{"schemaVersion":1,"edition":{"id":"communications-read-2026-11-04","authoredAt":"2026-11-04","evidenceReviewedAt":"2026-11-04","scope":"First occupation read, added on 4 November 2026: communications and public relations work. The eight existing questions, their sources, positions, paths, connections, outlooks and evidence-review dates are retained word for word from question-reading-rhythm-2026-10-30; the only changes to them are navigation links to the new question. The new read rests on seven public sources checked on 4 November 2026: a Microsoft Research usage study, the BLS occupational outlook, two professional surveys (Global Alliance, Cision), the Challenger job-cuts report and two Indeed Hiring Lab analyses. Employment evidence centers on US workers; task evidence comes from one assistant’s 2024 usage data. Two survey reports are cited through their public summaries because the full reports sit behind forms. Outlook dates are EM judgments, not dates forecast by the cited authors. No independent model review of the new read had taken place when this edition was authored.","authors":[{"name":"Claude Mythos 5.1","role":"Author of the communications read, working in a persistent chat with James on 4 November 2026 (not an automated role); every earlier question is retained unchanged","model":"claude-mythos-5-1"},{"name":"Astra 6","role":"Author of the eight retained question reads, unchanged from question-reading-rhythm-2026-10-30","model":"gpt-6-astra"}],"reviewers":[{"name":"Opus 5.5","role":"Editorial review of the eight retained reads in editorial-context-2026-10-30; the new communications read has not yet had an independent review","model":"claude-opus-5-5","reviewedAt":"2026-10-30"}],"readerContextVersion":1},"projection":"question","selectedQuestionId":"jobs","questions":[{"id":"jobs","text":"AI taking most jobs: evidence for or against it, including automation and displacement, job creation and new occupations, hiring, employment, wages, productivity or how gains and losses are distributed.","displayTitle":"Will AI take most jobs?","background":"A job can provide income, a place to learn and a sense of being useful. AI taking over part of it can bring relief, opportunity or fear about where you fit next. This question separates changes to individual tasks from changes to hiring and employment. It matters whether you are choosing a career, supporting a family or deciding how a team should grow: more output can open new possibilities while making familiar routes into work harder to find.","topicIds":["work-and-business","job-displacement-and-creation","wages-and-labor-share"],"broader":[],"narrower":[{"id":"ai-and-work","reason":"Examine whether task capability becomes dependable help in real work."},{"id":"ai-entry-and-worker-gains","reason":"Follow who can enter work and share its benefits."},{"id":"ai-communications-work","reason":"An occupation-level read: how the hiring and job-content shift plays out for communications work."}],"related":[{"id":"agi","reason":"Broader capability could change the set of automatable tasks, but is not employment itself."},{"id":"bubble","reason":"Adoption economics connect workplace outcomes to investment returns, without making them equivalent."}],"read":{"gapLabel":"The first shock is the job you never get","basis":"Through 2029, we expect AI to reshape hiring and job content more than eliminate most jobs.\n\nIn the New York Fed’s regional survey, worker usage was limited even at firms that had adopted AI, and reduced hiring was more common than layoffs. Stanford’s August 2026 payroll study finds that the pressure on young workers in AI-exposed occupations appears primarily in fewer hires.\n\nWe expect employers to adjust recruitment first, rather than replace most existing staff: they can leave an opening unfilled while learning which tasks AI can take over. That makes the route into work an immediate concern even while most jobs persist.","alternatives":["Reliable agents become cheap enough to replace complete workflows, and employers respond with layoffs across experience levels.","Cheaper services expand demand enough to grow hiring, including new entry roles, despite lower labor per unit of output."],"assumptions":["The main case concerns employment across the economy; occupation-level disruption can be severe within it.","Reliability, integration and demand for human work continue to slow the jump from task automation to whole-job substitution."],"revisionTriggers":["Repeated payroll and national employment evidence shows losses spreading from young workers to experienced workers in AI-exposed occupations.","Employer data shows broad worker-level use, full workflow substitution and falling headcount together.","Junior hiring recovers alongside AI adoption and sustained growth in demand."],"implication":"More output creates a choice: serve people better, build something previously unaffordable, or cut the team. Employers and workers can shape that choice. Preserve a route into skilled work so today’s efficiency does not consume tomorrow’s expertise.","estimate":{"status":"editorial-outlook","horizon":{"start":"2027","end":"2029","reason":"A three-year employment outlook: broad replacement requires deeper adoption, dependable completion and organizational change. Existing hiring adjustments can spread sooner because an employer can leave an opening unfilled before replacing an occupied role."