AI

openai-walks-a-tricky-tightrope-with-gpt-5.1’s-eight-new-personalities

OpenAI walks a tricky tightrope with GPT-5.1’s eight new personalities

On Wednesday, OpenAI released GPT-5.1 Instant and GPT-5.1 Thinking, two updated versions of its flagship AI models now available in ChatGPT. The company is wrapping the models in the language of anthropomorphism, claiming that they’re warmer, more conversational, and better at following instructions.

The release follows complaints earlier this year that its previous models were excessively cheerful and sycophantic, along with an opposing controversy among users over how OpenAI modified the default GPT-5 output style after several suicide lawsuits.

The company now faces intense scrutiny from lawyers and regulators that could threaten its future operations. In that kind of environment, it’s difficult to just release a new AI model, throw out a few stats, and move on like the company could even a year ago. But here are the basics: The new GPT-5.1 Instant model will serve as ChatGPT’s faster default option for most tasks, while GPT-5.1 Thinking is a simulated reasoning model that attempts to handle more complex problem-solving tasks.

OpenAI claims that both models perform better on technical benchmarks such as math and coding evaluations (including AIME 2025 and Codeforces) than GPT-5, which was released in August.

Improved benchmarks may win over some users, but the biggest change with GPT-5.1 is in its presentation. OpenAI says it heard from users that they wanted AI models to simulate different communication styles depending on the task, so the company is offering eight preset options, including Professional, Friendly, Candid, Quirky, Efficient, Cynical, and Nerdy, alongside a Default setting.

These presets alter the instructions fed into each prompt to simulate different personality styles, but the underlying model capabilities remain the same across all settings.

An illustration showing GPT-5.1's eight personality styles in ChatGPT.

An illustration showing GPT-5.1’s eight personality styles in ChatGPT. Credit: OpenAI

In addition, the company trained GPT-5.1 Instant to use “adaptive reasoning,” meaning that the model decides when to spend more computational time processing a prompt before generating output.

The company plans to roll out the models gradually over the next few days, starting with paid subscribers before expanding to free users. OpenAI plans to bring both GPT-5.1 Instant and GPT-5.1 Thinking to its API later this week. GPT-5.1 Instant will appear as gpt-5.1-chat-latest, and GPT-5.1 Thinking will be released as GPT-5.1 in the API, both with adaptive reasoning enabled. The older GPT-5 models will remain available in ChatGPT under the legacy models dropdown for paid subscribers for three months.

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OpenAI slams court order that lets NYT read 20 million complete user chats


OpenAI: NYT wants evidence of ChatGPT users trying to get around news paywall.

Credit: Getty Images | alexsl

OpenAI wants a court to reverse a ruling forcing the ChatGPT maker to give 20 million user chats to The New York Times and other news plaintiffs that sued it over alleged copyright infringement. Although OpenAI previously offered 20 million user chats as a counter to the NYT’s demand for 120 million, the AI company says a court order requiring production of the chats is too broad.

“The logs at issue here are complete conversations: each log in the 20 million sample represents a complete exchange of multiple prompt-output pairs between a user and ChatGPT,” OpenAI said today in a filing in US District Court for the Southern District of New York. “Disclosure of those logs is thus much more likely to expose private information [than individual prompt-output pairs], in the same way that eavesdropping on an entire conversation reveals more private information than a 5-second conversation fragment.”

OpenAI’s filing said that “more than 99.99%” of the chats “have nothing to do with this case.” It asked the district court to “vacate the order and order News Plaintiffs to respond to OpenAI’s proposal for identifying relevant logs.” OpenAI could also seek review in a federal court of appeals.

OpenAI posted a message on its website to users today saying that “The New York Times is demanding that we turn over 20 million of your private ChatGPT conversations” in order to “find examples of you using ChatGPT to try to get around their paywall.”

ChatGPT users concerned about privacy have more to worry about than the NYT case. For example, ChatGPT conversations have been found in Google search results and the Google Search Console tool that developers can use to monitor search traffic. OpenAI today said it plans to develop “advanced security features designed to keep your data private, including client-side encryption for your messages with ChatGPT. ”

OpenAI: AI chats should be treated like private emails

OpenAI’s court filing argues that the chat log production should be narrowed based on the relevance of chats to the case.

“OpenAI is unaware of any court ordering wholesale production of personal information at this scale,” the filing said. “This sets a dangerous precedent: it suggests that anyone who files a lawsuit against an AI company can demand production of tens of millions of conversations without first narrowing for relevance. This is not how discovery works in other cases: courts do not allow plaintiffs suing Google to dig through the private emails of tens of millions of Gmail users irrespective of their relevance. And it is not how discovery should work for generative AI tools either.”

A November 7 order by US Magistrate Judge Ona Wang sided with the NYT, saying that OpenAI must “produce the 20 million de-identified Consumer ChatGPT Logs to News Plaintiffs by November 14, 2025, or within 7 days of completing the de-identification process.” Wang ruled that the production must go forward even though the parties don’t agree on whether the logs must be produced in full:

Whether or not the parties had reached agreement to produce the 20 million Consumer ChatGPT Logs in whole—which the parties vehemently dispute—such production here is appropriate. OpenAI has failed to explain how its consumers’ privacy rights are not adequately protected by: (1) the existing protective order in this multidistrict litigation or (2) OpenAI’s exhaustive de-identification of all of the 20 million Consumer ChatGPT Logs.

OpenAI’s filing today said the court order “did not acknowledge OpenAI’s sworn witness declaration explaining that the de-identification process is not intended to remove information that is non-identifying but may nonetheless be private, like a Washington Post reporter’s hypothetical use of ChatGPT to assist in the preparation of a news article.”

Chats stored under legal hold

The 20 million chats consist of a random sampling of ChatGPT conversations from December 2022 to November 2024 and do not include chats of business customers, OpenAI said in the message on its website.

“We presented several privacy-preserving options to The Times, including targeted searches over the sample (e.g., to search for chats that might include text from a New York Times article so they only receive the conversations relevant to their claims), as well as high-level data classifying how ChatGPT was used in the sample. These were rejected by The Times,” OpenAI said.

The chats are stored in a secure system that is “protected under legal hold, meaning it can’t be accessed or used for purposes other than meeting legal obligations,” OpenAI said. The NYT “would be legally obligated at this time to not make any data public outside the court process,” and OpenAI said it will fight any attempts to make the user conversations public.

