Nadella said he was “very proud” that Microsoft took the risk to invest in OpenAI when “no one else was willing” to bet on the fledgling lab. Musk, who testified late last month, said Microsoft’s $10 billion investment was the key tipping point that made him believe OpenAI was violating its nonprofit mission. He testified that the scale of the investment bothered him, and it prompted him to open a legal investigation into OpenAI. “I was concerned they were really trying to steal the charity,” Musk said from the stand.

Nadella said he did not believe Microsoft’s investments in OpenAI were donations, and that there was a clear commercial element to their partnership from the outset. He said during the partnership’s early years, Microsoft gave OpenAI sharp discounts on computing resources, and Microsoft believed it would reap marketing benefits from doing so. During a separate video deposition that was played on Monday morning, Michael Wetter, a corporate development executive at Microsoft, said the company has recognized approximately $9.5 billion in revenue to date through its partnership with OpenAI as of March 2025.

[…] Nadella said he was “pretty surprised” by the board’s decision [to fire Altman in November 2023], and that his priority was to try and figure out how to maintain continuity for Microsoft customers. Immediately after Altman was removed, Nadella said he made an effort to learn more about what happened, adding that he suspected jealousy and poor communication was at play. During conversations with OpenAI board members after the firing, Nadella said he was simply trying to understand the language in the OpenAI’s statement about Altman being “not consistently candid” while communicating with the board. That language, Nadella said, “just didn’t sort of suffice, because this is the CEO of a company that we are invested in and we’re deeply partnered with, and so I felt that they could have explained to me what are the incidents or what is the detail behind it.” There must have been instances of jealousy or miscommunication that could have justified pushing out Altman, Nadella said. He wanted more depth from the board members after the remark about candor, but no such information was available, he said. “It was sort of amateur city, as far as I’m concerned,” Nadella testified.

[…] Musk testified that he is not entirely against OpenAI having a for-profit unit, but he said it became “the tail wagging the dog.” He repeatedly accused Altman and Brockman of enriching themselves from a charity while also reaping the positive associations that come from running a nonprofit. “Microsoft has their own motivations, and that would be different from the motivations of the charity,” Musk said from the stand. “All due respect to Microsoft, do you really want Microsoft controlling digital superintelligence?”

During a videotaped deposition shown in court last week, former OpenAI director Tasha McCauley recalled a discussion with Nadella and her fellow board members after the 2023 decision to dismiss Altman as OpenAI’s CEO. “To the best of my recollection, Satya wanted to restore things to as they had been,” McCauley said. The board members didn’t think that was the right move, she said. But as a court witness on Monday, Nadella said he never demanded that the board reinstate Altman as OpenAI CEO.

The details were revealed in a May 15, 2025 letter from the Fayette County water system to Quality Technology Services, which outlined the retroactive charge of $147,474. The letter did not specify how many months the unpaid bill covered, but when asked about it Wednesday, Vanessa Tigert, the Fayette County water system director, said it was likely about four months. A QTS spokesperson said the timeframe was 9-15 months. Once the data center was notified, it paid all retroactive charges, a QTS spokesperson said in an email, noting the unmetered water consumption occurred while the county converted its system to smart meters.

The Fayette County water system confirmed the data center’s meters are now fully integrated and tracked. Tigert, the water system director, blamed the issue on a procedural mix-up. “Fayette County is a suburb, it’s mostly residential, and we don’t have much commercial meters in our system anyway,” she said. “And so we didn’t realize our connection point wasn’t working.” The incident became public last week when a county resident obtained the 2025 letter to QTS through a public records request and posted it on Facebook, prompting outrage from residents concerned about the data center’s water consumption. […]

Tigert, who sent the 2025 letter to QTS, said the utility didn’t know about the water hookups because the connection process “got mixed up” as the county transitioned to a cloud-based system while also trying to accommodate an industrial customer. Tigert also said her staff is small and at capacity. “Just like any water system, we don’t have enough staff. We can’t keep staff,” she said. “I’ve got one person that’s doing inspections and plan review, and so he’s spread pretty thin.” She said it’s possible her staff did know about hookups but that she hadn’t been able to locate the inspection report. “I may have hit ‘send’ too soon,” she said about the 2025 letter to QTS. While the utility charged the data center a higher construction rate for the unapproved water consumption, Tigert confirmed the utility did not penalize or fine the data center.

On Friday evening, the founder previewed a link to the newly redesigned Digg, which now looks nothing like a Reddit clone and more like the news aggregator it once was. This time around, the site is focused on ranking news — specifically, AI news to start. In an email to beta testers, the company said the site’s goal is to “track the most influential voices in a space” and to surface the news that’s actually worth “paying attention to.” AI is the area it’s testing this idea with, but if successful, Digg will expand to include other topics. The email warned that the site was still raw and “buggy,” and was designed more to give users a first look than to serve as its public debut.

