sam altman

OpenAI CEO declares “code red” as Gemini gains 200 million users in 3 months

AI, AI assistants, AI competition, Biz & IT, chatbots, chatgpt, Gemini, Gemini 3, Google, Google Gemini, GPT-5, large language models, machine learning, Marc Benioff, openai, sam altman, simulated reasoning, SR model, Sundar Pichai, vibemark, vibemarking / Kris Guyer / December 2, 2025

In addition to buzz about Gemini on social media, Google is quickly catching up to ChatGPT in user numbers. ChatGPT has more than 800 million weekly users, according to OpenAI, while Google’s Gemini app has grown from 450 million monthly active users in July to 650 million in October, according to Business Insider.

Financial stakes run high

Not everyone views OpenAI’s “code red” as a genuine alarm. Reuters columnist Robert Cyran wrote on Tuesday that OpenAI’s announcement added “to the impression that OpenAI is trying to do too much at once with technology that still requires a great deal of development and funding.” On the same day Altman’s memo circulated, OpenAI announced an ownership stake in a Thrive Capital venture and a collaboration with Accenture. “The only thing bigger than the company’s attention deficit is its appetite for capital,” Cyran wrote.

In fact, OpenAI faces an unusual competitive disadvantage: Unlike Google, which subsidizes its AI ventures through search advertising revenue, OpenAI does not turn a profit and relies on fundraising to survive. According to The Information, the company, now valued at around $500 billion, has committed more than $1 trillion in financial obligations to cloud computing providers and chipmakers that supply the computing power needed to train and run its AI models.

But the tech industry never stands still, and things can change quickly. Altman’s memo also reportedly stated that OpenAI plans to release a new simulated reasoning model next week that may beat Gemini 3 in internal evaluations. In AI, the back-and-forth cycle of one-upmanship is expected to continue as long as the dollars keep flowing.

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Tech giants pour billions into Anthropic as circular AI investments roll on

AI, AI bubble, AI chips, AI infrastructure, Anthropic, azure, Biz & IT, Cloud Computing, datacenters, enterprise AI, machine learning, microsoft, NVIDIA, openai, sam altman, Satya Nadella / Mike M. / November 18, 2025

On Tuesday, Microsoft and Nvidia announced plans to invest in Anthropic under a new partnership that includes a $30 billion commitment by the Claude maker to use Microsoft’s cloud services. Nvidia will commit up to $10 billion to Anthropic and Microsoft up to $5 billion, with both companies investing in Anthropic’s next funding round.

The deal brings together two companies that have backed OpenAI and connects them more closely to one of the ChatGPT maker’s main competitors. Microsoft CEO Satya Nadella said in a video that OpenAI “remains a critical partner,” while adding that the companies will increasingly be customers of each other.

“We will use Anthropic models, they will use our infrastructure, and we’ll go to market together,” Nadella said.

Anthropic, Microsoft, and NVIDIA announce partnerships.

The move follows OpenAI’s recent restructuring that gave the company greater distance from its non-profit origins. OpenAI has since announced a $38 billion deal to buy cloud services from Amazon.com as the company becomes less dependent on Microsoft. OpenAI CEO Sam Altman has said the company plans to spend $1.4 trillion to develop 30 gigawatts of computing resources.

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Forget AGI—Sam Altman celebrates ChatGPT finally following em dash formatting rules

AI, AI assistants, AI writing, Biz & IT, chatbots, custom instructions, GPT-5.1, large language models, machine learning, openai, sam altman / Rejus Almole / November 14, 2025

Next stop: superintelligence

Ongoing struggles with AI model instruction-following show that true human-level AI still a ways off.

Em dashes have become what many believe to be a telltale sign of AI-generated text over the past few years. The punctuation mark appears frequently in outputs from ChatGPT and other AI chatbots, sometimes to the point where readers believe they can identify AI writing by its overuse alone—although people can overuse it, too.

On Thursday evening, OpenAI CEO Sam Altman posted on X that ChatGPT has started following custom instructions to avoid using em dashes. “Small-but-happy win: If you tell ChatGPT not to use em-dashes in your custom instructions, it finally does what it’s supposed to do!” he wrote.

