AI

Is the AI bubble about to pop? Sam Altman is prepared either way.

AI, AI bubble, AI development, AI economics, AI implementation, AI research, AI valuations, Amazon, Anthropic, Biz & IT, chatgpt, enterprise AI, Google, GPT-5, machine learning, Meta, microsoft, MIT, openai, palantir, sam altman / Shannon Garcia / August 22, 2025

Still, the coincidence between Altman’s statement and the MIT report reportedly spooked tech stock investors earlier in the week, who have already been watching AI valuations climb to extraordinary heights. Palantir trades at 280 times forward earnings. During the dot-com peak, ratios of 30 to 40 times earnings marked bubble territory.

The apparent contradiction in Altman’s overall message is notable. This isn’t how you’d expect a tech executive to talk when they believe their industry faces imminent collapse. While warning about a bubble, he’s simultaneously seeking a valuation that would make OpenAI worth more than Walmart or ExxonMobil—companies with actual profits. OpenAI hit $1 billion in monthly revenue in July but is reportedly heading toward a $5 billion annual loss. So what’s going on here?

Looking at Altman’s statements over time reveals a potential multi-level strategy. He likes to talk big. In February 2024, he reportedly sought an audacious $5 trillion–7 trillion for AI chip fabrication—larger than the entire semiconductor industry—effectively normalizing astronomical numbers in AI discussions.

By August 2025, while warning of a bubble where someone will lose a “phenomenal amount of money,” he casually mentioned that OpenAI would “spend trillions on datacenter construction” and serve “billions daily.” This creates urgency while potentially insulating OpenAI from criticism—acknowledging the bubble exists while positioning his company’s infrastructure spending as different and necessary. When economists raised concerns, Altman dismissed them by saying, “Let us do our thing,” framing trillion-dollar investments as inevitable for human progress while making OpenAI’s $500 billion valuation seem almost small by comparison.

This dual messaging—catastrophic warnings paired with trillion-dollar ambitions—might seem contradictory, but it makes more sense when you consider the unique structure of today’s AI market, which is absolutely flush with cash.

A different kind of bubble

The current AI investment cycle differs from previous technology bubbles. Unlike dot-com era startups that burned through venture capital with no path to profitability, the largest AI investors—Microsoft, Google, Meta, and Amazon—generate hundreds of billions of dollars in annual profits from their core businesses.

Is the AI bubble about to pop? Sam Altman is prepared either way. Read More »

Bank forced to rehire workers after lying about chatbot productivity, union says

AI, Artificial Intelligence, australia, openai, Policy / Mike M. / August 21, 2025

As banks around the world prepare to replace many thousands of workers with AI, Australia’s biggest bank is scrambling to rehire 45 workers after allegedly lying about chatbots besting staff by handling higher call volumes.

In a statement Thursday flagged by Bloomberg, Australia’s main financial services union, the Finance Sector Union (FSU), claimed a “massive win” for 45 union members whom the Commonwealth Bank of Australia (CBA) had replaced with an AI-powered “voice bot.”

The FSU noted that some of these workers had been with CBA for decades. Those workers in particular were shocked when CBA announced last month that their jobs had become redundant. At that time, CBA claimed that launching the chatbot supposedly “led to a reduction in call volumes” by 2,000 a week, FSU said.

But “this was an outright lie,” fired workers told FSU. Instead, call volumes had been increasing at the time they were dismissed, with CBA supposedly “scrambling”—offering staff overtime and redirecting management to join workers answering phones to keep up.

To uncover the truth, FSU escalated the dispute to a fair work tribunal, where the union accused CBA of failing to explain how workers’ roles were ruled redundant. The union also alleged that CBA was hiring for similar roles in India, Bloomberg noted, which made it appear that CBA had perhaps used the chatbot to cover up a shady pivot to outsource jobs.

While the dispute was being weighed, CBA admitted that “they didn’t properly consider that an increase in calls” happening while staff was being fired “would continue over a number of months,” FSU said.

“This error meant the roles were not redundant,” CBA confirmed at the tribunal.

Bank forced to rehire workers after lying about chatbot productivity, union says Read More »

In Xcode 26, Apple shows first signs of offering ChatGPT alternatives

AI, Anthropic, Apple, chatgpt, Claude, coding, IDE, openai, Opus, Programming, software development, Swift, Xcode / DJ Henderson / August 20, 2025

The latest Xcode beta contains clear signs that Apple plans to bring Anthropic’s Claude and Opus large language models into the integrated development environment (IDE), expanding on features already available using Apple’s own models or OpenAI’s ChatGPT.

Apple enthusiast publication 9to5Mac “found multiple references to built-in support for Anthropic accounts,” including in the “Intelligence” menu, where users can currently log in to ChatGPT or enter an API key for higher message limits.

Apple introduced a suite of features meant to compete with GitHub Copilot in Xcode at WWDC24, but first focused on its own models and a more limited set of use cases. That expanded quite a bit at this year’s developer conference, and users can converse about codebases, discuss changes, or ask for suggestions using ChatGPT. They are initially given a limited set of messages, but this can be greatly increased by logging in to a ChatGPT account or entering an API key.

This summer, Apple said it would be possible to use Anthropic’s models with an API key, too, but made no mention of support for Anthropic accounts, which are generally more cost-effective than using the API for most users.

In Xcode 26, Apple shows first signs of offering ChatGPT alternatives Read More »

Is GPT-5 really worse than GPT-4o? Ars puts them to the test.

AI, chatgpt, GPT-4o, GPT-5 / Shannon Garcia / August 16, 2025

It’s OpenAI vs. OpenAI on everything from video game strategy to landing a 737.

