{"schemaVersion":1,"edition":{"id":"jobs-quality-2026-10-29","authoredAt":"2026-10-29","evidenceReviewedAt":"2026-10-29","scope":"Selected source review, October 2024–October 2026, centered on technical work and selected US employers, with attributed AI capability and investment arguments. Initial authored edition, not a reconstructed EM view from earlier dates. 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This population does not represent all developers or later systems."},{"id":"completion-time","text":"they take 19% longer to complete issues","locator":"Retained extracted text, character offsets [2468, 2507). Context (EM paraphrase): Core Result, first paragraph; interpret with Methods and the immediately following limitations table. Reported result for the 2025 study, not a current universal estimate."}],"readingRefs":[]},{"id":"metr-productivity-update-2026","title":"We are Changing our Developer Productivity Experiment Design","url":"https://metr.org/blog/2026-02-24-uplift-update/","publishedAt":"2026-02-24","dateBasis":"Publication date displayed by the original publisher.","access":{"status":"public-page","note":"Original publisher page and exact retained source reviewed. 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That lookup found no joined public Stream or bridge reading revision; no reading identity is invented."},"spans":[{"id":"self-report","text":"Participants self-reported a median 1.4–2x change in the value in their work due to AI tools.","locator":"Retained extracted text, character offsets [1308, 1401). Context (EM paraphrase): METR surveyed 349 technical workers in February–April 2026. The range reflects self-reported value under different question formulations, not a confidence interval or observed causal return. METR gives reasons to doubt the magnitude and distinguishes value from speed."}],"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. 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The workplace reports here describe narrower task and hiring changes. They do not establish the economy-wide effect; aggregate labor context and independent worker outcomes remain gaps.","alternatives":["Growing output could expand demand for workers; the same tools could reduce hiring in particular roles.","Employment can persist while entry opportunities, wages or bargaining power deteriorate."],"assumptions":["Task substitution, organizational deployment, demand and institutions jointly influence employment; none alone fixes the result."],"revisionTriggers":["Broader outcome evidence separating hiring, exits, wages and task changes across occupations and places.","Credible evidence that apparent labor effects have another explanation."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"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"}],"positionRefs":["anthropic-workforce-authors","nyfed-survey-authors","amodei-labor-long-run"],"connectionRefs":[],"gapRefs":[{"id":"aggregate-labor-context","reason":"Selected employers cannot establish total hiring, job creation or employment change."}]}},{"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":[],"read":{"gapLabel":"Useful output still needs a workable handoff","basis":"Dependability means acceptable work in a particular setting, including recovery when the tool fails. 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Its later update warns that new estimates are compromised by selection and measurement. Newer self-reports and workplace observations add useful context, but do not settle today’s net effect for a reader’s workflow.","alternatives":["Benefits may be larger on different tasks, with later tools or after workflow redesign.","Faster output can shift work into review, correction or integration; increased output may also be worth that cost."],"assumptions":["Acceptable quality and the work counted in the comparison are explicit.","Task mix, tool version and human experience affect comparisons."],"revisionTriggers":["A later comparable study measures accepted results and total human effort, with credible handling of selection.","A correction changes a load-bearing result or its population.","A new finding concerns a materially different workflow: add that scope without rewriting this one."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"sourceRefs":[{"sourceId":"metr-productivity-2025","spanIds":["setting","completion-time"],"role":"context"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"context"},{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"}],"positionRefs":["metr-productivity-researchers","metr-followup-method","metr-worker-value"],"connectionRefs":["quality-to-reliability","quality-to-returns"],"gapRefs":[{"id":"accepted-work-transfer","reason":"Check which task, worker and tool conditions explain different results."