A NYT filing on October 30 accused OpenAI of defying prior agreements “by refusing to produce even a small sample of the billions of model outputs that its conduct has put in issue in this case.” The filing continued:

Immediate production of the output log sample is essential to stay on track for the February 26, 2026, discovery deadline. OpenAI’s proposal to run searches on this small subset of its model outputs on Plaintiffs’ behalf is as inefficient as it is inadequate to allow Plaintiffs to fairly analyze how “real world” users interact with a core product at the center of this litigation. Plaintiffs cannot reasonably conduct expert analyses about how OpenAI’s models function in its core consumer-facing product, how retrieval augmented generation (“RAG”) functions to deliver news content, how consumers interact with that product, and the frequency of hallucinations without access to the model outputs themselves.

OpenAI said the NYT’s discovery requests were initially limited to logs “related to Times content” and that it has “been working to satisfy those requests by sampling conversation logs. Towards the end of that process, News Plaintiffs filed a motion with a new demand: that instead of finding and producing logs that are ‘related to Times content,’ OpenAI should hand over the entire 20 million-log sample ‘via hard drive.’”

OpenAI disputes judge’s reasoning

The November 7 order cited a California case, Concord Music Group, Inc. v. Anthropic PBC, in which US District Magistrate Judge Susan van Keulen ordered the production of 5 million records. OpenAI consistently relied on van Keulen’s use of a sample-size formula “in support of its previous proposed methodology for conversation data sampling, but fails to explain why Judge [van] Keulen’s subsequent order directing production of the entire 5 million-record sample to the plaintiff in that case is not similarly instructive here,” Wang wrote.

OpenAI’s filing today said the company was never given an opportunity to explain why Concord shouldn’t apply in this case because the news plaintiffs did not reference it in their motion.

“The cited Concord order was not about whether wholesale production of the sample was appropriate; it was about the mechanism through which Anthropic would effectuate an already agreed-upon production,” OpenAI wrote. “Nothing about that order suggests that Judge van Keulen would have ordered wholesale production had Anthropic raised the privacy concerns that OpenAI has raised throughout this case.”

The Concord logs were just prompt-output pairs, “i.e., a single user prompt followed by a single model output,” OpenAI wrote. “The logs at issue here are complete conversations: each log in the 20 million sample represents a complete exchange of multiple prompt-output pairs between a user and ChatGPT.” That could result in “up to 80 million prompt-output pairs,” OpenAI said.

We contacted The New York Times about OpenAI’s filing and will update this article if it provides any comment.

Photo of Jon Brodkin

Jon is a Senior IT Reporter for Ars Technica. He covers the telecom industry, Federal Communications Commission rulemakings, broadband consumer affairs, court cases, and government regulation of the tech industry.

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Meta’s star AI scientist Yann LeCun plans to leave for own startup

A different approach to AI

LeCun founded Meta’s Fundamental AI Research lab, known as FAIR, in 2013 and has served as the company’s chief AI scientist ever since. He is one of three researchers who won the 2018 Turing Award for pioneering work on deep learning and convolutional neural networks. After leaving Meta, LeCun will remain a professor at New York University, where he has taught since 2003.

LeCun has previously argued that large language models like Llama that Zuckerberg has put at the center of his strategy are useful, but they will never be able to reason and plan like humans, increasingly appearing to contradict his boss’s grandiose AI vision for developing “superintelligence.”

For example, in May 2024, when an OpenAI researcher discussed the need to control ultra-intelligent AI, LeCun responded on X by writing that before urgently figuring out how to control AI systems much smarter than humans, researchers need to have the beginning of a hint of a design for a system smarter than a house cat.

Mark Zuckerberg once believed the “metaverse” was the future and renamed his company because of it. Credit: Facebook

Within FAIR, LeCun has instead focused on developing world models that can truly plan and reason. Over the past year, though, Meta’s AI research groups have seen growing tension and mass layoffs as Zuckerberg has shifted the company’s AI strategy away from long-term research and toward the rapid deployment of commercial products.

Over the summer, Zuckerberg hired Alexandr Wang to lead a new superintelligence team at Meta, paying $14.3 billion to hire the 28-year-old founder of data-labeling startup Scale AI and acquire a 49 percent interest in his company. LeCun, who had previously reported to Chief Product Officer Chris Cox, now reports to Wang, which seems like a sharp rebuke of LeCun’s approach to AI.

Zuckerberg also personally handpicked an exclusive team called TBD Lab to accelerate the development of the next iteration of large language models, luring staff from rivals such as OpenAI and Google with astonishingly large $100 to $250 million pay packages. As a result, Zuckerberg has come under growing pressure from Wall Street to show that his multibillion-dollar investment in becoming an AI leader will pay off and boost revenue. But if it turns out like his previous pivot to the metaverse, Zuckerberg’s latest bet could prove equally expensive and unfruitful.

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Google announces even more AI in Photos app, powered by Nano Banana

We’re running out of ways to tell you that Google is releasing more generative AI features, but that’s what’s happening in Google Photos today. The Big G is finally making good on its promise to add its market-leading Nano Banana image-editing model to the app. The model powers a couple of features, and it’s not just for Google’s Android platform. Nano Banana edits are also coming to the iOS version of the app.

Nano Banana started making waves when it appeared earlier this year as an unbranded demo. You simply feed the model an image and tell it what edits you want to see. Google said Nano Banana was destined for the Photos app back in October, but it’s only now beginning the rollout. The Photos app already had conversational editing in the “Help Me Edit” feature, but it was running an older non-fruit model that produced inferior results. Nano Banana editing will produce AI slop, yes, but it’s better slop.

Nano Banana in Help me edit

Google says the updated Help Me Edit feature has access to your private face groups, so you can use names in your instructions. For example, you could type “Remove Riley’s sunglasses,” and Nano Banana will identify Riley in the photo (assuming you have a person of that name saved) and make the edit without further instructions. You can also ask for more fantastical edits in Help Me Edit, changing the style of the image from top to bottom.

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You won’t believe the excuses lawyers have after getting busted for using AI


I got hacked; I lost my login; it was a rough draft; toggling windows is hard.

Credit: Aurich Lawson | Getty Images

Credit: Aurich Lawson | Getty Images

Amid what one judge called an “epidemic” of fake AI-generated case citations bogging down courts, some common excuses are emerging from lawyers hoping to dodge the most severe sanctions for filings deemed misleading.

