The bottleneck moved
Code generation has made producing a first draft dramatically cheaper. What it has not made cheaper is deciding whether that draft is correct, secure and appropriate to the system it is joining.
The teams getting real leverage are the ones who invested in review capacity, test coverage and clear architectural boundaries first. Without those, faster code generation simply produces technical debt more efficiently.
Evaluation is the discipline that matters
Any AI feature that reaches production needs an evaluation set before it ships — a fixed collection of realistic inputs with known-good outputs, scored automatically on every change.
Without one you cannot answer the two questions that always arrive: did this change make things better, and has quality drifted since launch? Teams that skip this step end up reasoning about model behaviour from anecdotes.
Where ML earns its keep in delivery
Beyond product features, models are quietly improving the delivery process itself:
- Test generation and coverage gap detection
- Flaky-test identification from historical CI data
- Anomaly detection on production telemetry
- Triage and routing of incoming issues
- Predictive capacity and cost forecasting
A realistic adoption path
Start with an internal workflow where mistakes are cheap and feedback is fast. Instrument it properly. Prove the evaluation harness works on something low-stakes before pointing it at anything a customer touches.
The pattern we see succeed is narrow, well-measured, and boring — and it compounds far faster than an ambitious project that never gets past the pilot.
Working on something like this?
We are happy to talk through the approach, whether or not it turns into a project.
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