Only about two-thirds of long-tenured workers displaced between 2021 and 2023 had found new employment by January 2024, and older workers accounted for a disproportionate share of those who left the labor force entirely rather than landing a new role. As companies accelerate the adoption of AI-driven tools, the workers closest to retirement face the steepest climb back, with longer jobless spells, fewer retraining options, and, in at least one documented federal case, automated hiring software that screened them out before a human ever reviewed their applications.
Displacement data reveal a widening age gap
The Bureau of Labor Statistics released its most recent Displaced Workers Summary covering the 2021 to 2023 period, and the headline number is sobering. Roughly 65.7 percent of long-tenured displaced workers were reemployed by January 2024. That means about one in three workers who had held a job for at least three years before losing it still had not secured a new position. The BLS data explicitly note that older displaced workers’ lower reemployment rates are partly explained by higher rates of exiting the labor force altogether, a pattern distinct from younger cohorts who tend to keep searching.
Separate Current Population Survey tables tracking unemployment duration by age, including breakdowns for workers 55 to 64 and those 65 and older, show that late-career jobseekers endure longer spells of unemployment than their younger counterparts. The gap matters because extended time without a paycheck erodes retirement savings, reduces future Social Security benefits tied to earnings history, and shrinks the window available to rebuild financial security before leaving the workforce for good.
Age also interacts with industry and tenure. Workers who have spent decades in a single sector or occupation often possess highly specific skills that do not transfer easily to new roles, especially when those roles demand familiarity with rapidly evolving digital tools. When layoffs hit such workers late in their careers, they are less likely to move laterally into similar jobs and more likely to confront the prospect of starting over in a field where entry-level positions are geared toward much younger applicants.
When the algorithm says no: age bias in automated hiring
Longer job searches are not caused solely by skills mismatches or weak demand. At least one federal enforcement action has documented how automated screening tools can lock older applicants out before any hiring manager gets involved. The Equal Employment Opportunity Commission reached a settlement with iTutorGroup after the agency found the company’s software had been programmed to reject applicants who exceeded set age thresholds. The company agreed to pay $365,000 to resolve the discriminatory hiring suit and entered a consent decree.
While the iTutorGroup case involved a specific employer and a narrow slice of the labor market, it underscores how algorithmic tools can operationalize age bias at scale. When screening software is configured to favor recent graduates, short career gaps, or narrowly defined “cultural fit,” older applicants can be filtered out without any explicit age-based rule. Because these systems are often proprietary and opaque, rejected candidates may never know whether they were eliminated for legitimate reasons or because an automated model treated age as a liability.
That case is a concrete example of a broader concern flagged by the Urban Institute, whose research on AI and older workers identifies skills and training gaps as the primary barriers that displaced late-career employees face when trying to re-enter the job market. The institute’s analysis also points to limited access to publicly funded retraining programs, a gap that hits hardest in sectors where AI adoption is moving fastest. Workers who spent decades in roles now being automated often lack both the digital fluency employers demand and the institutional support to acquire it quickly.
Without targeted interventions, AI tools risk amplifying existing inequities. Employers that rely heavily on automated assessments may unintentionally favor applicants whose résumés mirror the recent past of the firm, sidelining career changers and those with non-linear work histories. For older workers, whose résumés often reflect steady advancement over many years rather than rapid job-hopping, that bias can be especially costly.
Missing data and the questions that remain for workers near retirement
Federal statistics confirm the broad pattern, but critical details are still absent from the public record. The BLS displacement data do not break out AI-specific layoffs by age group or industry, leaving policymakers to infer the technology’s impact from aggregate trends. Nor do standard labor surveys systematically capture whether automated tools played a role in hiring decisions, making it difficult to distinguish between traditional age discrimination and new, technology-mediated forms.
That lack of granularity complicates efforts to design effective safeguards. Regulators and lawmakers weighing rules for AI in employment must rely on case studies, individual enforcement actions, and research from independent organizations rather than a comprehensive federal dataset. For older workers already navigating long job searches, the absence of clear information about where AI is used and how it affects hiring means they have little guidance on how best to present their skills or which retraining paths are most likely to pay off.
For now, the available evidence points to a simple conclusion: workers nearing retirement who lose long-held jobs face higher risks of permanent exit from the labor force, longer stretches of unemployment, and growing exposure to opaque technologies that can shape their prospects without their knowledge. Filling the data gaps around AI-related displacement and screening, while expanding accessible training tailored to late-career workers, will be essential if the next wave of automation is to widen opportunity rather than close the door early on millions of experienced employees.
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David M. Keller is a finance writer based in Columbus, Ohio, covering personal finance and consumer-focused economic topics. He earned his degree in journalism from Ohio University and began his career reporting on local business and economic trends for a regional media outlet. Since then, he has contributed to a variety of online publications, focusing on clear, practical coverage of topics such as cost of living, debt, and everyday financial decision-making.
