Venture Data Insights, Market Realities, and More with Prof. Ilya Strebulaev

Hello everybody, welcome to Venture Trailblazers. This is one of my favorite events. We always have amazing speakers, we always talk about amazing things. It ends up just being a lot better than I always expect. So we're up for another one here today. And what a great guest we have, Ilya from Stanford GSB. He's the professor of venture capital and private equity. Done a lot of amazing things. I'll talk about that more in a second. Let me just introduce myself, give some updates about how the session will run, and then we'll get started. So hi everyone, my name is Adeo. I'm CEO of the Dassault Group. I'm also chairman of Founder Institute. This is a joint event. Thank you for the emojis, everyone. The love goes right back at you. In fact, everybody here should be using emojis. If you hear something you like that we're talking about at the bottom of the screen, you're gonna see this little like smiley yellow face. Click that, click an emoji, and it's really great feedback. So thanks everybody. And just, we put this event on as a combination between the Founder Institute, which brings the entrepreneurs. Big hearts for Founder Institute out there, all Founder Institute people emoji maybe. All right, there we go. And we combine it with the VC Lab Network, which brings venture capitalists, limited partners, and others. So a lot of emojis there as well. So it's the combination of these two groups that make this event really interesting, and we bring on people who bridge the gap. They talk about entrepreneurial issues on one hand, and they also talk about investor and limited partner issues on the other hand. And there couldn't be a better guest than who we're bringing on today, who sits really at the intersection of all that and is an academic. So this is going to be really special. I'm excited about that because traditionally we brought in practitioners, and so this will be our first academic. I can't wait. A couple things of note: we talked about the emojis, use them away. You're going to see chat on the right-hand side of the screen, so you can chat away, share your profile, share your background. If you're an investor, don't share your thesis, but you can share your LinkedIn and your interests. So talk among peers on the right. There's a combination of limited partners, general partners, and entrepreneurs on the webinar today. You'll see a Q&A button, uh, kind of in the top right area, kind of off to the side of the chat, it says Q&A in two bubbles. Uh, if you have a question that you want Ilya and I to cover in the webinar, go to that Q&A button, add your question there, and we'll get to it. I did take some questions, uh, from social. I took some questions, so I have a whole set of questions we'll go through, but this is an opportunity for you to participate and ask questions as well. And we want to get your questions, so make sure to ask them. And if you have a question right now, don't wait, get it up there. Because if you— what always happens in these things is we get to the end and 100 questions come in and we don't have enough time. So if you get your questions in now, uh, we'll definitely get to them. Okay, so that's questions. Last thing, when Ilya and I are done and I'll bring him on stage in one second. We're gonna go and you're gonna see tables on screen, and this will go around in an hour from now. And so you'll have an opportunity to sit with your peers and discuss what Ilya and I discussed, share insights, share ideas, discuss what you like, what you didn't like. So If you want to hang around a little longer, there's going to be a lot of opportunities to socialize with peers. Okay. So without further ado, maybe we can bring Ilya on screen. So he's a globally recognized leader of venture capital, the David S. Lobel Professor of Private Equity and Professor of Finance at Stanford Graduate School of Business. Taught and influenced over 1,000 GSB students. In fact, some of your students were like, you've got to get Ilya on Venture Trailblazers. And so I'm really, really honored to have you. I see all the emojis coming here that people are excited as well. Is there anything you want to add about your background, which is pretty esteemed, that I didn't cover?

Thank you, Adele. Thank you for inviting me. First of all, I'm really very, very happy to be here. Uh, any of my former students who are here, hello. Uh, but I would love to— I would love to meet everybody else who is especially sending me emojis right now. Couple of things to add. First of all, indeed, more than 1,000 students took my venture capital class, but in total, in my 20 years at Stanford, I taught 5,000 Stanford students, and I'm very proud of that accomplishment. And second, About 10 years ago, I founded the Venture Capital Initiative at the Graduate School of Business at Stanford, which is really now is the major center that gets together, you know, all the data, all the insights, all our understanding about not just venture capital, not just startups, but the entire innovation ecosystem. So I really hope that I will be able to share today with you a lot of insights that we have accumulated over the last 10 years.

Yeah, you've done a lot of work. In fact, I have tons of questions about the different areas of work that you've done. But what— let's get started with the Venture Capital Initiative because that was my first question. Uh, like, what motivated or inspired you to start that initiative? And tell us— you gave us a little, a very high level, but if you want to provide any more detail about how it works as well, I'd love to hear that.

As so many things at Stanford, Adele, it started with my students. I came to Stanford 20 years ago, okay, in 2004. And I taught not venture capital. I in fact did not really know what venture capital was. I was a professor of finance teaching corporate finance. And many of my very, very first-year students, okay, I taught them for the first time and then they graduated and I kept in touch with them. And some of them became CEOs, CFOs, COOs, and so on, or hedge fund managers. And you know what? They followed very carefully my prescriptions I gave them in my corporate finance class, okay? They used all those finance rules. And then others became venture capitalists. And what I quickly realized is that those who became venture capitalists kind of violated almost every single rule I gave them in the finance class. And initially I thought, well, let me prove them wrong, because of course they have to follow my rules, okay, the golden rules of finance. So I decided, uh, to study venture capital. What I realized quickly, first of all, we don't know much about how venture capitalists make decisions. We don't know much about the venture capital industry because it's a private, semi-secretive industry. And frankly, at the time 2004, 2005, many people kind of either did not know about it, uh, or did not have a very high opinion of that. Remember, it was several years after the bubble burst and so on.

