A bipartisan push to stymie partisan discrimination in disaster assistance could be complicated by election-year politicking.

The progressive megadonor is back to talking about climate policy after an unsuccessful run for governor.

Staff must deal with patients too sweaty to receive IV drips, medication at risk of degrading in high temperatures and colleagues collapsing with heat fatigue.

The summertime ideal — lazy days, warm nights and escape from classes or parents or work or rules — seems dead this year.

The EU and regional authorities are helping cheese producers as dairy herds suffer in heat waves that are becoming the norm.

Tornadoes, heavy rains and flash flooding brought serious damage to communities from Illinois to western Pennsylvania in recent days.

The passing storm's strong winds took down trees, utility poles and transmission towers. The Big Island's power transmission system suffered "extensive damage," the Hawaiian Electric utility said.

You’ve heard of gig workers, freelancers, and temporary employees. But do you know about marginal workers? 

Accounting for about one in six U.S. jobs, it’s a huge category of people, who are going nowhere fast in the workplace — and don’t really have much say about that. 

“Marginal workers are employees who have no career prospects at their organizations,” says MIT Professor Emeritus Paul Osterman, author of a new book on the subject. “They are employees of the organization for whom they work, but the organization does not intend to keep them, and these workers are much less attached to any career ladder.” 

As such, marginal workers are part of a larger trend in U.S. employment. According to Osterman’s analysis, 35 percent of U.S. workers are either marginal employees, freelancers, contractors, or gig employees finding work on online platforms like ridesharing services. 

“That’s a big number,” says Osterman, who is the Nanyang Technological University Professor Emeritus at the MIT Sloan School of Management, where he is also a professor emeritus of work and organization studies. “That’s over 55 million people in the American work force.”

Osterman scrutinizes this employment landscape in his new book, “Disposable Workers: The Transformation of Employment,” published this month by Harvard University Press. In it, he examines the different categories of “disposable” workers in the U.S., while making the case that they are all part of a still-growing movement by firms to control labor costs, leaving many workers in precarious positions. 

“I wanted to present a unified way of thinking about these trends,” Osterman says.

Cutting costs

Osterman is a longtime labor economist and author of several previous books, whose work has often focused on job quality and labor-market fairness. 

He was motivated to write “Disposable Workers,” he says, because of how significantly marginal workers have been overlooked. Indeed, the category and term “marginal workers” comes from Osterman. 

In researching the book, Osterman conducted an original survey of over 6,000 workers, which helped shed light on the concept of marginal workers. They can fit a range of professions: staff attorneys at a law firm, adjunct faculty, and many kinds of part-time employees with few opportunities for advancement.

Overall, Osterman finds that about 17 percent of U.S. employees are marginal workers. Roughly 12 percent are contract workers, who are often employed by staffing agencies but then assigned to work at varying locations. Another 5 percent are organizational freelancers, working for firms without being part of the permanent staff. This includes gig workers, who account for a little more than 1 percent of the workforce and draw work from online platforms such as rideshare services. (Beyond this, there are also freelancers who work individually for multiple clients.) 

The common denominator among these categories is that each has evolved as a result of firms trying to cut back on labor expenses while trying to gain flexibility and more managerial discretion. The result is fewer workers with promotion prospects, health benefits, and employment stability.

“I’m putting the discussion of freelancing, contracting, and marginal workers into a coherent story that shows they’re all of a piece, they’re all part of the same thing, in terms of how employers are thinking about it,” Osterman says. 

Long term versus short term

How employers think about it, to be clear, revolves primarily around employee costs. By deploying employees in a variety of marginal, freelance, and contract roles, and making some of those positions part-time, businesses have constructed a system in which fewer employees have rising wages or additional benefits, and the portion of firm revenues plowed back into paying for workers can shrink. 

“This is not a book that argues that there’s dishonesty or that anyone’s evil, but at the end of the day, firms only care about one thing, which is to maximize profits, period, end of story,” Osterman says. 

He adds: “I’m very careful to say it’s a good thing that firms create jobs and develop new products — all good.” Still, he notes, for people who prioritize the plight of workers, the expansion of a disposable work force is a significant issue. 

To be sure, many scholars have found that short-term labor cost reductions can be counterproductive. Many firms have appeared to benefit from having a more stable, committed, motivated work force, which seems to result in greater productivity. What Osterman finds is that firms are likely aware of this tradeoff, and still willing to have a less-committed, less-expensive staff.

“The firms are obviously making a decision that the costs outweigh the value of commitment,” Osterman says. He also notes that the evidence on the matter is not entirely clear-cut. 

“There’s a debate on both sides of that question,” Osterman says. “I can’t prove that firms are being smart or stupid. But I can just tell you what they’re doing. And what they’re doing is making the decision that they benefit from having a large fraction of their workforce be disposable.”

