Honestly, Try Coding With It — AI Is Far Dumber Than People

Honestly, try coding with it. AI is considerably dumber than people.

More than the claim that AI will replace developers, what matters is who handles a dumb AI better.

These days I keep hearing that AI is smarter than people.

It writes code in seconds, finds bugs, explains complex concepts. Problems that once took hours of searching now get a plausible answer the moment you ask.

Judged from the outside, it really does look superhuman.

But build an actual product with AI, and you start to think differently.

Honestly, try coding with it.

AI is considerably dumber than people.

Of course AI is fast. It knows a lot. It handles repetitive work well. It can generate hundreds of lines of code in one go.

But being fast and knowing a lot is an entirely different ability from judging properly.

AI easily forgets the purpose of the very code it just wrote. Ask it to fix one bug and it breaks another feature. It re-creates problems that were already solved. Even when the whole architecture is at stake, it looks at the single file in front of it and produces a plausible-sounding answer.

Most dangerous of all: it delivers wrong answers with total confidence.

The code runs but may not match the actual requirements. The feature works but security may be broken underneath. The error message is gone, but data may be quietly corrupting.

If the output looks plausible, AI believes it has done a good job.

But a developer asks:

Why is this feature needed?

Will this architecture hold when users multiply?

How does this change affect other features?

Will this code still be maintainable in six months?

Should this problem even be solved with code in the first place?

AI can generate code, but it doesn't know what the product should be.

AI doesn't understand the whole

Real product development is not the work of writing lots of code.

It is interpreting user needs, setting priorities, designing data structures, anticipating countless edge cases, and choosing among features, cost, speed, and security.

Look at any one part in isolation and every choice seems right.

Store more data and analysis gets better.

Harden security and things are safer.

Add more features and users get more utility.

Provision bigger servers and things are more stable.

But across a whole product, you cannot have everything at once.

More than deciding what to add, you must decide what to drop. Shipping first may beat perfecting now. A simple structure customers can actually use may be worth more than a technically beautiful one.

There is no single right answer to these judgments.

You have to weigh the state of the business, user behavior, operating costs, the team's capability, and the direction of future expansion — all at once.

AI talks as if it understands this context, but in reality it only produces the most plausible answer within the sentences it was given.

So when you hand everything to AI, things seem to move incredibly fast at first. Screens get built, buttons respond, data gets saved.

Then at some point, one fix starts creating three new problems.

Code keeps getting bolted on without understanding of the structure, similar logic gets duplicated everywhere, and you reach a state where no one can explain the whole system.

AI produced code — but no product was built.

What AI does well and what developers must do are different

This doesn't mean AI is useless.

On the contrary, I use AI aggressively. I have no desire to return to the days before it.

AI is tremendously powerful for writing repetitive code, exploring unfamiliar libraries, tracing the cause of errors, and rapidly comparing implementation approaches.

The question is what you delegate to it.

Hand AI the purpose and the judgment too, and the result collapses easily. But when a person sets the direction, designs the structure, and verifies the output, AI becomes an extraordinary productivity tool.

In the end, the person who matters in the AI era is not the one who types the most characters of code.

The person who defines problems precisely.

The person who distinguishes good architecture from bad.

The person who can spot the errors AI produces.

The person who connects many features into one product.

The person who knows what result the user actually needs.

When someone like that uses AI, development speed explodes.

Conversely, when someone without basic judgment uses AI, they produce wrong results faster and in greater volume.

AI is not a tool that erases skill.

It is a tool that amplifies the difference in skill.

The fantasy of building a product in a few clicks

These days everyone says you can build a service with a few clicks.

And it's true that building a single screen has gotten easy. Adding login, connecting a database, wiring up a simple AI chat feature — all far easier than before.

But a screen existing and a product working are different things.

Can it hold when users flood in at once? How do you recover when a payment fails? What happens when a user enters something you never anticipated? Where is personal data stored and who can access it? Start asking these questions and the story changes.

A product reveals its quality not in normal conditions but in abnormal ones.

AI builds the most common path quickly.

The developer is responsible for the hundreds of situations that fall off that path.

Pasting in code AI wrote is easy. Understanding why that code works, judging when it won't, and recovering yourself when things break is an entirely different job.

So AI has lowered the barrier to entry for development — but the barrier to building a polished product has not disappeared.

If anything, as plausible-looking output becomes something anyone can produce quickly, the gap between a real product and a demo will only become clearer.

Developers aren't disappearing — their role is changing

The prediction that developers will vanish because AI writes code starts from seeing development as mere code entry.

But a developer's core work was never typing.

A developer is someone who turns ambiguous requirements into working structure — who chooses between conflicting constraints, anticipates invisible risks, and gives a whole system its coherence.

The more code AI writes, the more important this role becomes.

Someone has to review the mountains of code AI produces. Someone has to block features that are technically possible but commercially pointless. Someone has to decide the balance between short-term speed and long-term maintainability.

The good developer of the future will not be the one who writes the most code.

It will be the one who completes the most accurate product with the fewest missteps.

Using AI well doesn't just mean the skill of entering good prompts, either.

It includes deciding what to delegate, doubting the output, finding what's wrong, and integrating it safely into the whole system.

Giving AI work is easy.

Making AI work properly is hard.

And that is why humans matter more

Saying AI is dumber than people does not mean humans can relax.

AI is dumb but tireless. It is astonishingly fast, cheap, and constantly improving.

So the work of producing ordinary results will be automated quickly.

But deciding what to build, choosing one path among countless possibilities, and taking responsibility for that choice — that remains human work.

AI presents possibilities.

Humans decide direction.

AI makes code.

Humans make products.

AI outputs answers.

Humans judge whether the answers are right.

Ultimately, competitiveness in the AI era is not about writing code better than AI.

It is about seeing what AI cannot see, understanding the context AI cannot grasp, and taking responsibility — to the end — for outcomes AI can never own.

Honestly, try coding with it.

AI is considerably dumber than people.

And precisely because of that, the people who can truly handle AI will become far more powerful than before.