Okay.

And so what I quickly realized is that first, the VC industry is amazingly interesting, that VCs make decisions differently from traditional— what traditional finance prescribes, and there are reasons behind that. And I became really, really fascinated by this. And this led me to realizing that venture capital industry is also very impactful. In fact, much more impactful than not only I, but anybody else in the world, I think, at the time, including venture capitalists realized. And when I realized that, I went to, to the dean of the graduate school, the Graduate School of Business Stanford at the time, God Salone, and I told him all of this, and he got excited. And I said, Garth, we in fact need to establish research initiative to be able to get the data. And with his help and the help of other Stanford people, I founded, I think around 2014, 2015, the Venture Capital Initiative. So the initial idea was very simple. Let's try to get better data to understand how really venture capital industry works. So that was the initial idea. It's then, it's as always happens with successful initiatives. It then expanded. So that is the brief history.

Uh, okay, awesome. Uh, I was— so, um, well, I have lots of questions just about that, so let's stay on that for a minute. I mean, it is the most opaque industry out there, right? Because, um, it's not really regulated, so there's not regulators getting information. Uh, it's not real— there's no you know, a lot of VCs, if an LP says, I'm a public LP, so I've got to report our numbers publicly, they're like, I don't want you as an LP. So they actually will shy away from having LPs that will expose their data. So the data is, is largely unknown. And then you have things like PitchBook, which to the best of my knowledge, because I have pretty good data, is publishing garbage. So what— like, talk to me about just the data, any— the how what you're seeing, if you think it's like— yeah, I'd love to just dive in on the data a little bit, right?

Well, as an academic, I would not use the word garbage, um, right? So, so I think that all the datasets, including all the commercial datasets, do have some positive signs in them. That would include PitchBook. The truth is, is that, um, every single dataset that is commercially available— and you can think about Crunchbase, Venture Source, that is no longer exists, CB Insights, and there are many, many, many other datasets, um, including global datasets. They are, I would say, very incomplete. Okay, and so what our first task was is to get the data about companies but also about returns. So return-wise, for LPs here, of course, you would use— be using Burgess, Cambridge Associates, Prequin, and so on. So all the data is also incomplete about the returns of venture funds. And so we got, um, the, the data and completed it or reconciled it. Let me give you a specific example because you mentioned PitchBook. So let's say we take a specific company that is venture-backed, okay? Let's say PitchBook reports 2 rounds of venture funding. So we need to confirm that those 2 rounds existed, but also PitchBook will not say what its post-money valuation was. Or PitchBook would say that it was founded in 2017. Let's look at Crunchbase, it says the company was founded in 2012. There are many cases like this. So we, in fact, our team at the Venture Capital Initiative, reconciling this to the extent possible. Sometimes we contacted investors or founders, uh, or other providers, so we reconcile all the, all the data. Is it possible all the time? No, uh, but now, but at least we have the confidence score. So for every single variable we have, we actually have a confidence score, like how confident we are that, that this company was founded in this year, raised Series A in that year from that investor at that price, and so on. Okay, it's the same goes for fund returns. So, uh, we have data on every, more or less every single institutional venture capital fund, at least in North America. On every single firm, every single fund, uh, starting from the late 1980s, early 1990s. And again, we're reconciling data so that we know about multiples, about investments they made, about partners, uh, that work there, and so on and so forth. Again, is our data perfect? Absolutely not. Is our data better than most other, at least commercial data sets available? I think so.

So what—

how—

okay, so I'm like, wow. All right, let's just start with— because I don't think any— the only people that have half decent data on venture are the large LPs, um, because they get to at least see what the GPs write, and they can also sometimes go direct into companies. So I would say that you might, if you're doing what you're saying, you probably have. So can you access— I'm looking at the website right now— how accessible can— is that data available at all, or how do— how does One, uh, beg, borrow, or steal.

Well, we're using this data for academic purposes. Um, some of this data is under NDA. In fact, in fact, I work with a number of providers, some of them anonymous providers, who give the data. We incorporate this data in our dataset, or we can merge, but we of course cannot reveal this data. It's interesting idea you mentioned large LPs, because I work with a number of large LPs. And indeed, they have a lot of data. Here's the challenge for large LPs here who are listening to me, is that your data typically suffers from what is known as a selection bias. For example, large LPs, because they're large, they can invest only in some kind of funds. Let's say only large venture funds or large private equity funds. Or you typically invest— even the largest LPs in the world invest in a very limited number of funds because of their portfolio allocation strategies, which means that something that I believe is critically important to do for any investor is to construct their anti-portfolio, which means that the, let's say, the funds that you could have invested in but you did not invest, okay? That is very difficult to do. So as a result of this, Adeo, yes, large LPs have typically better fund-level data about the funds that they invested in, but they in fact don't have almost any data apart from generic, you know, Cambridge Associates or frequent data for the funds that they did not invest in. And of course, they didn't invest in most of the funds.

