Anthropic Economic Index report: Economic primitives
Figure 1.1: Usage shares among top 10 tasks over time by platform, Claude.ai and 1P API. Share of conversations assigned to the ten most prevalent O*NET tasks, by platform and report version.
Figure 1.2: Claude.ai and API usage over time. Each panel shows the share of sampled conversations on Claude.ai and 1P API records associated with tasks from each Standard Occupation Classification (SOC) major group.
Figure 1.3: Collaboration mode share over time by platform, Claude.ai and 1P API. Collaboration mode frequencies across Anthropic Economic Index Reports.Figure 1.4: Directive, Task Iteration, and Learning collaboration shares by Standard Occupation Classification (SOC) major group. For each SOC major group we calculate the share of conversations on Claude.ai associated with Directive, Task Iteration, and Learning from among O*NET tasks that have at least 100 observations in our sample. We weight observations by number of records to construct a representative sample.Figure 1.5: Prominent words from among O*NET task titles and bottom-up request groupings by key collaboration type. Word clouds constructed from among the top quartile of O*NET tasks and bottom-up request groups, ordered by the share of records classified as Directive, Task Iteration, and Learning from among tasks/requests with at least 1,000 observations. Directive interactions emphasize production ('create,' 'develop,' 'draft'); Task Iteration centers on refinement and iteration ('edit,' 'rewrite,' 'revise'); Learning focuses on explanation and knowledge transfer ('help,' 'explain,' 'provide'). Patterns are consistent across both classification methods. This analysis is not based on the words used in the underlying transcripts but rather groupings constructed using privacy-preserving methods.
Figure 1.6: AUI concentration around the world and within the US in this and the prior report. Lorenz curves for the Anthropic AI Usage Index (AUI) around the world and within the US, August and November 2025. A curve that is closer to the 45-degree line indicates less concentration. The plot on the right shows, for example, that the top 20 percent of US states accounted for 40 percent of population-adjusted usage in the US.
Figure 1.7: AUI and share of workers in Computer & Mathematical occupations in each US State. This figure shows that the share of workers in Computer & Mathematical occupations across US states is highly correlated with the Anthropic AI Usage Index (AUI). This is consistent with the view that overall Claude usage patterns—and associated capabilities—are shaping regional adoption patterns within the US. This pattern holds more generally when formally calculating the KL divergence between each state’s workforce distribution and global Claude.ai usage shares by SOC major group.
Figure 1.8: Anthropic AI Usage Index (AUI) across the US, August 2025 (V3) and November 2025 (V4). By comparing the AUI in November 2025 with its value in August 2025 we can estimate the implied rate of diffusion of Claude usage within the US. Under a model of proportional convergence toward a steady state in which AUI = 1 for all US states, the estimated elasticity can be used to calculate the pace of diffusion (see text for more details). Our range of estimates implies a pace of regional convergence of AUI in 2-5 years.
Table 2.1: Economic primitives added in this report.The table shows the new economic primitives added in this report, beyond collaboration patterns (automation/augmentation) from prior reports. The first column shows the primitive category, the second column the name of the primitive, and the third column the operationalization of the primitives as the prompts provided to Claude which we use a classifier to map conversations to primitives. See online appendix at https://huggingface.co/datasets/Anthropic/EconomicIndex for full prompt texts.
Figure 2.1: Education years needed to understand the human prompt and share of workers with at least a Bachelor’s Degree.Education data from “Educational attainment for workers 25 years and older by detailed occupation” (BLS), based on microdata from the 2022 and 2023 American Community Survey2. We calculate average years of schooling for tasks associated with a particular occupation. We then calculate the percentage of workers with a bachelor's degree or higher in that occupation.
Figure 2.2: Descriptive statistics of economic primitives overall and for two example request clusters.For this figure, we focus on descriptive statistics for the primitives across the whole Claude.ai sample as well as two request clusters at the lowest level of granularity. N indicates the overall count of conversations or the count of conversations belonging to the request clusters.
Figure 3.1: Share of work use of Claude.ai globally.The share of conversations for a given country that are classified as work, as opposed to personal or coursework. The different tiers reflect a country’s position within the global distribution of the Anthropic AI Usage Index as defined in chapter 1345. We only include countries with at least 200 observations in our sample for this figure because of the uncertainty of the measure for low-usage countries in our random sample. The underlying data includes Claude.ai Free, Pro and Max usage.
