Machine learning & AI on the blockchain

The intersection of machine learning, artificial intelligence, and blockchain technology represents one of the most ambitious frontiers in computing. DFINITY, now known as the Internet Computer, is among the most prominent projects attempting to bring decentralized computation, including AI workloads, to blockchain infrastructure.

DFINITY was founded by Dominic Williams in 2016 and launched its Internet Computer mainnet in May 2021. The project aims to extend the public internet with a decentralized computing platform capable of hosting software, smart contracts, and web applications at scale. Unlike traditional blockchains that are limited to simple token transfers and basic smart contracts, the Internet Computer is designed to run full web applications, including frontend code, directly on-chain. It uses a novel consensus mechanism called Chain Key Technology and is governed by the Network Nervous System (NNS), an algorithmic governance framework that allows token holders to vote on protocol upgrades and network configuration.

The relationship between AI and blockchain is multifaceted. On one hand, blockchain can provide infrastructure for decentralized AI: storing and distributing machine learning models, creating marketplaces for data and compute resources, and enabling verifiable AI inference where users can confirm that a specific model produced a specific output. On the other hand, AI can enhance blockchain systems through more efficient consensus algorithms, automated smart contract auditing, and predictive analytics for network optimization.

Several projects have explored this convergence. SingularityNET, founded by AI researcher Ben Goertzel, operates a decentralized marketplace for AI services on the Ethereum and Cardano blockchains. Developers can publish, discover, and monetize AI algorithms through the platform, creating a peer-to-peer economy for artificial intelligence. The project's native token, AGIX, facilitates transactions between AI service providers and consumers.

Fetch.ai takes a different approach by building an AI-native blockchain where autonomous agents can perform tasks such as supply chain optimization, energy grid management, and decentralized finance operations. These agents use machine learning to make decisions and negotiate with other agents, creating a network of autonomous economic actors. Fetch.ai merged with SingularityNET and Ocean Protocol in 2024 to form the Artificial Superintelligence Alliance (ASI), consolidating several major blockchain-AI projects under one umbrella.

Ocean Protocol focuses specifically on data markets, enabling data owners to monetize their datasets while maintaining privacy through compute-to-data technology. Rather than sharing raw data, computation is brought to the data, allowing AI models to be trained on sensitive datasets without exposing the underlying information. This approach addresses one of the fundamental challenges in AI development: access to quality training data while respecting privacy regulations.

Decentralized machine learning addresses the challenge of training AI models without centralizing data. Federated learning, where models are trained locally on distributed devices and only model updates are shared, is being combined with blockchain to create verifiable, incentivized training networks. Projects like Bittensor have built decentralized neural networks where participants contribute compute resources for model training and inference, earning rewards based on the quality of their contributions.

The Internet Computer (DFINITY) has positioned itself to support AI workloads through its canister smart contracts, which can store and process significantly more data than traditional smart contracts on Ethereum. In 2024, DFINITY announced AI-focused features including on-chain inference capabilities, allowing machine learning models to run directly on the Internet Computer without relying on external servers.

Privacy-preserving AI is another area where blockchain technology shows promise. Zero-knowledge proofs, homomorphic encryption, and secure multi-party computation can be combined with blockchain to enable AI training and inference on encrypted data. This is particularly important for sensitive domains like healthcare and finance, where data privacy regulations restrict how information can be used for model training.

Despite the excitement, significant challenges remain. Running complex AI models on blockchain infrastructure is computationally expensive, and current blockchain throughput is far below what centralized cloud providers offer. The economic incentive structures for decentralized AI are still being refined, and questions about model quality, liability, and governance in decentralized settings are largely unresolved. Nevertheless, the convergence of AI and blockchain continues to attract substantial investment and research attention, driven by the vision of a more open, transparent, and democratized AI ecosystem. As AI capabilities become increasingly concentrated among a small number of technology conglomerates, decentralized approaches offer a meaningful counterbalance, ensuring that access to intelligence is not gated by a few dominant providers.

AI, ML, Dfinity, Blockchain, Governance