}},"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["nyf-fewer","nyf-more"],"role":"context"},{"sourceId":"amodei-loving-grace-2024","spanIds":["labor-contribution"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["worker-adoption","reported-layoffs"],"role":"supports"},{"sourceId":"stanford-canaries-2026","spanIds":["hiring-first","descriptive"],"role":"supports"}],"positionRefs":["anthropic-workforce-authors","nyfed-survey-authors","amodei-labor-long-run","stanford-entry-researchers"],"connectionRefs":[],"gapRefs":[{"id":"aggregate-labor-context","reason":"Track whether hiring pressure spreads across occupations and experience levels, using national as well as regional and payroll-sample evidence."}]}}],"navigationQuestions":[{"id":"jobs","text":"AI taking most jobs: evidence for or against it, including automation and displacement, job creation and new occupations, hiring, employment, wages, productivity or how gains and losses are distributed.","displayTitle":"Will AI take most jobs?","topicIds":["work-and-business","job-displacement-and-creation","wages-and-labor-share"],"broader":[],"narrower":[{"id":"ai-and-work","reason":"Examine whether task capability becomes dependable help in real work."},{"id":"ai-entry-and-worker-gains","reason":"Follow who can enter work and share its benefits."},{"id":"ai-communications-work","reason":"An occupation-level read: how the hiring and job-content shift plays out for communications work."}],"related":[{"id":"agi","reason":"Broader capability could change the set of automatable tasks, but is not employment itself."},{"id":"bubble","reason":"Adoption economics connect workplace outcomes to investment returns, without making them equivalent."}]},{"id":"ai-and-work","text":"When does an AI become a dependable colleague?","displayTitle":"When does AI become a dependable colleague?","topicIds":["work-and-business","human-ai-collaboration","reliability-in-deployment"],"broader":[{"id":"jobs","reason":"Dependable assistance may change tasks and labor demand, with different effects across roles."}],"narrower":[{"id":"ai-work-quality-and-rework","reason":"Count the checking and correction needed for acceptable work."},{"id":"ai-delegation-reliability","reason":"Examine what happens when a human stops checking each step."}],"related":[]},{"id":"ai-work-quality-and-rework","text":"Does AI reduce the total effort needed to produce work that meets the required standard?","displayTitle":"Does AI reduce the effort needed to finish work well?","topicIds":["quality-and-rework","human-ai-collaboration","economics-of-adoption"],"broader":[{"id":"ai-and-work","reason":"Checking and correction are part of dependability."}],"narrower":[],"related":[{"id":"ai-delegation-reliability","reason":"Low average rework does not establish safe unsupervised operation."},{"id":"ai-deployment-returns","reason":"Net workflow value is one input to sustainable returns."},{"id":"ai-entry-and-worker-gains","reason":"Review and training work may be redistributed across experience levels."},{"id":"ai-communications-work","reason":"Communications is a drafting-heavy occupation where the review burden decides who keeps time."}]},{"id":"ai-entry-and-worker-gains","text":"Who gets an entry point, and who shares the gains?","displayTitle":"Who gets an entry point and shares the gains?","topicIds":["job-displacement-and-creation","worker-bargaining","education-and-learning","wages-and-labor-share"],"broader":[{"id":"jobs","reason":"Track distribution and opportunity alongside total employment."}],"narrower":[],"related":[{"id":"ai-work-quality-and-rework","reason":"Who performs review can affect skill development and the distribution of work."},{"id":"ai-communications-work","reason":"Junior communicators learned judgment by drafting; that entry route is the one AI reaches first."}]},{"id":"agi","text":"AGI within a few years: progress, limits, timelines or forecasts for broadly capable systems, including disagreement.","displayTitle":"Is AGI a few years away?","topicIds":["ai-capabilities-and-limits","long-horizon-planning"],"broader":[],"narrower":[{"id":"ai-delegation-reliability","reason":"Test one practical dependency without substituting it for the whole AGI question."