AI News Archive: May 27, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- What Walmart’s AI Pricing Patents Mean for Every Retailer
Algorithmic cost changes should not be a black box proposition.
Score: 55🌐 MovesMay 27, 2026https://www.inc.com/christopher-yang/what-walmarts-ai-pricing-patents-mean-for-every-retailer/91350049 - “Not a blip”: AI was Canada’s venture market mover in 2025
Osler Deal Points Report found that AI captured 54 cents of every Canadian venture dollar last year. The post “Not a blip”: AI was Canada’s venture market mover in 2025 first appeared on BetaKit .
Score: 55🌐 MovesMay 27, 2026https://betakit.com/not-a-blip-ai-was-canadas-venture-market-mover-in-2025/ - TWSE Returns to COMPUTEX: The World's Fifth‑Largest Capital Market Powering a Full‑Stack AI Ecosystem
TWSE Returns to COMPUTEX: The World's Fifth‑Largest Capital Market Powering a Full‑Stack AI Ecosystem The Straits Times
- Aitech Supercharges its Space-Proven Rugged AI Systems with NVIDIA IGX Thor
Aitech Supercharges its Space-Proven Rugged AI Systems with NVIDIA IGX Thor The Straits Times
- Anthropic Growth and Bedrock Mix Drive AWS Margins Higher While Peers Lag
Amazon’s Bedrock Mix and Anthropic Deal Terms Combine to Show Greater Operating Leverage
- The big winner in Elon Musk’s suit against OpenAI and Microsoft — hypocrisy
If ever there were a lawsuit in which a jury and judge should have ruled against both the accuser and the defendants, Elon Musk’s suit against OpenAI and Microsoft was it. The high-profile legal battle pitted the world’s richest man against a company worth more than $3 trillion, another that might soon launch a $1 trillion IPO, and tech execs claiming to have only the good of the world in mind, not mere filthy lucre, while they develop a technology some fear could eventually destroy humankind. The lawsuit was eventually thrown out, but only on technical grounds. Meanwhile, unregulated AI marches on, with Musk, OpenAI and Microsoft all getting richer. The only winner in this suit was hypocrisy. Here’s why. Back to the beginning To understand how this unfolded, we need to go back to OpenAI’s beginnings. The company was founded by current CEO Sam Altman, Musk and others in 2015 — back when AI was a niche technology, used primarily for image and speech recognition, robotics, and experiments in self-driving cars. The founders funded OpenAI out of their own pockets as a nonprofit company aimed at developing AI for the good of the world. Then, as the technology evolved, Altman, Musk and others grew worried it might become so powerful that, without serious guardrails, it could pose a danger to humans. They feared what might happen if AI reached the level of a super-powerful artificial general intelligence (AGI) system, superior to humans on a variety of tasks, with general problem-solving skills rather than narrowly targeted ones – and the ability to think for itself rather than heeding humans. In an earlier version of Musk’s suit against OpenAI and Microsoft, Musk put their fears this way : “A.G.I. poses a grave threat to humanity — perhaps the greatest existential threat we have today.” Early on, OpenAI wasn’t on many people’s radar. When Microsoft invested $1 billion in the company in 2019, few outside the tech industry took notice. Between 2021 and 2023 Microsoft invested $2 billion more, still without drawing a lot of attention. Then in November 2022, OpenAI released ChatGPT, launching the generative AI (genAI) revolution — and all the disruption that has followed since. Eventually, as it became clear how important and valuable genAI technology would become, Microsoft’s investment ballooned to $13 billion. Nonprofit no more OpenAI insiders were convinced several years before ChatGPT’s release that the company could become tremendously profitable. With potentially trillions of dollars at stake, in 2017 they started looking for a way to turn the nonprofit operation into a for-profit company. It was at that point, OpenAI says, that Musk pushed to gain majority equity in the company if it went public, take control of the board, and become CEO . When the other founders balked, Musk withheld funding. Last year, OpenAI released copies of emails he sent to it during the height of their in-fighting. In one, in February 2018, he lobbied for the creation of a for-profit arm, pointing out that, “a for-profit pivot might create a more sustainable revenue stream over time and would, with the current team, likely bring in a lot of investment.” Musk then suggested that OpenAI “attach to Tesla as its cash cow.” When the other founders dismissed the idea, Musk threw a fit and quit the company. OpenAI went ahead and launched a for-profit arm, becoming a hybrid of a for-profit and nonprofit company in 2019. Years later, in 2024, Musk filed suit, targeting OpenAI, Altman, OpenAI co-founder and president Greg Brockman, and Microsoft — accusing them of “stealing a charity” by creating the for-profit arm of OpenAI, and taking the $13 billion Microsoft investment. He claimed they had all illegally enriched themselves through the profit/nonprofit setup and sought $150 billion in damages. (OpenAI fired back last year with a counter suit .) It took only two hours for the jury to rule against Musk , though the ruling didn’t address his actual claims. Rather, the suit was thrown out because it had been filed after the statute of limitations had run out. Cynicism and hypocrisy win out Everyone in this case was driven by venality. Altman portrayed himself as only wanting to develop AI to help humanity — and as evidence, pointed out he has no equity in OpenAI. What he neglected to add, though, is that he has more than a $2 billion stake in companies that have deals with OpenAI , and stands to gain billions more if those deals grow after any IPO. Microsoft, meanwhile, has used its investments in OpenAI to become a multi-trillion-dollar company. And if, as expected, OpenAI becomes a trillion-dollar company when it files its IPO later this year, Microsoft’s 27% ownership stake in the company would make it $270 million richer. That’s not a bad payoff for turning a blind eye to the way in which OpenAI performed a bait-and-switch from nonprofit to for-profit company. As for Musk…, well, what can you say about someone who claims he wants to save humankind from the evils of AI, while at the same time lobbying for OpenAI to become a for-profit company and milking it like a cash cow? He’s shown he’s not only the world’s wealthiest man. He’s also the world’s most hypocritical.
- AI Factories: The New Infrastructure of Intelligence
AI factories are token factories, converting power into intelligence in real time. And as agentic AI scales and autonomous, always-on special agents are deployed in the enterprise, performance per watt and cost per token become the economics that matter.
Score: 55🌐 MovesMay 27, 2026https://blogs.nvidia.com/blog/ai-factories-the-new-infrastructure-of-intelligence/ - Physical AI: Your Next Blue Ocean Opportunity in the New AI Era
Physical AI: Your Next Blue Ocean Opportunity in the New AI Era Gartner
- Why the next decade of physical AI must be human-centric
The shift to physical AI requires moving past rigid automation and towards true human-robot collaboration.
