2026-04-06 19:00:00
For years Mike McClary sold the Guardian LTE Flashlight, a heavy-duty black model, online through his small outdoor brand. The product, designed for brightness and durability, became one of his most popular items ever. Even after he stopped offering it around 2017, customers kept sending him emails asking where they could buy it.
When McClary decided to revisit the Guardian flashlight in 2025, he didn’t begin the way he might have in the past, by combing through supplier listings and sending inquiries to factories. Instead, he opened Accio, an AI sourcing and researching tool on Alibaba.com.
For small entrepreneurs in the US, deciding what to sell and where to make it has traditionally been a slow, labor-intensive process that can take months. Now that work is increasingly being done by AI tools like Accio, which help connect businesses with manufacturers in countries including China and India. Business owners and e-commerce experts told MIT Technology Review that these AI tools are making sourcing more accessible and significantly shortening the time it takes to go from product idea to launch.
McClary, 51, who runs his business from his Illinois living room, has sold products ranging from leather conditioner to camping lights, including one rechargeable lantern that brought in half a million dollars. Like many small online merchants, he built his business by being extremely scrappy—spotting demand for a product, tweaking existing designs, finding a factory, doing modest marketing, and getting the goods in front of customers fast.
This time, though, he began by telling Accio about the flashlight’s original design, production cost, and profit margin. Then Accio suggested several changes, making it smaller and slightly less bright and switching its charging method to battery power. It also identified a manufacturer in Ningbo, China, that McClary said could cut the manufacturing cost from $17 to about $2.50 per unit.
McClary took the process from there, contacting the supplier himself to discuss the revised design. Within a month, the new version of the Guardian flashlight was back up for sale on Amazon and on his brand’s website.
Although Alibaba is better known for owning Taobao, the biggest shopping site in China, its first business was Alibaba.com, the primary website that lists Chinese factories open for bulk orders. Placing an order with a manufacturer usually requires far more than clicking “Buy.” Sellers often spend days or weeks browsing listings, comparing suppliers’ reviews and manufacturing capacities, asking about minimum order quantities, requesting samples, and negotiating timelines and customization options.
But Accio has gained significant momentum by changing how that sourcing gets done. Launched in 2024, Accio exceeded 10 million monthly active users in March 2026, according to the company. That means about one in five Alibaba users consults with AI about product sourcing.
Accio’s interface looks a lot like ChatGPT or Claude: Users type a question into an empty box and choose between “fast” and “thinking” modes. But when asked about products, the tool returns more than text, offering charts, links, and visuals and asking follow-up questions to clarify the buyer’s needs. It then narrows the field to one or a handful of suppliers that appear capable of delivering. After that, the human work begins: Users still have to reach out to suppliers themselves and negotiate the details.
Zhang Kuo, the president of Alibaba.com, told MIT Technology Review that the tool is built on multiple frontier models, including the company’s own Qwen series, a popular family of open-source large language models. The system is able to pull from the site’s millions of supplier profiles and is trained on 26 years of proprietary transaction data.
For tasks like product research and sourcing analysis, the tool “blows it away” compared with general AI tools like ChatGPT, says Richard Kostick, CEO of the beauty brand 100% Pure.
Many websites have tried using AI to assist shopping, but Alibaba has been one of the most aggressive. In March, Eddie Wu, CEO of the site’s parent company Alibaba Group, told managers that integrating the company’s core services with Qwen’s AI capabilities is a top priority. During a Chinese New Year promotion of Qwen’s personal shopping AI agent, where the company gave away cash, customers placed 200 million orders, the firm says.
Vincenzo Toscano, an e-commerce seller and consultant, recommended Accio to his clients before deciding to try it himself for a new sunglasses brand. He came in with a rough vision: a brand shaped by his Italian heritage, his personal style, and a boutique aesthetic. He says the AI helped turn that concept into something more concrete, suggesting materials, refining the look, and pointing to design ideas that felt current.
But the tool has clear limits. McClary, who uses AI tools regularly, says Accio is strongest when it comes to product ideation, but less helpful on marketing questions such as advertising and social media outreach. To use it well, he says, buyers still need to challenge its recommendations, since some can be generic.
