"I love these machines," writes long-time Slashdot reader Shayde: I was super-active in the Unix-PC Usenet groups back in the 90s... We hacked the hell out of them. They were small, sexy, and... they ran Unix!

Unfortunately, they were a commercial failure. There were so many things wrong with them — not just stuff that broke, but the baseline configuration was nigh on worthless. I recently was able to get another machine and got it up and running (with a few hiccups). I whipped up a video showing all the cool things it can do, but also running through what went wrong and why it ultimately failed.
The video shows the ancient green-on-black screen of 1984's AT&T Unix PC (with the OS running on a silicon drive emulation). The original machine had 512K of memory and a 10-megabyte hard drive described as slow, failure-prone, and noisy. There's also a drive for inserting floppy disks, and a separate MS-DOS board (with its own CPU) that could be plugged into the expansion slot — but the device was "remarkably heavy," weighing in aqt 40 pounds

See the strange 1984 mouse, and its keyboard with both a Return key and a separate Enter key. There's even plug-in ports for phone landlines. "It looked great," Shayde says in the video, showing off its Spirograph demo and '80s-era games like Pong, Conway's Game of Life, GNU Chess, "Trk", and NetHack. But besides slow startup times, it was expensive — in today's dollars, it would've cost roughly $15,000 — and suffered from Unix's lack of spreadsheets, word processing software and other office productivity tools at the time. At that price the Unix PCs couldn't compete with IBM's home computers and their desktop applications. "It just didn't have the resources, the software, the capabilities and the price point that made it attractive."

A satirical but working tool called Malus uses AI to create "clean room" clones of open-source software, aiming to reproduce the same functionality while shedding attribution and copyleft obligations. "It works," Mike Nolan, one of the two people behind Malus, who researches the political economy of open source software and currently works for the United Nations, told 404 Media. "The Stripe charge will provide you the thing, and it was important for us to do that, because we felt that if it was just satire, it would end up like every other piece of research I've done on open source, which ends up being largely dismissed by open source tech workers who felt that they were too special and too unique and too intelligent to ever be the ones on the bad side of the layoffs or the economics of the situation." 404 Media reports: Malus's legal strategy for bypassing copyright is based on a historically pivotal moment for software and copyright law dating back to 1982. Back then, IBM dominated home computing, and competitors like Columbia Data Products wanted to sell products that were compatible with software that IBM customers were already using. Reverse engineering IBM's computer would have infringed on the company's copyright, so Columbia Data Products came up with what we now know as a "clean room" design.

It tasked one team with examining IBM's BIOS and creating specifications for what a clone of that system would require. A different "clean" team, one that was never exposed to IBM's code, then created BIOS that met those specifications from scratch. The result was a system that was compatible with IBM's ecosystem but didn't violate its copyright because it did not copy IBM's technical process and counted as original work.

This clean room method, which has been validated by case law and dramatized in the first season of Halt and Catch Fire, made computing more open and competitive than it would have been otherwise. But it has taken on new meaning in the age of generative AI. It is now easier than ever to ask AI tools to produce software that is identical in function to existing open source projects, and that, some would argue, are built from scratch and are therefore original work that can bypass existing copyright licenses. Others would say that software produced by large language models is inherently derivative, because like any LLM output, it is trained on the collective output of humans scraped from the internet, including specific open source projects.

Malus (pronounced malice), uses AI to do the same thing. "Finally, liberation from open source license obligations," Malus's site says. "Our proprietary AI robots independently recreate any open source project from scratch. The result? Legally distinct code with corporate-friendly licensing. No attribution. No copyleft. No problems." Copyleft is a type of copyright license that ensures reproductions or applications of the software keep it free to share and modify.

An anonymous reader quotes a report from Wired: Today at a hearing of the Colorado Senate Business, Labor, and Technology committee, lawmakers voted unanimously to move Colorado state bill SB26-090 -- titled Exempt Critical Infrastructure from Right to Repair -- out of committee and into the state senate and house for a vote. The bill modifies Colorado's Consumer Right to Repair Digital Electronic Equipment act, which was passed in 2024 and went into effect in January 2026. While the protections secured by that act are wide, the new SB26-090 bill aims to "exempt information technology equipment that is intended for use in critical infrastructure from Colorado's consumer right to repair laws."

