The Most Important Skill in AI Right Now: How to Know When to Stop
Maximum productivity also means maximum burnout and other, worse things
Hey there, I’m Alberto! 👋 Each week, I publish long-form AI analysis covering culture, philosophy, and business for The Algorithmic Bridge. Paid subscribers also get Monday news commentary and Friday how-to guides. I publish occasional extra articles. If you’d like to become a paid subscriber, here’s a button for that:
Today’s post is this week’s paid how-to guide. Should have published yesterday (Friday), and I apologize for the delay. I’m super-focused on the practical side of AI lately because getting this right will define how good your 2026 ends up being. This post, in particular, is about how to approach AI tools so that you don’t fry your brain in the process.
Developer Siddhant Khare writes:
[T]he real skill of the AI era is . . . knowing when to stop. Knowing when the AI output is good enough. Knowing when to write it yourself. Knowing when to close the laptop. Knowing when the marginal improvement isn’t worth the cognitive cost. Knowing that your brain is a finite resource and that protecting it is not laziness - it’s engineering. . . . AI is the most powerful tool I’ve ever used. It’s also the most draining. Both things are true. The engineers who thrive in this era won’t be the ones who use AI the most. They’ll be the ones who use it the most wisely.
Khare’s fix is definite boundaries: time-boxed sessions (he doesn’t use AI forever but sets a timer and then writes it himself), a three-prompt rule (if AI doesn’t get to 70% usable in three attempts, he writes it himself), and deliberate morning hours without AI to keep his own reasoning sharp. His approach to this is personal—might be a bit constraining, although I find it to be a good starting point—but the underlying principle is universal: you have to figure out how to make AI work for you rather than against you. My golden rule is: AI should always enhance—never erode—your brain.
The fact that you can do more doesn’t mean you always should. AI enables a bunch of new cognitive costs and risks associated with this change from being a “maker” to being a “manager” that no one knows how to prevent or fix. There’s no manual because we’re collectively figuring it out on the go. One is burnout, but there are others. I’ve found six that I think belong to different categories; let me know if you have found others. I explain them below and provide specific solutions for each of them (I use them in my own practice):
The perennial rookie
Cognitive surrender
The infinity trap
Invisible unproductivity
Workflow fetishism
Dilettantes and myopics