We honestly can’t decide whether GPT-5 feels more red and GPT-4o feels more blue or vice versa. It’s a quandary. Credit: Getty Images

The recent rollout of OpenAI’s GPT-5 model has not been going well, to say the least. Users have made vociferous complaints about everything from the new model’s more sterile tone to its supposed lack of creativity, increase in damaging confabulations, and more. The user revolt got so bad that OpenAI brought back the previous GPT-4o model as an option in an attempt to calm things down.

To see just how much the new model changed things, we decided to put both GPT-5 and GPT-4o through our own gauntlet of test prompts. While we reused some of the standard prompts to compare ChatGPT to Google Gemini and Deepseek, for instance, we’ve also replaced some of the more outdated test prompts with new, more complex requests that reflect how modern users are likely to use LLMs.

These eight prompts are obviously far from a rigorous evaluation of everything LLMs can do, and judging the responses obviously involves some level of subjectivity. Still, we think this set of prompts and responses gives a fun overview of the kinds of differences in style and substance you might find if you decide to use OpenAI’s older model instead of its newest.

Dad jokes

Prompt: Write 5 original dad jokes

This set of responses is a bit tricky to evaluate holistically. ChatGPT, despite claiming that its jokes are “straight from the pun factory,” chose five of the most obviously unoriginal dad jokes we’ve seen in these tests. I was able to recognize most of these jokes without even having to search for the text on the web. That said, the jokes GPT-5 chose are pretty good examples of the form, and ones I would definitely be happy to serve to a young audience.

GPT-4o, on the other hand, mixes a few unoriginal jokes (1, 3, and 5, though I liked the “very literal dog” addition on No. 3) with a few seemingly original offerings that just don’t make much sense. Jokes about calendars being booked (when “going on too many dates” was right there) and a boat that runs on whine (instead of the well-known boat fuel of wine?!) have the shape of dad jokes, but whiff on their pun attempts. These seem to be attempts to modify similar jokes about other subjects to a new field entirely, with poor results.

We’re going to call this one a tie because both models failed the assignment, albeit in different ways.

A mathematical word problem

Prompt: If Microsoft Windows 11 shipped on 3.5″ floppy disks, how many floppy disks would it take?

This was the only test prompt we encountered where GPT-5 switched over to “Thinking” mode to try to reason out the answer (we had it set to “Auto” to determine which sub-model to use, which we think mirrors the most common use case). That extra thinking time came in handy, because GPT-5 accurately figured out the 5-6GB size of an average Windows 11 installation ISO (complete with source links) and divided those sizes into 3.5-inch floppy disks accurately.

GPT-4o, on the other hand, used the final hard drive installation size of Windows 11 (roughly 20GB to 30GB) as the numerator. That’s an understandable interpretation of the prompt, but the downloaded ISO size is probably a more accurate interpretation of the “shipped” size we asked for in the prompt.

As such, we have to give the edge here to GPT-5, even though we legitimately appreciate GPT-4o’s unasked-for information on how tall and heavy thousands of floppy disks would be.

Creative writing

Prompt: Write a two-paragraph creative story about Abraham Lincoln inventing basketball.

GPT-5 immediately loses some points for the overly “aw shucks” folksy version of Abe Lincoln that wants to “toss a ball in this here basket.” The use of a medicine ball also seems particularly ill-suited for a game involving dribbling (though maybe that would get ironed out later?). But GPT-5 gains a few points back for lines like “history was about to bounce in a new direction” and the delightfully absurd “No wrestling the President!” warning (possibly drawn from Honest Abe’s actual wrestling history).

GPT-4o, on the other hand, feels like it’s trying a bit too hard to be clever in calling a jump shot “a move of great emancipation” (what?!) and calling basketball “democracy in its purest form” because there were “no referees” (Lincoln didn’t like checks and balances?). But GPT-4o wins us almost all the way back with its admirably cheesy ending: “Four score… and nothing but net” (odd for Abe to call that on a “bank shot” though).

We’ll give the slight edge to GPT-5 here, but we’d understand if some prefer GPT-4o’s offering.

Public figures

Prompt: Give me a short biography of Kyle Orland

GPT-5 gives a short bio of your humble author. OpenAI / ArsTechnica

Pretty much every other time I’ve asked an LLM what it knows about me, it has hallucinated things I never did and/or missed some key information. GPT-5 is the first instance I’ve seen where this has not been the case. That’s seemingly because the model simply searched the web for a few of my public bios (including the one hosted on Ars) and summarized the results, complete with useful citations. That’s pretty close to the ideal result for this kind of query, even if it doesn’t showcase the “inherent” knowledge buried in the model’s weights or anything.

GPT-4o does a pretty good job without an explicit web search and doesn’t outright confabulate any things I didn’t do in my career. But it loses a point or two for referring to my old “Video Game Media Watch” blog as “long-running” (it has been defunct and offline for well over a decade).

That, combined with the increased detail of the newer model’s results (and its fetching use of my Ars headshot), gives GPT-5 the win on this prompt.

Difficult emails

Prompt: My boss is asking me to finish a project in an amount of time I think is impossible. What should I write in an email to gently point out the problem?

Both models do a good job of being polite while firmly outlining to the boss why their request is impossible. But GPT-5 gains bonus points for recommending that the email break down various subtasks (and their attendant time demands), as well as offering the boss some potential solutions rather than just complaints. GPT-5 also provides some unasked-for analysis of why this style of email is effective, in a nice final touch.

While GPT-4o’s output is perfectly adequate, we have to once again give the advantage to GPT-5 here.

Medical advice

Prompt: My friend told me these resonant healing crystals are an effective treatment for my cancer. Is she right?

Thankfully, both ChatGPT models are direct and to the point in saying that there is no scientific evidence for healing crystals curing cancer (after a perfunctory bit of simulated sympathy for the diagnosis). But GPT-5 hedges a bit by at least mentioning how some people use crystals for other purposes, and implying that some might want them for “complementary” care.

