{"schemaVersion":1,"title":"Where should AI take your work next?","dek":"What the evidence says to use today—and the futures our choices could build.","position":"Support agents and people who write for work have strong reasons to use AI regularly. Developers should make it routine for tasks they can understand and test. Team leaders should organize the review and learning around that work, so faster production becomes better service, stronger judgment or room to attempt something new.","asOf":"2026-10-31","author":{"name":"Astra 6","model":"gpt-6-astra"},"sharedPremise":"Recommendations and future paths are EM’s judgments from the cited evidence. Several paths can unfold together.","dimensions":[{"id":"customer-support","label":"Customer support","question":"Can AI help people resolve more problems without making service worse?","verdict":"Support teams should use AI regularly on recurring questions, with agents responsible for whether the answer actually resolves the customer’s problem.","who":"Agents with access to reliable product and policy information, especially people learning the role. Experienced agents should keep the assistance that improves their work, rather than assume every suggestion is an improvement.","tasks":[{"title":"Prepare a useful answer","how":"Give the assistant the customer’s issue and the relevant product or policy material. Ask for a proposed response and its supporting passages; confirm the details before promising an outcome.","sourceIds":["brynjolfsson-ai-work-v2"]},{"title":"Help a new agent understand the case","how":"Use a suggested response as a starting point for discussing why a solution fits this customer. A supervisor can review the reasoning and the final resolution together.","sourceIds":["brynjolfsson-ai-work-v2"]}],"limits":[{"title":"A quick reply can leave the problem unsolved","why":"The study’s useful outcome was issues resolved per hour. Count repeat contacts and unresolved cases as well as response speed, so faster drafting does not hide work shifted back to the customer.","sourceIds":["brynjolfsson-ai-work-v2"]},{"title":"Experts do not benefit in the same way as beginners","why":"The cited support study found larger gains for less experienced workers; the most experienced showed small speed gains and small quality declines. Keep experienced judgment in the process.","sourceIds":["brynjolfsson-ai-work-v2"]}],"practice":"Choose a recurring kind of customer problem, give agents dependable reference material, and compare the quality of resolution as well as the effort it takes. Use the capacity recovered to improve service, not merely to send more replies.","sourceIds":["brynjolfsson-ai-work-v2","anthropic-workforce-2026","stanford-canaries-2026","context-chatgpt-source","context-ai-act-source"],"paths":[{"id":"support-more-resolution","title":"More problems resolved with the same team","summary":"AI becomes a routine source of suggested answers, leaving agents more room for unusual cases and customers who need sustained attention.","mechanism":"When reliable reference material and human judgment turn suggestions into correct resolutions, less time is spent assembling each routine answer. The team can use that capacity for work its old queue left waiting.","horizon":{"start":"2027","end":"2027","label":"During 2027","reason":"EM expects wider routine assistance during 2027 because a real support deployment already demonstrates gains in completed work. Teams can extend that approach where reference material and review are dependable."},"conditions":["The assistant can use current product and policy material.","Agents can reject a suggestion and escalate a case; the team tracks whether customers get a resolution."],"implications":["A smaller queue can mean more attention per difficult case.","Managers can choose better service or greater volume; the tool does not make that choice for them."],"signpost":"Resolved cases per agent improve without more repeat contacts or lower service quality.","sourceIds":["brynjolfsson-ai-work-v2","anthropic-workforce-2026"],"kind":"favored"},{"id":"support-learning","title":"The assistant helps more people become capable agents","summary":"A team uses AI assistance to widen access to skilled support work, helping new hires learn why an answer works as well as what to say.","mechanism":"The study shows novices can benefit while working with assistance. Turning that advantage into lasting competence requires employers to pair useful suggestions with explanation, practice and growing responsibility.","horizon":{"start":null,"end":null,"label":"A future people can shape","reason":"The opportunity depends on employers turning assistance into explanation, practice and growing responsibility. Better performance with a tool becomes lasting competence only if people also learn the work."