Google and OpenAI struck a cloud computing deal in May, according to a Reuters report. The deal surprised the industry as the two are seen as major AI rivals. Signs of friction between OpenAI and Microsoft may have also fueled the move. The partnership is a win-win.OpenAI gets more badly needed computing resources while Google profits from its $75B investment to boost its cloud computing capacity in 2025. In a surprise move, Google and OpenAI inked a deal that will see the AI rivals partnering to address OpenAI’s growing cloud computing needs. The story, reported by Reuters, cited anonymous sources saying that the deal had been discussed for months and finalized in May. Around this time, OpenAI has struggled to keep up with demand as its number of weekly active users and business users grew in Q1 2025. There’s also speculation of friction between OpenAI and its biggest investor Microsoft. Why the Deal Surprised the Tech Industry The rivalry between the two companies hardly needs an introduction. When OpenAI’s ChatGPT launched in November 2022, it posed a huge threat to Google that triggered a code red within the search giant and cloud services provider. Since then, Google has launched Bard (now known as Gemini) to compete with OpenAI head-on. However, it had to play catch up with OpenAI’s more advanced ChatGPT AI chatbot. This led to numerous issues with Bard, with critics referring to it as a half-baked product. A post on X in February 2023 showed the Bard AI chatbot erroneously stating that the James Webb Telescope took the first picture of an exoplanet. It was, in fact, the European Southern Observatory’s Very Large Telescope that did this in 2004. Google’s parent company Alphabet lost $100B off its market value within 24 hours as a result. Two years on, Gemini made significant strides in terms of accuracy, quoting sources, and depth of information, but is still prone to hallucinations from time to time. You can see examples of these posted on social media, like telling a user to make spicy spaghetti with gasoline or the AI thinking it’s still 2024.  And then there’s this gem: With the entire industry shifting towards more AI integrations, Google went ahead and integrated its AI suite into Search via AI Overviews. It then doubled down on this integration with AI Mode, an experimental feature that lets you perform AI-powered searches by typing in a question, uploading a photo, or using your voice. In the future, AI Mode from Google Search could be a viable competitor to ChatGPT—unless of course, Google decides to bin it along with many of its previous products. Given the scope of the investment, and Gemini’s significant improvement, we doubt AI + Search will be axed. It’s a Win-Win for Google and OpenAI—Not So Much for Microsoft? In the business world, money and the desire for expansion can break even the biggest rivalries. And the one between the two tech giants isn’t an exception. Partly, it could be attributed to OpenAI’s relationship with Microsoft. Although the Redmond, Washington-based company has invested billions in OpenAI and has the resources to meet the latter’s cloud computing needs, their partnership hasn’t always been rosy.  Some would say it began when OpenAI CEO Sam Altman was briefly ousted in November 2023, which put a strain on the ‘best bromance in tech’ between him and Microsoft CEO Satya Nadella. Then last year, Microsoft added OpenAI to its list of competitors in the AI space before eventually losing its status as OpenAI’s exclusive cloud provider in January 2025. If that wasn’t enough, there’s also the matter of the two companies’ goal of achieving artificial general intelligence (AGI). Defined as when OpenAI develops AI systems that generate $100B in profits, reaching AGI means Microsoft will lose access to the former’s technology. With the company behind ChatGPT expecting to triple its 2025 revenue to $12.7 from $3.7B the previous year, this could happen sooner rather than later. While OpenAI already has deals with Microsoft, Oracle, and CoreWeave to provide it with cloud services and access to infrastructure, it needs more and soon as the company has seen massive growth in the past few months. In February, OpenAI announced that it had over 400M weekly active users, up from 300M in December 2024. Meanwhile, the number of its business users who use ChatGPT Enterprise, ChatGPT Team, and ChatGPT Edu products also jumped from 2M in February to 3M in March. The good news is Google is more than ready to deliver. Its parent company has earmarked $75B towards its investments in AI this year, which includes boosting its cloud computing capacity. In April, Google launched its 7th generation tensor processing unit (TPU) called Ironwood, which has been designed specifically for inference. According to the company, the new TPU will help power AI models that will ‘proactively retrieve and generate data to collaboratively deliver insights and answers, not just data.’The deal with OpenAI can be seen as a vote of confidence in Google’s cloud computing capability that competes with the likes of Microsoft Azure and Amazon Web Services. It also expands Google’s vast client list that includes tech, gaming, entertainment, and retail companies, as well as organizations in the public sector. As technology continues to evolve—from the return of 'dumbphones' to faster and sleeker computers—seasoned tech journalist, Cedric Solidon, continues to dedicate himself to writing stories that inform, empower, and connect with readers across all levels of digital literacy. With 20 years of professional writing experience, this University of the Philippines Journalism graduate has carved out a niche as a trusted voice in tech media. Whether he's breaking down the latest advancements in cybersecurity or explaining how silicon-carbon batteries can extend your phone’s battery life, his writing remains rooted in clarity, curiosity, and utility. Long before he was writing for Techreport, HP, Citrix, SAP, Globe Telecom, CyberGhost VPN, and ExpressVPN, Cedric's love for technology began at home courtesy of a Nintendo Family Computer and a stack of tech magazines. Growing up, his days were often filled with sessions of Contra, Bomberman, Red Alert 2, and the criminally underrated Crusader: No Regret. But gaming wasn't his only gateway to tech.  He devoured every T3, PCMag, and PC Gamer issue he could get his hands on, often reading them cover to cover. It wasn’t long before he explored the early web in IRC chatrooms, online forums, and fledgling tech blogs, soaking in every byte of knowledge from the late '90s and early 2000s internet boom. That fascination with tech didn’t just stick. It evolved into a full-blown calling. After graduating with a degree in Journalism, he began his writing career at the dawn of Web 2.0. What started with small editorial roles and freelance gigs soon grew into a full-fledged career. He has since collaborated with global tech leaders, lending his voice to content that bridges technical expertise with everyday usability. He’s also written annual reports for Globe Telecom and consumer-friendly guides for VPN companies like CyberGhost and ExpressVPN, empowering readers to understand the importance of digital privacy. His versatility spans not just tech journalism but also technical writing. He once worked with a local tech company developing web and mobile apps for logistics firms, crafting documentation and communication materials that brought together user-friendliness with deep technical understanding. That experience sharpened his ability to break down dense, often jargon-heavy material into content that speaks clearly to both developers and decision-makers. At the heart of his work lies a simple belief: technology should feel empowering, not intimidating. Even if the likes of smartphones and AI are now commonplace, he understands that there's still a knowledge gap, especially when it comes to hardware or the real-world benefits of new tools. His writing hopes to help close that gap. Cedric’s writing style reflects that mission. It’s friendly without being fluffy and informative without being overwhelming. Whether writing for seasoned IT professionals or casual readers curious about the latest gadgets, he focuses on how a piece of technology can improve our lives, boost our productivity, or make our work more efficient. That human-first approach makes his content feel more like a conversation than a technical manual. As his writing career progresses, his passion for tech journalism remains as strong as ever. With the growing need for accessible, responsible tech communication, he sees his role not just as a journalist but as a guide who helps readers navigate a digital world that’s often as confusing as it is exciting. From reviewing the latest devices to unpacking global tech trends, Cedric isn’t just reporting on the future; he’s helping to write it. View all articles by Cedric Solidon Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

