NVIDIA's New AI Makes Cars Fly...Sort Of!

Why California Sold Its Water to Two Billionaires

How A Chinese Villager Shook Silicon Valley [DeepSeek Founder]

Jurassic Park Trilogy Limited Edition Steelbook (4K Blu-ray) $60 | Release date TBD Preorder at Amazon Jurassic World Trilogy Limited Edition Steelbook (4K Blu-ray) $60 | Release date TBD Preorder at Amazon Jurassic World Rebirth hits theaters this summer on July 2, and it’s shaping up to be another massive blockbuster for the long-running series. It's not the only way to have some prehistoric fun this year, however, as two gorgeous 4K Blu-ray steelbook collections are now up for preorder-- the Jurassic Park Trilogy Limited Edition Steelbook and the Jurassic World Trilogy Limited Edition Steelbook. Release dates are yet to be revealed for the duo, though preorders are now open at Amazon. Both steelbooks are limited edition, so lock in your preorder before they go extinct.Reserving a copy at Amazon means you won’t be charged until the item ships. And if it happens to get a discount between now and launch, you’ll be eligible for the savings.Continue Reading at GameSpot

The long-rumored remake of The Elder Scrolls IV: Oblivion is indeed real, and fans don't have to wait another minute to play it. Bethesda has officially announced the remaster and it's out now on Game Pass for Xbox and PC, and it's also available for purchase on those platforms, starting at $50 for the standard edition and scaling to $60 for the deluxe edition. A PS5 version is available, too.The game is developed by Virtuos in partnership with Bethesda, running on Unreal Engine 5 and sporting better visuals. The Remastered version includes the base game and story expansions Shivering Isles and Knights of the Nine. Beyond an updated look, the game features a new leveling system.A first look at the Oblivion remaster.Gallery There are also new lines of dialogue, sprinting support, blood splatter effects where previously there were none, and better lighting effects, Bethesda said. You can get a closer look at the game in the announcement trailer below.Continue Reading at GameSpot

Sunderfolk brings to life the feeling of playing a tabletop game with friends in video game form. Players work together to protect the town of Arden, nestled deep within the often dangerous Sunderlands. The meaning of the game's title, Sunderfolk, is not immediately obvious. Does it simply refer to the townspeople of Arden themselves, or the player characters and their party, the foes they face, or all or perhaps none of the above? As the team at Dreamhaven and Secret Door explained, the meaning of "Sunderfolk" is a multi-layered one that effectively captures multiple elements of the game.

Stylish Wild West-themed Soulslike game Tombwater has officially debuted a new playable demo, giving players the chance to test out its horror-inspired action for the first time. "Soulslike" games have become a staple of the gaming industry throughout recent years, with many developers looking to capitalize on the popularity of the Dark Souls franchise's brutal difficulty. The trend has even spread to indie games, with smaller studios and creators giving their own spin on the growing genre. Now, a new entry is bringing the Soulslike experience to the Old West.

