Limitless Podcast - How Gavin Baker Invests in AI, and Where the Bubble is Going Next

Episode Date: May 28, 2026

In this episode, we discuss Gavin Baker’s view that AI is a super cycle, with the biggest opportunities in infrastructure, chips, memory, and power rather than software. It's interesting to pit him against Leopold Aschenbrenner, who has taken a higher octane approach to similar overarching theses. Gavin's portfolio includes Astera Labs, Cerebras, NVIDIA, Micron, and Unity Software. DYOR!------🌌 LIMITLESS HQ ⬇️NEWSLETTER:    https://limitlessft.substack.com/FOLLOW ON X:   https://x.com/LimitlessFTSPOTIFY:             https://open.spotify.com/show/5oV29YUL8AzzwXkxEXlRMQAPPLE:                 https://podcasts.apple.com/us/podcast/limitless-podcast/id1813210890RSS FEED:           https://limitlessft.substack.com/------TIMESTAMPS0:00 AI Infrastructure Thesis1:47 Gavin’s Track Record2:57 Bottlenecks in Chips5:26 Unity and World Models7:40 Inference11:20 Four AI Constraints17:35 Energy and Space Compute19:08 This Isn’t Dot-Com24:10 Supply Constraints26:06 Conclusion------RESOURCESJosh: https://x.com/JoshKaleEjaaz: https://x.com/cryptopunk7213------Not financial or tax advice. See our investment disclosures here:https://www.bankless.com/disclosures⁠

Gavin Baker is one of the most prolific AI investors that almost no one has heard of. He spent the last 20 years investing in some of the biggest AI companies before they became household names. He was an early backer of Ambidia, as well as Cerebrus, which IPOed very recently. And he has a very concrete thesis, which is AI isn't in a bubble. It's quite the contrary. It's in a super cycle. He says that by looking at the Watts, Wafers and Tokens, the infrastructure of AI, He's identified some of the key bottlenecks and constraints, and he has one simple thesis. The biggest returns that you can get in AI is in electricity, power, and silicon fabrication.

It's got nothing to do with SaaS software as a service. It's got nothing to do with chatbots such as Anthropic or OpenAI. It all filters downstream from semiconductors, the picks and shovels that build the entire AI industry. And he's been expressing that interest to the tune of $4.1 billion. While most people are calling the AI industry a complete bubble, He thinks that it is a generational buying opportunity specifically for AI infrastructure, and he makes the thesis very clearly in his fund. And if you hear these constraints that he's talking about, this AI infrastructure,

you realize that this kind of sounds pretty familiar. We've heard this thesis before, and that's because on the show, many times we've covered a investor by the name of Leopold Asherbrenner. He has been around for three years. He just published his most recent 13F, and he has a lot of the same core philosophies. Now, here's the difference. Leopold's been around for what, three years. Gavin's been doing this for 20 plus years.

And when we're comparing these two, Leopold actually has almost three times the assets under management. But there's this great quote that I was reminded up from our producer Luke before the show is like, okay, you can beat Warren Buffett over a year, but can you beat him over multiple decades? And Gavin Baker has a track record that proves that he might have a slightly different outlook

on this investment thesis. For those who don't know, Gavin Baker, he is the basically founder of this company named to trade his management. They are in investing fund. and he has spent 20 years investing in NVIDIA. Now, if you've invested in NVIDIA for 20 years, it's a miracle he's still working because those are some pretty incredible return.

Some of the early wins that we've recently seen include companies like Cerebris, which he was a very early investor in. Cerebus, if you'll remember, just IPOed for an ungodly amount of money. Same with this company, Astera Labs. And there's a lot of other companies that I don't think you've probably heard of before that we're going to cover in this episode as we walk through his portfolio and his guidance on where he sees the, the AI industry going in terms of investment and where all the opportunity lies?

