Something for the weekend - why enterprise AI progress is not where the industry thinks! Time to chow down on some snake tail?

Enterprise AI is often described as a pipeline. Data goes in, answers come out. In practice, it behaves more like a feedback loop. Each time organizations add new data sources, tools, and constraints, the system reshapes itself. Expectations rise, margins for error shrink, and weaknesses that were once invisible become harder to ignore.

There is an old image of a snake eating its own tail – the Ouroboros – used to describe systems that evolve by consuming themselves. Enterprise AI increasingly follows this pattern. Progress does not simply expand capability; it tightens the conditions under which accuracy is judged.

The research from Databricks suggests that many failures in enterprise AI emerge from this dynamic. As models become more capable and use cases more ambitious, the limiting factor is no longer language generation itself. Instead, it is the system’s ability to consistently retrieve the right information, apply the right constraints, and connect ...