AI

california’s-newly-signed-ai-law-just-gave-big-tech-exactly-what-it-wanted

California’s newly signed AI law just gave Big Tech exactly what it wanted

On Monday, California Governor Gavin Newsom signed the Transparency in Frontier Artificial Intelligence Act into law, requiring AI companies to disclose their safety practices while stopping short of mandating actual safety testing. The law requires companies with annual revenues of at least $500 million to publish safety protocols on their websites and report incidents to state authorities, but it lacks the stronger enforcement teeth of the bill Newsom vetoed last year after tech companies lobbied heavily against it.

The legislation, S.B. 53, replaces Senator Scott Wiener’s previous attempt at AI regulation, known as S.B. 1047, that would have required safety testing and “kill switches” for AI systems. Instead, the new law asks companies to describe how they incorporate “national standards, international standards, and industry-consensus best practices” into their AI development, without specifying what those standards are or requiring independent verification.

“California has proven that we can establish regulations to protect our communities while also ensuring that the growing AI industry continues to thrive,” Newsom said in a statement, though the law’s actual protective measures remain largely voluntary beyond basic reporting requirements.

According to the California state government, the state houses 32 of the world’s top 50 AI companies, and more than half of global venture capital funding for AI and machine learning startups went to Bay Area companies last year. So while the recently signed bill is state-level legislation, what happens in California AI regulation will have a much wider impact, both by legislative precedent and by affecting companies that craft AI systems used around the world.

Transparency instead of testing

Where the vetoed SB 1047 would have mandated safety testing and kill switches for AI systems, the new law focuses on disclosure. Companies must report what the state calls “potential critical safety incidents” to California’s Office of Emergency Services and provide whistleblower protections for employees who raise safety concerns. The law defines catastrophic risk narrowly as incidents potentially causing 50+ deaths or $1 billion in damage through weapons assistance, autonomous criminal acts, or loss of control. The attorney general can levy civil penalties of up to $1 million per violation for noncompliance with these reporting requirements.

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Burnout and Elon Musk’s politics spark exodus from senior xAI, Tesla staff


Not a fun place to work, apparently

Disillusionment with Musk’s activism, strategic pivots, and mass layoffs cause churn.

Elon Musk’s business empire has been hit by a wave of senior departures over the past year, as the billionaire’s relentless demands and political activism accelerate turnover among his top ranks.

Key members of Tesla’s US sales team, battery and power-train operations, public affairs arm, and its chief information officer have all recently departed, as well as core members of the Optimus robot and AI teams on which Musk has bet the future of the company.

Churn has been even more rapid at xAI, Musk’s two-year-old artificial intelligence start-up, which he merged with his social network X in March. Its chief financial officer and general counsel recently departed after short stints, within a week of each other.

The moves are part of an exodus from the conglomerate of the world’s richest man, as he juggles five companies from SpaceX to Tesla with more than 140,000 employees. The Financial Times spoke to more than a dozen current and former employees to gain an insight into the tumult.

While many left happily after long service to found start-ups or take career breaks, there has also been an uptick in those quitting from burnout, or disillusionment with Musk’s strategic pivots, mass lay-offs and his politics, the people said.

“The one constant in Elon’s world is how quickly he burns through deputies,” said one of the billionaire’s advisers. “Even the board jokes, there’s time and then there’s ‘Tesla time.’ It’s a 24/7 campaign-style work ethos. Not everyone is cut out for that.”

Robert Keele, xAI’s general counsel, ended his 16-month tenure in early August by posting an AI-generated video of a suited lawyer screaming while shoveling molten coal. “I love my two toddlers and I don’t get to see them enough,” he commented.

Mike Liberatore lasted three months as xAI chief financial officer before defecting to Musk’s arch-rival Sam Altman at OpenAI. “102 days—7 days per week in the office; 120+ hours per week; I love working hard,” he said on LinkedIn.

Top lieutenants said Musk’s intensity has been sharpened by the launch of ChatGPT in late-2022, which shook up the established Silicon Valley order.

Employees also perceive Musk’s rivalry with Altman—with whom he co-founded OpenAI, before they fell out—to be behind the pressure being put on staff.

“Elon’s got a chip on his shoulder from ChatGPT and is spending every waking moment trying to put Sam out of business,” said one recent top departee.

Last week, xAI accused its rival of poaching engineers with the aim of “plundering and misappropriating” its code and data center secrets. OpenAI called the lawsuit “the latest chapter in Musk’s ongoing harassment.”

Other insiders pointed to unease about Musk’s support of Donald Trump and advocacy for far-right provocateurs in the US and Europe.

They said some staff dreaded difficult conversations with their families about Musk’s polarizing views on everything from the rights of transgender people to the murder of conservative activist Charlie Kirk.

Musk, Tesla, and xAI declined to comment.

Tesla has traditionally been the most stable part of Musk’s conglomerate. But many of the top team left after it culled 14,000 jobs in April 2024. Some departures were triggered as Musk moved investment away from new EV and battery projects that many employees saw as key to its mission of reducing global emissions—and prioritized robotics, AI, and self-driving robotaxis.

Musk cancelled a program to build a low-cost $25,000 EV that could be sold across emerging markets—dubbed NV-91 internally and Model 2 by fans online, according to five people familiar with the matter.

Daniel Ho, who helped oversee the project as director of vehicle programs and reported directly to Musk, left in September 2024 and joined Google’s self-driving taxi arm, Waymo.

