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My guest today is Gavin Baker, the founding partner and CIO of Atreides Management, and this is our sixth conversation.
The central theme is watts and wafers, the two physical constraints that, in Gavin's view, will dictate the next phase of AI.
On power.
He thinks the near-term shortage starts to ease in 2027 and 28 as new sources of energy come online and that orbital compute helps solve this problem in the long term.
On Wafers.
He explains what is different this time from the dot-com bubble and why TSMC's capacity decisions may be the single most important variable to watch.
We also discuss Elon's TeraFab, the disaggregation of GPUs, the role of new chip companies and whether economic value of AI will keep accruing to the frontier models.
Please enjoy this awesome sixth conversation with Gavin Baker.
All right, so this is our sixth time doing this, if you can believe it, which puts you back into first place, or at least tied first place with Gurley, back into esteemed territory.
Always my favorite conversation about markets and everything going on, even since last time when we did this, which was so exciting and spectacular.
I think we're in an even more interesting time now.
Maybe just start by riffing on how it felt for you living through March and April of this year, which felt to me just like a completely unique economic technology and market environment.
And you're the biggest student of history and of these times.
So what does it feel like?
I would say, broadly speaking, there are two kinds of drawdowns.
There are drawdowns where you're wrong.
Company missed estimates, your hypothesis was invalidated and you have to take your medicine and you crystallize that loss.
And then their drawdowns are periods of underperformance where you're underperforming because of companies you know really, really well and where you profoundly disagree with the price action and you can lean in.
And instead of crystallizing negative performance you can kind of build pent up alpha, pent up future performance.
And for me, that is what March felt like.
The NASDAQ was selling off.
At the same time, what was happening in AI was, I think, the most extraordinary moment in the history of capitalism, the history of American business.
What I just mean by that is that Anthropic, they added $11 billion of AR.
And what is astonishing to me about this is that the SaaS and cloud revolution it created we'll call it between 5 and 10 trillion of value.
I would say arguably.
The three highest profile SaaS companies in the last 10 12 years are Palantir, Snowflake and Databricks.
And these three companies employ thousands of people, tens of thousands collectively.
They've all spent 10 years building their businesses.
And Anthropic added their combined businesses in one month.
Nothing like that has ever happened in the history of capitalism.
Forget my career.
Just the flat-out history of capitalism.
The history of business.
It's wild.
And then Krishna comes on this show and shares some stats.
500% in DR?
Yeah, you do the math on that for three years.
So there's just no precedent for this.
And we tech investors, we hear a lot of discussions about S-curves and investing in exponentials.
I've just never seen an exponential like this.
It felt even more extreme than DeepSeek. which was a very similar setup.
They happened at about the same time.
If we go back to 25, there's a huge sell-off on DeepSeek, which was very strange because the paper gets published seven days before DeepSeek Monday.
It got published yesterday.
I believe on a Monday that was a holiday in America.
I read it.
I thought, hmm, this feels like it might not read that positively for the AI trade.
I took action.
And then we had Deep Seek Monday, where AI really imploded.
A week later.
That was really strange because by Deep Seek Monday it was super clear that this was going to be the most positive thing that had ever happened to compute demand.
Prices of the AWS availability zones in Asia had already doubled dramatically.
You were seeing GPU availability go down.
And this was just the first time we saw how much more compute-hungry reasoning models are during inference than non-reason models.
And so that was a similar setup.
You had to do some work to see that.
I mean, it's not that hard to say, oh, wow, stocks are selling off.
The price of DRAM is going vertical.
The price of GPUs in Asia are going vertical.
GPU availability is going down.
And then like two or three days later, GPU prices in America started going up, GPU rental prices.
All you had to do in March was simply observe what was happening to Anthropic.
And there's all these people who seem to regret not buying during 22, not buying during COVID, not buying during deep seek.
You had the same valuation set up at the beginning of April, and an even clearer AI inflection.
So there have been all these chances to buy into AI.
And then, of course, what complicated it was the straightforwardness.
I became a believer and am a believer that I think, maybe one thing, that the market was mispricing.
I'm no macro expert.
I do do a lot of pro national security investing.
So I do have access to people who are experts that are excited to share their thoughts and opinions with me, and that the Strait of Hormuz being closed is actually relatively awesome for America.
Why?
Particularly for the goals of the current administration.
So electricity is a very important industrial or manufacturing input.
The key input into American electricity prices, which feeds into AI, is in G1 natural gas.
One on Bloomberg.
That was down 20%.
And natural gas in Asia, Europe, everywhere else doubled or tripled.
Our relative manufacturing competitiveness improved overnight.
And for better or worse, that is what the Trump administration seems to care about.
They are very focused on America's relative position.
And I think a lot of people had memories of the 1970s.
What made the 70s so traumatic was it wasn't just that prices went up.
It's that there were actual gas shortages.
Then you go through, okay, well, the US economy is dramatically less energy intensive than it was.
The United States is now the world's largest producer of oil and gas.
And we've become now the world's largest exporter of oil and gas.
And on top of that, there's this relative manufacturing advantage that made it easier to stay focused on AI fundamentals, stay focused on what were historically attractive valuations.
I think on a relative basis, tech essentially got as cheap as it's been versus the rest of the market has at any point over the last 10 years.
And just think about that in the context of market efficiency.
We have the most extraordinary moment in the history of capitalism.
It's wildly bullish for AI, and you get a chance to buy AI at really attractive valuations.
What do you make of the multiples that specifically Anthropic and OpenAI which, in my mind, are like the reference assets that are the most pure play takes on, this trend really being not that crazy.
Like if you just look at the sales multiple and compare it to maybe what Databricks and Snowflake and these companies traded at at their peak, how do you process it?
How do you make sense of it?
I do think OpenAI and Anthropic are pretty different animals from a capital efficiency perspective.
And Anthropic clearly has a dramatically lower cost per token than OpenAI.
They just do.
And you can just see that in the amount of money that they have burned to get to a roughly similar revenue scale.
I think they burned maybe 80% less than OpenAI.
So his businesses, they clearly have very different structural ROICs.
I think Sarah Fryer is one of the most exceptional CFOs.
I think they're doing a lot of things to try to improve this.
And they've secured a lot of compute.
They've secured a lot of compute.
That's another big difference.
It turns out being aggressive really paid.
Anthropic at $900 billion for $50 billion in ARR, growing at ridiculous rates.
And I think maybe a true statement is that if Anthropic could just wave a magic wand and get all the compute they wanted,
They'd probably be doing well north of $100 billion today, maybe 150.
They have clearly deprecated the intelligence of Claude.
There's analysis, Claude is even on Opus is generating 70% less tokens for the exact same question.
As we talked about last time, token quantity equals quality of answer and quality of thinking at some level.
And there is an intelligence density per token that also matters.
I felt that as a user.
So I think they would be doing materially more, 100, 150, maybe 200 billion.
So you might be buying it at more like five times unconstrained.
I'm going to make up a new number.
URR, unconstrained revenue growth.
Why do you think they don't raise $100 billion at a $3 trillion valuation or something like this?
