At its Delivering the Future event in Dortmund, Germany, Amazon announced the debut of Vulcan, a groundbreaking robot that introduces a fundamental new capability to warehouse automation: the sense of touch.

Unlike previous robotic systems that rely primarily on vision and pre-programmed motions, Vulcan is engineered to physically “feel” the objects it handles—offering a major leap forward in dexterity, safety, and efficiency across Amazon’s vast network of fulfillment centers.

“Vulcan represents a fundamental leap forward in robotics,”

said Aaron Parness, Amazon’s Director of Applied Science.

“It’s not just seeing the world, it’s feeling it—enabling capabilities that were impossible for Amazon robots until now.”

The announcement was made via Amazon’s official blog post and showcased how Vulcan is already transforming warehouse operations in locations such as Spokane, Washington, and Hamburg, Germany.

From “Numb and Dumb” to Tactile Intelligence

While robots have become adept at tasks ranging from autonomous driving to cleaning pet hair, most commercial units lack a sense of touch—rendering them fragile or clumsy in environments that require nuance.

“In the past, when industrial robots have unexpected contact, they either emergency stop or smash through that contact,”

said Parness.

“They often don’t even know they have hit something.”

This is the problem Vulcan was built to solve.

Equipped with advanced force-feedback sensors and an “end of arm tooling” system that mimics human-like grip adjustments, Vulcan can manipulate items with care—gently repositioning objects inside densely packed bins without causing damage.

Its design resembles a ruler attached to a hair straightener, where:

• The ruler component makes space in crowded bins,
• The paddles grip and insert items using built-in conveyor belts that help “zhoop” them into place.

Vulcan’s ability to pick and stow items makes our associates’ jobs easier—and our operations more efficient.

Built for the Bin: Solving Amazon’s Unique Storage Challenge

In Amazon’s warehouses, items are stored in fabric-covered pods split into one-foot square compartments—each containing up to ten items. The irregularity and density of these compartments have long posed a challenge to robotic systems.

While earlier robots like Sparrow, Cardinal, and Robin relied on computer vision and suction cups to handle packages, they lacked the tactile intelligence to finesse objects in tight spaces. Vulcan, however, changes the game.

Using a suction-based picking arm guided by a camera and stereo vision system, Vulcan identifies items and the best gripping points while avoiding accidental co-extraction of surrounding objects—an error engineers refer to as “co-extracting non-target items.”

Vulcan can pick and stow about 75% of the diverse inventory found in fulfillment centers, performing at speeds that rival Amazon’s human workers. And when it encounters an object it can’t confidently handle, it’s smart enough to call in a human colleague—striking a balance between AI autonomy and human judgment.

Vulcan uses an arm that carries a camera and a suction cup to pick items from our storage pods.

Enhancing Safety and Ergonomics for Employees

One of Vulcan’s key contributions is in improving worker safety and reducing ergonomic strain.

Traditionally, reaching items stored in the top or bottom rows of pods—some as high as eight feet—required workers to use ladders or stoop to floor level. Vulcan now handles these less ergonomic zones, allowing employees to work comfortably at waist height.

“Working alongside Vulcan, we can pick and stow with greater ease,”

said Kari Freitas Hardy, a front-line employee at Amazon’s Spokane facility.

“It’s great to see how many of my co-workers have gained new job skills and taken on more technical roles.”

The company has already deployed over 750,000 robots across its operations, including systems like Proteus, Titan, and Hercules, all built to handle physically demanding tasks. Vulcan is the latest and most advanced addition in this line—focusing on precision and adaptability rather than brute strength.

Vulcan will let our associates spend less time on step ladders and more time working in their power zone.

A Decade of Robotics Innovation

Amazon’s approach to robotics has never been about building flashy tech for its own sake. Instead, the company zeroes in on specific operational problems and builds purpose-driven solutions.

“We pick out important problems and find or develop solutions—we don’t create interesting tech and then look for ways to use it,”

Parness emphasized.

