Beth Washington

Scientists built a badminton-playing robot with AI-powered skills

AI, ai in sports, badminton, robot, Science / Beth Washington / June 11, 2025

It also learned fall avoidance and determined how much risk was reasonable to take given its limited speed. The robot did not attempt impossible plays that would create the potential for serious damage—it was committed, but not suicidal.

But when it finally played humans, it turned out ANYmal, as a badminton player, was amateur at best.

The major leagues

The first problem was its reaction time. An average human reacts to visual stimuli in around 0.2–0.25 seconds. Elite badminton players with trained reflexes, anticipation, and muscle memory can cut this time down to 0.12–0.15 seconds. ANYmal needed roughly 0.35 seconds after the opponent hit the shuttlecock to register trajectories and figure out what to do.

Part of the problem was poor eyesight. “I think perception is still a big issue,” Ma said. “The robot localized the shuttlecock with the stereo camera and there could be a positioning error introduced at each timeframe.” The camera also had a limited field of view, which meant the robot could see the shuttlecock for only a limited time before it had to act. “Overall, it was suited for more friendly matches—when the human player starts to smash, the success rate goes way down for the robot,” Ma acknowledged.

But his team already has some ideas on how to make ANYmal better. Reaction time can be improved by predicting the shuttlecock trajectory based on the opponent’s body position rather than waiting to see the shuttlecock itself—a technique commonly used by elite badminton or tennis players. To improve ANYmal’s perception, the team wants to fit it with more advanced hardware, like event cameras—vision sensors that register movement with ultra-low latencies in the microseconds range. Other improvements might include faster, more capable actuators.

“I think the training framework we propose would be useful in any application where you need to balance perception and control—picking objects up, even catching and throwing stuff,” Ma suggested. Sadly, one thing that’s almost certainly off the table is taking ANYmal to major leagues in badminton or tennis. “Would I set up a company selling badminton-playing robots? Well, maybe not,” Ma said.

Science Robotics, 2025. DOI: 10.1126/scirobotics.adu3922

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Ocean acidification crosses “planetary boundaries”

climate, climate change, ocean acidification, Science, syndication / Beth Washington / June 10, 2025

A critical measure of the ocean’s health suggests that the world’s marine systems are in greater peril than scientists had previously realized and that parts of the ocean have already reached dangerous tipping points.

A study, published Monday in the journal Global Change Biology, found that ocean acidification—the process in which the world’s oceans absorb excess carbon dioxide from the atmosphere, becoming more acidic—crossed a “planetary boundary” five years ago.

“A lot of people think it’s not so bad,” said Nina Bednaršek, one of the study’s authors and a senior researcher at Oregon State University. “But what we’re showing is that all of the changes that were projected, and even more so, are already happening—in all corners of the world, from the most pristine to the little corner you care about. We have not changed just one bay, we have changed the whole ocean on a global level.”

The new study, also authored by researchers at the UK’s Plymouth Marine Laboratory and the National Oceanic and Atmospheric Administration (NOAA), finds that by 2020 the world’s oceans were already very close to the “danger zone” for ocean acidity, and in some regions had already crossed into it.

Scientists had determined that ocean acidification enters this danger zone or crosses this planetary boundary when the amount of calcium carbonate—which allows marine organisms to develop shells—is less than 20 percent compared to pre-industrial levels. The new report puts the figure at about 17 percent.

“Ocean acidification isn’t just an environmental crisis, it’s a ticking time bomb for marine ecosystems and coastal economies,” said Steve Widdicombe, director of science at the Plymouth lab, in a press release. “As our seas increase in acidity, we’re witnessing the loss of critical habitats that countless marine species depend on and this, in turn, has major societal and economic implications.”

Scientists have determined that there are nine planetary boundaries that, once breached, risk humans’ abilities to live and thrive. One of these is climate change itself, which scientists have said is already beyond humanity’s “safe operating space” because of the continued emissions of heat-trapping gases. Another is ocean acidification, also caused by burning fossil fuels.

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IBM now describing its first error-resistant quantum compute system

Computer science, computing, ibm, logical qubits, Physics, quantum computing, quantum mechanics, Science / Beth Washington / June 10, 2025

Company is moving past focus on qubits, shifting to functional compute units.

A rendering of what IBM expects will be needed to house a Starling quantum computer. Credit: IBM

On Tuesday, IBM released its plans for building a system that should push quantum computing into entirely new territory: a system that can both perform useful calculations while catching and fixing errors and be utterly impossible to model using classical computing methods. The hardware, which will be called Starling, is expected to be able to perform 100 million operations without error on a collection of 200 logical qubits. And the company expects to have it available for use in 2029.

Perhaps just as significant, IBM is also committing to a detailed description of the intermediate steps to Starling. These include a number of processors that will be configured to host a collection of error-corrected qubits, essentially forming a functional compute unit. This marks a major transition for the company, as it involves moving away from talking about collections of individual hardware qubits and focusing instead on units of functional computational hardware. If all goes well, it should be possible to build Starling by chaining a sufficient number of these compute units together.

“We’re updating [our roadmap] now with a series of deliverables that are very precise,” IBM VP Jay Gambetta told Ars, “because we feel that we’ve now answered basically all the science questions associated with error correction and it’s becoming more of a path towards an engineering problem.”

New architectures

Error correction on quantum hardware involves entangling a group of qubits in a way that distributes one or more quantum bit values among them and includes additional qubits that can be used to check the state of the system. It can be helpful to think of these as data and measurement qubits. Performing weak quantum measurements on the measurement qubits produces what’s called “syndrome data,” which can be interpreted to determine whether anything about the data qubits has changed (indicating an error) and how to correct it.

There are lots of potential ways to arrange different combinations of data and measurement qubits for this to work, each referred to as a code. But, as a general rule, the more hardware qubits committed to the code, the more robust it will be to errors, and the more logical qubits that can be distributed among its hardware qubits.

Some quantum hardware, like that based on trapped ions or neutral atoms, is relatively flexible when it comes to hosting error-correction codes. The hardware qubits can be moved around so that any two can be entangled, so it’s possible to adopt a huge range of configurations, albeit at the cost of the time spent moving atoms around. IBM’s technology is quite different. It relies on qubits made of superconducting electronics laid out on a chip, with entanglement mediated by wiring that runs between qubits. The layout of this wiring is set during the chip’s manufacture, and so the chip’s design commits it to a limited number of potential error-correction codes.

Unfortunately, this wiring can also enable crosstalk between neighboring qubits, causing them to lose their state. To avoid this, existing IBM processors have their qubits wired in what they term a “heavy hex” configuration, named for its hexagonal arrangements of connections among its qubits. This has worked well to keep the error rate of its hardware down, but it also poses a challenge, since IBM has decided to go with an error-correction code that’s incompatible with the heavy hex geometry.

A couple of years back, an IBM team described a compact error correction code called a low-density parity check (LDPC). This requires a square grid of nearest-neighbor connections among its qubits, as well as wiring to connect qubits that are relatively distant on the chip. To get its chips and error-correction scheme in sync, IBM has made two key advances. The first is in its chip packaging, which now uses several layers of wiring sitting above the hardware qubits to enable all of the connections needed for the LDPC code.

We’ll see that first in a processor called Loon that’s on the company’s developmental roadmap. “We’ve already demonstrated these three things: high connectivity, long-range couplers, and couplers that break the plane [of the chip] and connect to other qubits,” Gambetta said. “We have to combine them all as a single demonstration showing that all these parts of packaging can be done, and that’s what I want to achieve with Loon.” Loon will be made public later this year.

Two diagrams of blue objects linked by red lines. The one on the left is sparse and simple, while the one on the right is a complicated mesh of red lines. — On the left, the simple layout of the connections in a current-generation Heron processor. At right, the complicated web of connections that will be present in Loon. Credit: IBM

The second advance IBM has made is to eliminate the crosstalk that the heavy hex geometry was used to minimize, so heavy hex will be going away. “We are releasing this year a bird for near-term experiments that is a square array that has almost zero crosstalk,” Gambetta said, “and that is Nighthawk.” The more densely connected qubits cut the overhead needed to perform calculations by a factor of 15, Gambetta told Ars.

