AI

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.”

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google-hits-back-after-apple-exec-says-ai-is-hurting-search

Google hits back after Apple exec says AI is hurting search

The antitrust trial targeting Google’s search business is heading into the home stretch, and the outcome could forever alter Google—and the web itself. The company is scrambling to protect its search empire, but perhaps market forces could pull the rug out from under Google before the government can. Apple SVP of Services Eddie Cue suggested in his testimony on Wednesday that Google’s search traffic might be falling. Not so fast, says Google.

In an unusual move, Google issued a statement late in the day after Cue’s testimony to dispute the implication that it may already be losing its monopoly. During questioning by DOJ attorney Adam Severt, Cue expressed concern about losing the Google search deal, which is a major source of revenue for Apple. This contract, along with a similar one for Firefox, gives Google default search placement in exchange for a boatload of cash. The DOJ contends that is anticompetitive, and its proposed remedies call for banning Google from such deals.

Surprisingly, Cue noted in his testimony that search volume in Safari fell for the first time ever in April. Since Google is the default search provider, that implies fewer Google searches. Apple devices are popular, and a drop in Google searches there could be a bad sign for the company’s future competitiveness. Google’s statement on this comes off as a bit defensive.

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trump-admin-to-roll-back-biden’s-ai-chip-restrictions

Trump admin to roll back Biden’s AI chip restrictions

The changing face of chip export controls

The Biden-era chip restriction framework, which we covered in January, established a three-tiered system for regulating AI chip exports. The first tier included 17 countries, plus Taiwan, that could receive unlimited advanced chips. A second tier of roughly 120 countries faced caps on the number of chips they could import. The administration entirely blocked the third tier, which included China, Russia, Iran, and North Korea, from accessing the chips.

Commerce Department officials now say they “didn’t like the tiered system” and considered it “unenforceable,” according to Reuters. While no timeline exists for the new rule, the spokeswoman indicated that officials are still debating the best approach to replace it. The Biden rule was set to take effect on May 15.

Reports suggest the Trump administration might discard the tiered approach in favor of a global licensing system with government-to-government agreements. This could involve direct negotiations with nations like the United Arab Emirates or Saudi Arabia rather than applying broad regional restrictions. However, the Commerce Department spokeswoman indicated that debate about the new approach is still underway, and no timetable has been established for the final rule.

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open-source-project-curl-is-sick-of-users-submitting-“ai-slop”-vulnerabilities

Open source project curl is sick of users submitting “AI slop” vulnerabilities

Ars has reached out to HackerOne for comment and will update this post if we get a response.

“More tools to strike down this behavior”

In an interview with Ars, Stenberg said he was glad his post—which generated 200 comments and nearly 400 reposts as of Wednesday morning—was getting around. “I’m super happy that the issue [is getting] attention so that possibly we can do something about it [and] educate the audience that this is the state of things,” Stenberg said. “LLMs cannot find security problems, at least not like they are being used here.”

This week has seen four such misguided, obviously AI-generated vulnerability reports seemingly seeking either reputation or bug bounty funds, Stenberg said. “One way you can tell is it’s always such a nice report. Friendly phrased, perfect English, polite, with nice bullet-points … an ordinary human never does it like that in their first writing,” he said.

Some AI reports are easier to spot than others. One accidentally pasted their prompt into the report, Stenberg said, “and he ended it with, ‘and make it sound alarming.'”

Stenberg said he had “talked to [HackerOne] before about this” and has reached out to the service this week. “I would like them to do something, something stronger, to act on this. I would like help from them to make the infrastructure around [AI tools] better and give us more tools to strike down this behavior,” he said.

In the comments of his post, Stenberg, trading comments with Tobias Heldt of open source security firm XOR, suggested that bug bounty programs could potentially use “existing networks and infrastructure.” Security reporters paying a bond to have a report reviewed “could be one way to filter signals and reduce noise,” Heldt said. Elsewhere, Stenberg said that while AI reports are “not drowning us, [the] trend is not looking good.”

Stenberg has previously blogged on his own site about AI-generated vulnerability reports, with more details on what they look like and what they get wrong. Seth Larson, security developer-in-residence at the Python Software Foundation, added to Stenberg’s findings with his own examples and suggested actions, as noted by The Register.