If you were the anthropic CFO, Krishna's awesome, we just had him on.
Or if you're Sarah, it seems to me like, if the inbound I received following the Krishna episode is any indication, everyone I've ever met is trying to invest in both these companies.
I think it's wise.
The future is uncertain.
You are clearly in a very capital intensive game.
Even if you are Anthropic, I am sure is at very positive gross margins on inference today.
I can drop.
It probably starts generating cash this year, if they are not already generating cash, which I think is probably the case.
But still, you probably want to be able to raise more capital, access more compute.
The world is uncertain.
Ukraine is starting to really, really win.
How is Russia going to respond?
I think there's still a lot of uncertainty in Iran.
All this uncertainty, I think, probably amplifies geopolitical uncertainty over time.
So it's an uncertain world.
If I think about Elon, Elon has always made investors money.
He treats it like a sacred covenant.
And as a result, because he's made people money for now 20 years, he has a superpower.
That is, he can essentially raise as much capital as he wants whenever he wants.
I do think being focused on making investors money is wise and creates benefits that don't just last for like a year or two.
They can last for the next 20 to 30 years.
And the way Elon did this was systematically underpricing SpaceX or whatever else.
What is the actual method?
Just never being greedy on valuation, never pushing valuation.
Just that simple.
My friend Antonio pointed out SpaceX compounded at low 30% per year for a decade.
And that was just because Elon was, I think, focused on preserving the superpower and having trying to strike a fair balance between investors and employees.
I think it's wise, but could Anthropic raise money at probably at least a 100 premium to this rumored latest mark?
Of course.
Let's get to the Watson wafers part of the discussion.
Always my favorite thing to talk about with you.
The importance of this infrastructure build out every time I feel like it's getting overheated.
And then the next time I talk to you, it seems like we should have done way more than we did.
You studied S-curves and the steepness of those S-curves a lot.
And you know a lot about history.
Talk us through how you're thinking about Watts and wafers today as the key to inputs into this whole thing.
I think capitalism is going to solve the Watts shortage, absent big regulatory political blowback, which I think is a real possibility.
The head of data center infra investing at one of the big PE firms, Blackstone Apollo KKR, said it used to be energy and chips were our biggest gating factors.
Now it's zoning and approval, much more important.
I think a lot of companies are waiting till after the midterms to take action in terms of maybe workforce reductions.
Nobody wants to be a pinata during the midterms.
You've seen a lot of companies that make turbines announce plans to significantly increase capacity.
There's like two of these machines that can cast these big blades.
We haven't made one in 80 years of the West.
We don't know how to make them anymore.
All of that is true.
By no means am I minimizing the industrial engineering magic and artistry that goes into those, but capitalism is very good at solving problems like these over time.
There's other sources of energy besides these turbines with a longer timeframe.
So I think the watts shortage will probably begin to alleviate 27, 28.
And then I think orbital compute will really solve that.
I do want to reframe orbital compute because I think when people hear data centers in space which we discussed our last episode They picture a pentagon-sized building in space.
They're like, well, we can't do that.
That's not what it is.
A Blackwell rack weighs 3,000 pounds.
It's eight feet high.
It's four feet deep, three feet wide.
It's racks in space.
And SpaceX has showed you an illustration.
And it's a rack.
That's the satellite.
But it's probably about the size of a Blackwell rack.
It has these solar wings that are probably 500 feet long on each side.
You keep it in a sun-secretous orbit, so those solar panels are always in the sun.
Because it's in an exactly sun-secretous orbit, the radiator which extends behind it for hundreds of feet.
This is a common criticism.
Yeah.
I've spent a lot of time at Starbase over the years and I've talked to a lot of SpaceX engineers and I do think it is the most talented group of engineers on planet Earth.
And they're very confident they have solved this.
And they're not always confident.
There's some engineering that needs to happen to turn the Starship into a Mars colonial transporter.
Will they do that?
Absolutely.
What are they more focused on?
I'd say probably the repair and maintenance.
These are the two big responses the radiator and how do you repair the?
Whatever issue goes wrong in the rack.
And the answer is, until you have probably floating optimuses, you don't.
Starship is going to change the space economy in ways we cannot imagine.
And particularly if regulation becomes a constraint to data centers, none of it's going to matter.
You're going to sell as much orbital compute as you can make.
And then obviously, you link these racks using lasers traveling through vacuum, which are already on every Starlink.
And it's just mind blowing to me that SpaceX operates the world's largest satellite fleet, which is 98 or 99 of all satellites in orbit.
Every Starlink, they're cooling it today.
I think Starlink V3 is going to operate at 20 kilowatts.
A Blackwell rack is only 100 kilowatts and people talk a lot about density.
Well, if you're connecting the racks with lasers through vacuum, you can make the rack bigger.
Physically, you're focused on weight, not size.
In a data center on earth where you're trying to connect Racks, ideally using copper.
Minimize lengths.
Cabling is a big cost.
You do want that rack to be small.
Copper when you can, optics when you must.
But in space, you know, there's all sorts of things that SpaceX can do.
That I think maybe some of these naysayers are not contemplating.
They operate more satellites than anyone.
They have a 20 kilowatt satellite today.
So maybe you just scale that up to 60 kilowatts to start.
They seem very confident they're gonna go right to 100 to 120.
The same company now, also operates the largest data center on Earth.
They have the world's best hardware engineers and all sorts of people, almost all of whom are not smart enough or practical enough to work at SpaceX.
Are these armchair skeptics?
I don't want to quote Larry Ellison, but somebody was being skeptical.
And Larry was just like, listen, he's out there landing rockets.
I don't see anybody else landing rockets.
And the reality is, 10 years later, No other company is consistently capable of landing and fully reusing an orbital rocket.
None of this makes sense without reusability.
That means you have to land it.
I would like to redefine orbital compute as racks in space.
Not giant, floating, pentagon-sized data centers in space.
That's silly.
What makes a data center is you're connecting these racks with lasers.
So it'll be racks in space that are connected with lasers into a virtual data center.
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And if you think about that state of the world, let's say that all happens and we're really good at getting these things up economically and running matrix multiplication all over space.
What does that mean for terrestrial data centers?
Someone once said America was going to suck as hard as it can on every energy source it can get.
And I just think the same is true of compute.
It's why I'm probably less worried about like an edge AI bear case than I was.
We're going to consume as much compute as we can.
Inference, I think, is very sensible for Orville compute.
Training will be done on Earth for a long time.
So I don't think that this is super bearish for terrestrial data centers.
I think those are gonna be valuable for my lifetime.
But I do think if you're in this ecosystem of power production and cooling and you are massively ramping capacity, a lot of these capacity ramps are going to be hitting, just as, I think, all of the silly skeptics start to understand that orbital compute is very real.
I think it's worth thinking long and hard about that if you're one of those companies.
And then all sorts of cool stuff is happening in the interim.
We're getting really good at repurposing jet engines.
There's that boom aerospace that is doing this.
Capitalism is hard at work.
On WAFers, though it's just this group of 20 older humans in Taiwan who are the most important humans in Taiwan, whatever they are.
