The Rise of AI Factories: Transforming Intelligence at Scale

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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 […]

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:

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Southwest Power Pool’s board of directors on Tuesday approved a proposed Expedited Resource Adequacy Study, or ERAS, which aims to “significantly accelerate the addition of new generating resources to the grid,” according to the regional grid operator. SPP plans to file amendments to its governing documents with the Federal Energy Regulatory Commission later this month in order to implement the ERAS proposal, it said in a Thursday announcement. “ERAS offers utilities who are responsible for keeping the lights on a clearly defined and impactful opportunity to address real and immediate needs,” SPP President and CEO Lanny Nickell said. “It’s not a replacement for broader interconnection reforms, but this complementary effort will ensure reliability isn’t compromised during a transitional period while we work to implement more permanent solutions.” SPP expects its excess capacity will fall to 5% in 2029, down from 24% in 2020, Nickell said in April at a trade group meeting focused on transmission issues. “Excess generating capacity is dwindling, and it’s dwindling to a point where it’s becoming dangerous,” he said. If ERAS is approved by federal regulators, staff of the regional transmission organization will work with qualified load responsible entities, or LREs, to submit projects for inclusion in the process as early as August, SPP said. Interconnection rights could be granted as soon as April 2026. Eligibility for the ERAS process is limited to new generation nominated by LREs, and projects must be capable of reaching commercial operation within five years of executing a generation interconnection agreement, the grid operator said. ERAS will be a “one-time process [that] will run separately” from SPP’s standard generation interconnection queue, it said. Projects submitted in SPP’s most recent batch of interconnection study requests will be given the option to transfer their submissions to the ERAS queue, the operator said. While SPP

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The implications are clear: without dramatic improvements to the US energy infrastructure, the nation’s AI ambitions could be significantly constrained by simple physical limitations – the inability to power the massive computing clusters necessary for advanced AI development and deployment. Streamlining permitting processes The tech executives have offered specific recommendations to address these challenges, with several focusing on the need to dramatically accelerate permitting processes for both energy generation and the transmission infrastructure needed to deliver that power to AI facilities, the report added. Intrator specifically called for efforts “to streamline the permitting process to enable the addition of new sources of generation and the transmission infrastructure to deliver it,” noting that current regulatory frameworks were not designed with the urgent timelines of the AI race in mind. This acceleration would help technology companies build and power the massive data centers needed for AI training and inference, which require enormous amounts of electricity delivered reliably and consistently. Beyond the cloud: bringing AI to everyday devices While much of the testimony focused on large-scale infrastructure needs, AMD CEO Lisa Su emphasized that true AI leadership requires “rapidly building data centers at scale and powering them with reliable, affordable, and clean energy sources.” Su also highlighted the importance of democratizing access to AI technologies: “Moving faster also means moving AI beyond the cloud. To ensure every American benefits, AI must be built into the devices we use every day and made as accessible and dependable as electricity.”

Networking errors pose threat to data center reliability

Still, IT and networking issues increased in 2024, according to Uptime Institute. The analysis attributed the rise in outages due to increased IT and network complexity, specifically, change management and misconfigurations. “Particularly with distributed services, cloud services, we find that cascading failures often occur when networking equipment is replicated across an entire network,” Lawrence explained. “Sometimes the failure of one forces traffic to move in one direction, overloading capacity at another data center.” The most common causes of major network-related outages were cited as: Configuration/change management failure: 50% Third-party network provider failure: 34% Hardware failure: 31% Firmware/software error: 26% Line breakages: 17% Malicious cyberattack: 17% Network overload/congestion failure: 13% Corrupted firewall/routing tables issues: 8% Weather-related incident: 7% Configuration/change management issues also attributed for 62% of the most common causes of major IT system-/software-related outages. Change-related disruptions consistently are responsible for software-related outages. Human error continues to be one of the “most persistent challenges in data center operations,” according to Uptime’s analysis. The report found that the biggest cause of these failures is data center staff failing to follow established procedures, which has increased by about 10 percentage points compared to 2023. “These are things that were 100% under our control. I mean, we can’t control when the UPS module fails because it was either poorly manufactured, it had a flaw, or something else. This is 100% under our control,” Brown said. The most common causes of major human error-related outages were reported as:

Liquid cooling technologies: reducing data center environmental impact

“Highly optimized cold-plate or one-phase immersion cooling technologies can perform on par with two-phase immersion, making all three liquid-cooling technologies desirable options,” the researchers wrote. Factors to consider There are numerous factors to consider when adopting liquid cooling technologies, according to Microsoft’s researchers. First, they advise performing a full environmental, health, and safety analysis, and end-to-end life cycle impact analysis. “Analyzing the full data center ecosystem to include systems interactions across software, chip, server, rack, tank, and cooling fluids allows decision makers to understand where savings in environmental impacts can be made,” they wrote. It is also important to engage with fluid vendors and regulators early, to understand chemical composition, disposal methods, and compliance risks. And associated socioeconomic, community, and business impacts are equally critical to assess. More specific environmental considerations include ozone depletion and global warming potential; the researchers emphasized that operators should only use fluids with low to zero ozone depletion potential (ODP) values, and not hydrofluorocarbons or carbon dioxide. It is also critical to analyze a fluid’s viscosity (thickness or stickiness), flammability, and overall volatility. And operators should only use fluids with minimal bioaccumulation (the buildup of chemicals in lifeforms, typically in fish) and terrestrial and aquatic toxicity. Finally, once up and running, data center operators should monitor server lifespan and failure rates, tracking performance uptime and adjusting IT refresh rates accordingly.

