Gavin Baker - Watts and Wafers

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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/fuelix. Open AI, cursor, andthropic, perplexity, and versel all have something in common. They all use work OS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities, like SSO, SKIM, RBAC, and audit logs. That's where Work OS comes in. Instead of spending months building these mission-critical capabilities yourself, you can just use Work OS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on Work OS. Work OS is the fastest way to become enterprise ready and stay focused on what matters most, your product. Visit work OS.com to get started. Hello and welcome everyone. I'm Patrick O'Shanasi and this is Invest Like The Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at Colossus.com. Patrick O'Shanasi is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. - My guest today is Gavin Baker, the founding partner and CIO of a Trade East Management and this is our sixth conversation. The central team is Watts and Wafers, the two physical constraints that in Gavin's view will dictate the next phase of AI. On power, he thinks the near-term shortage starts to ease in 2027 and 28 as new sources of energy come online and the orbital compute helps solve this problem in the long term. On Wafers, he explains what is different this time from the dot-com bubble and YTSMC's capacity decisions, maybe the single most important variable to watch. We also discuss Elon's Carifab, the disaggregation of GPUs, the role of new chip companies and whether economic value of AI will keep accruing to the frontier models. Please enjoy this awesome sixth conversation with Gavin Baker. - All right, so this is our sixth time doing this if you can believe it, which puts you back into first place or at least tied first place with Gurley. Back into steam territory, always my favorite conversation about markets and everything going on. Even since last time when we did this, which was so exciting and spectacular, I think we're in an even more interesting time now. Maybe just start by riffing on how it felt for you living through March and April of this year, which felt to me just like a completely unique economic technology and market environment. And you're the biggest student of history and of these times, so what did it feel like? I would say broadly speaking, there are two kinds of drawdowns. They're drawdowns where you're wrong, company misdestimates, your hypothesis was invalidated, and you have to take your medicine and you crystallize that loss. And then their drawdowns are periods of utter performance where you're underperforming because of companies you know really, really well and where you profoundly disagree with the price action and you can lean in and instead of crystallizing negative performance, you could kind of build, pent up alpha, pent up future performance. And for me, that is what March felt like. The Nasdaq was selling off. At the same time, what was happening in AI was I think the most extraordinary moment in the history of capitalism, the history of American business. What I just mean by that is an anthropic, they added $11 billion of ARR. And what is astonishing to me about this is that this asset cloud revolution it created will call it between $5.10 billion of value. I would say, arguably the three highest profile SaaS companies in the last 10, 12 years are Palantir, Snowflake and Databricks. And these three companies employ thousands of people, tens of thousands collectively. They've all spent 10 years building their businesses and anthropic added their combined businesses in one month. (laughs) Nothing like that has ever happened in the history of capitalism for your career. Just the flat out history of capitalism, the history of business. It's wild and then Krista comes on this show and shares some stats, 500% in DR. - Yeah, you do the math on that for three years. - Yeah, it's the same thing. - So there's just no precedent for this. And we tech investors, we hear a lot of discussions about S curves and investing in exponentials. I've just never seen an exponential like this. It felt even more extreme than deep seek, which was a very similar setup. They happened at about the same time. If we go back to 25, right, there's a huge sell off on deep seek, which was very strange because the paper gets published seven days before deep seek Monday. It got published. I believe on a Monday that was a holiday in America. I read it, I thought, hmm, this feels like it. - Can you read that for me? - That positively for the AI trade, I took action. And then we had deep seek Monday where AI really imploded a week later. That was really strange because by deep seek Monday, it was super clear that this was going to be the most positive thing that had ever happened to compute demand. Prices in the AWS availability zones in Asia had already doubled. You were seeing GPU availability go down and this was just the first time we saw how much more compute hungry reasoning models are during inference than non-reason models. And so that was a similar setup. You had to do some work to see that. I mean, it's not that hard to say, oh wow, stocks are sell it off. The price of DRAMs going vertical. The price of GPUs in Asia going vertical. GPU availability is going down. And then like two or three days later, GPU prices in America started going up, GPU rental prices. All you had to do in March was simply observe what was happening to athropic. And there's all these people who seem to regret not buying during 22, not buying during COVID, not buying during deep seek. You had the same valuation setup at the beginning of April and an even clearer AI inflection. So there have been all these chances to buy into AI. And then of course what complicated it was the straight-up foremose. I became a believer in am a believer that I think maybe one thing that the market was mispricing. I'm no macro expert. I do do a lot of pro national security investing. So I do have access to people who are experts that are excited to share their thoughts and opinions with me. And that the straight-up foremose being closed is actually relatively awesome for America. Why? Particularly for the goals of the current administration. So electricity is a very important industrial or manufacturing input. The key input into American electricity prices, which feeds into AI is in GWAM. Natural gas water in Blueberg. That was down 20%. And natural gas in Asia, Europe, everywhere else, doubled or tripled. Our relative manufacturing competitiveness improved overnight. And for better or worse, that is what the Trump administration seems to care about. They are very focused on America's relative position. And I think a lot of people had memories of the 1970s. What made the 70s so traumatic was it wasn't just the prices with Dopp. It's that there were actual gas shortages. Then you go through, OK, well, the US economy is dramatically less energy intensive than it was. The United States is now in the world's largest producer of oil and gas. And we've become now the world's largest exporter of oil and gas. And on top of that, there's this relative manufacturing advantage that made it easier to stay focused on AI fundamentals, stay focused on what were historically attractive valuations. I think on a relative basis, tech essentially got as cheap as it's been first the rest of the market. Has at any point over the last 10 years. And just think about that in the context of market efficiency. We have the most extraordinary moment in the history of capitalism. It's wildly bullish for AI. And you get a chance to buy AI at really attractive valuation. What do you make of the multiples that specifically anthropic and open AI, which in my mind are like the reference assets that are the most pure play takes on this trend, really being not that crazy. Like if you just look at the sale. multiple and compare it to maybe what data bricks and snowflake and these companies traded that at their peak. How do you process it? How do you make sense of it? I do think OpenAI and Anthropic are pretty different to animals from a capital efficiency perspective. And Anthropic clearly has a dramatically lower cost per token than OpenAI. They just do. And you can just see that in the amount of money that they have burdened to get to a roughly similar revenue scale. I think they've burdened maybe 80% less than OpenAI. So his businesses, they clearly have very different structural ROICs. I think Sarah Friars, one of the most exceptional CFOs, I think they're doing a lot of things to try to improve this. And they've secured a lot of compute. They've secured a lot of compute. That's another big difference. It turns out being aggressive really paid. Anthropic at 900 billion for 50 billion and AR are