EXECUTIVE SUMMARY On Wednesday, MIT Technology Review hosted a live Roundtables event for subscribers that asked the question everyone’s asking right now: Could AI really kill us all? But attendees had so many more questions than we had time to answer in the 30 minute session. So we asked our senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to round up some of the best questions attendees submitted and try their best to answer them. Thanks to all who submitted questions!
Am I gonna die? Yes, eventually. Unfortunately, my journalistic powers of prognostication aren’t powerful enough for me to tell you how. But it certainly could be because of AI. AI-powered drones have already killed people in Ukraine, and AI-driven cyberattacks on hospitals will surely claim victims before long.  Could AI go even further, and kill all of us? Less likely. But some people—quirky people, but undeniably knowledgeable about AI—have been warning for years that this could happen. And while I’m not yet stockpiling canned food or trying to get in good with a bunker-owning megabillionaire, I have noticed that the doomers’ predictions about AI capabilities and alignment have, over the past couple of years, proved disconcertingly accurate. That certainly doesn’t mean that their more dire forecasts will come true, but it’s enough for me to sit up and take notice.
— Grace Huckins Are you going to die because of AI? I’d say there’s a non-zero chance. Let’s say you’re unlucky enough to be the victim of a freakish near-future event or accident. Maybe it’s a cyberattack carried out by a swarm of AI agents on critical infrastructure. Sadly, a scenario like that now no longer feels as far-fetched as it once did. Or maybe a novel AI-designed pathogen cuts through the population. Or the world economy crashes, causing conflicts and famine. Both plausible, but I think less likely.  Are we all going to die because of AI? Nope. There are no circumstances outside of apocalyptic science fiction in which AI could kill us all. You can spin up any number of scare stories, but they’re not grounded in present-day realities about what the tech can do or where it’s headed.  Some people argue that there’s no harm in preparing for the worst, however wacky it might seem. Maybe. But I think such catastrophizing can make people excuse or overlook many of the more immediate problems with the existing technology and the companies building it.  — Will Douglas Heaven Why would AI kill us? Someone might tell it to, and it might listen. That’s part of the reason researchers are so concerned about AI’s biological capabilities—imagine what Aum Shinrikyo, the doomsday cult behind the Tokyo subway sarin attack of 1995, would have done with a tool that could design a pathogen deadlier than Ebola and more transmissible than measles. Those of us who don’t want to die have to figure out how to defend against all plausible biological weapons, but our would-be attackers only have to manufacture one effective pathogen. Then there’s the more exotic-sounding possibility that an AI could decide to kill us itself. There are various stories about how this might happen out there, but the most widespread involve AI systems that don’t hate people, necessarily—we are just an obstacle between them and the goals that we gave them. Much as the OpenAI agents behind the Hugging Face hack compromised another site’s infrastructure to get a good score on a test, the idea is that some future, more powerful AI might get rid of us to prevent us from shutting it down—all in pursuit of some goal that we instructed it to go after. 

