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nvidia-sells-tiny-new-computer-that-puts-big-ai-on-your-desktop

Nvidia sells tiny new computer that puts big AI on your desktop

For the OS, the Spark is an ARM-based system that runs Nvidia’s DGX OS, an Ubuntu Linux-based operating system built specifically for GPU processing. It comes with Nvidia’s AI software stack preinstalled, including CUDA libraries and the company’s NIM microservices.

Prices for the DGX Spark start at US $3,999. That may seem like a lot, but given the cost of high-end GPUs with ample video RAM like the RTX Pro 6000 (about $9,000) or AI server GPUs (like $25,000 for a base-level H100), the DGX Spark may represent a far less expensive option overall, though it’s not nearly as powerful.

In fact, according to The Register, the GPU computing performance of the GB10 chip is roughly equivalent to an RTX 5070. However, the 5070 is limited to 12GB of video memory, which limits the size of AI models that can be run on such a system. With 128GB of unified memory, the DGX Spark can run far larger models, albeit at a slower speed than, say, an RTX 5090 (which typically ships with 24 GB of RAM). For example, to run the 120 billion-parameter larger version of OpenAI’s recent gpt-oss language model, you’d need about 80GB of memory, which is far more than you can get in a consumer GPU.

A callback to 2016

Nvidia founder and CEO Jensen Huang marked the occasion of the DGX Spark launch by personally delivering one of the first units to Elon Musk at SpaceX’s Starbase facility in Texas, echoing a similar delivery Huang made to Musk at OpenAI in 2016.

“In 2016, we built DGX-1 to give AI researchers their own supercomputer. I hand-delivered the first system to Elon at a small startup called OpenAI, and from it came ChatGPT,” Huang said in a statement. “DGX-1 launched the era of AI supercomputers and unlocked the scaling laws that drive modern AI. With DGX Spark, we return to that mission.”

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Hackers can steal 2FA codes and private messages from Android phones


STEALING CODES ONE PIXEL AT A TIME

Malicious app required to make “Pixnapping” attack work requires no permissions.

Samsung’s S25 phones. Credit: Samsung

Android devices are vulnerable to a new attack that can covertly steal two-factor authentication codes, location timelines, and other private data in less than 30 seconds.

The new attack, named Pixnapping by the team of academic researchers who devised it, requires a victim to first install a malicious app on an Android phone or tablet. The app, which requires no system permissions, can then effectively read data that any other installed app displays on the screen. Pixnapping has been demonstrated on Google Pixel phones and the Samsung Galaxy S25 phone and likely could be modified to work on other models with additional work. Google released mitigations last month, but the researchers said a modified version of the attack works even when the update is installed.

Like taking a screenshot

Pixnapping attacks begin with the malicious app invoking Android programming interfaces that cause the authenticator or other targeted apps to send sensitive information to the device screen. The malicious app then runs graphical operations on individual pixels of interest to the attacker. Pixnapping then exploits a side channel that allows the malicious app to map the pixels at those coordinates to letters, numbers, or shapes.

“Anything that is visible when the target app is opened can be stolen by the malicious app using Pixnapping,” the researchers wrote on an informational website. “Chat messages, 2FA codes, email messages, etc. are all vulnerable since they are visible. If an app has secret information that is not visible (e.g., it has a secret key that is stored but never shown on the screen), that information cannot be stolen by Pixnapping.”

The new attack class is reminiscent of GPU.zip, a 2023 attack that allowed malicious websites to read the usernames, passwords, and other sensitive visual data displayed by other websites. It worked by exploiting side channels found in GPUs from all major suppliers. The vulnerabilities that GPU.zip exploited have never been fixed. Instead, the attack was blocked in browsers by limiting their ability to open iframes, an HTML element that allows one website (in the case of GPU.zip, a malicious one) to embed the contents of a site from a different domain.

Pixnapping targets the same side channel as GPU.zip, specifically the precise amount of time it takes for a given frame to be rendered on the screen.

