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fleg writes:

https://www.forbes.com/sites/jonmarkman/2026/08/13/claude-just-broke-a-math-record-that-stood-for-37-years/

"Number theorists spent 37 years moving one number by less than a single percentage point. The number is the proven share of the Riemann zeta function's zeros that sit on the critical line, the place where one of the most famous unsolved problems in mathematics says all of them belong. Decades of human refinement had carried it to 41.6%. On August 10, Anthropic published a result from an unreleased research version of Claude that moved it to 67.2% in roughly a day and a half.

The Riemann hypothesis has been open since 1859. It predicts the hidden structure behind how prime numbers are distributed, and it carries a million-dollar Clay Institute bounty. The hypothesis itself remains open. What Claude established is that at least two-thirds of the zeros behave the way it predicts, the largest single advance in the record's history."

Original Submission

hubie writes:

"Always finish your antibiotics" is no longer considered medical best practice for all conditions:

Surprised? You're not alone.

A new study has found that almost 90% of Americans believe that it's always best to take the full course of antibiotics, even when you feel better—consistent with long-running but now outdated health campaigns. The reality is more complicated. Sometimes shorter courses are safer, and sometimes longer courses are best.

The survey demonstrates a need for better communication between doctors and patients about what's healthiest.

"Historically, there was very strong guidance by major health organizations and clinicians that you must always finish the course," says Alistair Thorpe, PhD, research assistant professor of population health sciences at University of Utah Health and first author on the study. "Now, we're seeing a growing body of evidence saying that that is not always the case. And oftentimes, shorter durations of antibiotics are as effective and safe as longer alternatives."

[...] One of the main reasons people gave for preferring longer antibiotic courses was that they had been told to "always finish their antibiotic course"—88% of respondents had heard of, and agreed with, this common mantra. Most had been told this by their clinician, and many had also heard it via a public health campaign.

A strong body of scientific evidence shows that, for many common infections, shorter courses of antibiotics work as well as longer courses and are less likely to cause side effects. Still, there are some cases, like tuberculosis, where longer courses are most effective.

The study authors suggest that doctors and public health campaigns use several evidence-informed strategies to better communicate the complex reality of antibiotic course length—for instance, avoiding overly simplistic claims that either shorter or longer courses are universally better, and acknowledging that as scientific evidence accumulates over time, health recommendations can change.

[...] "Discuss with your clinician what the right duration is for you and when the right time is to stop your course," Thorpe says. "Getting advice directly from a clinician on a one-to-one basis about what is most appropriate for you in that situation is the right way to go."

Thorpe emphasizes that the changing recommendations are a positive outcome of increasing knowledge.

"Evidence is growing and guidance is evolving on antibiotic use, which is a normal process and a good sign that we are working to improve how we provide care," he says. "Our knowledge about how best to use antibiotics has changed, but it has changed because we're learning more, and it's important that we make sure we are communicating this well to the public."

Journal Reference: Alistair Thorpe, Rachael A Lee, Julia E Szymczak, et al. US Adults' Perspectives on Antibiotic Durations and Adherence to Therapy for Common Bacterial Respiratory Infections: A National Survey, Open Forum Infectious Diseases, Volume 13, Issue 7, July 2026, ofag407, https://doi.org/10.1093/ofid/ofag407

Original Submission

hubie writes:

The mashup of a black hole and an enormous star has never been seen before and could explain the mysterious little red dots often found in deep-space images:

Astronomers at MIT and elsewhere have spotted an extremely bright red spot in the early universe. The object resembles an enormous star, spanning the size of our solar system. But it also is putting out 100 billion times more energy than any known star can physically produce. In fact, such energies are closer to what a black hole might generate.

The curious combination suggests that the red spot is an entirely new type of astrophysical source. The astronomers are calling it a "black hole star."

In a paper appearing today in the journal Nature, the team presents their analysis of the new object, which they discovered using NASA's James Webb Space Telescope (JWST). The telescope spotted the bright red dot in the very early universe, just a few hundred million years after the Big Bang.

