AI at scale
Not an organization's rollout — one person, ten projects deep, trying not to let quality quietly slide on the ones getting less attention.
What actually breaks first
Using AI well on one focused task is a different skill from using it well across a dozen projects running in parallel — and the gap between them shows up faster than expected. It's rarely that the AI gets worse. It's that context switching costs more than it looks like on paper, review quality quietly drops on whichever project got looked at last, and a technique that worked well on Monday's project silently never makes it into Thursday's, because there was never a system for carrying it over.
The specific things that go wrong at volume
Context switching tax
Re-orienting to a different project's context repeatedly costs more focus than it looks like.
Uneven attention
Review quality drifts toward whatever's freshest, not whatever actually needs the most scrutiny.
Lost lessons
A fix discovered on one project stays there instead of improving the other nine.
Personal systems that actually hold up
A simple per-project log — what worked, what didn't, one line each — turns lessons that would otherwise stay siloed into something reusable across every project after it. A shared prompt file organized by task type, not by project, means a technique learned once gets applied everywhere it's relevant instead of being reinvented under deadline pressure for the fifth time.
Keeping quality even, not just keeping up
Review effort naturally drifts toward recency and toward whichever project feels most urgent — which has nothing to do with which one actually carries the most risk if something's wrong. Assign review depth deliberately, by stakes, the same personal trust threshold described in Everyday AI Best Practices, just applied on purpose across many projects instead of by instinct on one.
The "good enough on average" trap
Ten projects reviewed at 80% effort each isn't the same as ten projects reviewed at the right depth for each one — the two or three genuinely high-stakes projects in that group needed more than an even split would ever give them, and the low-stakes ones didn't need nearly that much. Averaging attention evenly across projects with uneven stakes is quietly worse than allocating it deliberately, even when the total effort is identical.
When scale itself is the wrong goal
Not everything benefits from being run across more projects at once. Work that depends on deep, singular focus — the genuinely hard problem, the relationship-sensitive conversation, the thing that needs your best attention rather than your average attention — often gets worse, not more efficient, when it's just one more item in a rotation. Recognizing which project that is, and protecting it from the volume treatment, is itself part of the skill.
The short version
Scale doesn't fail because AI gets worse under volume — it fails because attention doesn't naturally distribute itself by what actually matters, and lessons learned on one project don't automatically travel to the next one without a system carrying them over. Log what works, allocate review depth deliberately instead of evenly, and protect the one or two projects that genuinely need your full attention from getting the same rotation treatment as everything else. That's most of what separates someone who's actually good at this from someone who's just doing a lot of it.