Oh my God, I have so many questions for you. I mean, can we get some emojis if this is getting people excited? All right, there There we go. All right. So it's only an hour. What do I do? All right. So I'm going to invite you back. Yeah, we might need to do this again. Should we do it again? Let's start with what is— let's— I just love you to do whatever you're comfortable with sharing about data that may not be conventionally known or understood, or stuff that you've seen that just totally confirms conventional wisdom. Either, I'd be very interested interested in hearing.

Okay, uh, that's very simple. Uh, in fact, some of this data is now published, and I will put some into, in the chat, by the way, in a second. But let me first of all introduce—

I'll put the link in the chat as you talk to your, your initiative.

Okay. Oh, fantastic, fantastic. But I'll put a specific link because we now have a, have a report produced as well. So, um, let me give you two examples of that. Okay, one is the following. So data that is not conventional We talk about venture-backed companies, but Adeo, you are not a company, you're a founder. And as we know, not a big surprise, as venture capitalists know, it is the identity of founders that is critically important, especially for early-stage companies. So in fact, you know, if Adeo comes to me and says, I'm starting a new company called— little company called the Sile Group, and I'm a venture capitalist, I really will first of all think about whether I would like to back Adeo, and then I will think about whether DeSalle Group makes sense or not. Because if I decide Adeo isn't good, I really don't care about DeSalle Group. If I care, if I actually love Adeo, I think he's a great founder, but I don't think DeSalle Group is great, I will talk to Adeo. In fact, I may, I may take, I may ask him, you know what, Adeo, come to my venture fund, become my entrepreneur in residence. Your DeSalle Group sucks, sorry, Adeo. But, but you can come up with something else. So as a result of this, not a big surprise, founder characteristics are extremely important. But what kind of founder characteristics? Okay, that is much more challenging. So this is what we did. We identified, uh, in the United States for now, uh, every single unicorn. So for this audience, I don't need to define what unicorn is. Yeah. For every single unicorn, we identified every single founder or co-founder, okay? And then for every single founder, co-founder, we found hundreds of characteristics. So for example, your educational background, your social demographic background, okay? Your work experience, like, are you a serial entrepreneur? Um, did you work at McKinsey before you started your unicorn? Um, whether Uh, what kind of titles you had? Was your startup backed by venture capitalists in the past? Uh, did you educate in the Ivy League? And so on and so forth. And okay, and some variables are obvious, some variables are in fact much, much less obvious. Okay, that was only the start, because if I know— if I tell about statistics about unicorn founders, kind of cute, but what can you do with it? For every single venture-backed company we identified other companies that were also funded by venture capitalists but that did not necessarily become unicorns. In fact, most of them did not. So my statistics show that on average, only 1 out of 100 US venture-backed companies becomes a unicorn. 1 out of 100. Now, if you're in California, it's 1 out of 70. If you're in Utah, it's 1 out of 61. But on average, in the US, it's 1 out of 100. So We took what I call academically random sample, okay, which is effectively it's a venture-backed companies, uh, that were funded in the same year as a unicorn and in the US. But apart from that, no other constraints. Um, and then we also identify their founders and then we compare. And so the question really, let's say for example I look at Adeo, okay, and Adeo founded two companies before, let's say and one of them was venture-backed before his Decile Group. And I asked a question: so if Adele founded the venture-backed company before and now is founding another company, does it increase his chance to founding a unicorn? It's impossible— it's important for a founder, it's really important for me if I'm an investor. Okay, um, and now I can ask those questions. Okay, and we did this for like hundreds of variables. And by the way, now we are expanding the data to non-US, and also our original data is stopped in 2021. And let me tell you, it takes a lot of time and a lot of people to, to get all this data. And even in the world of AI, it's still a lot of manual stuff, manual work. So now we're expanding to 2024, um, and what I'm going to do is I'm pretty All the results are completely available free on my LinkedIn, and we just produced a unicorn report. And frankly, I decided to charge money for this simply because this will pay for the venture capital industry for, for the research. Okay, um, I'll just put this in the chat, which is this unicorn report. And it's, uh, we're also updating this once every quarter. I think we actually were going to update this this weekend. I think so. Um, and going back to your question, there's a lot of unconventional things. I already mentioned one which is not about founders. We found out that, um, that, you know, if you're in Utah, your probability of unicorn is actually higher. Now, for those— I recently went to Utah to, um, to give a talk at Peterson Ventures, the annual LP meeting. And I think they were not surprised because obviously they invested in Peterson Ventures that invest in a lot of Utah companies. But others I talked to are, wow, surprised. Why is that? Why is it the case that per capita or per dollar invested, Utah seems to be more successful than California? Okay, and we can go, can go ahead and identify this deeply. Um, so here are some interesting other examples. It turns out to be that some companies are much more likely to produce unicorns. And you should not be surprised that, you know, if you worked at Google and then left Google and started a unicorn, you are more likely to become a unicorn founder than like a random, random guy who started a VC-backed company. Not surprising, I think. Um, but what about McKinsey? I already mentioned It turns out to be it's also the case, which is if you were a consultant at McKinsey, doesn't sound, you know, on the, on the, on the face of it as the most entrepreneurial job in your life. But if you did start a company after working at McKinsey, you have a much higher chance to found a unicorn. And so what is really interesting is that you can slice and dice and look at this data Once you spent 3,000 hours, uh, per year, which is like tens of thousands of hours trying to collect all this data, and sometimes you find some amazing, amazing correlations. I'm not going to claim it's causation at this point, okay? But I think from the investor point of view, even correlations are extremely useful.

