This article strikes at the heart of the weirdness in the software industry right now. Programmers making $200K+ are no longer writing code but instead simply supervising Claude Code which writes the code for them.

But instead of being afraid for their jobs, they are excited by how much more productive they are.

https://www.nytimes.com/2026/03/12/magazine/ai-coding-programming-jobs-claude-chatgpt.html?unlocked_article_code=1.SlA.udVF.I5aQFIDnpN1t&smid=url-share

Coding After Coders: The End of Computer Programming as We Know It

In the era of A.I. agents, many Silicon Valley programmers are now barely programming. Instead, what they’re doing is deeply, deeply weird.

The New York Times
@carnage4life I got bored a third of the way through, but it seems like all the enthusiasts are already financially secure. It’s easy to love steamrollers if you’re just watching it from a safe distance
@carnage4life I am… optimistic… my job is secure. There’s this paper that was submitted to arXiv on the 5th that evaluates a couple of models on how well they deal with maintaining a long term codebase all of which I believe showed a non-zero regression rate. https://arxiv.org/abs/2603.03823
SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration

Large language model (LLM)-powered agents have demonstrated strong capabilities in automating software engineering tasks such as static bug fixing, as evidenced by benchmarks like SWE-bench. However, in the real world, the development of mature software is typically predicated on complex requirement changes and long-term feature iterations -- a process that static, one-shot repair paradigms fail to capture. To bridge this gap, we propose \textbf{SWE-CI}, the first repository-level benchmark built upon the Continuous Integration loop, aiming to shift the evaluation paradigm for code generation from static, short-term \textit{functional correctness} toward dynamic, long-term \textit{maintainability}. The benchmark comprises 100 tasks, each corresponding on average to an evolution history spanning 233 days and 71 consecutive commits in a real-world code repository. SWE-CI requires agents to systematically resolve these tasks through dozens of rounds of analysis and coding iterations. SWE-CI provides valuable insights into how well agents can sustain code quality throughout long-term evolution.

arXiv.org

@carnage4life another part of the problem is the person writing the prompts. There are those who use AI to codegen because they have no experience, and there are those who use it because it’s essentially a keystroke and therefore time saver to some degree.

The latter case will be more likely to correct their AI response when they’re architecturally wrong “long term”. You HAVE to be like Fletcher from Whiplash if you don’t want this codebase you’re using AI on to quickly turn to a ball of mud.

@carnage4life it feels like pair programming or doing a live code review

@carnage4life @cate a very weird take, like a glimpse into another reality. “You might imagine this would unsettle and demoralize programmers. Some of them, certainly. But I spoke to scores of developers this past fall and winter, and most were weirdly jazzed about their new powers.”

yeah because a decent number of us who are “unsettled and demoralized” (and got laid off thanks to AI boosterism) are simply… not around to be interviewed about workflows anymore. survivorship bias at its best 🤷🏻