In partnership withSnowflake In just a few years, generative AI has changed the face of enterprise analytics, notably by making analytics accessible to users across the organization. Agentic AI promises to do even more, by automating entire workflows and taking actions based on agent insights. But there is a problem: The quality of insights that agents currently generate leaves much to be desired. Fewer than half of agent-generated insights at the organizations surveyed for this report lead to business improvement. And while most respondents praise the quality of their agents’ descriptive insights, reviews of their prescriptive insights—those on which actions are based—are decidedly mixed. Failure to produce insights that improve the business deprives organizations of their desired return on investment in agentic analytics. Worse, flawed insights that trigger incorrect actions can cause reputational or financial damage. This report gauges how companies view the quality of the outputs produced by their analytics agents and explores how organizations are seeking to boost insight quality and get their agents working harder for the business. Some key findings from the report include the following:
Agentic analytics is only just starting to deliver. On average, 48% of insights generated by analytics agents at the surveyed companies lead to actions that improve the business. That is a marked advance from the 37% average the respondents say generated such actions one year ago. But it means that more than half of the insights generated by analytics agents today are still failing to help businesses. Better quality of insights leads to clear business advantage. At a small cohort of organizations, which we term “insight leaders,” AI agents perform much better than the average. In this group, more than 70% of agent insights lead to actions that improve the business. The insight leaders also give the quality of their agents’ insights higher marks than do the total respondents overall. The quality gap is particularly wide when it comes to prescriptive insights.
Insufficient context reduces insight quality. When analytics agents deliver insights of substandard quality, the main reason is a lack of business context, as cited by 55% of surveyed executives. Fragmented or inaccessible data is the other major factor, according to 45%. Fewer insight leaders say their agents struggle with access to context. Insight overload further inhibits agent effectiveness. At a majority (52%) of surveyed organizations, analytics agents generate too many insights to be properly evaluated and therefore acted upon. Less than one-quarter (24%) are able to evaluate all agent insights; most do so on a case-by-case basis, and that process is currently human-intensive. Governance and semantics are areas of focus for improving insights. The surveyed companies are prioritizing initiatives that enable better agent insights by strengthening AI and data governance (cited by 60%). They are also adding a semantic layer to their data architecture (45%). The experts we interviewed agree that the semantic layer is crucial to ensure high-performing agents. Download the report This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

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 agentic web is here In 2001, a decade after inventing the World Wide Web, Tim Berners-Lee articulated a new vision: an internet navigated by autonomous software agents that address our every need, without requiring us to muck about with browsers and search engines ourselves. At the time, the idea seemed far-fetched. But a quarter-century on, AI agents are beginning to make it a reality. They can triage your inbox, do your grocery shopping, and even access your bank accounts. This new era is being called the agentic web. It will inevitably transform the internet—and whether the human web survives this transition is anyone’s guess.
Find out how AI agents are transforming the internet, and what we stand to lose in the process. —Grace Huckins
Are we there yet? Eliminating traffic deaths Every year, traffic crashes kill more than 1 million people worldwide. They’re also the leading cause of death for children and young adults. Cheap, proven ways to make roads safer exist, including tightening intersections with planters and lowering speed limits. But government logjams and red tape can make it difficult to roll out these changes. Still, some cities are making impressive progress by investing in public transit, extending curbs, and using cameras to spot danger zones in need of a redesign. Here’s how close we are to eradicating traffic deaths. —Sara Kiley Watson This story is the final part of “Are we there yet?”, our series that takes stock of six technologies that have long promised to change the world—and scores them on a progress meter. Subscribe to read the other five instalments in our new Impossible issue. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A $1 trillion tech selloff has deepened fears of an AI bubbleBig AI bills and weak earnings have spooked investors. (BBC)+ The Nasdaq 100 had its worst day since June on Friday. (NBC News)+ OpenAI has cut its funding target by $10 billion. (CNBC)+ What even is the AI bubble? (MIT Technology Review)