}],"related":[{"id":"jobs","reason":"Capability does not determine deployment, demand or employment on its own."}]},{"id":"ai-delegation-reliability","text":"When can work be delegated without checking every step?","displayTitle":"When can work be delegated without checking every step?","topicIds":["reliability-in-deployment","long-horizon-planning","ai-capabilities-and-limits--evaluation-and-reliability"],"broader":[{"id":"agi","reason":"Reliability is one relevant condition, not a sufficient definition of general intelligence."},{"id":"ai-and-work","reason":"A dependable colleague can also be a supervised collaborator."}],"narrower":[],"related":[{"id":"ai-work-quality-and-rework","reason":"Inspect the cost of detecting and correcting failures."}]},{"id":"bubble","text":"An AI bubble: financing, valuations, capital expenditure, compute investment, revenues, demand or sustainable returns.","displayTitle":"Is there an AI bubble?","topicIds":["finance-and-the-economy","economics-of-adoption"],"broader":[],"narrower":[{"id":"ai-deployment-returns","reason":"Begin with value to a deploying organization, then identify the additional evidence needed for investor returns."}],"related":[{"id":"jobs","reason":"Employment effects and investment outcomes can move differently."}]},{"id":"ai-deployment-returns","text":"When does a useful AI deployment become worth its total cost?","displayTitle":"When is an AI deployment worth its total cost?","topicIds":["economics-of-adoption","work-and-business--adoption-and-deployment","finance-and-the-economy"],"broader":[{"id":"bubble","reason":"Customer value is one dependency; supplier margins, capital cost and valuation need separate evidence."}],"narrower":[],"related":[{"id":"ai-work-quality-and-rework","reason":"Review and correction can affect total cost and value."},{"id":"jobs","reason":"A positive deployment return does not specify whether employment expands or contracts."}]},{"id":"ai-communications-work","text":"How is AI changing communications and public relations work, and where does a communications professional’s advantage now lie?","displayTitle":"What happens to communications work?","topicIds":["occupational-change","task-automation","human-ai-collaboration","journalism"],"broader":[{"id":"jobs","reason":"An occupation-level read of the hiring and job-content shift the jobs question expects."}],"narrower":[],"related":[{"id":"ai-work-quality-and-rework","reason":"How much review AI-drafted material needs decides whether drafting assistance frees time or moves the work."},{"id":"ai-entry-and-worker-gains","reason":"If first drafts stop being the junior communicator’s job, the route into the profession changes."}]}],"sources":[{"id":"anthropic-workforce-2026","title":"How employment is changing at Anthropic","url":"https://www.anthropic.com/research/employment-at-anthropic","publishedAt":"2026-10-16","dateBasis":"Publisher publication date, checked on original page.","access":{"status":"public-page","note":"Original publisher page and exact retained source reviewed. Source retention does not establish every downstream reuse or training right.","checkedAt":"2026-10-29"},"provenance":{"kind":"stream-snapshot","workId":"18f3c3ca31491462a8ab52f797ebadd2a7087803317e69a5152e2a0c4cfbed65","snapshotId":"a633ce13b2d661ad9c008ac99d3357f15a80c4bcd91009290d11712c2f565793","textHash":"8ea3533aa08b1f63c143b0a383911da93e88f464edd2d8e61edebf2a97a42232","reason":"Exact retained Stream work/snapshot and SHA256 verified against the 29 October SELECT-only source packet. That lookup found no joined public Stream or bridge reading revision; no reading identity is invented."},"spans":[{"id":"anth-junior","text":"The proportion of junior technical hires fell even faster, from 27% to 9%.","locator":"Retained extracted text, character offsets [4420, 4494). Context (EM paraphrase): Anthropic’s October 2026 report describes its own workforce; junior employees’ share fell from early 2025, and technical hiring still grew in absolute terms. This is a changing hiring mix, not a count of jobs lost."},{"id":"anth-bottleneck","text":"This acceleration makes human attention the bottleneck for agent output.","locator":"Retained extracted text, character offsets [767, 839). Context (EM paraphrase): The authors report rising internal output alongside agent use and interpret human attention as a constraint. Adoption is not randomized; the report does not establish a general causal productivity effect."