[00:00] What was happening in AI was I think the most extraordinary moment in the history of capitalism, the history of American business. Anthropic they added $11 of AR. Three highest profile SaaS companies founded in the last 10 12 years are Palantir, Snowflake, and Databricks. And these three companies spent 10 [music] years building their businesses. Anthropic added their combined businesses in 1 month.
[00:30] That's just nothing like that has ever happened in the history of capitalism. Forget my career. Just the flat-out history of capitalism. The history of business. >> [music] >> All right, so this is our sixth time doing this if you can believe it, which puts you back into first place. At least tied for first place with Gurley. And I
[01:01] think 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 which felt to me just like a completely unique economic, technology, and market environment. And you're the biggest student of the history and of these times, so what did it feel like? Now I'd say broadly speaking, there are two kinds of drawdowns. There are drawdowns where you're wrong, a company
[01:32] misestimates, your hypothesis was invalidated, and you have to take your medicine and you crystallize that loss. And then there are drawdowns or periods of underperformance 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 uh negative performance, you can kind of build pent-up alpha, pent-up future performance. And for me, that is what
[02:02] March felt like. It felt like uh you know, the NASDAQ was selling off, and 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. And 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 the SaaS and cloud revolution it created will call it between 5 and 10 trillion dollars of value. And I would say
[02:33] arguably the three highest profile SaaS companies to have kind of been founded in the last 10, 12 years are Palantir, Snowflake, and Databricks. And these three companies have spent and employed thousands of people, tens of thousands collectively. They've all spent 10 years building their businesses. And Anthropic added their combined businesses in 1 month. >> [laughter] >> That's just nothing like that has ever
[03:04] happened in the history of capitalism. Forget my career. Just the flat-out history of capitalism. The history of business. I mean, it's wild that that Krishna comes out this show and shares some stats, 500% in DR. >> Yeah, you do the math on that for 3 years. Insanity. We So, there's just no precedent for this, and we you know, tech tech investors you you hear a lot of discussions about S-curves and investing in exponentials. I've just never seen an exponential like this. It
[03:35] felt even more extreme than Deep Seek, which was a very similar setup. If we go back to 25, there was a huge sell-off at Deep Seek, which was very strange because the paper gets published 7 days before Deep Seek Monday. It got published I believe on a Monday that was a holiday in America. And I read it, I thought, "Hmm, you know, this this feels like it might not read >> [laughter] >> that positively for
[04:06] uh you know, the AI trade. Yeah, I I took action. We had Deep Seek Monday where AI really imploded a week later. And 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 available availability zones in Asia had already like doubled. You were seeing GPU availability go down.
[04:37] And this was just the first time we saw how much more compute-hungry reasoning models are during inference than non-reasoning models. And so that was a similar setup. But you you had to do some work to see that. I mean, it's not that hard to say, "Oh, wow, stocks are selling off. The price of DRAMs going vertical. The price of GPUs in Asia are going vertical. Um GPU availability's going down. And then like two or three days later, you know, GPU prices in in in America started going up, GPU rental prices."
[05:08] All you had to do in in March was just simply observe what was happening to Anthropic. There's all these people who seem to regret you know, not buy during '22, not buy during COVID, not buy during Deep Seek. You had the same valuation setup at the beginning of April. And and even clearer AI inflection. And so there've been all these chances to buy into AI. And then of course, what
[05:38] complicated it was the straight-up FOMO. I became a believer, an every believer, that I think maybe one thing that the market was mispricing. it. I'm no background 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 Strait of Hormuz being closed is actually relatively awesome for America. Why?
[06:08] Because, 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 G 1. Natural gas went up in Bloomberg. That was down 20%. And natural gas in Asia, Europe, everywhere else doubled or tripled. So, our relative manufacturing competitiveness
[06:38] 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. And what made the '70s so dramatic was it wasn't just that prices went up. It's that there were actual gas shortages. And then you go through, okay, well, the US economy is dramatically less energy intensive than it was. The US economy The United States
[07:08] is now the world's largest producer of oil and gas. And we've become now the world's largest exporter of oil and gas. And then on top of that, there's this relative manufacturing advantage. And so, that made it, I think, 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
[07:39] versus 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 that'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 OpenAI, 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
[08:10] just look at the sales multiple and compare it to maybe what Databricks and Snowflake and these companies traded at at their peak. Like how do you make sense of it? I do think OpenAI and Anthropic are pretty different animals from a capital efficiency perspective. And Anthropic clearly is 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 burned to get to a roughly similar revenue scale. I think they have they have they've burned maybe 80% less than OpenAI.
