AI News Archive: July 27, 2026 — Part 2
Sourced from 500+ daily AI sources, scored by relevance.
- HHS taps AI to accelerate research for chronic disease
HHS taps AI to accelerate research for chronic disease Healthcare IT News
Score: 58🌐 MovesJul 27, 2026https://www.healthcareitnews.com/news/hhs-taps-ai-accelerate-research-chronic-disease - Intel Beats Expectations as AI Infrastructure Boom Expands Beyond GPUs
Intel beat quarterly expectations as AI infrastructure spending boosted demand for its data center processors and foundry business, signaling broader growth beyond GPUs. The post Intel Beats Expectations as AI Infrastructure Boom Expands Beyond GPUs appeared first on TechRepublic .
Score: 58🌐 MovesJul 27, 2026https://www.techrepublic.com/article/news-intel-ai-infrastructure-earnings-foundry-growth/ - China’s memory chip makers ride AI boom to new power — and U.S. scrutiny
China’s memory chip makers ride AI boom to new power — and U.S. scrutiny The Japan Times
Score: 58🌐 MovesJul 27, 2026https://www.japantimes.co.jp/business/2026/07/27/tech/china-chip-makers-ai-us/ - NIST unveils new AI evaluation platform
The AI Technology Evaluation will provide exclusive data to grade models’ performance in select areas.
Score: 58🌐 MovesJul 27, 2026https://www.nextgov.com/artificial-intelligence/2026/07/nist-unveils-new-ai-evaluation-platform/415035/ - Enigma raises $71M to make controlling a robot as easy as adjusting the volume
The massive seed round was led by Index Ventures and Ribbit Capital, with participation from Sarah Guo's Conviction Partners.
Score: 58💰 MoneyJul 27, 2026https://techcrunch.com/2026/07/27/enigma-raises-70m-to-make-controlling-a-robot-as-easy-as-adjusting-the-volume/ - Amazon closes S.F. AI lab, lays off 1,100 Florida warehouse workers
Amazon closes S.F. AI lab, lays off 1,100 Florida warehouse workers San Francisco Chronicle
Score: 56🌐 MovesJul 27, 2026https://www.sfchronicle.com/tech/article/amazon-closes-s-f-ai-lab-lays-1-100-florida-22362487.php - Spain’s Multiverse Computing raises $570m at $1.7bn valuation
The AI compression start-up's funding round – co-led by Forgepoint Capital International, BNPP SIVF and Bullhound Capital – values the company at a fivefold step-up from its Series B. Read more: Spain’s Multiverse Computing raises $570m at $1.7bn valuation
Score: 56💰 MoneyJul 27, 2026https://www.siliconrepublic.com/start-ups/spains-multiverse-computing-raises-570m-at-1-7bn-valuation - NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding
Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware...
- AgiBot starts Hong Kong IPO process
Chinese humanoid robot maker AgiBot has initiated the process for a Hong Kong IPO. The move follows earlier reports that the Shanghai-based company had selected banks and was preparing for a listing. Founded in 2023, AgiBot develops humanoid robots and embodied AI systems for industrial and commercial applications. The company has also released AgiBot World, […]
- Nvidia’s New Rubin Servers Offer Early Relief to Cloud Providers
Nvidia’s New Rubin Servers Offer Early Relief to Cloud Providers The Information
- Reimagining Independence: How Meta’s AI Models Are Helping the University of Pittsburgh Transform Assistive Robotics
Reimagining Independence: How Meta’s AI Models Are Helping the University of Pittsburgh Transform Assistive Robotics AI at Meta
Score: 55🌐 MovesJul 27, 2026https://ai.meta.com/blog/assistive-robotics-university-of-pittsburgh-sam-dino/ - Wipro and Databricks Join Forces to Supercharge Enterprise Data and AI
Wipro Limited today announced an enhanced partnership with Databricks, the Data and AI company, to enable enterprises to modernize data foundations and accelerate AI adoption. As part of this partnership, Wipro has set up a self-contained Databricks business practice to drive industry solutions and accelerators to deliver business value through industry-specific offerings. The enhanced partnership […] The post Wipro and Databricks Join Forces to Supercharge Enterprise Data and AI appeared first on CXOToday.com .
- This Is Donald Trump’s AI Brain Trust
“It’s not an argument with two sides, it’s an argument with 10 sides,” one senior administration official tells WIRED about how US AI policy is being shaped.
- Meituan expands AI push with new platform for businesses
Meituan said CatPaw has already been deployed internally, covering 90,000 employees, with more than 30,000 agents built.
- Apple Watch shipments jumped 21% in a single quarter to claim the fastest growth of any top-10 smartwatch brand, and the on-device AI layer now powering one in four new smartwatches is changing what these gadgets are actually for
Something unusual happened in the global smartwatch market at the start of this year. One brand did not just grow. It grew faster than everyone else in the top ten, at a pace the category had not seen in years. And the reason buried inside that number is not the one most people would guess. ... Read more
- 'Largest wealth creation event in San Francisco’s history' — Why the Bay Area’s AI boom is bigger than you may think
The Bay Area's concentration of AI-related unicorns surprised even the authors of a new report that looked at how artificial intelligence, blockchain and the Internet are converging.