The scientists conclude that the most likely explanation for the strange red dot is that it is a mashup of a black hole and a star — a combination that has never been observed until now. The object is likely a hugely dense cloud of gas, powered not by standard nuclear fusion, but by a central black hole.

"Our picture of this object is evolving very rapidly," says lead author Rohan Naidu, a NASA Hubble Fellow and Pappalardo Fellow at MIT's Kavli Institute for Astrophysics and Space Research (MKI). "We think there is a central black hole that is 100,000 times as massive as the sun. And around this black hole, there would be this very extended envelope of gas that looks like a star the size of the solar system. It's huge."

If the bright red dot is indeed a black hole star, it would help to solve the identity of other mysterious "little red dots" that have appeared in nearly every deep space image JWST has taken to date.

"These little red dots seem to be everywhere in the early universe but essentially disappear by the present day," Naidu says. "What exactly these objects are has been one of the most debated topics of the JWST era."

[...] "When we see something very red in the universe, we often assume that it is surrounded by dust, like soot or ash," Simcoe explains. "The same way that the wildfire smoke from Canada recently made the sky in Boston look bright red, astronomical objects can also appear redder than their intrinsic color when you see them through a veil of dust."

But there were other signatures in the light that didn't quite match up with what physicists expect from dust. The team also observed another strange pattern: The dot's light was extremely bright, except below certain wavelengths, where the light completely disappeared.

"fab23" writes:

If the Markets Reject OpenAI and Anthropic, the US Should Nationalize Them:

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian .

If the Markets Reject OpenAI and Anthropic, the US Should Nationalize Them

OpenAI, and then Anthropic , were each formed by AI developers who feared unrestrained corporate AI development—specifically, that companies like Google and Meta would steer the technology towards deleterious, maybe even catastrophically unsafe, outcomes for society. Their founders proclaimed that their new labs, uniquely, could be trusted to develop the technology in humanity's best interest. But each, in turn, were themselves co-opted by the same market incentives, themselves becoming corporate behemoths zealously guarding future investor value rather than the public interest.

It was only a few weeks ago, in June, when OpenAI and Anthropic each filed for their IPOs and were met with buzz about trillion-dollar valuations. The hype around their valuations is so extreme that many worry about their potential for concentrating wealth on a global scale. In an effort to leave something for the rest of us, some observers have proposed that the federal government seize a share of these companies' stock to create a US sovereign wealth fund , or redistribute their revenues to produce a dividend for taxpayers.

Now the headlines are about public backlash to AI datacenters and the AI chip giant Nvidia's slumping stock. The tech and AI giant SpaceX's newly minted stock price tanked just weeks after its IPO. There are even questions about whether the leading AI labs will ever be sustainably profitable . All of a sudden, the makers of ChatGPT and Claude face strong headwinds as they seek to generate the massive equity assets that once felt all but assured.

In fact, evidence suggests the market itself could reassess that these companies offer nothing of financial value. In that case, perhaps we can return them both to their original purposes. If these AI companies should fail in the financial markets, the US should nationalize them and convert them into national labs operated under democratic control that preserve their benefit to the public interest.

The economics of the big AI labs hardly guarantee a booming return on investment. Frontier AI models are both expensive to train and depreciate within months, when a newer model appears. This means that the payback window to extract profit from them is very narrow. Meanwhile, enterprise clients are getting smart about minimizing AI token usage. Even worse, the models are basically commodities; the best ones largely perform and behave similarly, which depresses prices. Perhaps most importantly, open-source and Chinese competitors— lagging only a few months behind the leading labs in capability—give away for free the kinds of models Anthropic and OpenAI sell.

Even setting aside the model training costs, it's not clear whether the unit economics of AI as it's currently conceived will ever be sustainably profitable. Many of these free and open-source models can be run locally: the large ones on private clouds and high-end servers, the smaller ones on anyone's laptop or even cellphone, putting to question the companies' exorbitant capital investment in datacenters.

