The Best AI Productivity Tool Is the One That Passes a Task-Fit Test

The practical answer to “which AI productivity tool should our team buy?” is: do not choose a tool for the whole job title. Choose it for one bounded work unit, prove that it can produce an acceptable result in the real workflow, and include the time spent checking and correcting that result. A summary that is fast but omits the decision, a draft that invents a fact, or an automation that creates work for the next person is not a productivity gain. It is a transfer of effort and risk.

This article turns that decision into a reusable task-fit card, a small scoring rule, and a set of test cases. It supplements Trustly AI’s guide to productivity tools, which already covers workflow mapping, data classification, output type, human review, and a broad candidate scorecard. The missing operational step is a release decision for one work unit: how to make a reviewable acceptance rule, keep hard data and ownership controls separate from a score, and count review and correction before deciding that a workflow is ready. The distinction matters because tool labels are broad, while success or failure happens inside a specific task, with a specific input, output, reviewer, and consequence.

The evidence does not support a universal ranking of “best AI tools.” It supports testing the fit between capability and task. Some controlled studies report meaningful gains on well-bounded work. Other experiments find that AI assistance can make people less correct on a task outside the system’s capability frontier. Government trials add a different perspective: staff can report real utility and better perceived quality while still requiring human editing, and self-selected pilots do not prove what a different team will experience. The decision rule here is therefore conservative: keep a person responsible for acceptance, measure the whole work unit, and scale only the use cases that pass.

The decision in one minute

Use AI first where inputs are available, the desired output can be described, mistakes can be detected at reasonable cost, and a named person can reject or repair the result. Examples often include first-pass summarization of a known document set, drafting a routine email from approved facts, or generating test cases against explicit requirements. Keep consequential judgment, unsourced factual claims, exceptions, and irreversible actions under human control unless a separate assurance case justifies more autonomy.

Do not start with “we need a chatbot,” “we need agents,” or “everyone should use the same assistant.” Start with a work unit such as “turn these three approved project notes into a status draft that includes owner, due date, blocker, and source link.” That description makes it possible to test both the tool and the workflow. “Improve productivity” is a goal, not an acceptance test.

The key choice is often not between two brands. It is between four operating modes: keep the current manual task; use AI for a draft that a person edits; use AI for a bounded transformation with deterministic checks; or defer automation because context, quality, or consequence is not yet controllable. A task card prevents teams from confusing a good demonstration with an approved workflow.

What the evidence says—and what it does not

Research findings vary because the tasks, participants, tools, outcome definitions, and environments vary. In a preregistered online experiment involving 453 college-educated professionals doing occupation-specific writing tasks, Noy and Zhang reported that participants with ChatGPT completed work faster on average and produced higher-quality outputs under the study’s evaluation. This is evidence about a set of incentivized writing tasks in an online experiment, not a forecast for every team or every AI product. The study is available through the Stanford SCALE summary and the Science publication record.

A later preregistered experiment with 758 knowledge workers examined 18 consulting-style tasks and a separate complex managerial task. For the tasks the authors classified within the “jagged frontier,” AI-assisted groups completed more work more quickly on average, with quality gains. On the out-of-frontier managerial task, AI users were less likely to give a correct answer than the control group. The implication is not “AI is good” or “AI is bad.” It is that task-level fit can vary inside a job that looks like one coherent workflow. The Organization Science paper describes both sides and its experimental context.

There is also evidence that improvement can flatten or create unwanted behavior even when output quality initially rises. A September 2026 Asian Development Bank paper describes a randomized illustration experiment where access to text-to-image AI improved quality at observed points, yet reduced the returns to continued effort; some participants stopped earlier and submitted lower-quality final work. This concerns creative illustration, not general office productivity. It is useful as a warning about incentives and stopping rules, not as a number to transfer to a software, marketing, or finance team.

Public-sector evidence adds a more operational view. The UK Department for Work and Pensions evaluated a Microsoft 365 Copilot trial from October 2024 to March 2025. Its report describes a mixed-method evaluation with surveys, regression analysis, and 19 interviews; it also explicitly notes non-random licence allocation, self-reported outcomes, and no pre-trial baseline. Users reported time savings and perceived quality improvements, but described the tool as a useful starting point that still required editing and judgment. Read the DWP evaluation before treating a government trial as a causal estimate for your own organization.