The bill is supported by tech manufacturers like Cisco and IBM, according to lobbying disclosures. These are companies that have vested interests in manufacturing things like routers, server equipment, and computers and stand to profit if they can control who fixes their products and the tools, components, and software used to make those upgrades and repairs. They also cite cybersecurity concerns, saying that giving people access to the tools and systems they would need to repair a device could also enable bad actors to use those methods for nefarious means. (This is a common argument manufacturers make when opposing right-to-repair laws.)

[...] During the hearing, more than a dozen repair advocates spoke from organizations like Pirg, the Repair Association, and iFixit opposing the bill. YouTuber and repair advocate Louis Rossmann was there. The main problem, repair advocates say, is that the bill deliberately uses vague language to make the case for controlling who can fix their products. [...] The Colorado Labor and Technology committee advanced the bill, but it still needs to go through votes on the Colorado Senate and House floors before going into effect. Those votes may take place as early as next week. Regardless of how the bill goes in the state, it's likely that manufacturers will continue their push to alter or undo repair legislation in other states across the country. "The 'information technology' and 'critical infrastructure' thing is as cynical as you can possibly be about it," says Nathan Proctor, the leader of Pirg's US right-to-repair campaign. "It sounds scary to lawmakers, but it just means the internet."

The current wording of the bill "leaves it up to the manufacturers to determine which items they will need to provide repair tools and parts to owners and independent repairers and which ones they don't," says Danny Katz, executive director CoPIRG, the Colorado branch of the consumer advocate group Pirg. "This is a bad policy and would be a big step back for Coloradans' repair rights."

iFixit CEO Kyle Wiens said in the hearing: "There's a general principle in cybersecurity that obscurity is not security," iFixit CEO Kyle Wiens said in the hearing. "The money that's behind the scenes, that's what's driving the bill."

IBM and Arm are teaming up to let Arm-based software run on IBM Z mainframes. Network World reports: The two companies plan to work on three things: building virtualization tools so Arm software can run on IBM platforms; making sure Arm applications meet the security and data residency rules that regulated industries must follow; and creating common technology layers so enterprises have more software options across both platforms, IBM said in a statement.

IBM has not said whether the virtualization work will happen at the hypervisor level, through its existing PR/SM partitioning technology, or via containers -- a question enterprise architects will need answered before they can assess the collaboration's practical value. IBM described the effort as serving enterprises that run regulated workloads and cannot simply move them to the cloud, the statement said. IBM mainframe customers have largely missed out on the efficiency and price-performance gains Arm has already delivered in the cloud. "Arm says close to half of all compute shipped to top hyperscalers in 2025 runs on Arm chips, with AWS, Google, and Microsoft deploying their own Arm silicon through Graviton, Axion, and Cobalt, respectively," reports Network World.

That gap is precisely what IBM and Arm's collaboration intends to address. "This is a mainframe adjacency play," says Rachita Rao, senior analyst at Everest Group. "The intent is to extend IBM Z and LinuxONE environments by enabling Arm-compatible workloads to run closer to systems of record. While hyperscalers use Arm to lower their own internal power costs and pass savings to cloud-native tenants, IBM is targeting the sovereign and air-gapped market."

"IBM says its quantum computer can now simulate real magnetic materials and match actual lab experiment results," writes Slashdot reader BrianFagioli, "which is something people have been waiting years to see." Instead of just theoretical output, the system reproduced neutron scattering data from a known material, meaning it lines up with real world physics. It still relies on a mix of quantum and classical computing and this is a narrow use case for now, but it is one of the first times quantum hardware has produced results that scientists can directly validate against experiments, which makes it a lot more interesting than the usual hype.
Classical computers "are not great at modeling quantum systems," according to this article at Nerds.xyz. "The math gets messy fast, and scientists end up relying on approximations... Quantum computers are supposed to solve that problem..." If this direction continues, it could start to matter in areas like superconductors, battery tech, and even drug development. Those are the kinds of problems where better simulations can actually lead to better outcomes, not just nicer charts in a research paper.
"I am extremely excited about what this means for science," said study co-author Allen Scheie from the Los Alamos National Laboratory. In an announcement from IBM, Scheie calls this "the most impressive match I've seen between experimental data and qubit simulation, and it definitely raises the bar for what can be expected from quantum computers."