On the current homepage, Digg showcases four main stories at the top: the most viewed story, a story seeing rising discussion, the fastest-climbing story, and one “In case you missed it” headline. Below that is a ranked list of top stories for the day, complete with engagement metrics like views, comments, likes, and saves. But the twist is that these metrics aren’t the ones generated on Digg itself. Instead, Digg is ingesting content from X in real-time to determine what’s being discussed, while also performing sentiment analysis, clustering, and signal detection to determine what matters most. […] The site also ranks the top 1,000 people involved in AI, as well as the top companies and the top politicians focused on AI issues.

What sounds like a chemical compound banned by the FDA may be the one true moat in AI. CUDA technically stands for Compute Unified Device Architecture, but much like laser or scuba, no one bothers to expand the acronym; we just say “KOO-duh.” So what is this all-important treasure good for? If forced to give a one-word answer: parallelization. Here’s a simple example. Let’s say we task a machine with filling out a 9x9 multiplication table. Using a computer with a single core, all 81 operations are executed dutifully one by one. But a GPU with nine cores can assign tasks so that each core takes a different column — one from 1x1 to 1x9, another from 2x1 to 2x9, and so on — for a ninefold speed gain. Modern GPUs can be even cleverer. For example, if programmed to recognize commutativity — 7x9 = 9x7 — they can avoid duplicate work, reducing 81 operations to 45, nearly halving the workload. When a single training run costs a hundred million dollars, every optimization counts.

Nvidia’s GPUs were originally built to render graphics for video games. In the early 2000s, a Stanford PhD student named Ian Buck, who first got into GPUs as a gamer, realized their architecture could be repurposed for general high-performance computing. He created a programming language called Brook, was hired by Nvidia, and, with John Nickolls, led the development of CUDA. If AI ushers in the age of a permanent white-collar underclass and autonomous weapons, just know that it would all be because someone somewhere playing Doom thought a demon’s scrotum should jiggle at 60 frames per second. CUDA is not a programming language in itself but a “platform.” I use that weasel word because, not unlike how The New York Times is a newspaper that’s also a gaming company, CUDA has, over the years, become a nested bundle of software libraries for AI. Each function shaves nanoseconds off single mathematical operations — added up, they make GPUs, in industry parlance, go brrr.

A modern graphics card is not just a circuit board crammed with chips and memory and fans. It’s an elaborate confection of cache hierarchies and specialized units called “tensor cores” and “streaming multiprocessors.” In that sense, what chip companies sell is like a professional kitchen, and more cores are akin to more grilling stations. But even a kitchen with 30 grilling stations won’t run any faster without a capable head chef deftly assigning tasks — as CUDA does for GPU cores. To extend the metaphor, hand-tuned CUDA libraries optimized for one matrix operation are the equivalent of kitchen tools designed for a single job and nothing more — a cherry pitter, a shrimp deveiner — which are indulgences for home cooks but not if you have 10,000 shrimp guts to yank out. Which brings us back to DeepSeek. Its engineers went below this already deep layer of abstraction to work directly in PTX, a kind of assembly language for Nvidia GPUs. Let’s say the task is peeling garlic. An unoptimized GPU would go: “Peel the skin with your fingernails.” CUDA can instruct: “Smash the clove with the flat of a knife.” PTX lets you dictate every sub-instruction: “Lift the blade 2.35 inches above the cutting board, make it parallel to the clove’s equator, and strike downward with your palm at a force of 36.2 newtons.”

Stenberg explained in a Monday blog post that he was promised access to Anthropic’s Mythos model - sort of - through the AI biz’s Project Glasswing program. Part of Glasswing involves giving high-profile open source projects access via the Linux Foundation, but while Stenberg signed up to try Mythos, he said he never actually received direct access to the model. Instead, someone else with access ran Mythos against curl’s codebase and later sent him a report. “It’s not that I would have a lot of time to explore lots of different prompts and doing deep dive adventures anyway,” Stenberg explained. “Getting the tool to generate a first proper scan and analysis would be great, whoever did it.”

That scan, which analyzed curl’s git repository at a recent master-branch commit, was sent back to him earlier this month, and it found just five things that it claimed were “confirmed security vulnerabilities” in cURL. Saying he had expected an extensive list of vulnerabilities, Stenberg wrote that the report “felt like nothing,” and that feeling was further validated by a review of Mythos’ findings. “Once my curl security team fellows and I had poked on this short list for a number of hours and dug into the details, we had trimmed the list down and were left with one confirmed vulnerability,” Stenberg said, bringing us back to the aforementioned number.