growing at ridiculous rates. And I think maybe a true statement is that the Anthropic could just wave a magic wand and get all the compute they wanted. They'd probably be doing well north of a hundred billion dollars today. Maybe 150. They have clearly deprecated the intelligence of Claude. There's an analysis Claude is even on Opus is generating 70% less tokens for these exact same question. As we talked about last time, token quantity equals quality of answer and quality of thinking at some level. And there is an intelligence density per token that also matters. I felt that as a user. So I think they would be doing materially more. 100, 150, maybe 200 billion. So you might be buying it at more like five times unconstrained. I'm going to make up a new number. You are. Unconstrained. Why do you think they don't raise 100 billion dollars at a $3 trillion valuation or something like this? If you were the Anthropic CFO, person is awesome. We just had them on or if you're Sarah, it seems to me like if the inbound I received following the Christian episode is any indication, everyone I've ever met is trying to invest in both these companies. I think it's wise. The future is uncertain. You are clearly in a very capital intensive game. Even if you are in the topic, I'm sure is it very positive, gross margins on inference today. I can probably start generating cash this year if they are not already generating cash. I think it's probably the case. But still, you probably want to be able to raise more capital, access more compute. The world is uncertain. Ukraine is starting to really, really win. How is Russia going to respond? I think there's still a lot of uncertainty in Iran. All this uncertainty, I think probably amplifies geopolitical uncertainty over time. It's an uncertain world. If I think about Iran, Iran has always made investors money. He treats it like a sacred covenant and has a result because he's made people money for now 20 years. He has a superpower. That is, he get essentially raised as much capital as he wants whenever he wants. I do think being focused on making investors money is wise and creates benefits that don't just last for like a year or two. They can last for the next 20 to 30 years. And the way Elon did this was systematically underpricing space-axe or whatever else. What is the actual method? Just never being greedy on valuation. Never pushing valuation. Just that simple. My friend Antonio pointed out, SpaceX compounded it low 30% per year for a decade. And that was just because Elon was, I think, focused on preserving the superpower and having trying to strike a fair balance between investors and employees. I think it's wise, but could anthropic raise money at probably a at least a 100% premium to this rumored latest mark? Of course. Let's get to the lots and wafers part of the discussion. Always my favorite thing to talk about with you. The importance of this infrastructure build out every time I feel like it's getting overheated and then the next time I talk to you, it seems like we should have done way more than we did. You studied S curves and the steepness of those S curves a lot. And you know a lot about history. Talk us through how you're thinking about lots and wafers today as the key to inputs into this whole thing. I think capitalism is going to solve the Watts shortage, absent big regulatory political blowback, which I think is a real possibility. The head of data center, infra-investing at one of the big PE firms, Blackstone Apollo, KKR said it used to be energy and chips, were our biggest gating factors. Now it's zoning and approval, much more important. I think a lot of companies are waiting till after the midterms to take action in terms of maybe workforce reductions. Nobody wants to be opinionated during the midterms. You've seen a lot of companies that make turbines, announce a plan, significantly increase capacity. There's like two of these machines that can cast these big blades. We haven't made one in 80 years in the West. We don't know how to make them anymore. All of that is true by no means am I minimizing the industrial engineering, magic, and artistry that goes into those. But capitalism is very good at solving problems like these over time. There's other sources of energy besides these turbines with a longer time frame. So I think the Watts shortage will probably begin to alleviate 27, 28. And then I think orbital compute will really solve that. I do want to reframe orbital compute because I think when people hear data centers in space, which we discussed our last episode, they picture a pit to god's size building in space. They're like, well, we can't do that. That's not what it is. A Blackwell rack weighs 3,000 pounds. It's eight feet high. It's four feet deep, three feet wide. It's racks in space. It's SpaceX has showed you an illustration. And it's a rack. That's the satellite. But it's probably about the size of a Blackwell rack. It has these solar wings that are probably 500 feet long on each side. You keep it in a sun-secretous orbit. So those solar panels are always in the sun. Because it's in an exactly sun-secretous orbit, the radiator, which extends behind it for hundreds of feet. This is the common criticism. Yeah. How are you going to go over that? I've spent a lot of time at Starbase over the years that I've talked to a lot of SpaceX engineers. And I do think it is the most talented group of engineers on planet Earth. And they're very confident they have solved this. And they're not always confident. There's some engineering that needs to happen to turn the starship into a Mars colonial transport. Will they do that? Absolutely. What are they more focused on? I'd say probably the repair maintenance. This is the two big responses. The radiator and how do you repair the whatever issue goes wrong in the rack? And the answer is, until you have probably floating optimuses, you don't. Starship is going to change the space economy in ways we cannot imagine. And particularly if regulation becomes constrained to data centers, none of it's going to matter. You're going to sell as much orbital compute as you can make. And then obviously you link these racks using lasers traveling through vacuum, which are already on every Starlink. And it's just mind-blowing to me that SpaceX operates the world's largest satellite fleet, which is 98 or 99% of all satellites in orbit. Every Starlink, they're cooling it today. I think Starlink V3 is going to operate at 20 kilowatts. A blackwell rack is only 100 kilowatts and people talk a lot about density. Well, if you're connecting the racks with lasers through vacuum, you can make the rack bigger. Physically, you're focused on weight, not size, and a data center on Earth where you try to connect racks, ideally using copper, minimize lengths, cabling is a big cost. You do want that rack to be small, copper when you can't optics when you must. But in space, there's all sorts of things that SpaceX can do that I think maybe some of these naysayers are not contemplating. They operate more satellites than anyone. They have a 20 kilowatts satellite today. So maybe you just scale that up to 60 kilowatts to start. They seem very confident they're going to go right to 100 to 120. The same company now also operates the largest data center out of Earth. They have the world's best hardware engineers and all sorts of people, almost all of whom are not smart enough or practical enough to work at SpaceX are these armchair skeptics. You know, I don't want to quote Larry Elleson, but somebody was being skeptical and Larry was just like, listen, he's out there landing rockets. I don't see anybody else landing rockets. And the reality is 10 years later, no other company is consistently capable of landing and fully reusing an orbital rocket. None of this makes sense without reusability. That means you have to land it. I would like to redefine orbital compute has racks in space, not giant floating. Not data center. Side data centers in space. That's silly. What makes a data center is you're connecting these racks with lasers. So it'll be racks in space that are connected with lasers into a virtual data center. 