Is the ultimate goal of AI to develop more powerful models or to create a more open network?

Most people focus on the former because it's more intuitive, but the true determinant of value distribution is often the latter.
Recently, I've been observing @dgrid_ai, and a clear signal is that they haven't focused on building the strongest model but are trying to distribute existing model capabilities across an accessible network.
In other words, they are more like working on the "infrastructure layer" of AI rather than the application layer.
This approach is actually quite similar to the early internet, transitioning from centralized servers to a distributed network, but the process won't be so smooth.
Because AI's computational costs are much higher than traditional services, it demands greater network stability and performance.
So the question is straightforward: can this model find a balance between cost and performance?
If not, it will be hard to compete with centralized services. If yes, it could change who owns AI.
Many people are still comparing model effectiveness, but what truly matters is who is reshaping the underlying operational methods.
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