But that can be the wrong lever. When you examine the full end-to-end transaction, the model usually isn’t the problem. Take the extraordinary scale of AI traffic as it moves from a branch office to a centralized security stack, then to a cloud region, then to a data-hosted backend somewhere — and then add inconsistent local internet performance, packet loss and congestion — and the AI experience deteriorates.
The challenge also varies widely depending on where in the world you’re standing. At an AI symposium in Africa recently, the attendees might have been expected to discuss which model to use, but the conversation instead was “how do we actually deploy this.” In Latin America, the discussion I hear most often from customers isn’t about raw bandwidth, but latency, specifically, because of how the region’s infrastructure routes traffic back to major cloud regions. Silicon Valley and São Paulo are not the same infrastructure conversation, and treating them as though they were is exactly how well-intentioned rollouts stall.
This is not because the model got worse, but because everything underneath it was never built to carry this kind of load across this kind of distance. When an AI application performs poorly, our instinct is to look up the stack, but the answer is often underneath it.
