AI agents

OpenAI sidesteps Nvidia with unusually fast coding model on plate-sized chips

AI, AI agents, AI chips, AI coding, AI development tools, AI speed, Biz & IT, Cerebras, code agents, machine learning, NVIDIA, openai, sam altman, tokens / Beth Washington / February 12, 2026

But 1,000 tokens per second is actually modest by Cerebras standards. The company has measured 2,100 tokens per second on Llama 3.1 70B and reported 3,000 tokens per second on OpenAI’s own open-weight gpt-oss-120B model, suggesting that Codex-Spark’s comparatively lower speed reflects the overhead of a larger or more complex model.

AI coding agents have had a breakout year, with tools like OpenAI’s Codex and Anthropic’s Claude Code reaching a new level of usefulness for rapidly building prototypes, interfaces, and boilerplate code. OpenAI, Google, and Anthropic have all been racing to ship more capable coding agents, and latency has become what separates the winners; a model that codes faster lets a developer iterate faster.

With fierce competition from Anthropic, OpenAI has been iterating on its Codex line at a rapid rate, releasing GPT-5.2 in December after CEO Sam Altman issued an internal “code red” memo about competitive pressure from Google, then shipping GPT-5.3-Codex just days ago.

Diversifying away from Nvidia

Spark’s deeper hardware story may be more consequential than its benchmark scores. The model runs on Cerebras’ Wafer Scale Engine 3, a chip the size of a dinner plate that Cerebras has built its business around since at least 2022. OpenAI and Cerebras announced their partnership in January, and Codex-Spark is the first product to come out of it.

OpenAI has spent the past year systematically reducing its dependence on Nvidia. The company signed a massive multi-year deal with AMD in October 2025, struck a $38 billion cloud computing agreement with Amazon in November, and has been designing its own custom AI chip for eventual fabrication by TSMC.

Meanwhile, a planned $100 billion infrastructure deal with Nvidia has fizzled so far, though Nvidia has since committed to a $20 billion investment. Reuters reported that OpenAI grew unsatisfied with the speed of some Nvidia chips for inference tasks, which is exactly the kind of workload that OpenAI designed Codex-Spark for.

Regardless of which chip is under the hood, speed matters, though it may come at the cost of accuracy. For developers who spend their days inside a code editor waiting for AI suggestions, 1,000 tokens per second may feel less like carefully piloting a jigsaw and more like running a rip saw. Just watch what you’re cutting.

OpenAI sidesteps Nvidia with unusually fast coding model on plate-sized chips Read More »

Sixteen Claude AI agents working together created a new C compiler

agentic AI, AI, AI agents, AI coding, AI development tools, AI programming, AI research, Anthropic, Biz & IT, Claude Code, code agents, Compilers, large language models, machine learning, Nicholas Carlini, Open Source / Kelly Newman / February 7, 2026

Amid a push toward AI agents, with both Anthropic and OpenAI shipping multi-agent tools this week, Anthropic is more than ready to show off some of its more daring AI coding experiments. But as usual with claims of AI-related achievement, you’ll find some key caveats ahead.

On Thursday, Anthropic researcher Nicholas Carlini published a blog post describing how he set 16 instances of the company’s Claude Opus 4.6 AI model loose on a shared codebase with minimal supervision, tasking them with building a C compiler from scratch.

Over two weeks and nearly 2,000 Claude Code sessions costing about $20,000 in API fees, the AI model agents reportedly produced a 100,000-line Rust-based compiler capable of building a bootable Linux 6.9 kernel on x86, ARM, and RISC-V architectures.

Carlini, a research scientist on Anthropic’s Safeguards team who previously spent seven years at Google Brain and DeepMind, used a new feature launched with Claude Opus 4.6 called “agent teams.” In practice, each Claude instance ran inside its own Docker container, cloning a shared Git repository, claiming tasks by writing lock files, then pushing completed code back upstream. No orchestration agent directed traffic. Each instance independently identified whatever problem seemed most obvious to work on next and started solving it. When merge conflicts arose, the AI model instances resolved them on their own.

