AI – Page 21

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 / Mike M. / 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that Musk waged to harm OpenAI’s business by forcing the company to divert resources or expend money on things like withdrawn legal claims and fake buyouts.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 / Paul Patrick / 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.

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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 / Paul Patrick / 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.

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

AI, bci, brain computer interface, Neuralink, Science, syndication / Mike M. / 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.

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Why it’s a mistake to ask chatbots about their mistakes

AI, Biz & IT, machine learning / Kris Guyer / August 13, 2025

The only thing I know is that I know nothing

The tendency to ask AI bots to explain themselves reveals widespread misconceptions about how they work.

When something goes wrong with an AI assistant, our instinct is to ask it directly: “What happened?” or “Why did you do that?” It’s a natural impulse—after all, if a human makes a mistake, we ask them to explain. But with AI models, this approach rarely works, and the urge to ask reveals a fundamental misunderstanding of what these systems are and how they operate.

A recent incident with Replit’s AI coding assistant perfectly illustrates this problem. When the AI tool deleted a production database, user Jason Lemkin asked it about rollback capabilities. The AI model confidently claimed rollbacks were “impossible in this case” and that it had “destroyed all database versions.” This turned out to be completely wrong—the rollback feature worked fine when Lemkin tried it himself.

And after xAI recently reversed a temporary suspension of the Grok chatbot, users asked it directly for explanations. It offered multiple conflicting reasons for its absence, some of which were controversial enough that NBC reporters wrote about Grok as if it were a person with a consistent point of view, titling an article, “xAI’s Grok offers political explanations for why it was pulled offline.”

Why would an AI system provide such confidently incorrect information about its own capabilities or mistakes? The answer lies in understanding what AI models actually are—and what they aren’t.

There’s nobody home

The first problem is conceptual: You’re not talking to a consistent personality, person, or entity when you interact with ChatGPT, Claude, Grok, or Replit. These names suggest individual agents with self-knowledge, but that’s an illusion created by the conversational interface. What you’re actually doing is guiding a statistical text generator to produce outputs based on your prompts.

There is no consistent “ChatGPT” to interrogate about its mistakes, no singular “Grok” entity that can tell you why it failed, no fixed “Replit” persona that knows whether database rollbacks are possible. You’re interacting with a system that generates plausible-sounding text based on patterns in its training data (usually trained months or years ago), not an entity with genuine self-awareness or system knowledge that has been reading everything about itself and somehow remembering it.

Once an AI language model is trained (which is a laborious, energy-intensive process), its foundational “knowledge” about the world is baked into its neural network and is rarely modified. Any external information comes from a prompt supplied by the chatbot host (such as xAI or OpenAI), the user, or a software tool the AI model uses to retrieve external information on the fly.

In the case of Grok above, the chatbot’s main source for an answer like this would probably originate from conflicting reports it found in a search of recent social media posts (using an external tool to retrieve that information), rather than any kind of self-knowledge as you might expect from a human with the power of speech. Beyond that, it will likely just make something up based on its text-prediction capabilities. So asking it why it did what it did will yield no useful answers.

The impossibility of LLM introspection

Large language models (LLMs) alone cannot meaningfully assess their own capabilities for several reasons. They generally lack any introspection into their training process, have no access to their surrounding system architecture, and cannot determine their own performance boundaries. When you ask an AI model what it can or cannot do, it generates responses based on patterns it has seen in training data about the known limitations of previous AI models—essentially providing educated guesses rather than factual self-assessment about the current model you’re interacting with.

A 2024 study by Binder et al. demonstrated this limitation experimentally. While AI models could be trained to predict their own behavior in simple tasks, they consistently failed at “more complex tasks or those requiring out-of-distribution generalization.” Similarly, research on “Recursive Introspection” found that without external feedback, attempts at self-correction actually degraded model performance—the AI’s self-assessment made things worse, not better.

This leads to paradoxical situations. The same model might confidently claim impossibility for tasks it can actually perform, or conversely, claim competence in areas where it consistently fails. In the Replit case, the AI’s assertion that rollbacks were impossible wasn’t based on actual knowledge of the system architecture—it was a plausible-sounding confabulation generated from training patterns.

