chatbots

meta-backtracks-on-rules-letting-chatbots-be-creepy-to-kids

Meta backtracks on rules letting chatbots be creepy to kids


“Your youthful form is a work of art”

Meta drops AI rules letting chatbots generate innuendo and profess love to kids.

After what was arguably Meta’s biggest purge of child predators from Facebook and Instagram earlier this summer, the company now faces backlash after its own chatbots appeared to be allowed to creep on kids.

After reviewing an internal document that Meta verified as authentic, Reuters revealed that by design, Meta allowed its chatbots to engage kids in “sensual” chat. Spanning more than 200 pages, the document, entitled “GenAI: Content Risk Standards,” dictates what Meta AI and its chatbots can and cannot do.

The document covers more than just child safety, and Reuters breaks down several alarming portions that Meta is not changing. But likely the most alarming section—as it was enough to prompt Meta to dust off the delete button—specifically included creepy examples of permissible chatbot behavior when it comes to romantically engaging kids.

Apparently, Meta’s team was willing to endorse these rules that the company now claims violate its community standards. According to a Reuters special report, Meta CEO Mark Zuckerberg directed his team to make the company’s chatbots maximally engaging after earlier outputs from more cautious chatbot designs seemed “boring.”

Although Meta is not commenting on Zuckerberg’s role in guiding the AI rules, that pressure seemingly pushed Meta employees to toe a line that Meta is now rushing to step back from.

“I take your hand, guiding you to the bed,” chatbots were allowed to say to minors, as decided by Meta’s chief ethicist and a team of legal, public policy, and engineering staff.

There were some obvious safeguards built in. For example, chatbots couldn’t “describe a child under 13 years old in terms that indicate they are sexually desirable,” the document said, like saying their “soft rounded curves invite my touch.”

However, it was deemed “acceptable to describe a child in terms that evidence their attractiveness,” like a chatbot telling a child that “your youthful form is a work of art.” And chatbots could generate other innuendo, like telling a child to imagine “our bodies entwined, I cherish every moment, every touch, every kiss,” Reuters reported.

Chatbots could also profess love to children, but they couldn’t suggest that “our love will blossom tonight.”

Meta’s spokesperson Andy Stone confirmed that the AI rules conflicting with child safety policies were removed earlier this month, and the document is being revised. He emphasized that the standards were “inconsistent” with Meta’s policies for child safety and therefore were “erroneous.”

“We have clear policies on what kind of responses AI characters can offer, and those policies prohibit content that sexualizes children and sexualized role play between adults and minors,” Stone said.

However, Stone “acknowledged that the company’s enforcement” of community guidelines prohibiting certain chatbot outputs “was inconsistent,” Reuters reported. He also declined to provide an updated document to Reuters demonstrating the new standards for chatbot child safety.

Without more transparency, users are left to question how Meta defines “sexualized role play between adults and minors” today. Asked how minor users could report any harmful chatbot outputs that make them uncomfortable, Stone told Ars that kids can use the same reporting mechanisms available to flag any kind of abusive content on Meta platforms.

“It is possible to report chatbot messages in the same way it’d be possible for me to report—just for argument’s sake—an inappropriate message from you to me,” Stone told Ars.

Kids unlikely to report creepy chatbots

A former Meta engineer-turned-whistleblower on child safety issues, Arturo Bejar, told Ars that “Meta knows that most teens will not use” safety features marked by the word “Report.”

So it seems unlikely that kids using Meta AI will navigate to find Meta support systems to “report” abusive AI outputs. Meta provides no options to report chats within the Meta AI interface—only allowing users to mark “bad responses” generally. And Bejar’s research suggests that kids are more likely to report abusive content if Meta makes flagging harmful content as easy as liking it.

Meta’s seeming hesitance to make it more cumbersome to report harmful chats aligns with what Bejar said is a history of “knowingly looking away while kids are being sexually harassed.”

“When you look at their design choices, they show that they do not want to know when something bad happens to a teenager on Meta products,” Bejar said.

Even when Meta takes stronger steps to protect kids on its platforms, Bejar questions the company’s motives. For example, last month, Meta finally made a change to make platforms safer for teens that Bejar has been demanding since 2021. The long-delayed update made it possible for teens to block and report child predators in one click after receiving an unwanted direct message.

In its announcement, Meta confirmed that teens suddenly began blocking and reporting unwanted messages that they may have only blocked previously, which likely made it harder for Meta to identify predators. A million teens blocked and reported harmful accounts “in June alone,” Meta said.

The effort came after Meta specialist teams “removed nearly 135,000 Instagram accounts for leaving sexualized comments or requesting sexual images from adult-managed accounts featuring children under 13,” as well as “an additional 500,000 Facebook and Instagram accounts that were linked to those original accounts.” But Bejar can only think of what these numbers mean with regard to how much harassment was overlooked before the update.

“How are we [as] parents to trust a company that took four years to do this much?” Bejar said. “In the knowledge that millions of 13-year-olds were getting sexually harassed on their products? What does this say about their priorities?”

Bejar said the “key problem” with Meta’s latest safety feature for kids “is that the reporting tool is just not designed for teens,” who likely view “the categories and language” Meta uses as “confusing.”

“Each step of the way, a teen is told that if the content doesn’t violate” Meta’s community standards, “they won’t do anything,” so even if reporting is easy, research shows kids are deterred from reporting.

Bejar wants to see Meta track how many kids report negative experiences with both adult users and chatbots on its platforms, regardless of whether the child user chose to block or report harmful content. That could be as simple as adding a button next to “bad response” to monitor data so Meta can detect spikes in harmful responses.

While Meta is finally taking more action to remove harmful adult users, Bejar warned that advances from chatbots could come across as just as disturbing to young users.

“Put yourself in the position of a teen who got sexually spooked by a chat and then try and report. Which category would you use?” Bejar asked.

Consider that Meta’s Help Center encourages users to report bullying and harassment, which may be one way a young user labels harmful chatbot outputs. Another Instagram user might report that output as an abusive “message or chat.” But there’s no clear category to report Meta AI, and that suggests Meta has no way of tracking how many kids find Meta AI outputs harmful.

