Gold In The Machine: Scaling Infrastructure For The Age Of AI

During the gold rush, hopeful prospectors flooded the west to make their fortunes in gold. Today, technology pioneers are looking to stake their claim in the realm of artificial intelligence (AI). Price Waterhouse Cooper (PWC) estimates that 45% of total global economic gains by 2030 will be driven by AI as more sectors embrace the productivity and product enhancement benefits of AI. PWC’s research further suggests that AI could add an additional $15.7 trillion to the global gross domestic profit (GDP), which amounts to an increase of roughly 14%. This era-defining economic opportunity is driving demand for computational power and power density past what current capacity can handle.

Whether it is supplying goods, services, and security to gold prospectors or housing servers, storage systems, networking equipment, and data storage for enterprises, infrastructure enables progress. As AI adoption increases, the demand for AI-ready compute, storage, and network capacity is already exceeding its availability. This era-defining economic opportunity is driving demand for computational power and power density past what current capacity can handle.

Global government and business leaders have their eyes cast towards the possibilities that artificial intelligence (AI) promises. Policy frameworks like the European Union’s AI Act and government investment schemes like the US CHIPS and Science Act provide frameworks and funding to stake their claims in the AI gold rush. Most recently, the newly inaugurated Trump administration announced the Stargate Project, a new company formed by OpenAI, Oracle, and Softbank that will invest $500 billion in building new AI infrastructure across the United States.

The private sector is also investing heavily in infrastructure to support future innovation. In November, Forbes estimated that AI spending from the Big Tech sector will exceed a quarter trillion dollars in 2025, with the bulk of that going towards infrastructure. Microsoft alone is on track to invest $80 billion this year to build out data centers with the power and speed necessary to train powerful AI models. While these numbers seem staggering, the investment is necessary to keep pace with the demand for the computational capacity required to ensure that AI innovation does not stagnate.

Computational power: The modern world’s most precious resource

AI data centers enable AI innovation because they offer the immense data storage, lightning-fast networking, and high-performance computing (HPC) capabilities required for AI workloads. They also have sophisticated cooling and power management systems that address the challenges associated with the high-density power demands of AI hardware. Without these unique features, the pioneering innovation pushing AI to its limits today would not be possible. However, as more organizations look to capitalize on the potential of AI, AI data center designs — and the systems they employ — must evolve.

AI systems are rooted in machine learning (ML) and deep learning techniques, both of which are notorious for their computational intensity. AI models process vast quantities of data when they are being trained. They adapt and refine parameters throughout training to optimize performance. Even for basic models, this is a computationally intensive process.

In just a few short years, AI-based applications have advanced to a point that they’re not subject to the law of diminishing returns. Increasingly complex models are needed to enhance existing use cases and to push the boundaries of emerging ones like generative AI. However, the computational power required to train advanced AI and ML algorithms increases by orders of magnitude as the models become more advanced and more demands are made of it. This magnitude can be illustrated by looking at OpenAI’s early generative AI (gen AI) machine models. Over the course of six years, the company’s machine learning models saw a staggering 300,000-fold increase in the computing power required to run their models.

Six years ago, OpenAI had little competition for the resources required to train the models that became ChatGPT. Now, there are significantly more players on the field training gen AI models – all vying for access to only modestly more resources. Computational power on the scale necessary to produce the next ChatGPT has become a precious, finite resource. Expanding access to this resource is a costly affair, as evidenced by the size of investments being made to build them. With AI evolving at a breakneck speed, AI data center developers are looking for solutions to ensure that these critical innovation enablers can adapt and scale to meet future demand.

Planning for the unpredictable

Building a data center for the age of AI means ensuring that these facilities can accommodate the power consumption of large-scale GPU clusters, adapt to shifting balances between cloud and edge computing, and increase capacity to keep pace with rising demand without disruptions or downtime. In addition to increasing capacity by constructing new data centers, it is crucial to ensure they are reliable and secure. Currently, traditional data center testing solutions are used to design and test both the components and the systems comprising AI data centers. This approach has been pushed past its limits, necessitating a new approach.

AI data centers are comprised of intricate systems built from a complex web of individual components. A flaw in any one of these pieces weakens the infrastructure that enables the innovation and market capex it promises. An AI data center is only as reliable as its weakest link. On the cutting edge of performance, every chip, cable, interconnect, switch, server, and GPU represents both vast potential and equally steep risk. To mitigate this risk, each individual component must work independently and cohesively as a system – under relentless and growing demand.

Building networks capable of handling the crushing demands of AI workloads means validating every component, connection, and configuration. With the stakes and scale this high, even the smallest efficiency gain, operational improvement, or performance enhancement can push back against innovation’s diminishing returns. Staking a successful, profitable claim in this modern gold rush requires a new technology stack that can withstand whatever the future brings.

Future-proofing AI innovation

Meeting future demand for AI-ready networks, semiconductors, and data center equipment demands an AI-ready tech stack of test and simulation tools. This gold rush will see countless digital prospectors vying to strike a rich seam. AI-ready test and simulation tools will set the successful apart. Keysight is helping AI data center designers to future-proof designs and build out a robust tech stack of tools tailored to the dynamic needs of such complex environments. With a full-stack portfolio of simulators, emulators, and test hardware, Keysight solutions make it easy to emulate real-world AI workloads, validate network components, and optimize system-level performance across every layer from physical hardware to application-level behavior.