AI News Archive: May 28, 2026 — Part 1
Sourced from 500+ daily AI sources, scored by relevance.
- As Anthropic launches Claude Opus 4.8, it raises $65B in new funding
Anthropic PBC today introduced a new large language model, Claude Opus 4.8, that’s significantly better than its predecessor at complex coding tasks. The company announced the LLM alongside another major business milestone. Anthropic has raised $65 billion in new funding at a $965 billion valuation to buy more computing infrastructure. The company evaluated Claude Opus […] The post As Anthropic launches Claude Opus 4.8, it raises $65B in new funding appeared first on SiliconANGLE .
Score: 88💰 MoneyMay 28, 2026https://siliconangle.com/2026/05/28/anthropic-launches-claude-opus-4-8-raises-65b-new-funding/ - Illinois set to OK regulatory framework for big AI companies, including independent safety audits
Illinois set to OK regulatory framework for big AI companies, including independent safety audits Chicago Tribune
Score: 85🌐 MovesMay 28, 2026https://www.chicagotribune.com/2026/05/28/illinois-artificial-intelligence-audit-bill/ - Ohio suspends data center tax break as tech firms face pressure to pay the cost to power AI
Ohio suspends data center tax break as tech firms face pressure to pay the cost to power AI Toronto Star
- ByteDance develops in-house CPUs for AI needs
The move comes as rising chip costs and persistent supply shortages increasingly challenge the company's expansion plans.
Score: 78🌐 MovesMay 28, 2026https://www.techinasia.com/bytedances-doubao-tops-120-trillion-daily-tokens - AI just changed everything about how we forecast the weather
Last October, days before Hurricane Melissa slammed into Jamaica, it wasn’t obvious how quickly the storm would intensify or the path it would take. But inside Google, an experimental AI model was spinning through dozens of scenarios, including the possibility that it might be the strongest hurricane on record to hit the island. Five days before the storm made landfall—while traditional weather models were undecided on whether it would weaken and turn in another direction—the AI model, called WeatherNext , predicted with 80% confidence that Melissa would rapidly intensify from a Category 1 storm to a Category 5 and land in Jamaica. Google sent its predictions to the U.S.’s National Hurricane Center, which used the models to help make a record-breaking high-intensity forecast. That early forecast “was critical,” says Evan Thompson, principal director of the Meteorological Service of Jamaica. “We want to get the information as soon as possible and then continuously drill that message to the public.” A Category 5 hurricane had never made landfall on the island. The weather office warned residents that anything they had experienced before “would pale in comparison,” Thompson says, and urged people to prepare however they could. Evan Thompson , principal director, Meteorological Service of Jamaica [Photo: Google DeepMind] When the storm finally hit, the forecast was correct. Wind speeds topped 131 miles per hour, and the damage was catastrophic: Roofs were torn off more than 120,000 buildings, and tens of thousands of others were destroyed, leaving families homeless. Forty-five people were killed. But the early warnings—and the fact that they stayed consistent as the storm approached—meant that people took them seriously, and likely saved additional lives. A Hurricane Melissa forecast visualization produced by Google [Image: Google DeepMind] The Google DeepMind model was more accurate than any other model the National Hurricane Center used during the storm. Now, as the new hurricane season begins on June 1, the NHC will work with Google again. Last year, the model ran a set of 50 possible futures every six hours; this year, it will look at 1,000 futures every six hours, making it even more likely that it can predict unusual storms. “This significant increase should provide more stable and consistent guidance,” says Philippe Papin, NHC senior hurricane specialist. Google is one of several companies working to use AI to reshape forecasting as weather becomes more extreme. That includes other large tech companies like Microsoft, Nvidia, and Huawei, and startups like Atmo, Tomorrow.io, and WindBorne—some of which are also collecting better data through cheap satellites or redesigned weather balloons. The AI tools are much cheaper and faster than traditional weather models. In many cases, they’re also more accurate. As climate change fuels stronger and less-predictable storms, forecasters are under pressure to issue accurate warnings earlier. And AI companies are eager to offer some of their compute to help, showcasing a use case for the technology that helps scientists and saves lives. The improved forecasts could also transform how businesses respond to weather risk—from rerouting packages during snowstorms to helping utilities better predict how much solar or wind power will be available on the grid—and expand the market for weather prediction, as more accurate, longer-term forecasts become valuable to a wider variety of companies. Nvidia powers the forecasting revolution For decades, weather forecasts have worked the same way: Expensive supercomputers, often owned by governments, run complicated physics-based models that try to mimic what’s happening in the atmosphere. AI works differently, using decades of past weather data to predict what will happen next. “It’s a completely new way to simulate the atmosphere,” says Mike Pritchard, an atmospheric physicist and director of climate simulation research at Nvidia. A decade ago, he says, experts in the field didn’t expect it to work as well as it does. But early research had suggested that even complex, chaotic systems like the weather could potentially be reproduced by machine learning, and academics with access to powerful Nvidia computers had shown that it was possible to train large, complex models. Inside the research departments of tech companies, working on the challenge of weather was a natural next step. [Image: Nvidia] Already, companies were working on AI videos—training models to predict the next frame of a video based on the previous frame. Weather is a more complicated problem, but it turns out the technology is transferable. At the time, “a lot of the progress in image and video emulation with AI was pretty low dimensional,” Pritchard says. “But AI researchers like challenges that feel like the future. A challenge back then was, could you possibly do video at the scale of thousands by thousands of pixels and dozens and dozens of channels?” Tech companies and academic researchers started to roll out the first major AI models for weather forecasting in 2022. Google DeepMind put out a model called GraphCast . Huawei, the Chinese tech giant, released Pangu-Weather , a model that looked at the atmosphere three-dimensionally. Nvidia, working with partners at the California Institute of Technology and Lawrence Berkeley National Laboratory, released a model called FourCastNet . Quickly, it became clear that forecasts from AI models could compete with traditional forecasting on accuracy—and even outperform the older models. The approach also has other advantages. AI can be 100 to 1,000 times more efficient to run than a supercomputer because it learns patterns from past data