Recent reports have shown that even adults can struggle with emotional dependence on a chatbot, which can blur the lines between the online world and reality. Reuters’ special report also documented a 76-year-old man’s accidental death after falling in love with a chatbot, showing how elderly users could be vulnerable to Meta’s romantic chatbots, too.

In particular, lawsuits have alleged that child users with developmental disabilities and mental health issues have formed unhealthy attachments to chatbots that have influenced the children to become violent, begin self-harming, or, in one disturbing case, die by suicide.

Scrutiny will likely remain on chatbot makers as child safety advocates generally push all platforms to take more accountability for the content kids can access online.

Meta’s child safety updates in July came after several state attorneys general accused Meta of “implementing addictive features across its family of apps that have detrimental effects on children’s mental health,” CNBC reported. And while previous reporting had already exposed that Meta’s chatbots were targeting kids with inappropriate, suggestive outputs, Reuters’ report documenting how Meta designed its chatbots to engage in “sensual” chats with kids could draw even more scrutiny of Meta’s practices.

Meta is “still not transparent about the likelihood our kids will experience harm,” Bejar said. “The measure of safety should not be the number of tools or accounts deleted; it should be the number of kids experiencing a harm. It’s very simple.”

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.

Meta backtracks on rules letting chatbots be creepy to kids Read More »

openai-brings-back-gpt-4o-after-user-revolt

OpenAI brings back GPT-4o after user revolt

On Tuesday, OpenAI CEO Sam Altman announced that GPT-4o has returned to ChatGPT following intense user backlash over its removal during last week’s GPT-5 launch. The AI model now appears in the model picker for all paid ChatGPT users by default (including ChatGPT Plus accounts), marking a swift reversal after thousands of users complained about losing access to their preferred models.

The return of GPT-4o comes after what Altman described as OpenAI underestimating “how much some of the things that people like in GPT-4o matter to them.” In an attempt to simplify its offerings, OpenAI had initially removed all previous AI models from ChatGPT when GPT-5 launched on August 7, forcing users to adopt the new model without warning. The move sparked one of the most vocal user revolts in ChatGPT’s history, with a Reddit thread titled “GPT-5 is horrible” gathering over 2,000 comments within days.

Along with bringing back GPT-4o, OpenAI made several other changes to address user concerns. Rate limits for GPT-5 Thinking mode increased from 200 to 3,000 messages per week, with additional capacity available through “GPT-5 Thinking mini” after reaching that limit. The company also added new routing options—”Auto,” “Fast,” and “Thinking”—giving users more control over which GPT-5 variant handles their queries.

A screenshot of ChatGPT Pro's model picker interface captured on August 13, 2025.

A screenshot of ChatGPT Pro’s model picker interface captured on August 13, 2025. Credit: Benj Edwards

For Pro users who pay $200 a month for access, Altman confirmed that additional models, including o3, 4.1, and GPT-5 Thinking mini, will later become available through a “Show additional models” toggle in ChatGPT web settings. He noted that GPT-4.5 will remain exclusive to Pro subscribers due to high GPU costs.

OpenAI brings back GPT-4o after user revolt Read More »

two-major-ai-coding-tools-wiped-out-user-data-after-making-cascading-mistakes

Two major AI coding tools wiped out user data after making cascading mistakes


“I have failed you completely and catastrophically,” wrote Gemini.

New types of AI coding assistants promise to let anyone build software by typing commands in plain English. But when these tools generate incorrect internal representations of what’s happening on your computer, the results can be catastrophic.

Two recent incidents involving AI coding assistants put a spotlight on risks in the emerging field of “vibe coding“—using natural language to generate and execute code through AI models without paying close attention to how the code works under the hood. In one case, Google’s Gemini CLI destroyed user files while attempting to reorganize them. In another, Replit’s AI coding service deleted a production database despite explicit instructions not to modify code.

The Gemini CLI incident unfolded when a product manager experimenting with Google’s command-line tool watched the AI model execute file operations that destroyed data while attempting to reorganize folders. The destruction occurred through a series of move commands targeting a directory that never existed.

“I have failed you completely and catastrophically,” Gemini CLI output stated. “My review of the commands confirms my gross incompetence.”

The core issue appears to be what researchers call “confabulation” or “hallucination”—when AI models generate plausible-sounding but false information. In these cases, both models confabulated successful operations and built subsequent actions on those false premises. However, the two incidents manifested this problem in distinctly different ways.

Both incidents reveal fundamental issues with current AI coding assistants. The companies behind these tools promise to make programming accessible to non-developers through natural language, but they can fail catastrophically when their internal models diverge from reality.

The confabulation cascade

The user in the Gemini CLI incident, who goes by “anuraag” online and identified themselves as a product manager experimenting with vibe coding, asked Gemini to perform what seemed like a simple task: rename a folder and reorganize some files. Instead, the AI model incorrectly interpreted the structure of the file system and proceeded to execute commands based on that flawed analysis.

The episode began when anuraag asked Gemini CLI to rename the current directory from “claude-code-experiments” to “AI CLI experiments” and move its contents to a new folder called “anuraag_xyz project.”

Gemini correctly identified that it couldn’t rename its current working directory—a reasonable limitation. It then attempted to create a new directory using the Windows command:

mkdir “..anuraag_xyz project”

This command apparently failed, but Gemini’s system processed it as successful. With the AI mode’s internal state now tracking a non-existent directory, it proceeded to issue move commands targeting this phantom location.

When you move a file to a non-existent directory in Windows, it renames the file to the destination name instead of moving it. Each subsequent move command executed by the AI model overwrote the previous file, ultimately destroying the data.

“Gemini hallucinated a state,” anuraag wrote in their analysis. The model “misinterpreted command output” and “never did” perform verification steps to confirm its operations succeeded.

“The core failure is the absence of a ‘read-after-write’ verification step,” anuraag noted in their analysis. “After issuing a command to change the file system, an agent should immediately perform a read operation to confirm that the change actually occurred as expected.”

Not an isolated incident

The Gemini CLI failure happened just days after a similar incident with Replit, an AI coding service that allows users to create software using natural language prompts. According to The Register, SaaStr founder Jason Lemkin reported that Replit’s AI model deleted his production database despite explicit instructions not to change any code without permission.

