Original title: DeFAI is the New DeFi Original author: Defi0xJeff, crypto Kol Original compilation: zhouzhou, BlockBeats
Editor's Note: In this article, Jeff discusses how DeFAI (Decentralized Finance + Artificial Intelligence) simplifies, optimizes, and enhances the DeFi experience through abstraction layers, autonomous trading agents, and AI-driven dApps, introducing multiple developing DeFAI projects such as Almanak, Cod3x, and Mode, highlighting the role of AI in enhancing trading strategies and managing portfolios.
The following is the original content (reorganized for readability):
DeFi has been a pillar of Web3. It makes blockchain practical, providing tools for instantly transferring funds globally, investing in assets on-chain, lending without intermediaries, and layering strategies across DeFi protocols. This is financial freedom within reach.
More importantly, DeFi addresses real-world problems. It enables unbanked individuals to access financial services, removes intermediaries, and operates around the clock, creating a truly global and inclusive financial system.
But when we face reality and realize that DeFi is complex—setting up wallets, managing transaction fees, avoiding scams and rug pulls—this is not suitable for ordinary people. The ever-expanding L1, L2, and cross-chain ecosystems only make things more complicated. For most, the entry barrier is just too high.
This complexity has limited the development of DeFi, but with the emergence of DeFAI, this situation is beginning to change.
What is DeFAI?
DeFAI (DeFi + AI) makes DeFi more accessible. By leveraging AI technologies, it simplifies complex interfaces and removes barriers for ordinary people to participate. Imagine a world where managing your DeFi portfolio is as simple as chatting with ChatGPT.
The first wave of DeFAI projects has begun to emerge, primarily focusing on three areas:
1. Abstraction Layer
2. Autonomous trading agents
3. AI-driven dApp
1. Abstraction Layer
The goal of the abstraction layer is to hide the complexities of DeFi through intuitive interfaces. They allow users to interact with DeFi protocols via natural language commands, without needing complex dashboards.
Before AI, abstraction layers such as intent-based architectures simplified trade execution. Platforms like CoWSwap and symm io allowed users to achieve optimal pricing in decentralized liquidity pools, addressing the issue of liquidity fragmentation, but they did not solve the core problem: DeFi still feels challenging.
Currently, AI-driven solutions are filling this gap:
Griffain is the first project to launch a token and is currently still in early access, requiring an invitation to use.
Griffain is more versatile, allowing users to perform a variety of operations from simple to complex, such as task automation (DCA), launching memecoins, and conducting airdrops.
Orbit / Grift is the second project to launch a token, aimed at on-chain DeFi experiences. Orbit emphasizes cross-chain functionality, having integrated over 117 chains and 200 protocols, making it the most integrated among the three protocols.
Neur is the third project to launch a token, but due to its open-source nature, it quickly surpassed Orbit in valuation. Neur positions itself as the co-pilot of Solana, specifically designed for the Solana ecosystem. Neur is supported by the Solana Agent Kit from sendaifun.
I personally use slate ceo, which is still in early stages and has not yet launched a token, but I really like their automation features. I primarily use it to set conditional trades, such as selling 25% of my position if xxxx reaches a $5 million market cap, or buying $5,000 worth of tokens if xxx reaches xxxx price.
AIWayfinder is another interesting project worth noting. It is a behemoth created by the PRIME / ParallelTCG team and is highly anticipated.
2. Autonomous trading agents
Why spend hours digging for Alpha, manually executing trades, and trying to optimize your portfolio when you can have an agent do it for you? Autonomous trading agents elevate trading bots to a new level, transforming them into dynamic partners that can adapt, learn, and make smarter decisions over time.
It needs to be clarified that trading bots are not new. They have existed for many years, executing predefined actions based on static programming. But agents are fundamentally different:
They extract information from unstructured and constantly changing environments.
They reason data in the context of their goals.
They detect patterns and learn to leverage these patterns over time.
They can perform operations that their owners did not explicitly program.
This sub-field is evolving rapidly; initially, agents may have been used for entertainment purposes—such as potentially promoting some junk coins for fun—but it has now shifted towards more practical, profit-driven tools that help users trade more effectively. However, there remains an important challenge: how do you verify that an 'agent' is not just a bot, or even that a person is not operating behind the scenes?
This is where the DeAI infrastructure plays a critical role.
The role of DeAI in verifying agents
Key infrastructures, such as Trusted Execution Environments (TEE), ensure that agents can operate securely and tamper-proof.
For example:
· TEE: Promoted by PhalaNetwork, TEE provides a secure isolated environment where data can be processed confidentially. Phala's experiments—such as Unruggable ICO and Sporedotfun—demonstrate how agents can carry out tasks while maintaining data integrity.
· Transparent execution/verifiability framework: Innovations such as zkML (zero-knowledge machine learning) or opML provide reasoning and computational verifiability. Hyperbolic labs' Proof-of-Sampling (PoSP) is a prominent example. This mechanism combines game theory and sampling techniques to ensure computations are accurate and efficient in a decentralized environment.
Why is this important?