instead of repeatedly solving physics equations in millions of grid cells across the atmosphere. In the past, the cost of those supercomputers—which can run to tens or hundreds of millions of dollars—meant that poorer countries didn’t have their own. “Realistically, only about eight countries have had forecasting models,” says Julian Green, a serial entrepreneur who is now CEO of Brightband, an AI weather startup with a mission to democratize weather forecasting. “And they tend to be the richer countries. Both those models, and where the money has been spent making observations, mean that rich-country forecasts are much, much better than poor-country forecasts. There’s some data that the seven-day forecast for a rich country is better than tomorrow’s forecast for a poor country.” At first, AI forecasts were tested only in experiments. But last year, the European Center for Medium-Range Weather Forecasts rolled out the first operational forecast system powered by AI. For headline weather metrics, AI models are 10% to 20% more accurate than the best physics models. (Research is ongoing to compare the performance for specific weather phenomena, but for monsoons , for example, AI forecasts have far fewer errors than traditional forecasts.) It doesn’t work perfectly yet: One recent study highlighted the fact that AI can struggle to predict record-breaking weather, since it’s trained on data from the past. But researchers are testing ways to tweak it for the most extreme weather, such as training it not just with real-world data, but mixing in more extreme, theoretical examples. And the models continue to show radical improvement. In the past, weather forecasting improved slowly. Roughly every decade has yielded a day of accuracy, so a seven-day forecast now is about as accurate as a five-day forecast was 20 years ago. But AI is rapidly speeding up the pace of improvement. In a single generation of AI models, “we were able to make the progress that used to take a decade or more,” says Ferran Alet, a research scientist at Google DeepMind. Ferran Alet , research scientist, Google DeepMind [Photo: Google DeepMind] Traditional weather forecasts “have imperfect human assumptions about how some processes in the atmosphere work,” says Nvidia’s Pritchard, noting that we still don’t fully understand the physics behind weather formation. AI avoids those assumptions by learning only from data. And the models keep getting smarter. “A physicist can come in and poke it and look at the response and realize it’s learned physics,” Pritchard says. “You can see that by doing some credibility tests.” And figuring out how to model systems with “high-dimensional chaos” like the weather will help inform future innovation, like air quality predictions. The rate of change “is insane, to be frank,” says meteorological scientist Monte Flora, who previously worked on AI weather models for the National Oceanic and Atmospheric Administration (NOAA) before last year’s layoffs. He’s now developing an internal model for the Weather Co. “I kind of get overwhelmed as someone who tries to really stay up [to date].” It’s not clear exactly how far the technology can go; since weather is naturally chaotic, some errors are inevitable. But two-week forecasts are likely to keep getting much more accurate. As AI improves, better data is helping it forecast even further out. Michael Brennan , director of the National Hurricane Center [Photo: Google DeepMind] A new balloon could transform how business uses weather data Inside a factory in Redwood City, California, WindBorne is scaling its assembly line for its custom, 8-meter-tall weather balloon. The balloons are designed to stay aloft for weeks (a typical weather balloon, by contrast, might last only a couple of hours before popping) and navigate remotely by changing altitude to catch different wind streams, so they can gather much more data. The startup now has 400 balloons in the air at any given time, and is working to grow that number to 10,000. [Photo: WindBorne] Launched by Stanford grads in 2019, WindBorne started out collecting better data for traditional forecasting. Two years ago, the team began racing to also improve AI. Now almost all AI models are trained on a dataset called ERA5 , with decades of historical data reconstructed from weather observations and forecasting models. But ERA5 only estimates conditions at grid points roughly 25 kilometers apart. In the space in between, “there’s basically zero training data available,” says cofounder Kai Marshland. Because WindBorne’s balloons can fly much longer than traditional weather balloons, and also can be steered to specific locations if needed, it’s possible to cover the Earth in much more detail. The company now launches its balloons from strategically chosen sites around the planet. In South Korea, its balloons drift over the Pacific, providing crucial data before storms hit the West Coast of the U.S. At its latest launch site, in Uruguay, the company is providing the first sounding data—atmospheric measurements from a balloon—that has ever been collected in the area. [Photo: WindBorne] The combination of AI and unique data is important, says Bill Clerico, founder and managing partner of Convective Capital , a VC firm focused on wildfire tech , which invested in WindBorne in 2024. (For wildfires, the data from balloons can be critical both for prevention—for example, helping a utility company understand when it needs to shut off power lines—and for anticipating how a fire is likely to spread once it starts.) “There are a lot of companies that are just innovating at the software layer,” Clerico says. “I think those companies have some really difficult questions about what their long-term defensibility will be. The state of the art in models is changing so quickly. There are so many companies getting funded, so many companies building things. You can have the best AI model in the world right now, and then that could change 30 days from now.” [Image: Tomorrow.io] Real-time weather data plus agents can equal key business insights Tomorrow.io , another company in the space, is gathering data from its own fleet of 13 satellites that sample every point of Earth roughly every 60 minutes. “The gap that we saw is that 90% of the Earth, or more than 5 billion people, are blind to real-time weather data,” says CEO Shimon Elkabetz. Like sounding data from balloons, the company’s satellite data can help improve forecasts. At the same time, AI is making it possible to churn through the growing pile of measurements. “In the past it took an hour to six to process global physical models,” Elkabetz says. “With AI you can do it within a minute, if not seconds. And because of that you have the ability to consume in an efficient way much more data. . . . Now data becomes even more important.” [Image: Tomorrow.io] The company also builds AI agents for different use cases. For airlines, for example, it shares not just the forecast but also specific recommendations, such as how many deicing trucks need to be in place by a certain time to deal with snow at an airport. For a company like Uber, Tomorrow.io can recommend where drivers should be positioned before rider demand spikes because of a rainstorm. For companies delivering time-sensitive products like pharmaceuticals, the software can