AI and the Layered Evolution of Crypto Assets: Technology-Driven vs Financial Packaging

Recently, there has been a view that Ethereum's Rollup centralization strategy seems to have failed, and there is dissatisfaction with the layered nesting model of L1-L2-L3. Interestingly, the development in the AI field over the past year has also undergone a similar rapid evolution of L1-L2-L3. By comparing the development paths of the two, we can discover some interesting differences.

The layered logic of AI addresses core problems that each layer cannot solve. L1's large language model tackles the basic capabilities of language understanding and generation, but there are shortcomings in logical reasoning and mathematical calculations. L2's reasoning model is specifically designed to overcome these weaknesses, with some models capable of solving complex mathematical problems and debugging code, filling the cognitive blind spots of large language models. On this basis, L3's AI Agent integrates the capabilities of the first two layers, transforming AI from passive responses to active execution, enabling it to autonomously plan tasks, call tools, and handle complex workflows.

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

In contrast, the layered logic of Crypto Assets seems to be that each layer is patching the issues of the previous layer, inadvertently bringing about new and larger problems. L1 public chains face performance bottlenecks, leading to the introduction of L2 expansion solutions. However, after intense competition in L2 infrastructure, although Gas fees have decreased and TPS has increased, liquidity is dispersed and ecological applications are still lacking, with excessive L2 infrastructure becoming a new problem. To address this, L3 vertical application chains are being developed, but these application chains operate independently, unable to enjoy the ecological synergy of universal chains, resulting in a more fragmented user experience.

This layered evolution has led to "problem shifting": L1 has bottlenecks, L2 provides patches, and L3 is even more chaotic and decentralized. Each layer seems to merely shift the problem from one place to another, giving the impression that all solutions revolve around the purpose of "issuing coins."

The fundamental reason for this difference may lie in: AI stratification is driven by technological competition, with major AI companies striving to enhance model capabilities; while the stratification of Crypto Assets seems to be constrained by token economics, with each L2 project's core metrics concentrated on total locked value (TVL) and token prices.

In short, one field focuses on solving technical challenges, while the other is more akin to packaging financial products. There may not be a standard answer to which is right or wrong; it depends on individual perspectives and positions.

Of course, this abstract analogy is not absolute; it is merely an interesting insight drawn from comparing the developmental contexts of the two fields. This line of thinking may provide us with a new perspective to examine the balance between technological innovation and financial motivations.