I mean the foundations didn't go away, they just got more profound (advances in programming language design, distributed algorithms, formal methods etc.). Previously closed down layers just got open sourced (RISC-V, FPGAs). I estimate that 98% of all engineering efforts are always hidden beneath some facade that takes away its spotlight (through politics, committees, marketing etc.). I'm close to 15 years in and there are still programming languages, data structures or protocols I never heard of.
The world was never as complex as it is today, advancements were never that accelerated, and expectations on scalable software were never this high. Do you really buy the marketing fuzz that the work is "done" just because your software runs on hyperscaler #3 or in a k8s cluster? The amount of available open source projects steadily increases, those can (and should) be used to learn from and contribute something back. Free and open source software is used everywhere and whole businesses are built on some, yet Linux and all those other projects are just increasing in complexity. Sure, everybody wants to be the expert and yet nobody really is. Fact is, unfinished projects are everywhere and there's a lot of work to be done.
LLMs have the chance to make personal computing even more personal and should be treated as valuable assistents to learn with. LLMs won't ever be the desired oracles of some kind (yes, I don't buy that "AGI is near" crap), they'll rather be good personal sparing partners. APIs still break constantly and there are transient errors everywhere. I can imagine some small shops and personalized apps, yet people that aren't into tech won't magically get into it because of some progress in machine learning. If you're in it just for the money times might get challenging here and there (what isn't?), but if you're in it for the engineering times can look pretty bright, as long as we make good use of our ambitions. There are still some engineering efforts to take before a smartwatch can also act smart in isolation. Our tooling just took a leap ahead - go make use of it, that's it.
The world was never as complex as it is today, advancements were never that accelerated, and expectations on scalable software were never this high. Do you really buy the marketing fuzz that the work is "done" just because your software runs on hyperscaler #3 or in a k8s cluster? The amount of available open source projects steadily increases, those can (and should) be used to learn from and contribute something back. Free and open source software is used everywhere and whole businesses are built on some, yet Linux and all those other projects are just increasing in complexity. Sure, everybody wants to be the expert and yet nobody really is. Fact is, unfinished projects are everywhere and there's a lot of work to be done.
LLMs have the chance to make personal computing even more personal and should be treated as valuable assistents to learn with. LLMs won't ever be the desired oracles of some kind (yes, I don't buy that "AGI is near" crap), they'll rather be good personal sparing partners. APIs still break constantly and there are transient errors everywhere. I can imagine some small shops and personalized apps, yet people that aren't into tech won't magically get into it because of some progress in machine learning. If you're in it just for the money times might get challenging here and there (what isn't?), but if you're in it for the engineering times can look pretty bright, as long as we make good use of our ambitions. There are still some engineering efforts to take before a smartwatch can also act smart in isolation. Our tooling just took a leap ahead - go make use of it, that's it.