The artificial intelligence boom is increasingly being defined not by model releases alone, but by the infrastructure required to run them. A new 100-page report from ResearchAndMarkets.com examines the technologies converging inside AI data centers, from accelerated computing and networking to storage, power and advanced cooling, as enterprises, hyperscalers and governments confront the physical limits of AI deployment.
The AI infrastructure market is moving into a more complicated phase. Building an AI-ready data center now involves far more than installing large numbers of GPUs: operators must coordinate high-performance processors, low-latency networking, storage architectures, power delivery and cooling systems capable of supporting increasingly dense compute environments.
That changing landscape is the focus of “Guide to New Technologies for the AI Data Center,” a 100-page global market intelligence report published by ResearchAndMarkets.com. The report is aimed at business leaders, government professionals, academics and investors seeking competitive intelligence around the technologies underpinning AI data center development.
The report covers the rise of AI data centers and examines AI servers, processors and chipmakers, along with networking and interconnect technologies. It also addresses storage technologies such as data lakes, object storage and NVMe SSDs, as well as power systems and advanced cooling approaches. Quantum computing is included as an emerging area of potential relevance to future infrastructure.
That breadth matters because AI infrastructure is becoming a systems-engineering problem.
For traditional enterprise workloads, compute, storage and networking could often be expanded relatively independently. AI workloads are changing that equation. Training and inference can require tightly coupled accelerated computing, high-bandwidth communication and rapid movement of large datasets. Increasing rack densities also make heat removal and power availability central considerations in infrastructure design.
The scale of spending reflects the shift. IDC estimates that worldwide AI infrastructure spending reached $318 billion in 2025, more than twice the $153 billion recorded in 2024. IDC expects spending to reach $487 billion in 2026 and exceed $1 trillion by 2029.
For companies such as NVIDIA, Microsoft, Amazon and Google, this represents an enormous opportunity to supply the hardware, cloud capacity and software platforms required for AI workloads. But it also creates dependencies across the wider technology ecosystem, including semiconductor manufacturing, networking, storage, utilities, construction and data center operators.
The energy question may ultimately prove just as important as the semiconductor question.
The International Energy Agency estimates that global data center electricity consumption will roughly double from about 485 terawatt-hours in 2025 to around 950 TWh in 2030. Electricity consumption from AI-focused data centers is expected to grow even faster, potentially tripling during the same period.
That puts power availability alongside compute capacity as a strategic constraint. The IEA says data centers are already encountering bottlenecks involving energy equipment, grid connections, advanced chips and other components.
For enterprise technology teams, the implication is straightforward: AI infrastructure decisions cannot be evaluated solely on GPU performance or cloud pricing.
Organizations planning large-scale AI deployments will increasingly need to consider workload characteristics, inference economics, data locality, networking requirements, cooling architecture, power availability and the flexibility to adopt new accelerator generations. In some cases, the choice may also be between public cloud infrastructure, dedicated enterprise facilities, colocation providers or specialized AI infrastructure companies.
The competitive environment is also becoming less straightforward. NVIDIA remains central to accelerated AI computing, while hyperscalers including Microsoft, Google and Amazon are developing their own chips and infrastructure architectures. Cloud providers can abstract much of this complexity from customers, but enterprises adopting AI at scale still need to understand the infrastructure underneath those services.
That is where a competitive-intelligence report can be useful—provided buyers treat it as one input into a broader technical and financial evaluation rather than a substitute for vendor due diligence.
The ResearchAndMarkets report is particularly relevant to product managers, infrastructure planners, investors and strategy teams because the AI data center market spans multiple technology categories simultaneously. A decision about processors, for example, can affect networking, power consumption, cooling requirements and software compatibility.
The report’s timing also reflects a broader transition in the AI market. The industry is moving from experimentation toward sustained infrastructure investment. IDC’s spending figures suggest that AI infrastructure is becoming a major category of enterprise and service-provider capital expenditure rather than a short-lived extension of conventional IT spending.
But scale does not eliminate uncertainty. AI architectures are still evolving, hardware generations are turning over rapidly, and the economics of training versus inference continue to change. Enterprises therefore face a difficult balancing act: invest early enough to support AI ambitions without locking themselves into infrastructure that becomes inefficient or obsolete.
The emerging AI data center market is consequently less about a single breakthrough technology than about how compute, networking, storage, power and cooling fit together.
ResearchAndMarkets.com’s new guide offers a consolidated view of those technologies. The larger industry question is whether infrastructure providers can continue expanding capacity quickly enough—and efficiently enough—to keep pace with AI’s rapidly growing computational appetite.
Market Landscape
The AI data center market is being shaped by three simultaneous forces.
First, accelerated computing is becoming the infrastructure baseline. IDC says servers accounted for almost 98% of global AI infrastructure spending in Q4 2025, demonstrating how heavily current investment remains concentrated in compute.
Second, infrastructure is becoming energy-constrained. The IEA expects global data center electricity consumption to approach 950 TWh by 2030, with AI among the principal drivers. Power generation, grid connections, transformers and cooling therefore increasingly influence where AI capacity can be deployed.
Third, the competitive stack is broadening. NVIDIA’s accelerator ecosystem remains influential, while Google, Amazon and Microsoft are pursuing increasingly differentiated AI infrastructure and silicon strategies. For enterprise buyers, that creates more choice but also makes interoperability, software ecosystems and total cost of ownership more important.
The result is an AI infrastructure market where the winning architecture may depend less on peak benchmark performance and more on how efficiently an organization can operate an entire workload from data ingestion through inference.
Top Insights
- ResearchAndMarkets.com’s 100-page AI data center report maps compute, networking, storage, power and cooling technologies shaping enterprise infrastructure decisions.
- IDC expects global AI infrastructure spending to exceed $1 trillion by 2029, underscoring a sustained shift toward large-scale AI capacity.
- AI data center expansion increasingly depends on power availability, with the IEA projecting roughly 950 TWh of global data center electricity demand by 2030.
- NVIDIA, Google, Amazon and Microsoft are influencing the competitive infrastructure landscape through accelerators, cloud platforms, networking and increasingly specialized silicon.
- Enterprise AI teams must evaluate infrastructure holistically, balancing compute performance with networking, storage, cooling, energy availability and long-term total cost of ownership.
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