Vulcan’s development began with a simple observation: each time a worker uses a ladder to access a high shelf, efficiency drops and injury risk increases. Tackling this required breakthroughs in physical AI, including:

• Real-world training based on tactile feedback rather than simulation,
• Algorithms to identify item types and bin availability,
• Adaptive grip mechanics to handle everything from tubes of toothpaste to delicate electronics.

Vulcan was trained on thousands of real-world scenarios, and like a child learning through experience, it improves its understanding of object properties through trial and error.

“This is a technology that three years ago seemed impossible,”

Parness said,

“but is now set to help transform our operations.”

Vulcan represents “a technology that three years ago seemed impossible but is now set to help transform our operations,” says Aaron Parness, Amazon’s director of robotics AI.

Empowering the Workforce of the Future

The ripple effects of Vulcan extend beyond automation. As robots take on more of the physical burden, Amazon is investing in reskilling its workforce through programs like Career Choice, helping employees transition into roles like robotics maintenance and automation systems engineering.

With robots now assisting in 75% of customer orders, Amazon’s strategy appears to be less about replacing humans and more about augmenting them—with Vulcan standing as the latest proof that automation and human labor can coexist and even elevate each other.

Images/Photos: Amazon

IBM has introduced a suite of new technologies aimed at scaling enterprise AI across hybrid environments, as announced today at its annual THINK conference. This move reinforces IBM’s commitment to breaking down barriers to AI deployment with integrated solutions that unify data, orchestrate AI agents, and simplify enterprise operations.

With over one billion applications projected to emerge by 2028, businesses are under increasing pressure to streamline operations across fragmented systems. IBM is responding with hybrid technologies, enhanced agent capabilities, and the consulting expertise of IBM Consulting, aiming to help clients accelerate AI adoption and realize measurable business value.

Arvind Krishna, Chairman and CEO of IBM

“The era of AI experimentation is over,”

said Arvind Krishna, Chairman and CEO of IBM.

“Today’s competitive advantage comes from purpose-built AI integration that drives measurable business outcomes.”

Enterprise AI Agents Powered by watsonx Orchestrate

Central to IBM’s announcement is the expanded functionality of watsonx Orchestrate, designed to help businesses create, deploy, and manage AI agents across a wide array of enterprise tools.

Key features include:

• Agent Builder: Enables users to create custom AI agents in under five minutes using no-code to pro-code tools.
• Pre-built Domain Agents: Ready-to-use agents tailored for HR, sales, procurement, and more.
• Wide Integration: Supports over 80 leading enterprise applications, including solutions from Adobe, AWS, Microsoft, Oracle, Salesforce Agentforce, SAP, ServiceNow, and Workday.
• Agent Orchestration: Coordinates multi-agent workflows and tools across vendors.
• Agent Observability: Provides monitoring, guardrails, and lifecycle governance.

IBM also unveiled the Agent Catalog, offering access to over 150 agents and tools co-developed with partners like Box, Mastercard, Symplistic.ai, 11x, and others. Sample integrations include a Salesforce-native prospecting agent and a conversational HR agent for Slack.

Tackling Integration Challenges with webMethods Hybrid Integration

A common obstacle to enterprise AI adoption is integration complexity. IBM is addressing this with the launch of webMethods Hybrid Integration, a solution for automating workflows across applications, APIs, partners, and clouds.

According to a Forrester Total Economic Impact (TEI) study, organizations using webMethods realized:

• 176% ROI over three years
• 40% reduction in downtime
• Up to 67% time savings on project execution
• Improved ease of use, security, and visibility

This integration technology complements IBM’s existing automation offerings and is enhanced by collaborations with HashiCorp, including integrations with Terraform and Vault, to support secure, scalable hybrid cloud operations.

Unlocking the Value of Unstructured Data

Unstructured data—such as contracts, spreadsheets, and presentations—represents a largely untapped asset in enterprise AI. IBM is advancing watsonx.data to help organizations activate this data with greater accuracy.

