Tokenomics is AI economics: the discipline of turning energy and capital into AI, then using that AI well enough to show business value. It's not the crypto kind. Its first rule sounds odd for something named after tokens: don't count tokens. Count what they bought.
That's the pitch from J.R. Storment, executive director of the new Tokenomics Foundation at the Linux Foundation, in a five-minute keynote at AGNTCon + MCPCon on October 22. He opened with two questions for the room. How many of you know what your agents are costing you? A few hands. How many can tie that to value and ROI? One, at the back.
Energy in, value out
His slide gives the definition: take energy and capital, put them through chips to create intelligence, consume that intelligence efficiently, and drive measurable business value at the other end. His shorter version: energy creating intelligence driving value.
The definition is still a draft. The Linux Foundation launched the Tokenomics Foundation on August 4 to write open, vendor-neutral standards, benchmarks and best practices for the economics of AI, and it works closely with the FinOps Foundation.
Energy creating intelligence driving value
- Energy and capitalgo in
- Intelligenceis created
- Business valuemeasurable, at the other end
DraftVersion 0.5.2, dated September 1, 2026. It will move.
Cost per outcome beats cost per token
Token counts are not the important value metric, he said. Even cost per token isn't right. Cost per task is better, cost per outcome is better still, and what you finally need is business value from the total cost of AI, not just the cost of tokens.
A token count tells you how busy the models were. It can't tell you whether the work was worth doing. My TokenOps note has the other half of this problem: why the cost of one task can grow much faster than the number of turns it takes.
From counting tokens to business value
- SkipToken countNot the important value metric
- Not rightCost per tokenEven this is not the right one
- BetterCost per taskWhat one piece of work cost
- BetterCost per outcomeWhat one result cost
- The pointBusiness valueFrom the total cost of AI, not just tokens
The bill is more than the model
The total cost of AI includes everything under the models. He listed what agents quietly pull in: idle compute, storage, network, data, and often licensing and software. Then labor: your time, your colleagues' time and the company's time to manage all of it, plus what it does to hiring and staffing plans over time.
A token meter sees none of that below the line. My own Meterlex prices every model call from the session logs, which is exactly the visible slice. Useful, and only the top of the answer.
Above the line a meter sees it. Below, it does not.
- Idle compute
- Storage
- Network
- Data
- Licensing and software
- LaborYour time, your colleagues' time, the company's time to manage it, and the effect on hiring and staffing plans
The business counts something else
The business, he said, does not care how many agents you have, how many tasks they finish, how many lines of code you write, how many PRs you ship or even how many features. It cares about revenue, outcomes, and how the organization changed. And that side is less cool than what you can ship with an agent this afternoon, which is why it's talked about less.
He sees a pattern from cloud coming back. There it went too expensive, so people pulled workloads back: repatriation. With AI the first signs are there already, in the stories moving from token maxing to how we realize value on the other end.
What we count
The shipping
Agents
Tasks accomplished
Lines of code
PRs shipped
Features shipped
vs
What the business counts
The change
Revenue
Outcomes
How the organization changed
Cloud, again?Too expensive, pull it back. He sees the first signs with AI.
Three layers, and a gap at the end
The Foundation looks at the whole supply chain, in three layers. Production is the data center side: energy, chips, the AI factories and the models. Consumption is how well we use what comes out: the KV cache, prompt engineering, model selection, demand forecasting, even the cost of the harness. Value is not only cost per outcome or intelligence per watt but business impact, including pricing models and margins.
Their first survey, the State of Tokenomics, ran in September. Plenty of respondents were confident they could see their spending. Very few were confident they could show value on it to the person who signs off the AI budget, the CFO. He called that a real disconnect. The talk gave no figures and I haven't read the report, so I won't quote any.
1 · Production
- Energy
- Chips
- Data centers
- AI factories
- Models
2 · Consumption
- KV cache
- Prompt engineering
- Model selection
- Demand forecasting
- Cost of the harness
3 · Value
- Cost per outcome
- Intelligence per watt
- Business impact
- Pricing models
- Margins
First survey: who is confident
Seeing the spendPlenty
Showing the CFO the valueVery few
What the community is building
Work is going on across all three. He named caching optimization, model routing and gateways, and Big T, a riff on Big O for tokens. On the Foundation's site Big T writes token growth as T of n times k times a: n is requests or input size, k is model calls per request, a is agent depth. The page lists more levels between them; I've only checked the two ends and the default:
| Level | What it is |
|---|---|
| T of 1 | A cache hit, no model call at all |
| T of n | The healthy default: one call per request |
| T of infinity | Unbounded: a loop with no termination condition |
The page's advice is to bound depth and add budgets. The launch announcement adds two more items of planned work: token cost telemetry in FOCUS, the FinOps cost-reporting format, and value measurement that starts with the share of work completed without human involvement.
All of it, he said, cascades into one job: take what your AI did and show the business in terms it recognizes. Revenue. Cost avoidance. Capacity gain. Labor augmentation. Speed to market, and not just code to market.
Work under way
- Caching optimization
- Big T: Big O for tokens
- Model routing and gateways
What the business hears
- Revenue
- Cost avoidance
- Capacity gain
- Labor augmentation
- Speed to marketnot just code to market
The jobShow the impact of your AI in terms the business already uses.
Try it
The Foundation's conference in San Diego, Tokenomicon with FinOps X, runs June 7 to 10, 2027. Before then:
- Watch the five minutes: the keynote on the AGNTCon livestream, starting at 39:41.
- Read the Foundation's page: tokeneconomics.com has the definition, the three layers and Big T.
- Read the launch: the Linux Foundation's announcement.
- Read the cost side in detail: TokenOps, why a 40-turn agent costs 16×, not 4×.
Next time you open an AI bill, write one outcome next to it in the CFO's words. If you've managed a real cost per outcome, I'd like to hear how you defined the outcome.
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