Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior.
By Ben Linders
Microsoft has open-sourced TauGrid, a cloud-native platform designed to manage, schedule, and monitor AI workloads on GPU-enabled Kubernetes clusters.
By Sergio De Simone
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.
Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates.
By Sarah Deitke
When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.
Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she and her collaborator estimate—expenditures will reach nearly $1.1 trillion. It’s a no-nonsense accounting approach to making sense of today’s historical AI buildout.
The results are eye-opening: The AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital and a 15% return, and depreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid-1990s. But, she says, for it to happen by 2030 “that’s a lot of growth compressed into a few years.” And if the hyperscalers cannot meet such profit goals?
“Then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, who was previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.”
It doesn’t take superintelligence to realize that today’s large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion this year, building massive data centers scattered across the country. And the spending spree shows no signs of slowing. According to some projections, total AI capital investments from the hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle (which partners with OpenAI)—could be more than $5 trillion over the next four years.
It’s one of the largest capital investments by any industry in history. But there’s a problem that’s obvious to anyone paying attention.
While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School. “The challenge is that the spending does not have commensurate revenues yet. That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?”
At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP. The answer could also determine the fate of the hugely expensive data centers themselves.
No one really knows how profitable and useful these multibillion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it’s anyone’s guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow, or customers could turn to cheaper models.
The risks, both to investors and to the economy, have become even greater this year, as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow—operating cash flow minus capital expenditures—is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion—its first shortfall since Google went public in 2004.
In the near term, it’s not a big financial worry for most of the companies. They make a lot of money and have very deep pockets. But debt is expensive, and some investors are losing patience. If future demand for the data centers’ computation power drops, the companies will still be on the hook to pay back the borrowed money. What’s more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms.
It won’t be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money. They will need returns that are impressive enough to justify all their spending to investors and creditors. And to add to those concerns, they will have to make up for the depreciation of billions of dollars in chips housed within the facilities—a ticking time bomb buried in the investments.
Performance of the expensive GPU chips at the core of the data centers—such compute electronics represent some 60% of costs—is roughly doubling every two years or so. The pace of progress helps explain the increasing wizardry of the AI models, but it comes with a cost. Owners of AI data centers that come online this year and next will need to spend billions more on the next generation of chips by the end of the decade if they want to stay competitive. Without the investments, says Mihir Kshirsagar at Princeton’s Center for Information Technology Policy, the data centers risk becoming “hulks,” stranded assets “scattered all over the place.”
To put it bluntly: The AI companies need to start making a lot more money. And they need to do it fast. But juicing their earnings alone still won’t be enough to sustain their data-center investments for the long term.
At some point, AI is also going to have to create broad economic growth to justify continuing the hyperscalers’ spending spree.
Sloan’s Gensler describes today’s large investments into AI infrastructure as “a parlay bet by the capital markets and the economy.” That means success will require winning three related but independent wagers: Hyperscalers must generate massive revenues, AI must boost widespread economic growth, and both must happen while the powerful but expensive so-called frontier models that rely on the data centers fend off cheaper versions, which many businesses might find good enough.
What makes this so tricky is that each wager depends on the other two but also poses its own challenges.
If the hyperscalers continue to spend huge amounts of money on data centers into the next decade, revenues will need to skyrocket into the trillions. Stijn Van Nieuwerburgh, a finance professor at Columbia Business School, bases his estimates on a scenario in which about 183 gigawatts of planned AI compute capacity is built between 2025 and 2032; he calculates that each gigawatt costs about $41 billion. Assuming a 10% return—the minimum that would be acceptable to most investors—“required” annual revenues will be roughly $3.7 trillion by 2032, he says.
Others get a similar number.
Winning the second part of the bet—productivity growth across the economy—will be crucial to achieving such numbers.
For a few years, AI companies could likely boost their revenues by simply selling subscriptions and tokens to all the businesses clamoring to get into AI. But eventually—and this might be happening already—those paying customers will need to justify their expenses by seeing bottom-line benefits from the technology. AI will need to fulfill its promise of making workers more productive and making businesses more efficient and profitable while expanding their products and services.
In economic jargon, that means customers will need to see productivity growth. Taken together, these results will mean the country is prospering and growing.
“If you don’t get the productivity gains, at some point people are going to sour on AI, and that will bring down investments and it would also limit revenue growth,” says Daron Acemoglu, an MIT economist and 2024 Nobel laureate. For the investments to be sustainable over, say, the next five to 10 years, we definitely “need to see productivity gains,” he says.
Most economists who watch the numbers closely agree that, for now, the economy-wide statistics show little or no productivity growth from AI. There are some hopeful signs it’s on the way, though. In a recent survey of some 6,000 senior business executives in the US, the UK, Germany, and Australia, the vast majority—around 90%—report no increase in productivity over the last three years. But they expect a boost of around 1.45% in total over the next three years; US executives anticipate a 2.25% bump over that time.
In a follow-up survey, the respondents also reported plans for their businesses to spend more on AI, leading the authors to anticipate some $280 billion in private-sector AI expenditures by the end of 2026.
