OpenAI Releases ChatGPT Work Usage Data Across 100 Countries
OpenAI released the OpenAI Signals dataset, detailing how ChatGPT is used for work across nearly 100 countries. Mapped to 165 Intermediate Work Activities (IWAs) via the O*NET framework, the data covers tasks like programming and marketing, offering researchers a quantitative basis to study AI's economic impact.
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Today, we are releasing OpenAI Signals data that explores 165 work activities across nearly 100 countries, revealing how consumers use ChatGPT for work. This data gives researchers a new way to study AI’s role in the economy, with new information on the work activities people bring to ChatGPT—from preparing instructional materials and developing marketing content to programming and analyzing business data.
Researchers can use this data to explore how ChatGPT use for work differs across countries, track changes over time, and match this data to other economic indicators. The release connects ChatGPT usage to 165 work activity categories called Intermediate Work Activities (IWAs). IWAs are commonly-used categories of tasks for the kinds of work people do across occupations. The data is represented quarterly, spanning a period from the first quarter of 2025 through the third quarter of 2026.
Understanding how people use AI for work at a deeper level
Our new data draws on O*NET, a standardized framework that groups occupation-specific tasks into broader categories of work activities shared across occupations. Using a common framework helps researchers compare ChatGPT use with other types of occupational and employment data.
The interactive chart below can be used to explore these work activities within a specific country over time, moving from broad activity groups to more specific activities. The data shows wide variation across countries in how people use ChatGPT for work.
Work activities in ChatGPT
% of work-related messages
Q1 2025
Work activities
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Explore the work people bring to ChatGPT. Select an activity group to see the tasks within it, see how those change over time, or choose a country to explore its mix of work-related messages.
Connecting this data to IWAs revealed that work-related use became less concentrated in the most common activities. That means that countries tended to specialize in their work over time, and different countries specialized in different types of work. These differences might reflect variation in the kind of work done across countries or varying access to AI tools. Exploring these questions is one example of many that researchers can pursue with this new data.
Across 74 countries observed in every quarter, the ten most common activities represented an average of 51.1% of each country’s classified work messages in Q3 2026, down from 56.0% in Q1 2025.
How researchers can use this data
Connecting ChatGPT usage data with IWAs lets researchers compare ChatGPT messages with activities performed across U.S. jobs. The paired visualizations below compare how people use ChatGPT with the activities performed across U.S. jobs. This comparison shows the difference in how people use ChatGPT for work and the actual work being done in the U.S. economy. The two distributions differ substantially, largely because about 43% of the employment-weighted task mix for mapped U.S. occupations falls into Other IWAs, outside the individually reported categories. These include activities such as moving materials, operating vehicles, and maintaining health or medical records. These omitted categories did not meet our threshold for consistent representation in the original U.S. ChatGPT sample.
Select an activity to see how much it appears in ChatGPT usage versus the overall U.S. economy.
Explore the data
This release builds on OpenAI Signals, our ongoing effort to share evidence about how people use AI. The data covers work-related messages on individual ChatGPT plans and does not include enterprise messages or Codex use. We invite researchers to explore the data, connect it with other sources, and investigate what these patterns mean for workers and businesses.
To protect users’ privacy, researchers do not read individual conversations. Automated systems classify messages after identifying information is removed. We publish aggregated statistics, withhold results for small groups, and add statistical noise to protect individual messages.