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AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, consider the foundation of the web itself. The web was not designed for the automated discovery and retrieval that new AI applications demand. Overcoming this inherent design constraint requires infrastructure. The next frontier in AI may depend on a new web data infrastructure layer that can enable models to discover and map this ever-expanding digital realm. This layer must be able to navigate hundreds of millions of existing web domains and billions of new URLs created each week, delivering real-time information and overcoming technical barriers. “The data suggests there’s far more data out there,” says Or Lenchner, CEO of Bright Data, a web data collection platform. “Think of the universe: It’s out there, but you don’t know what you don’t know.” Enabling access to fresh, relevant, and trustworthy data While early AI breakthroughs were driven by scaling training data and model size, organizations are now encountering a fundamental bottleneck: They need to keep pace with the dynamic, unstructured, and constantly evolving nature of web data in order to ground outputs in current and verifiable information. AI performance increasingly depends not just on model architecture but on a system’s compute, networking, retrieval, and data engineering capabilities—that is, the system’s ability to quickly and reliably retrieve data that is fresh, relevant, and trustworthy. Traditional model training relies on snapshots of information collected at a particular point in time. Training AI on such static data is no longer sufficient. To track fluctuations such as competitor pricing, consumer sentiment, and market trends, companies need a constant feed of new information, pulling data in real time along with relevant context. Their infrastructure must therefore be able to handle millions of simultaneous interactions across websites that vary by geography, language, format, and access rules. “If it can’t retrieve real-time information, it lacks context,” Lenchner says. “In a business setting, that’s not acceptable anymore. Stale answers lead to bad decisions and disappointed consumers.” Speed is not merely a matter of convenience; it’s a matter of necessity. Today’s organizations operate in environments where prices, inventory, markets, security threats, and customer behavior change continuously. Delayed data retrieval can reduce the usefulness of an otherwise sophisticated model. Using live, high-quality web data can also reduce AI hallucinations because the model has a more relevant knowledge base. This builds user trust. In fact, one survey found that 56% of AI practitioners said businesses need access to real-time web data to improve trust in AI outputs. To ensure the model runs efficiently and effectively, the information must also be pared down to the appropriate essentials.  Despite the introduction of retrieval-augmented generation (RAG), where models pull in external data at the moment of a query, many AI systems still struggle to deliver outputs that are current, contextually relevant, and trustworthy in operational settings. According to Gartner, 60% of AI projects that are not supported by AI-ready data—accurate, structured, organized, and contextualized—will be abandoned by the end of the year.  This is because large-scale retrieval alone does not solve the problem. As Lenchner puts it, “You need to retrieve data at scale, but also in real time. Latency becomes an issue because of the end user who is waiting for the output.”  Accessing fresh, AI-ready data at scale introduces technical and structural challenges. In practice, many enterprise systems combine public web retrieval with APIs, licensed datasets, and proprietary internal data in their AI applications. Integrating these fragmented sources into a timely and usable knowledge layer requires specialized capabilities. Some research has found that 97% of AI organizations depend on real-time web data infrastructure, but 90% feel boxed in by various restrictions. Companies are increasingly developing technical approaches to navigate these constraints. Lenchner draws this metaphor: “Think of the trained model as intelligence and relevant data as knowledge. A powerful intelligence layer sitting on top of a hollow knowledge layer is like a genius who knows nothing—useless in practice. Intelligence and knowledge have to come together.” The promise of new infrastructure A new layer of web data infrastructure can address this developing need for stronger AI inputs by enabling discovery of data, real-time access, and tailoring to a specific context. As Lechner describes it, “It’s all about collecting data at scale, super-low latency, without being blocked.” Rather than relying on increased computing power, this type of platform emulates human browsing behavior to access available content and transform raw code into structured data feeds. It can work with websites that might not interact with traditional scraping tools, such as those heavy in JavaScript, or with aggressive antibot software.  As Lenchner explains, “It’s basically having infrastructure that can mimic a web user with identifying information—IP address, location, and 1,000 more parameters. And at scale. Think of doing that 80 billion times a day for millions of websites. And every single time, you are looking exactly as the website expects you to look.” Of course, continuous retrieval introduces new data governance challenges. To address them, platforms can enforce strict compliance protocols aligned with global privacy frameworks, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). They can also be limited to openly accessible, public information, avoiding paywalls or private logins. Any networks used can be vetted and consent-based, and incentives can be provided to owners of IP addresses. In this way, systems can be designed to comply with tightening regulation. Such complex capabilities do not come easy. “When this is critical infrastructure for a company,” Lenchner says, “doing it in-house becomes a full-time engineering problem that competes with the actual AI work.” Addressing this complexity requires organizations to commit significant resources, leading many to seek specialized platforms designed specifically for data retrieval, orchestration, and observability. Infrastructure for the real world Real-time data retrieval is changing what AI

