Source: Uncoveralpha
As inference efficiency improves, the cost advantage of running smaller, fine-tuned models on commodity hardware shrinks. Mid-market AI workloads are moving back toward centralized frontier models controlled by a handful of companies. This reverses the open-source democratization narrative because efficiency gains primarily benefit those with scale to amortize training costs and infrastructure to serve models at volume. The split isn't between "best model" and "good enough model" workloads, but between problems that need frontier reasoning—where hyperscalers have the advantage—and everything else, which increasingly requires renting compute from those same players rather than deploying independently.