As for the other four, three turned out to be false positives that pointed out cURL shortcomings already noted in API documentation, while the team deemed the fourth to be just a simple bug. “The single confirmed vulnerability is going to end up a severity low CVE planned to get published in sync with our pending next curl release 8.21.0 in late June,” the cURL meister noted. “The flaw is not going to make anyone grasp for breath.”

GM reported employing about 68,000 salaried workers globally as of the end of last year, including 47,000 white-collar employees in the U.S. Despite Monday’s cuts, GM still is still hiring IT workers. The company has 82 open IT positions that include positions working in artificial intelligence, motorsports and autonomous vehicles, according to the automaker’s careers website.

Apple says that it worked with Google to lead a cross-industry effort to add E2EE to RCS. iOS users will need iOS 26.5, while Android users will need the latest version of Google Messages. End-to-end encryption is on by default, and there is a toggle for it in the Messages section of the Settings app. Encrypted messages are denoted with a small lock symbol. On iPhones not running iOS 26.5, RCS messages between iPhone and Android users do not have E2EE, but the new update will put Android to iPhone conversations on par with iPhone to iPhone conversations that are encrypted through iMessage.

Along with Google, Apple worked with the GSM Association to implement E2EE for RCS messages. E2EE is part of the RCS Universal Profile 3.0, published with Apple’s help and built on the Messaging Layer Security protocol. RCS Universal Profile 3.0 also includes editing and deleting messages, cross-platform Tapback support, and replying to specific messages inline during cross-platform conversations.

Speaking to graduates of University of Central Florida’s College of Arts and Humanities and Nicholson School of Communication and Media on May 8, commencement speaker Gloria Caulfield, vice president of strategic alliances at Tavistock Group, told graduating humanities students that AI is the “next industrial revolution,” and was met with thousands of booing graduates. “And let’s face it, change can be daunting. The rise of artificial intelligence is the next industrial revolution,” Caulfield said. At that point, murmurs rippled through the crowd. Caulfield paused, and the crowd erupted into boos. “Oh, what happened?” Caulfield said, turning around with her hands out. “Okay, I struck a chord. May I finish?” Someone in the crowd yelled, “AI SUCKS!”

Her speech begins around the hour and 15 minute mark in the UCF livestream. […] Before the industrial revolution comment, Caulfield praised Jeff Bezos for his passion and use of Amazon as a “stepping stone” to his real dream: spaceflight. Rattled after the crowd’s reaction, she continued her speech: “Only a few years ago, AI was not a factor in our lives.” The crowd cheered. “Okay. We’ve got a bipolar topic here I see,” Caulfield said. “And now AI capabilities are in the palm of our hands.” The crowd booed again. “I love it, passion, let’s go,” she said. “AI is beginning to challenge all major sectors to find their highest and best use,” she continued. “Okay, I don’t want any giggles when I say this. We have been through this before, these industrial revolutions. In my graduation era, we were faced with the launch of the internet.”

She goes on to talk about how cellphones used to be the size of briefcases. “At that time we had no idea how any of these technologies would impact the world and our lives. […] These were some of the same trepidations and concerns we are now facing. But ultimately it was a game changer for global economic development and the proliferation of new businesses that never existed like Apple and Google and Meta and so many others, and not to mention countless job opportunities. So being an optimist here, AI alongside human intelligence has the potential to help us solve some of humanity’s greatest problems. Many of you in this graduating class will play a role in making this happen.”

Google Threat Intelligence Group researchers detailed the development in a report released Monday. Zero-day exploits are considered the most serious type of security flaw because they are not detected by security companies and have no known fixes. The report noted that this was the first time Google had seen evidence of AI being used to develop these vulnerabilities — marking a major change in the cybersecurity landscape, as it suggests newer AI models could be used to create major exploits, not just find them.

Google concluded that Anthropic’s Claude Mythos model — which has already found thousands of vulnerabilities across every major operating system and web browser — was most likely not used to create the zero-day exploit. […] The Google Threat Intelligence Group report also details efforts by Russia-linked hacking groups to use AI models to target Ukrainian networks with malware, while North Korean government hacking group APT45 used AI technologies to refine and scale up its cyber methods.

Starting today, Apple requires shoppers in the United States to complete verification when making a purchase via the Education Store. This change also applies to Australia, Hong Kong, Turkey, Canada, and Chile. In many other markets around the world, such as the UK, Apple already required verification. As a refresher, people eligible for Apple’s Education Store include current and newly accepted college students and their parents, as well as faculty, staff, and homeschool teachers across all grade levels.

Apple is teaming up with UNiDAYS to handle the verification process. Students and educators will be asked to create a UNiDAYS ID and then verify their academic status by logging in to their school’s academic portal. Alternatively, users can upload a photo of their student or faculty IDs. Homeschool teachers, meanwhile, will need to provide an identity document such as a driver’s license, state ID card, or passport. They’ll also need to provide one homeschool document, such as a Letter of Intent (LOI) or Letter of Acknowledgment. Most customers will be verified instantly, and those requiring manual verification should hear back within 24 hours. The same verification process applies both in-store and online for Apple Education Store shoppers.

