AI News Archive: May 24, 2026 — Part 2
Sourced from 500+ daily AI sources, scored by relevance.
- Why Mythos could be launching sooner than you think? Leaked code exposes preparations for Claude Mythos 1
Anthropic's Mythos AI model could be coming to public soon as a new source leak gives a major hint at the company's rollout strategy.
- The Middle East war is testing the Gulf's ambitions to become an AI hub
Attacks on data centers in the Middle East and persistently high energy prices have altered the calculus for operators of the facilities, analysts say.
Score: 52🌐 MovesMay 24, 2026https://www.cnbc.com/2026/05/24/middle-east-war-testing-gulfs-ambitions-to-become-ai-hub.html - Why Nuro thinks being a robotaxi ‘second mover’ gives it an advantage
Waymo is the undisputed leader in the robotaxi space, operating a fleet of over 3,000 driverless cars in at least 10 cities across the US. A number of companies, including Tesla, Zoox, Avride, and Motional, are racing to catch up with the Alphabet-owned firm. But what if being No. 2 was actually better? Nuro, the […]
Score: 51🌐 MovesMay 24, 2026https://www.theverge.com/transportation/936126/nuro-robotaxi-dave-ferguson-interview-uber-lucid-waymo - Chinese tech giants dangle record pay packets to court Singapore-trained AI graduates
Chinese tech giants dangle record pay packets to court Singapore-trained AI graduates The Straits Times
- As SpaceX and OpenAI Prepare for Public Debuts, What Recent IPO History Signals
As SpaceX and OpenAI Prepare for Public Debuts, What Recent IPO History Signals The Information
Score: 51🌐 MovesMay 24, 2026https://www.theinformation.com/articles/spacex-openai-prepare-public-debuts-recent-ipo-history-signals - AI could kill the brokerage industry’s cash cow
Shares of Charles Schwab and other firms have swooned on concerns that AI tools could erode the profit they derive from cash that clients hold in so-called sweep accounts.
Score: 50🌐 MovesMay 24, 2026https://www.livemint.com/ai/artificial-intelligence/charles-schwab-ai-ai-in-fintech-11779599429420.html - Hackers Find That Inaudible Sounds Hidden in Podcasts or Random Videos Can Hijack Your AI Voice Chatbot
Be careful. The post Hackers Find That Inaudible Sounds Hidden in Podcasts or Random Videos Can Hijack Your AI Voice Chatbot appeared first on Futurism .
Score: 50🌐 MovesMay 24, 2026https://futurism.com/artificial-intelligence/hackers-inaudible-recordings-hijack-ai-voice-chatbots - How AI Talks People Out of Conspiracy Theories—and What We Can Learn From That
Research shows that the key is to clearly explain relevant facts. That isn’t always easy to do.
Score: 50🌐 MovesMay 24, 2026https://www.wsj.com/tech/ai/ai-debunks-conspiracy-theories-92eff2c5?mod=rss_Technology - The AI Bill Just Arrived. And Nobody Budgeted for It
Uber burned its entire 2026 AI budget in four months. Microsoft is pulling the plug on Claude Code. Is StackOverflow about to have its… Continue reading on Towards AI »
- Deepmind's Hassabis sees humanity "in the foothills of the singularity" while LeCun says current AI isn't intelligent
Yann LeCun says current AI systems aren't genuinely intelligent. Demis Hassabis thinks humanity is already "standing in the foothills of the singularity." And Gemini co-lead Oriol Vinyals splits the difference: today's models would've looked like AGI seven years ago, but they still can't learn from experience or produce real breakthroughs. The article Deepmind's Hassabis sees humanity "in the foothills of the singularity" while LeCun says current AI isn't intelligent appeared first on The Decoder .
- Why AI Likely Means More Work For Humans
The paradox of AI is that replacing some aspects of expert work may only accentuate the need for human experts.