[08:41] So as businesses, they clearly have very different structural ROICs. I think OpenAI is doing a lot I think Sarah Friar is 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 more more than others >> They secured a lot of compute. That's another big difference. Um it turns out being aggressive really paid. But yeah, I just Anthropic at 900 billion for 50 billion and you know, ARR and you know, I I But growing a thousand Yeah, growing at
[09:11] ridiculous rates. Maybe a true statement is that if Anthropic had all the compute, they'd probably be doing well north of a hundred billion dollars today. Maybe 150. And I do you know, they have clearly deprecated the intelligence of Claude. There's analysis Claude is even on Opus is generating 70% less tokens for the exact same question. And you know, as we talked about last time, token quantity equals quality of answer and quality of thinking at some level. You know, and
[09:41] there is an intelligence density per token that also matters. You know, I think I felt that as 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. URR unconstrained run rate revenue. >> [laughter] >> Yes. Why do you think they don't raise a 100
[10:11] billion dollars at a 3 trillion dollar valuation or something like this? Like if if you were the Anthropic CFO, uh Krishna's awesome, we just had him on. Or if you're the if you're Sarah, certainly if if the inbound I received following the Krishna episode is any indication, everyone I've ever met is trying to invest in in both these companies. So, I think it's wise. It the future is uncertain. You are clearly in a very capital intensive game, even if you are you
[10:41] know, Anthropic um I'm sure is at very positive gross margins on inference today. I can Anthropic probably starts generating cash this year if they are not already generating cash, which I think is 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? You know, I think there's still a lot of uncertainty in Iran. All this uncertainty I think probably amplifies
[11:11] geopolitical uncertainty over Taiwan. So, it's an uncertain world. If if I think about Elon, Elon has always made investors money. He treats it like a sacred covenant. And as a result, because he's made people money for now 20 years, he has a superpower. And that is he can essentially raise as much capital as he wants whenever he wants. And I think it's wise that these companies are taking I don't know if that's how they think about it, but I do think being focused on making
[11:42] 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 sort of systematically underpricing SpaceX or whatever else. What is the actual method? Just never being greedy on valuation. Never pushing valuation. Just that simple. You know, my friend Antonio pointed out SpaceX compounded it, you know, low 30% per year for
[12:13] whatever that was, a decade. And and that was just cuz Elon was I think focused on preserving the superpower and having to trying to strike a fair balance between investors and employees. But I I think it's wise. But could Anthropic raise money at probably at least a 100% premium to this rumored latest mark? Of course. Most software companies try to maximize your time on their app to juice engagement. Ramp does the exact [music]
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[14:14] get started. >> Let's get to the Watson wafers part of the discussion. [laughter] Always my favorite thing to talk about with you. Uh the importance of this infrastructure build-out. I feel like 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. And you've studied S-curves and the steepness of those S-curves a lot. Uh and you know a lot about history. Talk us through how you're thinking about Watson wafers today as the key to inputs into this whole thing. Yeah, I would say I think capitalism is going to
[14:45] solve the watts shortage absent big regulatory or political blowback, which I think is a real possibility. The head of kind of data center infra investing at one of the big PE firms, you know, think 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. And I think a lot of companies are waiting till after the midterms
[15:15] to take action in terms of maybe workforce reductions. Nobody wants to be you know, a piñata during the midterms. But, you know, you've seen a lot of companies that make turbines significant announce a plans to 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, etc. etc. etc. All of that is true and I and by no means am I
[15:45] minimizing, you know, the industrial engineering, you know, 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. And I do I do want to like reframe orbital compute because [snorts] I think when
[16:15] people hear data centers in space, they, you know, which we discussed in our last episode, they picture a Pentagon-sized building in space. They're like, "Well, we can't do that." That's not what it is. A Blackwell rack weighs 3,000 lb. It's 8 ft high. It's 4 ft deep, 3 ft wide. It's racks in space. It's SpaceX has showed you an illustration. And it's a rack. That's the satellite. Um it's probably about the size of a
[16:45] Blackwell rack. It has these solar wings that are probably 500 ft long on each side. You keep it in a sun synchronous orbit so those solar panels are always at the sun. And then because it's in an exactly sun synchronous orbit, the radiator, which extends behind it for hundreds of feet, This is a common criticism, yeah. How are you going to cool it? >> Yeah. I've spent a lot of time at Starbase over the years and I've talked to a lot of SpaceX engineers.
[17:15] 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. Like I think probably, you know, there's some engineering that needs to happen to turn the Starship into a Mars colloidal transporter. Will they do that? Absolutely. What are they more focused on? I'd say probably, you know, the repair and maintenance. Those are the two big, you know, the two big responses. The radiator and the and how do you repair the whatever issue goes wrong in the rack. And the answer is
[17:46] like until you have probably an, you know, floating optimists, you don't. Now, I do think Starship is going to change the space economy in ways we cannot imagine, particularly if regulation becomes a constraint 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 it's just mind-blowing to me that SpaceX operates the world's largest
[18:17] satellite fleet, which is like 98 or 99% of all satellites in orbit. Every Starlink, they're cooling it today. And, you know, I think Starlink V3 is going to operate at 20 kilowatts. A Blackwell rack is only a 100 kilowatts. And people talk a lot about density. Well, if you're connecting the racks with lasers through vacuum, you know, you can make the rack bigger physically. You're focused on weight, not size. In a data center on Earth
[18:49] where you're trying to connect racks ideally using copper, minimize lengths, etc., etc. Cabling is a big cost. Um, you do want that rack to be small cuz, you know, copper when you can, optics when you must. But in space, you know, there's all sorts of things that SpaceX can do that I think maybe some of these naysayers are not contemplating. But it's just they operate more satellites than They have a 20 kilowatt satellite today. So maybe you just scale that up to 60 kilowatts to start. They seem very confident they're going to go
[19:19] right to 100 to 120. And they also the same company now also operates the largest data center on 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? >> [laughter] >> You know, I don't want to quote Larry Ellison, but somebody was, you know, being skeptical. And Larry And Larry was just like, "Listen, he's out there landing rockets. I don't see anybody
[19:51] else landing rockets." And the reality is is that 10 years later, no other company is consistently capable of landing and fully reusing an orbital rocket. And none of this works makes sense without reusability. That means you have to land it. I would like to redefine orbital compute as racks in space. Not giant floating Pentagon-sized data centers in space, which is just, you know, that's silly. But you can, you know, what makes a data center is you're connecting these racks with lasers.
