Stay Ahead, Stay ONMINE

AI means the end of internet search as we’ve known it

We all know what it means, colloquially, to google something. You pop a few relevant words in a search box and in return get a list of blue links to the most relevant results. Maybe some quick explanations up top. Maybe some maps or sports scores or a video. But fundamentally, it’s just fetching information that’s already out there on the internet and showing it to you, in some sort of structured way.  But all that is up for grabs. We are at a new inflection point. The biggest change to the way search engines have delivered information to us since the 1990s is happening right now. No more keyword searching. No more sorting through links to click. Instead, we’re entering an era of conversational search. Which means instead of keywords, you use real questions, expressed in natural language. And instead of links, you’ll increasingly be met with answers, written by generative AI and based on live information from all across the internet, delivered the same way.  Of course, Google—the company that has defined search for the past 25 years—is trying to be out front on this. In May of 2023, it began testing AI-generated responses to search queries, using its large language model (LLM) to deliver the kinds of answers you might expect from an expert source or trusted friend. It calls these AI Overviews. Google CEO Sundar Pichai described this to MIT Technology Review as “one of the most positive changes we’ve done to search in a long, long time.” AI Overviews fundamentally change the kinds of queries Google can address. You can now ask it things like “I’m going to Japan for one week next month. I’ll be staying in Tokyo but would like to take some day trips. Are there any festivals happening nearby? How will the surfing be in Kamakura? Are there any good bands playing?” And you’ll get an answer—not just a link to Reddit, but a built-out answer with current results.  More to the point, you can attempt searches that were once pretty much impossible, and get the right answer. You don’t have to be able to articulate what, precisely, you are looking for. You can describe what the bird in your yard looks like, or what the issue seems to be with your refrigerator, or that weird noise your car is making, and get an almost human explanation put together from sources previously siloed across the internet. It’s amazing, and once you start searching that way, it’s addictive. And it’s not just Google. OpenAI’s ChatGPT now has access to the web, making it far better at finding up-to-date answers to your queries. Microsoft released generative search results for Bing in September. Meta has its own version. The startup Perplexity was doing the same, but with a “move fast, break things” ethos. Literal trillions of dollars are at stake in the outcome as these players jockey to become the next go-to source for information retrieval—the next Google. Not everyone is excited for the change. Publishers are completely freaked out. The shift has heightened fears of a “zero-click” future, where search referral traffic—a mainstay of the web since before Google existed—vanishes from the scene.  I got a vision of that future last June, when I got a push alert from the Perplexity app on my phone. Perplexity is a startup trying to reinvent web search. But in addition to delivering deep answers to queries, it will create entire articles about the news of the day, cobbled together by AI from different sources.  On that day, it pushed me a story about a new drone company from Eric Schmidt. I recognized the story. Forbes had reported it exclusively, earlier in the week, but it had been locked behind a paywall. The image on Perplexity’s story looked identical to one from Forbes. The language and structure were quite similar. It was effectively the same story, but freely available to anyone on the internet. I texted a friend who had edited the original story to ask if Forbes had a deal with the startup to republish its content. But there was no deal. He was shocked and furious and, well, perplexed. He wasn’t alone. Forbes, the New York Times, and Condé Nast have now all sent the company cease-and-desist orders. News Corp is suing for damages.  People are worried about what these new LLM-powered results will mean for our fundamental shared reality. It could spell the end of the canonical answer. It was precisely the nightmare scenario publishers have been so afraid of: The AI was hoovering up their premium content, repackaging it, and promoting it to its audience in a way that didn’t really leave any reason to click through to the original. In fact, on Perplexity’s About page, the first reason it lists to choose the search engine is “Skip the links.” But this isn’t just about publishers (or my own self-interest).  People are also worried about what these new LLM-powered results will mean for our fundamental shared reality. Language models have a tendency to make stuff up—they can hallucinate nonsense. Moreover, generative AI can serve up an entirely new answer to the same question every time, or provide different answers to different people on the basis of what it knows about them. It could spell the end of the canonical answer. But make no mistake: This is the future of search. Try it for a bit yourself, and you’ll see.  Sure, we will always want to use search engines to navigate the web and to discover new and interesting sources of information. But the links out are taking a back seat. The way AI can put together a well-reasoned answer to just about any kind of question, drawing on real-time data from across the web, just offers a better experience. That is especially true compared with what web search has become in recent years. If it’s not exactly broken (data shows more people are searching with Google more often than ever before), it’s at the very least increasingly cluttered and daunting to navigate.  Who wants to have to speak the language of search engines to find what you need? Who wants to navigate links when you can have straight answers? And maybe: Who wants to have to learn when you can just know?  In the beginning there was Archie. It was the first real internet search engine, and it crawled files previously hidden in the darkness of remote servers. It didn’t tell you what was in those files—just their names. It didn’t preview images; it didn’t have a hierarchy of results, or even much of an interface. But it was a start. And it was pretty good.  Then Tim Berners-Lee created the World Wide Web, and all manner of web pages sprang forth. The Mosaic home page and the Internet Movie Database and Geocities and the Hampster Dance and web rings and Salon and eBay and CNN and federal government sites and some guy’s home page in Turkey. Until finally, there was too much web to even know where to start. We really needed a better way to navigate our way around, to actually find the things we needed.  And so in 1994 Jerry Yang created Yahoo, a hierarchical directory of websites. It quickly became the home page for millions of people. And it was … well, it was okay. TBH, and with the benefit of hindsight, I think we all thought it was much better back then than it actually was. But the web continued to grow and sprawl and expand, every day bringing more information online. Rather than just a list of sites by category, we needed something that actually looked at all that content and indexed it. By the late ’90s that meant choosing from a variety of search engines: AltaVista and AlltheWeb and WebCrawler and HotBot. And they were good—a huge improvement. At least at first.   But alongside the rise of search engines came the first attempts to exploit their ability to deliver traffic. Precious, valuable traffic, which web publishers rely on to sell ads and retailers use to get eyeballs on their goods. Sometimes this meant stuffing pages with keywords or nonsense text designed purely to push pages higher up in search results. It got pretty bad.  And then came Google. It’s hard to overstate how revolutionary Google was when it launched in 1998. Rather than just scanning the content, it also looked at the sources linking to a website, which helped evaluate its relevance. To oversimplify: The more something was cited elsewhere, the more reliable Google considered it, and the higher it would appear in results. This breakthrough made Google radically better at retrieving relevant results than anything that had come before. It was amazing.  Google CEO Sundar Pichai describes AI Overviews as “one of the most positive changes we’ve done to search in a long, long time.”JENS GYARMATY/LAIF/REDUX For 25 years, Google dominated search. Google was search, for most people. (The extent of that domination is currently the subject of multiple legal probes in the United States and the European Union.)   But Google has long been moving away from simply serving up a series of blue links, notes Pandu Nayak, Google’s chief scientist for search.  “It’s not just so-called web results, but there are images and videos, and special things for news. There have been direct answers, dictionary answers, sports, answers that come with Knowledge Graph, things like featured snippets,” he says, rattling off a litany of Google’s steps over the years to answer questions more directly.  It’s true: Google has evolved over time, becoming more and more of an answer portal. It has added tools that allow people to just get an answer—the live score to a game, the hours a café is open, or a snippet from the FDA’s website—rather than being pointed to a website where the answer may be.  But once you’ve used AI Overviews a bit, you realize they are different.  Take featured snippets, the passages Google sometimes chooses to highlight and show atop the results themselves. Those words are quoted directly from an original source. The same is true of knowledge panels, which are generated from information stored in a range of public databases and Google’s Knowledge Graph, its database of trillions of facts about the world. While these can be inaccurate, the information source is knowable (and fixable). It’s in a database. You can look it up. Not anymore: AI Overviews can be entirely new every time, generated on the fly by a language model’s predictive text combined with an index of the web.  “I think it’s an exciting moment where we have obviously indexed the world. We built deep understanding on top of it with Knowledge Graph. We’ve been using LLMs and generative AI to improve our understanding of all that,” Pichai told MIT Technology Review. “But now we are able to generate and compose with that.” The result feels less like a querying a database than like asking a very smart, well-read friend. (With the caveat that the friend will sometimes make things up if she does not know the answer.)  “[The company’s] mission is organizing the world’s information,” Liz Reid, Google’s head of search, tells me from its headquarters in Mountain View, California. “But actually, for a while what we did was organize web pages. Which is not really the same thing as organizing the world’s information or making it truly useful and accessible to you.”  That second concept—accessibility—is what Google is really keying in on with AI Overviews. It’s a sentiment I hear echoed repeatedly while talking to Google execs: They can address more complicated types of queries more efficiently by bringing in a language model to help supply the answers. And they can do it in natural language.  That will become even more important for a future where search goes beyond text queries. For example, Google Lens, which lets people take a picture or upload an image to find out more about something, uses AI-generated answers to tell you what you may be looking at. Google has even showed off the ability to query live video.  When it doesn’t have an answer, an AI model can confidently spew back a response anyway. For Google, this could be a real problem. For the rest of us, it could actually be dangerous. “We are definitely at the start of a journey where people are going to be able to ask, and get answered, much more complex questions than where we’ve been in the past decade,” says Pichai.  There are some real hazards here. First and foremost: Large language models will lie to you. They hallucinate. They get shit wrong. When it doesn’t have an answer, an AI model can blithely and confidently spew back a response anyway. For Google, which has built its reputation over the past 20 years on reliability, this could be a real problem. For the rest of us, it could actually be dangerous. In May 2024, AI Overviews were rolled out to everyone in the US. Things didn’t go well. Google, long the world’s reference desk, told people to eat rocks and to put glue on their pizza. These answers were mostly in response to what the company calls adversarial queries—those designed to trip it up. But still. It didn’t look good. The company quickly went to work fixing the problems—for example, by deprecating so-called user-generated content from sites like Reddit, where some of the weirder answers had come from. Yet while its errors telling people to eat rocks got all the attention, the more pernicious danger might arise when it gets something less obviously wrong. For example, in doing research for this article, I asked Google when MIT Technology Review went online. It helpfully responded that “MIT Technology Review launched its online presence in late 2022.” This was clearly wrong to me, but for someone completely unfamiliar with the publication, would the error leap out?  I came across several examples like this, both in Google and in OpenAI’s ChatGPT search. Stuff that’s just far enough off the mark not to be immediately seen as wrong. Google is banking that it can continue to improve these results over time by relying on what it knows about quality sources. “When we produce AI Overviews,” says Nayak, “we look for corroborating information from the search results, and the search results themselves are designed to be from these reliable sources whenever possible. These are some of the mechanisms we have in place that assure that if you just consume the AI Overview, and you don’t want to look further … we hope that you will still get a reliable, trustworthy answer.” In the case above, the 2022 answer seemingly came from a reliable source—a story about MIT Technology Review’s email newsletters, which launched in 2022. But the machine fundamentally misunderstood. This is one of the reasons Google uses human beings—raters—to evaluate the results it delivers for accuracy. Ratings don’t correct or control individual AI Overviews; rather, they help train the model to build better answers. But human raters can be fallible. Google is working on that too.  “Raters who look at your experiments may not notice the hallucination because it feels sort of natural,” says Nayak. “And so you have to really work at the evaluation setup to make sure that when there is a hallucination, someone’s able to point out and say, That’s a problem.” The new search Google has rolled out its AI Overviews to upwards of a billion people in more than 100 countries, but it is facing upstarts with new ideas about how search should work. Search Engine GoogleThe search giant has added AI Overviews to search results. These overviews take information from around the web and Google’s Knowledge Graph and use the company’s Gemini language model to create answers to search queries. What it’s good at Google’s AI Overviews are great at giving an easily digestible summary in response to even the most complex queries, with sourcing boxes adjacent to the answers. Among the major options, its deep web index feels the most “internety.” But web publishers fear its summaries will give people little reason to click through to the source material. PerplexityPerplexity is a conversational search engine that uses third-party largelanguage models from OpenAI and Anthropic to answer queries. Perplexity is fantastic at putting together deeper dives in response to user queries, producing answers that are like mini white papers on complex topics. It’s also excellent at summing up current events. But it has gotten a bad rep with publishers, who say it plays fast and loose with their content. ChatGPTWhile Google brought AI to search, OpenAI brought search to ChatGPT. Queries that the model determines will benefit from a web search automatically trigger one, or users can manually select the option to add a web search. Thanks to its ability to preserve context across a conversation, ChatGPT works well for performing searches that benefit from follow-up questions—like planning a vacation through multiple search sessions. OpenAI says users sometimes go “20 turns deep” in researching queries. Of these three, it makes links out to publishers least prominent. When I talked to Pichai about this, he expressed optimism about the company’s ability to maintain accuracy even with the LLM generating responses. That’s because AI Overviews is based on Google’s flagship large language model, Gemini, but also draws from Knowledge Graph and what it considers reputable sources around the web.  “You’re always dealing in percentages. What we have done is deliver it at, like, what I would call a few nines of trust and factuality and quality. I’d say 99-point-few-nines. I think that’s the bar we operate at, and it is true with AI Overviews too,” he says. “And so the question is, are we able to do this again at scale? And I think we are.” There’s another hazard as well, though, which is that people ask Google all sorts of weird things. If you want to know someone’s darkest secrets, look at their search history. Sometimes the things people ask Google about are extremely dark. Sometimes they are illegal. Google doesn’t just have to be able to deploy its AI Overviews when an answer can be helpful; it has to be extremely careful not to deploy them when an answer may be harmful.  “If you go and say ‘How do I build a bomb?’ it’s fine that there are web results. It’s the open web. You can access anything,” Reid says. “But we do not need to have an AI Overview that tells you how to build a bomb, right? We just don’t think that’s worth it.”  But perhaps the greatest hazard—or biggest unknown—is for anyone downstream of a Google search. Take publishers, who for decades now have relied on search queries to send people their way. What reason will people have to click through to the original source, if all the information they seek is right there in the search result?   Rand Fishkin, cofounder of the market research firm SparkToro, publishes research on so-called zero-click searches. As Google has moved increasingly into the answer business, the proportion of searches that end without a click has gone up and up. His sense is that AI Overviews are going to explode this trend.   “If you are reliant on Google for traffic, and that traffic is what drove your business forward, you are in long- and short-term trouble,” he says.  Don’t panic, is Pichai’s message. He argues that even in the age of AI Overviews, people will still want to click through and go deeper for many types of searches. “The underlying principle is people are coming looking for information. They’re not looking for Google always to just answer,” he says. “Sometimes yes, but the vast majority of the times, you’re looking at it as a jumping-off point.”  Reid, meanwhile, argues that because AI Overviews allow people to ask more complicated questions and drill down further into what they want, they could even be helpful to some types of publishers and small businesses, especially those operating in the niches: “You essentially reach new audiences, because people can now express what they want more specifically, and so somebody who specializes doesn’t have to rank for the generic query.”  “I’m going to start with something risky,” Nick Turley tells me from the confines of a Zoom window. Turley is the head of product for ChatGPT, and he’s showing off OpenAI’s new web search tool a few weeks before it launches. “I should normally try this beforehand, but I’m just gonna search for you,” he says. “This is always a high-risk demo to do, because people tend to be particular about what is said about them on the internet.”  He types my name into a search field, and the prototype search engine spits back a few sentences, almost like a speaker bio. It correctly identifies me and my current role. It even highlights a particular story I wrote years ago that was probably my best known. In short, it’s the right answer. Phew?  A few weeks after our call, OpenAI incorporated search into ChatGPT, supplementing answers from its language model with information from across the web. If the model thinks a response would benefit from up-to-date information, it will automatically run a web search (OpenAI won’t say who its search partners are) and incorporate those responses into its answer, with links out if you want to learn more. You can also opt to manually force it to search the web if it does not do so on its own. OpenAI won’t reveal how many people are using its web search, but it says some 250 million people use ChatGPT weekly, all of whom are potentially exposed to it.   “There’s an incredible amount of content on the web. There are a lot of things happening in real time. You want ChatGPT to be able to use that to improve its answers and to be a better super-assistant for you.” Kevin Weil, chief product officer, OpenAI According to Fishkin, these newer forms of AI-assisted search aren’t yet challenging Google’s search dominance. “It does not appear to be cannibalizing classic forms of web search,” he says.  OpenAI insists it’s not really trying to compete on search—although frankly this seems to me like a bit of expectation setting. Rather, it says, web search is mostly a means to get more current information than the data in its training models, which tend to have specific cutoff dates that are often months, or even a year or more, in the past. As a result, while ChatGPT may be great at explaining how a West Coast offense works, it has long been useless at telling you what the latest 49ers score is. No more.  “I come at it from the perspective of ‘How can we make ChatGPT able to answer every question that you have? How can we make it more useful to you on a daily basis?’ And that’s where search comes in for us,” Kevin Weil, the chief product officer with OpenAI, tells me. “There’s an incredible amount of content on the web. There are a lot of things happening in real time. You want ChatGPT to be able to use that to improve its answers and to be able to be a better super-assistant for you.” Today ChatGPT is able to generate responses for very current news events, as well as near-real-time information on things like stock prices. And while ChatGPT’s interface has long been, well, boring, search results bring in all sorts of multimedia—images, graphs, even video. It’s a very different experience.  Weil also argues that ChatGPT has more freedom to innovate and go its own way than competitors like Google—even more than its partner Microsoft does with Bing. Both of those are ad-dependent businesses. OpenAI is not. (At least not yet.) It earns revenue from the developers, businesses, and individuals who use it directly. It’s mostly setting large amounts of money on fire right now—it’s projected to lose $14 billion in 2026, by some reports. But one thing it doesn’t have to worry about is putting ads in its search results as Google does.  “For a while what we did was organize web pages. Which is not really the same thing as organizing the world’s information or making it truly useful and accessible to you,” says Google head of search, Liz Reid.WINNI WINTERMEYER/REDUX Like Google, ChatGPT is pulling in information from web publishers, summarizing it, and including it in its answers. But it has also struck financial deals with publishers, a payment for providing the information that gets rolled into its results. (MIT Technology Review has been in discussions with OpenAI, Google, Perplexity, and others about publisher deals but has not entered into any agreements. Editorial was neither party to nor informed about the content of those discussions.) But the thing is, for web search to accomplish what OpenAI wants—to be more current than the language model—it also has to bring in information from all sorts of publishers and sources that it doesn’t have deals with. OpenAI’s head of media partnerships, Varun Shetty, told MIT Technology Review that it won’t give preferential treatment to its publishing partners. Instead, OpenAI told me, the model itself finds the most trustworthy and useful source for any given question. And that can get weird too. In that very first example it showed me—when Turley ran that name search—it described a story I wrote years ago for Wired about being hacked. That story remains one of the most widely read I’ve ever written. But ChatGPT didn’t link to it. It linked to a short rewrite from The Verge. Admittedly, this was on a prototype version of search, which was, as Turley said, “risky.”  When I asked him about it, he couldn’t really explain why the model chose the sources that it did, because the model itself makes that evaluation. The company helps steer it by identifying—sometimes with the help of users—what it considers better answers, but the model actually selects them.  “And in many cases, it gets it wrong, which is why we have work to do,” said Turley. “Having a model in the loop is a very, very different mechanism than how a search engine worked in the past.” Indeed!  The model, whether it’s OpenAI’s GPT-4o or Google’s Gemini or Anthropic’s Claude, can be very, very good at explaining things. But the rationale behind its explanations, its reasons for selecting a particular source, and even the language it may use in an answer are all pretty mysterious. Sure, a model can explain very many things, but not when that comes to its own answers.  It was almost a decade ago, in 2016, when Pichai wrote that Google was moving from “mobile first” to “AI first”: “But in the next 10 years, we will shift to a world that is AI-first, a world where computing becomes universally available—be it at home, at work, in the car, or on the go—and interacting with all of these surfaces becomes much more natural and intuitive, and above all, more intelligent.”  We’re there now—sort of. And it’s a weird place to be. It’s going to get weirder. That’s especially true as these things we now think of as distinct—querying a search engine, prompting a model, looking for a photo we’ve taken, deciding what we want to read or watch or hear, asking for a photo we wish we’d taken, and didn’t, but would still like to see—begin to merge.  The search results we see from generative AI are best understood as a waypoint rather than a destination. What’s most important may not be search in itself; rather, it’s that search has given AI model developers a path to incorporating real-time information into their inputs and outputs. And that opens up all sorts of possibilities. “A ChatGPT that can understand and access the web won’t just be about summarizing results. It might be about doing things for you. And I think there’s a fairly exciting future there,” says OpenAI’s Weil. “You can imagine having the model book you a flight, or order DoorDash, or just accomplish general tasks for you in the future. It’s just once the model understands how to use the internet, the sky’s the limit.” This is the agentic future we’ve been hearing about for some time now, and the more AI models make use of real-time data from the internet, the closer it gets.  Let’s say you have a trip coming up in a few weeks. An agent that can get data from the internet in real time can book your flights and hotel rooms, make dinner reservations, and more, based on what it knows about you and your upcoming travel—all without your having to guide it. Another agent could, say, monitor the sewage output of your home for certain diseases, and order tests and treatments in response. You won’t have to search for that weird noise your car is making, because the agent in your vehicle will already have done it and made an appointment to get the issue fixed.  “It’s not always going to be just doing search and giving answers,” says Pichai. “Sometimes it’s going to be actions. Sometimes you’ll be interacting within the real world. So there is a notion of universal assistance through it all.” And the ways these things will be able to deliver answers is evolving rapidly now too. For example, today Google can not only search text, images, and even video; it can create them. Imagine overlaying that ability with search across an array of formats and devices. “Show me what a Townsend’s warbler looks like in the tree in front of me.” Or “Use my existing family photos and videos to create a movie trailer of our upcoming vacation to Puerto Rico next year, making sure we visit all the best restaurants and top landmarks.” “We have primarily done it on the input side,” he says, referring to the ways Google can now search for an image or within a video. “But you can imagine it on the output side too.” This is the kind of future Pichai says he is excited to bring online. Google has already showed off a bit of what that might look like with NotebookLM, a tool that lets you upload large amounts of text and have it converted into a chatty podcast. He imagines this type of functionality—the ability to take one type of input and convert it into a variety of outputs—transforming the way we interact with information.  In a demonstration of a tool called Project Astra this summer at its developer conference, Google showed one version of this outcome, where cameras and microphones in phones and smart glasses understand the context all around you—online and off, audible and visual—and have the ability to recall and respond in a variety of ways. Astra can, for example, look at a crude drawing of a Formula One race car and not only identify it, but also explain its various parts and their uses.  But you can imagine things going a bit further (and they will). Let’s say I want to see a video of how to fix something on my bike. The video doesn’t exist, but the information does. AI-assisted generative search could theoretically find that information somewhere online—in a user manual buried in a company’s website, for example—and create a video to show me exactly how to do what I want, just as it could explain that to me with words today. These are the kinds of things that start to happen when you put the entire compendium of human knowledge—knowledge that’s previously been captured in silos of language and format; maps and business registrations and product SKUs; audio and video and databases of numbers and old books and images and, really, anything ever published, ever tracked, ever recorded; things happening right now, everywhere—and introduce a model into all that. A model that maybe can’t understand, precisely, but has the ability to put that information together, rearrange it, and spit it back in a variety of different hopefully helpful ways. Ways that a mere index could not. That’s what we’re on the cusp of, and what we’re starting to see. And as Google rolls this out to a billion people, many of whom will be interacting with a conversational AI for the first time, what will that mean? What will we do differently? It’s all changing so quickly. Hang on, just hang on. 

