ChatGPT 不断扩大的工作世界
OpenAI经济研究发布数据供研究人员更深入地探索AI和劳工的问题。
今天，我们正在发布OpenAI信号数据，该数据探索了近100个国家的165个工作活动，揭示消费者如何将ChatGPT用于工作。 这些数据为研究人员提供了研究AI在经济中作用的新途径，提供了人们给ChatGPT带来的工作活动的新信息——从编写教学材料和开发营销内容到编程和分析商业数据。
研究人员可以利用这些数据探索ChatGPT用于工作在各国有何不同，跟踪随时间的变化，并将这些数据与其他经济指标相匹配。 该发布将ChatGPT的使用与165个工作活动类别联系起来，称为中间工作活动(IWAs). 职业介绍所是不同职业工作者常用的任务类别。 数据为季度数据，从2025年第一季度到2026年第三季度。
我们的新数据利用了O*NET，这是一个标准框架，将特定职业的任务分为职业间共享的更广泛的工作活动类别。 使用共同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。
以下互动图可用于探讨特定国家内随时间推移开展的这些工作活动，从广泛的活动组转向更具体的活动。 在人们如何使用ChatGPT工作方面，各国的数据显示差异很大。
将这些数据与国际水协会连接表明，与工作有关的使用不再集中在最常见的活动中。 这意味着各国往往会随着时间的推移专门从事其工作，而不同国家则专门从事不同类型的工作。 这些差异可能反映各国所做工作的类型不同或获得人工智能工具的机会不同。 探索这些问题是许多研究者可以用这个新数据来追求的一个例子。
将ChatGPT使用数据与IWAS连接，使研究人员能够将ChatGPT信息与全美国工作范围内开展的活动进行比较。 下面的配对可视化将人们如何使用ChatGPT与全美工作的活动进行比较。 这种比较是显示，人们如何使用ChatGPT进行工作，以及美国经济中的实际工作的不同。 这两种分布差别很大，主要是因为美国按就业加权计算的职业组合中约有43%属于其他国际职业协会，不属于个别报告的类别。 这些活动包括移动材料、操作车辆以及保存健康或医疗记录等活动。 这些省略的类别没有达到我们在最初的美国ChatGPT样本中一致代表的门槛。
这个发布基于OpenAI信号，我们不断努力分享人们如何使用AI的证据。 数据涵盖单个ChatGPT计划中与工作相关的信息，不包括企业信息或代码的使用。 我们邀请研究人员探索数据，将其与其他来源连接起来，并调查这些模式对工人和企业的意义。
为了保护用户隐私，研究人员不阅读个人谈话。 自动系统在识别信息后对电文进行分类。 我们公布汇总统计数据，为小群体保留结果，并增加统计噪音以保护个人信息。
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ChatGPT’s expanding world of work
OpenAI Economic Research releases data for researchers to explore questions on AI and labor more deeply.
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.
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.
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.
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.
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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