I’m not a lawyer, but I was on the high school debate team. And I was pretty darn good. I was basically the Kevin G (Mean Girls reference) of the debate club, although I’d like to think I could also crush as a mathlete.
Real footage of me in high school
Since my experience with law is limited to watching my wife obsess over Law & Order SVU and high school debate, I decided to look into the biggest name in Legal AI.
This week, I’m doing a deep dive into Harvey and breaking down the repeatable strategy that they used to turn an AI novelty into a billion-dollar business.
TL;DR
The thirty-second version of what Harvey did.
If you're not a lawyer, Harvey probably isn't on your radar, so a quick primer. Harvey is basically ChatGPT built for law firms. (The name is a two-for-one: it sounds like Harvard, and it's a callback to Suits' slick superlawyer, Harvey Specter.) A lawyer can ask it to draft a contract, research how courts have ruled on some obscure issue, or plow through ten thousand documents and pull out what actually matters, the tedious and expensive work that junior associates normally grind through by hand.
Harvey was only created four years ago by two roommates. Those roommates were Winston Weinberg, a first-year litigation associate buried in grunt work, and Gabe Pereyra, an AI researcher (Google Brain, DeepMind, Meta) who mostly used GPT-3 to run their Dungeons and Dragons campaigns. Between them, they had zero enterprise sales experience, zero connections at the firms they wanted, and, in Winston's case, about eight months of legal experience. But a blind test on real attorneys convinced them the technology was ready, and convinced OpenAI to cut them a check before they had a single customer.
So how did these two win over the most skeptical buyers alive? Three bets. They started absurdly narrow, on the one corner of law where a 2022 model was already reliable. They made their sales demo a live attack on the lawyer's own court filings, which turned a cold pitch into a challenge no litigator could walk away from. And they built for where the models were heading, not where they sat (a recurring theme of ours).
Harvey now has 700 customers and is north of $100M in revenue, a majority of the biggest law firms in America, and an $8 billion valuation.
The Shift: Demo Against the Buyer's Own Work.
A generic AI demo lets a skeptical buyer imagine every reason it wouldn't work for them. A demo built from their own data eliminates the imagination step. Every skeptical enterprise buyer has public or shared work you can turn into a personalized demo. Use it.
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Under The Hood
The three strategies that make it work.
1. Find a wedge that your model can excel at
You don't have to make AI do the whole job on day one. You find the one slice where today's model is already good, and you prove it there.
Harvey's slice that Winston chose was California landlord-tenant law. There's "so much public data" on it, as he put it, and the rules are tight enough that every fact pattern falls into one of a handful of buckets: security deposits, notice periods, the usual. Narrow enough that even a clunky 2022 model could get it right. When he tried the same trick on securities law, the model fell short.
They then validated the model by bringing in experts. They grabbed about 100 landlord-tenant questions off r/legaladvice (the subreddit where, in Winston's words, "almost every single answer is 'so who do I sue'"), ran them through a long chain-of-thought prompt, and showed the answers to three practicing landlord-tenant attorneys without telling them a machine wrote them. One question: would you send this to a client, as is? On 86 of the 100, the answer was yes.
That 86% success rate gave them the conviction to productize the model in a very narrow domain. And by the time Winston was demoing to BigLaw litigators, the models had already improved and were able to handle more types of legal work.
2. Demo with real client data
Litigators file their work in federal court, and those filings are public, sitting in a system called PACER. So before a call with a litigation partner, Winston would look up the most recent brief that partner had filed, drop it into Harvey, and ask the model to switch sides and rip the argument apart. He's joked that his prompts basically came down to "this is bad." Find the weak spots, write the counterarguments, and lay out how opposing counsel would take it apart.
Then he'd hop on the call, where the partner is usually half-listening with the camera on, mentally drafting an email. Winston would say: I saw you filed that motion to dismiss. Do you want to watch Harvey argue against your own brief? Every time, they took the bait. They'd read every line of the critique, hunting for the spot where the AI got it wrong.
You can’t show a litigator a teardown of their own argument and expect them not to engage. Their whole job is winning that argument (this is basically the professional version of telling someone their baby is ugly). In Winston's words: "Because they're a litigator and I'm basically attacking something that they just wrote, they would instantly read the screen.”
A normal demo shows the product working on some made-up example, and the buyer still has to squint and imagine it holding up on their actual mess. Here, there was nothing to imagine. The proof was their own case, getting picked apart in real time.
It was a coin flip every time, though. If Harvey hallucinated a case or fumbled the law, the partner caught it in about four seconds, and the call was toast. But when it landed, in Winston's words, "the times that they got it right, it was over.”
Those demos won over individual lawyers, one ugly baby at a time. Landing a whole firm was a different beast, and the first one came down to a warm intro: a Stanford Business School contact who'd worked at Allen & Overy vouched for them to a partner there. In February 2023, A&O rolled Harvey out to 3,500 lawyers while the company still had four employees (one of whom had started the day before).
3. Build for where the model is going, not where it is
This is a common theme that we see among AI startups. You have to look ahead of the curve and build beyond the current capabilities. Harvey took the same approach. Back in 2022, Winston was convinced the models were about to get a lot better, fast, so he built for that version instead of the one sitting in front of him. His own investors thought he was a little nuts. He turned out to be right, and as his investor, Sarah Guo put it, "you don't look as crazy when you're right.”
Winston skipped the easy, low-end work on purpose, the "review my lease" stuff, because he bet plain ChatGPT would handle that kind of thing on its own soon enough. So instead, they built for the harder problems the models couldn't quite do yet, and waited for the models to improve. When they got early access to GPT-4, Winston locked himself in his room for 14 hours and re-ran every legal task GPT-3 had choked on. Stuff that had been impossible a year earlier suddenly worked. The jump in reasoning, he said, was "astronomical.”
So which future problem do you chase? Winston's filter is value per (extra 👀) token: find the work where a few pages of output are worth an absurd amount of money. His go-to example is a merger agreement, a couple hundred pages of paperwork that can rack up $20 to $30 million in legal fees. The day a model can draft that reliably, the value it unlocks is bananas, which makes it exactly the kind of thing worth building toward before it's even possible.
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Extra Credit
“Humans essentially have a tendency to believe that machines have more knowledge than they do, don’t break, and are infallible.” Basically, we humans are too trusting. This piece by Scientific American is a great callout of the dangers of relying too much on AI for legal work. This likely happens more if lawyers use ChatGPT as opposed to Harvey, which has hallucination guardrails built in, but the risk is still there.
Never skip a Paul Graham essay, and this one is a classic. PG argues that startups take off because founders do laborious, unscalable things by hand at the start. You have to earn the right to scale, and to do that, you need to first manually acquire users.
He tells founders to start in a "deliberately narrow market" to get the fire hot before adding logs (landlord-tenant wedge), and to act like a consultant "building something just for that one user" (demo run on a partner's own brief).
Hope you enjoyed reading this issue as much as I enjoyed writing it. I love stories about how companies got their start, especially if they did something unconventional.
I hope you have a great holiday weekend if you’re celebrating July 4th, and reply to this email with any company origin stories you know about.
— Amaraj (aka not good enough to be debate captain)
The Meme
Team Gryffindor Forever