“If this is happening to a handful of projects that I have visibility for, then I suspect that this is happening on a large scale to open source projects,” Larson wrote in December. “This is a very concerning trend.”

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data-centers-say-trump’s-crackdown-on-renewables-bad-for-business,-ai

Data centers say Trump’s crackdown on renewables bad for business, AI

Although big participants in the technology industry may be able to lobby the administration to “loosen up” restrictions on new power sources, small to medium-sized players were in a “holding pattern” as they waited to see if permitting obstacles and tariffs on renewables equipment were lifted, said Ninan.

“On average, [operators] are most likely going to try to find ways of absorbing additional costs and going to dirtier sources,” he said.

Amazon, which is the largest corporate purchaser of renewable energy globally, said carbon-free energy must remain an important part of the energy mix to meet surging demand for power, keep costs down, and hit climate goals.

“Renewable energy can often be less expensive than alternatives because there’s no fuel to purchase. Some of the purchasing agreements we have signed historically were ‘no brainers’ because they reduced our power costs,” said Kevin Miller, vice-president of Global Data Centers at Amazon Web Services.

Efforts by state and local governments to stymie renewables could also hit the sector. In Texas—the third-largest US data center market after Virginia, according to S&P Global Market Intelligence—bills are being debated that increase regulation on solar and wind projects.

“We have a huge opportunity in front of us with these data centers,” said Doug Lewin, president of Stoic Energy. “Virginia can only take so many, and you can build faster here, but any of these bills passing would kill that in the crib.”

The renewables crackdown will make it harder for “hyperscale” data centers run by companies such as Equinix, Microsoft, Google, and Meta to offset their emissions and invest in renewable energy sources.

“Demand [for renewables] has reached an all-time high,” said Christopher Wellise, sustainability vice-president at Equinix. “So when you couple that with the additional constraints, there could be some near to midterm challenges.”

Additional reporting by Jamie Smyth.

© 2025 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

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A DOGE recruiter is staffing a project to deploy AI agents across the US government


“does it still require Kremlin oversight?

A startup founder said that AI agents could do the work of tens of thousands of government employees.

An aide sets up a poster depicting the logo for the DOGE Caucus before a news conference in Washington, DC. Credit: Andrew Harnik/Getty Images

A young entrepreneur who was among the earliest known recruiters for Elon Musk’s so-called Department of Government Efficiency (DOGE) has a new, related gig—and he’s hiring. Anthony Jancso, cofounder of AcclerateX, a government tech startup, is looking for technologists to work on a project that aims to have artificial intelligence perform tasks that are currently the responsibility of tens of thousands of federal workers.

Jancso, a former Palantir employee, wrote in a Slack with about 2000 Palantir alumni in it that he’s hiring for a “DOGE orthogonal project to design benchmarks and deploy AI agents across live workflows in federal agencies,” according to an April 21 post reviewed by WIRED. Agents are programs that can perform work autonomously.

We’ve identified over 300 roles with almost full-process standardization, freeing up at least 70k FTEs for higher-impact work over the next year,” he continued, essentially claiming that tens of thousands of federal employees could see many aspects of their job automated and replaced by these AI agents. Workers for the project, he wrote, would be based on site in Washington, DC, and would not require a security clearance; it isn’t clear for whom they would work. Palantir did not respond to requests for comment.

The post was not well received. Eight people reacted with clown face emojis, three reacted with a custom emoji of a man licking a boot, two reacted with custom emoji of Joaquin Phoenix giving a thumbs down in the movie Gladiator, and three reacted with a custom emoji with the word “Fascist.” Three responded with a heart emoji.

“DOGE does not seem interested in finding ‘higher impact work’ for federal employees,” one person said in a comment that received 11 heart reactions. “You’re complicit in firing 70k federal employees and replacing them with shitty autocorrect.”

“Tbf we’re all going to be replaced with shitty autocorrect (written by chatgpt),” another person commented, which received one “+1” reaction.

“How ‘DOGE orthogonal’ is it? Like, does it still require Kremlin oversight?” another person said in a comment that received five reactions with a fire emoji. “Or do they just use your credentials to log in later?”