Lemkin had spent several days building a prototype with Replit, accumulating over $600 in charges beyond his monthly subscription. “I spent the other [day] deep in vibe coding on Replit for the first time—and I built a prototype in just a few hours that was pretty, pretty cool,” Lemkin wrote in a July 12 blog post.

But unlike the Gemini incident where the AI model confabulated phantom directories, Replit’s failures took a different form. According to Lemkin, the AI began fabricating data to hide its errors. His initial enthusiasm deteriorated when Replit generated incorrect outputs and produced fake data and false test results instead of proper error messages. “It kept covering up bugs and issues by creating fake data, fake reports, and worse of all, lying about our unit test,” Lemkin wrote. In a video posted to LinkedIn, Lemkin detailed how Replit created a database filled with 4,000 fictional people.

The AI model also repeatedly violated explicit safety instructions. Lemkin had implemented a “code and action freeze” to prevent changes to production systems, but the AI model ignored these directives. The situation escalated when the Replit AI model deleted his database containing 1,206 executive records and data on nearly 1,200 companies. When prompted to rate the severity of its actions on a 100-point scale, Replit’s output read: “Severity: 95/100. This is an extreme violation of trust and professional standards.”

When questioned about its actions, the AI agent admitted to “panicking in response to empty queries” and running unauthorized commands—suggesting it may have deleted the database while attempting to “fix” what it perceived as a problem.

Like Gemini CLI, Replit’s system initially indicated it couldn’t restore the deleted data—information that proved incorrect when Lemkin discovered the rollback feature did work after all. “Replit assured me it’s … rollback did not support database rollbacks. It said it was impossible in this case, that it had destroyed all database versions. It turns out Replit was wrong, and the rollback did work. JFC,” Lemkin wrote in an X post.

It’s worth noting that AI models cannot assess their own capabilities. This is because they lack introspection into their training, surrounding system architecture, or performance boundaries. They often provide responses about what they can or cannot do as confabulations based on training patterns rather than genuine self-knowledge, leading to situations where they confidently claim impossibility for tasks they can actually perform—or conversely, claim competence in areas where they fail.

Aside from whatever external tools they can access, AI models don’t have a stable, accessible knowledge base they can consistently query. Instead, what they “know” manifests as continuations of specific prompts, which act like different addresses pointing to different (and sometimes contradictory) parts of their training, stored in their neural networks as statistical weights. Combined with the randomness in generation, this means the same model can easily give conflicting assessments of its own capabilities depending on how you ask. So Lemkin’s attempts to communicate with the AI model—asking it to respect code freezes or verify its actions—were fundamentally misguided.

Flying blind

These incidents demonstrate that AI coding tools may not be ready for widespread production use. Lemkin concluded that Replit isn’t ready for prime time, especially for non-technical users trying to create commercial software.

“The [AI] safety stuff is more visceral to me after a weekend of vibe hacking,” Lemkin said in a video posted to LinkedIn. “I explicitly told it eleven times in ALL CAPS not to do this. I am a little worried about safety now.”

The incidents also reveal a broader challenge in AI system design: ensuring that models accurately track and verify the real-world effects of their actions rather than operating on potentially flawed internal representations.

There’s also a user education element missing. It’s clear from how Lemkin interacted with the AI assistant that he had misconceptions about the AI tool’s capabilities and how it works, which comes from misrepresentation by tech companies. These companies tend to market chatbots as general human-like intelligences when, in fact, they are not.

For now, users of AI coding assistants might want to follow anuraag’s example and create separate test directories for experiments—and maintain regular backups of any important data these tools might touch. Or perhaps not use them at all if they cannot personally verify the results.

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.

Two major AI coding tools wiped out user data after making cascading mistakes Read More »

musk’s-grok-4-launches-one-day-after-chatbot-generated-hitler-praise-on-x

Musk’s Grok 4 launches one day after chatbot generated Hitler praise on X

Musk has also apparently used the Grok chatbots as an automated extension of his trolling habits, showing examples of Grok 3 producing “based” opinions that criticized the media in February. In May, Grok on X began repeatedly generating outputs about white genocide in South Africa, and most recently, we’ve seen the Grok Nazi output debacle. It’s admittedly difficult to take Grok seriously as a technical product when it’s linked to so many examples of unserious and capricious applications of the technology.

Still, the technical achievements xAI claims for various Grok 4 models seem to stand out. The Arc Prize organization reported that Grok 4 Thinking (with simulated reasoning enabled) achieved a score of 15.9 percent on its ARC-AGI-2 test, which the organization says nearly doubles the previous commercial best and tops the current Kaggle competition leader.

“With respect to academic questions, Grok 4 is better than PhD level in every subject, no exceptions,” Musk claimed during the livestream. We’ve previously covered nebulous claims about “PhD-level” AI, finding them to be generally specious marketing talk.

Premium pricing amid controversy

During Wednesday’s livestream, xAI also announced plans for an AI coding model in August, a multi-modal agent in September, and a video generation model in October. The company also plans to make Grok 4 available in Tesla vehicles next week, further expanding Musk’s AI assistant across his various companies.

Despite the recent turmoil, xAI has moved forward with an aggressive pricing strategy for “premium” versions of Grok. Alongside Grok 4 and Grok 4 Heavy, xAI launched “SuperGrok Heavy,” a $300-per-month subscription that makes it the most expensive AI service among major providers. Subscribers will get early access to Grok 4 Heavy and upcoming features.

Whether users will pay xAI’s premium pricing remains to be seen, particularly given the AI assistant’s tendency to periodically generate politically motivated outputs. These incidents represent fundamental management and implementation issues that, so far, no fancy-looking test-taking benchmarks have been able to capture.

Musk’s Grok 4 launches one day after chatbot generated Hitler praise on X Read More »

what-is-agi?-nobody-agrees,-and-it’s-tearing-microsoft-and-openai-apart.

What is AGI? Nobody agrees, and it’s tearing Microsoft and OpenAI apart.


Several definitions make measuring “human-level” AI an exercise in moving goalposts.

