Is AI Making Us Dumber? 🤔

A few years ago, when I was just getting started with programming, I built a proper game using Pygame. It wasn't anything revolutionary, and it certainly wasn't going to win any awards, but I remember the process. I had to actually think. How should the player move? How should collisions work? What happens when something hits something else? How should I structure the logic? Why isn't this thing working?

I'd search through documentation, read things I didn't completely understand, try something, break something, debug it, change it, run it again, and eventually figure out what was wrong. Sometimes I'd spend hours solving something that, looking back, was probably a very simple problem.

But the important part wasn't that I eventually wrote the code. I had to do the thinking required to get there.

And that's what has been bothering me lately.

Not because AI can't do these things. It absolutely can. In many cases, it can do them faster than I can. And that's exactly where the problem gets interesting.

We used to do the thinking first

When AI first started becoming a serious part of everyday software development, I think most of us viewed it primarily as a tool for executing tasks. You still had to understand what you wanted, research the problem, go through documentation, figure out why something wasn't working, make decisions, and then ask the machine to help you implement it. AI was helping with the execution; the thinking was still largely ours.

But that boundary has been moving.

Today, I can describe an idea to an AI and have it research the problem, read documentation, decide on an approach, write the implementation, encounter an error, debug it, change the implementation, and try again. Suddenly, the entire process that used to require me to sit down and mentally wrestle with a problem can happen with a few prompts.

That's incredible.

It's also a little unsettling.

Because we usually call this productivity. And technically, it is. If I can produce the same result in two hours instead of ten, I've become more productive.

But there's another ratio we don't really talk about:

Output produced vs. thinking performed.

What happens to that ratio when we keep pushing more and more of the thinking onto AI?

The thing we don't measure

Imagine two people building the same application. The first person spends ten hours building it. They read documentation, make architectural decisions, write code, encounter bugs, debug them, change their approach, and eventually finish.

The second person gives the requirements to an AI agent. The agent researches the relevant libraries, writes most of the code, runs it, fixes the errors, and produces something usable in two hours.

Who was more productive? Obviously, the second person — at least if we're measuring productivity by output divided by time.

But did the second person learn as much? Did they exercise the same problem-solving ability? Did they develop the same intuition for recognizing bad architecture? Did they understand the same number of failure modes?

Maybe. Maybe not.

And that's the difficult part to measure.

We've become very good at measuring what AI helps us produce. We're not nearly as good at measuring what happens to the abilities we no longer need to exercise.

It's a bit like going to the gym and hiring someone else to lift the weights for you. You still got the weights moved. You just didn't get stronger.

Obviously, thinking isn't as simple as muscle strength, and AI isn't literally doing all of our thinking for us. But the analogy raises an uncomfortable question:

If we constantly outsource a particular kind of thinking, do we eventually become worse at doing it ourselves?

And this isn't just about coding

This is where I think the conversation gets much bigger than software development. AI is slowly moving into almost every domain where humans perform cognitive work. Students can ask AI to explain an entire chapter, solve their homework, summarize a book, create flashcards, and even write their assignments. Writers can ask AI to research a topic, create an outline, generate a draft, and rewrite it. Designers can describe an idea and get a visual representation in seconds.

Researchers can use AI to search through enormous amounts of information and summarize findings. Analysts can feed data into AI and ask it to find patterns. People can ask AI to write emails, make presentations, plan trips, analyze documents, explain complicated concepts, and make decisions between different options.

And the technology is only getting better.

The important part isn't that AI can now do these things. The important part is how much of the process we're willing to hand over to it.

Because there's a difference between:

"Help me do this."

and:

"Do this for me."

That difference might seem small.

It isn't.

The convenience trap

Let's take something extremely ordinary. Suppose you're trying to understand a new technical concept. Before AI, you might have searched for documentation, found three different explanations, rejected one because it was terrible, found another that was too advanced, watched a video, read some Stack Overflow answers, tried an example yourself, encountered an error, and gone back to the documentation.

It was inefficient.

It was also learning.

Today, you can simply ask:

"Explain this to me like I'm a beginner."

And you'll probably get a pretty good explanation immediately.

That's fantastic.

But here's the problem: the difficult part of learning something isn't always understanding the final explanation. Sometimes the difficult part is getting to the explanation yourself. Searching, comparing, being confused, following the wrong path, realizing you're wrong, trying again, and connecting two seemingly unrelated pieces of information are all part of the process.

That entire process is mentally expensive.

And that's exactly why we want to avoid it.

AI is incredibly good at removing that friction, which is precisely what makes it so useful — and potentially, precisely what makes it dangerous when we use it for everything.

We are optimizing for the wrong thing

Modern technology has spent decades trying to remove friction from our lives, and we've gotten extremely good at it. We have calculators because doing arithmetic manually is slow. Search engines because looking through encyclopedias is slow. GPS because figuring out routes manually is slow. Autocorrect because typing everything perfectly is annoying.

And now we have AI because thinking through certain tasks is slow.

In isolation, none of these things are bad. In fact, they're incredible. But there's a subtle difference between removing unnecessary work and removing useful mental effort.

If I use a calculator to multiply two large numbers, I'm probably not losing anything important. But if I use AI to solve every programming problem I encounter without ever trying to understand the problem myself, I'm not just saving time. I'm also skipping the mental exercise that comes with solving it.

And unlike a calculator, AI doesn't just automate one narrow operation. It can increasingly handle entire chains of cognitive work.

That's a very different kind of tool.

The productivity paradox

Here's the weird part: the better AI gets, the harder this problem becomes.