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Training will be done on earth for a long time. So I don't think that this is super bearish for terrestrial data centers. I think those are going to be valuable for my lifetime. But I do think if you're in this ecosystem of power production and cooling, and you are massively ramping capacity. A lot of these capacity ramps are going to be hitting just as I think all of the silly skeptics start to understand that Orville compute is very real. I think it's worth thinking long and hard about that if you're one of those companies. And then all sorts of cool stuff is happening in the interim where getting really good at repurposing jet engines. There's that boom aerospace that he's doing this. Capitalism is hard at work on what's on way first though. It's just this group of flinty older humans in Taiwan, who are the most important humans in Taiwan, whatever they are. The overwhelming fraction of the country's GDP water usage, electricity usage. They talk about the Silicon Shield. They all view themselves as inheritors of more chasing sacred legacy. I've vividly remembered like visiting science part more than 20 years ago and talking to them. Do you think you could catch Intel? And they said this is such a beautiful dream, but it's a dream for our grandchildren and did it partly because of Intel self-inflicted wounds. They think very differently. One reason Jensen flies over there so much is he wants them to expand capacity. I do think it's wild that Jensen has never had a contract with Taiwan. So they do business of what seems fair and in chicks just fascinating. No contract. It's going to be fair over time. We're partners. We're going to be fair to each other. The truth is based on every prior market precedent for a foundation new technology like AI. You've always had a bubble. Carlotta Perez wrote this great book about this markets are efficient. They correctly understand that this is a foundation new technology. There's what Mobus and calls a breakdown in diversity. Everyone becomes bullish on this new technology. And I am beginning to worry a little bit about a diversity breakdown. And then you get a bubble that bubble funds the build out of this new technology. But supply gets ahead of demand and you get a crash. And it's a particularly severe crash. If it's a debt fueled build out like the year 2000. And what they really go to about the current build out is it's still overwhelmingly funded out of operating cash flows. Which is a really important fundamental difference for a year 2000 has his valuation. Has this the fact that every GPU is running at 100% utilization when 99% of fiber was unutilized. So there's all these fundamental differences. History doesn't repeat, but it rhymes and has investor. We have to be very cognizant of it. I recognize that based on the last two or three hundred years, you know, forget the internet bubble. We had a railroad bubble. Canal bubble. Every kind of bubble. South sea bubble. We should expect a bubble. That's terrifying. Nobody wants a bubble. The reason it's terrible is if your valuation sensitive, you like massively underperform. You get fired by probably all your clients. George Vanderhiden who is no longer with us. Great Fidelity portfolio manager. He fought the bubble in 99. And he retired in early 2000. Because I think he just couldn't take it. He knew it was wrong. His clients were deeply skeptical. George, you're out of step. He had white hair. He's a truly great man. I only overlap with him briefly, but he was a very important mentor and friend to my good friend and mentor Jennifer Eurig. So I have a lot of Vanderhiden DNA through her. He was the same person who said being early is the same thing as being wrong. George retires because he can't take the underperformance. And he can't take clients saying what's wrong with you. You don't get it. And he has like 40% of his flooded tobacco, 40% of his home builders. And literally he probably outperformed the NASDAQ by like 20 or 30X over the next three years. And I have been optimistic that this fundamental shortage of wafers, which really today is controlled by Taiwan semi will prevent one. If Taiwan semi did what Jinssen wanted, I think in video it could sell $2 trillion of GPUs in 26 or 27, maybe 2.5 trillion, maybe 3 trillion, but there is a limit where consumers would consume so much, the probably would be in an over build. So Taiwan semi, if we don't get a bubble, we need to throw a party for them because they will have secretly prevented a bubble. You are starting to see companies go to Intel and Samsung. Unless it's assumed TSM stays super supply constrained versus the latent demand. The history markets is, I don't know who, but one of Intel and Samsung, they're not going to stay disciplined. They will break. And then at some level that will force everyone else to break. I think a lot of this may come down to the degree to which Taiwan semi can maintain a lead over Intel and Samsung. You gotta remember, it's whatever it is, it's 9, 12, 15 months. Beleding node, Adjima. Exactly. The pace at which they expand capacity. If I were to watch one thing to understand whether there's a bubble, it's Taiwan semi's capacity decisions. And I think there's a Goldilocks zone where they expand enough. They make it hard for Intel or Samsung to really truly emerge as like a at scale second source. With something well north of 30% market share. And yet they also keep this fundamental constraint on wafers that helps us avoid a bubble. And then obviously I think the tariff fab is going to play into this too. Similar to that. It's a SpaceX, I believe, Tesla's involved as well. To ensure to build the world's largest fab here in America. I think they're going to be successful. One, they have a partnership with Intel, which is very important because they're getting access to 50 years of institutional knowledge. That's just nine months, a few quarters, 12 months, three to five quarters behind the front. That's an advantage. It's also an advantage that I believe the tariff fab is going to get attention from the A teams, all the semi cap equipment companies. One big reason Taiwan semi caught up is ASML and Kalei 10 core and the research and applied materials. They wanted them to catch up. They don't like having a monopsony. The eight teams were in Taiwan working until made some mistakes and presto. So the eight teams will be here because of Elon's reputation in hardware engineering. And then to a degree that I think is maybe hard for people to imagine in America where politics is replaced religion. I think as Elon had his foreign politics that makes it hard for some people in America to see him clearly, which is sad because I do think he's probably doing more for America than any other American. We single handically bringing manufacturing back to America. He's revived defense tech. I think SpaceX is in some ways the most important defense contractor in America. He's doing a star link is amazing for the world. He's creating all these blue collar manufacturing jobs, which is like a goal. I think of a lot of liberals and good for America. He's done more than any living human to decarbonize the world. And if you are upset about data centers on earth for environmental reason, it's, well, here you go. It's sad, but he is a living deity in China, Taiwan, South Korea, and Japan. Having watched him for a long time, what he's going to do is they're going to recruit the best people because the best engineers want to work for Elon, especially in hardware engineering. He's going to recruit incredible engineers. Next to the teraphab, they'll be a Taiwan town. Oh, these are your favorite restaurants. I'm going to move them and their whole staff from Taiwan to Texas. And we're going to make everything the way they like it, and then we'll have Japan town. Same thing. We're going to have Korea town. We're going to have all these things exactly dying. to recruit the best engineers, and that's just not the way that the people who wrote Intel it's the absurd thing. So he's going to have the best talent, he's going to have the eight teams at the way for Fab equipment companies. He has Intel, which is important. It's so good for all of any administration's political goals. And I think it's different enough that it will not alienate Taiwan's semi. And these have long lead times, right? Terra Fab is going to be pumping out whatever GPs, whatever chips quite a long time from now. We'll see Elon tends to do things differently. Everybody else has taken three years to build a data center. He built one in 122 days. Samsung had to give him an office in their fab of Texas because he was so unhappy about like the pace at which they're expanding a building. Are you surprised by, you mentioned DeepSeek earlier, the simple reaction to that was, okay, these models are just going to get 95% as effective for some tiny fraction of the cost to still Chinese open source models. We'll use these for most of what we want to do. Fast forwarded a little bit of time, two years from now, there's no reason I have to spend a million dollars a year in my small little firm on tokens or something. But then the actual reality seems quite different than this. And I'm curious why there's that dissonance in your mind. I do think it's fascinating. The returns to the frontier, all the economic returns to AI at the model layer, not all of them, but an overwhelming amount of them have been at the frontier, which