The Tokenomics Foundation

On June 3, 2026, The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced the intent to launch the Tokenomics Foundation, a new foundation that will focus on establishing open industry standards, benchmarks, and best practices for the economics of AI infrastructure. The Tokenomics Foundation will operate in close partnership with the FinOps Foundation, extending the discipline of variable technology spend into the era of token-based AI.

The Tokenomics Foundation will bring together large token consumers with hardware providers, frontier model providers, neoclouds, hyperscalers, inference companies, consulting companies, platform companies, etc to build the primitives of tokenomics. It will cover token production (i.e., token factory effectiveness), consumption (i.e., FinOps for AI) and monetization (i.e., AI value) and help tie AI use to business value and outcomes.

The Foundation will serve both sides of the AI economy: the buyer side, made up of enterprises operating at scale that need transparent, vendor-neutral standards for the economics of AI token consumption, and the supplier side, including frontier model providers, NeoClouds, and the broader token factory supply chain. 

The Tokenomics Foundation Governing Board will help set industry direction and deploy funds to support the project. A Technical Committee will develop open specifications, benchmarks, and frameworks, and the Foundation will jointly fund and support the FOCUS (focus.finops.org) specification’s expansion into token based spending models.

Tokenomics is not just about the cost of tokens, it’s about the entire layer of AI that they drive from production, to consumption to monetization. It is about getting more value from AI systems: systems that are efficient, observable, accountable, and designed with cost, performance, and value in mind from the start.

Why Tokenomics Matters

As AI becomes embedded into products, workflows, platforms, and decision-making systems, every interaction carries a cost and offers an opportunity for value optimization. Prompts, embeddings, retrieval steps, agent loops, model calls, generated outputs, evaluations, and infrastructure all form part of a growing economic layer. At small scale, these costs can feel invisible. At product and enterprise scale, they become strategic.

The first wave of AI adoption focused on capability. Could the model answer the question? Could it generate the content? Could it automate the task?

The current wave is focused on efficiency and value. Should this task use a frontier model, a smaller model, a cached response, a retrieval step, a rules-based workflow, or no AI at all?

Tokenomics helps teams make those decisions. It connects product design, engineering, finance, operations, and governance around a shared understanding of AI cost and value, using FinOps principles.

Our work

We support the industry by developing shared language, frameworks, benchmarks, best practices, and community-led guidance for managing AI usage responsibly at scale.

We aim to help organisations move from experimental AI adoption to sustainable AI operations.

What We Do

We bring together AI leaders, practitioners, product leaders, finance teams, researchers, and AI builders to define emerging best practices in token economics.

Our work focuses on:

• Understanding the true cost of AI systems
• Improving visibility into token usage and model spend
• Designing efficient AI architectures
• Comparing model performance, cost, and suitability
• Encouraging responsible use of large and small models
• Supporting open standards, shared metrics, and practical guidance
• Helping teams connect AI investment to business value

Our Principles

Efficiency is a design choice
AI cost is shaped by architecture, not just usage. Good system design can reduce waste while improving performance.

Bigger is not always better
The best AI system is not always the one using the most expensive model. It is the one using the right tool for the job.

Visibility comes before optimisation
Teams cannot manage what they cannot see. Token usage, model calls, retries, retrieval steps, and agent behaviour need to be observable.

Value matters more than volume
More tokens, more calls, and more automation do not automatically mean better outcomes. Tokenomics focuses on the relationship between cost, quality, and value.

Open knowledge benefits everyone
AI economics is still emerging. Shared standards, community learning, and transparent practices will help the whole ecosystem mature.

Who We’re For

The Tokenomics Foundation is for anyone building, operating, funding, or governing AI systems.

That includes CAIOs, CTOs, CIOs, CFOs, software engineers, AI product teams, platform teams, FinOps practitioners, cloud cost specialists, researchers, consultants, startups, enterprises, and public sector organisations.

Whether you are just beginning to explore AI costs or already managing complex multi-model systems, our goal is to provide practical resources that help you make better decisions.

Building the Future of AI Economics

AI will not simply be judged by what it can do. It will be judged by whether it can do it reliably, affordably, responsibly, and at scale.

Tokenomics gives teams the language and tools to make that possible.

The Tokenomics Foundation exists to build that language, support that community, and help define the economic operating model for the AI era.