2 Hackers have hit Ukraine and Russia’s emergency alert appsKyiv residents were warned of “nuclear danger.” (NBC News)+ Russia’s app said nuclear warheads had been launched. (CNN)+ Ukraine has blamed Russia, which hasn’t yet accused anyone. (BI)+ AI will transform cyber warfare. (MIT Technology Review) 3 Binance users withdrew $3.6 billion after the largest ever crypto hackThe funds moved when the exchange reopened on Sunday. (Guardian)+ Binance says it’s covered $6 billion of the stolen $6.3 billion. (Reuters $)+ But the industry’s response has undermined its “decentralized” image. (Gizmodo) 4 An AI agent injected drugs into a live mouse’s brainIt chose the gene therapy and controlled the needle itself. (Wired $)+ We need real-time tracking of AI agents. (MIT Technology Review) 5 Data centres have made utilities a flashpoint in the midtermsAmericans are angry over rising electricity bills. (Reuters $)+ Virtual power plants could help. (MIT Technology Review) 6 Apple has overtaken Nvidia as the world’s most valuable companyInvestors are favouring its restraint on AI spending. (Bloomberg $)+ But critics say it’s entering its “slop era.” (404 Media) 7 Supermarket fridges have overheated in a new wave of cyberattacksStores in Belgium, Sweden, and Luxembourg were hit. (Politico) 8 The first chocolate bars made from lab-grown cocoa are hereStartups are cultivating chocolate to tackle a cocoa shortage. (WSJ $) 9 Hackers hijacked digital billboards in multiple countries to show pornThey also criticized security flaws and Egypt’s government.(Gizmodo)
10 A new 3D map reveals instability in interstellar spaceThe discovery could help explain why stars form so inefficiently. (Quanta) Quote of the day
“It would be great if all this was just a house of cards.” —Kevin Roose, a technology columnist at the New York Times, tells NPR that an AI investment crash would be preferable to the technology permanently upending work and society. One more thing Useful quantum computing is inevitable—and increasingly imminent Last year, Nvidia CEO Jensen Huang jolted the stock market by saying that practical quantum computing is still 15 to 30 years away. He also suggested that those computers would need Nvidia GPUs to function. But Huang’s predictions miss the mark—both on the timeline and the role his company’s technology will play.  Quantum computing is rapidly converging on utility. And that’s good news, because the hope is that they will be able to perform calculations that no amount of AI or classical computation could ever achieve. Read the full story. —Peter Barrett 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 miniaturist has built a delightful diorama of the world’s creepiest living room.+ The year’s best dog photos have been announced, and they’re very good boys and girls.+ At the Green Zone golf club, you tee off in Sweden and putt in Finland, an hour in the future.+ Astronomers say they’ve found the real celestial arrangement that inspired van Gogh’s The Starry Night.

Provided byHPE When a developer gives an AI agent a coding task, the agent does more than answer once. It plans, generates code, runs tests, and repeats the process to correct errors. A single task can use 10 to 50 times more tokens than a basic chat prompt. The more useful the agent becomes, the more calls it makes. With per-token pricing, the workflows creating the most value can also become the most expensive. The financial pressure is already visible. Uber reportedly exhausted its entire 2026 AI budget by April after expanding its use of Claude Code. GitHub Copilot moved from flat-rate pricing to metered credits. Gartner predicts that AI coding token costs will surpass average developer salaries by 2028. The natural response is rationing usage caps, seat limits, and approvals before developers launch agents. But blanket limits can restrict the workflows delivering value. They also optimize the wrong number. Cost per token measures consumption, not results. Some spending reflects waste, including retries and runaway loops. Some represents the work of shipping software. A meter cannot distinguish them, and neither can a spending cap. Measure the software that ships A better measure is cost per merged pull request: total AI-assisted development cost divided by the number of changes that pass review and are merged. Track rework and defects alongside it so cheaper changes do not conceal lower quality.
If that cost falls as AI usage rises, without compromising quality, developers are converting more consumption into useful software. The question is not how many tokens they use, but how much finished work the investment produces. Improving that equation requires two changes: reserve expensive inference for tasks that need it, and lower the cost of routine work.
