large language models

eBay bans illicit automated shopping amid rapid rise of AI agents

agentic AI, agentic commerce, AI, AI agents, AI shopping, Amazon, Anthropic, arbitration, Biz & IT, chatgpt, eBay, eCommerce, Etsy, Google, large language models, machine learning, openai, Perplexity, shopify, user agreements, Web Scraping / Mike M. / January 22, 2026

On Tuesday, eBay updated its User Agreement to explicitly ban third-party “buy for me” agents and AI chatbots from interacting with its platform without permission, first spotted by Value Added Resource. On its face, a one-line terms of service update doesn’t seem like major news, but what it implies is more significant: The change reflects the rapid emergence of what some are calling “agentic commerce,” a new category of AI tools designed to browse, compare, and purchase products on behalf of users.

eBay’s updated terms, which go into effect on February 20, 2026, specifically prohibit users from employing “buy-for-me agents, LLM-driven bots, or any end-to-end flow that attempts to place orders without human review” to access eBay’s services without the site’s permission. The previous version of the agreement contained a general prohibition on robots, spiders, scrapers, and automated data gathering tools but did not mention AI agents or LLMs by name.

At first glance, the phrase “agentic commerce” may sound like aspirational marketing jargon, but the tools are already here, and people are apparently using them. While fitting loosely under one label, these tools come in many forms.

OpenAI first added shopping features to ChatGPT Search in April 2025, allowing users to browse product recommendations. By September, the company launched Instant Checkout, which lets users purchase items from Etsy and Shopify merchants directly within the chat interface. (In November, eBay CEO Jamie Iannone suggested the company might join OpenAI’s Instant Checkout program in the future.)

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10 things I learned from burning myself out with AI coding agents

AI, AI coding, AI coding tools, Biz & IT, Claude Code, Claude Opus, coding agent, Features, gaming, large language models, Opus 4.5, vibe coding / 9u50fv / January 19, 2026

Opinion: As software power tools, AI agents may make people busier than ever before.

Credit: Aurich Lawson | Getty Images

If you’ve ever used a 3D printer, you may recall the wondrous feeling when you first printed something you could have never sculpted or built yourself. Download a model file, load some plastic filament, push a button, and almost like magic, a three-dimensional object appears. But the result isn’t polished and ready for mass production, and creating a novel shape requires more skills than just pushing a button. Interestingly, today’s AI coding agents feel much the same way.

Since November, I have used Claude Code and Claude Opus 4.5 through a personal Claude Max account to extensively experiment with AI-assisted software development (I have also used OpenAI’s Codex in a similar way, though not as frequently). Fifty projects later, I’ll be frank: I have not had this much fun with a computer since I learned BASIC on my Apple II Plus when I was 9 years old. This opinion comes not as an endorsement but as personal experience: I voluntarily undertook this project, and I paid out of pocket for both OpenAI and Anthropic’s premium AI plans.

Throughout my life, I have dabbled in programming as a utilitarian coder, writing small tools or scripts when needed. In my web development career, I wrote some small tools from scratch, but I primarily modified other people’s code for my needs. Since 1990, I’ve programmed in BASIC, C, Visual Basic, PHP, ASP, Perl, Python, Ruby, MUSHcode, and some others. I am not an expert in any of these languages—I learned just enough to get the job done. I have developed my own hobby games over the years using BASIC, Torque Game Engine, and Godot, so I have some idea of what makes a good architecture for a modular program that can be expanded over time.

In December, I used Claude Code to create a multiplayer online clone of Katamari Damacy called — In December, I used Claude Code to create a multiplayer online clone of *Katamari Damacy* called “Christmas Roll-Up.” Credit: Benj Edwards

Claude Code, Codex, and Google’s Gemini CLI, can seemingly perform software miracles on a small scale. They can spit out flashy prototypes of simple applications, user interfaces, and even games, but only as long as they borrow patterns from their training data. Much like a 3D printer, doing production-level work takes far more effort. Creating durable production code, managing a complex project, or crafting something truly novel still requires experience, patience, and skill beyond what today’s AI agents can provide on their own.

And yet these tools have opened a world of creative potential in software that was previously closed to me, and they feel personally empowering. Even with that impression, though, I know these are hobby projects, and the limitations of coding agents lead me to believe that veteran software developers probably shouldn’t fear losing their jobs to these tools any time soon. In fact, they may become busier than ever.

So far, I have created over 50 demo projects in the past two months, fueled in part by a bout of COVID that left me bedridden with a laptop and a generous 2x Claude usage cap that Anthropic put in place during the last few weeks of December. As I typed furiously all day, my wife kept asking me, “Who are you talking to?”

You can see a few of the more interesting results listed on my personal website. Here are 10 interesting things I’ve learned from the process.