Score: 49🌐 MovesMay 24, 2026https://www.forbes.com/sites/joemckendrick/2026/05/24/why-ai-likely-means-more-work-for-humans/ - Hackers are learning to exploit chatbot ‘personalities’
This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on AI mischief, follow Robert Hart. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started Hacking the first generation of AI chatbots was a laughably simple affair. […]
- AI smart glasses gain momentum globally, but India’s adoption may take time
The category got a boost this week as Google unveiled its Android XR-powered AI glasses at Google I/O 2026, showcasing partnerships with Samsung and eyewear brands Gentle Monster and Warby Parker
- ‘AI washing’: firms are scrambling to rebrand themselves as tech-focused
PR executives say UK companies are forcing them to present ordinary automation as artificial intelligence UK companies are performing “yoga-level” stretches to describe themselves as AI specialists in an attempt to capitalise on the buzz around the technology, public relations firms have said. Weary communications executives tasked with securing media coverage for brands have complained that bosses in low-tech industries or running businesses that use automation but not generative AI, are increasingly demanding they are pitched to journalists as artificial intelligence companies. Continue reading...
Score: 48🌐 MovesMay 24, 2026https://www.theguardian.com/technology/2026/may/24/ai-washing-pr-firms-scrambling-rebrand - Smaller, easier, smarter: what special operations forces need from AI, now
AI agents are coming to a special operations mission near you—if they can fit in the pack.
- The Real Risk of AI Isn’t Replacement. It’s Influence
AI isn’t taking over your job. It might just be taking over your perspective.
Score: 48🌐 MovesMay 24, 2026https://www.inc.com/andrea-olson/what-happens-when-employees-stop-questioning-the-system/91344396 - The Next AI Revolution Isn’t Chatbots. It’s Robotics
Robots are getting an upgrade, and world models are leading the charge.
Score: 47🌐 MovesMay 24, 2026https://www.inc.com/heather-wilde/the-next-ai-revolution-isnt-chatbots-its-robotics/91344941 - De Anza College rolling out applied AI degree program in fall
De Anza College rolling out applied AI degree program in fall The Mercury News
Score: 47🌐 MovesMay 24, 2026https://www.mercurynews.com/2026/05/24/de-anza-college-rolling-out-applied-ai-degree-program-in-fall/ - XPENG Offers More Human-Like Autonomous Driving
In recent articles on XPENG, I have focused on the development of human employees who make technology possible and the technology tools that they use. However, the output of the people using automation and AI tools is what matters the most to customers. It is especially noticeable in autonomous driving ... [continued] The post XPENG Offers More Human-Like Autonomous Driving appeared first on CleanTechnica .
Score: 47🌐 MovesMay 24, 2026https://cleantechnica.com/2026/05/24/xpeng-offers-more-human-like-autonomous-driving/ - Microsoft Research Releases Webwright: A Terminal-Native Web Agent Framework That Scores 60.1% on Odysseys, Up from Base GPT-5.4's 33.5%
Microsoft Research Releases Webwright: A Terminal-Native Web Agent Framework That Scores 60.1% on Odysseys, Up from Base GPT-5.4's 33.5% MarkTechPost
- TechCrunch Mobility: Robotaxi reality check
Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation.
Score: 47🌐 MovesMay 24, 2026https://techcrunch.com/2026/05/24/techcrunch-mobility-robotaxi-reality-check/ - Blind Waymo Users Revel in the Joy of Riding Alone
Waymo has been an object of frustration to some in California. For visually impaired people, it can also bring a rare feeling of independence.
- This Zurich startup built a four-armed robot for space stations. Each astronaut hour it saves is worth $140,000.
