Startups are increasingly using AI to handle legal and accounting tasks themselves, avoiding high professional fees. This signals a significant market need for tools that formalize and support this DIY approach, especially as startups scale and require more robust solutions for investors.

To ensure accuracy in its legal AI, LexisNexis unexpectedly hired a large number of lawyers, not just data scientists. These legal experts are crucial for reviewing AI output, identifying errors, and training the models, highlighting the essential role of human domain expertise in specialized AI.

Parvy's founders validated their idea by applying GPT-3 to 100 legal questions from Reddit. They sent the AI-generated answers to attorneys, who approved 86% without edits. This simple, real-world test was so effective it surprised even OpenAI's own legal team about their model's capabilities.

While building a legal AI tool, the founders discovered that optimizing each component was a complex benchmarking challenge involving trade-offs between accuracy, speed, and cost. They built an internal tool that quickly gained public traction as the number of models exploded.

Unlike coding with its verifiable unit tests, complex legal work lacks a binary success metric. Harvey addresses this reinforcement learning challenge by treating senior partner feedback and edits as the "reward function," mirroring how quality is judged in the real world. The ultimate verification is long-term success, like a merger avoiding future litigation.

Venture capitalist Keith Rabois observes a new behavior: founders are using ChatGPT for initial legal research and then presenting those findings to challenge or verify the advice given by their expensive law firms, shifting the client-provider power dynamic.

The legal profession's core functions—researching case law, drafting contracts, and reviewing documents—are based on a large, structured corpus of text. This makes them ideal use cases for Large Language Models, fueling a massive wave of investment into legal AI companies.

Harvey intentionally avoids self-serve and focuses on the most complex enterprise legal work first. The strategy is to build a business around problems so difficult they will outlast the next decade of foundational model advancements, preventing commoditization.

Harvey is building agentic AI for law by modeling it on the human workflow where a senior partner delegates a high-level task to a junior associate. The associate (or AI agent) then breaks it down, researches, drafts, and seeks feedback, with the entire client matter serving as the reinforcement learning environment.