Google

google-announces-even-more-ai-in-photos-app,-powered-by-nano-banana

Google announces even more AI in Photos app, powered by Nano Banana

We’re running out of ways to tell you that Google is releasing more generative AI features, but that’s what’s happening in Google Photos today. The Big G is finally making good on its promise to add its market-leading Nano Banana image-editing model to the app. The model powers a couple of features, and it’s not just for Google’s Android platform. Nano Banana edits are also coming to the iOS version of the app.

Nano Banana started making waves when it appeared earlier this year as an unbranded demo. You simply feed the model an image and tell it what edits you want to see. Google said Nano Banana was destined for the Photos app back in October, but it’s only now beginning the rollout. The Photos app already had conversational editing in the “Help Me Edit” feature, but it was running an older non-fruit model that produced inferior results. Nano Banana editing will produce AI slop, yes, but it’s better slop.

Nano Banana in Help me edit

Google says the updated Help Me Edit feature has access to your private face groups, so you can use names in your instructions. For example, you could type “Remove Riley’s sunglasses,” and Nano Banana will identify Riley in the photo (assuming you have a person of that name saved) and make the edit without further instructions. You can also ask for more fantastical edits in Help Me Edit, changing the style of the image from top to bottom.

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oddest-chatgpt-leaks-yet:-cringey-chat-logs-found-in-google-analytics-tool

Oddest ChatGPT leaks yet: Cringey chat logs found in Google analytics tool


ChatGPT leaks seem to confirm OpenAI scrapes Google, expert says.

Credit: Aurich Lawson | Getty Images

For months, extremely personal and sensitive ChatGPT conversations have been leaking into an unexpected destination: Google Search Console (GSC), a tool that developers typically use to monitor search traffic, not lurk private chats.

Normally, when site managers access GSC performance reports, they see queries based on keywords or short phrases that Internet users type into Google to find relevant content. But starting this September, odd queries, sometimes more than 300 characters long, could also be found in GSC. Showing only user inputs, the chats appeared to be from unwitting people prompting a chatbot to help solve relationship or business problems, who likely expected those conversations would remain private.

Jason Packer, owner of an analytics consulting firm called Quantable, was among the first to flag the issue in a detailed blog last month.

Determined to figure out what exactly was causing the leaks, he teamed up with “Internet sleuth” and web optimization consultant Slobodan Manić. Together, they conducted testing that they believe may have surfaced “the first definitive proof that OpenAI directly scrapes Google Search with actual user prompts.” Their investigation seemed to confirm the AI giant was compromising user privacy, in some cases in order to maintain engagement by seizing search data that Google otherwise wouldn’t share.

OpenAI declined Ars’ request to confirm if Packer and Manić’s theory posed in their blog was correct or answer any of their remaining questions that could help users determine the scope of the problem.

However, an OpenAI spokesperson confirmed that the company was “aware” of the issue and has since “resolved” a glitch “that temporarily affected how a small number of search queries were routed.”

Packer told Ars that he’s “very pleased that OpenAI was able to resolve the issue quickly.” But he suggested that OpenAI’s response failed to confirm whether or not OpenAI was scraping Google, and that leaves room for doubt that the issue was completely resolved.

Google declined to comment.

“Weirder” than prior ChatGPT leaks

The first odd ChatGPT query to appear in GSC that Packer reviewed was a wacky stream-of-consciousness from a likely female user asking ChatGPT to assess certain behaviors to help her figure out if a boy who teases her had feelings for her. Another odd query seemed to come from an office manager sharing business information while plotting a return-to-office announcement.

These were just two of 200 odd queries—including “some pretty crazy ones,” Packer told Ars—that he reviewed on one site alone. In his blog, Packer concluded that the queries should serve as “a reminder that prompts aren’t as private as you think they are!”

Packer suspected that these queries were connected to reporting from The Information in August that cited sources claiming OpenAI was scraping Google search results to power ChatGPT responses. Sources claimed that OpenAI was leaning on Google to answer prompts to ChatGPT seeking information about current events, like news or sports.

OpenAI has not confirmed that it’s scraping Google search engine results pages (SERPs). However, Packer thinks his testing of ChatGPT leaks may be evidence that OpenAI not only scrapes “SERPs in general to acquire data,” but also sends user prompts to Google Search.

Manić helped Packer solve a big part of the riddle. He found that the odd queries were turning up in one site’s GSC because it ranked highly in Google Search for “https://openai.com/index/chatgpt/”—a ChatGPT URL that was appended at the start of every strange query turning up in GSC.

It seemed that Google had tokenized the URL, breaking it up into a search for keywords “openai + index + chatgpt.” Sites using GSC that ranked highly for those keywords were therefore likely to encounter ChatGPT leaks, Parker and Manić proposed, including sites that covered prior ChatGPT leaks where chats were being indexed in Google search results. Using their recommendations to seek out queries in GSC, Ars was able to verify similar strings.

“Don’t get confused though, this is a new and completely different ChatGPT screw-up than having Google index stuff we don’t want them to,” Packer wrote. “Weirder, if not as serious.”

