Anthropic Economic Index report: Learning curves

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.

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 grown, it is spread across many task categories rather than concentrated in a few. As a result, task concentration in the API remained roughly flat despite the influx of coding activity.

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 migration of code out of Claude.ai is not the only factor driving decreased concentration. Part of the drop is due to changes in the mix of use cases between the two periods. Coursework fell from 19% to 12% of conversations, while personal use rose from 35% to 42% of conversations. Some of the drop in coursework can be explained by academic calendars in countries where students were on winter break during our sample period. 4 At the same time, increasing signups beginning around February brought more casual AI users.

Another way to measure the change in the mix of tasks done on Claude is to look at the change in the average value of tasks, which we define as the average hourly wage of US workers who perform that task (Figure 1.4). 6 This estimate of the value of tasks in Claude.ai has dropped slightly from $49.3 to $47.9 mostly due to an increase in simple factual questions (e.g., sports outcomes, weather) and a decrease in coding as it shifts to the API. As mentioned in our previous report , the tasks we see on Claude tend to require higher education. The plot shows that these tasks also tend to earn higher wages than the US national average.

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.

In the first chapter, we revisit findings from our previous Economic Index report , published in January 2026. We find that:

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.