Orbit Robotics, a Zurich-based startup, has unveiled Helios, a four-armed robot designed to work inside space stations. In microgravity, legs are useless. Helios replaces them with two extra arms that serve as both mobility aids and working hands. The design logic is simple. Two arms anchor the robot to the station interior while the other […] This story continues at The Next Web
Score: 46🌐 MovesMay 24, 2026https://thenextweb.com/news/orbit-robotics-helios-four-armed-space-robot - BofA says you’ll be 10x more productive with AI. Ignore the 0.1% result so far
BofA says you’ll be 10x more productive with AI. Ignore the 0.1% result so far Fortune
Score: 46🌐 MovesMay 24, 2026https://fortune.com/2026/05/24/is-ai-bubble-bigger-than-internet-electricity-dotcom-bofa-panmure/ - Why one NC founder says businesses that ignore AI will not survive the next three years
The former Capgemini and IBM executive founded msg2ai, co-founded Rethink Labs, and launched the AI Innovation Council. His prediction for holdouts is stark.
- How AI is changing stock trades for retail investors
How AI is changing stock trades for retail investors Nikkei Asia
Score: 45🌐 MovesMay 24, 2026https://asia.nikkei.com/business/markets/trading-asia/how-ai-is-changing-stock-trades-for-retail-investors - NVIDIA AI Releases Gated DeltaNet-2: A Linear Attention Layer That Decouples Erase and Write in the Delta Rule
NVIDIA AI Releases Gated DeltaNet-2: A Linear Attention Layer That Decouples Erase and Write in the Delta Rule MarkTechPost
- Robotaxis need to be tested in real traffic
To achieve safe, cost-effective autonomy we need to see how other road users react to the vehicles
- AI agents are quietly generating chaos engineering failures enterprises don’t track yet
There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template. The agent initiated an action. The action was technically correct given the agent's context. The context was incomplete. The infrastructure cascaded. And, by the time the incident review happened, three teams were arguing about whether it was an agent failure or an infrastructure failure, because the frameworks for thinking about these two things have never been connected. The scale of this exposure is no longer theoretical. Seventy-nine percent of organizations now have some form of AI agent in production, with 96% planning expansion. Gartner predicts 33% of enterprise software will include agentic AI by 2028, but separately warns that 40% of those projects will be canceled due to poor risk controls. What neither statistic captures is the failure mode happening between those two numbers: Agents that are running, that are not canceled, and that are quietly generating infrastructure events no one has categorized as risk. I've spent six years building infrastructure automation systems at enterprise scale, first at Cisco (leading AI-driven lifecycle platforms deployed across 20-plus global enterprise customers), then at Splunk (designing AI-assisted root cause analysis and observability workflows across thousands of enterprise environments). During that time I also filed a patent on intent-based chaos engineering methodology. And across all of it, I kept watching organizations make the same structural mistake: Treating autonomous agents and chaos engineering as separate disciplines. They are not. They are the same discipline, and the gap between them is quietly generating the next wave of major production incidents. The judgment call that agents skip To understand why this matters, you need to understand what's actually broken in how enterprises govern chaos today, before you add agents to the picture. Most mature engineering organizations have invested in chaos engineering programs. Game days, blast radius controls, SLO-gated experiments. When a human engineer initiates a chaos experiment, the sequence has a critical property: A human is making a judgment call about whether the system has capacity to absorb the perturbation right now. They check dashboards. They look at the error budget burn rate. They assess whether dependencies are stable. It's imperfect and often intuitive, but there is at least a person in the loop asking the right question before anything runs. When you introduce an autonomous remediation agent, one that can restart services, reroute traffic, scale resources, or modify configurations in response to detected anomalies, that question disappears. The agent sees an anomaly. The agent takes an action. The action is a chaos event. No SLO burn rate check. No blast radius calculation. No human judgment about whether right now is the right moment to introduce additional stress into a system that may already be under pressure from three other directions. Here is the specific failure mode I have watched play out. A remediation agent detects elevated latency on a microservice and responds by restarting the service cluster; a reasonable action given its training data and its narrow view of the incident. What the agent doesn't know: Three other services are in the middle of handling peak traffic. The shared connection pool is already at 87% utilization. A dependent database is running a background index rebuild. The restart triggers a thundering herd against the recovering service. What started as a latency spike the agent was designed to fix becomes a cascade the agent was never designed to model. 