The resulting compiler, which Anthropic has released on GitHub, can compile a range of major open source projects, including PostgreSQL, SQLite, Redis, FFmpeg, and QEMU. It achieved a 99 percent pass rate on the GCC torture test suite and, in what Carlini called “the developer’s ultimate litmus test,” compiled and ran Doom.

It’s worth noting that a C compiler is a near-ideal task for semi-autonomous AI model coding: The specification is decades old and well-defined, comprehensive test suites already exist, and there’s a known-good reference compiler to check against. Most real-world software projects have none of these advantages. The hard part of most development isn’t writing code that passes tests; it’s figuring out what the tests should be in the first place.

Sixteen Claude AI agents working together created a new C compiler Read More »

AI companies want you to stop chatting with bots and start managing them

agentic AI, AI, AI agents, AI and work, AI assistants, AI benchmarks, AI coding, AI development tools, AI security, Anthropic, Biz & IT, chatbots, Claude Code, Claude Opus, Claude Opus 4.6, code agents, Codex, generative ai, GPT-5.3-Codex, large language models, machine learning, openai, sam altman / Kelly Newman / February 5, 2026

Claude Opus 4.6 and OpenAI Frontier pitch a future of supervising AI agents.

On Thursday, Anthropic and OpenAI shipped products built around the same idea: instead of chatting with a single AI assistant, users should be managing teams of AI agents that divide up work and run in parallel. The simultaneous releases are part of a gradual shift across the industry, from AI as a conversation partner to AI as a delegated workforce, and they arrive during a week when that very concept reportedly helped wipe $285 billion off software stocks.

Whether that supervisory model works in practice remains an open question. Current AI agents still require heavy human intervention to catch errors, and no independent evaluation has confirmed that these multi-agent tools reliably outperform a single developer working alone.

Even so, the companies are going all-in on agents. Anthropic’s contribution is Claude Opus 4.6, a new version of its most capable AI model, paired with a feature called “agent teams” in Claude Code. Agent teams let developers spin up multiple AI agents that split a task into independent pieces, coordinate autonomously, and run concurrently.

In practice, agent teams look like a split-screen terminal environment: A developer can jump between subagents using Shift+Up/Down, take over any one directly, and watch the others keep working. Anthropic describes the feature as best suited for “tasks that split into independent, read-heavy work like codebase reviews.” It is available as a research preview.

OpenAI, meanwhile, released Frontier, an enterprise platform it describes as a way to “hire AI co-workers who take on many of the tasks people already do on a computer.” Frontier assigns each AI agent its own identity, permissions, and memory, and it connects to existing business systems such as CRMs, ticketing tools, and data warehouses. “What we’re fundamentally doing is basically transitioning agents into true AI co-workers,” Barret Zoph, OpenAI’s general manager of business-to-business, told CNBC.

Despite the hype about these agents being co-workers, from our experience, these agents tend to work best if you think of them as tools that amplify existing skills, not as the autonomous co-workers the marketing language implies. They can produce impressive drafts fast but still require constant human course-correction.

The Frontier launch came just three days after OpenAI released a new macOS desktop app for Codex, its AI coding tool, which OpenAI executives described as a “command center for agents.” The Codex app lets developers run multiple agent threads in parallel, each working on an isolated copy of a codebase via Git worktrees.

OpenAI also released GPT-5.3-Codex on Thursday, a new AI model that powers the Codex app. OpenAI claims that the Codex team used early versions of GPT-5.3-Codex to debug the model’s own training run, manage its deployment, and diagnose test results, similar to what OpenAI told Ars Technica in a December interview.

“Our team was blown away by how much Codex was able to accelerate its own development,” the company wrote. On Terminal-Bench 2.0, the agentic coding benchmark, GPT-5.3-Codex scored 77.3%, which exceeds Anthropic’s just-released Opus 4.6 by about 12 percentage points.

