The Linux Foundation, the nonprofit organization enabling mass innovation through open source, announced 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 : 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 AI TCO
AI Value Frameworks: the Foundation is laying out a roadmap for showing ROI from the TCO (Total Cost of Ownership) of AI, as well as how Tokenomics impacts business models, COGS and labor planning.
The Tokenomics Foundation’s creation brings together industry participants to create shared measurement methods, vendor-neutral frameworks and best practices for organizations managing AI investments.
The Tokenomics Foundation will operate a dedicated Governing Board, Technical Steering Committee, and IP-managed member working groups to develop vendor-neutral specifications, benchmarks and best practices.
The Tokenomics Foundation operates as part of the technology value umbrella within the Linux Foundation, which serves a global community of more than 120,000. The technology value Foundations share community events such as Tokenomicon + FinOps X, San Diego June 7-10 2027, and operational resources while maintaining separate governance. They will also support FOCUS, the open billing data specification that provides the substrate for normalizing cost and usage data across providers.
Jim Zemlin, Executive Director of the Linux Foundation
Open source proved that shared foundations beat closed ones, and open weight models are now extending that lesson to AI itself. But an open AI ecosystem needs more than open models; it needs open economics. Tokens have become the commercial expression of the entire AI economy, yet there is no shared way to connect AI spending to value. 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 this community has done for software and for cloud economics.
J.R. Storment, Executive Director of the Tokenomics Foundation
Businesses are reinventing how they deliver value with AI faster than they can measure it. Every model release changes the math on cost, consumption, and ROI, and tokens are only the visible tip. The real total cost of AI spans compute, storage, data, and the people who build with it. Every CEO is being asked to show returns on all of that without a shared way to count it. That is why this Foundation exists and is working together on pre-competitive frameworks, benchmarks, and specifications, built in the open, so the entire global economy can accelerate value from AI rather than just account for the spend.
Mike Eisenstein, Managing Director, Accenture
We work with thousands of enterprises reinventing themselves around AI, and the hardest conversation is no longer whether to adopt it but how to prove the return. Token spend is climbing fast and the discipline to govern it has not kept pace. When the bill arrives, companies face a tough choice between pouring more money in, or having to pull back and risk slowing innovation. Open, vendor-neutral standards for token economics give our clients a common language to manage that spend and quantify the investment and its returns. That is the gap the Tokenomics Foundation fills, and we are excited to help build it.
Leigh-Ann Russell, CIO and Global Head of Engineering, BNY
As organizations invest more heavily in AI, transparent and consistent approaches to measuring total cost of ownership and business value will become increasingly important. BNY looks forward to collaborating with the Tokenomics Foundation and other members to help advance standards, shared language, and practical benchmarks for AI economics.
Laurent Gil, Co-Founder and President, Cast AI
Enterprises are under pressure to show ROI on tokens, but almost none can say what a token costs to produce. Our research puts enterprise GPU utilization at 5%, so most of a token's cost is idle hardware. The return shows up at the business outcome, and almost no one measures it. The industry is standardizing the meter in the middle and ignoring both ends. Cast AI joined the Tokenomics Foundation to help measure both.
Amit Kinha, Field CTO, DoiT
Every organization running AI is now asking the same question they once asked about cloud: What is this actually costing me, and what am I getting for it? Tokenomics is the discipline that answers it. FinOps gave us a shared language for cloud spend, and the Tokenomics Foundation does the same for tokens, models, and GPU cycles. DoiT is proud to be a founding member, and I'm honored to help steer this work as a member of the Governing Board.
Jay Litkey, Senior Vice President of Cloud and FinOps, Flexera
Gartner forecasts worldwide AI spending will grow 47% this year alone — the fastest-moving line item in enterprise IT, and one most finance teams still can't reliably forecast. That's not a maturity problem, it's a measurement problem. Most technology spend has matured around stable, predictable units — a license, a seat, a compute-hour. Token-based AI pricing has no such stability, and no equivalent billing standard yet. That's the gap a neutral standard needs to close, and why Flexera supports the Tokenomics Foundation.
John Ridd, CEO and Co-Founder, Greenpixie
Executives are scrambling for the productivity benefits of AI while struggling to reconcile the cost, energy and carbon implications. Tokenomics reconciles this and safeguards long-term growth with a sustainable AI strategy. The Tokenomics Foundation will play a hugely important role in establishing best practices and Greenpixie is ecstatic to apply its expertise to this new frontier.
