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

Tony Kim is Managing Director and Head of the Global Technology Team within Fundamental Equities at BlackRock. Recorded at the RAISE Summit in Paris, where Tony spoke on 4 panels, next-gen accelerators with d-Matrix, quantum computing with PsiQuantum, optics in data center design with Lumentum, and XPU and AI chip co-design with Broadcom. Tony breaks down the shift from a software-centric world to a compute-centric one, and why roughly $1 trillion of CapEx this year, and $10 trillion over the next 5 years, is being spent to move data centimeters and millimeters. He maps the market cap transformation, roughly $10 trillion in software, services, and internet, $20 trillion in Mag 7, and $30 trillion in non-Mag 7 chips and hardware. We cover the RAMpocalypse and why memory intensity is skyrocketing as AI models start to mirror the human brain, the 3 to 4 year duration mismatch between fab buildouts and today's demand, and the move from copper to light inside the data center. Tony also lays out his capital allocation framework, 90% in the 3-year AI vortex and the rest on frontier bets like quantum, orbital data centers, SMRs, and 800-volt power architectures, all converging on 2030. Plus, the coming wave of 30 to 40 Chinese robotics IPOs, mixing Chinese robot bodies with Western brains, his contrarian case for social robots addressing loneliness and aging populations, token flow as the new enterprise framework, and the mentors who shaped his career. We cover: › Why AI is forcing a complete rebuild of data centers › The shift from software to compute › Why memory becomes the next critical bottleneck › The future of AI chip co-design › How investors should think about AI infrastructure › Quantum, orbital data centers and the road to 2030 › Why robotics could become AI's next trillion-dollar market › China's robotics advantage › The enterprise AI stack and "token flow" › How BlackRock evaluates long-term technology investments Tony Kim: https://www.linkedin.com/in/tony-kim-3150053/ Molly O’Shea: https://x.com/MollySOShea Sourcery: ⁠https://x.com/sourceryy 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊 YouTube: https://youtu.be/zXB-LI7skL8 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 • Brex https://brex.com/sourcery—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. https://brex.com/sourcery • MongoDB https://mongodb.com/ai–Millions of developers and more than 65,200+ customers across industries, including ~75% of the Fortune 100, rely on MongoDB for their most important applications. With integrated capabilities for operational data, search, real-time analytics, & AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, & simplify complex architectures. https://mongodb.com/ai • AssemblyAI http://assemblyai.com/sourcery–Millions of developers use AssemblyAI to power their voice ai applications and features. One API gives you access to best-in-class speech-to-text, voice agent, and speech understanding models for both pre-recorded and real-time audio. Granola, ClickUp & HeyGen are scaling with AssemblyAI - get $50 of free credits today at http://AssemblyAI.com/sourcery http://assemblyai.com/sourcery 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Tony Kim, Head of BlackRock Fundamental Equities Global Technology (01:10) Secret Location, Croissants, and the RAISE Summit (03:52) Why Compute now rules everything (05:17) Why old data centers can't survive AI (07:43) Why data centers are ditching Copper for Light (10:44) The shortage nobody saw coming: RAM (15:20) Only three companies control memory (16:03) Tony's Playbook for Investing in the AI Era (18:16) The 20-year lie: "Compute is just a Commodity" (22:38) How Hardware quietly became bigger than Software (27:49) The Investing Rule: Will you still be cool in 5 years? (32:41) 2030: the year every frontier technology bet converges (38:16) Chips were never a Commodity (41:30) Rack design, materials science, and the physical-world renaissance (43:30) Inside the architecture of a Robot's mind

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Tony Kim. Tony Kim from BlackRock, who runs the global technology team there.

• 0:04Speaker 01

  AI happens. It's like BCE on a dominie. And bam, 23 happens. Everything changed. So the base layer of compute went up 10,000x. $10,000 server is a $1 million server. There's roughly 10-plus trillion market cap in software and services and internet. There's 22, 23 trillion in Mag7. 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, 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.

• 0:49Speaker 01

  And there's 130, 140 relaxed companies in China. I see 30, 40 potential IPOs this year in China, alone this year. And there's what, 0, 1, 2 in the United States maybe this year?

• 1:09Molly O'Shea?

  Tony Kim, welcome to Sorcery.

• 1:12Speaker 01

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

• 1:15Molly O'Shea?

  In Paris, at the Rais Summit. We're in a, we're in a secret off-location that has AC and some croissants. So it's, it's quite nice.

• 1:22Speaker 01

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

• 1:27Molly O'Shea?

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

• 1:31Speaker 01

  I am doing four panels in my involvement with Rais. So one on accelerators with D-matrix, kind of the next-gen compute architectures. Another one with, with side quantum and around quantum computing. A third with Lumentum and kind of bringing optics to kind of next-gen data center design. And then finally with, with Broadcom and XPU and AI co-design for chips.