The post, which came two days after the release of OpenAI’s new GPT-5.1 AI model, received mixed reactions from users who have struggled for years with getting the chatbot to follow specific formatting preferences. And this “small win” raises a very big question: If the world’s most valuable AI company has struggled with controlling something as simple as punctuation use after years of trying, perhaps what people call artificial general intelligence (AGI) is farther off than some in the industry claim.

Sam Altman @sama Small-but-happy win: If you tell ChatGPT not to use em-dashes in your custom instructions, it finally does what it's supposed to do! 11:48 PM · Nov 13, 2025 · 2.4M Views — A screenshot of Sam Altman’s post about em dashes on X. Credit: X

“The fact that it’s been 3 years since ChatGPT first launched, and you’ve only just now managed to make it obey this simple requirement, says a lot about how little control you have over it, and your understanding of its inner workings,” wrote one X user in a reply. “Not a good sign for the future.”

While Altman likes to publicly talk about AGI (a hypothetical technology equivalent to humans in general learning ability), superintelligence (a nebulous concept for AI that is far beyond human intelligence), and “magic intelligence in the sky” (his term for AI cloud computing?) while raising funds for OpenAI, it’s clear that we still don’t have reliable artificial intelligence here today on Earth.

But wait, what is an em dash anyway, and why does it matter so much?

AI models love em dashes because we do

Unlike a hyphen, which is a short punctuation mark used to connect words or parts of words, that lives with a dedicated key on your keyboard (-), an em dash is a long dash denoted by a special character (—) that writers use to set off parenthetical information, indicate a sudden change in thought, or introduce a summary or explanation.

Even before the age of AI language models, some writers frequently bemoaned the overuse of the em dash in modern writing. In a 2011 Slate article, writer Noreen Malone argued that writers used the em dash “in lieu of properly crafting sentences” and that overreliance on it “discourages truly efficient writing.” Various Reddit threads posted prior to ChatGPT’s launch featured writers either wrestling over the etiquette of proper em dash use or admitting to their frequent use as a guilty pleasure.

In 2021, one writer in the r/FanFiction subreddit wrote, “For the longest time, I’ve been addicted to Em Dashes. They find their way into every paragraph I write. I love the crisp straight line that gives me the excuse to shove details or thoughts into an otherwise orderly paragraph. Even after coming back to write after like two years of writer’s block, I immediately cram as many em dashes as I can.”

Because of the tendency for AI chatbots to overuse them, detection tools and human readers have learned to spot em dash use as a pattern, creating a problem for the small subset of writers who naturally favor the punctuation mark in their work. As a result, some journalists are complaining that AI is “killing” the em dash.

No one knows precisely why LLMs tend to overuse em dashes. We’ve seen a wide range of speculation online that attempts to explain the phenomenon, from noticing that em dashes were more popular in 19th-century books used as training data (according to a 2018 study, dash use in the English language peaked around 1860 before declining through the mid-20th century) or perhaps AI models borrowed the habit from automatic em-dash character conversion on the blogging site Medium.

One thing we know for sure is that LLMs tend to output frequently seen patterns in their training data (fed in during the initial training process) and from a subsequent reinforcement learning process that often relies on human preferences. As a result, AI language models feed you a sort of “smoothed out” average style of whatever you ask them to provide, moderated by whatever they are conditioned to produce through user feedback.

So the most plausible explanation is still that requests for professional-style writing from an AI model trained on vast numbers of examples from the Internet will lean heavily toward the prevailing style in the training data, where em dashes appear frequently in formal writing, news articles, and editorial content. It’s also possible that during training through human feedback (called RLHF), responses with em dashes, for whatever reason, received higher ratings. Perhaps it’s because those outputs appeared more sophisticated or engaging to evaluators, but that’s just speculation.

From em dashes to AGI?

To understand what Altman’s “win” really means, and what it says about the road to AGI, we need to understand how ChatGPT’s custom instructions actually work. They allow users to set persistent preferences that apply across all conversations by appending written instructions to the prompt that is fed into the model just before the chat begins. Users can specify tone, format, and style requirements without needing to repeat those requests manually in every new chat.