Consider what happens when you ask an AI model why it made an error. The model will generate a plausible-sounding explanation because that’s what the pattern completion demands—there are plenty of examples of written explanations for mistakes on the Internet, after all. But the AI’s explanation is just another generated text, not a genuine analysis of what went wrong. It’s inventing a story that sounds reasonable, not accessing any kind of error log or internal state.

Unlike humans who can introspect and assess their own knowledge, AI models don’t have a stable, accessible knowledge base they can query. What they “know” only manifests as continuations of specific prompts. Different prompts act like different addresses, pointing to different—and sometimes contradictory—parts of their training data, stored as statistical weights in neural networks.

This means the same model can give completely different assessments of its own capabilities depending on how you phrase your question. Ask “Can you write Python code?” and you might get an enthusiastic yes. Ask “What are your limitations in Python coding?” and you might get a list of things the model claims it cannot do—even if it regularly does them successfully.

The randomness inherent in AI text generation compounds this problem. Even with identical prompts, an AI model might give slightly different responses about its own capabilities each time you ask.

Other layers also shape AI responses

Even if a language model somehow had perfect knowledge of its own workings, other layers of AI chatbot applications might be completely opaque. For example, modern AI assistants like ChatGPT aren’t single models but orchestrated systems of multiple AI models working together, each largely “unaware” of the others’ existence or capabilities. For instance, OpenAI uses separate moderation layer models whose operations are completely separate from the underlying language models generating the base text.

When you ask ChatGPT about its capabilities, the language model generating the response has no knowledge of what the moderation layer might block, what tools might be available in the broader system, or what post-processing might occur. It’s like asking one department in a company about the capabilities of a department it has never interacted with.

Perhaps most importantly, users are always directing the AI’s output through their prompts, even when they don’t realize it. When Lemkin asked Replit whether rollbacks were possible after a database deletion, his concerned framing likely prompted a response that matched that concern—generating an explanation for why recovery might be impossible rather than accurately assessing actual system capabilities.

This creates a feedback loop where worried users asking “Did you just destroy everything?” are more likely to receive responses confirming their fears, not because the AI system has assessed the situation, but because it’s generating text that fits the emotional context of the prompt.

A lifetime of hearing humans explain their actions and thought processes has led us to believe that these kinds of written explanations must have some level of self-knowledge behind them. That’s just not true with LLMs that are merely mimicking those kinds of text patterns to guess at their own capabilities and flaws.

Why it’s a mistake to ask chatbots about their mistakes Read More »

Perplexity offers more than twice its total valuation to buy Chrome from Google

AI, artificial inteligence, Browsers, Google, Perplexity, Tech / Rejus Almole / August 13, 2025

Google has strenuously objected to the government’s proposed Chrome divestment, which it calls “a radical interventionist agenda.” Chrome isn’t just a browser—it’s an open source project known as Chromium, which powers numerous non-Google browsers, including Microsoft’s Edge. Perplexity’s offer includes $3 billion to run Chromium over two years, and it allegedly vows to keep the project fully open source. Perplexity promises it also won’t enforce changes to the browser’s default search engine.

An unsolicited offer

We’re currently waiting on United States District Court Judge Amit Mehta to rule on remedies in the case. That could happen as soon as this month. Perplexity’s offer, therefore, is somewhat timely, but there could still be a long road ahead.

This is an unsolicited offer, and there’s no indication that Google will jump at the chance to sell Chrome as soon as the ruling drops. Even if the court decides that Google should sell, it can probably get much, much more than Perplexity is offering. During the trial, DuckDuckGo’s CEO suggested a price of around $50 billion, but other estimates have ranged into the hundreds of billions. However, the data that flows to Chrome’s owner could be vital in building new AI technologies—any sale price is likely to be a net loss for Google.

If Mehta decides to force a sale, there will undoubtedly be legal challenges that could take months or years to resolve. Should these maneuvers fail, there’s likely to be opposition to any potential buyer. There will be many users who don’t like the idea of an AI startup or an unholy alliance of venture capital firms owning Chrome. Google has been hoovering up user data with Chrome for years—but that’s the devil we know.

