ChatGPT Curious

This episode is about giving you a clear, simple definition of a word that gets thrown around constantly: AI. You’re hearing it in every commercial, on every app, and in every new feature rollout. But what is it, really? I share a plain-language definition — “software that can guess” — contrast everyday…

In this episode I give an introduction to using ChatGPT for content creation with a focus on practical strategies for repurposing your own work. You’ll hear about reverse prompting, using Projects to save instructions, and how to keep your taste and voice at the center of what you create. Main Topics Covered…

In this episode, I dig into whether you can trust ChatGPT, and what that question reveals about trust in general. From model changes and user backlash to the three pillars of trust (benevolence, integrity, competency), I share why double-checking is anon-negotiable and how personal responsibility plays into using AI. We talk about…

So… is ChatGPT actually coming for your job? In this episode, I walk you through the GPT-5 release, what’s new, what’s noise, and why I think most folks won’t even notice a difference. We’ll talk about how AI is being used at work, why the headlines can be misleading, where the tech…

In this episode I answer the question “Is ChatGPT killing creativity?” with a resounding no, and then unpack why I think that question even exists in the first place. I talk about what people are really reacting to, how our systems reward speed over depth, and why I see ChatGPT not as…

In this episode I dig into the environmental impact of using ChatGPT: energy use, water consumption, and carbon emissions. I walk through the research, acknowledge the risk of whataboutism, and explain why comparisons to other systems matter when we’re talking about true impact. Adopting a both/and approach, this episode is absolutely not…

In this episode of ChatGPT Curious, I lay the groundwork for understanding what ChatGPT actually is, without getting too lost in the weeds. I break down key terms like LLM and parameters, explain how the model was trained (hint: lots of math), why it sometimes spits out wrong info, and what all…