Nighthawk is a 2025 release on a parallel roadmap that you can think of as user-facing. Iterations on its basic design will be released annually through 2028, each enabling more operations without error (going from 5,000 gate operations this year to 15,000 in 2028). Each individual Nighthawk processor will host 120 hardware qubits, but 2026 will see three of them chained together and operating as a unit, providing 360 hardware qubits. That will be followed in 2027 by a machine with nine linked Nighthawk processors, boosting the hardware qubit number over 1,000.

Riding the bicycle

The real future of IBM’s hardware, however, will be happening over on the developmental line of processors, where talk about hardware qubit counts will become increasingly irrelevant. In a technical document released today, IBM is describing the specific LDPC code it will be using, termed a bivariate bicycle code due to some cylindrical symmetries in its details that vaguely resemble bicycle wheels. The details of the connections matter less than the overall picture of what it takes to use this error code in practice.

IBM describes two implementations of this form of LDPC code. In the first, 144 hardware qubits are arranged so that they play host to 12 logical qubits and all of the measurement qubits needed to perform error checks. The standard measure of a code’s ability to catch and correct errors is called its distance, and in this case, the distance is 12. As an alternative, they also describe a code that uses 288 hardware qubits to host the same 12 logical qubits but boost the distance to 18, meaning it’s more resistant to errors. IBM will make one of these collections of logical qubits available as a Kookaburra processor in 2026, which will use them to enable stable quantum memory.

The follow-on will bundle these with a handful of additional qubits that can produce quantum states that are needed for some operations. Those, plus hardware needed for the quantum memory, form a single, functional computation unit, built on a single chip, that is capable of performing all the operations needed to implement any quantum algorithm.

That will appear with the Cockatoo chip, which will also enable multiple processing units to be linked on a single bus, allowing the logical qubit count to grow beyond 12. (The company says that one of the dozen logical qubits in each unit will be used to mediate entanglement with other units and so won’t be available for computation.) That will be followed by the first test versions of Starling, which will allow universal computations on a limited number of logical qubits spread across multiple chips.

Separately, IBM is releasing a document that describes a key component of the system that will run on classical computing hardware. Full error correction requires evaluating the syndrome data derived from the state of all the measurement qubits in order to determine the state of the logical qubits and whether any corrections need to be made. As the complexity of the logical qubits grows, the computational burden of evaluating grows with it. If this evaluation can’t be executed in real time, then it becomes impossible to perform error-corrected calculations.

To address this, IBM has developed a message-passing decoder that can perform parallel evaluations of the syndrome data. The system explores more of the solution space by a combination of randomizing the weight given to the memory of past solutions and by handing any seemingly non-optimal solutions on to new instances for additional evaluation. The key thing is that IBM estimates that this can be run in real time using FPGAs, ensuring that the system works.

A quantum architecture

There are a lot more details beyond those, as well. Gambetta described the linkage between each computational unit—IBM is calling it a Universal Bridge—which requires one microwave cable for each code distance of the logical qubits being linked. (In other words, a distance 12 code would need 12 microwave-carrying cables to connect each chip.) He also said that IBM is developing control hardware that can operate inside the refrigeration hardware, based on what they’re calling “cold CMOS,” which is capable of functioning at 4 Kelvin.

The company is also releasing renderings of what it expects Starling to look like: a series of dilution refrigerators, all connected by a single pipe that contains the Universal Bridge. “It’s an architecture now,” Gambetta said. “I have never put details in the roadmap that I didn’t feel we could hit, and now we’re putting a lot more details.”

The striking thing to me about this is that it marks a shift away from a focus on individual qubits, their connectivity, and their error rates. The error hardware rates are now good enough (4 x 10^-4) for this to work, although Gambetta felt that a few more improvements should be expected. And connectivity will now be directed exclusively toward creating a functional computational unit.

That said, there’s still a lot of space beyond Starling on IBM’s roadmap. The 200 logical qubits it promises will be enough to handle some problems, but not enough to perform the complex algorithms needed to do things like break encryption. That will need to wait for something closer to Blue Jay, a 2033 system that IBM expects will have 2,000 logical qubits. And, as of right now, it’s the only thing listed beyond Starling.

John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.

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A history of the Internet, part 2: The high-tech gold rush begins

AOL, browser wars, Dotcom, Features, history, Internet, Netscape, Tech, World Wide Web / Beth Washington / June 10, 2025

The Web Era arrives, the browser wars flare, and a bubble bursts.

Welcome to the second article in our three-part series on the history of the Internet. If you haven’t already, read part one here.

As a refresher, here’s the story so far:

The ARPANET was a project started by the Defense Department’s Advanced Research Project Agency in 1969 to network different mainframe computers together across the country. Later, it evolved into the Internet, connecting multiple global networks together using a common TCP/IP protocol.

By the late 1980s, investments from the National Science Foundation (NSF) had established an “Internet backbone” supporting hundreds of thousands of users worldwide. These users were mostly professors, researchers, and graduate students.

In the meantime, commercial online services like CompuServe were growing rapidly. These systems connected personal computer users, using dial-up modems, to a mainframe running proprietary software. Once online, people could read news articles and message other users. In 1989, CompuServe added the ability to send email to anyone on the Internet.

In 1965, Ted Nelson submitted a paper to the Association for Computing Machinery. He wrote: “Let me introduce the word ‘hypertext’ to mean a body of written or pictorial material interconnected in such a complex way that it could not conveniently be presented or represented on paper.” The paper was part of a grand vision he called Xanadu, after the poem by Samuel Coleridge.

A decade later, in his book “Dream Machines/Computer Lib,” he described Xanadu thusly: “To give you a screen in your home from which you can see into the world’s hypertext libraries.” He admitted that the world didn’t have any hypertext libraries yet, but that wasn’t the point. One day, maybe soon, it would. And he was going to dedicate his life to making it happen.

As the Internet grew, it became more and more difficult to find things on it. There were lots of cool documents like the Hitchhiker’s Guide To The Internet, but to read them, you first had to know where they were.

The community of helpful programmers on the Internet leapt to the challenge. Alan Emtage at McGill University in Montreal wrote a tool called Archie. It searched a list of public file transfer protocol (FTP) servers. You still had to know the file name you were looking for, but Archie would let you download it no matter what server it was on.

An improved search engine was Gopher, written by a team headed by Mark McCahill at the University of Minnesota. It used a text-based menu system so that users didn’t have to remember file names or locations. Gopher servers could display a customized collection of links inside nested menus, and they integrated with other services like Archie and Veronica to help users search for more resources.

Gopher is a text-based Internet search and retrieval system. It’s still running in 2025! Jeremy Reimer

A Gopher server could provide many of the things we take for granted today: search engines, personal pages that could contain links, and downloadable files. But this wasn’t enough for a British computer scientist who was working at CERN, an intergovernmental institute that operated the world’s largest particle physics lab.

The World Wide Web

Hypertext had come a long way since Ted Nelson had coined the word in 1965. Bill Atkinson, a member of the original Macintosh development team, released HyperCard in 1987. It used the Mac’s graphical interface to let anyone develop “stacks,” collections of text, graphics, and sounds that could be connected together with clickable links. There was no networking, but stacks could be shared with other users by sending the files on a floppy disk.

The home screen of HyperCard 1.0 for Macintosh. Jeremy Reimer

Hypertext was so big that conferences were held just to discuss it in 1987 and 1988. Even Ted Nelson had finally found a sponsor for his personal dream: Autodesk founder John Walker had agreed to spin up a subsidiary to create a commercial version of Xanadu.