[20:23] So it'll be racks in space that are connected with lasers into a virtual data center. And And if you think about that state of the world, let's say that all happens and we're really good at getting these things up economically, running matrix multiplication all over space. What does that mean for terrestrial data centers? Someone once said, um you know, America was going to suck as hard as it can on every energy source it can get. And I just think the same is true of compute. It's why I'm probably less worried about
[20:53] like an edge AI bear case than I was. We're going to consume as much compute as we can. And inference, I think is very sensible for orbital compute. 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 are in this
[21:23] ecosystem of power production and cooling and you are massively ramping capacity and you know, a lot of these capacity ramps are going to be hitting just as I think, you know, all of the silly skeptics start to understand that orbital compute is very real. Like, 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, you know, we're getting really good at repurposing jet engines, you know, there's that Boom Aerospace
[21:54] that is doing this. So, there's a lot of capitalism is hard at work on on watts. On wafers though, it's just this group of, you know, plenty older humans in Taiwan who are the most important humans in Taiwan. 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, you know,
[22:25] Morris Chang's sacred legacy. I vividly remember like visiting Science Park more than 20 years ago and, you know, 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 they did it. Partly because of Intel's self-inflicted wounds, but just they don't they think very differently. You know, one reason, you know, Jensen flies over there so much is
[22:55] he wants them to expand capacity. I do think it's wild that Jensen has never had a contract with Taiwan Semi. They do business on what seems fair in handshakes. Just fascinating. No contract. It's going to be fair over time. We're partners. We're going to be fair to each other. And the truth is, you know, based on every every prior market precedent for a foundational new technology like AI, you've always had a bubble. You know, Carlotta Perez wrote this great book about this. And basically, markets are efficient. They correctly understand
[23:26] that this is a foundational new technology. There's what Simpson 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.
[23:56] And one thing I'm really happy about really good about the current build out is it's still overwhelmingly funded out of operating cash flows, which is a a really important fundamental difference versus year 2000. As is valuation, as is the fact that every GPU is running at 100% utilization when 99% of fiber was unutilized. So, there's all these fundamental differences. But we do have to History doesn't repeat, but it rhymes. And and as investor, we have to be very cognizant of it. And recognize that based on the last 200
[24:27] years, you know, forget the internet bubble. We had a railroad bubble. A canal bubble. We should expect a bubble. And that's terrifying. Like nobody wants a bubble. A bubble is terrible. Reason it's terrible is if you're valuation sensitive, you like massively underperform. You get fired by probably all your clients. George Vanderheide, who um is is is no longer with us, great uh Fidelity portfolio manager. He fought the bubble in '99. And he retired in two in early 2000 cuz
[24:58] I think he just couldn't couldn't take it. He knew it was wrong. And you know, his his clients were deeply skeptical. George, you're out of step. You know, he had he had white hair. He's truly great man. I only overlapped with him briefly, but he was a very important mentor and friend to my good friend and mentor Jennifer Yurig. So, I have a lot of Vander Heiden DNA through her. Like he was the same person who said being early is the same thing as being wrong. George retired cuz he can't take the underperformance and he can't take
[25:29] clients saying, "What's wrong with you? You don't get it." And he has like 40% of his fund did tobacco, 40% in home builders. And literally he under he probably outperformed the Nasdaq by like 20 or 30X over the next 3 years, okay? 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 Jensen wanted, I
[26:00] think Nvidia could sell $2 trillion of GPUs in 20 in 26 or 27. Maybe 2 and 1/2 trillion. Maybe 3 trillion. But there is a limit where consumers would consume so much that you probably would be in an overbuild. And so, Taiwan Semi, if we don't get a bubble, like we need to throw a party for them because they will have single-handedly prevented a bubble, okay? You are starting to see companies go to Intel and Samsung. Let's just assume TSMC
[26:30] stays super supply constrained versus, you know, the latent demand. Like what what happens? one of, >> [snorts] >> you know, the history of 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. So, like I think a lot of this may come down to the degree to which Taiwan Semi can maintain a lead over Intel and Samsung. And you got to remember it's whatever it
[27:01] is, it's 9, 12, 15 months. >> Sort of like the leading node edge, you mean? Exactly. You know, the pace at which they expand capacity. Like if I were to watch one thing to understand where 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 um at scale second source with something,
[27:31] you know, well north of 30% market share. And yet they also keep this fundamental constraint on wafers that you know, helps us avoid a bubble. And then obviously, I think the Terra Fab um is going to play into this, too. Say more about that. For people that are not familiar. >> the Terra Fab. It's a SpaceX, I believe Tesla's involved as well. Um joint venture to build the world's largest fab here in America. And I'm I think they're
[28:02] going to be successful. One, they have a partnership with Intel, which is very important. Um because they're getting access to 50 years of institutional knowledge. That's just, you know, a 9 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 Terra Fab is going to get attention from the A teams at all the semi-cap equipment companies. Like one big reason Taiwan Semi caught up is ASML and KLA Tencor and Lam Research and Applied
[28:33] Materials. They wanted them to catch up. They didn't They don't like having a monopsony. And so the A teams were in Taiwan working Intel made some mistakes. And presto. And so the A teams will will be here cuz of Elon's reputation in in hardware engineering. And then just to a degree that I think is uh maybe hard for people to imagine in America. Um where, you know, politics has replaced religion cuz Elon had his foray into politics that makes it hard for
[29:04] some people in America to see him clearly, which is sad because I do think you know, he's probably doing more for America than any other American. You know, he's single-handedly bringing manufacturing back to America. He's revived defense tech. SpaceX is in some ways the most important defense contractor in America. You know, what he's doing with Starlink 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
[29:34] than any living human to decarbonize the world. And if you are upset about data centers on Earth for environmental reasons, well, here you go. >> [laughter] >> Uh so it's it's sad, but he is a living deity in China, Taiwan, South Korea, and Japan. And 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
[30:05] want to work for Elon, especially in hardware engineering. He's going to recruit incredible engineers. And then they'll be next to the next to Terrific Um 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 [clears throat] but dialed to recruit the best
[30:35] engineers. And that's just not the way that the people who run Intel and Samsung think. So, he's going to have the best talent. He's going to have the A teams at the wafer fab equipment companies. He's 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 semi. >> And these have long lead times, right? So, like TerraFab is going to be pumping out Nvidia cheaper whatever GPUs
[31:06] whatever chips like quite quite a long time from now. Elon tends to do things differently. Everybody else is taking 3 years to build a data center. He built one in 122 days. >> [laughter] >> You know. Samsung had to give him an office in their fab in Texas cuz he was so unhappy about like the pace at which they're expanding and building. We'll see. Are you surprised by You mentioned Deep Seek 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 of still Chinese open source
[31:37] models like we'll be able to use these for most of what we want to do. Fast forward it a little bit of time, you know, 2 years from now, there's no reason I have to spend a million dollars a year in my small little firm on 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 the 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,
[32:07] which is surprising to me. I think it's been surprising to a lot of people. And I think this is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor. Are frontier tokens going to continue capturing the overwhelming majority of economic value created at the model layer? And it is surprising. Like I just I remember when Jim and I 3.1 Pro came out. And it was it was mind-blowing to me. It
[32:37] was so good. And today, it's intolerable. Intolerable. And, you know, 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 for attacks or, you know, open source. But still, it is 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.