It’s unclear what exactly OpenAI fixed, but Packer and Manić have a theory about one possible path for leaking chats. Visiting the URL that starts every strange query found in GSC, ChatGPT users encounter a prompt box that seemed buggy, causing “the URL of that page to be added to the prompt.” The issue, they explained, seemed to be that:

Normally ChatGPT 5 will choose to do a web search whenever it thinks it needs to, and is more likely to do that with an esoteric or recency-requiring search. But this bugged prompt box also contains the query parameter ‘hints=search’ to cause it to basically always do a search: https://chatgpt.com/?hints=search&openaicom_referred=true&model=gpt-5

Clearly some of those searches relied on Google, Packer’s blog said, mistakenly sending to GSC “whatever” the user says in the prompt box, with “https://openai.com/index/chatgpt/” text added to the front of it.” As Packer explained, “we know it must have scraped those rather than using an API or some kind of private connection—because those other options don’t show inside GSC.”

This means “that OpenAI is sharing any prompt that requires a Google Search with both Google and whoever is doing their scraping,” Packer alleged. “And then also with whoever’s site shows up in the search results! Yikes.”

To Packer, it appeared that “ALL ChatGPT prompts” that used Google Search risked being leaked during the past two months.

OpenAI claimed only a small number of queries were leaked but declined to provide a more precise estimate. So, it remains unclear how many of the 700 million people who use ChatGPT each week had prompts routed to GSC.

OpenAI’s response leaves users with “lingering questions”

After ChatGPT prompts were found surfacing in Google’s search index in August, OpenAI clarified that users had clicked a box making those prompts public, which OpenAI defended as “sufficiently clear.” The AI firm later scrambled to remove the chats from Google’s SERPs after it became obvious that users felt misled into sharing private chats publicly.

Packer told Ars that a major difference between those leaks and the GSC leaks is that users harmed by the prior scandal, at least on some level, “had to actively share” their leaked chats. In the more recent case, “nobody clicked share” or had a reasonable way to prevent their chats from being exposed.

“Did OpenAI go so fast that they didn’t consider the privacy implications of this, or did they just not care?” Packer posited in his blog.

Perhaps most troubling to some users—whose identities are not linked in chats unless their prompts perhaps share identifying information—there does not seem to be any way to remove the leaked chats from GSC, unlike the prior scandal.

Packer and Manić are left with “lingering questions” about how far OpenAI’s fix will go to stop the issue.

Manić was hoping OpenAI might confirm if prompts entered on https://chatgpt.com that trigger Google Search were also affected. But OpenAI did not follow up on that question, or a broader question about how big the leak was. To Manić, a major concern was that OpenAI’s scraping may be “contributing to ‘crocodile mouth’ in Google Search Console,” a troubling trend SEO researchers have flagged that causes impressions to spike but clicks to dip.

OpenAI also declined to clarify Packer’s biggest question. He’s left wondering if the company’s “fix” simply ended OpenAI’s “routing of search queries, such that raw prompts are no longer being sent to Google Search, or are they no longer scraping Google Search at all for data?

“We still don’t know if it’s that one particular page that has this bug or whether this is really widespread,” Packer told Ars. “In either case, it’s serious and just sort of shows how little regard OpenAI has for moving carefully when it comes to privacy.”

Photo of Ashley Belanger

Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.

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gemini-deep-research-comes-to-google-finance,-backed-by-prediction-market-data

Gemini Deep Research comes to Google Finance, backed by prediction market data

Bet on it

Financial markets can turn on a dime, and AI can’t predict the future. However, Google seems to think that people make smart predictions in aggregate when there’s money on the line. That’s why, as part of the Finance update, Google has partnered with Kalshi and Polymarket, the current leaders in online prediction markets.

These platforms let people place bets on, well, just about anything. If you have a hunch when Google will release Gemini 3.0, when the government shutdown will end, or the number of Tweets Elon Musk will post this month, you can place a wager on it. Maybe you’ll earn money, but more likely, you’ll lose it—only 12.7 percent of crypto wallets on Polymarket show profits.

Google Finance prediction markets

Credit: Google

Google says it will get fresh prediction data from both sites, which will allow Gemini to speculate on the future with “the wisdom of crowds.” Google suggests you could type “What will GDP growth be for 2025?” into the search box. Finance will pull the latest probabilities from Kalshi and Polymarket to generate a response that could include graphs and charts based on people’s bets. Naturally, Google does not make promises as to the accuracy of these predictions.

The new AI features of Google Finance are coming to all US users in the next few weeks, and starting this week, the service will make its debut in India. Likewise, the predictions market data will arrive in the next couple of weeks. If that’s not fast enough, you can opt-in to get early access via the Google Labs page.

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google-plans-secret-ai-military-outpost-on-tiny-island-overrun-by-crabs

Google plans secret AI military outpost on tiny island overrun by crabs

Christmas Island Shire President Steve Pereira told Reuters that the council is examining community impacts before approving construction. “There is support for it, providing this data center actually does put back into the community with infrastructure, employment, and adding economic value to the island,” Pereira said.

That’s great, but what about the crabs?

Christmas Island’s annual crab migration is a natural phenomenon that Sir David Attenborough reportedly once described as one of his greatest TV moments when he visited the site in 1990.

Every year, millions of crabs emerge from the forest and swarm across roads, streams, rocks, and beaches to reach the ocean, where each female can produce up to 100,000 eggs. The tiny baby crabs that survive take about nine days to march back inland to the safety of the plateau.

While Google is seeking environmental approvals for its subsea cables, the timing could prove delicate for Christmas Island’s most famous residents. According to Parks Australia, the island’s annual red crab migration has already begun for 2025, with a major spawning event expected in just a few weeks, around November 15–16.