BrianFagioli shares a report from NERDS.xyz: An international team of scientists has done something chemistry has never seen before. IBM, working alongside researchers from the University of Manchester, Oxford University, ETH Zurich, EPFL, and the University of Regensburg, has created and characterized a molecule whose electrons travel through its structure in a corkscrew-like pattern, fundamentally altering its chemical behavior. The findings were published today in Science. The molecule, known as C13Cl2, is the first experimental observation of what scientists call a half-Mobius electronic topology in a single molecule. To the researchers' knowledge, nothing like it has ever been synthesized, observed, or even formally predicted. And proving why it behaves the way it does required something equally extraordinary -- a quantum computer.

The whole thing started at IBM, where the molecule was assembled atom by atom from a custom precursor synthesized at Oxford. Working under ultra-high vacuum at near-absolute-zero temperatures, researchers used precisely calibrated voltage pulses to remove individual atoms one at a time. The result is an electronic structure that undergoes a 90-degree twist with each circuit through the molecule, requiring four complete loops to return to its starting phase. That is a topological property that has no counterpart anywhere in chemistry's existing record. What makes it even more interesting to folks who follow materials science is that this topology can be switched. The molecule can move reversibly between clockwise-twisted, counterclockwise-twisted, and untwisted states. That means electronic topology is not just a curiosity to be stumbled upon in nature -- it can be deliberately engineered. That is a big deal.

The quantum computing angle here is not just a supporting role. Electrons within C13Cl2 interact in deeply entangled ways, each influencing the others simultaneously. Modeling that requires tracking every possible configuration of those interactions at once -- something that causes computational demands to grow exponentially and can quickly overwhelm classical machines. A decade ago, researchers could exactly model 16 electrons classically. Today that number has crept to 18. Using IBM's quantum computer, the team was able to explore 32 electrons. Quantum computers can represent these systems directly rather than approximate them, because they operate according to the same quantum mechanical laws that govern electrons in molecules. In this case, that capability helped reveal helical molecular orbitals for electron attachment -- a fingerprint of the half-Mobius topology -- and exposed the mechanism behind the unusual structure: a helical pseudo-Jahn-Teller effect.

A 7,000-word "doomsday" thought experiment from Citrini Research helped trigger an 800-point drop in the Dow, "painting a dark portrait of a future in which technological change inspires a race to the bottom in white-collar knowledge work," reports the Wall Street Journal. From the report: Concerns of hyperscalers overspending are out. Worries of software-industry disruption don't go far enough. The "global intelligence crisis" is about to hit. The new, broader question: What if AI is so bullish for the economy that it is actually bearish? "For the entirety of modern economic history, human intelligence has been the scarce input," Citrini wrote in a post it described as a scenario dated June 2028, not a prediction. "We are now experiencing the unwind of that premium."

Many of Monday's moves roughly aligned with the situation outlined by Citrini, in which fast-advancing AI tools allow spending cuts across industries, sparking mass white-collar unemployment and in turn leading to financial contagion. Software firms DataDog, CrowdStrike and Zscaler each plunged more than 9%. International Business Machines' 13% decline was its worst one-day performance since 2000. American Express, KKR and Blackstone -- all name-checked by Citrini -- tumbled. That anxiety, coupled with renewed uncertainty about trade policy from Washington, weighed down major indexes Monday. The Dow Jones Industrial Average led declines, falling 1.7%, or 822 points. The S&P 500 shed 1%, while the Nasdaq composite retreated 1.1%.

[...] Monday's market swings extended a run of AI-linked volatility. A small research outfit that has garnered a huge Substack following for macro and thematic stock research, Citrini said in its new post that software firms, payment processors and other companies formed "one long daisy chain of correlated bets on white-collar productivity growth" that AI is poised to disrupt. [...] Shares in DoorDash also veered 6.6% lower Monday after Citrini's Substack note called the delivery app a "poster child" for how new tools would upend companies that monetize interpersonal friction. In the research firm's scenario, AI agents would help both drivers and customers navigate food deliveries at much lower costs.

IBM shares plunged nearly 13% on Monday after Anthropic published a blog post arguing that its Claude Code tool could automate much of the complex analysis work involved in modernizing COBOL, the decades-old programming language that still underpins an estimated 95% of ATM transactions in the United States and runs on the kind of mainframe systems IBM has sold for generations.

Anthropic said the shrinking pool of developers who understand COBOL had long made modernization cost-prohibitive, and that AI could now flip that equation by mapping dependencies and documenting workflows across thousands of lines of legacy code. The sell-off deepened a rough 2026 for IBM, whose shares are now down more than 22% year to date.

