NNs for example are (mostly) a sequence of matrix multiplication operations, and GPUs are very good at those. Much better than CPUs. AI is hot at the moment, and Nvidia is producing the kind of hardware that can run large models efficiently which is why it's a 2 trillion-dollar company right now.

However, in the Spiral series, I aim to go beyond just making an ML library for running NN models and break new ground.

Newer GPUs actually support dynamic memory allocation, recursion, and the GPU threads have their own stacks, so you could in fact treat them as sequential devices and write games and simulators directly on them. I think once I finish the NL Holdem game, I'll be able to get over 100x fold improvements by running the whole program on the GPU versus the old approach of writing the sequential part on a CPU and only using the GPU to accelerate a NN model powering the computer agents.

I am not sure if this is a good answer, but this is how GPU programming would be helpful to me. It all comes down to performance.

The problem with programming them is that the program you are trying to speed up needs to be specially structured, so it utilizes the full capacity of the device.