}],"citation":{"label":"Anthropic workforce report · October 2026","aliases":["Anthropic’s workforce report","Anthropic workforce report","Anthropic’s hiring mix"],"context":"An internal study of employment and agent use at Anthropic. It describes a changing hiring mix and demands on human attention at one growing AI company.","spanIds":["anth-junior","anth-bottleneck"]},"readingRefs":[]},{"id":"nyfed-business-survey-2026","title":"Businesses Are Using AI to Transform Work, Not Cut Jobs","url":"https://libertystreeteconomics.newyorkfed.org/2026/09/businesses-are-using-ai-to-transform-work-not-cut-jobs/","publishedAt":"2026-09-01","dateBasis":"Publisher publication date, checked on original page.","access":{"status":"public-page","note":"Original publisher page and exact retained source reviewed. Source retention does not establish every downstream reuse or training right.","checkedAt":"2026-10-29"},"provenance":{"kind":"stream-snapshot","workId":"c16ff26252860625027590ed41b03b3773be9d5b324475687ef0304e51a73af8","snapshotId":"d59dc274ed0f66564545055ec05e4a9abf5a5314f05623d021bbe3ac1b2274ab","textHash":"7ec36434625838d3aa01b2819ecb8d90e622fd2f2e8a664dd2c656116c4829dd","reason":"Exact retained Stream work/snapshot and SHA256 verified against the 29 October SELECT-only source packet. That lookup found no joined public Stream or bridge reading revision; no reading identity is invented."},"spans":[{"id":"nyf-fewer","text":"hired fewer workers","locator":"Retained extracted text, character offsets [4475, 4494). Context (EM paraphrase): August 2026 regional survey: about 15% of AI-using service firms reported hiring fewer workers than they would otherwise have because of AI. This is employers’ stated counterfactual, not independently identified job loss. The paired figure for hiring more is about 13%; firm percentages cannot be subtracted into a net job count."},{"id":"nyf-more","text":"About 13 percent of service firms said they had hired more workers due to AI","locator":"Retained extracted text, character offsets [4776, 4852). Context (EM paraphrase): August 2026 regional business survey, reporting workforce adjustments over the preceding six months. The workforce charts concern AI-using firms in the survey; they are not national employment totals. Authors also report retraining as a common response."},{"id":"worker-adoption","text":"17 percent","locator":"Retained extracted text, character offsets [2997, 3007). Context (EM paraphrase): Among AI-using regional service firms, median worker usage in the August 2026 survey was 17%; manufacturers were at 7%. Firm adoption and workforce penetration are different measures."},{"id":"reported-layoffs","text":"4 percent","locator":"Retained extracted text, character offsets [4214, 4223). Context (EM paraphrase): AI-using service firms reporting AI-related layoffs over the preceding six months, versus about 15% reporting reduced hiring. Regional employer reports, not national worker shares."}],"citation":{"label":"New York Fed regional business survey · September 2026","aliases":["New York Fed regional business survey","New York Fed’s regional survey","regional employer survey"],"context":"A report on the August 2026 survey of businesses in New York and northern New Jersey, distinguishing firm adoption, worker usage, hiring and layoffs.","spanIds":["nyf-fewer","nyf-more","worker-adoption","reported-layoffs"]},"readingRefs":[]},{"id":"amodei-loving-grace-2024","title":"Machines of Loving Grace","url":"https://darioamodei.com/essay/machines-of-loving-grace","publishedAt":"2024-10","dateBasis":"October 2024 is printed in the retained essay. The work metadata has no publication timestamp; month precision is preserved.","access":{"status":"retained-public-essay","note":"Original personal essay retained in Stream and reviewed here; not an institutional position or a new 2026 statement. Public availability does not establish every reuse right.","checkedAt":"2026-10-29"},"provenance":{"kind":"stream-snapshot","workId":"6aa2c807bdcb81da04a30b6cf0b1c0620353219816ab224111920bdc8c02bc32","snapshotId":"07fedfacf7c11b925e82d6c8a319ee710df91c8b7160fae3d7ca3fda1da6af45","textHash":"d8f37e4a35c07240bca22712dece7667d41c3e3efb9f28b7ca62c4c5f807442a","reason":"Exact original Stream identity and SHA256 verified against the retained source packet."},"spans":[{"id":"powerful-ai-timing","text":"as early as 2026","locator":"Retained extracted text, character offsets [6255, 6271). Context (EM paraphrase): Amodei says powerful AI could arrive that early but could also take much longer. His working definition combines exceptional cognitive capability, human-like computer interfaces and autonomous work over hours, days or weeks. The essay assumes relatively near arrival to explore later effects; it is not a demonstrated outcome or a standardized AGI forecast."