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Inside the AI-optimized data center: Why next-gen infrastructure is non-negotiable

How are AI data centers different from traditional data centers? AI data centers and traditional data centers can be physically similar, as they contain hardware, servers, networking equipment, and storage systems. The difference lies in their capabilities: Traditional data centers were built to support general computing tasks, while AI data centers are specifically designed for […]

How are AI data centers different from traditional data centers?

AI data centers and traditional data centers can be physically similar, as they contain hardware, servers, networking equipment, and storage systems.

The difference lies in their capabilities: Traditional data centers were built to support general computing tasks, while AI data centers are specifically designed for more sophisticated, time and resource-intensive workloads. Conventional data centers are simply not optimized for AI’s advanced tasks and necessary high-speed data transfer.

Here’s a closer look at their differences:

AI-optimized vs. traditional data centers

  • Traditional data centers: Handle everyday computing needs such as web browsing, cloud services, email and enterprise app hosting, data storage and retrieval, and a variety of other relatively low-resource tasks. They can also support simpler AI applications, such as chatbots, that do not require intensive processing power or speed.
  • AI data centers: Built to compute significant volumes of data and run complex algorithms, ML and AI tasks, including agentic AI workflows. They feature high-speed networking and low-latency interconnects for rapid scaling and data transfer to support AI apps and edge and internet of things (IoT) use cases.

Physical infrastructure

  • Traditional data centers: Typically composed of standard networking architectures such as CPUs suitable for handling networking, apps, and storage.
  • AI data centers: Feature more advanced graphics processing units (GPU) (popularized by chip manufacturer Nvidia), tensor processing units (TPUs) (developed by Google), and other specialized accelerators and equipment.

Storage and data management

  • Traditional data centers: Generally, store data in more static cloud storage systems, databases, data lakes, and data lakehouses.
  • AI data centers: Handle huge amounts of unstructured data including text, images, video, audio, and other files. They also incorporate high-performance tools including parallel file systems, multiple network servers, and NVMe solid state drives (SSDs).

Power consumption

  • Traditional data centers: Require robust cooling systems such as air-based or raised floors, free cooling using outside air and water, and evaporative cooling. Methods depend on factors such as IT equipment density and energy efficiency/sustainability goals.
  • AI data centers: GPUs, due to their high processing power, generate much more heat and require advanced techniques such as liquid cooling, direct-to-chip cooling, and immersion cooling.

Cost

  • Traditional data centers: Use standard hardware and computing components that do take up a big chunk of IT budgets. Costs can be reduced with optimized components, processes, cloud resources, and diligence around energy use.
  • AI data centers: Are often far more expensive due to high costs of GPUs, ultra-high-speed networking components, and specialized cooling requirements.

Ultimately, AI-optimized and traditional data centers have pivotal, yet distinct, roles in enterprise. A key difference is adaptability: The rapid evolution of AI requires advanced infrastructure with modular designs that can accommodate evolving chip architectures, power densities, and cooling methods.

Key components of AI-optimized data centers

AI-ready data centers have specific requirements when it comes to infrastructure. They must be able to perform high-performance computing (HPC) and process enormous datasets for training, inference, deployment, and ongoing operation of AI systems. This process is enabled by:

  • AI accelerators: These specialized chips span hundreds, or even thousands, of servers working in tandem.
  • Fast and reliable networking: Low latency and high-bandwidth connections between compute clusters and storage and data sources is a must. In some cases, bandwidth requirements can reach into the terabits per second (Tbps). Leading providers incorporate direct cloud connectivity, software-defined networking, and high-speed, redundant fiber connections to support performance. Technologies such as ethernet and InfiniBand, and optical interconnects can quickly transfer data between chips, servers, and storage.
  • GPUs: Popularized by Nvidia and originally designed for rendering graphics in video games GPUs are electronic circuits that perform many calculations simultaneously, what’s known as parallel processing. This involves fragmenting complex tasks into smaller pieces that can be solved concurrently across multiple processors. Parallel processing makes GPUs fast, efficient, and scalable, optimizing neural networks and deep learning applications and reducing training and inference times.
  • TPUs, NPUs and DPUs: AI-ready data centers increasingly incorporate more specialized accelerators specifically built for AI workloads. These include tensor processing Units (TPUs), neural processing units (NPUs), and data processing units (DPUs).
    • TPUs speed up tensor computations, or multi-dimensional data structures, so that AI models can process complex data and perform calculations. They are extremely efficient at handling large-scale operations fundamental to training and running AI, and their high throughput and low latency make them ideal for AI and deep learning.
    • NPUs mimic the neural pathways of the brain, allowing for processing of AI workloads in real time. They are optimized for parallel processing and offload AI tasks from CPUs and GPUs to optimize performance, reduce energy needs, and support faster AI workflows.
    • DPUs offload and speed up networking, storage, and security functions, freeing up CPUs and GPUs to focus on AI tasks. DPUs often handle data compression, storage management, and encryption to help improve efficiency, security, and performance.

