Microwaves, chefs, and restaurants – Part 3

Restaurants: Dynamic Learning Organizations in the Age of AI By:Daniel Cabrera, Felix Ankel We live in exponential times. As artificial intelligence (AI) transforms healthcare and education, we are called not just to rethink how we teach and learn, but to create a new architecture of how we provide care and learn. In previous posts, we introduced AI tools as “microwaves” and educators as “chefs” — metaphors for how technology and human expertise reshape the educational kitchen. Now we focus on what type of restaurants and food we need to create. What kind of “restaurant”, or learning organization, do we need? The answer isn’t shiny or clear, but we have to create a more efficient version of what we’ve had, versions aligned with a knowledge economy instead of a pre-industrial economy. It’s a whole new blueprint. The future healthcare professions education restaurant is a Distributed Autonomous Organization of Learning (DAOL): flexible, distributed, autonomous ecosystems where humans and machines learn together, driven by shared purpose, not hierarchy. From Fine-Dining to Street Food: A Shift in Structure From Gusteau to Bourdain. Traditional education, especially in medicine, has long mirrored a fine-dining, elegant, hierarchical, rigid, and expensive kitchen. Knowledge trickled down from experts to learners. Curricula were fixed menus. Authority came from tenure, prestige, and tradition. But in a world of rapid technological evolution, interconnected knowledge, rapid change of locations, multiple cultural influences, and uncertain futures, this model cracks under pressure. What’s needed now is a decentralized “food truck” model — open, mobile, adaptive, unconstrained, and collaborative. We are witnessing a foundational shift: From hierarchies to networks From expert-driven to co-created learning From institutional control to autonomous agency We are moving from traditional single-style food (think Coq-au-Vin) to rapidly evolving fusion food (Think Doritos-Tacos-Locos Korean BBQ hot chicken with pistachio humus). The same is happening with health professions education (HPE). Medical knowledge is expanding rapidly, connecting and creating new domains. The interphase between knowledge and learning is becoming rhizomatic learning — a concept inspired by Deleuze and Guattari and adapted for medical education. In rhizomatic systems, learning doesn’t follow a top-down syllabus. Instead, it spreads like roots underground: nonlinear, responsive, and regenerative. In rhizomatic learning networks: There’s no fixed menu: learning is emergent, driven by context and curiosity. Everyone is both a node and a nutrient: chefs, learners, machines, and communities. Knowledge is fluid: shared, challenged, and co-created in real time. AI is not the teacher: it’s the fertilizer that accelerates the ecosystem without controlling it. These networks thrive on uncertainty. They flourish where rigid outcomes fail. This organic systematization of HPE is a solid foundation for building robust frameworks. These new frameworks rest on the monumental technological advances of the last 25 years, including AI, blockchain, and digital twins. Now imagine combining rhizomatic learning with the architecture of a Distributed Autonomous Organization of Learning (DAOL). Our recent concept paper proposed this DAO model for future health professions education. Here, learning organizations become: Distributed: The work is done by many system nodes, including learners, teachers, educational organizations, regulators, etc. Autonomous: The system is organized by automatic contracts, with no centralized source of authority. Organization: All nodes have control and authority and work cohesively to address the needs of society. Think blockchain meets peer learning. Think communities of practice (CoP), scaled and amplified by AI. Think of a deliberately developmental organization (DDO) where human capital is central. Whether as a rhizome, a CoP, a DDO, a DAO, or a learning network, these future-ready organizations share six core traits: Adaptive Infrastructure and Curriculum. Learning systems evolve through real-time feedback. AI doesn’t just automate, it senses, reflects, and adapts. Radical Transparency. Like DAOs, these environments are open-source; everyone can see the kitchen, the recipe, and the reviews. Collective Intelligence. Silos are impossible. Learning becomes social, interdisciplinary, and distributed. No one owns the kitchen; we all share the table. Human-AI Coevolution. Machines scale reach; humans bring ethics, meaning, and storytelling. Together, they create synergy, not substitution. Continuous Re-skilling. Roles are fluid. Faculty become designers. Learners become architects. Everyone stays in motion. Purpose-Driven Learning. Tools are secondary. What matters is the mission: equity, care, curiosity, and community. In this new paradigm, learning isn’t a goal; it’s a living system. Communities of practice thrive across boundaries. Learning networks adapt and evolve—and organizational design shifts from controlling content to cultivating conditions. Success isn’t just grades or credentials, it’s contribution, adaptability, and connection. The Check, Please So, what kind of restaurant do we want to build? We want one that learns faster than the world changes. Where learners and educators co-evolve. Where AI deepens our humanity rather than replacing it. Where learning networks grow like rhizomes, not walls. Let’s not just upgrade our microwaves or refine our chefs. Let’s reimagine the entire restaurant as a distributed, purpose-driven, dynamic learning network. If you missed part 1, here is the link : https://icenet.blog/2025/04/29/microwaves-chefs-and-restaurants/ If you missed part 2, here is the link: https://icenet.blog/2025/06/24/microwaves-chefs-and-restaurants-part-2/ References/Further Reading Cabrera D, Nickson CP, Roland D, Hall E, Ankel F. Distributed Autonomous Organization of Learning: Future Structure for Health Professions Education Institutions. JMIR Med Educ. 2022 Jan 4;8(1):e28770. doi: 10.2196/28770. PMID: 34982722; PMCID: PMC8767473. Kegan, R., & Lahey, L. (2016). An Everyone Culture: Becoming a Deliberately Developmental Organization. Stanford Social Innovation Review. https://doi.org/10.48558/X1XV-3Z72 Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge University Press. https://doi.org/10.1017/CBO9780511803932