BlackRock's Tony Kim on AI's Next Winners? Chips, Memory, Robotics & Quantum

Speaker 0:00 Tony Kim.

Speaker 0:00 Tony Kim.

Molly O'Shea 0:01 Tony Kim.

Tony Kim 0:01 Tony Kim from BlackRock who runs the Global Technology Team there. AI happens. It's like BCE anno domini, and bam, '23 happens. Everything changed. So the base layer of compute went up 10,000X. $10,000 server is a million-dollar server. There's roughly 10-plus trillion market cap in software and services and internet. There's 22, 23 trillion in Mag 7, and then there's another 30-plus trillion in chips and hardware. I don't think people would realize that we are that compute hardware-centric. Before AI, in BCE era, it was probably reversed, and so you've seen in the last four years a transformation in value that has systematically been happening for the last four years. The realization came to me that when we hit AD era, AI era, that you needed to rethink everything. The Chinese are coming, and there's 130, 140 robotics companies in China. I see 30, 40 potential IPOs this year in China alone this year, and there's what? Zero, one, two in the United States maybe this year.

Molly O'Shea 1:10 Tony Kim, welcome to Sourcery.

Tony Kim 1:13 Thank you. Pleasure to be here in Paris.

Molly O'Shea 1:15 In Paris-

Tony Kim 1:15 In Paris. Yeah

Molly O'Shea 1:15 ... at the RAISE Summit.

Tony Kim 1:17 Yeah, yeah.

Molly O'Shea 1:17 We're in a, we're in a secret off location that has AC-

Tony Kim 1:20 I know

Molly O'Shea 1:20 ... and some croissants, so it's, it's quite nice. [laughs]

Tony Kim 1:22 It's beautiful here. It's so, so, uh, classic French. I love it. It's fantastic.

Molly O'Shea 1:27 So you're on-

Tony Kim 1:28 [laughs]

Molly O'Shea 1:28 ... stage a bit this year. What are you covering?

Tony Kim 1:32 I am doing four panels in my involvement with RAISE, so, um, one on accelerators with, uh, d-Matrix, kind of the next gen compute architectures. Another one with, with PsiQuantum and, um, around quantum computing. A third with Lumentum and kind of, uh, bringing optics to kinda next gen data center design, and then finally with, um, with, uh, Broadcom and, uh, XPU and AI code design for chips.

Molly O'Shea 2:07 So light agenda.

Tony Kim 2:08 Light agenda.

Molly O'Shea 2:09 [laughs]

Tony Kim 2:09 Yeah, yeah, yeah.

Molly O'Shea 2:10 So as the head of Global Tech for BlackRock-

Tony Kim 2:13 Sure

Molly O'Shea 2:13 ... what, what brings you here? How did you get involved with RAISE?

Tony Kim 2:17 So I got involved last year. One of my companies I was involved with, uh, SambaNova, Lipu was supposed to be one of the speakers, um, and, uh, he couldn't make it, so, uh, I decided to fill in for that. And, um, and then I, you know, I, I, I saw RAISE, um, this thing in Paris at the Louvre, and I was like, "Oh, this is interesting." It's, uh, it was the second year of, of development that, uh, Henri, uh, had kind of pioneered and built this, uh, this event. Um, I saw something there. I saw a lot of my, uh, my colleagues and friends from San Francisco all congregating here in Paris, and, uh, I said, "Well, I'd like to, uh, uh, help foster this, uh, get it going," and so last year I, I was here. Um, and then this year, uh, it's, I don't know, probably tripled again in size. It, it seems to be Europe's biggest or most targeted, targeted, uh, AI conference. So, uh, yeah, so I continue to, uh, to help and, uh, do what I can to help build an AI presence for, uh, in, in Europe.

Molly O'Shea 3:26 It's massive. I don't know how they get all the names that they get, but I remember last year seeing Eric Schmidt on stage, and, like, I had not heard of the conference before-

Tony Kim 3:35 No, and it's just-

Molly O'Shea 3:35 ... and I was just amazed. [laughs]

Tony Kim 3:37 Yeah, and I think they're outgrowing it after- I think next year it might out- outgrow the Louvre even, so it's, it's really becoming something. And, uh, and well, you're here, testament to-

Molly O'Shea 3:46 [laughs]

Tony Kim 3:46 ... to where it's come. Yeah.

Molly O'Shea 3:47 Well, they're amazing, so I'm happy-

Tony Kim 3:49 Yeah

Molly O'Shea 3:49 ... to get involved in any way that I can.

Tony Kim 3:51 Yeah.

Molly O'Shea 3:52 So between all of the panels that you're doing-

Tony Kim 3:54 Yeah

Molly O'Shea 3:54 ... you're doing four panels. What are the through lines and the macro themes?

Tony Kim 3:58 Through lines, macro themes. Okay. Well, I, I think one, one of the big ideas obviously, clearly is, um, and we see that in the stock market, and we see it in, in the investment market, is that, uh, the primacy of compute and how the- actually you're seeing it already, the how much the stock market and the capitalization, uh, in Silicon Valley has changed. And, um, we went from a, let's call it a, uh, a software-centric world to a compute-centric world. And, and, and you're, and you're seeing the emergence of companies, um, so that's number one, this, this move to compute. And, you know, uh, to me, in my opinion, you know, the models and the compute are kind of like s- uh, symbiotic and, and, uh, synonymous with each other, so the primacy of compute. Within that, secondly, is, um, since now that is the dominant, uh, theme, it is a dominant where the CapEx, where the money, where the capitalization is all gone, it then, uh, engenders a whole rethink of the data center. And, uh, so I think there is a redesign of the data center, and we're going through stages of the data

Tony Kim 5:17 center rebuild. And so we had, think of data centers pre-AI, kind of like the scramble to build data centers today, where there's a massive shortage of compute, but on the other hand then, you know, we're hitting the laws of physics are, are, are driving a yet another transformation of data center design going forward. And, and so that's the second. And, and the ramifications of this data center redesign kind of flow through every layer of the AI stack. Third, within that, then you go, you go another layer deep in this data center redesign. Um, you know, one of those is around power density. It's incredible what's happening is that the data centers, as we put more and more computation- Because the requirements of these, of the, of these models are requiring more and more compute density. That you need to pack more and more bits into a smaller, smaller footprint. And when you do that, bandwidth, power, heat. And so you have these logarithmic effects of, of the data center design driven by AI, uh, necessity. And effectively, we, the data center is changing from, you know, before we would transmit data kilometers, and now building to building, and then it's within the building, and then rack to rack, and then now it's within the rack, and then, and the next is within the chip. And so you're going from kilometers to meters to centimeters to millimeters. And so as you go one order of magnitude smaller in, in distance, the bandwidth goes up, the power goes up, the heat goes up. So this is the, the irony of it all, that we're-- the trillion dollars of CapEx this year and the $10 trillion over the next five years that are coming is to move data centimeters and millimeters. That's AI. And so when you think of it in that context, how does the data center need to change? And it's not just one of those things is around... Everything, you know, everything being done is to, is to optimize around this, these new physics. And, and a lot of the action is gonna be around the energy and the power density and the grid.

