The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

Anthropic was the star of the show at San Francisco's AI-centric conference.

OpenAI ignored three warnings that a ChatGPT user was dangerous — including its own mass-casualty flag — while he stalked and harassed his ex-girlfriend, a new lawsuit alleges.

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.

Constellations 

—Constellations is a short story by Jeff VanderMeer, the author of the critically acclaimed, bestselling Southern Reach series.  

A spacecraft has crash-landed on a hostile planet. The only survivors are three members of the exploration team and the ship’s AI mind.  

Little exists on the planet except deserts of snow. But alien artifacts lie nearby, in the form of 13 domes, spread across the terrain. Linked by cables threaded through metal posts, the domes form a series of paths—the only hope for life support. 

As the team treks across the frozen hellscape, they discover the remains of countless astronauts from unknown species who followed the same route before them. Is their trail a path to salvation, or a cosmic trap?

Read the rest of this short story in full. 

This story is from the next issue of our print magazine, packed with stories all about nature. Subscribe now to read the full thing when it lands on Wednesday, April 22. 

The must-reads 

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

1 OpenAI has joined Anthropic in curbing an AI release over security fears 
Only select partners will get its new cybersecurity tool. (Axios)  
+ Anthropic said only yesterday that its new AI is too dangerous for the public. (NBC News) 
+ Top models may not be so public going forward. (Bloomberg $)  
+ The US has summoned bank CEOs to discuss the risks. (FT $)  
 
2 Florida is investigating OpenAI over an alleged role in a shooting  
ChatGPT may have helped someone plan a mass shooting in Florida. (WSJ $)  
+ OpenAI has backed a bill that would limit AI liability for deaths. (Wired $)  
+ The family of a victim plans to sue the company. (Guardian)  
+ AI’s role in delusions is dividing opinion. (MIT Technology Review)  
 
3 Volkswagen is ditching EV production for more gasoline models  
The carmaker will stop making its top electric vehicle in the US. (NYT $)  
+ Instead, it will concentrate on developing a new SUV. (Ars Technica)  
+ Western carmakers are retreating from electric vehicles. (Guardian) 
 
4 Elon Musk’s xAI has sued Colorado over an AI anti-discrimination law  
It’s the first state bill of its kind. (Bloomberg $)  
+ xAI says it will force the firm to “promote the state’s ideological views.” (FT $) 

5 A fifth of US employees say AI now does parts of their job  
The survey found half of US adults used AI in the past week. (NBC News)  
+ Missing data could shed light on AI’s job impact. (MIT Technology Review)  
 
6 Google DeepMind’s CEO wants to automate drug design  
He hopes to develop AI capable of curing all diseases. (The Economist)  
+ A scientist is using AI to hunt for antibiotics. (MIT Technology Review) 

7 China’s Unitree is launching a viral robot on the international market
R1, its cheapest humanoid, will go on sale outside China next week. (SCMP)
+ Gig workers are training humanoids at home. (MIT Technology Review)

8 An experiment on Artemis II astronauts could reshape space medicine
Chips containing their cells will model spaceflight’s effects. (WP $)

9 A pro-Iran meme machine is trolling Trump with AI Lego cartoons
The videos have racked up millions of views. (Wired $) 
+ You can learn to love AI slop. (MIT Technology Review)

10 Short breaks could erase 10 years of social media brain damage 
Studies show that a two-week detox could have a dramatic benefit. (WP $) 

Quote of the day 

“AI should advance mankind, not destroy it. We’re demanding answers on OpenAI’s activities that have hurt kids, endangered Americans, and facilitated the recent FSU mass shooting.”

—Florida Attorney General James Uthmeier explains on X why he’s probing OpenAI. 

One More Thing 

TOM HUMBERSTONE

It’s time to retire the term “user” 

People have been called “users” for a long time. Often, it’s the right word to describe people who use software. But “users” is also unspecific enough to refer to just about everyone. It can accommodate almost any big idea or long-term vision. 

We use—and are used by—computers and platforms and companies. The label “user” suggests these interactions are deeply transactional, but they’re frequently quite personal. Is it time for a more human vocabulary? Read the full story. 

—Taylor Majewski 

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.) 

+ This flawless levitation trick will leave you questioning the laws of physics.
+ The World Press Photo winners expose the beauty (and brutality) of our planet.
+ Over 3 million pink flamingos gathered to create a stunning pink horizon.
+ Behold the galaxy’s enormity in this comparison of its largest known star to Earth

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 fake grass a bad idea? The AstroTurf wars are far from over. 

