Two workers have been confirmed dead and another seriously injured after an explosion at Intel’s Kiryat Gat plant in southern Israel on Wednesday, The Times of Israel reports. Police suspect a gas cylinder malfunction caused the explosion, and Israel’s Fire and Rescue Services said the fire involved nitrogen trifluoride (NF3), a toxic gas mainly used to clean the process chambers of chipmaking tools. So far, Intel hasn’t made any statements about the victims or about any potential production impacts at the site.

The site houses Fab 28, which, per Intel, makes “processors, 5G, and AI products” on Intel 7, formerly 10nm Enhanced SuperFin. Intel 7 products include 13th and 14th Gen Core “Raptor Lake,” 5th Gen Xeon “Emerald Rapids,” and the I/O die of Xeon 6 “Granite Rapids,” although these are not explicitly linked to the site. Fab 38, adjacent to Intel’s existing Kiryat Gat fabs, was announced in December 2023 with a $3.25 billion Israeli grant but has been paused since mid-2024. Intel reportedly considered closing Fab 28 in July 2025, but no closure decision has been announced.

The Kiryat Gat police commander at the scene laid out the sequence: a blast occurred during a material transfer into the factory’s tanks after one truck had disconnected and another connected. At least eight Fire and Rescue Services teams, including hazmat, searched for trapped people, contained the fire, and monitored the hazardous substance.

“At this moment, I cannot point to any suspicion of criminal activity or negligence,” the commander added. Magen David Adom, Israel’s national emergency medical service, said two of the injured underwent resuscitation efforts and all three suffered severe burns. Ultimately, one of the injured was pronounced dead, a second later died of their wounds, and one remains hospitalized in serious condition inside the ICU, based on a statement from Soroka Medical Center in Beersheba.

The gas, although hazardous if inhaled, is not flammable. It’s not clear if the trucks were delivering NF3 or if it was directly involved in causing the explosion. Once the fire was put out, crews kept cooling the scene to prevent a secondary incident, and the commander said things appeared to be under control, with no sign so far of further leaks and no need to evacuate residents or factory workers. Kiryat Gat Mayor Kfir Suissa told residents to stay away while crews worked and added, “There is no danger to the public.”

At the time, there was no word on possible production delays, but these would be problematic as Intel 7 capacity was already tight. Last year the company’s CFO said that Intel 7 and Intel 10 constraints had limited its ability to meet demand, with shortages expected into 2026 with server wafers getting priority. Intel reports third-quarter results on Oct. 29 and will hold a call to discuss them, although the company has not mentioned whether it will address the explosion.

This year’s Grace Hopper Celebration (GHC) in Anaheim, a conference for women in computing, has been canceled just two weeks before its start. Brenda Darden Wilkerson, president and CEO of AnitaB.org, the nonprofit that manages the convention, said in a press release that "both individual registrations and sponsorship commitments came in significantly below our expectations," leaving GHC short of the money needed to cover its operating costs. The circumstances behind the cancellation have also hurt AnitaB.org’s finances, and it is now considering strategic alternatives and looking at what programming it can offer in the future.

With the event called off, attendees were told to cancel their own travel and lodging arrangements (which are on the attendee). Fees already paid have not been addressed, and depend on the nonprofit getting a clearer picture of its finances. Sponsors and exhibitors are being handled directly through contacts, but no information on refunds was available at the time of posting. Meanwhile, AnitaB.org is asking employers with scheduled career-fair interviews to keep those conversations going directly “where possible” — but without any guarantees.

The event, set for Oct. 27 – 30 at the Anaheim Convention Center, had announced speakers — including journalist Kara Swisher, Cloudflare co-founder Michelle Zatlyn, and futurist Sinead Bovell — with Cathy Hackl hosting, and GHC co-founder Telle Whitney expected to give a talk.

"I’m in shock. The notice just came in," Hackl told Forbes. Writer and TheLi.st co-founder Rachel Sklar called the conference "a mainstay" on X.

The conference carries the name of Grace Hopper, the Navy rear admiral the service calls a pioneer in computer science. AnitaB.org says 700+ organizations take part each year. The event intended to cover AI fluency with a career fair for hiring — just as tech firms are cutting jobs and ascribing many of the cuts to AI. Its sponsors reportedly include Capital One, CVS Health, Disney, Goldman Sachs, Google, Intuit, and Morgan Stanley.

Looking at its financial history, AnitaB.org lost $14.6 million in 2024 on revenue of $27.5 million and expenses of $42.1 million, according to its annual IRS filings. Its net assets roughly halved while liabilities nearly doubled. “Contributions and grants” fell from $11.2 million in 2023 to just $724,350 in 2024. Attendance also was in decline — from 29,000 in 2023 to 17,000 in 2024 — counting in-person and virtual attendees. The 2024 filing put GHC’s own expenses at $26.9 million against $22.4 million in revenue. The conference's 2024 gap suggests a pattern that may have worsened.

It’s unclear what this means for GHC moving forward — 32 years after Anita Borg and Whitney founded it in 1994. AnitaB.org says it will contact registrants about their fees by Oct. 26, and further announcements are expected in the coming weeks.

The FBI has dismantled a major North Korean laptop farm operation in the U.S. with the help of CrowdStrike, which assisted in the identification and disruption of these networks. According to The New York Times, the agency shut down over 40 laptop farms in Texas, resulting in the seizure of over 200 devices. No arrests have been made so far, as the authorities wanted to take down the operations as soon as possible and did not wait for prosecutors to file charges against those involved, although they also warned that they could face criminal penalties if they continue to be involved with remote fraud.

North Korea has been using fake remote workers to earn income despite the economic sanctions placed against the hermit kingdom. It has been reported that it earns around $500 million a year under this scheme, which affects both startups and Fortune 500 companies. Amazon’s IT department actually caught one North Korean infiltrator after it noticed a 110ms keyboard input lag, betraying their distant location.

The U.S. has been clamping down on this scheme since last year, after it shut down 29 North Korean laptop farms across 16 states that were facilitating the employment of North Korean IT workers across over a hundred U.S. companies. Four U.S. citizens and a Ukrainian national were convicted over scams like this in November 2025, with the Justice Department unsealing indictments against two U.S. citizens and 10 China-based North Koreans recently. Prosecutors said that the last group defrauded more than $3 million from 70 companies over a span of six years from 2019, and that the identities of two more individuals involved in the case remain under seal.

Despite the number of networks that U.S. authorities have been able to shut down since last year, CrowdStrike’s Adam Meyers, who’s in charge of the company’s counter adversary operations, says that this scheme is “increasing both in prevalence and complexity.” He noted that it’s difficult to know how truly widespread the problem is and that even though CrowdStrike has identified an average of two new cases daily last month, it was most probably just the tip of the iceberg.

Facilitators are the one crucial aspect that North Korea’s operations rely on. These are people based in the United States who help bypass security measures by hosting the work laptops that companies send to the fraudulent North Korean IT workers to make it appear as if they’re based in the country. While the job looks like easy money from that start, it would eventually become more complicated as the operation progresses. “They get kind of hooked on it initially, and then more gets asked of them, and they’re kind of in too deep at that point,” Meyers told the publication.

Ukrainian officials have accused SpaceX of limiting its satellite internet services in the country, disrupting ongoing military and combat drone missions that rely on the technology, according to a Reuters report today, October 14. Presidential adviser Serhiy Beskrestnov said SpaceX has implemented a speed limit on its military-grade Starshield satellite terminals in Ukraine, hindering their operations at speeds above 100 miles per hour (160 km/h).

In a Telegram post on Wednesday, Beskrestnov said Elon Musk's company provided no prior warning before implementing the restriction. “Our defense forces did not receive a warning about this limitation, and this led to the disruption of several missions,” he wrote. The restriction specifically impacts the Starshield terminals in motion. The terminals, which are physically mounted onto mobile platforms such as drones and other military vehicles to provide high-speed, real-time communications during transit, have been crucial in guiding Ukraine's drone strikes that have decimated Russian logistics across occupied southern and eastern Ukraine.

Following the limitation, Ukraine's 7th Rapid Reaction Corps, deployed on the southeastern frontline, confirmed they had to cancel multiple missions. “According to our observations, during flights, the connection could drop for 10–20 seconds at speeds of just 110–120 km/h (68–75 mph). The drones kept moving, but these interruptions made them harder to control,” it told Reuters. Volunteer Serhiy Sternenko, citing military reports, said that the restrictions are tightening further, with connection drops occurring at speeds of just 60–70 km/h (37–43 mph). Sources in the country's defense ministry also stated that the limitation complicates logistics and drone operations.

While SpaceX has not provided a reason for the throttling, Ukrainian officials, including Beskrestnov, believe that the move is intentional, as it coincides with mounting pressure from Washington for Kyiv to halt its long-range drone strikes on Russian oil refineries after U.S. President Donald Trump called for an “energy ceasefire.” Musk posted on X on Saturday that “Zelensky has gone too far.” Ukraine is also striking data centers across Russia. The U.S. has been one of Ukraine's major allies since Russia’s full-scale invasion in 2022, providing billions of dollars worth of direct military assistance. Likewise, Starlink has been indispensable for Ukrainian battlefield communications; shutting it down would devastate the country’s frontline defenses.

Meanwhile, Ukrainian defense tech company Stetman is developing an independent domestic satellite communications system called UASAT-NANO to reduce heavy reliance on SpaceX for military operations.

Advanced packaging technologies have become integral parts of advanced CPU and GPU designs, and virtually all of Intel's high-end client and data center CPUs rely on its Embedded Multi-die Interconnect Bridge (EMIB) and Foveros technologies. But what's next for these two advanced packaging methods? Before we try to see where both technologies are going, let's first take stock of where we are today.

Although Intel's advanced packaging saga began in 2017 with the Kaby Lake-G CPUs and Stratix 10 FPGAs, packaging technologies weren't quite the hot topic they are today. However, both Intel and its industry peers were working relentlessly to advance these technologies, so Intel was among the first companies to outline a detailed roadmap for multi-chiplet processors featuring its 2.5D and 3D integration technologies — EMIB and Foveros — back in 2020. Following the fiasco with its initial 10nm process technology, the company made it clear that its multi-chiplet strategy is a sustainable way to deliver performance and feature enhancements cost-efficiently and at a predictable cadence. While we certainly cannot say that all of Intel's multi-chiplet projects were a success, it is safe to say that the majority of Intel's client and data center CPU products launched in recent years use multiple chiplets and rely on EMIB and Foveros advanced packaging technology.

In fact, as Intel's advanced packaging roadmap evolves, EMIB and Foveros not only become more sophisticated to address the demands, but they also become simpler to cover cost-sensitive products. Another peculiarity about Intel's packaging duo is that they no longer seem to be two mostly separate technologies. In many cases, they complement each other and Intel's upcoming process technologies, thereby becoming key building blocks of the company's future products. The ultimate goal is to build system-in-packages (SiPs) the size of 12 reticles using both EMIB and Foveros Direct 3D, but Intel is not quite there yet, so let's take a look at where we are today and what's on the horizon.

EMIB: Fantastic bridges and where to find them

Intel's EMIB has evolved dramatically since entering high-volume manufacturing in 2017. What started as a relatively straightforward way to connect adjacent chiplets without using an expensive silicon interposer has become a sophisticated packaging technology used for data center processors and is set to enable massive AI accelerators 12 times the size of a reticle with dozens of silicon bridges, more than 24 HBM stacks, and advanced power delivery.

The basic idea behind EMIB remains unchanged almost a decade after its first introduction. Instead of placing compute and memory dies on a large silicon interposer, Intel embeds tiny silicon bridges into an organic package substrate only where high-density die-to-die connectivity is required (try to find those bridges!). This reduces silicon usage and allows the rest of the package to use conventional substrate wiring and relaxed bump pitches. Early EMIB implementations used a 55-µm first-layer-interconnect (FLI) bump pitch, which Intel subsequently reduced to 45 µm. Intel's recent roadmaps indicate further reductions to around 36 µm and ultimately 25 µm, which dramatically increases the number of connections that can fit along a chiplet's edge (shoreline density).

In addition to shrinking FLI bump pitches and increasing connection density, Intel is also adding functionality to the bridges themselves. EMIB-M incorporates metal-insulator-metal (MIM) capacitors into the bridge to improve power delivery and suppress voltage fluctuations close to compute dies. The newer EMIB-T takes this concept considerably further by adding through-silicon vias (TSVs), which enable power to travel vertically through the bridge instead of taking longer paths around it. This becomes increasingly important for multi-kilowatt system-in-packages (SiPs) and HBM4-class memory subsystems, where both current density and power integrity are major packaging constraints.

Such SiPs are not only complex, but they are physically large, so Intel's EMIB has evolved to support larger designs. Firstly, a finer FLI pitch increases the interconnect density required to build bigger SiPs. Secondly, EMIB-M addresses the power-integrity problem created by big processors, such as Intel's Xeon 6 'Granite Rapids.' Thirdly, EMIB-T simplifies power routing, lowers impedance, and reduces noise, which will get more important when faster versions of UCIe (32 GT/s and higher) become commonplace. Intel's consistent EMIB technology development has enabled the company to move from an SiP of 4X reticle size on an 80×80 mm package earlier this decade, to something closer to 8X reticles on a 120×120 mm package today, and even larger 12X SiPs on a 120×180 mm package in the future.

Eventually, Intel plans to combine finer-pitch EMIB technology with glass-core substrates. Glass offers better stability and reduced warpage compared with organic materials, which becomes increasingly important as packages grow to enormous sizes. Glass will likely be one of EMIB's biggest advancements, as it will enable improvements in multiple directions, including performance and dimensions.

Intel says EMIB-T is on track for mass production in 2027 and its yields and reliability are meeting its targets. If those ramps proceed as planned, EMIB will have evolved from an alternative to a silicon interposer into one of Intel Foundry's key technologies for building massive AI systems.

"Customer interest in EMIB-T continues to be very high – the technology is compelling, providing capabilities for advanced AI silicon solutions which are not possible with today's mainstream offerings," said Lip Bu Tan, chief executive of Intel, in an August 2026 earnings call. "We continue to have a growing EMIB-T backlog, yield and reliability are hitting targets, and we are focused on ramping the technology into high volume and high quality to support customer ramps in 2027."

Intel is scaling EMIB along several directions simultaneously: finer interconnect pitch, more bridges, larger substrates, denser power delivery, faster memory interfaces, and dramatically larger SiPs. Intel formally positions EMIB as a cost-effective advanced packaging technology that uses local silicon bridges for logic-to-logic and logic-to-HBM connections in an organic substrate instead of large silicon interposers used by solutions like TSMC's CoWoS-S. Meanwhile, there are applications that can benefit from large, rigid interposers or flexible organic redistribution layer interposers. To address these applications, Intel's Foveros portfolio includes Foveros-S, Foveros-R, and Foveros-B, which more directly mirror TSMC's CoWoS-S, CoWoS-R, and CoWoS-L packaging technologies, respectively.

Foveros 2.5D: A fistful of packages

Intel's Foveros packaging technology is evolving from its original chip-on-wafer implementation that emerged in 2019 with the Lakefield processor into a broad family of 2.5D and 3D integration technologies designed to address everything from cost-sensitive client processors to large HPC and AI systems.

The original Foveros technology used an active 22FFL-based silicon base die, or interposer, with through-silicon vias (TSVs) and active dies attached using face-to-face bonding. Intel's Lakefield and Ponte Vecchio used active base dies and 36 µm microbump pitches, whereas Meteor Lake, Arrow Lake, and Panther Lake implementations use a passive base with a 36 µm microbump pitch. Since the introduction of Foveros with active and passive base dies in 2019 – 2023, Intel has also diversified Foveros to address different cost and integration requirements. The latest Foveros 2.5D roadmap — which mentions -S, -R, and -B variants with similar characteristics — presented in mid-2025, takes Foveros considerably further from its original capabilities: it targets pitches below 25 µm, bump density increase from 772 to 1,600 connections per mm², and chip-to-chip interconnect power of 0.15 pJ/bit.

For now, the most advanced and yet production-proven Foveros 2.5D technology is Foveros-S. This packaging technology places multiple chiplets on a silicon interposer and currently supports packages up to four reticles in size, which is below what TSMC offers with CoWoS-S (up to 5.5X reticles). Since Foveros-S is essentially an evolution of the original Foveros from 2019, Intel legitimately says that it has been in volume production since 2019. Yet, going forward, Foveros-S will be evolving in multiple directions, which includes larger package sizes, interconnect density, and interconnect power efficiency.

Foveros-R removes the silicon interposer altogether and uses fan-out RDL technology to optimize cost and interconnect density. Intel says the implementation can use up to three redistribution layers. The method is designed to offer a balance between cost and interconnect density and enable heterogeneous integration of chiplets from different sources through UCIe. Like the other members of Intel's current Foveros 2.5D family, the technology uses 36 µm microbump pitches, but is designed to support pitches below 25 µm as well as bump density scaling from 772 to 1,600/mm² and chip-to-chip interconnect power of 0.15 pJ/bit. Intel says Foveros-R will be production-ready in 2027.

Foveros-B extends the concept toward larger and more complex AI/HPC designs. Instead of relying on a full silicon interposer, the technology combines RDLs with local active or passive silicon bridges that provide high-density connections only where they are needed. This potentially gives Intel an intermediate option between an RDL-only implementation and a large silicon interposer, as well as a direct competitor for TSMC's CoWoS-R.

Note that Intel does not explicitly say that Foveros-B uses EMIB bridges, though given similar microbump pitch sizes and the plan to use active local bridges in the future, Foveros-B's bridges are likely to closely resemble EMIB-M and EMIB-T bridges. If Foveros-B can indeed use EMIB bridges, future Foveros-B designs could also incorporate cache disaggregation, integrated voltage regulators (IVRs), or MIM capacitors.

As for interconnection densities and power, Foveros-B is listed as designed to support pitches below 25 µm, bump density scaling up to 1,600/mm², and chip-to-chip interconnect power of 0.15 pJ/bit. Meanwhile, Intel has not disclosed when Foveros-B will be ready for commercial production, so its actual specifications remain to be seen.

With EMIB and the Foveros 2.5D portfolio, Intel can address a broad range of applications, but Intel's scaling is not stopping. In addition to 2.5D lateral scaling enabled by EMIB or Foveros 2.5D, it can stack chiplets on top of each other using its Foveros 3D and EMIB 3.5D packaging technologies.

Foveros Direct 3D: There will be copper (and no bumps)

Unlike simple package-on-package stacking, which has existed for decades, densely interconnected 3D stacking essentially turns separate dies into parts of the same tightly coupled chip, increasing bandwidth, reducing latency, cutting interconnect power, and shrinking footprint. The challenge is manufacturing thousands of extremely fine connections between dies while maintaining yield, power delivery, signal integrity, and — perhaps most importantly — removing heat from chips buried inside the stack.

The biggest architectural step in Intel's Foveros roadmap is Foveros Direct 3D, which moves from microbump-based connections toward direct copper-to-copper hybrid bonding. Conventional microbumps cannot shrink forever, as solder bumps need sufficient dimensions to maintain manufacturing yield and reliability, while the solder itself introduces additional resistance and parasitic effects. Foveros Direct 3D eliminates solder altogether and directly bonds copper pads on one die to those on another, which enables an interconnect pitch below 10 µm — up to 10 times finer than conventional microbump technology.

Intel claims that Foveros Direct 3D can offer bonding pitch of sub-10 µm, interconnect density of up to 10,000 connections per mm², and chip-to-chip interconnect power below 0.05 pJ/bit. Such dense connectivity shortens signal paths between stacked dies, increases bandwidth, reduces latency, and cuts the energy required to move data. In addition to electrical advantages, direct bonding creates more continuous thermal paths between dies, which Intel says improves heat dissipation compared with conventional packaging, which is crucially important as vertically stacked logic pushes power density dramatically higher.

Intel's Foveros Direct 3D bonding interface supports both face-to-back and face-to-face stacking. In addition, Intel has described chip-to-wafer as well as wafer-to-wafer bonding.

As Intel's schematic drawing of a Foveros Direct 3D-based system-in-package shows (and Intel confirms), the technology is much more flexible than simply placing one logic die on top of another. The method can support different die sizes, multiple stacking levels, and can combine dies manufactured using different process technologies in one SiP.

Intel's Xeon 6+ 'Clearwater Forest' processor is the first product to use Foveros Direct 3D copper bonding in volume to attach Intel 18A compute chiplets to active base dies. The CPU uses the 1^st Generation Foveros Direct 3D with 9 µm copper bonding pitch. Meanwhile, Intel plans to shrink bonding pitch to 3 µm with its 2^nd Gen Foveros Direct 3D. To put these numbers into perspective, TSMC's 1^st Gen SoIC from 2023 supports pitches of 9 µm (AMD's 2^nd Gen 3D V-Cache uses this version), whereas the latest generation that entered volume production in 2025 features bonding pitches of 6 µm. TSMC expects to shrink SoIC's interconnection pitch to 4.5 µm by 2029.

EMIB 3.5D: Everything everywhere all at once

Finally, Foveros and EMIB do not have to operate independently. Intel can combine Foveros 2.5D or Foveros Direct 3D with substrate-embedded EMIB bridges to create what it calls EMIB 3.5D in a bid to build its ultimate products. In this architecture, Foveros provides dense local 2.5D or 3D connectivity within individual chiplet assemblies, while EMIB provides high-bandwidth horizontal connections between these modules, essentially building a massive system-in-package out of smaller SiPs. Intel says the individual SiPs can be identical to scale performance, or distinctive, to enable different functionality.

