| Here would be my guess: * GPU programming (GPUs have consistently kept up with Moore-like laws) * FPGA/ASIC design (hard but price for all of these is dropping rapidly, so becoming more accessible) * Bitcoin/cryptocurrency related tech, including standing up your own miner, full node, or understanding how to build applications on top of it (web3/etc.) (despite the hate, cryptocurrencies are still around and thriving) * Solar and battery related tech (solar prices continue to drop, as does battery technology. Consumers ROI on solar installations are approaching 2-5 years instead of 10+). Understanding "fundamentals", either in terms of computer science education or mathematics, I think is also critical but I don't really know what fundamental math should be focused on, in the short term. It's easy to say "neural networks" but proficiency in that area is mostly about learning frameworks (as a snapshot of right now) and little to do with some underlying theoretical understanding. In terms of specific languages or frameworks, just a word of warning. What language/frameworks that were popular 10 years ago are still relevant today? Many people gain utility both from using and from being paid to manage frameworks (and to a certain extent languages) but they tend to be ephemeral. One piece of advice that I think was pretty good was to avoid the "stampeding hoards". One can "win" at the game of being the best at what's fashionable now but the greater utility is in understanding more fundamental skills with the added benefit of, should a skill become fashionable later, being well versed in it when it does. |