So then the question becomes, what is he investing in and why is he investing in it? Well, if we look at his recent 13F for a Trades Management, which is the name of his fund, they have about $4 billion worth of AOM, we look at some of his biggest positions and unpack what some of these companies actually solve. It kind of points towards the bottleneck that Gavin references in a bunch of his different interviews as to where AI is going. So he has some pretty sizable positions in some companies that a lot of people may not have heard of purely because they're quite unsexy. So he has an almost 9% position, so 10% position of the fund in this thing called Astero Labs. Now, Astero Labs can be described as kind of like the connectivity layer

between GPUs. So if you imagine in a data center, you have GPUs. They're kind of like the engine that can like pre-train, post-train your model and also like inference your model. But in order for these GPUs to work, they need to traverse a bunch of different. data. They need to send data between themselves. They also need to access data from all these memory chips that they store this data on. Now, in order to access this, you need some kind of plumbing system, and I'm being very high level on this, because I'm not going to pretend that I know the intricacies of all of this. Astero Labs is a company that essentially fixes that. The problem it solves is, as AI clusters scale to hundreds of thousands of chips, the bottleneck stops becoming

GPU specifically, but it starts becoming that transfer window and sending the right data at the right time, accessing the right data at the right time, is what the plumbing system that Astero Labs builds. Now, I haven't heard of Astero Labs until we started researching for this episode, but I remember another company being the exact same case called Cerebrus Labs, which is what Gapin was talking about almost like, I think, like six months ago, which is quite long, given the runs of scale or timeline of AI. And then the next thing I know is that they IPOed for like, what was it, like $60 billion?

And it's just been like, it's up 40% since the IPO. So just kind of point towards these different trends. Astroabs might be something significant on that horizon. Yeah, Cerebus is one of his earliest investments. I mean, he was in Cerebus very early in the company's lifetime, which means he's bet on this thesis for years. There's also a few other ones that he's been betting on for a long time. I mean, Nvidia being that flagship one,

being involved in Nvidia for 20 plus years is pretty incredible and still having conviction all the way through is impressive. Gavin was recently on two podcasts where I was listening to him speak about his Nvidia position. And it's very clear that he believes that they're going to be able to maintain these profit margins and maintain the demand as well, which means he's putting NVIDA on a clear path to getting close to $10 trillion in MarketCath. It's only halfway there right now. A few other noteworthy mentions are Micron, which we discussed on a previous episode, which I would highly

recommend going to watch in terms of the AI investment stack and where all these companies lie. Micron is one of the largest memory makers and a crazy statistic about Micron. A year ago, it was sub-100 billion dollars. And as of recording this, it just eclipsed a trillion dollars in Market cap. It got a 10x in a single year. And it's a testament to how important that memory problem is. Now, perhaps some of the less noteworthy companies that are interesting, EGEST, is one I want to highlight for you in particular, because I feel like you're going to like this one, is Unity Software. And for those that don't know, Unity, I mean, I know this very well, as a gamer, Unity is a game engine. There's a lot of popular games that are built using this 3D rendering

software. So why would someone who's investing in AI be investing in Unity Software, the thing that makes my video games. And the answer is the 3D game engine. Unity is a world model builder, and it has a really deep understanding of physics and the way the world works and the understanding of textures and lighting. And when these AI companies are trying to build AGI, they're trying to build humanoid robots. A big part of that is simulating virtual environments and virtual datasets that allow these robots to be trained in. And Unity just still happens to be one of the best ones. So I feel like that one I wanted to highlight specifically for you, as the world model Maxi, there is a clear path in which a gaming company that is known for a gaming engine

becomes a pretty serious player in the world of AI. Yeah, the whole thesis behind world models is pretty simple. It's AI models or LLMs currently understand the world through text, through books. It's kind of like a student sitting in a library, but it doesn't actually have experience of the real world. World models basically unlock that. It's like putting a game character into a simulated environment and understanding the physical reality of how the world works if it works. If I drop this phone, if I kick a ball, like what happens? What are the next consequential steps? What do you do? World models effectively fix that. And there are very few players that have like built this out at scale. I think currently the leader is probably Google with

