By Jacqueline Mairghread Logan

I’ve been thinking about this more today—

We’re trying to build AI to be as accurate as possible. To pull from everything. To get closer and closer to “right.”

There’s an assumption built into that: that accuracy is the goal, and that the closer something gets to being correct, the better it becomes.

But humans don’t operate that way.

We’re inconsistent. We hesitate. We misread things. We bring emotion into decisions that aren’t purely logical. We interpret the same situation differently depending on context, history, even mood. And we get things wrong—often.

Yet that inconsistency isn’t just a flaw. It’s part of how we understand each other.

A perfectly structured answer doesn’t always feel like a truthful one. Sometimes what makes something feel real is the hesitation, the partial understanding, the imperfection in how it’s expressed.

That’s where this starts to shift.

Because if AI continues to move toward something that is always clear, always composed, always “right,” it may also move away from the way people actually think and interact.

And we tend to notice that.

There’s something about perfection that stands out—not in a good way, but in a way that feels slightly off.

In nature, things aren’t perfect. They’re irregular. Layered. Uneven. Even patterns that repeat still carry variation. That variation is what signals something is organic, something is real.

When something becomes too precise, too polished, too exact, it starts to feel artificial.

You see this in small ways. A conversation that feels overly structured. A response that is technically correct but emotionally flat. A statement that doesn’t leave room for uncertainty.

It doesn’t feel wrong, exactly. But it doesn’t feel fully human either.

So what happens if AI gets too close to that kind of perfection?

If it consistently produces answers that are clean, confident, and optimized for correctness—what gets lost in the process?

Because in many cases, we’re not just using AI to retrieve information. We’re using it to simulate interaction.

We ask it to explain things, to reason through problems, to engage in dialogue. In some cases, we ask it to take on roles that are inherently human—creative, interpretive, even emotional.

And those roles are not built on perfection.

If you imagine AI being used to play a human character in a film, or to simulate a conversation that’s meant to feel natural, or even to operate in a space that resembles therapy or guidance—the expectation isn’t flawless output.

It’s something that feels real.

And real includes imperfection.

Real includes pauses, uncertainty, misinterpretation, correction. It includes responses that are shaped by perspective rather than purely by optimization.

So there’s a tension here.

On one side, we’re pushing AI toward something that is more accurate, more refined, more consistent. On the other, we’re asking it to replicate or interact within systems that depend on inconsistency, nuance, and variation.

Those two directions don’t fully align.

Which raises a different kind of question.

What are we actually trying to build?

A system that is perfectly correct?

Or a system that reflects human experience well enough to feel real?

Because those may not be the same thing.

If the goal is correctness, then reducing error makes sense. Minimizing variability makes sense. Removing ambiguity makes sense.

But if the goal includes interaction—if it includes something that feels human—then complete optimization may not be the endpoint.

There may need to be room for imperfection.

Not in the sense of being careless or unreliable, but in the sense of allowing for variability. Allowing for uncertainty. Allowing for responses that aren’t always perfectly aligned or perfectly resolved.

That idea is uncomfortable, especially in systems that are expected to be dependable.

We tend to think of improvement as a straight line—more accurate, more efficient, more refined.

But there’s something in that line that doesn’t quite make sense when you sit with it long enough.

Perfection is what people are taught to move toward.

Better decisions. Better outcomes. Fewer mistakes. More precision.

The closer something gets to perfect, the more valuable it’s assumed to be.

But perfection doesn’t really exist in the natural world.

Things shift. They adapt. They carry variation. Even the most stable systems have irregularity built into them.

And humans are no different.

We don’t think in straight lines. We don’t respond the same way twice. We bring context, memory, and emotion into everything we do. That inconsistency isn’t a failure—it’s part of how we function.

So there’s a contradiction in what we’re building.

We push toward something that is, by definition, not fully attainable—something perfectly consistent, perfectly correct, perfectly optimized.

And then, when we get closer to it, we start to adjust it back.

We try to make it feel more human.

Less rigid.

Less exact.

More natural.

Which raises the question—

If the end goal is something that feels human,

why are we building toward something that removes the very qualities that define it?

If imperfection is part of what makes human interaction meaningful,

what happens when we start designing systems that remove it?