# Flywheel

* Data Demand Growth:

Browser agents, AI startups, and model builders need clean, structured web interaction data. As more agents deploy, they must purchase access using credits, which are bought with the token and burned. Higher demand drives more burn.

* Token Buy Pressure&#x20;

All data credit purchases go through a one-way token sink. Buyers acquire tokens from the market, exchange them for credits, and credits are destroyed when used. Continuous burn reduces circulating supply, increasing scarcity.

* Contributor Rewards&#x20;

Producers, curators, and verifiers are rewarded from a fixed daily emission pool. As token price rises, real-world value of rewards increases, attracting more and better contributors, which raises data quality and network utility.

* Higher Data Quality

&#x20;Better data attracts premium enterprise buyers and improves agent performance. Quality and diversity raise perceived value per dataset, allowing higher pricing in credits, which deepens the burn cycle.

* Governance Expansion&#x20;

Token holders vote on new dataset schemas, reward weights, and fee splits. As governance value increases with adoption, holding tokens conveys real influence over valuable data markets.

* Treasury Reinforcement&#x20;

A portion of burns and protocol fees flows to the treasury. The treasury funds audits, new schema development, and incentive programs that grow usage further. Treasury valuation (in token and in external assets) backs long-term confidence.
