We're All Managers Now, Congratulations
Everyone becomes a manager, we’re told. In skill-acquisition terms, that’s a demotion.
Hey reader, ever have a manager that you feel like is from another planet? A middle manager that acts like it’s their first week on Earth? Did that then increase your desire to become a manager? Not the boss, but just a manager. How about a manager of a computer that tricks people into thinking it’s conscious?
Well, you’re in luck, AI would like to allow you to become a manager. No need for new skills, just stop doing the work and start directing it. You will review outputs, approve and reject, orchestrate a little staff of tireless 01010101 subordinates who never take PTO and only occasionally lie to you. As the meme goes, “YOU HAVE BEEN PROMOTED”!
Except you didn’t ever want this promotion. No kid has ever stood up at career day and announced that when they grow up, they want to review documents1. If you look how humans actually acquire skill, “everyone becomes a manager” starts to look less like a promotion and more like a demotion with a robot attached. AI shoves experts back down the skill ladder into the exact cognitive mode they spent a decade climbing out of.
This is really all about expertise though. Expertise comes from skill acquisition, not just from reading a lot of books. Its the combination of learning and doing. The psych student in me still looks towards the Dreyfus model of skill acquisition2 which describes five stages: novice, advanced beginner, competence, proficiency, expertise. Let’s break this down using food. A simple tomato sauce is a good test case here. The novice version is a recipe: sauté the garlic for exactly two minutes, add the tomatoes, simmer for forty. The advanced beginner starts noticing situational stuff, like that the garlic browns faster in the thinner pan. At competence, you’re planning consciously. You weigh options, you consider the acidity of this particular batch of tomatoes, you make what are essentially little pro/con lists in your head, and you take responsibility for the analysis.
The intriguing transitions occur after that. At proficiency, you begin perceiving the situation as a whole rather than as a collection of individual features. At expertise, you cease all deliberation entirely. Experts don’t even think anymore; they simply observe the pot and comprehend it. Dreyfus and Dreyfus3 describe this phenomenon precisely: when things are proceeding normally, experts don’t solve problems or make decisions. Instead, they perform what is typically effective.
The aspect of the model that is relevant to my argument is the section that discusses what hinders the ascent. Adhering to conscious rules and procedures impedes your progress to competence and prevents intuition from ever developing. To overcome stage three, you must relinquish the checklist and engage directly with the complex reality of the domain. In this interpretation, expertise is a perceptual capacity. What we casually refer to as taste is essentially intuition at the expert level. It’s an effortless recognition that this situation resonates with countless personal experiences.
Also from psychology, Ericsson’s deliberate practice research4 outlines the purchasing mechanism: structured, effortful repetition on your specific weaknesses, immediate feedback, and time to retry. While the replication wars have significantly altered his original numbers, all parties involved in the debate concur on the fundamental principle: intuition is acquired through repetition, and only effortful repetitions are accepted by the store.
AI disrupts all of this on a few levels. Previous automation ate the bottom of the ladder. Spreadsheets and compilers took over the novice-level rule-following, the stuff you could proceduralize, and pushed human output upward toward judgment. This was and is, historically, a pretty good deal.
Current AI produces output that lacks any prior precedent. It generates drafts that resemble the writing of your most accomplished senior on a particularly good day, code that resembles the work of your best engineer, and analysis that bears the surface texture of two decades of accumulated experience.
Consequently, the human in the loop adopts a new default posture. Instead of generating from their own perception of the situation, they now review generated candidates and select among them. They weigh alternatives, evaluate them against criteria, and make analytical choices with full conscious deliberation.
Now go back to the five stages and find that activity.
It’s competence, specifically stage three of five, the stage the entire model warns you about getting stuck at. The expert-turned-reviewer is performing stage-three work on top of stage-five output, and everywhere you look, this arrangement is being portrayed as an advancement. You’ve been demoted, and HR will handle it.
If this were merely a static demotion, you could perhaps dismiss it. Well, now I’m an evaluator, and my evaluations are expert-grade. The system appears to be functioning correctly.
