Questions below come from posts in the AI category, newest first. Each answer reads as a citable claim and links back to the source post for the data, the chart, or the dissenting view.

The angle: AI as an economic problem, not a personality. Scaling laws cost money. Enterprise adoption hits coordination problems before it hits model-quality problems. Benchmark gains and real-world utility are not the same number. “Agentic” is a useful label only after you specify the orchestration, memory, and tool-use layers separately.

What the answers actually cover: foundation-model unit economics (OpenAI’s standalone P&L, hyperscaler capex sustainability), enterprise deployment failure modes (why 85% of AI projects don’t reach production), the agent stack (MCP vs A2A, episodic memory beyond vector search), and the labor-market data on what AI displaces and what it complements.

Answers tend to lead with a number, because the more useful question on AI in 2026 isn’t “what can it do” but “what does it deliver, and at what cost.”