[33:08] And I'm much more open-minded to that having had the experience I've had with Gemini 3.1. And then Opus. Um and then I do use Grok 4.3. It is on the Pareto frontier. Like the companies that are on the Pareto 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 as a result of making very conservative design decisions with TPU v8 to try and take it away partially from Broadcom and Nvidia
[33:39] um continuing to make aggressive choices. Uh but Google dominated the Pareto frontier. The Pareto frontier being intelligence first cost. And I think this is the most important thing to look at to analyze AI labs. Google dominated that 9 months ago. At every point on the Pareto frontier, OpenAI, xAI, and Anthropic were inside of them. Now, the Pareto frontier is dominated by Anthropic, OpenAI, and then Grok 4.3 is on the Pareto frontier. It's clearly like the,
[34:09] you know, the best lowest cost 500 billion parameter model. And then Gemini 3.1 is like hanging onto the Pareto frontier. And if I were to bet, I'd bet that they're subsidizing that out of pride. I'd just say, well, one, violation of Richard Sutton's bitter lesson is for sure the biggest risk to this trade. To all of AI. Now, the closer someone is to AI, the more skeptical they are this will occur. One thing I think contributed to weakness in March was, you know, a much more stupid version of deep seek, which
[34:39] is a thing called turbo quad. And turbo quad 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 Hynix to sign, you know, some LTA that would lock in really high prices for a long time, they released this. You know, 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
[35:09] single AI engineer on planet Earth who believed that turbo quad 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 compute and data are chin beyond Chinchilla optimal, I guess what what people increasingly do today. That's a real risk, man. And I think the people who are building these models are skeptical of that risk. The reason I am a little less skeptical
[35:39] is I think we are very close to ASI. And who knows if the bitter lesson holds for 400 IQ models. Just, you know, or maybe we get a temporary period where these, you know, if you get to ASI, the first thing it wants 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 that humans, the bitter lesson, literally I believe includes humans in it.
[36:09] So, we're about to find out whether the bitter lesson will find out if applies to a 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 two that people seem to be most focused on as things that might create yet another, you know, new paradigm that we would
[36:39] 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 used to think of it as like a a runtime that the model operates in. It knows where the pool tools are. It you know, it creates context, memory,
[37:09] state. Um, you know, has very specific, you know, prompts or instructions. And just Makes a huge difference. Even [clears throat] simple versions. It makes an incredible difference. And I think the last time I was on here or one of the other times I just said like, "Hey, as an investor, it's very important that you pay for the $250 a month version to get like your own intuitive sense." That's no longer possible. To understand what Frontier AI is capable of today, even for like a non-coding use case, you
[37:40] need to have Claude code or Codex. And you need to be on an enterprise plan. And the reason for this is and this is another, I think 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're on that $250 or $300 or $280 a month plan or whatever it is, you're getting severely rate limited. You're getting a lobotomized version of the AI. Because like we talked about, Claude now
[38:10] produces 70% less tokens. You want the tokens that Claude 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 by way, this is so bullish for AI. I was a telecom analyst in '05 to '07. And cellular had been a great growth industry really for 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. And when did cellular stop being a great
[38:42] growth industry? When everybody just went to all you can eat. And 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 to usage-based pricing is probably why you will see OpenAI and Anthropic exceed well over $200 in ARR
[39:13] this year. Because 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. But it's it's sad. It's sad for the world. And cuz it just means if you can't afford that, you're not at the frontier. But yeah, continual learning, man. I mean, if we solve that How do you conceptualize that? Like There's so many mysteries about the human mind. Like we're such sample efficient learners relative to AI. Like I forget what it is, but like an AI
[39:44] needs >> Orders of magnitude. Yeah, many orders of magnitude. Now, we have a crude variant of continual learning today when something is verifiable. And that's just you know, reinforcement learning during mid-training. But yeah, continual learning is a model that dynamically adjusts it its weights or adjusts in some way in real time. Like as a human, That's what you do. Yeah, like if I the first time I touch or, you know, put my hand in a fire, I've learned I never put it in there before. That model today needs to put its hand
[40:15] in the fire a million times and then have, you know, the designers effectively put a fire in the next training 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
[40:45] is kind of just around the corner. And I do think this is like the third big question. Bitter lesson violation as result of ASI are less likely. Human ingenuity. Will frontier tokens still command the premium they do? And will you get continual learning and if so, when? As your business scales up, everything gets more complex, especially your compliance and security needs. With so many tools offering band-aids and patches, [music] it's unfortunately far too easy for something to slip through the cracks. Fortunately, Vanta is a powerful tool designed to simplify and
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[42:17] 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, what role they'll play. So, I think this is good and healthy for the world. It's good for Jensen, too. Um you know, because a different administration might take a different view. Competition, I think, is good for everyone. In in tank design, they talk about the iron triangle. The iron triangle's tank design is that all designers of a tank, they have to make
[42:47] trade-offs between attack, defense, and mobility. And you know, for obvious reasons. The more defense you have, which is your armor, the heavier the tank is, the less mobile it is. So, you have to live in this triangle and make trade-offs. Okay? Like the Merkava in Israel, it's optimized for defense. Russian tanks and like the Leopard are generally more optimized for mobility. Chip design is the same. And you there there are these fundamental constraints imposed by the laws of
[43:17] physics as embedded in the Taiwan semi design rules that you need to live within. And you have TPU, Trainium, and AMD, which are all um you know, essentially trying to be a better GPU. And today, I think probably Trainium is doing the best. Now, nobody's a better GPU. But Trainium is is, I think, they're you know, they're they're tugging on Superman's cape. And and this is that I'm starting yet.
