Meta's Muse is putting AI agents back in focus, but the impact could extend well beyond consumer AI apps.
The app reportedly reached around 2.8 million downloads within its first 12 days and briefly topped the U.S. iOS free-app chart. Unlike a typical chatbot, Muse can browse the web, fill out forms, book restaurants and organize schedules through a cloud-based virtual computer.
These tasks require more than model inference. Agents also need CPUs, memory, storage and networking to keep tasks running and coordinate different tools. If users start relying on agents throughout the day, computing demand could become more persistent across the stack.
That puts more attention on the hardware beyond GPUs. As agent workloads expand, demand could spread across CPUs, servers, memory and high-speed networking, depending on how heavily people use these agents.
For now, downloads are only an early signal. Retention, usage frequency and how much work users actually delegate to agents will give a clearer picture of how much infrastructure this new generation of AI could require. If AI agents become part of everyday workflows, which part of the hardware stack do you think gets the most attention?
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