Economic Index March 2026 Report

Anthropic Economic Index report: Learning curves \ Anthropic Economic Research Anthropic Economic Index report: Learning curves Mar 24, 2026 Read in PDF

The Anthropic Economic Index uses our privacy-preserving data analysis system to track how Claude is being used across the economy. It’s part of our effort to understand the economic impacts of AI as early as possible, so that researchers and policymakers have adequate time to prepare. This latest report studies Claude usage in February 2026, building on the economic primitives framework introduced in our previous report (which used data from November 2025). Our sample covers February 5 to February 12, three months following the release of Claude Opus 4.5 and coincident with the release of Claude Opus 4.6. We first document how usage has changed relative to our previous reports: the rate of augmentation, collaborative interaction where the AI complements the user’s abilities, increased slightly in both Claude.ai and API traffic. In Claude.ai, usage diversified, with the top 10 tasks accounting for a smaller share of usage last month than in November 2025. As a result of this diversification, the average conversation in Claude.ai had a slightly lower-wage task than in previous reports. We then focus on an important determinant of Claude’s impact on the labor market and the broader economy: learning curves in Claude adoption. We present evidence that high-tenure users have developed habits and strategies that allow them to better harness Claude’s capabilities. Indeed, we document that more experienced users not only attempt higher-value tasks, but are also more likely to elicit successful responses in their conversations. What has changed since our last report In the first chapter, we revisit findings from our previous Economic Index report , published in January 2026. We find that: Use cases on Claude.ai diversified. Coding tasks continue to migrate from augmentative usage in Claude.ai to more automated workflows in our first-party API traffic. 1 In this report, Claude.ai usage was less concentrated: the top 10 tasks made up 19% of all traffic in February, down from 24% in November. That said, almost all tasks in this sample appeared in at least one of our previous samples. About 49% of jobs have seen at least a quarter of their tasks performed using Claude. Claude adoption broadened to lower-wage tasks. As use cases have diversified, the average economic value of work done on Claude—as measured by US wages paid to workers in the associated occupations—has decreased slightly. This is caused, mechanically, by a rise in personal queries around sports, product comparisons, and home maintenance. The pattern is consistent with a standard “adoption curve” story, in which early-adopters favor specific high-value uses like coding, and later adopters take on a much wider range of tasks. Inequality in global usage has persisted. Usage remains heavily concentrated: the top 20 countries account for 48% of all per-capita usage, up from 45%, underscoring a persistent gap in global adoption. However, Claude usage per capita continued to converge within the United States: the share of usage accounted for by the 10 highest usage states decreased from 40% to 38% since our last report.

Learning curves A central finding in the Economic Index is that early adoption of Claude is very uneven: Claude is used more intensely in high-income countries, within the US in places with more knowledge workers, and for a relatively small set of specialized tasks and occupations. An important question is how inequality of adoption might determine where and to whom the benefits of AI will accrue. If, for example, effective AI use requires complementary skills and expertise—which we argued in our previous report —and if such skills can be acquired through use and experimentation, then the benefits from early adoption may be self-reinforcing. In our second chapter we investigate how users appear to shape the value that they get out of Claude: how they match model capability to the task at hand, and how usage patterns and outcomes shift with experience on the platform. Model selection matches the task. We show that users choose our most intelligent model class, Opus, for tasks that normally receive higher wages in the labor market. For example, among paying Claude.ai users, Opus is used 4 percentage points more than average for coding tasks and 7 percentage points less than average for tutoring-related tasks. This model switching is about twice as stark for API users. Higher tenure, higher success. In general, the most seasoned Claude users employ it more often for higher-education tasks and less often for personal use cases. For example, people who have been using Claude for 6 months or more have 10% fewer personal conversations and a 6% higher education level reflected in their inputs. Most strikingly, people in this higher-tenure group have a 10% higher success rate in their conversations, an association that is not explained by their task selection, country of origin, or other factors. While this could reflect sophistication of early adopters, it could also be evidence of learning-by-doing, where people get better at using Claude through experience.

What has changed since our last report Diversification of use cases in Claude.ai We first look at the kinds of tasks that Claude is asked to perform. We use our privacy-preserving system , which allows us to describe behavior at an aggregated level without revealing the content of individual transcripts. We sample 1 million conversations from both Claude.ai, our consumer-facing web product, and our first-party API, the developer-facing interface for integrating Claude into products and workflows. 2 Coding remains the most common use on our platforms, with tasks associated with Computer and Mathematical occupations accounting for 35% of conversations on Claude.ai (see Appendix ). 3 However, between November 2025 and February 2026, use cases on Claude.ai became less concentrated: the top 10 most common O*NET tasks went from 24% of conversations to just 19% (Figure 1.1). This decline in concentration partly reflects coding tasks migrating from Claude.ai to our first-party API, where Claude Code has grown to represent a large share of sampled traffic. Claude Code’s agentic architecture splits coding work into smaller API calls, which are labeled as distinct tasks. So while coding’s overall share of API traffic has…