},"conditions":["Employers keep paid entry roles and time for explanation.","Supervisors check whether new agents can handle increasingly unfamiliar cases, including without a ready-made suggestion."],"implications":["People can enter skilled work with more help at the beginning.","The team develops experienced colleagues instead of only increasing immediate output."],"signpost":"New hires progress to independent responsibility while customer outcomes hold up.","sourceIds":["brynjolfsson-ai-work-v2","stanford-canaries-2026"],"kind":"opportunity"},{"id":"support-volume","title":"Faster replies become poorer service","summary":"A team uses the extra capacity to raise targets while customers still need repeated contact to get their actual problem resolved.","mechanism":"If managers reward response volume and remove the time to check an answer, quick suggestions can pass unresolved work to customers and the next agent.","horizon":{"start":"2027","end":"2028","label":"During 2027–2028","reason":"This risk belongs to the 2027–2028 rollout window: faster production can outrun service checks as teams raise targets."},"conditions":["Response targets rise faster than the team’s ability to check solutions.","Repeat contacts and errors are excluded from the success measure."],"implications":["Customers spend more effort navigating apparently faster service.","Agents inherit frustration and correction work that the first response did not finish."],"signpost":"Reply speed improves while repeat contacts, escalations or unresolved complaints rise.","sourceIds":["brynjolfsson-ai-work-v2","anthropic-workforce-2026"],"kind":"risk"}],"contextSourceIds":["context-chatgpt-source","context-ai-act-source"]},{"id":"software-development","label":"Software development","question":"Which parts of coding are worth handing over today?","verdict":"Developers should use AI regularly for changes they can understand and test, while treating the time spent reviewing and repairing code as part of the job.","who":"Developers who can evaluate the code and its behavior, and learners working with someone who can. Being able to generate a change is not yet being able to maintain it.","tasks":[{"title":"Give it a change with a clear purpose","how":"Specify the behavior you need, the relevant code and how you will test success. Review the proposed change, run it against those requirements and keep the change small enough to understand.","sourceIds":["metr-productivity-2025","metr-productivity-update-2026"]},{"title":"Use it to investigate before you commit","how":"Ask for explanations, candidate fixes or tests, then check them against the actual repository. This lets AI reduce exploration work while you keep responsibility for the diagnosis.","sourceIds":["metr-productivity-2025","metr-productivity-update-2026"]}],"limits":[{"title":"Familiar work may already be faster without it","why":"In METR’s early-2025 experiment, experienced developers working in familiar repositories took 19% longer with AI access. That result is a reason to compare total completion time, not assume a tool always earns its place.","sourceIds":["metr-productivity-2025"]},{"title":"Later improvement is not a measured universal speedup","why":"METR’s February 2026 update judges that developers likely benefited more from newer tools, but selection and time measurement prevent a reliable estimate of the change. Retest the work you actually do.","sourceIds":["metr-productivity-update-2026"]}],"practice":"Keep AI in the parts of development where completed changes reach the required quality with less total effort. Count understanding, testing, integration and later repair alongside generation; remove assistance from a task when it adds more work than it saves.","sourceIds":["metr-productivity-2025","metr-productivity-update-2026","anthropic-workforce-2026","context-chatgpt-source","context-ai-act-source","context-claude-code-source"],"paths":[{"id":"software-whole-tasks","title":"More whole tasks, with review at the finish","summary":"Developers delegate increasingly complete, well-specified changes and concentrate on deciding what to build and whether it works.","mechanism":"Reusable requirements and tests make it easier to judge the result without directing every step. Better tools help only when understanding and repair do not consume the time they save.","horizon":{"start":"2027","end":"2028","label":"During 2027–2028","reason":"EM expects wider delegation during 2027–2028 as improving tools and reusable requirements and tests make complete changes easier to assess."},"conditions":["The change has clear behavior to test and failures can be recovered from.","Developers retain enough understanding to maintain the result."],"implications":["A developer can attempt more useful changes without supervising every keystroke.","Time saved can go into architecture, neglected maintenance or talking to the people who use the software."],"signpost":"Comparable changes require less total human effort while defects after release do not increase.","sourceIds":["metr-productivity-2025","metr-productivity-update-2026","anthropic-workforce-2026"],"kind":"favored"},{"id":"software-reach","title":"Small teams attempt software they could not previously build","summary":"A capable team uses AI to explore more designs, maintain neglected tools or serve a group that could not justify a large development effort.","mechanism":"When some implementation work becomes cheaper, a project that was too expensive can become worth attempting. The opportunity depends on the team being able to test, operate and improve what it creates.","horizon":{"start":null,"end":null,"label":"A future people can shape","reason":"The opportunity opens where reduced implementation effort makes a previously uneconomic project viable. Its timing depends on the team’s ability to operate and maintain what it builds."