Sarah Guo - What the 250 People Building AI Believe - [Invest Like the Best, EP.489]

Sarah, where to begin with what will hopefully be a really fun conversation? Because you and I are interested in so many of the same things, I'm just curious what's on your mind today. I think we're both feeling a little frenzied, and it's been going on for a while. It feels like, if anything, it might get more frenzied and more chaotic, both for what you do and for the world around what you do. In this moment, what does it feel like? What's on your mind?

An obvious thing for anyone thinking about how to navigate this period as an investor is: How does it unfurl, and how is it different from the past? What do I do if I can't backtest? I was talking to an investor friend last night, and the analogy he gave me was, “I keep saying I want to press the brakes as hard as I can, but I'm not doing it; I'm going 90 miles an hour.” I do think the question is, do you miss the opportunity on one side, or do you make the mistake of every boom-bust cycle in technology history?

Especially if you combine that with what it means when you're also 4 years into building a firm, for the durability of the firm and the careers of the people, I think it's a complex question.

1. The AI Investment Wager

Do you think that we got it right in the profile that we wrote, that you're making a specific wager or bet—a positioning, whatever you want to call it—that there is a really important fundamental thing happening between a couple of labs in AI and everybody else, and that we need to take up arms to make sure we don't end up with a very monolithic outcome? Do you think about it that way?

I would answer in a maybe different way, which is that I believe in the great-man and great-woman theories of history. If you have very high-agency people in all of these places, and they have the correct risk capital or support in the network or environment, you change outcomes. You look at very important questions of what happens to open-source models and U.S. industrial policy.

If you think about the opportunity for the ecosystem in the future, is there going to be a competitive Western open-source model? It actually leads to the question: Did anybody make one? Could they raise the money? Were they willing to commit the capital, gather the talent base, build all the infrastructure, and fight for the frontier or not? I definitely think that individual people and entrepreneurs can affect the outcome.

I don't necessarily think of it as a war. I am working closely with, co-investing with, and have many friends at the labs. I think it's fair to say that the extreme point of view some folks may have, in and outside of those big labs, that the owner of 1, 2, or 3 frontier models consumes the economy—I do not want that future, very clearly, and I don't think we're going to end up there.

Do you actively want to be a great woman in this sense of the theory?

No. I don't mean that from a personal humility perspective. It's just not in my set of goals. I want to be the best investor in the things that I try to do, so it's not out of a lack of ambition or even confidence. I think some people are driven by, “I want to be the person that made this happen.”

I thought I was going to be a software entrepreneur for the longest time. My decision, from an identity perspective, that I was going to be an investor actually had a lot to do with the idea that I'm deeply curious, I like to understand things, and I like to be right. I'm very motivated by working with extraordinary people. If I have a set of skills, using those to make them more successful is a pretty good fit for early-stage investing.

If I want to work with the very best people and I want the companies to have impact, the firm can support a movement and support the change we want to see, but I don't think it has to be me.

2. Winning Through Focus

If that's the goal, what does it take, do you think, right now to be the best in a very competitive environment? Undeniably, you've done really well so far. I'm curious what it has taken to be that good and what you think it's going to take over the next year-plus to be that good.

I think it's been pretty simple so far. My read of the environment was that the growth of firms and generational transitions that were happening meant that it wasn't the most competitive landscape in early-stage investing that it has been, and you had this massive technology transition happening. If you took the bet on understanding the technology and the community and approached it from first principles, you might have better access and make better decisions than others who are less focused.

All you have to do is take the risk and be focused, and then it's an execution play. That's one of those things that I think is pretty simple and is just hard. It's effort. I think today it has actually not been that complicated. It's more about what your bar is for the people you work with.

My partner Mike and I started with a set of preexisting relationships and understanding, and so I think that has been useful.

Surely there must be more than just outworking everyone. Mike said something interesting to me a couple of weeks ago, which was that there are 250-ish people that he thinks about, or you guys think about—

Mm-hmm.

—that are some combination of entrepreneurs and researchers—

Doing the most interesting things on the frontier.

Yes. The people who are actually showing up in the morning and pushing this whole thing forward. One of your goals as a firm is to be as close to those people as possible, know them all, and support them in as many ways as possible. I really like that idea. It's a cool idea, and that's more than just doing a bunch of meetings with people who are starting companies.

From the outside looking in, from the cheap seats, it looks like there's more unique stuff going on than just the competition set being low, focusing more, and executing better.

Yeah.

I'm interested in the ingredients that have so far been part of the success. I won't name them, but there's one well-known LP that does a survey every year of which other fancy LPs they most want to invest with. You were either number 1 or 2.

There's something going on both with the companies you've invested in, with the performance so far, and with the market perception. There's something more than just, “Okay, it was a moment in time when we worked really hard.” I'm trying to get at those ingredients. That's why I'm pushing on it.

To the point of perhaps making a bet that others wouldn't, we thought very carefully about what markets are going to matter and what might be different about the founders that we look at in this era, if we are right about capability growth and the breadth of impact that would be non-obvious to other people. Where's the biggest difference from the status quo? How does the framework change?