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. Nobody's chaos engineering program had tested for that specific combination. Nobody's blast radius calculation had included the agent as an actor. Because we don't think of agents as chaos injectors. We should. According to the AI Incidents Database , reported AI-related incidents rose 21% from 2024 to 2025. That count almost certainly understates the actual exposure, because most organizations have no incident classification that captures an autonomous agent action as the initiating cause of a cascade. The incident gets logged as a service restart, a connection pool saturation, or a latency event. The agent is invisible in the postmortem. Absorb capacity is a resource; most systems don't treat it that way The underlying problem is that enterprise systems have no shared language for absorb capacity — the real-time estimate of how much additional stress a system can take before it breaches its SLO commitments. Chaos engineering programs manage it implicitly, through human judgment and static thresholds that fire after a limit has already been crossed. Agents don't manage it at all. Through structured primary research with site reliability engineering (SRE) and platform engineering practitioners across organizations including Intuit and GPTZero, I've been developing a resilience budget model. The core idea is to treat absorb capacity as a continuously recomputed, consumable resource rather than a static threshold you try not to breach. A resilience budget draws on four live signal classes. SLO burn rate is the primary input, because it directly encodes the distance between current system behavior and the commitment that actually matters. If a system is burning its monthly error budget at five times the expected rate, the resilience budget is near zero regardless of what CPU utilization looks like. P99 latency trend matters more than absolute latency, because a service trending upward over forty minutes tells you something different than a service that has been stable at the same absolute value. Dependency saturation state is the most commonly missed signal; a chaos experiment or an agent action that assumes a shared connection pool is freely available when it's sitting at 87% will produce failure modes that nobody designed for. Application behavioral signals, session completion rates, API call pattern shifts, conversion degradation, and surface system stress earlier than infrastructure metrics do, because users feel the degradation before Prometheus reports it. What makes this a budget rather than a threshold is that it is consumable. Every chaos experiment draws from the available capacity. Every agent action draws from it. In multi-team organizations where multiple experiments and multiple agents may be acting simultaneously, the budget is shared. Without a shared ledger of consumption, two teams running experiments against overlapping dependencies produce a combined blast radius that neither team planned. Add autonomous agents acting completely outside the ledger, and the accounting collapses. Where language models help, and exactly where they fail Several engineering organizations are now running experiments using large language models (LLMs) to generate chaos hypotheses from dependency graphs and incident postmortem corpora. The results are directionally useful. Language models surface plausible failure modes that experienced SREs recognize as worth testing, and they generate hypotheses faster than manual processes, particularly when working from rich postmortem history. The limit is dependency graph staleness, and it is a hard limit. A hypothesis generated from a graph that doesn't reflect last month's service extraction, or a new shared library dependency added two sprints ago, will propose an experiment with incorrect blast radius assumptions. The problem is not that the model makes a mistake, it's that the model doesn't know it's making one. It will be confidently incorrect about a system boundary that no longer exists, and in chaos engineering, confident incorrectness in production means an unplanned outage. Stanford's Trustworthy AI Research Lab found that model-level guardrails alone are insufficient: Fine-tuning attacks bypassed leading models in the majority of tested cases. The implication for chaos hypothesis generation is direct, a model that cannot reliably hold its own safety boundaries cannot be trusted to accurately model the blast radius of an action it has never seen in a dependency graph it has not verified. When hypothesis generation draws instead from postmortem corpora, the