Agentic coding and persistent returns to expertise \ Anthropic<br>Try Claude
Economic Research<br>Agentic coding and persistent returns to expertise<br>Jun 17, 2026<br>Read in PDF
Key findings<br>Building on prior work, we introduce a framework for studying interactive agentic coding based on a privacy-preserving analysis of ~400,000 Claude Code sessions from between October 2025 and April 2026. We evaluate the composition of tasks, human-AI collaboration, and success rates.<br>In a typical session, people make most of the planning decisions (what to do) and Claude makes most of the execution decisions (how to do it). The greater domain expertise a person brings to a session, the more work Claude does per instruction. On coding tasks, every major occupation succeeds––accomplishes what the person set out to do, with verifiable evidence like passing tests or committed work––at nearly the same rate as software engineers, on average.<br>The more domain expertise a person has, the more often the session ends in success—though the gap between intermediate and expert users is modest. Over the seven months we observe, the share of sessions spent debugging fell by nearly half, and usage shifted toward more end-to-end agentic use: deploying and running code, analyzing data, and writing non-code documents.<br>Over those seven months, the value of the typical task, which we estimate through a comparison to freelance job postings, rose in almost every kind of work—about 25% on average.<br>Introduction<br>Agentic coding has taken off. The share of GitHub projects with coding agent activity has more than doubled since late 2025,1 and Claude Code users now spend an average of 20 hours per week using the tool.2 Can people without formal coding experience successfully direct an agent through complex technical work? And what will rapid adoption and improvement of these tools mean for knowledge work broadly? While we don’t have full answers to these questions yet, we look to Claude Code usage data for early signals.
This report provides evidence on how Claude Code is used in practice, based on a privacy-preserving analysis of ~400,000 interactive sessions from ~235,000 people between October 2025 and April 2026. It builds on prior work focused on measures of autonomy in Claude Code sessions, and how Claude Code is changing work at Anthropic.3 Here, we introduce a framework for describing interactive AI coding-assistant usage: what kind of work is being done, who is doing it, and whether it succeeds. We focus on Claude Code usage through a command-line interface (CLI), Claude.ai, or the Claude Code desktop app.4 By tracking how agentic coding usage changes as models get more capable, we can better understand how these tools affect the labor market for coding professionals and knowledge workers.
What happens on Claude Code may be a preview of where knowledge work is headed, as agents become embedded in non-coding work. We find that Claude is handling more complex and more valuable tasks. At the same time, there remains a clear division of labor in agentic coding: People decide what to build, and the agent decides how to build it.
We also see evidence that domain expertise, and not coding proficiency, amplifies effective use of the tool. In particular, domain experts succeed more often, and more easily recover from errors and misunderstandings. However, the gap between experts and intermediates is modest—suggesting that proficiency in a domain is enough to use the tool almost as effectively as those with deep mastery.
These findings give us an early read on possible transitions in the labor market. In our data, success is determined by how well a person understands the problem they are trying to solve, not whether they’re trained in coding. If these patterns hold across the economy, it suggests that while agentic coding tools may be absorbing some implementation-heavy work, they are also rewarding those with firm understanding of the problems they solve on the job. Coding agents are not substituting for domain expertise—the more understanding a worker brings to an agent, the more quality work the agent is able to do.
The division of labor<br>What people use Claude Code for<br>To understand what people are using Claude Code for, we classify each session into one of nine work modes—the single activity that best describes what the session is trying to accomplish.5 Four modes involve writing or maintaining code directly: building something new, fixing something broken, testing code, and orchestrating other agents or automated pipelines. Another category is operating software—deploying, configuring, running pipelines, monitoring systems. Two categories are more about working out what to do: understanding how an existing system works, and planning a change before making it. And two take actions unrelated to code, or where code is incidental to the final product: analyzing data, and communicating via presentations and other prose-based documents.
About 56% of...