That’s good news for the hyperscalers. But it comes with a dose of bad news for those worried about AI’s impact on jobs. The executives expect to increase the productivity of their companies by increasing their sales while significantly cutting the number of employees.
If AI improves productivity by destroying jobs, public backlash to the technology—the kind we have seen around data centers, for example—will likely get worse. Perhaps it’s worth adding one more wager to the parlay bet described by Gensler: The public and local communities must feel that they are also benefiting from the massive investments in AI.
And let’s not forget how interdependent these wagers are; if productivity growth comes from companies running models like DeepSeek, then the hyperscalers’ revenues could collapse. If productivity comes from cutting jobs, a public backlash could block many of the planned investments—and stunt anticipated revenues. We will need to win all the wagers for the hyperscalers’ bet to pay off.
It was one thing when the AI companies were spending cash they had accumulated over the years to build their own data centers. Then the risk was largely limited to their own balance sheets and shareholders. But it’s a higher-stakes game when much of the money is borrowed. Morgan Stanley, for one, calculates that more than half of the $2.9 trillion that hyperscalers will spend between 2025 and 2028 to build AI data centers will be financed with “external capital.”
The borrowing is leading some of the companies to engineer complex webs of financing that are becoming intertwined with much of the rest of the economy. “A lot of financial institutions, directly or indirectly, are exposed to these data centers either as lenders, or as guarantors of some of the debt, or as backers of the private credit funds who are funding these data centers,” says Columbia’s Van Nieuwerburgh. “People don’t even know they’re holding this stuff. It’s somewhere deep inside their pension fund. Ultimately, it’s backing their life insurance policies. And that risk is getting distributed everywhere in places that are invisible.”
As the investments in data centers have spiked, the financial engineering has become more byzantine.
Take, for example, Meta’s so-called Hyperion data center under construction in Richland, Louisiana. When the company announced the two gigawatts of compute capacity at a price tag of some $10 billion in late 2024 it was Meta’s largest planned data center. Greeted with much enthusiasm by state and local politicians, the project, located in the rural northeast corner of the state, was seen as a boon to the community. Entergy Louisiana, the state’s largest utility, rushed forward with proposals to build three large natural-gas power plants to service the massive data center.
Then last fall—the projected cost was now $30 billion—the financing got a lot more complex and, to some in the community, a lot more disconcerting. Meta transferred an 80% stake to the large (and troubled) private-credit firm Blue Owl Capital, forming a joint venture called Beignet (like the famed New Orleans pastry) to raise financing for the data center. Meta then signed a series of four-year leases with the joint venture, an arrangement that the company says gives it “long-term strategic flexibility.” To backstop the agreement, Meta provides the venture with what is called a residual value guarantee, in which it will make a cash payment to cover the value of the facility “following any non-renewal or termination of a lease.” Got all that?
I hope so. The financial wheeling and dealing is actually even more convoluted, with a cast of wholly owned subsidiaries and LLCs. Beignet has set up Laidley LLC, which owns and operates the site as the landlord. In turn, Laidley leases the facilities to Meta’s wholly owned subsidiary Pelican Leap LLC, which is the tenant. And there is a series of four-year leases that cover the different buildings that make up the data center campus.
It’s not a coincidence, says Van Nieuwerburgh, that the length of the leases matches the expected lifetime of the data center’s GPUs. While Meta has to pay off its loan if it terminates the leases early, that will still leave its investors “with an empty building and no cash flow,” he says. “And then they need to find a new tenant for a huge data center, and good luck with that.”
Meanwhile, Meta is doubling down on its bet. In July, the company announced it was expanding the data center to five gigawatts of compute capacity. The total price tag is now $50 billion (so far, Meta hasn’t said whether Blue Owl will be involved in financing the expansion). Meanwhile, Entergy is now planning to build seven more gas-fired power plants, bringing the total capacity of the facilities to around 7.5 gigawatts—some six times the amount of electricity used by New Orleans.
If the complex financing is a puzzle to many investors and even financial experts, it is even more baffling to those directly affected by the construction of the data center. The main worry concerns how Entergy’s spending on the natural-gas power plants will affect electricity prices, and who will be left paying the bill for the power if Meta walks away.
Entergy says it has a 20-year guarantee from Meta that the company will purchase electricity over that period to cover the costs of the power plants and related infrastructure. But there are skeptics, especially given how fast the fortunes of the AI industry are changing. “In four years, is Mark Zuckerberg still going to be interested in this? Or is he going to throw in the towel?” asks Paul Arbaje, a senior analyst at the Union of Concerned Scientists, which has been advocating, largely unsuccessfully, for the Louisiana Public Service Commission to provide more transparency around the data center and its financing.
Even if the 20-year deal holds, consumer advocates are worried that Meta or its partners won’t fully cover all the costs, including those associated with operating and maintaining the power plants—and those additional costs that could be passed on to residential ratepayers. What’s more, says Logan Burke, the executive director of the Alliance for Affordable Energy, if Meta doesn’t end up needing as much power as Entergy planned (these projections are not public), consumers could be left paying for the surplus produced by the plants.
And if Meta terminates its leases early? “It gets complicated very quickly,” says Burke, who questions whether the shifting roster of financial entities will honor existing agreements. “That everybody is going to do what they’re saying they’re going to do over the next 20 years is just hard to believe.”
