You’re Old and You Want to Work. Too Bad.

I turn 60 next year, an age traditionally associated with the beginning of the professional endgame. The assumption is that by now I should be thinking about winding down, protecting what I have accumulated, and preparing for retirement. I do not feel that way at all. I am actively building the career I want for 2030 rather than preserving the one I had in 2005. After turning 50, I completed software engineering bootcamps, earned a master’s degree in business analytics, and began pursuing a Doctor of Educational Technology. I continue adding technical skills in machine learning and artificial intelligence, and I am considering additional doctoral study in computer science or theoretical physics. To me, reinvention at this stage of life is not a desperate attempt to remain relevant. It is the discovery of what becomes possible when decades of experience are combined with capabilities that did not exist earlier in a career.

Increasingly, I see experienced professionals on LinkedIn learning AI, earning certifications, changing industries, starting businesses, returning to school, and navigating a labor market obsessed with transformation. I think of us as adult active applicants: people who still want to work, contribute, learn, compete, build, and reinvent ourselves. A large portion of this population belongs to Generation X, which Pew Research Center generally defines as those born between 1965 and 1980. We have spent our entire professional lives adapting to technological disruption. We moved from typewriters, fax machines, paper files, and landlines to personal computers, email, broadband, smartphones, cloud computing, and now generative artificial intelligence. AI is not our first technological revolution. It may simply be the biggest one yet. That history makes the stereotype that older workers cannot or will not adapt to technology particularly frustrating. In many cases, adaptation is precisely what our careers have required us to do repeatedly.

Artificial intelligence should theoretically create an extraordinary opportunity for experienced workers. AI can write code, analyze data, summarize documents, generate images, model scenarios, and perform technical tasks that once required specialized knowledge. But the ability to generate an answer is not the same thing as knowing whether the answer makes sense. Someone still needs to understand the problem. Someone must recognize when an AI system is confidently wrong. Someone needs to understand the organization, the customer, the industry, the ethical implications, and the consequences of a decision. Someone must exercise judgment. Those capabilities often take decades to develop.

Research from the AARP Public Policy Institute and Urban Institute makes exactly this point. Their 2026 analysis identifies critical thinking, creative problem-solving, ethical oversight, and judgment as capabilities organizations will need to effectively adopt AI. These are also precisely the types of durable skills experienced workers frequently possess. That should make older workers extremely valuable in an AI-driven economy. Yet something very different appears to be happening.

One explanation sometimes offered for the displacement of older workers is that they simply do not want to learn new technology. The evidence does not support that assumption. AARP’s 2026 research on AI and workers age 50-plus found that 52 percent of workers 50 and older were familiar with AI in the workplace, while 49 percent were interested in learning more about using AI for work. Only 12 percent, however, reported actually having taken AI-related training or classes for work. Nearly two-thirds of employed workers age 50-plus said their employers were not doing enough to train workers to use AI, and two-thirds disagreed that their employers encouraged and offered AI training to workers regardless of age.

Think about the contradiction. We are entering an economy in which workers are increasingly expected to understand AI. Experienced employees are being told that their skills may become obsolete. Yet many of the same organizations making that determination are not providing those workers with meaningful opportunities to learn the technology. Then, when those employees enter the job market, they may discover that employers want candidates with recent AI experience. It creates a nearly impossible loop: you need current experience to remain competitive, but you are not given access to the opportunities that would allow you to acquire it.

Age discrimination rarely arrives in a job posting that says people over 55 need not apply. It is much more sophisticated than that. An experienced applicant is overqualified. Their experience is not recent enough. Their salary expectations are assumed to be too high. A hiring manager worries they might not stay. Someone wonders whether they will fit the culture. Their résumé goes back too far. They are encouraged to remove graduation dates. They are advised to hide portions of their experience so that employers will not realize how old they probably are. At some point, we should recognize how bizarre this has become. We tell people to accumulate experience for their entire careers and then eventually penalize them for having accumulated too much of it.