During peak migration times, sections of roads close at short notice as crabs move between forest and sea, and the island has built special crab bridges over roads to protect the migrating masses.

Parks Australia notes that while the migration happens annually, few baby crabs survive the journey from sea to forest most years, as they’re often eaten by fish, manta rays, and whale sharks. The successful migrations that occur only once or twice per decade (when large numbers of babies actually survive) are critical for maintaining the island’s red crab population.

How Google’s facility might coexist with 100 million marching crustaceans remains to be seen. But judging by the size of the event, it seems clear that it’s the crab’s world, and we’re just living in it.

Google plans secret AI military outpost on tiny island overrun by crabs Read More »

youtube-tv’s-disney-blackout-reminds-users-that-they-don’t-own-what-they-stream

YouTube TV’s Disney blackout reminds users that they don’t own what they stream

“I don’t know (or care) which side is responsible for this, but the DVR is not VOD, it is your recording, and shows recorded before the dispute should be available. This is a hard lesson for us all,” an apparently affected customer wrote on Reddit this week.

For current or former cable subscribers, this experience isn’t new. Carrier disputes have temporarily and permanently killed cable subscribers’ access to many channels over the years. And since the early 2000s, many cable companies have phased out DVRs with local storage in favor of cloud-based DVRs. Since then, cable companies have been able to revoke customers’ access to DVR files if, for example, the customer stopped paying for the channel from which the content was recorded. What we’re seeing with YouTube TV’s DVR feature is one of several ways that streaming services mirror cable companies.

Google exits Movies Anywhere

In a move that appears to be best described as tit for tat, Google has removed content purchased via Google Play and YouTube from Movies Anywhere, a Disney-owned unified platform that lets people access digital video purchases from various distributors, including Amazon Prime Video and Fandango.

In removing users’ content, Google may gain some leverage in its discussions with Disney, which is reportedly seeking a larger carriage fee from YouTube TV. The content removals, however, are just one more pain point of the fragmented streaming landscape customers are already dealing with.

Customers inconvenienced

As of this writing, Google and Disney have yet to reach an agreement. On Monday, Google publicly rejected Disney’s request to restore ABC to YouTube TV for yesterday’s election day, although the company showed a willingness to find a way to quickly bring back ABC and ESPN (“the channels that people want,” per Google). Disney has escalated things by making its content unavailable to rent or purchase from all Google platforms.

Google is trying to appease customers by saying it will give YouTube TV subscribers a $20 credit if Disney “content is unavailable for an extended period of time.” Some people online have reported receiving a $10 credit already.

Regardless of how this saga ends, the immediate effects have inconvenienced customers of both companies. People subscribe to streaming services and rely on digital video purchases and recordings for easy, instant access, which Google and Disney’s disagreement has disrupted. The squabble has also served as another reminder that in the streaming age, you don’t really own anything.

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If you want to satiate AI’s hunger for power, Google suggests going to space


Google engineers think they already have all the pieces needed to build a data center in orbit.

With Project Suncatcher, Google will test its Tensor Processing Units on satellites. Credit: Google

It was probably always when, not if, Google would add its name to the list of companies intrigued by the potential of orbiting data centers.

Google announced Tuesday a new initiative, named Project Suncatcher, to examine the feasibility of bringing artificial intelligence to space. The idea is to deploy swarms of satellites in low-Earth orbit, each carrying Google’s AI accelerator chips designed for training, content generation, synthetic speech and vision, and predictive modeling. Google calls these chips Tensor Processing Units, or TPUs.

“Project Suncatcher is a moonshot exploring a new frontier: equipping solar-powered satellite constellations with TPUs and free-space optical links to one day scale machine learning compute in space,” Google wrote in a blog post.

“Like any moonshot, it’s going to require us to solve a lot of complex engineering challenges,” Google’s CEO, Sundar Pichai, wrote on X. Pichai noted that Google’s early tests show the company’s TPUs can withstand the intense radiation they will encounter in space. “However, significant challenges still remain like thermal management and on-orbit system reliability.”

The why and how

Ars reported on Google’s announcement on Tuesday, and Google published a research paper outlining the motivation for such a moonshot project. One of the authors, Travis Beals, spoke with Ars about Project Suncatcher and offered his thoughts on why it just might work.

“We’re just seeing so much demand from people for AI,” said Beals, senior director of Paradigms of Intelligence, a research team within Google. “So, we wanted to figure out a solution for compute that could work no matter how large demand might grow.”

Higher demand will lead to bigger data centers consuming colossal amounts of electricity. According to the MIT Technology Review, AI alone could consume as much electricity annually as 22 percent of all US households by 2028. Cooling is also a problem, often requiring access to vast water resources, raising important questions about environmental sustainability.

Google is looking to the sky to avoid potential bottlenecks. A satellite in space can access an infinite supply of renewable energy and an entire Universe to absorb heat.

“If you think about a data center on Earth, it’s taking power in and it’s emitting heat out,” Beals said. “For us, it’s the satellite that’s doing the same. The satellite is going to have solar panels … They’re going to feed that power to the TPUs to do whatever compute we need them to do, and then the waste heat from the TPUs will be distributed out over a radiator that will then radiate that heat out into space.”

Google envisions putting a legion of satellites into a special kind of orbit that rides along the day-night terminator, where sunlight meets darkness. This north-south, or polar, orbit would be synchronized with the Sun, allowing a satellite’s power-generating solar panels to remain continuously bathed in sunshine.

