Everyone thinks AI is coming for people’s jobs – just not their own

A survey of 2,000 Canadian workers found 26% now believe machines or computers will be doing much of their jobs within a few years, up from 17% in 2023. The article argues that workers are not broadly panicked about their own roles, but media-driven expectations of AI disruption may be creating pluralistic ignorance and giving employers cover to frame layoffs and restructuring as AI-driven. Several company cuts have been attributed to AI, though New York filings from 160 firms in 2025 did not cite technology as a reason for job reductions.

Analysis

The important market takeaway is not that workers fear AI, but that management now has a cheap narrative for restructuring before the technology is fully deployed. That creates a near-term asymmetry: labor-cost actions can accelerate in the next 1-3 quarters even if productivity gains remain incremental, because boards get immediate optics and investors reward “discipline” today while deferring proof of AI-driven ROI. The risk is that AI is becoming a governance excuse faster than a cash-flow engine, which can pull forward layoffs, outsourcing, and headcount freezes across software, internet, and white-collar service sectors.

META is especially exposed because it sits at the intersection of automation narrative and credibility risk. If the market starts penalizing firms that cite AI for cuts without showing a measurable increase in revenue per employee or operating margin durability, META’s multiple could compress despite intact ad fundamentals. The second-order effect is that enterprise buyers may also resist aggressive AI pricing until vendors can prove replacement rather than augmentation, slowing monetization across the broader AI stack.

The contrarian view is that the article may be understating how quickly “AI-washing” can become self-fulfilling. Once enough firms act as if displacement is inevitable, wage growth in susceptible roles can cool before actual automation is widespread, which improves margins and helps AI-sensitive equities in the medium term. But the immediate trade is less about AI capability and more about management behavior: expect higher scrutiny on headcount trends, severance charges, and productivity claims over the next 2-4 earnings cycles.

Key Decisions for Investors

• Short META on rallies over the next 2-6 weeks; thesis is not ad demand deterioration but multiple risk from AI-led restructuring stories that investors may increasingly discount unless margin expansion is visibly attributable to product monetization.
• Pair trade: long high-quality enterprise software with demonstrated AI revenue attachment, short lower-confidence AI beneficiaries that lean on narrative and cost-cutting. Use a 3-6 month horizon and size for dispersion as investors separate true productivity gains from AI-washing.
• Buy 3-6 month put spreads on a basket of white-collar labor-exposed names if available, targeting companies with heavy SG&A and recent layoffs justified by automation. Risk/reward improves if markets start rewarding cost cuts without evidence of volume growth.
• Avoid chasing broad AI beta after sharp rallies; instead, wait for the next 1-2 earnings seasons where guidance language on headcount, opex, and productivity will reveal which firms can quantify AI returns versus merely frame cuts around it.
• For existing META exposure, hedge with short-dated calls or collars into earnings if management is likely to emphasize AI-driven efficiency; the setup is for headline support but potential post-call multiple compression if monetization proof remains thin.