AI News Archive: May 25, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- Google AI Overviews glitch misreads search queries as commands: Report
Google's AI Overviews feature is reportedly misinterpreting words like "stop" and "disregard" as commands, leading to broken search responses and blank spaces
- Google’s AI Overviews Reportedly Broke Down When Users Searched ‘Disregard’ or ‘Stop’
Google Search’s integrated artificial intelligence (AI) experience, AI Overviews, reportedly suffered a snag over the weekend. When users searched specific keywords, such as “disregard” or “stop,” the dedicated space is said to glitch and stop showing any information. This results in a wide blank space that users reportedly had to scroll past to see any resu...
- India's AI ambitions hinge on workforce re-skilling, IBM India head says
India's AI ambitions hinge on workforce re-skilling, IBM India head says Reuters
- Target pushes deeper AI adoption, reviews usage‑based pricing, India head says
Target pushes deeper AI adoption, reviews usage‑based pricing, India head says Reuters
- Ammune.ai Launches First Runtime AI Governance Platform to Discover and Map Shadow AI
Ammune.ai Launches First Runtime AI Governance Platform to Discover and Map Shadow AI azcentral.com and The Arizona Republic
- Q&A: How video helps build robot brains for physical AI
Robots could well be the next trillion-dollar tech opportunity, in no small part thanks to AI. Not surprisingly, that’s led to race by a variety of robotics companies to build industrial and humanoid robots to help (or replace) humans. And to help orient those devices visually in the real world, robot brains are being fed Youtube videos. The idea is to help them understand the environment in which they would work and to spur physical AI. Kate Shen , co-founder of startup Anaxi Labs , is following a different approach to training robot brains. She is crowdsourcing and supplying videos of people performing tasks, which she then shares with robotics makers. Human-scale video, she argues, is critical to train robots because it more accurately captures how robots should perform their tasks, depending on the circumstances around them. More broadly, the technique can also provide a clearer roadmap for physical AI . With that in mind, Computerworld spoke recently with Shen about Anaxi Labs’ physical AI initiatives and how they differ from what other companies are doing. Kate Shen , co-founder of startup Anaxi Labs . Anaxi Labs Tell me about your company and why you started it. “This is very much a … [Carnegie Mellon University] startup. We started this company [when] we realized that when it comes to AI-building [large language models] (LLMs), everybody knows that there are two things on the infra level, chips and data. The same things were happening to robotics as we moved from digital to physical AI. “Except this time…, everybody is aware of [the] difficulty, everybody’s using infrastructure. But when it comes to data, we have to build the data infrastructure from scratch, because unlike LLM, the training data for robots can’t be from the internet. “We realized that it would become a [barrier] sooner or later, and it will turn into a major, major industry. And that’s how we started the company.” Isn’t physical AI data mostly collected from YouTube? What are you doing differently as a company? “You mentioned two approaches, one,using YouTube video, and two, using a simulation. And unfortunately, the two paths were [taken] back then because [of a] lack of better paths. The sheer volume of data needed to train physical AI far exceeds what’s available on the internet, and it needs physical interaction many, many times for each scenario [more] than can be found on YouTube. “We realized, by talking to pretty much all the industry [players] since last year, [there is a] shift to egocentric, meaning like human-based training videos, data. We started investing heavily in building a world-scale data pipeline. We started working with industrial- dense regions…who usually have business covering multiple scenarios — for example, construction, logistics, and especially factory floors. “And the second pipeline is, we can use [a] community model for this and tap into this worldwide [pool of] individuals, consumers who are wanting to upload videos for training purpose[s]. We’re launching, starting this summer, our data collection and annotation app.” What exactly