help shift logistics so deliveries arrive in advance of a storm. At a recent Formula One event, Tomorrow.io’s data helped change the schedule. Like Clerico, Elkabetz says that having the best AI model isn’t enough. “We have 30 PhDs in the company focused just on building AI models, and we do that day in and day out. But we remember that it’s not enough to create an impact,” he says. “Everybody has smart teams. Everybody’s building. I think what really matters is, how do you operationalize this model? How do you put it in the hands of the decision-maker so they can make the right decision in the right time?” Open science, private stakes For several of the companies racing to improve AI forecasting, part of the goal right now is to share the technology. Nvidia’s Earth-2, a set of forecasting tools released earlier this year that can process observational data and support forecasts up to 15 days in advance, is open source and available for anyone to use, from national governments to energy companies. [Image: Nvidia] “Nvidia isn’t a weather service provider,” Pritchard says. “We have no intention of becoming one. Our goal is to produce really great software—that happens to run great on our GPUs—to accelerate and stimulate this ecosystem, and do it transparently, so that everyone can exchange notes and everyone can experience the end-to-end process of training and testing and interrogating weather simulations.” He points out that the motivation is help the world better respond to extreme weather, and to grow broader AI adoption. Nvidia’s free model is designed to be a base that others can use to build a bigger ecosystem of models customized for different domains. A capture from Nvidia’s Earth-2 weather analytics [Image: Nvidia] The startup Brightband is using Nvidia’s tech, along with other AI models that are public, including those from Google and Microsoft. In turn, when it runs the models and creates forecast data, it shares the results so that anyone can see how AI is performing and where it needs to be improved. “The first thing that we think about is, we can’t be successful as a company if AI weather forecasting isn’t any good,” Green, Brightband’s CEO, says. “And we’re not the only ones who are going to be able to make that happen. So we want lots of smart people to come into weather forecasting and bring AI into it, and we open source a bunch of stuff and try and increase innovation in general.” Google, like others in the space, has published papers about its innovations in weather forecasting so that others can learn from them. “I think all the best [models] are published scientific papers, and give all the details,” says Peter Battaglia, senior director of research at Google DeepMind. “They’re mostly open-source code.” For tech companies, it’s partly a way to build reputation and prove the potential of AI. As the models continue to be proven and accepted, there’s obvious economic opportunity for the companies building them, and there are questions about how much may be privatized. On the data side, when the Trump administration cut hundreds of jobs at NOAA in 2025, and others left through early retirements and resignations, the agency started to buy some data from companies like WindBorne. On the AI side, as forecasting becomes less dependent on expensive supercomputers and can be run on relatively cheap AI models, it’s possible to imagine private companies eventually playing a bigger role. And with that, there’s some risk that the best forecasts will be available only to those who can pay for them. For now, there’s a clear upside to the changes. In India, AI forecasting is helping predict monsoons and giving farmers forecasts about the optimal times to sow fields or use fertilizer. (A University of Chicago study found that giving an accurate monsoon forecast has helped some farmers nearly double their incomes.) In sub-Saharan Africa, which historically had very little weather data, WindBorne worked with the Gates Foundation to gather data more cost-effectively than was ever possible before, so it can be used in AI weather forecasts and give farmers advance warning about extreme weather. Tomorrow.io is working with farmers in Kenya and the Philippines and with governments in Indonesia and Thailand. Setting up a state-of-the-art national weather forecasting system no longer requires large teams and expensive equipment; an AI system can run on a relatively inexpensive inference unit like Nvidia’s DGX Spark, which costs roughly $4,500. Training the AI still requires more equipment, but that’s happening at a global scale, and countries can use that data without needing to retrain the AI themselves. “There’s such a huge opportunity in the developing world,” Pritchard says. “It used to take a small legion of people to stand up the personnel and the computing that was required to do niche weather forecasting. But that’s totally changed in the age of AI.”
- Medidata’s Next-Gen AI Imaging Solution Delivers Unprecedented Speed and Precision for Clinical Trials
Medidata’s Next-Gen AI Imaging Solution Delivers Unprecedented Speed and Precision for Clinical Trials Toronto Star
- Microsoft to Release New Coding Model Next Week in Comeback Attempt
Microsoft to Release New Coding Model Next Week in Comeback Attempt The Information
Score: 72🤖 ModelsMay 28, 2026https://www.theinformation.com/newsletters/ai-agenda/microsoft-release-new-coding-model-next-week-comeback-attempt - Microsoft Build 2026 Preview: The AI Takeover of Windows Has Officially Begun
Microsoft Build 2026 Preview: The AI Takeover of Windows Has Officially Begun PCMag
Score: 67🌐 MovesMay 28, 2026https://www.pcmag.com/news/microsoft-build-2026-preview-the-ai-takeover-of-windows-has-officially - Apple working to cram massive Gemini model into iPhone to power new Siri
As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.
- Penn Medicine, K Health partner to deploy AI clinical agents
The agents will first be deployed across the system’s virtual urgent care program, with plans to expand to in-person primary care and certain specialties.
- CNN sues Perplexity over ‘verbatim’ copycat articles
Perplexity is accused of scraping CNN’s work without permission and providing users with content locked behind its paywall.
Score: 64🌐 MovesMay 28, 2026https://www.theverge.com/ai-artificial-intelligence/938893/cnn-perplexity-ai-copyright-lawsuit - IBM, Red Hat Pledge $5 Billion for AI-Driven Open Source Security Initiative
Under the new project, dubbed Project Lightwell, the companies said they will deploy a global force of 20,000 engineers, supported by advanced AI, to establish a trusted enterprise clearinghouse.
- Apollo, Blackstone work on $36 billion debt deal for Anthropic, Bloomberg News reports
Apollo, Blackstone work on $36 billion debt deal for Anthropic, Bloomberg News reports Reuters
- Anthropic Says a Mythos-Class AI Model Will Be Available Soon
The new Claude Opus 4.8 is a "modest but tangible improvement," but a Mythos model you can use may be just weeks away.
Score: 63🤖 ModelsMay 28, 2026https://www.cnet.com/tech/services-and-software/anthropic-claude-opus-4-8-release-mythos-class-ai-model-soon/ - Meta Tests AI Subscriptions and Rolls Out New Paid Plans for Facebook, Instagram
The company is seeking to supplement its advertising business as the costs of its AI buildout mount.
