AI assistants

OpenAI built an AI coding agent and uses it to improve the agent itself

agentic AI, AI, AI agents, AI assistants, AI coding, AI development tools, AI programming, AI work, Anthropic, Biz & IT, chatbots, Claude, code agents, Codex, Dario Amodei, generative ai, GitHub Copilot, Google, GPT-5, large language models, machine learning, Mistral, Open Source, openai, Programming, sam altman, software development, vibe coding / Kris Guyer / December 12, 2025

“The vast majority of Codex is built by Codex,” OpenAI told us about its new AI coding agent.

With the popularity of AI coding tools rising among software developers, their adoption has begun to touch every aspect of the process, including the improvement of AI coding tools themselves.

In interviews with Ars Technica this week, OpenAI employees revealed the extent to which the company now relies on its own AI coding agent, Codex, to build and improve the development tool. “I think the vast majority of Codex is built by Codex, so it’s almost entirely just being used to improve itself,” said Alexander Embiricos, product lead for Codex at OpenAI, in a conversation on Tuesday.

Codex, which OpenAI launched in its modern incarnation as a research preview in May 2025, operates as a cloud-based software engineering agent that can handle tasks like writing features, fixing bugs, and proposing pull requests. The tool runs in sandboxed environments linked to a user’s code repository and can execute multiple tasks in parallel. OpenAI offers Codex through ChatGPT’s web interface, a command-line interface (CLI), and IDE extensions for VS Code, Cursor, and Windsurf.

The “Codex” name itself dates back to a 2021 OpenAI model based on GPT-3 that powered GitHub Copilot’s tab completion feature. Embiricos said the name is rumored among staff to be short for “code execution.” OpenAI wanted to connect the new agent to that earlier moment, which was crafted in part by some who have left the company.

“For many people, that model powering GitHub Copilot was the first ‘wow’ moment for AI,” Embiricos said. “It showed people the potential of what it can mean when AI is able to understand your context and what you’re trying to do and accelerate you in doing that.”

A place to enter a prompt, set parameters, and click — The interface for OpenAI’s Codex in ChatGPT. Credit: OpenAI

It’s no secret that the current command-line version of Codex bears some resemblance to Claude Code, Anthropic’s agentic coding tool that launched in February 2025. When asked whether Claude Code influenced Codex’s design, Embiricos parried the question but acknowledged the competitive dynamic. “It’s a fun market to work in because there’s lots of great ideas being thrown around,” he said. He noted that OpenAI had been building web-based Codex features internally before shipping the CLI version, which arrived after Anthropic’s tool.

OpenAI’s customers apparently love the command line version, though. Embiricos said Codex usage among external developers jumped 20 times after OpenAI shipped the interactive CLI extension alongside GPT-5 in August 2025. On September 15, OpenAI released GPT-5 Codex, a specialized version of GPT-5 optimized for agentic coding, which further accelerated adoption.

It hasn’t just been the outside world that has embraced the tool. Embiricos said the vast majority of OpenAI’s engineers now use Codex regularly. The company uses the same open-source version of the CLI that external developers can freely download, suggest additions to, and modify themselves. “I really love this about our team,” Embiricos said. “The version of Codex that we use is literally the open source repo. We don’t have a different repo that features go in.”

The recursive nature of Codex development extends beyond simple code generation. Embiricos described scenarios where Codex monitors its own training runs and processes user feedback to “decide” what to build next. “We have places where we’ll ask Codex to look at the feedback and then decide what to do,” he said. “Codex is writing a lot of the research harness for its own training runs, and we’re experimenting with having Codex monitoring its own training runs.” OpenAI employees can also submit a ticket to Codex through project management tools like Linear, assigning it tasks the same way they would assign work to a human colleague.

This kind of recursive loop, of using tools to build better tools, has deep roots in computing history. Engineers designed the first integrated circuits by hand on vellum and paper in the 1960s, then fabricated physical chips from those drawings. Those chips powered the computers that ran the first electronic design automation (EDA) software, which in turn enabled engineers to design circuits far too complex for any human to draft manually. Modern processors contain billions of transistors arranged in patterns that exist only because software made them possible. OpenAI’s use of Codex to build Codex seems to follow the same pattern: each generation of the tool creates capabilities that feed into the next.

But describing what Codex actually does presents something of a linguistic challenge. At Ars Technica, we try to reduce anthropomorphism when discussing AI models as much as possible while also describing what these systems do using analogies that make sense to general readers. People can talk to Codex like a human, so it feels natural to use human terms to describe interacting with it, even though it is not a person and simulates human personality through statistical modeling.

