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

[ad_1] 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 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 value tags awarded to AI firms, and Harvey’s potential to win over main regulation companies and company authorized departments.

Actually, the startup now claims 235 shoppers throughout 63 nations, together with a majority of the highest 10 U.S. regulation companies. 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 in regards to the wild experience that he and co-founder Gabe Pereyra have been on to date. Throughout that chat, he shared how a chilly electronic mail despatched a number of summers in the past to Sam Altman modified every part; why he believes legal professionals will profit relatively 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 frivolously for size. For the total 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 at first, I swear to God, the primary use case I had for it was working a Dungeons and Dragons sport with associates 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.

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My co-founder Gabe and I found out 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 3 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'd ship it with zero edits. That was the second after we had been like, wow, this whole business may be remodeled by this expertise.

TC: What occurred subsequent?

Weinberg: We cold-emailed Sam Altman and Jason Kwon, who was the final counsel at OpenAI. We figured we needed to electronic mail a lawyer as a result of in any other case the individual wouldn’t know if the outputs had 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 type of the remainder of the C-suite at OpenAI, and we made our pitch.

TC: Did they write a test straight away?

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 every part else from there we had been doing ourselves. I really didn’t have any associates that labored in tech. I didn’t develop up in San Francisco. I didn’t know who the highest VCs had been. I didn’t perceive the way you’re presupposed 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 lift a lot?

Weinberg: I'd say one thing the VC neighborhood won't love, however I strongly imagine that the easiest way to lift cash is to simply be sure that your organization is doing tremendous properly. I believe there’s a variety of recommendation on the market about networking, however to me, an important factor is to spend virtually your entire time on your enterprise, after which discover VCs who need to do this with you.

It is advisable discover a number of companions who you assume are going to go the gap with you. So, 99% of your time, deal with the enterprise going properly, after which spend time looking for a number of people who you actually assume you'll be able to accomplice with and who can 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 a variety of different issues. We’re working in additional than 60 nations with information residency legal guidelines in all of them. For a very long time, should 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 nations, even should 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 outdoors of these nations. We’d arrange Azure or AWS situations in each single a type of nations, however we’d solely use them to shut three or 4 giant shoppers. Our margins look superb on a token foundation, however they’re worse as a result of we have now to spend a lot on upfront compute throughout so many jurisdictions. That may get solved over time.

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

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

However what was fascinating is as soon as we obtained adoption at regulation companies, the regulation companies 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 companies would really assist us promote to corporates as a result of they need to collaborate within the system.

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

Weinberg: It is a big drawback. You’ve seen bulletins from OpenAI and Microsoft about shared threads and firm reminiscence. That’s exhausting — you need to get the permissioning proper so brokers can entry the suitable programs. However you’re solely fixing it for one entity at a time.

The secondary drawback we have now is: How do you remedy that for an organization plus all its regulation companies? It is advisable get the permissioning proper internally and externally. There’s an idea in regulation referred to as moral partitions. Take into consideration a regulation agency within the valley that works with 20 VCs. For those who’re engaged on a deal for Sequoia, but additionally engaged on one other deal for Kleiner Perkins, what occurs should you by accident give all the information on the Sequoia deal to Kleiner Perkins? Big, astronomical drawback. We now have to unravel inside permissioning and exterior permissioning so brokers can work accurately, and should you get it incorrect, you’re going to have disastrous impacts on the business.

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 completed 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 drawback is far simpler as a result of they’ve already gone by way of safety evaluate.

TC: How are legal professionals primarily utilizing Harvey immediately?

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 rather more transactional use instances — M&A and fund formation. These are nonetheless extremely popular, and we’re constructing modules particularly for these issues. The realm that’s rising sooner is litigation, and a variety of that's since you wanted the information earlier than you may do it.

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

Weinberg: The most important benefit we have now over time is 2 issues. One, we’re accumulating an incredible quantity of workflow information — what are the primary use instances these fashions can really do? Analysis turns into a reasonably robust moat, as a result of how do you consider the standard of a merger settlement? That turns into actually exhausting. You need to arrange analysis frameworks and agentic programs that may self-eval all of the totally different steps.

The second strongest moat is our product is changing into very strongly multiplayer. This business has two sides — suppliers of authorized providers and customers. It is advisable construct a platform that’s in between each. To this point, I haven’t seen a competitor doing that. We now have rivals doing what we do for regulation companies, 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, a variety of the ability behind the product is actually the mannequin, plus front-end work that makes the UI and UX simpler. However should you’re making an attempt 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 largely seats, however we’re shifting 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 which you could guarantee have the very same degree of accuracy as a human, or higher, with very excessive velocity. However the actuality is, you’re going to desire a lawyer within the loop for a lot of labor.

For at the very least the following yr or two, it’s a productiveness suite offered seat-based and multiplayer between regulation companies and their in-house groups. Slowly over time, we’ll construct extra consumption-based workflows because the programs get higher and extra correct than people in some areas. However it’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 cross, then have legal professionals bounce 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 an excellent low share. There are 8 or 9 million legal professionals on Earth. However the extra fascinating level is we're within the unbelievably early innings on how complicated work these programs can do. They’re very useful and individuals are getting unbelievable ROI, but when you consider what share of authorized work these programs can do immediately versus what I believe 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 hundreds of thousands of {dollars}. The artifact you could have 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 sorts 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 having the ability to do this precisely is extremely excessive.

TC: What occurs to junior legal professionals who're now not getting the apprenticeship they could have had up to now?

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

I believe proper now, that’s partially the purpose, however partially the purpose is we rent armies of associates and invoice them out so much. 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 programs can’t do what they do, an important factor financially for a regulation agency is to be sure to’re hiring, coaching and growing legal professionals that get to being a accomplice as quick as humanly doable.

For those who 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 a variety of regulation faculties. You may think about in some unspecified time in the future you could have an AI merger that you just do in Harvey — the system’s instructing you, providing you with real-time suggestions. That’s an unbelievable coaching system. For those who can construct programs that may really do a variety of the duties, there’s no purpose you couldn’t flip that into the most effective schooling platforms doable.

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 is just not one thing we have now deliberate anytime quickly. We don’t want that a lot cash, and we aren’t burning a loopy quantity. The explanation I did a variety of fundraising this yr is there are analysis instructions which might be going to require a variety of compute, and we needed to organize ourselves for that. By way of public markets, that’s positively what we’re desirous about long run. I can’t offer you something near a timeline, however we’re .