86 Out of 100: Harvey's Playbook for Selling AI Into Law

Sarah Guo opens this No Priors episode with Winston Weinberg, cofounder and CEO of Harvey, which builds domain-specific AI for law, professional services, and the Fortune 500. The company has raised more than $500 million from OpenAI, Sequoia, Kleiner Perkins, GV, Elad Gil, and Guo herself. Guo wants to talk about end-to-end workflows, conservative users, impostor syndrome, and what lawyers will actually do five years from now. Weinberg says the two and a half years since he and cofounder Gabe Pereyra started have been wild.

The spark was not a grand plan to found a company. Weinberg had met Gabe a couple of years earlier, knew nothing about the startup world, and had no intention of doing one. Gabe showed him GPT-3, already public, and Weinberg was stunned that nobody was talking about it or putting it to work in any serious way. He showed Gabe his legal workflows in return.

The aha moment arrived on r/legaladvice, a subreddit where people ask legal questions and the most common reply is some version of "so who do I sue?" They pulled about a hundred landlord-tenant questions and wrote what would now be called chain-of-thought prompts, before anyone was using that phrase. Three landlord-tenant attorneys were handed the questions and answers with no mention of AI. The only ask was whether they would send each answer to a client without edits, and whether that would be ethical and good enough. Eighty-six of the hundred answers were a yes.

They cold-emailed those results to OpenAI's general counsel, whose response was that he had no idea the models were this good at legal work. A couple of weeks later they sat with OpenAI's senior leadership. The view in the room was that the models were going to be good enough, so the two of them should build a company around that fact.

Weinberg says the reason they were confident was that even GPT-3 could complete a lot of legal tasks if you brute-forced the context and spelled out every step. The idea was that over time either the models would get better, or the team would get better at providing context and evaluating results. After enough time playing with the tools, that direction was already visible. Harvey is no longer a property-law experiment; what they are building, he says, is the AI platform for legal and professional services as a whole.

He admits that sentence sounds vague, and says the vagueness is on purpose. If you are using these tools and cannot see AI applied to an industry until the whole industry is transformed, "I don't think you're thinking ambitiously enough." The constraint is that models still cannot one-shot complex legal work, or the equivalent in tax and other professional services. So the product has to constantly expand and constantly collapse: ship specific features and agentic workflows for pieces of a task, then combine them so the interface stays simple instead of becoming a tentacle monster.

Guo remembers thinking Harvey sounded a little crazy when it described the sophistication of the end-to-end tasks it intended to complete. Weinberg traces the conviction to actually using the tools, and to how few executives have spent real time doing that. When the company got GPT-4 access, he went into a room for 24 hours and retried every task GPT-3 could not do, or could only do with heavy brute force and no efficiency gain. It did not complete all of them, but the improvement was extreme, and almost nobody was talking about it.

He noticed a second pattern when he showed GPT-3 and GPT-4 to friends. They would try to get the model to do one thing, fail to get a perfect result, and stop. There is a large gap between logging into ChatGPT a few times and sitting there until the task actually works. Do that often enough, he argues, and you develop intuition for where the models are going and how much better they can get. That intuition is what later becomes product strategy.

From a product standpoint, Harvey treats expand-and-collapse as two themes. It needs productivity tools that are useful for the largest number of seats, and it needs streamlined vertical workflows that run a job from start to finish. Those vertical workflows can then be chained into more powerful systems. One example he likes: upload your documents and the target company's, and the system tells you in 72 countries where you need to file antitrust, what to file, and what other information is still missing.

Spreading that instinct across research, engineering, domain knowledge, and users is hard to put in a single person. The company is landing on what it calls AI patterns, the 30 to 50 systems that should be integrated into every part of the product. A model that is excellent at case-law research can go into a motion for summary judgment and a huge range of other litigation use cases, even if it does not finish those jobs alone. One set of teams builds the patterns; another implements them across the platform. Weinberg says it has been working well, and is still a work in progress.

