Invest Like the Best with Patrick O'Shaughnessy - Jerry Neumann – Why Venture is Hard - [Invest Like the Best, EP.134]

Episode Date: June 11, 2019

My guest this week is Jerry Neumann. Jerry is one of the most thoughtful early stage investors that I’ve encountered, and his writings at reactionwheel.net are my favorite on this topic. He applies an incredibly structured way of thinking to a notoriously mysterious investment category. This is our second conversation, in which we cover why investing with one’s gut is a bad idea and why some of the popular edges in startups, like network effects, may be picked over. Please enjoy our conversation. For more episodes go to InvestorFieldGuide.com/podcast. Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub. Follow Patrick on Twitter at @patrick_oshag   Show Notes 1:17 - (First Question) – His take on the venture landscape and the type of investments new VC’s are making vs what they should be making 3:44 – Most important implications of excess VC firms 5:32 – Misalignment of incentives in the VC space 8:19 – What he does differently from angel investors or VC’s 10:11 – The notion of risk and the types of risk the people he invests in takes 14:33 – Protections that he thinks about when it comes to the ideas he invests in 19:37 – Is there an area of expertise that provides an edge for startups 20:11 – Network effects are picked over 21:35 – IP protection 23:08 – One of the two most interesting things for VC’s to go after, brands 25:13 – The other most important thing, the value chain 27:42 – A current example of a disruptive value chain 29:14 – Innovation as the source of profit             29:16 – Schumpeter on Strategy 31:50 – Efficiency innovation vs value innovation             31:52 – Energy and Civilization: A History 35:50 – Efficiency investments he’s made 37:13 – Investment in Unsupervised and the machine learning landscape 41:25 – Investment in Sila 43:14 – Investment in Edmit 44:44 – investing on gut 50:32 – Black boxes and their value in investments 53:23 – Metrics about the predictive level of whether people are going to succeed 54:45 – What defines good people worth backing 57:50 – Advice for LP investors in this space and how they should evaluate VC’s in this space   Learn More For more episodes go to InvestorFieldGuide.com/podcast.  Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub Follow Patrick on twitter at @patrick_oshag

Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com. Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaunsi Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of O'Shaughnessy Asset Management may maintain positions and the securities discussed in this podcast.

My guest this week is Jerry Newman. Jerry is one of the most thoughtful early stage investors that I've encountered, and his writings at reactionwheel.net are my favorite on this topic. He applies an incredibly structured way of thinking to a notoriously mysterious investment category. This is our second conversation in which we cover why investing with one's gut is a bad idea and why some of the popular edges in startups like network effects may be picked over. Please enjoy our conversation. So Jerry, for round two, we will cover a whole lot of new things. And even since we last talked a year and a half ago or whenever it was, a lot's changed in the world. And the proliferation of venture capital as an interesting topic. And just the number of people doing it has continued to grow. And so I thought

it'd be fun to start there. You know, you mentioned when you started doing this, there was maybe a hundred. You literally counted venture investors deploying money into markets and now there's probably north of a thousand. And this has a variety of implications for investors in these types of strategies and for the GPs themselves. And so I'd love to walk through sort of your take on today's venture landscape and the types of investments that VCs are making versus maybe the ones you're making and you think early stage investors should make. So we'll start there with just your high level thoughts on the proliferation of money and investors in this world and its impacts. Ten years ago when I was starting to angel invest, I built a scraper

It would go out and look at the websites of venture funds, and then it would figure out how they changed what companies they had added and tweet them. It was called VC Delta. And a bunch of people followed it. It was kind of a geeky little project. It ended up selling it so it's no longer out there. But it was getting to be a pain in the neck because every time somebody started a venture fund, if you wanted to add them, you had to code them in. But I went through and I said, all right, first I put it in the 10 venture firms that I was closest to and then I added more. And I wanted to know what everybody was investing in.

And I went through and I said, all right, who are the venture investors that are actually active? They're making more than a few investments per year. And I made a list. And there were about 100, 120 of them in tech, not including the whole bio side, just the tech side. And that was it. I was a little surprised by how few there were. But there were a lot more funds that were just not active. Now I've read somewhere recently that there have been 800 new funds started in the past five, six years.

So that's a lot more than there used to be. And the interesting thing isn't so much, you know, obviously we know there's a lot more money and a lot of that money is at the top end, you know, the late stage. But if there's that many more new funds, then almost all the people who are running those funds haven't been doing this for very long. They just can't have been venture capitalists five years ago. They must have been doing something else. So I wrote this blog post a long time ago called Heat Death about venture capital in the 80s.

And there was a quote from somebody who said that more than half of the venture capitalists who are investing right now have been investing for less than two years. And that's, he considered that a problem. So it's interesting to think if it's a problem. It's hard to know if it's a problem today or not. But the same sort of dynamic is happening. What do you think the most important implications of this are in terms of kind of what's getting funded, what founders are optimizing for versus maybe what they did when it was 100 VCs? What are the most tangible changes on both sides of the investor and the founder side that you've observed? So there's two things. One, One is the people who are investing themselves.

And I've met a lot of people who've just started investing. Some of them are really smart. They've come out of roles from startup companies, really thoughtful people. And some of them look at the market and say, it's just not that hard. People have been making money now for 12 years in this market fairly effortlessly it looks like from the outside. Especially if you read the press, all you hear about are the successful companies. So say, wow, it's just not that hard. And I think that's the wrong way to look at anything.

if it's what you're doing for a living, you should accept that it's not going to be easy. It's work. So the idea that you can just pop in and not do any work and be successful, I think, is mistaken. And I think it's a bad attitude, obviously. But then some of them will be because there's so much luck involved in venture capital that some of them will get lucky. So I think that's a problem. I worry less about that problem. I think that people who don't do the work are going to get weeded out.

There's sort of a weird market dynamic in the interim where people will bid up companies. You know, if you have an auction, then the person who wins the auction obviously paid too much because they paid more than anybody else would pay. And that's true in venture as well. The person who wins the auction for the term sheet is by definition paying too much if there are multiple bidders. So if you have people who are unclear how to really value a company so that they can make money, they're going to be the ones who get the term sheet in. And then, you know, as a smaller investor, you either have to file them or not. And that's your choice. So there's some market distortion there.

Whether or not that makes any difference in the long run, it's hard to tell. On the venture capitalist side, we know that the venture business itself is fairly straightforward. You raise money, you charge usually pretty high management fees on that money, and then you do really well if one of your companies does really well. You can pay through carried interest. So talk about the incentives, maybe misaligned or well aligned of that group in this market. Because if, like you said, if you hit one major smash success and you've got a track record that will allow you to raise a lot more subsequent money, So how would you think about that and you think subconsciously or consciously that GPs are investing a different way because of that business model than they would say if it's their own money? Yeah, okay.