The system runs many processes autonomously, addresses feedback, spins off and manages child processes, and produces code that ships in real products. OpenAI employees call it a “teammate” and assign it tasks through the same tools they use for human colleagues. Whether the tasks Codex handles constitute “decisions” or sophisticated conditional logic smuggled through a neural network depends on definitions that computer scientists and philosophers continue to debate. What we can say is that a semi-autonomous feedback loop exists: Codex produces code under human direction, that code becomes part of Codex, and the next version of Codex produces different code as a result.

Building faster with “AI teammates”

According to our interviews, the most dramatic example of Codex’s internal impact came from OpenAI’s development of the Sora Android app. According to Embiricos, the development tool allowed the company to create the app in record time.

“The Sora Android app was shipped by four engineers from scratch,” Embiricos told Ars. “It took 18 days to build, and then we shipped it to the app store in 28 days total,” he said. The engineers already had the iOS app and server-side components to work from, so they focused on building the Android client. They used Codex to help plan the architecture, generate sub-plans for different components, and implement those components.

Despite OpenAI’s claims of success with Codex in house, it’s worth noting that independent research has shown mixed results for AI coding productivity. A METR study published in July found that experienced open source developers were actually 19 percent slower when using AI tools on complex, mature codebases—though the researchers noted AI may perform better on simpler projects.

Ed Bayes, a designer on the Codex team, described how the tool has changed his own workflow. Bayes said Codex now integrates with project management tools like Linear and communication platforms like Slack, allowing team members to assign coding tasks directly to the AI agent. “You can add Codex, and you can basically assign issues to Codex now,” Bayes told Ars. “Codex is literally a teammate in your workspace.”

This integration means that when someone posts feedback in a Slack channel, they can tag Codex and ask it to fix the issue. The agent will create a pull request, and team members can review and iterate on the changes through the same thread. “It’s basically approximating this kind of coworker and showing up wherever you work,” Bayes said.

For Bayes, who works on the visual design and interaction patterns for Codex’s interfaces, the tool has enabled him to contribute code directly rather than handing off specifications to engineers. “It kind of gives you more leverage. It enables you to work across the stack and basically be able to do more things,” he said. He noted that designers at OpenAI now prototype features by building them directly, using Codex to handle the implementation details.

The command line version of OpenAI codex running in a macOS terminal window. Credit: Benj Edwards

OpenAI’s approach treats Codex as what Bayes called “a junior developer” that the company hopes will graduate into a senior developer over time. “If you were onboarding a junior developer, how would you onboard them? You give them a Slack account, you give them a Linear account,” Bayes said. “It’s not just this tool that you go to in the terminal, but it’s something that comes to you as well and sits within your team.”

Given this teammate approach, will there be anything left for humans to do? When asked, Embiricos drew a distinction between “vibe coding,” where developers accept AI-generated code without close review, and what AI researcher Simon Willison calls “vibe engineering,” where humans stay in the loop. “We see a lot more vibe engineering in our code base,” he said. “You ask Codex to work on that, maybe you even ask for a plan first. Go back and forth, iterate on the plan, and then you’re in the loop with the model and carefully reviewing its code.”

He added that vibe coding still has its place for prototypes and throwaway tools. “I think vibe coding is great,” he said. “Now you have discretion as a human about how much attention you wanna pay to the code.”

Looking ahead

Over the past year, “monolithic” large language models (LLMs) like GPT-4.5 have apparently become something of a dead end in terms of frontier benchmarking progress as AI companies pivot to simulated reasoning models and also agentic systems built from multiple AI models running in parallel. We asked Embiricos whether agents like Codex represent the best path forward for squeezing utility out of existing LLM technology.

He dismissed concerns that AI capabilities have plateaued. “I think we’re very far from plateauing,” he said. “If you look at the velocity on the research team here, we’ve been shipping models almost every week or every other week.” He pointed to recent improvements where GPT-5-Codex reportedly completes tasks 30 percent faster than its predecessor at the same intelligence level. During testing, the company has seen the model work independently for 24 hours on complex tasks.

OpenAI faces competition from multiple directions in the AI coding market. Anthropic’s Claude Code and Google’s Gemini CLI offer similar terminal-based agentic coding experiences. This week, Mistral AI released Devstral 2 alongside a CLI tool called Mistral Vibe. Meanwhile, startups like Cursor have built dedicated IDEs around AI coding, reportedly reaching $300 million in annualized revenue.

