AI News Archive: May 27, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- Gavin Newsom takes a populist turn on AI ahead of a possible 2028 presidential run
AI is becoming a dominant political issue, with many on the right and the left coalescing around concerns about the rapid growth of the industry.
Score: 45🌐 MovesMay 27, 2026https://www.nbcnews.com/politics/2028-election/gavin-newsom-populist-ai-2028-presidential-run-rcna346926 - A team at Polytechnique Montréal is tackling one of the biggest barriers to scaling artificial intelligence
A team at Polytechnique Montréal is tackling one of the biggest barriers to scaling artificial intelligence EurekAlert!
- Exclusive: Unravel Data launches autonomous optimization engine for Databricks, Snowflake and BigQuery
Unravel Data Systems Inc. is expanding beyond observability and FinOps software with a new autonomous optimization engine designed to automatically tune and remediate enterprise data platforms running on Databricks Inc., Snowflake Inc. and Google LLC BigQuery platforms. The Palo Alto-based company today unveiled Arvix AI, an agentic artificial intelligence system embedded within the Unravel platform […] The post Exclusive: Unravel Data launches autonomous optimization engine for Databricks, Snowflake and BigQuery appeared first on SiliconANGLE .
- Laptop prices are exploding in 2026 — and AI is the biggest reason why
Laptop prices are exploding in 2026 — and AI is the biggest reason why Gulf News
- Majority of Android users aren’t sold on Gemini Intelligence, survey reveals
Google wants Gemini to control your Android phone but users aren't buying it yet.
Score: 45🌐 MovesMay 27, 2026https://www.androidauthority.com/google-gemini-intelligence-on-android-survey-3671460/ - Arab women's voices 'must be heard' in AI revolution
Arab women's voices 'must be heard' in AI revolution The National
Score: 45🌐 MovesMay 27, 2026https://www.thenationalnews.com/news/uk/2026/05/27/plea-for-arab-womens-voices-to-be-heard-in-ai-revolution/ - The race to regulate AI systems has lawyers getting creative
Can an artificial intelligence tool, such as a chatbot, be held responsible if people committed crimes based on information they got from the tool? Should chatbots, which are programmed to closely mimic human thought patterns and are increasingly treated as companions, be subject to legal checks that acknowledge those tools' developing independence or autonomy?
- A G.O.P. Mini-Rebellion & The Mystery of the A.I. Executive Order
A G.O.P. Mini-Rebellion & The Mystery of the A.I. Executive Order Puck
Score: 44🌐 MovesMay 27, 2026https://puck.news/newsletter_content/a-gop-mini-rebellion-the-mystery-of-the-ai-executive-order/ - MPs question minister’s handling of revised AI policy
Parliament’s oversight committee raises questions about the communication ministry’s presentation on withdrawing the AI policy and is unimpressed with remedial processes.
Score: 44🌐 MovesMay 27, 2026https://www.itweb.co.za/article/mps-question-ministers-handling-of-revised-ai-policy/lwrKxv3Y2nNMmg1o - The AI fight brewing inside The New York Times
Unionized tech employees at the Times say the company is violating their contract by using AI tools to monitor employee performance.
- Synopsys raises annual forecast on demand for AI chip design software
Synopsys raises annual forecast on demand for AI chip design software Reuters
Score: 44🌐 MovesMay 27, 2026https://www.reuters.com/business/synopsys-raises-annual-forecast-demand-ai-chip-design-software-2026-05-27/ - 👨🏿🚀TechCabal Daily – SA delays AI policy to 2027
In today's edition: SA sets January 2027 for revised AI policy || Namibia Telecom faces regulatory scrutiny || Pepkor’s phone financing business is growing || PayPal, Vodacom partner in Tanzania
- India's GCC model shifts from cost to capability as AI, talent strains bite
India's dominance in global capability centres is evolving. These hubs are now integrated, managing complex functions beyond support. Rising costs and talent shortages present challenges. Artificial intelligence is poised to reshape operations, demanding adaptation. India's future edge depends on its ability to adjust to these changes and global competition.
- U.S. companies have an AI problem. Indian IT wants to be the solution
As firms find ROI elusive, India’s tech giants are betting they can fill the AI “deployment gap” for U.S. clients, before automation eats their own back-office business.
- Data centre owner DigitalBridge buys energy PE firm ArcLight for $1bn
Tie-up comes as Wall Street heavyweights strike partnerships to uncover new sources of power
- AI is rewiring digital media’s power structure
AI is restructuring how media is produced, distributed, and monetised, forcing platforms to compete less on content scale.