Tony Kim 7:43 An-another will be around the chip architectures. And then, then another one will be around the movement of data. And so we're going from a regime of copper to a regime of light. And, and, and, you know, and these things help address this power density and energy and the, and the laws of physics that are pushing data center design to its very limits. So, so that's, uh, another one. And then, and then, and then one more thing I would say around, um, around AI a-and this conference and things is around my, my first comments around the symbiotic relationship between compute and LLMs and, uh, AI models. And, um, and I think what you're seeing is that the best models obviously are trained on and built on the best compute and most optimized inference. But this co-design, this notion of co-design, of tightly integrating the design of your silicon to match the p- the parameters and the specs of the model, and the model specs informing the design of the compute. And then this kind of co-co-design is like the new path that many of the leading foundation labs are, are pursuing, and this is what I'll talk with, uh, Charlie at Broadcom about. Uh, obviously they did that with, uh, with the new, uh, Jalapeno chip that recently came out. So, um, so those are some of the big ideas. Uh, my panels are today mostly all focused, as you can tell, on the physical layer of AI, the compute stack, and, and, and the co-integration. Not so mu- uh, I'm not doing panels on the, on the software layer for, uh, at this conference.

Molly O'Shea 9:31 It's okay. Software, you know, it's a little sleepy right now.

Tony Kim 9:34 [laughs]

Molly O'Shea 9:35 [laughs]

Tony Kim 9:35 Yeah. Yeah. So l- yeah, a lot happening. It's interesting. A-and-and, you know, and then, and then one more thing I would say is that there's this whole, whole another revolution going on in, in the energy and the power side-

Molly O'Shea 9:47 Hmm

Tony Kim 9:47 ... around, you know, around grid, behind the meter, all kinds of new generation sources. And, you know, I'm sure you've done stuff around nuclear SMRs and, and things. And then, um, a new power architecture, the rise of 800 volt, is gonna have a transformative effect. And, uh, and then ultimately a solid state, solid state transformers. And so again, this goes to the point around r- this, this-- not only is the chip layer, the, um, the energy layer, the, the, the, the... Every time you have these step downs in voltage, y-you lose efficiency and energy. So again, things around making it more efficient, packing more in, reducing the distance, getting things up. And so this is like kind of a, a sub-narrative that is going around in the future design of data center. So, um, that's interesting. And I think you mentioned one thing, RAMpocalypse. Did you say RAMpocalypse

Tony Kim 10:44 or-

Molly O'Shea 10:44 You, you said that, by the way

Tony Kim 10:46 ... yeah. Yeah

Molly O'Shea 10:46 We were on a call beforehand-

Tony Kim 10:47 Yeah

Molly O'Shea 10:47 ... and you brought up RAMpocalypse. [laughs]

Tony Kim 10:48 Well, somebody... Yeah. Somebody-- It wasn't my call.

Molly O'Shea 10:51 I won't take credit for it either.

Tony Kim 10:52 I won't take credit. Somebody took credit for it. Uh, there's a lot of the memory companies here. But, uh, this goes to also w- yeah, maybe I'll add a fifth or sixth topic o-on this around the data center design of the future is, again, back to this co-design element around the model and the compute. And, you know, as from my observation, uh, again, I'm not building the models, but, but I observe the compute architectures and what the, what model guys are doing. Increasingly, more and more of, uh, chip and, uh, model development is starting to mirror the human, human brain. And, um, so in the initial early days, you know, you notice that we had a ton of compute, ton of parallelism compute, and the models didn't have much memory. And now we're adding memory to the, to the, to the, to the models. You know, it's remembering things about your behavior, what you're doing. And then you look in the future, you know, everyone talks more and more about having, uh, AIs that... Your personal AI that will, you know, the, these agentic harnesses that build in the intes- i-institutional context within an enterprise and, and memory and storing memory. And the human brain is, has a lot more storage of memory than maybe Compute. You know, it depends on how you look at synapses and neurons and things, but a, a- the human brain is, is very memory-intensive. And today is more compute-intensive. But as you see it with RAMpocalypse, the memory intensity has just skyrocketed.

Molly O'Shea 12:31 Wow.

Tony Kim 12:31 And, and, and going forward, you'll see more and more and more memory. And then when you look at the chip architectures, it is all about arbitrating memory in some form with your compute. Different kinds of memory, SRAM or DRAM or stack DRAM or HBM or high bandwidth flash. All of these memory and storage are methods t- tightly packed with your chip, your computer architecture, so that it, it aligns to how maybe these AI- AIs will be built that start to emulate more and more human brain. And so I think that's what's d- fueling the RAM, the RAMpocalypse-

Molly O'Shea 13:15 Mm-hmm

Tony Kim 13:15 ... the shortage of RAM. And, uh, you know, the other thing about that is that there's, uh, you know, there's a mismatch of what I call duration. Because it takes three or four years to build a chip fab or a memory fab, but yet everyone, there's a shortage today. So yet you're trying to build for a fut- you gotta build, and it takes three or four years to build the capacity. But what do you do about today's demand? And so there's like, but yet you gotta spend so much money to get chips out- output. And so there's this like mismatch of, um, demand, supply, duration, and this is causing a lot of, uh, angst in the market. But underlying all that, I think we are going to more and more memory than, uh, or let's just say memory in, in co- in concert with compute. Today, we all talk about compute, compute, compute. I think the primacy of memory will become even more important.

Molly O'Shea 14:24 This episode is brought to you by Brex, my favorite. You become what you spend on, and I refuse to spend my time on work that shouldn't exist. Expense reports, receipt chasing, and manual closes. The companies building what's next from Vercel, OpenAI, Anthropic, Granola, and Deepgram all made the same call. They all run on Brex. Brex is the intelligent finance platform that combines cards, expenses, and banking into a single stack with agentic finance built in. AI agents that handle expenses automatically enforce policy before spend happens and close your books in minutes. That's why Sourcery runs on Brex, so I can spend time on building and not busy work. It's time to get Brex AF. Learn more at brex.com/sourcery. That's B-R-E-X.com/S-O-U-R-C-E-R-Y.

Molly O'Shea 15:21 Bye. So we've been seeing this trend in a lot of our conversations and on the macro side-

Tony Kim 15:28 Yes

Molly O'Shea 15:28 ... with news and everything. We had a conversation with Jamin from Cotu. He's the CIO-

Tony Kim 15:34 Okay

Molly O'Shea 15:34 ... of public markets over there.

Tony Kim 15:35 Yeah.

Molly O'Shea 15:36 And they were talking about with the proliferation of agents, memory is only increasing more.

Tony Kim 15:40 Yes.

Molly O'Shea 15:40 They also talked about the chip flip, which we'll talk about a bit. But on the memory side, to your point, there's only three main players.

Tony Kim 15:46 There's only three.

Molly O'Shea 15:47 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, right, like, how do you underwrite that? Because the demand premium is, is massive because there's limited supply, and how long does that last? And how do you catch

Molly O'Shea 16:03 up to that? And so I would love to go deeper into all of these topics a bit more, but I guess to start more on the macro side. So we are entering, and we have entered the new era of AI. This has increased an entire rebuild-

Tony Kim 16:17 Yeah

Molly O'Shea 16:17 ... 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. So 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?

