New research from LinkedIn has found that women account for only a quarter of new hires into AI roles. The reason isn’t a lack of interest — it’s a workplace that judges women differently for using the same tools.
The paper titled ‘The Triple Penalty: Mapping the Gender Gap in the AI Economy’ found that women aren’t represented in many AI company boardrooms and hold only 13 percent of c-suite positions at AI companies across 27 countries including the UK, Germany, France, Italy and Spain.
AI jobs pay twice as much as non-AI jobs and women are missing out on being involved in developing groundbreaking technology – with AI tools also missing out on not having input from women.
There are numerous potential explanations for the lack of women in AI roles. Fewer women in STEM than men to put into these positions is one major factor.
However, a perceived notion that women don’t want to be in AI roles, don’t use AI as much as men, or are even more likely to be opposed to AI has so far constructed a narrative around the relationship between women and the new technology – a relationship that is resistant and hostile.
In this article, we ask whether there is any truth to this narrative. And if it’s something that, so far, has held professional women from finding themselves in AI-focused jobs.
Women Don’t Care About AI
So far, the narrative around gendered AI use has centered around an assumption that women aren’t using AI as much as men. Reasons given for this lack of use have been everything from concerns about AI’s energy consumption and its contribution to climate change to job displacement fears.
“The narrative that women use AI less as they are tech-averse is a convenient myth,” says Charlotte Bunyan, Head of Innovation at experience agency, We Are Collider.
Bunyan points out that the AI gender gap isn’t a product of women not having the AI skills needed to succeed, with women upskilling in this field on the rise. “ Women’s GenAI course enrollment is climbing faster than men’s, up from 32 percent to 36 percent year-on-year. Which raises a critical question: if visible AI use invites critical scrutiny, what’s being built off-camera?
“Particularly when women face systemic friction that AI could help solve; across care, health, and financial exclusion. Build without them in the room, and blind spots get baked into tomorrow’s tools.”
This supposed scepticism from women towards AI has, so far, often served as a justification for why there are so few women in AI roles, and why women are supposedly less likely to be engaging with AI in the workplace, or be completely against it altogether.
“The urgent task has never been about teaching women how to use tech; it’s dismantling the human bias that penalises them when they do. Fix this, and the AI divide won’t just narrow; it will vanish.”
A Question of Competence
Conducted by Zehra Chatoo, a former Meta strategist and founder of Code for Good Now, a recent study evaluated how 1000 adults reviewed two identical CVs that had been created with AI, with the only difference between the resumes being the name at the top – either Emily or James Clarke.
The study found that employers were twice as likely to question a woman’s competency over AI use, viewing use of the tech as a sign of inadequacy compared to seeing it as pragmatic when male candidates used it.
This research might challenge the idea that women simply aren’t interested in AI. For Carlotta Costanzo, Director of Creative Performance at GAIN, AI simply gives historical gender gaps a new place to show up. “Why is a man using AI so easily interpreted as being efficient, resourceful or ahead of the curve, while a woman doing exactly the same thing can be perceived as taking a shortcut or lacking the expertise to do the work herself?” she explains.
From this perspective, AI might be a new technology but it’s only highlighting something that was already existing in companies – men are more likely to be perceived as competent over women in the workplace.
She continues: “The answer shouldn’t simply be to tell women to use AI more, or to prove that they can do everything without it. It should be to create workplaces where AI is treated as what it increasingly is: a tool that can amplify human capability, rather than a proxy for whether someone is capable in the first place.”
These findings point to the fact that the barrier for women when it comes to AI isn’t a lack of appetite, but how their use of it is judged. Closing that gap means shifting the burden away from women having to prove themselves and onto workplaces rethinking how they evaluate skill and competence in the first place.
“The urgent task has never been about teaching women how to use tech; it’s dismantling the human bias that penalises them when they do. Fix this, and the AI divide won’t just narrow; it will vanish,” concludes Bunyan.
