From travel planning in Hawaii to scientific research in Massachusetts and web development in India, users are applying AI in diverse ways that reflect regional economic strengths. While software engineering remains the dominant use of Claude globally, certain regions show distinct preferences: Massachusetts users lean heavily into scientific inquiry, while Brazilians use the AI for language learning and translation at six times the global average. These insights come from the latest Anthropic Economic Index, which maps early AI adoption patterns shaping labor and economic activity.
The report introduces geographic analysis, revealing that the U.S. leads in overall Claude usage, followed by India, Brazil, Japan, and South Korea. Adjusting for working-age population, smaller, tech-advanced nations such as Singapore rank highest in per capita adoption. A strong correlation exists between GDP per capita and AI usage: a 1% increase in income correlates with a 0.7% rise in AI adoption. This suggests that wealthier nations, with stronger digital infrastructure and knowledge-based economies, are better positioned to integrate AI tools.
Within the United States, economic structure plays a key role. The District of Columbia has the highest adjusted usage (AUI of 3.82), driven by tasks like document editing and information retrieval—common in policy and administrative work. California shows high engagement in coding, while New York stands out in finance-related queries. Even in lower-adoption states like Hawaii, usage aligns with local industries: tourism-related requests occur twice as often as the national average.
Over time, user behavior has shifted significantly. Since December 2024, the share of directive automation—where AI acts with minimal human input—has surged from 27% to 39%. This marks the first time automation (49.1%) surpasses augmentation (47%), indicating growing user trust in AI’s ability to deliver reliable results. This trend may reflect improvements in model performance, such as the evolution from Claude Sonnet 3.6 to newer versions.
Interestingly, higher AI adoption at the national level correlates with *less* automation. Countries with greater per capita usage tend to use AI more collaboratively, while lower-adoption nations rely more on automated outputs. A 1% rise in population-adjusted usage links to a 3% drop in automation rates, suggesting that as familiarity grows, users shift toward interactive, exploratory modes.
Business use, analyzed via Anthropic’s API customers, reveals even starker patterns. Forty-four percent of API traffic involves computer and mathematical tasks, compared to 36% on the consumer-facing Claude.ai. Automation dominates in enterprise settings: 77% of API interactions are automated, mostly directive, versus just 12% augmentation. In contrast, consumer use is nearly evenly split between automation and collaborative workflows.
Cost does not deter business investment. More expensive tasks—those requiring more tokens—are used more frequently, indicating that companies prioritize value and capability over expense. This suggests that businesses view AI as a productivity driver worth the investment.
The findings underscore uneven AI integration: wealthier nations and knowledge-intensive sectors lead adoption, while usage patterns reflect local economic identities. Businesses are further ahead in automation, potentially signaling future labor market shifts. As AI becomes more capable, users are delegating more responsibility, though how this evolves remains to be seen.
An interactive platform allows public exploration of the data across U.S. states and occupations. Anthropic has also released the full dataset, supporting further research into AI’s economic impact.
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Anthropic Economic Index: Tracking AI’s role in the US and global economy
Travel planning in Hawaii, scientific research in Massachusetts, and building web applications in India. On the face of it, these three activities share very little in common. But it turns out that they’re the particular uses of Claude that are some of the most overrepresented in each of these places. n nThat doesn’t mean these are the most popular tasks: software engineering is still by far in the lead in almost every state and country in the world. Instead, it means that people in Massachusetts have been more likely to ask Claude for help with scientific research than people elsewhere – or, for instance, that Claude users in Brazil appear to be particularly enthusiastic about languages: they use Claude for translation and language-learning about six times more than the global average. n nThese are statistics we found in our third Anthropic Economic Index report. In this latest installment, we’ve expanded our efforts to document the early patterns of AI adoption that are beginning to reshape work and the economy. We measure how Claude is being used differently… n n…within the US: we provide the first-ever detailed assessment of how AI use differs between US states. We find that the composition of states’ economies informs which states use Claude the most per capita – and, surprisingly, that the very highest-use states aren’t the ones where coding dominates. n n…across different countries: our new analysis finds that countries’ use of Claude is strongly correlated with income, and that people in lower-use countries use Claude to automate work more frequently than those in higher-use ones. n n…over time: we compare our latest data with December 2024-January 2025 and February–March 2025. We find that the proportion of ‘directively’ automated tasks increased sharply from 27% to 39%, suggesting a rapid increase in AI’s responsibility (and in users’ trust). n n…and by business users: we now include anonymized data from Anthropic’s first-party API customers (in addition to users of Claude.ai), allowing us to analyze businesses’ interactions for the first time. We find that API users are significantly more likely to automate tasks with