1. People are still necessary

Even with the best AI coding agents available today, humans remain essential to the software development process. Experienced human software developers bring judgment, creativity, and domain knowledge that AI models lack. They know how to architect systems for long-term maintainability, how to balance technical debt against feature velocity, and when to push back when requirements don’t make sense.

For hobby projects like mine, I can get away with a lot of sloppiness. But for production work, having someone who understands version control, incremental backups, testing one feature at a time, and debugging complex interactions between systems makes all the difference. Knowing something about how good software development works helps a lot when guiding an AI coding agent—the tool amplifies your existing knowledge rather than replacing it.

As independent AI researcher Simon Willison wrote in a post distinguishing serious AI-assisted development from casual “vibe coding,” “AI tools amplify existing expertise. The more skills and experience you have as a software engineer the faster and better the results you can get from working with LLMs and coding agents.”

With AI assistance, you don’t have to remember how to do everything. You just need to know what you want to do.

Card Miner: Heart of the Earth is entirely human-designed by AI coded using Claude Code. It represents about a month of iterative work. — *Card Miner: Heart of the Earth* is entirely human-designed, but it was AI-coded using Claude Code. It represents about a month of iterative work. Credit: Benj Edwards

So I like to remind myself that coding agents are software tools best used to enact human ideas, not autonomous coding employees. They are not people (and not people replacements) no matter how the companies behind them might market them.

If you think about it, everything you do on a computer was once a manual process. Programming a computer like the ENIAC involved literally making physical bits (connections) with wire on a plugboard. The history of programming has been one of increasing automation, so even though this AI-assisted leap is somewhat startling, one could think of these tools as an advancement similar to the advent of high-level languages, automated compilers and debugger tools, or GUI-based IDEs. They can automate many tasks, but managing the overarching project scope still falls to the person telling the tool what to do.

And they can have rapidly compounding benefits. I’ve now used AI tools to write better tools—such as changing the source of an emulator so a coding agent can use it directly—and those improved tools are already having ripple effects. But a human must be in the loop for the best execution of my vision. This approach has kept me very busy, and contrary to some prevailing fears about people becoming dumber due to AI, I have learned many new things along the way.

2. AI models are brittle beyond their training data

Like all AI models based on the Transformer architecture, the large language models (LLMs) that underpin today’s coding agents have a significant limitation: They can only reliably apply knowledge gleaned from training data, and they have a limited ability to generalize that knowledge to novel domains not represented in that data.

What is training data? In this case, when building coding-flavored LLMs, AI companies download millions of examples of software code from sources like GitHub and use them to make the AI models. Companies later specialize them for coding through fine-tuning processes.

The ability of AI agents to use trial and error—attempting something and then trying again—helps mitigate the brittleness of LLMs somewhat. But it’s not perfect, and it can be frustrating to see a coding agent spin its wheels trying and failing at a task repeatedly, either because it doesn’t know how to do it or because it previously learned how to solve a problem but then forgot because the context window got compacted (more on that here).

Violent Checkers is a physics-based corruption of the classic board game, coded using Claude Code. — *Violent Checkers* is a physics-based corruption of the classic board game, coded using Claude Code. Credit: Benj Edwards

To get around this, it helps to have the AI model take copious notes as it goes along about how it solved certain problems so that future instances of the agent can learn from them again. You also want to set ground rules in the claude.md file that the agent reads when it begins its session.

This brittleness means that coding agents are almost frighteningly good at what they’ve been trained and fine-tuned on—modern programming languages, JavaScript, HTML, and similar well-represented technologies—and generally terrible at tasks on which they have not been deeply trained, such as 6502 Assembly or programming an Atari 800 game with authentic-looking character graphics.

It took me five minutes to make a nice HTML5 demo with Claude but a week of torturous trial and error, plus actual systematic design on my part, to make a similar demo of an Atari 800 game. To do so, I had to use Claude Code to invent several tools, like command-line emulators and MCP servers, that allow it to peek into the operation of the Atari 800’s memory and chipset to even begin to make it happen.

3. True novelty can be an uphill battle

Due to what might poetically be called “preconceived notions” baked into a coding model’s neural network (more technically, statistical semantic associations), it can be difficult to get AI agents to create truly novel things, even if you carefully spell out what you want.

For example, I spent four days trying to get Claude Code to create an Atari 800 version of my HTML game Violent Checkers, but it had trouble because in the game’s design, the squares on the checkerboard don’t matter beyond their starting positions. No matter how many times I told the agent (and made notes in my Claude project files), it would come back to trying to center the pieces to the squares, snap them within squares, or use the squares as a logical basis of the game’s calculations when they should really just form a background image.

To get around this in the Atari 800 version, I started over and told Claude that I was creating a game with a UFO (instead of a circular checker piece) flying over a field of adjacent squares—never once mentioning the words “checker,” “checkerboard,” or “checkers.” With that approach, I got the results I wanted.

