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The Real Challenges of AI (and Why Most Data Centers Aren’t Ready)

The conversation around AI is everywhere. Most of it focuses on models, new applications, and what AI might do next. Very little of it talks about what it actually takes to run these workloads at scale.

That’s the problem.

AI isn’t just another application category. It is a fundamentally different class of computationally intense workloads. When organizations start infusing AI into their existing applications and processes, they quickly discover that it changes how infrastructure must be designed, deployed, and operated.

Power requirements rise sharply. Cooling becomes a primary constraint. Systems grow significantly more complex — and more expensive. At the same time, expectations for performance and time-to-solution only increase.

This is not a minor upgrade. It is a structural shift.

The organizations best prepared for this shift aren’t necessarily the ones with the biggest budgets or the newest hardware. They’re the ones that have already been operating under similar constraints for years.

High-performance computing environments have long dealt with these exact challenges: running large, complex workloads at scale, optimizing every watt and every cycle, and constantly balancing performance against cost and power limits.

Now those same pressures are appearing across a much wider range of data centers. Enterprise applications are becoming AI-infused. New workloads are emerging that demand HPC-class infrastructure. Many environments are being pushed well beyond what they were originally designed to handle.

The technology itself is rarely the limiting factor. How it is deployed and operated usually is.

Where the Pressure Shows Up

Power is the first and most visible constraint. Traditional racks that once ran comfortably at 15–20 kW are now being asked to support 40 kW, 60 kW, or more — with rapid fluctuations as AI workloads spin up and down.

Cooling follows close behind. Air-cooled systems that worked reliably for years are hitting hard limits, forcing organizations to seriously evaluate liquid cooling and other alternatives.

Cost is the third pressure point. These systems are not only more powerful, they’re significantly more expensive to acquire and run. Underutilized or poorly matched infrastructure is no longer just inefficient — it’s financially unsustainable.

Efficiency Becomes the Differentiator

The real key going forward is not simply adding more hardware. It’s using what you already have far more effectively.

That means gaining clear visibility into where power and compute are actually being used (and wasted), matching workloads to the right systems, reducing idle resources, and continuously measuring and adjusting operations.

High-performance computing environments have been doing this for decades. The rest of the industry is now being forced to learn the same lessons — quickly.

This site exists to help bridge that gap.

We examine these issues through a practical Infrastructure Efficiency Framework organized around six core areas:

• Data Center Assessment — Understanding what you actually have and where inefficiencies hide.
• Cooling Technology — Managing the massive new heat loads AI systems generate.
• Power Infrastructure — Delivering reliable power efficiently at high densities.
• Systems OEMs & Integrators — Choosing the right partners for system-level design.
• System Components — Selecting accelerators and other critical technologies wisely.
• Operational Efficiency — Maximizing useful work from the resources you have.

Each area plays a vital role in helping data centers adapt to the demands of AI and other high-intensity workloads.

The website itself isn’t in line with today’s fashions. You won’t find flowing pictures of landscapes or abstract art to scroll past. It’s designed to be straightforward, with a high meat-to-fluff ratio. Pretty? Not so much. Useful? That’s the goal.

Let us know if it is — or isn’t.

The Data Center Perfect Storm (video)

A quick video introducing “The Data Center Perfect Storm” and Olds Research

Enterprise data centers will need to evolve toward supercomputing

As workloads become more demanding, efficiency across the entire system becomes the key to performance

The Data Center Perfect Storm

The forces reshaping modern data center infrastructure.

Enterprise data centers are entering a period of rapid, disruptive change - and many aren't prepared for it.

The rapid adoption of artificial intelligence is driving computational demands to levels that traditional enterprise infrastructure was never designed to handle. Power consumption, heat density, operational complexity, and costs are rising dramatically — creating what we call The Data Center Perfect Storm.

These challenges are not entirely new. High-performance computing (HPC) environments have been dealing with extreme compute density, aggressive power and cooling requirements, and intense efficiency pressure for decades.

What HPC organizations learned long ago is that simply throwing more hardware at the problem is not enough. Real progress requires system-wide efficiency gains across power, cooling, system architecture, workload management, and operations.

This site exists to help non-HPC data centers navigate this storm.

We explain the real problems in clear, practical terms, show what actually works to reduce their impact, and point you to the vendors and solutions that can help — all focused on making on-prem data centers efficient and viable for the long term.

The Explosion of Compute Demand AI is quietly infiltrating every corner of your business — and it's bringing friends: pricey, massive, power-hungry, heat-generating friends.

AI is no longer just a specialized workload — it is rapidly becoming embedded in everyday business and technical applications, analytics, customer experiences, and operational systems.

Unlike traditional enterprise workloads, which are typically predictable, bursty, and relatively modest in their compute needs, AI workloads differ in several critical ways:

They are highly compute-intensive and require parallel processing to deliver timely results
They scale poorly without the right hardware and software stack
They create extreme power density and heat loads
They require tight integration between compute, memory, and networking

Even modest AI adoption can dramatically increase power consumption, heat generation, storage demands, and network traffic. More advanced use cases — such as large language models, real-time inference, computer vision, and AI-driven modeling and simulation — push these demands much higher.