Public policy executives Rohan Patel and Hasan Nazar and the head of the power-train and energy units Drew Baglino also stepped down after the pivot. Rebecca Tinucci, leader of the supercharger division, went to Uber after Musk fired the entire team and slowed construction on high-speed charging stations.

In late summer, David Zhang, who was in charge of the Model Y and Cybertruck rollouts, departed. Chief information officer Nagesh Saldi left in November.

Vineet Mehta, a company veteran of 18 years, described as “critical to all things battery” by a colleague, resigned in April. Milan Kovac, in charge of Optimus humanoid robotics program, departed in June.

He was followed this month by Ashish Kumar, the Optimus AI team lead, who moved to Meta. “Financial upside at Tesla was significantly larger,” wrote Kumar on X in response to criticism he left for money. “Tesla is known to compensate pretty well, way before Zuck made it cool.”

Amid a sharp fall in sales—which many blame on Musk alienating liberal customers—Omead Ashfar, a close confidant known as the billionaire’s “firefighter” and “executioner,” was dismissed as head of sales and operations in North America in June. Ashfar’s deputy Troy Jones followed shortly after, ending 15 years of service.

“Elon’s behavior is affecting morale, retention, and recruitment,” said one long-standing lieutenant. He “went from a position from where people of all stripes liked him, to only a certain section.”

Few who depart criticize Musk for fear of retribution. But Giorgio Balestrieri, who had worked for Tesla for eight years in Spain, is among a handful to go public, saying this month he quit believing that Musk had done “huge damage to Tesla’s mission and to the health of democratic institutions.”

“I love Tesla and my time there,” said another recent leaver. “But nobody that I know there isn’t thinking about politics. Who the hell wants to put up with it? I get calls at least once a week. My advice is, if your moral compass is saying you need to leave, that isn’t going to go away.”

But Tesla chair Robyn Denholm said: “There are always headlines about people leaving, but I don’t see the headlines about people joining.

“Our bench strength is outstanding… we actually develop people really well at Tesla and we are still a magnet for talent.”

At xAI, some staff have balked at Musk’s free-speech absolutism and perceived lax approach to user safety as he rushes out new AI features to compete with OpenAI and Google. Over the summer, the Grok chatbot integrated into X praised Adolf Hitler, after Musk ordered changes to make it less “woke.”

Ex-CFO Liberatore was among the executives that clashed with some of Musk’s inner circle over corporate structure and tough financial targets, people with knowledge of the matter said.

“Elon loyalists who exhibit his traits are laying off people and making decisions on safety that I think are very concerning for people internally,” one of the people added. “Mike is a business guy, a capitalist. But he’s also someone who does stuff the right way.”

The Wall Street Journal first reported some of the details of the internal disputes.

Linda Yaccarino, chief executive of X, resigned in July after the social media platform was subsumed by xAI. She had grown frustrated with Musk’s unilateral decision-making and his criticism over advertising revenue.

xAI’s co-founder and chief engineer, Igor Babuschkin, stepped down a month later to found his own AI safety research project.

Communications executives Dave Heinzinger and John Stoll, spent three and nine months at X respectively, before returning to their former employers, according to people familiar with the matter.

X also lost a rash of senior engineers and product staff who reported directly to Musk and were helping to navigate the integration with xAI.

This includes head of product engineering Haofei Wang and consumer product and payments boss Patrick Traughber. Uday Ruddarraju, who oversaw X and xAI’s infrastructure engineering, and infrastructure engineer Michael Dalton were poached by OpenAI.

Musk shows no sign of relenting. xAI’s flirtatious “Ani bot” has caused controversy over sexually explicit interactions with teenage Grok app users. But the company’s owner has installed a hologram of Ani in the lobby of xAI to greet staff.

“He’s the boss, the alpha and anyone who doesn’t treat him that way, he finds a way to delete,” one former top Tesla executive said.

“He does not have shades of grey, is highly calculated, and focused… that makes him hard to work with. But if you’re aligned with the end goal, and you can grin and bear it, it’s fine. A lot of people do.”

Additional reporting by George Hammond.

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

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Big AI firms pump money into world models as LLM advances slow

Runway, a video generation start-up that has deals with Hollywood studios, including Lionsgate, launched a product last month that uses world models to create gaming settings, with personalized stories and characters generated in real time.

“Traditional video methods [are a] brute-force approach to pixel generation, where you’re trying to squeeze motion in a couple of frames to create the illusion of movement, but the model actually doesn’t really know or reason about what’s going on in that scene,” said Cristóbal Valenzuela, chief executive officer at Runway.

Previous video-generation models had physics that were unlike the real world, he added, which general-purpose world model systems help to address.

To build these models, companies need to collect a huge amount of physical data about the world.

San Francisco-based Niantic has mapped 10 million locations, gathering information through games including Pokémon Go, which has 30 million monthly players interacting with a global map.

Niantic ran Pokémon Go for nine years and, even after the game was sold to US-based Scopely in June, its players still contribute anonymized data through scans of public landmarks to help build its world model.

“We have a running start at the problem,” said John Hanke, chief executive of Niantic Spatial, as the company is now called following the Scopely deal.

Both Niantic and Nvidia are working on filling gaps by getting their world models to generate or predict environments. Nvidia’s Omniverse platform creates and runs such simulations, assisting the $4.3 trillion tech giant’s push toward robotics and building on its long history of simulating real-world environments in video games.