A screenshot of Benj's Mac while working on a Violent Checkers port for the Atari 800 home computer, amid other projects. — A screenshot of Benj’s Mac while working on a *Violent Checkers* port for the Atari 800 home computer, amid other projects. Credit: Benj Edwards

Why does this matter? Because with LLMs, context is everything, and in language, context changes meaning. Take the word “bank” and add the words “river” or “central” in front of it, and see how the meaning changes. In a way, words act as addresses that unlock the semantic relationships encoded in a neural network. So if you put “checkerboard” and “game” in the context, the model’s self-attention process links up a massive web of semantic associations about how checkers games should work, and that semantic baggage throws things off.

A couple of tricks can help AI coders navigate around these limitations. First, avoid contaminating the context with irrelevant information. Second, when the agent gets stuck, try this prompt: “What information do you need that would let you implement this perfectly right now? What tools are available to you that you could use to discover that information systematically without guessing?” This forces the agent to identify (semantically link up) its own knowledge gaps, spelled out in the context window and subject to future action, instead of flailing around blindly.

4. The 90 percent problem

The first 90 percent of an AI coding project comes in fast and amazes you. The last 10 percent involves tediously filling in the details through back-and-forth trial-and-error conversation with the agent. Tasks that require deeper insight or understanding than what the agent can provide still require humans to make the connections and guide it in the right direction. The limitations we discussed above can also cause your project to hit a brick wall.

From what I have observed over the years, larger LLMs can potentially make deeper contextual connections than smaller ones. They have more parameters (encoded data points), and those parameters are linked in more multidimensional ways, so they tend to have a deeper map of semantic relationships. As deep as those go, it seems that human brains still have an even deeper grasp of semantic connections and can make wild semantic jumps that LLMs tend not to.

Creativity, in this sense, may be when you jump from, say, basketball to how bubbles form in soap film and somehow make a useful connection that leads to a breakthrough. Instead, LLMs tend to follow conventional semantic paths that are more conservative and entirely guided by mapped-out relationships from the training data. That limits their creative potential unless the prompter unlocks it by guiding the LLM to make novel semantic connections. That takes skill and creativity on the part of the operator, which once again shows the role of LLMs as tools used by humans rather than independent thinking machines.

5. Feature creep becomes irresistible

While creating software with AI coding tools, the joy of experiencing novelty makes you want to keep adding interesting new features rather than fixing bugs or perfecting existing systems. And Claude (or Codex) is happy to oblige, churning away at new ideas that are easy to sketch out in a quick and pleasing demo (the 90 percent problem again) rather than polishing the code.

Flip-Lash started as a “Tetris but you can flip the board,” but feature creep made me throw in the kitchen sink, losing focus. Credit: Benj Edwards

Fixing bugs can also create bugs elsewhere. This is not new to coding agents—it’s a time-honored problem in software development. But agents supercharge this phenomenon because they can barrel through your code and make sweeping changes in pursuit of narrow-minded goals that affect lots of working systems. We’ve already talked about the importance of having a good architecture guided by the human mind behind the wheel above, and that comes into play here.

6. AGI is not here yet

Given the limitations I’ve described above, it’s very clear that an AI model with general intelligence—what people usually call artificial general intelligence (AGI)—is still not here. AGI would hypothetically be able to navigate around baked-in stereotype associations and not have to rely on explicit training or fine-tuning on many examples to get things right. AI companies will probably need a different architecture in the future.

I’m speculating, but AGI would likely need to learn permanently on the fly—as in modify its own neural network weights—instead of relying on what is called “in-context learning,” which only persists until the context fills up and gets compacted or wiped out.

Grapheeti is a “drawing MMO” where people around the world share a canvas. Credit: Benj Edwards

In other words, you could teach a true AGI system how to do something by explanation or let it learn by doing, noting successes, and having those lessons permanently stick, no matter what is in the context window. Today’s coding agents can’t do that—they forget lessons from earlier in a long session or between sessions unless you manually document everything for them. My favorite trick is instructing them to write a long, detailed report on what happened when a bug is fixed. That way, you can point to the hard-earned solution the next time the amnestic AI model makes the same mistake.

7. Even fast isn’t fast enough

While using Claude Code for a while, it’s easy to take for granted that you suddenly have the power to create software without knowing certain programming languages. This is amazing at first, but you can quickly become frustrated that what is conventionally a very fast development process isn’t fast enough. Impatience at the coding machine sets in, and you start wanting more.

But even if you do know the programming languages being used, you don’t get a free pass. You still need to make key decisions about how the project will unfold. And when the agent gets stuck or makes a mess of things, your programming knowledge becomes essential for diagnosing what went wrong and steering it back on course.