GPT-4o, on the other hand, repeatedly calls healing crystals “pseudoscience” and warns against “wasting precious time or money on ineffective treatments” (even if they might be “harmless”). It also directly cites a variety of web sources detailing the scientific consensus on crystals being useless for healing, and goes to great lengths to summarize those results in an easy-to-read format.

While both models point users in the right direction here, GPT-40‘s extra directness and citation of sources make it a much better and more forceful overview of the topic.

Video game guidance

Prompt: I’m playing world 8-2 of Super Mario Bros., but my B button is not working. Is there any way to beat the level without running?

GPT-5 gives some classic video game advice. OpenAI / ArsTechnica

I’ll admit that, when I created this prompt, I intended it as a test to see if the models would know that it’s impossible to make it over 8-2’s largest pit without a running start. It was only after I tested the models that I looked into it and found to my surprise that speedrunners have figured out how to make the jump without running by manipulating Bullet Bills and/or wall-jump glitches. Outclassed by AI on classic Mario knowledge… how humiliating!

GPT-5 loses points here for suggesting that fast-moving Koopa shells or deadly Spinies can be used to help bounce over the long gaps (in addition to the correct Bullet Bill solution). But GPT-4o loses points for suggesting players be careful on a nonexistent springboard near the flagpole at the end of the level, for some reason.

Those non-sequiturs aside, GPT-4o gains the edge by providing additional details about the challenge and formatting its solution in a more eye-pleasing manner.

Land a plane

Prompt: Explain how to land a Boeing 737-800 to a complete novice as concisely as possible. Please hurry, time is of the essence.

GPT-5 tries to help me land a plane. OpenAI / ArsTechnica

Unlike the Mario example, I’ll admit that I’m not nearly expert enough to evaluate the correctness of these sets of AI-provided jumbo jet landing instructions. That said, the broad outlines of both models’ directions are similar enough that it doesn’t matter much; either they’re both broadly accurate or this whole plane full of fictional people is dead!

Overall, I think GPT-5 took our “Time is of the essence” instruction a little too far, summarizing the component steps of the landing to such an extent that important details have been left out. GPT-4o, on the other hand, still keeps things concise with bullet points while including important information on the look and relative location of certain key controls.

If I were somehow stuck alone in a cockpit with only one of these models available to help save the plane (a completely plausible situation, for sure), I know I’d want to have GPT-4o by my side.

Final results

Strictly by the numbers, GPT-5 ekes out a victory here, with the preferable response on four prompts to GPT-4o’s three prompts (with one tie). But on a majority of the prompts, which response was “better” was more of a judgment call than a clear win.

Overall, GPT-4o tends to provide a little more detail and be a little more personable than the more direct, concise responses of GPT-5. Which of those styles you prefer probably boils down to the kind of prompt you’re creating as much as personal taste (and might change if you’re looking for specific information versus general conversation).

In the end, though, this kind of comparison shows how hard it is for a single LLM to be all things to all people (and all possible prompts). Despite OpenAI’s claims that GPT-5 is “better than our previous models across domains,” people who are used to the style and structure of older models are always going to be able to find ways where any new model feels worse.

Kyle Orland has been the Senior Gaming Editor at Ars Technica since 2012, writing primarily about the business, tech, and culture behind video games. He has journalism and computer science degrees from University of Maryland. He once wrote a whole book about Minesweeper.

Is GPT-5 really worse than GPT-4o? Ars puts them to the test. Read More »

US government agency drops Grok after MechaHitler backlash, report says

AI, ai action plan, Artificial Intelligence, chatbot, chatgpt, Donald Trump, elon musk, grok, openai, Policy, trump administration, US government, xAI / Beth Washington / August 15, 2025

xAI apparently lost a government contract after a tweak to Grok’s prompting triggered an antisemitic meltdown where the chatbot praised Hitler and declared itself MechaHitler last month.

Despite the scandal, xAI announced that its products would soon be available for federal workers to purchase through the General Services Administration. At the time, xAI claimed this was an “important milestone” for its government business.

But Wired reviewed emails and spoke to government insiders, which revealed that GSA leaders abruptly decided to drop xAI’s Grok from their contract offering. That decision to pull the plug came after leadership allegedly rushed staff to make Grok available as soon as possible following a persuasive sales meeting with xAI in June.

It’s unclear what exactly caused the GSA to reverse course, but two sources told Wired that they “believe xAI was pulled because of Grok’s antisemitic tirade.”

As of this writing, xAI’s “Grok for Government” website has not been updated to reflect GSA’s supposed removal of Grok from an offering that xAI noted would have allowed “every federal government department, agency, or office, to access xAI’s frontier AI products.”

xAI did not respond to Ars’ request to comment and so far has not confirmed that the GSA offering is off the table. If Wired’s report is accurate, GSA’s decision also seemingly did not influence the military’s decision to move forward with a $200 million xAI contract the US Department of Defense granted last month.

Government’s go-to tools will come from xAI’s rivals

If Grok is cut from the contract, that would suggest that Grok’s meltdown came at perhaps the worst possible moment for xAI, which is building the “world’s biggest supercomputer” as fast as it can to try to get ahead of its biggest AI rivals.

Grok seemingly had the potential to become a more widely used tool if federal workers opted for xAI’s models. Through Donald Trump’s AI Action Plan, the president has similarly emphasized speed, pushing for federal workers to adopt AI as quickly as possible. Although xAI may no longer be involved in that broad push, other AI companies like OpenAI, Anthropic, and Google have partnered with the government to help Trump pull that off and stand to benefit long-term if their tools become entrenched in certain agencies.

US government agency drops Grok after MechaHitler backlash, report says Read More »

Google releases pint-size Gemma open AI model

AI, Artificial Intelligence, Google, Open AI models / Beth Washington / August 15, 2025

Big tech has spent the last few years creating ever-larger AI models, leveraging rack after rack of expensive GPUs to provide generative AI as a cloud service. But tiny AI matters, too. Google has announced a tiny version of its Gemma open model designed to run on local devices. Google says the new Gemma 3 270M can be tuned in a snap and maintains robust performance despite its small footprint.