staleness problem shrinks considerably. Postmortems describe failures that actually occurred in the system at a specific moment in time. The signal is inherently validated by production reality. This is the tractable near-term AI application in this space, and it is genuinely useful for organizations with mature incident documentation practices. What AI cannot do, and should not be asked to do, is make the execution decision when signals are ambiguous. That judgment requires awareness of things that live entirely outside any monitoring system: Pending deployments that changed the dependency landscape an hour ago, on-call staffing levels on a holiday weekend, a customer commitment that makes any additional risk unacceptable until Monday. A model without access to that context should not be making that call. This is not a temporary limitation pending a more capable model. It is a structural constraint of what machine observability can represent, and building an agent architecture that ignores it is building one that will eventually make a consequential decision with incomplete information — and no human in the loop to catch it. What this means for how enterprises govern agents in production The governance implication is straightforward to describe and harder to implement than it sounds. Every autonomous agent action that touches infrastructure needs to register against the same live signal layer that governs chaos experiments. The same SLO burn rates, latency trends, dependency saturation states that a human engineer would check before initiating an experiment should gate what an agent is permitted to do and when. If the resilience budget is below a defined floor, the agent waits or escalates. It does not act. Agent actions also need to be modeled as experiments, not just logged as events. When an agent restarts a service, the question isn't only whether the restart completed successfully. It's whether the blast radius of that action was proportionate to the available absorb capacity, and what cascading effects it produced across dependencies. That is chaos engineering data. It belongs in the budget model, feeding the next decision the agent or the team needs to make. And when signals are genuinely ambiguous, when the budget score is unclear, when a recent deployment has changed the topology in ways the agent's context window doesn't capture, when dependency states are in flux, the execution decision needs to go to a human. Not as a permanent limitation on agent autonomy, but as a hard engineering requirement for the current state of the technology. A circuit breaker that hands ambiguous cases to a human is not a weakness in the agent architecture. It is the thing that makes the architecture trustworthy enough to actually run in production. Intent-based verification formalizes exactly this: Defining what correct agent behavior looks like before deployment, then continuously probing whether those boundaries hold under live system conditions. The organizations that operate autonomous agents reliably at scale are not the ones with the most sophisticated models. They are the ones that understood, before something went badly wrong, that every agent action is a chaos event and built their governance layer accordingly. The practical first step is unglamorous: Audit every autonomous agent currently touching infrastructure, map its action surface against your live SLO burn rate signals, and define explicit floor conditions below which the agent is required to wait or escalate. That audit will surface agents acting entirely outside your resilience accounting. Most organizations running agents at scale today have several. Find them before production does. Sayali Patil has spent 6-plus years at Cisco Systems and Splunk building the reliability and automation systems that keep enterprise AI infrastructure running at scale.
- AI bonus frenzy raises image of blue-collar jobs as more workers seek to join 'kingsanjik'
Employees at KG Mobility’s Pyeongtaek plant work on underbody components for the the company's Torres EVX model in this photo provided on April 24, 2024. [KG MOBILITY] [BEHIND THE NUMBERS] In Korea, manufacturing workers at major conglomerates are broadly called "kingsanjik," a portmanteau of “king” and the Korean word for production worker to refer to the hefty compensation associated with the job. The word symbolizes a broader reassessment of the country’s traditional career hierarchy that often viewed white-collar roles as higher than blue-collar ones. University degrees and white-collar careers have long been regarded as clear markers of success in Korea, where office jobs at conglomerates are seen not only as symbols of financial stability but also of elite status. Related Article SK hynix to begin construction on second phase of Yongin chip cluster fab Korea grows intoxicated on semiconductor windfall (KOR) Korea's exports jump 43.7% on year in first 10 days of May due to overseas semiconductor demand Cash windfalls