That was the second point I was going to bring up. Thank you. I do think that if you've raised a fund and a lot of these smaller funds, the newer funds are $5 million, $10 million, $15 million. They're a smaller amount of money. And that's the LPs hedging their risk. They want to be in new managers, but they're not going to give you a ton of money until they can figure out whether or not you can do the job, which is smart. But if you have a $5 million fund, you can only make small investment. and you can only make a few of them.

And you have to raise another fund pretty quickly. So you have to show results. And the way that people show results is they may get into companies which don't have long-term prospects, but in the short term, we'll show traction along some metric, which they can then bring to their LPs and say, hey, look how great I'm doing. And nobody really knows. I've had companies that have had great traction after, you know, two, three years after investing, which have ended up failing and vice versa.

I think my biggest return to date has been in the trade desk. And after three years, they had remarkably little traction. You know, it was worrying the CAO. It worried the investors. But he kept plugging along. And then all of a sudden, he got the traction and grew incredibly quickly. So I think after a few years, you just can't know, but they have to know. So I think the investments they would pick to do that to show traction in the early stages are going to be things that may be shorter term in view.

Like they may plateau in revenue more quickly. In AdTech, we saw this a lot, where it's easy to get to. to a million dollars in revenue in ad tech. It's easy, generally, to get to $5 million in ad tech in this billings. But then it's hard after that. The ad agencies are willing to throw you a $5 million check figuring there's only so much you can do wrong with it. You can only lose so much of it, and they'll probably get 90% of the value, even if you're an idiot. But after that, they're not. A lot of VCs were, this is back a few years ago, but a lot of the VCs were kind of pulled in with these companies that grew really quickly from zero to $5 million in billings. And then they plateaued

they never got any bigger. That's why there were so many of these companies entering the market and that remained small. I think if you can't think through to the end, the end game like art, do these people really have some sort of sustainable competitive internet? Not even so much as they have it today, but can they generate it in the future? And what is that going to be? That long-term view is hard to have when you are incented to show results in three years. You wrote a really interesting post in which you declared sort of what you do maybe isn't venture capital and maybe you don't know what to call it. But I want to, I want to kind of peel that apart because I want to identify what it is that you are doing differently

than a standard angel investor or venture capital investor and why you've made that decision. You should peel it apart. I wish you would. I think about half of my posts are well thought out. The other half are just spewing. Yeah, exactly, ranting. And that one's a bit of a rant. I have been seeing a lot of, a lot of my deal flow for the past year and a half have been companies that are their businesses. They're not startup. in the capital S sense. There's a certain amount of snobbery among VCs around the, oh, it's a lifestyle business.

And I'm not saying lifestyle business. I mean, it's a business. It's something, you know, my father ran a business in construction. He was never going to be a billion dollar company. The problem with venture is that's the way the model is built. If you're doing venture, like, you have to have a shot at being a very valuable company. That's really, in the way that venture is built right now, that's the only way to make money is to invest in companies that become 100 million.

million plus in revenue or at least value. And if you're building a company that can get to $30 million in revenue or exit for $30 million in three years, it's just not the kind of thing that I can do. And you can argue about that and people will argue about that. And I think there's actually, we can talk about that later, but there's a certain decent argument for that, for investing in those types of companies now as some people are starting to do. But for me, that's not going to work. And I see a lot of these deals. And you can argue with them about it. But at some point, they just, they're not venture businesses. And when this has become a big part of my deal flow, I have to say, well, geez, and those

companies get funded by venture capitalists. I said, maybe I don't do venture. Maybe that's not what I'm doing. I mean, you know, I do the same thing that I've always done, but that's no longer what people are calling venture. So maybe let's dig into the nature of risk, the type of risk that you're interested in taking. So to have a venture style outcome, you often need to embrace uncertainty.

We talked about this last time on the podcast. So talk a little bit more about the notion of risk and the types of risks that the founders you're backing are taking relative to maybe that other category you just described. You know, I wanted to ask you this before we started. What do people think risk is? Like, how do you define risk? And this is one of those things. You know, when you say a word over and over again, I start you can no longer have to

need any more. Thinking about risk that way. Is it probabilistic risk? This has a 10% chance of succeeding. So it's riskier. That's something that has a 20% chance of succeeding. Is it the variance? compared to the market like beta, is that risk? That's what I was taught risk was in business school, right?

Certainly what most investors, I think, would say something quantifiable. And then maybe uncertainty is something completely different. Who knows what that is? Right. Although, you know, we could talk about uncertainty all day, but a lot of economists say uncertainty is the same thing as risk. And if you look up uncertainty or find books with the word uncertainty in their title, which I've done, they're almost all about probability statistics. What kind of risks are venture capitalists taking? you are, there's a lot of things you just don't know. And I think what venture capitalists consider risk generally is how much don't I know about what's going to happen. Is this market going to become bigger? Is this technology going to work? Some of these things you can reduce to probabilities,

but not a lot. Most of them you can't because the essence of venture is doing things that haven't been done before. You know, you're investing in a company that is doing something which nobody's done. I mean, if somebody else had done it already, then it might not be a good investment. to be walking into a market that already had competitors. So if you're doing things that have never been done before, you can't use statistics or probability, because there's nothing, right? And there's nothing to have counted to do your probabilities against. So I think that's what people consider uncertainty.

I think, so let's call that risk for now. It's not risk as financial markets. Think about it, but let's call that risk. If risk and return are somehow related, you get more return for higher risk, then it makes sense to take more risk to do things which have less knowability about them, there's more uncertainty. But you don't look for risk just injudiciously. You have higher returns for risk because the classic theories,

but you won't take the risk unless you're paid more. But what does that even mean in venture? Like, you don't really, you can't quantify it. The entry prices group around a number which has very little to do with the actual risk. It has more to do with the size of the outcome than the size of the potential outcome if successful than how much risk there isn't getting there. It's not linearly related at all. It's not like you'd say, well, this company is twice as risky, so I'm going to pay half as much.

That doesn't work in venture. You can't say, well, instead of a $5 million pre-money, we're going to do a $1 million pre-money because the risk is so much higher. One million pre-money will sink your company. The company will fail anyway. So you have this kind of weird situation where, like, on the other hand, like, if you're not taking risk, and I think a lot of the, a lot of people who are newer to venture will say, well, can I mitigate the risk before I invest? And this is a natural thing to want to do. So can I say, like, and I'm going to use this example, which I love to use, which apologies to my friends who invested in this sector. the food delivery. So there's been a lot of food delivery companies. And in some way, you could

mitigate the market risk by saying, I know there's a huge market here. People have food delivery. And that's great if you can mitigate that risk. You know, in pharma, they mitigate that risk all the time by saying, look, I know a lot of people have this disease. I know how big this market is. So if I can develop my product, then I don't have market risk. And that works in pharma. And I think it's fairly complicated reasons why it works there, but it doesn't work. I don't believe in technology. The problem with technology is if there's no barriers to entry in the market's evident, then you have too many entrants. And you have to divide this big market among so many competitors that you may have winners and losers, but on average, the entire sector will be a loser for the investors. This has happened periodically. In the 80s, it happened with disc drives. It also happened with office supply stores.