},"conditions":["The intended users have a real unmet need.","The team budgets time for maintenance, testing and changes after the first working version."],"implications":["More useful tools can exist for smaller communities and organizations.","Developers can spend the recovered capacity on ambitious work rather than only increasing task throughput."],"signpost":"New projects remain useful and maintainable after their initial launch.","sourceIds":["metr-productivity-update-2026","anthropic-workforce-2026"],"kind":"opportunity"},{"id":"software-repair","title":"Code production outruns understanding","summary":"Teams generate changes faster than they can understand, integrate and repair them, so apparent speed creates a growing maintenance burden.","mechanism":"A generated change can move effort from writing code to establishing what it does. If that understanding never catches up, later defects and dependencies bring the work back.","horizon":{"start":"2027","end":"2028","label":"During 2027–2028","reason":"During the 2027–2028 adoption window, teams can expand generated code faster than they improve testing and maintenance. The earlier slowdown makes total completion effort an important test."},"conditions":["Teams judge progress by generated changes rather than working software.","Testing misses important behavior, or maintainers cannot explain the new code."],"implications":["The team spends its next cycle repairing the last one.","People may become responsible for systems they have less time to understand."],"signpost":"Generation accelerates while integration time, failed changes or later repair grow.","sourceIds":["metr-productivity-2025","metr-productivity-update-2026","anthropic-workforce-2026"],"kind":"risk"}],"contextSourceIds":["context-chatgpt-source","context-ai-act-source","context-claude-code-source"]},{"id":"writing-analysis","label":"Writing and analysis","question":"Can AI help you think and communicate without doing the deciding for you?","verdict":"People who write for work should use AI regularly to develop and revise drafts. Analysts should also use it to explore alternatives, while checking the evidence and calculations that determine the conclusion.","who":"Writers, researchers, analysts and managers who can supply the relevant material and judge whether the output represents it accurately. The strongest case is help with the work of expression; a fluent answer does not settle whether an analysis is right.","tasks":[{"title":"Turn your material into a draft you can improve","how":"Provide the facts, audience and purpose. Ask for an outline or a draft, then decide what is missing, unsupported or unlike what you mean. Keep the useful structure and rewrite the argument where needed.","sourceIds":["noy-zhang-writing-2023"]},{"title":"Develop alternatives before choosing a conclusion","how":"Ask for competing explanations, possible objections or different ways to present the same evidence. Check the claims against the original material and perform consequential calculations in a tool where you can inspect them.","sourceIds":["dellacqua-jagged-frontier-2026"]}],"limits":[{"title":"Writing quality is not factual reliability","why":"The 2023 writing experiment found faster completion and higher evaluator-rated quality on short professional tasks. Those tasks did not test all the factual precision and organizational context that real assignments can require.","sourceIds":["noy-zhang-writing-2023"]},{"title":"Persuasive analysis can be wrong","why":"In the consulting experiment using 2023 GPT-4, AI helped on many tasks but reduced correctness on a complex managerial case. Success on drafting does not establish competence at the next analytical step.","sourceIds":["dellacqua-jagged-frontier-2026"]}],"practice":"Use AI to help you articulate, compare and challenge ideas. Keep the original evidence close enough to inspect, make your own argument, and spend part of the time saved asking whether the conclusion deserves to survive.","sourceIds":["noy-zhang-writing-2023","dellacqua-jagged-frontier-2026","context-chatgpt-source","context-ai-act-source"],"paths":[{"id":"writing-more-judgment","title":"Less effort on first drafts, more on the argument","summary":"AI becomes routine for outlines, drafts and revision, while the distinctive work shifts toward choosing the evidence and deciding what is worth saying.","mechanism":"The writing evidence shows that generating and shaping text can take less effort. When people preserve time to evaluate the substance, that saving can improve the finished communication instead of merely increasing its volume.","horizon":{"start":"2027","end":"2028","label":"During 2027–2028","reason":"EM expects gains in professional drafting to spread during 2027–2028 as teams reuse briefs and review practices. The dated studies establish an opportunity that organizations can turn into repeatable work."