Making the issue matter

“Disposable Workers” has drawn praise from other scholars. David Weil, a professor in the Heller School for Social Policy and Management and the Department of Economics at Brandeis University, has called it “a carefully researched and engaging book documenting the degradation of employment in recent decades.” 

Indeed, as “Disposable Workers” makes clear, the workplace has been challenging for many employees for a while now. Add artificial intelligence into this setting, and the outlook would seem to get even tougher for employees. Indeed, Osterman thinks AI could increase the use of disposable workers, if only for indirect reasons.

“I think this trend is going to be exacerbated by AI, because AI introduces a lot of uncertainty to firms about what their staffing needs are, and if firms are uncertain, they’re going to want disposable workers,” Osterman says. However, he emphasizes, “Disposable Workers” is not a book about AI. 

In any case, if jobs in the U.S. have become more precarious, what can be done to reverse that trend? One answer might be more expansive worker protections stemming from union negotiations. But these days, Osterman notes, only about 6 percent of U.S. employees are in a union, so that will only go so far.

Still, Osterman points out that nonunion organizations can help the situations of workers, such as the advocacy groups that lobbied for a $15/hour minimum wage in many places several years ago. 

Then too, he observes, sometimes customer pressure gets firms, even large multinationals, to improve working conditions, either for the firm’s own workers, or along its supply chain. 

“There is no magic solution,” Osterman says. “There is a set of tools.”

A key reason he wrote “Disposable Workers” is to bring attention to the topic in the first place, and the full extent to which the U.S. now has a workforce without much security or prospects of upward mobility. Without recognition of that point, no effort to change things will unfold, Osterman believes.

“The bigger policy point is: This issue has to become salient,” Osterman says. “If it does, then public and political pressure will come to bear on firms. If it doesn’t, then it won’t.” 

Shannon Knight attributes her interest in neuroscience to an experience she had in high school. She and her sister attended a medical day for students at the nearby University of Illinois Chicago. As they were on their way out of the event, they walked past a room with a person holding a brain.

“We stopped and backpedaled into the room, and I was so fascinated,” says Knight. “I was able to hold the brain of a patient who had passed away of Alzheimer’s. The brain holds so much emotion, decision-making — everything. I realized that this man’s entire memory was in my hands, and something clicked for me. I decided that I really wanted to learn much more about this organ.”

Now in her sixth year of doctoral studies at MIT’s McGovern Institute for Brain Research, Knight is working on developing a novel gene therapy for childhood-onset epilepsy, specifically SYNGAP1 haploinsufficiency. This rare genetic disorder is caused by a mutation in the SYNGAP1 gene, rendering one of the two copies of the gene nonfunctional. 

SYNGAP1 is important for brain development and neuronal communication, and the disorder leads to seizures in children starting as young as 4 months old. Other symptoms include intellectual disabilities, challenges with eating and sleeping, and difficulties with movement.

While there are currently methods to address the symptoms of the disorder, such as anti-seizure medications and dietary restrictions, as the child ages, the seizures often become resistant to medications. Knight is working to develop a therapeutic using CRISPR, a biotechnology tool used to edit genes. This therapeutic aims to address the root cause of this medication resistance by focusing on the gene itself.

“The idea of leading science with empathy is something that I feel very deeply,” she says. “I hope my efforts in the lab work toward the benefit of the people affected, rather than just for the benefit of my own science.”

Researching gene therapies

Knight’s interest in the brain flourished as a neuroscience major at Bowdoin College, working with Professor Hadley Horch. While she had originally planned to be pre-med, Knight ultimately decided that it wasn’t the best fit. She enjoyed the research she did as part of her honors thesis, exploring the regeneration of neurons in the auditory system of crickets, and decided that she wanted to pursue more research in molecular neuroscience, as well as genetics.

After graduating, Knight worked at the Perrimon Lab at Harvard University, where she first learned about CRISPR, applying it in a fruit fly model. She worked for two years in the lab, co-authoring a few papers and applying to graduate schools. 

She ultimately landed in the lab of MIT Professor Guoping Feng, studying the potential of utilizing CRISPR to develop a gene therapy treatment for Phelan-McDermid Syndrome, a rare genetic disorder caused by a deletion or mutation on the 22nd chromosome.

“Many of our graduate students are passionate about making a positive impact to society through cutting-edge research, and Shannon is a perfect example,” says Feng, the James W. and Patricia T. Poitras Professor and associate director at the McGovern Institute. “She is developing gene therapy technologies that have the potential to help many kids with devastating neurodevelopmental disorders.”

Building off of the gene therapy research around Phelan-McDermid syndrome, which is now in clinical trials in patients, Knight is now in the early phases of testing gene therapy for SYNGAP1 disorder. The goal is to go through the same process for the SYNGAP1 gene therapy as for the Phelan-McDermid gene therapy — eventually obtaining U.S. Food and Drug Administration approval and beginning clinical trials. 