So you also realize, by the way, that a lot of the unicorns are not unicorns. Unicorns, correct? That was another—

Absolutely. Absolutely.

So that was interesting as well. Do you want to just quickly talk about that? And if you want to add any other finding— emoji if you want to hear any other findings from the unicorn research.

So I have, I have so many findings, we will not have time for it.

But this— so let's talk about the not unicorns, if you will, because I thought that was very interesting. And then maybe two or three other quick findings that you have, and then I have lots of other interesting questions.

Okay, let me, let me talk about unicorns, and then let me talk about two findings, one for LPs and one for, you know, just people who care about life. Uh, so, okay, non-unicorns. First of all, the history, which I think is really important. And again, you know what, about 99% of my, all of my research projects and insights start with the questions that my Stanford students ask So I'm teaching this venture capital class for the first time ever. It was very difficult to bring this class about. Again, it's not traditional finance, okay? And the smart student is asking me a question: so how we're— Professor, how we're going to value venture-backed companies? Because they are pre-revenue, um, they, you know, there are no cash flows. If you're expert in finance, we cannot really apply net present value kind easily. Okay, what should we do? And that led me on a journey with my PhD student on how to value venture-backed companies. So to cut the story short, we came up with a new valuation framework that, um, that allows us to value not just unicorns but every single venture-backed company, as long as it really raised a bona fide, you know, venture round. And we did so. And what we realized very quickly, even before we came up with this valuation framework, is that the word valuation that is always used, uh, in, in the venture capital is not really valuation in the world of finance. Like, journalists make mistakes all this time. They will say, oh, this company like OpenAI, you know, raised round at the valuation of so many tens of billions of dollars. Well, that is not the valuation. It is post-money valuation. If you ever raised around, you know what PMV is? Post-money valuation. Post-money valuation is not valuation, okay, in, in the finance kind of sense. So we created the valuation framework that kind of allows to convert post-money valuation into fair valuation, which is effectively— think about this intuitively— what would be the valuation had this company right now be traded on the New York Stock Exchange or NASDAQ? So that's kind of the intuition. And yes, Adele, we found out, we found out is that first, the post-money valuation for most venture-backed companies is larger, is above the fair value. Okay, so there is a discount effectively. So if you say that, oh, this company is like 8 billion post-money, the reality is the fair value would be lower. We found out that on average, discount is large. It's like 30% on average. So you take $8 billion on it, if you don't know anything else about the company, you go down by like 30%. Second, we found out is that the, um, for some companies, for some companies, the discount could be dramatically larger, and that depends on the venture terms. So entrepreneurs here, or venture investors, You know, if you have terms such as multiple liquidation preference, participation, IPO ratchets, okay, and so on and so forth, um, add all those terms and then discount will be much, much larger. Sometimes we see discount of 6 times in the data, okay, which really means, really mean that, that unicorns, some unicorns that, um, that have post-money valuation of let's say $1.5 billion Once you discount them by 50%, 70%, 80%, they're no longer unicorns if we take a definition of unicorn as $1 billion valuation. Okay, uh, that is a— are they correct? By the way, the final point about this is that, um, how can we check that our valuation framework kind of reasonable? Here's what we did. We valued every single unicorn In fact, since then we valued every single venture-backed company, and then we looked at the exit. So for unicorns, you know, we value unicorn, and then maybe 18 months later the company would go public or be sold, and we compare the post-money valuation with the exit valuation or liquidity event valuation, and we compare our valuation, our fair valuation implied by our framework. Okay, and it turns out to be that our fair value estimate, uh, really, uh, is much more correlated, so much better predictor of the fair value of the eventual value, you know, like 18, 24 months down the road, than the post-money valuation. So that is, that is the valuation. That is the evaluation I do.

I have a business crush on you right now. I think if everyone else here has All right, so I want to ask—

I want only business, only business.

So I gotta ask about, um, so this fair market, you said the terms of the round affect the fair market value. So if they're— in a sense, this is a, this is a fascinating point that may change venture capital forever, because essentially what you're saying is these onerous terms that VCs put on companies— and I saw this in your research, so I wanted to hear you talk about this direct, and it's great that you brought it up— are actually hurting the, the growth value of the, the business, right? So now it might help the VC, but it ultimately hurts the VC if it drags the exit value down. Is, is that what you— I just want to double-click on this.

