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 annual US labor productivity growth by 1.8% 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 what might happen once AI models improve further.

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.