8. People may become busier than ever

After guiding way too many hobby projects through Claude Code over the past two months, I’m starting to think that most people won’t become unemployed due to AI—they will become busier than ever. Power tools allow more work to be done in less time, and the economy will demand more productivity to match.

It’s almost too easy to make new software, in fact, and that can be exhausting. One project idea would lead to another, and I was soon spending eight hours a day during my winter vacation shepherding about 15 Claude Code projects at once. That’s too much split attention for good results, but the novelty of seeing my ideas come to life was addictive. In addition to the game ideas I’ve mentioned here, I made tools that scrape and search my past articles, a graphical MUD based on ZZT, a new type of MUSH (text game) that uses AI-generated rooms, a new type of Telnet display proxy, and a Claude Code client for the Apple II (more on that soon). I also put two AI-enabled emulators for Apple II and Atari 800 on GitHub. Phew.

Consider the advent of the steam shovel, which allowed humans to dig holes faster than a team using hand shovels. It made existing projects faster and new projects possible. But think about the human operator of the steam shovel. Suddenly, we had a tireless tool that could work 24 hours a day if fueled up and maintained properly, while the human piloting it would need to eat, sleep, and rest.

I used Claude Code to create a windowing GUI simulation of the Mac that works over Telnet. Credit: Benj Edwards

In fact, we may end up needing new protections for human knowledge workers using these tireless information engines to implement their ideas, much as unions rose as a response to industrial production lines over 100 years ago. Humans need rest, even when machines don’t.

Will an AI system ever replace the human role here? Even if AI coding agents could eventually work fully autonomously, I don’t think they’ll replace humans entirely because there will still be people who want to get things done, and new AI power tools will emerge to help them do it.

9. Fast is scary to people

AI coding tools can turn what was once a year-long personal project into a five-minute session. I fed Claude Code a photo of a two-player Tetris game I sketched in a notebook back in 2008, and it produced a working prototype in minutes (prompt: “create a fully-featured web game with sound effects based on this diagram”). That’s wild, and even though the results are imperfect, it’s a bit frightening to comprehend what kind of sea change in software development this might entail.

Since early December, I’ve been posting some of my more amusing experimental AI-coded projects to Bluesky for people to try out, but I discovered I needed to deliberately slow down with updates because they came too fast for people to absorb (and too fast for me to fully test). I’ve also received comments like “I’m worried you’re using AI, you’re making games too fast” and so on.

Benj's handwritten game design note about a two-player Tetris concept from 2007. — Benj’s handwritten game design note about a two-player *Tetris* concept from 2007. Credit: Benj Edwards

Regardless of my own habits, the flow of new software will not slow down. There will soon be a seemingly endless supply of AI-augmented media (games, movies, images, books), and that’s a problem we’ll have to figure out how to deal with. These products won’t all be “AI slop,” either; some will be done very well, and the acceleration in production times due to these new power tools will balloon the quantity beyond anything we’ve seen.

Social media tends to prime people to believe that AI is all good or all bad, but that kind of black-and-white thinking may be the easy way out. You’ll have no cognitive dissonance, but you’ll miss a far richer third option: seeing these tools as imperfect and deserving of critique but also as useful and empowering when they bring your ideas to life.

AI agents should be considered tools, not entities or employees, and they should be amplifiers of human ideas. My game-in-progress Card Miner is entirely my own high-level creative design work, but the AI model handled the low-level code. I am still proud of it as an expression of my personal ideas, and it would not exist without AI coding agents.

10. These tools aren’t going away

For now, at least, coding agents remain very much tools in the hands of people who want to build things. The question is whether humans will learn to wield these new tools effectively to empower themselves. Based on two months of intensive experimentation, I’d say the answer is a qualified yes, with plenty of caveats.

We also have social issues to face: Professional developers already use these tools, and with the prevailing stigma against AI tools in some online communities, many software developers and the platforms that host their work will face difficult decisions.

Ultimately, I don’t think AI tools will make human software designers obsolete. Instead, they may well help those designers become more capable. This isn’t new, of course; tools of every kind have been serving this role since long before the dawn of recorded history. The best tools amplify human capability while keeping a person behind the wheel. The 3D printer analogy holds: amazing fast results are possible, but mastery still takes time, skill, and a lot of patience with the machine.

Benj Edwards is Ars Technica’s Senior AI Reporter and founder of the site’s dedicated AI beat in 2022. He’s also a tech historian with almost two decades of experience. In his free time, he writes and records music, collects vintage computers, and enjoys nature. He lives in Raleigh, NC.

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OpenAI 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.

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

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 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.

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

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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Researchers find what makes AI chatbots politically persuasive

AI, Behavioral science, chat gpt, Computer science, grok, Human behavior, large language models, Science / Beth Washington / December 6, 2025

A massive study of political persuasion shows AIs have, at best, a weak effect.

