Generative AI has captured the world's attention for its power to create, accelerate, and enhance—but what happens when these same tools are misused? In this episode of the Professor Insight Podcast, we turn the spotlight to the darker side of GenAI. Drawing on a groundbreaking new paper from DeepMind titled Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data, we explore nearly 200 real-life cases where generative tools were used to deceive, defraud, and manipulate.

From deepfake impersonations and non-consensual imagery to AI-powered phishing scams and fake social media botnets, the study reveals how GenAI is being exploited in ways that are surprisingly low-tech but highly effective. These misuse tactics often require little technical skill and rely on easy-to-access tools, making them more widespread and harder to track. We break down the taxonomy of tactics and the motivations behind them, including disinformation, monetisation, harassment, and even digital resurrection. This episode is a deep dive into how misuse is already reshaping online trust and public perception. We also discuss the broader implications for AI governance, content authenticity, and digital safety.

In this episode of the Professor Insight Podcast, we explore what it really takes to build infrastructure capable of supporting generative AI at scale. Based on Google’s 2025 State of AI Infrastructure Report, this conversation cuts through the buzz to focus on the practical realities facing tech and business leaders. From adoption trends to infrastructure strategy, we look at how organizations are navigating the shift from experimentation to production.

With 98 percent of companies already working with generative AI, the urgency to get infrastructure right has never been greater. We unpack the report’s key findings, including the challenges around data governance, the growing importance of cost-efficiency, and the role of secure, scalable platforms. Hybrid cloud and edge computing are gaining momentum, and a robust AI foundation is now seen as essential for future competitiveness.

This episode covers the strategic decisions shaping enterprise AI—from managing costs and scaling models to deploying across distributed systems. We also discuss the infrastructure features most valued by leaders today, including flexibility, security, and performance. If your organization is planning for long-term AI integration, this episode highlights the groundwork needed to get there.

In this episode of the Professor Insight Podcast, we take a closer look at how generative AI is fundamentally changing the way we search for information, products, and services online. With ChatGPT rolling out new shopping features and AI search tools gaining traction, it's clear we're entering a new era of digital discovery. But what does this shift mean for the businesses, marketers, and platforms that rely on traditional search behavior to connect with customers?

We draw from Algolia’s report on How Generative AI Affects Search: A Full Stack Story, the Future of Professionals Report to explore how AI is disrupting search engine marketing, SEO strategy, and consumer habits. From voice-driven search to conversational queries and AI Overviews, this episode breaks down what’s changing, where the search volume is going, and how brands can stay relevant in a fragmented, AI-first ecosystem.

Generative AI is not only changing how people find information but also how businesses think about content strategy, advertising investments, and customer engagement. Understanding these shifts is crucial as traditional search models evolve and new opportunities emerge across both established and emerging platforms. This episode offers context and insights into how the future of search is likely to unfold.

In this episode of the Professor Insight Podcast, we dive into the rapidly evolving intersection of artificial intelligence and the legal profession. Drawing from two powerful resources — Prompt Engineering for Lawyers, a joint publication by Microsoft and the Singapore Academy of Law, and the 2024 Future of Professionals Report by Thomson Reuters — we explore how AI is not just automating tasks but redefining the way legal professionals work, think, and serve their clients.

We break down how AI is being used right now in law firms and legal departments, from contract review and research to drafting and client communication. More importantly, we focus on one emerging superpower every legal professional should pay attention to: prompt engineering. We unpack how lawyers can write better AI prompts to improve outcomes, increase efficiency, and stay compliant with ethical and professional standards. This is practical, applicable knowledge for anyone navigating the shift from traditional practice to AI-powered productivity.

If you work in law, support legal teams, or are simply fascinated by how AI is transforming high-trust professions, this episode is your guide. You’ll walk away with insights into the tools, techniques, and mindset needed to thrive in the AI-enabled future of legal work.

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.

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.