Score: 43🌐 MovesMay 27, 2026https://www.techmonitor.ai/comment-2/ai-is-rewiring-digital-medias-power-structure - Tech giants back new data center climate initiative
Microsoft, Google, Amazon and Meta are teaming up with a nonprofit investor to accelerate new technologies using data centers as test cases. Why it matters: The initiative, led by Elemental Impact, is among the clearest attempts yet to turn the massive buildout of AI infrastructure into a proving ground for new technologies rather than just a new source of emissions. Those technologies include advanced cooling, energy storage and low-carbon building materials. "We see data centers as really important customers for entrepreneurs to commercialize technologies that we've been working on for a long time," said Dawn Lippert, CEO and founder of Elemental Impact, the nonprofit investment firm spearheading the initiative. The big picture: The AI boom is driving a historic surge in electricity demand, increasing fossil fuel use and putting climate goals — including those of major tech companies — on the back burner. Opposition to data centers is also growing, fueled by concerns about rising power prices and AI displacing jobs. "This initiative is coming at the most perfect time because the priorities they have are literally the same priorities that you're hearing from communities," said Ryan Panchadsaram, a top advisor to venture capitalist John Doerr at Kleiner Perkins. Panchadsaram, whose firm is not involved in the initiative, said those include Elemental Impact's focus on community engagement and supporting technologies that enable more efficient use of energy and water. Follow the money: Elemental will invest between $500,000 and $5 million in up to 10 startups through 2027 to support pilot projects in data centers. The tech companies are not formally committing investment dollars, though that remains a possible outcome, Lippert and others involved said. They did provide funding to help launch the effort and will pay annual membership fees. Yes, but: That investment range is relatively small in the world of cleantech. Lippert said the group settled on that amount partly to move quickly over the next year — and because the initiative's value extends beyond funding. Getting connected to data center developers is valuable to startups looking to demo their technologies, Lippert said. That was echoed by Chris Graves, CEO of energy startup Noon Energy, who said faster customer connections can help speed tech development. "It does sound quite valuable to accelerate things," said Graves, whose company has received financial support from Elemental Impact previously but did not know yet the full details of the initiative before its Wednesday release. The intrigue: The initiative also includes philanthropic backing from organizations including the Bill Gates-backed Breakthrough Energy and Builders Vision Philanthropy, backed by Lukas Walton, an heir to the Walmart fortune. The groups are providing multi-year grant funding to Elemental to support its investment into startups, though grant sizes were not disclosed. What they're saying: "There are plenty of promising technologies that exist, but they do struggle to move from the pilot to large-scale deployment," said Melanie Nakagawa, chief sustainability officer at Microsoft. "There's a real need right now to apply the pace and agility of an [investment] accelerator and the venture funding," she added. How it works: Wednesday's announcement of the initiative will also kick off a request for proposals from startups. Technologies under consideration include cleaner materials such as green cement and steel, alternatives to copper wiring, energy storage and new cooling methods, Lippert said. Friction point: Nakagawa declined to predict to what degree this could soften opposition to data centers, but said this initiative is part of Microsoft's overall approach to building the projects. "We recognize that communities are asking really important questions around energy use, water and local impact," Nakawaga said. Reality check: Opposition to data centers runs far deeper than what this initiative alone can solve, including concerns about AI-driven job losses. Still, Lippert said the goal is both to accelerate innovation and ensure local communities are considered. "This is not funding data center expansion itself , but it's working with entrepreneurs to shape how that happens and to help them commercialize ideas," said Lippert, adding that innovation in the spaces of climate, energy and water "can be really beneficial locally." What we're watching: Elemental may expand the initiative if it's successful after 2027. Sign up here for Axios' Future of Energy newsletter.
Score: 43🌐 MovesMay 27, 2026https://www.axios.com/2026/05/27/tech-giants-data-center-climate-initiative - Malware dev tries to steal Claude users' secrets, writes npm slop, leaks own GitHub private token
Script kiddies these days
- Enterprise AI Has A Readiness Problem, Not A Model Problem
Most leadership teams are still asking which AI tool they should buy. The better question is whether their company is actually ready to use the AI tool.
- Building self-improving tax agents with Codex
See how OpenAI, Thrive, and Crete built a self-improving tax agent with Codex, automating filings, improving accuracy, and accelerating workflows.
