The SuperSkills Glossary

Every term used in this research, defined in one place, with the primary source for each. Four groups: terms introduced by this work, established concepts it relies on, the vocabulary institutions have coined, and the general AI words you are likely to meet in the press. If a term is missing, that is an omission rather than a judgement, and worth telling us about.

# Terms introduced by this research

The vocabulary of drift versus design, defined by the person who uses it on stage.

Capability debt#

Capability debt is the accumulated cost of decisions not made, skills not developed, and judgement not exercised. Like technical debt, it is invisible while the system runs and expensive the moment it is tested, and it accrues in five forms: accountability debt, skill debt, dependency debt, trust debt and culture debt. It comes due when you can least afford it, because the moment that requires human judgement is rarely scheduled. At the scale of a single person the shorter form is the gap between what you can produce and what you could still do if the machine were switched off. Rahim Hirji has used the term since at least 8 June 2025, as a section heading in "The Half-Life of Skills" for Box of Amazing, and develops it in SuperSkills (Kogan Page, 2026). The term has carried three senses across his own essays, which is part of why no single definition has settled around it: skills held past their retirement in June 2025, the organisational gap between skills held and skills needed in August 2025, and from January 2026 the cost of cognitive offloading, "every skipped rep saves time now, and costs judgement later". That is a date rather than a claim of coinage: the phrase is in independent use elsewhere, by Wolfgang Rohde in a working paper of April 2026 for the human layer, and by Jeremy Jarrell in software delivery for a different thing entirely. No claim of first use is made. Read the full argument.

Drift versus design#

Drift versus design is the difference between an organisation that adopts AI through a thousand small decisions nobody quite made, and one that decides in advance where human judgement has to remain. Drift is not incompetence; it is competence with no one behind it, because every individual step is reasonable and only the accumulation is not. Where an organisation sits depends on two things: awareness of what is shaping its choices, and the agency to act on what it sees. Rahim Hirji has used the framework since at least 16 November 2025, in "Drift vs Design" for Box of Amazing, where he writes "this is what I call Drift" and points to the Drift vs Design Matrix as a framework from his book. The pairing appears three weeks earlier still, on 26 October 2025 in "Why curiosity is the only moat left", where drift is defined as letting algorithms, conventions and first-draft answers shape your trajectory. He develops it in "The Architecture of Drift" of 15 March 2026 and in SuperSkills (Kogan Page, 2026). The four positions are the Sleepwalkers, the Programmed, the Stuck and the Designers. Read the full article.

The Half-Life of Skills#

The half-life of a skill is the time it takes for half of its value to decay. The idea long predates AI in workforce literature, but the interval has compressed to the point where a capability learned at the start of a role can be worth half as much by the end of it. The response is not faster reskilling but building the capabilities that do not decay: the human skills underneath the technical ones. The term is established in workforce literature and no claim of first use is made. Rahim Hirji has used it since at least 8 June 2025, in "The Half-Life of Skills" for Box of Amazing, and develops it in SuperSkills (Kogan Page, 2026).

The Great Unbundling of Work#

The great unbundling of work is the separation of a job into its component tasks, so that each can be priced, automated or reassigned on its own, leaving the role as a container rather than a thing anyone was hired to do. Unbundling is an established idea in economics and in technology strategy, and no claim of first use is made. What this research adds is what it does to capability: the tasks that get unbundled first are the routine ones, and those were also the repetitions through which judgement was built, which is the argument at the missing rungs. Rahim Hirji has used the term since at least 25 May 2025, in "The Great Unbundling of Work" for Box of Amazing, and develops it in SuperSkills (Kogan Page, 2026). Read the argument.

The missing rungs#

The missing rungs are the junior tasks that used to build senior judgement, removed by automation before anyone noticed they were load-bearing. Every profession has a ladder, and the lower rungs were never really about the output; they were the repetitions that made someone good. Organisations that automate the bottom of the ladder without deliberately building new rungs discover the gap only when they need someone to have climbed it. Rahim Hirji uses the term and develops it in SuperSkills (Kogan Page, 2026). Read the full article.