NVIDIA is working with companies worldwide to build out AI factories — speeding the training and deployment of next-generation AI applications that use the latest advancements in training and inference. The NVIDIA Blackwell architecture is built to meet the heightened performance requirements of these new applications. In the latest round of MLPerf Training — the 12th since the benchmark’s introduction in 2018 — the NVIDIA AI platform delivered the highest performance at scale on every benchmark and powered every result submitted on the benchmark’s toughest large language model (LLM)-focused test: Llama 3.1 405B pretraining. The NVIDIA platform was the only one that submitted results on every MLPerf Training v5.0 benchmark — underscoring its exceptional performance and versatility across a wide array of AI workloads, spanning LLMs, recommendation systems, multimodal LLMs, object detection and graph neural networks. The at-scale submissions used two AI supercomputers powered by the NVIDIA Blackwell platform: Tyche, built using NVIDIA GB200 NVL72 rack-scale systems, and Nyx, based on NVIDIA DGX B200 systems. In addition, NVIDIA collaborated with CoreWeave and IBM to submit GB200 NVL72 results using a total of 2,496 Blackwell GPUs and 1,248 NVIDIA Grace CPUs. On the new Llama 3.1 405B pretraining benchmark, Blackwell delivered 2.2x greater performance compared with previous-generation architecture at the same scale. On the Llama 2 70B LoRA fine-tuning benchmark, NVIDIA DGX B200 systems, powered by eight Blackwell GPUs, delivered 2.5x more performance compared with a submission using the same number of GPUs in the prior round. These performance leaps highlight advancements in the Blackwell architecture, including high-density liquid-cooled racks, 13.4TB of coherent memory per rack, fifth-generation NVIDIA NVLink and NVIDIA NVLink Switch interconnect technologies for scale-up and NVIDIA Quantum-2 InfiniBand networking for scale-out. Plus, innovations in the NVIDIA NeMo Framework software stack raise the bar for next-generation multimodal LLM training, critical for bringing agentic AI applications to market. These agentic AI-powered applications will one day run in AI factories — the engines of the agentic AI economy. These new applications will produce tokens and valuable intelligence that can be applied to almost every industry and academic domain. The NVIDIA data center platform includes GPUs, CPUs, high-speed fabrics and networking, as well as a vast array of software like NVIDIA CUDA-X libraries, the NeMo Framework, NVIDIA TensorRT-LLM and NVIDIA Dynamo. This highly tuned ensemble of hardware and software technologies empowers organizations to train and deploy models more quickly, dramatically accelerating time to value. The NVIDIA partner ecosystem participated extensively in this MLPerf round. Beyond the submission with CoreWeave and IBM, other compelling submissions were from ASUS, Cisco, Dell Technologies, Giga Computing, Google Cloud, Hewlett Packard Enterprise, Lambda, Lenovo, Nebius, Oracle Cloud Infrastructure, Quanta Cloud Technology and Supermicro. Learn more about MLPerf benchmarks.