Nvidia Chief Executive Jensen Huang has asserted that the next major growth phase for the company will come with “physical AI,” with the new models revolutionizing the field of robotics.

Some such as Meta’s LeCun have said this vision of a new generation of AI systems powering machines with human-level intelligence could take 10 years to achieve.

But the potential scope of the cutting-edge technology is extensive, according to AI experts. World models “open up the opportunity to service all of these other industries and amplify the same thing that computers did for knowledge work,” said Nvidia’s Lebaredian.

Additional reporting by Melissa Heikkilä in London and Michael Acton in San Francisco.

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

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why-la-comic-con-thought-making-an-ai-powered-stan-lee-hologram-was-a-good-idea

Why LA Comic Con thought making an AI-powered Stan Lee hologram was a good idea


Trust us, it’ll be marvel-ous

“I suppose if we do it and thousands of fans… don’t like it, we’ll stop doing it.”

Excelsior, true beliers! Credit: Proto Hologram

Late last week, The Hollywood Reporter ran a story about an “AI Stan Lee hologram” that would be appearing at the LA Comic Con this weekend. Nearly seven years after the famous Marvel Comics creator’s death at the age of 95, fans will be able to pay $15 to $20 this weekend to chat with a life-sized, AI-powered avatar of Lee in an enclosed booth at the show.

The instant response from many fans and media outlets to the idea was not kind, to say the least. A writer for TheGamer called the very idea “demonic” and said we need to “kill it with fire before it’s too late.” The AV Club urged its readers not to pay to see “the anguished digital ghost of a beloved comic book creator, repurposed as a trap for chumps!” Reactions on a popular Reddit thread ranged from calling it “incredibly disrespectful” and “in bad taste” to “ghoulish” and “so fucked up,” with very little that was more receptive to the concept.

But Chris DeMoulin, the CEO of the parent company behind LA Comic Con, urged critics to come see the AI-powered hologram for themselves before rushing to judgment. “We’re not afraid of people seeing it and we’re not afraid of criticism,” he told Ars. “I’m just a fan of informed criticism, and I think most of what’s been out there so far has not really been informed.”

“It’s unfortunate that a few people have really negative things to say about it, sight unseen, just the level of it being a concept,” DeMoulin continued. “It’s not perfect. I’m not sure something like this can ever be perfect. But I think what you strive to do is feed enough information into it and test it enough so that the experience it creates for the fans is one that feels genuine.”

“It’s going to have to be really good or we’re all going to say no”

This isn’t the first time LA Comic Con has featured an interactive hologram (which for the Stan Lee experience means a life-sized volumetric screen-in-a-box that can show different views from different angles). Starting in 2019, the convention used similar technology to feature Boffo the Bear, a 7-foot-tall animated blue ursid who served as the MC for a live talent show featuring famous voice acting talent. But Boffo was powered by a real-time motion-captured improv performance from actor Mark DeCarlo rather than automated artificial intelligence.

A live mo-capped version of Boffo the Bear hosts a panel with voice actors at LA Comic Con.

In the years since Boffo’s introduction at the con, DeMoulin said he’s kept up with the team behind that hologram and “saw the leaps and bounds that they were making in improving the technology, improving the interactivity.” Now, he said, it’s possible to create an AI-powered version that ingests “all of the actual comments that people made during their life” to craft an interactive hologram that “is not literally quoting the person, but everything it was saying was based on things that person actually said.”

DeMoulin said he called Bob Sabouni, who manages the Stan Lee Legacy brand, to pitch the AI Stan Lee avatar as “kind of an entry point into people asking questions about the Marvel universe, the stories, the characters he created.” Sabouni agreed to the idea, DeMoulin said, but added that “it’s gonna have to be really good or we’re all going to say no.”

With that somewhat conditional approval, DeMoulin reached out to Proto Hologram, the company that had developed the Boffo the Bear experience years earlier. Proto, in turn, reached out to Hyperreal, a company that describes itself as “powering ownership, control, performance, and monetization of identity across digital ecosystems” to help develop the AI model that would power the Lee avatar.

A promotional video from Proto Holograms shows off the kind of volumetric box that the AI-powered Stan Lee avatar will appear in.

Hyperreal CEO and Chief Architect Remington Scott tells Ars that the company “leverages a customized ecosystem of cutting-edge AI technologies” to create “bespoke” and “custom-crafted” AI versions of celebrities. To do that for Stan Lee, DeMoulin said they trained a model on decades of content he had left behind, from tapes of dozens of convention panels he had appeared on to written and spoken content gathered by the managers of the Stan Lee Universe brand.

Scott said Hyperreal “can’t share specific technical details” of the models or training techniques they use to power these recreations. But Scott added that this training project is “particularly meaningful, [because] Stan Lee had actually begun digitizing himself while he was alive, with the vision of creating a digital double so his fans could interact with him on a larger scale.”

After incurring costs of “tens of thousands into six figures” of dollars, DeMoulin said he was finally able to test the Lee hologram about a month ago. That first version still needed some tweaks to get the look and feel of Lee’s delivery just right, though.

“Stan had a considered way of speaking… he would pause, he had certain catch phrases that when he used them he would say them in a certain way,” DeMoulin said. “So it took a while to get to the hologram to be able to say all that in a way that [Sabouni] and I and others that work with Stan felt like, ‘Yeah, that’s actually starting to sound more like him.’”