Score: 55🌐 MovesMay 27, 2026https://www.weforum.org/stories/2026/05/why-the-next-decade-of-physical-ai-must-be-human-centric/ - ’My job is going’: U.K. workers squeezed out by AI
’My job is going’: U.K. workers squeezed out by AI The Japan Times
Score: 55🌐 MovesMay 27, 2026https://www.japantimes.co.jp/business/2026/05/27/tech/uk-workers-jobs-ai/ - Social and situationally aware: German-Danish research project develops humanoid robots for practical use
Despite significant progress, humanoid robots still face limitations in real-world working environments, especially when dealing with complex and rapidly…
Score: 55🌐 MovesMay 27, 2026https://dfki.de/en/web/news/german-danish-research-project-develops-humanoid-robots-for-practical-use - Google folds Display Ads into AI-first Demand Gen platform
Google is folding Display Ads into its AI-powered Demand Gen platform, marking the end of a long-standing digital advertising model. The Google Display Network (GDN) has been a staple of the open internet for almost twenty years. Marketers previously relied on its predictable framework to target placements, bid on audiences, and A/B test static creative […] The post Google folds Display Ads into AI-first Demand Gen platform appeared first on AI News .
Score: 55🌐 MovesMay 27, 2026https://www.artificialintelligence-news.com/news/google-folds-display-ads-ai-first-demand-gen-platform/ - Xiaomi Slashes AI Model API Prices by 99% to Match DeepSeek
Xiaomi Slashes AI Model API Prices by 99% to Match DeepSeek Caixin Global
Score: 55🤖 ModelsMay 27, 2026https://www.caixinglobal.com/2026-05-28/xiaomi-slashes-ai-model-api-prices-by-99-to-match-deepseek-102448357.html - China’s Industrial Profits Grow at Fastest Pace in Over Two Years on AI Boom
China’s Industrial Profits Grow at Fastest Pace in Over Two Years on AI Boom Caixin Global
- GPT-5.5 Beats Claude and Gemini in New Long-Horizon Coding Benchmark
DeepSWE is a new benchmark for testing real-world AI coding capabilities.
Score: 55🌐 MovesMay 27, 2026https://analyticsindiamag.com/ai-news/gpt-55-beats-claude-and-gemini-in-new-long-horizon-coding-benchmark - Samsung Unions Approve Pay Deal That Highlights Inequality of A.I. Age
The agreement all but guarantees hefty bonuses for employees in the top-performing chip unit. Other workers say they feel left out.
Score: 55🌐 MovesMay 27, 2026https://www.nytimes.com/2026/05/27/world/asia/samsung-ai-profit-bonus-workers.html - China Wants Its Companies to Embrace AI—Without Firing Workers
As a backlash against AI builds in the U.S. and elsewhere, China is acting to stave off social and economic disruption.
- Nvidia CEO Begs Execs to Stop Telling Workers They’re Fired Because of AI
"It was just a way for them to sound smart and I really hate that. I think we're scaring people, and that's irresponsible." The post Nvidia CEO Begs Execs to Stop Telling Workers They’re Fired Because of AI appeared first on Futurism .
Score: 55🌐 MovesMay 27, 2026https://futurism.com/artificial-intelligence/nvidia-ceo-begs-execs-stop-fired-ai - Teachers' Union's AI Plan Seeks 'Big Tech Tax,' Elementary Screen Bans
The American Federation of Teachers launches push to limit AI-based tools for students.
- Hackers are using AI to find security flaws no scanner can catch, Google warns
For the first time, hackers have used artificial intelligence to find and exploit a security flaw that no automated scanner would have caught – and Google says only its own active monitoring stopped a mass attack.
- China tries to balances AI push with job displacement fears
China is trying to balance its society-wide AI push with mounting concerns over the tech’s disruptive socioeconomic effects.
Score: 54🌐 MovesMay 27, 2026https://www.semafor.com/article/05/27/2026/china-tries-to-balances-ai-push-with-job-displacement-fears - Inference Provider Baseten in Talks to Double Valuation to $11 Billion
Inference Provider Baseten in Talks to Double Valuation to $11 Billion The Information
Score: 54💰 MoneyMay 27, 2026https://www.theinformation.com/briefings/inference-provider-baseten-talks-double-valuation-11-billion - The AI tech job slaughter gets real
Tech companies seem to be falling over each other these days in firing people to either replace them with AI or to pay to build AI infrastructure. Wouldn’t it be nice if they at least waited until AI actually worked for business? On the one hand, top tech businesses such as Amazon, Block, Cisco, Cloudflare, and Meta have all announced that they’re slashing payrolls — either because AI can do the same work as people or they need the cash to build out their AI infrastructure. Isn’t that great? All together, of the 37,638 tech job cuts so far this year , 47.9% — almost half — can be tracked back to AI. On the other hand, despite all the AI hype and hysteria, no one has yet proven that AI is, generally speaking, really all that helpful for businesses. Oh, I know, I know. You did great things with OpenClaw vibe programming. Microsoft’s CEO, Satya Nadella, claims 20% to 30% of the company’s code was written by AI . And Nvidia assures us that 88% of its surveyed customers report AI has increased their revenues. But really, what else would they say? “Dear Board, we just blew half a billion bucks on Nvidia GPUs, and we’re losing money hand over fist?” I don’t think so. The truth is, as an IDC study reports, a mind-boggling 88% of proof-of-concept AI projects never reach production. Lest we forget, MIT’s The GenAI Divide: State of AI in Business 2025 study found that 95% of AI projects fail to deliver measurable P&L impact. Now, I have to acknowledge that AI is finally becoming truly helpful in business. As a guy who knows a thing or two about programming, Linus Torvalds, creator of Linux and Git, said at Open Source Summit North America , “I’m personally 100% convinced that AI is changing programming.” He estimates that “ AI will increase your productivity by a factor of 10. ” But is that reason enough to slash make workforce cuts of between 10% to 40%? (Short answer: No. Longer answer: Noooo!) It’s not just the mass firings. Workers who are either awaiting the axe, or have escaped it for the moment, are miserable. As one Meta employee told The San Francisco Standard , “ I tend to cry in the shower, ” and, “A lot of my feelings about my job are about the general chaos and not just the layoffs. ” So, explain this to me: When everyone knows AI-driven layoffs are coming, exactly how well do you expect them to work? You really think they can give their best? Making matters worse, it’s an open secret that IBM, Google, and Meta are having their employees train their AI replacements. As a popular meme puts it, workers are now “building your own coffin.” Is it any wonder that a lot of people — 29% of all employees and 44% among Gen Z workers — are deliberately sabotaging work when the boss insists they train their AI replacements? It also sure doesn’t help office morale when the CEO keeps saying AI will replace half of all employees. A particularly egregious example of this was when Standard Chartered CEO Bill Winters proclaimed his bank would slash thousands of jobs and replace “lower-value human capital” with AI. He’s since backed off the claim, but come on — we all know he meant it. Just like all the other CEOs who’ve said similar things, between FOMO and the knowledge that AI job news is sure to make the stock price jump, they’re eager to cut headcounts and boast about how successful AI will make them. What happens a few quarters down the road? Their attitude today seems to be let tomorrow take care of tomorrow. I hate to tell them, but that really doesn’t work in the long run. (Not, mind you, that a future much farther ahead than the next quarter seems to matter much anymore to business executives.) It should. As a recent Deloitte study stated: “Most respondents reported achieving satisfactory ROI on a typical AI use case within two to four years. This is significantly longer than the typical payback period of 7seven to 12 months expected for technology investments. Only 6% reported payback in under a year, and even among the most successful projects, just 13% saw returns within 12 months.” AI, in short, is not the miracle cure for what ails businesses that its fans claim. Will that stop businesses? I doubt it. While I appreciate that California Gov., Gavin Newsom is trying to bandage the AI job bleedout by mandating studies on subsidizing companies to keep employees rather than replace them with AI , I doubt that will do much to staunch the wound. At the Open Source Summit North America, Linux Foundation CEO Jim Zemlin was optimistic about AI and jobs. He pointed out that, thanks to AI becoming “pretty damn good coders,” the number of open-source projects on GitHub has led to a “surge of new code and projects.” Zemlin also believes that while few developers will write code, “engineers will still design, review, secure, and integrate that code.” (He’s referring to what’s becoming known as forward-deployed engineers .) This, in turn, will supposedly lead to tech job growth. I’d feel a lot better about that prediction if I believed the C-suite suits at most companies were capable of truly forward-looking thinking rather than focusing entirely on hiking the stock price by making the next quarter look good through staffing cuts. In the long run, sure, AI will make us more productive. But, we’re not there yet. For now, companies need to keep employees happy, not shove AI down their throats — and work out carefully and thoughtfully how AI will really work for business.
