Chapter Timestamps
00:00 Why AI investing cannot be backtested
01:52 Great-person theory and resisting monolithic AI
05:03 How Conviction builds access to 250 frontier people
08:16 Harvey and the technology-first investment thesis
12:06 Why researchers now discuss exponential intelligence
15:58 Energy, compute, and physical supply-chain bottlenecks
17:38 Storytelling versus technical intuition in capital markets
20:05 Sunday Robotics and solving the robotics-data problem
24:03 Sarah’s investment decision process
29:11 Portfolio work, new investments, and deliberate learning
31:45 Raising a fund with an honest, evolving thesis
34:50 Independent thought and positive-sum competition
39:06 Open-source models, access, and safety testing
44:42 Automation, compute independence, and industrial policy
51:29 New venture markets in semiconductors, energy, and biology
57:00 Conviction, ownership, and finding wrongly priced truth
1:00:43 Learning from founders instead of grand lab strategy
1:04:15 AI productivity, Jevons’ paradox, and employment
1:06:40 Mentorship and betting on people before the evidence
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Full video:
Sarah Guo is the founder of Conviction and one of the most closely watched investors in AI. In this conversation, Sarah and Patrick discuss what the people at the frontier of AI actually believe, why some researchers think exponential intelligence may be only a few years away, and how she’s navigating an investing environment that can’t be backtested.
Transcript
00:02-02:22
Sarah Guo: It is a violently competitive landscape. I think people are very concerned that this is a globally competitive landscape. There’s insecurity in that because I think it’s a bit narrative-breaking as well. The belief is like with recursive self-improvement of AI research models that can improve the models themselves, we are a year or two years away from some sort of exponential intelligence.
Patrick O’Shaughnessy: Sarah, where to begin? What will hopefully be a really fun conversation, I guess, 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 for a while, and it kind of feels like if anything, it might get more frenzied and more chaotic, both for what you do but also the world around what you do.
Sarah Guo: An obvious thing for anyone like thinking about how to navigate this period as an investor is just like, well, how does it unfurl and how’s it different from the past, and what do I do if I can’t back test, right? I was talking to an investor friend last night, and the analogy he gave me was like, 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. And I do think the question is, do you miss the opportunity on one side or, you know, do you make the mistake of every boom-bust cycle in technology history? I think it’s a complex question.
Patrick O’Shaughnessy: Do you think that we got it right in the profile that we wrote, that you’re sort of making a specific—I think we called it a wager, a bet, a positioning—whatever you want to call it.
Sarah Guo: Yes, I’m a long. Yeah, that there is a really important fundamental thing happening between a couple of labs in AI and kind of everybody else, and that we need to sort of take up arms to make sure we don’t end up in a very monolithic outcome.
Patrick O’Shaughnessy: Do you think about it that way?
02:22-03:57
Sarah Guo: I would answer in a maybe different way, which is I believe in the “great man” and “great woman” theories of history. I just believe that 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, like, yeah, you change outcomes. So you look at very important questions of what happens to open-source models and U.S. industrial policy. You know, if you think about the opportunity for the ecosystem in the future, is there going to be a competitive Western open-source model? Like, did anybody make one? Could they raise the money or were they willing to commit the capital, gather the talent base, and build all the infrastructure and fight for the frontier or not? So I definitely think that individual people and entrepreneurs can affect the outcome. I don’t necessarily think of it as a war. I’m working closely with, co-invested with, and have many friends at the labs—the big labs and small—but I think it’s fair to say the extreme point of view that some folks may have, in and outside of those big labs, of the owner of one, two, three frontier models consuming the economy—I do not want that future, very clearly, and I don’t think we are going to end up there.
Patrick O’Shaughnessy: Do you actively want to be a great woman in this sense of the theory?
03:57-06:15
Sarah Guo: No, I think—and I don’t mean that from a personal humility perspective; it’s just not in my set of goals, right? Like, 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. It’s—I think some people are driven by like, “I want to be the person that made this happen.” And my realization—I thought I was going to be a software entrepreneur for the longest time—and my decision that from an identity perspective I was going to be an investor actually had a lot to do with the idea of like, I’m deeply curious. I like to understand things; I like to be right. And I care—I’m very motivated by working with extraordinary people, and then, if I have a set of skills, using those to make them more successful. And that looks like a pretty good fit for early-stage investing, right? If I want to work with the very best people and I want the companies to have impact, I think the firm can support a movement and support change we want to see, but I don’t think it has to be me.
Patrick O’Shaughnessy: If that’s the goal, what does it take, do you think, right now to be the best in a very competitive environment? What would you ascribe—you’ve done really well so far.
Sarah Guo: We’re still very early.
Patrick O’Shaughnessy: I know, but 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 in the next year plus to, you know, within a horizon, to be that good.
Sarah Guo: I think it’s been pretty simple so far, actually. Right? We talked about this—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 that it had 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, like, you might have better access and make better decisions than others who are less focused, right? So 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 just hard. It’s effort. Today, it’s actually been not that complicated. It’s more about what your bar is for the people you work with. And my partner Mike and I started with a set of pre-existing relationships and understanding. And so, I think that’s been useful.
