AI Can Write Code, But It Can’t Understand It

blog ai software-engineering sustainability cognition

Let’s get one thing straight: AI can write code.

Sometimes it’s brilliant. Sometimes it’s uncanny. Tools like Cursor, Copilot,
and ChatGPT have changed how I work — and I’m not going back.

But we need to talk about what AI can’t do.

Because writing code is easy. Understanding it is hard.

And right now, we’re at risk of building faster than we can comprehend.

Code is not just syntax

AI can generate a Rails model or a React hook or a Terraform module.
It can refactor a method. It can fill in your tests. That’s not the hard part.

The hard part is understanding why the code exists, how it behaves under
stress, what it breaks when it changes, and what it means to the people
who use or maintain it.

That’s not pattern-matching. That’s thinking.

And no LLM is doing that — not yet.

Understanding is multi-layered

AI sees the tokens. It doesn’t see the context.

You and I look at a piece of code and think:

  • What’s the business rule here?
  • What assumption is this making?
  • Who’s going to be woken up if this breaks at 2am?
  • Is this the right place for this logic to live?
  • How does this shape our ability to evolve?

An LLM is doing autocomplete. You’re doing analysis.
Those are different games.

AI scales productivity. Humans scale sustainability.

AI lets us write more code faster.

But more code isn’t always good. It can mean more bugs, more interfaces, more
complexity, more entropy — unless someone is actively shaping the direction
of the system.

That someone has to be you.

We are the ones who understand trade-offs. Who teach teams how things work.
Who refactor when the design is wrong. Who build mental models.

AI doesn’t do that. It’s not a partner in design — it’s a mirror of the
existing patterns.

And it can’t tell when those patterns are broken.

You don’t scale understanding with tokens

You scale it with documentation, tests, naming, constraints, pair programming,
reviews, and stories.

You scale it with culture.

If we want to use AI sustainably, we have to build the systems it can’t see:
boundaries, feedback loops, conversations. Because that’s where the real
understanding happens — not in a GPT prompt, but in the space between humans
trying to do the right thing.

Final thought

I’m not scared of AI writing code.

I’m scared of teams thinking that’s all there is to engineering.

Because good software isn’t just functional — it’s intentional. It has
structure. It has stories. It has signals of care.

And we can’t prompt our way to that.