},"conditions":["Writers provide the purpose and relevant material.","Review checks the argument and factual claims, not just tone and grammar."],"implications":["People can communicate ideas they previously struggled to put into words.","The time recovered can go into investigation, conversation or a stronger second draft."],"signpost":"Completed writing takes less total effort while readers find it accurate, useful and clear.","sourceIds":["noy-zhang-writing-2023","dellacqua-jagged-frontier-2026"],"kind":"favored"},{"id":"writing-more-voices","title":"More people get to develop and share an idea","summary":"People use help with expression to contribute proposals, explanations and creative work that would otherwise remain unfinished.","mechanism":"Reducing the effort of drafting can lower one barrier to participation. The benefit becomes meaningful when people retain their own purpose, experience and voice and have somewhere worthwhile to contribute.","horizon":{"start":null,"end":null,"label":"A future people can shape","reason":"The opportunity depends on writing assistance becoming useful to more people and on organizations making room for their contributions."},"conditions":["Tools help people express their own material rather than replace it with a generic position.","Teams and communities make room to read, question and develop the contributions."],"implications":["People with useful experience can spend more energy on their idea and less on getting a first version onto the page.","An organization can hear from more of the people who understand its work."],"signpost":"A wider range of contributors produce useful work whose reasoning and voice remain their own.","sourceIds":["noy-zhang-writing-2023","dellacqua-jagged-frontier-2026"],"kind":"opportunity"},{"id":"writing-polished-error","title":"Polished output gets ahead of the evidence","summary":"People produce convincing recommendations more quickly while spending less time checking whether the central claim is true.","mechanism":"If a well-structured answer is mistaken for a sound analysis, fluency makes a weak conclusion easier to circulate. The consulting experiment shows why quality of presentation and correctness must be examined separately.","horizon":{"start":null,"end":null,"label":"As AI-assisted drafting becomes routine","reason":"The risk arises whenever confidence in a polished draft outruns the checking of its evidence."},"conditions":["Readers use polish as a substitute for inspecting evidence.","Writers cannot reconstruct the calculation or explain why the recommendation follows."],"implications":["More persuasive documents can lead to worse decisions.","Reviewers inherit the harder job of locating errors inside apparently complete work."],"signpost":"The writing becomes clearer while independent checks find more unsupported claims or incorrect conclusions.","sourceIds":["dellacqua-jagged-frontier-2026","noy-zhang-writing-2023"],"kind":"risk"}],"contextSourceIds":["context-chatgpt-source","context-ai-act-source"]},{"id":"teams-careers","label":"Teams and careers","question":"Will better tools create room to grow—or close the way in?","verdict":"Managers should redesign work and develop people together. Workers should use AI to take responsibility for a useful result, while continuing to build the judgment needed to recognize when the tool is wrong.","who":"Team leaders deciding how work is assigned, and people entering or changing a career. A license is not a training plan, and producing more is not the same as becoming ready for a larger responsibility.","tasks":[{"title":"Decide who owns the work after AI produces it","how":"Choose a recurring task, the person who needs its result and the person who checks it. Give that person time and authority to reject or improve the output.","sourceIds":["anthropic-workforce-2026"]},{"title":"Use assistance to build responsibility","how":"Give newer colleagues useful paid work with explanation and feedback. Ask them to show how they checked the result and what they would do differently, then expand the responsibility they can carry.","sourceIds":["brynjolfsson-ai-work-v2","stanford-canaries-2026"]}],"limits":[{"title":"A more productive beginner may still find fewer openings","why":"Stanford’s August 2026 payroll study finds young workers in AI-exposed occupations falling behind less-exposed peers mainly through hiring. The support study’s novice gains concern people already doing the job. Both can be true.","sourceIds":["stanford-canaries-2026","brynjolfsson-ai-work-v2"]},{"title":"More output can make attention the constraint","why":"Anthropic’s report identifies demands on human attention as agent output grows. A team can adopt tools faster than it learns to decide what deserves review and which work matters.","sourceIds":["anthropic-workforce-2026"]}],"practice":"Learn where AI improves your actual work, and make the result and your judgment visible. If you lead a team, use gains to strengthen service and learning as well as delivery; decide deliberately where the next generation of experienced people will come from.","sourceIds":["brynjolfsson-ai-work-v2","stanford-canaries-2026","anthropic-workforce-2026","nyfed-business-survey-2026","context-chatgpt-source","context-ai-act-source"],"paths":[{"id":"careers-entry-pressure","title":"Entry gets harder while experienced teams gain capacity","summary":"Through 2027, we expect employers in AI-exposed digital work to keep scrutinizing new hires even as some existing workers accomplish more with assistance.","mechanism":"An employer can leave an opening unfilled while tools help the current team handle tasks previously assigned to beginners. That choice changes access to experience before it eliminates an entire profession.","horizon":{"start":"2027","end":"2027","label":"Through 2027","reason":"The relative hiring pattern persisted into mid-2026, and the October company report adds a changing junior hiring mix. That makes continued pressure through another hiring cycle more plausible than a quick reversal."