AI News Archive: June 2, 2026 — Part 2
Sourced from 500+ daily AI sources, scored by relevance.
- OpenAI turns ChatGPT into a career platform with job search and CV editor
OpenAI turns ChatGPT into a career tool: A new job search feature surfaces personalized listings from Indeed, Upwork, and Appcast, initially US-only. Resumes can also be created directly in ChatGPT and tailored to specific roles. The article OpenAI turns ChatGPT into a career platform with job search and CV editor appeared first on The Decoder .
Score: 55🌐 MovesJun 2, 2026https://the-decoder.com/openai-turns-chatgpt-into-a-career-platform-with-job-search-and-cv-editor/ - Build 2026: Microsoft's MDASH exits preview with 100+ specialized threat-hunting AI agents
Microsoft's Build 2026 security news centers on an agentic AI vulnerability system designed to find real exploitable flaws, connect them to Defender and GitHub, and help developers fix them faster.
- Google just made an $80 billion AI bet—and Wall Street isn’t loving it
Alphabet is looking for greater cash flow to spend on AI advancements. Google’s parent company has announced plans to sell $80 billion worth of its stock (Nasdaq: GOOG), with net proceeds earmarked for “general corporate purposes, including capital expenditures to scale AI infrastructure and global compute.” The company produces generative AI chatbot Gemini alongside a series of offerings through Google DeepMind . Alphabet’s expected $80 billion influx will come from three sources. For one, Berkshire Hathaway has agreed to invest $10 billion into Alphabet. This stake builds on the Omaha-based conglomerate’s $4.3 billion investment in Alphabet last fall. Outside of Berkshire Hathaway, Alphabet has proposed $30 billion in underwritten public offerings, with $15 billion “in depositary shares representing mandatory convertible preferred stock.” The remaining $15 billion will be in Class A Common Stock and Class C Capital Stock. Similarly, Alphabet plans to offer $40 billion of Class A Common Stock and Class C Common Stock at-the-market starting in this year’s third quarter. As of publication, Alphabet’s shares had fallen about 2.6% in premarket trading. Alphabet is all in on AI The announcement follows Alphabet’s quarter-one earnings report in which it raised 2026’s expected capital expenditures from between $175 to $185 billion to $180 and $190 billion. In the first quarter alone, the company reached $35.7 billion—jumping 107% year-over-year (YOY). Alphabet further predicts its 2027 capital expenditures to increase even further. The attitude at Alphabet is clear: investment is necessary for further AI development. “AI is driving an expansionary moment for Alphabet. The company is experiencing strong demand for its AI solutions and services from enterprises and consumers, at levels that are exceeding the company’s available supply,” Alphabet stated in this week’s announcement. “By scaling its investments, the company seeks to expand its foundational infrastructure to support the significant growth opportunity ahead.”
- Alibaba's Qwen3.7-Plus supports text, video and imagery inputs at low cost of $0.4/$1.6 per 1M token — but it's proprietary
Alibaba this week released Qwen3.7-Plus , the latest AI large language model (LLM) in its globally beloved and increasingly expansive Qwen family, boasting more multimodal capabilities and a 60% lower cost than the prior, text-only Qwen3.7-Max model released just weeks ago. However, like its immediate predecessor Qwen3.7-Plus is available only under a "closed" commercial license via proprietary application programming interfaces (API) and Qwen Chat. That marks a big departure from the Qwen strategy to date, which was focused mainly on releasing powerful,near state-of-the-art open source models. Those enterprises and users who relied on the open source Qwen models — among them, U.S. giants such as Airbnb — will no doubt be disappointed to see that Alibaba is going closed for its newer releases. Still, the model is worth a look because of its low cost and high performance on multimodal tasks like creating enterprise-grade visuals or analyzing video, imagery and screenshots, which Qwen3.7-Max cannot do (it's text-only). It is among the cheaper powerful AI models available now, coming in price-wise just above Chinese rival's new MiniMax-M3's limited-time discount pricing. VentureBeat Frontier AI Model API Pricing Snapshot Model Input Output Total Cost Source MiMo-V2.5 Flash $0.10 $0.30 $0.40 Xiaomi MiMo deepseek-v4-flash $0.14 $0.28 $0.42 DeepSeek deepseek-v4-pro $0.435 $0.87 $1.305 DeepSeek MiniMax-M3 $0.30 $1.20 $1.50 MiniMax Qwen3.7-Plus $0.40 $1.60 $2.00 Alibaba Cloud Gemini 3.1 Flash-Lite $0.25 $1.50 $1.75 Google MiMo-V2.5 $0.40 $2.00 $2.40 Xiaomi MiMo Grok 4.3 low context $1.25 $2.50 $3.75 xAI GLM-5 $1.00 $3.20 $4.20 Z.ai Kimi-K2.6 $0.95 $4.00 $4.95 Moonshot/Kimi GLM-5.1 $1.40 $4.40 $5.80 Z.ai Grok 4.3 high context $2.50 $5.00 $7.50 xAI Qwen3.7-Max $2.50 $7.50 $10.00 Alibaba Cloud Gemini 3.5 Flash $1.50 $9.00 $10.50 Google Gemini 3.1 Pro Preview ≤200K $2.00 $12.00 $14.00 Google GPT-5.4 $2.50 $15.00 $17.50 OpenAI Gemini 3.1 Pro Preview >200K $4.00 $18.00 $22.00 Google Claude Opus 4.8 $5.00 $25.00 $30.00 Anthropic GPT-5.5 $5.00 $30.00 $35.00 OpenAI Maintaining continuity during complex tool execution loops For technical decision-makers deploying autonomous agents, the primary bottleneck has rarely been initial model intelligence. Instead, it is state decay —the tendency of an agent framework to lose its analytical trajectory over multi-step, long-horizon tasks. Qwen3.7-Plus addresses this architectural vulnerability through a combined approach to context management and reasoning state preservation. The model ships with a 1-million token context window and allocates up to 256K tokens specifically for internal chain-of-thought processing. To contextualize this capacity, imagine an automated cloud migration agent: it can ingest an entire codebase, map out the dependencies, and spend thousands of tokens quietly evaluating edge cases before executing a single line of bash script. Crucially, the API exposes a parameter called ' preserve_thinking .' Across Alibaba's ecosystem, the capability serves as a standardized architectural bridge rather than a tiered perk. Alibaba introduced the feature during the prior Qwen 3.6 generation, integrating it into both the open-weight Qwen3.6-27B and the proprietary Max models. At its core, the parameter operates at the API and template level to retain internal blocks across continuous conversational turns. This structural continuity solves a critical bottleneck for developers engineering long-horizon tasks. By keeping these internal logic loops intact, the feature prevents the model from dropping its context or needlessly recomputing its cached history midway through an operation. When a model executes complex, multi-step agentic coding assignments, this retention allows the system to hold onto its original train of thought without losing the plot or forgetting the underlying logic of its previous actions. Alibaba remains far from alone in recognizing this technical necessity, as the underlying concept now dictates the architecture of nearly all major artificial intelligence laboratories. Anthropic deploys this exact capability under the moniker "Extended Thinking" for its advanced models, including its latest Claude Opus 4.8. This framework requires developers to feed unmodified thinking blocks directly back into the API on subsequent turns to maintain an unbroken chain of reasoning. OpenAI tackles the same challenge through an encrypted reasoning pass-back mechanism for models like GPT-5.5. Within the OpenAI ecosystem, developers must return