Cultivating Intelligence

When someone steals a bicycle, everyone knows what happened. But when the thing taken is an idea, a melody, a voice, or a lifetime of expertise, the original owner still has it, and yet something is gone: credit, income, opportunity, control. In this episode of Cultivating Intelligence, Christine asks a harder question than whether AI is stealing from us. She asks whether it's making it easier for us to steal from one another.Artists have always borrowed, and culture grows by passing ideas along. What AI changes is the scale and the friction. A few typed words can now produce something that sounds like a specific artist, and neither the person asking nor the person whose work made it possible may ever know the other exists. To make sense of this, Christine turns to the natural world, where frigate birds steal food, cuckoos trick other birds into raising their young, and the dodder plant drains its neighbors. Nature can show us what works, she argues, but it can't tell us what's right. It does offer one warning, though: a parasite that exhausts its host hasn't found a winning strategy.From there, the conversation moves closer to home. Christine looks at the pressure to adopt AI before we've thought it through, the old habit of taking credit for a colleague's idea in its new AI-assisted forms, and a quieter kind of theft, where we use AI to skip the effort that would have built our own skills and judgment. The real danger, she suggests, isn't that AI gets good at taking from us. It's that taking becomes so easy we stop noticing when we're doing it.Connect with Cultivating Intelligence:BlueskyYouTubeLinkedIn

At the John C. Campbell Folk School in Brasstown, North Carolina, people spend a week learning to forge metal, weave cloth, throw clay, or carve a spoon. There are no grades and no competition, just the experience of learning to do something you couldn't do before. In a world where AI can generate a photo, a strategy, or a financial forecast in seconds, that kind of slow, hands-on learning is starting to feel almost radical.In this episode of Cultivating Intelligence, Christine explores what craft can teach us about the moment we're living through. Drawing on the folk school's hundred-year history, recent research from Microsoft on AI and critical thinking, and new OECD reports on skills in the AI age, she makes the case that output is not expertise. AI can make us capable of producing something without making us capable of understanding it. And as AI gets better at producing work that looks competent, we may need more human expertise to spot when it isn't, even as we quietly remove the experiences that build that expertise in the first place.This isn't an argument against AI. It's an argument for being deliberate about which human capabilities we keep developing. Christine looks at why some friction is actually learning, why organizations need their own "imperfect bowls," and why leaders may soon need to track capability risk alongside financial and operational risk. If you've ever wondered what gets lost when the answer arrives before the effort, this episode is for you.Connect with Cultivating Intelligence:BlueskyYouTubeLinkedInResources:OECD's June 2026 AI and Skills: What We Know So Far  https://www.oecd.org/en/publications/ai-and-skills_f843b352-en/full-report.htmlOECD's July 2026 Skills in the AI Age  https://www.oecd.org/en/publications/skills-in-the-ai-age_972bd15e-en/full-report.htmlMicrosoft Research critical thinking study  https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/John C. Campbell Folk School history  https://www.folkschool.org/folk-school-experience/our-history/

When an octopus sleeps, its skin flashes with color, its eyes move, and its body twitches. It looks like dreaming. Science can't confirm that, but what researchers have found is remarkable in its own right. In this episode, Christine uses the sleeping octopus and a series of viral animal stories to explore one of the most important spaces we move through as humans: the small gap between what we observe and what we conclude.Otters that keep a favorite rock for life. Cows with best friends. Cats that purr to heal their bones. Crows that warn their children about you. Goats with accents. Penguins proposing with pebbles. Some of these stories are exaggerated and some are surprisingly close to the truth, but each one shows how quickly the human mind turns fragments into a finished story. That same instinct gives us art, scientific hypotheses, and innovation. It also gives us misinformation, organizational myths, and bad decisions made from half a dashboard.AI is now working in that same space between fragments, and it can fill the gaps faster and more fluently than we ever could. Christine looks at what that means for leaders: why coherence is not evidence, why repetition is not verification, and why disruption only reveals what was already there. The answer isn't to stop imagining. It's to know when we're doing it, and to practice a disciplined kind of belief that sits somewhere between gullibility and cynicism.Connect with Cultivating Intelligence:BlueskyYouTubeLinkedInResources:Pophale, A., Shimizu, K., Mano, T. et al. Wake-like skin patterning and neural activity during octopus sleep. Nature 619, 129–134 (2023). https://doi.org/10.1038/s41586-023-06203-4 Cornell HN, Marzluff JM, Pecoraro S. Social learning spreads knowledge about dangerous humans among American crows. Proc Biol Sci. 2012 Feb 7;279(1728):499-508. doi: 10.1098/rspb.2011.0957. Epub 2011 Jun 29. PMID: 21715408; PMCID: PMC3234554. From <https://pubmed.ncbi.nlm.nih.gov/21715408/>   Elodie F. Briefer, Alan G. McElligott,Social effects on vocal ontogeny in an ungulate, the goat, Capra hircus,Animal Behaviour,Volume 83, Issue 4,2012,Pages 991-1000,ISSN 0003-3472,https://doi.org/10.1016/j.anbehav.2012.01.020.(https://www.sciencedirect.com/science/article/pii/S0003347212000401) https://www.nps.gov/places/000/fire-followers.htm https://www.ablesci.com/scholar/paper?id=3JxmmgqK8&utm

