The Report Calls It Wasted Time. I Call It Tuition.
Either way, the brain fry is real.
Tuesday morning I pasted the same three paragraphs of context into a model for what was probably the fourth time that week, watched it hand back something fluent and confident and wrong in a way I only caught because I already knew the answer, fixed it myself, and moved on without thinking much about the ten minutes it cost me. That not thinking is the part that matters.
Over the past year I’ve gotten faster at my work than I’ve ever been. I’ve also been more tired at the end of the day than the work alone explains. For a while I filed that under getting older, running a one person shop, the general weather of the year. I started paying attention in March, when Julie Bedard of Boston Consulting Group went on the podcast Hard Fork to talk about a study her team had just put in Harvard Business Review. They surveyed about fifteen hundred full-time workers, gave the exhaustion a name, AI brain fry, and tied it to a number: self-reported productivity climbed as people added AI tools, up to around three, then fell off a cliff past four. I run five pretty regularly. By their count, I’m the cautionary tale, and I’ll come back to that.
A separate institute survey has been sitting with me more recently. This one comes from Glean’s Work AI Index, six thousand workers rather than fifteen hundred, and it isn’t asking how many tools fry you. It’s asking where the time goes. Eighty seven percent using AI. Seventy three percent saying it makes them more productive. On average they report saving about thirteen hours a week, close to a third of a workweek. And only thirteen percent say their organization is actually performing significantly better because of any of it. The gains are real. The gap is real. The report exists to ask where the thirteen hours went, since they clearly didn’t all land where they were supposed to.
One answer is bot sitting: the tracked and untracked labor of making the tool usable. Feeding it context. Watching its output. Debugging the parts that come back broken. Cleaning up after it. The report puts that work at roughly six and a half hours a week, about half of everything the AI just saved you. Not all of that is wasted time, some of it is iterative and healthy, but a lot of it is tedious, invisible, and tiring.
The single most draining piece is feeding the tool context, the part it feels like it should already know. Which documents matter. Where they live. Which ones are authoritative. Second is debugging, because the thing is probabilistic rather than predictable. You fix something and you can’t quite tell what you fixed or whether it will stay fixed. It adds up to a quiet tax that shows up as fatigue long before it shows up as a performance problem.
Then comes the finding underneath the finding. Forty percent of workers admit to shipping AI work they couldn’t explain if someone asked them to. The report gives that its own cruder name. The path from bot sitting to that failure mode is short, and it runs straight through exhaustion. You sit the bot until you can’t anymore, and somewhere in there “good enough” quietly becomes permission to ship.
Researchers have a polite term for settling at good enough: satisficing. Creatives have heard the slogans that make that feel reasonable. Done is better than perfect. If you’re not embarrassed by the first version of your product, you’ve launched too late. Document, don’t create. All of them were originally answers to a real problem, perfectionism that kept people from shipping anything at all. They work when the rough thing is still yours, when you can explain what you tried, what you learned, and what you’ll change. They stop working the moment the rough thing is something the tool wrote and you can’t defend it. That’s the edge where iteration turns into surrender and where “good enough” stops being a stepping stone and starts being a ceiling.
What the report describes is satisficing scaled into a workplace norm, work going out the door that the person who sent it could not defend. That’s the surrender of agency written as a survey statistic. It’s the generic photographer seen from the inside: competent, smooth, and indistinguishable from anyone else. It doesn’t get fixed by a better prompt or a faster model, because the variable was never the tool. The variable is whether you’re still in the loop or just clearing the machine’s throat for it until you stop checking.
The generic photographer
The generic photographer is what AI produces without voice infrastructure: writing that is competent, inoffensive, and indistinguishable from anyone else’s.
Rebecca Hinds, who runs the Work AI Institute, said the line that I’ve been pondering since. Most people are too exhausted to be curious. I believe her. I also know that’s not the only way this goes, because it isn’t the way it went for me, and the difference between the two is where this issue earns its keep.