Score: 63🌐 MovesMay 28, 2026https://www.wsj.com/tech/meta-starts-rollout-of-subscriptions-for-facebook-instagram-9f00821f?mod=rss_Technology - How Bayesian Health’s Sepsis AI Tool Is Decreasing Alerts & Saving Lives
How Bayesian Health’s Sepsis AI Tool Is Decreasing Alerts & Saving Lives MedCity News
- TikTok owner ByteDance said to weigh spending as much as $89 billion in AI push
TikTok owner ByteDance said to weigh spending as much as $89 billion in AI push The Straits Times
- Waymo’s newest robotaxi is Chinese-made, built to make money, and now accepting riders
The launch of the Ojai minivan robotaxi comes after years of development and testing, but arrives amid a challenging time for Waymo.
- Beyond the EHR: Why one CIO believes AI will dwarf every prior health IT shift
Beyond the EHR: Why one CIO believes AI will dwarf every prior health IT shift Healthcare IT News
Score: 61🌐 MovesMay 28, 2026https://www.healthcareitnews.com/news/beyond-ehr-why-one-cio-believes-ai-will-dwarf-every-prior-health-it-shift - YouTube to begin automatically detecting, labeling photorealistic AI content
The YouTube app is displayed on an iPad in Baltimore on March 20, 2018. [AP/YONHAP] YouTube will begin automatically tagging videos that contain substantial AI content, adding a detection layer on top of the self-disclosure system that it has used since 2024, the company announced on Wednesday. While YouTube will still require creators to manually flag their use of AI, the company's in-house system will start to identify videos with a significant amount of photorealistic AI material and apply a label to them, even when their uploader does not. The autodetection system is set to roll out this month. Related Article Chef Anh Sung-jae suspends YouTube activities after restaurant's wine service controversy YouTube 'drunk driving hunter' and subscribers sentenced for causing fatal crash Court orders YouTube channel Sojang operator to pay $115,000 to SM Entertainment over defamatory videos “We've heard consistently from our community that they value transparency when it comes to generative AI content,” the YouTube team said in a blog post announcing the change. The platform is also consolidating its various AI disclosures into a single label and giving it more visible placement. For long-form videos that are detected to have been photorealistically altered or generated by AI, a label indicating AI use will now appear directly below the video player and above the description. A screenshot from YouTube's official blog, announcing the new AI labeling system [SCREEN CAPTURE] On Shorts, YouTube's short-form vertical video service, the label will appear as an overlay on the video itself. Disclosures for animated, unrealistic or lightly altered content will continue to be located within the expanded description. Uploaders can update their settings in YouTube Studio if they believe their content was wrongly identified as AI-generated. The label will, however, remain permanently in place for videos made with YouTube's in-house AI tools, Veo and Dream Screen, and for content whose C2PA metadata identifies it as fully AI-generated. C2PA is an industry-standard system for digitally certifying the origin and editing history of content. The AI label does not affect how a video is recommended or whether it is eligible to earn money, according to YouTube. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom. BY KIM JI-HYE [cho.yongjun1@joongang.co.kr]
- AI-powered atlas reveals new insights into tertiary lymphoid structures as prognostic and response biomarkers in cancer
AI-powered atlas reveals new insights into tertiary lymphoid structures as prognostic and response biomarkers in cancer EurekAlert!
- Waymo introduces new vehicles: ‘Living room on wheels’
The new vehicles share some similarities with Waymo competitor Zoox.
- This map tracks 4,000 AI data centers being built—and reveals where the biggest boom is
Environmental activist Erin Brockovich has launched a crowdsourced map that tracks more than 4,000 major AI data centers across the United States. The Brockovich AI Data Center Reporting website aims to give the public “a platform to speak up and voice concerns about AI data centers in their communities” by naming where they are “already operating, under construction, rumored or proposed projects . . . focusing on locations where communities are actively voicing concerns.” “The RACE to build AI infrastructures is unfolding town by town across America,” Brockovich said on the website. “In some places, data centers are welcomed. In others, they are delayed, contested or abandoned altogether. This MAP captures the real-world footprint of that race—revealing patterns of growth, conflict and uncertainty.” Brockovich rose to fame after actor Julia Roberts played her in the 2000 movie Erin Brockovich , based on the activist’s relentless attempts to help a small California community hold the Pacific Gas and Electric Co. (PG&E) accountable for polluting the local water supply . That work eventually led to a class-action lawsuit settlement of $333 million on behalf of the affected plaintiffs. [Screenshot: Brockovich AI Data Center Reporting ] Scientists and community activists argue that massive data centers, needed to power Big Tech’s AI boom, create a whole host of problems that drain local communities by raising energy demand and utility bills, tapping water supplies, and polluting the environment. They also argue that the data centers are disproportionately located in lower-income areas. As Fast Company recently reported , a new study from Arizona State University shows data centers also create “ heat islands ,” making already-warm American cities even hotter by as many as 4 degrees. Where are the AI data centers located? The map reveals that of the current 2,716 crowdsourced data center reports to date, a majority are in Texas (612), trailed by Pennsylvania (195), Ohio (155), and Georgia (126). Here’s a breakdown of the U.S. locations with the most AI data centers, ranked in order, according to the map: Sulphur Springs, Texas (297) Lusby, Maryland (36) Box Elder County, Utah (31) Archbald, Pennsylvania (30) Abilene, Texas (18) Madison, Indiana (16) Fort Meade, Florida (14) Lebanon, Indiana (13) Lufkin, Texas (13) Maysville, Georgia (12) Based on the Brockovich AI Data Center Reporting’s crowdsourced data, most people are concerned about how the centers will affect their community’s water supply (41.2%), electric supply grid (22.2%), and overall health (18.1%). Check out the full interactive map and breakdown here .
- Taiyo Yuden Sees ‘Scary’ AI Demand Straining Supply Chain
Taiyo Yuden Co. is fielding “scary” levels of demand for its high-end AI server components, stretching capacity and increasing the risk of supply chain hiccups.
- Corgi announces $106M raise at $2.6B valuation — double what it was worth 3 weeks ago
While startups raising back-to-back rounds at steep step-ups have become almost routine, a company whose valuation doubles in three weeks is unusual enough to raise questions, particularly given the investor set in both rounds is the same.