Perplexity offers more than twice its total valuation to buy Chrome from Google Read More »

Musk threatens to sue Apple so Grok can get top App Store ranking

AI, App Store, Apple, Artificial Intelligence, chatbot, chatgpt, DeepSeek, elon musk, Google, grok, openai, play store, Policy, Twitter, X, xAI / Kris Guyer / August 12, 2025

After spending last week hyping Grok’s spicy new features, Elon Musk kicked off this week by threatening to sue Apple for supposedly gaming the App Store rankings to favor ChatGPT over Grok.

“Apple is behaving in a manner that makes it impossible for any AI company besides OpenAI to reach #1 in the App Store, which is an unequivocal antitrust violation,” Musk wrote on X, without providing any evidence. “xAI will take immediate legal action.”

In another post, Musk tagged Apple, asking, “Why do you refuse to put either X or Grok in your ‘Must Have’ section when X is the #1 news app in the world and Grok is #5 among all apps?”

“Are you playing politics?” Musk asked. “What gives? Inquiring minds want to know.”

Apple did not respond to the post and has not responded to Ars’ request to comment.

At the heart of Musk’s complaints is an OpenAI partnership that Apple announced last year, integrating ChatGPT into versions of its iPhone, iPad, and Mac operating systems.

Musk has alleged that this partnership incentivized Apple to boost ChatGPT rankings. OpenAI’s popular chatbot “currently holds the top spot in the App Store’s ‘Top Free Apps’ section for iPhones in the US,” Reuters noted, “while xAI’s Grok ranks fifth and Google’s Gemini chatbot sits at 57th.” Sensor Tower data shows ChatGPT similarly tops Google Play Store rankings.

While Musk seems insistent that ChatGPT is artificially locked in the lead, fact-checkers on X added a community note to his post. They confirmed that at least one other AI tool has somewhat recently unseated ChatGPT in the US rankings. Back in January, DeepSeek topped App Store charts and held the lead for days, ABC News reported.

OpenAI did not immediately respond to Ars’ request to comment on Musk’s allegations, but an OpenAI developer, Steven Heidel, did add a quip in response to one of Musk’s posts, writing, “Don’t forget to also blame Google for OpenAI being #1 on Android, and blame SimilarWeb for putting ChatGPT above X on the most-visited websites list, and blame….”

Musk threatens to sue Apple so Grok can get top App Store ranking Read More »

China tells Alibaba, ByteDance to justify purchases of Nvidia AI chips

AI, china, NVIDIA, Policy, syndication / Tim Belzer / August 12, 2025

Beijing is demanding tech companies including Alibaba and ByteDance justify their orders of Nvidia’s H20 artificial intelligence chips, complicating the US chipmaker’s business in China after striking an export arrangement with the Trump administration.

The tech companies have been asked by regulators such as the Ministry of Industry and Information Technology (MIIT) to explain why they need to order Nvidia’s H20 chips instead of using domestic alternatives, said three people familiar with the situation.

Some tech companies, who were the main buyers of Nvidia’s H20 chips before their sale in China was restricted, were planning to downsize their orders as a result of the questions from regulators, said two of the people.

“It’s not banned but has kind of become a politically incorrect thing to do,” said one Chinese data center operator about purchasing Nvidia’s H20 chips.

Alibaba, ByteDance, and MIIT did not immediately respond to a request for comment.

Chinese regulators have expressed growing disapproval of companies using Nvidia’s chips for any government or security related projects. Bloomberg reported on Tuesday that Chinese authorities had sent notices to a range of companies discouraging the use of the H20 chips, particularly for government-related work.

China tells Alibaba, ByteDance to justify purchases of Nvidia AI chips Read More »

The GPT-5 rollout has been a big mess

AI, Biz & IT, chatgpt, chatgtp, large language models, machine learning, openai / Tim Belzer / August 12, 2025

It’s been less than a week since the launch of OpenAI’s new GPT-5 AI model, and the rollout hasn’t been a smooth one. So far, the release sparked one of the most intense user revolts in ChatGPT’s history, forcing CEO Sam Altman to make an unusual public apology and reverse key decisions.