The Reverse Singularity#

The Reverse Singularity is the inversion of the story we were told: not machines becoming human, but humans becoming machine-like. The singularity everyone watched for was the moment AI matched us; the one that actually arrived is the slow standardisation of people into predictable, optimisable, interchangeable units of output. It matters because it is happening in the direction nobody is monitoring, one process at a time, and the people it reshapes are usually the last to notice. Rahim Hirji has used the term since at least 10 August 2025, in "The Reverse Singularity" for Box of Amazing, and develops it in SuperSkills (Kogan Page, 2026). Corrected on 4 September 2026: this page previously said 21 September 2025, which understated the dated first use by six weeks.

Synthetic seniority#

Synthetic seniority is when a junior professional produces work that looks like it came from someone with ten years of judgement, except the judgement is the model's. The work product is senior; the person is not, because the pattern recognition and contextual wisdom that used to come with producing the work were never built. The organisational consequence is a pipeline that looks productive for three years and produces no senior people in fifteen. Rahim Hirji uses the term and develops it in SuperSkills (Kogan Page, 2026). Read the full article.

The Unclaimed Hour#

The Unclaimed Hour is the capacity AI creates that nobody decides how to use. Every automation returns time, and in most organisations no one owns the question of where that time goes, so it is absorbed silently into more of the same. Where nobody decides, drift decides. The question for a leadership team is not how much time AI saves but who has claimed the hour. SuperSkills uses the term to describe this pattern. No claim of first use is made. Read the full article.

Usage Theatre#

Usage Theatre is what organisations perform when they cannot measure the value of AI and measure its use instead. Adoption dashboards rise, licence counts become KPIs, and employees learn to perform the metric rather than improve the work. Much of the adoption is real; what is being performed is the usage. The measure of an AI programme is whether decisions got better. That is harder to count, so few organisations count it. SuperSkills uses the term to describe this pattern. No claim of first use is made. Read the full article.

The Verifier's Discount#

The Verifier's Discount is what happens to the value of human work when the machine produces and the human checks: the accountability stays with the person while the pay and the status are repriced downward. The mechanism is subtle because verifying is real work, often harder than producing, but it is invisible in the output. Organisations that treat verification as residue rather than as the judgement layer end up paying least for the work they depend on most. SuperSkills uses the term to describe this pattern. No claim of first use is made. Read the full article.

# Established concepts this research relies on

These are not SuperSkills terms. Each comes from an existing literature, is named with its primary source, and most have a full page setting out what the evidence does and does not show.

Algorithm aversion. The disproportionate loss of confidence in an algorithmic forecaster after observing it err, relative to the loss of confidence in a human making the same error, resulting in the rejection of a system that performs better. Definition.

Automating versus informating. Automating replaces human judgement with a machine. Informating generates information that deepens the worker's understanding. The same system can do either, and which one happens is a management choice rather than a property of the technology. The estate's own canon note calls this the better-specified original of drift versus design. Naming the ancestor is stronger than not naming it. Shoshana Zuboff, In the Age of the Smart Machine, Basic Books, 1988 Zuboff's coinage.

Automation bias. The tendency to accept output from an automated system without applying the scrutiny that would be applied to the same claim from a person. Definition.

Automation complacency. A reduction in the frequency and depth with which a person monitors an automated system, arising from a history of reliable performance, and resulting in slower detection of the failures that do occur. Definition.

Configurations capacitantes and aliénantes. Capacitating and alienating configurations are the two outcomes an AI deployment can produce. Where an organisational compromise is reached, the arrangement increases human aptitude and skill. Where it fails, workers lose command of the work being done. The most important European idea in this vocabulary. Capability is a property of the arrangement, not of the person or the tool, and the same system produces either outcome depending on a negotiation. LaborIA (Ministère du Travail, Inria, Matrice), Rapport d'enquête LaborIA Explorer, May 2024 LaborIA's framing, from the French ergonomics tradition.

Desirable difficulty. A manipulation of learning conditions that impairs immediate performance while improving long-term retention and transfer. Definition.

Deskilling. The reduction of skill required or retained in a role, caused by the transfer of skilled elements of the work to a machine, a procedure or another group of workers. Definition.

Human-AI collaboration. A work arrangement in which a person and an automated system each contribute to a shared output, with the division of labour, the point of human entry, and the basis on which the human may override the system all specified in advance. Definition.

