Ya I actually bought an Nvidia GeForce RTX 2070, specifically to play around with its ray tracing capabilities. I kind of wish I could play around with a Google

Ya I actually bought an Nvidia GeForce RTX 2070, specifically to play around with its ray tracing capabilities. I kind of wish I could play around with a Google TensorFlow processor (TPU): https://en.wikipedia.org/wiki/Tensor_processing_unit https://en.wikipedia.org/wiki/Tensor_processing_unit It's an ok chip and has some of the ideas that I mentioned (like an 8 bit design that could possibly emulate 64 bit and higher via microcode or software) but I'm afraid that it's too narrowly scoped to be of much use in other domains. Companies seem to be exploring branches of the parallel/DSP problem space rather than providing general solutions like the Xeon Phi. I'm not totally sold on high multicore (say 64 or more cores), but it's the only way that I can see forward since Moore's law ended. It would be much better to have increased single-threaded performance since that can emulate multicore. Multicore can't emulate single-threaded above Amdahl's limit of about 4x, which is why very few chips today have more than 4 or 8 cores. That said, most of the problems that I find interesting are also embarrassingly parallel. And since there is currently no consumer high-multicore processor, I can't explore those problem spaces. From first principles, it should have gone: 1) Single core: 6502, z80, 286 2) Low multicore: Pentium, POWER 3) High multicore (MIMD): Xeon Phi, Parallella 4) Optimized vector processing (SIMD): GPU, TPU Without a viable #3, we have no way of knowing if #4 is an optimal solution or an evolutionary dead end. We have to drink the kool-aid and proceed out of trust. That's the part that I find extremely disappointing about the last 20-25 years. Another way to say that is: if there is no market fit for ever-faster processors at cheaper prices.. that consumers would rather have low-cost mobile processors and regressively rent time on data centers.. that they'd rather have photorealistic rasterized video games than true supercomputing at home, then computer science is at the very least wounded (and I would argue, slowly dying). This is a nuanced issue that the mainstream isn't even aware of. I'm excited to see some people trying to get projects like Parallella off the ground. From what I can tell, they are one of the only chips working from first principles: https://en.wikipedia.org/wiki/Adapteva https://en.wikipedia.org/wiki/Adapteva But as you pointed out, it may take more than enlightened engineering to bridge the gap to next-gen computing. That's the reason why we are only today seeing advances in machine learning that were fairly well understood by 2000. I just don't want to wait around another 20 years for the other good stuff to get here. Which is why I think it might take an open source design on inexpensive FPGA hardware to show people what they're missing.