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.

In this episode of the Professor Insight Podcast, we explore a fascinating and timely question: how is generative artificial intelligence beginning to reshape academic research itself? Drawing on a newly published academic paper from the Journal of the Academy of Marketing Science (April 2025), we examine how large multimodal models like ChatGPT-4o could revolutionise the way marketing scholars generate ideas, build theories, design experiments, and analyse data. Authored by Kiwoong Yoo, Michael Haenlein, and Kelly Hewett, the paper—titled “A whole new world, a new fantastic point of view”—offers a serious look at how generative AI might soon become a research partner, not just a productivity tool.

Rather than speculating, the authors put AI to the test by replicating the entire research process of 35 published consumer research articles using ChatGPT-4o. They applied advanced prompting strategies like chain-of-thought prompting and carefully evaluated the AI’s performance across key stages such as theory development, pilot testing, and data analysis. What emerged is a nuanced picture of where AI excels—like generating conceptual frameworks and simulating study designs—and where it still struggles, such as interpreting human behaviour or conducting robust statistical analyses.

This episode is a must-listen for anyone curious about the intersection of AI and knowledge creation. Whether you are a researcher, educator, PhD student, or simply someone fascinated by how ideas are shaped and shared, this conversation offers a rare look at how AI may redefine the boundaries of academic research. We explore the promises, the limitations, and the ethical questions that come with using AI as a co-investigator in scholarly work.