The Work That Changes Us
Oct 8, 2026 · 26 min · 10 segments
For most of history, getting the answer meant doing the work. To write something, you had to write. To solve a hard problem, you had to work through it yourself. Along the way, the work was also…
For most of history, getting the answer meant doing the work. To write something, you had to write. To solve a hard problem, you had to work through it yourself. Along the way, the work was also shaping us. Now AI can increasingly deliver the result without the process, and in this episode of Cultivating Intelligence, Christine asks what else we might be giving up when we remove the effort. Drawing on photography, woodworking, gardening, and the way young animals learn through play, the episode looks at how expertise really forms: not as information, but through encounter, repetition, and a lot of being wrong. Christine explores how schools are responding now that generative AI has broken the link between a polished essay and actual learning, from Denmark's new oral defenses to Copenhagen Business School's decision to allow AI by default in most exams. She also introduces the idea of capability debt, the quiet loss of expertise that happens when organizations optimize today's output at the expense of tomorrow's judgment. This isn't an argument against AI, and it isn't a case for struggle for its own sake. It's about learning to tell the difference between meaningless friction, which we should happily eliminate, and meaningful friction, which may need protecting. Whether you lead a team, teach a classroom, or practice a craft, this conversation offers a thoughtful way to decide which work to hand off and which work is quietly making you capable of what comes next. Connect with Cultivating Intelligence: - Bluesky - YouTube - LinkedIn Resources: 1. NIST Generative AI Risk Management Profile; https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence?utm\_source=chatgpt.com 2. OpenAI research on why language models hallucinate; https://openai.com/index/why-language-models-hallucinate/?utm\_source=chatgpt.com 3. University of Texas research on social sharing and knowledge overconfidence; https://news.utexas.edu/2022/08/30/sharing-on-social-media-makes-us-overconfident-in-our-knowledge/?utm\_source=chatgpt.com 4. UNESCO's work on assessment in the AI age; https://www.unesco.org/en/articles/whats-worth-measuring-future-assessment-ai-age?utm\_source=chatgpt.com 5. OECD/European Commission 2026 AI Literacy Framework; https://www.oecd.org/en/publications/empowering-learners-for-the-age-of-ai\65cd27d4-en.html?utm\source=chatgpt.com 6. World Economic Forum Future of Jobs 2025 workforce-strategy findings. https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/4-workforce-strategies/?utm\_source=chatgpt.com 7. Denmark August 2026; https://www.theguardian.com/technology/2026/aug/06/students-ai-cheating-schools-denmark?utm\_source=chatgpt.com
For most of history, getting the answer meant doing the work. To write something, you had to write. To solve a hard problem, you had to work through it yourself. Along the way, the work was also…
