AI News Archive: June 2, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- Nvidia's new PC chips represent CEO Huang's bid to win at every layer of AI stack
Nvidia's announced entry into the PC chip market sent shares of AMD, Intel and Qualcomm lower as Wall Street recognized the threat.
Score: 48🌐 MovesJun 2, 2026https://www.cnbc.com/2026/06/02/nvidias-new-pc-chips-are-ceos-bid-to-own-every-part-of-ai-stack.html - Salesforce Stake in Anthropic Reaches $5 Billion
Salesforce Stake in Anthropic Reaches $5 Billion The Information
Score: 48💰 MoneyJun 2, 2026https://www.theinformation.com/briefings/salesforce-stake-anthropic-reaches-5-billion - 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 - New bill aims to regulate military uses of AI
Sen. Kirsten Gillibrand also wants to establish a clear chain of human accountability for AI on the battlefield.
Score: 48🌐 MovesJun 2, 2026https://www.defenseone.com/policy/2026/06/bill-regulate-military-ai/413917/ - China robotics firms line up IPOs to pitch next phase of AI
China robotics firms line up IPOs to pitch next phase of AI The Straits Times
- Microsoft targets Anthropic with new model releases
Software giant’s AI chief Mustafa Suleyman says focus is on developing products for business users
- Microsoft Launches AI That Works Like an Executive Assistant
Microsoft Corp. launched new artificial intelligence software designed to function like an always-active executive assistant, the latest evolution of its workplace AI efforts.
- Zoom launches ZoomMate: the first AI teammate built to turn conversations into completed work
Generally available today, ZoomMate combines agentic search, AI-generated presentations and deliverables, and automated execution in Salesforce, Jira, Slack, ServiceNow, and more
- NVIDIA Just Fit a Giant LLM Into a Laptop. No Cloud Required.
NVIDIA’s new RTX Spark, unveiled at Computex this morning, fits a petaflop of AI compute and 128GB of memory into a thin Windows laptop. The marketing is loud, but underneath it is a genuine shift in where AI can actually run. The pitch sounds like a slide deck wrote it. A laptop that runs a 120-billion-parameter language model locally, with a million tokens of context, no cloud subscription, no API key, nothing leaving the machine. That is the headline claim NVIDIA made this morning in Taipei when Jensen Huang unveiled the RTX Spark, the company’s first chip built specifically for Windows PCs. And the reflexive reaction, especially if you have sat through a few of these keynotes, is to roll your eyes at the number and move on. Do not move on this time. Strip away the hype and there is a real change underneath, and it lands right on a question a lot of people have been quietly wrestling with: do I actually need the cloud to run serious AI, or can I just run it myself? For most of the current AI era, the answer was the cloud, full stop. The good models were too big to fit on anything you owned, so you rented access through an API and paid by the token. The RTX Spark is interesting because it pokes a real hole in that assumption, and understanding exactly how big the hole is, and where it is not, is the whole story. What actually makes this possible The number that matters here is not the petaflop of compute. It is the 128GB of unified memory. To see why, you have to understand the thing that has always made running big models locally so painful. In a normal computer, the processor has its own memory and the graphics card has its own separate memory, with a relatively narrow pipe between them. A language model has to live in the graphics card’s memory to run fast, and that memory has historically been small. A consumer card might have 24GB. A big model needs far more than that just to load. So either the model does not fit and you cannot run it at all, or you resort to awkward tricks that shuffle pieces of it back and forth and slow everything to a crawl. Unified memory erases that wall. Instead of two separate pools, the CPU and GPU share one large 128GB pool, and the model simply lives in it without the constant shuffling. This is not a brand-new idea. Apple proved it works for consumers with its M-series chips, which is exactly why a MacBook with a lot of memory has quietly been one of the better ways to run local AI for a couple of years now. What NVIDIA has done is bring that architecture to the Windows world and pair it with its own GPU and software stack. The result is that a thin Windows laptop can hold a model in memory that previously required a desktop workstation or a cloud instance. That is the mechanism behind the headline. A 120-billion-parameter model is genuinely large, not a toy, and being able to load one into a laptop’s memory and run it is a real capability that did not exist in this form factor before. The part that makes this matter: CUDA comes too Hardware alone