A friend of mine, an internationally renowned scientist, once told me something that stuck: “The most important words in science aren’t ‘Eureka, I found it.’ They’re ‘Hmm, that’s funny.’”
She was talking about the value of noticing the unexpected. Of being curious enough to wonder why something doesn’t fit your mental model. Of having the intellectual humility to admit you don’t know something, and the drive to figure it out.
I’d argue the same principle applies to AI adoption.
The Youth Myth
There’s a pervasive belief that young people have a natural advantage with AI. They grew up with technology. They’re digital natives. They adapt faster.
It’s a seductive narrative, but, in my personal experience, it’s dead wrong.
Yes, younger workers may be more comfortable with new interfaces and less intimidated by change. But comfort isn’t competence. And familiarity with consumer tech doesn’t translate to strategic AI implementation.
What actually matters, what makes a real difference, isn’t age. It’s curiosity.
Why Curiosity Wins
The leaders I see succeeding with AI share a common trait: they ask better questions.
Not “Can AI do this?” but “What happens if we try this?”
Not “What’s the best practice?” but “What don’t we know yet?”
Not “How does everyone else use this?” but “What could this mean for our specific context?”
This kind of questioning requires intellectual adventure. A willingness to explore without knowing the destination. The courage to experiment when you’re not sure what you’ll find.
That’s not a generational trait. It’s a mindset. For example, on my big screen, there’s a Post-It sticker which says “Can Claude Code do this better?”. It’s there to remind me to explore, to maintain a sense of playfulness and adventure.
Experience as Fuel, Not Barrier
Here’s what younger workers often lack: decades of pattern recognition.
Curiosity without context is just random exploration. But curiosity combined with experience? That’s when you notice the signals others miss.
When you’ve seen three market cycles, five technology shifts, and a dozen failed transformations, you develop intuition. You know which questions matter. You recognize when something that looks normal is actually “funny.”
The scientist who notices the anomaly in her data isn’t the one running the experiment for the first time. It’s the one who’s run it a hundred times and knows what “normal” looks like.
That pattern recognition is invaluable in AI adoption—if you pair it with curiosity.
The Dangerous Opposite
The real barrier to AI success isn’t age. It’s certainty.
The executive who’s sure they know how their industry works and doesn’t need to reconsider.
The manager who’s confident in their existing processes and sees no reason to experiment.
The consultant who’s built a career on best practices and isn’t interested in questioning them.
That kind of certainty calcifies over time—but it’s not inevitable. I know 60-year-olds who ask more curious questions than 25-year-olds. And I know young professionals who are already locked into rigid thinking.
Age doesn’t determine curiosity. Choice does.
What Curious AI Adoption Looks Like
In practice, curiosity-driven AI work looks different:
Less: “Here’s what OpenAI says to do.” More: “What happens if we test this in our specific context?”
Less: “We need an AI strategy.” More: “What small experiment could teach us something valuable this week?”
Less: “What’s the ROI?” More: “What did we just learn, and what should we try next?”
Less: “Let’s wait until we know more.” More: “Let’s try something and see what we discover.”
This isn’t reckless. It’s rigorous exploration. The scientific method applied to business transformation.
Your Unfair Advantage
If you’re reading this and you’re worried you’re “too old” for AI, stop.
Your experience is an advantage—if you pair it with curiosity.
You’ve seen what worked and what didn’t. You understand the nuances of your industry that newcomers miss. You have relationships, credibility, and institutional knowledge.
What you need isn’t youth. It’s the willingness to say “that’s funny” when AI produces an unexpected result.
To ask “why?” when something doesn’t match your assumptions.
To experiment without knowing the answer.
To learn in public, even when it’s uncomfortable.
The Real Question
The question isn’t whether you’re young enough to succeed with AI.
It’s whether you’re curious enough to keep learning.
Because in the intelligence economy, the winners won’t be those who know the most today. They’ll be those who learn the fastest tomorrow.
And that requires the same attribute that drives breakthrough science: not the thrill of finding answers, but the courage to notice when something’s funny—and the curiosity to figure out why.
What’s Next:
The most valuable AI experiments aren’t the ones that confirm what you already know. They’re the ones that make you say “that’s funny.”
What’s the last thing AI showed you that didn’t match your expectations? And what did you do about it?
