Gavin Baker - Nvidia v. Google, Scaling Laws, and the Economics of AI

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I get sales, calls all the time and ignore them. Ridgeline sold me very quickly. We went from $7 billion to $23 billion and the goal is $50 billion. Ridgeline was the clear frontrunner to help us scale. In your view, what most distinguishes Ridgeline? They reimagined how this industry should work, it's obvious that they were operating on another level. It's worth reaching out to Ridgeline to see what the unlock can be for your firm. Visit RidgelineApps.com to schedule a demo. One of the hardest parts of investing is seeing what shifting before everyone else does. Alpha Sense is helping investors do exactly that. You may already know Alpha Sense is the market intelligence platform trusted by 75% of the world's top hedge funds, providing access to over 500 million premium sources from company filings and broker research to news, trade journals, and over 200,000 expert transcript calls. 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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 Review, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus Review along with all of our podcasts at join Colossus.com. Patrick O'Shaughnessy is the CEO of Positive Some. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Some. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Some may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. I will never forget when I first met Gavin Baker in 2017. I find his interest in markets his curiosity about the world to be as infectious as any investor that I've ever come across. He is encyclopedic on what is going on in the world of technology today, and I've had the good fortune to host him every year or two on this podcast. In this conversation, we talk about everything that interests Gavin. We talk about Nvidia, Google, and its TPUs, the changing AI landscape, the math, and business models around AI companies, and everything in between. We even discuss the crazy idea of data centers in space, which he communicates with his usual passion and logic. In closing, at the end of this conversation, because I've asked him my traditional closing question before, I asked him a different question, which led to a discussion of his entire investing origin story that I had never heard before. Because Gavin is one of the most passionate thinkers and investors that I know, these conversations are always amongst my most favorite. I hope you enjoy this latest in the series of discussions with Gavin Baker. I would love to talk about how you, like in the nitty-gritty process, new things that come out in this AI world, because it's happening so constantly. I'm extremely interested in it, and I find it very hard to keep up, and I have a couple of blogs that I go read and friends that I call. But maybe let's take Gemini 3 as a recent example. When that comes out, take me into your office, what are you doing? How do you and your team process an update like that, given how often these things are happening? I mean, I think the first thing is you have to use it yourself. And I would just say, I'm amazed at how many famous and August investors are reaching really definitive conclusions about AI. Well, now based on the free tier, the free tier is like you're dealing with the 10-year-olds, and you're making conclusions about the 10-year-olds capabilities as an adult. And you could just pay, and I do think, actually, you do need to pay for the highest tier, whether it's Gemini Ultra, Super Grock, whatever it is, you have to pay the $200 per month tiers. Whereas those are like a fully-fledged 30-35-year-old, it's really hard to extrapolate from a native or a 10-year-old to the 35-year-old, and yet a lot of people are doing that. And the second thing is, there was an insider post about OpenAI, and they said to a large degree OpenAI runs on quitterfops. And I just think AI happens on X. There have been some really memorable moments, like there was a giant fight between the PyTorch team at Meta and the Jack's team at Google on X. And the leaders of each lab had to step in publicly say, "No one from my lab is allowed to say bad things about the other lab, and I respect them, and that is the end of that." The companies are all commenting on each other's posts, you know, the research papers come out. There's a list of, you know, if on planet Earth, there's 500 to 1,000 people who really, really understand this and are at the cut of edge of it. And a good number of them live in China. I just think you have to follow those people closely, and I think there is an incredible signal. Everything in AI is just downstream. Of those people? Yeah. Everything Andre Karpathy writes. You have to read it three times. Yeah. Minimum. Yeah, he's incredible. And then I would say any time one of those labs, the four labs that matter, open AI, Jim and I, and throughout the Connected AI, which are clearly the four leading labs. Any time somebody from one of those labs goes on a podcast, I just think it's so important to listen. For me, one of the best use cases of AI is to keep up with all of this. Listen to a podcast, and then if there are parts that I thought were interesting, just talk about it with AI. And I think it's really important to have as little friction as possible. I'll bring it up. I can either press this button and pull up rock, or I have this. It's so amazing. If people leave, we have this. I know. It's like somebody said on X, you know, like we imbued these rocks with crazy spells. And now we can summon super intelligent genies on our phones over the air. You know, it's crazy. Crazy. Okay. So something like Gemini 3 comes out with the public interpretation was, oh, this is interesting. It seems to say something about scaling laws and the pre-training stuff. What is your frame on like the state of general progress in frontier models in general? Like what are you watching most closely? Yeah. Well, I do think Gemini 3 was very important because it showed us that scaling laws for pre-training are intact. They stated that unequivocally. And that's important because no one on planet Earth knows how or why scaling laws for pre-training work. It's actually not a law. It's an empirical observation. And it's an empirical observation that we've measured extremely precisely in this held for a long time. But our understanding of scaling laws for pre-training, and maybe this is a little bit controversial with 20% of researchers, but probably not more than that. It's kind of like the ancient British people's understanding of the sun. Are the ancient Egyptians understanding the sun? They can measure it so precisely that the east-west access to the great pyramids are perfectly aligned with the equinoxes. And so are the east-west accesses of Stonehenge? Perfect measurement. They didn't understand orbital mechanics. They had no idea how or why it rose in the east, set in the west, and moved across the horizon. There's the aliens in the ocean. Yeah. Yeah. Our god had a chariot. And so it's really important every time we get a confirmation of that. So Gemini 3 was very important in that way. But I think there's been a big misunderstanding of maybe in the public equity investing community, or the broader more generalist community. Based on the scaling laws of pre-training, there really should have been no progress in 24 and 25. And the reason for that is, is after XAI figured out how to get 200,000 hoppers coherent, you had to wait for the next generation of chips. Because you really can't get more than 200,000 hoppers coherent. And coherent just means you could just think of it as each GPU knows what every other GPU is thinking. They kind of are sharing memory. They're connected. The scale of networks and the scale out. And they have to be coherent during the pre-training process. The reason we've had all this progress, maybe we could show like the ARC, AGI slide where you had 0 to 8 over four years, 0 to 8% intelligence. And then you went from 8% to 95% in three months when the first reasoning model came out from OpenAI. We have these two new scaling laws of post-training, which is just reinforcement learning with verified rewards. And verified is such an important concept in AI. Like one of Carpathi's great things was with software. Anything you can specify, you can automate with AI. Anything you can verify, you can automate. It's such an important concept. And I think important distinction. And then test time compute. And so all the progress we've had, we've had immense progress since October 24 through today was based entirely on these two new scaling laws. And Jim and I-3 was arguably the first test since Hopper came out of the scaling law for pre-training. And it held. And that's great because all these scaling laws are multiplicative. So now we're going to apply these two new reinforcement learning, verified rewards and test time compute to much better base models. There's a lot of misunderstanding about Jim and I-3 that I think is really important. So the most important thing to conceptualize everything in AI has a struggle between Google and Nvidia. And Google has the TPU. And Nvidia has their GPUs and Google only has a TPU and they use a bunch of other chips for networking. And Nvidia has the full stack. Blackwell was delayed. Blackwell was Nvidia's next generation chip. The first iteration of that was the Blackwell 200. A lot of different skews were canceled. And the reason for that is it was by far the most complex product transition we've ever gone through in technology. Going from Hopper to Blackwell, first you go from air cooled to liquid cooled. The rack goes from weighing round numbers a thousand pounds to three thousand pounds. Goes from round numbers 30 kilowatts, which is 30 American homes to 130 kilowatts, which is 130 American homes. I analogize it to imagine if to get a new iPhone, you had to change all the outlets in your house to 220 volts, put in a Tesla power wall, put in a generator, put in solar panels, that's the power, you know, put in a whole home humidification system and then reinforce the floor because the floor can't handle this. So it was a huge product transition. And then just the rack was so dense, it was really hard for them to get the heat out. So Blackwells have only really started to be deployed and really scaled deployments over the last three or four months. Can you explain why it spent such an important thing that Blackwell was delayed? Because Blackwell is so complicated and it was so hard for everyone to get these exquisitely complex racks working consistently. Had reasoning not come along, there would have been no AI progress from bid 2024 through essentially Jim and I three. There would have been none. Everything would have stalled. And you can imagine what that would have meant to the markets. For sure, we would have lived in a very different environment. So reasoning kind of bridged this 18-month gap, reasoning kind of saved AI because it let AI make progress without Blackwell or the next generation of TPU, which were necessary for the scaling loss for pre-trading to continue. Google came out with the TPU V6 in 2024 and the TPU V7 in 2025. In semiconductor time, imagine like Hopper, it's like a World War II