While spinning, the blades store a miniscule amount of kinetic energy.
After removing power even that small amount ends up as heat through friction ( both in the bearing but mostly in the air turbulence). And the blades end up in the same zero energy state: sitting still.
Overhead high voltage conductors are not insulated with a coating, probably for many reasons but certainly for cost and heat dissipation.
That means the path through the air to some conducting materials needs a certain distance, and that even when wet or iced over or whatever can happen up there.
This view is just very extreme, it is much less zig zag. It is just mounted to the wall at the high points and slack in between. Certainly there is also a reason for the exact amount of slack like thermal expansion.
The money you buy stock with l goes to the former/selling shareholder, which is most often not the company. It is possible the company is holding its own stock and selling for cash, or emitting new shares for cash, but that is much much rarer.
Remember that you are supposed to replace the entire thing because the other components like the sensor or simply capacitors also age. It is a very cheap safety device and simply not worth taking any risks by stretching it to say 15 years instead. The proper way would be to replace them while they were all still fine by making a note in the calendar.
There are two cases:
Your products are faulty and at least one has not made their intended 10 year lifespan. I'd change them all for better ones.
Or
They have reached their lifespan and you only noticed because the first one failed. I'd replace them all.
> Couldn't LLM provider just fine-tune their model for these tasks specifically - since they are static - to get ad value?
They could. They would easily be found out as they loose in real world usage or improved new unique benchmarks.
If you were in charge of a large and well funded model, would you rather pay people to find and "cheat" on LLM benchmarks by training on them, or would you pay people to identify benchmarks and make reasonably sure they specifically get excluded from training data?
I would exclude them as well as possible so I get feedback on how "real" any model improvement is. I need to develop real world improvements in the end, and any short term gain in usage by cheating in benchmarks seems very foolish.
After removing power even that small amount ends up as heat through friction ( both in the bearing but mostly in the air turbulence). And the blades end up in the same zero energy state: sitting still.
So it is correct that a 100% "end up" as heat