The Attention Commons
The attention commons is the third flow of the intelligence commons. Attention has always been finite — there are only so many hours in a day, only so many works that a person can evaluate with genuine care — but AI has accelerated the production of output to a degree that threatens to overwhelm the evaluative mechanisms on which quality depends. When anyone can produce a polished essay, a competent design, a functional application in a fraction of the time previously required, the volume of output increases dramatically, and the mechanisms through which the community identifies quality — critical review, peer assessment, editorial judgment — are strained beyond their capacity.
In the AI Story
Attention is subtractable in the most direct sense. Attention given to one piece of output is attention not given to another. When the volume of AI-generated content increases, the fraction of attention available for any individual piece decreases. Each producer's rational decision to maximize output degrades the evaluative environment for all producers, including the producer herself. This is the classic commons tragedy structure at informational scale.
The strain propagates through the quality-assessment infrastructure of entire domains. Editorial capacity at journals, peer review at conferences, curation at archives — the institutions that historically filtered signal from noise operate at capacities calibrated to pre-AI production rates. The capacity cannot be scaled indefinitely; editorial judgment requires time, and time is the scarce resource AI production has rendered scarcer.
The filter economy that emerges under these conditions concentrates value at the evaluative layer rather than the productive one, a shift with consequences for how the judgment economy must be institutionally organized.
Origin
The concept extends Herbert Simon's 1971 observation that information wealth produces attention poverty, applying Ostrom's common-pool resource framework to the specific case of AI-accelerated content production.
Key Ideas
Direct subtractability. Attention given to one piece is attention not given to another; volume increase lowers per-piece allocation.
Evaluative strain. Quality-assessment institutions operate at capacities calibrated to pre-AI production rates.
Classic commons tragedy. Each producer's rational maximization degrades the evaluative environment for all.
Value migration. The scarcity migrates from production to evaluation, reshaping the economic structure of creative work.
Appears in the Orange Pill Cycle
Further reading
- Herbert Simon, "Designing Organizations for an Information-Rich World" (1971)
- Chris Anderson, The Long Tail (2006)
- Ostrom, Governing the Commons (1990)