06:28-08:41
Patrick O’Shaughnessy: Surely there must be more than just outworking everyone. Your partner Mike said something interesting to me a couple weeks ago, which was there’s sort of 250-ish people that he thinks about, or you guys think about—
Sarah Guo: That are some combination of entrepreneurs and researchers...
Patrick O’Shaughnessy: Doing the most interesting things on the frontier. Yes. Just like the people that are actually showing up in the morning and pushing this whole thing forward. And one of your goals as a firm is to be as close to those people, know them all, and be as close to them 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 like doing a bunch of meetings with people that are starting companies. So it feels like from the outside looking in, from the cheap seats, it looks like there’s more unique stuff going on than just the competition set was low, and we focused more, and we’re executing better.
Patrick O’Shaughnessy: Yeah. And so I’m interested in the ingredients that have been, so far, a part of success. I won’t name them, but there’s one well-known LP that does this like survey—
Patrick O’Shaughnessy: A well-known LP does this survey every year of what all the other fancy LPs most want to invest with. And you were either number one or two.
Patrick O’Shaughnessy: And so there’s something going on, both with the companies you’ve invested in, with the performance so far, and with the market perception. It’s more than just, “Okay, it was a moment in time and we worked really hard.” So I’m trying to get at those ingredients. That’s why I’m pushing on it.
Sarah Guo: 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 we look at in this era. If we are right about capability growth and the breadth of impact, that would be nonobvious to other people. So where’s the biggest difference from the status quo? How does the framework change?
Sarah Guo: I’ll give you two examples. One of the things we were really looking for in the first year was application areas like workflows and professions—tasks that we thought were a good fit for the models. That’s a very technology-forward approach. Lots of people said, “This is nonsense, right? You have to think about the customer problem. Working from the customer back is the only way.” I think we want to do both.
08:41-11:06
Sarah Guo: And if you look at Harvey and the function of the law, rationally, if you think 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 think, “Okay, you need to read a lot of documents.” We had retrieval, and you need to generate text. There’s a lot of precedent text, both inside firms and in common law and history. That feels like a really good match. When I say focus, it’s like we also took a very specific view of what is now possible, what is valuable within what is possible, and who is aligned with us.
Sarah Guo: Like Winston and Gabe—they believed that AI would transform the practice of the law. It’s even cringey to say it now, but in a very “AI pill” way, right? They said, “We will do enormously complex work with lawyers. I don’t know when—could be next year or five years from now.” But from the kernel of “I can look at a landlord-tenant agreement in California and answer a question”—it’s somewhat trivial—to projecting to doing an Activision Blizzard M&A and doing 85% of the work... That’s a leap, right?
Sarah Guo: 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 at that moment.
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11:06-13:25
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Patrick O’Shaughnessy: What is that group—the 250 researchers, the frontiers group—talking about today? What’s the most interesting thing and the most notable difference between today and six months ago, 12 months ago, or something?
Sarah Guo: It is a violently competitive landscape. I think that was true 12 months ago, but even more true than it was 24 and 36 months ago. I think people are very concerned that this is a globally competitive landscape, and there’s insecurity in that because I think it’s a bit narrative-breaking as well. I’ll describe a belief, and then I’ll question it. The belief is that with recursive self-improvement of AI research models—models that can improve the models themselves—we are one or two years away from some sort of exponential intelligence. I think that belief is new within the last 12 months for a lot of researchers.
Patrick O’Shaughnessy: But some driver of it—like we’re two years away—and smarter people than me, Andre Karpathy, will actually say, in a very self-aware way, “I’ve thought it was two years away for about 10 years.”
Sarah Guo: Yeah.
Patrick O’Shaughnessy: Right. And he thinks it again, to be fair. But who can say?
13:25-15:19
Sarah Guo: I do think there is some sense that the major labs are so compute-intensive and so large from a headcount perspective now that the sense of contribution—like “I can move the needle”—if OpenAI is 200 people and there aren’t that many researchers, then the question of how we get there is really up to every single person. Now, if the question is, “Well, I need $750 billion of compute spend and we have many thousands of people working on this problem,” I think people feel less ownership of the outcome.
Patrick O’Shaughnessy: Oh, that’s interesting. So it’s just a function of getting compute—that’s the thing that’s going to push it over the line, not our own human efforts.
Sarah Guo: I definitely think there’s a large contingent of researchers who would feel that one of two things is now true: either 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.
Patrick O’Shaughnessy: So then what are they doing? If I believe both of those and I’m a top-five researcher or something...
Sarah Guo: That says something about your psychology, because you want to do something that matters. I think there’s also a set of scientists who
Sarah Guo: They want to work on the thing whether or not it matters that they’re working on it. I had an entrepreneur ask me yesterday, “Would anyone—would I 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?” For a couple of them, 5%, 10% max. They’re resourceful, really talented people; they find other investors. There are lots of smart investors in the world who want to take that risk. But maybe they wouldn’t have gotten the next hundred million dollars of compute.
15:19-17:38
Sarah Guo: I don’t think every researcher doing frontier work at these top couple labs right now feels like they’re essential to the machine.
