Why? Unlike loads involving a real physical process there is absolutely no need for AI-training to be constant.

I'm hoping the kind of gas turbines being installed on these datacenters are capable of quickly responding to a load change, meaning the primary source of energy could be solar during the day and complement when there isn't enough energy.

But I haven't looked into where these datacenter are being placed, I'm assuming that although solar is cheap now, the surface needed would make the purchase of nearby land probably not worth it. These new categories of datacenters are becoming very energy dense...

Solar power still can take a certain amount of load from any other source and saving money and CO2 while doing so.

And grid battery works and is cheap enough now.

but you can't trust especially hyperscalers with securely sealed HEUs in shipping containers

you're just incorrect. You probably missed 2 points :

- battery storage

- and in the article

"However, AI labs and some hyperscalers have relaxed those requirements as there is now a lower uptime tolerance applied to both inference and training, not just training. Many of Meta’s self-built AI datacenters, for example, target just two nines of uptime and forgo backup generators entirely, as detailed in our Industrials Model."

That's why we are getting clean, beautiful coal the likes of which nobodys ever seen before!