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AI and society

Delegation is not degradation: must a person be able to redo what the AI did?

Short answerNot being able to repeat the AI's work without the AI is not degradation: that is how any division of labour works. Degradation begins where a person hands the machine not the execution but the goal, the criteria of correctness and the stop button.

The AI horror story is well known and always told the same way. Today a person asks a model to write an email. Tomorrow, to make sense of a contract. The day after, to set up a server. And five years later they sit in front of a screen, cannot remember their times tables and wait for the machine to tell them what to think. Humanity has degraded. Curtain.

There is a real risk in this picture. But it is hidden not where people look for it and, more annoyingly, not where it is convenient to look.

If a person cannot repeat the AI’s work without the AI, that is not yet a diagnosis. A modern human cannot reproduce most of the world they use, and somehow lives with that without considering themselves an idiot. The division of labour exists precisely because mastering every profession you need in person is absurdly expensive. AI has simply made delegation cheap to the point of indecency. So the main question is not: “Could you do the same thing without the machine?”

It is: “What exactly did you hand to the machine: the execution, or the ability to understand, accept and control the result?”

That is where the line runs.

Not being able to repeat is not losing a skill

Picture the owner of a small business. He never knew how to set up a VPS, connect a bot to an automation system or configure backups. Hiring a specialist for an experiment that would most likely die within a month made no sense, so the experiment simply did not exist. Now he describes the task to a model, gets instructions, follows them, and it works. Sometimes even on the first try.

After this it is easy to say: “He depends on the model; without it he could not repeat this.”

True. But which skill exactly did he lose? The one he never had?

Before AI, the entrepreneur could not administer a server and did not have a server. After AI, he is still not a sysadmin, but he has an automation that does useful work. This is not the degradation of a former ability. It is a new capability without fully mastering someone else’s profession. Between “could not and did not have” and “cannot, but have” the difference is tangible, even though the words “cannot” sit in both.

Demanding that he first spend six months learning Linux, networking and security is roughly the same as demanding an accounting degree before hiring an accountant. Such a position looks like a defence of human competence, but in practice it forbids using the division of labour. And forbids it selectively: only where the helper is new and suspicious.

You cannot judge a tool only by the operation a person stopped performing. You also have to look at the task one level up that they started performing because of it. Sometimes it turns out there is no task one level up and the person simply stopped doing something. Then the complaint is fair. But that has to be checked, not assumed.

Skill, capability and three modes of a task

Debates about AI constantly mix up two different concepts, and the mix-up suits both sides because it lets the argument go on forever.

A skill is the ability to perform an operation: write code, translate a text, cost an estimate. A capability is the ability to achieve a result: launch a service, understand a foreign client, assess expenses. A tool can reduce the need for a skill and expand capabilities at the same time. In itself this trade is neither good nor bad. Everything depends on whether the skill handed over was the goal or only the means.

Hence three modes, each with its own exam. One universal exam, “do everything yourself”, does not work, however much examiners like it.

A learning task. A student solves an equation not for the answer but to change themselves. Nobody needs the answer; it is in the back of the textbook. When an AI produces a ready solution, it does not help with the task, it cancels it, leaving behind a grade in the register. Here there is no way around doing the core operation yourself: if you want to learn to write, write, do not just edit something ready-made. In this mode the AI should ask questions, point out mistakes and give hints, not replace the training with a pretty result.

A production task. An entrepreneur needs to reconcile a thousand rows in two price lists. Nobody becomes a better person by doing that with their eyes. It is a technical operation that is sensible to hand to a machine without feeling anything about it. What needs checking here is not the ability to reproduce every step but the ability to accept the work: understanding what function the system performs, which indicators mean success and how much its failure would cost.

A high-risk task. A medical conclusion, a legal position, a large payment, access to critical infrastructure. One user check is not enough; you need an independent competent loop. And no, “I asked another model to check the first one” is not independence: models repeat the same mistakes, were trained on the same data and are equally unaware of your real situation. It is like getting a second opinion from the first doctor’s twin.

Confuse the modes and you get absurdity in both directions. Either we force a business owner to study system administration for the sake of one bot, or we let a schoolchild skip maths because the answer is in the chat anyway. Both, incidentally, are already happening.

Why AI is still not an ordinary contractor

The accountant comparison is too convenient for the person delegating, so it needs a separate check. It is useful but incomplete. AI has properties that make control far easier to lose.

First, a normal contractor exists separately from the verification tool. He can explain a decision, sign off on the work, hand over documentation, carry contractual obligations. A model can produce a faulty configuration and a minute later, in the same confident voice, “check” it and confirm that everything is fine. It can invent a goal, draw up a plan, execute it, check itself and write a success report. The report will be excellent. When one system plays every role, the appearance of control remains, but the independent point of verification is gone. We looked at the same effect at the scale of an organisation separately: a human in the loop who confirms a thousand decisions an hour is not a controller.

Second, a human specialist shows the boundaries of their profession. An accountant does not pretend to be a cardiologist, an electrician does not pretend to be a lawyer, if only out of fear. A general-purpose chat happily moves from taxes to medicine and from programming to law while keeping the same smooth tone. The user takes stylistic confidence for competence, because in people the two usually correlate. In a model they do not correlate at all.