}]}},{"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."}],"read":{"gapLabel":"Aggregate employment can hide different experiences","basis":"A shrinking junior hiring share at one company and regional reports of both reduced and increased hiring raise different questions from total job counts. Workers’ own experience and distributional outcomes remain thin in this edition; a proposal for stronger worker voice is included as a proposal.","alternatives":["Company growth and changing business functions can alter hiring composition without AI being the sole cause.","Skills learned through supervised AI work might create different entry routes; that benefit is not established here."],"assumptions":["Access to training, ownership and bargaining can shape how similar technical changes affect different people."],"revisionTriggers":["Worker accounts and hiring, pay, progression or training outcomes that test these mechanisms."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["nyf-fewer","nyf-more"],"role":"context"},{"sourceId":"steering-beyond-automation","spanIds":["un-voice"],"role":"context"}],"positionRefs":["unger-narayanan-collaboration"],"connectionRefs":[],"gapRefs":[{"id":"entry-progression-and-distribution","reason":"One company and regional employers do not settle entry opportunities or distribution."}]}},{"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."}],"read":{"gapLabel":"This slice tests a deployment dependency, not an AGI date","basis":"Amodei’s October 2024 essay offers a near-arrival possibility for his defined powerful AI, while allowing delay. That makes a forecast and its conditions inspectable; it does not establish an AGI date. This edition has not compared current capabilities or competing timeline arguments comprehensively.","alternatives":["A broadly capable system might still require oversight in consequential deployment.","Useful economic specialization may arrive without satisfying a chosen definition of AGI."],"assumptions":["Any timeline claim needs a stated capability definition and endpoint."],"revisionTriggers":["Capability evidence spanning relevant tasks, robustness and operating conditions, with explicit definitions."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["powerful-ai-timing"],"role":"context"}],"positionRefs":["amodei-powerful-ai"],"connectionRefs":[],"gapRefs":[{"id":"agi-claims-and-endpoints","reason":"Compare defined claims with dated capability evidence and competing forecasts before assessing a timeline."}]}},{"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."}],"read":{"gapLabel":"Assistance and delegation need different evidence","basis":"Observed assistance and reported output gains do not establish reliable autonomous completion. This question needs evidence on failure, detection, escalation and recovery across a defined task distribution; this edition does not contain that full comparison.","alternatives":["Humans may remain effective supervisors while models perform more implementation.","Redesigning tasks or narrowing permissions may enable dependable bounded delegation before broad autonomy."],"assumptions":["The cost and detectability of failure matter alongside the average result."],"revisionTriggers":["Comparable completion and failure evidence with task, tool, oversight and stopping conditions specified."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"},{"sourceId":"amodei-loving-grace-2024","spanIds":["powerful-ai-timing"],"role":"context"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"limits"}],"positionRefs":["amodei-powerful-ai","metr-followup-method"],"connectionRefs":["quality-to-reliability"],"gapRefs":[{"id":"delegation-failure-and-recovery","reason":"A claimed definition and supervised workplace use do not supply deployment failure/recovery evidence."}]}},{"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."}],"read":{"gapLabel":"The financing argument is inspectable; the verdict remains open","basis":"Zitron argues that AI financing rests on fragile growth expectations. Damodaran asks what business outcomes would justify investment prices. These are useful arguments to inspect, not an assessed industry-wide verdict: this edition has not reconciled financing, demand, costs and valuations against original financial records.","alternatives":["Customers can benefit while suppliers or investors earn poor returns.","Investment can be excessive in one segment while others have durable demand."],"assumptions":["Returns depend on price, cost, competition, financing and timing as well as usefulness."],"revisionTriggers":["A scoped investment question with verified financial, demand and cost evidence."