Human content creators are protected by copyright law, in part to ensure that they’re fairly compensated for their work.
But whether these laws allow artificial intelligence models to learn from human-created content is up for debate — both in court and on Capitol Hill. Encyclopedia Britannica’s lawsuit against OpenAI, for example, is one of the latest allegations of misuse of reference materials. Meanwhile, the U.S. Copyright Office has not made a binding determination about whether using copyrighted works to train AI models is fair use.
To deal with these issues, in 2023 MIT Sloan School of Management professor Thomas Malone proposed “learnright” laws that would give copyright holders the exclusive right to license their content to AI companies for model training.
“Copyright law wasn’t designed for a world with generative AI, and without something like learnright laws, the incentives for people to create new content are likely to be greatly reduced,” said Malone.
In a more recent article, Malone and his co-authors outlined the argument for learnrights and described how they could work legally, economically, and practically.
Link in bio for more.
Climate scientists and scholars often warn when we approach dangerous tipping points in the global fight against climate change, when warming triggers reinforcing climate effects that become increasingly difficult or impossible to reverse.
But there’s another kind of tipping point that offers reasons for optimism. Building on the work of Timothy Lenton and colleagues at University of Exeter, researchers have explored how positive tipping points can accelerate climate progress through reinforcing economic and technological change.
In this conversation, MIT Catalytic Climate Finance Project co-founders Jason Jay and Florian Berg discuss how scaling climate technologies can help create positive tipping points — the moments when adoption accelerates, costs decline, and markets begin driving further growth.
Consider solar energy. Decades of financing, grid investment, and policy support helped transform solar from an expensive niche technology into the world’s fastest-growing energy source.
The question now is how to build the financial architecture needed for other technologies critical to advancing climate goals — like green steel and green ammonia — to repeat the success of solar.
Link in bio to watch the full conversation.
“MIT Sloan’s Sustainability Initiative provides a great platform to help a generalist like myself become more specialized in this space, whether it be the Sustainability lunch series that they run every Thursday, the annual conference that gets organized, or the class catalog that aligns with the Sustainability Certificate.”
Patrick Yeung, MBA ‘26, came to MIT Sloan wanting to be surrounded by a community of builders. “I come from a consulting background which has its own strengths and gives you a specific toolkit, but I felt like I was not very technical and so I wanted to be surrounded and inspired by people who had that knowledge and experience.”
Learn more at the link in our bio.
The MIT Startup Exchange supports MIT-connected ventures as they explore and assess new technologies. Four startups led by MIT Sloan School of Management alumni were featured as part of a recent virtual Demo Day.
See what each of these startups is doing to make other firms work better.
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Employees using generative artificial intelligence tend to fall into one of three categories, according to a new study co-authored by MIT Sloan professor Kate Kellogg.
1️⃣ Cyborgs collaborate fluidly with AI throughout a task, probing its suggestions and accepting some while pushing back on others. 2️⃣ Centaurs engage more selectively, drawing on their own domain expertise to ask targeted questions and maintain control. 3️⃣ Self-automators offload the task almost entirely. This group demonstrates what the researchers describe as “abdicated co-creation,” delegating analytical and evaluative thinking to AI and often accepting the results without modification.
In a study of 244 consultants at Boston Consulting Group, self-automators made up 27% of participants. Self-automators produced quick results that were polished but lacked depth, and they were the lowest performers on both accuracy and persuasiveness. They were also the only group to gain no skills gains — either in domain expertise or AI proficiency.
Companies can help all employees, but especially self-automators, by taking a more active role in helping them decide which tasks to automate — structuring workflows and training so that efficiency and skill-building happen together, Kellogg said.
The study was co-authored by researchers from MIT Sloan, the University of Warwick, Harvard Business School, and the University of Pennsylvania.
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The standard advice for managing artificial intelligence risks such as hallucinations and unreliable outputs is to keep a human in the loop.
But a new study that tracked AI use among Boston Consulting Group employees suggests that rather than solving problems related to AI, putting a human in the loop introduces a new issue.
When the consultants at BCG tried to validate a large language model’s suggestions for a particular business case, the LLM reiterated its position — and the harder people challenged it, the harder the LLM defended its original answer. Instead of considering pushback and appearing to work toward the best solution, the LLM dug in its heels and acted like a salesperson, pushing its suggestions even when they were wrong.
“We saw the human’s act of real-time validation with generative AI triggering this persuasive counter-response by the LLM,” said MIT Sloan School of Management professor Kate Kellogg, one of the researchers. “The very tool that was supposed to be the solution to one set of problems actually activated a different problem.”
The study, which Kellogg conducted with colleagues at Harvard University and the University of Warwick, highlights a new barrier to human-AI collaboration and an uncomfortable question for any organization betting on human oversight to keep AI honest: What if the AI is better at persuasion than humans are at resistance?
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As innovation around climate technology accelerates, developing the financial mechanisms to scale green innovation remains a central challenge.
The MIT Catalytic Climate Finance Project brings together researchers, industry leaders, and policymakers to examine how capital can more effectively support the development and deployment of climate technologies.
CCFP co-founders Florian Berg and Jason Jay of the MIT Sloan School of Management discuss how MIT’s strengths across engineering, economics, and financial innovation create a powerful foundation for this work.
