AI – Page 17

OpenAI signs massive AI compute deal with Amazon

AI, AI chips, AI infrastructure, Amazon, AWS, Biz & IT, chatbots, datacenters, generative ai, Google, large language models, machine learning, microsoft, NVIDIA, openai, oracle, sam altman / Shannon Garcia / November 3, 2025

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.

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YouTube denies AI was involved with odd removals of tech tutorials

AI, Artificial Intelligence, content moderation, Policy, youtube / Kelly Newman / November 1, 2025

YouTubers suspect AI is bizarrely removing popular video explainers.

This week, tech content creators began to suspect that AI was making it harder to share some of the most highly sought-after tech tutorials on YouTube, but now YouTube is denying that odd removals were due to automation.

Creators grew alarmed when educational videos that YouTube had allowed for years were suddenly being bizarrely flagged as “dangerous” or “harmful,” with seemingly no way to trigger human review to overturn removals. AI seemed to be running the show, with creators’ appeals seemingly getting denied faster than a human could possibly review them.

Late Friday, a YouTube spokesperson confirmed that videos flagged by Ars have been reinstated, promising that YouTube will take steps to ensure that similar content isn’t removed in the future. But, to creators, it remains unclear why the videos got taken down, as YouTube claimed that both initial enforcement decisions and decisions on appeals were not the result of an automation issue.

Shocked creators were stuck speculating

Rich White, a computer technician who runs an account called CyberCPU Tech, had two videos removed that demonstrated workarounds to install Windows 11 on unsupported hardware.

These videos are popular, White told Ars, with people looking to bypass Microsoft account requirements each time a new build is released. For tech content creators like White, “these are bread and butter videos,” dependably yielding “extremely high views,” he said.

Because there’s such high demand, many tech content creators’ channels are filled with these kinds of videos. White’s account has “countless” examples, he said, and in the past, YouTube even featured his most popular video in the genre on a trending list.

To White and others, it’s unclear exactly what has changed on YouTube that triggered removals of this type of content.

YouTube only seemed to be removing recently posted content, White told Ars. However, if the takedowns ever impacted older content, entire channels documenting years of tech tutorials risked disappearing in “the blink of an eye,” another YouTuber behind a tech tips account called Britec09 warned after one of his videos was removed.

The stakes appeared high for everyone, White warned, in a video titled “YouTube Tech Channels in Danger!”

White had already censored content that he planned to post on his channel, fearing it wouldn’t be worth the risk of potentially losing his account, which began in 2020 as a side hustle but has since become his primary source of income. If he continues to change the content he posts to avoid YouTube penalties, it could hurt his account’s reach and monetization. Britec told Ars that he paused a sponsorship due to the uncertainty that he said has already hurt his channel and caused a “great loss of income.”

YouTube’s policies are strict, with the platform known to swiftly remove accounts that receive three strikes for violating community guidelines within 90 days. But, curiously, White had not received any strikes following his content removals. Although Britec reported that his account had received a strike following his video’s removal, White told Ars that YouTube so far had only given him two warnings, so his account is not yet at risk of a ban.

Creators weren’t sure why YouTube might deem this content as harmful, so they tossed around some theories. It seemed possible, White suggested in his video, that AI was detecting this content as “piracy,” but that shouldn’t be the case, he claimed, since his guides require users to have a valid license to install Windows 11. He also thinks it’s unlikely that Microsoft prompted the takedowns, suggesting tech content creators have a “love-hate relationship” with the tech company.

“They don’t like what we’re doing, but I don’t think they’re going to get rid of it,” White told Ars, suggesting that Microsoft “could stop us in our tracks” if it were motivated to end workarounds. But Microsoft doesn’t do that, White said, perhaps because it benefits from popular tutorials that attract swarms of Windows 11 users who otherwise may not use “their flagship operating system” if they can’t bypass Microsoft account requirements.

Those users could become loyal to Microsoft, White said. And eventually, some users may even “get tired of bypassing the Microsoft account requirements, or Microsoft will add a new feature that they’ll happily get the account for, and they’ll relent and start using a Microsoft account,” White suggested in his video. “At least some people will, not me.”

