Episode summary
Why OpenAI and Anthropic Won't Win Finance
Invest Like the Best
· · 1 hr 7 min · Hosts: Patrick O'Shaughnessy · Guests: Gabe Stengel
Summary
Gabe Stengel argues that AI is already strong enough to handle large parts of finance work, but the real moat is not just model quality. The hard part is building the surrounding plumbing: auditability, compliance, data integrations, workflow automation, and the systems that let firms actually use the output inside real transactions. He says Rogo began as a search product, then moved into junior-analyst and banker tasks, and is now pushing toward autopilot workflows for dealmakers.
He frames finance as unusually attractive for applied AI because there is enormous spend, messy niche workflows, and lots of “human rate-limited” work that can be turned into software. Gabe says the best near-term wedge is private markets and banking, where firms lack standardized infrastructure, while public equities will require deeper domain expertise before the same tools can be fully applied. He expects that in a few years, firms will use AI to price risk, draft materials, run diligence, and even coordinate transactions far faster than today.
He also argues that AI businesses will evolve toward outcome-based pricing, not just seats or tokens, and that the biggest technical bottleneck now is compaction: persistent memory across many interactions and many users. For Rogo, he says the challenge is to keep reinventing the core system as the models improve, while helping humans become more productive and more auditable rather than trying to replace judgment outright.
Key takeaways
- Gabe Stengel says the moat in finance AI is the harness: compliance, auditability, workflows, and integrations, not just the model itself.
- He believes private markets and dealmaking are the best initial wedge because they still have lots of manual, unstandardized plumbing.
- He says public equities will matter later, but only after Rogo hires deep domain experts and adapts its systems to that workflow.
- Gabe expects AI pricing to move from seats and tokens toward outcome-based fees for ideas, reports, and simulations.
- He identifies compaction, or persistent memory across many conversations and users, as a major unresolved model limitation.
- He argues that finance firms will use AI to price risk, run diligence, and transact much faster, making markets more liquid and efficient.
Notable quotes
“What we're seeing right now though is the models are smarter than anyone I know, anyone I spend time with”
“I think figuring out how to apply AI into the investment life cycle is the biggest challenge over the next 5 years for every great investor”
“the models just became capable of basically anything a junior investment professional or junior banker was doing as long as you gave it the right instructions and context”
“I think it's actually more important to be auditable than it is to be accurate”
“What we're seeing right now though is that the models are smarter than anyone I know, anyone I spend time with”
How the episode opens
What we're seeing right now though is the models are smarter than anyone I know, anyone I spend time with. Imagine if every PM at a hedge fund had 10,000 agents that were just kind of fraternizing, talking about ideas, reading through the notes, pontificating, and then at the end of, you know, 24 hours of endless, you know, debate just gave you one idea. In 10 years, the world's best investment firms, best banks will be have 90% of their enterprise value, not in people, but in software and data and systems.
What would you start doing? You would start trying to figure out how do you take all of what lives in the latent minds of your best people and putting it into a system that you own and operate. There's no part of the business that you hold sacred that is immune to being revolutionized.
You and I have talked many times about this basic question that I'll start with over the last couple of years. You're effectively trying to build investing super intelligence 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 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?
I think 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, Jane Street took 15 years to build the dominant franchise.
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- ai agents
- ai-native finance
- autonomous capital markets
- deal analysis
- what humans still do better
Companies and tickers mentioned
- Rogo
- OpenAI
- Anthropic
Watch the episode
Watch “Why OpenAI and Anthropic Won't Win Finance” on YouTube
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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