Proof of Personhood at Scale

The internet is about to be flooded with agents that are indistinguishable from people. Almost everything online - accounts, votes, benefits, attention - assumes one human behind one identity. Proving uniqueness usually means surveillance: collect everything, compare everything. The problem is doing it the other way around - verifying that someone is one real human while learning as little about them as possible.

Machine learning under physics-grade constraints. Models whose failure modes are characterized before they ship, because at this scale 'mostly right' is wrong for millions of people. Privacy is a design input, not a compliance step: the systems are built to verify uniqueness without building a profile of the person being verified.

Production ML inside World's verification stack - learning systems doing real work, for real people, where the cost of being wrong is measured in trust rather than benchmarks.