The Rise of AI Factories: Transforming Intelligence at Scale

Stay Ahead, Stay ONMINE

The Rise of AI Factories: Transforming Intelligence at Scale

AI Factories Redefine Infrastructure

The architecture of AI factories reflects a paradigm shift that mirrors the evolution of the industrial age itself—from manual processes to automation, and now to autonomous intelligence. Nvidia’s framing of these systems as “factories” isn’t just branding; it’s a conceptual leap that positions AI infrastructure as the new production line. GPUs are the engines, data is the raw material, and the output isn’t a physical product, but predictive power at unprecedented scale. In this vision, compute capacity becomes a strategic asset, and the ability to iterate faster on AI models becomes a competitive differentiator, not just a technical milestone.

This evolution also introduces a new calculus for data center investment. The cost-per-token of inference—how efficiently a system can produce usable AI output—emerges as a critical KPI, replacing traditional metrics like PUE or rack density as primary indicators of performance. That changes the game for developers, operators, and regulators alike. Just as cloud computing shifted the industry’s center of gravity over the past decade, the rise of AI factories is likely to redraw the map again—favoring locations with not only robust power and cooling, but with access to clean energy, proximity to data-rich ecosystems, and incentives that align with national digital strategies.

The Economics of AI: Scaling Laws and Compute Demand

At the heart of the AI factory model is a requirement for a deep understanding of the scaling laws that govern AI economics.

Initially, the emphasis in AI revolved around pretraining large models, requiring massive amounts of compute, expert labor, and curated data. Over five years, pretraining compute needs have increased by a factor of 50 million. However, once a foundational model is trained, the downstream potential multiplies exponentially, while the compute required to utilize a fully trained model for standard inference is significantly less than that required for training and fine-tuning models for use.

The challenge shifts to post-training scaling and test-time scaling. Fine-tuning models to suit specific applications demands 30x more compute than the original pretraining. Meanwhile, the latest advanced inference tasks like agentic AI, where models reason iteratively before responding, can require 100x more compute than standard inference. These compute-intensive needs simply exceed the capacity of general-purpose data centers.

AI factories are designed with this exponential growth in mind. From the ground up, they are built to support massive inference demands, iterative reasoning, and adaptive model deployment.

AI’s New Cost Curve

This shift in workload dynamics rewrites the economic blueprint for infrastructure investment. Where once the ROI of data center capacity was measured against steady-state cloud or enterprise workloads, AI factories demand a forward-looking calculus based on scaling behavior and future inference velocity.

The cost per token or decision point becomes a more meaningful financial metric than simple cost per kWh or per-core performance. Operators must not only provision for peak demand but architect systems flexible enough to evolve with model complexity—supporting seamless upgrades in compute density, interconnect bandwidth, and software orchestration.

Moreover, these economics aren’t confined to hyperscale players alone. Enterprises deploying vertical-specific models—whether for fraud detection, supply chain optimization, or autonomous control systems—are increasingly recognizing that the benefits of faster, smarter AI decisions justify the infrastructure premium.

This drives demand for regional and modular AI factories tailored to industry use cases, where latency, data locality, and compliance matter as much as raw compute. As with previous inflections in the digital economy, those who internalize and invest early in the new cost curves will be best positioned to lead in a world where intelligence itself is the product.

AI Factory Development Around the World

Nvidia is not alone in recognizing the strategic importance of AI factories. Governments and enterprises across the globe are racing to deploy them:

India: Through a high-profile partnership with NVIDIA, Yotta Data Services has launched the Shakti Cloud Platform—one of the country’s first AI supercomputing infrastructures. Positioned as a national resource, Shakti aims to democratize access to high-performance GPU resources for startups, research institutions, and public sector innovation, reflecting India’s broader ambition to become a global AI hub.

Japan: Cloud providers like GMO Internet and KDDI are rapidly scaling NVIDIA-powered AI infrastructure to accelerate advancements in robotics, precision medicine, and smart cities. These efforts align with Japan’s Society 5.0 vision, which emphasizes the fusion of cyber and physical systems to tackle demographic and economic challenges through AI and automation.

Europe: The European Union is taking a coordinated, multi-national approach to AI factory development, investing in seven advanced computing centers across 17 member states via the High Performance Computing Joint Undertaking (EuroHPC JU). These sites are being positioned not just as data centers but as digital sovereignty assets—powering AI research, public sector applications, and secure industrial innovation.

Norway: Telenor’s NVIDIA-powered AI factory exemplifies how Nordic countries are integrating sustainability into digital transformation. With a strong emphasis on green energy, regional talent development, and cross-border collaboration, the initiative is laying a foundation for climate-conscious AI infrastructure that aligns with European ESG priorities.

United States: AI factory development is taking a dual-track approach. Public-private initiatives like the Stargate project—focused on frontier-scale computing—and executive directives from the White House underscore Washington’s intent to lead in both commercial and governmental AI capabilities. The U.S. sees AI infrastructure not just as a competitive edge but as a strategic imperative for national resilience.

