The research

The full index.

Every research page on this site, grouped. If you are not sure where to begin, the research homepage picks six places to start, and the questions map lists what this work does and does not yet answer.

Every research page on this site, grouped. Generated from the estate rather than maintained by hand, so nothing can be published and then be impossible to find.

Start here

The seven SuperSkills

Frameworks for using AI well

  • AI frameworks comparedSix teaching frameworks set against Bloom, SAMR, CLEAR, TCREI, SIFT, the CRAAP test and the levels of automation literature, with the peer-reviewed critique of SAMR applied to all of them including these. Offered as unique rather than best, and each page names the outside framework that does the job better.
  • Think, AI, thinkWrite your own position first, use the model, then decide what to keep. Skipping the last step produces a bad answer somebody eventually catches; skipping the first produces a good one nobody can check. Nearest published relatives are Dell'Acqua's centaurs and cyborgs and Mollick's seven approaches.
  • The four levels of AI useExtract, Explore, Examine, Extend: a ladder about the request rather than the tool. Bloom's taxonomy describes the learner and stops discriminating once a model can perform all six categories; SAMR describes the technology and carries a peer-reviewed critique that applies here too.
  • Goal, Context, Friction, StandardFour parts to a prompt, and Friction is the reason it exists. CLEAR, TCREI and CO-STAR all ask what the model should be told, and none has a slot for what the person should be made to keep doing, which is the difference between the two arms of the Bastani trial.
  • Keep, share, hand overThree columns for AI delegation, defaulting to Keep for anything you cannot classify. Sixty years of levels-of-automation research is better at everything except being usable in four seconds, and Swiss federal guideline four says the same thing in policy: responsibility must not be capable of being delegated to machines.
  • The five rungs of AI useChat, Project, Skill, Automation, Agent. The rungs are not evenly spaced: one to three are conveniences, and four removes the person from the moment of execution, which is where oversight quietly stops working. Anthropic draws the same line between workflows and agents.
  • The source ruleSearch, Open, Understand, Record, Cite. Written for the failure a model creates, a reference that looks perfect and does not exist. Caulfield's SIFT is the stronger instrument for a web page and this page says so; the amendment is that existence has to be checked before quality.

For students

  • How to use AI at university94 per cent of wholly AI-written exam answers went undetected at Reading, and they outscored real students, so "will I get caught" is the weakest argument available. The decision that replaces it, three columns, taken before the work rather than at midnight.
  • How to be honest about using AIThe question is not whether you are allowed but whether you could tell your tutor exactly what you did without leaving bits out. At Reading 94 per cent of wholly AI-written submissions went undetected; detectors flag 61.22 per cent of non-native English essays as AI. The HEPI 94 per cent and the 12 per cent answer different questions.
  • Using AI when your department does not want you toHistory, English, Law and Philosophy are strict because in those subjects the writing IS the assessment. If it touches the words that get marked, do not; if it touches your understanding, do. Six things that are almost always fine, six where people get caught, and three of those six are done by people who think they are safe.
  • How to handle forty readingsThe bottleneck is not reading speed, it is that you read forty things and kept none of them findable. Five free steps, fifteen minutes in week one. Step five, being tested on your own readings, is the best-evidenced study technique in psychology: testing beat restudying 56 to 42 per cent at one week.
  • Group work when everyone has AISomebody drops three paragraphs of slop in at midnight and the whole group wears the mark. Five things to agree in week one, and the Procter and Gamble field experiment with 776 professionals showing AI dissolved the functional split that groups usually divide along.
  • What actually happens if you get caught using AIExpulsion is rare and it is the wrong thing to picture. What usually happens is a zero or a failed module, settled quietly, and at the 2026/27 English fee cap of 9,790 pounds across six modules that is roughly 1,600 pounds. The cost question survives a falling detection risk where the fear question does not.
  • How often does AI invent a source?Two counted floors. One paper in 277 on PubMed cited a study that does not exist in early 2026, up from 1 in 2,828 in 2023. And 2,022 court decisions worldwide record fabricated material, of which 1,163 were filed by people representing themselves rather than by lawyers.
  • Running out of messages makes you worseEvery free tier has a limit and what it does to you before you reach it is the part nobody notices. You take the first answer, stop being tested, ask worse questions and hoard the good tool. Scarcity turns a capable user back into a beginner.