As platforms become more AI-driven, manufacturers are adjusting too. Sally Yan, a representative at a makeup packaging company in Wuhan, China, says her firm has started writing more detailed product descriptions and adding information about its equipment and manufacturing experience on Alibaba.com because it suspects those details make its listings more likely to be surfaced by AI.
Yan says manufacturers cannot tell whether an inquiry from a customer was generated or guided by AI, and that her firm is not using AI to negotiate pricing or product details.
“AI agents are increasingly used by people to assist decision making or even directly making transactions, and in certain situations, they can become extremely useful,”
“AI agents are increasingly used by people to assist purchase decisions and even directly making transactions, and with clear data guardrails, they can become extremely useful,” says Jiaxin Pei, a research scientist at the Stanford Institute for Human-Centered AI, “but agents need to act transparently, securely, and in the customer’s best interest.” Pei says developers of these tools should disclose the data they collect and the incentives built into them to ensure that the marketplace remains fair.
Zhang, of Alibaba.com, says Accio currently does not include advertising. Suppliers can pay for higher placement in Alibaba.com’s regular search results, but Zhang says Accio is “not integrated” with that system. “We haven’t had a clear answer in terms of how to monetize this tool,” he says. For now, users can pay for additional tokens to continue chatting with the agent after their free queries run out.
Sellers say that while AI tools have made it easier to come up with ideas and get a business off the ground, they do not replace the core skills that make someone good at e-commerce. McClary believes that even when sellers have access to the same market information, some are still better at making decisions, acting quickly, and actually delivering on orders. Those differences, he says, still go a long way.
Toscano, the brand founder and e-commerce consultant, feels good about officially launching his new brand of sunglasses in just a few months: “We [small business owners] always have to bootstrap a lot of decisions. Deciding what to sell often comes down to an educated guess,” he says, “And we’re now in an era when making those decisions is easier than ever.”
2026-04-04 01:03:19
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.
In January, Elon Musk’s SpaceX filed an application with the US Federal Communications Commission to launch up to one million data centers into Earth’s orbit. The goal? To fully unleash the potential of AI without triggering an environmental crisis on Earth. But could it work?
SpaceX is the latest in a string of high-tech companies extolling the potential of orbital computing infrastructure. Last year, Amazon founder Jeff Bezos said that the tech industry will move toward large-scale computing in space. Google has plans to loft data-crunching satellites, aiming to launch a test constellation of 80 as early as next year. And last November Starcloud, a startup based in Washington State, launched a satellite fitted with a high-performance Nvidia H100 GPU, marking the first orbital test of an advanced AI chip. The company envisions orbiting data centers as large as those on Earth by 2030.
Proponents believe that putting data centers in space makes sense. The current AI boom is straining energy grids and adding to the demand for water, which is needed to cool the computers. Communities in the vicinity of large-scale data centers worry about increasing prices for those resources as a result of the growing demand, among other issues.
In space, advocates say, the water and energy problems would be solved. In constantly illuminated sun-synchronous orbits, space-borne data centers would have uninterrupted access to solar power. At the same time, the excess heat they produce would be easily expelled into the cold vacuum of space. And with the cost of space launches decreasing, and mega-rockets such as SpaceX’s Starship promising to push prices even lower, there could be a point at which moving the world’s data centers into space makes sound business sense. Detractors, on the other hand, tell a different story and point to a variety of technological hurdles, though some say it’s possible they may be surmountable in the not-so-distant future. Here are four of the must-haves we’d need to make space-based data centers a reality.
AI data centers produce a lot of heat. Space might seem like a great place to dispel that heat without using up massive amounts of water. But it’s not so simple. To get the power needed to run 24-7, a space-based data center would have to be in a constantly illuminated orbit, circling the planet from pole to pole, and never hide in Earth’s shadow. And in that orbit, the temperature of the equipment would never drop below 80 °C, which is way too hot for electronics to operate safely in the long term.
Getting the heat out of such a system is surprisingly challenging. “Thermal management and cooling in space is generally a huge problem,” says Lilly Eichinger, CEO of the Austrian space tech startup Satellives.
On Earth, heat dissipates mostly through the natural process of convection, which relies on the movement of gases and liquids like air and water. In the vacuum of space, heat has to be removed through the far less efficient process of radiation. Safely removing the heat produced by the computers, as well as what’s absorbed from the sun, requires large radiative surfaces. The bulkier the satellite, the harder it is to send all the heat inside it out into space.