Patrick O’Shaughnessy: What do you think is the unhealthiest part of everything going on right now? What worries you about what people are trying to accomplish? What are the things that you’re most worried will inhibit the future that you want to see?
Sarah Guo: I think compute is one. People understand that very well: the question 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 said, “There is nothing that is going to move the needle for us at sufficient scale before 2030.” I was like, “Oh, that’s depressing.” It’s just, 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 is a technology or capability or capitalism problem. I think it is a regulatory problem and an alignment problem.
Sarah Guo: And 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 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.
Patrick O’Shaughnessy: Yeah.
Sarah Guo: 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. So that’s one. The physical supply chain is just a tough reality. That makes me worried: the learning to build things, having the tacit knowledge, the labor, and the raw materials—that can’t go as fast as software or even decision-making. But the only way through that is through. We just have to invest in it.
17:38-20:05
Sarah Guo: On the investing side, what worries me: I was talking to one of our founders—several of our founders who are researchers—and explaining that the quality of their storytelling and their ability to make the company’s bets legible to investors is obviously very important to their success if it’s going to be a capex-intensive play up front. The reality of the financial landscape for all of these people is, I’m not a research scientist, you’re not a research scientist, and all of the capital is not research scientists. So it’s on entrepreneurs to go explain their story.
Sarah Guo: 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 pedigree.
Sarah Guo: Proxying judgment to pedigree or to other legible signals, like who’s invested in you or what the references are, is always good. But the decision-making is less fundamental, right?
Sarah Guo: Right. I asked an extraordinarily good investor friend, I said, “Tell me—I like you, we had a debate—I don’t understand what this company is going to be that would be big. Explain it to me.”
Sarah Guo: And his explanation was essentially, “Do you know the quality of this person?” I’m like, “Yes, I’ve known the quality of the person for eight years.”
Sarah Guo: But the business, the technical theory, and the business don’t make sense to me. People are making large-scale research bets without any intuition for them or without any opinion on them. It’s not all going to work. I may not be any better at deciding, but we want to get to that intuition. Not having a point of view on the business beyond the pedigree of the person is very dangerous.
20:05-22:58
Patrick O’Shaughnessy: Who is a researcher that has most blown you away, and how did they do so? What’s something they did that felt extraordinary? Back to the great person theory—there are a number of these people whose contributions history books will write about as a truly extraordinary thing that created this kink. What’s an example of a person like that and something you’ve seen them do?
Sarah Guo: My partner Pranav and I were introduced to Tony Zhao and Cheng Chi at Sunday Robotics when they were PhD students at Stanford. They’d worked at Toyota Research, DeepMind, and Tesla, so they weren’t just academics by any means. They were young—I think they were like 25.
Patrick O’Shaughnessy: Yeah.
Sarah Guo: I think one of them didn’t even finish; they just started the company. What I thought was impressive about them then and now: I remember looking at their body of work, and it took me a while to get oriented, but I think they contributed more than most of the interesting ideas in robotics AI over the last four years, dual-handedly. That’s a pretty weird thing for two very young people to do.
Sarah Guo: If I try to characterize the type of ideas, it’s, “How can I use modern AI to solve the robotics generalization and robustness problem in a very practical way?” It’s 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.
Sarah Guo: One of the big research and practical problems is, “Where do you get that data?” Some of the ideas Tony and Chang 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?”
Sarah Guo: 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 it as a technical problem to solve: what is the shape of the data and the collection, how does it interact with the model, and how much of this cheap data collection can we transfer to model learning? That’s super interesting. It’s very outcomes-driven. I was blown away in the first meeting. We said yes immediately there, thankfully. It’s just under two years that the company has been around. Nothing is true until it is shipped, but this entire team...
22:58-24:27
Sarah Guo: ...is shipped. But this entire team believes that we are going to have general semi-humanoid robot thoughts—doing things in people’s homes, like first in beta at the end of this year. That’s not something that any of us believed. And it blows me away that you can move that quickly from a bunch of cardboard in a Stanford basement to building the full-stack thing. It is manufactured here. We’ve done hundreds of iterations of hardware, model collection, data collection, and translated it into tasks, and tested it in all these real-world environments, and it’s going to work.
Patrick O’Shaughnessy: The videos are very cool.
Patrick O’Shaughnessy: I just think the entire speed of that is mind-boggling. Because I still think the broad view—not everyone, but lots of people in robotics—are like, “Now it’s a question of when, not if.” And it surprised me that the team was like, “If not this year, next year.”
Patrick O’Shaughnessy: I’m very curious about the moments of your investment decisions. Like you just said, in the first meeting, you were sort of like, “Okay, we’re in.” Is it always like that? Or are there examples of things where you hem and haw, and you end up doing it, and it works? I’d love to hear more about how you and your team make investment decisions—like the actual in-the-room process for, “Okay, this thing is interesting, we’re going to do it,” or “We’re not,” or “We’re debating it.” What is that process like?
