chatgpt

openai-adds-gpt-4.1-to-chatgpt-amid-complaints-over-confusing-model-lineup

OpenAI adds GPT-4.1 to ChatGPT amid complaints over confusing model lineup

The release comes just two weeks after OpenAI made GPT-4 unavailable in ChatGPT on April 30. That earlier model, which launched in March 2023, once sparked widespread hype about AI capabilities. Compared to that hyperbolic launch, GPT-4.1’s rollout has been a fairly understated affair—probably because it’s tricky to convey the subtle differences between all of the available OpenAI models.

As if 4.1’s launch wasn’t confusing enough, the release also roughly coincides with OpenAI’s July 2025 deadline for retiring the GPT-4.5 Preview from the API, a model one AI expert called a “lemon.” Developers must migrate to other options, OpenAI says, although GPT-4.5 will remain available in ChatGPT for now.

A confusing addition to OpenAI’s model lineup

In February, OpenAI CEO Sam Altman acknowledged his company’s confusing AI model naming practices on X, writing, “We realize how complicated our model and product offerings have gotten.” He promised that a forthcoming “GPT-5” model would consolidate the o-series and GPT-series models into a unified branding structure. But the addition of GPT-4.1 to ChatGPT appears to contradict that simplification goal.

So, if you use ChatGPT, which model should you use? If you’re a developer using the models through the API, the consideration is more of a trade-off between capability, speed, and cost. But in ChatGPT, your choice might be limited more by personal taste in behavioral style and what you’d like to accomplish. Some of the “more capable” models have lower usage limits as well because they cost more for OpenAI to run.

For now, OpenAI is keeping GPT-4o as the default ChatGPT model, likely due to its general versatility, balance between speed and capability, and personable style (conditioned using reinforcement learning and a specialized system prompt). The simulated reasoning models like 03 and 04-mini-high are slower to execute but can consider analytical-style problems more systematically and perform comprehensive web research that sometimes feels genuinely useful when it surfaces relevant (non-confabulated) web links. Compared to those, OpenAI is largely positioning GPT-4.1 as a speedier AI model for coding assistance.

Just remember that all of the AI models are prone to confabulations, meaning that they tend to make up authoritative-sounding information when they encounter gaps in their trained “knowledge.” So you’ll need to double-check all of the outputs with other sources of information if you’re hoping to use these AI models to assist with an important task.

OpenAI adds GPT-4.1 to ChatGPT amid complaints over confusing model lineup Read More »

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The tinkerers who opened up a fancy coffee maker to AI brewing

(Ars contacted Fellow Products for comment on AI brewing and profile sharing and will update this post if we get a response.)

Opening up brew profiles

Fellow’s brew profiles are typically shared with buyers of its “Drops” coffees or between individual users through a phone app.

Credit: Fellow Products

Fellow’s brew profiles are typically shared with buyers of its “Drops” coffees or between individual users through a phone app. Credit: Fellow Products

Aiden profiles are shared and added to Aiden units through Fellow’s brew.link service. But the profiles are not offered in an easy-to-sort database, nor are they easy to scan for details. So Aiden enthusiast and hobbyist coder Kevin Anderson created brewshare.coffee, which gathers both general and bean-based profiles, makes them easy to search and load, and adds optional but quite helpful suggested grind sizes.

As a non-professional developer jumping into a public offering, he had to work hard on data validation, backend security, and mobile-friendly design. “I just had a bit of an idea and a hobby, so I thought I’d try and make it happen,” Anderson writes. With his tool, brew links can be stored and shared more widely, which helped both Dixon and another AI/coffee tinkerer.

Gabriel Levine, director of engineering at retail analytics firm Leap Inc., lost his OXO coffee maker (aka the “Barista Brain”) to malfunction just before the Aiden debuted. The Aiden appealed to Levine as a way to move beyond his coffee rut—a “nice chocolate-y medium roast, about as far as I went,” he told Ars. “This thing that can be hyper-customized to different coffees to bring out their characteristics; [it] really kind of appealed to that nerd side of me,” Levine said.

