Anthropic: Working at the AI Frontier Shapes Expectations for the Future

TL;DR

Anthropic published two reports showing that its internal AI adoption has changed hiring and productivity while creating a large gap between Anthropic employees' and the US public's expectations for AI's future economic impact. The findings matter because Anthropic presents its own workplace as a possible leading indicator of how AI-native companies may organize work, hire, and shape labor demand.

What Anthropic announced

Anthropic released two reports. The first covers how AI is changing employment inside Anthropic. The second examines the gap between how the public and Anthropic employees expect AI to affect work and the economy. The central claim is that Anthropic staff and US workers do not seem to have different theories about how AI affects jobs. Instead, they have different experiences of what AI capabilities are, and those experiences are closely related to perceptions about what will happen to work in the future.

Inside Anthropic: AI adoption is changing hiring and output

Anthropic reports that over the past year it has been hiring for a smaller share of junior positions, and a smaller share of its 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.

Anthropic says this has led to more code produced per person, though it calls that a notoriously imperfect measure. It also sees signs of increased productivity elsewhere: more sales closed per sales representative, and more contracts processed. When Anthropic researchers follow the same employee over time, they find that the employee's output rises as their adoption of AI tools increases.

Anthropic states that this acceleration increasingly makes human attention the bottleneck. As a result, employees spend more time building ways to better monitor the work their agents are doing.

Why Anthropic's internal data may be a leading indicator

Anthropic says it could be a leading indicator of how AI will shift work organization, hiring, and labor demand across the economy. It acknowledges that an AI lab is different from more traditional businesses in how it adopts AI into its work. Even so, Anthropic argues that research findings about its own workplace may still be instructive about broader transformations, including dynamics for future AI-native companies.

The expectations gap: Anthropic staff versus the US public

Anthropic surveyed its employees and compared them to an August survey of almost 11,000 US adults. That survey was 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. Several questions show large gaps between staff and the public. For example, 39% of US adults and 91% of Anthropic 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, while only 5% of Americans expect AI to be that capable.

There is considerable variation in both groups. Two out of ten Anthropic staff have expectations similar to those of the typical US adult.

Where staff and the public agree

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. Their personal experiences with AI lead them to different expectations for the economy of the future.

Related Anthropic research

The post links to related Anthropic research. One item describes giving Claude plain-language specifications for eight quantum circuits and an automated evaluator to check and score its designs; Anthropic says Claude improved on the best published circuits, in some cases by an order of magnitude or more. Another item says Claude revealed the origins of the ECDSA curve. A third item, The AI expectations gap, presents new survey evidence on disagreement about AI's future impact on the economy.