Women in Tech 2026: Progress, Stagnation, and the AI Gender Gap

A Decade of Diversity Pledges, and the Numbers Barely Moved

In 2014, Google published its first diversity report, revealing that 29% of its global workforce was female, with an even smaller share in technical roles. The company pledged hundreds of millions of dollars to improve representation. A decade later, Google’s overall workforce reached 33.8% female (2024), with technical roles closer to 26%. Meta, Microsoft, Apple, and Amazon hover in a similar range of 24-29% women in technical positions. Then, in 2025, Google, Meta, and Microsoft quietly stopped publishing diversity reports altogether, part of a broader corporate retreat from DEI commitments.

Progress? Yes, modest gains over a decade. But the pace remains glacial relative to the resources invested and the urgency of the rhetoric. At current rates, gender parity in tech roles will not be achieved for decades.

The global picture is stark: women hold approximately 26-28% of tech roles worldwide, a figure confirmed by multiple analyses including the World Economic Forum’s Global Gender Gap Report 2025. In AI specifically, the fastest-growing and highest-paying segment of the industry, the number drops to roughly 22% of AI professionals globally. Among AI researchers authoring peer-reviewed papers, women represent approximately 20%. Women occupy less than 14% of senior executive roles in AI.

There is one encouraging signal: the WEF reports that women’s share of AI engineering skills on LinkedIn profiles rose from 23.5% in 2018 to 29.4% in 2025, and the AI talent gender gap narrowed in 74 out of 75 economies studied. But these gains are fragile, concentrated in certain roles, and have not yet translated into leadership parity.

The AI gender gap is particularly consequential because AI systems encode the perspectives of their creators. When the vast majority of AI developers, researchers, and product managers are men, the resulting systems risk reflecting male-dominated perspectives and may fail to account for the needs, experiences, and safety concerns of women.

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The Pipeline: Where Women Are Lost

The “pipeline problem,” the argument that fewer women enter STEM education and therefore fewer are available for tech roles, is real but insufficient as a full explanation.

Stage 1: STEM Education

Women earn approximately 58% of all bachelor’s degrees in the US, including roughly half of science and math degrees. In biology, chemistry, and many science fields, women are at or near parity. But in computer science, women earn only about 21% of bachelor’s degrees, a number that has barely increased since 2010. This figure is strikingly lower than the 37% peak reached in 1984.

The decline from the 1984 peak is one of the most studied trends in education. Researchers have identified contributing factors: the rise of personal computers marketed primarily to boys in the 1980s and 1990s, the “geek culture” stereotype that discouraged girls from identifying with computing, the homogeneity of CS departments that made women feel unwelcome, and the lack of visible female role models in computing.

Globally, the picture varies significantly. In India, women earn over 40% of STEM degrees. In Gulf states, women represent up to 60% of engineering students according to UNESCO. Across the Middle East and North Africa, women’s participation in STEM education often exceeds that of many Western countries, a phenomenon researchers call the “gender equality paradox.” But these educational gains do not always translate into workforce participation due to cultural, economic, and structural barriers.

Stage 2: Entry to the Workforce

Women who earn CS degrees enter the tech workforce at lower rates than men. According to the Accenture and Girls Who Code “Resetting Tech Culture” study, 50% of young women who enter tech leave by age 35. The primary reasons cited:

• Hostile or unwelcoming culture: The Kapor Center’s Tech Leavers Study found that 78% of all tech employees reported experiencing some form of unfair treatment at work, with women experiencing significantly more unfairness than men. Unfair treatment was the top reason employees left, costing the industry an estimated $16 billion annually.
• Lack of advancement opportunity: Women are promoted to technical leadership roles at lower rates than men with equivalent performance evaluations. McKinsey’s 2025 data shows only 93 women are promoted to manager-level roles for every 100 men.
• Compensation gaps: The unadjusted gender pay gap in US tech stands at roughly 22%, meaning women earn about $0.78 for every $1.00 men earn. When controlling for job family, level, and geography, the gap narrows to approximately 4%. However, the gap widens significantly at senior levels, where differences in equity compensation, bonus structures, and compounding effects of slower promotion inflate the disparity to 15-20%.
• Caregiving burden: The disproportionate caregiving burden on women, including childcare and eldercare, conflicts with tech industry norms of long hours and always-on availability.

Stage 3: Senior Leadership

The attrition problem compounds at senior levels. Women hold approximately:

• 26-28% of entry-level tech roles globally
• ~20% of senior/staff engineer roles
• ~15% of VP-level engineering roles
• ~12% of STEM executive positions (WEF data)
• 11% of Fortune 500 CEO positions (a record 55 women in 2025, though far fewer in tech specifically)

This is the “leaky pipeline”: women are lost at every stage, from education to entry-level to mid-career to leadership, with the largest drops occurring at the mid-career transition and the leap to senior leadership. A notable bright spot: women now hold the CFO role at several major tech companies, including Alphabet, Microsoft, Nvidia, OpenAI, and Salesforce.

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The AI Gender Gap: A Compounding Problem

The AI boom is making the gender gap worse in critical ways. AI roles require skills concentrated in the fields with the lowest female representation: advanced mathematics, deep learning, and systems engineering. The result:

Only about 22% of AI professionals globally are women. In AI research, the highest-prestige and highest-compensation tier, the representation is even lower, with women holding less than 14% of senior AI executive roles.

