In a galaxy far, far away — well, California, actually — AI systems are being built that could reshape everything from how we work and bank to how we manage our health, and even how we age.
Engineers are training machines to solve complex problems, mimic human reasoning and bring robots one step closer to life.
And yet, while the AI advances emerging from Silicon Valley may resemble the futuristic tech of Star Wars, they’re missing a familiar story arc: the presence of an older, wiser guide. Luke had Obi-Wan. Oprah Winfrey had Maya Angelou. Bill Gates turned to Warren Buffett.
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In the past, young tech visionaries often sought out seasoned mentors to help turn raw innovation into lasting impact. Steve Jobs mentored a young Mark Zuckerberg, for example. Today, the future is being written mainly by the young.
That generational imbalance is striking, especially when you consider who will be most affected.
How AI is missing the wisdom of older adults
According to AARP, 59% of Americans over 50 say today’s technology isn’t designed with them in mind. That’s a massive blind spot, given that more than 120 million Americans are now over 50. By 2030, at least 21% of the population will be 65 or older, and the first wave of millennials will begin turning 50.
Steve Jobs once said, “Design is not just what it looks like and feels like. Design is how it works.”
That’s where many older adults feel left behind. “I think the biggest disconnect is in what’s prioritized in tech itself,” says Dr. Brittne Kakulla, Senior Research Advisor at AARP. “Older adults prioritize function over flash, which can be counter to the tech industry’s obsession with speed and novelty.”
This raises a central question: How well will AI work as it becomes integrated into everything from healthcare to personal finance if younger generations primarily program it?
Researchers and advocates say the stakes are high. Excluding older adults from the design, testing and governance of AI could lead to systems that not only overlook their needs but also fall short for everyone.
Ghost in the machine: how AI is trained and why bias is built in
Artificial intelligence systems are designed to recognize patterns and make decisions by analyzing vast amounts of data. Through machine learning, these systems “learn” from what they’re fed — whether it’s language, lab results or consumer behavior — and generate outputs based on that information.
Simply put: what goes in determines what comes out. When that input reflects bias, or when the teams building the models lack diversity, those flaws are embedded into the technology itself.
A review published by Nature identified age-related bias throughout the machine learning pipeline, from how data is selected and labeled to how models are evaluated.
Real-world examples are already raising red flags. A 2024 study found that AI chatbots, often powered by “large language models” (LLMs), such as Copilot and Perplexity, frequently returned responses laced with age-related stereotypes, evidence that bias isn’t theoretical, but built into the behavior of the tools themselves.
Compounding the issue is the malleability of these systems. While most large language models rely on massive datasets, their personality and tone can be quickly altered. Elon Musk’s Grok, for example, sparked backlash after parroting false narratives about a white genocide in South Africa. Even when users asked about unrelated topics like baseball, Grok responded with the false genocide story.
How to give feedback
When using an LLM or chatbot, pay attention to age-related content. If you find it to be biased or inaccurate, you can usually provide feedback by clicking on a thumbs-up or thumbs-down sign, or clicking on “report.”
As philosopher Matteo Pasquinelli told The New York Times, “AI needs us: living beings, producing constantly, feeding the machine. It needs the originality of our ideas and our lives.”
But what happens when that perspective is incomplete?
What happens when older adults are left out?
Take healthcare, where AI already plays an increasing role in diagnosing disease, managing treatment plans and predicting medical outcomes.
A policy brief issued by the World Health Organization warns that when these systems rely on data skewed toward younger populations, they can “systematically discriminate on a much larger scale than biased individuals.” The authors note that AI tools may miss symptoms, delay diagnoses or reinforce disparities in care for older adults – simply because they weren’t designed to recognize what aging looks like in the data.
The consequences extend well beyond healthcare. In the workplace, AI is rapidly reshaping hiring and skills expectations. Nearly one in four U.S. tech job postings sought candidates with artificial intelligence skills, according to job-listing data. Yet, older workers are being left behind, not due to their capabilities, but rather due to perception.
A survey by the nonprofit Generation found that just 32% of U.S. employers said they would “likely” consider candidates over 60 for roles involving AI tools, compared to 90% who would consider applicants under 35. Yet when those same hiring managers were asked to assess the performance of mid-career and older workers already on their teams, 89% said these employees performed as well as, if not better than, their younger peers.
When those biases shape who builds and trains AI, the result could be technology that’s less accurate, less inclusive and less effective for everyone
The case for inclusion: why AI needs older adults
Of course, the stereotype that older adults are tech-averse doesn’t hold up. In fact, AARP reports that generative AI use among Americans 50 and older doubled in 2024, from 9% to 18%, and another 30% express excitement about its potential.
Workplace adoption is rising, too. According to Generation, 15% of midcareer and older workers already use AI tools regularly, most as self-taught “power users” who turn to them multiple times a week.
“The growth in AI adoption suggests it’s becoming more relevant to older adults,” says Dr. Kakulla.
That’s why advocates argue that including older adults in AI development can lead to more functional, ethical and widely usable tools.
This can mean building age-diverse teams where mid- and late-career professionals help identify blind spots; ensuring training data reflects aging populations, not just digital natives; and creating AI oversight roles that draw on the experience and judgment that only decades in the workforce can provide.
Dr. Kakulla also emphasizes, “Tech companies need to account for diversity across the entire lifestage, and design for the lifestage.” She points to translation tools, popular among older adults, as a good example: the same AI feature could help someone in their 50s while traveling, support speech-to-text needs in their 70s, and aid communication with a caregiver in their 80s.
Ultimately, what AI becomes depends on who trains and guides it. To ignore older adults in that process is to repeat one of humanity’s most enduring mistakes, devaluing wisdom until it’s too late.
As Benjamin Franklin once put it: “Life’s tragedy is that we get old too soon and wise too late.”
It’d be wise not to program that tragedy into our machines.