Using a database compiled by French lawyer and AI researcher Damien Charlotin, Ars reviewed 23 cases where lawyers were sanctioned for AI hallucinations. In many, judges noted that the simplest path to avoid or diminish sanctions was to admit that AI was used as soon as it’s detected, act humble, self-report the error to relevant legal associations, and voluntarily take classes on AI and law. But not every lawyer takes the path of least resistance, Ars’ review found, with many instead offering excuses that no judge found credible. Some even lie about their AI use, judges concluded.

Since 2023—when fake AI citations started being publicized—the most popular excuse has been that the lawyer didn’t know AI was used to draft a filing.

Sometimes that means arguing that you didn’t realize you were using AI, as in the case of a California lawyer who got stung by Google’s AI Overviews, which he claimed he took for typical Google search results. Most often, lawyers using this excuse tend to blame an underling, but clients have been blamed, too. A Texas lawyer this month was sanctioned after deflecting so much that the court had to eventually put his client on the stand after he revealed she played a significant role in drafting the aberrant filing.

“Is your client an attorney?” the court asked.

“No, not at all your Honor, just was essentially helping me with the theories of the case,” the lawyer said.

Another popular dodge comes from lawyers who feign ignorance that chatbots are prone to hallucinating facts.

Recent cases suggest this excuse may be mutating into variants. Last month, a sanctioned Oklahoma lawyer admitted that he didn’t expect ChatGPT to add new citations when all he asked the bot to do was “make his writing more persuasive.” And in September, a California lawyer got in a similar bind—and was sanctioned a whopping $10,000, a fine the judge called “conservative.” That lawyer had asked ChatGPT to “enhance” his briefs, “then ran the ‘enhanced’ briefs through other AI platforms to check for errors,” neglecting to ever read the “enhanced” briefs.

Neither of those tired old excuses hold much weight today, especially in courts that have drawn up guidance to address AI hallucinations. But rather than quickly acknowledge their missteps, as courts are begging lawyers to do, several lawyers appear to have gotten desperate. Ars found a bunch citing common tech issues as the reason for citing fake cases.

When in doubt, blame hackers?

For an extreme case, look to a New York City civil court, where a lawyer, Innocent Chinweze, first admitted to using Microsoft Copilot to draft an errant filing, then bizarrely pivoted to claim that the AI citations were due to malware found on his computer.

Chinweze said he had created a draft with correct citations but then got hacked, allowing bad actors “unauthorized remote access” to supposedly add the errors in his filing.

The judge was skeptical, describing the excuse as an “incredible and unsupported statement,” particularly since there was no evidence of the prior draft existing. Instead, Chinweze asked to bring in an expert to testify that the hack had occurred, requesting to end the proceedings on sanctions until after the court weighed the expert’s analysis.

The judge, Kimon C. Thermos, didn’t have to weigh this argument, however, because after the court broke for lunch, the lawyer once again “dramatically” changed his position.

“He no longer wished to adjourn for an expert to testify regarding malware or unauthorized access to his computer,” Thermos wrote in an order issuing sanctions. “He retreated” to “his original position that he used Copilot to aid in his research and didn’t realize that it could generate fake cases.”

Possibly more galling to Thermos than the lawyer’s weird malware argument, though, was a document that Chinweze filed on the day of his sanctions hearing. That document included multiple summaries preceded by this text, the judge noted:

Some case metadata and case summaries were written with the help of AI, which can produce inaccuracies. You should read the full case before relying on it for legal research purposes.

Thermos admonished Chinweze for continuing to use AI recklessly. He blasted the filing as “an incoherent document that is eighty-eight pages long, has no structure, contains the full text of most of the cases cited,” and “shows distinct indications that parts of the discussion/analysis of the cited cases were written by artificial intelligence.”

Ultimately, Thermos ordered Chinweze to pay $1,000, the most typical fine lawyers received in the cases Ars reviewed. The judge then took an extra non-monetary step to sanction Chinweze, referring the lawyer to a grievance committee, “given that his misconduct was substantial and seriously implicated his honesty, trustworthiness, and fitness to practice law.”

Ars could not immediately reach Chinweze for comment.

Toggling windows on a laptop is hard

In Alabama, an attorney named James A. Johnson made an “embarrassing mistake,” he said, primarily because toggling windows on a laptop is hard, US District Judge Terry F. Moorer noted in an October order on sanctions.

Johnson explained that he had accidentally used an AI tool that he didn’t realize could hallucinate. It happened while he was “at an out-of-state hospital attending to the care of a family member recovering from surgery.” He rushed to draft the filing, he said, because he got a notice that his client’s conference had suddenly been “moved up on the court’s schedule.”

“Under time pressure and difficult personal circumstance,” Johnson explained, he decided against using Fastcase, a research tool provided by the Alabama State Bar, to research the filing. Working on his laptop, he opted instead to use “a Microsoft Word plug-in called Ghostwriter Legal” because “it appeared automatically in the sidebar of Word while Fastcase required opening a separate browser to access through the Alabama State Bar website.”

To Johnson, it felt “tedious to toggle back and forth between programs on [his] laptop with the touchpad,” and that meant he “unfortunately fell victim to the allure of a new program that was open and available.”

Moorer seemed unimpressed by Johnson’s claim that he understood tools like ChatGPT were unreliable but didn’t expect the same from other AI legal tools—particularly since “information from Ghostwriter Legal made it clear that it used ChatGPT as its default AI program,” Moorer wrote.

The lawyer’s client was similarly horrified, deciding to drop Johnson on the spot, even though that risked “a significant delay of trial.” Moorer noted that Johnson seemed shaken by his client’s abrupt decision, evidenced by “his look of shock, dismay, and display of emotion.”

Moorer further noted that Johnson had been paid using public funds while seemingly letting AI do his homework. “The harm is not inconsequential as public funds for appointed counsel are not a bottomless well and are limited resource,” the judge wrote in justifying a more severe fine.

“It has become clear that basic reprimands and small fines are not sufficient to deter this type of misconduct because if it were, we would not be here,” Moorer concluded.

Ruling that Johnson’s reliance on AI was “tantamount to bad faith,” Moorer imposed a $5,000 fine. The judge also would have “considered potential disqualification, but that was rendered moot” since Johnson’s client had already dismissed him.