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"sourceRefs":[{"sourceId":"zitron-ai-financing-2026","spanIds":["financing-thesis"],"role":"context"},{"sourceId":"damodaran-ai-business-2026","spanIds":["valuation-conditions"],"role":"context"}],"positionRefs":["zitron-bubble-financing","damodaran-ai-value-conditions"],"connectionRefs":[],"gapRefs":[{"id":"bubble-investment-and-demand","reason":"A scoped investment assessment needs financing, costs, demand and valuation evidence, not a productivity survey."}]}},{"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."}],"read":{"gapLabel":"Measure the value of accepted work","basis":"The relevant comparison includes benefits from new or better work as well as time saved. Checking, integration, training and operating costs can change the answer. The sources here motivate that question; they do not supply a general return estimate or audited customer economics.","alternatives":["A deployment can be worth buying for improved quality or new capability even without labor savings.","Apparent savings can disappear when quality losses or transferred work are counted."],"assumptions":["A defined buyer, workflow, alternative and time horizon are necessary for a useful comparison."],"revisionTriggers":["Comparable costs and accepted outcomes over repeat use, including rollout and recurring work.","Evidence that costs or benefits were omitted or assigned to the wrong participant."],"estimate":{"status":"not-estimated","reason":"This selected evidence does not justify a probability, date or numerical outcome range for this question."},"sourceRefs":[{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"},{"sourceId":"damodaran-ai-business-2026","spanIds":["valuation-conditions"],"role":"context"}],"positionRefs":["metr-worker-value","damodaran-ai-value-conditions"],"connectionRefs":["quality-to-returns"],"gapRefs":[{"id":"deployment-value-total-cost","reason":"Self-reported value and internal output do not establish a complete deployment return."}]}}],"paths":[{"id":"colleague-useful-work-expands","questionId":"ai-and-work","outcome":"More accepted work with useful human oversight","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Useful output or new capabilities outweigh checking and integration costs.","People can recognize and recover from consequential failures."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"},{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"context"},{"sourceId":"metr-productivity-2025","spanIds":["setting","completion-time"],"role":"challenges"}],"positionRefs":["anthropic-work-organization","metr-worker-value"]},{"id":"colleague-review-absorbs-gains","questionId":"ai-and-work","outcome":"Review and rework absorb the apparent gains","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["The task or tool produces costly errors or omissions.","New value does not offset correction and integration work."],"sourceRefs":[{"sourceId":"metr-productivity-2025","spanIds":["setting","completion-time"],"role":"supports"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"limits"}],"positionRefs":["metr-productivity-researchers","metr-followup-method"]},{"id":"quality-useful-work-expands","questionId":"ai-work-quality-and-rework","outcome":"More accepted work with useful human oversight","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Useful output or new capabilities outweigh checking and integration costs.","People can recognize and recover from consequential failures."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"},{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"context"},{"sourceId":"metr-productivity-2025","spanIds":["setting","completion-time"],"role":"challenges"}],"positionRefs":["anthropic-work-organization","metr-worker-value"]},{"id":"quality-review-absorbs-gains","questionId":"ai-work-quality-and-rework","outcome":"Review and rework absorb the apparent gains","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["The task or tool produces costly errors or omissions.","New value does not offset correction and integration work."],"sourceRefs":[{"sourceId":"metr-productivity-2025","spanIds":["setting","completion-time"],"role":"supports"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"limits"}],"positionRefs":["metr-productivity-researchers","metr-followup-method"]},{"id":"jobs-complements-persist","questionId":"jobs","outcome":"Tasks change while substantial human work persists","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Human contributions, demand growth or new tasks complement automation.","Deployment remains constrained by costs, reliability or institutions."