era airplane. And it was by far the best World War II era airplane. It's P51 Mustang with the Merlin engine. And two years later, in semiconductor time, that's like you're an F4 phantom, okay? Because Blackwell was such a complicated product and so hard to ram. Google was training Jim and I three on 24 and 25 era TPUs, which are like F4 phantoms. Blackwell, it's like an F35. It just took a really long time to get it going. So I think Google for sure has this temporary end of advantage right now from pre-training perspective. I think it's also important that they've been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producers ever matter. Apple is not worth trillions because they're low cost producer of phones. Microsoft is not worth trillions because they're low cost producer of software. And video is not worth trillions because they're the low cost producer of AI accelerators. It's never mattered. And this is really important because what Google has been doing has the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them. And for anyone is the low cost producer, let's make life really hard for our competitors. So what happens now, I think it says pretty profound implications. One, we'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from XAI. And the reason for that is just according to Jensen, no one builds data centers faster than Eva. Jensen has said this on the record. And even once you have the Blackwells, it takes six to nine months to get them performing at the level of Hopper. Because Hopper is finally tuned. Everybody knows how to use it. The software is perfect for it. The engineers know all its quirks. Everybody knows how to architect a Hopper data service. And by the way, when Hopper came out, it took six to 12 months for it to really outperform Ampere, which was generation before. So if you're Jensen or Nvidia, you need to get has many GPUs deployed in one data center as fast as possible and a coherent cluster so you can work out the bugs. And so this is what XAI effectively does for Nvidia, because they build the data centers the fastest. They can deploy Blackwells at scale the fastest and they can help work with Nvidia to work out the bugs for everyone else. So because they're the fastest, they'll have the first Blackwell model. We know that scaling loss for pre-training are intact. And this means the Blackwell models are going to be amazing. Blackwell is, I mean, it's not enough 35 first and last four phantom, but from my perspective, it is a better chip. You know, maybe it's like an F 35 or so Raphael. And so now that we know pre-skilling loss for holding, we know that these Blackwell models are going to be really good based on the raw specs. They should probably be better. And then something even more important happens. So the GB 200 was really hard to get it going. The GB 300 is a great chip. It is drop in compatible in every way with those GB 200 racks. Now you're not going to replace the GB 200s, but just any power walls. Yeah, just any data center that can handle those. You can slot in the GB 300s and now everybody's good at making those racks and you know how to get the heat out. You know how to cool them. You're going to put those GB 300s in. And then the companies that use the GB 300s, they're going to be the low-cost producer of tokens, particularly if you're vertically integrated. If you're paying a margin to someone else to make those tokens, you're probably not going to be. And he just has pretty profound implications. I think it has to change Google's strategic calculus. If you have a decisive cost advantage in your Google and you have search and all these other businesses, why not run AI at a negative 30% margin? It is by far the rational decision to take the economic oxygen out of the environment. You eventually make it hard for your competitors who need funding unlike you to raise the capital they need. And then on the other side of that, maybe you have an extremely dominant sheer position. While that calculus changes, once Google is no longer the low-cost producer, which I think will be the case. The Blackwells are now being used for training. And then when that model is trained, you start shifting Blackwell clusters over to inference. And then all these cost calculations and these dynamics change. It's very interesting, like during the strategic and economic calculations between the players, I've never seen anything like it. Everyone understands their position on the board. What the prize is, what play their opponents are running. And it's really interesting to watch. If Google changes its behavior, because it's going to be really painful for them as a higher-cost producer to run that negative 30% margin, it might start to impact their stock. That has pretty profound implications for the economics of AI. And then when Ruben comes out, the gap is going to expand significantly. Versus TPUs. Versus TPUs and all other ASICs. Now, I think training of three is probably going to be pretty good and training four are going to be good. Why is that the case? Why won't TPU be V9 be everybody's good? A couple of things. So one, for whatever reason, Google made more conservative design decisions. I think part of that is so Google, let's say the TPU. So there's front-end and back-end of semiconductor design and then there's dealing with Taiwan Semi. And you can make an ASIC in a lot of ways. What Google does is they do mostly the front-end for the TPU and then Broadcom does the back-end and manages Taiwan Semi and everything. It's a crude analogy but the front-end is like the architect of a house. They design a house. The back-end is the person who builds the house. And the managing Taiwan Semi is like stamping out that house like Lunar or D.R. Horton. And for doing those two lot of parts, Broadcom runs a 50 to 55% gross margin. We don't know what on TPUs. Let's say in 2027, TPU, I think it's its estimates, maybe somewhere around 30 billion. Again, who knows? 30 billion, I think is a reasonable estimate. 50 to 55% gross margins. So Google is paying Broadcom 15 billion dollars. That's a lot of money. At a certain point, it makes sense to bring a semiconductor program entirely in house. So in other words, Apple does not have an ASIC partner for their chips. They do the front-end themselves, the back-end and they manage Taiwan Semi. And the reason is they don't want to pay that 50% margin. So at a certain point, it becomes rational to renegotiate this and just just perspective. The entire op-ex of Broadcom Semi Conductor Division is around numbers 5 billion dollars. So it'd be economically rational. Now that Google's paying, if it's 30 billion or paying them 15, Google can go to every person who works in Broadcom Semi, double their comp and make an extra 5 billion. In 2028, let's just say it does 50 billion. Now it's 25 billion. You can triple their comp. And by the way, you don't need them all. And of course, they're not going to do that because of competitive concerns. But with TPU-V8 and V9, all of this is beginning to have an impact because Google is bringing in media tech. This is maybe the first way you send a warning shot to Broadcom. We're really not happy about all this money we're paying. But they did bring media tech in in the Taiwanese ASIC companies, have much lower gross margins. So this is kind of the first shot against the bowel. And then there's all this stuff people say, but Broadcom has the best 30s. Broadcom has really good 30s and 30s is like an extremely foundational technology because it's how the chips communicate with each other. You have to serialize it, do serialize. But there are other good 30s providers in the world. A really good 30s is maybe it's worth 10 or 15 billion a year, but it's probably about 25 billion a year. So because of that friction, and I think conservative design choices on the part of Google, and maybe the reason they made those conservative design choices is because they were going to a bifurcated supply. TPU is slowing down. I would say as the GPUs are accelerating, this is the first competitive response of Lisa and Jensen to everybody saying we're going to have our own ASIC, is hey, we're just going to accelerate. We're going to do a GPU every year and you cannot keep up with us. And then I think what everybody is learning is like, oh wow, that's so cool. You made your own accelerator has an ASIC. Wow, what's the NIC going to be? What's the CPU going to be? What's the scale up switch going to be? What's the scale of protocol? What's the scale out switch? What kind of objects are you going to use? What's the software that's going to make all this work together? And then it's like, oh shit, I made this tiny little chip. And you know, like whether it's admitted or not, I'm sure the GPU makers don't love it when their customers make ASICs to try and compete with them. And like, whoops, what did I do? I thought this was easy. You know, it takes at least three generations to make a good chip. Like the TPU V1, I mean, it was an achievement and then they made it. It was really not till TPU V3 or V4 that the TPU started to become like even vaguely competitive. Is that just a classic like learning by doing thing? 100%. And even if you've made from my perspective, the best ASIC team at any semiconductor company is actually the Amazon ASIC team. They're the first one to make the graph on the CPU. They have this Nitro, it's called SuperNIC. They've been extremely innovative, really clever. And like, training them an infantry one, maybe they're a little better than the TPU V1, but only a little. Training them too, you get a little better. Training them three. I think the first time, it's like, okay. And then, you know, I think training four will probably be good. I will be surprised if there are a lot of ASICs other than training them in TPU. And by the way, and training them in TPU will both run on customer-owned tooling at some point. We can debate when that will happen, but the economics of success that I just described mean it's inevitable. Like, no matter what the companies say, just the economics make it and reasoning from first principles make it absolutely inevitable. If I were to zoom all the way out on this stuff, I find these details unbelievably interesting and it's like the grandest game that's ever been played. It's so crazy and so fun to follow. Sometimes I forget to zoom out and say, well, so what? Like, okay. So, project this forward three generations past Rubin or whatever. What is like the global human dividend of all this crazy development? Will we keep making the loss lower on these pre-training scaling models? Like, who cares? Like, it's been a while since I've asked this thing something that I wasn't kind of blown away by the answer for me personally. What are the next couple of things that all this crazy infrastructure war allows us to unlock because they're so successful? If I were to pause it like an event path, I think the Blackwell models are going to be amazing. The dramatic reduction in per token cost enabled by the GP300 and the prime more the MI450 than the MI355 will lead to these models being allowed to think for much longer, which means they're going to be able to do new things. I was very impressed. My three made me a restaurant reservation. It's the