Artificial Intelligence Integration Complicates Software Acquisition Due Diligence
AI-assisted software development is making it more difficult for companies to fully understand what technology they are acquiring during transactions, requiring deeper scrutiny of code provenance and potential hidden liabilities. Recent research found that 98 percent of audited software contained open-source components, with 94 percent of deals involving license conflicts and 97 percent containing unpatched security vulnerabilities. Buyers now must investigate not only what technology they obtain but how it was developed and whether it supports the underlying assumptions of their deals.
Software transactions have traditionally required financial review, but technological assessment is becoming equally critical. When artificial intelligence participates in code creation, the origin and legitimacy of that code becomes harder to trace. Companies acquiring software now face compounded risks: open-source components embedded without clear attribution, licensing arrangements that conflict with acquisition plans, and security gaps that remain unpatched. The governance of AI tools themselves—including what data they access and how generated code is validated—introduces questions that didn't exist in traditional software development.
This shift could affect technology investors, acquiring companies, and software developers across industries. Buyers may face higher transaction costs due to more rigorous auditing requirements, potentially slowing deals or reducing acquisition appetite. Developers and smaller software firms might benefit from conducting self-audits before approaching acquirers, though this requires additional investment. The broader implication concerns market efficiency: as due diligence becomes more complex, information asymmetries between buyers and sellers may widen, affecting deal terms and valuations across the software sector.