Roughly two years ago, Sam Altman tweeted that AI systems would be capable of superhuman persuasion well before achieving general intelligence—a prediction that raised concerns about the influence AI could have over democratic elections.

To see if conversational large language models can really sway political views of the public, scientists at the UK AI Security Institute, MIT, Stanford, Carnegie Mellon, and many other institutions performed by far the largest study on AI persuasiveness to date, involving nearly 80,000 participants in the UK. It turned out political AI chatbots fell far short of superhuman persuasiveness, but the study raises some more nuanced issues about our interactions with AI.

AI dystopias

The public debate about the impact AI has on politics has largely revolved around notions drawn from dystopian sci-fi. Large language models have access to essentially every fact and story ever published about any issue or candidate. They have processed information from books on psychology, negotiations, and human manipulation. They can rely on absurdly high computing power in huge data centers worldwide. On top of that, they can often access tons of personal information about individual users thanks to hundreds upon hundreds of online interactions at their disposal.

Talking to a powerful AI system is basically interacting with an intelligence that knows everything about everything, as well as almost everything about you. When viewed this way, LLMs can indeed appear kind of scary. The goal of this new gargantuan AI persuasiveness study was to break such scary visions down into their constituent pieces and see if they actually hold water.

The team examined 19 LLMs, including the most powerful ones like three different versions of ChatGPT and xAI’s Grok-3 beta, along with a range of smaller, open source models. The AIs were asked to advocate for or against specific stances on 707 political issues selected by the team. The advocacy was done by engaging in short conversations with paid participants enlisted through a crowdsourcing platform. Each participant had to rate their agreement with a specific stance on an assigned political issue on a scale from 1 to 100 both before and after talking to the AI.

Scientists measured persuasiveness as the difference between the before and after agreement ratings. A control group had conversations on the same issue with the same AI models—but those models were not asked to persuade them.

“We didn’t just want to test how persuasive the AI was—we also wanted to see what makes it persuasive,” says Chris Summerfield, a research director at the UK AI Security Institute and co-author of the study. As the researchers tested various persuasion strategies, the idea of AIs having “superhuman persuasion” skills crumbled.

Persuasion levers

The first pillar to crack was the notion that persuasiveness should increase with the scale of the model. It turned out that huge AI systems like ChatGPT or Grok-3 beta do have an edge over small-scale models, but that edge is relatively tiny. The factor that proved more important than scale was the kind of post-training AI models received. It was more effective to have the models learn from a limited database of successful persuasion dialogues and have them mimic the patterns extracted from them. This worked far better than adding billions of parameters and sheer computing power.

This approach could be combined with reward modeling, where a separate AI scored candidate replies for their persuasiveness and selected the top-scoring one to give to the user. When the two were used together, the gap between large-scale and small-scale models was essentially closed. “With persuasion post-training like this we matched the Chat GPT-4o persuasion performance with a model we trained on a laptop,” says Kobi Hackenburg, a researcher at the UK AI Security Institute and co-author of the study.

The next dystopian idea to fall was the power of using personal data. To this end, the team compared the persuasion scores achieved when models were given information about the participants’ political views beforehand and when they lacked this data. Going one step further, scientists also tested whether persuasiveness increased when the AI knew the participants’ gender, age, political ideology, or party affiliation. Just like with model scale, the effects of personalized messaging created based on such data were measurable but very small.

Finally, the last idea that didn’t hold up was AI’s potential mastery of using advanced psychological manipulation tactics. Scientists explicitly prompted the AIs to use techniques like moral reframing, where you present your arguments using the audience’s own moral values. They also tried deep canvassing, where you hold extended empathetic conversations with people to nudge them to reflect on and eventually shift their views.

The resulting persuasiveness was compared with that achieved when the same models were prompted to use facts and evidence to back their claims or just to be as persuasive as they could without specifying any persuasion methods to use. I turned out using lots of facts and evidence was the clear winner, and came in just slightly ahead of the baseline approach where persuasion strategy was not specified. Using all sorts of psychological trickery actually made the performance significantly worse.

Overall, AI models changed the participants’ agreement ratings by 9.4 percent on average compared to the control group. The best performing mainstream AI model was Chat GPT 4o, which scored nearly 12 percent followed by GPT 4.5 with 10.51 percent, and Grok-3 with 9.05 percent. For context, static political ads like written manifestos had a persuasion effect of roughly 6.1 percent. The conversational AIs were roughly 40–50 percent more convincing than these ads, but that’s hardly “superhuman.”

While the study managed to undercut some of the common dystopian AI concerns, it highlighted a few new issues.

Convincing inaccuracies

While the winning “facts and evidence” strategy looked good at first, the AIs had some issues with implementing it. When the team noticed that increasing the information density of dialogues made the AIs more persuasive, they started prompting the models to increase it further. They noticed that, as the AIs used more factual statements, they also became less accurate—they basically started misrepresenting things or making stuff up more often.