It was in this environment that CERN fellow Tim Berners-Lee drew up his own proposal in March 1989 for a new hypertext environment. His goal was to make it easier for researchers at CERN to collaborate and share information about new projects.

The proposal (which he called “Mesh”) had several objectives. It would provide a system for connecting information about people, projects, documents, and hardware being developed at CERN. It would be decentralized and distributed over many computers. Not all the computers at CERN were the same—there were Digital Equipment minis running VMS, some Macintoshes, and an increasing number of Unix workstations. Each of them should be able to view the information in the same way.

As Berners-Lee described it, “There are few products which take Ted Nelson’s idea of a wide ‘docuverse’ literally by allowing links between nodes in different databases. In order to do this, some standardization would be necessary.”

The original proposal document for the web, written in Microsoft Word for Macintosh 4.0, downloaded from Tim Berners-Lee’s website. Credit: Jeremy Reimer

The document ended by describing the project as “practical” and estimating that it might take two people six to 12 months to complete. Berners-Lee’s manager called it “vague, but exciting.” Robert Cailliau, who had independently proposed a hypertext system for CERN, joined Berners-Lee to start designing the project.

The computer Berners-Lee used was a NeXT cube, from the company Steve Jobs started after he was kicked out of Apple. NeXT workstations were expensive, but they came with a software development environment that was years ahead of its time. If you could afford one, it was like a coding accelerator. John Carmack would later write DOOM on a NeXT.

The NeXT workstation that Tim Berners-Lee used to create the World Wide Web. Please do not power down the World Wide Web. Credit: Coolcaesar (CC BY-SA 3.0)

Berners-Lee called his application “WorldWideWeb.” The software consisted of a server, which delivered pages of text over a new protocol called “Hypertext Transport Protocol,” or HTTP, and a browser that rendered the text. The browser translated markup code like “h1” to indicate a larger header font or “a” to indicate a link. There was also a graphical webpage editor, but it didn’t work very well and was abandoned.

The very first website was published, running on the development NeXT cube, on December 20, 1990. Anyone who had a NeXT machine and access to the Internet could view the site in all its glory.

The original WorldWideWeb browser running on NeXTstep 3, browsing the world’s first webpage. Jeremy Reimer

Because NeXT only sold 50,000 computers in total, that intersection did not represent a lot of people. Eight months later, Berners-Lee posted a reply to a question about interesting projects on the alt.hypertext Usenet newsgroup. He described the World Wide Web project and included links to all the software and documentation.

That one post changed the world forever.

Mosaic

On December 9, 1991, President George H.W. Bush signed into law the High Performance Computing Act, also known as the Gore Bill. The bill paid for an upgrade of the NSFNET backbone, as well as a separate funding initiative for the National Center for Supercomputing Applications (NCSA).

NCSA, based out of the University of Illinois, became a dream location for computing research. “NCSA was heaven,” recalled Alex Totic, who was a student there. “They had all the toys, from Thinking Machines to Crays to Macs to beautiful networks. It was awesome.” As is often the case in academia, the professors came up with research ideas but assigned most of the actual work to their grad students.

One of those students was Marc Andreessen, who joined NCSA as a part-time programmer for $6.85 an hour. Andreessen was fascinated by the World Wide Web, especially browsers. A new browser for Unix computers, ViolaWWW, was making the rounds at NCSA. No longer confined to the NeXT workstation, the web had caught the attention of the Unix community. But that community was still too small for Andreessen.

“To use the Net, you had to understand Unix,” he said in an interview with Forbes. “And the current users had no interest in making it easier. In fact, there was a definite element of not wanting to make it easier, of actually wanting to keep the riffraff out.”

Andreessen enlisted the help of his colleague, programmer Eric Bina, and started developing a new web browser in December 1992. In a little over a month, they released version 0.5 of “NCSA X Mosaic”—so called because it was designed to work with Unix’s X Window System. Ports for the Macintosh and Windows followed shortly thereafter.

Being available on the most popular graphical computers changed the trajectory of the web. In just 18 months, millions of copies of Mosaic were downloaded, and the rate was accelerating. The riffraff was here to stay.

Netscape

The instant popularity of Mosaic caused the management at NCSA to take a deeper interest in the project. Jon Mittelhauser, who co-wrote the Windows version, recalled that the small team “suddenly found ourselves in meetings with forty people planning our next features, as opposed to the five of us making plans at 2 am over pizzas and Cokes.”

Andreessen was told to step aside and let more experienced managers take over. Instead, he left NCSA and moved to California, looking for his next opportunity. “I thought I had missed the whole thing,” Andreessen said. “The overwhelming mood in the Valley when I arrived was that the PC was done, and by the way, the Valley was probably done because there was nothing else to do.”

But his reputation had preceded him. Jim Clark, the founder of Silicon Graphics, was also looking to start something new. A friend had shown him a demo of Mosaic, and Clark reached out to meet with Andreessen.

At a meeting, Andreessen pitched the idea of building a “Mosaic killer.” He showed Clark a graph that showed web users doubling every five months. Excited by the possibilities, the two men founded Mosaic Communications Corporation on April 4, 1994. Andreessen quickly recruited programmers from his former team, and they got to work. They codenamed their new browser “Mozilla” since it was going to be a monster that would devour Mosaic. Beta versions were titled “Mosaic Netscape,” but the University of Illinois threatened to sue the new company. To avoid litigation, the name of the company and browser were changed to Netscape, and the programmers audited their code to ensure none of it had been copied from NCSA.

Netscape became the model for all Internet startups to follow. Programmers were given unlimited free sodas and encouraged to basically never leave the office. “Netscape Time” accelerated software development schedules, and because updates could be delivered over the Internet, old principles of quality assurance went out the window. And the business model? It was simply to “get big fast,” and profits could be figured out later.

Work proceeded quickly, and the 1.0 version of Netscape Navigator and the Netsite web server were released on December 15, 1994, for Windows, Macintosh, and Unix systems running X Windows. The browser was priced at $39 for commercial users, but there was no charge for “academic and non-profit use, as well as for free evaluation purposes.”

Version 0.9 was called “Mosaic Netscape,” and the logo and company were still Mosaic. Jeremy Reimer

Netscape quickly became the standard. Within six months, it captured over 70 percent of the market share for web browsers. On August 9, 1995, only 16 months after the founding of the company, Netscape filed for an Initial Public Offering. A last-minute decision doubled the offering price to $28 per share, and on the first day of trading, the stock soared to $75 and closed at $58.25. The Web Era had officially arrived.

The web battles proprietary solutions

The excitement over a new way to transmit text and images to the public over phone lines wasn’t confined to the World Wide Web. Commercial online systems like CompuServe were also evolving to meet the graphical age. These companies released attractive new front-ends for their services that ran on DOS, Windows, and Macintosh computers. There were also new services that were graphics-only, like Prodigy, a cooperation between IBM and Sears, and an upstart that had sprung from the ashes of a Commodore 64 service called Quantum Link. This was America Online, or AOL.

Even Microsoft was getting into the act. Bill Gates believed that the “Information Superhighway” was the future of computing, and he wanted to make sure that all roads went through his company’s toll booth. The highly anticipated Windows 95 was scheduled to ship with a bundled dial-up online service called the Microsoft Network, or MSN.

At first, it wasn’t clear which of these online services would emerge as the winner. But people assumed that at least one of them would beat the complicated, nerdy Internet. CompuServe was the oldest, but AOL was nimbler and found success by sending out millions of free “starter” disks (and later, CDs) to potential customers. Microsoft was sure that bundling MSN with the upcoming Windows 95 would ensure victory.

Most of these services decided to hedge their bets by adding a sort of “side access” to the World Wide Web. After all, if they didn’t, their competitors would. At the same time, smaller companies (many of them former bulletin board services) started becoming Internet service providers. These smaller “ISPs” could charge less money than the big services because they didn’t have to create any content themselves. Thousands of new websites were appearing on the Internet every day, much faster than new sections could be added to AOL or CompuServe.