The testing of the gene therapy on mice with a version of SYNGAP1 disorder has shown promising preliminary results in alleviating seizures and all of the behavioral phenotypes. This work is being accelerated by the Rare Brain Disorders Nexus, an MIT initiative that launched in the fall of 2025.

“Something I think about a lot is the idea of who ‘deserves’ the attention of a gene therapy. I feel that, regardless of how rare a genetic disorder might be, it still deserves care,” says Knight. “SYNGAP1 disorder is extremely rare, only impacting one to four out of every 10,000 children. I am very fortunate to be at an institution like MIT that has so many labs and brilliant researchers working on diseases that impact large portions of society, and it was really important to me to spend my PhD years helping a small, often unseen population. Although I don’t actually have a relationship with someone who has SYNGAP1 disorder, I know so many people who feel invisible in systems, and it is really important to me to be able to focus on people who feel unseen and give them hope.”

Inspiring others in the lab

In addition to her passion for neuroscience and genetic research, Knight has also developed a love of teaching. She has been a teaching assistant for 9.12 (Experimental Molecular Neurobiology), leading the lab portion of the course. She has enjoyed working closely with small classes of students, introducing them to the fundamentals of neuroscience lab research.

“We walked through the process of looking at a specific protein in neurons, and talked about how you can go from cell culture all the way up to a mouse brain — and all the steps in between. It was so important to me to be able to teach the students and help them to consider all of the different types of experiments they could do,” she says. “I’ve talked to many of the students since then, and many said it was one of their favorite classes.”

Knight received the Goodwin Medal in 2025 in recognition of her commitment to excellent teaching.

“Shannon has a rare combination of scientific excellence, teaching talent, and compassion,” says Laura Frawley, senior lecturer and teaching and curriculum development specialist in the Department of Brain and Cognitive Sciences. “Students trust her because she is approachable and invested in their success, and they learn from her because she has an exceptional ability to make complex ideas accessible and engaging. Her influence extends far beyond the laboratory skills she teaches.”

Knight has also invited high school and other college students into the lab and worked with them during the summers.

“It’s so exciting to bring in kids with no previous experience in a wet lab, and watch them be so amazed by all of the things that you can do,” she says. “Experiments that might seem so routine and relatively simple to me, at this point, are so exciting for them.”

Following the completion of her PhD program, Knight plans to do postdoctoral research and would ultimately like to be a faculty member at a small liberal arts college.

“It’s amazing to see students gain confidence over time, and then seeing them progress in their careers as scientists,” she says. “That’s very rewarding for me.”

Nature Climate Change, Published online: 18 August 2026; doi:10.1038/s41558-026-02676-z

Leading European cities race to reach net-zero emissions, but residual emissions are tied to easier-to-abate sectors and temporary, land-intensive carbon removal for compensation. To keep climate neutrality credible and fair, policy must tighten expectations on cutting emissions and set clear rules for carbon removal and credits.

A functional nervous system depends on the cooperation of many kinds of cells. So as developing organisms build their nervous systems, their neurons must take on different forms and functions to fulfill their designated roles. That carefully orchestrated process gives rise to thousands of different cell types in the human brain.

In the tiny worm known as C. elegans, the nervous system is far simpler, comprising a mere 118 classes of neurons.

At MIT, scientists in H. Robert Horvitz’s lab are studying the worms to learn about how nervous systems develop. Horvitz is the David H. Koch Professor of Biology at MIT, an investigator at the McGovern Institute for Brain Research at MIT, and an investigator at the Howard Hughes Medical Institute. His team has just discovered that a protein complex called cohesin, which helps shape the three-dimensional structure of the genome in both worms and humans, is critical for establishing some neurons’ identities as development unfolds.

The open-access findings, reported July 31 in the journal Science Advances, could help scientists find a way to treat a rare developmental disorder called Cornelia de Lange syndrome, which is caused by mutations that interrupt the cohesin complex.

Model organism

MIT postdoc Dongyeop Lee explains that C. elegans is a powerful model for studying neurodevelopment not just because its nervous system has been comprehensively mapped, but also because of the ease and speed with which scientists can study the function of its genes.

Because many of the worm’s genes have been retained through evolution, findings from studies of C. elegans often reveal important aspects of human biology. The current study began with worms that, because of a genetic mutation, make too many neurons of a certain type.

Adrenergic neurons, named for the kind of neurotransmitter they use to communicate with other neurons, are vital for enabling worms to respond to both their environment and their own internal state. Normally, C. elegans has just two pairs of adrenergic neurons: two RIM neurons and two RIC neurons. But the worms Lee studied had extras of both.

Takashi Hirose, a former member of the Horvitz lab, first observed this change in 2007.

Lee later continued the study and discovered that worms carrying a mutation in a gene called coh-1 have extra adrenergic neurons. The coh-1 gene encodes one part of the cohesin complex.

When Lee tested other mutations that disrupt cohesin, he found the same effect: Worms without fully functional cohesin had too many RIM neurons and too many RIC neurons.

