E68: How Theory Ventures Differentiates in a Crowded VC Market

0:05Welcome back to Turpentine VC, the podcast where we discuss the art and science of building successful venture firms, VC to VC. Today, we're airing excerpts from my talks with Tom Tungus, general partner at Theory Ventures, which just announced their$450 million second fund less than two years after launching. Tom and I spoke in January of 2024 to dig into Theory's approach to concentrated portfolios and his contrarian take on decentralized infrastructure and AI. Then we spoke a few months ago specifically about the future of data businesses, another key part of the firm's thesis. Please enjoy.

0:40Tom, welcome to the podcast or podcasts. Thanks so much for joining. Thrilled to be here. Thanks for inviting me. So Tom, let's start with the theory behind theory. Given that you're entering a very crowded venture market, how did you think about, hey, where do you want to fit in within the ecosystem? Where did you want to differentiate? Walk us through kind of the idea maze of how you thought about getting your fund off the ground and where you wanted to play when you thought about fund size, when you thought about portfolio construction, where you thought about where you wanted founders to see you in market compared to all these other amazing firms that have been around for a long time?

1:17Yeah, great question. I mean, there are a lot of wonderful firms out there. The way that we think about ourselves is we initially started by using math to create portfolio construction. And so we started running Monte Carlo analysis on historical venture returns to come up with basically a thesis-driven, highly concentrated portfolio where we research spaces for long periods of time, try to understand the entire landscape, and then invest at the early stage and continue to invest as companies grow. That's the core idea. And today, we're a team of six people all pursuing that vision. Yeah. And you have unique insights on portfolio construction in terms of you want it to be more concentrated, higher ownership.

1:59Talk about how you got to conviction on that being the right opportunity. Yeah, I think there's probably two dominant models in venture capital. I think there's the, you would call it like the Y Combinator build-in index, very, very broad, and then ideally have a growth fund or a mid-stage fund to be able to pick up the winners and then concentrate. And then the other is what you might call the classical venture model, which is very early bets with significant ownership where over time you're able to drive a lot of returns for investors. And we think the U.S. venture capital asset class grew from about$800 billion to$300 billion over the last 12 years, 81 % of those dollars, according to PitchBook, are from non-traditional VCs.

2:37And as we start our firm, one of the things that we need to do and the advice that we give to startups is we need to focus to be able to win. And so our goal is to be able to focus on categories that we really care about, go really deep. And then once we have a lot of conviction in those businesses, help them grow as quickly as we can, both with advice, but also capital. And that$300 billion number, where do you expect that to be in the next, in the next few years? Yeah, I think it settles to somewhere around like 150 to 180 would be my guess. So pretty significant correction. Obviously the vast majority of those dollars are kind of in the mid to late stage, just sort of definitionally.

3:16So I think, and where will it be in five or 10 years is much harder to predict, But I do think over the last 10 years, the venture capital asset class has become institutionalized. If you think about what David Swenson did at Yale, 30 % to 50 % in privates, that's here to stay. And one big part of it is index investing has dominated the public markets. And the second is the total number of publicly traded companies in the U.S. is significantly less. And the last part is 25 years ago, a company with$25 million in trailing could go public. Today, you probably need$150 million in trailing. So if you're an institutional investor and you want exposure to some of these mid-stage, early mid-stage companies, the only way to do that is through privates.

3:58Yeah. And so if we go down from 300 to 180 or 150, almost, you know, almost a cut in half, where does that get cut from? Is it multi-stage firms just lower their fund size significantly? Is it just that there's no new entrance? Is it that a lot go out of business? Is it all of the above? How does that happen? Yeah, I think, well, one, you have hedge funds who are public and private. They look at, I would imagine they're looking at public multiples on a relative basis and thinking that the public markets are more attractive. If you want to play the AI trend as an institutional investor, Microsoft is actually a really phenomenal way of doing that.

4:33And so I wonder if some of the hedge fund money moves out. The second is the really big growth funds will be much harder to raise than they have been in the past. And the top firms will continue to do that. But, you know, they called, and I just learned this term during the fundraiser, staple funds. And that term means raising the early stage fund and then I have a growth fund. And they're called staple because in order to invest$1 in the early fund, you must invest$2 or$3 in the growth fund. I think that dynamic may change. And so I think you'll see a lot less later stage capital for this reason.

