AI hiring is one of the fastest-growing parts of the job market, and it pays well. LinkedIn found that job postings for AI roles have roughly doubled since 2023, with a typical salary near $177,000. A regular, non-AI role pays about $80,000. That gap alone makes AI hiring one of the biggest pay opportunities in the economy right now.
The problem is that women are barely getting a share of it. LinkedIn's research, covering tens of millions of workers across 27 countries, found women made up only 26% of new AI hires in the U.S. in 2025. In non-AI roles, women got roughly half of all new hires. That is not a small gap. It means AI is becoming a new, well-paid track that mostly runs through men.
LinkedIn breaks this down into three separate problems, and each one compounds the next. First, women get fewer AI job offers to begin with. Second, companies built around AI simply employ fewer women across the board, even outside of AI-specific roles. Third, and most striking, women hold only 13% of C-suite AI leadership positions globally. The gap between men and women gets wider, not narrower, the higher up you look.
This lines up with older research too. A 2024 study from Zeki Research found women working in AI receive 30% less visibility and recognition than their peers as their careers go on, even when they contribute the same technical work. Talent alone is not translating into promotion or credit.
Now look at the other end of the labor market. A report from the National Partnership for Women and Families found women make up 83% of workers in the jobs considered most vulnerable to AI automation, roles in gig work, nursing support, and warehouses. Women of color account for close to a third of workers in the highest-risk jobs on that list.
Put both findings together and the picture is uncomfortable. Women are underrepresented in the AI jobs that pay the most and offer the most security, and overrepresented in the jobs AI is most likely to shrink or eliminate. This is not a story about individual ambition or skill. It is a structural gap in who gets pulled into the highest-value part of the AI economy and who gets left exposed to the riskiest part of it.
For any business building out AI teams or restructuring roles around AI tools, this is worth a hard look internally. A company that only recruits AI talent through the same narrow pipelines it has always used will end up competing for the same small pool of candidates everyone else is chasing, while missing half the available talent. It is also a retention risk: firms that let women drop out of technical and leadership tracks lose experienced people right when AI skills are becoming the most valuable asset a company can have.