Dr. Ben Lee explains the operational and energy efficiency advantages of hyperscale cloud data centers compared to enterprise facilities.
Prediction Not checkable as stated
Siting distributed edge compute will become easier than 1GW data centers
“I think finding capacity there may eventually become easier than finding the next thousand megawatts.”
Prediction Open · timeframe Dec 2035
80% of AI inference compute will move to the edge by 2035
“So I would say that we could be getting 80% of our compute done locally and leaving 20% of the heavy lifting or the more esoteric, the more corner case compute for the data center cloud. That is, of course, excluding the training. The training will Continue to…”
Prediction Not checkable as stated
Cyber-physical AI applications will require edge computing for low latency
“So I agree that there will be cases where we will need those really low latencies, and that is going to require edge computing much closer to the user, so we have much shorter internet delays, network delays.”
Prediction Not checkable as stated
If AI scaling slows, repurposed central GPUs will cannibalize edge data centers
“And if it turns out that maybe there are diminishing returns from training larger and larger models, or maybe we run out of data because we've exhausted all the data that's available on the internet. When those things happen, it may be that demand for these GP…”
Insight
Shifting inference to edge data centers will reduce efficiency and increase energy costs
“I think as you shrink the system down, you will get, you will lose an efficiency. You will be trying to build these 20 megawatt data centers and maybe footprints or facilities that weren't designed initially for those workloads. So yes, I think total energy co…”
Prediction Open · timeframe Dec 2030
Software agents, rather than human queries, will drive most AI inference workloads
“I think increasingly most of the inference workload will come from other software agents.”