The overwhelming fraction of the country's GDP, water usage, electricity usage.
They talk about the Silicon Shield.
They all view themselves as inheritors of Morris Chang's sacred legacy.
I vividly remember visiting Science Park more than 20 years ago and talking to them.
Do you think you could catch Intel?
And they said, this is such a beautiful dream, but it's a dream for our grandchildren.
And they did it, partly because of Intel's self-inflicted wounds.
They think very differently.
One reason Jensen flies over there so much is he wants them to expand capacity.
I do think it's wild that Jensen has never had a contract with Taiwan Semi.
They do business on what seems fair in handshakes.
Just fascinating.
No contract.
It's going to be fair over time.
We're partners.
We're going to be fair to each other.
The truth is based on every prior market precedent for a foundational new technology like AI.
You've always had a bubble.
Carlotta Perez wrote this great book about this.
Markets are efficient.
They correctly understand that this is a foundational new technology.
There's what Mobison calls a breakdown in diversity.
Everyone becomes bullish on this new technology.
And I am beginning to worry a little bit about a diversity breakdown.
And then you get a bubble.
That bubble funds the build-out of this new technology.
But supply gets ahead of demand and you get a crash.
And it's a particularly severe crash if it's a debt-fueled build-out like the year 2000.
And one thing that's really good about the current build-out is it's still overwhelmingly funded out of operating cash flows, which is a really important fundamental difference versus the year 2000.
Has this valuation?
Has this the fact that every GPU is running at 100 utilization, when 99 of fiber was unutilized?
So there's all these fundamental differences.
History doesn't repeat, but it rhymes.
And, as an investor, we have to be very cognizant of it and recognize that, based on the last two or 300 years, you know, forget the internet bubble.
We had a railroad bubble, a canal bubble, every kind of bubble, South Sea bubble.
We should expect a bubble.
That's terrifying.
Nobody wants a bubble.
And the reason it's terrible is if you're valuation sensitive, you like massively underperform.
You get fired by probably all your clients.
George Vander Heiden, who is no longer with us, great Fidelity portfolio manager.
He fought the bubble in 99 and he retired in early 2000 because I think he just couldn't take it.
He knew it was wrong.
His clients were deeply skeptical.
George, you're out of step.
He had white hair.
He's a truly great man.
I only overlapped with him briefly but he was a very important mentor and friend to my good friend and mentor, Jennifer Urich.
So I have a lot of van der Heiden DNA through her.
He was the same person who said being early is the same thing as being wrong.
George retires because he can't take the underperformance and he can't take clients saying what's wrong with you.
You don't get it.
And he has like 40% of his funded tobacco, 40% did home builders.
And literally, he probably outperformed the Nasdaq by like 20 or 30x over the next three years.
And I have been optimistic that this fundamental shortage of wafers, which really today is controlled by Taiwan Semi, will prevent one
If Taiwan Semi did what Jensen wanted, I think NVIDIA could sell 2 trillion of GPUs in 26 or 27, maybe 25 trillion, maybe 3 trillion.
But there is a limit where consumers would consume so much, they probably would be in an overbuild.
So Taiwan Semi...
If we don't get a bubble, we need to throw a party for them, because they will have single-handedly prevented a bubble.
You are starting to see companies go to Intel and Samsung.
Let's just assume TSM stays super supply constrained versus the latent demand.
What happens?
The history of markets is I don't know who, but one of Intel and Samsung.
They're not going to stay disciplined.
They will break.
And then at some level, that will force everyone else to break.
I think a lot of this may come down to the degree to which Taiwan Semi can maintain a lead over Intel and Samsung.
You got to remember, it's whatever it is.
It's 9, 12, 15 months.
The leading node at Jumin.
Exactly. the pace at which they expand capacity.
If I were to watch one thing to understand where there's a bubble, it's Taiwan Semi's capacity decisions.
And I think there's a Goldilocks zone where they expand enough they make it hard for Intel or Samsung to really truly emerge as like a at-scale second source with something well north of 30 market share.
And yet they also keep this fundamental constraint on wafers that helps us avoid a bubble.
And then obviously I think the TerraFab is going to play into this too.
Say more about that.
It's a SpaceX, I believe Tesla's involved as well, joint venture to build the world's largest fab.
Here in America, I think they're going to be successful.
One.
They have a partnership with Intel, which is very important because they're getting access to 50 years of institutional knowledge.
That's just nine months, a few quarters, 12 months, three to five quarters behind the front.
That's an advantage, also an advantage that I believe the TerraFab is going to get attention from the A-teams, all the semi-cap equipment companies.
One big reason Taiwan's semi caught up is ASML and KLA, Tincor and LAM, Research and Applied Materials.
They wanted them to catch up.
They don't like having a monopsony.
The A-teams were in Taiwan working until made some mistakes.
And presto.
So the A-teams will be here because of Elon's reputation in hardware engineering.
And then to a degree that I think is maybe hard for people to imagine in America, where politics has replaced religion.
I think because Elon had his foray into politics that makes it hard for some people in America.
To see him clearly, which is sad because I do think he's probably doing more for America than any other American.
He's single-handedly bringing manufacturing back to America.
He's revived defense tech.
I think SpaceX is in some ways the most important defense contractor in America.
He's doing a Starlink.
It's amazing for the world.
He's creating all these blue-collar manufacturing jobs, which is like a goal, I think, of a lot of liberals and good for America.
He's done more than any living human to decarbonize the world.
And if you are upset about data sitters on Earth for environmental reasons, well, here you go.
Yeah.
It's sad, but he is a living deity in China, Taiwan, South Korea, and Japan.
Having watched him for a long time.
What he's going to do is they're going to recruit the best people, because the best engineers want to work for Elon, especially in hardware engineering.
He's going to recruit incredible engineers.
Next to TIRFAB, there'll be a Taiwan town.
Oh, these are your favorite restaurants?
I'm gonna move them and their whole staff from Taiwan to Texas.
And we're gonna make everything the way they like it.
And then we'll have Japantown, same thing.
Then we're gonna have Koreatown.
We're gonna have all these things exactly dialed to recruit the best engineers, And that's just not the way that the people who run Intel at Samsung think.
So he's going to have the best talent.
He's going to have the A-teams at the wafer fab equipment companies.
He has Intel, which is important.
It's so good for all of any administration's political goals.
And I think it's different enough that it will not alienate Taiwan Semi.
And these have long lead times, right?
TerraFab is going to be pumping out whatever GPUs, whatever chips, quite a long time from now.
We'll see.
Elon tends to do things differently.
Everybody else has taken three years to build a data center.
He built one in 122 days.
SabSaga had to give him an office.
And they're fab in Texas because he was so unhappy about like the pace at which they're expanding and building.
Are you surprised by you mentioned DeepSeek earlier.
The simple reaction to that was okay, these models are just going to get 95 as effective for some tiny fraction of the cost to still Chinese open source models.
We'll use these for most of what we want to do.
Fast forwarded a little bit of time, two years from now.
There's no reason I have to spend a million dollars a year in my small little firm on tokens or something.