risk undermining Korea’s semiconductor edge(KOR) But amid the semiconductor boom and its accompanying hefty bonuses, some people are abandoning four-year universities to enroll in vocational colleges, a path that often leads to the production site. The number of these cases reached a record 2,500 this year, up 23 percent from a year earlier, according to Korean Council for University College Education. The changing perception is increasingly reflected in the applicant pool. “Even job seekers from four-year universities are seriously considering applying for production-line positions at SK hynix, which shows how much the applicant pool has broadened,” said a career consultant who worked at the chipmaker until last year and now reportedly handles around 1,000 consulting cases annually. He explained he is seeing applicants from “completely unrelated fields,” including people with “little to no basic knowledge” of semiconductors applying for blue-collar work in the industry. The sentiment is also felt on the ground. “I work on the production line at SK hynix, and life is sweet,” wrote a user on an anonymous workplace community. Describing himself as a factory worker in his 20s, he said he landed the job straight out of vocational high school and called it “the best value-for-money career path.” The rapid advance of AI in the workplace is further accelerating the change in attitude, as generative AI raises fears that many routine white-collar tasks — from data analysis to document drafting — could increasingly be automated, a trend already being aggressively pursued by U.S. tech giants such as Meta Platforms and Amazon. “This semiconductor windfall has accelerated a shift away from university credentialism,” said Seo Yong-gu, a business professor at Sookmyung Women’s University. “Just as gender discrimination has diminished, the gap between manual and office work is narrowing.” Cracks in Korea’s academic credentialism Korea’s highly competitive social order has long revolved around university rankings, which often determined access to prestigious corporate jobs. But eye-popping bonuses in the semiconductor industry are blurring the line between white- and blue-collar work and shifting the focus toward industry rather than job type. The shift comes as the semiconductor upcycle could present employees at SK hynix with an average of about 800 million won ($540,000) in performance bonuses early next year while chip division staff at Samsung Electronics are expected to receive roughly 600 million won per person on average. Although actual payouts for white- and blue-collar employees may differ, as compensation is tied to base salaries, bonus payout ratios are the same across all employees at SK hynix while the ratio slightly varies at Samsung Electronics’ chip division by the types of chip business. Even after accounting for those differences, average bonuses itself could far exceed the average annual income of 74 million won for workers at conglomerates, including bonuses, as of last year, according to data from Korea Enterprises Federation. Until now, white-collar jobs in Korea have been preferred because they offered stable employment and predictable income streams — a reliable path to the middle class — while an abundant labor supply in the past kept production wages from exceeding those of office jobs, according to Song Heon-jae, an economics professor at the University of Seoul. “But upward pressure on blue-collar wages from demographic-driven labor shortages, together with this year’s semiconductor bonus windfalls, has prompted a reassessment by showing that production-line workers — not traditionally part of the professional elite — can earn large compensation outside the credentialed professions,” Song said. Despite strong export performance, manufacturing employment has declined for 22 consecutive months through April, falling by 55,000 from a year earlier, according to the government data. For manufacturing firms with 300 or more employees, the average monthly total wages of regular workers reached 8.3 million won in 2025, up 6.9 percent from a year earlier. “If similar booms extend to industries such as shipbuilding and biotechnology, the revaluation of blue-collar work could broaden further across the economy and ultimately lead to changes in the college admissions system [that favor white-collar jobs], long resistant to reform due to parental influence,” he added. This reassessment of pay structures is being reflected among young job seekers. A recent survey of 1,800 Gen Z job seekers, born between 1997 and 2012, found that 60 percent preferred a 70 million won production job with shift work over a 30 million won office job without overtime, according to job platform Catch, showing that the prestige of white-collar jobs does not outweigh compensation and hours. The share favoring production roles rose 2 percentage points on-year. Perceptions of blue-collar jobs are also turning more positive, with 68 percent of