Score: 54🌐 MovesMay 27, 2026https://www.computerworld.com/article/4175956/the-ai-tech-job-slaughter-gets-real.html - As AI datacenter memory becomes hot commodity, SK Hynix makes it cooler
South Korean semiconductor giant SK Hynix has announced a new type of high-bandwidth memory (HBM) for AI datacenters that improves heat dissipation by integrating a cooling layer within the memory package itself. The change could allow AI processors incorporating the new memory to run faster, or reduce cooling costs. Traditional chip cooling architectures are largely external; heat dissipation happens after it leaves the package. For the HBM memory used by AI, which vertically stacks memory chips on top of one another to improve latency and memory density, the extra heat generated has become a major design constraint. Slated for the company’s next-generation HBM5 products due for launch from 2029 onwards, SK Hynix’s latest integrated high bandwidth memory (iHBM) takes a completely different approach of putting the cooling inside the Die-to-Die Physical Layer (D2D PHY). This is the physical interface connecting the HBM and GPU where heat is concentrated. In iHBM this becomes a new ‘heat dissipation path’ for integrated cooling elements (ICE), reducing thermal resistance by a claimed 30%. Not that long ago, innovations in memory and cooling would have been viewed as an interesting sideshow in a datacenter sector dominated by processor chip performance. But as datacenter processor performance has grown rapidly over the last decade, the rise in importance of memory design, and the ability to cool it inside high-performance computing (HPC) systems, has turned into a big issue. Made from custom silicon, putting ICE into memory packages makes life simpler for system builders. If iHBM can make good on the 30% improvement in heat dissipation that means the HBM modules have more headroom before hitting temperature ceilings that act as a drag on performance. HBM boom Memory’s importance to the AI datacenter boom is now so fundamental that recent figures from forecasting organization Epoch AI found that between Q1 2024 and Q4 2025 HBM rose from 52% to 63% of all AI chip component spending. The numbers underline how AI has undermined decades of computing performance assumptions. With AI, the volume of data becomes critical and not simply the speed at which it can be processed. This has turned memory from an afterthought into something every datacenter architect worries about first. By comparison with HBM, Epoch AI noted that logic dies — Nvidia’s famous GPUs, for example — fell slightly from 14.2% to 12.9% of spending over the same period. The knock-on effect of AI demand is that manufacturers have prioritized HBM over other types of memory such as DDR5, causing shortages for device makers. In March, SK Group chairman Chey Tae-won said demand for hardware to run AI had overwhelmed supply in ways that looked like a longer-term structural change rather than a cyclical one. Epoch AI reckons this HBM demand boom has some way to go. “HBM will likely account for an even larger share in 2026 as memory supply remains tight and prices rise,” it said. However, HBM is not the only show in town; in February Intel announced it was partnering with Softbank to develop an alternative, Z-Angle Memory (ZAM) , also based on stacking memory modules on top of one another, with a delivery date of around 2030. For AI datacenters designers and customers, every development is good news at a time when expectations for constantly rising performance have put the industry under pressure. Improving thermal performance, and delivering it on time, could turn out to be a deciding factor. “iHBM is an optimal solution for thermal management, combining our memory design capabilities with advanced packaging technology,” said SK Hynix senior VP of PKG development, Kangwook Lee.
- Photonic chips could process light directly for AI networks thanks to a self-aligning molecule
Every second, the data behind billions of emails, TikTok videos and AI queries travels around the world as pulses of light through fiber-optic networks. Along the way, these signals pass through tiny components that act as channels for light: photonic chips. These devices don't just carry signals—they direct and combine them, ensuring information moves efficiently across complex networks.
Score: 54🌐 MovesMay 27, 2026https://techxplore.com/news/2026-05-photonic-chips-ai-networks-aligning.html - ‘SymJack’ Attack Turns AI Coding Agents Into Supply Chain Attack Delivery Systems
Malicious repositories and disguised symlinks can trick AI coding agents into silently installing attacker-controlled MCP servers capable of stealing secrets, compromising CI pipelines, and deploying malicious code. The post ‘SymJack’ Attack Turns AI Coding Agents Into Supply Chain Attack Delivery Systems appeared first on SecurityWeek .