Besides running tech operations at the UK's Post Office, their interim CTO is also removing and replacing Fujitsu's Horizon system, which Computer Weekly describes as "the error-ridden software that a public inquiry linked to 13 people taking their own lives."

After over 16 years of covering the scandal they'd first discovered back in 2009, Computer Weekly now talks to CTO Paul Anastassi about his plans to finally remove every trace of the Horizon system that's been in use at Post Office branches for over 30 years — before the year 2030: "There are more than 80 components that make up the Horizon platform, and only half of those are managed by Fujitsu," said Anastassi. "The other components are internal and often with other third parties as well," he added... The plan is to introduce a modern front end that is device agnostic. "We want to get away from [the need] to have a certain device on a certain terminal in your branch. We want to provide flexibility around that...."

Anastassi is not the first person to be given the task of terminating Horizon and ending Fujitsu's contract. In 2015, the Post Office began a project to replace Fujitsu and Horizon with IBM and its technology, but after things got complex, Post Office directors went crawling back to Fujitsu. Then, after Horizon was proved in the High Court to be at fault for the account shortfalls that subpostmasters were blamed and punished for, the Post Office knew it had to change the system. This culminated in the New Branch IT (NBIT) project, but this ran into trouble and was eventually axed. This was before Anastassi's time, and before that of its new top team of executives....

Things are finally moving at pace, and by the summer of this year, two separate contracts will be signed with suppliers, signalling the beginning of the final act for Fujitsu and its Horizon system.
Anastassi has 30 years of IT management experience, the article points out, and he estimates the project will even bring "a considerable cost saving over what we currently pay for Fujitsu."

IBM said it will triple entry-level hiring in the US in 2026, even as AI appears to be weighing on broader demand for early-career workers. From a report: While the company declined to disclose specific hiring figures, it said the expansion will be "across the board," affecting a wide range of departments. "And yes, it's for all these jobs that we're being told AI can do," said Nickle LaMoreaux, IBM's chief human resources officer, speaking at a conference this week in New York.

LaMoreaux said she overhauled entry-level job descriptions for software developers and other roles to make the case internally for the recruitment push. "The entry-level jobs that you had two to three years ago, AI can do most of them," she said at Charter's Leading With AI Summit. "So, if you're going to convince your business leaders that you need to make this investment, then you need to be able to show the real value these individuals can bring now. And that has to be through totally different jobs."

The American economy's most valuable companies are now worth trillions of dollars more than their predecessors were a generation ago, yet they employ a fraction of the workers -- and a new analysis by the Wall Street Journal argues that this widening gap between capital and labor is the defining economic story of our time.

Labor received 58% of gross domestic income in 1980; by the third quarter of 2025, that figure had fallen to 51.4%. Corporate profits' share rose from 7% to 11.7% over the same period. Nvidia, the most valuable US company in 2026, is nearly 20 times as valuable as IBM was in 1985 in inflation-adjusted terms and employs roughly a tenth as many people. Since the end of 2019, real average hourly wages have risen 3% while corporate profits have climbed 43%.

Household stock wealth now equals almost 300% of annual disposable income, up from 200% in 2019. Yale economist Pascual Restrepo predicted that AI integration will shrink labor's share of revenue further, just as factory automation did for blue-collar workers in decades past.

Alphabet has lined up banks to sell a rare 100-year bond, stepping up a borrowing spree by Big Tech companies racing to fund their vast investments in AI this year. From a report: The so-called century bond will form part of a debut sterling issuance this week by Google's parent company, according to people familiar with the matter. Alphabet was also selling $15bn of dollar bonds on Monday and lining up a Swiss franc bond sale, the people said.

Century bonds -- long-term borrowing at its most extreme -- are highly unusual, although a flurry were sold during the period of very low interest rates that followed the financial crisis, including by governments such as Austria and Argentina. The University of Oxford, EDF and the Wellcome Trust -- the most recent in 2018 -- are the only issuers to have previously tapped the sterling century market.

Such sales are even rarer in the tech sector, with most of the industry's biggest groups issuing up to 40 years, although IBM sold a 100-year bond back in 1996. Big Tech companies and their suppliers are expected to invest almost $700bn in AI infrastructure this year and are increasingly turning to the debt markets to finance the giant data centre build-out. Michael Burry, writing on Substack: Alphabet looking to issue a 100-year bond. Last time this happened in tech was Motorola in 1997, which was the last year Motorola was considered a big deal.