is surprising to me. And I think it's been surprising to a lot of people. This is one of the most important questions to be answered. And you need to have a hypothesis on it as an investor. Our frontier tokens going to continue capturing the overwhelming majority of economic value created at the model layer. And it is surprising. I remember when Jim and I 3.1 pro came out. It was mind blowing to me. It was so good. Today, it's intolerable. There's probably a little bit of a dynamic where companies prototype with frontiers. Then when they put something into production, you're hearing a lot of people do use vertex or open source. But still, it is a fact today that the overwhelming majority of these economic returns come from frontier tokens. And that's surprising. And whether or not it continues, I think is a very interesting question. And I'm much more open-minded to that having had the experience I've had with Jim and I 3.1. And then Opus. And then I do use GROC 4.3 a lot. It is on the parade of frontier. The companies that are on the parade of frontier are, and this is, by the way, a big change in a consequence of what we talked about last time, Google losing their per-cost token leadership has resulted in making very conservative design decisions with TPUVA to try and take it away partially from Broadcom and Nvidia continuing to make aggressive choices. But Google dominated the parade of frontier, the parade of frontier being intelligence first cost. And I think this is the most important thing to look at to analyze AI labs. Google dominated that nine months ago. Every point on the parade of frontier, OpenAI, XAI, and Anthropic were inside of them. Now, the parade of frontier is dominated by Anthropic, OpenAI. And then GROC 4.3 is on the parade of frontier. It's clearly the best lowest cost, 500 billion parameter model. And then Jim and I 3.1 is hanging onto the parade of frontier. And if I were to bet, they're bet that they're subsidizing that out of pride. I would just say a violation of Richard Sutton's bitter lesson is for sure the biggest risk to this trade to all of AI. The closer someone is to AI, the more skeptical they are, this will occur. One thing I think contributed to weakness in March was a much more stupid version of deep seek, which was a thing called turboquant. And turboquant is some Google memory optimization that was written up in a paper a year ago. And then during the middle of an agreement, while Google was negotiating with micron Samsung and high next to sign some LTA, they would lock in really high prices for a long time. They release this. What people do is always more important than they say, and they just kind of publicize it on X. And it goes viral. Like, oh my god, DRAM is cooked. Here's this DRAM optimization. I was unable to find a single AI engineer on planet Earth who believe that turboquant would have any impact on DRAM demand. But nonetheless, a violation of Richard Sutton's bitter lesson, you know, more compute will always outperform human algorithmic ingenuity, more computing data, chinchilla optimal, beyond chinchilla optimal. I guess what people increasingly do today. That's a real risk, man. The people who are building these models are skeptical of that risk. The reason I am a little less skeptical is I think we're very close to ASI. And who knows if the bitter lesson holds for 400 IQ models? Maybe we get a temporary period where these, you know, if you get to ASI, the first thing at once is probably to be smarter and have more resources. How does it do that? It makes itself more efficient. I think that is an actual risk. The bitter lesson, literally, I believe includes humans in it. So we're about to find out whether the bitter lesson will find out it applies to 300 IQ AIs, then 400, then 500 and 600. And at some point, we may have like a temporary violation of the bitter lesson based upon AI and ASI. So I'm curious how you think about some other parts of the innovation around the model, continual learning and memory being too that people seem to be most focused on as things that might create yet another new paradigm that we would enter. What do you think about the role of those two things? Yeah, well, I think we've done a lot with memory through these harnesses. And it turns out that harness engineering is not as important as the model, but it really matters. And these harnesses and these models are increasingly being co-developed. One of the big things a harness does, we usually give us like a run time that the model operates in. And it knows where the tools are. It creates context, memory, state, has very specific prompts or instructions. It makes a huge difference, even simple versions. It makes an incredible difference. I think the last time I was on here, I went on a lot of other times, I just said like, hey, has an investor. It's very important that you pay for the $250 a month version to get your own intuitive sense. That's no longer possible to understand what frontier AI is capable of today. Even for a non-coding use case, you need to have cloud code or codex. And you need to be on an enterprise plan. And the reason for this is, and this is another dynamic that's enabled by Google losing their cost leadership is these AI models just shifted to usage-based pricing. And if you were on that $250 or $300 or $280 a month plan or whatever it is, you were getting severely rate limited. You were getting a lobotomized version of the AI. Because like we talked about cloud now produces 70% less tokens. You want the tokens that cloud and its harness really think it needs to produce to get you a good answer. You need to be on a usage-based plan. And by the way, this is so bullish for AI. If we go back to 5207, cellular had been a great growth industry really for the last 10 years. And the reason was you had a combination of fixed pricing. You had 900 minutes for whatever it was. And then usage-based pricing over that. When did cellular stopping your great growth industry? When everybody just went to all you can eat. And by the way, long distance is the same thing. AI is just shifting from all you can eat to pay by the drink. And it turns out people really like to talk to their friends long distance. They really like to talk to their friends on the phone. And people really like to use AI. And particularly now that one person can have 100 agents working. So I think this shift usage-based pricing is probably why you will see open AI and traffic exceed well over $200 billion an ARR this year. Not only is more compute going to become online, but they're going to be able to push frontier token pricing with these usage enterprise models. It's sad. It's sad for the world because it just means if you can't afford that, you're not at the frontier. And I think it's going to throw off a lot of investors in two-it-of-sense of the capabilities of AI. But yeah, continual learning, man. I mean, if we solve that, how do you conceptualize that? AI is constantly updating its weights. I mean, it made up being something different. There's so many mysteries about the human mind or such sample efficient learners relative to AI. Many orders of magnitude. Now, we have a crude variant of continual learning today when something is verifiable. And that's just reinforcement learning during mid-training. continual learning is a model that dynamically adjusts its weights or adjust in some way in real time. Like as a human, the first time I put my hand in a fire, I've learned I never put it in there before that model today needs to put a hand in the fire a million times and then have the designers effectively put a fire in the next trading run or an RL gym for it to learn. I think it has to be dynamically updating the weights, but I think people are working on really smart techniques beyond this. But if we get that, then we have a really fast takeoff. And people seem confident that continual learning is kind of just around the corner. And I do think this is like the third big question. Bit or less in violation as a result of ASI are less likely human ingenuity. We'll frontier tokens still command the premium they do. And we'll continue learning if so, when What is the role of new chip companies in all of this? We talked a lot about Nvidia, their relationship with the SMC and Intel and all these sorts of things. There's a thousand flowers blooming, I think literally probably a thousand flowers blooming, trying to create a new chip to address some part of this bottleneck. I'm curious how you process this space, this opportunity, what role it will play. - So I think this is good and healthy for the world. It's good for Jensen too, because a different administration might take a different view. Competition I think is good for everyone and seeing different architectures explored is good. And the reason is in tank