The issue is that expert-grade evaluation requires maintenance, and no one in this arrangement is paying for it. The skill-decay literature is straightforward on this: cognitive skills deteriorate faster than physical ones, and the decline accelerates with disuse. Even deep expert pattern recognition fades when you stop encountering the situations that keep it sharp. Researchers often refer to expert intuition as “resilient,” which sounds reassuring until you realize that resilient and permanent are different concepts.
So the loop runs like this. The AI removes effortful production, which removes your reps. Your skills decay, or if you’re a junior, never form in the first place. Decayed skill makes independent judgment feel slow and shaky, so you lean harder on the AI, which removes more reps, which decays more skill. There’s a 2024 research thread on AI-induced skill development hindrance5 documenting the front end of this loop in learners, and the finding is about what you’d guess: the abilities the AI handles are precisely the abilities the human never acquires. The machine does your homework and you fail the final, except the final is your career and it’s administered continuously for thirty years.
At the organizational level, some researchers have begun referring to the accumulated version as cognitive debt, which I find almost overly polite. It’s an erosion of human capabilities that goes unnoticed on any dashboard because the output remains satisfactory. However, it only surfaces on two specific days: the day when the AI makes an error that no one in the building can detect, and the day after, when recovery becomes impossible without it. Consequently, leaders approve work that they can no longer genuinely assess. The talent pipeline produces individuals who have reviewed a thousand expert-shaped documents but have written none.
And here’s the genuinely unsettling aspect. Your calibration drifts at the same rate as your standard for judging it. The output consistently surpasses a declining bar maintained by the same declining judgment. You can be years deep into atrophy with a performance review that claims you’ve never been more productive. In a sense, this is true; you haven’t. However, you haven’t accomplished anything either.
The answer is not abstinence. I use these systems every day and I build on top of them, so a purity argument from me would be laundered hypocrisy. The answer is treating human skill maintenance as a design constraint of the system instead of a personal virtue you’re supposed to exercise on the side, alongside flossing.
I’ve been running this experiment on myself with MicrowaveOS, the personal agent runtime I’ve written about here before. Two mechanisms in it exist purely because of the loop above, and both amount to me building a robot that makes me do homework before it helps me. Pre-game briefings force me to state my own read of a problem before the system shows me its work. That’s the perception rep, preserved, instead of letting me skip straight to the review chair. Checkpoint enforcement inserts mandatory friction at decision points, so I stay at minimum an active stage-three participant instead of a passive consumer of finished output. Neither mechanism makes the system more productive this quarter. Both exist so that my judgment is still worth having in year three.
Does it work? I honestly don’t know, and I want to flag that this is real uncertainty rather than essayist humility. We have a mountain of expertise research and a mountain of decay research and almost nothing connecting them in complex real-world domains. AI-augmented knowledge work is exactly that domain, and nobody has a validated answer yet. I’m one guy with a weird pasta-adjacent agent runtime and a hypothesis.
Here’s the hypothesis, and where I’ll end (for now). A system optimized to maximize your output this quarter and a system optimized to develop your judgment over years are architecturally incompatible. The first one wants to remove your friction and increase returns. The second one requires keeping some of it, on purpose, forever. Every AI product on the market today is built to the first objective, which means the second objective is currently nobody’s job. If Dreyfus is even directionally right about how expertise forms, that’s a design decision all of us are living inside of. And we made it the way most bad decisions get made, which is by not noticing we were making one.
And let’s be honest, you’re asking AI to review those too
In case you’re curious about more Dreyfus: https://en.wikipedia.org/wiki/Dreyfus_model_of_skill_acquisition
Yeah the Dreyfus model is actually two Dreyfus’ (Dreyfi? Dreyfus Squared). Brothers Hubert and Stuart.
One replication put deliberate practice at around 26% of the variance in expert performance, well below Ericsson’s original claims. Fine. A quarter of expertise is still an enormous amount of expertise to leave on the table because a model offered to do your reps for you.
https://link.springer.com/article/10.1186/s41235-024-00572-8