Limitless: An AI Podcast

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!

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TIMESTAMPS

0:00 AI Infrastructure Thesis
1:47 Gavin’s Track Record
2:57 Bottlenecks in Chips
5:26 Unity and World Models
7:40 Inference
11:20 Four AI Constraints
17:35 Energy and Space Compute
19:08 This Isn’t Dot-Com
24:10 Supply Constraints
26:06 Conclusion

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RESOURCES

Josh: https://x.com/JoshKale

Ejaaz: https://x.com/cryptopunk7213

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Not financial or tax advice. See our investment disclosures here:
https://www.bankless.com/disclosures⁠

Creators and Guests

Host
Ejaaz Ahamadeen
Host
Josh Kale

What is Limitless: An AI Podcast?

Exploring the frontiers of Technology and AI

Ejaaz:
Gavin Baker is one of the most prolific AI investors that almost no one has heard of.

Ejaaz:
He spent the last 20 years investing in some of the biggest AI companies before

Ejaaz:
they became household names.

Ejaaz:
He was an early backer of NVIDIA, as well as Cerebris, which IPO'd very recently.

Ejaaz:
And he has a very concrete thesis, which is AI isn't in a bubble.

Ejaaz:
It's quite the contrary.

Ejaaz:
It's in a super cycle. He says that by looking at the watts,

Ejaaz:
wafers and tokens, the infrastructure of AI.

Ejaaz:
He's identified some of the key bottlenecks and constraints,

Ejaaz:
and he has one simple thesis.

Ejaaz:
The biggest returns that you can get in AI is in electricity,

Ejaaz:
power, and silicon fabrication.

Ejaaz:
It's got nothing to do with SaaS software as a service.

Ejaaz:
It's got nothing to do with chatbots, such as Anthropic or OpenAI.

Ejaaz:
It all filters downstream from

Ejaaz:
semiconductors, the picks and shovels that build the entire AI industry.

Ejaaz:
And he's been expressing that interest to the tune of $4.1 billion.

Ejaaz:
While most people are calling the AI industry a complete bubble,

Ejaaz:
he thinks that it is a generational buying opportunity specifically for AI infrastructure,

Ejaaz:
and he makes the thesis very clearly in his fund.

Josh:
And if you hear these constraints that he's talking about, this AI infrastructure,

Josh:
you realize that this kind of sounds pretty familiar.

Josh:
We've heard this thesis before, and that's because on the show,

Josh:
many times we've covered an investor by the name of Leopold Aschenbrenner.

Josh:
He has been around for three years. He just published his most recent 13F,

Josh:
and he has a lot of the same core philosophies.

Josh:
Now, here's the difference. Leopold's been around for, what, three years?

Josh:
Gavin's been doing this for 20-plus years. And when we're comparing these two,

Josh:
Leopold actually has almost three times the assets under management.

Josh:
But there's this great quote that I was reminded of from our producer Luke before

Josh:
the show. It's like, okay, you can beat Warren Buffett over a year,

Josh:
but can you beat him over multiple decades?

Josh:
And Gavin Baker has a track record that proves that he might have a slightly

Josh:
different outlook on this investment thesis.

Josh:
For those who don't know, Gavin Baker, he is the basically founder of this company

Josh:
named a Trades Management.

Josh:
They are an investing fund, and he has spent 20 years investing in NVIDIA.

Josh:
Now, if you've invested in NVIDIA for 20 years, it's a miracle he's still working

Josh:
because those are some pretty incredible returns.

Josh:
Some of the early wins that we've recently seen include companies like Cerebris,

Josh:
which he was a very early investor in,

Josh:
Cerebus, if you'll remember, just IPO'd for an ungodly amount of money.

Josh:
Same with this company, Estera Labs. And there's a lot of other companies that

Josh:
I don't think you've probably heard of before that we're going to cover in this episode.

Josh:
As we walk through his portfolio and his guidance on where he sees the AI industry

Josh:
going in terms of investment and where all the opportunity lies.

Ejaaz:
So then the question becomes, what is he investing in and why is he investing in it?

Ejaaz:
Well, if we look at his recent 13F for a tradies management,

Ejaaz:
which is the name of his fund, They have about $4 billion worth of AUM.

Ejaaz:
We look at some of his biggest positions and unpack what some of these companies actually solve.

Ejaaz:
It kind of points towards the bottleneck that Gavin references in a bunch of

Ejaaz:
his different interviews as to where AI is going.

Ejaaz:
So he has some pretty sizable positions in some companies that a lot of people

Ejaaz:
may not have heard of purely because they're quite unsexy.

