ChatGPT Is Coming For White-Collar Work

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ChatGPT is a generative AI program; that is, it is unsupervised in the creation of text, images, video, audio, etc. Ask a general question and get a specific answer. ChatGPT is only the leading edge of this technology, but it promises to quickly displace a massive number of white-collar jobs. ⁃ TN Editor

In the next five years, it is likely that AI will begin to reduce employment for college-educated workers. As the technology continues to advance, it will be able to perform tasks that were previously thought to require a high level of education and skill. This could lead to a displacement of workers in certain industries, as companies look to cut costs by automating processes. While it is difficult to predict the exact extent of this trend, it is clear that AI will have a significant impact on the job market for college-educated workers. It will be important for individuals to stay up to date on the latest developments in AI and to consider how their skills and expertise can be leveraged in a world where machines are increasingly able to perform many tasks.

There you have it, I guess: ChatGPT is coming for my job and yours, according to ChatGPT itself. The artificially intelligent content creator, whose name is short for “Chat Generative Pre-trained Transformer,” was released two months ago by OpenAI, one of the country’s most influential artificial-intelligence research laboratories. The technology is, put simply, amazing. It generated that first paragraph instantly, working with this prompt: “Write a five-sentence paragraph in the style of The Atlantic about whether AI will begin to reduce employment for college-educated workers in the next five years.”

ChatGPT is just one of many mind-blowing generative AI tools released recently, including the image generators Midjourney and DALL-E and the video generator Synthesia. The upside of these AI tools is easy to see: They’re going to produce a tremendous amount of digital content, quickly and cheaply. Students are already using ChatGPT to help them write essays. Businesses are using ChatGPT to create copy for their websites and promotional materials, and to respond to customer-service inquiries. Lawyers are using it to produce legal briefs (ChatGPT passes the torts and evidence sections of the Multistate Bar Examination, by the way) and academics to produce footnotes.

Yet an extraordinary downside is also easy to see: What happens when services like ChatGPT start putting copywriters, journalists, customer-service agents, paralegals, coders, and digital marketers out of a job? For years, tech thinkers have been warning that flexible, creative AI will be a threat to white-collar employment, as robots replace skilled office workers whose jobs were once considered immune to automation. In the most extreme iteration, analysts imagine AI altering the employment landscape permanently. One Oxford study estimates that 47 percent of U.S. jobs might be at risk.

No single technology in modern memory has caused mass job loss among highly educated workers. Will generative AI really be an exception? No one can answer this question, given how new the technology is and given how slowly employment can adjust in response to technological change. But AI really is different, technology experts told me—a range of tasks that up until now were impossible to automate are becoming automatable. “Before, progress was linear and predictable. You figured out the steps and the computer followed them. It followed the procedure; it didn’t learn and it didn’t improvise,” the MIT professor David Autor, one of the world’s foremost experts on employment and technological change, told me. ChatGPT and the like do improvise, promising to destabilize a lot of white-collar work, regardless of whether they eliminate jobs or not.

People and businesses are just figuring out how to use emerging AI technologies, let alone how to use them to create new products, streamline their business operations, and make employees more efficient. If history is any guide, this process could take longer than you might think. Consider electricity. The circuit, electric lights, and rudimentary electric motors were developed in the early 1800s. But another century passed before the widespread adoption of electricity in the United States began to lift GDP. Or take computers. They became commercially available in the early 1950s but did not show up in the productivity stats until the late 1990s.

Some technologies clearly improve productivity and reduce the need for labor. Automated machine tools, for instance, depress manufacturing employment while lifting output and productivity, as do many of the forms of machinery invented and employed since the Industrial Revolution. But other technologies—even amazing ones—show surprisingly muted effects. How about the internet, which has revolutionized almost every facet of communications in the past four decades? Despite altering how we date and talk and read and watch and vote and emote and record our own life stories, launching a zillion businesses, and creating however many fortunes, the internet “fails the hurdle test as a Great Invention,” the economist Robert Gordon argued in 2000, because it “provides information and entertainment more cheaply and conveniently than before, but much of its use involves substitution of existing activities from one medium to another.” Nearly a quarter century later, the internet still hasn’t spurred a productivity revolution. Smartphones haven’t either.

So is AI like the smartphone or is it like an automated machine tool? Is it about to change the way that work gets done without eliminating many jobs in aggregate, or is it about to turn San Francisco into the Rust Belt?

Predicting where technology will cause job losses is hard, Autor noted. Remember the freak-out several years ago over the possibility of self-driving automobiles eliminating work for truck drivers? But AI is much more flexible than a system like Excel, much more creative than a Google Doc. What’s more, AI systems get better and better and better as they get more use and absorb more data, whereas engineers often need to laboriously and painstakingly update other types of software.

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