• 2:07Molly O'Shea?

  Light agenda?

• 2:08Speaker 01

  Light agenda, yeah, yeah, yeah.

• 2:10Molly O'Shea?

  So as the head of global tech for BlackRock, what brings you here? How did you get involved with Raise?

• 2:17Speaker 01

  So I got involved last year. One of my companies I was involved with, San Benova, Lipu was supposed to be one of the speakers.

• 2:26Speaker 01

  And he couldn't make it, so I decided to fill in for that. And then, you know, 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 that Henri had kind of pioneered and built this event.

• 2:49Speaker 01

  I saw something there. I saw a lot of my colleagues and friends from San Francisco all congregating here in Paris. And I thought, well, I'd like to help foster this, get it going. And so last year I was here. And then this year, it's, I don't know, probably tripled again in size. It seems to be Europe's biggest or most targeted AI conference. So, yeah, so I continue to help and do what I can to help build an AI presence in Europe.

• 3:26Molly O'Shea?

  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.

• 3:34Speaker 01

  No.

• 3:35Molly O'Shea?

  And I was just amazed.

• 3:37Speaker 01

  And I think they're outgrowing it. After, next year I might outgrow the Louvre even. So it's really becoming something. And, well, you're here, testament to where it's come.

• 3:47Molly O'Shea?

  Well, they're amazing, so I'm happy to get involved in any way that I can.

• 3:51Speaker 01

  Yeah.

• 3:52Molly O'Shea?

  So between all of the panels that you're doing, you're doing four panels. What are the through lines and the macro themes?

• 3:58Speaker 01

  Through lines, macro themes, okay. Well, I think one of the big ideas, obviously, clearly, is, and we see that in the stock market and we see it in the investment market, is that the primacy of compute.

• 4:13Speaker 01

  And how, actually, you're seeing it already, that how much the stock market and the capitalization in Silicon Valley has changed. And we went from a, let's call it a...

• 4:30Speaker 01

  A software centric world to a compute centric world. And you're seeing the emergence of companies. So that's number one, this move to compute. And you know, to me, in my opinion, you know, the models and the compute are kind of like symbiotic and synonymous with each other. So the primacy of compute. Within that, secondly, is since now that is the dominant theme, it is a dominant where the capex, where the money, where the capitalization is all gone. It then engenders a whole rethink of the data center. And so I think there is a redesign of the data center.

• 5:15Speaker 01

  And we're going through stages of the data 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 driving yet another transformation of data center design going forward. And so that's the second. And the ramifications of this data center redesign kind of flow through every layer of the AI stack. Third, within that, then you go another layer deep in this data center redesign. You know, one of those is around power density.

• 5:58Speaker 01

  It's incredible what's happening is that the data centers, as we put more and more computation, because the requirements of these of these models are requiring more and more compute density. You need to pack more and more bits into a smaller and smaller footprint. And when you do that, bandwidth, power, heat. And so you have these logarithmic effects of the data center design driven by AI necessity. And effectively, the data center is changing from, you know, before we would transmit data kilometers.

• 6:39Speaker 01

  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 next it's 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,

• 7:23Speaker 01

  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 going to be around the energy, and the power density, and the grid. And another will be around the chip architectures. And 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, 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.

• 8:06Speaker 01

  So, so that's another one. And then, and then, and then one more thing I would say around, around AI and this conference and things is around, my, my first comments around the symbiotic relationship between compute and LOMs and AI models. And, and I think what you're seeing is that...

• 8:32Speaker 01

  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 parameters and the specs of the model, and the model specs informing the design of the compute. And then this kind of co-design is like the new path that many of the leading foundation labs are pursuing. And this is what I'll talk with Charlie at Broadcom about. Obviously, they did that with the new jalapeno chip that recently came out.

• 9:14Speaker 01

  So those are some of the big ideas. My panels are today mostly all focused, as you can tell, 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.

• 9:31Molly O'Shea?

  It's OK. Software, you know, it's a little sleepy right now.

• 9:34Speaker 01

  Yeah, yeah. So, yeah, a lot happening. It's interesting. And then one more thing I would say is that there's this whole nother revolution going on in the energy, in the power side 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 things. And then a new power architecture, the rise of 800 volt is going to have a transformative effect. And then ultimately, a solid-state transformers.

• 10:12Speaker 01

  And so, again, this goes to the point around, not only is the chip layer, the energy layer, every time you have these step downs in voltage, you lose efficiency and energy. And 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 sub-narrative that is going around in the future design of data center. So that's interesting. And I think you mentioned one thing, Ram-... Did you say Ram-...