Most of us use "accuracy" and "precision" as if they mean the same thing. They don't. Accuracy is about getting close to the right answer, while precision is about getting the same answer again and again. In this episode of Cultivating Intelligence, Christine starts with a classic Lean Six Sigma diagram of four archery targets. She uses it to ask an urgent question for a world built on increasingly precise machines: what happens when we get very good at hitting the same spot without checking whether it's the right one?The answers come from some unexpected places. Migrating animals show how a group of imperfect individuals can find the right direction together, and a study of songbirds suggests that remembering yesterday's right answer can make it harder to adapt. Musicians, dancers, and photographers show that technical perfection and truth are not always the same thing. Then the conversation turns to AI: why fluent, confident answers can still be wrong, why models are sometimes rewarded for guessing instead of admitting uncertainty, and how an AI connected to outdated information can spread the wrong answer with remarkable consistency.From there, Christine looks at what this means for organizations, education, and leadership. She covers headline testing that optimizes for clicks over truth, the risk of chasing metrics that quietly replace the real goal, and why Finland teaches students to question their sources. She also makes the case for the expert intuition that is hard to measure but often right. As precision becomes cheap and widely available, the real advantage will belong to the people and organizations who know where to aim.Connect with Cultivating Intelligence:BlueskyYouTubeLinkedInFormal citations:https://asq.org/quality-resources/gage-repeatability?https://pmc.ncbi.nlm.nih.gov/articles/PMC5882979/Hermer, E., Murphy, B., Chaine, A.S. et al. Great tits who remember more accurately have difficulty forgetting, but variation is not driven by environmental harshness. Sci Rep 11, 10083 (2021). https://doi.org/10.1038/s41598-021-89125-3https://openai.com/index/why-language-models-hallucinate/https://www.nist.gov/itl/ai-risk-management-framework/ai-risk-management-framework-faqshttps://eur-lex.europa.eu/legal-content/en/TXT/?qid=1767721066212&uri=CELEX%3A32024R1689&utmhttps://www.unesco.org/en/articles/ai-competency-framework-students?hub=66903&utmhttps://apnews.com/article/fake-news-classrooms-finland-russia-194b32d8829838bfe47469d6ff357689https://cloud.google.com/blog/products/ai-machine-learning/rag-and-grounding-on-vertex-ai/https://www.anthropic.com/engineering/demystifying-evals-for-ai-agentsNickl, P., Moussaïd, M. & Lorenz-Spreen, P. The evolution of online news headlines. Humanit Soc Sci Commun 12, 364 (2025). https://doi.org/10.1057/s41599-025-04514-7Pennycook, G., Epstein, Z., Mosleh, M. et al. Shifting attention to accuracy can reduce misinformation online. Nature 592, 590–595 (2021). https://doi.org/10.1038/s41586-021-03344-2

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:BlueskyYouTubeLinkedInResources:NIST Generative AI Risk Management Profile; https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence?utm_source=chatgpt.comOpenAI research on why language models hallucinate; https://openai.com/index/why-language-models-hallucinate/?utm_source=chatgpt.comUniversity 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.comUNESCO's work on assessment in the AI age; https://www.unesco.org/en/articles/whats-worth-measuring-future-assessment-ai-age?utm_source=chatgpt.comOECD/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.comWorld 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.comDenmark August 2026; https://www.theguardian.com/technology/2026/aug/06/students-ai-cheating-schools-denmark?utm_source=chatgpt.com