machine learning

Anthropic CEO floats idea of giving AI a “quit job” button, sparking skepticism

AI, AI consiousness, AI ethics, Anthropic, Biz & IT, Claude, Dario Amodei, Kyle Fish, large language models, machine learning, Tech / 9u50fv / March 13, 2025

Amodei’s suggestion of giving AI models a way to refuse tasks drew immediate skepticism on X and Reddit as a clip of his response began to circulate earlier this week. One critic on Reddit argued that providing AI with such an option encourages needless anthropomorphism, attributing human-like feelings and motivations to entities that fundamentally lack subjective experiences. They emphasized that task avoidance in AI models signals issues with poorly structured incentives or unintended optimization strategies during training, rather than indicating sentience, discomfort, or frustration.

Our take is that AI models are trained to mimic human behavior from vast amounts of human-generated data. There is no guarantee that the model would “push” a discomfort button because it had a subjective experience of suffering. Instead, we would know it is more likely echoing its training data scraped from the vast corpus of human-generated texts (including books, websites, and Internet comments), which no doubt include representations of lazy, anguished, or suffering workers that it might be imitating.

Refusals already happen

A photo of co-founder and CEO of Anthropic, Dario Amodei, dated May 22, 2024. — Anthropic co-founder and CEO Dario Amodei on May 22, 2024. Credit: Chesnot via Getty Images

In 2023, people frequently complained about refusals in ChatGPT that may have been seasonal, related to training data depictions of people taking winter vacations and not working as hard during certain times of year. Anthropic experienced its own version of the “winter break hypothesis” last year when people claimed Claude became lazy in August due to training data depictions of seeking a summer break, although that was never proven.

However, as far out and ridiculous as this sounds today, it might be short-sighted to permanently rule out the possibility of some kind of subjective experience for AI models as they get more advanced into the future. Even so, will they “suffer” or feel pain? It’s a highly contentious idea, but it’s a topic that Fish is studying for Anthropic, and one that Amodei is apparently taking seriously. But for now, AI models are tools, and if you give them the opportunity to malfunction, that may take place.

To provide further context, here is the full transcript of Amodei’s answer during Monday’s interview (the answer begins around 49: 54 in this video).

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OpenAI pushes AI agent capabilities with new developer API

AI, AI agents, Biz & IT, chatgpt, chatgtp, Computer-Using Agent, large language models, machine learning, openai, Operator, Tech / Mike M. / March 12, 2025

Developers using the Responses API can access the same models that power ChatGPT Search: GPT-4o search and GPT-4o mini search. These models can browse the web to answer questions and cite sources in their responses.

That’s notable because OpenAI says the added web search ability dramatically improves the factual accuracy of its AI models. On OpenAI’s SimpleQA benchmark, which aims to measure confabulation rate, GPT-4o search scored 90 percent, while GPT-4o mini search achieved 88 percent—both substantially outperforming the larger GPT-4.5 model without search, which scored 63 percent.

Despite these improvements, the technology still has significant limitations. Aside from issues with CUA properly navigating websites, the improved search capability doesn’t completely solve the problem of AI confabulations, with GPT-4o search still making factual mistakes 10 percent of the time.

Alongside the Responses API, OpenAI released the open source Agents SDK, providing developers with free tools to integrate models with internal systems, implement safeguards, and monitor agent activities. This toolkit follows OpenAI’s earlier release of Swarm, a framework for orchestrating multiple agents.

These are still early days in the AI agent field, and things will likely improve rapidly. However, at the moment, the AI agent movement remains vulnerable to unrealistic claims, as demonstrated earlier this week when users discovered that Chinese startup Butterfly Effect’s Manus AI agent platform failed to deliver on many of its promises, highlighting the persistent gap between promotional claims and practical functionality in this emerging technology category.

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Why extracting data from PDFs is still a nightmare for data experts

AI, Biz & IT, chatgpt, chatgtp, Computational Journalism, Derek Willis, large language models, machine learning, Mistral, Mistral OCR, ocr, optical character recognition, PDFs, Ray Kurzweil, Simon Willison, Tech / Kelly Newman / March 11, 2025

Optical Character Recognition

Countless digital documents hold valuable info, and the AI industry is attempting to set it free.

For years, businesses, governments, and researchers have struggled with a persistent problem: How to extract usable data from Portable Document Format (PDF) files. These digital documents serve as containers for everything from scientific research to government records, but their rigid formats often trap the data inside, making it difficult for machines to read and analyze.

“Part of the problem is that PDFs are a creature of a time when print layout was a big influence on publishing software, and PDFs are more of a ‘print’ product than a digital one,” Derek Willis, a lecturer in Data and Computational Journalism at the University of Maryland, wrote in an email to Ars Technica. “The main issue is that many PDFs are simply pictures of information, which means you need Optical Character Recognition software to turn those pictures into data, especially when the original is old or includes handwriting.”

Computational journalism is a field where traditional reporting techniques merge with data analysis, coding, and algorithmic thinking to uncover stories that might otherwise remain hidden in large datasets, which makes unlocking that data a particular interest for Willis.

The PDF challenge also represents a significant bottleneck in the world of data analysis and machine learning at large. According to several studies, approximately 80–90 percent of the world’s organizational data is stored as unstructured data in documents, much of it locked away in formats that resist easy extraction. The problem worsens with two-column layouts, tables, charts, and scanned documents with poor image quality.

The inability to reliably extract data from PDFs affects numerous sectors but hits hardest in areas that rely heavily on documentation and legacy records, including digitizing scientific research, preserving historical documents, streamlining customer service, and making technical literature more accessible to AI systems.

“It is a very real problem for almost anything published more than 20 years ago and in particular for government records,” Willis says. “That impacts not just the operation of public agencies like the courts, police, and social services but also journalists, who rely on those records for stories. It also forces some industries that depend on information, like insurance and banking, to invest time and resources in converting PDFs into data.”

A very brief history of OCR

Traditional optical character recognition (OCR) technology, which converts images of text into machine-readable text, has been around since the 1970s. Inventor Ray Kurzweil pioneered the commercial development of OCR systems, including the Kurzweil Reading Machine for the blind in 1976, which relied on pattern-matching algorithms to identify characters from pixel arrangements.

These traditional OCR systems typically work by identifying patterns of light and dark pixels in images, matching them to known character shapes, and outputting the recognized text. While effective for clear, straightforward documents, these pattern-matching systems, a form of AI themselves, often falter when faced with unusual fonts, multiple columns, tables, or poor-quality scans.

Traditional OCR persists in many workflows precisely because its limitations are well-understood—it makes predictable errors that can be identified and corrected, offering a reliability that sometimes outweighs the theoretical advantages of newer AI-based solutions. But now that transformer-based large language models (LLMs) are getting the lion’s share of funding dollars, companies are increasingly turning to them for a new approach to reading documents.

