Authorized AI won’t sound just like the sexiest class in Silicon Valley, however Harvey‘s CEO Winston Weinberg has captured the eye of just about each top-tier investor within the Valley. The corporate’s cap desk reads like a who’s who of enterprise capital: the OpenAI Startup Fund (its first institutional investor), Sequoia Capital, Kleiner Perkins, Elad Gil, Google Ventures, Coatue, and most lately, Andreessen Horowitz.

The San Francisco-based firm’s valuation skyrocketed from $3 billion in February 2025 to $5 billion in June to $8 billion in late October — an increase that displays each the bonkers worth tags awarded to AI firms, and Harvey’s skill to win over main legislation corporations and company authorized departments.

In reality, the startup now claims 235 shoppers throughout 63 international locations, together with a majority of the highest 10 U.S. legislation corporations. It additionally says it surpassed $100 million in annual recurring income as of August.

TechCrunch spoke with Weinberg for this week’s StrictlyVC Obtain podcast to ask concerning the wild experience that he and co-founder Gabe Pereyra have been on up to now. Throughout that chat, he shared how a chilly e-mail despatched just a few summers in the past to Sam Altman modified every thing; why he believes legal professionals will profit fairly than undergo from AI; and the way Harvey is tackling the technically complicated problem of constructing a very multiplayer platform that navigates moral partitions and knowledge permissioning throughout dozens of nations.

This interview has been edited evenly for size. For the complete monty, take a look at the podcast.

TechCrunch: You began as a first-year affiliate at O’Melveny & Myers. When did you understand AI may remodel authorized work?

Winston Weinberg: So my co-founder was working at Meta on the time; he was additionally my roommate. He was displaying me GPT-3, and to start with, I swear to God, the principle use case I had for it was working a Dungeons and Dragons recreation with pals in LA. Then I used to be assigned to this landlord-tenant case at O’Melveny, and I didn’t know something about landlord-tenant legislation. I began utilizing GPT-3 to work on it.

My co-founder Gabe and I discovered we may do chain-of-thought prompting earlier than that was actually a factor. We created this tremendous lengthy chain-of-thought immediate over California landlord-tenant statutes. We grabbed 100 questions from r/legaladvice [on Reddit] and ran that immediate over them, then gave the question-answer pairs to a few landlord-tenant attorneys with out saying something about AI.

We simply stated, “A possible buyer requested this query, right here’s the reply—would you make any edits or would you ship this as is?” On 86 of the 100 samples, two out of three attorneys or extra stated they’d ship it with zero edits. That was the second once we have been like, wow, this complete business could be reworked by this know-how.

TC: What occurred subsequent?

Weinberg: We cold-emailed Sam Altman and Jason Kwon, who was the overall counsel at OpenAI. We figured we needed to e-mail a lawyer as a result of in any other case the particular person wouldn’t know if the outputs have been proper. On the morning of July 4 at 10 a.m. — I keep in mind this particularly as a result of it was July 4 — we received on a name with them and form of the remainder of the C-suite at OpenAI, and we made our pitch.

TC: Did they write a verify immediately?

Weinberg: Yeah. It’s the OpenAI Startup Fund [they are the second-largest investor in Harvey]. OpenAI launched us to our angel traders on the time, Sarah Guo and Elad Gil, after which every thing else from there we have been doing ourselves. I truly didn’t have any pals that labored in tech. I didn’t develop up in San Francisco. I didn’t know who the highest VCs have been. I didn’t perceive the way you’re speculated to fundraise. This was all simply internet new to me.

TC: For somebody who wasn’t accustomed to the VC scene, you’ve raised some huge cash. What enabled you to boost a lot?

Weinberg: I would say one thing the VC group won’t love, however I strongly consider that one of the best ways to boost cash is to simply be certain that your organization is doing tremendous properly. I feel there’s a whole lot of recommendation on the market about networking, however to me, an important factor is to spend virtually your entire time on your corporation, after which discover VCs who need to do this with you.

You’ll want to discover just a few companions who you suppose are going to go the space with you. So, 99% of your time, give attention to the enterprise going properly, after which spend time looking for just a few of us who you actually suppose you’ll be able to companion with and who will likely be there for you for the long term.

