The Feedback Vacuum.
Daniel Okafor had been writing Python for three years when his team asked him to pick up Trio, a concurrency library nobody else on staff knew well either. He worked through the self-guided tutorial the way he worked through everything now: with an AI assistant open in a side pane, ready to generate a working example the moment he got stuck. He finished the tutorial in the time the syllabus allotted. He built the sample project. Everything ran.
Three weeks later, a production job using Trio hung intermittently in a way the tutorial had never covered, and Daniel stared at the stack trace with the specific, sinking recognition of someone who has never actually debugged the thing he supposedly learned. He had finished the course. He had not learned the library. He had learned how to get an AI to finish the course for him, which turns out to be a different skill entirely, and one with a much shorter half-life once the assistant isn’t in the room.
Daniel is not a real name, but his experience is close to a real result. In January 2026, Anthropic researchers Judy Hanwen Shen and Alex Tamkin ran fifty-two software developers through exactly this scenario in a randomized controlled trial: learn the Trio library, half with an AI assistant able to generate correct code on demand, half without. The two groups finished in roughly the same amount of time — AI conferred no meaningful efficiency gain in this setting. What it did confer was a gap: the AI-assisted group scored 17% lower on a follow-up quiz testing conceptual understanding, code reading, and debugging, with the largest losses concentrated in debugging specifically — the skill you only build by sitting with an error until you understand it.
The researchers’ own summary of the result is blunt enough to serve as this edition’s epigraph.
“AI-enhanced productivity is not a shortcut to competence.”
— Judy Hanwen Shen and Alex Tamkin, “How AI Impacts Skill Formation”, Anthropic, 2026
That sentence deserves to be read twice, because it contradicts the entire pitch of the last three years of workplace AI adoption. The pitch was never “this will make you incompetent faster.” The pitch was speed, and speed delivered — sometimes. But underneath the speed, in domain after domain, researchers keep finding the same second effect, arriving quietly and mostly unmeasured: something that used to get built through the ordinary friction of work has stopped getting built, because the friction is gone.
That something is feedback. This is an account of the vacuum left behind when it disappears.
What feedback actually was
It is worth being precise about what got removed, because “feedback” has become one of those words flattened by overuse in performance-review culture until it barely means anything. Strip it back to the mechanism. A feedback loop, in the sense that matters here, has four steps: you attempt something, using your current best understanding; the world returns a signal about whether the attempt worked; you notice the gap between what you expected and what happened; and you update your understanding to close that gap next time. Attempt, signal, noticing, update. Run that loop enough times in a domain and the update process compresses — you stop consciously reasoning through problems you’ve solved before and start recognising them. That compression is what “expertise” is. It is not a personality trait or a certificate. It is thousands of completed loops, most of them too small and too fast to remember individually.
Every established profession built its training around this mechanism, usually without describing it in those terms. Apprenticeship systems put novices next to masters precisely so that attempts could be corrected quickly and cheaply, on low-stakes work, before the stakes rose. Medical residencies are built entirely around supervised attempts with rapid correction — the resident makes the call, the attending confirms or corrects it, and the resident updates in real time, thousands of times, before being trusted alone. Newsrooms had sub-editors whose entire function was to hand a piece back covered in corrections, which is a brutal but extremely efficient feedback mechanism, one many journalists will tell you they learned more from than journalism school. None of these systems were designed by anyone thinking about “feedback loops” as a formal concept. They were designed by people who noticed, empirically, that competence didn’t develop any other way.
What all of them share is a structural feature that AI assistance quietly breaks: the correction has to arrive after the attempt, and the person has to notice it as a correction. If the “attempt” step is skipped — if the AI generates the answer before you’ve formed your own — there is nothing for the correction to correct. If the correction happens invisibly, folded into a tool’s silent auto-fix, there is a correction but no noticing. Either way, the loop that builds competence never completes, even though, from the outside, work is visibly getting done, on schedule, to a high standard. That is the trap. The vacuum is invisible from the output side. It only shows up later, in the moment the tool isn’t there.
The mechanism: where AI actually intervenes
It’s tempting to describe this as AI simply “doing the work for you,” but that framing misses where the damage happens. AI doing routine, low-skill work for you is not the problem — nobody develops expertise from filing expense reports, and automating them is a pure gain. The Feedback Vacuum opens specifically when AI intervenes in tasks you are still in the process of learning, and it opens at one precise point in the loop: between the attempt and the noticing.