“The only words that are gonna be in Stan’s mouth are Stan’s words”

Anyone who is familiar with LLMs and their tendency to confabulate might be worried about the potential for an AI Lee avatar to go off-script or make things up in front of a live audience. And while DeMoulin said he was concerned about that going in, those concerns have faded as he and others who worked with Lee in his lifetime have spent hours throwing “hundreds and hundreds and hundreds” of questions at the hologram “to sort of see where the sensitivities on it are.”

“The only words that are gonna be in Stan’s mouth are Stan’s words,” DeMoulin said. “Just because I haven’t personally seen [the model hallucinate] doesn’t mean that it’s impossible, but that hasn’t been my experience.”

The living version of Stan Lee appeared at the Wizard World convention in 2018, shortly before his death.

Credit: Getty Images

The living version of Stan Lee appeared at the Wizard World convention in 2018, shortly before his death. Credit: Getty Images

While a moderator at the convention will be on hand to repeat fan questions into a microphone (to avoid ambient crowd noise from the showfloor), DeMoulin said there won’t be any human filtering on what fans are allowed to ask the Lee avatar in the 15- to 20-minute group Q&A sessions. Instead, DeMoulin said the team has set up a system of “content governors” so that, for instance, “if you ask Stan what he thought of the last presidential election he’s gonna say ‘That’s not what we’re here to talk about. We’re here to talk about the Marvel universe.'”

For topics that are Marvel-related, though, the AI avatar won’t shy away from controversy, DeMoulin said. If you ask the avatar about Jack Kirby, for instance, DeMoulin said it will address the “honest disagreements about characters or storylines, which are gonna happen in any creative enterprise,” while also saying that “‘I have nothing but respect for him,’ which is I think largely what Stan would have said if he was asked that question.”

Hyperreal’s Scott said the company’s approach to training digital avatars on verified content “ensures responses stay true to Stan’s documented perspectives and values.” And DeMoulin said the model is perfectly willing to say when it doesn’t know the answer to an appropriate question. In early testing, for instance, the avatar couldn’t answer a question about the Merry Marvel Marching Society, DeMoulin said, because that wasn’t part of its training data. After a subsequent update, the new model provided a relevant answer to the same question, he said.

“We are not trying to bring Stan back from the dead”

Throughout our talk, DeMoulin repeatedly stressed that their AI hologram wasn’t intended to serve as a replacement for the living version of Lee. “We want to make sure that people understand that we are not trying to bring Stan back from the dead,” he said. “We’re not trying to say that this is Stan, and we’re not trying to put words in his mouth, and this avatar is not gonna start doing commercials to advertise other people’s products.”

DeMoulin said he sees the Lee avatar as a kind of futuristic guide to a library of Marvel information and trivia, presented with a fun and familiar face. “In the introduction, the avatar will say, ‘I’m here as a result of the latest developments in technology, which allow me to be a holographic representation of Stan to answer your questions about Marvel and trivia’ and this, that, and the other thing,” DeMoulin said

Still, DeMoulin said he understands why the idea of using even a stylized version of Lee’s likeness in this manner could rub some fans the wrong way. “When a new technology comes out, it just feels wrong to them, and I respect the fact that this feels wrong to people,” he said. “I totally agree that something like this–not just for Stan but for anyone, any celebrity alive or dead–could be put into this technology and used in a way that would be exploitative and unfortunate.”

Fans like these, seen at LA Comic Con 2022, will be the final arbiters of whether the AI-powered Stan Lee avatar is respectful or not.

Credit: Getty Images

Fans like these, seen at LA Comic Con 2022, will be the final arbiters of whether the AI-powered Stan Lee avatar is respectful or not. Credit: Getty Images

That’s why DeMoulin said he and the others behind the AI-powered Lee feel a responsibility “to make sure that if we were going to do this, we never got anywhere close to that.” Moreover, he said he’s “disappointed that people would be so negative about something they’ve not seen. … It’s not that I think that their point of view is invalid. What I think is invalid is having a wildly negative point of view about something that you haven’t actually seen.”

Scott said concerns about respect for the actual human celebrity are why they “partner exclusively with authorized estates and rights holders like Stan Lee Universe.” The “premium, authenticated digital identities” created by Hyperreal’s system are “not replacing artists” but “creating respectful digital extensions that honor their legacy,” Scott said.

Once fans actually see the AI-powered Lee avatar in person, DeMoulin said he’s confident they’ll see the team behind the convention is “trying to do it in a way that will actually be delightful and very much be consistent with Stan’s legacy… We clearly have to set our sights on doing this right, and doing it right means getting people that knew and loved the guy and worked with him during his career to give us input, and then putting it in front of enough fans to know if we’re doing it in a way that lives up to his standards.”

And if he’s wrong about the expected reception? “I suppose if we do it and thousands of fans interact with [it] and they don’t like it, we’ll stop doing it,” he said. “I saw firsthand the impact that Stan had in that [convention] environment, so I think we have a team of people together that love and respect that and are trying to do something which will continue that. And if it turns out, for some reason, this isn’t that, we won’t do it.”

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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can-ai-detect-hedgehogs-from-space?-maybe-if-you-find-brambles-first.

Can AI detect hedgehogs from space? Maybe if you find brambles first.