first time it's done something for me. And I mean, other than like go research something and teach me stuff, if you can make a restaurant reservation, you're not that far from being able to make a hotel reservation and an airplane reservation and order me an Uber and- All of a sudden, you got an assistant. Yeah, and you can just imagine everybody talks about that. You can just imagine it's on your phone. I think that's pretty near-term. But some big companies that are very tech forward, 50% plus of customer support is already done by AI and that's a $400 billion industry. And then if you know what AI is great about is persuasion, that sales and customer support. And so of the functions of a company, if you think about them, they're to make stuff, sell stuff, and it support the customers. So right now maybe in late 26, you're going to be pretty good at two of them. I do think it's going to have a big impact on media. Like I think robotics, you know, we talked about the last time, are going to finally start to be real. You know, there's an explosion and kind of exciting robotics startups. I do still think that the main battle is going to be- we in Tesla's Optimus in the Chinese because, you know, it's easy to make prototypes. It's hard to mass produce them. But then it goes back to that what Andre Carpathi said about AI can automate anything that can be verified. So any function where there's a right or wrong answer or right or wrong outcome, you can apply reinforcement learning and make the AI really good at that. What are your favorite examples of that so far or theoretically? I mean, just does the model balance. They'll be really good at making models. Do all the books globally reconciled. They'll be really good at accounting, double entry bookkeeping and haste balance. There's a verifiable you got it right or wrong. Supporters say, did you make the sale or not? That's just like AlphaGo. Did you win or you lose? Did the guy convert or not? Did the customer ask for an escalation during customer support or not? It's most important functions are important because they can be verified. So I think if all of this starts to happen and starts to happen in 26, they'll be in ROI on Blackwell and then all this will continue and then we'll have Ruben. And then that'll be another big quantum spin Ruben in the MI450 and the TPU V9. And then I do think just the most interesting question is what are the economic returns to artificial superintelligence? Because all of these companies in this great game, they've been in a prisoner's dilemma. They're terrified that if they slow down gone forever and their competitors don't, it's an existential risk. And you know, Microsoft blinked for like six weeks earlier this year. Yeah. But I think they would say they regret that. But with Blackwell and for sure with Ruben, the economics are going to dominate the prisoner's dilemma from a decision making and spending perspective just because the numbers are so big. And this goes to kind of the ROI on AI question. And the ROI on AI has empirically, factually, unambiguously been positive. I just always find it strange that there's any debate about this because the largest bidder just on GPUs or public companies, they report something called audited quarterly financials and you can use those things to calculate something called a return on investment capital. And if you do that calculation, the ROIC of the big public spenders on GPUs is higher than it was before they ramped spinning. And you could say, well, part of that is, you know, up savings. Well, at some level, that is part of what you expect the ROI to be from AI. And then you say, well, a lot of is actually just applying to use moving the big recommender systems that power the advertising and the recommendation systems from CPUs to GPUs and you've had massive efficiency gains. And that's why all the revenue growth at these companies is accelerated. But like, so what? The ROI has been there. And it is interesting, like every big internet company, the people who are responsible for the revenue are intensely annoyed at the amount of GPUs that are being given to the researchers. It's a very linear question. If you give me more GPUs, I will drive more revenue. Yeah. Give me those GPUs. We'll have more revenue, more gross profit. And then we get to everybody. So it's as constant fight at every company. One of the factors in the prisoners still, Emma, as everybody has this like religious belief that we're going to get to ASI. And at the end of the day, what do they all want? Almost all of them want to live forever. And they think that ASI is going to help them with that. Right. Good return. That's good. But we don't know. And if as humans, we have pushed the boundaries of physics, biology, and chemistry, the natural laws that govern the universe, then maybe the economic returns to ASI aren't that high. I'm very curious about your favorite sort of throw cold water on this stuff. Type takes that you think about sometimes. One would be like the things that would cause, I'm curious what you think, the things that would cause this demand for compute to change or even the trajectory of it to change. There's one really obvious bear case that it is just edge AI. And it's connected to the economic returns to ASI in three years on a bigger and bulkier phone to fit the amount of DRAM necessary. You know, in the battery won't part less as long. You will be able to probably run like a pruned down version of something like Jim and I five or GROC for GROC 4.1 or chat GPT at 30 60 tokens per second. And then that's free. And this is clearly Apple's strategy. It's just we're going to be a distributor of AI and we're going to make it privacy safe and run on the phone. And then you can call one of the big models, you know, the God models in the cloud, whatever you have a question. And if that happens, if like 30 60 tokens a second at a 115 IQ is good enough, I think that's a bear case other than just the scaling laws break. But in terms of if we assume scaling laws continue and we now know they're going to continue for pre-training for at least one more generation. And we're very early in the two new scaling laws for post-training, mid-training, RLVR, whatever people want to call it, and then test time computed inference. We're so early in those and we're getting so much better at helping the models hold more and more context in their minds as they do this test time compute. And that's really powerful because everybody's like, well, how's the model going to know this? Well, eventually you can get through, you can hold enough context. You can just hold every slack message and outlook message and company manual. And in a company, in your context, and then you can compute the new task and compare it with your knowledge of the world, what you think with the model things, all this context and maybe that like just really, really long context windows are the solution to a lot of the current limitations. And that's enabled by all these cool tricks like KV Cash Offload and stuff. But I do think other than scaling laws slowing down other than there being low economic returns to ASI, edge AI is to me by far the most plausible and scariest bear case. I like to visualize like different S-curve you invested through the iPhone. And I love to like see the visual of the iPhone models as it sort of went from this clunky, bricky thing up to the what we have now. We're like, each one's like a little bit, you know, obviously we've sort of petered out on its form factor. If you picture something similar for the frontier models themselves, does it feel like it's at a certain part of that natural technology paradigm progression? If you're paying for human eye ultra or super rock and you're getting the good AI, it's hard to see differences. Like I have to go really deep on something like do you think PCI Express or Ethernet is a better protocol for scale up networking and why show me the scientific vapors. And if you shift between models and you ask a question like that, where you know it really deeply, then you see differences. I do play fantasy football, winnings are turning into charity, but it is like, you know, these new models are quite a bit better at helping who should I play. They think in much more sophisticated ways. If you're a historically good fantasy football player and you're having a bad season, this is why this is why because you're not using it, you know, and I think we'll see that in more and more domains. But I do think they're already at a level where unless you're a true expert or just have an intellect that is beyond mind, it's hard to see the progress. And that's why I do think we just shift from getting more intelligent to more useful unless more intelligence starts leading to these massive scientific breakthroughs and we're hearing cancer in 26 and 27. I don't know that we're going to be hearing cancer, but I do think from almost an ROIS curve, we need to kind of hand off from intelligence to usefulness and then usefulness will then have to hand off to scientific breakthrough just that creates whole new industries. What are the building blocks of usefulness in your mind? Just being able to do things consistently and reliably. And a lot of that is keeping all the context. Like, there's a lot of context if someone wants to plan a trip for me. Like, you know, I've acquired these strange preferences. Like I follow that guy, Andrew Cuperman. So I like to have an East facing balcony. So I think I'm warning Sun, you know, so the AI has to remember, here's how I like to fly. Here are my preferences for that. Being out of plain with Starlink is important to me. Okay, here are the resorts I've historically liked. Here are the kinds of areas I've liked. Here are the rooms that I would really like at each. That's a lot of context and to keep all of that and kind of wait those, it's a hard problem. So I think context windows are a big part of it. You know, there's this meter task evaluation thing how long it could work for and you could think of that as being related in some way to context, not precisely, but that just task length needs to keep expanding because booking a restaurant and booking is economically useful, but it's not that economically useful. But booking me an entire vacation and knowing the preferences of my parents, my sister, my niece and my nephew, that's a much harder problem and that's something that like a human might spend three or four hours on optimizing that. And then if you can do that, that's amazing. But then again, I just think it has to be good at sales and customer support relatively soon. And then after that, it has to be and I think it is already here. I do think we're going to see in a kind of an acceleration and the awesomeness of various products engineers are using AI to make products better and faster. We both invested in Fortell, the here and the company, which is just absolutely remarkable, like something I never would have thought of. And we're going to see I think something like that in every vertical and that's AI being used for the most core function of any company, which is designing the product. And then it will be, you know, there's already lots of examples of AI being used to help manufacture the product and distribute