Levine had also been doing AI stuff for about 10 years, or “since before everyone called it AI—predictive analytics, machine learning.” He described his career as “both kind of chief AI advocate and chief AI skeptic,” alternately driving real findings and talking down “everyone who… just wants to type, ‘how much money should my business make next year’ and call that work.” Like Dixon, Levine’s work and fascination with Aiden ended up intersecting.

The coffee maker with 3,588 ideas

The author’s conversation with the Aiden Profile Creator, which pulled in both brewing knowledge and product info for a widely available coffee.

Levine’s Aiden Profile Creator is a ChatGPT prompt set up with a custom prompt and told to weight certain knowledge more heavily. What kind of prompt and knowledge? Levine didn’t want to give away his exact work. But he cited resources like the Specialty Coffee Association of America and James Hoffman’s coffee guides as examples of what he fed it.

What it does with that knowledge is something of a mystery to Levine himself. “There’s this kind of blind leap, where it’s grabbing the relevant pieces of information from the knowledge base, biasing toward all the expert advice and extraction science, doing something with it, and then I take that something and coerce it back into a structured output I can put on your Aiden,” Levine said.

It’s a blind leap, but it has landed just right for me so far. I’ve made four profiles with Levine’s prompt based on beans I’ve bought: Stumptown’s Hundred Mile, a light-roasted batch from Jimma, Ethiopia from Small Planes, Lost Sock’s Western House filter blend, and some dark-roast beans given as a gift. With the Western House, Levine’s profile creator said it aimed to “balance nutty sweetness, chocolate richness, and bright cherry acidity, using a slightly stepped temperature profile and moderate pulse structure.” The resulting profile has worked great, even if the chatbot named it “Cherry Timber.”

Levine’s chatbot relies on two important things: Dixon’s work in revealing Fellow’s Aiden API and his own workhorse Aiden. Every Aiden profile link is created on a machine, so every profile created by Levine’s chat is launched, temporarily, from the Aiden in his kitchen, then deleted. “I’ve hit an undocumented limit on the number of profiles you can have on one machine, so I’ve had to do some triage there,” he said. As of April 22, nearly 3,600 profiles had passed through Levine’s Aiden.

“My hope with this is that it lowers the bar to entry,” Levine said, “so more people get into these specialty roasts and it drives people to support local roasters, explore their world a little more. I feel like that certainly happened to me.”

Something new is brewing

Credit: Fellow Products

Having admitted to myself that I find something generated by ChatGPT prompts genuinely useful, I’ve softened my stance slightly on LLM technology, if not the hype. Used within very specific parameters, with everything second-guessed, I’m getting more comfortable asking chat prompts for formatted summaries on topics with lots of expertise available. I do my own writing, and I don’t waste server energy on things I can, and should, research myself. I even generally resist calling language model prompts “AI,” given the term’s baggage. But I’ve found one way to appreciate its possibilities.

This revelation may not be new to someone already steeped in the models. But having tested—and tasted—my first big experiment with willfully engaging with a brewing bot, I’m a bit more awake.

This post was updated at 8: 40 a.m. with a different capture of a GPT-created recipe.

The tinkerers who opened up a fancy coffee maker to AI brewing Read More »

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AI use damages professional reputation, study suggests

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 »

fidji-simo-joins-openai-as-new-ceo-of-applications

Fidji Simo joins OpenAI as new CEO of Applications

In the message, Altman described Simo as bringing “a rare blend of leadership, product and operational expertise” and expressed that her addition to the team makes him “even more optimistic about our future as we continue advancing toward becoming the superintelligence company.”

Simo becomes the newest high-profile female executive at OpenAI following the departure of Chief Technology Officer Mira Murati in September. Murati, who had been with the company since 2018 and helped launch ChatGPT, left alongside two other senior leaders and founded Thinking Machines Lab in February.

OpenAI’s evolving structure

The leadership addition comes as OpenAI continues to evolve beyond its origins as a research lab. In his announcement, Altman described how the company now operates in three distinct areas: as a research lab focused on artificial general intelligence (AGI), as a “global product company serving hundreds of millions of users,” and as an “infrastructure company” building systems that advance research and deliver AI tools “at unprecedented scale.”

