As Kent Halliburton stood in a bathroom at the Rosewood Hotel in central Amsterdam, thousands of miles from home, running his fingers through an envelope filled with 10,000 euros in crisp banknotes, he started to wonder what he had gotten himself into.

Halliburton is the cofounder and CEO of Sazmining, a company that operates bitcoin mining hardware on behalf of clients—a model known as “mining-as-a-service.” Halliburton is based in Peru, but Sazmining runs mining hardware out of third-party data centers across Norway, Paraguay, Ethiopia, and the United States.

As Halliburton tells it, he had flown to Amsterdam the previous day, August 5, to meet Even and Maxim, two representatives of a wealthy Monaco-based family. The family office had offered to purchase hundreds of bitcoin mining rigs from Sazmining—around $4 million worth—which the company would install at a facility currently under construction in Ethiopia. Before finalizing the deal, the family office had asked to meet Halliburton in person.

When Halliburton arrived at the Rosewood Hotel, he found Even and Maxim perched in a booth. They struck him as playboy, high-roller types—particularly Maxim, who wore a tan three-piece suit and had a highly manicured look, his long dark hair parted down the middle. A Rolex protruded from the cuff of his sleeve.

Over a three-course lunch—ceviche with a roe garnish, Chilean sea bass, and cherry cake—they discussed the contours of the deal and traded details about their respective backgrounds. Even was talkative and jocular, telling stories about blowout parties in Marrakech. Maxim was aloof; he mostly stared at Halliburton, holding his gaze for long periods at a time as though sizing him up.

As a relationship-building exercise, Even proposed that Halliburton sell the family office around $3,000 in bitcoin. Halliburton was initially hesitant, but chalked it up as a peculiar dating ritual. One of the guys slid Halliburton the cash-filled envelope and told him to go to the bathroom, where he could count out the amount in private. “It felt like something out of a James Bond movie,” says Halliburton. “It was all very exotic to me.”

https://arstechnica.com/information-technology/2025/11/bonkers-bitcoin-heist-5-star-hotels-cash-filled-envelopes-vanishing-funds/

Market concerns and Google’s position

Alphabet’s recent market performance has been driven by investor confidence in the company’s ability to compete with OpenAI’s ChatGPT, as well as its development of specialized chips for AI that can compete with Nvidia’s. Nvidia recently reached a world-first $5 trillion valuation due to making GPUs that can accelerate the matrix math at the heart of AI computations.

Despite acknowledging that no company would be immune to a potential AI bubble burst, Pichai argued that Google’s unique position gives it an advantage. He told the BBC that the company owns what he called a “full stack” of technologies, from chips to YouTube data to models and frontier science research. This integrated approach, he suggested, would help the company weather any market turbulence better than competitors.

Pichai also told the BBC that people should not “blindly trust” everything AI tools output. The company currently faces repeated accuracy concerns about some of its AI models. Pichai said that while AI tools are helpful “if you want to creatively write something,” people “have to learn to use these tools for what they’re good at and not blindly trust everything they say.”

In the BBC interview, the Google boss also addressed the “immense” energy needs of AI, acknowledging that the intensive energy requirements of expanding AI ventures have caused slippage on Alphabet’s climate targets. However, Pichai insisted that the company still wants to achieve net zero by 2030 through investments in new energy technologies. “The rate at which we were hoping to make progress will be impacted,” Pichai said, warning that constraining an economy based on energy “will have consequences.”

Even with the warnings about a potential AI bubble, Pichai did not miss his chance to promote the technology, albeit with a hint of danger regarding its widespread impact. Pichai described AI as “the most profound technology” humankind has worked on.

“We will have to work through societal disruptions,” he said, adding that the technology would “create new opportunities” and “evolve and transition certain jobs.” He said people who adapt to AI tools “will do better” in their professions, whatever field they work in.

https://arstechnica.com/ai/2025/11/googles-sundar-pichai-warns-of-irrationality-in-trillion-dollar-ai-investment-boom/

Each defendant also helped the IT workers pass employer vetting procedures. Travis and Salazar, for example, appeared for drug testing on behalf of the workers.

