The Anthropic Economic Index

Introducing the Anthropic Economic Index \ Anthropic Societal Impacts Economic Research The Anthropic Economic Index Feb 10, 2025 Read the paper

In the coming years, AI systems will have a major impact on the ways people work. For that reason, we're launching the Anthropic Economic Index , an initiative aimed at understanding AI's effects on labor markets and the economy over time. The Index’s initial report provides first-of-its-kind data and analysis based on millions of anonymized conversations on Claude.ai , revealing the clearest picture yet of how AI is being incorporated into real-world tasks across the modern economy. We're also open sourcing the dataset used for this analysis, so researchers can build on and extend our findings. Developing policy responses to address the coming transformation in the labor market and its effects on employment and productivity will take a range of perspectives. To that end, we are also inviting economists, policy experts, and other researchers to provide input on the Index. The main findings from the Economic Index’s first paper are: Today, usage is concentrated in software development and technical writing tasks. Over one-third of occupations (roughly 36%) see AI use in at least a quarter of their associated tasks, while approximately 4% of occupations use it across three-quarters of their associated tasks. AI use leans more toward augmentation (57%), where AI collaborates with and enhances human capabilities, compared to automation (43%), where AI directly performs tasks. AI use is more prevalent for tasks associated with mid-to-high wage occupations like computer programmers and data scientists, but is lower for both the lowest- and highest-paid roles. This likely reflects both the limits of current AI capabilities, as well as practical barriers to using the technology.

See below for further details on our initial findings. Where and how AI is used across the economy, drawn from real-world usage data from Claude.ai. The numbers refer to the percentage of conversations with Claude that were related to those individual tasks, occupations, and categories. Mapping AI usage across the labor market Our new paper builds on a long line of research on the labor market impact of technologies, from the Spinning Jenny of the Industrial Revolution to the car-manufacturing robots of the present day. We focus on the ongoing impact of AI. We don’t survey people on their AI use, or attempt to forecast the future; instead, we have direct data on how AI is actually being used. Analyzing occupational tasks Our research began with an important insight from the economics literature : sometimes it makes sense to focus on occupational tasks rather than occupations themselves . Jobs often share certain tasks and skills in common: for example, visual pattern recognition is a task performed by designers, photographers, security screeners, and radiologists. Certain tasks lend themselves better to being automated or augmented by a new technology than others. We’d therefore expect AI to be adopted selectively for different tasks across different occupations, and that analyzing tasks—in addition to jobs as a whole—would give us a fuller picture of how AI is being integrated into the economy. Using Clio to match AI use to tasks This research was made possible by Claude insights and observations, or " Clio ", an automated analysis tool that allows us to analyze conversations with Claude while preserving user privacy 1 . We used Clio on a dataset of approximately one million conversations with Claude (specifically, Free and Pro conversations on Claude.ai ), and used it to organize the conversations by occupational task. We chose tasks according to the classification made by the U.S. Department of Labor, which maintains a database of around 20,000 specific work-related tasks called the Occupational Information Network, or O*NET . Clio matched each conversation with the O*NET task that best represented the role of the AI in the conversation (the process is summarized in the figure below). We then followed the O*NET scheme for grouping the tasks into the occupations they best represented, and the occupations into a small set of overall categories: education and library, business and financial, and so on. The process by which our Clio system translates conversations with Claude (which are kept strictly private; top left) into occupational tasks (top middle) and occupations/occupational categories derived from O*NET (top right). These can then be entered into various analyses (bottom row; discussed in more detail below). Results Uses of AI by job type. The tasks and occupations with by far the largest adoption of AI in our dataset were those in the “computer and mathematical” category, which in large part covers software engineering roles. 37.2% of queries sent to Claude were in this category, covering tasks like software modification, code debugging, and network troubleshooting. The second largest category was “arts, design, sports, entertainment, and media” (10.3% of queries), which mainly reflected people using Claude for various kinds of writing and editing. Unsurprisingly, occupations involving a high degree of physical labor, such as those in the “farming, fishing, and forestry” category (0.1% of queries), were least represented. We also compared the rates in our data to the rates at which each occupation appeared in the labor market in general. The comparisons are shown in the figure below. For each job type, the percentage of relevant conversations with Claude is shown in orange compared to the percentage of workers in the U.S. economy with that job type (from the U.S. Department of Labor’s O*NET categories) in gray. Depth of AI use within occupations. Our analysis found that very few occupations see AI use across most of their associated tasks: only approximately 4% of jobs used AI for at least 75% of tasks. However, more moderate use of AI is much more widespread: roughly 36% of jobs had some use of AI for at least 25% of their tasks. As we predicted, there wasn’t evidence in this dataset of jobs being entirely automated: instead, AI was diffused across the many tasks in the economy, having stronger impacts for some groups of tasks than others. AI use and salary. The O*NET database provides the median U.S. salary for each of the occupations listed. We added this information to our analysis, allowing us to compare professions’ median salaries and…