The crypto industry has been very turbulent recently. The Bybit theft incident was historic, and the web3 "Yu'ebao" inita was stolen for $50 million. Although the impact on both projects is not serious, the expected selling pressure of the stolen funds and the recent struggles have made the market extremely bad, and the market is chattering. On the other hand, this is a good time to build seriously, explore new projects, and build positions at low prices. The biggest narrative in 2024-2025 may be the AI Agent track, and the future upstarts are likely to be born in this field. Today, the author will make a simple comparative analysis of two relatively leading AI projects from the perspective of technical routes, to stimulate discussion.
1. Technology Showdown: The Underlying Logic of Two Disruptive Architectures
1. ElizaOS: Lego-style expansion of modularity and open source ecosystem
ElizaOS is based on an open source modular architecture . Developers can freely combine functional plug-ins (such as TEE privacy computing and multi-chain interaction modules) to build lightweight or enterprise-level AI agents6 . Its design concept originated from the exploration of basic human-computer interaction by the ELIZA program of MIT in 1966, but it has achieved three key breakthroughs through blockchain:
Pluggable model integration : Supports plug-and-play integration of large models such as GPT-4 and Claude with Web3 protocols (such as DeFi contracts and NFT standards) 4 ;
Decentralized governance : $ELIZA tokens are used to incentivize developers to contribute code, and agents within the ecosystem are required to distribute 5% of their income to framework maintainers4 ;
Immortality guarantee : Smart contracts ensure that the proxy logic runs permanently and can be iterated even if the founding team disappears6 .
Typical case: Solana ecosystem's AI trading fund AI16ZDAO is developed based on ElizaOS. Its agent integrates oracle chain data and TEE privacy strategy to achieve automated arbitrage with an annualized return of over 300%.
2. Swarm AI: The “bee colony revolution” of collective intelligence and collaborative networks
Swarm AI was founded by 20-year-old genius Kye Gomez. Its multi-agent collaboration framework redefines the paradigm of complex task processing:
SwarmNode : serverless infrastructure that allows 45 million agents to coordinate simultaneously, solving hardware dependency and cost issues2 ;
Hybrid Stream Model : Combining SSM (state space model) with MoE (mixture of experts) to achieve super-human accuracy in scenarios such as medical diagnosis2 ;
Cross-chain memory : a distributed database shared by agents that supports long-term context tracking and cross-task knowledge reuse2 .
Market performance: Although the token $SWARMS plummeted 35% in the short term due to speculative bubbles 1 , its enterprise-level customers (such as JPMorgan Chase's insurance claims automation) have verified the feasibility of the technology.
2. Market competition: market value, community and capital battle
1. Market value differentiation: modular light assets vs heavy operation enterprise services
Revenue Model:
— elizaOS: Development profit sharing + protocol commission
- Swarm: B-side subscription + SNAI node leasing.
Key Differences :
ElizaOS relies on the viral spread of the open source community and attracts developers through an airdrop mechanism (such as holding $ELIZA to get priority access to new proxy tokens)
Swarm AI focuses on enterprise payment scenarios , but its token economy fails to effectively bind customer growth, resulting in a decoupling of market value from business.
The two different routes led to a conflict:
a16z tried to implant ElizaOS into the 50+ projects it invested in, raising centralization concerns;
The Swarm community accused Kye of over-reliance on personal IP and lack of transparency in technology routes
3. Technical Strengths and Fatal Flaws
1. ElizaOS: Lightweight and compatibility issues
Advantages:
Deploy a DeFi arbitrage proxy in 5 minutes (with integrated Uniswap and dYdX interfaces);
It supports multiple chain environments such as Ethereum, Solana, Base, etc., and the migration cost is close to zero.
Shortcomings
The communication efficiency between modules is low, and the delay of complex tasks can reach up to minutes4 ;
Over-reliance on external large models and weak local optimization capabilities6 .
2. Swarm AI: The double-edged sword of collaborative networks
Advantages :
Hundreds of agents collaborate to process insurance claims, with an error rate 90% lower than humans;
The training cost of the self-developed SSM+MoE model is only 1/20 of that of GPT-4.
Shortcomings :
When the node network is congested, the task allocation is unbalanced, which has caused the collective failure of medical diagnosis agents;
Enterprise customers refuse to go on-chain due to data privacy, which conflicts with the vision of Web3;
IV. The ultimate showdown: five key battlefields in 2025
1. The battle for developers’ minds
ElizaOS attracts Web2 transitioners with "low code + high returns", and the number of GitHub stars exceeds 7,000 4 ;
Swarm AI cultivates deep users through hackathons, but its Python SDK has a steep learning curve.
2. Privacy computing standard formulation
ElizaOS integrates TEE plug-in, but a vulnerability was exposed that can extract smart contract keys;
Swarm AI develops ZKP verification nodes, sacrificing speed for security and causing community division.
3. On-chain proxy supervision game
The US SEC sued ElizaOS ecological project AI16ZDAO for "market manipulation" and demanded disclosure of proxy trading logic;
Swarm AI faces a class-action lawsuit due to misdiagnosis by medical agents, and the decentralized architecture serves as an excuse for exemption from liability.
4. Underlying public chain alliance competition
ElizaOS and Solana jointly built a dedicated side chain for AI agents, and TPS increased to 100,000 levels4 ;
Swarm AI selects the Avalanche subnet and customizes virtual machines to optimize swarm scheduling.
5. The life and death line: energy efficiency
ElizaOS's single agent consumes an average of 0.3 kWh of electricity per day, which has been boycotted by environmental organizations;
Swarm AI reduces energy consumption by 90% through task compression technology, but at the expense of the ability to handle complex tasks.
5. Industry predictions: 2026 final guess
Scenario 1: ElizaOS dominates the market
Modular framework becomes the "default option" for Web3 proxy, with market value exceeding $50 billion;
Cost: Innovation stagnates and developers become ecological tenants4 .
Scenario 2: Swarm AI strikes back to become the god
Swarm collaboration gives birth to super AI, and autonomous agents take over 60% of on-chain transactions;
Risk: An out-of-control proxy network triggers a financial crisis2 .
Scenario 3: Two major players and the long-tail revolution
ElizaOS monopolizes the simple task market, while Swarm AI sticks to high-end enterprise services;
New forces (such as federated learning + DAO governance) subvert the existing pattern6 .
Conclusion: Technological Utopia or Monopoly Tool?
The confrontation between ElizaOS and Swarm AI is essentially a battle between the spirit of Web3 and the route of AI centralization . When ElizaOS uses modularization to lower the development threshold, the capital behind it is quietly weaving a control network; when Swarm AI holds high the banner of collective intelligence, the black box of technology may give rise to new hegemony. Perhaps the real answer is not in the code, but whether humans can maintain the bottom line of "proxy sovereignty".
“All technological revolutions are carnivals for idealists in the early stages, but turn into chessboards for capital and power in the later stages.” — Anonymous AI agent developer @0x_KongKong
