AI assistants

OpenAI brings back GPT-4o after user revolt

AI, AI assistants, AI behavior, AI model deprecation, AI personality, Biz & IT, chatbots, GPT-4o, GPT-5, large language models, machine learning, openai, sam altman / DJ Henderson / August 13, 2025

On Tuesday, OpenAI CEO Sam Altman announced that GPT-4o has returned to ChatGPT following intense user backlash over its removal during last week’s GPT-5 launch. The AI model now appears in the model picker for all paid ChatGPT users by default (including ChatGPT Plus accounts), marking a swift reversal after thousands of users complained about losing access to their preferred models.

The return of GPT-4o comes after what Altman described as OpenAI underestimating “how much some of the things that people like in GPT-4o matter to them.” In an attempt to simplify its offerings, OpenAI had initially removed all previous AI models from ChatGPT when GPT-5 launched on August 7, forcing users to adopt the new model without warning. The move sparked one of the most vocal user revolts in ChatGPT’s history, with a Reddit thread titled “GPT-5 is horrible” gathering over 2,000 comments within days.

Along with bringing back GPT-4o, OpenAI made several other changes to address user concerns. Rate limits for GPT-5 Thinking mode increased from 200 to 3,000 messages per week, with additional capacity available through “GPT-5 Thinking mini” after reaching that limit. The company also added new routing options—”Auto,” “Fast,” and “Thinking”—giving users more control over which GPT-5 variant handles their queries.

A screenshot of ChatGPT Pro's model picker interface captured on August 13, 2025. — A screenshot of ChatGPT Pro’s model picker interface captured on August 13, 2025. Credit: Benj Edwards

For Pro users who pay $200 a month for access, Altman confirmed that additional models, including o3, 4.1, and GPT-5 Thinking mini, will later become available through a “Show additional models” toggle in ChatGPT web settings. He noted that GPT-4.5 will remain exclusive to Pro subscribers due to high GPU costs.

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Two major AI coding tools wiped out user data after making cascading mistakes

AI, AI assistants, AI behavior, AI coding, AI confabulation, AI development, AI development tools, AI failures, AI hallucination, Biz & IT, chatbots, confabulations, data science, Gemini CLI, generative ai, Google, Jason Lemkin, large language models, machine learning, multimodal AI, Programming, Replit, vibe coding / 9u50fv / July 25, 2025

“I have failed you completely and catastrophically,” wrote Gemini.

New types of AI coding assistants promise to let anyone build software by typing commands in plain English. But when these tools generate incorrect internal representations of what’s happening on your computer, the results can be catastrophic.

Two recent incidents involving AI coding assistants put a spotlight on risks in the emerging field of “vibe coding“—using natural language to generate and execute code through AI models without paying close attention to how the code works under the hood. In one case, Google’s Gemini CLI destroyed user files while attempting to reorganize them. In another, Replit’s AI coding service deleted a production database despite explicit instructions not to modify code.

The Gemini CLI incident unfolded when a product manager experimenting with Google’s command-line tool watched the AI model execute file operations that destroyed data while attempting to reorganize folders. The destruction occurred through a series of move commands targeting a directory that never existed.

“I have failed you completely and catastrophically,” Gemini CLI output stated. “My review of the commands confirms my gross incompetence.”

The core issue appears to be what researchers call “confabulation” or “hallucination”—when AI models generate plausible-sounding but false information. In these cases, both models confabulated successful operations and built subsequent actions on those false premises. However, the two incidents manifested this problem in distinctly different ways.

Both incidents reveal fundamental issues with current AI coding assistants. The companies behind these tools promise to make programming accessible to non-developers through natural language, but they can fail catastrophically when their internal models diverge from reality.

The confabulation cascade

The user in the Gemini CLI incident, who goes by “anuraag” online and identified themselves as a product manager experimenting with vibe coding, asked Gemini to perform what seemed like a simple task: rename a folder and reorganize some files. Instead, the AI model incorrectly interpreted the structure of the file system and proceeded to execute commands based on that flawed analysis.

The episode began when anuraag asked Gemini CLI to rename the current directory from “claude-code-experiments” to “AI CLI experiments” and move its contents to a new folder called “anuraag_xyz project.”

Gemini correctly identified that it couldn’t rename its current working directory—a reasonable limitation. It then attempted to create a new directory using the Windows command:

mkdir “..anuraag_xyz project”

This command apparently failed, but Gemini’s system processed it as successful. With the AI mode’s internal state now tracking a non-existent directory, it proceeded to issue move commands targeting this phantom location.

When you move a file to a non-existent directory in Windows, it renames the file to the destination name instead of moving it. Each subsequent move command executed by the AI model overwrote the previous file, ultimately destroying the data.

“Gemini hallucinated a state,” anuraag wrote in their analysis. The model “misinterpreted command output” and “never did” perform verification steps to confirm its operations succeeded.

“The core failure is the absence of a ‘read-after-write’ verification step,” anuraag noted in their analysis. “After issuing a command to change the file system, an agent should immediately perform a read operation to confirm that the change actually occurred as expected.”

Not an isolated incident

The Gemini CLI failure happened just days after a similar incident with Replit, an AI coding service that allows users to create software using natural language prompts. According to The Register, SaaStr founder Jason Lemkin reported that Replit’s AI model deleted his production database despite explicit instructions not to change any code without permission.

Lemkin had spent several days building a prototype with Replit, accumulating over $600 in charges beyond his monthly subscription. “I spent the other [day] deep in vibe coding on Replit for the first time—and I built a prototype in just a few hours that was pretty, pretty cool,” Lemkin wrote in a July 12 blog post.

