The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 13 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 2 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
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.”
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…”
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.”
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…”
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…”
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.”
AI inference spending must surge to justify current adoption optimism
“If the optimism about AI is to be justified, you're going to have to see inference costs go way up because that will be an indicator that adoption has gone up in a fairly significant way, both among individual users, but also among companies and enterprise use…”
Generative AI is reconditioning users to tolerate multi-second latency delays
“What is interesting with generative AI is that we are being reconditioned to tolerate much longer delays. So if you use something like GPT or you use something like Claude or your favorite chatbot, oftentimes it's just sitting there thinking for seconds and se…”
AI training compute cycles cause massive power fluctuations unlike inference workloads
“And some of the people in the energy space may know that there are massive energy fluctuations or power fluctuations we will see in data center usage when the GPUs go from this computational intensive phase where you're learning the model weights to this commu…”
LLM inference prompts are processed locally on one to eight GPUs
“When you send a prompt to for processing by a large language model, that prompt is probably handled by one GPU or maybe Eight GPUs inside a single machine. So, and the reason that is, is because the model sits in that machine, the data sits in that machine, an…”
Competition on AI latency will force local GPU deployments in major markets
“I think the catch there will be if one of these model providers or one of these application developers makes performance a distinguishing feature of their offering, right? If they start competing on performance rather than on capability, then we're going to se…”
Google achieves a data center power usage effectiveness near 1.1
“Google's PUE is close to 1.1, which is to say for every watt going to compute, there's an additional .1 watts going to the overheads of power delivery or cooling or whatever.”
Meta study: AI energy costs split evenly across pre-processing, training, and inference
“There was a study we did when I was a visiting research scientist at Meta where we Found that energy costs for AI were roughly broken into three categories. There's a data pre-processing aspect as well, and that's about a third. The training is another third, …”