24:28-27:39
Sarah Guo: Yeah. It varies based on the company. I’m personally very instinctive on people. And I don’t know that this is the best way to go about it now, describing it, but I often know I want to do something immediately. If I know what somebody has worked on, and then I interact with them and I hear the idea, and I have some background in the idea, then I’m like—we have a rating scale of 1 to 10—I’m immediately an eight or a nine. What I’m doing between that and a real decision is often figuring out what the holes in my understanding are, where my judgment of their premise or them is incomplete or wrong. Like, what do I not know? Because if you’re an investor looking for the most ambitious, impactful companies, you can’t know every domain, right? And we have biology, defense, robotics, and law. So I spend the next day to a few weeks desperately trying to ground myself, being like, “Okay, what does everybody else believe about this space? Are they the people I think they are?” That’s what my process looks like. And then I want feedback—I want 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 ship it off to a friend who was an investor I trusted for their perspective outside the fund. You and I have talked about what I value in a partnership. 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. So I used to take the memo and send it to John Lilly or Dylan Field or something. Now we run a memo. I see what Bella, Prof, or Mike wants to know about the person and try to complete the picture. Other people are more even in their decision-making. They’re thinking about it and thinking about it, and then they climb to conviction. I kind of start there and then work backwards. But a similarity between Mike and me is that we’ll start at a point and explain what will move us. So I met a really interesting company earlier this week with my partner Bella. Instinctively, I feel positive about it, but I just don’t know enough about the science here. I need to go make sure this makes sense. Can you really be an eight or a nine if you don’t get it? No. So that’s what I’m trying to do—I could get there. And 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?
27:39-29:58
Patrick O’Shaughnessy: Right. It’s not—I’m trying to think about if that’s fair. Because if Brett Taylor wanted to dig volcanoes or do dog streaming or something, I’d be like, “Yeah, of course, man.” But you know, he wouldn’t do that.
Sarah Guo: Yeah. So there is some class—there is 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.”
Patrick O’Shaughnessy: Brett, as an example.
Sarah Guo: Yes. But I feel like these things are so inextricably intertwined in my mind.
Patrick O’Shaughnessy: Yeah.
Sarah Guo: Because part of what makes people great—like, I was trying to help one of my companies with a candidate yesterday. They’re like, “Oh, what did you see in these people?” And I’m like, “They’re just so right.” That’s very useful. He’s right all the time. His industrial logic is impeccable. He has all these great character traits, too—amazing at recruiting. But he has a point of view that I think is going to be right in the world, and I’ve seen him just make repeatedly correct decisions, including when I am wrong. I have a lot of respect for that.
Sarah Guo: And so, when you say these people are amazing, part of what I think makes them amazing is that I believe in their judgment. And if I don’t understand what they’re doing, I can’t have an opinion on their judgment. Brett’s doing enterprise AI, right? I understand what he’s doing.
Patrick O’Shaughnessy: 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, talking to other people, talking to candidates. I have no sense of what it would be. What is it like if you had to sum it up? What are the things you spend your time on, and what’s the percentage allocation?
Sarah Guo: I think I spend two-thirds of my time working on portfolio company stuff—recruiting, helping people think through things, trying to influence the outside ecosystem in some way. Then raising money. And then the next largest piece is looking at companies.
30:01-31:44
Sarah Guo: It’s a piece of looking at companies right now. But I probably... I don’t know if this is right or wrong, I get paranoid about it and move the number. But I probably see four to six new companies a week. It’s not a very high volume. My first couple of months at my old firm, I saw 500 companies.
Patrick O’Shaughnessy: Wow.
Sarah Guo: So now I feel like I have more calibration. I have much more confidence. I can tell.
Patrick O’Shaughnessy: Yeah.
Sarah Guo: Right. And then the balance of my time, I am doing a combination of helping one of my partners look at something, meeting people that might teach me something about the world. That can be a researcher. If you are purely early stage at a larger firm, you can be very myopic because your ecosystem is big enough, right? And we are a very small firm, we’re very ecosystem-oriented. So I’ve learned so much from just getting to know investors who think differently than I do, including different asset classes, right? I never spent that much time with public markets people before, and it is very educational how they think about the world.
Patrick O’Shaughnessy: Right.
Sarah Guo: I spend time with other investors of all skills and asset classes. I spend time with companies, right? Like right now with a pharma company who is thinking about how AI is going to transform their 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 just learning, feeding curiosity. And then there are pieces that are other parts of bridges to DC, external communication.
31:45-33:29
Patrick O’Shaughnessy: What have you learned about raising money?
Sarah Guo: I stand by the belief that I advise entrepreneurs: 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. Right? When I started the fundraise for Fund One, I knew a lot of LPs over a long period of time already, so it was not very complicated. But I had one of my friends who is a private equity investor who stated that you have to have a very differentiated story for LPs, and every part of the funnel should be the specific thing you’re going to do. And I’m like, let’s be honest, I don’t know yet, but I need to raise some money so I can go experiment and figure it out. So I never made slides and told a specific story about all the things we attempt to do now, because I think you don’t know until you make contact with reality, think about it, and you’re in the market.
Patrick O’Shaughnessy: Right.
