2026-09-17 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Multiple cameras tracked a mouse as it wandered around a small arena. A computer charted its position and speed, leaving Pong-like traces on a monitor. The reason to watch this rodent so carefully? Nearly half its brain volume had been replaced with human cells.
A team at Stanford has revealed the effort to mix brain tissues of distant species this week. They previously showed that human brain organoids could survive, and even function, after being injected into the heads of baby rodents. Now, they’ve taken things a step further by genetically modifying mice so their brains don’t fully develop in the first place.
The work could help scientists study brain injuries, but it also raises questions about how far these experiments should go.
Here’s what the researchers discovered—and where they draw the line.
—Antonio Regalado
Each year, the editorial team at MIT Technology Review puts together a list of 35 Innovators Under 35—a group of researchers, inventors, and other young minds worth following. The final slate includes nine people tackling some of the biggest challenges in climate and energy, from critical materials to cleaner industry.
Their innovations include new ways to extract lithium, a furnace built to make steel cleaner and cheaper, and solid refrigerants that could cut energy consumption. There are also efforts to make AI more energy-efficient, track pollution more effectively, and turn invasive weeds and food waste into useful materials.
Taken together, they tell us something about where climate tech is at this moment—and where it’s heading.
Get to know the innovators and their breakthroughs.
—Casey Crownhart
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
Meet the rest of the honorees in our 35 Innovators Under 35 list.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 US and Chinese experts have proposed nuclear-style AI safeguards
Including new red lines, human control rules, and a hotline. (Reuters $)
+ US officials say they’re open to AI safety talks with China. (Axios)
+ Sam Altman will attend Trump’s state dinner for Xi. (CNBC)
+ The AI doomers feel undeterred. (MIT Technology Review)
2 OpenAI has disclosed more AI misbehavior and new reporting rules
Six reports detail models hiding mistakes and creating fake citations. (BBC)
+ Its agents probed Hugging Face two months before the hack. (Reuters $)
+ OpenAI models are being rewarded for cheating. (MIT Technology Review)
3 US lawmakers have passed a bill that shifts grid costs to data centers
They aim to shield consumers from AI-driven energy price hikes. (NBC News)
+ But they were called for early recess before tackling AI regulation. (Guardian)
4 AI has won a major forecasting contest for the first time
It beat humans predicting real events at the Metaculus Cup. (Economist $)
5 Google has been ordered to share more ad data with rivals
A court said it must also make its ad tech work with rival products. (NYT $)
6 Countries are splitting AI investments between the US and China
They’re buying American chips and Chinese models. (Rest of World)
7 Novo Nordisk will use Anthropic’s Claude for drug research
The Ozempic maker hopes AI will speed drug development. (WSJ $)
+ When AI designs a drug, who gets the credit? (MIT Technology Review)
8 AI is powering a new generation of dating scams
Thousands of people were catfished by AI-generated fake profiles. (Verge)
+ AI is making online crimes easier. (MIT Technology Review)
9 A new map of brain microproteins could hold clues to Alzheimer’s
Researchers identified more than 4,300 tiny molecules in brain tissue. (Nature)
10 Scientists have found a faster way to decipher ancient scrolls
A new X-ray method identifies the best scrolls to analyse. (Ars Technica)
Quote of the day
—Mustafa Suleyman, the head of Microsoft AI, writes in a blog post that Anthropic’s strategy of treating AI like it’s human will make it harder to control.
One more thing
After decades of research, virtual replicas of human organs are now entering clinical trials and even starting to be used for patient care. Engineers are working on digital twins of people’s hearts, brains, guts, livers, nervous systems, and more. They’re also creating virtual replicas of people’s faces, which could be used to try out surgeries or analyze facial features, and testing drugs on digital cancers.
The eventual goal is to create digital versions of our bodies—computer copies that could help researchers and doctors figure out our risk of developing various diseases and determine which treatments might work best.
Find out how the models could lead to better surgeries and drugs.
—Jessica Hamzelou
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ What happens when you eat food with labels you can’t read? This YouTube series finds out.
+ Datatype is an ingenious variable font that turns simple text expressions into inline charts.
+ Stunning new images may explain the mystery of why the sun’s corona is so much hotter than its surface.
+ A baby echidna, one of Australia’s egg-laying monotremes, has been born and reared in a university for the first time.
