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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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LG TVs’ unremovable Copilot shortcut is the least of smart TVs’ AI problems

AI, chatbots, copilot, generative ai, lg, microsoft, samsung, Tech, TVs / Mike M. / December 19, 2025

But Copilot will still be integrated into Tizen OS, and Samsung appears eager to push chatbots into TVs, including by launching Perplexity’s first TV app. Amazon, which released Fire TVs with Alexa+ this year, is also exploring putting chatbots into TVs.

After the backlash LG faced this week, companies may reconsider installing AI apps on people’s smart TVs. A better use of large language models in TVs may be as behind-the-scenes tools to improve TV watching. People generally don’t buy smart TVs to make it easier to access chatbots.

But this development is still troubling for anyone who doesn’t want an AI chatbot in their TV at all.

Some people don’t want chatbots in their TVs

Subtle integrations of generative AI that make it easier for people to do things like figure out the name of “that movie” may have practical use, but there are reasons to be wary of chatbot-wielding TVs.

Chatbots add another layer of complexity to understanding how a TV tracks user activity. With a chatbot involved, smart TV owners will be subject to complicated smart TV privacy policies and terms of service, as well as the similarly verbose rules of third-party AI companies. This will make it harder for people to understand what data they’re sharing with companies, and there’s already serious concern about the boundaries smart TVs are pushing to track users, including without consent.

Chatbots can also contribute to smart TV bloatware. Unwanted fluff, like games, shopping shortcuts, and flashy ads, already disrupts people who just want to watch TV.

LG’s Copilot web app is worthy of some grousing, but not necessarily because of the icon that users will eventually be able to delete. The more pressing issue is the TV industry’s shift toward monetizing software with user tracking and ads.

If you haven’t already, now is a good time to check out our guide to breaking free from smart TV ads and tracking.

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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.

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Google releases Gemini 3 Flash, promising improved intelligence and efficiency

AI, Artificial Intelligence, Gemini 3, generative ai, Google, search / Mike M. / December 17, 2025

Google began its transition to Gemini 3 a few weeks ago with the launch of the Pro model, and the arrival of Gemini 3 Flash kicks it into high gear. The new, faster Gemini 3 model is coming to the Gemini app and search, and developers will be able to access it immediately via the Gemini API, Vertex AI, AI Studio, and Antigravity. Google’s bigger gen AI model is also picking up steam, with both Gemini 3 Pro and its image component (Nano Banana Pro) expanding in search.

This may come as a shock, but Google says Gemini 3 Flash is faster and more capable than its previous base model. As usual, Google has a raft of benchmark numbers that show modest improvements for the new model. It bests the old 2.5 Flash in basic academic and reasoning tests like GPQA Diamond and MMMU Pro (where it even beats 3 Pro). It gets a larger boost in Humanity’s Last Exam (HLE), which tests advanced domain-specific knowledge. Gemini 3 Flash has tripled the old models’ score in HLE, landing at 33.7 percent without tool use. That’s just a few points behind the Gemini 3 Pro model.

Google is talking up Gemini 3 Flash’s coding skills, and the provided benchmarks seem to back that talk up. Over the past year, Google has mostly pushed its Pro models as the best for generating code, but 3 Flash has done a lot of catching up. In the popular SWE-Bench Verified test, Gemini 3 Flash has gained almost 20 points on the 2.5 branch.

The new model is also a lot less likely to get general-knowledge questions wrong. In the Simple QA Verified test, Gemini 3 Flash scored 68.7 percent, which is only a little below Gemini 3 Pro. The last Flash model scored just 28.1 percent on that test. At least as far as the evaluation scores go, Gemini 3 Flash performs much closer to Google’s Pro model versus the older 2.5 family. At the same time, it’s considerably more efficient, according to Google.

One of Gemini 3 Pro’s defining advances was its ability to generate interactive simulations and multimodal content. Gemini 3 Flash reportedly retains that underlying capability. Gemini 3 Flash offers better performance than Gemini 2.5 Pro did, but it runs workloads three times faster. It’s also a lot cheaper than the Pro models if you’re paying per token. One million input tokens for 3 Flash will run devs $0.50, and a million output tokens will cost $3. However, that’s an increase compared to Gemini 2.5 Flash input and output at $0.30 and $2.50, respectively. The Pro model’s tokens are $2 (1M input) and $12 (1M output).

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Merriam-Webster’s word of the year delivers a dismissive verdict on junk AI content

AI, AI criticism, ai slop, Biz & IT, dictionaries, generative ai, machine learning, merriam-webster, Oxford, Simon Willison, slop, Social Media, spam, word of the year / Shannon Garcia / December 15, 2025

Like most tools, generative AI models can be misused. And when the misuse gets bad enough that a major dictionary notices, you know it’s become a cultural phenomenon.