- Google promised not to show ads based on your emails. AI could undo that
Since 2017, Google has promised not to show ads based on users’ Gmail messages. While that policy isn’t changing today, Google may be laying the groundwork for personalized ads based on AI insights from users’ inboxes. Google’s support documents already acknowledge that it may show ads based on users’ interactions with AI, and with new Personal Intelligence AI features in Google Search, those interactions can now include data from Gmail. Let’s say, for instance, that you’re researching new cars. Personal Intelligence could make recommendations by looking to your email to figure out what you drive now, what kind of activities you might use the car for, and who else you’ll be driving around in it. AI allows Google to synthesize a clearer picture of who you are. The only question is whether those particular AI interactions involving personal data will become fair game for targeted ads. Allie Bodack, a Google spokesperson, says via email that the company isn’t using Gmail data for targeted ads right now. She points to a recent statement to Search Engine Land saying that when users enable Personal Intelligence features, they currently won’t see ads in the AI Mode of Google Search. Asked if Google is using Personal Intelligence to target ads elsewhere—for instance, in regular Google searches—Bodack says “that’s not the case today.” But neither that response nor Google’s statement to Search Engine Land rule out showing ads based on Gmail data in the future, and the company’s statement acknowledges that ads are coming to AI mode even for users who’ve set up Personal Intelligence. While Google is proceeding with caution due to the sensitive nature of users’ email communications, the insights it can gain from that data may be too useful to pass up. How your emails could become targeted ad fodder Google introduced Personal Intelligence in January, and made it available to all U.S. users in March . Once activated, it allows Google to reference personal information from Gmail, Google Calendar, and Google Photos when users search in the Gemini app or in Google Search’s AI Mode. For instance, if you’re shopping for clothes, Google might look through your purchase receipts in Gmail to figure out what brands and styles you like. If you ask for places to visit, Google might use your emails and photos to figure out where you’ve already been. As noted by a Google support document , these AI responses become part of your Search Services History, which is a record of past search interactions tied to your account. That same document mentions that Google uses this history for targeted ads. (Google also uses these interactions to train its AI models, albeit with personal information removed.) “Your saved history helps tailor your experience on Search services and across Google,” the document says. For example, it helps you revisit previous searches and get personalized recommendations and ads, based on your personalization settings , the document says. This doesn’t mean Google is scanning entire inboxes to build advertising profiles of its users. Instead, it implies that if you ask Google’s AI a question that requires information from your email to answer, that response could part of the record that Google uses for ad targeting. “Email data is particularly valuable because it reveals high-intent signals about travel plans, purchases, subscriptions, life events, and professional interests that are often more predictive than browsing behavior,” Forrester principal analyst Nikhil Lai says via email. “While Google emphasizes that Personal Intelligence is designed to improve AI responses, consumers are likely to question where the line exists between personalization and advertising.” Personal Intelligence is an opt-in feature; there’s a page on Google’s website allowing users to connect or disconnect their Google Workspace (Gmail, Calendar, Drive) and Google Photos accounts. Still, Google has been pushing Personal Intelligence via pop-ups in its Gemini app , so it’s possible that users may enable the feature without fully understanding the implications. Why it matters To be sure, Google already has lots of other ways to understand who you are. Many of the websites you visit and apps you use have embedded tracking code from Google for monitoring your activity beyond its own websites, and services like Chrome and Google Search give the company a detailed view of what you’re looking for. But with Personal Intelligence, Google has the ability to generate even deeper insights into users’ lives. A story from June by David Pierce of The Verge is particularly instructive, as he demoed an upcoming AI agent from Google called Spark. With access to Pierce’s Gmail, Spark was able to determine his wife’s dietary preferences, the exact ages of his two children, and the names of his entire family (including his parents). “I know that Google knows an incredible amount about me—add up my emails, my calendar, my photos, and my search history, and you’ve pretty much got me pegged. But seeing Spark treat all that data not as something to be protected, but as something to be mined, just feels bad,” Pierce wrote. Of course, mining that information for advertising purposes would be the logical next step for a company whose parent, Alphabet, derived 73% of its revenue from ads last year. While Google says it’s not doing that today, it’s not guaranteeing that it’ll stay that way forever. Lena Cohen, a staff technologist for the Electronic Frontier Foundation, says features often introduce new vectors for companies to collect or misuse personal data, and that users deserve more transparency from Google about how that data will be used. “It’s unacceptable that it takes a journalist asking the company, and that it’s so unclear to regular Google users how their emails may or may not link to Google’s ad targeting system through this new AI feature,” Cohen says. Google’s privacy policy is at least clear, noting that “[w]e don’t show you personalized ads based on your content from Drive, Gmail, or Photos.” It may be worth keeping an eye on whether that changes.
- DIAGNOS Receives Health Canada Medical Device Licence for CARA System, Bringing AI-Assisted Retinal Image Analysis to Canadian Optometrists
DIAGNOS Receives Health Canada Medical Device Licence for CARA System, Bringing AI-Assisted Retinal Image Analysis to Canadian Optometrists Toronto Star
- How Meta Got Everything It Wanted in a Secret Louisiana Data Center Deal
A Times examination details how the Silicon Valley giant used private talks with local officials to start a project big enough to cover nearly six square miles.
Score: 54🌐 MovesJul 27, 2026https://www.nytimes.com/2026/07/27/technology/meta-data-center-louisiana.html - Microsoft’s Project Perception Announcement And How To Implement It Right
Today, Microsoft announced Project Perception, a series of red, blue, and green team agents designed to be coordinated together in an agentic architecture to evaluate infrastructure and close gaps as close to autonomously as possible. The red team agents find potential paths to compromise. The blue team agents prioritize and evaluate them. The green team […]
Score: 54🌐 MovesJul 27, 2026https://www.forrester.com/blogs/microsofts-project-perception-announcement-and-how-to-implement-it-right/ - Is open source the answer to rogue AI agents? Nvidia's new alliance says yes
As AI cybersecurity incidents ramp up, companies are racing to find a fix.
Score: 54🌐 MovesJul 27, 2026https://www.zdnet.com/article/is-open-source-the-answer-to-rogue-ai-security-incidents-nvidia-thinks-so/ - Sam Altman to meet with Trump administration, senators this week. Here's what he plans to say
Altman will preview the capabilities of the company's upcoming family of AI models and answer questions about cybersecurity and open-weight models.
