Entrepreneurship

The Initiative Problem: The Skill AI Quietly Erodes

· 25 min read

A study landed this week that named something a lot of us have felt and not said out loud. TripleTen and Talker Research surveyed 2,000 US office workers and found what they call the AI Direction Deficit: 57% of the C-suite said they had been completely encouraged by their company to use AI, against just 27% of staff. Leaders feel 3.4 times more likely to be well ahead of their coworkers on AI. They are twice as likely to call it enjoyable. The gap they measured is about training and encouragement inside companies. The gap I keep seeing is quieter and lives one level down, inside the person.

Here is the durable version of the story, the part that will still be true when this week’s study is forgotten. AI is astonishingly good at the how. Ask it to write the email, refactor the function, draft the deck, plan the trip, and it delivers. It is silent, though, on the what. It will not tell you which email is worth sending, which product is worth building, which of your three ideas deserves the next six months. It answers the question you bring. It does not decide which question was worth asking.

So the skill that gets cheap is execution. And the skill that gets rare, the one almost nobody is guarding, is initiative. The plain human act of deciding what to do next when nothing and no one is prompting you. I have watched founders lose it without noticing, myself included on bad weeks. You open the model, you take its suggestion, you ship, and it feels like progress. Then one Tuesday you sit down with no prompt in front of you and realize you have not originated a direction in a month. You have only been approving them.

This is the Initiative Problem. Execution became free, and originating direction quietly became the scarce thing. This post is about the muscle that does that originating, why AI shrinks it faster than anything before it, and how to keep yours.

Table of contents

The skill that flipped

For most of work history, the bottleneck was doing the thing. Ideas were cheap and getting them made was expensive. You could sketch a business on a napkin in a minute, then spend two years and a payroll building it. Because execution was the hard part, that is where we trained, hired, and measured. The person who could actually produce the report, ship the code, close the deal, that person was rare and valued.

AI inverted the cost curve. The producing is now the fast part. What is left, sitting exposed where execution used to be, is the deciding. Not the small deciding of which button to click. The large deciding of what is worth pointing all this cheap production at.

And here is the uncomfortable finding. When people lean on AI for the thinking, the thinking gets worse. Michael Gerlich’s 2025 study in the journal Societies surveyed 666 adults across the UK and found a strong negative correlation, about minus 0.68, between frequent AI tool use and critical thinking scores, with the effect running through what researchers call cognitive offloading. The more you hand the reasoning to the tool, the less you do it yourself, and the less you can. Younger participants, who leaned hardest, scored lowest. Correlation is not destiny, and heavier thinkers may simply reach for AI differently. But the direction of the signal matches what anyone who has coasted on autocomplete for a week already suspects.

A 2026 paper in Scientific Reports, a Nature journal, went at the feeling side of it. People did occupation specific writing tasks under three conditions: no AI, passive AI where they copied what the model produced, and active collaboration where they drafted first and used AI to refine. Passive use cut their sense of ownership over the work by close to 20% and their sense that the work was meaningful by around 10%. The unsettling part: those drops lingered even after people went back to working on their own. You do not just feel less like the author while the model is helping. You feel less like the author afterward.

Put the two together and you get the shape of the Initiative Problem. The output stays high. The critical thinking that would have chosen the output drops. The felt ownership that would have made you care about choosing drops. The machine is happy to keep producing. You are the part quietly going quiet.

Who sets the direction

The cleanest way I have found to see this is to draw the loop you actually run each day. There are two versions, and they look almost identical from the outside. Same tools, same screens, similar output. The difference is one node: who originates the direction.

The Origination LoopYou decide what is worth doingYOU setdirectionAIexecutesYOUjudgeThe muscle stays yours and growsThe Approval LoopThe model decides what is nextyou setdirectionAIproposesYOUapproveThe muscle greys out and shrinks

Look at the left loop. The direction starts with you. You decide what is worth doing, the AI does the labor, you judge the result, and that judgment feeds your next decision about what is worth doing. Your origination node is lit up and load bearing. Every trip around the loop is a rep for the muscle that sets direction.

Now the right loop. It runs just as fast. Output comes out the other end. But the direction starts with the model. It proposes the next step, you approve, it proposes again. Your origination node is still on the diagram, greyed out, because technically you could have set the direction yourself. You just did not need to. And a muscle you do not need does not stay strong.

The trap is that both loops feel productive in the moment. You cannot tell from your calendar or your commit history which one you are running. You can only tell by asking a strange question: today, where did the what come from? If the honest answer is mostly the model, you have been living in the right loop, and the cost does not show up until the day you need to originate and find the node dark.