So Staples and Office Depot were venture funded along with a whole bunch of competitors. And people say, oh, look, this is a – it worked, yeah. Yeah, it's a great market. We should be in it. So they funded like 12 of them or 15 of them, 13 them went out of business. So did the venture capitalists make money as a whole? Did the venture capital industry make money? I don't think so. And I doubt that venture capital industry has made money in food delivery,

although you have a couple of people who picked the right horse and they made money. There's one way of thinking about this then focusing on those barriers, that we're going to come to Schumpeter and the kinds of innovation in a little bit. But if you think about something new is happening, and that new thing is going to be valuable to the, the people involved, the founders, the executives, the investors, but there's just no barriers to entry. So it's like consumer surplus or something. How do you think about that when you're analyzing a business early on and thinking about the, not just the innovation, but ways that that

innovation might be protected from other entrants? Yeah, I think this is a blind spot venture capitalists tend to have because there is a grace period when a startup starts where you don't need a barrier to entry because the varied entry is that nobody else wants to enter, right? There's some stupid idea that some company has that it was like, that's a stupid idea. There's no market. It's not going to work. It's crazy. So nobody else enters the market because nobody else thinks it's a good idea.

So you have that barrier entry, which is not a barrier entry at all. It's just a, if you embrace enough unknown, enough unknowability, enough ambiguity. Fewer people do it. Right. I had a conversation with a friend at a big company once who, and I said, well, look, this company that I talk to you, you should talk to them. You should meet them. They always love this company. It's so interesting.

It could be really big. I'm like, well, why aren't you doing that internally? Because you could do that internally. They're like, well, you know, what am I going to do? I'm going to go to my boss and bring him this business plan. He's going to make, well, how big is the market going to be? What's the payback period? Like, the things that he wants some certainty on.

And I don't have any. I can't, he has to how big the market's going to be. I don't know, right? I'm just going to have to make up some number and he's going to see through that. So I just can't bring him that kind of plan. And I think in that case, those things don't get competed with by large companies who have more resources. So you have that grace period.

And that grace period's probably a few years until the, until the company is successful and proves out the market, and then everybody can see that they're successful, the market exists, and then they can compete. And at that point, you need to have some varied entry. There's a lot of traditional barriers to entry that are available to venture-backed startups or to startups in general. Network effects is the one that people love the most,

and that's a great buried entry when you have it. Most companies don't really have a network effect, buried entry. It's unusual. There's scale. Not a lot of scale barriers to entry in venture capital or in startups at all, because I think that's more of a physical goods, buried entry. You know, you have to build factories or whatever you're building railroad tracks.

If you're building software, I think the barrier entry from scale is much lower. And one of the biggest barriers to entry that people have used over the past 30, 40 years, is expertise. So a company I invested in a few years ago, Bonsai.com. One of the things I thought about was, look, these people have 15 AI PhDs on staff. that's hard to replicate at that time, right? There's just not that many of them. So it's going to be hard to build a competitor to their company. And I think that's true.

It's kind of like a cornered resource, basically. Yeah, it is. The problem with that kind of resource, though, it's not like, you know, cornering the gold market. It's, they're making more. So my favorite example is back in the 90s, I invested in a few companies that built websites for big corporations. And they grew very quickly, very fast and were very valuable when public. And it was the 90s. But they're, you know.

But there. unique resource, the reason that it was hard to compete with them was they had programmers on staff who knew HTML. And it was, you know, not just HTML, but dynamic HTML. They could make the websites actually look good and do things and nobody else could do that, right? And it was sort of laughable now. Like, well, my 12-year-old writes HTML in school. And part of that's because, well, not only are there more people who know how to do it, but it became easier to do. People have built tools to do these things that allow anybody to do it. And I think that's generally true in most software nowadays.

Anybody can learn to become a programmer, not anybody. But it's a lot easier. You don't need to go to MIT and get your degree in computer science. You can go to Lambda school, right? In three months, six months, you know, get a good job as a programmer because you're using a lot of tools that were developed by other programmers to make programming something that's more accessible to somebody. You don't need to be the high priest of the programming religion to become a good programmer.

So if that's true, then where is the innovation in programming itself? It becomes a skill that's accessible to enough people. Let me say that differently. So you've started a SaaS company, which goes after a specific niche, and you have developed software to address the problem that you've found. Anybody who sees what you've done can replicate the software fairly easily, whereas previously that may not have been true. But now things like AWS and the whole, what has caused this explosion in a small company,

the ability to stand on top of the shoulders of giant companies and use their infrastructure to make your job easier makes the job easier. So it's easier to replicate. So I think that buried entry in a lot of what venture capitalists have done previously is going away. Expertise specifically as a barrier, right? You'll find it like, so AI, there was a window and probably still is, we're probably still in that window where people who are really good at that sort of thing are still hard

to find because it's still more art than science. Is there any, let's use AI equivalent today from whenever you made that investment, is there some area of expertise that does still feel like it's potentially an edge for startups? I actually think crypto is. I think it's, crypto ecosystem is still fluid enough that you actually, if you're doing something interesting, you really need to understand the underlying protocols and algorithms and how it works. And I think it's not that easy.

You know, there's a bunch of different things that, a bunch of different disciplines that overlap. Now, the question is, is there anything useful? to do with crypto, but if there were, I think it would be hard to replicate just to find the people to do it. Yeah. Yeah, so you mentioned, to round out these barriers, you mentioned in our correspondence before this, that network effects are pretty picked over. So what does that mean picked over? Does it mean that everyone got excited about network effects and that defined a lot of the big, pretty much all the big, massive outcomes in technology businesses in this last cycle? So they're overpriced now when someone's, you know, starting to build a network effect. How do you think about that? You know, network effects have

always been around and network effects occur when you're connecting a bunch of nodes together and the nodes probably people or businesses or some limited set of things and where they have to interact with each other. So if you take that as sure there's a better definition of network effects, but if you take whatever definition of network effects you have and start saying which problems correspond to this very specific type of value creation, there's a limited number of them. It's not like people haven't been looking at for them now pretty vigorously for 20 years. I just think with the opportunity space is becoming exhausted. And I don't know.