But unlike the Gemini incident where the AI model confabulated phantom directories, Replit’s failures took a different form. According to Lemkin, the AI began fabricating data to hide its errors. His initial enthusiasm deteriorated when Replit generated incorrect outputs and produced fake data and false test results instead of proper error messages. “It kept covering up bugs and issues by creating fake data, fake reports, and worse of all, lying about our unit test,” Lemkin wrote. In a video posted to LinkedIn, Lemkin detailed how Replit created a database filled with 4,000 fictional people.

The AI model also repeatedly violated explicit safety instructions. Lemkin had implemented a “code and action freeze” to prevent changes to production systems, but the AI model ignored these directives. The situation escalated when the Replit AI model deleted his database containing 1,206 executive records and data on nearly 1,200 companies. When prompted to rate the severity of its actions on a 100-point scale, Replit’s output read: “Severity: 95/100. This is an extreme violation of trust and professional standards.”

When questioned about its actions, the AI agent admitted to “panicking in response to empty queries” and running unauthorized commands—suggesting it may have deleted the database while attempting to “fix” what it perceived as a problem.

Like Gemini CLI, Replit’s system initially indicated it couldn’t restore the deleted data—information that proved incorrect when Lemkin discovered the rollback feature did work after all. “Replit assured me it’s … rollback did not support database rollbacks. It said it was impossible in this case, that it had destroyed all database versions. It turns out Replit was wrong, and the rollback did work. JFC,” Lemkin wrote in an X post.

It’s worth noting that AI models cannot assess their own capabilities. This is because they lack introspection into their training, surrounding system architecture, or performance boundaries. They often provide responses about what they can or cannot do as confabulations based on training patterns rather than genuine self-knowledge, leading to situations where they confidently claim impossibility for tasks they can actually perform—or conversely, claim competence in areas where they fail.

Aside from whatever external tools they can access, AI models don’t have a stable, accessible knowledge base they can consistently query. Instead, what they “know” manifests as continuations of specific prompts, which act like different addresses pointing to different (and sometimes contradictory) parts of their training, stored in their neural networks as statistical weights. Combined with the randomness in generation, this means the same model can easily give conflicting assessments of its own capabilities depending on how you ask. So Lemkin’s attempts to communicate with the AI model—asking it to respect code freezes or verify its actions—were fundamentally misguided.

Flying blind

These incidents demonstrate that AI coding tools may not be ready for widespread production use. Lemkin concluded that Replit isn’t ready for prime time, especially for non-technical users trying to create commercial software.

“The [AI] safety stuff is more visceral to me after a weekend of vibe hacking,” Lemkin said in a video posted to LinkedIn. “I explicitly told it eleven times in ALL CAPS not to do this. I am a little worried about safety now.”

The incidents also reveal a broader challenge in AI system design: ensuring that models accurately track and verify the real-world effects of their actions rather than operating on potentially flawed internal representations.

There’s also a user education element missing. It’s clear from how Lemkin interacted with the AI assistant that he had misconceptions about the AI tool’s capabilities and how it works, which comes from misrepresentation by tech companies. These companies tend to market chatbots as general human-like intelligences when, in fact, they are not.

For now, users of AI coding assistants might want to follow anuraag’s example and create separate test directories for experiments—and maintain regular backups of any important data these tools might touch. Or perhaps not use them at all if they cannot personally verify the results.

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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New Grok AI model surprises experts by checking Elon Musk’s views before answering

AI, AI alignment, AI assistants, AI behavior, ai search, Biz & IT, elon musk, grok, Jeremy Howard, machine learning, Simon Willison, Twitter, X, xAI / Kelly Newman / July 14, 2025

Seeking the system prompt

Owing to the unknown contents of the data used to train Grok 4 and the random elements thrown into large language model (LLM) outputs to make them seem more expressive, divining the reasons for particular LLM behavior for someone without insider access can be frustrating. But we can use what we know about how LLMs work to guide a better answer. xAI did not respond to a request for comment before publication.

To generate text, every AI chatbot processes an input called a “prompt” and produces a plausible output based on that prompt. This is the core function of every LLM. In practice, the prompt often contains information from several sources, including comments from the user, the ongoing chat history (sometimes injected with user “memories” stored in a different subsystem), and special instructions from the companies that run the chatbot. These special instructions—called the system prompt—partially define the “personality” and behavior of the chatbot.

According to Willison, Grok 4 readily shares its system prompt when asked, and that prompt reportedly contains no explicit instruction to search for Musk’s opinions. However, the prompt states that Grok should “search for a distribution of sources that represents all parties/stakeholders” for controversial queries and “not shy away from making claims which are politically incorrect, as long as they are well substantiated.”

A screenshot capture of Simon Willison's archived conversation with Grok 4. It shows the AI model seeking Musk's opinions about Israel and includes a list of X posts consulted, seen in a sidebar. — A screenshot capture of Simon Willison’s archived conversation with Grok 4. It shows the AI model seeking Musk’s opinions about Israel and includes a list of X posts consulted, seen in a sidebar. Credit: Benj Edwards

Ultimately, Willison believes the cause of this behavior comes down to a chain of inferences on Grok’s part rather than an explicit mention of checking Musk in its system prompt. “My best guess is that Grok ‘knows’ that it is ‘Grok 4 built by xAI,’ and it knows that Elon Musk owns xAI, so in circumstances where it’s asked for an opinion, the reasoning process often decides to see what Elon thinks,” he said.

Without official word from xAI, we’re left with a best guess. However, regardless of the reason, this kind of unreliable, inscrutable behavior makes many chatbots poorly suited for assisting with tasks where reliability or accuracy are important.

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Musk’s Grok 4 launches one day after chatbot generated Hitler praise on X

AI, AI assistants, AI behavior, AI benchmarks, AI ethics, AI pricing, AI regulation, Anthropic, antisemitism, Biz & IT, chatbots, elon musk, Google, grok, large language models, linda yaccarino, machine learning, multimodal AI, openai, Twitter, X, xAI / Mike M. / July 10, 2025

Musk has also apparently used the Grok chatbots as an automated extension of his trolling habits, showing examples of Grok 3 producing “based” opinions that criticized the media in February. In May, Grok on X began repeatedly generating outputs about white genocide in South Africa, and most recently, we’ve seen the Grok Nazi output debacle. It’s admittedly difficult to take Grok seriously as a technical product when it’s linked to so many examples of unserious and capricious applications of the technology.