• 10:44Molly O'Shea?

  You said that, by the way. We were on a call beforehand, and you brought up Ram-...

• 10:48Speaker 01

  Well, somebody, yeah, somebody, it wasn't my call.

• 10:51Molly O'Shea?

  I won't take credit for it either.

• 10:52Speaker 01

  I won't take credit. Somebody took credit for it. There's a lot of the memory companies here, but this goes to...

• 11:00Speaker 01

  Also, yeah, maybe I'll add a fifth or sixth topic on this around the data center design of the future is, again, back to this code design element around the model and the compute. And, you know, it's from my observation, again, I'm not building the models, but I observe the compute architectures and what the model guys are going. Increasingly, more and more of chip and model development is starting to mirror the human brain. And so in the initial early days, you know, you noticed that we had a ton of compute, ton of parallelism compute, and the models didn't have much memory.

• 11:43Speaker 01

  And now we're adding memory 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 AIs that be a personal AI that will, you know, these agentic harnesses that are built in the institutional context within an enterprise 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 the human brain is, is very memory intensive.

• 12:23Speaker 01

  And today is more compute intensive, but as you see it with RAMpocalypse, the memory intensity has just skyrocketed. And, and, and going forward, you will 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 stacked DRAM, or HBM, or high-band with flash. All of these memory and storage methods tightly packed with your chip, your compute

• 13:02Speaker 01

  architecture, so that it aligns to how maybe these AIs will be built that start to emulate more and more human brain. And so I think that's what's fueling the RAM, the RAMpocalypse, the shortage of RAM.

• 13:18Speaker 01

  And you know, the other thing about that is that there's, 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 future, 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 angst in the market.

• 14:03Speaker 01

  But underlying all that, I think we are going to more and more memory than uh, 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.

• 14:24Molly O'Shea?

  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 Sorcery runs on Brex, so I can spend time on building and not busy work.

• 15:08Molly O'Shea?

  It's time to get Brex AF. Learn more at brex.com/sorcery. That's B R E X dot com slash S O U R C E R Y. Bye. So we've been seeing this trend in a lot of our conversations and on the macro side.

• 15:27Speaker 01

  Yes.

• 15:28Molly O'Shea?

  News and everything. We had a conversation with Ja Min from Kotu. He's a C I O of Public Markets over there. And they were talking about with the proliferation of agents, memory is only increasing more.

• 15:40Speaker 01

  Yes.

• 15:40Molly O'Shea?

  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.

• 15:46Speaker 01

  It's only three.

• 15:47Molly O'Shea?

  And S K 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 massive, because there's limited supply. And how long does that last? And how do you catch 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 A I. This has increased 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. So I'm curious, from your standpoint, on the investor side, how do you think?

• 16:30Molly O'Shea?

  And how has your strategy evolved to now play offense on this type of field?

• 16:38Speaker 01

  I like your framing. It is a complete rebuild. So let's start with that 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, two thousand to two thousand twenty-ish in one framework, which is basically around, basically the birth and the dawn of cloud computing. And 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.

• 17:21Speaker 01

  And then these data centers were were small and they were.

• 17:29Speaker 01

  At the end of the day, cloud computing, everyone says it's software, but it was really reselling CPUs with hard drives.

• 17:39Speaker 01

  That was the compute stack, a CPU with a hard drive. And it was a considered commodity. And server prices were 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 think of, the other way I think about it is that, 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. And so, but if you think about the base unit to create a cloud was relatively small.

• 18:25Speaker 01

  CPUs and hard drives, basically, that was it. And some databases. And then so SaaS was king.

• 18:37Speaker 01

  And the margins went to that because the cost of compute was so low. And therefore, everyone said, compute is 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 building this massive application on a very, very thin layer of compute. And so that fueled the 20-year run in cloud and SaaS.

• 19:11Speaker 01

  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 hosted cloud services. And so that was a great business. Everyone was very happy and data centers were built for that spec.

• 19:39Speaker 01

  AI happens. It's like BCE, Anna, Dominic. 2023 is, you know, it's, it's going from BC to AD.

• 19:50Speaker 01

  And bam, 23 happens. Everything changed. So that, what was called the base layer of compute went up, I don't know, 10,000x.

• 20:05Speaker 01

  It's, you know, a $10,000 server is a million dollar server. Small HDD, big HDD. And by the way, 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, it was megawatts, now it's gigawatts. It's small data centers in cities to giant server farms in Texas. And so that is the data center and cloud of tomorrow. And they're, and they're, and they're selling tokens.

• 20:50Speaker 01

  The data center of past is like this old cloud, but yet it facilitated high margins.