The named concepts

Judgement, oversight and accountability

Thinking, learning and capability

Work, careers and the labour market

  • Who AI leaves behindAI's gains land on the least skilled, in customer support and in taxi driving. The detectors built to police it misclassify non-native English writers at 61.22 per cent. The same people the tool helps are the ones the checking penalises.
  • Proving you did the workDetection misclassifies over half of non-native English essays, a 61.22 per cent false positive rate, while labelling a reply as AI removes its advantage even where it beat humans. Why both detection and blanket disclosure fail, and what proof of process looks like instead.
  • The mid-career squeezeThe measured displacement is at 22 to 25, through reduced hiring. Mid-career exposure is different: AI's gains land on the least experienced, compressing the gap a mid-career salary pays for. Plus why a randomised trial found experienced developers 19 per cent slower and unable to detect it.
  • Will AI replace my job?
  • Will AI replace entry-level jobs?
  • Which jobs are safest from AI?
  • What is the AI employment gap?Employment of 22 to 25 year olds in AI-exposed occupations sits about 19 per cent below where it would have been had it tracked their less-exposed peers. The 19 is the distance between a fall of 11 and a growth of 10, not a fall of 19, and it runs through hiring rather than redundancy.
  • Does AI actually make people more productive?Large measured gains on narrow tasks: writing 40 per cent faster, a standardised coding task 55.8 per cent faster, support resolutions up 15 per cent an hour. Scattered or negative in real work. Nothing yet in the national statistics, and the perception gap runs in both directions.
  • Will AI replace programmers?No study shows replacement. The two most cited coding experiments sit seventy-five points apart because one built something new and the other changed a system somebody already knew. The measured risk is to how developers are made.
  • Should juniors use AI at all?Yes, and the evidence says which version of use is safe. The three experiments that removed the tool afterwards, and the four rules that follow. Plus four for whoever manages them, because the burden is in the wrong place.
  • Do apprenticeships still work?Germany has record unplaced applicants and 54,400 empty places in the same year. England shortened the apprenticeship and its flagship replacement drew 74 starts against a target of 1,000. Four systems read at source, and three claims the evidence does not support.
  • Should I still learn to code?
  • Staying valuable in the age of AIWhere human value moves as AI spreads: staying valuable, the entry-level question, and the human skills that matter most.
  • What should I tell my children to study?
  • Why "learn to prompt" is weak career advice
  • Four Generations of Disruption
  • Who captures the productivity gains from AI?
  • Which tasks do workers not want automated?Asked task by task about their own occupations, 1,500 US workers were positive about automating 46.1 per cent of them. Where they refused, distrust of accuracy outranked fear of replacement by two to one, and workers wanted more human involvement than experts thought necessary on 47.5 per cent of tasks.
  • Is deskilling real, or a rescaling of what counts as skill?Deskilling and rescaling have both been measured, in different people. Nineteen Polish endoscopists lost six percentage points of unassisted detection; a Japanese taxi fleet's skill gap closed by 14 per cent. The strong claim that there is no deskilling fails against a measurement, and the rescaling claim survives only as a question about which tasks were automated.
  • How do juniors become senior if AI does the junior work?Junior work was a by-product of senior workload, not a training scheme, so AI removes the reason it existed. Access is not the harm: in randomised trials with 1,222 people the withdrawal effect appeared after ten minutes, and across 26,811 Chinese students the exam losses fell almost entirely on those whose homework time collapsed.