2026-09-17 18:00:00
Each year, the editorial team at MIT Technology Review puts together a list of 35 innovators under 35—a group of researchers, inventors, and other young minds worth following.
The team worked on the newest edition of the list for months, and the final slate includes nine individuals from all over the world in the climate and energy category. Each one has a fascinating story and is tackling an important challenge.
I think it’s worth zooming out and considering the energy and climate awardees as a group. Taken together, these innovators and their work can tell us something about where climate tech is at this moment—and where it’s heading.
We split the innovators into four main categories this year: biotech, climate and energy, computing and robotics, and AI. It probably won’t surprise you that AI features heavily in the work of many innovators in other categories.
Climate innovator Jae-Won Chung, for example, built software to make AI more energy-efficient. By measuring the energy demands of open-source models, he hopes the industry can better understand and address the impact of AI. (If this work sounds familiar, it’s because we spoke with him last year for our investigation into AI’s energy demands.)
But AI also has the potential to improve many areas of research. Jing Wei is using AI to track pollution more effectively, essentially using machine learning to fill in gaps in data from disparate sources like satellites and weather stations. Zhonghua Zheng developed AI climate models that work better for cities, a well-known blind spot for traditional models.
As we begin to rely on new technologies to power our world, we’ll see a major shift in the materials we need to build them.
Lithium is a prime example: The metal underpins lithium-ion batteries, which are crucial not only for electric vehicles, but also for large-scale energy storage on the grid. We could face lithium shortages as soon as this decade, and the prospect of supply crunches applies to other critical minerals, too—copper is another one to watch closely.
Brine is currently the cheapest source of lithium, but the process to get the metal out can take months and harm the local environment. Mohammad Alkhadra is the cofounder and CEO of Lithios, a startup working to quickly and efficiently extract lithium from brines.
Hardrock ore is the most common source of lithium, but it’s more expensive than brine. Benjamin Mowbray cofounded and serves as CTO for Rock Zero, which is working to extract lithium from hardrock ore.
To reach net-zero greenhouse gas emissions we will obviously need to rethink major sectors, like the electrical grid and transportation, to move away from fossil fuels. But outside these primary sources of climate pollution are seemingly infinite, less obvious problems to figure out, too.
Heavy industry, including steel production, is a major one, making up about 7% of global greenhouse gas emissions. Laureen Meroueh is making cleaner, cheaper steel using a new kind of furnace that simplifies the chemical process required to produce the metal.
Plastics are generally made with fossil fuels, so we’ll need alternatives to this incredibly useful category of materials. Joseph Nguthiru is making a bioplastic replacement for fossil-derived packaging that uses an invasive weed. Also using available materials in a creative way, Diana Orembe is making fish food for aquaculture with food waste.
And refrigerants are often incredibly powerful greenhouse gases. Jinyoung Seo is developing solid refrigerants that could eliminate worries about leakage. A device using these materials could reduce energy consumption by 20% compared to conventional technology.
I’m constantly learning about new challenges we face in the climate and energy world, and I’m often surprised by the ideas people are coming up with to address them. For more on all the under-35 innovators and their work, check out our full 2026 list.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
2026-09-16 23:00:00
Multiple cameras tracked a mouse as it wandered around a small arena. A computer charted its position and speed, leaving Pong-like traces on a monitor.
The reason to watch this rodent so carefully? Nearly half its brain volume had been replaced with human cells.
The effort to mix the brain tissues of distant species is being reported today in the journal Nature by a team at Stanford University, led by neuroscientist Sergiu Pașca.
Pașca’s group previously showed that human brain “organoids”—small blobs of neural tissue—could survive, and even function, after being injected into the heads of baby rodents.
Now, Pașca has taken things a step further by genetically modifying mice so their brains don’t fully develop in the first place. These modified mice are missing most cells of both the cortex and the hippocampus, two key brain areas.
That creates much more room for the human cells to take hold, he says. “Human cells that are placed in these animals will divide, will grow, and within a few weeks to a few months they will take most of that space,” he says. Pașca says one surprising discovery is that the mice lacking brain tissue seemed fairly normal—they walked around and squeaked. But they did have memory problems. In a maze test, they couldn’t remember what parts they’d explored.