[43:48] The Trainium 3 needs to ramp into production cuz it has a switch scale-up network, which you really need to economically inference MoE models. You know, a lot of companies have a Taurus architecture. Um that that's where Google was. And AMD, we'll see. The MI450, we we we don't know yet. We'll see. We probably know more about Tridium 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
[44:19] do. So, I think the best path for these startups, like my rule of thumb is 1% market share is going to be worth 100 billion. 100 billion is a pretty good venture outcome. I think what Jensen would say is like, "Okay, if something somebody does something different and it gets to 1 or 2 or 3% share, we'll make that chip." And that's 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
[44:49] tradeoffs, you know, the disaggregation of prefill and inference really have opened the aperture um for making these different pre tradeoffs because you can make very aggressive tradeoffs for decode, aggressive tradeoffs for prefill. Prefill being taking in the context, decode being, you know, write the output. Yeah, I have a great colleague named Andrew Fox who said, "Picture, you know, a British naval ship from the 18th century. Prefill is loading the cannon, decode is firing it." And what prefill literally is is just the model understanding the question, the prompt,
[45:19] and then kind of keeping track of its own deco- if if it's own answer. And that is fundamentally a memory capacity bound problem. Decode is the process of generating new tokens and that is memory bandwidth constrained. And so, if you're a chip designer, this gives you a richer canvas to to paint on. But, even so, it needs to be hard cuz if you make different tradeoffs in that iron triangle to optimize for memory capacity and they're not hard tradeoffs to make, well then, Nvidia is going to make those same tradeoffs.
[45:49] They get better prices from Taiwan Semi than you're ever going to get. Um and good luck. Good luck. And they have the advantage of working with every model company and optimizing in designs. By the way, another very funny thing is if you're a VC and you're investing in a semiconductor company that is telling you they are going to have an advantage cuz of a Taiwan semi process that they have special access to. I promise you that Jensen saw that process when it was a twinkle in Taiwan semi's
[46:21] eyes and it they know more about it than this little company with 200 people can imagine. Taiwan semi, everybody supply chain is showing Jensen everything. The same way they're showing Amazon everything, AMD everything, TPU everything. And that's another reason don't go try to make a better GPU. So you can do something different. You can paint in the prefill canvas. You can paint in the decode canvas. But you also have to do something hard because if it gets to scale, you're going to have those four
[46:51] companies as very fast followers. My firm was a was a um venture investor in Cerebrus. What Cerebrus has done is something hard and fundamentally different. Wafer scale computing. And it it comes with a set of tradeoffs. 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. And you know, they're working on really cool things like um one of the problems Cerebrus has, once you start
[47:22] 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. And so Cerebrus has an overwhelming ratio of on-chip computer 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. Um I'm sure they're looking at hybrid bonding of DRAM, you know, to get around these
[47:52] alleged limitations that are not true. A Cerebrus machine can theoretically run any size model. So there are of models where they're much better than other sizes. So, Cerebras, what I think is interesting is they did something different that's hard to do. Really hard to do. Wafer scale computing. So, I do think there's a role for these. And, you know, I would just encourage them all. Make a different trade-off. And try and do something hard. Cuz everybody's going to get funded after the Cerebras IPO. It's not going to be a
[48:22] problem. But, it took it took Cerebras three generations of chips to get it right. And it's really hard. Like, 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? But, the one last thing on this topic, this is going to be amazing for the useful lives of
[48:54] GPUs and may single-handedly save private credit. >> about that. What do you What do you mean by the private credit? >> Well, just, you know, private credit, they're in pain from these SAS loans. And however much they're marked down, they probably need to be marked down more. Cuz if the public companies are struggling to to adapt, how's like a debt laden company going to going to adapt? Um and invest in what is a very different margin structure business. But, there's a lot of private credit in GPUs, too. They were underwriting that to I think three or four years. And but the disaggregation of inference
[49:24] 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. You know, the useful life of GPU is only a year or two. The useful life of a CPU is only four years cuz the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is mean that you you know, you can put a Cerebras system or Groq LPU's that Nvidia acquired and effectively in front of a hopper or
[49:54] even an ampere use that hopper and ampere for prefill and extend the useful life of that GPU until it melts. Now they do melt. They do melt so they have a time but you know maybe you don't have to run them as fast. This is going to be really good for the whole private credit industry. It's going to help finance the AI build out cuz if you can start to finance GPUs at more like you know 5% or 6% instead of I think CoreWeave's lowest financing was like low sevens. That actually mathematically changes the cost of finance this build out. We had
[50:24] this technological innovation that it's going to lower the cost of financing, extend the useful life of computer on earth. And then I do think the one last thing that's interesting about that is um my friend Jamin from Coatue just did a podcast and Coatue had a deck and they talked about hey you know the sellers of shortage are doing so much better than the buyers of shortage. Buyers shortage being you know the the hyperscalers. But if you own a giant installed base of what is currently in shortage that's
[50:55] also a very very good place to be. And we're hearing you know CPUs are way more important than they were in an agentic world. They do all these things around orchestration, tool calls, etc. etc. etc. The biggest CPU fleets in the world sit at the hyperscalers. So I think some of these hyperscalers may have you know may may catch up a little bit to the sellers of shortage. >> I want to talk about this idea of different and hard applied outside of the infrastructure piece of this. So now you're starting to interact with new founders, um existing CEOs and founders that have to adjust to this new world.