• 20:57Speaker 01

  And now that is what I call, that is, that still exists, but it's not going to 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 so, but that requires massive capital investment. But it also engenders a very different rethink of the margin stacking.

• 21:28Speaker 01

  Because before you would resell this base layer of low compute and massive

• 21:36Speaker 01

  SaaS app margins on top. Now you've got this massive compute stack and they're reselling those as tokens.

• 21:44Speaker 01

  Right? So you're, you're, that, that compute factory is creating tokens and then the model guys are then selling their tokens.

• 21:53Speaker 01

  And that just takes a lot of what I call, it 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, you must build these new data centers because at some point, all of your revenue will become here. 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 then that, because of this new rebuild, it then has triggered a reexamination of value.

• 22:38Speaker 01

  So, and so if you look at today, the tech stock market today, and I'm going to make some approximations. You know, there's about 1,500 companies globally over a billion dollars or more market cap, maybe 2,000, depending if you add China, not in China. In the US, let's just talk, a global stock market, there's roughly 10-plus trillion market cap in software and services and internet. They were the classic industry where most of the market cap was in the pre-AI era. That's 10 trillion, plus or minus.

• 23:23Speaker 01

  There's 22, 23 trillion in Mac 7. So I just put Mac 7 as a new category. So, Microsoft and Mac 7, it's not, I just have a non-Mag 7 software services internet category of 10 trillion, 22 trillion in Mac 7, and then there's another 30-plus trillion in chips and hardware. So 10, 20, 30, something like that, okay? 10 trillion in software services internet, 20 trillion in Mac 7, 30 trillion in non-Mag 7 compute, chips, hardware.

• 24:05Speaker 01

  That's a... I don't think people would realize that we are that compute hardware-centric now. 10, 20, 30. 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 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,

• 24:49Speaker 01

  have consumed the market cap out of software and services. It is just, you know, it's like the Borg. It is consumed. And for those foundational models to exist, it needs to live on the compute stack. 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,

• 25:32Speaker 01

  an investment philosophy and capital allocation philosophy that would mirror what is becoming the new reality. Where these intelligence, insatiable demand for intelligence. And as a function, intelligence begets compute. And intelligence...

• 25:55Speaker 01

  For compute equals basically revenue. 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 makes you rethink kind of the margin stacking and kind of where the value sits for companies. And and and the market is trying to adjudicate that right now. And so, you know, that's why you saw SaaS apocalypse earlier this year, end of last year. That's why you're seeing the RAMpocalypse. There's a lot of apocalypses, you know. And and, you know, and and the thing, if you believe these scaling laws and intelligence

• 26:37Speaker 01

  is getting better, I'd say at one order of magnitude a year. I mean, ten times ten times ten, that's a thousand 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. 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 mote a lot. Mote is a very defensive. Motes are always the breached, aren't they? So 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.

• 27:18Speaker 01

  I hope that kind of, yeah, that's how I think about the macro. Like you said, this complete redesign. That redesign facilitates a new rethink. And that rethink also, you know, has huge implications on the business models and the modes of companies. And so, and this is, you know, I'm I'm what my thesis is now, my change, we talk again in six months, it might be completely different. But that's kind of my current thinking.

• 27:50Molly O'Shea?

  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 want to talk about quantum. I mean, that's always 10 years, whatever year you're talking about it, to be clear. 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? Because you're obviously taking a risk on that. And I kind of bring that up in the point of quantum as well.

• 28:24Speaker 01

  Yes.

• 28:24Molly O'Shea?

  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?

• 28:35Speaker 01

  Yeah. An 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, you know, 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. But then they 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,

• 29:19Speaker 01

  you know, there's a lot around today, even the three year, the three year duration mismatch of, let's say, DRAM and foundries. So I call that kind of like the now, right? This is the now, the vortex of AI is like the now. And so, you know, if I 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 majority is going in.

• 29:52Speaker 01

  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,

• 30:11Speaker 01

  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, you, the belief in a future has a huge impact on your multiple. So if they do not believe in that, in that future, 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, you know, I always feel like you must be betting on, on the future as well. So, so things look cheap,

• 30:54Speaker 01

  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, versus like, the next three years is going to be great for memory, or compute, or data centers. But then like, well, 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?

• 31:39Speaker 01

  Where you didn't know that this LLM thing was going to happen. Some of these things gestate longer. My intuition was that we will need AI compute.

• 31:51Speaker 01

  Of some form, maybe machine learning, AI. And so I didn't know that this LLM, the wave would happen. But 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 have this longer context is around, let's say, you mentioned quantum, I think 2030, we will see. You know, I will start getting involved in 2019. So I'm already seven years in. You've got another five more years to go. But, you know, that's a decade long.