respondents viewing blue-collar work favorably, up 5 percentage points from a year earlier, driven largely by higher pay. A book on passing the SK hynix admission exam is displayed at a large bookstore in central Seoul on April 16. As the semiconductor industry benefits from the AI boom, interest in employment at chip companies has heated up the market for related study materials. [NEWS1] AI-driven fears The rapid adoption of AI in corporate workplaces is further eroding once-dominant white-collar roles, as companies worldwide cut corporate headcounts at scale while increasingly emphasizing the value of manual labor. Meta Platforms started reducing its workforce by about 10 percent, or around 8,000 jobs, starting mid-May, with more layoffs expected this year, while Amazon confirmed 16,000 corporate job cuts in January. Corporate executives are also increasingly acknowledging the growing importance of manual labor. During a commencement speech at Carnegie Mellon University in May, Nvidia CEO Jensen Huang told students that “electricians, plumbers, iron workers, technicians, builders — this is your time.” Tesla billionaire Elon Musk, who has repeatedly argued university education is overrated, said on the Moonshots podcast in January that even with AI at its current state, society is already “pretty close” to replacing “half of” white-collar jobs. Similar findings were reported by the Bank of Korea, which said in a November 2025 report that youth employment in white-collar jobs like information services, publishing and professional services declined by 23.8 percent, 20.4 percent and 8.8 percent, respectively, over the three years since November 2022. Other data suggest AI is having a more direct impact on white-collar jobs than on manufacturing roles in Korea. While 65.4 percent of nonmanufacturing companies have adopted AI in the workplace, only 42.6 percent of manufacturing had done so, according to a 2025 Korea Labor Institute survey of corporate executives. As concerns over shrinking corporate job opportunities grow, resistance to AI is also mounting. Hyundai Motor’s labor union earlier this year opposed the automaker’s plan to deploy Boston Dynamics’ AI-powered humanoid robot Atlas in manufacturing facilities, while the National Health Insurance Service’s call-center union protested the adoption of AI counselors, according to a local media report in March. Samsung Electronics' Pyeongtaek chip cluster in Gyeonggi [PYEONGTAEK CITY] The anxiety is also increasingly visible abroad, particularly among young job seekers. Former Google CEO Eric Schmidt was booed by students when he spoke about the rise of AI during his address at the University of Arizona’s graduation ceremony earlier this May. Similar sentiment was evident at the University of Central Florida earlier this month, when Gloria Caulfield, vice president of strategic alliances at Tavistock Development Company, mentioned that the rise of AI is “the next industrial revolution.” “Simple repetitive tasks are increasingly being replaced by robots, but blue-collar workers — particularly skilled manufacturing technicians — are likely to be less exposed to AI-related job threats than office workers, whose roles in report writing and analysis are increasingly performed by AI,” said Won Chae-hwan. a business professor at Sogang University. “Even within white-collar jobs, however, roles that require human judgment and emotional intelligence, such as personnel evaluations and customer service, will be difficult to replace with AI.” BY JIN MIN-JI [jin.minji@joongang.co.kr]
- New 'AI scientists' are improving—but reveal their fundamental limits
Many of the most exciting discoveries in science involve highly specialized knowledge and making connections between far-flung facts. Scientists must combine deep analysis with broad reasoning strategies.
Score: 43🌐 MovesMay 24, 2026https://phys.org/news/2026-05-ai-scientists-reveal-fundamental-limits.html - The AI paradox: More automation, more humans, more work | Dan Shipper
Why most work will happen inside Codex or Claude Code, the CLI era is over, every agent needs a human, and why he’s wildly bullish on PMs and designers
- Google’s plan to run your life could break the internet
Google’s plan to run your life could break the internet The Telegraph
Score: 43🌐 MovesMay 24, 2026https://www.telegraph.co.uk/business/2026/05/24/googles-plan-to-run-your-life-could-break-the-internet/ - AI-polished resumes force recruiters to rethink hiring filters
Data from Hunar.AI indicates that over 70% of white-collar candidates use AI to draft their CVs, and for Gen Z, this number exceeds 85%
- Etzioni on AI: The Virgin Unicorns
Twelve AI labs have raised more than $29 billion at a combined valuation approaching $130 billion, without shipping anything a customer can buy. Oren Etzioni examines what history says about how this story ends. Read More
- Everyone is navigating AI security in real time — even Google
We're in the transition period -- all of us.