In 2001, Americans installed just over 7 million square meters of synthetic turf. By 2024, that number was 79 million square meters—enough to carpet all of Manhattan and then some. The increase worries folks who study microplastics and environmental pollution.  

While the plastic-making industry insists that synthetic fields are safe if properly installed, lots of researchers think that isn’t so. Find out why AstroTurf has ignited heated debates.

—Douglas Main 

This story is from the next issue of our print magazine, packed with stories all about nature. Subscribe now to read the full thing when it lands on Wednesday, April 22. 

Mustafa Suleyman: AI development won’t hit a development wall anytime soon—here’s why 

—Mustafa Suleyman, Microsoft AI CEO and Google DeepMind co-founder 

The skeptics keep predicting that AI compute will soon hit a wall—and keep getting proven wrong. To understand why that is, you need to look at the forces driving the AI explosion.  

Three advances are enabling exponential progress: faster basic calculators, high-bandwidth memory, and technologies that turn disparate GPUs into enormous supercomputers. Where does all this get us? Read the full op-ed on the future of AI development to learn more. 
 

Desalination technology, by the numbers 

—Casey Crownhart 

When I started digging into desalination technology for a new story, I couldn’t help but obsess over the numbers. 

I knew on some level that desalination—pulling salt out of seawater to produce fresh water—was an increasingly important technology, especially in water-stressed regions including the Middle East. But just how much some countries rely on desalination, and how big a business it is, still surprised me.

Here are the extraordinary numbers behind the crucial water source. 

This story is from The Spark, our weekly newsletter on the tech that could combat the climate crisis. Sign up to receive it in your inbox every Wednesday. 

The must-reads 

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

1 Meta has launched the first AI model from its Superintelligence Labs
Muse Spark is the company’s first model in a year. (Reuters $) 
+ The closed model brings reasoning capabilities to the Meta AI app. (Engadget) 
+ It’s built by Meta’s Superintelligence Labs, the unit led by Alexandr Wang. (TechCrunch) 

2 Anthropic has lost a bid to pause the Pentagon’s blacklisting 
An appeals court in Washington, DC denied the request. (CNBC) 
+ A California judge had temporarily blocked the blacklisting in March. (NPR) 
+ The mixed rulings leave Anthropic in a legal limbo. (Wired $) 
+ And open doors for smaller AI rivals. (Reuters $) 

3 New evidence suggests Adam Back invented Bitcoin 
The British cryptographer may be the real Satoshi Nakamoto. (NYT $) 
+ Back denies the claims. (BBC) 
+ There’s a dark side to crypto’s permissionless dream. (MIT Technology Review) 

4 Gen Z is cooling on AI 
The share feeling angry about it has risen from 22% to 31% in a year. (Axios) 
+ Anti-AI protests are also growing. (MIT Technology Review) 

5 War in the Gulf could tilt the cloud race toward China 
Huawei is pitching “multi-cloud” resilience to Gulf clients. (Rest of World) 

6 Meta has killed a leaderboard of its AI token users 
It showed the top 250 users. (The Information $) 
+ Meta blamed data leaks for the shutdown. (Fortune) 
+ It encouraged “tokenmaxxing,” a growing phenomenon in Big Tech. (NYT $) 

7 Did Artemis II really tell us anything new about space? 
Or was it primarily a PR exercise? (Ars Technica) 

8 Israeli attacks have brutally exposed Lebanon’s digital infrastructure 
It’s managing a modern crisis without modern technology. (Wired $) 

9 AI models could offer mathematicians a common language 
They hope it will simplify the process of verifying proofs. (Economist)  

10 A “self-doxing’ rave is helping trans people stay safe online 
It’s among a series of digital self-defenses. (404 Media) 

Quote of the day 

“I feel like anything that I’m interested in has the potential of maybe getting replaced, even in the next few years.” 

—Sydney Gill, a freshman at Rice University, tells the New York Times why she’s soured on AI. 

One More Thing 

Inside the hunt for new physics at the world’s largest particle collider 

In 2012, data from CERN’s Large Hadron Collider (LHC) unearthed a particle called the Higgs boson. The discovery answered a nagging question: where do fundamental particles, such as the ones that make up all the protons and neutrons in our bodies, get their mass?

But now particle physicists have reached an impasse in their quest to discover, produce, and study new particles at colliders. Find out what they’re trying to do about it.

—Dan Garisto 

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.) 

+ Enjoy this tale of the “joke” sound that accidentally defined 90s rave culture. 
+ Take a nostalgic trip through the websites of the early 00s. 
+ One for animal lovers: sperm whales have teamed up to support a newborn. 
+ Here’s a long overdue answer to a vital question: can the world’s largest mousetrap catch a limousine? 