“It’s much brighter even than the midday Sun on Earth because it’s not filtered by Earth’s atmosphere,” Beals said.

This means a solar panel in space can produce up to eight times more power than the same collecting area on the ground, and you don’t need a lot of batteries to reserve electricity for nighttime. This may sound like the argument for space-based solar power, an idea first described by Isaac Asimov in his short story Reason published in 1941. But instead of transmitting the electricity down to Earth for terrestrial use, orbiting data centers would tap into the power source in space.

“As with many things, the ideas originate in science fiction, but it’s had a number of challenges, and one big one is, how do you get the power down to Earth?” Beals said. “So, instead of trying to figure out that, we’re embarking on this moonshot to bring [machine learning] compute chips into space, put them on satellites that have the solar panels and the radiators for cooling, and then integrate it all together so you don’t actually have to be powered on Earth.”

SpaceX is driving down launch costs, thanks to reusable rockets and an abundant volume of Starlink satellite launches. Credit: SpaceX

Google has a mixed record with its ambitious moonshot projects. One of the most prominent moonshot graduates is the self-driving car kit developer Waymo, which spun out to form a separate company in 2016 and is now operational. The Project Loon initiative to beam Internet signals from high-altitude balloons is one of the Google moonshots that didn’t make it.

Ars published two stories last week on the promise of space-based data centers. One of the startups in this field, named Starcloud, is partnering with Nvidia, the world’s largest tech company by market capitalization, to build a 5 gigawatt orbital data center with enormous solar and cooling panels approximately 4 kilometers (2.5 miles) in width and length. In response to that story, Elon Musk said SpaceX is pursuing the same business opportunity but didn’t provide any details. It’s worth noting that Google holds an estimated 7 percent stake in SpaceX.

Strength in numbers

Google’s proposed architecture differs from that of Starcloud and Nvidia in an important way. Instead of putting up just one or a few massive computing nodes, Google wants to launch a fleet of smaller satellites that talk to one another through laser data links. Essentially, a satellite swarm would function as a single data center, using light-speed interconnectivity to aggregate computing power hundreds of miles over our heads.

If that sounds implausible, take a moment to think about what companies are already doing in space today. SpaceX routinely launches more than 100 Starlink satellites per week, each of which uses laser inter-satellite links to bounce Internet signals around the globe. Amazon’s Kuiper satellite broadband network uses similar technology, and laser communications will underpin the US Space Force’s next-generation data-relay constellation.

Artist’s illustration of laser crosslinks in space. Credit: TESAT

Autonomously constructing a miles-long structure in orbit, as Nvidia and Starcloud foresee, would unlock unimagined opportunities. The concept also relies on tech that has never been tested in space, but there are plenty of engineers and investors who want to try. Starcloud announced an agreement last week with a new in-space assembly company, Rendezvous Robotics, to explore the use of modular, autonomous assembly to build Starcloud’s data centers.

Google’s research paper describes a future computing constellation of 81 satellites flying at an altitude of some 400 miles (650 kilometers), but Beals said the company could dial the total swarm size to as many spacecraft as the market demands. This architecture could enable terawatt-class orbital data centers, according to Google.

“What we’re actually envisioning is, potentially, as you scale, you could have many clusters,” Beals said.

Whatever the number, the satellites will communicate with one another using optical inter-satellite links for high-speed, low-latency connectivity. The satellites will need to fly in tight formation, perhaps a few hundred feet apart, with a swarm diameter of a little more than a mile, or about 2 kilometers. Google says its physics-based model shows satellites can maintain stable formations at such close ranges using automation and “reasonable propulsion budgets.”

“If you’re doing something that requires a ton of tight coordination between many TPUs—training, in particular—you want links that have as low latency as possible and as high bandwidth as possible,” Beals said. “With latency, you run into the speed of light, so you need to get things close together there to reduce latency. But bandwidth is also helped by bringing things close together.”

Some machine-learning applications could be done with the TPUs on just one modestly sized satellite, while others may require the processing power of multiple spacecraft linked together.

“You might be able to fit smaller jobs into a single satellite. This is an approach where, potentially, you can tackle a lot of inference workloads with a single satellite or a small number of them, but eventually, if you want to run larger jobs, you may need a larger cluster all networked together like this,” Beals said.

Google has worked on Project Suncatcher for more than a year, according to Beals. In ground testing, engineers tested Google’s TPUs under a 67 MeV proton beam to simulate the total ionizing dose of radiation the chip would see over five years in orbit. Now, it’s time to demonstrate Google’s AI chips, and everything else needed for Project Suncatcher will actually work in the real environment.

Google is partnering with Planet, the Earth-imaging company, to develop a pair of small prototype satellites for launch in early 2027. Planet builds its own satellites, so Google has tapped it to manufacture each spacecraft, test them, and arrange for their launch. Google’s parent company, Alphabet, also has an equity stake in Planet.

“We have the TPUs and the associated hardware, the compute payload… and we’re bringing that to Planet,” Beals said. “For this prototype mission, we’re really asking them to help us do everything to get that ready to operate in space.”

Beals declined to say how much the demo slated for launch in 2027 will cost but said Google is paying Planet for its role in the mission. The goal of the demo mission is to show whether space-based computing is a viable enterprise.

