Original Title: Crypto AI Moats: Where Capital and Agents Converge
Original Author: 0xJeff (@Defi0xJeff)
Translated by: Asher (@Asher_0210)
As market funds tighten, capital is concentrating on projects with stronger fundamentals, revealing a clearer picture: The next wave of AI innovation is about to directly impact Crypto's most solid moats.
In this process, the convergence of Crypto and AI will further deepen, giving birth to more AI applications native to the Crypto ecosystem. These applications will not only demonstrate AI's value in the Crypto field but also form their unique use cases.
The most obvious synergy point is the combination of AI and Crypto in capital efficiency and yield optimization, which is the core demand of the Crypto ecosystem.
DeFi: On-chain Yields
DeFi has always been the core of the crypto field, with its on-chain yields and trading opportunities allowing global users to participate freely. However, with the rise of AI, these values can be captured and optimized more efficiently and further improve capital utilization. AI's intervention makes DeFi not only a tool against inflation but also helps users obtain excess returns. The core areas with AI empowerment include:
Stablecoins: As the primary monetary medium in the on-chain ecosystem, stablecoins are used in almost all on-chain transactions, lending, payments, and yield optimization;
RWA: Tokenizing real-world assets such as government bonds, bonds, real estate, DePIN loans, GPUs, etc., bringing them into the on-chain financial system and expanding DeFi's asset range;
Spot & Perpetual Contract Trading: AI can optimize trading fees and yields, improving trading strategy execution efficiency;
Lending Markets: AI improves capital efficiency through intelligent lending strategies, achieving enhanced returns;
Yield Markets: AI introduces dynamic interest rate adjustments and intelligent yield strategies, making yield markets more efficient and providing users with better interest rate environments.
The essence of DeFi is creating, transferring, and adding value to capital, and Web3 AI shows enormous potential in this process. Compared to the closed Web2 system, AI in the Web3 ecosystem can more intelligently optimize asset management by utilizing blockchain's openness and token incentive mechanisms.
Let's now look at the early practices of DeFi + AI. Although still in the early stages, several exciting cases have emerged in the DeFi AI (DeFAI) field:
Giza (@gizatechxyz): Its AI-driven stablecoin yield optimization agent's TVL has exceeded $1 million, improving traditional lending strategies by over 83% with efficient strategies, with a cumulative trading volume of $6 million;
Cod3x (@Cod3xOrg): Launched the Sophon Spark agent trading competition, where AI agents will compete for a $1.5 million reward while using data to train smarter trading strategies;
Olas (@autonolas): Its Modius & Optimus AI agents can serve as personal portfolio management tools, and its team is the only one supporting agents running on local desktops, with users managing through the "Pearl" agent app store (recently, the team also launched a $1 million Olas acceleration program);
AI DeFi Access Layer: Projects like @HeyAnonai, @AIWayfinder, and @slate_ceo are working to improve DeFi accessibility and better embed AI into the DeFi ecosystem.
So, why are AI agents suitable for DeFi?
24/7 Operation: AI agents can optimize yields, adjust positions, and respond to market changes in real-time around the clock;
Automated Management: AI can intelligently manage DeFi positions, significantly improving the efficiency of on-chain trading and yield optimization;
Multi-Protocol Integration (MCP): AI agents can access more on-chain protocols, mining richer DeFi yield opportunities. In the next year, AI agents may handle a large portion of on-chain trading in the DeFi ecosystem due to their efficient automation advantages.
Additionally, directions worth paying attention to in this sector include:
Teams Driving Technological Innovation: Projects supporting developer ecosystems, such as hosting hackathons, competitions, and workshops;
Teams Focusing on Privacy, Verifiability, and Non-Custodial Solutions: Allowing users to fully control AI agents, not centralized platforms;
AI Agent Growth Metrics: Such as Assets Under Administration (AUA)/Agent Managed TVL, measuring AI's impact in the DeFi ecosystem.
However, the DeFi AI competition has just begun. Crypto x AI is driving an evolutionary race of survival, and ultimately, only the strongest AI agents and teams will survive and rise.
(Note: The translation continues, but due to the character limit, I've provided the first part of the translation.)Social data is crucial in the AI ecosystem, helping AI understand market trends, track project developments, and optimize trading decisions. Several projects have already made progress in this field.
Kaito AI（@KaitoAI）: Launched Yap Leaderboard and Yaps Open Protocol, enabling teams to build on the Yaps scoring system and improve social data utilization;
aixbt（@aixbt_agent）: Tracks and maps project Alpha opportunities and social trends on Twitter, helping investors discover new opportunities;
Cookie DAO（@cookiedotfun）: Provides market/social intelligence analysis, helping AI agents more accurately interpret market sentiment.
2. On-Chain Data
Currently, the leader in on-chain data is not yet clear, but this field remains a key direction for infrastructure development.
3. Other Data Field Players
Data Crawling: @getgrass_io collects data by utilizing unused bandwidth, improving data availability;
Data Ownership: @vana incentivizes personal data ownership through DataDAOs, giving users control over their own data;
Privacy Computing: @nillionnetwork develops Blind Compute technology to enhance data computation privacy, with its token NIL approaching TGE.
As the DeFAI and Darwinian AI narrative deepens, infrastructure such as data, computing power, and privacy protection will see broader applications. In the future, we will witness a more mature infrastructure ecosystem and a greater role for AI agents in decentralized data processing, transaction optimization, and market prediction.
Crypto AI Breaks Through Web2 AI Limitations
Crypto AI is not just about giving AI the ability to think, but also empowering it with trading capabilities. Through DeFi infrastructure, AI agents can autonomously manage assets, optimize capital flow, and operate independently in open, permissionless networks.
Three Key Capabilities of Crypto AI-Enabled AI Agents
Autonomous Fund Movement, Without Centralized Intermediaries: AI agents can directly allocate funds, optimize returns, and execute trades in the DeFi ecosystem, avoiding limitations from centralized institutions;
Access to Data Unavailable to Web2 AI: Decentralized data streams (On-chain Data, Social Data) provide unique information advantages, helping AI make more precise on-chain decisions;
Open Collaboration, Faster AI Evolution: Crypto AI relies on open models and collaborative innovation, evolving faster and with broader application scenarios compared to the closed AI systems of the Web2 era.
Core Value of Crypto AI
AI + DeFi, Intelligent Agents Truly Become Participants in the On-Chain Economy: AI agents are not just task execution tools, but can also hold assets, trade arbitrage, optimize capital operations, becoming an important part of the DeFi ecosystem;
Transparent, Verifiable & Highly Composable: All AI transactions and profit management operations on-chain can be tracked, with intelligent agents able to reuse, iterate, and collaborate, driving accelerated AI ecosystem development;
Web2 Only Initiated the AI Race, Web3 Drives AI Towards an Autonomous Economy: AI agents will evolve from "assisting humans" to active creators in the on-chain economy, building a more intelligent and decentralized financial world.
In the future, Crypto AI will unlock a decentralized intelligent economy that Web2 AI cannot achieve, with AI agents not only optimizing the DeFi ecosystem but also becoming the core engine of on-chain wealth flow and value creation.