specific reasoning items generated alongside previous function calls, ensuring the model explicitly remembers the rationale behind its tool executions. Ultimately, preserve_thinking simply represents Alibaba's terminology for what has rapidly become the undisputed table stakes for modern multi-turn reasoning. Benchmarks show a competitive, yet sub state-of-the-art model On raw capability metrics, this deep-thinking architecture translates to structural gains across multimodal and agentic benchmarks. However, it still falls below many of the leading and prior generations of U.S. proprietary models such as Anthropic's Claude Opus 4.6 and OpenAI's GPT-5.4. On Terminal Bench 2.0-Terminus , which measures an model's capability to run actual terminal-level code safely and iteratively, Qwen3.7-Plus scored 70.3 , outperforming DeepSeek-V4-Pro Max (67.9) and Gemini-3.1 Pro (63.5). On computer vision benchmarks that demand localized interface understanding, such as ScreenSpot Pro , the model hit 79.0 , significantly outpacing legacy industry standouts like GPT-5.4 (xhigh) at 67.4 and Claude-Opus-4.6 at 49.5. Agent Evaluation Metrics (Selected Benchmarks) What should enterprises consider Qwen3.7-Plus for? For an enterprise architect, the key question when analyzing Qwen3.7-Plus is clear: What does this replace in our current tech stack? The model is designed to step in as a direct replacement for premier frontier models (such as GPT-5-tier or Claude-Max-tier models) within high-frequency developer workflows, robotic process automation (RPA), and data engineering pipelines. Rather than deploying an expensive, general-purpose flagship model to handle repetitive system operations, technical teams can route these tasks to Qwen3.7-Plus. It handles visual interface interpretation, command execution, and code generation simultaneously. Alibaba has structured its API delivery to align with existing open-source and proprietary enterprise frameworks. The endpoints are fully OpenAI-compatible, meaning swapping out existing dependencies requires minimal infrastructure adjustment. For groups leveraging autonomous terminal frameworks, the integration is natively supported across multiple environments. Engineers can run Qwen3.7-Plus directly through their local terminal setups by altering base environment targets. From a pure cost perspective, running an agent framework that constantly references massive code repositories or visual layout histories can quickly become cost-prohibitive. Alibaba addresses this by exposing granular caching price points. Standard input processing sits at $0.40 per million tokens, but if the agent is reading from an explicitly created cache (e.g., a massive base repository or standard enterprise UI kit that remains static over hundreds of automated loops), the cost drops sharply to $0.04 per 1M tokens for subsequent reads. This tier makes high-frequency, multi-turn agent iterations economically practical at an enterprise scale. No open source license or open weights raises the compliance question for enterprises When evaluating any model in the Qwen ecosystem, a primary concern for legal and security teams is the licensing framework and operational boundary of the data pipeline. While previous iterations of the Qwen family gained significant enterprise traction via fully open-source weight availability under the Apache 2.0 or customized open-use licenses, Qwen3.7-Plus is delivered strictly as a managed, commercial cloud API via Alibaba Cloud Model Studio. For enterprise risk management, this distinction carries specific implications: No Local Weight Deployment : Organizations cannot download, sandbox, or locally host the weights of Qwen3.7-Plus within their completely air-gapped internal data centers. All data verification, visual processing, and execution calls must step through Alibaba Cloud's international endpoints (e.g., the Singapore instance highlighted in developer documentation). Compliance and Sovereignty : Since the model requires cloud-based inference, companies operating under strict sovereign data boundaries (such as healthcare entities subject to local HIPAA/GDPR constraints or defense contractors) must explicitly evaluate whether external API routing complies with their specific data-residency obligations. Managed Risk Mitigation : Conversely, a managed API structure removes the internal infrastructure burden of provisioning, optimizing, and maintaining multi-GPU clusters (such as dedicated Nvidia H100 arrays) simply to host an internal agent network. Still, Qwen3.7-Plus offers high intelligence across modalities at low cost The initial reception from developer communities and technical venture capital highlights the shifting economics of agent deployment. Prominent industry voice and Web3 venture capitalist @Boxmining highlighted the strategic cost advantage, stating: "Qwen 3.7 Plus being 40% cheaper than Max changes the conversation. If the output is close enough for most coding and much stronger for visual workflows, do you really need Max every day or only for the heavy terminal-only jobs?" This perspective aligns with the current trend of optimizing enterprise operational budgets: shifting away from raw, unconstrained compute toward targeted task automation.At the same time, specialized researchers deep within the ecosystem point out that this isn't merely an incremental optimization of text generation. Dunjie Lu, a research intern at Alibaba Qwen, remarked: "It shows clear gains over Qwen3.6-Plus in computer-use capabilities, with stronger generalization beyond general desktop tasks into professional workflows such as data engineering and scientific research." Ultimately, for enterprise buyers deciding on their next infrastructure roadmap, Qwen3.7-Plus presents a practical alternative. If your organization's primary objective is building resilient, visual-capable autonomous software loops that interact directly with developer environments and cloud consoles—without blowing out your inference budget—the model provides a compelling reason to shift execution away from more expensive frontier alternatives.
- Snowflake CoWork: The Personal Work Agent for Every Knowledge Worker
Snowflake CoWork (formerly Snowflake Intelligence) is a personal work agent for knowledge workers, combining deep business context, automation and governed actions across enterprise systems.
Score: 53🌐 MovesJun 2, 2026https://www.snowflake.com/content/snowflake-site/global/en/blog/snowflake-cowork-personal-work-agent - Trump executive order on AI gives central role to NSA
Trump executive order on AI gives central role to NSA Breaking Defense
Score: 53🌐 MovesJun 2, 2026https://breakingdefense.com/2026/06/trump-executive-order-on-ai-gives-central-role-to-nsa/ - Amazon offers AI agent tech to other retailers
The online retail behemoth wants to narrow the time it takes for retailers to launch AI shopping assistants, starting with Kate Spade.
Score: 53🌐 MovesJun 2, 2026https://www.retaildive.com/news/amazon-to-offer-ai-agents-to-other-retailers/821597/ - Mark Carney to unveil Canada’s AI strategy this week
Mark Carney to unveil Canada’s AI strategy this week Toronto Star
- Cisco Launches Agentic Platform to Defend and Automate Critical IT Infrastructure
In an agentic AI world, organizations must act and defend at machine speed and scale. Cisco Cloud Control, unveiled today at Cisco Live, is a unified platform built for humans and AI agents to manage, monitor and defend critical IT infrastructure — and the foundation for Cisco’s AgenticOps operating model. With one login, Cisco Cloud […] The post Cisco Launches Agentic Platform to Defend and Automate Critical IT Infrastructure appeared first on CXOToday.com .
