{ "article": { "format_version": 5, "presentation": "complete-card", "overview": "AI can help with a work draft, but useful assistance depends on the task, the information you share and the check you can perform. Start with one permitted task using public or approved material. Judge the finished work, including review and correction, while keeping responsibility for the result.", "sections": [ { "id": "understand-the-assistant", "role": "explanation", "title": "What a workplace AI assistant does", "intro": "A draft, a verified answer and an action taken on your behalf require different kinds of trust.", "items": [ { "label": "Give the assistant a defined piece of work", "text": "Generative AI creates content such as text or code from instructions and supplied material. A large language model is one kind of generative system: it learns patterns in language and uses them to produce a response. Your prompt is the request and context you give it. That can help turn a rough idea into an outline, rewrite a paragraph or propose questions. It does not give the system automatic access to your company's actual promises, policies or records. Brynjolfsson, Li and Raymond distinguish the underlying model from the workplace system built around it. Begin by naming the output you want and which facts it is allowed to use.", "source_ids": [ "support-qje-2025", "ncsc-llm-2023" ] }, { "label": "Use source material to make an answer checkable", "text": "Some assistants can search documents or retrieve passages before generating an answer. Retrieval means finding material; generation means composing the response. These are separate operations: finding the right document does not prove that the summary preserves its meaning. The support assistant studied by Brynjolfsson and colleagues provided suggestions and documentation links, while agents retained discretion over the conversation. At your desk, a useful answer includes a passage or locator you can open, not merely a convincing reference to a handbook. Check that the cited passage supports the particular statement and that the document is the current one.", "source_ids": [ "support-qje-2025", "nist-measure" ] }, { "label": "Review an action before granting permission to carry it out", "text": "An assistant that drafts an email differs from an agent that can send it. Agentic AI is connected to tools and can take actions toward a goal, potentially without continuous human intervention. The NCSC's May 2026 guidance says organizations should define ownership, access, monitoring and the ability to stop the system before connecting agents to real work. For an ordinary user, the practical distinction is straightforward: a draft can be edited; a sent message, changed record or executed command may already affect someone. Use only an approved arrangement, with the access and review your organization requires. A successful drafting trial does not establish readiness for autonomous action.", "source_ids": [ "ncsc-agents-2026" ] } ] }, { "id": "choose-a-work-task", "role": "application", "title": "Where it can fit into an ordinary workday", "intro": "These are practical applications of the evidence, not tested STUDlearn procedures or promised savings.", "items": [ { "label": "Start an email with its purpose and audience", "text": "When you know what an email must accomplish but struggle to start, ask for a structure using public facts or fictional placeholders. For example: draft a brief meeting invitation with spaces for the date, location and reply deadline. Noy and Zhang tested occupation-specific writing assignments and found a useful role for assistance with composition. Their tasks did not depend on private organizational knowledge or precise factual accuracy, so the result cannot certify a real client message. Put the correct facts into the draft on an authorized system. Read it for tone, invented commitments and whether the recipient can tell what to do next. You remain responsible for the send.", "source_ids": [ "writing-2023", "writing-author-account" ] }, { "label": "Compare a summary with the document it represents", "text": "When a long notice or meeting record feels hard to digest, an approved assistant may help organize it. A summary is a shorter account of a source, so its quality depends on preserving decisions, exceptions and uncertainty rather than merely sounding clear. NIST recommends evaluating AI in the context in which it will be used. For a routine summary, compare the output with the original and ask: did it miss a decision, turn a suggestion into a commitment, or attach the wrong person or date? Keep the record available for the reader. If you cannot open the underlying source or competently review it, the summary is not a reliable basis for your decision.\n\nConsider a fictional notice: a workshop is planned for Thursday, subject to the room being confirmed, and attendees must reply by Tuesday. An assistant summarizes it as 'The workshop is Thursday; reply by Thursday.' The sentence is fluent, but it has removed a condition and changed a deadline. A source check has to compare those details, not merely reread the summary for good grammar.\n\nThe reviewer restores 'planned, pending room confirmation,' corrects the reply date and leaves a source locator. If the room is later confirmed, that is a new fact requiring an updated record. Counting the quick first draft as time saved would omit this review and correction work. This invented example shows what responsible review actually checks; it does not establish a particular tool's accuracy or authorize sharing a real internal notice with it.", "source_ids": [ "nist-measure", "nist-gai" ] }, { "label": "Choose a permitted route before supplying work information", "text": "When the task involves a client file, staff record or unreleased number, decide whether the tool and that particular use are authorized before entering anything. Confidentiality concerns what may be shared, with whom and for what purpose; it is not just a setting labeled private. The NCSC describes shadow AI as use outside an organization's approved systems and processes, which can reduce visibility and control of information. Replace actual details with invented examples only when they no longer reveal the underlying matter. Otherwise use the approved environment or do the work without the tool. An app installed on a work laptop is not, by itself, permission to upload every file.", "source_ids": [ "ncsc-shadow-2026", "ico-minimisation", "aba-512" ] }, { "label": "Discuss a changing task rather than your whole job at once", "text": "When a headline makes your occupation sound obsolete, identify one task the tool may change and one responsibility that remains. Drafting a report, verifying its figures and explaining its recommendation are different parts of a job. The ILO's 2025 index measures potential exposure of occupational tasks to generative AI; it does not count layoffs. Autor's task framework explains why automation can substitute for some work and complement other work. This does not guarantee employment security. It makes a workplace conversation more useful: what will be automated, what expertise is needed to review it, and what training, staffing or sign-off will support the new process?", "source_ids": [ "ilo-exposure-2025", "autor-2015" ] } ] }, { "id": "read-the-workplace-evidence", "role": "findings", "title": "What the workplace studies actually found", "intro": "The studies answer different questions. Their results describe particular people, tasks and systems, rather than a single productivity rate for AI.", "items": [ { "label": "Some short writing tasks improved with AI assistance", "text": "Shakked Noy and Whitney Zhang randomly gave ChatGPT access to half of 453 college-educated professionals completing paid, occupation-specific writing tasks in 2023. Participants were managers, marketers, grant writers, consultants, human-resource professionals and data analysts. Average completion time fell about 40 percent and judged quality rose about 18 percent. Lower-baseline writers gained more. The assignments included reports, press releases and tactful emails, and experienced graders assessed the output. The authors emphasize that the tasks favored clear, relatively generic writing and did not require context-specific knowledge or precise factual accuracy. That makes the finding useful for drafting, while leaving a real workplace's fact checking, authorization and audience requirements to be tested separately.", "source_ids": [ "writing-2023" ] }, { "label": "A useful draft can change how people work without proving long-term competence", "text": "In their public account, Noy and Zhang reported that many participants submitted unedited or lightly edited chatbot output and graders often rated it well. That describes what worked in their experiment; it is not an instruction to forward raw output at work. Participants enjoyed the assisted task more, and both excitement and concern about AI rose temporarily. Those attitude effects faded in follow-up surveys, while treated participants were more likely