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

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

The Linux Foundation | 04 August 2026

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New Foundation brings together 30 industry leaders to develop open frameworks, specifications and best practices for measuring the cost, value and return on AI spend

Summary

The Linux Foundation has launched the Tokenomics Foundation to establish open, vendor-neutral industry standards, benchmarks and best practices for the economics of AI.

Backed by 30 initial members, the foundation will address the challenges of tracking the ROI of AI, replacing fragmented vendor pricing with a standard way to measure the true total cost of ownership.

The roadmap will aim to create shared language and measurement: defining tokenomics and the value metrics for AI ROI, creating AI Value Frameworks to measure business impact, vendor-neutral models for the full cost of AI, establishing standard methods for measuring cost to serve and the value AI actually returns, delivering token cost telemetry in the FOCUS specification and building the education and certification practitioners need.

SAN FRANCISCO, August 4, 2026 — The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today launched the Tokenomics Foundation to focus on establishing open industry standards, benchmarks and best practices for the economics of AI. At the time of launch the founding member organizations include 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 Tokenomics Foundation’s creation coincides with accelerating enterprise urgency around AI spend,with token consumption forecast to increase 24-fold by 2030, Goldman Sachs predicts. Organizations face a widening gap between what they spend on AI and their ability to measure, manage and monetize their AI investments.

The Tokenomics Foundation aims to help organizations better meet the challenge of AI value, right at the moment that the global 2000 are evaluating the ROI they are receiving from AI investments amidst historic investment into infrastructure and AI services.

AI Token Economics, or AI Tokenomics, is the emerging practice of managing the production, consumption and value of AI to generate business outcomes. It gives practitioners 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 AI costs) to drive intelligence and the outcomes and impacts to business models that the AI drives.

Tokenomics, as the industry is defining it, acknowledges that much of the cost of AI is not in the tokens themselves. There are a wide range of adjacent costs from compute to storage to database to cache and even, human labor in the form of engineers. But tokens are a consistent atomic unit of usage driving various costs. Tokens cover the entire spread of AI costs.

The inaugural Governing Board convened on July 30, and soon after will be the formation and meeting of the Technical Steering Committee, which aligns on key challenge areas for working groups to build best practice materials. The Tokenomics Foundation roadmap includes initial pieces like:

Definitions. Publish what tokenomics is, and define token value/density, including input, output, reasoning, and cache.

A reference model for the full cost of AI , not just tokens. Shared terms and the complete component picture, so the token line is understood as one part of the bill rather than the whole of it.

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, measured against what the process costs today.

Education and certification. A foundational course and credential, so practitioners and the teams around them can apply all of the above.

Projects like the Big-T Framework : provides a methodology for classifying the token and related AI cost complexity of workloads ahead of model routing activities 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...