Anthropic Reports Detail Internal Workforce Shifts and Diverging Future Expectations

Anthropic Reports Detail Internal Workforce Shifts and Diverging Future Expectations Anthropic published two reports examining how AI adoption reshapes its internal hiring and productivity, as well as the significant gap between employee and public expectations regarding future AI capabilities. By March 2026, AI agents logged more hours than Anthropic staff, growing to six times the employee headcount by August as individual output increased across coding, sales, and contracts. Anthropic's hiring has shifted toward smaller shares of junior and technical roles over the past year, with human attention becoming the key operational bottleneck. Surveys show 91% of Anthropic employees expect AI to make a Nobel-level discovery by 2030 compared to 39% of the US public, and over half expect AI to do 80% of their work versus 5% of the public. Both the public and Anthropic staff share similar economic models linking higher automation to higher unemployment, but their differing hands-on experiences drive divergent forecasts. Internal Employment and Productivity Dynamics Over the past year, Anthropic reduced its share of junior and technical hires while rapidly expanding agent deployment. By March 2026, AI agents worked more hours than human employees, and by August, agents outnumbered staff six to one. This growth accompanied productivity increases, measured by higher code output per person, more sales closed per representative, and increased contract processing. As individual productivity scaled with AI adoption, human attention emerged as the primary operational constraint, prompting employees to spend more time monitoring agent activity. Expectations Gap Between Staff and the Public Comparing internal employee responses to a representative survey of nearly 11,000 US adults conducted by Morning Consult revealed contrasting views on AI progress. While 39% of US adults expect AI to reach a Nobel-level discovery by 2030, 91% of Anthropic staff anticipate this milestone. Furthermore, more than half of Anthropic employees expect AI to perform at least 80% of their current workload by 2030, a view shared by only 5% of the public. Despite these differences, both groups exhibit a similar economic rationale, predicting higher unemployment when anticipating greater workplace automation.

Anthropic 发布研究：内部 AI 应用进展与公众预期存在显著差距 Anthropic 发布了两份研究报告，分别探讨 AI 对其内部雇佣模式的影响，以及公司员工与美国公众在 AI 影响未来工作与经济方面的预期差距。 Anthropic 招聘中初级岗位与技术岗位的占比下降，AI 智能体的工作时长和数量已大幅超过人类员工。 员工生产力在编程、销售和合同处理等领域显著提高，人类注意力和对智能体的监控成为新的瓶颈。 调查显示 Anthropic 员工对 AI 进展的预期远超公众：91%的员工认为 AI 到 2030 年可实现诺贝尔奖级别发现，而公众仅有 39%。 超过半数的 Anthropic 员工预计 AI 到 2030 年将完成其目前 80% 以上的工作，而持此观点的公众比例仅为 5%。 AI 对 Anthropic 内部组织与生产力的重塑 Anthropic 内部应用 AI 的经验显示，过去一年中初级职位及技术岗位的招聘占比有所减少。到 2026 年 3 月，AI 智能体的工作时长已超过人类员工；至 8 月，智能体数量达到员工总数的六倍。这带动了全方位的生产力提升，不仅人均代码产出增加，销售代表完成的单量和合同处理量也出现增长。随着流程加速，人类注意力逐渐成为主要瓶颈，员工因而将更多时间投入到构建监控智能体工作的机制上。 员工与公众对未来预期的认知差距 Anthropic 将员工调查结果与 Morning Consult 针对近 11,000 名美国成年人的加权调查进行了对比。数据显示，公众虽认同 AI 将迅速发展，但 Anthropic 员工的预期更为激进。例如，91% 的员工预计 AI 能在 2030 年前做出诺奖级科学发现，而公众仅为 39%；超过半数员工预计 AI 能接管自己目前 80% 以上的工作，而公众中这一比例仅有 5%。不过，双方在底层逻辑上高度一致：预计 AI 替代自身工作程度越高的群体，对失业率上升的预期也越相似，说明认知差异本质上源于对 AI 实际能力体验的不同。

We’re sharing two reports: [the first](https://www.anthropic.com/research/employment-at-anthropic) covers how AI is changing employment inside Anthropic, and [the second](https://www.anthropic.com/research/ai-expectations-gap) looks at the gap between how the public and Anthropic employees expect AI to affect work and the economy. Anthropic staff and US workers don’t seem to have different theories about how AI affects jobs. Instead, they have different experiences of what AI capabilities are. Those different experiences seem closely related to perceptions about what will happen to work in the future. Anthropic is a company at the frontier of AI adoption, and that adoption has changed the way we hire. We find that over the past year, we're hiring for a smaller share of junior positions, and a smaller share of our employees work in technical roles. At the same time, the rate of hours worked by AI agents has shot up. By March 2026, agents worked more hours than employees. By August, there were six times more agents than employees at Anthropic, and productivity increased. That’s led to Anthropic producing more code per person, but that’s a notoriously imperfect measure. We see signs of increased productivity elsewhere: we’ve closed more sales per sales representative, and processed more contracts. When our researchers follow the same employee over time, they find the employee’s output rises as their adoption of AI tools increases. All this acceleration means that, increasingly, the bottleneck is human attention. As a result, employees spend more time building ways to better monitor work their agents are doing. Anthropic could be a leading indicator of how AI will shift work organization, hiring, and labor demand across the economy. An AI lab is different from more traditional businesses in how it adopts AI into its work, but [research findings](https://www.anthropic.com/research/how-ai-is-transforming-work-at-anthropic) about our own workplace may still be instructive about broader transformations, including dynamics for future AI-native companies. Because Anthropic employees experience AI’s effect on work so differently, it stands to reason that they might have different predictions about AI’s effect on the *future* of work. That’s what our second report investigates. We surveyed Anthropic employees, and compared them to an August survey of almost 11,000 US adults, fielded by Morning Consult and weighted to be representative of the adult population. The public expects AI capabilities to advance rapidly in the coming years; Anthropic staff tend to expect even faster progress. There are a number of questions with big gaps between staff and the public. For example, 39% of US adults—and 91% of staff—expect AI will be able to make a Nobel-level discovery by 2030. More than half of Anthropic staff expect AI to be able to do at least 80% of their current work by 2030; only 5% of Americans expect AI to be that capable. That said, there’s considerable variation in both groups: 2 out of 10 Anthropic staff have expectations similar to those of the typical US adult. And Anthropic staff and the public also agree on some key questions. In each bucket, people who expect AI to do comparably more of their own work anticipate similar increases in unemployment. In other words, both the public and Anthropic staff have similar economic models of how AI affects the labor market, but ultimately their personal experiences with AI lead them to different expectations for the economy of the future.