• 32:36Speaker 01

  So you have some bets on kind of where the frontier is coming next. Space, right? You know, these orbital data centers, right? 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 air 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?

• 33:20Speaker 01

  2030, 2029, 2028, whatever. Then you say, when will we hit 800 volt power architectures? Late 2020s, 2030. You know, will we have solid state transformers? 2030. Will we fusion longer? But many, data centers in space, 2030. You know, when it starts to really scale. So you're like, you're sitting here, obviously you have the now, this AI train that is consuming everything, all my time and energy, you know, but that's 80, 90% of it. But, you know, you must be always betting on the tomorrow. And some of these are long dated things.

• 34:05Speaker 01

  And so I have, I spend time on the future.

• 34:11Speaker 01

  Allocating X resources of my time on that. What will really be transparent, not incremental. I want nonlinear asymmetric potential. And then I bet out of what is the primacy of today? That with not only a three-year financial forecastable window, but with then relevancy beyond. And then everything else that doesn't fit in that window, is it worthy of your time? And the opportunity cost to keep investing in that? And there are other strategies, other portfolios, other things that will, that can pursue those.

• 34:54Speaker 01

  It's just not my, that's not my focus, really. So I hope that kind of gives you a sense of where, how I, this is my opinion, how I frame capital allocation, portfolio, decision-making.

• 35:11Molly O'Shea?

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

• 35:18Speaker 01

  2030, you know, actually, it's not, 2030 is not far away.

• 35:23Molly O'Shea?

  No, it's not.

• 35:25Speaker 01

  Yeah, absolutely. I mean, you know, many companies, right? You gotta look 2031, I mean, that's, 2031 is five years. 10 years, 2036. So, I mean, 10 years is like almost an impossible forecasting feat. But five years, you know, a lot of companies will not even have free cash flow by 2031. So, so you then need to have a belief system that, that, you know, that could flip positive beyond. So, but yeah, I'd say at least a five-year, five-year window. You know, you always want to be betting on not what's cool today.

• 36:10Speaker 01

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

• 36:51Molly O'Shea?

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• 37:33Molly O'Shea?

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• 38:16Molly O'Shea?

  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 that really didn't exist before.

• 38:41Speaker 01

  No.

• 38:41Molly O'Shea?

  And that is a huge weight and a big weight, especially with SpaceX coming and the whole IPO on that. But there's also fun categories. I recently visited Figure AI, 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 robot Atlas. They're different. The Atlas one is hydraulic, so it can pick up like a fridge. Figures is, you know, more of like daily use, package sorting, commercial stuff, making cars and that sort of thing. But of these new categories, which ones are

• 39:26Molly O'Shea?

  you paying attention to? What are you excited about?

• 39:28Speaker 01

  So robotics, yeah. I mean, the first comment around these older categories,

• 39:36Speaker 01

  you know, like you said, semiconductors have been around a long time. And I don't understand why people, you know, it's called Silicon Valley for a reason.

• 39:45Molly O'Shea?

  People forget.

• 39:47Speaker 01

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

• 40:17Speaker 01

  And so this notion that they were a commodity was just a false notion, in my opinion. It was not, it really never was. 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 a 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.

• 41:00Speaker 01

  And then those that have survived are behemoths with huge pricing power. The complete opposite of commodity. And then, by the way, all those people are engineering nerds. So I always say it's the revenge of the nerds. It's not a revenge of the nerds, it's the lost ring of power that was found. And so, but it was always there. And so their time to shine is now. That said, you're right, these other industries, you know, it has spawned a renaissance in hardware, right? You know, when you look at that market cap shift that I was talking about, a lot of those go into,

• 41:45Speaker 01

  you know, servers are cool, fiber is cool, power is cool, you know, rack design is cool.

• 41:52Molly O'Shea?

  People love rack design.

• 41:53Speaker 01

  They go crazy about rack design. It's like, you know, it's like vending metal, copper, you know, like, you know, heat. You know, material science is cool because you need all kinds of new materials, you know? And then substrates and packaging. It's the physical world. These are all what I call the physical world, the physical sciences. That's cool again. Going to school to study material 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 like, is like being blacksmiths. And it's like very few, you know, how many friends of yours go into study new memory design?

• 42:38Speaker 01

  You know, I was talking at a dinner last night with, here at Ray's, with one of the biggest memory companies. Like, we cannot get people to design custom memory. Because like, like more of this 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. And, and so, like, all of this has happened, and 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 cognitive labor, and you gotta

• 43:22Speaker 01

  build these models for that. But then those models then can be repurposed and implemented in robotics. And so the robotics system chain is very interesting to me, but it, it, you know, 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, and, and the brain will have, I mean, kinda two parts to it. It'll be a, a baseline LLM that you, you and I will communicate as a translator, translate and talk to the, to the robot. But then there will be, it's like the human brain. But you also have the brain for the motor functions.