Ironies of automation. The ironies of automation are that automating the routine parts of a task leaves the human the hardest residue, monitoring and handling exceptions, while removing the practice that built the competence to do it. The foundation under every oversight argument in this research, and it predates AI by forty years. Lisanne Bainbridge, Automatica 19(6), 1983, pp. 775-779 Bainbridge's coinage, now established. Definition.

Judgement. The capability to recognise what a situation is, and what it requires, before any option is weighed. Definition.

Knowledge collapse. Knowledge collapse is the modelled steady state in which general knowledge eventually disappears despite high-quality personalised advice, once human effort is elastic enough and agentic recommendations pass an accuracy threshold. The formal economic statement of the erosion argument, from Acemoglu. It gives the thesis a modelled mechanism rather than only cases. Acemoglu, Kong and Ozdaglar, NBER Working Paper 34910, 2026 Their coinage.

Meaningful human oversight. Supervision by a person who understands the system's capacities and limitations well enough to detect anomalies, who is aware of their own tendency to over-rely on it, who can interpret its output correctly, and who has both the authority and the practical ability to disregard, override or stop it. Definition.

Moral crumple zone. A moral crumple zone is what forms when responsibility for a failure falls on the human nearest an automated system, who had limited real control over its behaviour. The sharpest available frame for the accountability argument. As Elish puts it, the crumple zone in a car protects the driver, while the moral crumple zone protects the integrity of the technological system at the expense of the nearest human operator. Madeleine Clare Elish, Engaging Science, Technology and Society 5, 2019, pp. 40-60. DOI 10.17351/ests2019.260 Elish's coinage. Definition.

Over-reliance. Dependence on an automated system beyond the point at which the person relying on it could detect that it was wrong. Definition.

Override authority. The assigned power of a named person to disregard, reverse or stop an AI system's output, held together with the competence, information and organisational standing required to use it. Definition.

Paradoxe de la facilitation. The facilitation paradox is that effort is part of satisfaction. Difficulty produces the good tiredness that comes from work done well, so removing it can strip work of the thing that made it worth doing. There is no American business vocabulary for the harm of making work easier, only for friction removed. This is the term that names it. LaborIA, Rapport d'enquête LaborIA Explorer, May 2024 LaborIA's framing.

Situation awareness. Situation awareness is the perception of what is happening around you, the comprehension of what it means, and the projection of what it will mean next. Sits underneath meaningful human oversight. Everyone in the field cites it; this estate has not. Mica R. Endsley, Human Factors 37(1), 1995, pp. 32-64 Endsley's three-level model is the standard formulation.

The out-of-the-loop performance problem. The out-of-the-loop performance problem is the loss of ability to take over manual operation when automation fails, caused by the reduced situation awareness and reduced practice that come from monitoring rather than doing. Endsley and Kiris found the decrement significantly worse under full automation than under intermediate levels, which is the empirical basis for keeping people in the work rather than at the end of it. Mica R. Endsley and Esin O. Kiris, Human Factors 37(2), 1995, pp. 381-394 Established in the human factors literature.

The verification bottleneck. The verification bottleneck is the finding that reliance on AI rises with task difficulty at the point where the ability to verify the output falls, widening the gap between believed and actual performance. The mechanism that makes 'just check the output' fail as advice. Checking is hardest when it matters most. Huemmer et al., arXiv:2601.17055, 2026 Their coinage.

The vigilance decrement. The vigilance decrement is the measurable fall in detection accuracy that occurs when a person monitors for rare signals over time, with most of the loss arriving in the first half hour. The unexamined assumption in every human-in-the-loop policy is that a person can watch indefinitely. Mackworth showed in 1948 that they cannot. N. H. Mackworth, Quarterly Journal of Experimental Psychology 1, 1948, pp. 6-21 Established. The Mackworth Clock is the originating apparatus.

What stays human. Not a list of tasks. Rahim Hirji argues three things survive because AI cannot be them rather than cannot do them: accountability, because responsibility requires someone answerable for a decision; recognition, because being seen depends on the identity of whoever chose to attend to you; and origination, because a model has no stake in which answer is right. Definition.