But Yves Durand, former director of technology at the European aerospace giant Thales Alenia Space, says that technology already exists to tackle the problem.
The company previously developed a system for large telecommunications satellites that can pipe refrigerant fluid through a network of tubing using a mechanical pump, ultimately transferring heat from within a spacecraft to radiators on the exterior. Durand led a 2024 feasibility study on space-based data centers, which found that although challenges exist, it should be possible for Europe to put gigawatt-scale data centers (on par with the largest Earthbound facilities) into orbit before 2050. These would be considerably larger than those envisioned by SpaceX, featuring solar arrays hundreds of meters in size—larger than the International Space Station.
The space around Earth is constantly battered by cosmic particles and lashed by solar radiation. On Earth’s surface, humans and their electronic devices are protected from this corrosive soup of charged particles by the planet’s atmosphere and magnetosphere. But the farther away from Earth you venture, the weaker that protection becomes. Studies show that aircraft crews have a higher risk of developing cancer because of their frequent exposure to high radiation at cruising altitude, where the atmosphere is thin and less protective.
Electronics in space are at risk of three types of problems caused by high radiation levels, says Ken Mai, a principal systems scientist in electrical and computer engineering at Carnegie Mellon University. Phenomena known as single-event upsets can cause bit flips and corrupt stored data when charged particles hit chips and memory devices. Over time, electronics in space accumulate damage from ionizing radiation that degrades their performance. And sometimes a charged particle can strike the component in a way that physically displaces atoms on the chip, creating permanent damage, Mai explains.
Traditionally, computers launched to space had to undergo years of testing and were specifically designed to withstand the intense radiation present in Earth’s orbit. These space-hardened electronics are much more expensive, though, and their performance is also years behind the state-of-the-art devices for Earth-based computing. Launching conventional chips is a gamble. But Durand says cutting-edge computer chips use technologies that are by default more resistant to radiation than past systems. And in mid-March, Nvidia touted hardware, including a new GPU, that is “bringing AI compute to orbital data centers.”
Nvidia’s head of edge AI marketing, Chen Su, told MIT Technology Review, that “Nvidia systems are inherently commercial off the shelf, with radiation resilience achieved at the system level rather than through radiation‑hardened silicon alone.” He added that satellite makers increase the chips’ resiliency with the help of shielding, advanced software for error detection, and architectures that combine the consumer-grade devices with bespoke, hardened technologies.
Still, Mai says that the data-crunching chips are only one issue. The data centers would also need memory and storage devices, both of which are vulnerable to damage by excessive radiation. And operators would need the ability to swap things out or adapt when issues arise. The feasibility and affordability of using robots or astronaut missions for maintenance is a major question mark hanging over the idea of large-scale orbiting data centers.
“You not only need to throw up a data center to space that meets your current needs; you need redundancy, extra parts, and reconfigurability, so when stuff breaks, you can just change your configuration and continue working,” says Mai. “It’s a very challenging problem because on one hand you have free energy and power in space, but there are a lot of disadvantages. It’s quite possible that those problems will outweigh the advantages that you get from putting a data center into space.”
In addition to the need for regular maintenance, there’s also the potential for catastrophic loss. During periods of intense space weather, satellites can be flooded with enough radiation to kill all their electronics. The sun has just passed the most active phase of its 11-year cycle with relatively little impact on satellites. Still, experts warn that since the space age began, the planet has not experienced the worst the sun is capable of. Many doubt whether the low-cost new space systems that dominate Earth’s orbits today are prepared for that.
Both large-scale orbiting data centers such as those envisioned by Thales Alenia Space and the mega-constellations of smaller satellites as proposed by SpaceX give a headache to space sustainability experts. The space around Earth is already quite crowded with satellites. Starlink satellites alone perform hundreds of thousands of collision avoidance maneuvers every year to dodge debris and other spacecraft. The more stuff in space, the higher the likelihood of a devastating collision that would clutter the orbit with thousands of dangerous fragments.
Large structures with hundreds of square meters of solar arrays would quickly suffer damage from small pieces of space debris and meteorites, which would over time degrade the performance of their solar panels and create more debris in orbit. Operating one million satellites in low Earth orbit, the region of space at the altitude of up to 2,000 kilometers, might be impossible to do safely unless all satellites in that area are part of the same network so they can communicate effectively to maneuver around each other, Greg Vialle, the founder of the orbital recycling startup Lunexus Space, told MIT Technology Review.