The Prompt Reflex

I call the habit that forms in the right loop the Prompt Reflex. It is the learned tendency to wait for a suggestion before you move. Not laziness exactly. Something more like a trained expectation that the next step will arrive on its own, so there is no reason to generate it.

Psychologists have a name for the deeper version of this. Martin Seligman’s work on learned helplessness showed that when your actions stop seeming to matter to the outcome, you stop initiating them, even later when initiating would work fine. The mechanism is not that you lack ability. It is that you have learned, correctly in the moment and wrongly in general, that originating is not your job. AI does not make you helpless. It makes originating optional, over and over, until optional quietly becomes never.

What makes this hard to catch is that each individual surrender is completely reasonable. A research project on the loss of human agency framed it well: people give away their thinking one small, sensible step at a time, and no single step ever feels like a loss. Of course you took the model’s outline, it was good. Of course you shipped its plan, it was faster. Of course you let it pick which three tasks mattered today, you were busy. Every one of those was the right local call. Stacked over months, they add up to a person who has not chosen a direction in a long time and has half forgotten how.

Self-determination theory, the Deci and Ryan framework that has held up for decades, puts autonomy at the center of motivation. People need to feel that their actions originate with them. Strip that out and motivation drains, which is exactly what the Nature study measured as lost ownership and lost meaning. The Prompt Reflex is not just a productivity issue. It is quietly an motivation issue, because work you did not choose is work you struggle to care about.

What shrinks when you stop originating

There is a clean physical analogy for what is happening, and it comes from London cab drivers. To earn a license they memorize a tangle of thousands of streets, a feat called the Knowledge. Eleanor Maguire’s 2000 study at PNAS scanned their brains and found the posterior hippocampus, the region tied to spatial memory, was measurably larger than in non drivers, and it grew with years on the job. The muscle responded to use.

Then GPS arrived. Later research found that heavy satnav users have weaker spatial memory and struggle more when they have to navigate unaided. The capacity did not vanish because the roads changed. It faded because the finding of the route got outsourced. You stop doing the thing, the thing shrinks. Your brain is efficient. It does not maintain expensive capacities you appear to have stopped needing.

Initiative works the same way, and it is a more expensive capacity than street memory. When you outsource the origination of direction, here is what quietly weakens.

What you outsource to AI The muscle that quietly shrinks
Deciding what to work on today Prioritizing from a blank page, not a menu
Generating options and ideas Divergent thinking, the ability to produce your own starting points
Framing the problem before solving it Asking the right question instead of answering the given one
Sitting with an unclear next step Tolerance for ambiguity, the nerve to move without a prompt
Owning the choice and its result The felt authorship that makes you care enough to choose well

None of these show up on a dashboard. That is the danger. Your output looks the same or better while the underlying capacity to point that output at the right thing gets thinner. This is close cousin to the cognitive debt founders take on when they offload reasoning, and it rhymes with the reps problem, where skipping the small hard repetitions costs you the judgment that only reps build. The Initiative Problem is the reps problem aimed at one specific muscle: the one that starts the whole thing.

Passive use costs more than you think

The Nature study points at the escape hatch, and it is not turning off the tools. The damage was concentrated in passive use, the copy and paste path where the model produces and you accept. Active collaboration, where people drafted first and then used AI to sharpen, kept their ownership and meaning close to the levels of working with no AI at all. Same tool. Opposite effect. The variable was who started.

That single word, who started, is the whole game. Look at what the two paths do to the two things that matter, ownership and meaning.

Who starts decides what you keepSense retained (relative)050100No AIthe baselineActiveyou draft first-20%-10%Passiveyou copy outputOwnershipMeaning

Notice what the chart does not say. It does not say AI is bad for you. Active use holds the line. It says passive AI use is where the initiative leaks out, and passive is the default because it is the path of least effort. The model offers a finished thing, and accepting a finished thing is easier than starting your own and having the model react to it. The easy path and the costly path are the same tool used in a different order.

This is why my rule is seed then draft, never blank then paste. Before the model touches a problem, I put down my own rough take, even three ugly bullet points. That small act of going first keeps me the author. The AI becomes an editor of my direction instead of the source of it. It is the difference between the left loop and the right loop, expressed as a habit you can actually run.

The compounding cost of a borrowed direction

There is a second cost that is easy to miss because it hides behind good execution. A borrowed direction compounds. When you originate the what, you own the whole chain, and if the direction is wrong you feel the friction early and course correct. When the direction came from the model and you only approved it, you have no gut stake in it. You will execute a mediocre direction beautifully and for longer, because nothing inside you is arguing with it.

