AI Anxiety Differences Between Men and Women

Posted on April 16, 2026 by JimS

By Jim Shimabukuro (assisted by Perplexity)
Editor

Recent work suggests that gender differences in AI anxiety are real but not just about anxiety alone: women tend to report higher AI anxiety and lower positive attitudes, use, and self-rated knowledge, yet the gender gap in attitudes shrinks when anxiety is high, because anxiety itself depresses attitudes for everyone.1 A newer 2026 study adds an important layer by showing that women’s greater skepticism toward AI is also tied to higher perceived risk and greater exposure to AI-related harms, especially when AI’s benefits are uncertain.2

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Russo et al. (2025) found that women in their sample reported higher AI anxiety, lower positive attitudes toward AI, lower use of AI, and lower perceived AI knowledge than men.1 They also found a negative link between AI anxiety and positive attitudes toward AI, plus a gender-by-anxiety interaction: at low anxiety, women were less positive than men, but at high anxiety the gender gap became much smaller. The authors interpret this as AI anxiety acting like a “gender differences leveler.”1

The newer 2026 PNAS Nexus study helps explain why the gap exists, showing that women were more likely than men to see AI as riskier, and that two mechanisms mattered: greater general risk aversion and greater exposure to AI-related risk, such as employment uncertainty and possible harms from automation. Importantly, women’s skepticism became especially pronounced when AI’s economic benefits were uncertain; when positive job effects were guaranteed, the gender difference largely disappeared.2 A related 2025 review of multidimensional AI anxiety also found small but persistent gender differences on some subscales, while emphasizing that psychological factors such as self-efficacy and AI experience were stronger predictors overall than demographics.3

These findings matter because anxiety and skepticism can shape who adopts AI, who gains from it, and who helps design it. If women are more likely to anticipate risk, feel less knowledgeable, or expect fewer benefits, they may adopt AI tools more cautiously, which can widen existing digital and occupational gaps.1,2 That matters in workplaces, education, and public policy because AI systems are spreading into decisions about hiring, learning, health, and productivity.

The policy implication is not simply “reduce anxiety,” but reduce the conditions that produce it: uncertainty, low transparency, weak AI literacy, and unequal exposure to harms.2,3 The 2025 Frontiers paper points toward interventions that build confidence, improve experience with AI, and address gendered barriers in technology access.1 The 2026 evidence suggests that clearer communication about risks and benefits may also reduce the gender gap, especially where people cannot assess AI outcomes confidently.2

Russo et al.’s study is useful background because it frames AI anxiety as part of a broader pattern of gendered technology adoption rather than as a standalone emotion. Their main contribution is showing that anxiety does not just differ by gender in level; it also changes how strongly gender predicts positive attitudes toward AI.1 That makes the topic especially relevant now, as AI becomes more embedded in daily life and the old digital divide increasingly looks like an AI divide as well.1,2

References

  1. Gender differences in artificial intelligence: the role of artificial intelligence anxiety — https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1559457/full
  2. Explaining women’s skepticism toward artificial intelligence: The role of risk orientation and risk exposure — https://academic.oup.com/pnasnexus/article/5/1/pgaf399/8429563
  3. The social anatomy of AI anxiety: gender, generations, and psychological drivers — https://pmc.ncbi.nlm.nih.gov/articles/PMC12679909/

AI anxiety and women’s career choices in tech

AI anxiety can push women away from tech careers in two main ways: by lowering confidence and career adaptability, and by making AI-related jobs feel riskier or less worth the cost. Recent evidence shows that higher AI anxiety is linked to poorer career decisions, partly because it weakens career adaptability and self-efficacy, which are key ingredients for choosing and persisting in technical fields.1

In practical terms, that means anxious women may be less likely to apply for AI-heavy roles, less willing to try AI tools at work, and more inclined to avoid fields like data science, software, or machine learning if they expect the technology to be opaque, ethically risky, or professionally punishing.1,2 A 2025 Harvard Business School summary of research on generative AI adoption found that women were less likely to use AI tools even when access was equal, and it noted that this reluctance could slow career advancement because productivity gains and skill-building opportunities are increasingly tied to AI use.2

This matters because tech careers increasingly reward AI fluency, not just technical ability. If AI anxiety leads women to opt out of early experimentation, they may lose practice, confidence, and signaling opportunities that employers value, which can compound over time into fewer promotions and narrower job options.1,2 The risk is especially large in roles where AI is becoming a default workflow rather than a niche specialization.