Organisations and leadership

  • The shape of the organisation after AIAcemoglu puts ten-year productivity gains under 0.66 per cent, Danish records find precise nulls on pay two years in, and US payroll data rules out widespread displacement. What the evidence supports about redesign and fragility, and why middle management is the layer nobody has counted.
  • What AI does to a teamEveryone improves and the room converges. Doshi and Hauser on individual creativity rising while collective diversity falls, Dell'Acqua on AI flattening the difference between an engineer and a marketer, and the sycophancy evidence on what happens when challenge moves from colleagues to a model that affirms half again as often.
  • Who should own AI strategy in an organisation?Ownership is decided by inheritance rather than argument, and it silently answers a bigger question: augmentation or replacement. Autor and Thompson on why which tasks you automate matters more than how many, what each placement is blind to, and the role missing from most implementation teams.
  • AI workforce strategy
  • The CHRO guide to AI
  • How leaders should respond to AI
  • What does AI literacy mean for leaders?A legal obligation since February 2025, enforced since this month, at every risk tier. What Article 4 requires, the clause almost every programme misses, and why a completed training module is not evidence of capability.
  • How do you write an AI use policy that works?Most are unenforceable and everyone involved knows it. Six elements that survive every model release, what EU law now makes auditable, and the one-afternoon test that beats legal review.
  • How do you measure AI adoption properly?
  • Why reskilling programmes mostly failA position page against the near-universal institutional answer to AI. Five structural reasons, the evidence stated honestly including where it is indirect, and the published data that would change the position.
  • AI Transformation Is Not a Change-Management Problem
  • Common AI Transformation Challenges
  • Should AI attend my meetings?
  • The Third Way
  • How do you keep expertise in an organisation?Documentation preserves what experts can say, and most of what they know is not that. Polanyi on the tacit part, Lave and Wenger on how it transfers, and why the work juniors learned from is the work most easily automated.
  • What happens to institutional memory?Retrieval is not retention. An organisation can get better at finding things while getting worse at knowing them, because the reasons, the rejected options and the trust calibration were never in the archive.
  • What happens when AI removes the visible work?Bainbridge, 1983: taking away the easy parts of a task can make the difficult parts harder. Why time saved prices only what was removed, why task productivity and workload are different variables, and what responsible de-automation requires.
  • What should we tell employees about AI and headcount?Which decisions a machine may make in the organisation's name, and what will and will not be done with the time it saves, declared by domain and dated. Deloitte's 25,000-worker UK survey found 31 per cent concealing use and 64 per cent of weekly users afraid their manager will decide AI can do their job; the PNAS experiments show the penalty they fear is real. Concealed use is delegation nobody has decided, checked, or could reconstruct.
  • What is a Shared Prompt Review?Four things on the table once a week: the prompt as typed, the raw output, what a person cut and why, and where it might be wrong. A field experiment with 776 professionals found AI erasing the difference between what a technical and a commercial specialist proposed. The problem is measured; the remedy is not.
  • What is automating versus informating?Zuboff, 1988. Automating replaces human judgement with a machine; informating makes the work more visible to the person doing it. The same model configured two ways, so output can improve while capability erodes with nothing on any dashboard showing the second half.
  • What is a capability audit?A proposed method, labelled as one. What people can still do unaided, where expertise actually sits, what has no redundancy and what would be hard to rebuild. Why confidence and output are both broken instruments.
  • When should an organisation reverse an AI deployment?NIST's five conditions for deactivating a system, Perrow on why adding a safeguard can reduce safety, and why a fallback that exists only as a document is not a fallback.
  • How do you preserve capability across vendors?Prahalad and Hamel's 1990 warning, applied. Keep enough to specify the work, judge it, handle exceptions and replace the supplier. Three things that are genuinely new, and one that is not new at all.
  • If every competitor has the same AI, where does the advantage come from?A licence every rival can buy at list price passes one of Barney's four tests. US Census data puts firm use at 18 per cent with 57 per cent of adopters in three or fewer functions, so parity has not arrived, and the assets that pass the test are the ones the invoice does not include.
  • How long should we give an AI investment before deciding whether it worked?The J-curve says an early read understates and a late one overstates, for the same reason. Danish administrative records found precise nulls on pay two years in, next to heavy task reorganisation. Three questions, three clocks, and most business cases wind only one.
  • What becomes more valuable in a business as AI gets cheaper?Three randomised trials found the same compression: the tool raises the floor far more than the ceiling. So the capability losing value fastest is being unusually good at exactly the work the model does well. Four complements that appreciate, chosen by standing rather than by capability.
  • What happens to work whose purpose was moving information around?Garicano's model says a layer exists because matching problems to knowledge is costly. Resume data on 3,100 firms finds hierarchies flattening after AI adoption, on tests the authors call under-powered. What the reporting job was doing that the report never showed.
  • Which AI investments should we stop?Between 30 and 40 per cent of information systems projects show some degree of escalation, and the founding case study of that literature was an expert system that ran for a decade. There is no credible non-vendor base rate for AI programme failure: the 80 per cent figure traces to a press article. Three questions that do not need a counterfactual.
  • How do humans and agents divide work across a process?Task-level allocation has been on the wall since 1951 and has never worked, because every assignment creates new work for the other party. The unanswered part is the boundary. Medicine calls those discontinuities gaps, the best multi-agent failure data puts 36.9 per cent of failure at the joins, and Article 14 says nothing about transitions.