- Pine Labs in talks with NPCI for ‘autonomous’ UPI payments on Model Context Protocol
As Pine Labs revealed it will enable "autonomous" UPI payments on its MCP server, questions remain over how it works and whether prompts and metadata will be retained for AI training. The post Pine Labs in talks with NPCI for ‘autonomous’ UPI payments on Model Context Protocol appeared first on MEDIANAMA .
Score: 43🌐 MovesMay 27, 2026https://www.medianama.com/2026/05/223-pine-labs-npci-autonomous-upi-payments-model-context-protocol/ - Triomics nabs $22M to bring oncology-specific AI to cancer centers
The Series B round was led by Battery Ventures.
Score: 43💰 MoneyMay 27, 2026https://techcrunch.com/2026/05/27/triomics-nabs-22m-to-bring-oncology-specific-ai-to-cancer-centers/ - AI Leader GE Vernova: Data-Center Customers 'Are Struggling To Get Projects Across The Line'
GE Vernova's CEO noted community pushback on AI data centers, but sees little risk to growth. GE Vernova stock fell but is near a buy point. The post AI Leader GE Vernova: Data-Center Customers 'Are Struggling To Get Projects Across The Line' appeared first on Investor's Business Daily .
- Cogent Security launches autonomous vulnerability response tools as AI-assisted exploits outpace scanners
Cogent Security Inc., a startup that employs agentic artificial intelligence for vulnerability management, today launched two new platform capabilities aimed at compressing enterprise vulnerability response from weeks to hours, as AI-assisted exploit development shrinks attacker timelines to a matter of minutes. The company introduced Zero Day Response and Autonomous Remediation, two features designed to close […] The post Cogent Security launches autonomous vulnerability response tools as AI-assisted exploits outpace scanners appeared first on SiliconANGLE .
- AI music: The fake artists filling up playlists
Is AI about to end music as we know it?
- Tabnine Named a Visionary in the 2026 Gartner® Magic Quadrant™ for Enterprise AI Coding Agents
Tabnine Named a Visionary in the 2026 Gartner® Magic Quadrant™ for Enterprise AI Coding Agents Toronto Star
- Tony Blair and the Reform party both want AI in government, though they can't agree on what for
Former UK PM demands modernization, and right wing party channels inner Musk to call for mass job cuts in public sector
- Why enterprise AI initiatives stall — and what CIOs can do about it
CIOs who launched AI projects over the past year that didn’t deliver on expectations, or failed entirely, aren’t alone. Investments in AI projects have surged, but nearly half of enterprises have struggled to demonstrate business value, according research from Gartner. “People jumped in with unrealistic expectations, and that’s where the biggest disappointments are coming from,” says Manav Misra, chief data and analytics officer at Regions Bank. He and many other practitioners say technology isn’t the problem, it’s everything that surrounds it. Organizational, operational, and structural failures have been derailing projects at every step in the journey. While the CIO’s team may be clear-eyed on model selection, platform architecture, and data pipelines, in many cases the wrong projects were chosen, business outcomes and success metrics weren’t defined with enough granularity, the level of collaboration with business leaders fell short, or users weren’t brought on board from the start. width="1240" height="827" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Manav Misra, Regions Bank Regions Bank AI projects may also be plagued by user fear, uncertainty, and doubt. “We got paid to train our offshore replacements, and now we’re getting paid to train the AIs to replace us,” says Keith Fulton, CDO at banking technology provider Jack Henry. The reality, at least for now, is that while LLMs can improve productivity , they need users to review and validate outputs. AIs, he says, act like brilliant interns that need guidance. “The playbooks of the past aren’t effective,” adds Afshean Talasaz, former chief technology and data officer at Colonial Pipeline. “You need to dig into the details of these things, figure out what you want to do, and make your expectations really clear.” Don’t let any of these unforced errors become your next AI project’s Achilles’ heel. Stop leading AI projects. Start co-leading them. Sumeet Gupta, senior managing director, and AI and digital transformation practice lead at FTI Consulting, says that if you ask a CIO to run an AI CoE on their own, it’s almost never a recipe for success. “CIOs may often end up driving it from a platform-centric view, and that’s not where most of the issues lie,” he says as these aren’t technology projects, but rather business projects that happen to use AI to achieve desired business outcomes. “You need to be co-leading this with the right business owners in the enterprise you’re in, because that’s where the fundamental business problems get solved.” width="1240" height="827" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Sumeet Gupta, FTI Consulting FTI Fulton agrees, saying AI projects need to be led by the business. “Business leaders find the opportunities first, and then AI experts can help them triage those possibilities to determine which should be on a shortlist,” he says, adding not to rely on consultants on this step. “You need an internal partner who will keep you conservative.” Fulton learned this lesson early on. “The only failed experiment was a consulting engagement a year ago that turned into a fishing expedition using AI in the finance department, which failed to find any useful cases based on the technology at that time.” Misra, on the other hand, has a team that includes product managers who come from the business side, so they have a clear understanding of business needs. They meet with business leaders and help them identify viable opportunities. “You need an executive sponsor willing to commit to the value and invest in building it properly, and the business owner must be clear about what success actually looks like,” he says. The business owner should set the success metrics CIOs should work with business leaders up front to determine and agree upon what metrics determine success in terms of addressing desired business outcomes. Both business and technology groups should review those metrics quarterly. “The person who owns the P&L