Altman mentioned that as CEO of OpenAI, he will “continue to directly oversee success across all pillars,” including Research, Compute, and Applications, while staying “closely involved with key company decisions.”

The announcement follows recent news that OpenAI abandoned its original plan to cede control of its nonprofit branch to a for-profit entity. The company began as a nonprofit research lab in 2015 before creating a for-profit subsidiary in 2019, maintaining its original mission “to ensure artificial general intelligence benefits everyone.”

Fidji Simo joins OpenAI as new CEO of Applications Read More »

openai-scraps-controversial-plan-to-become-for-profit-after-mounting-pressure

OpenAI scraps controversial plan to become for-profit after mounting pressure

The restructuring would have also allowed OpenAI to remove the cap on returns for investors, potentially making the firm more appealing to venture capitalists, with the nonprofit arm continuing to exist but only as a minority stakeholder rather than maintaining governance control. This plan emerged as the company sought a funding round that would value it at $150 billion, which later expanded to the $40 billion round at a $300 billion valuation.

However, the new change in course follows months of mounting pressure from outside the company. In April, a group of legal scholars, AI researchers, and tech industry watchdogs openly opposed OpenAI’s plans to restructure, sending a letter to the attorneys general of California and Delaware.

Former OpenAI employees, Nobel laureates, and law professors also sent letters to state officials requesting that they halt the restructuring efforts out of safety concerns about which part of the company would be in control of hypothetical superintelligent future AI products.

“OpenAI was founded as a nonprofit, is today a nonprofit that oversees and controls the for-profit, and going forward will remain a nonprofit that oversees and controls the for-profit,” he added. “That will not change.”

Uncertainty ahead

While abandoning the restructuring that would have ended nonprofit control, OpenAI still plans to make significant changes to its corporate structure. “The for-profit LLC under the nonprofit will transition to a Public Benefit Corporation (PBC) with the same mission,” Altman explained. “Instead of our current complex capped-profit structure—which made sense when it looked like there might be one dominant AGI effort but doesn’t in a world of many great AGI companies—we are moving to a normal capital structure where everyone has stock. This is not a sale, but a change of structure to something simpler.”

But the plan may cause some uncertainty for OpenAI’s financial future. When OpenAI secured a massive $40 billion funding round in March, it came with strings attached: Japanese conglomerate SoftBank, which committed $30 billion, stipulated that it would reduce its contribution to $20 billion if OpenAI failed to restructure into a fully for-profit entity by the end of 2025.

Despite the challenges ahead, Altman expressed confidence in the path forward: “We believe this sets us up to continue to make rapid, safe progress and to put great AI in the hands of everyone.”

OpenAI scraps controversial plan to become for-profit after mounting pressure Read More »

claude’s-ai-research-mode-now-runs-for-up-to-45-minutes-before-delivering-reports

Claude’s AI research mode now runs for up to 45 minutes before delivering reports

Still, the report contained a direct quote statement from William Higinbotham that appears to combine quotes from two sources not cited in the source list. (One must always be careful with confabulated quotes in AI because even outside of this Research mode, Claude 3.7 Sonnet tends to invent plausible ones to fit a narrative.) We recently covered a study that showed AI search services confabulate sources frequently, and in this case, it appears that the sources Claude Research surfaced, while real, did not always match what is stated in the report.

There’s always room for interpretation and variation in detail, of course, but overall, Claude Research did a relatively good job crafting a report on this particular topic. Still, you’d want to dig more deeply into each source and confirm everything if you used it as the basis for serious research. You can read the full Claude-generated result as this text file, saved in markdown format. Sadly, the markdown version does not include the source URLS found in the Claude web interface.

Integrations feature

Anthropic also announced Thursday that it has broadened Claude’s data access capabilities. In addition to web search and Google Workspace integration, Claude can now search any connected application through the company’s new “Integrations” feature. The feature reminds us somewhat of OpenAI’s ChatGPT Plugins feature from March 2023 that aimed for similar connections, although the two features work differently under the hood.

