AI Yield Optimization in DeFi: Maximizing Returns in a Volatile Market

In this entry in our series on the convergence of AI and blockchain technology will focus on the topic of dynamic prediction models and adaptive algorithms, and specifically how they can be applied for yield optimization in DeFi protocols.

This topic is not unfamiliar to those following our previous entries. In our first article, we looked at how AI is transforming the global food supply chain through efficiency gains from early detection and predictive analysis. 

Now that we have a firm grasp on how AI technology operates, we can explore its direct applications and where it can most effectively be used across the DeFi space. Our most recent entry examined AI’s use in optimizing and securing cross-chain interoperability. We see some interesting trends emerging if we apply the same methodology to explore where AI protocols can maximize yield farming returns, considering that DeFi has been historically volatile.

This article will first explain yield farming, adaptive algorithms, and dynamic prediction models and how real-time data can be introduced to enhance DeFi protocols. We will then transition to exploring the role of Yearn Finance and other ML-powered yield optimization protocols before concluding on the potential benefits and risks of such protocols.

Understanding Yield Farming and How to Leverage Your Digital Assets

Understanding yield farming is key to understanding the broader DeFi ecosystem. It refers to the strategy of how users deploy their digital assets into decentralized liquidity pools. Users can leverage their assets by providing liquidity as a lender, borrower, or staker. The process is essential to the success of DeFi because it provides liquidity for trading pairs, allows users to earn easy interest on their token holdings, and allows speculation on the future valuations of digital assets.

Yield farming utilizes smart contracts within decentralized exchanges that the user selects based on the rate of return the smart contract offers. Lenders provide an essential service for the DeFi landscape by establishing the liquidity for trading pairs. DeFi protocols establish the borrowing rate for swaps and trades. The protocols then provide the lender or staker with a portion of the return, similar to how legacy banks provide a savings rate to customers in exchange for lending out their funds.

AI Machine Learning and Adaptive Reinforcement

In our entry on AI risk assessment, we reviewed the process of how NLP and general AI training models work for use in predictive risk assessment. The key to their success was the ability to absorb a constant flow of new information for reinforcement machine learning.

However, AI training models didn’t always work that way. The release of the ChatGPT-3.5 LLM to the public in 2022 ushered in the modern era of AI development, but unlike the newer models that have been developed, the 3.5 version of the popular protocol could not access the internet for its adaptive reinforcement. This limited its ability and quickly became outdated as the information it was trained on’s half-life began to fade.

In the arena of DeFi, where the markets move at an accelerated pace, incorporating the most up-to-date information is critical for its security. When dealing with yield farming, the ability to autonomously reevaluate market rates for borrowers, stakers, and lenders can lead to more efficient yields for everyone.

Leveraging Dynamic Prediction Models for Maximum Returns

With a clearer understanding of how adaptive reinforcement works in AI model training, we can now explain how it can be utilized for dynamic predictions. We have already seen how AI models are incredibly efficient at analyzing massive volumes of data. Where reinforcement machine learning takes over is that it enables the models to adapt to their environment through new information from outputs or external events. The models can then combine their historical data with the new information to create accurate predictions.

Looking at retail sales and the global supply chain, we can see how dynamic prediction modeling can benefit merchants and suppliers enormously. Merchants have always placed a heavy influence on leveraging their sales history to project future income. Incorporating seasonal trends, company business decisions, and direct sales history from their POS service provider has been the standard operating procedure for most businesses. These new AI models enhance these practices because they can also account for external events and black swan events like extreme weather, the outbreak of war, or a hugely popular influencer speaking positively or negatively about their products in real-time. 

Predictive models similarly enable yield farming to track borrowing, lending, and staking rates in real time while continually adjusting for the most economically beneficial outcomes. Before AI models enabled this automation, it was done through an analog decision where the user would have to select the rate they wished to engage with. The automation of the process allows for more dynamic and efficient decision-making.

Real-Time Data and the Future of Web3

There has been no shortage of hyperbole regarding the potential of IoT and Web3, yet much of the early promise has yet to be realized. One of the lingering hurdles for these promises to become reality was identified in our entry on the limitations of the AI revolution. The issue of large independent data silos prevents the integration and interoperability of the technology. Trusted oracles are another pain point that Web3 needs to overcome in the future. Decentralized oracle networks like Chainlink are helping solve this issue by removing single points of failure.

AI adaptive algorithms can assist decentralized oracle protocols further through the implementation of real-time data CDC streaming. This process streamlines the data movement to the decentralized oracles before adding it to the blockchain ledger. AI further enhances this process by efficiently analyzing and sorting the data from multiple sources into one transaction before it is moved across CDC streaming services. AI could be the missing link for IoT and Web3 to realize their potential through efficiently analyzing the data from multiple sources to be streamed directly to oracles and onto the blockchain in real-time. This could allow for the development of highly interactive IoT devices while still maintaining the decentralized nature of the blockchain.

In DeFi, the implementation of real-time data allows for the development of more efficient markets. Yield and staking rates that are reacting in real-time to each demand. This helps stabilize the market and reduces the fallout from periods of high volatility.

Yearn Finance ML-Powered Yield Optimization 

Yearn Finance, an Ethereum decentralized finance protocol that leverages AI to maximize user yield returns, is driving the future of ML-powered yield optimization. The Yearn Vaults are one of the protocol's most popular options, allowing users to deposit their assets into a “savings” account and earn yield passively, similar to how a legacy bank savings account operates. The main advantage of leveraging a vault compared to traditional finance is the security and transparency that the protocol offers through being on a blockchain.

The protocol was one of the first to adopt AI learning models to quickly identify the best rate of returns for their users, allowing swift allocation of resources and identifying the most profitable trades. However, Yearn Finance is also not without risk. There is always a risk associated with any custodial protocols where the user gives up control of their digital assets. The collapse of the centralized yield-earning platform Celcius is a stark reminder of this fact. The crypto market is also extremely volatile. While AI protocols have gone a long way to helping risk mitigation, there is always the chance that a user's assets could be time-locked into a liquidity pool when the valuation of a token crashes.

The Future of All Trading

AI adaptive algorithms and dynamic prediction models based on real-time data are set to disrupt the status quo of trading in DeFi. They have the potential to help stabilize the markets through quicker responses and adaptations to shifts in the market but still come with their own set of risks. If AI protocols can disrupt the future of DeFi yield farming, then the future of legacy TradFi markets that are already less dynamic than their DeFi alternatives are surely equally or more susceptible.