for that business should be responsible for the measurement of that and should determine what the metric should be,” Gupta says. “How it’s measured is something the CIO can opine on, but what that business metric is that’ll feed into the P&L should always be determined by the owner of the P&L.” But you need more than management buy-in to succeed. Users need to be in on it or they won’t be on board with it The best laid plans for any IT project will amount to nothing if employees won’t use the technology, and this is especially true for AI. Gupta recalls an initiative at a firm where they invested in building a wrapper around an AI model that didn’t end up delivering any unique business value. “The AI team in the CoE thought it would be interesting to develop, but didn’t bring the larger user base into those discussions,” he says. There’s fear and cynicism about AI, Fulton adds. “The organizational change management aspects of getting people to see it as a helper and not a replacement is a big challenge,” he says. “But while AI can create recommendations, for example, human review is an essential verification step before delivering anything to clients.” That’s why users need to be involved from the very start and at every step in the process. “I can’t tell you the number of cases I’ve been through where technology teams have built an AI product that did all these things, and the business users didn’t use it because they weren’t in on the process itself,” Gupta says. “That never works in a transformation, especially with technology.” Misra recalls one business unit where they didn’t achieve the level of buy-in needed initially, which slowed down deployment. “Someone would say they’re not sure it’s going to work, and that would start a cycle of doubt,” he says. He recommends identifying and supporting early champions, creating workshops for their peers, and doing a phased rollout with quarterly measures of usage and adoption. Engaging with users from the earliest stages has another benefit of the change not feeling as big because they see it happening in increments. And at Jack Henry, Fulton puts a heavy emphasis on coaching. “We have a team of 10 who help people use these tools,” he says. “If we guide them the right way we can win them over. But if it’s not a fit, we don’t coerce them.” width="1240" height="698" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Keith Fulton, chief data officer, Jack Henry Jack Henry Ultimately, says Gupta, you need to convert users and make them accountable as part of the process. That means making them part of the discussion of how the AI might affect workflows and how any needed changes should be addressed. Think through workflow and change management implications Before using AI to automate, CIOs and stakeholders should review current and planned workflows and how they’ll impact productivity and how employees do their jobs. “Workflow redesign and change management go hand in hand,” says Gupta. “For example, there was an AI product at a firm that didn’t get used for a long time because they didn’t do any change management around it.” The company designed a fairly complex agentic AI workflow to eliminate a high degree of manual labor and improve process accuracy in a crucial part of their business operations. But for the workflow to be properly adopted, they had to address some workforce-related operating model and change management issues. “This wasn’t done in a timely manner,” he adds. “So, while the AI workflow itself was well conceived and properly developed, it wasn’t effectively used for many months.” He applies what he calls first principles to each project: know the problem you’re trying to solve with the workflow, the desired outcomes, and what your inputs are, then rethink how it might work in the future with AI. “You have to design it based on those constraints,” he says. “A lack of reinvention or reimagination around AI is issue number one.” That requires engaging with both business leaders and users. Fulton at Jack Henry has a different view to not redesign everything at first, but start with task automation and build from there. “Business process reengineering has been around for 20 years but has never lived up to its potential,” he says, because it’s expensive and large companies have hundreds, if not thousands, of business processes. Today, he adds, agentic AI works better on simpler business processes than on more complicated ones. That said, agents are starting to understand business processes and optimize them. “Instead of building a workflow diagram with an RPA tool, you just tell the LLM what the steps should be and it determines its own workflow for that,” Fulton says. However, agents capable of autonomously optimizing complex, multi-step enterprise workflows without extensive human configuration are still in the early stages and are unproven at scale . The best intentioned workflow designs and organizational change management plans may not work, however, without the right understanding of the data requirements. Data readiness anxiety paralyzes projects Conventional wisdom about having all of your data ready before embarking on an AI can stymie projects before they get started. “Many companies think if they don’t have the data, it won’t work,” says Gupta. But not all projects require pristine data. Too often, companies stumble on data concerns that don’t apply to their actual use cases. “Data is a big issue for machine learning models but not for most large language models,” Fulton says, unless the project focuses on complex forecasting, customer-level personalization, or sales lead scoring, for instance. But if you need the data in those cases, Misra says to identify the specific data the project requires, and focus only on that so the data problems you have become easier to solve due to AI-based data cleansing, integration, and preparation tools. width="1240" height="828" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Avivah Litan, VP and distinguished analyst, Gartner Gartner There are, however, plenty of legitimate data concerns that should give pause, says Avivah Litan, distinguished VP analyst at Gartner. These include data quality, metadata, and lineage gaps that prevent compliance, explainability, and regulator reporting, as well as siloed datasets and immature metadata management that block regulatory