Score: 59💰 MoneyMay 28, 2026https://techcrunch.com/2026/05/28/corgi-announces-106m-raise-at-2-6b-valuation-three-weeks-after-160m-series-b/ - The $6 Billion Chinese Startup Trying to Build Hands for Every Robot
LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories—and eventually replace human labor altogether.
Score: 58🌐 MovesMay 28, 2026https://www.wired.com/story/made-in-china-the-dollar6-billion-chinese-startup-making-hands-for-humanoids/ - Report: Apple Plans to Make On-Device AI a Key WWDC Focus
Apple reportedly plans to use next month's Worldwide Developers Conference (WWDC) to highlight its on-device AI capabilities as a competitive advantage, leaning on 15 years of custom silicon expertise to make the case for running AI models locally rather than in the cloud. People familiar with Apple's plans speaking to The Information say the company is expected to showcase how the chips designed for iPhones, Apple Watches, and Macs give it an edge in processing AI queries directly on devices. While cloud-based processing will remain necessary for complex queries, Apple will position local inference as a privacy-preserving, cost-saving alternative to the massive data center buildouts its rivals have pursued. As part of its agreement with Google , Apple is apparently set to use a large version of Google's Gemini model to train a smaller, distilled version capable of running locally on Apple hardware. Apple is also said to be scouting acquisitions to help advance its model-shrinking work, with one company it has reportedly considered being Liquid AI, a Massachusetts startup focused on running AI locally on devices. Some queries will still require cloud processing. Apple is believed to have approved the use of Nvidia's confidential compute technology within Google Cloud to handle processing of the larger Gemini-based model. The security feature encrypts data and AI models during processing, adding a modest performance cost but offering stronger privacy protections. The arrangement represents a noticeable departure from Apple's original Apple Intelligence announcement, in which the company said all cloud-bound queries would be handled exclusively by its own Private Cloud Compute infrastructure running on Apple silicon. Apple is likely to retain the Private Cloud Compute branding despite the change, people familiar with the partnership told The Information . There are also said to be material limits to how far Apple can push on-device processing. Google's full Gemini model runs into the trillions of parameters, and The Information claims that Apple has struggled to run it on its own Private Cloud Compute infrastructure, which uses the same Apple silicon chips found in Mac computers. Apple Intelligence was first announced at WWDC 2024 , but the rollout has been hampered by a tepid response to initial features and a protracted delay to the more personal version of Siri . Apple is now expected to use WWDC 2026 , which runs from June 8 to reframe the narrative, reintroduce the delayed features, and debut new ones. Related Roundup: WWDC 2026 Tags: Apple Intelligence , The Information , WWDC 2026 Related Forum: Apple, Inc and Tech Industry This article, " Report: Apple Plans to Make On-Device AI a Key WWDC Focus " first appeared on MacRumors.com Discuss this article in our forums
Score: 58🌐 MovesMay 28, 2026https://www.macrumors.com/2026/05/28/apple-to-make-on-device-ai-key-focus/ - A new AI model enables more efficient analysis of colorectal cancer samples
A new AI model enables more efficient analysis of colorectal cancer samples EurekAlert!
- UK cyberspying chief calls AI ‘an unstoppable force’ and warns about Russia
UK cyberspying chief calls AI ‘an unstoppable force’ and warns about Russia AP News
Score: 58🌐 MovesMay 28, 2026https://apnews.com/article/uk-cyberattacks-warning-gchq-russia-china-iran-d454c58bff93e60189c8816ccf3d41da - Fujitsu partners with Anthropic to drive AI transformation in Japan
Fujitsu partners with Anthropic to drive AI transformation in Japan IT Pro
- Korean tech giants in talks with gov't to join Anthropic's Project Glasswing in face of 'Mythos shock'
Silhouettes of laptop users are seen next to a screen projection of binary code in this illustration created on March 28, 2018. [REUTERS] Korea’s major tech firms are in discussions with the government about joining Project Glasswing, a cybersecurity initiative launched by U.S. AI giant Anthropic in response to the potentially disruptive capabilities of its own AI model, Mythos. The consultations come as the country became the latest to join OpenAI's Government and Institutional Trust-Based Access Program alongside Japan, which specializes in AI-powered cyber defense. Related Article Cybersecurity in 'turbulent transition period' as AI changes game, KAIST professor says The butterfly effect of the Anthropic contract termination Rise of AI raises fears of North Korean hacking capabilities “We are reviewing various measures to build a joint response framework for frontier AI models like Mythos, including discussions with government agencies and participation in domestic and overseas AI security cooperation projects such as Project Glasswing,” said a source at one of Korea's major tech companies, who spoke on the condition of anonymity as the discussions are ongoing. Ryu Je-myung, second vice minister of the Ministry of Science and ICT, far right, meets with OpenAI officials to discuss cooperation in responding to AI security threats at the Three IFC building in Yeouido, western Seoul, on May 26. [NEWS1] Mythos can detect flaws in what had been believed to be unbreakable code, meaning that in theory, a single prompt by a bad actor could have devastating repercussions. Anthropic claims that the impact could be so immense that it withheld Mythos from public access, limiting access only to select participants under the Glasswing network. Out of 1,000 open-source projects, which according to Anthropic “collectively underpin much of the internet,” the model found what it estimated were 6,202 high-or critical-severity vulnerabilities in these projects. Anthropic is accelerating its entry into Korea with the appointment of Choi Ki-young as Representative Director of Korea, a sign of increased focus and the growing popularity of their pre-existing Claude model in the Korean market. The Anthropic logo is seen in this illustration created on March 1. [REUTERS] Korean tech wants in Industry insiders project that interested firms may include Samsung Electronics, LG Electronics and SK hynix as well as financial firms, as their massive trove of intellectual property and privacy-related data could become subject to the growing security threats presented by advanced AI systems. The logo of Samsung Electronics is seen on a gate at the company's headquarters in Suwon, Gyeonggi, on May 22. [AP] Project Glasswing currently has approximately 50 partners, according to Anthropic, including firms such as Google, Nvidia, Microsoft and Apple. No Korean companies are currently included, according to multiple industry sources. The Japanese government is already scheduled to obtain Mythos in late May, followed closely by its megabanks Mitsubishi UFJ Financial Group, Mizuho Financial Group and Sumitomo Mitsui Financial Group, while Korean tech and finance firms are still in limbo. Still, the Korean government has so far adopted a measured approach despite calls from companies seeking quicker government backing for participation in the Anthropic project. “Past large-scale hacking incidents have shown