Anthropic Economic Index: Insights from Claude 3.7 Sonnet

Last month, we launched the Anthropic Economic Index —a new initiative where we’re regularly releasing data and research aimed at understanding AI's effects on labor markets and the economy over time.

Today, we’re releasing our second research report from the Index, covering usage data on Claude.ai following the launch of Claude 3.7 Sonnet —our newest and most capable model with strengths in agentic coding and a new “extended thinking” mode.

In addition, we’re releasing a first-of-its-kind bottom-up taxonomy of usage on Claude.ai. This new dataset covers 630 granular categories ranging from “Help resolve household plumbing, water, and maintenance issues” to “Provide guidance on battery technologies and charging systems.” We hope this bottom-up taxonomy will be useful for researchers, and reveal use-cases that might be missed by top-down approaches which map usage onto a list of predefined tasks.

As models continue to advance, so too must our measurement of their economic impacts. In our second report, covering data since the launch of Claude 3.7 Sonnet, we find relatively modest increases in coding, education, and scientific use cases, and no change in the balance of augmentation and automation. We find that Claude’s new extended thinking mode is used with the highest frequency in technical domains and tasks, and identify patterns in automation / augmentation patterns across tasks and occupations. We release datasets for both of these analyses.

Last month, we introduced Claude 3.7 Sonnet, our most capable model yet with an “extended thinking mode”. We reran our previous analysis on data from the 11 days following the launch, covering 1 million anonymized Claude.ai Free and Pro conversations. The vast majority of the data we analyzed was from Claude 3.7 Sonnet, as it is set as the default on Claude.ai and our mobile app.

As a reminder, our privacy-preserving analysis tool, Clio , maps each conversation to one of 17,000 tasks in the U.S. Department of Labor’s O*NET database. We then look at the overall patterns in the occupations and high-level occupational categories associated with those tasks.

When looking at the breakdown of these 1 million conversations, we see that the proportion of usage in several occupational categories has increased modestly, including coding, education and the sciences. While this increase in coding usage was expected due to Claude 3.7 Sonnet’s improved scores on coding benchmarks, the increase in these other categories could reflect either ongoing diffusion of AI throughout the economy, novel applications of coding to those domains, or unexpected capability improvements in the model.