Metis's exploration has opened a new path for the development of Layer2. In an era of severe technological homogenization, scene differentiation may be the key to breaking through.

Written by: Haotian

Many people believe that the Ethereum Layer2 ecosystem is beyond saving, but that is not the case. If we only look at it from the perspective of the TPS arms race, there is indeed a sense of decline. However, after the Pectra technology upgrade, if some Layer2 can reposition themselves correctly, they might still have a fighting chance. Recently, @MetisL2 released the strategic roadmap of "All in AI." Can this alternative choice break the current predicament of Layer2? Here are my observations:

1) To be honest, the fundamental problem facing the current Layer2 ecosystem is not a lack of technical capability, but the solidification of narrative boundaries. Most projects are still using the linear thinking of "faster speed, cheaper gas," which has led to too many generic Layer2s emerging, with diminishing technical differences, while the real pain point for users—the lack of killer applications—remains unresolved.

Upon further research into Metis's technical roadmap, I found that its true innovation lies not in a single technological breakthrough, but in a systematic architectural reconstruction. The dual-network strategy (Andromeda + Hyperion) is essentially a clever solution to the classic trade-off between "generality vs. specialization."

Clearly, Metis aims to maintain the stability and reliability of the existing Layer2, Andromeda, providing a mature DeFi and Web3 application infrastructure; on the other hand, it seeks to establish a high-performance execution layer specifically for AI scenarios, shifting from a general technology stack to specialized AI infrastructure. This not only avoids homogenized competition with other Layer2s but also finds a technical path for the integration of AI and Web3 (providing a feasible breakthrough idea for the Ethereum ecosystem?).

2) Previously, many people were familiar with Metis's decentralized Sequencer and Hybrid Rollup technology innovations on the Andromeda chain. What is special about the brand new Hyperion AI chain?

1. MetisVM, a virtual machine deeply customized for AI applications, improves execution efficiency by 30% compared to traditional EVM through dynamic opcode optimization, which is a qualitative leap for AI inference scenarios. More importantly, the MPEF parallel execution framework resolves the contradiction between blockchain serial processing and AI concurrent demands.

2. MetisDB, which uses memory-mapped Merkle trees and MVCC concurrency control, achieves nanosecond-level state access. This design completely eliminates storage bottlenecks, providing hardware performance guarantees for high-frequency AI computations.

With this background, it is easy to understand MetisSDK. In simple terms: MetisSDK builds a development toolkit specifically for AI applications based on modular components and standardized interfaces, abstracting complex chain-level technologies into combinable building blocks, effectively lowering the development threshold for AI applications.

3) Based on my personal observations of the web3AI industry, the biggest current problem is not a lack of technical capability, but a distortion in the value distribution mechanism. Large platforms monopolize most of the value, and data providers hardly benefit. In other words, today's AI is a black box: where does the training data come from? How do the algorithms work? Are the results trustworthy? These questions remain unclear.

LazAI attempts to change this situation through three core innovations:

1. The iDAO model redefines the AI governance structure. Unlike traditional DAOs, iDAO allows everyone or AI agents to become governance participants rather than passive data providers. To some extent, this is a "substitute" for the current centralized governance model in AI.

2. The design concept of DAT (Data Anchoring Token) is particularly clever. Unlike traditional NFTs that only record static ownership, it tracks the entire lifecycle of AI assets. This innovation can directly address the fundamental issue of quantifying data value in the AI economy.

3. Verifiable computation provides transparency for AI behavior. It is like equipping AI with a "black box," where all inference processes are verifiable, traceable, and accountable. This concept of "verifiable AI" provides a trust foundation for decentralized AI applications.

This set of designs is like building a new "value distribution engine" for the integration of AI and Web3. If DeFi established a financial value system using indicators like TVL and APR, LazAI is constructing a similar quantitative framework for AI.

In summary, the current Metis technical framework appears to me like a sandwich structure: at the bottom, Metis itself provides a unified governance mechanism and token incentives; in the middle, Hyperion specifically handles high-performance AI computations; at the top, LazAI defines the rules for value transfer. This layered design is not a simple technical stack; each layer is both independent and collaborative, avoiding the "universal" trap of traditional single-chain architectures.

As for the most concerning aspect, the $METIS token economics will naturally be upgraded in sync. As the native token of the dual network, METIS's sources of income are more diversified than traditional Layer2s: in addition to transaction fees, there are also new sources of income such as computation fees and data verification fees. The introduction of the Holders Mining revenue-sharing model further transforms token holders from passive speculators into participants in ecological value sharing.

Overall, Metis's exploration has opened a new path for the development of Layer2. In an era of severe technological homogenization, scene differentiation may be the key to breaking through. Whether it will succeed depends on specific execution, but at least the direction chosen is promising. (Looking back, the previous narrative positioning of the decentralized Sequencer was at least successful.)

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