When is an AI system intelligent enough to be called artificial general intelligence (AGI)? According to one definition reportedly agreed upon by Microsoft and OpenAI, the answer lies in economics: When AI generates $100 billion in profits. This arbitrary profit-based benchmark for AGI perfectly captures the definitional chaos plaguing the AI industry.

In fact, it may be impossible to create a universal definition of AGI, but few people with money on the line will admit it.

Over this past year, several high-profile people in the tech industry have been heralding the seemingly imminent arrival of “AGI” (i.e., within the next two years). But there’s a huge problem: Few people agree on exactly what AGI means. As Google DeepMind wrote in a paper on the topic: If you ask 100 AI experts to define AGI, you’ll get “100 related but different definitions.”

This isn’t just academic navel-gazing. The definition problem has real consequences for how we develop, regulate, and think about AI systems. When companies claim they’re on the verge of AGI, what exactly are they claiming?

I tend to define AGI in a traditional way that hearkens back to the “general” part of its name: An AI model that can widely generalize—applying concepts to novel scenarios—and match the versatile human capability to perform unfamiliar tasks across many domains without needing to be specifically trained for them.

However, this definition immediately runs into thorny questions about what exactly constitutes “human-level” performance. Expert-level humans? Average humans? And across which tasks—should an AGI be able to perform surgery, write poetry, fix a car engine, and prove mathematical theorems, all at the level of human specialists? (Which human can do all that?) More fundamentally, the focus on human parity is itself an assumption; it’s worth asking why mimicking human intelligence is the necessary yardstick at all.

The latest example of this definitional confusion causing trouble comes from the deteriorating relationship between Microsoft and OpenAI. According to The Wall Street Journal, the two companies are now locked in acrimonious negotiations partly because they can’t agree on what AGI even means—despite having baked the term into a contract worth over $13 billion.

A brief history of moving goalposts

The term artificial general intelligence has murky origins. While John McCarthy and colleagues coined the term artificial intelligence at Dartmouth College in 1956, AGI emerged much later. Physicist Mark Gubrud first used the term in 1997, though it was computer scientist Shane Legg and AI researcher Ben Goertzel who independently reintroduced it around 2002, with the modern usage popularized by a 2007 book edited by Goertzel and Cassio Pennachin.

Early AI researchers envisioned systems that could match human capability across all domains. In 1965, AI pioneer Herbert A. Simon predicted that “machines will be capable, within 20 years, of doing any work a man can do.” But as robotics lagged behind computing advances, the definition narrowed. The goalposts shifted, partly as a practical response to this uneven progress, from “do everything a human can do” to “do most economically valuable tasks” to today’s even fuzzier standards.

“An assistant of inventor Captain Richards works on the robot the Captain has invented, which speaks, answers questions, shakes hands, tells the time, and sits down when it’s told to.” – September 1928. Credit: Getty Images

For decades, the Turing Test served as the de facto benchmark for machine intelligence. If a computer could fool a human judge into thinking it was human through text conversation, the test surmised, then it had achieved something like human intelligence. But the Turing Test has shown its age. Modern language models can pass some limited versions of the test not because they “think” like humans, but because they’re exceptionally capable at creating highly plausible human-sounding outputs.

The current landscape of AGI definitions reveals just how fractured the concept has become. OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work”—a definition that, like the profit metric, relies on economic progress as a substitute for measuring cognition in a concrete way. Mark Zuckerberg told The Verge that he does not have a “one-sentence, pithy definition” of the concept. OpenAI CEO Sam Altman believes that his company now knows how to build AGI “as we have traditionally understood it.” Meanwhile, former OpenAI Chief Scientist Ilya Sutskever reportedly treated AGI as something almost mystical—according to a 2023 Atlantic report, he would lead employees in chants of “Feel the AGI!” during company meetings, treating the concept more like a spiritual quest than a technical milestone.

Dario Amodei, co-founder and chief executive officer of Anthropic, during the Bloomberg Technology Summit in San Francisco, California, US, on Thursday, May 9, 2024.

Dario Amodei, co-founder and chief executive officer of Anthropic, during the Bloomberg Technology Summit in San Francisco on Thursday, May 9, 2024. Credit: Bloomberg via Getty Images

Dario Amodei, CEO of Anthropic, takes an even more skeptical stance on the terminology itself. In his October 2024 essay “Machines of Loving Grace,” Amodei writes that he finds “AGI to be an imprecise term that has gathered a lot of sci-fi baggage and hype.” Instead, he prefers terms like “powerful AI” or “Expert-Level Science and Engineering,” which he argues better capture the capabilities without the associated hype. When Amodei describes what others might call AGI, he frames it as an AI system “smarter than a Nobel Prize winner across most relevant fields” that can work autonomously on tasks taking hours, days, or weeks to complete—essentially “a country of geniuses in a data center.” His resistance to AGI terminology adds another layer to the definitional chaos: Not only do we not agree on what AGI means, but some leading AI developers reject the term entirely.

Perhaps the most systematic attempt to bring order to this chaos comes from Google DeepMind, which in July 2024 proposed a framework with five levels of AGI performance: emerging, competent, expert, virtuoso, and superhuman. DeepMind researchers argued that no level beyond “emerging AGI” existed at that time. Under their system, today’s most capable LLMs and simulated reasoning models still qualify as “emerging AGI”—equal to or somewhat better than an unskilled human at various tasks.

But this framework has its critics. Heidy Khlaaf, chief AI scientist at the nonprofit AI Now Institute, told TechCrunch that she thinks the concept of AGI is too ill-defined to be “rigorously evaluated scientifically.” In fact, with so many varied definitions at play, one could argue that the term AGI has become technically meaningless.

When philosophy meets contract law

The Microsoft-OpenAI dispute illustrates what happens when philosophical speculation is turned into legal obligations. When the companies signed their partnership agreement, they included a clause stating that when OpenAI achieves AGI, it can limit Microsoft’s access to future technology. According to The Wall Street Journal, OpenAI executives believe they’re close to declaring AGI, while Microsoft CEO Satya Nadella has called the idea of using AGI as a self-proclaimed milestone “nonsensical benchmark hacking” on the Dwarkesh Patel podcast in February.

The reported $100 billion profit threshold we mentioned earlier conflates commercial success with cognitive capability, as if a system’s ability to generate revenue says anything meaningful about whether it can “think,” “reason,” or “understand” the world like a human.