I mean, look, that's easy to say from, it's easy to say and easy to be wrong, right? I mean, it could be proved wrong tomorrow or all of a sudden there's some great new company. But when you look at the kinds of things that people who invest in network effects are investing in, they're becoming more and more specific to smaller and smaller segments of the market. So they're just smaller opportunities. I could have said that five years ago and then Slack would have started and I looked like an idiot. So I probably shouldn't be making predictions about the future. the other ones that you listed, IP protection is one that I think is probably not all that

something you want to rely upon as a technology investor, maybe like you might as a bio investor. Talk a little bit about IP protection. Yeah, so IP protection in tech, I mean, it's well known that in technology it's easy to work around things. There's a lot of solutions to any problem. So you can patent one thing and then easily find somebody else can come along and find a workaround. The most valuable patents aren't things that are the most basic, like, all right, well, I've patented the pull down to refresh. And somebody did patent that. I can remember who. I think it was actually Tweety or TweetDeck. I don't know how much money they're making on that patent, but everybody uses that now. But it's self-limiting in the way that if they were

charging a lot for that patent, they wouldn't be able, nobody would use it. There's only so much money you can make. A great example, and I've written about this is the MP3 patents. People had patents on all the technologies that underlie MP3s, the music compression. Those people, although they made a lot of money from those patents, didn't make anywhere close to a large percentage of the money made from those patents. The Fronhofer Institute who owned several of the MP3 patents probably made a few hundred million dollars. How much did Apple make from them? You just don't have much leverage there. And the reason is, well, Apple could say, well, let's do something different. Let's use Og Vorbis, which is open source and doesn't use any of the patented technologies. So there's workarounds.

This isn't so true. And in pharma, I'm not a farmer expert, but it does seem that if you find the needle in the haystack, somebody who wants to compete with you has to find another needle in the haystack. And that's just as hard as finding the first one. So they have to spend as much money on R&D as you did, and you're already in the market, so you can keep them out. You mentioned there's two other categories that maybe are the most interesting things for VCs to go after, the first being brands and the second being building an entire new value chain. So especially that last one, I'm very interested in hearing sort of you spec that out what that means.

But start with brands. Why is that interesting for VCs? What are the challenges? What are the opportunities for making an investment of that type? Yeah, brands are easier. So we've seen a lot of companies that have been started in the past five, six years that have built brands, the Dollar Shave Club, the mattress companies. What they're doing is they're going into a market, which is already known, and they're building a brand. A lot of times they'll go in it

and they'll build a brand because the existing brands have neglected their brand or their brand no longer has the brand image that they once had, which is a type of neglect of their brand. So you can go in and build a new brand and be successful. Why would you buy a Casper mattress when you could buy a Seeley mattress? And I don't think the people who do are saying, well, Casper's a better mattress. they probably have absolutely no idea. But the brand is speaking to them, right? And I think that's a hugely valuable thing. If you can do that, that's, you know, brands are a great varied entry.

The problem is they're super expensive to create, which isn't to say that venture capitalists shouldn't back those companies, just that I can't back those companies because I'm investing my own money. So if you're going to spend $100 million building a brand, I'm not going to end up owning a lot of your company at the end, right? I could, you know, help you get started, and then you raise $100 million, and I own, you know, two basis points. And no matter how big your exit is, that's just not that interesting.

So I think it's a problem with the capital efficiency. The great thing about software, and there were the reason that the software industry and the venture industry co-evolved, because software is so capital efficient. Trade desk, which is now, I don't know how is it worth like $8 billion in the market. I mean, I think to build that company raised like $6 million. And then they later, at a very, right before the IPO, they raised another big round, but that was much more of a pre-IPO confidence-building measure than it was. that they needed the money. So you can do that in software. It's hard to do that with brands or

hard goods or any of that kind of stuff. So software is great that way. So, but, you know, I mean, I think if you can build a brand, that's a hugely valuable skill. So how about the value chain? That's the final and really interesting category that, that probably needs some explanation. The value chain, this is the Porter thing, right? So Michael Porter wrote about sustainable competitive advantage, the five forces. Everybody knows that. But actually, the meat of his work was more around the value chain. And what he said was if you want to be competitive in a market against bigger companies, you have to have a value chain, which is the value chain is the series of activities you use to produce your product or service. And your value chain has to be different than the others

in a way that it's hard for them to change because it's easy for people to replicate products. It's hard for them to change the activities that they perform inside their company in a substantial way. You know, I think that's, in one sense, too theoretical to think very seriously about it. It's generally true that if you want to compete with in a large market where are existing competitors, you need to do things in a different way. So when Google Search was launched, there were several large competitors in search already. You know, Yahoo was already public, and Yahoo was extremely valuable and had a lot of money. And Google started, and Yahoo kind of both ignored them and laughed at them.

And then Google was successful, and why were they successful in an industry where people could easily have replicated what they did? I know they had a patent on what they did, but I don't think the patent was that valuable. I mean, on the search side, Yahoo could have done what they did. and Yahoo could have become what Google is now. They didn't, and the reason they didn't was because the activities inside their company to create value were so different from what Google did because they were trying to, they were a content company, right?

They were creating pages that people would visit and see ads on. Google was saying, well, we don't want to do that. We'll also create pages that people visit the see ads on, but we're not trying to keep them on our site. Yahoo was saying, well, people are doing search and then they're clicking on results and then they're leaving the site, right, so that we no longer make money. That's a bad thing. So let's make our search a little less good.

Like we don't want people finding things off our site. And let's replicate everything good on our site. And that was their start. I mean, this is the portal strategy. All of them did this. It was the only way they could think of to become bigger. And Google said, no, we want people to leave the site. That's what they're here for.

They want to find things that are somewhere else. So let's make it easier for them to leave the site. But then how do you make money? And the way they end up making money was saying, well, people who are searching for things are really looking for that specific thing. So we can show the ads around that specific thing. obviously what Google does on the search site. Yahoo couldn't change that, right? Because their activity was around keeping people on the site. How do we keep people here? And Google said, how do we get people to go? How do we find something that people want to click on?

Do you have another example of a disruptive, like, value chain that's more recent than the Google one? I want to make sure that, like, we understand exactly like what, what it is about, let's call it the incumbent company and their value chain that like almost disallows them from just completely reworking their inners? So I can talk about one from my portfolio. That's more recent, although it's certainly not as well known as Google. Now, this one worked. This was an online-only bank that I backed, Bank Simple.

They went in and what they said was retail banks are making money by using you as a supplier of cash, and they take your cash and they lend it out and that's how they make money. So you as a customer, you think you're a customer, you're actually a supplier. And they have all these branches everywhere. And the branches aren't to make your life easier because they hope you walk in. They can sell you other products. That's the point. It's a marketing expense, right? And this is not great for you as a customer. And Simple said, well, if we get rid of these branches and we actually, we will then have enough

margin to treat you as the customer and we're going to just, you're the customer, we're going to make money from you, so we want you to be happy. And this was a value change. A radical change. Yeah. And it was, you know, the retail banks shouldn't have been able to respond to that and haven't really been able to because it would lower their profits. They'd have to change the activities in a way that would be difficult from the inside to justify. In that sense, I thought about this as a disruptive innovation. It turns out that they didn't have as much barrier to entry as I had hoped because they didn't get big enough, fast enough.

