BlackRock 的 Tony Kim 谈 AI 下一批赢家？芯片、存储、机器人与量子计算

AI happens. It's like BCE, Anno Domini, and bam—'23 happens. Everything changed. So the base layer of compute went up 10,000x. A $10,000 server is a million-dollar server.

There's roughly $10-plus trillion in market cap in software, services, and internet. There's $22–23 trillion in the Magnificent Seven, and then there's another $30-plus trillion in chips and hardware. I don't think people realize that we are that compute-hardware-centric.

Before AI, in the BCE era, it was probably reversed. You've seen, in the last 4 years, a transformation in value that has systematically been happening for the last 4 years. The realization came to me that when we hit the A.D. era—the AI era—you needed to rethink everything.

The Chinese are coming, and there are 130 or 140 robotics companies in China. I see 30 or 40 potential IPOs this year in China alone, and there's what? Zero, 1, or 2 in the United States, maybe, this year.

Tony Kim, welcome to Sourcery.

Thank you. It's a pleasure to be here in Paris.

In Paris—

In Paris. Yeah.

At the RAISE Summit.

Yeah, yeah.

We're in a secret off-location that has AC—

I know.

—and some croissants, so it's quite nice.

It's beautiful here. It's so classic French. I love it. It's fantastic.

You're on stage a bit this year. What are you covering?

I am doing 4 panels in my involvement with RAISE: one on accelerators with D-Matrix, around next-generation computer architectures; another one with PsiQuantum around quantum computing; a third with Lumentum, bringing optics to next-generation data-center design; and finally, one with Broadcom on XPU and AI co-design for chips.

So, light agenda.

Light agenda. Yeah, yeah, yeah.

As the head of global tech for BlackRock—

Sure.

—what brings you here? How did you get involved with RAISE?

I got involved last year. One of the companies I was involved with, SambaNova—Lipu was supposed to be one of the speakers, but he couldn't make it, so I decided to fill in for him.

Then I saw RAISE, this thing in Paris at the Louvre, and I was like, “Oh, this is interesting.” It was the second year of development of what Henri had pioneered and built. I saw something there. I saw a lot of my colleagues and friends from San Francisco all congregating here in Paris, and I said, “Well, I'd like to help foster this and get it going.”

So last year, I was here. This year, it's probably tripled in size again. It seems to be Europe's biggest, or most targeted, AI conference. I continue to help and do what I can to build an AI presence in Europe.

It's massive. I don't know how they get all the names they get, but I remember last year seeing Eric Schmidt on stage, and I had not heard of the conference before.

No, and this is—

I was just amazed.

Yeah. I think they're outgrowing it. I think next year it might outgrow the Louvre, even, so it's really becoming something. You're here, too—a testament to where it's come.

They're amazing, so I'm happy—

Yeah.

—to get involved in any way that I can.

Yeah.

So between all of the panels that you're doing—

Yeah.

—you’re doing 4 panels. What are the through lines and the macro themes?

1. Compute Becomes Primary

I think one of the big ideas, obviously, is the primacy of compute. We see that in the stock market and in the investment market. You're seeing already how much the stock market and the capitalization in Silicon Valley have changed.

We went from a software-centric world to a compute-centric world, and you're seeing the emergence of companies. That's number 1: this move to compute. To me, in my opinion, the models and the compute are symbiotic and synonymous with each other, so it's the primacy of compute.

Secondly, since that is now the dominant theme—where the CapEx, the money, and the capitalization have all gone—it engenders a whole rethink of the data center. I think there is a redesign of the data center, and we're going through stages of the data-center rebuild.

Think of data centers pre-AI versus the scramble to build data centers today, where there's a massive shortage of compute. On the other hand, we're hitting the laws of physics, which are driving yet another transformation of data-center design going forward. The ramifications of this data-center redesign flow through every layer of the AI stack.

You go another layer deep in this data-center redesign. One of those layers is power density. It's incredible what's happening as we put more and more computation in, because the requirements of these models require more and more compute density. You need to pack more and more bits into a smaller footprint.

When you do that, you run into bandwidth, power, and heat. You have these logarithmic effects on data-center design driven by the necessities of AI. Effectively, the data center is changing from transmitting data kilometers away to building-to-building, then within the building, rack-to-rack, within the rack, and next within the chip.

You're going from kilometers to meters to centimeters to millimeters. As you go 1 order of magnitude smaller in distance, the bandwidth goes up, the power goes up, and the heat goes up. That's the irony of it all: the trillion dollars of CapEx this year and the $10 trillion over the next 5 years that are coming are to move data centimeters and millimeters.