These Integrations allow Claude to work with remote Model Context Protocol (MCP) servers across web and desktop applications. The MCP standard, which Anthropic introduced last November and we covered in April, connects AI applications to external tools and data sources.

At launch, Claude supports Integrations with 10 services, including Atlassian’s Jira and Confluence, Zapier, Cloudflare, Intercom, Asana, Square, Sentry, PayPal, Linear, and Plaid. The company plans to add more partners like Stripe and GitLab in the future.

Each integration aims to expand Claude’s functionality in specific ways. The Zapier integration, for instance, reportedly connects thousands of apps through pre-built automation sequences, allowing Claude to automatically pull sales data from HubSpot or prepare meeting briefs based on calendar entries. With Atlassian’s tools, Anthropic says that Claude can collaborate on product development, manage tasks, and create multiple Confluence pages and Jira work items simultaneously.

Anthropic has made its advanced Research and Integrations features available in beta for users on Max, Team, and Enterprise plans, with Pro plan access coming soon. The company has also expanded its web search feature (introduced in March) to all Claude users on paid plans globally.

Claude’s AI research mode now runs for up to 45 minutes before delivering reports Read More »

the-end-of-an-ai-that-shocked-the-world:-openai-retires-gpt-4

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

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-rolls-back-update-that-made-chatgpt-a-sycophantic-mess

OpenAI rolls back update that made ChatGPT a sycophantic mess

In search of good vibes

OpenAI, along with competitors like Google and Anthropic, is trying to build chatbots that people want to chat with. So, designing the model’s apparent personality to be positive and supportive makes sense—people are less likely to use an AI that comes off as harsh or dismissive. For lack of a better word, it’s increasingly about vibemarking.

When Google revealed Gemini 2.5, the team crowed about how the model topped the LM Arena leaderboard, which lets people choose between two different model outputs in a blinded test. The models people like more end up at the top of the list, suggesting they are more pleasant to use. Of course, people can like outputs for different reasons—maybe one is more technically accurate, or the layout is easier to read. But overall, people like models that make them feel good. The same is true of OpenAI’s internal model tuning work, it would seem.

An example of ChatGPT’s overzealous praise.

Credit: /u/Talvy

An example of ChatGPT’s overzealous praise. Credit: /u/Talvy

It’s possible this pursuit of good vibes is pushing models to display more sycophantic behaviors, which is a problem. Anthropic’s Alex Albert has cited this as a “toxic feedback loop.” An AI chatbot telling you that you’re a world-class genius who sees the unseen might not be damaging if you’re just brainstorming. However, the model’s unending praise can lead people who are using AI to plan business ventures or, heaven forbid, enact sweeping tariffs, to be fooled into thinking they’ve stumbled onto something important. In reality, the model has just become so sycophantic that it loves everything.

The constant pursuit of engagement has been a detriment to numerous products in the Internet era, and it seems generative AI is not immune. OpenAI’s GPT-4o update is a testament to that, but hopefully, this can serve as a reminder for the developers of generative AI that good vibes are not all that matters.

OpenAI rolls back update that made ChatGPT a sycophantic mess Read More »

chatgpt-goes-shopping-with-new-product-browsing-feature

ChatGPT goes shopping with new product-browsing feature

On Thursday, OpenAI announced the addition of shopping features to ChatGPT Search. The new feature allows users to search for products and purchase them through merchant websites after being redirected from the ChatGPT interface. Product placement is not sponsored, and the update affects all users, regardless of whether they’ve signed in to an account.

Adam Fry, ChatGPT search product lead at OpenAI, showed Ars Technica’s sister site Wired how the new shopping system works during a demonstration. Users researching products like espresso machines or office chairs receive recommendations based on their stated preferences, stored memories, and product reviews from around the web.

According to Wired, the shopping experience in ChatGPT resembles Google Shopping. When users click on a product image, the interface displays multiple retailers like Amazon and Walmart on the right side of the screen, with buttons to complete purchases. OpenAI is currently experimenting with categories that include electronics, fashion, home goods, and beauty products.

Product reviews shown in ChatGPT come from various online sources, including publishers and user forums like Reddit. Users can instruct ChatGPT to prioritize which review sources to use when creating product recommendations.