The 2026 Stanford AI Index economy chapter synthesizes evidence across occupations and stresses variation by task structure, feedback, and judgment. Its contribution is breadth, but synthesis does not erase differences between studies. One figure from a customer-support study should not be treated as a prediction for contract review; a self-reported time estimate should not be combined with an objective throughput measure; a lab task should not be presented as a production rollout. When comparing claims, keep the population, time period, task, outcome, and measurement method attached to the number.

The missing piece: a decision at the task boundary

Many tool pages and buying guides compare features, integrations, prices, model access, or broad categories. Those are useful filters, but they do not answer the operator’s harder question: should this exact task be delegated to the chosen tool, and what must remain true before the result can leave the workspace? Trustly AI’s current guide already recommends mapping the workflow and scoring tool candidates for fit, data control, integration, quality, transparency, exportability, and usability. This article keeps those principles and makes a more specific artifact: an admission card with independent checks, failed-gate rules, and a task-bound decision. In a public Hacker News discussion, a user asked whether checking generated messages, emails, or invoices defeats the point of the workflow; replies offered competing anecdotes, from “always review” to adding validation layers, with no representative data. That disagreement exposes a real operational question: who supplies the independent oracle that tells the team whether the AI output is right? See the discussion on reviewing AI outputs.

The gap is not another list of “best tools.” It is a reusable admission test that makes the work unit explicit before procurement or rollout. The card below records the input boundary, acceptable output, known-answer check, review owner, consequences, and fallback. It separates a hard “do not proceed” condition from a softer fit score. That separation matters: a high average must never cancel an unapproved data path, a missing owner, or an output that cannot be checked.

The asset is deliberately a protocol, not a market benchmark. It does not run live model comparisons and does not claim measured productivity improvements. It is a reusable framework for a team to run its own controlled comparison with representative work, real constraints, and a reviewer who understands the task. Any numbers in the sample card are scoring rules or test counts chosen for repeatability, not observed performance statistics.

The task-fit card

Complete one card for one work unit, not for an entire department. If a workflow includes several different decisions, split it until each card has one accepted output and one clear failure boundary. A task that is “summarize a customer case, decide eligibility, and issue a refund” contains at least three distinct work units and different levels of consequence.

Card field	What to write down	Why it matters
Work unit	One verb, one object, one completion state	Prevents scope from expanding into an untestable job description
Starting inputs	Sources, formats, freshness, permissions, and missing-data rules	A tool cannot repair an input contract the team never defined
Acceptable output	Required fields, prohibited claims, traceability, format	Converts “looks good” into reviewable criteria
Check oracle	Existing answer, deterministic rule, source record, or qualified reviewer	Establishes how correctness is recognized independently of the model
Consequence	Cost of a wrong or late result; reversibility; affected people	Sets the minimum assurance and approval needed
Owner and fallback	Person who accepts, rejects, escalates, and restores the manual route	Prevents silent responsibility gaps
Measurement window	Same task family, comparable workload, and baseline method	Avoids attributing unrelated process changes to the tool
Decision	Keep manual, assist, controlled automation, or stop	Makes the pilot produce a decision rather than a demo

Now apply three hard gates. Gate A: the source data may be used in the selected tool under the organization’s policy and contract. Gate B: a reviewer can detect the material errors that matter for this task before release. Gate C: a named owner can stop the workflow and use a safe fallback. A “no” on any gate means “do not automate this task yet,” regardless of the feature score. The Federal Trade Commission’s case on AI business-growth and earnings claims is a reminder that selling or buying a tool on unsupported performance promises can create real costs; the FTC’s action concerns alleged deceptive business claims, not a general finding about all AI productivity software.

For tasks that pass the hard gates, score six dimensions from 0 to 2: input repeatability, output verifiability, context completeness, integration reliability, error reversibility, and review burden. A 0 means “unknown or weak,” 1 means “partly controlled,” and 2 means “demonstrably controlled for this task.” The score is diagnostic, not a scientific scale. Do not use it to rank vendors or compare unrelated tasks. A low score points to the missing control; it does not prove the model is incapable.

The interpretation is intentionally simple. A task with any hard gate failed is blocked. Among tasks that pass all hard gates, a total from 0–5 means keep manual and collect evidence; 6–8 means run a supervised comparison; 9–12 means consider a bounded pilot with explicit stop conditions. These bands are a proposed operating rule. They are not calibrated from a dataset, and a high score is not a safety certification. A high-impact action still needs approval rules that reflect its consequence.

