文章将生产力视为主要的非人口因素对冲手段，指出在多年疲弱后已有试探性改善，包括 2025 年劳动生产力的提升；但同时强调，当前数据并未显示清晰、全经济范围的 AI 就业破坏。采用 AI 的产业仍在招聘，而总体就业成长放缓，部分可由景气循环条件与移民下降解释，而不仅是自动化所致。Schrager 也质疑把工作简化为被取代的叙事，强调任务组成的重要性：许多职位包含关系互动与高度判断功能，因此 AI 可能把时间重新配置到更高价值的人类工作，而非直接消除劳动需求。
关键风险是转型失败：即使生产力上升，技能与技术错配也可能暂时或持续侵蚀人力资本价值，造成低度就业与较弱的福利结果。她指出中学与高等教育层级的教育品质正在走弱，且大学就读扩张的报酬可能递减，这意味著文凭数量正快于批判性思维品质的提升。政策含义是采取由两部分组成的人力资本策略，而非 AI 决定论：提升严谨教育并优先高技能移民，因为若劳动品质与适应力持续下降，仅靠高生产力可能不足以维持成长与债务承载能力。
In February 2026, Allison Schrager argues that US anxiety about AI-driven unemployment is part of a broader deterioration in human capital, the core asset behind long-run US economic strength. The central structural pressure is demographic: an aging population and reduced migration are shrinking labor-force growth, while a smaller population also weakens consumer demand and raises fiscal strain on younger workers financing retirements and public debt. Historically, the US offset this through immigration and rising worker capability, but she contends those buffers are now less reliable.
The article frames productivity as the main non-demographic offset, noting tentative improvement after weak years, including labor-productivity gains in 2025, yet stresses that current data do not show clear, economy-wide AI job destruction. Sectors adopting AI are still hiring, and slower aggregate job growth can be partly explained by cyclical conditions plus lower migration rather than automation alone. Schrager also challenges simplistic replacement narratives by emphasizing task composition: many roles include relational and judgment-intensive functions, so AI may reallocate time toward higher-value human work instead of eliminating labor demand outright.
The key risk is transition failure: even if productivity rises, a skills-technology mismatch can temporarily or persistently erode human-capital value, producing underemployment and weaker welfare outcomes. She highlights weakening education quality at secondary and post-secondary levels and possible diminishing returns from expanded college attendance, implying that credential quantity is outpacing critical-thinking quality. The policy implication is a two-part human-capital strategy rather than AI determinism: improve rigorous education and prioritize highly skilled immigration, because high productivity alone may be insufficient to sustain growth and debt capacity if labor quality and adaptability continue to decline.