[51:26] What are you seeing like 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 if you've observed differences? Well one 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
[51:58] scale, scale is the ultimate advantage, you're in trouble. And the great thing Amazon had was, you know, it was obvious to a lot of people, but it wasn't obvious to the retail CEOs. And 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 going to We're going to take our margins in that to negative 10,000%." And that's what like like the guys at Wayfair, they did something hard. And Amazon tried to kill them and they
[52:28] failed. Those were like tough operationally, like really competent CEOs. For me in venture, I always look, is this going to 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. Like I think people are becoming worried, you know, today in
[52:58] that in Jensen's five-layer cake of AI, you know, the profits, they're accruing to energy, they're accruing to data centers, they're accruing to chips, they're accruing to models, not really accruing to the applications. Cursor and Cognition, you know, got to a scale. You know, they focused on coding, you know, 18 months ago the people were focusing on coding. OpenAI was doing everything under the sun. The people focused on coding were Cursor, Cognition, and Anthropic. And it was really righteous focus on code.
[53:29] Um I'm John Massaad, the founder of Replit, tweeted something that I thought was so smart. Just it was something like, you know, bitter lesson adjacent is the fact that coding might be the shortest path to ASI and useful AI. Cuz if you really go to coding, you can write yourself code to do anything. And so I think it was really smart of those companies to focus intensely on coding. And I think they all probably got to a scale where they they have a place. I think Cognition is doing something really, really different. But I think a lot of founders are really struggling,
[53:59] man. They're really struggling. And you know, I think they're trying to get confidence that in niche areas that they can get to them and get like a you know, 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 a different outcome. >> Is this related to what you would call like the token path? I know you've used that phrase with me before. Yeah, he comes from a guy at Altimeter, Jamin
[54:29] Ball. 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 the token path. Comparable companies are in the token path. If you're not in the token path and you're not in some really niche thing, life may be hard. And 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 of the data that's, you
[55:00] know, being generated in these niches come 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. 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
[55:31] 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. They're doing some really cool things in pneumatronics. >> to monetize your compliment as Sklansky would say. >> don't think he wants to do that. That is what OpenAI and you know, Anthropic are kind of trying to do to him unsuccessfully.
[56:01] But so it's just like he's a very logical thinker. This is the logical counter move. And I think you will see that like open source frontier, which today consists of, you know, Chinese models with stolen American tokens, you know, somebody told me that like Deep Seek uh the latest one or maybe the original one was only 150,000 reasoning traces. There's many ways to launder this if you're Chinese company. You know, you can hit all these different APIs. You can make it hard.
[56:31] Now, the American labs are working really hard on anti-distillation technology, but I I 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 there not being enough compute to serve Mythos, just they did not want it to be distilled. They wanted to use Mythos, you know, distill it themselves, use it to RL their next model, whatever it is. And then I think what they and
[57:02] eventually I think if OpenAI gets to, you know, economics I feel good about anyone on the frontier will do is just say you know, there's going to be some very interesting game theory because it's it is it's a new kind of prisoner's dilemma. You know, we talked about the old prisoner's dilemma being just around like, "Hey, you you're in a prisoner's dilemma where you have to spend." The new prisoner's dilemma is going to be if you were at the frontier, do you release that model via API or not? And if everyone at the frontier agrees not to do that, then Chinese open
[57:32] sources quickly 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 resources equal intelligence, so they'll start to pull ahead and then that will lead to, you know, 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, Samsung and Intel. The reality is like if if a company like Nvidia were or AMD were to ever really really use one of these other foundries, that foundry
[58:02] would get better really quickly. So I do think Jensen is going to keep open source a certain time frame 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, they can, you know, they cost energy to produce, you need to make up on GPUs, and the open source model companies almost always get a revenue share. How are you preparing a trade ease for the world of Mythos 3,
[58:33] Mythos 4? We're just trying to over invest in cyber security, you know, something I've like, you know, said in multiple forums and I really believe is you everybody needs to have a safe word. Everybody needs to go leave your digital devices behind, literally go to the ocean and have a family safe word or a company safe word. And it can't be one that can be like socially engineered. And this is just to avoid like cyber crime where like what looks like your son or your daughter or your your grandparents or your parents or whatever FaceTimes you,
[59:04] it's an utterly accurate simulation of them. They know everything and can extrapolate based on what they've said, what they're likely to say, and says, you know, wire me a million bucks. That's defensive. What about What will you still be able to do that it won't be able to do, I guess? On the analytical side. So it's a good question. I did just have I just watched The Last Samurai and I asked um at my firm to watch it. And The Last Samurai, if you haven't seen it, I highly recommend watching it. It's actually a movie that's aged really well. Tom Cruise movie from 20 years ago, you
[59:35] know, the conceit is Tom Cruise is this like bitter, washed-up Civil War veteran who's actually a very good soldier. He's bitter and washed-up cuz he feels like he participated in negative actions against the Native Americans. He's hired by Japan to train 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 win even though they don't have guns. He fights valiantly, so the samurai decide not to kill him, take him to
[60:06] their village. He becomes a samurai. It feels like the Civil War to him. So he fights on the side of the samurai. And at the end, he's massacred by a peasant with a machine gun. And like the machine gun is here. And if we do not all become masters of the machine gun, we're going to get mastered. So I am trying to become a master of the machine gun. And then, you know, I'm optimistic there's a long period of time where just like if you were a 50-year-old samurai veteran of