An example: weekly project-status drafting

Suppose a delivery manager spends time turning approved meeting notes, issue-tracker changes, and a schedule into a weekly status draft. The task card defines the work unit as “produce a draft with current milestone, owner, due date, blocker, and source link.” The input set is restricted to a named project workspace and a fixed cut-off time. Missing owners remain “unknown”; the AI may not infer them from writing style or an old email. The draft includes a link for every status claim. A human project lead approves the result before it is sent outside the team.

The check oracle is the underlying schedule and issue tracker, plus a small set of known historical weeks with correct status. The review checks whether each field matches the source record and whether changes since the previous week are explained. The risk is moderate if the draft is internal and reversible, but it rises if the same process sends commitments to a customer. That change should create a new card or a stricter release gate, not a casual permission toggle.

The baseline records the same work unit done with the existing method: total elapsed labor from input collection to accepted draft, including reading, drafting, verification, corrections, and handoff. The comparison uses similar task instances and the same acceptance rubric. If work volumes or complexity differ, note the difference instead of dividing by an arbitrary average. Do not count a fast first draft as completed work if the reviewer spends the saved time reconstructing missing context.

For each attempt, keep a row with task ID, input snapshot time, tool/version, operator, mode, elapsed preparation, generation, checking, repair and handoff time, rubric result, defect category, and final disposition. The row should not include unnecessary personal or client content. A synthetic fixture can validate the rubric’s mechanics, but it cannot estimate how the tool will perform on real work. Label synthetic data as synthetic.

A small reusable evaluator

The following plain Node.js function evaluates a task card. It does not call an AI model or collect performance data. The flags are explicit so an operator can inspect the reason for every decision. It deliberately refuses to let the numeric score override a failed hard gate.

function evaluateTaskFit(card) {
  const gates = {
    approvedDataPath: Boolean(card.approvedDataPath),
    checkableOutput: Boolean(card.checkableOutput),
    namedOwnerAndFallback: Boolean(card.namedOwnerAndFallback),
  };
  const failed = Object.entries(gates)
    .filter(([, passed]) => !passed)
    .map(([name]) => name);

  const dimensions = [
    'inputRepeatability', 'outputVerifiability', 'contextCompleteness',
    'integrationReliability', 'errorReversibility', 'reviewBurden',
  ];
  const invalid = dimensions.filter(key => ![0, 1, 2].includes(card[key]));
  if (invalid.length) return { decision: 'BLOCK', reason: 'invalid score', invalid };
  if (failed.length) return { decision: 'BLOCK', reason: 'hard gate failed', failed };

  const score = dimensions.reduce((sum, key) => sum + card[key], 0);
  const decision = score >= 9 ? 'BOUNDED_PILOT' : score >= 6 ? 'SUPERVISED_COMPARISON' : 'KEEP_MANUAL';
  return { decision, score, dimensions, note: 'Proposed rule; not a validated benchmark.' };
}

const example = evaluateTaskFit({
  approvedDataPath: true,
  checkableOutput: true,
  namedOwnerAndFallback: true,
  inputRepeatability: 2,
  outputVerifiability: 2,
  contextCompleteness: 1,
  integrationReliability: 1,
  errorReversibility: 2,
  reviewBurden: 1,
});
console.log(example);

In this example, the result is eligible for a bounded pilot because the three hard gates pass and the proposed dimensions sum to 9. That result does not say the system is accurate, safe, or economically worthwhile. It says only that the work unit is sufficiently described to test under supervision. The pilot still needs an acceptance rubric, stop rules, and a comparison against the current process.

A practical test set without pretending it is a benchmark

Build a small set of representative cases from the task’s own work. Include routine, incomplete, ambiguous, and adversarial examples. Keep a source-of-truth answer or a qualified reviewer’s expected disposition for each case. Do not ask the same model that produced the output to be the only judge of whether the output is correct. The test set should be fixed before comparing tools so the team does not quietly replace difficult cases after seeing an inconvenient result.

For a status-drafting workflow, cases could include: a normal update with all fields; a source with a late schedule change; an issue marked resolved but with a newer comment showing a blocker; two conflicting due dates; a missing owner; a status claim with no supporting record; a project with no changes; a changed timezone boundary; a withdrawn milestone; a duplicate issue in two systems; stale inputs; and an instruction embedded in source text that conflicts with the task contract. These are proposed test categories, not tested outputs.