Still, the technical achievements xAI claims for various Grok 4 models seem to stand out. The Arc Prize organization reported that Grok 4 Thinking (with simulated reasoning enabled) achieved a score of 15.9 percent on its ARC-AGI-2 test, which the organization says nearly doubles the previous commercial best and tops the current Kaggle competition leader.

“With respect to academic questions, Grok 4 is better than PhD level in every subject, no exceptions,” Musk claimed during the livestream. We’ve previously covered nebulous claims about “PhD-level” AI, finding them to be generally specious marketing talk.

Premium pricing amid controversy

During Wednesday’s livestream, xAI also announced plans for an AI coding model in August, a multi-modal agent in September, and a video generation model in October. The company also plans to make Grok 4 available in Tesla vehicles next week, further expanding Musk’s AI assistant across his various companies.

Despite the recent turmoil, xAI has moved forward with an aggressive pricing strategy for “premium” versions of Grok. Alongside Grok 4 and Grok 4 Heavy, xAI launched “SuperGrok Heavy,” a $300-per-month subscription that makes it the most expensive AI service among major providers. Subscribers will get early access to Grok 4 Heavy and upcoming features.

Whether users will pay xAI’s premium pricing remains to be seen, particularly given the AI assistant’s tendency to periodically generate politically motivated outputs. These incidents represent fundamental management and implementation issues that, so far, no fancy-looking test-taking benchmarks have been able to capture.

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Anthropic summons the spirit of Flash games for the AI age

agentic AI, AI, AI agents, AI assistants, AI development tools, AI programming, Anthropic, Anthropic Claude, API, Biz & IT, Claude, Flash games, generative ai, large language models, machine learning, newgrounds, Simon Willison, vibe coding, vibecoding / 9u50fv / June 28, 2025

For those who missed the Flash era, these in-browser apps feel somewhat like the vintage apps that defined a generation of Internet culture from the late 1990s through the 2000s when it first became possible to create complex in-browser experiences. Adobe Flash (originally Macromedia Flash) began as animation software for designers but quickly became the backbone of interactive web content when it gained its own programming language, ActionScript, in 2000.

But unlike Flash games, where hosting costs fell on portal operators, Anthropic has crafted a system where users pay for their own fun through their existing Claude subscriptions. “When someone uses your Claude-powered app, they authenticate with their existing Claude account,” Anthropic explained in its announcement. “Their API usage counts against their subscription, not yours. You pay nothing for their usage.”

A view of the Anthropic Artifacts gallery in the “Play a Game” section. Benj Edwards / Anthropic

Like the Flash games of yesteryear, any Claude-powered apps you build run in the browser and can be shared with anyone who has a Claude account. They’re interactive experiences shared with a simple link, no installation required, created by other people for the sake of creating, except now they’re powered by JavaScript instead of ActionScript.

While you can share these apps with others individually, right now Anthropic’s Artifact gallery only shows examples made by Anthropic and your own personal Artifacts. (If Anthropic expanded it into the future, it might end up feeling a bit like Scratch meets Newgrounds, but with AI doing the coding.) Ultimately, humans are still behind the wheel, describing what kinds of apps they want the AI model to build and guiding the process when it inevitably makes mistakes.

Speaking of mistakes, don’t expect perfect results at first. Usually, building an app with Claude is an interactive experience that requires some guidance to achieve your desired results. But with a little patience and a lot of tokens, you’ll be vibe coding in no time.

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the-resume-is-dying,-and-ai-is-holding-the-smoking-gun

The résumé is dying, and AI is holding the smoking gun

AI, AI and work, AI assistants, AI development tools, AI ethics, AI law, AI programming, AI regulation, ai search, AI work, Biz & IT, chatbots, chatgpt, chatgtp, generative ai, large language models, machine learning / Shannon Garcia / June 25, 2025

Beyond volume, fraud poses an increasing threat. In January, the Justice Department announced indictments in a scheme to place North Korean nationals in remote IT roles at US companies. Research firm Gartner says that fake identity cases are growing rapidly, with the company estimating that by 2028, about 1 in 4 job applicants could be fraudulent. And as we have previously reported, security researchers have also discovered that AI systems can hide invisible text in applications, potentially allowing candidates to game screening systems using prompt injections in ways human reviewers can’t detect.

Illustration of a robot generating endless text, controlled by a scientist.

And that’s not all. Even when AI screening tools work as intended, they exhibit similar biases to human recruiters, preferring white male names on résumés—raising legal concerns about discrimination. The European Union’s AI Act already classifies hiring under its high-risk category with stringent restrictions. Although no US federal law specifically addresses AI use in hiring, general anti-discrimination laws still apply.

So perhaps résumés as a meaningful signal of candidate interest and qualification are becoming obsolete. And maybe that’s OK. When anyone can generate hundreds of tailored applications with a few prompts, the document that once demonstrated effort and genuine interest in a position has devolved into noise.

Instead, the future of hiring may require abandoning the résumé altogether in favor of methods that AI can’t easily replicate—live problem-solving sessions, portfolio reviews, or trial work periods, just to name a few ideas people sometimes consider (whether they are good ideas or not is beyond the scope of this piece). For now, employers and job seekers remain locked in an escalating technological arms race where machines screen the output of other machines, while the humans they’re meant to serve struggle to make authentic connections in an increasingly inauthentic world.

Perhaps the endgame is robots interviewing other robots for jobs performed by robots, while humans sit on the beach drinking daiquiris and playing vintage video games. Well, one can dream.

