inside harvey: how a first-year legal associate built one of silicon valley’s hottest startups

The Rise of Harvey: Legal AI and Silicon Valley Investment

While perhaps not the most glamorous sector in Silicon Valley, Legal AI has garnered significant attention, particularly surrounding Harvey and its CEO, Winston Weinberg. The company’s investor roster is impressive, featuring prominent venture capital firms such as the OpenAI Startup Fund, Sequoia Capital, Kleiner Perkins, Elad Gil, Google Ventures, Coatue, and Andreessen Horowitz.

Rapid Valuation Growth

Harvey, based in San Francisco, has experienced a remarkable increase in valuation. From $3 billion in February 2025, it surged to $5 billion in June, and ultimately reached $8 billion by late October. This rapid growth reflects both the high valuations currently assigned to AI companies and Harvey’s success in securing contracts with major law firms and corporate legal departments.

Client Base and Revenue

Currently, the startup boasts a client base of 700 across 63 countries. A majority of the top 10 U.S. law firms are among its customers. As of August, Harvey reported exceeding $100 million in annual recurring revenue.

The Genesis of Harvey

TechCrunch’s StrictlyVC Download podcast featured an interview with Weinberg, where he discussed the company’s journey with co-founder Gabe Pereyra. He revealed how a simple cold email to Sam Altman proved pivotal, his belief that AI will empower lawyers rather than displace them, and the technical challenges of creating a secure, multiplayer platform that respects data privacy and ethical considerations across numerous countries.

From Law Associate to AI Innovator

You began your career as an associate at O’Melveny & Myers. When did you foresee AI’s potential to revolutionize legal work?

My co-founder was employed at Meta at the time, and also happened to be my roommate. He demonstrated GPT-3 to me, and initially, I primarily used it for running Dungeons and Dragons games with friends in Los Angeles. However, when assigned a landlord-tenant case at O’Melveny, lacking expertise in that area, I began experimenting with GPT-3.

Early Experimentation with Chain-of-Thought Prompting

Gabe and I pioneered a form of chain-of-thought prompting before it became widely recognized. We developed an extensive prompt based on California landlord-tenant statutes. We then tested this prompt against 100 questions sourced from r/legaladvice on Reddit, presenting the question-answer pairs to three landlord-tenant attorneys without disclosing the use of AI.

We simply asked them if they would edit or approve the answers as is. Remarkably, in 86 out of 100 instances, at least two out of three attorneys indicated they would approve the responses without any modifications. This realization underscored the transformative potential of this technology for the entire industry.

Securing Early Investment and Building Momentum

What steps did you take following this discovery?

We directly contacted Sam Altman and Jason Kwon, OpenAI’s general counsel, recognizing the need for a legal perspective to validate the AI’s outputs. On the morning of July 4th – a date I distinctly remember – we connected with them and the OpenAI leadership team to present our concept.

Immediate Support from OpenAI

Did you receive funding immediately?

Yes, we did. The OpenAI Startup Fund became our second-largest investor. OpenAI also facilitated introductions to our initial angel investors, Sarah Guo and Elad Gil. From there, we navigated the fundraising process independently. I lacked connections within the tech industry, having not grown up in San Francisco and being unfamiliar with prominent VCs or fundraising strategies.

The Importance of Product-Market Fit

Given your unfamiliarity with the VC landscape, how did you manage to secure substantial funding?

I may express a view that some in the VC community might not favor, but I firmly believe that the most effective way to attract investment is to ensure your company is performing exceptionally well. While networking advice abounds, I contend that prioritizing your business is paramount. Then, seek out VCs who share your vision.

Focus on Long-Term Partnerships

Identify a few partners who you believe will support you throughout the journey. Dedicate 99% of your time to ensuring your business thrives, and then invest time in finding those key partners who will be with you for the long haul.

Scaling and Addressing Technical Challenges

Harvey achieved $100 million in ARR in August. With approximately 400 employees, how close are you to profitability?

Compute costs represent a significant expense for us, particularly given our operations in over 60 countries, each with its own data residency regulations. Previously, utilizing multiple models required purchasing a minimum compute capacity in each country, even if client volume didn’t justify the cost.

Navigating Data Residency Laws

Germany and Australia have particularly stringent data processing laws, prohibiting the transfer of financial data outside their borders. We established Azure or AWS instances in each of these countries, but initially, they were only utilized by a handful of large clients. While our margins appear strong on a per-token basis, they are impacted by these substantial upfront compute investments across numerous jurisdictions. This situation will improve over time.

Expanding Sales and Global Reach

Could you describe your sales process and your strategy for global expansion?

At the beginning of the year, 4% of our revenue came from corporate clients, with the remaining 96% from law firms. Currently, 33% of our revenue is generated by corporates, and I anticipate this figure will rise to around 40% by year-end.

Initially, we presented public litigation briefs from Pacer to partners, inputting them into Harvey to demonstrate how they could construct counterarguments. This approach garnered significant attention due to its direct relevance to their recent work.