But then the actual reality seems quite different than this.
And I'm curious why there's that dissonance in your mind.
I do think it's fascinating the returns to the frontier, all the economic returns to AI at the model layer.
Not all of them, but an overwhelming amount of them have been at the frontier, which is surprising to me.
And I think it's been surprising to a lot of people.
This is one of the most important questions to be answered and you need to have a hypothesis on it as an investor.
Are frontier tokens going to continue capturing the overwhelming majority of economic value created at the model layer?
And it is surprising.
I remember when Gemini 3.1 Pro came out.
It was mind-blowing to me.
It was so good.
Today, it's intolerable.
There's probably a little bit of a dynamic where companies prototype with frontiers.
Then when they put something into production you're hearing a lot of people do use Vertex or open source.
But still it is a fact today that the overwhelming majority of these economic turns come from frontier tokens.
And that's surprising.
And whether or not it continues, I think is a very interesting question.
And I'm much more open-minded to that, having had the experience I've had with Gemini 31 and then Opus, and then I do use Grok 43 a lot.
It is on the Pareto frontier.
The companies that are on the Pareto frontier are and this is, by the way, a big change and a consequence of what we talked about last time Google losing their per cost token leadership as a result of making very conservative design decisions, with TPV8 to try and take it away partially from Broadcom, and NVIDIA continuing to make aggressive choices.
But Google dominated the Pareto frontier, the Pareto frontier being intelligence versus cost.
And I think this is the most important thing to look at to analyze AI labs.
Google dominated that nine months ago.
Every point on the Pareto frontier, OpenAI, XAI, and Anthropic were inside of them.
Now the Pareto Frontier is dominated by Anthropic OpenAI, and then Grok 43 is on the Pareto Frontier.
It's clearly the best, lowest cost, 500 billion parameter model.
And then Gemini 3.1 is hanging on to the Pareto Frontier.
And if I were to bet, I would bet that they're subsidizing that out of pride.
I would just say a violation of Richard Sutton's bitter lesson is for sure the biggest risk to this trade, to all of AI.
The closer someone is to AI, the more skeptical they are that this will occur.
One thing I think contributed to weakness in March was, you know, a much more stupid version of DeepSeek, which was this thing called TurboQuant.
And TurboQuant is some Google memory optimization that was written up in a paper a year ago.
And then, during the middle of an agreement, while Google was negotiating with Micron, Samsung and Hynix to sign some LTA that would lock in really high prices for a long time, they released this.
What people do is always more important than they say, and they just kind of publicize it on X.
And it goes viral.
Like, oh my God, DRAM is cooked.
There's this DRAM optimization.
I was unable to find a single AI engineer on planet Earth who believed that TurboQuant would have any impact on DRAM demand.
But nonetheless a violation of Richard Sutton's bitter lesson more compute will always outperform human algorithmic ingenuity.
More compute and data, chinchilla optimal, beyond chinchilla optimal, I guess what people increasingly do today.
That's a real risk, man.
The people who are building these models are skeptical of that risk.
The reason I am a little less skeptical is I think we're very close to ASI.
And who knows if the bitter lesson holds for 400 IQ models.
Maybe we get a temporary period where these, if you get to ASI, the first thing it wants is probably to be smarter and have more resources.
How does it do that?
It makes itself more efficient.
I think that is an actual risk.
The bitter lesson literally, I believe, includes humans in it.
So we're about to find out whether the bitter lesson we'll find out if it applies to 300 IQ AIIs, then 400, then 500 and 600.
And at some point we may have like a temporary violation of the bitter lesson based upon AI and ASI.
So I'm curious how you think about some other parts of the innovation around the model, continual learning and memory being two that people seem to be most focused on as things that might create yet another new paradigm that we would enter.
What do you think about the role of those two things?
Yeah, well, I think we've done a lot with memory through these harnesses.
And it turns out that harness engineering is not as important as the model, but it really matters.
And these harnesses and these models are increasingly being co-developed.
One of the big things.
A harness does what you just think of as like a runtime that the model operates in.
And it knows where the tools are.
It creates context, memory, state, has very specific prompts or instructions.
It makes a huge difference.
Even simple versions.
It makes an incredible difference.
I think the last time I was on here, or one of the other times, I just said like hey, as an investor, it's very important that you pay for the 250 a month version to get your own intuitive sense.
That's no longer possible.
To understand what Frontier AI is capable of today, even for a non-coding use case, you need to have cloud code or Codex.
And you need to be on an enterprise plan.
And the reason for this is and this is another dynamic that's enabled by Google losing their cost leadership.
Is these AI models just shifted to usage-based pricing?
And if you're on that 250 or 300 or 280 a month plan or whatever it is, you are getting severely rate limited.
You are getting a lobotomized version of the AI.
Because like we talked about, Claude now produces 70% less tokens.
You want the tokens that Claude and its harness really think it needs to produce to get you a good answer.
You need to be on a usage-based plan.
And by the way, this is...
So bullish for AI.
If we go back to 5207, cellular had been a great growth industry really for the last 10 years.
And the reason was you had a combination of fixed pricing.
You had 900 minutes for whatever it was, and then usage-based pricing over that.
When did cellular stop being a great growth industry?
When everybody just went to all you can eat.
And by the way, long distance is the same thing.
AI is just shifting from all you can eat to pay by the drink.
And it turns out people really like to talk to their friends long distance.
They really like to talk to their friends on the phone.
And people really like to use AI.
And particularly now that one person can have 100 agents working.
So I think the shift to usage-based pricing is probably why you will see OpenAI and Anthropic exceed well over 200 billion in ARR this year.
Not only is more compute going to become online, but they're going to be able to push frontier token pricing with these usage enterprise models.
It's sad.
It's sad for the world because it just means if you can't afford that, you're not at the frontier.
And I think it's going to throw off a lot of investors' intuitive sense of the capabilities of AI.
But yeah, continual learning, man.
I mean, if we solve that.
How do you conceptualize that?
AI is constantly updating its weights.
I mean, it may end up being something different.
There's so many mysteries about the human mind.
We're such sample efficient learners relative to AI. many orders of magnitude.
Now we have a crude variant of continual learning today, when something is verifiable and that's just reinforcement learning during mid-training.
Continual learning is a model that dynamically adjusts its weights or adjusts in some way in real time.
Like as a human, The first time I put my hand in a fire.
I've learned I never put it in there before.
That model today needs to put its hand in the fire a million times and then have the designers effectively put a fire in the next training run or an RL gym for it to learn.
I think it has to be dynamically updating the weights.
But I think people are working on really smart techniques beyond this.
But if we get that, then we have a really fast takeoff.
And people seem confident that continual learning is kind of just around the corner.
And I do think...
This is like the third big question.
Bitter lesson violation as a result of ASI are less likely human ingenuity.
Will frontier tokens still command the premium they do?
And will we get continual learning?
And if so, when?
What is the role of new chip companies in all of this?
We talked a lot about Nvidia, their relationship with the SMC and Intel and all these sorts of things.
There's a thousand flowers blooming.