Ejaaz:
So he has an almost 9% position, so 10% position of the fund in this thing called

Ejaaz:
Astera Labs. Now, Astro Labs can be described as kind of like the connectivity layer between GPUs.

Ejaaz:
So if you imagine a data center, you have GPUs, they're kind of like the engine that can like pre-train

Ejaaz:
post-train your model and also like inference your model. But in order for these

Ejaaz:
GPUs to work, they need to traverse a bunch of different data.

Ejaaz:
They need to send data between themselves. They also need to access data from

Ejaaz:
all these memory chips that they store this data on.

Ejaaz:
Now, in order to access this, you need some kind of a plumbing system.

Ejaaz:
And I'm being very high level on this because I'm not going to pretend that

Ejaaz:
I know the intricacies of all of this.

Ejaaz:
Astero Labs is a company that essentially fixes that.

Ejaaz:
The problem it solves is as AI clusters scale to hundreds of thousands of chips,

Ejaaz:
The bottleneck stops becoming GPUs specifically, but it starts becoming that

Ejaaz:
transfer window and sending the right data at the right time,

Ejaaz:
accessing the right data at the right time is what the plumbing system that Astro Labs builds.

Ejaaz:
Now, I haven't heard of Astro Labs until we started researching for this episode,

Ejaaz:
but I remember another company being the exact same case called Cerebrus Labs,

Ejaaz:
which is what Gavin was talking about almost like, I think like six months ago, which is quite long,

Ejaaz:
given the relative scale or timeline of AI.

Ejaaz:
And then the next thing I know is that they IPO'd for like, what was it,

Ejaaz:
like $60 billion, and it's just been like, it's up 40% since the IPO.

Ejaaz:
So just kind of point towards these different trends. Asher Labs might be something

Ejaaz:
significant on that horizon.

Josh:
Yeah, Cerebrus is one of his earliest investments. I mean, he was in Cerebrus

Josh:
very early in the company's lifetime, which means he's bet on this thesis for years.

Josh:
There's also a few other ones that he's been betting on for a long time.

Josh:
I mean, NVIDIA being that flagship one, being involved in NVIDIA for 20 plus

Josh:
years is pretty incredible and still having conviction all the way through is impressive.

Josh:
Gavin was recently on two podcasts where I was listening to him speak about his NVIDIA position.

Josh:
And it's very clear that he believes that they're going to be able to maintain

Josh:
these profit margins and maintain the demand as well, which means he's putting

Josh:
NVIDIA on a clear path to getting close to $10 trillion in market cap.

Josh:
It's only halfway there right now.

Josh:
A few other noteworthy mentions are Micron, which we discussed on a previous

Josh:
episode, which I would highly recommend going to watch in terms of the AI investment

Josh:
stack and where all these companies lie.

Josh:
Micron is one of the largest memory makers and a crazy statistic about Micron.

Josh:
A year ago, it was sub $100 billion.

Josh:
And as of recording this, it just eclipsed a trillion dollars in market cap.

Josh:
It got a 10X in a single year. And it's a testament to how important that memory problem is.

Josh:
Now, perhaps some of the less noteworthy companies that are interesting,

Josh:
EGES, there's one I want to highlight for you in particular,

Josh:
because I feel like you're going to like this one, is Unity Software.

Josh:
And for those that don't know, Unity, I mean, I know this very well as a gamer, Unity is a game engine.

Josh:
There's a lot of popular games that are built using this 3D rendering software.

Josh:
So why would someone who's investing in AI be investing in Unity software,

Josh:
the thing that makes my video games? And the answer is the 3D game engine.

Josh:
Unity is a world model builder, and it has a really deep understanding of physics

Josh:
and the way the world works and the understanding of textures and lighting.

Josh:
And when these AI companies are trying to build AGI, they're trying to build humanoid robots.

Josh:
A big part of that is simulating virtual environments and virtual datasets that

Josh:
allow these robots to be trained in.

Josh:
And Unity just still happens to be one of the best ones. So I feel like that

Josh:
one I want to highlight specifically for you, EHS.

Josh:
As the world model Maxi, there is a clear path in which a gaming company that

Josh:
is known for a scanning engine becomes a pretty serious player in the world of AI.

Ejaaz:
Yeah, the whole thesis behind world models is pretty simple.

Ejaaz:
It's AI models or LLMs currently understand the world through text, through books.

Ejaaz:
It's kind of like a student sitting in a library, but it doesn't actually have

Ejaaz:
experience of the real world.

