The rapid expansion of artificial intelligence is creating an unexpected bottleneck: the materials that make it all work. While algorithms and computing power often dominate the conversation, the physical substances underlying semiconductors and data centers are hitting fundamental limits in performance, heat dissipation, energy efficiency, and durability. These constraints are driving demand for materials capable of meeting multiple demanding requirements simultaneously. Meanwhile, AI itself is revolutionizing how materials scientists explore the vast landscape of molecular possibilities and speed up the creation of new solutions.
For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, this intersection is reshaping what advanced materials can accomplish. «AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,» he explains.
As performance criteria multiply—including elevated temperatures, purity standards, electrical properties, chemical resistance, plasma resistance, and long-term reliability—materials migrate toward what Finelli describes as the «top of the pyramid.» Beyond merely supporting AI advancement, he argues that advanced materials are «actually increasingly defining what’s going to be possible.»
This challenge manifests throughout the infrastructure driving the AI boom. Syensqo is creating materials for high-voltage data center designs, specialized sealing compounds for semiconductor fabrication, and thermal management technologies such as fluids for direct immersion cooling. Some of these breakthroughs transcend industry boundaries. Materials originally engineered for electric vehicles, for instance, can address the elevated voltage and energy-density requirements emerging in data centers.
The very meaning of performance is evolving. An increasing number of customers expect materials to satisfy technical specifications while simultaneously minimizing environmental footprint. «Our goal is to remove the trade-off between performance and sustainability,» Finelli states. This requires embedding sustainability considerations at the outset of research rather than treating them as an afterthought once a material is fully developed.
AI is also transforming how materials are discovered. Syensqo employs AI agents to digitally synthesize millions of potential molecular configurations, forecast their performance and sustainability profiles, and distill them down to a much smaller subset for actual laboratory evaluation. According to Finelli, this enables the company to go «broader, deeper, and faster» while freeing scientists to focus on complex engineering challenges.
Looking ahead, Finelli envisions the potential for a self-reinforcing cycle: AI contributes to developing materials that enhance AI infrastructure, which in turn powers more capable AI to further accelerate materials discovery. Such a feedback loop could generate a continuous cycle of innovation and broaden what future technologies are capable of achieving.
«You end up in this accelerated materials, innovative cycle of materials innovation,» Finelli says. «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.
When asked to identify the key drivers of AI advancement, many of us might mention algorithms, data centers, or raw computing power. But equally essential to performance are the advanced materials that form the foundation of every layer of that innovation. As AI continues to develop, it is pushing semiconductors and data centers toward new physical boundaries, placing fresh demands on the advanced materials sector to keep up. Yet this relationship works in both directions. As the industry rises to meet these challenges, AI is simultaneously emerging as a powerful instrument for accelerating materials discovery and development, dramatically compressing 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, could I start by asking you to tell us a bit more about Syensqo and the role it plays in developing advanced materials?
*Mike:* Yes, absolutely. Syensqo is a global leader in specialty materials. Our mission is to help customers solve their most difficult technology challenges. We serve many different markets, but the simple way I like to put it is: if it flies, we’re on it. If it drives, we’re in it. In healthcare, our products are literally saving lives every day. And if you enjoy your mobile devices, if you enjoy AI, it’s our products that are actually enabling the advanced semiconductor chips required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performance, greater reliability, and increasingly more sustainable solutions. I would say this is at the heart of our business. In fact, it’s in our name, Syensqo. To put some numbers around it, 20% of our annual revenues come from new products and applications we’ve launched in the last five years, which is really evidence of a very strong innovation engine.
*Megan:* Yes, absolutely. And as you described, you operate across many different industries with a particular emphasis perhaps on electronics and semiconductors. Could you talk a bit more about that work and where those industries are headed?
