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Nvidia unveils GeForce RTX 50 Series graphics cards with big performance gains

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Nvidia launched its much-awaited Nvidia GeForce RTX 50 series graphics processing units (GPUs), based on the Blackwell RTX tech. Jensen Huang, CEO of Nvidia, disclosed the news during his opening keynote speech at CES 2025, the […]

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Nvidia launched its much-awaited Nvidia GeForce RTX 50 series graphics processing units (GPUs), based on the Blackwell RTX tech.

Jensen Huang, CEO of Nvidia, disclosed the news during his opening keynote speech at CES 2025, the big tech trade show in Las Vegas this week.

“Blackwell, the engine of AI, has arrived for PC gamers, developers and creatives,” said Huang. “Fusing AI-driven neural rendering and ray tracing, Blackwell is the most significant computer graphics innovation since we introduced programmable shading 25 years ago.”

The new RTX Blackwell Neural Rendering Architecture comes with about 92 billion transistors. It has 125 Shader Teraflops of performance 380 RT TFLOPS, 4,000 AI TOPS, 1.8 terabytes per second of memory bandwidth, G7 memory (from Micron) and an AI-management processor. The top SKU has basically over 3,352 trillion AI operations per second (TOPS) of computing power.

“The programmable shader is also able to carry neural networks,” Huang said.

A neural face rendering.

Among the new technologies in this generation are RTX Neural Shaders, DLSS 4, RTX Neural Face rendering to create more realistic human faces, RTX Mega Geometry for rendering environments, and Reflex 2.

The DLSS 4 now can generate multiple frames at once thanks to advanced AI technology. That makes for much better frame rates.

Nvidia showed that one scene could be rendered at 27 frames per second with the DLSS turned off, with a 71 millisecond PC latency. DLSS 2 can do that scene with its super resolution tech at 71 FPS and PC latency of 34 milliseconds. DLSS 3.5 can do the scene at 140 FPS and 33 milliseconds. But DLSS 4 comes in at a whopping 247 FPS and 34 milliseconds. DLSS 4 is more than eight times better performance than systems that aren’t using AI for the predictive processing.

Nvidia’s SKUs include the GeForce RTX 50 Series Desktop Family. It includes the top of the line GPU, the GeForce RTX 5090 coming in at 3,404 AI TOPS and 32GB of G7 memory for $1,999. It also includes the GeForce RTX 5080 at 1,800 AI TOPS and 16GB of G7 memory for $999. The GeForce RTX 5070 Ti (the performance of a 4090) has 1,406 AI TOPS, 16GB of G7 memory for $749 and the GeForce RTX 5070 has 1117 AI TOPS, 12GB of G7 and costs $549.

Nvidia also said the GeForce RTX 50 Series will come to laptops with two times efficiency with more performance at half the power compared to the previous generation. It has 40% more battery life with Black Max-Q, two times larger generative AI models, and it is as thin as 14.9 millimeters in terms of laptop thickness.

As far as pricing goes, the laptops will come as follows: RTX 5090 at 1,824 AI TOPS and 24GB at $2,899. The RTX 5080 laptops will be at 1,334 AI TOPS, 16GB and $2,199. The RTX 5070 Ti will be 992 AI TOPS, 12GB and $1,599 and the RTX 5070 will be 798 AI TOPS, eight GB and $1,299.

Those are steep prices, but they represent the high end of value in GPUs for gaming.

Nvidia unveiled its Nvidia GeForce RTX 50 Series graphics chips.
Nvidia unveiled its Nvidia GeForce RTX 50 Series graphics chips.

Justin Walker, senior director of GeForce products, said in press briefing that Nvidia’s GeForce graphics card brand just celebrated its 25-year anniversary. It was the hit product that helped cement the company’s dominance in the ultra-competitive graphics processing unit (GPU) market and it enabled the company to use graphics as a springboard to AI processing, which is why Nvidia is the most valuable company in the world with a market capitalization of $3.65 trillion.