that cybersecurity risks can no longer be confined to individual companies and may quickly escalate into national security concerns, underscoring the urgency for the government to establish AI-driven cyber threat responses, including potential participation in Project Glasswing,” the source said. An executive at a major Korean chipmaker raised the need for the government to play a leading role. “There are clear limits to how individual companies can respond on their own. Our responses are confined to monitoring of the news across the globe. Close coordination with the government is essential,” the executive said. The logo of SK Hynix is seen on one of its products during the 26th Semiconductor Exhibition in Seoul in 2024. [Reuters] A spokesperson from the Science Ministry told the Korea JoongAng Daily there were no regular meetings between the government and Korean tech companies to consult on discussions with Anthropic and OpenAI. Korea vulnerable to AI weaponization Kim Seung-joo, a professor of cybersecurity at Korea University, said Korea is “structurally more vulnerable” to what he called a “Mythos shock” while speaking at a seminar focused on AI defense on May 13 at Korea University. “Technology is not the problem,” said Prof. Kim. “We can acquire the technology. The problem is that the ecosystem and culture needed to actually utilize such technology is where Korea is more vulnerable.” Prof. Kim was referring to the lengthy chain of regulatory frameworks that slowed the government's response to cybersecurity issues. With a single malicious prompt, Mythos could potentially bring down large-scale security apparatuses in both public and private sectors within just 10 hours. Korea's financial sector is especially susceptible because it adopts a network separation rule as part of its current cybersecurity approach. Internal corporate networks are segregated from public internet networks, meaning that inside companies, people cannot use third-party AI models, which hampers their ability to respond to digital threats. A spokesperson at Hana Bank said AI tools such as Mythos cannot be put into action even if Anthropic granted access, because government regulation comes first, echoing Prof. Kim's remarks. “In Korea, banks first need regulatory approval before using these kinds of systems. The domestic financial sector operates under a licensing and approval framework. That means we need to comprehensively review all related restrictions and regulatory considerations. So we are not really at a stage where we can definitively say yes or no. It’s not at the stage where the bank can officially discuss plans one way or the other.” A currency trader watches monitors near a screen showing the Korea Composite Stock Price Index (Kospi) at the foreign exchange trading room of Hana Bank's headquarters in Jung District, central Seoul, on July 9, 2025. [AP] The spokesperson said “extensive discussions” generally take place with regulators and government authorities first. He said the financial industry was “unique” in that regard. Prof. Kim said Korea's financial sector was the most inflexible to rapid change and response, after government and public sectors. “The private sector, including telecoms, is equally inflexible,” said Kim. Korea and Japan are home to some of the largest tech and banking sectors in the world outside of the United States, making digital defense paramount in an AI boom. Governmental response lags Science Minister Bae Kyung-hoon, together with Foreign Affairs Minister Cho Hyun, National Intelligence Service Director Lee Jong-seok and other agency heads met on May 11 to “explore avenues for cooperation with Anthropic in the areas of artificial intelligence and cybersecurity — with the aim of securing AI safety and trust and promoting the domestic AI industry,” according to a statement released by the Science Ministry. Government officials “engaged in in-depth discussions on various cooperation topics” with Anthropic representatives, including Michael Sellitto, head of global affairs. A Science Ministry Spokesperson said the ministry could not disclose further discussions around access to Project Glasswing, only that there would be an announcement “if Korea decides to come on board.” OpenAI CEO Sam Altman attends a session with SoftBank group Chairman and CEO Masayoshi Son in Tokyo on February 3, 2025. [AFP] The Science Ministry has gone on to add that Mythos-specific measures would be announced sometime in the near future. Japan and Korea are unique in the fact that, outside of the United States, they house some of the biggest tech firms that are also globally dominant in their industries. Because being part of Project Glasswing acts almost like being on a “world's richest” list, these firms feel they’ve earned a spot. The Japanese government and banking sector have seemingly been granted access to Mythos, even as Korea held largely similar talks with Anthropic's executives days before that did not reach the same result. Korea's regulatory rigidity seems to be the reason for the delay. The Japanese Financial Services Agency (FSA) held a working group meeting on April 24. The purpose of the working group was to “deepen discussions to ensure that the financial industry, IT service providers and relevant public bodies — including the government and the Bank of Japan — share a common understanding of threats arising from advances in AI technology and jointly consider appropriate responses,” according to a statement from the FSA. While Korea's neighbor makes progress, its own cybersecurity environment remains structurally vulnerable, lacking not only a comprehensive legal framework but also sophisticated systems. “Korea lags behind major economies in areas such as the absence of a comprehensive cybersecurity law, with the lack of advanced systems that automatically match cyberthreats with affected IT assets and mandatory SBOMs [software bills of materials],” said Lee Sang-kyun, head of the AI Security Research Institute at Korea University, during a seminar hosted by the National Assembly Research Service. BY FERGUS GOODALL SMITH, PARK EUN-JEE [fergus.gs@joongang.co.kr]
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Mistral AI used its inaugural conference on Wednesday to announce a sweeping expansion into industrial manufacturing, a new inference data center south of Paris, and a rebranding of its consumer-facing assistant — moves that collectively signal the three-year-old French startup's ambition to become the enterprise AI provider of record for companies that refuse to hand their most sensitive data to American hyperscalers. At the AI NOW Summit , held at a venue in central Paris, co-founder and CEO Arthur Mensch took the stage alongside CTO Timothée Lacroix and Chief Scientist Guillaume Lample to lay out a strategy that stretches from bare-metal GPU clusters to physics simulations for aircraft wings. The company disclosed that it now employs 1,000 people and is targeting €1 billion ($1.17B USD) in revenue for 2026 — a figure that, if achieved, would be an extraordinary growth trajectory for a company that began with 15 employees collaborating with its first customer, BNP Paribas, in 2023. "We have two convictions at Mistral," Mensch told the audience. "The first is that in order to deploy AI in the enterprise, you actually need, as an AI provider, to own the full stack." He described Mistral's business as fundamentally about "transforming electrons into tokens and intelligence," arguing that physical infrastructure control