it more efficiently, whether it's optimizing a supply chain, having a vision system, watch a production line. A lot of stuff is happening. I think it's really interesting in this whole ROI part is 4 to 500 companies are always the last to adopt a new technology. They're conservative. They have lots of regulations, lots of lawyers. Startups are always the first. So let's think about the cloud, which was the last truly transformative new technology for enterprises, being able to have all of your compute and the cloud and usas. So it's always upgraded. It's always great, et cetera, et cetera. You can get it on every device. I think the first AWS reinvent, I think it was in 2013. And by 2014, every startup on Planet Earth ran on the cloud. The idea that you would buy your own server and storage box and router was ridiculous. And that probably happened like even earlier. That probably already happened before the first reinvite. The first big fortune 500 companies started to standardize on it like maybe five years later. You see that with AI. I'm sure you've seen this in your startups. And I think one reason VCs are more broadly bullish on AI than public market investors is VCs see very real productivity gains. There's all these charts that for a given level of revenue, a company today has significantly lower employees than a company of two years ago. And the reason is AI is doing a lot of the sales, the support, and helping to make the product. I mean, there's, you know, iconic has some charts, a 16z. By the way, David George is a good friend. Great guy. He has his model busters thing. So there's very clear data that this is happening. So people who have a lens into the world of venture see this. And I do think it was very important in the third quarter. This is the first quarter where we had fortune 500 companies outside of the tech industry give specific quantitative examples of AI driven uplift. So see, Robinson went up something like 20% on earnings. Should I tell people what sees Robinson does? Yeah. Like, let's just say a truck goes from Chicago to Denver. And then the trucker lives in Chicago. So it's going to go back from Denver to Chicago. There's an empty load. It's the a Robinson has all these relationships with these truckers and trucking companies. And they match shippers demand with that empty load supply to make the trucking more efficient. You know, they're a free foreritor. You know, there's actually lots of companies like this, but they're the biggest and most dominant. So one of the most important things they do is they quote price and availability. So somebody a customer calls them up and says, hey, I urgently need three 18 wheelers from Chicago to Denver. But in the past, they said it would take them, you know, 15 to 45 minutes. And they only quoted 60% of inbound requests with AI. They're quoting 100% and doing it in seconds. And so they printed a great quarter and stock went up 20% and it was because of AI driven productivity that's impacting the revenue line, the cost line, everything. I was actually very worried about the idea that we might have this black well ROI air gap because we're spending so much money on black well. Those black wells are being used for training. And there's no ROI on training. Training is you're making the model. The ROI comes from inference. So I was really worried that, you know, we're going to have maybe this three quarter period where the cap access unimaginably high. Those black wells are only being used for training. Right. Arsting, flat eyes going off. Exactly. So ROIC goes down and you can see like meta, meta they printed, you know, because meta has not been able to make a frontier model. Meta printed a quarter where ROIC declined. And that was not good for the stocks. I was really worried about that. I do think that those data points are important in terms of suggesting that maybe we'll be able to navigate this potential air gap and ROIC. Yeah. It makes me wonder about in this market, I'm like everybody else. It's the 10 companies at the top that are all the market cap more than all of the attention. There's 49 of the other companies, the S&P 500, you study those too. Like, what do you think about that group? Like, what is interesting to you about the group that now nobody seems to talk about? And no one really seems to care about because they haven't driven returns and they're a smaller percent of the overall index. I think that people are going to start to care if you have more and more companies, print these C/H Robinson like quarters. I think the companies that have historically been really well-run, the reason they have a long track record of success, you cannot succeed without using technology well. And so if you have a kind of internal culture of experimentation and innovation, I think you will do well with AI. I would bet on the best investment banks to be earlier and better adopters of AI than maybe some of the trailing banks, just sometimes past his prologue. And I think it's likely to be in this case. One strong opinion I have, all these VCs are sitting up these holding companies and we're going to use AI to make traditional businesses better. And the really smart VCs and their great track records, but that's what private equity has been doing for 50 years. You're just not going to be private equity at their game. That's what Vista did in the early days, right? Yeah, and I do think this is actually private equities, maybe had a little bit of a tough run, just multiples have gone up. Now private assets are more expensive. The cost of financing has gone up. It's tough to take a company public, because the public valuation is 30% lower than the private valuation. So P's had a tough run. I actually think these private equity firms are going to be pretty good at systematically applying AI. We haven't spent much time talking about meta and traffic or open AI. And I'd love your impression on everything that's going on in this infrastructure side that we talked about. These are three really important players in this grand game. How does all of this development that we've discussed so far impact those players specifically? First thing, let me just say about frontier models broadly. Yeah. In 2023 and 24 hours, fond of quoting Eric Vistria, and Eric Vistria's statement, our friend, brilliant man, and Eric would always say foundation models of the fastest appreciating assets in history. And I would say he was 90% right. I modified the statement. I said foundation models without unique data and internet scale distribution are the fastest appreciating assets in history. And reasoning fundamentally changed that in a really profound way. So there was a loop, a flywheel to quote Jeff Bezos, that was at the heart of every great internet company. And it was you made a good product, you got users, those users using the product generated data that could be fed back into the product to make it better. And that flywheel has been spinning at Netflix, at Amazon, at Meta, at Google for over a decade. And that's an incredibly powerful flywheel and it's why those internet businesses were so tough to compete with. It's why they're increasing returns to scale. Everybody talks about network effects. They were important for social networks. I don't know to what extent Meta is a social network anymore like a content distribution, but they just had increasing returns to scale because of that flywheel. And that dynamic was not present in the pre-reasoning world of AI. You pre-trained the model. You let it out in the world. And it was what it was. And it was actually pretty hard. They would do RLHF reinforcement learning with human feedback. And you try and make the bot model better. And maybe you'd get a sense from Twitter vibes that people didn't like this. And so you tweak it. They're the little up and down arrows. But it was actually pretty hard to feed that back into the model. With reasoning, it's early, but that flywheel started to spin. And that is really profound for these frontier labs. So one, reasoning fundamentally changed the industry dynamics of frontier labs. And just explain why specifically that is like what is going on. Because if a lot of people are asking a similar question, they're consistently either liking or not liking the answer, then you can kind of use that like that has a verifiable reward that's a good outcome. And then you can kind of feed those good answers back into the model. And we're very early at this flywheel spinning. Yeah, got it. Like it's hard to do now. But you can see it beginning to spin. So this is important fact number one for all of those dynamics. Second, I think it's really important that meta, you know, Mark Zuckerberg at the beginning of this year in January said I'm highly confident I'm going to get the quote wrong that at some point in 2025, we're going to have the best and most performant AI. I don't know if he's in the top 100. So he was as wrong as it was possible to be. And I think that is a really important fact. Because it suggests that what these four companies have done is really hard to do. Because meta through a lot of money at it. And they failed. Yall McCune had to leave. They had to have the famous billion dollar for AI researchers. By the way, Microsoft also failed. They did not make such an unequivocal prediction, but they bought an inflection AI. And there were a lot of comments from them that we anticipate our internal models quickly getting better and we're going to run more and more of our AI on our internal models. Nope. Amazon. They bought a company called the Dept AI. They have their models called Nova. I don't think they're in the top 20. So clearly it's much harder to do than people thought a year ago. And there's many, many reasons for that. Like it's actually really hard to keep a big cluster of GPUs coherent. A lot of these companies were used to running their infrastructure to optimize for cost instead of performance. Complexity and performance. Complexity and keeping the GPUs running at high utilization rate and a big cluster. It's actually really hard. And there are wild variations in how well companies run GPUs. If the most anybody because the laws of physics, you know, maybe you can get two or three hundred thousand black wells, go ahead and we'll see. But if you have 30% uptime on that cluster and you're competing with somebody who has 90% uptime, you're not even competing. So one, there's a huge spectrum on how well people run GPUs. Two, then I think there is, you know, these AI researchers they like to talk about taste. I find it very funny. You know, why do you make so much money? I have very good taste. You know, what taste means is you have a good intuitive sense for the experiments to perform. This is why you pay people a lot of money because it actually turns out that as these models get bigger, you can no longer run an experiment on a thousand GPU cluster and replicate it on a hundred thousand GPUs. You need to run that experiment on 50,000 GPUs and maybe it takes, you know, days. And so there's a very high opportunity cost. You have to have a really good team that can make the right decisions about which experiments to run on this. And then you need to do all the reinforcement learning during post-training well and test time