Okay, this is really interesting, Adele. That calls for a longer discussion. So, so there is a trade-off Now, first of all, from the point of view of the company, why we agree as founders or as, you know, CEO of the company, etc., why do we agree to those onerous terms? Well, there could be at least two reasons. The first reason is, Adele, and I'm sorry to say so, maybe your company is not doing well and you cannot raise money, um, at, you know, without those terms. So a new investor comes in and says, sorry, Adele, 'Your company is not doing well, and by the way, for me to invest, you know what, I need to get, let's say, senior liquidation preference,' which means that, you know, if the company is sold tomorrow, um, before I'm paid, nobody else is paid. That's kind of the idea, okay? And you may have to agree to this because otherwise you are not able to raise money at all. So that's one. So at this— from this point of view, this onerous terms in fact allow the company to survive. Okay, okay, so that's number one. Um, and by the way, I don't want to go down this rabbit hole, which is fascinating rabbit hole. Sometimes, often in fact, we find it allows the company to avoid a down round. Okay, so there's another possibility out there which is exactly the opposite one, which is, um, let's say now I am a venture investor, Adele, and you are a founder of an amazing company And you come to me and I say, Adele, this is amazing, here's the very clean term sheet, which is, you know, no, no onerous terms, and the post-money value of your company is going to be $750 million. And you tell me, Adele, you tell me, Ilya, this is fantastic, but you know what, my buddy from the Stanford GSB class just became a unicorn founder. I would like to be a unicorn founder too. And you give me the term sheet for $750. Okay, I don't like it. And I say, Adele, not a problem, let's change the term. So let's now— let's say I will have participation, I will have 1.5 liquidation, I will have an IPO ratchet. Okay, and now I'm going to give you exactly the same amount of money, but obviously my securities are now much more valuable, and that increases mechanically the post-money valuation. Remember, Adele, the fair valuation doesn't change. The fair valuation is not really a function of those kind of fancy terms. But the post-money valuation changes, and lo and behold, you're a unicorn founder. Okay, in fact, in fact, in, um, I used to give this very simple, um, kind of mini case study in my Stanford VC class, uh, where I asked students, here's the company, hypothetical company, okay, with a, with a, with a value of like $150 million. Make it a unicorn. And turns out to be relatively— it's relatively simple.

Okay, well, you just opened up a can— one more can of worms that I— we might as well— I just— let's close this with this question because I got lots of other questions for you that are equally good. Okay, you're gonna have to do another one. All right, so you— people are manufacturing unicorns, okay? I think your work showing was roughly half of all unicorns are not unicorns. Is that right? So people are manufacturing these unicorns. Do you think that's a good thing? Is that a bad thing? Is it just gone too far? Is half too many? Or is there— because you seem to have a very logical reason for everything, but what's your opinion?

Is it a good thing? It's really interesting, Adil, because So manufacturing, again, I would not use this word as an academic. I think that what's in fact my research shows, and again, we, you know, my team and I follow every single unicorn in, in the US, and in fact we're now expanding to covering all global unicorns, which is much more difficult, frankly, data-wise. But, uh, here is the interesting reality, is that, um, that in our data, of course, there are fascinating failed unicorns. You might remember the Theranos. Yeah, you might remember FTX. Okay, but the reality is, is that most unicorns are quite successful companies. So we'll look at what then happens to them, to those unicorns. And, uh, upon liquidity event, not so many of them fail, go bankrupt, etc., uh, or in fact go IPO at a very dramatically low value than $1 billion. So, so eventually, um, there's a very large correlation between being a unicorn and being successful. Okay, so from this point of view, I don't think that, using your terminology, manufacturing means that companies or venture capitalists back on average companies that have no future, that, you know, are not doing great stuff. Uh, it's just that from the point of view of pure valuation, it all depends on the definition. So whether, you know, a $1 billion company, a $10 billion company, or a $500 million company is, uh, is, is a success. But there's a very large correlation, very large correlation between being a unicorn and being successful. So the founders here I think that if your goal is still to become a founder of unicorn, it's a good goal.

That's amazing. Uh, we can leave that at that. I have so many other questions, but there are other questions on other areas. So I want to switch because you've done a lot of research on how venture capital has really helped innovation. In fact, you opened with that. Um, what are some of the, like You know, the question I had was what role has venture capital played in fostering innovation? But if you want to share any sort of anecdotes like you did or any other things, because you really have pioneered and realized that venture capital has a much bigger impact on the world than people realize. Yeah, we agree, by the way. So, right.

So, you know, we agree, but it's the big question about agreeing and quantifying. I think, uh, so I think intuition is very important, but it's also very, very, very important to be able to say precisely what the impact of the venture capital industry, especially in the academic world where people are asking not about correlations but also about causation. Like, the big question is, has the venture capital industry caused Okay, anything we see around, and I believe I have a very firm answer to this. Okay, and I think if you're founders, it's interesting. If you're LPs, it's interesting. But if you really care about the economy, if you happen to be care about politics, about regulation, about innovation, um, technology overall, I think that is really, really, really critical. Like $1 trillion— quite well, actually trillion is not a lot of money these days, so like $100 trillion question. Okay, um, here's what I find is that the venture capital industry caused the existence of about three-quarters of all the new largest companies in America in the last 50 years. Caused. So there's a causal relationship. Okay. And the way to see it intuitively, okay, and you can go back to my academic paper to check all the nitty-gritty details, but the intuition is the following. Have you ever heard a de of a French Tesla, of Italian Google, of Japanese Airbnb, of Canadian Microsoft? And I can continue the list. So all those countries I mentioned are also developed countries. They just don't produce new companies, new large companies. And the reason is venture capital. It's an amazing reason because, and this is another interesting point, what we found in our research is that the United States, prior to the invention of the venture capital industry in the 1970s, did not or had not produced companies at a faster rate than other countries. Like, the US between the 19th century and the 1970s was actually similar to Canada, Japan, and all those large European countries, okay? But something happened in the, in the mid to late 1970s, and suddenly there's a huge disparity between the US and everybody else. And we looked at too many, many explanations, and kind of we discussed all of them. And the one that is left is the venture capital industry. So that suggests that the venture capital industry has an absolutely huge impact. And by the way, I now talk a lot to regulators around the world because suddenly they realize this. By the way, not the regulators in Europe.