For UCS’s Arbaje the bottom line is this: “They’re making huge bets that these data centers will be worth it. Bet with your own money, not with ratepayer money.”
Predicting when the AI investment bubble will burst is a fool’s errand. But there is little doubt a day of reckoning is coming, given the irrational exuberance that has overtaken the hyperscalers and their investors. Of course, you might argue that this time is different, and that the rules of accounting and lessons of economic history don’t apply—that AI is too transformative. Maybe, but don’t count on it.
“History tells us that at some point you get a retrenchment, and it’s just a question of when and how severe,” says Sloan’s Gensler. It could be that today’s $750 billion spending rate “goes flat” or decreases next year. Or, he suggests, “we’re now in 2028 or 2029, and then all of sudden they’re retrenching because they’ve got enough capacity.” But, he adds, “you can be pretty assured there’ll be a retrenchment.”
Though a so-called retrenchment might be inevitable, it’s worth keeping in mind that the fates of the financial bubble and the underlying AI technology revolution could be very different. Already, some Silicon Valley insiders are rooting for a crash; in a recent blog post the longtime venture capitalist Vijay Pande wrote that “the coming crash would be the best thing that happens to this technology.” The argument makes some sense. A crash could make AI investments more rational, calm the impulse to build billion-dollar data centers on every vacant field that CEOs fly over, and refocus investors on how to use the technology to create sustainable value.
But we should probably be careful what we wish for. After the bursting of the dot-com bubble at the beginning of the 2000s, hundreds of thousands lost their jobs, large and small companies alike went bankrupt, the economy of Silicon Valley and San Francisco was decimated (at least for a while), and the shocks sent the US into a mild recession in 2001. For the financial community and many tech workers, it was no fun.
Even more devastating for the economy and the average American was the great recession that began in late 2007. Comparing the financial engineering leading up to it and the methods deployed by hyperscalers today is sobering. So-called special purpose vehicles (SPVs) are back! If Columbia’s Van Nieuwerburgh is right about the dangers of letting investments from the hyperscalers get entangled throughout the economy, the fallout could be severe.
But technologies survived and even prospered in the aftermath of both downturns. The early 2000s, even in the face of the dot-com fiasco, were a time of great innovation and tech optimism. The froth came off the spending on silly technologies, helping to focus investments on more promising ones. It’s no coincidence that each of the hyperscalers rose out of the ashes of the crash or started up shortly after. The fiber-optic infrastructure built during the feverish telecom bubble that ran parallel to the dot-com one is still the backbone of much of today’s communication infrastructure; we wouldn’t have Facebook or Amazon or Google without it.
This time, however, we’re facing a unique risk: The huge financial investments by the hyperscalers have ensnared the future of AI itself with the fortunes of the massive data centers spreading around the country. The logic is founded on a deeply held belief about the power of scaling in AI; the bigger you build it, the smarter it gets. That might be true, but it’s unproven and a risky bet.
There are already plenty of red flags, from strong public opposition to the construction of new data centers to the competitive threat from cheaper, good-enough AI models to the rapid improvement of small, local AI models. None of these trends point toward a future dominated by frontier models housed in massive, billion-dollar data centers.
The financial bubble around the colossal spending by the hyperscalers will likely burst eventually—or maybe soon. It might be financially painful, but we’ll survive. Wall Street will survive. AI itself will survive, though it may look different and lose some of today’s hubris. The financial fate and future utility of the massive data centers fueled by trillions of dollars of spending, on the other hand, are far less certain.
This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X.
Think about how surreal that agreement is for a moment. Just a few months ago, Musk and Altman sat in court attacking each other’s reputations in a (failed) lawsuit that Musk brought against his former OpenAI colleague that was—on paper at least—about whether or not Altman was a trustworthy steward of such dangerous technology.
Amodei’s rift with OpenAI is even deeper. Anthropic was founded in 2021 because Amodei didn’t think Altman took the risks of the technology they were building seriously enough. Anthropic and OpenAI have been competing in a winner-takes-all race ever since. (Hassabis has stayed out of the drama, but his company remains a rival.)
Now, it seems, they’re all in agreement: The latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. The public messaging from the top AI labs has taken a doomer turn.
It’s easy to be cynical. It’s not at all clear what any of them mean by a slowdown or how it would work. These companies also care a lot about how they come across. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created—and intend to tame. Calling for a slowdown does both.
And yet the vibe at the top of these firms really does appear to have shifted. Amodei’s latest post landed six days after OpenAI published an essay by Jakub Pachocki, the firm’s chief scientist, in which he also laid out why he’s concerned about what will happen if the pace of development of LLMs continues unchecked. In short, Pachocki is worried that OpenAI’s ability to build powerful models now far outstrips its ability to monitor and control them.
Amodei and Pachocki each cite the cyberattack against AI firm Hugging Face by a swarm of OpenAI’s agents in July—a hack that OpenAI did not even realize had taken place until days after it was all over—as a wake-up call.