The rise of AI language models in OCR

Unlike traditional OCR methods that follow a rigid sequence of identifying characters based on pixel patterns, multimodal LLMs that can read documents are trained on text and images that have been translated into chunks of data called tokens and fed into large neural networks. Vision-capable LLMs from companies like OpenAI, Google, and Meta analyze documents by recognizing relationships between visual elements and understanding contextual cues.

The “visual” image-based method is how ChatGPT reads a PDF file, for example, if you upload it through the AI assistant interface. It’s a fundamentally different approach than standard OCR that allows them to potentially process documents more holistically, considering both visual layouts and text content simultaneously.

And as it turns out, some LLMs from certain vendors are better at this task than others.

“The LLMs that do well on these tasks tend to behave in ways that are more consistent with how I would do it manually,” Willis said. He noted that some traditional OCR methods are quite good, particularly Amazon’s Textract, but that “they also are bound by the rules of their software and limitations on how much text they can refer to when attempting to recognize an unusual pattern.” Willis added, “With LLMs, I think you trade that for an expanded context that seems to help them make better predictions about whether a digit is a three or an eight, for example.”

This context-based approach enables these models to better handle complex layouts, interpret tables, and distinguish between document elements like headers, captions, and body text—all tasks that traditional OCR solutions struggle with.

“[LLMs] aren’t perfect and sometimes require significant intervention to do the job well, but the fact that you can adjust them at all [with custom prompts] is a big advantage,” Willis said.

New attempts at LLM-based OCR

As the demand for better document-processing solutions grows, new AI players are entering the market with specialized offerings. One such recent entrant has caught the attention of document-processing specialists in particular.

Mistral, a French AI company known for its smaller LLMs, recently entered the LLM-powered optical reader space with Mistral OCR, a specialized API designed for document processing. According to Mistral’s materials, their system aims to extract text and images from documents with complex layouts by using its language model capabilities to process document elements.

Robot sitting on a bunch of books, reading a book.

However, these promotional claims don’t always match real-world performance, according to recent tests. “I’m typically a pretty big fan of the Mistral models, but the new OCR-specific one they released last week really performed poorly,” Willis noted.

“A colleague sent this PDF and asked if I could help him parse the table it contained,” says Willis. “It’s an old document with a table that has some complex layout elements. The new [Mistral] OCR-specific model really performed poorly, repeating the names of cities and botching a lot of the numbers.”

AI app developer Alexander Doria also recently pointed out on X a flaw with Mistral OCR’s ability to understand handwriting, writing, “Unfortunately Mistral-OCR has still the usual VLM curse: with challenging manuscripts, it hallucinates completely.”

According to Willis, Google currently leads the field in AI models that can read documents: “Right now, for me the clear leader is Google’s Gemini 2.0 Flash Pro Experimental. It handled the PDF that Mistral did not with a tiny number of mistakes, and I’ve run multiple messy PDFs through it with success, including those with handwritten content.”

Gemini’s performance stems largely from its ability to process expansive documents (in a type of short-term memory called a “context window”), which Willis specifically notes as a key advantage: “The size of its context window also helps, since I can upload large documents and work through them in parts.” This capability, combined with more robust handling of handwritten content, apparently gives Google’s model a practical edge over competitors in real-world document-processing tasks for now.

The drawbacks of LLM-based OCR

Despite their promise, LLMs introduce several new problems to document processing. Among them, they can introduce confabulations or hallucinations (plausible-sounding but incorrect information), accidentally follow instructions in the text (thinking they are part of a user prompt), or just generally misinterpret the data.

“The biggest [drawback] is that they are probabilistic prediction machines and will get it wrong in ways that aren’t just ‘that’s the wrong word’,” Willis explains. “LLMs will sometimes skip a line in larger documents where the layout repeats itself, I’ve found, where OCR isn’t likely to do that.”

AI researcher and data journalist Simon Willison identified several critical concerns of using LLMs for OCR in a conversation with Ars Technica. “I still think the biggest challenge is the risk of accidental instruction following,” he says, always wary of prompt injections (in this case accidental) that might feed nefarious or contradictory instructions to a LLM.

“That and the fact that table interpretation mistakes can be catastrophic,” Willison adds. “In the past I’ve had lots of cases where a vision LLM has matched up the wrong line of data with the wrong heading, which results in absolute junk that looks correct. Also that thing where sometimes if text is illegible a model might just invent the text.”

These issues become particularly troublesome when processing financial statements, legal documents, or medical records, where a mistake might put someone’s life in danger. The reliability problems mean these tools often require careful human oversight, limiting their value for fully automated data extraction.

The path forward

Even in our seemingly advanced age of AI, there is still no perfect OCR solution. The race to unlock data from PDFs continues, with companies like Google now offering context-aware generative AI products. Some of the motivation for unlocking PDFs among AI companies, as Willis observes, doubtless involves potential training data acquisition: “I think Mistral’s announcement is pretty clear evidence that documents—not just PDFs—are a big part of their strategy, exactly because it will likely provide additional training data.”

Whether it benefits AI companies with training data or historians analyzing a historical census, as these technologies improve, they may unlock repositories of knowledge currently trapped in digital formats designed primarily for human consumption. That could lead to a new golden age of data analysis—or a field day for hard-to-spot mistakes, depending on the technology used and how blindly we trust it.

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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What does “PhD-level” AI mean? OpenAI’s rumored $20,000 agent plan explained.

AI, AI assistants, Biz & IT, chatgpt, chatgtp, Deep Research, Google, large language models, machine learning, openai, PhD, Tech / Shannon Garcia / March 8, 2025

On the Frontier Math benchmark by EpochAI, o3 solved 25.2 percent of problems, while no other model has exceeded 2 percent—suggesting a leap in mathematical reasoning capabilities over the previous model.

Benchmarks vs. real-world value

Ideally, potential applications for a true PhD-level AI model would include analyzing medical research data, supporting climate modeling, and handling routine aspects of research work.

The high price points reported by The Information, if accurate, suggest that OpenAI believes these systems could provide substantial value to businesses. The publication notes that SoftBank, an OpenAI investor, has committed to spending $3 billion on OpenAI’s agent products this year alone—indicating significant business interest despite the costs.

Meanwhile, OpenAI faces financial pressures that may influence its premium pricing strategy. The company reportedly lost approximately $5 billion last year covering operational costs and other expenses related to running its services.

News of OpenAI’s stratospheric pricing plans come after years of relatively affordable AI services that have conditioned users to expect powerful capabilities at relatively low costs. ChatGPT Plus remains $20 per month and Claude Pro costs $30 monthly—both tiny fractions of these proposed enterprise tiers. Even ChatGPT Pro’s $200/month subscription is relatively small compared to the new proposed fees. Whether the performance difference between these tiers will match their thousandfold price difference is an open question.

