Enterprise AI Analysis
How AI is transforming work at Anthropic
Dec 3, 2025
Executive Summary & Impact
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.
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of work with Claude
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Productivity Boost
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New Work Enabled
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Survey Data Overview
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.
Common Daily Coding Tasks (Figure 1)
Debugging (55% daily)
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Code Understanding (42% daily)
→
Implementing New Features (37% daily)
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Refactoring
→
Data Science
→
Front-End Development
→
High-Level Design/Planning
27%
of Claude-assisted work wouldn't have been done otherwise, enabling new projects.
Employees self-reported that 12 months ago, they used Claude in 28% of their daily work and got a +20% productivity boost from it, whereas now, they use Claude in 59% of their work and achieve +50% productivity gains from it on average. This suggests a more than 2x increase in both metrics in one year. When asking employees how Claude affects their overall time spent and work output volume, we see a net decrease in time spent, and a larger net increase in output volume across almost all task categories. Output volume increases are more straightforward and substantial; there is a larger net increase across all task categories.
Qualitative Interview Insights
While these survey findings reveal significant productivity gains and changing work patterns, they raise questions about how engineers are actually experiencing these changes day-to-day. We conducted in-depth interviews with 53 Anthropic engineers and researchers to understand the human dimension behind these metrics. Employees are developing intuitions for AI delegation, often giving Claude tasks that are easily verifiable, low-stakes, or boring. Skillsets are broadening into more areas, but some are getting less practice, raising concerns about atrophy of deeper skillsets. The relationship to coding craft is changing, with some embracing AI for outcomes, others missing the hands-on process. Workplace social dynamics may be shifting, with Claude becoming the first stop for questions, potentially reducing mentorship opportunities. Career evolution brings uncertainty, with some fearing automation while others see new high-level roles emerging.
Case Study: Full-Stack Expansion
One backend engineer described building a complex UI by iterating with Claude: “It did a way better job than I ever would’ve. I would not have been able to do it, definitely not on time... [The designers] were like ‘wait, you did this?’ I said “No, Claude did this - I just prompted it.’” This illustrates how AI enables engineers to work effectively outside their core expertise.
Case Study: Skill Atrophy Concerns
A senior engineer reflected on junior talent: “I’m primarily using AI in cases where I know what the answer should be or should look like. I developed that ability by doing SWE ‘the hard way’... But if I were [earlier in my career], I would think it would take a lot of deliberate effort to continue growing my own abilities rather than blindly accepting the model output.” This highlights the paradox of supervision and the importance of deliberate practice.
Case Study: Redefining Coding Craft
One engineer shared a perspective on the changing nature of work: “I expected that by this point I would feel scared or bored… however I don't really feel either of those things. Instead I feel quite excited that I can do significantly more. I thought that I really enjoyed writing code, and instead I actually just enjoy what I get out of writing code.”
Case Study: Future Career Uncertainty
Regarding long-term impact, one engineer stated, “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.” Another added, “It kind of feels like I'm coming to work every day to put myself out of a job.”
Claude Code Usage Trends
To complement self-reported data, we analyzed 200,000 internal transcripts from Claude Code from February and August 2025. Claude Code usage has shifted toward more difficult and autonomous coding tasks over the last six months. Employees are tackling increasingly complex tasks with Claude Code, with average task complexity increasing from 3.2 to 3.8. The maximum number of consecutive tool calls Claude Code makes per transcript increased by 116% (from 9.8 to 21.2), and the number of human turns decreased by 33% (from 6.2 to 4.1). These data corroborate that engineers delegate increasingly complex work and Claude requires less oversight.
Changes in Claude Code Usage (Feb 2025 → Aug 2025)
Increased Task Complexity (3.2 → 3.8)
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More Autonomous Actions (9.8 → 21.2 calls)
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Fewer Human Turns (6.2 → 4.1)
The distribution of tasks has also evolved significantly. The most striking change is the increase in Claude usage for implementing new features and code design or planning.
| Task Type | 6 months ago (Feb 2025) | Present Day (Aug 2025) |
|---|---|---|
| Implement New Features | 14.3% | 36.9% |
| Code Design/Planning | 1.0% | 9.9% |
8.6%
of Claude Code tasks involve "papercut fixes" – addressing minor quality-of-life improvements.
Claude also enables capability expansion across different teams. For example, the Pre-training team heavily uses Claude for building new features (54.6%), while the Security team utilizes it for code understanding (48.9%), analyzing security implications. Non-technical employees find Claude valuable for debugging (51.5%) and data science (12.7%), bridging gaps in technical knowledge. This demonstrates Claude's role in making everyone more "full-stack."
| Team | Primary Claude Code Usage |
|---|---|
| Pre-training | Building new features (54.6%) |
| Alignment & Safety | Front-end development (7.5%) for data visualizations |
| Security | Code understanding (48.9%) for security analysis |
| Non-technical Employees | Debugging (51.5%) and Data Science (12.7%) |
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