5:07Yeah. And so what's your advice to emerging managers? You are an emerging manager, although, you know, at quite a big fund. a lot of emerging managers listening to this thinking about, hey, what's the market like? How big of a fund can I raise? But also, what is an approach that will resonate with LPs in this market? When you're advising emerging managers, what is some of the non-common advice that you're giving? I think the most important thing, well, one, a strong track record with DPI, I think, is the golden ticket in this ecosystem. The other, so I would say like six months ago or nine months ago, the fundraising market was quite difficult.

5:43And that's just because many LPs were re-evaluating their investments. And there was obviously the US public markets crashed, geopolitical tensions with China, the real estate implications or the implications to real estate of 400 basis points of increase in the Fed funds rate, what that means for the value of those assets. I think at this point, a lot of the repricings of the private portfolios have come through and limited partners or institutional investors have a better sense of where they stand, which is a good thing. I think what I've been hearing from the institutional investor base is that smaller funds is where many institutional investors would like to be because the multiples are higher.

6:29And so the most important thing is to be able to find LPs who are interested in potentially moving from bigger multi-stage down to smaller funds and asking qualifying questions about how many emerging managers have you backed in the last 18 to 24 months is a really good way of figuring out whether that LP could be a fit. And what types of strategies do you think are most differentiating? People are asking, should I differentiate on sector-specific? Should I differentiate on geo-specific? Should I try to do something different with portfolio construction? What do you think are the types of strategies that really resonating in market outside of sort of, hey, we've got some DPI, it's great references, et cetera?

7:11I think that the really important question when you have a startup is, I have a product idea, I have a product and I have go-to-market strategy and they all need to align. And the companies that work really well are the ones that are able to draw a line through those three points. I think it's the same for early stage fund managers where you can focus on AI, super crowded category. If you have a proprietary network or access, you can make it work. You can come up with a different sort of fund strategy. There are a handful of friends who are starting SaaS roll-up funds where they buy companies that are not, they're growing at like 10 to 20%, 10 to 30 % profitable.

7:47And they're rolling them up as a way of creating a next generation constellation, which is a publicly traded company that does this. And so I think it's just about like the consistency of vision and some specialization that's defensible. And it's not that there's like a unique specialization, but just as long as it's like a very consistent story, you can be in a good place. Totally. I want to transition a little bit into, you mentioned AI earlier and how Microsoft could play. How are you approaching AI as a venture capitalist in terms of where do you think are opportunities uniquely suited for venture investors to play and invest and make money from?

8:25Yeah, great question. I mean, I think so at the highest level, who's earning the most money from AI right now? It's it's open AI, like one point three, one point five billion in run rate. That's what they've said. Microsoft has two businesses that when you total them are roughly the same size. And then there's sort of everyone else. The foundation model is a really difficult place, I think, for small venture firms like ours to play just because of the capital intensity of those businesses. Where we're spending a lot of time is trying to understand the. So one, the tooling. We interviewed a bunch of data leaders, about 25 of them over the last couple of months, and only one had actually deployed an LLM inside of production.

9:05And the major challenge there is just understanding the risks associated with moving data into and out of LLMs and containing it. So one of our themes is just enterprise readiness around large language models. If you're a Goldman Sachs, if you're a Fidelity, if you're a Nike, how do you become comfortable with these kinds of issues? I went to dinner with a friend yesterday who works at a publicly traded company, and she raised this issue around terms and conditions of what indemnification exists if I'm using an image generation software. And so there's just a lot of these questions that have yet to be asked and answered around the use of LLM.

9:44So we're spending a lot of time around the developer tooling infrastructure. And then the second part is at the application layer. So it's clear you can't beat Salesforce with an LLM-enabled CRM. You can't beat Zendesk with an LLM-enabled customer support tool. And our current working hypothesis is that there's an opportunity to redefine categories. So there's a CRM category and there's a marketing category. And those have been sort of etched into the fabric of how we sell and build and buy software. And we're starting to wonder whether these large language models, you can use English as an API.

10:24You can write something and it'll translate it to what a computer understands and bring you the data back. But what if you could combine CRM and a marketing software in one or the entire go-to-market functions in a single stack? What would that look like? So that's the theme that we're currently researching is how do you redefine the way that people think about these categories? That's helpful overview. you. Hey, we'll continue our interview in a moment after a word from our sponsors.

