Exploring the frontiers of Technology and AI
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For three years, the AI trade has been one simple idea.
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Buy the chips. NVIDIA became the most valuable company on Earth by making these
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chips, and they've made countless of investors rich because of this.
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But this investment thesis now has become overcrowded. Not because AI is slowing
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down, but because there's another problem that has surfaced.
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No one can turn these GPUs on. Trillions of dollars being spent in AI,
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and all these GPUs are collecting dust in data centers.
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The problem, energy, electrical grids, wiring, networking, the transformers,
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the web of infrastructure that sits around a GPU that allows it to keep alive,
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to talk to each other, to transmit petabytes of data between each other.
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This is the next constraint that hasn't been solved yet and where the majority
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of the AI capital will eventually flow.
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Now, there's four physical things that keep a chip alive, essentially.
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The power to run it, the light to transmit all the data between them,
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the silicon wiring between them all, and then somewhere to rent it all from.
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And almost nobody is really talking about this. And on this episode,
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we're going to unpack that specific layer of the infrastructure stack,
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the power layer, and why it's so important going forwards in terms of capital.
Josh:
Yeah, and this may feel a little bit different because we're always talking
Josh:
about whose model is the smartest. That's the common conversation.
Josh:
But once you strip away all that abstraction, a token is really just what,
Josh:
is output and that is just a series of electrons flowing through chips like
Josh:
flowing through fiber and then heat being pulled out of the system and all of
Josh:
that requires a lot of energy and electricity uh
Josh:
the money cycle that we're going to talk about has kind of flowed towards this
Josh:
direction as well i mean everyone started with the crowded gpu trade then it
Josh:
flowed to the semis that exist around them then it flowed to the memory trade
Josh:
and now it's kind of moving over to the
Josh:
seemingly the end game bottleneck, which is energy.
Josh:
I mean, US data power center demand, I think is projected to roughly double
Josh:
80 gigawatts in 2026 to 150 by 2028. So the grid's just not really built for that.
Josh:
And there's no shortage of demand for that. And when we think about energy as
Josh:
an idea, even if you believe that the AI bubble is towards the latter end of
Josh:
it, energy is still something that's not going away.
Josh:
We had energy problems prior to the LLM becoming a big deal from the chat GPT moment.
Josh:
This is just an extension and an exaggeration on top of that.
Josh:
And it creates a lot of interesting opportunities in the marketplace to actually
Josh:
participate in this bottleneck that is now known as, I guess we call it the energy bottleneck.
Ejaaz:
Yeah. And I think a lot of the reason why people don't talk about this is because
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it's sort of unsexy, you know, not everyone is an electrical grid expert.
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Not everyone knows how to network wires between these complex GPUs.
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I'm still trying to wrap my head around the GPU itself, So the fact that we're
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bringing in all these other things, it's quite complex.
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Now, if you were to look at a budget of spending for an AI lab or someone that's
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setting up a data center, only 10% of that budget gets allocated to the power
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and infrastructure side of things.
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The irony of it now is it's going close to 100% of the bottleneck of the entire
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thing. So even though it's not the majority of the cost, still 90% of the cost
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goes into the actual GPUs itself. That's the most expensive stuff,
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the memory and the silicon required to build those GPUs.
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Only 10% is allocated to power, but that is like the main bottleneck that we're facing today.
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Now, the lead time, if you are an AI lab, you could be the richest,
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most important person on earth right now.
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If you want to build a data center in the US, the lead time to get the necessary
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power to your GPUs is five years.
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And there are different sectors within like the power thing.
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It's not just the energy infrastructure grid you need to get access to.
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You need to get lead times for wiring and to get transformers designed,
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custom made and delivered to you. All of that takes a lot of time.
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And we're looking at basically like half a decade.
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So what we've seen a lot of these AI labs start to do now is figure out what
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alternatives they could potentially procure to help them get GPUs online.
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Now, you've all heard the news of Meta, Amazon, Microsoft, and Google spending
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trillions of dollars this year alone.
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I think it was something along the lines of, actually, it might have hit a trillion
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dollars for this year, right? Because I think after the last quarterly earnings,
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they like upped their budget.
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They haven't set those data centers up. In fact, there have been delays.
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Stargate from OpenAI, which said that was their biggest project to kind of get
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compute online, has been delayed multiple times.
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And the point around this is because of compute, and not many people are talking about this yet.
