Moore’s law died a long time ago. Engineers pretended it was going on for years by abusing the nanometer metric, by saying that if they cleverly find a way to use the space more effectively then it is as if they packed more transistors into the same nanometers of space, and so they would say it’s a smaller nanometer process node, even though quite literal they did not shrink the transistor size and increase the number of transistors on a single node.

This actually started to happen around 2015. These clever tricks were always exaggerated because there isn’t an objective metric to say that a particular trick on a 20nm node really gets you performance equivalent to 14nm node, so it gave you huge leeway for exaggeration. In reality, actual performance gains drastically have started to slow down since then, and the cracks have really started to show when you look at the 5000 series GPUs from Nvidia.

The 5090 is only super powerful because the die size is larger so it fits more transistors on the die, not because they actually fit more per nanometer. If you account for the die size, it’s actually even less efficient than the 4090 and significantly less efficient than the 3090. In order to pretend there have been upgrades, Nvidia has been releasing software for the GPUs for AI frame rendering and artificially locking the AI software behind the newer series GPUs. The program Lossless Scaling proves that you can in theory run AI frame rendering on any GPU, even ones from over a decade ago, and that Nvidia’s locking of it behind a specific GPU is not hardware limitation but them trying to make up for lack of actual improvements in the GPU die.

Chip improvements have drastically slowed done for over a decade now and the industry just keeps trying to paper it over.