On Sunday, Merriam-Webster announced that “slop” is its 2025 Word of the Year, reflecting how the term has become shorthand for the flood of low-quality AI-generated content that has spread across social media, search results, and the web at large. The dictionary defines slop as “digital content of low quality that is produced usually in quantity by means of artificial intelligence.”

“It’s such an illustrative word,” Merriam-Webster president Greg Barlow told the Associated Press. “It’s part of a transformative technology, AI, and it’s something that people have found fascinating, annoying, and a little bit ridiculous.”

To select its Word of the Year, Merriam-Webster’s editors review data on which words rose in search volume and usage, then reach consensus on which term best captures the year. Barlow told the AP that the spike in searches for “slop” reflects growing awareness among users that they are encountering fake or shoddy content online.

Dictionaries have been tracking AI’s impact on language for the past few years, with Cambridge having selected “hallucinate” as its 2023 word of the year due to the tendency of AI models to generate plausible-but-false information (long-time Ars readers will be happy to hear there’s another word term for that in the dictionary as well).

The trend extends to online culture in general, which is ripe with new coinages. This year, Oxford University Press chose “rage bait,” referring to content designed to provoke anger for engagement. Cambridge Dictionary selected “parasocial,” describing one-sided relationships between fans and celebrities or influencers.

The difference between the baby and the bathwater

As the AP points out, the word “slop” originally entered English in the 1700s to mean soft mud. By the 1800s, it had evolved to describe food waste fed to pigs, and eventually came to mean rubbish or products of little value. The new AI-related definition builds on that history of describing something unwanted and unpleasant.

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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.

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.

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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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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.

Disney invests $1 billion in OpenAI, licenses 200 characters for AI video app Sora Read More »

The NPU in your phone keeps improving—why isn’t that making AI better?

AI, Artificial Intelligence, Features, generative ai, Google, NPU, smartphones, Tech / DJ Henderson / December 4, 2025

Shrinking AI for your phone is no simple matter.

The NPU in your phone might not be doing very much. Credit: Aurich Lawson | Getty Images

Almost every technological innovation of the past several years has been laser-focused on one thing: generative AI. Many of these supposedly revolutionary systems run on big, expensive servers in a data center somewhere, but at the same time, chipmakers are crowing about the power of the neural processing units (NPU) they have brought to consumer devices. Every few months, it’s the same thing: This new NPU is 30 or 40 percent faster than the last one. That’s supposed to let you do something important, but no one really gets around to explaining what that is.

Experts envision a future of secure, personal AI tools with on-device intelligence, but does that match the reality of the AI boom? AI on the “edge” sounds great, but almost every AI tool of consequence is running in the cloud. So what’s that chip in your phone even doing?

What is an NPU?

Companies launching a new product often get bogged down in superlatives and vague marketing speak, so they do a poor job of explaining technical details. It’s not clear to most people buying a phone why they need the hardware to run AI workloads, and the supposed benefits are largely theoretical.

Many of today’s flagship consumer processors are systems-on-a-chip (SoC) because they incorporate multiple computing elements—like CPU cores, GPUs, and imaging controllers—on a single piece of silicon. This is true of mobile parts like Qualcomm’s Snapdragon or Google’s Tensor, as well as PC components like the Intel Core Ultra.

The NPU is a newer addition to chips, but it didn’t just appear one day—there’s a lineage that brought us here. NPUs are good at what they do because they emphasize parallel computing, something that’s also important in other SoC components.

Qualcomm devotes significant time during its new product unveilings to talk about its Hexagon NPUs. Keen observers may recall that this branding has been reused from the company’s line of digital signal processors (DSPs), and there’s a good reason for that.

“Our journey into AI processing started probably 15 or 20 years ago, wherein our first anchor point was looking at signal processing,” said Vinesh Sukumar, Qualcomm’s head of AI products. DSPs have a similar architecture compared to NPUs, but they’re much simpler, with a focus on processing audio (e.g., speech recognition) and modem signals.

Qualcomm chip design NPU — The NPU is one of multiple components in modern SoCs. Credit: Qualcomm

As the collection of technologies we refer to as “artificial intelligence” developed, engineers began using DSPs for more types of parallel processing, like long short-term memory (LSTM). Sukumar explained that as the industry became enamored with convolutional neural networks (CNNs), the technology underlying applications like computer vision, DSPs became focused on matrix functions, which are essential to generative AI processing as well.

