What if Anthropic had named its chatbot Claudia

By Jean Rhodes

I first raised this idea with my sister, a computer scientist. But I keep coming back to it. What if Anthropic had named its chatbot Claudia instead of Claude. It sounds like a trivial branding choice, but names shape perception in ways we rarely notice until we notice them, and now I can’t stop noticing this one.

That’s because every day, millions of people around the world open an app and ask Claude to write code, debug a model, explain a proof, or solve a hard technical problem. Every day, headlines and casual conversation describe what he did and how he solved it. He wrote the script. He caught the bug. He explained the theorem. Well done Claude, or shall I say Bien joué, since your name is vaguely French. It is such a small pronoun, and yet it reinforces an old and stubborn association, the one that links intelligence, mathematics, and technical mastery with maleness. If that assistant had been named Claudia, every one of those sentences would have been rewritten. She wrote the script. She caught the bug. She explained the theorem. Multiply that by the scale at which people now use these tools and you get a genuinely large cultural intervention, delivered for free, simply by choosing a different name.

This may have shaped countless identities, since computer science and artificial intelligence remain fields where women are still a minority at every level. Global estimates suggest women hold only around a quarter of AI related roles and stem, with the share shrinking dramatically further as seniority increases (Interface EU, 2024; World Economic Forum, 2025). The OECD’s Digital Economy Outlook found that men authored roughly 90 percent of scientific publications on AI in 2023, and that women were the sole authors of only about 8 percent of AI papers worldwide (OECD, 2024). These numbers describe a field that continues to code itself, quite literally, as a man’s domain.

The research on mentoring makes clear why representation of this kind matters so much. Dasgupta and her colleagues at UMass Amherst ran a multiyear field experiment in which incoming women engineering students were randomly assigned a female peer mentor, a male peer mentor, or no mentor at all. The results were striking. Women with female mentors showed greater belonging, motivation, and confidence in engineering, and by the end of the first year, essentially all of them remained in their engineering majors, compared with an 18 percent dropout rate among women assigned male mentors and 11 percent among those with no mentor (Dennehy & Dasgupta, 2017). A follow up study tracking the same women through graduation and beyond found that the benefits of having a female peer mentor in that first year persisted for years afterward, shaping not just retention but internship success and postgraduate aspirations (Wu et al., 2022). Male mentors simply did not produce the same protective effect. What mattered was seeing someone like yourself succeed in the space you were trying to enter.

This is the same mechanism that a name like Claudia could have activated at enormous scale. Representation does not need to come from a formal mentoring program to shift how young women see themselves in technical fields. It can come from language itself, from the pronouns and identities embedded in the tools people use every single day. When women interact with an AI system and are told, implicitly, that a female presence is capable of writing elegant code and solving difficult mathematical problems, that message accumulates the same way a mentor’s presence accumulates, building a sense of belonging that formal diversity statements rarely achieve. Nosek, Banaji, and Greenwald’s landmark study, memorably titled Math equals male, me equals female, therefore math does not equal me, found that college students, especially women, held strong implicit associations linking mathematics with maleness, and that these associations predicted more negative attitudes toward math and weaker math identity among women even when they had chosen math intensive majors (Nosek et al., 2002). Related research using the Implicit Association Test found that roughly 72 percent of nearly 300,000 website visitors exhibited an implicit stereotype linking science with men rather than women (Nosek, Smyth, et al., 2007. The Draw a Scientist literature tells much the same story from the other direction. A meta-analysis spanning five decades and nearly 21,000 children found that while the proportion of children drawing female scientists has risen over time, children still increasingly draw scientists as male as they age, moving from roughly equal representation at ages 5 and 6 to drawing male scientists three to four times as often by the time they reach high school (Miller et al., 2018). A related meta-analysis of ability stereotypes found that by age 6, children already believe boys are better than girls at computer science and engineering specifically, with that stereotype strengthening rather than weakening with age (American Institutes for Research, 2024).

These associations are are learned, absorbed from the images and language children and adults encounter, and what Claudia could have chipped away at, one interaction at a time.  Instead we got another entry in a long list of hes, another data point reinforcing the pattern the mentoring research and the implicit bias research both warn us about.

References

American Institutes for Research. (2024). The development of children’s gender ability stereotypes. https://www.air.org/sites/default/files/2024-12/gender-ability-stereotypes.pdf

Dennehy, T. C., & Dasgupta, N. (2017). Female peer mentors early in college increase women’s positive academic experiences and retention in engineering. Proceedings of the National Academy of Sciences, 114(23), 5964–5969. https://www.pnas.org/doi/10.1073/pnas.1613117114

Interface EU. (2024). AI’s missing link: The gender gap in the talent pool. https://www.interface-eu.org/publications/ai-gender-gap

Miller, D. I., Nolla, K. M., Eagly, A. H., & Uttal, D. H. (2018). The development of children’s gender science stereotypes: A meta-analysis of 5 decades of U.S. Draw-A-Scientist studies. Child Development, 89(6), 1943–1955. https://onlinelibrary.wiley.com/doi/full/10.1111/cdev.13039

Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = male, me = female, therefore math ≠ me. Journal of Personality and Social Psychology, 83(1), 44–59. https://banaji.sites.fas.harvard.edu/research/publications/articles/2002_Nosek_JPSP.pdf

OECD. (2024). OECD digital economy outlook 2024 (Vol. 2): The potential of women in the digital economy. https://www.oecd.org/en/publications/2024/11/oecd-digital-economy-outlook-2024-volume-2_9b2801fc.html

World Economic Forum. (2025). Gender parity in the intelligent age. https://reports.weforum.org/docs/WEF_Gender_Parity_in_the_Intelligent_Age_2025.pdf

Wu, D. J., Fowler, C., Dasgupta, N., et al. (2022). Female peer mentors early in college have lasting positive impacts on female engineering students that persist beyond graduation. Nature Communications, 13, Article 6837. https://pubmed.ncbi.nlm.nih.gov/36369261/