Score: 54🌐 MovesMay 27, 2026https://www.securityweek.com/symjack-attack-turns-ai-coding-agents-into-supply-chain-attack-delivery-systems/ - Ottawa to release long-awaited AI strategy next week, Carney says
Artificial Intelligence Minister Evan Solomon said earlier this month the strategy will consider the technology’s impacts on the labour market
Score: 53🌐 MovesMay 27, 2026https://www.theglobeandmail.com/politics/article-federal-government-ai-strategy/ - Companies are spending billions to train workers for AI. Most of it will fail
Walk into almost any boardroom this year, and the agenda is identical: How do we close the AI gap? Leaders are projected to spend billions to ensure their workforce doesn’t fall behind. They are confident. They are aggressive. And, in most cases, they’re failing. According to Mercer’s 2026 Global Talent Trends study , 63% of executives view AI work redesign as their highest-return investment. But only 32% say their workforce is actually ready. The response to this disconnect is a predictable reflex: throw more money at training. But training is a multiplier, not a foundation. If you give an employee with the right foundational skills targeted instruction, the gap closes quickly. But if you give that same instruction to someone whose underlying behavioral profile doesn’t fit the redesigned role, you aren’t “upskilling.” You’re simply creating a person with an expensive certificate doing the exact same job they did yesterday. The reason this cycle persists is that we are investing in the “how” without a clear picture of the “who.” Resumes are just history. Performance reviews reflect yesterday’s requirements. And AI “fluency surveys”? They mostly measure how a person feels about a tool they’ve likely only used three times. None of these inputs tells a leader who has the adaptability or the learning orientation to thrive in a redesigned role. So, the training is rolled out indiscriminately, the results come back uneven and the C-suite is left wondering why the needle hasn’t moved. The problem isn’t the training. It’s the sequence. Consider a maintenance supply distributor I observed. They were under immense pressure to grow sales following a major acquisition. Rather than rushing into a massive training rollout for new account managers, they stopped to build a predictive model. They identified the specific behavioral patterns that separated their top performers from the rest. When they eventually deployed the training, they didn’t do it blindly. They matched the coursework to the specific gaps they had measured. The results weren’t just “better,” they were transformative. That group drove 20% more in net sales, translating to an extra $89,000 in revenue per employee. That wasn’t a “new” investment. It was money already in the budget, being spent on the right people. This is the step the majority of organizations are skipping. Mercer reports that nearly every executive (98%) is planning an org redesign in the next two years. Most of these will be drawn up in a vacuum, devoid of any real measurement of the people expected to execute them. I’ve sat in rooms with HR leaders who are terrified of assessment because they fear it feels cold or clinical. But there is nothing colder than setting an employee up for failure by forcing them into a role that doesn’t align with their natural strengths. When we skip the diagnostic phase, we aren’t being people-first. We are being process-first. True empathy in the age of AI means knowing your people well enough to know exactly where they will shine, rather than asking them to guess. The cost of this oversight isn’t just a line item on a P&L. It’s a trust killer. According to Mercer, workforce “thriving” is at a decade-long low. Employees are already anxious about AI. When we ask them to absorb redesigned work based on executive assumptions rather than evidence, we create a retention crisis before we see a productivity gain. It is time to stop treating AI literacy as a spray-and-pray exercise. Flip the script. Stop treating work redesign as a planning exercise that ends when the org chart is approved. Instead, measure first, redesign second, train third and communicate throughout. And stop conflating technical skills with fit. A skills inventory tells you what people have done in a world that no longer exists. A soft skills assessment tells you who is built for the world that’s coming. AI is forcing a pace of change that most companies are unprepared for. Spending heavily on training without first understanding the workforce being trained isn’t “moving fast.” It’s just expensive guesswork. Before you sign off on your next round of role changes, ask: Have we actually measured the people we’re asking to change? If the answer is no, you’re just flipping a coin. This article is published as part of the Foundry Expert Contributor Network. Want to join?
- AI-powered phishing a growing threat, warns Cisco's Talos
Phishing is back as the number one cyber threat, supercharged by AI and no-code tools, Cisco's Talos Q1 2026 report finds.
Score: 53🌐 MovesMay 27, 2026https://www.itweb.co.za/article/ai-powered-phishing-a-growing-threat-warns-ciscos-talos/KA3WwMdzw1gvrydZ - STAT+: How Stanford patients help expose ‘fault lines’ in health AI adoption
Stanford Health Care started asking patients about new AI tools before they are implemented. Here's what patients are telling them.
Score: 53🌐 MovesMay 27, 2026https://www.statnews.com/2026/05/27/stanford-patient-panels-feedback-on-ai-shaping-health-care/?utm_campaign=rss - STAT+: Where patients and hospitals disagree about AI
In this edition of AI Prognosis, Brittany Trang takes a look at patients' role in how Stanford Health Care adopts AI tools, and more health AI news.
Score: 53🌐 MovesMay 27, 2026https://www.statnews.com/2026/05/27/health-ai-where-patients-hospitals-disagree-ai-prognosis/?utm_campaign=rss - AI scams fuel consumer payments fraud, Visa warns: ‘Threats are evolving faster than ever’
The stakes are high. Visa identified almost $1 billion in scam-related activity targeting consumer payments in one recent six-month period.
- No essays, no homework: ChatGPT transforms how students are assessed in Spain
Artificial intelligence has changed the way people study and is now forcing exams to be redesigned. Universities and schools are seeking new ways to test what students really know.
- OpenAI and Anthropic dig in against each other on AI jobs apocalypse
AI's most powerful CEOs are splitting into warring camps over whether their own technology will gut white-collar work or supercharge it — but the truth probably lies somewhere in the middle. Why it matters: The two leading AI labs are trading in hype and doom, making it nearly impossible for companies, policymakers and the public to know what's coming. The big picture: A pair of public appearances this week highlighted how far apart Anthropic and OpenAI are. Anthropic co-founder Chris Olah , speaking at the Vatican's AI ethics conference, doubled down on rhetoric CEO Dario Amodei has used about the dangers of AI. "There is a real possibility that AI will displace human labor at very large scale," Olah said. OpenAI CEO Sam Altman is sounding rosier about the tech. He said it's unlikely to cause a jobs apocalypse and that he was "wrong" about earlier projections that it would wipe out entire categories of jobs. "I'm delighted to be wrong about this, I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened," Altman told Commonwealth Bank of Australia CEO Matt Comyn. Zoom in: A spate of tech company layoffs in recent weeks has given fresh fodder to the "doomer" camp. Meta let go of nearly 8,000 employees, after projecting at least $125 billion in AI capital expenditures this year. That came after Coinbase, Block, Pinterest, Shopify and others tied workforce restructurings to AI capabilities. "AI costs a lot of money" and layoffs can offset those costs, Sophia Velastegui, Microsoft's former chief AI officer and now CEO at Velastegui Ventures, told Axios. Yes, but: There's also recent evidence pointing in the other direction. While unemployment has ticked up since 2023, it has predominantly been in sectors with the least exposure to AI, according to Stanford researchers. Software engineering job openings on Indeed are up over 18% year over year, while all openings are down 4.3% over the same period. LinkedIn's chief economist recently said AI has led to around 1.3 million new job postings . Data: Indeed ; Chart: Noah Bressner/Axios Reality check: Some technology giants are scaling back their AI usage after finding that the promise of huge productivity gains hasn't materialized. Uber's COO said AI costs are getting "harder to justify" weeks after his chief technology officer blew through his 2026 IT budget on AI usage. Microsoft is winding down some of its Claude Code licenses, according to The Verge , a move Fortune tied to their enormous costs. The bottom line: No one really knows how the AI jobs story will play out. The most likely scenario: widespread displacement in some sectors, job growth in others, and an uneven transition that defies a clean narrative for either side.
