How much should you check the AI’s work?
Two things go wrong when a practice starts using AI in earnest, and they look like opposites. One is work that arrives looking finished and isn’t: someone downstream has to redo it. The other is the person doing the checking wearing out. Harvard Business Review has now put numbers on both. The instrument below reads them together.
Two workplace phenomena documented in Harvard Business Review:
Workslop
AI output that looks polished but lacks the substance to advance the task, offloading the real work onto whoever receives it.
A survey of 1,150 full-time U.S. desk workers found 41% had received workslop in the past month; each instance took an average of 1 hour 56 minutes to resolve — an estimated $186 per employee per month, and over $9M a year for a 10,000-person organisation.
A follow-up locates the cause less in individuals than in management: mandating AI use without training or clear guidelines produces the low-effort output, and stricter reviews or harsher feedback don’t reduce it.
Brain fry
Acute mental fatigue from marshalling cognitive oversight of AI beyond capacity — the cost of judging it all by hand.
A survey of 1,488 full-time U.S. workers found 14% of AI users had experienced brain fry. Affected workers made 39% more major errors and 11% more minor errors, showed 33% more decision fatigue, and their intent to quit rose from 25% to 34%.
The evaluation performance loop · how good the output gets vs how hard you check — your skill level is the current ceiling (it rises as you learn); the amber line is what the stakes demand
- Novice
- Adv. Beginner
- Competent
- Proficient
- Expert
● near the sweet spot — sustainable
your skill 50/100 · Competent · at Competent you can stand behind output up to 50% of what a perfect judge would catch — above what these stakes demand, so you can sign off on the verdict. That ceiling isn’t fixed: it rises as you learn (drag the skill slider to see the climb), and the model itself is a strong teacher.
loop shape · advanced (endoreversible parameters)
How good the output gets (γ₂/η, closeness to goal) rises with checking effort, peaks, then folds back — that fold is brain-fry. Your skill — your current Dreyfus level (novice → expert) — is the ceiling it peaks at today; the amber line is the bar the stakes set. The ceiling is not fixed: it rises as you learn, and an LLM is a strong teacher — so a gap below the bar is a place to climb, not only to defer. How slowly checking pays off holds output quality low through the workslop region before the steep climb; slide how hard you check to see where a piece of work sits.
The two failures are one curve. Check too little and polished-looking output sails through — that’s the workslop region. Check everything by hand and you pass your own peak and start making more mistakes, not fewer — that’s brain-fry. In between is a zone where the checking is enough for what’s at stake and sustainable for the person doing it. Where that zone sits depends on two things you can actually measure: how good your people are at judging this kind of output today, and what the work demands.
That is what a Diagnose engagement measures — one workflow, one team, a week or two: where your people sit on this loop, what the stakes require, and where the gap is. A Build engagement then ships the evaluation suite that keeps the work in the structured zone when the model changes, the prompt changes, or the data drifts — so you know before your clients do. And because the ceiling rises as people learn, part of the answer is usually training, not tooling.