Molecular switch

With a series of experiments designed to tease apart how cohesin impacts neurons’ identities, Lee discovered that cohesin cooperates with a gene-regulating protein called EOR-1 (known in humans as PLZF) to direct some neurons to develop into neurons that communicate with the inhibitory neurotransmitter GABA.

By reorganizing the structure of the genome, cohesin can change the way gene regulators like EOR-1 interact with DNA. Lee’s experiments showed that when either cohesin or EOR-1 couldn’t do its job, cells that should have become GABA-producing neurons become adrenergic neurons instead.

“What we found is that there are two alternative possible fates of certain neurons, and cohesin acts as a molecular switch that decides one of the possible neuronal fates,” Lee explains. “This means the structure of genomic DNA in the nucleus is important for neuronal fate determination.”

Disease connection

Lee adds that extra adrenergic neurons were not the only abnormality he observed in worms with cohesin mutations. Cohesin is important for shaping cells and tissues throughout the body. “The mutants have severe developmental defects,” Lee says. “They grow slowly. They don’t move well, and they also have defects in reproduction.”

Notably, the problems Lee saw in the worms echo aspects of Cornelia de Lange syndrome, a rare genetic disorder that impacts physical, cognitive, and behavioral development. Cornelia de Lange syndrome can be caused by mutations in cohesin genes, and Lee says that the discovery of how cohesin mutations affect worm development and behavior opens new opportunities to study the disease and search for potential therapeutic targets in C. elegans.

The Horvitz lab already has some promising leads. Taking advantage of the quick genetic screens that are possible in worms, Lee has found additional mutations that can counteract impaired cohesin, improving the health of worms with cohesin mutations. The team is now working to identify the genes where these suppressor mutations occur, so they can investigate whether they might make good therapeutic targets in humans.

Meanwhile, the team is also exploring a potential role for cohesin in shaping the fates of other neuron types, as well as searching broadly for additional molecules that work with cohesin to guide development. “We expect we have opened up a new biology,” Lee says. “This paper is just the beginning.”

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and Engineering, in his latest book, “The Invisible Engine: Why Innovation Evades Control.”

Fitzgerald draws on a decade of work leading international research programs, including the MIT and Masdar Institute Cooperative Program and the MIT-Singapore Alliance for Research and Technology, where he explored innovation as the integration of market applications, technology, and implementation.

Written at a moment when artificial intelligence is reshaping how we think about knowledge, research, and innovation, “The Invisible Engine” examines a deeper question: How does innovation actually happen, and how should society invest in it?

In this interview, Fitzgerald discusses his own experiences with innovation — including his co-invention of strained silicon at AT&T Bell Laboratories in the 1990s, which helped extend Moore’s Law, the semiconductor industry’s long-standing trend of increasing the number of transistors on chips roughly every two years — while exploring common misconceptions about innovation, how to create the conditions for it, and novel ways to prepare institutions for future uncertainty.

Q: What inspired you to write this book? 

A: The book really grew out of the last 10 years of work in research-to-market activity, from the MIT Masdar program to the MIT-Singapore Alliance. In science, we have professional journals and things like that that capture discoveries within individual fields, but these larger-scale projects — where science, economics, industry, and society all intersect — don’t really have an academic thread that connects them.

I wanted to write a book that condensed all of those connections, because the innovation process at that scale is really the intersection of many different fields. The dominant ones are science and economics, because those are the underlying principles that drive how innovation happens.

So I was interested in marking this moment in history and documenting the experiments we’ve done at scale — trying to understand how knowledge of the innovation process can be incorporated into large collaborative research programs.

What started as a practical effort to make these programs work became a broader and somewhat unexpected interest in the innovation process itself.

Q: What is the “invisible engine?” 

A: The invisible engine is this decentralized collective intelligence of different actors, which are people and companies that eventually create surprise in the marketplace, which brings great profit.

This concept of “surprise” comes from Frank Knight, an economist from the early 1900s who was trying to understand the Industrial Revolution happening around him. So he takes a close look at the entrepreneur and asks, “What does the entrepreneur do?” And his answer is that the entrepreneur takes on uncertainty. They bring something into the world without knowing exactly what will happen, and their reward is surprise — everyone is surprised that people want it and that it can be done. Because the entrepreneur is the first to discover that opportunity, they can earn a profit.

Q: How did your experience developing semiconductor technologies shape the ideas in the book? 

A: It started with Bell Labs. My colleague and I made an important discovery — we found a way of straining silicon in a thin-film form with very few defects, which had never been done before. From the physics point of view, it was a big result. But I was always interested in having impact in the world, not just scientific recognition, so I went to my manager and asked, “What do we do next?”

He said, “Go talk to the marketing people at AT&T.” In hindsight, that made perfect sense. Bell Labs, like a lot of great industrial labs, created a lot of stuff, but they couldn’t always commercialize it.