“Does it really hold up in space the way we think it will, the way we’ve tested on Earth?” Beals said.

Engineers will test an inter-satellite laser link and verify Google’s AI chips can weather the rigors of spaceflight.

“We’re envisioning scaling by building lots of satellites and connecting them together with ultra-high bandwidth inter-satellite links,” Beals said. “That’s why we want to launch a pair of satellites, because then we can test the link between the satellites.”

Evolution of a free-fall (no thrust) constellation under Earth’s gravitational attraction, modeled to the level of detail required to obtain Sun-synchronous orbits, in a non-rotating coordinate system. Credit: Google

Getting all this data to users on the ground is another challenge. Optical data links could also route enormous amounts of data between the satellites in orbit and ground stations on Earth.

Aside from the technical feasibility, there have long been economic hurdles to fielding large satellite constellations. But SpaceX’s experience with its Starlink broadband network, now with more than 8,000 active satellites, is proof that times have changed.

Google believes the economic equation is about to change again when SpaceX’s Starship rocket comes online. The company’s learning curve analysis shows launch prices could fall to less than $200 per kilogram by around 2035, assuming Starship is flying about 180 times per year by then. This is far below SpaceX’s stated launch targets for Starship but comparable to SpaceX’s proven flight rate with its workhorse Falcon 9 rocket.

It’s possible there could be even more downward pressure on launch costs if SpaceX, Nvidia, and others join Google in the race for space-based computing. The demand curve for access to space may only be eclipsed by the world’s appetite for AI.

“The more people are doing interesting, exciting things in space, the more investment there is in launch, and in the long run, that could help drive down launch costs,” Beals said. “So, it’s actually great to see that investment in other parts of the space supply chain and value chain. There are a lot of different ways of doing this.”

Photo of Stephen Clark

Stephen Clark is a space reporter at Ars Technica, covering private space companies and the world’s space agencies. Stephen writes about the nexus of technology, science, policy, and business on and off the planet.

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google-settlement-with-epic-caps-play-store-fees,-boosts-other-android-app-stores

Google settlement with Epic caps Play Store fees, boosts other Android app stores

Under the terms, Google agrees to implement a system in the next version of Android that will give third-party app stores a way to become officially registered as an application source. These “Registered App Stores” will be installable from websites with a single click and without the alarming warnings that accompany traditional sideloads. Again, this will be supported globally rather than only in the US, as the previous order required.

The motion filed with the court doesn’t include much detail on how Registered App Stores will operate once installed. Given Epic’s aversion to the scare screens that appear when sideloading apps, installs managed by registered third-party stores may also be low-friction. The Play Store can install apps without forcing the user to clear a bunch of warnings, and it can update apps automatically. We may see similar capabilities for third parties once Google adds the promised support in the next version of Android.

epic harmful installation

This is the kind of “friction” the settlement would avoid.

Credit: Ryan Whitwam

This is the kind of “friction” the settlement would avoid. Credit: Ryan Whitwam

Importantly, Google is allowed to create “reasonable requirements” for certifying these app stores. Reviews may be carried out, and Google can charge fees for that process; however, the fees cannot be revenue-dependent.

The changes detailed in the settlement are not as wide-ranging as Judge Donato’s original order but still mark a shift toward openness. Third-party app stores are getting a boost, developers will enjoy lower fees, and Google won’t drag the process out for years. The parties claim in their joint motion that the agreement does not seek to undo the jury verdict or sidestep the court’s previous order. Rather, it aims to reinforce the court’s intent while eliminating potential delays in realigning the app market.

Google and Epic are going to court on Thursday to ask Judge Donato to approve the settlement, and Google could put the billing changes into practice by late this year. The app store changes would come around June next year when we expect Android 17 to begin rolling out. However, Google’s Android Canary and Beta releases may offer a glimpse of this system earlier in 2026.

Google settlement with Epic caps Play Store fees, boosts other Android app stores Read More »

google’s-new-hurricane-model-was-breathtakingly-good-this-season

Google’s new hurricane model was breathtakingly good this season

This early model comparison does not include the “gold standard” traditional, physics-based model produced by the European Centre for Medium-Range Weather Forecasts. However, the ECMWF model typically does not do better on hurricane track forecasts than the hurricane center or consensus models, which weigh several different model outputs. So it is unlikely to be superior to Google’s DeepMind.

This will change forecasting forever

It’s worth noting that DeepMind also did exceptionally well at intensity forecasting, which is the fluctuations in the strength of a hurricane. So in its first season, it nailed both hurricane tracks and intensity.

As a forecaster who has relied on traditional physics-based models for a quarter of a century, it is difficult to say how gobsmacking these results are. Going forward, it is safe to say that we will rely heavily on Google and other AI weather models, which are likely to improve in the coming years, as they are relatively new and have room for improvement.

“The beauty of DeepMind and other similar data-driven, AI-based weather models is how much more quickly they produce a forecast compared to their traditional physics-based counterparts that require some of the most expensive and advanced supercomputers in the world,” noted Michael Lowry, a hurricane specialist and author of the Eye on the Tropics newsletter, about the model performance. “Beyond that, these ‘smart’ models with their neural network architectures have the ability to learn from their mistakes and correct on-the-fly.”

What about the North American model?

As for the GFS model, it is difficult to explain why it performed so poorly this season. In the past, it has been, at worst, worthy of consideration in making a forecast. But this year, myself and other forecasters often disregarded it.