When I started digging into desalination technology for a new story, I couldn’t help but obsess over the numbers.

I’d known on some level that desalination—pulling salt out of seawater to produce fresh water—was an increasingly important technology, especially in water-stressed regions including the Middle East. But just how much some countries rely on desalination, and how big a business it is, still surprised me.

For more on how this crucial water infrastructure is increasingly vulnerable during the war in Iran, check out my latest story. Here, though, let’s look at the state of desalination technology, by the numbers.

Desalination produces 77% of all fresh water and 99% of drinking water in Qatar.

Globally, we rely on desalination for just 1% of fresh-water withdrawals. But for some countries in the Middle East, and particularly for the Gulf Cooperation Council countries (Bahrain, Qatar, Kuwait, the United Arab Emirates, Saudi Arabia, and Oman), it’s crucial.

Qatar, home to over 3 million people, is one of the most staggering examples, with nearly all its drinking water supplies coming from desalination. But many major cities in the region couldn’t exist without the technology. There are no permanent rivers on the Arabian Peninsula, and supplies of fresh water are incredibly limited, so countries rely on facilities that can take in seawater and pull out the salt and other impurities.

The Middle East is home to just 6% of the world’s population and over 27% of its desalination facilities.

The region has historically been water-scarce, and that trend is only continuing as climate change pushes temperatures higher and changes rainfall patterns.

Of the 17,910 desalination facilities that are operational globally, 4,897 are located in the Middle East, according to a 2026 study in npj Clean Water. The technology supplies not only municipal water used by homes and businesses, but also industries including agriculture, manufacturing, and increasingly data centers.

One massive desalination plant in Saudi Arabia produces over 1 million cubic meters of fresh water per day.

The Ras Al-Khair water and power plant in Eastern Province, Saudi Arabia, is one of a growing number of gigantic plants that output upwards of a million cubic meters of water each day. That amount of water can meet the needs of millions of people in Riyadh City. Producing it takes a lot of power—the attached power plant has a capacity of 2.4 gigawatts.

While this plant is just one of thousands across the region, it’s an example of a growing trend: The average size of a desalination plant is about 10 times what it was 15 years ago, according to data from the International Energy Agency. Communities are increasingly turning to larger plants, which can produce water more efficiently than smaller ones.

Between 2024 and 2028, the Middle East’s desalination capacity could grow by over 40%.

Desalination is only going to be more crucial for life in the Middle East. The region is expected to spend over $25 billion on capital expenses for desalination facilities between 2024 and 2028, according to the 2026 npj Clean Water study. More massive plants are expected to come online in Saudi Arabia, Iraq, and Egypt during that time.

All this growth could consume a lot of electricity. Between growth of the technology generally and the move toward plants that use electricity rather than fossil fuels, desalination could add 190 terawatt-hours of electricity demand globally by 2035, according to IEA data. That’s the equivalent of about 60 million households.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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.

Desalination plants in the Middle East are increasingly vulnerable 

As the conflict in Iran has escalated, a crucial resource is under fire: the desalinization technology that supplies water in the region. 
 
President Donald Trump has threatened to destroy “possibly all desalinization plants” in Iran if the Strait of Hormuz is not reopened. The impact on farming, industry, and—crucially—drinking in the Middle East could be severe. Find out why. 

—Casey Crownhart 

This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. 

AI is changing how small online sellers decide what to make 

For small entrepreneurs, deciding what to sell and where to make it has traditionally been a slow, labor-intensive process. Now that work is increasingly being done by AI.   

Tools like Alibaba’s Accio compress weeks of product research and supplier hunting into a single chat. Business owners and e-commerce experts say they’re making sourcing more accessible—and slashing the time from product idea to launch.  

Read the full story on how AI is leveling the path to global manufacturing. 

—Caiwei Chen 

The gig workers who are training humanoid robots at home 

When Zeus, a medical student in Nigeria, returns to his apartment from a long day at the hospital, he straps his iPhone to his forehead and records himself doing chores.  
 
Zeus is a data recorder for Micro1, which sells the data he collects to robotics firms. As these companies race to build humanoids, videos from workers like Zeus have become the hottest new way to train them.   
 
Micro1 has hired thousands of them in more than 50 countries, including India, Nigeria, and Argentina. The jobs pay well locally, but raise thorny questions around privacy and informed consent. The work can be challenging—and weird. Read the full story.  