Over a century ago, Henry Ford pioneered the mass production of cars and engines to provide transportation at an affordable price. Today, the technology industry manufactures the engines for a new kind of factory — those that produce intelligence. As companies and countries increasingly focus on AI, and move from experimentation to implementation, the demand for AI technologies continues to grow exponentially. Leading system builders are racing to ramp up production of the servers for AI factories – the engines of AI factories – to meet the world’s exploding demand for intelligence and growth. Dell Technologies is a leader in this renaissance. Dell and NVIDIA have partnered for decades and continue to push the pace of innovation. In its last earnings call, Dell projected that its AI server business will grow at least $15 billion this year. “We’re on a mission to bring AI to millions of customers around the world,” said Michael Dell, chairman and chief executive officer, Dell Technologies, in a recent announcement at Dell Technologies World. “With the Dell AI Factory with NVIDIA, enterprises can manage the entire AI lifecycle across use cases, from training to deployment, at any scale.” The latest Dell AI servers, powered by NVIDIA Blackwell, offer up to 50x more AI reasoning inference output and 5x improvement in throughput compared with the Hopper platform. Customers use them to generate tokens for new AI applications that will help solve some of the world’s biggest challenges, from disease prevention to advanced manufacturing. Dell servers with NVIDIA GB200 are shipping at scale for a variety of customers, such as CoreWeave’s new NVIDIA GB200 NVL72 system. One of Dell’s U.S. factories can ship thousands of NVIDIA Blackwell GPUs to customers in a week. It’s why they were chosen by one of their largest customers to deploy 100,000 NVIDIA GPUs in just six weeks. But how is an AI server made? We visited a facility to find out. Building the Engines of Intelligence We visited one of Dell’s U.S. facilities that builds the most compute-dense NVIDIA Blackwell generation servers ever manufactured. Modern automobile engines have more than 200 major components and take three to seven years to roll out to market. NVIDIA GB200 NVL72 servers have 1.2 million parts and were designed just a year ago. Amid a forest of racks, grouped by phases of assembly, Dell employees quickly slide in GB200 trays, NVLink Switch networking trays and then test the systems. The company said its ability to engineer the compute, network and storage assembly under one roof and fine tune, deploy and integrate complete systems is a powerful differentiator. Speed also matters. The Dell team can build, test, ship – test again on site at a customer location – and turn over a rack in 24 hours. The servers are destined for state-of-the-art data centers that require a dizzying quantity of cables, pipes and hoses to operate. One data center can have 27,000 miles of network cable — enough to wrap around the Earth. It can pack about six miles of water pipes, 77 miles of rubber hoses, and is capable of circulating 100,000 gallons of water per minute for cooling. With new AI factories being announced each week – the European Union has plans for seven AI factories, while India, Japan, Saudi Arabia, the UAE and Norway are also developing them – the demand for these engines of intelligence will only grow in the months and years ahead.

CoreWeave shares popped more than 19% after the renter of artificial intelligence data centers announced a $2 billion debt offering.