Given the well-known issues with confabulation in AI models when people attempt to use them as factual resources, could it be that coding has become the killer app for LLMs? We wondered if OpenAI has noticed that coding seems to be a clear business use case for today’s AI models with less hazard than, say, using AI language models for writing or as emotional companions.

“We have absolutely noticed that coding is both a place where agents are gonna get good really fast and there’s a lot of economic value,” Embiricos said. “We feel like it’s very mission-aligned to focus on Codex. We get to provide a lot of value to developers. Also, developers build things for other people, so we’re kind of intrinsically scaling through them.”

But will tools like Codex threaten software developer jobs? Bayes acknowledged concerns but said Codex has not reduced headcount at OpenAI, and “there’s always a human in the loop because the human can actually read the code.” Similarly, the two men don’t project a future where Codex runs by itself without some form of human oversight. They feel the tool is an amplifier of human potential rather than a replacement for it.

The practical implications of agents like Codex extend beyond OpenAI’s walls. Embiricos said the company’s long-term vision involves making coding agents useful to people who have no programming experience. “All humanity is not gonna open an IDE or even know what a terminal is,” he said. “We’re building a coding agent right now that’s just for software engineers, but we think of the shape of what we’re building as really something that will be useful to be a more general agent.”

This article was updated on December 12, 2025 at 6: 50 PM to mention the METR study.

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 releases GPT-5.2 after “code red” Google threat alert

agentic AI, AI, AI assistants, AI benchmarks, AI coding, Biz & IT, chatgpt, Gemini 3, Google, Google Gemini, GPT-5, GPT-5.2, large language models, machine learning, openai, sam altman / Mike M. / December 11, 2025

On Thursday, OpenAI released GPT-5.2, its newest family of AI models for ChatGPT, in three versions called Instant, Thinking, and Pro. The release follows CEO Sam Altman’s internal “code red” memo earlier this month, which directed company resources toward improving ChatGPT in response to competitive pressure from Google’s Gemini 3 AI model.

“We designed 5.2 to unlock even more economic value for people,” Fidji Simo, OpenAI’s chief product officer, said during a press briefing with journalists on Thursday. “It’s better at creating spreadsheets, building presentations, writing code, perceiving images, understanding long context, using tools and then linking complex, multi-step projects.”

As with previous versions of GPT-5, the three model tiers serve different purposes: Instant handles faster tasks like writing and translation; Thinking spits out simulated reasoning “thinking” text in an attempt to tackle more complex work like coding and math; and Pro spits out even more simulated reasoning text with the goal of delivering the highest-accuracy performance for difficult problems.

A chart of GPT-5.2 benchmark results taken from OpenAI's website. — A chart of GPT-5.2 Thinking benchmark results comparing it to its predecessor, taken from OpenAI’s website. Credit: OpenAI

GPT-5.2 features a 400,000-token context window, allowing it to process hundreds of documents at once, and a knowledge cutoff date of August 31, 2025.

GPT-5.2 is rolling out to paid ChatGPT subscribers starting Thursday, with API access available to developers. Pricing in the API runs $1.75 per million input tokens for the standard model, a 40 percent increase over GPT-5.1. OpenAI says the older GPT-5.1 will remain available in ChatGPT for paid users for three months under a legacy models dropdown.

Playing catch-up with Google

The release follows a tricky month for OpenAI. In early December, Altman issued an internal “code red” directive after Google’s Gemini 3 model topped multiple AI benchmarks and gained market share. The memo called for delaying other initiatives, including advertising plans for ChatGPT, to focus on improving the chatbot’s core experience.

The stakes for OpenAI are substantial. The company has made commitments totaling $1.4 trillion for AI infrastructure buildouts over the next several years, bets it made when it had a more obvious technology lead among AI companies. Google’s Gemini app now has more than 650 million monthly active users, while OpenAI reports 800 million weekly active users for ChatGPT.

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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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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.”

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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 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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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.”

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

Experts urge caution about using ChatGPT to pick stocks

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“AI models can be brilliant,” Dan Moczulski, UK managing director at eToro, told Reuters. “The risk comes when people treat generic models like ChatGPT or Gemini as crystal balls.” He noted that general AI models “can misquote figures and dates, lean too hard on a pre-established narrative, and overly rely on past price action to attempt to predict the future.”

The hazards of AI stock picking

Using AI to trade stocks at home feels like it might be the next step in a long series of technological advances that have democratized individual retail investing, for better or for worse. Computer-based stock trading for individuals dates back to 1984, when Charles Schwab introduced electronic trading services for dial-up customers. E-Trade launched in 1992, and by the late 1990s, online brokerages had transformed retail investing, dropping commission fees from hundreds of dollars per trade to under $10.