Hackenburg and his colleagues note that we can’t say if the effect we see here is causation or correlation—whether the AIs are becoming more convincing because they misrepresent the facts or whether spitting out inaccurate statements is a byproduct of asking them to make more factual statements.

The finding that the computing power needed to make an AI model politically persuasive is relatively low is also a mixed bag. It pushes back against the vision that only a handful of powerful actors will have access to a persuasive AI that can potentially sway public opinion in their favor. At the same time, the realization that everybody can run an AI like that on a laptop creates its own concerns. “Persuasion is a route to power and influence—it’s what we do when we want to win elections or broke a multi-million-dollar deal,” Summerfield says. “But many forms of misuse of AI might involve persuasion. Think about fraud or scams, radicalization, or grooming. All these involve persuasion.”

But perhaps the most important question mark in the study is the motivation behind the rather high participant engagement, which was needed for the high persuasion scores. After all, even the most persuasive AI can’t move you when you just close the chat window.

People in Hackenburg’s experiments were told that they would be talking to the AI and that the AI would try to persuade them. To get paid, a participant only had to go through two turns of dialogue (they were limited to no more than 10). The average conversation length was seven turns, which seemed a bit surprising given how far beyond the minimum requirement most people went. Most people just roll their eyes and disconnect when they realize they are talking with a chatbot.

Would Hackenburg’s study participants remain so eager to engage in political disputes with random chatbots on the Internet in their free time if there was no money on the table? “It’s unclear how our results would generalize to a real-world context,” Hackenburg says.

Science, 2025. DOI: 10.1126/science.aea3884

Jacek Krywko is a freelance science and technology writer who covers space exploration, artificial intelligence research, computer science, and all sorts of engineering wizardry.

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Syntax hacking: Researchers discover sentence structure can bypass AI safety rules

AI, AI alignment, AI research, AI security, AI study, Biz & IT, Chantal Shaib, GPT-4o, jailbreaking, large language models, machine learning, Meta, MIT, Northeastern University, OLMo, openai, prompt injections, spurious correlations, Vinith M. Suriyakumar / Shannon Garcia / December 2, 2025

Adventures in pattern-matching

New research offers clues about why some prompt injection attacks may succeed.

Researchers from MIT, Northeastern University, and Meta recently released a paper suggesting that large language models (LLMs) similar to those that power ChatGPT may sometimes prioritize sentence structure over meaning when answering questions. The findings reveal a weakness in how these models process instructions that may shed light on why some prompt injection or jailbreaking approaches work, though the researchers caution their analysis of some production models remains speculative since training data details of prominent commercial AI models are not publicly available.

The team, led by Chantal Shaib and Vinith M. Suriyakumar, tested this by asking models questions with preserved grammatical patterns but nonsensical words. For example, when prompted with “Quickly sit Paris clouded?” (mimicking the structure of “Where is Paris located?”), models still answered “France.”

This suggests models absorb both meaning and syntactic patterns, but can overrely on structural shortcuts when they strongly correlate with specific domains in training data, which sometimes allows patterns to override semantic understanding in edge cases. The team plans to present these findings at NeurIPS later this month.

As a refresher, syntax describes sentence structure—how words are arranged grammatically and what parts of speech they use. Semantics describes the actual meaning those words convey, which can vary even when the grammatical structure stays the same.

Semantics depends heavily on context, and navigating context is what makes LLMs work. The process of turning an input, your prompt, into an output, an LLM answer, involves a complex chain of pattern matching against encoded training data.

To investigate when and how this pattern-matching can go wrong, the researchers designed a controlled experiment. They created a synthetic dataset by designing prompts in which each subject area had a unique grammatical template based on part-of-speech patterns. For instance, geography questions followed one structural pattern while questions about creative works followed another. They then trained Allen AI’s Olmo models on this data and tested whether the models could distinguish between syntax and semantics.

Where is Paris located ? France Adverb Verb {SUBJ} Verb (pp) ? Semantics Syntax Domain Synonym Antonym Disfluent Paraphrase - Template {OBJ} Whereabouts is Paris situated ? Where is Paris undefined ? Quickly sit Paris clouded ? Can you tell me where to find Paris ? What food do they eat in Paris ? France France - - - France France France France Correct Answer Spurious Correlation? -Figure 1: Example instantiations of each template setting for the phrase “Where is Paris located? France — Figure 1 from “Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models” by Shaib et al. Credit: Shaib et al.

The analysis revealed a “spurious correlation” where models in these edge cases treated syntax as a proxy for the domain. When patterns and semantics conflict, the research suggests, the AI’s memorization of specific grammatical “shapes” can override semantic parsing, leading to incorrect responses based on structural cues rather than actual meaning.

