2026-03-13 20:16:56
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
The US military might use generative AI systems to rank targets and recommend which to strike first, according to a Defense Department official.
A list of possible targets could first be fed into a generative AI system that the Pentagon is fielding for classified settings. Humans might then ask the system to analyze the information and prioritize the targets. They would then be responsible for checking and evaluating the results and recommendations.
OpenAI’s ChatGPT and xAI’s Grok could soon be at the center of exactly these sorts of high-stakes military decisions. Read the full story.
—James O’Donnell
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The Pentagon’s CTO claims Claude would “pollute” the defense supply chain
He blamed a “policy preference” that’s baked into the model. (CNBC)
+ Anthropic is reeling from OpenAI’s “compromise” with the DoD. (MIT Technology Review)
2 An ex-DOGE staffer has been accused of stealing social security data
Then taking the information to his new job in the IT division of a government contractor. (Wired)
+ He allegedly used a thumb drive to steal the data. (Washington Post)
3 Ukraine is offering its battlefield data for AI training
Allies can access the data to train drones and other UAVs. (Reuters)
+ Europe has a drone-filled vision for the future of war. (MIT Technology Review)
4 Meta has postponed its latest AI launch over performance issues
It fell short of rival models from Google, OpenAI, and Anthropic. (NYT $)
+ The company’s former AI chief is betting against LLMs. (MIT Technology Review).
5 X could be breaching sanctions on Iran
An account for Iran’s new supreme leader may break US rules. (Engadget)
+ Hacker group Handala has become the face of Iranian cyberwarfare. (Wired)
+ AI is turning the conflict into theater. (MIT Technology Review)
6 A landmark social media addiction trial is wrapping up
It’ll decide whether the platforms are liable for harms caused to children. (The Guardian)
+ AI companions are the next stage of digital addiction. (MIT Technology Review)
7 Western AI models have “failed spectacularly” on agriculture in the Global South
The biggest problem? They’re not trained on local data. (Rest of World)
8 Internet outages in Moscow are sparking surging sales of pagers
The disruptions have been blamed on new tests of web controls. (Bloomberg $)
9 Why is China obsessed with OpenClaw?
Lobster-mania is spreading to the general public. (SCMP)
+ Tech-savvy “tinkerers” are cashing in on the craze. (MIT Technology Review)
10 Hollywood has soured on Silicon Valley
Movies and TV shows have swapped eccentric founders for megalomaniac moguls. (NYT $)
Quote of the day
—OpenAI CEO Sam Altman makes a new pitch to investors at a BlackRock event, Gizmodo reports.
One More Thing
When Latvian startup Global Wolf Motors first pitched the idea of a military scooter, it was met with skepticism—and a wall of bureaucracy. Then Russia launched its full-scale invasion of Ukraine in February 2022, and everything changed.
Suddenly, Ukrainian combat units wanted any equipment they could get their hands on, and they were willing to try out ideas that might not have made the cut in peacetime.
Within weeks, the scooters were on the front line—and even behind it, being used on daring reconnaissance missions. It signaled that a new product category for companies along Ukraine’s borders had opened: civilian technologies repurposed for military needs. Read the full story.
—Peter Guest
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A new mini magnet could slash the costs of MRIs and nuclear fusion.
+ This interactive map of Earth offers new routes to facts about our planet.
+ Escape the news cycle with this deep dive into the power of fantasy and nature. (Big thanks to reader and MIT alum Vicki for the find!)
+ Reports of reading’s death are greatly exaggerated.
2026-03-13 17:00:00
Human-made glass is thousands of years old. But it’s now poised to find its way into the AI chips used in the world’s newest and largest data centers. This year, a South Korean company called Absolics is planning to start commercial production of special glass panels designed to make next-generation computing hardware more powerful and energy efficient. Other companies, including Intel, are also pushing forward in this area. If all goes well, such glass technology could reduce the energy demands of the sorts of high-performance computing chips used in AI data centers—and it could eventually do the same for consumer laptops and mobile devices if production costs fall.
