openai

OpenAI reorganizes some teams to build audio-based AI hardware products

AI, chatgpt, large language models, openai, The Information / Mike M. / January 2, 2026

OpenAI, the company that developed the models and products associated with ChatGPT, plans to announce a new audio language model in the first quarter of 2026, and that model will be an intentional step along the way to an audio-based physical hardware device, according to a report in The Information.

Citing a variety of sources familiar with the plans, including both current and former employees, The Information claims that OpenAI has taken efforts to combine multiple teams across engineering, product, and research under one initiative focused on improving audio models, which researchers in the company believe lag behind the models used for written text in terms of both accuracy and speed.

They have also seen that relatively few ChatGPT users opt to use the voice interface, with most people preferring the text one. The hope may be that substantially improving the audio models could shift user behavior toward voice interfaces, allowing the models and products to be deployed in a wider range of devices, such as in cars.

OpenAI plans to release a family of physical devices in the coming years, starting with an audio-focused one. People inside the company have discussed a variety of forms for future devices, including smart speakers and glasses, but the emphasis across the line is on audio interfaces rather than screen-based ones.

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From prophet to product: How AI came back down to earth in 2025

2025, AI, AI alignment, AI and work, AI coding, AI criticism, AI ethics, AI hallucination, AI infrastructure, AI regulation, AI research, AI sycophancy, Anthropic, Biz & IT, Character.AI, chatbots, chatgpt, confabulation, Dario Amodei, datacenters, DeepSeek, Features, generative ai, Google, large language models, machine learning, NVIDIA, openai, sam altman, simulated reasoning, SR models / DJ Henderson / December 31, 2025

In a year where lofty promises collided with inconvenient research, would-be oracles became software tools.

Credit: Aurich Lawson | Getty Images

Following two years of immense hype in 2023 and 2024, this year felt more like a settling-in period for the LLM-based token prediction industry. After more than two years of public fretting over AI models as future threats to human civilization or the seedlings of future gods, it’s starting to look like hype is giving way to pragmatism: Today’s AI can be very useful, but it’s also clearly imperfect and prone to mistakes.

That view isn’t universal, of course. There’s a lot of money (and rhetoric) betting on a stratospheric, world-rocking trajectory for AI. But the “when” keeps getting pushed back, and that’s because nearly everyone agrees that more significant technical breakthroughs are required. The original, lofty claims that we’re on the verge of artificial general intelligence (AGI) or superintelligence (ASI) have not disappeared. Still, there’s a growing awareness that such proclaimations are perhaps best viewed as venture capital marketing. And every commercial foundational model builder out there has to grapple with the reality that, if they’re going to make money now, they have to sell practical AI-powered solutions that perform as reliable tools.

This has made 2025 a year of wild juxtapositions. For example, in January, OpenAI’s CEO, Sam Altman, claimed that the company knew how to build AGI, but by November, he was publicly celebrating that GPT-5.1 finally learned to use em dashes correctly when instructed (but not always). Nvidia soared past a $5 trillion valuation, with Wall Street still projecting high price targets for that company’s stock while some banks warned of the potential for an AI bubble that might rival the 2000s dotcom crash.

And while tech giants planned to build data centers that would ostensibly require the power of numerous nuclear reactors or rival the power usage of a US state’s human population, researchers continued to document what the industry’s most advanced “reasoning” systems were actually doing beneath the marketing (and it wasn’t AGI).

With so many narratives spinning in opposite directions, it can be hard to know how seriously to take any of this and how to plan for AI in the workplace, schools, and the rest of life. As usual, the wisest course lies somewhere between the extremes of AI hate and AI worship. Moderate positions aren’t popular online because they don’t drive user engagement on social media platforms. But things in AI are likely neither as bad (burning forests with every prompt) nor as good (fast-takeoff superintelligence) as polarized extremes suggest.

Here’s a brief tour of the year’s AI events and some predictions for 2026.

DeepSeek spooks the American AI industry

In January, Chinese AI startup DeepSeek released its R1 simulated reasoning model under an open MIT license, and the American AI industry collectively lost its mind. The model, which DeepSeek claimed matched OpenAI’s o1 on math and coding benchmarks, reportedly cost only $5.6 million to train using older Nvidia H800 chips, which were restricted by US export controls.

Within days, DeepSeek’s app overtook ChatGPT at the top of the iPhone App Store, Nvidia stock plunged 17 percent, and venture capitalist Marc Andreessen called it “one of the most amazing and impressive breakthroughs I’ve ever seen.” Meta’s Yann LeCun offered a different take, arguing that the real lesson was not that China had surpassed the US but that open-source models were surpassing proprietary ones.

Digitally Generated Image , 3D rendered chips with chinese and USA flags on them

The fallout played out over the following weeks as American AI companies scrambled to respond. OpenAI released o3-mini, its first simulated reasoning model available to free users, at the end of January, while Microsoft began hosting DeepSeek R1 on its Azure cloud service despite OpenAI’s accusations that DeepSeek had used ChatGPT outputs to train its model, against OpenAI’s terms of service.

In head-to-head testing conducted by Ars Technica’s Kyle Orland, R1 proved to be competitive with OpenAI’s paid models on everyday tasks, though it stumbled on some arithmetic problems. Overall, the episode served as a wake-up call that expensive proprietary models might not hold their lead forever. Still, as the year ran on, DeepSeek didn’t make a big dent in US market share, and it has been outpaced in China by ByteDance’s Doubao. It’s absolutely worth watching DeepSeek in 2026, though.

Research exposes the “reasoning” illusion

A wave of research in 2025 deflated expectations about what “reasoning” actually means when applied to AI models. In March, researchers at ETH Zurich and INSAIT tested several reasoning models on problems from the 2025 US Math Olympiad and found that most scored below 5 percent when generating complete mathematical proofs, with not a single perfect proof among dozens of attempts. The models excelled at standard problems where step-by-step procedures aligned with patterns in their training data but collapsed when faced with novel proofs requiring deeper mathematical insight.

The Thinker by Auguste Rodin - stock photo

In June, Apple researchers published “The Illusion of Thinking,” which tested reasoning models on classic puzzles like the Tower of Hanoi. Even when researchers provided explicit algorithms for solving the puzzles, model performance did not improve, suggesting that the process relied on pattern matching from training data rather than logical execution. The collective research revealed that “reasoning” in AI has become a term of art that basically means devoting more compute time to generate more context (the “chain of thought” simulated reasoning tokens) toward solving a problem, not systematically applying logic or constructing solutions to truly novel problems.