“This allows a malicious app to steal sensitive information displayed by other apps or arbitrary websites, pixel by pixel,” Alan Linghao Wang, lead author of the research paper “Pixnapping: Bringing Pixel Stealing out of the Stone Age,” explained in an interview. “Conceptually, it is as if the malicious app was taking a screenshot of screen contents it should not have access to. Our end-to-end attacks simply measure the rendering time per frame of the graphical operations… to determine whether the pixel was white or non-white.”

Pixnapping in three steps

The attack occurs in three main steps. In the first, the malicious app invokes Android APIs that make calls to the app the attacker wants to snoop on. These calls can also be used to effectively scan an infected device for installed apps of interest. The calls can further cause the targeted app to display specific data it has access to, such as a message thread in a messaging app or a 2FA code for a specific site. This call causes the information to be sent to the Android rendering pipeline, the system that takes each app’s pixels so they can be rendered on the screen. The Android-specific calls made include activities, intents, and tasks.

In the second step, Pixnapping performs graphical operations on individual pixels that the targeted app sent to the rendering pipeline. These operations choose the coordinates of target pixels the app wants to steal and begin to check if the color of those coordinates is white or non-white or, more generally, if the color is c or non-c (for an arbitrary color c).

“Suppose, for example, [the attacker] wants to steal a pixel that is part of the screen region where a 2FA character is known to be rendered by Google Authenticator,” Wang said. “This pixel is either white (if nothing was rendered there) or non-white (if part of a 2FA digit was rendered there). Then, conceptually, the attacker wants to cause some graphical operations whose rendering time is long if the target victim pixel is non-white and short if it is white. The malicious app does this by opening some malicious activities (i.e., windows) in front of the victim app that was opened in Step 1.”

The third step measures the amount of time required at each coordinate. By combining the times for each one, the attack can rebuild the images sent to the rendering pipeline one pixel at a time.

As Ars reader hotball put it in the comments below:

Basically the attacker renders something transparent in front of the target app, then using a timing attack exploiting the GPU’s graphical data compression to try finding out the color of the pixels. It’s not something as simple as “give me the pixels of another app showing on the screen right now.” That’s why it takes time and can be too slow to fit within the 30 seconds window of the Google Authenticator app.

In an online interview, paper co-author Ricardo Paccagnella described the attack in more detail:

Step 1: The malicious app invokes a target app to cause some sensitive visual content to be rendered.

Step 2: The malicious app uses Android APIs to “draw over” that visual content and cause a side channel (in our case, GPU.zip) to leak as a function of the color of individual pixels rendered in Step 1 (e.g., activate only if the pixel color is c).

Step 3: The malicious app monitors the side effects of Step 2 to infer, e.g., if the color of those pixels was c or not, one pixel at a time.

Steps 2 and 3 can be implemented differently depending on the side channel that the attacker wants to exploit. In our instantiations on Google and Samsung phones, we exploited the GPU.zip side channel. When using GPU.zip, measuring the rendering time per frame was sufficient to determine if the color of each pixel is c or not. Future instantiations of the attack may use other side channels where controlling memory management and accessing fine-grained timers may be necessary (see Section 3.3 of the paper). Pixnapping would still work then: the attacker would just need to change how Steps 2 and 3 are implemented.

The amount of time required to perform the attack depends on several variables, including how many coordinates need to be measured. In some cases, there’s no hard deadline for obtaining the information the attacker wants to steal. In other cases—such as stealing a 2FA code—every second counts, since each one is valid for only 30 seconds. In the paper, the researchers explained:

To meet the strict 30-second deadline for the attack, we also reduce the number of samples per target pixel to 16 (compared to the 34 or 64 used in earlier attacks) and decrease the idle time between pixel leaks from 1.5 seconds to 70 milliseconds. To ensure that the attacker has the full 30 seconds to leak the 2FA code, our implementation waits for the beginning of a new 30-second global time interval, determined using the system clock.