Sarah Guo: And I’d say there were definitely LPs who did not like that. They’re like... I think I gave people a two-pager on my background and investing history and claimed that I was good at identifying extraordinary people and good at being useful to that set of people, being a genuine supporter of those people. And if you combine that with investment judgment and the ability to recruit, you got a starting point. I mostly said, “Okay, here are some things I imagine about firm culture, and then we’ll go execute like hell and figure it out.” And I can see how this is tough from an LP perspective. 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.” Right? That’s tough, right? 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, but if you tell people what you are going to do, life is much simpler. And if you tell them what you actually believe, life is much simpler.
33:30-35:38
Sarah Guo: And for me as an investor, when somebody can convince me that the world works differently than I thought, I’m immediately incredibly excited, right? So I’m like, maybe I can convince people that this is just how it actually works. I’ve also met a lot more investment managers over the last four years than I knew before. I knew a lot of VCs, but people who are doing creative things. I really like entrepreneurial investment managers, as I think you do. And as you might imagine, I don’t want to build the firms that they’ve built, but the creativity with which Philippe and Thomas Cotto, or Josh at Thrive, approach their business, and the encouragement they have for others to approach their business with a sense that you can do new things and you should express your opinions in the form of your investment management firm, I think is amazing.
Patrick O’Shaughnessy: What was imprinted on you watching your parents, who were both entrepreneurs?
Sarah Guo: I think this is very helpful to me because there was no moment of my childhood or being a teenager where I felt like they were not there for me, even though they worked all the time, both of them, right? And if you try to resolve these things, one is that 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. I think our family values are very similar to theirs, right? There’s integrity, there’s thinking for yourself. It’s independence of thought, and then there’s focus and team spirit and family.
35:39-38:07
Sarah Guo: And 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. But for both my parents, it was a moral issue. They were like, “You cannot ever worry about what other people think.”
Patrick O’Shaughnessy: I think I’m too far on that side of the spectrum, but I do think about it sometimes. You do sometimes worry what other people think.
Sarah Guo: Yeah.
Patrick O’Shaughnessy: What do you want them to think that you worry they don’t?
Sarah Guo: I worry about raising people’s competitive hackles in the ecosystem.
Patrick O’Shaughnessy: Why?
Sarah Guo: Because I’m a friendly person. I want to be friends with everybody, right? I don’t mind competition, but I think the sometimes purely zero-sum competition stance of traditional Series A firms, 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 so you’ll pile in after them. And I’m like, this is an extraordinary situation, right? I would love to talk about why.
Sarah Guo: ...about why gaming and entertainment is going to be completely different, and people are super under-indexed on it.
Sarah Guo: And I think 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.
Patrick O’Shaughnessy: And 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?
Sarah Guo: I saw my parents build a company. I was like, “This is so cool.” It’s us against the man, and the man is very big companies. The man’s trying to kill us, and 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 goes to entrepreneurs because I think, “Okay, you can make something out of nothing because you see a better future, and you can do it really fast.”
Sarah Guo: There is so much courage in that, and optimism. And I actually think there is something poisonous that bothers me today, especially with the post-gen Z entrepreneurial crowd. They think it’s all about marketing, that brand 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—I think that’s nonsense. If you focus on value, treat people well, 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. I hate that.
38:07-39:27
Sarah Guo: I’m inspired by people who, and Pat is like this, I find him very inspiring. And Tuhin is like this. I find him very inspiring, where he’s just like, “If we just do the right thing by the customer, we will win.” And I think, “It feels a lot more complicated than that.” But I think he’s right; that seems to be working.
Patrick O’Shaughnessy: One of the huge debates right now is what to do about the fact that there are open source models, not American-made, which are competitive at the frontier of performance of AI models. And they 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 B10 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? It’s a big, hard, important, interesting question.
39:27-41:39
Sarah Guo: It’s a big question. I think there is my point of view on what is healthy for businesses, America, the ecosystem, and individuals, and then there’s what’s already actually happened. The reality is that over the last three years, we’ve had increasingly competitive open source models from all fronts, largely China, but also the US and Europe. Most recently, models from Thinky, Poolside, people waiting for Reflection, Nvidia models. We will have—and already do have—very powerful open source models from Western countries. So 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 ask, “How is the diffusion of capability going to happen in the economy?”
Sarah Guo: There are a huge number of instances where it is too expensive, too sensitive, or too slow to use a frontier model from today’s providers. I think that’s going to increase as we learn how to do more things with AI, because it’s actually quite expensive. So I’d say it’s objectively true that if the capabilities are more democratized, you will see them used in more ways, right?
Patrick O’Shaughnessy: Right. And I want to see that happen.
41:39-43:33
Sarah Guo: It would be irresponsible not to understand the safety profile of these models as they progress, because you can draw the line. You have companies that use frontier model capability for defensive cybersecurity and biology work. If it works for those use cases, it obviously also should work in similar ways for the offensive use cases or the bioweapons and biosecurity use cases.
Patrick O’Shaughnessy: Offensive use cases, or the bioweapons biosecurity use cases.
