The names that dominate technology coverage in 2026 are still predominantly male. This is a journalism problem before it is an industry problem — because if you look at who is actually building the systems, the tools, and the companies that will define the next decade of technology, women are doing significant work that is consistently underreported.

What follows is not a list of “women in tech” in the diversity-statement sense. It is an account of people doing genuinely important technical and strategic work, who happen to be women, whose work is worth knowing about because of its quality and consequence.

Building the Infrastructure of Intelligent Systems

Fei-Fei Li remains the most important active figure in the development of AI systems that benefit from human diversity rather than encoding human exclusion. Her current work at Stanford’s Human-Centered AI Institute focuses on healthcare AI — specifically on building diagnostic and treatment systems that perform equitably across gender, race, and socioeconomic backgrounds. The practical application: AI that can identify breast cancer as accurately in a woman of colour as in a white woman. The scientific challenge: assembling training data that represents the full diversity of the patient population rather than the populations historically overrepresented in clinical research.

Li’s argument — that AI safety is not separate from AI equity, that a system that works poorly for 40% of the population is not a safe system — is reshaping how the field thinks about evaluation criteria.

Chelsea Finn is among the most cited researchers in machine learning, working on meta-learning and multi-task learning — teaching AI systems to generalise from limited examples in the way humans do, rather than requiring massive training datasets for every new task. Her work on robot learning, on AI systems that can adapt to new environments from small numbers of examples, is foundational to the next generation of physical AI applications. She is 33.

Building for Health

Dr. Reshma Jagsi, a radiation oncologist and director of the Center for Bioethics and Social Justice at Michigan Medicine, is doing the structural work that makes equitable medical AI possible: defining the frameworks for how clinical AI systems should be evaluated for fairness, who should be consulted in their development, and what the standards should be for deployment in healthcare settings. She is not a technologist by training. She is building the policy and ethical infrastructure without which the technology cannot be deployed responsibly.

Anne Wojcicki, CEO and co-founder of 23andMe, spent 15 years building the world’s largest consumer genetic database before the company faced significant financial difficulties in 2023-2024. Whatever the company’s fate, the database she built — and the research it enabled on genetic correlates of disease, ancestry, and pharmacogenetics — is an extraordinary scientific asset. The research on genetic factors in women’s health, including BRCA variants and their implications for breast and ovarian cancer risk, has been materially advanced by the 23andMe data.

Building Economic Infrastructure

Anne Boden founded Starling Bank in the UK in 2014 at age 53, becoming the first woman to found a licensed bank in the UK. Starling has been profitable for multiple consecutive years and has a customer base of over 3 million, making it one of the most successful digital banking operations in Europe. Boden’s specific contribution to fintech: building a bank that took its product seriously as a product — the user experience, the data architecture, the mobile design — rather than as a compliance exercise with a mobile front-end.

Her 2022 memoir, Banking On It, is one of the most honest accounts of founding a technology company as a woman, describing the specific challenges of fundraising as an older woman founder, the dynamics of being the only woman in rooms of investors and regulators, and the particular combination of credibility and scepticism that female expertise in traditionally male fields attracts.

Cristina Junqueira co-founded Nubank in Brazil in 2013. Nubank is now the largest digital bank in the world by customer count — over 100 million customers across Latin America. Junqueira’s background was in finance, not technology, and she joined a team of two other co-founders (male engineers) to build a business that democratised access to banking and credit in countries where financial exclusion had been structural. Nubank is a specific kind of important: technology deployed for financial inclusion in developing economies, serving populations that traditional financial institutions had written off.

Building the Decentralised Web

Stani Kulechov is male, but the decentralised finance infrastructure he built — Aave — has enabled female founders in DeFi in ways worth noting. Mia Hoffman and Tara Tan are among the founders building decentralised finance tools specifically designed for the remittance market — the $700 billion annual flow of money from migrant workers to their families in developing countries, a transaction that is disproportionately performed by women and charged at extractive rates by legacy financial services.

The intersection of crypto/DeFi with financial inclusion for women is underreported and genuinely significant: women are more likely to be banked through mobile money rather than traditional banking in developing economies, and the infrastructure being built in this space is specifically addressing problems that affect women disproportionately.

Building the Tools of Creativity

Emad Mostaque founded Stability AI, but the most interesting development in AI image generation in 2026 has come from a cluster of smaller companies and open-source projects where women have played significant roles. The conversation about how AI-generated images represent women — whether they encode existing beauty standards or have the potential to expand representation — is being actively shaped by women working in the technical and design layers of these systems.

Margaret Mitchell, who co-led Google’s AI ethics team before joining Hugging Face as chief ethics scientist, is building the methodological infrastructure for responsible AI development: the frameworks, evaluation methods, and documentation standards that determine whether AI systems can be trusted to handle sensitive information about real people, including health data, legal status, and personal communications.

The Obstacle Picture

Profiling women in technology without describing the obstacles they navigate would be dishonest.

Women founders receive approximately 2% of venture capital funding — a figure that has stubbornly remained at or below 2-3% for over a decade despite enormous amounts of stated commitment to diversity from the VC industry. The 2022 All Raise study found that even with record-high total VC investment in 2021, the absolute amount going to female-founded companies did not increase proportionally.

The funding gap is not explained by a quality gap in pitches or companies. A 2019 Harvard Business School study found that VCs ask male founders predominantly promotion-focused questions (what can this company achieve?) and female founders predominantly prevention-focused questions (what are your risks and challenges?). The questioning pattern predicts funding outcomes: founders who receive promotion-focused questions raise significantly more money.

Harassment and discrimination in tech workplaces remain documented at rates that have not significantly improved despite multiple high-profile accountability moments — the Google walkout of 2018, the Uber cultural reckoning, the gaming industry’s ongoing crisis. The difference between 2016 and 2026 is that women are more likely to name what happened to them and to find communities of support. The cultures that produce the harassment have changed less than the cultures that respond to it.

Why This Matters Beyond Representation

The case for women in technology leadership is sometimes made on representation grounds — that women deserve to be there, that their absence is unjust. This is true. It is also, in the technology context, not the most powerful argument.

The most powerful argument is the one that Timnit Gebru and Joy Buolamwini have made repeatedly through their research: that AI systems built without the perspective of the people they will affect make different decisions than AI systems built with that perspective, and that the differences have real consequences for real people.

The women building the next internet are not building it for women. They are building it for everyone. But they are doing so with a set of questions about who the technology serves, who it excludes, and what it means for those excluded that their male counterparts have not always asked.

Those questions are not nice-to-have. They are the questions on which the legitimacy of the technology depends.

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