Ejaaz:
World models basically unlock that. It's like putting a game character into

Ejaaz:
a simulated environment and understanding the physical reality of how the world works.

Ejaaz:
If it works, if I drop this phone, if I kick a ball, like what happens?

Ejaaz:
What are the next consequential steps? What do you do?

Ejaaz:
World models effectively fix that. And there are very few players that have

Ejaaz:
like built this out at scale. I think currently the leader is probably Google with Genie 3.

Ejaaz:
They actually released a new model called Gemini Omni recently,

Ejaaz:
which kind of like does this at scale, but it's not quite like where it's meant

Ejaaz:
to be. It hasn't quite had its ChatGPT moment.

Ejaaz:
What I like about Gavin in particular is he kind of has this barbell.

Ejaaz:
I don't know if you noticed this, Josh, where he's kind of old school and he's like,

Ejaaz:
people are going to need GPUs. People are going to need memory.

Ejaaz:
I'm going to invest in the biggest players, Micron and NVIDIA.

Ejaaz:
But then he has this kind of forward-looking thing where he's like,

Ejaaz:
I think that's where the puck is going to go.

Ejaaz:
And so I think, let me invest in Cerebrus because I think inference is going to be super important.

Ejaaz:
And then let me invest in Unity because I think world models are going to be

Ejaaz:
the future of how we train robots and future LLMs.

Ejaaz:
So he has this kind of like barbell approach. Now, one thing that I see in his

Ejaaz:
portfolio over here as well is this, well, there's two companies, actually.

Ejaaz:
It's this company called Positron, which kind of creates inference chips.

Ejaaz:
Now, if that sounds similar, it's very similar to Cerebrus. That's exactly what they do.

Ejaaz:
And it's around this entire thesis, which Gavin has spoken about on his recent

Ejaaz:
interviews, which is the infrastructure stack, specifically the training stack

Ejaaz:
for AI models, is moving from pre-training to something more focused on post-training.

Ejaaz:
Now, if you've been involved in the AI sphere, generally, you kind of had an

Ejaaz:
idea that this shift was happening. But Gavin is all in on this thesis.

Ejaaz:
And if you have a model, it still needs to understand new information that comes in, new data.

Ejaaz:
It needs to update it. Just because you pre-train it on a specific data set

Ejaaz:
doesn't mean it's going to be a genius for the rest of its life.

Ejaaz:
It still needs to learn new information.

Ejaaz:
That happens in the post-training layer, and it requires a lot of inference.

Ejaaz:
Secondly, if you need the AI model to actually think about the problem more,

Ejaaz:
in the same way that we take in new information, we're like,

Ejaaz:
hmm, I wonder if this angle makes sense or if another thesis maybe applies.

Ejaaz:
That's known as reasoning. You need a lot of inference. Now,

Ejaaz:
the estimates are the cost or the revenue opportunity from inference alone is

Ejaaz:
worth around 5 to 10x more than the amount of compute that is being put into pre-training.

Ejaaz:
So, AI labs and chip makers are suddenly making this big shift.

Ejaaz:
Like you've seen NVIDIA create a bunch of different GPUs that are aligned to

Ejaaz:
inference to allow agentic kind of exposure.

Ejaaz:
And so we're seeing Gavin express this through his different investments on inference alone.

Ejaaz:
And the final point that I'll make is Gavin made a really cool point,

Ejaaz:
and we're talking about this before we started recording, Josh, on China specifically.

Ejaaz:
It's been very much like China versus the US when it comes to the AI race.

Ejaaz:
China has a very unique kind of setup where they have an abundance of energy

Ejaaz:
and the ability to scale chip manufacturers. That's something that the U.S.

Ejaaz:
Currently struggles with. That's why they outsource a ton of stuff to the likes of TSMC on Taiwan.

Ejaaz:
And what he basically explains is that China has a unique opportunity to create

Ejaaz:
infrastructure or chips specifically that are going to look very different to what the U.S.

Ejaaz:
Is creating because they're focused so much on inference.

Ejaaz:
So Gavin, you could say, is leading the charge in the U.S. through his investments

Ejaaz:
on building or taking a bet on the U.S.

Ejaaz:
Infrastructure setup for inference. And I think that could be a huge opportunity in the future.

Josh:
And it's worth noting that this bet also isn't only for the upside.

Josh:
There is a large put position here in an ETF named QQQ.

Josh:
Now, for those who are not familiar, it covers the NASDAQ 100. It's a basket of stocks.

Josh:
It's the second most traded ETF in the United States. And it's been performing incredibly.