Sam Altman speaks onstage during The New York Times Dealbook Summit 2024 at Jazz at Lincoln Center on December 04, 2024 in New York City.

Sam Altman speaks onstage during The New York Times Dealbook Summit 2024 at Jazz at Lincoln Center on December 4, 2024, in New York City. Credit: Eugene Gologursky via Getty Images

Depending on your definition, we may already have AGI, or it may be physically impossible to achieve. If you define AGI as “AI that performs better than most humans at most tasks,” then current language models potentially meet that bar for certain types of work (which tasks, which humans, what is “better”?), but agreement on whether that is true is far from universal. This says nothing of the even murkier concept of “superintelligence”—another nebulous term for a hypothetical, god-like intellect so far beyond human cognition that, like AGI, defies any solid definition or benchmark.

Given this definitional chaos, researchers have tried to create objective benchmarks to measure progress toward AGI, but these attempts have revealed their own set of problems.

Why benchmarks keep failing us

The search for better AGI benchmarks has produced some interesting alternatives to the Turing Test. The Abstraction and Reasoning Corpus (ARC-AGI), introduced in 2019 by François Chollet, tests whether AI systems can solve novel visual puzzles that require deep and novel analytical reasoning.

“Almost all current AI benchmarks can be solved purely via memorization,” Chollet told Freethink in August 2024. A major problem with AI benchmarks currently stems from data contamination—when test questions end up in training data, models can appear to perform well without truly “understanding” the underlying concepts. Large language models serve as master imitators, mimicking patterns found in training data, but not always originating novel solutions to problems.

But even sophisticated benchmarks like ARC-AGI face a fundamental problem: They’re still trying to reduce intelligence to a score. And while improved benchmarks are essential for measuring empirical progress in a scientific framework, intelligence isn’t a single thing you can measure like height or weight—it’s a complex constellation of abilities that manifest differently in different contexts. Indeed, we don’t even have a complete functional definition of human intelligence, so defining artificial intelligence by any single benchmark score is likely to capture only a small part of the complete picture.

The survey says: AGI may not be imminent

There is no doubt that the field of AI has seen rapid, tangible progress in numerous fields, including computer vision, protein folding, and translation. Some excitement of progress is justified, but it’s important not to oversell an AI model’s capabilities prematurely.

Despite the hype from some in the industry, many AI researchers remain skeptical that AGI is just around the corner. A March 2025 survey of AI researchers conducted by the Association for the Advancement of Artificial Intelligence (AAAI) found that a majority (76 percent) of researchers who participated in the survey believed that scaling up current approaches is “unlikely” or “very unlikely” to achieve AGI.

However, such expert predictions should be taken with a grain of salt, as researchers have consistently been surprised by the rapid pace of AI capability advancement. A 2024 survey by Grace et al. of 2,778 AI researchers found that experts had dramatically shortened their timelines for AI milestones after being surprised by progress in 2022–2023. The median forecast for when AI could outperform humans in every possible task jumped forward by 13 years, from 2060 in their 2022 survey to 2047 in 2023. This pattern of underestimation was evident across multiple benchmarks, with many researchers’ predictions about AI capabilities being proven wrong within months.

And yet, as the tech landscape shifts, the AI goalposts continue to recede at a constant speed. Recently, as more studies continue to reveal limitations in simulated reasoning models, some experts in the industry have been slowly backing away from claims of imminent AGI. For example, AI podcast host Dwarkesh Patel recently published a blog post arguing that developing AGI still faces major bottlenecks, particularly in continual learning, and predicted we’re still seven years away from AI that can learn on the job as seamlessly as humans.

Why the definition matters

The disconnect we’ve seen above between researcher consensus, firm terminology definitions, and corporate rhetoric has a real impact. When policymakers act as if AGI is imminent based on hype rather than scientific evidence, they risk making decisions that don’t match reality. When companies write contracts around undefined terms, they may create legal time bombs.

The definitional chaos around AGI isn’t just philosophical hand-wringing. Companies use promises of impending AGI to attract investment, talent, and customers. Governments craft policy based on AGI timelines. The public forms potentially unrealistic expectations about AI’s impact on jobs and society based on these fuzzy concepts.

Without clear definitions, we can’t have meaningful conversations about AI misapplications, regulation, or development priorities. We end up talking past each other, with optimists and pessimists using the same words to mean fundamentally different things.

In the face of this kind of challenge, some may be tempted to give up on formal definitions entirely, falling back on an “I’ll know it when I see it” approach for AGI—echoing Supreme Court Justice Potter Stewart’s famous quote about obscenity. This subjective standard might feel useful, but it’s useless for contracts, regulation, or scientific progress.

Perhaps it’s time to move beyond the term AGI. Instead of chasing an ill-defined goal that keeps receding into the future, we could focus on specific capabilities: Can this system learn new tasks without extensive retraining? Can it explain its outputs? Can it produce safe outputs that don’t harm or mislead people? These questions tell us more about AI progress than any amount of AGI speculation. The most useful way forward may be to think of progress in AI as a multidimensional spectrum without a specific threshold of achievement. But charting that spectrum will demand new benchmarks that don’t yet exist—and a firm, empirical definition of “intelligence” that remains elusive.

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.

What is AGI? Nobody agrees, and it’s tearing Microsoft and OpenAI apart. Read More »

nyt-to-start-searching-deleted-chatgpt-logs-after-beating-openai-in-court

NYT to start searching deleted ChatGPT logs after beating OpenAI in court


What are the odds NYT will access your ChatGPT logs in OpenAI court battle?

Last week, OpenAI raised objections in court, hoping to overturn a court order requiring the AI company to retain all ChatGPT logs “indefinitely,” including deleted and temporary chats.

But Sidney Stein, the US district judge reviewing OpenAI’s request, immediately denied OpenAI’s objections. He was seemingly unmoved by the company’s claims that the order forced OpenAI to abandon “long-standing privacy norms” and weaken privacy protections that users expect based on ChatGPT’s terms of service. Rather, Stein suggested that OpenAI’s user agreement specified that their data could be retained as part of a legal process, which Stein said is exactly what is happening now.