matters as much as model quality. The announcements come at a pivotal moment for Mistral and for the broader European AI ecosystem. The company has raised at least $3.9 billion across nine funding rounds, according to Clay's funding tracker, including a massive €1.7 billion Series C led by Dutch semiconductor equipment maker ASML in September 2025 at an €11.7 billion valuation, and an $830 million debt financing round in March 2026 from a consortium of seven banks to fund data center construction. Mistral now finds itself in a peculiar competitive position: too large to be dismissed as a research lab, but still dwarfed by the resources of OpenAI , Google DeepMind , and Anthropic . Its answer, articulated across nearly an hour of presentations Wednesday, is vertical depth — going industry by industry, workflow by workflow, and building the infrastructure to keep everything on premises. Why Mistral is betting that physics AI will reshape how Airbus and BMW design products The centerpiece announcement was Mistral for Industrial Engineering , a fully integrated AI stack that combines Mistral's large language models with physics simulation capabilities acquired through its purchase of Emmi AI , completed earlier in May 2026. The platform targets the aerospace, automotive, and semiconductor industries with tools for accelerating product design, validating simulations, and optimizing production. The launch came with headline partnerships. Mistral announced it is working with Airbus across its commercial aircraft, helicopter, defense, and space divisions, implementing AI from initial design through to on-board capabilities. For BMW Group , Mistral is serving as a central partner for what the automaker calls its "Large Industry Model" initiative, focused on multimodal reasoning models for crash simulation and other complex engineering tasks. ASML, already Mistral's largest shareholder, is also an early adopter. Mensch framed the industrial push as addressing a fundamental gap in how AI is currently deployed. "AI is great today at automating tasks for knowledge workers and for people that are doing software engineering," he told the summit audience. "But once you move to all the kind of engineers, well, they are underserved." The reason, he explained, is structural. Simulating the behavior of a wing or a factory process requires compute-intensive physics solvers that can take hours or weeks per design variant. Traditional simulation creates a bottleneck that makes AI-assisted iteration impractical. Mistral's answer is what it calls " physics AI " — data-driven models trained on solver outputs that can predict physical behavior in seconds rather than hours, running on a single GPU. As Mistral's own blog post on the technology acknowledges, physics AI is "not a replacement for first-principles solvers in every regime" — it is a throughput accelerator for the majority of design-loop iterations, with traditional solvers reserved for verification and edge cases. "We now have both the language intelligence and the physical intelligence models, and by combining them together we are building delegation loops that allow us to create better tools, that allow us to create better objects that actually have an impact on the physical world," Mensch said. The ASML partnership offered a concrete illustration. In a video testimonial shown at the summit, an ASML representative described how the company's lithography machines run around the clock at customer fabrication plants, and field service engineers need to diagnose issues as rapidly as possible. By combining ASML's internal engineering expertise with Mistral's models, "we were able to develop a solution that's 120 times faster with a similar accuracy as we have today," the representative said. Another ASML speaker described AI agents acting as "an always-on code reviewer" to catch software defects before they reach customers. Inside Mistral's €4 billion infrastructure gamble to build Europe's most powerful AI data centers Mistral's full-stack ambitions extend all the way down to the physical layer. Launched in June 2025, Mistral Compute is a €4 billion ($4.66B USD) investment in data centers in France and Sweden, with a stated roadmap of 200 MW of capacity by 2027 and 1 GW by 2030. Lacroix described the company's existing 40 MW facility at Bruyères-le-Châtel, south of Paris, which was built in collaboration with Eclarion and has been training models since early 2026. "It's been very interesting to see how we can transfer rigor, which is one of our company values, into down to the hardware layer," he said, describing the process of "fixing compute trays and fixing fibers, allowing us to reach the very best speeds possible on that hardware for training." On Wednesday, Mistral announced a new 10 MW facility at Les Ulis in the Essonne department, also south of Paris, dedicated to inference operations and scheduled to open in Q3 2026. Lacroix also referenced a site in Borlänge, Sweden, planned for development through 2027, which will host NVIDIA's next-generation Vera Rubin GPUs. "One of the benefits for us of owning the hardware layer is also that it lets us be at the very bleeding edge of what infrastructure provides," he told the audience. The infrastructure push is funded in part by the $830 million debt financing round announced in March 2026, which Clay's funding tracker attributes to a consortium of seven banks: Bpifrance, BNP Paribas, Crédit Agricole CIB, HSBC, La Banque Postale, MUFG, and Natixis CIB. And this infrastructure ownership is not merely a hedge against GPU scarcity — it is central to Mistral's pitch to security-conscious enterprise and government customers. The company's February 2026 acquisition of serverless platform Koyeb has been integrated into Mistral Studio to support both hosted and on-premises deployments, giving customers a choice between running inference on Mistral's hardware or their own. "More and more, the compute world has been getting supply constrained," Lacroix told the audience. "One of the reasons we've been doing all of this and developing all of this data center capacity is to secure compute capacity not only for ourselves but also for our customers." Le Chat is dead, long live Vibe: How Mistral's new agent platform takes aim at enterprise productivity In a consumer-facing rebrand with significant enterprise implications, Mistral announced that Le Chat — its conversational AI assistant launched in February 2024 — is being renamed Vibe and reimagined as a unified agent platform for enterprise productivity and software development. "We are transitioning Le Chat to the Vibe family," Lacroix told the audience, explaining that the evolution was driven by the growing power of agentic models, particularly the new Mistral Medium 3.5. As the team used Vibe's coding CLI internally with increasingly complex tasks, "we realized that this really didn't need to be bound to the CLI, it didn't need to be limited to code, and we could do a lot more with it," he said. Vibe encompasses two primary modes. Vibe for Work is a web and mobile agent that connects to enterprise tools — Google Workspace, Outlook, SharePoint, Slack, GitHub — to perform multi-step tasks such as summarizing emails, analyzing spreadsheets, drafting reports, and scheduling recurring workflows. Vibe for Code is a coding agent available through a web interface, a new VS Code extension, and the existing CLI, capable of building features, fixing bugs, refactoring code, and shipping pull requests. Critically, the same