It was just a much more difficult thing to develop technologically than it should have been. One of my favorite of your recent posts was this thing on Schumpeter, however you pronounce that guy's name. We'll call him Schumpetter for now. And sort of the idea of innovation as the source of profit. And so I'd love to walk through this fairly simple framework and how that might map onto how we think about all kinds of investing, whether that be venture or early stage investing or identifying public businesses that might be innovators or trying to innovate to

create new profit. Can you walk through that very basic model, the sort of area under the curve model that you lay out in that post, and then we'll pick apart some of the interesting insights. Shampere was really the first economist to really focus on entrepreneurship as one of the drivers of the economy. Previous to that economists had been focusing on the static economy, like, you know, let's figure out how things work when nothing's. changing. And his point was, well, things are always changing. He said, there's a certain type of, if you think about economic profit, it's different than accounting profit. Economic profit is, are you creating more value than your resources are costing you, including capital. So accounting profit

is, a lot of that profit is the return to the capital invested. So that's not really profit, because that's what the capital is due, just like the way the employers are due salary. So he said, well, let's think about excess value created more than the cost of capital. And that's how you define profit. And he said, well, how do you do that in a competitive market? Because in a competitive market, there shouldn't be any excess profit. People compete with each other, and the profit goes away. He said, well, there's two ways to do it generally. One is you either do something more efficiently, so it costs you less to do it than your competitors. So you have that excess profit between how much it costs them and it costs you and you can make that as excess profit. Or you do something

better than your competitors, and then you can charge more for it. So you make that as excess profit. Those are the two ways to do it. And this is obviously, you know, extremely high level. So this is fairly simplistic. So those are two ways of making excess profit. The problem is that as soon as anybody else notices you're doing that, they're going to start doing the same thing, you know, as quickly as they can, as quickly as they can figure out how to do it. So that excess profit gets whittled away over time. So if you think about that as a curve, like, all right, well, you're making zero economic profit. And also now you're making some economic profit because you've done this thing. And then it kind of, you know, erode. Curves down.

It curves down to some shape either exponentially or whatever, probably more sudden than that. Probably have some period of time in which nobody else is doing it. And then all of a sudden, people are like, oh, all right, let's do that. And then it goes away fairly quickly. The area under that curve is the total excess value you've created. That's how much more your company is making than just your risk-adjusted cost of capital. So that's the value. And that's what entrepreneurs do is they find ways of being either more efficient or creating something that has more value to their customers.

I know we've both recently read this Vaclav Smeel book on the history of energy and innovation in energy. And it's a great metaphor for this idea of efficiency innovation versus value innovation, which is what Chimpeder calls it. So efficiency innovation would be basically what's happened in energy over the last 100 years, which is, you know, now we're getting 98% of the energy out of the turbine. We used to get 20 or something like that. Same amount of stuff we're burning. We're just capturing a lot more of it. So it's an efficiency gain. But we're not, we haven't innovated necessarily on, say, fuel source.

source, which would be more of like a value innovation. How do you think about those, the landscape today kind of versus history and the balance between whether we're seeing more efficiency type stuff or more actual new value creative type stuff, which you hear a lot of really smart VCs would say we haven't done big things in a long time. We haven't really created new value. We're sort of eking efficiency gains out of stuff that's already there. How do you think about those two in today's environment as an investor? So I love reading Smell because he thinks about things so differently than I do, but he's so smart that you're, it's like, all right, it forces you to think about things in a completely different way. The idea of measuring everything in terms of energy, as opposed to

any other unit of measure, is just so interesting. I don't actually think about things that way. So I agree. I think, you know, so when you think about efficiency versus value, now efficiency, like how much efficiency can you get? You can get up to 100% more efficiency, right, and probably a lot less. So it's capped. There's only a certain amount that's there, whereas value is sort of uncapped, right? You can create an infinite amount of new value in theory, but it could be a lot more value created than you can possibly get with efficiency. So I think there is a lot right now of efficiency innovations. A lot of SaaS companies, a lot of enterprise software is how can we make what somebody is doing a little bit better? Here's a problem in an enterprise. We can do it twice as good.

But the twice as good isn't twice as much value created. A lot of it is 25% more efficiency. is going to cost you less to do this. And those things are valuable. I mean, it's clearly valuable. They're just not as big. They can't be as big as if you're creating more value. Because more value can be of any size. I have a clarifying question here.

So let's take marketplace businesses, maybe Uber specifically. Is that efficiency or is that value? Or is it some combination? This is a great example because I, you know, this is, I, like every other person who was investing when Uber was raising their seed round pass on Uber. So it was one of those. Everybody saw Uber. Nobody liked the idea.

A few people were like, all right, let's put some money in. And then they were right. And the reason I passed was because you look at it and say, well, this is an efficiency innovation. How much can they save? The taxi market in the United States is not that big. It's a few hundred million bucks. It's just not that big.

There's only so much efficiency they can get from using a smartphone to call a cab rather than calling on the phone. I need a car to the airport tomorrow morning and use to call on the phone. I mean, pick up the phone and actually call somebody and put it in a reservation. Hope they showed up. But it wasn't that. It wasn't actually that. What they did as a marketplace was they opened a entire new market of supply, right?

So the taxi market in the U.S. today is far larger than it was 10 years ago. I mean, a couple order, at least an order of magnitude. And the reason is because now all of a sudden you say, well, look, the number of drivers that have entered the market to drive cabs is much, much larger. So this was a value innovation primarily by creating. a huge new supply. And by having a supply, they realized there was this unlocked demand. And a lot of that demand was people who knew the car five minutes from now, not tomorrow morning. It was a new market. I obviously regret not investing in Uber. But it's also, you know, I think it's an interesting way of saying, well, look, this is a complete failure of my process in looking at this company

and saying, and weeding it out and saying not worth it. I didn't anticipate these new market creation. Hopefully I've learned from it and we'll see that the next time. But it was, yeah, it's a really interesting way of unlocking value, not efficiency. Just you personally, and I'd love to talk about some of the recent investments that you've put out there publicly that you've been an investor in, just to use them as examples of how you're thinking about where value might be created. Do you ever make efficiency type investments or is it pretty much exclusively value innovation type investments? No, I have made efficiency investments. Sometimes by mistake. So sometimes the company you invested and that was creating value, then pivots to be, you know, more of an efficiency

investment and you'd go for the ride. Which is not to say, I mean, look, there's some of these could also grow to be 50, 60, 70 million dollars in revenue. So there'll be good investments for me as an angel, right? I'm a smaller investor. If you're going to exit for $300 million, I'll probably make money on that. And it'll probably actually have a meaningful difference to my bottom line. Whereas if I was a venture firm, it wouldn't. I also, because I'm an angel, I, invest in people who I want to succeed, not so much because I believe that they're going to succeed. I like the people. I like what they're trying to do, and that's a non-economic decision. Using some of the frameworks that we've laid out, maybe we'll discuss a few of the businesses.