Consider the two most common patterns of use. In the first, the assistant generates the answer before you’ve formed your own hypothesis — you describe the problem, it returns working code, a finished paragraph, a completed diagnosis. There is no attempt for a correction to attach to, so there is no loop, just output. In the second, you do form an attempt, but the assistant “helps” by silently repairing the parts that were wrong — autocomplete finishing your syntax before your typo becomes an error message, a grammar tool smoothing your sentence before its awkwardness registers as a lesson, a code-review bot auto-patching an edge case you never saw. Here an attempt exists, but the signal that would have told you it was wrong is intercepted before it reaches you. Both patterns look identical from the outside: correct output, produced efficiently. Only one of them — neither, in fact — leaves anything behind.
METR’s 2025 randomized trial of sixteen experienced open-source developers is the sharpest illustration of how invisible this is even to the people it’s happening to. Given AI tools for real coding tasks in mature codebases, the developers took 19% longer to finish than the control group working unassisted. Beforehand, they had forecast that AI would cut their time by 24%. Afterward — having just been measurably slower — they estimated that AI had made them about 20% faster. The gap between the forecast, the outcome, and the post-hoc perception is not a rounding error. It is what a severed feedback loop looks like applied reflexively to the tool itself: the developers had no reliable internal signal for whether the AI was helping, because the very faculty that would normally supply that signal — noticing when something worked and when it didn’t — was itself one of the casualties.
A 2026 theoretical model from Venkat Ram Reddy Ganuthula and a co-author, published in Human Behavior and Emerging Technologies, formalises why this perception gap is not a coincidence. Their model treats skill as a running balance between learning-through-practice and forgetting-through-disuse, with AI assistance shifting both terms of the equation at once: it raises visible, current-period performance — you produce good output today — while simultaneously reducing the practice that maintains skill over time, because the assistant is doing the practising for you. Run the simulation forward and you get a curve that rises before it falls: performance improves in the short run, exactly as advertised, then erodes in the long run, exactly as nobody advertised, and the two phases are far enough apart in time that the erosion is easy to misattribute to something else entirely — a market downturn, a personal slump, “just not what I used to be.”
Whose loop, exactly
The Feedback Vacuum is often discussed as though it is purely a software engineering phenomenon, because software engineering happens to be the domain with the cleanest randomized trials. It is not confined there, and the clearest evidence of that comes from medicine, where the stakes make the pattern much harder to ignore.
In a study published in Radiology in 2023, twenty-seven radiologists — spanning inexperienced, moderately experienced, and highly experienced readers — assessed fifty mammograms with the assistance of a purported AI system offering a BI-RADS category suggestion. When the AI’s suggestion was correct, inexperienced readers matched it correctly almost 80% of the time — a strong result, on its face. When the researchers had the AI suggest an incorrect category, the same inexperienced readers’ accuracy collapsed to under 20%. They were not reading the mammogram and consulting the AI. For all practical purposes, they were reading the AI, and checking the mammogram only enough to notice that it hadn’t obviously contradicted it.
This is the Feedback Vacuum in a setting where the “attempt” step is a diagnosis and the “signal” is, eventually, a biopsy result — a genuinely high-stakes loop that took decades to build into radiology training. The AI did not remove that loop by design; it removed it by convenience, one confident BI-RADS suggestion at a time, in exactly the cases — inexperienced readers, still forming their own independent read — where the loop mattered most. The radiologists who resisted the bias were, unsurprisingly, the most experienced ones: readers who had already banked enough completed loops, before the tool arrived, to have an independent judgment worth defending. The vacuum does not open evenly. It opens fastest in exactly the people who have the least stored expertise to fall back on — which is to say, the people the profession most needs to be developing.
The four shapes of feedback loss
Not everyone loses the loop the same way. Four recognisable patterns of AI use show up across the research and across ordinary workplace observation, and only one of them keeps the loop intact.
The Vending Machine User. Describes the problem, receives the answer, ships it. No hypothesis is ever formed before the AI’s output arrives, so there is no attempt for a correction to attach to — the loop never opens in the first place. This is the largest group, the default mode most tools are designed to encourage, and the pattern behind the 17-point comprehension gap in the Anthropic study: not misuse, just the path of least resistance, taken by people with every reasonable intention of doing good work.
The Ghost Editor. Forms a genuine attempt, but works inside a tool that silently repairs the parts that were wrong before they’re consciously registered — autocomplete finishing the syntax, a smoothing pass cleaning the prose, an auto-fix patching the edge case. An error occurred. Nobody noticed, including the person who made it. This is the subtlest version of the vacuum, because from the inside it feels like fluency, not assistance.