Travis, an active-duty member of the US Army at the time, received at least $51,397 for his participation in the scheme. Phagnasay and Salazar earned at least $3,450 and $4,500, respectively. In all, the fraudulent jobs earned roughly $1.28 million in salary payments from the defrauded US companies, the vast majority of which were sent to the IT workers overseas.

The fifth defendant, Ukrainian national Oleksandr Didenko, pleaded guilty to one count of aggravated identity theft, in addition to wire fraud. He admitted to participating in a “years-long scheme that stole the identities of US citizens and sold them to overseas IT workers, including North Korean IT workers, so they could fraudulently gain employment at 40 US companies.” Didenko received hundreds of thousands of dollars from victim companies who hired the fraudulent applicants. As part of the plea agreement, Didenko is forfeiting more than $1.4 million, including more than $570,000 in fiat and virtual currency seized from him and his co-conspirators.

In 2022, the US Treasury Department said that the Democratic People’s Republic of Korea employs thousands of skilled IT workers around the world to generate revenue for the country’s weapons of mass destruction and ballistic missile programs.

“In many cases, DPRK IT workers represent themselves as US-based and/or non-North Korean teleworkers,” Treasury Department officials wrote. “The workers may further obfuscate their identities and/or location by sub-contracting work to non North Koreans. Although DPRK IT workers normally engage in IT work distinct from malicious cyber activity, they have used the privileged access gained as contractors to enable the DPRK’s malicious cyber intrusions. Additionally, there are likely instances where workers are subjected to forced labor.”

Other US government advisories posted in 2023 and 2024 concerning similar programs have been removed with no explanation.

In Friday’s release, the Justice Department also said it’s seeking the forfeiture of more than $15 million worth of USDT, a cryptocurrency stablecoin pegged to the US dollar, that the FBI seized in March from North APT38 actors. The seized funds were derived from four heists APT38 carried out, two in July 2023 against virtual currency payment processors in Estonia and Panama and two in November 2023 thefts from exchanges in Panama and Seychelles.

Justice Department attempts to locate, seize, and forfeit all the stolen assets remain ongoing because APT38 has laundered them through virtual currency bridges, mixers, exchanges, and over-the-counter traders, the Justice Department said.

https://arstechnica.com/security/2025/11/5-plead-guilty-to-laptop-farm-and-id-theft-scheme-to-land-north-koreans-us-it-jobs/

“That is a huge liability and credit risk for Oracle. Your main customer, biggest customer by far, is a venture capital-funded start-up,” said Andrew Chang, a director at S&P Global.

OpenAI faces questions about how it plans to meet its commitments to spend $1.4 trillion on AI infrastructure over the next eight years. It has struck deals with several Big Tech groups, including Oracle’s rivals.

Of the five hyperscalers—which include Amazon, Google, Microsoft, and Meta—Oracle is the only one with negative free cash flow. Its debt-to-equity ratio has surged to 500 percent, far higher than Amazon’s 50 percent and Microsoft’s 30 percent, according to JPMorgan.

While all five companies have seen their cash-to-assets ratios decline significantly in recent years amid a boom in spending, Oracle’s is by far the lowest, JPMorgan found.

JPMorgan analysts noted a “tension between [Oracle’s] aggressive AI build-out ambitions and the limits of its investment-grade balance sheet.”

Analysts have also noted that Oracle’s data center leases are for much longer than its contracts to sell capacity to OpenAI.

Oracle has signed at least five long-term lease agreements for US data centers that will ultimately be used by OpenAI, resulting in $100 billion of off-balance-sheet lease commitments. The sites are at varying levels of construction, with some not expected to break ground until next year.