At the heart of the controversy has been OpenAI’s decision to automatically remove access to all previous AI models in ChatGPT (approximately nine, depending on how you count them) when GPT-5 rolled out to user accounts. Unlike API users who receive advance notice of model deprecations, consumer ChatGPT users had no warning that their preferred models would disappear overnight, noted independent AI researcher Simon Willison in a blog post.

The problems started immediately after GPT-5’s August 7 debut. A Reddit thread titled “GPT-5 is horrible” quickly amassed over 4,000 comments filled with users expressing frustration over the new release. By August 8, social media platforms were flooded with complaints about performance issues, personality changes, and the forced removal of older models.

As of May 14, 2025, ChatGPT Pro users have access to 8 different main AI models, plus Deep Research. — Prior to the launch of GPT-5, ChatGPT Pro users could select between nine different AI models, including Deep Research. (This screenshot is from May 14, 2025, and OpenAI later replaced o1 pro with o3-pro.) Credit: Benj Edwards

Marketing professionals, researchers, and developers all shared examples of broken workflows on social media. “I’ve spent months building a system to work around OpenAI’s ridiculous limitations in prompts and memory issues,” wrote one Reddit user in the r/OpenAI subreddit. “And in less than 24 hours, they’ve made it useless.”

How could different AI language models break a workflow? The answer lies in how each one is trained in a different way and includes its own unique output style: The workflow breaks because users have developed sets of prompts that produce useful results optimized for each AI model.

For example, Willison wrote how different user groups had developed distinct workflows with specific AI models in ChatGPT over time, quoting one Reddit user who explained: “I know GPT-5 is designed to be stronger for complex reasoning, coding, and professional tasks, but not all of us need a pro coding model. Some of us rely on 4o for creative collaboration, emotional nuance, roleplay, and other long-form, high-context interactions.”

The GPT-5 rollout has been a big mess Read More »

Reddit blocks Internet Archive to end sneaky AI scraping

AI, ai scraping, AI training, Artificial Intelligence, Google, Internet Archive, openai, Policy, reddit / Tim Belzer / August 12, 2025

“Until they’re able to defend their site and comply with platform policies (e.g., respecting user privacy, re: deleting removed content) we’re limiting some of their access to Reddit data to protect redditors,” Rathschmidt said.

A review of social media comments suggests that in the past, some Redditors have used the Wayback Machine to research deleted comments or threads. Those commenters noted that myriad other tools exist for surfacing deleted posts or researching a user’s activity, with some suggesting that the Wayback Machine was maybe not the easiest platform to navigate for that purpose.

Redditors have also turned to resources like IA during times when Reddit’s platform changes trigger content removals. Most recently in 2023, when changes to Reddit’s public API threatened to kill beloved subreddits, archives stepped in to preserve content before it was lost.

IA has not signaled whether it’s looking into fixes to get Reddit’s restrictions lifted and did not respond to Ars’ request to comment on how this change might impact the archive’s utility as an open web resource, given Reddit’s popularity.

The director of the Wayback Machine, Mark Graham, told Ars that IA has “a longstanding relationship with Reddit” and continues to have “ongoing discussions about this matter.”

It seems likely that Reddit is financially motivated to restrict AI firms from taking advantage of Wayback Machine archives, perhaps hoping to spur more lucrative licensing deals like Reddit struck with OpenAI and Google. The terms of the OpenAI deal were kept quiet, but the Google deal was reportedly worth $60 million. Over the next three years, Reddit expects to make more than $200 million off such licensing deals.

Disclosure: Advance Publications, which owns Ars Technica parent Condé Nast, is the largest shareholder in Reddit.

Reddit blocks Internet Archive to end sneaky AI scraping Read More »

GitHub will be folded into Microsoft proper as CEO steps down

AI, GitHub, GitHub Copilot, microsoft, Tech / Mike M. / August 12, 2025

Putting GitHub more directly under its AI umbrella makes some degree of sense for Microsoft, given how hard it has pushed tools like GitHub Copilot, an AI-assisted coding tool. Microsoft has continually iterated on GitHub Copilot since introducing it in late 2021, adding support for multiple language models and “agents” that attempt to accomplish plain-language requests in the background as you work on other things.

