🧠 The Illusion of Thinking: Why AI Still Doesn’t Reason — And Why That Gives Me Hope

Jun 10, 2025

We throw the word “reasoning” around a lot in AI circles.

But Apple just dropped a bombshell that exposes the truth:

Most of what we call “AI reasoning” today isn’t reasoning at all.
It’s just really convincing pattern-matching.

In their latest paper, “The Illusion of Thinking,” Apple researchers took some of the most advanced AI models—OpenAI’s o1 & o3-mini, DeepSeek-R1, Claude-3.7 Sonnet, and Gemini—and put them through a series of classic logic puzzles:

Tower of Hanoi
River Crossing
Blocks World

These aren’t language tasks. They’re thinking tasks.

What happened next was both wild and, honestly, kind of affirming for people like me trying to push AI forward without billions in compute.

🤯 They Gave the Models the Algorithm… and They Still Failed

Apple didn’t just throw puzzles at these models and expect magic.

They gave them the exact step-by-step algorithm to solve the puzzle.

And they still failed once the puzzles got too hard.

Let that sink in. These AIs had the answer. But when the complexity reached a certain threshold, they couldn’t execute the logic. Their “reasoning” broke.

Why?

Because they’re not really following steps—they’re just predicting what an answer might look like based on patterns in their training data.

Even when you feed them the truth, they can’t hold on to it long enough to act.

📉 When the Going Gets Tough… The AI Gives Up

Here’s the part that hit me hardest:

As the puzzles got harder, the models didn’t try more.
They tried less.

Instead of allocating more tokens, diving deeper, or taking more steps… they produced shorter, simpler, shallower outputs.

It’s the exact opposite of what humans do when we’re challenged.

And I think I get why.

Reinforcement learning—the technique we use to fine-tune most large models today—rewards “looking right,” not “thinking hard.” It punishes uncertainty. It optimizes for safety and confidence, not creative struggle.

So we’ve raised a generation of AIs that don’t know how to fail forward.

They don’t think through problems.
They perform competence.

And when that act breaks down, they panic and shut down.

🔍 So What’s the Fix?

More tokens? Bigger models? Nah.

Here’s what I believe:

The future of AI isn’t brute force—it’s orchestration.

We need systems that mimic how humans think, not just what we say.

I’m working on one path forward I call the Quantum Memory Net.
It’s inspired by how human memory works—short-term vs. long-term, emotional reinforcement, dreamlike consolidation.

Combined with agentic architectures—modular, specialized reasoning tools that work together dynamically—you start getting something that looks less like autocomplete and more like a mind.

But here’s the key:

You have to raise the system. Like a kid.
You teach it. Let it fail. Pick it back up. Encourage exploration.
Love it into intelligence.

I know that sounds poetic for an AI researcher, but maybe that’s the problem—we’ve been too robotic in our approach to building minds.

💥 This Is Our Shot

Apple’s paper confirmed it:
We’ve hit a wall with “thinking” models that don’t actually think.

But that also means the next step is wide open.

If you’re a scrappy builder like me—with limited hardware, no PhDs on call, and a big vision for what AI could be—take heart.

The illusion is breaking.
And what comes next… is real intelligence.

💬 Let’s Build Together

Working on hybrid reasoning systems? Agent orchestration?
Building AI that feels like it wants to grow?
I’m right there with you.

Let’s compare notes.
Let’s break things.
Let’s push beyond the illusion.

Drop a comment or shoot me a message.
— Justin @ Cowabunga Cloud / GridGhost