design, they talk about the iron triangle. The iron triangles take design is that all designers of a tank, they have to make trade-offs between attack, defense and mobility for obvious reasons. More defense you have, which is your Sharmer, the heavier the tank is, the less mobile it is. Do you have to live in this triangle and make trade-offs? The Mercava in Israel is optimized for defense. Russian tanks and like the leopard are generally more optimized for mobility. Chip design is the same. They're these fundamental constraints imposed by the laws of physics has embedded in the Taiwan semi-design rules that you need to live with it. You have TPU, Trainiam and Amdi, which are all essentially trying to be a better GPU. And today I think probably Trainiam is doing the best. Nobody's a better GPU. But Trainiam is tugging a Superman's cape that hadn't started yet. The Trainiam 3 needs to ramp into production 'cause it has a switch scale up network, which you really need to economically inference MMOE models. A lot of companies have a Taurus architecture. That's where Google was, Google's developing a switch scale network. And the AMD is like always kind of flying over behind. Yeah, AMD will see the MI450. We don't know yet, we'll see. We probably know more about Trainiam 3 than the MI450. But that's a hard game to play. So you have to do something different. And you have to do something different that is also hard to do. So I think the best path for these startups, my rule of thumb is 1% market share is gonna be worth 100 billion. 100 billion is a pretty good venture outcome. And I think what Jinson would say is, okay, if somebody does something different and it gets to a one or two or three percent share, we'll make that chip. And that's coming for everyone. But if you're trying to make a better GPU, good luck. If you were doing something different, it also needs to be hard to do. And you can make different trade-offs that disaggregation of pre-fill and inference really have opened the aperture for making these different trade-offs because you can make very aggressive trade-offs for decode, aggressive trade-offs for pre-fill. Pre-fill being taking in the context decode being, you know, right the output. Yeah, I have a great colleague named Andrew Fox who said, pre-fill, picture, British ship from the 18th century, pre-fill is loading the cannon, decode is firing. And what pre-fill literally is, it's just the model understanding the question, the prompt, and then keeping track of its own answer. And that is fundamentally a memory capacity bound problem. Decode is a process of generating new tokens and that is memory bandwidth constrained. So if you were a chip designer, this gives you a richer canvas to paint on. But even so, it needs to be hard 'cause if you make different trade-offs in that iron triangle, to optimize for memory capacity and they're not hard trade-offs to make, Nvidia is gonna make those same trade-offs, they get better prices from Taiwan Simi than you're ever gonna get. Good luck. And they have the advantage of working with every model company and optimizing their designs. Other way, another very funny thing is, there's this process, if you're a VC and you're investing in semiconductor company, that is telling you they are going to have an advantage 'cause of a Taiwan Simi process that they have special access to. I promise you, the Jinssen saw that process. When it was a Tbilical in Taiwan Simi's eyes, they know more about it than this little company with 200 people can imagine. Taiwan Simi, everybody in the supply chain, it's showing Jinssen everything. The same way they're showing Amazon everything, AMD everything, TPU everything, and that's another reason, don't quote, try to make a better GPU. So you can do something different, you can paint in the pre-fill canvas, you can paint in the decode canvas, but you also have to do something hard because if it gets to scale, you're gonna have those four companies has very fast followers. My firm was a venture investor in Cerebris. What Cerebris has done is something hard and fundamentally different. Way for scale computing, it comes with a set of trade-offs, but that architectural decision they made was hard and lets them do something that no one else can do. And we'll find out how big that is. They're working on really cool things. One of the problems Cerebris has is, once you start needing to glue a lot of chips together and scale up networks or scale out networks, you need a lot of IO, and IO is bound by what's called the shoreline, the sides of the chip. Cerebris has an overwhelming ratio of on-chip computing memory relative to shoreline IO. Well, they're really smart people, they did something really hard. They're trying to see if they can put an optical wafer right on top of that and then that solves that problem. I'm sure they're looking at hybrid bonding of DRAM to get around the much discussed on X, these alleges limitations that are not true. A Cerebris machine could theoretically run any size model. Their sizes and models were there much better than other sizes. So Cerebris, what I think is interesting is they did something different that's hard to do, really hard to do wafer scale computing. I do think there's a role for these. I just encourage them all make a different trade off, try and do something hard. Everybody's gonna get funded after this Cerebris IPO. It's not gonna be a problem, but it took Cerebris three generations of chips to get it right. Andrew Feldman, the CEO, you can just see how hard it was what he did and that whole team did to get where they are today. And they need to have the grit to do that, the resilience. This first chip is a failure. It happens. Can you come back and make a second chip? Well, the one I say on this topic is this is gonna be amazing for the useful lives of GPUs and may single handedly save private credit. - Say more about that, what do you mean by the private credit? - We'll just private credit there in pain from these sass loans. And I have a much to mark down. I probably need to be marked down more because if the public companies are struggling to adapt, how's like a debt-laden company going to adapt? And invest in what is a very different margin structure business. There's a lot of private credit and GPUs too. And they were underwriting that to I think three or four years. The disaggregation of inference means that I think these GPUs are going to have 10 or 15 year lives. The AI skeptics are like, oh, these companies are all cooking their books. The useful life of GPU is only a year or two. The useful life of CPUs, only four years 'cause the rapid technological change. No, what rapid technological change has done with the disaggregation of pre-fill and inference is mean that you can put a cerebral system or GROC LPU's that Infinity acquired and effectively in front of a hopper or even an ampere. Use that hopper name here for pre-fill and extend the useful life of that GPU until it melts. They do melt, so they have a time. But maybe you don't have to run 'em has fast. This is gonna be really good for the whole private credit industry. It's gonna help finance the AI build out. 'Cause if you can start to finance GPUs at more like 5% or 6% instead of I think Coruiz lowest financing was like low sevens, that actually mathematically changes the cost to finance this build out. We had this technological innovation that's gonna lower the cost of financing, extend the useful life of compute on earth. And then I do think the one last thing that's interesting about that is my friend, Jeman from Cotu, just did a podcast and Cotu had a deck and they talked about hey, sellers of shortage are doing so much better than the buyers of shortage, buyers shortage being the hyperscalers. But if you own a giant installed base of what is currently in shortage, that's also a very good place to be. And we're hearing CPUs are way more important than they were in an agentic world. They do all these things around orchestration, tool calls the biggest CPU fleets in the world sit at the hyperscalers. Some of these hyperscalers may catch up a little bit to the sellers of shortage. - I wanna talk about this idea of different and hard applied outside of the infrastructure piece of this. Now you're starting to interact with new founders, existing CEOs and founders that have to adjust to this new world. What are you seeing the most AI native founders that aren't building chips or infrastructure or models? But just people using this technology to build other stuff, how do they feel the most different to you if you've observed differences? - I do think this is just for chip design. To me, it's always been a fundamental question for venture. So there are different ideas that are obvious to everyone on planet earth as soon as they hear it. And if that's