Tony Kim 16:38 Uh, I like your framing. It is a complete rebuild. So let's start with that and then, and then how we play offense as an investor. So you're absolutely right. It is a complete rebuild. So the internet as we know it was built, let's just say 2000 to 2020-ish in one framework, which is basically around, basically the birth and da- the dawn of cloud computing. And, um, and cloud computing, you know, necessitated a certain kind of data center. You, you remember the good old classic data center, megawatts, not gigawatts, right? So we had a order of magnitude increase today. A- and then these data centers were, were small and, and, and they were, um, at the end of the day, cloud computing, this is a big, everyone says it's software, but it, it was really reselling CPUs with hard drives. That was the compute stack, a CPU with a hard drive. And it was considered a commodity. And server prices were th- tens of thousands of dollars, thousands of dollars, and now those compute servers are millions and tens of millions of dollars. So compute was an afterthought. And so when you, when you think of-- The other way, the other way I think about it is then, and then these clouds built these classic compute stacks, and then they resold that as platform services, databases, and then SaaS built on top of that

Tony Kim 18:16 And, and, and, and so but the-- if you think about the base unit to create a cloud was relatively small. CPUs and hard drives, right? Basically, that was it. And, and some databases, okay? Uh, uh, and, and then so SaaS was, was king. And, and the margins went to that because the, the marg-- the cost of compute was so low. Uh, and, and, and, and, and therefore everyone said, "Compute is f-free, it's cheap, it's a commodity," and all the value went to this layer, right? Because that's what cloud computing was. You're reselling... You're, you're, you're building this massive application on a very, very thin layer of compute. And, and, and so that fueled a, a twenty-year run in cloud and SaaS. And that all of that data center infrastructure that was built, even with AWS and GCP and Azure, that was built for that. And that was built to basically bring on-prem software to, to hosted cloud services. And so that was a great business. Everyone was very happy, and data centers were built for that spec. AI happens. It's like, uh, BCE, Anno Domini.

Speaker 19:43 [laughs]

Tony Kim 19:44 2023 is, you know, it's, it's going from BC to AD. And, uh, bam, '23 happens, everything changed. So that, what was called the base layer of compute, went up, I don't know, 10000X. It's, you know, a $10,000 server is a million-dollar server. Small HDD, big HDD. Oh, and by the way, c-- DRAM was a commodity only used in smartphones. I need to pack all the DRAM, HBM I can, which is expensive DRAM, onto, onto this AI thing. And so this data center is now, like-- uh, and it was megawatts, now it's gigawatts. It's small data centers in cities to giant server farms in, in Texas. And, and so that is the data center and cloud of tomorrow, and, and they're, and they're, and they're selling tokens. Data center of past is like this old cloud, but yet it facilitated high margins. And now that is what I call-- That has started-- That still exists, but it's not gonna grow like this. But this business will facilitate radically more capital, a complete rebuild. This is an alien data center to this data center. And, and, and so, uh, but that requires h-massive capital investment. But it also engenders a very different rethink of the margin stacking. Because before you would resell this base layer of low compute and massive SaaS, uh, app margins on top, now you've got this massive compute stack, and they're reselling those as tokens, right? So your, your-- that, that compute factory is d-creating tokens, and then the model guys are then selling their tokens. And that just takes a lot of what I call-- takes a lot of margin out of that top layer of the stack. And so this is also facilitating-- this is driving what you just said, a complete rebuild. Like these-- you must build these new data centers because at some point, all of your revenue will become here. A-and, and this revenue over here in the old data center that was asset light, high margin, is now moving to asset heavy, lower margin, big dollars, but it's a completely new data center. And, and then that, because of this new rebuild, it, it, it then has triggered a reexamination of, of value.

Tony Kim 22:38 So and, and so if you look at today, the tech stock market today, and I'm gonna appro-- make some approximations. You know, there's about fifteen hundred companies globally, uh, over a billion dollars or more market cap, maybe two thousand, depend-- if you add China, not in China. In the US, let's just talk a global stock market. There's roughly ten plus trillion market cap in software and services and, and internet. They were the classic industry where most of the market cap was in the pre-AI era. That's ten trillion plus or minus. There's twenty-two, twenty-three trillion in Mag 7. So I, I just put Mag 7 as a new category. Yeah. So Microsoft is a Mag 7. It's not-- So I just have a, a non-Mag 7 software service and internet category of ten trillion. Twenty-two trillion in Mag 7. And there's another thirty-plus trillion in chips and hardware. So ten, twenty, thirty. Something like that. Okay? Ten trillion in software service internet, twenty trillion in Mag 7, thirty trillion in non-Mag 7 compute chips hardware. That's a-- I don't think people realize-- would realize that we are that compute hardware-centric now. Ten, twenty, thirty. Before AI, BCE era, it was probably reversed. Okay? And so you've seen in the last four years a transformation- In value that has systematically been happening for the last four years. And, and, and that follows the data center transformation because the primacy of compute, the plurality of the dollars, the creation of the models themselves, the models themselves, like it or not, have consumed the market cap out of software and services. It is just, you know, it's like the Borg, it is, it is [laughs] it has consumed. And, and, and for, for the, for those foundational models to exist, it needs to live on the compute stack. And, and so that has happened. And so to be offensive in this structure, you need to-- I needed to-- the realization came to me that when we hit AD era, AI era, that you needed to rethink everything. And then you need to align offensively, as you say, uh, an investment philosophy and capital allocation philosophy that would mirror what is bec- becoming the new reality, um, where these intelligence, the insatiable demand for intelligence and as a function, intelligence begets compute. And intelligence for compute equals basically revenue. And then, and then if you think that the basis of, of many companies is around this compute factories and, and then your ability to resell that intelligence, and then that re- makes you rethink kind of the margin stacking and kind of where the value sits for companies. And, and, and, and the market is trying to adjudicate that right now. And so, you know, that's why you saw SaaSpocalypse earlier this year, end of last year. That's why you're seeing the RAMpocalypse.

Molly O'Shea 26:28 [laughs]

Tony Kim 26:29 There's a lot of apocalypses, you know. Um, and, and, you know, and, and the thing, if you believe these scaling laws and intelligence is getting better, I'd say at one order of magnitude a year. I mean, 10 times 10 times 10, that's 1,000 times in three years. So it's not like these AIs are getting less capable. They're getting more capable. And more capable means more compute. [laughs] And, and if they can do more things, then it's a rethink around where the margins are, where is your defensibility, where people use the term moat a lot. Moat is a very defensive. Moats are always the breached, aren't they? So [laughs] it's more about offense in my opinion. Can you move faster? And so yeah, that's, that's a, that's a broad topic. I hope that kinda... Yeah, th- that's how I think about the macro. Like you said, this complete redesign. That redesign facilitates a rethink, and that rethink also, it, you know, has huge implications on the business models and the moats of companies. And so, uh, and this is, you know, I ma- I'm ha- what my thesis is now, might cha- we talk again in six months, it might be completely different. But, uh, that's kind of my current thinking.