Josh:
In 2023, it was up 55%. 2024, 25%. 2025, 20%. And so far in 2026,

Josh:
already up 17%. So QQQ, as an index fund, has been doing incredibly well.

Josh:
It's easy. It's a basket of the top 100 stocks. Gavin is saying,

Josh:
I'm shorting against that. I think that's going to go down. I think you're wrong.

Josh:
And what this tells me is that he believes strongly in the AI play in the sense

Josh:
that he's going to invest in the key makers who are solving these bottlenecks.

Josh:
But as a market-wide general sentiment, he doesn't appear to be very bullish.

Josh:
And this is a hedge against that downside protection, where if the market does

Josh:
start to fall apart in ways that are less favorable, even though AI still wins,

Josh:
he has that hedge with QQQ.

Josh:
We can kind of break this down into a few bottlenecks that he believes are going

Josh:
to be most important when it comes to investing.

Josh:
These key things that he is looking at in terms of what the world of AI is going

Josh:
to need as we progress forward. What are the actual constraints?

Josh:
What do they look like? And then how do you invest? How do you convert these

Josh:
into actual dollars that you could put into companies to earn you money?

Josh:
And there's four of them.

Josh:
The first one is verticalized small language models.

Ejaaz:
If you think about LLMs in general, like the chatbots that you talk to,

Ejaaz:
such as Claude and ChatGPT, they're generalized LLM.

Ejaaz:
So they have a wide understanding of the world in context, and they'll be able

Ejaaz:
to answer like specific questions.

Ejaaz:
But it's another thing, training a model around a specific vertical or specific

Ejaaz:
problem that you're trying to solve. Where do these specific problems exist?

Ejaaz:
Well, in enterprises that are deep on solving a particular problem or companies

Ejaaz:
that have made or formed a niche in their particular subsector.

Ejaaz:
Now, verticalized small language models basically address exactly this.

Ejaaz:
They're frontier models, but highly optimized towards running efficiently on

Ejaaz:
specific enterprise data or locally on device.

Ejaaz:
Now, we have spoken about on device or locally run models before,

Ejaaz:
purely for the case that there's a bunch of data in your phone or the devices

Ejaaz:
that you use that are highly personable to you, but you don't necessarily want to give up.

Ejaaz:
And companies don't necessarily have access to that. For example,

Ejaaz:
medical records, financial details.

Ejaaz:
I saw OpenAI release a financial AI agent that can get access to your bank account,

Ejaaz:
But it can't actually act on it because there's a lot of personal identifiable

Ejaaz:
information that you don't want to share, such as your social security number, banking details, etc.

Ejaaz:
Now, locally run models or these SLMs can solve that kind of a problem.

Ejaaz:
And Gavin is making a huge bet that these are going to become huge in the future.

Ejaaz:
One company that I've noted that Gavin is hugely bullish on is Apple.

Ejaaz:
Although he doesn't express an investment interest, he knows or thinks that

Ejaaz:
Apple is going to be the device maker, one of the major device makers,

Ejaaz:
Allows for these locally run models to run on their devices.

Ejaaz:
Now, in a world where that is the future, you can start thinking of a world

Ejaaz:
where maybe Claw doesn't need to be the model that you need to interface with every day.

Ejaaz:
Maybe you need a personalized AI agent trained on your own data,

Ejaaz:
and that's what these SLMs end up eventually becoming.

Ejaaz:
Now, that's the generalized version of it where you can run it on your own phone,

Ejaaz:
but a bunch of enterprises will run these highly optimized and specialized models

Ejaaz:
to train on their proprietary data, which ends up helping them sell a product

Ejaaz:
or market a product much better.

Josh:
Oh man, Apple's in such a good position there. So good. I can't wait for WWDC. It's coming.

Josh:
We are just a few weeks away from Apple's developer conference where they're

Josh:
going to unveil all of this new AI software that's coming and what that looks

Josh:
like integrating the hardware.

Josh:
That's going to be huge. We will be covering that. I'm so excited to talk about

Josh:
that. In terms of the next pillar of this, it is sovereign infrastructure.

Josh:
We always talk about this, that the speed of bits is so much faster than the speed of atoms.

Josh:
I mean, When you think about AI infrastructure, the quality of models has gone purely exponential.

Josh:
The amount of intelligence we could generate per watt, the amount of intelligence

Josh:
per token, is up into the right only.

Josh:
What isn't up into the right at nearly the same rate is the speed of physical

Josh:
deployment, because that itself is the mode.

