When was the last time you didn’t know something - and didn’t immediately look it up?
It’s harder than it sounds.
For example, someone asks a question in a meeting. Before anyone can think about it, phones are out. Three seconds later: “Here it is.” Discussion over.
Google trained us to find answers fast. AI made it even faster. And somewhere in all that speed, we stopped sitting with questions long enough to actually think about them.
What’s Changed
Not-knowing used to have a longer lifespan.
You’d notice something odd in your data. Mention it to a colleague over lunch. They’d suggest a theory. You’d push back or build on it. The question would sit with you for a while - bumping around in your head, connecting to other things you knew, maybe leading to related questions you hadn’t considered.
Now? Question → search → answer → move on.
Most of the time, this is just efficient. “What’s the pandas syntax for this?” doesn’t need contemplation. Look it up and keep working.
But the same pattern is starting to apply to every question. And that’s where something fundamental shifts.
What Gets Harder to Build
Comfort with uncertainty
When every question resolves in seconds, you don’t develop tolerance for not-knowing. That matters because useful analytical thinking often happens in the “I’m not sure yet” space - where you question your assumptions, notice what doesn’t fit, let patterns show themselves over time.
The thinking that happens while you wait
When finding answers took effort, your brain kept working. You’d try different angles, form theories, spot connections. That processing time wasn’t inefficiency. It was how understanding actually developed.
Questions as conversation starters
“Why does this metric spike here?” used to launch discussion. Now it launches a quick search. We’re gradually trading collaborative thinking for solo answer-hunting.
Deep understanding versus surface knowledge
AI can explain what a statistical technique does. Understanding when to use it, why it might fail in your specific context, what assumptions it makes - that comes from time spent with the concept, not from reading a clear explanation.
What This Looks Like
Junior analysts who can generate working code but get stuck explaining why it works. The prompting skill is there. The underlying understanding isn’t.
Teams solving problems before they’ve fully understood them. The friction that used to force examination is gone.
Meetings where “Let me think about that” rarely gets said. Immediate answers feel more competent than thoughtful pauses.
Classrooms where quick answers get rewarded more than questions that make people think harder.
None of this is dramatic or catastrophic. It’s subtle. But for many people, the habit of wondering gets less practice because it’s no longer necessary for getting answers.
So here are some ways to maintain it deliberately.
Some Things Worth Trying
The point isn’t to avoid AI or pretend search engines don’t exist. It’s about being deliberate with when you use them.
Distinguish between lookups and thinking questions
Syntax questions, definitions, facts - look those up immediately. No performance needed.
But “Why is this happening?” or “What should we do about this?” - those deserve some time before you search. Even just writing down what you think first changes how you process the answer when you find it.
Before asking AI, ask yourself
When something comes up that isn’t urgent, spend a few minutes thinking before searching.
What do you already know that might be relevant? What would a good answer look like? What are you assuming?
This activates your existing knowledge. When you do search, the new information connects to something instead of just floating alone.
Use AI as a thinking partner, not an answer vending machine
Instead of “What’s causing this anomaly?” try “I’m thinking this might be X - what else should I consider?” or “What questions would help me figure this out?”
You stay engaged in the reasoning. AI supplements your thinking instead of replacing it.
Keep some questions open in conversations
When someone asks something you could instantly resolve with a search, occasionally try: “What do you think?” or “Let’s work through it together.”
This feels less efficient. Sometimes it is. But the discussions that emerge often surface insights the quick answer would have missed.
Make room for wondering without a goal
Some time each week - could be 30 minutes, could be an hour - to explore something you’re curious about with no deliverable attached.
Read something unrelated to your work.
Follow a tangent.
Let your mind connect things in unexpected ways.
This isn’t direct productivity. But it keeps your thinking flexible and maintains your capacity for genuine curiosity.
Notice and acknowledge good questions
In meetings or discussions, when someone asks something that makes the group think harder, point it out. “That’s a really good question - it’s making us examine something we were taking for granted.”
Small recognition shifts the culture slightly. Questions become as valued as answers.
Where You’ll Feel Resistance
Wondering feels wasteful when answers are instantly available.
Your brain will tell you to “just check quickly.” You’ll rationalize it as being efficient. If you try this in meetings, someone might look impatient.
This is what can help:
Remember that speed-to-answer and depth-of-understanding aren’t the same thing. Pattern recognition, sound judgment, knowing which questions actually matter - these build through thinking, not through collecting answers.
Start small too. One question a day that you think about before searching. One meeting where you don’t immediately resolve everything. That’s enough to maintain the habit.
Why This Might Matter More
As AI handles more technical work, the valuable skills shift toward things AI can’t do as well:
Knowing which questions to ask
Synthesizing across contexts
Noticing gaps
Making judgment calls with incomplete information
Holding space for exploration before jumping to solutions
These capabilities develop through practice. If wondering becomes something you rarely do, these skills get less exercise.
The Basic Truth
We’re in the most answer-rich era in history. Questions have never been easier to resolve, and that’s genuinely useful most of the time.
But it’s made wondering optional. And optional habits often fade when they’re not actively maintained.
Some questions benefit from time. Not all - maybe not even most.
But some.
The ones where understanding matters more than just knowing. The ones where the process of thinking reveals something the answer alone wouldn’t show.
The skill is recognizing which questions those are - and giving them the space they need.
Til next time,
Donabel