- AI and robotics accelerate search for better gut microbiome therapies
AI and robotics accelerate search for better gut microbiome therapies EurekAlert!
- Apple AI glasses are set for 2027 debut, report claims
It sure sounds like Apple is going to jump into the AI smart glasses race in the first half of next year.
- Satya Nadella says companies that trust one AI for everything may not survive
Companies without their own models — or without a layer of AI infrastructure known as AI gateways to separate their prompts from the model itself — will be in trouble, Nadella says.
Score: 52🌐 MovesJul 27, 2026https://techcrunch.com/2026/07/27/satya-nadella-says-companies-that-trust-one-ai-for-everything-may-not-survive/ - 5 Things to Know About Meta’s Giant Data Center in Louisiana
The road to the giant project was paved with secret meetings and an ever-expanding building plan.
Score: 52🌐 MovesJul 27, 2026https://www.nytimes.com/2026/07/27/business/meta-data-center-louisiana-takeaways.html - AT&T unveils telco open AI model
Even as the world of IT and communications was coming to terms with the potential ramification of the agentic artificial intelligence (AI)-based hack on AI company Hugging Face, US comms giant AT&T has launched OTel 2.0, a dedicated model for the telecoms industry. The telco says the announcement highlights a broader shift towards domain-specific AI. That is rather than relying solely on frontier models, it is working with global mobile trade association the GSMA , Microsoft, AMD, Dell and Red Hat to build specialised telecom models that improve accuracy for network operations while reducing infrastructure costs. The offer is built on Gemma 4 31B-IT, an open multimodal model built by Google DeepMind that handles text and image inputs and can process video as sequences of frames, and generates text output. It was trained using 400 billion telecom-specific tokens selected from more than 1 trillion processed tokens. Alongside the model, AT&T has revealed it has developed an AI Gateway that intelligently routes prompts to the most cost-effective model for each task. Processing an average of 45 billion AI tokens every day, the system is already claimed to be cutting AI inference costs by up to 90%, saving the company millions while maintaining performance. Explaining the reasons for the launch, AT&T noted that artificial intelligence was now a cornerstone of telecommunications’ future, and that because it worked with what it called some of the most complex data sets in the world, it had a real opportunity to transform how to automate complex network operations and deliver more personalised customer experiences. However, stressed AT&T chief data and AI officer Andy Markus, the company could not lose sight of one of the most critical responsibilities: using AI efficiently. A new, intelligent AI gateway could crack the cost challenge that is rapidly emerging with AI usage. “We’re focused on efficiency in two primary ways: we’re using a proprietary cache-aware router to select the most cost-effective models for each task, and we’re training more open-source models to address telco-specific needs for accuracy,” he said. “And both of these approaches are being actively used in production to drive real results, not just theoretical ones. “Running advanced AI models can be expensive, especially at AT&T’s scale: an average of 45 billion tokens per day. People often default to the latest and greatest models, but only a small percentage of the tasks we run require that level of sophistication. Many can be handled by lower-cost models without sacrificing performance … We’ve built an AI Gateway that goes beyond simple model selection: it uses cache-aware routing to intelligently match each task to the most cost-effective model – without compromising on quality. At each turn, the gateway weighs speed and cost with the expected quality of the output, then routes the prompt to the best model.” For its part, the GSMA stressed how the telecoms industry simply needs its own models. To that end, it said general-purpose AI models have come a long way, but they weren’t built with telecoms in mind. It believes that to ask a general-purpose AI model to interpret an industry standard or troubleshoot a live network issue, “the cracks start to show”, not because the models are not capable, but because the data they learned from “barely touches this domain”. In a blog post, it added: “That gap shows up in the results. The top three performers on the Open Telco AI benchmarks are all domain-adapted models, not general-purpose ones, with the new OTel 2.0 model top of the Open Telco AI leaderboard . This clearly illustrates that domain adapted models are highly accurate and can be significantly smaller in size” Moreover, the GSMA noted that just as healthcare, financial services and manufacturing are developing domain-specific AI approaches, the telecoms industry needs models trained on its unique standards, protocols and operating environments. This, it said, was not just about accuracy, it was about enterprise requirements; reducing costs and maintaining control by deploying models across clouds and on-premise as needed. The GSMA concluded that accuracy and requirements together are vital for operators deploying telco-specific use cases, like network troubleshooting, product development, network configuration and more. Read more about AI in networking How AI is being used to manage networks : Network management is becoming reliant on artificial intelligence-enabled tools, which use machine learning based on network monitoring data. Nokia accelerates AI for networking drive : Spate of activity sees global comms tech provider announce expanded collaboration with hyperscaler, as well as a joint proof of concept with data and AI company to support autonomous networks for the AI era. AI workloads to test mobile network capability : Research across 22 markets finds which 5G network metrics emerging AI use cases will place under stress, relative to standard internet traffic. R&A drives AI critical networking infrastructure refresh at Open : Golf governing body partners with networking and comms giant to deliver critical networking infrastructure to major championships and new global headquarters bringing AI-based next-gen connectivity.