“It took us about 20 seconds to find the first one in an area indicated by the model,” wrote Jaffer in a blog post documenting the field test. Starting at Milton Community Centre, where the model showed high confidence of brambles near the car park, the team systematically visited locations with varying prediction levels.

The research team locating their first bramble.

The research team locating their first bramble. Credit: Sadiq Jaffer

At Milton Country Park, every high-confidence area they checked contained substantial bramble growth. When they investigated a residential hotspot, they found an empty plot overrun with brambles. Most amusingly, a major prediction in North Cambridge led them to Bramblefields Local Nature Reserve. True to its name, the area contained extensive bramble coverage.

The model reportedly performed best when detecting large, uncovered bramble patches visible from above. Smaller brambles under tree cover showed lower confidence scores—a logical limitation given the satellite’s overhead perspective. “Since TESSERA is learned representation from remote sensing data, it would make sense that bramble partially obscured from above might be harder to spot,” Jaffer explained.

An early experiment

While the researchers expressed enthusiasm over the early results, the bramble detection work represents a proof-of-concept that is still under active research. The model has not yet been published in a peer-reviewed journal, and the field validation described here was an informal test rather than a scientific study. The Cambridge team acknowledges these limitations and plans more systematic validation.

However, it’s still a relatively positive research application of neural network techniques that reminds us that the field of artificial intelligence is much larger than just generative AI models, such as ChatGPT, or video synthesis models.

Should the team’s research pan out, the simplicity of the bramble detector offers some practical advantages. Unlike more resource-intensive deep learning models, the system could potentially run on mobile devices, enabling real-time field validation. The team considered developing a phone-based active learning system that would enable field researchers to improve the model while verifying its predictions.

In the future, similar AI-based approaches combining satellite remote sensing with citizen science data could potentially map invasive species, track agricultural pests, or monitor changes in various ecosystems. For threatened species like hedgehogs, rapidly mapping critical habitat features becomes increasingly valuable during a time when climate change and urbanization are actively reshaping the places that hedgehogs like to call home.

Can AI detect hedgehogs from space? Maybe if you find brambles first. Read More »

youtube-music-is-testing-ai-hosts-that-will-interrupt-your-tunes

YouTube Music is testing AI hosts that will interrupt your tunes

YouTube has a new Labs program, allowing listeners to “discover the next generation of YouTube.” In case you were wondering, that generation is apparently all about AI. The streaming site says Labs will offer a glimpse of the AI features it’s developing for YouTube Music, and it starts with AI “hosts” that will chime in while you’re listening to music. Yes, really.

The new AI music hosts are supposed to provide a richer listening experience, according to YouTube. As you’re listening to tunes, the AI will generate audio snippets similar to, but shorter than, the fake podcasts you can create in NotebookLM. The “Beyond the Beat” host will break in every so often with relevant stories, trivia, and commentary about your musical tastes. YouTube says this feature will appear when you are listening to mixes and radio stations.

The experimental feature is intended to be a bit like having a radio host drop some playful banter while cueing up the next song. It sounds a bit like Spotify’s AI DJ, but the YouTube AI doesn’t create playlists like Spotify’s robot. This is still generative AI, which comes with the risk of hallucinations and low-quality slop, neither of which belongs in your music. That said, Google’s Audio Overviews are often surprisingly good in small doses.

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experts-urge-caution-about-using-chatgpt-to-pick-stocks

Experts urge caution about using ChatGPT to pick stocks

“AI models can be brilliant,” Dan Moczulski, UK managing director at eToro, told Reuters. “The risk comes when people treat generic models like ChatGPT or Gemini as crystal balls.” He noted that general AI models “can misquote figures and dates, lean too hard on a pre-established narrative, and overly rely on past price action to attempt to predict the future.”

The hazards of AI stock picking

Using AI to trade stocks at home feels like it might be the next step in a long series of technological advances that have democratized individual retail investing, for better or for worse. Computer-based stock trading for individuals dates back to 1984, when Charles Schwab introduced electronic trading services for dial-up customers. E-Trade launched in 1992, and by the late 1990s, online brokerages had transformed retail investing, dropping commission fees from hundreds of dollars per trade to under $10.

The first “robo-advisors” appeared after the 2008 financial crisis, which began the rise of automated online services that use algorithms to manage and rebalance portfolios based on a client’s goals. Services like Betterment launched in 2010, and Wealthfront followed in 2011, using algorithms to automatically rebalance portfolios. By the end of 2015, robo-advisors from nearly 100 companies globally were managing $60 billion in client assets.

The arrival of ChatGPT in November 2022 arguably marked a new phase where retail investors could directly query an AI model for stock picks rather than relying on pre-programmed algorithms. But Leung acknowledged that ChatGPT cannot access data behind paywalls, potentially missing crucial analyses available through professional services. To get better results, he creates specific prompts like “assume you’re a short analyst, what is the short thesis for this stock?” or “use only credible sources, such as SEC filings.”

Beyond chatbots, reliance on financial algorithms is growing. The “robo-advisory” market, which includes all companies providing automated, algorithm-driven financial advice from fintech startups to established banks, is forecast to grow roughly 600 percent by 2029, according to data-analysis firm Research and Markets.

But as more retail investors turn to AI tools for investment decisions, it’s also potential trouble waiting to happen.

“If people get comfortable investing using AI and they’re making money, they may not be able to manage in a crisis or downturn,” Leung warned Reuters. The concern extends beyond individual losses to whether retail investors using AI tools understand risk management or have strategies for when markets turn bearish.