—Michelle Kim 

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 Anthropic’s new model found security problems in every OS and browser 
Claude Mythos has been heralded as a cybersecurity “reckoning.” (The Verge)  
+ Anthrophic is limiting the rollout over hacking fears. (CNBC) 
+ It’s also launching a project that lets Mythos flag vulnerabilities. (Gizmodo) 
+ Apple, Google, and Microsoft have joined the initiative. (ZDNET) 

2 Iranian hackers are targeting American critical infrastructure 
Their focus is on energy and water infrastructure. (Wired) 
+ They’re targeting industrial control devices. (TechCrunch)  

3 Google’s AI Overviews deliver millions of incorrect answers per hour 
Despite a 90% accuracy rate. (NYT $) 
+ AI means the end of internet search as we’ve known it. (MIT Technology Review) 

4 Elon Musk is trying to oust OpenAI CEO Sam Altman in a lawsuit 
As remedies for Altman allegedly defrauding him. (CNBC) 
+ Musk wants any damages given to OpenAI’s nonprofit arm. (WSJ $) 

5 ICE has admitted it’s using powerful spyware 
The tools that can intercept encrypted messages. (NPR) 
+ Immigration agencies are also weaponizing AI videos. (MIT Technology Review) 

6 Greece has joined the countries banning kids from social media 
Under-15s will be blocked from 2027. (Reuters) 
+ Australia introduced the world’s first social media ban for children. (Guardian) 
+ Indonesia recently rolled out the first one in Southeast Asia. (DW)  
+ Experts say they’re a lazy fix. (CNBC) 

7 Intel will help Elon Musk build his Terafab in Texas 
They aim to manufacture chips for AI projects. (Engadget) 
+ Musk says it will be the largest-ever semiconductor factory. (Engadget) 
+ Future AI chips could be built on glass. (MIT Technology Review)  

8 TikTok is building a second billion-euro data center in Finland 
It’s moving data storage for European users. (Reuters) 
+ Finland has become a magnet for data centers. (Bloomberg $) 
+ But nobody wants one in their backyard. (MIT Technology Review) 

9 Plans for Canada’s first “virtual gated community” have sparked a row 
The AI-powered surveillance system has divided neighbors. (Guardian) 
+ Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review) 

10 The high-tech engineering of the “space toilet” has been revealed 
Artemis II is the first mission to carry one around the world. (Vox) 

Quote of the day 

“This case has always been about Elon generating more power and more money for what he wants. His lawsuit remains nothing more than a harassment campaign that’s driven by ego, jealousy and a desire to slow down a competitor.” 

—OpenAI criticizes Musk’s legal action in an X post. 

One More Thing 

Inside the US government’s brilliantly boring websites 

You may not notice it, but your experience on every US government website is carefully crafted. 

Each site aligns an official web design and a custom typeface. They aim to make government websites not only good-looking but accessible and functional for all. 

MIT Technology Review dug into the system’s history and features. Find out what we discovered. 

—Jon Keegan 

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.) 

+ Rejoice in the splendor of the “Earthset” image captured by Artemis II. 
+ Meet the fearless cat chasing off bears. 
+ This document vividly explains what makes the octopus so unique. 
+ Revealed: the rhythmic secret that makes emo music so angsty. 

The new model will be used by a small number of high-profile companies to engage in defensive cybersecurity work.

Anthropic bulked up its compute deal with Google and Broadcom as the company has seen its run-rate revenue surge to $30 billion.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

As the conflict in Iran has escalated, a crucial resource is under fire: the desalination technology that supplies water across much of the region.

In early March, Iran’s foreign minister accused the US of attacking a desalination plant on Qeshm Island in the Strait of Hormuz and disrupting the water supply to nearly 30 villages. (The US denied responsibility.) In the weeks since, both Bahrain and Kuwait have reported damage to desalination plants and blamed Iran, though Iran also denied responsibility.

In late March, President Donald Trump threatened the destruction of “possibly all desalinization plants” in Iran if the Strait of Hormuz was not reopened. Since then, he’s escalated his threats against Iran, warning of plans to attack other crucial civilian infrastructure like power plants and bridges.

Countries in the Middle East, particularly the Gulf states, rely on the technology to turn salt water into fresh water for farming, industry, and—crucially—drinking. The mounting attacks and threats to date highlight just how vital the industry is to the region—a situation made even more precarious by rising temperatures and extreme weather driven by climate change.

Right now, 83% of the Middle East is under extremely high water stress, says Liz Saccoccia, a water security associate at the World Resources Institute. Future projections suggest that’s going to increase to about 100% by 2050, she adds: “This is a continuing trend, and it’s getting worse, not better.”

