Comparative Analysis of the Development Paths of Artificial Intelligence and Crypto Assets

Recently, there has been a viewpoint that Ethereum’s multi-layer scaling strategy seems to have encountered setbacks, and there are quite a few criticisms of this layered nesting model. Interestingly, the development of artificial intelligence over the past year has also experienced a similar multi-layer evolution. So, what are the differences between these two fields in their development processes?

Firstly, the layered development in the field of AI follows a logic of progressive capabilities. Basic Large Language Models (LLMs) serve as the first layer, addressing the fundamental issues of language understanding and generation, but they fall short in logical reasoning and mathematical calculations. The second layer, reasoning models, focuses on bridging these gaps; for instance, some models are now capable of solving complex mathematical problems and debugging code, effectively supplementing the cognitive blind spots of LLMs. Building on this, the third layer of AI agents integrates the capabilities of the first two layers, transforming AI from passive response to active execution, enabling it to autonomously plan tasks, call tools, and handle complex workflows.

This layered structure reflects a significant enhancement of capabilities: the first layer lays the foundation, the second layer addresses deficiencies, and the third layer achieves integration. The development of each layer realizes a qualitative leap based on the previous layer, allowing users to genuinely feel that AI has become more intelligent and practical.

In contrast, the layered development in the field of Crypto Assets seems to have fallen into a cycle of problem shifting. Initially, to address the performance issues of public chains, the industry proposed second-layer scaling solutions. However, although these solutions have reduced Gas fees to some extent and improved transaction processing speeds, they have also led to new problems such as liquidity dispersion and a lack of ecological applications. To address these challenges, the concept of third-layer vertical application chains has emerged, but this has instead exacerbated the fragmentation of user experience, making it difficult to enjoy the ecological synergy brought by universal infrastructure.

This development model is essentially a transfer of problems: the first layer has bottlenecks, the second layer tries to solve them but introduces new issues, and the third layer creates even more chaos and decentralization. The solutions at each layer seem to merely shift the problems from one area to another, rather than truly addressing the core issues.

The fundamental reason for this difference may lie in the fact that the hierarchical development in the field of AI is primarily driven by technological competition, with major companies striving to enhance model capabilities. In contrast, the hierarchical development in the field of Crypto Assets is more influenced by token economics, with various projects focusing more on metrics such as capital lock-up amounts and token prices.

This contrast reveals the different development focuses of the two fields: one is focused on solving technical challenges, while the other is more oriented towards designing financial products. There may not be an absolute answer to the advantages and disadvantages of these two development paths; it depends on personal perspectives and value judgments.

It is important to note that this abstract comparison is not absolute; it is merely an interesting observation and reflection on the development context of the two fields.