Molly O'Shea 27:49 Because to your point earlier, we are investing in new areas, or we're investing in areas that have three to four to five-year lead times, even 10 years if you wanna talk about quantum. I mean, that's always 10 years, whatever year you're talking about it-

Tony Kim 28:05 Mm-hmm

Molly O'Shea 28:06 ... to be clear.

Tony Kim 28:06 Mm-hmm.

Molly O'Shea 28:07 But in, in terms of those types of outward investments and strategies, how do you think about where you're gonna spend time and which one of those is most effective right now? Because you're obviously taking a risk on that, and I, I kind of bring that up-

Tony Kim 28:22 Yeah

Molly O'Shea 28:22 ... in the point of quantum as well.

Tony Kim 28:24 Yes.

Molly O'Shea 28:24 And with these new chips and building them specific for models. So like, how do you think about that, and how do you weigh the different kinds of risks that come along with it?

Tony Kim 28:35 Yeah, um, a investor, a portfolio manager, you know, at the end of the day you're allocating capital, right? So, you know, you have only so many bullets. And so y- you know, my-- I'm a public investor, I'm a private investor. I do both. And, but at the end of the day, you're allocating capital and, and the creation of whatever portfolio you're creating for your, for your mandate, for your clients. At the end of the day, you're trying to arbitrate between risk, like you said, what is, what is the today and what is the tomorrow? And like a lot of the things around, around this AI today, y- you know, there's a lot around to today, even the three-year, the three-year duration mismatch of, let's say, DRAM and foundries. So I, I call that kind of like the now, right?

Molly O'Shea 29:31 Mm-hmm.

Tony Kim 29:32 This is the now. The vortex of AI is like the now. A- a- a- and so, you know, if I, in, in this kind of three-year window, that is probably where 90-plus percent of my investment, or 90-ish percent, something like that, you know, the p- the majority is going in. Um, you know, within this three-year window, what-- 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. So that's one. The second thing, or even in this three-year window, let's call it, is, is there, is there, is there life after the three years? So you, you kind of have to believe that this continues five-plus years, right? Because like that, the, the, the, the u- the belief in a future has a huge impact on your multiple. So if they do not believe in that, in that future, y- you know, even beyond the two or three-year horizon that most investors and, and Wall Street can forecast to, there is an implicit, there is an implicit understanding that if-- do you have a future or not? So y- you know, I always feel like you must be betting on, on the future as well. So, so things look cheap, but you know it's, it's atrophying and maybe in, in decline, or growth is decelerating and, and so is that the best allocation of capital put to that? So it w- versus, like, the next three years, it's gonna be great for memory or compute or data centers, but then, like, will-- but will that continue four, five, six years into the future? Question mark, yes or no. And then, but then there's also you gotta be betting on the frontier of the frontier. You know, I made some of these AI investments pre-gen AI, like pre-2020, '19, '20, '21, you know, where you didn't know that this LLM thing was gonna happen. Some of these things gestate longer. My intuition was that we will need AI compute of some form, maybe machine learning AI. And, and so I didn't know that this LLM w- w- the, the, the wave would happen. But you're, you're thinking about future architectures. And so now we sit six, seven years later, and the AI accelerator wars have begun, and the compute has taken off. And so I think about that in that kind of longer term context. And so, you know, the next set of companies that are-- have this longer context is around, let's say, you mentioned quantum. I, I, I think 2030 we will see. I, you know, I started getting involved in 2019, so I'm already seven years in. You got another five more years to go. But, you know, that's a decade-long con- so you have some bets on kind of where the frontier is coming next.

Tony Kim 32:41 Space, right? You know, these orbital data centers, right? That's also targeting 2030. It's a- it's int- very interesting when you look at these long data 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, 2030. When you, when you say, "Well, when was AGI happen for classical computing?" 2030, 2029, 2028, whatever. Then you say, "When will we hit 800-volt power architectures?" Late 20-- 2020s to 2030. You know, will we have solid state transformers? 2030. Will, will we, uh... Fusion, longer. But many-- uh, data centers in space, 2030, uh, you know, when it starts to really scale. So you're like, you're sitting here, obviously you have the now, this AI train [laughs] that is consuming everything, all my time and energy, you know, but that's eight- eighty, ninety percent of it. But, you know, you must be always betting on the tomorrow. And, and some of these are long-dated things. And so I have-- I spend time on the future, allocating X resources of my time on that. What will really be transformative, not incremental. I want nonlinear, asymmetric potential. And then I bet on what is the, the primacy of today that with not only a three-year financial forecastable window, but with then relevancy beyond. And then everything else [laughs] that doesn't fit in that window, is it worth- worthy of your time and the opportunity cost to keep investing in that? And, and ch- there are other strategies, other portfolios, other, other things that will... that can pursue those. It's just not my-- that's my-- not my focus, really. So I hope that kind of gives you a sense of, uh, of where, how I fra- this is my, my opinion how I frame capital allocation portfolio decision-making, you know.

Molly O'Shea 35:11 That's a super helpful explanation. I'm sure a lot of your investment memos have 2030 on them. [laughs]

Tony Kim 35:18 20 thir- you know, actually it's not-- 2030 is not far away.

Molly O'Shea 35:23 No, it's not.

Tony Kim 35:24 Um, yeah, absolutely. I mean, you know, many companies, right? You, you gotta look twenty-thirty-one. I mean, that's-- twenty-thirty-one is five years. Ten years, tw- twenty-thirty-six. So I mean, ten years is, like, almost an impossible forecasting p- but five years, you know, uh, you know, a lot of th- a lot of companies will not even have free cash flow by 2031. So, so, um, you then need to have a belief system that, that, uh, you know, that, that could flip positive, um, beyond, so. But yeah, I, I, I'd say at least a five-year, five-year window. You know, you always wanna be betting on not, not what's cool today. Will you still be cool in five years?

Molly O'Shea 36:12 Mm.

Tony Kim 36:12 Because if you're still, like... Because then you become yesterday's news in five years because you, you-- even though you are cool today. So maybe there's a little bit of that happening too, so. Uh, because, like, that really has a huge impact on your exit multiple, right? If you're just following the trend of today and, and, but you know that there's a half-life to this- It might be difficult to get, get a, a, a good good return on the exit because, um, it will not be what you think it is in five years, and the multiple that people will pay will, will go down. And, and your growth rates are decelerating-

Molly O'Shea 36:48 Mm

Tony Kim 36:48 ... and now you, now you're, now you're in a bind.

Molly O'Shea 36:51 If you're building what's next in AI, you need to know MongoDB, the database platform developers love and built for the agents you're running. MongoDB stores, searches, and reasons over your data in real time with vector search and embeddings from Voyage AI all in the same system. No separate pipelines, no stitching together 10 different tools. It's why 75% of the Fortune 100 and leading AI-native startups run on MongoDB. Build and scale from your first user to billions of vectors. Go to mongodb.com/ai to learn more. That's mongodb.com/ai to learn more. Bye. AssemblyAI is a voice AI infrastructure layer millions of developers build on. They build the industry's best speech-to-text, voice agent, and speech understanding models that serve as critical infrastructure for companies like Granola, HeyGen, Ashby, and ClickUp. Their speech-to-text models lead the industry in accuracy and quality, and their speech understanding models help you go beyond transcription by uncovering insights, identifying speakers, and highlighting key information from voice data. You can get started today at assemblyai.com/sourcery and get $50 of free credits to start building voice AI products. That's assemblyai.com/S-O-U-R-C-E-R-Y.