Score: 41🌐 MovesMay 24, 2026https://techcrunch.com/2026/05/24/everyone-is-navigating-ai-security-in-real-time-even-google/ - Report: watchOS 27 to improve heart-rate tracking; AI health coach may not debut at launch
For the better part of a year, we’ve been hearing about Project Mulberry : Apple’s AI-powered health coach. Back in February, it was reported that these efforts had been scaled back. According to Bloomberg’s Mark Gurman , the features should still be on track for iOS 27, though they may not release until later in the cycle. On the other hand, Apple is apparently going to greatly improve Apple Watch heart-rate tracking with watchOS 27, which could tie in nicely with Apple’s eventual health coach. more…
Score: 41🌐 MovesMay 24, 2026https://9to5mac.com/2026/05/24/apple-improving-heart-rate-tracking-in-watchos-27-mulberry-health-coach-delays/ - Google Cloud Security Uses Instruqt Platform to Train 150+ Practitioners on Agentic AI at Google Next 2026
Google Cloud Security Uses Instruqt Platform to Train 150+ Practitioners on Agentic AI at Google Next 2026 Toronto Star
- The End of the Chatbot Era: 7 Technical Takeaways from Google I/O 2026
Google I/O 2026 showed that AI is no longer just about better chat interfaces. It is becoming an execution layer for long-horizon tasks… Continue reading on Towards AI »
- Kore.ai Launches Artemis to Revolutionize Enterprise AI Agents
Kore.ai today launched the new-generation Kore.ai Agent Platform Artemis edition, the AI-programmable, AI-native foundation that builds, governs, and optimizes the agents, systems, and workflows running across the enterprise. The platform launches initially on Microsoft Azure, with broader cloud availability to follow. The new-generation Agent Platform enables enterprises to deploy production-ready multiagent AI systems in days […] The post Kore.ai Launches Artemis to Revolutionize Enterprise AI Agents appeared first on CXOToday.com .
- Google CEO Sundar Pichai says graduates booing AI will shape its future — and live with its consequences
Google CEO Sundar Pichai says graduates booing AI will shape its future — and live with its consequences Business Insider
Score: 40🌐 MovesMay 24, 2026https://www.businessinsider.com/sundar-pichai-google-graduation-speech-stanford-ai-backlash-eric-schmidt - To A.I. Executives, We’re All Just ‘Meat Computers’
A term first used in philosophy and cognitive science circles has lately taken on a more ominous cast. Moo.
Score: 40🌐 MovesMay 24, 2026https://www.nytimes.com/2026/05/24/business/meat-computer-brain-artificial-intelligence.html - How To Fact Check AI, According To Tech Experts
Learn how to fact check AI with tips and techniques to verify accuracy, avoid hallucinations, and ensure reliable information from tools like ChatGPT.
- The Sequence Radar #865: Last Week in AI: Last Week in AI: Karpathy, Google, Colossus, and the Coming IPO Wave
The next AI frontier might be capital structuring.
- Ubisoft reportedly testing generative AI in Far Cry 7, insider says it 'looks like sh*t' — company recently posted a record €1.3 billion loss
Ubisoft is using an early build of its unannounced Far Cry 7 as a testbed for generative AI tools, according to Tom Henderson of Insider Gaming.
- ByteDance study finds that asking LMMs questions beats making it transcribe text for long document training
ByteDance Seed shows that a 7B model can answer questions on long, image-heavy documents more reliably than much larger models, even when documents are four times longer than anything it saw during training. Instead of transcribing pages, the model learns by answering questions and finding the right passages on its own. The article ByteDance study finds that asking LMMs questions beats making it transcribe text for long document training appeared first on The Decoder .
- Government slammed for ‘shocking’ failure on hyperscale data centre emissions
Scottish government’s NPF4 national planning framework states that "green data centres" will have an "overall negligible impact" on nation’s emissions reduction goals
Score: 37🌐 MovesMay 24, 2026https://www.the-independent.com/news/uk/home-news/scottish-government-hyperscale-ai-data-emissions-b2982766.html - One Job That Is Growing in the A.I. Era? Cybersecurity Experts.
Demand for security engineers has surged as artificial intelligence generates a glut of new code and models like Anthropic’s Mythos create new concerns.
Score: 37🌐 MovesMay 24, 2026https://www.nytimes.com/2026/05/24/technology/one-job-that-is-growing-in-the-ai-era-cybersecurity-experts.html - Ask the Analyst: Audit Analytics in the Age of AI
Ask the Analyst: Audit Analytics in the Age of AI Gartner
- Ajman Chamber maps out AI integration across its services in support of the Ajman AI Programme
Organizes the “Artificial Intelligence and Its Role in Service Development – Agentic AI” Forum