Imagine AI becomes so good that it can build an entire application from a few paragraphs. Why would I spend six hours learning a framework? Why would I read the documentation? Why would I debug the issue myself? Why would I understand how the system works internally?

If AI can do it better and faster, the rational decision seems obvious:

Let AI do it.

And that's where I think the productivity conversation gets incomplete.

There are actually two different outcomes.

More output 📈

I can build more things, write more, research faster, automate more tasks, and learn certain things faster. That's genuinely valuable.

Less practice 📉

I solve fewer problems myself, spend less time being stuck, read less documentation, make fewer decisions from first principles, encounter fewer failures, and spend less time figuring things out.

And those things were never just obstacles.

They were also practice.

So we might end up in a strange situation where our output per hour keeps increasing while our independent problem-solving ability doesn't increase at the same rate — or perhaps even declines in some areas because we're practicing less.

I don't think we know yet exactly how large that effect will be.

But I think it's worth taking seriously.

What happens when AI enters the decision-making loop?

There's another step that worries me even more.

Using AI to execute a decision is one thing. Using AI to make the decision itself is another.

Imagine I tell an AI:

"Build me a web application."

It can generate the code. Fine.

Now imagine I say:

"Decide what architecture I should use, which database I should choose, which libraries I should use, and how the system should be structured."

Now I've delegated part of the engineering judgment.

Go one step further:

"Research the market, figure out what product I should build, identify the target users, create the strategy, and build the MVP."

Now AI isn't just executing. It's participating in deciding what should be done in the first place.

We're entering a world where a small team — or even one person — can use AI systems to perform work that previously required entire groups of people. You can build companies with AI, create products with AI, produce marketing material with AI, conduct research with AI, and automate operations with AI.

That's an incredible expansion of individual capability.

But it also means we're increasingly allowing machines to participate in the parts of the process that used to require human judgment.

And I don't think we should blindly assume that this has no cost.

The dependency loop 🔄

This is the part that I find the most interesting.

Imagine this happens gradually: AI gets better → we use it more → we practice certain skills less → those skills become less familiar → doing the task manually starts feeling harder → we rely on AI even more → AI gets better again.

And the cycle continues.

Eventually, something that you could do yourself starts feeling unnecessarily difficult because you haven't exercised that ability in a long time.

That's what I mean when I say:

Are we slowly getting dumber?

Not necessarily in the sense that our intelligence is disappearing. But perhaps we're becoming less capable of operating independently in areas where we've stopped practicing.

There's an important distinction there.

But should we just stop using AI?

Absolutely not.

At least, I don't think that's the answer.

It would be ridiculous to argue that we should go back to doing everything manually just because doing things manually requires more thinking. We didn't stop using calculators because people were worried about arithmetic skills. We didn't stop using search engines because people were worried about memorization. We didn't stop using compilers because programmers might forget how to write machine code.

Technology is supposed to make us more capable.

And AI can absolutely do that.

The question isn't:

"Should we use AI?"

The better question is:

"What should we use AI for?"

There are tasks where I have no problem handing things over completely. If I need to transform a large amount of repetitive data, automate some boring task, or generate a first draft that I will heavily modify, why would I waste hours doing it manually?

That's exactly what tools are for.

But there are other things where I think the struggle itself is valuable: learning, problem-solving, writing, research, designing systems, making decisions, and understanding why something works.

And sometimes, I think we should deliberately not take the shortcut.

Maybe we need an "AI gym" 🏋️

Here's the funny thing: we already understand this concept in other parts of our lives.

If you want to become physically stronger, you don't just buy a machine that lifts weights for you. You exercise. If you want to become better at playing an instrument, you don't listen to someone else play every song for you. You practice.

If you want to become better at solving problems, perhaps you also need to actually solve problems.

Maybe we need the equivalent of an AI gym — a place where we deliberately practice doing things without AI.

Not because AI is evil. Not because humans are inherently better. But because some abilities are worth maintaining.

Maybe that means occasionally reading documentation without asking an AI to summarize it, trying to debug a problem yourself before asking for the answer, writing something from scratch before asking AI to improve it, researching a topic yourself before asking AI to summarize the research, or trying to design a solution before asking AI to design one for you.

And then using AI afterward as a second opinion, a reviewer, a teacher, or an accelerator.

That feels very different from simply asking:

"Do this for me."

So, is AI making us dumber?

I don't know.

And I don't think anyone can confidently answer that question yet.

AI is still relatively new, and the long-term effects of widespread cognitive outsourcing are difficult to measure. Maybe we'll adapt. Maybe our definition of useful intelligence will change. Maybe AI will free humans from boring cognitive work and allow us to focus on more creative and meaningful problems.

I hope that's what happens.

But I also think there's a risk that we become so good at getting answers that we forget how to find them. So good at generating things that we forget how to create. So good at asking AI to solve problems that being stuck with a problem starts feeling unbearable.

And maybe that's the thing we should be careful about.

Because AI becoming smarter isn't necessarily the problem.

The problem would be if we became less willing — or less capable — of thinking for ourselves.

I still use AI. A lot. And honestly, I don't see myself stopping.

But sometimes, when I find myself about to ask AI to solve something, I try to stop for a moment and ask myself:

"Do I actually need help with this, or am I just avoiding the thinking?"

Maybe that's the balance we're going to have to figure out.

Because the goal of AI shouldn't be to make humans unnecessary.

It should be to make humans more capable.

And if we're not careful, we might optimize so hard for productivity that we forget to ask what capabilities we're trading for it.