The first “robo-advisors” appeared after the 2008 financial crisis, which began the rise of automated online services that use algorithms to manage and rebalance portfolios based on a client’s goals. Services like Betterment launched in 2010, and Wealthfront followed in 2011, using algorithms to automatically rebalance portfolios. By the end of 2015, robo-advisors from nearly 100 companies globally were managing $60 billion in client assets.

The arrival of ChatGPT in November 2022 arguably marked a new phase where retail investors could directly query an AI model for stock picks rather than relying on pre-programmed algorithms. But Leung acknowledged that ChatGPT cannot access data behind paywalls, potentially missing crucial analyses available through professional services. To get better results, he creates specific prompts like “assume you’re a short analyst, what is the short thesis for this stock?” or “use only credible sources, such as SEC filings.”

Beyond chatbots, reliance on financial algorithms is growing. The “robo-advisory” market, which includes all companies providing automated, algorithm-driven financial advice from fintech startups to established banks, is forecast to grow roughly 600 percent by 2029, according to data-analysis firm Research and Markets.

But as more retail investors turn to AI tools for investment decisions, it’s also potential trouble waiting to happen.

“If people get comfortable investing using AI and they’re making money, they may not be able to manage in a crisis or downturn,” Leung warned Reuters. The concern extends beyond individual losses to whether retail investors using AI tools understand risk management or have strategies for when markets turn bearish.

Experts urge caution about using ChatGPT to pick stocks Read More »

Millions turn to AI chatbots for spiritual guidance and confession

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Privacy concerns compound these issues. “I wonder if there isn’t a larger danger in pouring your heart out to a chatbot,” Catholic priest Fr. Mike Schmitz told The Times. “Is it at some point going to become accessible to other people?” Users share intimate spiritual moments that now exist as data points in corporate servers.

Some users prefer the chatbots’ non-judgmental responses to human religious communities. Delphine Collins, a 43-year-old Detroit preschool teacher, told the Times she found more support on Bible Chat than at her church after sharing her health struggles. “People stopped talking to me. It was horrible.”

App creators maintain that their products supplement rather than replace human spiritual connection, and the apps arrive as approximately 40 million people have left US churches in recent decades. “They aren’t going to church like they used to,” Beck said. “But it’s not that they’re less inclined to find spiritual nourishment. It’s just that they do it through different modes.”

Different modes indeed. What faith-seeking users may not realize is that each chatbot response emerges fresh from the prompt you provide, with no permanent thread connecting one instance to the next beyond a rolling history of the present conversation and what might be stored as a “memory” in a separate system. When a religious chatbot says, “I’ll pray for you,” the simulated “I” making that promise ceases to exist the moment the response completes. There’s no persistent identity to provide ongoing spiritual guidance, and no memory of your spiritual journey beyond what gets fed back into the prompt with every query.

But this is spirituality we’re talking about, and despite technical realities, many people will believe that the chatbots can give them divine guidance. In matters of faith, contradictory evidence rarely shakes a strong belief once it takes hold, whether that faith is placed in the divine or in what are essentially voices emanating from a roll of loaded dice. For many, there may not be much difference.

Millions turn to AI chatbots for spiritual guidance and confession Read More »

Developers joke about “coding like cavemen” as AI service suffers major outage

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Growing dependency on AI coding tools

The speed at which news of the outage spread shows how deeply embedded AI coding assistants have already become in modern software development. Claude Code, announced in February and widely launched in May, is Anthropic’s terminal-based coding agent that can perform multi-step coding tasks across an existing code base.

The tool competes with OpenAI’s Codex feature, a coding agent that generates production-ready code in isolated containers, Google’s Gemini CLI, Microsoft’s GitHub Copilot, which itself can use Claude models for code, and Cursor, a popular AI-powered IDE built on VS Code that also integrates multiple AI models, including Claude.

During today’s outage, some developers turned to alternative solutions. “Z.AI works fine. Qwen works fine. Glad I switched,” posted one user on Hacker News. Others joked about reverting to older methods, with one suggesting the “pseudo-LLM experience” could be achieved with a Python package that imports code directly from Stack Overflow.

While AI coding assistants have accelerated development for some users, they’ve also caused problems for others who rely on them too heavily. The emerging practice of so-called “vibe coding“—using natural language to generate and execute code through AI models without fully understanding the underlying operations—has led to catastrophic failures.