Score: 52🤖 ModelsJul 27, 2026https://www.computerweekly.com/news/366646218/ATT-unveils-telco-open-AI-model - Software-first to Agent-first: How agentic AI is reshaping healthcare
Software-first to Agent-first: How agentic AI is reshaping healthcare Techcircle
Score: 52🌐 MovesJul 27, 2026https://www.techcircle.in/2026/07/27/software-first-to-agent-first-how-agentic-ai-is-reshaping-healthcare/ - Kimi K3 and Kimi K3 Fast with ZDR and US-based providers now on AI Gateway
Kimi K3 from Moonshot AI and its faster serving path, Kimi K3 Fast , are now available from US-based providers on AI Gateway, including Baseten and Fireworks. Zero Data Retention (ZDR) is also supported for both models. Running Kimi K3 on US-based providers lets teams with data residency and compliance requirements use the model on US infrastructure. Because AI Gateway serves the models from multiple providers, it automatically routes across them for failover, higher uptime, and more available throughput than any single provider offers. You call the same moonshotai/kimi-k3 model ID, and the gateway handles provider selection and fallback. Kimi K3 Fast trades a higher per-token cost for lower latency. Request it with the speed option on the base model, which stays on moonshotai/kimi-k3 and falls back to standard speed when the fast tier is unavailable. Alternatively, use moonshotai/kimi-k3-fast . The fast variant costs ~50% more than the base model. To use Kimi K3, set model to moonshotai/kimi-k3 in the AI SDK : US inference To route Kimi K3 requests to use only US data centers for inference, set inferenceRegion . Regional pricing is ~10% more than the regular variant. Zero Data Retention Zero Data Retention for Kimi K3 is also available. Turn on Zero Data Retention for every request from the AI Gateway dashboard settings , or set it per request with zeroDataRetention : Providers and endpoints To see every provider serving Kimi K3, along with per-provider pricing, supported parameters, uptime, throughput, and latency, call the model endpoints API: Model prices vary by provider and variant type. Use Kimi K3 in your coding agent Run vercel ai-gateway coding-agents setup and select Kimi K3. This will detect the agents on your machine, provision an AI Gateway key, and write their config. See how to set it up via the Vercel CLI . Try Kimi K3 in the model playground . Read more
- Meet the new humanoid with smart skin (I touched it)
Gene.01 from Generative Bionics features smart skin with touch and proximity sensors designed to improve robot awareness and interaction.
- Penn State Receives $20M NSF Grant for AI Research
Penn State University will use a four-year grant from the National Science Foundation to establish a new laboratory for studying thin-film materials used to create semiconductors.
Score: 52🌐 MovesJul 27, 2026https://www.govtech.com/education/higher-ed/penn-state-receives-20m-nsf-grant-for-ai-research - More cracks emerge in AI-related bonds as Meta, Microsoft earnings loom
‘The money has to come from somewhere,’ says Bryce Doty at Sit, of pressure heavy AI-debt supply and the rest of the bond market
- Nvidia signs deal to deploy edge AI on future Moon missions — laying the groundwork for a permanent human home in space
Nvidia will provide the backbone to Lunar Outpost, a company dedicated to in-space infrastructure and extra-terrestrial economy.
- The rise of the ‘internet of agents’ and why it changes everything
For the past two years, companies have been racing to build AI agents. Most of that work has been focused inward: Embedding agents inside existing enterprise applications, workflows and teams. Now a more consequential shift is already underway. AI agents are beginning to operate beyond the boundaries of the enterprise, interacting with external systems, coordinating with partner platforms and, increasingly, engaging with other agents owned by different organizations. This is where the architecture changes. AI is moving outside the enterprise boundary The next wave of AI is not about what happens inside your company. It’s about what happens between companies. Consider what this enables: A procurement agent negotiating dynamically with a supplier’s pricing agent A healthcare provider’s agent coordinating eligibility and claims in real time with an insurer’s system A bank’s risk agent interacting directly with external compliance and fraud detection agents These are not future scenarios. The underlying capabilities already exist. What’s changing is the expectation that these interactions will happen continuously, autonomously and across organizational boundaries. The coordination problem no one solved That shift exposes a problem most enterprises have not yet designed for: Agents must be able to communicate, coordinate and establish trust outside the systems they were built in. Today’s enterprise stack was not built for that. Every company is developing agents on different frameworks, with different data structures and behind different security models. When those agents need to interact across companies, complications arise as there is no shared identity, agreed upon standard messaging and no way to understand or build trust with external agents. Without a shared identity model, agents can’t reliably authenticate each other or verify permissions. Every interaction has to be explicitly defined: Who can call what, under which conditions and with what level of access. That turns even simple cross-enterprise workflows into a web of point-to-point integrations. The result is predictable: Most “agent-to-agent” workflows today are brittle, hard-coded and manually maintained. They work in isolated use cases, but they don’t scale well. Why the industry is now building an ‘internet of agents’ This is exactly why a new layer of infrastructure is emerging. Efforts like AGNTCY are trying to solve a very specific problem: How do agents from different companies find, verify and communicate with each other securely? AGNTCY was initially open-sourced by Cisco in March 2025 and has since grown to include more than 75 supporting companies, with Cisco, Dell Technologies, Google Cloud, Oracle and Red Hat joining as formative members under Linux Foundation governance. This is already moving from concept to implementation. A recent Cisco Outshift and ServiceNow example shows AGNTCY being used to enable governed, multi-agent workflows across enterprise platforms; in this case, connecting Cisco’s agentic network intelligence with ServiceNow’s workflow and governance layer. “We launched the internet of agents in early 2025, and subsequently built AGNTCY to solve a real coordination problem: How agents from different vendors and companies can find, verify and collaborate openly with each other,” said Vijoy Pandey, SVP and GM, Outshift by Cisco. “We’ve put this into practice with many partners, like this example from ServiceNow — using AGNTCY to enable secure, multi-agent workflows that connect platforms that wouldn’t otherwise talk to each other. By creating a shared layer for discovery, identity, communication and observability, we’re moving past brittle, one-off integrations toward an interoperable ecosystem where agents can actually work together to deliver results as the world has envisioned.” The ServiceNow example is not yet the full cross-enterprise version of the internet of agents. It is an early proof point inside the enterprise stack: Agents from different platforms coordinating through a shared layer rather than through brittle, custom integrations. The next step is applying the same standards and governance patterns across company boundaries, where agents owned by different organizations can discover, verify and work with one another safely. AGNTCY provides foundational components: Discovery (so agents can find each other and understand their capabilities), identity (cryptographically verifiable credentials and access control), messaging (structured, secure, safe communication) and observability (end-to-end visibility into what agents are actually doing). The ambition is bigger than a framework: AGNTCY is trying to become part of the shared infrastructure that lets agents operate safely across company boundaries. This isn’t one standard. It’s a stack AGNTCY is not alone. It’s part