Data Infrastructure for AI

Extracted Attributes

• Geopolitical Context

  US vs China AI leadership race

• Key Hardware Components

  GPUs, TPUs, Wafer Scale Engine (WSE), high-performance networking, next-gen storage

• Emerging Energy Solutions

  Space-based solar power for orbital data centers

• Foundational Data Sources

  Unstructured data from industrial inspections, energy, and defense sectors

• Primary Terrestrial Bottleneck

  Electric power availability and energy infrastructure

• Projected Global Data Volume by 2030

  612 zettabytes

Timeline

• The U.S. National Science Foundation (NSF) announces the Integrated Data Systems and Services (IDSS) program and the NAIRR Pilot to expand national AI infrastructure. (Source: NSF Web Search Result)

  2024-01-01

• Tech leaders at the World Economic Forum in Davos discuss the critical importance of AI compute power and energy infrastructure buildouts. (Source: Document 67dd679b-d764-4b4b-b23b-46e6c18ea056)

  2026-01-20

• Projected date for global unstructured data volume to reach 612 zettabytes, driving massive demand for new infrastructure tooling. (Source: Bessemer Venture Partners Web Search Result)

  2030-12-31

Space-based data center

Space-based data centers or orbital AI infrastructure are proposed concepts to build AI data centers in the sun-synchronous orbit or other orbits utilizing space-based solar power. Electric power has become the main bottleneck for terrestrial AI infrastructure. Building AI data centers in space off-the-grid could become cost competitive with advancements in reusable rockets.

Web Search Results

• Roadmap: AI Infrastructure - Bessemer Venture Partners

  First, AI is powering the modern data stack, and incumbent data infrastructure companies have started incorporating AI functionalities for synthesis, retrieval, and enrichment within data management. Additionally, recognizing the strategic importance of the AI wave as a business opportunity, several incumbents have even released entirely new products to support AI workloads and AI-first users. For instance, many database companies now support embeddings as a data type, either as a new feature or standalone offering. [...] Next, data and AI are inextricably linked. Data continues to grow at a phenomenal rate to push the limits on current infrastructure tooling. The volume of generated data, especially unstructured data, is projected to skyrocket to 612 zettabytes by 2030, driven by the wave of ML/AI excitement and synthetic data produced by generative models across all modalities. (One zettabyte = one trillion gigabytes or one billion terabytes.) In addition to volume, data types and sources continue to grow in complexity and variety. Companies are responding by developing new hardware including more powerful processors (e.g., GPUs, TPUs), better networking hardware to facilitate efficient data movement, and next-gen storage devices. [...] Having partnered with category-defining data infrastructure and developer platform companies such as Auth0, HashiCorp, Imply, Twilio, Zapier, we know that building novel and foundational technologies within the infrastructure layer is challenging and often requires specialized knowledge and resources. As such, we have extensive networks and tailored resources to support AI infrastructure founders in their drive for innovation as they ride these tailwinds, including:

• Building the future of AI infrastructure: A comprehensive guide

  A strong AI infrastructure is more than just a technical requirement. It is a strategic asset that empowers enterprises to harness AI and big data for advanced analytics, process automation, and personalized customer experiences, leading to improved efficiency and competitiveness. As AI reshapes industries, having a scalable, flexible infrastructure is key to long-term success. ## Key components of AI infrastructure ### Data storage and management AI applications rely on large volumes of data for tasks such as training, validation, and inference. Reliable data storage and management systems are essential for supporting the demands of AI workloads. These systems can include databases, data warehouses, or data lakes deployed on-premises or in the cloud. [...] A well-designed infrastructure is critical for organizations looking to streamline AI development and deployment. It provides the tools and resources to scale AI projects, optimize machine learning tasks, and manage complex AI models. With the right AI infrastructure in place, businesses can fully harness the potential of AI, driving innovation and growth. ## The importance of AI infrastructure AI infrastructure is fundamental to the success of AI and machine learning initiatives. It supports every stage of the AI lifecycle, from data ingestion and processing to model training and deployment. Without an optimized infrastructure, organizations may struggle to scale workloads, limiting innovation and the ability to address real-world challenges. [...] ## The future of AI infrastructure As artificial intelligence continues to evolve, a new infrastructure paradigm is emerging—one purpose-built to meet the unique demands of AI, driving the next wave of enterprise data software. This shift reflects the growing need for AI infrastructure designed specifically to supercharge AI-native and embedded AI applications, paving the way for more advanced and efficient systems. Explore the State of AI Instructure Report The traditional infrastructure, originally developed for more general workloads, lacks the native tooling required to fully support AI’s complex demands. In response, a new AI infrastructure stack is being developed, focused on empowering AI-centric companies with the flexibility and power they need to innovate.