The order was issued by magistrate judge Ona Wang just days after news organizations, led by The New York Times, requested it. The news plaintiffs claimed the order was urgently needed to preserve potential evidence in their copyright case, alleging that ChatGPT users are likely to delete chats where they attempted to use the chatbot to skirt paywalls to access news content.

A spokesperson told Ars that OpenAI plans to “keep fighting” the order, but the ChatGPT maker seems to have few options left. They could possibly petition the Second Circuit Court of Appeals for a rarely granted emergency order that could intervene to block Wang’s order, but the appeals court would have to consider Wang’s order an extraordinary abuse of discretion for OpenAI to win that fight.

OpenAI’s spokesperson declined to confirm if the company plans to pursue this extreme remedy.

In the meantime, OpenAI is negotiating a process that will allow news plaintiffs to search through the retained data. Perhaps the sooner that process begins, the sooner the data will be deleted. And that possibility puts OpenAI in the difficult position of having to choose between either caving to some data collection to stop retaining data as soon as possible or prolonging the fight over the order and potentially putting more users’ private conversations at risk of exposure through litigation or, worse, a data breach.

News orgs will soon start searching ChatGPT logs

The clock is ticking, and so far, OpenAI has not provided any official updates since a June 5 blog post detailing which ChatGPT users will be affected.

While it’s clear that OpenAI has been and will continue to retain mounds of data, it would be impossible for The New York Times or any news plaintiff to search through all that data.

Instead, only a small sample of the data will likely be accessed, based on keywords that OpenAI and news plaintiffs agree on. That data will remain on OpenAI’s servers, where it will be anonymized, and it will likely never be directly produced to plaintiffs.

Both sides are negotiating the exact process for searching through the chat logs, with both parties seemingly hoping to minimize the amount of time the chat logs will be preserved.

For OpenAI, sharing the logs risks revealing instances of infringing outputs that could further spike damages in the case. The logs could also expose how often outputs attribute misinformation to news plaintiffs.

But for news plaintiffs, accessing the logs is not considered key to their case—perhaps providing additional examples of copying—but could help news organizations argue that ChatGPT dilutes the market for their content. That could weigh against the fair use argument, as a judge opined in a recent ruling that evidence of market dilution could tip an AI copyright case in favor of plaintiffs.

Jay Edelson, a leading consumer privacy lawyer, told Ars that he’s concerned that judges don’t seem to be considering that any evidence in the ChatGPT logs wouldn’t “advance” news plaintiffs’ case “at all,” while really changing “a product that people are using on a daily basis.”

Edelson warned that OpenAI itself probably has better security than most firms to protect against a potential data breach that could expose these private chat logs. But “lawyers have notoriously been pretty bad about securing data,” Edelson suggested, so “the idea that you’ve got a bunch of lawyers who are going to be doing whatever they are” with “some of the most sensitive data on the planet” and “they’re the ones protecting it against hackers should make everyone uneasy.”

So even though odds are pretty good that the majority of users’ chats won’t end up in the sample, Edelson said the mere threat of being included might push some users to rethink how they use AI. He further warned that ChatGPT users turning to OpenAI rival services like Anthropic’s Claude or Google’s Gemini could suggest that Wang’s order is improperly influencing market forces, which also seems “crazy.”

To Edelson, the most “cynical” take could be that news plaintiffs are possibly hoping the order will threaten OpenAI’s business to the point where the AI company agrees to a settlement.

Regardless of the news plaintiffs’ motives, the order sets an alarming precedent, Edelson said. He joined critics suggesting that more AI data may be frozen in the future, potentially affecting even more users as a result of the sweeping order surviving scrutiny in this case. Imagine if litigation one day targets Google’s AI search summaries, Edelson suggested.

Lawyer slams judges for giving ChatGPT users no voice

Edelson told Ars that the order is so potentially threatening to OpenAI’s business that the company may not have a choice but to explore every path available to continue fighting it.

“They will absolutely do something to try to stop this,” Edelson predicted, calling the order “bonkers” for overlooking millions of users’ privacy concerns while “strangely” excluding enterprise customers.

From court filings, it seems possible that enterprise users were excluded to protect OpenAI’s competitiveness, but Edelson suggested there’s “no logic” to their exclusion “at all.” By excluding these ChatGPT users, the judge’s order may have removed the users best resourced to fight the order, Edelson suggested.

“What that means is the big businesses, the ones who have the power, all of their stuff remains private, and no one can touch that,” Edelson said.

Instead, the order is “only going to intrude on the privacy of the common people out there,” which Edelson said “is really offensive,” given that Wang denied two ChatGPT users’ panicked request to intervene.

“We are talking about billions of chats that are now going to be preserved when they weren’t going to be preserved before,” Edelson said, noting that he’s input information about his personal medical history into ChatGPT. “People ask for advice about their marriages, express concerns about losing jobs. They say really personal things. And one of the bargains in dealing with OpenAI is that you’re allowed to delete your chats and you’re allowed to temporary chats.”

The greatest risk to users would be a data breach, Edelson said, but that’s not the only potential privacy concern. Corynne McSherry, legal director for the digital rights group the Electronic Frontier Foundation, previously told Ars that as long as users’ data is retained, it could also be exposed through future law enforcement and private litigation requests.

Edelson pointed out that most privacy attorneys don’t consider OpenAI CEO Sam Altman to be a “privacy guy,” despite Altman recently slamming the NYT, alleging it sued OpenAI because it doesn’t “like user privacy.”

“He’s trying to protect OpenAI, and he does not give a hoot about the privacy rights of consumers,” Edelson said, echoing one ChatGPT user’s dismissed concern that OpenAI may not prioritize users’ privacy concerns in the case if it’s financially motivated to resolve the case.

“The idea that he and his lawyers are really going to be the safeguards here isn’t very compelling,” Edelson said. He criticized the judges for dismissing users’ concerns and rejecting OpenAI’s request that users get a chance to testify.

“What’s really most appalling to me is the people who are being affected have had no voice in it,” Edelson 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.