AARP’s research on workplace age discrimination suggests that this is not simply a matter of perception. Older workers continue to report substantial exposure to age bias and concerns about being pushed out of their jobs. Artificial intelligence could make the problem worse. Research from Geoffrey Sanzenbacher at the Center for Retirement Research at Boston College raises a particularly troubling possibility. Following the introduction of ChatGPT, older workers in occupations highly exposed to generative AI became more likely to leave the workforce than workers in occupations with lower AI exposure. The pattern appeared in knowledge-based fields including computer programming, accounting, auditing, and management analysis. AARP subsequently examined the same phenomenon, asking whether AI may already be contributing to more early retirements.

That matters because it challenges one of our traditional assumptions about automation. For decades, technological displacement was often discussed as a threat to factory workers, clerical employees, and people performing routine physical tasks. Generative AI changes the equation. It can affect accountants, analysts, programmers, writers, managers, researchers, and other knowledge workers who spent decades accumulating expertise. These workers may suddenly find themselves competing against a technology capable of producing portions of their work in seconds and against younger applicants presumed to be more technologically fluent. The result could be one of the great ironies of the AI revolution: we may discard precisely the people whose judgment we need to use the technology responsibly.

There is another possible future. Instead of viewing AI as a reason to replace experienced workers, organizations could use it to amplify them. Imagine giving someone with 25 years of industry knowledge sophisticated AI capabilities. The technology can help that worker write code they never learned to write, analyze datasets they previously needed an analyst to interpret, create financial models, research unfamiliar subjects, automate repetitive work, build presentations, explore competing ideas, and learn new technical skills on demand. The experienced worker contributes something equally important: the ability to determine which problems matter, which assumptions are unrealistic, which outputs deserve skepticism, and what should actually be done. That combination, experience plus AI, may be far more valuable than either one independently.

AI also dramatically lowers the cost of intellectual reinvention. Someone who wants to learn Python at 58 no longer has to struggle alone through a 700-page programming textbook. An AI tutor can explain a concept, generate examples, examine errors, and adapt explanations until the learner understands. The same is increasingly true for statistics, analytics, finance, machine learning, engineering, and countless other technical domains. For perhaps the first time in history, sophisticated personalized learning is available to millions of adults almost instantly. It would be a profound contradiction if, at precisely the moment technology makes lifelong professional reinvention more achievable than ever, our hiring practices impose an arbitrary expiration date on the people attempting it.

So what are we going to do about it? This cannot end with telling older workers to update their résumés. The responsibility cannot belong entirely to the person being displaced. Employers need to train the workers they already have. If AI is strategically important enough to restructure an organization around, then AI literacy should be available across the workforce rather than concentrated among younger employees or a handful of technical teams. Hiring also needs to become genuinely skills-based. Employers should spend less time calculating how long ago someone acquired experience and more time asking what that person is learning, building, and experimenting with today.

The word overqualified should also stop functioning as a socially acceptable substitute for too old. If compensation, responsibilities, or retention are concerns, ask the applicant. Do not make the decision for them. Companies deploying AI hiring systems should also examine those systems for age-related outcomes. Automating hiring does not eliminate bias; it can simply automate the assumptions already embedded in the process. Organizations should intentionally combine generations rather than treating them as substitutes for one another. A 28-year-old employee with emerging technical expertise and a 58-year-old employee with decades of domain knowledge may be far more valuable together than an organization forced to choose between them.

Experienced workers have responsibilities too. We cannot simply demand that employers value our experience while refusing to acquire new skills. We must keep learning. We must experiment. We must understand AI rather than dismiss it. We must be willing to become beginners again. But give us the opportunity to do it. That is the bargain.

Do not guarantee us jobs because we are older. Do not protect us from competition. Do not lower the standard. Let us compete. Judge us on what we know, what we can learn, what we can build, how we think, and what value we can create now, not on the year printed on our birth certificate.

I will turn 60 next year. I have no intention of treating that milestone as the beginning of my professional decline. I am learning technologies that did not exist when I began my career, studying problems I could not have imagined twenty years ago, and preparing myself for work I hope to be doing years from now. There are millions of people like me.

The question facing employers is not whether older workers can contribute to the future of work. Many of us already are. The more important question is whether organizations will recognize our value before they push us out of it.

Contact

Robert Foreman
Doctoral Candidate, Educational Technology
Central Michigan University
Research Focus: AI-Augmented Exploratory Learning, Cognitive Apprenticeship, and Human-AI Interaction in Professional Skill Development
Email: forem1r@cmich.edu
Website: NhanceData.com