Something about OpenGradient kept bothering me the longer I looked at it.
An open network can make it easier for AI models to enter the market, but that doesn't mean users will spend time evaluating them.
In fact, the opposite may happen.
If OpenGradient successfully hosts more models and serves more inference requests, most users won't suddenly become better at comparing dozens of options. They'll look for shortcuts. They'll rely on familiar names, previous usage patterns, and whatever already appears trusted inside the network.
That creates a strange dynamic.
The barrier to joining the network can fall while the barrier to getting meaningful attention quietly rises.
A new model may technically have the same access to OpenGradient's infrastructure, yet still struggle to attract inference demand because users naturally cluster around what they already know.
The interesting part is that this isn't a compute problem or a verification problem. It's a behavior problem.
Open systems often assume that more choice automatically creates more competition. But users rarely distribute their attention evenly. They concentrate it.
If that pattern emerges inside OpenGradient, the biggest advantage may not belong to the best model.
It may belong to the model that gets noticed first.
That would mean the most valuable asset in an open intelligence network isn't infrastructure access.
It's attention.