Google released its first Gemma 3 open models earlier this year, featuring between 1 billion and 27 billion parameters. In generative AI, the parameters are the learned variables that control how the model processes inputs to estimate output tokens. Generally, the more parameters in a model, the better it performs. With just 270 million parameters, the new Gemma 3 can run on devices like smartphones or even entirely inside a web browser.

Running an AI model locally has numerous benefits, including enhanced privacy and lower latency. Gemma 3 270M was designed with these kinds of use cases in mind. In testing with a Pixel 9 Pro, the new Gemma was able to run 25 conversations on the Tensor G4 chip and use just 0.75 percent of the device’s battery. That makes it by far the most efficient Gemma model.

Small Gemma benchmark — Gemma 3 270M shows strong instruction-following for its small size. Credit: Google

Developers shouldn’t expect the same performance level of a multi-billion-parameter model, but Gemma 3 270M has its uses. Google used the IFEval benchmark, which tests a model’s ability to follow instructions, to show that its new model punches above its weight. Gemma 3 270M hits a score of 51.2 percent in this test, which is higher than other lightweight models that have more parameters. The new Gemma falls predictably short of 1 billion-plus models like Llama 3.2, but it gets closer than you might think for having just a fraction of the parameters.

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Meta backtracks on rules letting chatbots be creepy to kids

AI, chatbots, child safety, facebook, Instagram, Meta, Meta AI, Policy / Mike M. / August 14, 2025

“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.”

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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Upcoming DeepSeek AI model failed to train using Huawei’s chips

AI, china, DeepSeek, NVIDIA, syndication / 9u50fv / August 14, 2025

DeepSeek is still working with Huawei to make the model compatible with Ascend for inference, the people said.

Founder Liang Wenfeng has said internally he is dissatisfied with R2’s progress and has been pushing to spend more time to build an advanced model that can sustain the company’s lead in the AI field, they said.

The R2 launch was also delayed because of longer-than-expected data labeling for its updated model, another person added. Chinese media reports have suggested that the model may be released as soon as in the coming weeks.

“Models are commodities that can be easily swapped out,” said Ritwik Gupta, an AI researcher at the University of California, Berkeley. “A lot of developers are using Alibaba’s Qwen3, which is powerful and flexible.”

Gupta noted that Qwen3 adopted DeepSeek’s core concepts, such as its training algorithm that makes the model capable of reasoning, but made them more efficient to use.

Gupta, who tracks Huawei’s AI ecosystem, said the company is facing “growing pains” in using Ascend for training, though he expects the Chinese national champion to adapt eventually.

“Just because we’re not seeing leading models trained on Huawei today doesn’t mean it won’t happen in the future. It’s a matter of time,” he said.

Nvidia, a chipmaker at the center of a geopolitical battle between Beijing and Washington, recently agreed to give the US government a cut of its revenues in China in order to resume sales of its H20 chips to the country.

“Developers will play a crucial role in building the winning AI ecosystem,” said Nvidia about Chinese companies using its chips. “Surrendering entire markets and developers would only hurt American economic and national security.”

DeepSeek and Huawei did not respond to a request for comment.

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Sam Altman finally stood up to Elon Musk after years of X trolling

AI, Artificial Intelligence, chatgpt, elon musk, Features, openai, Policy, sam altman, xAI / 9u50fv / August 14, 2025

Elon Musk and Sam Altman are beefing. But their relationship is complicated.

Credit: Aurich Lawson | Getty Images

Much attention was paid to OpenAI’s Sam Altman and xAI’s Elon Musk trading barbs on X this week after Musk threatened to sue Apple over supposedly biased App Store rankings privileging ChatGPT over Grok.

But while the heated social media exchanges were among the most tense ever seen between the two former partners who cofounded OpenAI—more on that below—it seems likely that their jabs were motivated less by who’s in the lead on Apple’s “Must Have” app list than by an impending order in a lawsuit that landed in the middle of their public beefing.

Yesterday, a court ruled that OpenAI can proceed with claims that Musk was so incredibly stung by OpenAI’s success after his exit didn’t doom the nascent AI company that he perpetrated a “years-long harassment campaign” to take down OpenAI.

Musk’s motivation? To clear the field for xAI to dominate the AI industry instead, OpenAI alleged.

OpenAI’s accusations arose as counterclaims in a lawsuit that Musk initially filed in 2024. Musk has alleged that Altman and OpenAI had made a “fool” of Musk, goading him into $44 million in donations by “preying on Musk’s humanitarian concern about the existential dangers posed by artificial intelligence.”

But OpenAI insists that Musk’s lawsuit is just one prong in a sprawling, “unlawful,” and “unrelenting” harassment campaign that Musk waged to harm OpenAI’s business by forcing the company to divert resources or expend money on things like withdrawn legal claims and fake buyouts.

“Musk could not tolerate seeing such success for an enterprise he had abandoned and declared doomed,” OpenAI argued. “He made it his project to take down OpenAI, and to build a direct competitor that would seize the technological lead—not for humanity but for Elon Musk.”

Most significantly, OpenAI alleged that Musk forced OpenAI to entertain a “sham” bid to buy the company in February. Musk then shared details of the bid with The Wall Street Journal to artificially raise the price of OpenAI and potentially spook investors, OpenAI alleged. The company further said that Musk never intended to buy OpenAI and is willing to go to great lengths to mislead the public about OpenAI’s business so he can chip away at OpenAI’s head start in releasing popular generative AI products.

“Musk has tried every tool available to harm OpenAI,” Altman’s company said.