The tipping point happened very quickly. Before Windows 95 had even shipped, Bill Gates wrote his famous “Internet Tidal Wave” memo, where he assigned the Internet the “highest level of importance.” MSN was quickly changed to become more of a standard ISP and moved all of its content to the web. Microsoft rushed to release its own web browser, Internet Explorer, and bundled it with the Windows 95 Plus Pack.

The hype and momentum were entirely with the web now. It was the most exciting, most transformative technology of its time. The decade-long battle to control the Internet by forcing a shift to a new OSI standards model was forgotten. The web was all anyone cared about, and the web ran on TCP/IP.

The browser wars

Netscape had never expected to make a lot of money from its browser, as it was assumed that most people would continue to download new “evaluation” versions for free. Executives were pleasantly surprised when businesses started sending Netscape huge checks. The company went from $17 million in revenue in 1995 to $346 million the following year, and the press started calling Marc Andreessen “the new Bill Gates.”

The old Bill Gates wasn’t having any of that. Following his 1995 memo, Microsoft worked hard to improve Internet Explorer and made it available for free, including to business users. Netscape tried to fight back. It added groundbreaking new features like JavaScript, which was inspired by LISP but with a syntax similar to Java, the hot new programming language from Sun Microsystems. But it was hard to compete with free, and Netscape’s market share started to fall. By 1996, both browsers had reached version 3.0 and were roughly equal in terms of features. The battle continued, but when the Apache Software Foundation released its free web server, Netscape’s other source of revenue dried up as well. The writing was on the wall.

There was no better way to declare your allegiance to a web browser in 1996 than adding “Best Viewed In” above one of these icons. Credit: Jeremy Reimer

The dot-com boom

In 1989, the NSF lifted the restrictions on providing commercial access to the Internet, and by 1991, it had removed all barriers to commercial trade on the network. With the sudden ascent of the web, thanks to Mosaic, Netscape, and Internet Explorer, new companies jumped into this high-tech gold rush. But at first, it wasn’t clear what the best business strategy was. Users expected everything on the web to be free, so how could you make money?

Many early web companies started as hobby projects. In 1994, Jerry Yang and David Filo were electrical engineering PhD students at Stanford University. After Mosaic started popping off, they began collecting and trading links to new websites. Thus, “Jerry’s Guide to the World Wide Web” was born, running on Yang’s Sun workstation. Renamed Yahoo! (Yet Another Hierarchical, Officious Oracle), the site exploded in popularity. Netscape put multiple links to Yahoo on its main navigation bar, which further accelerated growth. “We weren’t really sure if you could make a business out of it, though,” Yang told Fortune. Nevertheless, venture capital companies came calling. Sequoia, which had made millions investing in Apple, put in $1 million for 25 percent of Yahoo.

Yahoo.com as it would have appeared in 1995. Credit: Jeremy Reimer

Another hobby site, AuctionWeb, was started in 1995 by Pierre Omidyar. Running on his own home server using the regular $30 per month service from his ISP, the site let people buy and sell items of almost any kind. When traffic started growing, his ISP told him it was increasing his Internet fees to $250 per month, as befitting a commercial enterprise. Omidyar decided he would try to make it a real business, even though he didn’t have a merchant account for credit cards or even a way to enforce the new 5 percent or 2.5 percent royalty charges. That didn’t matter, as the checks started rolling in. He found a business partner, changed the name to eBay, and the rest was history.

AuctionWeb (later eBay) as it would have appeared in 1995. Credit: Jeremy Reimer

In 1993, Jeff Bezos, a senior vice president at a hedge fund company, was tasked with investigating business opportunities on the Internet. He decided to create a proof of concept for what he described as an “everything store.” He chose books as an ideal commodity to sell online, since a book in one store was identical to one in another, and a website could offer access to obscure titles that might not get stocked in physical bookstores.

He left the hedge fund company, gathered investors and software development talent, and moved to Seattle. There, he started Amazon. At first, the site wasn’t much more than an online version of an existing bookseller catalog called Books In Print. But over time, Bezos added inventory data from the two major book distributors, Ingram and Baker & Taylor. The promise of access to every book in the world was exciting for people, and the company grew quickly.

Amazon.com as it would have appeared in 1995. Credit: Jeremy Reimer

The explosive growth of these startups fueled a self-perpetuating cycle. As publications like Wired experimented with online versions of their magazines, they invented and sold banner ads to fund their websites. The best customers for these ads were other web startups. These companies wanted more traffic, and they knew ads on sites like Yahoo were the best way to get it. Yahoo salespeople could then turn around and point to their exponential ad sales curves, which caused Yahoo stock to rise. This encouraged people to fund more web startups, which would all need to advertise on Yahoo. These new startups also needed to buy servers from companies like Sun Microsystems, causing those stocks to rise as well.

The crash

In the latter half of the 1990s, it looked like everything was going great. The economy was booming, thanks in part to the rise of the World Wide Web and the huge boost it gave to computer hardware and software companies. The NASDAQ index of tech-focused stocks painted a clear picture of the boom.

The NASDAQ composite index in the 1990s. Credit: Jeremy Reimer

Federal Reserve chairman Alan Greenspan called this phenomenon “irrational exuberance” but didn’t seem to be in a hurry to stop it. The fact that most new web startups didn’t have a realistic business model didn’t seem to bother investors. Sure, WebVan might have been paying more to deliver groceries than they earned from customers, but look at that growth curve!

The exuberance couldn’t last forever. The NASDAQ peaked at 8,843.87 in February 2000 and started to go down. In one month, it lost 34 percent of its value, and by August 2001, it was down to 3,253.38. Web companies laid off employees or went out of business completely. The party was over.

Andreessen said that the tech crash scarred him. “The overwhelming message to our generation in the early nineties was ‘You’re dirty, you’re all about grunge—you guys are fucking losers!’ Then the tech boom hit, and it was ‘We are going to do amazing things!’ And then the roof caved in, and the wisdom was that the Internet was a mirage. I 100 percent believed that because the rejection was so personal—both what everybody thought of me and what I thought of myself.”

But while some companies quietly celebrated the end of the whole Internet thing, others would rise from the ashes of the dot-com collapse. That’s the subject of our third and final article.

I’m a writer and web developer. I specialize in the obscure and beautiful, like the Amiga and newLISP.

A history of the Internet, part 2: The high-tech gold rush begins Read More »

Apple details the end of Intel Mac support and a phaseout for Rosetta 2

Apple, Apple silicon, Macs, Rosetta, Rosetta 2, Tech / Beth Washington / June 10, 2025

The support list for macOS Tahoe still includes Intel Macs, but it has been whittled down to just four models, all released in 2019 or 2020. We speculated that this meant that the end was near for Intel Macs, and now we can confirm just how near it is: macOS Tahoe will be the last new macOS release to support any Intel Macs. All new releases starting with macOS 27 will require an Apple Silicon Mac.

Apple will provide additional security updates for Tahoe until fall 2028, two years after it is replaced with macOS 27. That’s a typical schedule for older macOS versions, which all get one year of major point updates that include security fixes and new features, followed by two years of security-only updates to keep them patched but without adding significant new features.

Apple is also planning changes to Rosetta 2, the Intel-to-Arm app translation technology created to ease the transition between the Intel and Apple Silicon eras. Rosetta will continue to work as a general-purpose app translation tool in both macOS 26 and macOS 27.

But after that, Rosetta will be pared back and will only be available to a limited subset of apps—specifically, older games that rely on Intel-specific libraries but are no longer being actively maintained by their developers. Devs who want their apps to continue running on macOS after that will need to transition to either Apple Silicon-native apps or universal apps that run on either architecture.

Apple details the end of Intel Mac support and a phaseout for Rosetta 2 Read More »

Anti-vaccine advocate RFK Jr. fires entire CDC panel of vaccine advisors

ACIP, CDC, health, Robert F Kenned Jr., vaccines / Beth Washington / June 10, 2025

“Most likely aim to serve the public interest as they understand it,” he wrote. “The problem is their immersion in a system of industry-aligned incentives and paradigms that enforce a narrow pro-industry orthodoxy.”