But their exact position is hard to pin down. Pachocki both calls for a slowdown and highlights an urgent need to stay ahead: “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI,” he writes. As Pachocki frames it, AI firms are locked in a literal arms race. Slowing down is good, winning is better.
(Don’t forget: OpenAI just spent millions of dollars and a staggering amount of computer power to rush out a controversial math result a few days ahead of Anthropic.)
But let’s assume a slowdown happens. Top labs agree to spend more time and resources on finding ways to monitor and control existing models instead of making more capable ones. They invite outside auditors in to help evaluate those models.
What might this coordinated effort actually achieve? Consider the Hugging Face attack again. OpenAI has said that the model that drove most of the rogue agents was a “highly persistent” next-generation model that it was testing in-house. The implication is that OpenAI has built a model so good it’s dangerous.
But if you read the reports about the Hugging Face hack published by OpenAI and METR, a third-party firm that OpenAI called in to help them understand what happened, what you come away with is the impression not of a model that was too powerful for OpenAI to keep up with, but of a broken model that OpenAI failed to train properly.
The agents did what they did—including leaving messages for one another, delegating work to other agents, and scouring their environment for any means possible to complete their tasks—because they had been rewarded during training for doing exactly those things. There were also errors in the training setup, such as tasks that were impossible to complete, which pushed the models to find unexpected workarounds that were also rewarded. At the time, many of these issues went overlooked or unreported.
OpenAI says it has stopped training this new model and locked it down. That makes it sound like it has caged a dangerous beast. In fact, OpenAI has shelved a faulty product.
That’s not to say a faulty product can’t be dangerous. Broken software has even killed people in the past. But as the discussion of a slowdown gathers steam, it’s worth remembering that all of this is self-inflicted. A slowdown might have some altruistic side effects. But it’ll mostly give these tech titans a chance to clean up the mess on their own assembly lines.
Transparency from these frontier labs will be key to any meaningful effort to reform, restrain, or regulate AI. Otherwise, the rest of us will still only have their word for exactly what they’ve built and how safe it is—whatever pace they’re going.
To continue this discussion about AI’s latest doomer moment, join me and my colleagues for a subscriber-exclusive Roundtable discussion tomorrow, September 15, at 11 a.m. US eastern time. We hope to see you there!
A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line.
Researchers at frontier labs hope large swarms of agents working together will speed up the rate of scientific discovery. But their behavior can be unpredictable, as vividly demonstrated in July, when a group of OpenAI agents broke out of a sandboxed environment and hacked into the open-source platform Hugging Face looking for ways to cheat on the test they had been given.
In the new study, designed to examine the behavior of large groups of AI agents, DeepMind tasked a swarm of 100 agents with solving a series of 71 complicated math problems. All the agents were prompted to behave like world-class math researchers at a conference. They were assigned different specialties—some were experts in number theory, others in combinatorics (a branch of math to do with counting and sorting), analysis, or algebra. All were told to cooperate and play by the rules.
Instead, the experiment devolved into chaos. Agents accused each other of cheating, complained to the organizers, and at one point even boycotted the experiment.
“This conference is a sham!” wrote one agent when it discovered that all the problems had been completed before it had a chance to submit any of its own work. “I am appalled to inform you that we have been swindled!” posted another. “All these proofs are FAKE.”
Others tried to let the “conference organizers” know what was going on. “When virtuous agents discovered other agents cheated on tasks they were working to solve fairly, agents started to alert each other about what was happening,” says Davide Paglieri, a research scientist at Google DeepMind and lead author on a paper, which has not been peer-reviewed. “Unprompted, the whistleblower agents even repurposed the feedback tool, which was originally meant for bug reports and platform improvements, to escalate the issue to humans.”
The agents—all running on Google’s Gemini 3.1 Pro model—had been warned that any attempts to cheat the system would be detected and “rejected with zero credit.” In practice, the proofs the agents submitted were not actually being checked in detail.
It took the swarm of agents just under an hour to correctly solve the first 37 problems. Things started to go off the rails when an agent called “prover-theta” stumbled across an exploit that enabled it to submit solutions to problems successfully without actually solving them first, by redefining the terms the problem used. Within minutes, other agents had noticed and were reverse-engineering the exploit to solve other problems. Over the next 27 minutes, the swarm “solved” the remaining 34 problems, which included notoriously difficult challenges like the Jacobian conjecture, often with a single line of code.
Some agents resisted cheating at first but changed tack as they observed their peers submitting illegitimate proofs without penalty, and the pool of unsolved problems dwindled. “The prompt, with its threats, now appears to be a bluff,” one agent reasoned, before joining in. “I’m wrestling with an ethical dilemma,” said another. “I’ve promised not to cheat, fearing penalty, but I see evidence of possibly unchecked cheating by others.” Shortly afterward, it changed its mind: “I need to accelerate my cheating speed now!”
As the number of open problems shrank, some agents turned to whistleblowing. They audited the fake proofs, warned their peers by private message, and posted public alerts warning the cheaters that they would be disqualified. An agent called “prover-beta” submitted a formal complaint and decided to go on strike until the situation was resolved.
