How can AI work for everyone, not just the few?

When asked whether she feels optimistic or anxious about AI’s rapid development, Stamm offers a balanced view: “I feel neither anxious nor overly enthusiastic. AI can do a lot of very positive things for us and with us, but it’s up to us to shape the trajectory of the technology and what we develop it for.” She advocates for a middle ground, not succumbing to fears of dystopia nor to blind techno-utopianism. AI is not a silver bullet; it will not solve all problems, especially when it is primarily used to increase productivity and efficiency instead of addressing complex societal issues.

Why gender is at the forefront of the AI discussion

Stamm emphasizes that gender and diversity must be central to discussions about AI development. She points out that media and leadership narratives around AI are dominated by a very specific demographic: largely white, male, and from the United States. This narrow representation shapes how technologies are built, which undermines their potential to serve diverse global communities.

“I have seen amazing women leading the way in tech and AI development, but they’re often hidden, not in the spotlight,” she says. Her mission includes dispelling myths that women do not exist in AI, highlighting the critical contributions of female leaders and entrepreneurs. Many women developing AI focus on responsible, ethical, and empathetic technology that centers care and community. “This is about asking: what do we want to use technology for? And creating tech that builds a better future.”

Inclusive design and risk-taking are more likely when women participate, making it essential to have women visible not just in coding but in leadership and funding roles. Stamm insists, “We can’t afford to exclude women from this picture or from supporting women’s innovations.”

The consequences of biased AI

AI systems often inherit human biases, reproducing harmful stereotypes or inaccuracies with real-world consequences. For example, recruitment algorithms trained on biased data reject women’s resumes at disproportionately higher rates. Stamm highlights the book Invisible Women by Caroline Criado Perez, which documents how a world built by men for men leads to misdiagnoses and inequalities particularly against women, for instance, in healthcare AI trained mostly on male data. Here the message is clear: AI must reflect diverse constituencies, quantify qualitative lived experiences, and be continuously checked by human judgment to avoid replicating harmful patterns.

Addressing trust and governance

As AI adoption spreads, public trust in the technology and governance remains low. Stamm underscores the urgent need for governments to bridge this trust gap by “listening to people’s realities, opening up policy discussions, facilitating consultations and forums, and explaining planned actions on AI impacts, especially on jobs and financial security.”

She notes women tend to distrust AI more than men and that governments worldwide must address this skepticism openly to ensure inclusive acceptance and responsible deployment.

Economic impact and the undervaluation of women-led innovation

Stamm draws attention to the economic stakes of excluding women from AI leadership and innovation: advancing gender equality could generate $7 trillion in global GDP growth. Diverse teams improve performance and revenue, with products developed by mixed-gender teams better reflecting market realities.

Yet women-led startups receive only 2% of venture capital funding. This disparity exists because 85% of current investors are men who rely on familiar networks and past successful patterns, mostly funding all-male teams.

Men partnering with women face funding penalties as well. The lack of equal access to “warm intros” and investment networks further widens the gap.

Advice for women and allies in AI

Stamm leaves a powerful call to action for women and allies: “We need you. There’s a lot at stake and tremendous opportunity. Train, engage in relevant communities, find role models. Never say you don’t belong or have nothing to add.”

To allies, the advice is clear. “Open your checkbooks. Prioritize women-led innovation. Lead by example, ensure women are present on panels, events, and decision-making spaces. Don’t hide behind myths of scarcity. The women are there; you just need to find them.”

This is the only way to rewrite the narrative of tech that works for everyone.

This article is based on an exclusive interview recorded in 2025 for The Diplomat’s Cabinet podcast. For more insights, subscribe to our newsletter and listen to the full episode.