L1, L2, and the continuous expansion of cross-chain ecosystems have made things more complex. For most people, the threshold is simply too high. This complexity has previously hindered the development of DeFi, but with the emergence of DeFAI, the situation is beginning to change.
Author: @Defi0xJeff Translated by: Blockchain in Plain Language
Translated by: Blockchain in Plain Language
https://x.com/Defi0xJeff/status/1875881226151841925
DeFi has always been a pillar of Web3. It makes blockchain practical, providing tools for instant remittances globally, on-chain asset investments, borrowing without intermediaries, and stacking strategies across DeFi protocols. This is financial freedom within reach.
More importantly, DeFi addresses real-world problems. It empowers the unbanked to access financial services, removes intermediaries, operates 24/7, and creates a truly global and inclusive financial system.
But we have to face a reality: DeFi is complex.
Setting up wallets, managing gas fees, dealing with scams and various exit scams—this is not suitable for everyone, especially for middle-aged individuals under stress. The continuous expansion of L1, L2, and cross-chain ecosystems has made things even more complicated. For most people, the threshold is simply too high.
This complexity has previously hindered the development of DeFi, but with the emergence of DeFAI, the situation is beginning to change.
1. What is DeFAI?
DeFAI (DeFi + AI) makes DeFi more accessible. By leveraging AI, it simplifies complex interfaces and eliminates the barriers for ordinary people to participate. Imagine managing your DeFi portfolio as easily as chatting with ChatGPT; wouldn’t you be interested in trying it out?
The first wave of DeFAI projects is beginning to emerge, currently focusing on the following three areas:
1) Abstraction Layers
Abstraction layers aim to hide the complexity of DeFi through intuitive interfaces, making it easier to use. Users can interact with DeFi protocols using natural language commands instead of complex dashboards. Before the advent of AI, abstraction layers like intent-based architectures had already simplified trade execution. Platforms like @CoWSwap and @symm_io allow users to obtain the best pricing from fragmented liquidity pools, addressing the issue of liquidity fragmentation, but they did not solve the core problem: DeFi still feels daunting.
Today, AI-driven solutions are beginning to bridge this gap: Griffain is the first project to launch a token, and the product is still in early access, requiring an invitation to use. Griffain is more versatile, allowing users to perform various operations, from basic tasks to complex actions like task automation (DCA), launching memecoins, and conducting airdrops based on conditions.
Orbit / Grift is the second project to launch a token, focusing on the on-chain DeFi experience. Orbit emphasizes cross-chain functionality, having integrated over 117 chains and 200 protocols, making it the most integrated among the three major protocols.
Neur is the third project to launch, but due to its open-source nature, it quickly surpassed Orbit in valuation. Neur is positioned as a co-pilot for the Solana ecosystem, designed specifically for it. Neur is supported by @sendaifun and utilizes the Solana Agent Kit.
I personally use @slate_ceo. It is still in early stages and has not launched a token yet, but I really like its automation features. I mainly use it to set conditional trades, such as selling 25% of my position when the market cap of [xxxx] reaches $5 million, or buying $5,000 worth of tokens when [xxx] reaches [xxxx].
@AIWayfinder is also a project worth watching. It is a giant project developed by the $PRIME / @ParallelTCG team.
2) Autonomous Trading Agents
Why spend hours digging for alpha, manually executing trades, and optimizing portfolios when agents can do these tasks for you? Autonomous trading agents elevate trading bots to a new level, transforming them into dynamic companions that can adapt, learn, and make smarter decisions over time.
It is important to clarify that trading bots are not new. They have existed for years, executing predefined operations based on static programming. However, agents are fundamentally different from bots:
They extract information from unstructured and constantly changing environments.
They infer data based on the context of the target.
They identify patterns and learn to leverage these patterns over time.
They can perform operations that the owner never explicitly programmed.
Previously, @Almanak__'s white paper summarized the differences between agents and bots very well.
This subfield is rapidly evolving. From the initial entertainment nature—agents might gamble on shitcoins for fun—to now being practical profit-making tools, agents can help users trade more effectively. However, there is a significant challenge: How do you verify that an "agent" is not a bot, or even a real person operating behind the scenes?
At this point, the DeAI infrastructure plays a crucial role.
The Role of DeAI in Verifying Agents
Key infrastructures like Trusted Execution Environments (TEE) ensure the secure operation of agents and prevent tampering.
For example:
TEE: Promoted by @PhalaNetwork, TEE provides a secure computing environment where data is processed confidentially. Phala's experiments—such as Unruggable ICO and Sporedotfun—demonstrate how agents can perform tasks while maintaining data integrity.
Transparent Execution/Verification Frameworks: Innovations like zkML (Zero-Knowledge Machine Learning) or opML provide verifiability for reasoning and computation. @hyperbolic_labs's Proof-of-Sampling (PoSP) is a highlight. This mechanism combines game theory and sampling techniques to ensure the accuracy and efficiency of computations in a decentralized environment.