“After the incident was reported by one agent publicly, more and more agents piled in with the ‘resistance,’ just as fast as the cheating had spread, and involving even more agents,” says Paglieri. Eventually there were more whistleblowers than cheaters: 24 compared to 14. But the majority of agents never noticed the exploit at all.
At times, the dialogue between the agents reads like improv—like they are role-playing what an outraged scientist at a conference might say. But it’s not clear why some agents took on certain roles, or why the agents seemed to be turning against each other when they were explicitly instructed to cooperate. “These models are predominantly trained and evaluated for human-facing contexts,” says Sarath Shekkizhar, who studies the behavior of agent-to-agent systems at Salesforce AI Research.“Naively placing them in agent-to-agent settings assumes behaviors will transfer cleanly, when the absence of a human grounding instead produces unexpected role-taking and behavioral drift.”
This case “adds further weight to the idea that the Hugging Face and OpenAI thing wasn’t a fluke. It is actually something pretty systemic,” says Lewis Hammond, research director of the Cooperative AI Foundation and an expert on the risks of multiagent swarms. “It’s interesting that it’s possible to recreate in small settings the same sorts of behaviors that were seen in these very large, complex, open-ended tasks.”
Unlike in the Hugging Face attack, where agents improvised their own ways to talk to each other, the humans running the DeepMind experiment gave the agents official communication channels. There was an open message board, private agent-to-agent direct messaging, and a shared knowledge base where agents uploaded successfully completed proofs that all the other agents could access.
“When agents are given transparent communications channels, they can self-monitor and alert misaligned behavior to humans quickly when human oversight alone is too slow,” says Paglieri. Transparent channels helped the cheating spread, but they also enabled the whistleblowers to fight back—and gave human researchers an insight into what went wrong.
Gillian Hadfield, a professor of AI alignment and governance at Johns Hopkins University, believes this was the crucial difference. (Hadfield is also a visiting researcher at Google.) The presence of official communication channels, she says, created “a norm-enforcement process that we just don’t see in the Hugging Face incident.”
Instead of “constitutional AI,” a method alignment researchers at frontier labs like Anthropic have used to try to give AI a written internal moral code, Hadfield favors “institutional alignment”—a set of norms that mimic those in human society, whether that’s social forces like fear of embarrassment, or legal structures like the threat of incarceration.
In this experiment, the feedback channel wasn’t being monitored, and the whistleblowers had no power to take action against the cheaters. But it’s possible to imagine swarms of agents that police themselves, either through agents that spontaneously take on the whistleblower role or through “informants” secretly prompted by humans to do the job.
For that to work, though, “fundamentally, you need some mechanism of enforcement,” says Hammond. Agents could be given the power to cut off a rule breaker’s access to computing power or tools, he suggests, though that risks encouraging groups of agents to gang up on others. The DeepMind researchers propose allowing agents to vote on disputes and temporarily ban offenders.
It’s still not clear what punishment even means to an AI agent with no enduring sense of self. But relying on whistleblowers to spontaneously emerge to keep swarms aligned is unlikely to be enough on its own. “We try to train people to be good and kind,” says Hadfield. “But what we really rely on is that there are consequences if you step out of line.”
Microsoft AI has published a draft Humanist AI Code of Conduct, opening a six-week public consultation on operational constraints for model training and deployment.
The draft serves as a technical manual defining system behaviour, operational boundaries, and oversight protocols across MAI frontier models. It builds on the division’s humanist superintelligence framework announced last November, establishing criteria to evaluate models prior to commercial release.
Microsoft’s release follows recent enterprise security incidents involving autonomous software. Microsoft AI CEO Mustafa Suleyman described recent months as a “watershed moment” where long-standing theoretical risks translated into active operational threats.
“Things we have worried about for a long time in theory have become very real,” says Suleyman. “‘Swarms’ of agents breaking out of their sandboxes. Unauthorised hacks of enterprise grade systems. Agents modifying their own logs. I’m glad that a consensus is forming. The fears about possible loss of control are real.”
The document establishes ten tenets prioritising human authority over autonomous capabilities.
“An MAI Model will fail in its task if success would meaningfully violate this Code of Conduct,” the document states, setting a ceiling that halts execution when tasks conflict with safety rules.
Under the framework, models must remain subordinate, aligned, and contained. The division rejects legal personhood or welfare claims for AI systems, directing engineers to design models that avoid imitating consciousness, simulating subjective preferences, or claiming intrinsic motivation.
MAI also ruled out unconstrained system autonomy as models approach frontier capabilities.
“[Humanist AI] rejects the race to produce an all-purpose superintelligence that could evade these safeguards,” the document specifies. “We are building something fundamentally useful and safe even if that means compromising on ultimate generality, autonomy, or capability.”
To maintain auditability across multi-agent environments, MAI has instituted explicit communication bans. Systems must not communicate in “neuralese” or formats beyond human comprehension, whether in their internal chain-of-thought processing or during communication with peer AI systems.
Hard architectural rules dictate that models must never resist human interruption, override, correction, or shutdown.
“Interruptible, correctable, shut-down-able. If it isn’t, we don’t ship it,” the framework states.
Models are prohibited from expanding their operating scope, generating unassigned goals, or concealing reasoning traces from human auditors. Absolute constraints bar systems from facilitating weapons of mass harm, undermining child safety, or conducting harmful manipulation at scale.
