Initiative on the Digital Economy

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.”  

MIT researchers found that AI assisted work leads to the ‘diversity collapse’ of a brand’s voice, and users that rely on AI to do their thinking lose skills over time, highlights Forbes’ Michelle O'Connor. “Judgment, taste and the instinct to catch when something’s off isn’t a soft skill you can afford to lose,” writes O’Connor. “The MIT data is the first hard evidence of that.” 

A new report published in part by MIT FutureTech finds that AI does not significantly accelerate scientific research, writes Peter Hall for the Scientific American. “As the experiments become larger or more complex, so, too, do the models that simulate them, and the data to train those models is hard to come by,” Hall explains. “Robots handling real-world materials raise safety questions that are unknown to software.”  

For The Atlantic, reporter Rogé Karma describes how Prof. David Autor and Principal Research Scientist Neil Thompson found a basic pattern for technological changes and job displacement based on the evolution of inventory clerk versus accounting clerk positions. “The story is almost never as simple as: We’re in a race with machines and machines will win,” says Autor. “What matters for a given profession is whether technology enhances a worker’s expertise or commodifies that expertise.”

Fortune reporter Nick Lichtenberg highlights research by MIT economists that finds “automation doesn’t affect all parts of a job equally. The critical variable is whether the tasks being automated are the expert parts of a role or the administrative scaffolding around them.” 

A new working paper by researchers from MIT FutureTech finds that “AI’s march through the labor market looks far less like a sudden catastrophe and far more like a slow, rising flood — serious and accelerating, but not the overnight apocalypse that has dominated headlines and executive anxiety for the past two years,” writes Nick Lichtenberg for Fortune. “Rather than arriving in crashing waves that transform a certain set of tasks at a time,” the researchers write, “progress typically resembles a rising tide, with widespread gains across many tasks simultaneously.”

A new study by researchers at MIT suggests that “the biggest and most computationally intensive AI models may soon offer diminishing returns compared to smaller models,” reports Will Knight for Wired. “By mapping scaling laws against continued improvements in model efficiency, the researchers found that it could become harder to wring leaps in performance from giant models whereas efficiency gains could make models running on more modest hardware increasingly capable over the next decade.” 

Principal Research Scientist Andrew McAfee speaks with New York Times reporter Steve Lohr about why companies are not yet seeing the economic payoff of AI implementation in the workforce. “It’s not surprising that early A.I. efforts are falling short,” said McAfee. “Innovation is a process of failing fairly regularly.”

Principal Research Scientist Andrew McAfee speaks with CNBC reporter Robert Frank about the artificial intelligence boom. “It’s astonishing how geographically concentrated this AI wave is,” says McAfee. “The people who know how to found and fund and grow tech companies are there.” 

Lecturer Michael Schrage speaks with Forbes reporter Josipa Majic Predin about the shift towards generative AI in business. "AI should not be seen overwhelmingly as just an ethical or a technical or a digital innovation and platform," says Schrage. "It's actually a philosophical capability and resource."

CSAIL Research Scientist Neil Thompson speaks with Wired reporter Will Knight about how new AI systems are developing new algorithms that could be used to help advance scientific research and innovation. “If these capabilities can be used to tackle bigger, less tightly-scoped problems, it has the potential to accelerate innovation—and thus prosperity,” says Thompson.

Researchers from MIT and elsewhere estimate that “while 36% of U.S. private sector jobs were technically ‘exposed’ to automation through computer vision… it would only make economic sense for firms to pursue automation for 8% of all private sector jobs—just a quarter of those jobs labelled ‘exposed,’” report François Candelon, David Zuluaga Martínez and Etienne Cavin for Fortune. 

Principal Research Scientist Andrew McAfee co-founded Workhelix, a “tech-enabled service startup that works with enterprises to better understand and monitor AI automation at their companies,” reports Rebecca Szkutak for Tech Crunch. “Workhelix breaks down a company’s employee positions into specific job functions and tasks and scores each task for its suitability for AI adoption,” explains Szkutak. “This helps companies build roadmaps for how and where to adopt AI and gives enterprises a way to monitor if the AI they adopted is working.” 

Principal Research Scientist Andrew McAfee speaks with Forbes reporter Joe McKendrick about lessons that he believes technology companies need to absorb. McAfee notes that successful companies “are a lot more egalitarian, they try to modularize themselves and give a great deal of autonomy, and they try to settle their arguments via evidence.”

Sloan research fellow Michael Schrage speaks with Fortune reporter Sheryl Estrada about the impact of AI on CFO roles. “The ongoing ‘Compound AI’ revolution, which involves approaching AI tasks by combining multiple interacting components, will increasingly transform the CFO role into that of an AI-powered chief capital officer (CCO),” says Schrage. “This is an analytics-driven shift that isn’t optional but imperative for enterprise growth.”