are you trying to collect from the videos? ”The data we collect is simply exactly the task our clients want their robots to do — [an] egocentric view, basically like the two hands in the video doing exactly the same thing, sorting the packages and [having] their barcode scanned. In general, there are about 20 general steps, most commonly seen in industrial factory floor settings, and we’re doing all of them. Increasingly, we’re seeing household scenarios, like cleaning the kitchen, cleaning up the bedroom. “In order for the models to be able to understand [the videos], the second most important thing is annotation. At the early beginning, they only wanted segmentation, captioning and contact point[s]. “But now, in order to have the robot really understand the how and the why behind the scene, they’re increasingly demanding captioning in the format of almost like the chain of [thought]. “For example, a robot sees a slipper. And then we’re going to identify this is what happened, and then you’ve got to grip harder. And that’s the result.” What is your assessment of physical AI, and how does it impact jobs? ”One is surrounding the safety, and the second one is [the] impact on [the] job market. As compared to LLM, in the early LLM days everybody just [got] as much data as possible from the internet. But [for] physical AI, when they place the order, there is a specific category called [failure] and recovery cases, meaning what if something goes wrong, what should the robot do in each scenario. This is a huge difference from the LLM days. Definitely, all the physical AI companies realized that, and they’re building this into their model since the beginning. “[On jobs,] right now, at least at this stage, we’re seeing mostly the upside. There are a lot of small robotic companies making a lot of money by working with the companies affected by [labor shortages]. We’re seeing those demands coming from factories who are struggling with shortage of labor, factories who have a problem hiring because their tasks are too dangerous.”
Score: 46🌐 MovesMay 25, 2026https://www.computerworld.com/article/4175902/qa-how-video-helps-build-robot-brains-for-physical-ai.html - Why European businesses are not using AI tools
Eurostat's 2025 data show that a lack of technical expertise, data privacy concerns, and legal uncertainty remain the main barriers to the use of AI tools among European businesses, even as most companies recognise AI’s potential value.
Score: 46🌐 MovesMay 25, 2026http://www.euronews.com/next/2026/05/25/why-european-businesses-are-not-using-ai-tools - These AI Gurus Are Charging Wall Street Banks $25,000 a Day
Global banks are pouring billions into artificial intelligence yet struggling to automate workflows. Two ex-bankers say they are being hired to ignite the shift.
- Business Brief (May 25): China Pushes AI Chip Self-Reliance
Business Brief (May 25): China Pushes AI Chip Self-Reliance Caixin Global
Score: 45🌐 MovesMay 25, 2026https://www.caixinglobal.com/2026-05-25/business-brief-may-25-china-pushes-ai-chip-self-reliance-102447385.html - Mohamad Moosavi: Accelerating the search for climate solutions with AI
Mohamad Moosavi, Assistant Professor, Chemical Engineering, University of Toronto | Vector Institute Faculty Member The path to breakthrough climate technologies often moves at a frustrating pace. Consider metal-organic frameworks – […] The post Mohamad Moosavi: Accelerating the search for climate solutions with AI appeared first on Vector Institute for Artificial Intelligence .
Score: 45🌐 MovesMay 25, 2026https://vectorinstitute.ai/mohamad-moosavi-accelerating-the-search-for-climate-solutions-with-ai/ - Google’s latest attempt to fix token quotas is here: Say hello to Gemini 3.5 Flash Low
Google rolls out a low-effort variant of Gemini 3.5 Flash to stop simple Antigravity tasks from draining token limits.
- NASA may use a one-legged robot to explore a Saturn moon. Watch it hop.
Engineers are working on a mission concept for NASA to explore Enceladus, a Saturn moon with icy geysers spraying plumes in space.
Score: 45🌐 MovesMay 25, 2026https://mashable.com/science/nasa-enceladus-one-leg-hopping-robot-leap-mission - Introducing the Agent Toolkit for Amazon Web Services
It’s like having your own personal expert AWS solutions architect and data engineer rolled into one. The post Introducing the Agent Toolkit for Amazon Web Services appeared first on Towards Data Science .