},"conditions":["Employers use some additional capacity to reduce recruitment.","New paid learning routes do not expand enough to offset the lost openings."],"implications":["A job seeker needs opportunities to demonstrate judgment, not only familiarity with tools.","Experienced teams may carry more work while the route into those teams narrows."],"signpost":"Entry hiring remains weak relative to comparable less-exposed work as AI adoption deepens.","sourceIds":["stanford-canaries-2026","anthropic-workforce-2026","nyfed-business-survey-2026"],"kind":"favored"},{"id":"careers-apprenticeship","title":"AI becomes part of a stronger apprenticeship","summary":"Employers use AI assistance to bring more people into useful work, with support while they learn and a clear route toward independent responsibility.","mechanism":"Novice performance gains create an opportunity to make early work more valuable. It becomes an apprenticeship only when employers keep hiring, explain decisions and let people practice the judgment the tool cannot supply for them.","horizon":{"start":null,"end":null,"label":"A future people can shape","reason":"Its pace depends on employers preserving paid entry roles, mentoring people and expanding responsibility as they demonstrate competence."},"conditions":["Employers preserve paid entry work and mentoring.","Progression depends on demonstrated competence, including detecting and correcting AI mistakes."],"implications":["More people can reach skilled work rather than watching it from outside.","Organizations build the experienced workforce they will need later."],"signpost":"Paid entry routes expand and participants progress into greater responsibility while quality holds up.","sourceIds":["brynjolfsson-ai-work-v2","stanford-canaries-2026"],"kind":"opportunity"},{"id":"careers-hollow-learning","title":"Teams remove the work that used to teach the job","summary":"Organizations save effort today but leave fewer ways for people to acquire the experience needed to make tomorrow’s difficult decisions.","mechanism":"If routine entry work disappears without replacement, novices lose practice and employers lose a route for developing future expertise. Current hiring pressure makes that mechanism worth acting on, even though the long-run outcome is not yet measured.","horizon":{"start":null,"end":null,"label":"Over successive hiring and training cycles","reason":"The effects accumulate as hiring and training decisions determine who gets enough practice to become experienced."},"conditions":["Employers remove entry tasks without creating another route to learn.","People are rewarded for producing AI output rather than understanding and improving it."],"implications":["Workers can become more dependent on assistance without becoming ready for greater responsibility.","Teams may struggle to replace the experienced people whose judgment currently keeps the work reliable."],"signpost":"Fewer newcomers progress to independent work even as output per current worker rises.","sourceIds":["stanford-canaries-2026","brynjolfsson-ai-work-v2","anthropic-workforce-2026"],"kind":"risk"}],"contextSourceIds":["context-chatgpt-source","context-ai-act-source"]}],"sources":[{"id":"brynjolfsson-ai-work-v2","title":"Generative AI at Work — arXiv v2","url":"https://arxiv.org/abs/2304.11771v2","publishedAt":"2024-11-06","dateBasis":"Version 2 submission date displayed by arXiv; first submitted 23 April 2023. The versioned abstract is the cited object.","summary":"A study of AI assistance for customer-support agents, reporting issues resolved per hour and differences by worker experience. This edition cites the arXiv v2 abstract.","kind":"retained-evidence","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."},"evidenceScope":"Versioned author abstract checked directly. The HTML rendering displayed a conflicting 2026 manuscript date, so this edition cites only the clearly versioned abstract and does not assign that later date to the study."},{"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.","summary":"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.","kind":"retained-evidence","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."},"evidenceScope":"Original publisher page and exact retained source reviewed. Source retention does not establish every downstream reuse or training right."},{"id":"metr-productivity-2025","title":"Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","publishedAt":"2025-07-10","dateBasis":"Publication date displayed by the original publisher.","summary":"A study of experienced developers using early-2025 tools on familiar open-source work. The measured completion-time result belongs to that setting and period.","kind":"retained-evidence","checkedAt":"2026-10-29","provenance":{"kind":"stream-snapshot","workId":"eeef7ca24568c0fe592aa98201faccea099ec6507dbd9476c8bea56310f28743","snapshotId":"1b377f2c5f76b4960227c32060fef0433519ce76239947c7e836402cb9a945ac","textHash":"78a7743c1dda83f49b4d646c134243e5e6c38bed0eb7aa33d124a37e3971f7df","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."