- Microsoft launches MXC, an OS-level sandbox for AI agents, with OpenAI and Nvidia already on board
For the past two years, the technology industry has raced to make AI agents more capable — teaching them to write code, navigate software interfaces, manage files, and orchestrate multi-step workflows with increasing autonomy. What the industry has not done, at least not with any consistency, is answer the question that keeps chief information security officers awake at night: what happens when an agent goes wrong? On Tuesday at its annual Build developer conference, Microsoft offered what may become the definitive answer. The company introduced Microsoft Execution Containers , or MXC — a policy-driven execution layer, built into the Windows operating system itself, that lets developers and IT administrators declare exactly what an AI agent can and cannot access, with those boundaries enforced at runtime by the OS kernel. The announcement, buried within a sweeping set of developer-focused updates , is arguably the most consequential platform move Microsoft made at Build this year, and it has the potential to reshape how every enterprise on Earth thinks about deploying autonomous AI software. MXC is not a product you buy. It is an SDK and a policy model — a foundational primitive embedded in Windows and the Windows Subsystem for Linux — that provides what Microsoft calls a " composable sandbox spectrum ." That spectrum ranges from lightweight process isolation, already adopted by GitHub Copilot's command-line interface, all the way up to micro-virtual machines, Linux containers, and full cloud instances running on Windows 365. The system separates an agent's execution from the user's desktop, clipboard, user interface, and input devices. Critically, it binds every agent to a strong identity — either a local ID or a cloud-provisioned identity backed by Microsoft Entra — so that every action the agent takes can be attributed, audited, and governed. The implications are enormous. Until now, the enterprise deployment of AI agents has been stuck in a paradox: the more autonomous and useful an agent becomes, the more dangerous it is to let it operate on a corporate network without guardrails. MXC is Microsoft's attempt to break that paradox — not by making agents less capable, but by making the environment they operate in fundamentally more controlled. Why every autonomous AI agent is a security incident waiting to happen To understand why MXC matters, consider what an AI agent actually does when it runs on your computer. Unlike a traditional application, which operates within well-understood boundaries — a word processor reads and writes documents, a browser fetches web pages — an AI agent is, by design, unpredictable. It receives a goal in natural language, reasons about how to achieve it, and then takes actions: opening files, executing code, calling APIs, browsing the web, interacting with other software. Each of those interactions creates what security professionals call "attack surface." Microsoft's own blog post framed the challenge in stark terms. The company wrote that "as agents become more capable and autonomous, they're delivering material productivity gains. But they're also introducing new risk, and the issue isn't just the agent. It's the entire system the agent operates across." Every interaction between agents and humans, tools, applications, models, and other agents "exposes new attack surface and introduces different failure modes." Microsoft characterized this as "a multi-layer systems problem." This is not a theoretical concern. In the months leading up to Build , security researchers demonstrated numerous ways that AI agents could be manipulated — through prompt injection, through malicious tool calls, through data exfiltration disguised as normal workflow. For enterprises that handle sensitive data, proprietary models, and regulated information, the absence of a trusted execution environment has been the single biggest barrier to moving agents from demo to deployment. Microsoft's answer is a sandbox that scales from a single process to a full virtual machine MXC operates on a deceptively simple principle: declare what the agent can do before it runs, and let the operating system enforce those declarations at runtime. A developer or an IT administrator writes a policy that specifies which files, directories, and network resources an agent is allowed to access. MXC then creates a contained execution environment — a sandbox — that enforces those boundaries regardless of what the agent attempts to do. What makes MXC unusual, and potentially very powerful, is the breadth of its isolation options. Microsoft designed the system so that a single SDK and policy model can map to the appropriate isolation construct for any given workload. For a lightweight coding assistant that just needs to read the current project directory, fast process isolation may be sufficient. For an autonomous agent that executes arbitrary code downloaded from the internet, a full micro-VM may be required. The system is designed to be "dynamically composable based on intent and risk," meaning that the level of isolation can be adjusted based on what the agent is actually doing, not just what category it falls into. Session isolation is a particularly important feature. MXC separates the agent's execution from the user's desktop, clipboard, UI, and input devices. This directly mitigates several classes of attacks that security researchers have identified as particularly dangerous for AI agents: UI spoofing, where an agent manipulates what the user sees to trick them into approving a malicious action; input injection, where an agent sends keystrokes or mouse clicks to other applications; and cross-session data leakage, where information from one user's session bleeds into another. A live demo showed an AI agent trying to delete files — and failing, because the OS wouldn't let it During a pre-briefing with VentureBeat the night before the announcement, a Microsoft developer offered a vivid demonstration of the technology in action. He had set up the open-source agent framework OpenClaw running inside MXC's sandbox on his personal development machine. He then instructed the agent to delete all the files on his desktop. The agent attempted to comply — but the sandbox prevented it. "If you look at my desktop here, you see how clean my desktop is," the developer said during the demo. "That's a lie." The files, he explained, were completely safe because "the container won't allow it." The demonstration went further, showcasing the granularity of MXC's controls. Users can mark specific files as read-only for the agent, restrict access to the browser and screen capture, control whether the agent can see location data, and have all of those permissions managed centrally by an enterprise IT department through Intune policies. The agent operates inside what is effectively a one-way mirror: it can do the work it has been asked to do, but it cannot see or touch anything outside the boundaries that its policy defines. Pavan Davuluri, Microsoft's Executive Vice President for Windows and Devices, underscored during the pre-briefing that the primitives MXC introduces — security, containment, isolation, and user control — are essential to making AI agents commercially viable. He emphasized that these capabilities are "not unique to OpenClaw" and that "this pattern repeats itself over and over" for any agent running on a Windows device. The primitives that exist in the operating system now "for the file around security, containment, isolating them, having users in control," he said, are what will make agents safe enough for ordinary consumers and corporate deployments alike. Defender, Entra, Intune, and Purview integration arriving in July turns MXC into an enterprise control plane For corporate IT departments, the most significant element of the MXC announcement is not the SDK itself but its integration with Microsoft's existing enterprise security stack through what the company calls Agent 365. Arriving in preview in July, Agent 365 layers Microsoft's Entra identity service and Intune device management platform on top of MXC, so that IT administrators can govern agent containment centrally while developers choose the level of isolation their workload demands. The integration goes further: Microsoft Defender will provide runtime threat protection, Entra will handle identity and access management, Intune will enforce device-level policies, and Microsoft Purview will extend its data governance and compliance capabilities to agent activity. This means that an enterprise could, in theory, allow employees to run AI agents on their corporate machines — even powerful, autonomous agents that execute code and manage files — while maintaining the same kind of centralized visibility and control that IT departments currently have over traditional applications. Microsoft described the identity layer in its official blog : "Windows assigns agents a local ID or a cloud provisioned identity backed by Entra and attributes all activity from the container to that identity, so you can clearly differentiate human from agent." For regulated industries — financial services, healthcare, government — the ability to produce an audit trail that distinguishes between human actions and agent actions on the same machine could prove to be a regulatory requirement, not merely a nice-to-have feature. Every agent action attributable to a specific identity, every containment boundary enforceable through the same policy infrastructure that already governs hundreds of millions of Windows devices — this is the architecture that could finally move AI agents from pilot programs to production. OpenAI, Nvidia, Manus, and Nous Research are already building on MXC — and that changes the calculus Platform announcements at developer conferences are often aspirational. What distinguishes the MXC launch is the breadth and specificity of the partners already building on it. Microsoft named five: OpenAI , Nvidia , Manus , Nous Research (maker of the Hermes agent), and the OpenClaw open-source project. Each is integrating MXC in a distinct way that illuminates a different use case for the technology. OpenAI's involvement is particularly striking. David Wiesen, a member of OpenAI's technical staff, said that "working with Microsoft on the Microsoft Execution Containers (MXC) allows us to explore new patterns for AI agents to safely and efficiently generate and execute code." He added that by combining Codex's capabilities with MXC's execution environment, the goal is "to help developers move from intent to reliable execution faster, while maintaining the security and control enterprises need." The reference to Codex — OpenAI's code-generation agent — suggests that MXC could become the default execution environment for one of the most widely anticipated agent products in