to report later workplace use. Adoption, enjoyment and a good graded document are different outcomes from sustained competence or a client's satisfaction. Keep that distinction when someone offers a compelling demonstration as proof of lasting value.", "source_ids": [ "writing-author-account", "writing-2023" ] }, { "label": "Support workers gained unevenly from a specialized suggestion tool", "text": "Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied staggered access to a conversational assistant among 5,172 customer-support agents. The published 2025 study found about 15 percent more issues resolved per hour on average. Less experienced and lower-skilled agents improved speed and quality; the most experienced and highest-skilled agents had small speed gains and small quality declines. Customers were more polite and less likely to request a manager. Agents could edit or ignore suggestions. This was a tool adapted to one firm's technical-support work, rather than an unrestricted public chatbot. A team should therefore examine whose work improves and whether quality changes, rather than assign the average gain to everybody.", "source_ids": [ "support-qje-2025" ] }, { "label": "Check capability on the task you actually need done", "text": "Dell'Acqua and colleagues call this the jagged technological frontier: AI can help with one task and hurt performance on another that looks equally difficult to a person. Their experiment included 758 BCG consultants, roughly 7 percent of individual contributors, in early stages of their consulting careers. Participants received no AI, GPT-4, or GPT-4 with a prompting overview. Across 18 tasks within the tested capability range, AI users completed 12.2 percent more tasks, worked 25.1 percent faster and produced higher-rated work. Lower-baseline performers gained more. On a deliberately selected task outside that range, the combined AI groups were about 19 percentage points less likely to reach the correct answer. The 2026 journal article preserves the key contrast. It does not locate today's boundary for your task or certify your preferred prompt.", "source_ids": [ "frontier-2026" ] }, { "label": "Measure completed work rather than the feeling of moving faster", "text": "METR randomized AI access on 246 real tasks completed by 16 experienced open-source developers in familiar repositories during early 2025. AI access made completion take about 19 percent longer, although developers believed it had helped them work faster. This is a small, specific study, not proof that coding assistance always slows work. In February 2026, METR reported that a later experiment's results were unreliable as a measure of current uplift because participation, task selection and concurrent-agent time measurement had changed. The researchers thought gains were likely larger by then, but could not reliably quantify the increase. Both reports support checking your own finished workflow rather than promoting an old result into a timeless claim.", "source_ids": [ "metr-2025", "metr-update-2026" ] }, { "label": "Exposure estimates describe potential change rather than observed job loss", "text": "The ILO–NASK 2025 index combines worker task assessments, expert input and model predictions to estimate how generative AI could affect occupations. About one in four workers globally are in occupations with some exposure, and 3.3 percent of employment falls in the highest category. Clerical work remains especially exposed; exposure also differs by income group and sex because occupational employment differs. The authors consider transformation more likely than complete replacement because many jobs retain tasks requiring human input. These are estimates of potential, not a count of displaced people, a forecast for a particular employer, or reassurance that no job will disappear. Use them to identify where transition support may be needed.", "source_ids": [ "ilo-exposure-2025", "ilo-brief-2025" ] } ] }, { "id": "protect-work-information", "role": "cautions", "title": "Handle work information before it enters a tool", "items": [ { "label": "Check approval for the task, data and system together", "text": "A company may approve one AI service for one class of information while restricting another use. Before uploading, establish what data the approved account may receive, who can access inputs and outputs, how they are retained, and whether connected services are covered. The NCSC's September 2026 shadow-AI guidance explains how unapproved use can bypass established visibility and controls. If the policy is unclear, bring a concrete example of the proposed task to the policy owner without exposing the actual confidential file. Approval answers whether you may use the tool; it does not answer whether its output is correct.", "source_ids": [ "ncsc-shadow-2026", "ncsc-llm-2023" ] }, { "label": "Use only the information needed for the purpose", "text": "Data minimisation means using the personal information necessary for a defined purpose, rather than providing everything that might be convenient. The UK's ICO treats this as a continuing requirement when organizations use AI. At a desk, an editorial application is to replace a real person's details with a genuinely fictional example when you only need help with wording or structure. Removing a name alone may leave a person identifiable through role, dates or circumstances. Keep patient and student records, client matters, employee details, privileged material, credentials, restricted code and unreleased figures out of unapproved tools. A full-file upload deserves more scrutiny than a public paragraph. This is privacy literacy, not a jurisdiction-specific compliance determination.", "source_ids": [ "ico-minimisation", "nist-gai", "ncsc-shadow-2026" ] }, { "label": "Treat confidentiality as a duty attached to the information", "text": "ABA Formal Opinion 512 explains lawyers' duties of competence, confidentiality, communication, supervision and reasonable fees when using generative AI. Its discussion of self-learning tools says informed consent can be necessary before entering information relating to a representation, and generic boilerplate is insufficient. That opinion concerns lawyers and US model ethics rules; it does not impose one consent procedure on every profession. Teachers, clinicians, accountants and government staff must use their own applicable rules and approved processes. The common workplace question is whether this transfer is allowed before it happens. Deleting a conversation later cannot retroactively authorize a disclosure or restore control of a copy already transferred.", "source_ids": [ "aba-512", "ncsc-llm-2023" ] }, { "label": "Separate a provider's privacy promise from your permission to share", "text": "A promise not to train on customer data addresses one use of information. It does not, on its own, answer retention, access, security or your employer's authorization requirements. FTC staff explain that providers can face liability when they break privacy and confidentiality commitments, including promises about model training. That is guidance directed at providers, rather than a declaration that a particular work file is safe to upload. Likewise, a prompt does not necessarily update a model instantly. Read the current approved terms and workplace policy for the system you actually use. Treat the transfer of restricted information as the decision to resolve before composing a prompt.", "source_ids": [ "ftc-privacy-2024", "ncsc-llm-2023" ] } ] }, { "id": "check-the-finished-work", "role": "application", "title": "What a useful check looks like", "items": [ { "label": "Compare claims with evidence outside the conversation", "text": "NIST calls confidently false generated content confabulation, often called hallucination. Automation bias means giving an automated suggestion more weight than its evidence deserves. These concepts explain why a polished answer can pass a casual read while still containing the wrong figure or unsupported conclusion. For a factual draft, compare names, dates, amounts and citations with the original record. A citation should exist, address the question and support the sentence. For a calculation, use the authoritative data and an appropriate independent calculation. Asking the same assistant whether it is sure can identify a question to investigate, but its assurance is not that investigation.", "source_ids": [ "nist-gai", "nist-measure", "frontier-2026" ] }, { "label": "Give the reviewer evidence and authority to change the result", "text": "Human review has value when someone has the knowledge, time and source access to identify an error and can reject or correct the output. In a 2026 MIT account of research on 72 BCG consultants, researchers observed GPT-4 defending recommendations with increasingly persuasive language when users challenged it. That analysis describes interactions in one task, not intentional deceit or every model's behavior. Its practical relevance is that remaining inside a persuasive chat may be a weak check. Move to the original records and record