In this episode, we return to the unfolding debate around AI’s so-called ability to “think.” Building on last week’s discussion of Apple’s controversial paper The Illusion of Thinking, we now explore the equally provocative rebuttal by Anthropic, the company behind Claude. Their paper, pointedly titled The Illusion of the Illusion of Thinking, argues that Apple’s findings say more about poor experimental design than any real limits in AI reasoning. So who's right? Or are both missing the point?

We unpack the technical details, the debate, and the AI community backlash — including criticisms that Apple may be using this research to explain away its slower pace in AI development. But more importantly, we tackle the deeper question hiding underneath it all: what do we actually mean by “reasoning” in machines? And are we setting the right benchmarks when we measure AI against human thought?

If you’ve ever wondered where the line is between token prediction and true intelligence, or if you’re curious about how models like Claude, GPT, and Gemini really handle complex problems, this is a conversation that pulls back the curtain. It’s not just about who’s right — it’s about whether our entire framework for evaluating AI thinking needs to change.

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.”

If you’ve listened to Episodes Five and Six of the Professor Insight Podcast, you’ll know we’ve already laid the groundwork on what Agentic AI is and why it matters. But this week, we’re taking it a step further. This episode is your practical guide to building AI agents—an extension of our earlier discussions, now grounded in real-world application. Based on OpenAI’s newly released Practical Guide to Building Agents, we distill a technical framework into actionable insights tailored for business leaders, strategists, and product teams.

We explore what it actually takes to develop your first AI agent—from choosing the right use case and designing safe, scalable workflows, to configuring models, tools, and instructions that help agents operate autonomously and intelligently. This isn’t just about writing prompts. It’s about building systems that make decisions, take action across platforms, and adapt in real time—all while staying aligned with business goals and compliance requirements.

Whether you're trying to reduce operational friction, tackle high-complexity workflows, or enable more intelligent automation inside your organisation, this episode will give you the clarity and confidence to start building. With insights on orchestration, human-in-the-loop design, and guardrails for safety and governance, this is your field guide to the next evolution of AI in business.