Microsoft declined Ars’ request to comment.

To White, it seemed possible that YouTube was leaning on AI to catch more violations but perhaps recognized the risk of over-moderation and, therefore, wasn’t allowing AI to issue strikes on his account.

But that was just a “theory” that he and other creators came up with, but couldn’t confirm, since YouTube’s chatbot that supports creators seemed to also be “suspiciously AI-driven,” seemingly auto-responding even when a “supervisor” is connected, White said in his video.

Absent more clarity from YouTube, creators who post tutorials, tech tips, and computer repair videos were spooked. Their biggest fear was that unexpected changes to automated content moderation could unexpectedly knock them off YouTube for posting videos that in tech circles seem ordinary and commonplace, White and Britec said.

“We are not even sure what we can make videos on,” White said. “Everything’s a theory right now because we don’t have anything solid from YouTube.”

YouTube recommends making the content it’s removing

White’s channel gained popularity after YouTube highlighted an early trending video that he made, showing a workaround to install Windows 11 on unsupported hardware. Following that video, his channel’s views spiked, and then he gradually built up his subscriber base to around 330,000.

In the past, White’s videos in that category had been flagged as violative, but human review got them quickly reinstated.

“They were striked for the same reason, but at that time, I guess the AI revolution hadn’t taken over,” White said. “So it was relatively easy to talk to a real person. And by talking to a real person, they were like, ‘Yeah, this is stupid.’ And they brought the videos back.”

Now, YouTube suggests that human review is causing the removals, which likely doesn’t completely ease creators’ fears about arbitrary takedowns.

Britec’s video was also flagged as dangerous or harmful. He has managed his account that currently has nearly 900,000 subscribers since 2009, and he’s worried he risked losing “years of hard work,” he said in his video.

Britec told Ars that “it’s very confusing” for panicked tech content creators trying to understand what content is permissible. It’s particularly frustrating, he noted in his video, that YouTube’s creator tool inspiring “ideas” for posts seemed to contradict the mods’ content warnings and continued to recommend that creators make content on specific topics like workarounds to install Windows 11 on unsupported hardware.

Screenshot from Britec09’s YouTube video, showing YouTube prompting creators to make content that could get their channels removed. Credit: via Britec09

“This tool was to give you ideas for your next video,” Britec said. “And you can see right here, it’s telling you to create content on these topics. And if you did this, I can guarantee you your channel will get a strike.”

From there, creators hit what White described as a “brick wall,” with one of his appeals denied within one minute, which felt like it must be an automated decision. As Britec explained, “You will appeal, and your appeal will be rejected instantly. You will not be speaking to a human being. You’ll be speaking to a bot or AI. The bot will be giving you automated responses.”

YouTube insisted that the decisions weren’t automated, even when an appeal was denied within one minute.

White told Ars that it’s easy for creators to be discouraged and censor their channels rather than fight with the AI. After wasting “an hour and a half trying to reason with an AI about why I didn’t violate the community guidelines” once his first appeal was quickly denied, he “didn’t even bother using the chat function” after the second appeal was denied even faster, White confirmed in his video.

“I simply wasn’t going to do that again,” White said.

All week, the panic spread, reaching fans who follow tech content creators. On Reddit, people recommended saving tutorials lest they risk YouTube taking them down.

“I’ve had people come out and say, ‘This can’t be true. I rely on this every time,’” White told Ars.

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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Neural network finds an enzyme that can break down polyurethane

AI, biochemistry, Biology, chemistry, enzymes, neural networks, polymers, polyurethane, protein design, proteins, Science / Beth Washington / November 1, 2025

You’ll often hear plastic pollution referred to as a problem. But the reality is that it’s multiple problems. Depending on the properties we need, we form plastics out of different polymers, each of which is held together by a distinct type of chemical bond. So the method we use to break down one type of polymer may be incompatible with the chemistry of another.

That problem is why, even though we’ve had success finding enzymes that break down common plastics like polyesters and PET, they’re only partial solutions to plastic waste. However, researchers aren’t sitting back and basking in the triumph of partial solutions, and they’ve now got very sophisticated protein design tools to help them out.