The rest, in brief#

Algorithm appreciation. Algorithm appreciation is the tendency to weight algorithmic advice more heavily than the same advice from a person. Domain experts are the exception. The exact mirror of algorithm aversion, which this estate already defines. Defining one without the other leaves the picture half drawn. Jennifer Logg, Julia Minson and Don Moore, Organizational Behavior and Human Decision Processes 151, 2019 Their coinage.

Calibration. The correspondence between the confidence a system states and how often it is correct. Definition.

Capability audit. A structured assessment of whether an organisation still possesses the human knowledge, judgement and practical ability its operations depend on, tested by removing assistance rather than by surveying confidence. Definition.

Capability masking. Capability masking is the appearance that organisational capability has been replaced by AI while dependence on skilled human labour actually remains, which supports hiring restraint while the cost accumulates. Independent arrival at the capability-debt argument, which is the most useful kind of corroboration. Wolfgang Rohde, AiSuNe Foundation, SSRN 6577818, 2026 The author's coinage. Definition.

Cognitive load. The total demand placed on working memory by a task, conventionally divided into intrinsic load inherent to the material, extraneous load imposed by presentation, and germane load, the effortful processing that builds understanding. Definition.

Conflit de rationalité. A conflict of rationalities is the unresolved disagreement between what an organisation wants from an AI system and what the work actually requires. Whether a compromise is reached decides whether the result builds capability or removes it. LaborIA, Rapport d'enquête LaborIA Explorer, May 2024 LaborIA's framing.

Epistemic debt. Epistemic debt is the gap between being able to produce working output with AI and being able to understand or repair it, producing practitioners whose functional usefulness masks low corrective competence. The repair-competence form of capability debt. Notable because the author reaches it independently. Sankaranarayanan, arXiv:2602.20206, 2026 The author's coinage. Definition.

Komplementäre Arbeitsgestaltung. Complementary work design treats human and machine complementarity as permanent and functional, grounded in structural limits of automation rather than in the current weakness of models. Refuses the assumption that the human role is simply whatever automation has not yet reached. Norbert Huchler, ISF München, Zeitschrift für Arbeitswissenschaft 76(2), 2022 Huchler's framing.

Legitimate peripheral participation. Legitimate peripheral participation is how newcomers acquire competence: by doing real but peripheral work alongside practitioners, moving from the edge of a community of practice towards its centre. Names the mechanism AI has broken. The missing rungs are the peripheral work being removed. Jean Lave and Etienne Wenger, Situated Learning, Cambridge University Press, 1991 Their coinage.

Misuse, disuse, abuse. Misuse is over-reliance on automation, disuse is unwarranted rejection of it, and abuse is deploying it without regard for the human consequences. Appropriate use is the fourth case. The estate's canon argues this vocabulary is better than over-reliance alone, because it names the opposite failure too. Raja Parasuraman and Victor Riley, Human Factors 39(2), 1997, pp. 230-253 Their coinage.

Retrieval practice. Retrieval practice is the finding that recalling information from memory strengthens it more durably than studying it again. Definition.

Substitution myth. The assumption that new technology can be introduced as a simple substitution of machines for people, preserving the basic system while improving it on some output measures. Definition.

Tacit knowledge. Knowledge that resists full articulation, acquired through experience and practice rather than instruction, and typically transmitted through shared work rather than documentation. Definition.

The expertise reversal effect. The expertise reversal effect is the finding that instructional support which helps a novice becomes useless or harmful once the learner has expertise. The direct answer to who should use AI assistance and when, which is a question this research is asked constantly. Kalyuga, Ayres, Chandler and Sweller, Educational Psychologist 38(1), 2003, pp. 23-31 Their coinage.

The Google effect. A shift in what people encode to memory when they believe information will remain externally accessible, favouring the location or retrieval route over the content. Definition.

The illusion of competence. The illusion of competence is the gap between how capable people feel and how capable they are. Definition.

# The vocabulary institutions have coined

Terms owned by the bodies that publish about this: Microsoft, the World Economic Forum, the OECD, PwC, the consultancies, the European institutions and the frontier labs. Worth knowing who wrote each one, because the definition usually carries the position of whoever wrote it.

Agent boss. An agent boss is Microsoft's term for a human manager of one or more AI agents. It describes a promotion in title and says nothing about whether the person can evaluate what the agents produce. That gap is where oversight readiness fails. Microsoft, Work Trend Index Annual Report 2025 Microsoft's coinage.

