Human-computer interaction

Prof. David Autor speaks to Financial Times reporter Christian Davies about the increased use of physical AI systems at U.S. manufacturing companies. “[Autor says] that while individual companies might increase headcount as they grow more productive, it is a fallacy to assume this will be replicated across the manufacturing sector as a whole, noting that previous waves of automation in the US have all driven large-scale declines in blue-collar employment,” writes Davies.  

MIT Media Lab researchers found that using large language models for writing assistance was associated with weaker recall and lower ownership of the work produced, writes Forbes’ Glenn Llopis. “Notice the pattern: when AI does too much, the person may retain too little,” writes Llopis.  

Prof. Emeritus Kerry Emanuel speaks to The New York Times’ William J. Broad about the use of AI models in predicting hurricanes. “It’s human beings who have to make these calls,” says Emanuel. “[Eventually] AI will be treated as just another form of guidance” along with satellite images and readings from hurricane hunter aircraft that pierce the tempests. 

Prof. Regina Barzilay, graduate student Aziz Ayed and alumna Sydney Pham ‘24 are featured in an article about AI models and the workforce, written by Nature’s Ben Deighton. “Sometimes models can make sense of unexpected observations, which can guide further iterations of studies,” Pham says. “It’s helpful to understand how the models generate their results.” 

A study by researchers from the MIT Media Lab found up to 55% lower neural connectivity in participants who used AI for writing assistance, and flagged children as especially vulnerable, writes Forbes’ Dana Williams. “83% of participants who used a chatbot could not quote a single sentence from an essay they had finished minutes earlier, versus roughly 11% of those who wrote unaided,” Williams notes. “The words were produced, but they never entered a mind.”  

Sloan Senior Lecturer Paul Cheek’s new book, “No One Works Here” is outlined by Forbes’ Joe McKendrick. “The companies that are most likely to fall behind their peers are the ones that insist on keeping a human in the loop for decisions that a machine could have made yesterday,” Cheek writes in his book.  

Prof. Daniela Rus, Director of MIT CSAIL, and Prof. David Autor, department head of Economics, are featured on Time’s “TIME100 AI 2026” list of 100 innovators, leaders, and thinkers reshaping the world through their advances in AI. “Over three decades, [Rus] has conducted pioneering work in robotics, extending the understanding of what form robots can take,” writes Tharin Pillay, while Steven Freiss describes Autor as “one of the nation’s most vocal and prominent economists aiming to ascertain AI’s potential impacts on labor.” 

Prof. Emeritus Rodney Brooks speaks to Scientific American’s Mary Randolph about humanoid robots interacting with the real world. “When you see a performance of an [artificial intelligence] system or a robot on one thing,” says Brooks, “that fools us into thinking that it has the same general competence as a human. And that’s a mistake people make.” 

Forbes’ Satya Krishnaswamy highlights a study co-authored by Lawrence Schmidt, a research affiliate at Sloan, that found AI can contribute to growth in certain roles where workers focus on higher-value tasks. “Schmidt’s research found that increased AI use was linked to both employment and sales growth,” writes Krishnaswamy.  

Forbes’ Bryan Robinson highlights Prof. Paul Osterman’s new book, “Disposable Workers: The Transformation of Employment,” and identifies what constitutes disposable work. “We’re not becoming a gig economy; we’re becoming a disposable one,” says Osterman. “Marginal workers are employees who have no career prospects at their organizations.” 

In a STAT opinion piece, Arya Rao, a candidate in the Harvard/MIT MD-PhD program, and Marc Succi write about how AI impacts clinical reasoning. “[C]linical reasoning and model reasoning are not the same thing,” Rao and Succi explain. “AI not only lacks this hidden framework and data repository for learning, but it arrives at conclusions using a fundamentally different method.” 

In a study of developers using GitHub Copilot, MIT researchers found AI shifted time away from teamwork and toward coding, writes Forbes’ Sarah Davis. “If AI reduces the need to ask colleagues for help and advice, workplaces become the mechanism for creating human interactions that promote connection and knowledge sharing,” writes Davis.  

The New York Times’ Dana Goldstein highlights an MIT study that found individuals who used AI for writing assistance recorded lower brain activity than those who worked without LLMs. “The writers who used AI struggled to remember what, exactly, they had written, and felt little ownership over their work,” writes Goldstein. “The findings join a growing body of research suggesting that frequent AI use can degrade critical thinking.” 

Prof. Max Tegmark joins Steve Clemons, host of Al Jazeerah’s, “The Bottom Line,” to discuss regulating AI. “Normally with technology we have a problem we want to solve: cure cancer, be able to get faster from point A to point B, and then we develop technology to meet those needs, to solve those problems—and that’s how we should deal with AI also,” says Tegmark. “We should look at what we would like to have accomplished in our society and then companies can sell products that solve those problems without causing a bunch of harm.” 

In an article about AI’s impact on the job market, The Guardian’s Varsha Bansal highlights Prof. Paul Osterman’s new book, “Disposable Workers: The Transformation of Employment.” Bansal writes: “About 35% of the US workforce is already considered easily replaceable, according to his [Osterman’s] research. AI will only exacerbate this, he said. But not all jobs are in the same boat, and neither are their options for adapting, said MIT’s Osterman.”