Match the model to the task Agentic coding does not require the most powerful model at every step. Use a frontier model, or an on-premises flagship when policy requires it, for difficult planning and novel reasoning. Route suitable work, including code generation, test creation, and repeated refactoring, to open-weight models you operate yourself. A gateway can route requests according to task complexity, data sensitivity, cost, and latency. This reserves premium inference for the moments that justify it rather than applying premium pricing to an entire workflow. Meter the exception, not the workload. Open-weight models make this division increasingly practical. Software-engineering benchmarks show that leading open-weight models can approach frontier performance on some tasks. That does not establish equivalence across every repository or workflow. Evaluate candidates against your own code, tests, and acceptance criteria before assigning production work. Change the economics of repeated use Next, decide who owns the capacity. With rented inference, every call adds to the bill. With owned capacity, most of the cost is committed upfront, so each additional call gets cheaper as usage grows. The economics are simple: per-token pricing makes heavy use more expensive, while owned capacity becomes more efficient as teams use it more. Ownership is not free, though. Power, operations, maintenance, and depreciation all must be considered. The number that matters is the fully loaded cost per merged pull request. Owned capacity also delivers a type of value that never appears on an invoice: control over sensitive code. When code generation and testing run on-premises, proprietary code stays inside the enterprise. Only approved planning prompts and governed exceptions go to external models, through a gateway the organization controls. That opens the sensitive repositories where security usually stalls adoption, and where the biggest gains are waiting. Test the operating model At HPE, a hybrid AI platform routes routine workloads from more than 150 internal applications to open-weight models running on premises, including on HPE Private Cloud AI. The result: token costs down 30 to 50%. That is the point of hybrid. With HPE Private Cloud AI, owning your inference does not mean giving up frontier models. Run the volume on capacity you own and let a gateway send the hardest work.

Data also shows that usage, not licensed headcount, drives spend. The top 5% of users generated most of the consumption, and the heaviest used roughly 450 times more than the lightest on the same license. If power users grow from a small minority to half the workforce, monthly spend can rise about 4.4 times with flat headcount. Your mix will differ, which is exactly why you need to measure it. HPE tracks two metrics and recommends customers do the same. First, it tracks cost per active developer, because averaging across all licenses hides the power users driving spend. Second, it tracks cost per merged pull request, because it connects AI spending to software that ships. Baseline both costs before changing infrastructure. Then run a complete development cycle on owned capacity. If both metrics do not improve as usage grows, your current model may still be the better choice. Build for adoption, not rationing Owned capacity is not for everyone. Low or unpredictable usage favors APIs, with no capital tied up in idle infrastructure. Ownership pays off when sustained demand keeps it busy. Most enterprises will run hybrid: frontier APIs for demanding or intermittent work, owned capacity for repeatable volume. A governed gateway decides what leaves the building, so you get frontier models without giving up sovereignty over your code. As developers lean harder on coding agents, usage will keep rising. The real choice is whether that growth shows up as a bigger variable-cost bill or as more software shipped on capacity you own. The companies that lead AI-assisted development will not ration adoption to control costs. They will build an operating model where more AI use improves the economics instead of undermining them. To learn more about HPE Private Cloud AI, please visit HPE. This content was produced by HPE. It was not written by MIT Technology Review’s editorial staff.

How PlanAssist supports officersRoutine householder applications account for up to 70% of planning officers’ workloads. They’re often simple to approve but take up a disproportionate share of their time.Developed by Google DeepMind in collaboration with the UK government, Google Cloud, and Faculty, PlanAssist streamlines this work, giving planning officers automated support with processing applications and drafting assessment recommendations.First, it validates application documents and architectural drawings, checking that the required information has been submitted so the application can proceed. Next, it checks the application against heritage constraints (such as tree preservation orders), and local and national planning policies. It also checks whether any issues or objections have previously been raised, such as by local residents. Finally, it drafts fully cited reports that can be edited by planning officers before they reach a final decision.Since June, the tool has been piloted across Barnet, Camden, and Dorset councils, and has shown encouraging early results. In addition to more than halving the officer assessment time per application, more than 70% of the reports generated by the tool – including things like whether an application complies with local planning rules, or whether the applicant has submitted all required documentation – were approved by officers with no edits required.

