Estimating Productivity Gains

Estimating AI productivity gains \ Anthropic Economic Research Estimating AI productivity gains from Claude conversations Nov 25, 2025 Read as a PDF

Overview What do real conversations with Claude tell us about the effects of AI on labor productivity? Using our privacy-preserving analysis method , we sample one hundred thousand real conversations from Claude.ai , estimate how long the tasks in these conversations would take with and without AI assistance, and study the productivity implications across the broader economy. Based on Claude’s estimates, these tasks would take on average about 90 minutes to complete without AI assistance, and Claude speeds up individual tasks by about 80%. Extrapolating these estimates out suggests current-generation AI models could increase US labor productivity growth by 1.8% annually over the next decade — roughly twice the run rate in recent years. But this isn’t a prediction of the future, since we don’t take into account the rate of adoption or the larger productivity effects that would come from much more capable AI systems. Our analysis has limits. Most notably, we can’t account for additional time humans spend on tasks outside of their conversations with Claude, including validating the quality or accuracy of Claude's work. But as AI models get better at time estimation, we think our methods in this research note could become increasingly useful for understanding how AI is shaping real work. Here’s a more detailed summary of our results: Across one hundred thousand real world conversations, Claude estimates that AI reduces task completion time by 80%. We use Claude to evaluate anonymized Claude.ai transcripts to estimate the productivity impact of AI. According to Claude’s estimates, people typically use AI for complex tasks that would, on average, take people 1.4 hours to complete. By matching tasks to O*NET occupations and BLS wage data, we estimate these tasks would otherwise cost $55 in human labor. The estimated scope, cost, and time savings of tasks varies widely by occupation. Based on Claude’s estimates, people use Claude for legal and management tasks that would have taken nearly two hours, but for food preparation tasks that would have taken only 30 minutes. And we find that healthcare assistance tasks can be completed 90% more quickly, whereas hardware issues see time savings of 56%. This doesn’t account for the time that humans might spend on these tasks beyond their conversation on Claude.ai , however, so we think these estimates might overstate current productivity effects to at least some degree. Extrapolating these results to the economy, current generation AI models could increase annual US labor productivity growth by 1.8% over the next decade. This would double the annual growth the US has seen since 2019, and places our estimate towards the upper end of recent estimates . Taking as given Claude’s estimates of task-level efficiency gains, we use standard methods to calculate a 1.8% implied annual increase in US labor productivity over the next ten years. However, this estimate does not account for future improvements in AI models (or more sophisticated uses of current technology), which could significantly magnify AI’s economic impact. As AI accelerates some tasks, others may become bottlenecks : We see large speedups for some tasks and much smaller ones in others, even within the same occupational groups. Where AI makes less of a difference, these tasks might become bottlenecks, potentially acting as a constraint on growth.

This gives us a new lens for understanding how AI’s economic impacts over time, which we will track going forward as part of our Economic Index : Computing these estimates based on real-world Claude conversations gives us a new lens to understand AI productivity. This complements other approaches, like lab studies in narrow domains, or government statistics which provide more coarse-grained insights. We will track how these estimates change over time to get an evolving picture of these issues as capabilities and adoption continue to progress. An overview of our method and some of our main results. See below for how we validate Claude’s estimates, the assumptions we make, and limitations of our analysis. Introduction As part of the Anthropic Economic Index , we have documented how people use Claude across different tasks, industries, and places. We’ve captured the breadth of uses—how people use Claude for legal, scientific, and programming tasks—but not their depth . How substantial are the tasks for which people use Claude, and how much time does Claude save them? The current version of the Economic Index can't capture this within-task heterogeneity —for instance, it can’t distinguish report-writing tasks that take five minutes from those that take five days, or financial modeling tasks that take an afternoon from those that take a few weeks. This makes it difficult to assess AI's economic effects: a software developer might use Claude to write ten pull requests in a day, but if nine are minor documentation updates and one is a critical infrastructure change, simply counting the number of these tasks performed with Claude misses the point. Not only that, but as model capabilities improve, we want to understand whether they do higher-value work. To understand how AI is reshaping work and productivity, we need to know not just which tasks Claude handles, but how substantial those tasks and time savings are. Several groups have begun conducting randomized controlled trials to measure productivity gains in narrow domains, including software engineering tasks , writing , and customer service . METR's work on measuring AI’s ability to complete long tasks has demonstrated that AI systems can independently tackle extended, multi-step challenges. But these evaluations consider a narrow set of problems, rather than broad real-world use. To assess AI’s overall impact on the economy, we need a way to analyze hundreds or thousands of real-world AI applications. This report takes a first step toward that goal. It uses Claude to estimate how much time it would take a human to complete the tasks that Claude handles, compares that to how long Claude and the human took together, and thereby calculates how much time the AI has saved. While AI models lack context about users' expertise, workflows, and constraints, we find that model-estimated times show promising accuracy for a dataset of…