The hidden costs of curiosity
Taking on expertise in the AI era requires a greater commitment to what we want to learn.
Not too long ago, applying a new skill was a far greater commitment—a measured, deliberate investment of time. You actively sought out learning material. You built a mental model. You practiced. You built a foundation. These days? Well, that makes for the start of quite an interesting debate.
There can be no doubting that AI gives us speed. But the greater the speed, the more the illusion of expertise. Which sounds like a bummer, and in some ways is. Perhaps the biggest being the satisfaction that comes with skill. Because while we can ship (or learn) faster than ever, the means have always been a deeper and more fulfilling topic.
So while the core competencies we once dreamed of learning are no longer untouchable, that doesn’t necessarily shorten the road to expertise. Nor does it eschew the long-term (and emotional) value of obtaining that expertise.
But it does mean there are some additional pitfalls to obtaining that expertise.
The distraction trap: how ease and speed cost us time
I’d like to think of myself as smart and curious. Both because I’ve gone to school (I was a bit more curious in my six months of dev bootcamp than my four years of undergrad), and also because I’m a self-motivated learner. I’ve studied (and I mean truly studied), a long list of different topics. Those include:
User experience
Graphic Design
Software System Design
Algorithms
Music Theory
Novel writing
Audio Engineering
Social media strategy
Copywriting
And most of those things, I continue to study today (both because they evolve and there’s just that much to soak up). Trouble is, curiosity can lead me (and the royal we) to chase things without realizing what’s at stake. Over that same course of time, I’ve also spent time on:
Stock trading
Data mining
Machine Learning
Cryptocurrency
Sales strategy
Video strategy
SEO
Space travel
Quantum Physics
What happens when we take that sort of curiosity into the age of AI? A land where Perplexity and Manus write us research reports and briefs in minutes. Where NotebookLM lets us upload entire library shelves worth of knowledge and ask questions of it. And where Claude and ChatGPT give us a whole new vector to “the ends justify the means”.
That just puts things into hyperdrive.
When curiosity becomes a detour
It’s tempting to know anything on that second list is more attainable. And that I can explore the foundation in the name of satisfying curiosity. But there’s a point where curiosity provides diminishing returns.
That might be because you don’t have any real application for data mining. Or maybe because you’re learning something that (if you were being honest with yourself) you don’t even care to get good at. But that line is most likely to be crossed when the things in list B (the curiosities) take away from list A (your expertise).
How can we concretely tell when that’s happening? Here are a few signals:
When you’re doing something because you can
You’re learning a skill you’d never want to use practically
When we don’t read the research we create
You feel compelled to ask the next question (like about space travel), because you asked the first one
These aren’t things that will raise red flags (unless you’re avoiding work with them). They’re drops in the wrong bucket that accrue over a long swath of time.
Expertise is a lot of things, but perhaps most importantly, it’s deeply intentional. Which is where fluency and foundation come into play.
Fluency versus foundation
There’s a certain kind of person who can walk into a room, rattle off the right concepts, and help you believe they’ve mastered what they’re describing. In other words, they’re fluent.
Foundation, on the other hand, is quieter. You can spot it in how someone asks follow-up questions. In how they pause to check their assumptions. In how they recover when something doesn’t work. Foundation means you’ve done the work—enough to know where the cracks are.
Fluency can be slippery: it rewards rhythm, vocabulary, and reading the room. Maybe you are that person—or maybe you know that person. They can speak confidently about topics, sometimes even when they’re called on it.
That’s just the world: we reward articulation over application. We assume knowledge based on delivery.
The truth is building real expertise requires both. You can do the thing and talk about the thing. Without foundation, you’re putting a nice roof on an empty house. Without fluency, you’ve built a fully livable woodshed.
Being in touch with our expertise is more important than ever
So how do we meaningfully grow our expertise? It’s analog: an honest reckoning.
It’s about your goals and a time-value alignment around those goals. Imagine your expertise as a heatmap. Some areas are glowing, others are fading. Your goal isn’t to turn off the fading lights entirely. It’s to notice where your energy goes, and that the energy is being applied where you want it to be.
That’s not easy. Not all knowledge is created equal and valuable knowledge to you can be worth far less to the next person. But by value, I don’t just mean monetary value.
Some honest questions to ask about the expertise that’s valuable for you:
What do I love?
What do I want to be good at?
What do I want to share?
The point is, choosing what’s sellable isn’t the only reason to choose expertise (and there’s a case to be made that any expertise has a market, even if that market is low).