Despite their benchmark performances, these simulated reasoning models still struggle with confabulations—instances where they generate plausible-sounding but factually incorrect information. This remains a critical concern for research applications where accuracy and reliability are paramount. A $20,000 monthly investment raises questions about whether organizations can trust these systems not to introduce subtle errors into high-stakes research.

In response to the news, several people quipped on social media that companies could hire an actual PhD student for much cheaper. “In case you have forgotten,” wrote xAI developer Hieu Pham in a viral tweet, “most PhD students, including the brightest stars who can do way better work than any current LLMs—are not paid $20K / month.”

While these systems show strong capabilities on specific benchmarks, the “PhD-level” label remains largely a marketing term. These models can process and synthesize information at impressive speeds, but questions remain about how effectively they can handle the creative thinking, intellectual skepticism, and original research that define actual doctoral-level work. On the other hand, they will never get tired or need health insurance, and they will likely continue to improve in capability and drop in cost over time.

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CMU research shows compression alone may unlock AI puzzle-solving abilities

AI, AI benchmarks, AI compression, ARC-AGI, Biz & IT, compression, François Chollet, machine learning, Tech / Mike M. / March 7, 2025

Tis the season for a squeezin’

New research challenges prevailing idea that AI needs massive datasets to solve problems.

A pair of Carnegie Mellon University researchers recently discovered hints that the process of compressing information can solve complex reasoning tasks without pre-training on a large number of examples. Their system tackles some types of abstract pattern-matching tasks using only the puzzles themselves, challenging conventional wisdom about how machine learning systems acquire problem-solving abilities.

“Can lossless information compression by itself produce intelligent behavior?” ask Isaac Liao, a first-year PhD student, and his advisor Professor Albert Gu from CMU’s Machine Learning Department. Their work suggests the answer might be yes. To demonstrate, they created CompressARC and published the results in a comprehensive post on Liao’s website.

The pair tested their approach on the Abstraction and Reasoning Corpus (ARC-AGI), an unbeaten visual benchmark created in 2019 by machine learning researcher François Chollet to test AI systems’ abstract reasoning skills. ARC presents systems with grid-based image puzzles where each provides several examples demonstrating an underlying rule, and the system must infer that rule to apply it to a new example.

For instance, one ARC-AGI puzzle shows a grid with light blue rows and columns dividing the space into boxes. The task requires figuring out which colors belong in which boxes based on their position: black for corners, magenta for the middle, and directional colors (red for up, blue for down, green for right, and yellow for left) for the remaining boxes. Here are three other example ARC-AGI puzzles, taken from Liao’s website:

Three example ARC-AGI benchmarking puzzles. Credit: Isaac Liao / Albert Gu

The puzzles test capabilities that some experts believe may be fundamental to general human-like reasoning (often called “AGI” for artificial general intelligence). Those properties include understanding object persistence, goal-directed behavior, counting, and basic geometry without requiring specialized knowledge. The average human solves 76.2 percent of the ARC-AGI puzzles, while human experts reach 98.5 percent.

OpenAI made waves in December for the claim that its o3 simulated reasoning model earned a record-breaking score on the ARC-AGI benchmark. In testing with computational limits, o3 scored 75.7 percent on the test, while in high-compute testing (basically unlimited thinking time), it reached 87.5 percent, which OpenAI says is comparable to human performance.

CompressARC achieves 34.75 percent accuracy on the ARC-AGI training set (the collection of puzzles used to develop the system) and 20 percent on the evaluation set (a separate group of unseen puzzles used to test how well the approach generalizes to new problems). Each puzzle takes about 20 minutes to process on a consumer-grade RTX 4070 GPU, compared to top-performing methods that use heavy-duty data center-grade machines and what the researchers describe as “astronomical amounts of compute.”

Not your typical AI approach

CompressARC takes a completely different approach than most current AI systems. Instead of relying on pre-training—the process where machine learning models learn from massive datasets before tackling specific tasks—it works with no external training data whatsoever. The system trains itself in real-time using only the specific puzzle it needs to solve.

“No pretraining; models are randomly initialized and trained during inference time. No dataset; one model trains on just the target ARC-AGI puzzle and outputs one answer,” the researchers write, describing their strict constraints.

When the researchers say “No search,” they’re referring to another common technique in AI problem-solving where systems try many different possible solutions and select the best one. Search algorithms work by systematically exploring options—like a chess program evaluating thousands of possible moves—rather than directly learning a solution. CompressARC avoids this trial-and-error approach, relying solely on gradient descent—a mathematical technique that incrementally adjusts the network’s parameters to reduce errors, similar to how you might find the bottom of a valley by always walking downhill.

A block diagram of the CompressARC architecture, created by the researchers. Credit: Isaac Liao / Albert Gu

The system’s core principle uses compression—finding the most efficient way to represent information by identifying patterns and regularities—as the driving force behind intelligence. CompressARC searches for the shortest possible description of a puzzle that can accurately reproduce the examples and the solution when unpacked.

While CompressARC borrows some structural principles from transformers (like using a residual stream with representations that are operated upon), it’s a custom neural network architecture designed specifically for this compression task. It’s not based on an LLM or standard transformer model.

Unlike typical machine learning methods, CompressARC uses its neural network only as a decoder. During encoding (the process of converting information into a compressed format), the system fine-tunes the network’s internal settings and the data fed into it, gradually making small adjustments to minimize errors. This creates the most compressed representation while correctly reproducing known parts of the puzzle. These optimized parameters then become the compressed representation that stores the puzzle and its solution in an efficient format.

An animated GIF showing the multi-step process of CompressARC solving an ARC-AGI puzzle. Credit: Isaac Liao

“The key challenge is to obtain this compact representation without needing the answers as inputs,” the researchers explain. The system essentially uses compression as a form of inference.

This approach could prove valuable in domains where large datasets don’t exist or when systems need to learn new tasks with minimal examples. The work suggests that some forms of intelligence might emerge not from memorizing patterns across vast datasets, but from efficiently representing information in compact forms.

The compression-intelligence connection

The potential connection between compression and intelligence may sound strange at first glance, but it has deep theoretical roots in computer science concepts like Kolmogorov complexity (the shortest program that produces a specified output) and Solomonoff induction—a theoretical gold standard for prediction equivalent to an optimal compression algorithm.

To compress information efficiently, a system must recognize patterns, find regularities, and “understand” the underlying structure of the data—abilities that mirror what many consider intelligent behavior. A system that can predict what comes next in a sequence can compress that sequence efficiently. As a result, some computer scientists over the decades have suggested that compression may be equivalent to general intelligence. Based on these principles, the Hutter Prize has offered awards to researchers who can compress a 1GB file to the smallest size.