That's AI. When you think of it in that context, how does the data center need to change? Everything being done is to optimize around this new physics. A lot of the action is going to be around energy, power density, and the grid.

Another area will be chip architectures, and then another will be the movement of data. We're going from a regime of copper to a regime of light. These things help address power density, energy, and the laws of physics that are pushing data-center design to its very limits.

One more thing I would say around AI, this conference, and things like that goes back to my first comments around the symbiotic relationship between compute, LLMs, and AI models. I think what you're seeing is that the best models are trained on and built on the best compute, with the most optimized inference.

But this co-design—the notion of co-design, of tightly integrating the design of your silicon to match the parameters and specifications of the model, with the model specifications informing the design of the compute—is the new path that many of the leading foundation labs are pursuing. This is what I'll talk with Charlie at Broadcom about. Obviously, they did that with the new Jalapeño chip that recently came out.

Those are some of the big ideas. My panels today are mostly focused on the physical layer of AI, the compute stack, and the co-integration. I'm not doing panels on the software layer at this conference.

It's okay. Software is a little sleepy right now.

Yeah, yeah. A lot is happening. It's interesting.

There's this whole other revolution going on in energy and power—

Mm.

—around the grid, behind the meter, and all kinds of new generation sources. I'm sure you've done stuff around nuclear SMRs and things. Then a new power architecture—the rise of 800-volt—is going to have a transformative effect.

Ultimately, there are solid-state transformers. Again, this goes to the point that it's not only the chip layer but also the energy layer. Every time you have these step-downs in voltage, you lose efficiency and energy.

Again, it's about making things more efficient, packing more in, reducing the distance, and getting things up. This is a sub-narrative that's developing around the future design of data centers. I think you mentioned one thing: RAMpocalypse. Did you say “RAMpocalypse,” or—

You said that—

Yeah, yeah.

—by the way. We were on a call beforehand—

Yeah.

—and you brought up RAMpocalypse.

Well, somebody—yeah, somebody. It wasn't my credit.

I won't take credit for it either.

I won't take credit. Somebody took credit for it. There are a lot of memory companies here.

This also goes to—yeah, maybe I'll add a 5th or 6th topic on the data center design of the future. Again, it's back to this co-design element around the model and the compute. From my observation—I'm not building the models, but I observe the compute architectures and what the model guys are doing—increasingly, more and more of chip and model development is starting to mirror the human brain.

In the early days, we had a ton of compute, a ton of parallel compute, and the models didn't have much memory. Now we're adding memory to the models. It's remembering things about your behavior and what you're doing.

Then you look to the future. Everyone talks more and more about having AIs—your personal AI, these agentic harnesses that build in the institutional context within an enterprise, with memory and memory storage. The human brain has a lot more memory storage than maybe compute. It depends on how you look at synapses and neurons and things, but the human brain is very memory-intensive. Today is more compute-intensive, but as you see with RAMpocalypse, the memory intensity has just skyrocketed.

Mm.

Going forward, you will see more and more and more memory. Then, when you look at chip architectures, it is all about arbitrating memory in some form with your compute: different kinds of memory—SRAM, DRAM, stacked DRAM, HBM, or high-bandwidth flash.

All of these memory and storage methods are tightly packed into your chip and computer architecture so that they align with how maybe these AIs will be built to start to emulate more and more of the human brain. I think that's what's fueling the RAMpocalypse—the shortage of RAM.

There's a mismatch in what I call duration: it takes 3 or 4 years to build a chip fab or a memory fab, yet there's a shortage today. You're trying to build for the future. You've got to build, and it takes 3 or 4 years to build the capacity, but what do you do about today's demand?

You've got to spend so much money to get chip output. There's this mismatch of demand, supply, and duration, and this is causing a lot of angst in the market. Underlying all that, I think we're going to more and more memory—or let's just say, memory in concert with compute. Today, we're all talking about compute, compute, compute. I think the primacy of memory will become even more important.

So we've been seeing this trend in a lot of our conversations and on the macro side—

Yes.

—news and everything. We had a conversation with Jamin from Coatue. He's the CIO—

Okay.

—of Public Markets—

Yeah.

—over there. They were talking about how, with the proliferation of agents, memory is only increasing.

Yes.

They also talked about the chip flip, which we'll talk about a bit. But on the memory side, to your point, there are only 3 main players.

There are only 3.

And SK Hynix is about to go public, and I don't know if, by the time we put this out, it might be public. But the big questions around that are: How do you underwrite that? The demand premium is massive because there's limited supply, and how long does that last? How do you catch up to that?

