Prof. Jonathan Gruber joins GBH’s All Things Considered to discuss stock market jitters, AI hype and interest rates, urging calm and a long-term view. “No one who’s in the market should be overreacting to one day’s movement,” Gruber says. “These short-run reactions are really overreactions to individual bits of news.”
Prof. Simon Johnson and Prof. David Autor speak with New York Times reporter Emma Goldberg about the anticipated impact of AI on the job market. “We should be concerned about eliminating them,” says Prof. Simon Johnson, of the risks posed by automating jobs. “This is the hollowing out of the middle class.”
In an essay for Project Syndicate, Prof. Simon Johnson underscores for the need for countries around the world to “make strategic investments in key technologies, to create more good jobs, and to stay ahead of increasingly aggressive geopolitical competitors.” Johnson emphasizes: “If you want more good jobs, invest in science, facilitate the commercialization of the technology that results from it, and make it easy for people to build companies where the product was invented.”
Forbes reporter Rodger Dean Duncan spotlights “The Skill Code: How to Save Human Ability in an Age of Intelligent Machines,” a new book by Research Affiliate Matt Bean SM '14, PhD '17. Duncan “explains Beane’s take on AI tools, collaboration and remote work, who suggests traditional mentoring is at risk in the workplace. Beane says today’s successful people have ‘discovered new tactics that others can use to get skills without throwing out the benefits of hybrid working arrangements.’”
A new working paper by Prof. Anna Stansbury and Research Associate Kyra Rodriguez looks at the “class gap” among US Ph.D.-holders in science, social science, engineering and health, reports Soumaya Keynes for the Financial Times. The paper found “those whose parents did not have a college degree are 13 per cent less likely to end up with tenure at a top university than those with more educated parents. They also tend to end up at lower-ranked institutions,” Keynes explains.
Institute Prof. Daron Acemoglu speaks with CNBC Last Call host Brian Sullivan about what he describes as exaggerated claims about the macroeconomic effects of AI. “I am completely convinced that there are some impressive changes and there are some things that AI can really help us with, but it's not going to suddenly revolutionize everything we do,” Acemoglu says. “And if it's going to do it, it's going to take a while.”
Research Scientist Eva Ponce, director of Online Education for the MIT Center for Transportation & Logistics, speaks with Supply Chain Management Review reporter Bridget McCrea about the growing demand for professional certifications in the field of supply chain management. “There’s going to be a bigger emphasis on ‘crafting your own educational pathway,’” says Ponce of the future of executive supply chain education, “and with more emphasis on lifelong learning.”
Reuters reporter Felix Martin highlights Prof. Daron Acemoglu’s research demonstrating how AI may not help improve productivity in the developed world. Acemoglu has found that broad productivity growth from reasonable estimates of AI replacing humans in the workplace “would increase by only around half a percentage point over 10 years. That is barely a third of the ground lost since 2008,” Martin explains.
Research Scientist Neil Thompson speaks with New York Times reporter Hank Sanders about the economic and social impact of AI technology in the fast-food industry. Thompson explains that “voice A.I. is inaccurate often enough that it requires some level of human oversight, which decreases cost savings,” writes Hank.
Wall Street Journal reporter Justin Lahart spotlights the work of Prof. David Autor, an economist whose “thinking helped change our understanding of the American labor market.” Harvard Prof. Lawrence Katz says Autor has “probably been the most insightful and influential scholar of the labor market” in decades. “To me, the labor market is the central institution of any society,” says Autor. “The fastest way to improve people’s welfare is to improve the labor market.”
Prof. Daron Acemoglu speaks with Greg Rosalsky of NPR’s Planet Money about AI’s potential effect on jobs, specifically the translation business. “I think how good AI has become is often exaggerated,” says Acemoglu. “But there is pretty much nothing that humans do as meaningful occupation that generative AI can now do. So in almost everything it can at best helps humans, and at worst, not even do that.”
Researchers at MIT and elsewhere have “outlined the costs and benefits of artificial intelligence in health care, education and the workforce,” reports Ruth Reader, Daniel Payne, and Carmen Paul for Politico. The researchers say “policymakers should study the real-world implications of the technology and consider using AI to inform policy,” writes Politico.
Researchers at MIT have found that prospective job applicants who utilized basic AI modules in their application process were, on average, more likely to get hired and receive higher wages, reports Maria Gracia Santillana Linares for Forbes. “[Applicants] with access to the technology are more likely to get hired without any negative implications [from] employers,” says graduate student Emma Wiles.
A new paper by Prof. Daron Acemoglu and Prof. Simon Johnson uses the impact of automation in the textile industry to predict potential similar effects from AI, writes Bloomberg’s Andy Mukherjee. Noting the parallels between the Indian textile industry and disruption currently underway in tech outsourcing, the economists write “the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages.”
New York Times columnist Thomas Edsall spotlights recent research by Profs. Daron Acemoglu, David Autor and Simon Johnson, in which they explore whether artificial intelligence could be a beneficial tool for workers. “It is quite possible to leverage generative AI as an informational tool that enables various different types of workers to get better at their jobs and perform more complex tasks,” explains Acemoglu. However, he notes “to turn generative AI pro-worker, we need a major course correction.”