Where the math is defensible.
Long-form research on live enterprise decisions. Publication is selective. Every number traces to a named source. No takes without evidence.
AI inference cost decline 2026: the trajectory and what it forces buyers to plan for
Token prices have fallen roughly 10x per year for equivalent capability since 2023, and the buyers who treat inference as a fixed line item are mispricing every AI roadmap they own.
Inference token pricing has compressed faster than almost any input cost in modern enterprise computing, with frontier model prices falling roughly an order of magnitude per year for any fixed capability tier between 2023 and 2026. The decline is driven by the Hopper to Blackwell hardware step, kernel and serving optimizations, FP8 and FP...
Hyperscaler GPU Procurement 2026: H200 vs B200 vs GB200 in Honest Deployment Math
Blackwell is no longer a roadmap promise, it is a procurement reality, and the only honest comparison runs on workload-weighted utilization rather than peak FLOPS. The hyperscalers that win in 2026 are the ones who match SKU mix to inference share, post-training intensity, and the Rubin cadence sitting one fiscal year out.
The 2026 GPU procurement cycle is the messiest in a decade. AWS, Azure, GCP, and Meta are running three NVIDIA generations in parallel while merchant clouds (CoreWeave, Lambda, Crusoe, Nscale) chase liquid-cooled GB200 NVL72 racks at terms designed for sovereign and frontier-lab buyers. The honest math is not B200 versus H100 peak FLOPS, ...
The 3.2x compute curve: what GB200 actually changes about training ROI
NVIDIA says 4x. A CFO needs the number closer to 3.2x, and only above 55 percent utilization.
NVIDIA positioned GB200 NVL72 as a 4x training improvement over H100. On a workload-weighted, all-in-cost basis the number is closer to 3.2x, and only if sustained utilization clears a 55 to 60 percent threshold. This brief unpacks the curve, where the gains come from in hardware terms, and the three places the ROI tends to break in real ...