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So I think on today's episode, as we walk down this infrastructure stack,
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and we did a previous episode before, where we covered all the layers of the
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AI infrastructure stack, we
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Talked about going from layer zero, which is the model labs down to layer one,
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which was the hyperscalers, GPU semiconductors, we didn't dig as much into layer
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five, which is the power and infrastructure side of things.
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And that's what we're going to do today. And we're going to do it through the
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lens of four specific companies, starting with Bloom Energy.
Josh:
Yeah. And just before we get into Bloom, there's one thing. Well,
Josh:
one of the things is, I think there's like these bottlenecks within bottlenecks.
Josh:
You mentioned the lead time, but also the actual infrastructure is becoming much more demanding.
Josh:
Vera Rubin, the new chips, I believe, those server racks take...
Josh:
Over 100 kilowatts, which is a 10x increase from what these companies and these
Josh:
data centers were preparing for.
Josh:
And then also before that, I want to kind of loosely define the power for people
Josh:
who are not familiar, because there's watts, there's watts hours,
Josh:
there's gigawatts, it's just like a brief one on one quickly on what everything is.
Josh:
A watt is a rate, it's how fast these data centers draw electricity,
Josh:
a watt hour is an amount, which is the total you drew. So a gigawatt is a billion of those watts drawn.
Josh:
One gigawatt is equal to roughly one large nuclear reactor or enough to power
Josh:
750 to a million U.S. homes. So this is a tremendous amount of data that we're
Josh:
talking about or a tremendous amount of energy that we're talking about.
Josh:
And these data centers are demanding tens to hopefully soon hundreds of gigawatts.
Josh:
Now back to Bloom Energy.
Josh:
Bloom Energy has a solution for this. It is solving the most painful solution
Josh:
in AI, which is this energy data source.
Josh:
They use these kind of modular energy reactors that allow them to allow these
Josh:
data centers to remove themselves off of the grid and generate a little bit
Josh:
more energy closer to the data center.
Ejaaz:
Yeah, actually, remember, the first time I heard about Bloom Energy at all was
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in Leopold Ashenbrenner's 13 filings at the end of last year.
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It was one of his biggest positions. I think in his recent one,
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I think it makes up about 12.7% of his entire portfolio.
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So he's very bullish, this particular company. And the reason why is,
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As I mentioned earlier, the lead times to getting power generation on site for
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your data center, if you go through the old traditional way, is around half a decade.
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Bloom energy will reduce that down to 90 days they have a product it's a gas
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turbine which they can custom build and deliver on site they kind of like fly
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it in and they reassemble it
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and it can generate enough power for you to run your gpus now obviously that's
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A very attractive thing a ton of companies have signed contracts with them now
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the one caveat that i will say is they haven't really delivered this at scale
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bloom energy has yet to prove themselves from a manufacturing capacity and delivery capacity, but it is
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a company that a lot of people like to talk about and kind of speak highly of.
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Now, the revenue kind of speaks for itself as well, because obviously there's
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a lot of lead time here, there's a lot of demand here. And as we mentioned,
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that money is going to flow down.
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As of Q1, 2026, their revenue hit $750 million.
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That is up 130% of revenue year over year, which is just like a crazy kind of swing.
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And of that, they're making around 70 million profit, which isn't large in the
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context of some of these big hyperscaling infrastructure companies.
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But for a relatively small company, this is pretty impressive.
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And Oracle is powering its 2.4 gigawatt data center, primarily using Bloom Energy.
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So they've signed some pretty major contracts as well.
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So Bloom Energy is one of these companies, and there's various different versions
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of this, where it's not just gas turbines, maybe it's solar energy or nuclear
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energy. But Bloom Energy is basically the first company that is having an impact
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almost immediately after this constraint has been identified.
Josh:
Yeah, well, in terms of gas turbines, I mean, that's one way of doing it.
Josh:
And the line to get a gas turbine extends back to 2029. So if you actually want gas turbines.
Josh:
A gas turbine at your data center and you place an order today.
Josh:
And for those who aren't familiar, a gas turbine is basically a jet engine,
Josh:
which is spinning a generator.
Josh:
And it's so funny how this works. It's like the way we generate energy today
Josh:
is the same way we did with like a steam engine way back in the day.
Josh:
You're just basically creating energy to propel a spinning thing.
Josh:
In this case, it's a jet engine.