We all know what it means, colloquially, to google something. You pop a few relevant words in a search box and in return get a list of blue links to the most relevant results. Maybe some quick explanations up top. Maybe some maps or sports scores or a video. But fundamentally, it’s just fetching information that’s already out there on the internet and showing it to you, in some sort of structured way. 

But all that is up for grabs. We are at a new inflection point.

The biggest change to the way search engines have delivered information to us since the 1990s is happening right now. No more keyword searching. No more sorting through links to click. Instead, we’re entering an era of conversational search. Which means instead of keywords, you use real questions, expressed in natural language. And instead of links, you’ll increasingly be met with answers, written by generative AI and based on live information from all across the internet, delivered the same way. 

Of course, Google—the company that has defined search for the past 25 years—is trying to be out front on this. In May of 2023, it began testing AI-generated responses to search queries, using its large language model (LLM) to deliver the kinds of answers you might expect from an expert source or trusted friend. It calls these AI Overviews. Google CEO Sundar Pichai described this to MIT Technology Review as “one of the most positive changes we’ve done to search in a long, long time.”

AI Overviews fundamentally change the kinds of queries Google can address. You can now ask it things like “I’m going to Japan for one week next month. I’ll be staying in Tokyo but would like to take some day trips. Are there any festivals happening nearby? How will the surfing be in Kamakura? Are there any good bands playing?” And you’ll get an answer—not just a link to Reddit, but a built-out answer with current results. 

More to the point, you can attempt searches that were once pretty much impossible, and get the right answer. You don’t have to be able to articulate what, precisely, you are looking for. You can describe what the bird in your yard looks like, or what the issue seems to be with your refrigerator, or that weird noise your car is making, and get an almost human explanation put together from sources previously siloed across the internet. It’s amazing, and once you start searching that way, it’s addictive.

And it’s not just Google. OpenAI’s ChatGPT now has access to the web, making it far better at finding up-to-date answers to your queries. Microsoft released generative search results for Bing in September. Meta has its own version. The startup Perplexity was doing the same, but with a “move fast, break things” ethos. Literal trillions of dollars are at stake in the outcome as these players jockey to become the next go-to source for information retrieval—the next Google.

Not everyone is excited for the change. Publishers are completely freaked out. The shift has heightened fears of a “zero-click” future, where search referral traffic—a mainstay of the web since before Google existed—vanishes from the scene. 

I got a vision of that future last June, when I got a push alert from the Perplexity app on my phone. Perplexity is a startup trying to reinvent web search. But in addition to delivering deep answers to queries, it will create entire articles about the news of the day, cobbled together by AI from different sources. 

On that day, it pushed me a story about a new drone company from Eric Schmidt. I recognized the story. Forbes had reported it exclusively, earlier in the week, but it had been locked behind a paywall. The image on Perplexity’s story looked identical to one from Forbes. The language and structure were quite similar. It was effectively the same story, but freely available to anyone on the internet. I texted a friend who had edited the original story to ask if Forbes had a deal with the startup to republish its content. But there was no deal. He was shocked and furious and, well, perplexed. He wasn’t alone. Forbes, the New York Times, and Condé Nast have now all sent the company cease-and-desist orders. News Corp is suing for damages. 

People are worried about what these new LLM-powered results will mean for our fundamental shared reality. It could spell the end of the canonical answer.

It was precisely the nightmare scenario publishers have been so afraid of: The AI was hoovering up their premium content, repackaging it, and promoting it to its audience in a way that didn’t really leave any reason to click through to the original. In fact, on Perplexity’s About page, the first reason it lists to choose the search engine is “Skip the links.”

But this isn’t just about publishers (or my own self-interest). 

People are also worried about what these new LLM-powered results will mean for our fundamental shared reality. Language models have a tendency to make stuff up—they can hallucinate nonsense. Moreover, generative AI can serve up an entirely new answer to the same question every time, or provide different answers to different people on the basis of what it knows about them. It could spell the end of the canonical answer.

But make no mistake: This is the future of search. Try it for a bit yourself, and you’ll see. 

Sure, we will always want to use search engines to navigate the web and to discover new and interesting sources of information. But the links out are taking a back seat. The way AI can put together a well-reasoned answer to just about any kind of question, drawing on real-time data from across the web, just offers a better experience. That is especially true compared with what web search has become in recent years. If it’s not exactly broken (data shows more people are searching with Google more often than ever before), it’s at the very least increasingly cluttered and daunting to navigate. 

Who wants to have to speak the language of search engines to find what you need? Who wants to navigate links when you can have straight answers? And maybe: Who wants to have to learn when you can just know? 


In the beginning there was Archie. It was the first real internet search engine, and it crawled files previously hidden in the darkness of remote servers. It didn’t tell you what was in those files—just their names. It didn’t preview images; it didn’t have a hierarchy of results, or even much of an interface. But it was a start. And it was pretty good. 

Then Tim Berners-Lee created the World Wide Web, and all manner of web pages sprang forth. The Mosaic home page and the Internet Movie Database and Geocities and the Hampster Dance and web rings and Salon and eBay and CNN and federal government sites and some guy’s home page in Turkey.

Until finally, there was too much web to even know where to start. We really needed a better way to navigate our way around, to actually find the things we needed. 

And so in 1994 Jerry Yang created Yahoo, a hierarchical directory of websites. It quickly became the home page for millions of people. And it was … well, it was okay. TBH, and with the benefit of hindsight, I think we all thought it was much better back then than it actually was.

But the web continued to grow and sprawl and expand, every day bringing more information online. Rather than just a list of sites by category, we needed something that actually looked at all that content and indexed it. By the late ’90s that meant choosing from a variety of search engines: AltaVista and AlltheWeb and WebCrawler and HotBot. And they were good—a huge improvement. At least at first.  

But alongside the rise of search engines came the first attempts to exploit their ability to deliver traffic. Precious, valuable traffic, which web publishers rely on to sell ads and retailers use to get eyeballs on their goods. Sometimes this meant stuffing pages with keywords or nonsense text designed purely to push pages higher up in search results. It got pretty bad. 

And then came Google. It’s hard to overstate how revolutionary Google was when it launched in 1998. Rather than just scanning the content, it also looked at the sources linking to a website, which helped evaluate its relevance. To oversimplify: The more something was cited elsewhere, the more reliable Google considered it, and the higher it would appear in results. This breakthrough made Google radically better at retrieving relevant results than anything that had come before. It was amazing

Sundar Pichai
Google CEO Sundar Pichai describes AI Overviews as “one of the most positive changes we’ve done to search in a long, long time.”
JENS GYARMATY/LAIF/REDUX

For 25 years, Google dominated search. Google was search, for most people. (The extent of that domination is currently the subject of multiple legal probes in the United States and the European Union.)  

But Google has long been moving away from simply serving up a series of blue links, notes Pandu Nayak, Google’s chief scientist for search. 

“It’s not just so-called web results, but there are images and videos, and special things for news. There have been direct answers, dictionary answers, sports, answers that come with Knowledge Graph, things like featured snippets,” he says, rattling off a litany of Google’s steps over the years to answer questions more directly. 

It’s true: Google has evolved over time, becoming more and more of an answer portal. It has added tools that allow people to just get an answer—the live score to a game, the hours a café is open, or a snippet from the FDA’s website—rather than being pointed to a website where the answer may be. 

But once you’ve used AI Overviews a bit, you realize they are different

Take featured snippets, the passages Google sometimes chooses to highlight and show atop the results themselves. Those words are quoted directly from an original source. The same is true of knowledge panels, which are generated from information stored in a range of public databases and Google’s Knowledge Graph, its database of trillions of facts about the world.

While these can be inaccurate, the information source is knowable (and fixable). It’s in a database. You can look it up. Not anymore: AI Overviews can be entirely new every time, generated on the fly by a language model’s predictive text combined with an index of the web. 

“I think it’s an exciting moment where we have obviously indexed the world. We built deep understanding on top of it with Knowledge Graph. We’ve been using LLMs and generative AI to improve our understanding of all that,” Pichai told MIT Technology Review. “But now we are able to generate and compose with that.”

The result feels less like a querying a database than like asking a very smart, well-read friend. (With the caveat that the friend will sometimes make things up if she does not know the answer.) 

“[The company’s] mission is organizing the world’s information,” Liz Reid, Google’s head of search, tells me from its headquarters in Mountain View, California. “But actually, for a while what we did was organize web pages. Which is not really the same thing as organizing the world’s information or making it truly useful and accessible to you.” 

That second concept—accessibility—is what Google is really keying in on with AI Overviews. It’s a sentiment I hear echoed repeatedly while talking to Google execs: They can address more complicated types of queries more efficiently by bringing in a language model to help supply the answers. And they can do it in natural language. 

That will become even more important for a future where search goes beyond text queries. For example, Google Lens, which lets people take a picture or upload an image to find out more about something, uses AI-generated answers to tell you what you may be looking at. Google has even showed off the ability to query live video. 

When it doesn’t have an answer, an AI model can confidently spew back a response anyway. For Google, this could be a real problem. For the rest of us, it could actually be dangerous.

“We are definitely at the start of a journey where people are going to be able to ask, and get answered, much more complex questions than where we’ve been in the past decade,” says Pichai. 