readiness. AI can be smart, but not always reliable Talasaz says governance is a big deal around orchestration and observability. “Agents can do things they weren’t intended to do, and you need visibility into that,” he says. The root cause of big failures is companies ask too much of LLMs and trust them without verification steps, adds Fulton. LLMs, he says, bump into things, make dumb mistakes, and lack context about specific domains. “When assessing outputs, don’t confuse smartness with experience and context, so every AI output needs human review before production use,” he says. For more complex tasks, an LLM can also lose its way when attacking problems that require more context than the available token window can handle. At that point, everything gets compressed to fit, and the LLM can lose its rhythm and start hallucinating. “LLMs can do a large number of these things, but the bigger they get, the more they lose track,” Fulton says.” He’s found that, in practice, shorter, more bounded tasks produce more reliable results than long-running autonomous processes, and he’s structured Jack Henry’s AI use accordingly. Failures may also come from unsanctioned projects that come out of left field. “With shadow AI, the concern is someone builds something for critical work processes in an ungoverned way,” Talasaz says. “Then when it’s wrong or it breaks, there’s no observability, creating sustainability challenges and risks for the organization.” width="1240" height="698" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Afshean Talasaz, former chief technology and data officer, Colonial Pipeline CIO.com While a Gartner survey says 69% of organizations suspect or have evidence of employees using prohibited AI tools, Jack Henry has taken steps to minimize that risk by maintaining strict data governance through mandatory training and approval processes. Policies strictly prohibit unapproved tools, such as public chatbots, and the company provides more than 100 AI-based applications so users hopefully don’t feel the need to go elsewhere. Too many PoCs make an untimely demise Pilots can fail when there’s a disconnect between those who run the pilots and those who expected something different to come from it, Talasaz says. “Be clear about what the pilot is intended to do, whether it’s more about experimentation or if there’s a high degree of certainty of getting an intended outcome,” he says. Some pilots move forward without a complete understanding of the business benefits . Gupta recalls one firm that embarked on an AI initiative without conducting a proper economic analysis on the financial value of the project. This, he says, is an important go or no-go stage that should be undertaken after an AI use case is conceived, but before the business deploys capital into it. “When we came in and did the financial analyses, we found that the AI initiative’s one-time and ongoing operating costs would be more than the median projected savings,” he says. Other projects fall short of what’s needed. The pilot might work 90% of the time, and getting to 99% might take six months of tuning and cleaning up the data. But financial services businesses like Jack Henry require 100% correctness, Fulton says. “A business process automation tool that works 99% of the time is not valuable,” he says, since just one mistake can lose the confidence of business leaders, leaving a pilot dead in the water. It’s not uncommon to discover that how engineers built a PoC doesn’t work at scale or is just too expensive at scale. “You can build a PoC but not have the right design and engineering talent or infrastructure to scale it,” Talasaz says. Another potential setback is time. The original team often goes back to their day jobs once the pilot concludes, taking their knowledge with them as a new team takes over, and that can slow down the project. “I like to have the same team take it from PoC to production,” Talasaz adds. Gupta recommends having explicit proof-of-life milestones, user simulations of agentic workflows, and accuracy thresholds that the PoC must achieve before it can move from one stage to the next. “If you don’t have the right people involved and the right milestones, that’s why these pilots get parked,” he says. Production attained but operational sustainability is unsure Your pilot is now in production and it works — for now. So how do you maintain momentum? “Most teams haven’t fully considered sustainability if they’re having trouble getting projects into production, but you have to think about that,” Talasaz says. Monitoring for ongoing operational issues such as model drift are important, but so are more fundamental issues like risk of vendor lock-in and rising technical debt that can delay and add expenses to upgrades down the road. If unaccounted for during the design and pilot phase, these issues may be baked in by the time you’re in production. Gartner warns against allowing your data, models, or workflows to be locked into a single vendor’s APIs, data lakes or platform tools. Instead, follow open standards, and use open APIs and modular architectures in AI stack design. As for technical debt, Gartner predicts that in the next four years, 50% of enterprises will face delayed AI upgrades as well as rising maintenance costs due to unmanaged gen AI tech debt. It recommends enterprises maintain an AI registry, enforce model cards, implement drift monitoring, and require vendors to provide model-change notices. In other words, apply the same IT lifecycle management disciplines you would to any other IT project. Devil in the details There’s a lot of figuring out to do with AI projects, but the key to success, Talasaz says, is in the detail work, not only on the technology side with the data, governance, and so on, but on how things work in the business. “Get into the nitty-gritty of what the work entails,” he says. “Work down from the desired business outcome and up from the technology foundation.” And if the technology’s part of the problem, don’t discard the idea. The timing just might be off. “Last year we tried several different code assist tools for developers, and they weren’t capable of working on systems as large as ours,” Fulton says. But eight months later, the team tried the same tools again, and now adoption is like wildfire. “Sometimes it’s not never, it’s just not yet,” he says.