Lawyers on staff, and later domain experts in other verticals, matter for two reasons. They are design partners who can say what the models must be taught, what the step-by-step thinking looks like, and what output the user actually wants. They are also the evaluators, because most public benchmarks are completely useless for Harvey. A lab will share early access to a model that wins every leaderboard, and Harvey will reply that it is less useful than a previous checkpoint. Those evaluators cannot be too junior, because "if they were too junior and they were able to eval it, they would be senior."

Asked which end-to-end task he most wants Harvey to complete this year, Weinberg names filing an S-4. The process is a mix of external data, internal data, and a million steps, which is how he thinks about knowledge work in general. Workflows are agentic systems that combine internal context, external context from data or from the client, and process knowledge such as how you do an LBO, plus the still more specific version of that process for a particular private equity firm. The more complicated the job, the more of those layers have to be fused.

Trust works differently across the two halves of the product. On productivity tools sold by the seat, the minimum viable quality of the output can be lower because several people will review it. The important move is to show your work: why the system did this, what information it pulled, and an inline citation to the literal sentence, so checking is cheap and a partially correct draft is still useful. That mimics the law-firm pyramid of junior, mid-level, senior, partner, then client. Specialized start-to-finish systems need a much higher quality floor, but they are narrower, so evaluation can happen at each step and recurse.

Guo was surprised, in 2023 and early 2024, by who Harvey chose to work with first: A&O Shearman, PwC, and other large, high-quality brands. A decade of technology investing assumed you start in the mid-market, with friends and startups whose requirements are lower and who will take more risk. Weinberg did the opposite. If the ambition is to apply AI to an entire industry, you have to partner with that industry, and the brand you are building has to make sense to what those firms already care about.

A&O Shearman is almost a hundred years old, and became famous advising on the abdication of King Edward VIII. Working with firms like that means treating them as design partners and building the workflows together. Harvey also wanted the data providers the industry already trusts. LexisNexis is involved in the current round, bringing goodwill, trust, and existing products to combine with AI. The company thought for a while about a self-serve, product-led growth model, then dropped it, because you eventually have to win the trust of the industry anyway, and the best way is to go after the hardest people first.

Guo asks whether AI finally lets enterprise software collapse back into something simple, instead of becoming untrainable once it is labeled enterprise. Weinberg says models are extraordinary at orchestration. Harvey can build separate workflows for share purchase agreements that extract reps and warranties, produce a complex summary, or handle closing conditions, then collapse them: when a user uploads an SPA, Harvey asks which of seven things they want to do to it. The actual interface of professional services, he notes, is email. What you do not want is a system with 10,000 workflows that users have to filter through, which is exactly the business software people already hate.

Harvey now serves more than 250 clients and more than $50 million in ARR. The reaction to automation, Weinberg says, changed massively over time. In 2023 there was a lot of press-fueled fear from people who had not actually used the product. Once lawyers use it, especially if they used Harvey a year ago and use it now, they see that it is good, that it is getting better, and that lawyer collaboration is still required. The junior ranks are the happiest of all.

The legal career path explains why. People come out of strong schools, having studied things with little to do with practice, then spend years reviewing documents in discovery or a data room. Strategic client work often waits ten years, or five if they are lucky. Presenting at his old firm, O'Melveny & Myers, a partner joked that there are easier ways to get out of doc review than automating it. Weinberg's point is that the tools compress that timeline, and the distinction that matters is task displacement, not job displacement: getting rid of those tasks does not mean the legal industry falls apart, it will evolve.

Advice for someone who wants to be a star lawyer in five years is almost the same advice he would have given before the startup, only more so: get as much hands-on experience navigating what is actually best for the client, even at a less prestigious seat. He does not think the billable hour disappears. Tasks that AI can automate with a lawyer in the loop will move toward fixed fees, while high-level advisory work stays hourly and may get more expensive. A specialist who has seen every kind of pharmaceutical merger should perhaps bill closer to 10x a junior in the data room, not 3x, because deep specialization in professional services is going to be more valuable over time.

