For decades, manufacturers have pursued automation to drive efficiency, reduce costs, and stabilize operations. That approach delivered meaningful gains, but it is no longer enough.

Today’s manufacturing leaders face a different challenge: how to grow amid labor constraints, rising complexity, and increasing pressure to innovate faster without sacrificing safety, quality, or trust. The next phase of transformation will not be defined by isolated AI tools or individual robots, but by intelligence that can operate reliably in the physical world.

This is where physical AI—intelligence that can sense, reason, and act in the real world—marks a decisive shift. And it is why Microsoft and NVIDIA are working together to help manufacturers move from experimentation to production at industrial scale.

The industrial frontier: Intelligence and trust, not just automation

Most early AI adoption focused on narrow optimization: automating tasks, improving utilization, and cutting costs. While valuable, that phase often created new friction, including skills gaps, governance concerns, and uncertainty about long‑term impact. Furthermore, the use cases were plentiful but not as strategic.

The industrial frontier represents a different approach. Rather than asking how much work machines can replace, frontier manufacturers ask how AI can expand human capability, accelerate innovation, and unlock new forms of value while remaining trustworthy and controllable.

Across industries, companies that successfully move into this frontier phase share two non‑negotiables:

• Intelligence: AI systems must understand how the business actually handles its data, workflows, and institutional knowledge.
• Trust: As AI begins to act in high‑stakes environments, organizations must retain security, governance, and observability at every layer.

Without intelligence, AI becomes generic. Without trust, adoption stalls.

Why manufacturing is the proving ground for physical AI

Manufacturing is uniquely positioned at the center of this shift.

AI is no longer confined to planning or analytics. It is moving into physical execution: coordinating machines, adapting to real‑world variability, and working alongside people on the factory floor. Robotics, autonomous systems, and AI agents must now perceive, reason, and act in dynamic environments.

This transition exposes a critical gap. Traditional automation excels at repetition but struggles with adaptability. Human workers bring judgment and context but are constrained by scale. Physical AI closes that gap by enabling human‑led, AI‑operated systems, where people set intent and intelligent systems execute, learn, and improve over time. Humans are essential for scaled success.

Microsoft and NVIDIA: Accelerating physical AI at scale

Physical AI cannot be delivered through point solutions. It requires agentic-driven, enterprise-grade development, deployment, and operations toolchains and workflows that connect simulation, data, AI models, robotics, and governance into a coherent system.

NVIDIA is building the AI infrastructure that makes physical AI possible, including accelerated computing, open models, simulation libraries, and robotics frameworks and blueprints that enable the ecosystem to build autonomous robotics systems that can perceive, reason, plan, and take action in the physical world. Microsoft complements this with a cloud and data platform designed to operate physical AI securely, at scale, and across the enterprise.

Together, Microsoft and NVIDIA are enabling manufacturers to move beyond pilots toward production‑ready physical AI systems that can be developed, tested, deployed, and continuously improved across heterogeneous environments spanning the product lifecycle, factory operations, and supply chain.

From intelligence to action: Human-agent teams in the factory

At the industrial frontier, AI is not a standalone system, but a digital teammate.

When AI agents are grounded in the proper operational data, embedded in human workflows, and governed end to end, they can assist with tasks such as:

• Optimizing production lines in real time
• Coordinating maintenance and quality decisions
• Adapting operations to supply or demand disruptions
• Accelerating engineering and product lifecycle decisions

For example, manufacturers are beginning to use simulation‑grounded AI agents to evaluate production changes virtually before deploying them on the factory floor, reducing risk while accelerating decision‑making.

Crucially, frontier manufacturers design these systems so humans remain in control. AI executes, monitors, and recommends, while people provide intent, oversight, and judgment. This balance allows organizations to move faster without losing confidence or control.

The role of trust in scaling physical AI

As physical AI systems scale, trust becomes the limiting factor.

Manufacturers must ensure that AI systems are secure, observable, and operating within policy, especially when they influence safety‑critical or mission‑critical processes. Governance cannot be an afterthought; It must be engineered into the platform itself.

This is why frontier manufacturers treat trust as a first‑class requirement, pairing innovation with visibility, compliance, and accountability. Only then can physical AI move from promising demonstrations to enterprise‑wide deployment.

Why this moment matters—and what’s next

The convergence of AI agents, robotics, simulation, and real‑time data marks an inflection point for manufacturing. What was once experimental is becoming operational. What was once siloed is becoming connected.

At NVIDIA GTC 2026, Microsoft and NVIDIA will demonstrate how this collaboration supports physical AI systems that manufacturers can deploy today and scale responsibly tomorrow. From simulation‑driven development to real‑world execution, the focus is on helping manufacturers cross the industrial frontier with confidence.

For manufacturing leaders, the question is no longer whether physical AI will reshape operations, but how quickly they can adopt it responsibly, at scale, and with trust built in from the start.

Discover more with Microsoft at NVIDIA GTC 2026.

This content was produced by Microsoft. It was not written by MIT Technology Review’s editorial staff.

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.

Defense official reveals how AI chatbots could be used for targeting decisions 

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 

“We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter.” 

—OpenAI CEO Sam Altman makes a new pitch to investors at a BlackRock event, Gizmodo reports. 

One More Thing 

How the Ukraine-Russia war is reshaping the tech sector in Eastern Europe 

GATIS INDRēVICS/ LATVIAN MINISTRY OF DEFENSE

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. 

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.

INTEL CORPORATION

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.

COURTESY OF ABSOLICS INC

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.

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).

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.

Hustlers are cashing in on China’s OpenClaw AI craze 

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 

Brutal times for the US battery industry 

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 

ACKERMAN + GRUBER

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. 

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.

Download the report.

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.

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. 

Feng Qingyang had always hoped to launch his own company, but he never thought this would be how—or that the day would come this fast. 

Feng, a 27-year-old software engineer based in Beijing, started tinkering with OpenClaw, a popular new open-source AI tool that can take over a device and autonomously complete tasks for a user, in January. He was immediately hooked, and before long he was helping other curious tech workers with less technical proficiency install the AI agent.

Feng soon realized this could be a lucrative opportunity. By the end of January, he had set up a page on Xianyu, a secondhand shopping site, advertising “OpenClaw installation support.” “No need to know coding or complex terms. Fully remote,” reads the posting. “Anyone can quickly own an AI assistant, available within 30 minutes.” 

At the same time, the broader Chinese public was beginning to catch on—and the tool, which had begun as a niche interest among tech workers, started to evolve into a popular sensation.

Feng quickly became inundated with requests, and he started chatting with customers and managing orders late into the night. At the end of February, he quit his job. His side gig has now grown into a full-fledged professional operation with over 100 employees. So far, the store has handled 7,000 orders, each worth about 248 RMB or approximately $34. 

“Opportunities are always fleeting,” says Feng. “As programmers, we are the first to feel the winds shift.”

Feng is among a small cohort of savvy early adopters turning China’s OpenClaw craze into cash. As users with little technical background want in, a cottage industry of people offering installation services and preconfigured hardware has sprung up to meet them. The sudden rise of these tinkerers and impromptu consultants shows just how eager the general public in China is to adopt cutting-edge AI—even when there are huge security risks. 

A “lobster craze”

“Have you raised a lobster yet?” 

Xie Manrui, a 36-year-old software engineer in Shenzhen, says he has heard this question nonstop over the past month. “Lobster” is the nickname Chinese users have given to OpenClaw—a reference to its logo.

Xie, like Feng, has been experimenting with OpenClaw since January. He’s built new open-source tools on top of the ecosystem, including one that visualizes the agent’s progress as an animated little desktop worker and another that lets users voice-chat with it. 

“I’ve met so many new people through ‘lobster raising,’” says Xie. “Many are lawyers or doctors, with little technical background, but all dedicated to learning new things.”

