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. Find tell me something negative.
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 PE of the last 10 years. The market 100% thinks they are significantly overrated.
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 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. There are some fundamental negatives which we should talk but 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 been out here for the summer. Have you heard a single negative quantitative metric about AI?
In fact, every metric is accelerating. And to your point, not just blind optimism from people excited about AI. Like here's some data that they can show you and from their different vantage points. 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, base 10, modal together. And the picture looks very different when you see that because open source has accelerated massively because of GLM 5.2, Kimmy K3 and then you know Neatron continues to kind of chug along. We had a great you know very small American open source model release. OpenAI has accelerated and Anthropic 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 GPU prices if you're really bullish you thought they would to price to rate 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 Neo clouds, 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 it 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 was an unusual amount of one-times this this quarter and if you from 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 Reubins which will come at a meaningful premium before these contracts repric. It's been a challenging month and it's almost
you know like is it helpful to kind of like walk through the month how we got here?
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 the 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 were thinking is like hey we will show on a small chunk of capacity that we can generate really strong IRRs then we'll go raise equity capital and and we'll be off to the races and probably raise capex. That doesn't look like what 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 metaccapex plans. None of that telemetry had shifted at all. If anything, it was, you know, 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. It was overshadowed by Croc 4.5, but it was a good model. Way better than you think in two years. So just no chance they're taking their foot off the gas. Then Kimmy 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 too. And and and then Kimmy, 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 debate 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. and 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 by thereby you know there is elasticity thereby driving token demand. You need more demand for compute and the margins, you know, anthropic and open source, they all run on the same underlying cloud providers who charge the same amount of compute, you know, 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 a catalyst. Well, this combination of things. Well, yeah. Then it kept it's 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 world 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 and comes term with it. Then China has a DUV machine and this causes, you know, everybody's in these baskets. This causes a huge sell off in semicap 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 is still funded out of operating cash flows and so real yields go up and spreads widen. 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 and maybe a CDS was blowing out all of these CDS for everybody is blowing out. And you know very smart private capital people just like that hey this is just exactly what you'd expect. These are just banks, you know, kind of hedging their commitments. But nonetheless, it doesn't look good. And these are undeniable facts. CDS is up, spreads widened, real yield real yields are up. And that is that would be really really scary if we needed debt to finance this buildout. And that's where I think it's this differential between spot and contract pricing for the installed base of compute is so important.
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It's so important to understand what the financing will be like for the next six months or something.
The degree to which this buildout is going to require credit, right? Which would be the classic like capital cycle. We start to overextend ourselves with debt and that's where things get
100% and then you know debt fueled buildouts you know they demand immediate repayment. So if supply and demand get a little bit out of whack, things can unwind 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 buildout, this is like a significant negative. And if you model it out, 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 Reuben. Ruben 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're not I think it's very unlikely they monetize at the rate of Ampere and we could go into why 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 in a discount to current Blackwells then it's more like two 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 repric we're going to continue accelerating consensus is modeling 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 choose to do that or not, we'll see. But 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 black wells. They're mostly being used for trading initially. trading does not generate, you know, a return and that this could be a risk. And we 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 as the operating cash flow started to really accelerate. And it's 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. operating cash flows go up and you could fund most of this out of operating cash flows, maybe all of it. Like if it reprices at current rates, you could probably fund all of it for the next several years. And so it has 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 deepseek you know what it's worried about liberation day you know what it's worried about there's something very clear in a weird way That is comforting reassuring. 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. Um, 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 definitionally 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, 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, B200s, 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 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 have 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 blackwells 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
Find tell me something negative
you know like you know the question I asked you have you is there one negative quantitative metric you've you've heard
has been what I've been asking everyone 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 I think that may very well be true. But then you have open AI and open source massively accelerating the comp and if you look at the sub it is net accelerating may 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 and 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 Kimmy K3 which I think we're going to see continue. I think you're going to see Nvidia bring Neotron 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.
The only time the civies have been cheaper were liberation day and deepseek and that was those were kind of V-bottoms. And that means to you just that the market thinks they're significantly over earning. Yeah, the market 100% thinks they're significantly over earning and you we need to be humble. Maybe they are. Um 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 d 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 anthropic third party data I would say that seems to be hotly contested by the anthropic shareholders who are who are bound or kind of like chopping 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. I mean it's hard for me to believe that open source and OpenAI have accelerated to the extent they did and but yeah, Enthropic is clearly, you know, kind of 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 in Fidelity who just says the way to have navigated 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?