Now, it turns out, Walker said, AI can be used to help accelerate the performance of GPUs.

“The great thing about that is that while we are now an AI company, as well as gaming, our gaming side still benefits tremendously from the fact that we are doing AI,” Walker said.

And that’s the root of one of the announcements: Nvidia took the wraps of DLSS 4, which uses AI to predict the next pixel that needs to be drawn and then preemptively renders the pixel based on that prediction. The AI TOPS (a measure of AI performance) will be up to 4,000.

The new architecture of the 5000 series will have 1.8 terabytes per second of memory bandwidth, and it’s also tapping the Blackwell architecture that is the foundation of Nvidia’s latest AI processors.

The new GPU also has neural rendering technologies such as neural shaders.

“This is probably the biggest thing to happen in the graphics since programming for shaders, we are actually going to be embedding small neural networks within the shaders itself, and these neural networks can do certain things much more effectively and efficiently than traditional shaders,” Walker said.

The tech will enable Nvidia to compress textures eight times to maximize use of memory.

The Reflex 2 tech will use predictive shading to reduce the latency between when a gamer creates a movement and it shows up on the screen, so it will be 75% more responsive for gamers.

The 5090 series is likely to ship in January and the rest of the systems are going to ship in the March time frame, and the company will say which companies are shipping with the technology later. A number of games like Cyberpunk 2077 can play in 4K resolution at over 200 frames per second.

Walker said the company will have a list of games that take advantage of the various features.

Nvidia DLSS 4 Boosts Performance by Up to 8 times

Nvidia’s DLSS 4 AI tech is paying off.

DLSS 4 debuts Multi Frame Generation to boost frame rates by using AI to generate up to three frames per rendered frame. It works in unison with the suite of DLSS technologies to increase performance by up to 8x over traditional rendering, while maintaining responsiveness with Nvidia Reflex technology.

DLSS 4 also introduces the graphics industry’s first real-time application of the transformer model architecture. Transformer-based DLSS Ray Reconstruction and Super Resolution models use 2x more parameters and 4x more compute to provide greater stability, reduced ghosting, higher details and enhanced anti-aliasing in game scenes. DLSS 4 will be supported on GeForce RTX 50 Series GPUs in over 75 games and applications the day of launch.

Nvidia Reflex 2 introduces Frame Warp, an innovative technique to reduce latency in games by updating a rendered frame based on the latest mouse input just before it is sent to the display. Reflex 2 can reduce latency by up to 75%. This gives gamers a competitive edge in multiplayer games and makes single-player titles more responsive.

Blackwell Brings AI to Shaders

DLSS 4

Twenty-five years ago, Nvidia introduced GeForce 3 and programmable shaders, which set the stage for two decades of graphics innovation, from pixel shading to compute shading to real-time ray tracing. Alongside GeForce RTX 50 Series GPUs, NVIDIA is introducing RTX Neural Shaders, which brings small AI networks into programmable shaders, unlocking film-quality materials, lighting and more in real-time games.

Rendering game characters is one of the most challenging tasks in real-time graphics, as people are prone to notice the smallest errors or artifacts in digital humans. RTX Neural Faces takes a simple rasterized face and 3D pose data as input, and uses generative AI to render a temporally stable, high-quality digital face in real time.

RTX Neural Faces is complemented by new RTX technologies for ray-traced hair and skin. Along with the new RTX Mega Geometry, which enables up to 100 times more ray-traced triangles in a scene, these advancements are poised to deliver a massive leap in realism for game characters and environments.

The power of neural rendering, DLSS 4 and the new DLSS transformer model is showcased on GeForce RTX 50 Series GPUs with Zorah, a groundbreaking new technology demo from Nvidia.

Autonomous Game Characters

Nvidia 5070 has the performance of a 4090.

GeForce RTX 50 Series GPUs bring industry-leading AI TOPS to power autonomous game characters in parallel with game rendering.