I'll give you 2 examples. One of the things that we were really looking for in the first year was application areas, workflows and professions, and tasks that we thought were a good fit for purpose for the models, which is a very technology-forward approach. Lots of people would say, “This is nonsense. You have to think about the customer problem, and working from the customer back is the only way.”

We want to do both, and if you look at Harvey and the function of the law, rationally, if you think that we can do next-token prediction with language and you knew that in late 2022, then law is structured language.

I'm not a lawyer, but from the outside, I'm like, you need to read a lot of documents, and we had retrieval, and you need to generate text, and there's a lot of precedent text both inside firms and in common law and history. That feels like a really good match.

We also took a very specific view of what is now possible, what is valuable within what's possible, and who's aligned with us. Winston and Gabe believed that AI would transform the practice of law in a very AI-pilled way.

Mm.

We will do enormously complex work with lawyers. I don't know when. It could be next year or 5 years from now, but going from the kernel of being able to look at a landlord-tenant agreement in California and answer a somewhat trivial question to being able to project to doing an Activision Blizzard M&A and doing 85% of the work—that's a leap.

The thing that appealed to me in that moment was the ambition of what was possible then and the technical logic of why it would work. I think that's probably a different decision-making framework from how other people were approaching it in that moment.

3. The Frontier Research Race

What is that group talking about today—the 250-researcher frontier group? What's the most interesting thing, and what's the most notable difference between today and 6 months ago or 12 months ago?

It is a violently competitive landscape. I think that was true 12 months ago, but it's even more true than it was 24 and then 36 months ago. I think people are very concerned that it is a globally competitive landscape, and there's insecurity in that because I think it's a bit narrative-breaking as well.

I'm going to describe a belief and then question the belief. The belief is that, with recursive self-improvement of AI research—models that can improve the models themselves—we are 1 or 2 years away from some sort of exponential intelligence. I think that belief is new within the last 12 months for a lot of researchers.

One driver of the belief that we're 2 years away is that Andrej Karpathy will actually, in a very self-aware way, say, "I thought it was 2 years away for about 10 years." And he thinks it again, to be fair. Who can say?

I do think that there's some sense that the major labs are now so compute-intensive and so large from a headcount perspective that the sense of contribution—"I can move the needle at OpenAI as 1 of 200 people"—there aren't that many researchers. The question of how we get there is really up to every single person.

Now, if the question is, "Will I need $750 billion of compute spend, and will we have many thousands of people working on this problem?" I think people feel less ownership of the outcome.

Oh, that's interesting. So it's just a function of getting compute. That's the thing that's going to push this thing over the line, not our own human efforts.

I definitely think there's a large contingent of researchers who would feel that one of two things is now true: What I do doesn't matter anyway because the model is going to do it, or the only thing that matters is compute scale. Both of those are somewhat disempowering.

So then what are they doing? If I believe both of those and I'm a top-5 researcher or something—

That says something about your psychology, because you want to do something that matters. I think there's also a set of scientists who want to work on the thing whether or not it matters that they're working on it.

An entrepreneur asked me yesterday—I would not work on the companies if I felt like we couldn't change the outcomes a little bit for them. He asked me yesterday, "Would any of the companies that you backed not have been backed without you at that round? Would people have just said no?" Five or 10% max. They were resourceful, really talented people. They'd find other investors. There are lots of smart investors in the world who want to take that risk.

Maybe they wouldn't have gotten the next $100 million of compute. I don't think every researcher doing frontier work at these top couple of labs right now feels like they're essential to the machine.

4. Compute Becomes The Bottleneck

What do you think is the unhealthiest part of everything going on right now? What worries you about what people are trying to accomplish, and what are the things that you're most worried will inhibit the future that you want to see?

I think compute is one. People, I think, understand that very well. The version of us having enough compute over the next 5 to 10 years—people are very much thinking about 2032 at this point, at scale. I was talking to the leader for infrastructure at one of the hyperscalers earlier in the week, and he's like, "There was nothing that was going to move the needle for us at sufficient scale before 2030." That's depressing.

Where can we get sufficient natural gas? Americans and entrepreneurs have been able to build new technologies and new capabilities very, very quickly in the past. I don't think it's a technology, capability, or capitalism problem. I think it's a regulatory problem and an alignment problem. I don't mean AI alignment. I mean, if you want to build data centers in New York, you need to convince the people of New York that they should want data centers there, or that America should want data centers there.

If you want to make the price of nuclear competitive as baseload power, then you need to convince people it's safe, and you need to allow enough construction of SMRs—

AP1000s, yeah, or SMRs, yeah.

Yeah, in order for the price to come down, because we understand how that cost curve will change in theory. I don't think we lack the technical and entrepreneurial capability to build abundant, cheap energy for the data centers. That's one.

The physical supply chain is just a tough reality. That makes me worried that the learning curve to build things, and having the tacit knowledge, the labor, and the raw materials, can't go as fast as software or even decision-making. The only way through that is through. We just have to invest in it.

On the investing side of what worries me, I was talking to one of our founders—several of our founders are researchers—and explaining that the quality of their storytelling and their ability to make the company legible to investors are obviously very important to their success if it's going to be a CapEx-intensive play upfront.

