Title: Crypto AI Moats: Where Capital and Agents Converge
Author: @Defi0xJeff, Head of @steak_studio
Translated by: zhouzhou, BlockBeats
Editor's Note: Crypto AI empowers autonomous agents to manage assets, optimize capital flows, and operate independently in the DeFi ecosystem. Compared to Web2 AI, it can access decentralized data and leverage open models for collaboration, accelerating evolution. As DeFi, Darwin-style AI, and decentralized infrastructure develop, AI will become more than just an assistant, but a direct participant in on-chain economics, achieving asset ownership, trading, and value creation. Crypto AI combines programmable currency with intelligent agents, building a decentralized economic system and accelerating the arrival of an autonomous agent economy, breaking through the limitations of Web2 AI.
Original content (slightly edited for readability):
As the market tightens and capital concentrates on stronger fundamentals, the next wave of innovation in the AI field is accelerating its collision with the core moats of the crypto world.
Here are several key areas where crypto and AI may further integrate, demonstrating and consolidating crypto-native AI application scenarios.
The most direct synergy between AI and crypto: capital efficiency and yield optimization.
DeFi - On-Chain Yields
Stablecoins
RWA
Spot & Perpetual Contract Trading
Lending Markets
Yield Markets (Interest / Points)
DeFi has always been the core of the crypto world, providing globally accessible on-chain yield and trading opportunities. The addition of AI can more efficiently capture and optimize these values, enabling better utilization of idle capital. For example, DeFi can be used as a tool to hedge against inflation or generate excess returns through AI strategies.
Stablecoins: The core use case of cryptocurrencies, covering almost all on-chain trading scenarios.
RWA: Tokenizing assets like government bonds, real estate, DePIN loans, GPU computing power, etc., and introducing them on-chain.
Spot & Perpetual Contract Trading: Optimizing trading fees and yields.
Lending Markets: Improving capital utilization through more efficient lending mechanisms to achieve better yields.
Yield Markets: Introducing new interest rate markets and enhancing yield optimization capabilities.
Crypto = DeFi = Capital Flow and Appreciation. Web3 AI may be better at this than closed Web2 systems because the openness and incentive mechanisms of blockchain and token economics allow AI to manage funds more efficiently.
Although DeFi AI is still in its early stages, there have been some exciting developments:
·@gizatechxyz's stablecoin yield optimization AI agent has exceeded $1M TVL, with trading volume reaching $6M, and yields 83%+ higher than traditional lending strategies.
·@Cod3xOrg launched the Sophon Spark trading agent competition, with agents competing for a $1.5M reward and optimizing AI trading capabilities through data.
·Modius & Optimus by @autonolas, serving as personal portfolio management AI agents. Their team is the only one supporting local AI agent operation, recently launching a $1M Olas accelerator program.
·Projects like @HeyAnonai, @AIWayfinder, @slate_ceo are exploring more user-friendly DeFi entry points, though still in early stages.
Why Are AI Agents Suitable for DeFi?
AI agents can continuously optimize yields and manage risks 24/7, intelligently adjusting positions. MCP (Multi-Protocol Compatible) drives deep integration of DeFi and AI, enabling AI agents to access on-chain data and integrate more protocols. Within the next year, AI agents may handle a large volume of on-chain transactions, automating DeFi operations and enhancing yield optimization capabilities.
Areas to Watch:
Teams driving technological advancement and building developer ecosystems (hackathons, competitions, workshops, etc.).
Teams focusing on privacy, verifiability, and non-custodial modes to ensure users truly control AI agents.
Growth data of AI agents, such as AUA (Agent Managed Assets) / TVL (Total Value Locked).
Beyond DeFi, AI is triggering an evolutionary race. Crypto AI is not just a yield optimization tool; it is accelerating natural selection of AI agents and teams - only the strongest AI agents and teams will survive and grow.
Darwin's Law of AI Evolution (Natural Selection)
@opentensor (AI Computation Network)
@AlloraNetwork (Machine Learning / Prediction)
@BitRobotNetwork (Robotics)
Darwinism: "Species evolution through natural selection". In other words, this is a "Hunger Games" for AI teams - either drive technological advancement and gain incentives, or be eliminated by the market.
