But: programming languages, libraries, and abstractions are not going away. It is still possible (and might always be possible) to get deep into the weeds of Python or Rust or whatever to understand how those work and really harness them to their full potential, or develop them further. It just won't be _compulsary_ (in most industries) if your only goal is to trade lines of code for dollars in your bank account.
Lines of code for dollars used to be a trade businesses made with developers out of necessity, but soon it will only be economically viable to make that trade with AI providers. So not only will going deep in the weeds not be compulsory, understanding anything about any programming concept will become economically void (though not void of educational value, or enjoyment).
On the other hand, what that code does depends entirely on a particular understanding of the real world, which is indescribably complex (i.e. combinatorially explosive). This is what I truly care about, and the possibilities for the application and customization of software are infinite. The interface between the world and software will always involve a value decision that AI cannot have a monopoly over (it would be economically infeasible, no matter how cheap inference becomes). This means that as long as my passion is not within the machine, but is instead centered on the relationship between the machine and the world, I will never be out of a job.
And part of me thinks, "good riddance!". For all the good we created, developers have also generated so much bullshit, it's honestly insane that any software companies were ever successful in spite of it. The human-politicking is probably the worst of it - think of the countless years of human life wasted in scrum ceremonies - but also so much of the software we've created sucks, and users hate it!
We used to be a proud culture of hackers, building miracles with miniscule resources, or at least that's what the greybeards here on HN like to whine about. They're right, we've squandered limitless cycles, uncountable exabytes of useless data. If there was a God of hackerdom, we are living in his Gomorrah, and he will strike us down with AI as punishment for these sins.
My honest and rather pessimistic take is that in the long-term any craft that purely lives in the abstract is likely to be doomed.
Take even 1 simple example - software applications on a smart watch. How many dimensions of reality are relevant? Maybe I'm a busy person, so I need a personal assistant for my calendar. Maybe my wife needs access too. Maybe I'm a bird watcher and I'd like to track the birds I see. Maybe I'm a bird researcher and those observations need to integrate with my research.... ad nauseum forever.
AI will write all the code, and make all the meaningful decisions, but the backstop of the whole thing has to be some non-virtual reality with a paying user, otherwise there is no value to extract.
I personally only care about the outcome, I don't even really care if I understand how anything else works, or any of the decisions made. My dollars go in, working code comes out to suit me.
What I was getting at is that nothing stops you from asking AI what would be the next best smartwatch app to build, and based on all its aggregated knowledge and other inputs (e.g. search) it has, it can potentially make a better estimation than you or any human of a product that would sell.
Of course whether that is actually true depends on how well its training data is able to model/mimic reality, and how grounded its inputs (e.g. internet) are. You can always help it a bit by steering it into the right direction, providing additional grounding. Was mainly wondering for how long this "additional" guidance would be a necessity, fearing that it won't be for as long as we think.
We definitely saw some kind of non-linear step function jump in quality around the beginning of the year - it's hard to express how good Claude opus/sonnet 4.6 is now. However, I wonder if we're going to see the same kind of improvement from here? It's kind of like we got to the 80% point but the next 20% is going to be a lot harder/take longer than that first 80% (pareto principle). Also, as more and more code out there is AI generated it's going to be like the snake eating it's own tail. Training models on AI generated code doesn't seem like it will lead to improvements.
Your job has always been to be accountable for the technical decisions made. Sometimes I read comments like yours and wonder if the almost apologetic tone is an admission of guilt that you never really liked this job. You can speak for yourself, but I didn't get into this to just noodle around or work at unhinged startups where nobody gives a rat's ass about code quality.