Artificial Influencer? Research Suggests AI is Widening the Pay Gap

The wage divide is growing, and artificial intelligence (AI) may be the source, according to new research released in December by Revelio Labs. The workforce intelligence company found that while the top quartile of salaries has grown by more than 30% since January 2023, the bottom quartile has only seen a 10% increase.

The study showed automation pressures appear to be a major force behind this new pattern.

“Average AI adoption rates by occupations are negatively correlated with wage growth at the bottom of the wage distribution, and only weakly correlated for top-quartile jobs. This suggests that automation pressures are disproportionately suppressing wage gains in lower-paid roles, which is contrary to narratives that AI is mostly impacting high-earning, white-collar jobs,” said Revelio experts.

Workspan Daily (WD) recently interviewed Lisa K. Simon, the chief economist at Revelio Labs, to discuss what the research findings mean for wage inequalities and how AI adoption will impact the future of compensation.

Lisa K. Simon, chief economist, Revelio Labs

WD: What kind of data was used in the research, and how was it collected?

Simon: The research used Revelio Public Labor Statistics (RPLS) data built from job postings, including posted salary information and hiring demand trends over time. They track how wage levels and job demand shift by occupation and wage quartile. We also used our COSMOS job postings data for RPLS, which is our unified job postings data, including sources such as job boards, LinkedIn job postings, regional aggregators and company website postings. 

WD: What are some key findings you can share from your research?

Simon: The main finding is a clear return of a K-shaped labor market. High-wage roles are growing in demand and wages, while low-wage roles face falling demand and slower wage growth.

WD: How are current salary increase practices contributing to the widening pay gap?

Simon: Rising wage inequality is primarily driven by the fact that relatively more people enter high-wage jobs than low-wage jobs currently. But we are also seeing within occupation salary increases. Salary increases at the in high paying jobs may in part be driven by productivity gains — if workers are able to achieve more work with less with the help of AI, employers may be rewarding this accordingly. 

WD: Based on the research, what occupations are most impacted by automation?

Simon: The most noticeable divergence appeared in high-wage and specialized fields like arts, design and entertainment, which saw the highest year-over-year salary growth (53%). Meanwhile, lower-wage resilience shows up in sales, legal and protective services, where automation potential is low.

WD: Based on the study, how do you see AI adoption influencing wage growth?

Simon: AI adoption appeared to suppress wage growth in lower-wage jobs. The research found a strong negative relationship between AI adoption rates and wage growth at the bottom of the wage distribution. This suggests automation pressure is limiting wage gains for lower-paid roles more than for top-wage roles. That is because lower-wage jobs with high levels of AI exposure have seen the lowest wage growth. Since low-wage jobs don’t all necessarily adopt AI, there is higher propensity to be automated from the outside because of lower task complexity. 

WD: How can HR and total rewards (TR) professionals use this research to identify and address wage inequalities?

Simon: HR and TR teams can use these insights to:

• Monitor compensation trends (e.g., your relevant labor market, whether that is a local labor market or industry) by wage quartile, not just averages.
• Identify roles where automation could reduce bargaining power and adjust pay strategy proactively.
• Protect lower-wage roles with skills-based progression paths and internal mobility.
• Benchmark wages frequently, especially for roles with high AI adoption.

WD: Looking ahead, what do your findings suggest about the future of compensation?

Simon: If AI adoption continues, wage inequality may rise further. Compensation may likely become more polarized: high-wage specialized roles continue gaining, while automation-exposed lower-wage roles may see slower growth and fewer openings. This could push organizations to rethink pay structures, reskilling investments and equity-focused compensation policies.

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