AI reasoning explained: smarter models still need context

The article argues that while AI reasoning models represent an advancement in LLM capabilities, they do not solve production AI failures — context quality is the real bottleneck that determines whether AI agents work reliably. The piece outlines five key limitations of reasoning models (cost/latency scaling, persistent hallucination, overthinking, diminishing returns, and untrustworthy reasoning traces) and explains how a strong data layer with fresh, structured retrieval is more critical to production AI reliability than model intelligence alone.