Harvey, OpenAI, and the race to use AI to revolutionize Big Law
The life of a junior associate at a prestigious law firm involves hours of research and analyzing contracts. Three years ago, Winston Weinberg found himself buried in these kinds of tasks as a first-year antitrust and litigation associate at O’Melveny & Myers in Los Angeles.
And there Weinberg might have remained, diligently climbing the BigLaw ranks from associate to partner, logging thousands of hours of drudgery along the way. Instead, he’s cofounder and CEO of Harvey, the high-flying legal AI platform that’s raised more than $800 million by promising to handle much of this work.
“A lot of the tasks junior (associates) do are going to get automated,” Weinberg says. “That doesn’t mean their job’s going to get automated. It’s just going to be a different job.”
Built atop language models from OpenAI, Anthropic, and Google, Harvey’s platform streamlines legal workflows by helping lawyers with drafting, contract analysis, legal research, due diligence, regulatory compliance, and case law review. In addition, the technology cuts down on reading time by summarizing complex legal documents and combining databases to research and summarize legal issues.
Harvey, which is used by some 250 law firms—including 42% of The American Lawyer’s list of biggest 100 firms in the U.S.—announced in June that it raised a $300 million series D led by Sequoia, bringing its total haul to more than $800 million and driving its valuation to $5 billion. It’s now the highest valued startup in a growing field of AI companies that are all focused on overhauling how the legal profession works.
The company’s annualized revenue run rate hit $100 million in August, up from $50 million earlier this year, propelled by an aggressive sales strategy targeting big law firms. Harvey now has 460 employees, 20% of whom are lawyers. The company is also hiring dozens of engineers, sales leads, and account executives as it seeks to increase the moat between itself and its competitors.
Whether Harvey ultimately upends the legal procession or winds up burning a lot of cash, time, and effort could reveal that fates of industries as far afield as finance, music, and film. All these sectors—and more—are on a similar curve: exploring whether AI tools can be truly transformative and seeing just how many jobs they’ll reimagine—or disappear altogether.
Despite its sizable moat, Harvey’s success is far from assured. Investors are pouring money into firms working on rival legal AI agents: Canada-based Clio raised $900 million last summer; Sweden’s Legora raised an $80 million Series B led by ICONIQ Venture & Growth and General Catalyst in September; London-based Luminance took in $75 million in January.
Meanwhile, a growing chorus of critics—sounding off on Reddit and elsewhere—question how original Harvey’s offerings are. “ChatGPT wrapper” is the most common dig thrown by these disaffected apparent users, who note the similarities between the information retrieval capabilities of Harvey and ChatGPT (made by Harvey’s early investor, OpenAI).
Even Harvey’s cofounders have called OpenAI an indirect competitor. But critics also say the company could be steamrolled by OpenAI, pointing out that the LLM could simply build its own legal-focused model.
“I’m hearing from more and more attorneys that OpenAI’s Deep Research is the single best research product on the market, and it’s what most attorneys use (even when it’s not a firm approved tool),” former lawyer and legaltech investor Zack Abramowitz wrote on his popular substack Legally Disrupted in June.
OpenAI itself has begun testing the waters of legal technology. In September, the company published a blog post about creating an internal database to review its own contracts—the feature is not available to consumers.
One Redditor, who claimed to be a former employee, recently went even further, drawing a direct comparison to Silicon Valley’s most notorious startup: “think of theranos and overinflated claims of what a product can do,” the person posted in a thread that reached more than 300 of comments within a day.
The post generated enough attention to prompt an indirect reply from Weinberg himself: On LinkedIn, he stated that the company’s Gross Revenue Retention (GRR) is at 98% and its Net Dollar Retention (NDR) is at 167—signs that Harvey is both retaining and growing revenue from its customers.
The Redditor has since deleted their post, though comments agreeing with it remain on the page. (When Fast Company reached out to verify the person’s employment at Harvey, a spokesperson said: “There was nothing in the post to suggest that the author was a recent employee at the company.”)
Harvey’s customers, in the meantime, seem satisfied. “(Harvey is) totally embedded in the workday of our lawyers. It’s really become embedded throughout their daily workflows,” says Gina Lynch, chief knowledge and innovation officer at Paul, Weiss, Rifkind, Wharton & Garrison, the white-shoe law firm with more than a thousand lawyers.
From D&D to r/legaladvice
Weinberg’s sliding doors moment from junior law associate to AI entrepreneur came via his roommate after law school: Gabe Pereyra, who was a machine learning engineer at Meta and a research scientist at Google DeepMind before that. (Pereyra is now president of Harvey.)
In the spring of 2022, while Weinberg was working as a lawyer, Pereyra was focused on finding real-world, assistant-like applications for large language models. It didn’t take long before he and Weinberg started testing out Weinberg’s legal workflows on language models, most notably OpenAI’s GPT-3, which was publicly available. (The pair had initially started running GPT-3 to augment their Dungeons and Dragons games, but quickly realized the potential of its chain of thought prompting abilities.)