TC: You hit $100 million in ARR in August. With round 400 workers, how shut are you to break-even?

Weinberg: Compute prices are dearer for us than a whole lot of different issues. We’re working in additional than 60 international locations with knowledge residency legal guidelines in all of them. For a very long time, in case you used a number of fashions in your product, you had to purchase a bucket of compute — a minimal threshold — in each single a type of international locations, even in case you didn’t have sufficient shoppers but to assist that value.

Germany and Australia have extremely strict knowledge processing legal guidelines. You can’t ship monetary knowledge exterior of these international locations. We’d arrange Azure or AWS situations in each single a type of international locations, however we’d solely use them to shut three or 4 giant shoppers. Our margins look excellent on a token foundation, however they’re worse as a result of we’ve to spend a lot on upfront compute throughout so many jurisdictions. That can get solved over time.

TC: Inform us about your gross sales course of. How are you increasing globally?

Weinberg: At first of this yr, about 4% of our income was from corporates and 96% from legislation corporations. Proper now, 33% of our income is from corporates, and my intestine says, by the tip of the yr, that appears nearer to 40%.
To start with, we might take public litigation briefs from Pacer, discover the companion who wrote it, put them into Harvey, and present them how they may argue towards their very own transient. That received huge consideration as a result of it was related to what they only did.

However what was attention-grabbing is as soon as we received adoption at legislation corporations, the legislation corporations themselves would assist us pitch to corporates. A agency like Latham will introduce Harvey to shoppers and say, “Hey, do you know that is how we will use AI to do XYZ?” So what began occurring was legislation corporations would truly assist us promote to corporates as a result of they need to collaborate within the system.

TC: You check with this as “multiplayer.” Are you able to expound on this as a rising space of focus?

Weinberg: This can be a large downside. You’ve seen bulletins from OpenAI and Microsoft about shared threads and firm reminiscence. That’s arduous — you must get the permissioning proper so brokers can entry the suitable methods. However you’re solely fixing it for one entity at a time.

The secondary downside we’ve is: How do you clear up that for a corporation plus all its legislation corporations? You’ll want to get the permissioning proper internally and externally. There’s an idea in legislation known as moral partitions. Take into consideration a legislation agency within the valley that works with 20 VCs. When you’re engaged on a deal for Sequoia, but in addition engaged on one other deal for Kleiner Perkins, what occurs in case you by chance give all the info on the Sequoia deal to Kleiner Perkins? Enormous, astronomical downside. We’ve got to unravel inner permissioning and exterior permissioning so brokers can work appropriately, and in case you get it fallacious, you’re going to have disastrous impacts on the business.

TC: Have you ever solved this?

Weinberg: It’s undoubtedly in course of. We’re doing all the safety and the permissioning first. The primary model of this at scale will in all probability be carried out in December. The good factor is as a result of such a excessive share of our buyer base are already corporates utilizing Harvey, the safety downside is way simpler as a result of they’ve already gone by means of safety evaluation.

TC: How are legal professionals primarily utilizing Harvey right now?

Weinberg: Primary is drafting. Quantity two is analysis — that’s rising as a result of we simply have a partnership with LexisNexis. And the third is analyze. What I imply by analyze is working 10 questions over 100,000 paperwork, like what you do in diligence or discovery.

To start with, we had way more transactional use instances — M&A and fund formation. These are nonetheless very fashionable, and we’re constructing modules particularly for these issues. The world that’s rising quicker is litigation, and a whole lot of that’s since you wanted the info earlier than you can do it.

TC: Some critics have stated Harvey is only a wrapper for ChatGPT. How do you reply?

Weinberg: The most important benefit we’ve over time is 2 issues. One, we’re gathering an incredible quantity of workflow knowledge — what are the principle use instances these fashions can truly do? Analysis turns into a fairly robust moat, as a result of how do you consider the standard of a merger settlement? That turns into actually arduous. It’s a must to arrange analysis frameworks and agentic methods that may self-eval all of the totally different steps.

The second strongest moat is our product is turning into very strongly multiplayer. This business has two sides — suppliers of authorized companies and customers. You’ll want to construct a platform that’s in between each. To date, I haven’t seen a competitor doing that. We’ve got rivals doing what we do for legislation corporations, and rivals doing what we do for in-house, however I haven’t seen somebody construct a very multiplayer platform.