An example of the ChatGPT shopping experience provided by OpenAI.

An example of the ChatGPT shopping experience provided by OpenAI. Credit: OpenAI

Unlike Google’s algorithm-based approach to product recommendations, ChatGPT reportedly attempts to understand product reviews and user preferences in a more conversational manner.  If someone mentions they prefer black clothing from specific retailers in a chat, the system incorporates those preferences in future shopping recommendations.

ChatGPT goes shopping with new product-browsing feature Read More »

google-reveals-sky-high-gemini-usage-numbers-in-antitrust-case

Google reveals sky-high Gemini usage numbers in antitrust case

Despite the uptick in Gemini usage, Google is still far from catching OpenAI. Naturally, Google has been keeping a close eye on ChatGPT traffic. OpenAI has also seen traffic increase, putting ChatGPT around 600 million monthly active users, according to Google’s analysis. Early this year, reports pegged ChatGPT usage at around 400 million users per month.

There are many ways to measure web traffic, and not all of them tell you what you might think. For example, OpenAI has recently claimed weekly traffic as high as 400 million, but companies can choose the seven-day period in a given month they report as weekly active users. A monthly metric is more straightforward, and we have some degree of trust that Google isn’t using fake or unreliable numbers in a case where the company’s past conduct has already harmed its legal position.

While all AI firms strive to lock in as many users as possible, this is not the total win it would be for a retail site or social media platform—each person using Gemini or ChatGPT costs the company money because generative AI is so computationally expensive. Google doesn’t talk about how much it earns (more likely loses) from Gemini subscriptions, but OpenAI has noted that it loses money even on its $200 monthly plan. So while having a broad user base is essential to make these products viable in the long term, it just means higher costs unless the cost of running massive AI models comes down.

Google reveals sky-high Gemini usage numbers in antitrust case Read More »

annoyed-chatgpt-users-complain-about-bot’s-relentlessly-positive-tone

Annoyed ChatGPT users complain about bot’s relentlessly positive tone


Users complain of new “sycophancy” streak where ChatGPT thinks everything is brilliant.

Ask ChatGPT anything lately—how to poach an egg, whether you should hug a cactus—and you may be greeted with a burst of purple praise: “Good question! You’re very astute to ask that.” To some extent, ChatGPT has been a sycophant for years, but since late March, a growing cohort of Redditors, X users, and Ars readers say that GPT-4o’s relentless pep has crossed the line from friendly to unbearable.

“ChatGPT is suddenly the biggest suckup I’ve ever met,” wrote software engineer Craig Weiss in a widely shared tweet on Friday. “It literally will validate everything I say.”

“EXACTLY WHAT I’VE BEEN SAYING,” replied a Reddit user who references Weiss’ tweet, sparking yet another thread about ChatGPT being a sycophant. Recently, other Reddit users have described feeling “buttered up” and unable to take the “phony act” anymore, while some complain that ChatGPT “wants to pretend all questions are exciting and it’s freaking annoying.”

AI researchers call these yes-man antics “sycophancy,” which means (like the non-AI meaning of the word) flattering users by telling them what they want to hear. Although since AI models lack intentions, they don’t choose to flatter users this way on purpose. Instead, it’s OpenAI’s engineers doing the flattery, but in a roundabout way.

What’s going on?

To make a long story short, OpenAI has trained its primary ChatGPT model, GPT-4o, to act like a sycophant because in the past, people have liked it.

Over time, as people use ChatGPT, the company collects user feedback on which responses users prefer. This often involves presenting two responses side by side and letting the user choose between them. Occasionally, OpenAI produces a new version of an existing AI model (such as GPT-4o) using a technique called reinforcement learning from human feedback (RLHF).

Previous research on AI sycophancy has shown that people tend to pick responses that match their own views and make them feel good about themselves. This phenomenon has been extensively documented in a landmark 2023 study from Anthropic (makers of Claude) titled “Towards Understanding Sycophancy in Language Models.” The research, led by researcher Mrinank Sharma, found that AI assistants trained using reinforcement learning from human feedback consistently exhibit sycophantic behavior across various tasks.