I have watched this play out. A founder lets the model shape the roadmap, and the roadmap is reasonable, so the team builds it, fast and clean. Six weeks later the thing is shipped and no one wants it, and the founder cannot quite say why they built it, because they did not decide to. They approved it. Approval leaves no fingerprints. When you cannot reconstruct why you are doing something, you cannot tell when to stop, and a wrong direction executed efficiently is more expensive than a wrong direction executed slowly, because you get further down it before the pain arrives.

This connects to something I wrote about in the velocity illusion: motion is not the same as progress, and AI is a motion machine. It will happily take you at high speed in a direction you never actually chose. The faster the execution, the more it matters who chose the heading, and the Prompt Reflex is precisely the habit of not choosing it.

The cost is not one bad project. It is a slow drift where more and more of your work is directed by whatever the model found reasonable, which trends toward the average of what everyone else is also being shown. Originate less and your work converges on the median. The distinct thing you would have built, the one that came from your specific taste and context, never gets started, because starting is the exact step you handed away.

The Direction Deficit at scale

Zoom out from the person to the company and the same pattern is visible in the TripleTen data, which is why the study named it a deficit at all. When a firm hands out AI tools but not the training and encouragement to use them with intent, staff default to the passive path. The tools get used and the people get quieter. Leaders, who tend to keep setting direction because that is their job, feel the tailwind. Everyone downstream feels a subtle loss of authorship they cannot name, and it reads in the survey as a big gap in enjoyment and confidence between the top and the middle.

An organization full of people running the approval loop is fragile in a specific way. It can execute anything and originate almost nothing. It waits, capably, for direction. That is fine while the person at the top is a strong originator and terrible the moment they are not, because there is no bench of people who kept the muscle warm. The company optimized for output and quietly stopped growing the capacity to decide what the output should be.

If you lead people, this is a design choice you are making whether or not you notice. Every workflow that goes model proposes, human approves is training your team toward the Prompt Reflex. Every workflow that goes human frames, model assists, human decides is training them out of it. It costs a little more friction now. It buys you a team that can still think when the map runs out, which is the only kind worth having. I sketched the broader version of this in the founder operating system for the AI age, and it starts with protecting origination as a first class activity, not a thing you hope survives.

Why more AI cannot fix this

The tempting fix is to point AI at the problem. Have it prompt you, nudge you, ask you the daily what. Build an agent that surfaces the decisions. And it will work, in the sense that decisions will get surfaced. It will not work in the sense that matters, because the thing that atrophied is the act of originating, and a model that originates for you is more of the disease presented as the cure.

Initiative is not information you are missing. It is a capacity you build by exercising. You cannot buy it back with a better tool for the same reason you cannot get fit by watching someone else run. The doing is the point. When the model asks you the question, you are still in the approval loop, just with an extra step. You did not originate the question. You reacted to it.

There is a hard asymmetry here that is worth sitting with. Execution capacity is now something you can rent by the token, near infinite and cheap. Initiative is not rentable. It lives only in you, it grows only when you use it, and it shrinks whenever you let something else use it for you. The whole pull of AI runs one direction, toward cheaper doing, and that same force pushes the other direction on initiative, toward less of it, unless you actively hold the line. More AI does not neutralize that. More AI is the current you are swimming against, and the tool that created the pull cannot also cancel it. I made the same argument about oversight in AI decision fatigue: the fix for a problem AI created is rarely a bigger dose of AI.

Initiative sits upstream of every other AI-era skill

Most of the advice about thriving alongside AI points at skills that sit one layer above initiative, and misses that they all depend on it. Take taste, the ability to tell good from mediocre, which I have argued is one of the few things that stays valuable when output is cheap. Taste is real and it matters. But taste is a judgment you apply to something that already exists. It answers is this good. It cannot answer what should exist in the first place, and if you never originate the candidate, there is nothing for your taste to judge. Initiative comes first. I wrote the case for taste in how to build taste, and everything there assumes you already generated the thing being judged.

Same with learning. There is a real trap where AI makes you feel like you understand a subject because the explanations are so smooth, while nothing actually lodged, which I called the fluency trap. That trap is downstream of initiative too. Fluency without retention is what happens when the model drives the learning and you ride along. The fix in both cases is the same move: go first, struggle a little, then let AI assist. The person who decides what to learn and takes a rough swing before opening the model keeps both the understanding and the direction. The person who lets the model lead keeps neither.

Even trust in AI output, the skill of knowing when to believe what the model hands you, sits on top of initiative. I wrote about the danger of automation bias, the tendency to accept a machine’s answer because it is a machine’s answer. Notice that automation bias and the Prompt Reflex are the same reflex pointed at two different objects. One is accepting the model’s answer without checking. The other is accepting the model’s direction without originating. Both are the atrophy of the part of you that pushes back and starts fresh. Fix the deeper habit of going first, and both problems shrink together.