Josh:
It's very difficult to take hardware that is incredibly complicated.

Josh:
We're talking about transistors that are down to the atomic level of precision

Josh:
and deploying them at scale in a world in which our infrastructure is already

Josh:
suffering. I mean, with the acceleration of electric cars, the grids have already

Josh:
been feeling a little bit more restrained. They're kind of at max capacity.

Josh:
Now they have the energy problem. Now they have the chip problem.

Josh:
Gavin is very strongly betting on the fact that infrastructure is hard.

Josh:
It's going to take many, many days to months to years to do.

Josh:
And he's betting on the people that can compress that into weeks.

Josh:
So the speed of the physical deployment itself is the moat.

Josh:
He's kind of narrowing in on who the companies are that are able to deploy as

Josh:
fast as possible. When I think about this, my first thought is SpaceX.

Josh:
And how quickly they've been able to build Colossus and then rent that out to

Josh:
Anthropic and I'm sure companies in the future.

Josh:
But that infrastructure pillar is one of the key ones that he's looking at.

Josh:
I think everyone, we looked at.

Josh:
Leopold's portfolio as well, that was a core component of that.

Josh:
It's just, it's really hard to build things. And whoever can build things,

Josh:
they can sell it for a lot of money.

Josh:
SpaceX, their largest line item now in terms of revenue is the data center that

Josh:
they're renting out. It has nothing to do with rocket ships.

Josh:
And I think that's a testament to how important this pillar is.

Ejaaz:
So it's speed that he cares about and he thinks that it's important,

Ejaaz:
but it's also like the cost, right?

Ejaaz:
He keeps referencing this metric, which is performance per watt.

Josh:
Perf per watt.

Ejaaz:
Yeah, perf per watt. And what he's talking about here is companies are increasingly,

Ejaaz:
companies being AI labs, are increasingly caring about how many tokens can you

Ejaaz:
generate per watt, right?

Ejaaz:
Because if you think about spending billions and trillions of dollars, which is what...

Ejaaz:
Five companies are currently spending this year alone on GPUs and compute to

Ejaaz:
kind of like power these things or electricity to power these things,

Ejaaz:
You want to get a lot of bang for buck, especially if you're scaling to the

Ejaaz:
size that most of these hyperscalers are doing.

Ejaaz:
So if you think about it, if I prompt Claude and if I prompt ChatGPT and Claude

Ejaaz:
gives me an answer that cost me two cents and ChatGPT gives me an answer that cost me $1,

Ejaaz:
I'm probably going to end up using Claude, even if it's like hypothetically

Ejaaz:
like say 95% of the intelligence hypothetically that ChatGPT has.

Ejaaz:
Because the point is you can prompt

Ejaaz:
it even more and eventually get to the answer for a lot less of cost.

Ejaaz:
So cost becomes a huge thing. This week alone, Microsoft and Uber announced

Ejaaz:
that they are effectively reducing their exposure to clawed code because their

Ejaaz:
annual budgets kind of like got sequestered in about four months.

Ejaaz:
So the point is like the cost of getting access to this intelligence matters a lot.

Ejaaz:
And you see this across Gavin's investment portfolio with Cerebras,

Ejaaz:
with Alexa, Positron, and with Astro Labs, which basically, like,

Ejaaz:
what I've noticed is he identifies these really niche infrastructure bottlenecks,

Ejaaz:
and he basically makes one simple bet.

Ejaaz:
He's like, yeah, if this company solves that, the performance per watt gets

Ejaaz:
to this specific level, which means that AI Labs are probably going to buy more

Ejaaz:
of these GPUs or more of these companies or more of these things,

Ejaaz:
and you end up with a bottleneck being resolved by one of these different companies that he's betting on.

Ejaaz:
So his thesis actually is quite simple, although it's quite complex,

Ejaaz:
which is I'm just going to focus on the AI bottlenecks at the infrastructure level.

Ejaaz:
And if I can identify a company that can increase performance for what by this

Ejaaz:
amount, that can make tokens this amount cheaper, then my bet is those companies

Ejaaz:
are going to be valuable and will either IPO or get acquired for a large sum of money.

Josh:
And the thinkers to know for the section, for those looking to copy trade,

Josh:
Gavin, Estera Labs, we have Cerebrus, we have Sci-5, and we have Positron.

Josh:
Those are the four companies that are really critical in this sector.

Josh:
Now, the fourth and final is a combination of energy and space.

Josh:
Because, I mean, like we talked about in the previous point,

Josh:
the terrestrial grid very much limits energy.