the industry. Nvidia is bringing its OpenShell framework to Windows built on MXC, providing what Microsoft described as "an easy-to-deploy package for autonomous, always-on agents safely." Manus, the Chinese-born AI agent startup that gained viral attention earlier this year, is also integrating. Tao Zhang, Manus's Chief Product Officer, said that MXC "gives developers a policy-driven way to define what an agent can access and enforce those boundaries at runtime, so more autonomous agents can operate safely in enterprise environments." And Dillon Rolnick, the CEO of Nous Research, offered what may be the most concise articulation of why MXC matters: "Continuously-running local agents, like Hermes Agent, require intentional isolation. Developers need control over what an agent can access and trust that those controls will hold." How an open-source agent framework became Microsoft's proving ground for AI safety on Windows One of the more revealing stories behind the MXC announcement involves OpenClaw . During the press pre-briefing, a Microsoft developer described how the partnership came together organically — Peter Steinberger, OpenClaw's creator, sent him a direct message in January expressing interest in collaborating. What began as a casual conversation evolved into a full-fledged platform partnership, with Microsoft developers contributing to the OpenClaw Windows companion app, built as a native WinUI application rather than a wrapped web app. The OpenClaw integration serves as what Scott called "the ultimate test app for all the stuff that [the Windows platform team] is making." If OpenClaw — which by its nature gives agents broad autonomy to execute tasks on a user's machine — can run securely within MXC's containment boundaries, then the containment system is robust enough for any agent. Scott explained the philosophy driving the work: "Think of OpenClaw Windows as the ultimate test app... If OpenClaw can succeed on Windows, that means that the Linux support is there, the container support is there, the containment is there." The companion app demonstrates the full spectrum of MXC's enterprise controls — file permissions, network access, screen capture restrictions, location data — all manageable centrally through Intune policies. Microsoft donated the project to OpenClaw and plans to continue contributing to it as open source. As one member of the Windows leadership team put it during the briefing: "All agents, all comers, everyone is welcome on Windows... It's going to run great on Windows, because the primitives are there. The base of the pyramid is solid." Building containment into the OS gives Microsoft a strategic edge over Apple's walled garden and Google's cloud-first model MXC arrives at a moment when the technology industry is grappling with a fundamental tension. AI agents represent what may be the most significant new category of software since mobile applications, and every major technology company is racing to build them. But the security and governance infrastructure required to deploy these agents responsibly in enterprise environments barely exists. Microsoft's approach is distinctive because it locates the trust layer at the operating system level rather than in the agent framework, the model provider, or a third-party security product. This is a deliberate architectural choice. By building containment into Windows itself, Microsoft ensures that the security guarantees hold regardless of which agent, which model, or which framework a developer chooses. It also means that the hundreds of millions of Windows devices already managed through Intune and secured through Defender can, in principle, become agent-ready through a software update rather than a rip-and-replace deployment. Apple's approach to AI agents leans heavily on its walled-garden ecosystem, offering security through restriction — limiting which agents can run and what they can do. Google's approach, centered on its cloud infrastructure, offers security through centralization. Microsoft's approach offers security through declaration and enforcement — allowing any agent to run, but containing its impact through OS-level policy. For enterprises that operate in heterogeneous environments with diverse toolchains and multiple AI providers, the Microsoft model may prove the most practical. The competitive dynamics are already shifting: with OpenAI's Codex , Nvidia’s OpenShell , and independent agent frameworks like Manus and Hermes all building on MXC, Microsoft is positioning Windows not just as the platform where agents run, but as the platform where agents can be trusted to run. The hardest part isn't building the sandbox — it's writing the policies that go inside it MXC is available now in early preview, meaning developers can begin building against the SDK and testing containment policies. The Agent 365 integration with Defender, Entra, Intune, and Purview is scheduled for preview in July — a timeline aggressive enough to suggest that much of the engineering work is already done, but far enough out to allow for refinement based on developer feedback. The real test, however, will come when enterprises begin deploying agents at scale on production networks. Containment is only as good as the policies that govern it, and writing effective agent policies for complex enterprise environments will be an entirely new discipline — one that IT departments have not yet developed and that no vendor has yet figured out how to teach. The technology is promising, but an empty sandbox is just an empty box. Filling it with the right rules, for the right agents, in the right contexts, will require a level of organizational sophistication that most companies are only beginning to contemplate. Still, the significance of what Microsoft announced on Tuesday is difficult to overstate. For the first time, a major operating system vendor has proposed a comprehensive, kernel-level answer to the question of how autonomous AI software should be contained, identified, and governed on the devices where most of the world's work actually gets done. The industry spent two years teaching agents to act. Microsoft is now betting that the bigger business — and the harder engineering problem — is teaching the operating system to watch.
- Jensen Huang says Nvidia wants to 'reinvent the single most important tool of humanity' with RTX Spark — Nvidia CEO touts support of 'literally every computer maker in the world' for its agentic AI PC platform
In a press Q&A held at Computex 2026, Nvidia CEO Jensen Huang discussed why the company is entering the PC market now and its ambitions for the future of computing.
- Snowflake and Anthropic deepen partnership to build governed enterprise AI ecosystems
Snowflake and Anthropic have expanded their strategic collaboration to accelerate enterprise adoption of governed, production-ready AI systems, reflecting a broader industry shift from AI experimentation towards operational AI embedded directly […] The post Snowflake and Anthropic deepen partnership to build governed enterprise AI ecosystems appeared first on Express Computer .
- Microsoft debuts Surface RTX Spark Dev Box to run large AI models without cloud costs
Microsoft on Monday unveiled the Surface RTX Spark Dev Box , a compact desktop computer designed to let software developers run large AI models on their desks instead of paying for cloud computing — a move that directly challenges the per-token pricing model that has defined the AI industry's economics since ChatGPT launched three and a half years ago. The device, announced at Microsoft Build 2026 , packs Nvidia’s new Blackwell-architecture RTX Spark processor and 128 gigabytes of unified memory into a small-form-factor chassis, delivering what Nvidia rates at one petaflop of AI compute. In practical terms, that means a developer can load, run and interact with AI models exceeding 120 billion parameters without sending a single API call to the cloud. "These class of devices, we think, will get to about 100 billion parameter model running," Pavan Davuluri, Microsoft's executive vice president of Windows and Devices, said during a press briefing ahead of the event. He emphasized that raw model size is only part of the equation: "The model size is one thing, but for the model to be effective, it kind of needs to be able to have enough context, because a larger model, you feed it larger context." At 100,000 tokens of context, he noted, the key-value cache alone can consume 40 to 50 gigabytes of memory — which is precisely why Microsoft and Nvidia engineered the device around a 128-gigabyte unified memory pool shared dynamically between the CPU and GPU. The machine will be available later this year in the United States, sold exclusively through Microsoft.com. The company did not disclose pricing. Why Microsoft is betting that AI's future runs on fixed costs, not cloud meters The Surface RTX Spark Dev Box arrives at a moment when the economics of AI development have become a boardroom-level concern. Companies large and small are grappling with cloud GPU bills that scale unpredictably: every fine-tuning run, every inference call, every agentic workflow that loops through a frontier model accumulates cost. For a developer iterating rapidly on a prototype — running the same model dozens or hundreds of times a day — those charges compound fast. Microsoft is framing the Dev Box as a release valve for that pressure. Andrew Hill, corporate vice president of Surface, wrote in the announcement blog post that the device "changes that equation" by letting developers "reserve frontier model calls for truly frontier problems and handle the rest on their own hardware." The pitch is not that cloud computing is obsolete, but that much of the work currently being sent to remote data centers does not require state-of-the-art models and would be better served by capable local hardware with predictable, fixed costs. This is a significant strategic shift for Microsoft, a company that derives tens of billions of dollars in annual revenue from Azure cloud services . By selling hardware that explicitly reduces customers' cloud dependency, Microsoft is acknowledging a tension that has been building across the industry: the marginal cost of AI inference at scale is unsustainable for many teams, and the market is demanding alternatives. The bet appears to be that developers who prototype locally will still deploy to Azure when they need to scale — and that owning both ends of that workflow is more valuable than owning only the cloud. Inside the 128GB unified memory architecture that makes local AI possible The technical architecture of the Dev Box reflects a set of deliberate engineering choices aimed at sustained, not peak, performance — a distinction that matters enormously for AI workloads that can run for hours. At the center is Nvidia’s RTX Spark system-on-chip , which combines an ultra-efficient ARM-based CPU with