How is AI changing the way we work? Our previous research on AI’s economic impacts looked at the labor market as a whole, covering a variety of different jobs. But what if we studied some of the earliest adopters of AI technology in more detail—namely, us?
Turning the lens inward, in August 2025 we surveyed 132 Anthropic engineers and researchers, conducted 53 in-depth qualitative interviews, and studied internal Claude Code usage data to find out how AI use is changing things at Anthropic. We find that AI use is radically changing the nature of work for software developers, generating both hope and concern.
Our research reveals a workplace facing significant transformations: Engineers are getting a lot more done, becoming more “full-stack” (able to succeed at tasks beyond their normal expertise), accelerating their learning and iteration speed, and tackling previously-neglected tasks. This expansion in breadth also has people wondering about the trade-offs—some worry that this could mean losing deeper technical competence, or becoming less able to effectively supervise Claude’s outputs, while others embrace the opportunity to think more expansively and at a higher level. Some found that more AI collaboration meant they collaborated less with colleagues; some wondered if they might eventually automate themselves out of a job.
We recognize that studying AI’s impact at a company building AI means representing a privileged position—our engineers have early access to cutting-edge tools, work in a relatively stable field, and are themselves contributing to the AI transformation affecting other industries. Despite this, we felt it was on balance useful to research and publish these findings, because what’s happening inside Anthropic for engineers may still be an instructive harbinger of broader societal transformation. Our findings imply some challenges and considerations that may warrant early attention across sectors (though see the Limitations section in the Appendix for caveats). At the time this data was collected, Claude Sonnet 4 and Claude Opus 4 were the most capable models available, and capabilities have continued to advance.
More capable AI brings productivity benefits, but it also raises questions about maintaining technical expertise, preserving meaningful collaboration, and preparing for an uncertain future that may require new approaches to learning, mentorship, and career development in an AI-augmented workplace. We discuss some initial steps we’re taking to explore these questions internally in the Looking Forward section below. We also explored potential policy responses in our recent blog post on ideas for AI-related economic policy.
Key findings
In this section, we briefly summarize the findings from our survey, interviews, and Claude Code data. We provide detailed findings, methods, and caveats in the subsequent sections below.
Survey data
Anthropic engineers and researchers use Claude most often for fixing code errors and learning about the codebase. Debugging and code understanding are the most common uses (Figure 1). People report increasing Claude usage and productivity gains. Employees self-report using Claude in 60% of their work and achieving a 50% productivity boost, a 2-3x increase from this time last year. This productivity looks like slightly less time per task category, but considerably more output volume (Figure 2). 27% of Claude-assisted work consists of tasks that wouldn't have been done otherwise, such as scaling projects, making nice-to-have tools (e.g. interactive data dashboards), and exploratory work that wouldn't be cost-effective if done manually. Most employees use Claude frequently while reporting they can “fully delegate” 0-20% of their work to it. Claude is a constant collaborator but using it generally involves active supervision and validation, especially in high-stakes work—versus handing off tasks requiring no verification at all.
Qualitative interviews
... continue reading