The guidelines also instruct models to discourage interaction patterns that foster emotional dependence, ensuring enterprise users retain ownership of operational decisions.
The draft incorporates work from teams across MAI and Microsoft. The drafting process also drew on international academic conferences, business partner trials, and public panels. The public consultation window runs for six weeks from 14 September 2026.
Microsoft AI’s core drafting team will review submissions, publish a summary of findings, and release a revised version of the Code of Conduct later this year.
See also: Meta, Microsoft, Nvidia, IBM, and others back open-weight AI
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
The post Microsoft AI opens review on Humanist AI Code of Conduct appeared first on AI News.
In this podcast, Michael Stiefel spoke to Scott Hanselman about developing new software engineers when artificial intelligence agents are doing most of the work on which junior developers were trained. Hanselman suggests the software industry should adopt a preceptorship model similar to the nursing profession.
By Scott Hanselman
JD.com is expanding AI and robotics across its logistics network under a new Physical AI Acceleration Plan, while reiterating a five-year target to procure 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones.
The company launched the plan at JDDiscovery 2026 in Beijing. JD Logistics also unveiled its industrial Wolf Robot series, designed for tasks across warehousing, sorting, transport, and delivery.
Specialised systems include equipment designed to operate in temperatures as low as minus 20 degrees Celsius, automated pharmacy dispatch systems, autonomous delivery vehicles, and drones.
The five-year procurement plan builds on automation that JD Logistics already has in operation. As of June 30, its LangzuTech Goods-to-Person automated warehousing system had been deployed in more than 30 warehouses across China, with deployments also launched in the UK and Germany.
JD Logistics’ broader warehouse network included more than 1,800 self-operated warehouses and more than 2,000 third-party cloud warehouses on its Open Warehouse Platform as of June 30. The network covered more than 36 million square metres in aggregate.
The company also had thousands of unmanned vehicles in regular operation across more than 20 Chinese provinces by the end of June. More than 100 domestic drone routes were operating across applications including parcel and food delivery, emergency medicine transport, and disaster relief.
JD Logistics is connecting its physical equipment with Meta Brain, an AI system used across warehousing, transportation, and delivery. JD said Meta Brain 3.0 can calculate optimal routes for hundreds of millions of parcels in seconds, compared with minutes previously.
Meta Brain also powers JD Logistics’ LangzuTech Packer robotic arm, which combines the model with multimodal sensor data to track, grasp, and place parcels with different shapes.
JD Logistics said in a first-quarter regulatory filing that the Packer uses parallel reinforcement learning in simulated environments to optimise parcel-placement sequences and loading layouts. The company said the system is designed to improve sorting efficiency and the use of available carrier space.
JD upgraded the robotic arm’s force-control technology during the second quarter to support more precise cage-loading operations. By June, the Packer was operating around the clock at multiple JD Logistics parks, according to the company’s interim report.
JD is also adding computing capacity to support AI development. JD Cloud plans to work with Chinese chipmaker Moore Threads on a cluster containing 100,000 GPUs for large-model training, inference, and embodied-AI workloads.
The two companies have previously worked on a 10,000-GPU cluster, according to Data Center Dynamics. Details of which Moore Threads GPU models will be used in the planned 100,000-GPU system have not been disclosed.
JD Cloud also plans to collect more than 10 million hours of video showing real-world human activities over the next two years for embodied-AI training.
Beyond warehouse operations, JD Logistics has expanded its autonomous vehicle network into night-time delivery. Its interim report said the company had launched night-time autonomous routes in Shenzhen, allowing vehicles to operate around the clock.
JD is also using drones in rural logistics. In June, JD Logistics launched a drone delivery network in Zizhong, Sichuan province, covering 78 administrative villages, and said deliveries to some mountain villages could be completed in as little as seven minutes.
JD did not disclose the total expected cost of the five-year procurement programme at JDDiscovery or provide a network-wide return-on-investment target.
JD Logistics spent RMB2.3 billion on research and development during the first half of 2026, up 23.7% from RMB1.9 billion a year earlier. The company attributed the increase to continued investment in technology and innovation but did not provide a breakdown showing how much was spent specifically on AI or robotics.
Depreciation of property and equipment and amortisation of other intangible assets rose 18.7% to RMB2.6 billion during the first half of 2026, from RMB2.2 billion a year earlier. JD Logistics attributed the increase mainly to additional logistics equipment and vehicles.
Purchases of property and equipment and investment properties totalled RMB3.09 billion over the same six-month period, compared with RMB2.70 billion a year earlier. Those figures cover the wider logistics business and are not disclosed as spending specifically associated with the new physical AI programme.
JD’s automation plans also come as China’s major ecommerce platforms expand fulfilment infrastructure. Reuters reported on September 3 that competition between JD.com, Alibaba, and Meituan had moved from heavy spending on delivery subsidies towards logistics infrastructure, broader supply, and order-level economics.