Sarah Guo: I think we just need to look at that reality and think about the other ways you control this. But attempting to stop technological progress and openness around it—like if you restricted use of open-source models in the United States, you’d basically just restrict law-abiding American businesses, slow them down, move profits to different pockets, and prevent certain uses. Because the actual attackers or people with adversarial uses are not affected by your restrictions, right? So you’re just restricting your own people.
Sarah Guo: My view is there should be testing and understanding of these models at the frontier. People are very worried about backdoor behaviors in Chinese models. And I say, well, the thing to do would be to have a very rigorous set of safety testing on that. Let’s go find out as much as we can, instead of speculating about an issue where there hasn’t been nearly enough actual research. We can do it—it’s not trivial, but we can.
43:33-45:45
Sarah Guo: I think 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 and capacity. If given those models and the increasing democratization of skills to post-train these models, build harnesses, and use tasks, that’s how you get it into the economy. The economy is so big that every individual has use cases that won’t be imagined by a researcher in a frontier lab. You can’t imagine the diversity of reality. Even if you trusted the models to figure out what to do, they have to get there. And I think the best way for that to happen is an ecosystem of businesses, as we’ve always had in the economy, and cheap infrastructure.
Patrick O’Shaughnessy: Do you ever worry that there’s a version of US history where there was energy too cheap to meter because we built a thousand AP-1000s, like China is doing now in nuclear, and 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 will have abundant, accessible, low-cost, valuable intelligence. Can you imagine a world where we don’t?
Sarah Guo: Yes, absolutely. And I also think I can 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.
Patrick O’Shaughnessy: Yeah.
Patrick O’Shaughnessy: Right. Like let me posit that 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...
Sarah Guo: ...it just doesn’t add up.
Patrick O’Shaughnessy: Yeah, what are you going to do? Who’s going to produce this stuff, right?
45:45-47:53
Sarah Guo: 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’s inevitable that we are competitive. I think we need to make that decision actively. The version that I think is very possible is that people are very rationally afraid of the impact of AI on jobs, or dislike the capture of rent by a small number of technology firms and, you know, reject the idea of being in a permanent underclass, and then connect that to an anti-capitalist orientation. That contingent of thought can slow down the buildout of energy, infrastructure, and industrial capacity. One of the most important inputs is compute. If we don’t have it, we’re naturally not competitive, or at least not independent, right? I think we’re going to start talking much more about compute independence.
Patrick O’Shaughnessy: And so that seems like a big problem.
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Patrick O’Shaughnessy: So on this point of compute independence, what does that mean? What are the missing pieces? Like, the most obvious one might be more fab capacity—more leading-edge fab capacity here in the US or something like that.
Sarah Guo: There are all sorts of things upstream. There are particular kinds of glass—very important—that are controlled by basically one company that TSMC has a monopoly on the supply of. There are all these component parts.
Patrick O’Shaughnessy: 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.
49:22-51:49
Sarah Guo: Well, if you just work backwards from a data center full of GPUs—cooling, powering, training, and inference, so we can support the use cases—all of those inputs, I think it looks a great deal like energy independence or something like that. All of those inputs have a global supply chain. There are parts of that—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 US and allies. So a different version of the world that will take a bunch of investment and national security and energy policy is, for example, Jacob Helberg is working on something called Pax Silica. 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 all has to be created in the United States. Comparative advantage is real, right? But having more than one source is a position everybody wants to be in. When we think about the components 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, because part of this is the actual capacity buildup. But that’s a very operational business, somewhere between operations, some technology, and real estate.
Patrick O’Shaughnessy: Financing. Yeah.
Sarah Guo: And financing, right? Absolutely. That’s probably the dominant thing.
Sarah Guo: We have not invested in it yet despite looking very closely. But I think it’s probably because, you know, I’m a technology investor. I want to understand what the durable product asset people are building.
Patrick O’Shaughnessy: What are the biggest debates inside Conviction? You’ve got such an interesting team. Mike, your partner, is a very technical—incredibly technical person, an amazing engineer. The young talent at your firm brings really interesting perspectives. So I’m imagining great lively debates about things that matter. What are the big debates today?
51:51-54:06
Sarah Guo: A lot. And I have a podcast called “No Priors”—the premise is that some of the things you believed, especially about markets based on the past, are no longer true. It’s an interesting one. And regularly we look at companies in domains that are not traditional software domains—not even traditional software domains—but like, 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, right?
Patrick O’Shaughnessy: Everyone told me this.
Sarah Guo: Yes, it was really bad. But I think that’s an example of...
Sarah Guo: An example is Bella, a partner on our team. She started looking at a bunch of these companies, and it’s obvious the demand is there. Now I think we’ve arrived at the conclusion that others have as well, which is that the market is different today. We’re seeing consolidated, at-scale demand for accelerators, or even supply chain independence.
Sarah Guo: Because the big buyers want it too. We can’t all be stuck on one line at TSMC. All of that is very, very valuable, and that changes the risk equation for these companies.
Sarah Guo: We start with pretty aligned beliefs about the direction of travel or the problems worth working on. Then a lot of the debates are like, is the market friendly to a venture-backed company or not? Space is not a friendly market to a venture-backed company, but it’s possible. Is the distribution of outcomes worth betting on?
