A defining hardware shift of 2026 is the move from spending on training ever-larger models to spending on serving them efficiently in production. Recent funding figures show inference-focused firms taking the majority of AI-chip capital, and product roadmaps increasingly emphasise cost-per-query rather than peak training performance.
Training a model is a large one-off cost; serving it is a recurring cost that scales with every user and every request, so at scale it dominates the bill.
What it means: the industry is optimising for the phase that businesses actually live in, which is daily use. Cheaper, more efficient serving hardware lowers the running cost of AI features over time, which is the number that determines whether an AI product is sustainable.