It's not that OpenAI and Anthropic are not valuable as organizations. They have remarkably talented AI scientists and engineers that are continuously producing innovations driving a global mania for their offerings. These leading labs might not ever be profitable, but their products are doing a lot of good in the world. You may or may not be a user of or believer in their technology, but their staggering, ongoing usage growth suggests that an awful lot of people would be disappointed if the companies simply disappeared.

The problem isn't the people or the products, it's the system. As constituted, OpenAI and Anthropic may not be valuable as market equities. If the market assesses they are not capable of producing a growing financial return on investment for shareholders, the companies will collapse.

Maybe private, for-profit is just not the right economic model under which to develop AI. Perhaps OpenAI should be returned to its private non-profit roots, the legacy they fought so hard to change and which Anthropic's founders spurned . Or possibly both could be reorganized as research centers at universities, returning to academia the scores of high-profile research faculty they have poached .

But a better outcome for society would be to establish public ownership and operation of their product-oriented capabilities. Turn OpenAI and Anthropic into US government agencies producing AI as a public good.

Arthur T Knackerbracket writes:

https://www.zdnet.com/article/linux-desktop-use-surged-on-one-workday-cloudflare-data-shows/

Web traffic analysis site StatCounter recently showed that desktop Linux had reached an all-time high of 10.65% in the North American desktop market. Many people, even many Linux fans, dismissed this proportion as fake news. However, the last time I looked at the numbers in 2025, Statcounter only showed a high of just over 5%. As I mentioned in my recent report, Linux doubling its market share in just over a year should get everyone's attention.

I dug deeper and found that data from popular content delivery network Cloudflare also showed that the Linux desktop has been gaining popularity. By its count, desktop Linux has now reached 6.4%. That proportion is not as high as StatCounter's, but it represents significant growth given that Linux accounted for 4.1% of North American operating systems for the same period last year.

Cloudflare's dataset is much larger than StatCounter's. StatCounter monitors about a million websites, while Cloudflare keeps an eye on over 24 million websites. Cloudflare itself claims to cover about 20% of the web. 

A deeper look also reveals that desktop Linux use tends to peak during the work week. For example, believe it or not, on Monday, 6 July, when people were back after the 4 July holiday, Linux usage jumped to a high of 22%. 

The users of these systems are not AI agents or bots, but real people. I set Cloudflare to count humans rather than bots. If you include people and bots, Linux has a 9.7% market share. That proportion is similar to StatCounter's score. By Cloudflare's count, bots alone come to 19%.

These numbers tell us that workers are leading the way to the Linux desktop. That result is not surprising. Historically, they're people who prefer control over their PCs, like open systems, or have a specific workload, such as programming or system administration, where Linux works well. 

Microsoft has long known this requirement. Windows Subsystem for Linux (WSL), for example, is designed for Windows developers who also need Linux's power and tools. It's been years since Microsoft released WSL user numbers, but in 2020, there were 3.5 million WSL users. 

hubie writes:

When it comes to recording, tracking, and storing data, the concept of "permanence" can be a lot trickier than you'd expect:

More and more, our data—private photos, work documents, confidential information, and so on—are migrating to digital clouds and platforms. In a more recent and perhaps controversial turn of events, some gaming firms are pushing for digital downloads in lieu of physical CDs.

One recurring argument against digital options has been that it's not as "permanent" as their physical counterparts. After all, aren't physical storage systems safer from the whims of a tech firm wiping your cloud storage—or, even worse, forcing you to choose a subscription plan?

But CDs break. Books burn. USBs go defunct. Personally, I—a stickler for physical media—have a very bad track record of breaking my game chips and scratching my movie discs. There are real challenges in dealing with physical storage systems, too, and big players like Microsoft are investing significant resources to overcome the inevitable decay caused by, well, the passage of time.

[...] The following responses may have been lightly edited for length and clarity.

Mohiuddin Ahmed, Computer scientist, Adelaide University, Australia.