I would love to go deeper into all of these topics a bit more. But I guess, to start more on the macro side, we are entering—and we have entered—the new era of AI. This has led to an entire rebuild of everything that's going on in tech because we need inference, we need things faster, and agents are now coming to market. It's no longer just chat. I'm curious, from your standpoint, on the investor side, how do you think—and how has your strategy evolved—to now play offense on this type of field?

2. AI Rebuilds Tech Investing

I like your framing. It is a complete rebuild. So let's start with that and then how we play offense as an investor. You're absolutely right. It is a complete rebuild.

The internet, as we know it, was built, let's just say, from 2000 to 2020-ish, in one framework, which was basically around the birth and dawn of cloud computing. Cloud computing necessitated a certain kind of data center. You remember the good old classic data center: megawatts, not gigawatts, right? So we had an order-of-magnitude increase today.

These data centers were small. At the end of the day, cloud computing—everyone says it's software, but it was really reselling CPUs with hard drives. That was the compute stack: a CPU with a hard drive. It was considered a commodity. Server prices were thousands of dollars, and now those compute servers are millions and tens of millions of dollars. So compute was an afterthought.

The other way I think about it is that these clouds built these classic compute stacks, and then they resold that as platform services and databases, with SaaS built on top of that. SaaS was king, and the margins went to that because the cost of compute was so low. Everyone said, "Compute is free, it's cheap, it's a commodity," and all the value went to this layer, right?

You were building this massive application on a very, very thin layer of compute. That fueled a 20-year run in cloud and SaaS. All of that data center infrastructure that was built, even with AWS, GCP, and Azure, was built for that. It was built to bring on-premises software to hosted cloud services. That was a great business. Everyone was very happy, and data centers were built to that spec.

AI happens. It's like BCE, Anno Domini. 2023 is going from B.C. to A.D. Bam, 2023 happens, and everything changed. What was called the base layer of compute went up—I don't know—10,000×. A $10,000 server is a $1 million server. Small HDD, big HDD.

Oh, and by the way, DRAM was a commodity used only in smartphones. Now I need to pack all the DRAM and HBM I can—HBM, which is expensive DRAM—onto this AI thing. This data center is now— it was megawatts, and now it's gigawatts. It's small data centers in cities to giant server farms in Texas.

That is the data center and cloud of tomorrow, and they're selling tokens. The data center of the past was like this old cloud, but it facilitated high margins. That still exists, but it's not going to grow like this. This business will facilitate radically more capital—a complete rebuild. This is an alien data center compared to this data center.

That requires massive capital investment. But it also engenders a very different rethink of margin stacking. Before, you would resell this base layer of low compute, with massive SaaS app margins on top. Now you've got this massive compute stack, and they're reselling those as tokens.

That compute factory is creating tokens, and then the model guys are selling their tokens. That takes a lot of margin out of the top layer of the stack. This is driving what you just said: a complete rebuild. You must build these new data centers because, at some point, all of your revenue will come here.

The revenue from the old data center—which was asset-light and high-margin—is now moving to asset-heavy, lower-margin, big dollars, but it's a completely new data center. Because of this new rebuild, it has triggered a reexamination of value.

If you look at today's tech stock market—and I'm going to make some approximations—there are about 1,500 companies globally with a market cap of $1 billion or more, maybe 2,000 if you add China. That's not including China.

In the US, let's just talk about the global stock market. There's roughly 10 trillion-plus in market cap in software, services, and internet. They were the classic industries where most of the market cap was in the pre-AI era. That's 10 trillion, plus or minus. There's 22 or 23 trillion in the Mag Seven, so I just put the Mag Seven in a new category.

Microsoft is in the Mag Seven. So I just have a non-Mag Seven software, services, and internet category of 10 trillion, 22 trillion in the Mag Seven, and another 30 trillion-plus in chips and hardware. So, 10, 20, 30, something like that. Okay? 10 trillion in software, services, and internet; 20 trillion in the Mag Seven; 30 trillion in non-Mag Seven compute, chips, and hardware. I don't think people would realize that we are that compute-hardware-centric now.

10, 20, 30. Before AI, in the BCE era, it was probably reversed. You've seen, in the last 4 years, a transformation in value that has been systematically happening for the last 4 years. And that follows the data-center transformation because of the primacy of compute, the plurality of the dollars, and the creation of the models themselves.

The models themselves, like it or not, have consumed the market cap out of software and services. It's like the Borg; it has consumed. And for those foundational models to exist, they need to live on the compute stack. So that has happened.

To be offensive in this structure, the realization came to me that when we hit the AD era, the AI era, we needed to rethink everything. Then you need to align offensively, as you say, an investment philosophy and capital-allocation philosophy that would mirror what is becoming the new reality: this insatiable demand for intelligence, and, as a function, intelligence begets compute. Intelligence for compute equals revenue.

