Hannah Ji-Otto, Jianfei Chen and Heather Buchta Write Article for Bloomberg Law on Risks for M&A Involving AI Assets
Quarles & Brady attorneys Hannah Ji-Otto, Jianfei Chen and Heather Buchta wrote an article for Bloomberg Law about the complex risks involved when artificial intelligence (AI) is a key part of mergers and acquisition (M&A) deals and what buyers can do to manage those risks. Ji-Otto and Buchta are partners in the firm’s Intellectual Property (IP) Practice Group, and Chen is an IP associate.
As AI has shifted from a supporting feature in deals to a primary driver of deal value, buyers need to understand how to effectively manage risk associated with post-closure issues with the AI assets. In the article, the authors warn that many “AI-heavy dealmakers” still assume that code defects can be fixed after closing, even though the loss of the asset itself often is a significant risk and the classic M&A toolkit of monetary risk allocation isn’t enough when the core asset may be irreparably impaired.
Ji-Otto, Chen and Buchta also write that buyers of AI technology should consider assessing the legal use and retention of training data, mapping model components and intended uses, and making verification a closing condition.
An excerpt:
Artificial intelligence has gone from a supporting feature to a primary driver of deal value. Yet many AI-heavy dealmakers still assume the classic software premise that code defects can be fixed after closing.
AI breaks that assumption.
…
Lately, we’ve observed a trend in AI-heavy deals where buyers are negotiating technical walk rights, allowing termination of the deal if a deep-dive audit reveals unverifiable model lineage or foundational licensing issues. Buyers increasingly require a forensic “pedigree log” of every dataset and fine-tuning weight, and if the target can’t produce a verifiable history of data sets and training logs, the asset may be treated as impaired from the outset.
These practices reflect an emerging standard: AI assets are treated not just as intellectual property, but as operationally material technology that requires validation before closing.