Inside Harvey: How a first-year legal associate built one of Silicon Valley’s hottest startups

Authorized AI won’t sound just like the sexiest class in Silicon Valley, however Harvey‘s CEO Winston Weinberg has captured the eye of nearly 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 recently, 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 corporations, and Harvey’s potential to win over main regulation 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. regulation 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 trip that he and co-founder Gabe Pereyra have been on up to now. Throughout that chat, he shared how a chilly e-mail despatched a number of summers in the past to Sam Altman modified all the things; why he believes legal professionals will profit slightly 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 information permissioning throughout dozens of nations.

This interview has been edited calmly for size. For the complete monty, check out the podcast.

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

Winston Weinberg: So my co-founder was working at Meta on the time; he was additionally my roommate. He was exhibiting me GPT-3, and at first, I swear to God, the primary use case I had for it was operating a Dungeons and Dragons recreation with buddies 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 regulation. 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 mentioned, “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 mentioned they might ship it with zero edits. That was the second once we have been like, wow, this whole trade will be remodeled by this expertise.

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 individual 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 obtained on a name with them and sort of the remainder of the C-suite at OpenAI, and we made our pitch.

TC: Did they write a examine instantly?

Weinberg: Yeah. It’s the OpenAI Startup Fund [they are the second-largest investor in Harvey]. OpenAI launched us to our angel buyers on the time, Sarah Guo and Elad Gil, after which all the things else from there we have been doing ourselves. I truly didn’t have any buddies 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 imagined to fundraise. This was all simply web new to me.

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

Weinberg: I would say one thing the VC neighborhood won’t love, however I strongly consider that one of the simplest ways to lift cash is to only ensure your organization is doing tremendous properly. I feel there’s numerous recommendation on the market about networking, however to me, an important factor is to spend nearly your entire time on your enterprise, after which discover VCs who wish to do this with you.

It’s essential to discover a number of 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 a number of of us who you actually suppose you may companion with and who will probably be there for you for the long term.

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

Weinberg: Compute prices are dearer for us than numerous different issues. We’re working in additional than 60 international locations with information residency legal guidelines in all of them. For a very long time, when 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 when you didn’t have sufficient shoppers but to help that value.

Germany and Australia have extremely strict information processing legal guidelines. You can not ship monetary information exterior of these international locations. We’d arrange Azure or AWS cases 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: Firstly of this 12 months, about 4% of our income was from corporates and 96% from regulation corporations. Proper now, 33% of our income is from corporates, and my intestine says, by the tip of the 12 months, 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 might argue towards their very own temporary. That obtained large consideration as a result of it was related to what they simply did.

However what was attention-grabbing is as soon as we obtained adoption at regulation corporations, the regulation 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 taking place was regulation corporations would truly assist us promote to corporates as a result of they wish to collaborate within the system.

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

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

The secondary downside we’ve is: How do you resolve that for an organization plus all its regulation corporations? It’s essential to get the permissioning proper internally and externally. There’s an idea in regulation known as moral partitions. Take into consideration a regulation agency within the valley that works with 20 VCs. In the event you’re engaged on a deal for Sequoia, but additionally engaged on one other deal for Kleiner Perkins, what occurs when you by accident give all the info on the Sequoia deal to Kleiner Perkins? Big, astronomical downside. We now have to unravel inside permissioning and exterior permissioning so brokers can work appropriately, and when you get it fallacious, you’re going to have disastrous impacts on the trade.

TC: Have you ever solved this?

Weinberg: It’s positively in course of. We’re doing the entire safety and the permissioning first. The primary model of this at scale will in all probability be achieved 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 assessment.

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 operating 10 questions over 100,000 paperwork, like what you do in diligence or discovery.

To start with, we had far 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 sooner is litigation, and numerous that’s since you wanted the info earlier than you might do it.

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

Weinberg: The biggest benefit we’ve over time is 2 issues. One, we’re accumulating an amazing quantity of workflow information — what are the primary 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 exhausting. It’s important 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 trade has two sides — suppliers of authorized providers and shoppers. It’s essential to construct a platform that’s in between each. To date, I haven’t seen a competitor doing that. We now have rivals doing what we do for regulation corporations, and rivals doing what we do for in-house, however I haven’t seen somebody construct a very multiplayer platform.

By way of the “ChatGPT wrapper” criticism, for 2023 and 2024, numerous the facility behind the product is actually the mannequin, plus front-end work that makes the UI and UX simpler. However when you’re attempting to construct one thing the place I’ve 100,000 paperwork on this information 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 enterprise mannequin?

Weinberg: Proper now it’s principally seats, however we’re transferring to extra outcome-based pricing because the workflows get extra complicated. You wish to do each. You need outcome-based pricing for very small issues that you may 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 no less than the subsequent 12 months or two, it’s a productiveness suite offered seat-based and multiplayer between regulation 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 whole M&A — it’s going to be particular items of diligence the place you may have disclosure brokers automate the primary cross, then have legal professionals bounce in and do the remainder.

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

Weinberg: What share of the legal professionals on Earth are utilizing Harvey proper now? It’s an excellent 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 unbelievable ROI, but when you consider what share of authorized work these methods can do right now versus what I feel it could 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 thousands and thousands of {dollars}. The artifact you’ve got after that merger is a merger settlement and an SPA — possibly 200 pages whole. 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 up to now?

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

I feel proper now, that’s partially the purpose, however partially the purpose is we rent armies of associates and invoice them out rather a lot. Whether or not it’s as a result of issues turn 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 regulation agency is to be sure you’re hiring, coaching and growing legal professionals that get to being a companion as quick as humanly potential.

In the event you can construct instruments that may do the primary cross of an M&A, that may be a one-on-one tutor for a junior affiliate. We work with numerous regulation colleges. You may think about sooner or later you’ve got an AI merger that you just do in Harvey — the system’s educating you, providing you with real-time suggestions. That’s an unbelievable coaching system. In the event you can construct methods that may truly do numerous the duties, there’s no purpose you couldn’t flip that into probably the greatest schooling platforms potential.

TC: Together with your valuation leaping from $3 billion to $8 billion in lower than a 12 months, 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 explanation I did numerous fundraising this 12 months is there are analysis instructions which can be going to require numerous compute, and we wished to organize ourselves for that. By way of public markets, that’s positively what we’re all in favour of long run. I can’t provide you with something near a timeline, however we’re .