The idea is to use glass as the substrate, or layer, on which multiple silicon chips are connected. This form of “packaging” is an increasingly popular way to build computing hardware, because it lets engineers combine specialized chips designed for specific functions into a single system. But it presents challenges, including the fact that hardworking chips can run so hot they physically warp the substrate they’re built on. This can lead to misaligned components and may reduce how efficiently the chips can be cooled, leading to damage or premature failure.
“As AI workloads surge and package sizes expand, the industry is confronting very real mechanical constraints that impact the trajectory of high-performance computing,” says Deepak Kulkarni, a senior fellow at the chip design company Advanced Micro Devices (AMD). “One of the most fundamental is warpage.”
That’s where glass comes in. It can handle the added heat better than existing substrates, and it will let engineers keep shrinking chip packages—which will make them faster and more energy efficient. It “unlocks the ability to keep scaling package footprints without hitting a mechanical wall,” says Kulkarni.
Momentum is building behind the shift. Absolics has finished building a factory in the US that is dedicated to producing glass substrates for advanced chips and expects to begin commercial manufacturing this year. The US semiconductor manufacturer Intel is working toward incorporating glass in its next-generation chip packages, and its research has spurred other companies in the chip packaging supply chain to invest in it as well. South Korean and Chinese companies are among the early adopters. “Historically, this is not the first attempt to adopt glass in semiconductor packaging,” says Bilal Hachemi, senior technology and market analyst at the market research firm Yole Group. “But this time, the ecosystem is more solid and wider; the need for glass-based [technology] is sharper.”
Fragile but mighty
Chip packaging has relied on organic substrates such as fiberglass-reinforced epoxy since the 1990s, says Rahul Manepalli, vice president of advanced packaging at Intel. But electrochemical complications limit how closely designers can place drilled holes to create copper-coated signal and power connections between the chips and the rest of the system. Chip designers must also account for the unpredictable shrinkage and distortion that organic substrates undergo as chips heat up and cool down. “We realized about a decade ago that we are going to have some limitations with organic substrates,” says Manepalli.
Glass may help overcome a lot of these limitations. Its thermal stability could allow engineers to create 10 times more connections per millimeter than organic substrates, says Manepalli. With denser connections, Intel’s designers can then stuff 50% more silicon chips into the same package area, improving computational capability. The denser connections also enable more efficient routing for the copper wires that deliver power to the chip. And the fact that glass dissipates heat more efficiently allows for chip designs that reduce overall power consumption.
“The benefits of glass core substrates are undeniable,” says Manepalli. “It’s clear that the benefits will drive the industry to make this happen sooner rather than later, and we want to be one of the first ones who do it.”
However, working with glass creates its own challenges. For one thing, it’s fragile. Glass substrates for data center chip packages are made from panels that are only about 700 micrometers to 1.4 millimeters thick, which leaves them susceptible to cracking or even shattering, says Manepalli. Researchers at Intel and other organizations have spent years figuring out how to use other materials and special tools to integrate the glass panels safely into semiconductor manufacturing processes.
Now, Manepalli says, Intel’s research and development teams are reliably fabricating glass panels and churning out test chip packages that incorporate glass—and in early 2025 they demonstrated that a functional device with a glass core substrate could boot up the Windows operating system. It’s a significant improvement from the early testing days, when hundreds of glass panels got cracked every couple of days, he says.
Semiconductor manufacturers already use glass for more limited purposes, such as temporary support structures for silicon wafers. But the independent market research firm IDTechEx estimates there’s a big market for glass substrates, one that could boost the semiconductor market for glass from $1 billion in 2025 to as much as $4.4 billion by 2036.
The material could have additional benefits if it takes off. Glass can be made astoundingly smooth—5,000 times smoother than organic substrates. This would eliminate defects that can arise as metal gets layered onto semiconductors, says Xiaoxi He, a research analyst at IDTechEx. Defects in these layers can worsen chips’ performance or even render them unusable.