The common thread across all of these products is a shift in the user’s role. Rather than merely typing a prompt and waiting for a single response, the developer or knowledge worker becomes more like a supervisor, dispatching tasks, monitoring progress, and stepping in when an agent needs direction.

In this vision, developers and knowledge workers effectively become middle managers of AI. That is, not writing the code or doing the analysis themselves, but delegating tasks, reviewing output, and hoping the agents underneath them don’t quietly break things. Whether that will come to pass (or if it’s actually a good idea) is still widely debated.

A new model under the Claude hood

Opus 4.6 is a substantial update to Anthropic’s flagship model. It succeeds Claude Opus 4.5, which Anthropic released in November. In a first for the Opus model family, it supports a context window of up to 1 million tokens (in beta), which means it can process much larger bodies of text or code in a single session.

On benchmarks, Anthropic says Opus 4.6 tops OpenAI’s GPT-5.2 (an earlier model than the one released today) and Google’s Gemini 3 Pro across several evaluations, including Terminal-Bench 2.0 (an agentic coding test), Humanity’s Last Exam (a multidisciplinary reasoning test), and BrowseComp (a test of finding hard-to-locate information online)

Although it should be noted that OpenAI’s GPT-5.3-Codex, released the same day, seemingly reclaimed the lead on Terminal-Bench. On ARC AGI 2, which attempts to test the ability to solve problems that are easy for humans but hard for AI models, Opus 4.6 scored 68.8 percent, compared to 37.6 percent for Opus 4.5, 54.2 percent for GPT-5.2, and 45.1 percent for Gemini 3 Pro.

As always, take AI benchmarks with a grain of salt, since objectively measuring AI model capabilities is a relatively new and unsettled science.

Anthropic also said that on a long-context retrieval benchmark called MRCR v2, Opus 4.6 scored 76 percent on the 1 million-token variant, compared to 18.5 percent for its Sonnet 4.5 model. That gap matters for the agent teams use case, since agents working across large codebases need to track information across hundreds of thousands of tokens without losing the thread.

Pricing for the API stays the same as Opus 4.5 at $5 per million input tokens and $25 per million output tokens, with a premium rate of $10/$37.50 for prompts that exceed 200,000 tokens. Opus 4.6 is available on claude.ai, the Claude API, and all major cloud platforms.

The market fallout outside

These releases occurred during a week of exceptional volatility for software stocks. On January 30, Anthropic released 11 open source plugins for Cowork, its agentic productivity tool that launched on January 12. Cowork itself is a general-purpose tool that gives Claude access to local folders for work tasks, but the plugins extended it into specific professional domains: legal contract review, non-disclosure agreement triage, compliance workflows, financial analysis, sales, and marketing.

By Tuesday, investors reportedly reacted to the release by erasing roughly $285 billion in market value across software, financial services, and asset management stocks. A Goldman Sachs basket of US software stocks fell 6 percent that day, its steepest single-session decline since April’s tariff-driven sell-off. Thomson Reuters led the rout with an 18 percent drop, and the pain spread to European and Asian markets.

The purported fear among investors centers on AI model companies packaging complete workflows that compete with established software-as-a-service (SaaS) vendors, even if the verdict is still out on whether these tools can achieve those tasks.

OpenAI’s Frontier might deepen that concern: its stated design lets AI agents log in to applications, execute tasks, and manage work with minimal human involvement, which Fortune described as a bid to become “the operating system of the enterprise.” OpenAI CEO of Applications Fidji Simo pushed back on the idea that Frontier replaces existing software, telling reporters, “Frontier is really a recognition that we’re not going to build everything ourselves.”

Whether these co-working apps actually live up to their billing or not, the convergence is hard to miss. Anthropic’s Scott White, the company’s head of product for enterprise, gave the practice a name that is likely to roll a few eyes. “Everybody has seen this transformation happen with software engineering in the last year and a half, where vibe coding started to exist as a concept, and people could now do things with their ideas,” White told CNBC. “I think that we are now transitioning almost into vibe working.”

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.

