Economic-Index_compressed

The Anthropic Economic Index report: Uneven geographic and enterprise AI adoption Authors: Ruth Appel*, Peter McCrory*, Alex Tamkin* Miles McCain, Tyler Neylon, Michael Stern Acknowledgements: Helpful comments, discussions, and other assistance: Alex Sanchez, Andrew Ho, Ankur Rathi, Asa Kittner, Ben Merkel, Bianca Lindner, Biran Shah, Carl De Torres, Cecilia Callas, Daisy McGregor, Dario Amodei, Deep Ganguli, Dexter Callender III, Esin Durmus, Evan Frondorf, Heather Whitney, Jack Clark, Jakob Kerr, Janel Thamkul, Jared Kaplan, Jared Mueller, Jennifer Martinez, Kaileen Kelly, Kamya Jagadish, Katie Streu, Keir Bradwell, Kelsey Nanan, Kevin Troy, Kim O’Rourke, Kunal Handa, Landon Goldberg, Linsey Fields, Lisa Cohen, Lisa Rager, Maria Gonzalez, Mengyi Xu, Michael Sellitto, Mike Schiraldi, Olivia Chen, Paola Renteria, Rebecca Jacobs, Rebecca Lee, Ronan Davy, Ryan Donegan, Saffron Huang, Sarah Heck, Stuart Ritchie, Sylvie Carr, Tim Belonax, Tina Chin, Zoe Richards *Lead authors. Contributed equally to this report. Published: September 15, 2025 The Anthropic Economic Index Report2 Introduction AI differs from prior technologies in its unprecedented adoption speed. In the US alone, 40% of employees report using AI at work, up from 20% in 2023 two years ago. 1 Such rapid adoption reflects how useful this technology already is for a wide range of applications, its deployability on existing digital infrastructure, and its ease of use—by just typing or speaking—without specialized training. Rapid improvement of frontier AI likely reinforces fast adoption along each of these dimensions. Historically, new technologies took decades to reach widespread adoption. Electricity took over 30 years to reach farm households after urban electrification. The first mass-market personal computer reached early adopters in 1981, but did not reach the majority of homes in the US for another 20 years. Even the rapidly-adopted internet took around five years to hit adoption rates that AI reached in just two years. 2 Why is this? In short, it takes time for new technologies—even transformative ones—to diffuse throughout the economy, for consumer adoption to become less geographically concentrated, and for firms to restructure business operations to best unlock new technical capabilities. Firm adoption, first for a narrow set of tasks, then for more general purpose applications, is an important way that consequential technologies spread and have transformative economic effects. 3 In other words, a hallmark of early technological adoption is that it is concentrated—in both a small number of geographic regions and a small number of tasks in firms. As we document in this report, AI adoption appears to be following a similar pattern in the 21st century, albeit on shorter timelines and with greater intensity than the diffusion of technologies in the 20th century. To study such patterns of early AI adoption, we extend the Anthropic Economic Index along two important dimensions, introducing a geographic analysis of Claude.ai conversations and a first-of-its-kind examination of enterprise API use. We show how Claude usage has evolved over time, how The Anthropic Economic Index Report3 adoption patterns differ across regions, and—for the first time—how firms are deploying frontier AI to solve business problems. Changing patterns of usage on Claude.ai over time In the first chapter of this report, we identify notable changes in usage on Claude.ai over the previous eight months, occurring alongside improvements in underlying model capabilities, new product features, and a broadening of the Claude consumer base. We find: • Education and science usage shares are on the rise. While the use of Claude for coding continues to dominate our total sample at 36%, educational tasks surged from 9.3% to 12.4%, and scientific tasks from 6.3% to 7.2%. • Users are entrusting Claude with more autonomy. “Directive” conversations, where users delegate complete tasks to Claude, jumped from 27% to 39%. We see increased program creation in coding (+4.5pp) and a reduction in debugging (-2.9pp)—suggesting that users might be able to achieve more of their goals in a single exchange. The geography of AI adoption For the first time, we release geographic cuts of Claude.ai usage data across 150+ countries and