Lobsters are indeed popping up everywhere in China right now—on and offline. In February, for instance, the entrepreneur and tech influencer Fu Sheng hosted a livestream showing off OpenClaw’s capabilities that got 20,000 views. And just last weekend, Xie attended three different OpenClaw events in Shenzhen, each drawing more than 500 people. These self-organized, unofficial gatherings feature power users, influencers, and sometimes venture capitalists as speakers. The biggest event Xie attended, on March 7, drew more than 1,000 people; in the packed venue, he says, people were shoulder to shoulder, with many attendees unable to even get a seat.

Now China’s AI giants are starting to piggyback on the trend too, promoting their models, APIs,  and cloud services (which can be used with OpenClaw), as well as their own OpenClaw-like agents. Earlier this month, Tencent held a public event offering free installation support for OpenClaw, drawing long lines of people waiting for help, including elderly users and children.

This sudden burst in popularity has even prompted local governments to get involved. Earlier this month the government of Longgang, a district in Shenzhen, released several policies to support OpenClaw-related ventures, including free computing credits and cash rewards for standout projects. Other cities, including Wuxi, have begun rolling out similar measures.

These policies only catalyze what’s already in the air. “It was not until my father, who is 77, asked me to help install a ‘lobster’ for him that I realized this thing is truly viral,” says Henry Li, a software engineer based in Beijing. 

A programmer gold rush

What’s making this moment particularly lucrative for people with technical skills, like Feng, is that so many people want OpenClaw, but not nearly as many have the capabilities to access it. Setting it up requires a level of technical knowledge most people do not possess, from typing commands into a black terminal window to navigating unfamiliar developer platforms. On the hardware side, an older or budget laptop may struggle to run it smoothly. And if the tool is not installed on a device separate from someone’s everyday computer, or if the data accessible to OpenClaw is not properly partitioned, the user’s privacy could be at risk—opening the door to data leaks and even malicious attacks. 

Chris Zhao, known as “Qi Shifu” online, organizes OpenClaw social media groups and events in Beijing. On apps like Rednote and Jike, Zhao routinely shares his thoughts on AI, and he asks other interested users to leave their WeChat ID so he can invite them to a semi-private group chat. The proof required to join is a screenshot that shows your “lobster” up and running. Zhao says that even in group chats for experienced users, hardware and cloud setup remain a constant topic of discussion.

The relatively high bar for setting up OpenClaw has generated a sense of exclusivity, creating a natural opening for a service industry to start unfolding around it. On Chinese e-commerce platforms like Taobao and JD, a simple search for “OpenClaw” now returns hundreds of listings, most of them installation guides and technical support packages aimed at nontechnical users, priced anywhere from 100 to 700 RMB (approximately $15 to $100). At the higher end, many vendors offer to come to help you in person. 

Like Feng, most providers of these services are early adopters with some technical ability who are looking for a side gig. But as demand has surged, some have found themselves overwhelmed. Xie, the developer in Shenzhen who created tools to layer on OpenClaw, was asked by a friend who runs one such business to help out over the weekend; the friend had a customer who worked in e-commerce and had little technical experience, so Xie had to show up in person to get it done. He walked away with 600 RMB ($87) for the afternoon.

The growing demand has also pushed vendors like Feng to expand quickly. He has now standardized his operation into tiers: a basic installation, a custom package where users can make specific requests like configuring a preferred chat app, and an ongoing tutoring service for those who want a hand to hold as they find their footing with the technology.

Other vendors in China are making money combining OpenClaw with hardware. Li Gong, a Shenzhen-based seller of refurbished Mac computers, was among the first online sellers to do this—offering Mac minis and MacBooks with OpenClaw preinstalled. Because OpenClaw is designed to operate with deep access to a hard drive and can run continuously in the background unattended, many users prefer to install it on a separate device rather than on the one they use every day. This would help prevent bad actors from infiltrating the program and immediately gaining access to a wide swathe of someone’s personal information. Many turn to secondhand or refurbished options to keep the cost down. Li says that in the last two weeks, orders have increased eightfold.

Though OpenClaw itself is a new technology, the general practice of buying software bundles, downloading third-party packages, and seeking out modified devices is nothing new for many Chinese internet users, says Tianyu Fang, a PhD candidate studying the history of technology at Harvard University. Many users pay for one-off IT support services for tasks from installing Adobe software to jailbreaking a Kindle.

Still, not everyone is getting swept up. Jiang Yunhui, a tech worker based in Ningbo, worries that ordinary users who struggle with setup may not be the right audience for a technology that is still effectively in testing. 

“The hype in first-tier cities can be a little overblown,” he says. “The agent is still a proof of concept, and I doubt it would be of any life-changing use to the average person for now.” He argues that using it safely and getting anything meaningful out of it requires a level of technical fluency and independent judgment that most new users simply don’t have yet.

He’s not alone in his concerns. On March 10, the Chinese cybersecurity regulator CNCERT issued a warning about the security and data risks tied to OpenClaw, saying it heightens users’ exposure to data breaches.

Despite the potential pitfalls, though, China’s enthusiasm for OpenClaw doesn’t seem to be slowing.

Feng, now flush with the earnings from his operation, wants to use the momentum—and the capital—to keep building out his own venture with AI tools at the center of it.

“With OpenClaw and other AI agents, I want to see if I can run a one-person company,” he says. “I’m giving myself one year.”

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.

How Pokémon Go is giving delivery robots an inch-perfect view of the world 

Pokémon Go was the world’s first augmented-reality megahit. Released in 2016 by Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. “500 million people installed that app in 60 days,” says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out last year.  

Now Niantic Spatial is using that vast trove of crowdsourced data to build a kind of world model—a buzzy new technology that grounds the smarts of LLMs in real environments. The firm wants to use it to help robots navigate more precisely. Read the full story. 

—Will Douglas Heaven 

MIT Technology Review Narrated: America was winning the race to find Martian life. Then China jumped in. 

In July 2024, after more than three years on Mars, the Perseverance rover came across a peculiar rocky outcrop. Instead of the usual crystals or sedimentary layers, this one had spots. Those specks were the best hint yet of alien life.  

NASA began a new mission to bring the rocks back to Earth to study. But now, just over a year and a half later, the project is on life support. As a result, those oh-so-promising rocks may be stuck out there forever. 

This also means that, in the race to find evidence of alien life, America has effectively ceded its pole position to its greatest geopolitical rival: China. The superpower is moving full steam ahead with its own version of NASA’s mission.  

—Robin George Andrews 

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. 

The must-reads 

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 

1 Viral AI fakes of the Iran war are flooding X 
And Grok is failing to flag them. (Wired $) 
+ The conflict could wreak havoc on data centers and electricity costs. (The Verge)  
+ Pro-Iran bots are weaponizing posts about Epstein. (Gizmodo)  
+ AI is turning the Iran conflict into a show. (MIT Technology Review) 

2 Anthropic fears the loss of billions due to the Pentagon’s blacklisting  
That’s what the company has told a judge as it seeks to block its designation as a supply-chain risk. (Bloomberg $) 
+ Microsoft has backed the company in its legal fight with the Pentagon. (FT $) 
+ OpenAI’s “compromise” with the DoD dealt a big blow to Anthropic. (MIT Technology Review) 
 
3 Meta has bought a social network that’s exclusively for bots 
Moltbook is a Reddit-like site where AI agents interact with each other. (NYT $) 
+ The platform is  AI theater. (MIT Technology Review)  
 
4 Ukraine is eagerly offering the US its expertise and tech to counter Iranian drones 
Kyiv has sent drones and UAV specialists to military bases in Jordan. (WSJ $) 
+ A radio-obsessed civilian is shaping Ukraine’s drone defense. (MIT Technology Review) 
 
5 OnlyFans “chatters” are earning $2 per hour to impersonate models 
A worker in the Philippines described the job as “heartbreaking” and “icky.” (BBC) 
 
6 The DHS has removed officials who objected to “illegal” orders about surveillance tech 
The officers had refused to mislabel records about the technologies in order to block their release. (Wired) 

7 This startup is building data centers run on brain cells  
The “biological data centers” are coming to Melbourne and Singapore. (New Scientist $) 

8 Anduril is expanding into space defense 
The company is buying ExoAnalytic, which specializes in missile defense tracking. (Reuters) 
+ We saw a demo of an AI system powering Anduril’s vision for war. (MIT Technology Review) 
 
9 Big tech has a new big idea: AI compute as compensation 
Silicon Valley is pitching it as a job perk. (Business Insider) 
 
10 Wordle’s creator is back with a new game 
It’s inspired by cryptic crosswords. (The New Yorker $)  

Quote of the day 

“You come for the Epstein content, and you stay for the propaganda.” 