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11:53To claim your match, go to givewell.org and pick podcast and enter econ102 with Noah Smith and Eric Torenberg at checkout. Make sure they know that you heard about GiveWell from econ102 with Noah Smith and Eric Torenberg to get your donation matched. Again, that's givewell.org to donate or find out more. um what are other areas that you're particularly interested in in investing in or sort of uh where you think there's lots of opportunities for startups you'd like to see more startups pursue um companies in in this area or companies of this type yeah we're keen on the modern data stack so if you look at the vendor q3 data bi databases that's a single fastest growing category for spend the second is security and so we spend a lot of time in the modern data stack next generation databases streaming that kind of thing and then the last is blockchains as databases so we look at ethereum it's worth about 250 billion fastest growing database company of all time worth five snowflakes put together and one of our long-term theses is that many future software products were built with web3 components embedded inside of them and so those are the three areas where we spend time in addition to ai it's contrarian to be thinking about blockchain applications in 2024 before it is it is i mean i think we've got we've gone through this bear market and it's been a really healthy development because what we would have called like the projects of the 2020 and 2021 era where a pitch deck didn't have any notion of like revenue or cost of customer acquisition or any of the economics of a business now that's changing many web3 companies and i think it's a very healthy development are now starting to think about what kind of businesses can we build which buyers do we sell to how do we package up the core technologies that are fundamental innovations and really meaningful and and solve an end user problem that doesn't have to do with stable coin movement or decentralized finance but is solving a real real enterprise need yeah that's well said you gave a talk at angel list confidential uh a year ago where you kind of uh talked about some of the biggest data trends of the of the year in in the space if you were giving that same talk this year and it just happened.

14:05So maybe you did give that talk. What would be some of the biggest points that you'd, you would emphasize in that talk today? Yeah, I think the data teams are becoming software engineering teams. Historically, they've lived sort of downstream of the analysis, right? You build a product, the product produces data, your customers do stuff that produces data. And then you ship it to the data team and they do a bunch of post hoc analysis. Now data is becoming fundamental to building of applications, personalization or risk scoring or chatbots. And so the data teams and the software engineering teams are converging.

14:39And there's a lot of opportunity to build software to enable that to happen because that hasn't existed before. The second one are the applications of machine learning within data. So there's two big problems with these large language models that are relevant to data. The first is they're non-deterministic. That's a fancy word that means if I ask it the same question twice, it will give me two different answers, which is really hard. If you're the CEO of a business and you ask a BI system, what is my revenue by region for a particular product? It will give you one answer. But if you ask it, what is my revenue by region and product?

15:19And then if I flip it and if I say, what is the revenue by product and region, I'll get two different answers. That's a big problem for data. And then the second is the hallucinations. hallucinations. So I think there's a significant amount of opportunity to solve those problems and then also bring more sophisticated data analysis to people who may not speak SQL. Text to SQL is a huge movement and many companies are spending a lot of time. And then the last is what I would call the small data movement. So the laptop that is in front of me today is more powerful than the server, the third most powerful server that Snowflake used to build Snowflake in 2012.

16:00So I can manage exactly the same volumes of data on this MacBook as Snowflake could in that era. And so there's this big drive now to take advantage of the fact that I can develop locally, I can process huge reams of data. I'm running some large language models on these machines. And as a way of both reducing costs and improving the developer experience for people manipulating data, you have next generation databases that operate and data analysis systems that operate and take advantage of the fact that I have this incredibly beefy machine at home. Those would be the top three. Well said. Talk a little bit about how LPs think about different venture firms, right?

16:40Because when Andreessen pitches an LP, it's different than when first round pitches an LP, right? And so why don't you explain a little bit the land of LPs and the different kinds of LP products that are better suited for, you know, in terms of matches between VCs and LPs? Why don't you give a little bit of a lay of the land there? Okay, great. So let's talk about the universe of LPs. And then let's talk about the financial products that venture capitalists offer to LPs. Let's cut it up in that way. There are many different kinds of LPs, right? There are individuals, family offices, then there's endowments and foundations fund to funds and then like pension plans and health care plans and each one of those has a different investment mandate endowments and foundations are typically much more focused on privates because of swenson's influence and then others and what i mean by that is like 30 to 40 percent of assets maybe sometimes more will be in private so about 80 percent of that will be in private equity and buyout and then single digit percent will be in venture capital.