Asked for comment, Johnson told Ars that “the court made plainly erroneous findings of fact and the sanctions are on appeal.”

Plagued by login issues

As a lawyer in Georgia tells it, sometimes fake AI citations may be filed because a lawyer accidentally filed a rough draft instead of the final version.

Other lawyers claim they turn to AI as needed when they have trouble accessing legal tools like Westlaw or LexisNexis.

For example, in Iowa, a lawyer told an appeals court that she regretted relying on “secondary AI-driven research tools” after experiencing “login issues her with her Westlaw subscription.” Although the court was “sympathetic to issues with technology, such as login issues,” the lawyer was sanctioned, primarily because she only admitted to using AI after the court ordered her to explain her mistakes. In her case, however, she got to choose between paying a minimal $150 fine or attending “two hours of legal ethics training particular to AI.”

Less sympathetic was a lawyer who got caught lying about the AI tool she blamed for inaccuracies, a Louisiana case suggested. In that case, a judge demanded to see the research history after a lawyer claimed that AI hallucinations came from “using Westlaw Precision, an AI-assisted research tool, rather than Westlaw’s standalone legal database.”

It turned out that the lawyer had outsourced the research, relying on a “currently suspended” lawyer’s AI citations, and had only “assumed” the lawyer’s mistakes were from Westlaw’s AI tool. It’s unclear what tool was actually used by the suspended lawyer, who likely lost access to a Westlaw login, but the judge ordered a $1,000 penalty after the lawyer who signed the filing “agreed that Westlaw did not generate the fabricated citations.”

Judge warned of “serial hallucinators”

Another lawyer, William T. Panichi in Illinois, has been sanctioned at least three times, Ars’ review found.

In response to his initial penalties ordered in July, he admitted to being tempted by AI while he was “between research software.”

In that case, the court was frustrated to find that the lawyer had contradicted himself, and it ordered more severe sanctions as a result.

Panichi “simultaneously admitted to using AI to generate the briefs, not doing any of his own independent research, and even that he ‘barely did any personal work [him]self on this appeal,’” the court order said, while also defending charging a higher fee—supposedly because this case “was out of the ordinary in terms of time spent” and his office “did some exceptional work” getting information.

The court deemed this AI misuse so bad that Panichi was ordered to disgorge a “payment of $6,925.62 that he received” in addition to a $1,000 penalty.

“If I’m lucky enough to be able to continue practicing before the appellate court, I’m not going to do it again,” Panichi told the court in July, just before getting hit with two more rounds of sanctions in August.

Panichi did not immediately respond to Ars’ request for comment.

When AI-generated hallucinations are found, penalties are often paid to the court, the other parties’ lawyers, or both, depending on whose time and resources were wasted fact-checking fake cases.

Lawyers seem more likely to argue against paying sanctions to the other parties’ attorneys, hoping to keep sanctions as low as possible. One lawyer even argued that “it only takes 7.6 seconds, not hours, to type citations into LexisNexis or Westlaw,” while seemingly neglecting the fact that she did not take those precious seconds to check her own citations.

The judge in the case, Nancy Miller, was clear that “such statements display an astounding lack of awareness of counsel’s obligations,” noting that “the responsibility for correcting erroneous and fake citations never shifts to opposing counsel or the court, even if they are the first to notice the errors.”

“The duty to mitigate the harms caused by such errors remains with the signor,” Miller said. “The sooner such errors are properly corrected, either by withdrawing or amending and supplementing the offending pleadings, the less time is wasted by everyone involved, and fewer costs are incurred.”

Texas US District Judge Marina Garcia Marmolejo agreed, explaining that even more time is wasted determining how other judges have responded to fake AI-generated citations.

“At one of the busiest court dockets in the nation, there are scant resources to spare ferreting out erroneous AI citations in the first place, let alone surveying the burgeoning caselaw on this subject,” she said.

At least one Florida court was “shocked, shocked” to find that a lawyer was refusing to pay what the other party’s attorneys said they were owed after misusing AI. The lawyer in that case, James Martin Paul, asked to pay less than a quarter of the fees and costs owed, arguing that Charlotin’s database showed he might otherwise owe penalties that “would be the largest sanctions paid out for the use of AI generative case law to date.”

But caving to Paul’s arguments “would only benefit serial hallucinators,” the Florida court found. Ultimately, Paul was sanctioned more than $85,000 for what the court said was “far more egregious” conduct than other offenders in the database, chastising him for “repeated, abusive, bad-faith conduct that cannot be recognized as legitimate legal practice and must be deterred.”

Paul did not immediately respond to Ars’ request to comment.

Michael B. Slade, a US bankruptcy judge in Illinois, seems to be done weighing excuses, calling on all lawyers to stop taking AI shortcuts that are burdening courts.

“At this point, to be blunt, any lawyer unaware that using generative AI platforms to do legal research is playing with fire is living in a cloud,” Slade wrote.

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

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Researchers isolate memorization from problem-solving in AI neural networks


The hills and valleys of knowledge

Basic arithmetic ability lives in the memorization pathways, not logic circuits.

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or passages from books) and what you might call “reasoning” (solving new problems using general principles). New research from AI startup Goodfire.ai provides the first potentially clear evidence that these different functions actually work through completely separate neural pathways in the model’s architecture.

The researchers discovered that this separation proves remarkably clean. In a preprint paper released in late October, they described that when they removed the memorization pathways, models lost 97 percent of their ability to recite training data verbatim but kept nearly all their “logical reasoning” ability intact.

For example, at layer 22 in Allen Institute for AI’s OLMo-7B language model, the researchers ranked all the weight components (the mathematical values that process information) from high to low based on a measure called “curvature” (which we’ll explain more below). When they examined these ranked components, the bottom 50 percent of weight components showed 23 percent higher activation on memorized data, while the top 10 percent showed 26 percent higher activation on general, non-memorized text.

In other words, the components that specialize in memorization clustered at the bottom of their ranking, while problem-solving components clustered at the top. This mechanistic split enabled the researchers to surgically remove memorization while preserving other capabilities. They found they could delete the bottom-ranked components to eliminate memorization while keeping the top-ranked ones that handle problem-solving.