Score: 45🌐 MovesMay 25, 2026https://towardsdatascience.com/introducing-the-agent-toolkit-for-amazon-web-services/ - Meta blocks Environmental Reporting Collective’s Instagram reel on Google’s AI data center in Andhra Pradesh
Meta blocked an ERC Instagram reel in India showing Andhra Pradesh farmers alleging forced land acquisition for Google's AI data center, raising concerns about journalistic censorship and transparency. The post Meta blocks Environmental Reporting Collective’s Instagram reel on Google’s AI data center in Andhra Pradesh appeared first on MEDIANAMA .
- AI Expands From Multibillion-Dollar Enterprises to Main Street
Artificial-intelligence agents scrape a bakery’s spreadsheets to help manage its growth.
- Anthropic’s restricted Claude Mythos model may be coming to Claude Code
Anthropic appears to be preparing for the public rollout of the Mythos model, which was announced in April as a restricted model that poses major security risks to private and public software. [...]
- Agentic AI will scale only when enterprises redesign processes, say tech leaders
At ETCIO Annual Conclave 2026, leaders from Axis Max Life Insurance, BSE, Adani Group and Thoughtworks said enterprises must move beyond AI pilots by fixing workflows, defining measurable outcomes and building governance for autonomous systems.
- Japan Cablemaker Rout Exposes Cracks in AI Infrastructure Rally
A $40 billion selloff in a 141-year-old Japanese cable firm has served as a reality check on the fragility of the global AI-driven stock rally.
- Managers Are Struggling to Keep Up with the AI Productivity Boom
To avoid being a bottleneck, they need to change how they work, give feedback, and communicate.
Score: 44🌐 MovesMay 25, 2026https://hbr.org/2026/05/managers-are-struggling-to-keep-up-with-the-ai-productivity-boom - Google adds open source Agent Executor to support AI agents in production
Google adds open source Agent Executor to support AI agents in production InfoWorld
- From test case to control tower: How DXC and ServiceNow are governing enterprise AI at scale
As AI agents take on real-world decisions, governance is becoming the defining challenge of the agentic era.
- Delivery robots are spreading across LA. Residents ‘both pity and hate them’
A region known for its lack of walkability now has more obstacles for pedestrians to contend with Robots have taken over Los Angeles. It’s not just the AI-generated videos that have caused angst in Hollywood. Our streets are full of driverless Waymo vehicles, covered in more sensors and gadgets than the Batmobile. And our walkways are home to fleets of boxes on wheels, hurrying past pedestrians and navigating outdoor bar-hoppers as the robots deliver smoothies and keto-friendly salads. Continue reading...
Score: 43🌐 MovesMay 25, 2026https://www.theguardian.com/us-news/2026/may/25/los-angeles-delivery-robots - How Qwen is enabling AI adoption across Southeast Asia
Artificial intelligence is rapidly becoming part of everyday business operations, with companies embedding AI into workflows, products, and decision-making. Large language models are central to this shift, enabling automation and insight at scale. In Southeast Asia, adoption is accelerating, but many startups and SMEs still face barriers around cost, access, and implementation. This creates demand […] The post How Qwen is enabling AI adoption across Southeast Asia appeared first on e27 .
Score: 43🌐 MovesMay 25, 2026https://e27.co/how-qwen-is-enabling-ai-adoption-across-southeast-asia-20260525/ - This big university system is embracing AI. Students and faculty aren't all on board
The California State University system offers an early look at what happens when an administration commits to a technology that its own community isn't convinced will improve education.
- Venture Capitalist John Doerr Says AI Is the Biggest Tech ‘Tsunami’ Ever
The well-known venture capitalist who bet on Google says that the AI revolution is, if anything, underhyped.
Score: 42🌐 MovesMay 25, 2026https://www.wsj.com/tech/ai/john-doerr-ai-opinion-1d64ee60?mod=rss_Technology - How Google is starting to win the AI race | Commentary
Despite its early stumbles, Google’s Gemini has leapfrogged ChatGPT in relevance and usefulness.
