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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. Why this battery company is pivoting to AI  Qichao Hu doesn’t mince words about the state of the battery industry. “Almost every Western battery company has either died or is going to die. It’s kind of the reality,” he says.   Hu is the CEO of SES AI, a Massachusetts-based battery company. It previously developed advanced lithium batteries for major industries, but is now shifting to AI materials discovery. Read our story to find out why.   —Casey Crownhart  This startup wants to change how mathematicians do math  Axiom Math, a California startup, has released a free AI tool with a big ambition: discovering mathematical patterns that could unlock solutions to long-standing problems.  Most of the successes with AI tools have involved finding solutions to existing problems. But that’s not all they could do. There are lots of problems in math that require new ideas nobody has ever had, which could come from spotting patterns that have never been spotted before.   Axiom Math’s new tool aims to find these hidden links. Read the full story to discover their plans—and how AI in general could change mathematics.  —Will Douglas Heaven  Are high gas prices good news for EVs? It’s complicated.  As the conflict in Iran has escalated, fossil-fuel prices have been on a roller-coaster—and some EV owners are celebrating.   They believe the volatility will create an opportunity for electric vehicles to make headway. But even the carless among us should be concerned about a sustained rise in fossil-fuel prices.   To find out why, read the full story.  —Casey Crownhart  This article is from The Spark, our weekly climate newsletter. 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 and YouTube have been fined for designing addictive products They must pay damages of $6 million for harming young people. (Guardian) + The verdicts will reshape legal protections for Big Tech. (WSJ $) + They could also ripple through social media markets worldwide. (Rest of World) + Juries have started taking the lead in the push for child online safety. (NYT)  2 SpaceX aims to file for IPO as soon as this week It’s hoping to raise more than $75 billion. (The Information) + Rocket stocks soared on the report. (BBC)  + But rivals are challenging SpaceX’s dominance. (MIT Technology Review)  3 A new AI safety bill would halt data center construction It was introduced by Bernie Sanders. (Wired) + Nobody wants a data center in their backyard. (MIT Technology Review + One solution: launch them into space. (MIT Technology Review)   4 Meta has laid off 700 employees After raising compensation for top earners. (NYT $)  5 Elon Musk wants a Delaware judge to recuse herself over an emoji She liked a LinkedIn post criticizing him. (CNBC) + The case had ruled Musk misled investors during the Twitter purchase. (Reuters)  6 Reddit will require “fishy” accounts to verify that a human runs them The process aims to combat the deluge of bots. (Ars Technica)  7 Uber and Pony AI aim to launch Europe’s first robotaxi service in Croatia Pony AI is also running trials in Luxembourg, while Uber is testing in London. (The Verge)  8 Google says quantum computers could break all cryptographic security by 2029 It’s set a timeline to secure the quantum era. (Gizmodo) + Quantum computers could soon solve health care problems. (MIT Technology Review)  9 New research shows cloning doesn’t produce perfect copies Clones have lots of extra, potentially dangerous mutations. (New Scientist)  10 The landmark AI Scientist has just completed peer review  It’s billed as the first AI tool built to fully automate the scientific process. (Nature)  Quote of the day  “For years, social media companies have profited from targeting children while concealing their addictive and dangerous design features. Today’s verdict is a referendum—from a jury, to an entire industry.”  —Attorney Rachel Lanier offers her view on yesterday’s fines for Meta and YouTube, the Washington Post reports.   One More Thing  GETTY IMAGES Longevity enthusiasts want to create their own independent state. They’re eyeing Rhode Island.   It’s incredibly difficult and expensive to study innovative ways to slow or reverse aging. In response, longevity enthusiasts have devised an ambitious plan: establish an independent state for life-extension experiments.   They envision a jurisdiction that slashes red tape, encourages self-experimentation with unproven treatments, and eliminates laws that limit how companies develop drugs.   Exactly where their longevity state might emerge is still being worked out—but one appealing location is Rhode Island. Read the full story to learn more about the plans.   —Jessica Hamzelou  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.)  + These gleaming photos of ancient insects in amber are time capsules of the dinosaur age. + Paint with pixels across a world map at this unique digital canvas. + Hands have a new shield against hammers: a nail holder that protects your fingers. + This new audio player uses cartridges to give digital music a soul. 

