PC Gaming

This is kind of the opposite of that idea though. This is saying that not everything put on the screen needs to be computed from the game engine. Some of the content on the screen can be inferred from a predictive model. What remains to be seen is if that requires less computing power from the GPU.

Yes, but with DLSS we're adding ML models to the mix where each one has been trained on different aspects:

Interpreting between frames
For instance, normally you might get 30FPS, but between the frames the ML model has an idea of what everything should look like (based off of what it has been trained on), so it can insert additional frames to boost your framerate up to 60FPS or more.

Upscaling (making the picture larger) - the CPU and other hardware can do work on a smaller resolution which makes their job easier, while the ML model here has been trained on how to make the image larger while filling in the correct pixels so that everything still looks good.

Optical Flow -
This ML model has been trained in motion which objects/pixels go where so that better prediction of frame generation can be achieved.

Not only that but Nvidia can update us with the latest ML models that have been trained on specific game titles using their driver updates.

While each of these could be accomplished with older techniques, I think the results we're already seeing speak for themselves.

Edit: added some sources below and fixed up optical flow description.

https://www.digitaltrends.com/computing/everything-you-need-to-know-about-nvidias-rtx-dlss-technology/
https://www.youtube.com/watch?v=pSiczcJgY1s

It has yes, however the techniques Carmack used in Doom's engine probably don't have much of an impact on something like Cyberpunk 2077.

In this context it is being used to reduce rendering load and therefore be less intensive on computer resources.

If you're trying to compare "AI" and electrical use you need to compare every use case to how we traditionally do things vs how any sort of "AI" does it. Even then we need to ask ourselves if there's a better way to do it, or if it's worth the increase in productivity.

For example, a rain sensor on your car.
Now, you could setup some AI/ML model with a camera and computer vision to detect when to turn on your windshield wipers.
But why do that when you could use this little sensor that shoots out a small laser against the window and when it detects a difference in the energy that's normally reflected back it can activate the windshield wipers.
The dedicated sensor with a low power laser will use far less energy and be way more efficient for this use case.

On the other hand, I could spend time/electricity to watch a Video over and over again trying to translate what someone said from one language to another, or I could use Whisper (another ML model) to quickly translate and transcribe what was said in a matter of seconds. In this case, Whisper uses less electricity.

In the context of this article we're talking about DLSS where Nvidia has trained a few different ML models for upscaling, optical flow (predicting where the pixels/objects are moving to next), and frame generation (being able to predict what the in-between frames will look like to boost your FPS).

This can potentially save energy because it puts less of a load on the CPU, and most of the work is being done at a lower resolution before upscaling it at the end. But honestly, I haven't seen anyone compare the energy use differences on this yet... and either way you're already using a lot of electricity just by gaming.

This is a reference to upscaling algorithms informed by machine learning a la Nvidia's DLSS -- seems like AMD is finally going to add the inference hardware to their GPUs that will let them close that technological gap with the competition. I'm guessing it won't come until RDNA5, though.