Jos Benschop is climbing a ladder to get to the top of his newest machine.  It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground, it looks like a futuristic V8 engine. When I reach the top with Benschop we’re looking down from about 15 feet in the air, with bunny-suited technicians scurrying around below. It’s more than 200 cubic meters of tech—“mechatronic devices that hold a few mirrors in a position with atomic precision,” he says, gesturing at the gargantuan apparatus. Benschop, a tall and grizzled 66-year-old, has spent over a decade working with his engineers to design this thing, but even so, he’ll sometimes look at it and go: Oh my God. Benschop is the executive vice president of technology for ASML, a Dutch company that is the linchpin of the microchip industry. If you want to make powerful chips to power phones or AI, a lithography machine like the one we’re standing on is what you need to create increasingly tiny circuitry. Lithography is the art and science of shining light on a silicon wafer to pattern out the transistors, wiring, and other components of the microchips that will be cut from it. The chipmaking field is essentially controlled by only two big players: ASML, which creates the lithography machines, and TSMC, the chipmaking giant. Nine years ago, ASML began selling machines that use a daring new way of patterning chip features. These machines employ extreme-ultraviolet light, or EUV—radiation well outside the visible spectrum that they produce by shooting lasers at tiny molten drops of tin, tens of thousands of times a second. Those first machines—the result of an R&D moonshot that lasted 16 years and cost about $10 billion—can craft transistor features with a resolution of 13 nanometers. This new machine can do even better: It has a resolution of just eight nanometers, the width of about 40 silicon atoms. The devices are now shipping to chipmaking factories, or fabs, at an eye-watering price: $400 million each. But chipmakers will fork that cash over, because they are in a desperate race to produce new and improved chips every year. That means getting their mitts on machines that can make ever smaller components and cram them together ever more densely—part of a long-standing recipe for creating faster and more energy-­efficient chips.  For years now, ASML’s tools have been critical to keeping Moore’s Law alive. Without the company’s advanced chipmaking technology it is very possible that chip density—and the ability to perform ever more calculations—would have plateaued.  The AI industry has produced new and ravenous demand for denser chips, as firms like OpenAI and Anthropic scramble to erect server farms that train and deploy new, ever-more-powerful models, which require new, ever-more-powerful hardware. ASML’s latest machine promises to help keep the AI party raging for at least another decade.  “We can allow customers to go to smaller and smaller features, and that opens up the space for whatever we see now today in AI, which is absolutely mind-blowing,” Marco Pieters, ASML’s CTO, told me. “I think we’ve only seen the tip of the iceberg.”  Its relentless push for “shrink”—as they call it in the chipmaking industry—has made ASML a dominant force: The company produces about 90% of all chip-­lithography tools worldwide. If you make chips, ASML is unavoidable. But that monopoly position makes some people, and governments, uneasy. The chipmaking field is essentially controlled by only two big players: ASML, which creates the lithography machines, and TSMC, the chipmaking giant in Taiwan, which uses ASML’s machines to craft the vast majority of all microchips. This duopoly is so powerful that it has geopolitical implications. In an effort to prevent China from developing advanced AI, the US government pressured the Dutch government to impose an embargo in 2019: ASML isn’t allowed to sell high-end machines to any Chinese firm. Geopolitically, “chips are the new oil,” says Marc Hijink, the author of Focus: The ASML Way. Being deprived of them can be as disastrous as being deprived of oil. And in that metaphor, you might say, ASML is the Strait of Hormuz. James Proud, the cofounder and CEO of the lithography startup Substrate, says the situation is not ideal. The US is “dangerously reliant” on a supply chain that’s overseas and increasingly pricey, Substrate says on its website. “There’s a huge concentration in a small number of players,” Proud says. “And the supply chain is just very expensive.”  Which is why, after two decades of ASML’s dominance, would-be competitors are now gunning for its territory. China is hungrily pouring billions into trying to replicate ASML’s tech. And startups like Substrate are trying to get in the game as well, setting their sights on creating lithography machines that are cheaper, smaller, and even more capable than ASML’s behemoths. Will any of them succeed? The near future clearly belongs to ASML, but as its engineers well know, you can unseat a giant with the right trick of the light. Making chips is, oddly, a bit like silk-screening a T-shirt. To print a pattern on a silicon wafer, you start with a pattern on a reticle—a mask that carries the design. Shining a light on the reticle transfers that pattern to the wafer. The light interacts with a layer of chemicals on the wafer, fixing the pattern in place.  The size of a chip’s features is partly set by the wavelength of light the machine uses: The smaller the wavelength, the teensier the circuitry you can create. You can stretch the capabilities of a wavelength somewhat; increasing what’s known as the numerical aperture, which usually means swapping in a bigger lens, can further focus the light and thus lay down patterns for smaller and smaller components. Eventually, though, this trick hits its limit, and you need to find a new form of light with a smaller wavelength. 