- HR is dead: AI and leadership are taking over
HR is dead: AI and leadership are taking over
Score: 42🌐 MovesMay 25, 2026https://www.khaleejtimes.com/business/innovation-city/hr-is-dead-ai-and-leadership-are-taking-over - Alibaba Cloud lifts MaaS revenue with deeper focus on AI coding
Agent-driven coding could be the next test of its AI cloud strategy.
Score: 42🌐 MovesMay 25, 2026https://kr-asia.com/alibaba-cloud-lifts-maas-revenue-with-deeper-focus-on-ai-coding - The AI Era Is Creating a Bug Hunting Arms Race
As attackers ramp up their AI exploit development, the search for software vulnerabilities is changing rapidly.
Score: 41🌐 MovesMay 25, 2026https://www.wired.com/story/the-ai-era-is-creating-a-bug-hunting-arms-race/ - AI streamlines auto lending and qualifies more buyers for loans
AI streamlines auto lending and qualifies more buyers for loans Automotive News
Score: 41🌐 MovesMay 25, 2026https://www.autonews.com/sponsored/executive-insights/ai-streamlines-auto-lending-qualifies-more-buyers-loans/ - Ask the Analyst: How Can Security Teams Govern Data and AI With Existing Sovereignty Demands?
Ask the Analyst: How Can Security Teams Govern Data and AI With Existing Sovereignty Demands? Gartner
- John Grisham’s New Legal Drama Is a Real Life Fight Against AI Audiobooks on YouTube
Why oh why are people listening to this garbage?
- AI edge will depend on building hard-to-copy systems, says McKinsey
Companies can no longer gain a competitive edge with standard AI models, as nearly 90% of organisations now use large language models said report by McKinsey & Company. True advantage lies in building unique, integrated AI systems and workflows that are difficult for rivals to replicate, turning cognitive work into scalable infrastructure.
- WorkOS Releases auth.md: An Open Agent Registration Protocol Built on OAuth Standards
WorkOS Releases auth.md: An Open Agent Registration Protocol Built on OAuth Standards MarkTechPost
- Apple Is Reportedly Working on a New Gen AI Website Ahead of WWDC 2026
Ahead of Apple’s annual Worldwide Developers Conference (WWDC), a new development has surfaced. As per the report, the Cupertino-based tech giant is readying a new “Gen AI” website, which could be showcased at WWDC 2026. It is currently non-functional and shows a connection timed out error, but the website appears to be registered.
- Maveric Systems Wants Banks to Walk Away From Flashy AI Pilots
CEO Ranga Reddy says Maveric now rejects AI work unless banks meet three strict readiness conditions.
Score: 40🌐 MovesMay 25, 2026https://analyticsindiamag.com/it-services/maveric-systems-wants-banks-to-walk-away-from-flashy-ai-pilots - Schneider Electric sees India data center business outpacing core growth on AI boom
Schneider Electric sees India data center business outpacing core growth on AI boom Reuters
- Asan Medical Center unveils AI-powered private search system
Asan Medical Center unveils AI-powered private search system Healthcare IT News
Score: 39🌐 MovesMay 25, 2026https://www.healthcareitnews.com/news/asia/asan-medical-center-unveils-ai-powered-private-search-system - India CIOs say AI value will come from process readiness, not adoption speed
At ETCIO Annual Conclave 2026, CIOs from Aditya Birla Group, Dr. Reddy’s and Wipro said boards are asking sharper questions on AI ROI, security, governance and measurable business impact.
