Christopher Kercher
- Lecturer in Law
Biography
Chris Kercher is trial‑tested litigator whose practice involves bet-the-company disputes spanning corporate control and M&A litigation, financial‑markets/securities matters, and other complex commercial disputes. He advises public and private companies, investment funds, and senior executives from pre‑litigation strategy through trial and appeal. Recent results include post‑trial specific performance compelling the closing of a nine‑figure merger and a buyer‑side victory affirmed on appeal in a COVID‑era terminated‑deal case.
As Founder and Head of Quinn Emanuel’s AI & Data Analytics Group, Chris leads the first dedicated AI litigation practice at a major law firm. Chris earned recognition from The American Lawyer for leading the first documented “AI-enabled trial team” after resolving Desktop Metal from complaint to final order in six weeks. The Group partners with litigation teams to deploy AI on high-stakes matters and advises clients on building AI capabilities for their legal departments. Through 2,000+ hours of hands-on experimentation in live cases, Chris has developed proprietary expertise in instruction architecture and context engineering. By creating structured context-rich AI environments that surface patterns, maintain document consistency, and accelerate strategic work product, the Group’s approach builds on a core insight: elite trial lawyers already excel at the precise, contextual communication that makes AI effective. This natural alignment has enabled the Group to train dozens of partners and hundreds of attorneys firm-wide, creating institutional expertise that compounds with each deployment.
Chris has also been recognized as a Litigator of the Week by ALM | Law.com for a decisive victory for Elon Musk and Tesla’s directors in the Delaware Supreme Court.
Featured Spring Short Course
A Litigator’s Guide to AI. Attention Is All You Need?
Taught from a litigator’s perspective, this course examines using LLMs in everyday practice. The course will teach how attention, the skill that defines great advocacy, bridges legal practice and the transformer architecture powering LLMs. Lawyers already structure arguments, visuals, and case narratives to direct attention; large language models allocate it through a structurally parallel process. A model’s context window demands the same discipline as a jury’s attention span: structure, selection, and sequence matter. From that foundation, students develop a practitioner’s understanding of transformers, tool use, reasoning, and agentic systems, and apply them to case development, context assembly, strategic analysis, and advocacy. Students then design and build their own AI-powered workflows for case development and analysis. The course shows why the litigator’s craft is already the foundation of AI-augmented practice, and gives students the tools to prove it. What remains irreducibly human: judgment, strategy, and framing. No coding required.