a Blackwell-generation RTX GPU. In a traditional Windows PC, Davuluri explained during the briefing, this configuration would require four separate components: a CPU, a discrete GPU, dedicated graphics memory and system RAM. The RTX Spark collapses all of that into a single chip paired with a single unified memory pool. That unification is the critical design decision. Conventional gaming laptops with high-end Nvidia GPUs top out at roughly 24 gigabytes of GPU-accessible memory. The Dev Box's 128 gigabytes of unified memory — accessible to both the CPU and GPU through what Nvidia calls its Unified Memory Access architecture — is what makes it possible to load models that would otherwise require cloud GPU instances with specialty high-bandwidth memory configurations. Microsoft did substantial work at the operating system level to exploit this architecture. The company implemented new memory management logic in Windows that raises the ceiling on how much system memory the GPU can address, introduces smarter page-size allocation for shared memory regions and ensures that heavy GPU workloads do not starve the CPU of the resources it needs for multitasking. The Windows scheduler was also optimized for RTX Spark's heterogeneous core layout, routing demanding workloads to performance cores while keeping efficiency cores available for background tasks. How a 3D-printed aluminum chassis doubles as a heatsink The thermal design is equally deliberate. The Dev Box operates within an approximately 100-watt sustained thermal envelope — modest by desktop standards, but meaningful for a device intended to run training jobs and inference workloads continuously. The aluminum chassis itself is engineered to function as a passive heatsink, and the method Microsoft used to build it is among the most striking details about the machine. The top panel is manufactured using metal 3D printing, a process that enables internal geometries too complex for conventional CNC machining or injection molding. The perforations are not simple through-holes; they are angled in multiple directions around the internal fan to optimize airflow from cold-air intake through heat dissipation. During the press briefing, Harry, a Surface industrial designer, explained the rationale: "The complexity is something other manufacturers wouldn't be able to do, like CNC, or like any molding, because of the complexity of shape." When asked whether 3D printing would constrain mass production, the designer acknowledged the challenge but suggested Microsoft had developed a process robust enough to scale. The result is a machine that runs quietly enough for an open office while sustaining the kind of continuous GPU workloads that would throttle most conventional desktops of similar size. For a device that Microsoft expects developers to leave running overnight on fine-tuning jobs, quiet sustained performance is not a luxury — it is a requirement. A developer-first setup that eliminates hours of configuration Microsoft is shipping the Dev Box with Windows 11 Pro pre-configured at the image level for development work — a detail that sounds minor but reflects a growing recognition that the out-of-box experience for developer hardware has historically been poor. The machine boots into a dark theme with a simplified taskbar, widgets removed and Do Not Disturb enabled. Developer Mode is turned on. PowerShell 7 is the default shell. WSL 2 — the Windows Subsystem for Linux — comes pre-installed with GPU passthrough and CUDA support already configured. Visual Studio Code, GitHub Copilot, Git, Python and Node.js are all installed and ready. "We've said, 'Hey, you know what, we got you, you want to go fast,'" a Microsoft engineer who demonstrated the configuration during the briefing told VentureBeat. The philosophy, he explained, is that developers were going to install all of these tools anyway — the friction was in the hours of setup and configuration that stood between unboxing a machine and writing the first line of code. The Dev Box also ships with integration points across Microsoft's AI stack: AI Toolkit for VS Code for model conversion and fine-tuning, Windows ML and Windows Copilot Runtime for local inference, and Microsoft Foundry for connecting local prototypes to cloud deployment pipelines. For enterprises, the device integrates with Entra ID and Intune for identity and device management, and includes Secured-core PC architecture, BitLocker encryption and Microsoft Defender. Why Apple's Mac Mini may not be the real competition anymore The most obvious competitive comparison is Apple's Mac Mini , which has dominated the compact-desktop category and has been widely adopted by developers drawn to Apple Silicon's unified memory architecture and power efficiency. Davuluri addressed the comparison directly during the briefing, saying the Dev Box is "in a different class of performance than Mac Minis, intentionally." He declined to share specific benchmarks, noting that detailed specifications and performance targets would come closer to the fall launch. But the architectural advantage Microsoft is claiming is clear: while the current Mac Mini with M4 Pro tops out at 48 gigabytes of unified memory and the M4 Max configuration reaches 128 gigabytes, the RTX Spark Dev Box pairs its 128 gigabytes with a Blackwell-class GPU that has a fundamentally different CUDA-based compute model — one that the vast majority of the AI/ML ecosystem's tooling (PyTorch, TensorRT, llama.cpp, Hugging Face frameworks) is already optimized for. That CUDA ecosystem advantage is difficult to overstate. While Apple's Metal framework has made progress, the overwhelming majority of AI training and inference frameworks are built and tested first against Nvidia’s CUDA stack. A developer running models on the Dev Box can use the same code, the same libraries and the same workflows they would use on a cloud GPU instance — a level of portability that Apple Silicon cannot currently match. From laptop to supercomputer: Microsoft's three-tier plan for local AI hardware The Dev Box is one piece of a three-tier hardware strategy Microsoft laid out at Build. The Surface Laptop Ultra , announced days earlier at Computex, brings the same RTX Spark silicon into a 15-inch laptop form factor for developers and creators who need portability. At the other end of the spectrum, the DGX Station for Windows — built on Nvidia's GB300 Grace Blackwell Ultra Superchip — targets organizations that need to run frontier models up to one trillion parameters on a deskside system. That machine is expected in the fourth quarter of this year. The three devices map to a tiered computing model that Microsoft is calling "unmetered intelligence": small on-device language models (the company's new Aion 1.0 family) handle lightweight tasks at zero marginal cost; RTX Spark-class hardware runs mid-range models locally for the bulk of development work; and cloud resources are reserved for genuinely frontier-scale problems. The GitHub Copilot CLI is getting a concrete implementation of this model with a new feature called /fleet, which allows a cloud-based primary agent to build a plan, assess the complexity of each task and route appropriate subtasks to a local model running on the developer's hardware. The cloud agent handles what requires frontier capability; the local model handles what does not. The result, in theory, is lower cost without lower quality. The real question is whether hybrid AI can shift from buzzword to business model Whether Microsoft's bet pays off depends on questions that will take months to answer. How does the Dev Box actually perform under sustained, real-world workloads? What will it cost? How quickly will the open-source model ecosystem continue to produce capable models in the 70-to-120-billion-parameter range that fit within its memory envelope? And perhaps most critically: will enterprise procurement teams, trained to think of AI as a cloud line item, accept a capital expenditure on desk hardware as an alternative? The strategic logic, however, is difficult to dismiss. For three years, the AI industry has operated on an implicit assumption: serious AI work happens in the cloud, and the economics of that arrangement are simply the cost of doing business. Microsoft, a company with every incentive to reinforce that assumption, is now selling a machine that undermines it. That is not a contradiction — it is a recognition that the market is moving, and that the company that controls the developer's local environment and the cloud they deploy to has a more durable advantage than one that controls only the cloud. Every dollar a developer does not spend on cloud inference is a dollar that can fund another experiment, another iteration, another prototype. For years, the AI industry told developers they needed to rent their intelligence by the token. Microsoft is now asking a different question: what if you could just buy it?
- Samsung reveals HBM5 mockup in bid to regain AI memory lead
Samsung Electronics unveiled a mockup of its HBM5 memory and a new heat-management technology at Computex 2026, signaling its ambition to regain momentum in the artificial intelligence memory race as competition with SK hynix intensifies. At the annual technology exhibition in Taiwan on Tuesday, Samsung showcased the planned architecture of its seventh-generation high-bandwidth memory, or HBM5, featuring a new thermal solution called Heat Path Block designed to improve heat dissipation in increa
- Thanks largely to robots, Ukraine is now talking about winning, not just surviving
Uncrewed and autonomous systems—and the willingness to adapt to them—have neutered Russian advantages.
Score: 52🌐 MovesJun 2, 2026https://www.defenseone.com/technology/2026/06/ukraine-robots-winning/413902/ - Anthropic Expanding Project Glasswing
Anthropic today announced it is expanding Project Glasswing, its collaborative defensive cybersecurity initiative, with participation of some 150 new organizations to continue work on making AI models more secure. The goal of Project Glasswing is to help organizations scan their code for vulnerabilities, using the Claude Mythos Preview model. When Anthropic developed the model, the … continue reading The post Anthropic Expanding Project Glasswing appeared first on SD Times .
- Travelers deploys AI-powered claims countrywide with OpenAI
Travelers built an AI-powered Claim Assistant with OpenAI to guide customers through filing claims, provide 24/7 support, and scale operations during peak demand.
- OpenAI models now available on Amazon Web Services
OpenAI is making GPT-5.5, GPT-5.4, and Codex available through Amazon Bedrock at the same prices as OpenAI's own platform. The models run in commercial and government AWS regions but are limited to the US for now. Usage counts toward existing AWS contracts. The article OpenAI models now available on Amazon Web Services appeared first on The Decoder .
