KAITO

Kaito applies AI search and analysis to crypto information, which is scattered across forums, governance discussions, social platforms and documentation with no unified index. The information problem it targets is genuine — most of what matters in this sector is not on any single site.

The information really is fragmented

Protocol decisions happen in governance forums, technical detail lives in repositories and documentation, and sentiment forms on social platforms. No conventional search covers this well, and the effort to follow even a few protocols properly is substantial. Aggregating and summarising it is a legitimate product.

Attention metrics as a data product

The platform measures which projects and narratives are gaining attention, which is information traders act on. It also creates a reflexive dynamic: when attention is measured and rewarded, participants optimise for the metric rather than for substance.

Reward mechanics shape the input

Where contributors earn for content that scores well on attention metrics, the incentive is to produce content that scores well. That produces volume, and it does not necessarily produce insight. Any system rewarding measured engagement encounters this, and it is worth naming rather than assuming quality follows.

AI summarisation carries known limits

Language models summarise fluently and can misrepresent sources, particularly on technical detail and where sources conflict. For financial decisions, a confident summary of a contested question is worse than no summary. Verification against primary sources remains necessary.

What to weigh

A real information problem with a plausible approach, and a reward structure that risks optimising for engagement rather than accuracy. Treat outputs as a starting point for research rather than as research.