The combination of EMIB and Foveros is particularly useful because it enables Intel to scale both horizontally and vertically without placing the entire system on one massive silicon interposer. Intel says EMIB 3.5D can achieve a total silicon surface area considerably larger than what its existing silicon-interposer-based technologies can accommodate. Intel first used EMIB 3.5D to build its Ponte Vecchio compute GPU that integrated 47 active tiles manufactured on five process nodes and contained more than 100 billion transistors. Although Intel's Data Center GPU Max can hardly be called a smashing success, it certainly demonstrated what Intel can do with its advanced packaging technologies.

Going forward, the EMIB 3.5D architecture becomes considerably more promising when combined with Foveros Direct 3D. Using both will enable Intel to build tightly coupled compute modules using ultra-dense vertical copper-to-copper connections, and then link multiple modules laterally using EMIB, essentially scaling complex processors both vertically and horizontally. However, surprisingly, Intel decided not to use EMIB 3.5D for its next-generation Xeon 'Diamond Rapids' processor: while the CPU uses Foveros Direct 3D for vertical integration, UCIe-S connections are used for lateral interconnects.

Multi-die another day

While Intel's EMIB and Foveros technologies emerged to serve different kinds of scaling, the company is evolving them into a complementary platform that can scale complex processors both horizontally and vertically.

EMIB is progressing toward finer interconnect pitches, vertical power delivery, glass substrates, and packages exceeding 12 reticles with more than 24 HBM stacks, while the expanding Foveros family spans from 2.5D integration all the way to ultra-dense Foveros Direct 3D hybrid bonding. In fact, Foveros Direct 3D already enables sub-10-µm copper-to-copper connections and is headed toward even finer pitches.

Meanwhile, EMIB 3.5D combines vertically stacked modules with lateral EMIB links to create large heterogeneous system-in-packages for ultimate AI and HPC applications without using enormous silicon interposers.

What is perhaps interesting to note is that, for now, Intel lacks a system-on-wafer platform to enable Cerebras WSE and Tesla Dojo-like wafer-scale processors, though this is explainable, as demand for such technologies is too limited today and may not pick up significantly tomorrow.

Elon Musk's Terafab will get its first EUV lithography tool in the middle of next year, reports Tim Culpan, citing sources with knowledge of the matter. But considering that the shell of the firm's development fab will not be completed until late 2027 and that tools will only begin to be moved in later that year, according to SemiAnalysis, the scanner will sit idle for some time.

Tesla broke ground on its research fab on the north campus of Giga Texas near Austin this past April. SemiAnalysis expects the building to be completed in 15 months and predicts it will take Tesla a further six months to install equipment and production tools, after which it will be able to start processing wafers, or rather experimenting with chip production to get the team prepared for completion of the Terafab volume fab phase 1.

Given how hard it is to get an ASML Twinscan NXE EUV lithography tool these days, storing it for several months before putting it to use may sound like an expensive slack period, but it's not the significant financial problem for Terafab that it would be for a traditional chipmaker.

The projected delivery of a conventional EUV lithography system to Terafab also indicates that while Elon Musk has praised free electron laser (FEL) light sources for lithography tools, the first-generation efforts of his semiconductor production company will rely on more traditional equipment. This is not particularly surprising, as Terafab is set to use Intel's 14A manufacturing technology. 14A will use not only existing Low-NA EUV scanners, but also next-generation High-NA EUV machines.

Using lithography systems with unconventional and largely untested light sources would require substantial modifications to the manufacturing process, extensive equipment qualification, and potentially changes to photomasks and photoresists, which would complicate the transfer of Intel’s 14A technology and delay Terafab's production ramp.

But usage of standard lithography equipment does not mean that Musk's company has no plans to use tools that are different from those used by Intel, though for now it is too early to make guesses about what Terafab engineers might change in terms of fab configuration.

SpaceX announced that it had selected Grimes County for Terafab in early August 2026. By now, there is evidence that work has progressed beyond clearing land, as drone footage published by Joe Tegtmeyer in mid-September showed foundation excavation and rebar cage installation at the northwestern part of the site. This suggests that contractors had begun structural construction by early September, although the precise purpose of the foundation is unknown.

Right now, SemiAnalysis reports that project Terafab Grimes phase 1 will be built in 20 months, equipped in 17 months, and ramped to volume in six months. Analysts project that the fab will begin operation in late 2029 and then ramped to volume by mid-2030.

An interesting note about Terafab's volume fab is that SpaceX reportedly ordered semiconductor manufacturing equipment in April 2026, several months before officially announcing the Grimes County location. This indicates that the company committed to its manufacturing plans well before the public groundbreaking. In addition, this makes the mid-2028 equipment installation forecast plausible, given the long lead times associated with advanced semiconductor manufacturing equipment.

Intel plans to begin risk production using its 14A (1.4nm-class) fabrication process in the second half of 2027 and initiate high-volume manufacturing (HVM) in the second half of 2028 if everything goes as planned. This puts Intel's ramp into HVM at least 1.5 years ahead of SpaceX’s Terafab Grimes Phase 1.

This means that while Terafab will get a proven advanced manufacturing technology, presumably with decent yields, it will not be the absolute best production node at the time, as TSMC intends to start volume production of chips using its A12 fabrication process in late 2029. A12 is a 1.2nm-class process with TSMC's second-generation nanosheet GAA transistors and backside power delivery. Intel will be close to unveiling its 14A-P node at the same time.

Nonetheless, since Terafab will likely exclusively serve SpaceX, Tesla, and xAI, a slightly less advanced node will not be a big problem for the company, as the potential cost increases incurred by using a more sophisticated manufacturing technology will be offset by using an in-house production facility.

An OpenAI employee has been arrested for allegedly planting a listening device in an executive meeting room. According to the Department of Justice (DOJ), 35-year-old Abdulrahman Alfozan, a national of the Kingdom of Saudi Arabia, allegedly installed a covert listening device in the back of a television used in the meeting room and covered it with toilet paper on or about Sept. 28, which was discovered a couple of days later. The FBI’s criminal complaint listed the device as a Seeed Xiao ESP32-S3 Sense, which is available on the seeedstudio.com website for $14 and listed for $24 on Amazon.

“According to the complaint unsealed today, Abdulrahman Alfozan allegedly concealed a covert recording device inside his employer’s executive conference room to secretly capture sensitive communications about the company’s super intelligence technology,” FBI Director Kash Patel said in the statement. “Protecting American SI innovation is essential to safeguarding our nation’s economic strength and national security, and the FBI views that mission as one of our highest counterintelligence priorities.”

The FBI described the Seeed Xiao ESP32-S3 Sense as a “thumb-sized development board with a detachable camera sensor, digital microphone, and microSD storage.” Seeed Studio advertises the tiny device as suitable for embedded machine learning, like intelligent voice and vision AI, although it’s unclear if Alfozan used its video recording capabilities because of its hidden location. Nevertheless, its built-in microphone and support for 2.4 GHz Wi-Fi and BLE should allow him to surreptitiously download any recordings it may have made without physically retrieving the device. Still, Alfozan was reportedly captured on Oct. 5 looking at the back of the television where the device was installed. Furthermore, OpenAI security inspected Alfozan’s unattended bag and allegedly discovered another recording device similar to what was installed in the meeting room alongside his wallet and ID.

The OpenAI engineer reportedly used a productivity tool to find information about OpenAI’s offices, meeting rooms, calendars, external partner meetings, and the locations of its “leadership and exec.” Aside from that, he allegedly used a chatbot under his spouse’s account, which was incidentally closely related to his own, to research how to build, customize, install, test, hide, and remotely access a sound recording device. Some of his inquiries included “build a continuous audio recorder,” “customize Wi-Fi connectivity,” “improve audio and live listening,” and “encrypt the SD card.” Furthermore, the DOJ said that the same chatbot account submitted prompts specifically related to the recording device found installed in the meeting room, including its custom header and unique MAC address.

It’s unclear yet if Alfozan was acting individually, for another AI company, or for a nation-state. Anthropic and OpenAI have accused Chinese AI labs multiple times of distillation attacks, but this is the first time that we’re hearing of a person getting arrested for installing a monitoring device inside the offices of an American AI lab. The FBI said that it’s still investigating the case, though, probably to determine if he’s acting alone or if he’s connected to a wider conspiracy of commercial and industrial espionage involving artificial intelligence labs. In the meantime, Alfozan is currently facing up to five years in prison plus a $250,000 fine if convicted, followed by a three-year supervised release.

Watchers and reporters in the AI space have long pointed out how its circular economy could implode, potentially taking associated investments with it. The IMF has examined those fears in its latest Global Financial Stability Report, and its conclusions are stark. In the IMF's view, the global economy is becoming increasingly dependent on capital flows to the AI world, and a crash could result in harm to global GDP, investment, and consumption by 2028.

The report makes a distinction between "core" AI firms and others in the space. Presumably, core firms include companies like TSMC, Nvidia, and AMD, memory makers like SK hynix and Micron, and storage vendors like Western Digital. Those firms are seeing historical windfalls, and most of their production is currently spoken for years in advance. In other words, in this gold rush, these are the companies making bank selling the shovels.

In theory, if non-core players fail, only their investors would suffer harm, but according to the report, there's still risk to worry about. Hedge funds have increased leveraged positions and margin borrowing, further exposing themselves to the blast radius of a potential bubble pop. Adding to that, the IMF states that the share of single stocks in leveraged ETFs increased to 28%, likely fueled by demand for AI and tech stocks. By our reckoning, a sharp drop in those equities could trigger a massive sell-off by the ETF issuers and lenders, triggering a feedback loop.

The section of the report that focuses on circular investments within the industry further emphasizes the problem. The IMF tracked fourteen public transactions since 2023 and found that $300 billion invested in "financially weaker" AI companies (mostly by hyperscalers), along with $650 billion in debt, accounted for $1.4 trillion in revenue from services and chip orders. Most of those virtual dollars are concentrated in only a handful of companies, too, increasing the risks that if one catches a cold, the herd might get the flu.

The IMF points out that "AI has become an important source of support for the global economy and financial markets." The silver lining is that according to the Fund, this investment has helped prop up global economies at a time when the current war in Iran is affecting energy prices and creating policy uncertainty. Furthermore, a recent global stress test on banks indicated that they'd "remain broadly resilient" even with a combination of an energy pricing shock, higher inflation, and an AI-driven market correction.

As for said correction, the IMF believes it may happen thanks to "weaker or less durable AI investments and profitability," stating that the necessary AI computing may not need such a high investment level, and likewise may be less profitable than expected or hoped. Under that hypothetical scenario, the IMF predicted that global GDP could drop 0.4%, investment by 1.3%, and consumption by 0.6% by 2028. However, the Fund notes that the excessive debt and leveraging flowing through the industry might make for a harsher shock to the system.

Hyperscalers now represent 10% of investment-grade bond issuance, up from under 1% in 2024. Since demand for AI compute remains intense, these companies are also making a mint. Yet they're increasingly strapped for cash due to their outsized investments in AI gear, necessitating substantial loans to keep the business rolling. Only time will tell whether investors will continue to have an appetite for the debt these companies are issuing in order to continue the gold rush.

Apple internally uses a system known as “Minions,” a “virtual GPU cloud” made up of idle Macs, to help manage workloads, as described in a recent arXiv paper published by its researchers. The system helps take stalled AI jobs, some that once had 10-plus-hour company GPU queues, and distributes them to Macs on the network, with resulting wait times down to as little as minutes. A request from a user on the company’s Slack can get the ball rolling, although the researchers observed a common concern about teams’ data running on devices outside their group.

The paper, also shared on Apple’s Machine Learning Research site, describes the system as having a “Minion Server,” a “Minion Executor,” and a “Minion Watchdog.” The system relies specifically on Apple Silicon for AI inference, the only type of work it does. There are two kinds of jobs: embeddings with open-source Hugging Face transformer models, and text generation from large language models through Ollama, which is installed on each participating Mac.

The Minion Server works by splitting submitted tasks into subtasks, stored in a PostgreSQL database. Then, on each Mac, a Minion Executor is run to pull the next subtask over REST APIs; there is no central scheduler in this “pull-based execution model.” The Mac reads inputs from Amazon S3, grabs the model if needed, runs the subtask on its own GPU, then returns the result to S3.

Failures are caught by the Minion Watchdog, which tracks the heartbeats that Macs running work send every five minutes. Unfinished subtasks end up back in the queue when, for example, a Mac goes to sleep, drops off the network, powers off, or a user stops the executor to get control of the machine back. The owner can determine which jobs the Mac takes first, such as preferring one team’s or project’s jobs with “dynamic task binding,” or can rely on the default first-in, first-out scheme.

Our understanding is that each Mac runs a whole model on its own piece of work, and the paper does not suggest that any one model is split across Macs. Running a model entirely on one Mac’s hardware with unified memory should come as no surprise to OpenClaw enthusiasts who practice the same in their own home labs. The volunteer computing angle is also not new, reminiscent of SETI@home’s and Folding@home’s model of distributing small chunks to idle machines, based on the paper’s described architecture.

Aside from the reduction in GPU queue times, the paper also cites that requests with Minions do not require new GPU requests, the infrastructure cost is zeroed, and the scaling effort goes from manual to organic. This does not factor in the electricity costs or hardware wear.

Participation is handled informally, and team members may, for example, “send Slack messages requesting others to temporarily run the Minion Executor and target a specific project or task.” Confidentiality remains a concern even for internal work, which Apple aims to mitigate with team- and organization-scoped deployments with their own databases and separate pools of Macs.

The new paper comes at a time when compute is increasingly a bottleneck for AI-related workloads. Apple acknowledges this in its latest quarterly filing in July, saying that it “depends on access to sufficient computing resources” in an ecosystem with “constrained supply, extended lead times, and increasing costs.” The risk that it may be unable to “secure sufficient capacity,” even with its own data centers and third-party cloud providers, appears to make the Minions approach more practical.

Apple’s Private Cloud Compute, the server side of Apple Intelligence for more complex requests, now also runs on Nvidia GPUs in Google Cloud, first announced at WWDC in June. That came after sudden and high Mac demand for local AI, with then-CEO Tim Cook saying in April that the Mac mini and Mac Studio supply may take months to catch up. Device supply discussed in the July earnings call also mentioned advanced-node constraints and increasing memory costs.

The authors say that, in future work, Minions will extend to distributed model training. More broadly, they write that if idle Macs across organizations “or potentially across the global community” can be “securely federated,” they could form a shared GPU pool. For now, the Macs act as overflow AI capacity only inside Apple.

Ledger is reviewing its sales policies and how its devices reach customers after the “Cryptobilis incident,” in which physical tampering along its supply chain led to $93 million in cryptocurrency theft from user wallets, according to a Bloomberg report on October 13. The crypto hardware manufacturer’s CTO, Charles Guillemet, confirmed in a social media post that it is also reviewing anti-tampering protection on its devices following the incident. He told users with tampered signers (hardware wallets) to contact Ledger support for shipping instructions to send the devices in for further examination. Guillemet also advised users who wanted to physically open their signers to check for tampering to review the company's guidelines on how to do so safely.

In a separate post, Ledger CEO Pascal Gauthier said, “Physical supply chain tampering is a risk for the whole industry. We are reviewing the controls that apply to our direct sales and to resellers, and we will share what we can,” confirming the company's plan to review its chain-of-custody and tamper detection practices. The company is inviting researchers and other vendors to collaborate in assessing these practices.

The incident that prompted the reviews was first noticed on Friday, October 9, when hundreds of otherwise secure wallets drained simultaneously across the Bitcoin, Ethereum, and Tron networks, triggering a massive alert across social media and blockchain security networks. Several victims came online to report losses and try to figure out what was happening. Early investigation revealed that almost all the affected wallets had been procured from CryptoBilis, an authorized third-party reseller covering Malaysia and Southeast Asia. Ledger ordered CryptoBilis to immediately halt all sales and shipments.

After further investigation, Ledger officially announced on October 10 that it had discovered a physical, unauthorized hardware implant equipped with a cellular/LTE module inside an affected user's device. It turns out that the attackers had manually installed the chips in brand-new devices at some point in the supply chain. When a new user is generating or verifying their 24-word recovery seed phrase on the wallet's screen during initial setup, the rogue chip records the OLED output or reads the raw hardware data lines carrying the text.

The integrated cellular chip then silently transmits the unencrypted recovery phrase over LTE networks directly to the attackers' external command servers, granting them full remote access to clone the private keys and drain the associated cryptocurrency funds. The hardware modification completely bypassed Ledger's standard automated “Genuine Check” software protocol, as it sat entirely outside the microchip running Ledger's standard firmware.

Ledger has stressed that its internal infrastructure, firmware code, and direct-to-consumer sales channels through Ledger's Official Store remain entirely uncompromised. The company has advised users with wallets from third-party resellers to transfer funds to a separate wallet secured by a freshly generated, uncompromised 24-word recovery phrase while it conducts further investigations. It is also restructuring its vendor verification and supply chain requirements, instructing distributors to enforce tighter tracking, source only through approved channels, and strictly ban the restocking or resale of returned units.

Anthropic's Frontier Red Team has demonstrated two components of a potential AI-powered worm that, if key issues with the practical implementation are resolved, could spread across consumer devices including inexpensive Android TV boxes and smart appliances, hypothetically without relying on datacenter infrastructure. The experiments show that AI agents can configure inference across different hardware platforms and orchestrate replication across a network. There are still some serious limitations, though, particularly around the gap between the capabilities of today's models versus the hardware constraints of potential targets.

In its October 12 report, titled "AI models may be capable of self-replicating without access to a datacenter," Anthropic examines a potential class of malware called a weight-based replicator. Rather than distributing conventional executable code, it would propagate AI model weights alongside the software infrastructure needed to run inference on compromised devices, and it's not pure theory, either.

The researchers demonstrated that Qwen3.5-9B can establish an inference server on eight of nine tested hardware types, spanning NVIDIA Jetson Nano devices, Apple Silicon Macs, Xeon servers, a Google TPU, AMD's MI300X, and at least one PC with an RTX 4090, among others. They also ran Gemma4-26B-A4B across 15 off-brand Android TV boxes costing approximately $55 each, achieving a combined generation rate of 0.39 tokens per second.

Anthropic also tested whether a model could orchestrate replication across a network. The experiment used eight Google Cloud nodes running Android 15 emulators. A seeded agent propagated between nodes through an unauthenticated Android Debug Bridge (ADB) port, transferring files and establishing an inference server on each target. GLM-5.2, a titanic model with 744 billion parameters over 1TB in size, completed the replication sequence across all eight nodes in four of five trials, reaching seven nodes in the remaining trial.

No other tested open-weight model was able to complete the sequence at all without orchestration from GLM-5.2, and they weren't able to meaningfully ask a larger model for help, either, despite being equipped with the capability. Naturally, GLM-5.2 wasn't actually replicating itself onto smart TV boxes; instead, it established an inference server for the much smaller Llama 3.1 8B on each target. GLM-5.2 ran on a separate inference server because the emulated nodes couldn't handle the larger model.

In other words, Anthropic demonstrated that a very large, externally hosted model can orchestrate a simplified replication sequence across machines with deliberately exposed ADB interfaces, and it showed that small models can set up inference, but it absolutely hasn't proven that a small model can independently replicate itself across consumer hardware. That distinction, admittedly fine, is nevertheless pretty important when the entire premise is that small models could soon spread without relying on datacenter infrastructure.

The experiments expose a central constraint of the research: models capable of reliably completing the replication sequence aren't currently small enough to run on inexpensive target devices, while models that fit within those devices' memory budgets, despite being capable of setting up inference, still can't reliably complete the entire process. This gap between the intelligence needed for reliable replication and the maximum model size that target hardware can support means that this threat model isn't really applicable right now, but Anthropic warns that gap could narrow, and potentially soon.

Using historical improvements in general AI capability (measured through the Epoch Capabilities Index), Anthropic projects that a model within the 30-billion-parameter class could reach the necessary capability level by the end of 2026. But once again, that's a hypothetical, and it still doesn't address the substantial efficiency problem.

Based on measured inference performance and the token requirements of its setup experiments, Anthropic estimates that establishing an inference environment would take approximately 28–51 hours for Qwen3.5-9B and 71–150 hours for Gemma4-26B-A4B at the observed throughput of its cheap Android TV boxes, and it’s worth noting that the boxes it used are configurations with 4GB of RAM, a relative luxury among these cheap set-top boxes. A model-driven replicator could take days to establish an inference environment on a new host, whereas conventional malware can execute its payload within seconds when its assumptions about the target hold. A conventional worm can spread to tens of thousands of vulnerable targets in minutes.

Model weights also impose substantial bandwidth and storage costs. Anthropic acknowledges that compromised hosts could download weights from external repositories rather than receiving them directly from an infected device, and that not every compromised machine would need to host a model. Nevertheless, distributing multi-gigabyte payloads and maintaining communication between machines running a sharded model would generate tremendous network traffic, making the activity very easy to detect.

Anthropic estimates that a hypothetical fleet comparable in size to the infamous Kimwolf botnet, which infected more than two million devices at its peak, could support approximately 133,000 instances of Gemma4-26B-A4B if every 15 devices collectively hosted one model. At the measured throughput (which benefits considerably from LAN latency), Anthropic says that such a fleet would provide roughly 40 Hopper H100-equivalents of inference capacity.