Score: 52🌐 MovesJun 2, 2026https://the-decoder.com/openai-models-now-available-on-amazon-web-services/ - OpenAI plans AI tools for finance, legal in race with Anthropic
OpenAI plans AI tools for finance, legal in race with Anthropic The Mercury News
Score: 52🌐 MovesJun 2, 2026https://www.mercurynews.com/2026/06/02/openai-ai-tools-finance-legal/amp/ - IMU closes $53m Series A to decode the immune system at scale
IMU closes $53m Series A to decode the immune system at scale BioXconomy
Score: 51💰 MoneyJun 2, 2026https://xconomy.com/investment/imu-closes-53m-series-a-to-decode-the-immune-system-at-scale - Nvidia Extends Its Grip On The AI Datacenter Outwards
Nvidia Extends Its Grip On The AI Datacenter Outwards
Score: 51🌐 MovesJun 2, 2026https://www.nextplatform.com/ai/2026/06/02/nvidia-extends-its-grip-on-the-ai-datacenter-outwards/5250344 - AWS adds database features and license options aimed at simplifying agent deployment
Amazon Web Services Inc. today enhanced its database services to simplify the process of building and operating agentic artificial intelligence applications while also lowering the barriers to cloud migration for users of Microsoft Corp.’s SQL Server. The announcements add built-in durability for Amazon ElastiCache for Valkey and a new bring-your-own-media option for Amazon RDS for […] The post AWS adds database features and license options aimed at simplifying agent deployment appeared first on SiliconANGLE .
- Snowflake CoCo: AI Coding Agent for the Modern Data Stack
Snowflake CoCo is the AI coding agent built for data teams, with desktop, mobile, Slack, cloud agents, and async APIs grounded in governed enterprise data.
- Alibaba's Qwen Team Enters Embodied AI With Qwen-VLA Model
Alibaba's Tongyi Qianwen team launched Qwen-VLA, its first vision-language-action model for embodied AI, signaling the company's entry into the physical world AI race.
- Nvidia has capacity to supply robust AI growth despite constraints, says CEO
Nvidia has capacity to supply robust AI growth despite constraints, says CEO Reuters
- Intel Announces New AI Innovations at Computex
TAIPEI, Taiwan, June 2, 2026 – Today at Computex 2026, Intel unveiled new innovations that address customers’ chip- to-systems-level AI needs with solutions tailored to address their specific industry challenges, including: New rackscale AI infrastructure: Intel announced rackscale AI infrastructure for customers interested in scaling their inference and agentic workloads based on Intel® Xeon® processors … The post Intel Announces New AI Innovations at Computex appeared first on Newsroom .
Score: 50🌐 MovesJun 2, 2026https://newsroom.intel.com/artificial-intelligence/intel-announces-new-ai-innovations-at-computex - Snowflake recasts its AI strategy around action, not answers, with CoWork
Snowflake is adding workflow automation, multi-agent orchestration, and persistent user context to its AI-based enterprise data query platform, Intelligence — and renaming it CoWork. It’s a sign the company wants to move beyond simply generating insights and help CIOs translate their AI investments into operational outcomes, analysts said. Snowflake is previewing a new User Skills feature in CoWork with which developers will be able to turn routine tasks into automated workflows, then use CoWork’s MCP connectors (now generally available) to take those actions across multiple systems. Independent consultant David Linthicum welcomed this ability to take actions without leaving CoWork, as it reduces handoffs between analytics, apps, and operations. Another CoWork feature, multi-agent orchestration, is designed to break down complex requests and route work among different agents without requiring users to manually select or manage them, Snowflake said. That could be one of the more strategically significant additions for CIOs, Linthicum said, as most enterprise workflows span retrieval, reasoning, approvals, actions, and follow-up. “Snowflake is trying to package that complexity behind a single user experience. For CIOs, the appeal is better productivity and process continuity. The risk is that orchestration becomes difficult to audit, tune, and control at scale,” he said. Dion Hinchcliffe , lead of the CIO practice at The Futurum Group, said it appears that Snowflake is moving from passive analytics and query assistance toward digital labor orchestration around enterprise data. “The strategic distinction is that copilots largely answered questions, while agentic systems are increasingly expected to execute workflows, coordinate tasks, and operate semi-autonomously across systems,” he said. Giving AI agents business context As part of that broader push toward agentic workflows, Snowflake plans soon to offer a private preview of Cortex Sense, a metadata and context layer designed to give AI agents a consistent understanding of enterprise data, business definitions, and operational knowledge. This will automatically derive context from an enterprise’s data estate and make it available across agents and applications, reducing the need to recreate business context for individual workflows, the company said. For CIOs, Cortex Sense could improve consistency, reduce the risk of hallucinations, and make AI outputs more operationally useful as shared context is what makes enterprise agents trustworthy, because it aligns business meaning, governance, and execution patterns across teams, Linthicum said. Embedding business semantics, workflow intelligence, and agent skills all in the same vendor-specific orchestration layer could lead to high switching costs later for those wanting to change platforms, Hinchliffe warned: “Enterprises are likely entering an era where semantic lock-in may become as strategically important as data lock-in once was.” Snowflake is not the only software vendor looking to create a semantic context layer to improve AI-driven business workflows. Cortex Sense has similarities in this respect with Microsoft’s recently released concept of ontologies, Salesforce’s metadata-driven AI layers, and ServiceNow’s workflow context models, Hinchcliffe said. “All vendors are converging toward a shared realization: Enterprise AI systems require persistent organizational context to scale effectively,” Hinchcliffe said. “What differs is where each vendor starts from strategically. Microsoft begins from productivity and platform dominance, ServiceNow from workflows, Salesforce from CRM and metadata, and Snowflake from governed enterprise data,” Hinchcliffe added. Tougher platform choices ahead That convergence will lead to a tussle between software vendors and hyperscalers to become the control plane for AI across enterprises. “The long-term enterprise value in AI will likely accrue to vendors controlling orchestration, governance, policy enforcement, and workflow execution rather than raw model access alone,” Hinchcliffe said. “Frontier models are increasingly commoditizing at the infrastructure layer. The durable enterprise differentiation is shifting toward context management, trust, operational integration, and orchestration,” he added. The new features in CoWork don’t stop there: a new type of resusable artifacts , the conversational dashboard, will enable users to interact with governed views of live data, while Cortex Training, soon in public preview, will enable enterprises to train and customize foundation models using Snowflake-managed GPUs tailored to their own data and business requirements. The conversational dashboards will move Snowflake into the overlap among analytics, copilots, and workflow automation, said Mike Leone , principal analyst at Moor Insights and Strategy. “Power BI and Tableau are getting agentic, productivity copilots are extending into enterprise data, and CoWork is moving into productivity territory, all heading toward the same agent surface for knowledge workers,” he said. The increasingly similar ambitions of analytics, productivity, and AI vendors will make it challenging to distinguish between competing platforms. Linthicum suggested CIOs ask vendors whether their offerings just surface insights or can complete work autonomously. However, if a company’s governed data and policy logic already live in Snowflake, CoWork may become the natural choice. “Otherwise, incumbent analytics and productivity platforms still have serious distribution and workflow advantages,” Linthicum said. CIOs looking for greater control over how AI is built and deployed may find value in Cortex Training, said Hinchcliffe. “Many enterprises are becoming uncomfortable with complete dependence on external frontier model APIs for strategic workloads. Concerns around governance, sovereignty, cost predictability, latency, customization, and intellectual property are pushing enterprises toward more controllable AI architectures,” he said.
- CoreWeave-Tied Data Center Raises $900 Million in Junk-Bond Sale
A data center tied to CoreWeave Inc. raised $900 million from a high-yield note offering, joining a wave of junk issuers tapping debt markets to fund artificial intelligence infrastructure.