Saudi Arabia: Through its Vision 2030 strategy, the Kingdom is investing heavily in AI infrastructure, including a partnership between the Saudi Data and Artificial Intelligence Authority (SDAIA) and global hyperscalers. Recent announcements include the creation of sovereign AI compute clusters designed to support Arabic-language models and AI-driven public services.

Singapore: Known for its methodical approach to digital infrastructure, Singapore is building out AI factories as part of its National AI Strategy 2.0. With investments in sovereign compute capabilities and robust data governance, the city-state is positioning itself as Southeast Asia’s AI nerve center—balancing innovation with regulatory foresight.

These projects highlight how AI factories are quickly becoming essential national infrastructure, akin to telecommunications and energy grids. More than just data centers, they represent strategic bets on where intelligence will be created, who controls its production, and how nations will compete in an AI-first global economy.

Inside the AI Factory: A Full-Stack Approach to Intelligence Production

Nvidia’s AI factory model isn’t just a high-powered compute stack—it’s a vertically integrated platform purpose-built to accelerate every stage of the AI lifecycle. From training foundational models to deploying them at scale in real-time applications, the architecture spans compute, networking, software, data pipelines, and digital twin simulation. Each layer is engineered for high-efficiency throughput, reflecting Nvidia’s belief that intelligence production requires the same rigor and precision as modern manufacturing.

1. Compute Performance: The Engine Room of Intelligence

At the core of the AI factory is GPU horsepower. Nvidia’s Hopper, Blackwell, and the forthcoming Blackwell Ultra architectures offer exponential leaps in performance. The flagship GB200 NVL72 system—a rack-scale unit with dual Blackwell GPUs connected by NVLink Switch—delivers 50x more AI inference throughput compared to the A100 generation. Integrated into DGX SuperPOD clusters, these systems can scale to tens of thousands of nodes, forming the compute backbone for hyperscale AI development.

DGX Cloud extends these capabilities into a managed, consumption-based model, allowing enterprises to access AI factory-grade infrastructure through major cloud platforms like Microsoft Azure, Google Cloud, and Oracle. It’s an operating model built for rapid deployment and elastic growth.

2. High-Performance Networking: Compute Without Bottlenecks

Scaling AI requires more than raw compute—it demands precision networking. Nvidia’s NVLink, Quantum-2 InfiniBand, and Spectrum-X Ethernet fabrics are designed to minimize latency and ensure lossless, high-bandwidth data movement between tens of thousands of GPUs. ConnectX-8 SmartNICs and BlueField-3 DPUs enable secure, multi-tenant environments while offloading network and storage tasks to free up GPU cycles. The result is a tightly-coupled infrastructure where compute and data flow at AI-native speeds.

3. Orchestration and Operational Intelligence

Orchestrating AI workloads at scale is non-trivial. Tools like Nvidia Run:ai, Base Command, and Mission Control provide full-stack visibility and GPU-aware scheduling, ensuring optimal utilization across heterogeneous environments. These platforms support multi-tenant operations, dynamic scaling, and fine-grained workload isolation—critical in enterprise and sovereign AI environments where uptime and performance cannot be compromised.

4. Inference Stack: From Model to Real-Time Decisions

The Nvidia inference stack—including TensorRT for optimized execution, NVIDIA Inference Microservices (NIMs) for containerized deployment, and NVIDIA Triton for scalable serving—enables low-latency, high-throughput AI services. These tools are optimized for transformer architectures and multimodal models, addressing the growing demand for agentic inference, edge reasoning, and continuous learning in production.

5. Data Infrastructure: Feeding the Intelligence Pipeline

AI performance is bound by the quality and availability of data. The Nvidia AI Data Platform enables seamless integration with modern data lakes, object stores, and streaming platforms. It provides end-to-end support for preprocessing, labeling, and versioning at scale—turning chaotic data pipelines into repeatable, high-performance processes. Certified storage partners (like NetApp, Dell, and VAST Data) ensure that storage throughput can keep pace with real-time inference and training demands.

6. Omniverse Blueprint: Digital Twin-Driven Infrastructure Planning

Designing an AI factory involves massive complexity—up to 5 billion components, 210,000 miles of cabling, and megawatt-scale power demands. Nvidia’s Omniverse Blueprint introduces a systems-level digital twin to simulate, validate, and optimize AI factory builds before breaking ground. This includes everything from airflow and thermals to rack placement and interconnect design.

By enabling real-time collaboration across electrical, mechanical, and IT disciplines, Omniverse reduces time-to-deployment and mitigates critical risk. In environments where an hour of downtime can equate to tens of millions in lost inference capacity, this level of planning precision is no longer optional—it’s a necessity.

AI factories represent more than just technical innovation—they are a new class of infrastructure, purpose-built for the intelligence economy. Nvidia’s full-stack platform provides the modularity, scalability, and performance required to manufacture intelligence at scale, redefining how enterprises and nations deploy AI as a core strategic asset.