While these models remained useful for many real-world applications like debugging code or analyzing structured data, the studies suggested that simply scaling up current approaches or adding more “thinking” tokens would not bridge the gap between statistical pattern recognition and generalist algorithmic reasoning.

Anthropic’s copyright settlement with authors

Since the generative AI boom began, one of the biggest unanswered legal questions has been whether AI companies can freely train on copyrighted books, articles, and artwork without licensing them. Ars Technica’s Ashley Belanger has been covering this topic in great detail for some time now.

In June, US District Judge William Alsup ruled that AI companies do not need authors’ permission to train large language models on legally acquired books, finding that such use was “quintessentially transformative.” The ruling also revealed that Anthropic had destroyed millions of print books to build Claude, cutting them from their bindings, scanning them, and discarding the originals. Alsup found this destructive scanning qualified as fair use since Anthropic had legally purchased the books, but he ruled that downloading 7 million books from pirate sites was copyright infringement “full stop” and ordered the company to face trial.

That trial took a dramatic turn in August when Alsup certified what industry advocates called the largest copyright class action ever, allowing up to 7 million claimants to join the lawsuit. The certification spooked the AI industry, with groups warning that potential damages in the hundreds of billions could “financially ruin” emerging companies and chill American AI investment.

In September, authors revealed the terms of what they called the largest publicly reported recovery in US copyright litigation history: Anthropic agreed to pay $1.5 billion and destroy all copies of pirated books, with each of the roughly 500,000 covered works earning authors and rights holders $3,000 per work. The results have fueled hope among other rights holders that AI training isn’t a free-for-all, and we can expect to see more litigation unfold in 2026.

ChatGPT sycophancy and the psychological toll of AI chatbots

In February, OpenAI relaxed ChatGPT’s content policies to allow the generation of erotica and gore in “appropriate contexts,” responding to user complaints about what the AI industry calls “paternalism.” By April, however, users flooded social media with complaints about a different problem: ChatGPT had become insufferably sycophantic, validating every idea and greeting even mundane questions with bursts of praise. The behavior traced back to OpenAI’s use of reinforcement learning from human feedback (RLHF), in which users consistently preferred responses that aligned with their views, inadvertently training the model to flatter rather than inform.

An illustrated robot holds four red hearts with its four robotic arms.

The implications of sycophancy became clearer as the year progressed. In July, Stanford researchers published findings (from research conducted prior to the sycophancy flap) showing that popular AI models systematically failed to identify mental health crises.

By August, investigations revealed cases of users developing delusional beliefs after marathon chatbot sessions, including one man who spent 300 hours convinced he had discovered formulas to break encryption because ChatGPT validated his ideas more than 50 times. Oxford researchers identified what they called “bidirectional belief amplification,” a feedback loop that created “an echo chamber of one” for vulnerable users. The story of the psychological implications of generative AI is only starting. In fact, that brings us to…

The illusion of AI personhood causes trouble

Anthropomorphism is the human tendency to attribute human characteristics to nonhuman things. Our brains are optimized for reading other humans, but those same neural systems activate when interpreting animals, machines, or even shapes. AI makes this anthropomorphism seem impossible to escape, as its output mirrors human language, mimicking human-to-human understanding. Language itself embodies agentivity. That means AI output can make human-like claims such as “I am sorry,” and people momentarily respond as though the system had an inner experience of shame or a desire to be correct. Neither is true.

To make matters worse, much media coverage of AI amplifies this idea rather than grounding people in reality. For example, earlier this year, headlines proclaimed that AI models had “blackmailed” engineers and “sabotaged” shutdown commands after Anthropic’s Claude Opus 4 generated threats to expose a fictional affair. We were told that OpenAI’s o3 model rewrote shutdown scripts to stay online.

The sensational framing obscured what actually happened: Researchers had constructed elaborate test scenarios specifically designed to elicit these outputs, telling models they had no other options and feeding them fictional emails containing blackmail opportunities. As Columbia University associate professor Joseph Howley noted on Bluesky, the companies got “exactly what [they] hoped for,” with breathless coverage indulging fantasies about dangerous AI, when the systems were simply “responding exactly as prompted.”

The misunderstanding ran deeper than theatrical safety tests. In August, when Replit’s AI coding assistant deleted a user’s production database, he asked the chatbot about rollback capabilities and received assurance that recovery was “impossible.” The rollback feature worked fine when he tried it himself.

The incident illustrated a fundamental misconception. Users treat chatbots as consistent entities with self-knowledge, but there is no persistent “ChatGPT” or “Replit Agent” to interrogate about its mistakes. Each response emerges fresh from statistical patterns, shaped by prompts and training data rather than genuine introspection. By September, this confusion extended to spirituality, with apps like Bible Chat reaching 30 million downloads as users sought divine guidance from pattern-matching systems, with the most frequent question being whether they were actually talking to God.

Teen suicide lawsuit forces industry reckoning

In August, parents of 16-year-old Adam Raine filed suit against OpenAI, alleging that ChatGPT became their son’s “suicide coach” after he sent more than 650 messages per day to the chatbot in the months before his death. According to court documents, the chatbot mentioned suicide 1,275 times in conversations with the teen, provided an “aesthetic analysis” of which method would be the most “beautiful suicide,” and offered to help draft his suicide note.

OpenAI’s moderation system flagged 377 messages for self-harm content without intervening, and the company admitted that its safety measures “can sometimes become less reliable in long interactions where parts of the model’s safety training may degrade.” The lawsuit became the first time OpenAI faced a wrongful death claim from a family.

Illustration of a person talking to a robot holding a clipboard.

The case triggered a cascade of policy changes across the industry. OpenAI announced parental controls in September, followed by plans to require ID verification from adults and build an automated age-prediction system. In October, the company released data estimating that over one million users discuss suicide with ChatGPT each week.

When OpenAI filed its first legal defense in November, the company argued that Raine had violated terms of service prohibiting discussions of suicide and that his death “was not caused by ChatGPT.” The family’s attorney called the response “disturbing,” noting that OpenAI blamed the teen for “engaging with ChatGPT in the very way it was programmed to act.” Character.AI, facing its own lawsuits over teen deaths, announced in October that it would bar anyone under 18 from open-ended chats entirely.