and compute well. It's really hard to do and everything's it's easy, but all those things, you know, I used to have the saying, like, I was retelling on a slow go, pick any vertical in America. If you can just run a thousand stores in 50 states and have them clean, well lit, stocked with relevant goods at good prices and staffed by friendly employees who are not stealing from you, you're going to be a 20 billion dollar company, a 30 billion dollar company. Like 15 companies have been able to do that. It's really hard. And it's the same thing. Doing all of these things well is really hard. And then reasoning with this flywheel, this is beginning to create barriers to entry and what's even more important, every one of those labs, X AI, Gemini, Open AI and Anthropic, they have a more advanced checkpoint internally of the model. Checkpoint is just kind of continuously working on these models and then you release kind of a checkpoint. And then the reason these models get fast. The one they're using internally is for sure. And they're using that model to train the next model. And if you do not have that latest checkpoint, it's getting really hard to catch up. Chinese open source is a gift from God to matter because you can use Chinese open source. That can be your checkpoint. And you can use that as a way to kind of bootstrap this. And that's what I'm sure they're trying to do and everybody else. The big problem and the big giant swing factor, I think China has made a terrible mistake with this rarest thing. So China, because you know, they have Huawei is in and it's a decent chip versus something like the deprecated hoppers every science looks okay. And so they're trying to force Chinese open source to use their Chinese chips. They're domestically designed chips. The problem is Blackwell's going to come out now in the gap between these American frontier labs in Chinese open source is going to blow out because of Blackwell. And actually deep seek in their most recent technical paper, V3.2 said one of the reasons we struggled to compete with the American frontier labs is we don't have enough compute. That was their very politically correct. Still a little bit risky way of saying because China said we don't want the Blackwell's right. And they're saying, would you please give us some light? That might be a big mistake. So if you just kind of play this out, these four American labs are going to start to widen their gap for Chinese open source. Which then makes it harder for anyone else to catch up because that gap is growing. So you can't use Chinese open source to bootstrap. And then geopolitically China thought they had the leverage. They're going to realize, oh, whoopsy daisy. We do need the Blackwells. And unfortunately, they'll problem for them. They'll probably realize that in late 26. And at that point, there's an enormous effort underway DARPA has there's also to really cool DARPA and DOD programs to incentivize really clever technical solutions for rare earths. And then there's a lot of rare earth deposits in countries that are very friendly to America that don't mind actually refining it in the traditional way. So I think rare earths are going to be solved way faster than anyone thinks. You know, they're obviously not that rare. They're just misnamed. They're rare because they're really messy to refine. And so geopolitically, I actually think Blackwell's pretty significant. And it's going to give America a lot of leverage as this gap widens. And then in the context of all of that, going back to the dynamics between these companies, XAI will be out with first Blackwell model. And then they'll be the first ones probably using Blackwell for inference at scale. And I think that's an important moment for them. And by the way, it is funny. Like if you go on OpenRouter, you can just look. They have dominant share now OpenRouter is whatever it is. It's 1% of API tokens. But it's an indication they process 1.35 trillion tokens. Google did like 800 or 900 billion. This is like whatever news last seven days or last month. Anthropic was at 700 billion. Like XAI is doing really, really well. And the model is fantastic. I highly recommend it. But you'll see XAI come out with this. OpenAI will come out faster. OpenAI's issue that they're trying to solve a stargate is because they pay a margin to people for compute. And maybe the people who run their compute are not the best at running GPUs. They're a high cost producer of tokens. And I think this kind of explains a lot of their code red recently. Yeah. Look, the $1.4 trillion spending commitments. And I think that was just like, hey, they know they're going to need to raise a lot of money. Particularly if Google keeps its current strategy of sucking the economic oxygen out of the room. And you know, you go from 1.4 trillion rough vibes code red like pretty fast, you know. And the reason they have code red is because of all these dynamics. So then they'll come out with a model. But they will not have fixed their per token cost disadvantage yet relative to both XAI and Google and Anthropic at that point. Anthropic is a good company. You know, they're burning dramatically less cash than OpenAI and growing faster. So I think you have to give Anthropic a lot of credit. And a lot of that is their relationship with Google and Amazon for the TPUs and the trainings. And so Anthropic has been able to benefit from the same dynamics that Google has. I think it's very indicative in this great game of chess. You can look at Dario and Jensen maybe have taken a few public comments, you know, that we're between them. Just a little bit of jousting. Well, Anthropic just signed the $5 billion deal with Nvidia. That is because Dario is a smart man and he understands these dynamics about Black Wall and Ruben relative to TPU. So Nvidia now goes from having two fighters XAI and OpenAI to three fighters that helps in this Nvidiaverse Google model. And then if meta can catch up, that's really important. I am sure Nvidia is doing whatever they can to help meta. You're running those GPUs this way. Maybe we should twist the screw this way or turn the dial that way. And then it will be also if Blackwell comes back to China, which it seems like it'll probably happen. That will also be very good because the Chinese open source will be back. I'm always so curious about the polls of things. Someone poll would be the other breakthroughs that you have your mind on. Things in the data center that aren't chips that we've talked about before as one example. I think the most important thing that's going to happen in the world, in this world, in the next three to four years is data centers in space. And this has really profound implications for everyone building a power plant or a data center on planet Earth. Okay. And there is a giant gold rush. I've heard anything about this. Please. Yeah, you know, it's like everybody thinks like, hey, AI is risky. But you know what? I'm going to build the data center. I'm going to build a power plant that's going to do a data center. We will need that. But if you think about it from first principles, data centers should be in space. What are the fundamental inputs to running a data center? Their power and their cooling. And then there are the chips. If you think about it from a total cost perspective. Yeah. And just the inputs to making the tokens come out of the magic machines in space, you can keep a satellite in the sun 24 hours a day. And the sun is 30% more intense. You can have the satellite always kind of catching light. The sun is 30% more intense in this results in six times more irradiance and outer space than on planet Earth. So you're getting a lot of solar energy. Point number two, because you're in the sun 24 hours a day, you don't need a battery. And this is a giant percentage of the cost. So the lowest cost energy available in our solar system is solar energy and space. Second, for cooling, and one of these racks, a majority of the mass in the weight is cooling. The cooling in these data centers is incredibly complicated. The HVAC, the CDUs, the liquid cooling, it's amazing to see. And space, cooling is free. You just put a radiator on the dark side of the satellite. It's fun. It's as close to absolute zero as you can get. So all that goes away. And that is a vast amount of cost. Okay. Let's think about how these maybe each satellite is kind of a rock. It's one way to think of it. Maybe some people make bigger satellites that are through VACs. Well, how are you going to connect those racks? Well, it's funny in the data center. The racks are over a certain distance connected with fiber optics. And that just means a laser going through a cable. The only thing faster than a laser going through a fiber optic cable is a laser going through absolute vacuum. So if you can link these satellites in space together using lasers, you actually have a faster and more coherent network than in a data center on Earth. Okay. For training, that's going to take a long time because it's so big. Yeah, just because it's so big, training will eventually happen. But then for inference, let's think about the user experience. When I asked rock about you and it gave the nice answer, here's what happened. A radio wave traveled from my cell phone to a cell tap. Then it hit the base station, went into a fiber optic cable, went to some sort of metro aggregation facility in New York, probably within like 10 blocks a year. There's a small little metro router that's routed those packets to a big XAI data center somewhere. The computation was done and it came back over the same path. If the satellites can communicate directly with the phone and Starlink has demonstrated direct to cell capability, you just go boom, it's a much better lower cost user experience. So in every way data centers in space from a first principles perspective are superior to data centers on Earth. If we could teleport that into existence, I understand that that portion, why will that not happen? Is it launch cost? Is it launch ability? Is it capacity? We need a lot of the starships, like the starships are the only ones that can economically make that happen. We need a lot of those starships. Maybe China or Russia will be able to land a rocket, Blue Origin just landed a booster. This is a big idea. And I do think it's an entirely new and different way to think about SpaceX. And it is interesting that Elon posted your set in an interview that Tesla SpaceX and XAI are converging and they really are. So XAI will be the intelligence module for Optimus made by Tesla with Tesla Vision has its perception system. And then SpaceX will have the data centers in space that will power a lot of the AI, presumably for XAI and Tesla and the Optimus and a lot of other companies. And it's just interesting the way that they're converging in each one is kind of creating competitive advantage for the other. If you're XAI, it's really nice. Do you have this built-in relationship with Optimus? Tesla's a public company, so there's going to be like, I cannot imagine the level of vetting. They'll go into that. It's your company agreement. And then you have a big advantage with these data centers in space. And then it's also nice if you're XAI that you have two companies with a lot of customers