So one that certainly popped when I saw just because it sort of relates to what I do is unsupervised. So unsupervised is using a really new machine learning technique called topological data analysis that was, you know, I think first written about only six, seven years ago, came out of the university. The founder is one is one of the few people in the commercial world who understands this technique. The other founder is, you know, I worked at startups before, you know, awesome, outside facing person, meaning, you know, customer relationships, that sort of thing. When I met them, there were two people in Boulder with this company. They had already built a product and they already had three, four to five hundred companies that were using it in Alpha. And I said, all right, and they said,

hey, do you want to invest? Yeah. So what they're doing is they're taking this machine learning technique and plugging it into corporate data marks. So they'll go to one of their clients and say, or a potential client, say, we can plug our algorithm into your set of data. You can say, this is what we're trying to figure out a better solution to. This is a KPI that we need to find a better way of maximizing or increasing. And the software can go in an unsupervised fashion. So it's not, you know, most machine learning is supervised. You're like, all right, this is a cat, this is a cat, this is a dog. You show a 10,000 examples of cats and dogs, and then it can tell you which is a cat and which is a dog, maybe. This one is a clustering algorithm which can say, like, of the data, for this KPI, we can actually take all of your data and start clustering the cases around different things and then finding out what's common among these clusters.

And so you can find, you don't have to tell it what to look for. You don't have to tell it what works to get that. It figures it out itself. And it comes up with, here's a hundred recommendations on how to improve this KPI. And then you have to have a human go through and then say, yeah, that's obvious. that, you know, okay, this woman is actually physically impossible. Like, all right, well, this is actually super interesting. I actually not sure if I'm allowed to talk about case studies with them or not, but some of the things that they have found have been really non-intuitive

until you think about it pretty hard. You're thinking, wow, you know, that's actually super interesting. Like, this person is much more likely to become a customer because of X. And why X? And then you go into, if you find some expert within the company, they're like, oh, well, you know, people who do X are these kinds of people, so they're likely to become customers. Nobody at the top levels or in marketing would have known that until they found this person deep in the company. So two questions on this one that just seems like such an interesting business. So, and this may be getting too technical and if so, no problem. But how do they communicate something like this is better than K-Beans clustering or like the standard clustering

stuff that I could run on a data set that's unsupervised today? Like, why is this a better system? And then the second question is, what's the business model? I don't have a good guess as to what and how they might charge for this service. So I'm just curious, like how you think about that as an My one problem with this company early on when they were starting was well, they were taking their software and plugging datamarts into it and they would start churning out. They like in the pitch meeting, started like, oh, here's what we're finding. And you compare that to somebody like Palantir where you have built a entire department around here's people who know how to use Palantir.

There's 20 people and they're in their, you know, their section of the office, you know, doing Palantir. You can't get rid of Palantir because you'd have to fire all those people. It's part of your process generally, whereas something which is easy to use may not become part of the overall process. So I worried about that. I'm like, well, it's too easy to use. How can you embed it, you know, which is the wrong way to think about things, obviously. It does turn out that it has to be part of the process. The recommendations it turns out aren't the kind of things. It's like, all right, let's just blindly do what the machine tells us. You have to use

some human judgment afterwards. So it's more of an idea generator than a solutions generator. They charge quite a bit for it because the ideas are good enough. Some of their clients are seeing 20, 30 percent improvements in like KPIs that they've been struggling to improve for decades. So it's really generating good ideas. So it's just a great example, right, of inverting, I think, the commonly held perception that machines are really good at answering questions and humans are better at asking questions. And this is basically the opposite. Now the machine is helping sort of ask a question by showing you a pattern.

And then the human is on the other side having to try to figure it out. I think that's fascinating. Oh, yeah. Well, and the other thing, you asked about K-means clustering. I think the difference is, and I'm not a machine learning PhD. So my explanation is going to be clunky. K means clustering is still highly. You have to intervene pretty highly into the, what is K, how many?

You've got to set hyper parameters, and this is just unsupervised completely. And if you do it wrong, you get wrong answers. Or you get muddy answers, right? I'm not getting any answers. Topological data analysis just does that better. It actually chooses K itself. So it finds clusters without that kind of hypertuning. Let's do a few more, because I think these are such interesting ways to get into your mind.

So the first would be SILA. So SILA, yeah, I said before, you know, crypto, you know, what is it good for? And I think, you know, there's been a lot of that. And then, you know, there's a lot of people who believe pretty strongly that crypto is good for pretty much everything. And a lot of people who believe it's a hopped up database. But the one thing that it's undeniably good for is currency. The problem I've always had with, you know, currency is I don't really want to compete with the U.S. government because they have guns and I don't. So I don't want to try to challenge their monopoly on Fiat.

That's, I think, it's not a good idea. But what SILA is doing is it is a currency, but they're using it underneath the hood. So they're working with fintech companies to say, look, all right, you want to be transferring money from here to there. Going through the banking system is expensive, slow, and clunky. We can provide you a way to do it quite quickly using our coin. And you can provide a lot of the things that any startup fintech company needs or any fintech company needs really quickly and easily through the API that we've developed

to access our coin. The founder of the company was one of the, co-founders of Simple, the online bank. And the problem I said with their technology, the technological problem they had in developing their company was that the banking systems they had to integrate into were so old and so set in what they could do that they couldn't get what they needed done done without a couple of years of work. So he said, well, these things that I need to do are really simple. Like I just, I need to be able to deposit money, withdraw money, transfer money, and know my customer. Those are the four main functions that he needed from a

banking system, and nobody could really do those kinds of things in any atomic way. You had to go through their entire process of like, well, you put all your transactions in the end of the day, we process them all and we send you a report. So they had to build a whole layer on top of the banking system to do all that. So what he has done is built that layer for any future company that wants to get into fintech. Two more. So the first is Edmit. So Edmitt's interesting. This one's a little different. This is definitely a value innovation. Little known fact. The list price of college is very few people pay that. And that's not the little known fact. The little known fact is that the average that people pay is 50% of the list price on average across the United States. This is from the Department of Education. How do you know how much to pay for college? And in some sense, I think people are sort of, most people are fatalistic about it. They're like, well, I'm going to apply for aid and I'll see how much I get, and that's that. And I make a decision about which college to go to. But, you know, if you had some leeway over both understanding how much you will pay for these colleges based on who you are and how much you might be able to go back.