However, there have been problems, too. Copilot inadvertently exposed the private code repositories of a few major companies earlier this year. And a recent Stack Overflow survey showed that trust in AI-assisted coding tools’ accuracy may be declining even as usage has increased, citing the extra troubleshooting and debugging work caused by “solutions that are almost right, but not quite.”

It’s unclear whether Dohmke’s departure and the elimination of the CEO position will change much in terms of the way GitHub operates or the products it creates and maintains. As GitHub’s CEO, Dohmke was already reporting to Julia Liuson, president of the company’s developer division, and Liuson reported to Core AI group leader Jay Parikh. The CoreAI group itself is only a few months old—it was announced by Microsoft CEO Satya Nadella in January, and “build[ing] out GitHub Copilot” was already one of the group’s responsibilities.

“Ultimately, we must remember that our internal organizational boundaries are meaningless to both our customers and to our competitors,” wrote Nadella when he announced the formation of the CoreAI group.

GitHub will be folded into Microsoft proper as CEO steps down Read More »

Trump strikes “wild” deal making US firms pay 15% tax on China chip sales

AI, AI chips, AMD, Donald Trump, Huawei, Micron, NVIDIA, Policy, semiconductor, tariffs, us-china trade war / Paul Patrick / August 11, 2025

“Extra penalty” for US firms

The deal won’t resolve national security concerns.

Ahead of an August 12 deadline for a US-China trade deal, Donald Trump’s tactics continue to confuse those trying to assess the country’s national security priorities regarding its biggest geopolitical rival.

For months, Trump has kicked the can down the road regarding a TikTok ban, allowing the app to continue operating despite supposedly urgent national security concerns that China may be using the app to spy on Americans. And now, in the latest baffling move, a US official announced Monday that Trump got Nvidia and AMD to agree to “give the US government 15 percent of revenue from sales to China of advanced computer chips,” Reuters reported. Those chips, about 20 policymakers and national security experts recently warned Trump, could be used to fuel China’s frontier AI, which seemingly poses an even greater national security risk.

Trump’s “wild” deal with US chip firms

Reuters granted two officials anonymity to discuss Trump’s deal with US chipmakers, because details have yet to be made public. Requiring US firms to pay for sales in China is an “unusual” move for a president, Reuters noted, and the Trump administration has yet to say what exactly it plans to do with the money.

For US firms, the deal may set an alarming precedent. Not only have analysts warned that the deal could “hurt margins” for both companies, but export curbs on Nvidia’s H20 chips, for example, had been established to prevent US technology thefts, secure US technology leadership, and protect US national security. Now the US government appears to be accepting a payment to overlook those alleged risks, without much reassurance that the policy won’t advantage China in the AI race.

The move drew immediate scrutiny from critics, including Geoff Gertz, a senior fellow at the US think tank Center for a New American Security, who told Reuters that he thinks the deal is “wild.”

“Either selling H20 chips to China is a national security risk, in which case we shouldn’t be doing it to begin with, or it’s not a national security risk, in which case, why are we putting this extra penalty on the sale?” Gertz posited.

At this point, the only reassurance from the Trump administration is an official suggesting (without providing any rationale) that selling H20 or equivalent chips—which are not Nvidia’s most advanced chips—no longer compromises national security.

Trump “trading away” national security

It remains unclear when or how the levy will be implemented.

For chipmakers, the levy is likely viewed as a relatively small price to pay to avoid export curbs. Nvidia had forecasted $8 billion in potential losses if it couldn’t sell its H20 chips to China. AMD expected $1 billion in revenue cuts, partly due to the loss of sales for its MI308 chips in China.

The firms apparently agreed to Trump’s deal as a condition to receive licenses to export those chips. But caving to Trump could bite them back in the long run, AJ Bell, investment director Russ Mould, told Reuters—perhaps especially if Trump faces increasing pressure over feared national security concerns.

