The next phase of AI infrastructure investment may depend as much on the changing economics and geography of computing as on the expansion of hyperscale data centers. Here's why that matters. The Evolving AI Infrastructure Demand The artificial intelligence infrastructure conversation has largely been dominated by the construction of increasingly enormous data centers, massive investments in GPU capacity, and the race among hyperscalers to develop the computing resources necessary to train increasingly sophisticated AI models. That investment cycle is far from over. However, another development is beginning to reshape the industry's longer-term infrastructure requirements: the growing importance of AI inference. Training establishes the capabilities of an AI model. Inference puts those capabilities to work, generating responses, analyzing information, executing tasks, and increasingly coordinating autonomous workflows. As enterprise organizations move beyond experimentation tow...
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