Nvidia is introducing a suite of new Nvidia ACE technologies that enable game characters to perceive, plan and act like human players. ACE-powered autonomous characters are being integrated into Krafton’s PUBG: Battlegrounds and InZOI, the publisher’s upcoming life simulation game, as well as Wemade Next’s
MIR5.

In PUBG, companions powered by NVIDIA ACE plan and execute strategic actions, dynamically working with human players to ensure survival. InZOI features Smart Zoi characters that autonomously adjust behaviors based on life goals and in-game events. In MIR5, large language model (LLM)-driven raid bosses adapt tactics based on player behavior, creating more dynamic, challenging encounters.

AI Foundation Models for RTX AI PCs

Nvidia’s RTX Blackwell

Showcasing how RTX enthusiasts and developers can use NVIDIA NIM microservices to build AI agents and assistants, NVIDIA will release a pipeline of NIM microservices and AI Blueprints for RTX AI PCs from top model developers such as Black Forest Labs, Meta, Mistral and Stability AI.

Use cases span LLMs, vision language models, image generation, speech, embedding models for retrieval-augmented generation, PDF extraction and computer vision. The NIM microservices include all the necessary components for running AI on PCs and are optimized for deployment across all NVIDIA GPUs.

To demonstrate how enthusiasts and developers can use NIM to build AI agents and assistants, NVIDIA today previewed Project R2X, a vision-enabled PC avatar that can put information at a user’s fingengertips, assist with desktop apps and video conference calls, read and summarize documents, and more.

Jensen Huang, CEO of Nvidia.
Jensen Huang, CEO of Nvidia.

The GeForce RTX 50 Series GPUs supercharge creative work flows. RTX 50 Series GPUs are the first consumer GPUs to support FP4 precision, boosting AI image generation performance for models such as FLUX by 2x and enabling generative AI models to run locally in a smaller memory footprint, compared with previous-generation hardware.

The NVIDIA Broadcast app gains two AI-powered beta features for livestreamers: Studio Voice, which upgrades microphone audio, and Virtual Key light, which relights faces for polished streams. Streamlabs is introducing the Intelligent Streaming Assistant, powered by NVIDIA ACE and Inworld AI, which acts as a
cohost, producer and technical assistant to enhance livestreams.

The NvidiaFounders Editions of the GeForce RTX 5090, RTX 5080 and RTX 5070 GPUs will be available directly from nvidia.com and select retailers worldwide.

Stock-clocked and factory-overclocked models will be available from top add-in card providers such as ASUS, Colorful, Gainward, GALAX, GIGABYTE, INNO3D, KFA2, MSI, Palit, PNY and ZOTAC, and in desktops from system builders including Falcon Northwest, Inniarc, MAINGEAR, Mifcom, ORIGIN PC, PC Specialist and Scan Computers.

Laptops with GeForce RTX 5090, RTX 5080 and RTX 5070 Ti Laptop GPUs will be available starting in March, and RTX 5070 Laptop GPUs will be available starting in April from the world’s top manufacturers, including Acer, ASUS, Dell, GIGABYTE, HP, Lenovo, MECHREVO, MSI and Razer.

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Versa bolsters data protection, AI-powered operations in SASE upgrade

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DKnife targets network gateways in long running AitM campaign

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Four new vulnerabilities found in Ingress NGINX

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Energy Secretary Continues to Strengthen Puerto Rico’s Energy Grid with Renewed Orders