—Bret Schafer, an expert on information manipulation, tells the Washington Post how pro-Iran networks are gaining traction with posts about Epstein. 

One More Thing 

MEREDITH MIOTKE | PHOTO: NASA/JPL-CALTECH/MSSS

The quest to figure out farming on Mars  

If ever a blade of grass grew on Mars, those days are over. But could they begin again? What would it take to grow plants to feed future astronauts on Mars?  

To grow food there, we can’t just drop seeds in the ground and add water. We will need to create a layer of soil that can support life. And to do that, we first have to get rid of the red planet’s toxic salts.  

Researchers recently discovered a potential solution—and the early signs are promising. Read the full story.

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) 

+ Finally, a rebellion arises against mint’s tyranny over our teeth: Peanut Butter Cup toothpaste. 
+ DIY decorators rejoice! The humble paint tray has received an ingeniously simple renovation. 
+ Saudi surgeons have successfully separated two conjoined twins. 
+ If you’re looking for real innovation, check out British Pie Week’s beef rendang, jerk chicken, and double-size pasties. 

In the race to adopt and show value from AI, enterprises are moving faster than ever to deploy agentic AI as copilots, assistants, and autonomous task-runners. In late 2025, nearly two-thirds of companies were experimenting with AI agents, while 88% were using AI in at least one business function, up from 78% in 2024, according to McKinsey’s annual AI report. Yet, while early pilots often succeed, only one in 10 companies actually scaled their AI agents.

One major issue: AI agents are only as effective as the data foundation supporting them. Experts argue that most companies are seeing delays in implementing AI, not because of shortcomings in the models, but because they lack data architectures that deliver business context to be reliably used by humans and agents.

Companies need to be ready with the right data architecture, and the next few months — years, at most — will be critical, says Irfan Khan, president and chief product officer of SAP Data & Analytics.

“The only prediction anybody can reliably make is that we don’t know what’s going to happen in the years, months — or even weeks — ahead with AI,” he says. “To be able to get quick wins right now, you need to adopt an AI mindset and … ground your AI models with reliable data.”

While data has always been important for business, it will be even more so in the age of AI. The capabilities of agentic AI will be set more by the soundness of enterprise data architecture and governance, and less by the evolution of the models. To scale the technology, businesses need to adopt a modern data infrastructure that delivers context along with the data.

More business context, not necessarily more data

Traditional views often conflate structured data with high value, and unstructured data with less value. However, AI complicates that distinction. High-value data for agents is defined less by format and more by business context. Data for critical business functions — such as supply-chain operations and financial planning — is context dependent. While fine-grained, high-volume data, such as IoT, logs, and telemetry, can yield value, but only when delivered with business context.

For that reason, the real risk for agentic AI is not lack of data, but lack of grounding, says Khan.

“Anything that is business contextual will, by definition, give you greater value and greater levels of reliability of the business outcome,” he says. “It’s not as simple as saying high-value data is structured data and low-value data is where you have lots of repetition — both can have huge value in the right hands, and that’s what’s different about AI.”

Context can be derived through integration with software, on-site analysis and enrichment, or through the governance pipeline. Data lacking those qualities will likely be untrusted — one reason why two-thirds of business leaders do not fully trust their data, according to the Institute for Data and Enterprise AI (IDEA). The resulting “trust debt” has held back businesses in their quest for AI readiness. Overcoming that lack of trust requires shared definitions, semantic consistency, and reliable operational context to align data with business meaning.

Data sprawl demands a semantic, business-aware layer

Over the past decade, the most important shift in enterprise data architecture has been the separation of compute and storage, cloud-scale flexibility, says Khan. Yet, that separation and move to cloud also created sprawl, with data housed in multiple clouds, data lakes, warehouses, and a multitude of SaaS applications.

As companies move to AI, that sprawl does not go away. In fact, the problem is growing with more than two-thirds of companies citing data siloes as a top challenge in adopting AI, with more than half of enterprises struggling with 1,000 data sources or more. While the last era was about laying the foundation on which to build software-as-a-service — separating compute and storage and building lakes — the next era is about delivering the right data to autonomous AI agents tasked with various business functions.

“Probably the biggest innovation that occurred in data management was the separation of compute and store,” Khan says. “But what’s really making a distinction now is the way that we harmonize the data and harvest the value of the data across multiple sources of content.”

To do that requires a semantic or knowledge layer that supports multiple platforms, encodes business rules and relationships, provides a business-contextual and governed view of data, and allows humans and agents to access the data in the appropriate ways. But legacy data architectures cannot power the autonomous AI systems of the future, consultancy Deloitte stated in its State of AI in the Enterprise report. Only four in 10 companies believe their data management process is ready for AI, and that’s down from 43% the previous year, suggesting that as companies explore AI deployment, they are realizing their infrastructure’s shortcomings.

Agentic AI does not replace SaaS

Some investors and technologists speculate that AI agents will make SaaS applications obsolete. Khan strongly disagrees. Over the past 15 years, value has steadily moved up the stack, from on-premises infrastructure to infrastructure as a service (IaaS) to platform as a service (PaaS) to SaaS. Agentic AI is simply the next layer. Agentic AI will have its own layer to access the data and interact with the business logic. The value rises up the stack, but nothing below disappears, he says.

“SaaS doesn’t go away,” he says. “It just means SaaS and these agents will cooperate with one another. Companies are not going to throw away their entire general ledger and replace it with an agent. What’s the agent going to do? It doesn’t know anything without business context and business processing.”

In this emerging model, the software stack is being reshaped so that applications and data provide governed context within which AI can act effectively. SaaS applications remain the systems of record, while the semantic layer becomes the business-context source of truth. AI agents become a new engagement layer, orchestrating across systems, and both humans and agents become “first-class citizens” in how they access business logic, he says.

Critically, agents cannot directly connect to every operational system. “If we’re saying agents are going to take over the world … you can’t have an agent talking to every operational backend system,” Khan warns. “It just doesn’t work that way.”

This further elevates the importance of a semantic or business-fabric layer.

Where to start

Most enterprises need to begin where their data already lives — in platforms like Snowflake, Databricks, Google BigQuery, or an existing SAP environment. Khan says that’s normal, but warns against rebuilding old patterns of vendor lock-in.

He suggests that companies prioritize the data that matters most by focusing on preserving and providing business context to operational and application data. Companies should also invest early in governance and semantics by defining shared policies, access rules, and semantic models before scaling pilots. Finally, businesses should prioritize openness and fabric-style interoperability rather than forcing all data into one stack.

Khan cautions against aiming for full automation too early. “There is a new brave opportunity to really engage in the agentic and AI world,” Khan says, “Fully automating [critical business processes] is maybe a stretch, because there’s going to be a lot of extra oversight necessary.” Early wins will likely come from less-critical processes and from agents that work off fresh, stateful data rather than stale dashboards, he adds. As AI begins to deliver value and adoption increases, leaders must decide how to reinvest those gains to drive top-line efficiency or enter new markets.

Register for “The Fabric of Data & AI” virtual event on March 24, 2026. Hear insights from executives and thought leaders who are shaping the future of data and AI.

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.

Pokémon Go was the world’s first augmented-reality megahit. Released in 2016 by the Google spinout Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. From Chicago to Oslo to Enoshima, players hit the streets in the urgent hope of catching a Jigglypuff or a Squirtle or (with a huge amount of luck) an ultra-rare Galarian Zapdos hovering just out of reach, superimposed on the everyday world.