At the same time, the latest evidence suggests the problem is not simply “women are less interested in tech.” Women’s caution often reflects rational concerns about ethics, fairness, surveillance, or being judged for using AI tools, and those concerns can become stronger when the benefits of AI are unclear or when job loss feels plausible.2,3 That means the career effect of AI anxiety is partly structural: it is shaped by workplace norms, uncertainty, and unequal exposure to AI-related risk, not just individual confidence.1,3

What this implies for schools and employers is straightforward: build AI literacy, normalize low-stakes AI use, and make the rules around acceptable use explicit. When women feel more competent and less likely to be penalized for using AI, they are more likely to enter and stay in tech pathways that now depend on it.1,2

References

  1. The impact of AI anxiety on career decisions of college students — https://pmc.ncbi.nlm.nih.gov/articles/PMC12972058/
  2. Women Are Avoiding AI. Will Their Careers Suffer? — https://www.library.hbs.edu/working-knowledge/women-are-avoiding-using-artificial-intelligence-can-that-hurt-their-careers
  3. Explaining women’s skepticism toward artificial intelligence: The role of risk orientation and risk exposure — https://academic.oup.com/pnasnexus/article/5/1/pgaf399/8429563

AI anxiety gender differences across cultures

Across cultures, the clearest pattern in the recent literature is that women tend to report higher AI anxiety or skepticism than men, but the size and meaning of that gap depend on local norms, institutions, and exposure to AI risk. In the 2025 Frontiers study, the gender gap appeared in AI anxiety, attitudes, knowledge, and use, and the authors argued that AI anxiety can act as a “gender differences leveler,” reducing the gap when anxiety is high.1 A 2025 cross-cultural organizational study on AI readiness also found that culture shapes AI perceptions, with social context changing how people interpret uncertainty and adoption pressure.2

In the newer North American evidence, women were more likely than men to judge AI as riskier, and that difference was explained partly by general risk orientation and partly by exposure to AI harms such as job loss, bias, and inequality.3 That matters cross-culturally because countries differ in how visible those harms are. Where AI is presented mainly as a productivity tool, the gender gap may be smaller; where AI is linked to surveillance, employment insecurity, or weak protections, women’s anxiety tends to be more salient.3

The most useful way to compare cultures is to separate three layers. First is baseline gender socialization: cultures with stronger gendered STEM stereotypes or lower female representation in technical fields are more likely to amplify AI anxiety among women.1 Second is institutional trust: in settings where people trust employers, regulators, and tech firms less, AI anxiety is less likely to be interpreted as a personal weakness and more as a rational response to uncertainty.2,3 Third is exposure: women in cultures or sectors that experience more displacement, harassment, or algorithmic bias are likely to show more caution because the perceived costs of AI are higher.3

The implication is that AI anxiety is not a fixed cultural trait. It is a response shaped by local opportunity structures, job security, role models, and norms about technology competence.1,3 That means cross-cultural interventions should not just “encourage women to be less anxious”; they should improve transparency, demonstrate safer use cases, reduce workplace penalties for experimentation, and increase visible female participation in AI-related roles. When those conditions improve, the gender gap in AI anxiety should narrow, and women’s participation in AI-enabled careers should rise.1.3

References

  1. Gender differences in artificial intelligence: the role of artificial intelligence anxiety — https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1559457/full
  2. The social anatomy of AI anxiety: gender, generations, and psychological drivers — https://pmc.ncbi.nlm.nih.gov/articles/PMC12679909/
  3. Explaining women’s skepticism toward artificial intelligence: The role of risk orientation and risk exposure — https://academic.oup.com/pnasnexus/article/5/1/pgaf399/8429563

AI anxiety: age, generation, gender differences

Current evidence suggests that age or generation does not strongly moderate gender differences in AI anxiety. The clearest recent study on this point found that age-group differences were non-significant, while gender differences persisted for several AI-anxiety dimensions, implying that gender matters more than generational membership in that sample.1

That said, the story is a little more nuanced. Russo et al. found that women reported higher AI anxiety and lower positive attitudes overall, but the gender gap in attitudes narrowed when AI anxiety was high, which means anxiety can partially “level” gender differences rather than simply amplify them.2 In other words, gender differences are present, but they do not appear to be driven mainly by older-vs-younger cohort effects.1,2

Why this likely happens is that AI anxiety seems to be shaped more by psychological dispositions and actual technology experience than by age alone. The 2025 multidimensional study found that technology self-efficacy and AI learning orientation were negative predictors of AI anxiety, while sociotechnical blindness and technoparanoia were the strongest positive predictors.1 That points to experience, confidence, and perceptions of risk as the main drivers, which can cut across age groups.

So the practical takeaway is that interventions should not assume younger women are automatically less anxious or older women are automatically more anxious. What seems more important is whether people, regardless of age, have usable AI experience, feel competent, and trust that AI will not expose them to avoidable harms.1,2

References

  1. The social anatomy of AI anxiety: gender, generations, and psychological drivers — https://pmc.ncbi.nlm.nih.gov/articles/PMC12679909/
  2. Gender differences in artificial intelligence: the role of artificial intelligence anxiety — https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1559457/full

Generational differences in AI adoption rates

Recent evidence suggests generational differences in AI adoption do exist, but they are not perfectly linear or uniform across studies. In work settings, younger cohorts generally adopt AI more often than older ones, with Gen Z and Millennials typically reporting higher use than Gen X and Baby Boomers.1-3

The recent pattern is strongest when adoption is defined as regular workplace use or weekly use of generative AI tools. One 2025 workplace survey reported higher AI usage among Gen Z and Millennials than among Gen X and Boomers, and a 2026 university summary of Deloitte data found the same broad gradient: Gen Z and Millennials used AI more than older cohorts.1,2 Another 2026 report noted that younger workers were more likely to receive recent AI training, and that training was strongly associated with adoption across all ages.2

That said, the divide is not just about age itself. Some data show that Gen Z can be more skeptical than expected when they are non-users, and that older adults may adopt once trust and training improve.1,2 This means generational differences often reflect access, training, job context, and perceived usefulness as much as birth cohort alone.