• 44:08Speaker 01

  That control our muscles and our bodies and our reactions and then the brain for language and memory. So they'll have two, like, kind of 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 really, obviously, embodiment of intelligence in, like, an L L. So you're gonna build these brains, two brains into, you know, and then you take the brain and then, those are like labs, right? There'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, the hardest thing,

• 44:52Speaker 01

  as you, I'm sure you know, but the body, the physical embodiment, that is a manufacturing hardware business. And when you think about that, the Chinese are coming. And there's, I think, a hundred thirty, a hundred forty robotics companies in China. I'm looking at the current pipeline, I see thirty, forty 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?

• 45:35Speaker 01

  And the reason for that, though, is also 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 proxy, funding mechanism, unlike in the US. So they're earlier. They're gonna come earlier and they're coming in waves. There's a hundred forty of them. And, and the thing about China is, in that physical layer, the body, the motion, they're, maybe they're, they're, they're, the, the, the robotic brain development, the world model, they, they may be behind, right? That's probably, most people would say that.

• 46:14Speaker 01

  The West is ahead on, on, on the model development, but make no mistake, Chinese and Asia, and you, you mentioned 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.

• 46:38Speaker 01

  I mean, and it's also an extension of kind of EV platforms, right? If you have the physical scale of manufacturing, you then avail yourself to potentially have lower cost to manufacture in mass these robots. 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. I know people, I just want the robotic. Those Chinese robots are amazing, right? So why don't we stick a Western brain in there, inside? And then permutations of this will continue. And so, so I think it's a wild west, a lot happening and

• 47:23Speaker 01

  but it will be a huge market, a huge market. And I have a, I have a soft spot. I mean, my view is one of the things I'm most interested in, in the robotic side is around, not so much the manufacturing robot, I mean, of course, that will, but, and you're seeing this come out of China already, it's around, around loneliness, around social embodiment, around, you know, around education, around elderly, around young people, and to bring consumer and/or commercial-like social robots,

• 48:07Speaker 01

  more so than industrial use. So.

• 48:13Molly O'Shea?

  That's kind of a hot take.

• 48:15Speaker 01

  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 you're facing demographic population cliffs around the world. You look at, actually, some of the best performing companies in the world are nursing home companies. And, and when you look at elderly, they really want companionship.

• 48:51Speaker 01

  And even amongst young people, you know, there's loneliness epidemics and things like that, and I think robots, even though you don't mind having that 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 Terminator-like.

• 49:12Molly O'Shea?

  Yeah.

• 49:13Speaker 01

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

• 49:23Molly O'Shea?

  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 of some sort, and it was like the girlfriend like yelling at the boyfriend.

• 49:40Speaker 01

  Yeah.

• 49:41Molly O'Shea?

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

• 49:45Speaker 01

  I mean, you, I'm not sure you've seen, have you seen Star Wars?

• 49:50Molly O'Shea?

  Yeah.

• 49:51Speaker 01

  Okay. And who are two of your favorite, did you like C3PO and R2?

• 49:55Molly O'Shea?

  Yeah.

• 49:56Speaker 01

  Okay. Now imagine, and then you have the current embodiment of Optimus and many other, the figure robot, and all these sleek, you know, amazing, Westworld-like things. But like, I hearken to like R2 and C3PO. What if you had like half-size robot? Even half-size, doesn't have to be full-size. That is approachable, friendly, not masculine, you know, something that, and then that robot, but that has the intelligence of Shakespeare

• 50:35Speaker 01

  and Einstein, and speaks every language like C3PO. And, and then you interact. You know, I, I always think, often think about the elderly, you know, it's like, imagine them having conversations with my mother and others, and then also.

• 50:52Speaker 01

  Empathetic to their stories and then you can record their stories, record their life histories. And you know, I think,

• 51:01Speaker 01

  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 think that's possible. I think that I think that will be a fascinating market to see. So yeah, I think that'd be a very new use case.

• 51:23Molly O'Shea?

  Yeah.

• 51:24Speaker 01

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

• 51:30Molly O'Shea?

  Really cool, yeah. It's been interesting to see, because I cover a lot of high-growth companies in Silicon Valley. The proliferation, of course, coding agents is a big thing, right?

• 51:41Speaker 01

  Absolutely.

• 51:42Molly O'Shea?

  But to your theme of the brain, speech model companies are crushing it. They are growing faster than most other companies out there. There's a company called Assembly AI that is like growing incredibly fast 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. 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. It's coming downstream and so like the Databricks, 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 MongoDB is, those databases are reaching.

• 52:27Molly O'Shea?