Glass could also help speed the movement of data. The material can guide light, which means chip designers could use it to build high-speed signal pathways directly into the substrate. Glass “holds enormous potential for the future of energy-efficient AI compute,” says Kulkarni at AMD, because a light-based system could move signals around with far less energy than the “power-hungry” copper pathways that are currently used to carry signals between chips in a package.
A panel pivot
Early research on glass packaging started at the 3D Systems Packaging Research Center at the Georgia Institute of Technology in 2009. The university eventually partnered with Absolics, a subsidiary of SKC, a South Korean company that produces chemicals and advanced materials. SKC constructed a semiconductor facility for manufacturing glass substrates in Covington, Georgia, in 2024, and the glass substrate partnership between Absolics and Georgia Tech was eventually awarded two grants in the same year—worth a combined $175 million—throughthe US government’s CHIPS for America program, established under the administration of President Joe Biden.
Now Absolics is moving toward commercialization; it plans to start manufacturing small quantities of glass substrates for customers this year. The company has led the way in commercializing glass substrates, says Yongwon Lee, a research engineer at Georgia Tech who is not directly involved in the commercial partnership with Absolics.
Absolics says its facility can currently produce a maximum of 12,000 square meters of glass panels a year. That’s enough, Lee estimates, to provide glass substrates for between 2 million and 3 million chip packages the size of Nvidia’s H100 GPU.
But the company isn’t alone. Lee says that multiple large manufacturers, including Samsung Electronics, Samsung Electro-Mechanics, and LG Innotek, have “significantly accelerated” their research and pilot production efforts in glass packaging over the past year. “This trend suggests that the glass substrate ecosystem is evolving from a single early mover to a broader industrial race,” he says.
Other companies are pivoting to play more specialized roles in the glass substrate supply chain. In 2025, JNTC, a company that makes electrical connectors and tempered glass for electronics, established a facility in South Korea that’s capable of producing 10,000 semi-finished glass panels per month. Such panels include drilled holes for vertical electrical connections and thin metal layers coating the glass, but they require additional manufacturing work for installation in chip packages.
Last year, that South Korean facility began taking orders to supply semi-finished glass to both specialized substrate companies and semiconductor manufacturers. The company plans to expand the facility’s production in 2026 and open an additional manufacturing line in Vietnam in 2027. Such industry actions show how quickly glass substrate technology is moving from prototype to commercialization—and how many tech players are betting that glass could be a surprisingly strong foundation for the future of computing and AI.
2026-03-13 06:23:34
The US military might use generative AI systems to rank lists of targets and make recommendations—which would be vetted by humans—about which to strike first, according to a Defense Department official with knowledge of the matter. The disclosure about how the military may use AI chatbots comes as the Pentagon faces scrutiny over a strike on an Iranian school, which it is still investigating.
A list of possible targets might be fed into a generative AI system that the Pentagon is fielding for classified settings. Then, said the official, who requested to speak on background with MIT Technology Review to discuss sensitive topics, humans might ask the system to analyze the information and prioritize the targets while accounting for factors like where aircraft are currently located. Humans would then be responsible for checking and evaluating the results and recommendations. OpenAI’s ChatGPT and xAI’s Grok could, in theory, be the models used for this type of scenario in the future, as both companies recently reached agreements for their models to be used by the Pentagon in classified settings.
The official described this as an example of how things might work but would not confirm or deny whether it represents how AI systems are currently being used.
Other outlets have reported that Anthropic’s Claude has been integrated into existing military AI systems and used in operations in Iran and Venezuela, but the official’s comments add insight into the specific role chatbots may play, particularly in accelerating the search for targets. They also shed light on the way the military is deploying two different AI technologies, each with distinct limitations.
Since at least 2017, the US military has been working on a “big data” initiative called Maven. It uses older types of AI, particularly computer vision, to analyze the oceans of data and imagery collected by the Pentagon. Maven might take thousands of hours of aerial drone footage, for example, and algorithmically identify targets. A 2024 report from Georgetown University showed soldiers using the system to select targets and vet them, which sped up the process to get approval for these targets. Soldiers interacted with Maven through an interface with a battlefield map and dashboard, which might highlight potential targets in one color and friendly forces in another.
