I’ve been watching a weird trend lately. One person opens an AI tool, types a prompt, and walks away rolling their eyes, saying, “I don’t get the hype; this thing is generic.” Then, someone with ten years of experience uses that exact same tool and comes away with a massive breakthrough.
It’s the same software. So why is the gap between the results so huge?
After thinking about it, I realized the difference isn't how well you prompt. It's about how much experience you're bringing to the table before you even start typing.
Beginners Want Answers; Experts Want Contrast
When you’re starting something new, you’re usually desperate for certainty. You want the right answer or the perfect framework so you don't mess up. Naturally, you ask the AI:
“What should I do?”
The problem is that AI is built on averages. If you ask a generic question, you get a generic, average answer. It feels meh because it’s not tailored to anything real.
Experts don't go to AI for the answer—they already have an opinion. They go to the AI to have that opinion challenged. They ask things like:
“Here’s my plan—tell me exactly where it’s going to fail.”
“What am I assuming here that’s actually a mistake?”
“Give me the strongest counter-argument to this approach.”
AI only starts to feel powerful when you give it something solid to push back against.
You Can’t Skip the “Bad” Reps
This is the part that sucks to hear: you can’t use AI to skip the phase where you’re bad at something.
Your first few wrong decisions, the moments where you’re confused, the projects that fail—those are actually what build your judgment. Without that scar tissue, you don't have the intuition to know if what the AI is telling you is brilliant or total garbage.
Trying to skip the struggle doesn't make learning faster; it just makes your understanding thinner.
The “Speed” Trap
There’s this quiet lie going around that AI is all about speed. But if you’re a beginner, speed can actually be your enemy.
Real learning is about the mess—it's the struggle of figuring things out that makes the lesson stick in your brain. When you use AI to get a clean explanation too early, you get the answer, but you don't get the understanding.
It feels productive in the moment, but you’re not actually building a mental model you can use later.
AI Is a Multiplier, Not an Addition
I’ve started looking at AI as a multiplier. If you have a zero in experience, it doesn't matter how powerful the AI is—anything times zero is still zero.
If you have a shallow understanding, AI just helps you produce shallow work at 10× speed.
If you have strong judgment, AI amplifies that leverage and makes you unstoppable.
It feels a bit unfair, but it’s honest. AI doesn't close the gap between a novice and an expert; it actually makes the gap wider. It rewards the people who have already done the heavy lifting of earning their context.
I’m Still Earning Mine
I’m definitely not writing this as an expert. I still have those days where AI feels noisy and useless. But I’ve noticed that those are usually the days when I haven't done the work myself first.
I’m trying to use the tool as a partner instead of a shortcut.
AI is a great tool for explaining things after you’ve tried to understand them, or for showing you a new perspective on a mistake you’ve already made. But it’s a terrible replacement for the confusion that comes with learning.
I’m learning to be okay with that confusion.
It’s not a sign that the tool isn't working—
it’s a sign that I'm actually learning.