AI continues to evolve at pace. The novelty of generative models producing their own content is already giving way to the buzz around agentic systems that can set goals autonomously and execute multi-step workflows without human involvement. Each staging post on AI's journey raises the bar for compute resources and demands more powerful processing. But in the headlong dash towards newer and more specialized architectures, and amid the fuss that greets each silicon innovation, the industry is overlooking something important: beyond a certain scale, AI's biggest challenge is not compute but data storage. AI's trajectory towards human-like reasoning is driving exponential growth in data volume. Better AI needs better data in ever-increasing quantities, which raises the question of what to do with all that information. Picturing today's AI datacenters as giant compute systems is only half right. They are also data storage systems. The principal battleground in the AI arms race lies just as much at the storage layer as in chip development. The focus on compute capabilities alone in AI discourse is a legacy of the technology's origins, argues Nicolas Frapard, senior manager and regional lead, EMEAI at WD (also known as Western Digital). "Early on, AI's challenge was simply to make models work reliably and at scale," he points out. "That naturally put the spotlight on GPUs, accelerators, interconnect bandwidth, and overall compute density. Questions centered on how quickly models could be trained, how large they could become, and how efficiently clusters could scale. In that context, compute became a clear and measurable indicator of progress." Sustained, large-scale deployment expands the focus to the management and housing of information resources. The profit and loss implications of AI also change with scale: "As AI adoption expands to billions of interactions, data growth becomes structural rather than incidental," believes Frapard. "That has direct economic consequences. While compute tends to become more efficient over successive generations, data volumes continue to expand, driving sustained storage demand." Total cost of ownership (TCO) and return on investment (ROI) increasingly depend on how well organizations store and manage large-scale data estates. At exabyte scale, even small inefficiencies become magnified, which makes a lifecycle-driven approach to storage essential. Getting back to a balanced approach Those exploring AI-friendly storage strategies will have to grapple with a common myth. In recent years, IT decision-makers have been drip-fed the idea that storage tiers are irrelevant, and that hybrid architectures spreading data across different media according to need are redundant. For enterprises operating at scale, HDDs remain the backbone of storage architecture, providing the only viable way to store vast and growing datasets economically. Despite persistent narratives around all-flash environments, disk continues to underpin majority of enterprise data. But just as with the debate over compute versus storage, economics poses awkward questions once teams reach a certain scale of deployment. On a properly measured cost-per-terabyte basis, flash can clock in at up to 20 times more expensive than HDD. This does not negate the value of flash, but reinforces its role as one component within a broader architecture. The cost gap between flash and HDD was once expected to narrow until it would threaten the logic of disk drives. That never happened . In fact the gap has widened. Flash doesn't look set to become applicable across the board in any foreseeable timeframe . "The 'flash everywhere' approach emerged for similar reasons as the compute-centric view – at small scale when performance was the primary concern and data volumes were manageable, it was a good choice," explains Frapard. Flash offers low latency and high throughput, both critical for latency-sensitive workloads such as real-time inference. In early architectures, these requirements were often generalized across the entire system. But scale and maturity change the calculus. "At production scale, only a small proportion of data requires high-speed access," he points out. "The majority, such as logs, historical outputs, and training artifacts, should be stored reliably, accessed predictably, and retained economically over long periods. This is where HDDs becomes essential." High-performance storage such as flash has its place in any tiered arrangement. It sits close to compute, but the bulk of data is best housed in capacity-optimized HDD layers, where cost efficiency, density, durability, and energy consumption become the deciding factors. AI that delivers any meaningful return on investment requires a strategy that recognizes this and aligns storage technologies with data lifecycles, rather than applying a single performance standard across all data regardless of cost. Striking that balance reconciles scalability with long-term sustainability. HDD solutions optimized for the age of AI Data storage veteran WD has repositioned itself to help guide IT decision makers through this AI data conversation. Roughly 90 percent of its revenue now comes from AI and cloud. WD's latest storage roadmap reinvents the hard drive for AI needs. It has already produced a new generation of storage technologies spanning scalable capacity, performance optimizations, and power efficiency innovations. It also offers a API that will help accelerate platform storage deployment with cost-effective economics. "WD's position is rooted in a simple but important principle," explains Frapard: "At scale, AI is fundamentally a data system, and high-capacity HDD storage is what enables it to function sustainably and economically. Our strength lies in supporting that foundation across different tiers, particularly where scale and economics intersect." WD's portfolio, spanning high-capacity, energy-efficient drives as well as performance-oriented solutions, is designed with hyperscale and cloud use cases in mind. It focuses on balancing density, durability, and cost efficiency for large-scale AI workloads. The vendor's mission centers on enabling a system-level approach in which data can move between tiers, be retained economically, and remain accessible for ongoing use. "Ultimately, the long-term success of AI will not be defined solely by peak compute performance, but by how effectively organizations can manage and build value from their data over time," concludes Frapard, "That is the layer where WD is focused." Looking to the future At scale, IT decision-makers must prioritize data management rather than fixating on how much compute they need to deploy. Getting this balance right determines whether AI delivers sustained business value or drains resources for no obvious return. This is the central AI challenge for enterprises, and equally for emerging AI scalers such as neoclouds, sovereign clouds, and AI labs. All must control their data storage now or face bottlenecks, economic shortfalls, and failed AI initiatives further down the line. Hyperscalers and large cloud datacenters operating mixed-fleet storage architectures are already demonstrating the way forward. These systems incorporate enough flash to drive performance-sensitive workloads, combined with high-capacity HDD storage to support the vast amount of data that underpins AI systems at scale. The winners have figured out that IT infrastructure at scale is a nuanced, multi-tiered matter. The prize for getting this balance right goes beyond keeping a lid on accumulated data. It is the chance to lift the whole organization to a new level. "The real opportunity lies not simply in governing data, but in building systems where data remains accessible, reusable, and economically viable over time," explains Frapard. "When this is achieved, data becomes a strategic asset rather than an operational burden." AI systems, he says, improve through iteration when supported by the right data strategy. The ability to retain and reintroduce historical data enables continuous refinement, richer context, and better outcomes. Over time, this creates a compounding effect that can set organizations apart competitively. "However, this depends on architecture," he says. "If storage becomes constrained – either economically or operationally – organizations are forced into trade-offs about what to retain or discard. Those decisions directly impact the effectiveness of AI systems." The organizations that lead, he concludes, will be those that design their infrastructure with data longevity in mind, enabling them to build knowledge progressively rather than losing their critical institutional data over time . Sponsored by WD.
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