What can one million sampled ChatGPT messages a month reveal about work?
OpenAI's new dataset maps consumer messages to 165 work activities across nearly 100 countries. It shows a broader mix over time, but measures classified messages—not workers, jobs, productivity or outcomes.
The short version
- What happened
- Signals v3.0 classifies a global sample of 1,000,000 consumer ChatGPT messages in each month from July 2024 through September 2026, from accounts with a declared age of at least 18.
- Who it affects
- Workers and educators may benefit from more transparent evidence about actual AI use, but policy decisions still need representative surveys and measured outcomes beyond one platform's prompts.
- What remains unproven
- Provider-specific consumer sample
Keep in mind
Provider-specific consumer sample
Excludes Enterprise and Codex
Messages are not unique users or tasks
Automated classifications can vary by language and context
Differential privacy adds noise
No productivity, wage, quality or employment outcome
Source analysis · 3 linked sources. Primary analysed source date: 26 October 2026.
Read the full report · 4 min The evidence, methods, implications and limitations
The dataset opens a useful window—not a labour census
OpenAI has released quarterly data linking work-related consumer ChatGPT messages to 165 O*NET intermediate work activities across nearly 100 countries. The methods document says every monthly global sample contains 1,000,000 messages from accounts with a declared age of at least 18, covering July 2024 to September 2026. Enterprise messages and Codex use are excluded, as are deleted accounts and people who did not allow training use.
That denominator makes Signals unusually large and repeatable, but the unit is a message, not a person or completed task. One worker can send many prompts; another may never use ChatGPT or may use a competing tool. The sample cannot estimate the share of workers using AI, jobs changed, time saved, pay, output quality or displacement. It describes what selected consumer traffic looked like after classification.[1][2][3]
Work prompts became less concentrated in the top activities
Among 74 countries observed in every quarter, the ten most common activities accounted for an average 51.1% of each country's classified work messages in the third quarter of 2026, down from 56.0% in the first quarter of 2025. OpenAI interprets the spread as countries becoming more specialized in the work they bring to ChatGPT. Different occupational mixes or differences in access could also produce the pattern.
For teachers, analysts and managers, the dataset can support better questions: which activities appear often, which rise or fall, and where a local survey or controlled evaluation is needed. It cannot tell whether a message helped. O*NET is a US occupational framework applied to global traffic, so a label may travel imperfectly across languages, informal work, public services and occupations organized differently elsewhere.[1]
The distinction that matters
From private messages to public statistics
Each stage protects privacy or enables comparison, while also narrowing what the numbers can mean.
Sample
1m eligible consumer messages each month
Large provider-specific traffic view
Classify
Automated work and O*NET labels
Comparable activity shares with label error
Protect
Withholding, weighting and privacy noise
Safer aggregates with added uncertainty
Privacy protection and automated labels both shape the result
Researchers did not read raw message text. OpenAI says automated language-model classifiers operated on de-identified messages after personal information was removed. Daily weights adjust for changes in logging, small groups are withheld, and Gaussian noise provides message-level differential privacy before aggregated statistics are published. Fine-grained country-and-activity cells therefore have more sampling and privacy variation than global totals.
The categories—including work-relatedness, asking, doing and expressing—are inferred labels rather than user-reported facts. OpenAI produced the dataset from its own service and has a commercial interest in showing breadth of use. Transparent classifier validation by language, uncertainty intervals and outside replication would strengthen it. For now, Signals is a valuable public research input, provided analysts resist turning message shares into claims about worker benefit or economic transformation.[2][3]