Nvidia Predictions: AI Infrastructure Set to Shift in 2025
The integration of AI into enterprise operations has reached an inflection point. According to Forrester Research’s 2024 State of AI Survey, two-thirds of organisations would consider their AI initiatives successful with a return on investment of less than 50%.
This commercial momentum has created pressure on technology infrastructure. Data centres require new cooling systems, networking architectures need redesign, and enterprises face decisions about whether to build or rent AI computing capacity. These changes arrive as companies navigate the emergence of agentic AI – autonomous systems that use multiple language models and advanced data architectures.
With enterprise adoption of generative AI (Gen AI) set to generate US$1.02 trillion in revenue by 2032, according to Bloomberg Intelligence, executives from AI hardware leader Nvidia have outlined their predictions for AI development in 2025, focusing on changes to computing infrastructure, quantum advances and the emergence of autonomous systems.
Nvidia predicts quantum computing development through error correction
Ian Buck, VP of Hyperscale and High-Performance Computing at Nvidia, expects quantum computing to advance through error correction techniques. “Error correction requires quick, low-latency calculations,” he says. He forecasts quantum hardware will be physically located within supercomputers, supported by specialised infrastructure.
The development addresses a key challenge in quantum computing, where qubits – the basic unit of quantum information – become unstable after performing thousands of operations. This instability currently prevents quantum hardware from solving useful problems.
Infrastructure changes required for enterprise AI deployment
Charlie Boyle, VP of DGX Platforms at Nvidia, says enterprises will transition to liquid cooling to maximise performance and energy efficiency in AI data centres. This shift comes as organisations deploy hundreds of thousands of AI accelerators, networking equipment and software.
“Enterprises will increasingly choose to deploy AI infrastructure in colocation facilities rather than build their own – in part to ease the financial burden of designing, deploying and operating intelligence manufacturing at scale,” says Charlie.
The change in cooling infrastructure accompanies a transformation in data centre architecture. Gilad Shainer, SVP of Networking at Nvidia, says the term ‘networking’ will become outdated as data centres evolve into integrated compute fabrics that enable thousands of accelerators to communicate.
“Scale-out communication across networks will be crucial to large-scale AI data centre deployments — and key to getting them up and running in weeks versus months or years,” he says.
Nvidia forecasts rise in AI agents and orchestration
Kari Briski, VP of Generative AI Software at Nvidia, predicts enterprises will deploy multiple AI agents – semi-autonomous trained models that work across internal networks for tasks including customer service and data security.
“These orchestrators will have access to deeper content understanding, multilingual capabilities and fluency with multiple data types, ranging from PDFs to video streams,” says Kari.
The company expects AI query engines to transform how businesses mine data. These enterprise-specific search engines will process structured and unstructured data, including text, images and videos, using natural language processing to interpret user intent.
Construction and engineering sectors embrace AI
Bob Pette, VP of Enterprise Platforms at Nvidia, forecasts AI adoption in construction and engineering. AI systems will analyse data from onsite sensors and cameras to improve project timelines and budget management.
“AI will evaluate reality capture data (lidar, photogrammetry and radiance fields) 24/7 and derive mission-critical insights on quality, safety and compliance — resulting in reduced errors and worksite injuries,” he says.
“For engineers, predictive physics based on physics-informed neural networks will accelerate flood prediction, structural engineering and computational fluid dynamics for airflow solutions tailored to individual rooms or floors of a building — allowing for faster design iteration.
“In design, retrieval-augmented generation will enable compliance early in the design phase by ensuring that information modeling for designing and constructing buildings complies with local building codes. Diffusion AI models will accelerate conceptual design and site planning by enabling architects and designers to combine keyword prompts and rough sketches to generate richly detailed conceptual images for client presentations. That will free up time to focus on research and design.”
New job roles emerge from AI implementation
The adoption of AI systems is creating new employment categories. Nader Khalil, Director of Developer Technology, identifies prompt engineers and AI personality designers as emerging roles.
"Just as the rise of computers spawned job titles like computer scientists, data scientists and machine learning engineers, AI will create different types of work,” he predicts, “expanding opportunities for people with strong analytical skills and natural language processing abilities.”
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