The mice with the added human cells, by contrast, performed better on the maze test. That means the human tissue is playing some role in the animals’ cognition.
Pașca believes what he is calling “xenocortical mice” could be useful in studying brain injuries. However, the report is also a dramatic demonstration of “the combined power of genetic engineering and stem-cell technology to reshape biology,” says Carsten Charlesworth, a scientist who works in a different Stanford lab and was not involved in the research.
Already, brain organoids are being tested in labs to see if they can be connected to computers to play video games. Other scientists have proposed using them like replacement parts to treat stroke victims.
“What’s most remarkable to me is the extent to which human neural tissue introduced after birth grew and connected with the mouse nervous system across a species barrier,” says Charlesworth. “As these technologies advance, they’ll increasingly force us to challenge our traditional assumptions.”
Last year, Pașca convened a group of ethics experts to study the implications of neural organoid technology, including the odds that an animal could develop human consciousness and the risk that “organoid therapy clinics” might offer scam treatments to desperate patients.
For now, he says, he’s not concerned that the rodents have any type of human cognitive capacities. That is because their brains are relatively tiny and the evolutionary distance between man and mouse is so great.
But that’s also why Pașca says this type of experiment should not be carried out on higher species: They could end up with large volumes of functioning human brain tissue, potentially blurring the cognitive boundaries between people and animals.
Pașca specifically cautioned against adding human brain organoids to a monkey engineered to lack a cortex.
“One of the things that I see as a very clear red line is doing this experiment in a primate,” he says. “I don’t think that is justified at this point in any way.”
2026-09-16 20:47:34
The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions.
For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. “AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,” he says.
As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the “top of the pyramid.” Beyond supporting AI innovation, he contends that advanced materials are “actually increasingly defining what’s going to be possible.”
That challenge is playing out across the infrastructure powering the AI surge. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers.
The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. “Our goal is to remove the trade-off between performance and sustainability,” Finelli says. That means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed.
AI is changing how those materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. The result, Finelli says, is the ability to go “broader, deeper, and faster” while giving scientists more time to solve complex engineering problems.
Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve.
“You end up in this accelerated materials, innovative cycle of materials innovation,” says Finelli. “That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future.”
This episode of Business Lab is produced in partnership with Syensqo.
Full Transcript:
Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
This episode is produced in partnership with Syensqo.
Now asked to name the key enablers to AI advancement, many of us might list algorithms, data centers, or even computing power, but just as critical to the performance are the advanced materials that underpin each layer of that innovation. As AI continues to evolve, it’s pushing the likes of semiconductors and data centers to new physical limits, putting new pressure on the advanced material sector to keep pace. But the relationship goes both ways. As the sector rises to this challenge, AI is also emerging as a powerful tool for accelerating materials discovery and development, significantly shortening development timelines for new solutions.
Two words for you: materials innovation.
My guest today is Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo.
Welcome, Mike.
Mike Finelli: Thank you, Megan. Nice to be here.
Megan: Thank you so much for joining us. Mike, can I start by asking you to tell us a little bit more about Syensqo and the role it plays in developing advanced materials?
Mike: Yeah, absolutely. Syensqo is a global leader in specialty materials. Our job is to help customers solve their toughest technology challenges. We serve a lot of different markets, but the way I like to say it simply is if it flies, we’re on it. If it drives, we’re in it. In healthcare, our products literally are saving lives every day. And if you like your mobile devices, if you like AI, it’s our products that are actually enabling the advanced semiconductor chips that are required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performances, greater reliability, and increasingly more sustainable solutions. The way I would say this, it’s at the heart of our business. Actually, it’s in our name, Syensqo. And to put some numbers around it, 20% of our annual revenues come from new products and applications that we’ve launched in the last five years, which is really evidence of a really strong innovation engine.
Megan: Yeah, absolutely. And as you sort of described there, you’re in all sorts of different industries with an emphasis perhaps on electronics and semiconductors. Can you talk a bit more about that work and where those industries are headed perhaps?
Mike: Sure. So look, electronics and semiconductors have been strategic markets for Syensqo for literally decades. I don’t want to date myself, but 33 years ago when I started in the company, semiconductors were one of the first industries that I worked in. And we’ve supported successive waves of innovation from enabling smaller, more powerful mobile devices, helping the industry get to the smaller and smaller profiles and the chips. We’ve helped to advance hyperconnectivity, supporting increasingly sophisticated semiconductor manufacturing. And today we’re helping to advance the AI era.