However, the feature has not always worked reliably because LLMs do not work reliably (even OpenAI and Anthropic freely admit this). An LLM takes an input and produces an output, spitting out a statistically plausible continuation of a prompt (a system prompt, the custom instructions, and your chat history), and it doesn’t really “understand” what you are asking. With AI language model outputs, there is always some luck involved in getting them to do what you want.

In our informal testing of GPT-5.1 with custom instructions, ChatGPT did appear to follow our request not to produce em dashes. But despite Altman’s claim, the response from X users appears to show that experiences with the feature continue to vary, at least when the request is not placed in custom instructions.

So if LLMs are statistical text-generation boxes, what does “instruction following” even mean? That’s key to unpacking the hypothetical path from LLMs to AGI. The concept of following instructions for an LLM is fundamentally different from how we typically think about following instructions as humans with general intelligence, or even a traditional computer program.

In traditional computing, instruction following is deterministic. You tell a program “don’t include character X,” and it won’t include that character. The program executes rules exactly as written. With LLMs, “instruction following” is really about shifting statistical probabilities. When you tell ChatGPT “don’t use em dashes,” you’re not creating a hard rule. You’re adding text to the prompt that makes tokens associated with em dashes less likely to be selected during the generation process. But “less likely” isn’t “impossible.”

Every token the model generates is selected from a probability distribution. Your custom instruction influences that distribution, but it’s competing with the model’s training data (where em dashes appeared frequently in certain contexts) and everything else in the prompt. Unlike code with conditional logic, there’s no separate system verifying outputs against your requirements. The instruction is just more text that influences the statistical prediction process.

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

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

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

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

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

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

AI, AI assistants, AI behavior, AI model, AI programming, API, Biz & IT, chatbots, generative ai, GPT-5, GPT-5.1, large language models, machine learning, multimodal AI, openai, sam altman, simulated reasoning, SR model, sycophancy / Tim Belzer / November 12, 2025

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

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

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

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

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

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

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

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

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

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OpenAI signs massive AI compute deal with Amazon

AI, AI chips, AI infrastructure, Amazon, AWS, Biz & IT, chatbots, datacenters, generative ai, Google, large language models, machine learning, microsoft, NVIDIA, openai, oracle, sam altman / Paul Patrick / November 3, 2025

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

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

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

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

Massive AI compute requirements

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

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

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Sam Altman wants a refund for his $50,000 Tesla Roadster deposit

Cars, sam altman, tesla roadster / Tim Belzer / October 31, 2025

2017 feels like another era these days, but if you cast your mind back that far, you might remember Tesla CEO Elon Musk’s vaporware Roadster 2.0. Full of nonsensical-sounding features that impressed people who know a little bit about rockets but nothing about cars, the $200,000 electric car promised to have a suction fan and “cold gas thrusters,” plus 620 miles (1,000 km) of range and a whole load of other stuff that’s never happening.

Plenty of other electric automakers have introduced electric hypercars in the eight years since Musk declared the second Roadster a thing, with no sign of it being any closer to reality, if the latest job postings are accurate. And it seems that over time, a lot of the people who gave the company a hefty deposit—some say interest-free loan—have become tired of waiting and want their money back.

And that’s not quite so easy, it turns out. Musk’s current Silicon Valley rival is the latest to discover this. According to Sam Altman’s social media account, he placed an order for a Roadster on July 11, 2018, with a deposit of $45,000 ($58,206 in today’s money). But after emailing Tesla for a refund, he discovered the email address associated with preorders had been deleted.

A screenshot of Sam Altman's X posts about cancelling his car — Credit: Twitter

Perhaps Altman forgot to ask ChatGPT how best to go about getting his money. If he had, he might have stumbled across the experience of streamer Marques Brownlee, who eventually had to pick up a telephone and call someone to get most of his $50,000 back. Or perhaps some of the threads at Reddit or the Tesla forums, where other people who fell for the cold gas thruster-equipped two-seater with Lucid-busting range and F1-beating acceleration have gathered to share stories of how best to make Tesla return their money.

Sam Altman wants a refund for his $50,000 Tesla Roadster deposit Read More »

ChatGPT maker reportedly eyes $1 trillion IPO despite major quarterly losses

AI, AI infrastructure, Biz & IT, chatgpt, CoreWeave, generative ai, IPO, large language models, machine learning, microsoft, NVIDIA, openai, sam altman, SoftBank / Mike M. / October 30, 2025

An OpenAI spokesperson told Reuters that “an IPO is not our focus, so we could not possibly have set a date,” adding that the company is “building a durable business and advancing our mission so everyone benefits from AGI.”