Imagine telling a digital agent, “Use my points and book a family trip to Italy. Keep it within budget, pick hotels we’ve liked before, and handle the details.” Instead of returning a list of links, the agent assembles an itinerary and executes the purchase. That shift, from assistance to execution, is what makes agentic AI different. It also changes the operating speed of commerce. Payment transactions are already clear in milliseconds. The new acceleration is everything before the payment: discovery, comparison, decisioning, authorization, and follow-through across many systems. As humans step out of routine decisions, “good enough” data stops being good enough. In an agent-driven economy, the constraint isn’t speed; it’s trust at machine speed and scale. Automated markets already work because identity, authority, and accountability are built in. As agents transact across businesses, that same clarity is required. Master data management (MDM)—the discipline of creating a single master record—becomes the exchange layer: tracking who an agent represents, what it can do, and where responsibility sits when value moves. Markets don’t fail from automation; they fail from ambiguous ownership. MDM turns autonomous action into legitimate, scalable trust. To make agentic commerce safe and scalable, organizations will need more than better models. They will need a modern data architecture and an authoritative system of context that can instantly recognize, resolve, and distinguish entities. It is the difference between automation that scales and automation that needs constant human correction. The agent is a new participant Digital commerce has long been built on two primary sides: buyers and suppliers/merchants. Agentic commerce adds a third participant that must be treated as a first-class entity: the agent acting on the buyer’s behalf. That sounds simple until you ask the questions every enterprise will face: Who is the individual, across channels and devices, with enough certainty for automation? Who is the agent, and what permissions and limits define what it can do? Who is the merchant or supplier, and are we sure we mean the right one? Who holds liability if the agent acts with permission, but against user intent? The practical risk is confusion. Humans, for example, can infer that “Delta” means the airline when they are booking a flight, not the faucet company. An agent needs deterministic signals. If the system guesses wrong, it either breaks trust or forces a human confirmation step that defeats the promise of speed. Why ‘good enough’ data breaks at machine speed Most organizations have learned to live with imperfect data. Duplicate customer records are tolerable. Incomplete product attributes are annoying. Merchant identities can be reconciled later. Agentic workflows change that tolerance. When an agent takes action without a human checking the output, it needs data that is close to perfect, because it cannot reliably notice when data is ambiguous or wrong the way a person can. The failure modes are predictable, and they show up in places that matter most: Product truth: If the catalog is inconsistent, an agent’s choices will look arbitrary (“the wrong shirt,” “the wrong size,” “the wrong material”), and trust collapses quickly. Payee truth: Agentic commerce expands beyond cards to account-to-account and open-banking-connected experiences, broadening the universe of payees and the need to recognize them accurately in real time. Identity truth: People operate in multiple contexts (work versus personal). Devices shift. A system that cannot distinguish amongst these contexts will either block legitimate activity or approve risky activity, both of which damage adoption. This is why unified enterprise data and entity resolution move from nice to have to operationally required. The more autonomy you want, the more you must invest in modern data foundations that ensure it is safe. Context intelligence: The missing layer When leaders talk about agentic AI, they often focus on model capability: planning, tool use, and reasoning. Those are necessary, but they are not sufficient. Agentic commerce also requires a layer that provides authoritative context at runtime. Think of it as a real-time system of context that can answer instantly and consistently: • Is this the right person?• Is this the right agent, acting within the right permissions?• Is this the right merchant or payee?• What constraints apply right now (budget, policy, risk, loyalty rules, preferred suppliers)? Two design principles matter. First, entity truth must be deterministic enough for automation. Large language models are probabilistic by nature. That is helpful for creating options for writing and drawing. It is risky for deciding where money goes, especially in B2B and finance workflows, where “probably correct” is not acceptable. Second, context must travel at the speed of interaction and remain portable across the entire connected network value chain. Mastercard’s experience optimizing payment flows is instructive: the more services you layer onto a transaction, the more you risk slowing it down. The pattern that scales pre-resolves, curates, and packages the signal so that execution is lightweight. This is also where tokenization is heading. Initiatives like Mastercard’s Agent Pay and Verifiable Intent signal a future in which consumer credentials, agent identities, permissions, and provable user intent are encoded as cryptographically secure artifacts — enabling merchants, issuers and platforms to deterministically verify authorization and execution at machine speed. What leaders should do in the next 12 to 24 months Adoption will not be uniform. Early traction will often depend less on industry and more on the sophistication of an organization’s systems and data discipline. That makes the next two years a window for practical preparation. Five moves stand out. Treat agents as governed identities, not features. Define how agents are onboarded, authenticated, permissioned, monitored, and retired. Prioritize entity resolution where the cost of being wrong is highest. Start with payees, suppliers, employee-versus-personal identity, and high-volume product categories. Build a reusable context service that every workflow and agent can call. Do not force each system to reconstruct identity and relationships from scratch. Precompute and compress signals. Resolve and curate context upstream so that runtime decisioning stays fast and predictable. Expand autonomy only as trust is earned. Build a governance framework to address disputes, keep humans in the loop

