A dedicated input type is better than reusing your domain model at the API boundary. Test layers matter. Writing log statements as you go saves the poor soul (probably you) debugging blind at 10pm. You know all of this.

This isn’t about any of that.

It’s about the fact that none of those decisions show up in the metrics that matter to the people making hiring and delivery calls. The cost is immediate and visible. The return is delayed, quiet, and arrives in the form of things that didn’t happen — the investigation that took two hours instead of two days, the API change that didn’t bleed into the domain model, the bug that the structure caught before it shipped.

Sprint velocity captures the extra day. It doesn’t capture what that day bought.

This is not a new problem. Most engineers who’ve been around long enough have felt it from both sides - made the careful call and got measured on the slowness, or inherited the codebase built entirely for speed and paid the tax. The measurement system was already broken. It has been rewarding the appearance of velocity over the thing velocity is supposed to serve.

This was true long before anyone was generating code with AI. The PR process in a lot of teams was already largely theatrical — review comments on naming conventions while the architectural decisions slipped through unquestioned, approvals given because the diff was too large to meaningfully read. The gate was already not doing much. We brushed it under the carpet and moved on.

AI tooling is changing the volume of code moving through that process by an order of magnitude. The pressure to remove the gate entirely — to trust the output, to ship faster - is only growing. The faster-is-better incentive that was already making review ineffective is about to be handed a much larger surface to work on.

Many years ago, I pitched full redevlopment of a ticketing system from a PHP based system to a Java EE system because it was struggling to scale.

It probably needed a couple of years to build. They wanted it in six months. I accepted the challenge.

We built and deployed the system in eight months. We spent the next year fixing it.

The client then rebuilt it in-house.

When AI runs this experiment at scale, who takes it back?