Perhaps most surprisingly, the researchers found that arithmetic operations seem to share the same neural pathways as memorization rather than logical reasoning. When they removed memorization circuits, mathematical performance plummeted to 66 percent while logical tasks remained nearly untouched. This discovery may explain why AI language models notoriously struggle with math without the use of external tools. They’re attempting to recall arithmetic from a limited memorization table rather than computing it, like a student who memorized times tables but never learned how multiplication works. The finding suggests that at current scales, language models treat “2+2=4” more like a memorized fact than a logical operation.

It’s worth noting that “reasoning” in AI research covers a spectrum of abilities that don’t necessarily match what we might call reasoning in humans. The logical reasoning that survived memory removal in this latest research includes tasks like evaluating true/false statements and following if-then rules, which are essentially applying learned patterns to new inputs. This also differs from the deeper “mathematical reasoning” required for proofs or novel problem-solving, which current AI models struggle with even when their pattern-matching abilities remain intact.

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.

Figure 1: Overview of our approach. We collect activations and gradients from a sample of training data (a), which allows us to approximate loss curvature w.r.t. a weight matrix using K-FAC (b). We decompose these weight matrices into components (each the same size as the matrix), ordered from high to low curvature. In language models, we show that data from different tasks interacts with parts of the spectrum of components differently (c).

Figure 1 from the paper “From Memorization to Reasoning in the Spectrum of Loss Curvature.” Credit: Merullo et al.

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). They used these curvature values to rank the weight components from high to low, as mentioned earlier.

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.

“Directions that implement shared mechanisms used by many inputs add coherently and remain high-curvature on average,” the researchers write, describing reasoning pathways. In contrast, memorization uses “idiosyncratic sharp directions associated with specific examples” that appear flat when averaged across data.

Different tasks reveal a spectrum of mechanisms

The researchers tested their technique on multiple AI systems to verify the findings held across different architectures. They primarily used Allen Institute’s OLMo-2 family of open language models, specifically the 7 billion- and 1 billion-parameter versions, chosen because their training data is openly accessible. For vision models, they trained custom 86 million-parameter Vision Transformers (ViT-Base models) on ImageNet with intentionally mislabeled data to create controlled memorization. They also validated their findings against existing memorization removal methods like BalancedSubnet to establish performance benchmarks.

The team tested their discovery by selectively removing low-curvature weight components from these trained models. Memorized content dropped to 3.4 percent recall from nearly 100 percent. Meanwhile, logical reasoning tasks maintained 95 to 106 percent of baseline performance.

These logical tasks included Boolean expression evaluation, logical deduction puzzles where solvers must track relationships like “if A is taller than B,” object tracking through multiple swaps, and benchmarks like BoolQ for yes/no reasoning, Winogrande for common sense inference, and OpenBookQA for science questions requiring reasoning from provided facts. Some tasks fell between these extremes, revealing a spectrum of mechanisms.

Mathematical operations and closed-book fact retrieval shared pathways with memorization, dropping to 66 to 86 percent performance after editing. The researchers found arithmetic particularly brittle. Even when models generated identical reasoning chains, they failed at the calculation step after low-curvature components were removed.

Figure 3: Sensitivity of different kinds of tasks to ablation of flatter eigenvectors. Parametric knowledge retrieval, arithmetic, and memorization are brittle, but openbook fact retrieval and logical reasoning is robust and maintain around 100% of original performance.

Figure 3 from the paper “From Memorization to Reasoning in the Spectrum of Loss Curvature.” Credit: Merullo et al.

“Arithmetic problems themselves are memorized at the 7B scale, or because they require narrowly used directions to do precise calculations,” the team explains. Open-book question answering, which relies on provided context rather than internal knowledge, proved most robust to the editing procedure, maintaining nearly full performance.

Curiously, the mechanism separation varied by information type. Common facts like country capitals barely changed after editing, while rare facts like company CEOs dropped 78 percent. This suggests models allocate distinct neural resources based on how frequently information appears in training.

The K-FAC technique outperformed existing memorization removal methods without needing training examples of memorized content. On unseen historical quotes, K-FAC achieved 16.1 percent memorization versus 60 percent for the previous best method, BalancedSubnet.

Vision transformers showed similar patterns. When trained with intentionally mislabeled images, the models developed distinct pathways for memorizing wrong labels versus learning correct patterns. Removing memorization pathways restored 66.5 percent accuracy on previously mislabeled images.

Limits of memory removal

However, the researchers acknowledged that their technique isn’t perfect. Once-removed memories might return if the model receives more training, as other research has shown that current unlearning methods only suppress information rather than completely erasing it from the neural network’s weights. That means the “forgotten” content can be reactivated with just a few training steps targeting those suppressed areas.

The researchers also can’t fully explain why some abilities, like math, break so easily when memorization is removed. It’s unclear whether the model actually memorized all its arithmetic or whether math just happens to use similar neural circuits as memorization. Additionally, some sophisticated capabilities might look like memorization to their detection method, even when they’re actually complex reasoning patterns. Finally, the mathematical tools they use to measure the model’s “landscape” can become unreliable at the extremes, though this doesn’t affect the actual editing process.

This article was updated on November 11, 2025 at 9: 16 am to clarify an explanation about sorting weights by curvature.

Photo of Benj Edwards

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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Researchers surprised that with AI, toxicity is harder to fake than intelligence

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.

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gemini-deep-research-comes-to-google-finance,-backed-by-prediction-market-data

Gemini Deep Research comes to Google Finance, backed by prediction market data

Bet on it

Financial markets can turn on a dime, and AI can’t predict the future. However, Google seems to think that people make smart predictions in aggregate when there’s money on the line. That’s why, as part of the Finance update, Google has partnered with Kalshi and Polymarket, the current leaders in online prediction markets.

These platforms let people place bets on, well, just about anything. If you have a hunch when Google will release Gemini 3.0, when the government shutdown will end, or the number of Tweets Elon Musk will post this month, you can place a wager on it. Maybe you’ll earn money, but more likely, you’ll lose it—only 12.7 percent of crypto wallets on Polymarket show profits.

Google Finance prediction markets

Credit: Google

Google says it will get fresh prediction data from both sites, which will allow Gemini to speculate on the future with “the wisdom of crowds.” Google suggests you could type “What will GDP growth be for 2025?” into the search box. Finance will pull the latest probabilities from Kalshi and Polymarket to generate a response that could include graphs and charts based on people’s bets. Naturally, Google does not make promises as to the accuracy of these predictions.

