The useful shift here is treating the token as an economic unit, not just a model output.
Every prompt travels through a chain of admission control, routing, scheduling, memory, kernels, interconnect and power. The model gets the attention, but much of the margin is won or lost in the plumbing between the request and the response.
That also explains why falling token prices do not necessarily destroy the infrastructure trade. Cheaper inference unlocks longer contexts, more agent loops and more frequent use. The bottleneck simply migrates from raw compute toward memory bandwidth, caching, networking and power.
The model may write the answer. The data center decides whether the economics work.
DATA GRAVITY's trace of a prompt through gateway, scheduler, KV cache, GPU, and network is the missing diagram most security teams never look at. They see the bill, not the path. That matters because my own take on token economics starts exactly where this infrastructure map ends: once you understand what each component actually does to serve a single inference call, the per-token cost stops looking arbitrary and starts looking like the first honest cost signal a SOC has ever had. The two pieces are complementary. DATA GRAVITY explains the physical reality, mine argues that reality finally gives security leaders a unit of work they can budget around instead of guessing.
What this diagram makes visible is how many org boundaries a single inference call crosses. The token travels farther than most incident reviews do. That gap between the path of the request and the path of accountability is where AI reliability problems tend to hide.
The useful shift here is treating the token as an economic unit, not just a model output.
Every prompt travels through a chain of admission control, routing, scheduling, memory, kernels, interconnect and power. The model gets the attention, but much of the margin is won or lost in the plumbing between the request and the response.
That also explains why falling token prices do not necessarily destroy the infrastructure trade. Cheaper inference unlocks longer contexts, more agent loops and more frequent use. The bottleneck simply migrates from raw compute toward memory bandwidth, caching, networking and power.
The model may write the answer. The data center decides whether the economics work.
DATA GRAVITY's trace of a prompt through gateway, scheduler, KV cache, GPU, and network is the missing diagram most security teams never look at. They see the bill, not the path. That matters because my own take on token economics starts exactly where this infrastructure map ends: once you understand what each component actually does to serve a single inference call, the per-token cost stops looking arbitrary and starts looking like the first honest cost signal a SOC has ever had. The two pieces are complementary. DATA GRAVITY explains the physical reality, mine argues that reality finally gives security leaders a unit of work they can budget around instead of guessing.
https://cyrilsimonnet.substack.com/p/the-token-bill-is-the-first-honest
What this diagram makes visible is how many org boundaries a single inference call crosses. The token travels farther than most incident reviews do. That gap between the path of the request and the path of accountability is where AI reliability problems tend to hide.
Excellent points!