About 15% of the world’s population — over a billion people — are affected by neurological disorders, from commonly known diseases like Alzheimer’s and Parkinson’s to hundreds of lesser-known, rare conditions. BrainStorm Therapeutics, a San Diego-based startup, is accelerating the development of cures for these conditions using AI-powered computational drug discovery paired with lab experiments using organoids: tiny, 3D bundles of brain cells created from patient-derived stem cells. This hybrid, iterative method, where clinical data and AI models inform one another to accelerate drug development, is known as lab in the loop. “The brain is the last frontier in modern biology,” said BrainStorm’s founder and CEO Robert Fremeau, who was previously a scientific director in neuroscience at Amgen and a faculty member at Duke University and the University of California, San Francisco. “By combining our organoid disease models with the power of generative AI, we now have the ability to start to unravel the underlying complex biology of disease networks.” The company aims to lower the failure rate of drug candidates for brain diseases during clinical trials — currently over 93% — and identify therapeutics that can be applied to multiple diseases. Achieving these goals would make it faster and more economically viable to develop treatments for rare and common conditions. “This alarmingly high clinical trial failure rate is mainly due to the inability of traditional preclinical models with rodents or 2D cells to predict human efficacy,” said Jun Yin, cofounder and chief technology officer at BrainStorm. “By integrating human-derived brain organoids with AI-driven analysis, we’re building a platform that better reflects the complexity of human neurobiology and improves the likelihood of clinical success.” Fremeau and Yin believe that BrainStorm’s platform has the potential to accelerate development timelines, reduce research and development costs, and significantly increase the probability of bringing effective therapies to patients. BrainStorm Therapeutics’ AI models, which run on NVIDIA GPUs in the cloud, were developed using the NVIDIA BioNeMo Framework, a set of programming tools, libraries and models for computational drug discovery. The company is a member of NVIDIA Inception, a global network of cutting-edge startups. Clinical Trial in a Dish BrainStorm Therapeutics uses AI models to develop gene maps of brain diseases, which they can use to identify promising targets for potential drugs and clinical biomarkers. Organoids allow them to screen thousands of drug molecules per day directly on human brain cells, enabling them to test the effectiveness of potential therapies before starting clinical trials. “Brains have brain waves that can be picked up in a scan like an EEG, or electroencephalogram, which measures the electrical activity of neurons,” said Maya Gosztyla, the company’s cofounder and chief operating officer. “Our organoids also have spontaneous brain waves, allowing us to model the complex activity that you would see in the human brain in this much smaller system. We treat it like a clinical trial in a dish for studying brain diseases.” BrainStorm Therapeutics is currently using patient-derived organoids for its work on drug discovery for Parkinson’s disease, a condition tied to the loss of neurons that produce dopamine, a neurotransmitter that helps with physical movement and cognition. “In Parkinson’s disease, multiple genetic variants contribute to dysfunction across different cellular pathways, but they converge on a common outcome — the loss of dopamine neurons,” Fremeau said. “By using AI models to map and analyze the biological effects of these variants, we can discover disease-modifying treatments that have the potential to slow, halt or even reverse the progression of Parkinson’s.” The BrainStorm team used single-cell sequencing data from brain organoids to fine-tune foundation models available through the BioNeMo Framework, including the Geneformer model for gene expression analysis. The organoids were derived from patients with mutations in the GBA1 gene, the most common genetic risk factor for Parkinson’s disease. BrainStorm is also collaborating with the NVIDIA BioNeMo team to help optimize open-source access to the Geneformer model. Accelerating Drug Discovery Research With its proprietary platform, BrainStorm can mirror human brain biology and simulate how different treatments might work in a patient’s brain. “This can be done thousands of times, much quicker and much cheaper than can be done in a wet lab — so we can narrow down therapeutic options very quickly,” Gosztyla said. “Then we can go in with organoids and test the subset of drugs the AI model thinks will be effective. Only after it gets through those steps will we actually test these drugs in humans.” View of an organoid using Fluorescence Imaging Plate Reader, or FLIPR — a technique used to study the effect of compounds on cells during drug screening. This technology led to the discovery that Donepezil, a drug prescribed for Alzheimer’s disease, could also be effective in treating Rett syndrome, a rare genetic neurodevelopmental disorder. Within nine months, the BrainStorm team was able to go from organoid screening to applying for a phase 2 clinical trial of the drug in Rett patients. This application was recently cleared by the U.S. Food and Drug Administration. BrainStorm also plans to develop multimodal AI models that integrate data from cell sequencing, cell imaging, EEG scans and more. “You need high-quality, multimodal input data to design the right drugs,” said Yin. “AI models trained on this data will help us understand disease better, find more effective drug candidates and, eventually, find prognostic biomarkers for specific patients that enable the delivery of precision medicine.” The company’s next project is an initiative with the CURE5 Foundation to conduct the most comprehensive repurposed drug screen to date for CDKL5 Deficiency Disorder, another rare genetic neurodevelopmental disorder. “Rare disease research is transforming from a high-risk niche to a dynamic frontier,” said Fremeau. “The integration of BrainStorm’s AI-powered organoid technology with NVIDIA accelerated computing resources and the NVIDIA BioNeMo platform is dramatically accelerating the pace of innovation while reducing the cost — so what once required a decade and billions of dollars can now be investigated with significantly leaner resources in a matter of months.” Get started with NVIDIA BioNeMo for AI-accelerated drug discovery.

