In my experience, poor performance is only very occasionally the fault of abstractions and frameworks. The problem is near universally between the chair and the

In my experience, poor performance is only very occasionally the fault of abstractions and frameworks. The problem is near universally between the chair and the keyboard. I don't think we'll be throwing away the old abstractions, but adding new ones in beside them. As an example, I made a demo a couple years back which processed and rendered about 64 MB of CNC G Code (a programming language for telling robots how to move and spin) in a web browser to match CNC toolpaths (the path of the robots motion) to their realtime run statistics (show me a realtime 3d map of where we can speed up the robot). To do this, you need to simulate the movements of the machine to build a 3d model, and then pair the model to collected data. You then need to cull/average data which is too fine to be visible, and ship this processed data to the browser as arrays of floating point numbers, where it gets rendered as -effectively- thousands of colored 3d arcs. The naive implementation took minutes to run. The final version took about 1 second. I didn't have to throw away any abstractions or frameworks to make things faster. I had to use my language's new abstractions for massively parallel processing and stack allocated data to speed up server side processing, and then use libraries for decoding base64 binary into floating point arrays on the browser side to speed up data transfer and parsing. I guess my point is that the end of Moore's law doesn't mean throwing old things away, it means using existing solutions when appropriate, and approaching new problems in different ways.