77 » contractual restrictions on IP transfer. By contrast, in physical AI transactions, the buyer is not only acquiring code and data rights but also inheriting a system that interacts with real-world conditions. The legal and economic risk therefore tends to expand to include: » safety and performance under diverse operating conditions; » product defect exposure and regulatory certification dependencies; » hardware-software integration risks and embedded firmware licensing; » cyber-physical vulnerabilities and safety-critical algorithmic decision-making; » post-deployment update governance; and » the potential for recalls, warnings, and claims that may arise even where the underlying model performed accurately in a controlled environment. Converging and Diverging Risk Profiles Across both software and physical AI transactions, buyers increasingly treat training data as a core value driver whose legal usability must survive closing. Acquisition agreements therefore often require disclosure of training datasets and representations that all necessary licenses, permissions, and consents have been obtained. These provisions address more than backward-looking compliance. Their central purpose is forward-looking: ensuring the buyer can continue operating and improving the model, refresh datasets, and integrate the AI stack into its broader R&D and product environment. The issue becomes more acute in physical AI systems, where telemetry and sensor data frequently support both model improvement and safety monitoring. The key transactional question is whether those data streams remain usable post-closing without contractual or regulatory restrictions that could undermine system maintainability or performance. More broadly, these R&Ws reflect a growing contractual allocation of AI-specific risks, including regurgitation and misappropriation. In practice, they both force disclosure of governance controls and create a remedy path for claims linked to pre-closing training practices or tool use. Post-closing integration is another area where physical AI diverges from software AI. Post-closing changes, such as retraining, fine-tuning, and dataset refresh can materially alter system performance and risk profile. In software AI, this typically presents an output reliability risk. In physical AI, a model update can alter device behavior in safety-critical ways. Buyers increasingly evaluate whether the target has documented postclosing change control and validation governance. Where gaps exist, parties may respond through post-closing covenants, conditions precedent, or price protection mechanisms such as escrows tied to remediation milestones. Finally, the remedy structure differs. In software AI transactions, breaches of AI, IP, or data R&Ws are often present as financial harm manageable via caps, baskets, and survival periods. In physical AI, harm may be non-linear: a single incident can cascade into claims, regulatory inquiries, corrective actions, and recall programs. This is why product-oriented R&Ws and targeted indemnity packages, often with separate liability caps and extended survival periods, become more prominent in physical AI acquisitions. Conclusion Israeli M&A in 2025 illustrates both the scale of strategic appetite for deep-tech innovation and the growing complexity of acquiring AI-intensive businesses. As AI becomes an enterprise-defining asset, and as physical AI expands the liability surface into the real world, diligence and documentation are adapting accordingly. AI diligence is increasingly distilled into the R&W package, with provisions focused on training datasets and non-infringement, tool dependencies, model and output ownership, and controls aimed at AI-specific failure modes. In physical AI transactions, these provisions sit alongside product-liability representations addressing claims history, regulatory compliance, and recalls. Together, these trends reflect a broader shift: in modern AI M&A, enterprise value is determined not solely by what the company owns at signing, but by whether the buyer can lawfully, safely, and sustainably operate and evolve the acquired system over time. For practitioners, these developments carry concrete implications. Sellers contemplating exits should conduct AI governance audits well before a transaction process begins, ensuring that training data provenance, tool licenses, and internal AI policies are documented and defensible. Buyers should integrate AI-specific diligence into their standard playbooks rather than treating it as a separate workstream, recognizing that AI risk cuts ISRAEL — MERGERS & ACQUISITIONS
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