Agentic AI changes cloud economics

The fundamental mechanic of cloud computing is that it pools usage across hardware. Canonically, Netflix had peak traffic in the evening when people watch tv shows, and much less traffic the rest of the day. If they had built their own data centers they would need 5x the hardware to serve peak traffic compared to their average. Doing so would have been super expensive so they used AWS EC2 instead.

But for AWS it’s nowhere near as expensive to support Netflix’s peak load. Because AWS mixes the usage patterns of 10,000s of companies. For AWS the goal is to get their hardware as close to 100% utilization while maintaining enough nines of uptime to keep their customers happy.

I wrote about this long ago here.

Most services have a peak to trough pattern. Usage changes over the day based on when humans are doing things. If you combine thousands of slightly different usage patterns they average out. For AWS this lets them get far higher utilization patterns than any private cloud ever could. This economy of scale makes the Cloud extremely profitable for hyper scalers.

Agentic AI changes this because if I’m able to make money off of tokens I want to hit my GPUs with 100% utilization 24/7.

There is no peak to trough for Agentic AI. If I’m running one hundred agents via agent flywheel, my load pattern is a flat 100% utilization all day long. I’m not scaling up and scaling down. My live human traffic is bumping agent traffic to lower in the queue. But the instant my humans are done consuming resources, AI agents can go right back to consuming every GPU flop.

This makes a huge difference in the accounting when buying GPUs. We aren’t trying to fit a peak to trough usage pattern onto a static stack of servers. Instead we are comparing the cost of X peak GPU flops on-premise vs X peak GPU flops on cloud.