Are today's most advanced AI models really capable of “thinking”? Or are we simply projecting human-like reasoning onto machines that are fundamentally limited in how they solve complex problems? In this episode of the Professor Insight Podcast, we dive into a provocative new paper from Apple titled The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models. It explores how some of the most powerful reasoning models — like Claude 3.7 Sonnet Thinking, Gemini Thinking, and OpenAI's o1 and o3 — struggle when problems get even modestly more complex.

The researchers tested these models on classic puzzle environments like Tower of Hanoi, River Crossing, and Blocks World — environments that allow precise measurement of reasoning complexity. The findings are surprising: despite their promise, these models hit a “reasoning wall.” They collapse in accuracy as complexity grows, underutilise their available thinking capacity, and even “overthink” simple problems. Apple identifies three distinct regimes where these models either outperform, flounder, or completely fail — and the implications are significant.

But the paper hasn't landed without controversy. Critics argue Apple’s conclusions are overstated and possibly self-serving, especially as the company faces pressure over lagging behind in AI development. Is this research a serious warning about the current limits of reasoning in AI? Or is it a carefully timed narrative to reshape public expectations? Tune in as we unpack the science, the backlash, and the broader debate on what it really means for AI to “think.”