Why it's worth your time

A 79-minute pressure-test of the AI trade from one of its most-cited public-markets bulls, recorded into the current selloff — the gap between falling stocks and accelerating fundamentals is the whole episode. It runs straight down our lanes: rising prices for old GPUs, the memory supply war, debt-vs-cashflow financing of the buildout, and SpaceX as an orbital compute story. Full transcript below.

Full transcript

Machine transcription of the full 79-minute episode video from the linked post. No speaker labels; light transcription errors are possible; paragraph breaks added for readability. All views quoted belong to the speakers — Patrick O'Shaughnessy and Gavin Baker, Invest Like the Best (recorded into the early-August 2026 AI selloff).

"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 percent thinks they are significantly over it. 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, I was sensitive to criticism that, 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 has 2022 in a month. Yeah. There are some fundamental negatives, which, which we should talk, but like on, on the whole, the ballots of fundamentals, I think is improving significantly. Loads of AI names are down 50, 60% from their highs. We'll call 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, every metric is accelerating. And to your point, not just blind optimism from people excited about AI. Yeah. 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 based in modal together. And the picture looks very different when you see that because open source has accelerated massively because of 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're really bullish, you thought that GPU prices, if you're really bullish, you thought they would price to rent a GPU would, you know, decline slowly. You know, 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 contracted base of installed compute trading at a massive discount to the current spot market and has 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 was an unusual amount of one timers this quarter. And if you 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 that 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'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 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 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 really, 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, 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 the catalyst. Well, yeah. This combination of things. Well, yeah. It's like Jensen is the world's largest supporter of open source. Do we really... And he is 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?

You know, he'd still support if it was the right thing for the world. Yeah. But maybe it wouldn't be a 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 and comes to trouble with it. Then China has a DUV machine. And this causes, you know, every... comes true 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 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 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, all of these, 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 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 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. 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.

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Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.ai slash Felix. 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 capital cycle. Absolutely. We start to overextend ourselves with debt, and that's where things get through. 100%. 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 get unwind very, very quickly. That's what happened to 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 can, we could go into why. And some of it comes from just, you know, seeing what is happening on the ground with demand here for 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 Blackwells. They're mostly being used for trading. Initially, trading does not generate, you know, a return. And 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 as 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 chunk 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 seen 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 this out of 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's, it's been a, 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. Deep seek, you know what it's worried about liberation day. You know what it's worried about. Uh, 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, 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 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 blackwells 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, 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 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 is like pressure test. Yeah. Yeah. Fine. Tell me something negative. Yeah. Yeah. You know, like, you know, the question I asked you, have you, is there one negative quantitative metric you've, you've heard? 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 i think that's i think that may very well be true but then you have open ai and open source massively accelerating yeah the complex and if you look at the sum it is net accelerating maybe 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 have 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 deep seek and that was those were kind of uh v bottoms uh 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 they're there 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 i normally come to silicon valley and you know there's a mixture of like okay here's 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 um by the anthropic shareholders who are 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 you know um i mean it's hard for me to believe that um 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 grok and cursor have also you can see from third-party data like july was a pretty transformational month with um grok 4.5 grok builds coming out so it has been a tricky month and um and i have a friend um i have a friend of 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 yeah 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 by mike bobison's theory that like a breakdown of 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 clod and clod clod code sometimes you know a clod agent and you know clod 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 uh 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 clod it's kind of walter cronkite for the stock market and everybody just believes whatever 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 it's not um 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 it feels like in the market there is this here's this piece of news it gets fed through clod clod interpreted this way 90 a huge chunk of people trade on clod'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 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 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 it's like oh there seem 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 um 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 trade something on 10 trillion tokens and then let it out into the world and learn sample efficiently you know that that doesn't sound good for training demand but like training 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 well 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 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 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 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 cash flow thing doesn't play out and therefore we just need to yeah the operating cash flow 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 open ai grok cursor which called 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 like that not a single person and it's not like it's the opposite it sounds like a drug market or something 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 open ai if the sub of these labs you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know if you know things, you know, if like Anthropic OpenAI, 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.

