"Always", in the same way that five years ago we'd "never" have an AI that can do a code review.
Don't get me wrong: I've watched a decade of promises that "self driving cars are coming real soon now honest", latest news about Tesla's is that it can't cope with leaves; I certainly *hope* that a decade from now will still be having much the same conversation about AI taking senior programmer jobs, but "always" is a long time.
And the LLMs can use the static analysis tools.
I did not say that.
That it can *also* use tools to help, doesn't mean it can *only* get there by using tools.
They can *also* just do a code review themselves.
As in, I cloned a repo of some of my old manually-written code, cd'd into it, ran `claude`, and gave it the prompt "code review" (or something close to that), and it told me a whole bunch of things wrong with it, in natural language, even though I didn't have the relevant static analysis tools for those languages installed.
Well sure, but was the result any better than that of installing and running the tools? If the AI can provide better or at least different (but accurate!) PR feedback from conventional tools, that's interesting. If it's just offering the same thing (which is not really "code review" as I'd define it, even if it is something that code reviewers in some contexts spend some of their time on) through a different interface, that's much less interesting.
Someone with a name, an employment contract, and accountability is needed to sign off on decisions. Tools can be infinitely smart, but they cannot be responsible, so AI will shift how developers work, not whether they are needed.
If AI becomes powerful enough to generate entire systems, the person supervising and validating those systems is, functionally, a developer — because they must understand the technical details well enough to take responsibility for them.
Titles can shift, but the role dont disappear. Someone with deep technical judgment will still be required to translate intent into implementation and to sign off on the risks. You can call that person "developer", "AI engineer" or something else, but the core responsibility remains technical. PMs and QA do not fill that gap.
LLMs can already do that.
What they can't do is be legally responsible, which is a different thing.
> Responsibility is about being able to judge whether the system is correct, safe, maintainable, and aligned with real-world constraints.
Legal responsibility and technical responsibility are not always the same thing; technical responsibility is absolutely in the domain of PM and QA, legal responsibility ultimately stops with either a certified engineer (which software engineering famously isn't), the C-suite, the public liability insurance company, or the shareholders depending on specifics.
Ownership requires legal personhood, which isn't the same thing as philosophical personhood, which is why corporations themselves can be legal owners.
They are powerful tools but they are not engineers.
And its not about legal responsibility at all. Developers dont go to jail for mistakes, but they are responsible within the engineering hierarchy. A pilot is not legally liable for Boeing's corporate decisions, and the plane can mostly fly on the autopilot, but you still need a pilot in the cockpit.
AI cannot replace the human whose technical judgment is required to supervise, validate, and interpret AI-generated systems.
Like everything else they do, it's amazing how far you can get even if you're incredibly lazy and let it do everything itself, though of course that's a bad idea because it's got all the skill and quality of result you'd expect if I said "endless hoarde of fresh grads unwilling to say 'no' except on ethical grounds".
regulation still very much a thing
Musk's claims about what Tesla's would be able to do wasn't limited to just "a few locations" it was "complete autonomy" and "you'll be able to summon your car from across the country"… by 2018.
And yet, 2025, leaves: https://news.ycombinator.com/item?id=46095867