Experience used to mean having answers.
AI is changing that.
Early in my career, I thought the most experienced person in the room was the person who knew everything.
Someone asks a question?
You answer.
A problem comes up?
You already know the solution.
You walk into meetings prepared with answers because saying
“I don’t know” somehow feels like admitting you aren’t experienced enough.
I used to think seniority worked like this:
More years → more experience → more answers.
But after spending years working in technology and products, I’m starting to see it differently.
Technology simply moves too fast.
A tool you learned two years ago may already be replaced.
A framework everyone was excited about last year might barely come up today.
And AI has accelerated this even more.
Every few weeks there seems to be another model, agent, framework, protocol or new way of building something.
Trying to know everything has become an impossible game.
And strangely, the more experienced people I meet seem more comfortable admitting that.
They say:
“I don't know.”
“Let me check.”
“I haven't looked at that yet.”
“I was wrong about this.”
Earlier in my career, those sentences sounded like uncertainty.
Today, I see them differently.
They show someone knows the limits of what they know.
Because experience doesn't necessarily give you more answers.
Experience gives you better questions.
A junior person might ask:
“How do we build this?”
Someone with more context might ask:
���Why are we building this?”
“Who actually has this problem?”
“What happens if we don't build it?”
“What assumption are we making here?”
“How will we know this worked?”
And now there’s another interesting change happening.
AI has made answers incredibly cheap.
I can ask ChatGPT, Claude, Gemini or another tool a question and get a confident-looking answer in seconds.
Ten years ago, finding information itself was valuable.
Today, information is everywhere.
The harder skill is figuring out:
Is this the right answer to the right question?
That distinction matters.
Because AI can generate an excellent solution to a badly framed problem.
It can write the code.
Create the PRD.
Analyze the data.
Draft the strategy.
Even argue convincingly for why the idea makes sense.
But somebody still needs to ask:
“Wait… are we solving the right problem?”
That might become one of the most valuable skills in the AI era.
Not knowing everything.
Not prompting faster than everyone else.
But having enough experience, curiosity and skepticism to know what to ask next.
Maybe seniority was never about having all the answers.
We just lived in a world where answers were harder to get.
Now that answers are becoming almost free, we’re finally discovering the value of a good question.
And yes…
I still Google the question after the meeting. 😄
Every AI demo is a well-behaved intern on day one.Then real users arrive, paste a novel, upload a photo of a receipt from 2014, and type “idk just fix it.”That’s when the engineering starts.Prototypes prove the idea.
Production proves users will never follow the https://t.co/SiSxjcrPHN for the edge cases. The slide already got applause. 🛠️
Been learning AI product management for a while now.
Funny thing — the more AI I learn, the less I think PMs need to “master AI.”
We need to get better at knowing where AI actually helps.
That part is still very human.
"🕒 Time spent doing a job doesn't magically enhance leadership skills. 💼 To level up, it's all about deliberate prioritization. 🚀 #ProductManagement#SoftSkills#Leadership"
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