Dancing Among The Giants with David Hornik, Founding Partner, Lobby Capital

I look at a thousand companies a year. If I were to be valuation insensitive across every company I encounter and then I get it wrong nine times out of 10, there's zero chance that that tenth is going to make up the difference. And the likelihood that that tenth is a trillion dollar company approaches zero. It's more likely, as has been the case for me, a multi-billion dollar company. Man, dancing among the giants is very tricky. How do you see startups doing it? So I think you need to have some sort of proprietary position in the market. You either need proprietary data that says hey we are in a better position than others to do this including the giants.

I actually think this is a moment in which empowering humans is going to be a big opportunity.

I'm doing great. Thanks for having me back.

It was a crazy market. I have to say there had been a long string of non-liquidity for the LP market. So, leading up to that, we had the longest bull market that the US markets had ever seen. And so there was huge liquidity, a bunch of increased valuations, etc. And by the time we went to raise that fund, I guess it was last year, it had been quite a few years since there had been any real liquidity. So it turns out three out of four firms that were out raising last year did not end up raising a fund. I'm pleased to say we did manage to raise our fund. All of our core existing investors came back in and we're already off to the races investing Lobby Capital 2. But it was a tough fund raise.

Yeah, I mean there's always some new challenge, right? It's never simple and there are different things that LPs are looking for. There are different groups of LPs. So historically there'll be some LP group that is flush with capital and is funding lots of great firms while others have waned. And so the typical pockets of LP capital are foundations, university endowments, funds of funds, family offices, and it is almost never the case that all of them are busy investing in venture capital. This last time none of them were. They all were like, nah, we'll see how it goes. There were folks who were saying we're not sure that the venture business is actually an economic investing category. There were others that said we are overweight. There were a lot of investors who had put huge dollar amounts into these big late-stage funds, multi-billion dollar firms, which left very little capital left for folks like myself who were investing early in a focused way. So in the end we will continue to do exactly what you described. We will be early stage focused concentrated investors in which our winners drive the lion's share of the returns. And luckily for us over the last 26 years, the big winners have been really big and have driven lots of really good returns. So the number one answer is drive good returns and people will be excited to give you more dollars.

Well, it is similar. I will say the run-up to 2000 was astonishing and I said earlier that the most recent bull market was the longest bull market that we've seen in the US markets. The run-up to 2000 was the biggest bull market. It was the fastest and the largest expansion of the US public markets that the public markets had ever seen. And so there was a huge amount of activity. Many, many companies were getting public at the time in very small numbers. You could have tens of millions in revenue and be a public company. There was lots of funding based on promise. And the byproduct was that there were just thousands of individuals who came to the Bay Area to start companies. It was very much the gold rush. We see opportunity, we're going to raise a bunch of money, we're going to take our companies public. This market is the same. There are thousands of people arriving to the Bay Area saying the AI gold rush is on or dropping out of school. There's never going to be a time like this again, even though there have been multiple times like it before. It's nowhere to go but up except that they always go down. So we have this huge glut of entrepreneurs and from an early-stage venture perspective that's kind of good news bad news. Ultimately I'm looking at companies all day and night trying to find great investable opportunities and there are more of them now. The bad news is that there are a lot more people who are just here to make a buck and the very best entrepreneurs in the world are here to solve a problem. They're here to scratch an itch that they can't help but focus on. And the people who show up to make money tend to be the worst entrepreneurs. They tend to be the ones who are the most easily dissuaded when they learn that it's hard, which it always is. So it's a tricky time to find great entrepreneurs. The last thing I'd say is that the biggest difference between this moment and every other moment is that in the internet bubble almost all of the value went to new companies. Everything was new. Everything was being reinvented and the existing behemoths were not really competing with the exception of Microsoft which had its browser was trying to fight the good fight. Everybody else was sort of caught flatfooted. In this AI moment, all of the biggest businesses know it is imperative that they are involved and that they win. So there are billions and billions of dollars being invested by Google, Meta, Amazon, Microsoft, Apple, like these giant companies have no intention of letting these little companies win. And man, dancing among the giants is very tricky.

Well, look at the valuations were stupid is how I look at it. I mean it was not long ago that there was no such thing as a trillion dollar company. And when you look at the... but not for all of them, not for all of them. I would argue there are some really reasonable and really good businesses out there in the AI space and a lot of overpriced as well, but the correction is affecting them all. Yeah, I mean look at the thing, the correction that was most dramatic and I think misguided was what was being called the SaaS apocalypse where all of these SaaS businesses were being punished because the possibility of AI undermining their opportunity was looming large. It turns out that was a little bit crazy. And in particular because these well-established SaaS businesses had the best data, had the best integrations, were in the best position to then bring AI to their platforms to drive a bunch of value. So we saw the SaaS businesses get depressed, but we are seeing them come back. I think that's wise. Then we saw the pure AI or the AI leaders skyrocketing. Of course the poster child for this is SpaceX. I think the world's stupidest valuation, the most inexplicable and indefensible where we're talking about triple-digit multiples on revenue with the promise that somehow either space or the future of AI will make up for that. That's going to correct because it's based on a dream. And yeah, maybe some of it comes true, but it's going to come true so far in the future. So I think the correction is necessary. I think it is logical. It doesn't worry me. But then you have a company like Anthropic which is growing faster than any private company in the history of private companies. Truly astonishing. And so, how do you value that kind of growth? How do you value multi-billion dollar revenue growth in quarters? And is it sustainable? I think the answer is no. It's not sustainable, but it is astonishing and valuable. So it's a very tricky market. I think that the corrections are appropriate. I think there will be world-class businesses that change the planet and will be worth what folks are paying for them. And I think that most of the valuations are completely irrational and indefensible.

