From Idea to $650M Exit: Lessons in Building AI Startups

Today I’m walking you through how we built an AI product so strong it led to a six hundred fifty million dollar exit—and how you can pull that off too. We’ll hit what to build, how to build it so it actually works, and how to get it into customers’ hands.

Quick background so you know where I’m coming from. I grew up coding, detoured into law, was stunned by how inefficient the work was, and started Casetext in 2013 with a bet that AI could change legal work. We went deep on large language models early, got early access to GPT‑4 in the summer of 2022, paused everything, built CoCounsel, and later joined Thomson Reuters.

Picking ideas just got easier because the market already tells you what it wants. Follow the money people spend on human labor and build AI that helps those jobs, replaces them, or makes previously impossible work doable at scale, like reading millions of documents. The opportunity is no longer seat‑based pricing; it’s the salary pool you can unlock, which is orders of magnitude larger.

This isn’t dystopian. It frees people to do higher‑value work and spreads access to services that used to be unaffordable. Think legal help that’s faster and cheaper, or world‑class assistants available to everyone.

To build the product, start by truly understanding the job. Get domain expertise yourself or partner with someone who has it, and map real workflows. Ask how the best pro would do the task with unlimited time and resources, then break that into concrete steps.

Translate those steps into simple software where you can and prompts where judgment is needed. When the path is stable, make it a straightforward workflow. When it depends on context, add the logic to branch. Stay grounded in how the work is actually done; don’t guess.

Reliability is the hard part. Most demos hover at sixty to seventy percent accuracy, which is not usable in production. Define what good looks like for the full task and each micro‑step, then build evaluations that are scorable and objective, like true or false or a relevance score.

Use an eval framework, start with a small realistic set, and iterate until you hit high‑nineties accuracy with misses you can explain. Ship a beta once you’re close, set expectations, and turn every real‑world failure into a new test. Users will type wild inputs; make those part of your suite.

Keep improving. Try new models, tweak prompts daily, and remember that a single word can move accuracy by a meaningful percent when stakes are high. If you mirror expert workflows and obsess over evals, you’ll outrun flashy demos that never harden into real products.

On go‑to‑market, a great product makes everything else easier. Word of mouth and press show up when you create real value, so don’t let anyone tell you product matters less than marketing.

You may not be selling classic software anymore; you might be selling a service powered by AI. Price to the value delivered, not a default seat price. That could look like per‑contract reviews at a fraction of a law firm’s fee. Still, ask customers how they want to pay; many prefer predictable annual budgets even if it costs a bit more.

Enterprises are eager yet wary. Build trust with head‑to‑head comparisons against their current approach, pilots with clear metrics, and published results. And remember, the sale starts after signature. Invest in onboarding, training, and field engineers who sit with users and make the product land. Beware of pilot revenue that never converts.

Don’t obsess over competitors. The markets are enormous, and once you start building, you’ll often find incumbents are easier to outrun than you feared. Aim at work that’s already outsourced, go where the pain is broad, and pick arenas where you have real insight and access.

As CEO, keep the product and product‑market fit at the center at every stage. Hiring, culture, fundraising, and sales are means to that end, not ends on their own.

Think bigger. Before modern models, legal budgets capped the impact software could have. With today’s AI, you can transform entire categories of work—consumer tasks, back‑office functions, and core professional services. Chase the largest solvable problems your skills and tech can handle.

On pricing over time, you can start near what humans charge, then expect competition to drive costs down—which is good for society. Value‑based pricing, like a small share of the savings, is a strong starting point.

Here’s the kicker. Once you build it well, you’ll see how many integrations, checks, and prompt details it took, and you’ll realize you’ve created something hard to copy. I’m not worried about fast followers, and you shouldn’t be either. Go build the thing, make it reliable, win trust, and then keep improving.