Cognitive debt, capability debt, and the rest

Cognitive debt, capability debt, epistemic debt, culture debt, distributed de-skilling. Five terms, published between June 2025 and June 2026, for versions of the same worry: that using AI well in the short term costs something that only shows up later. This page maps them. The finding that came out of doing so was not the one expected. Almost nobody claims to have coined any of them.

Every source in this map was read at the primary source

The terms below were checked against the paper or the publisher's own page, not against summaries. Where a source could not be reached, it is named at the foot of this page and left out of the map rather than described from secondary coverage. Three sources fell into that category, and one of them would have supported a point this page would otherwise have liked to make.

The debt family, and who actually claims what

A coinage claim is a specific thing. It reads "we introduce", "we term", "we propose the term". Using a phrase, even defining it carefully, is weaker than claiming it. That distinction turns out to matter more than the terms themselves.

• Cognitive debt. Kosmyna and colleagues, MIT Media Lab, arXiv 2506.08872, June 2025 with a revision on 31 December 2025. The phrase appears four times in 216 pages. It is not in the abstract. There is no "we introduce" or "we coin" anywhere in the paper, and no citation attached to any of the four appearances. The paper never connects it to technical debt, the analogy most coverage assumes it is making. Defined once, on page 151, as "a condition in which repeated reliance on external systems like LLMs replaces the effortful cognitive processes required for independent thinking."
• Capability debt. Rohde, "Short-Term Gain, Long-Term Fragility", SSRN, written 20 April 2026, revised 27 April 2026. Defined in a paragraph that also defines institutional debt, both introduced by way of the technical debt analogy. Rohde makes no coinage claim. What he claims is a mechanism: his stated contribution is "to identify and formalize a mechanism of capability masking and capability erosion". The paper is, in his own words, "a conceptual synthesis rather than a new empirical study".
• Epistemic debt. Sankaranarayanan, arXiv 2602.20206, February 2026, revised March 2026. The author puts the term in quotation marks in his own title, which signals borrowed usage rather than ownership, and explicitly builds on Kirschner's distinction between cognitive offloading and outsourcing. What he does claim as novel is an instrument, the "Explanation Gate".
• Culture debt. Deloitte, press release of 4 March 2026, defined inline as "the negative consequences an organization accumulates by neglecting its culture". Introduced in quotation marks, with no authorship claim.
• Distributed de-skilling. BCG, 17 June 2026. The only firm ownership language in the whole set: "We call this distributed de-skilling, a collective erosion of human skills that undermines organizational intelligence and resilience over time."

The actual finding: convergent metaphor, no shared lineage

Rohde, Sankaranarayanan and Deloitte each reach for a debt metaphor within four months of each other. None of them cites either of the others. Three separate authors, working in organisational economics, computing education and human capital consulting, independently arrived at the same accounting figure for the same phenomenon.

That convergence is more interesting than any individual coinage would have been. It suggests the metaphor is doing something the field genuinely needs, which is to describe a cost that is incurred now and paid later, invisible on any current measure. It also means the question "who said it first" is close to unanswerable and probably not worth answering.

What the most-cited term actually rests on

Cognitive debt is the term that reached the public. The study underneath it deserves reading rather than citing.

Fifty-four participants, aged 18 to 39, recruited from five universities in the Boston area, writing essays across three sessions. Three groups of eighteen: one using ChatGPT, one using a search engine, one unaided. EEG on 32 channels. The finding most quoted, that removing AI left the LLM group unable to quote their own work, comes from session four, which only 18 participants attended, nine per arm. The widely repeated 78 per cent and 11 per cent are seven of nine and one of nine.

The paper still carries "Preprint, under review" in the footer of all 216 pages of its December 2025 revision. It has not been peer reviewed. The authors state their own limits clearly, including that findings "are context-dependent and are focused on writing an essay in an educational setting and may not generalize across tasks", and they hedge the central passage themselves: "This next finding should be considered preliminary, as a larger participant sample is needed to confirm the claim."

On 29 December 2025 a formal Comment was posted by Stanković and colleagues at Vienna and TU Dresden, arXiv 2601.00856. Their power analysis puts the required sample at roughly 159. Their sharpest point is about the term itself: the search engine group used an external tool and showed no impairment, with the comparison against the unaided group returning p = 1. If offloading to a tool produced cognitive debt, that group should have shown it. The Comment is also a preprint, offered as a critique rather than a refutation.

None of this makes the study worthless. It makes it a small, unreviewed, contested pilot carrying a term that a great deal of subsequent commentary treats as settled.

What the most influential survey rests on

BCG's June 2026 article will be quoted in boardrooms for the rest of the year, and its headline numbers are worth reading with their provenance attached. Seventy C-suite leaders and senior executives. Half already observing de-skilling. More than 60 per cent expecting it to be a material threat within three to five years. Judgement and decision-making named as carrying the highest de-skilling risk.

The article states no countries, no fieldwork dates, no sampling method, no response rate, no industry breakdown and no question wording. It has no limitations section, no endnotes and no reference list. A "de-skilling risk score" is reported as though it were a defined metric and is never defined. By the grading used across this estate, that places it as an institutional survey of unknown representativeness, useful as a signal of what senior people now believe and not usable as a measurement of what is happening.