Here’s a look at desalination technology in the Middle East and what wartime threats to the critical infrastructure could mean for people in the region. 

A vital resource

Desalination technology has helped provide water supplies in the Middle East since the early 20th century and became widespread in the 1960s and 1970s.

There are two major categories of desalination plants. Thermal plants use heat to evaporate water, leaving salt and other impurities behind. The vapor can then be condensed into usable fresh water. The alternative is membrane-based technology like reverse osmosis, which pushes water through membranes that have tiny pores—so small that salt can’t get through.

Early desalination plants in the Middle East were the first type, burning fossil fuels to evaporate water, leaving the salt behind. This technique is incredibly energy-intensive, and over time, processes that rely on filters became the dominant choice.

Membrane technologies have made up essentially all new desalination capacity in recent years; the last major thermal plant built in the Gulf came online in 2018. Many reverse osmosis plants still rely on fossil fuels, but they’re more efficient. Since then, membrane technologies have added more than 15 million cubic meters of daily capacity—enough to supply water to millions of people.

Capacity has expanded quickly in recent years; between 2006 and 2024, countries across the Middle East collectively spent over $50 billion building and upgrading desalination facilities, and nearly that much operating them.

Today, there are nearly 5,000 desalination plants operational across the Middle East.

And looking ahead, growth is continuing. Between 2024 and 2028, daily capacity is expected to grow from about 29 million cubic meters to 41 million cubic meters.

Uneven vulnerabilities

Some countries rely on the technology more than others. Iran, for example, uses desalination for about 3% of its municipal fresh water. The country has access to groundwater and some surface water, including rivers, though these resources are being stretched thin by agriculture and extreme drought.

Other nations in the region, particularly the Gulf countries (Bahrain, Qatar, Kuwait, the United Arab Emirates, Saudi Arabia, and Oman), have much more limited water resources and rely heavily on desalination. Across these six nations, all but the UAE get more than half their drinking water from desalination, and for Bahrain, Qatar, and Kuwait the figure is more than 90%.

“The Gulf countries are much, much more vulnerable to attacks on their desalination plants than Iran is,” says David Michel, a senior associate in the global food and water security program at the Center for Strategic and International Studies.

There are thousands of desalination facilities across the region, so the system wouldn’t collapse if a small number were taken offline, Michel says. However, in recent years there’s been a trend toward larger, more centralized plants.

The average desalination plant is about 10 times larger than it was 15 years ago, according to data from the International Energy Agency. The largest desalination plants today can produce 1 million cubic meters of water daily, enough for hundreds of thousands of people. Taking one or more of these massive facilities offline could have a significant effect on the system, Michel says.

Escalating threats

Desalination facilities are quite linear, meaning there are multiple steps and pieces of equipment that work in sequence—and the failure of a component in that chain can take an entire facility down. Attacks on water inlets, transportation networks, and power supplies can also disrupt the system, Michel says. 

During the Gulf War in 1991, Iraqi forces pumped oil into the gulf, contaminating the water and shutting down desalination plants in Kuwait. 

The facilities are also generally located close to other targets in this conflict. Desalination is incredibly energy intensive, so about three-quarters of facilities in the region are next to power plants. Trump has repeatedly threatened power plants in Iran. In response, Iran’s military has said that if civilian targets are hit, the country will respond with strikes that are “much more devastating and widespread.” Other governments and organizations, including the United Nations, the European Union, and the Red Cross, have broadly condemned threats to infrastructure as illegal. 

But war isn’t the only danger facing these plants, even if it is the most immediate. Some studies have suggested that global warming could strengthen cyclones in the region, and these extreme weather events could force shutdowns or damage equipment.

Water pollution could also cause shutdowns. Oil spills, whether accidental or intentional, as in the case of the Gulf War, can  wreak havoc. And in 2009, a red algae bloom closed desalination plants in Oman and the United Arab Emirates for weeks. The algae fouled membranes and blocked the plants from being able to take water in from the Persian Gulf and the Gulf of Oman.

Desalination facilities could become more resilient to threats in the future, and they may need to as their importance continues to grow. 

There’s increasing interest in running desalination facilities at least partially on solar power, which could help reduce dependence on the oil that powers most facilities today. The Hassyan seawater desalination project in the UAE, currently under construction, would be the largest reverse osmosis plant in the world to operate solely with renewable energy. 

Another way to increase resilience is for countries to build up more strategic water storage to meet demand. Qatar recently issued new policies that aim to improve management and storage of desalinated water, for example. Countries could also work together to invest in shared infrastructure and policies that help strengthen the water supply through the region. 