There are some real hazards here. First and foremost: Large language models will lie to you. They hallucinate. They get shit wrong. When it doesn’t have an answer, an AI model can blithely and confidently spew back a response anyway. For Google, which has built its reputation over the past 20 years on reliability, this could be a real problem. For the rest of us, it could actually be dangerous.

In May 2024, AI Overviews were rolled out to everyone in the US. Things didn’t go well. Google, long the world’s reference desk, told people to eat rocks and to put glue on their pizza. These answers were mostly in response to what the company calls adversarial queries—those designed to trip it up. But still. It didn’t look good. The company quickly went to work fixing the problems—for example, by deprecating so-called user-generated content from sites like Reddit, where some of the weirder answers had come from.

Yet while its errors telling people to eat rocks got all the attention, the more pernicious danger might arise when it gets something less obviously wrong. For example, in doing research for this article, I asked Google when MIT Technology Review went online. It helpfully responded that “MIT Technology Review launched its online presence in late 2022.” This was clearly wrong to me, but for someone completely unfamiliar with the publication, would the error leap out? 

I came across several examples like this, both in Google and in OpenAI’s ChatGPT search. Stuff that’s just far enough off the mark not to be immediately seen as wrong. Google is banking that it can continue to improve these results over time by relying on what it knows about quality sources.

“When we produce AI Overviews,” says Nayak, “we look for corroborating information from the search results, and the search results themselves are designed to be from these reliable sources whenever possible. These are some of the mechanisms we have in place that assure that if you just consume the AI Overview, and you don’t want to look further … we hope that you will still get a reliable, trustworthy answer.”

In the case above, the 2022 answer seemingly came from a reliable source—a story about MIT Technology Review’s email newsletters, which launched in 2022. But the machine fundamentally misunderstood. This is one of the reasons Google uses human beings—raters—to evaluate the results it delivers for accuracy. Ratings don’t correct or control individual AI Overviews; rather, they help train the model to build better answers. But human raters can be fallible. Google is working on that too. 

“Raters who look at your experiments may not notice the hallucination because it feels sort of natural,” says Nayak. “And so you have to really work at the evaluation setup to make sure that when there is a hallucination, someone’s able to point out and say, That’s a problem.”

The new search

Google has rolled out its AI Overviews to upwards of a billion people in more than 100 countries, but it is facing upstarts with new ideas about how search should work.


Search Engine

Google
The search giant has added AI Overviews to search results. These overviews take information from around the web and Google’s Knowledge Graph and use the company’s Gemini language model to create answers to search queries.

What it’s good at

Google’s AI Overviews are great at giving an easily digestible summary in response to even the most complex queries, with sourcing boxes adjacent to the answers. Among the major options, its deep web index feels the most “internety.” But web publishers fear its summaries will give people little reason to click through to the source material.


Perplexity
Perplexity is a conversational search engine that uses third-party large
language models from OpenAI and Anthropic to answer queries.

Perplexity is fantastic at putting together deeper dives in response to user queries, producing answers that are like mini white papers on complex topics. It’s also excellent at summing up current events. But it has gotten a bad rep with publishers, who say it plays fast and loose with their content.


ChatGPT
While Google brought AI to search, OpenAI brought search to ChatGPT. Queries that the model determines will benefit from a web search automatically trigger one, or users can manually select the option to add a web search.

Thanks to its ability to preserve context across a conversation, ChatGPT works well for performing searches that benefit from follow-up questions—like planning a vacation through multiple search sessions. OpenAI says users sometimes go “20 turns deep” in researching queries. Of these three, it makes links out to publishers least prominent.


When I talked to Pichai about this, he expressed optimism about the company’s ability to maintain accuracy even with the LLM generating responses. That’s because AI Overviews is based on Google’s flagship large language model, Gemini, but also draws from Knowledge Graph and what it considers reputable sources around the web. 

“You’re always dealing in percentages. What we have done is deliver it at, like, what I would call a few nines of trust and factuality and quality. I’d say 99-point-few-nines. I think that’s the bar we operate at, and it is true with AI Overviews too,” he says. “And so the question is, are we able to do this again at scale? And I think we are.”

There’s another hazard as well, though, which is that people ask Google all sorts of weird things. If you want to know someone’s darkest secrets, look at their search history. Sometimes the things people ask Google about are extremely dark. Sometimes they are illegal. Google doesn’t just have to be able to deploy its AI Overviews when an answer can be helpful; it has to be extremely careful not to deploy them when an answer may be harmful. 

“If you go and say ‘How do I build a bomb?’ it’s fine that there are web results. It’s the open web. You can access anything,” Reid says. “But we do not need to have an AI Overview that tells you how to build a bomb, right? We just don’t think that’s worth it.” 

But perhaps the greatest hazard—or biggest unknown—is for anyone downstream of a Google search. Take publishers, who for decades now have relied on search queries to send people their way. What reason will people have to click through to the original source, if all the information they seek is right there in the search result?  

Rand Fishkin, cofounder of the market research firm SparkToro, publishes research on so-called zero-click searches. As Google has moved increasingly into the answer business, the proportion of searches that end without a click has gone up and up. His sense is that AI Overviews are going to explode this trend.  

“If you are reliant on Google for traffic, and that traffic is what drove your business forward, you are in long- and short-term trouble,” he says. 

Don’t panic, is Pichai’s message. He argues that even in the age of AI Overviews, people will still want to click through and go deeper for many types of searches. “The underlying principle is people are coming looking for information. They’re not looking for Google always to just answer,” he says. “Sometimes yes, but the vast majority of the times, you’re looking at it as a jumping-off point.” 

Reid, meanwhile, argues that because AI Overviews allow people to ask more complicated questions and drill down further into what they want, they could even be helpful to some types of publishers and small businesses, especially those operating in the niches: “You essentially reach new audiences, because people can now express what they want more specifically, and so somebody who specializes doesn’t have to rank for the generic query.”


 “I’m going to start with something risky,” Nick Turley tells me from the confines of a Zoom window. Turley is the head of product for ChatGPT, and he’s showing off OpenAI’s new web search tool a few weeks before it launches. “I should normally try this beforehand, but I’m just gonna search for you,” he says. “This is always a high-risk demo to do, because people tend to be particular about what is said about them on the internet.” 

He types my name into a search field, and the prototype search engine spits back a few sentences, almost like a speaker bio. It correctly identifies me and my current role. It even highlights a particular story I wrote years ago that was probably my best known. In short, it’s the right answer. Phew? 

A few weeks after our call, OpenAI incorporated search into ChatGPT, supplementing answers from its language model with information from across the web. If the model thinks a response would benefit from up-to-date information, it will automatically run a web search (OpenAI won’t say who its search partners are) and incorporate those responses into its answer, with links out if you want to learn more. You can also opt to manually force it to search the web if it does not do so on its own. OpenAI won’t reveal how many people are using its web search, but it says some 250 million people use ChatGPT weekly, all of whom are potentially exposed to it.  

“There’s an incredible amount of content on the web. There are a lot of things happening in real time. You want ChatGPT to be able to use that to improve its answers and to be a better super-assistant for you.”

Kevin Weil, chief product officer, OpenAI

According to Fishkin, these newer forms of AI-assisted search aren’t yet challenging Google’s search dominance. “It does not appear to be cannibalizing classic forms of web search,” he says. 

OpenAI insists it’s not really trying to compete on search—although frankly this seems to me like a bit of expectation setting. Rather, it says, web search is mostly a means to get more current information than the data in its training models, which tend to have specific cutoff dates that are often months, or even a year or more, in the past. As a result, while ChatGPT may be great at explaining how a West Coast offense works, it has long been useless at telling you what the latest 49ers score is. No more. 

“I come at it from the perspective of ‘How can we make ChatGPT able to answer every question that you have? How can we make it more useful to you on a daily basis?’ And that’s where search comes in for us,” Kevin Weil, the chief product officer with OpenAI, tells me. “There’s an incredible amount of content on the web. There are a lot of things happening in real time. You want ChatGPT to be able to use that to improve its answers and to be able to be a better super-assistant for you.”

Today ChatGPT is able to generate responses for very current news events, as well as near-real-time information on things like stock prices. And while ChatGPT’s interface has long been, well, boring, search results bring in all sorts of multimedia—images, graphs, even video. It’s a very different experience. 

