Winston Weinberg is the co-founder and CEO of legal AI startup Harvey. Founded in 2022, Harvey is currently valued at $11 billion, with annual revenue exceeding $200 million. 1. Transition from Lawyer to AI Entrepreneur Background: Winston was an accomplished litigation attorney, initially aiming to establish his own law firm. Opportunity: After encountering the GPT-3 API at the end of 2021, he realized the potential of AI in the legal field. Although the initial model was not perfect, through "Chain of Thought" prompt engineering, he found that AI could answer complex legal inquiries with high accuracy. Cold Start: He submitted 100 AI-generated legal answers to senior lawyers for review, resulting in 86% of the answers being recognized as "ready to send to clients without modification." With this data, he directly contacted OpenAI CEO Sam Altman. 2. Sales Strategy: Extreme Personalization in Demos Addressing Pain Points: As a former litigation attorney, Winston would find recent defense submissions or arguments from target law firms through public federal court documents and demonstrate how Harvey could refute or improve these in demos. This "challenge-based" presentation greatly captured the partners' attention. Breaking the Tradition of "Not Buying Technology": The legal industry has historically been indifferent to technology because past technologies mostly involved backend management (invoicing, time tracking) rather than legal practice itself. Harvey is the first technology that can directly change the core working methods of lawyers. 3. Four Stages of Company Development Winston believes Harvey will go through four iterative stages: Productivity Suite: Basic functional assistance, which is likely to be commoditized in the future. Workflow Integration: Deep integration into every aspect of legal practice, incorporating systems, context, and institutional knowledge. Infrastructure: Becoming the operating system that coordinates complex transactions between law firms, clients, and tax advisors (e.g., fund formation involving hundreds of LPs). Legal Operating System: Serving as the foundational layer for all legal operations. 4. Major Lessons from the Startup Process The Illusion of "Quick Wins": In early 2024, Harvey attempted to achieve explosive growth by acquiring a company ten times its size to "shortcut" the process. Reflection and Return: This failed acquisition attempt made Winston realize that there are no shortcuts in company operations; it must rely on continuous execution, recruiting top talent, and refining systems. He believes that if the acquisition had succeeded, Harvey's current situation would be worse. Executive Recruitment: He adheres to one principle—before hiring C-suite executives, he must personally run and manage that department for a period to ensure he fully understands the role's requirements and can assess whether candidates can scale with the company. 5. Addressing Doubts: AI Labs vs. Application Layer Moat: In response to doubts about whether labs like OpenAI will consume application-layer startups, Winston believes that law is a highly regulated industry with high barriers to entry, involving a large amount of non-public data and extremely complex domain expertise, which general models cannot solve with a single prompt. Focus and Secrecy: Early on, Harvey was relatively secretive and had a waiting list, not as a marketing tactic, but because the team had only 3-5 people while supporting 8,000 users, leaving no energy for public promotion. 6. Understanding the Legal Industry Law is Not Easy: He criticizes the tech industry (especially Silicon Valley) for often underestimating the complexity of legal work. Top lawyers are "craftsmen," and their value lies in high-level judgment, not just paperwork handling. Changing Pricing Models: With the introduction of AI, law firms are shifting from traditional "hourly billing" to "service packaging." By improving efficiency with AI, firms can take on entire transactions that were previously unprofitable due to high costs, thus increasing total revenue. 7. Internal Culture and Advice External Noise vs. Internal Morale: Winston believes that as long as clients are satisfied with the product, founders need not worry about the impact of Twitter/X opinions on internal morale. Learning Speed: In the application layer, CEOs must possess strong self-iteration abilities, as company revenue doubles every six months, meaning leaders must learn a completely new set of management skills every few months. Core Conclusion: Winston Weinberg presents a calm, pragmatic, and extremely optimistic leader regarding AI. He believes that Harvey's core competitiveness lies not in general AI technology, but in a deep understanding of the complex logic of the legal vertical and the reconstruction of workflows.