A buzzword is a term or phrase that becomes particularly popular or fashionable during a specific period. In the tech world, these words typically come with big promises, but often are used more for their catchy appeal than actual value. The cycle usually involves adopting, overusing, and then discarding these terms, waiting for the next big idea to replace the last one.
Take, for example, terms like decentralized finance (DeFi) and artificial intelligence (AI). While these were once considered buzzwords, both phrases have proven to be much more than fads. While early discussions around technologies are often speculative, their applications have been more than impactful.
And now, merging these two technologies has produced a growing number of use cases, leading to a new concept known as DeFAI. This subfield focuses on automating on-chain activities through AI-driven mechanisms, including autonomous trading agents, abstraction layers, and AI-powered decentralization applications (dApps).
Digital Finance Group (DFG), a leading crypto and Web3 investment firm, has shared a report titled “DeFAI: How AI is Unlocking DeFi’s Potential.” The report addresses the potential of this emerging sector, where developers are leveraging AI’s efficiency to build tools that automate and simplify complex on-chain operations. Here are the top four takeaways from the report:
- So, what is DeFAI?
As mentioned, DeFAI is the intersection between AI and DeFi, where AI enhances DeFi specifically through automation, risk management, and capital optimization, functioning across multiple layers. The blockchain forms the base, allowing AI agents to execute transactions and enforce smart contracts. Above the base layer, data and computation provide historical data and analytics for training AI models. Privacy layers secure sensitive information, while agentic frameworks allow for AI-driven applications like trading bots and governance tools.
- The ecosystem is made up of 3 top categories
While the DeFAI ecosystem continuously expands, most projects are grouped into one of the three main subsectors. These include:
- Abstraction layer: The abstraction layer is for protocols that operate similar to ChatGPT, allowing users to input prompts for on-chain execution. These are typically integrated with multiple chains and dApps, eliminating manual steps in complex transactions. Protocols in this category include Griffain, Hey Anon, and Orbit.
- Autonomous trading agents: Unlike traditional trading bots, which rely on a pre-defined set of rules, autonomous trading agents learn and adjust their strategies based on real-time market conditions. Key protocols in this category include Almanak, Cod3x, and Spectral.
- AI-powered dApps: DeFi apps enhance traditional decentralized applications by integrating AI agents to optimize services like lending, swapping, and yield farming. The AI and agents enhance services by optimizing liquidity provisions through rebalancing liquidity provider (LP) positions for better annual percentage yield (APY). In this category, key protocols include Giza, Sturdy, and Derive.
- Data plays a critical role in DeFAI’s success
AI’s effectiveness depends on the quality of its data. For AI agents to function as optimally as possible in DeFAI, access to real-time, structured, and verifiable data is crucial. High-quality datasets enhance the AI agent’s ability to make accurate predictions about future price movements, allowing it to offer trade recommendations or adjust bias for long or short positions.
According to DFG, DeFAI has four main data providers, each offering distinct features:
- Mode Synth creates synthetic data for financial forecasting, helping AI agents accurately predict price movements and manage liquidity pools.
- Chainbase provides structured, omnichain datasets optimized for AI agents, which enables efficient trading and data analysis.
- sqd.ai offers a high-speed, open database network for AI agents. It indexes real-time data with zk (zero-knowledge) proofs for cross-chain privacy. By adding more processing nodes, it can handle increasing data loads, supporting the growing number of AI agents.
- Cookie delivers a modular layer focused on social data, providing real-time market sentiment and trend insights from its 7TB data swarm.
- What’s next?
In its next phase, DeFAI will focus on integrating valuable data layers to develop autonomous platforms and agents. This will require in-depth on-chain data, such as whale movements, liquidity shifts, and other key metrics, all while utilizing synthetic data for predictive analysis.
The ultimate goal of this subsector is for AI agents to generate and execute trading strategies from a single interface with no human intervention. However, more accurate information is needed as more protocols integrate data layers and systems.