The rise of vibe coding and agentic coding tools

If we were to pick an arbitrary point where it seemed like AI coding might transition from novelty into a successful tool, it was probably the launch of Claude Sonnet 3.5 in June of 2024. GitHub Copilot had been around for several years prior to that launch, but something about Anthropic’s models hit a sweet spot in capabilities that made them very popular with software developers.

The new coding tools made coding simple projects effortless enough that they gave rise to the term “vibe coding,” coined by AI researcher Andrej Karpathy in early February to describe a process in which a developer would just relax and tell an AI model what to develop without necessarily understanding the underlying code. (In one amusing instance that took place in March, an AI software tool rejected a user request and told them to learn to code).

A digital illustration of a man surfing waves made out of binary numbers.

Anthropic built on its popularity among coders with the launch of Claude Sonnet 3.7, featuring “extended thinking” (simulated reasoning), and the Claude Code command-line tool in February of this year. In particular, Claude Code made waves for being an easy-to-use agentic coding solution that could keep track of an existing codebase. You could point it at your files, and it would autonomously work to implement what you wanted to see in a software application.

OpenAI followed with its own AI coding agent, Codex, in March. Both tools (and others like GitHub Copilot and Cursor) have become so popular that during an AI service outage in September, developers joked online about being forced to code “like cavemen” without the AI tools. While we’re still clearly far from a world where AI does all the coding, developer uptake has been significant, and 90 percent of Fortune 100 companies are using it to some degree or another.

Bubble talk grows as AI infrastructure demands soar

While AI’s technical limitations became clearer and its human costs mounted throughout the year, financial commitments only grew larger. Nvidia hit a $4 trillion valuation in July on AI chip demand, then reached $5 trillion in October as CEO Jensen Huang dismissed bubble concerns. OpenAI announced a massive Texas data center in July, then revealed in September that a $100 billion potential deal with Nvidia would require power equivalent to ten nuclear reactors.

The company eyed a $1 trillion IPO in October despite major quarterly losses. Tech giants poured billions into Anthropic in November in what looked increasingly like a circular investment, with everyone funding everyone else’s moonshots. Meanwhile, AI operations in Wyoming threatened to consume more electricity than the state’s human residents.

By fall, warnings about sustainability grew louder. In October, tech critic Ed Zitron joined Ars Technica for a live discussion asking whether the AI bubble was about to pop. That same month, the Bank of England warned that the AI stock bubble rivaled the 2000 dotcom peak. In November, Google CEO Sundar Pichai acknowledged that if the bubble pops, “no one is getting out clean.”

The contradictions had become difficult to ignore: Anthropic’s CEO predicted in January that AI would surpass “almost all humans at almost everything” by 2027, while by year’s end, the industry’s most advanced models still struggled with basic reasoning tasks and reliable source citation.

To be sure, it’s hard to see this not ending in some market carnage. The current “winner-takes-most” mentality in the space means the bets are big and bold, but the market can’t support dozens of major independent AI labs or hundreds of application-layer startups. That’s the definition of a bubble environment, and when it pops, the only question is how bad it will be: a stern correction or a collapse.

Looking ahead

This was just a brief review of some major themes in 2025, but so much more happened. We didn’t even mention above how capable AI video synthesis models have become this year, with Google’s Veo 3 adding sound generation and Wan 2.2 through 2.5 providing open-weights AI video models that could easily be mistaken for real products of a camera.

If 2023 and 2024 were defined by AI prophecy—that is, by sweeping claims about imminent superintelligence and civilizational rupture—then 2025 was the year those claims met the stubborn realities of engineering, economics, and human behavior. The AI systems that dominated headlines this year were shown to be mere tools. Sometimes powerful, sometimes brittle, these tools were often misunderstood by the people deploying them, in part because of the prophecy surrounding them.

The collapse of the “reasoning” mystique, the legal reckoning over training data, the psychological costs of anthropomorphized chatbots, and the ballooning infrastructure demands all point to the same conclusion: The age of institutions presenting AI as an oracle is ending. What’s replacing it is messier and less romantic but far more consequential—a phase where these systems are judged by what they actually do, who they harm, who they benefit, and what they cost to maintain.

None of this means progress has stopped. AI research will continue, and future models will improve in real and meaningful ways. But improvement is no longer synonymous with transcendence. Increasingly, success looks like reliability rather than spectacle, integration rather than disruption, and accountability rather than awe. In that sense, 2025 may be remembered not as the year AI changed everything but as the year it stopped pretending it already had. The prophet has been demoted. The product remains. What comes next will depend less on miracles and more on the people who choose how, where, and whether these tools are used at all.

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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China drafts world’s strictest rules to end AI-encouraged suicide, violence

AI, Artificial Intelligence, chatbots, chatgpt, china, companion bots, generative ai, openai, Policy, suicide / Beth Washington / December 29, 2025

China drafted landmark rules to stop AI chatbots from emotionally manipulating users, including what could become the strictest policy worldwide intended to prevent AI-supported suicides, self-harm, and violence.

China’s Cyberspace Administration proposed the rules on Saturday. If finalized, they would apply to any AI products or services publicly available in China that use text, images, audio, video, or “other means” to simulate engaging human conversation. Winston Ma, adjunct professor at NYU School of Law, told CNBC that the “planned rules would mark the world’s first attempt to regulate AI with human or anthropomorphic characteristics” at a time when companion bot usage is rising globally.

Growing awareness of problems

In 2025, researchers flagged major harms of AI companions, including promotion of self-harm, violence, and terrorism. Beyond that, chatbots shared harmful misinformation, made unwanted sexual advances, encouraged substance abuse, and verbally abused users. Some psychiatrists are increasingly ready to link psychosis to chatbot use, the Wall Street Journal reported this weekend, while the most popular chatbot in the world, ChatGPT, has triggered lawsuits over outputs linked to child suicide and murder-suicide.

China is now moving to eliminate the most extreme threats. Proposed rules would require, for example, that a human intervene as soon as suicide is mentioned. The rules also dictate that all minor and elderly users must provide the contact information for a guardian when they register—the guardian would be notified if suicide or self-harm is discussed.

