The author challenges the assumption that understanding AI requires deep technical expertise and that only credentialed AI experts should shape discourse around it. The argument rests on a simple observation: most questions about AI aren't actually technical questions. They're questions about how AI will reshape labor markets, institutions, psychology, culture, and power structures—domains where economists, philosophers, sociologists, and historians have relevant expertise that technologists often lack. When AI researchers speculate about these broader impacts, they frequently rely on shaky assumptions borrowed from other fields. The author uses the Industrial Revolution as a parallel: steam engines were revolutionary technologies, but the great thinkers who grappled with the transformation—Ricardo, Marx, Durkheim, Weber—distinguished themselves through understanding society, not through mastering mechanical engineering. The author argues transformative AI is coming regardless of current skepticism. The reasoning is straightforward: brains are information-processing systems, so building machines that exceed human cognitive capabilities is possible in principle. We've already done it in narrow domains. The space of possible machine intelligences is vast—they can be copied, run in parallel, operate at superhuman speeds, and redesign themselves. Given the economic and military incentives driving progress, we should expect far more capable systems within our lifetimes. This remains true even if you think current AI is overhyped, the technology will plateau, or deployment will be slowed by institutional bottlenecks. But here's the deeper problem: even purely technical questions about AI remain genuinely mysterious. Researchers can't fully explain why deep neural networks work the way they do. More fundamentally, understanding how AI will actually unfold depends on unsettled questions about the nature of intelligence, agency, and motivation itself. Does intelligence exist on a single scale? What does superhuman reasoning enable in the real world? Can we align AI systems with human values? These aren't engineering problems—they're philosophical ones. The author suggests the AI revolution threatens the modular division of expertise that modern knowledge relies on, creating space for polymaths and philosophers, but also pointing toward what he calls "moderate epistemic despair" as our world becomes less knowable.