I think literally probably a thousand flowers blooming trying to create a new chip to address some part of this bottleneck.
I'm curious how you process this space, this opportunity, what role it will play.
So I think this is good and healthy for the world.
It's good for Jensen, too, because a different administration might take a different view.
Competition, I think, is good for everyone.
And seeing different architectures explored is good.
And the reason is, in tank design, they talk about the iron triangle.
The iron triangle of tank design is that all designers of a tank.
They have to make trade-offs between attack, Defense and mobility, for obvious reasons.
The more defense you have, which is your charmer, the heavier the tank is, the less mobile it is.
So you have to live in this triangle and make trade-offs.
The Merkava in Israel is optimized for defense.
Russian tanks, unlike the Leopard, are generally more optimized for mobility.
Chip design is the same.
There are these fundamental constraints imposed by the laws of physics.
Has embedded in the Taiwan Simi design rules that you need to live with it.
You have TPU, Tranium, and AMD, which are all essentially trying to be a better GPU.
And today, I think probably Tranium is doing the best.
Nobody's a better GPU.
But Tranium is tugging on Superman's cape.
They haven't started yet.
The Tranium 3 needs to ramp into production because it has a switch scale up network which you really need to economically inference MOE models.
A lot of companies have a Taurus architecture.
That's where Google was.
Google's developing a switch scale up network.
And then AMD is like always kind of flying a little bit behind.
Yeah.
Yeah.
AMD, we'll see.
The MI450, we don't know yet.
We'll see.
We probably know more about Trinium III than the MI450.
But that's a hard game to play.
So you have to do something different.
And you have to do something different that is also hard to do.
So I think the best path for these startups.
My rule of thumb is 1.
Market share is going to be worth 100 billion.
100 billion is a pretty good venture outcome.
I think what Jensen would say is okay if somebody does something different and it gets to one or two or 3 share, we'll make that chip.
And that's coming for everyone.
But if you're trying to make a better GPU, good luck.
If you are doing something different, it also needs to be hard to do.
And you can make different trade-offs.
The disaggregation of prefill and inference really have opened the aperture for making these different trade-offs, because you can make very aggressive trade-offs for decode, aggressive trade-offs for prefill.
Prefill being taking in the context, decode being, you know, write the output.
Yeah, I have a great colleague named Andrew Fox who said pre-fill picture a British naval ship from the 18th century.
Pre-fill is loading the cannon, decode is firing.
And what pre-fill literally is, is just the model understanding the question, the prompt and then keeping track of its own answer.
And that is fundamentally a memory capacity bound problem.
Decode is the process of generating new tokens, and that is memory bandwidth constraint.
So if you're a chip designer, this gives you a richer canvas to paint on.
But even so it needs to be hard because if you make different trade-offs in that iron triangle to optimize for memory capacity and they're not hard trade-offs to make NVIDIA is going to make those same trade-offs.
They get better prices from Taiwan Semi than you're ever going to get.
Good luck.
And they have the advantage of working with every model company. and optimizing their designs.
By the way, another very funny thing is there's this process.
If you're a VC and you're investing in semiconductor company, that is telling you they are going to have an advantage because of a Taiwan Semi process that they have special access to.
I promise you the Jensen's saw that process When it was a twinkle in Taiwan Simi's eyes.
They know more about it than this little company with 200 people can imagine.
Taiwan Simi, everybody in the supply chain is showing Jensen everything.
The same way they're showing Amazon everything, AMD everything, TPU everything.
And that's another reason, don't go try to make a better GPU.
So you can do something different.
You can paint in the pre-fill canvas.
You can paint in the decode canvas.
But you also have to do something hard because if it gets to scale, you're going to have those four companies as very fast followers.
My firm was a venture investor in Cerebrus.
What Cerebrus has done is something hard and fundamentally different.
Way for scale computing.
It comes with a set of trade-offs.
But that architectural decision they made was hard and lets them do something that no one else can do.
And we'll find out how big that is.
They're working on really cool things.
One of the problems Cerebras has is once you start needing to glue a lot of chips together and scale up networks or scale out networks, you need a lot of IO, and IO is bound by what's called the shoreline, the sides of the chip.
Cerebrus has an overwhelming ratio of on-chip computer memory relative to shoreline IO.
Well, they're really smart people.
They did something really hard.
They're trying to see if they can put an optical wafer right on top of that.
And then that solves that problem.
I'm sure they're looking at hybrid bonding of DRAM to get around the much discussed on X, these alleged limitations that are not true.
A 3D machine can theoretically run any size model.
There are sizes of models where they're much better than other sizes.
So Cerebras, what I think is interesting is they did something different.
That's hard to do, really hard to do wait for scale computing.
I do think there's a role for these.
I would just encourage them all, make a different trade-off, try and do something hard.
Everybody's gonna get funded after the Cerebras IPO.
It's not gonna be a problem, but it took Cerebras three generations of chips To get it right.
Andrew Feldman, the CEO.
You can just see how hard it was what he did and that whole team did to get where they are today.
And they need to have the grit to do that, the resilience.
This first chip is a failure.
It happens.
Can you come back and make a second chip?
But the one last thing on this topic is this is going to be amazing for the useful lives of GPUs and may single-handedly save private credit.
Say more about that.
What do you mean by the private credit?
We'll just...
Private credit, they're in pain from these SaaS loans.
And however much they're marked down, they probably need to be marked down more.
Because if the public companies are struggling to adapt, how's like a debt-laden company going to adapt and invest in what is a very different margin structure business?
There's a lot of private credit in GPUs too.
And they were underwriting that to, I think, three or four years.
The disaggregation of inference means that I think these GPUs are going to have 10 or 15 year lives.
The AI skeptics are like, oh, these companies are all cooking their books.
The useful life of GPU is only a year or two.
The useful life of CPU is only four years because the rapid technological change.
No, what rapid technological change has done with the disaggregation of pre-fill and inference is mean that you can put a Cerebra system or Grok LPUs that NVIDIA acquired effectively in front of a hopper or even an Ampere.
Use that hopper and Ampere for prefill and extend the useful life of that GPU until it melts.
They do melt, so they have a time, but maybe you don't have to run them has fast.
This is going to be really good for the whole private credit industry.
It's going to help finance the AI build out.
Because if you can start to finance GPUs at more like 5 or 6 instead of, I think Corby's lowest financing was like low sevens.
That actually mathematically changes the cost to finance this build-out.
We have this technological innovation that's going to lower the cost of financing, extend the useful life of compute on earth.
And then I do think the one last thing that's interesting about that is my friend Jamin from Kotu just did a podcast and Kotu had a deck.
And they talked about hey, sellers of shortage are doing so much better than the buyers of shortage, buyer shortage being the hyperscalers.
But if you own a giant installed base of what is currently in shortage, that's also a very good place to be.
And we're hearing CPUs are way more important than they were in an agentic world.
They do all these things around orchestration, tool calls.
The biggest CPU fleets in the world sit at the hyperscalers.
Some of these hyperscalers may catch up a little bit to the sellers of Shortage.