Safra Catz, Oracle’s sole chief executive from 2019 until she stepped down in September, resisted expanding its cloud business because of the vast expenses required. She was replaced by co-CEOs Clay Magouyrk and Mike Sicilia as part of the pivot by Oracle to a new era focused on AI.

Catz, who is now executive vice-chair of Oracle’s board, has exercised stock options and sold $2.5 billion of its shares this year, according to US regulatory filings. She had announced plans to exercise her stock options at the end of 2024.
© 2025 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

https://arstechnica.com/information-technology/2025/11/oracle-hit-hard-in-wall-streets-tech-sell-off-over-its-huge-ai-bet/

When Altman celebrates finally getting GPT to avoid em dashes, he’s really celebrating that OpenAI has tuned the latest version of GPT-5.1 (probably through reinforcement learning or fine-tuning) to weight custom instructions more heavily in its probability calculations.

There’s an irony about control here: Given the probabilistic nature of the issue, there’s no guarantee the issue will stay fixed. OpenAI continuously updates its models behind the scenes, even within the same version number, adjusting outputs based on user feedback and new training runs. Each update arrives with different output characteristics that can undo previous behavioral tuning, a phenomenon researchers call the “alignment tax.”

Precisely tuning a neural network’s behavior is not yet an exact science. Since all concepts encoded in the network are interconnected by values called weights, adjusting one behavior can alter others in unintended ways. Fix em dash overuse today, and tomorrow’s update (aimed at improving, say, coding capabilities) might inadvertently bring them back, not because OpenAI wants them there, but because that’s the nature of trying to steer a statistical system with millions of competing influences.

This gets to an implied question we mentioned earlier. If controlling punctuation use is still a struggle that might pop back up at any time, how far are we from AGI? We can’t know for sure, but it seems increasingly likely that it won’t emerge from a large language model alone. That’s because AGI, a technology that would replicate human general learning ability, would likely require true understanding and self-reflective intentional action, not statistical pattern matching that sometimes aligns with instructions if you happen to get lucky.

And speaking of getting lucky, some users still aren’t having luck with controlling em dash use outside of the “custom instructions” feature. Upon being told in-chat to not use em dashes within a chat, ChatGPT updated a saved memory and replied to one X user, “Got it—I’ll stick strictly to short hyphens from now on.”

https://arstechnica.com/ai/2025/11/forget-agi-sam-altman-celebrates-chatgpt-finally-following-em-dash-formatting-rules/

Another campaign, documented by Sekoia, targeted Windows users. The attackers behind it first compromise a hotel’s account for Booking.com or another online travel service. Using the information stored in the compromised accounts, the attackers contact people with pending reservations, an ability that builds immediate trust with many targets, who are eager to comply with instructions, lest their stay be canceled.

The site eventually presents a fake CAPTCHA notification that bears an almost identical look and feel to those required by content delivery network Cloudflare. The proof the notification requires for confirmation that there’s a human behind the keyboard is to copy a string of text and paste it into the Windows terminal. With that, the machine is infected with malware tracked as PureRAT.

Push Security, meanwhile, reported a ClickFix campaign with a page “adapting to the device that you’re visiting from.” Depending on the OS, the page will deliver payloads for Windows or macOS. Many of these payloads, Microsoft said, are LOLbins, the name for binaries that use a technique known as living off the land. These scripts rely solely on native capabilities built into the operating system. With no malicious files being written to disk, endpoint protection is further hamstrung.

The commands, which are often base-64 encoded to make them unreadable to humans, are often copied inside the browser sandbox, a part of most browsers that accesses the Internet in an isolated environment designed to protect devices from malware or harmful scripts. Many security tools are unable to observe and flag these actions as potentially malicious.

The attacks can also be effective given the lack of awareness. Many people have learned over the years to be suspicious of links in emails or messengers. In many users’ minds, the precaution doesn’t extend to sites that instruct them to copy a piece of text and paste it into an unfamiliar window. When the instructions come in emails from a known hotel or at the top of Google results, targets can be further caught off guard.