This is why I treat initiative as the base of the stack, not one skill among many. Taste, learning, judgment, and trust all assume a human who still originates. Knock out that foundation and the skills above it have nothing to stand on. You can have exquisite taste and never make anything, because you stopped starting. You can be a fast learner who only ever learns what the model queued up. The whole point of the AI opportunity map is that the openings go to people who can see what is worth building before it is obvious, and seeing what is worth building is initiative wearing a business suit. Protect the base and the rest of the stack has somewhere to grow. Let the base erode and no amount of skill on top will save you, because you will be superbly equipped to execute directions you did not choose.

There is a wellbeing angle here too, and it is not soft. Work you originated is work you can care about, which is most of what protects you from the slow grind of doing more and feeling less, the pattern behind a lot of AI burnout. When every direction comes from outside you, the work turns into a treadmill of tasks you never chose, and that is exhausting in a way no amount of rest fixes. Keeping the origination yours is not only how you stay effective. It is how the work stays worth doing at all.

The Initiative Map

To make this usable, I map any piece of work on two axes. Who originated the direction, running from the model to you. And who executed it, running from you to the AI. Those two questions produce four quadrants, and knowing which one you are in tells you exactly what is at stake.

The Initiative MapWho originates the directionYouAIWho executesYouAISelf-ReliantYou choose, you build.Strong but slow.Founder-Led★You choose, AI builds.Fast and still yours.BusyworkAI picks, you grind.Effort without ownership.PassengerAI picks, AI builds.You are along for the ride.

Founder-Led is the target, the starred quadrant. You originate the direction and let AI carry the execution. This is the whole promise of the tools used well: you keep the scarce thing, the deciding, and rent out the cheap thing, the doing. It is fast and it is still yours.

Self-Reliant, top left, is the old default. You originate and you execute. Nothing wrong with it, and for the highest stakes work you may want to stay there on purpose to keep the muscle warm. But you cannot run everything here, it does not scale, and that is fine.

The two bottom quadrants are where the Initiative Problem lives. Passenger, bottom right, is the pure approval loop. The model picks and the model does, and you are a spectator with a login. Busywork, bottom left, is sneakier and more common than it looks. The model or the tool decided what mattered, and you are down there executing it by hand, feeling busy and productive, with no authorship at all. A day full of AI suggested tasks that you dutifully did is a day spent in Busywork, and it reads as effort, which is why it fools you.

The move is not to live in one quadrant. It is to notice which one each piece of work is in, and to make sure the important work sits up top where you originated it. If you find your week is mostly bottom half, you do not have a productivity problem. You have an initiative problem, and no amount of speed fixes a heading you did not set.

Quadrant What it feels like What to do about it
Founder-Led Fast, and you can say why you are doing it Put your most important work here on purpose
Self-Reliant Slow, effortful, deeply yours Keep the highest stakes calls here to stay sharp
Passenger Smooth, easy, oddly hollow Fine for the trivial, dangerous for anything that matters
Busywork Busy, productive-seeming, forgettable Stop and ask who chose this before you keep grinding

The contrarian take

Here is where I will argue against the easy read of my own post. The obvious objection is that this is just smart delegation, and delegation has always been good. When you hired an assistant to run your calendar, you did not lose the ability to schedule. You freed yourself for higher work. Is AI proposing your next step not the same healthy handoff? Why treasure a muscle that a tool now performs better?

The objection is partly right, and the honest counterweight matters. Some direction should be seeded by AI. You cannot originate everything from scratch, and pretending otherwise is its own kind of vanity. A model that scans a space you do not know and hands you three starting options is a gift, not a theft. The real skill was never originating every single thing. It is knowing which decisions must stay yours and which can be safely seeded, and that sorting is itself an act of initiative. Treat that as the line, not a blanket refusal to accept the model’s help.

But the delegation analogy breaks in one place, and it is the place that makes this worse than a normal handoff. When you delegated to a person, you still owned the direction. The assistant scheduled the meetings you decided to take. AI is not offering to execute the direction you set. It is increasingly offering to set the direction too, and to do it so smoothly that you never notice the handoff happened. Here is the twist that inverts the usual advice: the better AI’s suggestion is, the more dangerous it is to your initiative, because a bad suggestion you would reject and a great one you accept without thinking, and it is the accepting without thinking that shrinks the muscle. The quality of the model is not your protection. It is the exact thing that makes the Prompt Reflex so easy to slip into.