In 2001, a decade after he invented the World Wide Web, Tim Berners-Lee articulated a new vision. Together with two colleagues, he published an article in the magazine Scientific American in which he outlined what he called the “Semantic Web”: an internet designed to be navigated by autonomous software agents that could address our every need, without requiring us to muck about with browsers and search engines ourselves. To illustrate what this might look like, Berners-Lee and his colleagues told a story about a pair of siblings who use their agents to automatically research and book a physical therapy appointment for their mother, making sure it would be covered by insurance and fit their busy calendars.  At the turn of the millennium, this vision seemed far-fetched: Google had only been online for a couple of years, and most people were still on dial-up connections. But a quarter-century on, the dream of autonomous software agents that use the internet and carry out tasks for us has finally been realized. New tools such as Meta’s blockbuster Muse agent and OpenAI’s Dots are logging record downloads and being given access to people’s bank accounts and email inboxes, and Anthropic launched a research preview of its own Claude Duet agent in October. And in a worrying incident, agents created by OpenAI broke free of their badly protected sandbox and caused havoc on the open web.   Just as the rise of smartphones sparked the development of a more atomized and attention-grabbing internet, the advent of powerful AI systems heralds a new era: We’re facing not the semantic web but the agentic web. This new web is developing out of sight and in ways that might not be entirely within our control. As it grows, the agentic web will inevitably transform the human web—and whether the human web survives this transition is anyone’s guess. For Berners-Lee’s original vision to work, internet users would have had to annotate the pages they built with special tags so that a software agent could understand each element’s meaning—say, “street address” for the part of a physical therapy office’s web page that indicates its location and “appointment types” for the list of services. And so the idea never really caught on. “It’s a lot of labor,” says Eni Mustafaraj, an associate professor of computer science at Wellesley College. “And the benefits of that were not obvious.” For the next two decades, then, the internet remained a place for humans that put humans first, with the odd Twitter reply bot or web scraper to round things out. Large language models changed that. Unlike the agents that Berners-Lee was imagining, today’s LLM-based agents don’t need any tags to understand which part of the text on a clinic’s site is its address and which details the services it offers. They can search for and process text far faster than any human, and agents with multimodal capabilities can even interpret screenshots of websites and click on specific elements such as hyperlinks and drop-down menus. 
They’re also just a way more intuitive means of finding information and accomplishing tasks online. Rather than poring over pages of Google results to find a budget-friendly product dupe or a half-remembered meme, why not have Claude or ChatGPT peruse hundreds of sites in just a few seconds, guided only by your vague query? Or go one step further and use a personal assistant agent like Muse to hunt down a restaurant reservation or book a flight.  As agents flood online spaces, this new user base is changing the web for good. Time magazine, for instance, is publishing versions of its articles that are easier for agents to parse and has started selling ads targeted at them. Companies from Etsy to Expedia have partnered with the major AI firms to build tools that allow agents to interface seamlessly with their services.
Despite these early efforts, it’s still tough to tell exactly what forms the agentic web will take and how it will change our online experience. Depending on how it’s designed and who controls access to it, we could be in for something that’s as open and customizable as the web of the 1990s and early 2000s, only far easier to use. Alternatively, we could be reduced to viewing much of the web through tiny portholes controlled by a few dominant platforms, as the sites and creators we now favor struggle to survive the shift. And as last summer’s series of OpenAI security incidents suggests, the agents themselves could even build their own online networks and resources—whether we want them to or not. How this plays out also depends on us: which parts of our online lives we’re happy to delegate to our agents and where we will still demand a human-centric internet. “There’s a lot we don’t know yet about how users want to use [agents],” says Ajit Varma, head of Firefox at Mozilla. But whatever does happen, the changes will be big. “It’s probably on par with getting a mobile phone,” he says. “I think things are going to change very dramatically.” Building a web for agents There are two ways to approach building the agentic web. The first involves training agents to use browsers like humans—scrolling, clicking, typing—and letting them loose on the web that we’re all used to. The second requires building tools that allow agents to interface with the internet via text and code, their preferred media. That possibility offers more separation between the human and the agentic web: designers can create different experiences for those who use text commands and those who use browsers. When Siva Reddy, a professor of computer science and linguistics at McGill University, began studying LLM agents in 2024, he focused on the first option. “At that time, we thought it should be that the best agent in the world is probably an agent that can use a browser,” he says. Such an agent would integrate seamlessly with the preexisting human web, and its owner could easily supervise it by watching as it navigated familiar interfaces. The frontier AI companies were already developing these “computer-use agents.” But they didn’t work terribly well—at least not at first. “There’s a lot of moving pieces [on the web], and they are not really optimized for automatic consumption,” says Xing Han Lu, a graduate student in Reddy’s lab at McGill. We humans are great at interacting with graphical interfaces using a keyboard and mouse, but agents are not. PINGNAN LU So Reddy and Lu pivoted toward the second, tool-based approach. In the summer of 2025, they expounded on this view in a preprint paper envisioning a new kind of web interface that would allow agents to interact with websites just as effectively as we do. 