Yeah, they know everything.

Not yet. Not yet. Okay. But, but regulators in other countries, in emerging countries, in developing countries, um, that suddenly realized that, you know, if you really would like to become a fast growth economy, not in one year, but like over the last, over the next 30, 40 years, you cannot survive, you cannot achieve this without a very active venture capital innovation ecosystem, and you should do it in a very smart way. And as a result of this, um, as a result of this, there's a lot of, by the way, insights about how to, how to, how to do it. Okay. And, uh, I now talk to regulators, um, in, in, in many around, many around the globe. And by the way, the same applies to large corporations out there. If you're alive, if you are the CEO of large corporation then unless the change inside your company is happening at a faster rate than the change outside your company, okay, you're up for a very unpleasant surprise. So we looked at every single company in the original Fortune 500. So most of them are no longer with us. Only 1 out of 6 survived, and most of them survived because they were very— they were innovative. And so my prediction is that a lot of companies that are very successful, successful today will not actually survive over 10, 15 years. And I work a lot with large, large corporations on how in fact survive those wave of, wave of disruptions.

So, so basically the internal innovation of an organization must exceed the external, or at least be on par with the external innovation for it to survive. So survival there's this disruption paradigm that's constantly happening. And that's probably why the big companies in foreign nations survive because there's no venture capital to disrupt them. So the external disruption is relatively low. So the Myanmar telco or the Indian telegraph service can survive because there's not enough external forces. Very, very— okay, give us—

that's a great idea, that's a great insight. You know, the key point is that not that they survive, but that they don't need to innovate. Historically, they do not need to innovate because there was no competition. And now it's changing because now, you know, startups, not just in Silicon Valley or in the US, but around the world are starting to compete with large companies, and large companies waking up and see all those companies they never thought about, and suddenly they're competing with them. And by the way, what is the key lesson for large companies? Um, is, um, uh, they need to acquire the venture mindset. And what I mean by that, they've got to be the LPs, right? They need to be— well, they need to be the LPs and the VCs. So, so the venture mindset, and, and, you know, um, again, all my research at Stanford, I summarized the key principles of the venture mindset. And initially I thought they're for VCs, they're for founders, and now I think in fact they're for those who are regulators, those who are large companies. I actually wrote a book recently, um, that became a bestseller, The Venture Mindset. I'll put the link into chat as well. So check this book out. And by the way, Adele, I've just got what I would say the highest praise for this, for The Venture Mindset. I woke up recently, Adele, and I got a message from my former student who is now— who came from the US military to Stanford and now is back to the US military. And he texted me, texted me, Professor, have you, have you, um, read the LinkedIn post by the, um, chairman of the Joint Chief of Staff of the United States military, General, uh, Brown? And I haven't. I looked it up and I, um, I, um, saw that General Brown read my article in the Harvard Business Review about the book. And wrote a very, very long post about how the US military has to change. Okay, so in fact, it's not only large companies, it's not only regulators, but even the military, the defense industry has to go and acquire the Venture Mindset principles. So I do hope that everybody is— every single decision maker will read the Venture Mindset. And acquire the venture mindset.

I just bought your book. I'm sure many other people did too. Uh, I, I've heard about it actually. So, uh, interestingly enough, the hardcover is cheaper on Amazon right now, at least for me, than the Kindle, which is kind of unique. I'm like, how did—

what, use this option, buy 100 copies.

I was very positively surprised about that. Okay, one last question for me and then we'll jump into questions from the audience, 'cause I could just talk forever. So I'd like you to, you've done all this research on unicorns, you've actually done a lot of, we didn't get to cover it, so we have to do another one on venture decision-making, which we'll bookmark for another time. But talk to me about, given all your research and your knowledge and, What do you see as the future of the asset class? Do you see it— I'll leave that as an open-ended question.

