AI tokenomics (also called 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. This mind map lays out the field in three layers, from the hardware that produces tokens to the business outcomes they deliver.

## How to use the map

Scroll to zoom and drag to pan. Click a node to expand or collapse it, hover to read its definition, and use the filters to show only concepts or only KPIs. [Open the map full screen](https://tokenomics-foundation.github.io/tokenomics_mindmap/mindmap.html).

## The three layers

### Production

The supply side of AI: the energy, capital, and infrastructure needed to build and run AI in production, and how capacity is planned, costed, and brought online.

- **Tokenization:** How raw text and media become the tokens that are billed and processed.
- **Model / Architecture:** The model's design, which sets the cost floor before any training or serving.
- **Hardware:** The physical infrastructure that produces tokens.
- **Training:** Creating and refining a model.
- **Serving:** Running trained models to produce tokens for users, also called inference.
- **Sustainability:** The energy and environmental accounting of token production.

### Consumption

How AI services are delivered: how much is consumed, where waste occurs, and how to serve efficiently without degrading outcomes.

- **Prompt / Context Engineering:** Per-request techniques that control how many tokens a call uses.
- **Optimization:** Levers that reduce the number or price of tokens consumed.
- **Workloads / Use Cases:** The application types that consume AI capacity, each with a distinct cost profile.
- **Access & Delivery:** How requests are sent and results returned, which affects price.
- **Guardrails:** Controls that keep output safe and correct, some of which cost extra tokens.
- **Demand:** Understanding, measuring, and forecasting what drives AI consumption.
- **Purchase Options:** How usage is bought from providers, including rate cards and commercial structures.
- **Observability:** Visibility into the usage, cost, and quality of AI systems.

### Value

Measuring the business value AI produces, so investment is judged on outcomes rather than cost alone.

- **Business Outcomes:** What the AI changed and by how much, measured against a stated baseline.
- **Unit Economics:** The per-unit costs and efficiency ratios behind token spend.
- **Monetization:** How the value of tokens is captured as revenue.
- **Business Case:** The justification for AI spend against its returns.
- **Market Structure:** The supply landscape that sets what a buyer can get and at what price.

## Source and contributions

The map is a working draft maintained in the open. Its full content lives in a single YAML file that people and AI tools can read directly: [tokenomics_mindmap.yaml](https://raw.githubusercontent.com/tokenomics-foundation/tokenomics_mindmap/main/tokenomics_mindmap.yaml). To propose a change, open a pull request on [GitHub](https://github.com/tokenomics-foundation/tokenomics_mindmap).