How I Invest with David Weisburd
E149: From $0 to $1B in 3 Years: Fin Capital's Meteoric Rise in Fintech Investing
25 Mar 2025
In this episode of How I Invest, Logan Allin, Managing Partner and Founder of Fin Capital, shares how his firm uses AI-powered predictive models to evaluate startups. We explore how AI can assess founder DNA, predict billion-dollar outcomes, and transform the venture capital landscape. Logan also highlights the surprising traits that lead to startup success, the advantages of repeat founders, and what the future holds for fintech. This conversation offers valuable insights for investors, entrepreneurs, and anyone fascinated by the intersection of AI and venture capital.
Full Episode
There's a professor at Stanford named Ilyas Zhubelev, who was one of my professors while I was at GSB, and he's spent a lot of time on this. He produced a book called The Venture Mindset, and that was coming out of a lot of his research. And he started his research by looking at what do unicorns have in common? And he started doing that research when I was there in 2012, 2013.
And I kind of laughed. I was like, well, that's a fun academic exercise, but that's not how you make money. How you make money is figuring out what they had in common at the seed stage. which was really two things. One was effectively who the founders were and their backgrounds. And then two was the business model.
And for us, we believe, and certainly the Flights of Quality has demonstrated this, that software investing is where you derive capital returns within venture and then growth equity in late stage. The data is pretty demonstrable within fintech specifically that repeat founders dramatically outperform. There is an 80 plus percent correlation between over a billion dollar outcome
When we last caught up in New York, you mentioned that your AI can predict startup success to 90% certainty at the seed and series stage. How is that possible?
We are still obviously testing and refining the model. We have a pretty incredible Gen AI driven predictive model for effectively evaluating founder DNA. Gen AI has changed not only what we invest in, but how we invest. And we have a director of AI at the firm. And he and I and the rest of the team have been refining this model. We invest strictly in repeat founders and seasoned entrepreneurs.
So how do you evaluate them and score them ABC from the attributes that... We've assigned, which are about 35 weighted criteria. And from there, we want to predict, is this the type of founder that's going to generate a 5 to 10x net outcome at the earliest stages? To be inside stack ranks, the top 250 fintechs every year. There's a couple of other Fortune 50, etc.
They all have these ranking lists or these lists of the top fintechs. 50, top 250. So we've done backtesting against those. We've also done backtesting against our own portfolio. And we've found a number of interesting signals from that. With the Gen AI teaching the model, it's refining our percentages, refining our weightings. That started with
kind of multiple basis points, like three to 5% changes in weightings. Now it's down to, you know, small basis point tweaks. So we really feel like we're at a point now where we just keep feeding it more and more data and then on the margins, obviously continue to refine it. So we've looked at in training data sets, YC data, we've looked at a pitch book data.
We've looked at companies that have exited for over a billion dollars, companies that have exited over $500 million and And we've also looked at in terms of how much money have these companies been able to raise as a proxy of success. And we've used all those training data sets to refine our model.
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