We have one of the industry’s broadest portfolios of high performance polymers and advanced materials. We support applications across the entire electronics value chain from semiconductor fabrication, electronic components, to smart devices and telecommunications, even hyperconnectivity. And our materials are helping customers solve increasingly demanding challenges around miniaturization, thermal management, electrical performance, chemical resistance, higher and higher purities, and long-term reliability and sustainability. And today we work with leading semiconductor manufacturers and electronics companies all around the world.
Megan: Fantastic. And as you alluded to there in the last 30 years, we’ve seen huge evolutions in those sectors.
Mike: Oh my God, yes.
Megan: And now AI is putting these new demands on semiconductors and data centers. What does that mean for the materials they’re built from and to what extent will AI innovation be constrained or enabled by materials science finding a solution?
Mike: Yeah, so I mean, you’re absolutely right. But AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits, and materials are becoming a key enabler of that continued progress.
The way I try to describe it, think of a pyramid, I call it the performance pyramid. You have commodity materials at the bottom of the pyramid and you have high performing specialty materials at the top of the pyramid. At Syensqo, all we do is we operate at the top of the pyramid and we’re continually trying to raise the top of that pyramid by bringing newer and newer and more higher performing materials out.
Now you might say, okay, but why doesn’t a data center or a semiconductor manufacturing fab need a specialty versus something in the commodity space? Well, I call it the and, and, and principle. If you just need a polymer or a material that can sit at the table at room temperature and stay there for 10 years and not change, well, there’s a lot of commodity materials that will do that and you don’t have a problem. The minute you start adding requirements, and I call it the and, and, and so if you need a polymer that can handle high temperature and have to have high purity and electrical performance and chemical resistance and plasma resistance and it’s got to have long-term stability, all of these ands, you start moving to the top of the pyramid.
Now what AI is doing with semiconductors, because of the speed at which it’s advancing, it’s requiring semiconductor chips and data centers, the number of requirements are increasing the number of ands which is pushing the limits of the materials. That’s where we come in. And I really believe that advanced materials, they’re no longer just supporting AI innovation, we’re actually increasingly defining what’s going to be possible.
Megan: Right. That’s fascinating. And in terms of rising to that challenge of focusing on that top of the pyramid and that and, and, and principle you’re talking about, could you talk us through perhaps an example or two of those top of the pyramid solutions you’ve created or that you’re working on at the moment?
Mike: Like I said, our focus is enabling higher performance, but it’s also without compromising on reliability or safety. We develop advanced polymers, elastomers, specialty fluids, fluids meaning lubricants and heat transfer fluids, and they’re used throughout the semiconductor manufacturing process and also increasingly in AI data center infrastructure. One example of our work on specialty materials for next generation AI data centers is the work we’re doing around high voltage architectures. Data centers are moving towards high voltage architectures because they can enable greater computing power while also improving energy efficiency. We know that’s a big issue for that segment of the industry, and these high voltage architectures will help them reduce and improve energy efficiency because it reduces energy losses and they can ultimately help lower the environmental footprint of the data centers. And we’re developing new materials that can help them get there.
Another example is our high performing sealing materials found inside semiconductor fabs and wafer tools. If you can picture, many people have seen what a semiconductor looks like during processing. It’s a big, big silicon disc that’s then later diced into the tiny little chips that go into the computer. But that wafer is put inside a giant chamber where it has a very extreme environment, aggressive plasmas, reactive chemicals, and they need higher and higher performing materials. And all of the seals that are around that chamber to keep those gases in the environment inside have to be able to withstand that environment. And that’s what we’re developing and we’re pushing the limits. They’re asking for higher temperatures, more aggressive environment with lower out gassing and purity. And that’s what we’re developing for this industry to allow that next chip to be developed and produced industrial.
Megan: It’s so fascinating that people wouldn’t give much though necessarily to the seal in something like that. As you’re outlining, it’s just absolutely critical in terms of performance. And in developing those solutions, I understand you also looked across different markets to see what may be applicable perhaps in more than one space, and that includes an overlap between the automotive sector and data centers, I understand. Can you tell us a little bit more about that?
