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Now coding itself is being automated. To outsiders, what programmers are facing can seem richly deserved, and even funny: American white-collar workers have long fretted that Silicon Valley might one day use A.I. to automate their jobs, but look who got hit first! Indeed, coding is perhaps the first form of very expensive industrialized human labor that A.I. can actually replace. A.I.-generated videos look janky, artificial photos surreal; law briefs can be riddled with career-ending howlers. But A.I.-generated code? If it passes its tests and works, it’s worth as much as what humans get paid $200,000 or more a year to compose.

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His answer echoed what I’ve heard from pretty much every developer I’ve spoken to: A coder is now more like an architect than a construction worker. Developers using A.I. focus on the overall shape of the software, how its features and facets work together. Because the agents can produce functioning code so quickly, their human overseers can experiment, trying things out to see what works and discarding what doesn’t. Several programmers told me they felt a bit like Steve Jobs, who famously had his staffers churn out prototypes so he could handle lots of them and settle on what felt right. The work of a developer is now more judging than creating.

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For most of the coders I met, learning to work with A.I. means learning to talk to A.I. This struck me as an unexpected paradox of this new age, because traditionally coding was a haven for introverts who preferred to talk as little as possible to others at work. But now their entire job involves constantly chatting with this alien life form.

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A vast majority of software developers aren’t working in greenfield contexts. They’re “brownfield,” employed by mature companies, where the code was written years (or decades) earlier and already reaches millions or billions of lines. Rapidly adding new functions is usually a terrible idea — they might accidentally conflict with another part of the code and break something that millions of customers rely on. At most mature software firms, coders historically spent a minority of their time — sometimes barely more than an hour per day — actually writing code. The rest was planning, hashing out priorities and meeting to discuss progress.

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The A.I. took about eight minutes to figure things out, he told me. “By the time I’d opened my laptop, it’s ready.” One customer recently told him that Amazon’s A.I. agent fixed a problem in only 15 minutes; when a similar problem occurred months before, it had taken a full team of engineers eight hours to debug.

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It certainly could mean terrible job prospects. New computer-science graduates are particularly concerned. Companies used to hire junior developers to do the menial labor for their senior colleagues, but who is going to hire a neophyte when a senior engineer can be even more productive with an army of deathless code-writing ghosts?

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But there’s evidence that A.I. is now eroding entry-level coding jobs. Last year, Erik Brynjolfsson, an economist who directs the Stanford Digital Economy Lab, and his colleagues analyzed industries based on their age group and how easily their jobs could be done by A.I. He found that computer programmers had one of the most “A.I.-exposed” jobs — and junior developers were hit the hardest. The number of jobs for those between the ages 22 and 25 (when one is most likely to be entering the field) had declined by 16 percent since 2022, while older programmers saw no significant decrease.

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Several developers suggested, in fact, that the number of software jobs might actually grow. An untold number of small firms around the country would love to have their own custom-made software, but were never big enough to hire, say, a five-person programmer team necessary to produce it. But if you could hire a single A.I.-assisted coder to do that same work, or even a part-time one? This is, as Brynjolfsson notes, a version of the “Jevons paradox”: When something gets cheaper to do, we don’t just pocket the savings — we do more of it. Though it could also be that these software jobs won’t pay as well as in the past, because, of course, the jobs aren’t as hard as they used to be. Acquiring the skills isn’t as challenging.

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Writing code is now so highly abstracted that nearly anyone could crack open a L.L.M. and describe an app. Maybe not a complex one. But if they needed some simple software for personal use? An A.I. could likely craft it.

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This is the cultural side effect of coding becoming conversational: The realms of programmers and everyday people, separated for decades by an ocean of arcane know-how, are drifting closer together. If code-writing A.I. continues to improve, there will likely be far more people in Cuisy’s situation — the Jevons paradox in action. “Maybe they don’t label themselves as software engineers, but they’re creating code,” Brynjolfsson says. “A lot of people have ideas.” The world becomes flooded with far more software than ever before — written by individuals, for individuals.

How things will shake out for professional coders themselves isn’t yet clear. But their mix of exhilaration and anxiety may be a preview for workers in other fields. Anywhere a job involves language and information, this new combination of skills — part rhetoric, part systems thinking, part skepticism about a bot’s output — may become the fabric of white-collar work. Skills that seemed the most technical and forbidding can turn out to be the ones most easily automated. Social and imaginative ones come to the fore. We will produce fewer first drafts and do more judging, while perhaps feeling uneasy about how well we can still judge. Abstraction may be coming for us all.