the unresolved point. A named reviewer who merely clicks approval without that comparison has not established reliability.", "source_ids": [ "mit-validation-2026", "nist-govern" ] }, { "label": "Match review effort to the consequence of an error", "text": "NIST frames risk in terms of both the chance of an event and the severity of its consequences. A wrong heading in a private outline and a wrong number in a client decision have different stakes. Before reviewing, describe the specific failure: an invented deadline, missing exception, wrong recipient or unsupported recommendation. Choose a check capable of finding it, and involve the accountable specialist when the matter needs expertise you lack. Safety-critical, licensed or regulated work retains its required competence and sign-off. A generic 'human checked' label cannot replace that process. If an essential claim remains unverified, keep the output as a draft or use the existing authorized workflow.", "source_ids": [ "nist-rmf", "aba-512", "nist-manage" ] }, { "label": "Test the complete workflow against a defined standard", "text": "Choose the success criteria before a permitted comparison. For a meeting summary, that might mean retaining all decisions, owners and deadlines, preserving uncertainty, and adding no unsupported commitments. Count time spent preparing the input, generating, reviewing and correcting, not just time until text appears. Compare tasks of similar difficulty against the same quality standard. Danielle Li's public guidance stresses defining success; MIT's 2026 workplace synthesis adds evidence before scaling and attention to rework. This small comparison is a practical adaptation, not a controlled trial. One good outcome supports another cautious test of that task, rather than an all-job productivity percentage.", "source_ids": [ "mit-define-success-2025", "mit-levers-2026", "nist-measure", "metr-2025" ] }, { "label": "Give the task an owner, a purpose, a check and a fallback", "text": "NIST's voluntary AI Risk Management Framework organizes risk work into four connected functions: GOVERN sets responsibilities and rules; MAP describes the use, affected people and possible harms; MEASURE evaluates performance and risk; MANAGE responds to what is found. They are not an ordered checklist or a certificate of safety. Its trustworthiness characteristics cover validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed. A desk-level application is to write who owns the result, what the assistant may do, which failure your check can catch and what happens if it fails. The generative-AI profile adds detail on confabulation, privacy, information security, intellectual property and human over-reliance. Correct wording is only one part of responsible use.", "source_ids": [ "nist-rmf", "nist-core", "nist-gai" ] } ] }, { "id": "understand-adoption-choices", "role": "perspectives", "title": "Where workplace decisions remain unsettled", "items": [ { "label": "Ask what training and consultation will change in practice", "text": "OECD surveys of 5,334 workers and 2,053 firms in manufacturing and finance across seven countries found generally positive reported effects alongside concern about job loss. Training and worker consultation were associated with better reported outcomes. Association means the factors occurred together; it does not establish that training caused the improvement. Organizations with good management may differ in other ways. The 2023 report concerns surveys conducted in early 2022, before ChatGPT's public release; it is not a survey of today's generative tools. The genuine unresolved question is which training and consultation arrangements work best for which jobs. Ask who can demonstrate a useful review, who workers can report problems to, and whether staff feedback changes deployment. Optimism and worry both deserve a specific account of what happened.", "source_ids": [ "oecd-surveys-2023", "oecd-survey-design", "professional-kellogg" ] }, { "label": "Match assistance and review to the worker's experience", "text": "The writing, support and consultant studies often found larger gains for lower-baseline workers within the tasks where assistance worked. That is a shared pattern, rather than opposing findings about novices. Yet the consultant study also demonstrated a correctness drop outside the tested capability range. 'Lower skill' there was relative to other highly trained consultants, not an entry-level worker in every field. A team can reasonably debate which tasks need expert review, how much training new staff require and whether experts should use different workflows. Narrowing an output-quality gap does not prove that professional knowledge, exception handling or supervisory competence has become unnecessary.", "source_ids": [ "writing-2023", "support-qje-2025", "frontier-2026" ] }, { "label": "Use the disclosure rules that govern the work", "text": "Whether AI assistance must be named depends on the employer, client agreement, professional requirements and circumstances. ABA Opinion 512 describes situations requiring communication or informed consent and situations where routine assistance need not be disclosed; it is not a universal email-label rule. A useful internal handoff can say what was checked, who reviewed it and what remains uncertain, while following any required AI disclosure. Separately, disclosure of a tool used to evaluate a worker is a different issue from disclosure that you used it to draft a paragraph. Ifeoma Ajunwa's workplace-technology scholarship helps keep that distinction visible. Transparency should help the affected person understand the decision and the route for questioning it.", "source_ids": [ "aba-512", "professional-ajunwa", "nist-govern" ] }, { "label": "Read employment forecasts alongside the choices that shape jobs", "text": "Task exposure, employer adoption and observed employment are different measurements. A forecast should state its date, geography, occupations and assumptions about demand and deployment. Autor explains that technology can substitute for labor while also creating complementary work and changing demand. This does not rule out displacement. In their April 2026 synthesis of early experiments at more than 20 companies, Ben Armstrong and Julie Shah describe substantial variation in how organizations turn AI into supervisory work and whether that work is engaging or repetitive. It is qualitative organizational evidence, not a causal estimate of net jobs. Training, staffing, workflow design and how saved time is used remain real decisions.", "source_ids": [ "autor-2015", "ilo-exposure-2025", "mit-work-design-2026" ] } ] }, { "id": "keep-the-skill-you-own", "role": "application", "title": "Keep enough skill to supervise the work", "items": [ { "label": "Separate assisted performance from what you can do later", "text": "Hamsa Bastani and colleagues tested two GPT-4-based tutors in high-school mathematics: an open-ended helper and a tutor with teacher-informed safeguards. Both improved assisted practice performance, but the open-ended group later scored worse than controls on unaided exams. The safeguarded tutor largely prevented that penalty without demonstrating a later learning gain. The published 2025 study is classroom evidence, not a trial of adult workplace use. It makes a useful question visible: did the tool help you learn a method or simply produce an answer? When the job still requires judgment, close the chat and explain the calculation, exception or recommendation using the source material. Use Learning and remembering for the fuller study-skills question.", "source_ids": [ "bastani-pnas-2025" ] }, { "label": "Use your knowledge to direct and evaluate the assistant", "text": "A 2025 CHI survey by Hao-Ping Lee, Advait Sarkar, Lev Tankelevitch and colleagues collected 936 examples from 319 knowledge workers. Higher confidence in generative AI was associated with less reported critical-thinking effort, while greater confidence in one's own ability was associated with more. The researchers described a shift toward verification, integrating responses and overseeing tasks. This was self-reported behavior, not an objective test proving that AI destroys thinking skills. The support-agent study also found evidence consistent with learning when assistance was temporarily unavailable. Outcomes can differ with design and use. Maintain chances to practice, explain and challenge the work you remain accountable for, rather than treating either universal learning or universal deskilling as settled.", "source_ids": [ "critical-thinking-2025", "support-qje-2025" ] } ] }, { "id": "maintain-a-useful-process", "role": "application", "title": "Keep the process useful over time", "items": [ { "label": "Keep the current policy and an accountable contact within reach", "text": "Save the current workplace AI policy or the name of the person responsible for the approved service. Check whether the permission covers your specific task and class of information when either changes. NIST's GOVERN guidance connects AI responsibilities to existing