The AI narrative this cycle feels louder than ever, but building something real gives you a very different perspective.
I’ve been working on an on-chain automated trading agent, and honestly, the biggest struggle hasn’t been the model or the strategy. It’s the data. On most mainstream L2s, what you receive from the chain is raw and messy — logs, hashes, low-level outputs. To extract actual trading meaning, I had to decode events, structure activity, and even run my own indexing server. That’s a lot of extra infrastructure just to make the data usable for an AI system.
Many projects branding themselves as AI-focused seem to concentrate mainly on compute distribution, similar to Render Network or Akash Network. But AI doesn’t just need GPUs. It needs clean, structured information. If the ledger only provides fragmented technical outputs, the model spends more effort translating than reasoning.
When I tested Vanar Chain, I noticed a different design philosophy. Calling contract state through a simple Python script returned structured semantic data instead of raw noise. It felt like the chain was already preparing information in a machine-friendly way, reducing the need for heavy middleware. That’s a meaningful shift.
There are trade-offs, though. Under heavier concurrent calls, RPC latency fluctuated and performance wasn’t always stable. It seems like deeper data structuring may come at the cost of raw speed. In a market obsessed with TPS metrics, that’s definitely unconventional.
Compared to something like NEAR Protocol, which emphasizes user-facing abstraction, this approach feels more focused on machine readability. Almost like building infrastructure designed for intelligent agents from the ground up.
At this point, it feels clear to me that the industry doesn’t lack computing power. What it lacks is a ledger that AI systems can actually understand without layers of translation. The hype may be elsewhere, but the real long-term value might come from chains that prioritize structure over noise.