The Deferred Debtor. The loop is not erased so much as pushed downstream, past the point where it’s still useful. An AI-drafted client email goes out with a subtly wrong tone; the client doesn’t reply for six weeks, and the account quietly cools for reasons nobody connects back to that one email. A hiring rubric drafted with AI screens out a strong candidate; the gap in the team’s output shows up as a vague sense that “we’re just not hiring as well as we used to,” eighteen months and several unrelated decisions later. The feedback still technically arrives. It arrives too late, and too disconnected from its cause, to teach anyone anything.
The Sparring Partner. The protective pattern, and the rarest. Forms an independent attempt first — a real hypothesis, a real first draft, a real diagnosis — before consulting AI at all, then uses the AI’s response specifically as a check against that independent view, actively seeking out the disagreement rather than the confirmation. This maps closely onto the specific interaction patterns the Anthropic researchers found did preserve learning: participants who asked for explanations rather than finished answers, or asked conceptual questions rather than requesting generated code, scored no worse than the unassisted control group. The loop survived because they kept forcing themselves back into the “attempt” step the tool was otherwise happy to skip on their behalf.
Most people are not consistently one archetype. A single professional is often a Sparring Partner in their specialism and a Vending Machine User everywhere else — which is, if anything, the right allocation of a scarce resource: the friction is worth defending exactly where you’re still building expertise, and worth discarding everywhere you already have it.
What to actually do about it
For individuals, the operating rule is simple to state and hard to practise: identify which parts of your job you are still learning, as opposed to already competent at, and change your AI usage pattern accordingly. In the domain you’re building, form your own attempt before you open the assistant, and when you do consult it, ask for critique of your reasoning rather than a replacement for it — the research is specific here, not vague self-improvement advice: explanation-seeking and concept-checking preserved learning outcomes; answer-generation didn’t. In domains where you’re already expert, hand over the routine freely. The friction is only valuable where the loop is still under construction.
For managers, the practical move is to stop treating “time to completion” as a sufficient quality metric, because it is exactly the metric the Feedback Vacuum leaves undisturbed while the underlying skill erodes underneath it. Build in comprehension checks that don’t allow the assistant in the room — a debugging question asked cold, a diagnosis defended without the tool’s suggestion visible, a draft explained line by line. This will feel like an unnecessary tax on people who are visibly delivering good output on schedule. It is not a tax. It is the only way to distinguish real judgment from a well-camouflaged vacuum before the day arrives that the tool is wrong and no one in the room can tell.
For leaders, the Ganuthula model’s central warning belongs in every AI adoption plan: the performance gain and the skill loss are on different clocks, and the gain will always report first. A rollout that looks unambiguously successful at the six-month mark can be quietly hollowing out the judgment it will need at the three-year mark, and the metrics that would catch this — comprehension without assistance, debugging under time pressure, an independent second read — are not metrics most AI adoption dashboards currently track, because nobody built the dashboard with the vacuum in mind.
For hiring, treat “fast with AI tools” and “skilled” as two different claims requiring two different tests. A candidate who has spent two years as a fluent Vending Machine User will interview well on anything the assistant can be present for and poorly on anything it can’t — and the gap will not show up unless you specifically design for it to show up, by asking them to debug, diagnose, or explain with the tool switched off.
The tuition nobody budgeted for
There is a version of this argument that lands as nostalgia for productive suffering, and that is not what this is. Nobody is arguing for friction as a virtue in itself, or for a return to debugging without a debugger, reading film negatives instead of digital scans, or drafting on a typewriter. The argument is narrower and, hopefully, more useful: friction was never decorative. It was the delivery mechanism for a signal that competence has always depended on, and removing the friction without replacing the signal does not make the signal unnecessary. It just makes it disappear, silently, exactly where you can least afford not to notice.
The uncomfortable truth of the Feedback Vacuum is that it is invisible by construction. Every measure an organisation typically watches — output quality, time to completion, error rates on delivered work — can look flat or improving while the underlying capacity to produce that output without assistance quietly erodes underneath it, because none of those measures were built to detect the absence of a loop that used to run silently in the background of ordinary work. You do not find out you are in the vacuum by looking at your metrics. You find out the day the assistant is wrong, or unavailable, or simply not in the room — and discover, in that moment, whether the last several years actually taught you anything, or just taught you how to ask.