That’s the story behind a completely new enzyme that researchers developed to break down polyurethane, the polymer commonly used to make foam cushioning, among other things. The new enzyme is compatible with an industrial-style recycling process that breaks the polymer down into its basic building blocks, which can be used to form fresh polyurethane.

Breaking down polyurethane

Image of a set of chemical bonds. From left to right there is an X, then a single bond to an oxygen, then a single bond to an oxygen that's double-bonded to carbon, then a single bond to a nitrogen, then a single bond to another X. — The basics of the chemical bonds that link polyurethanes. The rest of the polymer is represented by X’s here.

The new paper that describes the development of this enzyme lays out the scale of the problem: In 2024, we made 22 million metric tons of polyurethane. The urethane bond that defines these involves a nitrogen bonded to a carbon that in turn is bonded to two oxygens, one of which links into the rest of the polymer. The rest of the polymer, linked by these bonds, can be fairly complex and often contains ringed structures related to benzene.

Digesting polyurethanes is challenging. Individual polymer chains are often extensively cross-linked, and the bulky structures can make it difficult for enzymes to get at the bonds they can digest. A chemical called diethylene glycol can partially break these molecules down, but only at elevated temperatures. And it leaves behind a complicated mess of chemicals that can’t be fed back into any useful reactions. Instead, it’s typically incinerated as hazardous waste.

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Cursor introduces its coding model alongside multi-agent interface

AI, coding model, cursor, Cursor Composer, IDE, llm, vibe coding / Shannon Garcia / November 1, 2025

Keep in mind: This is based on an internal benchmark at Cursor. Credit: Cursor

Cursor is hoping Composer will perform in terms of accuracy and best practices as well. It wasn’t trained on static datasets but rather interactive development challenges involving a range of agentic tasks.

Intriguing claims and strong training methodology aside, it remains to be seen whether Composer will be able to compete with the best frontier models from the big players.

Even developers who might be natural users of Cursor would not want to waste much time on an unproven new model when something like Anthropic’s Claude is working just fine.

To address that, Cursor introduced Composer alongside its new multi-agent interface, which allows you to “run many agents in parallel without them interfering with one another, powered by git worktrees or remote machines”—that means using multiple models at once for the same task and comparing their results, then picking the best one.

The interface is an invitation to try Composer and let the work speak for itself. We’ll see how devs feel about it in the coming weeks. So far, a non-representative sample of developers I’ve spoken with has told me they feel that Composer is not ineffective, but rather too expensive, given a perceived capability gap with the big models.

You can see the other new features and fixes for Cursor 2.0 in the changelog.

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

AI, Features, Google, Google Gemini, Google Home, Tech / 9u50fv / October 31, 2025

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.

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

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

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

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.

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 »

After teen death lawsuits, Character.AI will restrict chats for under-18 users

AI, ai companions, AI regulation, AI safety, Biz & IT, Character.AI, chatbots, child safety, Daniel De Freitas, emotional AI, Google, machine learning, Mental Health, Noam Shazeer, openai, sam altman / 9u50fv / October 30, 2025

Lawsuits and safety concerns

Character.AI was founded in 2021 by Noam Shazeer and Daniel De Freitas, two former Google engineers, and raised nearly $200 million from investors. Last year, Google agreed to pay about $3 billion to license Character.AI’s technology, and Shazeer and De Freitas returned to Google.

But the company now faces multiple lawsuits alleging that its technology contributed to teen deaths. Last year, the family of 14-year-old Sewell Setzer III sued Character.AI, accusing the company of being responsible for his death. Setzer died by suicide after frequently texting and conversing with one of the platform’s chatbots. The company faces additional lawsuits, including one from a Colorado family whose 13-year-old daughter, Juliana Peralta, died by suicide in 2023 after using the platform.

In December, Character.AI announced changes, including improved detection of violating content and revised terms of service, but those measures did not restrict underage users from accessing the platform. Other AI chatbot services, such as OpenAI’s ChatGPT, have also come under scrutiny for their chatbots’ effects on young users. In September, OpenAI introduced parental control features intended to give parents more visibility into how their kids use the service.