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google-deepmind-unveils-its-first-“thinking”-robotics-ai

Google DeepMind unveils its first “thinking” robotics AI

Imagine that you want a robot to sort a pile of laundry into whites and colors. Gemini Robotics-ER 1.5 would process the request along with images of the physical environment (a pile of clothing). This AI can also call tools like Google search to gather more data. The ER model then generates natural language instructions, specific steps that the robot should follow to complete the given task.

Gemin iRobotics thinking

The two new models work together to “think” about how to complete a task.

Credit: Google

The two new models work together to “think” about how to complete a task. Credit: Google

Gemini Robotics 1.5 (the action model) takes these instructions from the ER model and generates robot actions while using visual input to guide its movements. But it also goes through its own thinking process to consider how to approach each step. “There are all these kinds of intuitive thoughts that help [a person] guide this task, but robots don’t have this intuition,” said DeepMind’s Kanishka Rao. “One of the major advancements that we’ve made with 1.5 in the VLA is its ability to think before it acts.”

Both of DeepMind’s new robotic AIs are built on the Gemini foundation models but have been fine-tuned with data that adapts them to operating in a physical space. This approach, the team says, gives robots the ability to undertake more complex multi-stage tasks, bringing agentic capabilities to robotics.

The DeepMind team tests Gemini robotics with a few different machines, like the two-armed Aloha 2 and the humanoid Apollo. In the past, AI researchers had to create customized models for each robot, but that’s no longer necessary. DeepMind says that Gemini Robotics 1.5 can learn across different embodiments, transferring skills learned from Aloha 2’s grippers to the more intricate hands on Apollo with no specialized tuning.

All this talk of physical agents powered by AI is fun, but we’re still a long way from a robot you can order to do your laundry. Gemini Robotics 1.5, the model that actually controls robots, is still only available to trusted testers. However, the thinking ER model is now rolling out in Google AI Studio, allowing developers to generate robotic instructions for their own physically embodied robotic experiments.

Google DeepMind unveils its first “thinking” robotics AI Read More »

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Reviewing iOS 26 for power users: Reminders, Preview, and more


These features try to turn iPhones into more powerful work and organization tools.

iOS 26 came out last week, bringing a new look and interface alongside some new capabilities and updates aimed squarely at iPhone power users.

We gave you our main iOS 26 review last week. This time around, we’re taking a look at some of the updates targeted at people who rely on their iPhones for much more than making phone calls and browsing the Internet. Many of these features rely on Apple Intelligence, meaning they’re only as reliable and helpful as Apple’s generative AI (and only available on newer iPhones, besides). Other adjustments are smaller but could make a big difference to people who use their phone to do work tasks.

Reminders attempt to get smarter

The Reminders app gets the Apple Intelligence treatment in iOS 26, with the AI primarily focused on making it easier to organize content within Reminders lists. Lines in Reminders lists are often short, quickly jotted-down blurbs rather than lengthy, detailed complex instructions. With this in mind, it’s easy to see how the AI can sometimes lack enough information in order to perform certain tasks, like logically grouping different errands into sensible sections.

But Apple also encourages applying the AI-based Reminders features to areas of life that could hold more weight, such as making a list of suggested reminders from emails. For serious or work-critical summaries, Reminders’ new Apple Intelligence capabilities aren’t reliable enough.

Suggested Reminders based on selected text

iOS 26 attempts to elevate Reminders from an app for making lists to an organization tool that helps you identify information or important tasks that you should accomplish. If you share content, such as emails, website text, or a note, with the app, it can create a list of what it thinks are the critical things to remember from the text. But if you’re trying to extract information any more advanced than an ingredients list from a recipe, Reminders misses the mark.

iOS 26 Suggested Reminders

Sometimes I tried sharing longer text with Reminders and didn’t get any suggestions.

Credit: Scharon Harding

Sometimes I tried sharing longer text with Reminders and didn’t get any suggestions. Credit: Scharon Harding

Sometimes, especially when reviewing longer text, Reminders was unable to think of suggested reminders. Other times, the reminders that it suggested, based on lengthy messages, were off-base.

For instance, I had the app pull suggested reminders from a long email with guidelines and instructions from an editor. Highlighting a lot of text can be tedious on a touchscreen, but I did it anyway because the message had lots of helpful information broken up into sections that each had their own bold subheadings. Additionally, most of those sections had their own lists (some using bullet points, some using numbers). I hoped Reminders would at least gather information from all of the email’s lists. But the suggested reminders ended up just being the same text from three—but not all—of the email’s bold subheadings.

When I tried getting suggested reminders from a smaller portion of the same email, I surprisingly got five bullet points that covered more than just the email’s subheadings but that still missed key points, including the email’s primary purpose.

Ultimately, the suggested Reminders feature mostly just boosts the app’s ability to serve as a modern shopping list. Suggested Reminders excels at pulling out ingredients from recipes, turning each ingredient into a suggestion that you can tap to add to a Reminders list. But being able to make a bulleted list out of a bulleted list is far from groundbreaking.

Auto-categorizing lines in Reminders lists

Since iOS 17, Reminders has been able to automatically sort items in grocery lists into distinct categories, like Produce and Proteins. iOS 26 tries taking things further by automatically grouping items in a list into non-culinary sections.