Data storage is a contextual matter. Given the transient nature of everything around us, permanent data storage is some sort of a myth at this stage. That said, multiple copies of data and moving them to a new storage every couple of years can be thought of as a practical solution toward permanent data storage.

Francesca Musiani, Research director, Center for Internet and Society at the National Center for Scientific Research, France.

I find the idea of "permanent" data storage quite elusive, not only in our digital "era." Every medium we have ever invented eventually fails, from stone inscriptions that erode to paper burning, CDs' "disc rot" and solid-state drives that gradually lose stored charge. Further, even if a storage medium could survive for centuries, the devices and software needed to read it may not; a perfectly preserved floppy disk is of little use if no one can build or maintain a compatible drive. In this sense, data loss is a techno-historical problem, I would say: preserving information requires preserving the means of interpreting it.

[...]

Jack Cushman, Director, Library Innovation Lab at Harvard Law School; creator of the Public Data Project.

I know of one data storage form that can be permanent: a library. Or rather, a world full of libraries. Libraries aren't perfect storage devices (nothing is), but they are fiercely committed to self-repair. A good library is a collection of people, technologies, and practices that pass an obligation—to remember the things we must remember—from the last generation to the next.

Libraries, rather than better hard drives, are what solve correlated failure, cataloged in the LOCKSS (Lots Of Copies Keep Stuff Safe) threat model. A correlated failure is when you make really great, durable copies of the data you care about, and then they all get hit by an asteroid. Shouldn't have had them all in one physical location. Or a hacker marks them all for deletion (they were all behind one sysadmin), or a government orders them all destroyed (all in one regulatory regime), or they all rot at the same time (all from one flawed batch), or the money to store them dries up (all on one funding source).

On a long enough timeline any copy will break. You need multiple copies with different vulnerabilities so they don't all break at the same time. Then you need to repair the broken ones. A worldwide network of libraries does the repair work, whether the objects are 30 medieval Magna Carta manuscripts or 300,000 government datasets.

[...]

owl writes:

https://ludens.cl/photo/spectra/spectra.html

The biggest lie in color photography is that you can accurately represent the colors of objects by simply recording the amount of red, green and blue in them. This technique - the only one in current mainstream use - gives good results only when the spectral sensitivity curves of the camera precisely match those of the human eye, and when the spectrum of the light used to make the photo is perfectly smooth, and no different kind of light will ever be used!

It's clear that the first of these requirements is hard enough to meet, and that the second one is, simply and plainly, never true.

And that's why photographers are always battling to get the right colors - and never do get them!

Not only in photography is this matter an important one. In daily life it is, too. Lots of electronic technicians hate that stupid problem of not being able to correctly read resistors. It happens that old-style resistors, and some other parts too, are labelled with color bands or dots, instead of numbers. Under some lighting conditions it can be hard to tell a red from an orange, or a green from a blue. This leads to the wrong resistors being installed in equipment, and thus more troubleshooting work.

Original Submission

hubie writes:

Firefox says it will keep supporting uBlock Origin even as Microsoft Edge and other Chromium browsers phase it out under Manifest V3:

Firefox recently announced via Bluesky post: "Our support for uBlock Origin isn't going anywhere." The moment comes in response to news that Microsoft Edge is soon going to lock out uBlock Origin and other ad-blocking extensions that run on Manifest V2 architecture.

Once Microsoft Edge moves to Manifest V3, ad-blocking extensions won't have access to the functions needed to properly identify and block ads that occur while browsing websites and watching videos.

Microsoft's move isn't surprising, as Edge is based on Chromium, the open-source browser engine that powers most web browsers today, including Opera, Brave, Vivaldi, and Samsung Browser. Google initiated the migration from Manifest V2 to V3 in Chrome/Chromium, and Microsoft Edge is now following Google's lead.

But Firefox is one of the few web browsers remaining that isn't based on Chromium, and it's now the only major browser to still support uBlock Origin. Neither Safari nor DuckDuckGo—the two other major non-Chromium browsers out there—support uBlock Origin.

