Two numbers that do not add up. Adoption of AI coding agents is at a record in 2026, and teams have no plans to give them up. Meanwhile the share of developers who say they trust the output has collapsed to almost nothing.
So people work every day with a tool they do not believe. Here is why that is reasonable and how to live with it.
Why trust fell
The models did not get worse. They got better, and that is why they now get bigger jobs.
A model used to finish a line, and a mistake showed at once. Now it edits ten files and the mistake hides in the links between them. Checking costs more than writing.
The second reason: the tone never changed. A model delivers a right answer and a wrong one with the same composure, and the code carries no "I am unsure here" signal.
What to do instead of trusting blindly
Treat AI code as someone else's pull request. It is not worse. You just did not watch it being written. You already have habits for a stranger's PR: read it, ask questions, run the tests.
Require a failing test before the fix. If an agent is fixing a bug, have it write the test that catches the bug first. A test that failed before the change and passes after it proves the work better than any explanation.
Cap the step size. A ten-file task is harder to review than five two-file tasks. Splitting costs time when you frame the work and gives it back in review.
Do not accept an explanation as proof. Models describe what they did very well. That is not verification. Run the code.
What this means for spend
Distrust has a price. A substantial share of industry token spend goes into fixing what the model itself produced.
That draws a straight line from how sharply you frame a task to what you pay at month end. Sharper input, fewer iterations, smaller bill.
How it looks in a team
Three rules remove most of the pain:
- The author of a change is the human who submitted it, not the model. They answer for it.
- Agents do not merge on their own. Under any configuration.
- Spend is visible per key and per project, so you can see where iterations eat the budget.
We configure the last one separately: per-key usage stats, and sub-organizations with their own budget per team.
The short version
You do not need to trust generated code. You need a process where trust is not required: small steps, a failing test before the fix, ordinary review, and visible spend.