In other words, the workloads now emerging across enterprise data centers increasingly resemble the kinds of computational problems long associated with high-performance computing.

While AI workloads can technically run on almost any computer, training and operating modern models within practical timeframes requires highly parallel systems equipped with accelerators such as GPUs, large memory pools, high-speed interconnects, and fast storage. These architectural characteristics closely mirror the systems used for decades in scientific computing and supercomputing environments.

The core idea behind Olds Research is to help data centers apply the hard-won wisdom from supercomputing and radically increase the amount of useful compute per dollar spent.

The Power Problem: Power used to be background noise. Now it's the loudest voice in the room. As AI racks push into 50–130+ kW territory, electricity and cooling costs are no longer side issues — they're becoming the main event.

As computing demand increases and becomes more computationally demanding, so does the electricity required to support it.

For many years, improvements in processor efficiency helped offset rising compute capability. That trend has now reversed. Modern CPUs, GPUs, and accelerators are pushing the limits of current semiconductor technology, consuming significantly more power than previous generations.

High-performance GPUs used for AI training and inference now operate at power levels that would have seemed extreme only a few years ago. Future generations are expected to push those limits even further. At the same time, high-speed networking, large memory configurations, and fast storage are also driving up power consumption inside modern servers.

The result is a rapid increase in power density. Rack configurations that once fit comfortably within traditional enterprise power envelopes are being replaced by systems that demand far more electrical capacity. In many environments, rack power levels once considered extreme are becoming the new normal.

One thing computers are exceptionally good at is converting electricity into heat. A lot of heat.

These trends have major implications for data center infrastructure. Power distribution, electrical capacity, and facility design are now critical considerations when deploying AI and other high-performance workloads.

Air Cooling Has Reached Its Limits. It had a good run. It's over. On the bright side, liquid cooling will often pay for itself faster than you expect.

For decades, air cooling was sufficient for most enterprise data centers. It was simple, well-understood, and relatively inexpensive.

That era is ending.

Modern AI servers generate far more heat per square foot than traditional systems. A single high-density GPU rack can now produce heat loads that rival small industrial equipment. Air simply cannot remove this much heat efficiently enough to keep components within safe operating temperatures, especially as rack densities continue to climb.

The physics are unforgiving. Air has low thermal conductivity and low heat capacity. As power density increases, fans must spin faster, consuming more power and generating more noise. Eventually, you hit a wall where adding more fans or bigger heat sinks no longer provides meaningful improvement.

Many organizations are discovering that they can no longer rely on air cooling alone for their most demanding AI workloads. The transition to liquid cooling — whether direct-to-chip, rear-door heat exchangers, or immersion — is becoming a practical necessity rather than a future consideration.

We explore the different liquid cooling approaches, their trade-offs, and the vendors who can help implement them in the Cooling Technology section.

A New Cost Reality: Computing is more important than ever before — and the costs have never been higher. The only way forward is to get the absolute most for every dollar and every watt you spend.

The old assumption that computing would keep getting cheaper over time has broken down.

Each new generation of processors and accelerators is cheaper when measured per unit of compute. But with the advent of AI, the demand for compute has never been higher and is still increasing rapidly. At the same time, the absolute cost of the highest-end components has risen sharply, and power consumption has increased even more dramatically.

This shift is changing the economics of data centers in a fundamental way. What used to be dominated by capital expenses (the upfront cost of servers and accelerators) is increasingly driven by operating expenses — especially power and cooling. For example, on the CapEx side, here’s a quick history of top-end compute processors.

Server CPUs – Price and Power Comparison

2016/2017 era: Intel Xeon Platinum 8180 (28 cores) had an MSRP of ~$10,000 (street prices up to $13,000) with a TDP of 205 W.
Current (2025/2026): Flagship Intel Xeon Platinum models (e.g., 6980P or similar high-core Granite Rapids) carry MSRPs of $12,000–$17,800+ with TDPs of 500 W.

Compute GPUs – Price and Power Comparison

2016/2017 era: NVIDIA Tesla P100 (data center compute GPU) launched at ~$5,700–$7,400 with a TDP of 250 W. The Tesla V100 followed shortly after at similar or higher pricing with 250–300 W TDP.
Current (2025/2026): NVIDIA H100 (SXM) typically costs $35,000–$40,000+ with a TDP up to 700 W (PCIe versions are lower power but still significantly more expensive than previous generations).

Memory follows the same pattern. A high-end server in 2016 might have used 256–512 GB of DDR4 at a total memory power draw of ~100–200 W. Today’s AI-capable servers routinely carry 1–2 TB+ of DDR5, with the memory subsystem often consuming 300–600 W or more under load — even though DDR5 runs at a slightly lower voltage.

