Econometrics for your tokens
Stop guessing what AI costs. Start measuring it.
Tokenometrics brings the discipline of econometrics to AI spend: the quantitative analysis of how your tokens are actually consumed — turning raw usage into empirical answers about cost, value and what to do next.
// Start metering before token prices do the deciding for you.
The mandate is real
Finance leaders are already on the hook. The numbers don't lie.
65% of finance leaders are dedicating 10%+ of their 2026 budget to AI and automation — yet majority fears it's a bubble. The gap between commitment and accountability is where Tokenometrics lives.
"You can't reduce payroll if the tokens are costing more than the payroll itself. Have we got more productive, or are we just burning and spending a lot of money on tokens? Because it's a fashion fad right now."
Dheeraj Pandey · CEO, DevRev (ex-CEO & co-founder, Nutanix)"Tokenmaxxing as a measure of productivity is basically flawed. It's a great measure of input, but not a great measure of output or outcomes."
Neel Sundaresan · GM of Automation & AI, IBM (IBM Bob)"The companies that confused token consumption with engineering will spend 2026 explaining their AI line items to a CFO who has stopped finding the dashboard charming."
Kylan Gibbs · CEO, Inworld AI// — "Why 'Tokenmaxxing' Is Out And 'Valuemaxxing' Is In" — Tim Keary, Forbes (Jun 2 2026)
// — "Tokenmaxxing Explained: Why AI Use Is Becoming a Workplace Status Symbol" — Built In (Apr 22 2026)
// — "AI cost crisis hits tech giants as employee tokenmaxxing backfires" — Tom's Hardware (May 2026)
// — "Tokenmaxxer: AI should cost as much as your rent" — Axios (May 13 2026)
// — Emburse "AI-Powered Finance: New Practices, ROI and Future Outlook" (May 2025)
The quantitative analysis of actual AI usage — based on the concurrent development of theory and observation, related by appropriate methods of inference.
Every token, prompt and agent call, metered as it happens — the raw data of how AI is really used.
Statistical methods attribute spend to tasks and isolate what actually drives cost and value.
A working model of your AI economy — which models, which processes, which dollars pay off.
The blind spot
Everyone adopted AI overnight. Nobody is counting.
Prompts fly across every team, but the tokens behind them vanish into one undifferentiated cloud bill. Econometrics turned hunches about the economy into measurable laws — your AI spend is still stuck at hunches.
The trend already points up — and to the right.
Reasoning models, longer context and multi-agent chains multiply tokens per task. Prices are viable today; the baseline you collect now is the only thing that makes next year's bill defensible.
Where's the dumpster fire — and where's the win?
Plot every workflow by what it burns against what it returns. Tokenometrics turns the gut feeling into a map you can act on.
Quiet winners
Cheap models doing real work. Scale these up.
Worth the spend
Expensive but it earns it. Protect, don't cut.
Background noise
Harmless today. Watch the trend line.
The dumpster fire
Premium tokens, thin output. Refine or kill it.
How it works
From invisible spend to a decision in three moves.
Observation, inference, theory — the econometric method, applied to tokens. Drop in once; you get the empirical answers.
Meter every token
A lightweight proxy or SDK tags each call with task, team, model and prompt — no app rewrites. Every token is accounted for the moment it's spent.
Attribute & benchmark
Spend rolls up by task, workflow, team and outcome. Compare model choices on the same job and see cost-per-result, not just cost-per-million-tokens.
Act on the numbers
Route the right model to the right job, retire the burn, double down on what pays. Build the historical baseline that makes next year's budget defensible.
The product
The token P&L, live.
One view that finance and platform teams finally agree on — what AI costs, per task, per model, per dollar of output.
| Workflow | Model | Tokens / task | Cost / task | Output quality | 30d spend | Verdict |
|---|---|---|---|---|---|---|
| Contract review agentlegal · multi-agent | opus-4 | 412k | $6.18 | $48.2k | refine | |
| Support triagecx · classify + route | haiku-4 | 3.1k | $0.004 | $2.9k | scale up | |
| Sales deck draftingrevops · generation | sonnet-4 | 88k | $0.61 | $19.4k | keep | |
| Codebase Q&Aeng · RAG | sonnet-4 | 141k | $0.98 | $22.7k | watch | |
| Meeting summarizerops · summarize | opus-4 | 61k | $0.92 | $14.1k | downshift |
Early access
Start counting today. Decide on data tomorrow.
Token prices are still survivable. The orgs that win the next two years are the ones building their per-task baseline right now. Join the waitlist and meter your first workflow this week.