all U.S. states. To study diffusion patterns, we introduce the Anthropic AI Usage Index (AUI) to measure whether Claude.ai use is over- or underrepresented in an economy relative to its working age population. We find: • The AUI strongly correlates with income across countries. As with previous technologies, we see that AI usage is geographically concentrated. Singapore and Canada are among the highest countries in terms of usage per capita at 4.6x and 2.9x what would be expected based on their population, respectively. In contrast, emerging economies, including Indonesia at 0.36x, India at 0.27x and Nigeria at 0.2x, use Claude less. • In the U.S., local economy factors shape patterns of use. DC leads per- capita usage (3.82x population share), but Utah is close behind (3.78x). We The Anthropic Economic Index Report4 see evidence that regional usage patterns reflect distinctive features of the local economy: For example, elevated use for IT in California, for financial services in Florida, and for document editing and career assistance in DC. • Leading countries have more diverse usage. Lower-adoption countries tend to see more coding usage, while high-adoption regions show diverse applications across education, science, and business. For example, coding tasks are over half of all usage in India versus roughly a third of all usage globally. • High-adoption countries show less automated, more augmented use. After controlling for task mix by country, low AUI countries are more likely to delegate complete tasks (automation), while high-adoption areas tend toward greater learning and human-AI iteration (augmentation). The uneven geography of early AI adoption raises important questions about economic convergence. Transformative technologies of the late 19th century and the early 20th centuries—widespread electrification, the internal combustion engine, indoor plumbing—not only ushered in the era of modern economic growth but accompanied a large divergence in living standards around the world. 4 If the productivity gains are larger for high-adoption economies, current usage patterns suggest that the benefits of AI may concentrate in already-rich regions—possibly increasing global economic inequality and reversing growth convergence seen in recent decades. 5 Systematic enterprise deployment of AI In the final chapter, we present first-of-its-kind insight on a large fraction of our first-party (1P) API traffic, revealing the tasks companies and developers are using Claude to accomplish. Importantly, API users access Claude programmatically, rather than through a web user interface (as with Claude. ai). This shows how early-adopting businesses are deploying frontier AI capabilities. We find: • 1P API usage, while similar to Claude.ai use, differs in specialized ways. Both 1P API usage and Claude.ai usage focus heavily on coding tasks. However, 1P API usage is higher for coding and office/admin tasks, while The Anthropic Economic Index Report5 Claude.ai usage is higher for educational and writing tasks. • 1P API usage is automation dominant. 77% of business uses involve automation usage patterns, compared to about 50% for Claude.ai users. This reflects the programmatic nature of API usage. • Capabilities seem to matter more than cost in shaping business deployment. The most-used tasks in our API data tend to cost more than the less frequent ones. Overall, we find evidence of weak price sensitivity. Model capabilities and the economic value of feasibly automating a given task appears to play a larger role in shaping businesses’ usage patterns. • Context constrains sophisticated use. Our analysis suggests that curating the right context for models will be important for high-impact deployments of AI in complex domains. This implies that for some firms costly data modernization and organizational investments to elicit contextual information may be a bottleneck for AI adoption. Open source data to catalyze independent research As with previous reports, we have open-sourced the underlying data to support independent research on the economic effects of AI. This comprehensive dataset includes task-level usage patterns for both Claude.ai and 1P API traffic (mapped to the O*NET taxonomy as well as bottom-up categories), collaboration mode breakdowns by task, and detailed documentation