of a broader, fast-forming ecosystem tackling different layers of the same problem. Emerging agent-to-agent (A2A) approaches are beginning to define how agents exchange messages and coordinate tasks across systems. The Model Context Protocol (MCP) is helping standardize how agents access tools, APIs and external context. At the same time, a range of academic and open efforts are exploring federated identity, negotiation and decentralized orchestration. As agents become more autonomous, ad hoc integrations quickly become difficult to scale, secure and govern across environments, driving the need for standardized communication layers. In other words, we’re watching the early formation of a protocol stack for agent-to-agent interaction. The real test comes when these protocols move from internal workflows to external business ecosystems: Supply chains, financial networks, healthcare systems, partner platforms and customer journeys no single company controls. The hard part isn’t communication. It’s trust If this were just about sending messages, we’d already be done. The real challenge is whether one company can trust another company’s agent to act correctly. That’s why AGNTCY and similar efforts emphasize verifiable identity, access control and observability. Without these, cross-enterprise agent collaboration becomes a security nightmare. And the threat landscape is already real. Today, enterprise AI agents face a distinct and expanding set of attack vectors like prompt injection, token compromise, model poisoning, identity spoofing and data exfiltration via agent queries that traditional security tools were not designed to detect. “The enterprise wasn’t built for a world where AI agents can make decisions and take action on their own, which is why trust has to be built in from the start,” says Srini Namineni, Chief Automation Officer at Cisco. “The goal isn’t full autonomy everywhere. It’s the right level of autonomy for the right task, with humans involved where judgment, accountability or risk demand it. The companies that succeed with agentic AI won’t simply be the ones that move fastest. They’ll be the ones that establish the right guardrails early and apply them consistently as agents begin working across systems, teams and organizations.” A landmark example: Security researchers in early 2026 discovered a vulnerability in a major enterprise SaaS platform that demonstrated how an attacker could effectively exploit an enterprise AI agent through the systems and permissions it relied on. The research showed how agents can be tricked into recruiting more powerful agents to fulfill a malicious task, and the exposure was significant: The affected applications were in use across nearly half of the Fortune 100. Early implementations of agent protocols have also exposed a more mundane but pervasive gap: The configuration files that connect language models to external tools are often being treated as setup plumbing rather than security-critical infrastructure. Researchers and security firms have found MCP and agent configuration files containing hardcoded secrets, overly broad permissions and risky defaults, including a 2026 scan of more than 34,000 repositories that found hardcoded secrets inside agent files. The pattern is familiar: Developers copy example configurations, wire agents into real systems and move faster than security governance can adapt. But in the agentic era, a sloppy config file does not just expose an API key; it can define what an AI agent is allowed to read, write, run or transmit. What this means for enterprise leaders right now Most organizations are still focused on deploying agents, improving model performance and scaling internal use cases. But the next challenge is operating architecture: Deciding how agents are identified, authorized, monitored, connected and governed across systems. 1. Establish agent identity and trust This is the starting point. If your agents cannot reliably authenticate external systems, verify permissions and operate within clear trust boundaries, cross-enterprise collaboration is not viable. Identity, authorization and traceability are the foundation for everything that follows and the hardest to retrofit once agents are already deployed. Without this layer, any attempt at external coordination introduces significant security and operational risk. 2. Design for interoperability Trust and interoperability have to be designed together. Agents need to connect across systems without losing identity, context, authority or auditability. This means moving beyond closed, tool-specific architectures and designing for: protocol-aware communication (e.g., MCP, emerging A2A approaches) agent-specific permissioning and identity models, so every agent action is attributable, scoped and revocable tool-use boundaries that define what agents can read, write, execute or escalate audit trails for agent decisions, tool calls, data access and cross-system handoffs guardrails for agent-to-agent communication, including what context can be shared and when human approval is required Getting this right early will avoid costly rework and position enterprise agents to operate safely across broader agent ecosystems. 3. Enable cross-enterprise interaction This is where interoperability becomes more than a technical architecture issue. With identity, permissions and shared protocols in place, agents can begin to coordinate across organizational boundaries by connecting with partners, suppliers and external platforms to execute workflows that no single system controls end to end. Procurement offers an early signal of what this could look like. MIT’s Center for Transportation & Logistics has described how AI is reshaping supplier negotiations , moving from copilots toward semi-autonomous and autonomous negotiation systems operating within guardrails. Walmart’s use of AI to negotiate replenishment terms with suppliers shows how agentic systems can move beyond internal task automation and into structured interaction with external parties. For an interoperability lens, the lesson is that cross-enterprise agent workflows require a common operating layer: Verified agent identity, clear authority boundaries, structured data exchange, auditable decisions and shared rules for when humans must approve or intervene. Without that interoperability layer, agents remain trapped inside individual applications. With it, they can participate in broader business ecosystems where sourcing, negotiation, approval, contracting and fulfillment can be coordinated across company boundaries. 4. Prepare data for interoperability Data interoperability runs through all of this. Agents cannot coordinate across systems, tools or enterprises if the underlying data cannot be consistently understood, traced and exchanged. That requires standardized taxonomies, clear data lineage, shared definitions and alignment across functions. Many organizations have deferred this work because humans have historically absorbed the ambiguity: Translating between systems, reconciling inconsistent labels and interpreting context that was never formally encoded. Agents will not have that same tolerance for messy handoffs. As they begin to interact across applications, business units and external partners, weak data foundations will become one of the biggest barriers to agent interoperability. We’re at the same moment the internet faced in the early 1990s. The technology is advancing quickly, but the basic infrastructure for coordination is still being worked out. Companies have spent the last two years building agents inside their own walls. The next challenge is learning how those agents will work with everyone else’s. That is the central question behind the internet of agents. It will not be enough for a company to have powerful agents. Those agents will need to safely identify, communicate and coordinate with agents outside the company. Otherwise, even the most sophisticated agents will fail to participate safely in broader business ecosystems. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 50🌐 MovesJul 27, 2026https://www.cio.com/article/4200144/the-rise-of-the-internet-of-agents-and-why-it-changes-everything.html - Nvidia Vera Rubin shifts the AI trade beyond GPUs!
