Many people believe that the Ethereum Layer2 ecosystem is beyond redemption, but this is not the case.
If we only look at it from the perspective of the TPS arms race, it does seem like an old and declining company. But after the Pectra technology upgrade, if some layer2s can reposition their direction, maybe they can still fight?
Recently, Metis released a strategic roadmap of All in AI. Can this alternative choice break the current dilemma of layer2?
Here, my observations:
1) To be honest, the fundamental problem facing the current Layer2 ecosystem is not the lack of technical capabilities, but the solidification of narrative boundaries. Most projects are still using the linear thinking of "faster speed, cheaper gas". This homogeneous competition situation has led to the emergence of too many general layer2s, and the technical differences are getting smaller and smaller, while the real pain point of users - the lack of killer applications - has never been solved.
However, after an in-depth study of Metis's technology route, I found that its real innovation lies not in the breakthrough of a single technology, but in the systematic architecture reconstruction. The dual network strategy (Andromeda + Hyperion) is essentially a clever solution to the classic trade-off of "universality vs. specialization".
Obviously, Metis needs to maintain the stability and reliability of the existing Layer2, Andromeda, and provide mature DeFi and Web3 application infrastructure. On the other hand, it needs to develop a high-performance execution layer that specifically serves AI scenarios, and shift from a general technology stack to a specialized AI infrastructure. This not only avoids homogeneous competition with other Layer2s, but also finds a technical landing path for the integration of AI+Web3 (giving the Ethereum ecosystem a feasible breakthrough idea?).
2) Many people are familiar with the previous Andromeda chain, Metis's decentralized sequencer and Hybrid Rollup technological innovations. What is special about the new Hyperion AI chain this time?
1. MetisVM is a virtual machine deeply customized for AI applications. Through dynamic opcode optimization, the execution efficiency is 30% higher than that of traditional EVM, which is a qualitative leap for AI reasoning scenarios. More importantly, the MPEF parallel execution framework solves the contradiction between blockchain serial processing and AI concurrency requirements;
2. MetisDB uses memory-mapped Merkle trees and MVCC concurrency control to achieve nanosecond state access. This design completely eliminates storage bottlenecks and provides hardware performance guarantees for high-frequency AI computing. Based on the above background, it is not difficult to understand MetisSDK. In short: MetisSDK is based on modular components and standardized interfaces to build a development toolkit specifically for AI applications, abstracting complex chain-level technologies into composable building blocks, effectively lowering the development threshold for AI applications.
3) Based on my personal observation of the web3AI industry, the biggest problem at present is not the lack of technical capabilities, but the distortion of the value distribution mechanism. Large platforms monopolize most of the value, and data providers hardly get any benefits. In other words, AI is now a black box. Where does the training data come from? How does the algorithm work? Is the result credible? These questions are not clear. LazAI attempts to change this situation through three core innovations:
1. The iDAO model redefines the AI governance structure. Unlike traditional DAO, iDAO allows everyone or AI agent to become a governance participant rather than a passive data provider. To some extent, this is a "replacement" for the current AI centralized governance model.
2. The design idea of DAT (Data Anchor Token) is particularly clever. Unlike traditional NFTs that only record static ownership, it tracks the entire life cycle of AI assets. This innovation can directly solve the fundamental problem of the difficulty in quantifying the value of data in the AI economy.
3. Verifiable computing provides transparency for AI behavior. This is like putting a "black box" in AI, and all reasoning processes are verifiable, traceable, and accountable. This idea of "verifiable AI" provides a trust basis for decentralized AI applications. The design of this combination of punches is like building a new "value distribution engine" for the integration of AI+Web3. If DeFi uses indicators such as TVL and APR to establish a financial value system, LazAI is building a similar quantitative framework for AI.
above.
Finally, to summarize, the current Metis technical framework looks like a sandwich structure to me. The bottom layer is Metis itself providing a unified governance mechanism and token incentives, the middle layer is Hyperion specializing in high-performance AI computing, and the top layer is LazAI defining the value transfer rules. This layered design is not a simple technology stacking. Each layer is both independent and collaborative, avoiding the "universal" trap of the traditional single-chain architecture.
As for what everyone is most concerned about, the $METIS token economics will naturally be upgraded synchronously. As the native token of the dual network, METIS's revenue sources are more diversified than traditional Layer2: in addition to transaction fees, there are new sources of income such as computing fees and data verification fees. The introduction of the Holders Mining revenue sharing model will transform token holders from passive speculators to sharers of ecological value.
In general, Metis's exploration has opened up a new path for the development of Layer2. At a time when technology is highly homogenized, scenario differentiation may be the key to breaking through. Whether it will succeed or not depends on the specific implementation, but at least the direction is well chosen. (Looking back, the narrative positioning of the previous decentralized sequencer was at least successful).