For die-hard uBlock Origin fans, Firefox appears to be the only browser left without compromises. With any other browser, you'll need to settle for uBlock Origin Lite (with fewer features and less ad-blocking success) or whatever built-in ad-blocking feature comes with the browser.

Original Submission

Arthur T Knackerbracket writes:

Cloudflare built an AI agent workspace for its employees. Now it's open source:

Cloudflare has open-sourced its Cloudflare OS platform, which it first developed as an internal workspace for employees to build apps using AI agents—including people who are not software developers or engineers. The company also touts a security framework designed to reduce the risk of employee vibe-coding sessions creating serious security flaws or leading to data breaches.

The tech company spent several months building and internally testing Cloudflare OS, which allows employees to describe workflows in natural language so that an AI agent can code them into applications. In an August 5 blog post announcing the open source version's availability on GitHub, the company claims thousands of Cloudflare employees use the platform on a daily basis to "create documents and slides, automate repeatable tasks, and build small apps to visualize data and help them do their work."

"This is a full-on personal app vibe coding platform, in which the sandbox is so secure that you can pretty much go wild—the AI cannot introduce a significant security bug," said Kenton Varda, principal engineer at Cloudflare, in a post on the social media platform X. "We believe a company's security team can feel comfortable giving non-technical users permission to vibe code and then sleep soundly at night."

The security model relies on creating fine-grained app instances so that a document editor app would run each document as a separate instance in a separate sandbox, Varda explained. The Cloudflare OS platform manages who has permission to access each instance, and each individual runs their own copy of the code that they can freely modify.

[...] Cloudflare OS can work with just about any AI model and allows organizations to select the most suitable model for the job at hand. "Not every user needs access to the max thinking mode of the latest frontier lab model," said Sam Rhea, chief information officer at Cloudflare, in a separate blog post. "And we do not need team members spending $20 to summarize their email inbox every hour."

looorg writes:

Owners of Petlibro pet feeders have "service issue" that prevents feeding of the pets. If you can't even bother to feed your own pet? Why do you have them? Also if we can't even depend on the machines to feed pets how are we depending on them to do other more important things. But why do you need cloud access to set a schedule to feed a pet? Or that is to say to open a tray so the pet in question can access the food. As it was when it was not opening it was mocking or taunting the pet with food it could not access.

Pets should now be on their normal feeding schedule again ... Perhaps the service issue was just there to put some of the furries on a diet.

Petlibro says it's working to resolve a 'service issue' that's causing its smart feeders not to dispense food on schedule.

Smart pet-feeder company Petlibro suffered an outage on Tuesday, giving users a harsh reminder of the risks of relying on smart devices.

https://www.theverge.com/tech/979295/petlibro-outage-smart-pet-feeders
https://arstechnica.com/gadgets/2026/08/pet-owners-say-smart-pet-feeder-outage-led-to-furry-ones-going-unfed/

Original Submission

JoeMerchant writes:

Meta's recent open-weight release of Muse Glimmer 30B has re-ignited the debate over whether locally hosted models can replace subscription-based cloud APIs like Claude 3.5 Sonnet, GPT-4o, and Gemini 2.0 Pro. But what does it actually take in terms of silicon, power draw, and real-world performance to run Meta's latest agentic model at best capacity?

To run Muse Glimmer 30B at "best capacity"—meaning unquantized (FP16) or high-precision 8-bit (FP8/Q8_0) with its full 131,072-token context window loaded into VRAM alongside the DFlash speculative decoder—hardware requirements land between 48 GB and 64 GB of VRAM. A dual NVIDIA RTX 5090 workstation (~64 GB VRAM) requires an initial hardware investment of roughly $4,500–$6,000, while an Apple Mac Studio (128 GB Unified Memory) costs around $3,800–$4,800.