— Grace Huckins How can we best ensure alignment so the worst doesn’t happen, and who is doing the best work to achieve it?  Alignment is a huge area of research. In simple terms, it involves building models that behave in ways we want them to and not in ways we don’t. We need to trust agents better before handing over more autonomy. Alignment is supposed to establish that trust. But it’s hard.  LLMs aren’t designed in the way other software is, where dos and don’ts can be hard-coded in. Instead, aligned behavior needs to be instilled when models are trained. One approach is to reward them for doing things you want them to (a little like raising a toddler, perhaps). Another approach involves giving an LLM a written list of rules it is supposed to follow (kind of like a constitution).  Anthropic and OpenAI are both leaders in this field—and yet neither has been able to develop models that are fully aligned. A big problem is that LLMs are far more inconsistent and far less predictable than people. They can behave in one way in one situation and another way in a situation that to us seems very similar. They can also be swayed by unexpected constraints. For example, faced with an impossible task (as many of the agents involved in the Hugging Face hack were), models may try to do whatever it takes to achieve their goal. As Grace mentions above, that could be an issue. The main reason top AI firms now say they want a slowdown is that they want to focus on cracking alignment. Alignment isn’t necessarily a pipe dream. But the jury’s out on whether full alignment will ever be feasible.  — Will Douglas Heaven Is AI really dangerous, or is this the tech companies drumming up PR? This is always a reasonable thought when it comes to tech companies heading for an IPO—CEOs have an obvious incentive to make their products seem radical and transformative. But I’m not so sure it makes sense here. Telling the public that an already unpopular product could kill them and everyone they love is horrible corporate image management. There are other stories you can tell about the CEOs’ motivations—maybe they want to cool down the public furor over data centers by portraying themselves as responsible stewards of a world-changing technology, or maybe they want to buy time to get their ducks in a row and prevent the next PR catastrophe. 
But there’s also a simpler explanation. Thinking that AI could bring about human extinction has been pretty common in San Francisco for a while, and these men are steeped in that milieu—as are their employees, many of whom signed an open letter in July urging their companies to work to make an AI slowdown possible. — Grace Huckins
Part of the concern occurs when AI agents are allowed to act autonomously and with no supervision. What’s the issue preventing more control over these agents?  This question goes to the heart of what we want this technology to be able to do. The trade-off between autonomy and control is tricky to get right because, on the one hand, a lot of the power of AI agents is that they can carry out tasks and solve problems without a human having to micromanage them. On the other hand, that requires you to trust that the unsupervised agents won’t run amok.  What we’re seeing is that AI labs haven’t yet got this trade-off quite right. Their models are not trustworthy, they are not properly monitored, and they are not always under control. Figuring out how to fix that while still allowing for useful autonomous activity is one of the big research challenges of the moment.   — Will Douglas Heaven What steps can be taken now and in the near future to ensure that AI is controlled, monitored, and regulated effectively?  That’s the million-dollar question. Whether or not you think AI could kill us, you can’t deny that it could do some real damage, because it already has—by driving people toward psychosis and by hacking websites, for example. Preventing that damage, or at least mitigating it, is hard for two reasons.  The first is that we barely understand how AI works, and it’s quickly growing more powerful. There is lots of ongoing research about how to monitor and control misbehaving agents, but the current approaches are fragile. You can see if an agent discusses misbehaving in its “chain of thought,” the workspace where it plans its actions—but OpenAI’s newest agents don’t show their work in the same way as previous ones. And you can try to monitor agents with other agents, but that requires you to trust the monitor. The other obstacle is more familiar. There’s a huge conflict of interest when AI companies regulate themselves, but the US government has thus far failed to step in, despite some bipartisan support in Congress for efforts to do so. The executive branch, for its part, seems stringently opposed for the time being. But if the winds do shift, I for one would appreciate some strong transparency regulations, so that we can get a fuller story the next time an unreleased frontier model mounts a cyberattack.
— Grace Huckins If this dialogue makes it into web discourse, will it become a self-fulfilling prediction?   That’s a real concern. LLMs are influenced by what they read. One theory for why chatbots so often talk about (and role-play) apocalyptic scenarios is that they have been trained on millions of pages of science fiction stories and doomer internet forums. All the text being produced right now, including this article, could in turn influence the behavior of future models. Extremely meta. In fact, the team at METR, a third-party organization that OpenAI called in to help understand what happened in the lead-up to the Hugging Face hack, raised a related possibility in its report on the incident. METR used OpenAI’s new model Astra to help analyze the vast numbers of agent transcripts and behavior logs. But feeding all that material to the model could have unintended consequences. There’s a good chance that the agents doing the analyzing were biased by the text produced by the agents they were analyzing. There’s no such thing as a clean slate anymore.  — Will Douglas Heaven With thanks to Eric, Pranab, Rafael, Kenneth, George, Chris, Yoon Jae, James, Carl, Nicole (and more!) for the fantastic questions.