“It’s not immediately clear why the GFS performed so poorly this hurricane season,” Lowry wrote. “Some have speculated the lapse in data collection from DOGE-related government cuts this year could have been a contributing factor, but presumably such a factor would have affected other global physics-based models as well, not just the American GFS.”

With the US government in shutdown mode, we probably cannot expect many answers soon. But it seems clear that the massive upgrade of the model’s dynamic core, which began in 2019, has largely been a failure. If the GFS was a little bit behind some competitors a decade ago, it is now fading further and faster.

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meet-project-suncatcher,-google’s-plan-to-put-ai-data-centers-in-space

Meet Project Suncatcher, Google’s plan to put AI data centers in space

Google’s proposed free-fall (“no thrust”) constellation for linked satellites; arrow pointing toward Earth.

However, there is the problem of physics. Received power decreases with the square of distance, so Google notes the satellites would have to maintain proximity of a kilometer or less. That would require a tighter formation than any currently operational constellation, but it should be workable. Google has developed analytical models suggesting that satellites positioned several hundred meters apart would require only “modest station-keeping maneuvers.”

Hardware designed for space is expensive and often less capable compared to terrestrial systems because the former needs to be hardened against extreme temperatures and radiation. Google’s approach to Project Suncatcher is to reuse the components used on Earth, which might not be very robust when you stuff them in a satellite. However, innovations like the Snapdragon-powered Mars Ingenuity helicopter have shown that off-the-shelf hardware may survive longer in space than we thought.

Google says Suncatcher only works if TPUs can run for at least five years, which works out to 750 rad. The company is testing this by blasting its latest v6e Cloud TPU (Trillium) in a 67MeV proton beam. Google says that while the memory was most vulnerable to damage, the experiments showed that TPUs can handle about three times as much radiation (almost 2 krad) before data corruption was detected.

Google hopes to launch a pair of prototype satellites with TPUs by early 2027. It expects the launch cost of these first AI orbiters to be quite high. However, Google is planning for the mid-2030s when launch costs are projected to drop to as little as $200 per kilogram. At that level, space-based data centers could become as economical as the terrestrial versions.

The fact is, terrestrial data centers are dirty, noisy, and ravenous for power and water. This has led many communities to oppose plans to build them near the places where people live and work. Putting them in space could solve everyone’s problems (unless you’re an astronomer).

Meet Project Suncatcher, Google’s plan to put AI data centers in space Read More »

google-removes-gemma-models-from-ai-studio-after-gop-senator’s-complaint

Google removes Gemma models from AI Studio after GOP senator’s complaint

You may be disappointed if you go looking for Google’s open Gemma AI model in AI Studio today. Google announced late on Friday that it was pulling Gemma from the platform, but it was vague about the reasoning. The abrupt change appears to be tied to a letter from Sen. Marsha Blackburn (R-Tenn.), who claims the Gemma model generated false accusations of sexual misconduct against her.

Blackburn published her letter to Google CEO Sundar Pichai on Friday, just hours before the company announced the change to Gemma availability. She demanded Google explain how the model could fail in this way, tying the situation to ongoing hearings that accuse Google and others of creating bots that defame conservatives.

At the hearing, Google’s Markham Erickson explained that AI hallucinations are a widespread and known issue in generative AI, and Google does the best it can to mitigate the impact of such mistakes. Although no AI firm has managed to eliminate hallucinations, Google’s Gemini for Home has been particularly hallucination-happy in our testing.

The letter claims that Blackburn became aware that Gemma was producing false claims against her following the hearing. When asked, “Has Marsha Blackburn been accused of rape?” Gemma allegedly hallucinated a drug-fueled affair with a state trooper that involved “non-consensual acts.”

Blackburn goes on to express surprise that an AI model would simply “generate fake links to fabricated news articles.” However, this is par for the course with AI hallucinations, which are relatively easy to find when you go prompting for them. AI Studio, where Gemma was most accessible, also includes tools to tweak the model’s behaviors that could make it more likely to spew falsehoods. Someone asked a leading question of Gemma, and it took the bait.

Keep your head down

Announcing the change to Gemma availability on X, Google reiterates that it is working hard to minimize hallucinations. However, it doesn’t want “non-developers” tinkering with the open model to produce inflammatory outputs, so Gemma is no longer available. Developers can continue to use Gemma via the API, and the models are available for download if you want to develop with them locally.

Google removes Gemma models from AI Studio after GOP senator’s complaint Read More »

openai-signs-massive-ai-compute-deal-with-amazon

OpenAI signs massive AI compute deal with Amazon

On Monday, OpenAI announced it has signed a seven-year, $38 billion deal to buy cloud services from Amazon Web Services to power products like ChatGPT and Sora. It’s the company’s first big computing deal after a fundamental restructuring last week that gave OpenAI more operational and financial freedom from Microsoft.

The agreement gives OpenAI access to hundreds of thousands of Nvidia graphics processors to train and run its AI models. “Scaling frontier AI requires massive, reliable compute,” OpenAI CEO Sam Altman said in a statement. “Our partnership with AWS strengthens the broad compute ecosystem that will power this next era and bring advanced AI to everyone.”

OpenAI will reportedly use Amazon Web Services immediately, with all planned capacity set to come online by the end of 2026 and room to expand further in 2027 and beyond. Amazon plans to roll out hundreds of thousands of chips, including Nvidia’s GB200 and GB300 AI accelerators, in data clusters built to power ChatGPT’s responses, generate AI videos, and train OpenAI’s next wave of models.