Sarah Guo: So that could be true in solar, batteries, nuclear, turbine manufacturing, robotics, and 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’ve now strongly moved to one side of the debate.
Patrick O’Shaughnessy: What side is that?
54:06-56:15
Sarah Guo: You can create and capture enormous value with models in biology. There could be different AI software in biology. We were the first check in a company called Chai Discovery. Chai is working with a number of top 10 pharma companies in really significant ways to accelerate some part of the R&D process.
Sarah Guo: The answer is, the conventional wisdom when we invested in this company—and I’d been looking at computational biology companies of different sorts for five plus years at that point—the conventional wisdom was that the only way you make money in biotech or serving pharma is by...
Patrick O’Shaughnessy: ...making a drug.
Sarah Guo: Make a drug, get bi deals, and decide how far along that risk path you want to take. What that means for the capital structure of the company is that the great traditional biotech firms find these principal investigators, they own 40% of the company, they’re assembling these things and turning them into candidates, but most of it doesn’t work.
Sarah Guo: So it’s a very different distribution of outcomes and structure and way to invest in businesses. Dumb software investors think you can’t make money selling software to pharma or build platform businesses in pharma. So the debate is, does it change with models? I’m a strong yes now. We still have a question to solve on regulatory, and the speed of the physical world and safety are not things you can overcome easily. But I think we should see a massive acceleration in cures.
Sarah Guo: Part of the value creation will come from young companies, and we are excited to invest in them.
Patrick O’Shaughnessy: What flipped that for you? What bit of evidence did you see from “we don’t know yet” to “this is clearly something”?
Sarah Guo: Oh, we invested when we didn’t know yet?
Patrick O’Shaughnessy: Understood.
Sarah Guo: Yeah, that’s part of the fun adventure. We thought it is possible and it is worth trying. There’s no genius here. That’s a $10 million contract. I think the other piece is you just talk to the scientist at the customer, or somebody who leads a tool, or you see end-user adopted, product-led growth tools working in the space. The customer knows if it’s valuable or not.
56:15-58:23
Sarah Guo: I think the light bulb moment for the industry will be when we have a new indication or a new drug where the trajectory was clearly changed or created by AI. Then I think we should see a huge wave of investment, and rightfully so. But it’s going to happen.
Sarah Guo: I’m very impressed by the speed with which pharma and healthcare overall has said, “Yes, this is going to make a difference, and we actually think it’s going to change the business.”
Patrick O’Shaughnessy: Why’d you call it “Conviction”?
Sarah Guo: It’s aspirational, right? 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 or it dies. I think there’s wonderful alignment and simplicity to that.
Sarah Guo: 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 it wasn’t obvious at the beginning. Figma, Notion, Rippling—you wouldn’t bet on a company hoping it’s going to take four or five years to find the thing, right?
Sarah Guo: I think everybody is a product of their own experiences, including investing experiences. The first couple years at Base 10 were very non-obvious as well. The ability to take a point of view that is not obvious in the market—because of people’s backgrounds or whatever reason—and then suspend doubt and act with full belief until it’s true or not. I feel like that’s a great way to be a partner to somebody building a business.
Sarah Guo: I’m trying to build a partnership, and I deeply believe in this as a team sport. But I was talking to a friend who runs another investing firm, and he said, “We don’t have individual ownership of our investments.” I said, “That is nonsense to me. How could you run a business that way?” Because somebody has to own the decision.
58:23-1:00:42
Sarah Guo: 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?” So I think it’s all inputs to a single person.
Patrick O’Shaughnessy: Yeah. What have you learned about risk-taking and 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.
Sarah Guo: Yeah.
Patrick O’Shaughnessy: So is there always a leap of faith? Is that risk-taking by another name? You’ve been doing this enough, you’ve made enough investments. Now...
Sarah Guo: Unlike my good friends at Founders Fund, I don’t have an instinct to be contrarian. But I do think it is fundamental to decide what you think and not worry too much about what other people think. Other people are the dominant narratives of the period, or even what you know.
Sarah Guo: ...period, or even what different players in the ecosystem that are really important declare one way or another. I think you just need to find the truth. The way I would relate that to risk-taking is, if you find the truth and it is wrongly priced, and you hold on to 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. So 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.
Patrick O’Shaughnessy: 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 two things? Both having the truth and protecting yourself from the noise.
1:00:43-1:02:47
Sarah Guo: Yeah. I want to spend my time when I’m learning about the world from somebody who is making it happen—like our portfolio founders, or the many founders who are doing amazing things outside the portfolio, who are doing something that surprises me, advances their frontier, or really smart people who believe something I don’t. There are three different categories. And I’ll give you an example. Mikey Shulman at Suno is building an amazing business doing music generation, and shame on me. I knew Mikey—a mutual friend of ours who was an investor—asked me to invest, and I stupidly said no. I was just like, “Ah, I don’t think that many people want to make music.” I make music, right? But is that something you can turn into a social network? How much consumption is there going to be? A lot of questions, and my intuition was just wrong. It has been actually somewhat wrong because I have underestimated the amount of expression, entertainment, and creation for a lot of AI tools. And I’m like, “Oh, I’ve learned something here by talking to Mikey about his business and what people are trying to do.”