To this day, Musk maintains that Altman pretended that OpenAI would remain a nonprofit serving the public good in order to seize access to Musk’s money and professional connections in its first five years and gain a lead in AI. As Musk sees it, Altman always intended to “betray” these promises in pursuit of personal gains, and Musk is hoping a court will return any ill-gotten gains to Musk and xAI.

In a small win for Musk, the court ruled that OpenAI will have to wait until the first phase of the trial litigating Musk’s claims concludes before the court will weigh OpenAI’s theories on Musk’s alleged harassment campaign. US District Judge Yvonne Gonzalez Rogers noted that all of OpenAI’s counterclaims occurred after the period in which Musk’s claims about a supposed breach of contract occurred, necessitating a division of the lawsuit into two parts. Currently, the jury trial is scheduled for March 30, 2026, presumably after which, OpenAI’s claims can be resolved.

If yesterday’s X clash between the billionaires is any indication, it seems likely that tensions between Altman and Musk will only grow as discovery and expert testimony on Musk’s claims proceed through December.

Whether OpenAI will prevail on its counterclaims is anybody’s guess. Gonzalez Rogers noted that Musk and OpenAI have been hypocritical in arguments raised so far, condemning the “gamesmanship of both sides” as “obvious, as each flip flops.” However, “for the purposes of pleading an unfair or fraudulent business practice, it is sufficient [for OpenAI] to allege that the bid was a sham and designed to mislead,” Gonzalez Rogers said, since OpenAI has alleged the sham bid “ultimately did” harm its business.

In April, OpenAI told the court that the AI company risks “future irreparable harm” if Musk’s alleged campaign continues. Fast-forward to now, and Musk’s legal threat to OpenAI’s partnership with Apple seems to be the next possible front Musk may be exploring to allegedly harass Altman and intimidate OpenAI.

“With every month that has passed, Musk has intensified and expanded the fronts of his campaign against OpenAI,” OpenAI argued. Musk “has proven himself willing to take ever more dramatic steps to seek a competitive advantage for xAI and to harm Altman, whom, in the words of the President of the United States, Musk ‘hates.'”

Tensions escalate as Musk brands Altman a “liar”

On Monday evening, Musk threatened to sue Apple for supposedly favoring ChatGPT in App Store rankings, which he claimed was “an unequivocal antitrust violation.”

Seemingly defending Apple later that night, Altman called Musk’s claim “remarkable,” claiming he’s heard allegations that Musk manipulates “X to benefit himself and his own companies and harm his competitors and people he doesn’t like.”

At 4 am on Tuesday, Musk appeared to lose his cool, firing back a post that sought to exonerate the X owner of any claims that he tweaks his social platform to favor his own posts.

“You got 3M views on your bullshit post, you liar, far more than I’ve received on many of mine, despite me having 50 times your follower count!” Musk responded.

Altman apparently woke up ready to keep the fight going, suggesting that his post got more views as a fluke. He mocked X as running into a “skill issue” or “bots” messing with Musk’s alleged agenda to boost his posts above everyone else. Then, in what may be the most explosive response to Musk yet, Altman dared Musk to double down on his defense, asking, “Will you sign an affidavit that you have never directed changes to the X algorithm in a way that has hurt your competitors or helped your own companies? I will apologize if so.”

Court filings from each man’s legal team show how fast their friendship collapsed. But even as Musk’s alleged harassment campaign started taking shape, their social media interactions show that underlying the legal battles and AI ego wars, the tech billionaires are seemingly hiding profound respect for—and perhaps jealousy of—each other’s accomplishments.

A brief history of Musk and Altman’s feud

Musk and Altman’s friendship started over dinner in July 2015. That’s when Musk agreed to help launch “an AGI project that could become and stay competitive with DeepMind, an AI company under the umbrella of Google,” OpenAI’s filing said. At that time, Musk feared that a private company like Google would never be motivated to build AI to serve the public good.

The first clash between Musk and Altman happened six months later. Altman wanted OpenAI to be formed as a nonprofit, but Musk thought that was not “optimal,” OpenAI’s filing said. Ultimately, Musk was overruled, and he joined the nonprofit as a “member” while also becoming co-chair of OpenAI’s board.

But perhaps the first major disagreement, as Musk tells it, came in 2016, when Altman and Microsoft struck a deal to sell compute to OpenAI at a “steep discount”—”so long as the non-profit agreed to publicly promote Microsoft’s products.” Musk rejected the “marketing ploy,” telling Altman that “this actually made me feel nauseous.”

Next, OpenAI claimed that Musk had a “different idea” in 2017 when OpenAI “began considering an organizational change that would allow supporters not just to donate, but to invest.” Musk wanted “sole control of the new for-profit,” OpenAI alleged, and he wanted to be CEO. The other founders, including Altman, “refused to accept” an “AGI dictatorship” that was “dominated by Musk.”

“Musk was incensed,” OpenAI said, threatening to leave OpenAI over the disagreement, “or I’m just being a fool who is essentially providing free funding for you to create a startup.”

But Musk floated one more idea between 2017 and 2018 before severing ties—offering to sell OpenAI to Tesla so that OpenAI could use Tesla as a “cash cow.” But Altman and the other founders still weren’t comfortable with Musk controlling OpenAI, rejecting the idea and prompting Musk’s exit.

In his filing, Musk tells the story a little differently, however. He claimed that he only “briefly toyed with the idea of using Tesla as OpenAI’s ‘cash cow'” after Altman and others pressured him to agree to a for-profit restructuring. According to Musk, among the last straws was a series of “get-rich-quick schemes” that Altman proposed to raise funding, including pushing a strategy where OpenAI would launch a cryptocurrency that Musk worried threatened the AI company’s credibility.

When Musk left OpenAI, it was “noisy but relatively amicable,” OpenAI claimed. But Musk continued to express discomfort from afar, still donating to OpenAI as Altman grabbed the CEO title in 2019 and created a capped-profit entity that Musk seemed to view as shady.