would be a nice curiosity. The reason this is more than that is the software that comes with it, and this is the piece most of the spec-sheet coverage underplays. The entire AI world runs on NVIDIA’s software layer, CUDA, and the ecosystem built on top of it. When you use almost any AI tool, framework, or model today, somewhere underneath it is CUDA doing the work on an NVIDIA chip. That ecosystem has been a data-center and desktop thing. The RTX Spark brings the same CUDA, the same inference tooling, the same workflows that an AI developer already uses every day, onto a portable Windows machine. For someone who builds with this stuff, that means the laptop is not a new environment to learn. It is the environment they already work in, now running locally in front of them. That continuity is the difference between a gimmick and a tool. A developer can prototype, fine-tune a smaller model, and run inference on a large one, all on the machine in their bag, using the exact tooling they would use against a cloud GPU. No other portable platform offers that specific combination, because no one else owns that software layer. Where this actually changes the math Here is where it connects to a decision a lot of teams and individuals are making right now: when does it make sense to run AI yourself instead of renting it from the cloud? The honest answer, most of the time, has been to rent. Calling an API is cheaper and simpler than owning hardware for the large majority of workloads, and that does not suddenly stop being true because a powerful laptop exists. If your usage is light or occasional, a device like this is wildly more machine than you need, and the cloud is still the right call. Nobody should buy a premium AI laptop to send a few prompts a day. But there are specific situations where local genuinely wins, and the RTX Spark makes them more accessible than they have ever been. The clearest is privacy. If you are working with data that cannot leave your control, legal documents, medical records, proprietary code, confidential financials, then running the model on your own machine is not a cost question, it is the only acceptable option. Local means the data never touches someone else’s server. For people in regulated fields who have wanted to use AI but could not send their data to a cloud provider, a laptop that runs a capable model entirely offline is a real unlock. The second is cost at steady volume. If you are running inference constantly, all day, every day, the per-token cloud charges add up, and at some point owning the hardware is cheaper than renting it forever. A developer running models continuously, or a small team with heavy steady usage, can reach the point where local pays for itself. The catch, and it is a real one, is utilization. The economics only work if you actually keep the machine busy. An expensive AI laptop that mostly idles is worse value than the cloud, the same way a self-hosted server that sits at low load is worse value than an API. The hardware is only cheaper if you use it hard. The third is independence from latency and connectivity. A local model responds without a network round trip and works on a plane, in a secure facility, or anywhere the connection is bad or forbidden. For some workflows that reliability is worth more than raw speed. The honest limits A piece that only sold you the dream would not be useful, and there are real caveats that the launch-day excitement is glossing over. Running a 120-billion-parameter model locally is not the same as running it the way a cloud data center runs it. On a single laptop you are working with a quantized version, a compressed model, and the speed will be a fraction of what a rack of data-center GPUs delivers. It works, and for many tasks it works well, but anyone expecting frontier-cloud performance from a laptop will be disappointed. The earlier desktop version of this same silicon drew exactly that criticism from reviewers: impressive for local development, not a replacement for a serious rig on raw speed. There is also the Windows-on-Arm question. This is an Arm chip, not the x86 architecture most Windows software was written for, and while compatibility has improved a lot, it is still not perfect, particularly for some older applications and certain games. And NVIDIA gave no pricing today, with every signal pointing to these landing at the premium end, which means the people who can act on this first are professionals and enthusiasts, not the mainstream. So the realistic framing is this. The RTX Spark does not make the cloud obsolete, and it does not turn every laptop into a data center. What it does is move the line. Things that used to require a cloud subscription or a dedicated workstation now fit, with real compromises, into a machine you can carry. That is a meaningful shift even if it is not the