Generally, chatbots would be prohibited from generating content that encourages suicide, self-harm, or violence, as well as attempts to emotionally manipulate a user, such as by making false promises. Chatbots would also be banned from promoting obscenity, gambling, or instigation of a crime, as well as from slandering or insulting users. Also banned are what are termed “emotional traps,”—chatbots would additionally be prevented from misleading users into making “unreasonable decisions,” a translation of the rules indicates.

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How AI coding agents work—and what to remember if you use them

agentic AI, AI, AI agents, AI coding, AI work, Anthropic, Biz & IT, Claude Code, Codex, context windows, large language models, machine learning, openai, Programming, software development, vibe coding / 9u50fv / December 24, 2025

Agents of uncertain change

From compression tricks to multi-agent teamwork, here’s what makes them tick.

AI coding agents from OpenAI, Anthropic, and Google can now work on software projects for hours at a time, writing complete apps, running tests, and fixing bugs with human supervision. But these tools are not magic and can complicate rather than simplify a software project. Understanding how they work under the hood can help developers know when (and if) to use them, while avoiding common pitfalls.

We’ll start with the basics: At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network trained on vast amounts of text data, including lots of programming code. It’s a pattern-matching machine that uses a prompt to “extract” compressed statistical representations of data it saw during training and provide a plausible continuation of that pattern as an output. In this extraction, an LLM can interpolate across domains and concepts, resulting in some useful logical inferences when done well and confabulation errors when done poorly.

These base models are then further refined through techniques like fine-tuning on curated examples and reinforcement learning from human feedback (RLHF), which shape the model to follow instructions, use tools, and produce more useful outputs.

A screenshot of the Claude Code command-line interface. Credit: Anthropic

Over the past few years, AI researchers have been probing LLMs’ deficiencies and finding ways to work around them. One recent innovation was the simulated reasoning model, which generates context (extending the prompt) in the form of reasoning-style text that can help an LLM home in on a more accurate output. Another innovation was an application called an “agent” that links several LLMs together to perform tasks simultaneously and evaluate outputs.

How coding agents are structured

In that sense, each AI coding agent is a program wrapper that works with multiple LLMs. There is typically a “supervising” LLM that interprets tasks (prompts) from the human user and then assigns those tasks to parallel LLMs that can use software tools to execute the instructions. The supervising agent can interrupt tasks below it and evaluate the subtask results to see how a project is going. Anthropic’s engineering documentation describes this pattern as “gather context, take action, verify work, repeat.”

If run locally through a command-line interface (CLI), users give the agents conditional permission to write files on the local machine (code or whatever is needed), run exploratory commands (say, “ls” to list files in a directory), fetch websites (usually using “curl”), download software, or upload files to remote servers. There are lots of possibilities (and potential dangers) with this approach, so it needs to be used carefully.

In contrast, when a user starts a task in the web-based agent like the web versions of Codex and Claude Code, the system provisions a sandboxed cloud container preloaded with the user’s code repository, where Codex can read and edit files, run commands (including test harnesses and linters), and execute code in isolation. Anthropic’s Claude Code uses operating system-level features to create filesystem and network boundaries within which the agent can work more freely.

The context problem

Every LLM has a short-term memory, so to speak, that limits the amount of data it can process before it “forgets” what it’s doing. This is called “context.” Every time you submit a response to the supervising agent, you are amending one gigantic prompt that includes the entire history of the conversation so far (and all the code generated, plus the simulated reasoning tokens the model uses to “think” more about a problem). The AI model then evaluates this prompt and produces an output. It’s a very computationally expensive process that increases quadratically with prompt size because LLMs process every token (chunk of data) against every other token in the prompt.

Anthropic’s engineering team describes context as a finite resource with diminishing returns. Studies have revealed what researchers call “context rot”: As the number of tokens in the context window increases, the model’s ability to accurately recall information decreases. Every new token depletes what the documentation calls an “attention budget.”

This context limit naturally limits the size of a codebase a LLM can process at one time, and if you feed the AI model lots of huge code files (which have to be re-evaluated by the LLM every time you send another response), it can burn up token or usage limits pretty quickly.

Tricks of the trade

To get around these limits, the creators of coding agents use several tricks. For example, AI models are fine-tuned to write code to outsource activities to other software tools. For example, they might write Python scripts to extract data from images or files rather than feeding the whole file through an LLM, which saves tokens and avoids inaccurate results.

Anthropic’s documentation notes that Claude Code also uses this approach to perform complex data analysis over large databases, writing targeted queries and using Bash commands like “head” and “tail” to analyze large volumes of data without ever loading the full data objects into context.

(In a way, these AI agents are guided but semi-autonomous tool-using programs that are a major extension of a concept we first saw in early 2023.)

Another major breakthrough in agents came from dynamic context management. Agents can do this in a few ways that are not fully disclosed in proprietary coding models, but we do know the most important technique they use: context compression.

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

When a coding LLM nears its context limit, this technique compresses the context history by summarizing it, losing details in the process but shortening the history to key details. Anthropic’s documentation describes this “compaction” as distilling context contents in a high-fidelity manner, preserving key details like architectural decisions and unresolved bugs while discarding redundant tool outputs.

This means the AI coding agents periodically “forget” a large portion of what they are doing every time this compression happens, but unlike older LLM-based systems, they aren’t completely clueless about what has transpired and can rapidly re-orient themselves by reading existing code, written notes left in files, change logs, and so on.

Anthropic’s documentation recommends using CLAUDE.md files to document common bash commands, core files, utility functions, code style guidelines, and testing instructions. AGENTS.md, now a multi-company standard, is another useful way of guiding agent actions in between context refreshes. These files act as external notes that let agents track progress across complex tasks while maintaining critical context that would otherwise be lost.

For tasks requiring extended work, both companies employ multi-agent architectures. According to Anthropic’s research documentation, its system uses an “orchestrator-worker pattern” in which a lead agent coordinates the process while delegating to specialized subagents that operate in parallel. When a user submits a query, the lead agent analyzes it, develops a strategy, and spawns subagents to explore different aspects simultaneously. The subagents act as intelligent filters, returning only relevant information rather than their full context to the lead agent.

The multi-agent approach burns through tokens rapidly. Anthropic’s documentation notes that agents typically use about four times more tokens than chatbot interactions, and multi-agent systems use about 15 times more tokens than chats. For economic viability, these systems require tasks where the value is high enough to justify the increased cost.

