Hm since the discourse is mostly on how AI changes this, I still want to make a point that it's worth learning programming (and more), even if I'm a huge proponent of AI being economically valuable.
Humans at the moment are still better at building on our own knowledge and compounding our effort:output ratio, due to our superior plasticity. See https://joyemang33.github.io/blog/2026/humans-dont-just-samp... for a small experiment. That's why for programmers experienced with a particular language/codebase/domain, it feels like minimal effort to know where and what to change. "Why use LLMs when I can do it in 10 minutes"?
Of course, relying on our long term memory instead of re-ingesting the code, is also a problem for having assumptions that don't get updated, e.g. revisiting a codebase that's been updated by many others. With the rate of change/churn of codebases I'm wondering if this is naturally forcing us to use AI anyways, since it's really hard to keep track of a lot of code changes by AI as a human. Once you start down the path of not reading code yourself, the friction to start reading code gets higher in a feedback loop. I know people (incl. myself) who are still reading code in their own coding/agent sessions, but that's unsustainable in an organizational setting - you won't be able to ingest N'0,000s of LoC of changes every week if there is no incentive to do so.
Is that a good thing? It's unlikely LLMs in their current state will be "neuroplastic" for a single person's use case (mostly market reasons - cost of owning, running, tuning models will likely be unattainable for the foreseeable future). In a few generations, this would be a total surrender of all foundational coding to LLMs (unsure about knowledge work in other domains). Will text files with superintelligent models be enough to encode long term and broad understanding of a domain? Is it necessary if a model is superintelligent? What if a person/nation/species loses access to LLMs through supernatural / political reasons at that point? Will it be like losing the last remaining COBOL programmers to your mainframe? I'm sure we're resilient enough to re-learn all of that, but it will be a huge crutch like never before.
All that to say, it's still worth learning programming, and doing some things by hand, even if it's not "economically valuable" (at least on the surface). Even with web frameworks, it's still worth manually plumbing between frontend/backend/database. Even with interpreters and compilers, it is still worth understanding and doing a bit of assembly. Even with assemblers, it's still worth understanding machine code, CPU and architecture quirks. It's worth looking at the historical artifacts (both senses of the word) and understanding intents, constraints, and creativity (as another comment [1] has more beautifully said).
Two "further than programming" courses that also embody the spirit of the article (get interested, hands-on, play) have been impactful on my appreciation of computing systems are nand2tetris [2] and OS in 1000 lines [3]. Both are "easy" in the sense that they make certain design decisions so you don't have to dig through thousands of pages of manuals. If you wish, during or after the process, you can explore specific concepts further. It's broken down in the right pieces and checkpoints, so you get quick feedback on whether something is working or not. It's still very much a puzzle you have to solve, but the right difficulty/complexity ladder (great instructional design).
[1] https://news.ycombinator.com/item?id=49093468
[2] https://www.nand2tetris.org/
[3] https://operating-system-in-1000-lines.vercel.app/en/