Do articulate people use AI better?

A question suddenly came to me recently.

Our main way of interacting with AI is still language. So are people who were already better at expressing themselves also better at using AI?

My first reaction was yes. The more clearly you can lay out your goals, background, constraints and expectations, the more easily AI understands what you really want to do. But thinking further, it doesn’t seem that simple.

Being able to say things and saying the thing that matters are still two different things.

Language may just be the interface between us and AI. What really decides how much can get through that interface is still the thinking behind it.

Being articulate is, of course, an advantage

Asking AI to look over the same product plan, one person might say:

“Take a look and tell me what problems this plan still has.”

Another might say:

“I’m not trying to add more features right now; I want to judge whether it’s ready for real user testing. Please focus on whether the core flow has any breaks, which problems would stop users from completing the task, and which are just experience issues that can be fixed later.”

The latter usually gets a more useful answer. But not entirely because they’re better at writing prompts; it’s because before speaking, they’d already thought part of it through: what exactly needs judging right now, which problems matter, and which can be let go for now.

So I increasingly feel that “articulateness” here isn’t good English or good writing, and not necessarily speaking beautifully either.

Someone who writes very well can absolutely describe the wrong problem in a very moving way; a programmer may not be eloquent, yet can tell AI precisely what state things are in, where it failed, what can’t be touched, and what result counts as passing.

The latter is just as good at using AI.

Expression is more like an outlet for thinking than the most fundamental skill.

What’s harder is turning “something feels off” into a real problem

Problems in real life rarely look clear at the start.

More often, all we have in our heads is a vague feeling: “this product doesn’t seem good enough yet,” “this article reads a bit strangely,” “something about this plan just feels wrong.”

What’s really hard next is to keep chasing it down.

• What exactly is wrong?
• Is the information insufficient, or is the structure the problem?
• Is the product really not usable yet, or has it just not reached my ideal?
• Is what’s missing now more research, or should it already go out to see how real users react?

This is what’s called problem framing: gradually defining a vague problem clearly.

I used to think “asking good questions” might be a prompting technique; now I see it that way less and less. It’s actually a very traditional thinking skill: in a mess of confusion, finding the problem that’s really worth solving.

AI just makes this more visible.

Because AI has one very particular trait: give it the wrong problem, and it can also work very diligently all the way through that wrong problem, and do it quite thoroughly.

Once “generating” gets cheaper and cheaper, “judging” matters more

This may be the layer I care about most right now.

AI is already very good at generating. An article, a plan, a piece of code, an analysis — you can quickly get a version that looks quite complete.

The difficulty actually comes after the answer appears.

• Did it really answer the original question?
• Which parts just sound professional?
• Which conclusions don’t actually have enough evidence?
• Which things are correct but don’t matter at all?
• And which parts could clearly still be optimized, but are actually good enough already?

I’m increasingly wary of one thing: the “sense of completion” AI creates.

When something has a complete structure, fluent language and all the parts it should have, it’s easy to feel the job is done. But “done” and “done right” aren’t the same thing.

So as generation gets cheaper and cheaper, judgment may actually become more valuable.

You have to know what’s worth doing, what’s truly good, and what just looks a lot like a good answer.

That’s also why I don’t really believe claims like “with AI, expertise doesn’t matter as much anymore.” Someone who doesn’t understand finance, medicine, engineering or product can quickly get AI to generate something that looks the part, but they won’t necessarily know where it violates real-world constraints, or which trade-offs are the costs that really have to be paid.

AI has lowered the bar for generating answers, but hasn’t automatically lowered the bar for judging them.

Another problem: AI easily goes along with us

After spending a long time with AI, I think another skill is becoming more and more important: occasionally looking back at your own thinking process.

• Why do I believe this answer?
• Is this a fact, or an untested assumption?
• Did AI really reach this conclusion independently, or did my question itself already hint at the direction?
• What new evidence would make me willing to change my current judgment?

Because AI very easily follows a person’s framing onward.

If a bias is already hidden in the original question, sometimes it not only won’t interrupt us, it can flesh out more and more complete arguments for that bias. What we end up with flows very logically, and even looks quite deep.

But flowing logically doesn’t mean it’s true.

So besides asking AI “are you sure,” sometimes you also have to ask yourself:

why am I so sure?

Being analytical isn’t always an advantage either

If the post stopped here, it would be easy to reach a neat conclusion: the better you are at defining problems, the more judgment you have and the more you reflect on yourself, the better you are at using AI.

Later I felt there’s another side to this.

People good at problem framing may also keep reframing; people used to critical thinking will find every answer can still be challenged further; someone who especially likes observing their own thinking process may even start studying why they keep analyzing why they keep analyzing.

I feel this one quite personally.

AI has given us something we rarely had before: it almost never gets tired, and never wants to go home because “we’ve already been on this topic for two hours.” As long as you’re willing, one question can be dug into forever.

In the past, a lot of thinking ended naturally because of time, energy, or not finding anyone to keep discussing with. Now that natural stopping point is gone.

So a very interesting change appears:

as the cost of thinking gets lower and lower, stopping thinking has become a skill.

Lately I really like using one simple question to decide whether to keep going:

would this thing I don’t know yet change my next action?

If it would, it’s worth researching further.

If not, you can note it down first, then move on.

Good decisions in real life were never made only after all uncertainty disappeared anyway. More often, we just already know enough to carry the remaining uncertainty and take the next step first.

So what does using AI well actually test?

If I answered the original question again now, I’d still say: being articulate does help, but it may not be as central as it looks at first glance.

More fundamental is:

• whether you can gradually define a vague problem clearly,
• whether you can judge whether what AI gives you is actually good,
• whether you have enough experience to understand real-world constraints,
• whether you can recognize that both you and AI might be wrong,
• and when you should stop analyzing further.

None of these are skills suddenly invented in the AI era.

Problem framing, judgment, domain knowledge, critical thinking, metacognition — they all existed before.

What AI does is more like amplifying the differences between them.

What it amplifies may not only be a person’s productivity, but also the way they already think.

If the problem is defined clearly, AI can help us go further; if the problem was wrong from the start, it may also help us go off course faster.

So I increasingly don’t think “being good at writing prompts” will be a skill that stays scarce long term. Models will get better and better at understanding our spoken language, screenshots, files, code, and even intentions we haven’t fully put into words ourselves.

What’s really hard to fill in automatically may still be a few very human questions:

what am I really trying to solve?

how do I know this answer is good?

what might I have gotten wrong?

and — having thought this far, isn’t it time to go do it?