underlying agent powers both modes. "When you access it through our web app or through the CLI, you have access to the same connections, the same tools, the same understanding of who you are, what you do, and what you're trying to achieve," Lacroix said. Pricing starts at free for basic use, $14.99 per month for Pro, $24.99 per user per month for Teams, and custom pricing for Enterprise deployments. Alongside Vibe, Mistral also launched Search Toolkit, an open-source framework for building production search pipelines already in use by shipping giant CMA CGM, which uses it alongside Voxtral to process audio from multiple data sources and return alerts within 15 seconds. Mistral's model strategy signals a new phase: fewer products, more capabilities per model Chief Scientist Guillaume Lample used his portion of the keynote to describe a philosophical shift in Mistral's model strategy: consolidation of capabilities into fewer, more versatile models rather than maintaining separate specialized products. Mistral Medium 3.5 , the company's current flagship, absorbs capabilities that previously required distinct models. Pixtral (image processing), Magistrale (reasoning), and DevStral (coding) have all been deprecated as standalone products, with their capabilities folded natively into Medium 3.5. "Now all our models are natively multimodal," Lample said. "We no longer have Magistrale. This model is deprecated, because all our models will natively be doing reasoning." The company is also working on Mistral Large 4 , which Lample said would arrive "in a couple of months at most, during the summer," with expanded capabilities in industrial applications such as fluid dynamics, computational chemistry, computer-aided design, and cybersecurity. On the smaller end of the spectrum, Lample highlighted Mr. Lossier, a 1-billion-parameter OCR model that can process thousands of pages per minute on a single GPU, and the Voxtral speech model family, which has expanded from automatic speech recognition to include text-to-speech with voice cloning. A "duplex" model for real-time conversational speech is planned for release within months. Lample also made the case for open-weight models becoming more — not less — important in the agentic era. "Today we are building these agentic workflows, these models are running in the background, they are doing a lot of actions, a lot of tool calls, so they are extremely token-hungry, much more than before," he said. "What we are seeing today is actually a comeback of this small model and the efficient model." Upcoming models will be trained on more than 200 languages, a multilingual strength now powering a partnership with Amazon to improve non-English interactions on Alexa+. How Mistral's enterprise playbook stacks up against OpenAI and Anthropic Mistral's positioning stands in sharp contrast to the strategies of its most prominent American rivals. While OpenAI and Anthropic have each attracted hundreds of millions of consumer users and derive significant revenue from subscription products, Mistral has leaned almost entirely into enterprise and government deployments. As TechCrunch reported in March when Mistral announced its Forge customization platform at Nvidia GTC, CEO Mensch has described the company as being " on track to surpass $1 billion in annual recurring revenue " — a figure driven largely by corporate clients. The Forge platform , which lets enterprises train custom models on their own data rather than simply fine-tuning or applying retrieval-augmented generation to existing models, represents the foundation on which the company's industry-specific solutions are built. As Mistral's head of product, Elisa Salamanca, told TechCrunch, Forge "lets enterprises and governments customize AI models for their specific needs." Early partners include Ericsson, the European Space Agency, Italian consulting company Reply, and Singapore's DSO and HTX, alongside ASML. Mistral has also built an expanding network of systems integration partnerships to drive enterprise adoption. In February 2026, Accenture and Mistral announced a multi-year strategic collaboration , with Accenture itself becoming a Mistral customer. Mauro Macchi, Accenture's CEO for Europe, Middle East, and Africa, said at the time that the partnership brings together "sovereign models and the capability to scale technology across industries, geographies and business functions." The BNP Paribas relationship offers the most detailed public case study. In a video testimonial at the summit, a BNP Paribas representative described deploying Mistral's models on-premises to satisfy strict security requirements, developing AI agents for KYC processes that reduced incomplete files from 80% to 10% and compressed processing time from weeks to days. The bank's LLM platform at its Corporate and Institutional Banking division has now rolled out to 65,000 users. Mensch noted the significance: "We started to collaborate in 2023 where we were 15 people, so that was, I think, really a leap of faith at the time." The industrial vertical is also being extended to government clients. Mistral disclosed that it is working with France, Luxembourg, Singapore, Morocco, Greece, and Slovakia to build citizen-facing AI services — from deploying agents that help job-seekers through France Travail to building models that understand Moroccan Darija and Amazigh languages. "We think that AI needs to be specialized and understand structural nuances," Mensch told the audience. "It needs to speak languages as good as it speaks English." The road ahead for Europe's most ambitious AI company For Mistral , Wednesday's announcements amount to a declaration that the company intends to compete not by matching American AI giants on any single dimension, but by assembling capabilities none of them are willing or able to offer in combination: open-weight models, owned infrastructure, on-premises deployment, physics simulation, and deep vertical customization — all under a single roof. The strategy demands execution on multiple fronts simultaneously, each requiring enormous capital and specialized talent. The competition is formidable and accelerating. OpenAI has been rapidly expanding its enterprise offerings. Anthropic , backed by billions from Amazon, is building its own corporate AI practice. Google , Microsoft , and Amazon all offer AI platforms deeply integrated with cloud infrastructure that most enterprises already use. But Mistral is wagering that the world's most consequential AI deployments — the ones governing how aircraft get designed, how banks process compliance, how governments interact with citizens — will ultimately go to providers that offer sovereignty over data, models, and compute. "AI is too strategic to be left in the hands of a few," Mensch said, echoing the conviction he described from Mistral's founding three years ago. Three years in, the company that started as a Paris research lab with a handful of employees now trains models in its own data centers, simulates physics for the manufacturers that build the world's planes and cars, and is rewriting its assistant into an agent that can file your pull requests and summarize your inbox in the same conversation. Whether that sprawling ambition coheres into a durable business or stretches Mistral too thin is the €11.7 billion ($13.6B USD) question. The 1,000 people now working there are betting that in enterprise AI, owning the full stack is not a liability — it is the product.
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