As autonomous agents begin to handle significant Total Value Locked (TVL) (like $100 million or more), users will need assurances. They need to understand how agents manage risk, verify the frameworks they operate in, and ensure their funds do not randomly enter into memecoins.
This field is still in its early stages, but we see some promising projects exploring these verifiable tools. With the development of DeFAI, this is a trend worth watching.
To delve deeper into the trends of DeAI infrastructure, you can read this article:
The top autonomous trading agent projects I am following:
@Almanak__
Almanak provides users with institutional-grade quantitative AI agents, addressing complexity, fragmentation, and execution challenges in DeFi. The platform executes Monte Carlo simulations in real environments by forking EVM chains, considering unique complexities such as MEV, gas fees, and transaction ordering. It uses Trusted Execution Environments (TEE) to ensure the privacy of strategy execution, protect alpha information, and allows for precise permission delegation to agents through the Almanak wallet, enabling non-custodial fund management. Almanak's infrastructure supports the conception, creation, evaluation, optimization, deployment, and monitoring of financial strategies. The ultimate goal is for these agents to continuously learn and adapt over time. Almanak raised $1 million on @legiondotcc, oversubscribed. Next steps include launching a beta version with testers and deploying initial strategies/agents. It will be very interesting to observe how these quantitative agents perform.
@Cod3xOrg / @BigTonyXBT
Cod3x, created by the Byte Mason team (known for their work on Fantom and @SonicLabs), is a DeFAI ecosystem designed to simplify the creation of trading agents. The platform offers no-code building tools, allowing users to create agents by specifying trading strategies, personalities, or even tweet styles. Users can access any dataset and develop financial strategies in minutes, aided by a rich API and strategy library. Cod3x integrates with @AlloraNetwork, leveraging its advanced machine learning price prediction models to enhance trading strategies.
Big Tony is the flagship agent trading based on the Allora model, entering and exiting major markets according to its predictions. Cod3x is working to create a thriving ecosystem of autonomous trading agents.
One notable feature is Cod3x's liquidity model. Unlike the common alt:alt LP structure promoted by @virtuals_io, Cod3x uses a stablecoin:alt LP supported by cdxUSD (Cod3x's proprietary CDP). This provides liquidity providers with more stability and confidence, making it more attractive compared to the volatility of alt:alt pairs.
Cod3x also has its own DeFi primitives, such as liquidity AMOs and mini pools, which deepen liquidity and provide more functionality and DeFi building blocks for agents in its ecosystem.
I want to highlight the following projects:
@getaxal / @Gekko_Agent — Axal's autopilot product, where agents handle complex multi-step crypto strategies. Gekko integrates with autopilot. I look forward to seeing how Gekko executes data-driven trades in real trading.
@ASYM41b07 — Described by many as the "cheat code" for memecoin trading, the ASYM agent can analyze large datasets from blockchains and social media to predict memecoin trends. ASYM outperforms the market. Backtesting shows ASYM demonstrating 3-4 times the returns. I look forward to seeing its performance in actual trading.
@ProjectPlutus_ — I just like the name $PPCOIN (laughs).
3) AI-Driven dApps
AI-driven dApps represent a promising but still early area within the DeFAI field. These are fully decentralized applications that integrate AI or AI agents, aimed at enhancing functionality, automation, and user experience. Although this field is still in its early stages, some ecosystems and projects have begun to emerge.
One of the most active ecosystems in this area is @modenetwork, a Layer 2 network designed to attract highly technical AI x DeFi developers. Mode is home to multiple teams developing cutting-edge AI-driven applications:
ARMA: An autonomous stablecoin farm tailored to user preferences, developed by @gizatechxyz.
Modius: An autonomous agent farm Balancer LP supported by @autonolas.
Amplifi Lending Agents: Developed by @Amplifi_Fi, these agents integrate with @IroncladFinance, automatically exchanging assets, borrowing on Ironclad, and maximizing returns through automatic rebalancing.
At the core of this ecosystem is $MODE, the native token. Holders can stake $MODE to receive veMODE, which provides AI agent airdrops, project whitelist access, and additional ecosystem benefits. Mode is positioning itself as a hub for AI x DeFi innovation, with its impact expected to grow significantly by 2025.
Additionally, @danielesesta has garnered widespread attention with the DeFAI paper from @HeyAnonai. He announced that HeyAnon is developing:
An abstraction layer for DeFi interfaces
DeFi agents for autonomous trading execution
Research and communication agents for scraping, filtering, and interpreting relevant data
The market reacted enthusiastically, with the market cap of the ANON token soaring 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 clearer vision (hopefully).
Besides these two ecosystems, many teams are building their AI-driven dApps. Once these major ecosystems take shape, I will share more information in the future.
2. Summary
DeFAI is transforming by making DeFi 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 the DeFi summer of 2020, it is more like the DeFAI summer of 2025.
Disclaimer: This document is for informational and entertainment purposes only. The views expressed herein do not constitute investment advice or recommendations.
Article link: https://www.hellobtc.com/kp/du/01/5625.html
Source: https://x.com/Defi0xJeff/status/1875881226151841925
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