“You can fit roughly four to five thousand satellites in one orbital shell,” Vialle says. “If you count all the shells in low Earth orbit, you get to a number of around 240,000 satellites maximum.”
And spacecraft must be able to pass each other at a safe distance to avoid collisions, he says.
“You also need to be able to get stuff up to higher orbits and back down to de-orbit,” he adds. “So you need to have gaps of at least 10 kilometers between the satellites to do that safely. Mega-constellations like Starlink can be packed more tightly because the satellites communicate with each other. But you can’t have one million satellites around Earth unless it’s a monopoly.”
On top of that, Starlink would likely want to regularly upgrade its orbiting data centers with more modern technology. Replacing a million satellites perhaps every five years would mean even more orbital traffic—and it could increase the rate of debris reentry into Earth’s atmosphere from around three or four pieces of junk a day to about one every three minutes, according to a group of astronomers who filed objections against SpaceX’s FCC application. Some scientists are concerned that reentering debris could damage the ozone layer and alter Earth’s thermal balance.
The longer hardware survives in orbit, the better the return on investment. But for orbital data centers to make economic sense, companies will have to find a relatively cheap way to get that hardware in orbit. SpaceX is betting on its upcoming Starship mega-rocket, which will be able to carry up to six times as much payload as the current workhorse, Falcon 9. The Thales Alenia Space study concluded that if Europe were to build its own orbital data centers, it would have to develop a similarly potent launcher.
But launch is only part of the equation. A large-scale orbital data center won’t fit in a rocket—even a mega-rocket. It will need to be assembled in orbit. And that will likely require advanced robotic systems that do not exist yet. Various companies have conducted Earth-based tests with precursors of such systems, but they are still far from real-world use.
Durand says that in the short term, smaller-scale data centers are likely to establish themselves as an integral part of the orbital infrastructure, by processing images from Earth-observing satellites directly in space without having to send them to Earth. That would be a huge help for companies selling insights from space, as many of these data sets are extremely large, and competition for opportunities to downlink them to Earth for processing via ground stations is growing.
“The good thing with orbital data centers is that you can start with small servers and gradually increase and build up larger data centers,” says Durand. “You can use modularity. You can learn little by little and gradually develop industrial capacity in space. We have all the technology, and the demand for space-based data processing infrastructure is huge, so it makes sense to think about it.”
Smaller facilities probably won’t do much to offset the strain that terrestrial data centers are placing on the planet’s water and electricity, though. That vision of the future might take decades to come to fruition, some critics think—if it even gets off the ground at all.
2026-04-02 20:10:00
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.
As the war in Iran continues, one of the most visible global economic ripple effects has been fossil-fuel prices. But looking ahead, further consequences could be looming for plastics.
Plastics are made from petrochemicals, and the supply chain impacts from the conflict are starting to build up. Americans will likely feel the ripples.
Read the full story to grasp the unpredictable impacts.
