearly kind of a gap, at least, you know, given everybody's got free cashflow negative, until the operating cashflow accelerates enough that you can internally fund this. It's almost like, I mean, it's, um, very opportunistic, and it like significant, in a good way, and it significantly increases their revenue per gigawatt. And then it also strengthens their competitive position. You know, that's, you know, you and I, we both have startups, but okay, that's, that's, that's great. Use that startup's chip. Um, well, what prices are they paying at Taiwan Semi? Higher than NVIDIA and all these guys. What prices are they paying for, um, HBM DRAM? Higher. Um, can you finance those chips easily at the same rate as NVIDIA? No. And so it's always like, you know, there's, there's a real burden, particularly if you use HBM DRAM, like you're just, you're in the crosshairs of this. Um, unless like etched, you know, maybe etched, like they made really different architectural choices. Everything that's happening is actually pretty good for him, which are going back to game theory. Anthropic, if they had been as aggressive on compute as open AI had been, they would have run away with it. And so now open AI is back in the game. I think Grok is in the game. Those are the companies on the Pareto frontier. And they have the compute. And do you think after watching that anyone is going to let off the gas? Right. Cause you just, you know, it was, I think four months ago that Dario was talking about how, you know, it was a re it was a really thoughtful commentary, but it's like, it's really, really hard because you know, if you buy too much compute, you could go bankrupt at the scale of these things. But if you don't buy enough, you could lose. Well, we saw it happen. Open AI just got back into the game and now space X is in the game in a big way with Grok four, five and cursor. And like, after watching that from a game theory perspective, is anybody going to back off anytime sued, especially if it could be funded out of operating cashflow. Vanta automates security and [00:48:34] Speaker 2: compliance for over 16,000 fast moving companies like ramp cursor and Harvey, keeping them audit ready around the clock. It's the number one agentic trust platform. And it now helps companies like yours watch for the risks that show up between audits across your vendors, your AI tools, and your whole environment. Every new tool your team signs up for every vendor that turns on AI features is an opportunity for something to go wrong. And most security programs weren't built for AI's pace of growth. 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The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data, and no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation. You can request a demo at ridgeline.ai. Have you met anyone in your travels out here that you would say is like way more bullish than you? And if so, what do they believe that you don't? I mean, essentially, everyone out here is more bullish than me, man. I read this thing that [00:50:32] Speaker 1: Dorkesh wrote and I was like... The 3x compute price thing or whatever? Yeah, well, I forget what it was. No, no, it was like 15x or something. Yeah, but no, but just basically that, you know, renting an H100 for a year would cost $250,000, you know. And that's 15x the current spot or something. Exactly. Like, wow. You know, that was just like... That wasn't in my book. That wasn't in my, you know, forget my like Bayesian probability space of expected outcomes. That wasn't even in my considered but dismissed his totally [00:51:08] Speaker 3: unlikely outcomes. You know, and then that guy is, you know, he's very Dorkesh. He's a very smart guy. [00:51:13] Speaker 1: He's very plugged in. And, you know, and then he pointed out that like, hey, the, you know, something like, I think he just said, margins on compute are going up. The amount of compute is going up and inference margins going up. And if you multiply those three, that's how you're getting this crazy acceleration in the sum of the labs plus open source or though obviously open source, the margins on open source are not really going up. But I mean... So everyone's... Yeah, yeah. Like, you know, I just, I look at what's happening in the stock market and I feel like a foolish optimist. And then when I talk to people, whether it's people at the labs, whether anyone in this ecosystem, like I'm like bearish relative to essentially everyone, which is just a strange state of affairs. [00:52:07] Speaker 2: What do you make of the DUV news out of China where I've seen reactions really along a spectrum of like, this is the equivalent of like what ASML had in 2001 or something? Or like, no, this is actually the first bit of news in a new story for how we should think about the global supply of cutting edge compute. I think both could be true. You know, it's just like, like, let's just [00:52:31] Speaker 1: make an analogy. Like, let's just say a DUV machine was a jet turbine and now like an EUV machine is like a warp drive, you know, or whatever it could be. You know, a DUV machine is like a propeller plane, but like they didn't have it before. And now they allegedly do. And that is like a phase transition. You know, you, it's like, you've gone from like liquid to solid. Now that solid, that, you know, jet engine prop plane, whatever is 25 years behind, but still it's important. And I don't think should be dismissed, but I