By Daniel Foelber, The Motley Fool
Publication Date: 2026-06-02 07:04:00
The initial boom in artificial intelligence (AI) data center investment centered around hardware for training AI models — workloads that demand massive parallel-processing capabilities. Graphics processing units (GPUs) are powerful and flexible parallel processors, and that property propelled the rise of GPU specialist Nvidia (NASDAQ: NVDA) from an ordinary large cap worth around $350 billion at the start of 2023 to its current position as the world’s most valuable company. Today, it’s worth more than $5 trillion.
But data centers are growing in size, creating cost constraints and an AI energy bottleneck. What’s more, hyperscalers’ needs are evolving as AI inference becomes a growing part of the overall workload. While training is needed to build a model’s intelligence, inference applies that intelligence in real-world applications, such as through AI chatbots, AI agents, robotics, and self-driving cars.
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