Gender confidence gap in AI literacy revealed by new survey

Survey reveals gender gap in AI knowledge claims

A survey of more than 5,400 Americans, reported by WIRED, found that men are far more likely than women to claim familiarity with AI figures, including fictional ones. According to the survey, 73% of male respondents said they knew who Sam Altman was, compared to a lower percentage of women. Even more striking, 53% of men claimed to recognize fictional AI executives named Milner and Dalton, who do not exist. In contrast, 40% of women made the same claim.

The survey also revealed that men use AI tools more frequently and answer factual questions about AI more accurately. However, both genders answered incorrectly at similar rates, suggesting that men’s confidence may not always align with their actual knowledge. Women were more likely to admit uncertainty, averaging 3.3 "not sure" responses out of 12 questions, compared to 2.3 for men.

Media representation favors male AI experts

WIRED’s analysis of media coverage over a year found that men were featured in AI-related headlines more than 14 times as often as women. This disparity persists despite women making up 29% of the AI talent pool, according to the study. Additionally, three-quarters of AI subject experts quoted in media stories were men.

Laura Manley, Executive Director of Harvard Kennedy School’s Shorenstein Center, emphasized that this imbalance reinforces stereotypes about who is considered an expert in AI. The findings suggest that media representation may contribute to the confidence gap observed in the survey.

Emotional responses to AI differ by gender

The survey also explored emotional responses to AI, revealing further gender differences. While both men and women rated "concern" as their top emotional response, women reported higher levels of fear and worry. Men, on the other hand, rated positive emotions like curiosity, enthusiasm, and excitement significantly higher than women did.

These differences highlight how perceptions of AI may be shaped by broader societal attitudes toward technology and risk.

AI’s growing role in scientific problem-solving

AI’s ability to solve complex theoretical problems in scientific fields has surged between mid-2024 and mid-2026. Researchers found that AI models improved from solving just 2% of such problems to 35% during this period. The models were tested on problems published after their training cutoff, ensuring they solved them independently rather than relying on existing data.

OpenAI’s solution to the Navier-Stokes problem, a long-standing grand challenge in mathematics, was a notable milestone. However, the achievement sparked controversy when a mathematician at NYU accused OpenAI of gazumping their work after learning of their progress. This incident reflects broader tensions about AI’s role in research and whether the process of discovery is as valuable as the solution itself.

Some mathematicians are now calling for limits on AI’s use in problem-solving, arguing that the journey of discovery often yields insights that the final answer alone cannot provide. Murati, CEO of Thinking Machine Labs and former CTO of OpenAI, cautioned that while AI can produce solutions, verifying their accuracy remains a challenge. She noted that it is "very easy for a model to tell you ‘this is the right answer,’ and when you look at the entire solution, you start to see maybe it is not."

Background: AI’s expanding influence

AI’s rapid advancement has sparked debates about its role in society, from consumer products like AI-powered wearables to its integration into scientific research. For example, AI startups are exploring new form factors, such as pendants and robotics, to make AI more accessible in daily life.

Companies like Anthropic are expanding their global presence, emphasizing AI safety and collaboration with governments. Meanwhile, predictions about AI’s future capabilities, such as a "ChatGPT moment" for robotics, highlight both excitement and caution within the industry.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

This survey exposes more than just a gap in AI knowledge—it reveals a broader cultural issue about how confidence and expertise are perceived and represented. For founders and operators in the AI space, the findings underscore the importance of fostering inclusive environments where all voices are heard and represented. The media’s role in shaping perceptions cannot be overlooked; if women are underrepresented as experts, it reinforces the idea that AI is a male-dominated field, which may discourage women from engaging with the technology.

The debate over AI’s role in scientific research is equally critical. While AI’s problem-solving capabilities are undeniably powerful, the pushback from mathematicians highlights a tension between efficiency and the value of human-driven discovery. For startups building AI tools for research or other fields, this suggests a need to balance automation with transparency and ethical considerations. If users or stakeholders cannot verify AI-generated solutions, trust in the technology could erode.

Key takeaways

• Founders should prioritize inclusive messaging and representation in AI to avoid perpetuating gender stereotypes and confidence gaps.
• Media and industry narratives shape public perception—ensure diverse voices are amplified to encourage broader participation in AI.
• AI’s rapid advancements in problem-solving require transparency and verification mechanisms to maintain trust, especially in high-stakes fields like research.
• Women’s higher levels of uncertainty about AI suggest a need for targeted education and outreach to build confidence and engagement.
• The controversy over AI solving theoretical problems highlights the need to balance automation with the value of human-driven discovery.

FAQ

Why do men claim to know more about AI than women?

The survey suggests that men may overstate their AI knowledge due to higher confidence, even when incorrect. This aligns with broader patterns of gender differences in self-assessed expertise, though both genders answered questions incorrectly at similar rates.

How does media representation contribute to the gender gap in AI?

Men are featured in AI-related media headlines far more often than women, despite women making up 29% of the AI talent pool. This imbalance reinforces stereotypes about who is considered an AI expert, potentially discouraging women from engaging with the field.

What are the implications of AI solving complex scientific problems?

While AI’s ability to solve problems like the Navier-Stokes equation is a milestone, it has sparked debate about the value of the discovery process. Some researchers argue that AI may undermine the insights gained through human-driven problem-solving, while others emphasize the need for verification and transparency in AI-generated solutions.

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