World Model Readiness How AI-written code enters your repo

Module · How AI-written code enters your repo

The Engineering Team AI Check

Your team ships more code than it used to, and a growing share of it was drafted by a model. That is fine, until the day something breaks and nobody can say who wrote the line or whether anyone read it. This module checks the five habits that keep AI-assisted engineering honest: review discipline, test coverage on generated code, security scanning, clear ownership, and the prompts and configs that shape it all living in the repo.

Question 1 of 5 · AI code gets reviewed

Does AI-generated code get the same review as anything a human wrote?

A model produces plausible code fast, which makes it easy to wave through. The failure mode is a reviewer skimming a large diff because it looks finished. Same bar, same scrutiny, regardless of who or what typed it.

Question 2 of 5 · Generated code is tested

Does the code your team generates ship with tests that actually exercise it?

Models are happy to write both the code and the tests, and tests that were written to pass can hide the bug they should catch. The question is whether generated logic carries real coverage a human trusts, not green checkmarks the model produced to satisfy itself.

Question 3 of 5 · Security scanning runs

Does generated code pass through security scanning before it merges?

Models reproduce the vulnerable patterns they learned: hardcoded secrets, injectable queries, outdated dependencies pulled in without a thought. Automated scanning in the pipeline catches the common cases that a tired reviewer at speed will miss.

Question 4 of 5 · Someone owns the module

When AI writes a module, does a named human own it afterwards?

Code that no person understands is code no person can fix at 3am. Ownership means a named engineer who can explain the module, extend it, and answer for it, not a git blame that points at a prompt. Generation does not transfer understanding by itself.

Question 5 of 5 · Prompts live in the repo

Are the prompts and AI configs your team relies on versioned in the repo?

If the prompt that generates your code or the config that governs an AI feature lives in someone's chat history, you cannot review it, roll it back, or reproduce a result. Prompts and model configs are source: they belong in version control with everything else that shapes behaviour.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

How many engineers are on your team?

1 to 3
4 to 8
9 to 20
Over 20

What share of your team uses an AI coding assistant on most days?

Under a quarter
A quarter to half
Half to most
Nearly everyone
We do not track it

Roughly what share of code merged last month was AI-drafted?

Under 10 percent
10 to 30 percent
30 to 60 percent
Over 60 percent
We cannot tell

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.