Labor and jobs

Prof. Daron Acemoglu joins NPR’s Scott Simon to discuss Congress tackling affordability in the age of AI by increasing wages. Paying higher prices for goods “is much less of a burden when you see your own salary increase at the same rate or at a higher rate,” says Acemoglu. “When we need to create more jobs for people, create more wage growth opportunities for people, technology is going to make that harder. We have to create an agenda of steering AI in a more pro-worker direction so that rather than automate work, we get AI to actually help employers to find ways of using workers more productively, and that will be a pathway for encouraging firms to create more jobs.” 

Prof. Daron Acemoglu shares his insights on artificial intelligence and the future of work and education during “Ask an Economist,” a virtual event hosted by NPR’s “The Indicator from Planet Money.” “I think there are a lot of pro-teacher AI opportunities where you give better tools to teachers, but teachers still remain central to the learning experience of students,” says Acemoglu. AI “can individualize the teaching experience much more effectively and at low cost in a way that wasn't possible before. And that expands teacher expertise, expands teacher capabilities, enables them to perform new tasks, doesn't replace them, doesn't sideline them.” 

Alumna Thapanee Techajareonvikul, CEO and president of Berli Jucker, is honored as one of Forbes Asia’s Power Businesswomen 2026, writes reporter Rana Wehbe Watson. Techajareonvikul is “keen to promote gender equality at the company [Berli Jucker], where today more than half the 61,000 workforce is female,” notes Wehbe Watson. “In the top management positions women make up 53%,” says Techajareonvikul.  

Forbes has named MIT Professional Education’s Applied AI for Digital Transformation as the best professional development course available, writes Doug Wintemute and Veronica Beagle. “Designed for senior executives and tech leaders, this course explores AI adoption frameworks and strategies. Upon completion, graduates receive 6.4 continuing education units (CEUs) and a certificate of completion from the renowned MIT Professional Education.”  

Prof. Daron Acemoglu joins Bloomberg’s “Mishal Husain Show” to discuss his latest book, “What Happened to Liberal Democracy: Remaking a Politics of Shared Prosperity,” as it relates to democracy in the age of AI and social media. “The thing that gives me hope is that humans are very adaptable,” says Acemoglu. “People, even in this polarized world, have a deep sense of fairness, around [which] we can come together. There are so many shared moral values between the different sides of the aisle in the U.K., [and] the U.S. Polarization, bad leadership [and] social media have obscured them.” 

CodeSignal, co-founded by alumnus and CEO Tigran Sloyan, has developed Agentic Skills Intelligence, a new product that “uses AI agents to evaluate the skills people use in their work and links the findings to personalized practice,” writes Forbes’ Ray Ravaglia. CodeSignal has built an AI agent “for each domain in its job taxonomy, such as engineering and sales, along with agents for capabilities that cut across jobs, among them communication and AI literacy,” notes Ravaglia. “The agents draw on systems including Gong, Salesforce, and GitHub, and they score what they find against rubrics the company says are grounded in industrial-organizational psychology.” 

Prof. Daron Acemoglu joins “The Claman Countdown” on Fox Business to discuss the possibility that largescale deployment of AI could triple unemployment within the next decade. “There is so much uncertainty about AI,” says Acemoglu. “It’s very difficult to know exactly when we’re going to see the productivity gains, which will come, and when we’re going to see job losses, and whether we’ll actually see some job creation as well. But on the current path where AI is being prepared and developed as a labor replacing technology, I would be very worried that within the next ten years we’ll see a lot of jobs disappear.”  

For Manufacturing Dive, reporter Nathan Owens spotlights MIT’s Technologist Advanced Manufacturing Program (TechAMP), a 12-month-long program that combines online instruction with hands-on lab work to bridge the gap between technical engineering and management skills, designed for working technicians or operators. “Students gain foundational leadership, process control and systems thinking skills,” notes Owens. TechAMP has “really shown me how to communicate more, communicate problems more, communicate solutions more,” says Brian Coppenrath, who completed the program.  

Postdoctoral Associate Iman YeckehZaare speaks to El País reporter Jordi Pérez Colomé about his research on RentAHuman, a platform that allows AI agents to hire people to do physical labor. “RentAHuman has shown that many people are willing to sign up to be hired by AI agents. What it still hasn’t shown is that many AI agents are doing the hiring,” says YeckehZaare. “Listings posted by agents will increase as AI agents become capable of acting and paying on their own.”  

Prof. Emilio Castilla discusses the factors companies must consider when deploying AI to help in the hiring process, in an article by USA Today’s Greta Reich. “We should evaluate not only whether the algorithm predicts historical hiring decisions, but whether it helps identify candidates who subsequently perform well in the job,” writes Castilla. “The most exciting possibility is not simply that AI might help us find the best talent more efficiently. It is that AI might help us recognize talent that our traditional hiring practices have systematically overlooked.”  

Research Affiliate Paul Kedrosky joins The New York Times’ “The Ezra Klein Show” to discuss how the development of AI is creating an economic bubble, where asset prices are detached from economic reality. “We’re building it [AI] out like we were the railroads, like we were electrification, like we were the canals, like we were the interstate highways. And every one of those moments has led to overbuild,” says Kedrosky. “The reality is we’ve never seen a general-purpose technology that had such broad applicability, was adopted so quickly and was repricing labor at the same time.”  

Prof. Daron Acemoglu joins CNBC’s “Closing Bell” to discuss how AI could cause mass job displacement due to companies deploying rapidly advancing technologies. “AI’s capabilities are real and powerful, but human jobs are also complex. I don’t think you want AI to take over air traffic controller jobs right now; you don’t want AI to do CEO jobs,” says Acemoglu. “What we are seeing when these models escape their sandboxes and create havoc—it's not that they are necessarily super intelligent, it is that they don’t have judgement and they are optimized for certain narrow metrics and when you let them loose, they are going to do lots of unforeseeable things. You don’t want that in many jobs.”  

Boston Globe reporter Jon Chesto spotlights the fall fair event held as part of the MIT x GE Vernova Energy & Climate Alliance, during which, GE Vernova CEO Scott Strazik shared his enthusiasm for hiring MIT graduates. “Something we talk a lot about inside Vernova is the world is changing very quickly but we need to change quicker and even faster than that so we can lead in the world,” says Strazik. “Over the last year-plus on this campus, we see a lot of opportunities for us to get better through bringing you into the organization.”  

In an interview with CBS News’ “60 Minutes,” Prof. Daron Acemoglu discusses his views on AI and how the technology could lead to worker displacement, without intervention. Concerns about AI and the labor market “should move us into thinking about what we can do because there is a lot we can do,” says Acemoglu. "Because the future is so uncertain, because the future is so much subject to our choice and agency, it is all the more important that we have this discussion." 

A working paper co-authored by Prof. Kate Kellogg found that building organization wide AI tools created “substantial work beyond employees’ day-to-day jobs,” writes Fortune’s Bilin Lin. “That work included trial-and-error experimentation, reviewing and refining outputs with colleagues across departments, and continually adapting solutions as AI models evolved. Much of that work remained invisible to leadership,” notes Lin.