I want to talk about this idea of different and hard applied outside of the infrastructure piece of this.
Now you're starting to interact with new founders, existing CEOs and founders that have to adjust to this new world.
What are you seeing?
The most AI native founders that aren't building chips or infrastructure or models, but just people using this technology to build other stuff.
How do they feel the most different to you if you've observed differences?
I do think this is just for chip design.
To me, it's always been a fundamental question for venture.
So there are different ideas that are obvious to everyone on planet Earth as soon as they hear it.
And if that's where you are in venture...
If it's not hard to do, if it becomes obvious to the world before you have built scale.
Scale is the ultimate advantage.
You're in trouble.
And the great thing Amazon had was.
I think it was obvious to a lot of people, but it wasn't obvious to the retail CEO.
Amazon, they were very smart.
Any e-commerce company that VCs invested in, they would destroy.
They'd be like, oh, that's so cute.
We're going to take our margins of that to negative 10,000%.
And that's why the guys at Wayfair, they did something hard.
Amazon tried to kill them and they failed.
Those were like tough, operationally really competent CEOs.
For me in venture I always look is this going to be obvious to the world before this company could build scale?
Or is this both not obvious, different, and really hard to do?
I think a lot of founders are really struggling with this. in AI.
I think people are becoming worried today in Jensen's five-layer cake of AI.
The profits are accruing to energy, accruing to data centers, accruing to chips.
They're accruing to models, not really accruing to the applications.
I think cursor and cognition got to a scale.
They focused on coding.
18 months ago, the people were focusing on coding.
OpenAI was doing everything under the sun.
People focused on coding where cursor, cognition, And it was really right to focus on code.
Amjad Massad, the founder of Replit, tweeted something that I thought was so smart.
Just, it was something like bit or less than adjacent is the fact that coding might be the shortest path to ASI and useful AI.
Because if you're really good at coding, you can write yourself code to do anything.
So I think it was really smart of those companies to focus intensely on coding.
They all probably got to a scale where they have a place.
I think Cognition is doing something really, really different.
But I think a lot of founders are really struggling, man.
I think they're trying to get confidence that in nichier areas they can get to them and get like a data moat before the model companies get to that niche, or that it's a small enough niche that the model companies won't do it themselves but it can still produce their venture outcome.
Is this related to what you would call like the token path?
I know you've used that phrase with me before.
Yeah, I think it comes from a guy, an altimeter, Jamin Ball.
But he just said if you're a software company or an AI company of any kind, you have to be in the token path.
So Databricks, that's in the token path.
Comparable companies are in the token path.
If you're not in the token path and you're not in some really niche thing, life may be hard.
And even for these vertical niches.
I think if you talk to the people at the model companies, they're even skeptical of some of these, because all of the data that's being generated in these niches come from humans.
But then you're betting that you're able to use that proprietary data in this narrow vertical to train a model that's lower cost than the frontier labs can ever get to.
And maybe that's a good bet.
But I just think you have to be very, very careful.
Now, on the other hand, if...
The returns to these frontier tokens relative to other tokens come down.
There's going to be an explosion in value creation at the application layer.
And I think another really important point is I have a belief that whenever he wants, Jensen can probably get pretty close to the frontier.
With his own model.
I don't think he wants to do that, but that is what open AI and Anthropic are kind of trying to do to him, unsuccessfully.
He's a very logical thinker.
This is the logical counter move.
You will see that open source frontier, which today consists of Chinese models with stolen American tokens.
Somebody told me that like DeepSeek, maybe the original one was only 150,000 reasoning traces.
There's many ways to launder this if you're a Chinese company.
You can hit all these different APIs.
You can make it hard.
Now the American labs are working really hard on anti-distillation technology, but I just think Chinese open source.
They're doing really impressive things in a very resource constrained way, but there's a lot of distillation.
And this is why I think, in addition to there not being enough compute to serve Mythos, they did not want it to be distilled.
They wanted to use Mythos, distill it themselves, use it to RL their next model, whatever it is, And then I think what they and eventually I think OpenAI, anyone on the frontier will do, is just say there's going to be some very interesting game theory because it's a new kind of prisoner's dilemma.
We talked about the old prisoner's dilemma being just around, like hey, you're in a prisoner's dilemma where you have to spend.
The new prisoner's dilemma is going to be if you were at the frontier, do you release that model via API or not?
If everyone at the frontier agrees not to do that, then Chinese open source.
If one person defects, they're going to have the best model.
They're going to have a lot of revenue and cashflow.
And then of course, resources equal intelligence.
So they'll start to pull ahead and then that will lead to everybody else releasing it.
So it's a new game theory.
It's kind of the same game theory that you have with Taiwan Semi, Samsung and Intel.
The reality is, if a company like NVIDIA or AMD were to ever really really use one of these other foundries, that foundry would get better really quickly.
So I do think Jensen is going to keep open source a certain timeframe behind the frontier.
I think that's going to be a very interesting thing to watch.
And then by the way, open source gets monetized.
There's this misnomer that open source is free.
Open source tokens.
They cost energy to produce.
You need to make up on GPUs and the open source model companies almost always get a revenue share.
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How are you preparing Atreides for the world of Mythos 3, Mythos 4?
We're just trying to overinvest in cybersecurity.
And I really believe everybody needs to have a safe word.
Everybody needs to go.
Leave your digital devices behind, literally go to the ocean and have a family say for it or a company say for it.
And it can't be one that can be like socially engineered.
And this is just to avoid cyber crime, where what looks like your son or your daughter or your grandparents or your parents or whatever FaceTimes you.
It's an utterly accurate simulation of them.
They know everything and can extrapolate based on what they're likely to say and says you know, wire me a million bucks.
So doing everything we can with cybersecurity.
That's defensive.
What about analytical or processing?
What will you still be able to do that it won't be able to do, I guess, on the analytical side?
So it's a good question.
I just watched The Last Samurai and I asked people at my firm to watch it.
And The Last Samurai, if you haven't seen it, I highly recommend watching it.
It's actually a movie that's aged really well.
It's a Tom Cruise movie from 20 years ago.
You know, the conceit is, Tom Cruise is this bitter washed up Civil War veteran who's actually a very good soldier.
And he's bitter and washed up because he feels like he participated in negative actions against the Native Americans.
It's during the Meiji Restoration.
And he's hired by the modern elements of the Japanese government to train, like an army of peasants, how to fight the samurai.
There's a first battle.
Of course, the samurai win, even though they don't have guns.
He fights valiantly.
So the samurai decide not to kill him, take him to their village.
He becomes a samurai.
It feels like the Civil War to him.
So he fights on the side of the samurai at the end of it.
He's massacred by a peasant with a machine gun.
The machine gun is here.
If we do not all become masters of the machine gun, where are you a master?
So I am trying to become a master of the machine gun.
And then I'm optimistic.
There's a long period of time where, just like if you were a 50-year-old samurai veteran of many wars, I fought many wars, Master Dwarf.
You will have advantages using the machine gun.
I'm optimistic as a lifelong student of investing.