- Ask the Analyst: Risks & Considerations for AI in Government
Ask the Analyst: Risks & Considerations for AI in Government Gartner
- China launches first humanoid robot lifecycle management platform in Beijing
China has launched the country’s first full lifecycle management service platform for humanoid robots in Beijing. The platform gives each robot a unique digital identity and enables end-to-end tracking from production to recycling. The platform, led by the Ministry of Industry and Information Technology’s Standardization Technical Committee for Humanoid Robots and Embodied Intelligence, assigns every […]
Score: 39🌐 MovesMay 25, 2026https://technode.com/2026/05/25/china-launches-first-humanoid-robot-lifecycle-management-platform-in-beijing/ - Persistent Systems Teams Up With Kong to Simplify Enterprise AI Governance
The collaboration will combine Persistent’s enterprise modernisation expertise with Kong’s API connectivity platform to help businesses govern, integrate and manage AI workloads at scale.
Score: 39🌐 MovesMay 25, 2026https://analyticsindiamag.com/ai-news/persistent-systems-teams-up-with-kong-to-simplify-enterprise-ai-governance - Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk
Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear. AI systems are introducing new layers of technical debt that live across prompts, models, and data dependencies — making these layers less visible, harder to measure, and often more dangerous than traditional debt. A crisis hiding in plain sight The complexities of AI systems and their associated failures have been well documented. A 2025 MIT study found that 95% of AI projects fail to reach production or deliver value. A similar study by S&P Global Market Intelligence found that 42% of businesses scrapped multiple AI initiatives in 2025 — a sharp increase from 17% the previous year. Various reasons are cited for these failures, but most of them point to poorly designed and implemented systems that are complex to manage and have multiple hard-to-monitor failure points, leading to a rapid accumulation of AI debt. Traditional technical debt was localized to the codebase, and bugs were usually easily reproducible. Consequently, bugs could be easily identified during tests and fixed through rearchitecting the codebase. However, AI debt is much more distributed, manifesting across prompts, models, data pipelines, and all associated infrastructure. It is also more intermittent: Due to the probabilistic nature of AI, systems do not always respond the same way, leading to intermittent failures. This makes it much more challenging to identify risks during testing, and also creates a need for more continuous monitoring even post-deployment to prevent gradual drift and worsening performance. The new forms of AI debt AI debt typically manifests across four new forms, each of which comes with its own set of risks. Prompt debt is the most visible of these. A modern version of ‘spaghetti code,' this can include undocumented prompt tweaks, accumulated ‘quick-fix’ prompts that lead to inconsistencies, neglected version control of prompts, and ‘prompt stuffing’ (the cramming of extraneous data or context directly into AI prompts). All these combine to make prompts a form of untyped, untested code without any version control, leading to increased brittleness and vulnerabilities. Model dependency debt is another increasingly common form of AI debt. Most enterprises now depend on a mixture of external models developed by leading foundation model providers; applications and agents are built on top of API calls to these models. Consequently, application logic now depends on models that are external to the core system, and that cannot be clearly controlled. As models update, performance varies and reproducibility is lost — prompts tuned for one model may fail or perform poorly when switched to another model, whether an update from the same provider or from another provider. Most enterprise AI deployments today use retrieval-augmented generation (RAG), which pulls in additional context from enterprise data repositories. Retrieval debt is a consequence of these repositories having messy data, duplicated documents, and outdated information. This causes AI to return technically correct answers that are outdated and no longer relevant, causing downstream failures. Unlike hallucinations, these are harder to detect because they were correct, perhaps even until recently, and hence look correct to any tester. Evaluation debt reflects the lack of standardization in testing and monitoring for AI models and applications. While AI benchmarks exist, they tend to focus on narrow tests and reflect point-in-time results. Most enterprises lack consistent testing standards, ground truth datasets, and real-time monitoring of deployments; there is no equivalent yet of continuous integration /continuous delivery (CI/CD) for prompts. As a consequence, CIOs and CTOs do not have clear visibility into model performance and cannot track improvements or worsening of models. All of these are in addition to traditional forms of technical debt, which