Sarah Guo: So if it’s founders who have companies that are creating a behavior you don’t understand, somebody working on research in an interesting direction, or businesses that are like, “Here’s my plan for AI,” all of that is super educational. I think the circular logic sometimes of “What do people believe about the big lab strategy today?” and “How can any of the applications live?” is actually not that instructive for your decision-making. The way I think of it is, any organization has a couple of key priorities. Let’s assume the priority for OpenAI, Anthropic, and DeepMind is AGI, right? Or ASI, in a safe way where they capture a lot of profit.
1:02:47-1:04:53
Sarah Guo: ...capture a lot of profit. The priorities that lead into that probably look like ChatGPT, ads, coding, and then maybe there’s an expansion after that. Co-work like the ability to get different types of users to do richer tasks in 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 our companies or the opportunities we’re looking at. But coming up with some grand strategic framework for what layer is going to win here, I think, is not useful to me. And I feel like people spend so much of their investing energy thinking about that.
Sarah Guo: Versus I want to spend my energy figuring out like, okay, if we’re 1% of the way in, what is the next 99% of diffusion? So that’s where we try to direct our energy.
Patrick O’Shaughnessy: For fun, as we wind down here—understanding this is a purely speculative question, 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 you’re close with, the research community, the entrepreneurs, like 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?
Sarah Guo: 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. Right—you have companies, I have companies where they’re like, “We are just going way faster.” And I expect that some analogy like that will happen everywhere else.
Patrick O’Shaughnessy: Everywhere else.
1:04:53-1:06:48
Sarah Guo: Everywhere else, right. And we see it in our companies. For example, in one of our portfolio companies, the marketing department is like a person in a house.
Patrick O’Shaughnessy: Right?
Sarah Guo: This is a company that serves lots of customers, and they need to do very traditional things like sales enablement content, right? What happened was the guy in charge of marketing is very interested in creating leverage for himself, and he’s like, “I made an autonomous marketing department for our company.” So I think in every function, as you learn faster and do less of the mundane—
Patrick O’Shaughnessy: You will repurpose that time somehow, right? Do you work less now that you are more productive with AI?
Sarah Guo: Yeah, I work more. Right. And I think this is
Patrick O’Shaughnessy: [In response to Sarah] People will work more. And I think this is a core wisdom of Jensen’s, which is that we’re all going to be more employed. We need to make sure people are given access and education to the tooling that will allow that to happen.
Patrick O’Shaughnessy: Well, what you’ve built is incredible.
Patrick O’Shaughnessy: We’ve loved getting to know your whole world through Colossus. I think it’s so distinctive. I think you’re a great example of that.
Patrick O’Shaughnessy: There’s always room for great. Meaning, there were plenty of early-stage investment firms when you started Conviction, and yet here we are four years later. If you ask the people you’ve worked with, you’ve made a real difference in their lives. There’s just always room for great. I think that’s a great lesson. I especially love how you described the early pitch—it wasn’t about how you’re differentiated at every level of the funnel. It was just, “We’re going to run at this thing.”
Sarah Guo: By the end, I got so frustrated that I was just like, “It’s an execution game.” It really is.
Patrick O’Shaughnessy: Yeah, totally. I ask everyone the same traditional closing question: What is the kindest thing anyone’s ever done for you?
Sarah Guo: 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...
1:06:48-01:09:14
Sarah Guo: ...who care very little for pedigree. As soon as they have a conversation with you and they’re like, “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.
Sarah Guo: So, Ashim Cha, Anil Buster, Joseph Ansanelli, and Reid Hoffman—who hired me at Greylock when I started at 23. People love to make fun of young VCs: “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?” And I’m like, “Well, one, my job was just to make other people successful at the time,” and like, learn. You can take any task in any job and just try to be great at that task with mimicry and first principles thinking.
Sarah Guo: And so, 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,” and then investing the time to teach me how to be an investor. I think there were a few people who gave me advice.
Sarah Guo: When I started the firm, the people who I think would find this entirely trivial—because they’re just giving me their opinion—but Ravi Gupta, who’s now co-CEO of a new thing called Ithaca; Dylan Field and Elena Natalinsky; John Lily, who’s been a longtime partner and friend; there were a few folks who were just like, “You can definitely do it, right?”
Sarah Guo: And I was going to do it either way, but having the encouragement of people who believed there was room to be great, and including some of our first LPs—I will be forever grateful to the people who took a risk on me.
Patrick O’Shaughnessy: It’s beautiful. The world runs on faith—belief without evidence yet—and still conviction in someone’s ability to do something. Pretty cool.
01:09:15-01:10:47
Sarah Guo: Yeah, and I think it’s faith in people, right? We talked a lot about how people’s ideas and our opinions of them are intertwined. But I think that’s a beautiful thing, because you don’t need any particular advantage to have an idea.
Sarah Guo: And so the fact that folks will evaluate that and put faith in us—I could not be more grateful.
Patrick O’Shaughnessy: I’ve learned a lot watching you operate and talking to you. This has been really fun. Thanks for having me.
Sarah Guo: Thanks.
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