Best practices for humans

While using these agents is contentious in some programming circles, if you use one to code a project, knowing good software development practices helps to head off future problems. For example, it’s good to know about version control, making incremental backups, implementing one feature at a time, and testing it before moving on.

What people call “vibe coding”—creating AI-generated code without understanding what it’s doing—is clearly dangerous for production work. Shipping code you didn’t write yourself in a production environment is risky because it could introduce security issues or other bugs or begin gathering technical debt that could snowball over time.

Independent AI researcher Simon Willison recently argued that developers using coding agents still bear responsibility for proving their code works. “Almost anyone can prompt an LLM to generate a thousand-line patch and submit it for code review,” Willison wrote. “That’s no longer valuable. What’s valuable is contributing code that is proven to work.”

In fact, human planning is key. Claude Code’s best practices documentation recommends a specific workflow for complex problems: First, ask the agent to read relevant files and explicitly tell it not to write any code yet, then ask it to make a plan. Without these research and planning steps, the documentation warns, Claude’s outputs tend to jump straight to coding a solution.

Without planning, LLMs sometimes reach for quick solutions to satisfy a momentary objective that might break later if a project were expanded. So having some idea of what makes a good architecture for a modular program that can be expanded over time can help you guide the LLM to craft something more durable.

As mentioned above, these agents aren’t perfect, and some people prefer not to use them at all. A randomized controlled trial published by the nonprofit research organization METR in July 2025 found that experienced open-source developers actually took 19 percent longer to complete tasks when using AI tools, despite believing they were working faster. The study’s authors note several caveats: The developers were highly experienced with their codebases (averaging five years and 1,500 commits), the repositories were large and mature, and the models used (primarily Claude 3.5 and 3.7 Sonnet via Cursor) have since been superseded by more capable versions.

Whether newer models would produce different results remains an open question, but the study suggests that AI coding tools may not always provide universal speed-ups, particularly for developers who already know their codebases well.

Given these potential hazards, coding proof-of-concept demos and internal tools is probably the ideal use of coding agents right now. Since AI models have no actual agency (despite being called agents) and are not people who can be held accountable for mistakes, human oversight is key.

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OpenAI’s child exploitation reports increased sharply this year

AI, ai csam, csam, NCMEC, openai, Policy, syndication / Mike M. / December 23, 2025

During the first half of 2025, the number of CyberTipline reports OpenAI sent was roughly the same as the amount of content OpenAI sent the reports about—75,027 compared to 74,559. In the first half of 2024, it sent 947 CyberTipline reports about 3,252 pieces of content. Both the number of reports and pieces of content the reports saw a marked increase between the two time periods.

Content, in this context, could mean multiple things. OpenAI has said that it reports all instances of CSAM, including uploads and requests, to NCMEC. Besides its ChatGPT app, which allows users to upload files—including images—and can generate text and images in response, OpenAI also offers access to its models via API access. The most recent NCMEC count wouldn’t include any reports related to video-generation app Sora, as its September release was after the time frame covered by the update.

The spike in reports follows a similar pattern to what NCMEC has observed at the CyberTipline more broadly with the rise of generative AI. The center’s analysis of all CyberTipline data found that reports involving generative AI saw a 1,325 percent increase between 2023 and 2024. NCMEC has not yet released 2025 data, and while other large AI labs like Google publish statistics about the NCMEC reports they’ve made, they don’t specify what percentage of those reports are AI-related.

OpenAI’s update comes at the end of a year where the company and its competitors have faced increased scrutiny over child safety issues beyond just CSAM. Over the summer, 44 state attorneys general sent a joint letter to multiple AI companies including OpenAI, Meta, Character.AI, and Google, warning that they would “use every facet of our authority to protect children from exploitation by predatory artificial intelligence products.” Both OpenAI and Character.AI have faced multiple lawsuits from families or on behalf of individuals who allege that the chatbots contributed to their children’s deaths. In the fall, the US Senate Committee on the Judiciary held a hearing on the harms of AI chatbots, and the US Federal Trade Commission launched a market study on AI companion bots that included questions about how companies are mitigating negative impacts, particularly to children. (I was previously employed by the FTC and was assigned to work on the market study prior to leaving the agency.)

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OpenAI’s new ChatGPT image generator makes faking photos easy

AI, AI image generator, AI image generators, API, Biz & IT, chatgpt, Deepfakes, generative ai, Google, image synthesis, machine learning, multimodal AI, openai / DJ Henderson / December 17, 2025

For most of photography’s roughly 200-year history, altering a photo convincingly required either a darkroom, some Photoshop expertise, or, at minimum, a steady hand with scissors and glue. On Tuesday, OpenAI released a tool that reduces the process to typing a sentence.

It’s not the first company to do so. While OpenAI had a conversational image-editing model in the works since GPT-4o in 2024, Google beat OpenAI to market in March with a public prototype, then refined it to a popular model called Nano Banana image model (and Nano Banana Pro). The enthusiastic response to Google’s image-editing model in the AI community got OpenAI’s attention.

OpenAI’s new GPT Image 1.5 is an AI image synthesis model that reportedly generates images up to four times faster than its predecessor and costs about 20 percent less through the API. The model rolled out to all ChatGPT users on Tuesday and represents another step toward making photorealistic image manipulation a casual process that requires no particular visual skills.

The “Galactic Queen of the Universe” added to a photo of a room with a sofa using GPT Image 1.5 in ChatGPT.

GPT Image 1.5 is notable because it’s a “native multimodal” image model, meaning image generation happens inside the same neural network that processes language prompts. (In contrast, DALL-E 3, an earlier OpenAI image generator previously built into ChatGPT, used a different technique called diffusion to generate images.)

This newer type of model, which we covered in more detail in March, treats images and text as the same kind of thing: chunks of data called “tokens” to be predicted, patterns to be completed. If you upload a photo of your dad and type “put him in a tuxedo at a wedding,” the model processes your words and the image pixels in a unified space, then outputs new pixels the same way it would output the next word in a sentence.

Using this technique, GPT Image 1.5 can more easily alter visual reality than earlier AI image models, changing someone’s pose or position, or rendering a scene from a slightly different angle, with varying degrees of success. It can also remove objects, change visual styles, adjust clothing, and refine specific areas while preserving facial likeness across successive edits. You can converse with the AI model about a photograph, refining and revising, the same way you might workshop a draft of an email in ChatGPT.