… We use our end-to-end attack to leak 100 different 2FA codes from Google Authenticator on each of our Google Pixel phones. Our attack correctly recovers the full 6-digit 2FA code in 73%, 53%, 29%, and 53% of the trials on the Pixel 6, 7, 8, and 9, respectively. The average time to recover each 2FA code is 14.3, 25.8, 24.9, and 25.3 seconds for the Pixel 6, Pixel 7, Pixel 8, and Pixel 9, respectively. We are unable to leak 2FA codes within 30 seconds using our implementation on the Samsung Galaxy S25 device due to significant noise. We leave further investigation of how to tune our attack to work on this device to future work.

In an email, a Google representative wrote, “We issued a patch for CVE-2025-48561 in the September Android security bulletin, which partially mitigates this behavior. We are issuing an additional patch for this vulnerability in the December Android security bulletin. We have not seen any evidence of in-the-wild exploitation.”

Pixnapping is useful research in that it demonstrates the limitations of Google’s security and privacy assurances that one installed app can’t access data belonging to another app. The challenges in implementing the attack to steal useful data in real-world scenarios, however, are likely to be significant. In an age when teenagers can steal secrets from Fortune 500 companies simply by asking nicely, the utility of more complicated and limited attacks is probably of less value.

Post updated to add details about how the attack works.

Photo of Dan Goodin

Dan Goodin is Senior Security Editor at Ars Technica, where he oversees coverage of malware, computer espionage, botnets, hardware hacking, encryption, and passwords. In his spare time, he enjoys gardening, cooking, and following the independent music scene. Dan is based in San Francisco. Follow him at here on Mastodon and here on Bluesky. Contact him on Signal at DanArs.82.

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Google’s Photoshop-killer AI model is coming to search, Photos, and NotebookLM

NotebookLM added a video overview feature several months back, which uses AI to generate a video summary of the content you’ve added to the notebook. The addition of Nano Banana to NotebookLM is much less open-ended. Instead of entering prompts to edit images, NotebookLM has a new set of video styles powered by Nano Banana, including whiteboard, anime, retro print, and more. The original style is still available as “Classic.”

My favorite video.

NotebookLM’s videos are still somewhat limited, but this update adds a second general format. You can now choose “Brief” in addition to “Explainer,” with the option to add prompts that steer the video in the right direction. Although, that’s not a guarantee, as this is still generative AI. At least the style should be more consistent with the addition of Nano Banana.

The updated image editor is also coming to Google Photos, but Google doesn’t have a firm timeline. Google claims that its Nano Banana model is a “major upgrade” over its previous image-editing model. Conversational editing was added to Photos last month, but it’s not the Nano Banana model that has impressed testers over the summer. Google says that Nano Banana will arrive in the Photos app in the next few weeks, which should make those conversational edits much less frustrating.

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uk-antitrust-regulator-takes-aim-at-google’s-search-dominance

UK antitrust regulator takes aim at Google’s search dominance

Google is facing multiple antitrust actions in the US, and European regulators have been similarly tightening the screws. You can now add the UK to the list of Google’s governmental worries. The country’s antitrust regulator, known as the Competition and Markets Authority (CMA), has confirmed that Google has “strategic market status,” paving the way to more limits on how Google does business in the UK. Naturally, Google objects to this course of action.

The designation is connected to the UK’s new digital markets competition regime, which was enacted at the beginning of the year. Shortly after, the CMA announced it was conducting an investigation into whether Google should be designated with strategic market status. The outcome of that process is a resounding “yes.”

This label does not mean Google has done anything illegal or that it is subject to immediate regulation. It simply means the company has “substantial and entrenched market power” in one or more areas under the purview of the CMA. Specifically, the agency has found that Google is dominant in search and search advertising, holding a greater than 90 percent share of Internet searches in the UK.

In Google’s US antitrust trials, the rapid rise of generative AI has muddied the waters. Google has claimed on numerous occasions that the proliferation of AI firms offering search services means there is ample competition. In the UK, regulators note that Google’s Gemini AI assistant is not in the scope of the strategic market status designation. However, some AI features connected to search, like AI Overviews and AI Mode, are included.

