Everyone says that Ethereum's Rollup Centric strategy seems to have failed? And deeply abhor the L1-L2-L3 nesting game, but interestingly, the development of the AI track has also undergone a rapid evolution of L1-L2-L3 in the past year. By comparison, where exactly is the problem? 1) The hierarchical logic of AI is that each layer is solving core problems that cannot be solved by the upper layer. For example, L1 LLMs address the fundamental abilities of language comprehension and generation, but logical reasoning and mathematical calculations are indeed weaknesses; So we reached L2, where the inference model specifically tackled this weakness. DeepSeek R1 can solve complex mathematical problems and debug code, directly filling the cognitive blind spots of LLMs; After completing these preparations, L3's AI Agent naturally integrates the first two layers of capabilities, transforming AI from passive answering to active execution, capable of planning tasks, calling tools, and handling complex workflows on its own. You see, this layering is a "progressive ability": L1 lays the foundation, L2 fills the gaps, and L3 integrates. Each layer has made a qualitative leap based on the previous layer, and users can clearly feel that AI has become smarter and more useful. 2) The hierarchical logic of Crypto is that each layer is patching the problems of the previous layer, but unfortunately it brings new and bigger problems. For example, if the performance of L1 public chain is not sufficient, it is natural to think of using layer 2 expansion solution. However, after the internal competition of layer 2 Infra wave, it seems that the gas has decreased, TPS accumulation has improved, but liquidity has been dispersed, and ecological applications continue to be scarce, making excessive layer 2 Infra a big problem. So we started working on the Layer 3 vertical application chain, but each application chain was independent and unable to enjoy the ecological synergy of the Infra universal chain, resulting in a more fragmented user experience. In this way, this layering becomes a 'problem transfer': L1 has bottlenecks, L2 is patched, and L3 is chaotic and scattered. Each layer simply transfers the problem from one place to another, as if all solutions are just for the sake of 'coin issuance'. At this point, everyone should understand the crux of this paradox: AI layering is driven by technological competition, with OpenAI, Anthropic, and DeepSeek all striving to roll up model capabilities; Crypto layering is hijacked by Tokenomic, where the core KPIs for each L2 are TVL and Token prices. So， Essentially, one is solving technical problems and the other is packaging financial products? There may not be an answer to who is right or wrong, it depends on one's own opinion. Of course, this abstract analogy is not so absolute, I just find it very interesting to compare the development trends of the two. Let's do a mental massage on the weekend 💆。