Josh:
And it's very difficult to earn new energy. I think a statistic like 40% of

Josh:
new data centers have very strong petitions against them, or lobbying against

Josh:
them, people protesting they do not want these data centers.

Josh:
There's a lot of resistance to them. And the way that we solve this is twofold.

Josh:
One is creating energy out of the box. It's kind of portable energy you could

Josh:
bring to these data centers, power them with a smaller phone.

Josh:
That's companies like Bloom Energy, who Leopold is very bullish on.

Josh:
But then there's also the orbital compute part of this, which is what Gavin

Josh:
has really shifted his focus to.

Josh:
Now, the first largest company in this sector, of course, is SpaceX.

Josh:
They're the only one who is capable of being the highway to space,

Josh:
to delivering actual payload to orbit, to getting maths and data centers in

Josh:
low-end orbit that can generate enough intelligence and power that can funnel this.

Josh:
I think the space stack is a little bit bigger than just SpaceX.

Josh:
I was surprised to see there wasn't more allocation of space stocks in his portfolio,

Josh:
given the fact that he believes this is such a huge industry.

Josh:
Perhaps the reality is that it's just too early and that SpaceX is the linchpin

Josh:
to unlocking this industry and just kind of closely evaluating Starship V3 launches.

Josh:
We had a Starship launch last week. It performed very well.

Josh:
Without Starship functioning, we don't get energy in space. We don't get racks

Josh:
to orbit. It is required because the amount of payload is so large.

Josh:
So I'm sure SpaceX is the one to watch. There's a lot of other second order

Josh:
companies that can be impacted by this.

Josh:
I think we want to get to the end of this with the question that a lot of people

Josh:
are going to be asking, which is why is this not just the dot-com bubble again?

Josh:
And Gavin was asked this question many times. He has some pretty strong answers

Josh:
and I kind of believe him. It seemed his case that he made was pretty convincing.

Ejaaz:
Okay, so the way I think of his case is as follows.

Ejaaz:
In the dot-com bubble in the 2000s, it was debt-fueled.

Ejaaz:
You had people borrowing a hell of a lot of money for unproven theses and products

Ejaaz:
that people didn't actually care about or use.

Ejaaz:
Now, if you compare this to the current AI super cycle, as he describes it,

Ejaaz:
just from OpenAI and Anthropic alone, they're on track to reach $200 billion

Ejaaz:
of ARR this year, just two companies.

Ejaaz:
And this isn't made up money. This is money that they've signed through contracts

Ejaaz:
that are already prepaid in large part, I think 40 to 60% from a bunch of enterprise

Ejaaz:
and retail customers that are funding this. So this is real money exchanging hands.

Ejaaz:
Now, if you look at the GPU computer, so let's not look at the model labs,

Ejaaz:
let's look at the infrastructure, the people who are buying the goods from NVIDIA.

Ejaaz:
Google, Microsoft, Amazon, and Meta are all paying from their own cash reserves.

Ejaaz:
So they also haven't borrowed any money at all.

Ejaaz:
They're just spending free cash flow on this.

Ejaaz:
Amazon just came to the end of their free cash flow. Now, if they start borrowing

Ejaaz:
money, then we can start to get worried. But the point is, they are not levered up either.

Ejaaz:
Also, this is like five of the top companies in the entire world who are arguably

Ejaaz:
like some of the smartest companies because they are where they are in terms

Ejaaz:
of like market cap, value and size.

Ejaaz:
So the fact is like back to the dotcom bubble, you had a bunch of like no-name

Ejaaz:
companies that had raised a ton of money that was spending money in ways that

Ejaaz:
didn't really make sense.

Ejaaz:
In this cycle, you have like some of the smartest companies in the world spending

Ejaaz:
money that isn't levered at all, that isn't kind of like compressed into like

Ejaaz:
various different margins.

Ejaaz:
And all the quarterly reports that we've spoken about on previous episodes very

Ejaaz:
recently over the last couple of weeks have proven that profit is optimizing

Ejaaz:
through a bunch of these different movements that models are progressing,

Ejaaz:
they're getting more intelligent.

Ejaaz:
So the single argument that Gavin has is, this isn't a dot com bubble,

Ejaaz:
because we're not levered on money, but also because the bottlenecks themselves

Ejaaz:
that we're speaking about is constrained by physical atoms.

Ejaaz:
It's one thing being like, OK, I'm going to buy a bunch of memory chips and

Ejaaz:
I'm going to buy a bunch of GPUs.

