How AI Stands to Affect Gen X and Millennial Workers

Artificial intelligence is transforming workplaces, and for mid- and late-career employees, it brings both risk and opportunity.

Many workplaces are increasingly using artificial intelligence tools to evaluate employee performance, but some company leaders warn that such platforms cannot fully analyze situations the way humans can. Hackcapital/Unsplash

As companies reconfigure jobs around artificial intelligence tools, middle- and late-stage career workers face a twofold problem exacerbated by the rise of the new technology: age discrimination and potential managerial reluctance to retrain older employees in existing roles relative to younger peers.

However, some say AI represents an unprecedented opportunity for Gen X and millennial workers to double down on the experience and skill sets that make them irreplaceable.

Despite a federal law on the books for more than half a century that is meant to prevent age-related discrimination, it remains a significant problem in corporate America.

Between fiscal years 2021 and 2024, workplace age discrimination claims rose from 12,965 (2021) to 16,223 (2024), according to the Equal Employment Opportunity Commission.

Research from the International Monetary Fund concluded that workers older than 46 have been negatively affected in areas in which AI models reduce the value of existing skills.

Compounding this, a Pew Research Center report from Feb. 25 suggests that there is hesitation among workers 40 and older to use AI tools at work, putting Gen X and millennial employees at risk as the technology scales.

Evidence also points to a quiet reluctance among team leaders to retrain older workers in Europe and the UK. Legal experts say it is also a problem in the United States.

“In our recent workplace discrimination cases, we’ve defended against claims where employers assumed workers over 50 couldn’t adapt to new systems,” Michael Weiss, partner at Lerner & Weiss, told The Epoch Times. “These assumptions cost them six-figure settlements.”

Weiss said companies that think that they can simply phase out mid-career workers not only face massive liability, but also lose employees with critical skills.

“The managerial reluctance to retrain older workers isn’t just morally questionable, it’s legally dangerous,” he said.

“When managers document reluctance to invest in older workers, they’re creating evidence for pattern discrimination claims.”

From a litigation perspective, Weiss said the most vulnerable industries are health care and technology, “not because older workers can’t perform, but because these sectors often have youth-oriented cultures that create hostile environments,” he said. “We’ve handled multiple cases where ‘digital native’ preferences became age discrimination smoking guns.”

Weiss said workers older than 50 should focus on compliance and risk management roles in which AI augments rather than replaces human judgment.

“In our insurance premium audit practice, experienced auditors who use AI for data analysis but rely on their expertise for anomaly detection become indispensable,” Weiss said. “They understand regulatory implications that algorithms miss entirely.”

Age discrimination is prevalent in work environments, in health care, and among social connections. Dreamstime/TCA

One survey from MyPerfectResume in December 2024 revealed that 99 percent of respondents older than age 40 experience “some degree” of ageism in their workplace.

These claims are not without precedent. Elon Musk acquired Twitter, now known as X, in 2022 and laid off nearly 2,000 workers. A California court ruled in 2024 that the approximately 150 of those workers who were older than 50 could sue for age discrimination.

In that case, plaintiff John Zeman, who worked in X’s communications department, sued in 2023. He said in his lawsuit that X laid off 60 percent of employees who were 50 or older and nearly three-quarters of those who were older than 60, compared with 54 percent of employees younger than 50.

Sam Altman, CEO of OpenAI, has also expressed concern for how AI will affect the aging workforce of the United States. During an episode of the podcast Huge If True with Cleo Abram, Altman said he is more worried about what AI means for the 62-year-old who does not want to retrain or reskill than about what it means for the 22-year-old just graduating college.

According to Department of Labor estimates, Gen X workers make up about 31 percent of the U.S. labor force and millennials make up about 36 percent.

Getting Sharper

With the overall rise in cost of living, saving for retirement has become more difficult for the mid-career workforce.

A 2024 New York Life survey shows that the average Gen X adult does not have enough saved to fully fund retirement, with individual savings of less than $7,500 in 2024. In that same analysis, 43 percent of respondents said they carry credit card debt.

Millennial workers have not fared any better, with just 48 percent claiming that they have enough saved for retirement, according to an Allianz study published in May.

Nathan Lugo-Montanez at Blue Peak Strategies said this is why mid-career workers must adapt.

