How memory and storage became the backbone of the AI era

The dominant narrative of AI has centered on the GPU and the accelerator, which became the symbols of the era. That story made sense when training the biggest model was the goal. It no longer tells the whole story. As of mid-2026, system performance is increasingly shaped by data movement, latency and bandwidth, not compute alone.

The real story, the one that will define the next decade, is memory and storage. AI is no longer constrained by compute alone. It is constrained by how efficiently data can be moved, accessed and processed. That shift changes everything: how AI infrastructure is built, where investment flows and how organizations scale AI effectively.

The overlooked shift in AI infrastructure

In 2023, roughly two-thirds of AI compute spend went to training. By the end of 2026, that ratio will have inverted, with inference — the act of deploying AI and putting it to work in the real world — accounting for two-thirds of all AI compute spend.¹

The question is no longer who can train the biggest model. It is who can deploy AI at scale, efficiently and economically. For leaders planning AI infrastructure, that means evaluating memory and storage as strategic design choices, not supporting components added after compute decisions are made.

This shift represents a fundamental restructuring of the AI infrastructure landscape. As AI moves from episodic training runs to always-on inference and agentic workloads, success depends increasingly on the ability to support AI workloads continuously, at speed and within practical power and thermal limits. Memory and storage sit at the center of that challenge.

An analysis by International Data Corporation (IDC) reinforces the point: Accelerator utilization now depends on sustained high-bandwidth, low-latency access to data, while data movement and data locality are emerging as key constraints on AI scalability.²

Four workloads, one foundation

As AI adoption expands, infrastructure requirements are being shaped by four distinct workloads, each with its own demands and growth trajectory.

• Training: GPU-dominant, compute-intensive and centralized, with continued growth but at a measured 25% pace.³
• Inference: Distributed close to users, increasingly efficient and hungry for a balance of memory capacity and bandwidth. At a projected 79 percent compound annual growth rate, it is the fastest-growing workload.⁴
• Agentic: Maintains a persistent state, runs continuously, orchestrates multi-step autonomous tasks and places substantially greater demands on memory, context and data access than a simple chatbot query. Gartner projects that 40 percent of enterprise applications will embed AI agents by the end of 2026, up from roughly 5 percent just a couple of years prior.⁵ These agents require massive memory capacity (e.g., persistent key value (KV) cache, context windows exceeding 1 million tokens, etc.) and are expected to run across hybrid cloud and edge infrastructure simultaneously.
• General-purpose computing: CPU-centric and growing modestly. It completes the picture of a balanced silicon stack where CPUs, accelerators (like GPUs), memory and networking each play an important role.

The result: The industry is shifting from a “GPU supercycle” to a balanced silicon stack, where memory, CPUs and networking are as strategically important as accelerators.

Across each of these workloads, the same pattern is emerging: AI performance increasingly depends on how efficiently data moves through memory and storage, not just how much compute is available.

The efficiency equation

Global data center electricity consumption is projected to rise from roughly 415 terawatt-hours in 2024 to approximately 1,000 terawatt-hours by 2026. AI demand could consume between 4.2 billion and 6.6 billion cubic meters of water by 2027 for cooling alone. Rack power densities have already more than doubled, with AI training clusters pushing toward 80 to 120 kilowatts per rack.⁶

But the efficiency curve is bending faster than most people realize, and memory is leading the way. IDC’s framing is especially useful here: Performance per watt is becoming a defining metric for AI infrastructure because power availability and thermal limits are increasingly constraining deployment. In that environment, efficient memory and storage are not technical footnotes. They are increasingly important drivers of AI scalability, accessibility and cost efficiency. A few examples from Micron’s portfolio illustrate the trend:

• Micron HBM3E consumed 30 percent less power than competing products while delivering 50 percent higher memory capacity.⁷
• The company’s 1γ (1-gamma) process node — its most advanced, using extreme ultraviolet (EUV) lithography — pushes DDR5 solutions to 15 percent faster performance with meaningfully increased energy efficiency.⁸
• LPDDR5X delivers exceptional performance-per-watt, making it well-suited for inference and AI workloads where efficiency is a primary design consideration.
• HBM4, now in high-volume production, promises more than 50 percent performance improvement over HBM3E with continued power efficiency leadership.⁹

These are not incremental gains. They are a compounding efficiency advantage, running in parallel with AI’s compounding growth. And liquid cooling is accelerating the trend further. Direct liquid cooling removes heat at the chip level, reducing water use and power consumption and creating a virtuous cycle that compounds across every rack, every facility, every region.¹⁰

When AI becomes accessible

AI inference costs have fallen sharply (by about 280 times over two years), but enterprise AI spending continues to rise as usage scales significantly faster than per-unit cost reductions.¹¹ As the economics of deploying AI improve, organizations are finding new ways to apply intelligence across healthcare, research, logistics and everyday business operations. Memory and storage play an important role in this trend. Faster data movement, better utilization and more efficient infrastructure help reduce the cost of delivering AI, enabling broader adoption across more use cases:

• A manufacturer can deploy AI-assisted quality inspection that was previously too expensive to operate continuously.
• A clinic in a rural location can afford AI-assisted diagnostics.
• A researcher at an underfunded research institution can run models that previously required supercomputer access.

This is the democratization of expertise, and it is not a distant promise. Agentic AI, with its massive memory and storage demands, is what makes it possible. As AI usage scales exponentially and the cost of deploying intelligence falls, memory and storage become even more strategically important because they enable that expansion at scale. Organizations that eliminate data bottlenecks will be best positioned to realize the full value of AI.

The future enabled by memory and storage

Three converging forces are already reshaping the AI infrastructure landscape:

1. Silicon efficiency — memory and storage that deliver more intelligence per watt with every generation.
2. Architectural evolution — inference and agentic AI nodes distributed across the network edge, co-located with renewable energy.
3. Workload maturation — the shift from training to always-on inference and agentic AI, where AI’s value plays out across millions of use cases.

Memory feeds compute. Efficient memory feeds the balanced silicon stack. The future being built on that stack is one where AI-powered opportunity is accessible to anyone, anywhere.

IDC’s conclusion aligns: As AI becomes increasingly embedded across healthcare, research, manufacturing, logistics and everyday business operations, organizations that treat memory and storage as core enablers rather than afterthoughts will be best positioned to unlock its full value.

The future of AI will be shaped not only by how intelligence is created, but by how efficiently it can be delivered to the people, organizations and communities that depend on it.