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 repric. 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 was an interesting essay that got sent to me you know I think we've talked before about Mike Mikeson's theory that like a breakdown in diversity is kind of what leads you know to bubbles and crashes 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, but 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 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 Claude is kind of Walter Cronkite for the stock market and everybody just believes whatever it says and this is leading to like funny really and by the way it's really smart but it's not always right. Its 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 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 TBU TBU. He's like 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 6 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 6 weeks. What's your sense of being out here especially 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 etc 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. like a 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 then it's like these models are trained on 300 trillion tokens and if you know you can trade something on 10 trillion tokens and then let it out into the world and learn sample efficiently you know 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 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 to be clear this would be awesome for the world
yeah we all want we want this for
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 you just don't know.
Well, yeah. And also like Nvidia is heavily involved with all of these startups. So what would like if I was forced to if you were just forced to come up with the set of circumstances that would really switch you around and get you really scared is it ju would it just be that this operating cash flow thing doesn't play out and therefore we just need to debt finance this operating cash flow does not continue to accelerate that that would be negative um and and that to some degree is going to be a function of how anthropic openai gro cursor which Cook Rock and open source do you know if like 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
like not not a single person
in fact it's the opposite 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 entropic open AI, if the sum 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. Uh, and I do really think the future is like multi multimodel. Yeah, I think particularly for the AI natives, they're going to want to take an open source model. It's gotten, you know, all these inference clouds have gotten really good at 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 is 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. And 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 on different cycle times. You know all these you know big public companies are like oh my god my AI spend is 20xed. I've burned my budget in 3 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 in their spend or even a decline, but it actually has nothing to do with the amount of, you know, GPU compute hours they are 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 are 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 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 Yak as like 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 eight 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 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 push back 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 as 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 Satcha's comments like either we're going to start growing 10% or we're not 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 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 percent of percent tokens as a percent of like total comp spend or something like this. What what are the rages you've seen? I mean like in the really pled companies like it gets really high 20% 25% something like that.
Well um our Fred Dylan Patel at the company so he's an ASI back seat but he's at 30%.
he probably that's probably the highest one I've heard. Um 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 let's take your 20% number that's five trillion and that either comes out of labor substitution or faster economic growth and we really really really want as you know humans it to come 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 and controlled companies and adjust for some of the like COVID era overhiring like nobody's really laying people off like these are the people that would probably be most quick to adopt AI to 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 100k token spend
100%. Well, in the bull case,
so growth, not labor, labor growth in the bull case and you know, you've seen charts from cognition, ramp, and stripe that the companies that are spending the most on AI are growing 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, and 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, everything's in a shortage. Everything's in 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 policy is moving in a good way. You the turbine manufacturers, the diesel gen 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 of the market and like a transition in 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 they are trading short-term upside for these you know what they call supply chain agreements long-term agreements LTA where they essentially, you know, agree there's many flavors, but the 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 during during COVID were really reluctant to lay people off and 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 Traniums, 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. Mhm. 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. Um, and this is like at some level like a giant Game of Thrones or IPERS 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 pre-purchased. So if you break the LTA, and this is assuming we're not in a severe overcapacity situation, but the logic almost the game theory even holds in a severe overcapacity 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. You know, like let's just say Google breaks an LTA. You know, there's an overcapacity. I'm making this up. And 2028, 2029 they break their LTAs. Well, if they're breaking their LTAs, it probably means, you know, you're oversupplied, prices are coming down and then, you know, capacity naturally contracts. Well, like what do you think's going to happen to Google's allocations? And then, you know, 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, you know, going back three, four, five years. They know they can do whatever they want with no consequences because their volume is so big, you know, that even if they like super screw Hynix, Micron will of course take them.
This is just different. You know, you have at least four players and you have all the startups. You're an investor in Etched and if you break an LTA and 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 Nvidia's dominance, I think the current environment, the extent to which it favors Nvidia, 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 financable 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 in 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.
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 cash flow mismatch. Like, hey, we're making all the cash. And like this isn't really vendor financing because they're not loaning them the money. Somebody else is loaning the GPU buyer the money. So it's not quite vendor financing. They're still making equity investments but it's not like you're just putting money into someone in return for them. And then some of that money, you know, is used to buy chips even though Nvidia said that they write into all their equity investments that the money can't be used to buy Nvidia chips, but obviously money is fungible.
Just like a funny little thing.
