Large language models can now produce code that looks correct, compiles and sometimes even passes tests. That is not the same as saying it is correct. A recent analysis by Trail of Bits makes the point plainly: code generated by chatbots should be treated as trustworthy as code from a first-time contributor. It may be fine. It may also contain subtle flaws that only become visible under scrutiny or in production.
Fluency is not correctness
The most important distinction for engineering leaders is between syntactic fluency and semantic correctness. A model can generate a function that uses the right libraries, follows a sensible structure and reads cleanly. What it cannot reliably do is understand the full context of your system: the invariants that must hold, the failure modes you have seen before, the security assumptions embedded in your architecture.
This matters because generated code often looks more authoritative than it is. A junior developer’s first pull request is usually reviewed carefully precisely because the reviewer knows the author lacks context. An AI-generated change can slip through with less scrutiny because it looks polished.
Why the bar should stay the same
The safest policy is to apply the same review and testing standards to AI-generated code as to any other contribution. That means:
- Every change is reviewed by a human who understands the surrounding code.
- Generated code runs through the same CI pipeline, including linting, type checking, static analysis and security scanning.
- Tests are required, not optional, and those tests should cover edge cases and failure paths.
- Architectural and dependency choices are evaluated against existing standards, not accepted because the model suggested them.
Lowering the bar for AI-generated code does not just increase defect risk. It also erodes the trust developers have in their own codebase. Once inconsistencies and vulnerabilities accumulate, every subsequent change becomes harder and more expensive.
Practical steps for teams
If your team is using coding assistants, clarify the workflow. Require developers to explain what the generated code does and why it is the right approach before submitting it. Use pull request templates that flag AI-assisted changes so reviewers know to look more carefully at correctness rather than style.
Consider treating generated code as a starting point, not a finished product. The value of the assistant is in accelerating draft work and exploring unfamiliar APIs. The value of the engineer is in validating, integrating and owning the result.
The bottom line
AI coding tools are a genuine productivity aid, but they are not a replacement for engineering judgement. Trail of Bits is right: the only sustainable approach is to hold generated code to the same standards as everything else in your repository. Trust should be earned, not assumed.