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. This scientist rewarmed and studied pieces of his friend’s cryopreserved brain  L. Stephen Coles’s brain sits in a vat at a storage facility in Arizona. It has been held there at a temperature of around −146 degrees °C for over a decade, largely undisturbed. Before he died in 2014, Coles had the brain frozen with an ambitious goal in mind: reanimation.  His friend, cryobiologist Greg Fahy, believes it could be revived one day. But other experts are less optimistic.   Still, Fahy’s research could lead to new ways to study the brain. And using cryopreservation for organ transplantation is becoming a viable reality.   Read the full story to find out what the future holds for the technology.  —Jessica Hamzelou  The AI Hype Index  Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Take a look at this month’s edition.   MIT Technology Review Narrated: 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.  —Will Douglas Heaven  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 next era of space exploration  Our footprint in the solar system is rapidly expanding. Programs to build permanent Moon bases and find life on Mars have transitioned from science fiction to active space agency missions. The scientists behind them will not only shed new light on the cosmos, but also reveal where humanity is headed.  To examine what the future holds in store, MIT Technology Review features editor Amanda Silverman will sit down today with award-winning science journalist and author Robin George Andrews for an exclusive subscriber-only Roundtable conversation about “The Next Era of Space Exploration.” Register here to join the session at 16:00 GMT / 12:00 PM ET / 9:00 AM PT.  The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 OpenAI is shutting down AI video generator Sora  The app attracted at least as much controversy as acclaim. (CNBC) + Closing it means saying goodbye to $1 billion from Disney. (BBC) + OpenAI is cutting back on side projects ahead of an expected IPO. (WSJ $) + But it’s focusing its efforts on building a fully automated researcher. (MIT Technology Review)  2 A judge suspects the Pentagon is illegally punishing Anthropic She labelled the DoD’s ban “troubling.” (Bloomberg) + Anthropic and the Pentagon are facing off in court. (Guardian) + The DoD wants AI companies to train on classified data. (MIT Technology Review)  3 Meta has been ordered to pay $375 million for endangering children online Prosecutors said the company knew it put children at risk. (Engadget) + Meta is offering its top talent stock options as incentives for its AI push. (CNBC)  4 Arm will sell its own computer chips for the first time It’s aimed at data centers that run AI tasks. (NYT $) + Arm stock jumped 13% on the news. (CNBC)  5 Manus’s founders have been barred from leaving China following Meta’s takeover Beijing is reviewing the $2 billion acquisition of the AI startup. (FT $)  6 Baltimore has sued xAI over Grok’s fake nude images  The chatbot allegedly violated consumer protections. (Guardian) + There’s a big market for pornographic deepfakes of real women. (MIT Technology Review)  7 NASA plans to send a nuclear-powered spacecraft to Mars in 2028 It’ll take a payload of Ingenuity-class helicopters to the Red Planet. (NYT $) + NASA also wants to put a $20 billion base on the Moon. (The Verge)  8 A company is secretly turning Zoom meetings into AI-generated podcasts WebinarTV turns the calls into content without telling anyone. (404 Media)  9 Iranian volunteers have built their own missile warning map It fills the gap left by Iran’s lack of a public emergency alert tool. (Wired $) + Here’s where OpenAI’s tech could show up in Iran. (MIT Technology Review)  10 A nonprofit is sending basic income payments to AI-impacted workers It’s starting by giving 25-50 people $1,000 per month. (Gizmodo)  Quote of the day  “I am first and foremost a scientist. My goal is to understand nature. But doing science is, sort of, like reading the mind of God.”  —DeepMind CEO Demis Hassabis shares his approach to AI strategy with the FT.  One More Thing  EVA REDAMONTI Inside the hunt for the most dangerous asteroid ever   As asteroid 2024 YR4 hurtled toward Earth, astronomers determined that this massive rock posed a higher risk of impact than any object of its size in recorded history. Then, just as quickly as history was made, experts declared that the danger had passed.  This is the inside story of the network of global scientists who found, followed, planned for, and finally dismissed the most dangerous asteroid ever found—all under the tightest of timelines and with the highest of stakes. Find out how they did it.  —Robin George Andrews  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.)  + Soothe subscription fatigue with this simple cancellation tool. + Takashi Murakami’s reimagined Monets are pop-art magic. + Jump into a rabbit hole with this app that visualizes links between Wikipedia pages. + This playful lynx that snatched the top prize in a photo competition is a delight. 

My friend who is a developer once asked an LLM to generate documentation for a payment API.

If you look at the architecture diagram of almost any AI startup today, you will see a large language model (LLM) connected to a vector store.