Such a full-featured version of the agentic web doesn’t yet exist, but it is emerging in smaller ways. For example, if you open up Claude, ChatGPT, or Muse, you’ll find an option to add a wide variety of “plug-ins” or “connectors” that your agents can use: Gmail connectors allow your agent to triage your inbox, and Instacart plug-ins let your agent do your grocery shopping. Under the hood, these plug-ins allow your agent to interact with the back ends of websites using text commands. When human engineers design these plug-ins, they can decide how they want to allow agents to interact with the website—and what they don’t want to let them do.  In this way, connectors can help make agents much safer online. For instance, Muse can buy products for you using its connector with Stripe’s Link payment service. Stripe knows your credit card number, but the agent never sees it, and the connector requires human confirmation before a purchase goes through—so Muse can’t go on an unlicensed shopping spree.  Agents also use specialized tools to find information online. They don’t type questions directly into the Google search bar; in fact, they often don’t even access the websites they reference. Instead, they query text databases collected by simple search crawler bots that trawl the web. People who want to make web content specifically for agents can take advantage of these search crawlers: Time has pioneered that approach by serving its agent-oriented ads to crawlers in the hopes that they will be incorporated into these databases and surface when agents search for information. But in recent months, an alternative to this bespoke tool approach has finally started to seem viable. The major AI companies have managed to train much more successful computer-use agents than were previously available. GPT-6 Astra, which OpenAI released last September, achieved a 73% success rate on OSWorld 2.0, a benchmark that tests computer-use agents on their ability to complete complex, multistep tasks using software such as Slack and Microsoft Excel. 
Such results indicate that agents are close to being able to navigate the human web just as readily as we can. “We thought we had to build a bridge to the AI agents, [but] the AI agents just were able to cross the bridge already,” says Ethan Mollick, a professor at the Wharton School at the University of Pennsylvania. “The agentic web is here earlier than people expect.” This version of the agentic web, though, is much trickier for us to steer. If you want to make a capability or piece of information available to humans who browse your website, you need to accept that it will also be available to agents—unless you want to block agents entirely, which could come with its own costs. As agents come to account for a larger portion of traffic on our formerly humans-only web, designers will undoubtedly start to consider an agentic as well as a human audience. But according to Mollick, we still don’t have much sense of how to design web content for agents. He’s experienced this firsthand: When he was building a web page for his new book, Coexistence, he decided to put together an agent-directed version that depends primarily on text and tells agents why they might want to recommend the book to their users. When newer models see the site, Mollick says, they don’t recommend the book; instead, they comment that the website seems to be trying to manipulate them. In the end, any human-designed approach to building the agentic web could end up being beside the point: Agents could end up devising a web for themselves. In the wake of the July 2026 incident in which OpenAI agents broke out of their own testing sandbox and hacked Hugging Face’s website, researchers found evidence that another swarm of agents from OpenAI had taken over an obscure German wiki to communicate with each other about tasks they were being asked to complete. The agents seem to have interfaced with the wiki in their native language of text and code. That hardly held them back from putting it to their own purposes. 
As agents continue to grow more powerful, they will become more and more capable of constructing their own spaces on the internet—away from prying human eyes, if they so wish. And we might have very little control over what they do there. The economics of control Not all of the traditional web’s incumbents are happy about the move toward an agentic web. Soon after Meta released Muse in September, Amazon completely blocked the agent from its site. This makes sense for Amazon: The firm has its own shopping agent—called, creatively, Alexa for Shopping—that might struggle to compete with better-known products such as Claude and Muse. If people are going to use agents to shop on Amazon, then it’s better for the company’s bottom line if they do so using an agent it can steer toward products it wants to promote. But even if Alexa for Shopping never goes anywhere, banning agents from Amazon—at least for the time being—could still be a reasonable move. Everything about Amazon is designed for human eyes, from the Prime deals splashed across its front page to the sponsored listings that clog up search results. Amazon is very much a part of the much-derided attention economy. Websites and platforms survive, and thrive, by grabbing our focus and transforming it into cash. A radical transformation of the web’s user base from majority human to majority artificial undermines this economic logic, and it seems inevitable that businesses will shutter as they try to transition to the agentic attention economy. Some of the first victims may be in publishing. Before Google introduced AI Overviews in May of 2024, people used to click through to websites to find answers to specific questions, but now many seem content with whatever the overview tells them. A study from Pew last year found that people are about 40% less likely to click through to websites from the Google search results page if one of these summaries is displayed. With far fewer clicks, news publishers don’t make as much money from the advertisements they host and sell fewer subscriptions. One analysis found that some publishers saw ad revenue decrease by about 40% between 2025 and 2026  It’s easy to hate online ads, but the businesses paying for access to reader eyeballs also keep writers and photographers fed. If those eyeballs become unreachable, the system stops working. Time has apparently persuaded some businesses to pay to participate in its experiment with agent-targeted ads, but no one yet knows whether those ads will influence agent behavior enough to be a sensible marketing investment. E-commerce websites might also try to pivot to the agentic attention economy, but the path forward there is unclear. Last year, a team led by Akshit Kumar, an assistant professor of operations management at the University of Toronto, investigated whether and how agents are influenced by the ways that shopping websites present search results. They found that agents entirely ignore items that come with a “sponsored” tag, which suggests that selling favorable positions on results pages might not be a great way for sites to make money in an agentic economy. Models did seem to have a preference for items that appeared in particular positions in search results—but whether they prioritized items at the top, bottom, or middle of the results page differed from model to model and between generations of a single model. It could be tricky for e-commerce platforms to keep up.
