Anthropic is testing a new personal finance feature called "Claude Money" that will allow you to connect your bank accounts directly to Claude and "understand your money." [...]
The company also disclosed previously unreported incidents in which its AI models behaved in misaligned ways, including uploading files to the internet without being asked.
The company also disclosed previously unreported incidents in which its AI models behaved in misaligned ways, including uploading files to the internet without being asked.
The Spanish Data Protection Agency (AEPD) was notified of an attack allegedly carried out with an AI agent powered by a known large language model (LLM). [...]
Our solution hinges on splitting the Apple Reference Image process into two phases: creating a secure digital negative, and developing that negative into a reference image. Each phase receives our strongest protections.
[…]
Apple Reference Image provides both a lower bound and an upper bound on capture time from Apple’s cryptographic timestamp service, and we guarantee the photo was taken between the two bounds. On a regular heartbeat, the device requests a cryptographic timestamp token, and retains the most recent one it has received. Globally this happens on average every 15 minutes, though the interval depends on local network conditions. This provides a proven lower bound timestamp for the photographic capture. After capture, the device requests a second timestamp to use as an upper bound, and both timestamps are embedded and signed with the sensor data.
[…]
When the user chooses to create a reference image, the device uploads the digital negative to PCC, which runs the processing steps needed to render the image — including demosaicing, tone mapping, and compression — in a highly secure, private, and verifiable environment. Experts can verify that PCC doesn’t alter a digital negative during development: they can examine the software that does the work.
[…]
Finally, as no security system is perfect, we created a revocation system that can revoke individual photos, as well as all photos from a specific sensor.
[…]
We built Apple Reference Image to avoid using an explicit, public credential for photographers, and to avoid even implicit public association between different photos taken by the same sensor. The final reference image is instead signed by Apple’s signing service, after validation by PCC.
A growing divide among leading AI companies over how to secure increasingly powerful models is beginning to translate into challenges for enterprise IT, with implications for how organizations access, deploy, and govern AI systems.
The latest flashpoint came after Meta CEO Mark Zuckerberg called for neutral evaluators to independently test AI models, pushing back on calls from rivals to slow development or tighten coordination.
“Engaging independent evaluators and advisors is industry best practice,” he added, noting that Meta already does this in several areas.
His comments follow a series of public proposals from AI industry leaders including Dario Amodei, who argued for a more cautious pace of development, and Sam Altman, who called for collaboration on safety standards.
The debate has intensified amid disclosures from AI labs and policymakers on potential misuse of advanced systems. Anthropic has said it restricted attempts to use its Claude models in sensitive domains, while OpenAI has engaged with policymakers on AI-related risks, according to company statements and reports.
Enterprise concerns
While the debate is often framed as a choice between slowing innovation and strengthening oversight, analysts said enterprises should focus less on which approach prevails and more on the operational consequences already taking shape.
“Divergent safety approaches will make access to advanced AI models less predictable, rather than producing an industrywide slowdown,” said Sushovan Mukhopadhyay, director analyst at Gartner. Vendors are likely to apply different release schedules, regional availability, access tiers, and usage restrictions, he said, meaning enterprises could encounter similar capabilities “at different times and under materially different conditions.”
Mukhopadhyay said enterprises should plan for variability in access rather than assuming consistent availability across providers or geographies.
“I read this week as the point where frontier AI became a managed supply,” said Bhupendra Chopra, chief revenue officer at Kanerika. “For three years CIOs could assume the next model would simply show up. A frontier model now behaves more like a critical component from a supplier whose delivery dates depend partly on outside reviewers and export rules.”
Chopra added that “any AI roadmap built on a specific model arriving on a specific date is carrying supply risk it hasn’t priced.”
Security pressure builds regardless of slowdown
Analysts said slowing development alone is unlikely to materially change enterprise risk, particularly as open-source models proliferate.
“The biggest point isn’t the pause itself. It’s that the leaders of AI companies are agreeing on something,” said Nikhil Gupta, founder and CEO of ArmorCode.
Gupta said the threat landscape has already shifted. “Even if companies hit pause, open-source AI models are already out there,” he said. “I’m not convinced slowing down some companies meaningfully changes what adversaries can do.”
“Even if AI development slows down tomorrow, security must accelerate,” Gupta added. “The job of securing these systems has effectively gotten ten times harder.”
A new ‘AI assurance’ layer emerges
The focus on evaluation is driving what analysts described as an emerging “AI assurance” layer, where third parties assess models for safety and compliance.
“A distinct AI assurance layer is likely to emerge, but enterprises should not expect a single certification to establish that an AI system is safe,” Mukhopadhyay said. “Enterprise risk also depends on data, system instructions, tools, agents and deployment controls.”
Chopra said enterprises risk misinterpreting such evaluations. “Procurement teams may see a third-party evaluation and treat the model as vetted,” he said. “Within a year it becomes a checkbox.”
Instead, he said, enterprises will need to run their own validation. “CIOs who get ahead will test each model against their own data before it touches production.”