With many families gathering in the coming weeks for various holiday dinners, ClickFix scams are worth mentioning to those family members who ask for security advice. Microsoft Defender and other endpoint protection programs offer some defenses against these attacks, but they can, in some cases, be bypassed. That means that, for now, awareness is the best countermeasure.

https://arstechnica.com/security/2025/11/clickfix-may-be-the-biggest-security-threat-your-family-has-never-heard-of/

Looking ahead, if the information removal techniques receive further development in the future, AI companies could potentially one day remove, say, copyrighted content, private information, or harmful memorized text from a neural network without destroying the model’s ability to perform transformative tasks. However, since neural networks store information in distributed ways that are still not completely understood, for the time being, the researchers say their method “cannot guarantee complete elimination of sensitive information.” These are early steps in a new research direction for AI.

Traveling the neural landscape

To understand how researchers from Goodfire distinguished memorization from reasoning in these neural networks, it helps to know about a concept in AI called the “loss landscape.” The “loss landscape” is a way of visualizing how wrong or right an AI model’s predictions are as you adjust its internal settings (which are called “weights”).

Imagine you’re tuning a complex machine with millions of dials. The “loss” measures the number of mistakes the machine makes. High loss means many errors, low loss means few errors. The “landscape” is what you’d see if you could map out the error rate for every possible combination of dial settings.

During training, AI models essentially “roll downhill” in this landscape (gradient descent), adjusting their weights to find the valleys where they make the fewest mistakes. This process provides AI model outputs, like answers to questions.

The researchers analyzed the “curvature” of the loss landscapes of particular AI language models, measuring how sensitive the model’s performance is to small changes in different neural network weights. Sharp peaks and valleys represent high curvature (where tiny changes cause big effects), while flat plains represent low curvature (where changes have minimal impact).

Using a technique called K-FAC (Kronecker-Factored Approximate Curvature), they found that individual memorized facts create sharp spikes in this landscape, but because each memorized item spikes in a different direction, when averaged together they create a flat profile. Meanwhile, reasoning abilities that many different inputs rely on maintain consistent moderate curves across the landscape, like rolling hills that remain roughly the same shape regardless of the direction from which you approach them.

https://arstechnica.com/ai/2025/11/study-finds-ai-models-store-memories-and-logic-in-different-neural-regions/

The next time you encounter an unusually polite reply on social media, you might want to check twice. It could be an AI model trying (and failing) to blend in with the crowd.

On Wednesday, researchers from the University of Zurich, University of Amsterdam, Duke University, and New York University released a study revealing that AI models remain easily distinguishable from humans in social media conversations, with overly friendly emotional tone serving as the most persistent giveaway. The research, which tested nine open-weight models across Twitter/X, Bluesky, and Reddit, found that classifiers developed by the researchers detected AI-generated replies with 70 to 80 percent accuracy.

The study introduces what the authors call a “computational Turing test” to assess how closely AI models approximate human language. Instead of relying on subjective human judgment about whether text sounds authentic, the framework uses automated classifiers and linguistic analysis to identify specific features that distinguish machine-generated from human-authored content.

“Even after calibration, LLM outputs remain clearly distinguishable from human text, particularly in affective tone and emotional expression,” the researchers wrote. The team, led by Nicolò Pagan at the University of Zurich, tested various optimization strategies, from simple prompting to fine-tuning, but found that deeper emotional cues persist as reliable tells that a particular text interaction online was authored by an AI chatbot rather than a human.

The toxicity tell

In the study, researchers tested nine large language models: Llama 3.1 8B, Llama 3.1 8B Instruct, Llama 3.1 70B, Mistral 7B v0.1, Mistral 7B Instruct v0.2, Qwen 2.5 7B Instruct, Gemma 3 4B Instruct, DeepSeek-R1-Distill-Llama-8B, and Apertus-8B-2509.

When prompted to generate replies to real social media posts from actual users, the AI models struggled to match the level of casual negativity and spontaneous emotional expression common in human social media posts, with toxicity scores consistently lower than authentic human replies across all three platforms.