Molly O'Shea 38:16 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 of your takes on those. So in that respect, you did mention orbital data centers really didn't exist before.

Tony Kim 38:41 No.

Molly O'Shea 38:41 And that is a huge weight and a big weight, especially with SpaceX coming-

Tony Kim 38:45 Yeah

Molly O'Shea 38:46 ... and, and the whole IPO on that. But there's also fun categories. I did-- I recently visited Figure AI-

Tony Kim 38:54 Yeah

Molly O'Shea 38:54 ... the robo- the humanoid robotics company, and I was just at Config, Figma's conference, and Boston Dynamics was there, and one of their head of designs for human-robot interaction was talking about their humanoid ro- robot Atlas. They're different.

Tony Kim 39:11 Mm-hmm.

Molly O'Shea 39:12 The Atlas one is hydraulic, so it can pick up like a fridge.

Tony Kim 39:15 Mm-hmm.

Molly O'Shea 39:15 Figures is, you know, more of like daily use, package sorting, commercial stuff-

Tony Kim 39:21 Mm

Molly O'Shea 39:21 ... making cars and that sort of thing. But of these new categories, which ones are you paying attention to? What are you excited about?

Tony Kim 39:28 So robotics, yeah. I mean, the first comment around o- these older categories, [lips smack] you know, like you said, semiconductors have been around a long time, and, uh, they're always-- I don't know, I don't understand why pe- You know, it's called Silicon Valley for a reason.

Molly O'Shea 39:46 [laughs] People forget.

Tony Kim 39:47 But people forgot. It's kinda like the, the, the, the, the ring of power. It was lost, and then it was found. And, um, and, uh, people always said chips are a commodity, but yet they have the highest profitability of any company in the world, of the sector, is chip companies. They're 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 was ne- It really never was. And, and the other thing is that this industry is very interesting. There were hundreds of chip companies, and then systematically over 20 years, now there's just a few. And so in every category, you have duopolistic power. And by the way, venture capital, i.e. 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 new companies. So in fact, what you have is a shrinking effect. The number of companies have collapsed, and then those that have survived are behemoths with huge po- pricing power, the complete opposite of commodity. And the- By the way, all those people are engineering nerds. So I always say it's a revenge of the nerds. It's not a revenge of the nerds, it's the lost ring of power that was found. And, and so but it was always there. And, and, and, and so their time to sh- the time to, uh, to shine is now.

Tony Kim 41:30 That said, you're right. These other industries, you know, it has spawned a renaissance in hardware, right? You know, when you look at like that market cap shift that I was talking about, a lot of those go into, you know, servers are cool, fiber is cool-

Molly O'Shea 41:47 [laughs]

Tony Kim 41:48 ... power is cool, you know, rack design is cool.

Molly O'Shea 41:52 People love rack design. [laughs]

Tony Kim 41:53 People love it. They're going crazy about rack design. It's like, you know, it's like bending metal, copper, you know, like, you know, heat. You know, material science is cool-

Molly O'Shea 42:02 Mm-hmm

Tony Kim 42:02 ... because you need all kinds of new materials, you know, and then substrates and packaging. It's the physical world. These are all phys- what I call the physical world, the physical sciences. That's cool again. Going to school to study materials science is probably cool. It's very cool. There's not enough chip designs, chip designers in the world. People in the lost art, you know, like analog computers is, is like, is like a-- being blacksmiths. Uh, it's like very few pe- you know... How many friends of yours go into study new memory design? You know, I was talking to one, one- dinner last night with, here at RAISE, with one of the biggest memory companies. Like, we cannot get people to design custom memory, because like, like one of those custom co-design with the chip, they, they wanna co-design the memory. Well, where are the people? There are no people. Like, we gotta repurpose some of these software programmers into memory co-design architects. A- a- and so, like, all of this has happened in the physical, what I call the physical world, is this is the next unlock that AI will do. Obviously, we're gonna go hard in cognitive work and, and cognitive labor, and you gotta build these models for that. But then those models then can be repurposed and implemented in robotics.

Tony Kim 43:30 And so the robotics sys- chain is v- very interesting to me, but it, it, you know, it, it kinda is just a parallel to what was going on in AI. Because if you really think about it, what is, what is robotics, right? I mean, you got a brain that will be built, a- and the brain will have, uh, I mean, kinda two parts to it. The, it'll be a, a baseline LLM that w- you, you and I will communicate as a translator, translate and talk to the, the robot. But then there will be... It's like the human brain, but you also have a, uh, the brain for the motor functions that control our muscles and our bodies and our reactions, and then the brain for language and memory. So they'll have two r- uh, kinda like a world model, like that's kind of one of the new, a world model for, to perceive the world and motion and things, and then, and a, and a really obviously, uh, embodiment of intelligence in, uh, like an LLM. So you're gonna build these brains, two brains into one, [chuckles] uh, you know. And then you take the brain, and then the, those are like labs, right? They'll be like labs, and 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 the, probably- Yeah ... the hardest thing, as you, I'm sure you know. But the body, the physical embodiment, that is a manufacturing hardware business,

Tony Kim 45:03 and when you think about that, the Chinese are coming, and there's I think 130, 140 robotics companies in China. I'm looking at the current pipeline. I, I, I see 30, 40 potential IPOs this year in China. Yes. Yes. Alone this year. And there's what? Zero, one, two in the United States maybe this year. And, and the reason for that, though, is also th- the US, the, the, the lack of depth in the private markets in China, so they're using public markets as a, as a funding proce- funding mechanism- Mm-hmm ... unlike in the US. But, but- So they're earlier ... they're gonna come earlier. Okay. Yeah. And they're coming in waves. Mm-hmm. There's 140 of them. And, and the thing about China is in that physical layer, the body, the motion, they're, they maybe they're, they're, they're br- they're, the, the, the, the robotic brain development, the world model, th- th- they may be behind, right? That, that's probably most people would say that the West is ahead on, on, on the model development. But make no mistake, Chinese and Asia, and you, you mentioned, uh, Atlas Robot. That, that's, that's Korean, actually. That's, that's Hyundai that owns Boston Dynamics. But the Asian manufacturing complex, Japan, Korea, and China, I mean... And it's also a, an extension of kind of EV platforms, right? If you have the physical scale manufacturing, you then avail yourself to potentially have lower cost to manufacture in mass these robots. A- and then what you might have is ultimately you can mix and match Chinese physical robot with a Western brain, and I know that's happening. Mm-hmm. I know people are, "I just want the robotic..." They, th- those Chinese robots are amazing, right? So why don't we stick a Western brain in there, inside? And, and, then permutations of this will continue. And so, so I think it's a Wild West, a lot happening, and um, but it's, it, it will be a huge market, a huge market, and I, I have a, I have a soft spot. I mean, my view is, uh, one of my things I'm most interested in in the robotics side is around not so much the manufacturing robot.

