EVIDENCE ·
What do people actually ask AI to do at work?
OpenAI released an aggregated view of work-related ChatGPT messages. Learn how to read the categories without confusing a popular request with a completed job.
About 3 minutes of explanation · Announcement · Checked 2026-10-28 · Edition 2026-10-28.1
Can you use it?
The company’s public article links to interactive charts and downloadable data. You can read the explanation without an account. Availability of interactive features can depend on your browser and region.
Cost and access details
The published overview is publicly readable. The proposed pencil-and-paper exercise needs no account, subscription or API call. A separate assistant trial, if you choose one, uses your existing plan and its limits.
What this changes for you
A list of real requests can suggest a useful small project. It cannot, by itself, tell you that an assistant completed the work or saved its user time.
A request is the beginning of a job
Imagine a student asking an assistant to prepare a poster for a club. That message tells us someone wanted help with a poster. It does not tell us whether the poster was correct, whether anyone used it, or how long the person spent fixing it. To learn those things, we need information about the result as well as the request.
On 26 October, OpenAI released data about work-related messages from individual ChatGPT plans. The company maps messages to 165 work-activity categories, across nearly 100 countries, over quarters from early 2025 to the third quarter of 2026. An activity is a type of work, such as developing marketing content; it is not a count of finished products.
A person makes a request
An automated system classifies it
The published data aggregates categories
A separate check is needed for the outcome
A simplified reading aid. Follow the article for sources, conditions and limits.
What changed in the reported mix?
For 74 countries represented in every quarter, OpenAI reports that the ten most common activities accounted for an average of 56.0% of each country’s classified work-related messages in the first quarter of 2025 and 51.1% in the third quarter of 2026. Subtract 51.1 from 56.0: that is a change of 4.9 percentage points.
A percentage point measures the difference between two percentages. It is not the same as saying that every activity fell by 4.9%, or that ChatGPT use fell overall. The reported top-ten share became less concentrated within the classified work-message mix. Countries may differ in work, access and adoption; the data alone does not establish why.
Keep the missing messages in mind
The data covers individual plans. Enterprise messages and Codex usage are excluded, so technical and organisational uses may be underrepresented. OpenAI says automated systems classify messages after identifying information is removed; small groups are withheld and statistical noise is added. These steps protect privacy but make the result an estimate rather than a direct reading of every worker’s life.
The company also compares the mapped categories with a US employment-weighted activity mix. Around 43% of that mapped US mix falls in a residual Other group outside the individually reported activities. That makes a direct ranking comparison incomplete. Read the original methodology before treating the figures as a picture of the whole economy.
Make a tiny classification you can inspect
Try four invented requests: “Draft an event poster”, “Compare two public prices”, “Explain a spreadsheet formula” and “Correct a venue in a notice”. Put one request on each row. Choose your own simple labels, such as writing, comparison, learning and correction. If a request could fit two labels, write both possibilities rather than hiding the choice. These four rows are a classroom example, not observations from OpenAI.
Now make a second column for what a useful result would need: the poster needs an approved date, a price comparison needs dated sources, the formula needs a checkable example, and the correction needs an authoritative venue. That turns a vague activity category into a bounded task. You have not measured demand or proved that any assistant solved it.
Turn an observation into a careful next step
Choose one of the four tasks and make a five-minute checklist. For a price comparison, record the product, region, date, price and source link. If a price is missing, leave it unknown. Give an assistant a fictional or public example only if you already have access, then check its output against the supplied material. Keep a failed or incomplete row.
A recurring request could become an idea for a small service, but the next evidence is a real person voluntarily telling you what they need and checking a sample. A chart about messages is a starting clue, not a guarantee that customers exist or that a business will earn money.
What the evidence establishes
This is OpenAI’s company-reported analysis of classified messages from individual ChatGPT plans. It excludes enterprise messages and Codex use. Automated classification, small-group suppression and added statistical noise matter. It does not establish productivity, successful completion or the habits of all AI users.
- OpenAI: ChatGPT’s expanding world of workCompany research dataset announcement · Published 2026-10-26 · Checked 2026-10-28
Try a prediction
The ten most common categories fall from 56.0% to 51.1% of classified work-related messages. Does that prove AI made workers 4.9% more productive?
Show the explanation
No. The reported difference is 4.9 percentage points in the share of classified messages, not a productivity measurement. The data describes requests in individual ChatGPT plans and omits enterprise and Codex messages. You would need suitable outcome and comparison data to evaluate productivity.
One small thing to try
Write four fictional work requests, label their activity and list the checks needed for a usable result. For one request, make a small source checklist and try it with an existing assistant if available. Keep invented inputs separate from observed output; stop before connecting accounts, purchasing access or contacting a customer.
Practice brief, not an observed customer result. Use existing tools and public or invented inputs; stop before spending, connecting an account or publishing.
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