Law Firms as Sales Partners

Interestingly, once law firms adopted Harvey, they actively assisted us in pitching to corporate clients. Firms like Latham would introduce Harvey to their clients, highlighting its capabilities. This created a collaborative ecosystem where law firms facilitated sales to corporates, recognizing the benefits of joint utilization.

The "Multiplayer" Vision

You often refer to this as “multiplayer.” Could you elaborate on this as a key area of focus?

This presents a significant challenge. You’ve seen announcements from OpenAI and Microsoft regarding shared threads and company memory. Implementing this is complex, requiring accurate permissioning to ensure agents access the appropriate systems. However, this is typically addressed for a single entity.

Extending Multiplayer Capabilities

Our challenge is extending this functionality to encompass a company and all its associated law firms. This necessitates precise permissioning both internally and externally. The legal concept of “ethical walls” is crucial. Consider a law firm representing 20 VCs. If working on deals for Sequoia and Kleiner Perkins simultaneously, accidental data sharing could have catastrophic consequences. We must establish robust internal and external permissioning to ensure agents operate correctly and prevent such breaches.

Progress Towards a Solution

Have you resolved this issue?

We are actively working on it. Our priority is security and permissioning. We anticipate releasing the first large-scale version in December. Fortunately, a substantial portion of our customer base already consists of corporates utilizing Harvey, simplifying the security review process.

Current Use Cases and Future Outlook

How are lawyers currently utilizing Harvey?

The primary application is drafting. Research is the second most common use case, particularly with our recent partnership with LexisNexis. Analysis is also gaining traction – specifically, running multiple queries across large document sets, such as during due diligence or discovery.

Evolving Use Cases

Initially, we focused on transactional use cases like M&A and fund formation, which remain popular. We are now developing specialized modules for these areas. Litigation is experiencing faster growth, driven by increased data availability.

Addressing the "ChatGPT Wrapper" Criticism

Some critics suggest Harvey is merely a wrapper for ChatGPT. How do you respond?

Our key advantages lie in two areas. First, we are accumulating a wealth of workflow data – understanding the practical applications of these models. This data enables robust evaluation, which is a significant competitive advantage. Assessing the quality of a merger agreement, for example, requires sophisticated evaluation frameworks and agentic systems for self-assessment.

Building a Multiplayer Platform

Second, our product is evolving into a truly multiplayer platform. This industry involves both providers and consumers of legal services. We are building a platform that bridges both sides. While competitors offer solutions for law firms or in-house teams individually, we haven’t seen anyone create a genuinely multiplayer platform.

The Future of Legal AI

Regarding the “ChatGPT wrapper” criticism, for 2023 and 2024, the product’s power stems from the underlying model combined with front-end work that enhances the user interface and experience. However, if the goal is to analyze 100,000 documents, 5,000 emails, statutes, and codes with high accuracy, that’s the ultimate challenge. We’ve developed the necessary components and are now integrating them.

Business Model and Long-Term Vision

What is your business model?

Currently, it’s primarily seat-based, but we are transitioning towards outcome-based pricing for more complex workflows. We aim to offer both. Outcome-based pricing is suitable for tasks where accuracy can be guaranteed, while lawyer oversight remains essential for many areas of work.

A Productivity Suite for the Future

For the next year or two, we will focus on a seat-based productivity suite used collaboratively by law firms and their in-house teams. Over time, we will introduce more consumption-based workflows as the systems improve and surpass human accuracy in specific areas. However, complete automation of complex processes like M&A is unlikely; instead, we envision AI assisting with specific diligence tasks, with lawyers handling the remaining aspects.

Low Penetration and Untapped Potential

You mentioned earlier that AI penetration in the legal field is low. How low?

The percentage of lawyers globally using Harvey is extremely small. There are approximately 8 or 9 million lawyers worldwide, but the potential of these systems is far from fully realized. While current applications provide significant value and ROI, the capabilities of these systems in the next five years will be exponentially greater.

The Value Proposition of Legal AI

Consider the value per token. Legal fees for a merger can easily reach tens of millions of dollars. The resulting merger agreement and SPA typically comprise around 200 pages. What is the value of each token in that document, which required $20 million or $30 million in legal fees to create? These are the use cases where, despite our current low penetration, the potential is immense.

The Future of Junior Lawyers

What impact will AI have on junior lawyers and their traditional apprenticeship?

This is a concern I prioritize deeply, having recently been a junior lawyer myself. The goal of law firms in the next five to ten years is to accelerate the development of their best partners.

AI as a Training Tool

Currently, firms balance partner development with hiring and billing associates. AI can accelerate this process. Tools that handle the initial stages of an M&A serve as a one-on-one tutor for junior associates. We collaborate with law schools to explore this potential. Imagine an AI-driven merger simulation in Harvey, providing real-time feedback – an exceptional training platform.

Future Fundraising Plans

With your valuation increasing from $3 billion to $8 billion in under a year, what are your plans for future fundraising?

We don’t have immediate plans for large funding rounds. We don’t require substantial capital and aren’t experiencing excessive burn rates. This year’s fundraising was primarily to prepare for research directions requiring significant compute resources. Regarding a public offering, that’s a long-term goal, but I cannot provide a specific timeline.