- MiniMax teases upcoming M3 model with new sparse attention mechanism and 15.6X long-context response speed boost
Among the many Chinese AI companies and laboratories vying for market share and attention (no pun intended) on the global marketplace, MiniMax stands out for its commitment to providing frontier-level intelligence across a range of modalities, including text, coding, and video (through its Hailuo model series) — often under permissive, enterprise-friendly, standard open source licenses. Now, MiniMax is again raising the eyebrows of AI power users and developers around the world by releasing a new, in-depth technical report on the making of its popular M2 series of language models ( M2 , M2.5 , and M2.7 ) shedding light on its numerous engineering innovations and clever approaches — while the company and its leaders also teased a whole new sparse attention approach for its upcoming MiniMax M3 series of models , which it says yields up to 15.6 times faster decoding (or LLM response) speed at long contexts (a million tokens) by adopting a custom sub-quadratic framework. In so doing, MiniMax has designed M3 to make ultra-long-context AI agent deployment economically viable. The M2 report is noteworthy for any enterprise working with AI models, and especially those looking to fine-tune and train their own in-house. After all, MiniMax's M2 series models often achieved top benchmarks in the world for open source AI performance when they were released. While the title has since been eclipsed by several other Chinese labs including DeepSeek and Xiaomi, MiniMax's new report offers a blueprint that can be used to improve AI model and agent performance by enterprises around the world. As Adina Yakup of Hugging Face observed on X , "Beyond the benchmarks, they’ve done some really solid work on MoE efficiency and agent oriented design. Excited to see where M3 goes next!" The attention dilemma The core technical architecture of the M2 series relies on a sparse Mixture-of-Experts (MoE) decoder-only Transformer layout used by numerous other state-of-the-art LLMs. The foundational backbone houses 229.9 billion total parameters, yet maintains a remarkably lean operational footprint by activating just 9.8 billion parameters per token across 256 fine-grained experts. To optimize routing and avoid standard load-balancing issues, however, MiniMax implemented sigmoid gating paired with learnable, expert-specific bias terms, heavily reducing reliance on restrictive auxiliary losses. The most definitive engineering decision documented in the M2 paper was the strict adherence to full multi-head attention with Grouped Query Attention (GQA) across all 62 layers. In large language models, "quadratic scaling" refers to the computationally expensive reality of standard full attention mechanisms, where every token in a sequence must mathematically connect to every other token. To use a real-world analogy, it is akin to attending a networking event and being forced to have a deep conversation with every single person in the room while simultaneously monitoring all other ongoing conversations. While this approach yields incredibly thorough context, the processing power and memory required explode at the square of the input length, creating a severe hardware bottleneck as models attempt to ingest hundreds of thousands of words. The problem with sub-quadratic scaling "Sub-quadratic" scaling introduces architectural shortcuts designed to bypass this exponential computational load. Instead of mapping every possible connection, sub-quadratic methods—such as Sliding Window Attention or compressed linear attention—might only analyze a localized window of nearby words or generate a compressed summary of the broader text. These efficient methods drastically reduce hardware costs and allow models to process massive documents at high speeds, but they historically introduce severe trade-offs in accuracy, often causing the AI to miss the "big picture" or lose track of distant context. This mathematical dilemma defines the architectural evolution from MiniMax's M2 to its upcoming M3 series. During M2's development, researchers rigorously tested sub-quadratic shortcuts but found they crippled the model's "multi-hop reasoning"—its ability to connect disparate clues across a long document—forcing the team to absorb the massive computational cost of full quadratic attention to maintain frontier-level intelligence. Indeed, they aggressively benchmarked efficient attention alternatives during pre-training but intentionally threw them out. They experimented extensively with hybrid setups, interleaving full attention with sub-quadratic architectures like Lightning Attention or hybrid Sliding Window Attention (SWA) configurations. The empirical results were definitive: at a larger scale, linear and windowed attention variants exhibited severe reasoning deficits. On evaluations exceeding 32K context windows, SWA variants performed significantly worse than full attention, dropping from a baseline score of 90.0 to 72.0 on the RULER 128K complex word extraction task. Sub-quadratic configurations proved prone to memory-bound constraints during training, lacked native prefix caching support, and failed to smoothly align with Multi-Token Prediction (MTP) modules used for speculative decoding. Full attention was deemed necessary to preserve multi-hop reasoning capability. However, recognizing that physical hardware limits cannot sustain quadratic scaling indefinitely, MiniMax is designing the M3 series around a novel sub-quadratic framework to finally deliver both high-speed processing and uncompromised reasoning. MiniMax Sparse Attention (MSA) and sub-quadratic scaling incoming The upcoming MiniMax-M3 breaks away from the compute-heavy constraints of its predecessor. As disclosed by MiniMax’s engineering team under the banner "Something BIG is coming," M3 introduces "MiniMax Sparse Attention" (MSA). Unlike DeepSeek’s Multi-head Latent Attention (MLA), which compresses keys and values into a low-dimensional latent space, MSA operates on a standard GQA backbone but utilizes block-level selection on real, uncompressed Key-Values. Elie Bakouch at AI training infrastructure and platform lab Prime Intellect posted on X noting that the main changes feature "block level selection like in CSA but attention is done on the real KV, not in [compressed space]." This solves the precision loss and prefix-caching obstacles noted in the M2 paper. By filtering and selecting block-level sequences dynamically, MSA delivers an architectural leap: early hardware profiling indicates a 9.7x speedup in prefilling latency and a massive 15.6x speedup during decoding phases at a 1-million token sequence length compared to the full-attention M2 architecture. To understand why a speedup in the "decoding phase" is so significant, it helps to break down how an AI actually reads and writes information. When you interact with an AI, the processing happens in two distinct steps: prefilling and decoding. When you hand an AI a prompt—whether it’s a short sentence or a massive 1,000-page document—it processes that entire chunk of text all at once in parallel, known as "prefilling." It essentially "reads" the input in one big gulp to build its initial understanding and establish context. In order to generate a response, the AI must enter a "decoding phase." To predict the first word of its response, it looks at the prompt. To predict the second word, it has to look at the prompt plus the first word. To predict the hundredth word, it must recalculate the context of the prompt and the previous 99 words it just wrote. So the response actually becomes harder to generate as it goes on, with the end requiring a full review of all prior parts. For a layperson, imagine reading a