Yes. But you know, I think at some level it probably makes everybody feel better.
What would you do if you were the memory companies? Like if you were the CEO of Hynix?
I'd do the exact same thing Nvidia is doing right now, which is I would be going to the buyers of GPUs, tradeiums, and whoever, and saying 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 up front, so it's like they're not on the hook. I mean, I'm just making this up, but like do something like you can because you have money now and credit markets are reviving. There are many, you know, like the people, I'm sure our friends at Blackstone and Apollo are suggesting some variant of this to the memory companies, but hey, we will put up some amount of money from our cash flow today and then it's gone. It's, you know, shitty equity that makes the person who's extending the debt feel better, but we want some sort of a cut of the ongoing revenues as well.
Right? 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. You know, you can effectively get a royalty on recurring revenues. And 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. Essentially every time they haven't taken an equity stake in something, it's been a mistake. You know, they've taken equity stakes in everything essentially except the memory companies for a long while. Anthropic, then they took an equity stake getting Anthropic. But like why not if you have cash flow 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, safe superintelligence is now working with them. He sees everything and like what he sees makes him bullish. So one, have some equity upside, and then two, have a revenue share. And you're generating hundreds of billions of dollars of free cash flow and helping to kind of bridge what is clearly kind of a gap, at least given everybody's gone free cash flow negative until the operating cash flow accelerates enough that you can internally fund this. It's very opportunistic and significant in a good way and it significantly increases their revenue per gigawatt and then it also strengthens their competitive position.
You know, you and I, we both have startups. But okay, that's great, use that startup's chip. Well, what prices are they paying at Taiwan Semi? Higher than Nvidia. And all these guys, what prices are they paying for HBM DRAM? Higher. Can you finance those chips easily at the same rate as Nvidia? No. And so it's always like, you know, there's a real burden, particularly if you use HBM DRAM, like you're in the crosshairs of this. Unless they made really different architectural choices. Everything that's happening is actually pretty good for Nvidia. Just going back to game theory, Anthropic, if they had been as aggressive on compute as OpenAI had been, they would have run away with it.
And so now OpenAI is back in the game. I think Grok is in the game. Those are the companies on the frontier and they have the compute. And do you think after watching that anyone is going to let off the gas? Because, you know, it was, I think four months ago that Dario was talking about how it was a really thoughtful commentary, but he's like it's really, really hard because 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 what happened.
OpenAI just got back into the game and now SpaceX is in the game in a big way with Grok 4.5 and Cursor. And like after watching that from a game theory perspective, is anybody going to back off anytime soon, especially if it could be funded out of operating cash flow?
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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 like, you know, I read this thing that Dario wrote and I was like the 3x compute price thing or whatever. He was, I forget what it was. No, no, it was like 15x or something. Yeah, but just basically that 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 even in my, you know, forget my Bayesian probability space of expected outcomes. That wasn't even in my considered but dismissed as totally unlikely outcomes, you know. And then that guy is, you know, he's very smart, he's very plugged in. And then he pointed out that, hey, 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. Although obviously open source, the margins on open source are not really going up.
Yeah. I just, you know, 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 bearish relative to essentially everyone, which is just a strange state of affairs.
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 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 let's just make an analogy. Let's just say a DUV machine was a jet turbine and now like an EUV machine is like a warp drive. Or whatever it's going to be. DUV machine's like a propeller plane. EUV is like a jet turbine. But like they didn't have it before and now they allegedly do and that is like a phase transition. You know, you've gone from like liquid to solid. Now that solid, that 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. It's like the stock market massively overreacts and then like if this ever hits ASML's orders, maybe it hits it in 5 years and like the market has forgotten about it, gotten worried about it, forgotten about it, gotten 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, this is really important to you. And there, you know, there are some reports that like an EUV machine had been smuggled into China. And I mean, what a feat of espionage because those things are like giant, huge.
I don't know if that's true. You know, there's some noise about it, but China, they're really, really good. They're really, really smart. They work brutally hard and they see this as super important for them as a country.
But are they gonna go from the year 2001 to 2026 or even 2030? Are they going to?
It's a learning by doing. It's a learning by doing and you kind of have to, yeah. Like you can't accelerate the doing. 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 I think it's very hard as an American to really understand what is happening in China and have total conviction and clarity. You know, for better or worse, 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 that's unfortunate. But we are where we are and they're not going to stop. Neither are we.
Any commentary on like every other company in America? I feel like right now it is 10 companies, a couple private.
