This is a collection of observations and perspectives developed over time on how memory, data movement, and system constraints are reshaping AI infrastructure.
What began as isolated events has converged into a broader thesis: the next limiting factor in AI is not compute, but how data moves and how memory is accessed across systems.
Early observations on performance, latency, and the role of memory-adjacent technologies highlight how system constraints were already shifting away from compute alone.
The underlying constraint is not models, but how data moves through systems, how memory is accessed, and how those boundaries impact real-world AI performance.
A recurring pattern across enterprise AI adoption is the gap between successful pilots and production deployment. I’ve been referring to this as AI PoC Purgatory.
This is not a model failure. In many cases, the models are working as expected in controlled environments. The breakdown occurs when those models have to operate within real enterprise systems, where data access, governance, latency, and integration constraints become dominant.
While often attributed to governance, data quality, or organizational alignment, this is more fundamentally an execution problem tied to system-level constraints, particularly around data movement, memory locality, and orchestration.
AI PoC Purgatory represents the point where infrastructure realities meet enterprise expectations.