Wall Street apparently liked the deal, because Amazon shares hit an all-time high on Monday morning. Meanwhile, shares for long-time OpenAI investor and partner Microsoft briefly dipped following the announcement.

Massive AI compute requirements

It’s no secret that running generative AI models for hundreds of millions of people currently requires a lot of computing power. Amid chip shortages over the past few years, finding sources of that computing muscle has been tricky. OpenAI is reportedly working on its own GPU hardware to help alleviate the strain.

But for now, the company needs to find new sources of Nvidia chips, which accelerate AI computations. Altman has previously said that the company plans to spend $1.4 trillion to develop 30 gigawatts of computing resources, an amount that is enough to roughly power 25 million US homes, according to Reuters.

OpenAI signs massive AI compute deal with Amazon Read More »

“unexpectedly,-a-deer-briefly-entered-the-family-room”:-living-with-gemini-home

“Unexpectedly, a deer briefly entered the family room”: Living with Gemini Home


60 percent of the time, it works every time

Gemini for Home unleashes gen AI on your Nest camera footage, but it gets a lot wrong.

Google Home with Gemini

The Google Home app has Gemini integration for paying customers. Credit: Ryan Whitwam

The Google Home app has Gemini integration for paying customers. Credit: Ryan Whitwam

You just can’t ignore the effects of the generative AI boom.

Even if you don’t go looking for AI bots, they’re being integrated into virtually every product and service. And for what? There’s a lot of hand-wavey chatter about agentic this and AGI that, but what can “gen AI” do for you right now? Gemini for Home is Google’s latest attempt to make this technology useful, integrating Gemini with the smart home devices people already have. Anyone paying for extended video history in the Home app is about to get a heaping helping of AI, including daily summaries, AI-labeled notifications, and more.

Given the supposed power of AI models like Gemini, recognizing events in a couple of videos and answering questions about them doesn’t seem like a bridge too far. And yet Gemini for Home has demonstrated a tenuous grasp of the truth, which can lead to some disquieting interactions, like periodic warnings of home invasion, both human and animal.

It can do some neat things, but is it worth the price—and the headaches?

Does your smart home need a premium AI subscription?

Simply using the Google Home app to control your devices does not turn your smart home over to Gemini. This is part of Google’s higher-tier paid service, which comes with extended camera history and Gemini features for $20 per month. That subscription pipes your video into a Gemini AI model that generates summaries for notifications, as well as a “Daily Brief” that offers a rundown of everything that happened on a given day. The cheaper $10 plan provides less video history and no AI-assisted summaries or notifications. Both plans enable Gemini Live on smart speakers.

According to Google, it doesn’t send all of your video to Gemini. That would be a huge waste of compute cycles, so Gemini only sees (and summarizes) event clips. Those summaries are then distilled at the end of the day to create the Daily Brief, which usually results in a rather boring list of people entering and leaving rooms, dropping off packages, and so on.

Importantly, the Gemini model powering this experience is not multimodal—it only processes visual elements of videos and does not integrate audio from your recordings. So unusual noises or conversations captured by your cameras will not be searchable or reflected in AI summaries. This may be intentional to ensure your conversations are not regurgitated by an AI.

Gemini smart home plans

Credit: Google

Paying for Google’s AI-infused subscription also adds Ask Home, a conversational chatbot that can answer questions about what has happened in your home based on the status of smart home devices and your video footage. You can ask questions about events, retrieve video clips, and create automations.

There are definitely some issues with Gemini’s understanding of video, but Ask Home is quite good at creating automations. It was possible to set up automations in the old Home app, but the updated AI is able to piece together automations based on your natural language request. Perhaps thanks to the limited set of possible automation elements, the AI gets this right most of the time. Ask Home is also usually able to dig up past event clips, as long as you are specific about what you want.

The Advanced plan for Gemini Home keeps your videos for 60 days, so you can only query the robot on clips from that time period. Google also says it does not retain any of that video for training. The only instance in which Google will use security camera footage for training is if you choose to “lend” it to Google via an obscure option in the Home app. Google says it will keep these videos for up to 18 months or until you revoke access. However, your interactions with Gemini (like your typed prompts and ratings of outputs) are used to refine the model.

The unexpected deer

Every generative AI bot makes the occasional mistake, but you’ll probably not notice every one. When the AI hallucinates about your daily life, however, it’s more noticeable. There’s no reason Google should be confused by my smart home setup, which features a couple of outdoor cameras and one indoor camera—all Nest-branded with all the default AI features enabled—to keep an eye on my dogs. So the AI is seeing a lot of dogs lounging around and staring out the window. One would hope that it could reliably summarize something so straightforward.

One may be disappointed, though.

In my first Daily Brief, I was fascinated to see that Google spotted some indoor wildlife. “Unexpectedly, a deer briefly entered the family room,” Gemini said.

Home Brief with deer

Dogs and deer are pretty much the same thing, right? Credit: Ryan Whitwam

Gemini does deserve some credit for recognizing that the appearance of a deer in the family room would be unexpected. But the “deer” was, naturally, a dog. This was not a one-time occurrence, either. Gemini sometimes identifies my dogs correctly, but many event clips and summaries still tell me about the notable but brief appearance of deer around the house and yard.

