LinkedIn Data Shows Gender Gap in AI Leadership
LinkedIn's August 18 report showed women accounted for 26% of new US AI hires in 2025 and held 13% of executive AI roles at AI companies. The disparity was sharper in highly paid technical roles: Newser reported that men received 82% of "member of technical staff" hires, a role with a $223,000 median listed salary.
LinkedIn data reported by Inc.com and Newser shows women remain underrepresented in AI hiring and leadership, particularly in senior and highly paid technical positions. Newser reports that women accounted for 26% of new US hires into AI roles in 2025, compared with roughly half of hires in non-AI fields.
The disparity becomes more pronounced higher in the organizational hierarchy. Inc.com reports that women hold 13% of executive AI roles at AI companies. Its account of LinkedIn's analysis, covering 27 countries, also puts women at 29% of entry-level jobs at AI companies, 21% of vice president-level jobs, and 13% of C-suite jobs.
Pay and technical-role differences
Newser, citing reporting based on the LinkedIn data, identifies "member of technical staff" as a particularly imbalanced AI occupation. The role, described as sitting between advanced research and full-stack engineering, carried a median listed salary of $223,000 and went to men 82% of the time.
By comparison, lower-paid data annotator jobs had a median pay of $51,000 and were closer to gender parity, according to Newser. That distinction matters because technical staff and leadership roles commonly shape model development, engineering architecture, product decisions, and compensation trajectories, while annotation roles tend to sit elsewhere in the AI production pipeline.
A pipeline issue, not only an executive issue
Inc.com quotes a LinkedIn global public-policy partnerships leader describing the pattern as a hiring, training, advancement, and leadership-pipeline problem rather than solely a late-stage promotion problem. The reported figures support that framing: representation is already below parity among new AI hires and declines across seniority levels.
Comparable workforce patterns can affect whose domain expertise is represented in model requirements, evaluation design, human-feedback processes, and product research. Representation alone does not determine whether an AI system is reliable or trusted, but teams developing systems for broad user populations often need diverse technical and domain perspectives to surface failure modes that homogeneous teams may overlook.
The figures also distinguish between participation in the expanding AI labor market and access to its most influential roles. LinkedIn's data, as reported by the two outlets, indicates that women are participating in AI work but are substantially less represented in senior leadership and high-compensation technical positions.
Key Points
- 1LinkedIn data places women at 26% of new US AI hires in 2025, below their approximate share of non-AI hiring.
- 2Women held 13% of executive AI roles at AI companies, while representation declined from entry-level positions through the C-suite.
- 3Industry workforce patterns can influence which perspectives inform model evaluation, product requirements, and technical decision-making across AI development teams.
Scoring Rationale
The report provides relevant labor-market evidence for AI organizations recruiting technical and leadership talent. It does not introduce a new model, tool, or policy requirement, but the reported representation gaps have meaningful implications for AI workforce composition and governance.
Sources
Public references used for this report.
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