Anthropic Research: Scientists Use AI Where It Is Most Capable, Not Where They Consider It Most Important
Anthropic analyzed 207,000 research sessions and found that AI usage closely tracks its theoretical capabilities (related to reinforcement learning suitability and cognitive nature) rather than the stages scientists self-report as most important. Idea generation and data collection are considered critical but receive the least AI usage.
The Anthropic research team analyzed 207,049 research sessions conducted between August and September 2026, covering 34 disciplines and 7,262 research tasks, reaching a counterintuitive conclusion: scientists use AI where it is most capable, not where they believe it is most important. In surveys, researchers ranked idea generation and hypothesis formulation as the most critical tasks for producing impactful results, with data collection ranking second. However, in actual usage, data analysis accounted for approximately one-third of Claude sessions and nearly half of Claude Code sessions, while idea generation comprised only 4.2% of Claude sessions and 2.1% of Claude Code sessions.
This usage pattern is not random; it closely follows AI's theoretical capabilities. The research team constructed capability scores for each task using a Bradley-Terry model—the same methodology used for chess Elo ratings—by having language models compare pairs of task steps to determine which AI could perform better, then aggregating these into task-level scores. The results showed that the top 20% of tasks by capability score (such as data visualization and statistical testing) received two to four times more AI usage than the time researchers spent on them would predict. Conversely, the bottom 20% of tasks (such as purifying reaction mixtures or participant observation) saw almost no usage. Capability scores were highly correlated with three factors: whether the task was suitable for reinforcement learning (i.e., whether it could be repeatedly attempted in a computational environment with immediate feedback), whether the work was predominantly cognitive rather than physical or interpersonal, and whether regulatory constraints limited AI use. In short, scientists appear to use AI where it works best.