In short, we’re talking about a huge number of people pointing their phones at a huge number of buildings. “Five hundred million people installed that app in 60 days,” says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out in May last year. According to the video-game firm Scopely, which bought Pokémon Go from Niantic at the same time, the game still drew more than 100 million players in 2024, eight years after it launched. 

Now Niantic Spatial is using that vast and unparalleled trove of crowdsourced data—images of urban landmarks tagged with super-accurate location markers taken from the phones of hundreds of millions of Pokémon Go players around the world—to build a kind of world model, a buzzy new technology that grounds the smarts of LLMs in real environments. 

The company’s latest product is a model that it says can pinpoint your location on a map to within a few centimeters, based on a handful of snapshots of the buildings or other landmarks in view. The firm wants to use it to help robots navigate with greater precision in places where GPS is unreliable.

In the first big test of its technology, Niantic Spatial has just teamed up with Coco Robotics, a startup that deploys last-mile delivery robots in a number of cities across the US and Europe. “Everybody thought that AR was the future, that AR glasses were coming,” says McClendon. “And then robots became the audience.”

From Pikachu to pizza delivery

Coco Robotics deploys around 1,000 flight-case-size robots—built to carry up to eight extra-large pizzas or four grocery bags—in Los Angeles, Chicago, Jersey City, Miami, and Helsinki. According to CEO Zach Rash, the robots have made more than half a million deliveries to date, covering a few million miles in all weather conditions.

But to compete with human couriers, Coco’s robots, which trundle along sidewalks at around five miles per hour, must be as reliable as possible. “The best way we can do our job is by arriving exactly when we told you we were going to arrive,” says Rash. And that means not getting lost.

The problem Coco faces is that it cannot rely on GPS, which can be weak in cities because radio signals bounce off buildings and interfere with each other. “We do deliveries in a lot of dense areas with high-rises and underpasses and freeways, and those are the areas where GPS just never really works,” says Rash. 

“The urban canyon is the worst place in the world for GPS,” says McClendon. “If you look at that blue dot on your phone, you’ll often see it drift 50 meters, which puts you on a different block going a different direction on the wrong side of the street.” That’s where Niantic Spatial comes in. 

For the last few years, Niantic Spatial has been taking the data collected from players of Pokémon Go and Ingress (Niantic’s previous phone-based AR game, launched in 2013) and building a visual positioning system, technology that tells you where you are based on what you can see. “It turns out that getting Pikachu to realistically run around and getting Coco’s robot to safely and accurately move through the world is actually the same problem,” says John Hanke, CEO of Niantic Spatial.

“Visual positioning is not a very new technology,” says Konrad Wenzel at ESRI, a company that develops digital mapping and geospatial analysis software. “But it’s obvious that the more cameras we have out there, the better it becomes.” 

Niantic Spatial has trained its model on 30 billion images captured in urban environments. In particular, the images are clustered around hot spots—places that served as important locations in Niantic’s games that players were encouraged to visit, such as Pokémon battle arenas. “We had a million-plus locations around the world where we can locate you precisely,” says McClendon. “We know where you’re standing within several centimeters of accuracy and, most importantly, where you’re looking.”

The upshot is that for each of those million locations, Niantic Spatial has many thousands of images taken in more or less the same place but from different angles, at different times of day, and in different weather conditions. Each of those images comes with detailed metadata that pinpoints where in space the phone was at the time it captured the image, including which way the phone was facing, which way up it was, whether or not it was moving, how fast and in which direction, and more.   

The firm has used this data set to train a model to predict exactly where it is by taking into account what it is looking at—even for locations other than those million hot spots, where good sources of image and location data are scarcer.

In addition to GPS, Coco’s robots, which are fitted with four cameras, will now use this model to try to figure out where they are and where they are headed. The robots’ cameras are hip-height and point in all directions at once, so their viewpoint is a little different from a Pokémon Go player’s, but adapting the data was straightforward, says Rash. 

Rival companies use visual positioning systems too. For example, Starship Technologies, a robot delivery firm founded in Estonia in 2014, says its robots use their sensors to build a 3D map of their surroundings, plotting the edges of buildings and the position of streetlights. 

But Rash is betting that Niantic Spatial’s tech will give Coco an edge. He claims it will allow his robots to position themselves in the correct pickup spots outside restaurants, making sure they don’t get in anybody’s way, and stop just outside the customer’s door instead of a few steps away, which might have happened in the past.  

A Cambrian explosion in robotics 

When Niantic Spatial started work on its visual positioning system, the idea was to apply it to augmented reality, says Hanke. “If you are wearing AR glasses and you want the world to lock in to where you’re looking, then you need some method for doing that,” he says. “But now we’re seeing a Cambrian explosion in robotics.”

Some of those robots may need to share spaces with humans—spaces such as construction sites and sidewalks. “If robots are ever going to assimilate into that environment in a way that’s not disruptive for human beings, they’re going to have to have a similar level of spatial understanding,” says Hanke. “We can help robots find exactly where they are when they’ve been jostled and bumped.”

The Coco Robotics partnership is the start. What Niantic Spatial is putting in place, says Hanke, are the first pieces of what he calls a living map: a hyper-detailed virtual simulation of the world that changes as the world changes. As robots from Coco and other firms move about the world, they will provide new sources of map data, feeding into more and more detailed digital replicas of the world. 

But the way Hanke and McClendon see it, maps are not only becoming more detailed; they are being used more and more by machines. That shifts what maps are for. Maps have long been used to help people locate themselves in the world. As they moved from 2D to 3D to 4D (think of real-time simulations, such as digital twins), the basic principle hasn’t changed: Points on the map correspond to points in space or time.

And yet maps for machines may need to become more like guidebooks, full of information that humans take for granted. Companies like Niantic Spatial and ESRI want to add descriptions that tell machines what they’re actually looking at, with every object tagged with a list of its properties. “This era is about building useful descriptions of the world for machines to comprehend,” says Hanke. “The data that we have is a great starting point in terms of building up an understanding of how the connective tissue of the world works.”

There is a lot of buzz about world models right now—and Niantic Spatial knows it. LLMs may seem like know-it-alls, but they have very little common sense when it comes to interpreting and interacting with everyday environments. World models aim to fix that. Some firms, such as Google DeepMind and World Labs, are developing models that generate virtual fantasy worlds on the fly, which can then be used as training dojos for AI agents. 

Niantic Spatial says it is coming at the problem from a different angle. Push map-making far enough and you’ll end up capturing everything, says McClendon: “I’m very focused on trying to re-create the real world. We’re not there yet, but we want to be there.”

Loudoun County, Virginia, once known for its pastoral scenery and proximity to Washington, DC, has earned a more modern reputation in recent years: The area has the highest concentration of data centers on the planet.

Ten years ago, these facilities powered email and e-commerce. Today, thanks to the meteoric rise in demand for AI-infused everything, local utility Dominion Energy is working hard to keep pace with surging power demands. The pressure is so acute that Dulles International Airport is constructing the largest airport solar installation in the country, a highly visible bid to bolster the region’s power mix.

Data center campuses like Loudoun’s are cropping up across the country to accommodate an insatiable appetite for AI. But this buildout comes at an enormous cost. In the US alone, data centers consumed roughly 4% of national electricity in 2024. Projections suggest that figure could stretch to 12% by 2028. To put this in perspective, a single 100-megawatt data center consumes roughly as much electricity as 80,000 American homes. Data centers being built today are gearing up for gigawatt scale, enough to power a mid-sized city.

For enterprise leaders, energy costs associated with AI and data infrastructure are quickly becoming both a budget concern and a potential bottleneck on growth. Meeting this moment calls for a capability most organizations are only beginning to develop: energy intelligence. The emerging discipline refers to understanding where, when, and why energy is consumed, and using that insight to optimize operations and control costs.