They began using GPT-3 to solve problems on the r/legaladvice subreddit. “We found a hundred landlord-tenant questions and we were able to answer (them),” Weinberg says.
To further test the model’s accuracy, they fed it legal materials about California regulations and local statutes, then got it to answer questions. “We showed (the answers) to three California-based lawyers working on landlord-tenant issues. We just said, ‘Would you send this to a client?’ For 86 out of 200 questions, at least two attorneys answered thumbs up,” Weinberg says.
Their timing was auspicious. It was still months before the launch of ChatGPT would make GPT-3’s capabilities clear to everyone. In July 2022 Weinberg and Pereyra cold-emailed OpenAI’s general counsel at the time, Jason Kwon, and shared their ideas for how AI could change legal work (Kwon is now OpenAI’s chief strategy officer).
In November of that year, the company raised $5 million from the LLM’s startup fund, along with venture capitalists Elad Gil and Sarah Guo (all three also participated in the company’s most recent round). Within five months, blue chip venture capital firms, including Sequoia, also invested.
Signing on Big Law
Almost as soon as it launched, Harvey aggressively began pursuing enterprise contracts with big law firms. In December 2022, A&O Shearman, which has nearly 4,000 lawyers across 48 offices, started testing Harvey’s technology in its Markets Innovation Group.
Paul Weiss followed in January 2023 and began testing the technology throughout its practice. Both firms have since signed longer contracts. Using those clients’ reputations, Harvey has signed big contracts with other prominent U.S. firms, including Vinson & Elkins and Macfarlanes. The company now has 700 customers in 58 countries.
Other clients include general counsel offices at private equity firms and hedge funds like KKR and Bridgewater and accounting giant PwC. For bigger clients, Harvey embeds staff members within the company to personalize its services and features for their workflows. It also offers an off-the-shelf general application product for smaller companies.
Harvey’s close association with OpenAI has, in some ways, been a blessing and a curse. The AI giant gave Harvey a first-mover advantage, but has heightened the comparisons between ChatGPT and Harvey. After all, according to a March survey from Law360, ChatGPT is the tool most lawyers use for work—even if it’s not approved by their firm.
But although Harvey could still introduce hallucinations into its work (an issue that has bedeviled lawyers who rely too heavily on ChatGPT), it’s less likely to do so, says Weinberg, because it’s trained on legal data and purpose-built for corporate law firms. The company says that its 2024 version of Assistant, Harvey’s most popular product, reduces hallucinations by 60% and improves the accuracy of cited sources by 23% compared to other chatbots.
Harvey has also deepened its product by incorporating models from Anthropic and Google alongside OpenAI’s GPT. It also recently signed a key deal with LexisNexis that enables users to ask complex legal questions and get citation-backed answers.
Harvey, however, could find itself in a similar situation to Bloomberg’s ill-fated BloombergGPT as technology evolves. In 2023, the financial information giant Bloomberg spent more than $10 million training an LLM on its own financial data before finding out that an off-the-shelf GPT-4 provided more accurate answers to users.
As Ethan Mollick, a professor and codirector of the Generative AI Lab at Wharton, wrote on Linkedin, “There was a moment that we thought proprietary data would let organizations train specialized AIs that could compete with frontier models. It turns out that probably isn’t going to happen. The largest frontier models are just much better at most complex tasks than smaller models.”
Another concern is how long Harvey can maintain its lead, given the cost of its product. Harvey’s bespoke services cost $1,200 per seat, per month, with contracts stipulating that large companies need to purchase the service for at least 100 employees and for at least a year. The company justifies its prices by touting the productivity gains it passes onto employees.
“You can arm lawyers with tools that make them enormously productive,” says Harvey’s chief business officer John Haddock, who has a law degree from Stanford. And $100,000 a month is still less expensive than the salaries of an army of paralegals and junior associates.
Even so, Harvey will have to convince its customers to sign back up, even as more affordable products—aimed especially at smaller or midsized firms—enter the market. And it has to keep delivering for them amid an AI hype cycle where disappointment in enterprise AI products is growing.
After the recent flare-up on Reddit involving the apparent former employee, Maarten Truyens, founder and CEO of ClauseBase, a Belgium startup also working on an AI platform for legal drafting, took to LinkedIn to weigh in. He said the main problem is the hype and expectations around these AI platforms.
“GenAI is too good to ignore, yet simply not good enough for many legal use cases,” he wrote. “What’s really needed is vendors to be transparent about the limitations, and the legal community to learn what’s possible with GenAI, and where other technologies are a better fit. Both sides need to become much more realistic.”
AI associates
Though Harvey’s work needs to be checked—which can be a time-consuming process for paralegals and junior lawyers—the company aims to cut down their workload. A widely cited 2023 Thomson Reuters report estimated that AI technology could save lawyers on average 200 hours a year. That number has likely increased as AI becomes more sophisticated. Left unsaid: It could also cut down the number of lawyers and paralegals firms and companies need to hire.