Preparedness, resilience, and cooperation will be key for the Middle East broadly as critical infrastructure, including the water supply, is increasingly under threat. 

“The longer the conflict goes on, the more likely we’ll see significant water infrastructure damage,” says Ginger Matchett, an assistant director at the Atlantic Council. “What worries me is that after this war ends, some of the lessons will show how water can be weaponized more strategically than previously imagined.” 

On a recent episode of Equity, we talked to Arena Private Wealth to explore a growing trend: family offices bypassing VCs to gain direct exposure to AI startups, turning them from passive investors into active participants.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The one piece of data that could actually shed light on your job and AI 

Within Silicon Valley’s orbit, an AI-fueled jobs apocalypse is spoken about as a given. Now even economists who have downplayed the threat are coming around to the idea.  

Alex Imas, based at the University of Chicago, is one of them. He believes that any plan to address AI’s impact will depend on collecting one vital piece of data: price elasticity. 

Imas argues that “we need a Manhattan Project” for this. Read the full story to find out why. 

—James O’Donnell 

This article is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday. 

Four things we’d need to put data centers in space 

In January, Elon Musk’s SpaceX applied to launch up to 1 million data centers into Earth’s orbit. The goal? To fully unleash the potential of AI—without triggering an environmental crisis on Earth. 

SpaceX is among a growing list of tech firms pursuing orbital computing infrastructure. But can their plans really work? Here are four must-haves for making space-based data centers a reality. 

—Tereza Pultarova 

This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. 

The must-reads 

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

1 Trump has again proposed major cuts to US science and tech spending 
He wants to slash nearly every science-focused agency. (Ars Technica) 
+ If Trump gets his way, the US could face a costly brain drain. (NYT $)  
+ Top research talent is already fleeing the country. (Guardian)  
+ Basic science deserves our boldest investment. (MIT Technology Review) 

2 Sam Altman lobbied against AI regulations he publicly welcomed  
A bombshell report reveals many OpenAI insiders don’t trust him. (The New Yorker $) 
+ Some have called him a sociopath. (Futurism) 
+ OpenAI’s CFO fears it won’t be IPO-ready this year. (The Information $)  
+ A war over AI regulation is brewing in the US. (MIT Technology Review) 

3 NASA’s Artemis II has broken humanity’s all-time distance record 
The astronauts have flown farther than any humans before them. (BBC) 
+ Their mission includes MIT-developed technology. (Axios) 

4 Chinese tech firms are selling intel “exposing” US forces 
It comes from combining AI with open-source data.. (WP $) 
+ AI is turning the Iran conflict into theater. (MIT Technology Review) 

5 War is pushing countries to ditch hyperscalers 
Driven by Iran naming tech giants as military targets. (Rest of World) 
+ No one wants a data center in their backyard. (MIT Technology Review) 

6 OpenAI, Anthropic, and Google have united against China’s AI copying 
They’re sharing information on “adversarial distillation” (Bloomberg $) 

7 Anduril and Impulse Space are working on Trump’s “Golden Dome” 
They’re developing space-based missile tracking for the project. (Gizmodo)  

8 OpenAI has urged California to probe Elon Musk’s “anti-competitive behavior.” 
It accuses Musk of trying to “take control of the future of AGI.” (Reuters $) 
+ And claims he coordinated attacks with Mark Zuckerberg. (CNBC) 
+ A former Tesla president has revealed how he survived working for Musk. (WP $) 

9 DeepSeek’s new AI model will run on Huawei chips 
It’s expected to launch in the next few weeks. (The Information $) 

10 Memes have nuked our culture 
Internet “brain rot” has escaped our phones to take over everything. (NYT $) 

Quote of the day 

“I must say, it was actually quite nice.” 

 —Astronaut Victor Glover tells President Donald Trump what it was like when Artemis II was out of communication with the rest of humanity, The New York Times reports. 

One More Thing 

Inside the controversial tree farms powering Apple’s carbon-neutral goal  

In 2020, Apple set a goal to become net zero by the end of the decade. To hit that target, the company is offsetting its emissions by planting millions of eucalyptus trees in Brazil. 

Apple is betting that the strategy will lead to a greener future. But critics warn that the industrial tree farms will do more harm than good. 

Find out why the plans have sparked a backlash. 

—Gregory Barber 

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.) 