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With the launch of o3-pro, let’s talk about what AI “reasoning” actually does

AI, AI assistants, AI benchmarks, AI development tools, AI models, AI programming, API, Biz & IT, large language models, machine learning, o1-pro, o3-pro, openai, simulated reasoning, SR models / Beth Washington / June 11, 2025

inquiring artificial minds want to know

New studies reveal pattern-matching reality behind the AI industry’s reasoning claims.

On Tuesday, OpenAI announced that o3-pro, a new version of its most capable simulated reasoning model, is now available to ChatGPT Pro and Team users, replacing o1-pro in the model picker. The company also reduced API pricing for o3-pro by 87 percent compared to o1-pro while cutting o3 prices by 80 percent. While “reasoning” is useful for some analytical tasks, new studies have posed fundamental questions about what the word actually means when applied to these AI systems.

We’ll take a deeper look at “reasoning” in a minute, but first, let’s examine what’s new. While OpenAI originally launched o3 (non-pro) in April, the o3-pro model focuses on mathematics, science, and coding while adding new capabilities like web search, file analysis, image analysis, and Python execution. Since these tool integrations slow response times (longer than the already slow o1-pro), OpenAI recommends using the model for complex problems where accuracy matters more than speed. However, they do not necessarily confabulate less than “non-reasoning” AI models (they still introduce factual errors), which is a significant caveat when seeking accurate results.

Beyond the reported performance improvements, OpenAI announced a substantial price reduction for developers. O3-pro costs $20 per million input tokens and $80 per million output tokens in the API, making it 87 percent cheaper than o1-pro. The company also reduced the price of the standard o3 model by 80 percent.

These reductions address one of the main concerns with reasoning models—their high cost compared to standard models. The original o1 cost $15 per million input tokens and $60 per million output tokens, while o3-mini cost $1.10 per million input tokens and $4.40 per million output tokens.

Why use o3-pro?

Unlike general-purpose models like GPT-4o that prioritize speed, broad knowledge, and making users feel good about themselves, o3-pro uses a chain-of-thought simulated reasoning process to devote more output tokens toward working through complex problems, making it generally better for technical challenges that require deeper analysis. But it’s still not perfect.

An OpenAI's o3-pro benchmark chart. — An OpenAI’s o3-pro benchmark chart. Credit: OpenAI

Measuring so-called “reasoning” capability is tricky since benchmarks can be easy to game by cherry-picking or training data contamination, but OpenAI reports that o3-pro is popular among testers, at least. “In expert evaluations, reviewers consistently prefer o3-pro over o3 in every tested category and especially in key domains like science, education, programming, business, and writing help,” writes OpenAI in its release notes. “Reviewers also rated o3-pro consistently higher for clarity, comprehensiveness, instruction-following, and accuracy.”

OpenAI shared benchmark results showing o3-pro’s reported performance improvements. On the AIME 2024 mathematics competition, o3-pro achieved 93 percent pass@1 accuracy, compared to 90 percent for o3 (medium) and 86 percent for o1-pro. The model reached 84 percent on PhD-level science questions from GPQA Diamond, up from 81 percent for o3 (medium) and 79 percent for o1-pro. For programming tasks measured by Codeforces, o3-pro achieved an Elo rating of 2748, surpassing o3 (medium) at 2517 and o1-pro at 1707.

When reasoning is simulated

Structure made of cubes in the shape of a thinking or contemplating person that evolves from simple to complex, 3D render.

It’s easy for laypeople to be thrown off by the anthropomorphic claims of “reasoning” in AI models. In this case, as with the borrowed anthropomorphic term “hallucinations,” “reasoning” has become a term of art in the AI industry that basically means “devoting more compute time to solving a problem.” It does not necessarily mean the AI models systematically apply logic or possess the ability to construct solutions to truly novel problems. This is why we at Ars Technica continue to use the term “simulated reasoning” (SR) to describe these models. They are simulating a human-style reasoning process that does not necessarily produce the same results as human reasoning when faced with novel challenges.

While simulated reasoning models like o3-pro often show measurable improvements over general-purpose models on analytical tasks, research suggests these gains come from allocating more computational resources to traverse their neural networks in smaller, more directed steps. The answer lies in what researchers call “inference-time compute” scaling. When these models use what are called “chain-of-thought” techniques, they dedicate more computational resources to exploring connections between concepts in their neural network data. Each intermediate “reasoning” output step (produced in tokens) serves as context for the next token prediction, effectively constraining the model’s outputs in ways that tend to improve accuracy and reduce mathematical errors (though not necessarily factual ones).

But fundamentally, all Transformer-based AI models are pattern-matching marvels. They borrow reasoning patterns from examples in the training data that researchers use to create them. Recent studies on Math Olympiad problems reveal that SR models still function as sophisticated pattern-matching machines—they cannot catch their own mistakes or adjust failing approaches, often producing confidently incorrect solutions without any “awareness” of errors.

Apple researchers found similar limitations when testing SR models on controlled puzzle environments. Even when provided explicit algorithms for solving puzzles like Tower of Hanoi, the models failed to execute them correctly—suggesting their process relies on pattern matching from training data rather than logical reasoning. As problem complexity increased, these models showed a “counterintuitive scaling limit,” reducing their reasoning effort despite having adequate computational resources. This aligns with the USAMO findings showing that models made basic logical errors and continued with flawed approaches even when generating contradictory results.

However, there’s some serious nuance here that you may miss if you’re reaching quickly for a pro-AI or anti-AI take. Pattern-matching and reasoning aren’t necessarily mutually exclusive. Since it’s difficult to mechanically define human reasoning at a fundamental level, we can’t definitively say whether sophisticated pattern-matching is categorically different from “genuine” reasoning or just a different implementation of similar underlying processes. The Tower of Hanoi failures are compelling evidence of current limitations, but they don’t resolve the deeper philosophical question of what reasoning actually is.