While there is an architectural lineage here, it’s not quite right to say NPUs are just fancy DSPs. “If you talk about DSPs in the general term of the word, yes, [an NPU] is a digital signal processor,” said MediaTek Assistant Vice President Mark Odani. “But it’s all come a long way and it’s a lot more optimized for parallelism, how the transformers work, and holding huge numbers of parameters for processing.”

Despite being so prominent in new chips, NPUs are not strictly necessary for running AI workloads on the “edge,” a term that differentiates local AI processing from cloud-based systems. CPUs are slower than NPUs but can handle some light workloads without using as much power. Meanwhile, GPUs can often chew through more data than an NPU, but they use more power to do it. And there are times you may want to do that, according to Qualcomm’s Sukumar. For example, running AI workloads while a game is running could favor the GPU.

“Here, your measurement of success is that you cannot drop your frame rate while maintaining the spatial resolution, the dynamic range of the pixel, and also being able to provide AI recommendations for the player within that space,” says Sukumar. “In this kind of use case, it actually makes sense to run that in the graphics engine, because then you don’t have to keep shifting between the graphics and a domain-specific AI engine like an NPU.”

Livin’ on the edge is hard

Unfortunately, the NPUs in many devices sit idle (and not just during gaming). The mix of local versus cloud AI tools favors the latter because that’s the natural habitat of LLMs. AI models are trained and fine-tuned on powerful servers, and that’s where they run best.

A server-based AI, like the full-fat versions of Gemini and ChatGPT, is not resource-constrained like a model running on your phone’s NPU. Consider the latest version of Google’s on-device Gemini Nano model, which has a context window of 32k tokens. That is a more than 2x improvement over the last version. However, the cloud-based Gemini models have context windows of up to 1 million tokens, meaning they can process much larger volumes of data.

Both cloud-based and edge AI hardware will continue getting better, but the balance may not shift in the NPU’s favor. “The cloud will always have more compute resources versus a mobile device,” said Google’s Shenaz Zack, senior product manager on the Pixel team.

“If you want the most accurate models or the most brute force models, that all has to be done in the cloud,” Odani said. “But what we’re finding is that, in a lot of the use cases where there’s just summarizing some text or you’re talking to your voice assistant, a lot of those things can fit within three billion parameters.”

Squeezing AI models onto a phone or laptop involves some compromise—for example, by reducing the parameters included in the model. Odani explained that cloud-based models run hundreds of billions of parameters, the weighting that determines how a model processes input tokens to generate outputs. You can’t run anything like that on a consumer device right now, so developers have to vastly scale back the size of models for the edge. Odani says MediaTek’s latest ninth-generation NPU can handle about 3 billion parameters—a difference of several orders of magnitude.

The amount of memory available in a phone or laptop is also a limiting factor, so mobile-optimized AI models are usually quantized. That means the model’s estimation of the next token runs with less precision. Let’s say you want to run one of the larger open models, like Llama or Gemma 7b, on your device. The de facto standard is FP16, known as half-precision. At that level, a model with 7 billion parameters will lock up 13 or 14 gigabytes of memory. Stepping down to FP4 (quarter-precision) brings the size of the model in memory to a few gigs.

“When you compress to, let’s say, between three and four gigabytes, it’s a sweet spot for integration into memory constrained form factors like a smartphone,” Sukumar said. “And there’s been a lot of investment in the ecosystem and at Qualcomm to look at various ways of compressing the models without losing quality.”

It’s difficult to create a generalized AI with these limitations for mobile devices, but computers—and especially smartphones—are a wellspring of data that can be pumped into models to generate supposedly helpful outputs. That’s why most edge AI is geared toward specific, narrow use cases, like analyzing screenshots or suggesting calendar appointments. Google says its latest Pixel phones run more than 100 AI models, both generative and traditional.

Even AI skeptics can recognize that the landscape is changing quickly. In the time it takes to shrink and optimize AI models for a phone or laptop, new cloud models may appear that make that work obsolete. This is also why third-party developers have been slow to utilize NPU processing in apps. They either have to plug into an existing on-device model, which involves restrictions and rapidly moving development targets, or deploy their own custom models. Neither is a great option currently.

A matter of trust

If the cloud is faster and easier, why go to the trouble of optimizing for the edge and burning more power with an NPU? Leaning on the cloud means accepting a level of dependence and trust in the people operating AI data centers that may not always be appropriate.

“We always start off with user privacy as an element,” said Qualcomm’s Sukumar. He explained that the best inference is not general in nature—it’s personalized based on the user’s interests and what’s happening in their lives. Fine-tuning models to deliver that experience calls for personal data, and it’s safer to store and process that data locally.

