Jay Heidrick regularly advises clients in both transactional and litigation matters relying on his extensive experience in intellectual property, contracts, and trade secrets. He views his primary role as a problem solver who allows his clients to focus on their business instead of their legal concerns. To do this, Jay works hard to not only understand the problem at hand but also the client’s individual business and strategic goals to reach the best resolution possible for the long-term benefit of his clients. While Jay aggressively seeks creative and rapid resolutions to disputes, he litigates cases with the understanding that a trial may ultimately be necessary. And in trial, Jay is a storyteller who can take complicated issues and explain them in a manner that connects with and is understood by jurors of all backgrounds.

Jay regularly handles high-value, “bet the company” intellectual property cases where eight-figure damages are often at stake. He understands the stress this can create for his clients and personally invests in each of his clients and cases. 

Jay is licensed to practice in all state and federal courts in both Kansas and Missouri. He is also admitted in the United States District Courts for the Eastern and Western Districts of Texas, as well as numerous appellate jurisdictions. He has represented clients in numerous jurisdictions at both the state and federal level across the country.

Education

  • University of Kansas (J.D., 2002)
    • Kansas State University (B.A., 1999)
      • History; Men's Basketball Team Member

    Bar Admission

    • Kansas, 2002
    • Missouri, 2003

    Professional Affiliations

    • Kansas Bar Association
    • The Missouri Bar
    • Johnson County Bar Association
      • Former President
    • Tour de BBQ, Inc. (former)      
      • Founding Member, a Kansas City cycling event held to raise money to support cancer research in the Kansas City metropolitan area

    Recognition

    • Recognized as a Stellar Performance Lawyer by Thomson Reuters, 2026
    • Selected for inclusion in the IAM Patent 1000 list of the World's Leading Patent Practitioners, 2023-2025, Missouri - Recommended
    • Selected for inclusion in Best Lawyers in America® for:
      • Patent Law, 2024-2026
      • Litigation - Intellectual Property, 2023-2026
      • Commercial Litigation, 2023-2026
    • Selected for inclusion in "Under 40 Hot List" by Benchmark Litigation, 2018
    • Selected by Missouri Lawyers Weekly, Up and Coming Attorneys - Missouri, 2012
    • Named "Rising Star" by Missouri & Kansas Super Lawyers, 2012
    Publications
    AI Chats Are Discoverable—And Trigger Preservation Obligations
    Key Takeaways Courts are increasingly treating AI chatbot interactions as discoverable electronic records, with recent decisions confirming they fall under existing discovery rules. This classification means AI chats carry the same legal risks and obligations as other electronically stored information in litigation, and gaps in preserving these communications can expose companies to sanctions or adverse inferences. Companies should consider updating their information governance and litigation-hold practices to account for AI tools used across the organization. As generative AI tools become part of everyday business workflows, courts are treating chatbot interactions the same way they treat emails, text messages and internal messaging platforms. Recent court decisions confirm that AI chats are not a special category of communication. Rather, they are electronic records that may be
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    AI vs. Authors: Two California Judges, Two Directions and More Uncertainty on Fair Use and Copyright
    Key Takeaways Courts Lean Toward Fair Use for AI Training: Two California rulings suggest that using copyrighted works to train artificial intelligence (AI) may be considered fair use if outputs are transformative and do not replicate the original content. Pirated Libraries Raise Legal Risks: While courts accepted some use of pirated works for transformative AI purposes, they strongly criticized maintaining central libraries and training AI with pirated content for non-transformative purposes, signaling potential legal vulnerability. Legal Uncertainty Remains: With no clear precedent or updated legislation, both authors and tech companies face ongoing uncertainty; future guidance will likely need to come from Congress or the Supreme Court. The rapid advancement of AI large language models (LLMs) depends heavily on ingesting vast amounts of textual data,
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