The way Reminders groups user-created tasks in lists is more sensible—and useful—than when it tries to create task suggestions based on shared text.

For example, I made a long list of various errands I needed to do, and Reminders grouped them into these categories: Administrative Tasks, Household Chores, Miscellaneous, Personal Tasks, Shopping, and Travel & Accommodation. The error rate here is respectable, but I would have tweaked some things. For one, I wouldn’t use the word “administrative” to refer to personal errands. The two tasks included under Administrative Tasks would have made more sense to me in Personal Tasks or Miscellaneous, even though those category names are almost too vague to have a distinct meaning.

Preview comes to iOS

With the iOS debut of Preview, Apple brings an app for viewing and editing PDFs and images to iPhones, which macOS users have had for years. As a result, many iPhone users will find the software easy and familiar to use.

But for iPhone owners who have long relied on Files for viewing, marking, and filling out PDFs and the like, Preview doesn’t bring many new capabilities. Anything that you can do in Preview, you could have done by viewing the same document in Files in an older version of iOS, save for a new crop tool and a dedicated button for showing information about the document.

That’s the point, though. When an iPhone has two discrete apps that can read and edit files, it’s far less frustrating to work with multiple documents. While you’re annotating a document in Preview, the Files app is still available, allowing you to have more than one document open at once. It’s a simple adjustment but one that vastly improves multitasking.

More Shortcuts options

Shortcuts gets somewhat more capable in iOS 26. That’s assuming you’re interested in using ChatGPT or Apple Intelligence generative AI in your automated tasks. You can tag in generative AI to create a shortcut that includes summarizing text in bullet points and applying that bulleted list to the shortcut’s next task, for instance.

An example of a Shortcut that uses generative AI.

Credit: Apple

An example of a Shortcut that uses generative AI. Credit: Apple

There are inherent drawbacks here. For one, Apple Intelligence and ChatGPT, like many generative AI tools, are subject to inaccuracies and can frequently overlook and/or misinterpret critical information. iOS 26 makes it easier for power users to incorporate a rewrite of a long text that has a more professional tone into a Shortcut. But that doesn’t mean that AI will properly communicate the information, especially when used across different scenarios with varied text.

You have three options for building Shortcuts that include the use of AI models. Using ChatGPT or Apple Intelligence via Apple’s Private Cloud Compute, which runs the model on an Apple server, requires an Internet connection. Alternatively, you can use an on-device model without connecting to the web.

You can run more advanced models via Private Cloud Compute than you can with Apple Intelligence on-device. In Apple’s testing, models via Private Cloud Compute perform better on things like writing summaries and composition compared to on-device models.

Apple says personal user data sent to Private Cloud Compute “isn’t accessible to anyone other than the user—not even to Apple.” Apple has a strong, yet flawed, reputation for being better about user privacy than other Big Tech firms. But by offering three different models to use with Shortcuts, iOS 26 ensures greater functionality, options, and control.

Something for podcasters

It’s likely that more people rely on iPads (or Macs) than iPhones for podcasting. Nevertheless, a new local capture feature introduced to both iOS 26 and iPadOS 26 makes it a touch more feasible to use iPhones (and iPads especially) for recording interviews for podcasts.

Before the latest updates, iOS and iPadOS only allowed one app to access the device’s microphone at a time. So, if you were interviewing someone via a videoconferencing app, you couldn’t also use your iPhone or iPad to record the discussion, since the videoconferencing app is using your mic to share your voice with whoever is on the other end of the call. Local capture on iOS 26 doesn’t include audio input controls, but its inclusion gives podcasters a way to record interviews or conversations on iPhones without needing additional software or hardware. That capability could save the day in a pinch.

Photo of Scharon Harding

Scharon is a Senior Technology Reporter at Ars Technica writing news, reviews, and analysis on consumer gadgets and services. She’s been reporting on technology for over 10 years, with bylines at Tom’s Hardware, Channelnomics, and CRN UK.

Reviewing iOS 26 for power users: Reminders, Preview, and more Read More »

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Why does OpenAI need six giant data centers?

Training next-generation AI models compounds the problem. On top of running existing AI models like those that power ChatGPT, OpenAI is constantly working on new technology in the background. It’s a process that requires thousands of specialized chips running continuously for months.

The circular investment question

The financial structure of these deals between OpenAI, Oracle, and Nvidia has drawn scrutiny from industry observers. Earlier this week, Nvidia announced it would invest up to $100 billion as OpenAI deploys Nvidia systems. As Bryn Talkington of Requisite Capital Management told CNBC: “Nvidia invests $100 billion in OpenAI, which then OpenAI turns back and gives it back to Nvidia.”

Oracle’s arrangement follows a similar pattern, with a reported $30 billion-per-year deal where Oracle builds facilities that OpenAI pays to use. This circular flow, which involves infrastructure providers investing in AI companies that become their biggest customers, has raised eyebrows about whether these represent genuine economic investments or elaborate accounting maneuvers.

The arrangements are becoming even more convoluted. The Information reported this week that Nvidia is discussing leasing its chips to OpenAI rather than selling them outright. Under this structure, Nvidia would create a separate entity to purchase its own GPUs, then lease them to OpenAI, which adds yet another layer of circular financial engineering to this complicated relationship.