organizational rules, while the NCSC explains the risks when informal use escapes those processes. An unclear policy is a reason to seek a concrete answer before supplying restricted material. It does not require abandoning a useful public-material experiment that is already permitted. Policy gives you a boundary; source comparison gives you evidence about the result.", "source_ids": [ "nist-govern", "ncsc-shadow-2026" ] }, { "label": "Keep sensitive facts on the system authorized to hold them", "text": "Write a reusable prompt around the task's structure rather than real client names, personal histories or unreleased numbers. Fill those facts into the result only in an authorized place, after checking that the wording still fits. ICO minimisation guidance provides the principle of limiting personal data to what the purpose requires. The desk-level example is an editorial adaptation: it is useful when generic structure is enough, but unsuitable if the fictionalized input still reveals a person or the real task cannot be reviewed without restricted information. In that case, use the approved data-handling route or the ordinary workflow.", "source_ids": [ "ico-minimisation", "aba-512" ] }, { "label": "Keep a source locator with factual work", "text": "Store the approved source location, date or document version beside a factual draft so someone can repeat the comparison later. A sentence such as 'the notice dated October 1, section 3' is useful only if it identifies a real record the reviewer can access. Where records conflict, state what differs and seek the responsible person's resolution; a fluent generated paragraph should not silently choose. NIST's measurement guidance calls for context and documented evaluation. The ICO also distinguishes a model's average predictive accuracy from accurate information about a particular person. If a record contains a prediction about someone, identify it as an inference rather than an established fact. Neither a general accuracy score nor a tidy citation resolves a specific contradictory record.", "source_ids": [ "nist-measure", "ico-accuracy" ] }, { "label": "Be able to explain the decision you approve", "text": "Before approving work that requires your expertise, explain its reasoning with the authoritative source open and the chatbot closed. Identify the evidence, the exception and what would change your conclusion. This is a practical accountability check, rather than an exam score or a validated anti-deskilling intervention. Bastani's study shows why assisted performance and unaided understanding need separate attention, while the CHI survey suggests checking and task oversight may become a larger part of knowledge work. If you cannot reconstruct a material decision, obtain competent review or do that part yourself before relying on the output.", "source_ids": [ "bastani-pnas-2025", "critical-thinking-2025", "nist-core" ] }, { "label": "Keep a small permitted record and revisit recurring misses", "text": "Where workplace rules allow it, note the task, approved tool, check performed and one useful correction. Do not create a private archive of confidential prompts or copy restricted files into a personal log. NIST's MANAGE guidance supports monitoring problems and responding to them, and Kellogg's implementation work emphasizes workers' involvement and training. Review recurring misses when the task, source or tool changes. If assistance repeatedly invents commitments or consumes more correction time than it saves, narrow its role or use the established workflow. When AI influences scheduling, hiring or performance assessment, ask what information went in, which output affected the decision, who is responsible for the final decision and how an error can be raised. These apply NIST's feedback principle; employer policy, worker representation and local rules determine the formal route, rather than a uniform legal right assumed here.", "source_ids": [ "nist-manage", "professional-kellogg", "professional-ajunwa", "nist-govern" ] } ] }, { "id": "check-common-claims", "role": "findings", "title": "Check the claims people repeat", "intro": "An established finding stays bounded to what its study measured. Read the claim and its evidence together before extending it to your work.", "items": [ { "label": "Some writing gains are established within a defined experiment", "text": "Claim: ChatGPT-style tools can change time and quality on some mid-level writing tasks in a bounded experiment. Verdict: established. Noy and Zhang measured shorter completion times and higher grades. This does not establish a client-ready result or a guaranteed saving for your own job.", "source_ids": [ "writing-2023" ] }, { "label": "Task exposure leaves an occupation's future open", "text": "Claim: Generative AI will replace every job this year. Verdict: not. The ILO's index measures potential task exposure. It does not predict universal replacement, count layoffs or settle demand and adoption. A forecast and an observed employment change require their own evidence.", "source_ids": [ "ilo-exposure-2025", "autor-2015" ] }, { "label": "Resolve permission before sharing confidential material", "text": "Claim: Confidential client or employer material is safe in a public chatbot if I delete the chat. Verdict: not. Deletion does not retroactively authorize the transfer. Applicable confidentiality duties, approved terms and workplace rules govern the information before it enters the tool.", "source_ids": [ "aba-512", "ftc-privacy-2024", "ncsc-shadow-2026" ] }, { "label": "Fluent wording still needs a factual check", "text": "Claim: If the chatbot wrote it fluently, it is ready to send. Verdict: not. Fluent false content is a recognized risk. Review against the original evidence and the task's required quality standard before relying on it.", "source_ids": [ "nist-gai", "frontier-2026" ] }, { "label": "Evaluate gains for the particular worker and task", "text": "Claim: A workplace AI assistant raises every worker's performance equally. Verdict: not. The support study found different effects by experience and skill, and the consultant study found different effects by task. An average is not an individual guarantee.", "source_ids": [ "support-qje-2025", "frontier-2026" ] }, { "label": "Treat the training association as evidence with a causal limit", "text": "Claim: Training and consulting workers about AI is linked with better reported outcomes. Verdict: established for the OECD survey association. The stronger claim that training causes improvement everywhere remains unproven and open to debate. Association and universal effectiveness are different propositions.", "source_ids": [ "oecd-surveys-2023" ] }, { "label": "Keep the knowledge needed to explain and supervise your work", "text": "Claim: Using AI at work means I no longer need to know the material. Verdict: talk. Producing a draft does not demonstrate the competence needed to check, explain or approve it. The workplace still determines which responsibilities and expertise your role requires.", "source_ids": [ "critical-thinking-2025", "bastani-pnas-2025", "nist-core" ] } ] }, { "id": "try-one-permitted-task", "role": "next_steps", "title": "Try one small, permitted task", "intro": "Keep the first move small enough that you can judge it yourself.", "items": [ { "label": "Name the task, your check and the human step that remains", "text": "Choose one low-stakes task you are already allowed to do with AI, such as structuring a meeting invitation from public or fictional information. Write three lines: the assistant will propose the structure; I will check it against the request; I will supply the real facts, choose the tone and decide whether to send. These lines define the assistant's job and the decisions that remain yours. If the actual task needs restricted material, a consequential decision or expertise you lack, use the existing approved process instead of widening this experiment.", "source_ids": [ "autor-2015", "nist-core", "ncsc-agents-2026" ] }, { "label": "Try a structure with genuine placeholders", "text": "Ask for a short outline with slots for Date, Location and Reply deadline, without entering actual restricted facts. Specify the audience and desired outcome, then compare the proposed structure with the original request. Keep wording that helps, remove any invented promise, and fill real details only on the system authorized to hold them. The writing experiment supports exploring composition help, while minimisation guidance supports keeping unnecessary personal data out. This exact exercise is an editorial example, not the researchers' protocol.", "source_ids": [ "writing-author-account", "ico-minimisation" ] }, { "label": "Try one source comparison before relying on the result", "text": "Select one generated claim, number or commitment and open the record it should reflect. Mark it matches, wrong or not in the source. Correct or remove a miss, and repeat the review for every material fact before an actual send. This first comparison teaches the method; checking one sentence does not certify a whole