I'm going to be able to master the machine gun, this new technology, integrate it into my own process, integrate it into our firm's process in ways that let me contribute value as a human being for a long time.
Like everyone, I have agents running all the time now.
What's your most useful agent?
My single most useful agent is a really good summary of the points that would be interesting to me from podcasts.
There's just six hours a day of stuff that I feel like it's in my job description to watch.
Every time somebody from OpenAI XAI Google Cursor Fireworks, Base10.
I say nothing of Jensen, Elon Dario.
I feel compelled to watch and I just don't have that much time.
There's some real needles in haystacks.
That is what I would say for me is the most useful.
I do think there's a set of things that I always like to see.
Like I'm very sensitive to management compensation.
What are they incited to do?
Do they have stupid RSUs or do they have PSUs?
And if they have PSUs, what are those PSUs incentive to do?
And we now have... systems that do a very good first pass at that.
That saves people a lot of time.
It frees them up for more creative work than like going through the proxy, pulling the PSU thing, looking at how it's changed versus all the proxies, because there's signal in that.
And that's very labor intensive and that's so good for an AI.
And there's obviously all sorts of same things within investing.
Pressuring the organization in those ways, I think has been helpful.
This is the most exciting, thrilling time to be an investor.
I'm getting a little bit worried.
The diversity breakdown thing?
Yeah.
Say just like a little bit more about the kinds of people that are- I don't know anyone like me who's not really bullish on DRAM.
There's all these interesting things happening with AI right now.
One is cross-sectionally, the valuations do not make sense.
They just flat out do not make sense.
They cannot all be true.
In other words, you have semi-cap equipment companies trading at 40 times next quarter's annualized earnings and DRAM companies trading at mid-single digit.
At the peak of the last cycle, that was 5 versus 12.
At one point, it was like 3 versus 45.
Those can't both be true.
And yes, semiconductor CapEx business models have improved more than the memory business models.
We don't know how much HBM is going to improve memory business models yet.
Yes, they have some element of recurring revenue with parts and maintenance, but it's not worth a thousand percent multiple gap.
I think it's hard to square.
The valuation of something like NVIDIA, which is still in early April, was essentially as cheap as it gets relative to the market, like in the last 10 or 12 years or whatever it is, and very cheap absolute.
It's very hard to square that valuation with something like GE Vernova's valuation, because it builds in an unfathomable amount of share loss for NVIDIA.
So valuations cross-sectionally are really different.
Because we are in shortages, the lowest quality companies are doing the best.
So if you're an oil and gas investor, a mighty investor, natural resources investor, and you're well-versed in thinking of costs, this is very intuitive to you.
And a real bull market for a commodity.
The commodity suppliers with the highest costs go up the most, because it's the most beneficial to them.
They go from on the verge of bankruptcy to gushing cash.
And this is, I think, one reason.
Commodity investing is really really hard because quality outperforms during the cycles, but you get all of the outperformance during the downturns, when the high-cost guys that mooned during the shortages and the commodity bull markets go bankrupt or whatever.
You're seeing that happen in every industry.
The lowest quality players, companies that are hated and detested by the hyperscalers and the buyers because they have high costs.
They're unreliable, the parts fail at a high rate.
They're sold out and raising prices.
And then that activity gets the interest of these retail accounts on X and these stocks get bid to the moon.
Whereas some of the higher quality expressions have like, actually really underperformed as an investor.
It's hard because you know, within a shadow of a doubt, that that thing that's mooned 10x in three months or six months is going to go right back down, subject to what they do with all the cash.
And so it worries me a little bit that people who were very skeptical a year ago are no longer skeptical.
But then I just contrast that with the valuations of these high quality companies, which are just not extended.
And it makes me feel better.
I thought it was funny in 24 and 25 that anyone asked about an AI bubble or talked about it.
You have this nuclear bubble and this quantum bubble right here, right in front of you.
What are we talking about?
This is so real.
Some of that nuclear quantum silliness is maybe spread into more speculative, lower quality, smaller cap names where if you have a big presence on X or Reddit, it's easy to move them.
And that frightens me a little bit.
But I just wish there were more AI bears.
I wish there were more memory bears.
Astera is a stock I've been close to a long time.
There's a lot of bears on that.
I love that, great, I first invested in the Series C. Good luck thinking that's a copper loser.
And then there's also, you can feel the baskets in the market and the leverage baskets.
And what baskets you're in is really important.
You know, copper, optical, DRAM, NAND.
And a very interesting thing that's happened this year is in 24 and 25 the AI trade traded together.
You could be long GPU compute, scale up networking and optical scale across and short power or whatever it was that trade worked from.
Like a risk management sense.
Cause you know, I am very factor aware that all blew out in January of this year.
Scale-up networking would go crazy while scale-out was going down, or DRAMs massively underperforming NAND and HDDs, which had not happened.
So these cross-sectional correlations within AI really fell apart and you had to get very fine-grained.
You couldn't hedge your memory anymore with some semi-cap equipment or NAND.
Everything cross-sectionally really changed in a very interesting way in January.
And I think maybe one reason for that was AI got to a quality where it was all of a sudden really easy for a bunch of people to get really smart on these different subsectors, start trading them and then they get put into baskets and those baskets influence- AI, creating price efficiency yeah.
Yeah, exactly.
I think some of the biggest opportunities outside of these higher quality names that I think can compound for a long time and they're safe, unlike these low quality names which are terrifying is in names that are miscategorized.
Estera was in a lot of copper loser baskets.
Astera, their biggest product is going to be a switch.
You use both copper and optics to connect switches to accelerators.
Definitionally, if you're a switch company or an accelerator company, you cannot be a copper loser because you're going to be on the other side of that connection.
I wonder if you could riff just for like a sentence or two on each of the major companies.
Google, Microsoft, Amazon, the major players that are public.
All the conversation is centered around these exciting new companies.
Maybe run through them and riff.
Google was incredible last year because they had that TPU advantage, which is now gone.
The reason I think they're still in a great position is just they have the most compute of everyone.
We talked about the value of installed bases being higher as a result of shortages.
They have the biggest installed base of compute.
Google IO is this week.
If they don't release something that even slightly leapfrogs OpenAI and or Claude.
That's interesting.
And it's not a disaster for Google.
It's just interesting.
And it just means this NVIDIA effect we discussed is even more powerful than maybe I'd imagined.
But I'm very curious to see what the Pareto frontier looks like literally in five days after Google's announced its new stuff.
This is a big card for them, but Google, between the amount of data they have and the YouTube data, is actually really genuinely valuable.
It is valuable in a world of robotics.
The amount of compute they have, the search business they have, Google's never not going to be in a good position.
And then you see that with GCP going crazy.
You got to give Zuckerberg and Mintz credit what he's done in terms of making Meta an AI first company internally.
And I do think he is the only one of those true internet giants to have done that.
I give him a lot of credit for that.
I give him a lot of credit for paying up when he did for the billion dollar contracts, the talent.
And Muse, I think, was a really big upside surprise.
It was the first model from MSL.
And it's not on the Pareto Frontier with XAI, Google's one entrant, and then OpenAI and Cloud, but it's pretty close.