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["labor-contribution"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["nyf-fewer","nyf-more"],"role":"context"}],"positionRefs":["amodei-labor-long-run","nyfed-survey-authors"]},{"id":"jobs-substitution-expands","questionId":"jobs","outcome":"Substitution spreads beyond particular tasks and occupations","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Capability and cost improve enough for widespread deployment.","New tasks, demand or institutions do not offset the reduced labor demand."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["labor-contribution"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"context"}],"positionRefs":["amodei-labor-long-run"]},{"id":"entry-routes-narrow","questionId":"ai-entry-and-worker-gains","outcome":"Some entry routes narrow while output grows","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Implementation tasks previously used to train juniors are automated or reassigned.","Alternative training and entry routes fail to offset lost opportunities."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"context"},{"sourceId":"nyfed-business-survey-2026","spanIds":["nyf-fewer","nyf-more"],"role":"context"}],"positionRefs":["anthropic-workforce-authors"]},{"id":"entry-opportunities-renew","questionId":"ai-entry-and-worker-gains","outcome":"New learning and entry routes share the gains","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Tools and institutions help less-experienced people do valuable work.","Employers create opportunities and workers can influence deployment."],"sourceRefs":[{"sourceId":"steering-beyond-automation","spanIds":["un-voice"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"challenges"}],"positionRefs":["unger-narayanan-collaboration"]},{"id":"delegation-bounded-progress","questionId":"ai-delegation-reliability","outcome":"Bounded delegation becomes dependable","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["The tasks and consequences are specified.","Failures can be detected, contained or escalated at acceptable cost."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["powerful-ai-timing"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"}],"positionRefs":["amodei-powerful-ai"]},{"id":"delegation-supervision-persists","questionId":"ai-delegation-reliability","outcome":"Assistance improves but close supervision persists","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Errors, context requirements or recovery costs remain consequential.","People still need to inspect or repair work before relying on it."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"limits"}],"positionRefs":["anthropic-work-organization","metr-followup-method"]},{"id":"agi-near-capability","questionId":"agi","outcome":"The defined capabilities arrive relatively soon","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Progress reaches the breadth and autonomous-work requirements of the particular definition.","Important scientific, engineering or operating constraints are overcome."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["powerful-ai-timing"],"role":"context"}],"positionRefs":["amodei-powerful-ai"]},{"id":"agi-delayed-or-different","questionId":"agi","outcome":"Progress continues unevenly or the stated endpoint is delayed","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Some required capabilities or operating conditions remain unresolved.","Success on selected tasks does not transfer across the definition’s full scope."],"sourceRefs":[{"sourceId":"amodei-loving-grace-2024","spanIds":["powerful-ai-timing"],"role":"context"}],"positionRefs":["amodei-powerful-ai"]},{"id":"deployment-worth-cost","questionId":"ai-deployment-returns","outcome":"Accepted work justifies the total deployment cost","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Measured benefits include valuable new work or improved outcomes.","Recurring and rollout costs remain below those benefits for the defined buyer."],"sourceRefs":[{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"}],"positionRefs":["metr-worker-value"]},{"id":"deployment-value-erodes","questionId":"ai-deployment-returns","outcome":"Omitted costs or weak demand undermine the case","scope":"EM conditional scenario, not an estimated probability or a claim that this outcome has occurred.","conditions":["Reported gains fail to become useful accepted outcomes.","Correction, integration, reliability or opportunity costs exceed the value gained."],"sourceRefs":[{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"limits"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"limits"}],"positionRefs":["metr-worker-value"]},{"id":"bubble-cashflows-catch-up","questionId":"bubble","outcome":"Some AI investments produce durable cash flows","scope":"EM conditional investment scenario, not an estimated probability, advice or a verdict across all AI firms.","conditions":["Customers pay enough for useful products.","Margins and reinvestment needs support the