Chris Gee Consulting
🎧 An Audio Walkthrough of Apple’s “The Illusion of Thinking” Paper
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🎧 An Audio Walkthrough of Apple’s “The Illusion of Thinking” Paper

ai apple lrms reasoning models research Jul 10, 2025

We all have that one research paper we should read—until we see the 30-page PDF and suddenly remember we need to clean the junk drawer.

That’s why I created something a little different.

Using NotebookLM’s new Audio Overview feature, I generated a two-person podcast-style discussion that breaks down Apple’s provocative new paper, The Illusion of Thinkingand makes it actually enjoyable to absorb.

No jargon. No academic overload. Just two smart voices walking through what matters.

What’s the paper about?

Apple’s researchers set out to answer a simple question: “Do large AI models actually reason—or just sound like they do?”

And the answer is complicated but important.

Key insights covered in the audio:

  • “Thinking” ≠ understanding. Most AI models use “chain-of-thought” techniques to sound like they’re reasoning, but it’s often just pattern recognition.

  • Three zones of performance:

    • They struggle on simple problems.

    • They thrive on moderately complex tasks.

    • They collapse on the hardest ones and accuracy drops to near zero.

  • More tokens ≠ better thinking. When problems get really hard, these models actually stop trying. They reduce their reasoning effort, even when they still have room to think more.

  • Even step-by-step instructions don’t help. When researchers gave models a full algorithm for solving a logic puzzle, the AI still failed past a certain complexity.

  • Inconsistent behavior. One model nailed a 31-step problem but bombed on an 11-step version of a different puzzle. That’s not reasoning, it’s recall and luck.

Why I made this

Let’s face it: most people in PR, strategy, and communications aren’t reading arXiv papers over coffee. But these findings matter, especially in a world where clients and colleagues are convinced AI is ready to “replace” us.

This isn’t about dunking on AI. It’s about knowing where it works, where it fails, and how to lead responsibly in the face of hype.

📄 Want to read the original paper?
Download Apple’s full PDF

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