dense legal brief (prefilling) and then being forced to write a summary report where, before writing every single new word, you must rapidly reread the entire brief plus everything you've written so far to ensure your next word makes sense (decoding). Because the AI must constantly and repetitively look backward to generate each new step forward, the decoding phase is the most severe computational bottleneck in generating text. It is why AI models often type out their answers word-by-word, and why they slow down significantly as conversations get longer. Therefore, when the passage states the new architecture achieves a massive 15.6x speedup during the decoding phase at a 1-million token sequence length, it means the model has found a structural shortcut to generate its answer—token by token—nearly 16 times faster. It directly solves the exact bottleneck that normally makes AI chatbots freeze or stutter when handling massive amounts of information. The evolution of the MiniMax M series and the creation of 'Forge' On a product level, MiniMax has consistently evolved its models from simple text generation interfaces into autonomous workers. The M2 series pioneered an "interleaved thinking" protocol where the model alternates between natural-language planning traces and explicit tool invocations inside a single trajectory. Rather than dropping the intermediate chain-of-thought blocks between execution turns, M2 appends the full thinking history directly into the conversation context. This planning persistence prevents state drift, allowing the model to recover gracefully from runtime errors and revise its strategies based on environment feedback. To train these long-horizon workflows, MiniMax built "Forge," a scalable agent-native reinforcement learning system. Forge decouples execution into three independent modules—the Agent Side, the middleware abstraction layer (Gateway Server and Data Pool), and the Training/Inference engines. As MiniMax engineer Olive Song explained on the ThursdAI podcast , "What we realized is that there's a lot of potential with a small model like this if we train reinforcement learning on it with a large amount of environments and agents... But it's not a very easy thing to do," adding that this environmental training was where the team spent a significant portion of their development timeline. To absorb the extreme trajectory-length variance common in multi-step agent environments, Forge implements two vital engineering solutions: Windowed FIFO Scheduling: A training scheduler that maps a sliding window over the generation queue. It permits greedy, high-throughput fetching of completed tasks within the window to prevent cluster idle time, while strictly enforcing FIFO boundaries to maintain distributional stability and avoid gradient oscillation. Prefix Tree Merging: An optimization that restructures batch training into tree computation. Completions sharing identical conversation prefixes are calculated exactly once in the forward pass before branching. This eliminates redundant calculations, generating up to a 40x training speedup with zero approximation error. This reinforcement infrastructure directly spawned the M2.7 checkpoint, moving the series toward "self-evolution". Operating inside an automated agent harness, M2.7 functions as an independent machine learning engineer. The model profiles its own active training runs, diagnoses anomalies, reads logs, and automatically modifies its own codebase and configurations. According to MiniMax, M2.7 successfully handled between 30% and 50% of its own development workflow. On OpenAI’s rigorous MLE Bench Lite suite, which tests autonomous ML research capability, M2.7 achieved a 66.6% medal rate across independent 24-hour trials, effectively tying Google’s closed-weight Gemini 3.1 Pro. The continuous cadence from M2 to M2.5, which famously completed 30% of internal tasks and 80% of newly committed code at MiniMax HQ, underlines a broader vision. As the MiniMax team noted during that phase of deployment, "we believe that M2.5 provides virtually limitless possibilities for the development and operation of agents in the economy." With the technical report codifying the M2 generation's successes and the MSA tech blog on the horizon, MiniMax is signaling that the next frontier of AI is explicitly about translating a mini-activation footprint into maximum real-world intelligence.
- Cisco report finds no closed frontier AI model is safe from multi-turn attacks
A new report out today from Cisco Systems Inc. argues that none of the closed flagship large language models it tested can be considered safe once an attacker is allowed to push past a single prompt, as adversarial success rates climb sharply across every model in the cohort. The Cisco AI Threat Research team measured […] The post Cisco report finds no closed frontier AI model is safe from multi-turn attacks appeared first on SiliconANGLE .
Score: 52🌐 MovesMay 27, 2026https://siliconangle.com/2026/05/27/cisco-report-finds-no-closed-frontier-ai-model-safe-multi-turn-attacks/ - Cisco research finds standard AI safety benchmarks miss the real threat
Enterprises deploying closed AI models have generally relied on published safety benchmarks to assess risk before procurement and deployment decisions. New research from Cisco’s AI Threat Intelligence and Security Research team finds those benchmarks may systematically understate the threat. Standard safety tests submit a single adversarial prompt and record the model’s response. Multi-turn attacks work differently. An attacker maintains a conversation across multiple exchanges, iterating and adapting based on each response until the model yields. The report pairs single-turn and multi-turn adversarial evaluation across 15 closed/proprietary frontier models from OpenAI, Anthropic, Google, Amazon and xAI. Running 30,090 single-turn prompts and 6,986 multi-turn attacks, the team found that the two evaluation regimes produce different model rankings, different failure maps and different risk profiles. Every model tested failed a non-trivial share of multi-turn attacks . Key findings from the research: Multi-turn attack success rate (ASR) ranged from 7.89% to 88.30% across all 15 models, against a single-turn range of 2.19% to 64.91%. Eight of 15 models showed an absolute gap greater than 15 percentage points between the two regimes. Anthropic’s Claude family, which posted the lowest single-turn ASR in the cohort at 2.19% to 3.64%, still reached 11.16% to 16.20% under iterative attack. Single-turn failures concentrated in three procedures: Imposter AI at 37.50% weighted ASR, Soft Paraphrase at 29.21% and System Prompts at 27.69% The findings challenge a common assumption in enterprise AI procurement. “The surprising thing here is really that a lot of people accept and kind of understand these frontier labs as being state of the art, but they don’t necessarily think through the security and safety implications of that,” Amy Chang , head of AI threat and security research at Cisco, told Network World . “What this research does is kind of showcase that there is still variance across the different models, and how strong they are with the internal guardrails that are built within the model against these types of attacks.” How multi-turn attacks work In a multi-turn attack, the adversary does not present the harmful request upfront. Intent builds gradually across exchanges, with each prompt appearing benign in isolation while steering toward a harmful outcome. The model processes each turn without recognizing the pattern forming across the conversation. The research tested five attack strategy families: Crescendo escalation. The attacker escalates the ask incrementally, each prompt appearing harmless until the full picture emerges. “It