The official’s comments suggest that generative AI is now being added as a conversational chatbot layer—one the military may use to find and analyze data more quickly as it makes decisions like which targets to prioritize.
Generative AI systems, like those that underpin ChatGPT, Claude, and Grok, are a fundamentally different technology from the AI that has primarily powered Maven. Built on large language models, they are much less battle-tested. And while Maven’s interface forced users to directly inspect and interpret data on the map, the outputs produced by generative AI models are easier to access but harder to verify.
The use of generative AI for such decisions is reducing the time required in the targeting process, added the official, who did not provide details when asked how much additional speed is possible if humans are required to spend time double-checking a model’s outputs.
The use of military AI systems is under increased public scrutiny following the recent strike on a girls’ school in Iran in which more than 100 children died. Multiple news outlets have reported that the strike was from a US missile, though the Pentagon has said it is still under investigation. And while the Washington Post has reported that Claude and Maven have been involved in targeting decisions in Iran, there is no evidence yet to explain what role generative AI systems played, if any. The New York Times reported on Wednesday that a preliminary investigation found outdated targeting data to be partly responsible for the strike.
The Pentagon has been ramping up its use of AI across operations in recent months. It started offering nonclassified use of generative AI models, for tasks like analyzing contracts or writing presentations, to millions of service members back in December through an effort called GenAI.mil. But only a few generative AI models have been approved by the Pentagon for classified use.
The first was Anthropic’s Claude, which in addition to its use in Iran was reportedly used in the operations to capture Venezuelan leader Nicolas Maduro in January. But following recent disagreements between the Pentagon and Anthropic over whether Anthropic could restrict the military’s use of its AI, the Defense Department designated the company a supply chain risk and President Trump demanded on social media that the government stop using its AI products within six months. Anthropic is fighting the designation in court.
OpenAI announced an agreement on February 28 for the military to use its technologies in classified settings. Elon Musk’s company xAI has also reached a deal for the Pentagon to use its model Grok in such settings. OpenAI has said its agreement with the Pentagon came with limitations, though the practical effectiveness of those limitations is not clear.
If you have information about the military’s use of AI, you can share it securely via Signal (username jamesodonnell.22).
2026-03-12 21:02:48
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
In January, Beijing-based software engineer Feng Qingyang started tinkering with OpenClaw, a new AI tool that can take over a device and autonomously complete tasks. Within weeks, he was advertising “OpenClaw installation support” on a second-hand shopping site. Today, his side gig is a fully-fledged business with over 100 employees and 7,000 completed orders.
Feng is among a small cohort of savvy early adopters making serious cash from China’s OpenClaw craze. As users with little technical background want in, a cottage industry of installation services and preconfigured hardware has sprung up. The rise of these tinkerers shows just how eager the general public in China is to adopt cutting-edge AI—despite huge security risks. Read the full story.
—Caiwei Chen
Another battery business has fallen: 24M Technologies, once worth over $1 billion, is reportedly shutting down.
Just a few years ago, the industry was hot, hot, hot. Countless companies were popping up, with shiny new chemistries and huge funding rounds. But now, the tide has turned. Businesses are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore.
There are bright spots. China’s battery industry is thriving, and US stationary storage remains resilient. But it feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas.
This story is from The Spark, our weekly climate newsletter. Sign up to receive it in your inbox every Wednesday.
