What 81,000 people told us about the economics of AI
关于 AI 经济学，81,000 人告诉了我们什么
作者: @AnthropicAI
原文链接: https://anthropic.com/research/81k-economics
Economic Research 经济研究
What 81,000 people told us about the economics of AI
关于 AI 经济学，81,000 人告诉了我们什么
Apr 22, 2026 2026 年 4 月 22 日
Key findings:
核心发现：
- _Our recent survey of 81,000 Claude users shows that people who work in roles that are more exposed to AI have more concerns about AI-driven job displacement. These concerns are also higher among early-career respondents._
- _我们近期对 81,000 名 Claude 用户进行的调查显示，在 AI 暴露度较高的岗位上工作的人，对 AI 驱动的职业取代表现出更多的担忧。这种担忧在处于职业生涯早期的受访者中也更为显著。_
- _Those in the highest- and lowest-paid occupations report the largest productivity gains, most commonly from increases in scope (doing new tasks)._
- _薪资最高和最低的职业群体报告的生产力提升幅度最大，这通常源于工作范畴的扩大（即能够执行新的任务）。_
- _Respondents experiencing the largest speedups from AI express higher concern about job displacement._
- _那些经历过 AI 带来最大效率提升的受访者，对职业取代的担忧程度更高。_
In order to inform the public about the economic changes we’re observing with AI, our Economic Index shares what work Claude is being asked to do, and in which jobs Claude is doing the largest share of tasks. To date, however, we’ve lacked information on how these usage patterns map onto people’s thoughts and impressions of AI. 为了让公众了解我们观察到的 AI 带来的经济变革，我们的经济指数 (Economic Index) 分享了 Claude 被要求执行的工作内容，以及在哪些岗位中 Claude 承担的任务比例最高。然而到目前为止，我们还缺乏关于这些使用模式如何映射到人们对 AI 的看法和印象的信息。
Our recent survey study with 81,000 Claude users provides a way to connect people’s economic concerns with what we’ve quantified in Claude traffic. 我们近期对 81,000 名 Claude 用户进行的调查研究 提供了一种方法，将人们的经济担忧与我们在 Claude 流量中量化的数据联系起来。
The survey asked people about their visions and fears around advances in AI. Many of the thoughts that people shared touched on economic topics. We learned that many people fear job displacement—though they also feel more productive and empowered at work. In some cases, AI has enabled them to start businesses, or given them time for more important things; in others, AI feels stifling, or imposed on them by their employers. 这项调查询问了人们对 AI 进步的愿景和恐惧。人们分享的许多观点都涉及经济话题。我们了解到，许多人担心职业取代——尽管他们也感到在工作中更具生产力和获得更多赋能。在某些情况下，AI 让他们能够创业，或者给了他们时间去做更重要的事情；而在另一些情况下，AI 让他们感到压抑，或者觉得是雇主强加给他们的。
The survey’s results provide initial evidence that observed exposure (our measure of AI displacement risk) is correlated with economic concern around AI. People in highly exposed occupations—as defined by the tasks Claude is observed performing—were more nervous about economic displacement. This is consistent with people being broadly aware of AI’s diffusion and potential impacts. We expand on our findings below. 调查结果提供了初步证据，表明观察到的暴露度 (observed exposure)（我们衡量 AI 取代风险的指标）与围绕 AI 的经济担忧相关。在观察到 Claude 执行任务比例较高的职业中，人们对经济性取代更加感到紧张。这与人们普遍意识到 AI 的普及及其潜在影响的情况相符。我们在下文中详细阐述了我们的发现。
Who worries about job displacement?