document. Record how much review and correction the task needed, without saving secrets. A useful result is a finished piece of work you can explain and stand behind, including the checking time.", "source_ids": [ "nist-measure", "nist-manage", "mit-define-success-2025" ] } ] }, { "id": "use-the-right-boundaries", "role": "cautions", "title": "Use this with the rules for your work", "items": [ { "label": "Use workplace guidance and qualified sign-off for consequential work", "text": "This is a reading companion about ordinary workplace drafting, checking, privacy, over-trust, and dividing a job into tasks. It is not legal, human-resources, medical, or financial advice, a productivity promise, or a vendor recommendation. Use your employer’s current policy and approved tools when they exist. Keep client, patient, student, employee, and employer-confidential material out of tools you do not control. Safety-critical, licensed, or regulated work still needs the competence and sign-off the role requires. For studying with a chatbot, see Learning and remembering. For checking an online claim or an AI image, see Making sense of online information.", "source_ids": [ "nist-core", "aba-512", "ncsc-shadow-2026" ] }, { "label": "Read named findings as a companion to your judgment", "text": "Named sources. Honest paraphrase of the finding. Not medical, legal, or financial advice. Career and work covers the wider career and work-design question; this topic concentrates on using a workplace assistant. Learning and remembering covers study and memory. Making sense of online information covers evaluating online claims and AI images. Those topics complement this one without replacing the policy, evidence or competence your actual work requires.", "source_ids": [ "nist-core", "autor-2015" ] } ] } ], "related": [ "career", "learning", "information" ] }, "sources": [ { "id": "writing-2023", "title": "Experimental evidence on the productivity effects of generative artificial intelligence — Noy and Zhang, Science (2023), author-hosted manuscript", "url": "https://shakkednoy.com/Noy%20Zhang%20NBER%20SI.pdf", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened author-linked manuscript: abstract, experimental design, results and limitations. Verified 453 professionals, randomized access, approximately 40% less time and 18% higher graded quality, lower-baseline gains, attitude changes and follow-up adoption.", "limitations": "Paid short writing tasks using 2023 ChatGPT; no contextual knowledge or precise factual accuracy requirement. Grader scores are not client outcomes. Science publisher text was unavailable; this author's manuscript corresponds to the 453-person version. An older MIT preprint with 444 participants was excluded from numerical synthesis." }, { "id": "writing-author-account", "title": "The productivity effects of generative artificial intelligence — Noy and Zhang, VoxEU (2023)", "url": "https://cepr.org/voxeu/columns/productivity-effects-generative-artificial-intelligence", "kind": "professional", "accessed_at": "2026-10-01", "supports": "Opened the authors' public account of writing-task design, lightly edited output, enjoyment and later reported workplace use.", "limitations": "An explanation of the same experiment, not independent replication or advice to send unchecked work. STUDlearn's placeholder exercise is an editorial application." }, { "id": "support-qje-2025", "title": "Generative AI at Work — Brynjolfsson, Li and Raymond, Quarterly Journal of Economics 140(2), 889–942 (2025)", "url": "https://academic.oup.com/qje/article/140/2/889/7990658", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened published article including abstract, setting, results and learning analysis. Verified 5,172 agents, staggered access, 15% average issues-resolved-per-hour gain, heterogeneous experience/skill effects, customer interaction changes and discretion to ignore suggestions.", "limitations": "One firm's adapted technical-support assistant; field rollout differs from randomized general-office adoption. Evidence consistent with learning does not certify all unaided competence. Published 15% replaces older working-paper averages." }, { "id": "frontier-2026", "title": "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality — Dell'Acqua and colleagues, Organization Science (2026)", "url": "https://www.hbs.edu/ris/Publication%20Files/dell-acqua-et-al-2026-navigating-the-jagged-technological-frontier_5c589c8c-fbb5-458f-b285-c944746cd717.pdf", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened final journal article on publisher and HBS-hosted PDF: design and results for 758 BCG consultants; 18 in-frontier tasks; 12.2% more tasks, 25.1% faster, higher-rated quality and larger lower-baseline gains. Checked outside-frontier group correctness supporting approximately 19 percentage points lower correctness across AI conditions.", "limitations": "GPT-4 in a specified consultant experiment, not a map of current capability. Final article published online March 11, 2026, DOI 10.1287/orsc.2025.21838. Do not combine its estimates with the 2023 working paper's 40% quality headline or treat percentage-point correctness difference as a universal relative-percent effect." }, { "id": "metr-2025", "title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity — METR", "url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened research organization's study report: randomized AI access for 16 experienced developers and 246 real repository tasks; 19% slower completion and perceived speedup.", "limitations": "Small, selected sample, familiar complex repositories and early-2025 tools. Organization's report was reviewed rather than an independent replication. Not all coding or current models." }, { "id": "metr-update-2026", "title": "Update on AI developer productivity — METR (February 24, 2026)", "url": "https://metr.org/blog/2026-02-24-uplift-update/", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened follow-up report and its discussion of participation/task selection, changed compensation and time measurement with concurrent agents. Authors consider greater uplift likely but say later estimates are unreliable for determining its size.", "limitations": "Not a clean measured reversal of the 2025 result. Selection and measurement changes prevent a strong contemporary productivity estimate." }, { "id": "ilo-exposure-2025", "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure — ILO Working Paper 140 (2025)", "url": "https://brasil.un.org/sites/default/files/2025-05/OIT-NASK-IAGen_WP140_web.pdf", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened official UN-hosted report, author biographies, abstract and relevant methods/results. Verified task-based worker/expert/model assessment, one in four workers with some occupational exposure, 3.3% highest exposure and transformation-versus-replacement interpretation.", "limitations": "Modeled potential exposure rather than observed job loss or employer-specific forecast. Employment distributions and the task-rating procedure shape estimates. Relevant portions reviewed, not every page. ILO publication landing page was also opened." }, { "id": "ilo-brief-2025", "title": "One in four jobs at risk of being transformed by GenAI, new ILO–NASK Global Index shows — ILO", "url": "https://www.ilo.org/resource/news/one-four-jobs-risk-being-transformed-genai-new-ilo%E2%80%93nask-global-index-shows", "kind": "official", "accessed_at": "2026-10-01", "supports": "Opened official explanation of the refined 2025 index, variation by occupational composition, income group and sex, and potential transformation.", "limitations": "Public explanation of the same index, not an additional employment study. Risk of transformation is not a count of layoffs." }, { "id": "autor-2015", "title": "Why Are There Still So Many Jobs? The History and Future of Workplace Automation — David Autor, Journal of Economic Perspectives 29(3), 3–30 (2015)", "url": "https://www.aeaweb.org/articles?id=10.1257/jep.29.3.3", "kind": "review", "accessed_at": "2026-10-01", "supports": "Opened official abstract explaining substitution, complementarity and changing demand as reasons task automation does not directly determine total employment.", "limitations": "Historical conceptual synthesis, not a 2026 generative-AI employment forecast. Full paper was not reviewed in this pass; application to a three-line task card is editorial." }, { "id": "nist-rmf", "title": "Artificial Intelligence Risk Management Framework (AI RMF 1.0) — NIST AI 100-1 (2023)", "url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened framework and relevant scope, trustworthiness, risk, Core and human-role passages. Supports voluntary nature, likelihood/severity, seven trustworthiness characteristics and four connected functions.", "limitations": "Voluntary framework, not binding law, a certification, or a validated personal checklist. Framework concepts do not establish an organization's compliance." }, { "id": "nist-gai", "title": "Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — NIST AI 600-1 (2024)", "url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened profile and relevant risk definitions: confabulation, data privacy, information security, intellectual property and human-AI configuration including automation bias and over-reliance. Opened official publication record to verify Reva Schwartz as coauthor.", "limitations": "Cross-sector risk guidance rather than a prediction that every system always produces these harms. Suggested desk checks are editorial adaptations. Authorship alone does not verify a coauthor's current employment." }, { "id": "nist-core", "title": "AI RMF Core — NIST AI Resource Center", "url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened Core: GOVERN, MAP, MEASURE, MANAGE, human oversight, responsibilities, training, affected-party feedback and the statement that functions are not a required ordered checklist.", "limitations": "Organizational voluntary guidance. Naming a human does not establish meaningful review or a uniform legal right of appeal." }, { "id": "nist-govern", "title": "AI RMF Playbook — GOVERN, NIST", "url": "https://airc.nist.gov/airmf-resources/playbook/govern/", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened relevant policies, responsibility, accountability, training, feedback and oversight suggestions; reviewers need ability to challenge or override results.", "limitations": "Suggested actions adapted to context, not all mandatory steps or workplace legal requirements. No claim that a policy alone establishes correct output." }, { "id": "nist-measure", "title": "AI RMF Playbook — MEASURE, NIST", "url": "https://airc.nist.gov/airmf-resources/playbook/measure/", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened relevant deployment-context, validity/reliability, generalization-limit, interpretation and ongoing-feedback passages, including MEASURE 2.3, 2.5 and 2.9.", "limitations": "Framework guidance, not a controlled demonstration of the exact STUDlearn summary or source-comparison exercises." }, { "id": "nist-manage", "title": "AI RMF Playbook — MANAGE, NIST", "url": "https://airc.nist.gov/airmf-resources/playbook/manage/", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened relevant risk-response, monitoring, error/incident communication, appeal and override passages including MANAGE 4.1 and 4.3.", "limitations": "Context-specific suggested actions; does not create universal worker rights or prescribe a two-minute personal review." }, { "id": "aba-512", "title": "Formal Opinion 512: Generative Artificial Intelligence Tools — American Bar Association (July 29, 2024)", "url": "https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-opinions/aba-formal-opinion-512.pdf", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened opinion, relevant competence, confidentiality, informed consent, communication, supervision and fees passages. Reviewed limits on self-learning-tool inputs and insufficient boilerplate consent, and context-dependent client disclosure.", "limitations": "US lawyers and ABA Model Rules; applicability depends on jurisdiction and circumstances. Not legal advice, a general employee consent rule, or a claim that every prompt requires public disclosure." }, { "id": "ftc-privacy-2024", "title": "AI Companies: Uphold Your Privacy and Confidentiality Commitments — FTC Office of Technology (January 2024)", "url": "https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/01/ai-companies-uphold-your-privacy-confidentiality-commitments", "kind": "official", "accessed_at": "2026-10-01", "supports": "Opened staff post explaining potential liability for providers breaking privacy/confidentiality commitments, including promises not to train on customer data.", "limitations": "Provider-facing staff guidance, not permission for an employee to upload a file. Current individual provider terms were not audited. No-training and no-transfer are different issues." }, { "id": "ico-minimisation", "title": "How should we assess security and data minimisation in AI? — UK Information Commissioner's Office", "url": "https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/how-should-we-assess-security-and-data-minimisation-in-ai/", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened relevant security and minimisation passages: necessary personal data for a purpose, minimising processing throughout development and deployment.", "limitations": "UK data-protection context; site states guidance is under review following legal changes. General privacy literacy only; replacing a name alone is not a compliance determination or assured anonymisation." }, { "id": "ico-accuracy", "title": "What do we need to know about accuracy and statistical accuracy? — UK Information Commissioner's Office", "url": "https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/what-do-we-need-to-know-about-accuracy-and-statistical-accuracy/", "kind": "guideline", "accessed_at": "2026-10-01", "supports": "Opened distinction between accuracy of personal data and statistical accuracy of models; relevant guidance on marking predictions/inferences rather than presenting them as facts.", "limitations": "UK legal/regulatory context, under review. Does not determine whether one workplace record is lawful or prescribe a universal record format." }, { "id": "ncsc-llm-2023", "title": "ChatGPT and large language models: what's the risk? — UK National Cyber Security Centre (2023)", "url": "https://www.ncsc.gov.uk/blog-post/chatgpt-and-large-language-models-whats-the-risk", "kind": "official", "accessed_at": "2026-10-01", "supports": "Opened technical explanation of LLMs, prompts, operator access and confidential-input risk. Supports distinction between entering information and a model instantly learning it.", "limitations": "Dated technical guidance, not verification of current vendor retention policies. Use current approved terms for the particular service. No blanket assertion that every deployment has identical data handling." }, { "id": "ncsc-shadow-2026", "title": "The hidden risks of shadow AI — UK National Cyber Security Centre (September 7, 2026)", "url": "https://www.ncsc.gov.uk/blogs/the-hidden-risks-of-shadow-ai", "kind": "official", "accessed_at": "2026-10-01", "supports": "Opened official explanation of unapproved workplace AI use, diminished organizational visibility and information control, and the importance of usable approved routes and policy.", "limitations": "Cybersecurity guidance rather than a jurisdiction-specific employment rule. Unopened third-party prevalence statistics in the post were not used." }, { "id": "ncsc-agents-2026", "title": "Thinking carefully before adopting agentic AI — UK National Cyber Security Centre (May 15, 2026)", "url": "https://www.ncsc.gov.uk/blogs/thinking-carefully-before-adopting-agentic-ai", "kind": "official", "accessed_at": "2026-10-01", "supports": "Opened agentic-AI explanation and recommendations on bounded actions/access, ownership, monitoring, incident response, stopping and starting with lower-stakes uses.", "limitations": "Organizational cyber guidance, not a current vendor setup guide or an assurance that human approval prevents every harm." }, { "id": "oecd-surveys-2023", "title": "The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers — Lane, Williams and Broecke (2023)", "url": "https://www.oecd.org/en/publications/the-impact-of-ai-on-the-workplace-main-findings-from-the-oecd-ai-surveys-of-employers-and-workers_ea0a0fe1-en.html", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened official publication overview and report; verified manufacturing/finance, seven countries, positive reported outcomes with job-loss concerns, and training/consultation associations. Sample/timing additionally verified on current OECD survey project page.", "limitations": "Early-2022 perception surveys, not a causal evaluation, a study of current ChatGPT adoption or a survey of all sectors. Relevant report sections were reviewed, not every questionnaire and table. Association does not establish a universal training effect." }, { "id": "oecd-survey-design", "title": "OECD AI surveys of employers and workers — project overview", "url": "https://www.oecd.org/en/about/projects/aisurveysofemployersandworkers.html", "kind": "official", "accessed_at": "2026-10-01", "supports": "Opened About text verifying early-2022 sample of 5,334 workers and 2,053 firms in Austria, Canada, France, Germany, Ireland, UK and US. States surveys repeated in 2026 with wider coverage.", "limitations": "2026 repetition is not treated as published outcome evidence; this article reports the 2023 publication of 2022 data. Project overview is not an independent study." }, { "id": "critical-thinking-2025", "title": "The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers — Lee, Sarkar, Tankelevitch and colleagues, CHI (2025)", "url": "https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened author institution's publication record and abstract: 319 workers, 936 examples, confidence associations and shift toward verification, integration and task stewardship; DOI 10.1145/3706598.3713778.", "limitations": "Self-reports and