Then I came to MIT, which was an open aperture after Bell Labs. Here I could keep uncertainty open across all the elements and find convergence in different directions. Eventually I started a company, and going between institutions to stimulate things was an eye-opening experience. We eventually reached a settlement with Intel over a patent dispute because the industry discovered that strained silicon was needed to extend Moore’s Law — something we never expected.

A lot of people want things to be organized and say, “Oh yeah, look at all that chaos.” But no — the path from Bell Labs to MIT to a startup, and then to industry adoption, was the innovation process.

Q: What’s the biggest misconception about innovation? 

A: People think that all research investment works the same way if the goal is economic impact. But there are actually three different kinds of research investment, and they’re meant for different things.

There’s the one we all know about, which I call “altruistic science.” The purpose of altruistic science — in investing in an academic institution — is to produce educated people. It’s not done in the context of the world that ideas eventually have to succeed in. And if you honestly look at the direct economic yield over all these years, it’s basically zero.

Strategic research is the second investment category. As opposed to a single area of technology or science, it’s organized around a goal. A new F-35, for example, may need advancements in several fields, so the customer — in this case the government — wants them to come together. Basically, they’re taking economics out of the equation because they’re the only customer, but they have much broader uncertainty because they have multiple domains of technology that they have to deal with.

The third category is what I call “fundamental innovation.” It’s meant to represent the whole process from research to economic growth, even if it’s on 10-, 15-, or 20-year time horizons. Fundamental innovation is different because it has three variables: technology — what is physically possible; implementation — how it can be built and delivered; and market — who will adopt it, and why. Fundamental innovation involves all the necessary elements the whole time to converge on possible value. So you’re thinking about market applications the whole time, you’re thinking about new science and technology that could create new innovation options. Then you’re working in the real world saying, “OK, here’s how implementation would happen today, but maybe this could change, maybe that could change.” Not only are you doing your research, but the world is changing at the same time.

So that’s really the biggest misconception — that innovation is about an idea. It isn’t. It’s a process of working with things in the world until they become valuable.

Q: Who did you write the book for? 

A: I wrote it for multiple audiences: individual innovators and students; researchers and faculty; corporate leaders; research funders; and policy-makers. So, people who have a stake in trying to figure out, either with their careers or with their investments — whether it’s government or private — how to invest in the far future.

Q: What’s one lesson you hope readers take away?

A: For the policy people, I would say: Understand how innovation works in the economy, stop getting in its way, come up with new methods to drive it more efficiently, and realize there are three different streams of investment — altruistic, strategic, and this fundamental innovation stream that is not purposely being funded.

For students, I think understanding this is how you can actually have impact. What I point out in the book is that being involved in the innovation process makes you T-shaped: You have technical depth in one area and a broad working knowledge of many areas. If you’re doing research under these conditions, you start to learn about the world and all these different dimensions. It inherently includes business, economics, and applications. You’ve become broader, but then you still have the technical depth to drill down into any area.

For universities, this is who we should be. We should be teaching people how to do this and how to participate in these research corporations that I’m talking about. I call them third places: places that bring everybody together for this purpose — for investment, for everything else. Universities are the ones that can really trigger that, because companies aren’t going to have enough time. The government and universities should be targeting these third places for innovation, and students and faculty will be able to become more T-shaped through that interaction.

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and Engineering, in his latest book, “The Invisible Engine: Why Innovation Evades Control.”

Fitzgerald draws on a decade of work leading international research programs, including the MIT and Masdar Institute Cooperative Program and the MIT-Singapore Alliance for Research and Technology, where he explored innovation as the integration of market applications, technology, and implementation.

Written at a moment when artificial intelligence is reshaping how we think about knowledge, research, and innovation, “The Invisible Engine” examines a deeper question: How does innovation actually happen, and how should society invest in it?

In this interview, Fitzgerald discusses his own experiences with innovation — including his co-invention of strained silicon at AT&T Bell Laboratories in the 1990s, which helped extend Moore’s Law, the semiconductor industry’s long-standing trend of increasing the number of transistors on chips roughly every two years — while exploring common misconceptions about innovation, how to create the conditions for it, and novel ways to prepare institutions for future uncertainty.

Q: What inspired you to write this book? 

A: The book really grew out of the last 10 years of work in research-to-market activity, from the MIT Masdar program to the MIT-Singapore Alliance. In science, we have professional journals and things like that that capture discoveries within individual fields, but these larger-scale projects — where science, economics, industry, and society all intersect — don’t really have an academic thread that connects them.

I wanted to write a book that condensed all of those connections, because the innovation process at that scale is really the intersection of many different fields. The dominant ones are science and economics, because those are the underlying principles that drive how innovation happens.

So I was interested in marking this moment in history and documenting the experiments we’ve done at scale — trying to understand how knowledge of the innovation process can be incorporated into large collaborative research programs.

What started as a practical effort to make these programs work became a broader and somewhat unexpected interest in the innovation process itself.

Q: What is the “invisible engine?” 

