Anthropic Economic Index January 2026 Report

Anthropic Economic Index report: Economic primitives \ Anthropic Economic Research Anthropic Economic Index report: Economic primitives Jan 15, 2026 Download PDF

Introduction How is AI reshaping the economy?

This report introduces new metrics of AI usage to provide a rich portrait of interactions with Claude in November 2025, just prior to the release of Opus 4.5. These “primitives”—simple, foundational measures of how Claude is used, which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts—cover five dimensions relevant to AI’s economic impact: user and AI skills, how complex tasks are, the degree of autonomy afforded to Claude, how successful Claude is, and whether Claude is used for personal, educational, or work purposes.

The results reveal striking geographic variation, real-world estimates of AI task horizons, and a basis for revised assessments of Claude's macroeconomic impact.

The data we release alongside this report are the most comprehensive to date, covering five new dimensions of AI use, consumer and firm use, and country and region breakdowns for Claude.ai.

What has changed since our last report

In the first chapter, we revisit findings from our previous Economic Index report published in September 2025. We find:

Claude usage remains concentrated among certain tasks, most of them related to coding While we see over 3,000 unique work tasks in Claude.ai, the top 10 most common tasks account for 24% of our sampled conversations, a slight increase since our last report. Augmentation patterns (conversations where the user learns, iterates on a task, or gets feedback from Claude) edged to just over half of conversations on Claude.ai. In contrast, automated use remains dominant in 1P API traffic, reflecting its programmatic nature. Global usage remains persistently uneven while US states converge The US, India, Japan, the UK, and South Korea lead in overall Claude.ai use. Worldwide, uneven adoption remains well-explained by GDP per capita. Within the US, workforce composition plays a key role in shaping uneven adoption as states with more computer and mathematical professionals show systematically more Claude usage.

While substantial concentration remains, since our last report Claude usage has become noticeably more evenly distributed across US states. If sustained, usage per capita would be equalized across the country in 2-5 years. Introducing and analyzing our new economic primitives In the second chapter we discuss the motivation for and introduce our new economic primitives, including how they were selected and operationalized, and their limitations. We additionally present evidence that our primitives capture directionally accurate aspects of underlying usage patterns as compared to external benchmarks. In chapters three and four we use these primitives to further investigate implications for adoption and productivity. We find:

Claude use diversifies with higher adoption and income While the most common use of Claude is for work, coursework use is highest in countries with the lowest GDP per capita, while rich countries show the highest rates of personal use. This aligns with a simple adoption curve story: early adopters in less developed countries tend to be technical users with specific, high-value applications or use Claude for education, whereas mature markets see usage diversify toward casual and personal purposes. Claude succeeds on most tasks, but less so on the most complex ones We find that Claude generally succeeds at the tasks it is given, and that the education level of its responses tends to match the user's input. Claude struggles on more complex tasks: As the time it would take a human to do the task increases, Claude’s success rate falls, much like prominent evals measuring the longest tasks that AIs can reliably perform . Job exposure to AI looks different when success rates are factored in We also use the success rate primitive to better understand job exposure to AI, calculating the share of each occupation that Claude can perform by weighting task coverage by both success rates and the importance of each task within the job. For some occupations, like data entry keyers and database architects, Claude shows proficiency in large swaths of the job. Claude is used for higher-skill tasks than those in the broader economy The tasks we observe in Claude usage tend to require more education than those in the broader economy. If we assume that AI-assisted tasks diminish as a share of worker responsibilities, removing them would leave behind less-skilled work. But this simple task displacement would not affect white-collar workers uniformly—for some occupations it removes the most skill-intensive tasks, for others the least.

Without the tasks that we observe Claude performing, travel agents would experience deskilling as complex planning work gives way to routine ticket purchasing and payment collection. Property managers, by contrast, would experience upskilling as bookkeeping tasks give way to contract negotiations and stakeholder management.

A new window for understanding AI’s impact on the economy These results provide a new window into how AI is currently impacting the economy. Knowing the success rate of tasks gives a more accurate picture of which tasks might be automated, how impacted certain jobs might be, and how labor productivity will change. Measuring differential performance by user education sheds light on inequality effects.

Indeed, the close relationship between education levels in inputs and outputs signals that countries with higher educational attainment may be better positioned to benefit from AI, independent of adoption rates alone.

This data release aims to enable researchers and the public to better understand the economic implications of AI and investigate the ways in which this transformative technology is already having an effect.

Chapter 1: What has changed since our last report Overview Because frontier AI model capabilities are improving rapidly and adoption has been swift, it is important to regularly take stock of changes in how people and businesses are using such systems—and what this usage implies for the broader economy. 1

In this chapter we analyze how Claude usage and diffusion patterns changed from August 2025 to November 2025 just prior to the release of Opus 4.5. We make four observations: Usage remains highly concentrated…