*Mike:* Sure. 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 at the company, semiconductors were one of the first industries I worked in. We’ve supported successive waves of innovation—from enabling smaller, more powerful mobile devices, helping the industry achieve smaller and smaller chip profiles. We’ve helped 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 and electronic components to smart devices, telecommunications, and even hyperconnectivity. Our materials help customers solve increasingly demanding challenges around miniaturization, thermal management, electrical performance, chemical resistance, ever-higher purity levels, and long-term reliability and sustainability. Today we work with leading semiconductor manufacturers and electronics companies around the world.
*Megan:* Fantastic. And as you alluded to, over the last 30 years we’ve seen enormous evolution in those sectors.
*Mike:* Oh my goodness, yes.
*Megan:* And now AI is placing 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:* You’re absolutely right. AI is now, from a material standpoint, really pushing semiconductors and data centers to their physical limits, and materials are becoming a key enabler of that continued progress.
The way I try to describe it is to think of a pyramid—I call it the performance pyramid. You have commodity materials at the bottom and high-performing specialty materials at the top. At Syensqo, we operate exclusively at the top of the pyramid, and we’re continually trying to raise that top by bringing newer and higher-performing materials to market.
Now you might ask, why doesn’t a data center or a semiconductor fabrication plant need a specialty material rather than something in the commodity space? I call it the «and, and, and» principle. If you simply need a polymer or material that can sit at room temperature and remain unchanged for 10 years, there are plenty of commodity materials that will do that, and you don’t have a problem. The moment you start adding requirements—and I call it the and, and, and—if you need a polymer that can handle high temperature and have high purity and electrical performance and chemical resistance and plasma resistance and long-term stability, all of these ands, you start moving toward the top of the pyramid.
What AI is doing with semiconductors, because of the speed at which it’s advancing, is requiring semiconductor chips and data centers to meet an increasing number of requirements—an increasing number of ands—which is pushing the limits of materials. That’s where we come in. I genuinely believe that advanced materials are 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 the top of the pyramid and that and, and, and principle you mentioned, could you walk us through perhaps an example or two of those top-of-the-pyramid solutions you’ve created or are currently working on?
*Mike:* As I said, our focus is enabling higher performance 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 increasingly in AI data center infrastructure. One example of our work on specialty materials for next-generation AI data centers is what we’re doing around high-voltage architectures. Data centers are moving toward high-voltage architectures because they can enable greater computing power while also improving energy efficiency. We know that’s a major issue for that segment of the industry, and these high-voltage architectures will help them reduce energy losses and ultimately help lower the environmental footprint of data centers. 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 it, many people have seen what a semiconductor looks like during processing. It’s a large silicon disc that’s later diced into the tiny chips that go into computers. But that wafer is placed inside a giant chamber where it faces a very extreme environment—aggressive plasmas, reactive chemicals—and they need higher and higher performing materials. All the seals around that chamber that keep those gases contained within the environment must be able to withstand those conditions. That’s what we’re developing, and we’re pushing the limits. They’re asking for higher temperatures, more aggressive environments, with lower outgassing and higher purity. That’s what we’re developing for this industry to enable the next chip to be developed and produced.
*Megan:* It’s so fascinating that people wouldn’t necessarily give much thought to the seal in something like that. As you’re outlining, it’s absolutely critical in terms of performance. And in developing those solutions, I understand you also looked across different markets to see what might be applicable in more than one space, including an overlap between the automotive sector and data centers. Could you tell us a little more about that?
*Mike:* As I mentioned earlier, data centers are shifting to higher-voltage architectures. This is the next-generation data center, which can be more energy efficient but has a higher energy density. The power density increases, which raises temperatures. Many of the material challenges we’ll face there, we’ve already developed for the automotive industry in electric vehicles. I’ll give you an example of an application. Think about an electric vehicle. The powerhouse in an EV is no longer the motor—it’s the battery. That’s where all the energy resides. When you’re putting a hundred kilowatts of energy, driving it to the electric motor through wires and through what they call bus bars, you need to get that car up to 60 miles an hour pretty quickly. You’re driving massive amounts of energy, which dramatically increases temperatures.
All the electrical connections are in these bus bars, where there’s a polymer—an insulating polymer with copper in between for all the connections. That has to withstand that temperature increase, which can come on quite rapidly. We’ve developed new materials there, and those materials will be translatable to these data centers where they’ll have higher voltages with higher energy density.
