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

Authorized AI may not 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 corporations, and Harvey’s means to win over main legislation companies and company authorized departments.

In reality, the startup now claims 700 shoppers throughout 63 nations, together with a majority of the highest 10 U.S. legislation companies. It additionally says it surpassed $100 million in annual recurring income as of August.

Trendster spoke with Weinberg for this week’s StrictlyVC Obtain podcast to ask in regards to the wild trip that he and co-founder Gabe Pereyra have been on up to now. Throughout that chat, he shared how a chilly electronic mail despatched just a few summers in the past to Sam Altman modified all the things; why he believes legal professionals will profit reasonably than endure from AI; and the way Harvey is tackling the technically advanced problem of constructing a really multiplayer platform that navigates moral partitions and information permissioning throughout dozens of nations.

This interview has been edited calmly for size. For the total monty, try the podcast.

Trendster: 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 to start with, I swear to God, the primary use case I had for it was operating 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 legislation. I began utilizing GPT-3 to work on it.

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 complete trade might be reworked 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 particular person 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 sort of the remainder of the C-suite at OpenAI, and we made our pitch.

TC: Did they write a examine immediately?

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 had been doing ourselves. I truly 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 alleged to fundraise. This was all simply web new to me.

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

Weinberg: I’d say one thing the VC group may not love, however I strongly consider that the easiest way to lift cash is to simply make certain your organization is doing tremendous effectively. I believe there’s plenty of recommendation on the market about networking, however to me, a very powerful factor is to spend virtually the complete time on your enterprise, after which discover VCs who need to try this with you.

You have to discover just a few companions who you assume are going to go the gap with you. So, 99% of your time, give attention to the enterprise going effectively, after which spend time looking for just a few people who you actually assume you possibly can associate 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 staff, how shut are you to break-even?

Weinberg: Compute prices are costlier for us than plenty 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, 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 nations, even in case you didn’t have sufficient shoppers but to assist that price.

Germany and Australia have extremely strict information processing legal guidelines. You can’t ship monetary information exterior of these nations. We’d arrange Azure or AWS cases in each single a type of nations, however we’d solely use them to shut three or 4 massive shoppers. Our margins look excellent on a token foundation, however they’re worse as a result of we’ve got 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 the start of this 12 months, about 4% of our income was from corporates and 96% from legislation companies. 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%.
At first, we’d take public litigation briefs from Pacer, discover the associate who wrote it, put them into Harvey, and present them how they may argue towards their very own temporary. That obtained large consideration as a result of it was related to what they only did.

However what was attention-grabbing is as soon as we obtained adoption at legislation companies, the legislation 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 are able to use AI to do XYZ?” So what began occurring was legislation companies would truly assist us promote to corporates as a result of they need to collaborate within the system.

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

Weinberg: This can be a large drawback. 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 appropriate programs. However you’re solely fixing it for one entity at a time.

The secondary drawback we’ve got is: How do you resolve that for an organization plus all its legislation companies? You have 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. In the event 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 accident give all the information on the Sequoia deal to Kleiner Perkins? Large, astronomical drawback. Now we have to unravel inner permissioning and exterior permissioning so brokers can work accurately, and in case you get it incorrect, you’re going to have disastrous impacts on the trade.

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 most likely be achieved in December. The good factor is as a result of such a excessive proportion of our buyer base are already corporates utilizing Harvey, the safety drawback is far simpler as a result of they’ve already gone by means of safety assessment.

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

At first, we had way more transactional use instances — M&A and fund formation. These are nonetheless extremely popular, and we’re constructing modules particularly for these issues. The world that’s rising sooner is litigation, and plenty 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’ve got 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 sturdy 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 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 trade has two sides — suppliers of authorized companies and customers. You have to construct a platform that’s in between each. Thus far, I haven’t seen a competitor doing that. Now we have rivals doing what we do for legislation companies, and rivals doing what we do for in-house, however I haven’t seen somebody construct a really multiplayer platform.

When it comes to the “ChatGPT wrapper” criticism, for 2023 and 2024, plenty 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 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 need 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 advanced. You need to do each. You need outcome-based pricing for very small issues which you could guarantee have the very same stage of accuracy as a human, or higher, with very excessive velocity. However the actuality is, you’re going to need a lawyer within the loop for a lot of labor.

For at the least the following 12 months or two, it’s a productiveness suite bought seat-based and multiplayer between legislation 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. Nevertheless it’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 possibly can 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 proportion of the legal professionals on Earth are utilizing Harvey proper now? It’s a brilliant low proportion. 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 advanced work these programs can do. They’re very useful and persons are getting unimaginable ROI, but when you concentrate on what proportion of authorized work these programs can do immediately versus what I believe it will 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 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 kinds of use instances the place, once 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 try this precisely is extremely excessive.

TC: What occurs to junior legal professionals who’re not getting the apprenticeship they may have had prior 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 lately. The purpose of legislation companies within the subsequent 5 to 10 years is: how briskly are you able to practice the very best 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 change into outcome-based pricing or as a result of companions can cost extra if AI programs can’t do what they do, a very powerful factor financially for a legislation agency is to be sure you’re hiring, coaching and growing legal professionals that get to being a associate as quick as humanly doable.

In the event you can construct instruments that may do the primary cross of an M&A, that could be a one-on-one tutor for a junior affiliate. We work with plenty of legislation colleges. You may think about in some unspecified time in the future you could have 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. In the event you can construct programs that may truly do plenty of the duties, there’s no purpose you couldn’t flip that into probably the greatest schooling platforms doable.

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 massive rounds shouldn’t be one thing we’ve got deliberate anytime quickly. We don’t want that a lot cash, and we aren’t burning a loopy quantity. The rationale I did plenty of fundraising this 12 months is there are analysis instructions which can be going to require plenty of compute, and we needed to organize ourselves for that. When it comes to public markets, that’s undoubtedly what we’re fascinated about long run. I can’t offer you something near a timeline, however we’re .