Revenue grows as losses mount

The IPO preparations follow a restructuring of OpenAI completed on October 28 that reduced the company’s reliance on Microsoft, which has committed to investments of $13 billion and now owns about 27 percent of the company. OpenAI was most recently valued around $500 billion in private markets.

OpenAI started as a nonprofit in 2015, then added a for-profit arm a few years later with nonprofit oversight. Under the new structure, OpenAI is still controlled by a nonprofit, now called the OpenAI Foundation, but it gives the nonprofit a 26 percent stake in OpenAI Group and a warrant for additional shares if the company hits certain milestones.

A successful OpenAI IPO could represent a substantial gain for investors, including Microsoft, SoftBank, Thrive Capital, and Abu Dhabi’s MGX. But even so, OpenAI faces an uphill financial battle ahead. The ChatGPT maker expects to reach about $20 billion in revenue by year-end, according to people familiar with the company’s finances who spoke with Reuters, but its quarterly losses are significant.

Microsoft’s earnings filing on Wednesday offered a glimpse at the scale of those losses. The company reported that its share of OpenAI losses reduced Microsoft’s net income by $3.1 billion in the quarter that ended September 30. Since Microsoft owns 27 percent of OpenAI under the new structure, that suggests OpenAI lost about $11.5 billion during the quarter, as noted by The Register. That quarterly loss figure exceeds half of OpenAI’s expected revenue for the entire year.

ChatGPT maker reportedly eyes $1 trillion IPO despite major quarterly losses Read More »

OpenAI data suggests 1 million users discuss suicide with ChatGPT weekly

AI, AI assistants, AI behavior, AI ethics, AI safety, AI sycophancy, Biz & IT, chatbots, chatgpt, machine learning, Mental Health, openai, sam altman / Kris Guyer / October 28, 2025

Earlier this month, the company unveiled a wellness council to address these concerns, though critics noted the council did not include a suicide prevention expert. OpenAI also recently rolled out controls for parents of children who use ChatGPT. The company says it’s building an age prediction system to automatically detect children using ChatGPT and impose a stricter set of age-related safeguards.

Rare but impactful conversations

The data shared on Monday appears to be part of the company’s effort to demonstrate progress on these issues, although it also shines a spotlight on just how deeply AI chatbots may be affecting the health of the public at large.

In a blog post on the recently released data, OpenAI says these types of conversations in ChatGPT that might trigger concerns about “psychosis, mania, or suicidal thinking” are “extremely rare,” and thus difficult to measure. The company estimates that around 0.07 percent of users active in a given week and 0.01 percent of messages indicate possible signs of mental health emergencies related to psychosis or mania. For emotional attachment, the company estimates around 0.15 percent of users active in a given week and 0.03 percent of messages indicate potentially heightened levels of emotional attachment to ChatGPT.

OpenAI also claims that on an evaluation of over 1,000 challenging mental health-related conversations, the new GPT-5 model was 92 percent compliant with its desired behaviors, compared to 27 percent for a previous GPT-5 model released on August 15. The company also says its latest version of GPT-5 holds up to OpenAI’s safeguards better in long conversations. OpenAI has previously admitted that its safeguards are less effective during extended conversations.

In addition, OpenAI says it’s adding new evaluations to attempt to measure some of the most serious mental health issues facing ChatGPT users. The company says its baseline safety testing for its AI language models will now include benchmarks for emotional reliance and non-suicidal mental health emergencies.

Despite the ongoing mental health concerns, OpenAI CEO Sam Altman announced on October 14 that the company will allow verified adult users to have erotic conversations with ChatGPT starting in December. The company had loosened ChatGPT content restrictions in February but then dramatically tightened them after the August lawsuit. Altman explained that OpenAI had made ChatGPT “pretty restrictive to make sure we were being careful with mental health issues” but acknowledged this approach made the chatbot “less useful/enjoyable to many users who had no mental health problems.”