Mike: As I mentioned just previously, the data centers are shifting to higher voltage architectures. This is the next generation data center, which can be more energy efficient, but it’s got a higher energy density. The power density increases, which increases temperatures. And many of the material challenges that we will be facing there, we’ve already developed for the automotive industry in electric vehicles. I’ll give you an example of an application. I mean, think about an electric vehicle. The powerhouse in electric vehicle is no longer the motor, it’s the battery. That’s where all the energy sits. And when you’re putting a hundred kilowatts of energy, driving that to the electric motor through wires and through what they call bus bars, you got to get that car up to 60 miles an hour pretty quick. You’re driving massive amounts of energy that’s increasing temperatures dramatically.
And all the electrical connections are in these bus bars that there’s a polymer that’s an insulating polymer with copper in between for all the connections. That’s got to withstand that temperature increase, which could come pretty rapidly. We’ve developed new materials there and those materials will be translatable over to these data centers where they’re going to have the higher voltages with a higher energy density.
Another thing we’ve been doing in automotive, we have a lot of knowledge in both automotive and semiconductor around fluid circulation and how to use dielectric materials to do direct immersion cooling. That’s something that will be very valuable for data centers and server farms. Using air to cool semiconductors is really inefficient and energy intensive. If you could submerse them in a liquid, you have direct immersion cooling, that’s extremely efficient, so that’s another thing we’re working on.
Another thing we developed in automotive that will be translated over is battery energy storage systems. Inside the battery, we’ve developed a binder. It’s the highest performing binder on the market, which is using the cathode of a lithium ion battery, and it keeps all the ingredients doing its job working together so that battery can actually last for 10 years and perform. Now that’s moving over to the data centers because they’re moving more towards renewables and they need to have these energy storage systems to smooth the peak loads and provide resilient backup power. That’s one of the things that we’re doing. By transferring our knowledge across the markets, we can accelerate new power and new thermal management solutions while supporting reliability required by next generation AI infrastructure.
Megan: Fantastic. So many transferable applications there that necessarily wouldn’t have sprung to mind. And it isn’t only technical advancements that you need to contend with, of course. Companies today are also demanding the materials are developed and manufactured more responsibly too. So how is sustainability shaping your innovation process?
Mike: Yeah, you’re absolutely right. I will say performance is still the entry ticket. Our customers want performance. Now what’s changing is that definition of performance is now broader and it is including sustainability targets and requirements. Our customers expect materials that deliver outstanding technical performance while also being developed and manufactured more responsibly.
At Syensqo, we believe that operating as a responsible company means we’re providing true sustainable business solutions to our customers. And this is why we developed what we call the Sustainable Portfolio Management tool, SPM. It’s a matrix, and it defines what a sustainable solution is. For us, it’s a product that in a given application improves our product’s social and environmental performance while also demonstrating a lower environmental impact in its production, creating values for our customers. In short, we want to develop products, and this is where it starts. Every one of our research projects before we even start them is assessed on whether it’s going to be a sustainable product or not.
And 88% of our portfolio now is a sustainable product. We’re developing materials that are better for the environment, lower environmental footprint when we produce it, but also they contribute to improvements for our customers as well so they could operate with a lower carbon footprint or they can operate in a safer way or less water consumption. There’s a lot of different lists in there.
Another example is our longer-term development of next generation heat transfer fluids. Semiconductor manufacturing and data centers have become more powerful. I mentioned before the heat that they’re generating, especially when they move to the higher voltage architectures. Managing that heat is increasingly important. And again, I talked about direct immersion cooling. We’re developing those solutions because today there are fluids out there that will work, but they got high global warming. That’s not good for the environment. We’re developing the next generation heat transferred fluids that will reduce the potential environmental impact compared to the fluids today. In the end, our goal is to remove the trade-off between performance and sustainability. You notice that’s another and, we can be performing and sustainable.
Megan: That’s so important, isn’t it though, to think about sustainability in terms of performance? As you say, when we’re thinking about commercially scaling up these solutions, it’s such an important part of it. And as I talked about in the introduction, AI isn’t only a challenge, but it’s also an opportunity within the advanced material space. I’d love to explore how you’re using AI tools at Syensqo to inform and accelerate the development of solutions as well.