Even when companies say the right things about privacy in their cloud services, they’re far from guarantees. The helpful, friendly vibe of general chatbots also encourages people to divulge a lot of personal information, and if that assistant is running in the cloud, your data is there as well. OpenAI’s copyright fight with The New York Times could lead to millions of private chats being handed over to the publisher. The explosive growth and uncertain regulatory framework of gen AI make it hard to know what’s going to happen to your data.

“People are using a lot of these generative AI assistants like a therapist,” Odani said. “And you don’t know one day if all this stuff is going to come out on the Internet.”

Not everyone is so concerned. Zack claims Google has built “the world’s most secure cloud infrastructure,” allowing it to process data where it delivers the best results. Zack uses Video Boost and Pixel Studio as examples of this approach, noting that Google’s cloud is the only way to make these experiences fast and high-quality. The company recently announced its new Private AI Compute system, which it claims is just as safe as local AI.

Even if that’s true, the edge has other advantages—edge AI is just more reliable than a cloud service. “On-device is fast,” Odani said. “Sometimes I’m talking to ChatGPT and my Wi-Fi goes out or whatever, and it skips a beat.”

The services hosting cloud-based AI models aren’t just a single website—the Internet of today is massively interdependent, with content delivery networks, DNS providers, hosting, and other services that could degrade or shut down your favorite AI in the event of a glitch. When Cloudflare suffered a self-inflicted outage recently, ChatGPT users were annoyed to find their trusty chatbot was unavailable. Local AI features don’t have that drawback.

Cloud dominance

Everyone seems to agree that a hybrid approach is necessary to deliver truly useful AI features (assuming those exist), sending data to more powerful cloud services when necessary—Google, Apple, and every other phone maker does this. But the pursuit of a seamless experience can also obscure what’s happening with your data. More often than not, the AI features on your phone aren’t running in a secure, local way, even when the device has the hardware to do that.

Take, for example, the new OnePlus 15. This phone has Qualcomm’s brand-new Snapdragon 8 Elite Gen 5, which has an NPU that is 37 percent faster than the last one, for whatever that’s worth. Even with all that on-device AI might, OnePlus is heavily reliant on the cloud to analyze your personal data. Features like AI Writer and the AI Recorder connect to the company’s servers for processing, a system OnePlus assures us is totally safe and private.

Similarly, Motorola released a new line of foldable Razr phones over the summer that are loaded with AI features from multiple providers. These phones can summarize your notifications using AI, but you might be surprised how much of it happens in the cloud unless you read the terms and conditions. If you buy the Razr Ultra, that summarization happens on your phone. However, the cheaper models with less RAM and NPU power use cloud services to process your notifications. Again, Motorola says this system is secure, but a more secure option would have been to re-optimize the model for its cheaper phones.

Even when an OEM focuses on using the NPU hardware, the results can be lacking. Look at Google’s Daily Hub and Samsung’s Now Brief. These features are supposed to chew through all the data on your phone and generate useful recommendations and actions, but they rarely do anything aside from showing calendar events. In fact, Google has temporarily removed Daily Hub from Pixels because the feature did so little, and Google is a pioneer in local AI with Gemini Nano. Google has actually moved some parts of its mobile AI experience from local to cloud-based processing in recent months.

Those “brute force” models appear to be winning, and it doesn’t hurt that companies also get more data when you interact with their private computing cloud services.

Maybe take what you can get?

There’s plenty of interest in local AI, but so far, that hasn’t translated to an AI revolution in your pocket. Most of the AI advances we’ve seen so far depend on the ever-increasing scale of cloud systems and the generalized models that run there. Industry experts say that extensive work is happening behind the scenes to shrink AI models to work on phones and laptops, but it will take time for that to make an impact.

In the meantime, local AI processing is out there in a limited way. Google still makes use of the Tensor NPU to handle sensitive data for features like Magic Cue, and Samsung really makes the most of Qualcomm’s AI-focused chipsets. While Now Brief is of questionable utility, Samsung is cognizant of how reliance on the cloud may impact users, offering a toggle in the system settings that restricts AI processing to run only on the device. This limits the number of available AI features, and others don’t work as well, but you’ll know none of your personal data is being shared. No one else offers this option on a smartphone.

Galaxy AI toggle — Samsung offers an easy toggle to disable cloud AI and run all workloads on-device. Credit: Ryan Whitwam

Samsung spokesperson Elise Sembach said the company’s AI efforts are grounded in enhancing experiences while maintaining user control. “The on-device processing toggle in One UI reflects this approach. It gives users the option to process AI tasks locally for faster performance, added privacy, and reliability even without a network connection,” Sembach said.