17:45And then a lot of LPs might have one or 2 % in venture capital total. And the way they think about it is if you're a diversified LP, you have public company, public equities positions with hedge funds, and you might have index funds. And that's a big chunk. That's probably 40 % of your book. And that is super liquid. And it's meant to drive an S &P or marginally better than an S &P-like return. And then you have private equity and buyout, which is meant to drive something like, call it 15 to 18 % IRR. And then you have venture capital, which is the most expensive, longest duration, least liquid asset class, which is a small percentage portfolio, but is meant to drive a lot of alpha.

18:29That's typically the way that LPs think about it. And that's because we raise funds for 10 years with like two, one year extensions is pretty typical, although some firms are raising with 15 years. So that's the way that they kind of see the whole universe you know this kind of dawned on me during our fundraising where when a startup raises it raises a seed and then an a and then a b and then a c and then a d and the return expectations for each round change right if you invested a seed you might expect like a hundred x or a thousand x in the case of a really successful company and at the a your multiple is less because you know maybe you're expecting like a 20x and then at the series b maybe you're expecting like a 5x and the series c you're expecting a 2x and a 3x as you approach the IPO.

19:11It's the same for venture firms, right? So the way that LPs look at an emerging fund or a first-time fund is on average, first-time funds tend to produce, when they're successful, much higher multiples. And so that's a seed round. And then a second fund, vintage, typically is a little bit bigger. And so that's the A. And then the third one is the B. And then by the time you get to be a multi-stage, multi-platform fund where you're offering 17 different products to LPs, that's like an IPO stage, public company stage return profile. And so that's my mental model for it. If you were explaining kind of like what you think would happen to multi-stages over the next decade, what do you think would happen?

19:54Or I should say firms that have agglomerated or accumulated a lot of AUM and are, you know, raising or investing across every every stage do you think that those firms continue the same strategy um do you think that they change their strategies what advice might might you give them or what would you do if you if you were those firms and sitting on a ton of capital right now but knowing that the uh you know overall capital set will uh will decrease over time i think the the larger you are the the greater the likelihood you remain in the ecosystem um and that's just because as an LP, if I've decided to invest$250 million or$500 million with you, that's a 20 or 30 year commitment.

20:37And unless something goes seriously wrong, I will probably keep that commitment or increase it with inflation or even more. And so the way that I think about, like, if you're a multi-stage, multi-strategy asset, I mean, you're an asset manager. And the goal there is the firm has built a fundraising brand with both LPs and startups that allows it to hire people. And when those people come in, the brand in the calling card produces high response rates from customers on both sides, both the institutional investors and the startups. And the goal is to drive consistency and performance. That's the ultimate goal.

21:20It's trying to get to, you know and some people might disagree but it's trying to get to the beta of the asset class for larger and larger dollar sizes it's trying to sustain okay an lp puts in 250 million 500 million a billion what is the top quartile return for the asset class great can i have that in a really really large check size that's the product that's being offered and the way that it's done is across all these different strategies so i think those you know the multi-stage multi-asset firms they will continue to exist i would argue that they would thrive there are a lot of parallels between what's happening in the venture capital industry and what happened to private equity.

21:55So the original buyouts happened in the 1970s when KKR spun out, I think, a bear steams. And for about 20 years, it was a very, very small cottage industry. And then in the 80s, it was Milken and the junk bond market that really blew the private equity market up. And they went from single strategy firms to multiple strategy firms. Now KKR, huge asset manager, Many of them are publicly traded. And I think that evolution 40 years later is exactly what's happening in venture. Do you think that these firms will be publicly traded or that some of them may? I think some of them. Yeah, absolutely. I mean, you know, you have like Iconic has gone from zero to 75 billion in AUM in 10 years.

22:37I think the last time I looked was like zero to 35 billion in a similar timeframe. And there are publicly traded asset managers that are at that scale. So I think it's a firm by firm decision. I don't know anything about anybody's particular strategy, but there's no reason why they shouldn't or couldn't be publicly traded. Do you think that they will also get into like sort of public market investing or sort of real estate or just kind of things that have nothing to do with venture? I think so. I mean, I think, you know, the goal of most businesses is just to continue to increase in size. And if we trace like the history of venture capital, right, it was initially just like seed stage and then there's mid stage and then there's growth stage.