At the start of 1997, Motorola was a top 25 market cap and top 25 revenue corporation in America. Never again. The Motorola corporate brand in 1997 was ranked #1 in the US, ahead of Microsoft. In 1998, Nokia overtook Motorola in cell phones, and after the iPhone it fell out of the consumer eye. Today Motorola is the 232nd largest market cap with only $11 billion in sales.

An anonymous reader quotes a report from Network World: The global technology sector eliminated some 244,851 jobs in 2025, according to a report from RationalFX. The U.K.-based financial services company says the worldwide downsizing reflects how companies in 2025 restructured their operations to focus on efficiency, profitability, and AI-driven productivity. The RationalFX analysis, which examined layoffs reported by TrueUp, TechCrunch, and multiple state WARN databases, points to economic uncertainty, elevated interest rates, and accelerating AI and automation adoption as reasons that 2025 marked "another year of sustained downsizing following the post-pandemic correction that began in 2022."

Companies indicated that AI and automation were among the most frequently cited drivers for layoffs in 2025. Some companies retrained employees when faced with the technology; many replaced roles entirely, RationalFX reports. "Tech sector layoffs in 2025 displaced hundreds of thousands of workers worldwide as companies accelerated structural resets rather than short-term cost corrections," said Alan Cohen, analyst at RationalFX, in a statement. "While macroeconomic pressures such as high interest rates, trade restrictions, and geopolitical uncertainty continued to weigh on business confidence, the dominant force behind last year's job cuts was the rapid adoption of automation and artificial intelligence."

The analysis also uncovered that U.S.-headquartered technology companies were responsible for the majority of job losses, accounting for approximately 69.7% of all global tech layoffs. This resulted in more than 170,000 employees being cut across both domestic and offshore operations from U.S. tech companies. California spearheaded layoffs in the U.S. tech sector this year, with 73,499 job cuts accounting for roughly 43.08% of all tech layoffs in the country, according to the RationalFX report. The report also points out that Washington has seen 42,221 tech jobs cut since the start of the year, accounting for 24.74% of all U.S. tech layoffs. Intel contributed the single largest number of layoffs last year, reducing its headcount from 109,000 people at the end of 2024 to around 75,000 by the end of 2025. Other major U.S. tech companies with large-scale layoffs last year include Amazon (more than 20,000 jobs cut), Microsoft (approximately 19,215 layoffs), Verizon (15,000 employees), Accenture (11,000 employees), IBM (9,000 job cuts), and HP (6,000 roles).

An anonymous reader shared this report from CNBC: The Trump administration on Monday unveiled a new initiative dubbed the "U.S. Tech Force," comprising about 1,000 engineers and other specialists who will work on artificial intelligence infrastructure and other technology projects throughout the federal government.

Participants will commit to a two-year employment program working with teams that report directly to agency leaders in "collaboration with leading technology companies," according to an official government website. ["...and work closely with senior managers from companies partnering with the Tech Force."] Those "private sector partners" include Amazon Web Services, Apple, Google Public Sector, Dell Technologies, Microsoft, Nvidia, OpenAI, Oracle, Palantir, Salesforce and numerous others [including AMD, IBM, Coinbase, Robinhood, Uber, xAI, and Zoom], the website says.

The Tech Force shows the Trump administration increasing its focus on developing America's AI infrastructure as it competes with China for dominance in the rapidly growing industry... The engineering corps will be working on "high-impact technology initiatives including AI implementation, application development, data modernization, and digital service delivery across federal agencies," the site says.
"Answer the call," says the new web site at TechForce.gov.

"Upon completing the program, engineers can seek employment with the partnering private-sector companies for potential full-time roles — demonstrating the value of combining civil service with technical expertise." [And those private sector companies can also nominate employees to participate.] "Annual salaries are expected to be in the approximate range of $150,000 to $200,000."

IBM is buying Confluent for $11 billion in a major push to own real-time data streaming infrastructure essential for enterprise AI workloads. It marks Big Blue's biggest acquisition since Red Hat in 2019. Bloomberg reports: The AI boom has touched off billions of dollars in deals for businesses that build, train or leverage the technology, propelling the value of an entire ecosystem of data center developers, software makers, generative AI tool developers and data management firms. Mountain View, California-based Confluent sits in the data corner of that world, providing a platform for companies to gather -- or "stream" -- and analyze data in real time as opposed to shipping data in clunkier batches.