Genie 3. They actually released a new model called Gemini Omni recently, which kind of like does this at scale, but it's not quite like where it's meant to be. It hasn't quite had its chat chit moment. What I like about Gavin in particular is he kind of has this barbell. I don't know if you notice this, Josh, where he's kind of old school and he's like, people are going to need GPUs, people are going to need memory. I'm going to invest in the biggest players, Micron and Invidia. But then he has this kind of forward-looking thing where he's like, I think that's where the puck is going to go. And so I think let me invest in Cerebrus because I think inference is going to be super important. And then let me invest in Unity because I think world models are going to be the future

of how we train robots and future LLMs. So he has this kind of like barbell approach. Now, One thing that I see in his portfolio over here as well is this, well, there's two companies actually. It's this company called Positron, which kind of creates inference chips. Now, if that sounds similar, it's very similar to Cerebrus. That's exactly what they do. And it's around this entire thesis which Gavin has spoken about on his recent interviews, which is the infrastructure stack, specifically the training stack for AI models, is moving from pre-training to something more focused on post-training. Now, if you've been involved in the AI sphere, generally, you kind of.

kind of had an idea that this shift was happening. But Gavin is all in on this thesis. And if you have a model, it still needs to understand new information that comes in new data. It needs to update it. Just because you pre-training it on a specific dataset, doesn't mean it's going to be a genius for the rest of its life. It still needs to learn new information. That happens in the post-training layer, and it requires a lot of inference. Secondly, if you need the AI model to actually think about the problem more, you know, in the same way that like we take in new information, where like, hmm, I wonder if this angle makes sense or if another thesis maybe applies.

That's known as reasoning. You need a lot of inference. Now, the estimates are the cost or the revenue opportunity from inference alone is worth around 5 to 10x more than the amount of compute that is being put into pre-training. So AI labs and chipmakers are suddenly making this big shift. You've seen Nvidia create a bunch of different GPUs that are aligned to inference to allow agentic kind of exposure.

And so we're seeing Gavin Express this, through his different investments on inference alone. And the final point that I'll make is Gavin made a really cool point, and we're talking about this before we started recording, Josh, on China specifically. It's been very much like China versus the US when it comes to the AI race. China has a very unique kind of setup where they have an abundance of energy and the ability to scale chip manufacturers. That's something that the US currently struggles with.

That's why they outsource a ton of stuff to the likes of TSM on Taiwan. And what he basically explains is that China has a unique opportunity to create infrastructure or chips specifically that are going to look very different to what the US is creating because they're focused so much on inference. So Gavin, you could say, is leading the charge in the US

through his investments on building or taking a bet on the US infrastructure setup for inference. And I think that could be a huge opportunity in the future. And it's worth noting that this bet also isn't only for the upside. There is a large put position here

in an ETF named QQQQ. Now, for those who are not familiar, it covers the NASDAQ-100. It's a basket of stuff. stocks. It's the second most traded ETF in the United States. And it's been performing incredibly. In 2023, it was up 55 percent, 2024, 25 percent, 2025, 20 percent. And so far in 2026, already up 17 percent. So QQQ as an index fund has been doing incredibly well. It's easy. It's a basket of the top 100 stocks. Gavin is saying, I'm shorting against that. I think that's going to go

down. I think you are wrong. And what this tells me is that he believes strongly in the AI play, in the sense that he's going to invest in the key makers who are solving these bottlenecks. But as a market-wide general sentiment, he doesn't appear to be very bullish. And this is a hedge against that downside protection, where if the market does start to fall apart in ways that are less favorable, even though AI still wins, he has that hedge with QQQQ. Now, we can kind of break this down into a few bottlenecks that he believes are going to be most important when it comes to investing, these key things that he is looking at in terms of What the world of AI is going to need as we progress forward?