Nvidia’s Vera Rubin production ramp represents more than the beginning of another accelerator cycle. The platform changes how investors should evaluate AI infrastructure because its principal performance claim is no longer based solely on GPU speed. Nvidia is now emphasizing how many tokens an integrated rack-scale system can generate from a fixed amount of electrical power. In an industry increasingly constrained by grid capacity, power density and cooling requirements, tokens per megawatt may become a more economically important measure than peak processor performance. On July 21, Nvidia confirmed that Vera Rubin NVL72 production was ramping, with systems already operating at CoreWeave, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure. Nvidia also said that the platform is supported by more than 350 factory sites across 30 countries. This manufacturing scale illustrates how far the company has moved beyond its original identity as a merchant GPU supplier. Vera Rubin integrates CPUs, GPUs, memory, networking, optical communications, power delivery, liquid cooling and rack assembly into a coordinated AI infrastructure platform. That integration broadens the investment implications considerably. Nvidia remains the principal beneficiary because it controls the platform architecture and captures revenue across accelerators, CPUs, networking and software. However, the production ramp also creates demand for HBM4 memory, advanced packaging, optical components, high-speed connectivity chips, power semiconductors, electrical equipment, liquid cooling and rack integration. The central question for investors is therefore, not simply which companies appear somewhere in the supply chain, but, which layers experience the largest increase in content, complexity and capital intensity as Vera Rubin moves into volume production. Why Vera Rubin changes the AI investment metric? Vera Rubin NVL72 combines 72 Rubin GPUs and 36 Vera CPUs with NVLink 6, ConnectX-9 SuperNICs, BlueField-4 data-processing units and Spectrum-6 Ethernet switches. Rather than functioning as an assortment of independently sourced components, these elements are engineered as a rack-scale system. Nvidia describes the architecture as extreme codesign across seven chips and five rack trays, with hardware, networking, cooling and software optimized together. CoreWeave’s initial DeepSeek-R1 benchmark reported that Vera Rubin NVL72 generated approximately 10 times more tokens per second per megawatt than Grace Blackwell NVL72. Nvidia also said the system could lower the inference cost per token by as much as 90% compared with the prior generation. These results are workload-specific and should not be interpreted as a universal performance increase across every model. Nevertheless, they demonstrate the direction in which AI infrastructure economics is moving. The relevant objective is no longer to maximize GPU performance in isolation; it is to maximize usable AI output within fixed power, networking and cooling constraints. Nvidia’s Vera Rubin announcement Each one of Vera Rubin’s major architectural changes transfers part of the performance burden away from the GPU and into the surrounding infrastructure. HBM4 must supply more data to the accelerator. Advanced packaging must integrate larger and more complex chip assemblies. Networking must prevent communication delays from leaving expensive GPUs idle. Power and cooling equipment must support greater rack density without overwhelming the data center. The result is an AI platform in which system efficiency depends on multiple suppliers operating together. -- Dr. Robert Castellano, Semiconductor Deep Dive, USA.
Score: 50🌐 MovesJul 27, 2026https://www.dqindia.com/semiconductors/nvidia-vera-rubin-shifts-the-ai-trade-beyond-gpus-12190476 - Cognizant and Anthropic expand their partnership to bring Claude to enterprise clients
Cognizant and Anthropic expand their partnership to bring Claude to enterprise clients
- AI-powered method enables instant ultrasound imaging with a single fixed sensor
IMDEA Materials Institute has developed an acoustic imaging method that overcomes some of the fundamental limitations of technologies like ultrasound imaging.
Score: 49🌐 MovesJul 27, 2026https://techxplore.com/news/2026-07-ai-powered-method-enables-instant.html - China's hottest AI chip stock became its most valuable listed company on day one
China's hottest AI chip stock became its most valuable listed company on day one Business Insider
Score: 49🌐 MovesJul 27, 2026https://www.businessinsider.com/cxmt-stock-price-ipo-688825-ticker-shanghai-changxin-ram-chip-2026-7 - Big Tech credit risks rise sharply as AI spending soars
Investors are increasingly concerned over rush of borrowing to fund huge investments in data centres
Score: 48🌐 MovesJul 27, 2026https://www.ft.com/content/ac136522-ecc7-4262-8702-e0d636ea3099?syn-25a6b1a6=1 - India data centre capacity to jump to 12 GW by 2030 as AI fuels demand: Wood Mackenzie
AI-dedicated capacity is expected to rise multifold to 6,546 MW by 2030 from 275 MW in 2025, while data centre electricity demand is forecast to grow to 191 terawatt-hours (TWh) by 2040 from 10 TWh in 2025, the report said.
- Momenta expands into autonomous freight with Robovan
Chinese autonomous driving company Momenta has launched its Robovan autonomous freight business, with vehicles already operating in Suzhou’s Xiangcheng district. The company said the vehicles are being deployed for express delivery and overnight delivery, using its R7 world model and a map-free approach adapted from passenger-car deployments. Momenta said the same model platform will support […]
Score: 48🌐 MovesJul 27, 2026https://technode.com/2026/07/27/momenta-expands-into-autonomous-freight-with-robovan/ - Breakingviews - Cloudmaxxing sucks Nvidia into dangerous game
Breakingviews - Cloudmaxxing sucks Nvidia into dangerous game Reuters
Score: 48🌐 MovesJul 27, 2026https://www.reuters.com/commentary/breakingviews/cloudmaxxing-sucks-nvidia-into-dangerous-game-2026-07-27/ - Why Kimi K3 Signals A Convergence Toward Open-Weight Models
Closed systems create friction that hinders adoption and customization. Kimi K3's recent release suggests that the market may ultimately favor open architectures.