also, you know, it's kind of funny. You just see this in the stock market. You know, it's like the stock market massively overreacts. And then like, if this ever hits ASML's orders, maybe it hits it in five years. And like the market has forgotten about it, got worried about it, forgotten about it, got worried about it, forgotten about it multiple times along the way. So I do think that was probably an overreaction, but we shouldn't dismiss that either. And if you're China, like this is like really important to you. And there, you know, there are some reports that like an EV machine had been smuggled into China. And I mean, what a feat of espionage because those things are like, they're huge. I don't know if that's true. You know, there's some noise about it, but you know, China, they're really, really good. They're really, really smart. They work brutally hard and you know, they see this as super important for them as a country. But are they going to go from the year 2001 to 2026 or even 2030? Are they going to, because it really is, it is. It's a learning by doing. It's a learning by doing and you kind of have to, yeah, like you can't, you can't accelerate the doing. You can't, you can't teleport into the future. You actually have to go through those learning cycles. So is it significant? Yes. Did the market overreact? Probably. But like, I think a lot of, like, I think it's, it's very hard as an American to really understand what is happening in China and like, have like total conviction and clarity, you know, like for, for better or worse, like we are decoupling. And just that is a process that has been set in motion. And at this point, it almost feels like it's kind of self-reinforcing on each side. And you know, that's, that's unfortunate. But we are where we are. And they're not, [00:55:29] Speaker 2: they're not going to stop. Neither are we. Any commentary on like every other company in America? Like, I feel like right now it is 10 companies, couple private. Well, not, not last month. I mean, [00:55:39] Speaker 1: everything but AI was vertical. And I do think open, you know, open source getting closer to the frontier and companies like Fireworks making it really easy to customize a model such that you can get, in some cases, better than Frontier for performance for a meaningfully lower cost. That is a godsend for the software industry. And it's also a godsend for all these, like, you know, there's, there's a lot of AI natives and like all these AI natives, you know, it's like our friend Vishria, I think he said two years ago, I've never seen more companies go from like being founded to like $50 million a year in revenue, generating cash flow with like, whatever it is, nine months. And it's hard to know if any of them were durable. Because like, back then, like, it's like, hey, you know, these are a lot of people would dismiss them as chat GPT wrappers. Well, now with open source, you've actually, you've generated some data that's unique to your use case, whatever your vertical you're going after has a wrapper is. Fireworks, they did come out with a really cool product called Nexus. And if you're using Cloud Code, OpenAI Codex, Grok Build, it is literally three lines of code, like 20 words. And Fireworks ingest your data, kind of, you know, they can RL a model, and then there's a router that sends the query, and they've had amazing results. And this is kind of the solution for every AI native. And that's why you saw, you know, Harvey, before it was acquired, Cursor leads so heavily into this, Harvey, Lagora, all of them. Because if you can go from just using one, two or three frontier models, to using those frontier models, for whatever it is, 30 to 60% of your token consumption, and then use your own RL model, all of a sudden, [00:57:43] Speaker 2: you're not a wrapper, you're way more defensible. I was so interested by that Cursor thing that came out, I think it was Cursor, where it's sort of like AI speedrunning, like what we've learned amongst humans, which is you could use the frontier model to plan, and then farm out tasks to the dumber models, and it's 15 times more efficient, or whatever the metric was. [00:58:03] Speaker 1: And it may be that like, and this is like super ironic, but it may be that like, lower margin open source tokens that are just a little bit behind the frontier, and you know, we have friends who believe that, you know, frontier, once a frontier model hits RSI, it will actually have a dramatically lower cost, to serve at every level of intelligence by kind of distilling this, and then there's no place for open source. And I would say that's like a, you know, a anthropic, open AI, Grok, maximalist view. But you know, we shouldn't dismiss anything, I don't know, or really important, anything is possible. Like, you know, we want to like be very humble. I particularly want to be humble after the month I've had. But that doesn't seem that likely to be. And- Why? Well, one, because there are so many of these AI natives that have actually generated a decent amount of domain specific proprietary data. Yeah. And kind of before, like, open source had this moment to these inference clouds and these routers really developed, like you kind of didn't have a choice, like whatever the terms of service were, you accepted them. But if you can now kind of get off that treadmill, that gives you a degree of independence, maybe durability, safety. But kind of going back to your