Highlights include:

• Open Data Lakehouse: Now with data fabric features like lineage tracking and governance.
• watsonx.data integration: A unified tool for orchestrating data pipelines across formats.
• watsonx.data intelligence: AI-driven tools for deep insights from unstructured data.

Early testing indicates watsonx.data enables up to 40% more accurate AI compared to traditional retrieval-augmented generation (RAG) methods.

In support of this strategy, IBM recently announced plans to acquire DataStax, a leader in unstructured data handling for generative AI. Additionally, watsonx is now integrated with Meta’s Llama Stack, further enhancing its generative AI capabilities.

IBM’s Content-Aware Storage (CAS) is also now available on IBM Fusion, with support for IBM Storage Scale arriving in Q3. CAS enables contextual processing of unstructured data to speed up AI inferencing.

Infrastructure for AI at Scale: Introducing IBM LinuxONE 5

To support high-performance AI workloads, IBM introduced IBM LinuxONE 5, its most secure and powerful Linux platform to date. It can process up to 450 billion AI inference operations per day.

Innovations include:

• Telum II AI Processor & IBM Spyre Accelerator: High-speed processing for generative AI, with Spyre available in late 2025.
• Confidential Containers & Quantum-Safe Encryption: Advanced security for sensitive workloads.
• Cost and Energy Efficiency: Compared to x86 systems, LinuxONE 5 can lower total cost of ownership by up to 44% over five years.

IBM also expanded partnerships with AMD, Intel, CoreWeave, and NVIDIA to deliver new compute and storage solutions for AI-driven applications.

The Road Ahead

IBM’s latest announcements represent a strategic push toward operationalizing AI at enterprise scale through modular, secure, and hybrid-ready technologies. As businesses move from AI experimentation to enterprise-wide adoption, IBM aims to be the infrastructure backbone enabling that transformation.

Image: IBM video

Snyk has introduced a groundbreaking solution to tackle the next wave of cybersecurity challenges. The company announced the launch of Snyk API & Web, a next-generation Dynamic Application Security Testing (DAST) tool engineered to secure modern, AI-powered applications.

Bridging the Security Gap in AI-Driven Development

Traditional DAST solutions have long struggled to keep up with the rapidly evolving architectures and increasing complexity of APIs brought by AI-centric development. Recognizing this critical need, Snyk acquired Probely in 2024, integrating its advanced DAST capabilities into Snyk’s broader security ecosystem—complementing existing offerings like SAST, SCA, container, and Infrastructure as Code security.

The result is a unified platform that delivers a holistic, end-to-end view of application security throughout the entire Software Development Life Cycle (SDLC).

AI-Powered Innovations for Modern Applications

Snyk API & Web introduces several forward-thinking features designed specifically for the AI era:

• AI-Driven API Testing: Leveraging in-house fine-tuned Large Language Models (LLMs), the platform automates API discovery and vulnerability scanning—detecting complex issues like Broken Object Level Authorization (BOLA) more efficiently.
• Code-Informed Dynamic Testing: By correlating DAST results with SAST findings, Snyk provides deeper context for vulnerabilities, supporting more accurate prioritization and enabling AI-powered auto-remediation via DeepCode AI Fix.
• CI/CD-Ready: Designed with DevOps in mind, the tool offers seamless CI/CD integration, allowing developers to perform self-service scans within pipelines, guided by organizational AppSec policies.

Strategic Vision from the Top

“Our vision is to empower developers and AppSec teams to secure their entire application surface without slowing down innovation,”

said Geva Solomonovich, Snyk CIO.

“Snyk API & Web is the culmination of that vision—bringing together the speed of AI with the depth of full-lifecycle security.”

Momentum and Market Response

Since integrating Probely, Snyk has reported a 245% quarter-over-quarter growth in Annual Recurring Revenue (ARR) for DAST services—a clear signal of strong market demand for modern, AI-augmented security tools.

