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."

An anonymous reader shares a column: As the AI boom develops, consultants are in a tricky spot. The pandemic, inflation and economic uncertainty have encouraged many of their big clients to tighten expenditure. The U.S. government, one of the biggest spenders, has been cancelling multiple billion-dollar contracts in an effort to conserve cash. In March, 10 of the largest consultants including Deloitte, Accenture, Booz Allen Hamilton, IBM and Guidehouse were targeted by the Department of Government Efficiency to justify their fees. As a result, the largest listed players' shares have collapsed by up to 30% in the past two years, against the S&P 500's 50% jump.

AI is, in some respects, a boon. In September, Accenture said it had helped it cut 11,000 jobs, and CEO Julie Sweet is set to augment that with staff that cannot be retrained. Salesforce recently laid off 4000 customer support workers. Microsoft has halted hiring in its consulting business. Unfortunately, big clients are cottoning on to the advantages too. One finance chief of a large UK company outlined the issue for Breakingviews via an illustrative example. Say an outsourced project costs the client $1 million to do themselves, and Accenture and the like have historically been able to do the same job for $200,000. With the advent of machine learning, companies can do the same work for just $10,000. This gives clients considerable leverage. If consultants won't lower their prices to near the relevant level, the client can find one who will. Or just do the job itself.

OpenAI's rival Anthropic has a different approach — and "a clearer path to making a sustainable business out of AI," writes the Wall Street Journal. Outside of OpenAI's close partnership with Microsoft, which integrates OpenAI's models into Microsoft's software products, OpenAI mostly caters to the mass market... which has helped OpenAI reach an annual revenue run rate of around $13 billion, around 30% of which it says comes from businesses.

Anthropic has generated much less mass-market appeal. The company has said about 80% of its revenue comes from corporate customers. Last month it said it had some 300,000 of them... Its cutting-edge Claude language models have been praised for their aptitude in coding: A July report from Menlo Ventures — which has invested in Anthropic — estimated via a survey that Anthropic had a 42% market share for coding, compared with OpenAI's 21%. Anthropic is also now ahead of OpenAI in market share for overarching corporate AI use, Menlo Ventures estimated, at 32% to OpenAI's 25%. Anthropic is also surprisingly close to OpenAI when it comes to revenue. The company is already at a $7 billion annual run rate and expects to get to $9 billion by the end of the year — a big lead over its better-known rival in revenue per user.

Both companies have backing in the form of investments from big tech companies — Microsoft for OpenAI, and a combination of Amazon and Google for Anthropic — that help provide AI computing infrastructure and expose their products to a broad set of customers. But Anthropic's growth path is a lot easier to understand than OpenAI's. Corporate customers are devising a plethora of money-saving uses for AI in areas like coding, drafting legal documents and expediting billing. Those uses are likely to expand in the future and draw more customers to Anthropic, especially as the return on investment for them becomes easier to measure...

Demonstrating how much demand there is for Anthropic among corporate customers, Microsoft in September said Anthropic's leading language model, Claude, would be offered within its Copilot suite of software despite Microsoft's ties to OpenAI.
"There is also a possibility that OpenAI's mass-market appeal becomes a turnoff for corporate customers," the article adds, "who want AI to be more boring and useful than fun and edgy."

IBM announced that its quantum error-correction algorithm can now run in real time on standard AMD field-programmable gate array (FPGA) chips -- a major step toward making quantum computing more practical and affordable. Reuters reports: In June, IBM said it had developed an algorithm to run alongside quantum chips that can address such errors. In a research paper seen by Reuters to be published on Monday, IBM will show it can run those algorithms in real time on a type of chip called a field programmable gate array manufactured by AMD.

Jay Gambetta, director of IBM research, said the work showed that IBM's algorithm not only works in the real world, but can operate on a readily available AMD chip that is not "ridiculously expensive." "Implementing it, and showing that the implementation is actually 10 times faster than what is needed, is a big deal," Gambetta said in an interview. IBM has a multi-year plan to build a quantum computer called Starling by 2029. Gambetta said the algorithm work disclosed Friday was completed a year ahead of schedule.