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 gotten, 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 could 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, 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 a 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, you know, 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 are effectively consuming behind, you know, these, 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 it did, 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 on the coast, who maybe aren't as cutting, and then Europe, who's like, just trying to figure out how to regulate AI. 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 you access it out, like, I don't know, 500,000 people in the world, 250,000, maybe you're 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'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, it is compute reprices, their operating cash flow is gonna accelerate, and maybe we could fund this. But like, who, where's that operating cash flow gonna 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 gonna 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 percent of percent tokens as a percent of, like, total comp spend or something like this. And what are the ranges you've seen? I mean, like, in the really pilled companies, like, it gets really high, 20%, 25%, something like that.

Well, our friend Dylan Patel, he's an ASI maxi, but he's at 30%. Yeah. 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 really, really, really want his, you know, humans 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-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. 100%. Well, the bull case. So growth, not labor. Yeah, yeah, growth. And the bull case, 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, you know, regulatory policies moving in a good way.

The turbine manufacturers, the diesel jet manufacturers, you know, they're ramping up. Supplies responding. 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 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, you know, 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 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. shares are I think for the next several years are going to be determined by supply by supply chain allocations and kind of what you have kind of pre-purchased so if you break the LTA and you and this is 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 you know like let's let's just say google breaks an LTA you know there's there's an over there's an oversupply i'm making this up in 28 29 they break their LTAs well if they're breaking their LTAs it probably means you know your oversupply prices are coming down and then you know capacity that naturally contracts well like what do you think is 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 the over they're overwhelmingly the largest purchaser they know they can do what 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 hydex micron will of course take them this is this is just different you know you have at least four players did you have all the startups you're an investor in 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 gonna break the volume agreement and you know screw you we're gonna give the volume to your competitor you just you just lost share you know this so i think the the you know it like i think you know nvidia's dominance i think is um 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 um with a revenue share if gpu prices are above a floor yeah um 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 um you know cash flow mismatch like hey we're making all the cash yeah and like this isn't 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 um it's you know they're still making equity investments but it's not 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 said that they write into all their you know equity investments that um you know the money can't be used to buy nvidia chips but obviously money is fungible and um funny thing what's that it's just like a funny little thing yes um sense yeah but you know i think at some level it probably makes everybody feel better sure um what would you do if you were the member like if you were the ceo of hynix i'd do the exact same thing nvidia is doing right now which is i i would be going i say i would be going to the buyers of gpus tradiums and whoever it's 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 they're not i mean i'm just making this up but 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 cash flow today and then it's gone it's you know surety uh that you know makes the the person who's extending the debt feel better but we want 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 cash flow and you're bullish on ai and jinson 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 cash flow 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 cash flow negative until the operating cash flow accelerates enough that you can internally fund this it's almost like i mean it's um it's 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 city higher than nvidia and all these guys what prices are they paying for um hbmd ram 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 hbmd ram like you're just you're in the crosshairs of this um unless like actually you know maybe actually like they made really different architectural choices everything that's happening is actually pretty good for him just 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 yeah 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 because you just you know it was i think four months ago that dario was talking about how you know it was a really it was a really thoughtful commentary but he'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 opening i 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 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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 Dorkesh wrote, and I was like... The 3x compute price thing or whatever? Yeah. I forget what it was. No, no. It was like 15x or something. Yeah. No, but just basically that renting an H100 for a year would cost $250,000. 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 unlikely outcomes. You know, and then that guy is, you know, he's very Dorkesh. He's a very smart guy. He's very plugged in. And, you know, he pointed out that like, hey, the, you know, something like, I think you 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, although obviously open source, the margins on open source are not really going up.

But I mean, 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. 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 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's going to be, you know, a DUV machine is 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 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's 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.

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. 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, 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, 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, GrokBuild, 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, lead 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, 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 past the dumber models, and it's 15 times more efficient, or whatever the metric was.