And then, if you think the basis of many companies is around these compute factories and your ability to resell that intelligence, that makes you rethink the margin stacking and where the value sits for companies. The market is trying to adjudicate that right now. That's why you saw SaaSpocalypse earlier this year, at the end of last year. That's why you're seeing the RAMpocalypse. There are a lot of apocalypses.

If you believe these scaling laws and intelligence is getting better, let's say at 1 order of magnitude a year—10 times 10 times 10, that's 1,000 times in 3 years—it's not like these AIs are getting less capable. They're getting more capable, and more capable means more compute. If they can do more things, then it's a rethink around where the margins are and where your defensibility is. People use the term moat a lot.

A moat is very defensive. Moats are always breached, aren't they? So it's more about offense, in my opinion. Can you move faster? That's a broad topic.

That's how I think about the macro. Like you said, this complete redesign facilitates a new rethink, and that rethink also has huge implications for the business models and the moats of companies. This is my thesis now; it might change. We talk again in 6 months, and it might be completely different. But that's my current thinking.

Because, to your point earlier, we are investing in new areas, or we're investing in areas that have 3- to 4- to 5-year lead times, even 10 years if you want to talk about quantum. That's always 10 years, whatever year you're talking about it—

Mm-hmm.

To be clear.

Mm-hmm.

But in terms of those types of outward investments and strategies, how do you think about where you're going to spend time and which one of those is most effective right now? You're obviously taking a risk on that. And I bring that up—

Yeah.

In the context of quantum as well.

Yes.

And with these new chips and building them specifically for models, how do you think about that, and how do you weigh the different kinds of risks that come along with it?

3. Capital Allocation Favors The Future

Yes. An investor, a portfolio manager—at the end of the day, you're allocating capital, right? You have only so many bullets. I'm a public investor and a private investor. I do both. At the end of the day, you're allocating capital and creating whatever portfolio you're creating for your mandate, for your clients.

At the end of the day, you're trying to arbitrate between risk, as you said: what is today and what is tomorrow? A lot of what's around AI today is about today, even the 3-year duration mismatch of, let's say, DRAM and foundries. I call that the now, right? This is the now. The vortex of AI is the now.

In this 3-year window, that's probably where 90-plus percent of my investment—or 90-ish percent, something like that—is going. Within this 3-year window, who's winning, who's losing, what is on the ascendancy, what is on the decline, what is stagnating, and then you're arbitrating between these ideas. The second thing, even in this 3-year window, is: Is there life after the 3 years? You have to believe that this continues 5-plus years, right? Belief in a future has a huge impact on your multiple.

If they do not believe in that future, even beyond the 2- or 3-year horizon that most investors and Wall Street can forecast to, there's an implicit understanding: Do you have a future or not? I always feel like you must be betting on the future as well. Things look cheap, but it's atrophying and maybe in decline, or growth is decelerating, so is that the best allocation of capital to put into that? Versus the next 3 years, it's going to be great for memory, compute, or data centers, but will that continue 4, 5, or 6 years into the future? Question mark: yes or no.

But you also have to be betting on the frontier of the frontier. I made some of these AI investments pre-GenAI, in 2019, 2020, and 2021, when you didn't know this LLM thing was going to happen. Some of these things gestate longer. My intuition was that we will need AI compute of some form, maybe machine-learning AI. I didn't know that this LLM wave would happen, but you're thinking about future architectures.

And so now we sit 6 or 7 years later, and the AI accelerator wars have begun and compute has taken off. I think about that in that longer-term context. The next set of companies that have this longer context is around, let's say, quantum, which you mentioned. I think we'll see it in 2030. I started getting involved in 2019, so I'm already 7 years in. You've got another 5 years to go, so you have some bets on where the frontier is coming next.

Space: these orbital data centers. That's also targeting 2030. It's very interesting when you look at these long-dated technologies. All roads converge to 2030. It's like quantum computing: utility-scale, logically error-corrected, million-qubit quantum computer—2030. SMRs, fusion, small nuclear reactors with regulatory approval—you talk to these companies, and it's 2030.

When will AGI happen for classical computing? 2030, 2029, 2028, whatever. Then you say, "When will we hit 800-volt power architectures?" Late 2020s, 2030. Will we have solid-state transformers? 2030. Fusion? Longer. But many data centers in space—2030, when it starts to really scale.