—Casey Crownhart
This story is from The Spark, our weekly climate newsletter. Sign up to get 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 SpaceX has filed for an IPO
It’s set to be the largest ever, targeting a $1.75 trillion valuation. (NYT $)
+ Which would make Elon Musk the world’s first trillionaire. (Al Jazeera)
+ But the IPO could hinge on the success of Moon missions. (LA Times $)
+ And the conflicts of interest are staggering. (The Next Web)
+ Meanwhile, rivals are rising to challenge SpaceX. (MIT Technology Review)
2 Artemis II is on its way to the Moon
NASA successfully launched the four astronauts on its rocket yesterday. (Axios)
+ The lunar plans could violate international law. (The Verge)
+ But the potential scientific advances are tremendous. (Nature)
+ Check out our roundtable on the next era of space exploration. (MIT Technology Review)
3 Iran has struck Amazon’s cloud business in Bahrain again
It promised to hit US companies only yesterday. (FT $)
+ Other targets include Google, Microsoft, Apple, and Nvidia. (CNBC)
+ AWS data centers in Bahrain were also hit last month. (Reuters $)
4 OpenAI was secretly behind a child safety campaign group
It pushed for age verification requirements for AI. (The San Francisco Standard $)
+ OpenAI had backed the legislation as a compromise measure. (WSJ $)
+ Coincidentally, Sam Altman heads a company providing age verification. (Engadget)
5 Anthropic is scrambling to limit the Claude Code leak
It’s trying to remove 8,000 copies of the exposed code from GitHub. (Gizmodo)
+ An executive blamed the leak on “process errors.” (Bloomberg $)
+ Here’s what it reveals about Anthropic’s plans. (Ars Technica)
+ AI is making online crimes easier—and it could get much worse. (MIT Technology Review)
6 A new Russian “super-app” aims to emulate China’s WeChat
And give the Kremlin new surveillance powers. (WSJ $)
7 America’s AI boom is leaving the rest of the world behind
And it’s concentrating power and wealth in a handful of companies. (Rest of World)
8 Chinese chipmakers have claimed nearly half the country’s market
Nvidia’s lead is shrinking rapidly. (Reuters $)
9 The first quantum computer to break encryption is imminent
New research reveals how it could happen. (New Scientist)
10 The world’s oldest tortoise has been embroiled in a crypto scam
Reports that Jonathan died at just 194 years old are thankfully false. (Guardian)
Quote of the day
—Shay Boloor, chief market strategist at Futurum Equities, tells Reuters why SpaceX has such high hopes for its IPO.
One More Thing
Dried cells—it’s what’s for dinner. At least that’s what a new crop of biotech startups, armed with carbon-guzzling bacteria and plenty of capital, are hoping to convince us.
Their claims sound too good to be true: they say they can make food out of thin air. But that’s exactly how certain soil-dwelling bacteria work.
Startups are replicating the process to turn abundant carbon dioxide into nutritious “air protein.” They believe it could dramatically lower farming emissions—and even disrupt agriculture altogether. Read the full story.
—Claire L. Evans
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.)
+ Need more Artemis II in your life? This site takes you inside the flight.
+ Here’s a fascinating look at the recording errors that improved songs.
+ Good news: the elusive Nightjar bird is making a comeback.
+ Finally, a master chef has baked clam chowder donuts.
2026-04-02 18:00:00
As the war in Iran continues to engulf the Middle East and the Strait of Hormuz stays closed, one of the most visible global economic ripple effects has been fossil-fuel prices. In particular, you can’t get away from news about the price of gasoline, which just topped an average of $4 a gallon in the US, its highest level since 2022.
But looking ahead, further consequences for the global economy could be looming in plastics. Plastics are made using petrochemicals, and the supply chain impacts of the oil bottleneck near Iran are starting to build up.
Plastic production accounts for roughly 5% of global carbon dioxide emissions today. And our current moment shows just how embedded oil and gas products are in our lives. It goes far beyond their use for energy.
As I write this, I’m wearing clothes that contain plastic fibers, typing on a plastic keyboard, and looking through the plastic lenses of my glasses. It’s hard to imagine what our world looks like without plastic. And in some ways, moving away from fossil-derived plastic could prove even more complicated than decarbonizing our energy system.
Crude oil prices have been on a roller-coaster in recent weeks, and prices have recently topped $100 a barrel.
Crude oil contains a huge range of hydrocarbons, and it’s typically refined by putting it through a distillation unit that separates the raw material into different fractions according to their boiling point. Those fractions then go on to be further processed into everything from jet fuel to asphalt binder. We’ve already seen the price spikes for some materials pulled out of crude oil, like gasoline and jet fuel.
Let’s zoom in on another component, naphtha. It can be added to gasoline and jet fuel to improve performance. It can also be used as a solvent or as a raw material to make plastics.
The Middle East currently accounts for about 20% of global naphtha production and supplies about 40% of the market in Asia, where prices are already up by 50% over the last month.
We’re starting to see these effects trickle down already. The price of polypropylene (which is made from naphtha and used for food containers, bottle caps, and even automotive parts) is climbing, especially in Asia.
Typically, manufacturers have a bit of stock built up, but that’ll be exhausted soon, likely in the coming weeks. The largest supplier of water bottles in India recently announced that it would raise prices by 11% after its packaging costs went up by over 70%, according to reporting from Reuters. Toys could be more expensive this holiday season as manufacturers grapple with supply chain concerns.