So the law I keep coming back to is this. AI is brilliant at answering the question and silent on which question is worth asking. Execution became free, and originating direction quietly became the rare thing. The muscle that decides what is worth doing only grows when you use it, and it is the one capacity you cannot prompt your way back into. Guard it like it is the job, because increasingly it is the only part of the job that is still yours.

What to do Monday morning

None of this matters unless it changes what you do this week. Here is the practice, and it is deliberately small, because initiative is rebuilt with reps and not resolutions.

Run the blank-page test. Once tomorrow, before you open any AI tool, sit with the actual question of what is worth doing and write your own answer first. No model, no menu, just you and the blank page for five minutes. If it feels hard, that is the muscle telling you how weak it got, and the discomfort is the training working.

Seed before you draft, always. Make it a rule that the model never sees a problem before you have put down your own rough take. Three ugly bullets is enough. This one habit moves you from the passive path the Nature study punished to the active path it protected, and it keeps you the author of your own work.

Protect one origination block. Put a recurring block on your calendar, thirty minutes, where the only allowed activity is deciding what matters, with no tools open. Not executing, not consuming, not approving. Deciding. Guard it the way you would guard a meeting with your most important customer, because in a real sense it is one.

Run an initiative audit on last week. Go back through what you actually did and mark each block with who originated it, you or the model. Add up the two columns. The ratio is your real score, and it is usually more lopsided than it feels. If most of your week came from the right loop, you have found your problem.

Pre-commit your direction before the model speaks. For the next real decision, write down what you intend to do and why before you ask AI anything. Then let it critique, stress test, and execute. You will still get the speed. You will just keep the authorship, which is the whole point. I laid out the wider discipline of choosing what to build and learn in what to learn in the AI era and in the founder operating system, and every one of those systems starts here, with keeping the deciding yours.

The tools are going to keep getting better at the how. That is not the threat. The threat is that they get so good at it you forget the what was ever your job, and you wake up one Tuesday fluent at approving and rusty at deciding. Keep the muscle warm. It is the last thing on the whole stack that only you can run.

Frequently asked questions

What is the Initiative Problem?

The Initiative Problem is the quiet erosion of self-direction that happens when you rely on AI. Because AI handles the how of a task so well, the skill that atrophies is the what: deciding what is worth doing when nothing is prompting you. Execution became cheap and originating direction became the scarce capacity, and that capacity only grows when you exercise it.

Is using AI making me less capable?

It depends entirely on how you use it. A 2026 study in Scientific Reports found that passive use, copying what the model produces, cut people’s sense of ownership by close to 20% and meaning by around 10%, with the effects lingering afterward. Active collaboration, where you draft first and use AI to refine, held those close to normal. Same tool, opposite result. The variable is who starts the work.

What is the Prompt Reflex?

The Prompt Reflex is the learned habit of waiting for a suggestion before you move. Over time, letting AI propose every next step trains an expectation that direction will arrive on its own, so you stop generating it yourself. It echoes learned helplessness in psychology: when originating stops seeming like your job, you stop doing it, even when doing it would work fine.

How is this different from decision fatigue?

Decision fatigue is about the volume and depletion of decisions you already have to make. The Initiative Problem is upstream of that. It is about whether the decisions originate with you at all, or whether you are simply approving choices the model surfaced. One is running out of energy for decisions you own. The other is quietly ceasing to own them.

Does this mean I should stop using AI?

No. Turning off the tools is a bad trade and it is not what the evidence points to. The research shows the damage is in passive use, not AI itself. The move is to change the order: seed the problem with your own take before the model touches it, keep the direction yours, and let AI execute. Used that way, AI protects your initiative instead of draining it.

What is the blank-page test?

The blank-page test is a simple daily rep. Before you open any AI tool, sit with the question of what is worth doing and write your own answer first, with no model and no menu, for about five minutes. If it feels hard, that is a signal the muscle has weakened. Doing it regularly rebuilds the capacity to originate direction rather than react to it.

How do I know if my initiative is atrophying?

Run an initiative audit. Go through last week’s work and mark each block with who originated it, you or the model, then total the columns. If most of your week came from suggestions you approved rather than directions you set, your initiative is leaking. The signal is subtle because output stays high, so you have to measure origination directly rather than trust how busy you felt.

Can I train initiative back once it fades?

Yes, but only by using it, not by buying a better tool. Initiative is a capacity, like spatial memory or physical fitness, that responds to exercise. Protect a daily origination block where you only decide with no tools open, seed every problem before drafting, and pre-commit your direction before asking AI anything. The reps are small and the recovery is gradual, but the muscle does come back.