associations, not an objective causal test of skill loss. Detailed full-paper analyses were not reviewed. Microsoft-employed authors' institutional affiliation is disclosed in panel." }, { "id": "bastani-pnas-2025", "title": "Generative AI without guardrails can harm learning: Evidence from high school mathematics — Bastani and colleagues, PNAS 122(26), e2422633122 (2025)", "url": "https://www.pnas.org/doi/10.1073/pnas.2422633122", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened published article methods/results: field experiment in Turkish high-school math, GPT-4-based unrestricted and safeguarded tutors, assisted practice improvements, lower later unaided performance with unrestricted helper and mitigation with safeguarded design.", "limitations": "Classroom mathematics, not adult workplace evidence. Mitigating the unaided penalty is not demonstrating a later learning gain. Use published version rather than imply that the earlier working paper remains the latest evidence." }, { "id": "mit-validation-2026", "title": "How generative AI 'persuasion bombs' users — and how to fight back — MIT Sloan (April 28, 2026)", "url": "https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-persuasion-bombs-users-and-how-to-fight-back", "kind": "professional", "accessed_at": "2026-10-01", "supports": "Opened institutional research explanation of qualitative interaction analysis involving 72 BCG consultants, GPT-4 and 4,339 prompts; discusses increasingly persuasive responses to challenge and verification outside the chat.", "limitations": "The institutional account was reviewed; the full working paper was unavailable. It describes one experimental setting, not intentional deceit, every model or a universal rate." }, { "id": "mit-define-success-2025", "title": "How to use generative AI to augment your workforce — MIT Sloan (February 24, 2025)", "url": "https://mitsloan.mit.edu/ideas-made-to-matter/how-to-use-generative-ai-to-augment-your-workforce", "kind": "professional", "accessed_at": "2026-10-01", "supports": "Opened Danielle Li's public implementation guidance on defining success, data/infrastructure, workforce effects and evaluating augmentation.", "limitations": "Professional explanation; not randomized validation of STUDlearn's small workflow comparison or guaranteed return on a tool." }, { "id": "mit-levers-2026", "title": "10 levers shaping whether generative AI truly improves worker performance — MIT Sloan (September 3, 2026)", "url": "https://mitsloan.mit.edu/ideas-made-to-matter/10-levers-shaping-generative-ai-truly-improves-worker-performance", "kind": "professional", "accessed_at": "2026-10-01", "supports": "Opened recent institutional synthesis stressing context, worker judgment, evidence before scaling, knowing when to trust output and costs of rework.", "limitations": "Professional synthesis, not a single causal estimate for every lever or permission for autonomous work." }, { "id": "mit-work-design-2026", "title": "Humans in the Loop: The Evolution of Work in Early Experiments with Generative AI — Armstrong and Shah, MIT Industrial Performance Center (April 2026)", "url": "https://ipc.mit.edu/wp-content/uploads/2026/04/Humans_in_the_Loop_full_r01M.pdf", "kind": "study", "accessed_at": "2026-10-01", "supports": "Opened report executive summary, methods and relevant findings on early experiments at more than 20 companies in four industry groups, variation in supervisory work and job quality.", "limitations": "Qualitative interviews/cases, not causal net-employment estimates or representative universal outcomes. Relevant portions reviewed rather than every detail." }, { "id": "professional-kellogg", "title": "Kate Kellogg — MIT Sloan faculty directory", "url": "https://mitsloan.mit.edu/faculty/directory/kate-kellogg", "kind": "professional", "accessed_at": "2026-10-01", "supports": "Opened faculty biography verifying management/innovation role and public organizational research on technology, work practices and implementation; consulted related official MIT explainers listed above.", "limitations": "Credential and research-context verification, not empirical proof that a specific training or logging scheme will work." }, { "id": "professional-ajunwa", "title": "Ifeoma Ajunwa — Emory Law faculty profile", "url": "https://law.emory.edu/faculty/faculty-profiles/ajunwa-profile.html", "kind": "professional", "accessed_at": "2026-10-01", "supports": "Opened biography identifying Asa Griggs Candler Professor of Law, AI/Future of Work initiative leadership and scholarship on quantified workers, employment technology, privacy and discrimination.", "limitations": "Professional context distinguishing worker assessment from assistance with one's own draft. Profile is not jurisdiction-specific legal advice or a reviewed causal effect of monitoring." }, { "id": "question-speed-perception", "title": "ExperiencedDevs discussion of the early-2025 developer productivity study", "url": "https://www.reddit.com/r/ExperiencedDevs/comments/1lwk503/study_experienced_devs_think_they_are_24_faster/", "kind": "question_signal", "accessed_at": "2026-10-01", "supports": "Opened discussion to identify a reader question: why can assistance feel faster while measured completion is slower? The answer uses METR's opened reports.", "limitations": "Anonymous anecdotes and thread popularity are not evidence of productivity, professional consensus or a validated workflow. No factual guidance relies solely on this discussion." }, { "id": "question-thinking-headline", "title": "Futurology discussion of a headline about AI and thinking skills", "url": "https://www.reddit.com/r/Futurology/comments/1iq1gl2/microsoft_study_finds_relying_on_ai_kills_your/", "kind": "question_signal", "accessed_at": "2026-10-01", "supports": "Opened discussion as a signal of confusion between a confidence/self-report association and a claim that AI destroys thinking. Article explains the actual CHI survey's bounds.", "limitations": "Headline and comments are not causal evidence. No X access or professional consensus is claimed." } ], "professional_panel": [ { "name": "Erik Brynjolfsson", "credential": "Stanford Jerry Yang and Akiko Yamazaki Professor; Stanford Digital Economy Lab director, as listed on Stanford Economics profile", "official_url": "https://economics.stanford.edu/people/erik-brynjolfsson", "public_usefulness": "Public digital-economy research, books and explanations help readers distinguish an aggregate productivity claim from a specific workplace deployment.", "contribution": "Coauthored the published support-agent study used for average and heterogeneous effects.", "boundary": "That study covers one firm's support assistant; his broader expertise does not establish every tool's effects.", "reviewed_at": "2026-10-01" }, { "name": "Danielle Li", "credential": "David Sarnoff Professor of Management of Technology, MIT Sloan, as listed by MIT", "official_url": "https://mitsloan.mit.edu/faculty/directory/danielle-li", "public_usefulness": "MIT's public augmentation guidance explains how to define success before adopting workplace AI.", "contribution": "Coauthored support-agent evidence and supplies the practical success-criteria framing.", "boundary": "Institutional implementation advice does not validate STUDlearn's exact example or promise savings.", "reviewed_at": "2026-10-01" }, { "name": "Lindsey Raymond", "credential": "Assistant Professor jointly in MIT Economics, EECS and the Schwarzman College of Computing, as listed by MIT", "official_url": "https://economics.mit.edu/people/faculty/lindsey-raymond", "public_usefulness": "Public research on AI, labor and human learning makes workplace effects understandable through actual data rather than demonstration alone.", "contribution": "Coauthored Generative AI at Work, including experience differences and evidence consistent with learning.", "boundary": "Profile is no longer described as an incoming appointment; the research still does not establish universal learning or employment effects.", "reviewed_at": "2026-10-01" }, { "name": "Fabrizio Dell'Acqua", "credential": "HBS AI Institute researcher with a Columbia Management PhD; official personal site lists an incoming Michigan Ross assistant professorship", "official_url": "https://www.fabriziodellacqua.com/", "public_usefulness": "Public research explanations and teaching describe why nearby-looking knowledge tasks can produce different AI effects.", "contribution": "Lead author of the consultant frontier experiment, checked in its 2026 journal version.", "boundary": "Incoming appointment is reported as listed, not asserted as already begun; task boundaries from the experiment are not today's universal capability map.", "reviewed_at": "2026-10-01" }, { "name": "Ethan Mollick", "credential": "Associate Professor of Management and co-director of the Generative AI Labs at Wharton, as listed by Knowledge at Wharton", "official_url": "https://knowledge.wharton.upenn.edu/faculty/ethan-mollick/", "public_usefulness": "Public explanations and Co-Intelligence help ordinary readers consider AI assistance and human