We previously wrote about intelligence and compression in September 2023, when a DeepMind paper discovered that large language models can sometimes outperform specialized compression algorithms. In that study, researchers found that DeepMind’s Chinchilla 70B model could compress image patches to 43.4 percent of their original size (beating PNG’s 58.5 percent) and audio samples to just 16.4 percent (outperforming FLAC’s 30.3 percent).

That 2023 research suggested a deep connection between compression and intelligence—the idea that truly understanding patterns in data enables more efficient compression, which aligns with this new CMU research. While DeepMind demonstrated compression capabilities in an already-trained model, Liao and Gu’s work takes a different approach by showing that the compression process can generate intelligent behavior from scratch.

This new research matters because it challenges the prevailing wisdom in AI development, which typically relies on massive pre-training datasets and computationally expensive models. While leading AI companies push toward ever-larger models trained on more extensive datasets, CompressARC suggests intelligence emerging from a fundamentally different principle.

“CompressARC’s intelligence emerges not from pretraining, vast datasets, exhaustive search, or massive compute—but from compression,” the researchers conclude. “We challenge the conventional reliance on extensive pretraining and data, and propose a future where tailored compressive objectives and efficient inference-time computation work together to extract deep intelligence from minimal input.”

Limitations and looking ahead

Even with its successes, Liao and Gu’s system comes with clear limitations that may prompt skepticism. While it successfully solves puzzles involving color assignments, infilling, cropping, and identifying adjacent pixels, it struggles with tasks requiring counting, long-range pattern recognition, rotations, reflections, or simulating agent behavior. These limitations highlight areas where simple compression principles may not be sufficient.

The research has not been peer-reviewed, and the 20 percent accuracy on unseen puzzles, though notable without pre-training, falls significantly below both human performance and top AI systems. Critics might argue that CompressARC could be exploiting specific structural patterns in the ARC puzzles that might not generalize to other domains, challenging whether compression alone can serve as a foundation for broader intelligence rather than just being one component among many required for robust reasoning capabilities.

And yet as AI development continues its rapid advance, if CompressARC holds up to further scrutiny, it offers a glimpse of a possible alternative path that might lead to useful intelligent behavior without the resource demands of today’s dominant approaches. Or at the very least, it might unlock an important component of general intelligence in machines, which is still poorly understood.

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Will the future of software development run on vibes?

AI, Biz & IT, coding, machine learning, Tech, vibe coding, vibecoding / Shannon Garcia / March 6, 2025

Accepting AI-written code without understanding how it works is growing in popularity.

For many people, coding is about telling a computer what to do and having the computer perform those precise actions repeatedly. With the rise of AI tools like ChatGPT, it’s now possible for someone to describe a program in English and have the AI model translate it into working code without ever understanding how the code works. Former OpenAI researcher Andrej Karpathy recently gave this practice a name—”vibe coding”—and it’s gaining traction in tech circles.

The technique, enabled by large language models (LLMs) from companies like OpenAI and Anthropic, has attracted attention for potentially lowering the barrier to entry for software creation. But questions remain about whether the approach can reliably produce code suitable for real-world applications, even as tools like Cursor Composer, GitHub Copilot, and Replit Agent make the process increasingly accessible to non-programmers.

Instead of being about control and precision, vibe coding is all about surrendering to the flow. On February 2, Karpathy introduced the term in a post on X, writing, “There’s a new kind of coding I call ‘vibe coding,’ where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” He described the process in deliberately casual terms: “I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”

Karapthy tweet screenshot: There's a new kind of coding I call — A screenshot of Karpathy’s original X post about vibe coding from February 2, 2025. Credit: Andrej Karpathy / X

While vibe coding, if an error occurs, you feed it back into the AI model, accept the changes, hope it works, and repeat the process. Karpathy’s technique stands in stark contrast to traditional software development best practices, which typically emphasize careful planning, testing, and understanding of implementation details.

As Karpathy humorously acknowledged in his original post, the approach is for the ultimate lazy programmer experience: “I ask for the dumbest things, like ‘decrease the padding on the sidebar by half,’ because I’m too lazy to find it myself. I ‘Accept All’ always; I don’t read the diffs anymore.”

At its core, the technique transforms anyone with basic communication skills into a new type of natural language programmer—at least for simple projects. With AI models currently being held back by the amount of code an AI model can digest at once (context size), there tends to be an upper-limit to how complex a vibe-coded software project can get before the human at the wheel becomes a high-level project manager, manually assembling slices of AI-generated code into a larger architecture. But as technical limits expand with each generation of AI models, those limits may one day disappear.

Who are the vibe coders?

There’s no way to know exactly how many people are currently vibe coding their way through either hobby projects or development jobs, but Cursor reported 40,000 paying users in August 2024, and GitHub reported 1.3 million Copilot users just over a year ago (February 2024). While we can’t find user numbers for Replit Agent, the site claims 30 million users, with an unknown percentage using the site’s AI-powered coding agent.

One thing we do know: the approach has particularly gained traction online as a fun way of rapidly prototyping games. Microsoft’s Peter Yang recently demonstrated vibe coding in an X thread by building a simple 3D first-person shooter zombie game through conversational prompts fed into Cursor and Claude 3.7 Sonnet. Yang even used a speech-to-text app so he could verbally describe what he wanted to see and refine the prototype over time.

A photo of a MS-DOS computer with Q-BASIC code on the screen. — In August 2024, the author vibe coded his way into a working Q-BASIC utility script for MS-DOS, thanks to Claude Sonnet. Credit: Benj Edwards

We’ve been doing some vibe coding ourselves. Multiple Ars staffers have used AI assistants and coding tools for extracurricular hobby projects such as creating small games, crafting bespoke utilities, writing processing scripts, and more. Having a vibe-based code genie can come in handy in unexpected places: Last year, I asked Anthropic’s Claude write a Microsoft Q-BASIC program in MS-DOS that decompressed 200 ZIP files into custom directories, saving me many hours of manual typing work.

Debugging the vibes

With all this vibe coding going on, we had to turn to an expert for some input. Simon Willison, an independent software developer and AI researcher, offered a nuanced perspective on AI-assisted programming in an interview with Ars Technica. “I really enjoy vibe coding,” he said. “It’s a fun way to try out an idea and prove if it can work.”

But there are limits to how far Willison will go. “Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial.”

At some point, understanding at least some of the code is important because AI-generated code may include bugs, misunderstandings, and confabulations—for example, instances where the AI model generates references to nonexistent functions or libraries.

“Vibe coding is all fun and games until you have to vibe debug,” developer Ben South noted wryly on X, highlighting this fundamental issue.

Willison recently argued on his blog that encountering hallucinations with AI coding tools isn’t as detrimental as embedding false AI-generated information into a written report, because coding tools have built-in fact-checking: If there’s a confabulation, the code won’t work. This provides a natural boundary for vibe coding’s reliability—the code runs or it doesn’t.

