LinkedIn's Data Lays Bare the Gender Gap in AI Hiring — and It Widens at the Top
The News
LinkedIn research published August 18 found that women accounted for just 26% of AI hires in 2025 across the United States, despite making up half of all hires into non-AI roles. The gap narrows as the pay rises: across 27 countries, women hold only 13% of C-suite AI leadership positions at AI companies, and just 20% of head-of-AI roles. AI job postings on LinkedIn's platform have roughly doubled since 2023, with a median listed salary of $177,000 — more than double the $80,000 for non-AI roles. LinkedIn identified three compounding barriers: a role deficit (women are 10 percentage points less likely to hold AI roles than non-AI roles), a firm deficit (5 points lower representation at AI-focused companies), and a leadership deficit (the C-suite gap is 15 points wider than below-executive levels).Why It Matters
For a TA leader building AI teams, this is simultaneously a pipeline problem, a sourcing problem and a pay-equity problem. If three-quarters of your AI hires are men, the issue is not talent scarcity — it is where you are looking and how your funnel filters. The leadership gap is the sharpest point: 13% C-suite representation means the people designing your AI hiring processes and evaluating candidates are almost exclusively male, which compounds the pipeline problem rather than correcting it. And because AI roles pay more than twice as much as non-AI roles, this is also where your organisation's gender pay gap is being actively manufactured.Taking Action
All action is situated in your corporate context. These are starting points, not prescriptions — choose what fits.
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Who to talk with in your enterprise
Your head of DEI: ask for the gender breakdown of your last twelve months of AI-role hires set against non-AI hires. If the gap mirrors LinkedIn's 26%, you have a funnel problem worth naming publicly. Your talent sourcing lead: review whether your AI job advertisements, sourcing channels and screening criteria reach women at the same rate as your non-AI roles — LinkedIn's data suggests the gap starts at the top of the funnel, not the bottom.Inspect
What to take a closer look at
Pull the gender distribution of applicants, interviewees and hires for every AI-tagged role in the past year. Compare against your non-AI roles. If the applicant pool is more balanced than the hire count, the filtering is happening somewhere in your process — and that is where to look. Also check whether your AI role descriptions are written in a way that disproportionately deters female applicants, a pattern LinkedIn's research implies but does not directly test.Reflect
What to think about more deeply
If AI is the highest-paying, fastest-growing category in your hiring plan, and women are systematically excluded from it, at what point does 'we cannot find female AI talent' become an admission that your process is the problem — not the pipeline?