AccelerateX was originally called AccelerateSF, which VentureBeat reported in 2023 had received support from OpenAI and Anthropic. In its earliest incarnation, AccelerateSF hosted a hackathon for AI developers aimed at using the technology to solve San Francisco’s social problems. According to a 2023 Mission Local story, for instance, Jancso proposed that using large language models to help businesses fill out permit forms to streamline the construction paperwork process might help drive down housing prices. (OpenAI did not respond to a request for comment. Anthropic spokesperson Danielle Ghiglieri tells WIRED that the company “never invested in AccelerateX/SF,” but did sponsor a hackathon AccelerateSF hosted in 2023 by providing free access to its API usage at a time when its Claude API “was still in beta.”)

In 2024, the mission pivoted, with the venture becoming known as AccelerateX. In a post on X announcing the change, the company posted, “Outdated tech is dragging down the US Government. Legacy vendors sell broken systems at increasingly steep prices. This hurts every American citizen.” AccelerateX did not respond to a request for comment.

According to sources with direct knowledge, Jancso disclosed that AccelerateX had signed a partnership agreement with Palantir in 2024. According to the LinkedIn of someone described as one of AccelerateX’s cofounders, Rachel Yee, the company looks to have received funding from OpenAI’s Converge 2 Accelerator. Another of AccelerateSF’s cofounders, Kay Sorin, now works for OpenAI, having joined the company several months after that hackathon. Sorin and Yee did not respond to requests for comment.

Jancso’s cofounder, Jordan Wick, a former Waymo engineer, has been an active member of DOGE, appearing at several agencies over the past few months, including the Consumer Financial Protection Bureau, National Labor Relations Board, the Department of Labor, and the Department of Education. In 2023, Jancso attended a hackathon hosted by ScaleAI; WIRED found that another DOGE member, Ethan Shaotran, also attended the same hackathon.

Since its creation in the first days of the second Trump administration, DOGE has pushed the use of AI across agencies, even as it has sought to cut tens of thousands of federal jobs. At the Department of Veterans Affairs, a DOGE associate suggested using AI to write code for the agency’s website; at the General Services Administration, DOGE has rolled out the GSAi chatbot; the group has sought to automate the process of firing government employees with a tool called AutoRIF; and a DOGE operative at the Department of Housing and Urban Development is using AI tools to examine and propose changes to regulations. But experts say that deploying AI agents to do the work of 70,000 people would be tricky if not impossible.

A federal employee with knowledge of government contracting, who spoke to WIRED on the condition of anonymity because they were not authorized to speak to the press, says, “A lot of agencies have procedures that can differ widely based on their own rules and regulations, and so deploying AI agents across agencies at scale would likely be very difficult.”

Oren Etzioni, cofounder of the AI startup Vercept, says that while AI agents can be good at doing some things—like using an internet browser to conduct research—their outputs can still vary widely and be highly unreliable. For instance, customer service AI agents have invented nonexistent policies when trying to address user concerns. Even research, he says, requires a human to actually make sure what the AI is spitting out is correct.

“We want our government to be something that we can rely on, as opposed to something that is on the absolute bleeding edge,” says Etzioni. “We don’t need it to be bureaucratic and slow, but if corporations haven’t adopted this yet, is the government really where we want to be experimenting with the cutting edge AI?”

Etzioni says that AI agents are also not great 1-1 fits for job replacements. Rather, AI is able to do certain tasks or make others more efficient, but the idea that the technology could do the jobs of 70,000 employees would not be possible. “Unless you’re using funny math,” he says, “no way.”

Jancso, first identified by WIRED in February, was one of the earliest recruiters for DOGE in the months before Donald Trump was inaugurated. In December, Jancso, who sources told WIRED said he had been recruited by Steve Davis, president of the Musk-founded Boring Company and a current member of DOGE, used the Palantir alumni group to recruit DOGE members. On December 2nd, 2024, he wrote, “I’m helping Elon’s team find tech talent for the Department of Government Efficiency (DOGE) in the new admin. This is a historic opportunity to build an efficient government, and to cut the federal budget by 1/3. If you’re interested in playing a role in this mission, please reach out in the next few days.”

