
Anthropic study finds men use AI coding agents more than twice as often as women in social science research
Quick Answer
An Anthropic study reveals that researchers with typically male names utilize AI coding agents over twice as frequently as their female counterparts in social science, with economists at 39% usage compared to just 4% in education research.
Quick Take
An Anthropic study reveals that researchers with typically male names utilize AI coding agents over twice as frequently as their female counterparts in social science, with economists at 39% usage compared to just 4% in education research. This significant gender gap in coding agent usage exceeds that of general AI utilization.
Key Points
- Male-named researchers use AI coding agents over twice as often as female-named researchers.
- Economists lead in AI coding agent usage at 39%, while education researchers only reach 4%.
- The gender gap in coding agent usage is significantly larger than in general AI use.
Article Excerpt
From source RSS / original summaryResearchers with typically male names use coding agents more than twice as often as those with typically female names, even within the same discipline and career level, according to an Anthropic study. Economists lead at 39 percent, while education researchers sit at just four percent. The gender gap for coding agents is far wider than for general AI use. The article Anthropic study finds men use AI coding agents more than twice as often as women in social science research appeared first on The Decoder.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from The Decoder
See more →
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's MirrorCode benchmark reveals Claude Opus 4.7 as the leader with a 56% solve rate, reconstructing a 16,000-line toolkit in 14 hours. Despite this, all models tested struggle with the most complex tasks, highlighting limitations in current AI capabilities. The single task consumed $2,600 over 19 days, raising questions about cost-effectiveness in AI development.