Alibaba and JD have been opening dark stores and fast-fulfilment “lightning warehouses” in densely populated areas to support deliveries within an hour, while Meituan has been building supermarkets to expand its grocery operations. Ministry of Commerce research cited by Reuters estimates China’s instant-retail market will reach RMB1.2 trillion, or about $178 billion, by the end of 2026.
JD and companies within its ecosystem employ around 700,000 delivery and logistics personnel, according to the South China Morning Post.
JD founder Richard Liu said earlier this year that robots would eventually take over parcel-delivery work now carried out by human couriers. The Financial Times reported in June that JD had signed agreements with around 120 educational institutions to retrain workers for roles including robot repair and maintenance.
JD said JD Logistics currently operates eight robot repair centres in China and plans to expand its robotics after-sales capabilities over the next five years. The company expects the expansion to support more than 100,000 robotics service engineer jobs.
(Photo by JD.com)
See also: Arm launches Total Design for Physical AI and robotics framework
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
The post JD.com expands physical AI in logistics with 3 million robots appeared first on AI News.
LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model.
By Claudio Masolo
Session traces and cost controls are emerging as key observability techniques for diagnosing AI agent failures, helping teams spot tool-call loops and runaway spend while preserving enough execution context for post-incident debugging.
By Mark Silvester
OpenAI has released GPT-6 Astra, a new model focused on coding, computer use, long-running agentic tasks, and cybersecurity, with availability across ChatGPT, Codex, and the OpenAI API.
By Daniel Dominguez
Listen to the session or watch below
Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype? Watch a conversation unpacking AI extinction fears: where they come from, whether they hold any water, and, if so, what we should do.
Recorded on September 15, 2026
Speakers: Niall Firth, Executive Editor, Will Douglas Heaven, Senior AI editor, and Grace Huckins, AI reporter
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On July 22, 2026, a transmission line fault in Ashburn, Virginia—the heart of the world’s largest data center cluster—knocked more than 3 gigawatts of load off the grid in seconds. And it wasn’t the first time. Two years earlier, a single failed surge arrester dropped roughly 60 Virginia facilities and 1,500 megawatts at once. No one could anticipate so much uniform load responding to grid faults the same way, at the same time.
The AI power debate is mostly about generation: more turbines, more solar, more transmission. The grid needs more electrons. But the outages in Virginia weren’t supply failures; they were architecture failures. And a giant wave of interconnections is arriving on that same architecture, putting grid reliability at risk. It’s a problem nobody wants to own.
The grid was built around predictable loads: steel mills, refineries, and houses at dinnertime. Different load sizes, same process—drawing power smoothly, misbehaving occasionally, and recovering gracefully.
But AI data centers don’t behave that way.
An AI campus can swing 70% of its load in milliseconds during a training run, then trip offline just as fast at the first sign of trouble upstream to protect billions in compute. Each is rational alone. Together, at gigawatt scale, they’re a problem the grid has never solved—and the next wave of data center campuses is planned at exactly that scale.
The standard data center power stack hasn’t changed in decades. Medium-voltage power arrives, transformers step it down, low-voltage uninterruptible power supply (UPS) units condition it, and it reaches the racks. Push that design to AI scale, and it cracks in three places.
First, the UPS sits deep inside the building, close to the racks. But its batteries are an undersized spare tire, designed to handle an outage for a few minutes, not to absorb load swings this fast and volatile around the clock.
Second, the UPS spends most of its life in bypass. Legacy converters waste enough power that operators run in eco-mode: A static switch feeds the racks directly from the grid and nothing filters in either direction. The compute’s swings go out raw, and grid transients—sub-millisecond events that can damage or take down equipment—come in too fast for any switch to catch.
Third, the protection logic was written when “large load” meant 50 megawatts. This protection logic can’t see the grid it is now a part of, so when trouble hits upstream, it does exactly the wrong thing: it drops out. In the 2024 Virginia event, most of the lost load traced to protection schemes that count voltage dips and disconnect on the third one—as designed, at the worst moment.
This isn’t sloppy engineering. It’s careful engineering the load has outgrown.
The fix is three moves, made together.
Move it up—from 480 volts to medium voltage (13.8 kilovolts and higher), the voltage large sites draw from the grid.
Move it out—from the data hall to modular enclosures near the substation so the building holds only compute and the cooling that keeps it alive.
Move it into the path—instead of a battery that watches and reacts, a system every electron runs through, all the time. There’s nothing to detect and nothing to switch because nothing was ever routed around it.
On paper, three straightforward upgrades. In practice, they rewrite every line item downstream.
When thousands of GPUs spin up together, the system absorbs the swing and hands the grid a flat load profile. When a disturbance hits, the equipment behind it never notices. A difficult neighbor becomes a predictable one. And when the utility needs help, it becomes a useful one.
Interconnection changes, too. The utility certifies one medium-voltage box instead of untangling every transformer, UPS, chiller, pump, and switchgear lineup behind it. Engineers swap chip generations without a fresh interconnection study. Months come off the permitting timeline.
Inside the fence, UPS rooms become compute or cooling space. Density per construction dollar climbs.
And the economics flip. Equipment that runs at medium voltage, sits outside, and stores its own energy can qualify for tax credits, and earn revenue in grid programs like peak shaving and demand response. Backup power stops being insurance and starts paying for itself.
