Prof. Daron Acemoglu speaks with WBUR Here & Now host Scott Tong about the impact of AI on the white-collar job market and the economy. “The advances in AI models have been pretty impressive,” says Acemoglu. “But right now, much of this is still in the lab, so to speak, meaning it has not spread to the productive sector of the economy. We are not seeing massive job losses…but of course we should be planning for how these models will be used and what their impact should be.”
Profs. Robert Langer, Giovanni Traverso and former postdoctoral fellow Thomas von Erlach have founded Vivtex, a biotechnology startup specializing in “oral alternatives to drugs administered by injections,” reports Jonathan Satlzman for The Boston Globe. Vivtex, now working in collaboration with Novo Nordisk, is looking to develop a new class of “pills to treat obesity and diabetes,” explains Saltzman.
Guide Labs, co-founded by Julius Adebayo SM ’15, SM ’16, PhD ’22, has debuted a large language model designed to make “its actions easily interpretable,” reports Tim Fernholz for Tech Crunch. “Every token produced by the model can be traced back to its origins in the LLM’s training data,” explains Fernholz.
Prof. Daron Acemoglu speaks with Fortune reporter Jake Angelo about his work studying the “origins of economic and political decay,” and the need for the U.S. to crack down “on economic inequality and tempering with job destruction.” “If we go down this path of destroying jobs [and] creating more inequality, U.S. democracy is not going to survive,” says Acemoglu.
Prof. Anette “Peko” Hosoi and Jerry Lu MFin '24 speak with Eli Chavez of the Boston Business Journal about their work using AI technologies to help athletes improve their performance. Lu notes that the AI tool he created for figure skating allows athletes to not only evaluate themselves, “but it also lets you analyze pretty much everybody in history: your idols, your mentors, your coaches, or even your competitors, and you can make these measurements without having to ask them to do anything.”
Prof. Anette “Peko” Hosoi and Jerry Lu MFin ’24 speak with Edgar B. Herwick III, host of GBH’s Curiosity Desk, about their work at the intersection of sports and technology. “We founded the [MIT] Sports Lab about 10 years ago and the idea was to give MIT students and MIT faculty a chance to apply their technical expertise to problems in sports, to advance the state-of-the-art, to help athletes achieve the maximum that they can achieve,” says Hosoi.
Prof. Christopher Knittel speaks with New York Times reporter Claire Brown about the development of AI data centers and the potential of increased utility costs. “If it’s just a few industrial customers with behind-the-meter power plants, it doesn’t really matter,” says Knittel. [As data centers grow and expand] “these things are going to matter so much. We can get it right, but sadly, too, if we don’t do it right, we can get it really wrong.”
“In Event of Moon Disaster,” a short deepfake film on display at the MIT Museum’s “AI: Mind the Gap” exhibit depicts an alternate reality where the Apollo 11 mission ended in disaster, reports Mark Feeney for The Boston Globe. The “unnervingly realistic deepfake” depicts President Richard Nixon addressing the nation regarding the failed mission. The film “manages to be both frightening, in showing how convincing deepfakes can be, and, however paradoxically, inspiring,” writes Feeney.
Prof. Pat Pataranutaporn speaks with The Guardian reporter Andrew Gregory about the lack of safety warnings and disclaimers in AI overviews, specifically in AI-generated health materials. “The absence of disclaimers when users are initially served medical information creates several critical dangers,” says Pataranutaporn. “Disclaimers serve as a crucial intervention point. They disrupt this automatic trust and prompt users to engage more critically with the information they receive.”
Writing for The Atlantic, Prof. Deb Roy explores the impact of chatbots on language and learning development. “The ordinary forces that tether speech to consequence—social sanction, legal penalty, reputational loss—presuppose a continuous agent whose future can be made worse by what they say,” writes Roy. “With LLMs, there is no such locus. …When the speaker is an LLM, the human stakes that ordinarily anchor speech have nowhere to attach.”
Prof. David Autor speaks with Bloomberg reporter David Westin about the shift toward automation in the workforce and the impact on workers. “There are many ways for us to use AI,” says Autor. “It’s incredibly flexible, malleable, plastic technology. You could use it to try to automate people out of existence. You could also use it to collaborate with people to make them more effective. But I also think that it depends on how we invest, how we build out those technologies.”
Jerry Lu MFin ’24 speaks with Fast Company reporter Grace Snelling about his work developing a new AI tool that can be used to help figure skaters land their jumps and Olympic audiences better understand just how challenging a quadruple Axel is. “Some of the artistic sports were missing this data-driven storytelling ability—if you watch hockey on TV, it looks slow, but if you watch it in person, it looks fast,” Lu explains.
President Sally Kornbluth and MIT Corporation member Noubar Afeyan PhD '87 served as panelists at the 2026 Davos Imagination in Action event to discuss “upholding scientific principles in the era of LLMs,” reports John Werner for Forbes. “We want all of our students to have a foundational facility with AI,” said Kornbluth. “What we want them to know, now, is how they can really be passionate about the content that they care about, whether it's materials design, whether it's aerospace, whether it's biochemical innovation, and understanding the many ways in which AI can help in that innovation.”
Researchers at MIT have “developed a new technique that enables large language models to learn new skills and knowledge without forgetting their past capabilities,” reports Ben Dickson for Venture Beat. “Their technique, called self-distillation fine-tuning (SDFT), allows models to learn directly from demonstrations and their own experiments by leveraging the inherent in-context learning abilities of modern LLMs,” explains Dickson. “Experiments show that SDFT consistently outperforms traditional supervised fine-tuning (SFT) while addressing the limitations of reinforcement learning algorithms.”
Prof. Andrew Lo speaks with Wall Street Journal reporter Peter Coy about why he feels current AI systems aren’t suited to serving as financial advisors and his goal to create “an AI financial adviser that is a true fiduciary—namely, an entity that always puts the client’s interests first and tailors its advice to their particular needs, including emotional needs.” Lo notes that: “The AI people are using now can be dangerous, especially if the user isn’t fully aware of the biases, inaccuracies and other limits” of large language models.