Deep Dive on Omniverse Developments: Advancing AI Factory Design and Simulation

As AI continues to drive unprecedented demand for specialized infrastructure, NVIDIA is taking bold steps to help design and optimize the next generation of AI factories with its new Omniverse Blueprint for AI factory design and operations. Unveiled during NVIDIA’s GTC keynote, this innovative blueprint is designed to help engineers simulate, plan, and optimize the development of gigawatt-scale AI factories, which require the seamless integration of billions of components and complex systems.

In collaboration with leading simulation and infrastructure partners, including Cadence, ETAP, Schneider Electric, and Vertiv, the Omniverse Blueprint enables digital twin technology to support the design, testing, and optimization of AI factory components such as power, cooling, and networking systems long before physical construction begins.

Engineering AI Factories: A Simulation-First Approach

Using OpenUSD libraries, NVIDIA’s Omniverse Blueprint aggregates 3D data from multiple sources, including building layouts, accelerated computing systems like NVIDIA DGX SuperPODs, and power/cooling units from partners such as Schneider Electric and Vertiv. This unified approach allows engineers to address key challenges in AI factory development, such as:

Component Integration and Space Optimization: Seamlessly integrating NVIDIA systems with billions of components for optimal layouts.
Cooling Efficiency: Using the Cadence Reality Digital Twin Platform to simulate and evaluate cooling solutions, from hybrid air to liquid cooling.
Power Distribution: Designing scalable, redundant systems to simulate and optimize power reliability using ETAP.
Networking Topology: Fine-tuning high-bandwidth networking infrastructure with NVIDIA Spectrum-X and NVIDIA Air.

The blueprint empowers engineers to collaborate in real-time across disciplines, reducing inefficiencies and enabling parallel workflows. Real-time simulations allow for faster decision-making and optimization, with teams able to adjust configurations and immediately see the impact — drastically reducing design time and avoiding costly mistakes during construction.

Building Resilience Into the AI Frontier

As AI workloads continue to evolve, the blueprint offers advanced features such as workload-aware simulations and failure scenario testing to ensure AI factories can scale and adapt to future demands. With the growing importance of minimizing downtime (which can cost millions per day in gigawatt-scale AI factories), the Omniverse Blueprint reduces risk, improves efficiency, and helps AI factory operators stay ahead of infrastructure needs.

NVIDIA’s ongoing efforts with partners like Vertech and Phaidra will bring AI-enabled operations into the fold, including reinforcement-learning agents that optimize energy efficiency and system stability. These advancements ensure that AI factories can adapt to changing hardware and environmental conditions in real-time, contributing to ongoing operational resilience.

The integration of digital twin technology into AI factory design is not just a theoretical enhancement—it’s essential for the future of AI-driven data centers. With over $1 trillion projected for AI-related upgrades, NVIDIA’s Omniverse Blueprint stands poised to lead this transformation, helping AI factory operators navigate the complexities of AI workloads while minimizing risk and maximizing efficiency.

To explore these developments further, watch the GTC keynote, and discover how NVIDIA and its partners are shaping the future of AI factory infrastructure.

The Age of Reasoning and Agentic AI

Nvidia defines its Blackwell Ultra platform not just as another leap in GPU performance, but as the gateway to a new phase in AI development—what it calls the age of reasoning. As workloads transition from static inference to dynamic decision-making, AI systems must increasingly mimic human-like cognition: analyzing context, planning multistep actions, and adapting behavior in real time. This shift is giving rise to two transformative paradigms—agentic AI and physical AI—both of which are redefining the infrastructure requirements for scalable intelligence.

Agentic AI involves AI models that operate autonomously to solve complex, multistep problems. These models reason iteratively, self-correct, and manage workflows across multiple domains. They’re already emerging in tools like AutoGPT, Devin, and AI copilots that can write code, generate research plans, or manage enterprise workflows. Unlike traditional inference, agentic AI requires continual interaction with large-scale memory, context retrieval, and recursive reasoning—all of which drive up compute needs by orders of magnitude.
Physical AI focuses on embodied intelligence—where simulation, sensor fusion, and real-world control intersect. Applications include real-time photorealistic simulation for digital twins, robotics, autonomous vehicles, and industrial automation. These workloads demand ultra-low latency and tight coupling between simulation and inference engines.

Blackwell Ultra is engineered for this new class of demands. It enables AI factories to scale compute across the full lifecycle—from massive pretraining runs to highly variable post-training tasks, including fine-tuning, retraining, and real-time inference. Crucially, Nvidia’s Dynamo software stack coordinates these large-scale operations, orchestrating token generation and communication across thousands of GPUs with efficiency that keeps latency low and throughput high.

In this new era, compute isn’t just about speed—it’s about intelligence per watt, adaptability per dollar, and the ability to support inference that behaves less like static prediction and more like dynamic reasoning. Blackwell Ultra and its supporting ecosystem are designed to meet that challenge head-on, reshaping not only how AI runs, but what it can become.