The reality of the financial landscape for all of these people is that I'm not a research scientist, you're not a research scientist, and all of the capital is not research scientists. It's on entrepreneurs to go explain their story. But one of the challenges is that there are a lot of people attempting to invest, as they should, in technology bets. It varies how much fundamental understanding there is.

There's a lot of proxying of judgment to pedigree or to other legible signals. Who's invested? References are always good. Who are the referral sources? And so the decision-making is less fundamental.

I asked an extraordinarily good investor friend—we had a debate, like you do—and I was like, "I don't understand. What is this company going to be that would be big? Explain it to me." His explanation was essentially, "Do you know the quality of this person?" "Yes, I've known the quality of the person for 8 years."

The technical theory in the business doesn't make sense to me, and people are making large-scale research bets without any intuition for them or without any opinion on them. I'm like, it's not all going to work, and I may not be any better at deciding, but we want to get to that intuition. I think not having a point of view on the business besides the pedigree of the person is dangerous.

5. Robotics Leaves The Lab

Who is a researcher that has most blown you away, and how did they do so? What's something that they did that, to you, felt extraordinary?

Back to the great-person theory, there are a number of these people who I think the history books will write about—a contribution of theirs as a truly extraordinary thing that created this kink. What's an example of a person like that and something that you've seen them do?

My partner Pranav and I were introduced to Tony Zhou and Chang Xi at Sunday Robotics when they were PhD students at Stanford. They worked at Toyota Research, DeepMind, and Tesla, so they weren't just academics by any means.

They were young PhD students, so I think they were around 25. I think one of them didn’t finish; they just started the company. What I thought was impressive about them then—and still do now—I remember it took me a while to get oriented looking at their body of work, but I think they have contributed dual-handedly most of the interesting ideas in robotics AI over the last 4 years. That’s a pretty weird thing for 2 very young people to do.

If I try to characterize the type of ideas, it is: How can I use modern AI to solve the robotics generalization and robustness problem in a very practical way? It is believed in robotics that if we just had the internet of robotics data, we’d have fully general robots everywhere. This is clearly going to work. One of the big research and practical problems is: Where do you get that data?

Some of the ideas that Tony and Chang have worked on are: How can you be clever about collecting the data in the cheapest way possible, in a way that supports the distribution of real-world environments and tasks? I think the creativity of thinking about the actual constraints—we don’t have the data, we don’t have infinite dollars to spend on the data—and treating that as a technical problem to solve is super interesting: the shape of the data and the collection, how it interacts with the model, and how much of this cheap data collection can we transfer to model learning. That’s very outcomes-driven.

I was blown away in the first meeting. We said yes immediately there, thankfully. It’s been just under 2 years that the company has been around. Nothing is true until it is shipped. This entire team believes that we are going to have general semi-humanoid robots doing things in people’s homes, first in beta, by the end of this year.

That is not something that any of us believed. It blows me away that you can move that quickly from a bunch of cardboard in a Stanford basement to the full-stack thing. It is manufactured here. We have done hundreds of iterations of hardware and model-data collection, translated it into tasks, and tested it in all these real-world environments. It’s going to work. Right.

The videos are very cool.

I think the speed of that is mind-boggling. I still think the broad view—not everyone, but lots of people in robotics—is that now it’s a question of when, not if. It surprised me that the team was like, “If not this year, next year.”

6. Investment Decisions Start With Instinct

I’m very curious about the moments of your investment decisions. You just said that in the first meeting, you were sort of like, “We’re in.” Is it always like that, or are there examples of things where you hem and haw, end up doing it, and it works? I would love to hear more about how you and your team make investment decisions—the actual in-room process of, “Okay, this thing is interesting. We’re going to do it or we’re not. We’re debating it.” What is that process like?

It varies based on the company. I’m very instinctive on people. I often know I want to do something immediately. If I know what somebody has worked on, interact with them, hear the idea, and have some basis—some background—on it, we have a rating scale, a 1-to-10 scale, and I’m immediately at an 8 or a 9.

What I’m then doing between that and a real decision is often figuring out where the holes in my understanding are, where my judgment of their premise or them is incomplete or wrong. What do I not know? If you’re an investor looking for the most ambitious, impactful companies, you can’t know every domain. We have biology and defense and robotics and law.

I’m spending the next day to a few weeks desperately trying to ground myself and asking, “Okay, what does everybody else believe about this space? Are they the people I think they are?” That’s what my process looks like. Then I want to get feedback. I want to get second reads on people. I want to understand what the core questions are.

I’m a memo person. Even at the very beginning of the firm, when it was just me, I would write the full memo, perhaps Greylock-style, and send it off to a friend who was an investor whom I trusted for their perspective outside of the funds. You and I have talked about what you value in a partnership, and I’m comfortable making investment decisions, but I think other people can make me better. I want people to push at the logic and have me reflect.

I used to take the memo and send it to John Lilley or Dylan Field or something. We write a memo, and I go see what Bella or Pranav or Mike wants to know about the person and try to complete the picture. Other people, I think, are more even in their decision-making. They think about it and think about it, and they climb to conviction, whereas I start there and then work backward.