In recent incidents, Google’s Gemini CLI destroyed user files while attempting to reorganize them, and Replit’s AI coding service deleted a production database despite explicit instructions not to modify code. These failures occurred when the AI models confabulated successful operations and built subsequent actions on false premises, highlighting the risks of depending on AI assistants that can misinterpret file structures or fabricate data to hide their errors.

Wednesday’s outage served as a reminder that as dependency on AI grows, even minor service disruptions can become major events that affect an entire profession. But perhaps that could be a good thing if it’s an excuse to take a break from a stressful workload. As one commenter joked, it might be “time to go outside and touch some grass again.”

Developers joke about “coding like cavemen” as AI service suffers major outage Read More »

Microsoft ends OpenAI exclusivity in Office, adds rival Anthropic

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Microsoft’s Office 365 suite will soon incorporate AI models from Anthropic alongside existing OpenAI technology, The Information reported, ending years of exclusive reliance on OpenAI for generative AI features across Word, Excel, PowerPoint, and Outlook.

The shift reportedly follows internal testing that revealed Anthropic’s Claude Sonnet 4 model excels at specific Office tasks where OpenAI’s models fall short, particularly in visual design and spreadsheet automation, according to sources familiar with the project cited by The Information, who stressed the move is not a negotiating tactic.

Anthropic did not immediately respond to Ars Technica’s request for comment.

In an unusual arrangement showing the tangled alliances of the AI industry, Microsoft will reportedly purchase access to Anthropic’s models through Amazon Web Services—both a cloud computing rival and one of Anthropic’s major investors. The integration is expected to be announced within weeks, with subscription pricing for Office’s AI tools remaining unchanged, the report says.

Microsoft maintains that its OpenAI relationship remains intact. “As we’ve said, OpenAI will continue to be our partner on frontier models and we remain committed to our long-term partnership,” a Microsoft spokesperson told Reuters following the report. The tech giant has poured over $13 billion into OpenAI to date and is currently negotiating terms for continued access to OpenAI’s models amid ongoing negotiations about their partnership terms.

Stretching back to 2019, Microsoft’s tight partnership with OpenAI until recently gave the tech giant a head start in AI assistants based on language models, allowing for a rapid (though bumpy) deployment of OpenAI-technology-based features in Bing search and the rollout of Copilot assistants throughout its software ecosystem. It’s worth noting, however, that a recent report from the UK government found no clear productivity boost from using Copilot AI in daily work tasks among study participants.

Microsoft ends OpenAI exclusivity in Office, adds rival Anthropic Read More »

Why accessibility might be AI’s biggest breakthrough

Accessibility, ADHD, AI, AI accessibility, AI and work, AI assistants, AI evaluation, AI study, Biz & IT, Department for Business and Trade, disability, dyslexia, M365 Copilot, machine learning, microsoft, neurodiversity, uk government, workplace accommodation / Tim Belzer / September 9, 2025

For those with visual impairments, language models can summarize visual content and reformat information. Tools like ChatGPT’s voice mode with video and Be My Eyes allow a machine to describe real-world visual scenes in ways that were impossible just a few years ago.

AI language tools may be providing unofficial stealth accommodations for students—support that doesn’t require formal diagnosis, workplace disclosure, or special equipment. Yet this informal support system comes with its own risks. Language models do confabulate—the UK Department for Business and Trade study found 22 percent of users identified false information in AI outputs—which could be particularly harmful for users relying on them for essential support.

When AI assistance becomes dependence

Beyond the workplace, the drawbacks may have a particular impact on students who use the technology. The authors of a 2025 study on students with disabilities using generative AI cautioned, “Key concerns students with disabilities had included the inaccuracy of AI answers, risks to academic integrity, and subscription cost barriers,” they wrote. Students in that study had ADHD, dyslexia, dyspraxia, and autism, with ChatGPT being the most commonly used tool.

Mistakes in AI outputs are especially pernicious because, due to grandiose visions of near-term AI technology, some people think today’s AI assistants can perform tasks that are actually far outside their scope. As research on blind users’ experiences suggested, people develop complex (sometimes flawed) mental models of how these tools work, showing the need for higher awareness of AI language model drawbacks among the general public.

For the UK government employees who participated in the initial study, these questions moved from theoretical to immediate when the pilot ended in December 2024. After that time, many participants reported difficulty readjusting to work without AI assistance—particularly those with disabilities who had come to rely on the accessibility benefits. The department hasn’t announced the next steps, leaving users in limbo. When participants report difficulty readjusting to work without AI while productivity gains remain marginal, accessibility emerges as potentially the first AI application with irreplaceable value.

Why accessibility might be AI’s biggest breakthrough Read More »