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 $400 million machine powering the future of chipmaking It’s a bit of a schlep to get to the top of ASML’s newest machine. It’s about the size of a double-decker bus, weighs more than 150 tons, and costs $400 million. But if you want to make the world’s most powerful chips, a lithography system like this is essential. The AI era needs ever faster chips, and ASML’s machines make that possible. They pattern chip features with extreme-ultraviolet light, or EUV—radiation outside the visible spectrum, produced by shooting lasers at tiny molten drops of tin tens of thousands of times a second. ASML now makes about 90% of all chip-lithography tools worldwide. That dominance has made some people, and governments, uneasy. And would-be competitors are now gunning for its territory. Read the full story on ASML’s $400 million machine—and the growing threats to its position. —Clive Thompson Three things to watch amid Anthropic’s latest feud with the government In April, Anthropic said it had built an AI model called Mythos that could pose a cybersecurity risk. It then released a safer version called Fable. Days later, the US government placed export controls on it. Within hours, Anthropic revoked access to both models. “Doomers” have long warned about catastrophic AI risk. But this intervention came over a coding model—not a bioweapon or rogue AI—and the response so far looks less like a safety plan than a reactive policy move. Here are three things to watch in Anthropic’s standoff with Washington. —James O’Donnell This story 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. Longevity’s next frontier: “reprogramming” your body Billions of dollars are flooding into efforts to reverse aging as scientists explore ways to return cells to a younger state. But how far off are these experimental treatments? Will they really work? At an upcoming virtual Roundtables event, MIT Technology Review will examine the science behind the hype. Science editor Mary Beth Griggs and senior biotechnology reporter Jessica Hamzelou will explore longevity’s latest frontier in a subscriber-only discussion on Tuesday, June 30. Register here to join the session at 11:30 AM ET / 8:30 AM PT / 16:30 GMT. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Meta is pausing an AI training program that tracks workers’ keystrokesThe move comes after sensitive data was leaked. (Business Insider)+ Meta declined to say how ⁠long the pause would last. (Reuters $)+ The program tracked staff keystrokes and mouse movements. (BBC)+ AI is supercharging surveillance. (MIT Technology Review) 2 Trump is throwing his weight behind quantum computingHe’s signed an order for a system for scientific research by 2028. (Reuters $)+ A second order aims to protect government ‌systems from the tech. (TNW) 3 A trial was reportedly won using an AI lawyerAn AI law firm in England won the landmark case over an unpaid debt. (Guardian)+ Courts have been flooded with AI lawsuits. (MIT Technology Review) 4 Tesla faces a federal probe after a Model 3 killed a 76-year-oldThe car crashed into the woman’s Texas home. (NYT $)+ Police said Tesla’s driver-assistance system has been engaged. (CNBC) 5 Google DeepMind has partnered with movie studio A24 to build AI toolsIt’s invested $75 million into the company as well. (The Verge)+ The deal aims to develop new movie production tech. (WSJ $) 6 Nvidia says its new data center design can significantly cut water use The breakthrough lies in a “closed-loop” cooling system. (Gizmodo)+ But there are major caveats to Nvidia’s claims. (The Verge)+ We did the math on AI’s energy footprint. (MIT Technology Review) 7 SpaceX today plans to test a spacecraft for moving cargo from orbitStarfall is designed to deliver payloads anywhere on Earth. (Ars Technica)+ Commercial space stations are gaining traction. (MIT Technology Review) 8 People training new AI models admit they just get chatbots to do itWhich may reduce the usefulness of future models. (New Scientist $)+ AI trained on AI garbage spits out AI garbage. (MIT Technology Review) 9 A woman with Alzheimer’s regained speech after taking psilocybinShe had only spoken in monosyllables before the dose. (Vice)+ But psychedelics are falling short in clinical trials. (MIT Technology Review) 10 Elon Musk and NASA’s chief are dreaming of antimatter propulsionThey argue it could enable travel beyond our solar system. (Gizmodo) Quote of the day “If AI is to help build a better ​future, it must be honest about what it costs us now.”  —UN Secretary-General António Guterres calls on AI firms to come clean on environmental costs during his address at London Climate Action Week. One More Thing The entrepreneur dreaming of a factory of unlimited organs When her daughter was diagnosed with a fatal lung disease, entrepreneur Martine Rothblatt started a biotechnology company. Her goal was to create what she calls an “unlimited supply of transplantable organs.” Back then, Rothblatt’s vision seemed not only impossible but “phantasmagoric.” But genetically engineered pig organs have already been transplanted into humans, while United Therapeutics is also developing 3D-printed lungs.  Rothblatt believes every body part could eventually be 3D-printed. She envisions a pipeline of readily available transplantable organs, saving countless lives—including her daughter’s. Read the full story on her bet that the future of medicine lies in organs on demand. —Antonio Regalado 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.) + George Washington’s original beer recipe is a delicious taste of American history.+ Korn’s “Falling Away From Me” has been lovingly reconstructed as a jazz fusion brass track.+ Brighten your morning with a massive, cheering gallery of 99 incredibly derpy animal faces.+ This 20-legged omnidirectional robot moves seamlessly in any direction like a mechanical sea urchin.

The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.

The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.