A new business model is already showing up. Firms take the way they practice, Harvey translates that expertise into a specialized system, and the firm sells it to its own clients, usually because a couple of partners rally everyone else, a pattern that has snowballed in the past three to six months. They already do a lot of work at a discount or a loss, including private-equity work, in order to win the later LBO or M&A mandate, and software margins let even smaller firms compete for that feeder work. Reasoning models then unlock the next step of workflows such as antitrust, from identifying filings to actually completing them, while falling inference costs let Harvey raise quality for every user because it currently optimizes for quality rather than cost. Tax, audit, and deal diligence are next, because legal was the tip of the spear and the high-level problem is the same: apply a body of rules to a pile of documents.

Moving into tax diligence means engineers have to understand work they have never done. Harvey is working on context sharing, and Weinberg thinks one cultural piece landed: after talks on take-private deals, engineers came away with real respect for the professionals who do that work. Guo jokes about a weekly viewing of *Suits*; he jokes back that watching every season would do it, then admits it is a hard problem. What he hires for is agency: smart, hungry people who care, ship decisively, and iterate when they are wrong, because the environment changes every six months and experience without adaptation will not save you.

After two and a half years as a founder, the one mistake he would put above the others is figuring out when to scale himself. He still believes you should do every role for a stretch before hiring for it, because almost all of his mishires came from not understanding the job. You cannot scale a company, though, by staying hands-on in everything at all times. At the beginning of last year Harvey was 40 people, everyone had context because they worked with him directly, and he touched every customer and every piece of product. He has not fully solved the transition; he has only started treating it as a problem, while still arguing that founders need to keep absorbing as much signal as they can, because quick decisions come from that, not from a mysterious gut.

Guo notes that he works continuously and does little besides maybe working out, and asks where the drive comes from. Weinberg, who started the company at 27, says nothing in the years before came close: it is the most fun he has ever had, and the speed from idea to something built is addictive. Culturally he borrows a Kobe Bryant line, "the job's not finished," from an interview in which Bryant refused to celebrate a series lead. The expectation at Harvey is that people recognize or trust they are on a massively compressed timeline, and that they raise the bar without anyone tapping them on the shoulder.

Impostor syndrome is a live issue. He is scaling a very technical company and is not a research scientist, though his cofounder is. The most effective antidote has been spending time with a few people he trusts, because asking them very specific questions is overvalued and sitting with people who are incredible at what they do is undervalued: they hold almost none of your context, but proximity lets you absorb their intuition until it becomes yours. A concrete loop helped. Early last year many customers were blocked or did not know how to use Harvey; six or seven months later, after bets on follow-up questions, practice-area recommendations, and models getting better, usage had done a complete 180, and the intuition felt a little more earned.

The old debate over whether the application layer has value is, for Harvey, settled by the results. Weinberg's filter for other founders is price per token, looked at by industry and by task. In a 50-page merger agreement, producing each part of a word can be incredibly expensive, which is why legal was a good place to start. He does not want people to talk themselves out of a field because it has never been touched by tech, or because they do not already know it. You can learn what people actually do by talking to a lot of them.

Guo's version is that aspiring founders sit at home ideating when they should hang out in an industry they already find interesting and watch the work. Weinberg adds something that might upset some people: spend some time out of Silicon Valley. A tech company, he still thinks, needs to be in San Francisco, and Harvey itself has offices in San Francisco, New York, and London. Many industries are simply invisible inside a tech friend group, which may be a distorted sample of the work, so you have to go meet the industry itself.

Asked what has surprised him inside the company lately, he points to people without management experience who were put in management roles and improved at a rate that shocked him, the leadership team, and investors. He is willing to take those bets partly because of his own background. The signals he wants in a leader are care and ownership: obsession, no excuses, and walking up not to ask for praise but to ask what they got wrong. As people get more senior they start covering and protecting their position, and that is a terrible way to learn.

Looking beyond legal, he expects this year to bring sophisticated work completion in medicine and coding, a specialized replay of the ChatGPT moment, when a doctor or researcher sees something and cannot wait to show everyone else. Guo's implication for founders is that supposedly impenetrable industries are more open than Silicon Valley thinks, once someone shapes the capability into the task people actually want. Weinberg agrees: nobody wants Skynet taking their job, but people who care about their profession do want the tools once they have used them, because the spirit of law and medicine is still there under a pile of junk process. Those people will fight for a company that can finally make the change real enough to put their reputations on the line.