The same applies to Deloitte's March 2026 release, which reports that 85 per cent of leaders call adaptability critical while 7 per cent say they are leading on it, and gives no sample size, no countries and no fieldwork dates in the release itself.

The pattern across all three is the same. The vocabulary is ahead of the evidence, and the evidence is ahead of its own methodology sections.

Where this research sits, stated against itself

This estate uses capability debt. It makes no claim of first use, and the check above is the reason it never will: Rohde defines the term in print and does not claim it either, so the honest description is that two people arrived at an obvious metaphor for the same problem at roughly the same time, along with several others who reached for adjacent versions.

The same standard applies to the rest of the vocabulary here. Usage theatre and the verifier's discount carry no first-use claim. Synthetic seniority and the missing rungs do, and are dated. Applying to your own vocabulary the test you apply to everyone else's is the cheapest credibility available, and most of the sources on this page do not do it.

One further observation, offered as an impression rather than a measurement. The academic terms describe what happens inside an individual head: cognitive debt, epistemic debt, the verification bottleneck. The consulting terms describe what happens to an organisation: distributed de-skilling, culture debt. The gap between those two is where the mechanism lives, at human capability in the age of AI. It stays thinly occupied because working there needs both literatures at once.

Adjacent terms worth knowing

• Verification bottleneck and verification paradox. Huemmer and colleagues, arXiv 2601.17055, January 2026. A three-wave longitudinal pilot in an academic setting reporting that participants leaned hardest on AI for difficult tasks, 73.9 per cent, while accuracy on complex tasks fell to 47.8 per cent and belief-performance gaps widened to 34.6 percentage points. The authors put their own limitations in the abstract, including convenience sampling and no control condition. Neither phrase is claimed as a coinage.
• Fragile experts. Sankaranarayanan again, describing "developers whose high functional utility masks critically low corrective competence". His experiment, 78 participants across three conditions, found unrestricted AI users failed a subsequent AI-blackout maintenance task at 77 per cent against 39 per cent for a scaffolded group.
• Skill Automation Feasibility Index and AI Impact Matrix. Jadhav and Danve, arXiv 2604.06906, April 2026. These are claimed explicitly, "we present" and "we propose". Note what that means: in this whole corpus, the firm claims attach to instruments and frameworks, never to the debt vocabulary.
• The human advantage and changefulness. Deloitte, March 2026. Positioning language rather than defined concepts.

What could not be verified, and is therefore absent

Three sources were pursued and left out.

• "The AI Deskilling Paradox", Communications of the ACM. The page returns an empty body to every fetch. The title is confirmed; nothing else is. Secondary coverage attributes a byline and a date, and neither is used here.
• "AI Debris", arXiv 2606.12432. PDF-only with no extractable text. Secondary summaries suggest it contains a term close to "capability debris". That phrase could not be confirmed to exist in the paper, so it is not on the map.
• "The apprenticeship void", VILAKSHAN XIMB Journal of Management. The DOI resolves and the article exists. The publisher's bot wall prevented reading it, so its authors, method and any coinage claim are unknown.

Naming these is the point rather than an apology. A map of who said what, built partly from search summaries, would be a map of what search engines believe.

Key research and primary sources

• Kosmyna, N., Hauptmann, E., Yuan, Y.T., Situ, J., Liao, X.-H., Beresnitzky, A.V., Braunstein, I. and Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv 2506.08872, v2 31 December 2025. Preprint, under review.
• Stanković, M., Hirche, E., Kollatzsch, S. and Doetsch, J.N. (2025). Comment on: Your Brain on ChatGPT. arXiv 2601.00856, 29 December 2025. Preprint.
• Rohde, W. (2026). Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability. SSRN, written 20 April 2026, revised 27 April 2026. Preprint.
• Goel, S., Martin, D. and Kaffe, C. (2026). When Everyone Uses AI, Companies Risk Losing Critical Skills. Boston Consulting Group, 17 June 2026.
• Deloitte (2026). In a New Era of Work, Winning Organizations Will Build the Human Advantage. 4 March 2026.
• Sankaranarayanan, S. (2026). Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts. arXiv 2602.20206. Preprint.
• Huemmer, M., Durner, F., Shyiramunda, T. and Cummings-Koether, M.J. (2026). AI, Metacognition, and the Verification Bottleneck. arXiv 2601.17055. Preprint.
• Jadhav, R. and Danve, J. (2026). The AI Skills Shift. arXiv 2604.06906. Preprint.

Related SuperSkills research

For the category these terms are circling, human capability in the age of AI. For the mechanism, capability debt, the missing rungs and synthetic seniority. For how sources are graded here, the evidence base and what we actually know. For the oversight argument, human in the loop is not a safeguard.

About this research

Rahim Hirji is the author of SuperSkills (Kogan Page, 2026), keynote speaker on AI and human capability, and founder of The SuperSkills Intelligence Company. Every source on this page was read at the primary source on 28 August 2026, with the three exceptions named above. Coinage claims were tested by searching each paper for explicit claiming language rather than by inference from usage. This page describes other people's work and has an obvious interest in one of the terms on it, so the standard applied to everyone else has been applied here first.

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