revolution the keynote implied. The bigger picture Step back and the interesting thing is what NVIDIA is betting on. The company is wagering that the personal computer is becoming an AI device first, that people will increasingly want models running on the machine in front of them rather than in a distant server, and that owning the software layer the whole AI world runs on gives it the right to own that machine too. Whether that bet pays off depends on things we cannot see yet: real-world performance once these laptops ship this fall, the price, and whether developers and users actually want local AI badly enough to pay a premium for it. The cloud is convenient, and convenience usually wins. But there is a real and growing set of people, the privacy-bound, the high-volume, the offline, the simply independent-minded, for whom running their own model has always been the goal and never quite been practical on something portable. For them, the line just moved in their favor. You can now run a serious model on your laptop, no cloud required. For most people, the cloud is still the easier answer. But “can you” and “should you” are finally two different questions, and that, more than any benchmark number, is what changed this morning. NVIDIA Just Fit a Giant LLM Into a Laptop. No Cloud Required. was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Florida Supreme Court Posts New Rule on AI Hallucinations in Court Filings
Artificial intelligence hallucinations, also known as “AI slop,” have become such a concern in court filings that the Florida Supreme Court has issued new statewide rules designed to hold attorneys more accountable. The rules, which take effect June 15, will …
- Trump's AI E-(I)-O could let feds pick winners and losers
Government gets a say in 'trusted partner' access, and that worries policy experts
- Florida AG says OpenAI 'exposed' to billions in potential damages, cites evidence uncovered in investigation
Florida Attorney General says OpenAI faces potential billions in damages over alleged ChatGPT risks to children and lack of safety safeguards.
- Cisco bets on AI agents to redefine workplace collaboration
Cisco Systems Inc. today unveiled a set of collaboration and customer experience products that transform its Webex videoconferencing platform into what executives describe as an intelligent operating system for work. The updates span collaboration hardware, AI agents, contact center software and security enhancements that Cisco said will support a future in which AI agents work […] The post Cisco bets on AI agents to redefine workplace collaboration appeared first on SiliconANGLE .
Score: 46🌐 MovesJun 2, 2026https://siliconangle.com/2026/06/02/cisco-bets-ai-agents-redefine-workplace-collaboration/ - The Pentagon Is Running an AI Propaganda Mill Targeting Latin America
La Tilde publishes an unusual mix of personal finance guides and articles extolling American military efforts in Latin America. The post The Pentagon Is Running an AI Propaganda Mill Targeting Latin America appeared first on The Intercept .
Score: 46🌐 MovesJun 2, 2026https://theintercept.com/2026/06/02/la-tilde-propaganda-latin-america-pentagon/ - 'Resistance is futile,' says Qualcomm CEO. AI agents will be become invisible, inescapable, follow you across devices
Your personal Jarvis or an end to privacy as we know it?
- Microsoft Build 2026 Keynote Highlights video
See all the biggest announcements from Microsoft CEO Satya Nadella at Microsoft's Build conference in San Francisco. Microsoft showed off its native OpenClaw app for Windows, new Unmetered Agentic AI models and a preview of Microsoft's newest quantum computer, Majorana 2.
- Microsoft Targets Legal Fears to Sell Its Powerful New AI Model to Businesses
MAI-Thinking-1 is one of seven new models the company announced today, less than one year after unveiling its first in-house models.
- Safe Pro Secures U.S. Army Order for AI-Powered Threat Analysis Kit Integrated With Red Cat Black Widow Drones
Safe Pro Secures U.S. Army Order for AI-Powered Threat Analysis Kit Integrated With Red Cat Black Widow Drones Toronto Star
- Perplexity built an “air-traffic controller” that decides in real time whether your AI query runs on your PC or in the cloud
Perplexity AI has developed a platform that dynamically splits AI workloads between personal computers and cloud servers, deciding in real time which tasks can run locally on a PC’s processor and which need the power of data centre hardware. CEO Aravind Srinivas announced the system at Computex in Taipei on Tuesday, describing it as an “air-traffic […] This story continues at The Next Web
Score: 46🌐 MovesJun 2, 2026https://thenextweb.com/news/perplexity-ai-split-compute-pc-cloud-inference-cost - The Trump Administration Is at War With Itself Over AI Regulation
Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.