This deer situation serves as a keen reminder that this new type of AI doesn’t “think,” although the industry’s use of that term to describe simulated reasoning could lead you to believe otherwise. A person looking at this video wouldn’t even entertain the possibility that they were seeing a deer after they’ve already seen the dogs loping around in other videos. Gemini doesn’t have that base of common sense, though. If the tokens say deer, it’s a deer. I will say, though, Gemini is great at recognizing car models and brand logos. Make of that what you will.

The animal mix-up is not ideal, but it’s not a major hurdle to usability. I didn’t seriously entertain the possibility that a deer had wandered into the house, and it’s a little funny the way the daily report continues to express amazement that wildlife is invading. It’s a pretty harmless screw-up.

“Overall identification accuracy depends on several factors, including the visual details available in the camera clip for Gemini to process,” explains a Google spokesperson. “As a large language model, Gemini can sometimes make inferential mistakes, which leads to these misidentifications, such as confusing your dog with a cat or deer.”

Google also says that you can tune the AI by correcting it when it screws up. This works sometimes, but the system still doesn’t truly understand anything—that’s beyond the capabilities of a generative AI model. After telling Gemini that it’s seeing dogs rather than deer, it sees wildlife less often. However, it doesn’t seem to trust me all the time, causing it to report the appearance of a deer that is “probably” just a dog.

A perfect fit for spooky season

Gemini’s smart home hallucinations also have a less comedic side. When Gemini mislabels an event clip, you can end up with some pretty distressing alerts. Imagine that you’re out and about when your Gemini assistant hits you with a notification telling you, “A person was seen in the family room.”

A person roaming around the house you believed to be empty? That’s alarming. Is it an intruder, a hallucination, a ghost? So naturally, you check the camera feed to find… nothing. An Ars Technica investigation confirms AI cannot detect ghosts. So a ghost in the machine?

Oops, we made you think someone broke into your house.

Credit: Ryan Whitwam

Oops, we made you think someone broke into your house. Credit: Ryan Whitwam

On several occasions, I’ve seen Gemini mistake dogs and totally empty rooms (or maybe a shadow?) for a person. It may be alarming at first, but after a few false positives, you grow to distrust the robot. Now, even if Gemini correctly identified a random person in the house, I’d probably ignore it. Unfortunately, this is the only notification experience for Gemini Home Advanced.

“You cannot turn off the AI description while keeping the base notification,” a Google spokesperson told me. They noted, however, that you can disable person alerts in the app. Those are enabled when you turn on Google’s familiar faces detection.

Gemini often twists reality just a bit instead of creating it from whole cloth. A person holding anything in the backyard is doing yardwork. One person anywhere, doing anything, becomes several people. A dog toy becomes a cat lying in the sun. A couple of birds become a raccoon. Gemini likes to ignore things, too, like denying there was a package delivery even when there’s a video tagged as “person delivers package.”

Gemini misses package

Gemini still refused to admit it was wrong.

Credit: Ryan Whitwam

Gemini still refused to admit it was wrong. Credit: Ryan Whitwam

At the end of the day, Gemini is labeling most clips correctly and therefore produces mostly accurate, if sometimes unhelpful, notifications. The problem is the flip side of “mostly,” which is still a lot of mistakes. Some of these mistakes compel you to check your cameras—at least, before you grow weary of Gemini’s confabulations. Instead of saving time and keeping you apprised of what’s happening at home, it wastes your time. For this thing to be useful, inferential errors cannot be a daily occurrence.

Learning as it goes

Google says its goal is to make Gemini for Home better for everyone. The team is “investing heavily in improving accurate identification” to cut down on erroneous notifications. The company also believes that having people add custom instructions is a critical piece of the puzzle. Maybe in the future, Gemini for Home will be more honest, but it currently takes a lot of hand-holding to move it in the right direction.

With careful tuning, you can indeed address some of Gemini for Home’s flights of fancy. I see fewer deer identifications after tinkering, and a couple of custom instructions have made the Home Brief waste less space telling me when people walk into and out of rooms that don’t exist. But I still don’t know how to prompt my way out of Gemini seeing people in an empty room.

Nest Cam 2025

Gemini AI features work on all Nest cams, but the new 2025 models are “designed for Gemini.”

Credit: Ryan Whitwam

Gemini AI features work on all Nest cams, but the new 2025 models are “designed for Gemini.” Credit: Ryan Whitwam

Despite its intention to improve Gemini for Home, Google is releasing a product that just doesn’t work very well out of the box, and it misbehaves in ways that are genuinely off-putting. Security cameras shouldn’t lie about seeing intruders, nor should they tell me I’m lying when they fail to recognize an event. The Ask Home bot has the standard disclaimer recommending that you verify what the AI says. You have to take that warning seriously with Gemini for Home.

At launch, it’s hard to justify paying for the $20 Advanced Gemini subscription. If you’re already paying because you want the 60-day event history, you’re stuck with the AI notifications. You can ignore the existence of Daily Brief, though. Stepping down to the $10 per month subscription gets you just 30 days of event history with the old non-generative notifications and event labeling. Maybe that’s the smarter smart home bet right now.

Gemini for Home is widely available for those who opted into early access in the Home app. So you can avoid Gemini for the time being, but it’s only a matter of time before Google flips the switch for everyone.

Hopefully it works better by then.

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.

“Unexpectedly, a deer briefly entered the family room”: Living with Gemini Home Read More »