Companies are abandoning ‘peanut butter’ raises as pay-for-performance takes over the workplace in the AI era

Only about 4% of U.S. employers are using equal, across-the-board raises, versus 44% who had been considering them earlier this year. The article argues AI adoption is widening pay and promotion gaps, with AI super users reportedly three times more likely to have received a promotion and pay raise in the past year. The piece suggests companies are favoring performance-based compensation as AI fluency becomes more important.

Analysis

AI is turning compensation from a broad-based morale tool into a signal of skill dispersion. The key second-order effect is that wage-setting power is shifting from HR policy to productivity telemetry: firms that can measure AI leverage will increasingly pay for output density, not tenure or title. That should widen dispersion in labor returns across knowledge workers, with the biggest beneficiaries being software, consulting, and enterprise SaaS names that can credibly quantify and monetize AI adoption.

For the named names, the most meaningful read-through is not that equal raises disappear, but that adoption enforcement becomes a management tool. That supports ACN more than the others because consultancies can sell AI transformation, measurement, and change-management services when clients need to operationalize this bifurcation; it is modestly positive for MERC as compensation complexity rises, but the benefit is less direct and likely lower quality. GOOGL is the weakest on a near-term basis because incorporating AI into reviews may improve internal productivity, but it also increases scrutiny around whether the company’s AI tools are truly differentiated versus employee workarounds, which can slow monetization and raise internal execution friction.

The contrarian angle is that the market may be overestimating how quickly companies can separate super users from stragglers in a fair, legally defensible way. In the next 6-12 months, measurement noise, manager bias, and employee gaming can produce backlash that pushes firms back toward simpler raise structures, especially if turnover rises among mid-level performers. That creates a near-term risk that the headline AI productivity narrative outruns the actual comp reset; if adoption stalls, the premium on AI-fluent labor compresses and the supposed winners may not sustain their outperformance.

On balance, this is a slow-burn governance story with a faster tactical implication in software and consulting spending. The strongest catalyst is an earnings-season wave of management commentary linking promotions/raises to AI usage, which would confirm that AI is now embedded in operating cadence rather than pilot programs. If that language shows up broadly over the next 1-2 quarters, the dispersion trade should intensify; if not, the current market enthusiasm for AI-driven labor bifurcation may fade.

Key Decisions for Investors

• Long ACN vs. short a consulting basket into the next 1-2 earnings cycles: AI enforcement raises demand for implementation, measurement, and organizational redesign; target 8-12% relative outperformance if management commentary confirms client pull-through.
• Small tactical short GOOGL on any strength over the next 4-8 weeks via call spreads or a tight stop: internal AI-performance linkage is a productivity positive but a monetization and culture risk if adoption remains patchy; risk/reward favors fade on incremental overhead, not a structural short.
• Long MERC on pullbacks for a 3-6 month horizon: compensation complexity and differentiated pay architectures should lift workflow demand, but size modestly because the article implies only incremental rather than breakout benefit.
• Pair long enterprise software with AI-usage analytics / workflow tooling against broad HR-tech: the market is underpricing tools that help companies identify super users, measure output, and administer differentiated comp; best as a 6-12 month theme trade.
• Use call spreads rather than outright longs in AI-enabling names: the policy shift is gradual and vulnerable to backlash, so the cleanest expression is defined-risk upside tied to the next two earnings seasons.