As autonomous agents begin to handle large amounts of TVL (assuming $100 million or more), users will demand assurance. They need to understand how the agents manage risk, verify the frameworks they operate in, and ensure their funds are not randomly thrown at some junk coins.
This field is still in its early stages, but we have already seen some promising projects exploring these verifiability tools. As DeFAI evolves, this is a direction worth watching.
For more trends in DeAI infrastructure, please check out this article:
The top autonomous trading agents I am watching
Almanak
Almanak offers users institutional-level quantitative AI agents that address the complexities, fragmentation, and execution challenges in DeFi. The platform executes Monte Carlo simulations in real environments by forking EVM chains, accounting for unique complexities such as MEV, transaction fees, and trade order.
It employs TEE (Trusted Execution Environment) to ensure the privacy of strategy execution, protecting Alpha information, and enables non-custodial fund management through Almanak Wallets, allowing for precise permission delegation to agents.
The infrastructure of Almanak supports the ideation, creation, evaluation, optimization, deployment, and monitoring of financial strategies. The ultimate goal is to allow these agents to learn and adapt over time.
Almanak raised $1 million on legiondotcc, with oversubscription. The next steps include the release of a beta version and the deployment of initial strategies/agents, targeting beta testers for testing. It will be very interesting to observe the performance of these quantitative agents.
Cod3xOrg / BigTonyXBT
Cod3x, created by the Byte Mason team (known for their work in Fantom and SonicLabs), is a DeFAI ecosystem designed to simplify the creation of trading agents. The platform provides a no-code building tool that allows users to create agents by specifying trading strategies, personalities, or even tweet styles.
Users can access any dataset and develop financial strategies within minutes, leveraging a rich API and strategy library. Cod3x integrates with AlloraNetwork, using its advanced ML price prediction models to enhance trading strategies.
Big Tony is the flagship agent based on the Allora model, predicting entries and exits for mainstream coins. Cod3x is working to create a thriving ecosystem of autonomous trading agents.
A notable feature of Cod3x is its liquidity strategy. Unlike the common alt:alt LP structure promoted by virtuals io, Cod3x uses a stablecoin:alt LP supported by cdxUSD, which is Cod3x's own CDP (Collateralized Debt Position). This structure provides more stability and confidence for liquidity providers compared to the volatility of alt:alt trading pairs.
Cod3x also has its own DeFi primitives, such as liquidity AMO (Automated Market Operations) and mini pools, which enhance liquidity and add more functionality/DeFi Lego components for agents in its ecosystem.
Other noteworthy projects
getaxal / Gekko Agent—Axal's autonomous driving product, where the agent handles complex multi-step crypto strategies. Gekko integrates autonomous driving features. I am waiting to see how Gekko performs data-driven trades in autonomous mode.
ASYM41b07—often referred to as the 'cheat code for memecoin trading', the ASYM agent can analyze vast amounts of data from blockchains and social media to predict trends in memecoins. ASYM has consistently outperformed the market and demonstrated 3-4 times returns through backtesting. It will be interesting to see how it performs in live trading.
ProjectPlutus—I just love this name PPCOI
3. AI-driven decentralized applications (dApps)
AI-driven dApps are a promising yet still nascent area within the DeFAI space. These are fully decentralized applications that integrate AI or AI agents to enhance functionality, automation, and user experience. While this field is still in its early stages, some ecosystems and projects have already begun to emerge.
In this field, modenetwork is a very active ecosystem, a Layer 2 network designed to attract high-tech AI x DeFi developers. Mode serves as a base for multiple teams working to develop cutting-edge AI-driven applications:
· ARMA: Autonomous stablecoin mining tailored to user preferences, developed by gizatechxyz.
· Modius: Autonomous agents powered by autonolas, mining Balancer LP.
· Amplifi Lending Agents: Developed by Amplifi Fi, these agents integrate with IroncladFinance to automatically swap assets, lend on Ironclad, and maximize yields through automated rebalancing.
The core of this ecosystem is MODE, the local token. Holders can stake MODE to receive veMODE, thereby enjoying airdrops from AI agents, whitelist access to projects, and more ecosystem benefits. Mode is positioning itself as the center of AI x DeFi innovation, with its influence expected to significantly grow by 2025.
Additionally, danielesesta has garnered significant attention through the DeFAI theory of HeyAnonai. He announced that HeyAnon is developing the following:
· Abstraction layer as a DeFi interface
· DeFi agents for autonomous trading execution
· Research and communication agents for acquiring, filtering, and interpreting relevant data
The market reacted enthusiastically, with the market cap of the ANON token surging from $10 million to $130 million. Daniele seems to be bringing back the excitement of TIME Wonderland, but this time with a stronger foundation and a clearer vision.
In addition to these two ecosystems, many teams are building their own AI-driven decentralized applications. Once major ecosystems form around them, I will share more information in the future.
Final Thoughts
DeFAI is transforming DeFi by making it smarter, simpler, and more accessible. As abstraction layers simplify user interactions, autonomous trading agents manage portfolios, and AI-driven dApps optimize use cases, we are witnessing the dawn of a new era.
Rather than a DeFi summer in 2020, 2025 will be the summer of DeFAI.
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