[60:36] many wars, I fought many wars, Master Dwarf. Um you will have advantages using the machine gun. And I'm optimistic as a lifelong student of investing, I'm going to be able to master the machine gun, this new technology, um integrate it into my own process, integrate it into our firm's process in ways that, you know, let me contribute value as a human being for a long time. But, you know, like everyone, like, you know, I have agents running all the time now. >> What's your most useful agent? >> useful agent, honestly, is as And I
[61:07] think I told you this, and I don't want to hurt your business, but my single most useful agent is a really good summary of the points that would be interesting to me from podcasts. There's like 6 hours a day of stuff that I feel like it's in my job description to watch. You know, every time every time somebody from OpenAI, xAI, Google, you know, Cursor, Fireworks, Space 10, let's say nothing
[61:37] of like Jensen, Elon, Dario. Um, I feel compelled to watch and I just don't have that much time. And there's some real needles in haystacks. There's a set of things I always like to see like I'm very sensitive to management compensation. What are they incentivized to do? They do they have stupid RSUs? Or do they have PSUs? And if they have PSUs, what are those PSUs incentivized to do? I think systems that do a very good first pass at that. And you know, that saves people a lot of time. It frees them up for more creative
[62:08] work than like, you know, going through the proxy, pulling the PSU thing, looking at how it's changed versus all the proxies cuz 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. This is the most exciting, thrilling time to be an investor. And there is and it is I am a little I'm getting a little bit worried. >> The diversity breakdown thing? >> Yeah. I'm getting Say just like a little bit more about like the kinds of people that are >> know of anyone like me who's not really
[62:39] bullish on DRAM. No one. No one. There's all these interesting things happening with AI right now. So, one is cross-sectionally the valuations do not make sense. They just flat out do not make sense. They cannot all be true. 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 like five versus 12. At one point it was like three versus 45. Those can't both be true. And
[63:10] yes, semiconductor capex 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 like the valuation of something like Nvidia, which is still, you know, in in in early April was essentially as cheap as it gets relative to the market like in the last 10 or 12 years or whatever it is, and very cheap absolute.
[63:40] It's very hard to square that valuation with something like GE Vernova's valuation. Cuz it builds in like un- unfathomable amount of share loss for Nvidia. So, valuations 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 or, you know, a mining investor, natural resources investor, and you're, you know, you're well-versed in thinking of costs, this
[64:10] is very intuitive to you. In a real bull market for a 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 just gushing 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 mooned during the shortages and the commodity bull markets, you know, go bankrupt or whatever. You're seeing that happen in
[64:40] every industry. The lowest quality players in, you know, these different industries that are hated and detested by the hyperscalers and the buyers cuz they have high costs, they're unreliable, the parts fail at a high rate, etc., etc. They're sold out and raising prices. Um And then that activity gets the interest of like these retail accounts on X, and these stocks get bid to the moon. Whereas some of the higher quality expressions
[65:10] have like actually really underperformed. And, you know, as an investor, it's it's hard because you know within a like a shadow of a doubt that that thing that's moved, you know, 10x in 3 months or 6 months is going to go right back down subject to what they do with all the cash. But like these little quality companies really do smart stuff with cash. And so it worries me a little bit that people who were very skeptical a year ago are no longer skeptical. But then I just
[65:41] contrast that with like the valuations of these like high-quality companies, which are just not extended, and it makes me feel better. But it does kind of feel like, you know, I just thought it was funny in '24 and '25 that anyone asked about an AI bubble or talked about it. Cuz it's like 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 has maybe spread into more speculative, lower-quality, smaller-cap names
[66:14] where if you have a big presence on X or Reddit, it's easy to move them. And that frightens me a little bit. But I just wish there were more AI bears, like I wish there were more memory bears. You know, one reason I'm you know, Astera is a stock I've been close to a long time. There's a lot of bears on that. I love that. Great, you know, I first invested in the series C. Good luck thinking you're going to price that you know, differentially from me. You know, good luck thinking that's a copper loser. And then there's also you can feel the
[66:45] baskets in the market in the leverage baskets. And what baskets you're in is really important, you know, copper, optical, DRAM, NAND. Um and a very interesting thing that's happened this year um is in '24 and '25 the AI trade traded together. So like you could be long GPU compute, scale-up networking, and optical scale across and like short power that trade worked from like a risk management sense cuz you know I'm very factor aware.
[67:15] That all blew out in Jan- January of this year. It's like you know scale up networking would go crazy while scale out was going down or DRAMs massively underperforming NAND and HDDs which had not happened. So these cross-sectional correlations within AI really fell apart and you had to get very fine-grained. You couldn't hedge your memory anymore with like some semi-cap equipment or NAND. Everything
[67:46] cross-sectionally really changed and in a very interesting way in January. And I think maybe one reason for that was you know the AI got to a quality where it was all of a sudden really easy for a bunch of people to get really smart on these different subsectors, start trading them, and then they get put into baskets and those baskets in the >> Yeah, creating price efficiency. Yeah. Yeah, exactly. And then it's like if you like I think some of the biggest opportunities outside of these higher quality names that I think can compound
[68:16] for a long time and they're safe unlike these low quality names which are terrifying is in names that are miscategorized. Like Astera was in a lot of copper loser baskets. Astera their biggest product is going to be a switch. You use both copper and optics to connect switches to accelerators. >> [laughter] >> And so definitionally 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
[68:47] I wonder if you could riff just for like a sentence or two on each of the major companies. I feel like I always forget to ask you like Google, Microsoft, Amazon, you know, the the the major players that are public that all the conversation is centered around these exciting new companies. >> Yeah. So Google uh it 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 installed base of compute.