“Gen X and older millennials are in a tough spot,” Lugo-Montanez told The Epoch Times. “They can’t afford to retire, but the jobs most exposed to AI—administrative work, mid-level management, and back-office functions—are the ones they dominate.”

Lugo-Montanez said that while younger workers have more time to pivot and those close to retirement can step away, most members of Gen X and millennials are between a rock and a hard place.

“When Sam Altman says middle-aged workers resist retraining, he’s right,” he said. “But it’s not laziness, it’s math. A 50-year-old doesn’t get the same return on retraining as a 25-year-old.”

Lugo-Montanez stressed that middle and late-stage career workers should not quit adapting, though.

“It means you get sharper at what AI can’t replace: judgment, leadership, and people skills,” he said. “Pair that with competence in AI tools and data literacy, and you stay in the game.

“If you’re over 50, you don’t need to be a coder. You do need to know how AI reshapes your work and how to use it. The industries least forgiving here will be clerical, logistics, and middle management. Those roles are already on the chopping block.”

A smartphone and a laptop display the logo of the ChatGPT robot in Manta, Italy, on Oct. 4, 2023. Marco Bertorello/AFP via Getty Images

Leaning In

Neil Sahota, an AI adviser to the United Nations, said any job—regardless of the employee’s age—that has routinized judgment and rigid workflows risks replacement.

“Here’s the rule of thumb: If your value is keystrokes, you’re exposed,” he told The Epoch Times. “If it’s judgment under ambiguity, you’re essential.”

A National University roundup in May compiled data and trends on jobs being replaced by AI, job loss statistics, and the broader impact of AI on employment. The roundup states that 30 percent of current U.S. jobs could be automated by 2030, while 60 percent of employees could see their daily tasks “significantly modified” by AI. As it stands, more than 23 percent of U.S. companies say they have replaced workers with AI tools.

The same analysis states that 14 percent of all workers have already been displaced by AI, with a higher rate among younger and mid-career workers in tech and creative industries.

Drawing on his work with AI research group ACSI Labs, Sahota said the Gen X and millennial employees who come out on top amid the AI-integration shuffle will be the ones who pivot from task-drivers to outcome owners. “In other words, they become the people who orchestrate AI, data, and humans to deliver the results,” he said.

The key advantages middle-aged employees have are institutional memory and cross-functional judgment, according to Sahota.

“These are prime inputs for training, validating, and governing AI systems,” he said.

He used the example of a contact center and customer service-related roles: In this setting, workers could adapt to the integration of AI tools through conversation design, the creation of escalation playbooks, and quality analytics.

Sahota said that upskilling with AI tools is vital but that being involved in the operational capacity is, too.

“When upskilling, think: Prompts are entry level, process is executive level,” he said.

Ryan McEachron, CEO of ISU Insurance Services, said he has seen both the “policy side and business reality” of older workers. And what he has seen is a massive level-up.

“The biggest misconception is that older workers need to chase AI skills,” McEachron told The Epoch Times. “What they actually need is to understand how their existing expertise becomes more valuable, not less.”

In his line of work, he said, he has witnessed clients “doubling down” on relationship-heavy roles that AI cannot replicate.

“One client moved from accounting clerk to estate planning specialist at 52 [years old],” McEachron said. “Same financial background, but now focusing on the complex family conversations and trust-building that requires decades of life experience. His income increased 40 percent because he positioned himself where human judgment matters most.”

The areas in which he has seen the biggest industry age-outs are not necessarily tech-related. They are middle-management-heavy sectors such as retail and traditional manufacturing, in which companies can “flatten hierarchies using AI tools,” McEachron said.

On the other hand, McEachron has seen insurance and real estate businesses give preference to experienced workers, as clients prefer someone who has been through multiple market cycles.

“From my planning commission years, I learned that adaptation beats innovation every time,” he said. “Don’t learn AI coding, learn how to use AI tools like ChatGPT for research while you handle the relationship management and strategic thinking that comes from 25-plus years.”

Sahota said there has always been a “huge bias” in the perception that older workers cannot learn new technology as fast as younger generations. He called it “ironic,” considering the ages of many big tech leaders.

“This is a return on learning fallacy,” he said. “Experienced workers learn faster when training is anchored in their real workflows.

“Remember, institutional memory is training data. Don’t turn off your best dataset.”