Even so, the risk-reward calculation for vibe coding becomes far more complex in professional settings. While a solo developer might accept the trade-offs of vibe coding for personal projects, enterprise environments typically require code maintainability and reliability standards that vibe-coded solutions may struggle to meet. When code doesn’t work as expected, debugging requires understanding what the code is actually doing—precisely the knowledge that vibe coding tends to sidestep.

Programming without understanding

When it comes to defining what exactly constitutes vibe coding, Willison makes an important distinction: “If an LLM wrote every line of your code, but you’ve reviewed, tested, and understood it all, that’s not vibe coding in my book—that’s using an LLM as a typing assistant.” Vibe coding, in contrast, involves accepting code without fully understanding how it works.

While vibe coding originated with Karpathy as a playful term, it may encapsulate a real shift in how some developers approach programming tasks—prioritizing speed and experimentation over deep technical understanding. And to some people, that may be terrifying.

Willison emphasizes that developers need to take accountability for their code: “I firmly believe that as a developer you have to take accountability for the code you produce—if you’re going to put your name to it you need to be confident that you understand how and why it works—ideally to the point that you can explain it to somebody else.”

He also warns about a common path to technical debt: “For experiments and low-stake projects where you want to explore what’s possible and build fun prototypes? Go wild! But stay aware of the very real risk that a good enough prototype often faces pressure to get pushed to production.”

The future of programming jobs

So, is all this vibe coding going to cost human programmers their jobs? At its heart, programming has always been about telling a computer how to operate. The method of how we do that has changed over time, but there may always be people who are better at telling a computer precisely what to do than others—even in natural language. In some ways, those people may become the new “programmers.”

There was a point in the late 1970s to early ’80s when many people thought people required programming skills to use a computer effectively because there were very few pre-built applications for all the various computer platforms available. School systems worldwide made educational computer literacy efforts to teach people to code.

A brochure for the GE 210 computer from 1964. BASIC's creators used a similar computer four years later to develop the programming language. — A brochure for the GE 210 computer from 1964. BASIC’s creators used a similar computer four years later to develop the programming language that many children were taught at home and school. Credit: GE / Wikipedia

Before too long, people made useful software applications that let non-coders utilize computers easily—no programming required. Even so, programmers didn’t disappear—instead, they used applications to create better and more complex programs. Perhaps that will also happen with AI coding tools.

To use an analogy, computer controlled technologies like autopilot made reliable supersonic flight possible because they could handle aspects of flight that were too taxing for all but the most highly trained and capable humans to safely control. AI may do the same for programming, allowing humans to abstract away complexities that would otherwise take too much time to manually code, and that may allow for the creation of more complex and useful software experiences in the future.

But at that point, will humans still be able to understand or debug them? Maybe not. We may be completely dependent on AI tools, and some people no doubt find that a little scary or unwise.

Whether vibe coding lasts in the programming landscape or remains a prototyping technique will likely depend less on the capabilities of AI models and more on the willingness of organizations to accept risky trade-offs in code quality, maintainability, and technical debt. For now, vibe coding remains an apt descriptor of the messy, experimental relationship between AI and human developers—more collaborative than autonomous, but increasingly blurring the lines of who (or what) is really doing the programming.

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Eerily realistic AI voice demo sparks amazement and discomfort online

AI, AI assistants, Biz & IT, chatbots, chatgpt, chatgtp, emotional AI, machine learning, Sesame, Tech, voice synthesis / Mike M. / March 5, 2025

Sesame’s new AI voice model features uncanny imperfections, and it’s willing to act like an angry boss.

In late 2013, the Spike Jonze film Her imagined a future where people would form emotional connections with AI voice assistants. Nearly 12 years later, that fictional premise has veered closer to reality with the release of a new conversational voice model from AI startup Sesame that has left many users both fascinated and unnerved.

“I tried the demo, and it was genuinely startling how human it felt,” wrote one Hacker News user who tested the system. “I’m almost a bit worried I will start feeling emotionally attached to a voice assistant with this level of human-like sound.”

In late February, Sesame released a demo for the company’s new Conversational Speech Model (CSM) that appears to cross over what many consider the “uncanny valley” of AI-generated speech, with some testers reporting emotional connections to the male or female voice assistant (“Miles” and “Maya”).

In our own evaluation, we spoke with the male voice for about 28 minutes, talking about life in general and how it decides what is “right” or “wrong” based on its training data. The synthesized voice was expressive and dynamic, imitating breath sounds, chuckles, interruptions, and even sometimes stumbling over words and correcting itself. These imperfections are intentional.

“At Sesame, our goal is to achieve ‘voice presence’—the magical quality that makes spoken interactions feel real, understood, and valued,” writes the company in a blog post. “We are creating conversational partners that do not just process requests; they engage in genuine dialogue that builds confidence and trust over time. In doing so, we hope to realize the untapped potential of voice as the ultimate interface for instruction and understanding.”

Sometimes the model tries too hard to sound like a real human. In one demo posted online by a Reddit user called MetaKnowing, the AI model talks about craving “peanut butter and pickle sandwiches.”

An example of Sesame’s female voice model craving peanut butter and pickle sandwiches, captured by Reddit user MetaKnowing.

Founded by Brendan Iribe, Ankit Kumar, and Ryan Brown, Sesame AI has attracted significant backing from prominent venture capital firms. The company has secured investments from Andreessen Horowitz, led by Anjney Midha and Marc Andreessen, along with Spark Capital, Matrix Partners, and various founders and individual investors.

Browsing reactions to Sesame found online, we found many users expressing astonishment at its realism. “I’ve been into AI since I was a child, but this is the first time I’ve experienced something that made me definitively feel like we had arrived,” wrote one Reddit user. “I’m sure it’s not beating any benchmarks, or meeting any common definition of AGI, but this is the first time I’ve had a real genuine conversation with something I felt was real.” Many other Reddit threads express similar feelings of surprise, with commenters saying it’s “jaw-dropping” or “mind-blowing.”

While that sounds like a bunch of hyperbole at first glance, not everyone finds the Sesame experience pleasant. Mark Hachman, a senior editor at PCWorld, wrote about being deeply unsettled by his interaction with the Sesame voice AI. “Fifteen minutes after ‘hanging up’ with Sesame’s new ‘lifelike’ AI, and I’m still freaked out,” Hachman reported. He described how the AI’s voice and conversational style eerily resembled an old friend he had dated in high school.

Others have compared Sesame’s voice model to OpenAI’s Advanced Voice Mode for ChatGPT, saying that Sesame’s CSM features more realistic voices, and others are pleased that the model in the demo will roleplay angry characters, which ChatGPT refuses to do.

An example argument with Sesame’s CSM created by Gavin Purcell.