Ai literacy. In the European Union, yes. Article 4 of the EU AI Act has applied since 2 February 2025 and requires providers and deployers of AI systems to take measures to ensure, to their best extent, a sufficient level of AI literacy among their staff and other persons dealing with the operation and use of AI... Definition.

Borrowed competence. Borrowed competence is McKinsey's term for capability that appears in the output but disappears when the tool is withdrawn. The clearest external statement of what synthetic seniority produces, from a firm with no stake in the argument. McKinsey, Rethinking talent development in the age of AI, 14 July 2026 McKinsey's coinage.

Distributed de-skilling. Distributed de-skilling is BCG's term for the collective erosion of human skills across an organisation that undermines its intelligence and resilience over time. BCG frames it as a system design problem rather than a talent problem. BCG, When Everyone Uses AI, Companies Risk Losing Critical Skills, 17 June 2026 BCG coins it explicitly: 'We call this distributed de-skilling.' Definition.

Frontier Firm. A Frontier Firm is Microsoft's term for a company built on purchasable machine reasoning, human-agent teams, and a new role for every employee as a manager of agents. Worth knowing because of who wrote it. Microsoft publishes a glossary of this vocabulary for executives, and every definition in it is framed from the position of the buyer of digital labour rather than the people whose capability is at stake. Microsoft, Work Trend Index Annual Report 2025, 23 April 2025 Microsoft's coinage. Definition.

Human in command. The human in command principle holds that a person must retain authority over an AI system, as distinct from merely occupying a position in its process. The cleanest contrast in the whole vocabulary. Human in the loop describes where a person sits. Human in command describes what they are entitled and expected to be able to do, which requires competence they may no longer have. European Economic and Social Committee, OJ C/2025/1185, 21 March 2025 Attributed by the EESC to the 2020 Autonomous European Social Partners Framework Agreement on Digitalisation.

Human Reserved. Human Reserved is Bill Gates's term for work deliberately set aside for people only, by analogy with nature reserves: places we could develop but choose not to because the loss would be too great. It is the first serious proposal from a major technology figure that the boundary of automation should be decided rather than discovered. That makes it the counterpart to drift. Bill Gates, The turbulent AI era is here, gatesnotes, 26 August 2026 Gates coins it explicitly: 'I've started calling this domain Human Reserved.' Definition.

Learning by verifying. Learning by verifying is Bain's claim that juniors learn by reviewing, stress-testing and catching errors in AI output, and that the repetitions per hour go up rather than down. The most useful thing in this corpus because it contradicts the missing-rungs argument directly. A defended counter-position is worth more than another source agreeing. Bain & Company, The future of opex in the agent economy Bain's coinage.

Meaningful human involvement. Meaningful human involvement is the UK statutory test for whether a decision is solely automated: whether a human can exercise real influence before the decision is applied, and has the authority, discretion and competence to alter it, as opposed to rubber-stamping. Note the third requirement. Competence is written into the law, which means capability decay is a compliance exposure and not only a management problem. Information Commissioner's Office, Recruitment rewired, 2026; UK GDPR Article 22A Statutory UK term. Definition.

Mis-skilling. Mis-skilling is the acquisition of incorrect reasoning patterns, learned by uncritically adopting AI output that was erroneous or biased. The capability is built rather than lost, and built wrong. The third case the deskilling debate keeps missing. Both sides argue about whether skill is lost or retained, and neither asks what is learned when the teacher is confidently mistaken. Ke, Y. and colleagues, AI-induced never-skilling in medical education, Nature Medicine, 32(6), 22 May 2026 Named by Ke and colleagues alongside never-skilling, 22 May 2026. Not a SuperSkills term.

Never-skilling. Never-skilling is the failure to form foundational competence during training, because AI substituted for the cognitive effort that would have built it. It differs from deskilling in having no earlier capability to return to. The precise name for what synthetic seniority describes from the other side. Deskilling assumes a skill that decayed; never-skilling asks what happens when it is never laid down. Ke, Y. and colleagues, AI-induced never-skilling in medical education, Nature Medicine, 32(6), 22 May 2026 Named by Ke and sixteen colleagues in Nature Medicine, 22 May 2026. Not a SuperSkills term. The authors state that direct evidence for it in clinical trainees does not exist and present it as a risk model.