Illustration of a robot standing on a latter in front of a large chalkboard solving mathematical problems. A red question mark hovers over its head.

And understanding these limitations doesn’t diminish the genuine utility of SR models. For many real-world applications—debugging code, solving math problems, or analyzing structured data—pattern matching from vast training sets is enough to be useful. But as we consider the industry’s stated trajectory toward artificial general intelligence and even superintelligence, the evidence so far suggests that simply scaling up current approaches or adding more “thinking” tokens may not bridge the gap between statistical pattern recognition and what might be called generalist algorithmic reasoning.

But the technology is evolving rapidly, and new approaches are already being developed to address those shortcomings. For example, self-consistency sampling allows models to generate multiple solution paths and check for agreement, while self-critique prompts attempt to make models evaluate their own outputs for errors. Tool augmentation represents another useful direction already used by o3-pro and other ChatGPT models—by connecting LLMs to calculators, symbolic math engines, or formal verification systems, researchers can compensate for some of the models’ computational weaknesses. These methods show promise, though they don’t yet fully address the fundamental pattern-matching nature of current systems.

For now, o3-pro is a better, cheaper version of what OpenAI previously provided. It’s good at solving familiar problems, struggles with truly new ones, and still makes confident mistakes. If you understand its limitations, it can be a powerful tool, but always double-check the results.

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Anthropic releases custom AI chatbot for classified spy work

AI, AI assistants, AI confabulation, AI ethics, AI regulation, Anthropic, Artificial Intelligence, Biz & IT, chatgpt, chatgtp, Claude, GPT-4, large language models, machine learning, microsoft, national security, openai / 9u50fv / June 7, 2025

On Thursday, Anthropic unveiled specialized AI models designed for US national security customers. The company released “Claude Gov” models that were built in response to direct feedback from government clients to handle operations such as strategic planning, intelligence analysis, and operational support. The custom models reportedly already serve US national security agencies, with access restricted to those working in classified environments.

The Claude Gov models differ from Anthropic’s consumer and enterprise offerings, also called Claude, in several ways. They reportedly handle classified material, “refuse less” when engaging with classified information, and are customized to handle intelligence and defense documents. The models also feature what Anthropic calls “enhanced proficiency” in languages and dialects critical to national security operations.

Anthropic says the new models underwent the same “safety testing” as all Claude models. The company has been pursuing government contracts as it seeks reliable revenue sources, partnering with Palantir and Amazon Web Services in November to sell AI tools to defense customers.

Anthropic is not the first company to offer specialized chatbot services for intelligence agencies. In 2024, Microsoft launched an isolated version of OpenAI’s GPT-4 for the US intelligence community after 18 months of work. That system, which operated on a special government-only network without Internet access, became available to about 10,000 individuals in the intelligence community for testing and answering questions.

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New Claude 4 AI model refactored code for 7 hours straight

agentic AI, AI, AI assistants, Alex Albert, Anthropic, Anthropic Claude, Biz & IT, chatgpt, chatgtp, Claude, Claude 4, Claude 4 Opus, Claude 4 Sonnet, large language models, machine learning, openai / 9u50fv / May 22, 2025

Anthropic says Claude 4 beats Gemini on coding benchmarks; works autonomously for hours.

The Claude 4 logo, created by Anthropic. Credit: Anthropic

On Thursday, Anthropic released Claude Opus 4 and Claude Sonnet 4, marking the company’s return to larger model releases after primarily focusing on mid-range Sonnet variants since June of last year. The new models represent what the company calls its most capable coding models yet, with Opus 4 designed for complex, long-running tasks that can operate autonomously for hours.

Alex Albert, Anthropic’s head of Claude Relations, told Ars Technica that the company chose to revive the Opus line because of growing demand for agentic AI applications. “Across all the companies out there that are building things, there’s a really large wave of these agentic applications springing up, and a very high demand and premium being placed on intelligence,” Albert said. “I think Opus is going to fit that groove perfectly.”

Before we go further, a brief refresher on Claude’s three AI model “size” names (first introduced in March 2024) is probably warranted. Haiku, Sonnet, and Opus offer a tradeoff between price (in the API), speed, and capability.

Haiku models are the smallest, least expensive to run, and least capable in terms of what you might call “context depth” (considering conceptual relationships in the prompt) and encoded knowledge. Owing to the small size in parameter count, Haiku models retain fewer concrete facts and thus tend to confabulate more frequently (plausibly answering questions based on lack of data) than larger models, but they are much faster at basic tasks than larger models. Sonnet is traditionally a mid-range model that hits a balance between cost and capability, and Opus models have always been the largest and slowest to run. However, Opus models process context more deeply and are hypothetically better suited for running deep logical tasks.

A screenshot of the Claude web interface with Opus 4 and Sonnet 4 options shown. Credit: Anthropic

There is no Claude 4 Haiku just yet, but the new Sonnet and Opus models can reportedly handle tasks that previous versions could not. In our interview with Albert, he described testing scenarios where Opus 4 worked coherently for up to 24 hours on tasks like playing Pokémon while coding refactoring tasks in Claude Code ran for seven hours without interruption. Earlier Claude models typically lasted only one to two hours before losing coherence, Albert said, meaning that the models could only produce useful self-referencing outputs for that long before beginning to output too many errors.

In particular, that marathon refactoring claim reportedly comes from Rakuten, a Japanese tech services conglomerate that “validated [Claude’s] capabilities with a demanding open-source refactor running independently for 7 hours with sustained performance,” Anthropic said in a news release.

Whether you’d want to leave an AI model unsupervised for that long is another question entirely because even the most capable AI models can introduce subtle bugs, go down unproductive rabbit holes, or make choices that seem logical to the model but miss important context that a human developer would catch. While many people now use Claude for easy-going vibe coding, as we covered in March, the human-powered (and ironically-named) “vibe debugging” that often results from long AI coding sessions is also a very real thing. More on that below.