What are the actual constraints? What do they look like? And then how do you invest? How do you convert these into actual dollars that you can put into companies to earn you money? And there's four of that. The first one is verticalized small language models. If you think about LLMs in general, like the chat bots that you talk to, such as Claude and ChatGBT, GBT, they're generalized LLM. So they have a wide understanding of the world in context.

And they'll be able to answer like specific questions. but it's another thing training a model around a specific vertical or specific problem that you're trying to solve. Where do these specific problems exist? Well, in enterprises that are deep on solving a particular problem or companies that have made or formed a niche in their particular subsector. Now, verticalized small language models basically address exactly this. They're frontier models, but highly optimized towards running efficiently on specific enterprise data or locally on device. Now, we have spoken about on device or locally run models before. purely for the case that there's a bunch of data in your phone or the devices that you use

that are highly personable to you, but you don't necessarily want to give up, and companies don't necessarily have access to that. For example, medical records, financial details. I saw Open Air release a financial AI agent that can get access to your bank account, but it can't actually act on it because there's a lot of personal identifiable information that you don't want to share, such as your social security number, banking details, etc. Now, locally run models or these SLMs can solve that kind of a problem. And Gavin is making a huge bet that these are going to become huge in the future.

One company that I've noted that Gavin is hugely bullish on is Apple. Although he doesn't express an investment interest, he knows or thinks that Apple is going to be the device maker, one of the major device makers, that allows for these locally run models to run on their devices. Now, in a world where that is the future, you can start thinking of a world where maybe Claude doesn't need to be the model that you need to interface with every day, maybe you need a personalized AI agent trained on your own data, and that's what these SLMs end up eventually becoming. Now, that's the generalized version of it,

where you can run it on your own phone, but a bunch of enterprises will run these highly optimized and specialized models to train on their proprietary data, which ends up helping them sell a product or market a product much better. Oh, man, Apple's in such a good position there. So good. I can't wait for WWDC. It's coming. We are just a few weeks away from Apple's Developer Conference

where they're going to unveil all of this new, AI software that's coming and what that looks like interrating the hardware. That's going to be huge. We'll be covering that. I'm so excited to talk about that. In terms of the next pillar of this, it is sovereign infrastructure. We always talk about this, that the speed of bits is so much faster than the speed of atoms. When you think about AI infrastructure, the quality of models has gone purely exponential. The amount of intelligence we could generate per watt. The amount of intelligence per token is up into the right only. What isn't up into the right at nearly the same rate is the speed of physical deployment, because that itself is the mode.

It's very difficult to take hardware that is incredibly complicated. We're talking about transistors that are down to the atomic level of precision and deploying them at scale in a world in which our infrastructure is already suffering. I mean, with the acceleration of electric cars, the grids have already been feeling a little bit more of a strain. They're kind of at max capacity. Now they have the energy problem. Now they have the chip problem. Gavin is very strongly betting on the fact that infrastructure is hard.

it's going to take many, many days to months to years to do. And he's betting on the people that can compress that into weeks. So the speed of the physical deployment itself is the moat. He's kind of narrowing in on who the companies are that are able to deploy as fast as possible. When I think about this, my first thought is SpaceX and how quickly they've been able to build Colossus and then rent that out to Anthropic and I'm sure companies in the future. But that infrastructure pillar is one of the key ones that he's looking at. I think everyone, we looked at Leopold's portfolio as well.

that was a core component of that. It's just it's really hard to build things. And whoever can build things, they can sell it for a lot of money. SpaceX, their largest line item now in terms of revenue is the data center that they're renting out. It has nothing to do with rocket ships. I think that's a testament to how important this pillar is.