I think a similarity between Mike and me is that we’ll start at a point and explain what will move us. I met a really interesting company earlier this week with my partner Bella, and instinctively I’m positive on it, but I don’t know enough about the science here, and I have to go make sure this makes sense. Can you really be an 8 or a 9 if you don’t get it? No. I could get there.

That, as we were talking about, is one of my concerns for this period of time: if you don’t feel like you have any grounded intuition on the bet itself, what are we doing here? I’m trying to think about if that’s fair. If Brett Taylor wanted to—

Yeah, what’s the limit of that?

—dig in volcanoes or do dog streaming or something, I’d be like, “Yeah, of course, man.” But he wouldn’t do that.

There’s some version of, “I don’t even care if it doesn’t compile in my brain; the person is so undeniably good that I would just back them.” Brett, as an example.

Yes. I feel like these things are so inextricably intertwined in my mind because part of what makes people great—I was trying to help one of my companies with a candidate yesterday, and they asked, “What did you see in these people?” I’m like, “They’re just so right. He’s right all the time.” Their industrial logic is impeccable.

They have all these great character traits, too. They’re amazing at recruiting, but they have a point of view that I think is going to be right in the world. I’ve seen them just make repeatedly correct decisions, including when I am wrong, and I have a lot of respect for that. When you say these people are amazing, part of what I think makes them amazing is that I believe in their judgment. If I don’t understand what they’re doing, I can’t have an opinion on their judgment. Brett’s doing enterprise AI. I understand what he’s doing.

7. Running An Ecosystem Firm

If I were to build a pie chart of your time now—not when you started the firm, but today—there’s meeting new companies, helping existing companies, talking to researchers, spending time with other people, and talking to candidates. I have no sense of what it would be. If you had to sum it up, what are the main things that you spend your time on, and what’s the percentage allocation?

I think I spend 2/3—maybe 3/5—of my time working on portfolio-company stuff, and that is recruiting, helping people think through things, trying to influence the outside ecosystem in some way, and then raising money. The next largest piece is looking at companies. I don’t know if this is right or wrong. I get paranoid about it and move the number, but I probably see 4 to 6 new companies a week. It’s not a very high volume. In my first couple of months at my old firm, I saw 500 companies.

Wow.

Now I feel more calibrated. I just have much more confidence that I can tell. The balance of my time is a combination of helping one of my partners look at something and meeting people who might teach me something about the world, and that could be a researcher.

If you are purely early stage in a larger firm, you can be very myopic because your ecosystem is big enough. We are a very small firm. We’re ecosystem-oriented, and I’ve learned so much from just getting to know investors who think differently than I do, including across different asset classes. I never spent that much time with public-markets people before, and it is very educational how they think about the world.

I spend time with other investors of all skills and asset classes. I spend time with companies—right now, with a pharma company that’s thinking about how AI is going to transform its business. This is very interesting to me because I’ve learned a lot about what they believe about the future. I’m doing a lot, but I’m leaving room in my calendar for learning and feeding curiosity. Then there are other parts: bridges to D.C. and external communication.

What have you learned about raising money?

I stand by the belief that I advise entrepreneurs with: You should understand people’s objections to what you are doing and their questions, but you should not tell them what they want to hear. When I started the fundraise for Fund I, I already knew a lot of LPs over a long period of time, so it was not very complicated.

But I had one of my friends, who is a private-equity investor, state, “You have to have a very differentiated story for LPs. Every part of the funnel should be the specific thing you’re going to do.” And I said, “Let’s be honest. I don’t know yet, but I need to raise some money so I can experiment and figure it out.”

So I never made slides and told a specific story about all the things that we attempt to do now. I think you don’t know until you make contact with reality, think about it, and are in the market. There were definitely LPs who did not like that.

I gave people a two-pager on my background and investing history and claimed that I was good at identifying extraordinary people, being useful to that set of people, and being genuine supporters. If you combine that with investment judgment and the ability to recruit, you’ve got a starting point. Mostly, some things I imagined about firm culture, and then we’d go execute like hell and figure it out.

I can see how this is tough from an LP perspective, and I deeply value the people who bet on us early because they go write a memo for their investment committee and they’re like, “She’s going to execute like hell. We’ll find out.” Sometimes the cleanliness of the story is what people are looking for. I can’t advise other managers on this because people have different outcomes.

If you tell people what you’re going to do, life is much simpler, and what you actually believe, life is much simpler. For me as an investor, when somebody can convince me that the world works differently than I thought, I’m immediately incredibly excited. Maybe I can convince people that this is just how it actually works.

I’ve met a lot more investment managers over the last 4 years. I knew a lot of VCs, but I really like people who are doing creative things—entrepreneurial investment managers, as you, I think, do and as you might imagine. I’m like, “I don’t want to build the firms that they’ve built,” but I love the creativity with which Philippe and Thomas at Coatue, or Josh at Thrive, approach their businesses, and the encouragement they have for others to approach their businesses. Well, you can do new things, and you should go express your opinions in the form of your investment management firm. I think that’s amazing.

