Engineering note / AI-assisted delivery
AI throughput is a systems problem, not a typing speed problem
Why code generation only becomes engineering leverage when review design, evidence, operating feedback, and team capacity scale with it.
Generation is not the bottleneck
When producing code becomes cheaper, understanding and safely absorbing change become the constraints. A team can generate more pull requests than it can review, deploy more changes than it can observe, and create more surface area than it can support.
Scale the evidence with the output
Every AI-assisted change should arrive with a tighter problem statement, explicit assumptions, relevant tests, migration and rollback thinking, and a record of the checks that actually ran. The goal is not more prose; it is less uncertainty for the reviewer.
- Bound the requested change before generating it.
- Require executable checks for claimed behavior.
- Use adversarial review to search for missing branches and contract drift.
- Keep human review focused on intent, architecture, and risk.
Protect the team's understanding
Velocity that erodes collective understanding is borrowed time. Keep changes small, make architectural decisions visible, and rotate ownership so the system does not become legible only to the person who prompted the model.
Measure the whole loop
Useful signals include time to validated change, review latency, escaped defects, rework, operational load, and how quickly the team can explain an incident. Lines generated or prompts sent measure activity, not engineering leverage.