  So it's really interesting to see how like it's streaming downstream. Are you looking at any of the downstream winners?

• 52:32Speaker 01

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

• 52:39Molly O'Shea?

  I don't know either, so it could be the opposite.

• 52:41Speaker 01

  Well, no, I, no, I, no, 100%, you know, in supply chain hardware, is it upstream, downstream? Okay, it's like, I, I, I'm thinking vertically. So it's like compute, models, apps, or compute, models, data, apps, something like that. Right, going, I'm going up this stack.

• 53:00Molly O'Shea?

  You're going up, I'm going down.

• 53:02Speaker 01

  Going down this stack, whatever. Okay, so absolutely, it's so, you know.

• 53:08Speaker 01

  You know, like this whole data center redesign thing. I think the whole enterprise is redesigning. And the enterprise itself, if you really think about the future of what a big enterprise will be like, it'll be, on one hand, you'll be, you're bringing, you're bringing intelligence, tokens, you're bringing tokens in.

• 53:35Speaker 01

  And you will have data layer.

• 53:41Speaker 01

  It'll be, because that intelligence will need to interact and be orchestrated around this data layer. And 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 a, what they call like a context layer? A layer of the secrets and the ways of your company.

• 54:22Speaker 01

  You embody the cumulative knowledge of your employees into some context layer.

• 54:27Molly O'Shea?

  I think Palantir calls this ontology.

• 54:30Speaker 01

  Ontology, exactly. Yeah, yeah, exactly. So you have this ontology layer, this context layer, sitting on the data, the data foundation with tokens in. And then that's it. And then you, and then everyone builds agents. Agents go wild, right? And agents will interact through the context into your data with tokens. That's it. That is, that is the enterprise. 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.

• 55:17Speaker 01

  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 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 firing up a 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.

• 56:02Speaker 01

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

• 56:43Speaker 01

  And yeah, and so that's, you know, and 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, I will provide, I will do all of your claims processing. I'll 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, I'll take on all of your insurance. Pay me X.

• 57:20Speaker 01

  Pay me X. And so you don't know what are you? Are you an app company? Are you a service company? Are you a computer company? A token reseller? No, I'm just selling you the whole solution. Today, you used to pay 100. I'll charge you 20.

• 57:36Speaker 01

  And then now you're seeing this. 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.

• 57:56Molly O'Shea?

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

• 58:04Speaker 01

  Yeah, they're buying. They're getting customers or they're buying companies with customers. And they want to... They're basically trying to restructure the whole delivery of services. And if there's a lot of inefficiencies, fat cost in there, and then you could rip it all out? Okay, maybe that's a business. Let's see. I mean, I know people doing that are starting to do that. Yeah, that's interesting. I don't know.

• 58:34Molly O'Shea?

  I mean, it's working quite well. We've talked to a couple of them. Some on camera, some off camera. We talked to General Catalyst Creation Fund. And they've been doing a lot of PE rollups and then creating companies. One of them, Long Lake, just bought Amex Global. Their travel. I think their travel business. Which is interesting because you have a small player buying a large player.

• 58:57Speaker 01

  Yes.

• 58:58Molly O'Shea?

  Which was really interesting to see.

• 58:59Speaker 01

  Yeah, I mean, you know, there's this other framework, rubric that is emerging is, you know, when you have these traditional industries and you have a new company with 500 people.

• 59:15Speaker 01

  That can do and generate the revenue of 10,000 people.

• 59:20Speaker 01

  And, you know, and they're just approaching it in a radically rethought process. And so the efficiency. And, you know, 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 going to really just go at traditional industries. And it'll be up to can the traditional company, the incumbent, adjust in the face of, of these kind of companies coming? And, um, yeah, I think the jury's out. I, I think it's very, I'm very intrigued by that. I, I'm watching it. 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

• 1:00:05Molly O'Shea?

  Yeah.

• 1:00:05Speaker 01

  All of these traditional industries that have very little adoption of AI, that are still, their, their business workflow, the, how they've organized themselves. They could be all completely rethought and, and then 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, old employees drive that change versus maybe just buy the company, I'll do the change. And then, so it's an interesting idea. It's an interesting idea. I, I, yeah, it's all new. I'm, I'm watching it. It's, it's something to look out for, yeah.

• 1:00:48Molly O'Shea?

  We only have a few minutes

• 1:00:49Speaker 01

  Okay.

• 1:00:50Molly O'Shea?

  left, but I'll leave you with two questions.

• 1:00:53Speaker 01

  Sure.

• 1:00:53Molly O'Shea?

  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.

• 1:01:00Speaker 01

  Oh, boy, next 12 months, between now and June 27.

• 1:01:09Speaker 01

  Well, I mean, obviously, these big foundation labs. One of my most excited about, more concerned about, but... Take it either way, I mean.

