AI Hits Worker Paychecks, Not Payrolls, Apollo Study Finds
1h ago|4 min readStandard
Fazen Markets Editorial Desk
Collective editorial team · methodology
artificial-intelligencelabor-market
Fazen Markets Editorial Desk
Collective editorial team · methodology
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Apollo Global Management Chief Economist Torsten Slok announced on August 22, 2026, that early labor-market data indicates artificial intelligence is initially impacting worker wages more significantly than employment levels. A study of hundreds of occupations revealed that jobs with higher exposure to AI are experiencing weaker wage growth, while the direct effect on headcount remains relatively contained. This nuanced finding suggests companies are leveraging AI for productivity gains that suppress compensation rather than immediate workforce reduction. Former IBM HR chief Diane Gherson confirmed that corporate strategies are actively evaluating automation potential, retraining pathways, and how to realize cost savings. IBM stock traded at $235.68 as of 14:41 UTC today, down 0.62% on the session within a range of $233.12 to $238.72.
Labor economists have tracked technological disruption since the Industrial Revolution, but the pace of AI adoption distinguishes the current cycle. The last major wave of automation anxiety occurred during the robotics and software boom of the early 2010s, where manufacturing employment fell by 1.7 million jobs between 2010 and 2020 according to Bureau of Labor Statistics data. The current macro backdrop features a Federal Reserve policy rate of 4.25% and core PCE inflation running at 2.8%, creating a environment where businesses seek efficiency gains without triggering a consumer-led recession. The catalyst for this study is the unprecedented acceleration in generative AI tools reaching commercial viability within the last 24 months, forcing companies to develop immediate implementation strategies. This compression of the adoption curve has created a natural experiment in how AI permeates business operations before broader economic consequences become clear.
Torsten Slok's analysis measured AI exposure across occupational codes using a methodology that scores tasks based on their susceptibility to large language models and automation. Occupations in the top quartile of AI exposure showed median wage growth of 2.1% year-over-year, significantly trailing the 4.3% growth in occupations with minimal AI exposure. The current national average wage growth sits at 3.8% according to the latest Employment Cost Index, indicating that AI-exposed roles are already underperforming the broader market. Employment levels in high-exposure occupations declined by only 0.4% over the past year, compared to 1.2% growth in low-exposure fields. This divergence suggests that while companies maintain headcount, they are allocating fewer dollars toward compensation growth in roles where AI can augment or replace human effort. The S&P 500 information technology sector has gained 14% year-to-date, outperforming the broader index's 8% return as investors price in productivity benefits.
| Metric | High AI Exposure | Low AI Exposure |
|---|---|---|
| Wage Growth (YoY) | 2.1% | 4.3% |
| Employment Change | -0.4% | +1.2% |
IBM's stock performance reflects this transitional period, with shares declining 0.62% to $235.68 amid broader market consideration of how AI implementation affects various business models. The company's current market capitalization of approximately $256 billion positions it as a key player in both developing AI solutions and managing the workforce implications of those technologies.
The primary second-order effect of wage compression in AI-exposed fields is potential margin expansion for companies that successfully implement automation technologies. Sectors with high proportions of knowledge workers—including financial services, software development, and media—could see earnings upgrades if productivity gains outpace compensation costs. Technology infrastructure providers like NVIDIA and cloud service platforms stand to benefit from increased enterprise demand for AI tools. Conversely, companies that derive significant revenue from professional services fees face potential top-line pressure if automation reduces billable hours. A key limitation of the current data is its inability to capture potential job creation in new categories that don't yet exist in occupational classifications. Historical precedents like the ATM's impact on bank tellers initially reduced growth in that role but ultimately expanded the banking industry's overall employment. Current market positioning shows institutional investors increasing exposure to AI infrastructure plays while maintaining neutral weightings in sectors with high labor cost exposure. Flow data indicates net inflows into automation-themed ETFs totaling $2.4 billion quarter-to-date.
The September 6 release of the August Jobs Report will provide the next comprehensive look at wage growth trends across sectors, with particular attention to information services and professional and business services categories. The October 2 JOLTS (Job Openings and Labor Turnover Survey) data will reveal whether companies are reducing hiring plans for AI-exposed roles versus other occupations. Earnings calls throughout October, particularly for technology consulting firms and enterprise software providers, will yield management commentary on how AI is affecting workforce planning and capital allocation. Key levels to watch include whether wage growth in AI-exposed fields falls below 2.0%, which would signal accelerating pressure, and whether the employment component remains above -0.5%. The Federal Reserve's September 17 policy meeting may address whether disinflationary pressures from potential productivity gains affect their outlook on interest rates.
Companies appear to be using AI tools to augment existing workers rather than replace them entirely, allowing the same number of employees to handle increased workload without corresponding wage increases. This productivity gain suppresses compensation growth as businesses capture the efficiency benefit rather than passing it to workers through higher pay. The dynamic resembles earlier technology adoptions where initial workforce adjustments came through reduced hiring and natural attrition rather than immediate layoffs.
Occupations involving routine information processing show the highest exposure, including data analysts, technical writers, paralegals, and certain software development roles. These positions typically involve tasks that generative AI can perform or assist with, such as coding, document review, and data synthesis. By contrast, jobs requiring physical manipulation, emotional intelligence, or unpredictable problem-solving show lower exposure metrics in the Apollo study.
Historical technology adoption cycles ultimately created more jobs than they destroyed, but the transition period often involved significant displacement and required worker retraining. The Apollo study notes that AI is already contributing to record business formation, which typically generates new employment opportunities. The net effect will depend on whether new AI-enabled industries and roles emerge faster than traditional roles decline, a process that typically unfolds over 5-10 year periods rather than immediate timeframes.
AI's initial labor market impact manifests through suppressed wage growth rather than employment reduction, creating investment opportunities in companies that capture productivity gains.
Disclaimer: This article is for informational purposes only and does not constitute investment advice. CFD trading carries high risk of capital loss.
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