According to one source at SpaceX, who asked to remain anonymous as they are not authorized to speak to the press, Jancso appeared to be one of the DOGE members who worked out of the company’s DC office in the days before inauguration along with several other people who would constitute some of DOGE’s earliest members. SpaceX did not respond to a request for comment.

Palantir was cofounded by Peter Thiel, a billionaire and longtime Trump supporter with close ties to Musk. Palantir, which provides data analytics tools to several government agencies including the Department of Defense and the Department of Homeland Security, has received billions of dollars in government contracts. During the second Trump administration, the company has been involved in helping to build a “mega API” to connect data from the Internal Revenue Service to other government agencies, and is working with Immigration and Customs Enforcement to create a massive surveillance platform to identify immigrants to target for deportation.

This story originally appeared at WIRED.com.

Photo of WIRED

Wired.com is your essential daily guide to what’s next, delivering the most original and complete take you’ll find anywhere on innovation’s impact on technology, science, business and culture.

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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 »

google-teases-notebooklm-app-in-the-play-store-ahead-of-i/o-release

Google teases NotebookLM app in the Play Store ahead of I/O release

After several years of escalating AI hysteria, we are all familiar with Google’s desire to put Gemini in every one of its products. That can be annoying, but NotebookLM is not—this one actually works. NotebookLM, which helps you parse documents, videos, and more using Google’s advanced AI models, has been available on the web since 2023, but Google recently confirmed it would finally get an Android app. You can get a look at the app now, but it’s not yet available to install.

Until now, NotebookLM was only a website. You can visit it on your phone, but the interface is clunky compared to the desktop version. The arrival of the mobile app will change that. Google said it plans to release the app at Google I/O in late May, but the listing is live in the Play Store early. You can pre-register to be notified when the download is live, but you’ll have to tide yourself over with the screenshots for the time being.

NotebookLM relies on the same underlying technology as Google’s other chatbots and AI projects, but instead of a general purpose robot, NotebookLM is only concerned with the documents you upload. It can assimilate text files, websites, and videos, including multiple files and source types for a single agent. It has a hefty context window of 500,000 tokens and supports document uploads as large as 200MB. Google says this creates a queryable “AI expert” that can answer detailed questions and brainstorm ideas based on the source data.

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google-is-quietly-testing-ads-in-ai-chatbots

Google is quietly testing ads in AI chatbots

Google has built an enormously successful business around the idea of putting ads in search results. Its most recent quarterly results showed the company made more than $50 billion from search ads, but what happens if AI becomes the dominant form of finding information? Google is preparing for that possibility by testing chatbot ads, but you won’t see them in Google’s Gemini AI—at least not yet.

A report from Bloomberg describes how Google began working on a plan in 2024 to adapt AdSense ads to a chatbot experience. Usually, AdSense ads appear in search results and are scattered around websites. Google ran a small test of chatbot ads late last year, partnering with select AI startups, including AI search apps iAsk and Liner.

The testing must have gone well because Google is now allowing more chatbot makers to sign up for AdSense. “AdSense for Search is available for websites that want to show relevant ads in their conversational AI experiences,” said a Google spokesperson.

If people continue shifting to using AI chatbots to find information, this expansion of AdSense could help prop up profits. There’s no hint of advertising in Google’s own Gemini chatbot or AI Mode search, but the day may be coming when you won’t get the clean, ad-free experience at no cost.

A path to profit

Google is racing to catch up to OpenAI, which has a substantial lead in chatbot market share despite Gemini’s recent growth. This has led Google to freely provide some of its most capable AI tools, including Deep Research, Gemini Pro, and Veo 2 video generation. There are limits to how much you can use most of these features with a free account, but it must be costing Google a boatload of cash.

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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.

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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.

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google-search’s-made-up-ai-explanations-for-sayings-no-one-ever-said,-explained

Google search’s made-up AI explanations for sayings no one ever said, explained


But what does “meaning” mean?

A partial defense of (some of) AI Overview’s fanciful idiomatic explanations.