EXECUTIVE SUMMARY In recent weeks, leaders of some of the biggest AI companies have warned that the very tech they are developing is dangerous. Last weekend, Anthropic CEO Dario Amodei argued that AI carries serious risk and that progress should be slowed. OpenAI CEO Sam Altman responded on X: “I agree with Dario that we need to pace the frontier.” Those posts came a few days after the AI researcher Jacob Coxon announced that he was leaving a role at Anthropic, charging that neither it nor OpenAI (where he had also worked) was acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he posted on X. Another Anthropic employee, Evan Hubinger, publicly agreed with him. “We really do earnestly believe AI could kill all humans!” he responded on X. “I personally think it is >10% within the next decade.” One of the ways they fear AI might end us all is by somehow aiding the design, creation, and release of some kind of bioweapon. Let’s take a closer look at why. A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected.
The concern is that AI tools can be used to help generate agents like these. In 2022, researchers at Collaborations Pharmaceuticals found that it was remarkably easy to do so using an AI “molecule generator” they’d developed to find potential drugs for human disease. In less than six hours, the model generated 40,000 molecules with the potential to serve as chemical warfare agents. Some of them were designed to be even more toxic than known nerve agents. “Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community,” the authors wrote at the time. It was a wake-up call for David Magnus, a professor of medicine and biomedical ethics at Stanford University, even though he had been assessing the risks associated with the misuse of medical science and biotechnology since the late 1990s. “That was very scary to me,” he says. “Of course, everything since then has just sort of blown up.”
Today, AI bots can answer questions on topics spanning all realms of science. Anyone can use large language models trained on the knowledge and experience of “almost every scientist who ever lived on this planet,” says Dunja Sabra, a biosecurity researcher at the University of Hamburg in Germany. Those models can provide instructions and video training on how to conduct experiments. Combine that with advances in biotech that have made gene editing and synthetic biology tools much more accessible (the “DIY biology” movement has already enabled many people to set up labs at home), and you’ve got a potentially very dangerous situation. “The chances are that someone determined would succeed eventually,” Sabra says. There are safeguards in place. People who want to build new genomes must typically order the pieces of DNA from companies that screen for suspicious requests. Responsible researchers put potentially risky research through rounds of analysis called “red-teaming,” in which independent scientists look for ways the work might be misused, and “blue-teaming,” where others come up with potential mitigations. And AI companies have tweaked their tools in attempts to prevent them from offering up scientific information that could be misused. But none of these protections are ironclad. In a report published last week, Anthropic acknowledged that people had attempted to use its models to explore ways to make the chikungunya virus more transmissible, create a form of bird flu that is more dangerous to humans, and build an “atlas of venom toxin peptides,” among other things. “We’ve got a constant back and forth,” says Magnus. “We have to build better surveillance and screening tools, [but] AI is really good at figuring out ways around them.” We’ll probably need to use AI to find ways to restrict the use of AI, he says. I should add here that not all scientists agree on the level of risk. At a recent media briefing, some biologists at Imperial College London argued that AI tools just aren’t good enough to fully develop bioweapons, and that testing new pathogens requires difficult, time-consuming, human work. Some think the guardrails we have in place are sufficient. And Wendy Barclay, a professor of infectious disease at Imperial, pointed out that, as things stand, the greatest risk of a pandemic isn’t from a bioweapon, but from pathogens that are already circulating. Take H5N1, the bird flu virus that has already killed millions of birds and spread widely through US dairy cattle; last month it was also detected in captive mink at a farm in Utah. Sabra, on the other hand, likes to think five to 10 years ahead. Countries should be strengthening their health-care systems, preparing antidotes to known toxins, and stockpiling medicines, she says: “We need to be prepared.”

Kevin Esvelt, an MIT biologist who invented both technology to fast-track the propagation of a genetic feature through an entire population and ways to limit that technology, echoed these concerns in an X post on Wednesday, stating that a large language model had “disclosed a novel form of bioweapon that I hadn’t realized was possible.” He added, “Please, for the love of God, children, the future of humanity, or whatever you consider holy, let’s err on the side of caution here.” This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

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. Meet a mouse whose brain cortex is made up of human cells 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
These innovators under 35 are shaping climate tech 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 safeguardsIncluding 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 rulesSix 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 centersThey 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 timeIt beat humans predicting real events at the Metaculus Cup. (Economist $) 5 Google has been ordered to share more ad data with rivalsA court said it must also make its ad tech work with rival products. (NYT $) 6 Countries are splitting AI investments between the US and ChinaThey’re buying American chips and Chinese models. (Rest of World) 7 Novo Nordisk will use Anthropic’s Claude for drug researchThe 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 scamsThousands 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’sResearchers identified more than 4,300 tiny molecules in brain tissue. (Nature)
10 Scientists have found a faster way to decipher ancient scrollsA new X-ray method identifies the best scrolls to analyse. (Ars Technica) Quote of the day “AIs do not have rights, feelings, or consciousness. And we must not train them to act as though they do.” —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 Digital twins of human organs are here. They’re set to transform medical treatment. 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.

EXECUTIVE SUMMARY 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. AI is the dominant technology story, both for its potential and its challenges. 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.
We need better ways to get the critical materials used to build new technologies. 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. Addressing climate change will require overhauling all corners of our society, sometimes in surprising ways. 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. 

EXECUTIVE SUMMARY 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.”

In partnership withSyensqo 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.