So it's speed that he cares about and he thinks that it's important, but it's also like the cost, right? He keeps referencing this metric, which is performance per watt. Perth per watt. Yeah, Perf per what he's talking about here is companies are increasing. companies being AI labs,

are increasingly caring about how many tokens can you generate per what, right? Because if you think about spending billions and trillions of dollars, which is what like five companies are currently spending this year alone on GPUs and compute

to kind of like power these things or electricity to power these things, you want to get a lot of bang for buck, especially if you're scaling to the size that most of these hypers are doing. So if you think about it, If I prompt Claude and if I prompt ChatGBTGPT and Claude gives me an answer that costs me two cents and Chat Chapti gives me an answer that cost me $1, I'm probably going to end up using Claude

even if it's like hypothetically like say 95% of the intelligence hypothetically that ChatGPT has because the point is you can prompt it even more and eventually get to the answer for a lot less of cost. So cost becomes a huge thing. This week alone, Microsoft and Uber announced that they are effectively reducing their exposure to Claude Code because their annual budgets kind of like got sequestered in about four months. So the point is like the cost of getting access to this intelligence matters a lot.

And you see this across Gavin's investment portfolio with Cerebras with the likes of positron and with Astro Labs, which basically, like what I've noticed is he identifies these really niche infrastructure bottlenecks and he basically makes one simple bet. He's like, yeah, if this company solves that, the performance per what?

gets to this specific level, which means that AI labs are probably going to buy more of these GPUs or more of these companies or more of these things, and you end up with a bottleneck being resolved by one of these different companies that he's betting on. So this is actually is quite simple, although it's quite complex, which is I'm just going to focus on the AI bottlenecks at the infrastructure level. And if I can identify a company that can increase performance by this amount that can make tokens this amount cheaper, then my bet is those companies are going to be valuable and will either IPO or get acquired for a large sum of money. And the thinkers to know for the section, for those looking to copy trade Gavin,

Astera Labs, we have Cerebrus, we have Sci5, and we have Positron. Those are the four companies that are really critical in this sector. Now, the fourth and final is a combination of energy and space. Because, I mean, like we talked about in the previous point, the terrestrial grid very much limits energy. And it's very difficult to earn new energy. I think a statistic, like 40% of new data centers have very strong petitions against them, people are lobbying and can send people protesting, they do not want these data centers.

There's a lot of resistance to them. And the way that we solve this is twofold. One is creating energy out of the box. It's kind of portable energy you could bring to these data centers, power them with a smaller phone. That's companies like Bloom Energy who Leopold is very bullish on. But then there's also the orbital compute part of this, which is what Gavin has really shipped in his focus to. Now, the first largest company in this sector, of course, is SpaceX. They're the only one who is capable of being the highway to space, to delivering actual payload to orbit, to getting maths and data centers in north orbit that can generate enough intelligence and power

that can funnel this. I think the space stack is a little bit bigger than just SpaceX. I was surprised to see there wasn't more allocation of space stocks in his portfolio, given the fact that he believes this is such a huge industry. Perhaps the reality is that it's just too early, and that SpaceX is the linchpin to unlocking this industry, and just kind of closely evaluating Starship V3 launches. We had a Starship launch last week.

It performed very well. Without Starship functioning, we don't get energy in space. We don't get racks to orbit. It is required because the amount of payload is so large. So I'm sure SpaceX is the one to watch. There's a lot of other second order companies that can be impacted by this. But I think we want to get to the end of this with the question that a lot of people are going to be asking, which is why is this not just the dot-com bubble again?

And Gavin was asked this question many times. he has some pretty strong answers, and I kind of believe him. It seemed his case that he made was pretty convincing. Okay, so the way I think of his case is as follows. In the dot-com bubble, in the 2000s, it was debt-fueled. You had people borrowing a hell of a lot of money for unproven thesises and products that people didn't actually care about or use. Now, if you compare this to the current AI super cycle, as he describes it,

just from OpenAan Anthropic alone, they're on track to reach $200 billion of ARR this year, just two companies. And this isn't made up money. This is money that they've signed through contracts that are already prepaid in large part, I think 40 to 60% from a bunch of enterprise and retail customers

that are funding this. So this is real money exchanging hands. Now, if you look at the GPU computer, so let's not look at the model labs, let's look at the infrastructure, the people who are buying the goods from NVIDIA, Google, Microsoft, Amazon, and Meta are all paying from their own cash reserves. So they also haven't borrowed any money at all.