+ Japan’s automated bike garage is a cyclist’s dream come true.  
+ This deep dive into bird behavior reveals the secrets of their dining habits. (Big thanks to reader Terry Gordon for the find!) 
+ The first photo from the Artemis astronauts vividly captures the glow of our atmosphere. 
+ There’s a new contender for the world’s most gorgeous website: RobertDeNiro.com. 

arXiv:2604.03286v1 Announce Type: new Abstract: The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single-pixel camera or a scanning photocurrent microscope, we demonstrate how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, we further illustrate how LLM-assisted tools can be extended into autonomous AI agents capable of independently operating laboratory instruments and iteratively refining control strategies. This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress.

arXiv:2604.03376v1 Announce Type: new Abstract: Current literature on radiology report evaluation has focused primarily on designing LLM-based metrics and fine-tuning small models for chest X-rays. However, it remains unclear whether these approaches are robust when applied to reports from other modalities and anatomies. Which model and prompt configurations are best suited to serve as LLM judges for radiology evaluation? We conduct a thorough correlation analysis between expert and LLM-based ratings. We compare three existing LLM-as-a-judge metrics (RadFact, GREEN, and FineRadScore) alongside VERT, our proposed LLM-based metric, using open- and closed-source models (reasoning and non-reasoning) of different sizes across two expert-annotated datasets, RadEval and RaTE-Eval, spanning multiple modalities and anatomies. We further evaluate few-shot approaches, ensembling, and parameter-efficient fine-tuning using RaTE-Eval. To better understand metric behavior, we perform a systematic error detection and categorization study to assess alignment of these metrics against expert judgments and identify areas of lower and higher agreement. Our results show that VERT improves correlation with radiologist judgments by up to 11.7% relative to GREEN. Furthermore, fine-tuning Qwen3 30B yield gains of up to 25% using only 1,300 training samples. The fine-tuned model also reduces inference time up to 37.2 times. These findings highlight the effectiveness of LLM-based judges and demonstrate that reliable evaluation can be achieved with lightweight adaptation.

arXiv:2604.03506v1 Announce Type: new Abstract: Despite the large corpus of biology training text, the impact of reasoning models on biological research generally lags behind math and coding. In this work, we show that biology questions from current large-scale reasoning datasets do not align well with modern research topic distributions in biology, and that this topic imbalance may negatively affect performance. In addition, we find that methods for extracting challenging and verifiable research problems from biology research text are a critical yet underdeveloped ingredient in applying reinforcement learning for better performance on biology research tasks. We introduce BioAlchemy, a pipeline for sourcing a diverse set of verifiable question-and-answer pairs from a scientific corpus of biology research text. We curate BioAlchemy-345K, a training dataset containing over 345K scientific reasoning problems in biology. Then, we demonstrate how aligning our dataset to the topic distribution of modern scientific biology can be used with reinforcement learning to improve reasoning performance. Finally, we present BioAlchemist-8B, which improves over its base reasoning model by 9.12% on biology benchmarks. These results demonstrate the efficacy of our approach for developing stronger scientific reasoning capabilities in biology. The BioAlchemist-8B model is available at: https://huggingface.co/BioAlchemy.

arXiv:2604.03630v1 Announce Type: new Abstract: Spatial transcriptomics (ST) enables gene expression mapping within anatomical context but remains costly and low-throughput. Hematoxylin and eosin (H\&E) staining offers rich morphology yet lacks molecular resolution. We present \textbf{\ours} (\textbf{S}patial \textbf{T}ranscriptomics and hist\textbf{O}logy \textbf{R}epresentation \textbf{M}odel), a foundation model trained on 1.2 million spatially resolved transcriptomic profiles with matched histology across 18 organs. Using a hierarchical architecture integrating morphological features, gene expression, and spatial context, STORM bridges imaging and omics through robust molecular--morphological representations. STORM enhances spatial domain discovery, producing biologically coherent tissue maps, and outperforms existing methods in predicting spatial gene expression from H\&E images across 11 tumor types. The model is platform-agnostic, performing consistently across Visium, Xenium, Visium HD, and CosMx. Applied to 23 independent cohorts comprising 7,245 patients, STORM significantly improves immunotherapy response prediction and prognostication over established biomarkers, providing a scalable framework for spatially informed discovery and clinical precision medicine.