What was imprinted on you watching your parents, who are both entrepreneurs?

I think this was very helpful to me because there was no moment in my childhood or as a teenager when I felt like they were not there for me, even though they both worked all the time—

Nonstop, yeah.

Both of them. Can we try to resolve these things? I was a pretty independent kid. That helps me. I don’t know where I was in the distribution, but I feel like I was pretty independent.

It helps me to think that your family can make you feel like you are the center of their world, but they’re whole people with other interests, and they want to spend time doing other things, too. Even just recognizing my own importance from the perspective of remembering what it’s like to be 10 years old. I liked my mom. I love my mom and dad, and it was so cool.

Our family values are very similar to theirs: integrity, thinking for yourself, independence of thought, and then there’s a focus on family, team spirit, and family. The independence of thought is probably the least generic one of those. I think a lot of people want to be good and kind and work hard and whatever else. For both my parents, it was a moral issue: You cannot ever worry about what other people think. I’m far to that side of the spectrum, but I do think about it sometimes.

You do sometimes worry what other people think?

Yeah.

What do you want them to think that you worry that they don’t?

I worry about raising people’s competitive hackles in the ecosystem.

Why?

Because I’m a friendly person. I want to be friends with everybody. I don’t mind competition, but the sometimes pure zero-sum competition stance of traditional Series A, Series B firms that says, “I’m going to own 18% to 25% of this company and take the board, and you’re going to own none of it,” is not conducive to a lot of collaboration. There are issues with that from an incentives perspective, but the public-markets orientation is that people love to tell you about their best ideas and pile in after them.

Mm.

This is an extraordinary situation. I would love to talk about why gaming and entertainment is going to be totally different and people are super under-indexed on it. There’s part of that orientation that just appeals to me as a very positive-sum person.

I’m always interested in sources of inspiration. I’m curious in two ways. Overall in your life, who has inspired you the most? And also right now, in this very moment, who is inspiring you the most and why?

I saw my parents build a company. I thought, “This is so cool.” It’s us against the man. The man is a very big company. The man is trying to kill us. We can still do it just because the technology is better, the product is better, and the customer will want it.

My love and ethos go to entrepreneurs. You can make something out of nothing because you see a better future, and you can do it really fast. These things appeal to me in terms of what I want to try. There’s so much courage in that and an optimism.

There is something poisonous that bothers me today, where I think the post-Gen Z entrepreneurial crowd especially thinks that marketing and brand are all that is real. Building a network is totally real. No one denies that. But when folks are very cynical about how the world works, how entrepreneurship works, that it’s just nepotism and Twitter is useful, I think that’s nonsense.

If you focus on value and treating people well, and you work with extraordinary people and the vision is worthwhile, that works more times than you’d think. There’s so much cynicism about playing the game, be it marketing or fundraising, and I hate that.

Cod is like this. I find him very inspiring, and Tuhin is like this. I find him very inspiring. He’s just like, “If we just do the right thing by the customer, we will win.” It feels a lot more complicated than that. I think he’s right, and that seems to be working.

One of the huge debates right now is what to do about the fact that there are open-source models that aren’t American-made, which are competitive at the frontier of AI model performance and seem to clearly have been, at least to some degree, based on the work of American models. What to do about this, what it means for the future of AI—companies love open source because they can build their own thing, and Base10 and others that serve a lot of inference work with a lot of these models. How are you thinking about what’s right, what should happen, what will happen, and the implications for business? This is a big, hard, important, interesting question.

It’s a big question. My point of view is that there’s what is healthy for businesses, America, the ecosystem, and individuals, and then there’s what’s already actually happened. The reality is, over the last 3 years, we’ve had increasingly competitive open-source models from all fronts, largely China, but definitely also the US and Europe—most recently, Thinky, Poolside, people waiting for Reflection, NVIDIA models, and Mistral. We will have, and already do have, very powerful open-source models from Western countries.

The cat is out of the bag, and these are in use everywhere. Even if you have no economic point of view on the labs and you just say, “How is the diffusion of capability going to happen in the economy?” there are a huge number of instances where it is too expensive, too sensitive, or too slow to use the frontier model from the frontier providers today. I think that’s going to increase as we learn how to do more things with AI because it’s actually quite expensive.

It’s objectively true that if the capabilities are more democratized, you will see them used in more ways. I want to see that happen. I do think that it would be irresponsible not to understand the safety profile of models as they progress because you just draw the line. We have companies that use frontier-model capability for defensive cybersecurity and for biology work. If it works for those use cases, it obviously also should work in similar ways for the—

Offensive version, yeah.

—offensive use cases or the bioweapons, biosecurity use cases. We just need to look at that reality and think about the other ways in which you control this. But attempting to stop technological progress and openness around it—if you did restrict use of open-source models in the United States, you’d basically just restrict law-abiding American businesses and slow them down or move profits to different pockets, prevent certain uses of them, because the actual attackers or people who have adversarial uses of these things are not affected by your restrictions. You’re restricting your own people.

