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AI set to benefit from blockchain-based data infrastructure


With the development of decentralized protocols, apps, and business models since the release of the Bitcoin white paper in 2008, AI and blockchain are gaining industry momentum rapidly. Yet, for useful AI applications, a solid data infrastructure is vital. To ensure the highest quality data and AI applications, the data value chain needs to be approached completely. With the use of decentralized technologies, Web3 technologies may take advantage of artificial intelligence in fields like data storage, data transmission, and data intelligence.

Relationship between AI and data storage

The future of decentralized AI depends on decentralized data storage since centralized blockchain initiatives could have governance failures, legislative restrictions, or infrastructural problems. Decentralized storage solutions are required because of Ethereum’s “Merge” and dependency upon Amazon Web Services (AWS) cloud storage. Using several servers and computers throughout the world, decentralized storage entails employing the private key as the first line of defence when storing data. High-quality data storage can improve AI for practical use cases, even though decentralized storage is still in its early stages.

Transferring of Data

In the data value chain, decentralization can enhance data transfer and transmission, especially when oracles are involved. Oracles integrate blockchains into external data sources so that smart contracts can decide how to handle transactions. Oracles, however, are weak because they lack decentralized infrastructure and standards for data transport. Decentralized oracle networks (DONs), which include many nodes that provide high-quality data and end-to-end decentralization are a feasible solution for safe data transport. A wide range of oracles, including input, output, cross-chain, and compute-enabled oracles, support the data transmission method. Protocols also enable decentralized oracles, and platforms provide DONs with secure APIs for accessing off-chain data.

Data Intelligence

All infrastructure activities that store, share, and process data are fulfilled at the data intelligence layer. AI-powered blockchain applications can still use traditional APIs to get data. Yet doing so would increase centralization, which might reduce the ultimate solution’s resilience.

Yet, several blockchain and cryptocurrency apps are using machine learning and artificial intelligence.

Investing and trading

The fintech industry has employed machine learning and artificial intelligence to offer robo-advisory services to investors. Web3 is affected by AI applications and generates user-specific insights by employing market pricing, macroeconomic data, and social media. Examples include the AI-based trading signal providers Bitcoin Loophole and Numerai, which have an 85% success rate.

People Also read – AI And Blockchain: Benefits Of Decentralized Artificial Intelligence

Market for AI

Smart contracts are used in a decentralized AI marketplace to simplify business activities and transactions between companies, customers, and developers. Developers can set up price plans that include fixed retainer fees, payments according to information transactions, or insights. SingularityNET and are two startups that provide AI technologies and machine learning solutions. These applications make use of blockchain technology to handle smart contract transactions and track performance.

The Benefits of AI for Blockchain

• Enhanced commercial data models

• Internationalized verification methods

• Innovative methods for audits and compliance

• Financial responsibility

• Transparent leadership

• Adaptive retail

• Using intelligent prediction

• Rights to digital intellectual property

How AI Can Thrive With Blockchain

• All stakeholders’ identities can be tracked by employing the blockchain as a certificate authority.

• A permanent record of an AI model’s original purpose

• Make note of the AI model’s ongoing governance, evaluations, and rankings.

• Delivering a single point of reference for all algorithms and training data in the AI model.

• Creating a persistent experiment log for MLOps and AI developers.

• Delivering customer trust logos so that models’ blockchain backgrounds can be instantly verified

• To serve as an AI model’s portable, permanent memory bank.


Together with ChatGPT’s growth, a number of these AI projects have experienced an increase in token prices. Yet, user acceptance is the important litmus test; then only can we be convinced that these platforms aid users with their difficulties. Projects using decentralized data and AI remain in their infancy; however, the early signals are promising.