Josh:
If you want one of those, you're waiting at least three years,
Josh:
probably the next decade. So if you want something in the 2020s, that's not for you.
Josh:
The next option is nuclear or small nuclear reactors.
Josh:
Those are amazing. They're incredible. But if you want them in this decade,
Josh:
again, not possible. Those aren't probably coming online at scale until around 2030.
Josh:
And the licensing and legislation around that is pretty slow.
Josh:
So that leaves us with these fuel cells. And that's what Bloom has.
Josh:
And they're these modular boxes you stand up on site in 90 days instead of three to five years.
Josh:
And so far, people have really been liking them. We've seen that reflected in the stock price.
Josh:
With people desperate for energy, anyone who's able to actually deliver it.
Josh:
A accelerated timeline is going to win. I think about Elon all the time and
Josh:
how they were able to build the Colossus data center and how much premium it
Josh:
had to getting GPUs online quickly.
Josh:
I mean, think about how much Anthropic and Google are paying for these GPUs.
Josh:
The same thing is true with electricity.
Josh:
If a data center is bottlenecked by this power, anyone who's able to provide
Josh:
it for them is going to get paid a tremendous amount of money and a large margin for it.
Josh:
And so far, Bloom has been one of those companies through their fuel cell program
Josh:
that can be deployed in 90 days on site, they're collecting a lot of that value.
Josh:
So I think that's kind of the bull case for Bloom. That's why people are mostly excited about it.
Josh:
It is just a really difficult place for companies because it's hard to make
Josh:
energy. And a company that has figured out a way, I'm not sure this is the end
Josh:
state, but this is certainly a intermediary step.
Josh:
And right now, that's all people need. It's just power today.
Ejaaz:
Now, moving on, one of the other bottlenecks that is appearing,
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particularly with GPUs talking to each other, is data transfer specifically.
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Now, if we think of an AI model, typically it has grown exponentially in size
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and in quantity of data that is required to train a frontier AI model.
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We've gone from, if you remember, Josh, billions of parameters to trillions
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of parameters and now tens of trillions of parameters.
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So the model weights of these actual AI models are huge. People are carrying
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them around in briefcases.
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They're like incredibly dense.
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And when you're training a model, and when you're even inferencing a model,
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when you're trying to speak to it, when you're trying to send it a prompt and
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get a response, there is petabytes of data, which is an order of magnitude larger
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than anything we've ever really spoken about at scale in industry, at least, before.
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That are transferred between GPUs. So it's not just good enough to have the
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GPUs and wiring them up and having the power flowing to them.
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You need to transfer data between them. They need to talk to each other in rapid
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pace. If they talk to each other slowly, then they won't be able to work as
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quickly and as powerfully as you'd expect. Think of it like this.
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Every single way that a GPU requires to function, the lifeblood of it,
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the energy, the ability to communicate is a potential constraint.
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And this communication thing is another one.
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Now, typically, the way you achieve this is through copper wiring.
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Copper has a really interesting chemical property that allows you to transmit
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data at lightning speed without heating up, except we've run into a problem.
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We now have too much data. And so the copper wires are heating up,
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which causes energy to dissipate and energy efficiency and cost efficiency,
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therefore, to not really work in the favor of the AI capex that is being spent on this.
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So people have started scrambling around. They've started looking around for
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another type of material.
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And you wouldn't believe it. They've settled on light, optical fibers to transmit data using light.
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And one of the companies that is moving data as light is known as Lumenta.
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They're using optical fibers and laser transmitters to move data between chips
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as light because copper melts at a certain level where data becomes too burdensome, essentially.
Josh:
Yeah, the bottleneck was very much copper and it might still be to some extent
Josh:
because copper is the fastest way of transferring data just because it has so
Josh:
much bandwidth. It's so dense and you could transfer.
Josh:
I mean, now we have clusters of hopefully soon half a million GPUs.
Josh:
I know that's what people are working on.
Josh:
All those gpus need to communicate they need to do so as fast as possible copper
Josh:
has enough bandwidth unfortunately there's this direct correlation between the amount of data
Josh:
and the length it has to travel it's physically impossible to put that many
Josh:
gpus that close to each other to make it feasible there's just not enough space
Josh:
inside of a room to make it happen so as these data centers get larger the
Josh:
distance in which jane needs to travel gets further copper becomes a worse and
Josh:
worse option because i mean every single bit that has to flow through that it causes heat.
