this post was submitted on 21 Oct 2025
        
      
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I can give you one use case that has a public benefit. My brother works in research informatics at a children's hospital. They use ai to identify children with rare diseases. My understanding is it tracks patterns of appointments and symptoms and matches the patients with specialists. Typically these patients wouldn't be identified for years because doctors are looking for common ailments before any exotic disease.
There is lots of uses for urban planning related to population growth and census statistics as well.
I'd be curious to see data on the benefits, but assuming what you say is true: this example in medicine sounds like a pretty basic kind of machine learning and not something that requires massive energy-hungry data centers.
Same with the urban planning example. These are not the applications that require "sovereign AI compute" at scale. Those would be the generative AI applications like chatbots and image/video generators, as far as I understand these things.
AI data centres are usually about giant LLMs and agentic bots. “Ai” as in machine learning doesn’t need giant data centres and has been progressing quite well without them.
The term “AI” tends to get thrown around to claim all the benefits of the entire field to excuse the excesses of a very narrow slice.
These applications are great, but they’re not what these compute centers are for. For those applications, a regular supercomputer will do. Those gigantic and power hungry data centers are used for LLM training, which is a VC-funded arms race that we don’t actually need to partake in.