AI, technology, education, and ideas about what comes next.
Artificial intelligence, machine intelligence, and the emerging systems that are changing how humans think, create, decide, and interact with machines.
AI doom can be real without every AI-doom narrative being reliable. The Cute Tiger Cub Problem examines how satire, music, borrowed authority and visual certainty can blur the line between evidence, interpretation and speculation.
When AI and robotics make production increasingly abundant, the bottleneck may move from productivity to human attention, agency, meaning and choice.
AI can multiply cognitive productivity dramatically, but the real economy is constrained by human time, attention, consumption, infrastructure and demand.
AI is moving beyond answering questions toward reproducing parts of the expert teaching process. New experiments show why pedagogical structure—not raw intelligence alone—may be the real breakthrough.
AI does not need to replace teachers to disrupt teaching. What happens when scalable AI expertise enters a market built around selling instruction by the hour?
What happens if ASI succeeds? This thought experiment explores how abundance, automation and AI dependence could affect human agency, meaning and purpose.
A framework for separating AI capability, cognitive labor, work product, attribution, confidentiality, and rights—and understanding AI as a digital employee or agent.
When AI can reproduce expertise at machine scale, the economics of tutoring, education and knowledge work begin to change. What happens when intelligence becomes abundant?
AI may be weakening one of the traditional reasons companies needed to become large: the need to employ large numbers of specialists. As AI turns specialized capability into something that can be summoned on demand, a new organizational form may emerge—the lean micro-corporation, with a tiny human core commanding enormous productive capacity.
AI may create a new economic channel in which people contribute ideas, expertise, methods and cognitive systems to the development of machine intelligence—and potentially share in the value created.
AI can reason brilliantly inside the wrong frame. From the counterfeit-cap puzzle to the bat-and-ball problem, the deeper challenge is not merely solving a question but recognizing whether the question itself deserves to be solved.
AI could shift education from a fixed four-year journey to a continuous, adaptive capability system in which people learn, demonstrate, work and keep learning.
The same technology can amplify independent thinking—or accelerate cognitive outsourcing. AI may not produce one uniform cognitive future. It may widen the distance between people who use intelligence tools to think further and those who use them to think less.
AI can produce answers that look like understanding. But the deeper test is whether it can transfer concepts, detect contradictions, recognize boundaries, manage uncertainty, and connect different forms of intelligence.