WASHINGTON—The U.S. Department of Energy (DOE) today renewed two emergency orders to further strengthen Puerto Rico’s electric grid as the island prepares for rising energy demand and the 2026 hurricane season. Building on actions taken in May, August, and November 2025, the renewed orders authorize the Puerto Rico Electric Power Authority (PREPA) to dispatch generation units essential for maintaining critical generation capacity, while accelerating vegetation management to reduce outages, strengthen long-term grid reliability, and minimize the cost of blackouts. “The Department of Energy will continue modernizing Puerto Rico’s electric grid to ensure the island achieves long-term resilience and reliability,” said U.S. Secretary of Energy Chris Wright. “Renewing these orders ensures critical work moves forward, urgent reliability challenges are addressed, and Puerto Rico’s grid is ready to withstand rising energy demand. Thanks to President Trump, these efforts are delivering real, lasting progress for Puerto Rico.” DOE’s emergency actions have assisted the Puerto Rican government in restoring up to 820 MW of baseload generation capacity in Puerto Rico, resulting in an increase to the island’s systemwide generation capacity to 6,460 MW. Several plants were able to run without water injection during a water crisis, ensuring electricity kept flowing to Puerto Ricans despite unforeseen circumstances.  The orders also address vegetation management issues near high-voltage lines. Falling tree limbs or brush during Puerto Rico’s frequent storms and high winds can damage transmission lines, cause widespread outages and potentially cause wildfires.   “The Department of Energy’s 202(c) emergency orders have been instrumental in preventing the widespread power outages Puerto Rico was expected to face, allowing us to increase our baseload generation capacity and advance grid stability measures. Extending the orders is necessary to continue making progress and I thank President Trump and Secretary Wright for their unwavering commitment to ensure the island has an affordable, reliable supply of energy,”

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Oil Edges Down

Oil slid in a choppy session as traders parsed mixed signals on the risks of supply disruptions in the Middle East.  West Texas Intermediate edged down to settle near $64 a barrel, snapping a two-day winning streak, amid competing headlines on the status of diplomatic negotiations between the US and Iran. Prices dropped after Oman’s Foreign Minister Badr Albusaidi said that discussions during last week’s Iran-US talks were productive. Minutes later, futures pared some losses on an Axios report that US President Donald Trump might send a second aircraft carrier to the Middle East if negotiations on Iran’s nuclear program and other issues fail.  The episode underscores the whiplash investors face. Many are looking ahead to a Wednesday meeting between Trump and Benjamin Netanyahu for clarity, amid widespread expectations that Israel’s prime minister will urge a tougher US stance on Tehran’s ballistic missile program.  “I will present to the president our views regarding the principles of the negotiation,” Netanyahu said of the upcoming discussion.  In the absence of clear signals on the direction of the Middle East conflict, oil prices took cues from weaker equities.  Crude has risen more than 10% this year as recurrent geopolitical flare-ups eclipsed concerns that a global surplus would lift inventories and hurt prices.  The US said on Monday that American-flagged vessels should stay as far as possible from Iranian waters when passing through the Strait of Hormuz. Washington has amassed a powerful military force in the Middle East, even as it also pursues talks with Tehran over its nuclear ambitions. The Strait of Hormuz is a critical trade artery for Middle East energy shipments that links a slew of producers to global markets, especially in Asia. Tehran has threatened to close the maritime chokepoint during periods of geopolitical tension, though it hasn’t actually followed through. “Both

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Energy Department Awardee to Build First American Aluminum Smelter Since 1980

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USA Boards Venezuela-Linked Oil Tanker