Score: 46🌐 MovesJun 2, 2026https://www.wired.com/story/the-white-house-is-at-war-with-itself-over-ai-regulation/ - Chinese military has been acquiring Nvidia chips, even post-Washington export controls, research claims — multiple institutions linked to the PLA asked for Nvidia AI chips, according to publicly available documents
A business-intelligence researcher said that the Chinese military has been actively acquiring Nvidia AI chips, even after the U.S. put export controls on them. Public documents show that some institutions ask for these chips either through the specifications they demand or by directly asking for Nvidia chips by name.
- New trade secret rules: China says its AI data is none of your business
Under new Chinese regulations, any algorithm, dataset or program not publicly disclosed now counts as a trade secret.
- Build AI-powered Forge apps with Atlassian-hosted LLMs
Build AI-powered Forge apps with Atlassian-hosted LLMs Atlassian
- Intel and pals cram 36,864 CPU cores into a 100kW rack while chasing the agentic AI dragon
Meanwhile, Intel and SambaNova's disaggregated inference blueprint lands its first customer
- Android's June Update brings major security upgrades and even more AI features — what you need to know
Android's June Update brings major security upgrades and even more AI features — what you need to know Tom's Guide
- UAE launches new government media guidelines as officials prepare for ‘Agentic AI’ era
UAE launches new government media guidelines as officials prepare for ‘Agentic AI’ era Arabian Business
- Federal government’s new AI strategy will emphasize trust, minister says
Federal government’s new AI strategy will emphasize trust, minister says Toronto Star
- Mitsubishi Heavy to develop Japan-based defense AI with Preferred Networks
Mitsubishi Heavy to develop Japan-based defense AI with Preferred Networks Nikkei Asia
- MiniMax explores Star Market listing after Hong Kong debut
The company has hired advisors and signed a guidance agreement, but said any RMB share issuance remains preliminary.
Score: 45💰 MoneyJun 2, 2026https://kr-asia.com/minimax-explores-star-market-listing-after-hong-kong-debut - Supermicro Introduces DCBBS Blueprints for NVIDIA Vera Rubin NVL72 and NVIDIA HGX™ Rubin NVL8, Built to Scale from 5MW to 1GW as an End-to-End Total Solution
Supermicro Introduces DCBBS Blueprints for NVIDIA Vera Rubin NVL72 and NVIDIA HGX™ Rubin NVL8, Built to Scale from 5MW to 1GW as an End-to-End Total Solution
- Meta eyes broader AI wearable push with new glasses; pendant in works too
Meta is reportedly developing four new smart glasses, an AI-powered pendant, and a consumer AI assistant as competition from Google and Apple intensifies
- Cisco brings agentic ops platform and security overhaul to Cisco Live
Cisco built the networking infrastructure that underpins the internet and the cloud. At Cisco Live this week, the company is making its case to hold that same position as enterprises shift from AI chatbots to autonomous agents. Where chatbots answer questions, agents take actions: They execute tasks, call tools, make changes, and operate continuously at machine speed. That changes the requirements for networking, security, and observability, and it is the frame for a series of announcements. Among the key announcements from the Las Vegas event are: Cisco Cloud Control: A unified management platform spanning Meraki, Nexus, Intersight, Splunk, and Collaboration. Agentic Actions for networking: Closed-loop autonomous remediation for campus and branch networks. Cisco Multicloud Fabric: A cloud-delivered service connecting branches, data centers, and cloud workloads across AWS, Azure, Google Cloud, and neoclouds. Live Protect expansion: Runtime vulnerability shielding without reboots or maintenance windows, expanding to campus and branch Smart Switches. Agentic IAM: Ephemeral, task-scoped access controls for AI agents delivered through Cisco Secure Access. Cisco Data Fabric powered by Splunk: Federated Search, a Turnkey Machine Data Lake, an AI Toolkit, and an Agentic SOC with six purpose-built security agents. New hardware: C9550 Core switch, 8100/8200/8300/8600 Secure Routers, outdoor Wi-Fi 7, the IR1000 industrial router, and the Cisco Board Pro G3. “It’s no longer about humans clicking through dashboards, in a multitude of dashboards, trying to keep up with what the agents are doing,” DJ Sampath , senior vice president