What 1,000+ TikTok comments reveal about women's resistance to artificial intelligence
This analysis examines over 1,000 comments from a viral TikTok video by creator Dido Riot, which explores why women are using generative AI at lower rates than men. Through qualitative coding and AI-assisted sentiment analysis, we identified seven dominant themes in how women articulate their resistance to AI. The findings reveal that women's reluctance is not ignorance or technophobia—it is a complex, often well-reasoned response rooted in identity, distrust, ethics, and lived experience. This analysis was discussed on Episode 1 of Womansplaining AI, "On Women in AI and Who Gets a Seat."
Women are using generative AI at significantly lower rates than men. Multiple studies confirm the gender gap in AI adoption, and concern is mounting that this disparity will set women back in the workplace. But the conversation often stops at "women should use AI more" without asking why women are opting out—and whether their reasons carry weight.
Creator Dido Riot's viral TikTok addressed this directly, examining the research and arguing that women's resistance to AI deserves more nuance than a simple "get on board" message. The comment section became a de facto focus group: over 1,000 comments, overwhelmingly from women, articulating exactly why they are not using AI.
Dido Riot's video cites research showing women are using generative AI at lower rates than men, then unpacks why—pushing back against the default narrative that women simply need to "catch up." The video highlights how AI's development has been shaped by male-dominated industries, with explicit content and objectification baked into the technology's foundations.
The video's framing—that women's hesitation is neither irrational nor something to be overcome through better marketing—struck a chord. The comment section became an outpouring of specific, personal, and often deeply reasoned explanations for why women are resistant to AI adoption.
Seven dominant themes emerged from the comment data. The top five account for the vast majority of resistance narratives. Two additional themes surfaced with significant frequency.
The single most discussed theme. Commenters criticized tech bros and male-dominated AI development, referenced AI girlfriends and the sexualization built into these platforms, and shared stories of male coworkers over-trusting AI output while dismissing female colleagues' expertise. This wasn't abstract critique—it was rooted in daily workplace experience.
Women framed AI resistance as intellectual self-preservation. Comments expressed genuine pride in human cognition—not as technophobia, but as a values statement. The recurring sentiment: our ability to think independently is worth protecting.
Healthy skepticism about AI quality, backed by personal anecdotes of AI failures, hallucinations, and confidently wrong outputs. This was not ignorance of the technology—it was informed critique from people who had tried it and found it wanting.
Concerns about AI cannibalizing human creativity. Commenters raised stolen work, copyright violations, and the philosophical position that art is fundamentally human—defined by its imperfections. For many, using AI-generated content felt like participating in the theft of other creators' labor.
Recurring references to the material costs of AI infrastructure: water consumption for data center cooling, energy use, and carbon footprint. Women connected personal technology choices to larger ecological consequences.
Fear that relying on AI erodes cognitive capacity over time. Commenters cited research on cognitive offloading and expressed concern that outsourcing thinking to machines would make them less capable, not more.
A significant number of commenters described feeling pressured to use AI at work against their better judgment. The resentment was directed not at the technology itself but at the top-down mandate to adopt it without adequate consideration of its limitations or ethical implications.
Across all seven themes, a meta-pattern emerged: identity-based resistance. Women were not simply evaluating AI as a tool and finding it inadequate. They were asserting who they are and what they value—independent thinking, creative integrity, environmental stewardship, bodily autonomy, skepticism of male-dominated systems—and finding that AI, as currently built and marketed, conflicts with those values.
This is the "we don't need it" narrative. But it is more nuanced than blanket rejection. It is a statement of self-determination: I am choosing not to participate in a system that does not reflect my values or serve my interests.
| Framing | Characteristics |
|---|---|
| Values-Based Opt-Out | Ethical, environmental, or creative objections; principled stance |
| Practical Skepticism | Tried it, found it unreliable; evidence-based rejection |
| Systemic Critique | Gendered power dynamics; who benefits, who's harmed |
| Self-Preservation | Cognitive autonomy; protecting mental capacity |
| Ambivalent / Conflicted | Recognizes both risk of using and risk of not using |
Among the most striking comments were those expressing genuine ambivalence—women who recognized that opting out carries its own risks. These commenters held two contradictory truths simultaneously: AI is a patriarchal system that does not serve them, and refusing to engage with it may further entrench that patriarchy.
"Why is it always lose-lose? AI is a patriarchal system now because it's being designed, developed, and created primarily by men. We know AI is bad for the environment, women are nationally resisting AI and the use of it, but in doing so, we are all losing out on an equally balanced AI system." — TikTok comment
This tension—between principled resistance and pragmatic necessity—is the central thread of the Womansplaining AI podcast. As co-host Mara Bolis put it: opting out of AI is about as realistic as opting out of electricity. The question isn't whether to engage, but how to do so with integrity.
The following comments were selected for clarity of expression and representativeness of broader themes. All quotes are from the original TikTok comment section.
"Women deal with AI with an extension in mind, not as a leisure activity. We literally want AI to wash the dishes so that we can go do art or spend time with our friends." — On women's practical orientation
"Women are more focused and task-driven. They don't toy around with AI tools just for the sake of gaming with it." — On the gendered use gap
"The whole thing reminds me of World War I, when generals were excited about playing with new weaponry and therefore intentionally didn't suppress access to weapons. This all resulted in the massive destruction of Europe." — On unchecked technological enthusiasm
"I'm not going to redefine my professional identity to contribute to the destruction of humanity where no one's gonna have a job and there's no ecology anymore. Great. Sign me up." — On the ask being made of women
This comment analysis does not exist in a vacuum. It sits within a well-documented pattern:
Over 1,000 comments were extracted from Dido Riot's TikTok video on women and AI resistance using Apify's TikTok comments scraper. Data was exported as CSV, capturing comment text, timestamps, usernames, and engagement metrics (likes, replies).
Comments were analyzed using Claude Code (Anthropic) to identify patterns, themes, and sentiment. The AI processed all comments, extracting key phrases, categorizing emotional tone, and flagging recurring topics across the full dataset.
Recurring topics were grouped into seven major themes through qualitative analysis. Theme frequency was determined by comment count. Individual comments could address multiple themes; the gendered critique category alone contained 190+ comments, the highest single-theme count.
Representative quotes were selected based on engagement metrics, clarity of expression, and diversity of perspectives. All quotes are verbatim or closely paraphrased from the original comments.
Findings were discussed on Episode 1 of Womansplaining AI, "On Women in AI and Who Gets a Seat," providing additional context, interpretation, and counter-perspectives from hosts Logan Currie and Mara Bolis.
Limitations: This is a qualitative social listening analysis, not a rigorous quantitative study. The comment section of a TikTok video self-selects for people who felt strongly enough to respond, likely skewing toward those with negative experiences or strong opinions. Percentages are estimates based on AI-assisted pattern recognition. The goal is to surface themes and voices—not to produce statistically precise measurements. The demographics of TikTok's user base (skewing younger) and the creator's audience also shape who is represented. Additionally, the irony of using AI to analyze women's reasons for resisting AI is acknowledged and embraced.
Episode 1: "On Women in AI and Who Gets a Seat"
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Strong opinions. Loosely held.