Capability Leap vs. Problem Shifting: The "Layered Paradox" of AI and Crypto
Author:Haotian
Everyone says that Ethereum's Rollup-Centric strategy seems to have failed? And they hate this L1-L2-L3 nesting game, but what's interesting is that the development of the AI track in the past year has also gone through the rapid evolution of L1-L2-L3. In comparison, where exactly is the problem?
1) The hierarchical logic of AI is that each layer solves core problems that the upper layer cannot solve.
For example, LLMs in L1 solve the basic capabilities of language understanding and generation, but logical reasoning and mathematical calculations are indeed shortcomings; so when it comes to L2, the reasoning model specifically overcomes this shortcoming, and DeepSeek R1 can solve complex math problems and code debugging, directly filling the cognitive blind spots of LLMs; after completing these preparations, the L3 AI Agent naturally integrates the first two layers of capabilities, allowing AI to change from passive responses to active execution, and can plan tasks, call tools, and handle complex workflows on its own.
You see, this layering is "capability progression": L1 lays the foundation, L2 makes up for the shortcomings, and L3 does integration. Each layer produces a qualitative leap based on the previous layer, and users can clearly feel that AI has become smarter and more useful.
2) The layered logic of Crypto is that each layer patches the problems of the previous layer, but unfortunately it brings new and bigger problems.
For example, the performance of L1 public chain is not enough, so it is natural to think of using layer2 expansion solution, but after a wave of layer2 Infra, it seems that Gas is lower, TPS is cumulatively improved, but liquidity is dispersed, and ecological applications continue to be scarce, making too much layer2 infra a big problem. So they started to make layer3 vertical application chains, but the application chains are independent and cannot enjoy the ecological synergy effect of infra general chain, and the user experience is more fragmented.
In this way, this layering becomes a "problem transfer": L1 has a bottleneck, L2 is patched, and L3 is chaotic and scattered. Each layer just transfers the problem from one place to another, as if all the solutions are just for the purpose of "issuing coins".
At this point, everyone should understand the crux of this paradox: AI stratification is driven by technological competition, and OpenAI, Anthropic, and DeepSeek are all desperately trying to increase model capabilities; Crypto stratification is kidnapped by Tokenomic, and the core KPI of each L2 is TVL and Token price.
So, essentially, one is solving technical problems, and the other is packaging financial products? There is probably no answer to which is right or wrong, and it depends on one's own opinion.
Of course, this abstract analogy is not so absolute. I just think the comparison of the development context of the two is very interesting, and it can be used as a mental massage on the weekend.
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