If you or someone you know is feeling suicidal or in distress, please call the Suicide Prevention Lifeline number, 1-800-273-TALK (8255), which will put you in touch with a local crisis center.

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Ars Live recap: Is the AI bubble about to pop? Ed Zitron weighs in.

AI, AI agents, AI chips, AI criticism, AI economics, AI infrastructure, Benj Edwards, Biz & IT, bubble, Cerebras, datacenters, Ed Zitron, financial losses, generative ai, GPU, grok, large language models, machine learning, NVIDIA, openai, oracle, Power consumption, sam altman, Stargate, venture capital / Kris Guyer / October 16, 2025

Despite connection hiccups, we covered OpenAI’s finances, nuclear power, and Sam Altman.

On Tuesday of last week, Ars Technica hosted a live conversation with Ed Zitron, host of the Better Offline podcast and one of tech’s most vocal AI critics, to discuss whether the generative AI industry is experiencing a bubble and when it might burst. My Internet connection had other plans, though, dropping out multiple times and forcing Ars Technica’s Lee Hutchinson to jump in as an excellent emergency backup host.

During the times my connection cooperated, Zitron and I covered OpenAI’s financial issues, lofty infrastructure promises, and why the AI hype machine keeps rolling despite some arguably shaky economics underneath. Lee’s probing questions about per-user costs revealed a potential flaw in AI subscription models: Companies can’t predict whether a user will cost them $2 or $10,000 per month.

You can watch a recording of the event on YouTube or in the window below.

Our discussion with Ed Zitron. Click here for transcript.

“A 50 billion-dollar industry pretending to be a trillion-dollar one”

I started by asking Zitron the most direct question I could: “Why are you so mad about AI?” His answer got right to the heart of his critique: the disconnect between AI’s actual capabilities and how it’s being sold. “Because everybody’s acting like it’s something it isn’t,” Zitron said. “They’re acting like it’s this panacea that will be the future of software growth, the future of hardware growth, the future of compute.”

In one of his newsletters, Zitron describes the generative AI market as “a 50 billion dollar revenue industry masquerading as a one trillion-dollar one.” He pointed to OpenAI’s financial burn rate (losing an estimated $9.7 billion in the first half of 2025 alone) as evidence that the economics don’t work, coupled with a heavy dose of pessimism about AI in general.

Donald Trump listens as Nvidia CEO Jensen Huang speaks at the White House during an event on “Investing in America” on April 30, 2025, in Washington, DC. Credit: Andrew Harnik / Staff | Getty Images News

“The models just do not have the efficacy,” Zitron said during our conversation. “AI agents is one of the most egregious lies the tech industry has ever told. Autonomous agents don’t exist.”

He contrasted the relatively small revenue generated by AI companies with the massive capital expenditures flowing into the sector. Even major cloud providers and chip makers are showing strain. Oracle reportedly lost $100 million in three months after installing Nvidia’s new Blackwell GPUs, which Zitron noted are “extremely power-hungry and expensive to run.”

Finding utility despite the hype

I pushed back against some of Zitron’s broader dismissals of AI by sharing my own experience. I use AI chatbots frequently for brainstorming useful ideas and helping me see them from different angles. “I find I use AI models as sort of knowledge translators and framework translators,” I explained.

After experiencing brain fog from repeated bouts of COVID over the years, I’ve also found tools like ChatGPT and Claude especially helpful for memory augmentation that pierces through brain fog: describing something in a roundabout, fuzzy way and quickly getting an answer I can then verify. Along these lines, I’ve previously written about how people in a UK study found AI assistants useful accessibility tools.

Zitron acknowledged this could be useful for me personally but declined to draw any larger conclusions from my one data point. “I understand how that might be helpful; that’s cool,” he said. “I’m glad that that helps you in that way; it’s not a trillion-dollar use case.”

He also shared his own attempts at using AI tools, including experimenting with Claude Code despite not being a coder himself.

“If I liked [AI] somehow, it would be actually a more interesting story because I’d be talking about something I liked that was also onerously expensive,” Zitron explained. “But it doesn’t even do that, and it’s actually one of my core frustrations, it’s like this massive over-promise thing. I’m an early adopter guy. I will buy early crap all the time. I bought an Apple Vision Pro, like, what more do you say there? I’m ready to accept issues, but AI is all issues, it’s all filler, no killer; it’s very strange.”

