How Ai Is Transforming Work At Anthropic

How AI Is Transforming Work at Anthropic \ Anthropic Societal Impacts How AI is transforming work at Anthropic Dec 2, 2025

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 Employees are developing intuitions for AI delegation . Engineers tend to delegate tasks that are easily verifiable, where they “can relatively easily sniff-check on correctness”, low-stakes (e.g. “throwaway debug or research code”), or boring (“The more excited I am to do the task, the more likely I am to not use Claude”). Many describe a trust progression, starting with simple tasks and gradually delegating more complex work—and while they’re currently keeping most design or “taste” tasks, this boundary is being renegotiated as models improve. Skillsets are broadening into more areas, but some are getting less practice. Claude enables people to broaden their skills into more areas (of software engineering (“I can very capably work on front-end, or transactional databases... where previously I would've been scared to touch stuff”), but some employees are also concerned, paradoxically, about the atrophy of deeper skillsets required for both writing and critiquing code—“When producing output is so easy and fast, it gets harder and harder to actually take the time to learn something.” Changing relationship to coding craft. Some engineers embrace AI assistance and focus on outcomes (“I thought that I really enjoyed writing code, and I think instead I actually just enjoy what I get out of writing code”); others say that “there are certainly some parts of [writing code] that I miss.” Workplace social dynamics may be changing. Claude is now the first stop for questions that used to go to colleagues—some report fewer mentorship and collaboration opportunities as a result. (“I like working with people and it's sad that I ‘need’ them less now… More junior people don't come to me with questions as often.”) Career evolution and uncertainty. Engineers report shifting toward higher-level work managing AI systems and report significant productivity gains. However, these changes also raise questions about the long-term trajectory of software engineering as a profession. Some express conflicting feelings about the future: “I feel optimistic in the short term but in the long term I think AI will end up doing everything and make me and many others irrelevant.” Others emphasize genuine uncertainty, saying only that it was “hard to say” what their roles might look like in a few years’ time.

Claude Code usage trends Claude is handling increasingly…