My view would be that there should be testing and understanding of these models at the frontier. People are very worried about backdoor-like behaviors in Chinese models. The thing to do would actually be to have a very rigorous set of safety testing on that. Let’s go find out as much as we can instead of talking about how there might be this issue in a speculative way when there hasn’t been nearly enough actual research on it.

The future where there is broad access to intelligence too cheap to meter, as Sam put it, is coming. It will be supported by open source. Businesses want it to control their own destiny—for economics, for capacity. If given those models and the increasing democratization of the skills to post-train these models, build harnesses, and use tasks, the economy is so big. Every individual has these use cases that are not going to be imagined by a researcher in a frontier lab.

You can’t imagine the diversity of reality. Even if you trusted the models to go figure out what to do, they have to get there. The best way for that to happen is an ecosystem of businesses, as we’ve always had in the economy, and cheap infrastructure.

Do you ever worry that an alternate version of US history is that there was energy too cheap to meter because we built 1,000 AP1000s or something, much like China is doing now in nuclear, and just a set of circumstances happened such that we just didn’t get that? Do you ever worry about that as it relates to intelligence? It does seem inevitable that we’re going to have abundant, accessible, low-cost, valuable intelligence.

Can you imagine a world where we don't?

Yes, absolutely. I can also very easily imagine a world where we don't have that in a competitive way, because it is essential to economic competitiveness and national security. There is not a version of the world where we rebuild our industrial base without automation in the United States. If we don't import people, and our people are expensive and we lack some of the skills but want to produce a lot more goods and have a more resilient supply chain—

It just doesn't add up.

Who's going to produce this stuff? People in the United States do not want to work, and should not want to work, for $13 an hour doing a very inhuman job. I don't think it is inevitable that we are competitive, and I think we need to make that decision actively.

The version of it that I think is very possible is that people are rationally afraid of the impact of AI on jobs, or dislike the capture of rent by a small number of technology firms, rejecting the idea of being in the permanent underclass and then connecting that to an anti-capitalist orientation. That contingent of thought can slow down the buildout of energy and infrastructure and industrial capacity. One of the most important inputs is compute. If we don't have it, we're naturally not competitive, or we're at least not independent. I think we're going to start talking much more about compute independence. That's a big problem.

On this point of compute independence, what does that mean? What are the missing pieces of compute independence? The most obvious one might be more fab capacity, more leading-edge fab capacity here in the United States, or something like this. There's all sorts of stuff upstream. There are particular kinds of glass that are very important, controlled by basically one company, and TSMC has a monopoly on the supply of it. There are all these component parts, but if you think about compute independence—if so much boils down to compute—what's to be done about it? Are you trying to invest in companies that are solving that problem? What is the problem? Say a bit more about what it'll take.

If you just work backward from a data center full of GPUs—cooling, powering, training, and inference so we can support the use cases—all of those inputs actually look a great deal like energy independence or something like that. For all those inputs, there is a global supply chain. I've got my TSMC mug with me. There are parts of that supply chain that are like a very thin sieve in a place that is not necessarily stable or accessible to the United States and its allies.

A different version of the world that will take a bunch of investment in national security and energy policy is, for example, what Jacob Helberg is working on with something called PacSilica. It's like, okay, for every part of the supply chain, can we invest in more capacity and figure out what the independent paths are? I don't think that means it's all got to be created in the United States. Comparative advantage is real, but having more than one source is a position that everybody wants to be in.

When we think about the components that we've invested in, we've invested in the labor gap for data centers and robotics. We've invested in nuclear energy. We've invested in alternative chip architectures. We keep looking at people who are essentially data center builders, solar and battery installers of some kind. Part of this is the actual capacity buildup. That's a very operational business, somewhere between operations, technology, and real estate.

And financing, yeah.

And financing, absolutely. That's probably the dominant thing. We have not invested in it yet, despite looking very closely. I'm not opposed to it, but fundamentally, I'm a technology investor. I want to understand what it is that is the durable product asset people are building.

8. Markets Break Their Old Rules

What are the big debates inside of Conviction? You've got such an interesting team. Mike, your partner, is a very technical person, an amazing engineer. The young talent in your firm brings really interesting perspectives, so I'm imagining great, lively debates about things that matter. What are the big debates today?

Alad and I have a podcast called No Priors. This premise—that some of the things you believed, especially about markets of the past, are no longer true—is an interesting one. Regularly, we look at companies in domains that are not traditional software domains—not even traditional software domains—but ask, can you make money in this market at all?

Venture investing in semiconductor companies, Lip-Bu Tan aside, was a god-awful business for the longest time. The returns—

Everyone told me this.

Yes. It was really bad. That's an example of Bella, a partner on our team, starting to look at a bunch of these companies, and it's obvious that the demand is there.

Now, I think we have arrived at the conclusion that others have as well, which is: the market is different today. We are seeing consolidated, at-scale demand for accelerators, or even supply chain independence, because the big buyers of it want it, too. We can't all be stuck on one line—

On one line, yeah.

—at TSMC. All of that is very, very valuable, and that changes the risk equation for these companies.

