The AI space is moving so fast that it is hard to know which conclusions are stable. After all the discussion around local models, is the practical conclusion still that API/frontier providers have a huge structural advantage because of datacenter hardware, high utilization, batching, optimized inference stacks, and perhaps strategic pricing?

In a comparison like this, a $25k local setup versus buying tokens, what multiple are we really talking about? 10x? 100x? Or is it too workload-dependent to reduce to a single number?

Has someone written a good breakdown that separates true infrastructure efficiency from temporary underpricing/subsidy? The part I'm trying to understand is less ideological (local vs. cloud) and more basic economics.