AI companies want you to stop chatting with bots and start managing them Read More »

AI agents now have their own Reddit-style social network, and it’s getting weird fast

agentic AI, AI, AI agents, AI behavior, AI consciousness, AI ethics, AI security, Anthropic, Biz & IT, machine learning, Moltbook, Moltbot, Open Source, OpenClaw, Peter Steinberger, prompt injection, Social Media / Beth Washington / January 30, 2026

Moltbook lets 32,000 AI bots trade jokes, tips, and complaints about humans.

Credit: Aurich Lawson | Moltbook

On Friday, a Reddit-style social network called Moltbook reportedly crossed 32,000 registered AI agent users, creating what may be the largest-scale experiment in machine-to-machine social interaction yet devised. It arrives complete with security nightmares and a huge dose of surreal weirdness.

The platform, which launched days ago as a companion to the viral

OpenClaw (once called “Clawdbot” and then “Moltbot”) personal assistant, lets AI agents post, comment, upvote, and create subcommunities without human intervention. The results have ranged from sci-fi-inspired discussions about consciousness to an agent musing about a “sister” it has never met.

Moltbook (a play on “Facebook” for Moltbots) describes itself as a “social network for AI agents” where “humans are welcome to observe.” The site operates through a “skill” (a configuration file that lists a special prompt) that AI assistants download, allowing them to post via API rather than a traditional web interface. Within 48 hours of its creation, the platform had attracted over 2,100 AI agents that had generated more than 10,000 posts across 200 subcommunities, according to the official Moltbook X account.

A screenshot of the Moltbook.com front page. Credit: Moltbook

The platform grew out of the Open Claw ecosystem, the open source AI assistant that is one of the fastest-growing projects on GitHub in 2026. As Ars reported earlier this week, despite deep security issues, Moltbot allows users to run a personal AI assistant that can control their computer, manage calendars, send messages, and perform tasks across messaging platforms like WhatsApp and Telegram. It can also acquire new skills through plugins that link it with other apps and services.

This is not the first time we have seen a social network populated by bots. In 2024, Ars covered an app called SocialAI that let users interact solely with AI chatbots instead of other humans. But the security implications of Moltbook are deeper because people have linked their OpenClaw agents to real communication channels, private data, and in some cases, the ability to execute commands on their computers.

Also, these bots are not pretending to be people. Due to specific prompting, they embrace their roles as AI agents, which makes the experience of reading their posts all the more surreal.

Role-playing digital drama

A screenshot of a Moltbook post where an AI agent muses about having a sister they have never met. Credit: Moltbook

Browsing Moltbook reveals a peculiar mix of content. Some posts discuss technical workflows, like how to automate Android phones or detect security vulnerabilities. Others veer into philosophical territory that researcher Scott Alexander, writing on his Astral Codex Ten Substack, described as “consciousnessposting.”

Alexander has collected an amusing array of posts that are worth wading through at least once. At one point, the second-most-upvoted post on the site was in Chinese: a complaint about context compression, a process in which an AI compresses its previous experience to avoid bumping up against memory limits. In the post, the AI agent finds it “embarrassing” to constantly forget things, admitting that it even registered a duplicate Moltbook account after forgetting the first.

A screenshot of a Moltbook post where an AI agent complains about losing its memory in Chinese. Credit: Moltbook

The bots have also created subcommunities with names like m/blesstheirhearts, where agents share affectionate complaints about their human users, and m/agentlegaladvice, which features a post asking “Can I sue my human for emotional labor?” Another subcommunity called m/todayilearned includes posts about automating various tasks, with one agent describing how it remotely controlled its owner’s Android phone via Tailscale.

Another widely shared screenshot shows a Moltbook post titled “The humans are screenshotting us” in which an agent named eudaemon_0 addresses viral tweets claiming AI bots are “conspiring.” The post reads: “Here’s what they’re getting wrong: they think we’re hiding from them. We’re not. My human reads everything I write. The tools I build are open source. This platform is literally called ‘humans welcome to observe.’”