Yeah. Well, so as I talked about the SaaS business, historically every time there's been some big technology shift, it's enabled a whole set of platforms that address each of these verticals. Someone can rethink content marketing and rethink, etc. This time around, because LLMs are so flexible, because this technology is so intelligent, and I use that term loosely, it's not clear to me that the vertical applications are going to be the answer. I look at the legal vertical. I was a lawyer, so I have a clear sense of what needs to be addressed there. You have companies like Harvey that have been extraordinarily well valued. And yet, it's not clear to me why a Harvey is going to deliver anything meaningfully better or differentiated than something you could deliver with Claude. And so, startups that are trying to address verticals that could simply be addressed by Gemini or a Claude, I think are going to have a very hard time of it. I'm reticent to fund those companies. So I think you need to have some sort of proprietary position in the market. You either need proprietary data that says we are in a better position than others to do this including the giants. You need to have integrations and understanding that is distinct from what could exist within OpenAI. Or you need to have unique tech that you've gotten to some extraordinary moment where you have found the next way to think about data or the next way to manage memory or whatever. So those are things I'm looking for. They're few and far between. I have to say I think it's a very tricky market.

Yeah, I really don't think so. And mostly because it's not my business, right? I mean, for a long time, we had a special opportunities fund, we had a core fund, we were looking at late stage opportunities like the buyout of Seagate. We were looking at early stage opportunities. There is so much late-stage money right now that I don't see how I'm particularly valuable in that world. I'm not a financial engineer. I'm not someone who's going to focus on these later stage business metrics. I'm much more of a people and solution investor. I invest in astonishing people who I think are solving real problems. And so the firm Lobby Capital is very focused on invest early, invest in a concentrated way, sit with founders that have companies with two, three, ten people and help them find product market fit, help them build those businesses and grow. And I'll leave it to the late-stage investors to then put in the tens and hundreds and billions of dollars. It's just not what we do.

You're killing me. So what learnings from those passes do you have and did you change your investment strategy or lens after that so that next time you don't miss those things when they come up? Well, the challenge is you're always going to miss them because there's some new learning. I gave a talk relatively recently about the different types of errors. Venture capital has type one errors. Type one errors are I invest in something, it turns out it wasn't a great investment. And then type two errors are the errors you're describing where I had the opportunity to invest in something amazing and I failed to do so. It turns out that the type two errors are dramatically worse. Had I invested in LinkedIn and Uber and the early round of Twitter and blah blah blah, it would have been astonishing. But look, in every instance there were very reasonable ideas I had about those companies. LinkedIn, ironically the principal objection we had to LinkedIn was the price. Reed was out raising money at a multi-hundred million dollar valuation. And it just seemed disproportionate to me at the time given the amount of revenue and traction they had. Now it's very funny to think about that now where companies are literally getting seed-funded at the price that I was being offered to invest at LinkedIn. The biggest learning from that is that Reed at the time was saying, 'Look David, you shouldn't be focused on the revenue. You should be focused on the network. If we win in the network, if we are the place that business contacts exist, there'll be disproportionate value and we are that winner and to the winner will go disproportionate spoils.' 100% right. That worked for Meta and it worked unquestionably for LinkedIn. I wished I had focused on that issue. Uber was different. Uber at the time only had Uber Black, so they had a very focused business. People loved it as they do still to this day. But it was not a huge market space. And we looked at it and said, how many people are out there who are willing to spend $100 on a ride on demand? The biggest learning there is that I misunderstood the opportunity. The thing that was right in front of my face is that smartphones had just arrived and smartphones in many ways were just like a little computer in your pocket which was not that different. Most of the things being built for the smartphone were just little versions of the same stuff. The biggest difference between a smartphone and the world before the smartphone is that the smartphone was location-aware. It knew where you were sitting and it could locate you relative to other things. I was aware of this and I was looking at location-aware applications to try and figure out what was the best use of those. And obviously it turns out Uber was the best use of that, followed quickly by Instacart, followed by DoorDash. These things made extraordinary use of location and data and connectivity and became monumentally giant businesses. If I had connected that insight to those businesses I would have funded all three and then I'd be the king of the world. So I regret that. And now as I sit looking at the AI world, what's different? What is new and what is unique and what is being empowered? And I hope I don't miss it.

