Jevons paradox: large purpose-fit data centers increase efficiency such that you can use AI in more places, and use more tokens for those tasks.
The future is not a single chat bot session of bs=1. The future is many agents performing many tasks in parallel for a single user. Large GPU clusters will always have the edge in efficiency.
> The future is many agents performing many tasks in parallel for a single user. Large GPU clusters will always have the edge in efficiency.
Agentic AI has pluses and minuses for cloud efficiency. The plus is that usage could be very bursty, but the minus is that agents will more fully utilize a local system. The main disadvantage of local AI is that you would be paying a large amount for a system mostly doing nothing. If it's constantly working on different projects and integrating that data, you get use out of every penny that you spent. Every GPU you added would instantly make the thing smarter.
What's more, your local AI could offload an agent to the cloud if it needed to. It could do this rationally, based on your personal desire for privacy.
Jevon’s paradox suggests that total datacenter resource consumption will increase. It doesn’t say that people will choose datacenters over their own personal hardware when the latter is sufficient.
Personal hardware is only sufficient today for some tasks. As data center power efficiency, and large sparse MoE task efficiency increase, personal computing will continue to lose out.
The future is not a single chat bot session of bs=1. The future is many agents performing many tasks in parallel for a single user. Large GPU clusters will always have the edge in efficiency.