The great AI pricing reset is coming. Are you ready?
Google's token consumption hit 3.2 quadrillion a month. Your inference cost model is already broken.
Why it matters
Vendors have quietly subsidized AI inference costs; that subsidy is ending as agentic workloads push context into the hundreds of thousands of tokens. NVIDIA's CMX platform now ties inference economics directly to volatile NAND flash pricing, forcing infrastructure leaders to model storage media exposure and rethink all-flash architectures before committing capital.
The key facts
8 to knowGoogle token consumption: 9.7 trillion/month (2024) → 480 trillion/month (mid-2025) → 3.2 quadrillion/month (2026)
Goldman Sachs projects 24x industry-wide token consumption growth by 2030
NVIDIA CMX (Context Memory Storage Platform) standardizes KV cache offload to NVMe SSDs, claims 5x power efficiency and 5x throughput gains
12+ storage vendors (Dell, IBM, NetApp, VAST, WEKA) already supporting CMX or similar approaches
30TB TLC SSD pricing rose 472% between Q2 2025 and Q1 2026
QLC/HDD price multiple widened from 4.9x to 22x over same period; HDD pricing moved ~35%
Agentic workflows disproportionately increase input/context token consumption, not output tokens
Major hyperscalers (Google, Meta, Microsoft) run mixed storage fleets, not all-flash, to hedge volatility
Go to the source
CIOcio.com
Publisher excerpt: Per-token pricing has become the default commercial language of generative AI, and practically every budget conversation now happens in those terms (tokens consumed, tokens generated, cost per million tokens, etc., etc.). The problem is that this model treats every AI interaction as a discrete…