of our methodology. At present, geographic usage patterns are only available for Claude.ai traffic. Key questions we hope this data will help others to investigate include: • What are the local labor market consequences for workers and firms of AI usage & adoption? • What determines AI adoption across countries and within the US? What can be done to ensure that the benefits of AI do not only accrue to already-rich economies? • What role, if any, does cost-per-task play in shaping enterprise deployment patterns? • Why are firms able to automate some tasks and not others? What implications does this have for which types of workers will experience better The Anthropic Economic Index Report6 or worse employment prospects? 1 Gallup 2025, AI Use at Work Has Nearly Doubled in Two Years. 2 Bick, Blandin, Deming, 2024 The Rapid Adoption of Generative AI benchmark AI adoption against adoption of PC and the internet; Lewis & Severnini, 2020 Short- and long-run impacts of rural electrification: Evidence from the historical rollout of the U.S. power grid analyze the impact of bringing electricity to rural areas on economic outcomes. 3 Kalyani, Bloom, Carvalho, Hassan, Lerner and Ahmed Tahoun 2025 Diffusion of New Technologies. 4 See Gordon, 2012 Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds for a comparison of early and late 20th century innovations and their impact on productivity. Pritchett, 1997. Divergence, Big Time documents economic divergence that accompanied transition to era of modern economic growth. 5 Kremer, Willis, You, 2022 Converging to Convergence present evidence of growth convergence in recent decades. See Jones, Jones, and Aghion, 2017 Artificial Intelligence and Economic Growth for discussion of growth implications AI-powered automation of innovation. The Anthropic Economic Index Report7 Chapter 1: Claude.ai Usage Over Time Overview Understanding how AI adoption evolves over time can help predict its economic impacts—from productivity gains to workforce changes. With data spanning from December 2024 and January 2025 (from our first report, ‘V1’) to February and March 2025 (‘V2’) to our newest insights from August 2025 (‘V3’), we can track how AI usage has shifted over the past eight months as capabilities and product features have improved, new kinds of users have adopted the technology, and uses have become more sophisticated. We view the evidence presented below as suggesting that new product features have enabled new forms of work rather than simply accelerating adoption for existing tasks. How Claude.ai usage for economic tasks has changed Educational and scientific tasks continue their rise in relative importance While computer and mathematical tasks still dominate overall usage at 36%, we are seeing sustained growth in knowledge-intensive fields. Educational Instruction and Library tasks rose from 9% in V1 to 12% in V3. Life, Physical, and Social Science tasks increased from 6% to 7%. Meanwhile, the relative share of Business and Financial Operations tasks fell from 6% to 3%, and Management dropped from 5% to 3%. This divergence suggests AI usage may be diffusing especially quickly among tasks involving knowledge synthesis and explanation, compared to traditional business operations—possibly because these tasks benefit more from Claude’s reasoning capabilities. The Anthropic Economic Index Report8 Jan 2025Mar 2025Aug 2025 0 10 20 30 40 Percentage 37.2% 39.6% 36.9% Computer and Mathematical Jan 2025Mar 2025Aug 2025 0 2 4 6 8 10 Percentage 10.2% 9.4% 8.5% Arts, Design, Entertainment, Sports, and Media Jan 2025Mar 2025Aug 2025 0 2 4 6 8 10 12 14 Percentage 9.3% 11.0% 12.7% Educational Instruction and Library Jan 2025Mar 2025Aug 2025 0 2 4 6 8 Percentage 7.8% 7.0% 8.4% Office and Administrative Support Jan 2025Mar 2025Aug 2025 0 1 2 3 4 5 6 7 8 Percentage 6.3% 6.8% 7.4% Life, Physical, and Social Science Jan 2025Mar 2025Aug 2025 0 1 2 3 4 5 6 Percentage 5.9% 4.4% 3.1% Business and Financial Operations Jan 2025Mar 2025Aug 2025 0 1 2 3 4 5 Percentage 4.7% 3.8% 2.5% Architecture and Engineering Jan 2025Mar 2025Aug 2025 0 1 2 3 4 5 Percentage 4.5% 3.1% 2.7% Management Usage share trends across economic index reports (V1 to V3) Figure 1.1: Claude.ai usage over time Each panel shows the share of sampled conversations on Claude.ai associated with tasks