Cisco unveils prototype quantum networking chip

Clock synchronization allows for coordinated time-dependent communications between end points that might be cloud databases or in large global databases that could be sitting across the country or across the world, he said. “We saw recently when we were visiting Lawrence Berkeley Labs where they have all of these data sources such as radio telescopes, optical telescopes, satellites, the James Webb platform. All of these end points are taking snapshots of a piece of space, and they need to synchronize those snapshots to the picosecond level, because you want to detect things like meteorites, something that is moving faster than the rotational speed of planet Earth. So the only way you can detect that quickly is if you synchronize these snapshots at the picosecond level,” Pandey said. For security use cases, the chip can ensure that if an eavesdropper tries to intercept the quantum signals carrying the key, they will likely disturb the state of the qubits, and this disturbance can be detected by the legitimate communicating parties and the link will be dropped, protecting the sender’s data. This feature is typically implemented in a Quantum Key Distribution system. Location information can serve as a critical credential for systems to authenticate control access, Pandey said. The prototype quantum entanglement chip is just part of the research Cisco is doing to accelerate practical quantum computing and the development of future quantum data centers. The quantum data center that Cisco envisions would have the capability to execute numerous quantum circuits, feature dynamic network interconnection, and utilize various entanglement generation protocols. The idea is to build a network connecting a large number of smaller processors in a controlled environment, the data center warehouse, and provide them as a service to a larger user base, according to Cisco. The challenges for quantum data center network fabric

Zyxel launches 100GbE switch for enterprise networks

Port specifications include: 48 SFP28 ports supporting dual-rate 10GbE/25GbE connectivity 8 QSFP28 ports supporting 100GbE connections Console port for direct management access Layer 3 routing capabilities include static routing with support for access control lists (ACLs) and VLAN segmentation. The switch implements IEEE 802.1Q VLAN tagging, port isolation, and port mirroring for traffic analysis. For link aggregation, the switch supports IEEE 802.3ad for increased throughput and redundancy between switches or servers. Target applications and use cases The CX4800-56F targets multiple deployment scenarios where high-capacity backbone connectivity and flexible port configurations are required. “This will be for service providers initially or large deployments where they need a high capacity backbone to deliver a primarily 10G access layer to the end point,” explains Nguyen. “Now with Wi-Fi 7, more 10G/25G capable POE switches are being powered up and need interconnectivity without the bottleneck. We see this for data centers, campus, MDU (Multi-Dwelling Unit) buildings or community deployments.” Management is handled through Zyxel’s NebulaFlex Pro technology, which supports both standalone configuration and cloud management via the Nebula Control Center (NCC). The switch includes a one-year professional pack license providing IGMP technology and network analytics features. The SFP28 ports maintain backward compatibility between 10G and 25G standards, enabling phased migration paths for organizations transitioning between these speeds.

Engineers rush to master new skills for AI-driven data centers

According to the Uptime Institute survey, 57% of data centers are increasing salary spending. Data center job roles that saw the highest increases were in operations management – 49% of data center operators said they saw highest increases in this category – followed by junior and mid-level operations staff at 45%, and senior management and strategy at 35%. Other job categories that saw salary growth were electrical, at 32% and mechanical, at 23%. Organizations are also paying premiums on top of salaries for particular skills and certifications. Foote Partners tracks pay premiums for more than 1,300 certified and non-certified skills for IT jobs in general. The company doesn’t segment the data based on whether the jobs themselves are data center jobs, but it does track 60 skills and certifications related to data center management, including skills such as storage area networking, LAN, and AIOps, and 24 data center-related certificates from Cisco, Juniper, VMware and other organizations. “Five of the eight data center-related skills recording market value gains in cash pay premiums in the last twelve months are all AI-related skills,” says David Foote, chief analyst at Foote Partners. “In fact, they are all among the highest-paying skills for all 723 non-certified skills we report.” These skills bring in 16% to 22% of base salary, he says. AIOps, for example, saw an 11% increase in market value over the past year, now bringing in a premium of 20% over base salary, according to Foote data. MLOps now brings in a 22% premium. “Again, these AI skills have many uses of which the data center is only one,” Foote adds. The percentage increase in the specific subset of these skills in data centers jobs may vary. The Uptime Institute survey suggests that the higher pay is motivating workers to stay in the

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