Why are more and more AI projects emphasizing compute networks rather than the models themselves?

In the past, when discussing AI, everyone focused on the model's capabilities, such as parameter size and performance.
But now I realize that what truly limits AI development is often not the model, but the way compute resources are obtained.
@dgrid_ai has helped me re-understand this point. Its focus is not on the model, but on the organization of compute resources.
When compute can be connected and utilized more efficiently, the pace of AI development will naturally accelerate. This change is not just a superficial upgrade of features but an improvement in underlying efficiency.
From the user's perspective, you won't directly see the compute network, but you'll feel that AI services become more flexible.
I've started to realize that $DGAI represents not just an AI product, but a new direction in AI infrastructure.
That's also why I am willing to continue following its development, because real change often starts from the bottom layer.
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