Interest in edge AI might be a good thing even if you don’t use it. Planning for this AI-rich future can encourage device makers to invest in better hardware—like more memory to run all those theoretical AI models.

“We definitely recommend our partners increase their RAM capacity,” said Sukumar. Indeed, Google, Samsung, and others have boosted memory capacity in large part to support on-device AI. Even if the cloud is winning, we’ll take the extra RAM.

Ryan Whitwam is a senior technology reporter at Ars Technica, covering the ways Google, AI, and mobile technology continue to change the world. Over his 20-year career, he’s written for Android Police, ExtremeTech, Wirecutter, NY Times, and more. He has reviewed more phones than most people will ever own. You can follow him on Bluesky, where you will see photos of his dozens of mechanical keyboards.

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Microsoft drops AI sales targets in half after salespeople miss their quotas

agentic AI, AI, AI adoption, AI agents, AI infrastructure, AI spending, azure, Biz & IT, Cloud Computing, copilot, enterprise AI, foundry, generative ai, machine learning, microsoft, Microsoft Copilot, simulated reasoning, SR models / DJ Henderson / December 3, 2025

Microsoft has lowered sales growth targets for its AI agent products after many salespeople missed their quotas in the fiscal year ending in June, according to a report Wednesday from The Information. The adjustment is reportedly unusual for Microsoft, and it comes after the company missed a number of ambitious sales goals for its AI offerings.

AI agents are specialized implementations of AI language models designed to perform multistep tasks autonomously rather than simply responding to single prompts. So-called “agentic” features have been central to Microsoft’s 2025 sales pitch: At its Build conference in May, the company declared that it has entered “the era of AI agents.”

The company has promised customers that agents could automate complex tasks, such as generating dashboards from sales data or writing customer reports. At its Ignite conference in November, Microsoft announced new features like Word, Excel, and PowerPoint agents in Microsoft 365 Copilot, along with tools for building and deploying agents through Azure AI Foundry and Copilot Studio. But as the year draws to a close, that promise has proven harder to deliver than the company expected.

According to The Information, one US Azure sales unit set quotas for salespeople to increase customer spending on a product called Foundry, which helps customers develop AI applications, by 50 percent. Less than a fifth of salespeople in that unit met their Foundry sales growth targets. In July, Microsoft lowered those targets to roughly 25 percent growth for the current fiscal year. In another US Azure unit, most salespeople failed to meet an earlier quota to double Foundry sales, and Microsoft cut their quotas to 50 percent for the current fiscal year.

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Prime Video pulls eerily emotionless AI-generated anime dubs after complaints

AI, Amazon, amazon prime video, anime, culture, generative ai, streaming, Tech / Mike M. / December 3, 2025

[S]o many talented voice actors, and you can’t even bother to hire a couple to dub a season of a show??????????? absolutely disrespectful.

Naturally, anime voice actors took offense, too. Damian Mills, for instance, said via X that voicing a “notable queer-coded character like Kaworu” in three Evangelion movie dubs for Prime Video (in 2007, 2009, and 2012) “meant a lot, especially being queer myself.”

Mills, who also does voice acting for other anime, including One Piece (Tanaka) and Dragon Ball Super (Frieza) added, “… using AI to replace dub actors on #BananaFish? It’s insulting and I can’t support this. It’s insane to me. What’s worse is Banana Fish is an older property, so there was no urgency to get a dub created.”

Amazon also seems to have rethought its March statement announcing that it would use AI to dub content “that would not have been dubbed otherwise.” For example, in 2017, Sentai Filmworks released an English dub of No Game, No Life: Zero with human voice actors.

Some dubs pulled

On Tuesday, Gizmodo reported that “several of the English language AI dubs for anime such as Banana Fish, No Game No Life: Zero, and more have now been removed.” However, some AI-generated dubs remain as of this writing, including an English dub for the anime series Pet and a Spanish one for Banana Fish, Ars Technica has confirmed.

Amazon hasn’t commented on the AI-generated dubs or why it took some of them down.

All of this comes despite Amazon’s March announcement that the AI-generated dubs would use “human expertise” for “quality control.”

The sloppy dubbing of cherished anime titles reflects a lack of precision in the broader industry as companies seek to leverage generative AI to save time and money. Prime Video has already been criticized for using AI-generated movie summaries and posters this year. And this summer, anime streaming service Crunchyroll blamed bad AI-generated subtitles on an agreement “violation” by a “third-party vendor.”

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