Anthropic Economic Index: AI’s Impact on Software Development

Anthropic: “Jobs that involve computer programming are a small sector of the modern economy, but an influential one. The past couple of years have seen them changed dramatically by the introduction of AI systems that can assist with—and automate—significant amounts of coding work. In our previous Economic Index research, we found very disproportionate use of Claude by US workers in computer-related occupations: that is, there were many more conversations with Claude about computer-related tasks than one would predict from the number of people working in relevant jobs. It’s the same in the educational context: Computer Science degrees—which involve large amounts of coding—show highly disproportionate AI use. To understand these changes in more detail, we conducted an analysis of 500,000 coding-related interactions across Claude.ai (the “default” way that most people interact with Claude) and Claude Code (our new specialist coding “agent” that can independently accomplish chains of complex tasks using a variety of digital tools). We found three key patterns:

1. The coding agent is used for more automation. 79% of conversations on Claude Code were identified as “automation”—where AI directly performs tasks—rather than “augmentation,” where AI collaborates with and enhances human capabilities (21%). In contrast, only 49% of Claude.ai conversations were classified as automation. This might imply that as AI agents become more commonplace, and as more agentic AI products are built, we should expect more automation of tasks.
2. Coders commonly use AI to build user-facing apps. Web-development languages such as JavaScript and HTML were the most common programming languages used in our dataset, and user interface and user experience tasks were among the top coding uses. This suggests that jobs that center on making simple applications and user interfaces may face disruption from AI systems sooner than those focused purely on backend work.
3. Startups are the main early adopters of Claude Code, while enterprises lag behind. In a preliminary analysis, we estimated that 33% of conversations on Claude Code served startup-related work, compared to only 13% identified as enterprise-relevant applications. The adoption gap suggests a divide between nimbler organizations using cutting-edge AI tools, and traditional enterprises.