Comparison and Analysis of the Development Paths of AI and Crypto Assets Technology

Recently, there has been a viewpoint suggesting that Ethereum's Rollup-Centric strategy seems to have failed, and there is criticism regarding the L1-L2-L3 hierarchy. However, interestingly, the development in the field of artificial intelligence over the past year has also undergone a similar rapid evolution of L1-L2-L3. By comparing the development paths of these two fields, we can explore the differences and the issues at hand.

In the field of AI, the development of each layer is aimed at solving the core problems that the previous layer could not address. L1's large language models (LLMs) lay the foundational capabilities of language understanding and generation, but there are shortcomings in logical reasoning and mathematical calculations. L2's reasoning models specifically improve upon these weaknesses, as some models are now capable of solving complex mathematical problems and debugging code, filling the cognitive blind spots of LLMs. On this basis, L3's AI agents integrate the capabilities of the first two layers, transforming AI from passive answering to active execution, enabling it to autonomously plan tasks, invoke tools, and handle complex workflows.

This hierarchy reflects the characteristic of "incremental capability": L1 lays the foundation, L2 addresses shortcomings, and L3 integrates. Each layer achieves a qualitative leap based on the previous layer, and users can clearly feel that AI is becoming smarter and more practical.

In contrast, the hierarchical logic in the Crypto Assets field seems to be seeking solutions to the problems of the previous layer, but may bring about new and greater issues. For example, due to the inadequate performance of L1 public chains, L2 scaling solutions were introduced. However, after experiencing a wave of L2 infrastructure boom, although Gas fees have decreased and TPS has improved, liquidity has become fragmented, and ecological applications are still scarce. This has led to an excess of L2 infrastructure becoming a new problem. To address this issue, L3 vertical application chains have emerged, but these application chains often operate independently, unable to enjoy the ecological synergy of universal chains, which further fragments the user experience.

This layered evolution has led to "problem shifting": L1 has bottlenecks, L2 provides patches, and L3 appears chaotic and decentralized. Each layer seems to merely transfer the problem from one place to another, giving the impression that all solutions revolve around the goal of "issuing tokens."

The fundamental reason for this difference may lie in: AI layering is driven by technological competition, with major companies striving to enhance model capabilities; whereas cryptocurrency layering seems to be more influenced by token economics, with the core metrics of each L2 project often focusing on Total Value Locked (TVL) and token prices.

Essentially, one field is focused on solving technical problems, while the other is more about packaging financial products. There may be no definitive answer as to which is right or wrong; it depends on individual perspectives and positions.

Of course, this abstract comparison is not absolute; it merely provides an interesting perspective from the context of development. This comparative analysis can help us better understand the development characteristics and challenges faced by the two fields.