Reasoning Without Understanding: The Illusion Behind ‘Smart’ AI

Reasoning Without Understanding: The Illusion Behind ‘Smart’ AI

We started out with generation models — the early AIs that could quickly write text, generate images or draft code. What made them fast was also what made them limited: they matched patterns from training data and produced what looked right, not what was thought through.

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But we’re in a different phase now.

Today’s models take longer to respond — not because they’ve slowed down, but because they’re doing more under the hood. They don’t just guess, they try to reason.

They break a prompt into smaller steps, consider different paths and work through those to generate better answers. This is what’s called chain-of-thought reasoning and it’s a big reason AI feels smarter today — especially for complex tasks like math, logic or decision-making.

Why Reasoning Matters

What’s exciting is how these models are now improving at 2 critical things:

  • Cause and effect (Causality): AI is starting to connect actions with their consequences — like “rain” leading to “flooding” or “lack of sleep” causing “low focus.” It’s not flawless, but it’s getting better at identifying these links.
  • Context (Contextuality): Instead of treating every query as new, AI tries to consider what’s already been said, what matters to the user and how things relate.

These are major steps forward. Because real-world tasks aren’t always straightforward — they often need awareness of “why” something happens and “when” it makes sense.

The Illusion of Thinking

Apple’s paper “The Illusion of Thinking” highlights a key point: even the most advanced AI doesn’t actually think. It simulates the process.

Here’s what the paper brings to light:

  • It doesn’t “understand” the problem. It predicts what comes next based on patterns — not comprehension.
  • It may struggle more with complexity. Unlike humans who slow down and strategize when problems get harder, AI often becomes less accurate with more difficult tasks.
  • Execution is not understanding. Even when we provide detailed instructions or examples, AI often approximates instead of actually following them.

It’s like watching someone solve a puzzle using familiar steps — not because they get the puzzle, but because they’ve seen similar ones before.

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So, What Do We Do With This?

Knowing this helps us use AI better. It’s a powerful tool — one that’s fast, scalable and can generate structured responses. But it’s still not intuitive. It lacks lived experience, persistent memory and the ability to learn from context the way humans do.

When AI messes up, it’s not that your prompt was wrong. It’s that the model was never really “thinking” to begin with.

We need to stop expecting AI to behave like us — and start using it where it actually adds value.

Wrapping It Up

AI is improving. It’s gone from surface-level guesses to deeper, more structured responses. But what looks like thought is often just a well-practiced routine.

So let’s not treat it like a human — let’s treat it like a tool.

One that helps, supports and even surprises us…

But still needs our judgment, intuition and experience to make it useful.

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