April 24, 2024

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  • Experts say they will look to the past for clues about what the AI ​​boom will mean for work in the future.
  • There is a fine line between increasing productivity and making some jobs replaceable.
  • In many ways, AI is a generation unlike any technology before it; the future may still hold surprises.

They say history doesn’t repeat itself, but it often rhymes. That’s why top researchers are looking to the past as a guide to predict how generational AI might impact worker jobs in the years and years to come.

We asked four of these experts to unpack how new and pre-ChatGPT AI technologies have impacted jobs in the past, what this means for the workers of the future, and how you can prepare for the AI ​​boom.

Historically, automation has displaced some jobs due to the impact of technology, but it has created better jobs and increased the number of jobs in the long run, said Ethan Mollick, an associate professor of entrepreneurship and innovation at the University of Pennsylvania’s Wharton School who requires his students to use ChatGPT.

The big question is whether the new age of AI will be a similar story.

“We may eventually get better jobs, but in the short term, there’s a lot of disruption,” Mollick said. “We don’t have a very clear model. This is the fastest multi-purpose technology we’ve ever adopted, and we don’t know what its ultimate capabilities are.”

One thing everyone seems to agree on is that AI will increase the overall productivity of workers. Hope that helps sustaining the economy and markets going forward. But Carl Benedikt Frey — an Oxford economist who co-authored a highly publicized 2013 paper that estimated 47% of all US jobs were at risk of being replaced by automation as early as the 2020s — thinks there’s a fine line between AI helping workers and AI hurting them.

“History tells us that simplification is often just a step toward automation,” he said, adding, “AI assistants that analyze telemarketing calls and provide recommendations are being trained with the ultimate goal of replacing them.”

For historical reference, Frey focused on career lamp lamps, the 19th century workers who carried torches and heavy ladders to light the gas lamps that lit the streets at night. When electric street lights began arriving in the United States in the late 1800s, lampposts still had switches that required human operation, Frey said. But eventually, substations began to control the street lights, automating the jobs of lamplighters on a large scale.

More productivity often means more jobs

Another historical example could be in many industries, Lindsey Raymond — Ph.D. candidate at the MIT Sloan School of Management who previously worked as a White House economist – Insider said. She noted the invention of the cotton gin in the late 1700s.

“The cotton gin made people who made clothes or cotton for clothes more productive,” she said. “But the price fell so much that there was this huge change in demand for the amount of cotton that people bought. So employment increased.”

Although it is an apt historical example, we also have to admit that at that time in American history, employers filled many of the industries. vacancies with enslaved people.

Raymond pointed to a profession that has already been affected by AI for years – customer service representatives – as a possible example of a similar situation in action.

“As part of COVID and this shift towards buying more things online, most companies have seen huge increases in demand for their online customer support options,” she said. “So, with that happening, I wouldn’t expect a lot of negative employment effects.

But Raymond cautioned that AI could produce some less-than-desirable outcomes for customer service workers, especially if customer support conversations become much more capable and advanced. The productivity benefits created by jobs could lead to increased competition and lower wages. Furthermore, the same situation could arise across other professions.

April working paper the National Bureau of Economic Research offered data that supports this idea: The newest and worst customer service workers in their study saw the biggest productivity boost from AI assistance. Simply put, it closed the gap between experienced employees and potential replacements. This, in turn, could “significantly reduce the average wage,” Raymond said.

Oded Netzer, a Columbia business school professor, said he still sees a way for more experienced customer service workers to stand out in the remaining jobs.

“As AI improves, I expect AI to change some of the call center tasks, where the people left in these jobs have to be domain experts and deal with more difficult issues,” he said.

Can AI clear the critical ‘three hurdles’ of work?

The extent to which AI displaces jobs will depend on how quickly it scales what Mollick calls the “three levels” of work: tasks, jobs and systems. Even when AI becomes skilled at so many work tasks that it can arguably replace jobs, the final “systems” hurdle remains.

“We’ll say AI is better at diagnosing than your doctor in the short term, or AI is better at teaching a class than me,” he said. “There will still be a lot of time to change because it takes a long time to change the systems. The students expect to see a person in class, not an AI.”

He said studies have found that the “most creative, well-paid and educated jobs,” are the ones most likely to be transformed – not necessarily displaced – by AI, and that the closest thing to an AI “replacement story” is in the translation industry, where many human translators are still in the game but they may face further disruption in the coming years.

Mollick said the automation of telephone operators in the late 19th century – a common occupation for women at the time – suggests that older workers may struggle when job displacement occurs.

“When you got rid of operators, basically young women were able to adjust, find new jobs and they were able to adapt,” he said. “But older women took a lifelong hit in terms of pay – they couldn’t get a good job again.”

Mollick has one piece of advice for workers looking to adapt to the AI ​​boom in the coming years: Instead of focusing on the tasks AI can’t do, learn how to use it to make yourself more productive.

“Everyone expects these models to improve in power,” he said. “So you don’t have to make a bet that AI is bad at this now and it’s always going to be bad. Instead, what I’d be thinking about is: How do you know how to use it to do your job better?”

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