Josh:
It causes energy and then like you mentioned the copper it's going to start
Josh:
melting and it's really expensive to cool and it's really difficult so what's
Josh:
the next best thing is photonics you just use light and you use fiber optics
Josh:
and nothing travels faster than the speed of light so
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it's just a matter of how much bandwidth we could squeeze into those rays of
Josh:
light and by stacking this fiber and by using this glass fiber we're able to actually transfer,
Josh:
information from one ship to another at the speed of light and lumentum is a
Josh:
company that's working on that so i think that's why people are really excited
Josh:
about this company in particular as these data centers grow larger in footprint
Josh:
they are going to require
Josh:
a longer distance in which data has to travel and there's going to be a lot
Josh:
more data which means copper probably won't be ideal and unless we solve this
Josh:
material problem the next best thing
Josh:
is photonics it's just sending it right over a fiber optic line at the speed of light or i should say,
Josh:
many fiber optic lines to store as much data as possible in this transfer.
Josh:
And that's why Lumentum is, I think, one of the more exciting companies.
Josh:
This is part of Jensen's portfolio, if I am not mistaken, right?
Ejaaz:
Yeah, yeah. He's put in about $2 billion into Lumentum Holdings.
Ejaaz:
But to your point, it's important to mention that this is a relatively new type
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of technology. It hasn't quite hit that exponential wave that NVIDIA GPUs did two years ago.
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And so we're waiting on that. The bet is that that is going to happen for these
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particular types of transmitters and receivers. Now, their unit of sales is
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going up pretty aggressively.
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Last year, they sold, I think, around like 20 million units.
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This year, it's now 60 million units. So it's kind of like a two and a half
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to three X from what they were last year.
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But they have competitors in the form of Corning, GLW, who NVIDIA also invested $3.2 billion.
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Now, they're not direct competitors, but Corning creates the actual optical
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glass fibers that will be used to kind of like transmit light and data for this.
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And there are many other kind of like companies that are like highly focused on this.
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It's worth mentioning though that scaling any of these material goods,
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whether it's the glass fibers, whether it's transmitters, whether it's the power
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switches or whatever that might be, takes an incredible amount of lead time.
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Like one of the major critics of the memory bottleneck that everyone is quite
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familiar with right now is Apple just recently announced that they're raising
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the prices of all their devices because they can't get their hands on enough memory.
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NVIDIA is facing the same thing. And when they go to SK Hynix and the memory
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makers and they say, hey, can you just build more, please? They say, it takes time.
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The machinery is incredibly detailed. Like it takes a while and we're gonna
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be in this for a few years. And the optical fiber side of things,
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the material side of power infrastructure in itself is where this is also playing
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out. nothing's really changed in that sense.
Josh:
Also, I just sent you a custom link of a post that I'd like to share because
Josh:
this reminded me of an idea that I heard that I think is probably relevant because
Josh:
the metric underneath all of this is joules per bit. It's the energy cost to
Josh:
move one bit. And at scale, light wins decisively.
Josh:
Like there is nothing that flows smoother than light. And it reminded me of
Josh:
this post from John Carmack.
Josh:
He is the guy who created Dune. He's like this internet legend.
Josh:
And basically, John was saying that you can store data within this light if
Josh:
it is transferred over a certain.
Ejaaz:
Length of space. Oh, Elon responded to this, right?
Josh:
Yes. It is amazing. So when we think about memory and we think about data transfer
Josh:
in general, I think fiber optics is a really interesting industry to consider
Josh:
that can go much further than I think what we imagine it can do today.
Josh:
Because during the time in which this light or these bits are being transferred at the speed of light
Josh:
you can transfer a lot of data and if that's spun over a long enough distance
Josh:
it can actually store data in a way that is
Josh:
very fast and accessible way faster than we get in memory and RAM because it
Josh:
is actually moving at the speed of light and there's a lot of interesting science
Josh:
experiments and I think general experimentation as it relates to,
Josh:
fiber in general not only as a way of transferring data but as a way of storing
Josh:
data should you stack it and extend it over a long period of time and i just
Josh:
want to highlight this is
Josh:
like lumentum is hot right now but i think the industry of fiber and fiber optics
Josh:
and moving things around at the speed of light is ultimately where we're going
Josh:
to land on we talk about data centers in space
Josh:
They're going to be traveling at the speed of light. It has the lowest amount
Josh:
of friction. It has the highest velocity of information.