[69:18] I am a little surprised by their inability and Google IO is this is this week. And um like if they don't release something that even slightly leapfrogs OpenAI and or Claude like that that's interesting and it's not a disaster for Google. It's just
[69:48] interesting and it just means this Nvidia effect we discussed is even more powerful than maybe I'd imagined 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 you know between the amount of data they have and the YouTube data is actually really genuinely valuable. It's it is valuable in a world of robotics. The amount of compute they have and you know the search business they have. Google's never not going to be in a good
[70:18] position and then you see that with GCP going crazy. You got to give Zuckerberg a immense credit. 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. And I give him a lot of credit for that. I give him a lot of credit for paying up when he did for you know all those you know those billion dollar contracts that talent. And news I think it was a really big
[70:48] upside surprise. You know it was the first model from MSL and it's not on the Pareto of frontier with you know XAI Google's one entrant and then open AI and Claude but it's pretty close. That was very impressive to me. So I think that 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 like three year time frames over like
[71:19] long time frames level of competitive advantages tends to dominate but even within that you know the changes changes are really matter. Amazon I think is in a really strong position because of tranium. You're going to see like 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. Microsoft I think Satya is a really brilliant man but you know in in investor
[71:49] conversations people just don't talk about him the way that they did. I 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 you know he did go from we're going to make Google dance to being the product manager of co-pilot in like three years. I I would love to know during the coup attempt against open AI does Satya regret his decisions? Does Satya wish that he had supported
[72:20] Ilya and instead of Sam and that kind of Ilya and Mira were really running open AI today. In his heart of hearts I would love to know. Cuz I think the Microsoft open AI 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 like he is what he is doing now he's taking risk.
[72:50] So they could earn you know this goes to the decisions you have to make in that cone of uncertainty are 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. You know, they have this algorithm, we spend this much CapEx dollars, we get this return. That algorithm was kind of off. And if you flinch, you lose position. You lose all these allocations, and it's difficult to get it back. So, they flinched.
[73:20] And now the decision Satya is making, which the market has punished him for, but I think is the right decision, is we're going to use our compute, rather than making, 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. You know, 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.
[73:50] And they're trying to use their compute to train their own models. I don't I am a little skeptical that they have the right team to succeed there, but, you know, they can certainly, like, just like Meta, they can afford to hire maybe a maybe a different team. But I think he's making good decisions that are risky decisions to position Microsoft from for this world where frontier models are are no longer API accessible. And I think it's a really courageous
[74:20] decision that I give him a lot of credit for, and he is forgoing, I mean, Microsoft probably be an $800 stock today if they were using their GPUs to serve OpenAI solely OpenAI and Anthropic's capacity instead of using them for their own products. So, I give him a lot of credit for making a great decision. 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
[74:51] mile. Then there's a really intense engagement with Google. They're next most intense. Broadcom is engaged in a different way. They're just, you know, everybody's favorite ASIC supplier. Like it's, you know, if you're a startup, it'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. And I
[75:23] just, yeah, I mean, when I say zero, it's a little. And I just wonder about that decision. Because some of the best teams are no longer at 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
[75:53] um hyperscalers. As we wrap up, I'm curious for you to riff on any other like 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 impacts. We talked a little bit about the application layer and what would have to happen for there to be more value accruing to that layer of the stack. I'm curious like any other just fun knock-on things that you've been thinking about as this world changes so quickly. >> Yes, and it is wild. I mean, at the application layer, forget value accruing, just value has been destroyed. AI has net destroyed. Even if you count Cursor Cognition, the most successful AI
[76:25] natives, value has been trillions of dollars of value has been destroyed by AI at the application layer. And just in this context, I do think it's a little it's something we need to be aware of. The companies that are doing the best today that are are kind of their values increase the most that are creating economic value are the companies with the highest ratio a highest effective ratio of utilized GPUs per human. And you know, maybe this just means that every human's going to get a lot of
[76:55] 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 more more and more worried about personal safety. And I worry about this a lot more for people who are you know, have a much bigger public presence and are much more associated with AI. But I really worry about personal safety. I hope nothing tragic happens, but like there is this upsurge in political violence here in America. And as AI increasingly becomes political, I worry that's going
[77:26] to get directed at more and more AI political leaders, you know, just whatever we can agree you know, whatever whatever I may think or may not think of open AI. Like I think it is terrible that someone threw Molotov cocktails at Sam Altman'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 you know, for people who are big players on the chessboard. Think about what it
[77:57] means geopolitically. Like we're watching the Ukrainians are really starting to win. And the reason they're winning I I think is not really because they have better drones. I think they do have better drones. That's part of it. I think 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 like how do they respond? Like if the United States because of its edge in AI
[78:28] um 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 like educate the world on how awesome the West has been. Slavery was endemic to essentially almost every civilization and slavery was really ended by the British Empire. Tell that story. Um 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
[79:00] and now who are America's most reliable allies? Israel, South Korea, Japan. That's a testament to like the American spirit in our country. We didn't take over the the world. You know, there were these fears, you know, that were documented at the time that the American generals and you know, MacArthur was a little bit of an American emperor in Japan but um we're 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, you know this period of of great global stability
[79:30] between, you know, a scary there were terrible wars. Yeah, you had the Pax Americana. So maybe it's not destabilizing. Maybe it leads to the another Pax Ameri- Americana informed by our AI dominance and I'm so optimistic that AI is going to be amazing for the world. There's someone like me whose daughter was diagnosed with a very rare mutation. 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. Um we were made aware of what was happening.
[80:01] Spun up a immense 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. And like her life is already immeasurably different because of AI. So I'm like an AI I'm like an AI optimist maximalist, but I also just acknowledge it's like an event horizon. It for sure I think it's going to be a discontinuity. We need to navigate as soci- as society. I think the Luddites
[80:31] are going to be wrong, but we need to be like really thoughtful in 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. Like 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, "May you find something that you love as much as Gavin loves markets and companies and capitalism and history." Uh on display today as always, Gavin,
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