Tony Kim 47:37 I mean, of course, that will, but, and you're seeing this come out of China already. It's around, um, around loneliness, around social embodiment, around, um, you know, around education, around elderly, around y- young people, and to bring consumer and/or commercial, like, social robots more so than industrial use. So- That's kind of a hot take. Well, I think that's-- I mean, I think if you think about aging populations, when you look at Asia in particular, you know, the birth rates are well below 1.0, and you need 2.1, 2.2 to stay even. So y- you're facing demographic population cliffs around the world. You look at actually some of the best performing companies in, in the world are nursing home companies. And, and, and when you look at elderly, they really want companionship. And even amongst young people, you know, there's loneliness epidemics and things like that. And I, I think robots, even though you don't might have not perfect motor function, but if you could embody some intelligence, empathy. It could be many different form factors too. It does not have to be the, the Terminator, like ro-

Molly O'Shea 49:13 Yeah

Tony Kim 49:13 ... human ro-

Molly O'Shea 49:13 Mm-hmm

Tony Kim 49:14 ... I think that could unlock a really, really interesting market, a really big market. So, uh, that's my, that's my, that's my view. Yeah.

Molly O'Shea 49:24 That's interesting. I've seen, and I know this doesn't really count, but I saw videos on Instagram of, like, a long-distance relationship, and there was, like, a tiny little, like, pet robot on the ground. It was like a ball-

Tony Kim 49:36 Mm-hmm

Molly O'Shea 49:36 ... of some sort.

Tony Kim 49:37 Mm-hmm.

Molly O'Shea 49:37 And it was, like, the girlfriend, like, yelling at the boyfriend.

Tony Kim 49:40 Yeah

Molly O'Shea 49:41 And she's, like, in an entirely another country, like, following him around in the house.

Tony Kim 49:45 I mean, you... I'm, I'm not sure you, you've seen... Do you- Have you seen, um, Star Wars? You've seen Star Wars?

Molly O'Shea 49:50 Yeah. Mm-hmm.

Tony Kim 49:51 Okay. And who are two of your favorite? Did you like C-3PO and R2?

Molly O'Shea 49:55 Yeah.

Tony Kim 49:56 Okay. Now imagine- And then you have the current embodiment of Optimus and many other, the, the figure robot and all these sleek, you know, amazing, uh, Westworld-like things. But, like, I harken to, like, R2 and C-3PO. Um, and well, what if you had, like, half-size robot? Even half size. It doesn't even have to be full size. Um, that is approachable, friendly, not masculine, you know, something that... And then that robot, but that has the intelligence of Shakespeare and Einstein and speaks every language like C-3PO. And, and then you interact. You know, I, and I always think, often think about the elderly. You know? It's like imagine them having conversations with my mother and, and others, and then also empathetic to their stories, and then you can record their stories.

Molly O'Shea 50:55 Mm.

Tony Kim 50:56 Record their life histories. And, um, you know, I, I think, do you need to have a fully figured perfect motor function with, you know, all the hand articulation, or could you get something that can appeal to that? And I, I, I think that's possible. I think that, I think that will be a, uh, fascinating market to see. So, um, yeah, I think that'd be a very new use case, um-

Molly O'Shea 51:24 Yeah

Tony Kim 51:24 ... besides building, using robots to build lunar, the lunar base. Yeah. Which I think also that would be cool. Yeah.

Molly O'Shea 51:30 Really cool.

Tony Kim 51:31 Yeah.

Molly O'Shea 51:32 Yeah. Um, it's been interesting to see,

Molly O'Shea 51:34 'cause I cover high- a lot of high-growth companies in Silicon Valley.

Tony Kim 51:37 Okay. Yeah, yeah.

Molly O'Shea 51:38 The proliferation... Of course, coding agents is a big thing, right?

Tony Kim 51:42 Absolutely. Yeah.

Molly O'Shea 51:42 But to your theme of the brain, speech model companies-

Tony Kim 51:46 Yes

Molly O'Shea 51:46 ... are crushing it.

Tony Kim 51:47 Crushing it.

Molly O'Shea 51:48 They are growing faster than most other companies out there.

Tony Kim 51:51 Yes.

Molly O'Shea 51:51 There's a company called AssemblyAI that is, like, growing incredibly fast-

Tony Kim 51:54 Yes, yes

Molly O'Shea 51:54 ... and they're great. And then it's interesting to also see the downstream effects of all this. Okay. So we talked about... We didn't really cover at all any software, because it is what it is.

Tony Kim 52:03 Yeah, yeah.

Molly O'Shea 52:03 But the downstream effects of it now hitting the data layer, because now we are- we have so many agents that are just creating so much data.

Tony Kim 52:11 Mm-hmm.

Molly O'Shea 52:11 It's coming downstream. And so, like, the Databricks-

Tony Kim 52:14 Mm

Molly O'Shea 52:14 ... the Snowflakes, they're hitting some of that extra premium in the market, and they're getting some attention. And then you go downstream, like, more app size. And then so as agents create more apps, now MongoDBs, those databases are reaching. So it's really interesting to see how, like, it's streaming downstream. Are you looking at any-

Tony Kim 52:31 Oh, yeah

Molly O'Shea 52:31 ... of the downstream winners?

Tony Kim 52:33 Yeah, yeah. I'm invested in many of those companies. Streaming downstream or streaming upstream, I, I, I don't know what's down or up.

Molly O'Shea 52:39 I don't know either.

Tony Kim 52:40 Yeah. I, I-

Molly O'Shea 52:40 So I could be opposite.

Tony Kim 52:41 Well, no, I, I-

Molly O'Shea 52:42 [laughs]

Tony Kim 52:43 No, I, no, I 100%, you know? In, in, in supply chain hardware, is it upstream, downstream?

Molly O'Shea 52:48 [laughs]

Tony Kim 52:49 Okay. It's like, I, I, I, I, I, I'm thinking vertically. So it's like compute, models, apps. All right, compute, models, data, apps. Something like that.

Molly O'Shea 52:59 Okay.

Tony Kim 52:59 Right? Going, I'm going up the stack.

Molly O'Shea 53:00 You're going up.

Tony Kim 53:01 I'm going up the stack.

Molly O'Shea 53:01 I'm going down. [laughs]

Tony Kim 53:02 I'm going down the stack. Whatever. Okay. So absolutely. And so, you know,

Tony Kim 53:08 you know, like this whole data center redesign thing, I think the whole enterprise is, is redesigning.

Molly O'Shea 53:13 Mm-hmm.

Tony Kim 53:14 And, um, and the enterprise itself, if you really think about the future of what a big enterprise will be like, it, it, uh, uh, it'll be on one hand, you'll be, you're, you're, you're bringing, you're bringing intelligence, tokens. You're bringing tokens in, and you will have data layer that'll be, uh, the, the, so 'cause the, the, that intelligence will need to interact and, uh, be orchestrated it, uh, around this data layer. And this data layer will be the embodiment of your proprietary data and all your, all your external par- third-party data. And then companies will ul- ultimately, what is a company? Well, it's people, it's distribution, it's a brand, but at, at the end of the day, it's also can you embody all of the knowledge of your company in a, what they call like a context layer.