Americans will likely feel these ripples especially hard if disruptions continue. The average US resident used over 250 kilograms of new plastics in 2019, according to a 2022 report from the Organization for Economic Cooperation and Development. That’s an absolutely massive number—the global average is just 60 kilograms.
The effects of higher prices for both fuels and feedstocks could compound and multiply, and alternatives aren’t widely available. Bio-based plastics made with materials like plant sugars exist, but they still make up a vanishingly tiny portion of the market. As of 2025, global plastics production totaled over 431 million metric tons per year. Bio-based and bio-degradable plastics made up about 0.5% of that, a share that could reach 1% by 2030.
Bio-based plastics are much more expensive than their fossil-derived counterparts. And many are made using agricultural raw materials, so scaling them up too much could be harmful for the environment and might compete with other industries like food production.
Recycling isn’t the easy answer either. Mechanical recycling is the current standard method used for materials like the plastics that make up water bottles and disposable coffee cups. But that degrades the materials over time, so they can’t be used infinitely. Chemical recycling has its own host of issues—the facilities that do it can be highly polluting, and today plastics that go into advanced recycling plants largely don’t actually go into new plastics.
There’s been a lot of talk in recent weeks about how this energy crisis is going to push the world more toward renewable energy. Solar panels, electric vehicles, and batteries could suddenly become more attractive as we face the drastic consequences of a disruption in the global fossil-fuel supply.
But when it comes to plastic, the future looks far more complicated. Even though the plastics industry is facing much the same disruptions as the energy sector, there aren’t the same obvious alternatives available for a transition. Our lives are tied up in plastic, with uses ranging from the essential (like medical equipment) to the mundane (my to-go coffee cup). Soon, our economy could feel the effects of just how much we rely on fossil-derived plastics, and how hard it’s going to be to replace them.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
2026-04-01 20:10:00
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.
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
Our readers recently voted humanoid robots the “11th breakthrough” to add to our 2026 list of 10 Breakthrough Technologies. Check out what else officially made the cut.
For decades, AI has been evaluated based on whether it can outperform humans on isolated problems. But it’s seldom used this way in the real world.
While AI is assessed in a vacuum, it operates in messy, complex, multi-person environments over time. This misalignment leads us to misunderstand its capabilities, risks, and impacts.
We need new benchmarks that assess AI’s performance over longer horizons within human teams, workflows, and organizations. Here’s a proposal for one such approach: Human–AI, Context-Specific Evaluation.
—Angela Aristidou, professor at University College London and faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute.
In a laboratory on the outskirts of Oxford, a quantum computer built from atoms and light awaits its moment. The device is small but powerful—and also very valuable. Infleqtion, the company that owns it, is hoping its abilities will win $5 million at a competition.
The prize will go to the quantum computer that can solve real health care problems that “classical” computers cannot. But there can be only one big winner—if there is a winner at all.
—Michael Brooks
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 OpenAI just closed the biggest funding round in Silicon Valley history
It raised $122 billion ahead of its blockbuster IPO, which is expected later this year. (WSJ $)
+ It’s also prepping a push to “rethink the social contract.” (Vanity Fair $)
+ Campaigners are urging people to quit ChatGPT. (MIT Technology Review)
2 Iran has threatened to attack 18 US tech companies
It’s eyeing their operations in the Middle East. (Politico)
+ Targets include Nvidia, Apple, Microsoft, and Google. (Engadget)
+ Iran struck AWS data centers earlier this month. (Reuters $)
3 Artemis II is about to fly humans to the Moon. Here’s the science they’ll do
Their experiments will set the stage for future explorers. (Nature)
+ You can watch the launch attempt today. (Engadget)
4 Putin is trying to take full control of Russia’s internet
New outages and blockages are cutting the country off from the world. (NYT $)
+ Can we repair the internet? (MIT Technology Review)
5 A robotaxi outage in China left passengers stranded on highways
Baidu vehicles froze on the streets of Wuhan. (Bloomberg $)
+ Police are blaming a “system failure.” (Reuters $)
6 US government requests for social media user data are soaring
They’ve skyrocketed by 770% in the past decade. (Bloomberg $)
+ Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review)
7 Tesla has admitted that humans sometimes drive its robotaxis
Remote drivers occasionally control them completely. (Wired $)
8 A satellite-smashing chain reaction could spiral out of control
This data visualization captures the dangers of space collisions. (Guardian)
+ Here’s all the stuff we’ve put into space. (MIT Technology Review)
9 Meta’s smartglasses can turn you into a creep
According to one journalist who wore them for a month. (Guardian)
10 A Claude Code leak has exposed plans for a virtual pet
We could be getting a Tamagotchi for the GenAI era. (The Verge)
Quote of the day
—Iran’s Islamic Revolutionary Guard Corps (IRGC) threatens US tech firms in an affiliated Telegram, per CNBC.