A: The invisible engine is this decentralized collective intelligence of different actors, which are people and companies that eventually create surprise in the marketplace, which brings great profit.

This concept of “surprise” comes from Frank Knight, an economist from the early 1900s who was trying to understand the Industrial Revolution happening around him. So he takes a close look at the entrepreneur and asks, “What does the entrepreneur do?” And his answer is that the entrepreneur takes on uncertainty. They bring something into the world without knowing exactly what will happen, and their reward is surprise — everyone is surprised that people want it and that it can be done. Because the entrepreneur is the first to discover that opportunity, they can earn a profit.

Q: How did your experience developing semiconductor technologies shape the ideas in the book? 

A: It started with Bell Labs. My colleague and I made an important discovery — we found a way of straining silicon in a thin-film form with very few defects, which had never been done before. From the physics point of view, it was a big result. But I was always interested in having impact in the world, not just scientific recognition, so I went to my manager and asked, “What do we do next?”

He said, “Go talk to the marketing people at AT&T.” In hindsight, that made perfect sense. Bell Labs, like a lot of great industrial labs, created a lot of stuff, but they couldn’t always commercialize it.

Then I came to MIT, which was an open aperture after Bell Labs. Here I could keep uncertainty open across all the elements and find convergence in different directions. Eventually I started a company, and going between institutions to stimulate things was an eye-opening experience. We eventually reached a settlement with Intel over a patent dispute because the industry discovered that strained silicon was needed to extend Moore’s Law — something we never expected.

A lot of people want things to be organized and say, “Oh yeah, look at all that chaos.” But no — the path from Bell Labs to MIT to a startup, and then to industry adoption, was the innovation process.

Q: What’s the biggest misconception about innovation? 

A: People think that all research investment works the same way if the goal is economic impact. But there are actually three different kinds of research investment, and they’re meant for different things.

There’s the one we all know about, which I call “altruistic science.” The purpose of altruistic science — in investing in an academic institution — is to produce educated people. It’s not done in the context of the world that ideas eventually have to succeed in. And if you honestly look at the direct economic yield over all these years, it’s basically zero.

Strategic research is the second investment category. As opposed to a single area of technology or science, it’s organized around a goal. A new F-35, for example, may need advancements in several fields, so the customer — in this case the government — wants them to come together. Basically, they’re taking economics out of the equation because they’re the only customer, but they have much broader uncertainty because they have multiple domains of technology that they have to deal with.

The third category is what I call “fundamental innovation.” It’s meant to represent the whole process from research to economic growth, even if it’s on 10-, 15-, or 20-year time horizons. Fundamental innovation is different because it has three variables: technology — what is physically possible; implementation — how it can be built and delivered; and market — who will adopt it, and why. Fundamental innovation involves all the necessary elements the whole time to converge on possible value. So you’re thinking about market applications the whole time, you’re thinking about new science and technology that could create new innovation options. Then you’re working in the real world saying, “OK, here’s how implementation would happen today, but maybe this could change, maybe that could change.” Not only are you doing your research, but the world is changing at the same time.

So that’s really the biggest misconception — that innovation is about an idea. It isn’t. It’s a process of working with things in the world until they become valuable.

Q: Who did you write the book for? 

A: I wrote it for multiple audiences: individual innovators and students; researchers and faculty; corporate leaders; research funders; and policy-makers. So, people who have a stake in trying to figure out, either with their careers or with their investments — whether it’s government or private — how to invest in the far future.

Q: What’s one lesson you hope readers take away?

A: For the policy people, I would say: Understand how innovation works in the economy, stop getting in its way, come up with new methods to drive it more efficiently, and realize there are three different streams of investment — altruistic, strategic, and this fundamental innovation stream that is not purposely being funded.

For students, I think understanding this is how you can actually have impact. What I point out in the book is that being involved in the innovation process makes you T-shaped: You have technical depth in one area and a broad working knowledge of many areas. If you’re doing research under these conditions, you start to learn about the world and all these different dimensions. It inherently includes business, economics, and applications. You’ve become broader, but then you still have the technical depth to drill down into any area.

For universities, this is who we should be. We should be teaching people how to do this and how to participate in these research corporations that I’m talking about. I call them third places: places that bring everybody together for this purpose — for investment, for everything else. Universities are the ones that can really trigger that, because companies aren’t going to have enough time. The government and universities should be targeting these third places for innovation, and students and faculty will be able to become more T-shaped through that interaction.

The scales of a pine cone open in low humidity to scatter seeds, but close in damp conditions to protect seeds from moisture. An artificial material with the same behavior could be useful in applications like moisture-responsive shingles for passive cooling.

MIT researchers have now developed a system that simplifies the process of designing this type of bioinspired material. 

Their framework captures how mechanisms across length scales in a natural system, like the cells, fibers, and tissues inside a pine cone, work together to achieve unique properties. It then formally translates that behavior in an engineered system. 