arXiv:2604.03964v1 Announce Type: new Abstract: Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remains fragmented across heterogeneous artifacts that agents cannot readily operationalize. This gap between abundant scientific know-how and usable agent capabilities is a key bottleneck for building effective scientific agents. We present SkillFoundry, a self-evolving framework that converts such resources into validated agent skills, reusable packages that encode task scope, inputs and outputs, execution steps, environment assumptions, provenance, and tests. SkillFoundry organizes a target domain as a domain knowledge tree, mines resources from high-value branches, extracts operational contracts, compiles them into executable skill packages, and then iteratively expands, repairs, merges, or prunes the resulting library through a closed-loop validation process. SkillFoundry produces a substantially novel and internally valid skill library, with 71.1\% of mined skills differing from existing skill libraries such as SkillHub and SkillSMP. We demonstrate that these mined skills improve coding agent performance on five of the six MoSciBench datasets. We further show that SkillFoundry can design new task-specific skills on demand for concrete scientific objectives, and that the resulting skills substantially improve performance on two challenging genomics tasks: cell type annotation and the scDRS workflow. Together, these results show that automatically mined skills improve agent performance on benchmarks and domain-specific tasks, expand coverage beyond hand-crafted skill libraries, and provide a practical foundation for more capable scientific agents.

arXiv:2604.04145v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting plays a critical role in power system dispatch and market participation. Because PV generation is highly sensitive to weather conditions and cloud motion, accurate forecasting requires effective modeling of complex spatiotemporal dependencies across multiple information sources. Although recent studies have advanced AI-based forecasting methods, most fail to fuse temporal observations, satellite imagery, and textual weather information in a unified framework. This paper proposes Solar-VLM, a large-language-model-driven framework for multimodal PV power forecasting. First, modality-specific encoders are developed to extract complementary features from heterogeneous inputs. The time-series encoder adopts a patch-based design to capture temporal patterns from multivariate observations at each site. The visual encoder, built upon a Qwen-based vision backbone, extracts cloud-cover information from satellite images. The text encoder distills historical weather characteristics from textual descriptions. Second, to capture spatial dependencies across geographically distributed PV stations, a cross-site feature fusion mechanism is introduced. Specifically, a Graph Learner models inter-station correlations through a graph attention network constructed over a K-nearest-neighbor (KNN) graph, while a cross-site attention module further facilitates adaptive information exchange among sites. Finally, experiments conducted on data from eight PV stations in a northern province of China demonstrate the effectiveness of the proposed framework. Our proposed model is publicly available at https://github.com/rhp413/Solar-VLM.

arXiv:2604.04247v1 Announce Type: new Abstract: Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without parameter changes. For example, existing methods (like ACE or GEPA) can learn system prompts to improve accuracy based on previous agent runs. However, these methods primarily focus on single-agent or low-parallelism settings. This fundamentally limits their ability to efficiently learn from a large set of collected agentic traces. It would be efficient and beneficial to run prompt learning in parallel to accommodate the growing trend of learning from many agentic traces or parallel agent executions. Yet without a principled strategy for scaling, current methods suffer from quality degradation with high parallelism. To improve both the efficiency and quality of prompt learning, we propose Combee, a novel framework to scale parallel prompt learning for self-improving agents. Combee speeds up learning and enables running many agents in parallel while learning from their aggregate traces without quality degradation. To achieve this, Combee leverages parallel scans and employs an augmented shuffle mechanism; Combee also introduces a dynamic batch size controller to balance quality and delay. Evaluations on AppWorld, Terminal-Bench, Formula, and FiNER demonstrate that Combee achieves up to 17x speedup over previous methods with comparable or better accuracy and equivalent cost.

arXiv:2604.04258v1 Announce Type: new Abstract: The quality of AI-generated output is often attributed to prompting technique, but extensive empirical observation suggests that context completeness may be more strongly associated with output quality. This paper introduces Context Engineering, a structured methodology for assembling, declaring, and sequencing the complete informational payload that accompanies a prompt to an AI tool. Context Engineering defines a five-role context package structure (Authority, Exemplar, Constraint, Rubric, Metadata), applies a staged four-phase pipeline (Reviewer to Design to Builder to Auditor), and applies formal models from reliability engineering and information theory as post hoc interpretive lenses on context quality. In an observational study of 200 documented interactions across four AI tools (Claude, ChatGPT, Cowork, Codex), incomplete context was associated with 72% of iteration cycles. Structured context assembly was associated with a reduction from 3.8 to 2.0 average iteration cycles per task and an improvement in first-pass acceptance from 32% to 55%. Among structured interactions, 110 of 200 were accepted on first pass compared with 16 of 50 baseline interactions; when iteration was permitted, the final success rate reached 91.5% (183 of 200). These results are observational and reflect a single-operator dataset without controlled comparison. Preliminary corroboration is provided by a companion production automation system with eleven operating lanes and 2,132 classified tickets.