One More Thing
In a pine farm north of the tiny town of Tamarack, Minnesota, Talon Metals has uncovered one of America’s densest nickel deposits. Now it wants to begin mining the ore.
Products made from the nickel could net more than $26 billion in subsidies through the Inflation Reduction Act (IRA), which is starting to transform the US economy. To understand how, we tallied up the potential tax credits available. Read the full story to find out what we discovered.
—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.)
+ A selfless group of gluttons tried to taste-test every potato chip in the world.
+ Get romantic inspiration from these penguins’ engagement pebbles.
+ Good news: global terrorism has hit a 15-year low.
+ Enjoy endless new views through these windows around the world.
2026-04-01 19:00:00
When Zeus, a medical student living in a hilltop city in central Nigeria, returns to his studio apartment from a long day at the hospital, he turns on his ring light, straps his iPhone to his forehead, and starts recording himself. He raises his hands in front of him like a sleepwalker and puts a sheet on his bed. He moves slowly and carefully to make sure his hands stay within the camera frame.
Zeus is a data recorder for Micro1, a US company based in Palo Alto, California that collects real-world data to sell to robotics companies. As companies like Tesla, Figure AI, and Agility Robotics race to build humanoids—robots designed to resemble and move like humans in factories and homes—videos recorded by gig workers like Zeus are becoming the hottest new way to train them.
Micro1 has hired thousands of contract workers in more than 50 countries, including India, Nigeria, and Argentina, where swathes of tech-savvy young people are looking for jobs. They’re mounting iPhones on their heads and recording themselves folding laundry, washing dishes, and cooking. The job pays well by local standards and is boosting local economies, but it raises thorny questions around privacy and informed consent. And the work can be challenging at times—and weird.
Zeus found the job in November, when people started talking about it everywhere on LinkedIn and YouTube. “This would be a real nice opportunity to set a mark and give data that will be used to train robots in the future,” he thought.
Zeus is paid $15 an hour, which is good income in Nigeria’s strained economy with high unemployment rates. But as a bright-eyed student dreaming of becoming a doctor, he finds ironing his clothes for hours every day boring.
“I really [do] not like it so much,” he says. “I’m the kind of person that requires … a technical job that requires me to think.”
Zeus, and all the workers interviewed by MIT Technology Review, asked to be referred to only by pseudonyms because they were not authorized to talk about their work.
Humanoid robots are notoriously hard to build because manipulating physical objects is a difficult skill to master. But the rise of large language models underlying chatbots like ChatGPT has inspired a paradigm shift in robotics. Just as large language models learned to generate words by being trained on vast troves of text scraped from the internet, many researchers believe that humanoid robots can learn to interact with the world by being trained on massive amounts of movement data.
Editor’s note: In a recent poll, MIT Technology Review readers selected humanoid robots as the 11th breakthrough for our 2026 list of 10 Breakthrough Technologies.
Robotics requires far more complex data about the physical world, though, and that is much harder to find. Virtual simulations can train robots to perform acrobatics, but not how to grasp and move objects, because simulations struggle to model physics with perfect accuracy. For robots to work in factories and serve as housekeepers, real-world data, however time-consuming and expensive to collect, may be what we need.
Investors are pouring money feverishly into solving this challenge, spending over $6 billion on humanoid robots in 2025. And at-home data recording is becoming a booming gig economy around the world. Data companies like Scale AI and Encord are recruiting their own armies of data recorders, while DoorDash pays delivery drivers to film themselves doing chores. And in China, workers in dozens of state-owned robot training centers wear virtual-reality headsets and exoskeletons to teach humanoid robots how to open a microwave and wipe down the table.
“There is a lot of demand, and it’s increasing really fast,” says Ali Ansari, CEO of Micro1. He estimates that robotics companies are now spending more than $100 million each year to buy real-world data from his company and others like it.