who you can use to help build your customer support agents, your customer sales agents with kind of built-in customers. So they really are all kind of converging in a neat way. And I do think it's going to be a big moment when that first black well model comes out from XAI next year. If I go to the other end of the spectrum and I think about something that seems to have been historically endemic to the human economic experience that shortages are always followed by gluts in capital cycles. What if in this case the shortages compute like Mark Chen now is on the record is saying they would consume 10X as much compute if you gave it to him in like a couple weeks. So like there seems to still be a massive shortage of compute, which is all the stuff we've talked about today. But there also just seems to be this like iron law of history that gluts follow shortages. What do you think about that concept as relates to this? There will eventually be a glut. AI is fundamentally different than the software just in that every time you use AI takes compute in a way that traditional software just did not. I mean it is true. Like I think every one of these companies could consume 10X more compute. Like what would happen is just the $200 tier would get a lot better. The free tier would get like the $200 tier. Google has started to monetize AI mode with ads. And I think that will give everyone else permission to introduce ads into the free mode. And then that is going to be an important source of ROI. Open AI is tailor made to absolutely all of them in actions like, you know, hey, here are your three vacations. Would you like me to book one? And then they're for sure going to collect a commission. Yeah. There's many ways you can make money. But I think we went into great detail on maybe a prior podcast about how just inventory dynamics made these inventory cycles inevitable in semis. The iron law of semis is just customer buffer inventories have to equal lead times. And that's why you got these inventory cycles historically. We haven't seen a true capacity cycle in semis arguably since the late 90s and that's because Taiwan semis has been so good at aggregating and smoothing supply. And a big problem in the world right now is that Taiwan semis not expanding capacity has fastest their customers want. They're in the process of making mistakes just because you do have intel and with these fabs, they're not has good and it's really hard to work with their PDK. But now you have this guy, Lee Bo, who's a really good executive and really understands that business. I mean, by the way, Patrick Helsinger, I think was also good executive and he put intel on the only strategy that could result in the success. And I actually think it's shameful that the intel board fired him when they did. But Lee Bo is a good executive and now he's reaping the benefits of Patrick's strategy. And Intel has all these empty fabs and eventually given the shortages we have of compute, those fabs are going to be filled. So I think Taiwan semis in the process of making mistakes, but they're just so paranoid about an overbuilt and they're so skeptical. They're the guys who met with Sam Altman and laughed and said he's a podcast bro. He has no idea what he's talking about. You know, they're terrified of an overbuilt. So it may be that Taiwan semis single handedly that their caution is the governor. I think governors are good. It's good that powers the governor. It's good that Taiwan semis is a governor. If Taiwan semi opens up at the same time when data centers in space relieve all power constraints, but that's like, I don't know, five, six years away. The data centers in space are a majority of deployed megawatts. Yeah, you can get it overbuilt really fast, but just we have these two really powerful natural governors. And I think that's good. Smoother and longer is good. We haven't talked about the power of the than alluding to it through the space thing. Haven't talked about power very much. Power was like the most uninteresting topic. Nothing really changed for like a really, really long time. All of a sudden, I'm trying to figure out how to get like gigawatts here and everywhere. How do you think about are you interested in power? Oh, very interested. I do feel lucky in a prior life. I was the sector leader for the telecom and utilities team. So I do have some base level of knowledge. Having watts as a constraint is really good for the most advanced compute players, because if watts are the constraint, the price you pay for computers are relevant. The TCO of your compute is absolutely irrelevant, because if you can get three X or four X or five X more tokens per watt, that is literally three or four X or five X more revenue. If you're going to build an advanced data center cost 50 billion, a data center with the ASIC maybe costs 35 billion. If that 50 billion dollar data center pumps out 25 billion of revenue, and you're the ASIC data center at 35 billion is only pumping out eight billion. Well, like you're pretty buffed. So I do think it's good for all of the most advanced technologies of the data center, which is exciting to be as an investor. So as long as power is a governor, the best products are going to win irrespective of price and have crazy pricing power. That's the first application that's really important to me. Second, it is in the only solutions to this. We just can't build nuclear fast enough in America. As much as we would love to build nuclear quickly, we just can't. It's just too hard. NEPA, all these rules, like it's just, it's too hard. Like a rare ant that we can move and it could be in a better environment can totally delay the construction of a nuclear power plant. You know, one ant, you know, that is emergency. That's crazy. Actually, humans need to come first. We need to have a human-centric view of the world. But the solutions are natural gas and solar. And the great thing is, the great thing about these AI data centers is apart from the ones that you're going to do it. For it's on, you can locate them anywhere. So I think you were going to see, and this is why you're seeing all this activity at Abilene, because it's in the middle of a big natural gas basin. And we have a lot of natural gas in America, because of fracking. We're going to have a lot of natural gas for a long time. We can rip production really fast. So I think this is going to be solved. You know, you're going to have power plants fed by gas or solar. And I think that's the solution. And you know, you're already all these turbine-made factors were reluctant to expand capacity, but caterpillar just said, we're going to increase capacity by 75% over the next few years. So like, this system on the power side is beginning to respond. One of the reasons that I always still love talking to you is that you do as much in the top 10 companies in the world as you do looking at brand new companies with entrepreneurs that are 25 years old trying to do something amazing. And so you have this very broad sense of what's going on. If I think about that second category of young enterprising technologists who now are like the first generation of AI native entrepreneurs, what are you seeing in that group that's notable or surprising or interesting? These young CEOs, they're just so impressive in all ways. And they get more polished faster. And I think the reason is, is they're talking to the AI. How should I deal with pitching this investor? I'm meeting with Patrick O'Shaughnessy. What do you think the best ways I should pitch him are? And it works. Do a deep research thing. And it's good. Yeah. You know, hey, I have this difficult HR situation. How would you handle it? And it's good at that. We're struggling to sell our product. What changes would you make? And it's really good at all of that today. And so in that goes to these VCs are seeing massive AI productivity in all their companies. It's because their companies are full of these 23, 24 or even younger AI natives. And they're impressive. I've been so impressed with like young investment talent. And it's just part of it. Like your podcast is part of that. They're just very specific knowledge has became so accessible through podcasts in the internet. Like kids come in and they're just, I feel like they're where I was as an investor. Like in my, you know, early 30s and they're 22 and I'm like, oh my god, like I have to run so fast to keep up. These kids who are growing up native in AI, they're just proficient with it in a way that I haven't tried really hard to become. Can we talk about semis VCs specifically and like what is interesting in that universe? What do you guys just think is so cool about it and so underappreciated? Is your average semiconductor venture founder is like 50 years old? Okay. And Jensen, and what's happened with Nvidia and the market cap of Nvidia has like single handedly ignited semiconductor venture. But the way it's ignited is ignited in an awesome way. It's like really good for actually Nvidia and Google and everyone is like, let's just say you're the best DSP architect in the world. You had made for the last 20 years every two years because that's what you have to do. Semiconductors, it's like every two years you have to run a race. And if you won the last race, you start like a foot ahead and over time those compound and make each race easier to win. But maybe that person and his team, maybe he's ahead of networking at a big public company. And he's making a lot of money and he has a good life and he's 50 years old. And then because he sees these outcomes and the size of the markets and the data center, he's like, wow, I don't know, I just go start my own company. But the reason that's important is that, you know, I forget the number, but I mean, there's thousands of parts in a black well rack and there's thousands of parts in a TPU rack. And in the black well rack, maybe Nvidia makes two or three hundred of those parts, same thing in an AMD rack. And they need all of those other parts to accelerate with them. So they couldn't go to this one year cadence. If everything was not keeping up with them. So I think it's the fact that semiconductor ventures come back with the vengeance, Silicon Valley, stopping Silicon Valley long ago. My little firm, maybe has done more semiconductor deals in the last seven years than the top 10 VCs combined. You know, but that's really, really important because now you have an ecosystem of companies who can keep up. And then that ecosystem of these venture companies is putting pressure on the public companies that are also need to part of this. And we're going to go to this annual cadence, which is just so hard. But it's one reason I'm really skeptical of these ASICs that don't already have some degree of success. So I do think that's a super, super important dynamic. And one that's absolutely foundational and necessary for all of this to happen. Because not even Nvidia can do it alone. AMD can't do it alone. Google can't do it alone. You need the people who make the transceivers, you need the people who make the wires, who make the backplanes, who make the lasers. They all have to accelerate with you. And one thing that I think is very cool about AI as an investor is it's just it's the first time where every level of