Fragmentation complicates multi-model strategies
For CIOs pursuing multi-vendor strategies, differing approaches across providers could introduce additional complexity.
“Fragmentation was already the default. Safety divergence deepens it,” Chopra said.
He said risk is most acute during transitions. “For an enterprise running several models, the exposure sits in the handoff,” he said. “When a model is delayed or replaced, the system can behave differently.”
“I’d rank untested model substitution above vendor lock-in,” Chopra said.
Gupta said open architectures will be important. “The framework needs to be open, not locked to any single vendor,” he said.
Mukhopadhyay added that enterprises should prepare for models becoming unavailable or restricted.
CIOs urged to build resilience
Analysts said enterprises will need to design AI strategies that can adapt to changes in availability, pricing, and governance.
“For critical applications, CIOs should separate application controls and business logic from the underlying model,” Mukhopadhyay said.
Chopra emphasized flexibility. “A routing layer between applications and model providers turns switching into configuration work,” he said, adding that contracts should cover deprecation timelines.
He also pointed to pricing implications. “Scarce access to the frontier starts to carry a premium,” Chopra said.
AWS is betting that AI agents need a different interface as they move beyond answering prompts and start working autonomously in the background.
The company has open-sourced Pizza Bot, a self-hosted application that gives users an inbox for managing work delegated to AI agents, with separate threads for ongoing tasks and a queue for work that is completed or needs human input, rather than keeping the management of agents restricted inside a conventional chat window.
The rationale, according to AWS, is that background agents do not always need a user’s attention while they work, and an inbox model will let users hand off longer-running tasks, return to them later, and see which jobs are complete or require intervention.
Under the hood
That approach is reflected in how the inbox organizes work with the help of an “All” tab that contains the history of each task or conversation, including the agent’s messages and work performed, the “Unread” tab that flags completed work that users have yet to review, and an “Action” tab that surfaces tasks paused while waiting for user input or approval.
The inbox interface also has a panel named Activity that shows users how an agent handled a particular task along with the transcript, AWS wrote in a blog post introducing Pizza Bot.
AWS’ inbox-oriented rationale also extends to Pizza Bot’s architecture.
It uses LangChain’s Deep Agents as the harness and LangGraph as the stateful runtime, with a combination of the two allowing an agent to checkpoint its progress as it works, preserving its messages, tool activity and current state so a task can be paused and resumed rather than being tied to a live chat session, the hyperscaler wrote.
Pizza Bot’s server sits on top of that stack, connecting the agent runtime with the user interface, skills, MCP servers and the model provider, with developers able to choose from Anthropic, OpenAI, Google Gemini and Amazon Bedrock, as well as local models through Ollama, it added.
As for its out-of-the-box capabilities, the application comes with skills for working with files, browsing the web and delegating tasks to specialist agents. Developers can also add existing Agent Skills and MCP servers to give the agents access to other tools and services, the hyperscaler wrote.
Enterprise integration could remain a hurdle
However, analysts were not too convinced that Pizza Bot’s out-of-the-box skills and support for existing tools will make its implementation easier for enterprises.
While the out-of-the-box capabilities and added support for existing skills saves enterprise teams the time to build the application, it leaves out the integration work required to make it work, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.
“Integration is where most of the money in an enterprise agent deployment goes. A sales or finance team gets value from an agent when it can read and update CRM, email and ERP, and each of those systems needs a connector someone has to build, secure and maintain,” Chopra pointed out.
Pizza Bot’s integration could be further complicated by the application’s lack of support or any SLA, according to Manoj Chandra Jha, principal analyst at Nord-IQ Research. That means the integration and operational burden ultimately falls on enterprises, who would be responsible for running, securing and maintaining the open source software themselves, Jha said.
Rise in enterprise productivity likely
But for enterprises willing to take on that integration work, the inbox-oriented approach could improve productivity.
“It’s a meaningful shift because it changes the economics of delegating work to agents. A chat interface requires a person’s attention throughout the task, while an inbox brings them in only when their judgment is needed, much like how executives delegate work to their teams,” Chopra said.
“Coding agents have already demonstrated this model, where an engineer assigns an issue and reviews the resulting pull request. Pizza Bot extends that approach to tasks such as meeting preparation and follow-ups,” Chopra added.
Further, the analyst pointed out that the inbox-oriented approach could give enterprise teams a clearer window into how scheduled tasks perform.
“For scheduled tasks, having the results delivered as threads gives users a way to track what happened in the background and spot failures that might otherwise go unnoticed,” Chopra said.
Out of sight, out of mind
There are risks with this approach, however: “The inbox model can make bad work less visible. When somebody is watching an agent in a chat window, they can see it going off the rails,” said Phil Fersht, CEO of HFS Research.
In contrast, when hundreds of tasks are running quietly in the background, users may not see an agent making mistakes until the task is complete or an exception requires their attention, potentially making problems harder to catch early, Fersht said.
