Three Ways to Think About AI and Jobs - The Atlantic

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Sign up for Work in Progress, a newsletter where Rogé Karma investigates the mysteries of a complicated economy.<br>In 2016, the AI pioneer Geoffrey Hinton declared that “people should stop training radiologists now” because “it’s just completely obvious that within five years, deep learning is going to do better than radiologists.” He was half right. Today, the FDA has approved more than 1,000 AI radiology tools, some capable of analyzing medical images to detect injuries or diseases with greater accuracy than human specialists. Yet radiologists—human ones—are in more demand than ever. Since 2016, the number of radiologists has risen by 17 percent, the field’s vacancy rates are near all-time highs, and the average salary has increased from about $350,000 to $570,000, making radiology the third-highest-paid medical speciality in the United States.<br>Many people now fear that AI will make a huge number of careers obsolete. Last year, Anthropic CEO Dario Amodei claimed that AI would soon “wipe out half of all entry-level white-collar jobs.” But the radiologist story suggests that whether AI will replace a given profession is not so straightforward to predict. Answering the following three questions can help you determine how endangered a job really is.<br>Question 1: Is your job a weak bundle or strong bundle?<br>According to Luis Garicano, an economist and a co-author of the forthcoming book Messy Jobs, most white-collar jobs combine two very different kinds of work. “Clean” tasks involve predictable problems, objective standards of success, lots of written data, and little interpersonal interaction (think: approving an expense report or updating a spreadsheet). These are the easiest for AI systems to handle.<br>“Messy” tasks, however, involve dealing with unpredictable situations, meeting subjective measures of success, acting on tacit knowledge, and navigating complex webs of human relationships (think: choosing a new corporate logo, assuaging an upset client, or managing a team). AI isn’t so good at these kinds of tasks, at least not yet. This means that a job’s susceptibility to AI replacement depends, in part, on how easily the clean tasks can be cleaved off from the messy ones.<br>From the March 2026 issue: America isn’t ready for what AI will do to jobs<br>A trial lawyer has what Garicano and his co-authors call a “strong bundle” job, in which the various responsibilities are so tightly linked that delegating some of them to AI would actually be counterproductive. She might spend most of her time on the relatively clean tasks required to prepare for a given trial—reading relevant case law, studying the facts of the case, drafting an opening argument—and far less time on the messy tasks involved with appearing in court. In theory, much of that trial-prep work could be delegated to a large language model. In practice, doing so would be a huge mistake. During a trial, a lawyer can’t just read from an AI-generated script. She has to cross-examine witnesses, answer questions from the judge, respond to points made by the other counsel, and adjust her strategy based on constantly evolving circumstances. This all requires her to have a thorough understanding of the facts of the case, knowledge of relevant legal precedent, and a familiarity with potential counterarguments. To perform well at the messy part of her job, the lawyer needs to have done much of the clean part herself.<br>Other jobs are “weak bundles.” A friend of mine who works as a recruiter for a major HR firm used to spend most of his days sifting through résumés. Now AI can easily do that for him. So instead, he spends far more time sourcing potential recruits, talking to hiring managers, interviewing candidates, and negotiating offers. The fact that he’s no longer reading every single résumé doesn’t affect his ability to do the rest of the job. Likewise, delegating the basics of code-writing to AI doesn’t affect an experienced software developer’s ability to do more complex design and engineering tasks.<br>If AI makes an individual recruiter or software developer more efficient, does that mean that far fewer of them will soon be needed? Not necessarily. What exactly happens to weak-bundle jobs depends on how the rest of the economy responds. In some cases, a job that is largely automated can, somewhat counterintuitively, experience higher levels of employment precisely because it was automated. Whether this occurs depends on the answer to the next question.<br>Question 2: If what you produce got cheaper, how much more of it would people want?<br>When the first automobiles were being produced in the 1890s, each car had to be manually built by a large team of workers. Then, in 1913, Henry Ford introduced the assembly line, which could churn out far more cars with far less human labor. The partial automation of car assembly did not cause employment in the industry to collapse; instead, the...