They're just spending free cash flow on this. Amazon just came to the end of their free cash flow. Now, if they start borrowing money, then we can start to get worried. But the point is they are not levered up either. Also, this is like five of the top companies in the entire world who are arguably some of the smartest companies because they are where they are in terms of like market cap value and size. So the fact is, back to the dot-com bubble, you had a bunch of no-name companies that had raised a ton of money that was spending money in ways that didn't really make sense.

In this cycle, you have some of the smartest companies in the world spending money that isn't levered at all, that isn't kind of like compressed into like various different margins. And all the quarterly reports that we've spoken about on previous episodes very recently over the last couple of weeks have proven that profit is optimizing through a bunch of these different movements that models are progressing. They're getting more intelligent. So the single argument that Gavin has is this isn't a dot-com bubble because we're not levered on money, but also because the bottlenecks themselves that we're speaking about is constrained by physical atoms. It's one thing being like, okay, I'm going to buy a bunch of memory chips and I'm going to buy a bunch of GPUs, but it's not like Nvidia can oversell GPUs. It's not like Micron can oversell AI memory chips because they just don't have the chip production facilities to be able to do that.

So a simple argument is it's not a bubble if you can't oversupply the entire market. We are constrained by the fact that we don't have enough picks and shovels to do the thing. And that's what he's investing in. And there's one great point. If you scroll down just a hair on this artifact, you can see the $2 to $3 trillion number. This is something that Gavin believes is a reality that Nvidia could sell $2 to $3 trillion of GPUs this year and next year if only TSM could supply them. So he's saying that TSMC is actually one of the major linchpins of bubble territory. And I'll explain why for a second because I found this really interesting.

If TSM were able to supply the amount of demand that is required from these companies to actually provide them with that many chips, it will cost them a tremendous amount of money. And in fact, if you scroll up just a little bit more, you could see the earned income to the CAPEX. There's like this kind of bar chart there. Yeah, right here where you could see AI CapEx is not much different than the operating cash. And so far, companies are generating enough cash to fund the build out of these things. In the case that TSM came to Invidiates Marr and said, actually, we could try. triple our capacity overnight, and video wouldn't say no, and they would start spending an unbelievable amount of money to buy those chips. Other companies would then have to borrow money

to fund the purchase of all these chips, and therefore we would start to see that KAPX bubble really start to grow and separate from the operating cash for these companies. But because there are these supply constraints across the board, we have them in memory, we have them in chipmaking, we have them in energy, particularly as it relates to TSMC with the chips, we're not actually able to build out that fast. And therefore, TSM is creating a blocker on the rate of acceleration of this bubble. And so long as TSM stays limited and constrained the amount of shifts that they could produce, and so long as companies like Samsung and other chipmakers don't actually overtake that market share, which seems improbable because it's going to take an incredibly

long time and it's very difficult to do, then we're at a pretty sustainable rate of growth, where it feels fast, but there's still this overwhelming amount of demand that can't be satiated because we just can't build it out fast enough. And so long as that dynamic stays intact, I think we're probably good for now. Well, there's also this other thing, right? Because, like, you could assume that, like, demand stays static, but that also doesn't happen. You have an exponentially increasing demand side for all this AI stuff, which is outpacing the production supply that we have for all of these different chips. So, like, the only way that I see this thesis kind of being unproven is if somehow someone miraculously recreates ASML and we suddenly

have like ASML competitors all around. By the way, if those of you don't know, ASML produces these $400 million machines, which basically TSM and every major chip fab manufacturer needs. And ASML is only one team in, I believe, Norway that creates these things and it takes ages and they are backlogged for like five years right now. Or if we recreate a different type of LLM that doesn't require as many GPUs or doesn't require as much memory. But we're just simply not seeing that. I saw a story. break today about S.K. Hynix, which is the number one memory manufacturer and supplier for Nvidia GPUs. They are basically the top dog when it comes to AI memory. And they're currently