Oversight readiness. Oversight readiness is Google DeepMind's term for whether a future workforce will be able to judge the AI work it is nominally supervising, given that juniors are being deprived of the experience that builds strategic judgement. Names the problem in the future tense, which is the tense that gets budget. A workforce that can delegate but cannot judge. Tomašev, Franklin and Osindero, Google DeepMind, Intelligent AI Delegation, arXiv:2602.11865, 12 February 2026 DeepMind's framing. Definition.

Seniorised entry-level roles. Seniorised entry-level roles are junior jobs that now demand senior human skills. PwC found entry-level roles most exposed to AI are seven times more likely to require leadership, creativity or face-to-face interaction. The strongest external evidence for synthetic seniority. Entry-level work is not disappearing so much as being asked to arrive already senior. PwC, 2026 Global AI Jobs Barometer, June 2026 PwC's framing, 2026. Definition.

Shallow jobs. Shallow jobs are Bain's term for roles in which people rubber-stamp mostly correct AI output without engaging their judgement. The strongest counter to blanket human-in-the-loop prescriptions: universal review produces the appearance of oversight and the erosion of it. Bain & Company, What Financial Services Leaders Are Wrestling with on AI, 4 June 2026 Bain's coinage. Definition.

The AI employment gap. The AI employment gap is the Stanford Digital Economy Lab's finding that employment of workers aged 22 to 25 in AI-exposed occupations sits 19 per cent below where it would have been had it tracked their less-exposed peers, operating through reduced hiring rather than increased separations. The most-cited number in the entry-level debate, and the mechanism matters: the door is closing, not the jobs ending. Brynjolfsson, Chandar and Chen, Stanford Digital Economy Lab, revised August 2026 Stanford's framing. The authors describe their findings as 'canaries in the coal mine, rather than causal estimates'.

The experiential chasm. The experiential chasm is the gap between the small group who have spent real hours with frontier models and the majority still experimenting superficially. Bain describes it as neither a seniority gap nor a training gap, and as widening every week. It reframes AI capability as something acquired through hours rather than through instruction, which means training budgets aimed at awareness close none of it. Bain & Company, The Future of Opex in the Agent Economy, 14 May 2026 Bain's coinage.

The two-tier future. The two-tier future is the outcome financial services leaders fear most: lower-skilled people handling tasks AI is not set up for, a smaller group of experts training the system and handling exceptions, and no obvious bridge from the first group to the second. This is the missing rungs described independently, by executives, in their own words. The absent bridge is the point, and it is the strongest external corroboration the argument has. Bain & Company, What Financial Services Leaders Are Wrestling with on AI, 4 June 2026 Bain's framing of a concern raised by summit participants. Definition.

The rest, in brief#

AI-off zones. AI-off zones are BCG's term for tasks an organisation deliberately designates as off limits to AI, where originality, ethical judgement or synthesis matter most. The organisational counterpart to Human Reserved, at team rather than economy scale. BCG, When Everyone Uses AI, Companies Risk Losing Critical Skills, 17 June 2026 BCG's coinage.

Amplified oversight. Amplified oversight is Google DeepMind's term for an oversight signal as good as one a human would give if they understood all the reasons behind a decision. Names the problem of judging work you can no longer evaluate, which is the technical statement of the capability-debt endgame. Google DeepMind, An Approach to Technical AGI Safety and Security, April 2025 DeepMind's term.

Capacity gap. The capacity gap is Microsoft's term for the deficit between what a business demands and the maximum output humans alone can supply. It frames human limits as the problem to be solved. The same figures can be read as evidence that the demand is miscalibrated. Microsoft, Work Trend Index Annual Report 2025 Microsoft's coinage.

Complementary skills. Complementary skills are the OECD's term for teamwork, autonomy, problem solving, creative thinking, communication, collaboration and emotional intelligence: the capabilities that enable high-performance work and the ability to keep learning. The most carefully defined of the three names for one idea. Where three institutions have three names for one idea, the definition is worth owning. OECD, Skills in the AI age, OECD Artificial Intelligence Papers No. 60, July 2026 OECD's framing.