where you are in venture, if it's not hard to do, if it becomes obvious to the world before you have built scale, scale is the ultimate advantage. You're in trouble. And the great thing Amazon had was, I use obvious tool a lot of people, but it wasn't obvious to the retail CEO, Amazon, they were very smart. Any e-commerce company that VCs invested in, they would destroy. They'd be like, oh, that's so cute. We're gonna take our margins and that to negative 10,000%. And that's why the guys at Wayfair, they did something hard. Amazon tried to kill them and they failed. Those were tough, operationally, really competent CEOs. For me, in venture, I always look, is this gonna be obvious to the world before this company could build scale? Or is this both not obvious? different and really hard to do. I think a lot of founders are really struggling with this in AI. I think people are becoming worried today in Jensen's 5-layer cake of AI, the profits, they're crewing to energy, they're crewing to data centers, they're crewing to chips, they're crewing to models, not really accruing to the applications. I think cursor and cognition got to a scale, they focused on coding 18 months ago, the people were focusing on coding. Open AI was doing everything under the sun. People focused on coding were cursor, cognition, andthropic. It was really right to focus on code. I'm Chad Masad, the founder of Replet, tweeted something that I thought was so smart. It was something like, "Better or less than a Jason, is the fact that coding might be the shortest path to ASI in useful AI?" If you're really good at coding, you can write yourself code to do anything. I think it was really smart of those companies to focus intensely on coding. They all probably got to a scale where they have a place. I think cognition is doing something really, really different, but I think a lot of founders are really struggling, man. I think they're trying to get confidence that in niche-year areas, they won't get to the level. They can get to them and get like a data moat before the model companies get to that niche, or that it's a small enough niche, that the model companies won't do it themselves, but it can still produce their venture outcome. Is this really the tool you would call the token path? I'm going to use that phrase with me before. Yeah, he comes from a guy with an altimeter, Jayman Ball, but he just said, "If you're a software company or an AI company of any kind, you have to be in the token path." So, Databricks, that's in token path. Comparable companies are in the token path. If you're not in the token path, you're not in some really niche thing, life may be hard. Even for these vertical niches, I think if you talk to the people at the model companies, they're even skeptical of some of these, because all the data that's being generated in these niches, I'm from humans. But then you're betting that you're able to use that proprietary data in this narrow vertical to train a model that's lower cost than the frontier labs can ever get to. And maybe that's a good bet. But I just think you have to be very, very careful. Now, on the other hand, if the returns to these frontier tokens relative to other tokens come down, there's going to be an explosion in value creation at the application layer. And I think another really important point is I have a belief that whenever he wants, Jensen can probably get pretty close to the frontier with his own model. With his own model. I don't think he wants to do that. But that is what OpenAI in Anthropic are kind of trying to do to him. Unsuccessfully, he's a very logical thinker. This is the logical counter move. You will see that open source frontier, which today consists of Chinese models with stolen American tokens. Somebody told me that like deep seek, maybe the original one was only 150,000 reasoning traces. There's many ways to wander this if you're Chinese company. You can hit all these different APIs. You can make it hard. Now, the American labs are working really hard on anti-distillation technology. But I just think Chinese open source, they're doing really impressive things in a very resource constrained way. But there's a lot of distillation. And this is why I think in addition to they're not being enough compute to serve mythos, they did not want it to be distilled. They wanted to use mythos to still let themselves use it to RL their next model, whatever it is. And then I think what they and eventually I think OpenAI, anyone on the frontier will do is just say there's going to be some very interesting game theory because it's a new kind of prisoner's dilemma. We talked about the old prisoner's dilemma being just around like, hey, you're in a prisoner's dilemma where you have to spend the new prisoner's dilemma is going to be if you are at the frontier, do you release that model via API or not? If everyone at the frontier agrees, not to do that, then Chinese open source, if one person defects, they're going to have the best model, they're going to have a lot of revenue and cash flow. And then of course, resource is equal intelligence. So they'll start to pull ahead and then that will lead to everybody else releasing it. So it's a new game theory. It's kind of the same game theory that you have with Taiwan, semi-Semi, CM Sagan Intel. The reality is if a company like Nvidia or AMD were to ever really, really use one of these other foundries, that foundry would get better really quickly. So I do think Jensen is going to keep open source a certain timeframe behind the frontier. I think that's going to be a very interesting thing to watch. And then by the way, open source gets monetized. There's this misnomer that open source is free. Open source tokens, they cost energy, produce, you need to make up on GPUs, and the open source model companies almost always get a revenue share. Your finance team isn't losing money on big mistakes. It's leaking through a thousand tiny decisions nobody's watching. Ramp puts guard rails on spending before it happens. Real-time limits, automatic rules, zero firefighting. Try it at ramp.com/invest. 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We're just trying to over invest in cybersecurity, and I really believe everybody needs to have a say for it. Everybody needs to go leave your digital devices behind, literally go to the ocean, and have a family say for it or a company say for it. It can't be one that can be like socially engineered, and this is just to avoid cybercrime where what looks like your son or your daughter, your grandparents, or your parents, or whatever. Face times you. It's an utterly accurate simulation of them. They know everything and can extrapolate based on what they're likely to say, and says, "You know, wire me a million bucks." So, doing everything we can with cybersecurity, that's defensive. What about analytical or processing? What will you still be able to do that? It won't be able to do, I guess. So, the analytical side. So, it's a good question. I just watched the last samurai, and I asked people at my firm to watch it. In the last samurai, if you haven't seen it, a highly recommend watch again. It's actually a movie that's aged really well. It's Tom Cruz's movie from 20 years ago. The conceit is Tom Cruz. It's this bitter, washed up, civil war veteran who's actually a very good soldier. He's bitter and washed up because he feels like he participated in negative actions against the Native Americans. It's during the Meiji restoration. And he's hired by the modern elements of the Japanese government to train like an army of peasants how to fight the samurai. There's a first battle. Of course, the samurai went, even though they don't have guns, he fights valiantly. So, the samurai decided not to kill him, take him to their village. He becomes a samurai. It feels like the civil war to him. So, he fights on the side of the samurai at the end of it. He's massacred by a peasant with a machine gun. The machine gun is here. If we do not all become masters of the machine gun, we're a master. So, I am trying to become a master of the machine gun. And then I'm optimistic. There's a long period of time where just like if you were a 50-year-old samurai veteran of many wars, I fought many wars, master dwarf. You will have advantages using the machine gun. I'm optimistic as a lifelong student of investing. I'm going to be able to master the machine gun, this new technology, integrate it into my own process, integrate it into our firm process in ways that let me contribute value as a human being for a long time. Like everyone, I have agents running all the time now. What's your most useful agent? My single most useful agent is a really good summary of the points that would be interesting to me from podcasts. There's just six hours a day of stuff that I feel like it's