Why women aren't ‘missing’ the AI train

2026-04-07 · Source: Sifted · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ecosystem & Diversity · Depth: Fundamental Awareness, short

Summary

Malin Frithiofsson, cofounder and CEO of Daya Ventures, challenges the narrative that women are "missing the AI train," arguing that this perspective ignores systemic barriers. She highlights that women still undertake 70% of parental leave and the majority of unpaid labor, fragmenting their time and hindering their ability to engage in deep, uninterrupted work necessary for learning and building in AI. Frithiofsson uses her personal experience, where her husband manages domestic responsibilities, enabling her to have the uninterrupted time often afforded to men, as evidence that productivity is often subsidized. Furthermore, she points out that current AI tools and data stacks are better suited for problems historically addressed by men, making it harder and more capital-intensive for women to build in less represented areas like women's health. She concludes that unequal distribution of time, capital, and foundational data leads to predictable outcomes, not a mystery.

Key takeaway

For Directors of AI/ML or entrepreneurs seeking diverse talent, recognize that systemic factors, not individual shortcomings, limit women's participation. Your teams should critically assess how access to uninterrupted time, venture capital, and AI tool biases impact who can innovate. Prioritize creating equitable conditions, such as supporting balanced parental leave and household responsibilities, and advocating for diverse funding, to truly foster inclusive AI development.

Key insights

Unequal distribution of time, capital, and data access, not ambition, explains women's underrepresentation in AI.

Principles

• Productivity is often subsidized by someone else's unpaid labor.
• Access to uninterrupted time is critical for deep learning and building.
• AI tools reflect historical biases in data and problem-solving.

In practice

• Examine who bears the cost of "productivity" in your team.
• Assess if your AI stack supports diverse problem domains.
• Advocate for equitable parental leave and household chore distribution.

Topics

• Gender Equity in AI
• Unpaid Labor Burden
• Venture Capital Disparity
• AI Stack Bias
• Parental Leave Policies

Best for: Director of AI/ML, Entrepreneur, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sifted.