谁在担心职业取代？
_“Well like anyone who has a white collar job these days I'm 100% concerned, pretty much 24/7 concerned about losing my job eventually to A.I.”—Software engineer.1_ _“就像现在任何从事白领工作的人一样，我百分之百担心，几乎是全天候担心自己的工作最终会被 AI 取代。”——软件工程师。1_
One fifth of the respondents in our survey voiced concern about economic displacement. Some worried about this in the abstract: one software developer cautioned about “the possibility of AI in its current state being used to replace junior positions.” Others lamented that their jobs, or aspects of their jobs, were being automated away. One market researcher said, “In terms of improving my capability, it's no doubt. \[B\]ut in the future AI may replace my work.” In some jobs, people felt it made their work harder. One software developer observed that “when AI arrived, the project managers started giving harder and harder tickets and bugs to solve.” 我们调查中五分之一的受访者表达了对经济取代的担忧。有些人表达的是抽象的担忧：一位软件开发人员警告说，“目前的 AI 有可能被用来取代初级职位。”另一些人则哀叹他们的工作或工作的某些方面正在被自动化。一位市场研究员说：“在提高我的能力方面，这是毋庸置疑的。但未来 AI 可能会取代我的工作。”在某些岗位上，人们觉得 AI 让工作变得更难了。一位软件开发人员观察到，“当 AI 出现后，项目经理开始分配越来越难的任务和 Bug 来解决。”
Throughout this report, we use Claude-powered classifiers to infer people’s attributes and sentiments from their responses. For example, many participants mention their line of work in passing or give informative details about their work life, which allows us to infer their occupation. Similarly, we quantify concerns about job loss by prompting Claude to identify and interpret direct quotes in which respondents indicate that their own role is at risk of AI-driven displacement. We give example prompts in the Appendix. 在本报告中，我们使用由 Claude 驱动的分类器从受访者的回答中推断其属性和情绪。例如，许多参与者顺便提到了他们的工作领域，或提供了有关工作生活的详细信息，这使我们能够推断出他们的职业。同样，我们通过提示 Claude 识别并解释受访者的直接引语（其中受访者表示自己的角色面临 AI 驱动的取代风险），来量化对失业的担忧。我们在附录中给出了提示词示例。
Respondents’ perceived threat from AI was correlated with our own measure of observed exposure, which reflects the percentage of a job’s tasks for which Claude is used. A respondent was more concerned about AI when our observed exposure measure for that respondent was higher. Elementary school teachers were less worried about their own displacement than software engineers, for example, consistent with the fact that Claude usage skews toward coding tasks. 受访者感受到的 AI 威胁与其我们自己衡量的观察暴露度呈正相关，该指标反映了 Claude 在某项工作任务中所占的使用比例。当受访者的观察暴露度衡量指标较高时，他们对 AI 的担忧也更多。例如，小学教师对自身被取代的担忧程度低于软件工程师，这与 Claude 的使用偏向于编程任务的事实相一致。
We show this in Figure 1 below. The y-axis is the percentage of respondents in a given occupation who said that AI is already replacing their role or is likely to do so soon. The x-axis is observed exposure. The plot shows that, on average, people in more exposed occupations tended to express more concern about their jobs being automated away. For every 10-percentage-point increase in exposure, perceived job threat increased by 1.3 percentage points. People in the top 25% of exposure mentioned the worry three times as often as those in the bottom 25%. 我们在下面的图 1 中展示了这一点。y 轴是特定职业中表示 AI 已经开始取代或可能很快取代其角色的受访者百分比。x 轴是观察到的暴露度。该图显示，平均而言，处于较高暴露度职业的人往往对工作被自动化表现出更多的担忧。暴露度每增加 10 个百分点，感知到的职业威胁就会增加 1.3 个百分点。暴露度排名前 25% 的人提到这种担忧的频率是后 25% 的人的三倍。
