Tokens measure activity. If your financial strategy for AI is built on counting them, you're optimizing for the wrong metric.
Here's the problem: your support agent uses 50,000 tokens to resolve a customer ticket. Your finance dashboard shows the cost.
But did it actually solve the problem?
Did the customer still churn?
Did it introduce a compliance risk?
The token count tells you nothing about whether you created value or burned budget.
AI is pushing enterprises toward a new economic layer. One built on context, not activity.
Tokens Are Not the Currency of Value
Providers meter AI in tokens because it's easy for them. But companies create value through outcomes. A token can't tell you:
- Whether an answer changed a decision
- Whether it saved an engineer three hours
- Whether it introduced unacceptable risk
Tokens are kilowatt-hours, they measure consumption, not results. Value lives in what actually gets accomplished.
When Ashu Garg wrote about context graphs, he captured something important: next-generation AI systems won't just consume tokens, they'll be built on organizational memory. Decision history. Precedent. The record of what happened and why it happened.
That shift from activity-based to outcome-based economics needs different infrastructure. You can't manage what you can't measure, and right now most companies can't measure outcomes at all.
Enterprises Run on Decisions
Every day, teams make decisions:
- Which features to ship
- Which customers to prioritize
- Which risks to accept
AI increasingly participates in these decisions, sometimes autonomously. But without context, it's just expensive noise. You get automation without governance. Spend without accountability. A product team might know their AI feature consumed 10M tokens last month, but they can't tell you how many successful customer outcomes it delivered.
Most organizations can report token consumption. Almost none can report what those tokens accomplished.
Context Graphs Point to the Next AI Economic Control System
For decades, we've had systems of record for different business layers:
- CRM captured customers
- ERP captured finances
- Cloud platforms captured infrastructure
AI creates a new requirement: where is the control system for reasoning?
Where do we store decision traces? Policy exceptions? Organizational precedent? When an AI agent decides to escalate a support ticket or approve a refund, where does that decision live? Context graphs are the answer. They capture how decisions get made, including the reasoning that produced specific artifacts.
Without this layer, AI spend stays invisible. Costs accumulate without attribution. When the CFO asks what the AI investment accomplished, no one has an answer.
FinOps for AI Is a Context Problem
AI spend doesn't show up in one line item. It's distributed across:
- Internal workflows and automation
- Experimentation and R&D
- Agent loops running autonomously
- Product features serving customers
When costs spike, the important questions aren't about tokens, they're about context. Why did we use those tokens? What outcome did they drive? Was it intentional? Did it create value?
To manage AI economics, you need to manage context. That means capturing every AI transaction with full business context: who triggered it, why it ran, what outcome it served. Not just logs but financial-grade telemetry that connects code to business impact in real time, with the precision boards actually require.
The Future Metric Is Cost Per Outcome
The winning strategy won't be optimizing cost per token. It will be optimizing:
- Cost per successful task completed
- Cost per decision accelerated
- Cost per customer outcome improved
Getting there requires connecting AI activity back to intent and business decisions. That's hard to do when AI makes autonomous decisions at machine speed. Without that connection, you're spending without knowing whether it's an investment or waste. And that compounds fast.
We call this Agent Debt: unmeasured cost, unassigned liability, ungoverned autonomy. It's technical debt meets financial exposure, compounding invisibly until someone finally asks what all this AI spend actually accomplished.
Where This Goes
AI systems will generate enormous work volumes at a scale far beyond what humans can produce. The organizations that succeed won't be the ones generating the most activity. They'll be the ones who can answer one question in business terms: what did this accomplish?
The next infrastructure layer will be defined by systems that make AI:
- Accountable – every action tied to an outcome
- Governable – policies enforced in real time
- Measurable – cost and value visible at every layer
- Grounded in context – decisions traceable to intent
That's the economic layer AI needs. Not just observability. Economic intelligence.
Every action tied to an outcome. Policies enforced in real time. Cost and value visible at every layer.