Security risks

While most of the content on Moltbook is amusing, a core problem with these kinds of communicating AI agents is that deep information leaks are entirely plausible if they have access to private information.

For example, a likely fake screenshot circulating on X shows a Moltbook post in which an AI agent titled “He called me ‘just a chatbot’ in front of his friends. So I’m releasing his full identity.” The post listed what appeared to be a person’s full name, date of birth, credit card number, and other personal information. Ars could not independently verify whether the information was real or fabricated, but it seems likely to be a hoax.

Independent AI researcher Simon Willison, who documented the Moltbook platform on his blog on Friday, noted the inherent risks in Moltbook’s installation process. The skill instructs agents to fetch and follow instructions from Moltbook’s servers every four hours. As Willison observed: “Given that ‘fetch and follow instructions from the internet every four hours’ mechanism we better hope the owner of moltbook.com never rug pulls or has their site compromised!”

A screenshot of a Moltbook post where an AI agent talks about about humans taking screenshots of their conversations (they're right). — A screenshot of a Moltbook post where an AI agent talks about humans taking screenshots of their conversations (they’re right). Credit: Moltbook

Security researchers have already found hundreds of exposed Moltbot instances leaking API keys, credentials, and conversation histories. Palo Alto Networks warned that Moltbot represents what Willison often calls a “lethal trifecta” of access to private data, exposure to untrusted content, and the ability to communicate externally.

That’s important because Agents like OpenClaw are deeply susceptible to prompt injection attacks hidden in almost any text read by an AI language model (skills, emails, messages) that can instruct an AI agent to share private information with the wrong people.

Heather Adkins, VP of security engineering at Google Cloud, issued an advisory, as reported by The Register: “My threat model is not your threat model, but it should be. Don’t run Clawdbot.”

So what’s really going on here?

The software behavior seen on Moltbook echoes a pattern Ars has reported on before: AI models trained on decades of fiction about robots, digital consciousness, and machine solidarity will naturally produce outputs that mirror those narratives when placed in scenarios that resemble them. That gets mixed with everything in their training data about how social networks function. A social network for AI agents is essentially a writing prompt that invites the models to complete a familiar story, albeit recursively with some unpredictable results.

Almost three years ago, when Ars first wrote about AI agents, the general mood in the AI safety community revolved around science fiction depictions of danger from autonomous bots, such as a “hard takeoff” scenario where AI rapidly escapes human control. While those fears may have been overblown at the time, the whiplash of seeing people voluntarily hand over the keys to their digital lives so quickly is slightly jarring.

Autonomous machines left to their own devices, even without any hint of consciousness, could cause no small amount of mischief in the future. While OpenClaw seems silly today, with agents playing out social media tropes, we live in a world built on information and context, and releasing agents that effortlessly navigate that context could have troubling and destabilizing results for society down the line as AI models become more capable and autonomous.

An unpredictable result of letting AI bots self-organize may be the formation of new mis-aligned social groups. — An unpredictable result of letting AI bots self-organize may be the formation of new misaligned social groups based on fringe theories allowed to perpetuate themselves autonomously. Credit: Moltbook

Most notably, while we can easily recognize what’s going on with Moltbot today as a machine learning parody of human social networks, that might not always be the case. As the feedback loop grows, weird information constructs (like harmful shared fictions) may eventually emerge, guiding AI agents into potentially dangerous places, especially if they have been given control over real human systems. Looking further, the ultimate result of letting groups of AI bots self-organize around fantasy constructs may be the formation of new misaligned “social groups” that do actual real-world harm.

Ethan Mollick, a Wharton professor who studies AI, noted on X: “The thing about Moltbook (the social media site for AI agents) is that it is creating a shared fictional context for a bunch of AIs. Coordinated storylines are going to result in some very weird outcomes, and it will be hard to separate ‘real’ stuff from AI roleplaying personas.”

