A decade ago, I started covering the intricacies of the AI ecosystem. As early as 2016–17, I described AI as a multi-layered stack, almost like a layered cake of interconnected technologies and infrastructure — as explored in the economics of AI compute infrastructure — .

A few years later, right after the launch of ChatGPT, I began mapping the AI landscape in a far more granular way. The reason was simple: the ecosystem was finally crystallizing around a few critical building blocks.

More importantly, however, something I had emphasized for years became impossible to ignore. This entire ecosystem has been shaped by constraints, bottlenecks, chokepoints, and scarcity at every layer.

In an era where AI represents the next computing paradigm, this cascading ecosystem revealed how unprepared we actually were to make it work at scale.