and general manager for AI software and platform, said during a press briefing. “A true collaborative operating model starts when agents are doing the heavy lifting and humans are constantly staying in control of what matters.” Cisco Cloud Control Managing enterprise infrastructure today means logging into separate dashboards for networking, security, compute, observability, and collaboration. Cloud Control replaces that with a single environment where humans and agents work from the same data and the same interface. “With Cloud Control, what you’re getting is a secureness that allows you to be able to manage your infrastructure really, you know, effectively,” Sampath said. “It provides you with observability controls, it provides you with, you know, a safe AI gateway, guardrails for these agents. All of these come bundled along with Cloud Control.” Core capabilities in Cloud Control include: Cross-domain telemetry : Cloud Control aggregates data across networking, security, observability, AI infrastructure and collaboration into a shared data fabric that both operators and agents draw from simultaneously. Purpose-built models : Incoming tasks are routed to the most appropriate model rather than sent through a single large language model. Cisco’s own models include the Deep Network Model, trained on four decades of operational networking data, a Foundation Security Model, and a time-series model for telemetry analysis. Frontier models are available for broad reasoning tasks. Trusted agents : Agents are grounded in live telemetry, governed with enterprise guardrails and action-ready to execute at machine speed. The Cisco AI Canvas is the multiplayer workspace where operators and agents investigate and resolve incidents from shared live data. An Actions queue surfaces recommendations, root cause analyses and confidence scores for human review before any change is deployed. Cloud Control Studio : Targeted for late 2026, Studio adds an Agent Builder for creating custom agents with connectivity to more than 50 third-party platforms via native connectors or the Model Context Protocol, and an App Builder that embeds OpenAI’s Codex into the platform. Anything built inside Cloud Control inherits its observability and security controls automatically. Cloud Control Marketplace: Launches with integrations across IT service management (ServiceNow, Atlassian, BMC), identity (Okta, Ping Identity, Microsoft Entra ID, Jamf), network monitoring (LiveAction, Panduit), infrastructure knowledge (NetBox Labs, Device42, Vertiv) and AI-native platforms (Anthropic, OpenAI, NVIDIA, Collibra), among others. Agentic networking and Multicloud Fabric Network operations teams still rely on manual processes to detect problems and push fixes, while enterprise AI applications are increasingly split across multiple clouds. Cisco is addressing both with announcements this week. First up is Agentic Actions for networking. Entering beta in June 2026 via Meraki, the feature follows a five-stage loop: sense, diagnose, remediate, validate, deploy. Experience Metrics converts raw device telemetry into user-experience measurements in real time. Deep Reasoning applies Cisco’s purpose-built models to multi-step root cause analysis. Digital Twin runs an emulated replica of the production network using actual software images rather than a mathematical model, allowing agents to test changes before deployment. Digital Twin enters alpha in July 2026. The second announcement in this area is Cisco Multicloud Fabric. It connects branches, data centers, and cloud workloads across AWS, Azure, Google Cloud, and neocloud providers through a managed overlay with no customer-side hardware required. The fabric includes zero trust routing, cloud firewall service chaining and built-in ThousandEyes and Splunk observability. “This is a cloud-delivered service that Cisco builds and operates, so there’s nothing for the customer to install or deploy,” said Anurag Dhingra , senior vice president and general manager for enterprise connectivity and collaboration. “It’s instantly available, configured seamlessly with one button in Cisco Cloud Control, and it stitches all of this connectivity in minutes.” Security: Live Protect and Agentic IAM Frontier AI models have compressed the window between vulnerability discovery and exploitation from months to minutes. Cisco is responding with runtime defenses that operate at the