Basically had the same urge to write about this problem, prompted by the exact same comments around mental fatigue this week. Only got to the research stage.

Here's some of the literature I dug up when looking at what is the potential risk to cognition when you don't enjoy what you are doing.

Working memory is "gated"; you selectively process information relevant to a goal - or why you need to turn the radio off to reverse a car. (Numerous papers take it as a given, can't find a specific one developing the exact model of gating)

On working memory and trainability: https://www.nature.com/articles/nrn.2016.43 Working memory is (potentially) dopamine responsive, and expanded by use/training.

On building mental models, writing something down activates more of your brain than typing (cognitive offloading): https://www.scientificamerican.com/article/why-writing-by-ha...

I would argue that typing is better than just reading, and programming requires some extra elements - as you cut and paste to rearrange, run tests, iterate, spatially navigate to where various areas of your code is; so is likely closer to the findings around handwriting than the study. But I don't have specific studies on that.

On reward ($) as a proxy for enjoyment/flow state; and motivation; these two used similar basic designs to experiments https://www.nature.com/articles/s41598-025-09949-1

"Participants performed a delayed-estimation orientation working memory (WM) task with reward cues indicating reward levels at the beginning of trials. The results revealed that motivational incentives significantly improved WM performance and increased pupillary dilation during maintenance. These findings provide evidence for the modulation of WM maintenance by reward through enhanced top-down cognitive control processes."

https://www.jneurosci.org/content/39/43/8549 > "During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high-reward trials. Critically, high reward boosted neural coding of the active task rule, and the extent of this increase was associated with improvements in task performance"

You can also infer from their experiments that low reward = less care exercised.

I feel like a lot of these papers aren't really surprising, but they do measure something that many people have probably felt is true but can't prove.

While these papers don't talk about AI or decline in skills specifically, it's reasonable to say you don't get many of the benefits when it is low reward/passive task execution; where you are leaving review comments that are just reprompting a machine - you know it's not a person, so it feels even lower value to engage than a standard code review might.

I think overall, the rule of thumb around when to use AI should be closely linked to how painful / low reward a task is likely to be. Debugging something with a 10 minute build/test loop and a mystery problem that is not easy to control? AI party. Writing a complex but fun set of business rules? Run it on your wetwear while it is still giving you a sugar hit. An "easy" bug you have stuffed up fixing three times in a row? Push through a bit of discomfort and frustration; but fall back to tooling when you have invested reasonable efforts and are starting to feel slightly fatigued.