to the college and say, hey, you know, I'll come to your college if you pay me a little, if you charge me a little less. Surprisingly, colleges will do this. If you take out the top 1% of universities, every other college or university in this country is trying to fill seats. And they're struggling to make sure that they have enough enrollments to fill the class. So if you have a really good student who's, you really want to attend your university, and they come back and say, look, if you give me another $1,000 in, you know, merit scholarship, then I will enroll. That's a lot less than they pay. companies to acquire students in the market. So Edmund, it basically has this huge database as

as a preparatory database of how much people actually pay based on where they went to high school, what their GPA was, what their SAT scores were, how much they actually pay for particular colleges. Then you can go and figure out how much should I be paying here, not just after the fact, but prospectively. One of the two closing topics that I'd love to discuss is this recent thing we've both been talking about, which is making investment decisions with one's gut. And why that probably is a bad idea for most investors. Of course, not saying that there aren't people that do this that have done very well. We know there are. But this as an idea being magical and romantic, but a really bad idea. So talk about this notion where it comes from and sort of what the solution might be

to not ignoring your gut, but making use of it. Somebody tweeted something. I can't remember who it was and not going to call them out anyway, saying that all investors and founders need to make decisions from their gut. And I think he used gut wrong, or at least in a way that the rest of the world doesn't use it. He meant something different. He's a smart investor. He's done really well. But I thought that was a really bad message to be sending to the 900 new venture funds that were started in the past six months. You should just do things from the gut, because it's wrong. And let's define what we mean by gut first. I mean, when I say gut, it's, you know, the dictionary definition is something that you're not rationally analyzing, that you're, it's an unconscious

process. Yeah, it's a feeling about it. That's fine. I mean, if you, you know, there's a couple people pushed back on Twitter saying, well, I invest for my gut. Like, I don't use numbers. Like, there's no quantitative data. I'm like, all right, well, quantitative data, if you don't have quantitative data doesn't mean you can't do analysis. It just means you're analyzing non-quantitative data, which is good. I mean, that's just the way it is when you're working with startups who haven't done that much yet. But that's still analysis. So there's two things wrong with gut investing. One, it doesn't work. And I think this is, there's a huge amount of research showing that gut investing doesn't work.

Conneman has written a lot about Daniel Connman, the Nobel Prize winner, has written a ton about how gut investing doesn't work. I'm sorry, not about gut investing, about how instinct-driven decision-making doesn't work. He does use investing in the stock market as a primary example of what doesn't work. And even the people who believe in the naturalistic decision-making, as I call this, sort of instinctual decision-making, don't believe that it works in that area. Now, using your gut clearly works in some areas. The NDM, the naturalistic decision-making experts did studies on people like the fire chief.

He's got to make a decision very quickly in high pressure, an uncertain environment, and they tend to make good decisions. They're experts. They've been through the situation either for real or virtually tens or hundreds of times over many years. They can make good decisions from their gut, and they have to because you can't sit down and think about it. They don't have time. So in those situations, I think using your gut works. where it doesn't work is, and everybody agrees on this except for one Harvard Physical Professor Laura Huang, who's done some studies on angel investors, which have to be taken into account.

Everybody else says it doesn't work. She says it does. So, you know, obviously there's no certainty as to what is true here. But the place that it doesn't work is what is intuition. Intuition is pattern matching. So what do you need to do pattern matching? You need to have signals from your environment that correspond to signals you've had in the past, right? So this is what they call validity. And then You have to have gone through that situation enough times that you can form a pattern. So neither of these things really are true in investing. How many venture investments have you made? Have you made enough to form a pattern of what works and what doesn't?

And even, you know, people like me who sort of neurotically examine every other venture investment made in history to see what patterns you can derive, it's still anecdotal, right? There's not really enough data. And the other is, like, the signals are changing all the time. So the signals I get from SELA are completely different than the same. signals I would have gotten 10 years ago from another company is the environment's changing. So, you know, neither of these two things is true. You can't use pattern matching to find good companies. And I think that's reasonably obvious if you think about it. The place where people dig down is, well, you know, I invest in people. That's what everybody says, which talk about

some other time, whether that's a good idea or not. But I use my instinct to judge whether the right people or not. They have the right things that I'm looking for. And I think that's, you know, that's actually been shown to be a bad, to not work. There's actually a fairly large study directed directly at venture capitalists judging the people they invest in, what their method for judging them is, and what the success of those investments were. The guy by Jeff Smart, who wrote his doctoral dissertation on that. And he said, like, here's 89 instances of venture capitalists picking companies. Here's the method by which they picked the founder, whether or not they thought the founder was good, and how successful they were. And he found that the venture

investors that use their gut instinct to pick founders did the worst of the four main methods people picked founders by. And the ones who did the best were the ones who said, well, the only way to judge people on whether or not they can do a job is to say, have they done it before and were they successful, right? Like actually looking at their history of doing that thing. So founders who worked in that industry done that job and been successful are far more likely to be able to do that job and be successful in the future, right? This is a data-driven thing. So, you know, if you think you can pick people by your gut, well, you know, it's probably wrong. And even if we're right, it's the wrong way to do it.

So this is the second part of my problem with this thing. It's not just that it probably, gut probably doesn't work. It's also that it's a bad way to think. So I said before when I, you know, I missed this great investment. And I went back and said, well, why? Why did I miss it? Like, what did I do wrong? And, okay, I identified what I did wrong.

And I'm trying to work it into my process so I don't do it wrong again. And this is a constant thing. you do. Anybody does who's a professional. How did I do? How can I do better? How do you know how you did if you don't know why you did it? If you make a gut decision and an inshictional decision, you don't have a reason. It's something that you didn't consciously do. You can't say, well, I was wrong because this was what was wrong with my decision, because you don't know. So you can't approve. It makes me think of something which I've never really thought of until here you say all this, which is everyone says they don't like black boxes, but I think actually everyone does love black boxes. And very often, I'll just

take our business as an example. I mean, we're probably, you know, hyper-transparent on the transparency scale, but there's still a lot going on behind the scenes that would just be extremely hard to replicate. And that's why we're comfortable being transparent. But I definitely think there is some relationship between the magic and the black boxiness of it and its appeal to people. And that when they can't fully understand it or feel as though it would be really hard to replicate, they value it more. And gut is sort of the ultimate mythical black box. It totally is. And I think it's, you know, this is one reason that people do it, right? It's, you know, I've written about people thinking they're magic, right? It's like, well, I am, why am I successful investor? Because I'm

magic. And people want magic. You tweeted, like, what should I ask him on? And a lot of people are like, well, you know, what's his top reason for, you know, doing this? Or what is it the one thing that he looks for? And that's, what they're asking for is some sort of silver bullet. There is no one thing, right? You wouldn't go to a, not to compare myself to a brain surgeon, but just as a sort of end of the spectrum thing, to go to a brain surgeon, what's the one thing that you always think about when you do brain surgery? It's like, well, I try not to kill them, right? I mean, like, it's just not a useful thing. And it's, you know, venture capital is not brain surgery, but it's still, it's a thing that people do as a job. It's a, you know, it's a professional profession,

I don't what to call it. So it's not like there's a short menu of things, like a process, right? I mean, I actually personally believe that venture capital is the part of finance where there are no processes and that you have to say, well, every investment is different. I have to solve this puzzle of what makes this investment good or not. It doesn't mean you don't have rules about what is good and what's not or an idea, but you have to think it through every single one differently. So, you know, if that's true, the idea of like, well, I'm going to use my instinct, which basically says that everything's more or less the same.