• NSF expanding national AI infrastructure with new data systems and ...

  A robust data infrastructure is also critical to the success of the NSF-led NAIRR Pilot, a key initiative expanding access to AI research resources. As AI transforms sectors from health care and agriculture to energy and national defense, researchers face the challenge of accessing and integrating vast data to power advanced AI systems. Awarded systems and services through the IDSS program will be integrated into the NAIRR and other NSF-managed programs, such as the NSF Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support program, to be made easily discoverable and accessible to the nation's research and education communities. These systems will connect data with computing, instruments and software, making AI development, data analysis and scientific discovery faster, [...] "Data infrastructure and access to high-quality datasets are critical components of a thriving AI innovation ecosystem," said Katie Antypas, director of the NSF Office of Advanced Cyberinfrastructure. "But these efforts go beyond building data infrastructure — they will sharpen America's competitive edge and lay the foundation for a new era of leadership in science and innovation." Get more information about about IDSS and NAIRR. ## Research areas Directorate for Computer and Information Science and Engineering (CISE) Top [...] The U.S. National Science Foundation today announced two major advancements in America's AI infrastructure: the launch of the Integrated Data Systems and Services (NSF IDSS) program to build out national-scale data systems and the selection of 10 datasets for integration into the National Artificial Intelligence Research Resource (NAIRR) Pilot. These efforts directly align with priorities outlined in the White House AI Action Plan, which calls for investments in research infrastructure and datasets to strengthen U.S. leadership in AI research, education and innovation. ### NSF IDSS: Building a national integrated data infrastructure

• AI Storage and Infrastructure Solutions

  Contact Us Main Menu The Pure Advantage Services & Support 1. Solutions 2. AI Solutions AI moves fast. Your data infrastructure should, too. # AI moves fast. Your data infrastructure should, too. Accelerate AI training and inference with the simplicity, performance, value, and reliability of the Pure Storage platform. Get data ready for AI Tackle data preparation bottlenecks head on and streamline data pipelines. Rapidly move from raw data to real results. Learn More Build your enterprise AI factory Run on a validated, full-stack architecture that scales from pilot to production—securely and predictably. Read the Solution Brief Scale model training and fine-tuning

• Is your Data Infrastructure Ready for AI at the Point of Action? - Deloitte

  With our ecosystem of technology partners, we can help you identify the right hardware and infrastructure that aligns with business strategy and goals, and we then work with you to implement the right tools to prepare your data infrastructure for a future with real-time AI and HPC. ### Setting up your data infrastructure for real-time compute [...] Link opens in a new tab opens in new window Jump to: Contact Us Submit RFP Perspective: Print; "Print") Share Perspective # Accelerated Artificial Intelligence: Is your Data Infrastructure Ready for AI at the Point of Action? ## Next gen architecture from a business service view Once a vision is set for how the data architecture can be enhanced for high-performance computing and edge AI, the next step is to identify which pieces can be bought, which are better used as a service, and which could be built by the enterprise. These are not just technology considerations but instead they impact the wider business strategy and spending. Indeed, shifting the data architecture is a business decision. Print; "Print") Share ### Solving tomorrow’s challenges by accelerating AI innovation [...] Succeeding with AI requires computational power. Using GPU-accelerated computing for model creation and deployment in application delivers essential time savings, higher accuracy, and a greater capacity for experimentation. As enterprises refine and expand their AI strategies, the clear call is to identify where accelerated computing can be used to enhance existing capabilities and accelerate the entire AI lifecycle. ### Is your Data Infrastructure Ready for AI at the Point of Action? Artificial intelligence (AI) is increasingly a competitive necessity for many businesses—but not all AI and compute capabilities are the same. To activate the most powerful and differentiating AI applications, many organizations are missing some key technology pieces in their data architecture puzzle.