How to Build an AI-Native Services Company

**Charlie Warren** (0:09)
Some of the biggest companies of the next decade won't be software businesses at all. They'll be services companies like insurance carriers and law firms, rebuilt from scratch, with AI doing most of the work. These are what we call AI native service companies. And the markets are trillions of dollars in size, tax, audit, insurance, law, parts of healthcare and so forth. This opportunity didn't exist even a couple years ago. But advances in the models have unlocked this new type of business, where companies provide the outcome of the customer versus build a co-pilot that the customer uses internally. These companies also look and feel different than most startups today.
In this video, I'll walk through a playbook for founders starting AI services businesses from scratch. It's aimed at people thinking about starting a company, not if you're already running one. I'll share some obvious and non-obvious elements of building these businesses that we've observed here at YC. Topics include picking a market, forming a team, building the actual product, serving the customers, the P&L, and whether or not you should even buy a business. One general comment before we get started. We're still early here. Like most things in AI, the market is moving fast. We're learning as we go, but the early successes here should get you really excited. First, picking the right market. The same general advice for all startups applies here with some important caveats. You should pick a market you're excited to work in for a long time. These companies still take a decade or more. If you don't love some combination of the customers, or the market, or the technical problem, you're not going to make it. And that part isn't really new. But the best markets for AI services have four new, pretty unique traits. The first is low trust. Meaning the work is already outsourced, and the customer cares about the final product, not how they got there.
You're displacing a vendor, not asking the customer to do something fundamentally different.
That's a huge deal, because you're not changing behavior. You're showing up where the budget already lives and doing the work. Second, low judgment at the task level. If you can break the work into pieces, and every piece needs a human exercising actual judgment, you can't really scale. You need most of the steps to be automatable, with judgment focused in a few places where humans stay in the loop. The third is a high intelligence threshold. This sounds contradictory, but actually it isn't.
The overall work has to be hard, hard enough that models plus humans are need to actually deliver an outcome the customer accepts. The fourth is regulation could actually be good. Regulated industries have higher expectations and legal accountability that raises the bar and the moat for founders.
For instance, Panacea is a current YC company that provides FDA regulatory services for biotechs and medtechs. They actually hire experienced FDA consultants, pair them with an AI platform to deliver faster, higher quality FDA approvals. So what are some of the specific markets we have in mind here at YC?
The known good fit markets include tax, audit, insurance, mortgages, parts of healthcare, and parts of logistics. But there are plenty more markets nobody has touched yet. Don't hold yourself to the obvious ones or what people talk about on X. And here are a few more things to keep an eye on when it comes to the markets.
The first is on the models.
Will the models disrupt these businesses?
It depends on what I call the Sam Altman test. You should ask yourself, as the models get better, does your service get stronger, or does the model itself commoditize you?
You want to be in the first camp. Where to be careful?
Anything involving equipment and on-site labor. The software margin math doesn't apply when you own and operate physical things.
It's very hard to create real leverage, though these can be really good businesses.
Let's leave this area to the robotics founders. One more honesty check. Ask yourself sincerely, are you using humans because the work genuinely needs judgment, or are you compensating for product gaps? Be honest here so you're not papering over product shortcomings with actual humans. There are still great massive technology businesses to be built with humans in the loop. Second, and maybe most importantly, the right founding team. The same advice applies for all startups here, but again with some important caveats. You should build companies with people you already know and you've worked with. If you're so low, think about the best people you've ever worked with and ask them to join you.
You'd be surprised who says yes. For AI services specifically, there's three attributes that all the best founders share. The first is domain fluency.