Professions and sectors

  • Which professions face the greatest deskilling risk?
  • How will AI change medicine?
  • How will AI change law?
  • How will AI change consulting?
  • How will AI change accounting and audit?The oversight precedent everybody cites was replaced in April 2026, and its replacement puts generative AI expressly out of scope. Meanwhile the UK audit regulator found the six largest firms had not measured what their tools do to audit quality.
  • How will AI change journalism?Twenty-two broadcasters, 14 languages, 2,709 graded answers. Sourcing failed at 31 per cent against 20 per cent for accuracy, and the reputational cost arrives at the masthead that was cited rather than the assistant that cited it.
  • How will AI change customer service?The best-evidenced occupation there is, and the study watched the tool break. Gains ran to novices, the best agents got slightly worse, and the learning survived an outage only for the workers who had engaged with the suggestions.
  • How will AI change teaching?A school-randomised trial in England cut lesson planning time 31 per cent with no quality change a blinded panel could see. English teachers plan with it at 35 per cent and mark with it at 5 per cent, which is the profession quietly putting the tool where the risk is lowest.
  • How will AI change the public sector?Both flagship UK figures, 26 minutes a day and 19, are self-reported, and one was calculated from tick-box midpoints with its top tail capped by the analysts. The sector's deepest precedent is not a productivity study, it is the presumption that the computer is right.
  • How will AI change human resources?Retrieval models favoured White-associated names in 85.1 per cent of comparisons. The first algorithmic hiring audit law in the world produced published reports from 18 of 391 employers checked. And the function advising on capability is the one most exposed.

Everyday life

International

  • AI and work, country by country
  • AI and work in AsiaSingapore has written the loss of entry-level training grounds into a national AI framework. Hong Kong measures augmented reality adoption and does not ask about AI at all. Japan, Korea and the adoption gap, all read at source.
  • AI and work in the GulfSaudi Arabia, the UAE and Qatar read at the issuing body's own pages. The one Gulf instrument that names over-reliance, the region's only official AI adoption statistic, and the widely quoted claims that could not be verified.
  • AI and work in JapanThe country with the strongest imaginable economic case for adopting AI, an 11 million worker shortfall by 2040, and adoption running at roughly 18 per cent. The control condition this debate never had.

Definitions

Reference and record

  • The most-quoted AI statistics, checked
  • The AI reports worth reading
  • The official guidance on AI in educationUNESCO, UNICEF, the UK, the EU, Australia, the US and MIT, read at source and labelled by what each document is. Mostly schools: no government here has issued guidance for universities. Five primary-education mandates checked, one of which has already been reversed.
  • Does the brain mature at 25?It does not, and the number came from a 2004 magazine interview rather than a finding. Traced to source, checked against a 3,802-person 2025 study that puts the end of the adolescent epoch nearer 32, and reframed on the question that can actually be answered.
  • The evidence on AI and human capabilityA curated, dated reference to the best research in the field, from the World Economic Forum, PwC, MIT, the NBER, Harvard and BCG and the key academic studies.
  • Essential works on AI and human capabilityEssential works on human capability in the age of AI: hard data, experimental evidence, serious interpretation and the intellectual foundations most current writing rediscovers without attribution. Classified, and read rather than listed.
  • The timelineWeekly since January 2017, five years and ten months before ChatGPT. When each idea first surfaced, what form it took, and how it developed. The early years described honestly as curation rather than thesis.
  • The predictions recordThe dated provenance record since 2017, plus the reference layer: the annual predictions, the glossary, the people who shape AI, and the reading strategy.
  • The SuperSkills Glossary
  • AI People
  • The AI Reading Strategy
  • Accuracy and correctionsA public log of what this research got wrong: the original claim, what was found, what changed. Nothing is removed, including the corrections that weaken an argument made here.

Reference · SS-2026-225

Cite this page

Hirji, R. (2026). The full index. The SuperSkills evidence base, SS-2026-225. https://thesuperskills.com/research/all.

An evidence review by Rahim Hirji, not peer-reviewed research. For a material claim, cite the underlying study as well; every study here carries its own permanent link.

How citations and IDs work
Ask the evidence
What does the evidence actually show?What should our board be asking about this?Where does Rahim disagree with the consensus?
Bring this into your organisation

If this describes something happening in your teams, say so.

Keynotes, board sessions and advisory work, drawing on research across more than 200 organisations in 30 countries. Tell me the room, the date and the shift you need. A reply within 24 hours.

Start a conversation

Topics and audiences  ·  All research

Box of Amazing

Rahim’s free weekly letter on AI and human capability

If this was useful, the weekly letter is where the thinking happens first. Most of what ends up on this site starts there. Weekly essays on AI, capability and the future of work. Read by 25,000 people, every week since 2017. Free, and one click to stop.

Opens Substack to confirm. No pitch in it, unsubscribe in one click, and nobody follows up because you read something.

Running an event, or responsible for how AI arrives in your organisation? Keynotes  ·  Advisory for CEOs and boards  ·  Enquire