infrastructure layer and a new access control model built specifically for AI agents. Live Protect: Applies runtime compensating controls to network devices without reboots or maintenance windows, precise enough to target a specific process-to-file interaction on a running device. Agentic IAM : Rather than standing role-based access, agents receive ephemeral permissions scoped to a specific task, delivered through Cisco Secure Access via multi-turn LLM, API and MCP policy enforcement. “So instead of access control, we start to move to action control,” said Tom Gillis , senior vice president and general manager for infrastructure and security. “It’s just in time, it’s just enough access, and it’s just long enough, meaning it’s ephemeral. So you don’t get six months or a year’s worth of access, you get the access that you need to be able to do and perform a task and no more.” Non-human identity and agent protection : Cisco is building on technology it gained via the acquisition of Astrix Security to improve agentic AI security. The technology uses process-level inspection to distinguish agent activity from human activity. DefenseClaw, Cisco’s open-source runtime security framework for AI agents, is being embedded into Cisco Secure Client. With Secure Client deployed on more than 200 million enterprise devices, that means endpoint-level agent protections can be applied across the enterprise without requiring developers to instrument each agent individually. Cisco Data Fabric and the Agentic SOC Cisco Data Fabric , which debuted in September 2025, is getting a big update at Cisco Live. Powered by Splunk, it consolidates telemetry across network, application, security and third-party sources into a common layer that both human analysts and automated agents draw from, and serves as the data foundation for Cloud Control and the Agentic SOC. Among the enhancements is an improved federated search capability. “Instead of having to move data into Splunk, we bring Splunk to the data and we can query this data across different environments without copying, without moving it,” Kamal Hathi , sernior vice president and general manager for Splunk, said. There is also an AI Toolkit Agent Builder that provides domain-specific models for machine data operations as well as what Cisco is calling a Turnkey Machine Data Lake which automates schema management for raw machine data using AI. On top of the data fabric, Cisco is deploying an Agentic SOC with purpose-built agents covering the full detection and response lifecycle. “We’re reducing the time and sophistication required for security operations,” Hathi said. “We’re driving down from what used to take maybe days and hours down to minutes and seconds.” Going a step further Cisco is integrating an AI SRE capability that performs autonomous root cause analysis for application and infrastructure performance issues. Technology gained by the acquisition of Galileo earlier this year, adds trace-level observability into agent execution covering tool calls, LLM interactions and prompt injection detection. “Splunk then provides us full visibility into all aspects of the use of AI and agentic solutions and really makes all of this possible at scale in a trusted manner,” Hathi said.
- Cognition Aims to Be the Switzerland of AI Agents with App Makeover
Cognition Aims to Be the Switzerland of AI Agents with App Makeover The Information
Score: 45🌐 MovesJun 2, 2026https://www.theinformation.com/newsletters/ai-agenda/cognition-aims-switzerland-ai-agents-app-makeover - Top analyst sees 'opening of the floodgates for the IPO market' after Anthropic's filing as dotcom bubble comparisons fly
Top analyst sees 'opening of the floodgates for the IPO market' after Anthropic's filing as dotcom bubble comparisons fly Fortune
- Meet Microsoft Scout, Your AI Coworker That Never Logs Off
Microsoft’s OpenClaw-style agent appears in Teams, just like a human colleague, and automates your dull office tasks.
Score: 45🌐 MovesJun 2, 2026https://www.wired.com/story/meet-microsoft-scout-your-ai-coworker-that-never-logs-off/ - Cadence unveils fully autonomous virtual engineer for chip design, powered by NVIDIA
Level-5 ChipStack AI Super Agent framework and NVIDIA OpenShell runtime advance secure, agentic AI across semiconductor development The post Cadence unveils fully autonomous virtual engineer for chip design, powered by NVIDIA appeared first on Express Computer .
