Linux Foundation Launches the Tokenomics Foundation to Define the Economics and ROI of AI Value

The Linux Foundation, the nonprofit enabling mass innovation through open source, has launched the Tokenomics Foundation to establish open industry standards, benchmarks, and best practices for the economics of AI, including how to measure the true cost, value, and return on AI spend.

Backed by 30 founding members — including Accenture, BNY, Broadcom, Calero, Cast.ai, DoiT, Finout, Flexera, GoDaddy, Greenpixie, Hitachi, IBM, JPMorganChase, Kion, Lenovo, Nebius, North Cloud, Oracle, Pay-i, Pointfive, Revenium, SAP, ServiceNow, SHI, Stacklet, Vantage, WWT, XOsphere and Yarken — the foundation addresses a widening gap between what organizations spend on AI and their ability to measure, manage, and monetize those investments.

Why Tokenomics Matters Now

The foundation’s creation coincides with accelerating enterprise urgency around AI spend. According to Goldman Sachs, token consumption is forecast to increase 24-fold by 2030. As enterprises race to invest in AI infrastructure and services, the Global 2000 are increasingly evaluating the ROI they are getting back from those investments.

AI Token Economics, or “AI Tokenomics,” is the emerging practice of managing the production, consumption, and value of AI to generate business outcomes. It provides practitioners with a map for answering two challenging questions: what does AI actually cost, and what is the value of intelligence?

Tokenomics looks at the entire supply chain of how energy and capital are used to create tokens and AI services at the hardware layer, the consumption of AI services (and adjacent costs) that drive intelligence, and the outcomes and impacts to business models that the AI drives.

Importantly, much of the cost of AI is not in the tokens themselves. There is a wide range of adjacent costs — from compute to storage, to databases, to caching, and even human labor in the form of engineers. But tokens serve as a consistent, atomic unit of usage that drives various costs and covers the entire spread of AI spend.

The Roadmap

The inaugural Governing Board convened on July 30, with the Technical Steering Committee to follow. The roadmap includes:

• Definitions — Publishing what tokenomics is and defining token value/density, including input, output, reasoning, and cache.
• A reference model for the full cost of AI — not just tokens, so the token line is understood as one part of the bill rather than the whole.
• Cost to serve — a standard method for measuring the whole bill of materials, expressed as cost per call rather than cost per token, so the number maps to work actually performed.
• Value measurement — a framework for relating AI spend to outcomes, starting with the share of work completed without human involvement versus what the process costs today.
• Education and certification — a foundational course and credential so practitioners can apply all of the above.
• The Big-T Framework — a methodology for classifying the token and related AI cost complexity of workloads ahead of model routing between the most cost-effective frontier or open source models.
• Token Cost Telemetry — improved AI cost reporting schemas in FOCUS v1.5 and beyond (FinOps Open Cost and Usage Specification) to better understand AI total cost of ownership (TCO).
• AI Value Frameworks — a roadmap for showing ROI from AI TCO, and how Tokenomics impacts business models, COGS, and labor planning.

Open Economics for an Open Ecosystem

“Open source proved that shared foundations beat closed ones, and open weight models are now extending that lesson to AI itself,” said Jim Zemlin, Executive Director of the Linux Foundation. “But an open AI ecosystem needs more than open models; it needs open economics.”

As tokens become the commercial expression of the entire AI economy, the Tokenomics Foundation gives the consumers and suppliers of AI a common, vendor-neutral place to build those standards in the open — the same way the community has done for software and for cloud economics.