AI’s Risks to M&A Deals Require Early Planning and Model Testing
AI’s Risks to M&A Deals Require Early Planning and Model Testing
Publish Date: 2026-05-04 04:30:00
Source Domain: news.bloomberglaw.com
- In AI-driven mergers and acquisitions, the main risk is often not monetary but the potential loss of an irreparably impaired AI asset due to issues in its technical foundation.
- Buyers are negotiating technical walk rights to terminate deals if a deep-dive audit uncovers unverifiable model lineage or foundational licensing issues.
- The importance of a forensic “pedigree log” for every dataset and fine-tuning weight is rising, as buyers require verifiable histories to assess the quality and legality of training data.
- Legal considerations surrounding the use and retention of training data are becoming critical to the valuation and success of AI-driven deals.
- The prevalent developer mindset of prioritizing speed over compliance is shifting, influenced by significant settlements like Bartz v. Anthropic, which resulted in substantial penalties and legal measures.
- Regulatory frameworks and emerging AI governance principles are increasingly impacting deal dynamics, with frameworks like the EU AI Act and state-specific laws influencing legal exposure assessments.
- Ensuring data rights and risk controls consistent with regulatory frameworks and laws can heavily influence valuation and deal success.
- Buyers should assess training data legality, map model components for risk management, conduct deep-dive audits, and make verification a closing condition.
- AI developers should deliver up-to-date, well-documented records that comply with recognized frameworks and regulatory demands.