- Google Mystery: Why Tap Equity Instead Of Adding Debt To Fund AI?
Google stock fell after Alphabet announced equity offerings totaling $80 billion in a capital rise amid higher spending on AI data centers. The post Google Mystery: Why Tap Equity Instead Of Adding Debt To Fund AI? appeared first on Investor's Business Daily .
- Humanoid Robots Are Coming to Work. Here’s What You Need to Know Now.
Humanoid Robots Are Coming to Work. Here’s What You Need to Know Now. entrepreneur.com
- Google Is Daring Rivals To Keep Up in AI Spending Race
Plus, Anthropic gets the IPO ball rolling and Nvidia makes a play for the PC market.
- Microsoft Wants to 'Make People Addicted' to its New AI Assistant, Internal Documents Reveal
Planning documents for "Scout" say the plan is to "make people addicted" to the tool before adding new features.
- Anthropic surges as OpenAI struggles to keep up
Stock market filing illustrates AI company’s meteoric rise, while California’s tech billionaires pour cash into elections Hello, and welcome to TechScape. I’m your host, Blake Montgomery, US tech editor at the Guardian. This week in tech, we’re discussing Anthropic’s meteoric rise, both theological and financial, and California’s unprecedented infusion of political cash from Silicon Valley. ‘Like a billionaire on acid’: Star Wars director Gareth Edwards comes out in favour of AI To YouTube and beyond: how online gen Z directors stormed Hollywood Continue reading...
Score: 43🌐 MovesJun 2, 2026https://www.theguardian.com/technology/2026/jun/01/anthropic-openai-techscape - Fears of dotcom bubble 2.0 as trillion-dollar AI floats swamp the market
Fears of dotcom bubble 2.0 as trillion-dollar AI floats swamp the market The Telegraph
Score: 43🌐 MovesJun 2, 2026https://www.telegraph.co.uk/business/2026/06/02/ai-1tn-megafloats-risk-pushing-market-new-dotcom-bubble/ - Polish ecommerce major Allegro secures $275 million EIB loan for AI, research and development
The deal is the largest corporate R&D programme the EIB has backed in Poland and falls under the bank's TechEU initiative, which targets €250 billion ($291 billion) in new investment across Europe by 2027.
- ETH Zurich, EPFL, and Stanford HAI forge a strategic collaboration on human-centered AI
Long-term collaboration in AI research and education with a focus on open, large-scale foundation models.
- Cisco rolls out software tools to protect IT systems from AI agents
Cisco rolls out software tools to protect IT systems from AI agents Reuters
- Marvell enters the AI network fray with 102.4 Tbps switch silicon
High radix, low latency and low power is what AI datacenters crave, the chipmaker says
- All 8 laptops launching with Nvidia RTX Spark this fall — and what they can do
All 8 laptops launching with Nvidia RTX Spark this fall — and what they can do Tom's Guide
- Apollo Hospitals Chennai Performs World’s First Robotic Cancer Surgery for Lymph Node Removal Using Hugo RAS Platform via Lateral Approach
Apollo Hospitals Chennai Performs World’s First Robotic Cancer Surgery for Lymph Node Removal Using Hugo RAS Platform via Lateral Approach
- Palo Alto raises annual forecasts on strong AI cybersecurity demand, shares surge
Palo Alto raises annual forecasts on strong AI cybersecurity demand, shares surge Reuters
- Flush With Cash From OpenAI, Opal Is Making an AI-Powered Audio Gadget
Opal, the company famous for making a fancy webcam, has pivoted to making other consumer electronics. Fueled by big investments from OpenAI and Samsung, it’s working on an audio gadget first.
Score: 42💰 MoneyJun 2, 2026https://www.wired.com/story/opal-electronics-openai-investment-ai-powered-audio-gadget/