Josh:
If we can experiment and kind of iterate on the way that we do that,
Josh:
I think that's something worth noting.
Josh:
And Lumentum being the fiber optic company may stand to benefit over a much
Josh:
longer period of time than I think people think based on this current bottleneck.
Josh:
I just want to highlight that because I was like, huh, that's pretty cool.
Josh:
That reminded me of something I saw back in the day.
Ejaaz:
Oh, I love it. Now, you would think that the entire problem with the power infrastructure
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stack is just generating the power, right, Josh?
Ejaaz:
But there's also delivering the power, making sure the right amounts of power
Ejaaz:
go between the chips at this time. And then when they are really in demand,
Ejaaz:
delivering even more power, right?
Ejaaz:
That takes incredibly complex design patterns. Now, most people probably aren't
Ejaaz:
aware of this, but NVIDIA isn't a chip manufacturing company.
Ejaaz:
They're a chip design company.
Ejaaz:
They design what the silicon wafer is going to look like. And then they send
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that over to their friends in Taiwan and TSMC.
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And they actually manufacture the thing for them. Now, one key component of
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manufacturing this design is figuring out how the power is transmitted between
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these chips and within the chip itself.
Ejaaz:
And this is a huge role that is played within the power infrastructure because
Ejaaz:
it determines where the power gets dispersed. It's an incredibly hard thing to figure out. Now...
Ejaaz:
About three weeks ago, there was this very popular compute conference called
Ejaaz:
Computex, which was basically NVIDIA GTC 2.0. Jensen was like the bell of the bell there.
Ejaaz:
So he goes on stage and he talks about this company, Marvel,
Ejaaz:
and he says, this is the next trillion dollar company. And this isn't investment
Ejaaz:
advice, by the way. This is literally verbatim what he said in Taiwan.
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And the stock subsequently went up 76% after he mentioned this.
Ejaaz:
And I was looking up on this just to see, you know, have they like,
Ejaaz:
you know, rescinded since then?
Ejaaz:
Josh, they broke into the S&P 500 based off of this single use.
Ejaaz:
And the reason the TLDR of why Marvel is attracting so much attention and capital right now
Ejaaz:
is because they are experts in designing that part of the chip,
Ejaaz:
which determines where power gets dispersed, how it gets dispersed and where
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it gets used within the chip and between chips.
Ejaaz:
And if you're wondering, oh, okay, well, they're just solely reliant on NVIDIA, the answer is no.
Ejaaz:
Broadcom and every other single AI company that is trying to design their own
Ejaaz:
chip and diversify away from NVIDIA is using Marvel.
Josh:
Wow, it's up 80% on the month I'm seeing in one month since Jensen mentioned this company.
Josh:
I actually don't know much about Marvel and I feel bad about it because clearly
Josh:
I missed out. But it's one of those things where it's been very lucrative to
Josh:
copy trade the people in authority.
Josh:
It's like if someone who is running the largest company in the world says this
Josh:
company is going to a trillion dollars, people are going to react.
Josh:
And you have to just trust the fact that he has a good reason to do this.
Josh:
I mean, Jensen seems like he holds pretty strong generally on his morals.
Josh:
He's not trying to pump and dump. He genuinely believes this.
Josh:
He has made a sizable investment. What is the total amount? It's a couple of
Josh:
billion dollars, right?
Ejaaz:
I did $2 billion. Yeah. So he's up, he's up pretty big now.
Josh:
Man, my God, exactly. That's the point. And it's like, yeah,
Josh:
he, he actually has the ability to influence the success of this company,
Josh:
not just by talking about it, but by actually entering and the technology into his company.
Josh:
Same thing happens when you talk about the person in charge of the United States
Josh:
of America. Like when Trump says something, it generally, it goes up generally
Josh:
as a good reason. People are looking for projects to invest in,
Josh:
for companies to invest in.
Josh:
And Marvel has been one of the most recent winners. Now, there is another section
Josh:
of this stack in which you have to rent out the compute. Say that you are not,
Josh:
you don't have hundreds of billions of dollars of CapEx at your disposal this year.
Josh:
Sorry to hear that. Chances are you're not winning the AI race,
Josh:
but you can at least make an impact. You could do something novel,
Josh:
something interesting in AI. And if that's the case, you're going to need to
Josh:
rent that compute from someone.