Looking forward, the company plans to deepen its AI capabilities, broaden API coverage, and provide even richer context for faster, more accurate vulnerability remediation.

In a significant stride towards bolstering software supply chain security, Docker has introduced the Docker Maintained Community Packages (MCP) Catalog and Toolkit. This initiative is designed to offer developers and organizations a repository of secure, high-quality open-source packages maintained directly by Docker, addressing the growing concerns over the integrity and trustworthiness of software components in containerized applications.

The Docker MCP Catalog serves as a curated collection of frequently used open-source packages, selected based on community usage patterns and relevance to contemporary development workflows. By standardizing these images under Docker’s stewardship, the company provides a more trustworthy alternative to packages that are often sourced from less vetted or anonymous contributors.

Complementing the catalog is the Docker MCP Toolkit, an open-source suite of utilities aimed at simplifying the processes of building, testing, and verifying Docker images. This toolkit not only assists developers in reproducing Docker-maintained packages but also empowers contributors to create similarly secure and reliable images tailored to their specific use cases.

“Security remains a top priority for the open source and container ecosystems,”

stated Docker in its official blog post.

“By offering both the catalog and toolkit, we aim to reduce uncertainty in the software supply chain and help teams ship with confidence.”​

Docker’s MCP initiative aligns with broader industry trends emphasizing software supply chain integrity, especially in the wake of high-profile vulnerabilities and dependency attacks. It also supports best practices such as Software Bill of Materials (SBOMs) and digital signing, both of which are integrated into the MCP offerings.

The project is anticipated to see widespread adoption among developers seeking vetted base images, DevOps teams aiming to enhance CI/CD hygiene, and organizations striving to meet compliance standards related to open-source usage.​

Docker encourages community feedback and contributions to the MCP initiative via GitHub, promoting transparency and collaboration in building a more secure container ecosystem.

As threats to the software supply chain continue to evolve, Docker’s MCP Catalog and Toolkit represent a timely and proactive measure towards fortifying one of the most critical layers of modern application infrastructure.

“Images in ChatGPT are wayyyy more popular than we expected (and we had pretty high expectations),” Altman wrote.

GPT-4o’s Image Tools Initially Reserved for Paid Users

The delay comes just one day after OpenAI introduced its new GPT-4o model, which includes native support for image generation and editing. The update allows users to upload, modify, and interact with images directly within ChatGPT.

In a blog post published Tuesday, OpenAI said these new features would roll out to all ChatGPT users “soon.” However, as of Wednesday, the capabilities remain limited to paying subscribers—specifically those on ChatGPT Plus, Pro, and Teams plans.

Ongoing Capacity Challenges at OpenAI

This isn’t the first time OpenAI has scaled back or delayed product rollouts due to infrastructure limitations. The company has repeatedly cited insufficient compute capacity as a bottleneck for launching new features.

Earlier this year, OpenAI faced a similar issue following the debut of its text-to-video model Sora. Signups for the product were temporarily paused just days after launch due to demand.

Looking ahead, OpenAI is betting big on infrastructure to address these recurring issues. The company is reportedly planning a $500 billion initiative, dubbed Stargate, to build one of the largest data centers in the world in partnership with Microsoft. The project is aimed at dramatically increasing the compute power available for AI workloads.

Image: OpenAI

NVIDIA has partnered with GE HealthCare to drive innovation in autonomous imaging, with a focus on advancing AI-powered X-ray technologies and ultrasound applications.

Building autonomy into systems like X-ray and ultrasound requires medical imaging systems to understand and operate in the physical world. This enables the automation of complex workflows such as patient placement, image scanning and quality checking.

To accomplish this, GE HealthCare, a pioneering partner, is using the new NVIDIA Isaac™ for Healthcare medical device simulation platform, which includes pretrained models and physics-based simulations of sensors, anatomy and environments. The platform accelerates research and development workflows, enabling GE HealthCare to train, test and validate autonomous imaging system capabilities in a virtual environment before deployment in the physical world.