You're sitting here, obviously, and you have the now—this AI train that's consuming everything, all my time and energy—but that's 80% or 90% of it. But you must always be betting on tomorrow. Some of these are long-dated things, and so I spend time on the future, allocating X resources of my time to that. What will really be transformative, not incremental? I want nonlinear, asymmetric potential.

And then I bet on what is the primacy of today, with not only a 3-year, financially forecastable window, but also relevancy beyond. And then, for everything else that doesn't fit in that window, is it worthy of your time and the opportunity cost to keep investing in that? There are other strategies, other portfolios, other things that can pursue those. It's just not my focus, really. I hope that gives you a sense of how I frame capital allocation and portfolio decision-making. Yeah.

That's a super helpful explanation. I'm sure a lot of your investment memos have 2030 on them.

Twenty thirty—actually, it’s not far away.

No, it’s not.

Absolutely. Many companies—you’ve got to look to 2031. 2031 is 5 years; 10 years is 2036. Ten years is almost an impossible forecasting period, but in 5 years, a lot of companies will not even have free cash flow by 2031. So you then need to have a belief system that could flip positive beyond that. I’d say at least a 5-year window.

You always want to be betting on not what’s cool today. Will you still be cool in 5 years?

Mm.

Because then you become yesterday’s news in 5 years, even though you’re cool today. So there’s a little bit of that happening too. That really has a huge impact on your exit multiple, because if you’re just following today’s trend and you know there’s a half-life to this, it might be difficult to get a good return on the exit. It will not be what you think it is in 5 years, and the multiple that people will pay will go down. Your growth rates are decelerating, and now you’re in a bind.

It’s been really interesting to see how this new era has breathed life into older categories, or categories that have just been around—whether it is chips, whether it’s quantum. But it’s cool to see how entirely new opportunities have formed, and I’m curious about your takes on those. So in that respect, you did mention orbital data centers. That really didn’t exist before.

No.

And that is a huge wave and a big wave, especially with SpaceX coming—

Yeah.

—and the whole IPO on that. But there are also fun categories. I recently visited Figure AI—

Yeah.

—the humanoid robotics company, and I was just at Config, Figma’s conference. Boston Dynamics was there, and one of their heads of design for human-robot interaction was talking about their humanoid robot, Atlas. They’re different.

Mm-hmm.

The Atlas one is hydraulic, so it can pick up a fridge. Figure is more for daily use—package sorting, commercial stuff, making cars, and that sort of thing. But of these new categories, which ones are you paying attention to? What are you excited about?

4. Robotics Enters The Physical World

So robotics, yeah. The first comment around these older categories: like you said, semiconductors have been around a long time, and I don’t understand why people forget. It’s called Silicon Valley for a reason.

People forget.

But people forgot. It was kind of like the ring of power. It was lost, and then it was found.

People always said chips are a commodity, but yet chip companies have the highest profitability of any sector in the world. They have higher margins than software, pharmaceuticals, industrials, telecom—anything. And so this notion that they were a commodity was just a false notion, in my opinion. It really never was.

The other thing is that this industry is very interesting. There were hundreds of chip companies, and then, systematically, over 20 years, that number shrank. Now there are just a few. In every category, you have duopolistic power.

By the way, venture capital—that is, Silicon Valley—until recently never funded these companies. So there’s no money going in, and therefore, if you have no money going in, you have very few new companies. In fact, what you have is a shrinking effect. The number of companies has collapsed, and those that have survived are behemoths with huge pricing power—the complete opposite of a commodity.

By the way, all those people are engineering nerds. I always say it’s the revenge of the nerds. It’s not the revenge of the nerds; it’s the lost ring of power that was found. But it was always there. Their time to shine is now.

That said, you’re right. These other industries have spawned a renaissance in hardware. When you look at that market-cap shift that I was talking about, a lot of those are in areas like servers—servers are cool, fiber is cool, power is cool, and rack design is cool.

People love rack design.

People love it. They’re going crazy about rack design. It’s like bending metal, copper, and heat. Materials science is cool—

Mm-hmm.

—because you need all kinds of new materials. And then substrates and packaging. It’s the physical world. These are all physical sciences—what I call the physical world. Going to school to study materials science is probably cool. It’s very cool.

There aren’t enough chip designers in the world. It’s a lost art. Analog computing is like being a blacksmith. How many friends of yours go into studying new memory design? I was talking to someone at dinner last night here at Ray’s from one of the biggest memory companies. They said, “We cannot get people to design custom memory,” because they want to co-design custom memory with the chip. Well, where are the people? There are no people. We’ve got to repurpose some of these software programmers into memory co-design architects.

