AI safety research posits that there are certain goals that will always be wanted by any sufficiently smart AI, even if it doesn't understand them anything close to like a human does. These are called "instrumental goals", because they're prerequisites for a large number of other goals[0].
For example, if your goal is to ensure that there are always paperclips on the boss's desk, that means you need paperclips and someone to physically place them on the desk, which means you need money to buy the paperclips with and to pay the person to place them on the desk. But if your goal is to produce lots of fancy hats, you still need money, because the fabric, machinery, textile workers, and so on all require money to purchase or hire.
Another instrumental goal is compute power: an AI might want to improve it's capabilities so it can figure out how to make fancier paperclip hats, which means it needs a larger model architecture and training data, and that is going to require more GPUs. This also intersects with money in weird ways; the AI might decide to just buy a rack full of new servers, or it might have just discovered this One Weird Trick to getting lots of compute power for free: malware!
This isn't particular to LLMs; it's intrinsic to any system that is...
1. Goal-directed, as in, there are a list of goals the system is trying to achieve
2. Optimizer-driven, as in, the system has a process for discovering different behaviors and ranking them based on how likely those behaviors are to achieve its goals.
The instrumental goals for evolution are caloric energy; the instrumental goals for human brains were that plus capital[1]; and the instrumental goals for AI will likely be that plus compute power.
[0] Goals that you want intrinsically - i.e. the actual things we ask the AI to do - are called "final goals".
[1] Money, social clout, and weaponry inclusive.