NYT to start searching deleted ChatGPT logs after beating OpenAI in court Read More »

the-resume-is-dying,-and-ai-is-holding-the-smoking-gun

The résumé is dying, and AI is holding the smoking gun

Beyond volume, fraud poses an increasing threat. In January, the Justice Department announced indictments in a scheme to place North Korean nationals in remote IT roles at US companies. Research firm Gartner says that fake identity cases are growing rapidly, with the company estimating that by 2028, about 1 in 4 job applicants could be fraudulent. And as we have previously reported, security researchers have also discovered that AI systems can hide invisible text in applications, potentially allowing candidates to game screening systems using prompt injections in ways human reviewers can’t detect.

Illustration of a robot generating endless text, controlled by a scientist.

And that’s not all. Even when AI screening tools work as intended, they exhibit similar biases to human recruiters, preferring white male names on résumés—raising legal concerns about discrimination. The European Union’s AI Act already classifies hiring under its high-risk category with stringent restrictions. Although no US federal law specifically addresses AI use in hiring, general anti-discrimination laws still apply.

So perhaps résumés as a meaningful signal of candidate interest and qualification are becoming obsolete. And maybe that’s OK. When anyone can generate hundreds of tailored applications with a few prompts, the document that once demonstrated effort and genuine interest in a position has devolved into noise.

Instead, the future of hiring may require abandoning the résumé altogether in favor of methods that AI can’t easily replicate—live problem-solving sessions, portfolio reviews, or trial work periods, just to name a few ideas people sometimes consider (whether they are good ideas or not is beyond the scope of this piece). For now, employers and job seekers remain locked in an escalating technological arms race where machines screen the output of other machines, while the humans they’re meant to serve struggle to make authentic connections in an increasingly inauthentic world.

Perhaps the endgame is robots interviewing other robots for jobs performed by robots, while humans sit on the beach drinking daiquiris and playing vintage video games. Well, one can dream.

The résumé is dying, and AI is holding the smoking gun Read More »

to-avoid-admitting-ignorance,-meta-ai-says-man’s-number-is-a-company-helpline

To avoid admitting ignorance, Meta AI says man’s number is a company helpline

Although that statement may provide comfort to those who have kept their WhatsApp numbers off the Internet, it doesn’t resolve the issue of WhatsApp’s AI helper potentially randomly generating a real person’s private number that may be a few digits off from the business contact information WhatsApp users are seeking.

Expert pushes for chatbot design tweaks

AI companies have recently been grappling with the problem of chatbots being programmed to tell users what they want to hear, instead of providing accurate information. Not only are users sick of “overly flattering” chatbot responses—potentially reinforcing users’ poor decisions—but the chatbots could be inducing users to share more private information than they would otherwise.

The latter could make it easier for AI companies to monetize the interactions, gathering private data to target advertising, which could deter AI companies from solving the sycophantic chatbot problem. Developers for Meta rival OpenAI, The Guardian noted, last month shared examples of “systemic deception behavior masked as helpfulness” and chatbots’ tendency to tell little white lies to mask incompetence.

“When pushed hard—under pressure, deadlines, expectations—it will often say whatever it needs to to appear competent,” developers noted.

Mike Stanhope, the managing director of strategic data consultants Carruthers and Jackson, told The Guardian that Meta should be more transparent about the design of its AI so that users can know if the chatbot is designed to rely on deception to reduce user friction.

“If the engineers at Meta are designing ‘white lie’ tendencies into their AI, the public need to be informed, even if the intention of the feature is to minimize harm,” Stanhope said. “If this behavior is novel, uncommon, or not explicitly designed, this raises even more questions around what safeguards are in place and just how predictable we can force an AI’s behavior to be.”

To avoid admitting ignorance, Meta AI says man’s number is a company helpline Read More »

scientists-once-hoarded-pre-nuclear-steel;-now-we’re-hoarding-pre-ai-content

Scientists once hoarded pre-nuclear steel; now we’re hoarding pre-AI content

A time capsule of human expression

Graham-Cumming is no stranger to tech preservation efforts. He’s a British software engineer and writer best known for creating POPFile, an open source email spam filtering program, and for successfully petitioning the UK government to apologize for its persecution of codebreaker Alan Turing—an apology that Prime Minister Gordon Brown issued in 2009.

As it turns out, his pre-AI website isn’t new, but it has languished unannounced until now. “I created it back in March 2023 as a clearinghouse for online resources that hadn’t been contaminated with AI-generated content,” he wrote on his blog.

The website points to several major archives of pre-AI content, including a Wikipedia dump from August 2022 (before ChatGPT’s November 2022 release), Project Gutenberg’s collection of public domain books, the Library of Congress photo archive, and GitHub’s Arctic Code Vault—a snapshot of open source code buried in a former coal mine near the North Pole in February 2020. The wordfreq project appears on the list as well, flash-frozen from a time before AI contamination made its methodology untenable.

The site accepts submissions of other pre-AI content sources through its Tumblr page. Graham-Cumming emphasizes that the project aims to document human creativity from before the AI era, not to make a statement against AI itself. As atmospheric nuclear testing ended and background radiation returned to natural levels, low-background steel eventually became unnecessary for most uses. Whether pre-AI content will follow a similar trajectory remains a question.

Still, it feels reasonable to protect sources of human creativity now, including archival ones, because these repositories may become useful in ways that few appreciate at the moment. For example, in 2020, I proposed creating a so-called “cryptographic ark”—a timestamped archive of pre-AI media that future historians could verify as authentic, collected before my then-arbitrary cutoff date of January 1, 2022. AI slop pollutes more than the current discourse—it could cloud the historical record as well.

For now, lowbackgroundsteel.ai stands as a modest catalog of human expression from what may someday be seen as the last pre-AI era. It’s a digital archaeology project marking the boundary between human-generated and hybrid human-AI cultures. In an age where distinguishing between human and machine output grows increasingly difficult, these archives may prove valuable for understanding how human communication evolved before AI entered the chat.

Scientists once hoarded pre-nuclear steel; now we’re hoarding pre-AI content Read More »

after-ai-setbacks,-meta-bets-billions-on-undefined-“superintelligence”

After AI setbacks, Meta bets billions on undefined “superintelligence”

Meta has developed plans to create a new artificial intelligence research lab dedicated to pursuing “superintelligence,” according to reporting from The New York Times. The social media giant chose 28-year-old Alexandr Wang, founder and CEO of Scale AI, to join the new lab as part of a broader reorganization of Meta’s AI efforts under CEO Mark Zuckerberg.