Josh:
There's a bunch of companies that we've talked about on the show in which you can rent compute from.
Josh:
We talked about Iren, a lot of the NeoClouds, but we have a new one to talk
Josh:
about today, which is called Nebius.
Josh:
Ejaz, can you explain to everyone what Nebius is, what makes them so different and unique?
Ejaaz:
It's actually not what makes them different or unique. It's just,
Ejaaz:
it's a scarce service that they supply.
Ejaaz:
So if you've heard of CoreWeave or if you've heard of Iron that Josh just mentioned,
Ejaaz:
they're known as a neocloud.
Ejaaz:
A neocloud is basically a company that will come in, they will construct the
Ejaaz:
data center for you, they will handle all the wiring, they'll make sure that
Ejaaz:
the GPUs talk to each other, they'll make sure that the networking and transformers
Ejaaz:
and receivers, they'll make sure that the copper doesn't melt.
Ejaaz:
And they'll make sure that these beautiful, precious GPUs that you just spend
Ejaaz:
billions of dollars on function optimally.
Ejaaz:
The reason why someone will go to a Nebius or a CoreWeave is simple.
Ejaaz:
Google, Amazon, and all of those companies, they don't want to focus and spend
Ejaaz:
time operating and maintaining and designing the flow of a data center.
Ejaaz:
They want someone else to handle them. That's what Nebius does.
Ejaaz:
And they do it pretty damn well. I believe Nebius's background is they operated
Ejaaz:
a bunch of data centers that focus primarily on, maybe it was mining
Ejaaz:
in the Web3 side of things, maybe it was something else, but they pivoted pretty
Ejaaz:
aggressively to focus on AI specifically and their expertise of managing hardware.
Ejaaz:
And most importantly, the software that talks to each of these GPUs and the
Ejaaz:
different hardware components is their expertise. So the answer,
Ejaaz:
truthfully, is they do exactly what CoreWeave does. They do exactly what Iron
Ejaaz:
does, but they do it at a massive scale.
Ejaaz:
And their Q1 revenue is up almost 700% on the year. It's just insane.
Josh:
Yeah, it's crazy. I saw it at $400 million dollars and this is the thing with the demand
Josh:
issues is that like if you build it they will come it doesn't matter who you
Josh:
are one fun fact about nebius is they were previously known as yandex or this
Josh:
is what was built from the remains of yandex
Josh:
if you are from russia you probably know this because that was very much the
Josh:
google of russia up until 2024 when they severed and they started to build a new cloud
Josh:
so that's a yeah they are familiar with
Josh:
managing data and storing data and uptime with servers wow yeah so that was
Josh:
interesting and then i think one of the things that makes this company special
Josh:
again nvidia is a backer nvidia has placed
Josh:
two billion dollars of direct equity into this company nvidia should just like
Josh:
man if they weren't making gpus jensen could just make it as a vc because this.
Ejaaz:
Dude is making
Josh:
Pretty unbelievable,
Josh:
yeah two billion yeah maybe we just screenshot this portfolio and i just send
Josh:
it to my broker and say can you buy all of these for me just split it across
Josh:
my whole portfolio because,
Josh:
clearly jensen knows what he's doing but the the company is doing remarkably
Josh:
well and because they have contracts from companies that you actually have heard
Josh:
of they have 20 billion dollar contract from microsoft they have a 27 billion dollar contract from.
Josh:
Meta and then they have a backlog that's approaching 50 billion dollars for
Josh:
the years 2027 all the way out to 2031 and this is the thing that we talk about a lot too is that the,
Josh:
the useful life of a gpu has extended so far
Josh:
that i think these companies are getting revalued and re-underwritten based
Josh:
on that because a lot of the time
Josh:
people would say that oh if you have a gb100 today it's going to be worth 10
Josh:
of what it is today five years from now and it's a five-year depreciation schedule
Josh:
the reality is is that gpu from three to four years ago
Josh:
is worth more today per hour than it was back then
Josh:
and so long as this demand curve continues these gpus the useful life of them
Josh:
is going to continue to extend out over longer and longer durations,
Josh:
making the inventory of these companies more and more valuable.
Josh:
And if that trend continues, everyone's going to have to continue to re-underwrite
Josh:
these companies based on this new depreciation scale.
Josh:
And that is going to continue to bring the value of the company up.