The operational energy divergence between PC rigs and Apple Silicon is substantial:

• Dual-NVIDIA Workstation (750W Peak): Under heavy 8-hour daily agent workloads, power costs average ~$35/month ($420/year). Continuous 24/7 background operation increases power costs to ~$86/month ($1,036/year).
• Apple Silicon Mac Studio (130W Peak): Under the same 8-hour daily workload, power costs average ~$5.90/month ($71/year), or ~$15/month for continuous 24/7 agent loops—yielding an 80%+ energy reduction over PC GPU setups.

In terms of capability, Muse Glimmer 30B holds its ground surprisingly well against mega-parameter cloud models:

*SPOILER* (click to show) *SPOILER* (click to hide)

                        Benchmark / Workload
                        Muse Glimmer 30B (Local)
                        Claude 3.5 Sonnet (Cloud)
                        GPT-4o / o3-mini (Cloud)

                        SWE-bench Verified (Coding)
                        76.0
                        49.0
                        70.5 (o3-mini)

                        MCP Atlas (Tool Use)
                        75.5
                        81.2
                        80.0

                        DeepSearch QA (Research)
                        74.6
                        82.0
                        84.1

                        AIME 2026 (Math/Logic)
                        94.7
                        78.3
                        91.0

The Verdict: While frontier cloud models like Claude 3.5 Sonnet and Gemini 2.0 Pro maintain a clear lead in complex architectural design and massive context capacity (up to 2M tokens), Muse Glimmer 30B dominates in privacy, zero token costs, and low-latency local agent execution. For software developers and privacy-conscious users running 24/7 personal assistants, local hardware offers a compelling alternative to continuous cloud API fees.

Model weights and GGUF quantizations are currently available via Hugging Face under the Apache 2.0 license.

------

I'd call 3.5 Sonnet a bit of a strawman, over a year old, but still an impressive target to match locally, privately, at those prices.

Original Submission

Arthur T Knackerbracket writes:

AI-assisted research only used 20 prompts to find an exploit to hack hundreds of millions of people:

Critical 'Zoomsday' Flaw Enables Total Device Takeover During Zoom Calls — AI-Assisted Research Only Used 20 Prompts To Find An Exploit To Hack Hundreds Of Millions Of People.

The team claims it cooked the exploit with merely 20 prompts to an AI agent. The exploitable area is substantial, as recent estimates pin Zoom's monthly active users at around 220 million and an estimated 56% of the global conferencing market share.

With the exploit, the attacker was able to get full remote code execution, meaning they could effectively control the user's computer and their data — invisibly, to boot. Zoom isn't an application that runs with administrator privileges, so kernel-level rootkits are off the menu, but once you have the user's data, it's not like you need much else. Plus, it's easy to gain exploit persistence any number of other ways.

A.Security says a small team developed this exploit with a mere 20 prompts to an AI agent — pointing out how easy it was to come up with a nation-state-class vulnerability with meager resources. While the majority of AI-assisted vulnerability research focuses on open-source software or applications with published communications protocols or file formats, Zoom is fully proprietary, and it was still easily cracked open.

The firm further noted that "the model requiring elite teams, months of effort, and weapons-grade budgets has collapsed," and that "the barrier that kept these weapons scarce has collapsed, and it will not come back" — basically repeating what every security researcher has been yelling from the top of their lungs for the past year or so.

The vulnerability itself is, rather unsurprisingly, a buffer overrun: the program fails to check that an input is the right size, so you can push more data than it expects and overwrite part of the following memory with code that will be executed.

First, the scientists decompiled the Android package and asked an AI agent to rank the potential attack surfaces to relatively little success. They then turned their attention to the communications protocol. They found that the code library handling annotations received each object (rectangles, text, etc.) in serialized form, with count fields telling the recipient how much data to read next.

Crucially, they found that the code handling these reads didn't have a boundary check for maximum size, meaning one could simply lie about it and send a chunk of data that's too large and padded with exploit code at the end, as Norman Stansfield would say, bin-go!