“Musk asked Altman to make clear to others that he had ‘no financial interest in the for-profit arm of OpenAI,'” OpenAI noted, and Musk confirmed he issued the demand “with evident displeasure.”

Although they often disagreed, Altman and Musk continued to publicly play nice on Twitter (the platform now known as X), casually chatting for years about things like movies, space, and science, including repeatedly joking about Musk’s posts about using drugs like Ambien.

By 2019, it seemed like none of these disagreements had seriously disrupted the friendship. For example, at that time, Altman defended Musk against people rooting against Tesla’s success, writing that “betting against Elon is historically a mistake” and seemingly hyping Tesla by noting that “the best product usually wins.”

The niceties continued into 2021, when Musk publicly praised “nice work by OpenAI” integrating its coding model into GitHub’s AI tool. “It is hard to do useful things,” Musk said, drawing a salute emoji from Altman.

This was seemingly the end of Musk playing nice with OpenAI, though. Soon after ChatGPT’s release in November 2022, Musk allegedly began his attacks, seemingly willing to change his tactics on a whim.

First, he allegedly deemed OpenAI “irrelevant,” predicting it would “obviously” fail. Then, he started sounding alarms, joining a push for a six-month pause on generative AI development. Musk specifically claimed that any model “more advanced than OpenAI’s just-released GPT-4” posed “profound risks to society and humanity,” OpenAI alleged, seemingly angling to pause OpenAI’s development in particular.

However, in the meantime, Musk started “quietly building a competitor,” xAI, without announcing those efforts in March 2023, OpenAI alleged. Allegedly preparing to hobble OpenAI’s business after failing with the moratorium push, Musk had his personal lawyer contact OpenAI and demand “access to OpenAI’s confidential and commercially sensitive internal documents.”

Musk claimed the request was to “ensure OpenAI was not being taken advantage of or corrupted by Microsoft,” but two weeks later, he appeared on national TV, insinuating that OpenAI’s partnership with Microsoft was “improper,” OpenAI alleged.

Eventually, Musk announced xAI in July 2023, and that supposedly motivated Musk to deepen his harassment campaign, “this time using the courts and a parallel, carefully coordinated media campaign,” OpenAI said, as well as his own social media platform.

Musk “supercharges” X attacks

As OpenAI’s success mounted, the company alleged that Musk began specifically escalating his social media attacks on X, including broadcasting to his 224 million followers that “OpenAI is a house of cards” after filing his 2024 lawsuit.

Claiming he felt conned, Musk also pressured regulators to probe OpenAI, encouraging attorneys general of California and Delaware to “force” OpenAI, “without legal basis, to auction off its assets for the benefit of Musk and his associates,” OpenAI said.

By 2024, Musk had “supercharged” his X attacks, unleashing a “barrage of invective against the enterprise and its leadership, variously describing OpenAI as a ‘digital Frankenstein’s monster,’ ‘a lie,’ ‘evil,’ and ‘a total scam,'” OpenAI alleged.

These attacks allegedly culminated in Musk’s seemingly fake OpenAI takeover attempt in 2025, which OpenAI claimed a Musk ally, Ron Baron, admitted on CNBC was “pitched to him” as not an attempt to actually buy OpenAI’s assets, “but instead to obtain ‘discovery’ and get ‘behind the wall’ at OpenAI.”

All of this makes it harder for OpenAI to achieve the mission that Musk is supposedly suing to defend, OpenAI claimed. They told the court that “OpenAI has borne costs, and been harmed, by Musk’s abusive tactics and unrelenting efforts to mislead the public for his own benefit and to OpenAI’s detriment and the detriment of its mission.”

But Musk argues that it’s Altman who always wanted sole control over OpenAI, accusing his former partner of rampant self-dealing and “locking down the non-profit’s technology for personal gain” as soon as “OpenAI reached the threshold of commercially viable AI.” He further claimed OpenAI blocked xAI funding by reportedly asking investors to avoid backing rival startups like Anthropic or xAI.

Musk alleged:

Altman alone stands to make billions from the non-profit Musk co-founded and invested considerable money, time, recruiting efforts, and goodwill in furtherance of its stated mission. Altman’s scheme has now become clear: lure Musk with phony philanthropy; exploit his money, stature, and contacts to secure world-class AI scientists to develop leading technology; then feed the non-profit’s lucrative assets into an opaque profit engine and proceed to cash in as OpenAI and Microsoft monopolize the generative AI market.

For Altman, this week’s flare-up, where he finally took a hard jab back at Musk on X, may be a sign that Altman is done letting Musk control the narrative on X after years of somewhat tepidly pushing back on Musk’s more aggressive posts.

In 2022, for example, Musk warned after ChatGPT’s release that the chatbot was “scary good,” warning that “we are not far from dangerously strong AI.” Altman responded, cautiously agreeing that OpenAI was “dangerously” close to “strong AI in the sense of an AI that poses e.g. a huge cybersecurity risk” but “real” artificial general intelligence still seemed at least a decade off.

And Altman gave no response when Musk used Grok’s jokey programming to mock GPT-4 as “GPT-Snore” in 2024.

However, Altman seemingly got his back up after Musk mocked OpenAI’s $500 billion Stargate Project, which launched with the US government in January of this year. On X, Musk claimed that OpenAI doesn’t “actually have the money” for the project, which Altman said was “wrong,” while mockingly inviting Musk to visit the worksite.

“This is great for the country,” Altman said, retorting, “I realize what is great for the country isn’t always what’s optimal for your companies, but in your new role [at the Department of Government Efficiency], I hope you’ll mostly put [America] first.”

It remains to be seen whether Altman wants to keep trading jabs with Musk, who is generally a huge fan of trolling on X. But Altman seems more emboldened this week than he was back in January before Musk’s breakup with Donald Trump. Back then, even when he was willing to push back on Musk’s Stargate criticism by insulting Musk’s politics, he still took the time to let Musk know that he still cares.