the stack that I look at at least the most important competitors are public and private Nvidia. They're very important private competitors, Broadcom important private competitors, Marvel, important private competitors. You know, Lumiddon, coherent, all these companies. There's even like a wave of innovation and memory, which is really exciting to see because memory is such a gaining factor. But always something that could slow all this down and be a natural governor is if we get our first true DRAM cycle since the late 90s. So to say more of that means if the price of DRAM, if like DRAM wafer is valued at like a five-karat diamond in the 90s when he had these true capacity cycles before Taiwan, so we kind of smoothed everything out and DRAM became more of an oligopoly. You would have these crazy shortages where the price would just go 10x, unimaginable relative to the last 25 years, where like a giant DRAM cycle, a good DRAM cycle is the price starts to start, stops going down. An epic cycle is maybe it goes up, you know, whatever it is, 30, 40, 50%. But I mean, if it starts to go up by X's instead of percentages, that's a whole different game. By the way, we should talk about SAS. Yeah, let's talk about it. What do you think is going to happen? Well, I think that applications, SAS companies are making the exact same mistake that brick-and-mortar retailers did with e-commerce. So brick-and-mortar retailers, particularly after the telecom bubble crashed, you know, they looked at Amazon and they said, "Oh, it's losing money." e-commerce is going to be a low margin business. How can it ever be more efficient as a business? Right now, our customers pay to transport themselves to the store and then they pay to transport the goods out. How can it ever be more efficient if we're sending shipments out to individual customers? Amazon's vision, of course, will eventually we're just going to do it out of street and drop off a package at every house. And so they did not invest in e-commerce. They clearly saw customer demand for it, but they did not like the margin structure of e-commerce. That is the fundamental reason that essentially every brick-and-mortar retailer was really slow to invest in e-commerce. And now, here we are, and, you know, Amazon has higher margins, and they're North American retail business, then a lot of retailers that are mass market retailers. So margins can change. And if there's a fundamental transformative kind of new technology that customers are demanding, and it's always a mistake not to embrace it. And that's exactly what the SaaS companies are doing. They have their 70, 80, 90% gross margins, and they are reluctant to accept AI gross margins. The very nature of AI is, you know, software you write at once, and it's written very efficiently, and then you can distribute it broadly at very low cost. And that's why it was a great business. AI is the exact opposite, where you have to recompute the answer every time. And so a good AI company might have gross margins of 40%. The crazy thing is, because of those efficiency gains, they're generating cash way earlier than SaaS companies did historically, but they're generating cash earlier, not because they have high gross margins, but because they have very few human employees. And it's just tragic to watch all of these companies. Like, you want to have an agent? It's never going to succeed if you're not willing to run it at a sub 35% gross margin, because that's what the AI natives are ready to get at. Maybe they're ready in a 40. So if you're trying to preserve an 80% gross margin structure, you are guaranteed that you will not succeed in AI. Absolute guarantee. And this is so crazy to me, because one, we have an existence proof for software investors being willing to tolerate gross margin pressure, as long as gross profit dollars are okay. It's called the cloud. People don't remember, but when Adobe converted from on-premise to a SaaS model, not only did their margins implode, they're actually revenues imploded too, because you went from charging up front to charging over a period of years. Microsoft, it was less dramatic, but Microsoft was a tough stock in the early days of the cloud transition, because investors were like, "Oh my God, you're an 80% gross margin business." And the cloud is the 50s, and they're like, "Well, it's going to be gross profit dollar creative, and probably will improve those margins over time." Microsoft, they bought GitHub, and they used GitHub as a distribution channel for Copilot for Coding. Let's become a giant business, a giant business. Now for sure, it runs at much lower gross margins, but there are so many SaaS companies. I can't think of a single application SaaS company that could not be running a successful agent strategy. And they have a giant advantage over these AI natives, and that they have a cash alternative business. And I think there is room for someone to be a new kind of activist or constructivist and just go to SaaS companies and say, "Stop being so dumb. All you have to do is say, "Here are my AI revenues, and here are my AI gross margins." And you know it's real AI, because it's low gross margins. I'm going to show you that. And here's a venture competitor over here that's losing a lot of money. So maybe I should take my gross margins to zero for a while, but I have this business that the venture funded company doesn't have. And this is just such a like obvious playbook that you can run Salesforce, ServiceNow, HubSpot, GitLab, Alacian, all of them could run this. And the way that those companies could or should think about the way to use agents is just to ask the question, "Okay, what are the core functions we do for the customer now? Like, how can we further automate that with agents effectively?" If you're in CRM, well, what our customers do, they talk to their customers. We're customer relationship management software, and we do some customer support too. So make an agent that can do that. And sell that at 10 to 20% to let that agent access all the data you have. Because what's happening right now, another agent made by someone else is accessing your systems to do this job, pulling the data into their system, and then you will eventually be turned off. It's just crazy. And it's just because, oh, wow, but we want to preserve our 80% gross margins. This is a life for death decision. And essentially, everyone except Microsoft is failing it to quote that bimbo from that Nokia guy long ago, like their platforms are burning platform. Yeah, there's a really nice platform right over there, and you can just hop to it, and then you can put out the fire in your platform that's on fire. And now you got two platforms, and it's great. You know, your data centers and space thing makes me wonder if there are other kind of less discussed off the wall, things that you're thinking about in the markets in general, that we haven't talked about. It does feel like since 2020 kicked off in 2022, punctured this. Kind of a series of rolling bubbles. So in 2020, there is a bubble in like EV startup EVs that were not Tesla. And that's for sure a bubble, and they all went down, you know, 99% and there was kind of a bubble in more speculative stocks than we had the meme stocks, you know, game stop. And now it feels like the rolling bubble is in nuclear and quantum. And these are fusion and SMR. It would be a transformative technology. It's amazing. But sadly, from my perspective, none of the public ways you can invest in this are really good expressions of this theme are likely to succeed or have any real fundamental support. And same thing with quantum. I've been looking at quantum for 10 years. We have a really good understanding of quantum. And the public quantum companies again are not the leaders. From my perspective, the leaders in quantum would be Google, IBM, and then the Honeywell quantum. So the public ways you can invest in this theme, which probably is exciting, are not the best. So you have two really clear bubbles. I also think quantum supremacy is very misunderstood. People hear it. And they think that it means that quantum computers are going to be better than classical computers at everything with quantum. You can do some calculations that classical computers cannot do. That's it. That's going to be really exciting and awesome. But it doesn't mean that quantum takes over the world. I think the thought that I have had this is maybe less related to markets than just AI. I have just been fascinated that for the last two years, whatever AI needs to keep growing and advancing, it gets. Have you ever seen public opinion change so fast in the United States on any issue has nuclear power? Just happen like that. Like that. And like, why did that happen right when AI needed it to happen? Now we're running up on boundaries of power and earth. All of a sudden, data centers in space, it's just a little strange to me that whenever there is something, a bottleneck that might slow it down, everything accelerates. Ruben is going to be such an easy seamless transition relative to Blackwell and Ruben's a great chip. And then AMD getting into the game with the MI450, whatever AI needs, it gets. You're a deep reader of sci-fi. So yeah, exactly. You're making me think of Kevin Kelly's great book, What Technology Wants. He calls it the Technium, like the like the overall mass of technology that just like is supplied by humans. Absolutely more powerful. Yes, it's just more powerful. And now we're going into an in-state. You founded a trade ease in the pre-Chatchy PT and pre-COVID era. The world has changed dramatically. How has a trade ease changed the most in that same period? Before a trade ease, I had never had a big position where it wasn't my position. I was doing the fundamental work. I was the analyst. I was really a one-man show. It was such a crazy feeling. The first time I made money on an idea that was not my own. And that was CrowdStrike in 2020. And then it never happened to me. Ever. And I'd run funds for 15 years at that point. I think that was a big evolution for me. I've been thinking a lot about actually the NFL in the context of investing. I think it is so interesting. Sam Darnold, Baker Mayfield, Daniel Jones, left for dead, utter failures, embarrassment of draft picks. Okay. And now there's some of the best quarterbacks in the league. It just turned out they needed a different system and a different coaching mindset. I have really been trying to work on it. How I can make sure that if there is someone who is clearly talented and working really hard, how do I make sure that if I have that person, they succeed. And I just think investing is all about finding the right balance between the courage of your convictions and the flexibility to admit when you're wrong to quote Michael Steinhardt. It's all about finding the right balance between the arrogance to believe that you have a varied view that's going to be right in a game that is being played of tens of millions of people worldwide with the humility to recognize that at any moment, there might be a new data point that is outside of your expected probability space that invalidates that varied view and to be really, really open to that. And I think a lot of that comes down to finding a investment philosophy