The convergence of artificial intelligence (AI) and Web3 technologies is reshaping how decentralized applications (dApps), smart contracts, and blockchain ecosystems operate. While traditionally rigid and deterministic, smart contracts are now beginning to evolve into adaptive, context-aware programs thanks to the infusion of AI. This evolution could radically change how automation, decision-making, and trust are handled on-chain.

Smart contracts, as they were introduced on Ethereum and other Layer 1 blockchains, are self-executing contracts with the terms of the agreement directly written into code. While they provide transparency and remove the need for intermediaries, they are also limited by their deterministic nature and dependence on static data inputs.

This rigidity means they cannot handle nuanced decision-making, uncertainty, or dynamically changing environments — factors that are common in real-world scenarios.

AI, especially machine learning models, offers capabilities like pattern recognition, anomaly detection, and predictive analysis. When combined with smart contracts, these capabilities can enable:

• Adaptive Logic: Contracts that change behavior based on historical or real-time data.
• Fraud Detection: Using AI to identify abnormal behaviors or transactions.
• Dynamic Pricing: Predicting market trends or user behavior to adjust terms automatically.
• Risk Scoring: Evaluating counterparties’ on-chain behavior for risk assessment.

To integrate AI into blockchain workflows, one must bridge the gap between on-chain smart contracts and off-chain computation. This is where oracle networks like Chainlink, Witnet, and Band Protocol play a critical role.

AI models are often too large and compute-intensive to run on-chain. Oracles can query off-chain AI models, aggregate the results, and feed them back into smart contracts. Emerging tools like…