Manufacturers such as Michelin, for example, have used Confluent's platform to optimize their inventories of raw and semi-finished materials live. Instacart adopted Confluent to develop real-time fraud detection systems and gain more visibility into the availability of products sold on its grocery delivery platform. Businesses are increasingly tapping AI systems that manage tasks like this in real-time and require live flows of data to do so. IBM, which pioneered mainframe computers, has been trying to reposition its business around AI over the past few years. Under Chief Executive Officer Arvind Krishna, it's been buying software companies and selling generative AI-related services to enterprise clients. Software now makes up almost half its total revenue and continues to grow at a steady rate.

An anonymous reader quotes a report from the BBC: The island of Hokkaido has long been an agricultural powerhouse -- now Japan is investing billions to turn it into a global hub for advanced semiconductors. More than half of Japan's dairy produce comes from Hokkaido, the northernmost of its main islands. In winter, it's a wonderland of ski resorts and ice-sculpture festivals; in summer, fields bloom with bands of lavender, poppies and sunflowers. These days, cranes are popping up across the island -- building factories, research centers and universities focused on technology. It's part of Japan's boldest industrial push in a generation: an attempt to reboot the country's chip-making capabilities and reshape its economic future.

Locals say that beyond the cattle and tourism, Hokkaido has long lacked other industries. There's even a saying that those who go there do so only to leave. But if the government succeeds in turning Hokkaido into Japan's answer to Silicon Valley -- or "Hokkaido Valley", as some have begun to call it -- the country could become a new contender in the $600 billion race to supply the world's computer chips. At the heart of the plan is Rapidus, a little-known company backed by the government and some of Japan's biggest corporations including Toyota, Softbank and Sony.

Born out of a partnership with IBM, it has raised billions of dollars to build Japan's first cutting-edge chip foundry in decades. The government has invested $12 billion in the company, so that it can build a massive semiconductor factory or "fab" in the small city of Chitose. In selecting the Hokkaido location, Rapidus CEO Atsuyoshi Koike points to Chitose's water, electricity infrastructure and its natural beauty. Mr Koike oversaw the fab design, which will be completely covered in grass to harmonize with Hokkaido's landscape, he told the BBC. Local authorities have also flagged the region as being at lower risk of earthquakes compared to other potential sites in Japan.

IBM and Cisco plan to link quantum computers over long distances by the early 2030s, "with the goal of demonstrating the concept is workable by the end of 2030," reports Reuters. "The move could pave the way for a quantum internet, though executives at the two companies cautioned that the networks would require technologies that do not currently exist and will have to be developed with the help of universities and federal laboratories." From the report: The challenge begins with a problem: Quantum computers like IBM's sit in massive cryogenic tanks that get so cold that atoms barely move. To get information out of them, IBM has to figure out how to transform information in stationary "qubits" -- the fundamental unit of information in a quantum computer -- into what Jay Gambetta, director of IBM Research and an IBM fellow, told Reuters are "flying" qubits that travel as microwaves.

But those flying microwave qubits will have to be turned into optical signals that can travel between Cisco switches on fiber-optic cables. The technology for that transformation -- called a microwave-optical transducer -- will have to be developed with the help of groups like the Superconducting Quantum Materials and Systems Center, led by the Fermi National Accelerator Laboratory near Chicago, among others. Along the way, Cisco and IBM will also publish open-source software to weave all the parts together.

An anonymous reader quotes a report from ZDNet: At KubeCon North America 2025 in Atlanta, the Cloud Native Computing Foundation (CNCF)'s leaders predicted an enormous surge in cloud-native computing, driven by the explosive growth of AI inference workloads. How much growth? They're predicting hundreds of billions of dollars in spending over the next 18 months. [...] Where cloud-native computing and AI inference come together is when AI is no longer a separate track from cloud-native computing. Instead, AI workloads, particularly inference tasks, are fueling a new era where intelligent applications require scalable and reliable infrastructure. That era is unfolding because, said [CNCF Executive Director Jonathan Bryce], "AI is moving from a few 'Training supercomputers' to widespread 'Enterprise Inference.' This is fundamentally a cloud-native problem. You, the platform engineers, are the ones who will build the open-source platforms that unlock enterprise AI."

