Artificial intelligence is experiencing a renaissance, catalyzed by rapid technological advances, ballooning market demand, and rising competition. This accelerated growth has triggered a sharp uptick in M&A activity, with companies across financial services, semiconductors, life sciences, and other sectors racing to acquire artificial intelligence assets, data, and expertise. Despite a broader softening of tech deal valuations in 2023, artificial intelligence targets have commanded premium prices, a trend that persists into 2025 as businesses scramble to integrate generative artificial intelligence into their offerings or secure strategic datasets and talent that are otherwise beyond reach.
However, acquiring artificial intelligence companies is fraught with legal and regulatory risks, particularly regarding intellectual property, privacy, and compliance. Intellectual property considerations extend far beyond traditional software or patent ownership—they now encompass rights over vast and often controversial training datasets, the risk of infringing third-party IP during model development, and complex questions about the protectability and ownership of artificial intelligence-generated outputs. Use of unlicensed training data, open-source software, or inadvertent trade secret leakage through employee use of outside artificial intelligence tools could dramatically increase a buyer’s liabilities. Additionally, with the surge of class-action lawsuits alleging copyright infringement and regulatory attention on unfair practices, acquirers must carefully assess the provenance of all data and technology in play, as well as contractual risk allocations with vendors and customers.
Regulatory scrutiny is mounting in tandem with deal activity. Privacy and data protection regimes, particularly in the U.S. at the state level, the EU, and other major markets, now impose a complex landscape of consent, data minimization, and audit obligations—concerns compounded by artificial intelligence´s appetite for massive, sensitive datasets. Enforcement by the U.S. Federal Trade Commission has included destruction of technologies trained on improperly obtained personal data; the EU’s GDPR is actively enforced, with high-profile bans and investigations targeting artificial intelligence systems. Antitrust authorities worldwide are monitoring for anti-competitive consolidation, especially by big tech players, while national security frameworks like CFIUS in the U.S. and equivalent regimes in the UK, EU, and China demand careful navigation of foreign investment, data transfer, and export controls. Sector-specific rules further layer on: financial, health, and other regulated industries impose unique requirements on artificial intelligence vendors, and emerging laws such as the EU´s AI Act will soon raise compliance bars for high-risk applications.
Successful artificial intelligence acquirers are responding by enhancing due diligence, embedding detailed representations and warranties, securing indemnities for known and emergent risks, and preparing for pre- and post-closing remediation and integration. This includes rigorous audits of training data, scrutiny of data security controls, review of privacy practices, open-source scans, and proactive engagement with relevant regulators. As regulatory trends accelerate and artificial intelligence-specific laws mature, companies must stay agile to capture artificial intelligence opportunities—leveraging expert guidance and robust legal strategies to both unlock value and minimize exposure from rapidly evolving legal, ethical, and compliance threats.