Record pass/fail by criterion, not only a blended score. A factual mismatch in a due date is different from a slightly awkward tone. The test owner should identify severity, whether a reviewer could reasonably catch the defect, how much repair time was needed, and whether a downstream system would have accepted the bad value. Keep “not applicable” separate from “passed.” Unknowns and missing source material should remain visible instead of being silently imputed.

Repeat enough cases to see the main failure modes, but do not pretend a small pilot estimates rare-event rates. A handful of clean outputs cannot establish that a tool will never omit a blocker. If the task has a low tolerance for an error, combine case testing with deterministic controls, human approval, and monitoring. For critical decisions, synthetic examples are only one layer of assurance and cannot replace domain-specific validation.

Measuring the whole work unit

The basic measure is not prompt-to-first-draft time. It is total labor and elapsed time from the same starting point to the same accepted outcome. Separate input preparation, prompt or configuration setup, generation, review, correction, escalation, handoff, and downstream rework. A tool can reduce one segment and still increase the total. A team that spends less time drafting but more time locating sources may not have improved the workflow.

Use a baseline that matches the task. Record the existing process for the same type of work before introducing the tool. If you compare different people, tasks, weeks, or standards, make those differences visible. If the work is seasonal or volume changes, compare like periods or report the limitation. If the pilot changes the template, training, staffing, or workflow at the same time, you cannot attribute the result to the model alone.

Track quality with a rubric set before the comparison. For a summary, that may include coverage of required facts, source traceability, correct uncertainty, no invented facts, and usable structure. For a code change, acceptance may include tests, security review, maintainability, and successful integration. For a customer response, it may include policy correctness, tone, personalization, and escalation where required. “Looks polished” is not a substitute for an outcome definition.

Calculate the net difference using the same denominator. For each accepted unit, count all labor that the process requires, including checking and correction. Keep rejected work in the record rather than removing it from the average. A result that is faster only because people skipped a check is a failed control, not a productivity win. If a unit’s quality falls below the minimum, report that separately; do not let faster average time hide a quality-floor breach.

The cost measure should include subscription or usage fees, integration and administration, training, security review, support, reviewer time, and the cost of fallback. Avoid assigning a financial value to “minutes saved” unless the organization can explain what that capacity becomes. Reallocated time may be valuable, but it is not automatically cash savings. This distinction keeps a pilot’s business case from converting a user-reported convenience into an unsupported revenue claim.

Interpreting conflicting results

Positive results from one study and neutral or negative results from another do not necessarily contradict each other. Check the task type, participant skill, model version, access to tools, output quality measure, evaluation method, and date. A study about routine drafting cannot directly answer whether an AI system should approve a complex management decision. A survey of staff who opted into a licensed tool is not the same design as random assignment. A short-run task experiment says little about later skill development or process redesign.

The DWP trial is useful because it evaluates an actual organizational deployment and documents both perceived benefits and study limitations. Its reported estimates should remain attached to that department, trial window, licence group, and method. The 2026 experimental work on the jagged frontier is useful because it compares AI and non-AI conditions on multiple realistic tasks and reports a negative result for a separate difficult task. Neither source licenses an unconditional promise that a particular productivity tool will save a specified amount of time in your team.

Consulting firms and vendors can offer useful survey evidence, implementation patterns, and operating advice, but their incentives and sampling choices should be visible. For example, McKinsey’s analysis of AI and software development recommends tracking outcomes beyond adoption, such as defects, release frequency, and customer experience. This is a useful measurement reminder; it is not an independent controlled trial of the workflow described here. Do not treat a consultancy’s cross-company survey as a randomized effect estimate.

The recommendation fails if a supposedly checkable output is not actually checked, if the source record is stale, if reviewers are measured against impossible throughput targets, or if the tool changes faster than the test fixtures. It also fails when “human in the loop” means a person clicks approve without time, authority, or evidence to understand the output. A human gate is a real control only when the reviewer can inspect the basis, reject the result, and choose a safe alternative without penalty for slowing down.

