While legal AI may not be the most glamorous sector in Silicon Valley, Harvey’s CEO Winston Weinberg has managed to attract the interest of nearly every major investor in the region. The company’s list of backers reads like a roll call of top venture capitalists: the OpenAI Startup Fund (their first institutional backer), Sequoia Capital, Kleiner Perkins, Elad Gil, Google Ventures, Coatue, and most recently, Andreessen Horowitz.
Based in San Francisco, Harvey’s valuation soared from $3 billion in February 2025 to $5 billion by June, and then to $8 billion by late October. This dramatic increase highlights both the extraordinary valuations currently seen in AI and Harvey’s success in securing business from leading law firms and corporate legal teams.
Currently, the startup reports having 700 clients in 63 countries, including most of the top 10 U.S. law firms. As of August, Harvey also claims to have exceeded $100 million in annual recurring revenue.
TechCrunch interviewed Weinberg for this week’s StrictlyVC Download podcast to discuss the remarkable journey he and co-founder Gabe Pereyra have experienced so far. During the conversation, Weinberg recounted how a cold email to Sam Altman a few summers ago proved pivotal; why he’s convinced AI will help, not harm, lawyers; and how Harvey is addressing the technical challenges of building a collaborative platform that manages ethical barriers and data permissions across many countries.
This interview has been condensed for clarity and length. For the full conversation, listen to the podcast.
TechCrunch: You began your career as a first-year associate at O’Melveny & Myers. When did you first see the potential for AI to change legal work?
Winston Weinberg: At that time, my co-founder was working at Meta and was also my roommate. He introduced me to GPT-3, and honestly, my initial use for it was running a Dungeons and Dragons campaign with friends in LA. Later, I was assigned a landlord-tenant case at O’Melveny, an area I knew little about, so I started leveraging GPT-3 to help with the work.
Gabe and I realized we could use chain-of-thought prompting before it was widely known. We crafted a lengthy prompt based on California landlord-tenant laws, pulled 100 questions from r/legaladvice on Reddit, and ran our prompt on them. We then gave the resulting Q&A pairs to three landlord-tenant attorneys, without mentioning AI.
We simply told them, “A potential client asked this, here’s the answer—would you change anything or send it as is?” For 86 out of 100 samples, at least two of the three attorneys said they’d send the answer without any edits. That’s when we realized this technology could fundamentally reshape the legal industry.
TC: What did you do after that?
Weinberg: We sent cold emails to Sam Altman and Jason Kwon, who was OpenAI’s general counsel. We thought it was important to reach out to a lawyer, since only they could properly assess the outputs. On July 4th at 10 a.m.—I remember because it was Independence Day—we got on a call with them and the rest of OpenAI’s leadership, and presented our idea.
TC: Did they invest immediately?
Weinberg: Yes. The OpenAI Startup Fund became our second-largest investor. OpenAI also introduced us to our early angel investors, Sarah Guo and Elad Gil. Beyond that, we handled everything ourselves. I didn’t have friends in tech, didn’t grow up in San Francisco, and had no idea who the top VCs were or how fundraising worked. It was all completely new to me.
TC: For someone new to the VC world, you’ve raised a significant amount. What made that possible?
Weinberg: This might not be popular with VCs, but I truly believe the best way to raise capital is to ensure your company is thriving. There’s a lot of advice about networking, but I think the most important thing is to focus almost all your energy on building a great business, and then find investors who want to join you on that journey.
You need to identify a few partners who are committed for the long haul. So, spend 99% of your time making the business successful, and then look for a handful of people you genuinely want to work with and who will support you over time.
TC: You reached $100 million in ARR in August. With about 400 staff, how close are you to profitability?
Weinberg: Our compute expenses are higher than most other costs. We operate in over 60 countries, each with its own data residency requirements. For a while, if your product used multiple models, you had to purchase a minimum amount of compute in every country, even if you didn’t have enough customers there to justify it.
Countries like Germany and Australia have extremely strict regulations around data processing—you can’t transfer financial data out of those countries. We set up Azure or AWS in each country, but sometimes only needed them for a few major clients. While our margins are strong on a per-token basis, they’re reduced by the high upfront compute costs across so many regions. This should improve over time.
TC: Can you describe your sales strategy and how you’re growing internationally?
Weinberg: At the start of this year, about 4% of our revenue came from corporations and 96% from law firms. Now, corporates make up 33%, and I expect that to reach around 40% by year’s end.
Initially, we would take public litigation briefs from Pacer, find the authoring partner, run their brief through Harvey, and show them how they could argue against their own work. This approach got a lot of attention because it was directly relevant to their recent cases.
Interestingly, once law firms started using Harvey, they began introducing it to their corporate clients. For example, a firm like Latham would present Harvey to clients and explain how AI could help with specific tasks. So, law firms actually started helping us sell to corporates because they wanted to collaborate within the platform.