Yeah, so it's a great question, and I'm thinking a lot about this. And also because I work, you know, for example, with LPs that are so, uh, interested, or my students who say, well, should I go into the venture industry? Like, will it exist in the future? So here is my thinking about this. First, I think the venture industry has a very, very bright future, but I think the bright future is going to be different from the current state. And here is, and again, you know, as an academic, as an economist, I think in terms of principles. I think about what are the fundamental mechanisms that are changing, okay? I'm less interested in about what happened last week. I'm more interested in what are the mechanisms that are going to shape the future? So think about the following mechanisms. The first mechanism, I think that we are in a critical stage where technologies are becoming democratized. What this really means is the following, is that, you know, 20 years ago, my students who became founders, they needed to raise $5 million to start because they needed to, you know, get the expensive Oracle system, to hire 20 software engineers because there was no outsourcing, etc. There was no cloud, there was no et cetera. Uh, these days you can achieve the same for $50,000. It does not mean that, that you don't need money to scale. Actually, now you need hundreds of millions of dollars to scale potentially, but you can experiment. So as a result of this, the barriers to entry are lower. Okay, so we see the democratization of technology, democratization of the venture industry, and we see this from the supply and demand. So there are way more founders out there, okay? There were more entrepreneurs and there were more investors. So, Adel, you still may remember 20 years ago the word angel was relatively rare, okay? But these days, you know, everybody is an angel, kind of, more or less. So, so that means that traditional venture capitalists feel the heat from the early, very, very, very early stage investors, okay? On the other hand, and that is the second mechanism that I think is shaping the today, the reality today but also in the future, these unicorns we talked about and other companies stay private longer. What this means, this means is that they need to get funding. They need to get now hundreds of millions, billions of dollars. In the past, they would have gone public. In the past, they would have been acquired early on. But now they stay private. What this really means is that now there is an opportunity for large investors, uh, sovereign wealth funds, corporations, pension funds, private equity, growth equity funds, to invest in those companies later on. And again, they kind of pushing the traditional venture capital investors, uh, on the other end of the spectrum. And so traditional venture, as a result also has to reinvent itself. And we see this as a result of those kind of mechanisms. Okay, my final mechanism— I have way more, Adele, but I know we're, we're short on time. So my final mechanism is everybody talks to me, of course, about AI. Okay, what AI will do for sourcing, for due diligence, etc. And again, there's always a trade-off from the economics point of view. It's interesting because economists think about two types of equilibrium. If you never took an economics class, bear with me for a second. Two types of equilibrium: partial equilibrium and general equilibrium. So partial equilibrium means you just change one variable and you see what's going to change in the system, okay? A general equilibrium, well, people will readjust, or companies will readjust everything.

I think.

So, so, AI— well, partial equilibrium is relatively straightforward, which is, uh, nowadays AI allows venture capitalists to do due diligence better. That is the reality, I think, uh, to source better. It's easier to source. Seems like, seems like everybody's benefiting. But general equilibrium also means there's a lower barriers to entry. I think in fact In the past, the venture capital was a lot about proprietary deals. So Ade would say, I'm a venture capitalist, and Ade will go to his LPs who will say, you know what, I have access to these amazing founders, um, and by the way, I have access to them before anybody else sees them, and nobody ever will see them. And I think that is becoming quickly, um, the past, which is these days proprietary is much more difficult because of AI, because of sourcing. As a result of this, it's a lot about winning the deals rather than getting access to deals early on. In fact, in my venture capital class this quarter, we had a lot of guests. One of the guests was Roloff Botha, the managing partner at Sequoia, and he kind of poured a bit of cold water on my students who are all very excited, okay? And he, he asked a very, very important question, which is, why you? Like, if you want to become a venture capitalist, why you? Because even Sequoia has to, has to, has to fight to, to win the, the deals. Okay, so I think that in the general equilibrium sense of the world, in fact, I think this will kind of dramatically increase the nature of competition in the venture capital industry. And we will see, I think actually we will see a lot of new names in the venture capital industry in the, in the next 10 years. So there will be a lot of turnover, uh, as well. And we're keeping track of every single venture firm, so I think that's going to be interesting. I'm going to talk about this next time, Adele.

Yeah, so, you know, we're launching about 300 firms or so a year. Uh, cool. All right, so lots of questions. Maybe we could try and go fast on them, but we— I, I love what you're talking about, so we don't need to go too fast. So, um, you talked to, uh, ng is asking, you, you talked about breaking the rules of finance, and would you say that's more true for early-stage venture than later-stage venture? Because later-stage venture, they have a lot more data to go with.

That is correct. That is correct. So for example, from the point of valuation, you know, for later stage, very often once you get revenues, once you can forecast your cash flow, traditional finance quickly comes back. For early stage, especially for pre-revenue, uh, it's very difficult to apply traditional finance. The intuition, by the way, is still the same. It just— the— you have to, you have to adjust methodology.

Boris is asking sort of just what you said, but he said, look, uh, a lot of these top-tier VCs, they had some great returns a long time ago, and, you know, now their returns aren't so great. Uh, is that something you're seeing in the data, and what are your thoughts on that? And like, you know, I, I know that some of these firms have great returns.

Um, yeah, so great question. First, some firms still have great returns, okay? Uh, there is, uh, such a thing as persistent persistence, which means that you're very, very high return in the past, you tend to have higher returns. But yes, I think on average, um, there is a high degree of competition Uh, there's something else, Boris, which is many successful venture firms became very large, and as you become very, very large, it's more difficult to generate the same level of returns. So if you look at smaller firms, smaller venture firms, if they're successful, they tend to generate higher returns just because some large firms cannot afford to invest in very small startups. And so they, they are very unlike— large firms are very unlikely to generate, you know, 1,000x returns. Like they did in the past.

So there's a growing number of— Jeffrey's asking this— growing number of emerging managers, right? And he's asking, what do you think of the returns from like 2022 to 2026 for these emerging managers versus established firms?

Yeah.

Do you think this is a good vintage time? Do you think emerging managers will do better? You just brought up smaller funds will do better. Most likely.