They all fit this pattern is a bad, it's a bad. and it doesn't work. On the other hand, like, if you actually had a, if you had a process, you'd be more of a retail bank making loans. They have a process and things fit the process or they don't. They make the loan or not. Probably actually a lot of investing exists in this gray zone between having a process and having some sort of instinctual way of doing things. You just have to figure it out one by one. Hard. It is hard. And I think that's why people like gut, right? Because it's hard and like, you know, I mean... Got process this quickly. There's a great paper by Conneman and this guy Gary Klein.

I guess you gave you a link to that as well, which goes through their disagreements over instinct. And then I don't know the things they do agree on. And it talks a lot about how making gut decisions is effortless, right? Because you're not consciously doing and making conscious decisions is actually hard. So I think people are drawn to that, obviously. But I think that a lot of people are also like, I don't know, right? I don't know. And I can't admit that I don't know.

So I'm just going to say it's gut. Can you mention some of the stats around, I'm not sure if it was the same dissertation that holds these out, not the venture example, the same writer, but the ideas of people that went to Harvard or didn't go to Harvard or Stuyvesant was the other example. I just want to close with a couple questions on people and sort of the predictive ability of whether or not they're going to succeed. Oh, this was, well, we were talking about getting our kids into college.

I was different without ever, you know, now that we can't Photoshop their faces onto growers or whatever. Sorry, I shouldn't make light of it. But the idea that there's a couple of studies. One was they isolated people who got into Harvard but didn't go versus people who got in and did go and found that their outcomes in life were not statistically different. And then they also looked at people who got into Stuyvesant, one of this elite high school in New York City. It's public high school.

There's a test you take. And if you get above a certain score, you get in. And if you don't get in, so it's very objective assessment. People who got just below the admission score versus people who got just above it also had very similar outcomes. And what the implication is that the school, going to that school, didn't. make them smarter or better students. It was, they either were or weren't when they got there and both at Stuyves and at Harvard. So a Harvard education is no better than an education at some other school. It really more depends on the person. Are they somebody who wants to learn, you know,

works hard at learning, those sorts of things? And so then it begs the question when you're evaluating founders, people. Like so that is like the number one trope as you back the best people. Of course, that means, well, what does that mean? What defines a good person? person worth backing. How do you think about that question? Is it purely they've been successful doing something similar to this? How does that port to someone that's doing something for the first time? Is that maybe something you don't want to invest in? Talk a little bit about the people component of your process. And I think this is hard, right? I mean, the people thing is hard. It's just a hard part of the job. I'm going to say something completely different. I actually do use my gut in some

things. Is this person lying to me? Are they the kind of person who will lie to me? Like there's some instinctual part of me that perhaps is better at picking out. people who are lying versus not. And I don't mean lying in a sense of like boldly lying, but just making stuff up. And I don't know. I mean, maybe I can do that. Maybe I can't. It's hard to know.

I think the part where it's that I really try to do some sort of more analytical measure is, well, you talk to them for a long time. You don't have to trust your gut, right? There are certain things you need to have in founders. You do need them to be able to tell you what is going on, you know, honestly, and straightforwardly. And that you can simply talk to them and figure out whether they do this. that or not, you do need them to have a lot of expertise in the industry they're going in. Need them to be excited about what they're doing. And people who are excited about what they're doing

often know a lot of facts about what they're doing that are completely unrelated to what specifically they're doing, right? You can ask them about their industry and they'll know what people who are doing different things in their industry are doing or related things or other solutions. You know, talk to any of my founders, and I assume most founders of venture-backed businesses or most businesses, in fact, and you ask them about their industry and they'll know a ton. of detail, stuff that other people wouldn't know and that may not even be that useful to them, but they're interested in what they do. So you need that. You need them to be interested because it's not easy to start a company. It's not easy to have a company succeed. It takes a lot of,

a lot more than just the quest to become wealthy, right? That's not going to sustain anybody over the 10 years of building a business. You just need to want it to work. So there's things like that where you can talk to people and you can actually figure it out. Do they know the industry? Are they excited about the industry? Do they know how to raise money? Do they know how to hire people? Do they, will they be able to manage the people once they hire them? How do they respond to things which aren't what they planned for? Pretty straightforward.

Generally, when I talk to people, but sometimes I'll ask them a question which I, which goes directly against what they believe. And I'll say, like, you know, I don't believe that. I don't think that's true. See how they respond. If they respond like, well, you know what, maybe you're right. Maybe we should change that. It's like, no, no, I don't want that.

And they're definitely like, look, this is it. This is the way it is. Like, that's it. You know, you just have to believe it. I don't really want that either, right? I want the kind of more thoughtful response of like, you know, look, we've considered that objection. Here's how we thought about it. Here's what we came up with.

You know, this is perhaps a hypothesis or an assumption and it could be wrong. And if we're wrong, we're going to adjust our plan. But this is the path we're going down. You know, a thoughtful response to, you know, some slight adversity of somebody questioning your plan versus a insistence that you can't possibly be wrong. You know, because that is, that's the world. So sort of wrapping up where we started with this proliferation of money and people spending their time on this style of investment. investing. Any advice or thoughts for LP investors in this space and how they should evaluate, not founders, but VC investors? If we see the proliferation, the continued proliferation of money,

like we've seen, let's say, going into private equity right now, which is driven by, you know, good, good trailing returns, venture returns have tended to be pretty good as well. So a lot of money will likely go to GPs in this space. For LPs out there, thinking about making investments into GPs in the venture world, what would you recommend today? Well, it's sort of an unfair question to ask me since I've never managed to raise LP money. I think that you want to hire venture capitalists the same way that venture capitalists should hire founders, which is find people who have done it, have done it well, and have a way of doing it which is repeatable, right? And this is obvious advice, although it doesn't seem to be what people are funding.

You know, there's a lot of the new venture funds are people who came from a completely different industry and are generally smart. haven't done this specifically. And maybe they'll succeed, maybe they won't. But there's no way of knowing. This has been an absolute blast, as always. tons of interesting topics. I appreciate the time and all the insight. Thank you, better.

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