Skip to content
Tony Wood
Home

No, 20-Somethings Won’t Run Rings Around You With AI

Tony Wood |

why am i writing this blog post? Because every week I see the same line on LinkedIn: if you do not learn AI now, some twenty-something will run rings around you and take your job.

It is clickbait that lands, because it pokes at a very human fear. It is also lazy thinking that quietly bakes age bias into your AI strategy.

Here is the thing. Sometimes a younger colleague will move faster with new tools. Sometimes your most experienced person will. The real gap is not age. It is mindset, context and whether your organisation gives people space to practise.

As leaders, if we buy the age narrative, we design the wrong training, send the wrong signals and leave value on the table.

The Age Story Sounds True, But The Data Says “Mindset”

The age story feels neat. Younger people grew up with smartphones, therefore they must be better at generative AI. Older people did not, therefore they must struggle.

Some adoption data does show higher early use among younger workers. A 2024 survey of tech professionals found that younger employees were more likely to experiment with generative AI, which is not a shock.

Yet even that research warns against blaming or praising age alone:

"While younger employees have generally been more open to experimenting with generative AI, 'our data suggests mindset plays just as important a role as age,' said Dice Chief Product Officer Christian Dwyer."

So yes, younger cohorts may be first to poke at new tools. But what really predicts impact is whether someone is open to change, has permission to experiment and a practical reason to use the tech.

A separate analysis of workplace AI adoption goes further. It notes that there are gaps between age groups, then immediately qualifies the headline:

"While there are age-related disparities in generative AI exposure and adoption, 'workplace training, attitudes toward change, and opportunities to experiment' are influential, sometimes more so than age itself."

If you strip the fear out of the conversation, you get a much calmer picture. People use AI when four things are true:

  • They believe it is relevant to their work.
  • They feel safe to try and fail.
  • They have time to practise.
  • They have a basic growth mindset about learning.

None of those sit neatly on a birthday.

Mindset, Curiosity And Courage At Any Age

When you are young, almost everything is new. By definition you are in learning mode. You are not “learning new things”, you are learning things. There is less to unlearn, and social pressure often nudges you to try the shiny stuff.

By mid-career and beyond, your “context window” is much wider. You have made mistakes, shipped projects, learned what usually works and what ends badly. That bigger window changes how you approach learning.

You become more selective. You pick your spots. You ask better questions, like “How does this help our customers?” or “What risk does this create for our regulators?” That is not resistance, it is curation.

The danger is when selectivity hardens into a fixed mindset. You quietly move from “I choose what to learn” to “I am past the point of learning that”. For AI, that is fatal.

The good news is that growth mindset is not a young person’s game. A large multi-skill learning study with older adults found:

"A growth mindset predicted greater cognitive gains across tasks, suggesting that older adults who approached learning as an opportunity to grow demonstrated more pronounced benefits from the multi-skill intervention."

In other words, if someone in their 60s treats learning like a chance to grow, they can gain more from new skills than peers who assume their abilities are fixed. Swap “multi-skill intervention” for “AI tools at work” and the lesson holds.

For your leadership decisions, the practical takeaway is blunt:

  • Stop asking: “Can older staff adapt to AI?”
  • Start asking: “Where are the growth mindsets in our organisation, and are we giving them room to play with AI, regardless of age or job title?”

Context And Experience: Your Hidden AI Advantage

There is another reason the “20-somethings will replace you” story is off. It ignores the brutal truth that AI is confident, fast and often wrong.

You do not protect the business by finding the fastest prompt writer. You protect it by pairing AI with people who know what “good” looks like in your domain.

Recent research on trustworthy AI decision making is crystal clear:

"The combination of domain knowledge and AI tools is crucial: domain experts are essential in detecting, understanding and mitigating AI hallucinations...experienced human judgment remains a vital component of trustworthy AI decision-making."

Think about that in your own context.

  • A junior marketing analyst might be brilliant at prompting, but the director who has lived through three brand crises knows when a suggested message will blow up on social media.
  • A new engineer might generate code quickly, but the veteran understands the long-term cost of complexity in a safety-critical system.
  • A young data scientist might produce a model, but the underwriter with decades of pattern recognition spots when the output makes no commercial sense.

AI without experienced judgment is a risk amplifier. AI with deep context is a force multiplier.

So if you are in your 40s, 50s or 60s and worried that you are behind, reframe it. Your decades of pattern spotting, customer calls and board packs are not dead weight. They are exactly what turns AI from a toy into a strategic asset.

The job now is not to compete with the youngest person on token speed. It is to bring your context into the loop and learn enough of the tooling to direct it.

How Leaders Should Rethink Age, Teams And AI Learning

If you sit on a leadership team, you have outsized influence on how this plays out. The messages you send, the stories you repeat and the training you sponsor will either deepen age anxiety or build a healthier culture.

Some practical design moves:

  • Stop using fear-based age hooks. They get clicks, but they also make older staff feel expendable and younger staff feel tokenised.
  • Measure behaviour, not birthdays. Track who is using AI tools, what they are doing and what value it creates. Celebrate examples across age bands.
  • Invest in training that respects experience. Do not design AI basics as if everyone is starting from zero. Connect prompts and workflows directly to lived problems in operations, finance, customer support or product.
  • Normalise “reverse” and “mutual” mentoring. Let younger digital naturals share tips while older colleagues share context, decision criteria and risk signals.

One major multigenerational workplace study puts it plainly:

"Organizations should leverage the strengths of multigenerational teams, recognizing that mentorship can flow in both directions and digital skills development is a continuous process, not tied to one age cohort."

Notice the shift. The goal is not to create a fenced-off “AI kids table”. It is to build teams where curiosity is expected, and where every age group feels both useful and challenged.

Simple Daily Habits That Beat Generational Stereotypes

Mindset is not a slogan, it is a pattern of behaviour. You do not need a six-month transformation programme to start changing it.

For you and your team, a few low-friction habits go a long way:

  • 30–60 minutes a day with AI on real work. Replying to emails, summarising meetings, drafting content, checking code or policies. Work you already do, but with a co-pilot.
  • One “AI experiment” per week per person. Try a new tool, a fresh workflow or a different model, then share what you learned in a short note or stand-up.
  • Three questions for every AI output. “What assumption is this making?”, “What data might it be missing?” and “Does this align with what I know from experience?”
  • A regular cross-age show-and-tell. Short sessions where people bring one AI-assisted win or failure and everyone learns from it.

If you model this as a leader, the signal is powerful. You are not too senior, too old or too busy to learn in public. And you are willing to look slightly clumsy while you do it.

That is what makes it safe for everyone else.

From Generational War To A Community Of Learners

The loudest content about AI and age frames this as a zero-sum game. Either the youngsters take over, or the old guard digs in and blocks progress.

You do not need to run your organisation on that script.

You can choose a different story, where:

  • Curiosity is valued at 22, 42 and 62.
  • People with decades of context are invited into AI projects early, not treated as blockers.
  • Younger colleagues are recognised as teachers, not cheap prompt operators.
  • Training budgets are aimed at behaviour and environment, not stereotypes.

If you do that, you are not just being kind. You are de-risking your AI adoption and unlocking more value, faster.

The open question for you is simple.

What could you change this week to make AI learning safer, more curious and less age-obsessed for everyone on your team?

Links:

Quotes:

Share this post

Keep reading