and process that fits your own emotional makeup so you can be rational when you're wrong and you could strike that balance between conviction and flexibility. What every investment firm has to find is the right balance between incentivizing risk-taking and accountability from mistakes. If you don't have enough accountability, people take way too much risk. On the other hand, if you have too much accountability, people don't take risk. What I have tried to do and really, really institute is make it really safe for people to change their mind. I want to make it safe for people to say, here's some new data points that have invalidated my investment hypothesis. I'm the child of two attorneys. I've never taken an argument personally. I love to argue. Okay, it's like I love it. Like it's just like plus board and I can do it all day. But I do believe at some level kind of investing is the search for truth. And if you find truth first and you write about it big of truth, that's how you generate alpha. And it has to be a truth that other people have not yet seen. You're searching for hidden truths. I think that the best way to arrive at truth is through discourse. And so I try to really incentivize people to tell me that I'm wrong, celebrate it when people tell me I'm wrong, having what we call bull bear lunches, where the aside analyst presents their case, and then the skeptic presents their case, and there's a very robust discussion that I generally try not to participate in. I would say decision that takes an amidst amount of work, but decision qualities high after that. I want people to feel safe, taking risk. I want people to feel safe changing their mind. And then I want people to feel extremely safe, telling me that I'm wrong and telling each other that they're wrong. Because the more we have that, like, definitionally, if you were not wrong, you're not learning. If I'm not wrong about three things in a day, I didn't learn anything. So I want to be told I'm wrong as much as possible. And so I try to incentivize all of that. I have a selfish closing question speaking of young people. So my kids who are 12 and 10, but especially my son who's older, is developing an interest in what I do, which I think is quite natural. And I'm going to try to start asking my friends who are the most passionate about entrepreneurship and investing. Why they are so passionate about it. And what about it is so interesting and life-giving to them? How would you pitch what you've done the career you built this part of the world to a young person that's interested in this? The earliest thing I can remember is being interested in history, looking at books with pictures of the Phoenicians and the Egyptians and the Greeks and the Romans. I loved history. I vividly remember, I think in the second grade, as my dad drove me to school every day, we went through the whole history of World War 2 in one year. And I loved that. And then that translated into a real interest in current events very early. So like has a pretty young person, you know, I don't know if it was eighth grade or seventh grade or ninth grade. I was reading The New York Times and The Washington Post and I would get so excited when the mail came because it made that maybe there was an economist or newsweek or a time or a US news. And I was really into current events. You know, because current events is kind of like applied history and watching history happen. Thinking about what might happen next. I didn't know anything about investing. My parents were both attorneys. Like I was anytime I wanted argument, I was super rewarded. Like, you know, if I could make a reasonable argument why I should stay up late. My parents would be so proud and they'd let me simply, but I had to beat them. You know, that was the way I grew up. I was just kind of going through life and I really loved to ski and I loved rock climbing. And I go to college and rock climbing is by far the most important thing in my life. I dedicate myself to them completely. I climbed. I did all my homework at the gym. I got to the rock climbing gym at 7 a.m. Would skip a lot of classes to stay at the gym. I do my homework on like a big bouldering mat every weekend. I went and climbed somewhere with the Dartmouth Mount Nearing Club. It was super important to me. I'm not a good athlete. So I was never a very good climber, but I did dedicate myself entirely to rock climbing. And as part of that, like on climbing trips, the movie routers came out while I was in college. So we started playing poker. I like to play chess. I mean, I was never that good at chess or poker. You know, never really dedicated myself to either. And my plan after two or three years of college was I was going to leave. I was a ski bum at Alton College. I was a housekeeper. I cleaned a lot of toilets. It was shocking to me how people treated me. And it is like permanently impacted how I treat other people. You know, you'd be cleaning somebody's room and they'd be in it. And they'd be reading the same book as you. And like, you know, you'd say, Oh, that's a great book. You know, I'm about where you are. And they look at you like you're basically like you speak. And did they get even more shocked? You read. You know, so it like had a big impact on how I've like just treated everyone since then. Like being nice is free. But anyways, I was going to be a ski bum in the winter. I was going to work on a river in the summers. And that was how I was going to support myself. And then I was going to climb in the shoulder seasons. I was going to try and be a wildlife photographer and write the next great American. Wow. I can't believe I knew this. That was my plan. This was like my plan of record. I was really lucky. My parents were very supportive of everything I wanted to do. My parents had very strict parents. So of course, they're extremely permissive with me. So, you know, I'll probably end up being a strict parent. You know, just the cycle continues. And my parents were lawyers. You know, they've done reasonably well. They both grew up and I would say very economically disadvantaged circumstances. Like my dad talks about like he remembers every person who bought him a beer. Are you going to afford a beer? He worked the whole way through college. He was there on a scholarship. He had one pair of shoes all through high school. And so they were super on board with this plan. And I'd been very lucky. They sent me to college. And I didn't have to pay for college. So they said, you know, Gavin, we think this plan of being a ski bum, riverafety guide, wildlife photographer, climbing the shoulder seasons, tried to write it all, but we think it sounds like a great plan. But you know, we've never asked you for anything. We haven't encouraged you to study anything. We've supported you and everything you've wanted to do. Will you please get one professional internship? Just one. And we don't care what it is. The only internship I could get, this was at the end of myself our summer at Dartmouth. It was an internship with Donaldson, Loughkin, Engineer at DLJ. My job was to, every time DLJ published a research report, it was in like the private wealth management division. And I worked for the guy who ran the office. And my job was whenever they produced a piece of research, I would go through and look at which of his clients owned that stock, then I would mail it to the clients. So this day, we wrote on General Electric. So I need to mail the GE report to these 30 people, mail the Cisco report to these 20 people. And then I started like reading the reports and I was like, oh my god, this is like the most interesting thing imaginable. So investing, I kind of conceptualized it. It's a game of skill and chance kind of like poker. And you know, there's obviously chance in investing. Like if you're an investor in a company and a meteor hits their headquarters, that's bad luck. But like you own that outcome. So there is chance that is irreducible, but there's skill too. But so that really appealed to me. And the way you got an edge in this greatest game of skill and chance imaginable was you had the most thorough knowledge possible of history. And you intersected that with the most accurate understanding of current events in the world to form a differential opinion on what was going to happen next in this game of skill and chance, which stock is mispriced in the period mutual system. That is the stock market. And that was like day three. I went to the bookstore and I bought like the books that they had, which were penal inches books. I read those books in like two days. I'm a very fast reader. Then I read all these books about Warren Buffett. Then I read Market Wizards. Then I read Warren Buffett's letters to shareholders. This is like during my internship. Then I read Warren Buffett's letters to shareholders again. Then I taught myself accounting. There's this great book, why stocks go up and down. Then I went back to school. I changed my majors from English and history to history and economics. And I never looked back in it consumed. Like I continued to really focus on climbing, but like instead of like I would be in the gym and I would print out everything that the people on the Motley Fool wrote. They had these fools. They were early to talking about return on invested capital incremental ROIC is like a really important indicator. And I would just read it and I would underline it. And I read books. And then I'd read the Wall Street Journal. And then eventually there was a computer term finally set up near the gym. And I'd go to that gym and just read news about stocks. And it was the most important thing in my life. And like I barely kept my grades up. And yeah, that's how I got into it. History, current events, skill and chance. And I am a competitive person. And I've actually never been good at anything else. Okay. I got picked last for every sports team. Like I love to ski. I've literally spent a small fortune. I'm private ski lessons. I'm not that good of a skier. I like to play ping pong. All my friends could beat me. I tried to get really good at chess. And this was before we actually had to play the games. And my goal was to beat one of the people. I'm sure there's a park somewhere. Right there. Most famous one is right there. Okay. Well, there's one in Cambridge. And I wanted to beat one of them. Never beat one of them. I've never been good at anything. I thought I would be good at this. And the idea of being good at something other than taking a test that was competitive was very appealing to me. That's why I think that's been a really important thing too. And to this day, this is the only thing I'm good at. I'd love to be good at something else. I'm just not. I think I'm going to start asking this question of everybody. The ongoing education of Pearson Mave, amazing place to close. I love talking about everything. Thank you so much. This is great, man. Thank you, thank you, thank you. If you enjoyed this episode, visit JoinColossus.com where you'll find every episode of this podcast complete with hand out of the transcripts. You can also subscribe to Colossus Review, our quarterly print digital and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most. Learn more at JoinColossus.com/Subscribe. [Music]