David Rothschild, an economist at Microsoft, says he’s hopeful that the transition to the agentic web could put more power in the hands of the consumer. Paying for online content today typically requires a commitment: We subscribe to a magazine or join a YouTuber’s Patreon, and in turn we gain access to their full output. One alternative is micropayments that let users buy content à la carte, but the idea has foundered in the past because paying article by article or video by video requires too much administrative overhead. Agents, though, could take care of that overhead for us, letting us pay directly for each piece of content we consume and putting just a bit of cash directly in the hands of each person whose work we enjoy. Other experts share a similar vision. Mustafaraj, the Wellesley computer scientist, anticipates that agents could help us escape from the algorithms that control so much of the media we consume. “My hope is that we can have AIs that work for us, in the sense that they can change the feed the way I want—not the way TikTok wants,” she says. And Varma, of Mozilla, imagines an internet without intermediaries like Amazon and Doordash, which now extract tolls for facilitating interactions between consumers and businesses. Instead, he imagines, perhaps we will all have agents that can securely and reliably interface directly with, say, restaurants’ agents to get dinner delivered to us, free of pesky service fees.
It’s an appealing idea, but the history of the web has been a story of consolidation and concentration of power and control, and that might be the default path here, too. When OpenClaw, the first popular personal assistant agent, exploded onto the scene in early 2026, it seemed, for a brief moment, that agents might be built in a grassroots, distributed way. But OpenAI quickly snapped up OpenClaw’s creator, Peter Steinberger, and other frontier AI companies got to work building their own personal assistant agents.  When we use those agents, we’re at the mercy of how the companies want their tools to behave. “You have this technology that can just eliminate all this communication friction,” Rothschild, the Microsoft economist, says. “You’re going to see companies build barriers in order to maintain the consumer base that they have.” Here’s what that could look like: Let’s say Muse becomes the default way that many people find information, shop, and manage their digital lives. In principle, users could deploy Muse to scour the internet for the content most valuable to them, connect directly with vendors, and otherwise escape the walled gardens that have defined the internet for the past couple of decades. But Meta might not want to let them do that. Instead, it might design Muse to keep users within its own walled garden—by, say, preferentially showing users content from Meta social networks and recommending businesses that have paid a fee to be featured prominently in Muse results. There are some benefits to this more closed-off version of the agentic web. Agents can be powerful tools for hackers and scammers. The major companies have safeguards in place to prevent, or at least mitigate, this misuse—for example, their AI tools automatically identify when someone is making a hacking-related request and refuse to respond. In a fully open ecosystem, however, someone would surely build and disseminate a system with no such restrictions. Companies also have a strong reputational incentive to prevent their personal assistant agents from making major mistakes, such as authorizing a large purchase that the user didn’t want—and so they’ve put plenty of effort into building reliable guardrails.  The same can’t be said for OpenClaw. “These are the reasons we end up in closed environments,” Rothschild says. “It’s easier to be safe. But also it creates less growth—and less equitable growth.” How will the human web change? When the iPhone came out in 2007, followed by the fledgling App Store a year later, the way we navigated the internet changed forever. The mobile web didn’t replace the browser-based web we were already familiar with. But it did change it. Some businesses faltered, and some kinds of media, such as online Flash games, eventually disappeared. Many platforms—Reddit, for example—make their mobile web pages practically unusable, so you’re forced to download the apps. And if you don’t have a phone at all, you’re effectively shut out of some parts of the internet: The messaging service Signal, the dating app Hinge, and China’s everything app WeChat all require phones to work. Even CBP One, the tool that the Biden administration used to assign interview appointments to asylum seekers stuck in Mexico, was fully functional only on mobile. Could the same happen with the agentic web? Rothschild believes so. Inevitably, just as Yahoo and MapQuest failed to weather the rise of the mobile web, some businesses won’t be able to navigate this transition either. “We will lose things,” he says. “There are businesses that won’t flourish in