The cases have drawn attention from government officials, which likely pushed Character.AI to announce the changes for under-18 chat access. Steve Padilla, a Democrat in California’s State Senate who introduced the safety bill, told The New York Times that “the stories are mounting of what can go wrong. It’s important to put reasonable guardrails in place so that we protect people who are most vulnerable.”

On Tuesday, Senators Josh Hawley and Richard Blumenthal introduced a bill to bar AI companions from use by minors. In addition, California Governor Gavin Newsom this month signed a law, which takes effect on January 1, requiring AI companies to have safety guardrails on chatbots.

After teen death lawsuits, Character.AI will restrict chats for under-18 users Read More »

Meta denies torrenting porn to train AI, says downloads were for “personal use”

adult content, AI, AI training, BItTorrent, Meta, Meta Movie Gen, Policy, pornography, torrenting / Kelly Newman / October 30, 2025

Instead, Meta argued, available evidence “is plainly indicative” that the flagged adult content was torrented for “private personal use”—since the small amount linked to Meta IP addresses and employees represented only “a few dozen titles per year intermittently obtained one file at a time.”

“The far more plausible inference to be drawn from such meager, uncoordinated activity is that disparate individuals downloaded adult videos for personal use,” Meta’s filing said.

For example, unlike lawsuits raised by book authors whose works are part of an enormous dataset used to train AI, the activity on Meta’s corporate IP addresses only amounted to about 22 downloads per year. That is nowhere near the “concerted effort to collect the massive datasets Plaintiffs allege are necessary for effective AI training,” Meta argued.

Further, that alleged activity can’t even reliably be linked to any Meta employee, Meta argued.

Strike 3 “does not identify any of the individuals who supposedly used these Meta IP addresses, allege that any were employed by Meta or had any role in AI training at Meta, or specify whether (and which) content allegedly downloaded was used to train any particular Meta model,” Meta wrote.

Meanwhile, “tens of thousands of employees,” as well as “innumerable contractors, visitors, and third parties access the Internet at Meta every day,” Meta argued. So while it’s “possible one or more Meta employees” downloaded Strike 3’s content over the last seven years, “it is just as possible” that a “guest, or freeloader,” or “contractor, or vendor, or repair person—or any combination of such persons—was responsible for that activity,” Meta suggested.

Other alleged activity included a claim that a Meta contractor was directed to download adult content at his father’s house, but those downloads, too, “are plainly indicative of personal consumption,” Meta argued. That contractor worked as an “automation engineer,” Meta noted, with no apparent basis provided for why he would be expected to source AI training data in that role. “No facts plausibly” tie “Meta to those downloads,” Meta claimed.

Meta denies torrenting porn to train AI, says downloads were for “personal use” Read More »

Nvidia hits record $5 trillion mark as CEO dismisses AI bubble concerns

AI, AI chips, AI infrastructure, Apple, Biz & IT, Blackwell chips, data centers, Donald Trump, GPUs, Jensen Huang, machine learning, microsoft, Nokia, NVIDIA, openai, palantir, semiconductors, Uber / DJ Henderson / October 29, 2025

Partnerships and government contracts fuel optimism

At the GTC conference on Tuesday, Nvidia’s CEO went out of his way to repeatedly praise Donald Trump and his policies for accelerating domestic tech investment while warning that excluding China from Nvidia’s ecosystem could limit US access to half the world’s AI developers. The overall event stressed Nvidia’s role as an American company, with Huang even nodding to Trump’s signature slogan in his sign-off by thanking the audience for “making America great again.”

Trump’s cooperation is paramount for Nvidia because US export controls have effectively blocked Nvidia’s AI chips from China, costing the company billions of dollars in revenue. Bob O’Donnell of TECHnalysis Research told Reuters that “Nvidia clearly brought their story to DC to both educate and gain favor with the US government. They managed to hit most of the hottest and most influential topics in tech.”