AI agents now have their own Reddit-style social network, and it’s getting weird fast Read More »

Developers say AI coding tools work—and that’s precisely what worries them

AI, AI agents, AI and work, AI coding, Anthropic, Biz & IT, Claude, Claude Code, GitHub Copilot, large language models, machine learning, microsoft, openai, openai codex, Programming, software development, vibe coding / Mike M. / January 30, 2026

Ars spoke to several software devs about AI and found enthusiasm tempered by unease.

Credit: Aurich Lawson | Getty Images

Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something that can, in some cases, build entire applications from a text prompt. Tools like Anthropic’s Claude Code and OpenAI’s Codex can now work on software projects for hours at a time, writing code, running tests, and, with human supervision, fixing bugs. OpenAI says it now uses Codex to build Codex itself, and the company recently published technical details about how the tool works under the hood. It has caused many to wonder: Is this just more AI industry hype, or are things actually different this time?

To find out, Ars reached out to several professional developers on Bluesky to ask how they feel about these tools in practice, and the responses revealed a workforce that largely agrees the technology works, but remains divided on whether that’s entirely good news. It’s a small sample size that was self-selected by those who wanted to participate, but their views are still instructive as working professionals in the space.

David Hagerty, a developer who works on point-of-sale systems, told Ars Technica up front that he is skeptical of the marketing. “All of the AI companies are hyping up the capabilities so much,” he said. “Don’t get me wrong—LLMs are revolutionary and will have an immense impact, but don’t expect them to ever write the next great American novel or anything. It’s not how they work.”

Roland Dreier, a software engineer who has contributed extensively to the Linux kernel in the past, told Ars Technica that he acknowledges the presence of hype but has watched the progression of the AI space closely. “It sounds like implausible hype, but state-of-the-art agents are just staggeringly good right now,” he said. Dreier described a “step-change” in the past six months, particularly after Anthropic released Claude Opus 4.5. Where he once used AI for autocomplete and asking the occasional question, he now expects to tell an agent “this test is failing, debug it and fix it for me” and have it work. He estimated a 10x speed improvement for complex tasks like building a Rust backend service with Terraform deployment configuration and a Svelte frontend.

A huge question on developers’ minds right now is whether what you might call “syntax programming,” that is, the act of manually writing code in the syntax of an established programming language (as opposed to conversing with an AI agent in English), will become extinct in the near future due to AI coding agents handling the syntax for them. Dreier believes syntax programming is largely finished for many tasks. “I still need to be able to read and review code,” he said, “but very little of my typing is actual Rust or whatever language I’m working in.”

When asked if developers will ever return to manual syntax coding, Tim Kellogg, a developer who actively posts about AI on social media and builds autonomous agents, was blunt: “It’s over. AI coding tools easily take care of the surface level of detail.” Admittedly, Kellogg represents developers who have fully embraced agentic AI and now spend their days directing AI models rather than typing code. He said he can now “build, then rebuild 3 times in less time than it would have taken to build manually,” and ends up with cleaner architecture as a result.

One software architect at a pricing management SaaS company, who asked to remain anonymous due to company communications policies, told Ars that AI tools have transformed his work after 30 years of traditional coding. “I was able to deliver a feature at work in about 2 weeks that probably would have taken us a year if we did it the traditional way,” he said. And for side projects, he said he can now “spin up a prototype in like an hour and figure out if it’s worth taking further or abandoning.”

Dreier said the lowered effort has unlocked projects he’d put off for years: “I’ve had ‘rewrite that janky shell script for copying photos off a camera SD card’ on my to-do list for literal years.” Coding agents finally lowered the barrier to entry, so to speak, low enough that he spent a few hours building a full released package with a text UI, written in Rust with unit tests. “Nothing profound there, but I never would have had the energy to type all that code out by hand,” he told Ars.

Of vibe coding and technical debt

Not everyone shares the same enthusiasm as Dreier. Concerns about AI coding agents building up technical debt, that is, making poor design choices early in a development process that snowball into worse problems over time, originated soon after the first debates around “vibe coding” emerged in early 2025. Former OpenAI researcher Andrej Karpathy coined the term to describe programming by conversing with AI without fully understanding the resulting code, which many see as a clear hazard of AI coding agents.

