今天，我们发布OpenAI Signals数据，探索近100个国家的165种工作活动，揭示消费者如何使用ChatGPT进行工作。这些数据为研究人员提供了一种研究AI在经济中作用的新方式，并提供了关于人们向ChatGPT提出的工作活动的新信息——从准备教学材料和开发营销内容，到编程和分析业务数据。
研究人员可以利用这些数据探索ChatGPT在工作中的使用在不同国家之间有何差异、追踪随时间的变化，并将这些数据与其他经济指标相匹配。此次发布将ChatGPT的使用与165个工作活动类别相连接，这些类别被称为中间工作活动（Intermediate Work Activities，IWA）。IWA是人们在不同职业中所从事工作的常用任务类别。数据按季度呈现，时间跨度从2025年第一季度到2026年第三季度。
更深入地理解人们如何将AI用于工作
我们的新数据借鉴了O*NET，这是一个标准化框架，它将特定职业的任务归入跨职业共享的更广泛工作活动类别。使用一个通用框架有助于研究人员将ChatGPT的使用与其他类型的职业和就业数据进行比较。
下方的交互式图表可用于探索特定国家随时间变化的工作活动，从广泛的活动组深入到更具体的活动。数据显示，各国在人们如何使用ChatGPT进行工作方面存在很大差异。
探索人们向ChatGPT提出的工作。选择一个活动组以查看其中的任务，了解这些任务如何随时间变化，或选择一个国家以探索其工作相关消息的构成。
将这些数据与IWA相连接后发现，工作相关使用在最常见活动上的集中度有所下降。这意味着各国随着时间推移倾向于在工作上专业化，而不同国家专精于不同类型的工作。这些差异可能反映了各国所从事工作类型的差异，或对AI工具获取程度的不同。探索这些问题只是研究人员可以利用这些新数据追求的众多问题中的一个例子。
在每个季度都观察到数据的74个国家中，截至2026年第三季度，十种最常见活动平均占各国已分类工作消息的51.1%，低于2025年第一季度的56.0%。
研究人员如何使用这些数据
将ChatGPT使用数据与IWA相连接，使研究人员能够将ChatGPT消息与美国各类工作中执行的活动进行比较。下方的配对可视化图表比较了人们如何使用ChatGPT与美国各类工作中执行的活动。这一比较展示了人们将ChatGPT用于工作的方式与美国经济中实际完成的工作之间的差异。这两种分布差异很大，主要因为在美国被映射职业中，按就业加权的任务组合约有43%属于“其他IWA”，不在单独报告的类别之内。这些包括搬运材料、驾驶车辆以及维护健康或医疗记录等活动。这些被省略的类别未达到我们在原始美国ChatGPT样本中一致代表性的阈值。
选择一个活动，查看它在ChatGPT使用中相对于美国整体经济的出现频率。
探索数据
此次发布建立在OpenAI Signals的基础之上，这是我们持续努力分享有关人们如何使用AI的证据的一部分。数据涵盖个人ChatGPT套餐中的工作相关消息，不包括企业消息或Codex使用。我们邀请研究人员探索这些数据，将其与其他来源相连接，并研究这些模式对工人和企业意味着什么。
为保护用户隐私，研究人员不会阅读个人对话。自动化系统在移除身份识别信息后对消息进行分类。我们发布聚合统计数据，对小规模群体的结果不予公布，并添加统计噪声以保护个人消息。
引用
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.
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.
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