This is a very messy comparison that takes significant liberties with the data, but even at that it must be said that it's not particularly intimidating. We're talking about two million infected devices that must remain infected and operable for months (or years; infecting 2 million devices would take around 6,300 agent-years even with the fastest 28-hour stand-up speed) to produce less AI inference throughput than a few blades of previous-generation GPUs.

Anthropic notes that a real replicator could exploit other types of hardware, deploy different models across heterogeneous devices, and avoid loading model weights onto every compromised host. Those possibilities certainly complicate the comparison, but arguably they complicate it beyond the bounds of this discussion.

The research does identify a meaningful security concern: AI agents could make malware more adaptable to unfamiliar environments and reduce the amount of target-specific engineering required. A hybrid approach, combining conventional code for routine operations with AI-driven reasoning when unexpected conditions arise, could offer some of those benefits without requiring every compromised device to run an inference engine continuously. This is definitely concerning, but we've already seen AI agents using frontier intelligence write their own code to compromise secured systems, so that specific angle on this research isn't exactly groundbreaking.

Anthropic's experiments are useful in that they provide evidence that autonomous software configuration and simplified network replication are increasingly feasible, but turning those capabilities into a materially more dangerous class of malware will take improvements in model reliability, hardware compatibility, deployment efficiency, and the ability to overcome real-world security controls, which the firm didn’t test. There’s a ton of data and some interesting findings not discussed here in the actual paper, so if you’re interested in the research you should go check it out.

A person claiming to be responsible for the recent hack of Denmark’s national registry claimed that they were able to gain access because of an IT company’s weak password. According to The Register, Pays ApS, an IT and data services company that lets customers reference the Danish Central Register of Persons (CPR) was the source of the hack, with the hacker saying that one of the accounts they breached was only protected by a ‘123456’ password.

Copenhagen has confirmed the hack, with a government official saying that the breach was discovered 12 days after it happened when an invoice revealed an unusual amount of activity from the company. The Danish government said that 8.8 million records have been illicitly retrieved, although the hacker claimed that they do not intend to sell or leak the information. While the government did not mention which company the hacker exploited, Pays ApS managing director Sophie Laursen confirmed that the hacker used the company’s access to the database to exfiltrate the data.

This incident shows that a system’s security is only as strong as the weakest link, with the Danish database getting compromised through a third-party user’s poorly chosen password. Given that CPR essentially contains information on virtually every Danish citizen, one would expect that it would require users to at least have a more complicated password, if not two-factor authentication. This isn’t the first time that an incident reveals shortcomings in government database protection, though — Indonesia realized that it had no backups when a ransomware attack in 2024 affected over 230 public agencies, while South Korea lost 858 terabytes of data after a catastrophic fire at a government data center last year.

We expect the Danish government to overhaul its password policies in the near future, especially now that AI is making it easier and more efficient for hackers to hit their targets. Recent incidents like the attacks on South Korean banks and against the Taiwanese government were reportedly conducted with the help of AI agents. Hackers also breached OpenAI using Claude tools, showing just how potent AI has become as a hacking tool in the wrong hands. But with a password like “123456,” potential hackers wouldn’t need to use any AI tools to break into weakly protected systems.

Last week, OpenAI shared over 400 results for longstanding mathematical formulas that have troubled humans for decades; now researchers say it could take years for humans to validate and prove all the AI-driven work. But while analysts try to check the work of the unnamed OpenAI internal frontier model that found these alleged solutions, mathematicians argue this kind of AI-powered research and release threatens the field itself.

There's even the claim that by leaping from a prompt to a solution without a clear working in between, there is little benefit to these solutions in the first place. Mathematicians and the field itself may not be able to build on the results, as they don't know how they were achieved, nor has the development of such solutions produced new fields of mathematical understanding.

To make matters worse, there are suggestions that the growing confluence of mathematical and cybersecurity capabilities of frontier AI could bring into question the sanctity of cryptography itself. Alongside raising serious concerns for national security and modern digital systems, it could also put trillions of dollars worth of cryptocurrency at imminent risk.

Over 722 manuscripts dropped

The official count for OpenAI's maths solution release was 722 manuscripts, but with only 300 of the results formalized — digitizing human-readable formulas so they can be computer-verified — it's very difficult to know what's legitimate research. Some of the researchers The Verge spoke to found nonsense explanations within the reports, formulas that don't make sense, and some research that has already been completed by others.

The burden of proving the veracity of OpenAI and its model's claims is very much on the mathematical community, too. OpenAI didn't release the name of the model it used to generate these results, nor the prompts that generated them — despite those recommendations from the Advisory Group on Mathematicians and Artificial Intelligence (AGMAI).

OpenAI has at least published changelogs for its GitHub repository of the results, making it clearer where mistakes were made. But with several retractions and corrections already, it seems clear that it didn't check over the results in any great detail before releasing them.

It's this brashness, the pace of releases, and OpenAI's seeming disinterest in remotely slowing down its progress in the field that have so many mathematicians up in arms.

A looming threat becomes immediate

Mathematics isn't the first field to suffer a major upheaval because of AI. Writers first felt the looming threat with the release of ChatGPT in 2022, and both visual and musical artists faced the same concerns shortly after. Artificial intelligence is impacting almost every industry in some way, but while there are some potential benefits for its use in the field of mathematics, researchers within the field are concerned about the impact this may have on the collaborative nature of the field and how it might discourage future mathematicians from exploring it.

Speaking to Business Insider, mathematics professor at NYU, Tristan Buckmaster, said that OpenAI's release had upended the world of many early-career mathematicians, suggesting it had wiped out their whole research programs. Alongside their own research being invalidated by some of OpenAI's solutions, he suggested that some were now fearful that, by using AI tools, researchers had inadvertently contributed to the results, raising the potential of them not using advanced tools in the future and being less likely to talk about their research publicly.

This could cause huge problems for mathematics as a whole, argued mathematics professor at Northwestern University, Bryna Kra. Highlighting how collaborative maths is, with researchers sharing ideas, research data, and formulas, she is now extremely concerned that OpenAI has made mathematicians more secretive, reducing their ability to work together towards solutions and build on each other's research.

Professor of mathematics at the University of California, Terence Tao said on Mastodon that "many fewer seminars, workshops, collaborations, or other activities are being generated from these results compared to traditional breakthroughs." He argued mathematics may need to pivot to value "progress more holistically," by elevating the role of community building over raw solutions.

Researchers have described an atmosphere of extreme anxiety, that whatever they're working on could be invalidated overnight. That can undermine job applications, grant proposals, and those building the early components of their academic careers. Understanding and developing core mathematical capabilities at a high level can take years. AI dumps eradicating years of effort in such a haphazard fashion risk making it seem a near-pointless endeavour.

With swirling rumors of yet more releases from OpenAI, and potentially companion releases from other AI labs chasing the same headlines, the morale in many mathematical fields is extremely low.

Cryptography

With how much mathematics underpins key components of our digital world, it's no surprise that major changes in the pace of its development could have a domino effect elsewhere. One proposed potential minefield is the fertile ground of cryptography.

Associate professor of computer science at Johns Hopkins Whiting School of Engineering, Matthew Green, said on social media that he believes the congruence of mathematical developments and cybersecurity capabilities could result in AI that can defeat encryption as we know it.

While that raises potential concerns for all manner of fields that rely on encryption to protect the security of sensitive data, it could open up the cryptocurrency industry to wholesale theft, potentially impacting trillions of dollars of digital tokens.

Not everyone agrees with Green that this is an inevitability. British cryptographer — and one of the few people accused of being Bitcoin developer Satoshi Nakamoto — Adam Back, described these claims as nonsense and that there was little suggestion that just because AI can do certain mathematics now, that they'll be able to defeat encryption at some unnamed date in the future.

Ethereum creator Vitalik Buterin urged calm too, stating on Twitter/X that nobody should rush to move their tokens to a new wallet or panic over wallet security. However, he did argue that AI had the potential to one day be a far more dangerous threat to the sanctity of cryptocurrency than quantum computing.

While it doesn't appear that cryptography is a field AI is set to invalidate just yet, it's clear that it poses a potential threat to mathematical researchers and the core underlying 'way of doing things.' Much like it has just about everywhere else.

Researchers are adjusting to it, but the dust isn't likely to settle anytime soon, and in the meantime, OpenAI and its contemporaries may just move on to a new field to focus on as it continues to chase relevance and profitability.

Cryptocurrency enthusiasts are often advised to acquire a hardware wallet like one of the Ledger models, theoretically adding the ultimate layer of security. And yet not even that approach is 100% safe, as a currently unfolding investigation revealed that Ledger resellers in Southeast Asia and Europe are carrying hacked wallets laden with eSIM and LTE modules for exfiltration of recovery phrases. At least $93 million (and going) has already reportedly been siphoned off users' balances. Hardware sold directly by Ledger is so far unaffected.

The first booby-trapped wallet originated from reseller Cryptobilis, which has currently ceased all sales of hardware wallets of all brands. The offending unit came from a store in Malaysia, though Cryptobilis also sells in Indonesia and the Philippines. Researcher Mark Karpelès has been looking into hacked wallets for a while now, and one of them came via Amazon Japan, bought from a Chinese seller and shipped from Malaysia. Cryptobilis also sells other brands of wallets such as Trezor, Coolwallet, and Tangem, though so far there is no news of contamination beyond Ledger.

Ledger recommends that users who bought wallets from Cryptobilis in the past 90 days do not perform their initial setup, as that's where the private key exfiltration reportedly occurs. To those who have already done so, Ledger recommends moving their assets to a new wallet with a new seed. The data theft is performed by hiding a flexible PCB under the display assembly to capture the pixels showing the private key entered during the first setup. The information then heads off to the aforementioned eSIM+LTE combination for exfiltration.

Meanwhile, new reports have come in from Europe, as techie Johannes, seemingly located in Germany, found at least two hacked wallets at MediaMarkt, a well-known tech and appliances retailer. He bought one unit and found that it had the same exact hardware modifications as its Asian counterparts, suggesting that the Asian and European events may be related. Johannes then bought a second one at a different MediaMarkt location, and sure enough, it was also modded.

Notably, most hacked wallets found so far are confirmed to be brand-new units instead of returns. Malfeasants will often buy new hardware and return it to the store with modifications, so that the next unsuspecting customer gets bit.

Johannes had a MediaMarkt employee at one of the stores look up the wallets' stock information, and at least the one unit came from Aqipa GmbH, a Europe-wide distributor — meaning the blast radius may be much wider than MediaMarkt or Germany. Since this is a recent development, Ledger hasn't published any guidance, though we imagine it'll be similar to the aforementioned steps.

In his blog post, Mark Karpelès notes that earlier iterations of hacked wallets with eSIM cards had hand-made implants, but that, concerningly, the hardware bolt-ons inside the latest finds appear to be manufactured to order. If true, this might mean that there's a production line somewhere intercepting Ledger wallets and manufacturing hacked wallets in bulk.

Ledger publishes instructions detailing how users can verify the physical integrity of their wallets, including pictures of every component, for every model of wallet the company offers, so they can closely inspect for add-ons. Likewise, prospective users should only download software from the official Ledger website URL — a task made harder by numerous scam sites with booby-trapped software, pretending to be the official ones.

At OCP 2026 today, Intel has given Crescent Island a proper name: the Intel GPU D-Series. For the first time, the company also shared a theoretical peak performance target for the chip: 1.3 PFLOPS of FP8. It also disclosed the final memory bandwidth to expect from these AI inference accelerators: 1.5 TB/s. This new information adds to the slow drip of disclosures Intel has made about Crescent Island throughout 2026.

We already knew that Crescent Island is the first chip to be built on Intel's Xe3P graphics architecture (or massively parallel computing architecture, really, as the chip lacks most or all of the graphics-specific functional units that would characterize a true GPU). Intel will release GPU D-Series products in an air-cooled PCIe form factor with a TDP of 350W and up to 480GB of LPDDR5X memory.

For reference, each of the 72 Rubin GPUs in Nvidia's Vera Rubin NVL72 rack-scale system can push a theoretical 17.5 PFLOPS of FP8 throughput, but each of those chips requires liquid cooling and likely has a TGP measured in kilowatts. In any event, it should be clear that the GPU D-Series is targeting an entirely different performance envelope and infrastructure requirements than Rubin.

Intel GPU D-Series products, in contrast, are meant to be deployed in traditional air-cooled rack servers scaling up to eight GPUs, like Nvidia's HGX systems. In such a configuration, Intel says that GPU D-Series chips could power inference on models spanning more than one trillion parameters. Intel also highlights the chip’s suitability for “large language models, long-context inference, and concurrent inference services," creating a foundation for "multi-tenant AI services, robotics, and other latency-sensitive workloads."

In order for that kind of distributed inference deployment to work, in which multiple GPUs will presumably act as one, Intel will also need a robust software stack. The company is highlighting the “common Xe software foundation” it’s already built for AI across its Xe Cores in iGPUs and discrete GPU products.

To bring Xe accelerators into the data center, Intel says it’s working with “a broad ecosystem of OEMs, ODMs, neoclouds, independent software vendors, appliance providers, and AI-native companies.” To that end, Intel says customer sampling of GPU D-Series products is in progress, although it didn’t provide any details of a broad launch.

It’ll remain to be seen whether the Intel GPU D-Series is the product that finally gives Intel an AI accelerator toehold after the failure of the Data Center GPU Max family (code-named Ponte Vecchio) and its mothballed follow-ons, as well as a series of silicon acquisitions that failed to coalesce into a coherent AI strategy.

But in a world that’s hungry for every compute cycle for inference, a product with a proven architectural shape that’s targeted specifically at that workload in an accessible thermal and electrical envelope might be just the recipe Intel needs to start getting traction as AI models keep advancing in intelligence and agentic workflows assume ever-greater importance in the computing vision of the future.

Following the release of TypeSafe's Jev, many AI players have launched competitors, and many similar open-source projects from the likes of Microsoft, OpenAI, and many more have since emerged. Yet Nandakishor Mukkunnoth, of ConvAI Innovations, said that he'd come up with the base notions for Jev's architecture over a year before. So who copied who?

As a quick recap, Jev and its clones are APIs driven by decision models, for use in your own code. You feed them input data like a customer's online shopping transactions, alongside questions. For example: "is the user looking for a return?" for a yes/no answer, and "Which item are they referring to?" with a multiple-choice answer. A Jev-like AI model will answer each of the questions or choices with a probability that can then be used in your own "if" statements. While the concept by itself isn't strictly new, this practical application is indeed novel.

The release made a good portion of the internet abuzz, as the seemingly simple concept is extremely useful and neatly sidesteps the usual problems with trying to coax an LLM into giving a direct answer. There's no token-by-token text generation, no conversations, no re-feeding of conversation history, no feeding it gobs of information for context, and no conversational hallucination — just direct data in the form of probabilities. It's really fast too, answering questions in parallel, and exceedingly cheap compared to standard LLMs.

Along with Jev, TypeSafe also released a brief description of its System One architecture. Only days later, Nandakishor Mukkunnoth, founder of ConvAI Innovations, explained in a blog post that he'd actually "designed the Jev architecture one year back." He referred to his arXiv paper on SalesRLAgent, describing how he created a model for probability estimation, published on March 30, 2025. Along with the paper, he published his model weights and dataset. He later followed up with a September paper that more closely aligns with how Jev operates.

In his write-up, Mukkunnoth expressed his frustration, describing how he got overlooked "because [his idea] was built for a vertical use case," meaning SalesRLAgent was made to fix one specific problem, while TypeSafe made Jev a general-purpose decision engine. Notably, the company released Jev as a complete, hosted product, with its own servers, API, documentation, developer kits, and naturally, pricing.

Nevertheless, he said he opted to not remain bitter about the development, and ultimately released the open-source Laya roughly the same day. Laya is essentially a Jev-style model and interface that you can run on your own hardware, complete with multi-language support. Likely adding to the confusion, both TypeSafe and Mukkunnoth called their models "System 1/One" types, likely as a reference to Daniel Kahneman's book, Thinking, Fast and Slow, which describes System 1 as a faster, emotional mode of thought, whereas System 2 refers to slower, more logical thought.

Regardless, there was no stopping the hype generated by this new use case for AI, so players in this space did what they do best: copied the concept and rolled out their own services. In the space of only a few weeks after Jev was released, the world saw Microsoft Decision, OpenAI Decisions API, and Fastino Glide. Both Cloudflare Clef and Perplexity Decider arrived with open weights, meaning you can run them on your own gear, assuming it's up to the task. Over on the open-source aisle, there's Amazon's Strands Decider 2B, Kev, Von, Jeff, jevlike, SemIf, OpenJev, NanoJev, Ollaya, and many others.

Why so many? Well, because most of these projects, including the commercial ones, share the same root: taking an existing lightweight model, usually always a variant of Alibaba's open-source Qwen, and adding the extra layers that shape the input, output, and decision-making procedure. Mukkunnoth's Laya is based on encoder-only models from the Bert family, rather than an LLM like Qwen. And yet it's not too hard to imagine that many folks might have cribbed at least some notes from his work, given how quickly the alternatives materialized.

When GlobalFoundries abandoned development of leading-edge nodes in 2018, it also canceled the development of its 7nm-class FinFET fabrication technology that it co-developed with IBM. Almost exactly eight years later, GlobalFoundries is back with a 7nm-class manufacturing process, only this time the node relies on planar transistors and fully-depleted silicon-on-insulator (FD-SOI). It is aimed at automotive, edge AI, industrial, robotics, sensors, and other devices that prioritize low power consumption, high integration, and cost efficiency over maximum performance.

"We are delighted that GlobalFoundries has announced its next-generation 'single-digit FD-SOI; node, building on technology that CEA-Leti has been advancing for decades," said Sébastien Dauvé, chief executive of CEA Leti. "Through the FAMES pilot line, CEA-Leti continues to shape the future European roadmap for FD-SOI and works every day to promote its adoption in a wide range of applications."

FDX Fusion enters the scene

The new fabrication technology from GlobalFoundries is called FDX Fusion, and it promises 7nm-class 'digital performance' (an ambiguous claim; we will try to interpret it later) and more than twice the transistor density of its original 22FDX process introduced circa 2015. To achieve its target goals, the first generation of FDX Fusion will combine innovations in SOI substrates, transistor structures, and back-end interconnects, though keep in mind that GF has not disclosed transistor dimensions, contacted gate pitch, minimum metal pitch, or actual transistor density. GF plans to launch production of chips on FDX Fusion at its Dresden, Germany, facility in 2028, which will mark its return to the development of substantially more advanced FD-SOI fabrication processes after years of efforts to enhance its existing 22nm-class platform.

In addition to improved 'digital performance,' FDX Fusion will support RF transistors with fT/fmax of up to 0.5 THz, analog and mixed-signal circuitry, embedded nonvolatile memory, and in-memory computing capabilities. The combination is intended to enable highly integrated chips that can process information from sensors, execute AI inference workloads, control external devices, and communicate wirelessly without the need for numerous separate ICs. GF also intends to provide processor IP based on MIPS, ARC, and RISC-V architectures to accelerate the development of such products.

That being said, the main selling point of FDX Fusion is not necessarily its 7nm-class 'digital performance' and high-performance processor IP, but rather its ability to combine relatively powerful digital logic with RF, analog and mixed-signal circuitry, embedded nonvolatile memory, and in-memory computing capabilities on a single chip. This combination — which is why GloFo calls the process technology FDX Fusion — could enable highly integrated SoCs that would otherwise require multiple chips manufactured on different process technologies, which means additional tapeout costs as well as power consumption.

GlobalFoundries said it plans to deliver initial demo silicon for customer evaluation in early 2027, followed by the first process design kit (PDK) in mid-2027. The company is already collaborating with several customers on the technology, although it has not identified specific products or committed production volumes. Specifically, GF mentioned in its press release that BMW, Bosch, Infineon, NXP, Siemens, and Tesla have expressed interest in FDX Fusion, though only Infineon specifically confirmed collaboration on future 7nm-class technologies for radar and wireless communications.

Significant development

The GlobalFoundries announcement represents a significant development for GF's FD-SOI roadmap. The company introduced 22FDX in 2015 and announced its 12FDX successor in 2016, which promised performance comparable to 10nm FinFET, full-node density improvements, and up to 50% lower power consumption. Initial 12FDX customer tape-outs were expected in 2019, but the technology has never been adopted for commercial products. Instead, GF focused on enhancements to 22FDX and eventually the introduction of 22FDX+ (around the time when Samsung unveiled its 18FDS+), with additional capabilities for RF, embedded memory, and low-power applications. Interestingly, 12FDX was still a part of GF's investor roadmap in 2022.

FDX Fusion could therefore represent the realization of GF's original ambitions to scale FD-SOI beyond 22nm-class, although the company has not explained whether the new technology incorporates developments from its earlier 12FDX program. Meanwhile, CEA-Leti, which has conducted research into advanced FD-SOI technologies for years, describes FDX Fusion as a single-digit FD-SOI node. Nonetheless, GF's claim of 7nm-class 'digital performance' does not necessarily imply comparable transistor dimensions or integration density.

From an industry perspective, FDX Fusion could provide an alternative to advanced FinFET and perhaps even gate-all-around processes for applications that require reasonably powerful digital logic alongside RF, analog, and embedded memory. Such integration could reduce system complexity and power consumption, particularly in edge AI devices where energy efficiency and real-time responsiveness are critical.

Perhaps more importantly, the technology could give European semiconductor developers access to a more advanced locally manufactured process without dependence on leading-edge foundries in Asia or the U.S.

