Looking at this round of AI + Web3 projects, the outcome can actually be largely predicted.

Most failures are not due to insufficiently innovative technology, but because they fall into these three old traps.

First type: Only doing storytelling, not solving computing power and costs.
Models are large and narratives are full, but once entering the real operational stage, computing costs spiral out of control, deployment becomes unstable, and they can only revert to "demonstration AI."
It seems to be working towards the future, but in reality, it can't even sustain today.

Second type: Many Agents, but no economic closed loop.
Agents can collaborate, converse, and make decisions, but their contributions cannot be accurately recorded, and profit sharing relies on guesswork.
Without clear bookkeeping and incentive mechanisms, the more Agents there are, the more chaotic the system becomes.

Third type: Everything is on-chain, but no one actually uses it.
Decentralizing for decentralization's sake sacrifices experience, efficiency, and scalability.
In the end, the chain is lively, but outside the chain, no one is willing to stay.

These three paths seem different in direction, but fundamentally they share the same problem: rushing to produce results before the infrastructure is ready.

Against this backdrop, I can actually understand @OpenledgerHQ's choice.
They are not rushing or eager to prove how "smart" they are, but first focus on the layer most easily overlooked yet most prone to collapse—the computing power, storage, and verifiable contribution and settlement logic.

This path is indeed slower.

But in high-consumption, high-complexity tracks like AI + Web3, taking it slow often means being less likely to collapse.
Projects that truly go far are not usually the first to be noticed, but the last ones still standing.