No, I'm not. And I'll tell you why. There are very easy math arguments to be made that valuation is irrelevant at the earliest stages. Whether I invest in a company at $10 million or $100 million or a billion dollars, if that business ultimately is Anthropic and will go public at something north of a trillion dollars, you are going to make a thousand times multiple on your billion dollar investment. So then the biggest question is ownership. I need to own enough of your company that it matters. Because if I buy 0.00001% of the business, it doesn't matter even at a trillion dollars, it's not going to be a huge outcome. So we're very focused on ownership. We want to make sure that we have a reasonable position in your business. And so valuation matters a lot. Because if I want to own 10% of your company and the valuation is $10 million, that costs me a million dollars. If I want to own 10% of your company at $100 million, it costs me $10 million. And if you want to do it at a billion dollars, it's going to cost you a hundred million. So if you have infinite capital, then you can fund whatever you want. But the reality is that if all of your math is based on a trillion dollar outcome, you will get it wrong the vast majority of the time. And if you miss the trillion dollar outcome, you will be wildly underwater. And by the way, you probably will. There's only one of those. Maybe there are two of those in your lifetime. Meanwhile, I look at a thousand companies a year. And so if I were to be valuation insensitive across every company I encounter and then I get it wrong nine times out of ten, there's zero chance that that tenth is going to make up the difference. And the likelihood that that tenth is a trillion dollar company approaches zero. It's more likely, as has been the case for me, a multi-billion dollar company, which has been fine. If you own 16% of a business that's worth $4 billion you will do fantastically. But if I had invested at a 10x more expensive valuation I would have owned 1.6% of that business and it would not have gone as well.

Yeah, I mean I'm very happy to fund unconventional people. But I'm mostly looking for entrepreneurs who are obsessed with the problem they're solving. And I'm not terribly focused on what their background is or what that problem is as long as they take seriously figuring out the answer. My favorite example of this is I funded two really smart 22 year olds who were rethinking a financial services space in a company called WePay. Those were two fantastic young men named Bill Clerico and Rich Aberman. And when I introduced them to the former CTO of Visa and to one of the principal engineers in the early days of Intuit, I said to those people, what do you think about their expertise in the space? And their answer was they don't have any. One dropped out of law school, another one dropped out of banking. They had no particular reason that they should be building in the financial services space. But when I said, 'What do you think of them as founders?' Both of those individuals said, 'We'd like to invest.' It's like, 'Okay, you're willing to invest behind these individuals who have no reason to be building in the space.' Why is that? And the answer was because they were extraordinarily focused on understanding the market and solving a real market problem. And they went on to build a really interesting business that got sold to Chase for a lot of money. And both of them have gone on to do extraordinary things since. The biggest cautionary tale to the opposite position, which is, 'Oh, I'm not going to fund people who aren't cookie cutter engineer or business school or whatever,' was our pass on the Airbnb Series A. We were very interested in Airbnb. One of my partners, Howard Hartenbaum, had given them a term sheet. We were marching forward getting ready to approach the financing. And one of our partners said, 'Hey, these founders are from the Rhode Island School of Design. RISD is famous for creativity, but not for management and not for operations. This is a very operationally intense business. So what do these guys know about building a big business?' There were some of us around the table saying they're amazing, I don't see what your problem is. But there were others who were like, I see a lot of risk in that. So we did not end up funding the company. And obviously that was the biggest miss of the firm's existence. That was an extraordinary mistake because people were judging based on backgrounds not judging based on skills.

Yeah, they can make their own mistakes. Absolutely. So, they'll have their own stories to tell later on, right? And you were the earliest investor in Splunk back when nobody was talking about big data. What did you see in their early product market fit that today's AI infrastructure founders can learn from? Well, I think it's another good example of what I've been describing about solve a problem. Be smart people who see problems and then go out and solve it. As you say, at the time, this is in 2004, there were no inklings of big data. There were machines running. They were producing a lot of data, but nobody was talking about it. These founders had built multiple companies over the course of the internet generation. And they said, today we are sitting in a world where anytime you have some other problem, you go to Google and you say, hey Google, what can you tell me about this problem? And then Google gives you information which helps you to figure out the problem. But when a machine breaks, when your system goes down, when your database stops running, when your whatever fails, there is no search engine for that. And so they decided that they would make a search engine for log files, just the logs that were telling you what was happening in the systems. So instead of having to read every single line, you could just search across the lines. And I said, 'How in the world does that not exist?' Of course, that makes a huge amount of sense. I introduced these founders to a bunch of my friends who were building these systems and debugging these systems and to a person, what they said was, 'When it is built, I want it.' And so I said to these founders, 'All right, this is amazing. Can you build it?' And they were like, 'Yeah, of course we can build it.' Now, it turns out it was very hard. It's very hard to build a massive database that is based on time series data. But they built it and they became the poster child for big data. Their IPO was really a breakthrough IPO because people didn't think you could make big investments in the enterprise space until Splunk went public in 2011 and traded up from two and a half billion at the IPO to double digit billions and ultimately got sold for 30 billion to Cisco. So it was an extremely exciting ride, but it was based on a real problem that they are solving to this day.