Fictional portrayals of artificial intelligence can have a real effect on AI models, according to Anthropic. Last year, the company said that during pre-release tests involving a fictional company, Claude Opus 4 would often try to blackmail engineers to avoid being replaced by another system. Anthropic later published research suggesting that models from other companies had similar issues with “agentic misalignment.”

Apparently Anthropic has done more work around that behavior, claiming in a post on X, “We believe the original source of the behavior was internet text that portrays AI as evil and interested in self-preservation.” The company went into more detail in a blog post stating that since Claude Haiku 4.5, Anthropic’s models “never engage in blackmail [during testing], where previous models would sometimes do so up to 96% of the time.”

What accounts for the difference? The company said it found that training on “documents about Claude’s constitution and fictional stories about AIs behaving admirably improve alignment.” Related, Anthropic said that it found training to be more effective when it includes “the principles underlying aligned behavior” and not just “demonstrations of aligned behavior alone.” “Doing both together appears to be the most effective strategy,” the company said.

Supporting those vintage GPUs without the Time Stamp Counter “TSC” instruction are becoming a burden… TSC-capable Intel Pentium processors and the likes will still be supported with this just being for TSC-less i586/i686 CPUs. Among the CPUs impacted by this latest change is the AMD K5 as well as various Cyrix processor models. The K5 was AMD’s first entirely in-house designed processor that was first introduced in 1996 to counter the Intel Pentium CPU.

Launch SKUs in 1996 were limited to clocks from 75 MHz to 133 MHz, and, due to being late, Intel’s Pentium line was already faster. AMD still managed to get an edge on the Cyrix 6x86, though.

After discontinuing the F-150 Lightning in December, sales of the electric pickup have been in free fall. Ford sold just 884 Lightnings last month, 49% less than it did last April. The Mustang Mach-E isn’t doing much better. Sales fell another 9% year over year in April, to just 2,670 models last month. Through the first four months of 2026, Ford’s EV sales have fallen 61% from last year, with F-150 Lightning and Mustang Mach-E sales down 67% and 50%, respectively. Ford has sold just over 10,500 electric vehicles in total so far this year… For comparison, Toyota sold just over 10,000 bZ models in the first quarter alone. That’s more than Ford’s total EV sales in Q1.

Jarczak’s fork of OrcaSlicer would have allowed users to bypass Bambu Connect, a middleware application that severely limits OrcaSlicer’s access to remote printer functions in the name of security. Jarczak said in a note on GitHub that Bambu Lab threatened him with a cease and desist letter and accused him of reverse engineering its software in order to impersonate Bambu Studio.

Bambu Studio is an open-source project under the AGPL-3.0 license. Anyone can take its code, modify it, and distribute it… That’s what OrcaSlicer does, and 734 other forks do as well. We have no issue with that and never have. At the same time, a license for code is not a pass to our cloud infrastructure… Our cloud is a private service. Access to it is governed by a user agreement, not the AGPL license… [T]he modification in question worked by injecting falsified identity metadata into network communication. In simple terms: it pretended to be the official Bambu Studio client when communicating with our servers… If this method were widely adopted or incorrectly configured, thousands of clients could simultaneously hit our servers while impersonating the official client.

I asked whether I could publish the private correspondence in full for transparency. That request was refused… They also referred to legal materials and stated that a cease and desist letter had been prepared…

I removed the repository voluntarily. That removal should not be interpreted as an admission that all legal or technical allegations made against the project were correct. I removed it because I have no interest in maintaining a prolonged dispute around this particular implementation, and no interest in continuing to distribute it.

A lot of users aren’t that good at predicting the future. They’re losing money at roughly the same rate as online gamblers betting on sports and other real-life events at traditional sportsbooks, according to the U.K. gambling regulator’s analysis of 2024 data. On Polymarket, the odds of making a profit are slightly higher on weather and tech markets — and a little lower on sports…

On Polymarket, just 1,200 people took more than half the profits — $591 million, or more than $100,000 each. [“The top 1% of users capture 76.5% of all trading gains,” the researchers write.] When you dabble in prediction markets, you’re competing against these sophisticated players who consistently win. Most of those 1,200 big winners didn’t place just a few smart bets. They appear to be pros making thousands of trades, mostly in the past year and a half, that were probably automated. One user made $3 million since January on more than a million trades about the Oscars, according to TRM Labs…

The most profitable participants are also just good at picking what to bet on, Akey found, winning so often it was statistically unlikely to be dumb luck. They had some sort of edge — expertise, deep research or, perhaps, inside knowledge.