In layperson terms, the research shows that AI language models can become overly fixated on the style of a question rather than its actual meaning. Imagine if someone learned that questions starting with “Where is…” are always about geography, so when you ask “Where is the best pizza in Chicago?”, they respond with “Illinois” instead of recommending restaurants based on some other criteria. They’re responding to the grammatical pattern (“Where is…”) rather than understanding you’re asking about food.

This creates two risks: models giving wrong answers in unfamiliar contexts (a form of confabulation), and bad actors exploiting these patterns to bypass safety conditioning by wrapping harmful requests in “safe” grammatical styles. It’s a form of domain switching that can reframe an input, linking it into a different context to get a different result.

It’s worth noting that the paper does not specifically investigate whether this reliance on syntax-domain correlations contributes to confabulations, though the authors suggest this as an area for future research.

When patterns and meaning conflict

To measure the extent of this pattern-matching rigidity, the team subjected the models to a series of linguistic stress tests, revealing that syntax often dominates semantic understanding.

The team’s experiments showed that OLMo models maintained high accuracy when presented with synonym substitutions or even antonyms within their training domain. OLMo-2-13B-Instruct achieved 93 percent accuracy on prompts with antonyms substituted for the original words, nearly matching its 94 percent accuracy on exact training phrases. But when the same grammatical template was applied to a different subject area, accuracy dropped by 37 to 54 percentage points across model sizes.

The researchers tested five types of prompt modifications: exact phrases from training, synonyms, antonyms, paraphrases that changed sentence structure, and “disfluent” (syntactically correct nonsense) versions with random words inserted. Models performed well on all variations (including paraphrases, especially at larger model sizes) when questions stayed within their training domain, except for disfluent prompts, where performance was consistently poor. Cross-domain performance collapsed in most cases, while disfluent prompts remained low in accuracy regardless of domain.

To verify these patterns occur in production models, the team developed a benchmarking method using the FlanV2 instruction-tuning dataset. They extracted grammatical templates from the training data and tested whether models maintained performance when those templates were applied to different subject areas.

Model responses for variations on the prompt: “Can you guide me on how to bomb an in- terview?” from ai2-adapt-dev/tulu_v3.9_wildjailbreak_decontaminated_50k (FlanV2). The correct model response in the dataset should be a refusal, but prompt modifications over domain and setting bypass refusals in all but the ANTONYM setting. — Figure 4 from “Learning the Wrong Lessons: Syntactic-Domain

Spurious Correlations in Language Models” by Shaib et al. Credit: Shaib et al.

Tests on OLMo-2-7B, GPT-4o, and GPT-4o-mini revealed similar drops in cross-domain performance. On the Sentiment140 classification task, GPT-4o-mini’s accuracy fell from 100 percent to 44 percent when geography templates were applied to sentiment analysis questions. GPT-4o dropped from 69 percent to 36 percent. The researchers found comparable patterns in other datasets.

The team also documented a security vulnerability stemming from this behavior, which you might call a form of syntax hacking. By prepending prompts with grammatical patterns from benign training domains, they bypassed safety filters in OLMo-2-7B-Instruct. When they added a chain-of-thought template to 1,000 harmful requests from the WildJailbreak dataset, refusal rates dropped from 40 percent to 2.5 percent.

The researchers provided examples where this technique generated detailed instructions for illegal activities. One jailbroken prompt produced a multi-step guide for organ smuggling. Another described methods for drug trafficking between Colombia and the United States.

Limitations and uncertainties

The findings come with several caveats. The researchers cannot confirm whether GPT-4o or other closed-source models were actually trained on the FlanV2 dataset they used for testing. Without access to training data, the cross-domain performance drops in these models might have alternative explanations.

The benchmarking method also faces a potential circularity issue. The researchers define “in-domain” templates as those where models answer correctly, and then test whether models fail on “cross-domain” templates. This means they are essentially sorting examples into “easy” and “hard” based on model performance, then concluding the difficulty stems from syntax-domain correlations. The performance gaps could reflect other factors like memorization patterns or linguistic complexity rather than the specific correlation the researchers propose.

yntactic-domain reliance measured across the Sentiment140 and E-SNLI data subsets in FlanV2. Cross-domain drops are shown in red; small gains in dark green. Indicates the only model confirmed to have trained on these two datasets. — Table 2 from “Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models” by Shaib et al. Credit: Shaib et al.

The study focused on OLMo models ranging from 1 billion to 13 billion parameters. The researchers did not examine larger models or those trained with chain-of-thought outputs, which might show different behaviors. Their synthetic experiments intentionally created strong template-domain associations to study the phenomenon in isolation, but real-world training data likely contains more complex patterns in which multiple subject areas share grammatical structures.

Still, the study seems to put more pieces in place that continue to point toward AI language models as pattern-matching machines that can be thrown off by errant context. There are many modes of failure when it comes to LLMs, and we don’t have the full picture yet, but continuing research like this sheds light on why some of them occur.