Zitron and I agree that current AI assistants are being marketed beyond their actual capabilities. As I often say, AI models are not people, and they are not good factual references. As such, they cannot replace human decision-making and cannot wholesale replace human intellectual labor (at the moment). Instead, I see AI models as augmentations of human capability: as tools rather than autonomous entities.

Computing costs: History versus reality

Even though Zitron and I found some common ground about AI hype, I expressed a belief that criticism over the cost and power requirements of operating AI models will eventually not become an issue.

I attempted to make that case by noting that computing costs historically trend downward over time, referencing the Air Force’s SAGE computer system from the 1950s: a four-story building that performed 75,000 operations per second while consuming two megawatts of power. Today, pocket-sized phones deliver millions of times more computing power in a way that would be impossible, power consumption-wise, in the 1950s.

The blockhouse for the Semi-Automatic Ground Environment at Stewart Air Force Base, Newburgh, New York. Credit: Denver Post via Getty Images

“I think it will eventually work that way,” I said, suggesting that AI inference costs might follow similar patterns of improvement over years and that AI tools will eventually become commodity components of computer operating systems. Basically, even if AI models stay inefficient, AI models of a certain baseline usefulness and capability will still be cheaper to train and run in the future because the computing systems they run on will be faster, cheaper, and less power-hungry as well.

Zitron pushed back on this optimism, saying that AI costs are currently moving in the wrong direction. “The costs are going up, unilaterally across the board,” he said. Even newer systems like Cerebras and Grok can generate results faster but not cheaper. He also questioned whether integrating AI into operating systems would prove useful even if the technology became profitable, since AI models struggle with deterministic commands and consistent behavior.

The power problem and circular investments

One of Zitron’s most pointed criticisms during the discussion centered on OpenAI’s infrastructure promises. The company has pledged to build data centers requiring 10 gigawatts of power capacity (equivalent to 10 nuclear power plants, I once pointed out) for its Stargate project in Abilene, Texas. According to Zitron’s research, the town currently has only 350 megawatts of generating capacity and a 200-megawatt substation.

“A gigawatt of power is a lot, and it’s not like Red Alert 2,” Zitron said, referencing the real-time strategy game. “You don’t just build a power station and it happens. There are months of actual physics to make sure that it doesn’t kill everyone.”

He believes many announced data centers will never be completed, calling the infrastructure promises “castles on sand” that nobody in the financial press seems willing to question directly.

An orange, cloudy sky backlights a set of electrical wires on large pylons, leading away from the cooling towers of a nuclear power plant.

After another technical blackout on my end, I came back online and asked Zitron to define the scope of the AI bubble. He says it has evolved from one bubble (foundation models) into two or three, now including AI compute companies like CoreWeave and the market’s obsession with Nvidia.

Zitron highlighted what he sees as essentially circular investment schemes propping up the industry. He pointed to OpenAI’s $300 billion deal with Oracle and Nvidia’s relationship with CoreWeave as examples. “CoreWeave, they literally… They funded CoreWeave, became their biggest customer, then CoreWeave took that contract and those GPUs and used them as collateral to raise debt to buy more GPUs,” Zitron explained.

When will the bubble pop?

Zitron predicted the bubble would burst within the next year and a half, though he acknowledged it could happen sooner. He expects a cascade of events rather than a single dramatic collapse: An AI startup will run out of money, triggering panic among other startups and their venture capital backers, creating a fire-sale environment that makes future fundraising impossible.

“It’s not gonna be one Bear Stearns moment,” Zitron explained. “It’s gonna be a succession of events until the markets freak out.”

The crux of the problem, according to Zitron, is Nvidia. The chip maker’s stock represents 7 to 8 percent of the S&P 500’s value, and the broader market has become dependent on Nvidia’s continued hyper growth. When Nvidia posted “only” 55 percent year-over-year growth in January, the market wobbled.

“Nvidia’s growth is why the bubble is inflated,” Zitron said. “If their growth goes down, the bubble will burst.”