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, 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. 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.

Um, together modal, um, uh, base tip 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, let's go back to silly SAS metrics, like the, you know, the rule of 40 perspective. Like 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, something simple.

Like, 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 tokens processed. And just, again, going back, that's great for infrastructure demand because a token is a token. And it takes the same 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 like?

I think regulatory has to be the biggest risk. I mean, it's the most obvious risk. And so that was kind of one reason I was excited to be here this week 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, you know, watching these stocks relative to, you know, get more cheaper, thinking the expected forward returns are going up. You know, while, 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, 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 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. This is that like data center pledge that kind of Trump asked people to sign. Generally, the data center developer, you know, it used to be they just had to build a like, you know, whatever. They had to get the police department or 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 it also like it's just kind of wild how like what is the phrase like a lie could go around the world to get out of bed. But an author made a mistake in a book and 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. Did you ever hear that example? No. Well, the, you know, Popeye 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. Then people still say it has more iron. I literally had, I thought it had more iron. I mean, that's wild. Eight years ago. That's wild. I literally thought spinach had more iron. That's amazing. Crazy. Yeah.

You learn something new every day. 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 and then 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, yeah, they can't process that this is a true but wildly divergent view from most Americans. And so like, I think the industry really needs to tell its story better because this is like New York. It just feels like it's the first of many. And even in some of these deep red states that are 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. 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 HBMDRAM and are often made on older nodes that are not competing with like the latest, greatest GPUs. Because 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, it probably doesn't have HBMDRAM, do the attention on a super high powered chip with HBMDRAM, and then do the feed forward network on one of these SRAM chips. But like, the ROI on adding these SRAM accelerators, oh. 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 to 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 it to these three parts. Like I think this is gonna be really, really 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, it'd be like a sample answer to the question of someone that becomes as important as Anthropic, OpenAI, Microsoft, Amazon, you know, NVIDIA, SpaceX.

So like a Dark Horse Game of Thrones player? So some names that come to mind, Ike Leapu is probably a Dark Horse. I do think Lynn at Fireworks, she is like a, just an absolute killer. I think, you know, our friend Scott Wu, you know, Cognition is kind of like- You're here to that one. Yes. I think those are the most obvious names. What about SpaceX? What's it been like watching that be digested by public markets, at least initially? Do you think the market understands it as a company, the most important new company to be public? It doesn't really feel like 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 has clearly accelerated meaningfully. And then they have shown that they could, you know, 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 could, 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. 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 trade didn't slow down at all.

But, you know, a, you know, a sub-stack rider will fund a, fund a 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. 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 be like, I almost don't believe the funder report. But to this day, the only companies that have brought on more than 500 megawatts of power in a year are the hyperscalers, CoreWeave, 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. 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. 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 I 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 in 18 months. But I'm just quoting that because it's public. It's available to everyone. Like that, that, yes. 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. you know, there's like kind of a lot of truth to that. Yeah. Yeah. 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 thinking. I'm thinking of it. That funny meme that says SpaceX, the data center company. Absolutely. And then I would also just say like from, I did spend a lot of time at star base and, um, orbital compute feels more real every day. Pretty cool. We'll see that starship landing the other day. Pretty cool to see the starship landing. And then it's, you know, it is funny. There's our friends at benchmark. They funded star cloud. And I don't know. Last time star cloud 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. 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. And I mean, we should, should we say whose offices we're in? Yeah, we're sitting in the, we're sitting in the famous benchmark table. Yes. This is their famous table for their famous dinners. So thank you, benchmark. Thank you, benchmark for this episode. Yes. Thanks, Eric. And she, we should think of all, Eric coordinated for me. So he gets a special shout out. Thank you. Thank you. All the partners. Thank you, Eric. Well, you know, 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. Well, if we keep doing this on the model release cycle, I'll see you in a couple of weeks. Maybe you're going to benchmark. That's always a blast to do with you. You know how small advantages compound over time. That's true in investing and just as true in how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default, better data, better decisions, better economics over time.

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