Gavin Purcell, co-host of the AI for Humans podcast, posted an example video on Reddit where the human pretends to be an embezzler and argues with a boss. It’s so dynamic that it’s difficult to tell who the human is and which one is the AI model. Judging by our own demo, it’s entirely capable of what you see in the video.

“Near-human quality”

Under the hood, Sesame’s CSM achieves its realism by using two AI models working together (a backbone and a decoder) based on Meta’s Llama architecture that processes interleaved text and audio. Sesame trained three AI model sizes, with the largest using 8.3 billion parameters (an 8 billion backbone model plus a 300 million parameter decoder) on approximately 1 million hours of primarily English audio.

Sesame’s CSM doesn’t follow the traditional two-stage approach used by many earlier text-to-speech systems. Instead of generating semantic tokens (high-level speech representations) and acoustic details (fine-grained audio features) in two separate stages, Sesame’s CSM integrates into a single-stage, multimodal transformer-based model, jointly processing interleaved text and audio tokens to produce speech. OpenAI’s voice model uses a similar multimodal approach.

In blind tests without conversational context, human evaluators showed no clear preference between CSM-generated speech and real human recordings, suggesting the model achieves near-human quality for isolated speech samples. However, when provided with conversational context, evaluators still consistently preferred real human speech, indicating a gap remains in fully contextual speech generation.

Sesame co-founder Brendan Iribe acknowledged current limitations in a comment on Hacker News, noting that the system is “still too eager and often inappropriate in its tone, prosody and pacing” and has issues with interruptions, timing, and conversation flow. “Today, we’re firmly in the valley, but we’re optimistic we can climb out,” he wrote.

Too close for comfort?

Despite CSM’s technological impressiveness, advancements in conversational voice AI carry significant risks for deception and fraud. The ability to generate highly convincing human-like speech has already supercharged voice phishing scams, allowing criminals to impersonate family members, colleagues, or authority figures with unprecedented realism. But adding realistic interactivity to those scams may take them to another level of potency.

Unlike current robocalls that often contain tell-tale signs of artificiality, next-generation voice AI could eliminate these red flags entirely. As synthetic voices become increasingly indistinguishable from human speech, you may never know who you’re talking to on the other end of the line. It’s inspired some people to share a secret word or phrase with their family for identity verification.

Although Sesame’s demo does not clone a person’s voice, future open source releases of similar technology could allow malicious actors to potentially adapt these tools for social engineering attacks. OpenAI itself held back its own voice technology from wider deployment over fears of misuse.

Sesame sparked a lively discussion on Hacker News about its potential uses and dangers. Some users reported having extended conversations with the two demo voices, with conversations lasting up to the 30-minute limit. In one case, a parent recounted how their 4-year-old daughter developed an emotional connection with the AI model, crying after not being allowed to talk to it again.

The company says it plans to open-source “key components” of its research under an Apache 2.0 license, enabling other developers to build upon their work. Their roadmap includes scaling up model size, increasing dataset volume, expanding language support to over 20 languages, and developing “fully duplex” models that better handle the complex dynamics of real conversations.

You can try the Sesame demo on the company’s website, assuming that it isn’t too overloaded with people who want to simulate a rousing argument.

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Researchers surprised to find less-educated areas adopting AI writing tools faster

AI, AI research, AI writing, AI writing detectors, Biz & IT, chatgpt, chatgtp, large language models, machine learning, Stanford, Tech / DJ Henderson / March 4, 2025

From the mouths of machines

Stanford researchers analyzed 305 million texts, revealing AI-writing trends.

Since the launch of ChatGPT in late 2022, experts have debated how widely AI language models would impact the world. A few years later, the picture is getting clear. According to new Stanford University-led research examining over 300 million text samples across multiple sectors, AI language models now assist in writing up to a quarter of professional communications across sectors. It’s having a large impact, especially in less-educated parts of the United States.

“Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications,” wrote the researchers.

The researchers tracked large language model (LLM) adoption across industries from January 2022 to September 2024 using a dataset that included 687,241 consumer complaints submitted to the US Consumer Financial Protection Bureau (CFPB), 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations press releases.

By using a statistical detection system that tracked word usage patterns, the researchers found that roughly 18 percent of financial consumer complaints (including 30 percent of all complaints from Arkansas), 24 percent of corporate press releases, up to 15 percent of job postings, and 14 percent of UN press releases showed signs of AI assistance during that period of time.

The study also found that while urban areas showed higher adoption overall (18.2 percent versus 10.9 percent in rural areas), regions with lower educational attainment used AI writing tools more frequently (19.9 percent compared to 17.4 percent in higher-education areas). The researchers note that this contradicts typical technology adoption patterns where more educated populations adopt new tools fastest.

“In the consumer complaint domain, the geographic and demographic patterns in LLM adoption present an intriguing departure from historical technology diffusion trends where technology adoption has generally been concentrated in urban areas, among higher-income groups, and populations with higher levels of educational attainment.”

Researchers from Stanford, the University of Washington, and Emory University led the study, titled, “The Widespread Adoption of Large Language Model-Assisted Writing Across Society,” first listed on the arXiv preprint server in mid-February. Weixin Liang and Yaohui Zhang from Stanford served as lead authors, with collaborators Mihai Codreanu, Jiayu Wang, Hancheng Cao, and James Zou.

Detecting AI use in aggregate

We’ve previously covered that AI writing detection services aren’t reliable, and this study does not contradict that finding. On a document-by-document basis, AI detectors cannot be trusted. But when analyzing millions of documents in aggregate, telltale patterns emerge that suggest the influence of AI language models on text.

The researchers developed an approach based on a statistical framework in a previously released work that analyzed shifts in word frequencies and linguistic patterns before and after ChatGPT’s release. By comparing large sets of pre- and post-ChatGPT texts, they estimated the proportion of AI-assisted content at a population level. The presumption is that LLMs tend to favor certain word choices, sentence structures, and linguistic patterns that differ subtly from typical human writing.

To validate their approach, the researchers created test sets with known percentages of AI content (from zero percent to 25 percent) and found their method predicted these percentages with error rates below 3.3 percent. This statistical validation gave them confidence in their population-level estimates.

While the researchers specifically note their estimates likely represent a minimum level of AI usage, it’s important to understand that actual AI involvement might be significantly greater. Due to the difficulty in detecting heavily edited or increasingly sophisticated AI-generated content, the researchers say their reported adoption rates could substantially underestimate true levels of generative AI use.

Analysis suggests AI use as “equalizing tools”

While the overall adoption rates are revealing, perhaps more insightful are the patterns of who is using AI writing tools and how these patterns may challenge conventional assumptions about technology adoption.

In examining the CFPB complaints (a US public resource that collects complaints about consumer financial products and services), the researchers’ geographic analysis revealed substantial variation across US states.

