Executives Predict AI-Driven Layoffs, Worker Thriving Falls

A Mercer survey of 12,000 workers, including 825 executives, found 99% of C-suite leaders expect artificial intelligence to trigger layoffs within two years. The share of employees classified as "thriving" dropped from 66% in 2024 to 44% in 2026, which Mercer attributed to job-loss fears, thinner staffing, and uneven AI training access. A separate layoff tracker reported over 144,000 tech-sector job cuts in 2026 and projected more than 220,000 additional losses by year-end.

Executives Predict AI-Driven Layoffs, Worker Thriving Falls Newser reports that a Mercer survey of 12,000 workers, including 825 C-suite leaders, found 99% of executives expect artificial intelligence to trigger at least some layoffs within the next two years. Newser reports Mercer also found the share of employees classified as "thriving" fell from 66% in 2024 to 44% in 2026, which Mercer ties to job-loss fears, thinner staffing, and uneven access to AI tools and training. More than one third of respondents said they would consider quitting if left behind on AI training. Newser additionally cites a layoff tracker reporting more than 144,000 tech-sector job cuts so far in 2026 and projecting over 220,000 additional losses by year-end. What happened Newser reports that a Mercer survey of 12,000 workers, including 825 C-suite leaders, found 99% of executives expect artificial intelligence to prompt at least some layoffs within the next two years. Newser reports Mercer also recorded a decline in the share of employees the firm classifies as "thriving," from 66% in 2024 to 44% in 2026, and attributes that decline to job-loss fears, thinner staffing, and uneven access to AI tools and training. Newser further cites a layoff tracker reporting more than 144,000 tech-sector job cuts so far in 2026 and projecting over 220,000 additional losses by the end of the year. Editorial analysis - technical context Industry-pattern observations: large-scale AI adoption frequently triggers organisational redesigns that change role composition and toolsets. Firms that restructure work around automation typically need coordinated reskilling, clearer tooling access, and updated workflow integration to preserve productivity and morale. Public reporting on uneven training access is consistent with prior studies showing that tool availability and manager support are key determinants of whether employees adopt AI productively. Context and significance the Mercer findings and the layoff-tracker numbers together underscore two linked trends practitioners are watching: rising managerial expectation of workforce reduction tied to automation, and measurable declines in employee wellbeing metrics during early waves of AI adoption. For teams building or deploying AI, this matters because talent churn and uneven tool access raise operational risk for model deployment, monitoring, and maintenance. What to watch Industry observers should monitor three indicators: - •whether companies publish reskilling or internal-training uptake metrics - •changes in role-level headcount and job postings for AI-adjacent skills - •adoption signals for enterprise tooling that controls access and audit logging. Reported changes in those indicators will influence how easily organisations can stabilise production ML systems while managing workforce transitions Limitations of the reporting Newser's article summarises Mercer survey results and references a layoff tracker; the scraped article does not include full methodological detail from Mercer in-line, and no direct quotes from Mercer are provided in the scraped text. Where Mercer or the tracker is cited for numerical claims, those claims are presented as reported by Newser. Scoring Rationale The story combines a large Mercer survey with substantial layoff figures, highlighting workforce impacts of AI adoption that materially affect hiring, retention, and operational risk for AI teams. It is a notable industry development but not a new technical breakthrough. Practice with real Ad Tech data 90 SQL & Python problems · 15 industry datasets Active Search Campaigns by BudgetEasy /problems/sql/active-search-campaigns-by-budget High CPC Clicks & Poor Landing PagesMedium /problems/sql/high-cpc-clicks-poor-landing-page Campaign ROAS by Attribution ModelHard /problems/sql/campaign-roas-by-attribution-model 250 free problems · No credit card See all Ad Tech problems /problems/datasets/adtech