Claude than consumers are, which suggests that major labor market implications could be on the horizon. n nWe summarize the report below. In addition, we’ve designed an interactive website where you can explore our data yourself. For the first time, you can search for trends and results in Claude.ai use across every US state and all occupations we track, to see how AI is used where you live or by people in similar jobs. Finally, if you’d like to build on our analysis, we’ve made our dataset openly available, alongside the data from our previous Economic Index reports. n nGeography n nWe’ve expanded the Anthropic Economic Index to include geographic data. Below we cover what we’ve learned about how Claude is used across countries and US states. n nAcross countries n nThe US uses Claude far more than any other nation. India is in second place, followed by Brazil, Japan, and South Korea, each with similar shares. n nHowever, there is huge variation in population size across these countries. To account for this, we adjust each country’s share of Claude.ai use by its share of the world’s working population. This gives us our Anthropic AI Usage Index, or AUI. Countries with an AUI greater than 1 use Claude more often than we’d expect based on their working-age population alone, and vice-versa. n nFrom the AUI data, we can see that some small, technologically advanced countries (like Israel and Singapore) lead in Claude adoption relative to their working-age populations. This might to a large degree be explained by income: we found a strong correlation between GDP per capita and the Anthropic AI Usage Index (a 1% higher GDP per capita was associated with a 0.7% higher AUI). This makes sense: the countries that use Claude most often generally also have robust internet connectivity, as well as economies oriented around knowledge work rather than manufacturing. But it does raise a question of economic divergence: previous general-purpose technologies, like electrification or the combustion engine, led to both vast economic growth and a great divergence in living standards around the world. If the effects of AI prove to be largest in richer countries, this general-purpose technology might have similar economic implications. n nPatterns within the United States n nThe link between per capita GDP and per capita use of Claude also holds when comparing between US states. In fact, use rises more quickly within income here than across countries: a 1% higher per capita GDP inside the US is associated with a 1.8% higher population-adjusted use of Claude. That said, income actually has less explanatory power within the US than across countries, as there’s much higher variance within the overall trend. That is: other factors, beyond income, must explain more of the variation in population-adjusted use. n nWhat else could explain this adoption gap? Our best guess is that it’s differences in the composition of states’ economies. The highest AUI in the US is the District of Columbia (3.82), where the most disproportionately frequent uses of Claude are editing documents and searching for information, among other tasks associated with knowledge work in DC. Similarly, coding-related tasks are especially common in California (the state with the third-highest AUI overall), and finance-related tasks are especially common in New York (which comes in fourth).1 Even among states with lower population-adjusted use of Claude, like Hawaii, use is closely correlated to the structure of the economy: Hawaiians request Claude’s assistance for tourism-related tasks at twice the rate of the rest of America. Our interactive website contains plenty of other statistics like these. n nTrends in Claude use n nWe’ve been tracking how people use Claude since December 2024. We use a privacy-preserving classification method that categorizes anonymized conversation transcripts into task groups defined by O*NET, a US government database that classifies jobs and the tasks associated with them.2 By doing this, we can analyze both how the tasks that people give Claude have changed since last year, and how the ways people choose to collaborate—how much oversight and input into Claude’s work they choose to have—have changed too. n nTasks n nSince December 2024, computer and mathematical uses of Claude have predominated among our categories, representing around 37-40% of conversations. n nBut a lot has changed. Over the past nine months, we’ve seen consistent growth in “knowledge-intensive” fields. For example, educational instruction tasks have risen by more than 40 percent (from 9% to 13% of all conversations), and the share of tasks associated with the physical and social sciences has increased by a third (from 6% to 8%). In the meantime, the relative frequency of traditional business tasks has declined: management-related tasks have fallen from 5% of all conversations to 3%, and the share of tasks related to business and financial operations has halved, from 6% to 3%. (In absolute terms, of course, the number of conversations in each category has still risen significantly.) n nThe overall trend is noisy, but generally, as the GDP per capita of a country increases, the use of Claude shifts away from tasks in the Computer and Mathematical occupation group, and towards a diverse range of other activities, like education, art and design; office and administrative support; and the physical and social sciences. Compare the trend line in the first graph below to the remaining three: n nAll that said, software development remains the most common use in every single country we track. The picture looks similar in the US, although our sample size limits our ability to explore in more detail how the task mix varies with adoption rates. n nPatterns of interaction n nAs we’ve discussed previously, we generally distinguish between tasks that involve automation (in which AI directly produces work with minimal user input) and augmentation (in which the user and AI collaborate to get things done). We further break automation