Figure 3.2: Per capita income predicts how Claude is used across countries.Each plot shows the bivariate relationship between the share of a specific use case (work, coursework, or personal) for Claude.ai conversations and log GDP per capita. Labels show the ISO-3166-1 country codes. We only include countries with at least 200 observations in our sample for this figure because of the uncertainty of the measure for low-usage countries in our random sample. The underlying data includes Claude.ai Free, Pro and Max usage.
Figure 3.3: Relationship between the Anthropic AI Usage Index and five core economic primitives and GDP per capita at the country level.Each plot shows the bivariate relationship between the natural logarithm of the Anthropic AI Usage Index and a core economic primitive as well as log GDP per capita. Labels show the ISO-3166-1 country codes. We only include countries with at least 200 observations in our sample for this figure because of the uncertainty of the measure for low-usage countries in our random sample. The underlying data includes Claude.ai Free, Pro and Max usage. See chapter 2 for detailed definitions of human only time, human education, AI autonomy, work use case and task success.Figure 3.4: Relationship between the Anthropic AI Usage Index and five core economic primitives and GDP per capita at the US state level.Each plot shows the bivariate relationship between the natural logarithm of the Anthropic AI Usage Index and a core economic primitive as well as log GDP per capita. Labels show the ISO-3166-2 region codes6. We only include states with at least 100 observations in our sample for this figure because of the uncertainty of the measure for low-usage states in our random sample. The underlying data includes Claude.ai Free, Pro and Max usage. See chapter 2 for detailed definitions of human only time, human education, AI autonomy, work use case and task success.
Figure 3.5: Relationship between task success and human education.Plots on the left show the bivariate correlation between task success and years of education needed to understand the human prompts in the conversation. Plots on the right show partial regression where we additionally control for GDP per capita, AI autonomy, automation percent, share of work and coursework use cases, human without AI time, human with AI time, multitasking and human ability (see chapter 2 for detailed definitions of these variables). Labels show ISO-3166-1 country codes and ISO-3166-2 region codes. We only include countries with at least 200 and states with at least 100 observations in our sample for this figure because of the uncertainty of the measure for low-usage states in our random sample. The underlying data includes Claude.ai Free, Pro and Max usage.
Figure 4.1: Speed up (panel a) and Success rate (panel b) vs. Human years of schooling.The panel on the left shows a binned scatterplot of the bivariate relationship between speedup and human years of schooling, all measured at the O*NET task level and split by platform. The dashed lines show the fit from a linear regression. The panel on the right shows the same relationship with the success rate in the y-axis.
Figure 4.2: AI autonomy vs. human education.The plot shows a binned scatterplot of the bivariate relationship between AI autonomy and human education required, all measured at the O*NET task level. The dashed lines show the fit from a linear regression.
Figure 4.3: Task success vs. human-only time.The plot shows a binned scatterplot of the bivariate relationship between task success (%) and the time the task would require a human to complete alone, all measured at the O*NET task level and split by platform. The dashed lines show the fit from a linear regression.
Figure 4.4: Effective AI coverage vs. Task coverageThe plot shows a scatter of the bivariate relationship between task effective AI coverage (%) and task coverage, measured at the occupation level. Effective AI coverage tracks the share of a worker’s time-weighted duties that AI could successfully perform, based on Claude.ai data. Task coverage is the share of tasks that appear in Claude.ai usage. The dashed line shows where Effective AI coverage share equals task coverage.
Figure 4.5: Education level of all tasks vs. Claude-covered tasksThis shows two histograms. The blue bars give the distribution of the predicted task-level education required for all tasks in the O*NET database, weighted by employment. The orange bars show the same, restricting to tasks that appear in Claude.ai data.
Figure 4.6 Implied labor productivity effect from AI as a function of within-occupation task substitutabilityThis figure shows the implied aggregate labor productivity growth over the next decade based on efficiency gains estimated for tasks with at least 200 observations in our sample of 1M conversations on Claude.ai and 1M records from 1P API traffic. The elasticity of substitution governs how the degree to which non-AI enhanced tasks constrain the occupational productivity gains implied by Claude usage under a model in which occupational output is a CES index across tasks. An elasticity of =1 reproduces our unadjusted, baseline result of 1.8 percentage point increase in labor productivity growth over the next decade. Success-adjusted curves discount task-level speedups by task reliability. See text for more details.
