The inversion created two effects.

First, it broke the assumption that capability improvements had to come from larger training runs. A small, capable, efficient base model combined with adequate inference-time compute could match or exceed a much larger model run at a single forward pass.

Second, it shifted the unit of analysis from model capability to inference economics. The question stopped being “whose model is best” and became “whose serving infrastructure produces the highest quality output per dollar of inference compute.”