Score: 42🌐 MovesMay 27, 2026https://www.cio.com/article/4170940/why-enterprise-ai-initiatives-stall-and-what-cios-can-do-about-it.html - Post-Training Open Legal Agents With Baseten Research
Exploring post-training open legal agents with Baseten research
Score: 42🌐 MovesMay 27, 2026https://www.harvey.ai/blog/post-training-open-legal-agents-with-baseten-research - From Vision to Value: Operationalizing Your AI-Powered Marketing Organization
From Vision to Value: Operationalizing Your AI-Powered Marketing Organization Gartner
- xAI Cursor limits 🚫, DeepSWE 👨💻, China AI travel restrictions 🤖
xAI Cursor limits 🚫, DeepSWE 👨💻, China AI travel restrictions 🤖
- London data centre firm Pure DC secures $2.7BN for European and Middle East expansion
A London-headquartered data centre firm today said it had secured $2.7bn in financing, with the funds earmarked to expand across Europe and the Middle East amid soaring AI workload demands. Pure Dat...
Score: 42💰 MoneyMay 27, 2026https://tech.eu/2026/05/27/london-data-centre-firm-pure-dc-secures-27bn-for-european-and-middle-east-expansion/ - The AI data center race is getting way more complicated
Adjustments by Amazon and Microsoft reflect harsh realities: power grids that take years to expand, land speculators inflating prices, and overwhelmed utilities
Score: 42🌐 MovesMay 27, 2026https://qz.com/ai-data-centers-microsoft-amazon-electric-grid-land-1851782495 - Former Google and Apple Researchers Launch a Startup to Build AI’s Missing Feedback Loop
Trajectory is betting the rapid iteration cycle that supercharged vibe-coding can help all kinds of companies build AI products that learn continuously.
Score: 42🌐 MovesMay 27, 2026https://www.wired.com/story/ex-google-apple-ai-researchers-want-to-make-ai-that-gets-smarter-as-you-use-it/ - Synopsys Shares Fall Despite Earnings Beat With AI and Merger in Focus
Synopsys Shares Fall Despite Earnings Beat With AI and Merger in Focus Barron's
- The generalist is back, this time powered by AI
Last October, an old colleague sent me a message. Floyd and I go way back; we were both IT generalists who spent years climbing the corporate ladder in the Philippines before each of us left corporate to build our own companies. He’d been looking at my product and wanted to catch up. We eventually got […] The post The generalist is back, this time powered by AI appeared first on e27 .
- RevEng.AI raises $15M to reverse-engineer software binaries and hunt down malicious threats
British software supply chain security startup RevEng.AI today said has raised $15 million in early-stage funding after developing technology similar to Anthropic PBC’s Mythos model and working out a way to put it to good use. The startup, officially known as Binary AI Ltd., wants to help organizations use its technology to analyze software at […] The post RevEng.AI raises $15M to reverse-engineer software binaries and hunt down malicious threats appeared first on SiliconANGLE .
- AI’s true value is hiding in your customer conversations
How enterprises can transform raw customer conversations into a unified intelligence engine driving smarter decisions.
