Impact Software Development

Anthropic Economic Index: AI's impact on software development \ Anthropic Societal Impacts Economic Research Anthropic Economic Index: AI’s impact on software development Apr 28, 2025

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: 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. 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 . 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.

How we analyzed conversations on Claude Code and Claude.ai We analyzed the 500,000 total Claude interactions (split between Claude Code and Claude.ai 1 ) using our privacy-preserving analysis tool , which distills user conversations into higher-level, anonymized insights. Here, we used it to identify the topic of the conversation (e.g. “UI/UX component development”), or—as we’ll explain below—to categorize a conversation as focusing on “augmentation” versus “automation”. How do developers interact with Claude? In our previous Economic Index reports, we separated out “automation,” where AI directly performs tasks, from “augmentation,” where AI collaborates with a user to perform a task. Here, we found that Claude Code showed dramatically higher automation rates—79% of conversations involved some form of automation, compared to 49% on Claude.ai. We also split automation and augmentation into several subtypes (as discussed in our previous work ). “Feedback Loop” patterns, where Claude completes tasks autonomously but with help of human validation (for example, where the user sends any errors back to Claude), were nearly twice as common on Claude Code (35.8% of interactions) as Claude.ai (21.3%). “Directive” conversations, where Claude completed a task with minimal user interaction, were also higher on Claude Code (43.8%, versus 27.5% on Claude.ai). All the patterns of augmentation—including “Learning,” where the user acquires knowledge from the AI model—were substantially lower on Claude Code than on Claude.ai. Subtypes are defined as follows. Directive: Complete task delegation with minimal interaction; Feedback Loop: Task completion guided by environmental feedback; Task Iteration: Collaborative refinement process; Learning: Knowledge acquisition and understanding; Validation: Work verification and improvement. These results illustrate the differences between specialist, coding-focused agents (in this case, Claude Code) and the more “standard” way that users interact with large language models (i.e., through a chatbot interface like Claude.ai). As more agentic products are released, we might see differences in the way AI is integrated into people’s jobs. At least in the case of coding, this might involve more automation of tasks. This raises questions about the extent to which developers will still be involved as AI use becomes more common. Importantly, our results do show that even within automation, humans are still very often involved: “Feedback Loop” interactions still require user input (even if that input is simply pasting error messages back to Claude). But it’s by no means certain that this pattern will persist into the future, when more capable agentic systems will likely require progressively less user input. What are developers building with Claude? Overall, we found that developers commonly use Claude for building user interfaces and interactive elements for websites and mobile applications. Although no single language dominated, the primarily web-focused development languages of JavaScript and TypeScript together accounted for 31% of all queries, and HTML 2 and CSS (other languages for user-facing code) together added another 28%. Percentages represent total percentages of coding-related tasks across both platforms. Because Claude Code and Claude.ai are equally weighted, the portions of the bars that correspond to each of the platforms represent half of that platform's usage. Back-end development languages (used for behind-the-scenes logic, databases, and infrastructure, as well as API and AI development) were also represented: notably, Python was at 14% of queries. However, Python serves dual purposes—both for back-end development and data analysis. Combined with SQL (another data-focused language, making up 6% of queries), these languages likely included many data science and analytics applications beyond traditional back-end development. Percentages of coding language uses represent…