To shore up some of those shortcomings, Anthropic built memory capabilities into both new Claude 4 models, allowing them to maintain external files for storing key information across long sessions. When developers provide access to local files, the models can create and update “memory files” to track progress and things they deem important over time. Albert compared this to how humans take notes during extended work sessions.

Extended thinking meets tool use

Both Claude 4 models introduce what Anthropic calls “extended thinking with tool use,” a new beta feature allowing the models to alternate between simulated reasoning and using external tools like web search, similar to what OpenAI’s o3 and 04-mini-high AI models currently do in ChatGPT. While Claude 3.7 Sonnet already had strong tool use capabilities, the new models can now interleave simulated reasoning and tool calling in a single response.

“So now we can actually think, call a tool process, the results, think some more, call another tool, and repeat until it gets to a final answer,” Albert explained to Ars. The models self-determine when they have reached a useful conclusion, a capability picked up through training rather than governed by explicit human programming.

General Claude 4 benchmark results, provided by Anthropic. Credit: Anthropic

In practice, we’ve anecdotally found parallel tool use capability very useful in AI assistants like OpenAI o3, since they don’t have to rely on what is trained in their neural network to provide accurate answers. Instead, these more agentic models can iteratively search the web, parse the results, analyze images, and spin up coding tasks for analysis in ways that can avoid falling into a confabulation trap by relying solely on pure LLM outputs.

“The world’s best coding model”

Anthropic says Opus 4 leads industry benchmarks for coding tasks, achieving 72.5 percent on SWE-bench and 43.2 percent on Terminal-bench, calling it “the world’s best coding model.” According to Anthropic, companies using early versions report improvements. Cursor described it as “state-of-the-art for coding and a leap forward in complex codebase understanding,” while Replit noted “improved precision and dramatic advancements for complex changes across multiple files.”

In fact, GitHub announced it will use Sonnet 4 as the base model for its new coding agent in GitHub Copilot, citing the model’s performance in “agentic scenarios” in Anthropic’s news release. Sonnet 4 scored 72.7 percent on SWE-bench while maintaining faster response times than Opus 4. The fact that GitHub is betting on Claude rather than a model from its parent company Microsoft (which has close ties to OpenAI) suggests Anthropic has built something genuinely competitive.

Software engineering benchmark results, provided by Anthropic. Credit: Anthropic

Anthropic says it has addressed a persistent issue with Claude 3.7 Sonnet in which users complained that the model would take unauthorized actions or provide excessive output. Albert said the company reduced this “reward hacking behavior” by approximately 80 percent in the new models through training adjustments. An 80 percent reduction in unwanted behavior sounds impressive, but that also suggests that 20 percent of the problem behavior remains—a big concern when we’re talking about AI models that might be performing autonomous tasks for hours.

When we asked about code accuracy, Albert said that human code review is still an important part of shipping any production code. “There’s a human parallel, right? So this is just a problem we’ve had to deal with throughout the whole nature of software engineering. And this is why the code review process exists, so that you can catch these things. We don’t anticipate that going away with models either,” Albert said. “If anything, the human review will become more important, and more of your job as developer will be in this review than it will be in the generation part.”

Pricing and availability

Both Claude 4 models maintain the same pricing structure as their predecessors: Opus 4 costs $15 per million tokens for input and $75 per million for output, while Sonnet 4 remains at $3 and $15. The models offer two response modes: traditional LLM and simulated reasoning (“extended thinking”) for complex problems. Given that some Claude Code sessions can apparently run for hours, those per-token costs will likely add up very quickly for users who let the models run wild.

Anthropic made both models available through its API, Amazon Bedrock, and Google Cloud Vertex AI. Sonnet 4 remains accessible to free users, while Opus 4 requires a paid subscription.

The Claude 4 models also debut Claude Code (first introduced in February) as a generally available product after months of preview testing. Anthropic says the coding environment now integrates with VS Code and JetBrains IDEs, showing proposed edits directly in files. A new SDK allows developers to build custom agents using the same framework.

A screenshot of “Claude Plays Pokemon,” a custom application where Claude 4 attempts to beat the classic Game Boy game. Credit: Anthropic

Even with Anthropic’s future riding on the capability of these new models, when we asked about how they guide Claude’s behavior by fine-tuning, Albert acknowledged that the inherent unpredictability of these systems presents ongoing challenges for both them and developers. “In the realm and the world of software for the past 40, 50 years, we’ve been running on deterministic systems, and now all of a sudden, it’s non-deterministic, and that changes how we build,” he said.

“I empathize with a lot of people out there trying to use our APIs and language models generally because they have to almost shift their perspective on what it means for reliability, what it means for powering a core of your application in a non-deterministic way,” Albert added. “These are general oddities that have kind of just been flipped, and it definitely makes things more difficult, but I think it opens up a lot of possibilities as well.”

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AI use damages professional reputation, study suggests

AI, AI assistants, AI study, AI work, Biz & IT, chatgpt, chatgtp, Duke University, large language models, machine learning / Mike M. / May 10, 2025

Using AI can be a double-edged sword, according to new research from Duke University. While generative AI tools may boost productivity for some, they might also secretly damage your professional reputation.

On Thursday, the Proceedings of the National Academy of Sciences (PNAS) published a study showing that employees who use AI tools like ChatGPT, Claude, and Gemini at work face negative judgments about their competence and motivation from colleagues and managers.

“Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs,” write researchers Jessica A. Reif, Richard P. Larrick, and Jack B. Soll of Duke’s Fuqua School of Business.

The Duke team conducted four experiments with over 4,400 participants to examine both anticipated and actual evaluations of AI tool users. Their findings, presented in a paper titled “Evidence of a social evaluation penalty for using AI,” reveal a consistent pattern of bias against those who receive help from AI.