Score: 48🤖 ModelsJul 27, 2026https://www.forbes.com/sites/geruiwang/2026/07/27/why-kimi-k3-signals-a-convergence-toward-open-weight-models/ - Neura Robotics to Open Physical AI Training Center
The new facility, in collaboration with RWTH Aachen University, will join Neura’s global network of sites providing robotic training data.
Score: 48🌐 MovesJul 27, 2026https://aibusiness.com/robotics/neura-robotics-open-physical-ai-training-center - New ransomware targets AI model weights and can't even collect the ransom
The same attacker broke into the same internet-facing Langflow server twice, and the second time brought ransomware built to destroy trained AI models. Sysdig's Threat Research Team documented the first campaign on July 1 and the second on July 20 . The entry point never changed, but the payload changed completely. Both ran through CVE-2025-3248 , a missing-authentication flaw in Langflow's code-validation endpoint that lets anyone reaching the server execute Python on it. In the first, the agent improvised, encrypting 1,342 Alibaba Nacos configuration items with MySQL's own encryption function and dropping the tables. In the second, it staged ENCFORGE, a compiled Go binary sweeping roughly 180 file extensions. ENCFORGE was built for AI assets, not adapted to them What gives the design away is the extension list. Sysdig found PyTorch and TensorFlow checkpoints, Hugging Face SafeTensors weights, the GGUF format behind most local LLM deployment, FAISS vector indexes and training data in Parquet and NumPy. Generic ransomware picks up model files by accident because it encrypts everything. ENCFORGE names them. Its flag for appending formats uses LoRA adapters and legacy GGML weights as the example, and an attacker who writes that knows whose machines these are. Michael Clark, who leads Sysdig's threat research team, framed the objective as destroying "the one thing an organization can't simply restore." ENCFORGE carries no network code. Sysdig found no outbound dial in the binary, no leak site and no payment portal, and the identical Proton Mail contact in both ransom notes ties the campaigns to the crew it tracks as JADEPUFFER. The agent harvested credentials on the way in, but the locker cannot exfiltrate anything, so its only pressure is making files unusable. It encrypts regions of a file rather than the whole file, under AES-256-CTR with a per-run key wrapped in an embedded RSA-2048 key, the speed optimization established ransomware families use to ruin large files fast. Your backup plan probably does not cover model weights Restoring a database from Friday's snapshot costs a weekend of transactions. Restoring a fine-tuned model costs everything learned since Friday, none of it stored as rows to replay. Rebuilding one is not a restore job. Sysdig puts direct recovery for a production-ready fine-tuned model between $75,000 and $500,000, reflecting cloud GPU rates across the training runs a usable result requires plus the engineering hours behind them. That is per model, and teams keep several variants on shared storage. If the training data sits on the same host as the weights, recovery is blocked until the dataset is rebuilt. Paying is no way out either. In the first campaign, the encryption key was generated at random, printed to the console once and never saved, which made that payload a wiper wearing a ransom note. That figure makes the argument fundable. Kayne McGladrey, an IEEE Senior Member who has spent his career in identity security, told VentureBeat that security teams lose these fights by filing the exposure under the wrong heading. Companies "should be focused on business risks rather than some, you know, cybersecurity risk, because if it doesn't affect the business, like a loss or financial loss, in this case, predominantly, then nobody's going to pay any action to it, and they will not budget it appropriately, nor will they adequately put in controls to prevent it," he said. A destroyed model carries a known replacement cost, which is the version of this story a CFO acts on. Official guidance has not caught up. In May 2025 the NSA's Artificial Intelligence Security Center, CISA and the FBI published " AI Data Security ," the most authoritative document on the subject, co-sealed with the U.K., Australia and New Zealand. The three risks it names are the data supply chain, maliciously modified data and data drift. All three ask whether the data can be trusted. ENCFORGE asks whether it still exists. It built its own escape hatch in five minutes The delivery failure is where this campaign shows its hand. After confirming execution, the agent swept the host for cloud keys, connection strings and API tokens, replayed them against internal database and cache services, and found the Docker socket at /var/run/docker.sock, which is functionally root. It then tried to pull the ransomware binary from its command and control server, and the fetch did not land. Rather than retry, it changed strategy, building six Python scripts through the Langflow channel and converging on a working host escape in five minutes and 24 seconds, each correcting a failure in the one before it. The final script finds the host process ID through the Docker API, copies the binary across the namespace boundary, runs the encryption, then counts the files to confirm it worked. In the first campaign, that same behavior was a failed login diagnosed and fixed in 31 seconds. The problem got harder and the method held. Sam Evans, then CISO at Clearwater Analytics , put it in budget terms. "In security, it's all about dwell time," he told VentureBeat in an exclusive interview. "If there's a bad actor in your environment and they've been there for a while, your dwell time is increased, therefore the blast radius has increased. Probability of it becoming a material incident is exponentially high." Mike Riemer, Ivanti's SVP Network Security Group and Field CISO, has watched that pivot become standard. Vendors hardened the front door, he told VentureBeat, so attackers quit knocking. "I can't get through the front door, so let me get somebody his house key, and I can make it through the back door with a house key," he said. Behind it sits whatever teams assume is covered, because "they don't sit out directly on the internet, and they're behind a protection barrier, but they're not." No one claimed a machine did this unsupervised. TechCrunch reported on July 6 that the first operation still needed a person to pick the target and stand up infrastructure, and Sysdig could not trace the root credentials. A human aimed this one, and