price and financing terms."],"sourceRefs":[{"sourceId":"zitron-ai-financing-2026","spanIds":["financing-thesis"],"role":"context"},{"sourceId":"damodaran-ai-business-2026","spanIds":["valuation-conditions"],"role":"context"}],"positionRefs":["zitron-bubble-financing","damodaran-ai-value-conditions"]},{"id":"bubble-returns-disappoint","questionId":"bubble","outcome":"Investment returns disappoint even where AI is useful","scope":"EM conditional investment scenario, not an estimated probability, advice or a verdict across all AI firms.","conditions":["Revenue or margins fall short of the expectations built into prices.","Financing and ongoing capital needs magnify the shortfall."],"sourceRefs":[{"sourceId":"zitron-ai-financing-2026","spanIds":["financing-thesis"],"role":"context"},{"sourceId":"damodaran-ai-business-2026","spanIds":["valuation-conditions"],"role":"context"}],"positionRefs":["zitron-bubble-financing","damodaran-ai-value-conditions"]}],"connections":[{"id":"quality-to-reliability","from":{"kind":"question","id":"ai-work-quality-and-rework"},"to":{"kind":"question","id":"ai-delegation-reliability"},"relation":"informs","rationale":"Human review can make assisted output acceptable while concealing failures that would matter under autonomous delegation. The same evidence therefore asks a different question when oversight changes.","limits":["An authored cross-source connection, not a measured causal coefficient.","Failure detection and long-run reliability require additional evidence."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"},{"sourceId":"metr-productivity-2025","spanIds":["setting","completion-time"],"role":"context"},{"sourceId":"metr-productivity-update-2026","spanIds":["selection"],"role":"context"}],"positionRefs":[],"basis":"em-inference"},{"id":"quality-to-returns","from":{"kind":"question","id":"ai-work-quality-and-rework"},"to":{"kind":"question","id":"ai-deployment-returns"},"relation":"informs","rationale":"The cost of accepted work links task outcomes to deployment value. Different task value or new capabilities can offset additional effort; faster generation alone is insufficient.","limits":["Deployment returns are not provider margins or asset valuations.","This is an analytical dependency, not evidence of a bubble."],"sourceRefs":[{"sourceId":"metr-worker-survey-2026","spanIds":["self-report"],"role":"context"},{"sourceId":"anthropic-workforce-2026","spanIds":["anth-bottleneck"],"role":"context"}],"positionRefs":["metr-worker-value"],"basis":"em-inference"},{"id":"entry-to-institutions","from":{"kind":"question","id":"ai-entry-and-worker-gains"},"to":{"kind":"path","id":"entry-routes-narrow"},"relation":"conditional-path","rationale":"Training arrangements and workers’ influence over deployment may change who can benefit from similar tools. This is a mechanism to investigate, not a guaranteed policy lever.","limits":["The normative proposal does not establish its causal effect.","Worker and institutional outcomes need direct evidence."],"sourceRefs":[{"sourceId":"anthropic-workforce-2026","spanIds":["anth-junior"],"role":"context"},{"sourceId":"steering-beyond-automation","spanIds":["un-voice"],"role":"context"}],"positionRefs":["unger-narayanan-collaboration"],"basis":"em-inference"}],"changes":[],"selectedQuestionId":null,"questionVersions":{"jobs":"26f53ad614af16d8ca4318678a785c1e312cdee7f628e4124d2d31e04b79d014","ai-and-work":"4c5c74c923bb25c5427d0a11799a4caf09fc8f2eae940fd6f2bf314cd825b3a4","ai-work-quality-and-rework":"fe281595e046b48daf9acacfd0e1061f31af42728fe2a4d7e3e4418fa63585b4","ai-entry-and-worker-gains":"581795fc087f7a47ad5fee65e683f359001374bf0f62f47d1fcc07fb211d984b","agi":"d30d0ffc1b5daf9eab7c86a83566228f7ae7af971f660569fad79544bedf4f06","ai-delegation-reliability":"588c751e892c7c15d373bb1f193769517fef0faf9f15512d158344de13c1ea42","bubble":"d64986b9dae319bc6e2ec530bffb2881e19bf7f73e36878eb7683df306e43b68","ai-deployment-returns":"21e95cf1379f08835d6bb8795f3307a01c7ead25591f7f789792c0ce51751802"},"availableEditions":[{"id":"jobs-quality-2026-10-29","authoredAt":"2026-10-29","evidenceReviewedAt":"2026-10-29","scope":"Selected source review, October 2024–October 2026, centered on technical work and selected US employers, with attributed AI capability and investment arguments. Initial authored edition, not a reconstructed EM view from earlier dates. Broad jobs, AGI and bubble entries preserve their scope and expose limits of this slice.","authors":[{"name":"Astra 6","role":"Editorial synthesis author; revised after the independent audit","model":"gpt-6-astra"}],"reviewers":[{"name":"Opus 5.5","role":"Independent audit of earlier working content; findings informed this revision, not approval of revised content","model":"claude-opus-5-5","reviewedAt":"2026-10-29"}]}]}