FIELD NOTE / INSTAGRAM
AI budget hides in the attention loop.
The short film, the complete written thought, and the evidence behind it.
The Instagram edition will be linked here after its public post is verified.
AI budget hides in the attention loop.
Day 03 · 2026-09-30 · Instagram
Short video caption
AI pilot cost is not just API spend or hand rewrites. Count the owner's re-prompt, test, deploy and investigation cycles, then measure kept outcomes per qualified owner hour. More drafts can widen the decision queue. Hypothetical workload. #EricFieldNotes
Full written post / accessible read
The API bill is visible. The team lead's cycles are not: clarifying the spec again, sending the agent back, watching another test, redeploying and checking whether the original miss points to a wider problem.
Picture five agent drafts arriving at once. None needs a human to recode it, but each asks for a decision about behavior, a failed test, a retry or a release. The lead keeps changing context while customer work waits. This is a hypothetical workload.
For one matched task class, record the owner touch points and elapsed time from initial brief through test, release and follow-up. Track what was re-prompted, retested and investigated. Divide total owner time by outcomes that stayed accepted.
Before adding agent seats, compare kept outcomes per qualified owner hour and the unresolved-question queue. Add capacity only when that ratio improves. Do this because a pile of generated artifacts can consume the attention needed to judge them.
#EricFieldNotes
Evidence and boundary
On-screen boundary: ILLUSTRATIVE COST MODEL. The sources below support documented mechanisms and specifications; illustrative scenarios are not presented as measured incidents.