All of this has happened in the physical world—what I call the physical world. This is the next unlock that AI will do. Obviously, we’re going to go hard in cognitive work and cognitive labor, and you’ve got to build these models for that. But then those models can be repurposed and implemented in robotics.

The robotics system is very interesting to me, but it’s kind of just a parallel to what’s going on in AI. Because if you really think about it, what is robotics? You have a brain that will be built, and the brain will have kind of 2 parts to it. It’ll be a baseline LLM that you and I will communicate with, as a translator, to talk to the robot.

It’s like the human brain, but you also have the brain for the motor functions that control our muscles, our bodies, and our reactions, and then the brain for language and memory. So they’ll have 2 brains in 1. One will be a world model to perceive the world, motion, and things, and one will be, obviously, an embodiment of intelligence, like an LLM. So you’re going to build these 2 brains into 1.

And then you take the brain—those are like LLMs, right? They’ll be like LLMs. Many of the big labs are working on robotic brains, and then you’ll embody those brains into the body. But then the body: arms, legs, limbs, hands. Hands are probably the hardest thing, as I’m sure you know.

The body, the physical embodiment, is a manufacturing hardware business. When you think about that, the Chinese are coming, and there are, I think, 130 or 140 robotics companies in China. I’m looking at the current pipeline. I see 30 or 40 potential IPOs this year in China alone. And there are maybe 0, 1, or 2 in the United States this year.

The reason for that, though, is also the lack of depth in the private markets in China, so they’re using public markets as a funding mechanism, unlike in the US. But they’re earlier. They’re going to come earlier, and they’re coming in waves. There are 140 of them.

The thing about China is, in that physical layer—the body, the motion—they may be behind. The robotic brain development, the world model, may be behind. That’s probably what most people would say: the West is ahead in model development. But you mentioned Atlas. That’s Korean, actually. Hyundai owns Boston Dynamics.

But the Asian manufacturing complex—Japan, Korea, and China—is also an extension of EV platforms, right? If you have physical-scale manufacturing, you then avail yourself of the potential to manufacture these robots at a lower cost, at mass scale. And then what you might have is, ultimately, you can mix and match a Chinese physical robot with a Western brain, and I know that's happening. People are like, “Those Chinese robots are amazing, right? So why don't we stick a Western brain in there?”

And then permutations of this will continue. I think it's a Wild West, with a lot happening, but it will be a huge market, a huge market. I have a soft spot for it. My view is, one of the things I'm most interested in on the robotics side is not so much the manufacturing robot. You're seeing this come out of China already: it's around loneliness, social embodiment, education, the elderly, and young people—to bring consumer and/or commercial social robots, more so than industrial use.

That's kind of a hot take.

Well, I think if you think about aging populations, when you look at Asia in particular, the birth rates are well below 1.0, and you need 2.1 or 2.2 to stay even. So you're facing demographic population cliffs around the world. Some of the best-performing companies in the world are nursing home companies. And when you look at the elderly, they really want companionship.

Even amongst young people, there are loneliness epidemics and things like that. I think robots, even if they don't have perfect motor function, could embody some intelligence and empathy. They can have many different form factors, too. It doesn't have to be a Terminator-like humanoid robot.

Yeah.

I think that could unlock a really, really interesting market, a really big market. So that's my view.

That's interesting. I've seen—I know this doesn't really count, but I saw videos on Instagram of a long-distance relationship, and there was a tiny little pet robot on the ground. It was like a ball—

Mm-hmm.

—of some sort.

Mm-hmm.

And it was the girlfriend yelling at the boyfriend.

Yeah.

And she's in an entirely different country, following him around in the house.

Have you seen Star Wars?

Yeah. Mm-hmm.

Okay. Who are two of your favorites? Did you like C-3PO and R2-D2?

Yeah.

Now imagine. Then you have the current embodiment of Optimus and many other humanoid robots, and all these sleek, amazing, Westworld-like things. But I hearken to R2-D2 and C-3PO. What if you had a half-size robot? It doesn't even have to be full-size. It could be approachable, friendly, not masculine.

And that robot has the intelligence of Shakespeare and Einstein and speaks every language like C-3PO. Then you interact. I often think about the elderly. Imagine them having conversations with my mother and others, with the robot being empathetic to their stories, and then you can record their stories.

Mm.

Record their life histories. I think, do you need to have fully figured, perfect motor function with all the hand articulation, or could you get something that can appeal to that? I think that's possible. I think that will be a fascinating market to see. So, yeah, I think that'd be a very new use case.

Yeah.

Besides using robots to build the lunar base, which I think would also be cool.

Really cool.

Yeah.

Yeah. It's been interesting to see, because I cover a lot of high-growth companies in Silicon Valley.