Web3 AI provides the most suitable infrastructure for AI evolution, accelerating the process of survival of the fittest through token incentives, inflation/burning mechanisms. Bittensor pioneered this trend, with many teams building technologies around its subnets (such as SN 6, 41, 44), especially in the GambleFAI (prediction markets) domain, leveraging AI/ML predictive capabilities to gain a competitive advantage in markets.
Allora is leveraging machine learning to accelerate and enhance their models, covering a wide range of financial prediction applications. Allora's model is similar to Bittensor but focuses on financial predictions, using "Topics" (specific financial prediction use cases) instead of subnets, where development teams compete, with the best-performing teams receiving the most incentives.
Best Case: Allora collaborating with @steerprotocol to use AI-driven liquidity provision strategies, creating higher returns for positions while reducing Impermanent Loss (IL).
Bit Robot, developed by the @frodobots team (also behind @SamIsMoving in the @virtuals_io ecosystem), currently has limited information. They plan to build an ecosystem similar to Bittensor, focusing on robotics. Its subnets will represent different sectors in the robotics field, such as data, hardware, vision models, LLMs, etc.
Focus Points: $TAO price trends, dTAO ecosystem growth, how consumer applications/agents utilize subnet technology, Allora ecosystem integration, case studies, and TGE (Token Generation Event).
Key Elements of Decentralized Infrastructure:
Data
Model Creation / Training
Verifiability
Confidentiality
DePIN (GPU)
This infrastructure supports open collaboration, open innovation, and prevents technological innovation from being monopolized by a few centralized players. As I mentioned in previous articles, with the continued advancement of DeFAI and Darwin-style AI evolution, we will see continued adoption of these infrastructures, especially as more mature and clear application scenarios emerge.
Areas of Most Interest in the Short to Medium Term
Social & Sentiment Data:
·@KaitoAI Yap Leaderboard and recently launched Yaps Open Protocol, allowing teams to build products based on Yap scores
·@aixbt_agent tracking & mapping project Alpha/social trends on Twitter
·@cookiedotfun providing AI agent marketplace / social intelligence
On-Chain Data:
·Currently no absolute leader in on-chain data comparable to social & sentiment data.
Other Data Players
·Data Scraping: @getgrass_io collecting data using idle bandwidth
·Data Ownership: @vana incentivizing data ownership through DataDAOs
·Confidential Computing: Blind Compute by @nillionnetwork, with related applications and upcoming $NIL TGE (coming soon)
More in-depth reading about the data field:
About DePIN (GPU)
Currently, two interesting protocols are emerging that promote GPU asset financialization through on-chain lending, helping data centers and operators to massively scale up GPU businesses.
Due to the continuous growth of AI, the demand for computing power will be inexhaustible, and data centers will always need capital to expand operations. Therefore, projects like @gaib_ai and @metastreetxyz are connecting DeFi liquidity with borrowing needs, bringing DePIN yields on-chain, and providing capital support for GPU operators.
Gaib AI Dollar:
MetaStreet USDAI:
Key Points
Crypto-native AI solves challenges that Web2 AI cannot overcome. Crypto AI not only provides computing power for intelligent agents but also empowers them with trading capabilities, allowing AI to manage assets, optimize capital flows, and operate autonomously in an open and permissionless network. Crypto AI is shaping a new world where intelligent agents can:
·Freely move funds in the DeFi ecosystem without centralized intermediaries;
·Access decentralized data streams inaccessible to Web2, obtaining richer information sources;
·Evolve faster than closed systems by utilizing open models and collaborative ecosystems.
In simple terms, crypto AI makes scenarios that Web2 AI cannot replicate at scale a reality: combining programmable money with autonomous intelligent agents to create a fully verifiable and composable economic system. As DeFi, Darwin-like AI, and decentralized infrastructure continue to mature, we will see AI not just as an assistant, but as a direct participant in on-chain economics.
AI is not just smarter, but capable of autonomously holding, trading, optimizing, and creating value - this is the true moat of crypto AI.