Molly O'Shea 54:15 Mm-hmm.

Tony Kim 54:15 A layer of the secrets and, and the ways of, of your company, that you embody the, the cumulative knowledge of your, your employees into its own context layer.

Molly O'Shea 54:28 I think Palantir calls this ontology.

Tony Kim 54:30 Ontology. Exactly.

Molly O'Shea 54:31 [laughs]

Tony Kim 54:31 Yeah, yeah. Exactly. So you have this ontology layer, this context layer sitting on the r- the data, the data foundation with tokens in.

Molly O'Shea 54:41 Mm-hmm.

Tony Kim 54:42 And then that's it. And then you, and then you, everyone builds agents. A- agents go wild, right? And, and, and agents will interact through the context into your data-

Molly O'Shea 54:52 Mm

Tony Kim 54:52 ... with tokens. That's it. That is, that is the enterprise. And, and then, and then what I call that is a token flow. Follow the flow of tokens. Either you create tokens, compute. You, you, you then serve the tokens, foundation labs. And then you put a harness package 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 s- your very specific application. You 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, and firing up the token? If you're not in that flow, it's a, it's a problem. And then you mentioned these voice APIs and, you know, you mentioned the data foundation.

Molly O'Shea 56:02 Mm.

Tony Kim 56:02 They are in token flow. And, and, and so I think the app companies are struggling to f- to find their place. But some companies have, have moved into, you know, you're seeing the rise of what I call... I don't know what you call it, inference clouds, edge clouds-

Molly O'Shea 56:21 Yeah. Mm-hmm

Tony Kim 56:22 ... edge AI. They're basically kind of that last mile token. They're providing tokens and, and developer kits and, and things so that smaller businesses, medium business can take it kind of out of the box. And, and so they've, they've inserted themselves in this token flow. And, um, yeah. And so that's the... You know, and to me, I, 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, I will provide-- I will do all of your claims processing. I will do all of your insurance processing." And so you don't... You abstract away all of that, all of those layers of the stack and just say, "I will do all of your-- I will take on all of your insurance. Pay me X. Pay me X." And so you don't know what, what, what are you? 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 used to pay 100, I'll charge you 20. And, and then now you're seeing, you're seeing this, you, you're seeing certain private equity firms, you're seeing some venture firms saying, "You know what? Let's take an old industry. Let's buy these companies, and let's bring in this whole new stack, reimagine the stack, and just sell a whole new solution."

Tony Kim 57:56 So-

Molly O'Shea 57:56 Do you think-- I mean, it's really curious with those PE roll-ups, 'cause I think at the end of the day, they're just creating a new product, but they're buying the customers.

Tony Kim 58:04 Yeah. They're buying, they're buying, they're buying-- They're getting customers or they're buying companies with customers, and they want to-- They're basically trying to re- restructure the whole delivery of services, and there's a lot of inefficiencies, fat cost in there, and then you can rip it all out.

Molly O'Shea 58:24 Mm.

Tony Kim 58:25 Okay. Maybe, maybe that's a business. Let's see. I, I mean, um, I know people doing that, or starting to do that. Let's... Yeah, that's interesting. I don't know. It's-

Molly O'Shea 58:34 It's, I mean, it's working quite well. We've talked to a-

Tony Kim 58:36 Yeah

Molly O'Shea 58:36 ... a couple of them.

Tony Kim 58:38 Okay.

Molly O'Shea 58:38 Some on camera, some off camera.

Tony Kim 58:39 Yeah.

Molly O'Shea 58:40 We talked to General Catalyst Creation Fund.

Tony Kim 58:42 Yep. Yep.

Molly O'Shea 58:42 And they've been doing a lot of PE roll-ups-

Tony Kim 58:44 Yep

Molly O'Shea 58:44 ... and creating companies. Um, one of them, Long Lake, just bought Amex Global.

Tony Kim 58:50 Okay.

Molly O'Shea 58:50 Their travel, I think their travel business.

Tony Kim 58:52 Oh, wow. Yeah.

Molly O'Shea 58:52 Which is interesting 'cause you have a small-

Tony Kim 58:54 We, we use those

Molly O'Shea 58:54 ... player-

Tony Kim 58:54 [laughs] We use those guys

Molly O'Shea 58:55 ... buying a large player.

Tony Kim 58:58 Yes.

Molly O'Shea 58:58 Which was really interesting to see.

Tony Kim 58:59 Yeah. I mean, you know, there's this, you know, this other, this other ru- you know, framework rubric that is emerging is, um, you know, when you have these traditional industries, and you have a new company with 500 people that can do or generate the revenue of 10,000 people, and, you know, and, and they're, they're just approaching it in a radically rethought process. And so the efficiency. And, you know, and, and so maybe this is what you're kind of alluding to. You know, maybe that's the, that's the new framework for these newer companies that are gonna really just go at traditional industries. And it'll be up to can the traditional company, the incumbent, adjust in the face of, uh, of these kind of companies coming? And, and, and, um, yeah, I think the jury's out. I, I think it's very-

Molly O'Shea 59:54 Mm

Tony Kim 59:54 ... I'm very intrigued by that. I- I- I'm watching it. Um, yeah, you mentioned some of these. It could be, it could be quite disruptive. So I think that's the next, the next shoe to drop is-

Molly O'Shea 1:00:05 Yeah

Tony Kim 1:00:05 ... all of these traditional industries that have very little adoption of AI, that are still... The, the business workflow, the, how they've organized themselves, they could be all completely re- rethought and, and, and refactored with a new kind of... But that would require, like you say, maybe, you know, it's so hard to sell products, pieces, and then have your old c- old employees drive that change versus if you just buy the company, I'll do the change. And then, so it's an interesting idea. It's an interesting idea. I, I, um, yeah, it's all new. I'm, I'm watching it. Uh, it's, it's, uh, something to look out for. Yeah

Molly O'Shea 1:00:48 We only have a few minutes-

Tony Kim 1:00:50 Okay

Molly O'Shea 1:00:50 ... left, but I'll leave you with two questions.

Tony Kim 1:00:53 Sure.

Molly O'Shea 1:00:53 First, what are you most excited about in the next 12 months? I know 2030-

Tony Kim 1:00:57 Next 12 months

Molly O'Shea 1:00:57 ... is a big date, but-

Tony Kim 1:00:58 [laughs]

Molly O'Shea 1:00:58 ... let's talk about the next 12 months maybe.

Tony Kim 1:01:00 Oh, boy. Next 12 months, between now and June 27. Um, I, well, I mean, obviously these big foundation labs. What am I most excited about or more concerned about? Or, but, uh-

Molly O'Shea 1:01:17 Take it either way.

Tony Kim 1:01:18 Yeah, yeah.

Molly O'Shea 1:01:19 I mean [laughs]

Tony Kim 1:01:21 I, I, I, I just, um, I think it's a continuation of the same. What, what I would, uh... I know, like, there are these... It seems like every six months there's a scare.