Original Submission

An Anonymous Coward writes:

https://science.nasa.gov/missions/chandra/nasa-telescopes-create-colorful-craft-from-nearby-nebula/:

Like a collage made of layered sheets of colored cellophane, a vibrant new image layers observations of a famous star-forming nebula from NASA space telescopes. The resulting cosmic "craft" reveals new details about the star formation region known as 30 Doradus, or the Tarantula Nebula.

Located in the Large Magellanic Cloud, a small neighbor galaxy to the Milky Way about 160,000 light-years from Earth, the Tarantula has thousands of young stars embedded in a vibrant honeycomb-like structure of gas and dust.

NASA Tarantula image

The new composite image contains X-rays from NASA's Chandra X-ray Observatory, which has repeatedly observed the Tarantula Nebula over the course of its mission, in the layer that appears in blue. The X-ray data reveals gas blown away by winds from the surfaces of young, massive stars and heated to millions of degrees by shock waves, like sonic booms from supersonic jets.

The red represents infrared data from NASA's James Webb Space Telescope showing thousands of young stars, plus swaths of cool dust that will provide the ingredients to form new stars and planets. Optical data in the green layer from NASA's Hubble Space Telescope uncovers hydrogen gas that is warmer than seen with Webb, as well as some individual stars through the nebula.

The composite image shows the full Hubble and Webb images of this region, as well as a large section of the Chandra image, all recently published in a research paper in the Astrophysical Journal. In some regions the blue Chandra layer stands alone, and in others it combines with either the red Webb data or the green Hubble data. In the middle region all three images overlap to provide a holistic view in red, orange, yellow, green, and blue.

Arizona State University is introducing a degree in Content Creation. This is not an April fool's joke or a hoax.

The degree will teach the craft of influencing and online content creation which will include lessons on creating powerful content across video, podcasts, analytics, and social media strategy.

Online comments in response to this announceent have included "F**k. This. Timeline." and "Just imagine explaining to someone that your homework is 'go viral'".

Classes start this October.

Original Submission

Arthur T Knackerbracket writes:

Anthropic and OpenAI are racing to scale up while reducing dependence on Nvidia:

Anthropic is hiring a "custom silicon team" to design chips on which to run its models, the company has revealed.

Yesterday, Business Insider noticed a job listing for a senior engineer with experience shipping semiconductor designs. (You can see listings for a silicon engineer and a technical program manager, silicon on Anthropic's job board right now.) A spokesperson for Anthropic then confirmed the plans to both Business Insider and TechCrunch.

The spokesperson clarified that Anthropic will still take a "multi-chip approach," with plans to use hardware from other companies alongside its own designs as it continues to scale up.

This is confirmation of a rumor that has been circulating for a little bit; The Information previously reported that Anthropic was considering working with Samsung as a hardware manufacturing partner.

Anthropic is not alone in walking this path. Its competitor, OpenAI, recently announced a new custom chip called Jalapeño designed for large language model inference in data centers. OpenAI partnered with Broadcom to develop the chip. Google has been running its models on its own hardware for a while; Meta has also designed and deployed its own chips, and Mistral is reportedly looking into doing the same.

There are a few reasons AI providers are doing this. First, much of the industry is heavily reliant on Nvidia for the hardware the companies' models run on, and Nvidia's continued leverage there is a potential strategic vulnerability, especially as AI companies operate in an environment where compute infrastructure is highly competitive as demand continues to outstrip current capacity.

Second, designing chips for specific models and vice versa could lead to better performance. So, for example, if OpenAI can reap the rewards of that vertical integration, you can bet Anthropic and other frontier model providers will want that advantage as well.

To that point, Anthropic says its teams will co-design new hardware and models side by side. It has co-designed certain hardware with partners before, but the plan is now to bring more silicon expertise inside Anthropic itself.

Anthropic may also hope this could help its frontier models get some extra competitive edge as software developers and other users begin exploring running cheaper, smaller, or open-weight models on their own hardware or on edge devices.

However, since Anthropic is still in the process of hiring key team members, it will be some time before either the company or its users see any benefits.

Original Submission