AI Needs More Than Just Big Datacenters
For years, companies focused on building massive datacenters to train large AI models. This worked well when the goal was just to create the biggest possible software. Now that more businesses use AI, the focus is shifting toward day to day tasks.
Running AI applications requires a different approach than training them. While training takes place in huge, centralized hubs, real world tasks happen best near the user. This means we need smaller, faster systems located closer to where the work gets done.
The future of AI depends on this shift. We need infrastructure that prioritizes speed and efficiency over pure size. Success now relies on how well these systems perform when people actually use them.
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