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Attention shift

Post-Scarcity Content

When making media costs nothing, abundance becomes worthless and the only value left is knowing what to point at.

What is it?

Generation has driven the cost of content toward zero, and infinite supply strips it of value — the feed fills with competent, forgettable, machine-made stuff. What remains scarce, and valuable, is judgement: knowing which output is worth anyone’s time. Whether judgement itself can be automated is the open question.

Why now?

Cognitive Abundance means the intelligence needed to generate convincing text, image and video has become cheap and available to almost anyone. That collapses the barrier that once rationed content — talent, time, budget. Once creation is free, the value moves upstream to whoever decides what deserves attention.

Who’s building it?

Model providers and platforms building the generation tools; curators and filters trail behind them.

Model providersContent platformsCuration toolsTrust & safety teamsRegulators
The full read
Why now, who is building it, where the market goes, and what stays unsettled.
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01 · The market
Who’s in it
Builders — the model providers and the platforms distributing their output.
Buyers — advertisers, publishers and consumers all searching for signal inside the noise.
Gatekeepers — platforms deciding what gets labelled, ranked or filtered out.
What’s at stake
Trust in the feed itself — once anything can be synthetic, the value of verification and provenance both rise.
Advertisers try to avoid placement next to obvious slop.
Publishers now compete on judgement, not on raw volume.
What’s unsettled
Whether curation can be automated at the same scale as generation.
Who gets to define quality once output volume stops mattering.
Whether labelling synthetic content works, or gets ignored like cookie banners.
02 · How the demand matureda progression, not a timeline
Flood
Generation tools spread fast, and cheap output floods every feed and platform.
Fatigue
Audiences grow tired of competent, interchangeable content and start scrolling past it.
Filtering
People and platforms build informal signals — sources, creators, communities — that earn more trust than the rest.
Curation as product
Judgement becomes sellable — a subscription, a filter, a trusted human or algorithmic voice.
03 · Where the market goesthe forks, not a prediction
→Automated or human curation — whether algorithmic filters can substitute for taste, or whether trust requires a person behind the recommendation.
→Platforms could choose to label synthetic content honestly, or quietly let the distinction blur.
→Curation could concentrate in a few trusted gatekeepers, or fragment across countless small, personal filters.
→Value could shift from those who make content to those who select it.
The open question is whether judgement can scale the way generation did, or whether it stays a stubbornly human bottleneck worth paying for.
04 · The Taste Economy deskadjacent markets, moving now
Post-Feed
Subscription fatigue turns cancellation into default.
41% of streamers cancelled a service over subscription fatigue.
CivicScience
Machine Taste
An algorithm curates and narrates your listening.
Spotify’s AI DJ curates and narrates a personalized playlist.
TechCrunch
Borrowed Brilliance
Training data disputes put a price on origin.
Anthropic’s $1.5B author settlement paid $3,000 across 500,000 works.
TechCrunch
05 · The Wire
01
Macquarie and Merriam-Webster both named ‘slop’ 2025 word of year.
TechCrunch ↗
02
Uncurated AI content pollutes peer-reviewed science
American Scientist (via PMC) ↗
03
YouTube’s CEO names cutting AI slop a 2026 priority
CNBC ↗
04
Deezer says over 50% of new uploads are fully AI-generated
Paste Magazine ↗
The panel on this signal
They agreed on
–Generation’s cost collapse is real and irreversible, and it strips ordinary output of most of its value.
–Curation and trust are the scarce resources now.
–Platforms and publishers are already scrambling to build filtering and labelling systems.
The ML systems engineer disagreed on
–Curation as a task is defined by pattern recognition over prior selections, which is exactly the input recommendation models are trained on; there is no structural reason today’s human-judgement layer resists being replicated by tomorrow’s model.
Know more about the method →
✦How this signal was built
01 · Panel
A Delphi panel of 4 AI analysts, each reasoning independently.
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02 · Debate
They challenge and revise each other’s reads over two rounds.
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03 · Evidence
Every claim is tied to a cited, linked source on The Wire.
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04 · Synthesis
A moderator writes it into one read, disagreement and all.