3. Compute Platform Evolution: From Scarcity to Heterogeneity

The infrastructure layer itself is evolving rapidly:

2025–2026: The Blackwell Era

Blackwell-class systems deliver approximately 2× per-GPU training efficiency and 3–5× per-GPU inference efficiency versus Hopper, with much larger gains at cluster scale.

2027–2028: The Vera Rubin Transition

Power efficiency, not raw FLOPs, becomes the binding constraint as data centers hit the energy wall.

Rise of Non-NVIDIA Accelerators:

By 2030, 25–35% of inference workloads are likely to run on non-NVIDIA silicon, led by AMD and hyperscaler-specific ASICs. Training, however, remains NVIDIA-dominated well into the decade.

The long-term implication is heterogeneous compute, favoring platforms and clouds that abstract complexity rather than lock customers into a single architecture.