Home Content News Yarken Joins Linux Foundation to Build Open AI Cost Standards

Yarken Joins Linux Foundation to Build Open AI Cost Standards

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Linux Foundation
Linux Foundation

The Linux Foundation has launched the Tokenomics Foundation with 30 companies, including Yarken, to create vendor-neutral standards for measuring AI costs and value.

The Linux Foundation has launched the Tokenomics Foundation with 30 industry players to develop open, vendor-neutral frameworks for measuring the cost and value of AI investments. The initiative extends open-source governance into AI economics, addressing the growing challenge of tracking token consumption across models, platforms and AI processes.

Yarken, a founding member, is contributing its expertise in FinOps and Technology Business Management (TBM). The foundation aims to establish shared standards that let enterprises compare AI costs across vendors and models, govern investments, measure value against spending, and make more informed decisions.

The move comes as enterprises increasingly adopt AI models, agent frameworks and AI-driven processes from vendors across the US, China and elsewhere, making token consumption a growing component of technology spending.

The Linux Foundation’s experience in open-source governance across operating systems, cloud computing, networking, hardware and AI positions it to lead the initiative. According to Yarken, the foundation has 800 open-source projects to date.

“We are honoured to join up with 30 of our peers on the vital work of creating a vendor-neutral roadmap for future AI value. AI has truly redefined how enterprises look at their business and operating models. Every day our fast-growing teams see the risk and rewards this brings,” said Ravi Kuppan, Founder and CEO, Yarken.

“Connecting token consumption with business value is essential,” Kuppan added. The membership includes Accenture, Broadcom, IBM, JPMorganChase, Lenovo, Oracle, SAP, ServiceNow and others.

The open-source significance lies in creating common standards rather than open-sourcing an AI model or code, potentially reducing fragmentation in AI cost accounting.

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