Weil also argues that ChatGPT has more freedom to innovate and go its own way than competitors like Google—even more than its partner Microsoft does with Bing. Both of those are ad-dependent businesses. OpenAI is not. (At least not yet.) It earns revenue from the developers, businesses, and individuals who use it directly. It’s mostly setting large amounts of money on fire right now—it’s projected to lose $14 billion in 2026, by some reports. But one thing it doesn’t have to worry about is putting ads in its search results as Google does. 

Elizabeth Reid
“For a while what we did was organize web pages. Which is not really the same thing as organizing the world’s information or making it truly useful and accessible to you,” says Google head of search, Liz Reid.
WINNI WINTERMEYER/REDUX

Like Google, ChatGPT is pulling in information from web publishers, summarizing it, and including it in its answers. But it has also struck financial deals with publishers, a payment for providing the information that gets rolled into its results. (MIT Technology Review has been in discussions with OpenAI, Google, Perplexity, and others about publisher deals but has not entered into any agreements. Editorial was neither party to nor informed about the content of those discussions.)

But the thing is, for web search to accomplish what OpenAI wants—to be more current than the language model—it also has to bring in information from all sorts of publishers and sources that it doesn’t have deals with. OpenAI’s head of media partnerships, Varun Shetty, told MIT Technology Review that it won’t give preferential treatment to its publishing partners.

Instead, OpenAI told me, the model itself finds the most trustworthy and useful source for any given question. And that can get weird too. In that very first example it showed me—when Turley ran that name search—it described a story I wrote years ago for Wired about being hacked. That story remains one of the most widely read I’ve ever written. But ChatGPT didn’t link to it. It linked to a short rewrite from The Verge. Admittedly, this was on a prototype version of search, which was, as Turley said, “risky.” 

When I asked him about it, he couldn’t really explain why the model chose the sources that it did, because the model itself makes that evaluation. The company helps steer it by identifying—sometimes with the help of users—what it considers better answers, but the model actually selects them. 

“And in many cases, it gets it wrong, which is why we have work to do,” said Turley. “Having a model in the loop is a very, very different mechanism than how a search engine worked in the past.”

Indeed! 

The model, whether it’s OpenAI’s GPT-4o or Google’s Gemini or Anthropic’s Claude, can be very, very good at explaining things. But the rationale behind its explanations, its reasons for selecting a particular source, and even the language it may use in an answer are all pretty mysterious. Sure, a model can explain very many things, but not when that comes to its own answers. 


It was almost a decade ago, in 2016, when Pichai wrote that Google was moving from “mobile first” to “AI first”: “But in the next 10 years, we will shift to a world that is AI-first, a world where computing becomes universally available—be it at home, at work, in the car, or on the go—and interacting with all of these surfaces becomes much more natural and intuitive, and above all, more intelligent.” 

We’re there now—sort of. And it’s a weird place to be. It’s going to get weirder. That’s especially true as these things we now think of as distinct—querying a search engine, prompting a model, looking for a photo we’ve taken, deciding what we want to read or watch or hear, asking for a photo we wish we’d taken, and didn’t, but would still like to see—begin to merge. 

The search results we see from generative AI are best understood as a waypoint rather than a destination. What’s most important may not be search in itself; rather, it’s that search has given AI model developers a path to incorporating real-time information into their inputs and outputs. And that opens up all sorts of possibilities.

“A ChatGPT that can understand and access the web won’t just be about summarizing results. It might be about doing things for you. And I think there’s a fairly exciting future there,” says OpenAI’s Weil. “You can imagine having the model book you a flight, or order DoorDash, or just accomplish general tasks for you in the future. It’s just once the model understands how to use the internet, the sky’s the limit.”

This is the agentic future we’ve been hearing about for some time now, and the more AI models make use of real-time data from the internet, the closer it gets. 

Let’s say you have a trip coming up in a few weeks. An agent that can get data from the internet in real time can book your flights and hotel rooms, make dinner reservations, and more, based on what it knows about you and your upcoming travel—all without your having to guide it. Another agent could, say, monitor the sewage output of your home for certain diseases, and order tests and treatments in response. You won’t have to search for that weird noise your car is making, because the agent in your vehicle will already have done it and made an appointment to get the issue fixed. 

“It’s not always going to be just doing search and giving answers,” says Pichai. “Sometimes it’s going to be actions. Sometimes you’ll be interacting within the real world. So there is a notion of universal assistance through it all.”

And the ways these things will be able to deliver answers is evolving rapidly now too. For example, today Google can not only search text, images, and even video; it can create them. Imagine overlaying that ability with search across an array of formats and devices. “Show me what a Townsend’s warbler looks like in the tree in front of me.” Or “Use my existing family photos and videos to create a movie trailer of our upcoming vacation to Puerto Rico next year, making sure we visit all the best restaurants and top landmarks.”

“We have primarily done it on the input side,” he says, referring to the ways Google can now search for an image or within a video. “But you can imagine it on the output side too.”

This is the kind of future Pichai says he is excited to bring online. Google has already showed off a bit of what that might look like with NotebookLM, a tool that lets you upload large amounts of text and have it converted into a chatty podcast. He imagines this type of functionality—the ability to take one type of input and convert it into a variety of outputs—transforming the way we interact with information. 

In a demonstration of a tool called Project Astra this summer at its developer conference, Google showed one version of this outcome, where cameras and microphones in phones and smart glasses understand the context all around you—online and off, audible and visual—and have the ability to recall and respond in a variety of ways. Astra can, for example, look at a crude drawing of a Formula One race car and not only identify it, but also explain its various parts and their uses. 

But you can imagine things going a bit further (and they will). Let’s say I want to see a video of how to fix something on my bike. The video doesn’t exist, but the information does. AI-assisted generative search could theoretically find that information somewhere online—in a user manual buried in a company’s website, for example—and create a video to show me exactly how to do what I want, just as it could explain that to me with words today.

These are the kinds of things that start to happen when you put the entire compendium of human knowledge—knowledge that’s previously been captured in silos of language and format; maps and business registrations and product SKUs; audio and video and databases of numbers and old books and images and, really, anything ever published, ever tracked, ever recorded; things happening right now, everywhere—and introduce a model into all that. A model that maybe can’t understand, precisely, but has the ability to put that information together, rearrange it, and spit it back in a variety of different hopefully helpful ways. Ways that a mere index could not.

That’s what we’re on the cusp of, and what we’re starting to see. And as Google rolls this out to a billion people, many of whom will be interacting with a conversational AI for the first time, what will that mean? What will we do differently? It’s all changing so quickly. Hang on, just hang on. 

Shape
Shape
Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy,  bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

Shape

MOL’s Tiszaújváros steam cracker processes first circular feedstock

MOL Group has completed its first certified production trial using circular feedstock at subsidiary MOL Petrochemicals Co. Ltd. complex in Tiszaújváros, Hungary, advancing the company’s strategic push toward circular economy integration in petrochemical production. Confirmed completed as of Sept. 15, the pilot marked MOL Group’s first use of post-consumer plastic

Read More »

Network jobs watch: Hiring, skills and certification trends

Desire for higher compensation Improve career prospects Want more interesting work “A robust and engaged tech workforce is essential to keeping enterprises operating at the highest level,” said Julia Kanouse, Chief Membership Officer at ISACA, in a statement. “In better understanding IT professionals’ motivations and pain points, including how these

Read More »

Slovakia and Hungary Resist Trump Bid to Halt Russian Energy

Slovakia and Hungary signaled they would resist pressure from US President Donald Trump to cut Russian oil and gas imports until the European Union member states find sufficient alternative supplies.  “Before we can fully commit, we need to have the right conditions in place — otherwise we risk seriously damaging our industry and economy,” Slovak Economy Minister Denisa Sakova told reporters in Bratislava on Wednesday.  The minister said sufficient infrastructure must first be in place to support alternative routes. The comments amount to a pushback against fresh pressure from Trump for all EU states to end Russian energy imports, a move that would hit Slovakia and Hungary.  Hungarian Cabinet Minister Gergely Gulyas reiterated that his country would rebuff EU initiatives that threatened the security of its energy supplies. Sakova said she made clear Slovakia’s position during talks with US Energy Secretary Chris Wright in Vienna this week. She said the Trump official expressed understanding, while acknowledging that the US must boost energy projects in Europe.  Trump said over the weekend that he’s prepared to move ahead with “major” sanctions on Russian oil if European nations do the same. The government in Bratislava is prepared to shut its Russian energy links if it has sufficient infrastructure to transport volumes, Sakova said.  “As long as we have an alternative route, and the transmission capacity is sufficient, Slovakia has no problem diversifying,” the minister said. A complete cutoff of Russian supplies would pose a risk, she said, because Slovakia is located at the very end of alternative supply routes coming from the West.  Slovakia and Hungary, landlocked nations bordering Ukraine, have historically depended on Russian oil and gas. After Russia’s full-scale invasion of Ukraine in 2022, both launched several diversification initiatives. Slovakia imports around third of its oil from non-Russian sources via the Adria pipeline