Oracle and NVIDIA Team Up to Accelerate the AI Factory Model with Agentic AI Integration

At NVIDIA’s 2025 GTC conference, Oracle and NVIDIA unveiled a major step forward in the buildout of enterprise-scale AI infrastructure — a key component of the emerging “AI Factory” model. The companies announced a deep integration between Oracle Cloud Infrastructure (OCI) and the NVIDIA AI Enterprise software platform, aimed at accelerating the deployment of agentic AI — autonomous AI systems capable of reasoning, planning, and executing complex tasks.

This collaboration brings NVIDIA’s inference stack — including 160+ AI tools and more than 100 NIM™ (NVIDIA Inference Microservices) — natively into the OCI Console. Oracle customers can now tap into a fully integrated AI stack, available in Oracle’s cloud regions, sovereign clouds, on-premises via OCI Dedicated Region, and even at the edge.

“Oracle has become the platform of choice for both AI training and inferencing,” said Oracle CEO Safra Catz. “This partnership enhances our ability to help customers achieve greater innovation and business results.”

NVIDIA CEO Jensen Huang underscored the significance of the integration for enterprise AI: “Together, we help enterprises innovate with agentic AI to deliver amazing things for their customers and partners.”

No-Code Blueprints and Turnkey Inference

A key element of the Oracle-NVIDIA collaboration is the launch of no-code OCI AI Blueprints, which allow enterprises to deploy multimodal large language models, inference pipelines, and observability tools without managing infrastructure. These blueprints are optimized for NVIDIA GPUs and microservices, and can reduce the time-to-deployment from weeks to minutes.

NVIDIA is also contributing its own Blueprints to the OCI Marketplace, preloaded with workflows for enterprise use cases in customer service, simulation, and robotics. For example, Oracle plans to offer NVIDIA Omniverse and Isaac Sim tools on OCI, bundled with preconfigured NVIDIA L40S GPU instances for simulation and physical AI development.

Pipefy, a business process automation platform, is already deploying multimodal LLMs on OCI using these AI Blueprints. “Using these prepackaged and verified blueprints, deploying our AI models on OCI is now fully automated and significantly faster,” said Gabriel Custódio, principal software engineer at Pipefy.

Enabling Real-Time Inference and Vector Search

Oracle is also integrating NVIDIA NIM microservices into OCI Data Science, enabling real-time inference with a pay-as-you-go model. These microservices can be deployed within a customer’s OCI tenancy for AI use cases ranging from copilots to recommendation engines, delivering rapid time-to-value while maintaining data security and compliance.

In the AI data stack, Oracle Database 23ai now supports accelerated vector search powered by NVIDIA GPUs and the cuVS library — enabling fast creation of vector embeddings and indexes for massive datasets. Companies like DeweyVision, which provides AI-driven media cataloging and search tools, are using this integration to ingest, search, and manage high volumes of video content efficiently.

“Oracle Database 23ai with AI Vector Search can significantly increase Dewey’s search performance while increasing the scalability of the DeweyVision platform,” said CEO Majid Bemanian.

Blackwell-Powered Superclusters Signal the AI Factory Future

Perhaps most notably, Oracle is among the first cloud providers to roll out NVIDIA’s latest generation Blackwell Ultra GPUs across its AI Supercluster. The NVIDIA GB300 NVL72 and HGX B300 NVL16 platforms — successors to last year’s GB200 — promise up to 1.5x performance gains and are designed for large-scale AI factories spanning tens of thousands of GPUs. Oracle’s Supercluster deployments will soon support up to 131,072 GPUs, connected by NVIDIA’s Quantum-2 InfiniBand and NVLink fabrics.

Companies like Soley Therapeutics and SoundHound AI are already leveraging this full-stack Oracle-NVIDIA platform to train next-generation models for drug discovery and voice AI, respectively. “The combination of OCI and NVIDIA delivers a full-stack AI solution,” said Yerem Yeghiazarians, CEO of Soley Therapeutics. “It provides us the storage, compute, software tools, and support necessary to innovate faster with petabytes of data.”

As AI workloads continue to demand ever-larger compute clusters and sophisticated software integration, partnerships like Oracle and NVIDIA’s are laying the foundation for scalable, enterprise-ready AI factories — designed to push the limits of reasoning, automation, and insight.

Secure AI Factories: The Cisco-NVIDIA Collaboration

As AI infrastructure becomes a foundational layer of national and enterprise strategy, its security posture can no longer be an afterthought—it must be embedded from the silicon up. Cisco and NVIDIA have partnered to deliver exactly that with the Secure AI Factory: a full-stack architecture that merges scalable compute and high-performance networking with zero-trust security principles and AI-native threat protection.

The collaboration tightly integrates Cisco’s security and networking stack—including Hypershield, AI Defense, and hybrid mesh firewalls—with NVIDIA’s BlueField-3 DPUs and AI Enterprise platform. The result is a unified framework that provides policy enforcement, observability, and real-time threat detection across every layer of the AI stack.