Score: 42🌐 MovesMay 27, 2026https://www.techradar.com/pro/ais-true-value-is-hiding-in-your-customer-conversations - MediaTek Dimensity 8550 is ready for Gemini Intelligence on affordable Android flagships
MediaTek has just announced its new Dimensity 8550 chipset, a minor update in most ways, but with the foundational support needed for Android devices that support Gemini Intelligence. more…
- CodeIntegrity raises $5M to put permanent guardrails on unpredictable AI agents
While companies rush to deploy autonomous AI tools, CodeIntegrity has raised new funding to build runtime guardrails needed to keep unpredictable models from leaking sensitive enterprise data. The startup's co-founders recently relocated from the Seattle area to San Francisco. Read More
Score: 41💰 MoneyMay 27, 2026https://www.geekwire.com/2026/codeintegrity-raises-4-8m-to-put-permanent-guardrails-on-unpredictable-ai-agents/ - Perplexity most reliable AI chatbot for work tasks, ChatGPT ranks sixth: Report
Perplexity most reliable AI chatbot for work tasks, ChatGPT ranks sixth: Report
- This new chip could bring Gemini Intelligence to non-flagship Android phones
Don't expect big upgrades elsewhere, though.
Score: 41🌐 MovesMay 27, 2026https://www.androidauthority.com/mid-range-chip-gemini-intelligence-3671538/ - Why single-player AI is holding back the agentic enterprise
Why context and infrastructure are crucial to the agentic enterprise
Score: 40🌐 MovesMay 27, 2026https://www.techradar.com/pro/why-single-player-ai-is-holding-back-the-agentic-enterprise - Sonny reacts to Atlas learning football
Sonny reacts to Atlas learning football
Score: 40🌐 MovesMay 27, 2026https://www.hyundaimotorgroup.com/en/tv/sonny-reacts-to-atlas-learning-football - How to Build Reliable AI Agents Out of Unreliable AI Parts?
How to Build Reliable AI Agents Out of Unreliable AI Parts? Gartner
- Warp’s big bet on building open source with GPT-5.5
Warp uses GPT-5.5 and OpenAI models to coordinate coding agents across local, cloud, and open-source development workflows.
- Can AI really be conscious? Researchers call for more rigorous scientific standards
As artificial intelligence systems become increasingly sophisticated, questions once confined to philosophy are rapidly entering mainstream scientific and public debate: Can AI possess consciousness? Could animals, organoids, or even fetuses have subjective experiences?
Score: 40🌐 MovesMay 27, 2026https://techxplore.com/news/2026-05-ai-conscious-rigorous-scientific-standards.html - Marvell Boosts Annual Forecast, Citing AI-Fueled Demand
Marvell Technology Inc. delivered a quarterly forecast that exceeded analysts’ estimates and boosted its outlook for the year, citing demand for chips used in AI data centers.
- Build vs. Buy AI Agents in Enterprise Applications
Build vs. Buy AI Agents in Enterprise Applications Gartner
- When GPU utilization lies: The FinOps blind spot in secure AI training
Enterprise cloud teams are trained to act on utilization data. If a virtual machine is idle, resize it. If storage is overallocated, reclaim it. If a GPU appears underused, move the job to a smaller instance. That logic is central to modern FinOps. It helps organizations reduce waste, improve forecasting and keep cloud spending under control. But secure AI training introduces a different problem: sometimes the utilization signal is technically true and operationally misleading. A GPU can look underused even when the workload is not over-provisioned. In privacy-preserving machine learning, low accelerator utilization may indicate a memory-bound bottleneck, not excess capacity. If a cloud optimization process treats that signal as ordinary waste, the recommended fix can make the job slower and more expensive. For CIOs, this is not just a GPU tuning issue. It is a cloud governance issue. As I have noted previously, IT leaders must look beyond the cloud bill to understand the hidden operational costs of AI governance The utilization number does not explain the bottleneck Traditional cloud right-sizing depends on a simple assumption: low utilization usually means unused capacity. That assumption works for many enterprise workloads. It can work for web services, batch jobs, databases and standard compute jobs. But secure AI training can break that assumption because the workload shape changes. In my IEEE systems research on privacy and robustness in machine learning , I profiled what happens when trust controls are added to model training. The important lesson for CIOs was not only that secure training costs more, but it was that secure training can change what infrastructure metrics mean. On a controlled NVIDIA V100 GPU setup, privacy-preserving training increased cost by 3.55x on a vision workload and 2.96x on a tabular workload. Robustness training increased cost by 4.07x on the vision workload. Those cost multipliers matter. But for FinOps teams, the deeper finding is this: The workload became less aligned with the hardware signals that cloud teams often use for rightsizing. Why privacy-preserving training can look inefficient Modern AI accelerators are very good at large, dense mathematical operations. Standard model training often keeps these accelerator units busy because the work can be organized into large blocks of computation. Differential privacy training often requires per-example gradient computation and clipping. Instead