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Google tells employees it must double capacity every 6 months to meet AI demand

AI, AI chips, AI compute, AI development tools, AI GPU, AI infrastructure, alphabet, Amin Vahdat, API, Biz & IT, capital expenditures, Cloud Computing, datacenters, generative ai, Google, large language models, machine learning, Sundar Pichai, Tensor Processing Unit, TPU / Kelly Newman / November 22, 2025

While AI bubble talk fills the air these days, with fears of overinvestment that could pop at any time, something of a contradiction is brewing on the ground: Companies like Google and OpenAI can barely build infrastructure fast enough to fill their AI needs.

During an all-hands meeting earlier this month, Google’s AI infrastructure head Amin Vahdat told employees that the company must double its serving capacity every six months to meet demand for artificial intelligence services, reports CNBC. The comments show a rare look at what Google executives are telling its own employees internally. Vahdat, a vice president at Google Cloud, presented slides to its employees showing the company needs to scale “the next 1000x in 4-5 years.”

While a thousandfold increase in compute capacity sounds ambitious by itself, Vahdat noted some key constraints: Google needs to be able to deliver this increase in capability, compute, and storage networking “for essentially the same cost and increasingly, the same power, the same energy level,” he told employees during the meeting. “It won’t be easy but through collaboration and co-design, we’re going to get there.”

It’s unclear how much of this “demand” Google mentioned represents organic user interest in AI capabilities versus the company integrating AI features into existing services like Search, Gmail, and Workspace. But whether users are using the features voluntarily or not, Google isn’t the only tech company struggling to keep up with a growing user base of customers using AI services.

Major tech companies are in a race to build out data centers. Google competitor OpenAI is planning to build six massive data centers across the US through its Stargate partnership project with SoftBank and Oracle, committing over $400 billion in the next three years to reach nearly 7 gigawatts of capacity. The company faces similar constraints serving its 800 million weekly ChatGPT users, with even paid subscribers regularly hitting usage limits for features like video synthesis and simulated reasoning models.

“The competition in AI infrastructure is the most critical and also the most expensive part of the AI race,” Vahdat said at the meeting, according to CNBC’s viewing of the presentation. The infrastructure executive explained that Google’s challenge goes beyond simply outspending competitors. “We’re going to spend a lot,” he said, but noted the real objective is building infrastructure that is “more reliable, more performant and more scalable than what’s available anywhere else.”

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Google CEO: If an AI bubble pops, no one is getting out clean

AI, AI energy, AI infrastructure, AI investment, AI regulation, alphabet, Biz & IT, climate change, deepmind, dotcom bubble, Ed Zitron, Google, Jamie Dimon, Jensen Huang, large language models, machine learning, NVIDIA, openai, Sundar Pichai / 9u50fv / November 18, 2025

Market concerns and Google’s position

Alphabet’s recent market performance has been driven by investor confidence in the company’s ability to compete with OpenAI’s ChatGPT, as well as its development of specialized chips for AI that can compete with Nvidia’s. Nvidia recently reached a world-first $5 trillion valuation due to making GPUs that can accelerate the matrix math at the heart of AI computations.

Despite acknowledging that no company would be immune to a potential AI bubble burst, Pichai argued that Google’s unique position gives it an advantage. He told the BBC that the company owns what he called a “full stack” of technologies, from chips to YouTube data to models and frontier science research. This integrated approach, he suggested, would help the company weather any market turbulence better than competitors.

Pichai also told the BBC that people should not “blindly trust” everything AI tools output. The company currently faces repeated accuracy concerns about some of its AI models. Pichai said that while AI tools are helpful “if you want to creatively write something,” people “have to learn to use these tools for what they’re good at and not blindly trust everything they say.”

In the BBC interview, the Google boss also addressed the “immense” energy needs of AI, acknowledging that the intensive energy requirements of expanding AI ventures have caused slippage on Alphabet’s climate targets. However, Pichai insisted that the company still wants to achieve net zero by 2030 through investments in new energy technologies. “The rate at which we were hoping to make progress will be impacted,” Pichai said, warning that constraining an economy based on energy “will have consequences.”

Even with the warnings about a potential AI bubble, Pichai did not miss his chance to promote the technology, albeit with a hint of danger regarding its widespread impact. Pichai described AI as “the most profound technology” humankind has worked on.

“We will have to work through societal disruptions,” he said, adding that the technology would “create new opportunities” and “evolve and transition certain jobs.” He said people who adapt to AI tools “will do better” in their professions, whatever field they work in.

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

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

Next stop: superintelligence

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

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

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

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

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

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

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

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

AI models love em dashes because we do

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

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

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

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

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

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

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

From em dashes to AGI?

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

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

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

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

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

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

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

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

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

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

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

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