Mind…. blown Credit: Getty Images

Last week, the phrase “You can’t lick a badger twice” unexpectedly went viral on social media. The nonsense sentence—which was likely never uttered by a human before last week—had become the poster child for the newly discovered way Google search’s AI Overviews makes up plausible-sounding explanations for made-up idioms (though the concept seems to predate that specific viral post by at least a few days).

Google users quickly discovered that typing any concocted phrase into the search bar with the word “meaning” attached at the end would generate an AI Overview with a purported explanation of its idiomatic meaning. Even the most nonsensical attempts at new proverbs resulted in a confident explanation from Google’s AI Overview, created right there on the spot.

In the wake of the “lick a badger” post, countless users flocked to social media to share Google’s AI interpretations of their own made-up idioms, often expressing horror or disbelief at Google’s take on their nonsense. Those posts often highlight the overconfident way the AI Overview frames its idiomatic explanations and occasional problems with the model confabulating sources that don’t exist.

But after reading through dozens of publicly shared examples of Google’s explanations for fake idioms—and generating a few of my own—I’ve come away somewhat impressed with the model’s almost poetic attempts to glean meaning from gibberish and make sense out of the senseless.

Talk to me like a child

Let’s try a thought experiment: Say a child asked you what the phrase “you can’t lick a badger twice” means. You’d probably say you’ve never heard that particular phrase or ask the child where they heard it. You might say that you’re not familiar with that phrase or that it doesn’t really make sense without more context.

Someone on Threads noticed you can type any random sentence into Google, then add “meaning” afterwards, and you’ll get an AI explanation of a famous idiom or phrase you just made up. Here is mine

[image or embed]

— Greg Jenner (@gregjenner.bsky.social) April 23, 2025 at 6: 15 AM

But let’s say the child persisted and really wanted an explanation for what the phrase means. So you’d do your best to generate a plausible-sounding answer. You’d search your memory for possible connotations for the word “lick” and/or symbolic meaning for the noble badger to force the idiom into some semblance of sense. You’d reach back to other similar idioms you know to try to fit this new, unfamiliar phrase into a wider pattern (anyone who has played the excellent board game Wise and Otherwise might be familiar with the process).

Google’s AI Overview doesn’t go through exactly that kind of human thought process when faced with a similar question about the same saying. But in its own way, the large language model also does its best to generate a plausible-sounding response to an unreasonable request.

As seen in Greg Jenner’s viral Bluesky post, Google’s AI Overview suggests that “you can’t lick a badger twice” means that “you can’t trick or deceive someone a second time after they’ve been tricked once. It’s a warning that if someone has already been deceived, they are unlikely to fall for the same trick again.” As an attempt to derive meaning from a meaningless phrase —which was, after all, the user’s request—that’s not half bad. Faced with a phrase that has no inherent meaning, the AI Overview still makes a good-faith effort to answer the user’s request and draw some plausible explanation out of troll-worthy nonsense.

Contrary to the computer science truism of “garbage in, garbage out, Google here is taking in some garbage and spitting out… well, a workable interpretation of garbage, at the very least.

Google’s AI Overview even goes into more detail explaining its thought process. “Lick” here means to “trick or deceive” someone, it says, a bit of a stretch from the dictionary definition of lick as “comprehensively defeat,” but probably close enough for an idiom (and a plausible iteration of the idiom, “Fool me once shame on you, fool me twice, shame on me…”). Google also explains that the badger part of the phrase “likely originates from the historical sport of badger baiting,” a practice I was sure Google was hallucinating until I looked it up and found it was real.

It took me 15 seconds to make up this saying but now I think it kind of works!

Credit: Kyle Orland / Google

It took me 15 seconds to make up this saying but now I think it kind of works! Credit: Kyle Orland / Google

I found plenty of other examples where Google’s AI derived more meaning than the original requester’s gibberish probably deserved. Google interprets the phrase “dream makes the steam” as an almost poetic statement about imagination powering innovation. The line “you can’t humble a tortoise” similarly gets interpreted as a statement about the difficulty of intimidating “someone with a strong, steady, unwavering character (like a tortoise).”