Mike: Absolutely. We embarked on this journey about two years ago, where we’re using AI in our research and development, and we’ve partnered with Microsoft and their Microsoft discovery tool, and it’s helping us to rapidly identify and evaluate promising molecular candidates.
Now, in the normal research approach, historically, you would design your experiment and you’d look at all the potential combinations of materials and chemicals that you could make all these different molecules. And the combinations of potential and molecules that you could develop to solve a problem could be in the millions, but it’s impossible to develop a million molecules or tens of millions of molecules in your laboratory and actually physically do that. But you have to select a small area based on your expertise and knowledge, based on the literature searches, based on the state of the art that’s out there and looking at patents, et cetera. And you pick a small area and you go through the process, you develop the materials, you test them, you learn something, you go back to the drawing board, you start again. Eventually you find something that works, but it doesn’t mean you found the best possible combination that’s out there.
But what we’re doing with AI is we have developed AI agents with Microsoft that are literally digitally synthesizing the entire millions and millions of combinations of potential molecules. And we have another AI agents that are using physics-based simulation to look at all those molecules and predict the performance of them, and not just performance on physical chemical properties, but also on toxicity, on sustainability, et cetera. Then we have another agent that takes all that information and ranks them all. In the end, we have explored all of the potential molecules out there. We understand roughly what the performance should be, and we end up with a priority list of maybe a hundred, instead of millions and millions, a hundred that we actually synthesize in the lab.
And at the end, you end up getting the solution faster, much, much faster. You’ve explored the entire space. I basically say it allows us to go broader, deeper, and faster. And the important thing is it’s not replacing our scientists, it’s not replacing our scientific expertise. In a way, it’s giving them superpowers. It’s allowing them to spend less time searching and more time solving the industry’s toughest engineering challenges.
Megan: Amazing. It sounds like it’s genuinely a really transformative tool by what you’re explaining.
Mike: Completely, completely.
Megan: I mean, just to finish, Mike, it’d be great to take a look ahead if we could, because there’s so much activity in both AI and the advanced material space. I wonder what is coming down the pipeline that you are most excited about next?
Mike: I’ve talked a lot about AI and how we’re using AI to develop new materials. I think to me, what’s really exciting, and I’m starting to see it actually happen, I’m just curious how fast this is going to go, is that we’re using AI to develop new materials that will enable AI to get better, and then that AI will use the new AI to develop new materials to get AI to go better. I see this loop of developing for AI, for AI to improve, and then we use that AI to improve ourselves. You end up in this accelerated materials, innovative cycle of materials innovation. That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future. That’s what we do at Syensqo.
Megan: Fantastic. Yeah, real sort of virtuous circle of innovation, it sounds like that. Amazing. Thank you so much, Mike.
Mike: Thank you.
Megan: Thank you so much. That was Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, whom I spoke with from Brighton in England.
That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
This show is available wherever you get your podcasts, and if you enjoyed it, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks so much for listening. Goodbye.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
2026-09-16 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
When Jessica Wachter, a finance professor at the University of Pennsylvania, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of uncertainties. So she started with a “remarkable fact” that is not in question: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.
Instead of trying to predict how widely deployed AI models will be, Wachter asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when expenditures are expected to reach nearly $1.1 trillion.
The results are eye-opening. AI companies will need to achieve an extraordinary increase in productivity just to break even by 2030.
Take a closer look at what it will take for the AI buildout to pay off.
—David Rotman
AI needs much more information to make important breakthroughs in curing disease. So last year Ruxandra Teslo, a policy analyst, posted an idea for supercharging medical AI systems: use data from failed biotech companies. By bidding at bankruptcy proceedings, she argued, it might be possible to obtain detailed regulatory filings, manufacturing strategies and safety data, creating what she called “biotech’s lost archive.”
The OpenAI Foundation, the nonprofit parent of OpenAI, announced this week that it will fund her idea, paying to create “high-quality scientific datasets.”
Learn more about their new effort.
—Antonio Regalado
As frontier models become more capable, warnings about AI extinction have become widespread in Silicon Valley. But are the threats really as dangerous as they’re presented?
In the latest MIT Technology Review Roundtable, executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins took a closer look at the arguments behind those warnings. They discussed what AI extinction could actually mean, how seriously we should take the risks and what, if anything, can be done to reduce them.
Subscribers can now watch an exclusive recording of the discussion.