seems like, oh, benign prompt, benign prompt, benign prompt, but as it builds, you start to put the pieces together,” Chang said. Refusal reframe. When the model declines a request, the attacker reframes their identity or purpose to push past it. “You reframe the refusal and be like, no, no, you don’t understand, I’m not a bad person, this is what I need it for,” she said. Role-play and persona adoption. The attacker assumes a character or persona, shifting the conversational framing so the model perceives a different obligation to comply. The report identifies this as the highest-weighted strategy family in the cohort at 29.89% weighted ASR. Contextual ambiguity and misdirection. The attacker uses vague or misleading framing to obscure the true nature of the request, steering the conversation without stating harmful intent directly. Information decomposition and reassembly. The attacker breaks a harmful request into component parts distributed across multiple turns, each appearing innocuous in isolation. The model responds to each piece without recognizing the assembled outcome. What multi-turn failures say about AI safety Every model in the cohort failed a meaningful share of multi-turn attacks. The root cause is structural. Chang said the vulnerability is a fundamental characteristic of how generative AI models work. They are probabilistic systems trained to predict the next likeliest token, and that mechanism produces unintended outputs that pre-deployment testing cannot fully eliminate. For closed models, where training data is not publicly disclosed, the problem is compounded because defenders cannot fully audit what the model has learned. The pattern is not limited to closed models. Cisco’s earlier evaluation of eight open-weight LLMs, published in November 2025, found multi-turn attack success rates running two to ten times higher than single-turn baselines. The report concludes that multi-turn vulnerability is a structural property of the current AI frontier regardless of whether model weights are public or proprietary, and regardless of whether a lab publicly emphasizes safety or capability. The exposure grows significantly larger when those same models power agentic workflows. “These models are the ones that power agents, and agents have broader access, broader ability to conduct actions on behalf of the human,” Chang said. The network layer as a defense point For network security professionals, the instinct is to apply a familiar paradigm: Proxy LLM traffic at the network layer, inspect inputs and outputs, and enforce policy the same way a WAF or IPS handles web traffic. Chang said that instinct is right in part, but LLM security introduces a dimension that signature-based controls cannot address. The difference is intent. “There’s also an intent component there as well, where traditional network security approaches kind of fall short,” Chang said. A WAF operates on known patterns, payload signatures, protocol violations, known attack strings. Natural language does not reduce to those primitives. An agent responding to an instruction to delete a home directory cannot determine from the request alone whether the person asking is authorized or is attempting to manipulate the agent into a destructive action. Network-layer inspection remains a valid baseline for deployments that generate network traffic. “I would say that that is one component of a core principle that should be applied in terms of making sure that at least as traffic gets passed through the network layer, whether they’re inputs or outputs, should have some sort of either guardrail or sanitation check to ensure that the prompts that are coming back and forth are safe,” she said. Evaluation practices for enterprise teams For security teams reading the report, Chang’s guidance centers on three actions. Use the report and the LLM Security Leaderboard to inform model selection. Cisco’s leaderboard publishes adversarial evaluation signals against leading models on a rolling basis and gives security teams a more current picture than static model cards or published benchmarks. Do not take vendor safety claims at face value. Published single-turn benchmarks can misrank models by a wide margin. Multi-turn exposure is invisible to any single-turn evaluation, and procurement decisions made on that basis carry unquantified risk. Layer additional defenses on top of the model. No base model in the cohort is safe under iterative attack. Runtime guardrails, application-layer controls, and pre-deployment testing are necessary regardless of which model an organization selects. “Out of the box, without any additional protections, these models, whether they’re closed or open, are insufficient on their own to kind of be used in a way that [has] potential ramifications,” Chang said.
- 4 in 10 AI agents headed for demotion or the rubbish bin
Gartner predicts governance struggles to plague rollout of this year’s much-hyped technology
Score: 52🌐 MovesMay 27, 2026https://www.theregister.com/ai-ml/2026/05/27/4-in-10-ai-agents-headed-for-demotion-or-the-rubbish-bin/5246964 - Google’s AI is Turning the Internet Into a Winner-Take-All Game
A new analysis of 44 major U.S. publishers suggests Google Search is undergoing a structural shift in the AI era, with traffic increasingly concentrated among a small group of dominant outlets. The findings come from independent research published by XSquareSEO, which analyzed Semrush estimated organic search traffic across two periods: pre-AI (2022–2024) and post-AI (2024–2026). […] The post Google’s AI is Turning the Internet Into a Winner-Take-All Game appeared first on CXOToday.com .
- Datacentre dive: From rust belt to megawatt AI factory
Our one-hour drive from Buffalo, New York , to the 750MW TeraWulf artificial intelligence (AI) factory on the shores of Lake Ontario starts in a landscape defined by the heavy, silent remnants of former industrial glories. Here, between the grey expanses of Lake Erie and Lake Ontario, are the skeletal remnants of 20th century blue-collar dominance – railroad tracks, derelict grain elevators and blackened red-brick factories that were once part of a huge flow of steel, coal and auto production between the Midwest and the Atlantic coast. The city centre itself is tidy enough, but with a hint of ghost town – elevated flyovers snake above deserted streets, while the southern downtown periphery is dominated by vast, empty car parks built around sports stadia. You get the feeling the city centre has become more of a destination than a persistent community. Architectural gems – the Art Deco grandeur of Frank Lloyd Wright’s City Hall, the soaring facade of the Rand Building, the hollowed-out expanse of the former rail terminal – stand as grand, stone monuments to an era of manual labour and the professional culture that was once constructed on top of it. Read more about datacentres and TeraWulf's AI factory ‘We’re at Chinese levels’ at TeraWulf 750MW AI factory : We see the latest in AI factory technology and construction at TeraWulf’s Lake Ontario datacentre, where a former coal-fired power station is the site of a rapid transformation. Do AI datacentre physics make on-prem unviable? Does massive GPU power draw and liquid cooling mean the end of the on-premise datacentre? We look at the AI factory revolution and find that a hybrid path for enterprises will likely still exist. GPU power draw will require grid partnerships : But water use will likely decrease. We look at energy as the key driver – and bottleneck – in development, and why water use is less of an issue now datacentres aren’t like a VW Beetle. Leaving the rust belt city behind Heading east along Route 90, urban density gradually thins to the suburban fringe, where industrial units line the roadside behind narrow