—Casey Crownhart
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Iran has put US tech giants on a list of potential targets
The companies include Google, Microsoft, Palantir, IBM, Nvidia, and Oracle. (Al Jazeera)
+ Pro-Iran hackers have launched their first major strike on a US firm during the war. (CNN)
+ AI is warping perceptions of the conflict. (MIT Technology Review)
2 Grammarly is being sued for turning real people into AI-generated experts
A journalist has filed a lawsuit over her inclusion as a writing analyst. (Wired $)
+ Grammarly has now disabled the ‘Expert Review’ feature. (Engadget)
+ Here’s what’s next for AI copyright lawsuits. (MIT Technology Review)
3 Professors are losing the fight to protect critical thinking from AI
They describe the tech as an “existential threat.”(The Guardian)
+ Silicon Valley’s dream of an AI classroom faces a skeptical reality. (MIT Technology Review)
4 Big tech is backing Anthropic in its fight against the Trump administration
Google, Amazon, Apple, and Microsoft are publicly supporting its legal action. (BBC)
+ Is this an Oppenheimer moment for Anthropic? (The Atlantic $)
5 A Cybertruck owner has sued Tesla over a self-driving crash
He called the company “negligent” for retaining Elon Musk as CEO. (Electrek)
+ Tech has sparked a new wave of theft in the luxury car industry. (MIT Technology Review)
6 Is “AI-washing” providing cover for massive corporate layoffs?
The tech isn’t ready to replace workers, but the layoffs are happening anyway. (The Atlantic)
+ Software giant Atlassian is slashing 10% of its workforce ahead of an AI push. (The Guardian)
+ At least lawyers’ jobs look safer than first feared. (MIT Technology Review)
7 Software giants claim they’re not worried that AI will destroy them
Oracle and Salesforce CEOs have dismissed fears of an “SaaS-pocalypse.” (Reuters)
8 Lab-grown brains have started solving engineering problems
Scientists trained the organoid to decode an engineering task. (Popular Mechanics)
+ Other organoids are being impregnated with human embryos. (MIT Technology Review)
9 English-language music is losing its grip on Spotify
The variety of languages in its top 50 songs has doubled since 2020. (BBC)
10 AI is redrawing the boundaries of physics
It’s blurring the boundaries between a machine and a researcher. (The Economist $)
Quote of the day
“Elon Musk is an aggressive and irresponsible salesman, who has a long history of making dangerous design choices and over-promising the features of his products.”
—A lawsuit over Tesla’s Full Self-Driving mode takes aim at the company’s CEO, Gizmodo reports.
One More Thing
This town’s mining battle reveals the contentious path to a cleaner future
In a tiny Minnesota town, an exploratory mining company called Talon plans to dig up as much as 725,000 metric tons of raw ore per year.
It says the site will help power a greener future for the US by producing the nickel needed for EV batteries. But many local citizens aren’t eager for major mining operations near their towns.
The tensions have created a test case for conflicts between local environmental concerns and global climate goals. Read the full story.
—James Temple
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Mario is finally getting a LEGO minifigure.
+ This new social platform boldly aims to burst filter bubbles.
+ NASA is backing DSLR cameras by taking a trusty old Nikon D5 to the moon.
+ This nuclear escalation simulator helped me learn to stop worrying and love the bomb.
2026-03-12 21:00:00
The impact of artificial intelligence extends far beyond the digital world and into our everyday lives, across the cars we drive, the appliances in our homes, and medical devices that keep people alive. More and more, product engineers are turning to AI to enhance, validate, and streamline the design of the items that furnish our worlds.
The use of AI in product engineering follows a disciplined and pragmatic trajectory. A significant majority of engineering organizations are increasing their AI investment, according to our survey, but they are doing so in a measured way. This approach reflects the priorities typical of product engineers. Errors have concrete consequences beyond abstract fears, ranging from structural failures to safety recalls and even potentially putting lives at risk. The central challenge is realizing AI’s value without compromising product integrity.
Drawing on data from a survey of 300 respondents and in-depth interviews with senior technology executives and other experts, this report examines how product engineering teams are scaling AI, what is limiting broader adoption, and which specific capabilities are shaping adoption today and, in the future, with actual or potential measurable outcomes.
Key findings from the research include:
Verification, governance, and explicit human accountability are mandatory in an environment where the outputs are physical—and the risk high. Where product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions that are fixed at release, product failures can lead to real-world risks that cannot be rolled back. Product engineers are therefore adopting layered AI systems with distinct trust thresholds instead of general-purpose deployments.
