The AI-Generated Terraform Plan Looked Fine: Reviewing AI Infrastructure Code
The dangerous AI failure in infrastructure work is not the obviously broken output. It is the plausible one. A generated Terraform change that names sensible resources, reaches for the right provider blocks, and produces a plan that scrolls past without anything screaming is the change most likely to reach production carrying an assumption nobody checked — precisely because it never tripped the instinct that makes a reviewer slow down.
This is a five-minute talk with one job: name that pattern out loud and hand the room the review habits that catch it. The argument is not that AI writes bad infrastructure code. It is that AI writes infrastructure code plausible enough to stop the reviewer asking the right questions — and that the plan output, the one artifact that actually states what is about to change, is the thing we skim hardest when it looks calm.
What you’ll take away
- How to check the stated intent of a generated change against what its plan actually says it will do.
- Where to look for blast radius that a generated change hides rather than declares.
- Why provider behavior has to be validated rather than assumed from plausible-looking HCL.
- How to separate generated convenience from the operational ownership that outlives it.
- Where policy guardrails belong so they catch what a human skim misses.
Who it’s for
Anyone reviewing infrastructure changes an AI helped write — DevOps and platform engineers, SREs, and the leads approving the pull requests. Written for a five-minute lightning slot, so it works as a track opener or an AI-track short.
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