AI today functions as a layer of intelligent processing over data, using models that learn patterns to automate decisions, optimize processes, and generate predictions; what is interesting is that when you combine it with tokens on the blockchain, a kind of “AI economy” appears, where tokens are used to pay for computing, access to models, datasets, or even incentivize contributions (like training models or providing data), in addition to enabling decentralized governance. In practice, this allows the creation of platforms where AI does not depend on a single company, but is distributed among users who interact and are rewarded, although technical and security challenges also arise, such as data manipulation, adversarial attacks, or the malicious use of models, so the balance between decentralization, incentives, and control is the critical point of this type of system.

Imagine a decentralized platform like an "AI marketplace": a machine learning model (for example, one that detects fraud). Instead of hosting it on your own server, you upload it to a decentralized network. Users can make requests to that model (e.g., analyze transactions), and each time they use it, they pay with system tokens. These tokens are automatically distributed: a part for you (the creator of the model), another for the nodes that provide computing power, and another for those who contributed data to train it. Furthermore, if the platform has governance, the token holders vote on which models to prioritize, how to adjust fees, or which datasets to accept.