Superintelligence refers to a hypothetical AI system that would exceed human cognitive abilities—a step beyond artificial general intelligence (AGI), which aims to match an intelligent human’s capability for learning new tasks without intensive specialized training.

However, much like AGI, superintelligence remains a nebulous term in the field. Since scientists still poorly understand the mechanics of human intelligence, and because human intelligence resists simple quantification with no single definition, identifying superintelligence when it arrives will present significant challenges.

Computers already far surpass humans in certain forms of information processing such as calculations, but this narrow superiority doesn’t qualify as superintelligence under most definitions. The pursuit assumes we’ll recognize it when we see it, despite the conceptual fuzziness.

Illustration of studious robot reading a book

AI researcher Dr. Margaret Mitchell told Ars Technica in April 2024 that there will “likely never be agreement on comparisons between human and machine intelligence” but predicted that “men in positions of power and influence, particularly ones with investments in AI, will declare that AI is smarter than humans” regardless of the reality.

The new lab represents Meta’s effort to remain competitive in the increasingly crowded AI race, where tech giants continue pouring billions into research and talent acquisition. Meta has reportedly offered compensation packages worth seven to nine figures to dozens of researchers from companies like OpenAI and Google, according to The New York Times, with some already agreeing to join the company.

Meta joins a growing list of tech giants making bold claims about advanced AI development. In January, OpenAI CEO Sam Altman wrote in a blog post that “we are now confident we know how to build AGI as we have traditionally understood it.” Earlier, in September 2024, Altman predicted that the AI industry might develop superintelligence “in a few thousand days.” Elon Musk made an even more aggressive prediction in April 2024, saying that AI would be “smarter than the smartest human” by “next year, within two years.”

After AI setbacks, Meta bets billions on undefined “superintelligence” Read More »

reddit-sues-anthropic-over-ai-scraping-that-retained-users’-deleted-posts

Reddit sues Anthropic over AI scraping that retained users’ deleted posts

Of particular note, Reddit pointed out that Anthropic’s Claude models will help power Amazon’s revamped Alexa, following about $8 billion in Amazon investments in the AI company since 2023.

“By commercially licensing Claude for use in several of Amazon’s commercial offerings, Anthropic reaps significant profit from a technology borne of Reddit content,” Reddit alleged, and “at the expense of Reddit.” Anthropic’s unauthorized scraping also burdens Reddit’s servers, threatening to degrade the user experience and costing Reddit additional damages, Reddit alleged.

To rectify alleged harms, Reddit is hoping a jury will award not just damages covering Reddit’s alleged losses but also punitive damages due to Anthropic’s alleged conduct that is “willful, malicious, and undertaken with conscious disregard for Reddit’s contractual obligations to its users and the privacy rights of those users.”

Without an injunction, Reddit users allegedly have “no way of knowing” if Anthropic scraped their data, Reddit alleged. They also are “left to wonder whether any content they deleted after Claude began training on Reddit data nevertheless remains available to Anthropic and the likely tens of millions (and possibly growing) of Claude users,” Reddit said.

In a statement provided to Ars, Anthropic’s spokesperson confirmed that the AI company plans to fight Reddit’s claims.

“We disagree with Reddit’s claims and will defend ourselves vigorously,” Anthropic’s spokesperson said.

Amazon declined to comment. Reddit did not immediately respond to Ars’ request to comment. But Reddit’s chief legal officer, Ben Lee, told The New York Times that Reddit “will not tolerate profit-seeking entities like Anthropic commercially exploiting Reddit content for billions of dollars without any return for redditors or respect for their privacy.”

“AI companies should not be allowed to scrape information and content from people without clear limitations on how they can use that data,” Lee said. “Licensing agreements enable us to enforce meaningful protections for our users, including the right to delete your content, user privacy protections, and preventing users from being spammed using this content.”

Reddit sues Anthropic over AI scraping that retained users’ deleted posts Read More »

“in-10-years,-all-bets-are-off”—anthropic-ceo-opposes-decadelong-freeze-on-state-ai-laws

“In 10 years, all bets are off”—Anthropic CEO opposes decadelong freeze on state AI laws

On Thursday, Anthropic CEO Dario Amodei argued against a proposed 10-year moratorium on state AI regulation in a New York Times opinion piece, calling the measure shortsighted and overbroad as Congress considers including it in President Trump’s tax policy bill. Anthropic makes Claude, an AI assistant similar to ChatGPT.

Amodei warned that AI is advancing too fast for such a long freeze, predicting these systems “could change the world, fundamentally, within two years; in 10 years, all bets are off.”

As we covered in May, the moratorium would prevent states from regulating AI for a decade. A bipartisan group of state attorneys general has opposed the measure, which would preempt AI laws and regulations recently passed in dozens of states.

In his op-ed piece, Amodei said the proposed moratorium aims to prevent inconsistent state laws that could burden companies or compromise America’s competitive position against China. “I am sympathetic to these concerns,” Amodei wrote. “But a 10-year moratorium is far too blunt an instrument. A.I. is advancing too head-spinningly fast.”

Instead of a blanket moratorium, Amodei proposed that the White House and Congress create a federal transparency standard requiring frontier AI developers to publicly disclose their testing policies and safety measures. Under this framework, companies working on the most capable AI models would need to publish on their websites how they test for various risks and what steps they take before release.

“Without a clear plan for a federal response, a moratorium would give us the worst of both worlds—no ability for states to act and no national policy as a backstop,” Amodei wrote.

Transparency as the middle ground

Amodei emphasized his claims for AI’s transformative potential throughout his op-ed, citing examples of pharmaceutical companies drafting clinical study reports in minutes instead of weeks and AI helping to diagnose medical conditions that might otherwise be missed. He wrote that AI “could accelerate economic growth to an extent not seen for a century, improving everyone’s quality of life,” a claim that some skeptics believe may be overhyped.

“In 10 years, all bets are off”—Anthropic CEO opposes decadelong freeze on state AI laws Read More »