Members of the MIT community speak to Boston 25 News reporter Lisa Gresci about how they use AI, and their concerns with regulating the technology. Developers should consider, “how AI can help communities so [they are] not just building things, but are also deploying it [with purpose] out in the world,” says graduate student Anku Rani.
MIT researchers found that users preferred to consult professionals over AI chatbots, except when they are feeling embarrassment or shame, writes Forbes’ Joe McKendrick. “When we seek advice from other people, we’re often reluctant to share embarrassing details because we have a fear of being judged,” the researchers write. “People are more likely to turn to AI models to get professional advice when they’re embarrassed, because the model doesn’t judge them.”
Prof. Daron Acemoglu discusses the impact of AI models on jobs in an article by CNBC’s Trevor Laurence Jockims. “If AI continues to be developed as an automation technology, there will be more impacts. Right now, we don’t have many easy-to-use applications relevant for a large number of tasks/industries,” says Acemoglu. “Once these are developed, the labor market effects will be multiplied.”
Prof. Max Tegmark joins David Gura and Christina Ruffini, hosts of “Bloomberg This Weekend,” to discuss briefing the U.S Senate on regulating AI, in wake of recent reports of rogue agents. “I want to emphasize for anyone who gets stressed by this, that this dystopian future where we build smarter than human AIs and robots, super intelligence, and then they take over Earth, is not at all inevitable,” says Tegmark.
Prof. Justin Solomon joins GBH’s “The Curiosity Desk" to explain the Navier-Stokes problem and the significance of this longstanding challenge being solved by an AI platform. “There are concerns and there’s excitement,” says Solomon. “Obviously, working on the Navier-Stokes equations and other open problems shows that these AI tools are capable of assisting with proofs, or at least counter examples in this particular case, of really hard questions that have been open for centuries.”
Prof. Sherry Turkle speaks to Scientific American’s Brianne Kane about her forthcoming book, “Artificial Intimacy: Who We Become When We Talk to Machines.” “Social media got us into the attention economy, but chatbots are more toxic because they’re coming after the attachment economy,” says Turkle. “They’re coming after who you think you love, who you think you can count on, what you think empathy is.”
In a Scientific American article by reporter Joseph Howlett, Prof. Naomi Sweeting discusses the potential pitfalls of consulting artificial intelligence in mathematics. “I view math as part of a vast and beautiful human project,” says Sweeting. “It's this huge endeavor that puts us in communication with thousands of years of human thinking.”
Prof. Sherry Turkle’s latest book “Artificial Intimacy: Who We Become When We Talk to Machines,” is highlighted as one of the best new science books of September 2026 by New Scientist’s Liz Else. In her new book, Turkle explores how machines “are producing a generation more alienated, depressed and lonely than ever – and less equipped to reverse course, as machine relationships offer no practice for getting along with people,” writes Else.
Prof. Justin Reich, director of the MIT Teaching Systems Lab, speaks to Chalkbeat’s Lily Altavena about the need for policymakers to develop stronger guidelines to help teachers address the misuse of AI in their classrooms. “I think the situation teachers are in [without AI policies] feels bad, and feels like they ought to have a solution now that they don’t have,” says Reich. Existing documents on academic integrity “seem light on actual guidance and don’t include many citations, Reich said.”
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.
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.”
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.”
A new study by MIT researchers found that it is impossible to draw a direct link between AI diffusion model outputs and an artist’s body of work, writes Gizmodo’s Webb Wright. “If a [diffusion] model generates something, you want to be able to say, ‘Oh, this part of the training data was responsible,'” says Zheng Dai, PhD ’24, lead author on the study. “It’s important for us to understand how these models work to properly study them or regulate them.”