Layered Comparison of AI and Blockchain Technology Development

In the past year, both AI and Blockchain technology have undergone a similar L1-L2-L3 hierarchical development process. However, the evolution paths and effects of the two are quite different, which is worth our in-depth consideration.

The layered development of AI technology follows a logic of progressive capability. The large language models at the L1 layer lay the foundation for language understanding and generation. The reasoning models at the L2 layer focus on addressing the shortcomings of L1, such as complex logical reasoning and mathematical computation abilities. By the L3 layer, AI agents integrate the capabilities of the first two layers, achieving a leap from passive response to active execution, able to autonomously plan tasks, call tools, and handle complex workflows.

This hierarchical structure enables AI technology to achieve qualitative improvements at each layer, allowing users to genuinely feel that AI is becoming smarter and more practical. The development of each layer is built on the foundation of the previous layer and brings significant capability enhancements.

In contrast, the layered development of blockchain technology seems to be caught in a cycle of problem shifting. L1 public chains face performance bottlenecks, leading to the emergence of L2 scaling solutions. However, while L2 has addressed some issues, such as reducing Gas fees and increasing TPS, it has also brought new challenges, such as liquidity fragmentation and a lack of ecosystem applications. To tackle these new issues, L3 vertical application chains have started to appear, but this has further led to the fragmentation of the ecosystem.

This development model causes each layer of Blockchain technology to continuously shift problems from one domain to another, rather than truly addressing the core issues. The root of this phenomenon may lie in the fact that the development of Blockchain technology focuses too much on token economics rather than on the advancement of the technology itself.

The differences in the development paths of AI and Blockchain technology reflect the different driving forces behind them. The competition in the AI industry primarily focuses on improving technical capabilities, with major companies striving to enhance the performance of their models. In contrast, the blockchain industry seems to be more driven by token economics, with various projects paying more attention to metrics such as total locked value ( TVL ) and token prices.

This comparison reveals the starkly different development focuses of the two industries: one is dedicated to solving technical challenges, while the other is more focused on designing financial products. Of course, this comparison is not absolute, but it does provide us with an interesting perspective to examine these two rapidly evolving technological fields.