The framework organizes biological behavior into building blocks that can be used to design synthetic structures that can be mathematically validated to perform the same way, and fabricated using a 3D printer. 

By taking much of the guesswork out of this design process, the framework could help engineers more readily create new adaptive materials while cutting development time and eliminating costs from failed prototypes. This framework could one day be used to design soft robotic grippers that respond automatically to their environment without any complex electronics, or morphing structures for airplane wings that predictably change their shape in response to temperature shifts.

“I’ve always been fascinated with natural materials and how complex behavior emerges from very simple building blocks,” says Lee Marom, an MIT graduate student in the departments of Mechanical Engineering and Architecture and lead author of a paper on this framework. “What really excites me about this work is going beyond bio-inspiration to what we could call ‘bio-derivation,’ where we move past observing a unique behavior to capturing the relationships and mechanisms that are actually producing that behavior, and then finding a systematic way to translate them into an engineered system.”

Marom is joined on the paper by corresponding author Markus Buehler, the Jerry McAfee Professor of Engineering in the departments of Civil and Environmental Engineering and Mechanical Engineering; Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, a principal investigator in the Laboratory for Information and Decision Systems, and an affiliate faculty with the Institute for Data, Systems, and Society; and Skylar Tibbits, an associate professor in the Department of Architecture. The research appears in the Journal of the Mechanics and Physics of Solids.

Biological building blocks

Pine cones can open and close their scales in response to humidity because of complex interactions within the organism’s structure. 

Shifts in humidity cause changes in microscopic cellulose fibers, which then cause transformations in larger groupings of fibers called laminas, which impact tissue layers, and so on, all the way up to the pinecone we see hanging from a tree branch.

“We instantiated the framework on the pine cone because it gives us a relatively simple, well-understood mechanism to demonstrate how the framework works. But its value becomes even greater as we apply it to more complex systems,” Marom says.

For engineers, the challenge is not necessarily reproducing an individual behavior, but translating the mechanisms and relationships that produce it across length scales. Without an explicit framework, these relationships need to be reformulated for each new system. 

To streamline the material design process, MIT researchers created a mathematical framework that captures how the components at each scale in a natural object work together to exhibit a certain behavior. The framework carries the design all the way to fabrication, translating the engineered behavior into verified manufacturing specifications and executable code that is used to 3D-print the object.

“What we were missing was a way to connect the mathematical description of a natural system all the way to its physical realization. The goal of this framework is to make that entire chain explicit so we can reason about what has to be preserved at each step,” Marom says.

The framework utilizes tools from category theory, which is a systematic method to compose larger systems from smaller ones in a way that is guaranteed to succeed.

Using category theory, the system maps out how a stimulus, such as humidity, causes a response at each level of the biological hierarchy within an organism like a pine cone. It models each level of the biological hierarchy as a separate building block that is independently validated.

Then the framework constructs a larger system from these building blocks by employing mathematical rules to ensure there is a valid transition between each step in the hierarchy. 

It assigns each building block in the natural system to a synthetic counterpart. In this way, the engineered material preserves the stimulus-response interactions that cause the natural organism’s unique behavior.

The work extends a research program in Buehler’s laboratory spanning more than a decade. 

Earlier studies used category theory to describe hierarchical materials and determine when building blocks could be replaced while preserving higher-level function. In subsequent work, Buehler and colleagues introduced “categorical prototyping,” using the same mathematics to preserve selected molecular-scale mechanics when translating computational models into large-scale 3D-printed prototypes. 

The new framework takes the next step by closing the entire chain, from multiscale biological mechanics, through an engineered realization and fabrication specification, to an experimentally validated, machine-executable design.

“Biological materials derive their extraordinary functionality from relationships that span scales, from molecular and fiber-level mechanisms to whole structures. Category theory gives us a way to make those relationships explicit and transferable. Once that design logic is captured mathematically, nature becomes a library of composable mechanisms that can be translated, recombined, and realized in new material systems,” Buehler says.

Compositional structure 

“Once we know that the relationships we mapped are valid, we can start recombining them in new ways. That means the framework isn’t only describing existing systems, it can also help us reason about ones we haven’t built before,” Marom explains.

For instance, the engineers mapped the humidity-driven bending behavior in a pine cone and the humidity-driven twisting behavior of a wheat awn as separate sets of building blocks. 

Then they combined some building blocks from each to design and fabricate a new type of actuator that exhibits thermal twisting behavior, without the need to do any new design work. When tested, the twisting actuator performed as the researchers expected.

In the future, engineers could use this framework to reliably combine verified components into new, bio-inspired designs for adaptive materials in applications like robotics, biomedical devices, or wearable technology.

“The systematization of our framework allows you to reuse pieces without needing to start from scratch each time, saving a huge amount of computation. That’s the real-world payoff,” Zardini says.

Now that the researchers have laid the groundwork with this mathematical framework, they can apply it to objects with more complex mechanics. They also plan to incorporate artificial intelligence models into their pipeline to expedite the discovery of new adaptive materials. 

