Integrating the EVM with AI Protocols for Blockchain-based Decision-making

Building on prior entries about AI-enhanced DAOs, advanced smart contracts reasoning, and AI-powered predictive markets, this article explores the integration of AI protocols with the Ethereum Virtual Machine (EVM). With over $100 billion Total Value Locked (TVL) on the Ethereum blockchain, the push to integrate AI with blockchain-based decision-making is gaining significant attention.

This article will first explore the EVM, defining its role and operational mechanics. We’ll then delve into how AI integration can optimize smart contracts within dApps, DAOs, and DeFi through predictive analytics, machine learning, and dynamic decision-making using real-time EVM data. Finally, we will address the limitations of merging AI protocols with the EVM, propose potential solutions, and consider the future possibilities that AI protocols on the EVM might unlock.

Breaking Down the EVM

It is difficult to overstate the importance of the EVM to blockchain. As the world’s largest decentralized cloud computing engine, it executes the smart contracts that underpin the core deterministic infrastructure of all dApps, DAOs, and DeFi protocols on the Ethereum blockchain. The EVM enables self-executing programs based on code rather than approval from a third-party auditor, which establishes the trustless value of smart contracts and blockchains in general.

How does the EVM work?

Smart contracts are written in programming languages like Solidity, compiled into bytecode (a binary programming language of ones and zeros) and deployed to the EVM. The EVM processes this bytecode, performs computations, executes commands, and updates the blockchain with the contract’s new state.

.By executing code exactly as programmed, irrespective of the node, the EVM ensures deterministic outcomes that foster and reinforce trust within the network. Nodes are crucial to the EVM's operation; they run implementations of the Ethereum protocol software, validate transactions, and execute deployed smart contracts.   

Gas is an essential mechanism on the EVM that rewards nodes for executing transactions. Gas pricing helps provide security to the network by pricing out infinite loop attacks that can congest the network, inflate gas costs, and render regular transactions inefficient and expensive.

Nodes prioritize transactions with higher gas prices for inclusion in blocks; the more complex a contract, the more gas is needed for its execution. Contracts involving complex calculations, large datasets, and loops are the most gas-intensive. This system encourages efficient smart contract coding to minimize gas usage and reduce costs.  

Improving EVM with AI protocols

Introducing AI protocols on the EVM will have far-reaching impacts for dApps, DAOs, and the entire DeFi ecosystem, many of which we have explored in other blog posts. One of the most impactful and easy-to-integrate AI applications is to upgrade the UIs of dApps that interact with smart contracts in DAOs and DeFi protocols. AI-powered chatbots can simplify these interfaces, removing entry-level barriers and onboarding more users through familiar systems. Other dynamic applications that could establish the groundwork for future autonomous agentic operations within DeFi protocols and DAOs include:
Machine Learning-based Contract Execution

In earlier articles, we highlighted the importance of quality datasets for building AI models. Blockchain technology’s immutable distributed ledgers allow for some of the best opportunities for the development of AI models because of the data quality. The EVM provides a unique opportunity as the largest decentralized cloud execution layer with an ever-expanding set of smart contracts for AI models. Models can continue to incorporate new transaction data to enhance accuracy and development.

This application of continued machine learning could be applied to create increasingly accurate DeFi protocols for maximizing yield optimization, rebalancing lending contracts, or earlier detection in shifts to market sentiment while providing real-time adjustments. Within DAOs, machine learning models could learn governance preferences based on previous votes, determine member sentiment, and create dynamic proposals. This would establish a more efficient DAO model where issues could be addressed and resolved at a fraction of the current rate.

Predictive Analytics

We previously explored AI's role in improving yield optimization and flash loans across DeFi protocols within investment DAOs. AI integration allows for the creation of dynamic models that can leverage Natural Language Processing (NLP) to identify market trends and respond in real time. The EVM remains the world’s largest decentralized compute engine with access to unprecedented transaction data.

AI protocols can leverage this deep quality data resource to establish some of the most accurate predictive blockchain native models. AI protocols could also leverage the EVM’s historical data for further security enhancements and early identification of malicious attacks through advanced anomaly detection.

Dynamic Decision-making and Routing Optimization

Introducing AI protocols could enable dynamic decision-making and open the door to autonomous agents across the EVM. This is important for solving scalability issues by creating autonomous, efficient route optimization AI protocols. Protocols could analyze multiple blockchains' congestion, block space, and gas fees to determine the most economically viable route to execute.

Dynamic decision-making could also come in the AI protocols of investment DAOs, which can adjust smart contract parameters to maximize return or better align with the DAO's goals, rather than waiting for a collective governance vote from its members.

AI Protocol Limitations on the EVM

The more complex the smart contracts become, the higher the gas fees rise. As fully integrated AI protocols become more dynamic, autonomous, and reinforced, network congestion increases and gas fees rise. This ultimately creates a scalability problem with AI integrations becoming too large and expensive to justify their execution. Fortunately, this scalability issue can be resolved through novel layer two rollup solutions like Arbitrum, mainnet solutions like sharding, dynamic AI routing, and hybrid oracle operations like Chainlink.

The scraping of blockchain data across EVM smart contracts for use in enhancing machine learning AI protocols raises the issue of privacy.  Someone’s privacy being accidentally transgressed  during the output could dox a person’s identity. Fortunately, the introduction of decentralized IDs, zero-knowledge proofs, and federated smart contract applications can increase the protection of user data.

Conclusion

The EVM provides essential infrastructure to the blockchain industry and has demonstrated why it remains the dominant decentralized cloud computing engine on the market. The transition from a proof of work to a proof of stake consensus mechanism in 2022 further entrenched the EVM in the web3 ecosystem, and its integration with AI protocols is the next stage.