Decentralized AI
When working with decentralized AI, a networked approach that merges artificial intelligence models with blockchain‑based distribution and incentive layers. Also known as AI on the blockchain, it lets anyone run, train, or query models without a single controlling entity. This architecture hinges on three core pillars: machine learning, the algorithms that learn patterns from data, blockchain, the immutable ledger that records model provenance and execution results, and tokenomics, the economic rules that reward contributors and fund ongoing model improvement. Together they create a self‑sustaining ecosystem where data providers, compute miners, and AI developers all earn tokens for their work.
Why governance matters: the role of DAOs
In a truly decentralized AI system, decisions about model upgrades, data curation policies, and fee structures aren’t made by a single company. Instead, a DAO, a decentralized autonomous organization that uses on‑chain voting to manage protocol parameters holds the reins. The DAO model ensures that incentive alignment stays transparent: token holders propose and vote on changes, while smart contracts execute approved updates without human interference. This governance layer reduces the risk of central points of failure and makes the network adaptable to new research breakthroughs. For developers, it means access to a shared pool of compute resources and data, while investors can track token performance linked directly to the utility and adoption of AI services.
Below you’ll find a curated set of articles that unpack each component of decentralized AI. We cover everything from multi‑VM blockchains that can host both EVM and MoveVM smart contracts, to fast‑finality mechanisms that keep AI inference speedy and secure. You’ll also see how token‑based governance shapes the future of AI models, and why multi‑factor authentication is becoming a must‑have for protecting AI‑driven assets. Whether you’re looking to build a new AI dApp, evaluate token economics, or simply understand how decentralization changes the AI landscape, this collection gives you practical insights and real‑world examples. Dive in and see how the pieces fit together.
