Gabe Stengel - Building Investing Superintelligence - [Invest Like the Best, EP.492]
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How did Rogo’s product evolve alongside the frontier AI model eras?
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You and I have talked many times about this basic question that I'll start with over the last couple of years. You are effectively trying to build investing superintelligence tools to help investors do their job much faster, better, cheaper, easier, higher quality. But it's starting to feel like, wow, we're really eating a lot of the core functions that even a very smart analyst or even portfolio manager was doing a couple of years ago. How do you think about that? trajectory as you've seen it and lived it so far and where it's going over the next two years.
Two years is actually easier to reason about than 10 years or 20 years. Because in two years, the best investors are going to be figuring out how to reinvent their own firms and reinvent themselves. And if you look at what happened to market making and quant trading, James Street took 15 years to build the dominant franchise. And the world's best investors today are going to spend the next two to five years figuring out how to integrate AI into what they do. And Dario has the great line about everything. Everyone's gonna have a data center full of geniuses or a country full of geniuses in the data center. What would Goldman do? What would Millennium do? What would Citadel do if they had a country full of geniuses show up?
It would probably take them a while to figure out how to change the way they work, how to take advantage of that, how to integrate it into their system. I think figuring out how to apply AI into the investment lifecycle is the biggest challenge over the next five years.
Every great investor. There's been many companies on this trajectory where the product was cognition is very famous for like literally their ads now say, remember Devin, like it's good now. So lots of now clearly great companies with great products had a stage of an AI business where the product stunk. And now it's excellent. If you think about the couple increases in capability. that we've seen just from the raw models. Could you do the same thing for the eras of Rogo and its product? Like you pick how many eras it is, I don't know how you frame it up, but like what it could do at each level up to an including today. Yeah. I actually
tried starting Rogo two times before we got started. So in high school, I had a friend whose dad was an investment banker who wanted an app for trying to track the basically equity exchange rate of two public companies as they were merging. And so we tried using really old AI techniques to do that. Terrible. And then in college, before GBD three came out, we published a paper on AI assistance for econometrics and financial econometrics. Somebody tried commercializing at the time and nothing worked at all. And then when we actually started the business, it was when GBD3 came out, pre-Chat GPT. And so all the early days of Rogo, it was clear how kind of magical it was. You could demo things that were cool.
Nothing worked at all.
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Chapters
8 chapters
1
How did Rogo’s product evolve alongside the frontier AI model eras?
0:01–7:40
2
Why does the “last‑mile” integration matter more than raw model accuracy for finance?
7:40–16:19
3
Which investor skills will stay valuable as AI takes over routine analysis?
16:19–24:54
4
How does Rogo’s internal “company brain” (Shrek) improve workflow and auditability?
24:54–32:40
5
What pricing model (seat‑based, usage‑based, outcome‑based) makes sense for AI‑driven finance tools?
32:40–39:31
6
How can a fintech startup become a “black hole” for talent and capital?
39:31–46:51
7
What are the biggest uncertainties and risks Rogo faces in the next 2‑5 years?
46:51–54:21
8
What advice does Gabe give to founders building AI infrastructure for capital markets?
54:21–1:04:19
Speakers
1 identifiedMore from Invest Like the Best with Patrick O'Shaughnessy
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