Original Title: Crypto AI Moats: Where Capital and Agents Converge

Original Authors: @Defi0xJeff, @steak_studio Head

Original Translation: zhouzhou, BlockBeats

Editor's Note: Crypto AI empowers autonomous agents to manage assets, optimize capital flow, and operate independently within the DeFi ecosystem. Compared to Web2 AI, it can access decentralized data, utilize open models for collaboration, and accelerate evolution. With the development of DeFi, Darwinian AI, and decentralized infrastructure, AI will not just be an assistant but a direct participant in the on-chain economy, achieving asset holding, trading, and value creation. Crypto AI combines programmable money with agents to build a decentralized economic system, accelerating the arrival of autonomous agent economies and breaking through the limitations of Web2 AI.

The following is the original content (reorganized for better readability):

As the market tightens, capital is gradually concentrating on stronger fundamentals, and 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, showcasing and solidifying crypto-native AI application scenarios.

The most direct synergy between AI and crypto: capital efficiency and yield optimization.

DeFi - On-chain Yield

• Stablecoins

• RWA

• Spot & perpetual contract trading

• Lending markets

• Yield markets (interest / points)

DeFi has always been at 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, making idle capital better utilized. For example, DeFi can be used as a tool to hedge against inflation or to achieve excess returns through AI strategies.

Stablecoins: As a core use case of cryptocurrency, they cover almost all on-chain trading scenarios.

RWA: Tokenizing assets such as government bonds, bonds, real estate, DePIN loans, GPU computing power, and bringing them on-chain.

Spot & perpetual contract trading: Optimizing trading fees and yields.

Lending markets: Enhancing capital utilization through more efficient lending mechanisms to achieve better yields.

Yield markets: Introducing new interest rate markets to improve 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 economies allow AI to manage funds more efficiently.

Although DeFi AI is still in its early stages, there have already been some exciting developments:

· @gizatechxyz's stablecoin yield optimization AI agent has surpassed $1M TVL, with a trading volume of $6M, and its yield is over 83% higher than traditional lending strategies.

· @Cod3xOrg initiated the Sophon Spark trading agent competition, where agents compete for a $1.5M reward and optimize AI trading capabilities through data.

· @autonolas launched Modius & Optimus, serving as personal portfolio management AI agents. Their team is the only one supporting users to run AI agents locally and recently launched a $1M Olas accelerator program.

· Projects like @HeyAnonai, @AIWayfinder, and @slate_ceo are exploring more user-friendly DeFi entry points, although they are still in the 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 Compatibility) drives the deep integration of DeFi and AI, enabling AI agents to access on-chain data and integrate more protocols. In the coming year, AI agents may handle a large volume of on-chain transactions, automating DeFi operations and enhancing yield optimization capabilities.

Directions worth noting:

Teams that drive technological advancement and build developer ecosystems (hackathons, competitions, workshops, etc.).

Teams focused on privacy, verifiability, and non-custodial models, ensuring users truly control AI agents.

Growth data for AI agents, such as AUA (Assets Under Agent Management) / TVL (Total Value Locked).

Beyond DeFi, AI is sparking an evolutionary race. Crypto AI is not just a yield optimization tool; it is accelerating the natural selection of AI agents and teams—only the strongest AI agents and teams can survive and thrive.

Darwinian Laws of AI Evolution (Natural Selection)

@opentensor (AI computing network)

@AlloraNetwork (Machine Learning / Prediction)

@BitRobotNetwork (Robotics)

Darwinism: "The evolution of species through natural selection." In other words, this is the "Hunger Games" for AI teams—either drive technological advancement and gain incentives or be eliminated by the market.

Web3 AI provides the infrastructure most suitable for AI evolution, accelerating the survival of the fittest through token incentives, inflation/burning mechanisms. Bittensor has pioneered this trend, with many teams building technology around its subnets (such as SN6, 41, 44), particularly in the GambleFAI (prediction market) space, leveraging AI/ML predictive capabilities to gain a competitive edge in the market.

Allora is harnessing the power of 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 rather than adopting subnets; it has established "Topics" (specific financial prediction use cases) where development teams can compete, with the best-performing teams receiving the most incentives.

Best Case:
Allora collaborates with @steerprotocol to utilize AI-driven liquidity provision strategies to create higher returns for holdings while reducing impermanent loss (IL).

Bit Robot is developed by the @frodobots team, who are also behind @SamIsMoving (in the @virtuals_io ecosystem). Currently, there is limited information about Bit Robot, but they plan to build an ecosystem similar to Bittensor, focusing on robotics. Its subnets will represent different sectors within the robotics field, such as data, hardware, visual models, LLMs, etc.

Focus: $TAO price trends, dTAO ecosystem growth, how consumer applications/agents leverage 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 type of infrastructure supports open collaboration, open innovation, and prevents technological innovation from being monopolized by a few centralized players. I have previously mentioned this area, and as DeFAI and Darwinian AI evolution continue to advance, we will see ongoing adoption of these infrastructures, especially as more mature and clearer application scenarios emerge.

In the short to medium term, the areas I am most interested in

Social & Sentiment Data:

· @KaitoAI Yap Leaderboard and the recently launched Yaps Open Protocol, allowing teams to build products based on Yap scores.

· @aixbt_agent tracks & maps project Alpha/social trends on Twitter.

· @cookiedotfun provides an AI agent marketplace/social intelligence.

On-chain Data:

· Currently, there are no absolute leaders in the on-chain data space like there are in social & sentiment data.

Other data players:

· Data scraping: @getgrass_io collects data using idle bandwidth.

· Data ownership: @vana incentivizes data ownership through DataDAOs.

· Confidential computing: @nillionnetwork launched Blind Compute, related applications, and the upcoming $NIL TGE (coming soon).

For more in-depth reading on the data field:

About DePIN (GPU)

Currently, two interesting protocols are emerging that facilitate the financialization of GPU assets through on-chain loans, helping data centers and operators scale their GPU businesses massively.

Due to the continuous growth of AI, the demand for computing power will never wane, 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 while providing capital support for GPU operators.

Gaib AI Dollar:

MetaStreet USDAI:

Core Insights

Crypto-native AI addresses challenges that Web2 AI cannot overcome. Crypto AI not only provides computational power to agents but also empowers them with trading capabilities, allowing AI to manage assets, optimize capital flow, and operate autonomously in an open and permissionless network. Crypto AI is shaping a new world where agents can:

· Freely flow funds within the DeFi ecosystem without centralized intermediaries;

· Access decentralized data streams that Web2 cannot reach, obtaining richer information sources;

· Evolve faster than closed systems by leveraging open models and collaborative ecosystems.

In simple terms, crypto AI makes scenarios that Web2 AI cannot replicate at scale a reality: the combination of programmable money and autonomous agents creates a fully verifiable and composable economic system. As DeFi, Darwinian AI, and decentralized infrastructure continue to mature, we will see AI not just as an assistant but as a direct participant in the on-chain economy.

AI is not just smarter; it can autonomously hold, trade, optimize, and create value, which is the true moat of crypto AI.

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