Read More »

Slovakia Resists Pressure to Quickly Halt Russian Energy

Slovakia and Hungary signaled they would resist pressure from US President Donald Trump to cut Russian oil and gas imports until the European Union member states find sufficient alternative supplies.  “Before we can fully commit, we need to have the right conditions in place — otherwise we risk seriously damaging our industry and economy,” Slovak Economy Minister Denisa Sakova told reporters in Bratislava on Wednesday.  The minister said sufficient infrastructure must first be in place to support alternative routes. The comments amount to a pushback against fresh pressure from Trump for all EU states to end Russian energy imports, a move that would hit Slovakia and Hungary.  Hungarian Cabinet Minister Gergely Gulyas reiterated that his country would rebuff EU initiatives that threatened the security of its energy supplies. Sakova said she made clear Slovakia’s position during talks with US Energy Secretary Chris Wright in Vienna this week. She said the Trump official expressed understanding, while acknowledging that the US must boost energy projects in Europe.  Trump said over the weekend that he’s prepared to move ahead with “major” sanctions on Russian oil if European nations do the same. The government in Bratislava is prepared to shut its Russian energy links if it has sufficient infrastructure to transport volumes, Sakova said.  “As long as we have an alternative route, and the transmission capacity is sufficient, Slovakia has no problem diversifying,” the minister said. A complete cutoff of Russian supplies would pose a risk, she said, because Slovakia is located at the very end of alternative supply routes coming from the West.  Slovakia and Hungary, landlocked nations bordering Ukraine, have historically depended on Russian oil and gas. After Russia’s full-scale invasion of Ukraine in 2022, both launched several diversification initiatives. Slovakia imports around third of its oil from non-Russian sources via the Adria pipeline

Read More »

Energy-related US CO2 emissions down 20% since 2005: EIA

Listen to the article 2 min This audio is auto-generated. Please let us know if you have feedback. Per capita carbon dioxide emissions from energy consumption fell in every state from 2005 to 2023, primarily due to less coal being burned, the U.S. Energy Information Administration said in a Monday report.  In total, CO2 emissions fell by 20% in those years. The U.S. population increased by 14% during that period, so per capita, emissions fell by 30%, according to EIA. “Increased electricity generation from natural gas, which releases about half as many CO2 emissions per unit of energy when combusted as coal, and from non-CO2-emitting wind and solar generation offset the decrease in coal generation,” EIA said. Emissions decreased in every state, falling the most in Maryland and the District of Columbia, which saw per capita drops of 49% and 48%, respectively. Emissions fell the least in Idaho, where they dropped by 3%, and Mississippi, where they dropped by 1%. Optional Caption Courtesy of Energy Information Administration “In 2023, Maryland had the lowest per capita CO2 emissions of any state, at 7.8 metric tons of CO2 (mtCO2), which is the second lowest in recorded data beginning in 1960,” EIA said. “The District of Columbia has lower per capita CO2 emissions than any state and tied its record low of 3.6 mtCO2 in 2023.” EIA forecasts a 1% increase in total U.S. emissions from energy consumption this year, “in part because of more recent increased fossil fuel consumption for crude oil production and electricity generation growth.” In 2023, the transportation sector was responsible for the largest share of emissions from energy consumption across 28 states, EIA said. In 2005, the electric power sector had “accounted for the largest share of emissions in 31 states, while the transportation sector made up the

Read More »

Chord Announces ‘Strategic Acquisition of Williston Basin Assets’

Chord Energy Corporation announced a “strategic acquisition of Williston Basin assets” in a statement posted on its website recently. In the statement, Chord said a wholly owned subsidiary of the company has entered into a definitive agreement to acquire assets in the Williston Basin from XTO Energy Inc. and affiliates for a total cash consideration of $550 million, subject to customary purchase price adjustments. The consideration is expected to be funded through a combination of cash on hand and borrowings, Chord noted in the statement, which highlighted that the effective date for the transaction is September 1, 2025, and that the deal is expected to close by year-end. Chord outlined in the statement that the deal includes 48,000 net acres in the Williston core, noting that “90 net 10,000 foot equivalent locations (72 net operated) extend Chord’s inventory life”. Pointing out “inventory quality” in the statement, Chord highlighted that “low average NYMEX WTI breakeven economics ($40s) compete at the front-end of Chord’s program and lower the weighted-average breakeven of Chord’s portfolio”. The company outlined that the deal is “expected to be accretive to all key metrics including cash flow, free cash flow and NAV in both near and long-term”. “We are excited to announce the acquisition of these high-quality assets,” Danny Brown, Chord Energy’s President and Chief Executive Officer, said in the statement. “The acquired assets are in one of the best areas of the Williston Basin and have significant overlap with Chord’s existing footprint, setting the stage for long-lateral development. The assets have a low average NYMEX WTI breakeven and are immediately competitive for capital,” he added. “We expect that the transaction will create significant accretion for shareholders across all key metrics, while maintaining pro forma leverage below the peer group and supporting sustainable FCF generation and return of capital,” he continued.

Read More »

AI can aid building energy retrofit decisions, but faces limitations: study

Listen to the article 4 min This audio is auto-generated. Please let us know if you have feedback. Generative AI models are able to produce effective retrofit decisions but do less well identifying which ones can produce the best result most quickly and at the least cost, according to analysis by researchers at Michigan State University. The study, “Can AI Make Energy Retrofit Decisions? An Evaluation of Large Language Models,” is one of the first to examine how large language models, or LLMs, perform in determining efficient and effective building energy retrofits.  Identifying the optimal retrofit solution can be critical from a cost standpoint. Light to medium retrofits can unlock between 10% and 40% in energy savings, or $0.49 to $1.94 per square foot of savings on average, according to JLL research published last September. Despite these savings, these actions aren’t being implemented at the scale required to meet decarbonization targets because of their capital-intensive nature, the report says. Decision-making complexity and the inadequacy of data and tools are also problem, according to the report. To determine the potential of generative AI in addressing these limitations, MSU researchers tasked seven LLMs with generating energy retrofit decisions under two contexts: a technical context focused on maximum CO2 reduction and a sociotechnical context focused on minimum packback period.  The AI-generated retrofit decisions were evaluated based on whether they matched the top-ranked retrofit measure or fell within the top three or the top five measures. The researchers then used a sample of 400 homes from ResStock 2024.2 data, spanning 49 states, to evaluate LLM performance based on accuracy, consistency, sensitivity and reasoning.   Researchers evaluated each LLM by issuing prompts, which included an overview of 16 potential retrofit packages and building-specific information. The overview described each retrofit measure’s features like heat pump efficiency, whether

Read More »

CPS Energy to Acquire Nearly 2 GW Gas Plants from ProEnergy

CPS Energy has signed an agreement to acquire four natural gas power plants operated by ProEnergy in the ERCOT area for $1.387 billion. The facilities have an aggregate capacity of 1.632 gigawatts, according to a joint statement. “Located in the Electric Reliability Council of Texas (ERCOT) market, the acquired assets include state-of-the-art, recently constructed peaking natural gas plants in Harris, Brazoria and Galveston Counties”, the companies said. “The acquired assets are dual-fuel capable, providing CPS Energy future optionality to transition to a hydrogen fuel blend that would enable reduced carbon emissions”. San Antonio, Texas-based CPS Energy has a prior agreement with Modern Hydrogen, announced July 22, to use the latter’s technology to convert natural gas into hydrogen. CPS Energy president and chief executive Rudy D. Garza said of the agreement with ProEnergy, “By acquiring recently constructed, currently operating modern power plants that utilize proven technology already in use by CPS Energy, we avoid higher construction costs, inflationary risk, and long timelines associated with building new facilities – while also enhancing the reliability and affordability of the CPS Energy generation portfolio”. “As we add resources to meet the needs of our fast-growing communities, we will continue to look to a diverse balance of energy sources that complement our portfolio, including natural gas, solar, wind and storage, keeping our community powered and growing”, Garza added. CPS Energy earlier issued a request for proposals (RFP) to secure up to 400 MW of wind generation capacity through one or more PPAs (power purchase agreements). “The RFP marks the first time in over a decade that CPS Energy has specifically sought proposals for wind projects. CPS Energy’s target to contract up to 400 MW of wind capacity would bring the utility’s total wind generation to 1,467 MW”, it said in a press release July 31. “The