point, it may be that these cheaper tokens just massively inflate the value of the most cutting edge frontier tokens. Because if like today, if you have, you know, I'm going to make this up, you know, 120 IQ open source models, and they're really cheap to run, well, doesn't that make a 160 IQ model that can orchestrate them more valuable? And so just, we talked last time about how I've been really surprised that, you know, so much of the economic returns have accrued to the frontier. Now that that is changing with what we're seeing with these kind of inference clouds, together modal based in a very cash efficient way, what's shocking about those business models, is they're growing almost as fast as the frontier labs in the early days, but burning very little cash, like it's, it's pretty extraordinary, you know, from like, you know, look, go back to silly SAS metrics, like the, you know, the rule of 40 perspective, like, [01:00:51] Speaker 2: these are crazy numbers. Do you think there's a lot of instruction in just like the distribution of pay inside of an organization? Like the CEO makes X times more than the median person at a company, and maybe that's frontier tokens versus, you know, open source tokens. Absolutely. Yeah. [01:01:05] Speaker 1: Something simple. Yeah, it may be that what we discussed last time where, you know, frontier tokens, I think they may lose, like the pie is growing really, really fast. They may continue to capture the overwhelming majority of economic value, but kind of not all of it the way they have been. And open source tokens might be the majority of token source tokens processed. And just again, going back, that's great for infrastructure demand, because a token is a token, and it takes the same [01:01:34] Speaker 2: amount of flops, watts, space, cooling to make. What's the worst thing that could happen in AI? Is it regulatory? Is it some sort of life? I think regulatory has to be the biggest risk. I mean, [01:01:48] Speaker 1: it's the most obvious risk. And so that was kind of one reason I was excited to be here this week, [01:01:54] Speaker 3: was to just like, I want to be scared. You know, I like, I don't, I don't want to feel like a lunatic, [01:02:03] Speaker 1: you know, watching these stocks relative to, you know, get more cheaper, thinking the expected forward returns are going up. You know, well, you know, it feels like the on the ground fundamentals have like pretty materially improved in July relative to even June. But I still can't come away thinking like, you know, regulation, it just has to be the biggest risk, like you just can't ignore New York, making a data center moratorium. And just like we are, we're living in this weird, post factual post logical political world. And, you know, and I mean, I think the AI industry, it has done a terrible job of PR. And I do think that it at least realizes that now. Yeah, maybe if not fixed it, it's realizes it. Yeah, but like kind of the narrative in Washington, you know, the political narrative, you know, I think amongst a lot of ordinary Americans is like data centers, they're going to raise your electricity prices, they're going to take all your water, and then they're going to take your job. And the reality is, like, given the deals that are being cut now, when a data center goes in, electricity prices actually generally go down for everyone around there, because of behind the meter deals. And this is that like data center pledge that kind of Trump asked people to sign. Generally, the data center developer, it used to be they just had to build a like, you know, whatever, they had to get the police department and the fire departments, like, you know, new trucks and new cars and, you know, new body armor or whatever. Now it's like, well, we're going to build you a hospital, a school, a new police station and a fire station, and we're going to lower your power bills. How does that sound? And by the way, the jobs are ongoing because it turns out that you kind of need these plumbers, electricians, you know, HVAC contractors. And this is like data centers are like, are in a lot of ways the best thing to happen for blue collar wages in my lifetime. And yet you have the Democrats who ostensibly represent the, you know, the blue, you know, these blue collar workers taking those jobs away. And so, and also like, it's just kind of wild how like, what is the phrase? Like a lie could go around the world to, yeah, fashion truth gets out of bed. But an author made a mistake in a book. It overestimated the amount of water usage in data centers by 10,000 X, not a little bit, like not one order of magnitude, not two orders of magnitude, not three, you know. And she's admitted that mistake many times. I was completely wrong. It's like been super debunked. It's like the Popeye effect. Yeah. [01:05:00] Speaker 2: Did you ever hear that example? No. The, you know, Popeye eats spinach. The reason was same deal in academics, in an academic book, they placed the decimal two things wrong. So spinach does not have more iron than everything else. It was just this one source. And then that propagated that people still say it has more iron. I literally had, I thought it had more iron. I mean, that's wild. [01:05:20] Speaker 3: Like 80 years ago. That's wild. I literally thought spinach had more iron. That's amazing. You're crazy. Yeah. You learn something new every day. [01:05:27] Speaker 1: Same thing though. Yeah. It's the same thing. And