New Year, New Track——What Opportunities Can DeFAI Bring?
[TL;DR]:
The market has recently experienced repeated fluctuations, with overall sentiment in the AI Agent sector remaining subdued.
However, amidst this backdrop, the concept of DeFAI has risen against the tide, becoming a new market hotspot and bringing unprecedented opportunities for investors.
Introduction
DeFi has always been an important gateway for blockchain to attract mainstream users, but its complex operational experience has been a major obstacle to mass adoption. A new narrative has emerged—DeFAI (Decentralized Finance + Artificial Intelligence)—offering hope for breaking through these bottlenecks. Following the hype around AI frameworks, DeFAI is quietly becoming one of the hottest tracks in the crypto AI Agent sector. So, what exactly is DeFAI? How will it reshape the DeFi space? And where might it lead in the future?
What is DeFAI?
In simple terms, DeFAI is DeFi enhanced with AI capabilities. Introducing AI agents significantly improves user experience by replacing users with performing complex on-chain operations.
Take a typical DeFi operation as an example: if you want to exchange 0.1 ETH for SOL, the traditional method involves the following steps:
- Find a bridging protocol to transfer assets cross-chain to Solana;
- Locate a decentralized exchange (DEX) on Solana;
- Finally, ute the swap.
This process is complex, lengthy, and time-consuming, often discouraging beginners and hindering blockchain adoption. The emergence of DeFAI offers hope for change, potentially simplifying everything.
With DeFAI, performing the same operation—exchanging 0.1 ETH for SOL—would only require the user to input the exchange command. The AI handles the rest of the tedious steps. You no longer need to spend a significant amount of time learning how to use DeFi infrastructure such as wallets, DEXs, or swaps.
Imagine a future where DeFAI’s interface resembles ChatGPT. Users could not only interact with AI but also ute on-chain operations. DeFAI could enhance the efficiency and security of DeFi projects by leveraging the powerful computational and data analysis capabilities of AI. At the same time, the DeFi industry could evolve into a more user-friendly, intelligent, and efficient financial eco.
What imaginative possibilities does DeFAI bring?
According to data from CoinGecko, during recent market fluctuations, DeFAI’s market capitalization has continued to rise, almost forming an independent trend. Its market cap has now surpassed $2.7 billion, showcasing immense market potential.
However, from the perspective of actual project operations, DeFAI is still in the proof-of-concept stage. Its primary areas of exploration can be summarized into four categories:
AI Abstraction
AI abstraction refers to embedding the information-processing capabilities of large AI models into DeFi products, enabling users to perform operations with ease.
Such products often face criticism at this stage due to incomplete interaction experiences. For example, there may be discrepancies between vague commands input by users and the precise ution by the AI backend, leading to poor user experience. However, with continuous optimization and refinement of AI, these products also demonstrate long-term potential.
Through novel interaction models, these products could ultimately address basic transaction needs and even promote the formation of new trading paradigms.
Autonomous Portfolio Management & Yield Optimization
Autonomous portfolio management and yield optimization are essentially products of the previous round of DeFi market competition. Simply put, they involve real-time monitoring and analysis of on-chain data to develop and ute yield optimization strategies.
The core of such products lies in combining real-time on-chain data with the ability to capture trading opportunities, providing a comprehensive strategy optimization solution that includes fund allocation, arbitrage ution, yield forecasting, and risk control.
By integrating AI technology, the efficiency of these products is further improved. For example, AI agents can fine-tune personalized strategies, allowing users to have a dedicated advanced trading assistant that automatically identifies on-chain investment opportunities and utes transactions. This combination not only continues the refined operational logic of DeFi but also offers users a more efficient and personalized investment experience.
Market Analysis or Prediction
These products use AI to provide trading strategy analysis and market trend predictions, which have already become an important source of information for many traders.
Whether such products can sustain their growth in the long term depends on whether AI agents can autonomously manage user assets and automatically ute trading operations based on real-time information and decisions. Although this step has not yet been fully realized, the potential for traffic-driven and commercial monetization has already shown significant promise.
DeFAI Infrastructure or Platform
This category of projects provides foundational support for integrating AI and DeFi, encompassing aspects such as computational power, data, and fine-tuning.
The scope of these protocols is broad, including AI Agent-native platforms like #ai16z and #Virtual, as well as projects like Bittensor, Atheir, and Vana, which are related to AI computational power and data.
These infrastructures not only support the development and operation of AI applications but also drive innovation and growth across the AI+DeFi eco. As foundational support, they are critical for deploying and expanding other AI+DeFi applications, injecting more possibilities into the industry.
Where is DeFAI Heading?
Industry analysts generally believe that the future development of DeFAI will proceed through four stages:
Stage 1: Focus on Efficiency
The first stage of DeFi AI will center on efficiency, improving user experience through innovative tools that allow users to easily complete complex DeFi operations without requiring in-depth knowledge of protocols.
The focus of this stage is on the need for artificial intelligence to possess the ability to understand user prompts, accurately identify needs, and respond even when the user’s input format is irregular. Secondly, it should be able to efficiently complete swap operations within the shortest block time to enhance transaction efficiency. Finally, real-time market analysis should assist users in making optimal decisions based on their investment goals, enabling better participation in the DeFi eco.
If these innovations can be successfully implemented, they will significantly lower the barriers to on-chain transactions, saving users time and effort. This stage may initiate a “phantom moment” in the coming months, driving rapid development in the industry.
Stage 2: Autonomous Trading with Intelligent Agents
Once efficiency challenges are addressed, DeFi AI will enter the second stage—autonomous trading with intelligent agents.
Intelligent agents will ute trading strategies during this phase with minimal human intervention. They will dynamically adjust decisions based on third-party insights or data provided by other intelligent agents. Professional or experienced DeFi users can fine-tune models to create personalized agents, maximizing returns for themselves or their clients while significantly reducing reliance on manual monitoring.
Stage 3: Transparency and Verification
Following the development of the first two stages, as user demand for transparency increases, wallet management and AI verification will become key focus areas.
To meet these needs, technologies such as Trusted ution Environments (TEE) and Zero-Knowledge Proofs (ZKP) will be introduced to ensure the security and transparency of AI s. These technologies will further enhance user trust in DeFi AI.
Stage 4: No-Code Tools and Agent Economy
After completing the above stages, DeFi AI will enter its final phase: building an economy based on intelligent agents.
In this phase, no-code DeFi AI engineering toolkits or AI-as-a-Service protocols will become possible. Users without programming skills can create and deploy their own intelligent agents, driving further adoption and development of the DeFi eco.
Through these tools, AI models trained on cryptocurrency data can be directly used for trading, forming an economy driven by intelligent agents. This could expand DeFi from being a domain for professional users to a broader user base, potentially reshaping the financial services landscape.
Of course, the development of DeFAI still faces many challenges. Most existing tools are merely repackaged versions of ChatGPT, lacking genuine innovation. Additionally, there are no clear standards for uating tool quality or identifying high-quality projects, making it difficult for standout projects to emerge.
Moreover, the fragmentation of on-chain data is a significant issue. The decentralized and inconsistent nature of data makes AI models more inclined toward centralization rather than decentralization. How to build truly decentralized on-chain agents in such an environment remains a core challenge for the industry to address.