Arkansas showed the highest adoption rate at 29.2 percent (based on 7,376 complaints), followed by Missouri at 26.9 percent (16,807 complaints) and North Dakota at 24.8 percent (1,025 complaints). In contrast, states like West Virginia (2.6 percent), Idaho (3.8 percent), and Vermont (4.8 percent) showed minimal AI writing adoption. Major population centers demonstrated moderate adoption, with California at 17.4 percent (157,056 complaints) and New York at 16.6 percent (104,862 complaints).

The urban-rural divide followed expected technology adoption patterns initially, but with an interesting twist. Using Rural Urban Commuting Area (RUCA) codes, the researchers found that urban and rural areas initially adopted AI writing tools at similar rates during early 2023. However, adoption trajectories diverged by mid-2023, with urban areas reaching 18.2 percent adoption compared to 10.9 percent in rural areas.

Contrary to typical technology diffusion patterns, areas with lower educational attainment showed higher AI writing tool usage. Comparing regions above and below state median levels of bachelor’s degree attainment, areas with fewer college graduates stabilized at 19.9 percent adoption rates compared to 17.4 percent in more educated regions. This pattern held even within urban areas, where less-educated communities showed 21.4 percent adoption versus 17.8 percent in more educated urban areas.

The researchers suggest that AI writing tools may serve as a leg-up for people who may not have as much educational experience. “While the urban-rural digital divide seems to persist,” the researchers write, “our finding that areas with lower educational attainment showed modestly higher LLM adoption rates in consumer complaints suggests these tools may serve as equalizing tools in consumer advocacy.”

Corporate and diplomatic trends in AI writing

According to the researchers, all sectors they analyzed (consumer complaints, corporate communications, job postings) showed similar adoption patterns: sharp increases beginning three to four months after ChatGPT’s November 2022 launch, followed by stabilization in late 2023.

Organization age emerged as the strongest predictor of AI writing usage in the job posting analysis. Companies founded after 2015 showed adoption rates up to three times higher than firms established before 1980, reaching 10–15 percent AI-modified text in certain roles compared to below 5 percent for older organizations. Small companies with fewer employees also incorporated AI more readily than larger organizations.

When examining corporate press releases by sector, science and technology companies integrated AI most extensively, with an adoption rate of 16.8 percent by late 2023. Business and financial news (14–15.6 percent) and people and culture topics (13.6–14.3 percent) showed slightly lower but still significant adoption.

In the international arena, Latin American and Caribbean UN country teams showed the highest adoption among international organizations at approximately 20 percent, while African states, Asia-Pacific states, and Eastern European states demonstrated more moderate increases to 11–14 percent by 2024.

Implications and limitations

In the study, the researchers acknowledge limitations in their analysis due to a focus on English-language content. Also, as we mentioned earlier, they found they could not reliably detect human-edited AI-generated text or text generated by newer models instructed to imitate human writing styles. As a result, the researchers suggest their findings represent a lower bound of actual AI writing tool adoption.

The researchers noted that the plateauing of AI writing adoption in 2024 might reflect either market saturation or increasingly sophisticated LLMs producing text that evades detection methods. They conclude we now live in a world where distinguishing between human and AI writing becomes progressively more difficult, with implications for communications across society.

“The growing reliance on AI-generated content may introduce challenges in communication,” the researchers write. “In sensitive categories, over-reliance on AI could result in messages that fail to address concerns or overall release less credible information externally. Over-reliance on AI could also introduce public mistrust in the authenticity of messages sent by firms.”

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“It’s a lemon”—OpenAI’s largest AI model ever arrives to mixed reviews

AI, Biz & IT, chatgpt, chatgtp, GPT-4, GPT-4.5, GPT-4o, machine learning, o1, o3, openai, simulated reasoning, Tech / Beth Washington / March 2, 2025

Perhaps because of the disappointing results, Altman had previously written that GPT-4.5 will be the last of OpenAI’s traditional AI models, with GPT-5 planned to be a dynamic combination of “non-reasoning” LLMs and simulated reasoning models like o3.

A stratospheric price and a tech dead-end

And about that price—it’s a doozy. GPT-4.5 costs $75 per million input tokens and $150 per million output tokens through the API, compared to GPT-4o’s $2.50 per million input tokens and $10 per million output tokens. (Tokens are chunks of data used by AI models for processing). For developers using OpenAI models, this pricing makes GPT-4.5 impractical for many applications where GPT-4o already performs adequately.

By contrast, OpenAI’s flagship reasoning model, o1 pro, costs $15 per million input tokens and $60 per million output tokens—significantly less than GPT-4.5 despite offering specialized simulated reasoning capabilities. Even more striking, the o3-mini model costs just $1.10 per million input tokens and $4.40 per million output tokens, making it cheaper than even GPT-4o while providing much stronger performance on specific tasks.

OpenAI has likely known about diminishing returns in training LLMs for some time. As a result, the company spent most of last year working on simulated reasoning models like o1 and o3, which use a different inference-time (runtime) approach to improving performance instead of throwing ever-larger amounts of training data at GPT-style AI models.

OpenAI's self-reported benchmark results for the SimpleQA test, which measures confabulation rate. — OpenAI’s self-reported benchmark results for the SimpleQA test, which measures confabulation rate. Credit: OpenAI

While this seems like bad news for OpenAI in the short term, competition is thriving in the AI market. Anthropic’s Claude 3.7 Sonnet has demonstrated vastly better performance than GPT-4.5, with a reportedly more efficient architecture. It’s worth noting that Claude 3.7 Sonnet is likely a system of AI models working together behind the scenes, although Anthropic has not provided details about its architecture.

For now, it seems that GPT-4.5 may be the last of its kind—a technological dead-end for an unsupervised learning approach that has paved the way for new architectures in AI models, such as o3’s inference-time reasoning and perhaps even something more novel, like diffusion-based models. Only time will tell how things end up.

GPT-4.5 is now available to ChatGPT Pro subscribers, with rollout to Plus and Team subscribers planned for next week, followed by Enterprise and Education customers the week after. Developers can access it through OpenAI’s various APIs on paid tiers, though the company is uncertain about its long-term availability.

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New AI text diffusion models break speed barriers by pulling words from noise

AI, AI diffusion, Biz & IT, chatbots, chatgpt, chatgtp, dall-e, diffusion, image diffusion, image synthesis, language diffusion models, large language models, machine learning, MidJourney, Stable Diffusion, Tech, text diffusion, text synthesis / Mike M. / February 28, 2025

These diffusion models maintain performance faster than or comparable to similarly sized conventional models. LLaDA’s researchers report their 8 billion parameter model performs similarly to LLaMA3 8B across various benchmarks, with competitive results on tasks like MMLU, ARC, and GSM8K.

