Anthropic has launched a code-review tool intended to inspect the growing volume of software produced with AI. The product completes a revealing loop: one model helps generate code, and another is asked to identify the errors, security risks, and maintenance problems left behind. Automation increases the amount that can be written, then creates demand for a second layer of automation to make that output manageable. Review shifts from reading every line toward supervising a system that reads on the engineer's behalf.

This may catch routine mistakes quickly and give human reviewers time for architecture and difficult judgment. It can also produce a false sense of coverage. Models share blind spots, and generated explanations can sound certain when evidence is incomplete. If teams accept both creation and inspection from closely related systems, an error may pass through two confident interfaces without encountering genuine skepticism. The useful measure is not how many comments the reviewer generates, but whether failures are found before reaching users. Human responsibility therefore does not disappear. It moves to the design of tests, the selection of tools, and the decision to challenge an apparently clean report. Faster code requires stronger doubt, not less of it.