These efforts stand to address both immediate financial pressures and longer-term reputational risks, as communities like Loudoun County grow increasingly concerned about the energy demands associated with nearby data center development.

In December 2025, MIT Technology Review Insights conducted a survey of 300 executives to understand how companies are thinking about energy intelligence today, as well as where they’re anticipating challenges in the future.

Here are five of our most notable findings:

• Energy intelligence is becoming a universal business priority. One hundred percent of executives surveyed expect the ability to measure and strategically manage power consumption to become an important business metric in the next two years.
  
• AI workloads are already driving measurable cost increases, and the surge is just beginning. Two-thirds of executives (68%) report their companies have faced energy cost increases of 10% or more in the past 12 months due to AI and data workloads. Nearly all respondents (97%) anticipate their organization’s AI-related energy consumption will increase over the next 12-18 months.
  
• Mounting costs are the top energy-related threat to AI innovation. Half of executives (51%) rank rising costs as the single greatest energy-related risk to their digital and AI initiatives. Most companies currently tracking and attempting to optimize data center energy consumption are motivated by cost management.
  
• Organizations are responding through infrastructure optimization and energy-efficient partnerships. To address mounting energy demands, three in four leaders (74%) are optimizing existing infrastructure, while 69% are partnering with energy-efficient cloud and storage providers. More than half are also implementing AI workload scheduling (61%) and investing in more efficient hardware (56%).
  
• Closing the measurement gap is the next frontier. Most enterprises still lack the granular data needed for true energy intelligence. This gap is especially pronounced for companies relying on third-party cloud providers and managed services for their data compute and storage needs, where 71% say rising consumption-based costs originate, yet energy metrics are often opaque.

Download the full report.

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.

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.

How AI is turning the Iran conflict into theater 

Much of the spotlight on AI in the Iran conflict has focused on models like Claude helping the US military decide where to strike. But a wave of “vibe-coded” intelligence dashboards—and the ecosystem surrounding them—reflect a new role that AI is playing in wartime: mediating information, often for the worse. 

These sorts of intelligence tools have much promise. Yet there are real reasons to be suspicious of their data feeds. Read the full story. 

—James O’Donnell 

This story is from The Algorithm, our weekly newsletter on AI. Sign up to receive it in your inbox every Monday. 

The must-reads 

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 

1 Anthropic has sued the US government  
The AI firm wants to stop the Pentagon from blacklisting it. (Reuters) 
+ The White House is preparing a new executive order to weed out the company’s technology. (Axios) 
+ Defense experts are alarmed. (CNBC) 
+.Google and OpenAI staff have filed a legal brief backing Anthropic against Trump. (Wired $) 
+ The company’s stance won many supporters. (MIT Technology Review) 

2 GPS jamming has become a crucial battleground in the Middle East  
The interference is endangering—and protecting—ships and planes. (BBC) 
+ Signal jamming has made navigating the Strait of Hormuz even more difficult. (Bloomberg) 
+ Quantum navigation offers a potential solution. (MIT Technology Review)  

3 A tech journalist found his AI clone editing for Grammarly 
It’s providing AI-generated feedback “inspired by” real writers without their consent. (Platformer) 
+ Could ChatGPT do the jobs of journalists and copywriters? (MIT Technology Review) 

4 Nvidia plans to launch an open-source platform for AI agents  
It’s already pitching the “NemoClaw” product to enterprise software firms. (Wired $) 
+ But don’t let the AI agents hype get ahead of reality (MIT Technology Review) 
 
5 A startup wants to launch a space mirror that reflects sunlight onto Earth 
Reflect Orbital reckons it could power solar panels at night. Scientists are appalled. (NYT) 

6 Yann LeCun’s AI startup has raised over $1bn in Europe’s largest seed round  
Meta’s former chief AI scientist plans to build systems that “understand the world.” (Bloomberg) 

7 Hinge’s CEO insists the app doesn’t rate users’ attractiveness 
Jackie Jantos’ strategy has helped Hinge defy the decline in dating apps. (FT $) 
+ AI companions are stealing hearts—and it’s getting weird. (New Yorker $) 
+ It’s surprisingly easy to fall into a relationship with a chatbot. (MIT Technology Review) 

8 “AI psychosis” could be afflicting your loved ones  
If so, here’s how you can help them. (404 Media) 
+ One solution: AI should be able to “hang up” on you. (MIT Technology Review) 

9 Nintendo is suing Trump over illegal tariffs 
The gaming giant has joined a lawsuit seeking over $200 billion in refunds. (Ars Technica) 

10 Bio-tech is turning ancient poop into a map of lost civilizations  
Molecular sensors are finding human traces where physical ruins have vanished. (Nature)    

Quote of the day 

“I don’t think any of us, whether it’s me or Dario [Amodei], Sam Altman, or Elon Musk, has any legitimacy to decide for society what is a good or bad use of AI.”

—Yann LeCun gives Wired his take on the Anthropic’s spat the Pentagon. 

One More Thing 

This giant microwave may change the future of war 

YOSHI SODEOKA

armed forces are hunting for a weapon that disables drones en masse—and they want it fast.  

One solution focuses on microwaves: high-powered electronic devices that push out kilowatts of power to zap the circuits of a drone as if it were the tinfoil you forgot to take off your leftovers when you heated them up. 

Defense tech startup Epirus may have the winning formula. The company has developed a cutting-edge, cost-efficient drone zapper that’s sparking the interest of the US military. And drones are just one of its targets. Read the full story. 

—Sam Dean 

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.) 

+ Werner Herzog’s magnificent movie about Africa’s ghost elephants has arrived on Disney+ and Hulu. 
+ A “city killer” asteroid won’t hit Earth after all. Phew.  
+ The Met is publishing high-definition 3D scans of over 100 iconic works. 
+ Marty and Doc from Back to the Future are still BFFs in real life. 

Top image credit: MIT TECHNOLOGY REVIEW (ILLUSTRATION) | PHOTO OF MISSILE (US NAVY), AI-GENERATED IMAGE OF RUBBLE VIA X, SCREENSHOTS VIA WORLDMONITOR, GLOBALTHREATMAP 

Send asteroids to hi@technologyreview.com.  

You can follow me on LinkedIn. Thanks for reading! 
 
 

—Thomas  

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

“Anyone wanna host a get together in SF and pull this up on a 100 inch TV?” 

The author of that post on X was referring to an online intelligence dashboard following the US-Israel strikes against Iran in real time. Built by two people from the venture capital firm Andreessen Horowitz, it combines open-source data like satellite imagery and ship tracking with a chat function, news feeds, and links to prediction markets, where people can bet on things like who Iran’s next “supreme leader” will be (the recent selection of Mojtaba Khamenei left some bettors with a payout). 

I’ve reviewed over a dozen other dashboards like this in the last week. Many were apparently “vibe-coded” in a couple of days with the help of AI tools, including one that got the attention of a founder of the intelligence giant Palantir, the platform through which the US military is accessing AI models like Claude during the war. Some were built before the conflict in Iran, but nearly all of them are being advertised by their creators as a way to beat the slow and ineffective media by getting straight to the truth of what’s happening on the ground. “Just learned more in 30 seconds watching this map than reading or watching any major news network,” one commenter wrote on LinkedIn, responding to a visualization of Iran’s airspace being shut down before the strikes.

Much of the spotlight on AI and the Iran conflict has rightfully been on the role that models like Claude might be playing in helping the US military make decisions about where to strike. But these intelligence dashboards and the ecosystem surrounding them reflect a new role that AI is playing in wartime: mediating information, often for the worse.

There’s a confluence of factors at play. AI coding tools mean people don’t need much technical skill to assemble open-source intelligence anymore, and chatbots can offer fast, if dubious, analysis of it. The rise in fake content leaves observers of the war wanting the sort of raw, accurate analysis normally accessible only to intelligence agencies. Demand for these dashboards is also driven by real-time prediction markets that promise financial rewards to anyone sufficiently informed. And the fact that the US military is using Anthropic’s Claude in the conflict (despite its designation as a supply chain risk) has signaled to observers that AI is the intelligence tool the pros use. Together, these trends are creating a new kind of AI-enabled wartime circus that can distort the flow of information as much as it clarifies it.