Ejaaz:
But it's not like NVIDIA can oversell GPUs. It's not like Micron can oversell

Ejaaz:
AI memory chips because they just don't have the chip production facilities to be able to do that.

Ejaaz:
And so his simple argument is it's not a bubble if you can't oversupply the entire market.

Ejaaz:
We are constrained by the fact that we don't have enough picks and shovels to

Ejaaz:
do the thing. And that's what he's investing in.

Josh:
And there's one great point. If you scroll down just a hair on this artifact,

Josh:
you can see the $2 to $3 trillion number.

Josh:
This is something that Gavin believes is a reality, that NVIDIA could sell $2

Josh:
to $3 trillion of GPUs this year and next year if only TSMC could supply them.

Josh:
So he's saying that TSMC is actually one of the major linchpins of bubble territory.

Josh:
And I'll explain why for a second, because I found this really interesting.

Josh:
If TSMC were able to supply the amount of demand that is required from these

Josh:
companies to actually provide them with that many chips, it will cost them a

Josh:
tremendous amount of money.

Josh:
And in fact, EJS, if you scroll up just a little bit more, you could see the

Josh:
earned income to the CapEx.

Josh:
There's like this kind of bar chart there. Yeah, right here,

Josh:
where you could see AI CapEx is not much different than the operating cash.

Josh:
And so far, companies are generating enough cash to fund the build out of these things.

Josh:
In the case that TSMC came to NVIDIA tomorrow and said, actually,

Josh:
we could triple our capacity overnight.

Josh:
NVIDIA wouldn't say no. And they would start spending an unbelievable amount

Josh:
of money to buy those chips.

Josh:
Other companies would then have to borrow money to fund the purchase of all these chips.

Josh:
And therefore, we would start to see that CapEx bubble really start to grow

Josh:
and separate from the operating cash of these companies.

Josh:
But because there are these supply constraints across the board,

Josh:
we have them in memory, we have them in chip making, we have them in energy,

Josh:
particularly as it relates to TSMC with the chips, we're not actually able to build out that fast.

Josh:
And therefore, TSMC is creating a blocker on the rate of acceleration of this bubble.

Josh:
And so long as TSMC stays limited and constrained to the amount of chips that

Josh:
they could produce, and so long as companies like Samsung and other chip makers don't.

Josh:
Actually overtake that market share, which seems improbable because it's going

Josh:
to take an incredibly long time and it's very difficult to do,

Josh:
then we're at a pretty sustainable rate of growth where it feels fast,

Josh:
but there's still this overwhelming amount of demand that can't be satiated

Josh:
because we just can't build it out fast enough.

Josh:
And so long as that dynamic stays intact, I think we're probably good for now.

Ejaaz:
Well, there's also this other thing, right? Because like you could assume that

Ejaaz:
like demand stays static, but that also doesn't happen.

Ejaaz:
You have an exponentially increasing demand side for all this AI stuff,

Ejaaz:
which is outpacing the production supply that we have for all of these different chips.

Ejaaz:
So the only way that I see this thesis kind of being unproven is if somehow

Ejaaz:
someone miraculously recreates ASML and we suddenly have ASML competitors all around.

Ejaaz:
By the way, those of you who don't know, ASML produces these $400 million machines,

Ejaaz:
which basically tsmc and every major chip fab

Ejaaz:
manufacturer needs and asml is only

Ejaaz:
one team in i believe norway that creates

Ejaaz:
these things and it takes ages and they're backlogged for like five

Ejaaz:
years right now or if we recreate a different type of llm that doesn't require

Ejaaz:
as many gpus or doesn't require as much memory but we're just simply not seeing

Ejaaz:
that i saw a story break today about sk hynix which is the number one memory

Ejaaz:
manufacturer and supplier for nvidia gpus they are basically the top dog when it comes to AI memory.

Ejaaz:
And they're currently courting, I think it's like $50 billion,

Ejaaz:
$50 to $100 billion worth of offers from the likes of Google and Microsoft.

Ejaaz:
So two companies alone, just to pay for the equipment that they need to scale

Ejaaz:
up future supply over the next three years to lock in supply that they're going

Ejaaz:
to create three years from now.

Ejaaz:
This is how desperate some of these big companies are for memory.

Ejaaz:
This is one subsector of the AI component.

Ejaaz:
And SK Hanox is instead just saying, no, I don't want to give you guarantees of our supply.

Ejaaz:
Instead, we'll just hike up the price. They have like 70% operating margin,

Ejaaz:
by the way, which is just unheard of in semiconductor companies at all.

Ejaaz:
So it makes complete sense why Gavin is just going all in, because it doesn't look like a bubble.