Electricity. The Internet. Now it’s time for another major technology, AI, to sweep the globe. NVIDIA founder and CEO Jensen Huang took the stage at a packed Taipei Music Center Monday to kick off COMPUTEX 2025, captivating the audience of more than 4,000 with a vision for a technology revolution that will sweep every country, every industry and every company. “AI is now infrastructure, and this infrastructure, just like the internet, just like electricity, needs factories,” Huang said. “These factories are essentially what we build today.” “They’re not data centers of the past,” Huang added. “These AI data centers, if you will, are improperly described. They are, in fact, AI factories. You apply energy to it, and it produces something incredibly valuable, and these things are called tokens.” NVIDIA CUDA-X Everywhere: After showing a towering wall of partner logos, Huang described how companies are using NVIDIA’s CUDA-X platform for a dizzying array of applications, how NVIDIA and its partners are building 6G using AI, and revealed NVIDIA’s latest work to accelerate quantum supercomputing. “The larger the install base, the more developers want to create libraries, the more libraries, the more amazing things are done,” Huang said, describing CUDA-X’s growing popularity and power. “Better applications, more benefits to users.” More’s coming, Huang said, describing the growing power of AI to reason and perceive. That leads us to agentic AI — AI able to understand, think and act. Beyond that is physical AI — AI that understands the world. The phase after that, he said, is general robotics. All of this has created demand for much more computing power. To meet those needs, Huang detailed the latest NVIDIA innovations from Grace Blackwell NVL72 systems to advanced networking technology, and detailed huge new AI installations from CoreWeave, Oracle, Microsoft, xAI and others across the globe. “These are gigantic factory investments, and the reason why people build factories is because you know, you know the answer,” Huang said with a grin. “The more you buy, the more you make.” Building AI for Taiwan: It all starts in Taiwan, Huang said, highlighting the key role Taiwan plays in the global technology ecosystem. But Taiwan isn’t just building AI for the world; NVIDIA is helping build AI for Taiwan. Huang announced that NVIDIA and Foxconn Hon Hai Technology Group are deepening their longstanding partnership and are working with the Taiwan government to build an AI factory supercomputer that will deliver state-of-the-art NVIDIA Blackwell infrastructure to researchers, startups and industries – including TSMC. “Having a world-class AI infrastructure here in Taiwan is really important,” Huang said. NVIDIA NVLink Fusion: And moving to help its partners scale up their systems however they choose, Huang announced NVLink Fusion, a new architecture that enables hyperscalers to create semi-custom compute solutions with NVIDIA’s NVLink interconnect. This technology aims to break down traditional data center bottlenecks, enabling a new level of AI scale and more flexible, optimized system designs tailored to specific AI workloads. “This incredible body of work now becomes flexible and open for anybody to integrate into,” Huang said. Blackwell Everywhere: And the engine now powering this entire AI ecosystem is NVIDIA Blackwell, with Huang showing a slide explaining how NVIDIA offers “one architecture,” from cloud AI to enterprise AI, from personal AI to edge AI. DGX Spark: Now in full production, this personal AI supercomputer for developers will be available in a “few weeks.” DGX Spark partners include ASUS, Dell, Gigabyte, Lenovo and MSI. DGX Station: DGX Station is a powerful system with up to 20 petaflops of performance powered from a wall socket. Huang said it has the capacity to run a 1 trillion parameter model, which is like having your “own personal DGX supercomputer.” NVIDIA RTX PRO Servers: Huang also announced a new line of enterprise servers for agentic AI. NVIDIA RTX PRO Servers, part of a new NVIDIA Enterprise AI Factory validated design, are now in volume production. Delivering universal acceleration for AI, design, engineering and business, RTX PRO Servers provide a foundation for NVIDIA partners to build and operate on-premises AI factories. NVIDIA AI Data Platform: The compute platform is different, so the storage platform for modern AI is different. To that end, Huang showcased the latest NVIDIA partners building intelligent storage infrastructure with NVIDIA RTX 6000 PRO Blackwell Server Edition GPUs and the NVIDIA AI Data Platform reference design. Physical AI: Agents are “essentially digital robots,” Huang said, able to “perceive, understand and plan.” To speed up the development of physical robots, the industry needs to train robots in a simulated environment. Huang said that NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics. Huang introduced new tools to speed the development of humanoid robots: The Isaac GR00T-Dreams blueprint will help generate synthetic training data. And the Isaac GR00T N1.5 Humanoid Robot Foundation Model will power robotic intelligence. Industrial Physical AI: Huang said that companies are in the process of building $5 trillion worth of factories worldwide. Optimizing the design of those factories is critical to boosting their output. Taiwan’s leading manufacturers — TSMC, Foxconn, Wistron, Pegatron, Delta Electronics, Quanta, GIGABYTE and others — are harnessing NVIDIA Omniverse to build digital twins to drive the next wave of industrial physical AI for semiconductor and electronics manufacturing. NVIDIA Constellation: Lastly, building anticipation, Huang introduced a dramatic video showing NVIDIA’s Santa Clara office launching into space and landing in Taiwan. The big reveal: NVIDIA Constellation, a brand new Taiwan office for NVIDIA’s growing Taiwan workforce. In closing, Huang emphasized that the work Taiwanese companies are doing has changed the world. He thanked NVIDIA’s ecosystem partners and described the industry’s opportunity as “extraordinary” and “once in a lifetime.” “We are in fact creating a whole new industry to support AI factories, AI agents, and robotics, with one architecture,” Huang said.

CoreWeave's stock price has had by far its best week since joining the public market in March.

The renter of Nvidia-powered AI servers said it expects capex of $20 billion to $23 billion for the year.

CoreWeave's revenue growth slowed down from the feverish pace in 2024, but is still benefiting from the artificial intelligence boom.