“The Chinese market is significant for both these companies, so even if they have to give up a bit of the money, they would otherwise make it look like a logical move on paper,” Mould said. However, the deal “is unprecedented and there is always the risk the revenue take could be upped or that the Trump administration changes its mind and re-imposes export controls.”

So far, AMD has not commented on the report. Nvidia’s spokesperson declined to comment beyond noting, “We follow rules the US government sets for our participation in worldwide markets.”

A former adviser to Joe Biden’s Commerce Department, Alasdair Phillips-Robins, told Reuters that the levy suggests the Trump administration “is trading away national security protections for revenue for the Treasury.”

Huawei close to unveiling new AI chip tech

The end of a 90-day truce between the US and China is rapidly approaching, with the US signaling that the truce will likely be extended soon as Trump attempts to get a long-sought-after meeting with China’s President Xi Jinping.

For China, gutting export curbs on chips remains a key priority in negotiations, the Financial Times reported Sunday. But Nvidia’s H20 chips, for example, are lower priority than high-bandwidth memory (HBM) chips, sources told FT.

Chinese state media has even begun attacking the H20 chips as a Chinese national security risk. It appears that China is urging a boycott on H20 chips due to questions linked to a recent Congressional push to require chipmakers to build “backdoors” that would allow remote shutdowns of any chips detected as non-compliant with export curbs. That bill may mean that Nvidia’s chips already allow for US surveillance, China seemingly fears. (Nvidia has denied building such backdoors.)

Biden banned HBM exports to China last year, specifically moving to hamper innovation of Chinese chipmakers Huawei and Semiconductor Manufacturing International Corporation (SMIC).

Currently, US firms AMD and Micron remain top suppliers of HBM chips globally, along with South Korean firms Samsung Electronics and SK Hynix, but Chinese firms have notably lagged behind, South China Morning Post (SCMP) reported. One source told FT that China “had raised the HBM issue in some” Trump negotiations, likely directly seeking to lift Biden’s “HBM controls because they seriously constrain the ability of Chinese companies, including Huawei, to develop their own AI chips.”

For Trump, the HBM controls could be seen as leverage to secure another trade win. However, some experts are hoping that Trump won’t play that card, citing concerns from the Biden era that remain unaddressed.

If Trump bends to Chinese pressure and lifts HBM controls, China could more easily produce AI chips at scale, Biden had feared. That could even possibly endanger US firms’ standing as world leaders, seemingly including threatening Nvidia, a company that Trump discovered this term. Gregory Allen, an AI expert at a US think tank called the Center for Strategic and International Studies, told FT that “saying that we should allow more advanced HBM sales to China is the exact same as saying that we should help Huawei make better AI chips so that they can replace Nvidia.”

Meanwhile, Huawei is reportedly already innovating to help reduce China’s reliance on HBM chips, the SCMP reported on Monday. Chinese state-run Securities Times reported that Huawei is “set to unveil a technological breakthrough that could reduce China’s reliance on high-bandwidth memory (HBM) chips for running artificial intelligence reasoning models” at the 2025 Financial AI Reasoning Application Landing and Development Forum in Shanghai on Tuesday.

It’s a conveniently timed announcement, given the US-China trade deal deadline lands the same day. But the risk of Huawei possibly relying on US tech to reach that particular milestone is why HBM controls should remain off the table during Trump’s negotiations, one official told FT.

“Relaxing these controls would be a gift to Huawei and SMIC and could open the floodgates for China to start making millions of AI chips per year, while also diverting scarce HBM from chips sold in the US,” the official said.

Experts and policymakers had previously warned Trump that allowing H20 export curbs could similarly reduce access to semiconductors in the US, potentially disrupting the entire purpose of Trump’s trade war, which is building reliable US supply chains. Additionally, allowing exports will likely drive up costs to US chip firms at a time when they noted “projected data center demand from the US power market would require 90 percent of global chip supply through 2030, an unlikely scenario even without China joining the rush to buy advanced AI chips.” They’re now joined by others urging Trump to revive Biden’s efforts to block chip exports to China, or else risk empowering a geopolitical rival to become a global AI leader ahead of the US.

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.

Trump strikes “wild” deal making US firms pay 15% tax on China chip sales Read More »