courting, I think it's like $50 to $100 billion worth of offers from the likes of Google and Microsoft. So two companies alone, just to pay for the equipment that they need to scale up future supply over the next three years to lock in supply, that they're going to create three years from now. This is how desperate some of these big companies are for memory. This is just one subsector of the AI component. And SK Hanox is instead just saying, no, I don't want to give you guarantees of our supply. Instead, we'll just hike up the price. They have like 70% operating margin, by the way, which is just unheard of in semiconductor companies at all. So it makes complete sense why Gavin is just going all in because it doesn't look like a bubble. It might seem like a bubble. The market might

react to it. We opened our stock's portfolio today before we started recording. And everything was like down, but it's just reactionary because the directional goal of all of this is that we're just going to need more GPUs, we're going to need more semiconductor chips, and we don't have enough supply. We don't have enough manufacturers for this. So in conclusion, lots and wafers, that's it. Those are the two brick walls. Those are the two limiting factors, the limiting constraints that are going to prevent us from accelerating too fast. And so long as those watts and wafers stay valuable, stay in high demand, and stay limited in their supply, there will be good times ahead. Now, if you are looking for a TLVR on Gavin's portfolio, I will read out some of the

largest holdings for you. That way you can just, again, not financial advice. This is what Gavin's in. This is not what we're in. I have no idea if any of these are going to go up, down, or in circles. But his largest position, ironically, is that QQQPOT position. He's generally speaking bearish on the market, which I think is very noteworthy to talk about. Second to that is Astero Labs, 7.4%. A-Lab is the Tickr. Third is Unity, the 3D software. Then there's a whole bunch of others. Sienna, Micron, NVIDIA, Amazon, Lamentum, Alphabet, Coherent, Roblox, Echo Star, Twilio, Wayfair. Wayfair's a furniture company. This guy's in everything. There's a lot of investments. I think if you're interested, you can take a look at a 13-naf online.

We can link to it in the description. But that is the Gavin thesis, that the bottom link is Watson Wafers, so long as these things stay intact, we are up only. EJAS, how are you ingesting this information and what are you doing with it? So the market's been pretty rocky since the Leopold 13F. And I'm starting to realize as we're recording this episode that Gavin's kind of like an older, wiser Leopold. He's been around for a while. He may not have $13 billion in AUM, but I have a feeling he's going to be around a decade from now. And so if you're listening to this and you're like, listen, I don't want to keep up to date with AI every single minute, every single hour, every single day.

And I want to just kind of park my money and like kind of like see how it grows. over the next couple of months or years, Gavin's portfolio probably kind of makes a lot of sense. Again, this is not investment advice, but he takes a more cautious, long-term futuristic approach for a lot of these things. And if his trends indeed end up playing out, like he early back to Nvidia and Cerebrus,

you could end up having exponential gains over the next couple of years. But again, it's all based on his one thesis, which is we are not in a bubble. I'm curious whether the listeners of this show agree with this. Obviously, most people aren't as technical or in the weeds as Gavin, but just generally speaking off, you've heard this episode, do you think we're in a bubble?

Do you think we're not? What are the arguments for and against? Is there anything that we particularly missed? I don't know, Josh, do you think we're in a bubble as we round up? I think we're certainly in a bubble. Where we are in that bubble is to be debated. It seems like it's in the earlier stages. So hopefully it continues to be that way.

According to Gavin, so long as TSMC continues to limit their ability to produce chips, it will be fine. But that's the outlook. We have Leopold now that we've covered, whose success is measured in quarters. We have Gavin, whose success is matured in decades, and perhaps somewhere in the middle is where a lot of people are themselves. So if you did enjoy, please don't forget to share this with your friend.

Let us know which ones you are most. If it's not a thesis, perhaps there's a ticker that we should be looking at. It's exciting time because everything's moving quickly. Whether it be up or down, there is a lot of movement, there's a lot of volatility. It's fun to get involved in. See you guys tomorrow. See you tomorrow.