Strategies for a successful retirement

Artificial intelligence is no longer a distant concern reserved for recent graduates. New research shows it is already altering the career trajectories of workers in their mid-50s and beyond, with potential ripple effects on how long people remain employed and how policymakers approach Social Security.

A study from the Center for Retirement Research at Boston College finds that employees aged 55 and older in occupations highly exposed to AI are leaving their jobs at higher rates than before. Geoffrey Sanzenbacher, the paper’s author and an economics professor, notes that these exits stem roughly equally from job loss and voluntary decisions. “It’s a statistically significant effect,” he told CNBC. “For some occupations, it can be quite large.”

The research identifies three distinct pathways through which AI may influence the length of older workers’ careers. First, automation can directly displace workers, pushing them into unemployment or out of the labor force entirely. Second, the rapid pressure to master new tools may prompt some to seek roles less dependent on emerging technology or to retire earlier than planned. Third, and more optimistically, generative AI could extend working lives by boosting productivity, raising wages, and freeing people to concentrate on higher-value, more engaging tasks.

Data drawn from the Current Population Survey and AI-exposure measures developed by Tufts University’s Digital Planet initiative reveal a clear shift around the public release of ChatGPT. Before that launch, older workers in AI-exposed roles were significantly less likely to exit their jobs. Afterward, the pattern reversed: they became somewhat more likely to leave, including transitions into unemployment.

The occupations most exposed to AI tend to be white-collar and highly skilled. According to the study’s rankings, the five roles with the highest exposure scores are web and digital interface designers, web developers, database architects, computer programmers, and data scientists. At the opposite end sit physically intensive jobs with the lowest exposure: excavating and loading machine operators in mining, roof bolters, orderlies, painting and spraying machine operators, and fiberglass laminators and fabricators.

This distribution challenges conventional assumptions. Traditionally, workers in demanding physical jobs were expected to have shorter careers and retire earlier than college-educated professionals in higher-paying roles. Sanzenbacher’s findings suggest AI exposure could compress that gap. As he wrote, “AI exposure may reduce the gap in career length between low- and high-paying jobs.”

That possibility carries direct consequences for Social Security reform. Trustees project the program’s trust fund could be depleted by late 2032. One option frequently discussed is raising the full retirement age, as lawmakers did gradually from 65 to 67 after the 1983 reforms. Yet the workers most likely to face benefit reductions under many solvency proposals are higher-income earners—the same group whose jobs show the greatest AI exposure. “There’s a high probability that higher-income people see a bigger benefit cut than lower-income people from whatever happens with Social Security next,” Sanzenbacher observed. “These are the very people who therefore need to work longer.” Whether AI ultimately shortens or lengthens their ability to do so remains an open question.

Older workers are adopting the technology, though more slowly than their younger colleagues. AARP surveys show mixed attitudes: among adults 50 and older, 24 percent view AI primarily as a threat to their field, 19 percent see it mainly as an opportunity, and 37 percent regard it as both. Separate research from AARP and LinkedIn indicates that experienced professionals are somewhat more likely to hold roles insulated from generative-AI disruption, partly because those jobs emphasize collaboration, judgment, and leadership—skills that remain difficult for current systems to replicate.

Career experts advise a dual strategy. Vicki Salemi of Monster recommends that older professionals become fluent in the specific AI tools their employers already use, which can free time for deeper analytical work. At the same time, they should deliberately highlight soft skills—communication, relationship-building, and problem-solving—whether staying in a current role or applying elsewhere. “When you can show you possess strong soft skills coupled with the ability to evolve and grow with new technology, it can be a green light for your candidacy,” she said.

Monster’s own data underscores that many workers still have room to start: 42 percent of those surveyed reported not using AI at all. For those ready to begin, the most common entry points remain practical—email drafting, scheduling, and writing support—before progressing to more advanced applications such as data analysis or content creation.

The evidence so far is nuanced rather than alarmist. AI is neither an automatic ticket to early retirement nor a guaranteed career extender. Its effects vary by occupation, skill set, and individual adaptability. What is clear is that older workers, employers, and policymakers can no longer treat technological change as a problem that only younger cohorts need to solve. The decisions made in the next few years about training, job design, and retirement-age policy will help determine whether AI shortens or stretches the final chapters of many working lives.

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