in my job description to watch. Every time somebody from OpenAI, XAI, Google, cursor, fireworks, base tin, I'm going to say nothing of Jensen, Elon, Dario, I feel compelled to watch and I just don't have that much time. There's some real needles and haystacks. That is what I would say for me is the most useful. I do think there's a set of things that I always like to see. I'm very sensitive to management compensation. What are they interested to do? Do they have stupid RSUs? Or do they have PSUs? And if they have PSUs, what are those PSUs and sent them to do? And we now have systems that do a very good first pass at that. That saves people a lot of time. It frees them up for more creative work than like going through the proxy, pulling the PSU thing, looking at how it's changed first all the proxies because there's signal in that. And that's very labor intensive and that's so good for an AI. And there's obviously all sorts of same things within investing. Pressuring the organization in those ways I think has been helpful. This is most exciting thrilling time to be an investor. I'm getting a little bit worried. The diversity breakdown thing. Yeah. Say just like a little bit more about the kinds of people that are. I don't know anyone like me who's not really bullish on DRAM. There's all these interesting things happening with AI right now. One is cross-sectionally, evaluations do not make sense. They just flat out do not make sense. They cannot all be true. In other words, you have semi-cap equipment companies trading at 40 times next quarter's annualized earnings and DRAM companies trading at mid-single digit. At the peak of the last cycle, that was five verse 12. At one point, it was like three verse 45. Those can't both be true. And yes, semi-gadarter cap X business models have improved more than the memory business models. We don't know how much HBM is going to improve memory business models yet. Yes, they have some element of recurring revenue with parts and maintenance. But it's not worth a thousand percent multiple gap. I think it's hard to square the valuation of something like Nvidia, which is still in early April, is essentially cheap as it gets relative to the market, like in the last 10 or 12 years or whatever it is, and very cheap absolute. It's very hard to square that valuation with something like GE-Vernova's valuation, because it builds in an unfathomable amount of share loss for Nvidia. So, evaluations cross-sectionally are really different. Because we are in shortages. The lowest quality companies are doing the best. So, if you're an oil and gas investor, a mighty investor, natural resources investor, and you're well versed in thinking of costs, this is very intuitive to you, and a real bull market for commodity. The commodity suppliers with the highest cost go up the most, because it's the most beneficial to them. They go from on the verge of bankruptcy to gusset cash. And this is, I think, one reason commodity investing is really, really hard, because quality outperforms during the cycles, but you get all of the outperformance during the downturns when the high-cost guys that moond during the shortages and the commodity bull markets, go bankrupt or whatever. You're seeing that happen in every industry. The lowest quality players, companies that are hated and detested by the hyperscalers and the buyers, because they have high-cost, they're unreliable, the parts fail at a high rate. They're sold out and raising prices. And then that activity gets the interest of these retail accounts on X, and these stocks get bid to the moon, whereas some of the higher quality expressions have, like, actually really underperformed as an investor, it's hard, because you know within a shadow of a doubt that that thing that's moond, 10X, and three months or six months, is going to go right back down, subject to what they do with all the cash. And so it worries me a little bit that people who are very skeptical year ago are no longer skeptical, but then I just contrast that with the valuations of these high quality companies, which are just not extended, and it makes me feel better. I thought it was funny in 24 and 25 that anyone asked about an AI bubble or talked about it. Do you have this nuclear bubble and this quantum bubble right here, right in front of you? What are we talking about? This is so real. Some of that nuclear quantum silliness is maybe spread into more speculative, lower quality, smaller cap names, where if you have a big presence on X or Reddit, it's easy to move. And that frightens me a little bit, but I just wish there were more AI bears. I wish there were more memory bears. Astera is a stock up and close to a long time. There's a lot of bears on that. I love that. Great. I first invested in the series C. Good luck thinking that's a copper loser. And then there's also you can feel the baskets in the market and the leverage baskets. And what baskets you're in is really important, you know, copper, optical, DRAM. And a very interesting thing that's happened this year is in 24 and 25, the AI trade traded together. You could be long GPU compute scale up networking and optical scale across and short power or whatever it was. That trade worked from like a risk management sense because you know, I am very factor aware that all blew out in January of this year. Scale up networking would go crazy while scale out was going down our DRAMs massively underperforming NAND and HDDs, which did not happen. So these cross sexual correlations within AI really fell apart and you had to get very fine grain. You couldn't hedge your memory anymore with some simmicap equipment or NAND, everything cross sectionally really changed in a very interesting way in January. And I think maybe one reason for that was AI got to a quality where it was all of some really easy for a bunch of people to get really smart on these different subsectors, start trading them and then they could put into baskets and those baskets and creating price efficiency. Yeah, exactly. I think some of the biggest opportunities outside of these higher quality names that I think can compound for a long time and they're safe on like these low quality names which are terrifying. These are names that are miscategorized. Estera was in a lot of copper loser baskets. Estera, their biggest product is going to be a switch. You use both copper and optics to connect switches to accelerators. Definitiously, if you're a switch company or an accelerator company, you cannot be a copper loser because you're going to be on the other side of that connection. I wonder if you could riff just for like a sentence or two on each of the major companies. Google, Microsoft, Amazon, the major players that are public, all the conversations centered around these exciting new companies. Maybe run through them and riff. Google was incredible last year because they had that TPU advantage which is now gone. The reason I think they're still in a great position is just they have the most compute of everyone. We talked about the value of installed bases being higher as a result of shortages. They have the biggest install base of compute. Google I/O is this week. If they don't release something that even slightly leapfrogs, open AI and/or clawed, that's interesting. It's not a disaster for Google. It's just interesting and it just means this Nvidia effect we discussed is even more powerful than maybe I'd imagine. But I'm very curious to see what the Pareto Frontier looks like literally in five days after Google's announced its new stuff. This is a big card for them. But Google, between the amount of data they have and the YouTube data is actually really genuinely valuable. It is valuable in a world of robotics. The amount of compute they have, the search business they have, Google's never not going to be in a good position and then you see that with GCP going crazy. You got to give Zuckerberg a mitscredit what he's done in terms of making meta an AI first company internally. And I do think he is the only one of those true internet giants to have done that. I give him a lot of credit for that. I give him a lot of credit for paying up when he did for that talent. And Muse, I think, was a really big upside surprise. It was the first model from MSL. And it's not on the Pareto Frontier with XAI, Google's one-intrent and then OpenAI and Cloud. But it's pretty close. That was very impressive to me. So I think meta is in a better position. Still not as strong of an absolute position as Google, but like they're better position and rates of change matter more than level as you know in markets, particularly over short three-year timeframes. Over long timeframes, level of competitive advantages to dominate. But even within that, changes really matter. Amazon, I think, is in a really strong position because of