- Anthropic expands Glasswing as it promises public Claude Mythos-class model releases
Anthropic unveiled its Claude Mythos AI model in April, only sharing access with select partners like Apple. The controlled release is part of Anthropic’s Project Glasswing cybersecurity initiative. Now Anthropic says it’s sharing Claude Mythos access with more partners in more countries. Meanwhile, the company says Mythos-class models will be available to all customers in a matter of weeks. more…
- Fiduciary grade AI sets the bar as Thomson Reuters and Snowflake bring governed intelligence to the professions
Professionals who carry personal liability for their decisions — lawyers, tax accountants, auditors — cannot afford AI that gets it wrong. As enterprises accelerate deployment of agentic systems, the firms serving those professionals are discovering that fiduciary grade AI — intelligence built on governed, authoritative data — is not a constraint on adoption; it is […] The post Fiduciary grade AI sets the bar as Thomson Reuters and Snowflake bring governed intelligence to the professions appeared first on SiliconANGLE .
- Greece expands Shield AI V-BAT fleet for maritime operations
ATHENS (June 2, 2026) – Shield AI today announced that the Hellenic Army has signed a procurement agreement to grow its fleet of V-BAT vertical takeoff and landing (VTOL) uncrewed aircraft systems (UAS) in support of Maritime Domain Awareness (MDA) operations across the Aegean Sea. The Hellenic Army currently uses the V-BAT to deliver intelligence, […]
Score: 49🌐 MovesJun 2, 2026https://shield.ai/greece-expands-shield-ai-v-bat-fleet-for-maritime-operations/ - Salesforce Agentforce Life Sciences Selected by Viatris as Its Global Customer Engagement Platform
Salesforce today announced that Viatris, a global healthcare company, selected Agentforce Life Sciences for Customer Engagement as its global customer engagement platform to connect with customers and healthcare professionals worldwide to support end-to-end engagement across medical, commercial, and patient support functions. Viatris will use Salesforce to enable more connected, data-driven engagement across its global operations. […]
Score: 49🌐 MovesJun 2, 2026https://www.salesforce.com/news/stories/viatris-agentforce-life-sciences-global-commercial-transformation/ - What we learned at Microsoft Build: Autopilots, MAI-Thinking-1, and Nvidia RTX Spark
Microsoft Build, the Windows Maker
Score: 49🌐 MovesJun 2, 2026https://mashable.com/tech/microsoft-build-2026-keynote-everything-we-learned - Trump’s new AI executive order could slow down future model launches
The White House wants to review new AI models before they get a public release.
Score: 49🌐 MovesJun 2, 2026https://www.androidauthority.com/ai-model-review-executive-order-3673605/ - The AI boom is colliding with America’s aging power grid
PG&E entered 2026 expecting a year’s worth of new electricity demand. Barely two months later, nearly all of it was already spoken for. Interconnection requests were piling up faster than planners had expected, overwhelming a regulatory system built for an era when electricity demand barely moved. That world is gone. Load growth that historically ran below 1% annually hit 4% at some grid operators last year, according to a report by the Lawrence Berkeley National Laboratory . Bain and Company projects that AI data centers alone could consume up to 9% of total U.S. electricity by 2030, adding more than 150 terawatt-hours of demand that the current grid was never really built to handle. A third of that new demand is concentrated in Virginia, Texas, and California, according to Pew Research Center , putting extraordinary pressure on regional systems already straining to keep up. AI may dominate the headlines, but it is only part of the story. EVs , new factories, and industries shifting off diesel and gas are all pulling from the same aging grid at the same time. At Southwest Power Pool, which oversees electricity across 17 states, officials compared last year’s surge in demand to two large nuclear plants suddenly appearing on a grid with roughly 56 gigawatts of capacity. By late 2024, more than 2,600 gigawatts of proposed generation and storage projects were waiting to connect to the grid, according to Lawrence Berkeley National Laboratory, more than twice the country’s entire installed capacity. David Sawaya, PG&E’s director of rate-reducing load growth , says virtually every utility he knows has seen its interconnection queue swell by 50% to 150% in just two years. “The process does not move at the speed of business,” he tells Fast Company . “And right now, the business is moving very fast.” A decade behind and racing to catch up The utilities trying to manage this transformation are not exactly starting from a position of strength. Carlos Elena-Lenz, who leads digital enablement at Hitachi Energy, recalls a colleague who joined the company after decades in oil and gas offering a blunt assessment: Utilities globally are about a decade behind that industry in adopting AI. The culture inside many utilities has historically been built around what Elena-Lenz calls a “break-fix” model, waiting for equipment to fail rather than predicting and preventing failure. Many utilities, he says, still cannot tell you with GPS-level precision where their own assets sit on the grid. Then there is the data problem. Yuriy Yuzifovich, CTO of AI at GlobalLogic , a Hitachi subsidiary that builds digital systems for energy companies, says utilities often want AI systems their infrastructure simply cannot support. Equipment across the grid is already generating useful data, but much of it never reaches the enterprise systems designed to use it. And even when the data does make it through, utilities often lack the people or workflows needed to act on it. “Intelligence is just the tip of the iceberg,” he says. The much larger challenge lies beneath it: rebuilding data infrastructure, changing internal processes, and retraining workforces to actually use these systems effectively. Each new AI workload arriving on the grid creates pressure across three dimensions simultaneously: more power, more cooling, and more filtration. For an industry already a decade behind, that’s a significant amount of catching up to do. Some progress is visible, however. Utilities that spent years asking what they should avoid are now asking what they can do. GlobalLogic is deploying AI systems inside utility operations that continuously predict where grid stress will appear hours before it materializes. Hitachi Digital has begun using synthetic data to replicate entire power-grid networks for testing, allowing models to be stress-tested against simulated conditions before touching live infrastructure. THE REGULATORY WALL As is often the case with technology, the tools to modernize the grid are moving faster than the rules written to govern them. In conversation, Sawaya is careful to describe the California regulatory process as cumbersome rather than broken: a deliberate, stakeholder-driven structure built for a different era. Bringing a new proposal to the California Public Utilities Commission, gathering testimony, building a record, and securing a decision can take two years. For a data center developer operating on a business timeline, that can kill a project before it properly begins. Richard Schomberg, special envoy for smart electrification at the International Electrotechnical Commission (IEC), puts an even harder number on the broader bottleneck: Interconnection timelines across the U.S. can stretch to seven years, constrained by transmission capacity, substation readiness, and a queue system designed for far lower volumes. Jesse Jenkins, who leads the ZERO Lab at Princeton University, argues that the industry may be thinking about the problem in the wrong terms. “It doesn’t make sense to build a whole new transmission line that sits there 365 days a year when you only need it for a few hours a year,” he told Bloomberg News late last year. His research suggests that data centers willing to reduce their load during peak demand hours could connect to the grid years earlier—and at significantly lower cost—than those demanding guaranteed full power around the clock. PG&E has responded with reforms worth examining. A proposal called Rule 30 , approved by the California Public Utilities Commission in July 2025, requires large customers connecting to the transmission system to pay their full interconnection costs upfront, with reimbursement tied to the revenue their electricity consumption generates over time. If a data center commits, pays, and later scales back its power usage, minimum-demand fees ensure the shortfall does not fall on existing customers. At Southwest Power Pool, a consolidated planning process pending federal approval promises to cut interconnection study timelines from more than a year to six months. A separate fast-track program, recently approved by federal regulators, can bring certain large-load and generation projects online in as little as 90 days. These are real steps forward and, as Sawaya acknowledges, they come from a utility that has lived through enough operational and reputational crises to be deeply cautious about moving too fast on untested solutions. One utility in one state is still a long way from a national policy response. BUILDING AROUND THE GRID While utilities and regulators work through the constraints of the existing system, some companies have decided to build around them instead. For example, Charlotte Meerstadt, founder and CEO of Fram Energy , is building the financial infrastructure for an energy economy that is already reorganizing itself. The shift from centralized to decentralized energy generation, where businesses generate their own power and sell it directly to buyers through long-term agreements, is growing at a compound annual rate of 30%, according to Meerstadt. The drivers are straightforward: rising energy costs, unreliable grid supply, and the appeal of locking in a fixed electricity rate for 20 to 25 years rather than absorbing whatever the market delivers. The billing infrastructure for those transactions barely exists. Independent power producers, including solar farms , storage operators, and property owners selling power through direct agreements, typically manage billing through spreadsheets, manually shuttling data between operations and accounting teams. One of Fram Energy’s customers had been underbilling by $150,000 over six months without realizing it. Another was spending $50,000 a month simply to get invoices out correctly and on time. Fram’s platform automates the process, handling more than 200 variables per bill across thousands of monthly transactions. It also shows buyers exactly what they would have paid at standard grid rates versus what they actually paid—a calculation most independent producers cannot perform on their own. Meerstadt calls it the “Stripe for the decentralized energy future.” The decentralized energy market, she projects, could reach $2 trillion by 2034, making the infrastructure problem she is solving considerably larger than it might initially appear. WHO PAYS FOR ALL OF THIS? Schomberg is direct about where the cost burden currently sits. Tech companies are capturing enormous economic value from AI infrastructure while the capital costs required to enable that infrastructure are being socialized across millions of ratepayers who receive none of the upside. He advocates for what he calls a “cost causation” principle: Those whose demands drive the investment should bear its cost. PG&E’s Rule 30 moves in that direction, but applying the same logic nationally would require the kind of coordinated policy response the U.S. energy regulatory system has not historically been built to deliver. Rob Gramlich, president of the independent power sector consultancy Grid Strategies , thinks the political consequences of getting this wrong are already arriving. “I don’t think we’ve seen the end of the political repercussions,” he told CNBC in January. “And with a lot more elections in 2026 than 2025, we’ll see a lot of implications.” Retail electricity prices have already been rising faster than inflation since 2022 and are forecast to climb another 5% this year, according to a short-term energy outlook by the U.S. Energy Information Administration. For families and small business owners already stretched thin, those numbers are not mere abstractions. Hitachi, GlobalLogic, PG&E, Southwest Power Pool, and Fram Energy are each solving a real piece of a very large problem. But the grid is ultimately a shared resource, and the question of who bears the cost of transforming it is as much a political one as a technical one. That debate is just getting started.