Molly O'Shea 1:01:36 Yeah.

Tony Kim 1:01:37 Yeah. Ex-pocalypse, ex-pocalypse, war, interest rates, too much CapEx, not enough financing, what, and on and on and on. But I, I, I think through it all, I, I, I'm, I am optimistic that this compute wall, the, the memory wall, the compute demand, this data center redesign, that it will just plow through, and then our fears will be subsided. And then so therefore, the, we will s- we will be sitting here a year from now, and we'll be talking about many of the same things continuing on. So that's number one. I hope that's what I'm optimistic for. The second thing is the emergence of, um, is the progression along this, what I call this whole data center reimagination theme.

Molly O'Shea 1:02:32 Mm-hmm.

Tony Kim 1:02:33 And so I'd like to see more of the, just continued proof points along that, along that path. I, I am, uh, I'm hopeful, excited to see the big labs go public in the next 12 months. I think that would be, I think that'll be, I think it'll be interesting. It'll be exciting, and I, and I think there is huge market appetite for it. And, uh, I would love to see in the next 12 months, or I'm excited for, is, uh, the next forward step toward orbital data centers and... Because that, that also engenders a radical change in, in, in data centers, right? If, if you keep pushing on that progression, it, it could open, you know, it could unlock a rethink, you know, and, and, and moving the burden of terrestrial, terrestrial, uh, compute into space, and that could have a huge implications, huge implications of how current data centers are even being built. So I'm looking at that as, uh, to see if the, the progress being made there. So it, it's gonna be interesting 12 months.

Molly O'Shea 1:03:39 Amazing. Also sounds like a little bit of a manifestation going on over here.

Tony Kim 1:03:44 Manifestation?

Molly O'Shea 1:03:44 Yeah, you're manifesting.

Tony Kim 1:03:45 [laughs] I don't know. I'm, I'm just, I contemplate.

Molly O'Shea 1:03:49 [laughs]

Tony Kim 1:03:49 I contemplate, yeah. Yeah.

Molly O'Shea 1:03:51 Okay, so as we close out-

Tony Kim 1:03:52 Yes. Yes

Molly O'Shea 1:03:53 ... final question.

Tony Kim 1:03:53 Uh-huh.

Molly O'Shea 1:03:54 Okay, so one of our sponsors is Brex, and they're a performance platform for spending smarter and moving faster-

Tony Kim 1:04:01 Okay

Molly O'Shea 1:04:01 ... with corporate-

Tony Kim 1:04:02 Yes

Molly O'Shea 1:04:02 ... credit cards. So with this question, it's on the topic of performance. I take this on a personal bend, so no pressure here.

Tony Kim 1:04:08 Mm-hmm.

Molly O'Shea 1:04:08 But I believe performance personally really revolves around who you surround yourself with. I mean, people say you're a result of your five closest relationships-

Tony Kim 1:04:16 Okay

Molly O'Shea 1:04:16 ... and that kind of thing.

Tony Kim 1:04:17 Oh, yeah.

Molly O'Shea 1:04:17 But also, I'm curious, from your standpoint, you've built out a legendary career. Who are some of the people-

Tony Kim 1:04:24 Mm, I don't know about that

Molly O'Shea 1:04:24 ... it, it is true.

Tony Kim 1:04:25 Yeah.

Molly O'Shea 1:04:25 Who are some of the people that have inspired you or mentored you along the way?

Tony Kim 1:04:33 Wow, what a question. First of all, I'm not, I've no legendary career. I just, I'm just trying to survive. [laughs] Um, but who... Okay, so there, you know, um, what, what... I, I wouldn't say I had mentors, but you know what I had? I had people that believed in me at certain points in my life and, and, and basically gave me the freedom, the latitude, the keys to the kingdom and said, "You know what? There, I see something in this guy, and I will give you the latitude." And, and, and so there was a guy who brought me into BlackRock who basically gave me carte blanc- carte blanche and the freedom and latitude to... He believed in what, what I could do, so that's one. And, you know, I, I wasn't, you know, I actually did investment banking long ago, and there were a couple people there that took a shot on me, you know, some engineering kid out of the Midwest, growing up in the Midwest, and, you know. I thought I'd wanted, I wanted to go to consulting back then, and no consulting firm would hire me, so, uh, I wasn't good enough for them. And so there was some, some, some of these, uh, investment banking, somehow I found a fit there, and then someone else said, "Go west, young man."

Molly O'Shea 1:05:56 [laughs]

Tony Kim 1:05:56 Um, and this was right, like, before dot-com. So, um, so, you know, certain people that made a bet and, and just had faith, and it was, it wasn't like they were mentoring me per se. They, they just, they just had a belief. And I, you know, and so I try to do that. I try to always work with lots of young people to not mentor them, but just try to encourage them, give them a break if they can or give them a shot. But then the mentors I have are all dead. My mentors are, um, you know... I, I like to study... I'm a h- huge f- study of student of history, so, uh, Caesar, Alexander, Napoleon, Beethoven, Churchill. I, I like to, in, in, in many, in these kinds of, um, historians, uh, leaders, people that created their own destiny. That's who I, uh, those are my mentors, you know, because growing up where I did, you know, I, I, I was not a, was not a social kid. I was, I was somewhat ostracized, and so I grew up in libraries, and those historical figures in libraries became my mentors. And then when I went to the real world, some people gave you a shot. They, they just believed in me, uh, or the kindness. And, um, and so, uh, I'll never forget those people, but, uh, yeah, so there, there you go. It's kind of dead people and, and kind people. How's that?

Molly O'Shea 1:07:38 That's beautiful.

Tony Kim 1:07:38 Yeah.

Molly O'Shea 1:07:39 Wow.

Tony Kim 1:07:39 Yeah.

Molly O'Shea 1:07:40 Great place to end it.

Tony Kim 1:07:41 Okay. [laughs]

Molly O'Shea 1:07:42 Thank you-

Tony Kim 1:07:43 Thank you, Molly

Molly O'Shea 1:07:43 ... so much, Tony.

Tony Kim 1:07:44 Yeah, it's a pleasure. Yeah.

Molly O'Shea 1:07:45 Amazing.

Tony Kim 1:07:45 Yeah.

Molly O'Shea 1:07:46 Huge thank you to the entire RAISE team for an incredible event, and thank you to Brex, MongoDB, and AssemblyAI for making this trip and series possible. If you enjoyed this conversation, you're gonna love the rest of the RAISE series with Tony Kim from BlackRock, Scott Wu from Cognition, Andrew Feldman from Cerebras, Rodrigo Liang from SambaNova, Michael Hurlston from Lumentum, CJ Desai from MongoDB, and many, many more, like our hot takes that we did at a secret location that you can find on X, YouTube, and Instagram. Subscribe to Sourcery on YouTube for more conversations with the people shaping AI, and join the free newsletter, you can also do paid, at sourcery.vc for weekly insights on AI, robotics, enterprise software, consumer, semiconductors. Did I say AI? AI again, and everything that's coming next, like funding announcements and all big things in tech. Thank you. Bye.