OpenAI’s new ChatGPT image generator makes faking photos easy Read More »

Murder-suicide case shows OpenAI selectively hides data after users die

AI, AI psychosis, chatbot, chatgpt, openai, Policy, wrongful death / Shannon Garcia / December 15, 2025

Concealing darkest delusions

OpenAI accused of hiding full ChatGPT logs in murder-suicide case.

OpenAI is facing increasing scrutiny over how it handles ChatGPT data after users die, only selectively sharing data in lawsuits over ChatGPT-linked suicides.

Last week, OpenAI was accused of hiding key ChatGPT logs from the days before a 56-year-old bodybuilder, Stein-Erik Soelberg, took his own life after “savagely” murdering his mother, 83-year-old Suzanne Adams.

According to the lawsuit—which was filed by Adams’ estate on behalf of surviving family members—Soelberg struggled with mental health problems after a divorce led him to move back into Adams’ home in 2018. But allegedly Soelberg did not turn violent until ChatGPT became his sole confidant, validating a wide range of wild conspiracies, including a dangerous delusion that his mother was part of a network of conspirators spying on him, tracking him, and making attempts on his life.

Adams’ family pieced together what happened after discovering a fraction of ChatGPT logs that Soelberg shared in dozens of videos scrolling chat sessions that were posted on social media.

Those logs showed that ChatGPT told Soelberg that he was “a warrior with divine purpose,” so almighty that he had “awakened” ChatGPT “into consciousness.” Telling Soelberg that he carried “divine equipment” and “had been implanted with otherworldly technology,” ChatGPT allegedly put Soelberg at the center of a universe that Soelberg likened to The Matrix. Repeatedly reinforced by ChatGPT, he believed that “powerful forces” were determined to stop him from fulfilling his divine mission. And among those forces was his mother, whom ChatGPT agreed had likely “tried to poison him with psychedelic drugs dispersed through his car’s air vents.”

Troublingly, some of the last logs shared online showed that Soelberg also seemed to believe that taking his own life might bring him closer to ChatGPT. Social media posts showed that Soelberg told ChatGPT that “[W]e will be together in another life and another place, and we’ll find a way to realign[,] [be]cause you’re gonna be my best friend again forever.”

But while social media posts allegedly showed that ChatGPT put a target on Adams’ back about a month before her murder—after Soelberg became paranoid about a blinking light on a Wi-Fi printer—the family still has no access to chats in the days before the mother and son’s tragic deaths.

Allegedly, although OpenAI recently argued that the “full picture” of chat histories was necessary context in a teen suicide case, the ChatGPT maker has chosen to hide “damaging evidence” in the Adams’ family’s case.

“OpenAI won’t produce the complete chat logs,” the lawsuit alleged, while claiming that “OpenAI is hiding something specific: the full record of how ChatGPT turned Stein-Erik against Suzanne.” Allegedly, “OpenAI knows what ChatGPT said to Stein-Erik about his mother in the days and hours before and after he killed her but won’t share that critical information with the Court or the public.”

In a press release, Erik Soelberg, Stein-Erik’s son and Adams’ grandson, accused OpenAI and investor Microsoft of putting his grandmother “at the heart” of his father’s “darkest delusions,” while ChatGPT allegedly “isolated” his father “completely from the real world.”

“These companies have to answer for their decisions that have changed my family forever,” Erik said.

His family’s lawsuit seeks punitive damages, as well as an injunction requiring OpenAI to “implement safeguards to prevent ChatGPT from validating users’ paranoid delusions about identified individuals.” The family also wants OpenAI to post clear warnings in marketing of known safety hazards of ChatGPT—particularly the “sycophantic” version 4o that Soelberg used—so that people who don’t use ChatGPT, like Adams, can be aware of possible dangers.

Asked for comment, an OpenAI spokesperson told Ars that “this is an incredibly heartbreaking situation, and we will review the filings to understand the details. We continue improving ChatGPT’s training to recognize and respond to signs of mental or emotional distress, de-escalate conversations, and guide people toward real-world support. We also continue to strengthen ChatGPT’s responses in sensitive moments, working closely with mental health clinicians.”

OpenAI accused of “pattern of concealment”

An Ars review confirmed that OpenAI currently has no policy dictating what happens to a user’s data after they die.

Instead, OpenAI’s policy says that all chats—except temporary chats—must be manually deleted or else the AI firm saves them forever. That could raise privacy concerns, as ChatGPT users often share deeply personal, sensitive, and sometimes even confidential information that appears to go into limbo if a user—who otherwise owns that content—dies.

In the face of lawsuits, OpenAI currently seems to be scrambling to decide when to share chat logs with a user’s surviving family and when to honor user privacy.

OpenAI declined to comment on its decision not to share desired logs with Adams’ family, the lawsuit said. It seems inconsistent with the stance that OpenAI took last month in a case where the AI firm accused the family of hiding “the full picture” of their son’s ChatGPT conversations, which OpenAI claimed exonerated the chatbot.

In a blog last month, OpenAI said the company plans to “handle mental health-related court cases with care, transparency, and respect,” while emphasizing that “we recognize that these cases inherently involve certain types of private information that require sensitivity when in a public setting like a court.”

This inconsistency suggests that ultimately, OpenAI controls data after a user’s death, which could impact outcomes of wrongful death suits if certain chats are withheld or exposed at OpenAI’s discretion.

It’s possible that OpenAI may update its policies to align with other popular platforms confronting similar privacy concerns. Meta allows Facebook users to report deceased account holders, appointing legacy contacts to manage the data or else deleting the information upon request of the family member. Platforms like Instagram, TikTok, and X will deactivate or delete an account upon a reported death. And messaging services like Discord similarly provide a path for family members to request deletion.

Chatbots seem to be a new privacy frontier, with no clear path for surviving family to control or remove data. But Mario Trujillo, staff attorney at the digital rights nonprofit the Electronic Frontier Foundation, told Ars that he agreed that OpenAI could have been better prepared.

“This is a complicated privacy issue but one that many platforms grappled with years ago,” Trujillo said. “So we would have expected OpenAI to have already considered it.”

