Recently, I noticed that projects like OpenClaw have suddenly gained popularity, which is actually quite interesting.
On the surface, it seems that AI applications are exploding.
Various agents, automated strategies, on-chain interactions, suddenly all seem to be more "usable."
But if you think about it carefully, you'll find one problem:
These capabilities actually didn't just appear today.
The models have been around for a long time, and the logic of agents is not new.
So why is it that now, it's easier to "get started"?
I tend to lean towards one reason—underlying conditions have changed.
In the past, many AI products couldn't take off, not because the ideas weren't good enough, but because they lacked a key element: stable, callable data.
On-chain data was scattered and disorganized, making development costs very high, and many ideas ultimately stalled at the demo stage.
But now, that layer has slowly started to be filled in.
What @Chainbase Official is doing is actually quite basic, but very critical: organizing on-chain data into resources that can be directly called, allowing agents to no longer have to process data from scratch.
Once this layer is connected, many applications that originally "couldn't be done" start to become feasible.
So the emergence of projects like OpenClaw is more like a result rather than a cause.
What you see now as these "new things,"
may just be the first batch of amplified cases.
What will really happen next,
is not whether a particular application will succeed or fail,
but rather that the same type of things will start to appear in batches.
$C
On the surface, it seems that AI applications are exploding.
Various agents, automated strategies, on-chain interactions, suddenly all seem to be more "usable."
But if you think about it carefully, you'll find one problem:
These capabilities actually didn't just appear today.
The models have been around for a long time, and the logic of agents is not new.
So why is it that now, it's easier to "get started"?
I tend to lean towards one reason—underlying conditions have changed.
In the past, many AI products couldn't take off, not because the ideas weren't good enough, but because they lacked a key element: stable, callable data.
On-chain data was scattered and disorganized, making development costs very high, and many ideas ultimately stalled at the demo stage.
But now, that layer has slowly started to be filled in.
What @Chainbase Official is doing is actually quite basic, but very critical: organizing on-chain data into resources that can be directly called, allowing agents to no longer have to process data from scratch.
Once this layer is connected, many applications that originally "couldn't be done" start to become feasible.
So the emergence of projects like OpenClaw is more like a result rather than a cause.
What you see now as these "new things,"
may just be the first batch of amplified cases.
What will really happen next,
is not whether a particular application will succeed or fail,
but rather that the same type of things will start to appear in batches.
$C