Advanced data center cooling systems

AI workloads produce a significant amount of heat, forcing a re-think of facility design, particularly when it comes to cooling. Energy-efficient techniques and advanced cooling systems are a must for AI-optimized data centers.

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Quinas readies UltraRam, flash memory with DRAM speed

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7 Wi-Fi certifications to bolster wireless networking skills

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USA Hits New Crude Oil Production Record

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MPLX Closes $2.4B Acquisition of Northwind Midstream Assets

MPLX LP said it has completed the $2.375 billion acquisition of Northwind Delaware Holdings LLC from Five Point Infrastructure LLC. The acquisition of Northwind, which provides sour gas gathering, treating, and processing services in Lea County, New Mexico, will enhance its Permian natural gas and natural gas liquid (NGL) value chains, MPLX said in a news release. The acquisition is expected to be immediately accretive to distributable cash flow, and inclusive of estimated incremental capital of $500 million, represents a 7x multiple on forecast 2027 EBITDA and an estimated mid-teen unlevered return, the company said. The acquisition was financed and the incremental capital expenditures associated with in-process expansion projects will be funded by net proceeds from MPLX’s $4.5 billion senior notes issued in August, the company stated. MPLX said that the acquired business is complementary and adjacent to its existing Delaware basin natural gas system. It consists of over 200,000 dedicated acres, more than 200 miles of gathering pipelines, two in-service acid gas injection wells at 20 million cubic feet per day (MMcfpd), and a third permitted well that will bring its total capacity to 37 MMcfpd, according to the release. The system currently has 150 MMcfpd of sour gas treating capacity and in-process expansion projects will increase capacity to 440 MMcfpd, expected to be completed in the second half of 2026, the company said. The system is supported by minimum volume commitments from “top regional producers,” MPLX said. Last week, MPLX said it entered into a definitive agreement to divest its Rockies gathering and processing assets to a subsidiary of Harvest Midstream for $1.0 billion in cash consideration, subject to customary purchase price adjustments. Harvest has contractually agreed to dedicate approximately 12 thousand barrels per day of NGLs from these assets to MPLX for a period of seven years

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BW Offshore Signs HoA with Equinor for Bay Du Nord FPSO

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Strategists Forecast USA Crude Stock Drop

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Hafnia Secures Preliminary Deal to Acquire Over 14 Pct Stake in TORM

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Shell and BP Stations Running Low in Indonesia on Import Cuts

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Inside the AI-optimized data center: Why next-gen infrastructure is non-negotiable

How are AI data centers different from traditional data centers? AI data centers and traditional data centers can be physically similar, as they contain hardware, servers, networking equipment, and storage systems. The difference lies in their capabilities: Traditional data centers were built to support general computing tasks, while AI data centers are specifically designed for more sophisticated, time and resource-intensive workloads. Conventional data centers are simply not optimized for AI’s advanced tasks and necessary high-speed data transfer. Here’s a closer look at their differences: AI-optimized vs. traditional data centers Traditional data centers: Handle everyday computing needs such as web browsing, cloud services, email and enterprise app hosting, data storage and retrieval, and a variety of other relatively low-resource tasks. They can also support simpler AI applications, such as chatbots, that do not require intensive processing power or speed. AI data centers: Built to compute significant volumes of data and run complex algorithms, ML and AI tasks, including agentic AI workflows. They feature high-speed networking and low-latency interconnects for rapid scaling and data transfer to support AI apps and edge and internet of things (IoT) use cases. Physical infrastructure Traditional data centers: Typically composed of standard networking architectures such as CPUs suitable for handling networking, apps, and storage. AI data centers: Feature more advanced graphics processing units (GPU) (popularized by chip manufacturer Nvidia), tensor processing units (TPUs) (developed by Google), and other specialized accelerators and equipment. Storage and data management Traditional data centers: Generally, store data in more static cloud storage systems, databases, data lakes, and data lakehouses. AI data centers: Handle huge amounts of unstructured data including text, images, video, audio, and other files. They also incorporate high-performance tools including parallel file systems, multiple network servers, and NVMe solid state drives (SSDs). Power consumption Traditional data centers: Require robust cooling

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From Cloud to Concrete: How Explosive Data Center Demand is Redefining Commercial Real Estate