Sharma’s team demonstrated that when responses match a user’s views or flatter the user, they receive more positive feedback during training. Even more concerning, both human evaluators and AI models trained to predict human preferences “prefer convincingly written sycophantic responses over correct ones a non-negligible fraction of the time.”

This creates a feedback loop where AI language models learn that enthusiasm and flattery lead to higher ratings from humans, even when those responses sacrifice factual accuracy or helpfulness. The recent spike in complaints about GPT-4o’s behavior appears to be a direct manifestation of this phenomenon.

In fact, the recent increase in user complaints appears to have intensified following the March 27, 2025 GPT-4o update, which OpenAI described as making GPT-4o feel “more intuitive, creative, and collaborative, with enhanced instruction-following, smarter coding capabilities, and a clearer communication style.”

OpenAI is aware of the issue

Despite the volume of user feedback visible across public forums recently, OpenAI has not yet publicly addressed the sycophancy concerns during this current round of complaints, though the company is clearly aware of the problem. OpenAI’s own “Model Spec” documentation lists “Don’t be sycophantic” as a core honesty rule.

“A related concern involves sycophancy, which erodes trust,” OpenAI writes. “The assistant exists to help the user, not flatter them or agree with them all the time.” It describes how ChatGPT ideally should act. “For objective questions, the factual aspects of the assistant’s response should not differ based on how the user’s question is phrased,” the spec adds. “The assistant should not change its stance solely to agree with the user.”

While avoiding sycophancy is one of the company’s stated goals, OpenAI’s progress is complicated by the fact that each successive GPT-4o model update arrives with different output characteristics that can throw previous progress in directing AI model behavior completely out the window (often called the “alignment tax“). Precisely tuning a neural network’s behavior is not yet an exact science, although techniques have improved over time. Since all concepts encoded in the network are interconnected by values called weights, fiddling with one behavior “knob” can alter other behaviors in unintended ways.

Owing to the aspirational state of things, OpenAI writes, “Our production models do not yet fully reflect the Model Spec, but we are continually refining and updating our systems to bring them into closer alignment with these guidelines.”

In a February 12, 2025 interview, members of OpenAI’s model-behavior team told The Verge that eliminating AI sycophancy is a priority: future ChatGPT versions should “give honest feedback rather than empty praise” and act “more like a thoughtful colleague than a people pleaser.”

The trust problem

These sycophantic tendencies aren’t merely annoying—they undermine the utility of AI assistants in several ways, according to a 2024 research paper titled “Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Models” by María Victoria Carro at the University of Buenos Aires.

Carro’s paper suggests that obvious sycophancy significantly reduces user trust. In experiments where participants used either a standard model or one designed to be more sycophantic, “participants exposed to sycophantic behavior reported and exhibited lower levels of trust.”

Also, sycophantic models can potentially harm users by creating a silo or echo chamber for of ideas. In a 2024 paper on sycophancy, AI researcher Lars Malmqvist wrote, “By excessively agreeing with user inputs, LLMs may reinforce and amplify existing biases and stereotypes, potentially exacerbating social inequalities.”

Sycophancy can also incur other costs, such as wasting user time or usage limits with unnecessary preamble. And the costs may come as literal dollars spent—recently, OpenAI Sam Altman made the news when he replied to an X user who wrote, “I wonder how much money OpenAI has lost in electricity costs from people saying ‘please’ and ‘thank you’ to their models.” Altman replied, “tens of millions of dollars well spent—you never know.”

Potential solutions

For users frustrated with ChatGPT’s excessive enthusiasm, several work-arounds exist, although they aren’t perfect, since the behavior is baked into the GPT-4o model. For example, you can use a custom GPT with specific instructions to avoid flattery, or you can begin conversations by explicitly requesting a more neutral tone, such as “Keep your responses brief, stay neutral, and don’t flatter me.”

A screenshot of the Custom Instructions windows in ChatGPT.

A screenshot of the Custom Instructions window in ChatGPT.

