Who Vets AI’s Code? The Scale Challenge Facing Open Source Ingestion

The magic trick of AI coding tools is also their blind spot: they produce confident, compilable code built on a ghost stack of dependencies. If a model hallucinates a package name, that phantom can ride straight into your pipeline—no human, no policy, no second glance. The source article on BleepingComputer nails the real issue: it’s not that AI writes bad code, it’s that it scales bad trust decisions faster than we can vet them. This matters because traditional security reviews are retroactive. SBOMs and CVE scans catch what’s already in the tree. AI ingestion floods the tree with synthetic packages before anyone asks, "Who wrote this? Does it exist?" ActiveState is right: governance must happen at the point of selection, not after the commit lands. That’s the logical next step in supply chain security—call it shift-left with a hallucination filter. We’ve spent a decade building pipelines that assume human-authored dependencies. AI breaks that assumption at scale. The future is a vetting layer for every AI-suggested import, before it ever touches CI. Because the real question is no longer "does it compile?"—it’s "does it even exist outside the model's imagination?" {"key_insight":"AI coding tools migrate trust from the developer to the model, forcing governance to move upstream of the pipeline.","confidence":0}