it's just, so somebody just needs to tell the truth. Like, like I feel like the industry and I thought like, geez, maybe if nobody else is going to do it, like I'll do it. Like there needs to be some sort of foundation. Maybe it's a pack that runs ads during the final four, during NFL games, during college football games, World Series. Here's what a data center does. Your power, a data center that signed this pledge in your community, your power prices are going to go down. They're almost certainly going to, you know, like contribute to the community in a material way. You're going to see a massive influx of super high playing blue collar jobs that are going to persist. And I think a lot of people thought that they were one time and they're just not like, there's for sure a spike. And then that moves to the next data center. But there is an ongoing kind of, you know, need for kind of RMA that upgrades at these data centers and technology is changing. So you're going to have more jobs, you're going to have cheaper power. You're going to have a wealthier community. There's going to be no impact on water, no impact on the environment. You know, and it's easy to build the data center 10 miles out of town, you know. And so like that story needs to be told along with, you know, like there are, you know, we heard a story, I think we talked about it last time, about how AI is increasingly really saving lives, curing rare diseases. Like we, you know, I think, I can't remember if it was, I think it was at ASCO this year, you know, the kind of vibe, you know, the vibe was like, hey, we've, this is the most scientific breakthroughs we've ever seen at a single conference. And for sure, some of that is due to AI. And so we need to like tell those stories. Like, you know, if you have a sick child, you know, a sick parent, a sick loved one, like AI meaningfully increases the odds of them recovering. Like, we just, we, we need, it's, everybody needs to tell this. And I think people out here, it's all of this is so blindingly obvious to them that they, they can't, they can't, yeah, they can't process that this is a true but wildly divergent view from most Americans. Um, and so like, I think the industry really needs to tell its story better because this is like New York, it just feels like is the first of many. And even in some of these deep red states, they're super pro-growth. They're just like, hey, you guys are not doing a good job telling your story. Then we can't, we can't tell your story. If you tell your story though, we can retell it, but like, you're the experts. Um, you know, if you like, like something of, if you do not speak your own truth, no one else will. Yeah. What have we missed? I do think something that is missing from all of this conversation about compute is what is going to happen when you put these SRAM-based accelerators that are not constrained by HBM DRAM and are often made on older nodes that are not competing with like the latest, greatest GPUs. You can, whether you there's, when you disaggregate inference, there's people talk about pre-fill and decode, but decode is two parts attention and feed forward network. And like the ultimate holy grail is if you could do pre-fill on one chip, um, that probably doesn't have HBM DRAM, do the attention on a super high powered chip with HBM DRAM and then do the feed forward network on one of these SRAM chips. But like the ROI on adding these SRAM accelerators, to the existing installed base of compute and new compute. But like what we're seeing is like you do better. You just can't beat SRAM in particular for that feed forward network. And you just almost, you can't, no matter how much you try and get the ratio of compute to HBM DRAM to SRAM on the chip, correct. Like the workloads are always changing and there's different workloads and like being able to disaggregate into these three parts. Um, like, I think this is, this is going to be really, really [01:10:05] Speaker 2: positive for the ROI on AI. For some reason, I just thought of a funny question, which I love their framing of Game of Thrones versus all these people. Can you imagine a player that is not currently on everyone's mind becoming relevant at like the major Game of Thrones scale? Like that could be like Micron all of a sudden, you know, would be like a sample answer to the question of someone that becomes as important as Anthropic, OpenAI, Microsoft, Amazon, you know, NVIDIA, SpaceX, yeah. [01:10:31] Speaker 1: So like a dark horse Game of Thrones player? So some names that come to mind, um, like Lipu is probably a dark horse. Um, I do think, um, Lynn at fireworks. She is like a, just an absolute killer. Um, I think, uh, you know, our friend Scott Wu, you know, cognition is kind of like, um, you're here to that one. Yes. Um, [01:11:04] Speaker 2: I think those are, uh, the most obvious names. What about SpaceX? What's it been like watching that be digested by public markets, at least initially? Um, do you think the market understands it as a company, [01:11:20] Speaker 1: the most important new company to be public? It doesn't really feel like it, it does because it's kind of like such a, it's such a, like everything to me is the fundamentals have gotten better since an IPO, like rock 4.5, the cursor acquisition, you know, cursor, um, has clearly accelerated meaningfully. And then they have shown that they could, you know, they've, they've shown over the last three years, they