Women account for 26% of US AI hires as leadership gap persists

LinkedIn research found that women accounted for 26% of new US artificial intelligence hires in 2025, compared with 50% in non-AI occupations. Guardian reporting first published on 4 October 2026 brings those disparities together with women’s accounts of entering and staying in AI careers, highlighting unequal access to an expanding area of employment.

The Guardian page carries a later timestamp of 7 October, but does not identify the extent of its update. The workforce research predates October: these findings describe an existing employment divide, rather than a newly measured change this week.

What LinkedIn found about AI hiring and leadership

LinkedIn’s report, The Triple Penalty: Mapping the Gender Gap in the AI Economy, also examines leadership across 27 countries. Women held approximately 13% of C-suite AI positions at AI firms. That figure concerns a specific group of senior AI roles, rather than executives across the economy.

Women accounted for 20% of Head of AI hires, 26% of Director of AI hires and 18% of Member of Technical Staff hires. By contrast, women made up more than half of hires in data annotation, the lowest-paid AI occupation in the analysis.

The US pay analysis uses LinkedIn advertisements from 2023–2026, while hiring composition comes from member profiles. Those measures describe advertised compensation and platform-based hiring patterns; they do not establish population-wide earnings or equal-work pay discrimination. LinkedIn policy representative Sarah Steinberg told the Guardian: ‘Women are just strikingly underrepresented.’

Women describe barriers to staying in AI careers

Urvashi Batra, co-founder and chief executive of Prioriwise, an AI platform for IT service providers, told the Guardian that people take her less seriously than her male co-founder. She said their pitching experience led them to conclude that investment was more likely when he presented. This is her account of those encounters, rather than a controlled comparison of investors’ decisions.

Jayeeta Putatunda, an AI engineering lead at investment firm Turing, described being the only female engineer on teams and having to work harder to make her ideas heard. She said mentorship from other women could be difficult to find, while rapid technical change created pressure to work beyond normal hours.

After four months of maternity leave, Putatunda said, she returned to different frameworks and model capabilities. Catching up was overwhelming. She credited supportive colleagues and a husband who shared childcare equally with making the return possible. Her experience illustrates a retention challenge, without establishing how common it is across employers.

Brenda Darden Wilkerson, president of AnitaB.org, criticised AI companies’ reliance on familiar recruitment networks, referrals and filters that have historically limited women’s access. Her explanation identifies hiring practices as a possible mechanism; the reporting does not measure how much those practices contributed to LinkedIn’s figures.

AI exposure is not a count of jobs lost

Independent research published by the International Labour Organization on 5 March provides context for workers outside AI careers. It found that 29% of female-dominated occupations were exposed to generative AI, compared with 16% of male-dominated occupations.

In the highest exposure categories, the corresponding figures were 16% and 3%. These percentages describe occupations, not workers who have lost their jobs. Women were more exposed than men in 88% of countries analysed, but exposure measures potential technological effects rather than observed displacement.

The ILO linked the difference to women’s concentration in clerical, administrative and business-support work, including reception, payroll and accounting assistance. Many tasks in those roles are routine and codifiable. ‘Generative AI is not entering a neutral labour market,’ said co-author Anam Butt, pointing to unequal care responsibilities and occupational segregation.

What the ILO recommends for the workplace

The ILO expects generative AI’s effects on job quality to be more widespread than its effects on job numbers. It identifies potential changes to tasks, workloads, monitoring and autonomy, and calls for access to skills development, women’s participation in AI decisions and dialogue among governments, employers and workers.

Those are policy recommendations, not demonstrated remedies for the hiring gaps. Neither the LinkedIn findings nor the ILO exposure analysis quantifies realised AI-caused job losses among women. They address different questions: who is accessing AI careers, and whose existing work could be changed by the technology.