‘Can AI Do My Job?’ Is the Wrong Question

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.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.Question 1: Is your job a weak bundle or strong bundle? 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.“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 replacem