Invite Only + Governing Board
Tokenomics 100 SF
San Francisco
What is AI Tokenomics?
Don't count tokens. Realize value from the total cost of AI.
AI Tokenomics (i.e., "AI Economics") is the discipline of converting energy and capital into AI capabilities and efficiently consuming that intelligence across the organization, to realize measurable business value.
— Tokenomics draft definition v0.5.2, September 1 2026
State of Tokenomics: September 2026
Get insights on AI tokenomics, ROI proof, model routing, open weights, and pricing.
Tokenomics Foundation working groups
Tokenomics looks at the entire AI supply chain, from energy to labor to value
Production
Turning energy and capital into AI capabilities and intelligence.
Energy
Capital resources
AI factories
AI capacity creation
Capacity planning
Data Centers
HAM
Consumption
Using that intelligence well, and managing the entire bill of materials.
Model Selection
Forecasting
Prompt Routing
Caching Strategy
Efficiency
Licenses
Software
FinOps
ITAM
ITFM
Value
What the output is worth, and how it impacts the business.
Demand
Monetization
Pricing
Products
Labor
Accounting
Business Processes
Revenue
Events
Tokenomics Events Around the World
AGNTCon + MCPCon
San Jose, California
Invite Only (SVP/EVP/CxO)
Tokenomics Executive NYC
One World Trade Center
Tokenomicon + FinOps X
San Diego, California
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
Featured Project
Big-T Notation
Big-O for AI. A shared language for how token consumption grows as usage scales.
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