Kennedy, who is currently trying to shift the national attention to his idea of clean living and higher-quality foods, has a long history of advocating against vaccines, spreading misinformation and disinformation about the lifesaving shots. However, a clearer explanation of Kennedy’s war on vaccines can be found in his rejection of germ theory. In his 2021 book that vilifies infectious disease expert Anthony Fauci, he bemoaned germ theory as “the pharmaceutical paradigm that emphasized targeting particular germs with specific drugs rather than fortifying the immune system through healthy living, clean water, and good nutrition.”

As such, he rails against the “$1 trillion pharmaceutical industry pushing patented pills, powders, pricks, potions, and poisons.”

In Kennedy’s op-ed, he indicates that new ACIP members will be appointed who “won’t directly work for the vaccine industry. … will exercise independent judgment, refuse to serve as a rubber stamp, and foster a culture of critical inquiry.”

It’s unclear how the new members will be vetted and appointed and when the new committee will be assembled.

In a statement, the president of the American Medical Association, Bruce Scott, rebuked Kennedy’s firings, saying that ACIP “has been a trusted national source of science- and data-driven advice and guidance on the use of vaccines to prevent and control disease.” Today’s removal “undermines that trust and upends a transparent process that has saved countless lives,” he continued. “With an ongoing measles outbreak and routine child vaccination rates declining, this move will further fuel the spread of vaccine-preventable illnesses.”

This post has been updated to include a statement from the AMA. This story is breaking and may be updated further.

Anti-vaccine advocate RFK Jr. fires entire CDC panel of vaccine advisors Read More »

US air traffic control still runs on Windows 95 and floppy disks

air traffic control, aviation, Biz & IT, Chris Rocheleau, FAA, floppy disks, government technology, Infrastructure, legacy systems, modernization, retrotech, Sean Duffy, Tech, transportation, US government, Vintage computing, windows 95 / Beth Washington / June 9, 2025

On Wednesday, acting FAA Administrator Chris Rocheleau told the House Appropriations Committee that the Federal Aviation Administration plans to replace its aging air traffic control systems, which still rely on floppy disks and Windows 95 computers, Tom’s Hardware reports. The agency has issued a Request For Information to gather proposals from companies willing to tackle the massive infrastructure overhaul.

“The whole idea is to replace the system. No more floppy disks or paper strips,” Rocheleau said during the committee hearing. Transportation Secretary Sean Duffy called the project “the most important infrastructure project that we’ve had in this country for decades,” describing it as a bipartisan priority.

Most air traffic control towers and facilities across the US currently operate with technology that seems frozen in the 20th century, although that isn’t necessarily a bad thing—when it works. Some controllers currently use paper strips to track aircraft movements and transfer data between systems using floppy disks, while their computers run Microsoft’s Windows 95 operating system, which launched in 1995.

As Tom’s Hardware notes, modernization of the system is broadly popular. Sheldon Jacobson, a University of Illinois professor who has studied risks in aviation, says that the system works remarkably well as is but that an upgrade is still critical, according to NPR. The aviation industry coalition Modern Skies has been pushing for ATC modernization and recently released an advertisement highlighting the outdated technology.

While the vintage systems may have inadvertently protected air traffic control from widespread outages like the CrowdStrike incident that disrupted modern computer systems globally in 2024, agency officials say 51 of the FAA’s 138 systems are unsustainable due to outdated functionality and a lack of spare parts.

The FAA isn’t alone in clinging to floppy disk technology. San Francisco’s train control system still runs on DOS loaded from 5.25-inch floppy disks, with upgrades not expected until 2030 due to budget constraints. Japan has also struggled in recent years to modernize government record systems that use floppy disks.

If it ain’t broke? (Or maybe it is broke)

Modernizing the air traffic control system presents engineering challenges that extend far beyond simply installing newer computers. Unlike typical IT upgrades, ATC systems must maintain continuous 24/7 operation, because shutting down facilities for maintenance could compromise aviation safety.

US air traffic control still runs on Windows 95 and floppy disks Read More »

Startup puts a logical qubit in a single piece of hardware

Computer science, nord quantique, Physics, quantum computing, quantum mechanics, Science / Beth Washington / June 6, 2025

A bit over a year ago, Nord Quantique used a similar setup to show that it could be used to identify the most common form of error in these devices, one in which the system loses one of its photons. “We can store multiple microwave photons into each of these cavities, and the fact that we have redundancy in the system comes exactly from this,” said Nord Quantique’s CTO, Julien Camirand Lemyre. However, this system was unable to handle many of the less common errors that might also occur.

This time around, the company is showing that it can get an actual logical qubit into a variant of the same hardware. In the earlier version of its equipment, the resonator cavity had a single post and supported a single frequency. In the newer iteration, there were two posts and two frequencies. Each of those frequencies creates its own quantum resonator in the same cavity, with its own set of modes. “It’s this ensemble of photons inside this cavity that creates the logical qubit,” Lemyre told Ars.

The additional quantum information that can now be stored in the system enables it to identify more complex errors than the loss of a photon.

Catching, but not fixing errors

The company did two experiments with this new hardware. First, it ran multiple rounds of error detection on data stored in the logical qubit, essentially testing its ability to act like a quantum memory and retain the information stored there. Without correcting errors, the system rapidly decayed, with an error probability in each round of measurement of about 12 percent. By the time the system reached the 25th measurement, almost every instance had already encountered an error.

The second time through, the company repeated the process, discarding any instances in which an error occurred. In almost every instance, that meant the results were discarded long before they got through two dozen rounds of measurement. But at these later stages, none of the remaining instances were in an erroneous state. That indicates that a successful correction of the errors—something the team didn’t try—would be able to fix all the detected problems.

Startup puts a logical qubit in a single piece of hardware Read More »

GOP intensifies war against EVs and efficient cars

Cars, corporate average fuel efficiency, fuel efficiency, IRS clean vehicle tax credit, used EV tax credit / Beth Washington / June 6, 2025

Tesla CEO Elon Musk is on record as supporting the repeal of the EV tax credit, as it would hurt his rivals more than Tesla. But yesterday, Musk decried the fact that the spending bill does not cut subsidies for oil and gas, just EVs and solar.

No fines for you

Yesterday in the Senate, Republicans proposed another new measure that can only be seen as pro-pollution. Should it pass, the EPA would no longer be able to levy fines against carmakers that exceed fleet averages set out in the CAFE regulations. OEMs have paid the government hundreds of millions of dollars in these fines over the past decade. (Note that these fines are different from those imposed on Volkswagen and other automakers for circumventing efficiency standards.)

This would allow OEMs to save money by removing emissions equipment from their products, and it could potentially bring back older powertrains that would otherwise be prohibited on the roads. Tesla may well be the biggest loser here, as the bill removes incentives for other automakers to purchase carbon credits. The GOP is also attacking California’s ability to set its own emissions standards. That would remove another major source of emissions credits for Tesla, which are, again, increasingly important in keeping the company’s books out of the red.

Over at the Department of Transportation, similar efforts are underway. Secretary Sean Duffy’s first action as the head of DOT was to begin reviewing Biden-era fuel efficiency regulations, and today, the department decided that it makes no sense to include EVs as part of its CAFE rules.

At this rate, it’s a wonder they’re not trying to mandate coal-fired steam engines as an alternative.

GOP intensifies war against EVs and efficient cars Read More »

DOGE used flawed AI tool to “munch” Veterans Affairs contracts

AI, Policy / Beth Washington / June 6, 2025

Staffer had no medical experience, and the results were predictably, spectacularly bad.

As the Trump administration prepared to cancel contracts at the Department of Veterans Affairs this year, officials turned to a software engineer with no health care or government experience to guide them.