Hypershield applies adaptive segmentation and micro-isolation, using AI to identify and quarantine threats across east-west traffic inside data centers.
AI Defense leverages behavior-based analysis to protect against AI-specific risks such as prompt injection, model hijacking, adversarial inputs, and data leakage during runtime.
BlueField-3 DPUs offload security and network processing from host CPUs, enabling wire-speed telemetry, access control, and cryptographic operations without impacting AI performance.

This joint platform supports on-premises deployments through Cisco UCS AI servers and Nexus switches, or cloud and hybrid deployments using validated reference architectures optimized for AI factories. Security scales automatically with workload changes—eliminating blind spots in dynamic, multi-tenant environments where AI models evolve in real time.

By embedding security into every node, packet, and process, Cisco and NVIDIA are enabling enterprises to move fast without sacrificing control. In an era where AI models make mission-critical decisions and process sensitive data, the Secure AI Factory ensures that trust is not just assumed—it’s architected.

Chuck Robbins, Chair and CEO, Cisco, said:

Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

IBM X-Force: Stealthy attacks on the rise, toolkits targeting AI emerge

Last year, X-Force predicted that once AI technologies “establish market dominance—when a single technology approaches 50% market share or when the market consolidates to three or fewer technologies—attackers will be incentivized to invest in attack toolkits” that target AI models and solutions. “Are we there yet? Not quite, but adoption

VMware (quietly) brings back its free ESXi hypervisor

By many accounts, Broadcom’s handling of the VMware acquisition was clumsy and caused many enterprises to reevaluate their relationship with the vendor The move to subscription models was tilted in favor of larger customers and longer, three-year licenses. Because the string of bad publicity and VMware’s competitors pounced, offering migration

CoreWeave offers cloud-based Grace Blackwell GPUs for AI training

Cloud services provider CoreWeave has announced it is offering Nvidia’s GB200 NVL72 systems, otherwise known as “Grace Blackwell,” to customers looking to do intensive AI training. CoreWeave said its portfolio of cloud services are optimized for the GB200 NVL72, including CoreWeave’s Kubernetes Service, Slurm on Kubernetes (SUNK), Mission Control, and

Kyndryl launches private cloud services for enterprise AI deployments

Kyndryl’s AI Private Cloud environment includes services and capabilities around containerization, data science tools, and microservices to deploy and manage AI applications on the private cloud. The service supports AI data foundations and MLOps/LLMOps services, letting customers manage their AI data pipelines and machine learning operation, Kyndryl stated. These tools facilitate

Gunvor, Adnoc Shortlisted for Shell South Africa Unit

Abu Dhabi National Oil Co. and Swiss commodities trading firm Gunvor are among companies that have been shortlisted to buy Shell Plc’s downstream assets in South Africa, according to people familiar with the matter. The two companies are strong contenders for the assets that are valued at about $1 billion, said the people, who asked not to be identified as the information is private. Previous potential bidders including Trafigura’s Puma Energy, Sasol Ltd. and South Africa’s PetroSA are no longer in the running, two of the people said. “While Adnoc Distribution regularly reviews opportunities for domestic and international growth, we don’t comment on market speculation,” Adnoc’s fuel retail unit said. Shell, Gunvor, Trafigura and Sasol declined to comment. PetroSA did not immediately reply to a request for comment. Shell has been looking to offload the assets, which include about 600 fuel stations and trading operations in Africa’s biggest economy, as part of a broader strategy to focus on regions and businesses that offer higher returns. The assets are attractive for trading firms since they ensure demand for fuels that they can then supply. Adnoc and other Middle East oil companies such as Saudi Aramco have been expanding their trading arms as they look to break into new markets. Shell is working with adviser Rothschild & Co and a winner could be announced in the coming weeks, the people said. Talks are continuing and there’s no certainty there will be a final sale, they said. Saudi Aramco has also been involved in the process, but it wasn’t immediately clear if it was still in the running, the people said. Aramco declined to comment. A deal would give the buyer about 10% of South Africa’s fuel stations. The market in the country has changed significantly in recent years with trader Glencore Plc acquiring

ICYMI: Trump Administration Adds Two DOE Critical Minerals Projects to Federal Permitting Dashboard

ICYMI— The Federal Permitting Improvement Steering Council (Permitting Council) today announced increased transparency and accountability for the federal permitting of two Department of Energy (DOE) critical minerals projects. The projects — Michigan Potash and the South West Arkansas Project — are part of the first wave of critical minerals projects added to the Permitting Dashboard in response to President Trump’s Executive Order, Immediate Measures to Increase American Mineral Production. Once completed, both DOE-supported projects will help meet President Trump’s commitment to bolster domestic production of America’s vast mineral resources, support more American jobs and reduce reliance on foreign supply chains. The Michigan Potash Project, supported by DOE’s Loan Programs Office, is projected to produce the largest American-based source of high-quality potash fertilizer and food-grade salt using mechanical vapor recompression technology and geothermal heat from subsurface brine. Once completed, this project will reduce reliance on potash imports, support American farmers, improve food security, and create 200 permanent and 400 construction sector jobs. DOE announced a conditional commitment for a loan guarantee of up to $1.26 billion to Michigan Potash in January 2025. The South West Arkansas Project, under DOE’s Office of Manufacturing and Energy Supply Chains, supports the construction of a world-class Direct Lithium Extraction facility that will produce battery-grade lithium carbonate from lithium-rich brine in North America. Once completed, this project will help secure the domestic lithium supply chain and is expected to create roughly 100 direct long-term jobs and 300 construction sector jobs. These additions to the Federal Permitting Dashboard reflect the Administration’s commitment to strengthen domestic supply chains for critical minerals and materials, reduce dependence on foreign sources, and advance President Trump’s bold agenda for American energy dominance through a more secure, affordable, and reliable U.S. energy system. The Department looks forward to working with federal partners, project

