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If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring a really provocative piece by Wendy Liu in the Guardian titled I avoid AI tools because thinking is supposed to be hard. It's what makes us human. Now that's quite a statement, isn't it? Liu, a writer and software developer, makes a deeply personal and philosophical case for resisting the widespread adoption of AI, particularly in creative and cognitive domains. Her core argument is that the very act of struggling, of grappling with complex problems, is what builds our intelligence, our character, and ultimately our humanity. And she worries that AI, by making things convenient, is stripping us of that essential struggle. Liu opens by talking about her own journey, learning to code in the mid-2000s. She she describes countless painstaking hours of debugging and pouring over documentation, often for projects that never even saw the light of day. But what struck me was her insistence that those hours never felt wasted. Why? Because she was learning a craft, discovering a particular way of thinking, driven by curiosity and a desire to understand. She tells a similar story about becoming a writer, highlighting the transformative nature of the writing process itself, where you start with one idea and often end up somewhere entirely different, discovering your values along the way. For her, writing isn't just about outputting words, it's about thinking. Now she contrasts this with our current AI landscape, where, as she puts it, anyone can spin up a slick looking app using tools like OpenAI's Codex, or generate hundreds of books with prompt engineering. She sees both software development and writing as being de skilled by AI, turning complex creative processes into something much more automated and, in her view, less enriching. And this is the bit that really got me thinking, because it cuts right to the heart of something we talk about a lot, cognitive offloading. Liu admits how tempting it is to turn over tasks to a machine so I don't have to think so much. But she refuses. Thinking is the point. She says she doesn't want to get into the habit of avoiding it purely for the sake of convenience. And here I immediately thought of our own pillar about outsource the do and not the thinking. Liu is making a very strong case that for true learning, for true human development, we absolutely cannot outsource the thinking. She also raises a profound concern about young people coming of age in this AI boom. She fears they might start seeing technology as a black box, something opaque and beyond their control, managed by distant corporations. What does that do? She asks, to their relationship with technology and indeed with the world. This really resonated with our understanding of AI literacy. We've talked about how AI literacy isn't about technical skill or memorizing tool features. It's about developing that collaborative reasoning ability, understanding AI's limitations and failure modes, managing those conversations with precision, and developing reflective awareness about its influence. If we don't teach students to understand the inner workings, to peer into the black box, then Liu's fears become very real. We need to teach students to think with AI, not just use the tools. We need them to understand what the AI knows and perhaps more importantly, what it doesn't know. What she's also saying at a collective level is that by limiting our use of AI, we can protect our cognitive sovereignty, our individual ability to think. She even cites research suggesting a negative impact on cognition from just a few minutes of chatbot usage. Now, I always want to be careful not to exaggerate claims, and this is an emerging field, but the underlying principle is powerful. If we delegate too much, do we diminish our own capacities? She sees this as a political matter, a way to resist dependence on big Tech and prevent the world from becoming, in her words, a cold, inhospitable, and even more unequal place. Liu acknowledges that her stance makes her a less efficient coder and writer. In the time it took her to write this essay, she says she could have prompt engineered hundreds of books. But this leads to her ultimate and perhaps most challenging point. In a world where efficiency and convenience have become vehicles for the advancement of corporate greed, inconvenience and inefficiency may simply be the cost of preserving my humanity, of building character. Wow. Just think about that for a moment. In the context of our schools, so often we talk about AI's potential for efficiency, automating grading, generating lesson plans, speeding up administrative tasks. And these are valuable applications? Absolutely. They can give teachers back time, help us hold the complexity and free us up for creativity. That's part of the box. One linear innovation Doing what we do better. But Liu's argument forces us to consider the student experience. Are we, in our pursuit of efficiency in education, inadvertently stripping away the very productive struggle that builds character and deeper learning? This is where we need to be incredibly intentional about how we design learning. If we truly believe that process and productive struggle are where learning happens and that the real value is not in what the machine produces, but in how the student responds, then we have to design tasks that can't be faked. We have to design learning that demands depth, care, and imagination. Think About a year 8 geography lesson, an AI can quickly summarize a climate change report, but the cognitive stretch, the struggle, comes from analyzing that report, cross referencing it with local environmental data, debating the nuances of policy solutions, and perhaps designing a community action plan that requires human judgment, imagination and care for their local context. Those are the things machines cannot do. They cannot wonder, they cannot care. This is exactly why frameworks like our three Ps for assessment, looking at product, process and performance become so vital. We need to look beyond the final output and consider how the student got there, what their interaction with AI looked like, and then have them demonstrate their understanding live. It's about teaching students not to outsmart machines, but to outthink them. Liu's piece is a powerful reminder that our purpose in education isn't just to produce efficient outputs, but to nurture thoughtful, resilient and deeply human individuals. As we explore the potential of AI, we must always keep that human purpose at the forefront. We have to ask ourselves, how can AI enhance the journey of learning without diminishing the essential character building struggle of thinking? It's not an either or but a both and where we strategically leverage AI to outsource the doing for both teachers and students, but fiercely protect and even amplify the thinking. That's all for today. Thanks for listening.