These new statistics suggest that the spread of AI will not just amount to “more of the same,” and that the onset of AI will introduce new riddles into speculation about the future of work.
Given their difference from previous analyses purporting to discuss AI, Michael Webb’s novel procedures demonstrate that we have a lot to learn about artificial intelligence, and that these are extremely early days in our inquiries. What’s coming may not resemble what we have been experiencing or expect to experience.
Webb’s machine learning statistics suggest AI could bring new patterns of impact across the labor market—ones fundamentally different from those brought by previous technologies.
It’s clear that past automation analyses—including our own, with its amalgamation of robotics, software, and artificial intelligence—have likely obscured AI’s distinctive impact. Based on expert familiarity, previous analyses have almost certainly been dominated by the ways robotics and software have been able to take over numerous routine, highly structured, and repetitive tasks.13
These analyses have tended to suggest that automation’s main effects will be to displace work across the middle of the skill and wage spectrum (such as factory workers and office clerks) while leaving the status quo more or less intact for both high-pay and low-pay interpersonal or nonroutine work (such as chemical engineers and home health aides, respectively).
However, the more refined empirical research presented here suggests that AI’s ability to employ statistics and learning to carry out nonroutine work means that these technologies are set to affect very different parts of the WHAT JOBS ARE AFFECTED BY AI? 23 workforce than previous automation. Most strikingly, it now looks as if whole new classes of well-paid, white-collar workers (who have been less touched by earlier waves of automation) will be the ones most affected by AI.
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