AI

Coverage spans AI economics, enterprise deployment strategy, and the measurable effects of AI on labor markets and industries.

A recurring theme is the gap between AI benchmarks and real-world utility. Posts on model evaluations and scaling law economics examine whether trillion-dollar infrastructure bets translate into proportional capability gains. The enterprise AI coordination problem argues that most organizational failures stem from integration, not model quality, while the enterprise agent stack maps how orchestration, memory, and tool-use layers are separating into distinct infrastructure tiers.

On AI economics, the OpenAI standalone P&L analysis examines unit economics at scale, and the recommendation algorithm economics behind Netflix and Spotify shows why compute-optimal recommendations diverge from business-optimal ones. The a16z vs AQR AI valuation debate tests whether current AI infrastructure spending can generate proportional returns.

The AI and labor displacement analysis tracks measurable job market shifts, while The Impossible Backhand examines where AI quality ceilings make human domain expertise more valuable, not less. Applied AI work includes sentiment-based trading signals using news embeddings and hands-on agent-building projects.