Estimating AI productivity gains from Claude conversations

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.

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.

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.

Analyzing real-world transcripts enables us to account for intra-task variation . For instance, even if the overall share of designing manufacturing equipment tasks stays fixed, transcript-level information lets us see whether people tackle more complex, longer-timescale projects (or attain greater time savings) with AI over time. Our Economic Index will track how these estimates evolve over time, and share aggregate datasets that researchers can use to make their own forecasts and conclusions.

Human time estimates show that Claude handles tasks of very different lengths depending on the occupation. In the below plots, we show averages for each occupation category among the subset of tasks that Claude is used for 1 . The average management task where Claude is used (e.g. selecting investments) is estimated to take humans 2.0 hours to complete, followed by legal (1.8 hours), education (1.7), and arts/media tasks (1.6). At the other end of the spectrum, food preparation tasks (e.g. planning or pricing menu items), installation/maintenance, and transportation tasks all take 0.3-0.5hrs on average, suggesting more circumscribed tasks, or tasks with less waiting time. Given that Claude’s time estimates tend to underestimate long tasks and overestimate short tasks, it is possible that these differences might be even greater in practice.

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.