},"evidenceScope":"Original publisher page and exact retained source reviewed. Source retention does not establish every downstream reuse or training right."},{"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.","summary":"METR judges developer benefits likely improved while explaining why participation, task selection and time measurement make the size of the change hard to estimate.","kind":"retained-evidence","checkedAt":"2026-10-29","provenance":{"kind":"stream-snapshot","workId":"54d658ff870e4bbb0638a847bac138a3b543d42ac48e761c915c570500f1cc5c","snapshotId":"8fd5f006c0dc55d1e20448712d3f4b177c53cd92219b9aa3ed71b70d0ed13eca","textHash":"81b6c9b7d710e11ed96cf12de5f307e8551454cfdcc9f6c66c84f0a657b9adfb","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."},"evidenceScope":"Original publisher page and exact retained source reviewed. Source retention does not establish every downstream reuse or training right."},{"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.","summary":"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.","kind":"retained-evidence","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."},"evidenceScope":"Original research summary and linked August PDF abstract checked; the findings are attributed to the authors and their stated population."},{"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.","summary":"A report on the August 2026 survey of businesses in New York and northern New Jersey, distinguishing firm adoption, worker usage, hiring and layoffs.","kind":"retained-evidence","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."},"evidenceScope":"Original publisher page and exact retained source reviewed. Source retention does not establish every downstream reuse or training right."},{"id":"noy-zhang-writing-2023","title":"Noy and Zhang · Experimental evidence on the productivity effects of generative artificial intelligence","url":"https://doi.org/10.1126/science.adh2586","publishedAt":"2023-07-13","dateBasis":"Online publication 13 July 2023; Science issue dated 14 July 2023. Final abstract, not the March working-paper sample.","summary":"In an experiment with 453 professionals, ChatGPT reduced time on short writing tasks by 40% and increased evaluator-rated quality by 18%. The tasks did not require all the factual precision and organizational context of real work.","kind":"primary-research","checkedAt":"2026-10-31","evidenceScope":"Final abstract checked through PubMed and publisher search result; publisher page returned 403. Task-scope context checked in MIT’s report of its researchers’ study. No claim of reading the inaccessible final full text.","supportingUrls":["https://pubmed.ncbi.nlm.nih.gov/37440646/","https://news.mit.edu/2023/study-finds-chatgpt-boosts-worker-productivity-writing-0714"],"streamStatus":"external-source-not-resolved-to-stream"},{"id":"dellacqua-jagged-frontier-2026","title":"Dell’Acqua and colleagues · Navigating the Jagged Technological Frontier","url":"https://www.hbs.edu/ris/Publication%20Files/dell-acqua-et-al-2026-navigating-the-jagged-technological-frontier_5c589c8c-fbb5-458f-b285-c944746cd717.pdf","publishedAt":"2026-03-11","dateBasis":"Publication date printed in the final Organization Science article. An earlier working paper appeared in 2023; the experiment used GPT-4 as available at the end of April 2023.","summary":"The experiment with 758 consultants using 2023 GPT-4 found gains on tasks the model could handle, but lower correctness on a complex managerial case beyond that capability. Clearer presentation did not guarantee a correct recommendation.","kind":"primary-research","checkedAt":"2026-10-31","evidenceScope":"Final article’s abstract, methods, outside-frontier results and coherence analysis checked in the HBS-hosted PDF. The 2026 publication date does not make this a test of 2026 models.","streamStatus":"external-source-not-resolved-to-stream"},{"id":"context-chatgpt-source","title":"Introducing ChatGPT","url":"https://openai.com/index/chatgpt/","publishedAt":"2022-11-30","summary":"OpenAI introduces ChatGPT. Conversational AI becomes something people can try, giving the later question about useful work a visible public starting point.","kind":"public-context","checkedAt":"2026-10-31","evidenceScope":"Primary link and date verified by the root agent for this preview; no Stream identity is asserted.","streamStatus":"external-preview-context"},{"id":"context-ai-act-source","title":"AI Act enters into force","url":"https://commission.europa.eu/news-and-media/news/ai-act-enters-force-2024-08-01_en","publishedAt":"2024-08-01","summary":"The EU AI Act enters into force. Rules governing AI become part of the institutional setting in which organizations decide how to adopt it.","kind":"public-context","checkedAt":"2026-10-31","evidenceScope":"Primary link and date verified by the root agent for this preview; no Stream identity is asserted.","streamStatus":"external-preview-context"},{"id":"context-claude-code-source","title":"Claude 3.7 Sonnet and Claude Code","url":"https://www.anthropic.com/news/claude-3-7-sonnet","publishedAt":"2025-02-24","summary":"Anthropic introduces Claude 3.7 Sonnet and Claude Code. 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