Thousands? Maybe not, but hundreds? Yeah, for my freelancer/contracting gigs, it's easily worth $200/month to be able to say "How come X is like that and what change lead to Y being Z?", wait 20 minutes and then get an answer that jumpstarts understanding a completely new codebase. If AI/LLMs never evolved beyond their current skills and usefulness, I'd still be happy to pay $200/month for this.

However, I don't know a single developer who pays "thousands of dollars a month", not sure how you'd end up like that.

I most definitely am not.

From my vantage point AI consumption is being lead by tech leadership moreso than actual in-the-weeds programmers themselves. HN just happens to include more folk at the intersection of leadership and individual code contributor.

The top down push for AI is in line with the age old traditions of replacing highly skilled and highly compensated trade workers with automation. The writing is on the wall if folks care to look; many just don't want to. This has happened 1000 times before and it'll keep happening in the name of "progress" in capitalist systems for as long as there are "inefficiencies" to "resolve." AI is meant as our replacement, not as an extension of our skill as it happens to align with today.

Its increasingly obvious that the next phase in the evolution of the average programmer role will be as technical requirements writers and machine generated output validators, leaving the actual implementation outsourced to the machine. Even in that new role, there is no secret sauce protecting this "programmer" from further automation. Technical product managers eventually fall to automation given enough time and money poured into the automation of translating fuzzy, under specified ideas into concrete bulleted requirements where they can simply review the listed output, make minor tweaks and hit "send" to generate the list of jira-like units of work to farm out to a fleet of agents wearing various hats (architect, programming, validator, etc.)

The above is very much in progress already, and today I'm already spending the majority of my time reviewing the output of said AI "teams", and let me tell you: it gets closer and closer to "good enough" week by week. Last year's models are horse shit in comparison to what I'm using today with agentic teams of the latest frontier models (Opus 4.6 [1m] currently, with some Sonnet.)

Maybe we're at a plateau and the limitations inherent in GenAI tech will be insurmountable before we get to 100% replacement. But it literally won't matter in the end as "good enough" always prevails over the perfect, and human devs are far from perfect already.

I have been producing software (at fang scale) for several decades now, and I've been closely monitoring GenAI systems for coding specifically. Even just a few months ago I'd get a verbose, meandering sprawl of methods and logic scattered with the actual deliverables outlined in the prompt from these systems. Sometimes even with clear disregard of the requirements laid out, or "cheating" on validation via disabling tests or writing ones that don't actually do anything useful. Today I'm getting none of that. I don't know what changed, but I somehow get automated code with good separation of concerns, following best practices and proven architectural patterns. Sure, with a bunch of juniors let loose with AI you get garbage still, but that's simply a function of poor delegation of work units. Giving the individual developer and the AI too much leeway in the scope of changes is the bug there. Division of work into small enough units is the key and always has been for the de-skilling portion of automating away skilled human labor for machines. We're just watching Marxist theory on capitalist systems play out in real time in a field generally thought to be "safe." It certainly won't be the last.