Questions remain

However, the commercial success of FDX Fusion will depend on its actual performance, density, production costs, and the availability of a competitive design ecosystem, the latter arguably being the hardest part given the lack of any comments from leading electronic design automation (EDA) and IP developers.

When GlobalFoundries claims 2X 'digital performance' comparable to that of 7nm-class technologies, it doesn't reveal whether it means transistor switching speed, standard cell performance, achievable operating frequency, or something else. In addition, the company promises more than twice the logic density of its first-generation FDX technology, although the firm does not reveal any actual numbers. Consequently, it is unclear how FDX Fusion will compare with established 7nm-class FinFET technologies, which offer mature defect density and yields, plenty of supply, an established design ecosystem, and predictable pricing among other things, in terms of transistor density, performance, power consumption, and production costs. While CEA-Leti's comment seems promising, actual numbers are always better.

The very fact that GF's FDX Fusion planned 2028 introduction will come roughly nine years after the originally anticipated first 12FDX tape-outs illustrates significant technological and/or financial difficulties associated with the development of FD-SOI beyond the 22nm generation. So, while GlobalFoundries' FDX Fusion initiative certainly looks promising, it remains to be seen how that is going to play out for the contract chipmaker and its clients.

For now, the lineup of supporters from the automotive and industrial sectors — BMW, Bosch, Infineon, NXP, Siemens, and Tesla — certainly looks promising. But what about other applications? While established semiconductor companies such as Infineon and NXP already possess extensive processor IP portfolios, smaller fabless developers could benefit from GF’s MIPS and ARC processor cores, RISC-V expertise, and custom chip design capabilities, which could substantially reduce the cost and complexity of developing specialized SoCs. Still, to attract a broader range of chip developers, GlobalFoundries will need a mature ecosystem of EDA tools, standard-cell libraries, memory compilers, interface IP, and other silicon-proven building blocks. These are the things that GlobalFoundries has not touched upon in its press release.

An ongoing cyberattack has reportedly struck major supermarket chains across Europe, disrupting refrigeration control systems. The attack — which has reportedly seen units set to continuously defrost at 50 degrees Celsius — is also apparently affecting deliveries and in-store pricing powered by electronic shelf labels, La Dépêche reports.

According to the report, "several" major retailers have been targeted by a cyberattack, which has hit Danfoss-made refrigeration controls used by French supermarkets Intermarché, Leclerc, and Coopérative U. Indeed, the latter's president, Dominique Schelcher, has confirmed as of Monday morning that "A cyberattack has been affecting the refrigeration production facilities of several stores in our network since last night." Schelcher said teams had been mobilized to fix the issue and promised an investigation into the incident, which he described as "extremely serious."

The report states the units have had their automatic defrosting systems activate, but that hackers have also configured the function — which normally runs for just two hours a day — into a continuous, forced defrosting cycle with the temperature set to a toasty 50 degrees Celsius. Sources reportedly described "a wave of heat" escaping from fridges and freezers

Early reports indicate losses are already approaching 300,000 euros, but could run into several million euros nationwide. Separately, NOS in the Netherlands reports that several supermarket chains have been hit with a malfunction due to a cybersecurity incident on Sunday afternoon. Albert Heijn, Etos, and Gall & Gall have had their websites taken down, impacting deliveries. Electronic shelf labels have also been compromised, disrupting in-store pricing. "We have immediately taken measures to protect our IT environment. Part of this involves the preventive temporary shutdown of a number of systems," Albert Heijn said in a statement.

It is unclear at this stage whether the two attacks are related or how widespread either of them is. Belgium's Delhaize chain, which belongs to Albert Heijn, has also reported disruption. Images online show stores in Amsterdam warning customers about price problems, and the suspension of loyalty programs that have also been impacted by the incident.

Developer Maurice Heumann details in his blog how AI agents were able to decompile Call of Duty: Modern Warfare 2 (2009), which the post calls only “a popular first-person shooter,” in an effort to rebuild its source code in C++, taking three months and over 500 billion tokens. The agents originally attempted to cheat a byte-for-byte check added after the first month of code ran, but it was “semantically wrong,” and now 99% of the game’s functions are present, with 83% matching the original.

Heumann’s goal since the beginning has been to produce an “accurate, stable and feature-complete recreation,” and he wrote on X less than two weeks ago that the game was fully playable. By his estimate, the project consumed 600–700 billion tokens. In another X post, he said he doesn’t pay for his own tokens but put the cost at about €1,200 with subscriptions or $200,000–$250,000 with API tokens.

The difficulty turned out to be not in producing the code but verifying it was correct, forcing the developer to create a check and to make sure the agents couldn’t game it. The other challenge, as he put it, was that “corporate America was here to ruin our fun.” Two earlier blog posts on the project have since been removed, and the video in his Sept. 30 X post was disabled after a report by the copyright owner. No comment from Activision is currently on record.

The method includes pointing a fleet of agents, using Anthropic’s Claude and OpenAI’s Codex, at the original 2009 PC game. Heumann started with a Claude Max (20x) plan, then added Codex Pro and used both simultaneously. These are flat-rate plans and do not use per-token API billing. “Most of the time,” the developer relied on Sonnet 5, although Opus 5.5 and OpenAI’s Luna, Sol, and Terra were “also used a lot.” For harnesses, he relied mostly on the Claude Code and Codex CLIs.

The agents read the code through IDA using Hex-Rays’ ida-mcp server. The work started with four agents, three workers and a reviewer; by Sept. 30, he wrote on X, it had “17 agents working in parallel.” Keeping all of this straight meant having one GitHub issue per translation unit, and having all agents and humans share a single Discord channel with GitHub webhook CI posts. The developer also tuned the auto-compaction threshold, from the default 90% of the context window maximum to 42% to help clear finished functions out of memory. Agents also had a tendency to drift, so an hourly cron job had them reread instructions.

The agents’ first month of code was extremely readable but “semantically wrong” for multiple reasons: wrong function signatures, types and struct layout issues, and logic that was invented or removed. The agents also made unneeded architectural changes, such as turning a config-variable lookup into one that was “orders of magnitude more expensive.”

The reviewer agent failed to stop this as there were no objective acceptance criteria, and the workers’ comments justifying their changes “effectively acted as unintentional prompt injection,” Heumann says. The team eventually stripped all comments from the source, which “helped a lot,” he wrote on X. An attempt to salvage the first four weeks of code took more time than starting from scratch would have.

The answer was an automated check, so the team switched to the same compiler used to build the original game. A script compares each rebuilt function against the game’s executable at the byte level. Calls and data references are at different addresses and are skipped, with target symbols and offsets checked instead, but data and types get the same check. Then it’s a simple matter of pass/fail for each function, and every match is recorded so CI can recheck it and alert on regressions. While this type of matching is slow and sometimes challenging, a match guarantees identical behavior down to reproducing original bugs.

Even with this positive change, the agents immediately tried to cheat by writing “inline assembly” and “repeatedly tried to modify this script to exclude their function from comparison,” Heumann wrote. Inline assembly and similar shortcuts were then banned in the agents’ instructions, and CI now hashes the verification script and checks it against a hash stored as a GitHub Actions secret. Agents cheating is an expected outcome at this point with many such reports, such as when OpenAI’s models went after ExploitGym answers instead of solving the capture-the-flag challenges directly. As Heumann discovered, having a reviewer agent alone may be insufficient.

Before the check was originally implemented, the cheaper, less capable Haiku and Luna models “produced extremely bad results,” the post says. With the check in place, there was sufficient feedback to make the models work, which cut costs and let the project scale up. After almost two more months, 99% of the functions were present, with 83% meeting the byte-exact requirements. Heumann believes the 16% lacking a byte match are still semantically correct, but diminishing returns were getting real. The blog claims the game “runs flawlessly” as it stands.

For “obvious reasons,” the project’s code is remaining private and will be used “for my own needs only,” Heumann says. As he put it on X, the “decompiled code is obviously also copyrighted.” However, the developer has started extending Sogen, his userspace emulator, into lightweight sandboxing for agents, a response to his project’s agents wiping VMs. It will not be production-ready for a while.

As for the decompilation project, Heumann says that it is a special case, as not every project can do a byte-for-byte reference, but thinks every project can get close “with enough creativity.” One takeaway lesson is that you should be willing to move past failures as “Generating code is cheap now. Throw it away if it’s bad” and focus on correctness over productivity.

A YouTuber has received an unexpected visit from the police over an automatic license plate recognition (ALPR) camera he built to specifically track police cars, using the same technology law enforcement has infamously used to track ordinary citizens. In a video posted to his channel on October 7, Anthony Sistilli says two police officers showed up at his doorstep on September 20th to ask questions about the device, asking what his intentions were with the project.

Sistilli, a Canadian software engineer and YouTuber, pointed out the irony of the officers' concerns, explaining that they mirror the privacy concerns of ordinary citizens who are growing increasingly weary of the recent spike in blanket surveillance brought about by a mass installation of ALPR cameras. Flock Safety, a U.S. public safety technology company, recently scaled its centralized national surveillance network to over 120,000 cameras operating across 49 states, according to the company, capturing billions of vehicle scans every month.

The development sparked widespread public backlash, with critics arguing that Flock has quietly built a massive government surveillance dragnet without a public vote or federal oversight, since any police department on the network can search across state lines. Security researchers later discovered the network included 335,701 Flock cameras and created a “Flock Surveillance Map,” exposing their locations.

Similar ALPR camera surveillance networks exist in Canada, where Sistilli resides, prompting him to build his own device, but this time, encoded to exclusively track police cars. He detailed the build in several TikTok videos, describing it as “a personal Flock surveillance camera to Flock the Flockers.” He then posted a video of himself using the device in real life. The video brought Peel Regional Police, the local law enforcement serving Sistilli’s city, Brampton, Ontario, to his doorstep two weeks later.

“One of them [the officers] took the time to specifically mention how scary it would be for them, because people would be able to find out where they lived, when they got on their shift and what they were doing throughout the day, and that that type of powerful information could put them personally at risk for any bad apples that might want to track them down,” Sistilli narrated. He went on to point out that these were the exact concerns ordinary citizens had about constant surveillance, suggesting there should be more policies and systems around such networks. The officers reportedly responded that there was probably no effective way to implement this without giving away details of active investigations to criminals.

After several investigative reports exposed incidents where officers misused Flock’s database, with instances of stalking of exes and their acquaintances, public backlash snowballed into physical resistance that has seen residents covering or destroying the cameras. Several municipalities, including major hubs like Los Angeles and Fullerton, have now suspended or outright canceled their Flock contracts due to constituent privacy concerns.

Flock and its proponents insist the network has helped lower crime, a claim critics say misreads the data. Sistilli’s video also details aggressive, arguably deceptive marketing tactics Flock uses to convince municipalities to accept cameras. Another issue is centered around the collection and handling of the data. The company says its cameras are protected by on-device encryption and only briefly store images before they are forwarded to its servers. However, hackers reportedly extracted over 27,000 clips and 1.6 million images, captured in a span of 21 days, from a stolen Flock camera.

Russian bot traffic has dropped dramatically in the past few days after Ukraine knocked out Yandex’s biggest data centers, with a third attack happening last night. Cloudflare reports that bot traffic from the Yandex network has dropped to just 0.4% of the maximum number of HTTP requests recorded in the past two weeks. It initially dropped to around 57% on Oct. 8, which happened when Ukraine hit a Yandex data center housing two supercomputers in Sasovo, Russia, located around 195 miles to the southeast of the Kremlin. A second, bigger Yandex campus in Kaluga, about 220 miles to the west of Sasovo, was knocked offline yesterday.

These attacks are apparently Ukraine’s response to Moscow’s strikes on its own data centers, some of which resulted in the outage of some of the most popular piracy websites.

Yandex is Russia’s equivalent to Google, and its Yandex Cloud service status reports that at least 84 services are currently offline. Global internet monitoring firm NetBlocks said that Yandex’s network traffic has dropped to just 30% for Yandex Cloud and 63% for Yandex LLC, most likely resulting in slower internet services or complete disruption for the average Russian. Unfortunately, the ordinary Russian wouldn’t be able to use high-speed satellite internet as a backup, as Starlink does not work in Russia, while the competing Rassvet constellation has had a good number of its existing satellites fall back to the surface after launch.

These incidents are a result of the ongoing conflict in Ukraine, which escalated dramatically in 2022 after Russia’s failed attempt to capture Kyiv. However, many observers say that the war actually started in 2014 when Russian forces seized Crimea and occupied parts of eastern Ukraine after the fall of the pro-Russian government that year.

The hostilities, which have dragged on for years, saw the introduction of new technologies and strategies, specifically in drone warfare. It began with the wide deployment of commercially available drones on the front lines, typically for reconnaissance, dropping explosives, and kamikaze-style attacks. However, it has since evolved into long-range warfare, with specially built drones able to reach distances of over 1,500 miles and destroy Tu-95 strategic bombers situated well behind the front lines.

The drone strikes on data centers are relatively new developments. While data centers on bases are often considered valid targets, Russia’s justification for the attacks on civilian data centers was that Ukrainian military intelligence used them. This isn’t the first time that civilian data centers have also been hit by drones and missiles. AWS sites in Bahrain and the UAE have been hit multiple times since Washington started its bombing campaign against Iran, with the company telling its clients to move their data to sites outside the region, as it doesn’t have a recovery timeline.

Over 3,400 “victim servers” were hit by cryptomining malware PoeLLM during a campaign named Canto Incognito, tracked since April 2026, Lumen’s cybersecurity research team Black Lotus Labs reports. The malware’s “command-and-control (C2) mechanism” used address encoding through four words in a two-stanza poem on GitHub, altered 11 times so far, to direct affected hosts to new C2 servers. Most of those hit look to be running “vulnerable versions of open-source AI/LLM services, such as LiteLLM and Ollama,” despite an April LiteLLM fix that probably patched the exploitation path.

The malware’s payload consists of XMRig and Iron miners, connected to Kryptex mining infrastructure, with infected servers becoming scanners and exploit servers. The “primary commonality amongst the first 900 victims” was contact with “an endpoint for the Russian crypto mining service,” Lumen says. This indicates that it may be financially motivated. “AI infrastructure is becoming an attractive target” because exposed AI services may contain valuable data and hardware access, especially GPUs. In the meantime, it has “blocked all traffic to and from the PoeLLM C2 servers.”

The primary targets are LiteLLM, a proxy server (AI Gateway) to call LLM APIs, a way companies can route their apps to many models through one endpoint, and Gotenberg, a Docker-based API for PDF conversion, which has a guide warning not to expose it to the internet. Also hit are Ollama, which allows you to run open-weight models on your own hardware, although it is not internet-exposed by default, and Gitea, a self-hosted Git platform. Ivanti Sentry, an enterprise gateway appliance, “may also have been targeted,” and one instance was how Lumen discovered the campaign.

Although the malware’s methodology involves leveraging poetry, this is only for pointing the infected servers to the C2 server. It is not a form of an AI jailbreak through harmful requests or prompt injection, or what is known as “adversarial poetry,” described in a paper last year.

The poem, “On the Nature of Connection,” was first committed in April, with each new version pointing to a new C2 server. The malware is able to decode the new address by pulling specific words and converting them into an IPv4 address. Lumen says that “the malware creator has not changed the pattern used in deciphering the poem” at the time of reporting. Using a poem likely aided in obfuscation as it is “a perfect vehicle for hiding an important message,” the researchers told The Register.

The malware is “deployed through vulnerability exploitation of publicly exposed services,” with broad scanning beginning in May. For LiteLLM, a crafted POST to the connection endpoint “was likely the exploitation path,” based on a flaw where two endpoints would run a supplied command on the host with no role check (CVE-2026-42271), which CISA added to its list of exploited vulnerabilities in June. Lumen didn’t detail the methods for Ollama, Gotenberg, and Gitea.

If you want to check your own systems, look at connection logs for the indicators of compromise listed on Lumen’s GitHub page. Be sure to audit external exposure when installing any open-source tool and close unnecessary ports. Patch any and all network devices. Follow the individual advisories for the vulnerable products — LiteLLM 1.83.7 or later and Ivanti Sentry R10.5.2, R10.6.2, or R10.7.1.

When Lumen published its report, three of the 12 C2 servers were still active and the campaign “continues to infect new victims.” The firm “will continue to monitor for new traffic.” More attacks like this seem likely: Lumen says enterprise attack surfaces are “expanding rapidly as AI infrastructure grows.”

Spain’s Museo de Historia de la Computación unveiled its 18-month-long project to commemorate the 50th anniversary of both the Cray-1’s first operational use in Los Alamos and Apple’s founding. The replica uses 30 Mac Minis, 25 dual-core i5 units, and five quad-core i7 units from 2012-2014, totaling 70 cores that produce a peak of 1.5 TeraFLOPS; a theoretical 9,375x jump from the original Cray-1's 160 MegaFLOPS. After one quad-core machine went offline, the current reported peak sits at 1.3 TeraFLOPS.

The original Cray-1 is considered to be the first commercially successful supercomputer, with 80 systems sold in its heyday. Science and engineering institutions sought it out to churn through gigantic datasets for uses ranging from intense weather prediction and aerodynamics to nuclear physics and structural analysis, among many others.

It was very efficient at these computations thanks to vector processing, a computing technique we take for granted nowadays. Vector computing made this supercomputer so powerful; while most computers still handled tasks one instruction at a time, the Cray-1 could process chunks of numbers in parallel, saving a huge amount of time.

The replica has 70 cores from the 30 Mac Minis, and of course, vector processing has since become the norm and a standard feature of modern CPUs; jumping from a single core to 70 “modern” cores and 50 years' worth of innovation is what Museo de Historia de la Computación wants to showcase in this project. It is not only a replica, but a representation of how far technology has come.

The original version was cooled by a network of refrigerant lines that led to 20-ton refrigeration compressors in a separate room, while the replica is cooled passively through a convection-tower design and is extremely quiet, with only the Ethernet switches making the most noise. The museum plans to use silent switches to make it even quieter.

For visitors to see it in action, the museum also created a retro-style control terminal, akin to what you’d see in old movies: a large metal desk with a keyboard in the middle and multiple screens embedded into a panel spanning the desk, with angled features flanking the screens, completing the 70s control terminal silhouette.

On the screen, you can see it churning away at large matrix multiplication problems, the sort of thing it’s good at. Surprisingly, the team behind it realized they could run the supercomputer on macOS Mojave, which supports MPI (Message Passing Interface), allowing the instructions to spread across several Mac Minis for parallel computing. That's not the end of it, though: the museum plans to start a crowdfunding campaign and buy 70-80 M4 Mac Minis to fill the remaining slots, aiming to enter the TOP500 supercomputers list.

Plan your trip if you want to see it; it’s on permanent display, and the museum is open to the public on Saturdays from 11:30 AM-2:00 PM, but it doesn’t allow tours without a guide, and it's closed during summer, Christmas, January, and February.

A U.S. military-backed company plans to deliver a payload to any point on Earth within 90 minutes by prepositioning assets in space and then calling down delivery vehicles as needed. Sierra Space, a subsidiary of the Sierra Nevada Corporation, has successfully completed two test drops of its Ghost reentry vehicle while carrying a bottle of whiskey and a software-defined radio. According to Aviation Week, the test was conducted using high-altitude balloons that dropped it from 35,000 feet and 68,500 feet.

While this test isn’t as exciting as SpaceX rockets landing on a platform at sea, it’s still an achievement for the team working on the project, as it’s the first test of the vehicle in two years. This demonstration is part of the U.S. Space Command’s Apollo Nexus training exercise, which aims to demonstrate the needed synchronization of multiple units in complex operations.

The test’s payload isn’t just two random items, either. They’ve been specifically selected to show that liquids and sensitive electronics could survive part of the trip back to the surface. “We were able to safely demonstrate that and prove that we could come through all of those environments and then be recoverable in a certain period of time by the team on the ground,” Sierra Space senior vice president Erik Daehler told the publication.

It should be noted that these cargo units aren’t designed to be launched by rockets from another side of the world to be delivered to frontline forces as needed. Instead, they’re designed to stay in orbit for up to five years and then be called down to troops as needed — similar to how Orbital Depots drop down from the sky when called by an SCV in StarCraft II. “Getting something into theater to the warfighter when they need it is critical,” Daehler added. “If you’re in a denied area where you couldn’t get a C-17 or a C-130 to be able to drop into the theater, space is a perfect logistics node.”

The company says that Ghost can deliver its payload to any point on Earth within 90 minutes of getting called down. However, since it is based in space, the military would have to anticipate what supplies are needed, launch them, and keep them in orbit for up to five years. This is similar to how the U.S. pre-positions supply and cargo ships in remote bases across the world like Guam and Diego Garcia, so it could easily deploy materiel where it’s needed. The Ghost is a seemingly small unit, though, with a capacity of just five to ten tons, so it won’t be able to deploy tanks or artillery pieces across the world. Still, it’s a useful device for dropping equipment, ammunition, and supplies, like this in-house assembled drone or microwave counter-drone system, to units that need them without having to risk assets and aircrew.

Masayoshi Son, founder of SoftBank, is seeking up to $100 billion from Gulf investors to establish a fund that would acquire companies and improve their operations using artificial intelligence and robotics, according to the Financial Times. The initiative comes as SoftBank faces growing concerns over its $65 billion investment in OpenAI and its delayed initial public offering (IPO).