• 1:01:21Speaker 01

  I just...

• 1:01:27Speaker 01

  I think it's a continuation of the same. What I would... I know, like, there are these... It seems like every six months, there's a scare. Yeah. Yeah. X-pocalypse, X-pocalypse, war, interest rates, too much CapEx, not enough financing, what, on and on and on. But I think through it all, I am optimistic that this compute wall, the memory wall, the compute demand, this data center redesign, that it will just plow through. And then our fears will be subsided.

• 1:02:09Speaker 01

  And so therefore, 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...

• 1:02:27Speaker 01

  Is the progression along this, what I call, this whole data center reimagination theme. And so I'd like to see more of just continued proof points along that path. I am hopeful, excited to see the big labs go public in the next 12 months. I think that will be... I think that'll be... I think it'll be interesting, it'll be exciting. And I think there is huge market appetite for it. And I would love to see, in the next 12 months, or I'm excited for, is the next forward step toward orbital data centers. And because that also engenders a radical change in data centers.

• 1:03:10Speaker 01

  If you keep pushing on that progression, it could open, you know, it could unlock a rethink, you know, and moving the burden of terrestrial compute.

• 1:03:26Speaker 01

  Into space and that can have a huge implications, huge implications of how current data centers are even being built. So I'm looking at that as to see if the progress being made there. So it's gonna be interesting 12 months.

• 1:03:38Molly O'Shea?

  Amazing. Also, it sounds like a little bit of a manifestation going on over here.

• 1:03:43Speaker 01

  Manifestation?

• 1:03:44Molly O'Shea?

  Yeah, you're manifesting.

• 1:03:45Speaker 01

  I don't know. I'm just, I contemplate. I contemplate, yeah.

• 1:03:50Molly O'Shea?

  Okay, so as we close out.

• 1:03:52Speaker 01

  Yes.

• 1:03:53Molly O'Shea?

  Final question. Okay, so one of our sponsors is Brex and they're a performance platform for spending smarter and moving faster for corporate credit card. So with this question, it's on the topic of performance. I take this on a personal bend, so no pressure here. 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 and that kind of thing. But also, I'm curious, from your standpoint, you've built out a legendary career. Who are some of the people?

• 1:04:24Speaker 01

  I don't know.

• 1:04:25Molly O'Shea?

  It is true. Who are some of the people that have inspired you or mentored you along the way?

• 1:04:31Speaker 01

  Wow, what a question. First of all, I'm not, I have no legendary career. I just, I'm just trying to survive. But who, okay, so there, you know, what, what, 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, 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 blanche.

• 1:05:18Speaker 01

  And the freedom and latitude to, he believed in what I could do, so that's one. And, you know, I was, you know, I actually did investment banking long ago. And there were a couple of 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 I wasn't good enough for them. And so there was some, some of these investment banking. Somehow I found a fit there. And then someone else said, "Go west, young man." And this was like before.com.

• 1:05:59Speaker 01

  So, you know, certain people that made a bet and just had faith. And it wasn't, it wasn't like they're mentoring me per se, they just, they just had a belief. And I, and so I tried to do that. I tried 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, you know, I like to study, I mean, it'd be a huge study of, sort of, history.

• 1:06:39Speaker 01

  So Caesar, Alexander, Napoleon, Beethoven, Churchill. I like to, and many, and these kinds of historians, leaders, people that created their own destiny. That's who I, those are my mentors, you know, because growing up, where I did, you know, I was not a, was not a social kid, 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.

• 1:07:21Speaker 01

  Some people gave you a shot. They just believed in me, or the kindness. And so I'll never forget those people. But yeah, so there you go. It's kind of dead people and kind people. How's that?

• 1:07:38Molly O'Shea?

  That's beautiful.

• 1:07:38Speaker 01

  Yeah.

• 1:07:39Molly O'Shea?

  Wow.

• 1:07:39Speaker 01

  Yeah.

• 1:07:40Molly O'Shea?

  Great place to end it. Thank you so much, Tony.

• 1:07:44Speaker 01

  It's a pleasure.

• 1:07:44Molly O'Shea?

  Amazing. Huge thank you to the entire Raze team for an incredible event. And thank you to Brex, MongoDB, and Assembly AI for making this trip and series possible. If you enjoyed this conversation, you're going to love the rest of the Raze series with Tony Kim from BlackRock, Scott Wu from Cognition, Andrew Feldman from Cerebras, Rodrigo Yang from Samenova, 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 Sorcery on YouTube for more conversations with the people shaping AI and join the free newsletter, you can also do paid, at sorcery.vc for weekly insights

• 1:08:29Molly O'Shea?

  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.