# Interoperability For on-chain AI to thrive, it must have access to a wealth of data spanning various chains, decentralized applications, and oracles made available through interoperability protocols. Access to a diverse set of cross-chain data sources empowers AI models to engineer features from disparate sources, enhancing their capabilities and potential impact. For example, reputation AI models should be able to leverage historical data from multiple chains to improve their classification of whether a certain address is an authentic user or an airdrop-farming bot. Similarly, DeFi risk models should be able to leverage a wide variety of cross-chain data sources for more accurate risk assessments of the market. Interoperability unlocks a vast reservoir of data that facilitates the operation of on-chain AI. Vanna is leveraging cross-chain messaging features for the chain. This will allow a variety of dApps across a multitude of chains to all be able to access the compute that Vanna has to offer. Traditional chains like Ethereum or Arbitrum will be able to leverage cross-chain contract calls to inference models stored in Vanna's filestore on the Vanna network. The utility of on-chain AI would also be limited if it did not have access to programming interfaces that allowed it to operate in a multi-chain fashion. Providing access to a large number of chains and decentralized applications allows these models to perform more actions and simply have more impact.
Primarily, we foresee two types of cardinalities of on-chain AI interactions for increased scalability: one-to-many which allows the source chain to run on-chain inference and expose the result to multiple chains via a view function that can be accessed via interchain read queries, and many-to-many which allows parties on any chain to invoke cross-chain smart contract calls to run inference on the source chain in an ad-hoc fashion. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.vannalabs.ai/vision/interoperability.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.