These days, it feels like every second blockchain project is suddenly calling itself an “AI chain.” Most of them are just repainting old ideas with new buzzwords. A few demo pages, some technical jargon, and a flashy roadmap are enough to attract attention. But when you look deeper, there is rarely anything truly new. It is more about selling a story than building real technology.
I reached my own breaking point while testing a cross-chain data script. Ethereum gas fees were painfully high, and liquidity across L2s felt scattered and inefficient. Out of frustration, I decided to finally look into a project I had ignored for a long time, Vanar. At first, I assumed it was just another chain trying to ride the AI hype. Its earlier version, Virtua, never really stood out during the metaverse wave, so my expectations were low.
But when I actually ran Vanar’s latest Neutron testnet, my perspective shifted. It wasn’t trying to compete for speed or computing power like most so-called AI chains. It was solving a different problem. The real issue is not the lack of computing power. Platforms like Render and Akash have already made decentralized compute cheap and widely available. What we are missing is blockchain data that AI can truly understand.
Right now, blockchains are like basic calculators. They only record numbers and transfers, without any understanding of what those actions mean. A transaction may move tokens, but the chain has no idea whether it represents a payment, a loan, a game action, or something else. Because of this, AI agents must rely on large centralized databases to translate blockchain data, which defeats the purpose of decentralization.
Vanar approaches this problem from a different angle. Instead of focusing on raw speed, it adds meaning directly into the chain. When I deployed an NFT contract with emotional and descriptive parameters, I didn’t need any backend server or indexing service like I normally would on Ethereum. The chain itself returned structured, readable data. It felt like the difference between reading random symbols and reading a clear sentence.
Of course, the technology is not perfect. When I ran stress tests with high transaction volume, the system struggled. Memory usage spiked, node synchronization slowed, and I experienced a few RPC timeouts. This shows that the balance between decentralization and semantic complexity is still fragile. I also noticed that developer support is slow, which suggests the core team is likely small and stretched thin.
Even with all these limits, this is the kind of project that quietly builds while others chase noise. It is not about fast profits or viral trends. It is about preparing for a future where AI and blockchains must work together naturally. When that shift happens, systems that already understand context and meaning will stand far ahead of those that only know how to count numbers.