Many people believe that the Ethereum Layer2 ecosystem is beyond saving, but that's not the case.
If we look solely 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, Metis released its strategic roadmap titled "All in AI." Can this alternative choice break the current deadlock in Layer2?
Let me share my observations:
1) To be honest, the fundamental problem facing the current Layer2 ecosystem is not a lack of technical capability, but rather the solidification of narrative boundaries. Most projects are still relying on the linear thinking of "faster speeds, cheaper gas," which has led to an oversaturation of generic Layer2 solutions, with diminishing technical differences. Meanwhile, the real pain point for users—the lack of killer applications—remains unresolved.
Upon delving 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 infrastructure for DeFi and Web3 applications; at the same time, 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 Layer2 solutions but also finds a technical pathway for the integration of AI and Web3 (providing a feasible breakthrough idea for the Ethereum ecosystem?).
2) Previously, many were familiar with Metis's decentralized sequencer and hybrid rollup technology on the Andromeda chain. What’s special about the brand new Hyperion AI chain?
MetisVM, a virtual machine deeply customized for AI applications, optimizes execution efficiency by 30% compared to traditional EVM through dynamic opcode optimization. This represents a qualitative leap for AI inference scenarios. More critically, the MPEF parallel execution framework resolves the contradiction between blockchain's serial processing and AI's concurrent demands.
MetisDB employs memory-mapped Merkle trees and MVCC concurrency control to achieve nanosecond-level state access. This design completely eliminates storage bottlenecks, providing hardware performance guarantees for high-frequency AI computations. Given this background, it’s easy to understand MetisSDK: simply put, MetisSDK builds a development toolkit specifically for AI applications based on modular components and standardized interfaces, abstracting complex chain-level technology 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 issue is not a lack of technical capability, but rather a distortion in the value distribution mechanism. Large platforms monopolize most of the value, leaving data providers with little 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:
The iDAO model redefines the governance structure of AI. Unlike traditional DAOs, iDAO allows everyone or AI agents to become governance participants rather than passive data providers. To some extent, this serves as a "replacement" for the current centralized governance model of AI.
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 directly addresses the fundamental issue of quantifying data value in the AI economy.
Verifiable computation provides transparency for AI behavior. It’s 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 acts like a new "value distribution engine" for the integration of AI and Web3. If DeFi established a financial value system using metrics like TVL and APR, LazAI is building a similar quantifiable 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 circulation. This layered design is not merely a simple stacking of technologies; each layer is both independent and collaborative, avoiding the "universal" trap of traditional single-chain architectures.
As for what everyone is most concerned about, 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 Layer2: 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 opens a new path for the development of Layer2. In an environment where technical homogenization is severe, scenario 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 narrative positioning of the previous decentralized sequencer was at least successful.)
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