Another thing we’ve been doing in automotive: we have extensive knowledge in both automotive and semiconductor around fluid circulation and how to use dielectric materials for 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 submerge them in a liquid—direct immersion cooling—that’s extremely efficient, so that’s another thing we’re working on.
Another development from automotive that will translate over is battery energy storage systems. Inside the battery, we’ve developed a binder. It’s the highest-performing binder on the market, used in the cathode of a lithium-ion battery, and it keeps all the ingredients working together so that the battery can last for 10 years and perform reliably. Now that’s moving over to data centers because they’re moving more toward renewables and need these energy storage systems to smooth peak loads and provide resilient backup power. That’s one of the things we’re doing. By transferring our knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure.
*Megan:* Fantastic. So many transferable applications there that wouldn’t necessarily have sprung to mind. And it isn’t only technical advancements that you need to contend with, of course. Companies today are also demanding that materials be developed and manufactured more responsibly. So how is sustainability shaping your innovation process?
*Mike:* You’re absolutely right. I will say performance is still the entry ticket. Our customers want performance. What’s changing is that the definition of performance is now broader and includes 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 providing truly sustainable business solutions to our customers. This is why we developed what we call the Sustainable Portfolio Management tool, SPM. It’s a matrix that 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 value 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 begin them, is assessed on whether it’s going to be a sustainable product or not.
And 88% of our portfolio is now a sustainable product. We’re developing materials that are better for the environment, with a lower environmental footprint when we produce them, but they also contribute to improvements for our customers so they can operate with a lower carbon footprint, or operate more safely, or use less water. There are many different criteria 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 earlier the heat they’re generating, especially when they move to 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 have high global warming potential. That’s not good for the environment. We’re developing the next generation of heat transfer fluids that will reduce the potential environmental impact compared to today’s fluids. In the end, our goal is to remove the trade-off between performance and sustainability. You’ll notice that’s another «and»—we can be performing and sustainable.
*Megan:* That’s so important, isn’t it, to think about sustainability in terms of performance? As you say, when we’re thinking about commercially scaling up these solutions, it’s such a critical part of it. And as I mentioned in the introduction, AI isn’t only a challenge—it’s also an opportunity within the advanced materials 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, using AI in our research and development, and we’ve partnered with Microsoft and their Microsoft Discovery tool, which is helping us rapidly identify and evaluate promising molecular candidates.
In the traditional research approach, historically, you would design your experiment and look at all the potential combinations of materials and chemicals to make all these different molecules. The combinations of potential molecules you could develop to solve a problem could be in the millions, but it’s impossible to develop a million or tens of millions of molecules in your laboratory and actually physically do that. So you have to select a small area based on your expertise and knowledge, based on literature searches, based on the state of the art, looking at patents, and so on. You pick a small area and 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 out there.
What we’re doing with AI is we’ve developed AI agents with Microsoft that are literally digitally synthesizing the entire millions and millions of combinations of potential molecules. We have another AI agent that uses physics-based simulation to look at all those molecules and predict their performance—not just performance on physical and chemical properties, but also on toxicity, sustainability, and so on. Then we have another agent that takes all that information and ranks them. In the end, we’ve explored all 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 in the end, you get the solution faster—much, much faster. You’ve explored the entire space. I basically say it allows us to go broader, deeper, and faster. The important thing is it’s not replacing our scientists or our scientific expertise. In a way, it’s giving them superpowers. It allows 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 transformative tool from what you’re explaining.
*Mike:* Completely, completely.
*Megan:* Just to wrap up, Mike, it would be great to take a look ahead if we could, because there’s so much activity in both AI and the advanced materials space. I wonder what’s coming down the pipeline that you’re most excited about next?
*Mike:* I’ve talked a lot about AI and how we’re using it to develop new materials. I think what’s really exciting to me—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 make AI even 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 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. A real virtuous circle of innovation, it sounds like. 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, 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.
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*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.*