CONCEPT
The Discovery Problem
The structural challenge of finding valuable artifacts within an abundance that has overwhelmed every existing discovery mechanism — the specific form the lolcat problem takes at the scale of the second cognitive surplus.
The discovery problem is the challenge of identifying valuable contributions within creative outputs too numerous for any reader, reviewer, or recommendation system designed for previous scales to process. The first
cognitive surplus produced millions of blog posts, videos, and wiki edits; the discovery mechanisms built for that scale — search engines, social curation, collaborative filtering — were adequate because the volume, while large, remained within the capacity of indexing and ranking algorithms operating on textual and behavioral signals.
The second cognitive surplus will produce billions of software artifacts whose character differs fundamentally from the first surplus's outputs: they are functional rather than expressive, their value is use-contextual rather than broadly comparable, and their quality cannot be assessed without testing that exceeds the capacity of any lightweight review system.
In The You On AI Field Guide
The existing discovery mechanisms fail for second-surplus artifacts for three converging reasons. First, volume: when a billion people can build software, the signal-to-noise ratio