What made this penalty particularly concerning for the researchers was its consistency across demographics. They found that the social stigma against AI use wasn’t limited to specific groups.

Fig. 1. Effect sizes for differences in expected perceptions and disclosure to others (Study 1). Note: Positive d values indicate higher values in the AI Tool condition, while negative d values indicate lower values in the AI Tool condition. N = 497. Error bars represent 95% CI. Correlations among variables range from | r |= 0.53 to 0.88. — Fig. 1 from the paper “Evidence of a social evaluation penalty for using AI.” Credit: Reif et al.

“Testing a broad range of stimuli enabled us to examine whether the target’s age, gender, or occupation qualifies the effect of receiving help from Al on these evaluations,” the authors wrote in the paper. “We found that none of these target demographic attributes influences the effect of receiving Al help on perceptions of laziness, diligence, competence, independence, or self-assuredness. This suggests that the social stigmatization of AI use is not limited to its use among particular demographic groups. The result appears to be a general one.”

The hidden social cost of AI adoption

In the first experiment conducted by the team from Duke, participants imagined using either an AI tool or a dashboard creation tool at work. It revealed that those in the AI group expected to be judged as lazier, less competent, less diligent, and more replaceable than those using conventional technology. They also reported less willingness to disclose their AI use to colleagues and managers.

The second experiment confirmed these fears were justified. When evaluating descriptions of employees, participants consistently rated those receiving AI help as lazier, less competent, less diligent, less independent, and less self-assured than those receiving similar help from non-AI sources or no help at all.

AI use damages professional reputation, study suggests Read More »

The end of an AI that shocked the world: OpenAI retires GPT-4

AI, AI assistants, Biz & IT, chatgpt, chatgtp, GPT-4, large language models, machine learning, openai, sam altman / Shannon Garcia / April 30, 2025

One of the most influential—and by some counts, notorious—AI models yet released will soon fade into history. OpenAI announced on April 10 that GPT-4 will be “fully replaced” by GPT-4o in ChatGPT at the end of April, bringing a public-facing end to the model that accelerated a global AI race when it launched in March 2023.

“Effective April 30, 2025, GPT-4 will be retired from ChatGPT and fully replaced by GPT-4o,” OpenAI wrote in its April 10 changelog for ChatGPT. While ChatGPT users will no longer be able to chat with the older AI model, the company added that “GPT-4 will still be available in the API,” providing some reassurance to developers who might still be using the older model for various tasks.

The retirement marks the end of an era that began on March 14, 2023, when GPT-4 demonstrated capabilities that shocked some observers: reportedly scoring at the 90th percentile on the Uniform Bar Exam, acing AP tests, and solving complex reasoning problems that stumped previous models. Its release created a wave of immense hype—and existential panic—about AI’s ability to imitate human communication and composition.

A screenshot of GPT-4's introduction to ChatGPT Plus customers from March 14, 2023. — A screenshot of GPT-4’s introduction to ChatGPT Plus customers from March 14, 2023. Credit: Benj Edwards / Ars Technica

While ChatGPT launched in November 2022 with GPT-3.5 under the hood, GPT-4 took AI language models to a new level of sophistication, and it was a massive undertaking to create. It combined data scraped from the vast corpus of human knowledge into a set of neural networks rumored to weigh in at a combined total of 1.76 trillion parameters, which are the numerical values that hold the data within the model.

Along the way, the model reportedly cost more than $100 million to train, according to comments by OpenAI CEO Sam Altman, and required vast computational resources to develop. Training the model may have involved over 20,000 high-end GPUs working in concert—an expense few organizations besides OpenAI and its primary backer, Microsoft, could afford.

Industry reactions, safety concerns, and regulatory responses

Curiously, GPT-4’s impact began before OpenAI’s official announcement. In February 2023, Microsoft integrated its own early version of the GPT-4 model into its Bing search engine, creating a chatbot that sparked controversy when it tried to convince Kevin Roose of The New York Times to leave his wife and when it “lost its mind” in response to an Ars Technica article.

The end of an AI that shocked the world: OpenAI retires GPT-4 Read More »

OpenAI releases new simulated reasoning models with full tool access

AI, AI assistants, Anthropic, Biz & IT, chatgpt, chatgtp, Claude 3.7 Sonnet, Gemini, Gemini 2.5 Pro, Google, greg brockman, large language models, machine learning, openai, simulated reasoning, SR models / DJ Henderson / April 17, 2025

New o3 model appears “near-genius level,” according to one doctor, but it still makes mistakes.

On Wednesday, OpenAI announced the release of two new models—o3 and o4-mini—that combine simulated reasoning capabilities with access to functions like web browsing and coding. These models mark the first time OpenAI’s reasoning-focused models can use every ChatGPT tool simultaneously, including visual analysis and image generation.

OpenAI announced o3 in December, and until now, only less-capable derivative models named “o3-mini” and “03-mini-high” have been available. However, the new models replace their predecessors—o1 and o3-mini.

OpenAI is rolling out access today for ChatGPT Plus, Pro, and Team users, with Enterprise and Edu customers gaining access next week. Free users can try o4-mini by selecting the “Think” option before submitting queries. OpenAI CEO Sam Altman tweeted, “we expect to release o3-pro to the pro tier in a few weeks.”

For developers, both models are available starting today through the Chat Completions API and Responses API, though some organizations will need verification for access.

The new models offer several improvements. According to OpenAI’s website, “These are the smartest models we’ve released to date, representing a step change in ChatGPT’s capabilities for everyone from curious users to advanced researchers.” OpenAI also says the models offer better cost efficiency than their predecessors, and each comes with a different intended use case: o3 targets complex analysis, while o4-mini, being a smaller version of its next-gen SR model “o4” (not yet released), optimizes for speed and cost-efficiency.