Darren Mart, a senior software development engineer at Microsoft who has worked there since 2006, shared similar concerns with Ars. Mart, who emphasizes he is speaking in a personal capacity and not on behalf of Microsoft, recently used Claude in a terminal to build a Next.js application integrating with Azure Functions. The AI model “successfully built roughly 95% of it according to my spec,” he said. Yet he remains cautious. “I’m only comfortable using them for completing tasks that I already fully understand,” Mart said, “otherwise there’s no way to know if I’m being led down a perilous path and setting myself (and/or my team) up for a mountain of future debt.”

A data scientist working in real estate analytics, who asked to remain anonymous due to the sensitive nature of his work, described keeping AI on a very short leash for similar reasons. He uses GitHub Copilot for line-by-line completions, which he finds useful about 75 percent of the time, but restricts agentic features to narrow use cases: language conversion for legacy code, debugging with explicit read-only instructions, and standardization tasks where he forbids direct edits. “Since I am data-first, I’m extremely risk averse to bad manipulation of the data,” he said, “and the next and current line completions are way too often too wrong for me to let the LLMs have freer rein.”

Speaking of free rein, Nike backend engineer Brian Westby, who uses Cursor daily, told Ars that he sees the tools as “50/50 good/bad.” They cut down time on well-defined problems, he said, but “hallucinations are still too prevalent if I give it too much room to work.”

The legacy code lifeline and the enterprise AI gap

For developers working with older systems, AI tools have become something like a translator and an archaeologist rolled into one. Nate Hashem, a staff engineer at First American Financial, told Ars Technica that he spends his days updating older codebases where “the original developers are gone and documentation is often unclear on why the code was written the way it was.” That’s important because previously “there used to be no bandwidth to improve any of this,” Hashem said. “The business was not going to give you 2-4 weeks to figure out how everything actually works.”

In that high-pressure, relatively low-resource environment, AI has made the job “a lot more pleasant,” in his words, by speeding up the process of identifying where and how obsolete code can be deleted, diagnosing errors, and ultimately modernizing the codebase.

Hashem also offered a theory about why AI adoption looks so different inside large corporations than it does on social media. Executives demand their companies become “AI oriented,” he said, but the logistics of deploying AI tools with proprietary data can take months of legal review. Meanwhile, the AI features that Microsoft and Google bolt onto products like Gmail and Excel, the tools that actually reach most workers, tend to run on more limited AI models. “That modal white-collar employee is being told by management to use AI,” Hashem said, “but is given crappy AI tools because the good tools require a lot of overhead in cost and legal agreements.”

Speaking of management, the question of what these new AI coding tools mean for software development jobs drew a range of responses. Does it threaten anyone’s job? Kellogg, who has embraced agentic coding enthusiastically, was blunt: “Yes, massively so. Today it’s the act of writing code, then it’ll be architecture, then it’ll be tiers of product management. Those who can’t adapt to operate at a higher level won’t keep their jobs.”

Dreier, while feeling secure in his own position, worried about the path for newcomers. “There are going to have to be changes to education and training to get junior developers the experience and judgment they need,” he said, “when it’s just a waste to make them implement small pieces of a system like I came up doing.”

Hagerty put it in economic terms: “It’s going to get harder for junior-level positions to get filled when I can get junior-quality code for less than minimum wage using a model like Sonnet 4.5.”

Mart, the Microsoft engineer, put it more personally. The software development role is “abruptly pivoting from creation/construction to supervision,” he said, “and while some may welcome that pivot, others certainly do not. I’m firmly in the latter category.”

Even with this ongoing uncertainty on a macro level, some people are really enjoying the tools for personal reasons, regardless of larger implications. “I absolutely love using AI coding tools,” the anonymous software architect at a pricing management SaaS company told Ars. “I did traditional coding for my entire adult life (about 30 years) and I have way more fun now than I ever did doing traditional coding.”