“The healthcare industry is one of the most important applications of AI, as the demand for healthcare services far exceeds the supply,”

said Kimberly Powell, vice president of healthcare at NVIDIA.

“We are working with an industry leader, GE HealthCare, to deliver Isaac for Healthcare, three computers to give lifesaving medical devices the ability to act autonomously and extend access to healthcare globally.”

Expanding Access to Imaging With Physical AI

Ultrasounds and X-ray are the most common and widely used diagnostic imaging systems, yet nearly two-thirds of the global population lack access. Enhancing imaging systems with robotic capabilities will help expand access to care.

NVIDIA and GE HealthCare have been working together for nearly two decades, building innovative image-reconstruction techniques across CT and MRI, image-guided therapy and mammography.

“GE HealthCare is committed to developing innovative technologies that redefine and enhance patient care,”

said Roland Rott, president and CEO of Imaging at GE HealthCare.

“We look forward to taking advantage of physical AI for autonomous imaging systems with NVIDIA technology to improve patient access and address the challenges of growing workloads and staffing shortages in healthcare.”

Isaac for Healthcare Closes Gap Between Simulation and Reality

NVIDIA will also support other customers with Isaac for Healthcare for use cases including simulation environments. Simulation environments enable robotic systems to safely learn skills in a physically accurate virtual environment for real-world situations, such as surgery, that would otherwise be impossible to replicate.

Isaac for Healthcare is a physical AI platform built on NVIDIA’s three computers for robotics: NVIDIA DGX™, NVIDIA Omniverse™ and NVIDIA Holoscan. It includes AI models fine-tuned for healthcare robotics that can understand, act and see using enhanced vision and language processing. It also has a simulation framework for developers to accurately simulate medical environments and provides seamless deployment on NVIDIA Holoscan, an edge AI computing platform, to power robotic decision-making in the real world, in real time.

Simulation options for medical sensors are often limited. With Isaac for Healthcare, developers can now access physics-based digital twins of medical environments, allowing them to import custom sensors, instruments and even anatomies to teach robots how to respond to various scenarios. These virtual environments help close the gap between simulation and real-world implementation, and enable rapid digital prototyping.

Isaac for Healthcare allows for multi-scale simulation ranging from microscopic structures and surgery suites to full hospital facilities. Easy policy training in simulation allows robotic systems to learn how to respond in various medical scenarios in the operating room, and how to best support physician decision-making and patient care.

Healthcare Robotics Ecosystem Rapidly Expands

Isaac for Healthcare can help speed the development of robotic healthcare solutions by simulating complex medical scenarios, training AI models and optimizing robotic applications like surgery, endoscopy and cardiovascular interventions. Early adopters include Moon Surgical, Neptune Medical and Xcath.

Isaac for Healthcare is enabling ecosystem partners to seamlessly integrate their simulation tools, sensors, robot systems and medical probes into a domain-specific simulation environment. Among early ecosystem partners are Ansys, Franka, ImFusion, Kinova and Kuka.

Issac for Healthcare is now available in early access.

NVIDIA today announced the world’s most advanced enterprise AI infrastructure — NVIDIA DGX SuperPOD™built with NVIDIA Blackwell Ultra GPUs — which provides enterprises across industries with AI factory supercomputing for state-of-the-art agentic AI reasoning.

Enterprises can use new NVIDIA DGX™ GB300 and NVIDIA DGX B300 systems, integrated with NVIDIA networking, to deliver out-of-the-box DGX SuperPOD AI supercomputers that offer FP4 precision and faster AI reasoning to supercharge token generation for AI applications.

AI factories provide purpose-built infrastructure for agentic, generative and physical AI workloads, which can require significant computing resources for AI pretraining, post-training and test-time scaling for applications running in production.