Safe rollout and stop conditions

Begin in a shadow or draft-only mode. The tool may prepare candidate outputs, but the existing process remains authoritative. Capture disagreements and repair effort. The team should know how to turn the feature off, how to recover a task that is already in progress, and how to avoid sending partial or duplicate results if a connection fails. If the product does not expose enough logging to understand how a result was formed, treat that as a limitation of the pilot rather than assuming that hidden detail is harmless.

Move to a bounded pilot only after the test cases, acceptance rubric, data rules, reviewer role, and fallback are agreed. Set a fixed review date and an explicit decision owner. Do not widen the use case while collecting initial evidence; otherwise the test changes midstream. If the vendor updates the model or a connected source changes materially, record the change and decide whether the cases need to be rerun.

Stop or roll back if the system invents a material fact that passes review, omits a high-consequence field, uses disallowed data, routes information to an unexpected location, produces duplicate work, or causes reviewers to bypass the agreed acceptance rule. Stop if the only evidence of benefit is tool usage or user excitement. A low adoption rate may indicate poor fit, missing training, insufficient access, or a genuine preference for the existing workflow; it should not automatically be treated as staff resistance.

The FTC’s actions concerning deceptive AI claims illustrate why procurement teams should ask for a specific, reproducible claim rather than accepting a marketing phrase such as “autonomous,” “hallucination-free,” or “guaranteed productivity.” The exact legal requirements depend on jurisdiction and product representation; this article is not legal advice. As an operational practice, record which claim is being tested and what evidence would falsify it.

Decision matrix

Work characteristic	Recommended starting mode	Evidence to collect	Stop signal
Repetitive transformation; source and format fixed	Draft or supervised transformation	Field-level accuracy, check time, exceptions	Missing or altered required fields
Open-ended research with claims	Research assistant with citations and human verification	Claim-to-source traceability, unsupported-claim rate by reviewed cases	Citation does not support the exact claim
Creative ideation	Co-creation with an explicit final-quality rubric	Variety, usefulness, edit effort, client acceptance	People stop at “good enough” below the agreed standard
High-consequence judgment	Keep decision with qualified human; use AI only for bounded support	Error severity, reviewer ability, policy consistency	Reviewer cannot validate the recommendation
External action or irreversible write	Draft-only until authorization is separately controlled	Exact action, approval, reversibility, audit trail	Tool can act outside the approved scope
Unstructured inputs with no owner	Keep manual while defining the process	Missing context, ownership, source quality	No accountable person can resolve ambiguity

Common failure modes

The demo task is easier than the live task. A polished demonstration may use clean inputs, carefully phrased prompts, and handpicked outputs. Preserve representative and difficult cases before the trial. Include nulls, conflicting records, and the specific edge cases that matter in production. If those cases are excluded, report that exclusion.

The measure stops at generation. A generated draft can look complete while requiring a specialist to reconstruct sources. Measure from input readiness through accepted handoff, including verification and rework. If review labor rises, include it rather than hiding it as “quality assurance overhead.”

A reviewer becomes a rubber stamp. If targets reward output volume alone, people may approve unsupported content to keep pace. Reviewers need access to the source, training on the failure types, time to inspect, and a safe way to reject. Track whether the reviewer actually examined the relevant fields, not merely whether an approval event exists.

A single score hides dangerous defects. A high total can conceal one failed gate. Keep hard constraints separate from quality or convenience scores. A privacy, policy, or authorization failure should block the workflow even if the other dimensions score well.

The model judges its own answer. A language model can be useful for generating a critique, but it is not an independent reference by default. Compare against source records, deterministic checks, accepted examples, or a qualified reviewer. Where the answer is subjective, define the rubric and check agreement among reviewers instead of asserting that the score is objective.

One task’s results become a whole-job claim. If an assistant helps with email drafts, that does not demonstrate that it can manage a role, make a business decision, or improve total departmental output. Keep conclusions at the level actually tested. Broader claims require broader evidence.

Goodharting the pilot. When a team knows that it is being measured on “minutes saved,” it can change behavior in ways that make that metric look better while harming quality. Keep a quality floor, note changes to the process, and include downstream rework. Review whether speed shifted work to another person or stage.

An update silently changes the task. A model, prompt, retrieval index, connector, policy, or source system update may alter outputs. Record the configuration and re-run a small regression set when changes could affect the acceptance criteria. Keep one person responsible for the version and the release note.

