Twelve percent. That is the share of women among all AI researchers worldwide, according to 2026 data compiled by WomenHack and SheAI. Yet the women responsible for some of the most important advances in artificial intelligence are building billion-dollar startups, redesigning how models learn, and ensuring that technology serves all of society — not only those who build it.
This Mother’s Day, Entercast profiles the researchers, executives, and entrepreneurs shaping the future of AI in Brazil and around the world.
The Numbers Don’t Lie: Underrepresentation Is Still Real
The data is clear and persistent. According to 2026 surveys by the Women in Tech Network and interface-eu.org, women hold only 26.7% of technology roles globally. In the specific field of AI, the gap is even wider: just 12% of researchers are women, and they appear as first authors on only 14% of published AI papers.
In academia, only 16% of tenured AI faculty positions are held by women. Across the five biggest tech companies — Google, Apple, Meta, Amazon, and Microsoft — the average female share of the overall workforce sits at around 31%, but drops significantly at senior technical leadership levels.
These numbers are not just an equity issue. They are a technical problem: AI developed by homogeneous teams tends to reproduce the biases of those who built it. When women lead or participate in research teams, models tend to be more robust, less discriminatory, and more applicable to the real world.
The Global Pioneers Who Defined the Field
Fei-Fei Li, known as the “Godmother of AI,” is perhaps the most central figure in the recent history of artificial intelligence. She created ImageNet, the dataset that triggered the deep learning revolution in the early 2010s. In 2026, her startup World Labs — focused on giving AI spatial intelligence and 3D world understanding — raised $1 billion in funding, reaching a $5 billion valuation, according to USA Today. She also co-directs Stanford’s Human-Centered AI Institute, where she champions the idea that technology must be guided by human dignity, not only benchmark performance.
Mira Murati, former CTO of OpenAI, founded Thinking Machines Lab after leaving the company. In 2026, her new venture raised a $2 billion seed round — the largest in history for that stage — and reached a $12 billion valuation almost immediately, according to multiple industry sources. Her focus: models that are more interpretable and adaptable, where users better understand and control what the AI does.
Daphne Koller, co-founder of Coursera and now at the helm of Insitro, uses AI to accelerate drug discovery — one of the fields where the technology’s practical impact on human life is most direct.
Lisa Su, CEO of AMD, is the architect behind the company’s repositioning as an AI chip powerhouse. Under her leadership, AMD has become one of the leading alternatives to NVIDIA in the GPU market for model training.
Brazilian Women Building the Future of AI
Brazil also has its protagonists, even as significant ground remains to be won.
Gabriela de Queiroz, Director of AI at Microsoft for Startups, is the founder of AI Inclusive and R-Ladies — initiatives that have helped thousands of women enter the data and machine learning field in Brazil and beyond. Her work is simultaneously technical and structural: she not only codes, but rebuilds the pathways into the profession.
Cíntia Barcelos has led all of Bradesco’s technology as CTO since 2024, overseeing infrastructure, cybersecurity, data, and AI at one of Latin America’s largest banks. She is one of the most influential technology executives in the country.
Eliane Collins, a computer engineering Ph.D. specializing in AI and machine learning, leads research at EMBRAPII in Manaus on AI quality systems for mobile devices, coordinating a team where half the members are women.
Brazilian girls at Technovation Girls 2026 used AI to build applications targeting domestic violence, disinformation, and healthcare access in remote regions of the country, according to ESG Inside. They are Brazil’s next generation of AI researchers and tech founders.
Diversity in AI Is Not an HR Agenda — It’s an Engineering Issue
When AI teams are demographically homogeneous, models learn from a narrow slice of human reality. Facial recognition systems with higher error rates for Black women, assistants that reproduce gender stereotypes in professional contexts — these are not problems of intent, but of team composition and data design.
Companies that want to build AI products that are trustworthy and work for their entire customer base have a direct incentive to diversify their technical teams. This is not charity — it is engineering quality.
What Your Organization Can Do
- Audit the composition of your AI team. If it is entirely or predominantly male, you are missing perspectives that affect the quality of your models and products.
- Support initiatives like AI Inclusive and R-Ladies. Connecting women in your organization to these networks expands your talent pipeline and strengthens the community.
- Include diversity criteria in AI audits. When evaluating your models, ask: does this work equally well for all user profiles?
The history of AI has been written by women from the very beginning — from Ada Lovelace, history’s first programmer, to Fei-Fei Li and Mira Murati defining the field today. Recognizing that legacy this Mother’s Day is also a reminder that more diverse technology is better technology. Entercast follows this movement closely and will continue bringing the voices and stories shaping AI in Brazil and beyond. Follow us so you don’t miss the next analysis.
This article was published on May 10, 2026. Follow Entercast to stay ahead of the next update.