Josh:
They have the scarce resource, which is the ability to actually build these
Josh:
data centers, which means they have the land, they have the power, they have the shell.
Josh:
And this is another company that is doing it pretty well. So Nebius is a good
Josh:
example. Neoclads in general are a good.
Josh:
Insane demand that is insatiable it's like no one can actually provide enough
Josh:
energy enough gpus that are powered online and any company that's able to contribute
Josh:
to that is just going to continue to grow so long as this demand stays there
Josh:
and there's no signs of it's slowing down.
Ejaaz:
You know as we talk about this it's so interesting to see this sort of
Ejaaz:
It's like this entire ecosystem that has built around the gpu as gpus are basically
Ejaaz:
the new gold bar right there's this whole economy around this.
Ejaaz:
This is news that I was seeing earlier this week that the Chicago Mercantile Exchange, CMC,
Ejaaz:
I believe, is setting up a compute futures trading index so that you can hedge
Ejaaz:
trades and do trades based off of compute and GPU infrastructure.
Ejaaz:
It is becoming a financial instrument that you can eventually borrow money off
Ejaaz:
of. That's what NVIDIA and a ton of other companies are doing.
Ejaaz:
And so it's this kind of like pseudo or proxy investment vehicle where it's just based around GPUs.
Ejaaz:
And this whole ecosystem is now blossoming around it, whether it's neoclouds,
Ejaaz:
whether it's optical light fibers, whether it's the silicon itself,
Ejaaz:
whether it's the chip design that helps
Ejaaz:
transmit all this kind of power, it is formed around the gravitational pull
Ejaaz:
of these GPUs is pretty insane.
Ejaaz:
One final note that I wanted to mention for Nebius specifically,
Ejaaz:
And they have the same advantage that CoreWeave and Iron has is permitting and
Ejaaz:
licenses, like yawn, right? Like who the hell cares?
Ejaaz:
As we mentioned earlier, a lot of the five-year lead time to get like power
Ejaaz:
and energy to your GPUs is regulatory.
Ejaaz:
It's red tape. It's trying to jump through the government hoops to get all of this figured out.
Ejaaz:
Nebius, Iron, Coreweave all have this permitting in advanced stages.
Ejaaz:
And that's why they're signing these multi-billion dollar deals with Meta,
Ejaaz:
Google, and all the other hyperscalers because they have this advantage.
Ejaaz:
It's all a regulatory game. It's all a hardware game. Whoever can figure out
Ejaaz:
a way to arbitrage the red tape that they're currently facing will end up winning pretty hugely.
Ejaaz:
And if you look at what leopold ashenbrenner is investing and if you look at
Ejaaz:
what uh jen so puang is investing the people who are on the ground that are
Ejaaz:
seeing these bottlenecks in real time that can kind of give you an idea of where
Ejaaz:
that ai capital is eventually going to flow
Josh:
Yeah and then one one final point on the neoclabs which i think is fun and underrated
Josh:
and a lot of people don't recognize what the incentive is to go with them as
Josh:
if you don't need the gpus but if you're a large company like meta for example
Josh:
who has that 27 billion dollar deal with nebius
Josh:
you can actually write off that spend as an expense spread over years instead
Josh:
of just like hammering your free cash flow with this increased capex today.
Josh:
And for a lot of earnings reports, we see the first numbers we go to look at
Josh:
are the capex. How much are they going to be spending on building out compute?
Josh:
And if that number is too high based on what the market believes they can actually
Josh:
build, then they get crushed for it.
Josh:
So a way to kind of obfuscate that is by investing in these neoclouds,
Josh:
signing these deals that can then be extended out over the years and don't immediately
Josh:
crush your balance sheet like we're seeing a lot of these companies doing um
Josh:
google being one nvidia being one where they just raised 25 billion dollars
Josh:
so it's an interesting financial instrument as well that these companies are using but i think
Josh:
that is mostly it in the hardware energy
Josh:
stack that's where we are um how do we.
Ejaaz:
How do we do how do we do i'm curious like people have been asking for this
Ejaaz:
episode for a while i i feel like we did a good job kind of shepherding the
Ejaaz:
story between these companies? Josh, do you feel the same?
