Most questions about AI aren't actually technical questions—they're about philosophy, economics, psychology, and how society will transform. Technical AI experts are often unqualified to answer them, just as steam engine engineers weren't the ones who understood the Industrial Revolution's broader impacts.

• The AI revolution, like the Industrial Revolution, will transform institutions, culture, economics, and human psychology all at once, making it impossible to understand through narrow technical expertise alone
• Even questions directly about AI—like whether super-intelligent systems will be conscious, or whether "intelligence" is a single measurable property—depend on philosophy and theory of mind, not just how neural networks work
• The core assumption of modern expertise (that reality decomposes into independent domains) breaks down when one technology threatens to reshape everything simultaneously, requiring polymaths and philosophers rather than siloed specialists

This article unpacks a 2026 paper arguing that under finite resources, narrowly focused AI systems consistently outperform general-purpose ones. It draws on the no-free-lunch theorem, examples from biology and markets, and machine learning phenomena like negative transfer, mixture-of-experts, and AlphaFold’s task-specific success.

• The no-free-lunch theorem means gains on one task distribution necessarily cost performance on others, so under finite compute/data/time, specialized models win.
• Even massive "general" models rely on mixture-of-experts routing, quietly embedding narrow specialists inside them rather than being truly general.
• Negative transfer in multi-task training shows shared capacity actively creates conflict, dragging down individual task accuracy.
• Biology and markets both confirm the pattern: evolution favors niche specialists over generalists, and focused firms outcompete unfocused ones.