Yeah, so first of all, overall, I think this '23-'24 vintages are going to be pretty good. So in fact, historically, the vintages that were distressed vintages turn out to be much better. I'm pretty sure that '24 is going to be much better than '21. Um, you don't need to be a professor at Stanford kind of to, to, to, uh, to have intuition about this. So emerging managers, that's a very interesting question. Uh, first, I think the most successful managers are going to be emerging managers, um, for a number of reasons, from incentives to the fund size to who they are. However, and that is a very important point, there are many emerging managers, and so we will see a lot of dispersion. Are there going to be a lot of emerging managers that are going to be unsuccessful, that are going to fail? Absolutely. So in fact, we also tracking a lot of emerging managers Um, for many of them it's too early to judge, of course, but I think that especially for LPs here, uh, there are a lot of characteristics. Like, like I told you about unicorn founders, I think you also can— you should really be thinking about a lot of factors. And I've been working on this quite a lot. What kind of factors determine whether an emerging fund manager on average will have a much higher likelihood of success? And by the way, not just for this fund, But if you're an LP, much more important question is, like, if Adeo is an emerging manager and raising his first $25 million fund, much more important question is whether Adeo will have a higher chance of raising a $250 million third fund 5 or 6 or 7 years down the road. And I think there are, there are ways to predict that.

So we, we, you know, have almost very good data because we run the the source of truth. So you and I can have a discussion, and I'm happy to give anonymized data to your dataset, uh, to help. But we, we think we've unlocked some codes there. There's a great question from Kieran, um, how much of the gap in the fair value is due to the liquidity issue of private markets? It's a good question.

Uh, yeah, a lot. So LPs talk about denominator effect right now, which is related to this. Which is effectively, you know, a lot of venture firms, by the way, a lot of private equity firms as well, they kind of sit on paper gains, but because the markets have been relatively illiquid, they cannot return those money to LPs. And LPs say, well, because you are not returning money to us, we cannot return money to you. And as a result of this, the fundraising environment has been, has been very tough overall, I think, not just for emerging managers but even for established managers with successful past returns. And that generates gap in valuations as well.

Interesting. Yeah, so there are tons of great questions here. Um, well, first, uh, let me— before we run out of time, a bunch of people are asking, how can I get in touch? You put your LinkedIn there. Is there a better way to get in touch with you?

Um, so LinkedIn is the best way to, to, to get in touch— to get in touch with me. Um, read my book, have a look at the Unicorn Report. And, uh, on LinkedIn, we publish this week. We're on LinkedIn. I'm going to publish a lot of teaching-related stuff simply because it is the last week in class of my class. I'm teaching only once a year, so I'll produce a lot of stuff. Uh, but in fact, typically we— I post around 3, at least 3 posts, uh, on Unicorn Research a week. And then one or two other research insights, not just on unicorns, but on something else like venture firms, corporate venture capital, innovation, and so on. So just follow my LinkedIn.

Well, I will definitely be doing that as well. Um, Demit is asking, uh, this is a founder question. So, hey, I'm me and my co-founder, we're in Eastern Europe, what do you think the next best for us to— the step for us to take?

And I'm presuming, okay, okay, yeah, I said the second answer is the following, because that You know, a number of places in Eastern Europe are becoming innovation hubs very quickly. So from the point of view of starting a company, getting the first fundraising round, I think Eastern Europe is a good place. Also a good place if you're a software developer. There are a lot of software guys there. The challenge with Eastern Europe and a lot of other places around the world is that it's much more difficult to raise subsequent funding. And so the reality is, I think at least now, is that if you would like to raise Series A, Series B, very often you have to, you have to move your company. So that is, I think, the, the relative for most.

Yeah, these places, if they don't have later stage venture opportunities, Series A, growth stage, etc., you, you definitely need to think. Because as you were saying, the presence of venture capital is like a causal effect. And that you're not speaking causal in just plain English causal, you're speaking statistically causal.

Correct, correct.

That's amazing. Well, listen, everybody, I know Ilya, you gotta run at exactly the top of the hour, so let's do a big round of emoji applause. This is amazing. There's such good questions here. Um, let's do this, you and I will follow up separately, but let's get you on again, and maybe what we'll do is we'll give it a theme and take all audience questions because you were so amazing with your insights that I'd love to get the whole network participating. And based on this, it will even be bigger. And this was huge.

Adele, thank you so much. It's been great. I see a lot of questions in the chat. So what I would love to do, if you can do it, we will— if it's possible somehow to get all these questions in advance or at the very beginning, and then we'll just devote 1 hour or more to Q&A. As you, as you already can guess, and if you are my student, you know this, um, is this, you know, I love answering questions because I do think, I do think that these insights are very helpful, especially if you're a founder, a venture capitalist, an investor, or an LP. A lot of this, you know, a lot of these insights, one insight after another, and suddenly— and I think this, I, this happens, like, I recently worked with the CEO of a large company are providing him one insight, another insight, okay? And he's not sure, and then suddenly it shapes into an amazing picture, and suddenly he says, oh wow, now I see. And so hopefully for everybody here, there is this moment where there is an insight that makes you, uh, which leads you to a much, much better decision. Again, whoever, whoever—

there were a lot talking about the unicorns, talking about the impact adventure, talking You know, I'd love to talk in the future about decision-making, the economics, the fair market value. I mean, there was a, a lot of great insight. So thank you again, everybody. Go to the tables when we're done and can continue the conversation. Have a beautiful day, Ilya. Good luck with your—

Thank you. Bye, everybody, for now.