He also warned of broader consequences: “I think there’s a depression coming. I think once the markets work out that tech doesn’t grow forever, they’re gonna flush the toilet aggressively on Silicon Valley.” This connects to his larger thesis: that the tech industry has run out of genuine hyper-growth opportunities and is trying to manufacture one with AI.

“Is there anything that would falsify your premise of this bubble and crash happening?” I asked. “What if you’re wrong?”

“I’ve been answering ‘What if you’re wrong?’ for a year-and-a-half to two years, so I’m not bothered by that question, so the thing that would have to prove me right would’ve already needed to happen,” he said. Amid a longer exposition about Sam Altman, Zitron said, “The thing that would’ve had to happen with inference would’ve had to be… it would have to be hundredths of a cent per million tokens, they would have to be printing money, and then, it would have to be way more useful. It would have to have efficacy that it does not have, the hallucination problems… would have to be fixable, and on top of this, someone would have to fix agents.”

A positivity challenge

Near the end of our conversation, I wondered if I could flip the script, so to speak, and see if he could say something positive or optimistic, although I chose the most challenging subject possible for him. “What’s the best thing about Sam Altman,” I asked. “Can you say anything nice about him at all?”

“I understand why you’re asking this,” Zitron started, “but I wanna be clear: Sam Altman is going to be the reason the markets take a crap. Sam Altman has lied to everyone. Sam Altman has been lying forever.” He continued, “Like the Pied Piper, he’s led the markets into an abyss, and yes, people should have known better, but I hope at the end of this, Sam Altman is seen for what he is, which is a con artist and a very successful one.”

Then he added, “You know what? I’ll say something nice about him, he’s really good at making people say, ‘Yes.’”

Ars Live recap: Is the AI bubble about to pop? Ed Zitron weighs in. Read More »

ChatGPT erotica coming soon with age verification, CEO says

age verification, AI, AI behavior, AI censorship, AI companionship, AI ethics, AI paternalism, Biz & IT, chatbots, elon musk, machine learning, Mental Health, openai, sam altman, xAI / Rejus Almole / October 15, 2025

On Tuesday, OpenAI CEO Sam Altman announced that the company will allow verified adult users to have erotic conversations with ChatGPT starting in December. The change represents a shift in how OpenAI approaches content restrictions, which the company had loosened in February but then dramatically tightened after an August lawsuit from parents of a teen who died by suicide after allegedly receiving encouragement from ChatGPT.

“In December, as we roll out age-gating more fully and as part of our ‘treat adult users like adults’ principle, we will allow even more, like erotica for verified adults,” Altman wrote in his post on X (formerly Twitter). The announcement follows OpenAI’s recent hint that it would allow developers to create “mature” ChatGPT applications once the company implements appropriate age verification and controls.

Altman explained that OpenAI had made ChatGPT “pretty restrictive to make sure we were being careful with mental health issues” but acknowledged this approach made the chatbot “less useful/enjoyable to many users who had no mental health problems.” The CEO said the company now has new tools to better detect when users are experiencing mental distress, allowing OpenAI to relax restrictions in most cases.

Striking the right balance between freedom for adults and safety for users has been a difficult balancing act for OpenAI, which has vacillated between permissive and restrictive chat content controls over the past year.

In February, the company updated its Model Spec to allow erotica in “appropriate contexts.” But a March update made GPT-4o so agreeable that users complained about its “relentlessly positive tone.” By August, Ars reported on cases where ChatGPT’s sycophantic behavior had validated users’ false beliefs to the point of causing mental health crises, and news of the aforementioned suicide lawsuit hit not long after.

Aside from adjusting the behavioral outputs for its previous GPT-40 AI language model, new model changes have also created some turmoil among users. Since the launch of GPT-5 in early August, some users have been complaining that the new model feels less engaging than its predecessor, prompting OpenAI to bring back the older model as an option. Altman said the upcoming release will allow users to choose whether they want ChatGPT to “respond in a very human-like way, or use a ton of emoji, or act like a friend.”

ChatGPT erotica coming soon with age verification, CEO says Read More »

OpenAI, Jony Ive struggle with technical details on secretive new AI gadget

AI, Jony Ive, openai, sam altman, syndication, wearables / Tim Belzer / October 6, 2025

OpenAI overtook Elon Musk’s SpaceX to become the world’s most valuable private company this week, after a deal that valued it at $500 billion. One of the ways the ChatGPT maker is seeking to justify the price tag is a push into hardware.