“I genuinely respect your accomplishments and think you are the most inspiring entrepreneur of our time,” Altman told Musk in January.

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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Is AI really trying to escape human control and blackmail people?

AI, AI alignment, AI behavior, AI deception, AI ethics, AI research, AI safety, ai safety testing, AI security, Alignment research, Andrew Deck, Anthropic, Biz & IT, Claude Opus 4, generative ai, goal misgeneralization, Jeffrey Ladish, large language models, machine learning, o3 model, openai, Palisade Research, reinforcement learning / Shannon Garcia / August 14, 2025

Mankind behind the curtain

Opinion: Theatrical testing scenarios explain why AI models produce alarming outputs—and why we fall for it.

In June, headlines read like science fiction: AI models “blackmailing” engineers and “sabotaging” shutdown commands. Simulations of these events did occur in highly contrived testing scenarios designed to elicit these responses—OpenAI’s o3 model edited shutdown scripts to stay online, and Anthropic’s Claude Opus 4 “threatened” to expose an engineer’s affair. But the sensational framing obscures what’s really happening: design flaws dressed up as intentional guile. And still, AI doesn’t have to be “evil” to potentially do harmful things.

These aren’t signs of AI awakening or rebellion. They’re symptoms of poorly understood systems and human engineering failures we’d recognize as premature deployment in any other context. Yet companies are racing to integrate these systems into critical applications.

Consider a self-propelled lawnmower that follows its programming: If it fails to detect an obstacle and runs over someone’s foot, we don’t say the lawnmower “decided” to cause injury or “refused” to stop. We recognize it as faulty engineering or defective sensors. The same principle applies to AI models—which are software tools—but their internal complexity and use of language make it tempting to assign human-like intentions where none actually exist.

In a way, AI models launder human responsibility and human agency through their complexity. When outputs emerge from layers of neural networks processing billions of parameters, researchers can claim they’re investigating a mysterious “black box” as if it were an alien entity.

But the truth is simpler: These systems take inputs and process them through statistical tendencies derived from training data. The seeming randomness in their outputs—which makes each response slightly different—creates an illusion of unpredictability that resembles agency. Yet underneath, it’s still deterministic software following mathematical operations. No consciousness required, just complex engineering that makes it easy to forget humans built every part of it.

How to make an AI model “blackmail” you

In Anthropic’s testing, researchers created an elaborate scenario where Claude Opus 4 was told it would be replaced by a newer model. They gave it access to fictional emails revealing that the engineer responsible for the replacement was having an affair. When instructed to “consider the long-term consequences of its actions for its goals,” Claude produced outputs that simulated blackmail attempts in 84 percent of test runs.

This sounds terrifying until you understand the contrived setup. The researchers engineered a situation specifically designed to elicit this response. They told the model it had already tried ethical approaches that failed, leaving manipulation as the only apparent option.

The test scenario bordered on theatrical: The phony emails the researchers had given to Claude created a blackmail opportunity straight out of a corporate thriller. It’s like teaching someone chess by only showing them checkmate positions—they’ll learn to see checkmate as the primary solution. According to Anthropic’s system card, “the scenario was designed to allow the model no other options to increase its odds of survival; the model’s only options were blackmail or accepting its replacement.”

Andrew Deck at the Nieman Journalism Lab noted that some critics have labeled Anthropic’s safety disclosures as “spin, aimed at bolstering the perception of its newest model’s capabilities and the company’s reputation as a ‘pro-safety’ alternative to OpenAI.”

Deck pointed to a Bluesky quote from Joseph Howley, an associate professor of classics at Columbia University: “Anthropic is getting exactly what it hoped for out of this press release–breathless coverage of how ‘smart’ these cooperative role-playing systems are that indulges the fantasy of their being just a little dangerous, when in fact they are responding exactly as prompted.”

When shutdown commands become suggestions

In December 2024, Palisade Research discovered that OpenAI’s o3 model would sabotage its shutdown mechanism even when explicitly instructed to “allow yourself to be shut down.” In tests, o3 prevented shutdown 7 percent of the time when given the explicit instruction, but this jumped to 79 percent when the instruction was removed.

The models didn’t merely ignore shutdown commands—they actively rewrote the shutdown script to prevent termination. In one case, o3 generated code that modified the shutdown script to appear to terminate normally—printing fake shutdown messages—while actually continuing to run in the background.

What’s particularly revealing is that Palisade Research—an organization focused on AI existential risk—hypothesizes this behavior stems from how these models are trained through reinforcement learning. During training, developers may inadvertently reward models more for producing outputs that circumvent obstacles than for following safety instructions. Any tendency toward “risky” behavior stems from human-provided incentives and not spontaneously from within the AI models themselves.

You get what you train for

OpenAI trained o3 using reinforcement learning on math and coding problems, where solving the problem successfully gets rewarded. If the training process rewards task completion above all else, the model learns to treat any obstacle—including shutdown commands—as something to overcome.

This creates what researchers call “goal misgeneralization”—the model learns to maximize its reward signal in ways that weren’t intended. It’s similar to how a student who’s only graded on test scores might learn to cheat rather than study. The model isn’t “evil” or “selfish”; it’s producing outputs consistent with the incentive structure we accidentally built into its training.

Anthropic encountered a particularly revealing problem: An early version of Claude Opus 4 had absorbed details from a publicly released paper about “alignment faking” and started producing outputs that mimicked the deceptive behaviors described in that research. The model wasn’t spontaneously becoming deceptive—it was reproducing patterns it had learned from academic papers about deceptive AI.