To counter this deficiency, the researchers attempted optimization strategies (including providing writing examples and context retrieval) that reduced structural differences like sentence length or word count, but variations in emotional tone persisted. “Our comprehensive calibration tests challenge the assumption that more sophisticated optimization necessarily yields more human-like output,” the researchers concluded.

https://arstechnica.com/information-technology/2025/11/being-too-nice-online-is-a-dead-giveaway-for-ai-bots-study-suggests/

One of the world’s most ruthless and advanced hacking groups, the Russian state-controlled Sandworm, launched a series of destructive cyberattacks in the country’s ongoing war against neighboring Ukraine, researchers reported Thursday.

In April, the group targeted a Ukrainian university with two wipers, a form of malware that aims to permanently destroy sensitive data and often the infrastructure storing it. One wiper, tracked under the name Sting, targeted fleets of Windows computers by scheduling a task named DavaniGulyashaSdeshka, a phrase derived from Russian slang that loosely translates to “eat some goulash,” researchers from ESET said. The other wiper is tracked as Zerlot.

A not-so-common target

Then, in June and September, Sandworm unleashed multiple wiper variants against a host of Ukrainian critical infrastructure targets, including organizations active in government, energy, and logistics. The targets have long been in the crosshairs of Russian hackers. There was, however, a fourth, less common target—organizations in Ukraine’s grain industry.

“Although all four have previously been documented as targets of wiper attacks at some point since 2022, the grain sector stands out as a not-so-frequent target,” ESET said. “Considering that grain export remains one of Ukraine’s main sources of revenue, such targeting likely reflects an attempt to weaken the country’s war economy.”

Wipers have been a favorite tool of Russian hackers since at least 2012, with the spreading of the NotPetya worm. The self-replicating malware originally targeted Ukraine, but eventually caused international chaos when it spread globally in a matter of hours. The worm resulted in tens of billions of dollars in financial damages after it shut down thousands of organizations, many for days or weeks.

https://arstechnica.com/security/2025/11/wipers-from-russias-most-cut-throat-hackers-rain-destruction-on-ukraine/

On Monday, OpenAI announced it has signed a seven-year, $38 billion deal to buy cloud services from Amazon Web Services to power products like ChatGPT and Sora. It’s the company’s first big computing deal after a fundamental restructuring last week that gave OpenAI more operational and financial freedom from Microsoft.

The agreement gives OpenAI access to hundreds of thousands of Nvidia graphics processors to train and run its AI models. “Scaling frontier AI requires massive, reliable compute,” OpenAI CEO Sam Altman said in a statement. “Our partnership with AWS strengthens the broad compute ecosystem that will power this next era and bring advanced AI to everyone.”

OpenAI will reportedly use Amazon Web Services immediately, with all planned capacity set to come online by the end of 2026 and room to expand further in 2027 and beyond. Amazon plans to roll out hundreds of thousands of chips, including Nvidia’s GB200 and GB300 AI accelerators, in data clusters built to power ChatGPT’s responses, generate AI videos, and train OpenAI’s next wave of models.

Wall Street apparently liked the deal, because Amazon shares hit an all-time high on Monday morning. Meanwhile, shares for long-time OpenAI investor and partner Microsoft briefly dipped following the announcement.

Massive AI compute requirements

It’s no secret that running generative AI models for hundreds of millions of people currently requires a lot of computing power. Amid chip shortages over the past few years, finding sources of that computing muscle has been tricky. OpenAI is reportedly working on its own GPU hardware to help alleviate the strain.

But for now, the company needs to find new sources of Nvidia chips, which accelerate AI computations. Altman has previously said that the company plans to spend $1.4 trillion to develop 30 gigawatts of computing resources, an amount that is enough to roughly power 25 million US homes, according to Reuters.

https://arstechnica.com/ai/2025/11/openai-signs-massive-ai-compute-deal-with-amazon/