Investors and Harvey employees argue that legal tech is unlikely to take jobs away because the demand for legal services is likely to increase. “There’s a huge undelivered need for legal services. The right way to think about it is that if you can arm lawyers with tools that make them enormously productive, that will just expand the access to the types of services they can provide,” Haddock says.
Venture capitalist Sarah Guo, whose firm Conviction invested in Harvey in 2022, makes a similar argument: “People will not want less practice of the law. They will want more if it is more affordable, and the quality will go up.”
Employment for law school graduates hit a record high in 2024, according to the American Bar Association. But the incentive structure for law firms is complicated. Most big firms bill hourly, and when a task that typically took a law associate hours of research can be condensed into three or four minutes, they face the prospect of losing money. One way to save: hiring fewer associates.
Weinberg acknowledges that Harvey could change hiring. “Are there some firms that will change their business model and structure? Yes. I do think there are some firms that might explore (charging) fixed fees (rather than hourly billing) or having a leaner team,” he says.
Corporate legal teams could see the most impact from Harvey. Allison Zoellner, general counsel at advertising giant Dentsu, which started working with Harvey last year, says that while the company’s AI tools haven’t led to a workforce reduction, she hasn’t had to hire as much. “We’re being asked to do more with fewer resources. What Harvey does is allow us to keep our heads above water while not adding people.”
Weinberg, Haddock, and Guo all say that by eliminating rote tasks, tools like Harvey will free up time for junior lawyers to attend meetings, shadow senior leaders, and start doing more strategic work.
“So much of what an associate does today is the types of things that (are) not what they went to law school for. If we are able to give associates faster, thicker, deeper, higher-order thinking problems, that is great for everybody. It’s great for partners who can get more from their teams. It’s great for junior lawyers because it makes practice of law more intellectually satisfying sooner,” Haddock says. While that may be true, without billable hours to subsidize training opportunities like shadowing, firms may have trouble justifying the cost of so many associates.
The legal industry has weathered technological shifts before. “When I started as a lawyer in the ’90s, it was the time of transition from manual review of documents in litigation to computer-assisted review of documents. (That created) a bump in productivity,” NYU Law School professor Christopher Sprigman says. “How many more people would’ve been hired at those firms absent the introduction of that technology? There’s always an unknowable counterfactual, but I get the strong idea that it’s fewer than (if the technology hadn’t been adopted).”
These days, Sprigman says lawyers are having similar conversations. “A friend of mine who runs a law firm said to me that at this point, maybe 10 to 20% of what junior associates do can be automated through AI. But in 18 months it could be 30%,” he says. “The winners will be people who know how to use AI in their practice and also people who have deep expertise that allows them to exercise judgment.” It is too early to say how the technology will impact roles at the Paul, Weiss. Lynch expects that while it will not reduce positions at the company, it will bring change to certain roles. She has already seen wider acceptance by senior leadership than anticipated. “Partners very much gravitated towards AI because they were comfortable that they knew whether the output was good or bad.” More junior employees with less experience may not be able to make those distinctions early on in their careers.
Already, Paul Weiss is using its AI savvy as a sales pitch to prospective clients. “We want (clients) to know we’re using it and we are really earnest about the efficiency gains,” Lynch says.
Lynch and Sprigman see a new role emerging within law schools and firms that specializes in AI adoption. “You’re going to see more of a legal technologist role,” Lynch says, adding that, like many firms, Paul Weiss has invested heavily in training to get lawyers to use AI tools, and in building their own closed LLM to work alongside Harvey.
Sprigman says that senior lawyers may start to look for AI savvy in their juniors, and that means law schools may also have to change. “Law schools obviously want their students to be prepared. (That could be) teaching people how to prompt engineer. Maybe that’s something you hire an adjunct for. I’m sure school schools will be looking for expertise from the outside,” he says.
To gain an edge and get students used to its platform, Harvey has started partnering with law schools including the University of Chicago and the University of Pennsylvania to offer students, professors, and administrators access to its tools.
A sizable moat
The moat of capital Harvey has raised, its aggressive and successful pursuit of leading law firms, and its relationship with OpenAI have made it a dominant player in the legal AI race. But the company’s valuation may limit its exit possibilities.
“We’re voting that the company has the opportunity to be a tens of billions of dollars public company,” Guo says. The company’s success may ultimately hinge on how many big law firms decide to renew their contracts with Harvey once the mandatory year or two minimum expires, and whether the company can stay ahead of competitors from rivals like Legora to off-the-shelf LLMs.
Weinberg and Pereyra settled on the name Harvey because it sounds a little bit like Harvard and because of its associations with Suits’ superlawyer Harvey Specter. Ultimately though, Harvey acts more like the show’s other main character, Mike Ross, a college dropout with a photographic memory. Though he is unlicensed, Ross proves to be more useful to Specter than any other paralegal or associate at the fictional big law firm.
已发布: 2025-10-29 11:00:00