However, Mercury claims dramatic speed improvements. Their Mercury Coder Mini scores 88.0 percent on HumanEval and 77.1 percent on MBPP—comparable to GPT-4o Mini—while reportedly operating at 1,109 tokens per second compared to GPT-4o Mini’s 59 tokens per second. This represents roughly a 19x speed advantage over GPT-4o Mini while maintaining similar performance on coding benchmarks.

Mercury’s documentation states its models run “at over 1,000 tokens/sec on Nvidia H100s, a speed previously possible only using custom chips” from specialized hardware providers like Groq, Cerebras, and SambaNova. When compared to other speed-optimized models, the claimed advantage remains significant—Mercury Coder Mini is reportedly about 5.5x faster than Gemini 2.0 Flash-Lite (201 tokens/second) and 18x faster than Claude 3.5 Haiku (61 tokens/second).

Opening a potential new frontier in LLMs

Diffusion models do involve some trade-offs. They typically need multiple forward passes through the network to generate a complete response, unlike traditional models that need just one pass per token. However, because diffusion models process all tokens in parallel, they achieve higher throughput despite this overhead.

Inception thinks the speed advantages could impact code completion tools where instant response may affect developer productivity, conversational AI applications, resource-limited environments like mobile applications, and AI agents that need to respond quickly.

If diffusion-based language models maintain quality while improving speed, they might change how AI text generation develops. So far, AI researchers have been open to new approaches.

Independent AI researcher Simon Willison told Ars Technica, “I love that people are experimenting with alternative architectures to transformers, it’s yet another illustration of how much of the space of LLMs we haven’t even started to explore yet.”

On X, former OpenAI researcher Andrej Karpathy wrote about Inception, “This model has the potential to be different, and possibly showcase new, unique psychology, or new strengths and weaknesses. I encourage people to try it out!”

Questions remain about whether larger diffusion models can match the performance of models like GPT-4o and Claude 3.7 Sonnet, produce reliable results without many confabulations, and if the approach can handle increasingly complex simulated reasoning tasks. For now, these models may offer an alternative for smaller AI language models that doesn’t seem to sacrifice capability for speed.

You can try Mercury Coder yourself on Inception’s demo site, and you can download code for LLaDA or try a demo on Hugging Face.

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Grok’s new “unhinged” voice mode can curse and scream, simulate phone sex

AI, AI censorship, chatbots, elon musk, grok, Grok 3, machine learning, sex, Twitter, unhinged AI, xAI / Mike M. / February 26, 2025

On Sunday, xAI released a new voice interaction mode for its Grok 3 AI model that is currently available to its premium subscribers. The feature is somewhat similar to OpenAI’s Advanced Voice Mode for ChatGPT. But unlike ChatGPT, Grok offers several uncensored personalities users can choose from (currently expressed through the same default female voice), including an “unhinged” mode and one that will roleplay verbal sexual scenarios.

On Monday, AI researcher Riley Goodside brought wider attention to the over-the-top “unhinged” mode in particular when he tweeted a video (warning: NSFW audio) that showed him repeatedly interrupting the vocal chatbot, which began to simulate yelling when asked. “Grok 3 Voice Mode, following repeated, interrupting requests to yell louder, lets out an inhuman 30-second scream, insults me, and hangs up,” he wrote.

By default, “unhinged” mode curses, insults, and belittles the user non-stop using vulgar language. Other modes include “Storyteller” (which does what it sounds like), “Romantic” (which stammers and speaks in a slow, uncertain, and insecure way), “Meditation” (which can guide you through a meditation-like experience), “Conspiracy” (which likes to talk about conspiracy theories, UFOs, and bigfoot), “Unlicensed Therapist” (which plays the part of a talk psychologist), “Grok Doc” (a doctor), “Sexy” (marked as “18+” and acts almost like a 1-800 phone sex operator), and “Professor” (which talks about science).

A composite screenshot of various Grok 3 voice mode personalities, as seen in the Grok app for iOS.

Basically, xAI is taking the exact opposite approach of other AI companies, such as OpenAI, which censor discussions about not-safe-for-work topics or scenarios they consider too risky for discussion. For example, the “Sexy” mode (warning: NSFW audio) will discuss graphically sexual situations, which ChatGPT’s voice mode will not touch, although OpenAI recently loosened up the moderation on the text-based version of ChatGPT to allow some discussion of some erotic content.

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Claude 3.7 Sonnet debuts with “extended thinking” to tackle complex problems

AI, AI assistants, Anthropic, Anthropic Claude, chatbots, chatgpt, chatgtp, Claude, Claude 3.7 Sonnet, large language models, machine learning / DJ Henderson / February 25, 2025

Would the color be called 'magenta' if the town of Magenta didn't exist? The person is asking an interesting hypothetical question about the origin of the color name — An example of Claude 3.7 Sonnet with extended thinking is asked, “Would the color be called ‘magenta’ if the town of Magenta didn’t exist?” Credit: Benj Edwards

Interestingly, xAI’s Grok 3 with “thinking” (its SR mode) enabled was the first model that definitively gave us a “no” and not an “it’s not likely” to the magenta question. Claude 3.7 Sonnet with extended thinking also impressed us with our second-ever firm “no,” then an explanation.

In another informal test, we asked 3.7 Sonnet with extended thinking to compose five original dad jokes. We’ve found in the past that our old prompt, “write 5 original dad jokes,” was not specific enough and always resulted in canned dad jokes pulled directly from training data, so we asked, “Compose 5 original dad jokes that are not found anywhere in the world.”

Compose 5 original dad jokes that are not found anywhere in the world. The user is asking me to compose 5 original dad jokes. These should be jokes that follow the typical — An example of Claude 3.7 Sonnet with extended thinking is asked, “Compose 5 original dad jokes that are not found anywhere in the world.” Credit: Benj Edwards

Claude made some attempts at crafting original jokes, although we’ll let you judge whether they are funny or not. We will likely put 3.7 Sonnet’s SR capabilities to the test more exhaustively in a future article.

Anthropic’s first agent: Claude Code

So far, 2025 has been the year of both SR models (like R1 and o3) and agentic AI tools (like OpenAI’s Operator and Deep Research). Not to be left out, Anthropic has announced its first agentic tool, Claude Code.

Claude Code operates directly from a console terminal and is an autonomous coding assistant. It allows Claude to search through codebases, read and edit files, write and run tests, commit and push code to GitHub repositories, and execute command line tools while keeping developers informed throughout the process.

Introducing Claude Code.

Anthropic also aims for Claude Code to be used as an assistant for debugging and refactoring tasks. The company claims that during internal testing, Claude Code completed tasks in a single session that would typically require 45-plus minutes of manual work.

Claude Code is currently available only as a “limited research preview,” with Anthropic stating it plans to improve the tool based on user feedback over time. Meanwhile, Claude 3.7 Sonnet is now available through the Claude website, the Claude app, Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI.

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