Anthropic用户研究 - 8.1万人AI使用需求分析
What 81,000 people told us about the economics of AI
8.1万人告诉你AI的经济影响
作者: @AnthropicAI
来源: Anthropic Research
发布日期: 2026年4月22日
核心发现 | Key Findings
| English | 中文 | |---|---| | Our survey of 81,000 Claude users shows people in roles more exposed to AI have greater concerns about job displacement. | 我们对8.1万名Claude用户的调查显示，更易受AI影响的岗位工作者对工作被取代的担忧更为强烈。| | Early-career respondents showed higher concern about job displacement than senior workers. | 职业生涯早期的受访者比资深员工更担心工作被取代。| | Those in the highest- and lowest-paid occupations report the largest productivity gains. | 收入最高和最低的职业群体报告的生产力提升幅度最大。| | Most common productivity gain is expanded scope (doing new tasks), cited by 48% of users. | 最常见的生产力提升是工作范围扩展（承担新任务），48%的用户提及。| | Respondents experiencing the largest speedups also express higher concern about job displacement. | 感受到最大效率提升的受访者同时也更担忧工作被取代。|
完整文章（英文原文）
Economic Research
What 81,000 people told us about the economics of AI
Apr 22, 2026
Key findings:
- _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._
- _Those in the highest- and lowest-paid occupations report the largest productivity gains, most commonly from increases in scope (doing new tasks)._
- _Respondents experiencing the largest speedups from AI express higher concern about job displacement._
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.
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.
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.
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.
Who worries about job displacement?
_“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_
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.”
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.
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.
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%.
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.
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.
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.
Who benefits from AI?
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
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.
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.
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.
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.
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.”
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.
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.
Scope and speed
Respondents also shared where they experienced gains in productivity. We separate this into scope, speed, qua
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