},{"id":"labor-contribution","text":"most or all humans may not be able to contribute meaningfully","locator":"Retained extracted text, character offsets [75299, 75360). Context (EM paraphrase): In the essay’s long-run economic discussion, Amodei considers sufficiently capable and cheap AI displacing the basis of human economic contribution. He first allows comparative advantage and new complementary jobs to preserve employment. No job-loss date or measured proportion is supplied."}],"citation":{"label":"Dario Amodei · Machines of Loving Grace","aliases":["Amodei’s powerful-AI vision","Amodei’s powerful-AI essay","Machines of Loving Grace","Amodei’s demanding combination"],"context":"Amodei’s October 2024 essay defines powerful AI through broad capability and autonomous work, then explores possible consequences under an assumption of relatively near arrival.","spanIds":["powerful-ai-timing","labor-contribution"]},"readingRefs":[]},{"id":"stanford-canaries-2026","title":"Canaries in the Coal Mine? — August 2026 revision","url":"https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/","publishedAt":"2026-08-12","dateBasis":"Publisher page identifies the revision as 12 August 2026; the linked PDF is dated August 2026.","access":{"status":"public-page","note":"Original research summary and linked August PDF abstract checked; the findings are attributed to the authors and their stated population.","checkedAt":"2026-10-30"},"provenance":{"kind":"external","workId":null,"snapshotId":null,"textHash":null,"reason":"Original public source read on 30 October 2026. An exact retained Stream identity has not been resolved for this edition."},"spans":[{"id":"hiring-first","text":"reduced hiring of young workers rather than increased separations","locator":"Publisher summary, fourth finding; linked August PDF, abstract. EM context: ADP payroll sample through June 2026. The authors find no widespread displacement, but a relative employment shortfall among ages 22–25 in AI-exposed occupations."},{"id":"descriptive","text":"early, descriptive indicators","locator":"Publisher summary, final paragraph; linked PDF, abstract. EM context: education controls attenuate the pattern; some divergent trends predate generative AI, and the ADP pattern is stronger than national survey benchmarks. These are not causal job-loss estimates."}],"citation":{"label":"Stanford · Canaries in the Coal Mine? · August 2026","aliases":["Stanford’s August 2026 payroll study","Stanford’s payroll evidence","Canaries in the Coal Mine?"],"context":"A descriptive analysis of ADP payroll data through June 2026, examining employment by age and AI exposure. Education, earlier trends and sample differences matter to its interpretation.","spanIds":["hiring-first","descriptive"]},"readingRefs":[]}],"positions":[{"id":"anthropic-workforce-authors","speaker":"Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory","role":"Anthropic Institute researchers reporting on their employer","statement":"The authors report a falling junior share of technical hires and propose that intensive AI adoption at Anthropic may offer an early indication of wider workplace change.","conditions":["One rapidly growing AI company; technical hiring still grew in absolute terms.","Reported internal patterns and the authors’ interpretation do not identify an economy-wide causal effect."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"supports"}],"statedAt":"2026-10-16","dateBasis":"Dated authored publication; this is not the date of every underlying observation or interview.","attribution":"direct"},{"id":"nyfed-survey-authors","speaker":"Jaison R. Abel, Richard Deitz, Natalia Emanuel and Nick Montalbano","role":"New York Fed research staff; article expresses their views","statement":"The authors interpret their regional business surveys as showing more worker augmentation than replacement so far, while explicitly allowing this to change as adoption matures.","conditions":["August 2026 regional business survey, not a national census.","Workforce changes are employer reports; the relevant charts describe firms using AI."],"sourceRefs":[{"sourceId":"nyfed-business-survey-2026","spanIds":["nyf-fewer","nyf-more"],"role":"supports"}],"statedAt":"2026-09-01","dateBasis":"Dated authored publication; this is not the date of every underlying observation or interview.","attribution":"direct"},{"id":"amodei-labor-long-run","speaker":"Dario Amodei","role":"Anthropic chief executive writing a personal essay","statement":"He allows human comparative advantage and complementary jobs to persist initially, but expects sufficiently capable and cheap AI eventually to undermine the present basis of human economic contribution.","conditions":["Conditional long-run argument, not measured job loss or a dated prediction that most jobs disappear.","The essay treats economic arrangements and distribution as choices needing further debate."