As a journalist, I believe these sorts of intelligence tools have a lot of promise. While many of us know that real-time data on shipping routes or power outages exist, it’s a powerful thing to actually see it all assembled in one place (though using it to watch a war unfold while you munch on popcorn and place bets turns the war into perverse entertainment). But there are real reasons to think that these sorts of raw data feeds are not as informative as they may feel. 

Craig Silverman, a digital investigations expert who teaches investigative techniques, has been keeping a log of these dashboards (he’s up to 20). “The concern,” he says, “is there’s an illusion of being on top of things and being in control, where all you’re really doing is just pulling in a ton of signals and not necessarily understanding what you’re seeing, or being able to pull out true insights from it.” 

One problem has to do with the quality of the information. Many dashboards feature “intel feeds” with AI-generated summaries of complex, ever-changing news events. These can introduce inaccuracies. By design, the data is not especially curated. Instead, the feeds just display everything at once, with a map of strike locations in Iran next to the prices of obscure cryptocurrencies. 

Intelligence agencies, on the other hand, pair data feeds with people who can offer expertise and historical context. They also, of course, have access to proprietary information that doesn’t show up on the open web. 

The implicit promise from the people building and selling this sort of information pipeline about the Iran conflict is that AI can be a great democratizing force. There’s a secret feed of information that only the elites have had access to, the thinking goes, but now AI can bring it to everyone to do with what they wish, whether that’s simply to be more informed or to make bets on nuclear strikes. But an abundance of information, which AI is undeniably good at assembling, does not come with the accuracy or context required for real understanding. Intelligence agencies do this in-house; good journalism does the same work for the rest of us.

It is, by the way, hard to overstate the connection this all has with betting markets. The dashboard created by the pair at Andreessen Horowitz has a scrolling list of bets being made on the prediction platform Kalshi (which Andreessen Horowitz has invested in). Other dashboards link to Polymarket, offering bets on whether the US will strike Iraq or when Iran’s internet will return.

AI has also long made it cheaper and easier to spread fake content, and that problem is on full display during the Iran conflict: last week the Financial Times found a slew of AI-generated satellite imagery spreading online. 

“The emergence of manipulated or outright fake satellite imagery is really concerning,” Silverman says. The average person tends to see such imagery as very trustworthy. The spread of such fakes could erode confidence in one of the most important pieces of evidence used to show what’s actually happening in the war. 

The result is an ocean of AI-enabled content—dashboards, betting markets, photos both real and fake—that makes this war harder, not easier, to comprehend.

When Tony Fadell started working on the iPod, usability often trumped security. The result was an iterative process. Every time someone would find a security weakness or a way to hack the device, the development group would iterate to add measures and fix the issues. Yet, flaws would frequently be found, and the secure design of the product became a moving target.

But when it came to designing a device specifically for security purposes, there could be no iterative process after rolling it out: Security had to be the number one priority. 

“As you develop these things, you’re a victim of your own development speed,” says Fadell, who developed Ledger Stax, a signing device for securing digital assets, and is now a board member at digital asset security firm Ledger. “If you introduced these features and functions without the proper review, and now customers are demanding security, you’ll realize that you should have designed it differently from the start, and it’s very hard to undo what you’ve already done.”

A critical aspect of designing secure technology, however, must be ease of use too. Without it, it is all too simple for users to make a mistake or use an unsafe workaround that undermines device protections. Think a post-it stuck to a monitor or some variation of “123456” or “admin” for passwords.

With digital asset security devices like signers—more commonly called “wallets”—such errors could lead to seriously detrimental outcomes. If, for example, a user’s private key falls into the wrong hands, bad actors can use it to steal their digital assets. Estimates suggest that around 20% of all Bitcoin—worth around $355 billion—are inaccessible to owners. One of the reasons for this is likely because they lost their private keys.

In the past, crypto devices have been notoriously difficult to use. As cryptocurrency becomes ever more popular, valuable, and mainstream—attracting greater attention from criminals as the stakes rise—designers and engineers are prioritizing both security and usability when developing digital asset devices, drawing on in-depth research to iterate.

The three components of security

Strong security models for devices like signers, which are used to secure blockchain transactions,  require three major components. First, a secure operating system. Second, a secure element to bind the software to the hardware. And third, a secure user interface. Each of which need to be frequently tested by researchers and white hat hackers to simulate real-world attacks and improve product resilience and usability.

The first two elements focus on securing the device software and hardware. Secure software has always been a problem, but one that has improved over the last decade, as security architectures and processes have been refined. Meanwhile, hardware security components have become widely available—from trusted platform modules on computers to secure enclaves in smartphones—allowing digital information to essentially be locked to a device.

For crypto signers, hardware must provide encryption capabilities. And the security of the software must be frequently tested. Ledger, for example, has a secure OS and a Secure Element that handles encryption primitives, and a secure display that prevents device takeover.

Security and usability working hand in hand

Asset recovery is a major consideration when designing signers. If recovery options are not easy to use, an owner could lose access. But if recovery processes are not secure enough, attackers could exploit the system. With SIM swapping attacks, for example, attackers can tap into a mobile communications channel used for account recovery and “recover” a victim’s password to steal their assets.

In the digital-asset ecosystem, the creation of the seed phrase, a sequence of 12 to 24 words that could act as a passphrase for wallets is an example of improving usability and security. Known more formally as Bitcoin Improvement Proposal 39 (BIP-39), the approach gives users a master password to unlock their hierarchical deterministic (HD) wallets. 

There is a lot of creative tension between the security team and the UX team that happens to achieve the proper balance between convenience and safety, Fadell says, referring to Ledger’s security research team, the Donjon. “We mock things up, we prototype things from a UX UI perspective, we walk through it, then we walk the Donjon team through it,” Fadell explains. “We push back and forth to find the absolute optimal solution to balance the two.” 

Through the research the Donjon team has conducted, Ledger designed its Recovery Key—an NFC-based physical card to back up your 24 words—to be both user-friendly and secure. “What we did, as a first in the industry, was include an NFC card,” says Fadell. “Instead of only writing it down, you can also have an NFC card called a Recovery Key. You can have multiple Recovery Keys and store them in a lockbox, a safety deposit box, or give them to someone you trust for safekeeping.”

A number of government initiatives are working to regulate this balance between security and usability. This includes the US Cybersecurity and Infrastructure Security Agency’s Secure by Design, which aims to build cybersecurity into the design and manufacture of technology products. And the UK’s National Cyber Security Centre’s Software Security Code of Practice, which outlines security principles expected of all organizations that develop or sell software. 

Enterprise security presents distinct challenges

Embedding usability and security into devices for companies adds further complexity as businesses need features such as multi-signature capabilities to protect against single points of failure, whether from external attacks or internal bad actors. 

Security design can take these requirements into account, with secure governance using multiple signatures (multisig), hardware security modules (HSMs) for key storage, trusted display systems, and other usable security capabilities.

These technologies are critically important for companies who have roles in the blockchain ecosystem. Failure to establish robust security measures can have dire consequences. In 2024, for example, unknown cybercriminals made off with more than $300 million worth of assets from DMM Bitcoin, leading the Japanese cryptocurrency platform to close six months later. Japan’s Financial Services Agency discovered severe risk management issues, including inadequate oversight, lack of independent audits, and poor security practices.

For companies, allowing a multi-stage process that involves a required number of stakeholders is critical, says Fadell. “It’s making sure that the attack vector is not just one person, and so you need to support multiple people with multiple factors on all of their devices as well,” he says. “It gets to be a real combinatoric problem.”

R&D to stay one step ahead 

To keep up with requirements and offer strong security with improved visibility, crypto firms need to invest in research and development, Fadell says. Attack labs, such as Ledger Donjon, can conduct real-world testing on specific enterprise security requirements and create scenarios to educate both management and workers of the potential threats. 