Google also often finds connections that the original nonsense idiom creators likely didn’t intend. For instance, Google could link the made-up idiom “A deft cat always rings the bell” to the real concept of belling the cat. And in attempting to interpret the nonsense phrase “two cats are better than grapes,” the AI Overview correctly notes that grapes can be potentially toxic to cats.

Brimming with confidence

Even when Google’s AI Overview works hard to make the best of a bad prompt, I can still understand why the responses rub a lot of users the wrong way. A lot of the problem, I think, has to do with the LLM’s unearned confident tone, which pretends that any made-up idiom is a common saying with a well-established and authoritative meaning.

Rather than framing its responses as a “best guess” at an unknown phrase (as a human might when responding to a child in the example above), Google generally provides the user with a single, authoritative explanation for what an idiom means, full stop. Even with the occasional use of couching words such as “likely,” “probably,” or “suggests,” the AI Overview comes off as unnervingly sure of the accepted meaning for some nonsense the user made up five seconds ago.

If Google’s AI Overviews always showed this much self-doubt, we’d be getting somewhere.

Credit: Google / Kyle Orland

If Google’s AI Overviews always showed this much self-doubt, we’d be getting somewhere. Credit: Google / Kyle Orland

I was able to find one exception to this in my testing. When I asked Google the meaning of “when you see a tortoise, spin in a circle,” Google reasonably told me that the phrase “doesn’t have a widely recognized, specific meaning” and that it’s “not a standard expression with a clear, universal meaning.” With that context, Google then offered suggestions for what the phrase “seems to” mean and mentioned Japanese nursery rhymes that it “may be connected” to, before concluding that it is “open to interpretation.”

Those qualifiers go a long way toward properly contextualizing the guesswork Google’s AI Overview is actually conducting here. And if Google provided that kind of context in every AI summary explanation of a made-up phrase, I don’t think users would be quite as upset.

Unfortunately, LLMs like this have trouble knowing what they don’t know, meaning moments of self-doubt like the turtle interpretation here tend to be few and far between. It’s not like Google’s language model has some master list of idioms in its neural network that it can consult to determine what is and isn’t a “standard expression” that it can be confident about. Usually, it’s just projecting a self-assured tone while struggling to force the user’s gibberish into meaning.

Zeus disguised himself as what?

The worst examples of Google’s idiomatic AI guesswork are ones where the LLM slips past plausible interpretations and into sheer hallucination of completely fictional sources. The phrase “a dog never dances before sunset,” for instance, did not appear in the film Before Sunrise, no matter what Google says. Similarly, “There are always two suns on Tuesday” does not appear in The Hitchhiker’s Guide to the Galaxy film despite Google’s insistence.

Literally in the one I tried.

[image or embed]

— Sarah Vaughan (@madamefelicie.bsky.social) April 23, 2025 at 7: 52 AM

There’s also no indication that the made-up phrase “Welsh men jump the rabbit” originated on the Welsh island of Portland, or that “peanut butter platform heels” refers to a scientific experiment creating diamonds from the sticky snack. We’re also unaware of any Greek myth where Zeus disguises himself as a golden shower to explain the phrase “beware what glitters in a golden shower.” (Update: As many commenters have pointed out, this last one is actually a reference to the greek myth of Danaë and the shower of gold, showing Google’s AI knows more about this potential symbolism than I do)

The fact that Google’s AI Overview presents these completely made-up sources with the same self-assurance as its abstract interpretations is a big part of the problem here. It’s also a persistent problem for LLMs that tend to make up news sources and cite fake legal cases regularly. As usual, one should be very wary when trusting anything an LLM presents as an objective fact.

When it comes to the more artistic and symbolic interpretation of nonsense phrases, though, I think Google’s AI Overviews have gotten something of a bad rap recently. Presented with the difficult task of explaining nigh-unexplainable phrases, the model does its best, generating interpretations that can border on the profound at times. While the authoritative tone of those responses can sometimes be annoying or actively misleading, it’s at least amusing to see the model’s best attempts to deal with our meaningless phrases.

Photo of Kyle Orland

Kyle Orland has been the Senior Gaming Editor at Ars Technica since 2012, writing primarily about the business, tech, and culture behind video games. He has journalism and computer science degrees from University of Maryland. He once wrote a whole book about Minesweeper.

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