US forces boarded an oil tanker after a cat-and-mouse chase from the Caribbean to the Indian Ocean, the Pentagon said, as Washington expands its geographic scope in an ongoing crackdown on a global shadow fleet used to export sanctioned crude. The Aquila II had departed from the Jose terminal in Venezuela in early December and appeared to be bound for China, according to ship tracking data compiled by Bloomberg. The ship was intercepted while heading toward the Sunda Strait between the Indonesian islands of Java and Sumatra. It’s the latest Venezuela-linked ship the US has taken control of since December, and the furthest from Caribbean waters, underscoring how far Washington is prepared to go to enforce its energy quarantine worldwide. The Suezmax vessel, able to haul about 1 million barrels of oil, was sanctioned for its involvement in the Russian oil trade following Moscow’s invasion of Ukraine in 2022. Although it was sailing under the flag of Panama when it was sanctioned by the US, as a Treasury Sanctions list search shows, it now appears to be operating under an unknown flag, according to the Equasis international shipping database and Bloomberg data. Previous US tanker seizures were executed before and after US forces captured and removed former Venezuelan President Nicolas Maduro in a highly-coordinated operation that included air strikes on Caracas. The last such incident took place in late January, when Motor Vessel Sagitta was captured in the Caribbean Sea. The Trump administration has pledged to crack down on Venezuela’s use of sanctioned ships, which often deploy deceptive satellite positioning signals, false flags and other misleading techniques to illegally export oil and other goods.  On Monday, Defense Secretary Pete Hegseth said the Pentagon was pursuing dark fleet vessels carrying Venezuelan crude around the world.  “The only guidance I gave to my military commanders is,

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India Seizes Three Tankers

The Indian Coast Guard seized three tankers that it said were involved in oil smuggling, the first sign of the country getting tough on the so-called dark fleet.  The three ships were taken in the waters off Mumbai on Friday by the coast guard, which said in a post on X that it had “busted an international oil-smuggling racket” and that the vessels had been known to “frequently change identity.”  It’s the first time New Delhi has taken such action, according to people familiar with the Indian shipping industry, and comes as the US and Europe lead an effort to get tougher on vessels moving sanctioned oil. Many dark fleet tankers have sub-standard documentation, improper or fake flag registrations and poor maintenance, posing security and maritime safety risks.  The seizures are also happening as Washington pressures New Delhi to stop taking Russian crude, as part of a deal to cut import tariffs on the South Asian nation. India said early last year that it wouldn’t allow sanctioned tankers to discharge at its ports. Explainer: How Trump Is Testing India’s US-Russia Balancing Act The coast guard didn’t name the vessels it had seized, but shared photos of them in its post. The pictures matched past images of the Chiltern, Asphalt Star and Stellar Ruby that can be found on MarineTraffic, a ship-tracking intelligence platform.  Ship-intelligence platform TankerTracker.com identified the same vessels through their unique seven-digit IMO numbers.  All three ships were sanctioned by Washington last year for links to the Iranian oil trade.  Nobody responded to emails seeking comment at offices of the registered owners of the tankers as listed on the Equasis database. Calls made to the owners and manager of Chiltern and Asphalt Star were directed to voice mail. Calls made to the owner and manager of Stellar Ruby were not answered. The three vessels seized by

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Expand CEO Steps Down

Expand Energy Corp. said Domenic “Nick” Dell’Osso, Jr. stepped down as chief executive officer as the largest US natural gas producer plans to relocate its headquarters to Houston from Oklahoma City in mid-2026. Michael Wichterich, Expand’s chairman, will assume the role of interim CEO while the board searches for a permanent replacement, the company said Monday in a statement. Dell’Osso will serve as an external adviser for an unspecified period. “The relocation, which will primarily focus on the executive leadership team, will strengthen Expand Energy’s relationships with key industry and commercial partners,” the company said in the statement. Moreover, “virtually all Oklahoma City employees will be unaffected” by the change in headquarters, beyond those executive leaders, Wichterich told employees in an internal email seen by Bloomberg. Expand’s shares dropped as much as 8.9%, the most since July. The abrupt change in leadership comes less than six months after the Chief Financial Officer Mohit Singh left the company, which cited his “termination without cause.” RBC Capital Markets equity analyst Scott Hanold, who said he met with Expand’s leadership Monday morning, wrote in a note to clients that the conversation indicated “there were no disagreements or anything improper” and the executive change is the result of Dell’Osso choosing to stay in Oklahoma City. Expand’s board of directors views the move of executive functions to Houston as “urgent” to supporting “marketing efforts and commercial efforts,” Hanold said. Dell’Osso joined Chesapeake Energy, which was renamed Expand Energy after acquiring rival driller Southwestern Energy, in 2008 after working as an investment banker at Jefferies. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

