
What happens when AI enters a world where being wrong isn’t an inconvenience — it’s a liability? In this episode of Building One, Tomer Cohen sits down with Gabe Pereyra, co-founder of Harvey, to explore what it actually takes to build AI for one of the most complex and high-stakes industries in the world: legal. Harvey works with leading law firms and enterprises to draft, analyze, and reason through complex legal work — contracts, filings, cases, and internal workflows where precision, accountability, and trust are non-negotiable. On paper, legal is a perfect domain for AI.It’s language-heavy. Logic-heavy. High value. In reality, it’s one of the hardest. Every word matters.Every output has consequences.And “almost right” doesn’t count. In this conversation, Tomer and Gabe discuss: Why the hardest problem in AI today isn’t intelligence — it’s coordination What makes vertical AI companies like Harvey durable as foundation models improve Why legal systems must be auditable, permissioned, and accountable How conflicts and data isolation create unique infrastructure challenges in legal AI Why the future of work may look less like individuals using tools — and more like teams of humans and AI agents working together And why the next bottleneck in AI may not be generation — but human review and trust Gabe also shares his journey from aspiring professional soccer player to finance, AI research, and eventually co-founding Harvey — along with the contrarian thinking that led him to bet early on the future of AI. This episode is about what it takes to move AI from impressive demos into real-world systems — where the stakes are high, trust is fragile, and the tolerance for error is near zero.
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