"Cloud native and AI-native development are merging, and it's really an incredible place we're in right now," said CNCF CTO Chris Aniszczyk. The data backs up this opinion. For example, Google has reported that its internal inference jobs have processed 1.33 quadrillion tokens per month recently, up from 980 trillion just months before. [...] Aniszczyk added that cloud-native projects, especially Kubernetes, are adapting to serve inference workloads at scale: "Kubernetes is obviously one of the leading examples as of the last release the dynamic resource allocation feature enables GPU and TPU hardware abstraction in a Kubernetes context." To better meet the demand, the CNCF announced the Certified Kubernetes AI Conformance Program, which aims to make AI workloads as portable and reliable as traditional cloud-native applications.

"As AI moves into production, teams need a consistent infrastructure they can rely on," Aniszczyk stated during his keynote. "This initiative will create shared guardrails to ensure AI workloads behave predictably across environments. It builds on the same community-driven standards process we've used with Kubernetes to help bring consistency as AI adoption scales." What all this effort means for business is that AI inference spending on cloud-native infrastructure and services will reach into the hundreds of billions within the next 18 months. That investment is because CNCF leaders predict that enterprises will race to stand up reliable, cost-effective AI services.

An anonymous reader quotes a report from MIT Technology Review: The US- and UK-based company Quantinuum today unveiled Helios, its third-generation quantum computer, which includes expanded computing power and error correction capability. Like all other existing quantum computers, Helios is not powerful enough to execute the industry's dream money-making algorithms, such as those that would be useful for materials discovery or financial modeling. But Quantinuum's machines, which use individual ions as qubits, could be easier to scale up than quantum computers that use superconducting circuits as qubits, such as Google's and IBM's. "Helios is an important proof point in our road map about how we'll scale to larger physical systems," says Jennifer Strabley, vice president at Quantinuum, which formed in 2021 from the merger of Honeywell Quantum Solutions and Cambridge Quantum. Honeywell remains Quantinuum's majority owner.

Located at Quantinuum's facility in Colorado, Helios comprises a myriad of components, including mirrors, lasers, and optical fiber. Its core is a thumbnail-size chip containing the barium ions that serve as the qubits, which perform the actual computing. Helios computes with 98 barium ions at a time; its predecessor, H2, used 56 ytterbium qubits. The barium ions are an upgrade, as they have proven easier to control than ytterbium. These components all sit within a chamber that is cooled to about 15 Kelvin (-432.67 ), on top of an optical table. Users can access the computer by logging in remotely over the cloud. [...] Helios is noteworthy for its qubits' precision, says Rajibul Islam, a physicist at the University of Waterloo in Canada, who is not affiliated with Quantinuum. The computer's qubit error rates are low to begin with, which means it doesn't need to devote as much of its hardware to error correction. Quantinuum had pairs of qubits interact in an operation known as entanglement and found that they behaved as expected 99.921% of the time. "To the best of my knowledge, no other platform is at this level," says Islam.

[...] Besides increasing the number of qubits on its chip, another notable achievement for Quantinuum is that it demonstrated error correction "on the fly," says David Hayes, the company's director of computational theory and design, That's a new capability for its machines. Nvidia GPUs were used to identify errors in the qubits in parallel. Hayes thinks that GPUs are more effective for error correction than chips known as FPGAs, also used in the industry. Quantinuum has used its computers to investigate the basic physics of magnetism and superconductivity. Earlier this year, it reported simulating a magnet on H2, Quantinuum's predecessor, with the claim that it "rivals the best classical approaches in expanding our understanding of magnetism." Along with announcing the introduction of Helios, the company has used the machine to simulate the behavior of electrons in a high-temperature superconductor. Quantinuum is expanding its Helios line with a new system in Minnesota. It's also started developing its fourth-generation quantum computer, Sol, set for 2027 with 192 qubits. Then, a fifth-generation system, Apollo, is expected in 2029 with thousands of qubits and full fault tolerance.

IBM will cut thousands of roles this quarter while it continues to shift focus to higher-growth software and services, Bloomberg News reported on Tuesday. From a report: "We routinely review our workforce through this lens and at times rebalance accordingly," Bloomberg quoted a company spokesperson saying. "In the fourth quarter we are executing an action that will impact a low single-digit percentage of our global workforce."