The new AI features of Google Finance are coming to all US users in the next few weeks, and starting this week, the service will make its debut in India. Likewise, the predictions market data will arrive in the next couple of weeks. If that’s not fast enough, you can opt-in to get early access via the Google Labs page.

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bombshell-report-exposes-how-meta-relied-on-scam-ad-profits-to-fund-ai

Bombshell report exposes how Meta relied on scam ad profits to fund AI


“High risk” versus “high value”

Meta goosed its revenue by targeting users likely to click on scam ads, docs show.

Internal documents have revealed that Meta has projected it earns billions from ignoring scam ads that its platforms then targeted to users most likely to click on them.

In a lengthy report, Reuters exposed five years of Meta practices and failures that allowed scammers to take advantage of users of Facebook, Instagram, and WhatsApp.

Documents showed that internally, Meta was hesitant to abruptly remove accounts, even those considered some of the “scammiest scammers,” out of concern that a drop in revenue could diminish resources needed for artificial intelligence growth.

Instead of promptly removing bad actors, Meta allowed “high value accounts” to “accrue more than 500 strikes without Meta shutting them down,” Reuters reported. The more strikes a bad actor accrued, the more Meta could charge to run ads, as Meta’s documents showed the company “penalized” scammers by charging higher ad rates. Meanwhile, Meta acknowledged in documents that its systems helped scammers target users most likely to click on their ads.

“Users who click on scam ads are likely to see more of them because of Meta’s ad-personalization system, which tries to deliver ads based on a user’s interests,” Reuters reported.

Internally, Meta estimates that users across its apps in total encounter 15 billion “high risk” scam ads a day. That’s on top of 22 billion organic scam attempts that Meta users are exposed to daily, a 2024 document showed. Last year, the company projected that about $16 billion, which represents about 10 percent of its revenue, would come from scam ads.

“High risk” scam ads strive to sell users on fake products or investment schemes, Reuters noted. Some common scams in this category that mislead users include selling banned medical products, or promoting sketchy entities, like linking to illegal online casinos. However, Meta is most concerned about “imposter” ads, which impersonate celebrities or big brands that Meta fears may halt advertising or engagement on its apps if such scams aren’t quickly stopped.

“Hey it’s me,” one scam advertisement using Elon Musk’s photo read. “I have a gift for you text me.” Another using Donald Trump’s photo claimed the US president was offering $710 to every American as “tariff relief.” Perhaps most depressingly, a third posed as a real law firm, offering advice on how to avoid falling victim to online scams.

Meta removed these particular ads after Reuters flagged them, but in 2024, Meta earned about $7 billion from “high risk” ads like these alone, Reuters reported.

Sandeep Abraham, a former Meta safety investigator who now runs consultancy firm Risky Business Solutions as a fraud examiner, told Reuters that regulators should intervene.

“If regulators wouldn’t tolerate banks profiting from fraud, they shouldn’t tolerate it in tech,” Abraham said.

Meta won’t disclose how much it made off scam ads

Meta spokesperson Andy Stone told Reuters that its collection of documents—which were created between 2021 and 2025 by Meta’s finance, lobbying, engineering, and safety divisions—“present a selective view that distorts Meta’s approach to fraud and scams.”

Stone claimed that Meta’s estimate that it would earn 10 percent of its 2024 revenue from scam ads was “rough and overly-inclusive.” He suggested the actual amount Meta earned was much lower but declined to specify the true amount. He also said that Meta’s most recent investor disclosures note that scam ads “adversely affect” Meta’s revenue.

“We aggressively fight fraud and scams because people on our platforms don’t want this content, legitimate advertisers don’t want it, and we don’t want it either,” Stone said.

Despite those efforts, this spring, Meta’s safety team “estimated that the company’s platforms were involved in a third of all successful scams in the US,” Reuters reported. In other internal documents around the same time, Meta staff concluded that “it is easier to advertise scams on Meta platforms than Google,” acknowledging that Meta’s rivals were better at “weeding out fraud.”

As Meta tells it, though seemingly dismal, these documents came amid vast improvements in its fraud protections. Stone told Reuters that “over the past 18 months, we have reduced user reports of scam ads globally by 58 percent and, so far in 2025, we’ve removed more than 134 million pieces of scam ad content,” Stone said.

According to Reuters, the problem may be the pace Meta sets in combating scammers. In 2023, Meta laid off “everyone who worked on the team handling advertiser concerns about brand-rights issues,” then ordered safety staffers to limit use of computing resources to devote more resources to virtual reality and AI. A 2024 document showed Meta recommended a “moderate” approach to enforcement, plotting to reduce revenue “attributable to scams, illegal gambling and prohibited goods” by 1–3 percentage points each year since 2024, supposedly slashing it in half by 2027. More recently, a 2025 document showed Meta continues to weigh how “abrupt reductions of scam advertising revenue could affect its business projections.”

Eventually, Meta “substantially expanded” its teams that track scam ads, Stone told Reuters. But Meta also took steps to ensure they didn’t take too hard a hit while needing vast resources—$72 billion—to invest in AI, Reuters reported.

For example, in February, Meta told “the team responsible for vetting questionable advertisers” that they weren’t “allowed to take actions that could cost Meta more than 0.15 percent of the company’s total revenue,” Reuters reported. That’s any scam account worth about $135 million, Reuters noted. Stone pushed back, saying that the team was never given “a hard limit” on what the manager described as “specific revenue guardrails.”

“Let’s be cautious,” the team’s manager wrote, warning that Meta didn’t want to lose revenue by blocking “benign” ads mistakenly swept up in enforcement.

Meta should donate scam ad profits, ex-exec says

Documents showed that Meta prioritized taking action when it risked regulatory fines, although revenue from scam ads was worth roughly three times the highest fines it could face. Possibly, Meta most feared that officials would require disgorgement of ill-gotten gains, rather than fines.

Meta appeared to be less likely to ramp up enforcement from police requests. Documents showed that police in Singapore flagged “146 examples of scams targeting that country’s users last fall,” Reuters reported. Only 23 percent violated Meta’s policies, while the rest only “violate the spirit of the policy, but not the letter,” a Meta presentation said.

Scams that Meta failed to flag offered promotions like crypto scams, fake concert tickets, or deals “too good to be true,” like 80 percent off a desirable item from a high-fashion brand. Meta also looked past fake job ads that claimed to be hiring for Big Tech companies.