“We have shown that the boundaries between disciplines do not matter as much as we think they do. Some of the principles from category theory can be used to guide and empower materials design. These mathematical structures seem to really have no boundaries,” Zardini says.

“The larger vision is physical AI: intelligence that can reason in terms of physical mechanisms and then turn those ideas into matter. Here we are beginning to build the infrastructure for that — composable physical knowledge, mathematical rules for determining what can be combined, and a path from a new design concept all the way to machine instructions and fabrication. Ultimately, this could allow AI not only to discover new materials and mechanisms, but to physically realize and test what it discovers,” Buehler says.  

This research was supported, in part, by the MIT Lemelson Engineering Fellowship, Singapore DSO National Laboratories, and the MIT Generative AI Impact Consortium.

MIT researchers have engineered bacteria that can function as transistors, allowing the team to create living “circuit boards” that can be printed onto a growth medium in a Petri dish. 

In electrical circuits, transistors function as switches that can turn current on or off. In the biological circuits that the researchers have created, bacterial switches control the flow of small molecules, which send signals to downstream circuit components. 

The research team designed two different transistors, along with three bacterial strains that relay information between the transistors, giving them the building blocks they need to design nearly any type of circuit. In a new study, they used these cells to create circuits that can add two or three inputs, or send one input to a specific location in the circuit.

“We’ve built some initial computer architecture components that are commonly used, but any operation can be built with these five strains,” says Hamid Doosthosseini PhD ’25, an MIT postdoc and the lead author of the new study. 

Using this approach, the researchers hope to develop circuits that one day could coat plant leaves or roots, where they could compute to sense and respond to environmental conditions such as drought or attack by pests. 

Christopher Voigt, head of MIT’s Department of Biological Engineering, is the senior author of the paper, which was recently published in Nature Chemical Biology. Former MIT postdoc Haorong Chen is also an author of the paper.

Cells as transistors

When designing synthetic biology circuits, researchers typically engineer cells to express proteins and transcription factors that interact to perform a task such as sensing a target molecule, which then triggers production of a specific output.

These simple circuits can perform various logic functions, but they must use unique transcription factors to avoid crosstalk within the circuit. There is a limited number of transcription factors that can be used for these circuits, which limits the overall complexity that can be achieved in a single cell. Additionally, putting too many circuits in one cell can overburden the cell’s protein production machinery.

In the new paper, the researchers took a different approach: Instead of building an entire circuit into one cell, they designed cells that could act as transistors. These transistors can then be combined in different ways to create a variety of circuits.

To create the transistors, the researchers chose a bacterium called Pantoea agglomerans, which commonly grows on surfaces, including plants. Using these cells, they made two types of transistors that can be switched on or off by a molecule called OC-6. One of the transistors is switched on by this input, and the other is switched off. Each transistor also detects the presence of a target molecule, in this case, OC-12. Depending on whether that molecule is present, and whether the switch is active, the transistors produce an output molecule known as OHC-14.

The researchers also used three strains of Pantoea agglomerans to create relays, which translate the OHC-14 signal into an output that can be fed into another transistor. Using these relay strains, the researchers can “wire” the transistors together, just like an electronic circuit board.

For example, they could create a bidirectional switch with two transistors that sense OC-12, and then send that information to different relay strains based on a switch input, ultimately feeding into other transistors that further process the signal.

The researchers created their circuits by printing colonies of bacteria onto plates containing agar, a growth medium. Each colony is printed about 5 millimeters from the nearest one. This allows the signals to travel only to the nearest colony, which then relays them to the next one, so information flows only in one direction.

Complex calculations

In this paper, the researchers demonstrated a transistor that can perform several types of logic operations depending on its location in the circuit layout, including “multi-input,” “or,” and “imply” gates. They also combined the transistors to create more complex circuits that can add up two signals, process more signals simultaneously, or function as a demultiplexer — a circuit that takes one incoming signal and sends it to one of several possible destinations, depending on a control signal.

The largest of these circuits, which adds two inputs together, contains 24 bacterial colonies wired together.

“This work shows that we can get toward more complicated functions by linking up simpler functions in individual cells,” Voigt says. “Computationally, there’s nothing that your iPhone can do that these circuits couldn’t do.” 

Circuits made from these cells take about eight hours to perform each calculation, much longer than a computer circuit. But, for biological applications, that is a reasonable amount of time, the researchers say. 

“We’re not trying to replace computers, but rather put computational control into biology. If you have bacteria on the root of a plant, or the plant itself is doing the computing, running a simple calculation overnight is fast enough relative to a growth season,” Voigt says.

If developed for use in agriculture, this type of circuit could be applied to the roots of plants to detect different types of stress. Once a particular input is detected, it would trigger a response such as synthesizing a fungicide.

The research was funded, in part, by the U.S. Defense Advanced Research Projects Agency and by the U.S. Intelligence Advanced Research Projects Activity.

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