The engineer, working for the Department of Government Efficiency, quickly built an artificial intelligence tool to identify which services from private companies were not essential. He labeled those contracts “MUNCHABLE.”

The code, using outdated and inexpensive AI models, produced results with glaring mistakes. For instance, it hallucinated the size of contracts, frequently misreading them and inflating their value. It concluded more than a thousand were each worth $34 million, when in fact some were for as little as $35,000.

The DOGE AI tool flagged more than 2,000 contracts for “munching.” It’s unclear how many have been or are on track to be canceled—the Trump administration’s decisions on VA contracts have largely been a black box. The VA uses contractors for many reasons, including to support hospitals, research, and other services aimed at caring for ailing veterans.

VA officials have said they’ve killed nearly 600 contracts overall. Congressional Democrats have been pressing VA leaders for specific details of what’s been canceled without success.

We identified at least two dozen on the DOGE list that have been canceled so far. Among the canceled contracts was one to maintain a gene sequencing device used to develop better cancer treatments. Another was for blood sample analysis in support of a VA research project. Another was to provide additional tools to measure and improve the care nurses provide.

ProPublica obtained the code and the contracts it flagged from a source and shared them with a half-dozen AI and procurement experts. All said the script was flawed. Many criticized the concept of using AI to guide budgetary cuts at the VA, with one calling it “deeply problematic.”

Cary Coglianese, professor of law and of political science at the University of Pennsylvania who studies the governmental use and regulation of artificial intelligence, said he was troubled by the use of these general-purpose large language models, or LLMs. “I don’t think off-the-shelf LLMs have a great deal of reliability for something as complex and involved as this,” he said.

Sahil Lavingia, the programmer enlisted by DOGE, which was then run by Elon Musk, acknowledged flaws in the code.

“I think that mistakes were made,” said Lavingia, who worked at DOGE for nearly two months. “I’m sure mistakes were made. Mistakes are always made. I would never recommend someone run my code and do what it says. It’s like that ‘Office’ episode where Steve Carell drives into the lake because Google Maps says drive into the lake. Do not drive into the lake.”

Though Lavingia has talked about his time at DOGE previously, this is the first time his work has been examined in detail and the first time he’s publicly explained his process, down to specific lines of code.

Lavingia has nearly 15 years of experience as a software engineer and entrepreneur but no formal training in AI. He briefly worked at Pinterest before starting Gumroad, a small e-commerce company that nearly collapsed in 2015. “I laid off 75 percent of my company—including many of my best friends. It really sucked,” he said. Lavingia kept the company afloat by “replacing every manual process with an automated one,” according to a post on his personal blog.

Lavingia did not have much time to immerse himself in how the VA handles veterans’ care between starting on March 17 and writing the tool on the following day. Yet his experience with his own company aligned with the direction of the Trump administration, which has embraced the use of AI across government to streamline operations and save money.

Lavingia said the quick timeline of Trump’s February executive order, which gave agencies 30 days to complete a review of contracts and grants, was too short to do the job manually. “That’s not possible—you have 90,000 contracts,” he said. “Unless you write some code. But even then it’s not really possible.”

Under a time crunch, Lavingia said he finished the first version of his contract-munching tool on his second day on the job—using AI to help write the code for him. He told ProPublica he then spent his first week downloading VA contracts to his laptop and analyzing them.

VA Press Secretary Pete Kasperowicz lauded DOGE’s work on vetting contracts in a statement to ProPublica. “As far as we know, this sort of review has never been done before, but we are happy to set this commonsense precedent,” he said.

The VA is reviewing all of its 76,000 contracts to ensure each of them benefits veterans and is a good use of taxpayer money, he said. Decisions to cancel or reduce the size of contracts are made after multiple reviews by VA employees, including agency contracting experts and senior staff, he wrote.

Kasperowicz said that the VA will not cancel contracts for work that provides services to veterans or that the agency cannot do itself without a contingency plan in place. He added that contracts that are “wasteful, duplicative, or involve services VA has the ability to perform itself” will typically be terminated.

Trump officials have said they are working toward a “goal” of cutting around 80,000 people from the VA’s workforce of nearly 500,000. Most employees work in one of the VA’s 170 hospitals and nearly 1,200 clinics.

The VA has said it would avoid cutting contracts that directly impact care out of fear that it would cause harm to veterans. ProPublica recently reported that relatively small cuts at the agency have already been jeopardizing veterans’ care.

The VA has not explained how it plans to simultaneously move services in-house, as Lavingia’s code suggested was the plan, while also slashing staff.

Many inside the VA told ProPublica the process for reviewing contracts was so opaque they couldn’t even see who made the ultimate decisions to kill specific contracts. Once the “munching” script had selected a list of contracts, Lavingia said he would pass it off to others who would decide what to cancel and what to keep. No contracts, he said, were terminated “without human review.”

“I just delivered the [list of contracts] to the VA employees,” he said. “I basically put munchable at the top and then the others below.”

VA staffers told ProPublica that when DOGE identified contracts to be canceled early this year—before Lavingia was brought on—employees sometimes were given little time to justify retaining the service. One recalled being given just a few hours. The staffers asked not to be named because they feared losing their jobs for talking to reporters.

According to one internal email that predated Lavingia’s AI analysis, staff members had to respond in 255 characters or fewer—just shy of the 280 character limit on Musk’s X social media platform.

Once he started on DOGE’s contract analysis, Lavingia said he was confronted with technological limitations. At least some of the errors produced by his code can be traced to using older versions of OpenAI models available through the VA—models not capable of solving complex tasks, according to the experts consulted by ProPublica.

Moreover, the tool’s underlying instructions were deeply flawed. Records show Lavingia programmed the AI system to make intricate judgments based on the first few pages of each contract—about the first 2,500 words—which contain only sparse summary information.

“AI is absolutely the wrong tool for this,” said Waldo Jaquith, a former Obama appointee who oversaw IT contracting at the Treasury Department. “AI gives convincing looking answers that are frequently wrong. There needs to be humans whose job it is to do this work.”

Lavingia’s prompts did not include context about how the VA operates, what contracts are essential, or which ones are required by federal law. This led AI to determine a core piece of the agency’s own contract procurement system was “munchable.”

At the core of Lavingia’s prompt is the direction to spare contracts involved in “direct patient care.”

Such an approach, experts said, doesn’t grapple with the reality that the work done by doctors and nurses to care for veterans in hospitals is only possible with significant support around them.

Lavingia’s system also used AI to extract details like the contract number and “total contract value.” This led to avoidable errors, where AI returned the wrong dollar value when multiple were found in a contract. Experts said the correct information was readily available from public databases.

Lavingia acknowledged that errors resulted from this approach but said those errors were later corrected by VA staff.

In late March, Lavingia published a version of the “munchable” script on his GitHub account to invite others to use and improve it, he told ProPublica. “It would have been cool if the entire federal government used this script and anyone in the public could see that this is how the VA is thinking about cutting contracts.”

According to a post on his blog, this was done with the approval of Musk before he left DOGE. “When he asked the room about improving DOGE’s public perception, I asked if I could open-source the code I’d been writing,” Lavingia said. “He said yes—it aligned with DOGE’s goal of maximum transparency.”

That openness may have eventually led to Lavingia’s dismissal. Lavingia confirmed he was terminated from DOGE after giving an interview to Fast Company magazine about his work with the department. A VA spokesperson declined to comment on Lavingia’s dismissal.

VA officials have declined to say whether they will continue to use the “munchable” tool moving forward. But the administration may deploy AI to help the agency replace employees. Documents previously obtained by ProPublica show DOGE officials proposed in March consolidating the benefits claims department by relying more on AI.

And the government’s contractors are paying attention. After Lavingia posted his code, he said he heard from people trying to understand how to keep the money flowing.

“I got a couple DMs from VA contractors who had questions when they saw this code,” he said. “They were trying to make sure that their contracts don’t get cut. Or learn why they got cut.