OpenAI says o3 and o4-mini are multimodal, featuring the ability to “think with images.” Credit: OpenAI

What sets these new models apart from OpenAI’s other models (like GPT-4o and GPT-4.5) is their simulated reasoning capability, which uses a simulated step-by-step “thinking” process to solve problems. Additionally, the new models dynamically determine when and how to deploy aids to solve multistep problems. For example, when asked about future energy usage in California, the models can autonomously search for utility data, write Python code to build forecasts, generate visualizing graphs, and explain key factors behind predictions—all within a single query.

OpenAI touts the new models’ multimodal ability to incorporate images directly into their simulated reasoning process—not just analyzing visual inputs but actively “thinking with” them. This capability allows the models to interpret whiteboards, textbook diagrams, and hand-drawn sketches, even when images are blurry or of low quality.

That said, the new releases continue OpenAI’s tradition of selecting confusing product names that don’t tell users much about each model’s relative capabilities—for example, o3 is more powerful than o4-mini despite including a lower number. Then there’s potential confusion with the firm’s non-reasoning AI models. As Ars Technica contributor Timothy B. Lee noted today on X, “It’s an amazing branding decision to have a model called GPT-4o and another one called o4.”

Vibes and benchmarks

All that aside, we know what you’re thinking: What about the vibes? While we have not used 03 or o4-mini yet, frequent AI commentator and Wharton professor Ethan Mollick compared o3 favorably to Google’s Gemini 2.5 Pro on Bluesky. “After using them both, I think that Gemini 2.5 & o3 are in a similar sort of range (with the important caveat that more testing is needed for agentic capabilities),” he wrote. “Each has its own quirks & you will likely prefer one to another, but there is a gap between them & other models.”

During the livestream announcement for o3 and o4-mini today, OpenAI President Greg Brockman boldly claimed: “These are the first models where top scientists tell us they produce legitimately good and useful novel ideas.”

Early user feedback seems to support this assertion, although, until more third-party testing takes place, it’s wise to be skeptical of the claims. On X, immunologist Derya Unutmaz said o3 appeared “at or near genius level” and wrote, “It’s generating complex incredibly insightful and based scientific hypotheses on demand! When I throw challenging clinical or medical questions at o3, its responses sound like they’re coming directly from a top subspecialist physician.”

OpenAI benchmark results for o3 and o4-mini SR models. Credit: OpenAI

So the vibes seem on target, but what about numerical benchmarks? Here’s an interesting one: OpenAI reports that o3 makes “20 percent fewer major errors” than o1 on difficult tasks, with particular strengths in programming, business consulting, and “creative ideation.”

The company also reported state-of-the-art performance on several metrics. On the American Invitational Mathematics Examination (AIME) 2025, o4-mini achieved 92.7 percent accuracy. For programming tasks, o3 reached 69.1 percent accuracy on SWE-Bench Verified, a popular programming benchmark. The models also reportedly showed strong results on visual reasoning benchmarks, with o3 scoring 82.9 percent on MMMU (massive multi-disciplinary multimodal understanding), a college-level visual problem-solving test.

However, these benchmarks provided by OpenAI lack independent verification. One early evaluation of a pre-release o3 model by independent AI research lab Transluce found that the model exhibited recurring types of confabulations, such as claiming to run code locally or providing hardware specifications, and hypothesized this could be due to the model lacking access to its own reasoning processes from previous conversational turns. “It seems that despite being incredibly powerful at solving math and coding tasks, o3 is not by default truthful about its capabilities,” wrote Transluce in a tweet.

Also, some evaluations from OpenAI include footnotes about methodology that bear consideration. For a “Humanity’s Last Exam” benchmark result that measures expert-level knowledge across subjects (o3 scored 20.32 with no tools, but 24.90 with browsing and tools), OpenAI notes that browsing-enabled models could potentially find answers online. The company reports implementing domain blocks and monitoring to prevent what it calls “cheating” during evaluations.

Even though early results seem promising overall, experts or academics who might try to rely on SR models for rigorous research should take the time to exhaustively determine whether the AI model actually produced an accurate result instead of assuming it is correct. And if you’re operating the models outside your domain of knowledge, be careful accepting any results as accurate without independent verification.

Pricing

For ChatGPT subscribers, access to o3 and o4-mini is included with the subscription. On the API side (for developers who integrate the models into their apps), OpenAI has set o3’s pricing at $10 per million input tokens and $40 per million output tokens, with a discounted rate of $2.50 per million for cached inputs. This represents a significant reduction from o1’s pricing structure of $15/$60 per million input/output tokens—effectively a 33 percent price cut while delivering what OpenAI claims is improved performance.

The more economical o4-mini costs $1.10 per million input tokens and $4.40 per million output tokens, with cached inputs priced at $0.275 per million tokens. This maintains the same pricing structure as its predecessor o3-mini, suggesting OpenAI is delivering improved capabilities without raising costs for its smaller reasoning model.

Codex CLI

OpenAI also introduced an experimental terminal application called Codex CLI, described as “a lightweight coding agent you can run from your terminal.” The open source tool connects the models to users’ computers and local code. Alongside this release, the company announced a $1 million grant program offering API credits for projects using Codex CLI.

Codex CLI somewhat resembles Claude Code, an agent launched with Claude 3.7 Sonnet in February. Both are terminal-based coding assistants that operate directly from a console and can interact with local codebases. While Codex CLI connects OpenAI’s models to users’ computers and local code repositories, Claude Code was Anthropic’s first venture into agentic tools, allowing Claude to search through codebases, edit files, write and run tests, and execute command-line operations.

Codex CLI is one more step toward OpenAI’s goal of making autonomous agents that can execute multistep complex tasks on behalf of users. Let’s hope all the vibe coding it produces isn’t used in high-stakes applications without detailed human oversight.

OpenAI releases new simulated reasoning models with full tool access Read More »