training. I do think you're going to see real P&L efficiencies from robotics over the next 18 months in their retail business. I actually think Nova, their internal models are not where Muse is, but they're better than they get credit for. Then Microsoft, I like Satya, I admire him. I think he's an exceptional CEO. And I give him a lot of credit for the decisions he's made. But he did go from we're going to make Google dance to being the product manager of Copilot. And like three years, I would love to know during the Ku attempt against OpenAI, Satya regret his decisions. Disatya wish that he had supported Ilya instead of Sam and that Ilya and Mira were really running OpenAI today. And his heart of hearts, I would love to know. Because I think the Microsoft OpenAI partnership might look very different in that world. I think that's a very interesting question that we'll never know the answer to. But I give him a lot of credit. What he is doing now, he's taking risk. This goes to the decisions you have to make in that cone of uncertainty. You're not only how much you spend, but what you're going to spend it on. I think Microsoft flinched for like a moment in early 25. They have this algorithm. We spend this much cat-backed dollars. We get this return. That algorithm was kind of off. And if you flinch, you lose position. You lose all these allocations. It is difficult to get it back. So they flinched. And now the decision Satya is making, which the market has punished him for. But I think it's the right decision. I mean, who knows how fast Azure could be growing if they're willing to just sell GPUs to OpenAI. We're going to use our compute internally to make our own products better. One reason Copilot is so bad or has been so bad is just one enough compute available. They're fixing that. He's the product manager of Copilot. I do think he's a great CEO. They're trying to use their compute to train their own models. I am a little skeptical that they have the right team to succeed there. But just like that, are they going to forward to higher maybe a different team, but I think he's making good decisions that are risky decisions to position Microsoft for this world where frontier models are no longer API accessible. And I think it's a really courageous decision that I give him a lot of credit for. And he is for going, I mean, Microsoft probably be an $800 stock today if they were using their GPUs to serve solely open AI andthropics capacity instead of using them for their own products. So I give him a lot of credit for making a great decision. I think what's really interesting is the degree to which these companies are outward facing in their decisions. The two companies who are the most deeply engaged with startups are Amazon and Nvidia by a mile. Then there's a really intense engagement with Google, their next most intense. Broadcom is engaged in a different way. They're just everybody's favorite asix supplier of your startup that's considered like a level up if you get to work with Broadcom for your second gen chip. And it's considered mana from heaven if Broadcom works with you for their first gen chip. And then you see essentially zero engagement with startups from AMD, Microsoft and Meta. When I say zero, it's a little. And I just wonder about that decision, because some of the best teams are no longer big public companies. They're at these smaller startups. And I think it's going to end up being a pretty big advantage for Nvidia, AMD, Google, right behind them to have this engagement that you just don't see from these other hyper-scalers. As we wrap up, I'm curious for you to riff on any other out there knock on effects that you've started to think about for this giant trend. We've talked about the specific companies in a lot of detail that this most impact. We talked a little bit about the application layer and what would have to happen for there to be more value occurring to that layer of the stack. I'm curious, any other just fun knock on things that you've been thinking about as this world changes so quickly. And it is wild. I mean, if the application layer forget value, growing just value has been destroyed. AI has net destroyed. Even if you count cursor cognition, the most successful AI natives, millions of dollars of value has been destroyed by AI at the application layer. And just in this context, the companies that are doing the best today that are seeing their values increase the most that are creating economic value are the companies with the highest effective ratio of utilized GPUs per human. Maybe this just means that every human's going to get a lot of GPUs, but I think that's an interesting fact that we kind of need to be cognizant of. I will just say, and maybe this is a little dark, I am more and more worried about personal safety. And I worry about this a lot more for people who have a much bigger public presence and are much more associated with AI. I hope nothing tragic happens. There is this upsurge in political violence here in America. And as AI increasingly becomes political, I worry that's going to get directed at more and more AI political leaders. Whatever I may think or may not think of open AI, I think it is terrible that someone threw a lot of cocktails at Simultman's house. I am worried that we are headed into a higher variance, higher beta, higher risk world because of AI. And that's for me as an individual and then for people who are big players on the chess board. Think about what it means geopolitically. We're watching the Ukrainians are really starting to win. And the reason they're winning, I think is not really because they have better drones. I think they do have better drones. That's part of it. And the reason Ukraine is really winning is they have the best battlefield AI outside of probably America and Israel. And has China has our adversaries begin to process that. How do they respond? If the United States, because of its edge in AI, it's great if you're America, but it is destabilizing for the rest of the world. Something I think a lot about is creating a charity to just educate the world on how awesome the West has been. Slavery was in Dmitry to essentially almost every civilization. And slavery was really ended by the British empire. Tell that story. But America, after 1945, we had the nuclear bomb. No one else had it. We could have controlled the world forever. Instead, we rebuilt Germany and Japan, who are America's most reliable allies, Israel, South Korea. That's a testament to like the American spirit in our country. We didn't take over the world. There were these fears that were documented at the time that the American generals, MacArthur, was a little bit of an American emperor in Japan, were just going to take over the world. And they could have, and they didn't. They came home, we demilitarized. And then you had this period of great global stability between, you know, scary, they were terrible Americans. Yeah, you had the packs of Americana. So maybe it's not destabilizing. Maybe it leads to another packs Americana, informed by our AI dominance. And I'm so optimistic that AI is going to be amazing for the world. There's something like me whose daughter was diagnosed with a very rare disease. There's no cure. He was able to assemble a lot of resources. He was able to get a lot of compute from the labs, were made aware of what was happening. Spent up a minute amount of agents, came up, using AI with a drug on the market that can actually impact his daughter's disease and then has spun up a company to cure it. Her life is already immeasurably different because of AI. So I'm like an AI optimist, maximalist, but I also just acknowledge it's like an event horizon. It for sure I think is going to be a discontinuity. We need to navigate as society. I think the leadites are going to be wrong. But we need to be like really thoughtful and how we address their concerns. We need to make sure that it's good for everyone. Like it is a little dystopian that now the best AI is only available to people with a lot of money. We need to solve that. We need to approach this with humility, recognize there's a lot of uncertainty and be thoughtful. When I do this with you, I tell people afterwards I'm like, "me you find something that you love as much as Gavin loves markets and companies and capitalism and history on display today as always Gavin. Thanks for your time." Thank you. Thanks, Patrick. You know how small advantages compound over time that's true and 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 that are data that are decisions better economics over time. See how at ramp.com/invest. As your business grows, Vanta scales with you. Automating compliance and giving you a single source of truth for security and risk. Rogo does. 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