From token burn to business output.

Four months into the year, Uber's CTO admitted the company had blown through its annual AI budget. Engineers had embraced Claude Code and Cursor faster than finance could model it. R&D landed at $3.4B for the year. The budget didn't bend. It snapped.

Uber isn't alone. In nine workdays, one Disney employee asked Claude 460,000 questions. Not a person typing. An agent, running while she slept. Meta hit 60 trillion tokens in a month. Visa, 1.9 trillion. Indeed expects this year's AI bill to come in at four times last year's. The conversation is happening at the board now.

The CFO didn't approve any of it. The CFO got the invoice and couldn't tell you what it bought.

That's the part nobody's solving. Helicone shows usage. Langfuse shows traces. Vantage shows cloud cost. Internally, companies build token leaderboards ranking employees by consumption. Meta made one, then quietly took it down. Because dashboards aren't governance, and leaderboards reward the wrong behavior. The engineer who burned the most tokens isn't the most valuable. She might be the one who left an agent running all weekend.

None of it answers the question that matters: what did we get for the spend?

Walk through any AI-heavy company today. Engineering, marketing, support, ops: every team and every agent drawing from the same providers, draining the same shared bill, with no per-team budget, no agent leash, no accountability for what got built. The invoice shows up at month-end with no story attached.

This is what cloud looked like in 2015. Wild and exploding faster than the people paying for it could understand. Every great enterprise category started this way: a control gap widening in public, and one company building the system that closes it.

That system isn't another dashboard. It's a control plane that makes AI accountable.

Q1 starts. Pulse gives engineering a budget: 20M tokens across Claude, GPT, and your internal models, tied to the projects it's funding. Marketing gets 5M. Support gets 8M. Every agent gets a cap.

Then Pulse goes to work. When an agent starts compounding, it gets interrupted before the spike hits finance. When a prompt only needs Haiku but gets sent to Opus, it gets rerouted. When a workflow touches customer data, only approved providers can serve it. Two months in, marketing has used half its quota and engineering has blown through theirs. Pulse moves the slack across, with audit and approval, no Slack threads, no credit cards.

At quarter-end, when the CFO asks what the spend produced, Pulse answers in plain English. 1,240 pull requests merged. 14,000 support tickets closed. 18 priority projects shipped. All tied back to the $42K that paid for them.

Tokens stop being a sunk cost. They become a resource that flows toward output.

The wedge is concentrated. The full GenAI market is $644B. The model and token layer Pulse sits on is already $14.2B and growing fast. A 1–3% control-plane fee on that layer alone points to $140M–$425M of ARR available today. That's before agents multiply consumption, and before every Fortune 500 hires a Head of AI Operations who needs Pulse to do the job.

The bigger prize isn't taking 1% of model spend. It's becoming the system of record for enterprise AI: the place every CFO, CIO, and AI lead goes to answer who used what, for what work, at what cost, and what it produced.

Most people building this category will come from finance, FinOps, or cloud cost tooling. We're coming from a layer below all of them. We design the chips AI runs on.

We see the unit economics of every token before it's ever priced as an API call: what it costs in transistors, heat, joules, silicon area. We know which workloads are wasteful at the metal, which are expensive by accident, and which models companies should be routing toward and away from.

The token economy was always going to break under enterprise load. We happened to be sitting upstream of where it broke first. We got tired of watching companies downstream try to solve it with dashboards.