down into directive and feedback loop interactions, where directive conversations involve the minimum of human interaction, and in feedback loop tasks, humans relay real-world outcomes back to the model. We also break augmentation down into learning (asking for information or explanations), task iteration (working with Claude collaboratively), and validation (asking for feedback). n nSince December 2024, we’ve found that the share of directive conversations has risen sharply, from 27% to 39%. The shares of other interaction patterns (particularly learning, task iteration, and feedback loops) have fallen slightly as a result. This means that for the first time, automation (49.1%) has become more common than augmentation (47%) overall. One potential explanation for this is that AI is rapidly winning users’ confidence, and becoming increasingly responsible for completing sophisticated work. n nThis could be the result of improved model capabilities. (In December 2024, when we first collected data for the Economic Index, the latest version of Claude was Sonnet 3.6.) As models get better at anticipating what users want and at producing high-quality work, users are likely more willing to trust the model’s outputs at the first attempt. n nPerhaps surprisingly, in countries with higher Claude use per capita, Claude’s uses tend towards augmentation, whereas people in lower-use countries are much more likely to prefer automation. Controlling for the mix of tasks in question, a 1% increase in population-adjusted use of Claude is correlated with a roughly 3% reduction in automation. Similarly, increases in population-adjusted Claude use are associated with a shift away from automation (as in the chart below), not towards. n nWe’re not yet sure why this is. It could be because early adopters in each country feel more comfortable allowing Claude to automate tasks, or it could be down to other cultural and economic factors. n nBusinesses n nUsing the same privacy-preserving methodology we use for conversations on Claude.ai, we have begun sampling interactions from a subset of Anthropic’s first-party API customers, in a first-of-its-kind analysis.3 API customers, who tend to be businesses and developers, use Claude very differently to those who access it through Claude.ai: they pay per token, rather than a fixed monthly subscription, and can make requests through their own programs. n nThese customers’ use of Claude is especially concentrated in coding and administrative tasks: 44% of the API traffic in our sample maps to computer or mathematical tasks, compared to 36% of tasks on Claude.ai. (As it happens, around 5% of all API traffic focuses specifically on developing and evaluating AI systems.) This is offset by a smaller proportion of conversations related to educational occupations (4% in the API relative to 12% on Claude.ai), and arts and entertainment (5% relative to 8%). n nWe also find that our API customers use Claude for task automation much more often than Claude.ai users. 77% of our API conversations show automation patterns, of which the vast majority are directive, while just 12% show augmentation. On Claude.ai, the split is almost even. This could have significant economic implications: in the past, the automation of tasks has been associated with large economic transitions, as well as major productivity gains. n nFinally, given how API use is paid for, we can also explore whether differences in the cost of tasks (caused by differences in the number of tokens they consume) affect which tasks businesses choose to “buy”. Here, we find a positive correlation between price and use: higher-cost task categories tend to see more frequent use, as in the graph below. This suggests to us that fundamental model capabilities, and the economic value generated by the models, matters more to businesses than the cost of completing the task itself. n nConclusion n nThe Economic Index is designed to provide an early, empirical assessment of how AI is affecting people’s jobs and the economy. What have we found so far? n nAcross each of the measures we cover in this report, the adoption of AI appears remarkably uneven. People in higher-income countries are more likely to use Claude, more likely to seek collaboration rather than automation, and more likely to pursue a breadth of uses beyond coding. Within the US, AI use seems to be strongly influenced by the dominant industries in local economies, from technology to tourism. And businesses are more likely to entrust Claude with agency and autonomy than consumers are. n nBeyond the fact of unevenness, it’s especially notable to us that directive automation has become much more common in conversations on Claude.ai over the past nine months. The nature of people’s use of Claude is evidently still being defined: we’re still collectively deciding how much confidence we have in AI tools, and how much responsibility we should give them. So far, though, it looks like we’re becoming increasingly comfortable with AI, and willing to let it work on our behalf. We’re looking forward to revisiting this analysis over time, to see where—or, indeed, if—users’ choices settle as AI models improve. n nIf you’d like to explore our data yourself, you can do so on our dedicated Anthropic Economic Index website, which contains interactive visualizations of our country, state, and occupational data. We’ll update this website with more data in future, so you can continue to track the evolution of AI’s effects on jobs and the economy in the ways that interest you. n nOur full report is available here. We hope it helps policymakers, economists and others more effectively prepare for the economic opportunities and risks that AI provides. n nOpen data n nAs with our past reports, we’re releasing a comprehensive dataset for this release, including geographic data, task-level use patterns, automation/augmentation breakdowns by task, and an overview of API use. Data are available for download at the Anthropic Economic Index website. n nWork with us