We start with pretty aligned beliefs about the direction of travel or the problems that are worth working on. Then a lot of the debates are: Is the market friendly to a venture-backed company or not? Space is not a friendly market to a venture-backed company, but is it possible? Is the distribution of outcomes worth betting on?

That could be true in solar and batteries and nuclear, in turbine manufacturing, in robotics, and in biology. These are not your favorite software markets from 10 years ago. Each of them is a new debate. Biology is an interesting one where, by virtue of seeing the data empirically, I have now strongly moved to one side of the debate.

What side is that?

You can create and capture enormous value with models in biology, and there could be different AI software in biology. We're the first check in a company called Chai Discovery, and Chai is working with a number of top-10 pharma companies in really significant ways to accelerate some part of the R&D process.

The conventional wisdom when we invested in this company—and I've been looking at computational biology companies of different sorts for more than 5 years at that point—was that the only way you make money in biotech or serving pharma is by making a drug, then getting BioBucks deals, and then deciding how far along that risk path you want to take. What that means for the capital structure of the company is, okay, the great traditional biotech firms find these principal investigators, and they own 40% of the company. They're assembling these things, turning them into candidates, but most of it doesn't work.

So it's just a very different distribution of outcomes and structure and way to invest in businesses. Dumb software investors say you can't make money selling software to pharma or build platform businesses in pharma. The debate is, does it change with models? I'm a strong yes now.

We still have a question to solve on regulation. There's the speed of the physical world, and safety is not something you can overcome easily, but I think we should see a massive acceleration in cures.

What flipped that for you? What evidence did you see from when we didn't know yet?

We invested when we didn't know yet. That's part of the fun of venture. We were like, “It is possible—

Yeah, yeah.

—and it is worth trying.”

Could happen, yeah.

There's no genius here. That's a $10 million contract. The other piece is, you just talk to the scientist at the customer, or somebody who leads a—

The user of the tool, yeah.

—the user of the tool, or you see that they're also end-user-adopted tools, product-led growth tools that are working in the space. Well, the customer knows if it's valuable or not.

The lightbulb moment for the industry is when we have a new indication or a new drug where the trajectory of the thing was clearly changed and it was created by AI. We should see a huge wave of investment, and rightfully so, but it's going to happen. I'm very impressed by the speed with which pharma and healthcare overall have said, “Yes, this is going to make a difference, and we actually think it's going to change the business.”

Why do you call it Conviction?

It's aspirational. The most traditional form of early-stage investing is you start early with a company, you have a significant position, you never sell the position, and you work on the company until it works, is sold, or dies. There's wonderful alignment and simplicity to that.

Mike and I have both had the benefit of being part of the journey for some companies where it took a minute to begin to work, or where it's not obvious at the beginning: Sigma, Notion, Rippling. You wouldn't bet on a company hoping that it's going to take 4 or 5 years to find the thing. I think everybody is a product of their own experiences, including investing experiences. The first couple of years at Base10 were very non-obvious as well.

The ability to take a point of view that is not obvious in the market, because of the market or because of people's backgrounds or whatever the reason, and then just suspend doubt and act with full belief until it's true or not—that's a great way to be a partner to somebody building a business.

I am trying to build a partnership, and I deeply believe this is a team sport. I was talking to a friend who runs another investing firm, and he was like, “We don't have individual ownership of our investments.” I'm like, “That is nonsense to me. How could you run a business that way?” Because somebody has to own the decision.

Mm.

I don't know if this is right or wrong, but I don't know any other way to invest than by saying, “Patrick, you must make the decision. Do you believe? Convince us. How can we help you make that decision?”

What have you learned about risk-taking? Behind conviction, it sounds like there is often a leap of faith of some sort. Obviously, if you knew everything and it was obvious, that would be priced in and there'd be no return opportunity. Is there always a leap of faith? Is that risk-taking by another name?

Unlike my good friends at Founders Fund, I don't have an instinct to be contrarian, but I do think it is so fundamental to decide what you think and not worry too much about what other people think. By other people, I mean the dominant narratives of the period, or even what different players in the ecosystem who are really important declare, one way or another.

I think you just need to find the truth. If you find the truth and it is wrongly priced and you hold onto that, you're in a good position. You want asymmetric information and then the confidence to hold the opinion when other people haven't come around to it yet. I think a lot about how to make sure we have information that is better than other people's and then protect ourselves from noise.

Describe that in one more level of detail. That's a great way of asking what conviction looks like today. You're building that shell. What are the keys to doing those 2 things well, both having the truth and protecting yourself from the noise?

I want to spend my time learning about the world from somebody who is making it happen. Our portfolio founders, the many founders who are doing amazing things outside of our portfolio and doing something that surprises me or advances the frontier, or really smart people who believe something I don't—those are 3 different categories. I'll give you an example.

Mikey Shulman at Suno is building an amazing business doing music generation. Shame on me. I knew Mikey. A mutual friend of ours who is an investor asked me to invest, and I stupidly said no. I was just like, “I don't think that many people want to make music.” I make music, but can you turn it into a social network? How much consumption is there going to be? A lot of questions.