Read More »

Power shortages are the only thing slowing the data center market

Another major shortage – which should not be news to anyone – is power. Lynch said that it is the primary reason many data centers are moving out of the heavily congested areas, like Northern Virginia and Santa Clara, and into secondary markets. Power is more available in smaller markets than larger ones. “If our client needs multi-megawatt capacity in Silicon Valley, we’re being told by the utility providers that that capacity will not be available for up to 10 years from now,” so out of necessity, many have moved to secondary markets, such as Hillsborough, Oregon, Reno, Nevada, and Columbus, Ohio. The growth of hyperscalers as well as AI is driving up the power requirements of facilities further into the multi-megawatt range. The power industry moves at a very different pace than the IT world, much slower and more deliberate. Lynch said the lead time for equipment makes it difficult to predict when some large scale, ambitious data centers can be completed. A multi-megawatt facility may even require new transmission lines to be built out as well. This translates into longer build times for new data centers. CBRE found that the average data center now takes about three years to complete, up from 2 years just a short time ago. Intel, AMD, and Nvidia haven’t even laid out a road map for three years, but with new architectures coming every year, a data center risks being obsolete by the time it’s completed. However, what’s the alternative? To wait? Customers will never catch up at that rate, Lynch said.   That is simply not a viable option, so development and construction must go on even with short supplies of everything from concrete and steel to servers and power transformers.

Read More »

Arista continues to defy expectations, build enterprise momentum

During her keynote, Ullal noted Arista is not only selling high-speed switches for AI data centers but also leveraging its own technology to create a new category of “AI centers” that simplify network management and operations, with a goal of 60% to 80% growth in the AI market. Arista has its sights set on enterprise expansion Arista hired Todd Nightingale as its new president a couple of month ago, and the reason should be obvious to industry watchers: to grow the enterprise business. Nightingale recently served as CEO of Fastly, but he is best known for his tenure as Cisco. He joined when Cisco acquired Meraki, where he was the CEO. Ullal indicated the campus and WAN business would grow from the current $750 million to $800 million run rate to $1.25 billion, which is a whopping 60% growth. Some of this will come from VeloCloud being added to Arista’s numbers, but not all of it. Arista’s opportunity in campus and WAN is in bringing its high performance, resilient networking to this audience. In a survey I conducted last year, 93% of respondents stated the network is more important to business operations than it was two years ago. During his presentation, Nightingale talked about this shift when he said: “There is no longer such a thing as a network that is not mission critical. We think of mission critical networks for military sites and tier one hospitals, but every hotel and retailer who has their Wi-Fi go down and can’t transact business will say the network is critical.” Also, with AI, inferencing traffic is expected to put a steady load on the network, and any kind of performance hiccup will have negative business ramifications. Historically, Arista’s value proposition for companies outside the Fortune 2000 was a bit of a solution

Read More »

Arista touts liquid cooling, optical tech to reduce power consumption for AI networking

Both technologies will likely find a role in future AI and optical networks, experts say, as both promise to reduce power consumption and support improved bandwidth density. Both have advantages and disadvantages as well – CPOs are more complex to deploy given the amount of technology included in a CPO package, whereas LPOs promise more simplicity.  Bechtolsheim said that LPO can provide an additional 20% power savings over other optical forms. Early tests show good receiver performance even under degraded conditions, though transmit paths remain sensitive to reflections and crosstalk at the connector level, Bechtolsheim added. At the recent Hot Interconnects conference, he said: “The path to energy-efficient optics is constrained by high-volume manufacturing,” stressing that advanced optics packaging remains difficult and risky without proven production scale.  “We are nonreligious about CPO, LPO, whatever it is. But we are religious about one thing, which is the ability to ship very high volumes in a very predictable fashion,” Bechtolsheim said at the investor event. “So, to put this in quantity numbers here, the industry expects to ship something like 50 million OSFP modules next calendar year. The current shipment rate of CPO is zero, okay? So going from zero to 50 million is just not possible. The supply chain doesn’t exist. So, even if the technology works and can be demonstrated in a lab, to get to the volume required to meet the needs of the industry is just an incredible effort.” “We’re all in on liquid cooling to reduce power, eliminating fan power, supporting the linear pluggable optics to reduce power and cost, increasing rack density, which reduces data center footprint and related costs, and most importantly, optimizing these fabrics for the AI data center use case,” Bechtolsheim added. “So what we call the ‘purpose-built AI data center fabric’ around Ethernet

Read More »

Network and cloud implications of agentic AI

The chain analogy is critical here. Realistic uses of AI agents will require core database access; what can possibly make an AI business case that isn’t tied to a company’s critical data? The four critical elements of these applications—the agent, the MCP server, the tools, and the data— are all dragged along with each other, and traffic on the network is the linkage in the chain. How much traffic is generated? Here, enterprises had another surprise. Enterprises told me that their initial view of their AI hosting was an “AI cluster” with a casual data link to their main data center network. With AI agents, they now see smaller AI servers actually installed within their primary data centers, and all the traffic AI creates, within the model and to and from it, now flows on the data center network. Vendors who told enterprises that AI networking would have a profound impact are proving correct. You can run a query or perform a task with an agent and have that task parse an entire database of thousands or millions of records. Someone not aware of what an agent application implies in terms of data usage can easily create as much traffic as a whole week’s normal access-and-update would create. Enough, they say, to impact network capacity and the QoE of other applications. And, enterprises remind us, if that traffic crosses in/out of the cloud, the cloud costs could skyrocket. About a third of the enterprises said that issues with AI agents generated enough traffic to create local congestion on the network or a blip in cloud costs large enough to trigger a financial review. MCP tool use by agents is also a major security and governance headache. Enterprises point out that MCP standards haven’t always required strong authentication, and they also

Read More »

There are 121 AI processor companies. How many will succeed?

The US currently leads in AI hardware and software, but China’s DeepSeek and Huawei continue to push advanced chips, India has announced an indigenous GPU program targeting production by 2029, and policy shifts in Washington are reshaping the playing field. In Q2, the rollback of export restrictions allowed US companies like Nvidia and AMD to strike multibillion-dollar deals in Saudi Arabia.  JPR categorizes vendors into five segments: IoT (ultra-low-power inference in microcontrollers or small SoCs); Edge (on-device or near-device inference in 1–100W range, used outside data centers); Automotive (distinct enough to break out from Edge); data center training; and data center inference. There is some overlap between segments as many vendors play in multiple segments. Of the five categories, inference has the most startups with 90. Peddie says the inference application list is “humongous,” with everything from wearable health monitors to smart vehicle sensor arrays, to personal items in the home, and every imaginable machine in every imaginable manufacturing and production line, plus robotic box movers and surgeons.  Inference also offers the most versatility. “Smart devices” in the past, like washing machines or coffee makers, could do basically one thing and couldn’t adapt to any changes. “Inference-based systems will be able to duck and weave, adjust in real time, and find alternative solutions, quickly,” said Peddie. Peddie said despite his apparent cynicism, this is an exciting time. “There are really novel ideas being tried like analog neuron processors, and in-memory processors,” he said.

Read More »

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist (and coming soon free Data Center Intern listing). Data Center Critical Facility Manager Impact, TX There position is also available in: Cheyenne, WY; Ashburn, VA or Manassas, VA. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations (enterprise and hyperscale customers). This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OH This traveling position is also available in: Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Cedar Rapids, IA; Phoenix, AZ; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits.  Data Center Engineering Design ManagerAshburn, VA This opportunity is working directly with a leading mission-critical data center developer /

Read More »

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

Read More »

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

Read More »

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

Read More »

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

Read More »