The AI paradox: can AI and open source development co-exist? (www.techradar.com)

Modern AI coding assistants and open source development are at odds: open source relies on transparency, clear licensing and community maintenance, while today's top AI tools are closed, black-box models trained on vast corpora of public code. That training approach creates a “statistical amalgam” that can regurgitate snippets without provenance, raising legal and security risks—Snyk’s research finds developers frequently encounter vulnerabilities in AI-generated code and notes that an average application is roughly 70% open-source components. Corporations resist opening models or datasets for competitive reasons, which deepens mistrust in FOSS communities worried about uncredited reuse and license violations. Bridging the divide will require technical and policy work. Practical measures include building provenance and citation mechanisms, running real-time license and vulnerability scanners against generated code, and training models exclusively on permissively licensed or public-domain code to reduce reuse risk. Organizations should treat AI outputs like third‑party contributions: enforce review workflows, restrict what private code is shared with external models, and require disclosure/approval for AI-derived patches. Success depends on AI vendors adopting transparency and safeguards and on open source communities creating clear rules for AI-assisted contributions—only then can the productivity gains of generative AI coexist with the legal clarity and security guarantees that make open source sustainable.