Okay.

The proliferation of coding agents is a big thing, right?

Absolutely. Yeah.

But to your theme of the brain, speech model companies are crushing it.

Yes.

They are growing faster than most other companies out there.

Yes.

There's a company called AssemblyAI that's growing incredibly fast, and they're great.

Yes.

And then it's interesting to also see the downstream effects of all this. We talked about it, but we didn't really cover software at all because it is what it is.

Yeah.

But the downstream effects of it now hitting the data layer—because now we have so many agents that are just creating so much data. It's coming downstream. And so Databricks and Snowflake are hitting some of that extra premium in the market, and they're getting some attention.

As agents create more apps, MongoDB and those databases are reacting. Are you looking at any of the downstream winners?

5. Enterprise Follows The Token Flow

Yeah, yeah. I'm invested in many of those companies. Streaming downstream or streaming upstream, I don't know what's down or up.

I don't know either.

So it could be the opposite.

Well, no—100%. In supply chain hardware, is it upstream or downstream? I'm thinking vertically. So it's compute, models, apps, or compute, models, data, apps.

Okay.

I'm going up the stack.

You're going up.

I'm going up the stack.

I'm going down.

You're going down the stack. Whatever. So, absolutely. This whole data center redesign thing—I think the whole enterprise is redesigning.

Mm-hmm.

And the enterprise itself, if you really think about the future of what a big enterprise will be like, on one hand, you'll be bringing intelligence tokens. You're bringing tokens in. And you will have a data layer, because that intelligence will need to interact and be orchestrated around this data layer. This data layer will be the embodiment of your proprietary data and all your external third-party data.

And then companies will ultimately—what is a company? Well, it's people, it's distribution, it's a brand. But at the end of the day, it's also: can you embody all of the knowledge of your company in what they call a context layer? A layer of the secrets and the ways of your company, where you embody the cumulative knowledge of your employees into its own context layer.

I think Palantir calls this ontology.

Ontology. Exactly. Yeah, exactly. So you have this ontology layer, this context layer, sitting on the data foundation, with tokens in.

Mm-hmm.

And then that's it. Then everyone builds agents. Agents go wild, right? And agents will interact through the context into your data—

Mm.

—with tokens. That's it. That is the enterprise. And then what I call that is a token flow. Follow the flow of tokens.

You create tokens—compute. You then serve the tokens—foundation models. Then you put a harness, package, or context around the token—app services, et cetera. So you must be in this token flow to either resell or repackage the tokens with your context and your very specific application. You're serving the token with your intelligence.

Is it the proprietary, closed-source token? Is it the open-source token sitting on a compute foundation that's creating and firing up the token? If you're not in that flow, it's a problem. And then you mentioned these voice APIs, and you mentioned the data foundation. They are in the token flow.

Mm.

And so I think the app companies are struggling to find their place. But some companies have moved into—you’re seeing the rise of what I call—I don't know what you call it—inference clouds, edge clouds—

Yeah. Mm-hmm.

—edge AI. They're basically that last-mile token. They're providing tokens and developer kits and things so that smaller businesses and medium businesses can take it out of the box. And so they've inserted themselves in this token flow.

And, yeah, that's the—you know, to me, that's why I go back to this base foundation. Where can you earn your margin? Or you do the whole thing. You do the whole thing, and you just say, “I will do all of your claims processing. I will do all of your insurance processing.”

Pay me X. And so you don't know what you are. Are you an app company? Are you a service company? Are you a compute company? A token reseller? No, I'm just selling you the whole solution. Today, you pay 100; I'll charge you 20. And now you're seeing certain private equity firms and some venture firms saying, “You know what? Let's take an old industry, buy these companies, bring in this whole new stack, reimagine the stack, and just sell a whole new solution.”

Do you think—it's really curious with those PE roll-ups, because I think at the end of the day, they're just creating a new product, but they're buying the customers.

Yeah, they're getting customers, or they're buying companies with customers, and they're basically trying to restructure the whole delivery of services. There are a lot of inefficiencies, fat, and costs in there, and then you can rip it all out.

Mm.

Okay, maybe that's a business. Let's see. I know people doing that or starting to do that. Yeah, that's interesting. I don't know.

It's working quite well. We've talked to a couple of them, some on camera, some off camera. We talked to General Catalyst's Creation Fund, and they've been doing a lot of PE roll-ups and creating companies. One of them, Long Lake, just bought Amex GBT, their travel business, I think.

Okay.

Which is interesting because you have a small—

We use those—

—player—

We use those guys.

—buying a large player, which was really interesting to see.