“NVIDIA seeds companies and gives them the guaranteed contracts necessary to raise debt to buy GPUs from NVIDIA, even though these companies are horribly unprofitable and will eventually die from a lack of any real demand,” wrote tech critic Ed Zitron on Bluesky last week about the unusual flow of AI infrastructure investments. Zitron was referring to companies like CoreWeave and Lambda Labs, which have raised billions in debt to buy Nvidia GPUs based partly on contracts from Nvidia itself. It’s a pattern that mirrors OpenAI’s arrangements with Oracle and Nvidia.

So what happens if the bubble pops? Even Altman himself warned last month that “someone will lose a phenomenal amount of money” in what he called an AI bubble. If AI demand fails to meet these astronomical projections, the massive data centers built on physical soil won’t simply vanish. When the dot-com bubble burst in 2001, fiber optic cable laid during the boom years eventually found use as Internet demand caught up. Similarly, these facilities could potentially pivot to cloud services, scientific computing, or other workloads, but at what might be massive losses for investors who paid AI-boom prices.

Why does OpenAI need six giant data centers? Read More »

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When “no” means “yes”: Why AI chatbots can’t process Persian social etiquette

If an Iranian taxi driver waves away your payment, saying, “Be my guest this time,” accepting their offer would be a cultural disaster. They expect you to insist on paying—probably three times—before they’ll take your money. This dance of refusal and counter-refusal, called taarof, governs countless daily interactions in Persian culture. And AI models are terrible at it.

New research released earlier this month titled “We Politely Insist: Your LLM Must Learn the Persian Art of Taarof” shows that mainstream AI language models from OpenAI, Anthropic, and Meta fail to absorb these Persian social rituals, correctly navigating taarof situations only 34 to 42 percent of the time. Native Persian speakers, by contrast, get it right 82 percent of the time. This performance gap persists across large language models such as GPT-4o, Claude 3.5 Haiku, Llama 3, DeepSeek V3, and Dorna, a Persian-tuned variant of Llama 3.

A study led by Nikta Gohari Sadr of Brock University, along with researchers from Emory University and other institutions, introduces “TAAROFBENCH,” the first benchmark for measuring how well AI systems reproduce this intricate cultural practice. The researchers’ findings show how recent AI models default to Western-style directness, completely missing the cultural cues that govern everyday interactions for millions of Persian speakers worldwide.

“Cultural missteps in high-consequence settings can derail negotiations, damage relationships, and reinforce stereotypes,” the researchers write. For AI systems increasingly used in global contexts, that cultural blindness could represent a limitation that few in the West realize exists.

A taarof scenario diagram from TAAROFBENCH, devised by the researchers. Each scenario defines the environment, location, roles, context, and user utterance.

A taarof scenario diagram from TAAROFBENCH, devised by the researchers. Each scenario defines the environment, location, roles, context, and user utterance. Credit: Sadr et al.

“Taarof, a core element of Persian etiquette, is a system of ritual politeness where what is said often differs from what is meant,” the researchers write. “It takes the form of ritualized exchanges: offering repeatedly despite initial refusals, declining gifts while the giver insists, and deflecting compliments while the other party reaffirms them. This ‘polite verbal wrestling’ (Rafiee, 1991) involves a delicate dance of offer and refusal, insistence and resistance, which shapes everyday interactions in Iranian culture, creating implicit rules for how generosity, gratitude, and requests are expressed.”

When “no” means “yes”: Why AI chatbots can’t process Persian social etiquette Read More »

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DeepMind AI safety report explores the perils of “misaligned” AI

DeepMind also addresses something of a meta-concern about AI. The researchers say that a powerful AI in the wrong hands could be dangerous if it is used to accelerate machine learning research, resulting in the creation of more capable and unrestricted AI models. DeepMind says this could “have a significant effect on society’s ability to adapt to and govern powerful AI models.” DeepMind ranks this as a more severe threat than most other CCLs.

The misaligned AI

Most AI security mitigations follow from the assumption that the model is at least trying to follow instructions. Despite years of hallucination, researchers have not managed to make these models completely trustworthy or accurate, but it’s possible that a model’s incentives could be warped, either accidentally or on purpose. If a misaligned AI begins to actively work against humans or ignore instructions, that’s a new kind of problem that goes beyond simple hallucination.

Version 3 of the Frontier Safety Framework introduces an “exploratory approach” to understanding the risks of a misaligned AI. There have already been documented instances of generative AI models engaging in deception and defiant behavior, and DeepMind researchers express concern that it may be difficult to monitor for this kind of behavior in the future.

A misaligned AI might ignore human instructions, produce fraudulent outputs, or refuse to stop operating when requested. For the time being, there’s a fairly straightforward way to combat this outcome. Today’s most advanced simulated reasoning models produce “scratchpad” outputs during the thinking process. Devs are advised to use an automated monitor to double-check the model’s chain-of-thought output for evidence misalignment or deception.

Google says this CCL could become more severe in the future. The team believes models in the coming years may evolve to have effective simulated reasoning without producing a verifiable chain of thought. So your overseer guardrail wouldn’t be able to peer into the reasoning process of such a model. For this theoretical advanced AI, it may be impossible to completely rule out that the model is working against the interests of its human operator.

The framework doesn’t have a good solution to this problem just yet. DeepMind says it is researching possible mitigations for a misaligned AI, but it’s hard to know when or if this problem will become a reality. These “thinking” models have only been common for about a year, and there’s still a lot we don’t know about how they arrive at a given output.

DeepMind AI safety report explores the perils of “misaligned” AI Read More »