The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:30:02 · S1, Nik Storonsky
[00:30:02] S1: Can I ask you one on? You mentioned the liquidity to investors earlier. You've been unbelievable at maintaining a really tight control of your Cathayne role where other founders haven't. What have you done to maintain such good control of the cap table? Control it in the water. In preventing secondaries, preventing private transactions, preventing side deals? Yeah. Just because, again, it's pretty obvious. Right? [00:30:25] S0: As soon as you don't have control, so then every time when you try to raise a primary round, you're competing with all these early investors who want to sell at a large discount because, I mean, they make so much money. And they're okay to exit, as soon as possible because they they make, like, I don't know, like, x, 100 packs. Right? And it actually prevents the company to to raise primary because they're really kind of in the heading of the company. We had to put all these controls in place. [00:30:49] S1: In terms of competitors that you really respect around the world, who do you most respect
00:30:49 · S1, Nik Storonsky
[00:30:49] S1: In terms of competitors that you really respect around the world, who do you most respect [00:30:54] S0: and why them? Like, obviously, if I look at competition, NewBank is amazing what they do. Well, country's also great. Right? There's no competition there, and the rates are high. Credit card have, you know, very high rates as well. So I think, you know, new bank is very strong. Then I think JPMorgan, you know, have a good team in The US, so it's a bank, and very strong. If you look at Asia, right, so it's a bit different dynamic. So we have companies like WeChat, so they're very strong. I mean, already there are like many good companies in [00:31:23] S1: this Which will be a harder market for you to win, US or Asia? [00:31:26] S0: Nothing Asia. Why? Well, because they don't allow you a banking license, right? In The US, I mean, there is a lot of challenge. You get it. [00:31:33] S1: It's also a fundamentally different UI that they're used to. I mean, it's it's an entirely new product paradigm that you have to engage with. Culturally, it's not nearly as aligned as US and UK's are. You must have quantum like Nik.
00:31:44 · Nik Storonsky
[00:31:44] S0: So, yeah, let me maybe just spend one minute on explaining what quantum light is. So basically, because I was raising money, so many times, I've done it like seven or eight times, and, like, almost every time I experienced some very negative experience. For example, I think it was 2019, I agreed the term sheet with investor. I think it was, like, 7,000,000,000 valuation. And then next day, market dropped 10%. CP dropped 10%, then term sheet was withdrawn, and then it became like 5,000,000,000, however. Then, like even recently, like, you know, secondaries that were done, we kind of, you know, agreed everything with all the investors, and then, there was like, you know, market drop, Then luckily it went back, but we had some investors, you know, withdrawing term sheets. Or like when I was raising money for Series A. Like, one believed me. No one wanted to invest. I spent, like, you know, four, five, six months going through every single investor and saying to them, That's okay. It's really a great business. They looked at my unit economics, and unit economics was negative. Of course, it was negative because, you know, I just started. Right? It's a volume game. Anyway, what I learned that a lot of investors, they are A, following the crowd very often, and B, they're very emotions driven, right? Sometimes they like the founder, sometimes they don't like the founder. They don't like the founder and don't invest. So it's it's not very scientific. So two years ago, I stopped effectively a small team of data scientists and data engineers who collected a lot of data on startups. And then we trained machine learning models of the startups, selecting the best startups. And then the machine learning model, actually amazingly outperforms 95% of VCs over there on on backtest. That's how I started this business. I have an independent team of 20 people who are building, polishing, data science models. And then models give you a list of companies where to invest, and then we just go to these companies and invest.
00:33:36 · S1, Nik Storonsky
[00:33:36] S1: Where's the entry point? Because you need to have [00:33:38] S0: enough Serious B. [00:33:39] S1: Okay. So you need to have enough data. So it's series B. Okay. So you can select the best companies better than the best. That's fine. Generally, though, if you can select them better and there's data for that, the winners are more obvious. Yeah? And so if the winners are more obvious, selection is one thing, but winning is another. How do you think about winning when there's five others and you've got Sequoia and you've got Index and you have all the pretty
00:34:01 · Nik Storonsky, S1
[00:34:01] S0: And the reality is not that straightforward. Right? If if you look at companies that model selects and there we have... We tested for, like, 300 plus features, then we selected now, like, best 25, which statistically give you a signal, right, whether it's in the company or not. So the the features are, like, obviously, very important who are investors, who is the lead, and the the data of the lead. So we we actually track IRR for every single fund. And then based on this IRR, it's an input in the model, then model selects for that investor note. Then who is the founder? They have Already track IRRs of seed funds? No, we track IRR on the fund level for each investor. And then if we see a good investor with good IRR investing, it's obviously like a positive signal. Right? Anyway, model takes, say, 25 features. One of the features we're investing, which is dynamic evaluation, quality of employees, quality of founder, what they had on a outstanding education. Age is important as well. Why is age important? What do you find about age? If you look at statistics, so 20 to 25 is actually quite negative. Too early is negative. 25 to 30 is okay. 30 to 35 is the best age. After 35, I mean, there is a clear negative correlation with the success. So sweet spot is 25 to 35 for founders. That's the [00:35:21] S1: real view. So we have 25 to 35. You said education there. And you said investing in education early is is important. Why do you think it's so important? And do you think our current education system does it right?
00:35:21 · S1, Nik Storonsky
[00:35:21] S1: real view. So we have 25 to 35. You said education there. And you said investing in education early is is important. Why do you think it's so important? And do you think our current education system does it right? [00:35:32] S0: Well, I just look at the data, right, and then at stats, and then, statistics clearly show that top universities, stamping, we have a much more positive correlation with the success of the company. Obviously, it's not for any other profession out there, but particularly for startups. I mean, it is important. Purely based on stats. [00:35:51] S1: Which was the hardest funding round to raise? You mentioned earlier that the series A, for example, you spent five months on. Which was the hardest round to raise? [00:35:59] S0: I think our first one was very hard. Every single one was easy, easier than, think, 2019. We started raising 2019. We closed, I think, in February or in January 2020, just before COVID. We were very lucky, but it took time to close it. [00:36:14] S1: How big was that round? [00:36:15] S0: So relation was 5,000,000,000, which dropped from 7 because Oh, we did [00:36:19] S1: it at 5? [00:36:20] S0: Yeah. Yeah. We did it at five. I mean, I'm not sure. Yeah. Yeah. [00:36:23] S1: Wow. Yeah. So how much did you raise at five then? I think it was 300,000,000. Does that work? Because, like, as a relationship, it kind of dam... Like, I'd be damaged if you tried to haggle me down because the markets are moving.