Workers at Micro1 are vetted by an AI agent named Zara that conducts interviews and reviews samples of chore videos. Every week, they submit videos of themselves doing chores around their homes, following a list of instructions about things like keeping their hands visible and moving at natural speed. The videos are reviewed by both AI and a human and are either accepted or rejected. They’re then annotated by AI and a team of hundreds of humans who label the actions in the footage.
“There is a lot of demand, and it’s increasing really fast.”
Ali Ansari, CEO of Micro1
Because this approach to training robots is in its infancy, it’s not clear yet what makes good training data. Still, “you need to give lots and lots of variations for the robot to generalize well for basic navigation and manipulation of the world,” says Ansari.
But many workers say that creating a variety of “chore content” in their tiny homes is a challenge. Zeus, a scrappy student living in a humble studio, struggles to record anything beyond ironing his clothes every day. Arjun, a tutor in Delhi, India, takes an hour to make a 15-minute video because he spends so much time brainstorming new chores.
“How much content [can be made] in the home? How much content?” he says.
There’s also the sticky question of privacy. Micro1 asks workers not to show their faces to the camera or reveal personal information such as names, phone numbers, and birth dates. Then it uses AI and human reviewers to remove anything that slips through.
But even without faces, the videos capture an intimate slice of workers’ lives: the interiors of their homes, their possessions, their routines. And understanding what kind of personal information they might be recording while they’re busy doing chores on camera can be tricky. Reviews of such footage might not filter out sensitive information beyond the most obvious identifiers.
For workers with families, keeping private life off camera is a constant negotiation. Arjun, a father of two daughters, has to wrangle his chaotic two-year-old out of frame. “Sometimes it’s very difficult to work because my daughter is small,” he says.
Sasha, a banker turned data recorder in Nigeria, tiptoes around when she hangs her laundry outside in a shared residential compound so she won’t record her neighbors, who watch her in bewilderment.
“It’s going to take longer than people think.”
Ken Goldberg, UC Berkeley
While the workers interviewed by MIT Technology Review understand that their data is being used to train robots, none of them know how exactly their data will be used, stored, and shared with third parties, including the robotics companies that Micro1 is selling the data to. For confidentiality reasons, says Ansari, Micro1 doesn’t name its clients or disclose to workers the specific nature of the projects they are contributing to.
“It is important that if workers are engaging in this, that they are informed by the companies themselves of the intention … where this kind of technology might go and how that might affect them longer term,” says Yasmine Kotturi, a professor of human-centered computing at the University of Maryland.
Occasionally, some workers say, they’ve seen other workers asking on the company Slack channel if the company could delete their data. Micro1 declined to comment on whether such data is deleted.
“People are opting into doing this,” says Ansari. “They could stop the work at any time.”
With thousands of workers doing their chores differently in different homes, some roboticists wonder if the data collected from them is reliable enough to train robots safely.
“How we conduct our lives in our homes is not always right from a safety point of view,” says Aaron Prather, a roboticist at ASTM International. “If those folks are teaching those bad habits that could lead to an incident, then that’s not good data.” And the sheer volume of data being collected makes reviewing it for quality control challenging. But Ansari says the company rejects videos showing unsafe ways of performing a task, while clumsy movements can be useful to teach robots what not to do.
Then there’s the question of how much of this data we need. Micro1 says it has tens of thousands of hours of footage, while Scale AI announced it had gathered more than 100,000 hours.
“It’s going to take a long time to get there,” says Ken Goldberg, a roboticist at the University of California, Berkeley. Large language models were trained on text and images that would take a human 100,000 years to read, and humanoid robots may need even more data, because controlling robotic joints is even more complicated than generating text. “It’s going to take longer than people think,” he says.
When Dattu, an engineering student living in a bustling tech hub in India, comes home after a full day of classes at his university, he skips dinner and dashes to his tiny balcony, cramped with potted plants and dumbbells. He straps his iPhone to his forehead and records himself folding the same set of clothes over and over again.
His family stares at him quizzically. “It’s like some space technology for them,” he says. When he tells his friends about his job, “they just get astounded by the idea that they can get paid by recording chores.”
Juggling his university studies with data recording, as well as other data annotation gigs, takes a toll on him. Still, “it feels like you’re doing something different than the whole world,” he says.
