Invest Like the Best with Patrick O'Shaughnessy
· David George
[00:00:00] S1: I'll use a recent one, that the product is so good that people just naturally have gravitated to it is Cursor. And again, maybe in the fullness of time, that'll get harder. But GitHub is a great example of this. I'll tell a funny story about GitHub too. So GitHub was so special of a company that for a long period of time, they never actually talked to customers. So the first time I ever met GitHub, they were like, "We've got to tell you this, this is so awesome, we sold to Walmart and they're paying us 400,000 bucks and no one ever talked to them on the phone." We were like, "Wow, this is an incredibly magical product and an incredibly magical market. Just imagine if you had talked to them on the phone, what would they have paid you if you'd just called them on the phone? They'd probably have paid you $4 million." So unique product that leads to unique distribution with a founder that wants to optimize the situation. So the AI founders, I'm not the one involved with Cursor, but Michael is a very special founder and his team, they recognize what they have, and then they are aggressively pursuing the enterprise at the same time. So that's a really good combination, where you have unique product, you have great product people love, that leads to some uniqueness of distribution. And then, you can build on that advantage by saying, "Hey, we have all this bottoms-up use, we're going to go sell enterprises." And so, a big part of what we do as a firm is we help to facilitate customer introductions, new business, we call it our go to-market function, they're referred to as EBCs sometimes. And we get notes after every one. And this is the most fun thing in the world of AI, because we get these notes, and in the case of Cursor, every single time, it's like, "Immediately to POC, immediately to POC," proof of concept, whatever, "Immediately to POC," and, "Immediately to full sale deal." And you can see that, that's actually incremental data for us in making decisions, but you can see it, it is magic when it happens. So Martin led the A of Cursor, one of my partners who leads our infrastructure fund, and after one of these emails, he chimed in, and it's a big list, it's like a hundred people on the list or something, he wrote, "Product fucking market fit." So now, we're like, PMF is now PFMF.
· David George, Patrick O'Shaughnessy
So when you see that, that's unique product, that's unique distribution, and you have a founder, founding team or full set of employees who really wants to optimize it, that's magic. [00:00:00] S0: What are the trade-offs of the way that Andreessen is structured? No firm is perfect. There's choices for how you have structured and nested the team, lots of different groups, leaders of groups, like you. What are the negative parts of the trade-offs for how Andreessen is structured versus a more monolithic structure or something that was just different?
· David George
[00:00:00] S1: Our strategy for scaling is pretty well-covered. But effectively, we think scale allows us to bring more power to the entrepreneurs and give them a greater chance to be successful in the market. That's the fundamental thesis behind the scaling for us. And with more resources, you can bring more resources to bear for the entrepreneur. So for us, when I joined, every single Monday and every single Friday, we used to sit in a room together, all of us, and we'd hear all the pitches and then we'd have long meetings to talk about each of them, all as a group. So Dixon was leading our crypto fund and we'd have bio fund pitches, and we'd all listen to all of them and then we'd all debate. And then, we realized, at a point, that was not the optimal use of time, Dixon weighing in on a bio investment and vice versa probably doesn't make sense, and you could extrapolate that out to a bunch of our investment processes. Ben and Marc decided to decentralize the firm and put more power down into the investing teams that ran each investment fund, and the reason behind that is twofold. One, we thought it would allow us to have better expertise around the table. If you're only just fully deep in infrastructure or applications or American Dynamism or crypto or bio, that's an advantage. It's both an advantage in making decisions, but also an advantage in go-to-market with the entrepreneurs. And then, secondly, if we are going to scale, you can't scale an organization with 25 or 30 decision-makers around the table, it's too hard. You can't make a trade-off between, should we put an incremental dollar into a bio fund investment or a crypto investment, or how should we think about reserving this versus that, it's too hard. So we shrank the size of decision-makers by doing this to a smaller group who's in charge of their own funds, and so far, that's working really well, and I think that's mostly a function of the fact that our early-stage folks, they're really good and we're all really collaborative. The only trade-off that we have at the growth fund is selfishly, that process that I described where we all sit around the table, it's valuable for me. It's good for us to have access to all information at all times, because we sit across all of our early-stage funds, the way we operate is we invest across all of our sectors.
