AI's Future Depends on What Lies Beneath
By: Roger Cummings
Despite being once labeled “science fiction”, Artificial Intelligence (AI) has now become our new reality. Businesses are integrating AI into their operations, and headlines are filled with news covering numerous breakthroughs. Yet, beneath the excitement lies a critical issue: the infrastructure that drives entire systems is in urgent need of improvement.
Every intelligent response, real-time insight, or automated decision depends on a tightly coordinated network of computing, storage, and network systems that demand speed, accuracy, and
scalability to function as desired. The media awards Graphics Processing Units (GPUs) for the development of this new innovative technology, but it is only a fraction of the equation.
Organizations across the globe are prioritizing the development of AI applications to improve the efficiency of their operations. As industries rapidly integrate this technology, significant
pressure has been placed on the digital infrastructure and its ability to support large-scale computing systems.
The challenge is not AI alone; rather, it is the fast-evolving landscape of infrastructure that supports it. To stay competitive, organizations must learn how to adapt and be willing to
experiment and learn through its implementation. Scaling these technologies requires research into next-generation technologies, strategic investments, and strong partnerships.
Organizations are not questioning how to integrate AI into their operations, but rather how they can do so while focusing on minimizing costs and increasing efficiency.
While the previous focus of AI was directed toward achieving mass availability, it has now shifted toward performance efficiency. With this high demand, infrastructures are constantly put to the
test regarding the processes to train large-scale models and the vast components involved in delivering results instantaneously. Scaling AI is anything but cheap. A key contributor to vast
spending is the overprovisioning of hardware that is often derived from peak demand uncertainty. Training a variety of comprehensive models demands countless GPUs, accessible high-speed storage,
and extensive cooling resources. These demands require more than just extensive sources of power. They demand storage and network systems that also face significant loads of pressure in the
processes to deliver the desired data.
GPUs are not the only constraint; data is a massive bottleneck. AI teams are discovering limits on storage and bandwidth, deeming the modernization of infrastructure essential to preventing the
waste of valuable resources.
Modern AI demands exceed traditional IT infrastructure, as they were typically designed for general-purpose workloads. The pressure to maintain performance has resulted in organizations
oversupplying hardware and cloud capacity, which leads to an infeasible total cost of ownership (TCO). Investing in weak infrastructure delivers a significant blow to the ROI from AI, impedes
training, halts project momentum, impairs timelines, and ultimately undermines executive buy-in.
A New Face to Innovation is On the Horizon
A new era of innovation is taking shape that does not rely solely on adding more power to existing systems but on reimagining how infrastructure is built from the ground up. This next-generation approach is designed to meet the demands of AI at scale, not by stacking complexity, but by utilizing smarter, more adaptive systems that redefine what is possible.
The transition from traditional, monolithic systems to modular infrastructure has increased and is anticipated to continue growing. Instead of scaling in large, costly leaps, organizations are
now expanding in increments, such as node by node or workload by workload. This scalable model offers greater flexibility with performance and cost minimization tailored to the business’s
needs.
AI workloads also demand far more than just baseline computing. They rely on agile, high-bandwidth data pipelines that can move massive volumes of information with speed and precision. To meet
these demands, the implementation of software-defined storage is essential, combining commodity infrastructure with intelligent software to deliver the IOPS, bandwidth, and scalability AI
demands, all while diminishing inference costs. At the same time, as AI transitions into real-world environments, like