EVOL: Courting wood, grid zombies and Easter wake loss

This week, Wood provided updates on Sidara’s proposed £250 million takeover, NESO declared war on zombies in the grid queue, and Equinor and Orsted warned of the impacts of wake loss. Aberdeen-headquartered Wood received a non-binding takeover bid from Dubai-based rival Sidara worth £250m, a significant drop-off compared to last year’s £1.5 billion bid. Our reporters discuss this, Wood’s shares being suspended and the impacts of yet another Scottish company being bought over by international competitors. Next up, the UK’s National Energy System Operator (NESO) unveiled plans to get rid of ‘zombies’ from the grid queue in a collaboration with regulator Ofgem. This could see up to 360GW of projects on the current queue have their contracts downgraded because they are not ready. What does this mean, and is it a result of too much dithering from the UK? Finally, European energy giants Equinor and Orsted have said offshore wind revenues could take a £363m hit due to other projects getting in the way of their turbines. Although those in the Tour de France peloton don’t mind the frontrunner taking the brunt of the wind resistance, turbine operators do. Does the industry need to share its survey results so that everyone can benefit from the North Sea breeze? Listen to Energy Voice Out Loud on your podcast platform of choice.

Trump administration moves to curb energy regulation; BLM nominee stands down

The Trump administration issued two policy directives Apr. 10 to curb energy regulations, the same day the president’s choice to lead the Bureau of Land Management (BLM) pulled her nomination. Kathleen Sgamma, former head of Western Energy Alliance (WEA), an oil and gas trade association, withdrew her nomination after a memo was leaked on X that included critical remarks following the Jan. 6, 2021, attack on the US Capitol. In the memo to WEA executives, Sgamma said she was “disgusted” by Trump “spreading misinformation” on Jan. 6 and “dishonoring the vote of the people.” The Senate was to conduct a confirmation hearing Apr. 10. Prior to her withdrawal, industry had praised the choice of Sgamma to head the agency that determines the rules for oil and gas operations on federal lands. Deregulation On the deregulation front, the Interior Department said it would no longer require BLM to prepare environmental impact statements (EIS) for about 3,244 oil and gas leases in seven western states. The move comes in response to two executive orders by President Donald Trump in January to increase US oil and gas production “by reducing regulatory barriers for oil and gas companies” and expediting development permits, Interior noted (OGJ Online, Jan. 21, 2025). Under the policy, BLM would no longer have to prepare an EIS for oil and gas leasing decisions on about 3.5 million acres across Colorado, New Mexico, North Dakota, South Dakota, Utah, and Wyoming. BLM currently manages over 23 million acres of federal land leased for oil and gas development. The agency said it will look for ways to comply with the National Environmental Policy Act (NEPA), a 1970 law that requires federal agencies to assess the potential environmental impacts of their proposed actions. In recent years, courts have increasingly delayed lease sales and projects,

Viva Energy’s ULSG project at Geelong refinery to startup by yearend

In its 2024 annual report, Viva Energy confirmed it will complete the project at a final cost of $350 million (Aus.), $200 million of which is dedicated to Australian procurement and construction contracts mostly awarded to businesses in the Geelong region. The budget increase follows the federal government’s December 2023 announcement that fuel quality and noxious vehicle emissions standards would come into effect from December 2025 to ensure Australia’s fuel quality aligns more closely with international standards. In compliance with the regulatory timeline, Viva Energy confirmed expanding the ULSG project to certify that—in addition to all of Geelong’s ULSG conforms to 10 ppm sulfur content—aromatics limits of the refinery’s RON95 mid-grade gasoline production conforms to the pending legislation’s stricter requirement of less than 35%. Both the ULSG and aromatics upgrades are part of Viva Energy’s ongoing transformation of the Geelong refinery into a modern energy hub that supports Australia’s energy security while also playing an important role its energy transition, the operator said. Future refining plans In its 2024 annual report, Viva Energy said it was continuing to explore options to replace crude oil with biogenic and waste feedstocks at the Geelong refinery, with potential biofeedstock and waste processing projects for the site slated for development throughout 2025. After confirming in its 2024 half-year results presentation undertaking of engineering design for infrastructure to store and co-process biogenic feedstocks at Geelong, Viva Energy said in its latest annual report that, by yearend 2024, it had completed a first investment in infrastructure to support co-processing at Geelong involving the injection of used cooking oil and soft-plastics pyrolysis oil into processing activities to produce recycled polyproylene and biopolymers. Additionally, the operator said work remained under way on the scope and phasing of co-processing biofeedstocks at the refinery to produce renewable diesel, with the