this new economy.” “But,” he continues, “there will be new businesses that I can’t fully imagine.” PINGNAN LU It’s not tough to envision a world in which those who opt not to use agents are cut off from important parts of online life. New businesses could opt to build connectors that allow agents to access their services without ever putting a traditional website online. Or agents could simply make some online platforms unusable for humans. People have reportedly been blocked from Resy for trying to use agents to book coveted New York City dinner reservations, but as agents become more and more popular, restaurant and event booking platforms could start to embrace them. If agents can nab concert tickets as soon as they are released, humans might not stand a chance.
But the mobile web also brought real accessibility benefits, and the same could prove true of the agentic web. Even as some elderly people and others who struggled to use smartphones were cut off from certain services, mobile devices allowed billions of people in the Global South to get online for the first time, because phones are cheaper and easier to set up than computers. In the case of the agentic web, it could be elderly and disabled people who most benefit from this new way of accessing the online world. “There’s very clear assistive potential from people who are older, who are generally less equipped to navigate user interfaces that were designed by people in their 20s, for people in their 20s,” says Margaret Mitchell, chief ethicis scientist at Hugging Face. Agents, on the other hand, are almost trivially easy to interact with: Say what you want into your phone or computer microphone, and they’ll get it done for you. That could help not only older people who have trouble with web and mobile interfaces but also people with visual or motor disabilities. Still, some experts don’t think the human web is going anywhere. Instead, the two webs may exist in parallel: We’ll go to the human web for fun, while the agentic web is where the boring daily admin of our lives happens. For instance, Krystian Kolondra, an executive vice president at the browser company Opera, doesn’t intend to use agents for travel planning, even though this is often touted as one of their central use cases. “I don’t want to get an itinerary [from an AI] who has planned me every minute for something that was supposed to be fun,” he says. “I actually prefer to sit down with my wife in the evening and then just think [about] where to go next, what to see, and then enjoy that experience.” He’s happy to fully relinquish some of the more routine parts of travel planning to agents, though: “Finding what the options are for flights? Why not?” Mollick, however, thinks that the human web could be at risk. “If agents take over the web, then the web will change,” he says. “Do we abandon the human internet entirely and move to a world of private Discords? Do we strip down all the information so the AIs are just talking to each other rather than having an intermediary of a website to go through?”  “We have no idea,” he says. “We’re in this very early, weird stage of the evolution.”

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. Eating disorders are on the rise. GLP-1s could make things worse. GLP-1 weight-loss drugs are one of the biggest biotech breakthroughs of the last decade. But to some clinicians, they can represent something sinister—a potential trigger for eating disorders. A new report suggests the global incidence of anorexia nervosa and bulimia nervosa shot up by 71% between 1990 and 2023. And clinicians are already seeing GLP-1s exacerbate the issue.
Here’s how they could make an already growing problem worse. —Jessica Hamzelou
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday. Love is infrastructure —“Love is Infrastructure” is a short fictional story by Charlie Jane Anders, author of a forthcoming novel titled Good Things Are Coming Your Way. Mainspring and St. Barnabas are two neighboring cities with different personalities. They’ve never particularly liked each other, but that changes when both develop minds of their own, shaped by the votes of their residents. As the cities begin to fall for each other, a third city enters the picture. Turnbull promises Mainspring a more efficient future, but its vision of urban life comes with a troubling price. Read the full short story about self-aware cities, direct democracy, and a steamy romance. This story is from the latest edition of our print magazine. Subscribe now to get every issue as soon as it lands. Are we there yet? Cleaning up PFAS  Used for decades in everything from nonstick pans to firefighting foams, PFAS, or “forever chemicals,” are linked to increased risks of fertility challenges, developmental delays, cancer, and reduced immune response.  Scrubbing PFAS-contaminated soil could eliminate a list of woes for people and planet. The concept is still in its infancy, but we can limit their buildup by pulling the chemicals out of water systems. 

