Enterprise grade is a phrase people use to mean expensive. It is a property of the architecture, and it comes down to whether the system holds up under conditions it was not designed for.

Before I built anything, I spent weeks doing nothing but talking. The question that made it useful was not what to build. It was which decisions would be expensive to change later.

Every source a buyer uses to choose technology has a revenue model, and the revenue model determines what that source is able to tell you. That is not an accusation. It is arithmetic.

An academic will not build a better tool for an operating domain than an operator will. That is a claim about structure, not about intelligence.

The objection is not that a non-developer could not build a platform. It is that whatever he built must be a mess underneath. That objection is reasonable.

Thirty years in supply chain, no engineering background, and a platform running in production. The distinction that matters is not who writes the code.

A beta partner ran three reasonable requirements through the matching engine and it returned nothing at all. Here is what that broke, and what is underneath it.

Buyers across the supply chain ecosystem are choosing technology on sentiment, from a field too crowded to evaluate. Neither problem is theirs to fix.

The story the industry is telling itself is that AI is transforming supply chain. The data says otherwise, and the reason has nothing to do with the technology.