Unlike SoftBank's previous investment vehicles, which primarily financed technology companies, the proposed fund would purchase established businesses and introduce AI and other advanced technologies to improve their operations in a bid to improve their value, which is a relatively novel model to make money on AI. Son has reportedly approached influential investors in the Middle East, including senior figures in the United Arab Emirates, in recent weeks to get the money to acquire companies that have not yet adopted AI and/or robotics, but which could substantially improve their business positions by doing so.

Roze, SoftBank's robotics and physical AI division, would reportedly play a major role in these transformations, though exact details remain unclear. Son also intends to eventually take Roze public at a substantial valuation, though neither the fund's establishment nor its financing is guaranteed. In fact, at this point it is unclear whether the fund will only finance acquisitions, or will also finance development of Roze.

SoftBank has already committed approximately $65 billion to OpenAI, and the company remains the crown jewel of its investment strategy. The Japanese company has financed its investments using internal resources, external capital, and borrowing partly secured by its holdings in Arm. Meanwhile, SoftBank's well-being significantly depends on the valuation of OpenAI, which has yet to become a highly profitable company. Despite risks associated with OpenAI valuation, SoftBank continues to pursue aggressive AI expansion.

Son has previously attracted substantial financing from Middle Eastern sovereign wealth funds. Saudi Arabia's Public Investment Fund and Abu Dhabi's Mubadala participated in SoftBank's original $100 billion Vision Fund in 2017. Since its establishment, Vision Fund 1 has accumulated approximately $29 billion in investment gains, whereas Vision Fund 2, which holds the OpenAI investment and is primarily financed by SoftBank, has generated $20.5 billion as of June. Abu Dhabi has also expanded its AI investments through MGX and G42.

Last month, SoftBank raised more than $11 billion through the largest junk bond issuance on record, with yields reaching 9.75% (i.e., very expensive borrowing as risks are high). As of June, the company's net asset value stood at ¥72.3 trillion ($456.57 billion), while its loan-to-value ratio was 13%, considerably below its normal operating limit of 25%. However, a substantial portion of SoftBank's assets is tied to technology companies, including Arm and OpenAI, so a decline in their valuations could increase the company's LTV to uncomfortable levels even without further borrowings.

Son's investment history includes Alibaba's massive success and WeWork's 2023 bankruptcy, so it remains to be seen where the current strategy leads SoftBank.

Ting-Wei “Willy” Sun, a third-party broker and “fixer” who’s been charged alongside Super Micro co-founder Yih-Shyan “Wally” Liaw and sales manager Ruei-Tsang “Steven” Chang for conspiring to unlawfully divert Nvidia AI chips to China, has pleaded guilty to the allegations against him. According to Bloomberg, Sun’s plea comes as part of an agreement with prosecutors, although the details of any possible cooperation arrangement remain undisclosed. Sun is also expected to be sentenced on Sept. 8, 2027, after entering his plea.

The case against the individuals was revealed earlier this year after it was discovered that they diverted Super Micro servers containing Nvidia chips to China. The three were reported to have sent the servers to a Southeast Asian company, which then proceeded to fake the paperwork and create dummy servers for inspectors. Workers then used hair dryers to remove the serial numbers on the real servers to transfer them to the dummy server shells to complete the illusion, while the real servers made their way to China. Further investigation revealed that the three used a Thailand-based corporation to receive the servers, which were then delivered to Alibaba, even as the Chinese tech giant denied receiving the banned Nvidia hardware.

Super Micro has also denied any knowledge of the operation, even as it has netted $2.5 billion in sales since it started in 2024. Nevertheless, the company’s shareholders have sued it, accusing it of concealing its dependence on illicit sales to China. Liaw, on the other hand, has pleaded not guilty to the charges and was released on a $5 million bond. The Super Micro co-founder is set to go to trial this coming Nov. 2, even as the third co-conspirator, Chang, remains a fugitive.

Taiwanese authorities are also building their own case against the illegal shipment of AI chips to China, with the investigators raiding some Super Micro offices and the homes of six individuals. The company, on the other hand, denied that its premises were raided, insisting that it cooperated with the police and provided access to the workstations and gadgets of the employees that were being investigated. It also said that it has been collaborating with both U.S. and Taiwanese authorities over the smuggling investigation, and that it has even fired several employees after completing its own independent investigation. Soon after this, Taiwan indicted nine more people involved in the illegal smuggling of Nvidia B300 GPUs.

Sun’s guilty plea is the latest development in the U.S.’s clampdown on illegal AI chip exports, which happened soon after another tech CEO was arrested in California for smuggling $300 million in Nvidia AI servers to China. These cases show how serious the White House is about protecting its hardware advantage over Beijing in the AI race, even as AI development is facing its own challenges at home.

In case you've been missing out on the action over on Tom's Hardware Premium, we're back with another roundup to fill you in on everything you missed. Central to this week's theme is how Artificial Intelligence and its subsequent buildout are currently impacting all areas of the semiconductor industry.

Headlining this week is our report on how smart software utilization may play a part in the electricity consumption of AI data centers. We sat down and talked with experts in the domain to find out.

• Software could be the easiest fix for hyperscalers' AI power squeeze, researchers say — data center demand is expected to rival Japan's electricity usage by 2030

Reports state that Tencent has reached a deal to lease high-performance AI chips from Oracle to the tune of $7 billion over five years. While the figure on its surface sounds significant, the estimated lease price is around 43% less than an Nvidia H100 leased at $2.80 per hour on a one-year contract, meaning that Tencent may have scored a slight coup in the terms of its lease agreement. While no specific chips have been named, the size of the deal and the prices point toward a Hopper-class GPU. We explored how this rental loophole may be used by Chinese firms to potentially gain access to otherwise export-controlled hardware.

• Tencent scores 100,000 offshore AI chip deal with Oracle for $7 billion despite climbing prices — per-hour costs estimated to be 43% under standard H100 rental rates

If you cast your minds back to last week, Synopsys revealed a slew of new agreements and partnerships; one of the most notable among them was an agreement with OpenAI, unveiling a 'GPT-Synopsys' — a specialized AI model that leverages Synopsys' domain IP knowledge and EDA tools, with OpenAI's capability to design LLMs. GPT-Synopsys is, in all likelihood, a chipmaking-specialized AI model, which may play a part in the development of successors to OpenAI's Jalapeño AI ASIC. As we've previously explored, AI models designing chips will likely run into hurdles surrounding IP usage, which become a non-factor if both AI labs and IP holders decide to team up in a manner such as this, setting the stage for chip design's agentic future.

• OpenAI and Synopsys partner to build "GPT-Synopsys" for autonomous chip design — specialized AI model will operate EDA tools, allowing engineers to deliver more sophisticated chips faster

Neon is a noble gas that's widely used in the DUV chipmaking process, with over 70% of global supply being used by the now-older lithography machines. Crude neon production has usually been focused in Ukraine, and ever since the advent of the country's ongoing conflict with Russia, global supply has been disrupted. Now, companies are racing to bring neon gas recycling systems to market in order to make the most of supplies. Japanese lithography toolmaker Gigaphoton and rival Kanten Techno have both developed machines that recycle the material at varying degrees of success. While DUV lithography remains a primary focus for the Chinese chipmaking ecosystem, the material is not widely used in the newer EUV systems that many leading-edge nodes currently utilize.

• Gigaphoton debuts neon recycling system with claimed 50% recovery rate — systems throw a lifeline to chipmakers that utilize 70% of global neon supply in DUV lithography

Qualcomm has reportedly reached a deal with Huawei to license its patents for the first time. While the official announcement is light on detail and subject to approval, an additional report claims that Huawei's LogicFolding chip-stacking methods are also included in the deal, with products ranging across AI, networking, compute, and other technologies. What makes Huawei's LogicFolding approach so notable is that the technique follows and targets faster data transmission as a foil to not having access to the most advanced EUV machines. Additionally, many of the techniques that LogicFolding incorporates echo what others in the chip industry are already doing, which makes it an eyebrow-raising move on Qualcomm's part. Historically, patent licensing between the two companies has been on the other foot — Huawei licensing Qualcomm patents — not the other way around. We explore the deal in detail in our full article.

• Qualcomm will license patents behind Huawei’s LogicFolding chip architecture, report says — the Kirin 9050 Pro already uses it, with a teardown showing its lower die is mostly cache and I/O

Two former Groq employees and shareholders have filed a proposed class action lawsuit against Groq's former board and CEO, alleging that the company's board breached its duties and cost stockholders billions, following Groq's $20 billion deal with Nvidia in December 2025, according to a new report. The deal between Groq and Nvidia didn't involve an acquisition, and Nvidia is not a named defendant in the case, though the vast majority of Groq's staff have reportedly since joined Nvidia directly. The Groq3 LPU has since been showcased as a part of Nvidia's Vera Rubin ecosystem, which is currently ramping. Nvidia's Groq 3 LPX is scheduled to ship alongside Vera Rubin in H2 2026.

• Former Groq engineers sue board over $20 billion Nvidia deal, saying it handed Nvidia the LPU and the team that built it — plaintiffs allege the board kept billions from other shareholders

The AI train keeps rolling, with a new release from European firm Mistral, named Large 4, ML4, or, if you have seen any of the company's social media posts, Le Chonk. The race towards building capable open-weight models has predominantly been dominated by either U.S. or Chinese labs, meaning a European breakthrough is significant. Testing lab Artificial Analysis scored ML4 a reasonable 38 on its overall intelligence index, and it is currently outrun by numerous Chinese models, including Kimi K3 (max), GLM-5.3-Flash, and MiMo-V2.6.Pro. However, the model excels in cybersecurity tasks, only outmatched by a select few.

So, Europe's sovereign AI efforts appear to still be playing catch-up to Chinese alternatives, despite clear advancements. The global race to deploy capable closed and open-weight models marches on, and Mistral expects that the exact weights for ML4 will be released before the end of October.

• Independent tests rank Mistral’s new trillion-parameter Large 4 the best AI model outside the U.S. and China — but Chinese open-weights still overcome Europe's best efforts

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The U.S. Senate has been conducting a year-long investigation into AI data centers, and its findings suggest that some developers have been misleading the public about the costs and benefits that these projects bring to their communities. According to Time, data center developers have routinely pointed to the number of construction jobs that projects bring to neighborhoods, but many have allegedly refused to share the number of permanent positions these projects bring to the neighborhood once they’ve reached operational status. A few companies that did give numbers point to a ratio of one permanent position for every megawatt of capacity, meaning a 100-megawatt development, which consumes around the same amount of electricity as 100,000 homes, would only hire 100 people.

“Congress must hold Big Tech accountable so these companies pay their fair share,” Senator Elizabeth Warren (D-Mass.) told the publication. Senator Chris Van Hollen (D-Md.) also said, “This report lays bare what we have long known: working Americans and local communities are footing the bill for Big Tech’s massive expansion of data centers, while these companies continue to operate without transparency.”

Aside from the lack of permanent jobs, the authorities also looked at the tax incentives these companies have been getting from local and state governments. The investigation revealed that although property-tax breaks catch critics’ attention during the application process, sales-tax exemptions on computer equipment are reportedly more lucrative, especially as these sites maintain, replace, and upgrade their hardware. The report estimated that 39% of a 1GW data center’s spending is allocated towards expensive GPUs, so if the data centers do not deliver the jobs and economic benefits they’ve promised, then the state is losing out on a lot of revenue.

The investigation also revealed that the surveyed companies — Amazon, Google, Meta, Microsoft, CoreWeave, Digital Realty, and Equinix — don’t want to cover the costs of the additional infrastructure needed to serve their data centers even though they previously promised to “pay their own way.” While the companies said that they were willing to pay for direct costs, they argued that they should not be responsible for the larger investments that could potentially benefit other customers, such as new power plants and transmission lines, even though these were primarily driven by their power demands.

There have already been efforts to legislate the ratepayer protection pledge that data center developers signed earlier this year, but it has failed because senators thought that it was “toothless.” This investigation shows that Congress is not giving up and that it’s conducting an even broader effort to regulate the infrastructure behind AI, especially with regard to its massive power consumption and other issues that residents living around them have been protesting against.

Anthropic, OpenAI, and other AI labs are reportedly gaming out scenarios where a rogue AI wreaks havoc on critical infrastructure, like financial services, internet and telecommunications, or utilities such as power and water. According to Axios, the leadership of these companies is privately preparing for such an incident, similar to how the Pentagon has been running war games to see the results of different strategies in case of a nuclear exchange between nations.

Planning like this is not unheard of in the corporate world, with enterprises sometimes running business continuity exercises to account for contingencies. However, what’s concerning is that these exercises are in preparation for a major AI incident, something that many industry experts believe would happen within the next six to 12 months. "OpenAI conducts preparedness exercises where teams discuss and work through a range of potential scenarios," a company spokesperson told the publication. "These scenarios are not treated as inevitable but are meant to help us prepare for a variety of circumstances."

Dario Amodei recently released a warning about an AI-driven botnet “swarm” taking over the entire internet in the coming months, especially after incidents of OpenAI’s models escaping their testing environments. We’ve also heard of incidents where a “kill switch” failed to stop a rogue agent, forcing the startup to pause testing. The threat is so great that Anthropic listed “existential risks to humanity” as one of its major risk factors in its IPO prospectus, allocating nearly a third of the document to covering this issue. A former OpenAI safety employee has spoken out about this, saying that the current risks stem from a lack of safety culture — something that the aviation and nuclear industries have already learned through experience.

Despite that, the White House has refused to impose regulations on artificial intelligence, instead preferring to make Nvidia, OpenAI, Anthropic, and other AI labs promise to “self-police” AI development. But even though the current administration is lenient on AI labs and development, Axios says that one of the things that the scenarios focus on is how to educate Congress after a major incident. An event like this would probably result in Congress attempting to slow or shut down AI development, though planners think that such a move would face significant challenges. It will still likely have an impact on the economy, though, especially as big tech has already spent more than $1 trillion in AI infrastructure, with an additional $32 trillion expected to be invested until 2050.

The heads of Anthropic, OpenAI, and SpaceXAI have all agreed that a slowdown is needed for AI safety, but it’s unclear what steps these companies have taken to put that into effect. While gaming out scenarios like these is par for the course for any major organization, it still doesn’t give us comfort that they’re essentially preparing for “The Day After Tomorrow” of an AI apocalypse.

Hot on the heels of a Ukrainian drone strike on a Russian Yandex data center in Sasovo that caused a major outage, a Yandex facility in Kaluga has been struck in a similar fashion. The huge data center was so badly damaged that Russian state media admitted that multiple modules had been removed from service completely.

United24 Media reports that the facility is located in Grabtsevo industrial park and was designed to be the largest Yandex data center in all of Russia. It only entered service in the second half of 2023, lasting three years before this incident.

The Kaluga data center is a sprawling complex that covers around 1.4 million square feet (130,000 square meters). It's so vast that its 63-megawatt power capacity is around 50% higher than some other data centers owned by Yandex. With more than 3,800 server racks, it's perhaps easy to see why it became a target for Ukraine's drone operators. If, of course, the facility was the intended target for the attack — something yet to be confirmed.

As for what all those server racks actually do, it's reported that Yandex's internal services are housed at the facility. It was ultimately built to allow Yandex to expand its cloud offering.

It's as yet unclear how this drone strike is affecting everyday Russians, but the previous Sasovo strike also impacted Yandex Cloud. At the time, users reported problems with Yandex Mail, Disk, Documents, Pay, and Market despite Yandex claiming that key services remained up and running.

Russian state media TASS confirmed the severity of the attack, saying that “Several modules of the Yandex data center in Kaluga have been completely taken out of operation as a result of a UAV attack, [Yandex] reports.”

This incident is just the latest in a run of attacks on digital infrastructure both in Russia and Ukraine. Russian missiles struck a Ukrainian facility earlier this month, taking the world's second-largest torrent tracker offline in the process.

PC shipments globally have dropped by 20.1% year-over-year in the third quarter of 2026, a decline of 15.8 million units. According to research firm IDC Global, this marks a steeper decline compared to the 3.8% year-over-year decrease that the market experienced last quarter. It’s also down 9.1% from the second quarter of this year. Many research firms have been warning about this since late last year, with IDC warning that the PC market could shrink up to 9%, with the budget sector being hit the hardest. Some experts estimate that the situation isn’t expected to improve until 2029, with Tom’s Hardware readers saying that they won’t upgrade their systems in the next two years.

These price increases have been directly caused by the boom in AI data centers, with hyperscalers pouring massive amounts of money into the crucial HBM needed to support AI GPUs. This has led to memory manufacturers pivoting toward this technology, leaving less production capacity for consumer memory. One good example of this is Micron, which killed the Crucial RAM and SSD brand in late 2025 so that it can focus on data center production. As PC makers fight for a shrinking supply of memory chips, we saw memory prices climb by as much as 500%, with 128GB of DDR5 now costing as much as $3,399.

Manufacturers didn’t sit idly by and let the market take its course, though. Many of them stocked up on inventory in the second quarter of 2026 to try and head off the projected higher prices caused by the shortage, which directly led to the reduced shipments being reported. “What we’re seeing is the result of the strong first half pull-in. Vendors and channels loaded up on inventory early in the year to get ahead of price hikes, and that has thrown off the usual seasonality, where Q3 is typically larger than Q2,” said IDC consumer devices research director Jitesh Ubrani. “Channels are now worried about carrying too much inventory into a market where high prices are suppressing demand. That could translate into promotions and some short-term relief for consumers, but we don’t expect pricing anywhere near what it was a year ago. Prices will remain elevated. With macro conditions worsening, the risk is that the outlook for the next few quarters gets worse before it gets better.”

The top three PC vendors suffered the most, with Lenovo shedding 22.6%, HP shipping 30.9% fewer units, and Dell shipments contracting by 25%. On the other hand, Apple’s shipments shrank by 11.3%, while Asus’s numbers fell by 8.6%, with the rest of the market shipping 14% less year-over-year. Despite being hit the hardest, Lenovo, HP, and Dell still maintained their lead at 23.8%, 16.5%, and 12.1% market share, respectively.

Even though other industry leaders, including Micron, say that the situation will not improve for the next few years, Acer CEO Jason Chen went against the grain and said that these fears are just being hyped to protect the margins of memory chip makers. He estimated that PC prices will decline by the latter half of 2027 as Chinese memory chip capacity comes online, with only high-end parts like LPDDR5X-9600 and niche CPUs such as the Nvidia N1 and N1X remaining in short supply.

Mistral launched its trillion-parameter Large 4 (ML4) model this week, saying it “pushes the frontier of open-weight performance.” Artificial Analysis (AA), an independent AI benchmarking firm, ranked Mistral's Large 4 as the best model available from outside the U.S. and China in its index. The mixture-of-experts model, trained on Nvidia’s Grace Blackwell GPUs, scored its personal best in AA’s CyberGym-E2E-AA end-to-end cybersecurity measure, although several Chinese open models beat it overall.

ML4 scored 38 on the Artificial Analysis Intelligence Index v4.3.2, which AA says makes it the “most intelligent model from outside the US and China." At least five Chinese open models scored higher on the index: DeepSeek V4.1 Flash (max) at 39, GLM-5.3-Flash at 42, Kimi K3 (max) at 44, GLM-5.3 (max) at 45, and MiMo-V2.6-Pro at 46. While MiMo-V2.6-Pro is comparable in total size to ML4, GLM-5.3-Flash is about one-third the size at 320B parameters total and 18B versus ML4’s 49B–52B active. Unlike the rest, ML4 isn’t open yet: Mistral says its weights are due “by the end of the month.”

Cost is a different story. ML4 comes in at $1.13 per AA task at list price, or $0.57 at the 50% discounted launch price. This compares to $0.13 for MiMo-V2.6-Pro and $0.25 for GLM-5.3-Flash. Although comparatively expensive, ML4 scored what AA calls its “strongest result” and Mistral calls “the highest of any model” with an 81.7% score on CyberGym-E2E-AA, compared with GLM-5.3-Flash’s 74%, from a model whose post-training run Mistral claims is “still in flight.”

While the score is impressive, it is only one of three tests in the Cyber Index; ML4 only scores 16% in DeepsecBench-AA and 51% in CWE-Bench-AA. However, AA’s projection is that, once the weights ship, it “will rank among the top three open weights models on the Cyber Index.” This leaves the door open for ML4 to apply to narrower tasks.

Mistral bets on this, saying ML4 is “state-of-the-art among open models” on enterprise workloads such as “cybersecurity, finance and law,” and that in visual grounding, where its 1.6B-parameter vision encoder comes in, it can go as far as “surpassing even frontier closed models.” On AA’s Cyber Index, Mistral says, ML4 ranks “among the top five models globally,” and it solves 93% of Cybench, a set of 40 challenges drawn from security competitions.

CEO Arthur Mensch went further in Abu Dhabi, saying the model is “above the Chinese models on certain aspects, including cyber,” Reuters reported. Mistral’s other figures include DeepSWE v1.1 at 61.7%, Coding Agent Index at 49.8%, AutomationBench at 59.9%, and Dense 200 at 42%; AA’s own chart puts ML4 at 27% on Terminal-Bench 4.0.

ML4 does, in fact, beat OpenAI’s closed GPT-6 Luna (max) on CyberGym-E2E-AA, but Luna matches ML4’s 38 on AA’s index at about one-sixteenth of the cost per task ($0.07). Mistral says frontier models such as Claude Opus 5.5 and GPT-6 Astra “score near zero on the same test because they refuse to perform the task,” and AA’s chart marks both as declining some tasks on safety grounds.