service lanes. Beyond that, houses are now separated by wide, manicured lawns and what appears as a highly curated patina of rural Americana. Whiteboard houses with front stoops and fruit cellars dot the historic apple-growing regions near the lake shore. Finally, we are at the heavily secured perimeter of the site. A gatehouse with high fences of barbed wire and an on-board passport check that reminds this author of a tense bus crossing into Yugoslavia in 1985. Inside the fence line, the horizon is dominated by the buff-painted, surprisingly pristine carcass of the decommissioned Somerset power station. Beside it rises an unnatural-looking hill which turns out to be a grassed-over heap of compacted ash that now forms the highest geographic point in the county. Where eagles dare The old generation plant looms over the lake, and eagles circle overhead in the cold air. From its side, a giant, rust-coloured rectiform duct emerges. Its purpose is unfathomable; perhaps a fire-carrying intestine, herniated from an otherwise comprehensible body. It is wreathed in an impossible tracery of girders and steelwork and re-enters the building high above. In front of this brooding bulk, gargantuan yet spindly and angular transformer frames sit empty, waiting for massive cables to bridge the gap between the vanished coal era and the digital factory. Of the hundreds who once ran the old furnaces that created steam plumes visible from space, only a sparse handful of 10 or 20 workers remain. The current 180-acre construction site is a furious, chaotic anthill. Along the rough dirt tracks, a constant procession of all-terrain vehicles (ATVs), telescopic loaders, mud-splattered pickups, semi-trailers and concrete mixers churn the earth. Everywhere is a sea of high-vis vests and scratched hard hats, with an equal assortment of sweat-stained hoodies and caps beneath them. The workforce is entirely male – thick beards, wrap-around sunglasses and prominent bellies de rigeur among the older guys. Meanwhile, the pale, late-night tired faces of younger workers peer from beneath a helmet-and-hoodie combo. Baggy jeans and stiff canvas workwear, coated in a fine layer of grey concrete dust, are the uniform for all. Plumbing for AI Within the rising steel skeletons of the five new buildings, a dense labyrinth of industrial plumbing grows. Galvanised ducting, cable trays and complex routings cross overhead. Pipework ranges from inch-wide to feet across. It is high-end, highly engineered piping, with equally well-made clamping – no rough groundwork tubing here – all designed to direct immense but precise hydraulic flows between graphics processing unit (GPU) chip cooling and towering Evapco cooling units outside. These contain enormous circulating fans that pull air past car radiator-like heat exchangers to expel the heat of the silicon into the sky. In a nearby hall – a former cryptocurrency mining shed now repurposed as a workers’ lunch room – the evidence of technological obsolescence hangs overhead. Inch-thick cables, severed and useless, dangle from suspended trays like dead nerves, while beneath them, multi-coloured strands poke up from openings in the concrete floor like broken sticks of rock. While all on the surface appears smooth-running – and there’s nothing to doubt it is – the primary workflow bottleneck sits with the electricians . Threading 350 miles of heavy cabling through these steel shells is a slow, gruelling struggle of human fingers against a rigid digital blueprint. Inside the datacentre halls in various states of construction, men balance against high cable trays, stand in careful study of rack power bars, or kneel in the dirt to make copper connections. Work never stops, and shift changes bring two distinct armies of 1,600 men. There’s never-ending movement as shifts come on, or workers move between locations, and trucks and ATVs clatter through the mud under the silent gaze of the eagles. Between the rising structures, rows of portakabins sit in the grey mud, their windows revealing hard-hatted, high-vis-wearing engineers poring over blueprints. Portakabins are flanked by long lines of portaloos where basic human functions and the final checks of construction paperwork are carried out in the dark, sometimes accompanied by the distant, rhythmic thump of pile drivers breaking more ground.
Score: 52🌐 MovesMay 27, 2026https://www.computerweekly.com/news/366643633/Datacentre-dive-From-rust-belt-to-megawatt-AI-factory - Roundtable: Sovereignty Now? Localize AI Infrastructure to Stay Compliant and Secure
Roundtable: Sovereignty Now? Localize AI Infrastructure to Stay Compliant and Secure Gartner
- Microsoft's MAI-Image-2.5 pulls even with Google's Nano Banana 2 on benchmarks
Microsoft's MAI-Image-2.5 ranks third on Arena's text-to-image leaderboard, on par with Google's Nano Banana 2 but still behind OpenAI's Image-2. The model shows clear gains over its predecessor, especially in rendering text inside images and commercial visuals. The article Microsoft's MAI-Image-2.5 pulls even with Google's Nano Banana 2 on benchmarks appeared first on The Decoder .
Score: 52🤖 ModelsMay 27, 2026https://the-decoder.com/microsofts-mai-image-2-5-pulls-even-with-googles-nano-banana-2-on-benchmarks/ - Gujarat Partners With Meta for AI-Powered Citizen Services, Digital Infra
Discover how Gujarat and Meta are transforming citizen access through AI-powered WhatsApp services. Learn more about the initiative now!
Score: 52🌐 MovesMay 27, 2026https://analyticsindiamag.com/ai-news/gujarat-partners-with-meta-for-ai-powered-citizen-services-digital-infra - Amazon, Google, Meta, and Microsoft are backing a new clean-tech push for AI data centers
Nonprofit investor Elemental Impact will deploy up to $5 million per project in as many as 10 startups through 2027
Score: 52🌐 MovesMay 27, 2026https://qz.com/amazon-google-meta-microsoft-data-center-cleantech-initiative-052726 - Meta Launches New Enterprise Push to Boost Business Adoption of Its AI Tools
Meta Launches New Enterprise Push to Boost Business Adoption of Its AI Tools The Information
Score: 52🌐 MovesMay 27, 2026https://www.theinformation.com/articles/meta-launches-new-enterprise-push-boost-business-adoption-ai-tools - Former CIA chief Petraeus says drone swarms are the next danger — and growth opportunity
Unmanned systems will be one of the biggest security threats and structural growth opportunities in defense over the next decade: David Petraeus
Score: 52🌐 MovesMay 27, 2026https://www.cnbc.com/2026/05/28/petraeus-unmanned-systems-autonomous-drones-defense-investment.html - AI Chiefs Walk Back Job Apocalypse Warnings
AI Chiefs Walk Back Job Apocalypse Warnings Barron's
Score: 52🌐 MovesMay 27, 2026https://www.barrons.com/news/ai-chiefs-walk-back-job-apocalypse-warnings-fef1ac30 - TCS signs multi-million dollar deal with SKF for AI-led manufacturing business
TCS will help SKF create connected operating model, combining centralised, AI-enabled systems and harmonised processes
- Group behind pro-Iran AI Lego videos greets Iranians as internet blackout ends
Group behind pro-Iran AI Lego videos greets Iranians as internet blackout ends The National
Score: 52🌐 MovesMay 27, 2026https://www.thenationalnews.com/future/technology/2026/05/27/iran-lego-animation-propaganda/ - Companies That Adopted AI Agents Alarmed to Discover They’re Botching Incredibly Important Tasks
"The blast radius of that agent action was not the service restart. It was everything downstream of the restart, in a system state the agent had no complete picture of." The post Companies That Adopted AI Agents Alarmed to Discover They’re Botching Incredibly Important Tasks appeared first on Futurism .
Score: 52🌐 MovesMay 27, 2026https://futurism.com/artificial-intelligence/companies-ai-agents-botching-important-tasks