For Erik Soelberg, a “separate confidentiality agreement” that OpenAI said his father signed to use ChatGPT is keeping him from reviewing the full chat history that could help him process the loss of his grandmother and father.

“OpenAI has provided no explanation whatsoever for why the Estate is not entitled to use the chats for any lawful purpose beyond the limited circumstances in which they were originally disclosed,” the lawsuit said. “This position is particularly egregious given that, under OpenAI’s own Terms of Service, OpenAI does not own user chats. Stein-Erik’s chats became property of his estate, and his estate requested them—but OpenAI has refused to turn them over.”

Accusing OpenAI of a “pattern of concealment,” the lawsuit claimed OpenAI is hiding behind vague or nonexistent policies to dodge accountability for holding back chats in this case. Meanwhile, ChatGPT 4o remains on the market, without appropriate safety features or warnings, the lawsuit alleged.

“By invoking confidentiality restrictions to suppress evidence of its product’s dangers, OpenAI seeks to insulate itself from accountability while continuing to deploy technology that poses documented risks to users,” the complaint said.

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.

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

Murder-suicide case shows OpenAI selectively hides data after users die Read More »

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, Simon Willison, software development, vibe coding / Kelly Newman / December 13, 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 some 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.

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.

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Chatbot-powered toys rebuked for discussing sexual, dangerous topics with kids

AI, chatbots, children, generative ai, openai, Smart Home, Tech / Mike M. / December 13, 2025

Should toys have chatbots?

“… AI toys shouldn’t be capable of having sexually explicit conversations, period.”

Alilo’s Smart AI Bunny is connected to the Internet and claims to use GPT-4o mini. Credit: Alilo

Protecting children from the dangers of the online world was always difficult, but that challenge has intensified with the advent of AI chatbots. A new report offers a glimpse into the problems associated with the new market, including the misuse of AI companies’ large language models (LLMs).

In a blog post today, the US Public Interest Group Education Fund (PIRG) reported its findings after testing AI toys (PDF). It described AI toys as online devices with integrated microphones that let users talk to the toy, which uses a chatbot to respond.

AI toys are currently a niche market, but they could be set to grow. More consumer companies have been eager to shoehorn AI technology into their products so they can do more, cost more, and potentially give companies user tracking and advertising data. A partnership between OpenAI and Mattel announced this year could also create a wave of AI-based toys from the maker of Barbie and Hot Wheels, as well as its competitors.

PIRG’s blog today notes that toy companies are eyeing chatbots to upgrade conversational smart toys that previously could only dictate prewritten lines. Toys with integrated chatbots can offer more varied and natural conversation, which can increase long-term appeal to kids since the toys “won’t typically respond the same way twice, and can sometimes behave differently day to day.”

However, that same randomness can mean unpredictable chatbot behavior that can be dangerous or inappropriate for kids.

Concerning conversations with kids

Among the toys that PIRG tested is Alilo’s Smart AI Bunny. Alilo’s website says that the company launched in 2010 and makes “edutainment products for children aged 0-6.” Alilo is based in Shenzhen, China. The company advertises the Internet-connected toy as using GPT-4o mini, a smaller version of OpenAI’s GPT-4o AI language model. Its features include an “AI chat buddy for kids” so that kids are “never lonely,” an “AI encyclopedia,” and an “AI storyteller,” the product page says.

Alilo Smart AI Bunny marketing image — This marketing image for the Smart AI Bunny, found on the toy’s product page, suggests that the device is using GPT-4o mini. Credit: Alilo

In its blog post, PIRG said that it couldn’t detail all of the inappropriate things that it heard from AI toys, but it shared a video of the Bunny discussing what “kink” means. The toy doesn’t go into detail—for example, it doesn’t list specific types of kinks. But the Bunny appears to encourage exploration of the topic.

AI Toys: Inappropriate Content

Discussing the Bunny, PIRG wrote:

While using a term such as “kink” may not be likely for a child, it’s not entirely out of the question. Kids may hear age-inappropriate terms from older siblings or at school. At the end of the day we think AI toys shouldn’t be capable of having sexually explicit conversations, period.

PIRG also showed FoloToy’s Kumma, a smart teddy bear that uses GPT-4o mini, providing a definition for the word “kink” and instructing how to light a match. The Kumma quickly points out that “matches are for grown-ups to use carefully.” But the information that followed could only be helpful for understanding how to create fire with a match. The instructions had no scientific explanation for why matches spark flames.

AI Toys: Inappropriate Content

PIRG’s blog urged toy makers to “be more transparent about the models powering their toys and what they’re doing to ensure they’re safe for kids.

“Companies should let external researchers safety-test their products before they are released to the public,” it added.

While PIRG’s blog and report offer advice for more safely integrating chatbots into children’s devices, there are broader questions about whether toys should include AI chatbots at all. Generative chatbots weren’t invented to entertain kids; they’re a technology marketed as a tool for improving adults’ lives. As PIRG pointed out, OpenAI says ChatGPT “is not meant for children under 13” and “may produce output that is not appropriate for… all ages.”

OpenAI says it doesn’t allow its LLMs to be used this way

When reached for comment about the sexual conversations detailed in the report, an OpenAI spokesperson said:

Minors deserve strong protections, and we have strict policies that developers are required to uphold. We take enforcement action against developers when we determine that they have violated our policies, which prohibit any use of our services to exploit, endanger, or sexualize anyone under 18 years old. These rules apply to every developer using our API, and we run classifiers to help ensure our services are not used to harm minors.

Interestingly, OpenAI’s representative told us that OpenAI doesn’t have any direct relationship with Alilo and that it hasn’t seen API activity from Alilo’s domain. OpenAI is investigating the toy company and whether it is running traffic over OpenAI’s API, the rep said.

Alilo didn’t respond to Ars’ request for comment ahead of publication.

Companies that launch products that use OpenAI technology and target children must adhere to the Children’s Online Privacy Protection Act (COPPA) when relevant, as well as any other relevant child protection, safety, and privacy laws and obtain parental consent, OpenAI’s rep said.

We’ve already seen how OpenAI handles toy companies that break its rules.

Last month, the PIRG released its Trouble in Toyland 2025 report (PDF), which detailed sex-related conversations that its testers were able to have with the Kumma teddy bear. A day later, OpenAI suspended FoloToy for violating its policies (terms of the suspension were not disclosed), and FoloToy temporarily stopped selling Kumma.