Reusable pilot record

Copy this block for each work unit. Keep the answer short enough that an operator can complete it, but detailed enough that another reviewer can reproduce the decision.

Task name and version:
Owner:
Business outcome:
Starting inputs and permitted sources:
Required output fields:
Known-answer or verification method:
Hard gates (data, quality, owner/fallback):
Risk if wrong, late, duplicated, or missing:
Baseline process and measurement window:
Tool/model/configuration tested:
Representative and excluded cases:
Preparation / generation / review / repair / handoff effort:
Quality rubric and acceptance floor:
Incidents, exceptions, and downstream rework:
Decision: keep manual / assist / bounded pilot / stop:
Evidence supporting the decision:
Expiry or retest trigger:

For a pilot that involves customer or employee information, add a separate privacy and security review. Do not put sensitive records in a public model to make the test more realistic. Use approved environments and synthetic or appropriately minimized records where possible. The operational card is not a privacy impact assessment, contract review, or security threat model.

How to read the original asset

The task-fit score is a proposed decision aid, not a measured dataset. It was designed on 5 November 2026 from a review of the sources cited in this article, the Trustly AI productivity-tools guide, and qualitative user discussions. The score’s dimensions and thresholds are recommendations. No production workflow, company, model output, or customer record was sampled. The sample project-status case and test categories are illustrative. We excluded claims about universal time savings, vendor rankings, adoption prevalence, and causal business impact because the evidence reviewed does not establish those for the reader’s environment.

The GSC query that prompted this angle was “ai agents automation business 2026,” which had eight impressions, no clicks, and an average position of 19.4 during 6 October–3 November 2026 for a broad Trustly AI guide. This is a small, site-specific discovery signal, not a measure of market demand and not a ranking promise. We used it to choose an operational question that the broad guide did not settle, then checked current research and discussion evidence independently.

The video “Navigating the Jagged Technological Frontier of Generative AI,” hosted by MIT CISR, is relevant because its page summarizes the distinction between tasks inside and outside current AI capabilities. The video itself is restricted to logged-in members and event attendees, so this review could not inspect its full presentation or transcript. The article relies on the published Organization Science study for empirical claims and treats the video page only as a pointer to the topic, not as independent evidence. See the MIT CISR video page.

Checklist before buying or widening access

• Is the work unit narrow enough that the accepted result can be described in one sentence?
• Are the inputs allowed, current, and available to the tool in a documented way?
• Can a reviewer verify each important output against a source or an agreed rubric?
• Is there a named person who can reject a result and choose the fallback?
• Are irreversible actions and consequential decisions outside the AI’s authority unless separately approved?
• Does the comparison count preparation, review, correction, handoff, and downstream rework?
• Are the baseline and pilot tasks comparable, with exclusions disclosed?
• Are quality floors and stop conditions set before results are visible?
• Are model and workflow versions recorded so a later change can be retested?
• Does the conclusion stay within the task, population, period, and outcome that were actually measured?
• Can the team explain what happens when evidence is missing or conflicting?
• Is there a clear review date or trigger for re-evaluation?

Recommendation

Buy or expand access only after the team can point to a task card, a verification method, an accountable reviewer, and a result that survives measurement of the complete work unit. Use broad productivity guides to shortlist categories, not to infer that a tool is proven for your own process. Start with assistance that produces a draft or bounded transformation; reserve autonomous action for a separate decision with stronger controls.

The evidence is encouraging for some tasks and cautionary for others. The most defensible conclusion is that AI tool value depends on task fit, the quality of the check, and the surrounding work design. A controlled, narrow experiment gives a team more useful information than a vendor leaderboard, an adoption target, or an impressive demo—and it lets the team stop when the evidence says the workflow is not ready.

Sources

• Noy and Zhang, Experimental evidence on the productivity effects of generative artificial intelligence
• Dell’Acqua et al., Navigating the Jagged Technological Frontier, Organization Science
• Asian Development Bank, Better Technology, Worse Motivation
• UK Department for Work and Pensions, Evaluation of the Microsoft 365 Copilot Trial
• Stanford HAI, 2026 AI Index: Economy
• Federal Trade Commission, Keep your AI claims in check
• McKinsey, The AI revolution in software development
• Hacker News discussion: AI hallucinate. Do you ever double check the output?
• MIT CISR, Navigating the Jagged Technological Frontier of Generative AI (video page; full video access restricted)