Review is the bottleneck in those scenarios - and it's a _good thing_.

AI-gen code is often subtly (or obviously) incorrect. Human eyeballs are the cure. There is no way to go faster without compromising the value of the review.

What I've been super annoyed about recently is the number of times I've seen a completely stupid question on a PR - eg "why are you doing this?" when there's a comment right above that line about the why. Or "where else is this used?" - we all have IDEs and reviewers obviously have access to the code - stop being a lazy potato and check out the code locally.

I try to avoid adding these lazy people to PRs as they create more work than they help. But sometimes they end up on there anyway, because they're "important".

if this is a problem there is a problem in alignment in management or teams.

one team uses AI to create larger volume of work, but another does not use AI for whatever reason.

its really asking for problems.

either both should use it or neither.

if both is not perceived to be ok for quality reasons, then use neither.

We did some measurement at the beginning of the year and found that our PRs were getting bigger and review time was coming down even without us doing anything, which is not a good sign.

We ended up moving peer review to the implementation plan rather than the PR, then having a loop that validates the code against the plan when the PR is raised. That way the agent gets a CI failure if it deviates from the plan, which it then fixes or acknowledges. Anything with no differences gets merged without human review, differences get approved by the original person who peer reviewed the plan.

I puled some stats the other day for a presentation I'm working on about what we did:

Matched plan on first pass: 25% (169/677) Had differences: 75% (508/677)

Differences per PR: Median 2 Mean 2.60 P90 6 Max 20

1,757 findings:

- Missing (skipped planned work): 44.6% of findings, 51.7% of PRs - Changed (done differently): 42.7% of findings, 51.3% of PRs - Beyond (extra, still in scope): 9.1% of findings, 19.8% of PRs - Scope (unplanned feature): 3.6% of findings, 6.6% of PRs