The AI-Native PE Firm

For the past two years I’ve been building in the AI for finance space. My first thesis was an AI analyst, and on that thesis my timing was off in both directions.

First I was early. Then I was late. Along the way the product evolved into something I’m actually proud of, and the experience taught me where the real opportunity in this space is going to be.

The early version

The pitch was simple. A real analyst at a PE firm or investment bank costs $100K to $120K all-in. What if we could give you something that did the work for $20K?

The problem was that the agent wasn’t actually an analyst. It looked like one in a demo. It collapsed in real work.

I’d watch the supposed analyst write up a potential buyers list for a deal and the analysis would be wrong in ways that mattered. Not factually wrong. Wrong in nuance. It would miss the thing an actual analyst would catch in five minutes because they understand how the industry works, who the real strategics are, what a fair multiple looks like in this corner of the market. The model knew some things really well and other things not at all. And it had no idea which was which.

That’s the brutal part. Confident wrongness is worse than knowing nothing. A junior analyst who doesn’t know something asks. An agent that doesn’t know something writes a paragraph.

You can also see this in the pricing. Most AI analyst tools are priced at $200 a month, not $20K a year. If they were really doing the work of an analyst, the market would clear at the higher price. It doesn’t. The market is telling you what these things actually are.

The agents are much better now. They have more nuance. But by the time they got good enough, the second problem appeared.

The late version

The companies building in this space now are not bootstrapped startups. Anthropic and OpenAI are hiring ex-investment bankers. Harvey has raised hundreds of millions. The frontier labs are doing direct deals with the largest financial institutions in the world. This is not a space where a small team is going to win on insight. It’s a space won by capital and distribution, and both have already arrived.

That specific thesis (sell an analyst replacement) is dead, at least for me. But the work didn’t stop there. Over time the platform evolved into something broader: it helps on the deal side, but also after the deal closes. Automated scrapers that track what a portfolio company’s competitors are doing. A buyer finder for when a portfolio company needs a strategic exit. Tools for risk and compliance, market tracking, operational monitoring. The interesting thing is that none of this looks anything like the original “AI analyst” pitch. It looks like the connective tissue of a modern investment firm.

And that’s what pointed me toward where I think the real opportunity actually is.

Where the real opportunity is

Everyone in AI for finance is building on the deal side. Sourcing, due diligence, document parsing. Why is everyone there? Because that’s where the data is clean. You connect to a data room and you get a folder of PDFs. It’s the most structured part of the business, which makes it the most tractable.

The harder, more valuable opportunity is the opposite end of the firm: portfolio operations.

When a PE firm buys a company, the real value creation doesn’t happen in the deal. It happens in the next three to five years inside that portfolio company. Margin expansion, working capital, sales productivity, operational efficiency. Almost none of this work is being touched by AI right now.

The reason it’s not being touched is the reason it’s valuable. Portfolio companies are messy. Every business has a different topology. You can say “logistics companies are all similar” until you walk into one and discover their inventory taxonomy was invented by someone who left in 2008. There’s no clean data room. There’s a CFO who exports things to Excel and an ops manager who keeps everything in his head.

That mess is exactly why the opportunity exists. You can’t build one product and sell it to a thousand companies. But a PE firm doesn’t need to. It needs to deliver outsized returns across 5-7 portfolio companies, which is a completely different shape of problem.

In-house, not consulting

The instinct will be to outsource this to consultants. Bring in a boutique, run an assessment, deliver a deck, charge a fee. This will not work.

It won’t work because consultants give a view and then leave. The portfolio CEO nods, says “great, very interesting,” runs one fake project to satisfy the fund, and goes back to running the business the way they always did. Real implementation requires skin in the game.

PE firms already understand this for other functions. Every serious mid-market PE firm has an in-house operating partner team that goes into portfolio companies for six to nine months after a deal closes. What I’m describing is the same model with AI-native operating partners instead of supply-chain or finance ones. You parachute in a small team that has actually shipped real systems. They identify the three or four places where AI can create real ROI in this specific business. They build it, ship it, measure it, and move to the next portfolio company.

The moat isn’t the technology. It’s that the implementation is hard, the talent is scarce, and the value is captured by the fund instead of leaking out to a consultant.

Why almost no one will do this

I think this will get built. The interesting question is who builds it.

Most PE firms are run by excellent dealmakers. Dealmakers do deals. They are not spending their weekends building with the latest models, and they shouldn’t be. That’s not their job. But it means very few people sitting at the top of these firms will recognize the right AI talent when they see it, and the right talent won’t want to work for someone who doesn’t get it.

I think in five years a small handful of PE firms will operate this way and outperform the rest by a meaningful margin. Many more will try, hire the wrong people, run a few pilots, and quietly go back to their old playbook. The execution gap will be enormous. Most of the value will go to the few who actually do it.