That was very impressive to me.
So I think Meta's in a better position, still not as strong of an absolute position as Google, but like they're better positioned.
Position and rates of change matter more than level, as you know, in markets, particularly over short three-year timeframes.
Over long timeframes, the level of competitive advantages tends to dominate.
But even within that, changes really matter.
Amazon, I think, is in a really strong position because of Tranium.
I do think you're going to see real PL efficiencies from robotics over the next 18 months in their retail business.
I actually think Nova, their internal models are not where Muse is, but they're better than they get credit for.
Then Microsoft, I like Satya.
I admire him.
I think he's an exceptional CEO.
And I give him a lot of credit for the decisions he's made.
But he did go from we're going to make Google dance to being the product manager of Copilot in like three years.
I would love to know during the coup attempt against OpenAI, does Satya regret his decisions?
Does Satya wish that he had supported Ilya instead of Sam and that Ilya and Mira were really running OpenAI today?
In his heart of hearts, I would love to know.
Because I think the Microsoft OpenAI partnership might look very different in that world.
I think that's a very interesting question that we'll never know the answer to.
But I give him a lot of credit.
What he is doing now, he's taking risks.
This goes to.
The decisions you have to make in that cone of uncertainty are not only how much you spend, but what you're going to spend it on.
I think Microsoft flinched for like a moment in early 25.
They have this algorithm.
We spend this much CapEx dollars.
We get this return.
That algorithm was kind of off.
And if you flinch, you lose position.
You lose all these allocations.
And it's difficult to get it back.
So they flinched.
And now the decision Satya is making, which the market has punished him for but I think is the right decision.
I mean, who knows how fast Azure could be growing if they're willing to just sell GPUs to open AI.
We're going to use our compute internally to make our own products better.
One reason Copilot is so bad or has been so bad is just not enough compute available.
They're fixing that.
He's the product manager at Copilot.
I do think he's a great CEO.
They're trying to use their compute to train their own models.
I am a little skeptical that they have the right team to succeed there but, just like Beta, they can afford to hire maybe a different team.
But I think he's making good decisions that are risky decisions to position Microsoft for this world where frontier models are no longer API accessible.
And I think it's a really courageous decision that I give him a lot of credit for.
And he is foregoing.
I mean Microsoft probably be an 800 stock today if they were using their GPUs to serve solely OpenAI and Anthropix capacity, instead of using them for their own products.
So I give him a lot of credit for making a great decision.
I think what's really interesting is the degree to which these companies are outward facing in their decisions.
The two companies who are the most deeply engaged with startups are Amazon and Nvidia by a mile.
Then there's a really intense engagement with Google, their next most intense.
Broadcom is engaged in a different way.
They're just everybody's favorite ASIC supplier.
If you're a startup, it's considered like a level up if you get to work with Broadcom for your second gen chip.
And it's considered manna from heaven if Broadcom works with you for their first gen chip.
And then you see essentially zero engagement with startups from AMD, Microsoft, and Meta.
When I say zero, it's a little.
And I just wonder about that decision because some of the best teams are no longer at big public companies.
They're at these smaller startups.
And I think it's going to end up being a pretty big advantage for NVIDIA AMD, Google right behind them to have this engagement that you just don't see from these other hyperscalers.
As we wrap up, I'm curious for you to riff on any other out there knock-on effects that you've started to think about for this giant trend.
We've talked about the specific companies in a lot of detail that this most impacts.
We talked a little bit about the application layer and what would have to happen for there to be more value occurring to that layer of the stack.
I'm curious any other just fun knock-on things that you've been thinking about as this world changes so quickly.
And it is wild.
I mean, at the application layer, forget value accruing, just value has been destroyed.
AI has net destroyed.
Even if you count cursor cognition, the most successful AI natives, trillions of dollars of value has been destroyed by AI at the application layer.
And, just in this context, the companies that are doing the best today, that are seeing their values increase the most, that are creating economic value, are the companies with the highest effective ratio of utilized GPUs per human.
Maybe this just means that every human is going to get a lot of GPUs.
But I think that's an interesting fact that we kind of need to be cognizant of.
I will just say, and maybe this is a little dark, I am more and more worried about personal safety.
And I worry about this a lot more for people who have a much bigger public presence and are much more associated with AI.
I hope nothing tragic happens.
There is this upsurge in political violence here in America.
And as AI increasingly becomes political, I worry that's going to get directed at more and more AI political leaders.
Whatever I may think or may not think of open AI, I think it is terrible that someone threw Molotov cocktails at Sam Altman's house.
I am worried that we are headed into a higher variance, higher beta, higher risk world because of AI.
And that's for me as an individual and then for people who are big players on the chessboard.
Think about what it means geopolitically.
We're watching the Ukrainians are really starting to win.
And the reason they're winning, I think, is not really because they have better drones.
I think they do have better drones.
That's part of it.
I think the reason Ukraine is really winning is they have the best battlefield AI outside of probably America and Israel.
And has China, has our adversaries begin to process that?
How do they respond?
If the United States because of its edge in AI.
It's great if you're America, but it is destabilizing for the rest of the world.
Something I think a lot about is creating a charity to just educate the world on how awesome the West has been.
Slavery was endemic to essentially almost every civilization, and slavery was really ended by the British Empire.
Tell that story.
But America, after 1945, we had the nuclear bomb.
No one else had it.
We could have controlled the world forever.
Instead we rebuilt Germany and Japan, who were America's most reliable allies Israel, South Korea.
That's a testament to like the American spirit in our country.
We didn't take over the world.
There were these fears that were documented at the time that the American generals MacArthur was a little bit of an American emperor in Japan.
We're just going to take over the world.
And they could have.
And they didn't.
They came home.
We demilitarized.
And then you had this period of great global stability between, you know, it was scary.
They were terrible.
Pax Americana.
Yeah, you had the Pax Americana.
So maybe it's not destabilizing.
Maybe it leads to another Pax Americana. informed by our AI dominance.
And I'm so optimistic that AI is going to be amazing for the world.
There's someone like me whose daughter was diagnosed with a very rare disease.
There's no cure.
He was able to assemble a lot of resources.
He was able to get a lot of compute from the labs.
We were made aware of what was happening, spun up an immense amount of agents, came up, using AI, with a drug on the market that can actually impact his daughter's disease, and then has spun up a company to cure it.
Her life is already immeasurably different because of AI.
So I'm like an AI optimist maximalist, but I also just acknowledge it's like an event horizon.
It for sure, I think is going to be a discontinuity.
We need to navigate as society.
I think the Luddites are going to be wrong, but we need to be like, really thoughtful in how we address their concerns.
We need to make sure that it's good for everyone.
Like it is a little dystopian that now the best AI is only available to people with a lot of money.
We need to solve that.
We need to approach this with humility, recognize there's a lot of uncertainty and be thoughtful.
When I do this with you, I tell people afterwards.
I'm like, may you find something that you love as much as Gavin loves markets and companies and capitalism and history.
On display today as always, Gavin, thanks so much for your time.
Thank you.
Thanks, Patrick.
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