Traditionally, data centers have relied on air cooling — where mechanical chillers circulate chilled air to absorb heat from servers, helping them maintain optimal conditions. But as AI models increase in size, and the use of AI reasoning models rises, maintaining those optimal conditions is not only getting harder and more expensive — but more energy-intensive. While data centers once operated at 20 kW per rack, today’s hyperscale facilities can support over 135 kW per rack, making it an order of magnitude harder to dissipate the heat generated by high-density racks. To keep AI servers running at peak performance, a new approach is needed for efficiency and scalability. One key solution is liquid cooling — by reducing dependence on chillers and enabling more efficient heat rejection, liquid cooling is driving the next generation of high-performance, energy-efficient AI infrastructure. The NVIDIA GB200 NVL72 and the NVIDIA GB300 NVL72 are rack-scale, liquid-cooled systems designed to handle the demanding tasks of trillion-parameter large language model inference. Their architecture is also specifically optimized for test-time scaling accuracy and performance, making it an ideal choice for running AI reasoning models while efficiently managing energy costs and heat. Liquid-cooled NVIDIA Blackwell compute tray. Driving Unprecedented Water Efficiency and Cost Savings in AI Data Centers Historically, cooling alone has accounted for up to 40% of a data center’s electricity consumption, making it one of the most significant areas where efficiency improvements can drive down both operational expenses and energy demands. Liquid cooling helps mitigate costs and energy use by capturing heat directly at the source. Instead of relying on air as an intermediary, direct-to-chip liquid cooling transfers heat in a technology cooling system loop. That heat is then cycled through a coolant distribution unit via liquid-to-liquid heat exchanger, and ultimately transferred to a facility cooling loop. Because of the higher efficiency of this heat transfer, data centers and AI factories can operate effectively with warmer water temperatures — reducing or eliminating the need for mechanical chillers in a wide range of climates. The NVIDIA GB200 NVL72 rack-scale, liquid-cooled system, built on the NVIDIA Blackwell platform, offers exceptional performance while balancing energy costs and heat. It packs unprecedented compute density into each server rack, delivering 40x higher revenue potential, 30x higher throughput, 25x more energy efficiency and 300x more water efficiency than traditional air-cooled architectures. Newer NVIDIA GB300 NVL72 systems built on the Blackwell Ultra platform boast a 50x higher revenue potential and 35x higher throughput with 30x more energy efficiency. Data centers spend an estimated $1.9-2.8M per megawatt (MW) per year, which amounts to nearly $500,000 spent annually on cooling-related energy and water costs. By deploying the liquid-cooled GB200 NVL72 system, hyperscale data centers and AI factories can achieve up to 25x cost savings, leading to over $4 million dollars in annual savings for a 50 MW hyperscale data center. For data center and AI factory operators, this means lower operational costs, enhanced energy efficiency metrics and a future-proof infrastructure that scales AI workloads efficiently — without the unsustainable water footprint of legacy cooling methods. Moving Heat Outside the Data Center As compute density rises and AI workloads drive unprecedented thermal loads, data centers and AI factories must rethink how they remove heat from their infrastructure. The traditional methods of heat rejection that supported predictable CPU-based scaling are no longer sufficient on their own. Today, there are multiple options for moving heat outside the facility, but four major categories dominate current and emerging deployments. Key Cooling Methods in a Changing Landscape Mechanical Chillers: Mechanical chillers use a vapor compression cycle to cool water, which is then circulated through the data center to absorb heat. These systems are typically air-cooled or water-cooled, with the latter often paired with cooling towers to reject heat. While chillers are reliable and effective across diverse climates, they are also highly energy-intensive. In AI-scale facilities where power consumption and sustainability are top priorities, reliance on chillers can significantly impact both operational costs and carbon footprint. Evaporative Cooling: Evaporative cooling uses the evaporation of water to absorb and remove heat. This can be achieved through direct or indirect systems, or hybrid designs. These systems are much more energy-efficient than chillers but come with high water consumption. In large facilities, they can consume millions of gallons of water per megawatt annually. Their performance is also climate-dependent, making them less effective in humid or water-restricted regions. Dry Coolers: Dry coolers remove heat by transferring it from a closed liquid loop to the ambient air using large finned coils, much like an automotive radiator. These systems don’t rely on water and are ideal for facilities aiming to reduce water usage or operate in dry climates. However, their effectiveness