Open source has always had issues, but the benefits outweighed the costs/risks. AI is not merely exponentially accelerating tasks, it is disproportionately increasing risks.

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AI-generated code is flooding open source projects with low-quality pull requests, creating a dangerous asymmetry: code production has become nearly free and near-instant, but review and maintenance costs remain unchanged. Maintainers like those at Godot Engine are overwhelmed by AI slop—code that looks professional but may contain subtle logic errors, security flaws, or unmaintainable complexity. Enterprises moving to open source to escape hyperscaler AI risks are finding that AI-generated contributions have already infiltrated those codebases. Experts warn of supply chain security risks (including hallucinated package names being squatted by attackers), context rot, and a 'verification collapse' that erodes the trust infrastructure open source depends on. The core ROI calculation for AI coding needs rethinking: velocity gains on the generation side are being offset by unscaled review burdens and accumulating technical and legal debt.

Enterprise use of open source AI coding is changing the ROI calculation