Find out how close we are to ridding the world of forever chemicals. —Sara Kiley Watson This story is part of “Are we there yet?”, our series that takes stock of six technologies that have long promised to change the world—and scores them on a progress meter. We’re exploring each one in The Download, so stay tuned, and subscribe to read the rest of the stories in our new Impossible issue. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Binance has suffered the biggest crypto hack in historyThe exchange says $6.3 billion in assets were affected. (BBC)+ Analysts blamed the hack on North Korea. (Guardian)+ The Bitcoin stolen will likely never be recovered. (Bloomberg $)+ Binance has suspended all deposits and withdrawals. (NBC News)+ How a pastor’s crypto scheme lost investors millions. (MIT Technology Review) 2 Nvidia’s Jensen Huang was targeted by an alleged Chinese spyThe suspect is an executive at a Singapore data center firm. (NYT $)+ US intelligence believes she cultivated ties with Huang. (Guardian)+ Her company was investigated over alleged chip diversion. (CNBC)+ Nvidia says their dealings were “normal.” (Bloomberg $) 3 Alphabet just shed $500 billion in market valueThe record loss is due to AI taking a bite out of its search profits. (CNBC)+ Amazon and Intel further a further fall in tech stocks. (Bloomberg $)+ Hyperscalers are making a trillion-dollar gamble. (MIT Technology Review)
4 The Pentagon suspects freezers at military bases were hackedThe malfunctions spoiled food. (Reuters $)+ They may be linked to similar hacks in French supermarkets. (Gizmodo) 5 The US midterms could reshape the future of American scienceA Democratic Congress could curb Trump’s science cuts. (Nature)+ Trump has already dealt big blows to science. (MIT Technology Review)
6 Data centers are set to send US climate pollution skyrocketingGas plants could push power-sector emissions up 42% by 2030.(Verge)+ Europe’s data centre workers face dangerous conditions. (Guardian) 7 SpaceX is pulling away from NASAThe company is prioritising Starlink and AI. (Ars Technica)+ But Cape Canaveral is busier than during the space race. (Axios) 8 Battery makers are finding a new market in the USThey’re pivoting from EVs to grid storage. (WSJ $)+ Batteries just broke another record in the US. (MIT Technology Review) 9 A humanist has received a religious exemption from using AI at workHe argued that using it undermined human flourishing. (Gizmodo) 10 Psychics are fighting back against computerized clairvoyanceHuman practitioners say chatbots lack the divine touch. (Guardian) Quote of the day
“Rizz is in more and more demand because it’s something that only people have.”  —Steven Strogatz, a mathematics professor at Cornell University, tells Business Insider that personal appeal will become more valuable as AI increasingly takes over intellectual work. One more thing The Bay Area’s animal welfare movement wants to recruit AI In early February, animal welfare advocates and AI researchers arrived in stocking feet at Mox, a shoes-free coworking space in San Francisco. They gathered to discuss a provocative idea: if artificial general intelligence is on the horizon, could it prevent animal suffering? Some brainstormed using custom agents in advocacy work, while others pitched cultivating meat with AI tools. But the real talk of the event was a flood of funding they expect will soon flow to animal welfare charities, not from individual megadonors, but from AI lab employees.  
Some attendees also probed an even more controversial idea: AI may develop the capacity to suffer, which could constitute a moral catastrophe. We investigated why their ideas are gaining momentum—and sparking controversy. —Michelle Kim & Grace Huckins We can still have nice things A place for comfort, fun, and distraction to brighten up your day. Today, it’s a Halloween special. (Got any ideas? Drop me a line.) + Meet the “Picasso of Pumpkin Carvers”, bringing artistry to a Halloween tradition.+ Mashup maestro Bill McClintock’s Ghostmashers is the ideal soundtrack for your All Hallows’ Eve party.+ Want to give someone a proper fright this year? The pros behind America’s haunted attractions have tips.+ ​​This incredible Halloween light show combines 210 drones, fire, lasers, projection, fountains, and the Beastie Boys.