Ejaaz:
It might seem like a bubble. The market might react to it. We opened our stocks

Ejaaz:
portfolio today before we started recording and everything was like down.

Ejaaz:
But it's just reactionary because the directional goal of all of this is that

Ejaaz:
we're just going to need more GPUs. going to need more semiconductor chips and

Ejaaz:
we don't have enough supply we don't have enough manufacturers for this

Josh:
So in conclusion, watts and wafers, that's it. Those are the two brick walls.

Josh:
Those are the two limiting factors, the limiting constraints that are going

Josh:
to prevent us from accelerating too fast.

Josh:
And so long as those watts and wafers stay valuable, stay in high demand and

Josh:
stay limited in their supply, there will be good times ahead.

Josh:
Now, if you are looking for a TLDR on Gavin's portfolio, I will read out some

Josh:
of the largest holdings for you.

Josh:
That way you can just, again, not financial advice. This is what Gavin's in.

Josh:
This is not what we're in.

Josh:
I have no idea if any of these are gonna go up, down or in circles.

Josh:
But his largest position, ironically, is that QQQ put position.

Josh:
He's generally speaking bearish on the market, which I think is very noteworthy to talk about.

Josh:
Second to that is Astera Labs, 7.4%. Alab is the ticker. Third is Unity, the 3D software.

Josh:
Then there's a whole bunch of others. Sienna, Micron, NVIDIA,

Josh:
Amazon, Lumentum, Alphabet, Coherent, Roblox, EchoStar, Twilio, Wayfair.

Josh:
Wayfair is a furniture company. This guy's in everything. There's a lot of investments.

Josh:
I think if you're interested, you can take a look at a 13F online.

Josh:
We can link to it in the description. But that is the Gavin thesis,

Josh:
that the bottleneck is Watson wafers.

Josh:
So long as these things stay intact, we are up only. Ejaz, how are you ingesting

Josh:
this information and what are you doing with it?

Ejaaz:
So the market's been pretty rocky since the Leopold 13F.

Ejaaz:
And I'm starting to realize as we're recording this episode that Gavin's kind

Ejaaz:
of like an older, wiser Leopold.

Ejaaz:
He's been around for a while. He may not have $13 billion in AUM,

Ejaaz:
but I have a feeling he's going to be around a decade from now.

Ejaaz:
And so if you're listening to this and you're like, listen, I don't want to

Ejaaz:
keep up to date with AI every single minute of every single hour, every single day.

Ejaaz:
And I want to just kind of park my money and kind of like see how it grows over

Ejaaz:
the next couple of months or years.

Ejaaz:
Gavin's portfolio probably kind of makes a lot of sense. Again,

Ejaaz:
this is not investment advice, but he takes a more cautious,

Ejaaz:
long-term, futuristic approach to a lot of these things. and if his trends indeed

Ejaaz:
end up playing out like he early backed Nvidia and Cerebrus,

Ejaaz:
you could end up having like exponential gains over the next couple of years.

Ejaaz:
But again, it's all based on his one thesis, which is we are not in a bubble.

Ejaaz:
I'm curious whether the listeners of this show agree with this.

Ejaaz:
Obviously, most people aren't as technical or in the weeds as Gavin,

Ejaaz:
but just generally speaking, after you've heard this episode,

Ejaaz:
do you think we're in a bubble?

Ejaaz:
Do you think we're not? What are the arguments for and against?

Ejaaz:
Is there anything that we particularly missed?

Ejaaz:
I don't know, Josh, do you think we're in a bubble as we round up?

Josh:
I think we're certainly in a bubble. Where we are in that bubble is to be debated.

Josh:
It seems like it's in the earlier stages.

Josh:
So hopefully it continues to be that way. According to Gavin,

Josh:
so long as TSMC continues to limit their ability to produce chips, it will be fine.

Josh:
But that's the outlook. We have Leopold now that we've covered,

Josh:
whose success is measured in quarters.

Josh:
We have Gavin, whose success is measured in decades.

Josh:
And perhaps somewhere in the middle is where a lot of people think of themselves.

Josh:
So if you did enjoy, please don't forget to share this with your friend.

Josh:
Let us know which ones you are most. If it's not a thesis, perhaps there's a

Josh:
ticker that we should be looking at. I think it's exciting time because everything's moving quickly.

Josh:
Whether it be up or down, there is a lot of movement. There's a lot of volatility.

Josh:
It's fun to get involved in.

Ejaaz:
See you guys tomorrow.

Josh:
See you tomorrow.