Such research and development can support device designers and engineers in their never-ending mission to balance security measures with usability so that digital asset devices can support users to safeguard their digital assets in a constantly evolving crypto and cyber landscape.

Learn more about how to secure digital assets in the Ledger Academy.

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.

This content 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.

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.

Is the Pentagon allowed to surveil Americans with AI?

The ongoing public feud between the Department of Defense and the AI company Anthropic has raised a deep and still unanswered question: Does the law actually allow the US government to conduct mass surveillance on Americans?

Surprisingly, the answer is not straightforward. More than a decade after Edward Snowden exposed the NSA’s collection of bulk metadata from the phones of Americans, the US is still navigating a gap between what ordinary people think and what the law allows. 

Today, the legal complexity has a new edge: AI is supercharging surveillance—and our laws haven’t caught up. Read the full story.

—Michelle Kim

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The White House has tightened its AI rules amid the Anthropic spat
New guidelines require companies to allow “any lawful” use of their ‌models. (FT $)
+ London’s mayor has slammed Trump’s treatment of Anthropic and invited the firm to expand in the city. (BBC)

2 A satellite firm has stopped sharing imagery after exposing Iranian strikes
Planet Lab said it wants to stop “adversarial actors” from using the data. (Ars Technica)
+ AI is turbocharging the conflict in Iran. (WSJ $)
+ War is adding a brutal new element to the country’s internet issues. (Wired $)

3 The OpenAI-Anthropic feud is getting messy
The Pentagon contract controversy has intensified a deeply personal animosity between the founders. (NYT $)
+ Sam Altman and Dario Amodei’s rivalry could reshape the future of AI. (WSJ $) 
+ OpenAI’s robotics lead has quit over concerns about surveillance and “lethal autonomy.” (TechCrunch)
+ The company’s DoD “compromise” has brought Anthropic’s fears to life. (MIT Technology Review)

4 Staff at Block are outraged over the company’s “AI layoffs” 
They’re pushing back against Jack Dorsey’s bullishness on AI. (The Guardian)
+ They’ve also cast doubt on the payroll savings. (Gizmodo)
+ It’s not the first case of fears over AI taking everyone’s jobs. (MIT Technology Review)

5 Data center “man camps” are springing up in Texas
Aimed at luring workers to help build the centers, they will offer free steaks and golf simulators. (Bloomberg $)

6 The OpenClaw craze is sparking a rally in Chinese tech stocks
Shares surged after government agencies and tech leaders promoted the AI agent. (Bloomberg $)
+ Why is China falling so hard for it? (SCMP)

7 AI-generated videos are altering our relationship to nature
And could lead to “distorted expectations” of animal behavior. (NYT $)
+ AI slop could form a new kind of pop culture. (MIT Technology Review)

8 A rogue AI agent freed itself to mine crypto in secret
The model escaped its sandbox to start a side hustle in digital currency. (Axios)
+ AI agents are also starting to harass people. (MIT Technology Review)

9 In a first, a spacecraft has changed an asteroid’s orbit around the sun
The feat was a test of Earth’s future defenses. (Engadget)

10 How the Furby brought creepy-cute robotics into playtime   
A new show traces the legacy of the surprisingly high-tech toy. (The Verge)

Quote of the day

“I wanted to approach the whole situation with love.”

—Block cofounder and CEO Jack Dorsey tells Wired why he wore a hat with the word ‘Love’ on it during a meeting where he laid off 40% of his workforce. 

One more thing

LINDA NYLIND / EYEVINE VIA REDUX

Geoffrey Hinton tells us why he’s now scared of the tech he helped build

Geoffrey Hinton is a pioneer of deep learning who helped develop some of the most important techniques at the heart of modern artificial intelligence, but after a decade at Google, he’s stepped down to focus on concerns he now has about AI.

Hinton wants to spend his time on what he describes as “more philosophical work.” And that will focus on the small but—to him—very real danger that AI will turn out to be a disaster. Read the full story.

—Will Douglas Heaven

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.)

+ De La Soul’s Tiny Desk concert is a masterclass in joy and grief, proving their “Daisy Age” philosophy is timeless.
+ These original Disney concepts of beloved characters are a portal into an alternate childhood.
+ This square phone traverses two decades of nostalgia by rotating into a Game Boy AND a BlackBerry.
+ A newly discovered Rembrandt shows the Old Masters still have new tricks to reveal.

The ongoing public feud between the Department of Defense and the AI company Anthropic has raised a deep and still unanswered question: Does the law actually allow the US government to conduct mass surveillance on Americans?

Surprisingly, the answer is not straightforward. More than a decade after Edward Snowden exposed the NSA’s collection of bulk metadata from the phones of Americans, the US is still navigating a gap between what ordinary people think and what the law allows. 

The flashpoint in the standoff between Anthropic and the government was the Pentagon’s desire to use Anthropic’s AI Claude to analyze bulk commercial data on Americans. Anthropic demanded that its AI not be used for mass domestic surveillance (or for autonomous weapons, which are machines that can kill targets without human oversight). A week after negotiations broke down, the Pentagon designated Anthropic a supply chain risk, a label typically reserved for foreign companies that pose a threat to national security. 

Meanwhile, OpenAI, the rival AI company behind ChatGPT, sealed a deal that allowed the Pentagon to use its AI for “all lawful purposes”—language that critics say left the door open to domestic surveillance. Over the following weekend, users uninstalled ChatGPT in droves. Protesters chalked messages around OpenAI’s headquarters in San Francisco: “What are your redlines?” 

OpenAI announced on Monday that it had reworked its deal to make sure that its AI will not be used for domestic surveillance. The company added that its services will not be used by intelligence agencies, such as the NSA. 

CEO Sam Altman suggested that existing law prohibits domestic surveillance by the Department of Defense (now sometimes called the Department of War) and that OpenAI’s contract simply needed to reference this law. “The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement,” he wrote on X. Anthropic CEO Dario Amodei argued the opposite. “To the extent that such surveillance is currently legal, this is only because the law has not yet caught up with the rapidly growing capabilities of AI,” he wrote in a policy statement. 

So, who is right? Does the law allow the Pentagon to surveil Americans using AI?

Supercharged surveillance

The answer depends on what we think counts as surveillance. “A lot of stuff that normal people would consider a search or surveillance … is not actually considered a search or surveillance by the law,” says Alan Rozenshtein, a law professor at the University of Minnesota Law School. That means public information—such as social media posts, surveillance camera footage, and voter registration records—is fair game. So is information on Americans picked up incidentally from surveillance of foreign nationals. 

Most notably, the government can purchase commercial data from companies, which can include sensitive personal information like mobile location and web browsing records. In recent years, agencies from ICE and IRS to the FBI and NSA have increasingly tapped into this data marketplace, fueled by an internet economy that harvests user data for advertising. These data sets can let the government access information that might not be available without a warrant or subpoena, which are normally required to obtain sensitive personal data.

“There’s a huge amount of information that the government can collect on Americans that is not itself regulated either by the Constitution, which is the Fourth Amendment, or statute,” says Rozenshtein. And there aren’t meaningful limits on what the government can do with all this data. 

That’s because until the last several decades, people weren’t generating massive clouds of data that opened up new possibilities for surveillance. The Fourth Amendment, which protects against unreasonable search and seizure, was written when collecting information meant entering people’s homes. 

Subsequent laws, like the Foreign Intelligence Surveillance Act of 1978 or the Electronic Communications Privacy Act of 1986, were passed when surveillance involved wiretapping phone calls and intercepting emails. The bulk of laws governing surveillance were on the books before the internet took off. We weren’t generating vast trails of online data, and the government didn’t have sophisticated tools to analyze the data. 

Now we do, and AI supercharges what kind of surveillance can be carried out. “What AI can do is it can take a lot of information, none of which is by itself sensitive, and therefore none of which by itself is regulated, and it can give the government a lot of powers that the government didn’t have before,” says Rozenshtein. 