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Cisco amps up Silicon One line, delivers new systems and optics for AI networking

Those building blocks include the new G300 as well as the G200 51.2 Tbps chip, which is aimed at spine and aggregation applications, and the G100 25.6 Tbps chip, which is aimed at leaf operations. Expanded portfolio of Silicon One P200-powered systems Cisco in October rolled out the P200 Silicon One chip and the high-end, 51.2 Tbps 8223 router aimed at distributed AI workloads. That system supports Octal Small Form-Factor Pluggable (OSFP) and Quad Small Form-Factor Pluggable Double Density (QSFP-DD) optical form factors that help the box support geographically dispersed AI clusters. Cisco grew the G200 family this week with the addition of the 8122X-64EF-O, a 64x800G switch that will run the SONiC OS and includes support for Cisco 800G Linear Pluggable Optics (LPO) connectivity. LPO components typically set up direct links between fiber optic modules, eliminating the need for traditional components such as a digital signal processor. Cisco said its P200 systems running IOS XR software now better support core routing services to allow data-center-to-data-center links and data center interconnect applications. In addition, Cisco introduced a P200-powered 88-LC2-36EF-M line card, which delivers 28.8T of capacity. “Available for both our 8-slot and 18-slot modular systems, this line card enables up to an unprecedented 518.4T of total system bandwidth, the highest in the industry,” wrote Guru Shenoy, senior vice president of the Cisco provider connectivity group, in a blog post about the news. “When paired with Cisco 800G ZR/ZR+ coherent pluggable optics, these systems can easily connect sites over 1,000 kilometers apart, providing the high-density performance needed for modern data center interconnects and core routing.”

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NetBox Labs ships AI copilot designed for network engineers, not developers

Natural language for network engineers Beevers explained that network operations teams face two fundamental barriers to automation. First, they lack accurate data about their infrastructure. Second, they aren’t software developers and shouldn’t have to become them. “These are not software developers. They are network engineers or IT infrastructure engineers,” Beevers said. “The big realization for us through the copilot journey is they will never be software developers. Let’s stop trying to make them be. Let’s let these computers that are really good at being software developers do that, and let’s let the network engineers or the data center engineers be really good at what they’re really good at.”  That vision drove the development of NetBox Copilot’s natural language interface and its capabilities. Grounding AI in infrastructure reality The challenge with deploying AI  in network operations is trust. Generic large language models hallucinate, produce inconsistent results, and lack the operational context to make reliable decisions. NetBox Copilot addresses this by grounding the AI agent in NetBox’s comprehensive infrastructure data model. NetBox serves as the system of record for network and infrastructure teams, maintaining a semantic map of devices, connections, IP addressing, rack layouts, power distribution and the relationships between these elements. Copilot has native awareness of this data structure and the context it provides. This enables queries that would be difficult or impossible with traditional interfaces. Network engineers can ask “Which devices are missing IP addresses?” to validate data completeness, “Who changed this prefix last week?” for change tracking and compliance, or “What depends on this switch?” for impact analysis before maintenance windows.

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US pushes voluntary pact to curb AI data center energy impact

Others note that cost pressure isn’t limited to the server rack. Danish Faruqui, CEO of Fab Economics, said the AI ecosystem is layered from silicon to software services, creating multiple points where infrastructure expenses eventually resurface. “Cloud service providers are likely to gradually introduce more granular pricing models across cloud, AI, and SaaS offerings, tailored by customer type, as they work to absorb the costs associated with the White House energy and grid compact,” Faruqui said.   This may not show up as explicit energy surcharges, but instead surface through reduced discounts, higher spending commitments, and premiums for guaranteed capacity or performance. “Smaller enterprises will feel the impact first, while large strategic customers remain insulated longer,” Rawat said. “Ultimately, the compact would delay and redistribute cost pressure; it does not eliminate it.” Implications for data center design The proposal is also likely to accelerate changes in how AI facilities are designed. “Data centers will evolve into localized microgrids that combine utility power with on-site generation and higher-level implementation of battery energy storage systems,” Faruqui said. “Designing for grid interaction will become imperative for AI data centers, requiring intelligent, high-speed switching gear, increased battery energy storage capacity for frequency regulation, and advanced control systems that can manage on-site resources.”