Figure 1: Perceived job threat from AI and Observed Exposure. Percentage of respondents indicating some job threat from AI vs. the Observed Exposure measure from Massenkoff and McCrory (2026). A respondent was coded as indicating job threat if they said their role was already being replaced or substantially reduced, or that such changes were likely in the near term (coded using Claude). The green line shows a simple linear fit. 图 1：感知到的 AI 职业威胁与观察到的暴露度。 表示受到 AI 职业威胁的受访者百分比与来自 Massenkoff and McCrory (2026) 的观察暴露度衡量指标的对比。如果受访者表示他们的角色已经正在被取代或大幅缩减，或者这种变化在短期内很可能发生（使用 Claude 编码），则被编码为表示受到职业威胁。绿线表示简单的线性拟合。
Another important worker characteristic is career stage. In previous research, we reported tentative signs of a slowdown in the hiring of recent graduates and early-career workers in the United States. For about half of respondents in this survey, we were able to infer career stage from their answers.2 We found that early-career respondents were much more likely to express concern about job displacement than senior workers. 另一个重要的劳动力特征是职业阶段。在之前的研究中，我们报告了美国应届毕业生和处于职业生涯早期的工作者招聘放缓的初步迹象。在本次调查中，对于约一半的受访者，我们能够从他们的回答中推断出职业阶段。2 我们发现，职业生涯早期的受访者比资深工作者更有可能对职业取代表示担忧。
Figure 2: Concern about economic displacement by career stage. Percentage of respondents indicating some job threat from AI, by career stage. Both fields are inferred from free-form responses using Claude-powered classifiers. 图 2：各职业阶段对经济性取代的担忧。 按职业阶段划分，表示受到 AI 职业威胁的受访者百分比。这两个字段都是使用由 Claude 驱动的分类器从自由格式的回答中推断出来的。
Who benefits from AI?
谁从 AI 中获益？
Using Claude to assess the survey responses, we rated the extent of people’s self-reported productivity gains from AI on a 1–7 scale, where 1 is “less productive,” 2 is “no change,” and each subsequent level denotes a larger gain. Responses that scored 7 included testimonials like, “It used to take months to make the website I \[made\] in 4-5 days”; Claude gave a 5 to statements like, “What might have taken four hours was accomplished in half the time,” and a 2 to ones like, “Personally, I had AI help me fix code on a website. But it took multiple passes to get the result I was after.”3 利用 Claude 对调查回答进行评估，我们将人们自述的 AI 生产力提升程度按 1-7 级进行评分，其中 1 代表“生产力降低”，2 代表“无变化”，随后的每一级都代表更大的提升。评分为 7 的回答包括诸如“我用 4-5 天做出的网站，以前要花好几个月”之类的感言；Claude 对“原本需要四小时的事情，现在一半的时间就完成了”之类的陈述评分为 5，而对“就个人而言，我让 AI 帮我修复网站代码，但为了得到我想要的结果，反复试了好几次”之类的陈述评分为 2。3
Overall, people reported meaningful productivity gains on average. The mean productivity rating was 5.1, corresponding to “substantially more productive.” Our respondents were, of course, active Claude users who were willing to take a survey. This could make them more likely to report productivity benefits than the average user. Some 3% reported negative or neutral impacts, and 42% did not give a clear indication on productivity. 总体而言，人们报告的平均生产力提升是显著的。平均生产力评分为 5.1，对应于“生产力大幅提高”。当然，我们的受访者本身就是活跃的 Claude 用户，且愿意参加调查。这可能使他们比普通用户更有可能报告生产力收益。约 3% 的人报告了负面或中性的影响，42% 的人没有对生产力给出明确指示。
This splits somewhat across income lines. The left panel in Figure 3 shows that people in high-paying jobs, like software developers, conveyed the largest productivity gains from AI. This result is not driven only by coding; it holds when we leave out computer and math occupations. It echoes a previous Economic Index finding that also favored higher-paid workers: in tasks requiring greater levels of education, Claude tended to reduce the time taken to complete a task (relative to doing it without AI) by a higher percentage. 这种情况在收入水平上有所分化。图 3 的左面板显示，从事高薪工作的人（如软件开发人员）通过 AI 获得了最大的生产力提升。这一结果不仅仅是由编程驱动的；当排除计算机和数学职业时，结论依然成立。这呼应了之前经济指数的一项发现，该发现也倾向于高薪工作者：在需要更高受教育程度的任务中，Claude 往往能更大幅度地减少完成任务所需的时间（相对于不使用 AI 而言）。