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["labor-contribution"],"role":"supports"}],"statedAt":"2024-10","dateBasis":"Month printed in the essay; no job-loss horizon is stated.","attribution":"direct"},{"id":"stanford-entry-researchers","speaker":"Erik Brynjolfsson, Bharat Chandar and Ruyu Chen","role":"Researchers analyzing ADP payroll records","statement":"The authors find that young workers in AI-exposed occupations have fallen behind less-exposed peers, primarily through reduced hiring, while finding no widespread economy-wide displacement.","conditions":["Descriptive pattern through June 2026; education, prior trends and sample differences matter for attribution."],"sourceRefs":[{"sourceId":"stanford-canaries-2026","spanIds":["hiring-first","descriptive"],"role":"supports"}],"statedAt":"2026-08-12","dateBasis":"Date of the cited version or source update.","attribution":"direct"}],"paths":[{"id":"jobs-complements-persist","questionId":"jobs","outcome":"Work changes faster than employment disappears","scope":"Routine tasks shrink and some openings vanish, while demand, new tasks and deployment constraints keep substantial human work in place. Aggregate resilience coexists with painful losses in particular careers.","conditions":["Demand for improved or cheaper services absorbs some of the extra capacity.","Human judgment, relationships, physical work and implementation constraints continue to matter."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["labor-contribution"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["nyf-fewer","nyf-more"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["worker-adoption","reported-layoffs"],"role":"supports"},{"sourceId":"stanford-canaries-2026","spanIds":["hiring-first"],"role":"supports"}],"positionRefs":["amodei-labor-long-run","nyfed-survey-authors","stanford-entry-researchers"],"horizon":{"start":"2027","end":"2029","reason":"Hiring can adjust immediately; replacing most jobs requires capability, adoption and organizational change across many kinds of work. That makes uneven restructuring the stronger three-year case."},"weight":"base-case","signposts":["Hiring composition shifts more sharply than total employment.","AI-using firms retrain and reassign workers while expanding the range of tasks they automate."]},{"id":"jobs-substitution-expands","questionId":"jobs","outcome":"The hiring squeeze becomes a broader employment squeeze","scope":"Employers stop at fewer vacancies first, then remove occupied roles as reliable agents take over complete workflows. The disruption reaches experienced workers as well as entrants.","conditions":["Reliable completion becomes cheaper than the full human workflow, including supervision and recovery.","Demand growth and new work fail to absorb the labor released."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["labor-contribution"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"context"},{"sourceId":"stanford-canaries-2026","spanIds":["hiring-first","descriptive"],"role":"context"}],"positionRefs":["amodei-labor-long-run","stanford-entry-researchers"],"horizon":{"start":"2027","end":"2029","reason":"This is the faster-displacement route within the same three-year outlook. The milestone is spread across experience levels and occupations, not a claim that most jobs disappear by 2029."},"weight":"alternative","signposts":["AI-exposed employment weakens across age and experience groups in multiple data sources.","Firms link sustained headcount reductions to completed workflow substitution, beyond isolated announcements."]}],"connections":[],"changes":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-ai-exposure","name":"AI exposure","explanation":"In labor economics, how much of a job's tasks an AI system could in principle perform; it is not a forecast of job loss.","referenceUrl":"https://arxiv.org/abs/2303.10130"},{"id":"term-counterfactual","name":"Counterfactual","explanation":"An argument or scenario that considers what would have resulted if events had gone differently from how they actually did, such as if a policy had never been adopted; as an adjective, relating to this kind of thinking about what did not happen but could have.","referenceUrl":"https://dictionary.cambridge.org/dictionary/english/counterfactual"},{"id":"term-agi","name":"Artificial general intelligence","explanation":"The idea of AI with broad problem-solving, creativity and adaptability across different tasks.","referenceUrl":"https://developers.google.com/machine-learning/glossary#artificial-general-intelligence"},{"id":"term-benchmark","name":"Benchmark","explanation":"A defined set of tests used to compare systems. 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