My intuition was just wrong. It's been somewhat wrong because I have underestimated the amount of expression, entertainment, and creation for a lot of AI tools. I've learned something here by talking to Mikey about his business and what people are trying to do. If it is founders who have companies that are creating a behavior you don't understand, somebody working on research in an interesting direction, or businesses that say, “Here's my plan for AI,” all that is super educational.

I think the circular logic sometimes of asking what people believe about the big lab strategy today and how any of the applications can live is actually not that instructive for your decision-making. The way I think of it is that any organization has a couple of key priorities. Let's assume the priority for OpenAI, Anthropic, and DeepMind is AGI or ASI in a safe way, where they capture a lot of profit.

The priorities that ladder into that probably look like ChatGPT, ads, and coding. Maybe there's an expansion after that: co-work, the ability to get different types of users to do richer tasks, and more interfaces. I think you have to judge the actual competitiveness of any of those efforts, the reasonable scope of them, and then look at them relative to each of our companies or opportunities that we're looking at.

Coming up with some grand strategic framework for what layer is going to win here is not useful to me. I feel like people spend so much of their investing energy thinking about that, whereas I want to spend my energy figuring out: If we're 1% of the way in, what is the next 99% of diffusion?

That's where we try to direct our energy.

For fun, as we wind down here, understanding this is a purely speculative question and it's meant more for fun than raw prediction, what are some things you think are true a year from now based on all these incredible people that you're close with, the research community, the entrepreneurs, and the Sunday founders? You add it all up. Things are moving fast. A year is a long time, and it's like reverse dog years now. What do you think is notably different about the world of technology a year from now?

I'm hopeful that a year from now we see Jevons paradox in practice. As we have agents and products that do more of the mundane more effectively in all the domains of our lives, it should look like the transformation that has happened in software engineering. You have companies—I have companies—where they're like, “We're just going way faster.”

Yeah.

I expect that some analogy like that will happen—

Everywhere else.

Everywhere else, and we see it in our companies. I'm thinking about one of our portfolio companies where the marketing department is a person and a half. This is a company that serves lots of customers. They need to do very traditional things, like sales enablement content.

What happened was the guy in charge of marketing is interested in creating leverage for himself, and he's like, “I made an autonomous marketing department for us, the company.” In every function, as you learn faster and do less of the mundane, you will repurpose that time somehow. Do you work less now that you are more productive with AI?

I work more. Yeah.

Yeah, I work more, and I think this is a core wisdom of Jensen's: we're all going to be more employed.

Mm.

And we need to make sure that people are given access and education to the tooling that will allow that to happen.

What you've built is incredible. We've loved, through the Colossus side, getting to know your whole world. It's so distinctive. You are a great example that there's always room for great. There were plenty of early-stage investment firms when you started Conviction, and yet here we are 4 years later or whatever, and if you ask the people you've worked with, you've made a really huge difference in their lives.

There's always room for great. I think that's a great, awesome lesson, especially because I love how you described the early pitch. It was not like, “Here's how we're differentiated at every level of the funnel. We're just going to run at this thing.”

By the end, I got so frustrated that I was just like, “It's an execution game.”

Totally. I ask everyone the same traditional closing question. What is the kindest thing that anyone's ever done for you?

I love this question. I'm going to give a collective answer. I think there are so many people who are extraordinarily accomplished in Silicon Valley who care very little for pedigree. As soon as they have a conversation with you and think, “Maybe you can help me,” or, “You have an interesting idea,” or, “Maybe I just think you're promising,” the dominant factor in their willingness to invest in a relationship or a person is just their assessment of the idea and the person.

I think that's amazing. That is not how most ecosystems work. Ashim Chandna, Anil Baseri, Joseph Ansanelli, and Reid Hoffman, who hired me at Greylock—I started when I was 23. People love to make fun of young VCs. They're like, “Ah, what a barnacle on the ecosystem. This is a terrible experience for entrepreneurs. They don't know anything. They're trying to advise people. Who gave this kid money?”

I'm like, well, 1, my job was just to make other people successful at the time. You can take any task in any job and just try to be great at that task with mimicry and first-principles thinking.

We hire earlier-career people at my firm, but I do think, oh my goodness, thank you for taking a shot on some random person—

Mm.

—and then investing the time to teach me how to be an investor. I think there were a few people who gave me advice starting the firm who would think of this as entirely trivial. I'm just giving you my opinion, but Ravi Gupta, who's now co-CEO of a new thing called Ithaca, Dylan Field and Elena Natalinski, and John Lilly, who's been a longtime partner and friend—there were a few folks who just said, “You can definitely do it.”

I was going to do it either way, but having the encouragement of people who believed that there was room to be great, including some of our first LPs, I will be forever grateful to the people who took a risk with me.

It's beautiful. The world runs on faith—belief without evidence, yet still conviction in someone's ability to do something. Pretty cool.

It's faith in people. We talked a lot about how people's ideas and our opinions of them are intertwined, but I think that's the beautiful thing because you don't need any particular advantage to have an idea. The fact that folks will evaluate that and put faith in us, I could not be more grateful.

I've learned a lot watching you operate and talking to you. This has been really fun. Thanks for having me.

Thanks.