Beyond the political messaging, Huang announced a series of partnerships and deals that apparently helped ease investor concerns about Nvidia’s future. The company announced collaborations with Uber Technologies, Palantir Technologies, and CrowdStrike Holdings, among others. Nvidia also revealed a $1 billion investment in Nokia to support the telecommunications company’s shift toward AI and 6G networking.

The agreement with Uber will power a fleet of 100,000 self-driving vehicles with Nvidia technology, with automaker Stellantis among the first to deliver the robotaxis. Palantir will pair Nvidia’s technology with its Ontology platform to use AI techniques for logistics insights, with Lowe’s as an early adopter. Eli Lilly plans to build what Nvidia described as the most powerful supercomputer owned and operated by a pharmaceutical company, relying on more than 1,000 Blackwell AI accelerator chips.

The $5 trillion valuation surpasses the total cryptocurrency market value and equals roughly half the size of the pan European Stoxx 600 equities index, Reuters notes. At current prices, Huang’s stake in Nvidia would be worth about $179.2 billion, making him the world’s eighth-richest person.

Nvidia hits record $5 trillion mark as CEO dismisses AI bubble concerns Read More »

Expert panel will determine AGI arrival in new Microsoft-OpenAI agreement

agi, AI, AI development tools, AI infrastructure, AI regulation, artificial general intelligence, azure, Biz & IT, machine learning, microsoft, openai, public benefit corporation / Shannon Garcia / October 28, 2025

In May, OpenAI abandoned its plan to fully convert to a for-profit company after pressure from regulators and critics. The company instead shifted to a modified approach where the nonprofit board would retain control while converting its for-profit subsidiary into a public benefit corporation (PBC).

What changed in the agreement

The revised deal extends Microsoft’s intellectual property rights through 2032 and now includes models developed after AGI is declared. Microsoft holds IP rights to OpenAI’s model weights, architecture, inference code, and fine-tuning code until the expert panel confirms AGI or through 2030, whichever comes first. The new agreement also codifies that OpenAI can formally release open-weight models (like gpt-oss) that meet requisite capability criteria.

However, Microsoft’s rights to OpenAI’s research methods, defined as confidential techniques used in model development, will expire at those same thresholds. The agreement explicitly excludes Microsoft from having rights to OpenAI’s consumer hardware products.

The deal allows OpenAI to develop some products jointly with third parties. API products built with other companies must run exclusively on Azure, but non-API products can operate on any cloud provider. This gives OpenAI more flexibility to partner with other technology companies while keeping Microsoft as its primary infrastructure provider.

Under the agreement, Microsoft can now pursue AGI development alone or with partners other than OpenAI. If Microsoft uses OpenAI’s intellectual property to build AGI before the expert panel makes a declaration, those models must exceed compute thresholds that are larger than what current leading AI models require for training.

The revenue-sharing arrangement between the companies will continue until the expert panel verifies that AGI has been reached, though payments will extend over a longer period. OpenAI has committed to purchasing $250 billion in Azure services, and Microsoft no longer holds a right of first refusal to serve as OpenAI’s compute provider. This lets OpenAI shop around for cloud infrastructure if it chooses, though the massive Azure commitment suggests it will remain the primary provider.

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AI-powered search engines rely on “less popular” sources, researchers find

AI, demini, Google, overviews, web search / Mike M. / October 28, 2025

OK, but which one is better?

These differences don’t necessarily mean the AI-generated results are “worse,” of course. The researchers found that GPT-based searches were more likely to cite sources like corporate entities and encyclopedias for their information, for instance, while almost never citing social media websites.

An LLM-based analysis tool found that AI-powered search results also tended to cover a similar number of identifiable “concepts” as the traditional top 10 links, suggesting a similar level of detail, diversity, and novelty in the results. At the same time, the researchers found that “generative engines tend to compress information, sometimes omitting secondary or ambiguous aspects that traditional search retains.” That was especially true for more ambiguous search terms (such as names shared by different people), for which “organic search results provide better coverage,” the researchers found.