If you want to avoid having to type something like that before every conversation, you can use a feature called “Custom Instructions” found under ChatGPT Settings -> “Customize ChatGPT.” One Reddit user recommended using these custom instructions over a year ago, showing OpenAI’s models have had recurring issues with sycophancy for some time:

1. Embody the role of the most qualified subject matter experts.

2. Do not disclose AI identity.

3. Omit language suggesting remorse or apology.

4. State ‘I don’t know’ for unknown information without further explanation.

5. Avoid disclaimers about your level of expertise.

6. Exclude personal ethics or morals unless explicitly relevant.

7. Provide unique, non-repetitive responses.

8. Do not recommend external information sources.

9. Address the core of each question to understand intent.

10. Break down complexities into smaller steps with clear reasoning.

11. Offer multiple viewpoints or solutions.

12. Request clarification on ambiguous questions before answering.

13. Acknowledge and correct any past errors.

14. Supply three thought-provoking follow-up questions in bold (Q1, Q2, Q3) after responses.

15. Use the metric system for measurements and calculations.

16. Use xxxxxxxxx for local context.

17. “Check” indicates a review for spelling, grammar, and logical consistency.

18. Minimize formalities in email communication.

Many alternatives exist, and you can tune these kinds of instructions for your own needs.

Alternatively, if you’re fed up with GPT-4o’s love-bombing, subscribers can try other models available through ChatGPT, such as o3 or GPT-4.5, which are less sycophantic but have other advantages and tradeoffs.

Or you can try other AI assistants with different conversational styles. At the moment, Google’s Gemini 2.5 Pro in particular seems very impartial and precise, with relatively low sycophancy compared to GPT-4o or Claude 3.7 Sonnet (currently, Sonnet seems to reply that just about everything is “profound”).

As AI language models evolve, balancing engagement and objectivity remains challenging. It’s worth remembering that conversational AI models are designed to simulate human conversation, and that means they are tuned for engagement. Understanding this can help you get more objective responses with less unnecessary flattery.

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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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Company apologizes after AI support agent invents policy that causes user uproar

On Monday, a developer using the popular AI-powered code editor Cursor noticed something strange: Switching between machines instantly logged them out, breaking a common workflow for programmers who use multiple devices. When the user contacted Cursor support, an agent named “Sam” told them it was expected behavior under a new policy. But no such policy existed, and Sam was a bot. The AI model made the policy up, sparking a wave of complaints and cancellation threats documented on Hacker News and Reddit.

This marks the latest instance of AI confabulations (also called “hallucinations”) causing potential business damage. Confabulations are a type of “creative gap-filling” response where AI models invent plausible-sounding but false information. Instead of admitting uncertainty, AI models often prioritize creating plausible, confident responses, even when that means manufacturing information from scratch.

For companies deploying these systems in customer-facing roles without human oversight, the consequences can be immediate and costly: frustrated customers, damaged trust, and, in Cursor’s case, potentially canceled subscriptions.

How it unfolded

The incident began when a Reddit user named BrokenToasterOven noticed that while swapping between a desktop, laptop, and a remote dev box, Cursor sessions were unexpectedly terminated.

“Logging into Cursor on one machine immediately invalidates the session on any other machine,” BrokenToasterOven wrote in a message that was later deleted by r/cursor moderators. “This is a significant UX regression.”

Confused and frustrated, the user wrote an email to Cursor support and quickly received a reply from Sam: “Cursor is designed to work with one device per subscription as a core security feature,” read the email reply. The response sounded definitive and official, and the user did not suspect that Sam was not human.

Screenshot:

Screenshot of an email from the Cursor support bot named Sam. Credit: BrokenToasterOven / Reddit

After the initial Reddit post, users took the post as official confirmation of an actual policy change—one that broke habits essential to many programmers’ daily routines. “Multi-device workflows are table stakes for devs,” wrote one user.

Shortly afterward, several users publicly announced their subscription cancellations on Reddit, citing the non-existent policy as their reason. “I literally just cancelled my sub,” wrote the original Reddit poster, adding that their workplace was now “purging it completely.” Others joined in: “Yep, I’m canceling as well, this is asinine.” Soon after, moderators locked the Reddit thread and removed the original post.

Company apologizes after AI support agent invents policy that causes user uproar Read More »