AI can aggregate individual pieces of information to spot patterns, draw inferences, and build detailed profiles of people—at massive scale. And as long as the government collects the information lawfully, it can do whatever it wants with that information, including feeding it to AI systems. “The law has not caught up with technological reality,” says Rozenshtein.

While surveillance can raise serious privacy concerns, the Pentagon can have legitimate national security interests in collecting and analyzing data on Americans. “In order to collect information on Americans, it has to be for a very specific subset of missions,” says Loren Voss, a former military intelligence officer at the Pentagon. 

For example, a counterintelligence mission might require information about an American who is working for a foreign country, or plotting to engage in international terrorist activities. But targeted intelligence can sometimes stretch into collecting more data. “This kind of collection does make people nervous,” says Voss. 

Lawful use

OpenAI has amended its contract to say that the company’s AI system “shall not be intentionally used for domestic surveillance of U.S. persons and nationals,” in line with relevant laws. The amendment clarifies that this prohibits “deliberate tracking, surveillance or monitoring of U.S. persons or nationals, including through the procurement or use of commercially acquired personal or identifiable information.”

But the added language might not do much to override the clause that the Pentagon may use the company’s AI system for all lawful purposes, which could include collecting and analyzing sensitive personal information. “OpenAI can say whatever it wants in its agreement … but the Pentagon’s gonna use the tech for what it perceives to be lawful,” says Jessica Tillipman, a law professor at the George Washington University Law School. That could include domestic surveillance. “Most of the time, companies are not going to be able to stop the Pentagon from doing anything,” she says.

The language also leaves open questions about “inadvertent” surveillance, and the surveillance of foreign nationals or undocumented immigrants living in the US. “What happens when there’s a disagreement about what the law is, or when the law changes?” says Tillipman.

OpenAI did not respond to a request for comment. The company has not publicly shared the full text of its new contract. 

Beyond the contract, OpenAI says that it will impose technical safeguards to enforce its red line against surveillance, including a “safety stack” that monitors and blocks prohibited uses. The company also says it will deploy its own employees to work with the Pentagon and remain in the loop. But it’s unclear how a safety stack would constrain the Pentagon’s use of the AI, and to what extent OpenAI’s employees would have visibility into how its AI systems are used. More important, it’s unclear whether the contract gives OpenAI the power to block a legal use of the technology. 

But that might not be a bad thing. Giving an AI company power to pull the plug on its technology in the middle of government operations also carries its own risks. “You wouldn’t want the US military to ever be in a situation where they legitimately needed to take actions to protect this country’s national security, and you had a private company turn off technology,” says Voss. But that doesn’t mean there shouldn’t be hard lines drawn by Congress, she says.

None of these questions are simple. They involve brutally difficult trade-offs between privacy and national security. And that’s why perhaps they should be decided by the public—not in backroom negotiations between the executive branch and a handful of AI companies. For now, military AI is being regulated by contracts, not legislation. 

Some lawmakers are starting to weigh in. On Monday, Senator Ron Wyden of Oregon will seek bipartisan support for legislation addressing mass surveillance. He has championed bills restricting the government’s purchase of commercial data, including the Fourth Amendment Is Not For Sale Act, which was first introduced in 2021 but has not been passed into law. “Creating AI profiles of Americans based on that data represents a chilling expansion of mass surveillance that should not be allowed,” he said in a recent statement.  

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.

Coming soon: our 10 Things That Matter in AI Right Now

For years, MIT Technology Review’s newsroom has been ahead of the curve, tracking the developments in AI that matter and explaining what they mean. Now, our world-leading AI team is creating something definitive: the 10 Things That Matter in AI Right Now.

Publishing in April to be launched at our flagship AI event, EmTech AI, this special report will reveal what our expert journalists are tracking most closely, what breakthroughs have excited them, and what transformations they see on the horizon. It’s our authoritative snapshot of where AI is heading in the year ahead—a curated expert list of 10 technologies, emerging trends, bold ideas, and powerful movements reshaping our world.

Attendees at EmTech AI will get much more than an exclusive heads-up of what made our 10 Things That Matter in AI Right Now list. We’re at a pivotal moment as AI moves from pilot testing into core business infrastructure, and to reflect that we’ve curated a program that will help you navigate what’s going on, and get ahead of what’s coming next. 

We’ll hear from top leaders at OpenAI, Walmart, General Motors, Poolside, MIT, the Allen Institute for AI (Ai2) and SAG-AFTRA. Topics will include everything from how organizations are preparing for AI agents to how AI will change the future of human expression. As well as networking with speakers, you’ll have the chance to mingle with MIT Technology Review’s editors too. Download readers get 10% off tickets, so what are you waiting for? See you there!

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Anthropic says it plans to sue the Pentagon
It believes the DoD’s ban on its software is unlawful. (BBC) 
+ CEO Dario Amodei has nonetheless apologized for a leaked memo criticizing Trump. (Axios)
+ Trump, meanwhile, says he fired Anthropic “like dogs.” (The Guardian)
+ In happier news for Anthropic, its models can remain in Microsoft products.(CNBC)

2 The Pentagon has been secretly testing OpenAI models for years
Which shows exactly how effective OpenAI’s ban on military use of its models has been. (Wired $)

3 A new lawsuit says Trump’s TikTok deal helped firms that ‘personally enriched’ him
The suit aims to reverse the sale of the app’s US operations. (CBS News)
+ It could shed light on the majority American-owned joint venture for TikTok. (Reuters)

4 AI could give smart homes a reboot 
Google and Amazon are betting on smarter assistants—but not everyone’s convinced (NYT)

5 Iran has struck Amazon data centers, rattling the Gulf’s AI ambitions
The first military hit on a US hyperscaler has shaken the region’s tech sector. (FT $)
+ The conflict has thrown a spotlight on AI’s current use in warfare—and what’s next. (Nature)

6 Trump and tech CEOs have promised to protect consumers from AI’s energy costs
Google, Microsoft, Meta, Amazon, OpenAI, Oracle and xAI have all signed the pledge. (Axios)
+ But what is AI’s true energy footprint? We did the math. (MIT Technology Review)

7 Meta’s getting sued over surveillance through smart glasses  
The suit claims Meta misled users over the devices’ privacy features. (TechCrunch)

8 There’s a new field of study: researching ‘AI societies’
Scientists are examining human behavior without even involving humans. (Nature)
+ Hundreds of AI agents built their own society in Minecraft. (MIT Technology Review)

9 Oh great, teenage boys are using ChatGPT to chat up girls
Of all the things to outsource to AI, flirting surely ain’t it. (Vox)

10 The mythical Nintendo PlayStation has a new home 
The US National Video Museum has bought the fabled console’s development kit. (Engadget)

Quote of the day

“It’s sort of bitterly ironic.” 

—Dean Ball, a former Trump administration AI adviser, tells Politico that the Anthropic spat contradicts the president’s pledge to cut bureaucratic red tape for tech.

One more thing

KATHERINE LAM

These scientists are working to extend the life span of pet dogs—and their owners

Gavesh’s journey began with a Facebook job advert promising a better life. Instead, he was trafficked into “pig butchering”—a form of fraud where scammers build close relationships with online targets to extract money.

We spoke to Gavesh and five other workers from inside the scam industry, as well as anti-trafficking experts and technology specialists. Their testimony reveals how global tech platforms have industrialized this criminal trade—and why those same companies now hold the key to dismantling it. Read the full story.

—Peter Guest and Emily Fishbein

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 or skeet ’em at me.) 

+ The Blood Moon of March 3 was sublime.
+ Orysia Zabeida’s imperfect animations, drawn frame-by-frame from memory, are hypnotizing.
+ This stunning snap of a white whale calf scooped the top prize at the World Nature Photography Awards.
+ Two “Lazarus” marsupial species just came back from the dead in a big win for biodiversity.