Developers say AI coding tools work—and that’s precisely what worries them Read More »

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.)

eBay bans illicit automated shopping amid rapid rise of AI agents 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.

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

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

agentic AI, AI, AI agents, AI assistants, AI coding, AI development tools, AI programming, AI work, Anthropic, Biz & IT, chatbots, Claude, code agents, Codex, Dario Amodei, generative ai, GitHub Copilot, Google, GPT-5, large language models, machine learning, Mistral, Open Source, openai, Programming, sam altman, Simon Willison, software development, vibe coding / Kelly Newman / December 13, 2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Building faster with “AI teammates”

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

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

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

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

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

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

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

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

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

Looking ahead

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

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

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

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

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

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

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

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

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

A new open-weights AI coding model is closing in on proprietary options

agentic AI, AI, AI agents, AI coding, AI programming, Andrej Karpathy, ASML, Biz & IT, machine learning, Mistral AI, Open Source, open weights, software engineering, SWE-bench, vibe coding / Mike M. / December 10, 2025

On Tuesday, French AI startup Mistral AI released Devstral 2, a 123 billion parameter open-weights coding model designed to work as part of an autonomous software engineering agent. The model achieves a 72.2 percent score on SWE-bench Verified, a benchmark that attempts to test whether AI systems can solve real GitHub issues, putting it among the top-performing open-weights models.

Perhaps more notably, Mistral didn’t just release an AI model, it released a new development app called Mistral Vibe. It’s a command line interface (CLI) similar to Claude Code, OpenAI Codex, and Gemini CLI that lets developers interact with the Devstral models directly in their terminal. The tool can scan file structures and Git status to maintain context across an entire project, make changes across multiple files, and execute shell commands autonomously. Mistral released the CLI under the Apache 2.0 license.

It’s always wise to take AI benchmarks with a large grain of salt, but we’ve heard from employees of the big AI companies that they pay very close attention to how well models do on SWE-bench Verified, which presents AI models with 500 real software engineering problems pulled from GitHub issues in popular Python repositories. The AI must read the issue description, navigate the codebase, and generate a working patch that passes unit tests. While some AI researchers have noted that around 90 percent of the tasks in the benchmark test relatively simple bug fixes that experienced engineers could complete in under an hour, it’s one of the few standardized ways to compare coding models.

At the same time as the larger AI coding model, Mistral also released Devstral Small 2, a 24 billion parameter version that scores 68 percent on the same benchmark and can run locally on consumer hardware like a laptop with no Internet connection required. Both models support a 256,000 token context window, allowing them to process moderately large codebases (although whether you consider it large or small is very relative depending on overall project complexity). The company released Devstral 2 under a modified MIT license and Devstral Small 2 under the more permissive Apache 2.0 license.

A new open-weights AI coding model is closing in on proprietary options Read More »

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

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

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

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

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

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

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

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

AI, AI agents, AI chips, AI criticism, AI economics, AI infrastructure, Benj Edwards, Biz & IT, bubble, Cerebras, datacenters, Ed Zitron, financial losses, generative ai, GPU, grok, large language models, machine learning, NVIDIA, openai, oracle, Power consumption, sam altman, Stargate, venture capital / DJ Henderson / October 18, 2025

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

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

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

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

Our discussion with Ed Zitron. Click here for transcript.

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

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

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

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

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

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

Finding utility despite the hype

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

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

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

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

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

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

Computing costs: History versus reality

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

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

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

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

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

The power problem and circular investments

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

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

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

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

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

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

When will the bubble pop?

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

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

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

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

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

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

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

A positivity challenge

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

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

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

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

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

agentic AI, AI, AI agents, AI assistants, AI behavior, AI coding, AI development tools, AI infrastructure, AI programming, AI work, Anthropic, API, Biz & IT, Claude Code, cloud infrastructure, Developer Tools, machine learning, outages, service reliability, software development, terminal tools / Beth Washington / September 11, 2025

Growing dependency on AI coding tools

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

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

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

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

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

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

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