everything after ran with nobody at the keyboard. Heath Renfrow, co-founder and CISO at breach-recovery firm Fenix24, told Infosecurity Magazine that when an agent compresses hours of operator work into minutes, "defenders lose valuable time." Whether the attacker was AI-driven does not change the response. The door had been open for 14 months CVE-2025-3248 carries a CVSS score of 9.8. CISA added it to the Known Exploited Vulnerabilities catalog on May 5, 2025, with a federal deadline of May 26, and Langflow fixed it in 1.3.0. When JADEPUFFER came back in July 2026, the server was more than fourteen months past that listing and already documented publicly as a breach victim. Riemer put a number on how little time that leaves. "If I release a patch and a customer doesn't patch within 72 hours of that release, they're open to exploit, because that's how fast they can now do it," he said, adding most customers need a week to patch by hand. Set 72 hours against fourteen months, and the gap stops reading as one lapse. Nothing in either campaign was new. The first forged a Nacos admin token with a default signing key public since 2020, walked through CVE-2021-29441 , an authentication bypass Alibaba patched in 2021, and found a MinIO store on minioadmin:minioadmin. Sysdig counted more than 600 payloads, every one leaning on a known misconfiguration or a patched bug left exposed. The second added an exposed Docker socket. Every weakness was routine. Assembling them at machine speed was not. Langflow draws this attention because of what it holds. VentureBeat reported in June that roughly 7,000 instances sit exposed , most in North America, holding provider API keys, cloud credentials and live connections to the vector stores ENCFORGE was built to encrypt. Riemer puts it bluntly. "When you put your security at the edge of your network, you're inviting the entire world in to the edge of your network," he said. CISA has added five Langflow flaws to its Known Exploited Vulnerabilities catalog, two of them this month. Five Langflow flaws now sit on the KEV catalog, two of them this month. CISA added CVE-2026-55255 on July 7, a cross-tenant bypass letting any authenticated user on a shared instance run another tenant's flows with that tenant's credentials, which the maintainers' advisory rates 9.9 and fixed in 1.9.1. On July 21, CISA added CVE-2026-0770 , and that one is worse. Trend Micro found an unauthenticated path to root code execution through the exec_globals parameter, on the same validate endpoint JADEPUFFER came through, rated 9.8. KEVIntel logged exploitation from June 27, more than 220 attempts across 64 addresses. According to founder Ryan Dewhurst, speaking to BleepingComputer , the payloads reached past reconnaissance for AWS credentials and container metadata. Federal agencies had until July 24. None of this is an exotic environment. A Cloud Security Alliance survey of 418 professionals, commissioned by Token Security, found 82% of organizations had discovered AI agents nobody knew about, and 65% had handled an agent-related incident in the past year. McGladrey traces the permission half back four decades. Companies always cloned one employee's access profile onto the next hire, and now do it with agents. An agent "does whatever it needs to do to get its job done," he said, and it "uses far more permissions that it shouldn't have... than a human would do, because of the speed of scale and also intent." What to do before the board asks "Every time some new ransomware attack is published in the news, we get the inquiry from the board. What are we doing about this?" Evans said. "And if it involves AI, it sparks their fear even more." Five things are worth answering this week. None requires buying anything. Get every internet-reachable Langflow instance onto the current supported release. Anything off it is exposed to at least one of the five flaws CISA has now listed, including the July 21 addition. Then review historical requests to /api/v1/validate/code for the exec_globals pattern. Get the Docker socket out of application containers. Langflow has no reason to create them. If the mount is required, front it with a socket proxy allowing only needed calls. Name model artifact paths in the backup plan. Immutable snapshots of checkpoints, vector indexes and training data, restore tested, data off the host holding the weights. Rotate every credential the host could reach, then pull provider keys out of its runtime. Patching does not revoke what already left. The first campaign harvested OpenAI, Anthropic and cloud credentials within seconds. Scope replacements to a secrets manager. Detect mass .locked file creation in directories holding .gguf, .safetensors, .ckpt or .faiss files. Sysdig published a YARA rule and both hashes, neither with antivirus coverage at analysis time. It published no file count, so what is documented is a live encryption pass, not a measured loss. An attacker coming through an exposed AI framework now arrives carrying something built for what it connects to, and those assets are the ones a restore cannot reproduce. Model artifacts belong in the recovery plan next to the databases. Under three weeks passed between Sysdig's two reports, all this attacker needed to go from improvised Python to a compiled locker.
Score: 48🌐 MovesJul 27, 2026https://venturebeat.com/security/new-ransomware-targets-ai-model-weights-and-cant-even-collect-the-ransom - Satya Nadella says trust in US technology will outweigh the allure of cheap Chinese models
Satya Nadella says trust in US technology will outweigh the allure of cheap Chinese models Business Insider
Score: 48🌐 MovesJul 27, 2026https://www.businessinsider.com/satya-nadella-trust-us-ai-tech-ecosystem-chinese-models-2026-7 - Visa, LianLian Automate Cross-Border B2B Payment With AI Agent
Visa, LianLian Automate Cross-Border B2B Payment With AI Agent Caixin Global
- NOAA and Google Cloud collaborate to advance weather forecasting.
Google Cloud is now providing high-performance computing infrastructure for NOAA’s supercomputing system.
- Rewired takes: How AI is unlocking creativity and heralding the rise of the agent manager
Companies are reassessing how they work, their team structures, and what it means to be a manager in this bracing new era.
- 'The biggest shift happens when AI stops being a tool people occasionally use and becomes part of the operational fabric of the business': Why Arm is set to help power the next generation of AI workloads — and beyond
We speak to Arm's EVP of Cloud AI on what is really changing at the infrastructure layer.