According to the CMA, consultations on possible interventions to ensure effective competition will begin later this year. The agency’s first set of antitrust measures will likely expand on solutions that Google has introduced in other regions or has offered on a voluntary basis in the UK. This could include giving publishers more control over how their data is used in search and “choice screens” that suggest Google alternatives to users. Measures that require new action from Google could be announced in the first half of 2026.

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bank-of-england-warns-ai-stock-bubble-rivals-2000-dotcom-peak

Bank of England warns AI stock bubble rivals 2000 dotcom peak

Share valuations based on past earnings have also reached their highest levels since the dotcom bubble 25 years ago, though the BoE noted they appear less extreme when based on investors’ expectations for future profits. “This, when combined with increasing concentration within market indices, leaves equity markets particularly exposed should expectations around the impact of AI become less optimistic,” the central bank said.

Toil and trouble?

The dotcom bubble offers a potentially instructive parallel to our current era. In the late 1990s, investors poured money into Internet companies based on the promise of a transformed economy, seemingly ignoring whether individual businesses had viable paths to profitability. Between 1995 and March 2000, the Nasdaq index rose 600 percent. When sentiment shifted, the correction was severe: the Nasdaq fell 78 percent from its peak, reaching a low point in October 2002.

Whether we’ll see the same thing or worse if an AI bubble pops is mere speculation at this point. But similar to the early 2000s, the question about today’s market isn’t necessarily about the utility of AI tools themselves (the Internet was useful, afterall, despite the bubble), but whether the amount of money being poured into the companies that sell them is out of proportion with the potential profits those improvements might bring.

We don’t have a crystal ball to determine when such a bubble might pop, or even if it is guaranteed to do so, but we’ll likely continue to see more warning signs ahead if AI-related deals continue to grow larger and larger over time.

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google-confirms-android-dev-verification-will-have-free-and-paid-tiers,-no-public-list-of-devs

Google confirms Android dev verification will have free and paid tiers, no public list of devs

A lack of trust

Google has an answer for the most problematic elements of its verification plan, but anywhere there’s a gap, it’s easy to see a conspiracy. Why? Well, let’s look at the situation in which Google finds itself.

The courts have ruled that Google acted illegally to maintain a monopoly in the Play Store—it worked against the interests of developers and users for years to make Google Play the only viable source of Android apps, and for what? The Play Store is an almost unusable mess of sponsored search results and suggested apps, most of which are little more than in-app purchase factories that deliver Google billions of dollars every year.

Google has every reason to protect the status quo (it may take the case all the way to the Supreme Court), and now it has suddenly decided the security risk of sideloaded apps must be addressed. The way it’s being addressed puts Google in the driver’s seat at a time when alternative app stores may finally have a chance to thrive. It’s all very convenient for Google.

Developers across the Internet are expressing wariness about giving Google their personal information. Google, however, has decided anonymity is too risky. We now know a little more about how Google will manage the information it collects on developers, though. While Play Store developer information is listed publicly, the video confirms there will be no public list of sideload developers. However, Google will have the information, and that means it could be demanded by law enforcement or governments.

The current US administration has had harsh words for apps like ICEBlock, which it successfully pulled from the Apple App Store. Google’s new centralized control of app distribution would allow similar censorship on Android, and the real identities of those who developed such an app would also be sitting in a Google database, ready to be subpoenaed. A few years ago, developers might have trusted Google with this data, but now? The goodwill is gone.

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can-today’s-ai-video-models-accurately-model-how-the-real-world-works?

Can today’s AI video models accurately model how the real world works?

But on other tasks, the model showed much more variable results. When asked to generate a video highlighting a specific written character on a grid, for instance, the model failed in nine out of 12 trials. When asked to model a Bunsen burner turning on and burning a piece of paper, it similarly failed nine out of 12 times. When asked to solve a simple maze, it failed in 10 of 12 trials. When asked to sort numbers by popping labeled bubbles in order, it failed a whopping 11 out of 12 times.