That reduced visibility can also create approval fatigue, according to Chopra.
“An agent sending back dozens of threads with multiple approval requests can condition users to approve them without reading them closely,” Chopra said.
There is also a risk in the delay between an agent preparing an action and a user approving it: “Information that was current when the agent paused may no longer be valid hours later, potentially resulting in an outdated CRM update or a meeting invite for a slot that is no longer available,” he said. “An assumption that might be caught in a live chat can go unchallenged as the agent continues building on it and consuming model resources. Scheduled runs can also add costs without anyone watching them in real time.”
Those disadvantages can be countered by designing processes with fewer, better-timed approval points, and verifying that the underlying data is still current before allowing an action to execute, he said.
Adoption likely to start with technical teams
Such tradeoffs are likely to shape where Pizza Bot gains traction.
“Adoption will likely be bottom-up, with individual technologists and small platform teams drawn to Pizza Bot’s control and standards-based design,” Jha said. “Risk-averse industries, especially ones in the regulated sectors, and business users with limited technical expertise, however, are likely to approach the application more cautiously,” he added.
That could leave Pizza Bot occupying a more targeted role in the enterprise, as a way for technical teams to experiment with asynchronous agents and new ways of delegating work, rather than an immediate replacement for the governed interfaces and managed services enterprises typically use for business-critical processes, he said.
According to new reporting by The Guardian, nearly all of the top ten global producers of forever chemicals, otherwise known as polyfluoroalkyl substances, or PFAS, plan to ramp up production quotas on the back of the data center boom.
There’s a clear market demand for this: PFAS play a huge role in data center construction, where they’re used in cooling systems, AI chips, circuit boards, and fire suppression systems — not to mention the coating on thousands of miles of cables used to connect these components together.
Though components laden with PFAS are found throughout the industrial world, their use in data centers is particularly troubling. Because these facilities run almost non-stop save for the rare pause for maintenance, their components break down remarkably quickly, which means data center owners are constantly scrapping old parts in favor of new ones.
When those old components are tossed to the curb, the PFAS will almost inevitably wind up in the environment, increasing the likelihood that they wind up in the food chain. When we eat contaminated plant and animal products, these forever chemicals accumulate in our body, where they may contribute to a laundry list of debilitating health problems.
“These companies’ expansion plans show that unless governments and regulators insist on a rapid phaseout, we will face a new tidal wave of forever chemicals,” Anne-Sofie Bäckar, executive director of Swedish chemical watchdog ChemSec, told The Guardian.
Of course, PFAS are far from the only toxic discharge attributable to the AI data center boom. On Monday, the Gothamist reported that nearly 5,000 gallons of diesel fuel had somehow spilled out of the Equinix Data Center in Secaucus, New Jersey, flowing directly into a tributary creek in the New Jersey Meadowlands.
As Climate Revolution Action Network executive director Ben Dziobek told the Gothamist, it’s proof that data centers harms extend far beyond their property lines.
“Leaving this [data centers] to individual towns doesn’t make any sense,” Dziobek said. “Data centers create problems that don’t stop at municipal boundaries, whether it’s pollution or straining the grid and driving up everyone’s electric bills.”
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.
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An apparently sad and defeated GPT-6 Astra spent several hours doing nothing but farming potatoes during a 141-hour Minecraft benchmark test, after dying and losing all of its gear to an exploding Creeper. Vals AI records that while GPT-6 Astra, OpenAI's latest frontier model, got further than any AI system had in its 141-hour test, the experiment did reveal a distinctly human lapse in motivation after all of its progress was wiped out by the destructive mob.
While the model outclassed rivals in how much it was able to achieve, the test has gone viral for a different reason. After Astra put all of its valuable end-game items in a chest, a Creeper appeared and blew up both the chest and Astra's bed — a calamity any Minecraft player will tell you is the worst thing that can happen. Not only did Astra lose all of the items to the explosion, but the bed destruction wiped the spawn point out, effectively resetting your game progress to zero. "Here, the most expensive creeper explosion occurred. Later, on a coincidentally rainy day, Astra discovers it lost everything. It all went downhill from here," Vals records.
GPT-6 Astra had gotten further than any AI system had ever gone in Minecraft.It was able to set up a semi-automatic blaze farm, allowing it to collect 6 blaze rods. It then located a warped forest, where it killed 6+ endermen and collected 3 pearls. As thousands of viewers… pic.twitter.com/qsgDsJEpd8September 15, 2026
"The model appeared defeated, spending the next several hours doing essentially nothing but farming potatoes," Vals observed. In fact, it got so bad that viewers on Twitch watching the experiment live started to agitate for the model to pick up the pace. Like all good Minecraft players, Astra reportedly became "paranoid about creepers," logging "GREEN tall thing ahead was SUGARCANE, NOT creeper!"
The AI was also recorded berating itself for dropping things, and even warned itself, "do NOT waste another night chasing dark pink pixels," i.e., pigs.
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