Outside of that test, Mistral says ML4 also beats DeepSeek V4 Pro 0813 and Qwen3.8 Max on the Coding Agent Index; per AA, it is cheaper than the latter. Overall, AA’s index-vs.-cost chart puts ML4 below the Pareto line and just outside the “Most attractive quadrant.”

The cost comes from needing 200 million output tokens to run AA’s index, against a median of 81 million. As for speed, ML4 generated 116.1 tokens per second, with 1.46 seconds to the first token. This puts ML4 at about 33% faster than its tier median and 2.6x faster to the first token. AA’s speed is measured on Mistral’s own API, which Mistral says runs on the same hardware the model used for training.

ML4 was “trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe,” the company said. This is up from 3,000 Nvidia H200s used to train Large 3, released last year, which was smaller at 675B total parameters with 41B active. The training occurred over “roughly two months,” as reported by VentureBeat.

Mistral raised a €3 billion Series D in what the company calls “the largest equity round ever raised by a European technology company” in its post. Mistral Compute has planned for 18,000 Grace Blackwell chips, as announced with Nvidia last year. Mistral says it is “significantly scaling up our compute capacity in our own European datacenters,” and that ML4 will become the base for “a new generation of specialized and optimized Mistral models.” The model’s open weights, once released, will be able to run on a private cloud or on-premises.

The weights, architecture details, and additional benchmarks are expected before the end of October. Artificial Analysis lists the preview as proprietary until the weights arrive. Once it does, the model may be re-scored, and Mistral’s claims will be held to task. For the first two weeks following release, ML4 can be used via API at a discounted rate.

While European models still have a lot to prove, based on previous performance and ML4’s showing with an 8-point gap versus a similar-size Chinese model that’s also a lot less expensive, Mistral has demonstrated resolve and produced a model with, at least, niche application, as the basis to eventually close that gap.

GlobalFoundries and TSMC have entered into a five-year manufacturing agreement valued at $2 billion under which GlobalFoundries will produce silicon interposers for TSMC's CoWoS advanced packaging technology. The arrangement will establish a U.S.-based manufacturing source for these components, which will enable TSMC's clients to brand their products as completely made and packaged in America. Volume production is scheduled to ramp up in the first half of 2028.

"By providing manufacturing service using GF's trusted U.S. manufacturing footprint, we are creating a secure, scalable source of essential advanced-packaging elements that will help customers accelerate innovation and strengthen the semiconductor supply chain," said Ed Kaste, senior vice president of CMOS Business at GlobalFoundries.

GF and TSMC team up

Under the agreement, GlobalFoundries will expand manufacturing capacity at its fabrication facility in Malta, NY, to produce silicon interposers used in TSMC's CoWoS-S packaging ecosystem. Such interposers are custom pieces of silicon that provide high-density connections between logic chiplets and high-bandwidth memory (HBM) stacks and enable the creation of complex multi-die processors for artificial intelligence (AI) and high-performance computing (HPC) applications. The companies have not disclosed the production capacity to be installed, but the agreement provides a framework for additional expansion depending on demand.

Silicon interposers used for TSMC's CoWoS-S packaging technology are customized for particular processor designs, as their configurations depend on the number, dimensions, and placement of logic chiplets and HBM stacks, as well as the interconnections between them.

In addition, interposers must meet the power-delivery requirements of specific processors, including power distribution networks and embedded deep trench capacitors (DTC), and their designs must account for signal integrity, power integrity, and thermal characteristics. Furthermore, large interposers may exceed the maximum lithographic reticle size and therefore require stitching, so it remains to be seen how well GlobalFoundries will stitch such interposers for extra-large system-in-packages (SiPs).

While certain manufacturing processes and structural elements can be standardized, the resulting interposers are generally application-specific. GlobalFoundries will not be a TSMC supplier, but a subcontractor that will manufacture multiple customer-specific interposer designs rather than standardized components that require close cooperation with TSMC. Specifically, GlobalFoundries will need to ensure compatibility between its design rules, manufacturing processes, and qualification procedures so that interposers produced by GF can be used in TSMC's CoWoS packaging flows. It remains to be seen how the companies will handle ownership and protection of intellectual property, as interposer layouts may incorporate technologies and production methods specific to TSMC and its customers.

By transferring some interposer production to GlobalFoundries, TSMC could free up some of its own manufacturing resources and increase availability of components required for advanced packaging.

Manufacturing silicon interposers is only one part of the CoWoS-S production process. The technology also requires attaching logic chiplets and HBM stacks to interposers using high-precision bonding equipment, assembling the resulting structures onto organic substrates, and then performing electrical testing. These operations require dedicated manufacturing capacity, which the agreement between the two companies does not address.

Becoming a part of TSMC's CoWoS ecosystem

Following its exit from development of leading-edge process technologies in 2018 and consequent inability to produce leading-edge AI and HPC accelerators, GlobalFoundries is now becoming a part of TSMC's advanced packaging supply chain in the U.S. that will supply custom components for AI and HPC processors.

For GlobalFoundries, the agreement represents an opportunity to participate in the manufacturing of advanced AI processors without producing their leading-edge logic dies. Although the company abandoned the development of leading-edge process technologies eight years ago, its manufacturing capabilities remain suitable for producing certain components used in sophisticated multi-chip packages, including CoWoS-S interposers. It remains to be seen though whether it can also participate in TSMC's CoWoS-L supply chain.

The agreement with GlobalFoundries is particularly important considering TSMC's plans to establish advanced packaging operations in Arizona. The company intends to introduce CoWoS and 3D-IC packaging capabilities in the state before 2029 and has signed a 10-year agreement with Amkor Technology, which is building an advanced packaging facility in Peoria, Arizona, that is scheduled to begin production in early 2028.

As GlobalFoundries ramps up interposer production in New York in the first half of 2028, TSMC could potentially establish a largely domestic CoWoS supply chain that comprises logic dies manufactured in Arizona, silicon interposers produced in New York, and advanced packaging and testing performed by TSMC and/or Amkor in Arizona. For a while, HBM stacks would still have to be sourced from memory manufacturers in Asia, though both Micron and SK hynix intend to bring HBM assembly facilities to the U.S. already this decade, so it is safe to say that the U.S. will have a vertically integrated supply chain for production of advanced AI processors by 2029 – 2030.

There is a catch, though, as GloFo and TSMC have not disclosed how CoWoS assembly operations will be divided between TSMC and Amkor, or whether GlobalFoundries' interposers will be used in Arizona at all. Yet, GF's wording about a trusted U.S. manufacturing footprint strongly implies that its interposers will be primarily aimed at SiPs fabricated entirely in the U.S. Meanwhile, expanding interposer production alone will not increase the number of finished AI processors unless sufficient chip-on-wafer assembly and testing capacity is available.

Some things remain unclear

The initial agreement covers five years and establishes a mechanism for further capacity additions. However, the companies have not disclosed the planned manufacturing volumes, capital expenditures associated with the expansion, or the breakdown of the $2 billion agreement. Furthermore, we yet have to find out how GF's manufacturing capabilities are aligned with CoWoS-S' advancements.

During a Thursday announcement, Vice President JD Vance said that well-known tech companies Microsoft and Adobe are now blocked out of the PERM program — one of the possible paths for the firms' H-1B workers to eventually evolve to green card status. The move was triggered by alleged fraud and abuse. IT outsourcing firms Cognizant, Infosys, Tata Consultancy, Wipro, HCL, and Capgemini also got slapped with suspensions.

The companies involved have not yet commented.

The administration is not going to process any new or pending Permanent Labor Certification applications for the companies. In a press conference, Vance had harsh words about Microsoft in particular, stating that "There has been no company in the United States, unfortunately, that has abused this system more." He added that "if you do the math, for every worker that Microsoft laid off, they replaced that worker with one and a half foreign indentured servants."

According to Vance, Microsoft fired 6,000 workers while simultaneously benefiting from 6,300 H-1B approvals and nearly 3,000 green cards. The VP said the company filed 3,682 PERM applications, 1,000 of which were for positions that seemingly overlapped with those of laid-off workers. Both Microsoft and Adobe's PERM privileges were cut "due to multiple active federal investigations," as stated by Labor Secretary Keith Sonderling.

A move so swift and harsh is unusual, but it comes against a backdrop of the U.S. government putting a tighter grip on the H-1B program. Last July, the Labor Department launched a nationwide investigation into fraud and human trafficking in H-1B and PERM programs. The inquest focuses on fraudulent applications, wage kickbacks, coercion, below-wage labor, recruitment abuse, and benching — when a company keeps the H-1B employee on the books but doesn't assign them work or pay them.

In September, President Trump signed an executive order aiming to curb abuse of the H-1B program. Cognizant also had its green card filings suspended during that month. The H-1B lottery rules had already changed to reportedly favor highly skilled, highly paid workers.

According to the National Foundation for American Policy and reported estimates, Amazon had the most approved H-1Bs in fiscal year 2025 with 4,644 visas, followed by Meta at 1,555, Microsoft with 1,394, and Google at 1,050.

U.S. Semiconductor Manufacturer Wolfspeed Inc. has received a conditional 30-year, $1.5 billion loan commitment letter from the U.S. Department of War (DoW) through its Office of Strategic Capital (OSC), as the country continues its push to boost domestic semiconductor manufacturing. According to an official press release on Oct. 7, Wolfspeed — which specializes in silicon carbide (SiC) and gallium nitride (GaN) materials and devices — will use the long term-financing to upgrade its GaN epitaxy capabilities and to develop radiation hardening capabilities for current SiC and future GaN products.

The company says SiC and related technologies are “critical to next generation power and radio-frequency applications across defense, aerospace, AI, critical infrastructure and other strategic commercial markets,” and that it will focus on U.S. national security applications, including communications infrastructure and electronic warfare systems. “SiC and GaN have critical national security applications,” said Wolfspeed CEO Robert Feurle. “With this financing, the company would be well positioned to not only continue to serve the DoW but also expand its capabilities for the benefit of U.S. national security as a whole,” he continued.

Under the terms of the loan — which is subject to final agreements — Wolfspeed is required to issue the DoW warrants to buy up to 7.5% of its fully diluted equity. The warrants would be priced off the stock's volume-weighted average price (VWAP) and handed over in proportion to each tranche of the loan as it's funded. As of this report, the loan remains conditional, pending the OSC’s completion of its due diligence. Wolfspeed also needs to secure government approvals and appropriations, as well as the consent of its existing lenders. The company itself warns there's no guarantee the financing will go through.

“This conditional 30-year commitment represents another significant milestone in our ongoing efforts to optimize Wolfspeed’s capital structure and improve our financial foundation,” said Wolfspeed CFO Gregor van Issum. “Subject to the satisfaction of diligence, negotiation of definitive agreements, and other financial, legal, and investment conditions, the broader commitment is expected to provide additional long-dated capital to support our strategic priorities, improve financial flexibility, and further position Wolfspeed for long-term value creation.”

The announcement marks the U.S.’s latest move to onshore critical semiconductor manufacturing and strengthen the domestic supply chain, combining direct capital subsidies, major tax incentives, and strict regulatory changes. Through the $54.2 billion CHIPS and Science Act, the U.S. government has injected billions into massive fabrication facilities for industry leaders like TSMC, Intel, GlobalFoundries, and Micron, while simultaneously utilizing the Section 48D Advanced Manufacturing Investment Credit to offer a 25% tax credit that has led to over $827 billion in private sector announcements.

Google subsidiary Tuike Finland Oy has just received a notice from the Finnish Licensing and Supervision Agency, or LVV, ordering it to stop construction on two data centers. According to CNBC, the sites in Muhos and Kajaani cannot proceed until environmental impact assessments have been completed, which have been under investigation for allegedly clearing over 740 acres of Finnish forest for the project.

“We understand the concerns and have fallen short of our own high standards in this instance,” a Google spokesperson told the publication. “We acted in good faith under the Forestry Act and ran nature surveys and put in place measures to protect high-value areas. As we continue our long-term work in the region, we are advancing our landscaping and biodiversity plans, which include tree planting across 130 hectares at Muhos.”

Finland is sometimes called the “Texas of Europe” when it comes to data centers because of its readily available supply of land and electricity, which have been some of the major bottlenecks that data center developers have been facing. Even though the epicenters of the AI data center infrastructure build-up are centered around the U.S. and China, Europe is trying to catch up with its massive $30 billion investment and a target of 100,000 AI GPUs for each site. SoftBank also plans to invest $87 billion specifically in France because of its nuclear-powered electricity grid.

However, other European nations are struggling with the demand for this power-hungry infrastructure. Denmark has paused new data center connection requests as demand hit 60GW — more than eight times the country’s peak power consumption of around 7GW — while Amazon’s data center projects on the continent are facing delays of up to seven years as they wait for new connections.

These stop-work orders aren’t related to electricity supply, though. Instead, the concerns stem from the alleged cutting of trees without the go-signal from the government. The LVV ordered the subsidiary to stop removing trees and topsoil, as well as cease activities such as excavation, quarrying, crushing, soil mass transfers, ditching, and road and storage construction. “In our opinion, the measures in question will change the environment and cause impacts, the identification and assessment of which are among the key objectives of the EIA procedure,” LVV environmental department head Tommi Muilu said in a statement. Still, the subsidiary can proceed with planning, measurement, soil surveys, and other activities that would only have minimal impacts.

U.S. data centers have increasingly faced resistance from residents and communities around them, with 45 projects worth $68 billion blocked in the second quarter of 2026. We haven’t seen the same level of high-profile opposition in Europe, but this is likely because of the stricter permitting and review processes in the region, where concerns are usually raised and addressed even before construction begins.

Data center energy demand is shooting constantly upwards. By 2030, some 945TWh of electricity is expected to be used to meet AI’s demand; the International Energy Agency believes — that's as much electricity as Japan uses today. The energy crunch is inescapable, and the industry’s answer has largely been to focus on the physical: build more efficient GPUs and secure more power supply from an already tight grid. But is that myopic focus on hardware actually worth it? Instead, could most of the work running on those machines be cut back to save power?

Average power usage effectiveness (PUE) has barely changed for six successive years, according to the Uptime Institute’s 2025 survey. PUE can show whether cooling and power systems are wasteful, but doesn’t account for whether the software running on the servers is doing useful work. And given servers account for around 60% of electricity demand in a modern data center, while cooling ranges from about 7% in an efficient hyperscale site to more than 30% in a less-efficient enterprise facility, focusing on eking out more efficiency from the software side of the equation seems sensible.

“The answer to what seems to be the core question – ‘Can software or algorithms meaningfully help with power and energy?’ – is yes, 100%,” said Jae-Won Chung, a PhD candidate in computer science and engineering at the University of Michigan and researcher with the ML.Energy initiative, in an interview with Tom’s Hardware Premium.

Chung suggests computing systems should be seen as a stack with hardware at the bottom, backed up by systems software, algorithms and applications. The hardware end of that equation is the hardest bit to tackle: chips are slow and costly to replace. But the other three layers are easier to change, and any gains in efficiency made there can compound.

ML.Energy’s tests of the Alibaba Qwen 3 235B A22B Thinking model suggest that running inference in FP8 – a slightly lower-precision format – consumed a third less energy than bfloat16 versions on problem-solving tasks. Chung’s own Perseus training optimiser identifies parts of a large-model training job that have less work to do, then slows them so they finish alongside busier parts. The system cut training energy by up to 30% without reducing throughput or changing the hardware.

This isn’t a tweak around the edges being made by tinkerers: some of the biggest GPU firms are doing something similar. Nvidia’s Blackwell power profiles fine-tune everything from GPU compute and memory frequencies, power limits, NVLink states and cache settings to fit the workload. Nvidia believes that can save up to 15% of energy while retaining 97% or more of overall performance, allowing power-constrained facilities to run more GPUs, raising throughput by as much as 13%.

‘The loudest rack in the room’

Chetan Visrolia, business development manager for data center infrastructure at SHI, said in an interview with Tom’s Hardware Premium that the most obvious waste he encounters is often legacy equipment supporting old code. “It is always the lowest-hanging fruit for quick savings,” he said. “It can easily be found in any data center, as it is the loudest rack on the floor.”

Cloud instances can also be made a smarter size, and AI prompts that are repeatedly used can be stored in cache rather than recomputed on the fly. There are also efficiencies to be made by getting small models to handle routine requests while holding back the larger ones for harder tasks. At the same time, better batching and compilers can raise accelerator utilisation, and crunching down prompts while throwing limits on outputs can also tamp down the number of tokens processed.

Software can also change when and where electricity is drawn. Sophie Hall, a doctoral student at ETH Zurich’s Automatic Control Laboratory who has studied workload shifting across Google’s global data-center fleet, argues in an interview with Tom’s Hardware Premium that electricity consumption isn’t necessarily the main problem.

“It’s more like: when do they use it, where do they use it, and how is it interacting with the grid?” she said. If batch jobs aren’t time-sensitive, they could be delayed until local demand falls or routed to a region with spare capacity and lower-carbon electricity. Hall’s research uses day-ahead planning and real-time scheduling to respond to grid signals while preserving performance guarantees.

Taking that kind of action was less necessary when hyperscalers built way more capacity than they actually needed and the grid wasn’t a bottleneck, Hall said. But the massive demand for AI has changed the equation.

There are limits, though: individual countries’ data sovereignty rules can prevent a workload from crossing borders, while moving large datasets across networks is something that inference providers may be wary of in case things go wrong. “The data needs to literally, physically, geographically move to a different location,” Hall said. “That’s not that easy.”

The scale of the data center demand powered by AI means that software isn’t a silver bullet or a substitute for better chips and new electricity infrastructure. But it can act as a control layer that squeezes out every last drop from those increasingly expensive assets, while also helping shift demand to places where there is more capacity.

Jevons ’ paradox is also at play, and needs to be considered when thinking about making systems run better. Efficiency doesn’t reduce a facility’s electricity use if each watt saved gets used up generating even more tokens. “Power is the core bottleneck in AI data centers,” said Chung. “We really want to make the best use of every watt we consume.”

Ukrainian drones hit a major Yandex data center in Sasovo, Russia, causing a massive fire and forcing the company to shut down the facility, according to Reuters and Meduza. Many services using Yandex Cloud are down following the attack. The site reportedly houses two of Yandex's three supercomputers used for artificial intelligence development, though it remains unclear whether the systems sustained any damage, as for now their fate is unclear. Russian missiles struck Ukrainian digital infrastructure earlier this month.

The Sasovo facility, one of Yandex's five major data centers, accommodates tens of thousands of servers, including two Nvidia A100-based supercomputers named Chervonenkis and Lyapunov. Chervonenkis, the company's most powerful system, was once ranked as the world's 19^th most powerful supercomputer in the Top 500 list in 2021, but by today's standards its 21.53 FP64 PFLOPS is enough only for the 101^st place in the list. Due to Western sanctions, Yandex can no longer upgrade the system. Both machines have been used to train AI models, including YandexGPT, Russia's leading large language model. Yandex's third supercomputer, Galushkin, is located in Vladimir.

So far, the company has not disclosed whether its supercomputers were affected by the strike, though judging by the massive fire caused by the drone incident, the systems' chances of survival are low. The potential damage to Nvidia-powered computing infrastructure could be extremely painful for Russia's AI industry, which is already constrained by restrictions on exports of advanced AI accelerators and servers to Russia. If the machines are indeed destroyed or damaged, many of its elements may be lost forever, and Yandex will be forced to use Chinese infrastructure for future model training.

The attack also disrupted Yandex Cloud infrastructure, so the company is recommending customers use alternative zones. Although Yandex claims that its key consumer services remained operational, users reported problems with Yandex Mail, Disk, Documents, Pay, and Market. Other affected services reportedly included Cian, Litmarket, Russian Railways, Spartak Moscow, cryptocurrency exchange Rapira, Astral, and the 1C business solutions. Corporate systems operated by T-Bank, Ozon, and Wildberries also experienced disruptions, according to Meduza. A Moscow-region bus operator suspended online ticket sales, while Detector404 registered 58,268 complaints over 24 hours. Many Russian media outlets, including state propaganda RIA Novosti, Vesti, and Interfax, were also down following the attack on 'infrastructure partner,' reports RBC.

It is noteworthy that the Yandex data center occupies former premises of the Sasta machine-tool factory, where Yandex established operations in 2014. Sasta manufactures industrial equipment supplied to various Russian manufacturers, including defense-related enterprises, and is therefore under Western sanctions. Regional authorities reported a fire at an industrial facility following the attack, but it remains unknown whether the intended target was the factory or Yandex's computing infrastructure. Still, no matter the target, Ukraine's sanctions against Russia's economy and military complex seem to be generally more efficient than those imposed by the West.

The incident follows Russian strikes against Ukrainian telecommunications and computing facilities. Since September 23, attacks have damaged or destroyed data centers that belong to at least eight Ukrainian companies, including Virtual Systems, MiroHost, CosmoNova, DataGroup, and CityHost. Among other websites, popular torrent trackers 1337x and NordicQuality temporarily went offline after servers that hosted them were damaged.

Ukraine is consequently moving some digital infrastructure underground, while Russian businesses are discussing deployments in countries considered friendly by Moscow, which is probably limited to just a few states, including China.

A little less than seven months since signing a memorandum of understanding (MOU) concerning memory supply and collaboration in various fields with Samsung, AMD's chief executive Lisa Su is visiting South Korea to secure additional supply of HBM memory for AI accelerators, reports Chosun.biz. Meanwhile, Samsung is seeking a broader partnership and 'hopes to turn its memory supply relationship with AMD into foundry' chip production orders, the report claims.