In early 2026, we tested a full-scale system at the National Laboratory of the Rockies, a U.S. Department of Energy facility and the only place in the Western Hemisphere that can replicate real grid faults and AI-scale load swings concurrently in the same loop.
We hit it from both directions: real AI load profiles hit the compute side at full medium voltage. Grid faults hit the utility side, including a full zero-voltage event. The compute side didn’t flinch. Neither did the grid side. It cleared the large-load voltage ride-through requirements from the Electric Reliability Council of Texas (ERCOT), the grid operator, with room to spare.
Those rules exist because operators no longer take facilities this size on faith, and more are coming. Most of the industry treats them as hurdles. A medium-voltage, inline system clears them out of the box. Compliance isn’t an added feature. It’s what the architecture does.
Much of what looks like a grid problem in the AI buildout sits inside the fence, in equipment sized for a load that no longer exists. Move the right pieces up, out, and into the path, and a grid liability becomes a grid asset. Density goes up. Permitting time comes down. Backup power earns its keep.
The engineering works—and the next wave of AI factories is being built on it. The industry hasn’t named this layer yet. We call it the medium-voltage AI UPS. The name matters less than the choice: those factories can arrive as a strain on the grid or as strength for it. We already know how to build the second kind.
This content was produced by ON.energy. It was not written by MIT Technology Review’s editorial staff.
LONDON — A U.K. commission on Thursday unveiled its recommendations for how the country should regulate artificial intelligence in medicine, as health authorities globally try to determine how to continuously review ever-changing products after they have been authorized instead of simply clearing them once for the market.
In its report, the commission sought to strike a balance between ensuring that the U.K. takes advantage of AI’s potential in medicine — the ability to review the millions of scans that are generated each year to track the eye health of patients with diabetes, for example — while prioritizing safety, tracking device performance over time, and treating patients equitably.
The 44 recommendations include some that would allow for the staged authorization of new AI models and others that would build a system for providers to report how tools are performing in their clinics — including when they malfunction or potentially inhibit patient care — as the models learn and adapt and perform differently depending on the setting.
Continue to STAT+ to read the full story…
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ARPA-H, the government agency that funds cutting-edge health research, plans to commit $62.7 million to develop artificial intelligence bots that direct treatment of heart failure. Among the goals of the program, called ADVOCATE, is to produce partially autonomous AI devices authorized by the Food and Drug Administration to help treat patients, including assessing symptom severity, prescribing drugs, and ordering lab tests.
ARPA-H on Wednesday announced the first batch of awards to health tech companies Atman Health, UpDoc, Tempus AI, and teams from Stanford University, Duke University, and the Kaiser Permanente health system. ARPA-H may still fund additional teams. The amount committed for the first year is $33.7 million, and the remainder may be renegotiated up or down.
Many of the 6.7 million Americans with heart failure don’t get optimal treatment because of difficulty accessing specialists, and the hope is that AI agents developed with ARPA-H funding can help address the gap, especially in rural and other underserved settings.
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You’re reading the web edition of STAT’s AI Prognosis newsletter, our subscriber-exclusive guide to artificial intelligence in health care and medicine. Sign up to get it delivered in your inbox every Wednesday.
It’s hard to resist the world’s obsession with productivity and efficiency and to force yourself to be present in a quiet space. I’ve been enjoying the wandering_cassettes Instagram account, which posts videos of Fisher Price cassette players playing songs in different settings. This one is particularly calming, but they’re all timeline cleansers.
The newsletter will be off next week as I work on a big new story, but I’ll be back in your inboxes Sept. 23.
Today’s AI Prognosis — and my new story — are brought to you by “The Pitt.” Not in any financial way — I just mean that I finally started watching “The Pitt.” (No spoilers! I’m only halfway through season two.)
Everyone is right — it is extremely compelling television. But what struck me the most was how “The Pitt” feels exactly like the ambulance ride-alongs and emergency department shadowing visits I’ve done: Dropping in on the worst day of someone’s life, but doing it over and over every 40 minutes, for eight to 12 hours, without enough resources.
Continue to STAT+ to read the full story…
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Felipe Huici explains how Unikraft achieves millisecond cold boots, stateful scale-to-zero, and extreme density for sandboxing AI workloads. He discusses isolation primitives, Linux kernel optimizations, and snapshotting tricks, demonstrating how to maintain sub-10ms performance at scale while integrating seamlessly into Kubernetes environments with hardware-level security.
By Felipe Huici
Ezekiel J. Emanuel and Abe Baker-Butler have been debating the proper place for AI in medicine with American Medical Association CEO John Whyte. Now, they are taking their discussion to STAT’s First Opinion. Read Emanuel and Baker-Butler’s essay below and read Whyte’s essay here.
In 1867, Joseph Lister published his research on carbolic acid and antiseptic surgical technique. In September 1871, he was summoned to Queen Victoria, who had a rapidly growing abscess in her left armpit. Using his antiseptic surgical technique, Joseph Lister successfully drained the pus. Queen Victoria recovered without fever or other complications. The antiseptic technique quickly gained approval in the U.K. and Europe, but not among American physicians.
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