still manifest across the tools and systems that AI applications and agents interact with, read from, or write to. A rapid increase in the adoption of AI-generated code (often deployed without inadequate testing) is further aggravating inconsistencies within, and poor maintainability of traditional codebases. The new forms of AI debt combine with these earlier forms of technical debt to compound rapidly and create large-scale risks that can cause catastrophic failure of entire enterprise deployments. Solving for these risks is made even more challenging by the distributed nature of AI ownership – most systems span engineering, product, data, and business teams, leading to unclear accountability when an error is identified. As a result, these risks manifest in the form of escalating compute costs, inaccuracies in AI outputs, and increasing exceptions that need to be handled by humans — leading to projects often stalling and failing due to unclear return-on-investment stories and a lack of trust from users. How enterprises can prevent AI debt AI debt will not be solved by ‘better’ models — failure rates remain high despite models already having high accuracy. The solution to AI debt requires better system design, integration, controls, and changes in organizational culture. First, prompts need to be treated as code. This involves careful version control, documentation, and rigorous testing both pre- and post-deployment for all possible prompt configurations. Best practices from the traditional world of coding — such as the use of smaller prompt blocks instead of large prompt-stuffed walls, or reducing the use of hard-coded parameters — can also help mitigate AI debt. Second, evaluation needs to be built into the entire AI infrastructure stack. Continuous evaluation pipelines need to be established and must reflect a wide variety of metrics measuring both technical and business-aligned metrics. In addition, AI observability systems should be integrated to monitor output quality, failure rates, model drift, and data drift. Third, explainability should be included by default in all AI results to make up for limited reproducibility. Data lineage, models used, and the steps followed should be clearly traceable so as to allow auditability of results and correction in case of any systemic errors. This requires explicit AI debt reduction programs and associated budgets, similar to earlier waves of investment in security or in cloud modernization. These need to be driven at a CXO level by key leaders to prevent costly rework later. Conclusion: A stitch in time Enterprise AI deployments are not just static code; they are living systems that interact with the entire enterprise stack. As a result, the defining challenge in an agentic enterprise will not be building or deploying intelligent systems, it will be maintaining these systems to ensure continued reliability during real-world operation. Enterprises that seek to proactively identify and mitigate AI debt from the design phase itself are the likeliest to build sustainable AI platforms that deliver significant long-term productivity boosts across the organization. Vikram is a principal at Cota Capital, where he invests in early-stage enterprise tech and deep tech companies.
- Perplant raises €1 million to equip tractors with AI “eyes” to cut herbicide use and boost profits for farmers
Perplant, a Danish AgTech startup on a mission to democratise AI in agriculture by supporting sustainable farming with a cost-effective, plug & play and AI-based camera sensor, has raised €1 million in investment. The funding round was supported by private angel investors and industry leaders with deep expertise in agriculture and retail. This capital injection […] The post Perplant raises €1 million to equip tractors with AI “eyes” to cut herbicide use and boost profits for farmers appeared first on EU-Startups .
- Walmart Just Revealed Some Fascinating Numbers About Its AI Shopping Agent, and This Key Trend Mattered Most
One key stat: 35% more per order.
- Gemini’s camera AI thinks Aussie wildlife are people and cats are raccoons
It's also not doing a great job identifying some very Australian vehicles.
- Danny Hayes II and 33 Agency Expand Enterprise AI Governance and Procurement Readiness Advisory Services
Danny Hayes II and 33 Agency Expand Enterprise AI Governance and Procurement Readiness Advisory Services azcentral.com and The Arizona Republic
- How AI is shifting talent discovery from applications to real work
As AI reshapes how work is done, it is also changing how companies identify potential. Resumes reveal less about real capability, pushing employers to value visible execution over credentials. Platforms like ET AI Hackathon 2.0 highlight this shift by showcasing problem-solving, projects, and prototypes, offering companies clearer hiring signals and giving talent a credible path to be discovered through innovation.
- RBI appoints committee on quantum tech in finance
RBI appoints committee on quantum tech in finance
- Anthropic finds 10K flaws
Anthropic discovers 10,000 flaws, Microsoft cancels Claude Code licenses