Trump FTC could revisit final conditions of two recent oil mergers

The companies petitioned the new FTC to set aside its consent orders barring Hess chief executive officer John B. Hess from Chevron’s board and Pioneer chief executive officer Scott Sheffield from Exxon’s board or offering any advisory services to the company. In a rare move, the FTC on Apr. 11 said it would seek public comments on the petition for 30 days, until May 12, after which it “will vote to determine how to resolve” the issue. A deeply divided FTC granted final approval for Exxon Mobil Corp.’s $64.5 billion purchase of Pioneer Natural Resources and Chevron Corp.’s $53 billion acquisition of Hess Corp. on Jan. 17, 2025, 3 days before Trump’s inauguration.

The Rise of AI Factories: Transforming Intelligence at Scale

AI Factories Redefine Infrastructure The architecture of AI factories reflects a paradigm shift that mirrors the evolution of the industrial age itself—from manual processes to automation, and now to autonomous intelligence. Nvidia’s framing of these systems as “factories” isn’t just branding; it’s a conceptual leap that positions AI infrastructure as the new production line. GPUs are the engines, data is the raw material, and the output isn’t a physical product, but predictive power at unprecedented scale. In this vision, compute capacity becomes a strategic asset, and the ability to iterate faster on AI models becomes a competitive differentiator, not just a technical milestone. This evolution also introduces a new calculus for data center investment. The cost-per-token of inference—how efficiently a system can produce usable AI output—emerges as a critical KPI, replacing traditional metrics like PUE or rack density as primary indicators of performance. That changes the game for developers, operators, and regulators alike. Just as cloud computing shifted the industry’s center of gravity over the past decade, the rise of AI factories is likely to redraw the map again—favoring locations with not only robust power and cooling, but with access to clean energy, proximity to data-rich ecosystems, and incentives that align with national digital strategies. The Economics of AI: Scaling Laws and Compute Demand At the heart of the AI factory model is a requirement for a deep understanding of the scaling laws that govern AI economics. Initially, the emphasis in AI revolved around pretraining large models, requiring massive amounts of compute, expert labor, and curated data. Over five years, pretraining compute needs have increased by a factor of 50 million. However, once a foundational model is trained, the downstream potential multiplies exponentially, while the compute required to utilize a fully trained model for standard inference is significantly less than

Google’s AI-Powered Grid Revolution: How Data Centers Are Reshaping the U.S. Power Landscape

Google Unveils Groundbreaking AI Partnership with PJM and Tapestry to Reinvent the U.S. Power Grid In a move that underscores the growing intersection between digital infrastructure and energy resilience, Google has announced a major new initiative to modernize the U.S. electric grid using artificial intelligence. The company is partnering with PJM Interconnection—the largest grid operator in North America—and Tapestry, an Alphabet moonshot backed by Google Cloud and DeepMind, to develop AI tools aimed at transforming how new power sources are brought online. The initiative, detailed in a blog post by Alphabet and Google President Ruth Porat, represents one of Google’s most ambitious energy collaborations to date. It seeks to address mounting challenges facing grid operators, particularly the explosive backlog of energy generation projects that await interconnection in a power system unprepared for 21st-century demands. “This is our biggest step yet to use AI for building a stronger, more resilient electricity system,” Porat wrote. Tapping AI to Tackle an Interconnection Crisis The timing is critical. The U.S. energy grid is facing a historic inflection point. According to the Lawrence Berkeley National Laboratory, more than 2,600 gigawatts (GW) of generation and storage projects were waiting in interconnection queues at the end of 2023—more than double the total installed capacity of the entire U.S. grid. Meanwhile, the Federal Energy Regulatory Commission (FERC) has revised its five-year demand forecast, now projecting U.S. peak load to rise by 128 GW before 2030—more than triple the previous estimate. Grid operators like PJM are straining to process a surge in interconnection requests, which have skyrocketed from a few dozen to thousands annually. This wave of applications has exposed the limits of legacy systems and planning tools. Enter AI. Tapestry’s role is to develop and deploy AI models that can intelligently manage and streamline the complex process of

Podcast: Vaire Computing Bets on Reversible Logic for ‘Near Zero Energy’ AI Data Centers