Yes. Yeah, there's this other framework, this other rubric that's emerging: when you have these traditional industries and a new company with 500 people that can generate the revenue of 10,000 people, they're just approaching it through a radically rethought process. So the efficiency—and maybe this is what you're alluding to—maybe that's the new framework for these newer companies that are going to really go at traditional industries. It'll be up to the traditional company, the incumbent, to adjust in the face of these kinds of companies coming.

I think the jury's out. I'm very intrigued by that. I'm watching it. You mentioned some of these. It could be quite disruptive. So I think that's the next shoe to drop: all of these traditional industries that have very little adoption of AI, where the business workflow and how they've organized themselves could be completely rethought and refactored.

But that would require, like you say, it's so hard to sell products piece by piece and then have your old employees drive that change, versus saying, “Let me just buy the company. I'll make the change.” So it's an interesting idea. It's all new. I'm watching it. It's something to look out for.

We only have a few minutes left, but I'll leave you with 2 questions. First, what are you most excited about in the next 12 months? I know 2030 is a big date, but let's talk about the next 12 months, maybe.

6. The Compute Boom Keeps Advancing

Oh, boy. Next 12 months, between now and June 27th. Well, obviously, these big foundation labs. What am I most excited about—or more concerned about?

Take it either way.

I just think it's a continuation of the same. It seems like every 6 months there's a scare: AIpocalypse, war, interest rates, too much CapEx, not enough financing, and on and on and on. But through it all, I think I'm optimistic that this compute wall, the memory wall, the compute demand, and this data center redesign will just plow through, and then our fears will subside.

Therefore, we will be sitting here a year from now, talking about many of the same things continuing on. That's number 1. I hope that's what I'm optimistic about. The second thing is the progression along what I call this whole data center reimagination theme.

Mm-hmm.

I'd like to see more continued proof points along that path. I'm hopeful and excited to see the big labs go public in the next 12 months. I think that would be interesting. It'll be exciting, and I think there is huge market appetite for it. What I would love to see in the next 12 months—or what I'm excited about—is the next forward step toward orbital data centers.

That also engenders a radical change in data centers. If you keep pushing on that progression, it could open or unlock a rethink, moving the burden of terrestrial compute into space. That could have huge implications for how current data centers are even being built. So I'm watching to see the progress being made there. It's going to be an interesting 12 months.

Amazing. It also sounds like a little bit of a manifestation going on over here.

Manifestation?

Yeah, you're manifesting.

I don't know. I just contemplate.

Okay, as we close out—

Yes.

Final question.

Uh-huh.

With this question, it's on the topic of performance. I take this on a personal bent, so no pressure here. But I believe personal performance really revolves around who you surround yourself with. People say you're a result of your 5 closest relationships, and that kind of thing.

But I'm also curious: from your standpoint, you've built out a legendary career. Who are some of the people who have inspired you or mentored you along the way?

7. Tony Credits The People Who Believed

Wow, what a question. First of all, I don't have a legendary career. I'm just trying to survive.

I wouldn't say I had mentors, but you know what I had? I had people who believed in me at certain points in my life and basically gave me the freedom, the latitude, the keys to the kingdom, and said, “You know what? I see something in this guy, and I will give you the latitude.”

There was a guy who brought me into BlackRock who basically gave me carte blanche—the freedom and latitude to do what I could do. He believed in what I could do, so that's one. I actually did investment banking long ago, and there were a couple of people there who took a shot on me, some engineering kid out of the Midwest.

I thought I wanted to go into consulting back then, and no consulting firm would hire me, so I wasn't good enough for them. Somehow, I found a fit in investment banking, and then someone else said, “Go west, young man.” This was right before dot-com.

Certain people made a bet and just had faith, and it wasn't like they were mentoring me per se. They just had a belief. I try to do that. I try to always work with lots of young people—not to mentor them, but just to encourage them, give them a break if I can, or give them a shot.

But the mentors I have are all dead. I like to study. I'm a huge student of history, so Caesar, Alexander, Napoleon, Beethoven, Churchill. I like to study these kinds of historical leaders—people who created their own destiny. That's who my mentors are.

Growing up where I did, I wasn't a social kid. I was somewhat ostracized, and so I grew up in libraries. Those historical figures and libraries became my mentors. Then, when I went into the real world, some people gave me a shot. They believed in me or showed me kindness, and I'll never forget those people.

So there you go. It's kind of dead people and kind people. How's that?

That's beautiful.

Yeah.

Wow.

Yeah.

Great place to end it.

Okay.

Thank you—

Thank you so much.

So much, Tony.

Yeah, it's a pleasure. Yeah.

Amazing.

Yeah.