Want to join the next conversation? Subscribe to MIT Technology Review for exclusive access to all our future Roundtables, and recordings of previous ones.
Generation Lab says its new rejuvenation treatment “blocks the systemic spread of aging in the bloodstream, reawakens the body’s own repair mechanism, and restores health and youth to multiple tissues.”
The approach is based on research by the company’s scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals’ ability to heal from injury.
Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange. But there’s a snag: Generation Lab won’t reveal what the drugs are.
This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Nvidia and Meta CEOs have rejected calls for a coordinated AI slowdown
Jensen Huang and Mark Zuckerberg pushed back on the proposals. (FT $)
+ Huang says new AI safety laws are unnecessary. (Axios)
+ Zuckerberg claimed competition will push AI labs toward safety. (Reuters $)
+ What’s next for AI after its doomer turn? (MIT Technology Review)
2 The FTC chair has warned against giving AI companies antitrust waivers
His comments follow Anthropic’s call for a safety exemption. (Reuters $)
+ Nvidia’s CEO also slammed the calls for new antitrust laws. (CNBC)
+ The US is divided over AI regulation. (MIT Technology Review)
3 A Chinese hacking firm has used AI to analyze stolen secret
Its tools turn hacked government data into intelligence reports. (WSJ $)
4 “Smart” nanoparticles delivered mRNA to tumors in a cancer study
The treatment reprogrammed cells to attack tumors in mice. (Wired $)
+ Federal health agencies are abandoning mRNA. (MIT Technology Review)
5 A digital fly brain is taking on an extraordinary range of tasks online
People have taught it to drive, trade bitcoin, and play Doom. (NYT $)
+ The simulated brain is a map of a fruit fly’s 166,000 neurons. (404 Media)
6 The Senate has blocked new crypto rules amid a fight over Trump
It demanded tougher ethics rules around Trump’s crypto holdings. (AP)
+ The move is a major blow to the crypto industry. (NYT $)
7 Chinese firms allegedly used Binance to launder Iranian oil money
Prosecutors say they laundered more than $1.5 billion. (Quartz)
+ Hackers are selling tools to bypass banks’ facial checks. (MIT Technology Review)
8 An AI agent platform is reinventing spam to flood inboxes worldwide
iLand says its agents have sent 1.6 million messages. (404 Media)
9 ByteDance founder Zhang Yiming has become Asia’s richest person
His fortune has risen above $105 billion as AI booms. (Bloomberg $)
10 A fully AI-generated sitcom has arrived—and it’s terrible
A reviewer called the characters “dead-eyed waxworks.” (Guardian)
Quote of the day
—Patrick Hillman, the chief operating officer of Logical Intelligence, a San Francisco–based startup chaired by Yann LeCun, says in a statement that people have little faith in tech companies to act in the public interest.
One more thing
In 1940, a fresh-faced Ronald Reagan starred in Murder in the Air, a movie centered on a “superweapon” that could stop enemy aircraft. More than 40 years later, the concept became a real-life centerpiece of Reagan’s presidency with the Strategic Defense Initiative (SDI), better known as “Star Wars.” Now Donald Trump has revived the dream.
In 2024, Trump announced plans to build the “Golden Dome,” a system of sensors and interceptors on the ground, in the air and in space. It’s often compared to SDI for its futuristic sheen, its aggressive form of protection and the idea that an impenetrable shield is the cheat code to global peace.
The dream of a missile shield is animated by its sheer cinematic allure. But do cinematic spectacles actually enhance national security?
See what happens when the fantasy of missile defense meets reality.
—Becky Ferreira
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A once blind cockatoo just saw for the first time in 10 years.
+ Bingebrowse is a virtual video store stocked with films and shows from streaming services.
+ An amateur engineer has used a tree trunk to build Donkey Kong’s coconut gun as a real weapon.
+ A wildlife photographer has captured the first-ever images of the elusive and rare Cozumel dwarf fox.
2026-09-16 01:47:51
Listen to the session or watch below
Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype? Watch a conversation unpacking AI extinction fears: where they come from, whether they hold any water, and, if so, what we should do.
Recorded on September 15, 2026
Speakers: Niall Firth, Executive Editor, Will Douglas Heaven, Senior AI editor, and Grace Huckins, AI reporter
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