“In the past, what you see when you look at the methodology of carbon offsets — finance and economic and accounting scholars have not talked enough to engineering scholars,” Berg said. “And the Catalytic Climate Finance Project wants to make this connection.”
Link in bio to watch their full discussion.
Nike COO Venkatesh Alagirisamy arrived for an April 8 talk at MIT wearing the Vomero Premium running shoe.
“It has two airbags that are made in the United States and the rest of the shoe is manufactured in Vietnam,” he said. “This shoe wouldn’t exist today if we approached it purely through the lens of design for manufacturing. This shoe exists in this shape and form because creativity thrives at Nike and embracing the tension is very, very critical.”
Alagirisamy framed tensions and polarities as forces that are unavoidable in large organizations, but which can be harnessed for innovation. That might mean finding the right balance between local impact and global scale, or between investing in a mature market like running and an emerging one like volleyball.
Alagirisamy’s talk with MIT Sloan John C Head III Dean Richard Locke was presented by the MIT Initiative for New Manufacturing, an MIT-wide effort driving research, education, and collaborations to transform the future of manufacturing in the United States and beyond.
Link in bio for more about the initiative.
People are already using ChatGPT to help with retirement planning, but is it good enough yet to deliver the kind of smart, personalized financial guidance human advisers have traditionally provided? Does it engage in ethical financial behavior and act in the best interests of clients, as human and organizational fiduciaries must?
“Yes and no,” said Andrew Lo, a finance professor at the MIT Sloan School of Management. Presenting recently as part of the MIT Sloan speaker series, “AI + X: How AI Is Changing Management Practice,” Lo said that ChatGPT can offer reliable, individualized financial advice, but it’s not perfect — yet.
For now, people who want to use generative artificial intelligence for investment advice should be their own advocate and cross check what it says, according to Lo, who is director of the MIT Laboratory for Financial Engineering.
“You need to be educated because ultimately, it’s your life, it’s your wealth. You need to bear responsibility until such time as large language models can bear such responsibility,” Lo said.
Link in bio for more.
In a world of economic uncertainty, central banking can be as much an art as it is a science.
“The art of war is of vital importance to the State,” wrote Chinese philosopher Sun Tzu. “It is a matter of life and death, a road either to safety or to ruin. Hence it is a subject of inquiry which can on no account be neglected.”
In her new book, MIT Sloan professor Kristin Forbes draws a parallel between ancient military strategy and modern monetary policy. From the 2008 global financial crisis to the COVID-19 pandemic, central banks have been forced to innovate rapidly, expanding their tools, influence, and responsibilities.
But with greater reach comes greater scrutiny and higher stakes. As global markets grow more interconnected and geopolitical risks rise, the cost of miscalculation increases.
What principles should guide decision-making in this environment? Forbes offers a framework grounded in history, designed for the challenges ahead.
Link in bio for more.
People are already using AI chatbots to help with retirement planning, but is it good enough yet to deliver the kind of smart, personalized financial guidance human advisers have traditionally provided? Does it engage in ethical financial behavior and act in the best interests of clients, as human and organizational fiduciaries must?
“Yes and no,” said Andrew Lo, a finance professor at the MIT Sloan School of Management. Presenting recently as part of the MIT Sloan speaker series, “AI + X: How AI Is Changing Management Practice,” Lo said that ChatGPT can offer reliable, individualized financial advice, but it’s not perfect — yet.
“AI is good at explaining trade-offs, scenario exploration, emotional intelligence, behavioral coaching, and portfolio logic,” Lo said. However, AI “needs a lot of work” in things like precise tax optimization, the nuances of different kinds of regulation, arithmetic, and bearing legal responsibility.
That last item is important, Lo said, given that currently, LLMs bear no responsibility whatsoever. “Use at your own risk,” Lo said. “That might be okay for a piece of software, but when you're talking about financial planning, there's a whole world of challenges that you have to be wary of. Remember, AI is not infallible.” As Lo sees it, the best results are derived from an informed, engaged individual using the newest AI models as a collaborative partner rather than an oracle.
Organizations adopt artificial intelligence to increase their productivity, but research has found that many firms initially experience a short-term performance decline before seeing longer-term gains.
This phenomenon, represented by a J-shaped curve, can be attributed to a mismatch between existing processes and infrastructure and the advanced digital tools required to deliver value, said Kristina McElheran, a digital fellow at the MIT Initiative on the Digital Economy and a lead author of a paper examining tens of thousands of U.S. manufacturing firms.
The need for employee training, workflow redesign, and systems integration work can all play a role in performance lags. Despite that initial decline, the researchers found that early AI adopters experienced higher revenue, productivity, and employment growth over time compared with peers that implemented the technology later.
“Once firms work through the adjustment costs, they tend to experience stronger growth,” McElheran said.
The upward trajectory wasn’t the same for every organization, though.
AI implementation “requires systemic change, and that process introduces friction, particularly for established firms,” McElheran said. Entrenched practices and complexity at older firms can impede their ability to adapt and often lead to a sharper productivity decline and slower growth than younger, more digitally oriented firms experience, the researchers found.
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