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Intel teams with SoftBank to develop new memory type

However, don’t expect anything anytime soon. Intel’s Director of Global Strategic Partnerships Sanam Masroor outlined the plans in a blog post. Operations are expected to begin in Q1 2026, with prototypes due in 2027 and commercial products by 2030. While Intel has not come out and said it, that memory design is almost identical to HBM used in GPU accelerators and AI data centers. HBM sits right on the GPU die for immediate access to the GPU, unlike standard DRAM which resides on memory sticks plugged into the motherboard. HBM is much faster than DDR memory but is also much more expensive to produce. It’s also much more profitable than standard DRAM which is why the big three memory makers – Micron, Samsung, and SK Hynix – are favoring production of it.

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Nvidia’s $100 Billion OpenAI Bet Shrinks and Signals a New Phase in the AI Infrastructure Cycle

One of the most eye-popping figures of the AI boom – a proposed $100 billion Nvidia commitment to OpenAI and as much as 10 gigawatts of compute for the company’s Stargate AI infrastructure buildout – is no longer on the table. And that partial retreat tells the data center industry something important. According to multiple reports surfacing at the end of January, Nvidia has paused and re-scoped its previously discussed, non-binding investment framework with OpenAI, shifting from an unprecedented capital-plus-infrastructure commitment to a much smaller (though still massive) equity investment. What was once framed as a potential $100 billion alignment is now being discussed in the $20-30 billion range, as part of OpenAI’s broader effort to raise as much as $100 billion at a valuation approaching $830 billion. For data center operators, infrastructure developers, and power providers, the recalibration matters less for the headline number and more for what it reveals about risk discipline, competitive dynamics, and the limits of vertical circularity in AI infrastructure finance. From Moonshot to Measured Capital The original September 2025 memorandum reportedly contemplated not just capital, but direct alignment on compute delivery: a structure that would have tightly coupled Nvidia’s balance sheet with OpenAI’s AI-factory roadmap. By late January, however, sources indicated Nvidia executives had grown uneasy with both the scale and the structure of the deal. Speaking in Taipei on January 31, Nvidia CEO Jensen Huang pushed back on reports of friction, calling them “nonsense” and confirming Nvidia would “absolutely” participate in OpenAI’s current fundraising round. But Huang was also explicit on what had changed: the investment would be “nothing like” $100 billion, even if it ultimately becomes the largest single investment Nvidia has ever made. That nuance matters. Nvidia is not walking away from OpenAI. But it is drawing a clearer boundary around

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Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist Onsite Engineer – Critical FacilitiesCharleston, SC This is NOT a traveling position. Having degreed engineers seems to be all the rage these days. I can also use this type of candidate in following cities: Ashburn, VA; Moncks Corner, SC; Binghamton, NY; Dallas, TX or Indianapolis, IN. Our client is an engineering design and commissioning company that is a subject matter expert in the data center space. This role will be onsite at a customer’s data center. They will provide onsite design coordination and construction administration, consulting and management support for the data center / mission critical facilities space with the mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer Ashburn, VA This traveling position is also available in: New York, NY; White Plains, NY;  Richmond, VA; Montvale, NJ; Charlotte, NC; Atlanta, GA; Hampton, GA; New Albany, OH; Cedar Rapids, IA; Phoenix, AZ; Salt Lake City, UT; Dallas, TX; Kansas City, MO; Omaha, NE; Chesterton, IN or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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