could bring on more compute faster than anyone at lower prices. And now we know that they can even adjusting for the spot first contract gap, like their big advantage was they came into the market, you know, and just hit those spot highs. Um, and in a strange way, like one of the more bullish things for compute is like, you know, they put a vast amount of compute into the market overnight and it wasn't even really a blip. It was like the market just utterly absorbed it, you know, like just the freight train didn't slow down at all. Um, but you know, a, you know, a substack rider will fund a fund to AI. They think that SpaceX is going to try and bring on eight gigawatts of compute. I will never bet against Elon, but I mean, that would be a truly incredible feat. And they are, rates have gone up since they signed those last contracts, not down. And they're monetizing at something like 50 billion a gig. And consensus estimates for next year are 73 billion. So forget Starlink V3, forget Starlink direct to sell, Grok 4.5 and cursor, the sum of that probably hits a $10 billion ARR pretty quickly. Forget all of that. Um, you know, forget like the core base Starlink business. If they bring on anywhere near that, the consensus estimate is 73 billion and that's eight gigs at 50 billion a gig. And obviously that would not all be lit up at the beginning of 27. And it seems very implausible to me, like I almost don't believe the funder report. Um, but to this day, the only companies that have brought on more than 500 megawatts of power in a year are the hyperscalers, Corweave, Crusoe and SpaceX. SpaceX has kind of brought on the most, the fastest at the lowest cost. And then people do actually really like their clusters. Um, but again, it's kind of like the market is going to need to see that. That would not be the market's interpretation of SpaceX today. No, no. Um, and it does feel like, you know, there's this, there's, there's a big New York hedge fund short case on it. And I think they think, you know, oh, the spot price for compute is going to go down 90%. And, you know, you're going to bring on all this, you're going to bring all this on all this compute. It's not going to generate, you know, nearly as much revenue as you think maybe, but also want to be really clear. Like, like I have seen those, I've seen Elon's companies, you know, do really impressive things over the year, bringing the funder AI report of eight gigawatts at 18 months. Um, and I'm just quoting that because it's public, it's available to everyone. Like that, that, yes. Um, you know, I think one of Elon's phrases is we specialize in making the impossible late. I've never heard that. That's great. Yeah. Um, it, you know, there's like kind of a lot of truth to that. Um, but I just think very little is built in from my perspective to that stock for the amount of compute that they might be able to bring on. And again, I don't think it's anywhere near eight. Um, and it's going to be really hard and energizing these GPUs is really hard, but they've been good at it. And it doesn't feel like that's in estimates or really in people's [01:15:40] Speaker 2: thinking. I'm thinking about that funny meme that says SpaceX, the data center company. [01:15:43] Speaker 1: Oh no, a hundred percent. Yes, absolutely. Um, and then I would also just say like from, I did spend a lot of time at Starbase and, um, orbital compute feels more real every day. Pretty cool to see that Starship landing the other day. Pretty cool to see the Starship landing and that it's, you know, it is funny. There's our friends at Benchmark, they funded StarCloud. And I don't know, last time StarCloud is an orbital compute company that like SpaceX is kind of partnering with, uh, they're going to, I think, let them use the Starlink laser technology, which is really important for orbital compute. And like, but I do think that's like kind of a good sanity check. Last time I checked, you know, the benchmark guys were pretty smart and they're not coming from the Elon ecosystem at all. And they chose to fund an orbital compute company at like, you know, a decent valuation without the internal launch costs that SpaceX gets. And that's just, to me, that's a good like, hey, am I crazy? Am I crazy? And it's like, well, maybe I'm crazy and maybe Elon's crazy. And maybe Benchmark is also crazy. And maybe the SpaceX engineers are also crazy that, man, that just doesn't seem that probable to me. Um, and I mean, we should, should we say whose offices we're in? Yeah. We're sitting in the, we're sitting at the Benchmark office. Yes. This is their famous table for their famous dinners. Um, so thank you, Benchmark. Thank you, Benchmark for this episode. Yes. Thanks, Eric. Um, and, and Sheila, we should thank them all. Um, Eric, Eric coordinated for me. So he gets a special shout out. Thank you. Thank you all of the partners. Thank you, Eric. Well, you know, and just, you know, we will see where all of these stocks are in a year. And the great thing is, you know, time will tell, you know, people are going to be right or wrong. You know, the future is probabilistic, but we are at like, it's an exciting moment. [01:17:42] Speaker 2: Well, if we keep doing this on the, the model release cycle, I'll see you in a couple of weeks. Yeah. Maybe you're going to benchmark. That's always a blast to do with you. You know how small advantages compound over time. 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