AI, technology, education, and ideas about what comes next.
AI is becoming extraordinarily good at doing things. Yet it can still misunderstand what a person actually means. The deeper problem may not be intelligence, but judgment: knowing what matters in the first place.
An AI investment bust need not end the AI revolution. Explore how service-sector productivity, reusable data centres, real estate, capital flows, commodities and monetary policy could interact.
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.
IQ matters, but it is not destiny. Research suggests that success emerges from the interaction of cognitive ability, execution, education, environment, opportunity, persistence, timing, luck, and compounding.
If AI can teach you almost anything, why go to college?
AI’s breadth makes it powerful at exploration, but human expertise can be better at narrow execution. The future may be AI that knows when to explore—and when to stay in the box.
AI is making intelligence, coordination and information cheaper. Could that eventually change how money, local economies and even government work?
I tried to use Microsoft OneNote as a long-term archive for my ChatGPT work. The experiment exposed a deeper problem: storing AI-assisted work is easy; designing a useful system for organizing, retrieving and working with it is much harder.
A humorous but serious look at the gap between AI model capability and the amount of useful work a user can actually extract from it: fuel, interface bandwidth, context mobility, execution access and reliability.
A real-user review of ChatGPT’s model intelligence, memory, retrieval, UI/UX and tool limitations — and why a Pro upgrade is not the obvious answer when information flow is the real bottleneck.