of pushing most of the work through large, efficient operations, the system performs more fine-grained steps across individual training examples. That changes the performance profile. In my study, this pattern created memory-bound behavior and reduced effective use of specialized GPU compute units such as Tensor Cores. To a dashboard, that can look like underutilization. To a systems engineer, it means something more specific: the job is not waiting because the GPU is too large. It is waiting because the workload is constrained by memory movement and per-example operations, simply those are not the same problem. The FinOps risk: Right answer, wrong context Automated cloud recommenders are useful because they identify resources that appear oversized or idle. The problem is not that these tools exist. The problem is applying a generic right-sizing rule to a specialized AI workload. A standard recommendation workflow might ask, “Is the accelerator busy?: For secure AI training, CIOs need the team to ask, “Why is the accelerator not busy?” If the answer is idle capacity, downsizing may save money. If the answer is memory-bound privacy computation, downsizing may increase total cost. A smaller instance may have a lower hourly price, but cloud bills are not based only on hourly price. They are based on hourly price multiplied by runtime. If the smaller instance extends the training job enough, the total bill can rise. That is the FinOps blind spot: a recommendation can look correct on a utilization dashboard but fail when measured against the full training job. Secure AI needs a different exception policy Enterprise IT already treats some workloads differently. Regulated databases, security-sensitive systems and latency-critical applications often have special infrastructure policies. Secure AI training needs similar exception handling; a model training job that uses differential privacy or adversarial training should not be evaluated the same way as an idle development server. These workloads can produce unusual utilization patterns because the algorithm itself changes the way hardware is used. 1. Tag secure-AI training jobs FinOps teams need to know when a training job uses privacy-preserving or robustness-oriented methods. A simple workload tag can prevent the job from being evaluated as ordinary compute. The tag should tell cloud teams: Low utilization may be caused by the algorithm, not by waste. This gives FinOps, MLOps and infrastructure teams a shared signal before any right-sizing decision is made. 2. Treat rightsizing as a review trigger, not an automatic action For secure AI jobs, an automated recommendation should start an investigation. It should not automatically become a change request. Before moving the workload to a smaller instance, the team should answer four questions: Is the workload compute-bound or memory-bound? Is the bottleneck caused by data loading, memory bandwidth or per-example privacy operations? Would the smaller instance reduce total job cost, or only reduce hourly rate? Has the team measured runtime impact before approving the change? This shifts FinOps from simple utilization management to workload-aware cost governance. 3. Bring MLOps into FinOps decisions FinOps teams understand pricing, commitment plans, chargeback and utilization. But secure AI workloads require another layer of interpretation. Someone must understand what the training algorithm is doing. DP-SGD and PGD do not merely consume more GPU time. They change the computation pattern. That means utilization percentage alone is not enough to make an infrastructure decision. CIOs should connect FinOps, MLOps, AI governance and infrastructure engineering before applying cost recommendations to secure AI training workloads. 4. Measure total job economics, not only instance utilization The cheapest instance is not always the lowest-cost option. For secure AI training, CIOs should require teams to compare: Hourly cost Total runtime Energy use Job completion time Model utility impact Infrastructure bottleneck profile To truly optimize these economics, teams must look beyond the hardware and apply model-level deep cuts to slash AI training costs . Ultimately, a GPU that looks underused may still be the better economic choice if it completes the workload faster and avoids a longer memory-bound run. Failing to account for the model utility impact during these infrastructure changes can easily lead organizations into the AI accuracy trap , where cost savings inadvertently ruin real-world performance. The CIO takeaway The next phase of enterprise AI will require more than model accuracy and fast experimentation. Organizations will need AI systems that are private, robust, governable and economically sustainable. In ordinary cloud operations, low utilization often means waste. In secure AI training, low utilization may mean the workload has exposed a hardware-software mismatch. The rule for CIOs is simple: Do not right-size secure AI training jobs until you understand why the accelerator is underused. In trustworthy AI, utilization is not always truth. This article is published as part of the Foundry Expert Contributor Network. 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Score: 40🌐 MovesMay 27, 2026https://www.cio.com/article/4176754/when-gpu-utilization-lies-the-finops-blind-spot-in-secure-ai-training.html