The goal is to improve the “smart speakers” of the past decade, such as Amazon’s Echo speaker and its Alexa digital assistant, which are generally used for a limited set of functions such as listening to music and setting kitchen timers.

OpenAI and Ive are seeking to build a more powerful and useful machine. But two people familiar with the project said that settling on the device’s “voice” and its mannerisms were a challenge.

One issue is ensuring the device only chimes in when useful, preventing it from talking too much or not knowing when to finish the conversation—an ongoing issue with ChatGPT.

“The concept is that you should have a friend who’s a computer who isn’t your weird AI girlfriend… like [Apple’s digital voice assistant] Siri but better,” said one person who was briefed on the plans. OpenAI was looking for “ways for it to be accessible but not intrusive.”

“Model personality is a hard thing to balance,” said another person close to the project. “It can’t be too sycophantic, not too direct, helpful, but doesn’t keep talking in a feedback loop.”

OpenAI’s device will be entering a difficult market. Friend, an AI companion worn as a pendant around your neck, has been criticized for being “creepy” and having a “snarky” personality. An AI pin made by Humane, a company that Altman personally invested in, has been scrapped.

Still, OpenAI has been on a hiring spree to build its hardware business. Its acquisition of io brought in more than 20 former Apple hardware employees poached by Ive from his alma mater. It has also recruited at least a dozen other Apple device experts this year, according to LinkedIn accounts.

It has similarly poached members of Meta’s staff working on the Big Tech group’s Quest headset and smart glasses.

OpenAI is also working with Chinese contract manufacturers, including Luxshare, to create its first device, according to two people familiar with the development that was first reported by The Information. The people added that the device might be assembled outside of China.

OpenAI and LoveFrom, Ive’s design group, declined to comment.

OpenAI, Jony Ive struggle with technical details on secretive new AI gadget Read More »

OpenAI’s Sora 2 lets users insert themselves into AI videos with sound

AI, AI assistants, AI development tools, Ai video generator, Biz & IT, generative ai, image synthesis, machine learning, multimodal AI, openai, sam altman, Social Media, Sora, video synthesis / Mike M. / October 1, 2025

On Tuesday, OpenAI announced Sora 2, its second-generation video-synthesis AI model that can now generate videos in various styles with synchronized dialogue and sound effects, which is a first for the company. OpenAI also launched a new iOS social app that allows users to insert themselves into AI-generated videos through what OpenAI calls “cameos.”

OpenAI showcased the new model in an AI-generated video that features a photorealistic version of OpenAI CEO Sam Altman talking to the camera in a slightly unnatural-sounding voice amid fantastical backdrops, like a competitive ride-on duck race and a glowing mushroom garden.

Regarding that voice, the new model can create what OpenAI calls “sophisticated background soundscapes, speech, and sound effects with a high degree of realism.” In May, Google’s Veo 3 became the first video-synthesis model from a major AI lab to generate synchronized audio as well as video. Just a few days ago, Alibaba released Wan 2.5, an open-weights video model that can generate audio as well. Now OpenAI has joined the audio party with Sora 2.

OpenAI demonstrates Sora 2’s capabilities in a launch video.

The model also features notable visual consistency improvements over OpenAI’s previous video model, and it can also follow more complex instructions across multiple shots while maintaining coherency between them. The new model represents what OpenAI describes as its “GPT-3.5 moment for video,” comparing it to the ChatGPT breakthrough during the evolution of its text-generation models over time.

Sora 2 appears to demonstrate improved physical accuracy over the original Sora model from February 2024, with OpenAI claiming the model can now simulate complex physical movements like Olympic gymnastics routines and triple axels while maintaining realistic physics. Last year, shortly after the launch of Sora 1 Turbo, we saw several notable failures of similar video-generation tasks that OpenAI claims to have addressed with the new model.

“Prior video models are overoptimistic—they will morph objects and deform reality to successfully execute upon a text prompt,” OpenAI wrote in its announcement. “For example, if a basketball player misses a shot, the ball may spontaneously teleport to the hoop. In Sora 2, if a basketball player misses a shot, it will rebound off the backboard.”

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