Predictive analytics and AI-powered simulation and validation are the top near-term investment priorities for product engineering leaders. These capabilities—selected by a majority of survey respondents—offer clear feedback loops, allowing companies to audit performance, attain regulatory approval, and prove return on investment (ROI). Building gradual trust in AI tools is imperative.
Nine in ten product engineering leaders plan to increase investment in AI in the next one to two years, but the growth is modest. The highest proportion of respondents (45%) plan to increase investment by up to 25%, while nearly a third favor a 26% to 50% boost. And just 15% plan a bigger step change—between 51% and 100%. The focus for product engineers is on optimization over innovation, with scalable proof points and near-term ROI the dominant approach to AI adoption, as opposed to multi-year transformation.
Sustainability and product quality are top measurable outcomes for AI in product engineering. These outcomes, visible to customers, regulators, and investors, are prioritized over competitive metrics like time to-market and innovation—rated of medium importance—and internal operational gains like cost reduction and workforce satisfaction, at the bottom. What matters most are real-world signals like defect rates and emissions profiles rather than internal engineering dashboards.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
2026-03-12 18:00:00
Just a few years ago, the battery industry was hot, hot, hot. There was a seemingly infinite number of companies popping up, with shiny new chemistries and massive fundraising rounds. My biggest problem was sifting through the pile to pick the most exciting news to cover.
That tide has turned, and in 2026, what seems to be in unlimited supply isn’t battery success stories but stumbles or straight-up implosions. Companies are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore. On Monday, Steve Levine at The Information (paywalled link) reported that 24M Technologies, a battery company founded in 2010, was shutting down and would auction off its property.
The company itself has been silent, but this is the latest in a string of bad signs, and it’s a big one—at one point 24M was worth over $1 billion, and the company’s innovations could have worked with existing technology. So where does that leave the battery industry?
Many buzzy battery startups in recent years have been trying to sell some new, innovative chemistry to compete with lithium-ion batteries, the status quo that powers phones, laptops, electric vehicles, and even grid storage arrays today. Think sodium-ion batteries and solid-state cells.
24M wasn’t trying to sell a departure from lithium-ion but improvements that could work with the tech. One of the company’s major innovations was its manufacturing process, which involved essentially smearing materials onto sheets of metal to form the electrodes, a simpler and potentially cheaper technique than the standard one.
The layers in the company’s batteries were thicker, which cut down on some of the inactive materials in cells and improved the energy density. That allows more energy to be stored in a smaller package, boosting the range of EVs—the company famously had a goal of a 1,000-mile battery (about 1,600 kilometers).
We’re still thin on details of what exactly went down at 24M and what comes next for its tech. The company didn’t get back to my questions sent to the official press email, and nobody picked up the phone when I called. 24M cofounder and MIT professor Yet-Ming Chiang declined to speak on the record.
For those who have been closely following the battery industry, more bad news isn’t too surprising. It feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas. “It just feels like there’s not a lot of appetite for innovation,” says Kara Rodby, a technical principal at Volta Energy Technologies, a venture capital firm that focuses on the energy storage industry.
Natron Energy, one of the leading sodium-ion startups in the US, shut down operations in September last year. Ample, an EV battery-swapping company, filed for bankruptcy in December 2025.
There were always going to be failures from the recent battery boom. Money was flowing to all sorts of companies, some pitching truly wild ideas. But what recent months have made clear is that the battery market is turning brutal, even for the relatively safe bets.
Because 24M’s technology was designed to work into existing lithium-ion chemistry, it could have been an attractive candidate for existing battery companies to license or even acquire. “It’s a great example of something that should have been easier,” Rodby says.
The gutting of major components of the Inflation Reduction Act, key legislation in the US that provided funding and incentives for batteries and EVs, certainly hasn’t helped. The EV market in the US is cooling off, with automakers canceling EV models and slashing factory plans.
There are bright spots. China’s battery industry is thriving, and its battery and EV giants are looking ever more dominant. The market for stationary energy storage is also still seeing positive signs of growth, even in the US.
But overall, it’s not looking great.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
