AI Code Generation Is Cheap, But Its Context Engine Costs Scale Up
Generating code with LLMs may be cheap per token, but feeding them accurate, up‑to‑date organizational context requires costly retrieval, indexing, and knowledge‑graph pipelines. The hidden “context engineering” layer often becomes the performance bottleneck, forcing teams to invest in infrastructure that rivals—or exceeds—the savings from cheap generation.