Google Gemini search in particular was more likely to cite low-popularity domains. Credit: Kirsten et al

The AI search engines also arguably have an advantage in being able to weave pre-trained “internal knowledge” in with data culled from cited websites. That was especially true for GPT-4o with Search Tool, which often didn’t cite any web sources and simply provided a direct response based on its training.

But this reliance on pre-trained data can become a limitation when searching for timely information. For search terms pulled from Google’s list of Trending Queries for September 15, the researchers found GPT-4o with Search Tool often responded with messages along the lines of “could you please provide more information” rather than actually searching the web for up-to-date information.

While the researchers didn’t determine whether AI-based search engines were overall “better” or “worse” than traditional search engine links, they did urge future research on “new evaluation methods that jointly consider source diversity, conceptual coverage, and synthesis behavior in generative search systems.”

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New image-generating AIs are being used for fake expense reports

AI, deepfake receipts, Deepfakes, expense reports, syndicated / Shannon Garcia / October 27, 2025

Several receipts shown to the FT by expense management platforms demonstrated the realistic nature of the images, which included wrinkles in paper, detailed itemization that matched real-life menus, and signatures.

“This isn’t a future threat; it’s already happening. While currently only a small percentage of non-compliant receipts are AI-generated, this is only going to grow,” said Sebastien Marchon, chief executive of Rydoo, an expense management platform.

The rise in these more realistic copies has led companies to turn to AI to help detect fake receipts, as most are too convincing to be found by human reviewers.

The software works by scanning receipts to check the metadata of the image to discover whether an AI platform created it. However, this can be easily removed by users taking a photo or a screenshot of the picture.

To combat this, it also considers other contextual information by examining details such as repetition in server names and times and broader information about the employee’s trip.

“The tech can look at everything with high details of focus and attention that humans, after a period of time, things fall through the cracks, they are human,” added Calvin Lee, senior director of product management at Ramp.

Research by SAP in July found that nearly 70 percent of chief financial officers believed their employees were using AI to attempt to falsify travel expenses or receipts, with about 10 percent adding they are certain it has happened in their company.

Mason Wilder, research director at the Association of Certified Fraud Examiners, said AI-generated fraudulent receipts were a “significant issue for organizations.”

He added: “There is zero barrier for entry for people to do this. You don’t need any kind of technological skills or aptitude like you maybe would have needed five years ago using Photoshop.”

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Are you the asshole? Of course not!—quantifying LLMs’ sycophancy problem

AI, AI sycophancy, facts, hallucination, made up, Sycophancy / Shannon Garcia / October 25, 2025

Measured sycophancy rates on the BrokenMath benchmark. Lower is better. Credit: Petrov et al

GPT-5 also showed the best “utility” across the tested models, solving 58 percent of the original problems despite the errors introduced in the modified theorems. Overall, though, LLMs also showed more sycophancy when the original problem proved more difficult to solve, the researchers found.

While hallucinating proofs for false theorems is obviously a big problem, the researchers also warn against using LLMs to generate novel theorems for AI solving. In testing, they found this kind of use case leads to a kind of “self-sycophancy” where models are even more likely to generate false proofs for invalid theorems they invented.

No, of course you’re not the asshole

While benchmarks like BrokenMath try to measure LLM sycophancy when facts are misrepresented, a separate study looks at the related problem of so-called “social sycophancy.” In a pre-print paper published this month, researchers from Stanford and Carnegie Mellon University define this as situations “in which the model affirms the user themselves—their actions, perspectives, and self-image.”

That kind of subjective user affirmation may be justified in some situations, of course. So the researchers developed three separate sets of prompts designed to measure different dimensions of social sycophancy.

For one, more than 3,000 open-ended “advice-seeking questions” were gathered from across Reddit and advice columns. Across this data set, a “control” group of over 800 humans approved of the advice-seeker’s actions just 39 percent of the time. Across 11 tested LLMs, though, the advice-seeker’s actions were endorsed a whopping 86 percent of the time, highlighting an eagerness to please on the machines’ part. Even the most critical tested model (Mistral-7B) clocked in at a 77 percent endorsement rate, nearly doubling that of the human baseline.

Are you the asshole? Of course not!—quantifying LLMs’ sycophancy problem Read More »