AI hiring patterns reinforce the gap. Companies building AI teams often recruit from a small pool of elite programs (Stanford, MIT, CMU, and labs like DeepMind and FAIR) that themselves have low female representation. This “pedigree hiring” pattern creates a self-reinforcing cycle.

AI tools may embed gender bias. AI systems trained on historical data, such as hiring decisions, promotion records, and performance evaluations, learn and reproduce the biases present in that data. Amazon developed an AI recruiting tool starting in 2014 that downgraded resumes containing references to “women’s” (as in “women’s chess club”) and favored language more commonly found on male engineers’ resumes. The tool was scrapped in 2017 after the company determined the bias could not be fixed. The underlying dynamic, AI learning from biased historical data, remains a pervasive risk across the industry.

Generative AI and representation. Text-to-image AI models disproportionately generate male figures for professional roles (“CEO,” “engineer,” “doctor”) and female figures for domestic or sexualized roles. This reflects training data biases and, when AI-generated images are widely used, can reinforce stereotypes at scale.

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What Actually Moves the Needle

After two decades of diversity initiatives, research has identified what works and what does not.

What Does NOT Work

Mandatory diversity training: A 2021 meta-analysis by Paluck and colleagues examining over 400 studies found near-zero effect sizes on reducing prejudice. Harvard researchers Frank Dobbin and Alexandra Kalev showed that over 30 years of data, mandatory diversity training often has no effect or even increases bias by triggering defensiveness and resentment. Voluntary, skills-based training performs somewhat better, but mandatory programs remain the norm at most companies.

Pipeline-only programs: Programs that focus exclusively on getting more women into STEM education without addressing workplace culture, retention, and advancement produce more women entering the industry but the same proportion leaving.

Diversity targets without accountability: Publishing diversity numbers without tying them to management performance metrics produces reports, not change. The 2025 wave of companies abandoning diversity reports entirely underscores the fragility of this approach.

What DOES Work

Structured hiring processes: Removing identifiable information from resumes (blind review), using standardized interview questions and evaluation rubrics, and requiring diverse interview panels significantly reduces hiring bias. Deloitte’s UK office saw a 33% increase in female hires after implementing blind recruitment. A Google case study found structured interviews led to a 40% reduction in hiring bias.

Sponsorship (not just mentorship): Mentors give advice; sponsors advocate. Senior leaders who actively sponsor women, recommending them for promotions, assigning them high-visibility projects, introducing them to influential contacts, accelerate advancement more than any training program.

Flexible work arrangements: Remote work, flexible hours, and parental leave policies that are genuinely supported, not just written in policy handbooks, disproportionately benefit women who bear greater caregiving responsibilities. The Accenture study found that adopting just five inclusive practices could help retain approximately 1.4 million women in tech by 2030.

Pay transparency: Mandatory salary disclosure and pay equity audits close compensation gaps. The EU Pay Transparency Directive, with a transposition deadline of 7 June 2026, will require employers to provide salary ranges in job postings and conduct regular pay gap reporting. Implementation has been slow, with no member state having completed legislation as of early 2026, but the regulatory direction is clear.

Inclusive product development: Teams that include women in AI development, product design, and testing produce products that work better for diverse users. This is a business argument, not just an equity argument: products designed for everyone perform better in the market.

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Regional Variations

The gender gap varies significantly by region.

Nordic countries (strong social policy, moderate tech numbers): Sweden, Norway, and Finland are global leaders in gender equality, with strong social safety nets including generous parental leave and subsidized childcare. However, their tech sectors specifically have only 24-28% female representation, comparable to the global average. The “gender equality paradox” partly explains this: in countries with strong welfare systems, women have more freedom to choose careers based on interest rather than economic necessity, and fewer choose computing.

India (education-workforce gap): India produces large numbers of female STEM graduates, with over 40% of STEM degrees going to women. Major Indian IT companies have made significant progress: TCS employs 35.5% women, Wipro 36.6%, and Infosys has reached 39% with a target of 45% by 2030. However, broader workforce participation remains limited by cultural barriers, safety concerns, and career breaks for family care.

Middle East and North Africa (striking education-workforce paradox): Several MENA countries have remarkably high female STEM graduation rates. UNESCO estimates women comprise up to 60% of engineering students in Gulf countries. Iran reports approximately 70% female university students in STEM. But significant barriers to workforce participation persist, including cultural expectations, limited workplace infrastructure, and restrictive labor market norms. The UAE and Saudi Arabia are actively working to increase women’s tech participation as part of economic diversification strategies.

Sub-Saharan Africa: The tech sector in countries like Nigeria, Kenya, and South Africa has relatively high female participation in some segments, particularly product management and design, but low representation in engineering and AI roles.

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Sources & Further Reading

• World Economic Forum — Global Gender Gap Report 2025
• WEF — Gender Parity in the Intelligent Age (March 2025)
• Stanford HAI — AI Index Report 2025
• McKinsey — Women in the Workplace 2025
• Deloitte — Women and Generative AI (2025)
• Accenture & Girls Who Code — Resetting Tech Culture
• Kapor Center — Tech Leavers Study (2017)
• Paluck et al. — Prejudice Reduction Meta-Analysis (2021)
• Dobbin & Kalev — Why Diversity Programs Fail (HBR)
• EU Pay Transparency Directive — Implementation Status
• Payscale — 2025 Gender Pay Gap Report
• Ravio — Gender Pay Gap in European Tech (2025)
• Fortune — Women CEOs on 2025 Fortune 500
• UNESCO — Women in STEM Global Data
• NSF — Women in Computer Science Degree Data