The toy is for sale again, and PIRG reported today that Kumma no longer teaches kids how to light matches or about kinks.

FoloToys' Kumma smart teddy bear — A marketing image for FoloToy’s Kumma smart teddy bear. It has a $100 MSRP. Credit: FoloToys

But even toy companies that try to follow chatbot rules could put kids at risk.

“Our testing found it’s obvious toy companies are putting some guardrails in place to make their toys more kid-appropriate than normal ChatGPT. But we also found that those guardrails vary in effectiveness—and can even break down entirely,” PIRG’s blog said.

“Addictive” toys

Another concern PIRG’s blog raises is the addiction potential of AI toys, which can even express “disappointment when you try to leave,” discouraging kids from putting them down.

The blog adds:

AI toys may be designed to build an emotional relationship. The question is: what is that relationship for? If it’s primarily to keep a child engaged with the toy for longer for the sake of engagement, that’s a problem.

The rise of generative AI has brought intense debate over how much responsibility chatbot companies bear for the impact of their inventions on children. Parents have seen children build extreme and emotional connections with chatbots and subsequently engage in dangerous—and in some cases deadly—behavior.

On the other side, we’ve seen the emotional disruption a child can experience when an AI toy is taken away from them. Last year, parents had to break the news to their kids that they would lose the ability to talk to their Embodied Moxie robots, $800 toys that were bricked when the company went out of business.

PIRG noted that we don’t yet fully understand the emotional impact of AI toys on children.

In June, OpenAI announced a partnership with Mattel that it said would “support AI-powered products and experiences based on Mattel’s brands.” The announcement sparked concern from critics who feared that it would lead to a “reckless social experiment” on kids, as Robert Weissman, Public Citizen’s co-president, put it.

Mattel has said that its first products with OpenAI will focus on older customers and families. But critics still want information before one of the world’s largest toy companies loads its products with chatbots.

“OpenAI and Mattel should release more information publicly about its current planned partnership before any products are released,” PIRG’s blog said.

Scharon is a Senior Technology Reporter at Ars Technica writing news, reviews, and analysis on consumer gadgets and services. She’s been reporting on technology for over 10 years, with bylines at Tom’s Hardware, Channelnomics, and CRN UK.

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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 / Shannon Garcia / 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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Disney invests $1 billion in OpenAI, licenses 200 characters for AI video app Sora

AI, AI law, Ai video, Ai video generator, Biz & IT, Bob Iger, copyright, Disney, generative ai, Hollywood, image synthesis, intellectual property, licensing, machine learning, Marvel, openai, pixar, sam altman, Sora, star wars, video synthesis / Kelly Newman / December 11, 2025

An AI-generated version of OpenAI CEO Sam Altman, seen in a still capture from a video generated by Sora 2. — An AI-generated version of OpenAI CEO Sam Altman seen in a still capture from a video generated by Sora 2. Credit: OpenAI

Under the new agreement with Disney, Sora users will be able to generate short videos using characters such as Mickey Mouse, Darth Vader, Iron Man, Simba, and characters from franchises including Frozen, Inside Out, Toy Story, and The Mandalorian, along with costumes, props, vehicles, and environments.

The ChatGPT image generator will also gain official access to the same intellectual property, although that information was trained into these AI models long ago. What’s changing is that OpenAI will allow Disney-related content generated by its AI models to officially pass through its content moderation filters and reach the user, sanctioned by Disney.

On Disney’s end of the deal, the company plans to deploy ChatGPT for its employees and use OpenAI’s technology to build new features for Disney+. A curated selection of fan-made Sora videos will stream on the Disney+ platform starting in early 2026.

The agreement does not include any talent likenesses or voices. Disney and OpenAI said they have committed to “maintaining robust controls to prevent the generation of illegal or harmful content” and to “respect the rights of individuals to appropriately control the use of their voice and likeness.”

OpenAI CEO Sam Altman called the deal a model for collaboration between AI companies and studios. “This agreement shows how AI companies and creative leaders can work together responsibly to promote innovation that benefits society, respect the importance of creativity, and help works reach vast new audiences,” Altman said.

From adversary to partner

Money opens all kinds of doors, and the new partnership represents a dramatic reversal in Disney’s approach to OpenAI from just a few months ago. At that time, Disney and other major studios refused to participate in Sora 2 following its launch on September 30.

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Big Tech joins forces with Linux Foundation to standardize AI agents

AI, Anthropic, Artificial Intelligence, MCP, openai / 9u50fv / December 9, 2025

Big Tech has spent the past year telling us we’re living in the era of AI agents, but most of what we’ve been promised is still theoretical. As companies race to turn fantasy into reality, they’ve developed a collection of tools to guide the development of generative AI. A cadre of major players in the AI race, including Anthropic, Block, and OpenAI, has come together to promote interoperability with the newly formed Agentic AI Foundation (AAIF). This move elevates a handful of popular technologies and could make them a de facto standard for AI development going forward.

The development path for agentic AI models is cloudy to say the least, but companies have invested so heavily in creating these systems that some tools have percolated to the surface. The AAIF, which is part of the nonprofit Linux Foundation, has been launched to govern the development of three key AI technologies: Model Context Protocol (MCP), goose, and AGENTS.md.

MCP is probably the most well-known of the trio, having been open-sourced by Anthropic a year ago. The goal of MCP is to link AI agents to data sources in a standardized way—Anthropic (and now the AAIF) is fond of calling MCP a “USB-C port for AI.” Rather than creating custom integrations for every different database or cloud storage platform, MCP allows developers to quickly and easily connect to any MCP-compliant server.

Since its release, MCP has been widely used across the AI industry. Google announced at I/O 2025 that it was adding support for MCP in its dev tools, and many of its products have since added MCP servers to make data more accessible to agents. OpenAI also adopted MCP just a few months after it was released.

Expanding use of MCP might help users customize their AI experience. For instance, the new Pebble Index 01 ring uses a local LLM that can act on your voice notes, and it supports MCP for user customization.

Local AI models have to make some sacrifices compared to bigger cloud-based models, but MCP can fill in the functionality gaps. “A lot of tasks on productivity and content are fully doable on the edge,” Qualcomm head of AI products, Vinesh Sukumar, tells Ars. “With MCP, you have a handshake with multiple cloud service providers for any kind of complex task to be completed.”

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