Third, delegation has become almost free and instant. Hiring someone used to force you to formulate the task, discuss the price, describe the result and accept the work. That was inconvenient, and the inconvenience, it turns out, was half the benefit. Now a few seconds pass between a vague wish and a finished answer. A person hands out not only the execution but the framing of the question itself, simply because they never got round to formulating it.

So the danger of AI is not that it spares us manual work. The danger is that convenience lets us miss the moment when, along with the manual work, we handed over the criteria of correctness. And then we are pleased at how fast it all went.

What the research shows

The data so far supports neither the tale of universal dumbing-down nor the tale of unconditional human enhancement. This will disappoint both sides of the argument, but data has no obligation to please anyone.

A study of a real contact centre, published in The Quarterly Journal of Economics, followed 5,172 agents. Access to a generative AI assistant raised the number of issues resolved per hour by 15 percent on average. The largest gains went to less experienced agents. Moreover, during assistant outages some workers kept part of the improvement relative to their earlier level: the authors read this as a sign that the system not only suggested answers but passed the practices of strong agents on to newcomers. That is a counterexample to the thesis “delegation necessarily destroys skill”. The caveat: one firm, one occupation, and a system in which the agent kept the last word.

Another study, presented at CHI 2025, surveyed 319 knowledge workers and collected 936 examples of generative AI use. The higher a person’s confidence in the AI, the less critical-thinking effort they reported; the higher their confidence in their own ability, the more. Thinking did not disappear but shifted from doing to checking the answer and fitting it into the task. The caveat: these are self-reports, not a measurement of declining intelligence. What it does show well is the mechanism of the risk: if the user believes the system is more reliable than themselves, they switch on verification less often exactly where it is needed.

The difference between work and learning was shown by an experiment with nearly a thousand secondary-school students in maths lessons. During practice a standard GPT assistant noticeably improved results, but on the subsequent exam without AI that group did worse than students who had not used the assistant. A restricted tutor version, which gave hints prepared by teachers rather than ready solutions, mostly removed the negative effect. Here the AI became a crutch, because the goal was not to get the answer but to learn to solve.

From the three studies follows one thing: the design of the help must match what we consider the main result, the product now or the skill for later. A boring conclusion. Boring conclusions are usually the correct ones.

The symmetry test

There is a simple way to tell whether we are making a special demand of AI only because it is new and frightens us. Apply the same demand to everything else and see what is left of civilisation.

We do not ask a company owner whether he can redo all of his accountant’s entries. We ask whether he understands the company’s financial position, controls the payments, receives the reports and can change the contractor.

We do not require a tenant to take an electrical panel apart and put it back together. We require the work to be done to code, dangerous lines to be protected, the documentation to be kept, and in case of doubt another specialist to check the result.

We do not consider a person degraded because they cannot manufacture a processor or perform surgery. Honestly, most people cannot change a light bulb without a YouTube video, and civilisation somehow holds. It holds not on everyone’s ability to do everything but on a system of control: property rights, standards, reserves, accountability, the possibility of choosing and replacing the contractor.

The same criterion should apply to AI. If without the model a person cannot type a specific command but understands the goal, sees the result, limits the system’s authority and can hand the work to another contractor, the dependence exists, but it is normal. We depend in the same way on electricity, banks and millions of other people’s competences, and nobody writes anxious columns about that.

If, however, a person does not know what was done, cannot tell success from a convincing report of success, does not control the access and has no way out, the problem is no longer a lack of technical skill. And knowing Linux does not solve it.

Six questions you must be able to answer

Any work can be roughly divided into three levels: execution (write a command, lay out a table, translate a document), judgement (what result is needed and which errors are unacceptable) and governance (who has access, where the data lives, how to stop and roll back). Execution can and should be delegated. The other two levels cannot be handed over unnoticed along with it, and that is exactly what happens, because they are handed over for free and without a receipt. The same ladder at the level of states and economies is described in the article on invisible control: there it ends not in lost skill but in lost control.

In practice this is checked without an exam for sysadmin, editor and lawyer in one person. A person keeps control if they can answer six questions:

  1. What task does the system solve, and what observable result counts as success?
  2. Which errors are acceptable, and at which ones must the work be stopped?
  3. What data, money and actions can the AI, or the automation it built, access?
  4. Where are the accounts, configuration, source materials and backups, and who owns them?
  5. What happens if the service fails, and is there a manual or alternative way to keep working?
  6. Who can independently check the result where your own knowledge is not enough?

If the answers are there, a person can depend deeply on the tool and still remain the owner of the process. If they are not, the ability to retype individual commands by hand will not save them.

What remains after the check

Delegation turns into degradation not when a person stops performing every operation with their own hands. It turns into degradation when a person can no longer formulate the goal, evaluate the result, notice a dangerous error, limit the contractor’s authority and replace them. Far from everyone could do that before neural networks either; there was just nobody to blame for it.

AI can take away some of a person’s exercises. It can unlearn them from doing what they used to have to do themselves. Or it can give them access to tasks they could never take on before. The difference is decided not by the presence of a model and not by the volume of work handed over. It is decided by who, in the end, understands why this is being done, what came out of it and where the stop button is. And whether they have ever checked that the button works.

Sources

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