depends heavily on the temperature of the surrounding air. In warmer environments, they may struggle to keep up with high-density cooling demands unless paired with liquid-cooled IT systems that can tolerate higher operating temperatures. Pumped Refrigerant Systems: Pumped refrigerant systems use liquid refrigerants to move heat from the data center to outdoor heat exchangers. Unlike chillers, these systems don’t rely on large compressors inside the facility and they operate without the use of water. This method offers a thermodynamically efficient, compact and scalable solution that works especially well for edge deployments and water-constrained environments. Proper refrigerant handling and monitoring are required, but the benefits in power and water savings are significant. Each of these methods offers different advantages depending on factors like climate, rack density, facility design and sustainability goals. As liquid cooling becomes more common and servers are designed to operate with warmer water, the door opens to more efficient and environmentally friendly cooling strategies — reducing both energy and water use while enabling higher compute performance. Optimizing Data Centers for AI Infrastructure As AI workloads grow exponentially, operators are reimagining data center design with infrastructure built specifically for high-performance AI and energy efficiency. Whether they’re transforming their entire setup into dedicated AI factories or upgrading modular components, optimizing inference performance is crucial for managing costs and operational efficiency. To get the best performance, high compute capacity GPUs aren’t enough — they need to be able to communicate with each other at lightning speed. NVIDIA NVLink boosts communication, enabling GPUs to operate as a massive, tightly integrated processing unit for maximum performance with a full-rack power density of 120 kW. This tight, high-speed communication is crucial for today’s AI tasks, where every second saved on transferring data can mean more tokens per second and more efficient AI models. Traditional air cooling struggles at these power levels. To keep up, data center air would need to be either cooled to below-freezing temperatures or flow at near-gale speeds to carry the heat away, making it increasingly impractical to cool dense racks with air alone. At nearly 1,000x the density of air, liquid cooling excels at carrying heat away thanks to its superior heat capacitance and thermal conductivity. By efficiently transferring heat away from high-performance GPUs, liquid cooling reduces reliance on energy-intensive and noisy cooling fans, allowing more power to be allocated to computation rather than cooling overhead. Liquid Cooling in Action Innovators across the industry are leveraging liquid cooling to slash energy costs, improve density and drive AI efficiency: Vertiv’s reference architecture for NVIDIA GB200 NVL72 servers reduces annual energy consumption by 25%, cuts rack space requirements by 75% and shrinks the power footprint by 30%. Schneider Electric’s liquid-cooling infrastructure supports up to 132 kW per rack, improving energy efficiency, scalability and overall performance for GB200 NVL72 AI data centers. CoolIT Systems’ high-density CHx2000 liquid-to-liquid coolant distribution units provide 2MW cooling capacity at 5°C approach temperature, ensuring reliable thermal management for GB300 NVL72 deployments. Also, CoolIT Systems’ OMNI All-Metal Coldplates with patented Split-Flow technology provide targeted cooling of over 4,000W thermal design power while reducing pressure drop. Boyd’s advanced liquid-cooling solutions, incorporating the company’s over two decades of high-performance compute industry experience, include coolant distribution units, liquid-cooling loops and cold plates to further maximize energy efficiency and system reliability for high-density AI workloads. Cloud service providers are also adopting cutting-edge cooling and power innovations. Next-generation AWS data centers, featuring jointly developed liquid cooling solutions, increase compute power by 12% while reducing energy consumption by up to 46% — all while maintaining water efficiency. Cooling the AI Infrastructure of the Future As AI continues to push the limits of computational scale, innovations in cooling will be essential to meeting the thermal management challenges of the post-Moore’s law era. NVIDIA is leading this transformation through initiatives like the COOLERCHIPS program, a U.S. Department of Energy-backed effort to develop modular data centers with next-generation cooling systems that are projected to reduce costs by at least 5% and improve efficiency by 20% over traditional air-cooled designs. Looking ahead, data centers must evolve not only to support AI’s growing demands but do so sustainably — maximizing energy and water efficiency while minimizing environmental impact. By embracing high-density architectures and advanced liquid cooling, the industry is paving the way for a more efficient AI-powered future. Learn more about breakthrough solutions for data center energy and water efficiency presented at NVIDIA GTC 2025 and discover how accelerated computing is driving a more efficient future with NVIDIA Blackwell.