So the best takeaway is that AI adoption is generational, but the gap is shaped by support and incentives. When organizations provide training and clear use cases, older cohorts adopt more, and the generational spread narrows.2,3

References

  1. The Generational Divide in AI Adoption — https://www.randstadusa.com/business/business-insights/workplace-trends/generational-divide-ai-adoption/
  2. How every generation uses AI, from boomers to Gen Z — https://www.uc.edu/news/articles/2026/01/how-every-generation-uses-ai-from-boomers-to-gen-z.html
  3. AI Is Creating a Generational Divide at Work — and It’s Growing — https://builtin.com/articles/ai-generational-divide-work

Gen Z vs Boomer non-users: resistance to AI

Gen Z non-users often resist AI more strongly than Boomers because their objections are more values-based and identity-based, not just unfamiliarity-based. Recent reporting suggests Gen Z’s skepticism centers on authenticity, creativity, environmental impact, and the feeling that AI threatens meaningful human work, while older generations’ resistance is more often tied to privacy, transparency, or general caution about black-box systems.1,2

That difference matters. For Gen Z, AI can feel like a direct threat to originality and to the kind of professional identity they are trying to build, especially in creative, media, and knowledge work; for Boomers, resistance is more likely to reflect experience with earlier waves of automation and a warier stance toward algorithmic control.1,2 In other words, Gen Z’s non-use can be a principled refusal, whereas Boomer non-use is often a familiarity and trust gap.

It also helps explain why some studies find younger people adopting AI more overall, even while a subset of Gen Z non-users are unusually critical. Adoption and resistance are not opposites on the same line: Gen Z can be both the most active users and the most vocal skeptics, because the same cohort that experiments most with AI also sees most clearly how it can flatten style, intensify surveillance, or replace entry-level work.1,2

So the short answer is that Gen Z non-users are often resisting what AI represents, while Boomers are more often resisting how AI works and the risks it might create. That distinction is important for policy and workplace training, because the fix is different: Gen Z needs credible guardrails around authenticity, labor, and ethics; Boomers need transparency, support, and basic fluency.1,2

References

  1. The Generational AI Divide: Understanding Resistance and … — https://www.linkedin.com/pulse/generational-ai-divide-understanding-resistance-pathways-romero-p0soc
  2. Generational AI Trust Gap: Why Gen Z Scores 28 While Boomers Score 10 and What It Means for … — https://www.businessplusai.com/blog/generational-ai-trust-gap-why-gen-z-scores-28-while-boomers-score-10-and-what-it-means-for-b

AI adoption rates by generation in workplace vs personal use

Yes, but the gap looks different at work than in personal life. Workplace adoption is more age-stratified: younger workers use AI more often, while older workers are more likely to report never using it.1,2

In workplace data, Gen Z and Millennials lead. One survey reported that Gen Z workers use AI at work daily or multiple times per week more often than Gen X or Boomers, and that 15% of Gen Z and 13% of Millennials said they never use it, versus 46% of Boomers.1 Another 2026 survey found that AI use on the job rises in middle age and then drops sharply after 60, with 18–29-year-olds using AI at work more often than older adults, though the biggest discontinuity was still at the oldest ages.2 A Federal Reserve review also found broad workplace adoption, but with strong variation by occupation and role rather than age alone.3

Personal use is usually broader and less tied to job demands. The available evidence suggests that consumer AI use spreads more evenly across generations than workplace use, because people can experiment without employer rules or productivity pressure.4 The OpenAI workplace report also argued that widespread personal use is helping push AI into work settings, meaning some of the work gap may be a lag in organizational adoption rather than a pure generational divide.5

So the cleanest way to think about it is this: workplace adoption is more strongly shaped by age, occupation, and employer support, while personal use is more about individual curiosity, convenience, and access.1,2,5 That means a younger person may use AI heavily at home but only lightly at work if their employer discourages it, while an older worker may adopt AI on the job once training and clear use cases are provided.3,5

References

  1. The Generational Divide in AI Adoption — https://www.randstadusa.com/business/business-insights/workplace-trends/generational-divide-ai-adoption/
  2. How are Americans using AI? Evidence from a nationwide survey — https://www.brookings.edu/articles/how-are-americans-using-ai-evidence-from-a-nationwide-survey/
  3. The Fed – Measuring AI Uptake in the Workplace — https://www.federalreserve.gov/econres/notes/feds-notes/measuring-ai-uptake-in-the-workplace-20240205.html
  4. AI is a common workplace tool: half of employed AI users now use it for work — https://epoch.ai/blog/half-of-employed-ai-users-now-use-it-for-work
  5. ChatGPT usage and adoption patterns at work — https://cdn.openai.com/pdf/3c7f7e1b-36c4-446b-916c-11183e4266b7/chatgpt-usage-and-adoption-patterns-at-work.pdf

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