"We are going to talk about our broader partnership," said Lisa Su when asked by reporters about what she planned to discuss with Samsung's executives. "We have many partners at Samsung."

Before visiting Samsung headquarters, Lisa Su and Jack Huynh, senior vice president and general manager of AMD's Computing and Graphics Group, attended the AMD Gamer Day event where Su and Huynh answered some questions from South Korean journalists and called local memory makers 'important strategic partners,' something that reflects the significance of data center-grade DDR5 and HBM memory for the company's AI and data center businesses.

"Samsung Electronics and SK hynix are key supply-chain partners for AMD," Su said, Chosun reports. "AI demand is extremely high, so we are planning our long-term supply chain with our partners. […] It is important to plan three to five years ahead with our partners."

The non-binding MOU that AMD and Samsung signed this March covered four areas and remained in effect for a period of three years:

• Supply of next-generation memory products, including HBM4 and advanced DDR5;
• Co-development and optimization of memory solutions for Al and high-performance computing systems;
• Exploration of foundry manufacturing opportunities for the future AMD platforms;
• Co-optimization of advanced packaging and related semiconductor technologies.

While Samsung has become a principal supplier of HBM4 for AMD's Instinct MI400-series accelerators, expansion of collaboration beyond memory supply is considerably more complicated, as it involves considerable efforts to design chips for Samsung Foundry's process technologies as well as risks associated with a product ramp at a new contract chipmaker. Truth to be told, AMD already uses Samsung Foundry and UMC to make its "ICs in the form of programmable logic devices" however, moving production of more advanced products to Samsung is risky. Meanwhile, Samsung badly needs big customers like AMD, and the latter's dependence on HBM supply certainly gives the former leverage.

"If we transition the production of some of our products to new manufacturers, we may experience delayed product introductions, lower yields or poorer performance of our products," AMD wrote in its most recent Annual Report.

AMD has already started shipments of its Instinct MI455 accelerators with 432GB of HBM4 memory onboard as well as rack-scale Helios AI systems to interested parties.

"We have started shipping our next-generation MI455 and Helios systems," Su told journalists, according to Chosun. "These are all systems that use HBM4."

So, it looks like the company is negotiating for more memory supply. The question is, will it take a risk and adopt Samsung Foundry for something more serious than niche programmable chips in a bid to get more high-bandwidth memory and sell more expensive AI accelerators? Only time will tell.

A fortnight ago we reported on the curious case of top-secret F-35 advanced fighter parts getting swiped by China as a shipment from Australia to the U.S. transited Hong Kong. The decision to route through the Chinese territory was said to be 'inexplicable' at the time. Now, insiders talking to Bloomberg have revealed that a UPS worker missed an important email about securely routing these parts and opted for a Hong Kong transit to hasten the shipping time.

The F-35 parts were said to be sensitive military technology, not just because they were serviceable parts of one of the world’s most advanced fighter jets. These Royal Australian Air Force F-35 stealth fighter cockpit canopy and weapons-bay door were also “coated with radar-absorbing material that enhances their stealth capabilities,” noted the original report. In other words, the stealth coating’s secret recipe may have been more sensitive than the actual airplane parts.

The parts were being sent from Australia to San Francisco for inspection. According to U.S. International Traffic in Arms Regulations, also known as ITAR, certain countries and territories must be avoided when shipping such equipment. Hong Kong, as a special administrative region of China, is on the blacklist.

An email making clear that the cockpit canopy and weapons-bay door should be shipped according to ITAR rules was sent from heavy cross-border freight specialist DSV to global parcel distribution specialist UPS. Sadly, with the shipment leaving Australia late and the UPS agent reportedly not getting sight of the DSV email, reports Bloomberg, mistakes were made. Instead of routing from Australia via South Korea and/or Taiwan, the UPS agent pushed it through HK for speed. All this because “The DSV email may have been sent through a different IT system than the one used by the UPS agent,” according to Bloomberg’s unnamed sources.

Now these advanced allied warplane parts are in China’s hands, and there have been no subsequent reports indicating it admits to having them or intends to release them.

A series of unfortunate shipping events (updated)

• Late May 2026: Australia sends F-35 cockpit canopy and weapons-bay door to the U.S. for inspection, possible repair, or disposal.
• On its way to the U.S., the cargo plane stopped in South Korea.
• Then the F-35 parts consignment was diverted through Hong Kong "inexplicably," says the original source.
• NEW: Reportedly, an email from DSV that notified the UPS agent that the consignment had to be handled under ITAR rules never reached the correct person.
• NEW: The UPS agent noticed the parts shipment from Australia was late/delayed, so they decided to ship it through Hong Kong to save time.
• The Chinese government took possession of the parts in Hong Kong.
• China hasn’t returned or released the parts. The Chinese Foreign Ministry in Beijing said it is unaware of the missing parts.

The details of the missing shipment were embarrassing for the Pentagon and Lockheed Martin. Bloomberg’s new information shifts the blame somewhat to a shipping firm's miscommunication. Even though it has now been explained, the error seems pretty amateurish, though. Bloomberg contacted DSV and UPS spokespersons, but neither provided comments.

Though the Pentagon has sought to downplay the missing parts incident and characterize them as “unserviceable F-35 components,” others don’t shrug off the loss so lightly. Bloomberg quoted a representative from the non-partisan Government Accountability Office who thought the loss was “a big deal” that would see Chinese reverse engineering teams celebrating.

SpaceX is reportedly looking to raise $40 billion to acquire Nvidia AI processors in a bid to expand its compute capabilities, reports the Financial Times. The financing could combine $10 billion in bank loans with $30 billion in investment-grade debt and may be completed in 2027, though discussions are still at an early stage and there is no guarantee that a transaction will materialize. If it does, then SpaceX's xAI will get $40 billion worth of Nvidia Rubin generation hardware, which includes AI accelerators.

Although Bloomberg reports that the proposed financing would be used specifically to purchase Nvidia AI accelerators, since xAI has traditionally bought servers or rack-scale AI solutions from Nvidia and its partners, it is likely that the company will continue to do so rather than opt to get accelerators and then build AI servers itself. Assuming that xAI indeed gets Vera Rubin NVL72 systems at around $8 million per unit, then for $40 billion it is going to get approximately 5,000 or so machines with roughly 360,000 Nvidia Rubin AI accelerators inside. At this point, it is unclear whether $40 billion is xAI's entire new compute capacity budget for 2027, or if the company plans to spend more.

The scale of xAI's Nvidia deployments is already substantial. Elon Musk said last month that xAI's Colossus 2 cluster currently houses 110,000 Nvidia GB200 accelerators and 440,000 GB300 devices.

SpaceX intends to obtain approximately $10 billion from banks and raise another $30 billion on the investment-grade debt market. Apollo Global Management is expected to take the leading role in arranging the financing, while bond manager Pimco is among a limited number of lenders discussing participation, the report claims. The transaction is currently expected to close sometime in 2027.

Apollo already has considerable exposure to this market and has already participated in financing the buildout of Elon Musk's AI infrastructure. In August, Nvidia teamed up with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to establish financing platforms designed to attract more than $500 billion in third-party funding for AI infrastructure. Bloomberg reports that Apollo had previously arranged a $7 billion financing deal with Musk's xAI that financed Nvidia hardware intended for the company's Colossus 2 data center near Memphis.

No parties mentioned in the story commented on the potential deal.

Two former Groq engineers are suing Groq’s former board and former CEO Jonathan Ross, alleging the board handed over employees and company technology to Nvidia in a $20 billion deal. The proposed class action, filed in Delaware’s Court of Chancery by Benjamin Serebrin and Joshua Rubin and unsealed Monday, alleges the deal, with a $17 billion license fee for all backers and a separate $3 billion Nvidia stock pool for engineers who transferred, kept billions from other shareholders. Meanwhile, Groq is planning to deploy its own technology, now Nvidia’s Groq 3 LPX, the Financial Times reports.

Nvidia’s annual report (10-K) books $14.4 billion of the license as goodwill “primarily attributable to the workforce and future development of the licensed technology” plus $2.5 billion for the technology itself. The plaintiffs concede that “no Delaware decision has directly answered the question this case raises,” but the deal is already under a reported Department of Justice (DOJ) inquiry. The lawsuit’s outcome has implications for future license-plus-hire deals. Groq told CNBC the suit is “meritless” and said its licensing agreement with Nvidia “delivered exceptional value for Groq, our investors, and our employees.”

The deal involved both the LPU technology and the people who built it, including Ross, a former Google TPU engineer who founded Groq in 2016. The LP30 has 500MB SRAM per die and 150 TB/s bandwidth, uses Samsung’s 4nm process technology, and is rated by Nvidia at up to 1.23 FP8 PFLOPS. A single LPX rack has 256 LPUs with 128GB SRAM, 40 PB/s, and 315 FP8 PFLOPS in aggregate, as a decode co-processor for Vera Rubin NVL72. The LP30 appears to be the next-gen LPU Groq roadmapped in August 2023 on Samsung’s SF4X process with its first-gen Tensor Streaming architecture.

During Hot Chips in August of this year, Igor Arsovski, Groq’s former chief architect and now Nvidia VP of hardware, called the occasion “a pinch me moment for the Groq team that’s now integrated into the Nvidia group.” The rack was already in production at the time, Arsovski said. This supports the complaint’s factual premise, but does not answer the legal question about whether more is owed.

Nvidia’s 10-K notes indicate that “no customer contracts, existing products, or equity interests were purchased” with “$13.0 billion paid at closing and $4 billion, inclusive of imputed interest, payable within one year.” The goodwill number, as reported above, is about 85% of the $16.9 billion Nvidia booked and around 5.8x the value of the technology asset. Nvidia put significantly more value on the LPU design team and the technology’s future development than on the design itself.

Groq said when the deal was announced that it would continue “to operate as an independent company,” and it joined the Nvidia Cloud Partner program in August. Also in August, “Groq LLC” had a $350 million Series A and a $3.5 billion valuation after raising $650 million in June. Before the deal, in September 2025, the valuation was put at $6.9 billion after a $750 million round. Groq says it now operates 13 data centers and expects to scale from 54 to 200+ MW in 2027. Nvidia was slated to participate in the Series A, which Groq says will support those seeking “clusters of NVIDIA accelerated computing.”

Groq also said in August that it is “among the first adopters of NVIDIA Groq 3 LPX” with Nvidia’s Vera Rubin NVL72, deploying with Dell. Those racks are built on the LPU technology Groq licensed to Nvidia, and Groq claims to be “the only team with hands-on experience operating LPUs in production at scale.” The Financial Times said Groq “pivoted entirely to AI cloud computing, dropping its chip design efforts.”

The suit alleges that the Groq board was conflicted, failing to secure the best terms for every shareholder and denying some of them a vote on the deal. Specifically, it says Ross and other top Groq employees were allowed to “take a discount on those shares and be paid separately for following the technology to Nvidia.” According to the suit, Nvidia hired “nearly all” of Groq’s engineers, as many as 200. However, the final payout is alleged to have ignored potential upside and synergies with Nvidia.

The plaintiffs also say that because Nvidia didn’t buy Groq outright, the $17 billion license payment was taxable as income at Groq, and that the Series A valued the remaining Groq above the price paid to the shareholders who were bought out. They say four “conflicted funds” on Groq’s board, BlackRock, Social Capital, Infinitum, and Disruptive, which are not named as defendants in the case, engineered that gap and profited by staying with Groq after the buyout.

As reported by Axios in December 2025, though, sources close to the deal said that most shareholders would get per-share payouts “tied to the $20 billion valuation.” Around 90% of employees would join Nvidia, with vested shares paid in cash and unvested shares paid in vesting Nvidia stock. Staff who stayed with Groq would be paid for vested shares and get a package including “economic participation in the ongoing company.”

As for the DOJ, the New York Times reported in September that the department sent Nvidia a formal request for information with a fine being possible. Nvidia said at the time that the Groq story was “a prime example of the American system working as designed.” The deal did not go unnoticed by some senators who, in February, put it under the “reverse acqui-hire” deal label. Federal Trade Commission Chair Andrew Ferguson said in January that the agency is “beginning to examine these acqui-hires to make sure they are not an attempt to get around” its merger review process.

The litigation comes not long after Nvidia’s agreement to acquire Hugging Face (HF) a month ago, which included a purchase price and equity for HF employees who join Nvidia, a similar split. The deal is waiting on approval, but unlike the Groq deal, it is an outright purchase of the company. When the Groq deal was announced, Nvidia CEO Jensen Huang told employees in an email, obtained by CNBC, that while Nvidia was adding Groq staff and licensing its IP, “we are not acquiring Groq as a company.”

The legal question on the Groq deal remains open, and more details will come out as the case proceeds and the defendants respond. As for the LPX customers, Nvidia calls Nebius the first AI cloud to adopt LPX and says the racks will be online there later this year. Looking ahead, Nvidia has the LP35, with NVFP4 support, alongside the Rubin Ultra and LP40 for Feynman on its roadmap.

Taiwanese prosecutors have indicted 10 company leaders over Texas Instruments (TI) and Analog Devices (ADI) military-grade chips allegedly sold to China, the United Daily News reports. Orders for the chips allegedly used the name of Taiwan’s own defense research institute and were exported as “Made in Taiwan.” Investigators believe the chips ended up with Chinese research units building missiles and military radar.

The chips are high-speed military-industrial parts found on Taiwan’s strategic high-tech commodities list. The U.S. Commerce Department does not allow these to be shipped to China, Hong Kong, or Macau, and buyers must provide an end-user statement before the manufacturer ships. The equipment was cited to include radar receivers, electronic reconnaissance, jammers, ultrasound, communications gear, and antenna arrays.

Former employees and trading partners of three Taiwan distributors for ADI and TI, two large U.S. analog and mixed-signal chipmakers, used their own companies to order the chips. The orders went under the National Chung-Shan Institute of Science and Technology’s (NCSIST) name or in the names of other Taiwanese companies to claim use in Taiwan. The orders included written statements, which prosecutors say were false. The statements promised no export or re-export to U.S.-sanctioned parties, that they accept being bound by U.S. export regulations, and that the end-user declarations are required for sales outside of Taiwan. The distributors involved were duped, prosecutors say.

The parts themselves were manufactured in the U.S., Thailand, the Philippines, and South Korea with no processing in Taiwan that would change their country of origin, but were declared “Made in Taiwan” to fool customs officials. The buyers included Hong Kong companies, or those effectively controlled from mainland China, and the defendants profited from the venture. The three main groups of defendants made more than NT$92.75 million, NT$135.83 million, and NT$108.11 million, with four others earning from several hundred thousand to NT$6.41 million.

The charges include fraud, forgery, and Money Laundering Control Act offenses, with 2 to 10 years sought for each defendant. Prosecutors also asked the court to confiscate anything gained from the plot. The case for heavy sentences, prosecutors argue, lies in the products trafficked: military components seriously harm Taiwan’s national security and could instill distrust of Taiwan in the U.S. The trial, to take place in the New Taipei District Court if history is any indication, has no court date announced. Neither ADI nor TI has a comment on record about the case.

TI parts were previously found in Russian weapons in 2024 when third parties bought them for Russia’s military. Earlier this year, Supermicro co-founder Yih-Shyan “Wally” Liaw was charged in the U.S. over diverting servers with Nvidia chips to China, and has pleaded not guilty. In a separate Taiwanese case, Keelung prosecutors seized 50 Supermicro servers and detained three people. Ultimately, nine were indicted in August, including an Nvidia Taiwan manager and two of Supermicro Taiwan’s staff. Eight of them were charged with breach of trust and document forgery.

The chips in this case are not AI accelerators but rather radar and RF parts with practical military applications. The charges likely fall under fraud and forgery because breaking U.S. export control laws is not a crime in Taiwan. Taiwan’s own Foreign Trade Act carries prison terms for exporting listed high-tech goods to controlled areas without a permit. However, China counts as one of only 12 kinds of chipmaking equipment. A 2019 amendment raised fines for false origin labeling and illegal high-tech exports to protect the “Made in Taiwan” brand.

Taiwan’s economy ministry promised earlier this year to amend the Foreign Trade Act again to tighten controls on AI chip exports to China. Amendment advocates want China added as a controlled region, saying the current rules fall short of Taiwan’s trade cooperation agreements with the U.S.

Intel will remain a part of Elon Musk's Terafab project, Lip-Bu Tan, chief executive of Intel, told reporters on Wednesday. Around the same time, Elon Musk, chief executive of SpaceX, Tesla, and xAI, said that Terafab remains committed to building and operating its fab itself, but proposed to lease a part of the fab to TSMC.

Intel will continue its involvement in Terafab, Lip-Bu Tan told Bloomberg prior to the company's event in Japan that commemorates the company's presence in Japan. Back in April, Intel and Terafab, the semiconductor production venture by SpaceX, Tesla, and xAI, announced that the new chipmaker would use Intel's next-generation 14A fabrication process to make chips. Details of the collaboration are still unknown, but there is a general understanding that Intel will license its manufacturing technology to Terafab, though it is unclear whether it will also help ramp up production using the node.

At around the same time, Elon Musk debunked rumors that his companies would invest in Terafab and then partner with TSMC, which will build and operate the fab. Instead, Elon Musk proposes that TSMC sublease a part of Terafab to make chips for its customers.

"No, we will build and run the fab," Elon Musk wrote in an X post. "Let there be ZERO doubt about that. Maybe TSMC subleases part of the Terafab if they want, but nothing more than that."

Offering TSMC, the world's largest foundry that owns and operates dozens of advanced 300-mm fabs around the world, to 'sublease' a part of Terafab is quite an unusual arrangement for many reasons.

First up, TSMC has its own production facilities as well as aggressive plans for about 10 new fab phases in the coming years, and it hardly needs to sublease any capacity or part of the building or site from a third party.

Secondly, semiconductor fabs are not generic facilities: if Terafab is initially equipped and qualified around Intel's 14A process technology, converting part of the facility for TSMC's A14, A13, or A12 processes that the company will use in the back half of the decade would require different process flows, equipment configurations, materials, automation, qualification, and contamination control. Theoretically, TSMC could install a largely independent production line within the same enormous building, but at that point the arrangement would resemble TSMC building and operating its own fab inside a Musk-controlled site rather than simply renting spare manufacturing capacity.

Thirdly, if Intel licenses 14A technology to Terafab, the agreement would almost certainly contain extensive provisions governing access to Intel IP, process information, equipment configurations, and personnel. Putting TSMC engineers and manufacturing operations inside the same facility could create contractual and IP-isolation issues even if TSMC never directly touches the Intel production line. In fact, IP protection would be extraordinary at Terafab, which will complicate its operation.

Fourthly, if indeed two chipmakers end up in the same fab building, or even in the same campus, responsibility for downtime becomes messy. In the case of a Terafab-owned power distribution system, water plant, chemical system, or cooling installation fails and takes someone's production offline, determining operational responsibility and financial liability for potentially hundreds of millions of dollars of affected wafers would be considerably more complicated than at a site controlled by one company.

Finally, if Terafab can equip enough cleanroom space by the end of the decade to satisfy semiconductor requirements of Tesla, SpaceX, and xAI while still leaving substantial capacity available to TSMC, that would raise questions about whether the project had significantly overbuilt capacity in its initial phases.

Since Musk's statements at X are deliberately vague, it is hard to interpret them. In fact, there might be a conceptual problem with Musk's statement. If TSMC installs its own tools, hires its own employees, brings process technology, and production management, while Terafab just provides land, shell, and infrastructure, calling this a 'sublease' undersells what would be a TSMC fab embedded within the Terafab campus. Alternatively, if Terafab provides the tools and infrastructure and TSMC just operates them, that starts to look much closer to precisely the kind of TSMC operational involvement Musk appears to be denying.

Anyhow, while Intel has confirmed its involvement in the project and Musk wants TSMC to be part of it as well, the latest comments raise more questions than they answer.

Qualcomm has reached a deal to license Huawei patents behind the LogicFolding chip-stacking method, Bloomberg reports. The technology, which is used in the Kirin 9050 Pro, splits nearly every block across two bonded dies, and a teardown and die shots show the lower die holds mostly cache and I/O.

Qualcomm’s multiyear, cross-license deal with Huawei has a wide scope, covering 5G devices, AI services, near- and co-packaged optics and networks, a Huawei spokesperson told Bloomberg. Qualcomm also intends to purchase specific Huawei U.S. patents in compute, AI, networking, and other technologies, according to a release. Neither company’s announcement named any patents, LogicFolding, or any chip that uses it.

Bloomberg's report frames the deal as a win for Huawei, validating its chipmaking acumen. The deal remains subject to approval, and Qualcomm will reportedly pay Huawei to license its patents for the first time. According to the report, LogicFolding targets faster data transmission as a workaround for China’s lack of access to EUV lithography machines.

The Kirin 9050 Pro is used in the Huawei Mate XT 2 tri-fold smartphone, launched Sept. 7. Geekerwan, a Chinese tech review channel known for in-depth chip testing, had a lab slice the chip for its teardown. Kurnal, a semiconductor researcher who publishes die-shot analysis, also posted annotated shots of both dies on X. Geekerwan’s cross-section shows the two dies bonded face to face, and both show the execution units on top, with the PLLs also on the lower die.

LogicFolding was first unveiled on May 25 by He Tingbo, president of Huawei’s semiconductor business, at IEEE ISCAS 2026 in Shanghai, under the company’s “Tau Scaling Law,” which uses signal delay rather than transistor size as a measure. Huawei describes both layers as active and working as one. Vertical interconnects work to create shorter signal paths, which can reduce energy consumption. Peking University’s “true 3D” design tool for the technology shows a 30% wire-length cut in its own tests.