Core skills. Core skills are the World Economic Forum's term for the skills employers consider central to a role, and the unit in which it reports skill change: 39 per cent of workers' core skills are expected to change by 2030. The most likely WEF term to reach a board paper with a number attached. World Economic Forum, Future of Jobs Report 2025 WEF's framing. Definition.

Curriculum-aware task routing. Curriculum-aware task routing is Google DeepMind's proposal for systems that track a junior's skill progression and deliberately allocate tasks at the edge of their expanding competence, including work the system would otherwise have done itself. A designed answer to the missing rungs, from a frontier lab. It concedes that keeping people skilled requires deliberately not automating some work. Tomašev, Franklin and Osindero, Google DeepMind, arXiv:2602.11865, 12 February 2026 DeepMind's coinage.

Digital labour. Digital labour is Microsoft's term for AI agents purchased on demand to scale workforce capacity. The phrase moves AI from the capital column to the labour column in the reader's mind, which is the move Gates proposes taxing. Microsoft, Work Trend Index Annual Report 2025 Microsoft's coinage.

Human-agent ratio. The human-agent ratio is Microsoft's proposed business metric for the balance between human oversight and agent efficiency on a mixed team. A ratio implies oversight is a quantity. Vigilance research says it is a capacity that degrades with time on task, so the number tells you about headcount and not about whether anyone can still catch an error. Microsoft, Work Trend Index Annual Report 2025 Microsoft's coinage.

Hybrid intelligence. Hybrid intelligence is the European Policy Centre's proposed basis for skills policy: technical AI literacy combined with domain expertise and distinctively human capabilities, rather than AI skills alone. The emerging EU replacement for AI skills as a policy category, and explicitly a blend rather than an addition. European Policy Centre, Fostering AI resilience in the EU labour market, 19 March 2026 European Policy Centre framing, attributed to Kuiper and Świeboda.

Identity commoditisation. Identity commoditisation is the erosion of a professional's sense of uniqueness and dignity as their role narrows to supervising a system, until the work that carried their identity is no longer the work they do. Names the cost that skill measures cannot see. A practitioner can be as accurate as ever and still describe themselves as a bystander in their own practice, and nothing on a dashboard registers that. Ehsan, U. and colleagues, From Future of Work to Future of Workers, CHI '26, ACM Named by Ehsan and colleagues from twelve months of fieldwork in radiation oncology, published at CHI 2026. Their companion term is intuition rust, the dulling of expert judgement beneath output that still looks intact. Not SuperSkills terms.

Learning-conducive work environments. Learning-conducive work environments are workplaces designed so that continuous learning and the use of skills happen through the work itself rather than through separate training. Locates capability building in job design rather than in a course, which is where the evidence says it actually happens. Cedefop, Shaping learning and skills for Europe, publication 9208, 2026 Cedefop's framing. German counterpart: lern- und erfahrungsförderliche Arbeitsbedingungen.

Obligation to justify. The obligation to justify is Cedefop's proposal that employers should have to give reasons for introducing AI into a workplace. Turns drift into a decision that has to be defended, which is the procedural form of designing rather than drifting. Cedefop, publication 9201, 2025 Cedefop's proposal.

The answer-key model. The answer-key model is McKinsey's proposed training pattern in which the employee attempts the work first, the AI grades the attempt, and a manager reviews it with them. The cleanest named remedy for the broken apprenticeship available anywhere, and it inverts the usual order: attempt then check, rather than generate then edit. McKinsey, Rethinking talent development in the age of AI, 14 July 2026 McKinsey's coinage.

Work Chart. The Work Chart is Microsoft's proposed successor to the org chart, structured around jobs that need doing rather than functional expertise. Functional expertise is also how apprenticeship is organised. Removing it as the organising principle removes the ladder with it. Microsoft, Work Trend Index Annual Report 2025 Microsoft's coinage.

# The AI vocabulary, read for capability

The words that appear in the press and in board papers. Most glossaries stop at what they mean. Each entry here also says what the term implies for human capability, judgement and skill. Technical machine-learning vocabulary is deliberately excluded.

