The current secondary market is simply a patchwork scene; as long as it touches AI, it can be touted as a computational power revolution. After reading no less than fifty white papers, the vast majority of projects' so-called AI support is nothing more than a patch on the originally bloated EVM, and this AI-added approach contributes nothing substantial to computational power except for increasing gas fees. What we need is an AI-first infrastructure designed for agents from the bottom-up architecture.
A few days ago, I deeply experienced the Vanar Chain testnet, and the difference is obvious. It didn't choose the simple EVM compatibility but instead built a five-layer architecture. Especially the Neutron semantic memory layer, which hits the pain point. The current AI agents fear being mindless, forgetting after just a few sentences. The traditional method of linking the memory bank to Arweave is painfully slow, while Vanar directly supports semantic memory natively on-chain, paving the way for AI.
It’s even more interesting to compare it with Near or ICP horizontally. Near has good data availability, but the native interaction of agents is a bit lacking. When trying out Vanar's Creator Pad, I found that the barriers to issuing tokens and deployment have been lowered too much. The advantage is that developers don't need to rewrite code to transport Web2 logic; the downside is that if there is no filtering, junk projects may proliferate.
The core of AI-first is not about running larger models but whether the chain can understand the model's requirements. Kayon’s decentralized intelligent engine attempts to solve the verifiability of inference. Running AI models on-chain is a black box; how can we ensure the results are not tampered with? Vanar attempts to solve this through a bottom-level verification mechanism, which is a step above competitors who only focus on the application layer.
However, the current experience has its drawbacks. Although the official announcement claims a high TPS, there are occasional lags under high concurrency, and node synchronization has room for improvement. Additionally, the ecological framework is large, but there aren’t many killer applications that have emerged; it’s better to have practical competition than a grand vision. This is akin to decorating a luxurious mall where businesses have not fully settled in, making it feel a bit empty.
From a technical aesthetic perspective, encapsulating computational resources, semantic memory, and verification mechanisms at the L1 layer is undoubtedly the direction. We don’t need more L2 to clean up after Ethereum; we need a chain that allows AI to exist like native biology. When the market realizes that computational power is not the bottleneck, but trust integration is, the value of this native architecture will become apparent.