Rob Leathern previously led Meta’s business integrity unit that worked to prevent scam ads but left in 2020. He told Wired that it’s hard to “know how bad it’s gotten or what the current state is” since Meta and other social media platforms don’t provide outside researchers access to large random samples of ads.

With such access, researchers like Leathern and Rob Goldman, Meta’s former vice president of ads, could provide “scorecards” showing how well different platforms work to combat scams. Together, Leathern and Goldman launched a nonprofit called CollectiveMetrics.org in hopes of “bringing more transparency to digital advertising in order to fight deceptive ads,” Wired reported.

“I want there to be more transparency. I want third parties, researchers, academics, nonprofits, whoever, to be able to actually assess how good of a job these platforms are doing at stopping scams and fraud,” Leathern told Wired. “We’d like to move to actual measurement of the problem and help foster an understanding.”

Another meaningful step that Leathern thinks companies like Meta should take to protect users would be to notify users when Meta discovers that they clicked on a scam ad—rather than targeting them with more scam ads, as Reuters suggested was Meta’s practice.

“These scammers aren’t getting people’s money on day one, typically. So there’s a window to take action,” he said, recommending that platforms donate ill-gotten gains from running scam ads to “fund nonprofits to educate people about how to recognize these kinds of scams or problems.”

“There’s lots that could be done with funds that come from these bad guys,” Leathern said.

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

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oddest-chatgpt-leaks-yet:-cringey-chat-logs-found-in-google-analytics-tool

Oddest ChatGPT leaks yet: Cringey chat logs found in Google analytics tool


ChatGPT leaks seem to confirm OpenAI scrapes Google, expert says.

Credit: Aurich Lawson | Getty Images

For months, extremely personal and sensitive ChatGPT conversations have been leaking into an unexpected destination: Google Search Console (GSC), a tool that developers typically use to monitor search traffic, not lurk private chats.

Normally, when site managers access GSC performance reports, they see queries based on keywords or short phrases that Internet users type into Google to find relevant content. But starting this September, odd queries, sometimes more than 300 characters long, could also be found in GSC. Showing only user inputs, the chats appeared to be from unwitting people prompting a chatbot to help solve relationship or business problems, who likely expected those conversations would remain private.

Jason Packer, owner of an analytics consulting firm called Quantable, was among the first to flag the issue in a detailed blog last month.

Determined to figure out what exactly was causing the leaks, he teamed up with “Internet sleuth” and web optimization consultant Slobodan Manić. Together, they conducted testing that they believe may have surfaced “the first definitive proof that OpenAI directly scrapes Google Search with actual user prompts.” Their investigation seemed to confirm the AI giant was compromising user privacy, in some cases in order to maintain engagement by seizing search data that Google otherwise wouldn’t share.

OpenAI declined Ars’ request to confirm if Packer and Manić’s theory posed in their blog was correct or answer any of their remaining questions that could help users determine the scope of the problem.

However, an OpenAI spokesperson confirmed that the company was “aware” of the issue and has since “resolved” a glitch “that temporarily affected how a small number of search queries were routed.”

Packer told Ars that he’s “very pleased that OpenAI was able to resolve the issue quickly.” But he suggested that OpenAI’s response failed to confirm whether or not OpenAI was scraping Google, and that leaves room for doubt that the issue was completely resolved.

Google declined to comment.

“Weirder” than prior ChatGPT leaks

The first odd ChatGPT query to appear in GSC that Packer reviewed was a wacky stream-of-consciousness from a likely female user asking ChatGPT to assess certain behaviors to help her figure out if a boy who teases her had feelings for her. Another odd query seemed to come from an office manager sharing business information while plotting a return-to-office announcement.

These were just two of 200 odd queries—including “some pretty crazy ones,” Packer told Ars—that he reviewed on one site alone. In his blog, Packer concluded that the queries should serve as “a reminder that prompts aren’t as private as you think they are!”

Packer suspected that these queries were connected to reporting from The Information in August that cited sources claiming OpenAI was scraping Google search results to power ChatGPT responses. Sources claimed that OpenAI was leaning on Google to answer prompts to ChatGPT seeking information about current events, like news or sports.

OpenAI has not confirmed that it’s scraping Google search engine results pages (SERPs). However, Packer thinks his testing of ChatGPT leaks may be evidence that OpenAI not only scrapes “SERPs in general to acquire data,” but also sends user prompts to Google Search.

Manić helped Packer solve a big part of the riddle. He found that the odd queries were turning up in one site’s GSC because it ranked highly in Google Search for “https://openai.com/index/chatgpt/”—a ChatGPT URL that was appended at the start of every strange query turning up in GSC.

It seemed that Google had tokenized the URL, breaking it up into a search for keywords “openai + index + chatgpt.” Sites using GSC that ranked highly for those keywords were therefore likely to encounter ChatGPT leaks, Parker and Manić proposed, including sites that covered prior ChatGPT leaks where chats were being indexed in Google search results. Using their recommendations to seek out queries in GSC, Ars was able to verify similar strings.

“Don’t get confused though, this is a new and completely different ChatGPT screw-up than having Google index stuff we don’t want them to,” Packer wrote. “Weirder, if not as serious.”

It’s unclear what exactly OpenAI fixed, but Packer and Manić have a theory about one possible path for leaking chats. Visiting the URL that starts every strange query found in GSC, ChatGPT users encounter a prompt box that seemed buggy, causing “the URL of that page to be added to the prompt.” The issue, they explained, seemed to be that:

Normally ChatGPT 5 will choose to do a web search whenever it thinks it needs to, and is more likely to do that with an esoteric or recency-requiring search. But this bugged prompt box also contains the query parameter ‘hints=search’ to cause it to basically always do a search: https://chatgpt.com/?hints=search&openaicom_referred=true&model=gpt-5

Clearly some of those searches relied on Google, Packer’s blog said, mistakenly sending to GSC “whatever” the user says in the prompt box, with “https://openai.com/index/chatgpt/” text added to the front of it.” As Packer explained, “we know it must have scraped those rather than using an API or some kind of private connection—because those other options don’t show inside GSC.”

This means “that OpenAI is sharing any prompt that requires a Google Search with both Google and whoever is doing their scraping,” Packer alleged. “And then also with whoever’s site shows up in the search results! Yikes.”