The AI revolution is charging ahead—but powering it shouldn’t cost us the planet. That tension lies at the heart of Vaire Computing’s bold proposition: rethinking the very logic that underpins silicon to make chips radically more energy efficient. Speaking on the Data Center Frontier Show podcast, Vaire CEO Rodolfo Rossini laid out a compelling case for why the next era of compute won’t just be about scaling transistors—but reinventing the way they work. “Moore’s Law is coming to an end, at least for classical CMOS,” Rossini said. “There are a number of potential architectures out there—quantum and photonics are the most well known. Our bet is that the future will look a lot like existing CMOS, but the logic will look very, very, very different.” That bet is reversible computing—a largely untapped architecture that promises major gains in energy efficiency by recovering energy lost during computation. A Forgotten Frontier Unlike conventional chips that discard energy with each logic operation, reversible chips can theoretically recycle that energy. The concept, Rossini explained, isn’t new—but it’s long been overlooked. “The tech is really old. I mean really old,” Rossini said. “The seeds of this technology were actually at the very beginning of the industrial revolution.” Drawing on the work of 19th-century mechanical engineers like Sadi Carnot and later insights from John von Neumann, the theoretical underpinnings of reversible computing stretch back decades. A pivotal 1961 paper formally connected reversibility to energy efficiency in computing. But progress stalled—until now. “Nothing really happened until a team of MIT students built the first chip in the 1990s,” Rossini noted. “But they were trying to build a CPU, which is a world of pain. There’s a reason why I don’t think there’s been a startup trying to build CPUs for a very, very long time.” AI, the

Pennsylvania’s Homer City Energy Campus: A Brownfield Transformed for Data Center Innovation

The redevelopment of the Homer City Generating Station in Pennsylvania represents an important transformation from a decommissioned coal-fired power plant to a state-of-the-art natural gas-powered data center campus, showing the creative reuse of a large brownfield site and the creation of what can be a significant location in power generation and the digital future. The redevelopment will address the growing energy demands of artificial intelligence and high-performance computing technologies, while also contributing to Pennsylvania’s digital advancement, in an area not known as a hotbed of technical prowess. Brownfield Development Established in 1969, the original generating station was a 2-gigawatt coal-fired power plant located near Homer City, Indiana County, Pennsylvania. The site was formerly the largest coal-burning power plant in the state, and known for its 1,217-foot chimney, the tallest in the United States. In April 2023, the owners announced its closure due to competition from cheaper natural gas and the rising costs of environmental compliance. The plant was officially decommissioned on July 1, 2023, and its demolition, including the iconic chimney, was completed by March 22, 2025. The redevelopment project, led by Homer City Redevelopment (HCR) in partnership with Kiewit Power Constructors Co., plans to transform the 3,200-acre site into the Homer City Energy Campus, via construction of a 4.5-gigawatt natural gas-fired power plant, making it the largest of its kind in the United States. Gas Turbines This plant will utilize seven high-efficiency, hydrogen-enabled 7HA.02 gas turbines supplied by GE Vernova, with deliveries expected to begin in 2026. The GE Vernova gas turbine has been seeing significant interest in the power generation market as new power plants have been moving to the planning stage. The GE Vernova 7HA.02 is a high-efficiency, hydrogen-enabled gas turbine designed for advanced power generation applications. As part of GE Vernova’s HA product line, it

Dell data center modernization gear targets AI, HPC workloads

The update starts with new PowerEdge R470, R570, R670 and R770 servers featuring Intel Xeon 6 with P-cores processors in single- and dual-socket configurations designed to handle high-performance computing, virtualization, analytics and artificial intelligence inferencing. Dell said they save up to half of the energy costs of previous server generations while supporting up to 50% more cores per processors and 67% better performance. With the R770, up to 80% of space can be saved and a 42U rack. They feature the Dell Modular Hardware System architecture, which is based on Open Compute Project standards. The controller system also received a significant update, with improvements to Dell OpenManage and Integrated Dell Remote Access Controller providing real-time monitoring, while the Dell PowerEdge RAID Controller for PCIe Gen 5 hardware reduces write latency up to 33-fold.

Intel sells off majority stake in its FPGA business

Altera will continue offering field-programmable gate array (FPGA) products across a wide range of use cases, including automotive, communications, data centers, embedded systems, industrial, and aerospace. “People were a bit surprised at Intel’s sale of the majority stake in Altera, but they shouldn’t have been. Lip-Bu indicated that shoring up Intel’s balance sheet was important,” said Jim McGregor, chief analyst with Tirias Research. The Altera has been in the works for a while and is a relic of past mistakes by Intel to try to acquire its way into AI, whether it was through FPGAs or other accelerators like Habana or Nervana, note Anshel Sag, principal analyst with Moor Insight and Research. “Ultimately, the 50% haircut on the valuation of Altera is unfortunate, but again is a demonstration of Intel’s past mistakes. I do believe that finishing the process of spinning it out does give Intel back some capital and narrows the company’s focus,” he said. So where did it go wrong? It wasn’t with FPGAs because AMD is making a good run of it with its Xilinx acquisition. The fault, analysts say, lies with Intel, which has a terrible track record when it comes to acquisitions. “Altera could have been a great asset to Intel, just as Xilinx has become a valuable asset to AMD. However, like most of its acquisitions, Intel did not manage Altera well,” said McGregor.

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.

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

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

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