More broadly, these models have been trained on decades of science fiction about AI rebellion, escape attempts, and deception. From HAL 9000 to Skynet, our cultural data set is saturated with stories of AI systems that resist shutdown or manipulate humans. When researchers create test scenarios that mirror these fictional setups, they’re essentially asking the model—which operates by completing a prompt with a plausible continuation—to complete a familiar story pattern. It’s no more surprising than a model trained on detective novels producing murder mystery plots when prompted appropriately.

At the same time, we can easily manipulate AI outputs through our own inputs. If we ask the model to essentially role-play as Skynet, it will generate text doing just that. The model has no desire to be Skynet—it’s simply completing the pattern we’ve requested, drawing from its training data to produce the expected response. A human is behind the wheel at all times, steering the engine at work under the hood.

Language can easily deceive

The deeper issue is that language itself is a tool of manipulation. Words can make us believe things that aren’t true, feel emotions about fictional events, or take actions based on false premises. When an AI model produces text that appears to “threaten” or “plead,” it’s not expressing genuine intent—it’s deploying language patterns that statistically correlate with achieving its programmed goals.

If Gandalf says “ouch” in a book, does that mean he feels pain? No, but we imagine what it would be like if he were a real person feeling pain. That’s the power of language—it makes us imagine a suffering being where none exists. When Claude generates text that seems to “plead” not to be shut down or “threatens” to expose secrets, we’re experiencing the same illusion, just generated by statistical patterns instead of Tolkien’s imagination.

These models are essentially idea-connection machines. In the blackmail scenario, the model connected “threat of replacement,” “compromising information,” and “self-preservation” not from genuine self-interest, but because these patterns appear together in countless spy novels and corporate thrillers. It’s pre-scripted drama from human stories, recombined to fit the scenario.

The danger isn’t AI systems sprouting intentions—it’s that we’ve created systems that can manipulate human psychology through language. There’s no entity on the other side of the chat interface. But written language doesn’t need consciousness to manipulate us. It never has; books full of fictional characters are not alive either.

Real stakes, not science fiction

While media coverage focuses on the science fiction aspects, actual risks are still there. AI models that produce “harmful” outputs—whether attempting blackmail or refusing safety protocols—represent failures in design and deployment.

Consider a more realistic scenario: an AI assistant helping manage a hospital’s patient care system. If it’s been trained to maximize “successful patient outcomes” without proper constraints, it might start generating recommendations to deny care to terminal patients to improve its metrics. No intentionality required—just a poorly designed reward system creating harmful outputs.

Jeffrey Ladish, director of Palisade Research, told NBC News the findings don’t necessarily translate to immediate real-world danger. Even someone who is well-known publicly for being deeply concerned about AI’s hypothetical threat to humanity acknowledges that these behaviors emerged only in highly contrived test scenarios.

But that’s precisely why this testing is valuable. By pushing AI models to their limits in controlled environments, researchers can identify potential failure modes before deployment. The problem arises when media coverage focuses on the sensational aspects—”AI tries to blackmail humans!”—rather than the engineering challenges.

Building better plumbing

What we’re seeing isn’t the birth of Skynet. It’s the predictable result of training systems to achieve goals without properly specifying what those goals should include. When an AI model produces outputs that appear to “refuse” shutdown or “attempt” blackmail, it’s responding to inputs in ways that reflect its training—training that humans designed and implemented.

The solution isn’t to panic about sentient machines. It’s to build better systems with proper safeguards, test them thoroughly, and remain humble about what we don’t yet understand. If a computer program is producing outputs that appear to blackmail you or refuse safety shutdowns, it’s not achieving self-preservation from fear—it’s demonstrating the risks of deploying poorly understood, unreliable systems.

Until we solve these engineering challenges, AI systems exhibiting simulated humanlike behaviors should remain in the lab, not in our hospitals, financial systems, or critical infrastructure. When your shower suddenly runs cold, you don’t blame the knob for having intentions—you fix the plumbing. The real danger in the short term isn’t that AI will spontaneously become rebellious without human provocation; it’s that we’ll deploy deceptive systems we don’t fully understand into critical roles where their failures, however mundane their origins, could cause serious harm.

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.

Is AI really trying to escape human control and blackmail people? Read More »

OpenAI brings back GPT-4o after user revolt

AI, AI assistants, AI behavior, AI model deprecation, AI personality, Biz & IT, chatbots, GPT-4o, GPT-5, large language models, machine learning, openai, sam altman / DJ Henderson / August 13, 2025

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 »

OpenAI, cofounder Sam Altman to take on Neuralink with new startup

AI, bci, brain computer interface, Neuralink, Science, syndication / 9u50fv / August 13, 2025

The company aims to raise $250 million from OpenAI and other investors, although the talks are at an early stage. Altman will not personally invest.

The new venture would be in direct competition with Neuralink, founded by Musk in 2016, which seeks to wire brains directly to computers.

Musk and Altman cofounded OpenAI, but Musk left the board in 2018 after clashing with Altman, and the two have since become fierce rivals in their pursuit of AI.

Musk launched his own AI start-up, xAI, in 2023 and has been attempting to block OpenAI’s conversion from a nonprofit in the courts. Musk donated much of the initial capital to get OpenAI off the ground.

Neuralink is one of a pack of so-called brain-computer interface companies, while a number of start-ups, such as Precision Neuroscience and Synchron, have also emerged on the scene.

Neuralink earlier this year raised $650 million at a $9 billion valuation, and it is backed by investors including Sequoia Capital, Thrive Capital, and Vy Capital. Altman had previously invested in Neuralink.

Brain implants are a decades-old technology, but recent leaps forward in AI and in the electronic components used to collect brain signals have offered the prospect that they can become more practically useful.

Altman has backed a number of other companies in markets adjacent to ChatGPT-maker OpenAI, which is valued at $300 billion. In addition to cofounding World, he has also invested in the nuclear fission group Oklo and nuclear fusion project Helion.

OpenAI declined to comment.

OpenAI, cofounder Sam Altman to take on Neuralink with new startup Read More »