Josh:
I feel pretty good about it. I think there's this like,
Josh:
the layers go so deep and you could even go deeper than this with like the actual
Josh:
um manufacturing of this and like who is supplying all the raw materials in
Josh:
but i think this is about as low as you need to get
Josh:
because this is the this is the interesting part past this you start getting
Josh:
into all the science and physics that i'm not sure it's super interesting but i feel good about this
Josh:
i actually learned a lot in preparing and recording for this episode
Josh:
as it relates to just like where the bottlenecks are who's responsible for them
Josh:
i didn't even know what Marvel was until Jensen talked about them on stage.
Josh:
So we're learning a lot about them. There's a lot of alpha and coming across
Josh:
these early. I mean, all of the companies so far,
Josh:
they've done pretty well. It's no lie. Like people know about this.
Josh:
This is not this like deep alpha, but there is a really great opportunity.
Josh:
And these companies are building valuable goods and services for companies that
Josh:
need them. So yeah, I think that's probably it. That covers the whole thing.
Josh:
We went through the stack all the way down to the bottom. We previously covered
Josh:
the higher parts of the stack. If you're interested in that episode, you can find it.
Josh:
We'll link it in the description. It's also in our channel. You may have heard
Josh:
it if you've been around.
Josh:
But yeah, I think that's pretty much it. That is covering the base layer,
Josh:
the energy infralayer of this AI stack.
Ejaaz:
There is one more layer, Josh, that I, not today, not next week,
Ejaaz:
but maybe in a few months time that we need to prep hard for,
Ejaaz:
which is the substrate layer.
Ejaaz:
That is the one at the bottom of the bottom. That is what we're talking.
Ejaaz:
We're going back to old school days. We're talking about raw materials here
Ejaaz:
that actually help form the materials, the silicon itself, the wiring that we've
Ejaaz:
been speaking about, where do those materials come from?
Ejaaz:
And like fun little nugget, an Easter egg before you even hear anything about
Ejaaz:
this episode is the majority of those materials, they're in Japan.
Ejaaz:
And Japan has a company that manufactures toilets.
Ejaaz:
It's known as Toto. In fact, if you look at your toilet right now,
Ejaaz:
we filmed an episode about Toto.
Ejaaz:
They happen to have machines which get access to this really funky material
Ejaaz:
that is only accessible in Japan or primarily based in Japan that is used to make GPUs.
Ejaaz:
It is an essential component in NVIDIA's manufacturing stack.
Ejaaz:
Just a little fun fact for you. We're going to make an episode on that eventually.
Josh:
There's a teaser. What do toilet bowls and AI have in common? Ceramic is one answer.
Ejaaz:
Billions of dollars of market cap.
Josh:
Yeah, maybe we could do this again. We'll do it for the furthest down the stack.
Josh:
It's amazing how granular it gets. Like when we talk about these two nanometer
Josh:
chips that are coming down the line, the difference is a matter of atoms.
Josh:
Like they are literally constructing on the atomic level, five atoms.
Josh:
And if one of them is in the wrong spot, then these things don't work.
Josh:
So the precision required to make this happen is is like it's unbelievable there's
Josh:
nothing else in the history of humanity that has ever been so complex as this
Josh:
there's just nothing that humans have made at least and,
Josh:
going down the stack it's just unbelievable to see and the resulting output
Josh:
is you could type into your chat box and you get intelligence out and it seems
Josh:
imperceivable from magic so that is the ai energy stack episode i hope you enjoyed
Josh:
if you did please be sure to
Josh:
leave a comment share with a friend who you think might be interested be like
Josh:
yo bro you should check out this company.
Josh:
These guys on Limitless talked about it and they made a pretty interesting case.
Josh:
Again, none of this is investment advice. I'm not exposed to half these companies
Josh:
for better or worse, but we should start following your own book.
Ejaaz:
We should just copy
Josh:
Trade our own.
Ejaaz:
Episodes because everything that we mentioned has been going nuclear.
Josh:
That's the current state of the union. So if you invest, listen, that's on you.
Josh:
We're too stupid to take our own advice sometimes, But that is the episode.
Josh:
Thank you all so much for watching.
Josh:
Don't forget to share, like, subscribe, leave a comment on something we missed.
Josh:
Is there another one? Is there another layer of the stack that we're missing?
Josh:
Is there another company that is going to go nuclear?
Josh:
That'd be good to know. Just out of curiosity. But yeah, I think that's it.
Josh:
So we'll see you in the next one.
Ejaaz:
See you guys.