Distilled Reflections

The debate around AI coding assistants is missing something important: most of their limitations were predicted decades ago by classical cognitive and organizational theory. Drawing on the work of Nobel laureate Herbert A. Simon, Michael Polanyi, and complex systems research, this paper argues that AI struggles with software engineering for structural—not temporary—reasons. AI optimizes locally, while engineering requires global reasoning, tacit knowledge, and system-level judgment. The real question is no longer whether AI can generate code. It is whether prediction alone can ever replace engineering thinking. Hosted on Acast. See acast.com/privacy for more information.

AI can now sound remarkably human - but does it actually understand anything? This podcast explores the “great AI illusion”: the idea that machines may only be predicting patterns and imitating intelligence, not truly thinking or feeling. Through famous ideas like the Chinese Room and “stochastic parrots,” we examine whether AI is becoming intelligent… or whether humans are simply becoming easier to convince. Hosted on Acast. See acast.com/privacy for more information.

We are entering a strange phase of culture. Creating content is no longer difficult. Anyone can write, record, design, or generate something almost instantly. Distribution is immediate. Production is often automated. So the real limitation is no longer making things. It is getting attention.This raises an important question:When content becomes endless, what happens to value? And more quietly: does too much abundance reduce meaning? Hosted on Acast. See acast.com/privacy for more information.

This episode breaks down contradictions in large language models, showing how systems built on scaling laws now collide with messy limits in data, compute, law, and society. We argue that AI is moving from predictable scaling to a constrained, negotiated reality shaped by real-world systems. Hosted on Acast. See acast.com/privacy for more information.