AI is steel for organizations — when software carries the context, human communication stops being the load-bearing wall

Zhao’s structural metaphor: before steel, 19th-century buildings hit a ceiling of “six or seven floors” because iron was “strong but brittle and heavy” and collapsed under its own weight. Steel — “strong yet malleable” — let frames go lighter and buildings rise dozens of stories. He argues “AI is steel for organizations”: it “maintain[s] context across workflows and surface[s] decisions when needed without the noise,” so “human communication no longer has to be the load-bearing wall.” Concretely, “the weekly two-hour alignment meeting becomes a five-minute async review,” and orgs “can scale, truly scale, without the degradation we’ve accepted as inevitable.”

The load-bearing claim is the precise inverse of Context centralization is why coding AI works — git is a solved context repository, knowledge work has no equivalent: coding scaled because git already centralizes context; knowledge orgs degrade at scale because their context lives in meetings and human memory. Make software carry that context and the scaling limit lifts. This is the mechanism beneath When production constraints dissolve, the bottleneck shifts from execution to judgment at the firm level — communication overhead was a binding constraint, and removing it shifts the bottleneck upward to judgment and Organizational shape is the emerging moat in AI — what AI cannot copy is the institution underneath. It also depends on durable Persistent agent memory preserves institutional knowledge that walks out the door with employees, since context can only bear load if it persists.