TC: You call this “multiplayer.” Can you elaborate on this focus?
Weinberg: This is a major challenge. OpenAI and Microsoft have talked about shared threads and company memory, but those solutions only address single organizations. The real challenge is enabling collaboration between a company and all its law firms, with the right permissions both internally and externally. In law, there’s the concept of ethical walls. For example, a law firm in Silicon Valley might work with 20 VCs. If you’re handling a deal for Sequoia and another for Kleiner Perkins, what if you accidentally share Sequoia’s data with Kleiner Perkins? That’s a huge issue. We need to get both internal and external permissions right so agents can work properly—otherwise, the consequences could be severe for the industry.
TC: Have you solved this problem?
Weinberg: We’re actively working on it. Our priority is getting security and permissions right. The first large-scale version should be ready by December. The advantage is that a large portion of our customers are already corporates using Harvey, so the security aspect is easier since they’ve already completed security reviews.
TC: How are lawyers mainly using Harvey at the moment?
Weinberg: The primary use is drafting. The second is research, which is growing thanks to our partnership with LexisNexis. The third is analysis—by that, I mean running multiple questions over huge document sets, like in diligence or discovery.
Initially, our main use cases were transactional—M&A and fund formation. Those remain popular, and we’re developing modules for them. However, litigation is now growing faster, largely because we now have the necessary data to support it.
TC: Some critics say Harvey is just a ChatGPT wrapper. How do you respond?
Weinberg: Our biggest long-term advantages are twofold. First, we’re gathering a vast amount of workflow data—understanding which tasks these models can actually perform. Evaluation becomes a significant moat, since assessing the quality of something like a merger agreement is very complex. You need to build evaluation frameworks and agentic systems that can self-assess each step.
The second major advantage is that our platform is becoming highly collaborative. The legal sector has both service providers and clients, and you need a platform that connects both. So far, I haven’t seen competitors building a truly collaborative platform—some focus on law firms, others on in-house teams, but not both together.
Regarding the “ChatGPT wrapper” critique, in 2023 and 2024, much of the product’s strength does come from the underlying model, plus front-end work to improve the user experience. But if you want a system that can handle 100,000 documents, thousands of M&A emails, and various statutes and codes, and answer questions across all of them with high accuracy—that’s the ultimate goal. We’ve built the components, and our recent work has been about integrating them into a unified system.
TC: What is your revenue model?
Weinberg: Currently, we mainly charge per seat, but as workflows become more advanced, we’re moving toward more outcome-based pricing. Both models are important. Outcome-based pricing works well for smaller, well-defined tasks where you can guarantee accuracy and speed equal to or better than a human. But for much of legal work, you’ll still want a lawyer involved.
For at least the next year or two, we’ll continue offering a productivity suite sold per seat, enabling collaboration between law firms and their in-house teams. Over time, as our systems surpass human accuracy in certain areas, we’ll introduce more usage-based workflows. But you won’t see full automation of something like an M&A—rather, specific diligence tasks will be automated first, with lawyers handling the rest.
TC: You mentioned earlier that legal tech adoption is still very low. How low is it?
Weinberg: The percentage of lawyers worldwide using Harvey is extremely small. There are about 8 or 9 million lawyers globally. But what’s more interesting is that we’re still at the very beginning of what these systems can handle. They’re already delivering impressive ROI, but if you compare the share of legal work they can do today to what I expect in five years, it’s much less.
Consider the value per token. Legal fees for a merger can reach tens of millions of dollars, resulting in a merger agreement and SPA totaling maybe 200 pages. What’s the value per token in a document that cost $20–30 million in legal fees to produce? That’s why I say adoption is still incredibly low—we’re not yet at the point where such work can be automated, but the potential value is enormous.
TC: What about junior lawyers who might miss out on traditional training?
Weinberg: This is something I care deeply about, having recently been a junior lawyer myself. The main objective for law firms over the next five to ten years should be: how quickly can you develop top partners?
Currently, part of the goal is to train partners, but another part is to hire large numbers of associates and bill their time. Whether pricing shifts to outcome-based models or partners can charge more for work AI can’t do, the key financial priority for firms will be to recruit, train, and advance lawyers to partner as quickly as possible.
If you can create tools that handle the first pass of an M&A, it’s like giving junior associates a personal tutor. We collaborate with many law schools, and you could imagine a future where Harvey guides you through an AI-driven merger, offering real-time feedback. That would be an outstanding training platform. If these systems can handle many tasks, there’s no reason they couldn’t become one of the best educational tools available.
TC: With your valuation jumping from $3 billion to $8 billion in under a year, what are your future fundraising plans?
Weinberg: We’re not planning any major fundraising rounds soon. We don’t need a huge amount of capital and aren’t burning through cash rapidly. The main reason for our recent fundraising was to secure resources for research that will require significant compute. Looking ahead, we’re definitely interested in public markets, though I can’t provide a timeline yet.
