tokenomicon + FinOps X Amsterdam
Amsterdam, Netherlands
What is AI Tokenomics?
AI Tokenomics is the discipline of converting energy and capital into AI, then efficiently consuming AI services to enable intelligent outcomes and drive business value.
(Draft v0.2, August 2026)
Tokenomics 101: Tokenomics isn't about counting tokens. Tokenomics connects the total cost of AI (including labor) to its outputs.
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Foundations Work
The tokenomics map, and the group that owns each part
Tokenomics splits into three domains — production, consumption, and value — each with its own working group. Beneath all three sits a shared foundation: the vocabulary and data format every domain depends on.
Production
Turning energy and capital into AI capacity.
EnergyCapital resourcesAI factories
AI capacity creationCapacity planning
AI Factories & Energy
The supply side. Energy, capital resources, and the economics of building and running token production capacity. How capacity gets planned, costed, and brought online.
View working groups →Consumption
Using that capacity well, and knowing what it costs.
AllocationForecastingOptimization
EfficiencyFinOps for AI
Efficiency & Optimization
The practitioner core. Allocation, forecasting, optimization, and cost to serve: the whole bill of materials for an AI workload, expressed at cost per call rather than cost per token.
View working groups →Value
What the output is worth, and to whom.
DemandMonetizationPricing
Product implicationsLabor implications
Financial Reporting, Capitalization & Accounting Treatments
How AI spend shows up in the books, and how the return gets stated. Deflection rate, cost per event, and a defensible labor baseline that a CFO will accept.
View working groups →Shared foundation
Definitions, Personas & Frameworks
The vocabulary problem first. What tokenomics is, who practices it, and what a token actually is once you separate input, output, reasoning, and cache. Everything in the three domains above depends on this group landing it.
New to AI Tokenomics
Four steps, about an hour.
The order matters. Vocabulary before classification, classification before optimization, and instrumentation before all of it.
STEP 01
Learn the vocabulary
What tokenomics means, how production, consumption, and value connect, and who does what.
Read the overview →STEP 02
Classify your workloads
Big-T gives you a class for each workload and a way to see the cost curve before the invoice arrives.
Use Big-T Notation →STEP 03
Find the layer to act on
The stack tells you where a change is possible and what it is worth. Most first wins sit at L3 and L5.
Open the five-layer stack →STEP 04
State of AI Tokenomics Survey
The specifications get built from what practitioners actually report. The survey is the shortest way in.
State of Tokenomics →Projects
What the working groups are building.
Open specifications, benchmarks, and frameworks, built by the organizations doing the work.
Featured Project
The five-layer tokenomics stack
Open a layer to see what it controls
The order is load bearing. Each layer inherits the cost and availability of the ones beneath, so gains compound upward. Read the full stack
Featured Project
Big-T
Big-O for tokens. A shared language for how token consumption grows as usage scales, so teams can see the cost curve before the invoice arrives.
T(1)Constant. The model is not called per request — a cache hit, a static lookup.cache and precompute
T(log n)Sublinear. Deterministic code shrinks the input before the model sees it.filter before inference
T(n)Linear. One model call per request — the healthy default.trim per-call overhead
T(n·k)Multiplicative. k model calls per request, and k is usually invisible.compose tool pipelines
T(n·k·a)Agent-multiplicative. An orchestrator spawns sub-agents that spawn tool calls.bound depth, add budgets
T(∞)Unbounded. Loops with no termination condition.hard termination. always.
T(n · k · a) n = requests or input size · k = model calls per request · a = agent depth
Events