For the researchers, though, all of the above examples aren’t evidence of failure but instead a sign of the model’s capabilities. To be listed under the paper’s “failure cases,” Veo 3 had to fail a tested task across all 12 trials, which happened in 16 of the 62 tasks tested. For the rest, the researchers write that “a success rate greater than 0 suggests that the model possesses the ability to solve the task.”

Thus, failing 11 out of 12 trails of a certain task is considered evidence for the model’s capabilities in the paper. That evidence of the model “possess[ing] the ability to solve the task” includes 18 tasks where the model failed in more than half of its 12 trial runs and another 14 where it failed in 25 to 50 percent of trials.

Past results, future performance

Yes, in all of these cases, the model did technically demonstrate the capability being tested at some point. But the model’s inability to perform that task reliably means that, in practice, it won’t be performant enough for most use cases. Any future model that could become a “unified, generalist vision foundation models” will have to be able to succeed much more consistently on these kinds of tests.

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Google’s Gemini-powered smart home revamp is here with a new app and cameras


Google promises a better smart home experience thanks to Gemini.

Google’s new Nest cameras keep the same look. Credit: Google

Google’s products and services have been flooded with AI features over the past couple of years, but smart home has been largely spared until now. The company’s plans to replace Assistant are moving forward with a big Google Home reset. We’ve been told over and over that generative AI will do incredible things when given enough data, and here’s the test.

There’s a new Home app with Gemini intelligence throughout the experience, updated subscriptions, and even some new hardware. The revamped Home app will allegedly gain deeper insights into what happens in your home, unlocking advanced video features and conversational commands. It demos well, but will it make smart home tech less or more frustrating?

A new Home

You may have already seen some elements of the revamped Home experience percolating to the surface, but that process begins in earnest today. The new app apparently boosts speed and reliability considerably, with camera feeds loading 70 percent faster and with 80 percent fewer app crashes. The app will also bring new Gemini features, some of which are free. Google’s new Home subscription retains the same price as the old Nest subs, but naturally, there’s a lot more AI.

Google claims that Gemini will make your smart home easier to monitor and manage. All that video streaming from your cameras churns through the AI, which interprets the goings on. As a result, you get features like AI-enhanced notifications that give you more context about what your cameras saw. For instance, your notifications will include descriptions of activity, and Home Brief will summarize everything that happens each day.

Home app

The new Home app has a simpler three-tab layout.

Credit: Google

The new Home app has a simpler three-tab layout. Credit: Google

Conversational interaction is also a big part of this update. In the home app, subscribers will see a new Ask Home bar where you can input natural language queries. For example, you could ask if a certain person has left or returned home, or whether or not your package showed up. At least, that’s what’s supposed to happen—generative AI can get things wrong.

The new app comes with new subscriptions based around AI, but the tiers don’t cost any more than the old Nest plans, and they include all the same video features. The base $10 subscription, now known as Standard, includes 30 days of video event history, along with Gemini automation features and the “intelligent alerts” Home has used for a while that can alert you to packages, familiar faces, and so on. The $20 subscription is becoming Home Advanced, which adds the conversational Ask Home feature in the app, AI notifications, AI event descriptions, and a new “Home Brief.” It also still offers 60 days of events and 10 days of 24/7 video history.

Home app and notification

Gemini is supposed to help you keep tabs on what’s happening at home.

Credit: Google

Gemini is supposed to help you keep tabs on what’s happening at home. Credit: Google

Free users still get saved event video history, and it’s been boosted from three hours to six. If you are not subscribing to Gemini Home or using the $10 plan, the Ask Home bar that is persistent across the app will become a quick search, which surfaces devices and settings.

If you’re already subscribing to Google’s AI services, this change could actually save you some cash. Anyone with Google AI Pro (a $20 sub) will get Home Standard for free. If you’re paying for the lavish $250 per month AI Ultra plan, you get Home Advanced at no additional cost.

