i see many comments disregarding the take. while the arguments put forth may not be the strongest, but there is a point to this.
most people only chase the next thing you can build on top of what we have today. they only look back inside the layers when the current tech is not enough to achieve your goals out of the box.
a recent example i see is with the quantization and tinyml developments in machine learning. while it is easier than ever to create a model and to run it, the underlying architecture, previously only up to the people designing the frameworks, is now finally being looked at. only because the LLMs cannot fit inside the memory of elusive enterprise GPUs as easily as you'd like.
in no other instance would most people care about how numbers are stored in memory in the past 15-20 years in writing software. i think necessity is mother of invention, and that would probably still apply to dealing with abstractions going forward.
most people only chase the next thing you can build on top of what we have today. they only look back inside the layers when the current tech is not enough to achieve your goals out of the box.
a recent example i see is with the quantization and tinyml developments in machine learning. while it is easier than ever to create a model and to run it, the underlying architecture, previously only up to the people designing the frameworks, is now finally being looked at. only because the LLMs cannot fit inside the memory of elusive enterprise GPUs as easily as you'd like.
in no other instance would most people care about how numbers are stored in memory in the past 15-20 years in writing software. i think necessity is mother of invention, and that would probably still apply to dealing with abstractions going forward.