Reps & Remedies: A Practical Primer for M&A Deal Lawyers
This podcast uses AI-assisted writing and AI-generated audio; see the production note below and at www.repsandremedies.com. A $300 million AI target can turn into a paperweight if the model was trained on data the company never had the right to use. This deep dive gives deal lawyers a spotting-and-routing framework for AI risk across three postures: buying an AI company, buying a business that uses third-party AI, and buying a vendor or contractor selling or operating AI workflows for customers. The discussion covers training-data provenance, ownership of models, weights, outputs, open-source dependencies, bias and compliance reps, interim covenants around deployment changes, the agentic-AI contract checklist for vendor and customer paper, downstream liability allocation, and the still-forming RWI response to known AI issues. It also maps when pricing, earnouts, retention, escrows, or structure may need to move. Production disclosure: This podcast is for informational and educational purposes only. It is not legal advice, does not create an attorney-client relationship, and is not a substitute for advice from counsel licensed in your jurisdiction. Law and deal practice change, and information may become outdated. No confidential client, deal, or matter information is discussed. The views expressed are the author’s alone and do not reflect the views of the author’s firm or its clients. This podcast includes AI-assisted writing and AI-generated audio. The underlying instructional modules were developed with substantial assistance from OpenAI GPT-5.4, reviewed and refined with input from Anthropic Claude Opus 4.6, and further synthesized into audio by Google NotebookLM. Final topic selection, source curation, and editorial judgment remain with the creator. Some episodes may retain occasional stylistic or tonal artifacts from AI-assisted production.
20 episodios
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