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“I came to MIT Sloan intent on joining a vibrant ecosystem for entrepreneurship and leadership development,” says Alecia Asiamigbe, SFMBA ‘26.

It was MIT Sloan’s work to embed sustainability in new ventures that attracted Asiamigbe. Additionally, the MIT Sloan Fellows MBA Program gave her the opportunity to earn an MBA in one year. “I was anchored to my choice by the Disciplined Entrepreneurship framework and the potential to focus on climate and energy entrepreneurship.”

Currently, Asiamigbe is working to build out a sustainability-focused venture, Resilient Grid, a renewable energy company that aims to convert organic waste into sustainable natural gas able to produce reliable, dispatchable renewable power in fuel-import-dependent markets.

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 AI models to learn from human-created content is up for debate both in court and on Capitol Hill.

To address the issue, MIT Sloan professor Thomas Malone proposes “learnright” laws that would give copyright holders the exclusive right to license their content to AI companies for model training, enabling creators to be compensated fairly.

“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,” Malone said.

In a recent paper, Malone and co-authors Frank Pasquale and Andrew Ting outlined how learnrights could work legally, economically, and practically.

Link in bio for more.

In a new opinion piece, Greentown Labs’ Georgina Campbell Flatter and Ben Soltoff of the MIT Sloan School of Management argue that climate leadership and economic competitiveness are not opposing priorities — they are increasingly interconnected. As energy demand rises and industries evolve, the companies that succeed will be those that can deliver reliability, resilience, affordability, and lower emissions together.

The authors point to a growing opportunity for entrepreneurs and established organizations alike: building solutions that strengthen infrastructure, create economic value, and help shape a more resilient future.

Link in bio for more.

Many organizations struggle to convert data and artificial intelligence investments into assets that advance strategy and deliver measurable business impact.

To succeed, they need effective and efficient structures that support digital innovation and ensure that the time, money, and talent poured into digital initiatives aren’t wasted. One way to accomplish this: harnessing a troika of leaders, each with distinct areas of responsibility and complementary knowledge and expertise.

In a new research briefing, Nils Fonstad, Martin Mocker, and Jukka Salonen from the MIT Center for Information Systems Research highlight how successful organizations marshal initiative leaders, shared resource leaders, and portfolio leaders to weed out underperforming AI initiatives and increase bottom-line value.

“Traditional approaches worked when conditions were more stable, but in today’s dynamic environments, it’s impossible to assume any single leader or unit is capable of doing it all,” said Fonstad, an academic research fellow with @mitcisr. “What you actually need is a whole network of leaders.”

Link in bio for more.

Many organizations struggle to convert data and artificial intelligence investments into assets that advance strategy and deliver measurable business impact.

To succeed, they need effective and efficient structures that support digital innovation and ensure that the time, money, and talent poured into digital initiatives aren’t wasted. One way to accomplish this: harnessing a troika of leaders, each with distinct areas of responsibility and complementary knowledge and expertise.

In a new research briefing, Nils Fonstad, Martin Mocker, and Jukka Salonen from the MIT Center for Information Systems Research highlight how successful organizations marshal initiative leaders, shared resource leaders, and portfolio leaders to weed out underperforming AI initiatives and increase bottom-line value.

“Traditional approaches worked when conditions were more stable, but in today’s dynamic environments, it’s impossible to assume any single leader or unit is capable of doing it all,” said Fonstad, an academic research fellow with @mitcisr. “What you actually need is a whole network of leaders.”

Link in bio for more.

Many organizations struggle to convert data and artificial intelligence investments into assets that advance strategy and deliver measurable business impact.

To succeed, they need effective and efficient structures that support digital innovation and ensure that the time, money, and talent poured into digital initiatives aren’t wasted. One way to accomplish this: harnessing a troika of leaders, each with distinct areas of responsibility and complementary knowledge and expertise.

In a new research briefing, Nils Fonstad, Martin Mocker, and Jukka Salonen from the MIT Center for Information Systems Research highlight how successful organizations marshal initiative leaders, shared resource leaders, and portfolio leaders to weed out underperforming AI initiatives and increase bottom-line value.

“Traditional approaches worked when conditions were more stable, but in today’s dynamic environments, it’s impossible to assume any single leader or unit is capable of doing it all,” said Fonstad, an academic research fellow with @mitcisr. “What you actually need is a whole network of leaders.”

Link in bio for more.

Many organizations struggle to convert data and artificial intelligence investments into assets that advance strategy and deliver measurable business impact.

To succeed, they need effective and efficient structures that support digital innovation and ensure that the time, money, and talent poured into digital initiatives aren’t wasted. One way to accomplish this: harnessing a troika of leaders, each with distinct areas of responsibility and complementary knowledge and expertise.

In a new research briefing, Nils Fonstad, Martin Mocker, and Jukka Salonen from the MIT Center for Information Systems Research highlight how successful organizations marshal initiative leaders, shared resource leaders, and portfolio leaders to weed out underperforming AI initiatives and increase bottom-line value.

“Traditional approaches worked when conditions were more stable, but in today’s dynamic environments, it’s impossible to assume any single leader or unit is capable of doing it all,” said Fonstad, an academic research fellow with @mitcisr. “What you actually need is a whole network of leaders.”

Link in bio for more.

Many organizations struggle to convert data and artificial intelligence investments into assets that advance strategy and deliver measurable business impact.

To succeed, they need effective and efficient structures that support digital innovation and ensure that the time, money, and talent poured into digital initiatives aren’t wasted. One way to accomplish this: harnessing a troika of leaders, each with distinct areas of responsibility and complementary knowledge and expertise.

In a new research briefing, Nils Fonstad, Martin Mocker, and Jukka Salonen from the MIT Center for Information Systems Research highlight how successful organizations marshal initiative leaders, shared resource leaders, and portfolio leaders to weed out underperforming AI initiatives and increase bottom-line value.

“Traditional approaches worked when conditions were more stable, but in today’s dynamic environments, it’s impossible to assume any single leader or unit is capable of doing it all,” said Fonstad, an academic research fellow with @mitcisr. “What you actually need is a whole network of leaders.”

Link in bio for more.

Many organizations struggle to convert data and artificial intelligence investments into assets that advance strategy and deliver measurable business impact.

To succeed, they need effective and efficient structures that support digital innovation and ensure that the time, money, and talent poured into digital initiatives aren’t wasted. One way to accomplish this: harnessing a troika of leaders, each with distinct areas of responsibility and complementary knowledge and expertise.

In a new research briefing, Nils Fonstad, Martin Mocker, and Jukka Salonen from the MIT Center for Information Systems Research highlight how successful organizations marshal initiative leaders, shared resource leaders, and portfolio leaders to weed out underperforming AI initiatives and increase bottom-line value.

“Traditional approaches worked when conditions were more stable, but in today’s dynamic environments, it’s impossible to assume any single leader or unit is capable of doing it all,” said Fonstad, an academic research fellow with @mitcisr. “What you actually need is a whole network of leaders.”

Link in bio for more.

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.

Link in bio for more.

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.

Link in bio for more.

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.

Link in bio for more.

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.

Link in bio for more.

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.

Link in bio for more.

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.

Link in bio for more. #WorkingDefinitions

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?

Link in bio for more.

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?

Link in bio for more.

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?

Link in bio for more.

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?

Link in bio for more.

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.