At its October 8 analyst virtual summit, the Linux Foundation’s brought its technology value umbrella — Tokenomics, FinOps, Information Technology Asset Management (ITAM), and FinOps Open Cost and Usage Specification (FOCUS) — together around a bigger question: How do enterprises connect what AI costs to the business value it creates? Seven themes stood out.
1. TCA May Become AI’s Successor To TCO
Perhaps the summit’s most useful new terminology is TCA: total cost of AI. The key insight is that tokens represent only 10–25% of total AI spend once the broader cost stack is considered. TCA expands the denominator to infrastructure, energy, capital, software and licensing, labor, and other costs required to produce and consume AI.
For decades, technology leaders have relied on TCO: total cost of ownership. By contrast, TCA could become the more relevant construct for AI because AI is not simply an asset to own. Instead, it is a dynamic production and consumption system whose economics span infrastructure, intelligence, people, and outcomes.
Moreover, the discussion is moving into the CFO’s office. The event organizers indicated growing CFO involvement in the Tokenomics Foundation’s leadership and an increased emphasis on translating technology consumption into outcomes that can be recognized financially. In addition, the Foundation highlighted an upcoming December 8 CFO executive forum operating under the Chatham House Rule, creating an important venue for finance leaders to wrestle with AI cost attribution, revenue, and value.
2. Tokenomics Is About Production, Consumption, And Value, Not Tokens Alone
The emerging definition of AI tokenomics entails converting energy and capital into AI capabilities, consuming that intelligence, and realizing measurable business value. Therefore, infrastructure, data centers, software, labor, and outcomes all sit inside that economic system.
3. AI Has A Value Problem More Than A Cost Problem
The State of Tokenomics 2026 study encompasses 472 respondents across 11 industries and $4.6 trillion in respondent revenue. More importantly, its bigger question is whether enterprises can connect AI consumption and investment to measurable business outcomes. That changes the conversation from “How cheaply can we run AI?” to “What business value did the investment produce?”
4. Multimodel AI Makes Economics An Architecture Decision
Enterprises no longer consume AI through one channel. In fact, the study finds that 96% use frontier models, 87% cloud providers, and 64% AI embedded within existing products, with half operating across at least four of six AI channels. As AI estates fragment, model choice, routing, architecture, performance, and economics increasingly become integral in making the right architecture decision that optimizes the initiative.
5. Model Routing Becomes A Value Lever
Model routing provides a mechanism for matching workloads with appropriate models rather than automatically sending every task to the most capable or expensive option. Accordingly, the summit materials report that 86% of companies are evaluating or using model routers. The emerging opportunity is dynamic optimization across cost, performance, risk, and value.
6. FinOps, IT Financial Management, And Tokenomics Are Converging
AI crosses traditional technology management boundaries. Specifically, FinOps understands consumption and optimization; ITAM brings assets, contracts, licenses, and entitlements; information technology financial management (ITFM) connects technology economics with financial management; and tokenomics extends the lens into AI production, consumption, and value. As a result, the conversation broadens to technology value management rather than replacing one discipline with another.
7. FOCUS Is Emerging As AI’s Financial Data Layer
Cost and value management need normalized data. In that context, FOCUS is an open specification that normalizes billing datasets across AI, cloud, data center, and vendors to reduce complexity and unify technology value. It provides an important foundation for connecting cost, usage, pricing, attribution, and, eventually, financial accountability. The next challenge, however, is getting that data into language that finance understands: business units, cost centers, products, allocation rules, the chart of accounts, and ultimately the general ledger. The briefing explicitly raised mapping total direct and indirect costs to the general ledger and chart of accounts to reduce manual month-end allocation.
The Bottom Line: The Destination Is AI Value Management
Ultimately, the summit’s message is bigger than tokenomics. Enterprises need to connect production to consumption to cost to value. TCA provides a more complete denominator. Meanwhile, FOCUS helps standardize the underlying data. FinOps, ITAM, ITFM, and tokenomics contribute complementary disciplines. Finally, CFO engagement brings financial accountability. Therefore, the next frontier is AI value management: connecting the total cost of AI to the measurable value that AI creates. Forrester’s AI Value Matrix may help enterprises measure what matters.