Google announced Wednesday that its quantum computer achieved the first verifiable quantum advantage, running a new algorithm 13,000 times faster than a top supercomputer. The algorithm, called Quantum Echoes, was published in the journal Nature. The results can be replicated on another quantum computer of similar quality, something Google had not demonstrated before. The quantum computer uses a chip called Willow, which was announced in December 2024. Hartmut Neven, head of Google's Quantum AI research lab, called the work a demonstration of the first algorithm with verifiable quantum advantage and a milestone on the software track.

Michel H. Devoret, who won this year's Nobel Prize in Physics and joined Google in 2023, said future quantum computers will run calculations impossible with classical algorithms. Google stopped short of claiming the work would have practical uses on its own. Instead, the company said Quantum Echoes demonstrated a technique that could be applied to other algorithms in drug discovery and materials science.

A second paper published Wednesday on arXiv showed how the method could be applied to nuclear magnetic resonance. The experiment involved a relatively small quantum system that fell short of full practical quantum advantage because it was not able to work faster than a traditional computer. Google exhaustively red-teamed the research, putting some researchers to work trying to disprove its own results.

Prineha Narang, a professor at UCLA, called the advance meaningful. The quantum computer tested two molecules, one with 15 atoms and another with 28 atoms. Results on the quantum computer matched traditional NMR and revealed information not usually available from NMR. Google's research competes against Microsoft, IBM, universities and efforts in China. The Chinese government has committed more than $15.2 billion to quantum research. Previous claims of quantum advantage have been met with skepticism.

Longtime Slashdot reader kamesh shares a report from TechCrunch: Tech behemoth IBM is teaming up with AI research lab Anthropic to bring AI into its software. Armonk, New York-based IBM announced it will be adding Anthropic's Claude large language model family into some of its software products on Tuesday. The first product to tap Claude will be IBM's integrated development environment, which is already available to a select group of customers. IBM also announced it created a guide in partnership with Anthropic on how enterprises can build, deploy, and maintain enterprise-grade AI agents. Terms of the deal were not disclosed.

An anonymous reader shared this report from the Washington Post: Finnish tech firm Bluefors, a maker of ultracold refrigerator systems critical for quantum computing, has purchased tens of thousands of liters of Helium-3 from the moon — spending "above $300 million" — through a commercial space company called Interlune. The agreement, which has not been previously reported, marks the largest purchase of a natural resource from space.

Interlune, a company founded by former executives from Blue Origin and an Apollo astronaut, has faced skepticism about its mission to become the first entity to mine the moon (which is legal thanks to a 2015 law that grants U.S. space companies the rights to mine on celestial bodies). But advances in its harvesting technology and the materialization of commercial agreements are gradually making this undertaking sound less like science fiction. Bluefors is the third customer to sign up, with an order of up to 10,000 liters of Helium-3 annually for delivery between 2028 and 2037...

Helium-3 is lighter than the Helium-4 gas featured at birthday parties. It's also much rarer on Earth. But moon rock samples from the Apollo days hint at its abundance there. Interlune has placed the market value at $20 million per kilogram (about 7,500 liters). "It's the only resource in the universe that's priced high enough to warrant going out to space today and bringing it back to Earth," said Rob Meyerson [CEO of Interlune and former president of Blue Origin]...

[H]eat, even in small doses, can cause qubits to produce errors. That's where Helium-3 comes in. Bluefors makes the cooling technology that allows the computer to operate — producing chandelier-type structures known as dilution refrigerators. Their fridges, used by quantum computer leader IBM, contain a mixture of Helium-3 and Helium-4 that pushes temperatures below 10 millikelvins (or minus-460 degrees Fahrenheit)... Existing quantum computers have been built with more than a thousand qubits, he said, but a commercial system or data center would need a million or more. That could require perhaps thousands of liters of Helium-3 per quantum computer. "They will need more Helium-3 than is available on planet Earth," said Gary Lai [a co-founder and chief technology officer of Interlune, who was previously the chief architect at Blue Origin]. Most Helium-3 on Earth, he said, comes from the decay of tritium (an isotope of hydrogen) in nuclear weapons stockpiles, but between 22,000 and 30,000 liters are made each year...

"We estimate there's more than a million metric tons of Helium-3 on the moon," Meyerson said. "And it's been accumulating there for 4 billion years." Now, they just need to get it.
Interlune CEO Meyerson tells the post "It's really all about establishing a resilient supply chain for this critical material" — adding that in the long-term he could also see Helium-3 being used for other purposes including fusion energy.