“AI is advancing at light speed, and companies are racing to build AI factories that can scale to meet the processing demands of reasoning AI and inference time scaling,”

said Jensen Huang, founder and CEO of NVIDIA.

“The NVIDIA Blackwell Ultra DGX SuperPOD provides out-of-the-box AI supercomputing for the age of agentic and physical AI.”

DGX GB300 systems feature NVIDIA Grace Blackwell Ultra Superchips — which include 36 NVIDIA Grace™ CPUs and 72 NVIDIA Blackwell Ultra GPUs — and a rack-scale, liquid-cooled architecture designed for real-time agent responses on advanced reasoning models.

Air-cooled NVIDIA DGX B300 systems harness the NVIDIA B300 NVL16 architecture to help data centers everywhere meet the computational demands of generative and agentic AI applications.

To meet growing demand for advanced accelerated infrastructure, NVIDIA also unveiled NVIDIA Instant AI Factory, a managed service featuring the Blackwell Ultra-powered NVIDIA DGX SuperPOD. Equinix will be first to offer the new DGX GB300 and DGX B300 systems in its preconfigured liquid- or air-cooled AI-ready data centers located in 45 markets around the world.

NVIDIA DGX SuperPOD With DGX GB300 Powers Age of AI Reasoning

DGX SuperPOD with DGX GB300 systems can scale up to tens of thousands of NVIDIA Grace Blackwell Ultra Superchips — connected via NVIDIA NVLink™, NVIDIA Quantum-X800 InfiniBand and NVIDIA Spectrum-X™ Ethernetnetworking — to supercharge training and inference for the most compute-intensive workloads.

DGX GB300 systems deliver up to 70x more AI performance than AI factories built with NVIDIA Hopper™ systems and 38TB of fast memory to offer unmatched performance at scale for multistep reasoning on agentic AI and reasoning applications.

The 72 Grace Blackwell Ultra GPUs in each DGX GB300 system are connected by fifth-generation NVLink technology to become one massive, shared memory space through the NVLink Switch system.

Each DGX GB300 system features 72 NVIDIA ConnectX®-8 SuperNICs, delivering accelerated networking speeds of up to 800Gb/s — double the performance of the previous generation. Eighteen NVIDIA BlueField®-3 DPUs pair with NVIDIA Quantum-X800 InfiniBand or NVIDIA Spectrum-X Ethernet to accelerate performance, efficiency and security in massive-scale AI data centers.

DGX B300 Systems Accelerate AI for Every Data Center

The NVIDIA DGX B300 system is an AI infrastructure platform designed to bring energy-efficient generative AI and AI reasoning to every data center.

Accelerated by NVIDIA Blackwell Ultra GPUs, DGX B300 systems deliver 11x faster AI performance for inference and a 4x speedup for training compared with the Hopper generation.

Each system provides 2.3TB of HBM3e memory and includes advanced networking with eight NVIDIA ConnectX-8 SuperNICs and two BlueField-3 DPUs.

NVIDIA Software Accelerates AI Development and Deployment

To enable enterprises to automate the management and operations of their infrastructure, NVIDIA also announced NVIDIA Mission Control™ — AI data center operation and orchestration software for Blackwell-based DGX systems.

NVIDIA DGX systems support the NVIDIA AI Enterprise software platform for building and deploying enterprise-grade AI agents. This includes NVIDIA NIM™ microservices, such as the new NVIDIA Llama Nemotron open reasoning model family announced today, and NVIDIA AI Blueprints, frameworks, libraries and tools used to orchestrate and optimize performance of AI agents.

NVIDIA Instant AI Factory to Meet Infrastructure Demand

NVIDIA Instant AI Factory offers enterprises an Equinix managed service featuring the Blackwell Ultra-powered NVIDIA DGX SuperPOD with NVIDIA Mission Control software.

With dedicated Equinix facilities around the globe, the service will provide businesses with fully provisioned, intelligence-generating AI factories optimized for state-of-the-art model training and real-time reasoning workloads — eliminating months of pre-deployment infrastructure planning.

