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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 Cla

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 HTML2 and CSS (other languages for user-facing code) together added another 28%. 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

We also analyzed which groups of developers might be using Claude. We used our analysis system to identify the type of project (e.g. a personal project vs. a project done for a startup) that best described users’ coding-related interactions. Because we don’t know the real-world context in which Claude’s responses were being used, these analyses rely on uncertain inferences from incomplete data. We therefore treat these findings as more preliminary than the ones described above. Startups appear to be the primary early adopters of Claude Code, and enterprise adoption lags behind. Startup work ac

In our last report, we analyzed how AI usage varied between augmentative uses, like learning or iterating on an output, and automative uses, like asking the model to directly complete a task or debug errors. Our analysis shows the balance of augmentation and automation is essentially unchanged in our new data, with augmentation still comprising 57% of usage. However, we did see some change in types of automation and augmentation uses—for example, we saw learning interactions, where the user asks Claude for information or explanation about different topics, rise from ~23% to ~28%. We received a