“At the end of the day, humans are the ones terminating the contracts, but it is helpful for them to see how DOGE or Trump or the agency heads are thinking about what contracts they are going to munch. Transparency is a good thing.”

If you have any information about the misuse or abuse of AI within government agencies, Brandon Roberts is an investigative journalist on the news applications team and has a wealth of experience using and dissecting artificial intelligence. He can be reached on Signal @brandonrobertz.01 or by email brandon.roberts@propublica.org.

If you have information about the VA that we should know about, contact reporter Vernal Coleman on Signal, vcoleman91.99, or via email, vernal.coleman@propublica.org, and Eric Umansky on Signal, Ericumansky.04, or via email, eric.umansky@propublica.org.

This story originally appeared on ProPublica.org.

ProPublica is a Pulitzer Prize-winning investigative newsroom. Sign up for The Big Story newsletter to receive stories like this one in your inbox.

DOGE used flawed AI tool to “munch” Veterans Affairs contracts Read More »

Google’s nightmare: How a search spinoff could remake the web

AI, Artificial Intelligence, Features, Google, search, Tech / Beth Washington / June 6, 2025

Google has shaped the Internet as we know it, and unleashing its index could change everything.

Google may be forced to license its search technology when the final antitrust ruling comes down. Credit: Aurich Lawson

Google wasn’t around for the advent of the World Wide Web, but it successfully remade the web on its own terms. Today, any website that wants to be findable has to play by Google’s rules, and after years of search dominance, the company has lost a major antitrust case that could reshape both it and the web.

The closing arguments in the case just wrapped up last week, and Google could be facing serious consequences when the ruling comes down in August. Losing Chrome would certainly change things for Google, but the Department of Justice is pursuing other remedies that could have even more lasting impacts. During his testimony, Google CEO Sundar Pichai seemed genuinely alarmed at the prospect of being forced to license Google’s search index and algorithm, the so-called data remedies in the case. He claimed this would be no better than a spinoff of Google Search. The company’s statements have sometimes derisively referred to this process as “white labeling” Google Search.

But does a white label Google Search sound so bad? Google has built an unrivaled index of the web, but the way it shows results has become increasingly frustrating. A handful of smaller players in search have tried to offer alternatives to Google’s search tools. They all have different approaches to retrieving information for you, but they agree that spinning off Google Search could change the web again. Whether or not those changes are positive depends on who you ask.

The Internet is big and noisy

As Google’s search results have changed over the years, more people have been open to other options. Some have simply moved to AI chatbots to answer their questions, hallucinations be damned. But for most people, it’s still about the 10 blue links (for now).

Because of the scale of the Internet, there are only three general web search indexes: Google, Bing, and Brave. Every search product (including AI tools) relies on one or more of these indexes to probe the web. But what does that mean?

“Generally, a search index is a service that, when given a query, is able to find relevant documents published on the Internet,” said Brave’s search head Josep Pujol.

A search index is essentially a big database, and that’s not the same as search results. According to JP Schmetz, Brave’s chief of ads, it’s entirely possible to have the best and most complete search index in the world and still show poor results for a given query. Sound like anyone you know?

Google’s technological lead has allowed it to crawl more websites than anyone else. It has all the important parts of the web, plus niche sites, abandoned blogs, sketchy copies of legitimate websites, copies of those copies, and AI-rephrased copies of the copied copies—basically everything. And the result of this Herculean digital inventory is a search experience that feels increasingly discombobulated.

“Google is running large-scale experiments in ways that no rival can because we’re effectively blinded,” said Kamyl Bazbaz, head of public affairs at DuckDuckGo, which uses the Bing index. “Google’s scale advantage fuels a powerful feedback loop of different network effects that ensure a perpetual scale and quality deficit for rivals that locks in Google’s advantage.”

The size of the index may not be the only factor that matters, though. Brave, which is perhaps best known for its browser, also has a search engine. Brave Search is the default in its browser, but you can also just go to the URL in your current browser. Unlike most other search engines, Brave doesn’t need to go to anyone else for results. Pujol suggested that Brave doesn’t need the scale of Google’s index to find what you need. And admittedly, Brave’s search results don’t feel meaningfully worse than Google’s—they may even be better when you consider the way that Google tries to keep you from clicking.

Brave’s index spans around 25 billion pages, but it leaves plenty of the web uncrawled. “We could be indexing five to 10 times more pages, but we choose not to because not all the web has signal. Most web pages are basically noise,” said Pujol.

The freemium search engine Kagi isn’t worried about having the most comprehensive index. Kagi is a meta search engine. It pulls in data from multiple indexes, like Bing and Brave, but it has a custom index of what founder and CEO Vladimir Prelovac calls the “non-commercial web.”

When you search with Kagi, some of the results (it tells you the proportion) come from its custom index of personal blogs, hobbyist sites, and other content that is poorly represented on other search engines. It’s reminiscent of the days when huge brands weren’t always clustered at the top of Google—but even these results are being pushed out of reach in favor of AI, ads, Knowledge Graph content, and other Google widgets. That’s a big part of why Kagi exists, according to Prelovac.

A Google spinoff could change everything

We’ve all noticed the changes in Google’s approach to search, and most would agree that they have made finding reliable and accurate information harder. Regardless, Google’s incredibly deep and broad index of the Internet is in demand.

Even with Bing and Brave available, companies are going to extremes to syndicate Google Search results. A cottage industry has emerged to scrape Google searches as a stand-in for an official index. These companies are violating Google’s terms, yet they appear in Google Search results themselves. Google could surely do something about this if it wanted to.

The DOJ calls Google’s mountain of data the “essential raw material” for building a general search engine, and it believes forcing the firm to license that material is key to breaking its monopoly. The sketchy syndication firms will evaporate if the DOJ’s data remedies are implemented, which would give competitors an official way to utilize Google’s index. And utilize it they will.

Google CEO Sundar Pichai decried the court’s efforts to force a “de facto divestiture” of Google’s search tech. Credit: Ryan Whitwam

According to Prelovac, this could lead to an explosion in search choices. “The whole purpose of the Sherman Act is to proliferate a healthy, competitive marketplace. Once you have access to a search index, then you can have thousands of search startups,” said Prelovac.

The Kagi founder suggested that licensing Google Search could allow entities of all sizes to have genuinely useful custom search tools. Cities could use the data to create deep, hyper-local search, and people who love cats could make a cat-specific search engine, in both cases pulling what they want from the most complete database of online content. And, of course, general search products like Kagi would be able to license Google’s tech for a “nominal fee,” as the DOJ puts it.

Prelovac didn’t hesitate when asked if Kagi, which offers a limited number of free searches before asking users to subscribe, would integrate Google’s index. “Yes, that is something we would do,” he said. “And that’s what I believe should happen.”

There may be some drawbacks to unleashing Google’s search services. Judge Amit Mehta has expressed concern that blocking Google’s search placement deals could reduce browser choice, and there is a similar issue with the data remedies. If Google is forced to license search as an API, its few competitors in web indexing could struggle to remain afloat. In a roundabout way, giving away Google’s search tech could actually increase its influence.

The Brave team worries about how open access to Google’s search technology could impact diversity on the web. “If implemented naively, it’s a big problem,” said Brave’s ad chief JP Schmetz, “If the court forces Google to provide search at a marginal cost, it will not be possible for Bing or Brave to survive until the remedy ends.”

The landscape of AI-based search could also change. We know from testimony given during the remedy trial by OpenAI’s Nick Turley that the ChatGPT maker tried and failed to get access to Google Search to ground its AI models—it currently uses Bing. If Google were suddenly an option, you can be sure OpenAI and others would rush to connect Google’s web data to their large language models (LLMs).

The attempt to reduce Google’s power could actually grant it new monopolies in AI, according to Brave Chief Business Officer Brian Brown. “All of a sudden, you would have a single monolithic voice of truth across all the LLMs, across all the web,” Brown said.