- Daily Digest: Alphabet's $80 billion stock sale to fund AI, 40-acre Bay Area battery plant floated
Meanwhile, the California High-Speed Rail Authority has approved a contract with a consortium to install electrified track systems across over 100 miles.
- JetBrains Releases Mellum2: A 12B MoE Model for Fast, Specialized Tasks in Multi-Model AI Pipelines
JetBrains Releases Mellum2: A 12B MoE Model for Fast, Specialized Tasks in Multi-Model AI Pipelines MarkTechPost
- Snowflake Horizon Context: The Governed Context Layer for AI, BI and Apps
Discover Horizon Context, a new capability within Horizon Catalog that delivers a connected, governed semantic foundation with active context for AI and BI.
Score: 48🌐 MovesJun 2, 2026https://www.snowflake.com/content/snowflake-site/global/en/blog/horizon-context-governed-context - NVIDIA Jetson Brings Agentic AI to the Physical World
Agentic AI is getting physical. At COMPUTEX on Tuesday, NVIDIA announced NVIDIA JetPack 7.2 and NVIDIA NemoClaw support on NVIDIA Jetson. JetPack 7.2 brings agentic AI skills, Yocto project support, NVIDIA CUDA 13 on NVIDIA Jetson Orin, a substantial performance gain on Jetson AGX Orin 32GB module and Multi-Instance GPU (MIG) support on NVIDIA Jetson […]
- Snowflake’s Horizon Context aims to give AI agents a common understanding of the business
As enterprises move from AI experimentation to production deployments, one challenge is becoming increasingly apparent: AI systems are only as reliable as the business context they operate in. Snowflake is attempting to address that problem with Horizon Context, a new set of semantic and metadata-management capabilities, currently in preview, that it unveiled Tuesday at its annual Snowflake Summit conference. Artin Avanes , head of core data platform at Snowflake, told CIO.com that the offering, launched as part of Horizon Catalog , the company’s existing data discovery, management and governance suite, collects metadata from across an enterprise’s data estate, enriches it with business definitions, relationships, lineage, and governance information, and makes that context available across AI and analytics systems. These capabilities, according to Avanes, build on Snowflake’s acquisition last year of Select Star , a metadata management startup known for its integrations with database systems such as PostgreSQL and MySQL , business intelligence tools like Tableau and Power BI, and data pipeline/orchestration tools such as dbt and Airflow. Reducing operational complexity of agentic workflows For CIOs, Horizon Context should reduce operational complexity because it will provide a governed map of their organization’s data estate, said Stephanie Walter , practice lead of the AI Stack at HyperFRAME Research. “The value is not simply cataloging where data lives; it is giving AI systems the metadata, lineage, permissions, and business context needed to retrieve the right data safely,” Walter said. In fact, according to Robert Kramer , managing partner at KramerERP, the challenge of missing context for analytics and reporting is not new for enterprises. “Most enterprises were stitching together catalogs, BI semantic layers, governance tools, lineage, access controls, dbt models, security policies, and custom integrations. The issue was not that these pieces did not exist. The issue was that they were fragmented,” he said. That duct-taping together of different pieces, said Mike Leone , principal analyst at Moor Insights & Strategy, is the actual reason why most teams in an enterprise would end up with slightly different versions of a business metric, such as monthly active users, resulting in AI agents also being inconsistent downstream. But, said Walter, the stitching together of different catalogs and semantic layers only works for semi-autonomous workflows. “What changed with AI agents is that those systems increasingly need access to context at runtime rather than through documentation and human interpretation,” she said. “Snowflake is trying to pull those pieces closer to the data platform, so context, semantics, access control, and execution are part of the same operating environment” Automatically maintaining business context for AI agents To supplement Horizon Context, Snowflake is also adding Semantic Studio, currently in private preview, to help enterprises reduce the effort required to build and maintain business context for agents and agentic workflows. “Semantic Studio is a core part of the Enrich layer of Horizon Context, providing the AI-assisted workspace where teams define, test, and publish that business logic,” Avanes said. The Enrich layer itself, which consists of companion capabilities like the Semantic View Autopilot, automatically layers intelligence on data assets, providing insights such as which assets are most trusted, how they connect, what they mean, and how to correctly calculate metrics, Avanes added. According to Leone, Semantic Studio solves a critical challenge: “It will reduce the burden of SQL-savvy data engineers and let business owners author the shared definitions. That’s where most semantic-layer projects die today.” For Walter though, there is still a caveat to these offerings. “While these offerings reduce complexity, they do not remove the work. Someone still has to decide which metrics are authoritative, which data products are trusted, and who owns the business definitions,” she said. Addressing security and governance concerns Alongside these updates, Snowflake is also trying to address security concerns around agentic workflows with new capabilities inside Horizon Catalog’s Trust Center. These capabilities, which are focused on AI Security Posture Management (SPM) , include a new agent identity capability that enables enterprises to distinguish between human activity within a session and actions taken by an AI agent operating on a user’s behalf, Avanes said. “This should provide greater transparency and auditability for agents, while also enabling enterprises to apply existing data access controls, such as dynamic masking and row access policies, on an agent-by-agent basis,” he said. Another new capability, data exfiltration policies, currently in private preview, will help enterprises prevent unauthorized movement of sensitive data. These policies are part of Horizon Catalog’s broader governance framework, and will allow enterprises to define controls around how data can be accessed, shared, and moved across systems, Avanes said. For enterprises, Walter noted, these capabilities could help address one of the biggest barriers to deploying AI agents in production: governance. “Data exfiltration controls, AI Security Posture Management, and other centralized AI controls help CIOs move from experimentation to production by giving security teams a way to see and govern how AI workloads interact with enterprise data,” she said. Echoing Walter, Leone pointed out that the “security story” is the gating issue for almost every agent deployment that Moor Insights & Strategy is currently tracking. “Features such as data exfiltration policies and AI SPM are what convert a CISO from a ‘no’ to a ‘yes’ on moving an agent deployment into production,” he added.
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