NVIDIA DGX SuperPOD with DGX GB300 or DGX B300 systems are expected to be available from partners later this year. NVIDIA Instant AI Factory is planned to be available starting later this year.

In a defining moment for the cybersecurity landscape, Wiz has announced its agreement to be acquired by Google, marking a significant milestone in its five-year journey to revolutionize cloud security. While the deal remains subject to regulatory review, the acquisition will see Wiz become part of Google Cloud upon completion, ushering in a new chapter in its mission to redefine how organizations secure the cloud.

A Vision Born in the Cloud

Founded just five years ago, Wiz was born out of a simple yet powerful ambition: to build a platform that security and development teams truly love. From the outset, the company focused on listening to its customers, learning from their pain points, and innovating to solve real-world challenges in cloud security. This customer-first approach has propelled Wiz to become a leading player in modern cloud security—supporting organizations from startups to large enterprises and public sector entities.

A Platform for the Modern Cloud Security Operating Model

Wiz’s growth has been fueled by a relentless drive to change the way security is done. Its comprehensive platform spans the full spectrum of cloud risk management—from prevention and detection to response—covering everything from public clouds to data, AI, and the entire development lifecycle. This full-stack approach makes Wiz a unified platform for modern cloud security, bridging the gap between security and development teams.

Shared Vision with Google Cloud

At the heart of this acquisition is a shared philosophy between Wiz and Google Cloud: cloud security must be simpler, smarter, and more accessible. Together, they aim to democratize security—making it available and scalable for every organization, across any cloud environment.

Crucially, Wiz will remain a multicloud platform, continuing its strong collaborations with AWS, Azure, Oracle, and the broader cloud ecosystem. The company’s open approach will be preserved and strengthened, ensuring that organizations can benefit from best-in-class security tools, no matter where they build and run their systems.

Strapping a Rocket to Innovation

Joining Google Cloud isn’t just a strategic move—it’s an acceleration of Wiz’s mission. Google Cloud brings a legacy of engineering innovation in security, cloud-native technologies, and AI. From pioneering Kubernetes to setting new standards in Zero Trust and software supply chain security (SLSA), Google Cloud’s contributions have shaped the way organizations build and secure in the cloud. By aligning with this powerhouse, Wiz is poised to scale its innovations faster than ever before.

Wiz’s CEO likened the move to “strapping a rocket to our backs,” emphasizing how Google Cloud’s vast resources and AI expertise will turbocharge their development and reach.

Empowering Customers, Partners, and Employees

The benefits of this acquisition ripple outward:

• Customers will gain access to a richer, more integrated suite of cloud security, AI, and data solutions across all major cloud providers and hybrid environments.
• Partners will find renewed opportunity to collaborate with Wiz as it becomes an even more compelling choice for organizations of all sizes and sectors.
• Employees—the “Wizards”—celebrate a landmark achievement, ready to take their expertise to the next level by working alongside some of the brightest minds in cloud and AI.

A Future-Proof Security Mission

As cloud adoption and digital transformation accelerate, so too do the threats. Attackers are moving faster, powered by AI and advanced tools. Wiz and Google Cloud recognize the urgency of the moment—and the need to move even faster in response. This acquisition is not just about business synergy; it’s a bold move to future-proof cloud security and deliver the most advanced protection to organizations worldwide.

The message from Wiz is clear: the mission continues—stronger, faster, and more impactful than ever. With Google Cloud, Wiz is poised to build the most powerful security platform the world has ever seen.

Having operated in the city for years, Waymo has been methodically expanding its services. The company now facilitates tens of thousands of weekly trips, offering a safe, sustainable, and reliable transportation option for the local community and tourists alike.

