GPUs will remain essential for model inference, but autonomous agents could create a much broader infrastructure demand. Every task may require CPU capacity, networking, storage, databases, APIs and constant background processing.
That changes the investment question. Instead of simply asking how many GPUs AI needs, we should ask how much total infrastructure is required to support billions of agents working simultaneously.
I also find the ecosystem angle fascinating. An agent becomes far more useful when it can actually search, book, pay, communicate and execute tasks. That gives companies with strong consumer ecosystems another potential advantage.
To me, the next AI opportunity may not belong to one chip alone. It could be the entire stack that turns AI intelligence into real-world execution.
@Tiger_comments [真香]
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