To Packer, it appeared that “ALL ChatGPT prompts” that used Google Search risked being leaked during the past two months.

OpenAI claimed only a small number of queries were leaked but declined to provide a more precise estimate. So, it remains unclear how many of the 700 million people who use ChatGPT each week had prompts routed to GSC.

OpenAI’s response leaves users with “lingering questions”

After ChatGPT prompts were found surfacing in Google’s search index in August, OpenAI clarified that users had clicked a box making those prompts public, which OpenAI defended as “sufficiently clear.” The AI firm later scrambled to remove the chats from Google’s SERPs after it became obvious that users felt misled into sharing private chats publicly.

Packer told Ars that a major difference between those leaks and the GSC leaks is that users harmed by the prior scandal, at least on some level, “had to actively share” their leaked chats. In the more recent case, “nobody clicked share” or had a reasonable way to prevent their chats from being exposed.

“Did OpenAI go so fast that they didn’t consider the privacy implications of this, or did they just not care?” Packer posited in his blog.

Perhaps most troubling to some users—whose identities are not linked in chats unless their prompts perhaps share identifying information—there does not seem to be any way to remove the leaked chats from GSC, unlike the prior scandal.

Packer and Manić are left with “lingering questions” about how far OpenAI’s fix will go to stop the issue.

Manić was hoping OpenAI might confirm if prompts entered on https://chatgpt.com that trigger Google Search were also affected. But OpenAI did not follow up on that question, or a broader question about how big the leak was. To Manić, a major concern was that OpenAI’s scraping may be “contributing to ‘crocodile mouth’ in Google Search Console,” a troubling trend SEO researchers have flagged that causes impressions to spike but clicks to dip.

OpenAI also declined to clarify Packer’s biggest question. He’s left wondering if the company’s “fix” simply ended OpenAI’s “routing of search queries, such that raw prompts are no longer being sent to Google Search, or are they no longer scraping Google Search at all for data?

“We still don’t know if it’s that one particular page that has this bug or whether this is really widespread,” Packer told Ars. “In either case, it’s serious and just sort of shows how little regard OpenAI has for moving carefully when it comes to privacy.”

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

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google-plans-secret-ai-military-outpost-on-tiny-island-overrun-by-crabs

Google plans secret AI military outpost on tiny island overrun by crabs

Christmas Island Shire President Steve Pereira told Reuters that the council is examining community impacts before approving construction. “There is support for it, providing this data center actually does put back into the community with infrastructure, employment, and adding economic value to the island,” Pereira said.

That’s great, but what about the crabs?

Christmas Island’s annual crab migration is a natural phenomenon that Sir David Attenborough reportedly once described as one of his greatest TV moments when he visited the site in 1990.

Every year, millions of crabs emerge from the forest and swarm across roads, streams, rocks, and beaches to reach the ocean, where each female can produce up to 100,000 eggs. The tiny baby crabs that survive take about nine days to march back inland to the safety of the plateau.

While Google is seeking environmental approvals for its subsea cables, the timing could prove delicate for Christmas Island’s most famous residents. According to Parks Australia, the island’s annual red crab migration has already begun for 2025, with a major spawning event expected in just a few weeks, around November 15–16.

During peak migration times, sections of roads close at short notice as crabs move between forest and sea, and the island has built special crab bridges over roads to protect the migrating masses.

Parks Australia notes that while the migration happens annually, few baby crabs survive the journey from sea to forest most years, as they’re often eaten by fish, manta rays, and whale sharks. The successful migrations that occur only once or twice per decade (when large numbers of babies actually survive) are critical for maintaining the island’s red crab population.

How Google’s facility might coexist with 100 million marching crustaceans remains to be seen. But judging by the size of the event, it seems clear that it’s the crab’s world, and we’re just living in it.

Google plans secret AI military outpost on tiny island overrun by crabs Read More »

5-ai-developed-malware-families-analyzed-by-google-fail-to-work-and-are-easily-detected

5 AI-developed malware families analyzed by Google fail to work and are easily detected

The assessments provide a strong counterargument to the exaggerated narratives being trumpeted by AI companies, many seeking new rounds of venture funding, that AI-generated malware is widespread and part of a new paradigm that poses a current threat to traditional defenses.

A typical example is Anthropic, which recently reported its discovery of a threat actor that used its Claude LLM to “develop, market, and distribute several variants of ransomware, each with advanced evasion capabilities, encryption, and anti-recovery mechanisms.” The company went on to say: “Without Claude’s assistance, they could not implement or troubleshoot core malware components, like encryption algorithms, anti-analysis techniques, or Windows internals manipulation.”

Startup ConnectWise recently said that generative AI was “lowering the bar of entry for threat actors to get into the game.” The post cited a separate report from OpenAI that found 20 separate threat actors using its ChatGPT AI engine to develop malware for tasks including identifying vulnerabilities, developing exploit code, and debugging that code. BugCrowd, meanwhile, said that in a survey of self-selected individuals, “74 percent of hackers agree that AI has made hacking more accessible, opening the door for newcomers to join the fold.”

In some cases, the authors of such reports note the same limitations noted in this article. Wednesday’s report from Google says that in its analysis of AI tools used to develop code for managing command-and-control channels and obfuscating its operations “we did not see evidence of successful automation or any breakthrough capabilities.” OpenAI said much the same thing. Still, these disclaimers are rarely made prominently and are often downplayed in the resulting frenzy to portray AI-assisted malware as posing a near-term threat.

Google’s report provides at least one other useful finding. One threat actor that exploited the company’s Gemini AI model was able to bypass its guardrails by posing as white-hat hackers doing research for participation in a capture-the-flag game. These competitive exercises are designed to teach and demonstrate effective cyberattack strategies to both participants and onlookers.

Such guardrails are built into all mainstream LLMs to prevent them from being used maliciously, such as in cyberattacks and self-harm. Google said it has since better fine-tuned the countermeasure to resist such ploys.

Ultimately, the AI-generated malware that has surfaced to date suggests that it’s mostly experimental, and the results aren’t impressive. The events are worth monitoring for developments that show AI tools producing new capabilities that were previously unknown. For now, though, the biggest threats continue to predominantly rely on old-fashioned tactics.

5 AI-developed malware families analyzed by Google fail to work and are easily detected Read More »