· Patrick O'Shaughnessy, David George
[00:00:00] S0: What percent of the investments you make did the firm have a prior investment in? [00:00:00] S1: It's a little over half. And then, if you take the number of investments... So if you just do it by dollars, it's a little over half that are preexisting venture investments. And then, if you add the dollars that we're investing in preexisting investments that were preexisting growth fund originated investments, follow-ons, it's something like 70%. So 70% of the dollars that we're investing, we get deep knowledge on the companies. I call it game film. I talk about game film all the time, it's so important. When assessing an investment, when assessing a founder, game film is not just numbers, it's how has the founder done this. [00:00:00] S0: How do you do reserving in the growth fund, is it materially different than elsewhere? [00:00:00] S1: When we first started the growth fund, I was like, "Scott, zero reserves, let's do it. Every single dollar is going to have to be scrutinized, literally every dollar." It turns out that's not really practical, you need to reserve a little bit. So we reserve a tiny amount, and this is for small follow-ons where our participation is important, but we're not a lead. We do zero reserving for large investment amounts that we think we're going to make in a company, because I think that would lead to lazy decision-making. We'd say, "Oh, well, we reserved for it, let's do it." [00:00:00] S0: So you just treat it as a new investment next time?
· Patrick O'Shaughnessy, David George
[00:00:00] S0: So you just treat it as a new investment next time? [00:00:00] S1: Every single thing is a new investment. So if you look at our largest investments in the growth fund and just run down the list, Databricks, SpaceX, Anduril, OpenAI, xAI, Flock Safety, Figma, Stripe, Coinbase, most of them are across multiple funds, and that's by design. We want to be flexible and say, "Hey, if we're super excited about a new investment, it's fine, just keep going." We have no target metrics for industry, infrastructure versus American Dynamism versus crypto or whatever, it should always be best ideas win. But I manage the fund, and so I closely track how are we doing on those metrics. And generally speaking, thematically, do we feel like the fund is a good reflection of what we see as the opportunity set for the next 10 years? [00:00:00] S0: Can we talk about selling? This is such an interesting topic to me, because you can ask lots of investors that invest in private markets when and how they sell, and most of the answers you hear are fairly simple heuristics. One you hear a lot is when there's a crystallization, you sell a third, hold a third, hold a third forever. [00:00:00] S1: Yeah, it's like the Fred Wilson- [00:00:00] S0: Now, later or never, that'd be one example. There's lots of similar heuristics. How do you think about, especially because you're investing at the growth stage, probably closer to the opportunity to sell to another investor or the thing going public, talk about what you've learned about selling and just how you've done it so far.
· Patrick O'Shaughnessy, David George
[00:00:00] S0: Now, later or never, that'd be one example. There's lots of similar heuristics. How do you think about, especially because you're investing at the growth stage, probably closer to the opportunity to sell to another investor or the thing going public, talk about what you've learned about selling and just how you've done it so far. [00:00:00] S1: Selling is so hard to do this job. So we've tried a number of different variations. So I think it's different at the venture stage. Your Fred Wilson model, the third, third, third, I think he's totally sensible because he's coming in extremely early, so for him, that's relatively simple. We have our own version of... It's not algorithmic, but semi-algorithmic decision-making for the early-stage, and we take some very simple qualitative things, like is the founder still running the company, which we value a lot. And then, a qualitative, are they the market leader that we feel great about? And if so, we would bias to hold longer, and if not, we would bias to exit sooner. We also try to overlay an assessment of how it's valued versus performance, which is really, really hard. So I would say we've been fortunate in that generally we've gotten it pretty right. [00:00:00] S0: Why don't you buy whole companies?