What if you weren’t the product?

If white labeling Google does expand choice, even at the expense of other indexes, it will give more kinds of search products a chance in the market—maybe even some that shun Google’s focus on advertising. You don’t see much of that right now.

For most people, web search is and always has been a free service supported by ads. Google, Brave, DuckDuckGo, and Bing offer all the search queries you want for free because they want eyeballs. It’s been said often, but it’s true: If you’re not paying for it, you’re the product. This is an arrangement that bothers Kagi’s founder.

“For something as important as information consumption, there should not be an intermediary between me and the information, especially one that is trying to sell me something,” said Prelovac.

Kagi search results acknowledge the negative impact of today’s advertising regime. Kagi users see a warning next to results with a high number of ads and trackers. According to Prelovac, that is by far the strongest indication that a result is of low quality. That icon also lets you adjust the prevalence of such sites in your personal results. You can demote a site or completely hide it, which is a valuable option in the age of clickbait.

Kagi search gives you a lot of control. Credit: Ryan Whitwam

Kagi’s paid approach to search changes its relationship with your data. “We literally don’t need user data,” Prelovac said. “But it’s not only that we don’t need it. It’s a liability.”

Prelovac admitted that getting people to pay for search is “really hard.” Nevertheless, he believes ad-supported search is a dead end. So Kagi is planning for a future in five or 10 years when more people have realized they’re still “paying” for ad-based search with lost productivity time and personal data, he said.

We know how Google handles user data (it collects a lot of it), but what does that mean for smaller search engines like Brave and DuckDuckGo that rely on ads?

“I’m sure they mean well,” said Prelovac.

Brave said that it shields user data from advertisers, relying on first-party tracking to attribute clicks to Brave without touching the user. “They cannot retarget people later; none of that is happening,” said Brave’s JP Schmetz.

DuckDuckGo is a bit of an odd duck—it relies on Bing’s general search index, but it adds a layer of privacy tools on top. It’s free and ad-supported like Google and Brave, but the company says it takes user privacy seriously.

“Viewing ads is privacy protected by DuckDuckGo, and most ad clicks are managed by Microsoft’s ad network,” DuckDuckGo’s Kamyl Bazbaz said. He explained that DuckDuckGo has worked with Microsoft to ensure its network does not track users or create any profiles based on clicks. He added that the company has a similar privacy arrangement with TripAdvisor for travel-related ads.

It’s AI all the way down

We can’t talk about the future of search without acknowledging the artificially intelligent elephant in the room. As Google continues its shift to AI-based search, it’s tempting to think of the potential search spin-off as a way to escape that trend. However, you may find few refuges in the coming years. There’s a real possibility that search is evolving beyond the 10 blue links and toward an AI assistant model.

All non-Google search engines have AI integrations, with the most prominent being Microsoft Bing, which has a partnership with OpenAI. But smaller players have AI search features, too. The folks working on these products agree with Microsoft and Google on one important point: They see AI as inevitable.

Today’s Google alternatives all have their own take on AI Overviews, which generates responses to queries based on search results. They’re generally not as in-your-face as Google AI, though. While Google and Microsoft are intensely focused on increasing the usage of AI search, other search operators aren’t pushing for that future. They are along for the ride, though.

AI overview on phone — AI Overviews are integrated with Google’s search results, and most other players have their own version. Credit: Google

“We’re finding that some people prefer to start in chat mode and then jump into more traditional search results when needed, while others prefer the opposite,” Bazbaz said. “So we thought the best thing to do was offer both. We made it easy to move between them, and we included an off switch for those who’d like to avoid AI altogether.”

The team at Brave views AI as a core means of accessing search and one that will continue to grow. Brave generates AI answers for many searches and prominently cites sources. You can also disable Brave’s AI if you prefer. But according to search chief Josep Pujol, the move to AI search is inevitable for a pretty simple reason: It’s convenient, and people will always choose convenience. So AI is changing the web as we know it, for better or worse, because LLMs can save a smidge of time, especially for more detailed “long-tail” queries. These AI features may give you false information while they do it, but that’s not always apparent.

This is very similar to the language Google uses when discussing agentic search, although it expresses it in a more nuanced way. By understanding the task behind a query, Google hopes to provide AI answers that save people time, even if the model needs a few ticks to fan out and run multiple searches to generate a more comprehensive report on a topic. That’s probably still faster than running multiple searches and manually reviewing the results, and it could leave traditional search as an increasingly niche service, even in a world with more choices.

“Will the 10 blue links continue to exist in 10 years?” Pujol asked. “Actually, one question would be, does it even exist now? In 10 years, [search] will have evolved into more of an AI conversation behavior or even agentic. That is probably the case. What, for sure, will continue to exist is the need to search. Search is a verb, an action that you do, and whether you will do it directly or whether it will be done through an agent, it’s a search engine.”

Vlad from Kagi sees AI becoming the default way we access information in the long term, but his search engine doesn’t force you to use it. On Kagi, you can expand the AI box for your searches and ask follow-ups, and the AI will open automatically if you use a question mark in your search. But that’s just the start.

“You watch Star Trek, nobody’s clicking on links there—I do believe in that vision in science fiction movies,” Prelovac said. “I don’t think my daughter will be clicking links in 10 years. The only question is if the current technology will be the one that gets us there. LLMs have inherent flaws. I would even tend to say it’s likely not going to get us to Star Trek.”

If we think of AI mainly as a way to search for information, the future becomes murky. With generative AI in the driver’s seat, questions of authority and accuracy may be left to language models that often behave in unpredictable and difficult-to-understand ways. Whether we’re headed for an AI boom or bust—for continued Google dominance or a new era of choice—we’re facing fundamental changes to how we access information.

Maybe if we get those thousands of search startups, there will be a few that specialize in 10 blue links. We can only hope.

Ryan Whitwam is a senior technology reporter at Ars Technica, covering the ways Google, AI, and mobile technology continue to change the world. Over his 20-year career, he’s written for Android Police, ExtremeTech, Wirecutter, NY Times, and more. He has reviewed more phones than most people will ever own. You can follow him on Bluesky, where you will see photos of his dozens of mechanical keyboards.

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Xenomorphs are back and bad as ever in Alien: Earth trailer

Alien: Earth, culture, Entertainment, FX/Hulu, streaming television, Trailers / Beth Washington / June 5, 2025

Alien: Earth is set two years before the events of 1979’s Alien.

It’s been a long wait for diehard fans of Ridley Scott’s Alien franchise, but we finally have a fittingly sinister official trailer for the spinoff prequel series, Alien: Earth, coming this summer to FX/Hulu.

As previously reported, the official premise is short and sweet: “When a mysterious space vessel crash-lands on Earth, a young woman (Sydney Chandler) and a ragtag group of tactical soldiers make a fateful discovery that puts them face-to-face with the planet’s greatest threat.”

The series is set in 2120, two years before the events of the first film, Alien (1979), in a world where corporate interests are competing to be the first to unlock the key to human longevity—maybe even immortality. Showrunner Noah Hawley has said that the style and mythology will be closer to that film than Prometheus (2012) or Alien: Covenant, both of which were also prequels.

Chandler’s character is named Wendy; she’s a human/synth hybrid described as having “the body of an adult and the consciousness of a child.” Timothy Olyphant plays her synth mentor and trainer, Kirsh. The cast also includes Alex Lawther as a soldier named CJ, Samuel Blenkin as a CEO named Boy Kavalier, Essie Davis as Dame Silvia, Adarsh Gourav as Slightly, Kit Young as Tootles, David Rysdahl as Arthur, Babou Ceesay as Morrow, Jonathan Ajayi as Smee, Erana James as Curly, Lily Newmark as Nibs, Diem Camille as Siberian, and Adrian Edmondson as Atom Eins.

Xenomorphs are back and bad as ever in Alien: Earth trailer Read More »

Author name: Beth Washington