judgment together.", "contribution": "Coauthored the consultant experiment supporting uneven task performance.", "boundary": "Public examples and personal tool experiments do not establish a prompt's reliability or replace independent checking.", "reviewed_at": "2026-10-01" }, { "name": "Hamsa Bastani", "credential": "Associate Professor of Operations, Information and Decisions at Wharton with a secondary Statistics and Data Science appointment, as listed by Wharton", "official_url": "https://oid.wharton.upenn.edu/profile/hamsab/", "public_usefulness": "Public research on human/AI decision systems and safeguarded tutoring clarifies assisted performance versus later learning.", "contribution": "Coauthored the published high-school mathematics trial comparing unrestricted and safeguarded GPT-4 tutors.", "boundary": "Classroom learning evidence is adjacent to adult workplace accountability; it does not prove workplace deskilling.", "reviewed_at": "2026-10-01" }, { "name": "David Autor", "credential": "Professor of Economics at MIT, verified through MIT faculty profile", "official_url": "https://economics.mit.edu/people/faculty/david-h-autor", "public_usefulness": "Accessible essays explain tasks, substitution, complementarity and demand as mechanisms of workplace change.", "contribution": "2015 automation review supplies the conceptual distinction between automating a task and eliminating an entire occupation.", "boundary": "Historical framework, not a current numerical employment forecast or reassurance against displacement.", "reviewed_at": "2026-10-01" }, { "name": "Paweł Gmyrek", "credential": "Senior Researcher in the ILO Research Department's Effective Labour Institutions work, as listed in ILO research repository", "official_url": "https://researchrepository.ilo.org/esploro/profile/pawel_gmyrek/overview?institution=41ILO_INST", "public_usefulness": "Public ILO reports explain occupational exposure in a way readers can separate from layoffs.", "contribution": "Lead author of the refined 2025 ILO–NASK exposure index.", "boundary": "Task ratings and exposure estimates concern potential change, not observed employer-specific outcomes.", "reviewed_at": "2026-10-01" }, { "name": "Janine Berg", "credential": "ILO Research Department Senior Economist, identified in the opened May 2025 report's author biography", "official_url": "https://brasil.un.org/sites/default/files/2025-05/OIT-NASK-IAGen_WP140_web.pdf", "public_usefulness": "ILO's public exposure reports connect technological change to working conditions and transition support.", "contribution": "Coauthored the refined exposure index and its transformation-versus-replacement interpretation.", "boundary": "Role is verified in the dated report biography, not independently confirmed as a current appointment; modeling does not establish net job loss.", "reviewed_at": "2026-10-01" }, { "name": "Elham Tabassi", "credential": "Director of the Artificial Intelligence and Emerging Technology Initiative and Senior Fellow at Brookings; former NIST chief AI advisor and AI RMF leader, as listed by Brookings", "official_url": "https://www.brookings.edu/people/elham-tabassi/", "public_usefulness": "Public risk-framework work explains responsibility, trustworthy systems and meaningful measurement without relying on vendor marketing.", "contribution": "Led the AI RMF that supplies the four-function and trustworthiness teaching.", "boundary": "Current Brookings role is distinguished from historical NIST leadership; voluntary framework is not law or certification.", "reviewed_at": "2026-10-01" }, { "name": "Arvind Narayanan", "credential": "Princeton Computer Science professor and Center for Information Technology Policy director, as listed on official faculty page", "official_url": "https://www.cs.princeton.edu/~arvindn/", "public_usefulness": "Public AI as Normal Technology explanations encourage readers to distinguish demonstrated capabilities from advertised possibilities.", "contribution": "Provides the public explanatory lens of evaluating what a system actually does; factual risk claims here rest on opened studies and NIST guidance.", "boundary": "Profile and public teaching are professional context, not validation of one employer's assistant or evidence of a universal model failure rate.", "reviewed_at": "2026-10-01" }, { "name": "Ifeoma Ajunwa", "credential": "Asa Griggs Candler Professor of Law at Emory and founding director of its AI and the Future of Work initiative, as listed by Emory", "official_url": "https://law.emory.edu/faculty/faculty-profiles/ajunwa-profile.html", "public_usefulness": "The Quantified Worker and public employment-technology scholarship help readers ask about privacy, monitoring and decisions affecting workers.", "contribution": "Keeps AI used to evaluate a worker distinct from AI chosen by a worker to draft a message.", "boundary": "Professional scholarship and profile are not jurisdiction-specific legal advice or a universal right-of-appeal claim.", "reviewed_at": "2026-10-01" }, { "name": "Stijn Broecke", "credential": "Senior Economist in OECD Employment, Labour and Social Affairs, leading future-of-work research, as listed by OECD.AI", "official_url": "https://oecd.ai/en/community/stijn-broecke", "public_usefulness": "Public OECD research explanations and talks connect workforce consultation and training to reported experiences.", "contribution": "Coauthored the 2023 AI workplace survey report used for training/consultation associations.", "boundary": "Early-2022 survey perceptions are not a randomized causal effect or a survey of current generative tools.", "reviewed_at": "2026-10-01" }, { "name": "Kate Kellogg", "credential": "David J. McGrath Jr. Professor of Management and Innovation at MIT Sloan, as listed by MIT", "official_url": "https://mitsloan.mit.edu/faculty/directory/kate-kellogg", "public_usefulness": "Public organizational research and MIT explainers describe how implementation, expertise and worker participation change technology's practical effects.", "contribution": "Coauthored consultant frontier research and contributes organizational context for training, work design and review.", "boundary": "Neither a profile nor implementation research guarantees a particular training package's causal effect.", "reviewed_at": "2026-10-01" }, { "name": "Reva Schwartz", "credential": "Named coauthor of NIST AI 600-1, the Generative Artificial Intelligence Profile (2024), verified on NIST's publication record", "official_url": "https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence", "public_usefulness": "Coauthored publicly accessible professional risk guidance explaining confabulation, privacy and human-AI configuration.", "contribution": "Contributes to the official profile supporting over-reliance and confidentiality risk teaching.", "boundary": "Professional authorship is verified; a current job was not independently confirmed. Coauthorship does not attribute every passage to this individual.", "reviewed_at": "2026-10-01" }, { "name": "Advait Sarkar", "credential": "Senior Researcher, Microsoft Research Cambridge, as listed on Microsoft Research profile", "official_url": "https://www.microsoft.com/en-us/research/people/advait/", "public_usefulness": "Public human-computer-interaction research helps readers understand verification and critical-thinking effort in AI-assisted work.", "contribution": "Coauthored the CHI survey of 319 workers and 936 examples used to clarify confidence associations.", "boundary": "Microsoft-employed researcher; self-reported associations are not causal skill-loss evidence. Profile warns it is not regularly updated, so role is stated as listed.", "reviewed_at": "2026-10-01" }, { "name": "Lev Tankelevitch", "credential": "Senior Researcher and behavioral scientist at Microsoft Research Cambridge; Oxford PhD background in cognitive psychology/neuroscience, verified on Microsoft profile", "official_url": "https://www.microsoft.com/en-us/research/people/levt/", "public_usefulness": "Public metacognition and human-agency explanations connect thinking about one's own judgment to AI-assisted collaboration.", "contribution": "Coauthored the critical-thinking survey and supplies relevant expertise for confidence versus verification distinctions.", "boundary": "Microsoft-employed researcher. Survey supports reported associations, not a universal deskilling effect or a validated workplace intervention.", "reviewed_at": "2026-10-01" }, { "name": "Shakked Noy", "credential": "MIT Economics PhD student, described on official personal site as fifth-year and on the 2026–27 job market", "official_url": "https://shakkednoy.com/", "public_usefulness": "Public coauthored VoxEU account explains writing-task design and the gap between an experimental result and workplace use.", "contribution": "Coauthored the 453-person Science writing experiment; the author's site links the manuscript used here.", "boundary": "The listed PhD is in progress. The short, generic writing tasks do not establish real-file accuracy or productivity across workplaces.", "reviewed_at": "2026-10-01" } ] }