Some of the lowest-paid workers describe high productivity gains as well. This included a customer service representative using “AI to save me a lot of time with creating a response based on another one.” And in some cases, people in low-wage jobs were using AI on technical side projects. One delivery driver, for example, was using Claude to start an e-commerce business, and a landscaper was building a music application. 一些收入最低的工人也描述了很高的生产力提升。这包括一位客服代表使用“AI 帮我节省了大量时间，根据另一条回复生成新的回复。”在某些情况下，低薪岗位的个人正在利用 AI 开展技术性的副业项目。例如，一名快递司机正在利用 Claude 创办一家电子商务公司，一名园艺师正在开发一个音乐应用程序。
Figure 3: Inferred productivity gain by occupation. The left panel shows the mean inferred productivity benefit from AI (inferred using a Claude-powered classifier) by quartile of occupational median wage from the BLS. The right panel shows the same outcome, split by major occupational group. Error bars show 95% confidence intervals. 图 3：按职业推断的生产力提升。 左面板显示了按劳工统计局 (BLS) 职业薪资中位数四分位数划分的平均推断 AI 生产力收益（使用由 Claude 驱动的分类器推断）。右面板显示了相同的结果，但按主要职业组划分。误差线显示 95% 的置信区间。
We look at this in more detail in the right panel of Figure 3, showing the inferred productivity gain by major occupational group. At the top are management occupations. These respondents are mostly entrepreneurs using Claude to build a business.4 The next highest category is computer and math, which includes software developers. The two groups exhibiting the mildest productivity improvements were workers in scientific and legal professions. Some lawyers worried about AI’s ability to follow precise instructions. For example: “I have given very specific rules about what is where, how to read a legal document, what I want it to do… but it diverges every time.” 我们在图 3 的右面板中更详细地查看了这一点，展示了按主要职业类别划分的推断生产力提升。排在最前面的是管理类职业。这些受访者大多是使用 Claude 创业的企业家。4 下一个最高的类别是计算机和数学类，其中包括软件开发人员。生产力提升最不明显的两个群体是科学和法律专业的从业者。一些律师担心 AI 遵循精确指令的能力。例如：“我已经给出了关于什么在什么地方、如何阅读法律文件、我想要它做什么的非常具体的规则……但它每次都会偏离。”
A key question as AI diffuses through the economy is where the benefits will accrue—to workers, their managers, consumers, or corporations. Respondents indicated the recipient of these gains in about a quarter of interviews. Overall, most of these people cited benefits to themselves, through faster tasks, expanded scope, and freed-up time.5 But 10% of respondents who named a recipient said that employers or clients were asking for and getting more work. A smaller share mentioned benefits to AI companies, and an even smaller share said that AI would be a net negative. This depended on career stage: only 60% of early-career workers indicated that they personally benefited from AI, compared to 80% of senior professionals. 随着 AI 在经济中的普及，一个关键问题是收益将流向何处——是流向员工、他们的经理、消费者还是企业。在约四分之一的采访中，受访者指出了这些收益的接收者。总体而言，这些人中的大多数表示自己受益，因为任务完成更快、范畴扩大以及时间得到释放。5 但在指明了受益者的受访者中，10% 的人表示雇主或客户正在要求并获得更多的工作量。更少的一部分人提到了 AI 公司的受益，还有极少数人表示 AI 整体上是负面的。这取决于职业阶段：只有 60% 的早期职业工作者表示他们个人从 AI 中获益，而资深专业人士的这一比例为 80%。
Figure 4: Where does the surplus from AI productivity go? Among respondents who named a beneficiary of their AI productivity gains, the share identifying each destination. 图 4：AI 生产力带来的盈余流向了哪里？ 在指明了其 AI 生产力收益受益者的受访者中，指向各个去处的比例。