On the CapEx side of the ledger, power is now a major driver of total cost of ownership. Higher TDPs mean more electricity consumed and more heat generated, which must be removed by the cooling system. This creates a compounding effect on both power and cooling budgets.

Running a data center today is significantly more expensive than it was just a few years ago. Both the cost of modern AI-capable systems and the ongoing expenses for power and cooling have risen sharply.

And if you’re thinking that moving AI workloads to the public cloud is an easy escape from these realities, think again. Running sustained, moderate-to-high utilization workloads in the cloud is like selling your house to move into a hotel to save money — the numbers are very hard to make work over time.

What Supercomputing Already Learned: High-performance computing has been facing these exact challenges for decades. Enterprises can borrow the same technologies and practices to radically increase efficiency — without reinventing the wheel.

Many of the challenges now hitting enterprise data centers are not new — high-performance computing environments have been living with them for decades.

Supercomputing centers routinely manage extreme compute density, massive power demands, complex cooling requirements, and constant pressure to deliver maximum performance within limited budgets. In those environments, simply adding more hardware has never been a viable long-term strategy.

Instead, HPC organizations learned that real gains come from improving efficiency across the entire stack: better infrastructure design, advanced cooling, high-speed interconnects, optimized system architectures, intelligent workload scheduling, and relentless focus on utilization.

As AI workloads spread into mainstream enterprise IT, many organizations are now encountering the same physical and economic constraints that supercomputing centers have managed for years. Power availability and management, cooling capacity, infrastructure costs, and operational complexity are quickly becoming the dominant limiting factors.

The hard-won lessons from HPC — advanced cooling technologies, accelerator-based architectures, high-speed interconnects, sophisticated workload management, and a relentless drive for maximum efficiency — are becoming highly relevant to enterprise data centers.

The Infrastructure Efficiency Framework: The Data Center Perfect Storm can't be "solved," but it can be weathered maybe even in style. We break it down into six practical areas to help you cope, adapt, and get better results from your infrastructure.

Navigating the Data Center Perfect Storm requires more than simply deploying faster processors or larger systems. As computing demand grows and infrastructure constraints become more pronounced, organizations must focus on improving efficiency across the entire computing environment.

High-performance computing environments have long operated under this reality. Every watt of power, every unit of cooling capacity, and every cycle of compute time must be used as effectively as possible. Achieving that level of efficiency demands attention to multiple layers of the infrastructure stack.

At Olds Research, we examine these issues through a practical Infrastructure Efficiency Framework organized around six key areas:

Data Center Assessment Understanding how your existing infrastructure is being used, identifying inefficiencies, and finding opportunities to improve efficiency and free up capacity.
Cooling Technology Technologies and approaches for managing the dramatically increasing heat loads generated by modern AI systems.
Power Infrastructure Addressing rising electrical demands, reducing losses in power conversion and distribution, and improving overall power efficiency and capacity.
Systems OEMs & Integrators How to select the right partners and system-level designs to support high-performance, AI-driven workloads efficiently.
System Components Evaluating accelerators, composable/disaggregated infrastructure, and other critical components that determine real capability and efficiency.
Operational Efficiency Optimizing how compute resources are scheduled, monitored, and managed to maximize useful work delivered.

Each of these areas plays a vital role in helping organizations adapt their data centers to the demands of AI and other high-intensity workloads. Throughout this site we explore the practical technologies, operational practices, and vendor solutions that can make a real difference.

Looking Ahead

The forces shaping modern data centers are not temporary. Artificial intelligence, data-intensive applications, and increasingly complex digital services continue to drive compute demand at unprecedented levels. At the same time, the physical realities of power consumption, heat generation, and infrastructure cost are becoming impossible to ignore.

For many organizations, this represents a fundamental shift in how computing infrastructure must be designed and operated. Systems will become denser, power requirements will grow, and overall efficiency will determine how much useful work a data center can actually deliver.

In many ways, the future of enterprise data centers will look a lot like the environments the high-performance computing community has been managing for years. The technologies, architectural approaches, and operational practices developed in HPC provide valuable guidance for organizations navigating this transition.

The goal of Olds Research is simple: to help you understand these changes, explore the technologies that matter, and identify the practical solutions that can help your data center adapt and thrive.

Throughout this site we break down the key areas — assessment, cooling, power, systems, components, and operational efficiency — and show what actually works in real-world environments. We’re not picking winners and losers here. We present curated lists with clear differentiators and best-fit guidance based on our experience, along with direct links for further exploration.

The Data Center Perfect Storm is already here. The organizations that will succeed are those that recognize the shift happening now and begin preparing their infrastructure for the next generation of computing.

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Research Report

The Real Challenges of AI (and Why Most Data Centers Aren’t Ready)

Where the Pressure Shows Up

Efficiency Becomes the Differentiator

The Data Center Perfect Storm (video)

Enterprise data centers will need to evolve toward supercomputing

The Data Center Perfect Storm

Looking Ahead

Fake Science Racket Revealed!

Olds Research

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