Imagine you are an M&A associate preparing a comparison memo two hours before a signing call. The partner wants confirmation that the indemnification cap and survival periods in the final agreement reflect what was negotiated.

Using the Caterpillar–Siwei acquisition as a case study, this article examines why verification breaks down in complex M&A transactions when diligence relies on static snapshots instead of continuous alignment.

Most people in venture capital can recognize the terms inside a term sheet. Fewer people realize that understanding them is often what allows a dealmaker to push back when the drafting starts to move the deal.

Deal drift is the widening gap between what stakeholders believe the deal is and what drafts become. Learn how misalignment turns into structural risk before signing.

Most transaction data captures only the final agreement. But the real intelligence of a deal lies in the sequence of decisions that produced it. This article explores “deal memory,” the structured history of negotiations and revisions that reveals how judgment shaped a transaction, and why that history may become one of the most valuable datasets in AI-driven private markets.

Legal AI isn’t limited by model size. It’s limited by architecture. Learn why skills architecture, risk calibration, and citation discipline matter for defensible, transaction-ready AI outputs.

In M&A today, a simple belief is gaining momentum. Better data will produce better deals.

AI is clearly speeding up legal work, but this article argues that speed alone is not the right metric in transactional practice. It explores how acceleration can outpace verification, why traceability and defensibility matter more than ever, and how teams risk moving faster while becoming less able to show their work when conclusions are questioned later.

Aracor AI CEO Katya Fisher discussed AI's impact on dealmaking pace, precision, security, and compliance on TechTalk.