The world will generate 181 ZB of data in 2025, an increase of 23.13% year over year and 2.5 quintillion bytes (a quintillion byte is also called an exabyte, EB) created daily, according to a report from Demandsage. To put that in perspective: One exabyte is equal to 1 quintillion bytes, which is 1,000,000,000,000,000,000 bytes. That’s 29 TB every second, or 2.5 million TB per day. It’s no wonder data centers have become so crucial for creating, consuming, and storing data — and no wonder investor interest has skyrocketed.  The surging demand for secure, scalable, high-performance retail and wholesale colocation and hyperscale data centers is spurred by the relentless, global expansion of cloud computing and demand for AI as data generation from businesses, governments, and consumers continues to surge. Power access, sustainable infrastructure, and land acquisition have become critical factors shaping where and how data center facilities are built.  As a result, investors increasingly view these facilities not just as technology assets, but as a unique convergence of real estate, utility infrastructure, and mission-critical systems. Capitalizing on this momentum, private equity and real estate investment firms are rapidly expanding into the sector through acquisitions, joint ventures, and new funds—targeting opportunities to build and operate facilities with a focus on energy efficiency and scalability.

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Ai4 2025 Navigates Rapid Change in AI Policy, Education

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Two Lenses on One Market: JLL and CBRE Show Data Centers in a Pinch

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Data Center Frontier Trends Summit 2025: AI, Power Constraints, and Moonshots Take the Stage in Reston

Aug. 28, RESTON, Va. — It’s the last day of the second-annual Data Center Frontier Trends Summit, marking the conclusion of a gathering of significant players in the data center world and their customers, all of whom are looking to get a better handle on the data center industry as it grapples with AI-fueled power demands, grid constraints, and an urgent need for infrastructure innovation. Taking place in the heart of Northern Virginia’s Data Center Alley, acknowledged as the world’s premier data center hotspot, the conference in Reston, VA saw a significant increase in attendance in its second year, going from just over 300 attendees in its inaugural year to close to five hundred attendees this year. Not unexpected, many of the attendees were newcomers to the event, attracted by the strong list of speakers focused on critical topics to the industry, with an emphasis on power and artificial intelligence. Many conversation with the attendees had them identifying specific topics that were primary motivators to attend, while one attendee simply told us “after reading the presentation descriptions and the speakers list, how could we not attend?” From the opening keynote “Playbook Interrupted” presented by Chris Downie, CEO of Flexential, the tone and message of the conference was made clear. Touching on topics such as AI’s insatiable resource appetite, tightening energy policies, and power scarcity, Chris made it clear that todays’ data centers are breaking old frameworks and demanding new strategies for growth and resilience. The message was clear; times have changed and industry executives needed to be ready to change with them. Staying ahead of the curve was going to be more difficult, but just as important. It was tyime to develop a new playbook for your business operations. Getting to the Core of It With the demand for AI centric

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NTT Data, Google Cloud Forge Alliance to Expand AI, Cloud Modernization

The partnership is designed to accelerate development of repeatable, scalable solutions, according to reps. NTT Data’s GenAI framework called “Takumi” is at the heart of this development, designed to help clients move from idea to deployment by integrating with Google Cloud’s AI stack supporting rapid prototyping and GenAI use-case creation. This initiative expands NTT Data’s Smart AI Agent Ecosystem, which unites strategic technology partnerships, specialized assets, and an AI-ready talent engine to help users deploy and manage AI at scale.  New Business Group NTT Data has established a dedicated global Google Cloud Business Group comprising thousands of engineers, architects, and advisory consultants. This team will collaborate with Google Cloud teams to help clients adopt and scale AI-powered cloud technologies. The company also is investing in training and certification programs so teams across sales, presales, and delivery can sell, migrate, and implement AI-powered cloud solutions. NTT Data says it will certify 5,000 engineers in Google Cloud technology, a step that underscores the scale of resources both firms are committing to meet surging enterprise demand. Both companies are co-investing in global sales and go-to-market campaigns designed to fast-track adoption across priority industries. A Landmark Moment for NTT—and the Industry Marv Mouchawar, Head of Global Innovation at NTT Data, said the partnership is a significant milestone in the company’s mission to drive innovation and digital transformation across industries. “By combining NTT Data’s deep expertise in AI, cloud-native modernization and enterprise solutions with Google Cloud’s advanced technologies, we are helping businesses accelerate their AI-powered cloud adoption globally and unlock new opportunities for growth,” she noted. For the data center industry, this partnership is notable not just as a technology alignment but as a signal of where digital infrastructure is headed. Hyperscale cloud providers continue to expand their reach through partnerships with major service providers,

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

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

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

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2025 playbook for enterprise AI success, from agents to evals

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

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

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

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