Strengthening Community Ties

Waymo is becoming integral to San Francisco’s urban fabric. According to the company, 30% of its rides are directed to local businesses, boosting foot traffic to restaurants, music venues, coffee shops, and more. A recent survey highlighted that over half of Waymo’s users have utilized the service for medical appointments, while 36% connect to other transit systems such as BART or Muni. The service is even popular for wedding departures, adding a touch of modernity to traditional ceremonies.

“I’m thankful to be living in a city that embraces technology when it can improve our lives with convenient and safe modes of transit,” said Michelle Cusano, Executive Director at The Richmond Neighborhood Center.

Committing to Sustainability

In line with San Francisco’s environmental goals, Waymo’s fleet is entirely electric and powered by 100% renewable energy from the City’s CleanPowerSF program. Since its commercial launch in August 2023, Waymo has reduced carbon emissions by an estimated 570,000 kg. Survey results indicate that 53% of users feel that using Waymo has helped them adopt more environmentally friendly travel habits.

Enhancing Safety and Inclusivity

Safety remains a top priority for Waymo. More than half of its riders in San Francisco report an increased sense of personal safety when using the service. Charles Renfroe, Development Manager at Openhouse SF, praised the service for providing a harassment-free travel experience for transgender and gender non-conforming individuals.

Waymo also caters to tourists, with many enjoying rides to landmarks like the Presidio and the Ferry Building. The app supports Spanish and Chinese languages, ensuring accessibility for a diverse user base.

Milestones and Impact

Since Waymo’s waitlist opened, nearly 300,000 people have signed up for the service. With over 20 million rider-only miles and nearly 2 million paid trips, Waymo has demonstrated a strong commitment to safety. Data shows that Waymo’s autonomous vehicles are involved in fewer collisions and insurance claims compared to human-driven vehicles.

Addressing Road Safety

Waymo’s technology aims to enhance road safety, a pressing issue in San Francisco where traffic accidents remain a serious concern. The Waymo Driver is designed to avoid high-severity collisions more effectively than human drivers. Patricia Rillera, Executive Director of MADD California, highlighted the potential of autonomous vehicles to reduce accidents caused by impaired driving.

Community Engagement and Innovation

Waymo collaborates closely with city officials, first responders, and road safety advocates to ensure its service benefits the community. The company is committed to providing safe, eco-friendly transportation options that positively impact local mobility.

Waymo extends its gratitude to its riders, community partners, and the residents of San Francisco for their support. Together, they are helping to keep the city at the forefront of technological innovation, offering a safe, clean, and enjoyable transportation experience for all.

La inversión de @Microsoft en España se va a cuadruplicar y llegará hasta los 1.950 M€.

Quiero agradecer a su presidente, @BradSmi, su confianza en la economía española y en nuestra hoja de ruta para una transformación digital inclusiva y segura.

Además, hemos analizado las… pic.twitter.com/0PG1tZL08e

— Pedro Sánchez (@sanchezcastejon) February 19, 2024

Smith said it was not just about building data centres, but a commitment to help develop the “security, development and digital transformation of the country’s government, businesses and people”.

He added that the tech giant has been investing in Spain for 37 years. In September 2021, the company announced that a new artificial intelligence technology research and development centre would be established in Barcelona. It was then that Alberto Granados, president of Microsoft Spain, said that Microsoft’s investment is proof that Spain has the necessary potential to develop the digital sector.

This is the second major investment Microsoft has made in Europe within a month. Recall that last week the company invested 3 billion euros in the development of the artificial intelligence ecosystem in Germany.

Similar to its plans in Spain, over the next two years Microsoft has pledged to invest the funds in strengthening the AI infrastructure in Germany, building new data centres and training people to develop AI skills.

Major technology companies are increasing their presence in Europe by investing in the development of artificial intelligence, which in turn coincides with the implementation of EU legislation on artificial intelligence.

Read more:
1. European Commission Investigates TikTok
2. Equinix Announces Fully Managed Service for NVIDIA DGX AI Supercomputing
3. Android Releases First Preview of Android 15 for Developers