When it comes to the “ChatGPT wrapper” criticism, for 2023 and 2024, a whole lot of the ability behind the product is actually the mannequin, plus front-end work that makes the UI and UX simpler. However in case you’re making an attempt to construct one thing the place I’ve 100,000 paperwork on this knowledge room, 5,000 emails about this M&A, all these totally different statutes and codes, and I desire a system the place I can ask questions over all of these items mixed with excessive accuracy — that’s the holy grail. We’ve created all of the items, and what we’ve been constructing for the previous couple months is pulling that collectively.

TC: What’s your corporation mannequin?

Weinberg: Proper now it’s principally seats, however we’re transferring to extra outcome-based pricing because the workflows get extra complicated. You need to do each. You need outcome-based pricing for very small issues that you would be able to guarantee have the very same degree of accuracy as a human, or higher, with very excessive pace. However the actuality is, you’re going to desire a lawyer within the loop for a lot of labor.

For a minimum of the following yr or two, it’s a productiveness suite bought seat-based and multiplayer between legislation corporations and their in-house groups. Slowly over time, we’ll construct extra consumption-based workflows because the methods get higher and extra correct than people in some areas. But it surely’s not going to be such as you automate a complete M&A — it’s going to be particular items of diligence the place you’ll be able to have disclosure brokers automate the primary move, then have legal professionals leap in and do the remainder.

TC: You talked about to us earlier that penetration is actually low in authorized. How low?

Weinberg: What share of the legal professionals on Earth are utilizing Harvey proper now? It’s a brilliant low share. There are 8 or 9 million legal professionals on Earth. However the extra attention-grabbing level is we’re within the unbelievably early innings on how complicated work these methods can do. They’re very useful and persons are getting unimaginable ROI, but when you concentrate on what share of authorized work these methods can do right now versus what I feel it may possibly do within the subsequent 5 years, it’s a lot decrease.

Take into consideration the use case as, what’s the worth per token. The authorized charges for a merger may simply be tens of hundreds of thousands of {dollars}. The artifact you have got after that merger is a merger settlement and an SPA — perhaps 200 pages complete. What’s the worth per token on that doc that required $20 million or $30 million of authorized charges to generate? These are the varieties of use instances the place, after I say we’re at extremely low penetration, it’s that we aren’t on the level the place you are able to do one thing like that. And the worth of with the ability to do this precisely is extremely excessive.

TC: What occurs to junior legal professionals who’re not getting the apprenticeship they may have had previously?

Weinberg: I care about this doubtlessly greater than anything on the firm as a result of I used to be a junior lawyer very lately. The purpose of legislation corporations within the subsequent 5 to 10 years is: how briskly are you able to prepare the very best companions?

I feel proper now, that’s partially the purpose, however partially the purpose is we rent armies of associates and invoice them out loads. Whether or not it’s as a result of issues develop into outcome-based pricing or as a result of companions can cost extra if AI methods can’t do what they do, an important factor financially for a legislation agency is to ensure you’re hiring, coaching and growing legal professionals that get to being a companion as quick as humanly potential.

When you can construct instruments that may do the primary move of an M&A, that may be a one-on-one tutor for a junior affiliate. We work with a whole lot of legislation colleges. You possibly can think about sooner or later you have got an AI merger that you simply do in Harvey — the system’s educating you, supplying you with real-time suggestions. That’s an unimaginable coaching system. When you can construct methods that may truly do a whole lot of the duties, there’s no cause you couldn’t flip that into among the finest schooling platforms potential.

TC: Along with your valuation leaping from $3 billion to $8 billion in lower than a yr, what are your plans for future fundraising?

Weinberg: Fundraising giant rounds isn’t one thing we’ve deliberate anytime quickly. We don’t want that a lot cash, and we aren’t burning a loopy quantity. The rationale I did a whole lot of fundraising this yr is there are analysis instructions which are going to require a whole lot of compute, and we wished to arrange ourselves for that. When it comes to public markets, that’s undoubtedly what we’re fascinated with long run. I can’t provide you with something near a timeline, however we’re .