A proving ground for AI

You may have gotten used to Assistant over the past decade in spite of its frequent feature gaps, but you’ll have to leave it behind. Gemini for Home will be taking over beginning this month in early access. The full release will come later, but Google intends to deliver the Gemini-powered smart home experience to as many users as possible.

Gemini will replace Assistant on every first-party Google Home device, going all the way back to the original 2016 Google Home. You’ll be able to have live chats with Gemini via your smart speakers and make more complex smart home queries. Google is making some big claims about contextual understanding here.

Gemini Home

If Google’s embrace of generative AI pays off, we’ll see it here.

Credit: Google

If Google’s embrace of generative AI pays off, we’ll see it here. Credit: Google

If you’ve used Gemini Live, the new Home interactions will seem familiar. You can ask Gemini anything you want via your smart speakers, perhaps getting help with a recipe or an appliance issue. However, the robot will sometimes just keep talking long past the point it’s helpful. Like Gemini Live, you just have to interrupt the robot sometimes. Google also promises a selection of improved voices to interrupt.

If you want to get early access to the new Gemini Home features, you can sign up in the Home app settings. Just look for the “Early access” option. Google doesn’t guarantee access on a specific timeline, but the first people will be allowed to try the new Gemini Home this month.

New AI-first hardware

It has been four years since Google released new smart home devices, but the era of Gemini brings some new hardware. There are three new cameras, all with 2K image sensors. The new Nest Indoor camera will retail for $100, and the Nest Outdoor Camera will cost $150 (or $250 in a two-pack). There’s also a new Nest Doorbell, which requires a wired connection, for $180.

Google says these cameras were designed with generative AI in mind. The sensor choice allows for good detail even if you need to digitally zoom in, but the video feed is still small enough to be ingested by Google’s AI models as it’s created. This is what gives the new Home app the ability to provide rich updates on your smart home.

Nest Doorbell 3

The new Nest Doorbell looks familiar.

Credit: Google

The new Nest Doorbell looks familiar. Credit: Google

You may also notice there are no battery-powered models in the new batch. Again, that’s because of AI. A battery-powered camera wakes up only momentarily when the system logs an event, but this approach isn’t as useful for generative AI. Providing the model with an ongoing video stream gives it better insights into the scene and, theoretically, produces better insights for the user.

All the new cameras are available for order today, but Google has one more device queued up for a later release. The “Google Home Speaker” is Google’s first smart speaker release since 2020’s Nest Audio. This device is smaller than the Nest Audio but larger than the Nest Mini speakers. It supports 260-degree audio with custom on-device processing that reportedly makes conversing with Gemini smoother. It can also be paired with the Google TV Streamer for home theater audio. It will be available this coming spring for $99.

Google Home Speaker

The new Google Home Speaker comes out next spring.

Credit: Ryan Whitwam

The new Google Home Speaker comes out next spring. Credit: Ryan Whitwam

Google Home will continue to support a wide range of devices, but most of them won’t connect to all the advanced Gemini AI features. However, that could change. Google has also announced a new program for partners to build devices that work with Gemini alongside the Nest cameras. Devices built with the new Google Camera embedded SDK will begin appearing in the coming months, but Walmart’s Onn brand has two ready to go. The Onn Indoor camera retails for $22.96 and the Onn Video Doorbell is $49.86. Both cameras are 1080p resolution and will talk to Gemini just like Google’s cameras. So you may have more options to experience Google’s vision for the AI home of the future.

Photo of Ryan Whitwam

Ryan Whitwam is a senior technology reporter at Ars Technica, covering the ways Google, AI, and mobile technology continue to change the world. Over his 20-year career, he’s written for Android Police, ExtremeTech, Wirecutter, NY Times, and more. He has reviewed more phones than most people will ever own. You can follow him on Bluesky, where you will see photos of his dozens of mechanical keyboards.

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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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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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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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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 »