23:14And we talked about the hedge funds costs, crossing over, investing between public and private equity, offering that product. And some venture firms have started high net worth individual asset management. Right. So they're in that business. And there are a couple of firms have started secondaries businesses where they buy existing LP and GP stakes. So that's another financial product. One firm that I know of started like a structured credit product that lends dollars. to companies that may or may not be in the portfolio. So now all of a sudden there's a debt problem in there. And so starting from like a$3 million to$5 million check, all of a sudden you have all these different flavors, matrix that over a bunch of different geographies, and you have a pretty complex product suite.

24:00And again, if you think about the ultimate business of an asset manager, it's about being able to deliver returns and building a brand on both sides where LPs will take your call on and founders will take your call. And so if you continue to expand and you have that brand, you can hire some great people to use it effectively. It's kind of your, it's your motivation, right? Where a capital capitalist is in the job title. So I think that's, I think that's where we end up. Yeah. Hey, we'll continue our interview in a moment after a word from our sponsors. You're bearish on sort of the economy in the short to medium term.

24:40If I recall, you think there was a bit of an overcorrection. And as a result, I think you believe that rates will be, if not high, not super low, certainly not what they have been the past decade. Is that true? And if so, does that mean that you're bearish on industries that kind of depend on low rates or unpack that a little bit? So the futures market, the last time I looked at the bond market, is pricing a rate cut in Q3, Q4 of 24. The dynamic is there's a convexity to the curve. So cutting one percentage point from five and a half to four and a half. Yes, it's one percentage point cut and it has some impact, but cutting it from two to one has much more impact on what happens in the economy.

25:20So I think that the economy is strong, or at least that's what the numbers say. And what we're starting to see finally is like you look at like the data dog earnings, a couple of other companies that Microsoft earnings, people are starting to see a bottom where the growth, the decline in growth rates has stopped. And some of them are picking up growth again. It's not broad, but you're starting to see some, and even in the private markets, a bunch of portfolio companies are starting to get their footing underneath them after missing a couple quarters. And then the earnings surprise, which is a measure of how much more positive the earnings are for public companies is off the charts for a lot of software businesses.

26:02So I think we're, you know, who am I to predict? But I think we're at a place where we're kind of close to touching the bottom. And I would expect that the back half of next year is stronger. Gartner, again, trying to predict the future. But their overall numbers for growth, they predicted in 22, 9 % software growth year over year. In 23, which is this year, 13%. Next year, they've raised the expectations to 14%. So there are a lot of positive signs, I think, for, call it two to three years from now that you'll have good positive multiple expansion and there'll be a lot of appetite for software.

26:44Yeah, there are a lot of firms over the past few years, you know, there were SaaS specific firms or fintech specific firms that it feels like were built for a previous economy. But now we're entering sort of a world in which rates are higher and thus the sort of multiples. You know, we can't expect kind of the same ones we've been having. Do you think, do you agree with that framework? Do you think that's unique to SaaS or fintech or just kind of across the board? Or what does it mean for those two sectors in particular? yeah i so i think so software the median publicly traded multiple over the last 15 years about 5.5 x forward revenues to ev to enterprise value and we went through a period where like snow like traded like 89 times forward and there were a lot of companies in the 30 to 50 i think base case for a fast growing company you're looking at a multiple of something like 8 to 15 depending on how hot the market perceives it and we need to underwrite those investment cases to those multiples.

27:42And that's the way that it was for a really long time before we went into the zero interest rate environment. So what does that mean? Well, it means the cost of capital is significantly higher than it was. And there was a study on the cost of customer acquisition for software companies where over the last five years, it's increased 60 % during that time. Well, so what does that mean? Well, in a zero interest rate environment, it's fine because you can raise 60 % more dollars and you can grow at the same rate. You just pay the market fee for a new user, new customer. But in an environment where the capital is more expensive, that means that startups, they either need to give up more of their cap table to be able to grow at the same rate, or they must evolve to find a less expensive way of growing.

28:28And so I think the major driver, the major change in software will be, what are the new go-to-market strategies that are much more capital efficient? And open source was one for a while. Product-led growth was one for a while. there are these iterations or platform shifts, mobile acquisition was the third. And we'll be looking, I think we'll be looking here in the next couple of years for an innovative way of acquiring customers. We could argue that like the rippling suite strategy is another way of doing it. We're offering a whole bunch of bundled products as a single package is an innovation that is a pretty market departure from the best of breed last 15 years that Salesforce catalyzed.