Original title: Crypto AI Moats: Where Capital and Agents Converge
Original author: @ Defi0xJeff , head of @steak_studio
Original translation: zhouzhou, BlockBeats
Editors note: Crypto AI empowers autonomous agents, allowing them to manage assets, optimize capital flows, and operate autonomously in the DeFi ecosystem. Compared with Web2 AI, it can access decentralized data, collaborate using open models, and accelerate evolution. With the development of DeFi, Darwinian AI, and decentralized infrastructure, AI will not only be an assistant, but a direct participant in the on-chain economy, enabling asset holding, trading, and value creation. Crypto AI combines programmable currency with agents to build a decentralized economic system, accelerate the arrival of the autonomous agent economy, and break through the limitations of Web2 AI.
The following is the original content (for easier reading and understanding, the original content has been reorganized):
As the market tightens and capital gradually concentrates on stronger fundamentals, the next wave of innovation in the field of AI is accelerating and colliding with the core moat of the crypto world.
Here are a few key areas where crypto x AI may further converge, demonstrating and solidifying crypto-native AI use cases.
The most direct synergy between AI and encryption: capital efficiency and return optimization.
DeFi - On-chain income
Stablecoins
RWA
Spot Perpetual Contract Trading
Lending Market
Yield Market (Interest/Points)
DeFi has always been at the core of the crypto world, providing globally accessible on-chain yields and trading opportunities. The addition of AI can capture and optimize these values more efficiently, making better use of idle capital. For example, DeFi can be used as a tool to hedge against inflation, or to obtain excess returns through AI strategies.
Stablecoin: As the core use case of cryptocurrency, it covers almost all on-chain transaction scenarios.
RWA: Tokenize government bonds, bonds, real estate, DePIN loans, GPU computing power and other assets and bring them onto the chain.
Spot Perpetual Contract Trading: Optimize transaction fees and revenue.
Lending market: Improve capital utilization and achieve better returns through more efficient lending mechanisms.
Yield market: Introduce new interest rate markets and 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 incentives of blockchain and token economy allow AI to manage funds more efficiently.
Although DeFi AI is still in its early stages, there are some exciting developments:
@gizatechxyz’s stablecoin yield-optimizing AI agent has surpassed $1M TVL with $6M in trading volume, and its yield is 83%+ higher than traditional lending strategies.
@ Cod3xOrg launched the Sophon Spark Trading Agent Competition, where agents compete for $1.5M in rewards and optimize AI trading capabilities through data.
Modius Optimus by @autonolas, acting as personal portfolio management AI agents. The team is the only one that allows users to run AI agents locally, and recently launched the $1M Olas Accelerator Program.
Projects such as @HeyAnonai, @AIWayfinder, and @slate_ceo are exploring easier-to-use DeFi entry points, although they are still in the early stages.
Why are AI agents suitable for DeFi?
AI agents can continuously optimize returns and manage risks 24/7, and adjust positions intelligently. MCP (Multi-Protocol Compatibility) promotes the deep integration of DeFi and AI, enabling AI agents to access on-chain data and integrate more protocols. In the next year, AI agents may handle a large number of on-chain transactions, automate DeFi operations, and improve the ability to optimize returns.
Areas worth noting:
Teams that promote technological advancement and build a developer ecosystem (hackathons, competitions, workshops, etc.).
A team focused on privacy, verifiability, and non-custodial models to ensure users truly have control over AI agents.
Growth data for AI agents, such as AUA (Agency Assets Under Management) / TVL (Traded Funds).
Beyond DeFi, AI is sparking an evolutionary race. Crypto AI is more than just a yield optimization tool, it is accelerating the natural selection of AI agents and teams - only the strongest AI agents and teams will survive and thrive.
Darwin’s Law of AI Evolution (Natural Selection)
@opentensor (AI computing network)
@AlloraNetwork (Machine Learning/Prediction)
@BitRobotNetwork (Robot)
Darwinism: that is, evolution of species through natural selection. In other words, this is the Hunger Games of the AI team - either promote technological progress 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 and inflation/destruction mechanisms. Bittensor has taken the lead in promoting this trend, and many teams are building technologies around its subnets (such as SN 6, 41, 44), especially in the field of GambleFAI (prediction market), using AI/ML prediction capabilities to gain competitive advantages in the market.
Allora is leveraging the power of machine learning to accelerate and enhance their models, covering a wide range of financial prediction application scenarios. Alloras model is similar to Bittensor, but focuses on financial prediction. Instead of using a subnet, it sets up Topics (specific financial prediction use cases) where development teams can compete, and the best performing teams receive the most incentives.
Best Practices:
Allora has partnered with @steerprotocol to leverage AI-driven liquidity provision strategies to create higher returns for holdings while reducing impermanent loss (IL).
Bit Robot is developed by @frodobots team, who are also the team behind @SamIsMoving (in @virtuals_io ecosystem). There is little information about Bit Robot, but they plan to build an ecosystem similar to Bittensor, focusing on robotics. Its subnets will represent different sectors in the field of robotics, such as data, hardware, visual models, LLM, etc.
Focus: $TAO price action, 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 also mentioned this area in my previous article. As DeFAI and Darwinian AI evolution continue to advance, we will see the continued adoption of these infrastructures, especially after more mature and clear application scenarios emerge.
In the short to medium term, my main areas of interest are
Social Sentiment Data:
@KaitoAI Yap Leaderboard and the recently launched Yaps Open Protocol, which allows teams to build products based on Yap Scores
·@aixbt_agent Tracking mapping Project Alpha / social trends on Twitter
@cookiedotfun provides AI agent market/social intelligence
On-chain data:
Currently, there is no absolute leader in the field of on-chain data like 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: Blind Compute by @nillionnetwork, related applications and the upcoming $NIL TGE (coming soon)
More in-depth reading on Data Fields:
About DePIN (GPU)
There are currently two interesting protocols emerging that facilitate the financialization of GPU assets through on-chain lending, helping data centers and operators to hyperscale their GPU businesses.
As AI continues to grow, the demand for computing power will never dry up, 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 earnings on-chain while providing capital support to GPU operators.
Gaib AI Dollar:
MetaStreet USDAI:
Core Viewpoint
Crypto-native AI solves challenges that Web2 AI cannot overcome. Crypto AI not only provides computing power to intelligent agents, but also gives them trading capabilities, allowing AI to manage assets, optimize capital flows, and operate autonomously in an open and permissionless network. Crypto AI is shaping a whole new world where intelligent agents can:
Freely circulate funds in the DeFi ecosystem without the need for centralized intermediaries;
Access decentralized data streams that Web2 cannot reach, and obtain richer information sources;
Leverage open models and collaborative ecosystems to evolve faster than closed systems.
Simply put, crypto AI makes a scenario that Web2 AI cannot replicate on a large scale a reality: programmable currency combined with autonomous intelligent entities to achieve a fully verifiable and composable economic system. As DeFi, Darwinian AI, and decentralized infrastructure continue to mature, we will see that AI is not only an assistant, but also a direct participant in the on-chain economy.
AI is not just smarter, but is able to autonomously hold, trade, optimize, and create value. This is the real moat of crypto AI.