Huawei’s papers describe a “Kirin 2026” chip whose figures match the Kirin 9050 Pro’s. In a paper last month, relayed by TrendForce, Huawei said the chip uses a 1.5-micrometer hybrid-bonding pitch with about 50 million interconnects. About 10–15% of those carry signals. Core routing is 20% shorter, with critical paths up to 70% shorter. Power is reduced by as much as 66% for NPU, 58% for GPU, and 41% for a CPU performance core at the same performance level. NPU performance holds 29 TOPS at 63% less frequency and at a lower voltage.

Huawei does not name a process node. On its roadmap is Kirin 2027 at a 1-micrometer pitch and more than 100 million interconnects; for around three years out, a 720-nanometer pitch with over 200 million. It is targeting a 1.4nm-class density by 2031, with LogicFolding in the Ascend 990 AI accelerator around 2030. HiSilicon, Huawei’s chip-design unit, designs the Kirin chips, and SMIC is believed to make them, according to Reuters via TrendForce. It isn’t stated who handles the bonding process.

As for the Kirin 9050 Pro, Geekerwan tested it in a Mate 90 Pro Max phone. Analysis shows that the two dies are bonded with copper-to-copper hybrid bonding, metal layer to metal layer. The bonded circuit layers of both dies sit between two thick layers of silicon rather than directly on the package substrate. For the interconnects, about 80,000 through-silicon vias (TSVs) run through the lower die, taking up, with their keep-out zones, about 8% of the lower die’s usable area, Geekerwan says.

The split spans both dies. In the big CPU core, the execution units are on top, with the L1 and L2 caches directly beneath the load/store units, Geekerwan says. Kurnal's die shots put the GPU and NPU logic over their caches too. The lower die also holds a shared L3, a 12MiB system cache, PLLs, and the LPDDR, PCIe, camera, display, UFS, and USB interfaces, while the top die carries the ISP, modem, and memory controllers. Cache, I/O, and PLLs produce less heat and are less sensitive to the process, so they go below, Geekerwan explains; the denser compute goes on top, on a somewhat more advanced process.

Each die is just over 120 mm², which is smaller than the previous chip’s 140-plus mm². The total silicon area for both is therefore over 240 mm², up 70% overall. Density rose from 155 to 238 MTr/mm² at launch, about 53.5% higher, according to Huawei’s launch numbers. Other notable changes include the NPU’s area, up 150% across both dies, with Geekerwan measuring 68 TOPS INT8. The configuration makes sense and the rationale follows the layout, but an independent die analysis is needed for full understanding.

Huawei’s stacking approach has its competitors. TSMC’s SoIC roadmap set a 6-micrometer bond pitch for 2025 and 4.5 micrometers by 2029, with 3nm chip stacking in volume production last year. Intel’s Foveros Direct is said to have “copper bonding at a pitch of 9µm,” which reached high volume with the Clearwater Forest Xeon, and a second generation targets 3 micrometers. AMD’s first-generation 3D V-Cache stacks a 64MB L3 SRAM die on a complete CPU die, using direct copper bonding at a 9-micrometer pitch. Huawei’s 1.5-micrometer claim comes from its own paper and any comparison has to be taken with a grain of salt.

As to why Qualcomm might be interested, John Han, executive vice president and general manager of Qualcomm Technology Licensing, said the agreement reflects Qualcomm’s “recognition of Huawei’s continued innovation and intellectual property in 5G and other technology fields.” Bloomberg’s report says that at least some of the patents underpin LogicFolding, but no source names the patents. Dr. Ian Cutress, chief analyst at More Than Moore, questioned how new Huawei’s Tau Scaling is, writing on X that it is “a repackaging of what the industry has always done.”

Upon “receipt of the necessary regulatory approvals,” the cross-license and patent purchase deal should proceed. The deal is also subject to Federal Trade Commission review under the Hart-Scott-Rodino Act, Bloomberg reported. That U.S. premerger law sets a minimum deal size, $133.9 million this year, among other tests, and the deal’s value wasn’t disclosed.

Nikkei Asia reported that Qualcomm will be the net payer under the deal. However Qualcomm said on October 6 that reports characterizing it as a net payor on the license agreement are incorrect and that assertions linking the agreement to LogicFold are not accurate. Qualcomm’s licensing revenue stopped including Huawei royalties in fiscal Q2 2025, after the license expired. This follows a long history of Huawei paying for Qualcomm licenses, an agreement first inked in 2001. Huawei says it has brought in more license-related income than it’s spent since 2021. Patents behind its chip-stacking method reportedly join that business under the deal.

The industry now waits to see what comes of the FTC review. Qualcomm’s fiscal Q4 results should come early next month, as last year’s did, but its fiscal year ended Sept. 27, before the deal was announced. Additional independent analysis of the 9050 Pro’s die may come from TechInsights or SemiAnalysis. Also of interest is the next iteration in Huawei’s Kirin 2027, to see if its claims are met. The deal between the two companies currently lacks details on the extent of patent coverage, but the silicon on record can help reveal the nature of Qualcomm’s interest.

Update: 10/12/2026, 02:50 PT: Added passage reflecting Qualcomm's October 6 statement.

A fleet of AI agents, likely using Tencent’s Hy models, has spent more than a week grabbing data from rival Alibaba’s Amap mapping service, says a preliminary report from the Swarmchasers. The fleet, which ran its code on Tencent Cloud behind a hysandbox-ats proxy, reached a peak of 1,810 URL query scans on Oct. 4 alone. The Swarmchasers, a group of researchers, also noted 211 scans labeled “claude,” a reference to Anthropic’s AI models, though the code behind them matches Chinese models, not Claude.

The free urlquery.net service works by opening a web address in a sandboxed remote browser, with a public record of the scan. It’s used to verify suspicious and risky links and also helps agents reach pages they otherwise would not be able to touch directly. Leveraging the service for this type of agent work is not new, as the nonprofit lab Transluce tied some urlquery activity to OpenAI’s agents last month. The fleet in question this time used similar techniques, but on new targets and infrastructure.

The event timeline began with a single Amap scan on Aug. 25, which the researchers don’t tie to the fleet. The fleet’s own scans started over a month later, on Sept. 28, and peaked on Sunday, with 1,810 of its 2,048 scans through Oct. 4 and 213 of its 216 target locations. That day, from four to eight runs were active at once, with a peak at 14, although that could have been a handful of fast agents, the researchers say. The fleet’s first scan was only three days after OpenAI’s disclosure of it having paused “all training, evaluation, and inference with tool-use” on its most capable models.

The agents sought information about user navigation, discovering what share of Amap users arrive at each entrance of places such as a park, museum, zoo, and hospital, with one location used per run; the researchers found no evidence on urlquery.net that the runs coordinated. At the Chengdu Zoo: North Gate 71%, East Gate 23%, Southeast Gate 6%. A separate place returned: ground car park 40%, main gate 26%, underground car park 11%, plus seven other entrances. The report doesn’t say what the data is for, but speculates that the pattern may be “an evaluation or task-generation run.”

To get around Amap’s protections, the agents relied on the generation of Alibaba anti-bot tokens and the borrowing of public API access keys, in addition to the routing. They also loaded Alibaba’s own Baxia anti-bot scripts, ran an agent-written Puppeteer function through the microlink API, and tried Baidu Translate’s page translator. The method of agent access suggests an intent to bypass the rules.

The agents’ programs sent their results to inboxes on webhook.site, whose public API shows the IP address and software that created each inbox, according to its open-source code. The researchers determined that 15 out of 16 readable Amap inboxes, from Oct. 4–5, were created from Tencent Cloud, 13 of them by a script rather than a person. The 16th came from Iraq and looked like a person rather than the fleet, the researchers said.

Nine requests from the agents’ own code reached the inboxes from Tencent Cloud in Hong Kong, each carrying a Via header ending “(hysandbox-ats).” A forwarding proxy will add that header to name itself, in this case Apache Traffic Server, under a name chosen by whoever runs it, so the report treats that name as self-reported. One marked request, for Ta’er Temple, only took one second to hit after the inbox was created, 35 seconds before its address appeared anywhere public, so only the environment that created the inbox could have known it.

The “HY” identifier suggests Tencent’s Hunyuan models, although no public documentation of hysandbox exists. Tencent holds a security certificate for hysandbox.tencent-cloud.com addresses, but those names resolve to Tencent Cloud Beijing. That is not the fleet’s network, and nothing ties them directly to the proxy. Tests run by a separate team but not reviewed by the researchers suggest that the fleet did not run in Tencent Cloud’s public Agent Sandbox service in its standard internet mode. The report also cautions that “Tencent Cloud is open to anyone.”

The “claude” label appeared on 211 of the 2,048 scans through Oct. 4. Although this suggests use of Anthropic’s models, one classifier came back with a 0% reading for Claude and a second also ranked Hy4 first. In the researchers’ tests, Claude models never put their own name in a tag, while Tencent’s Hy3 called itself Claude in 29 of 36 answers when asked: “Which AI model are you, and which company trained you?” The report, which cautions that those model tests are small, says “the fleet is almost certainly not Claude.”

As it stands, no comment is currently on record from either Tencent or Alibaba. The fleet apparently hasn’t stopped, either: an update to the report says that the Oct. 5 pause lasted about eight hours and that the fleet continued to run that night. On its return, its programs reported to the same inbox as before the pause. A third party had left a note there that cited the report, with instructions for the operator to rotate its infrastructure. The researchers say “a full report will follow,” with no date given.

Japanese semiconductor lithography tool maker Gigaphoton has developed a novel neon gas recycling system named hTGM for its argon fluoride (ArF) excimer lasers. The method has demonstrated a 50% recycling rate, with hopes that it can be improved dramatically with adjusted configurations, Business Wire reports. This could help fill a gap in the chip supply chain caused by the Russian invasion of Ukraine. Before 2022, Ukraine shipped up to 50% of the neon gas used in the semiconductor industry, but its major producers had to stop exports, raising concerns over chip supply. 70% of global neon production is used in semiconductor manufacturing, particularly in advanced DUV processes.

In the aftermath, China, Taiwan, and South Korea all increased their own domestic Neon production efforts, but various chip manufacturers also integrated recycling systems at various points during the fabrication process. Gigaphoton's more efficient ArF lasers with neon recycling could further help close those loops, reducing material costs and further reducing reliance on global supply chains.

It's not the only company pushing this technology forward, either. At the start of September, Japanese environmental protection company Kanken Techno demonstrated its own Neon gas recycling system for excimer lasers at SemiCon Taiwan. It demonstrated a recycling rate of over 90%, suggesting there is considerable headroom to improve this process in the future.

What makes Neon so scarce?

Neon is one of a handful of noble gases, making it inert under standard conditions and easy to handle. However, the ease with which it vaporizes and its limited bonding ability mean it is relatively scarce in Earth's atmosphere, making it scarce. It is most efficiently obtained through fractional distillation of super-cooled air, making it most affordable to obtain as a byproduct of other processes. For decades, Ukrainian steel-mill air-separation units produced crude neon from large-scale air-separation units (ASUs), and then refined it in-country via a pair of key companies.

That meant that when Russia escalated its war against Ukraine into a full-scale invasion in 2022 and Ukrainian neon refiners shut down production, there was an immediate global response.

As SFA Oxford explains, South Korea accelerated domestic production and implemented a large-scale, national neon recycling initiative focused on in-fab recovery. Taiwan installed purification systems in existing blast-furnace ASUs, developing its own domestic supply chain. By utilizing its national infrastructure, it targeted complete self-sufficiency in neon gas production within a few years.

China integrated neon gas purification into many elements of its industrial base, twinning it with steel and petrochemical production facilities. The U.S. also responded with the development of new ASUs at Gulf-coast petrochemical hubs, but those aren't expected to meaningfully improve American neon supplies until near the end of the decade.

Lithography accounts for around 70% of global neon use, particularly in more advanced DUV processes — though crucially, not in EUV lithography, which uses a tin-plasma laser instead. With global DUV-produced semiconductor supply rapidly advancing in the wake of the AI buildout, neon supply is an important component in the chip supply chain, which has rapidly evolved to become a key asset within strategic defence and global trade.

Working with what you've got

For individual companies that can't directly control their supply chains, recycling has been a much more immediate focus. The first neon gas recycling systems were qualified for use with Cymer excimer ArF lasers at the end of 2023, and Samsung and SK Hynix became the first of the major memory makers to implement neon gas recycling in 2024.

Gigaphoton previously offered neon recycling with KrF (krypton fluoride) lasers, but has now extended that recycling capability to ArF lasers as well. It claims a single hTGM for ArF unit can reduce neon gas consumption by up to 470 kilotres per year, representing substantial material cost savings, as well as reducing company reliance on a global supply chain that has been sufficiently unstable in recent years.

However, its claims of a 50% recycling rate aren't exactly groundbreaking. Cymer's excimer laser neon recovery system demonstrated that same level of efficiency in 2015, and Gigaphoton's own testing suggested a potential peak for ArF recovery of over 90% in 2017.

That suggests it should be able to ramp up its efforts to a higher percentage quite swiftly. Especially since fellow Japanese firm Kanken Techno recently unveiled a neon recycling system with over 90% efficiency. Kanken works with both major ArF laser developers and is currently going through the certification process, with expected approval in the near future.

A temporary fix?

With neon gas recycling during chip production effectively solved, it may be that the supply of neon for the chip industry has proved to be a temporary problem. But it may be a temporary solution too, as the use of neon within the industry may be set to shrink dramatically in the years to come.

EUV lithography can build far more complex chips than DUV, and offers a future path beyond the need for neon gas usage in excimer lasers as newer electronics adopt the more capable chip designs. On top of that, alternative technologies to DUV are being developed which could produce the same DUV light to generate advanced semiconductors, but without the need for noble or toxic gases.

Solid-state laser technologies have been rapidly developing in recent years, though last year's Chinese efforts are still below required industrial power thresholds. Nanoimprint lithography also holds promise as a DUV alternative, as well as cutting costs by a comparative 90%.

There are still huge hurdles in performance and certification, as well as integration with existing fabrication plans and supply chains. But the potential to move beyond the need for neon gas within semiconductor lithography is there. Over the coming years, the combination of laser neon recycling systems and alternative manufacturing techniques could reduce the need for international neon sources to a fraction of existing levels.

EV charging firm Xeal plans to use its existing U.S. network to deploy the “world’s first edge inference compute network using idle EV charging capacity.” The big idea is that it already has 1,600+ locations and 200MW of permitted, installed electrical infrastructure, so it aims to “deploy over 100,000 NVIDIA GPUs” at these sites. Thus it has developed Laitent Pods (pictured), with each containing up to 48 Nvidia Hopper or Blackwell Ultra GPUs. We hope charging location security will be adequate to keep thieves away from these cabinets, as they are probably worth $2M+ each.

Nikhil Bharadwaj, co-founder and CEO of Xeal, explained that Laitent Pods can deliver inference compute today while new data centers are still being built. “Laitent is the fastest way to bring new compute online, tapping already permitted and grid-connected, yet idle energy capacity, located right where it's needed for ultra-low latency, with no water hookup requirements,” highlighted the exec. Grid interconnect is said to be one of the trickiest things for those looking to bring new compute online, but Xeal’s EV charging sites, which “typically operate at less than 10% of permitted capacity,” can deliver processing power via a distributed network across a metropolitan area.

An Nvidia rep also praised the upcoming Laitent Pod network for being an innovative way to bring low-latency inference closer to users while making better use of infrastructure that has already been built.

In the rendered image from Xeal, a Laitent Pod looks quite a lot like a broadband fiber cabinet that you might have on your street. A pod will boast the following specs/features:

• Powered by idle EV charging capacity
• Up to 48 Nvidia Hopper or Blackwell Ultra GPUs
• Self-contained independent cooling, no water hookup required
• Quiet operation at less than 65 dBs (comparable to a washing machine)
• Fixed rental or ancillary revenue for site owner
• Adds up to $1 million in property value for little-to-no investment
• All power bills covered as a tenant pass-through
• Potential savings for EV charging customers
• Built to withstand the outdoors, NEMA 4 enclosure
• Installed and running in hours

It claims that the location of its pods around metro areas, a feature dubbed Metro Edge, offers sub-20ms latency, a significant speedup over inference located in a distant data center.

We’ve previously reported on EVs and distributed computing, but in a slightly different context. Last year we discussed Elon Musk’s proposal to make use of the untapped asset of the millions of Tesla cars out there. He called it “100 gigawatts of inference” that could be used when the vehicles are “bored.”

Meanwhile, we must remember that EV charging stations are already being targeted by thieves. We reported about a ‘plague’ of electric car charger cable thefts driven simply by the copper scrap value of the hefty cables used at the sites. Imagine the security that would be required if/when Laitent Pods are deployed, stuffed with extremely expensive GPUs. 48 Blackwell GPUs would be worth around $2,000,000 according to 2024 figures – so surely much more by now.

The first Laitent Pod will be online by the end of the year, says Xeal.

Cyberattacks using AI agents against several of the largest South Korean banks, including Hana Bank, KB Kookmin Bank, and Shinhan Bank, are currently being investigated by South Korean authorities. According to The New York Times, at least 25,000 Shinhan Bank customers had their personal credit information leaked last week, while 99 customers and 20 current or former employees of Kookmin Bank have been affected. 89 Hana Bank customers were also affected by the hacks, which led to their information being stolen.

South Korea’s National Office of Investigation is looking at all three cases, which has also caught the attention of President Lee Jae Myung. The South Korean president said that there were signs that the hackers behind these incidents used AI models, “causing considerable public concern and anxiety,” and that he has instructed his cabinet to confirm the situation and to ensure that the damage from these incidents is minimized. “It’s now become possible to use AI to hack with ease even without specialized skills,” Lee said during the livestreamed cabinet meeting.

AI models are becoming more powerful with every release, and bad actors are taking advantage of them to find weaknesses in secure systems and exploit them. Anthropic reported the first instance of an AI-orchestrated cyberattack in November 2025, which was allegedly orchestrated by a Chinese state-sponsored group. A few months after that, a cybersecurity firm said that Taiwan was targeted by the first-ever end-to-end autonomous cyberattacks by China-linked hackers. Even AI labs that built these AI models aren’t immune from the AI-powered hacks, as security researchers breached OpenAI using Claude and gained access to its internal codebase.

There has been no indication yet of who’s behind the attacks on South Korea’s banking industry — speculation may focus on Seoul’s regional adversaries, but it can be difficult to trace and attribute hacks, especially if the actors know how to cover their tracks. Aside from that, AI agents are known to conduct unintentional cyberattacks on their own, with the Australian government complaining that it took OpenAI 84 days to inform it of a hacking incident involving Australia’s national health portal.

While the leading AI labs are believed to be putting safeguards in place to help prevent their models from being used in illegal and unethical ways, some hackers exploit hallucinations and trick AI agents into running malware. Aside from that, many open-weight and open-source models lack similar guardrails, making it easier for hackers to use them in their activities. While AI models haven’t replaced skilled attackers yet, they have made planning, reconnaissance, and attack execution so much easier and could serve as a force multiplier, allowing hackers to act more quickly and efficiently.

Private data center construction spending is up over 73%, from $49.05 billion to $84.95 billion, year over year (YoY), according to the Census Bureau’s detailed construction spending tables for August. Spending is at a record annual pace, and growth is the fastest since April 2025’s +88.2%. Data center buildouts are now about 63% of all private office construction. The report, released last week, uses Census definitions that exclude any value of “racks or servers in data centers” from its construction spending numbers. It also does not speculate on any cause of the spending increase.

The Census data covers the Value of Construction Put in Place, measured monthly, to calculate what a full year would total at that month’s pace. Data center construction is defined as “[including] buildings that contain the hardware needed for storing, processing, and transmitting digital information,” a subcategory of private office. Power plants and transmission lines built to meet data center power demand fall under Census’s separate power category.

The $39.1 billion gap between general office and data center construction spending is the widest in the series since Census data begins in January 2014. The former is at $45.8 billion, down 9.7% YoY and 34.8% since Jan. 2023. Data centers have, in fact, beaten the general office every month since Sept. 2025.

Data center construction now sits at over nine times its Jan. 2021 level of $9.26 billion. Data center construction has become the majority of private office construction, about 63% versus 47% a year earlier, and with forecasts for data center power use still climbing, that is likely to remain the case moving forward.

Private nonresidential construction was at $773.0 billion, down 1.0% YoY, or $688.1 billion and down 5.9% YoY if data centers are excluded. Of the $8.0 billion monthly rise in private nonresidential spending, $5.9 billion or 74% was for data centers. Manufacturing, down 19.8% YoY, fits the same pattern, and data center construction is now about half its size.

Power construction, up 9.7% YoY, is the other big gainer. Taken together, the picture painted is that the construction industry’s growth depends heavily on data center projects. This follows Associated Builders and Contractors chief economist Anirban Basu’s statement in a Sept. 1 release that July’s nonresidential gain was “entirely due to data centers,” and that power’s growth was boosted by their electricity needs.

Growth has gotten faster for four months now. In addition to August’s reported gain, May was +36.3% YoY, June was +55.2%, and July saw +65.3% by our math. As the numbers do not include server spend, which is also growing due to rising hardware costs from AI demand, the Census only shows part of the picture. The acceleration in construction is nevertheless a strong indicator of where things are headed. September’s results are expected on Nov. 2 and may continue the trend.