AI control as work. AI control is Narayanan and Kapoor's prediction that a steadily greater share of what people do in their jobs will consist of controlling AI rather than doing the underlying task. If most work becomes control, then the capacity to control well is the whole of human capability, and nothing currently builds it. Narayanan and Kapoor, AI as Normal Technology, Knight First Amendment Institute Existing safety term, repurposed as a labour category.

AI slop. AI slop is fast, plausible output that does not meet the standard. The word matters because it names a quality failure that reads as competence. Bain's finding is that catching it is a leadership discipline rather than a tooling problem: what gets measured, what gets rewarded, and what gets rejected. Bain & Company, What Financial Services Leaders Are Wrestling with on AI, 4 June 2026 In general circulation. Bain records it as the term that kept coming up among financial services executives.

Learn, unlearn, relearn. A widely repeated claim that the illiterate of the twenty-first century will be those who cannot learn, unlearn and relearn, universally attributed to Alvin Toffler's Future Shock. Toffler did not write it. Future Shock paraphrases the psychologist Herbert Gerjuoy, whose actual words were that tomorrow's illiterate will be the person who has not learned how to learn. The three-verb version is a later compression by an unknown hand. Alvin Toffler, Future Shock, 1970, paraphrasing Herbert Gerjuoy from an interview with the author Misattributed. Gerjuoy is the source of the idea; the famous phrasing has no identified author.

The jagged technological frontier. The irregular boundary between tasks an AI system performs well and tasks it performs badly, where the two can be almost indistinguishable in apparent difficulty and the system gives no signal of having crossed from one to the other. Definition.

Tokenomics. Tokenomics is the economics of token cost, and specifically its fall. As the price per token drops, tasks not worth handing to a machine become worth handing over, so the boundary of what is automated moves without anyone deciding to move it. The engine underneath drift. Almost nobody decides to offload a task. It becomes cheap, the reason not to disappears, and the decision is taken by default and noticed later. Not a single source. The usage here is the AI cost sense, not the cryptocurrency sense. Contested. In cryptocurrency the term means the design of a token economy. The AI usage is loose and recent. Definition.

The rest, in brief#

AI hallucination. Generated content presented as factual that is not supported by the model's training data, the provided context or reality. Definition.

Anthropological regression. Anthropological regression is the paradox in which material progress coincides with human and cultural impoverishment, through forced inactivity, absent responsibility and the loss of daily tasks and stimuli. The moral vocabulary for what capability debt costs a person rather than an organisation. Leo XIV, Magnifica Humanitas, §154, 15 May 2026 Coined in this formulation.

Centaur and cyborg work. Centaur work divides tasks cleanly between person and machine. Cyborg work interweaves them continuously, moving back and forth across the jagged frontier. A vocabulary for how someone works with AI rather than whether they do, which is the distinction most adoption metrics miss. Ethan Mollick, Centaurs and Cyborgs on the Jagged Frontier Adapted from advanced chess. The work-mode pairing is Mollick's. Definition.

Falling asleep at the wheel. Falling asleep at the wheel is what happens when people given high-quality AI become careless and less skilled in their own judgement, because the AI is good. Quality of the tool is not protective. The better the assistance, the faster the attention goes. Fabrizio Dell'Acqua, credited by Mollick Dell'Acqua's, credited explicitly by Mollick.

Research taste. Research taste is knowing what to study next, which experiment to run, and sensing where a new approach might lie. It has proved hard to train because the feedback loops are long and the data is thin. Presented in AI 2027 as a residual human skill. Notable because the reason it resists automation, long feedback loops, is the same reason it resists teaching. AI 2027, Kokotajlo et al. Established in machine-learning culture, explicitly defined there.

Shift left. Shift left means moving decisions closer to their source, removing the dilution that every handoff introduces. Handoffs are also where work becomes visible to other people. Removing coordination removes the moments when a colleague saw the work and could question it. Bain & Company, The Future of Opex in the Agent Economy, 14 May 2026 Predates Bain, from software testing. Used here for organisational decision-making.

The capability-reliability gap. The capability-reliability gap is the distance between what a model can do once and what it can do dependably. It is the main barrier to agents automating real work. Explains why the demonstration always looks better than the deployment, and why human checking keeps being reinvented as necessary. Narayanan and Kapoor, AI as Normal Technology, Knight First Amendment Institute Effectively named there, in scare quotes, unattributed to anyone else.