4 major North American CSPs are ramping up investment in AI infrastructure to stay competitive. This article explores AI infra trends through their strategies.
What Is AI Infrastructure?
AI Infrastructure, also known as the AI Stack, comprises software and hardware resources that
support the development, training, and deployment of Artificial
Intelligence (AI) and Machine Learning (ML) applications.
Built to handle the large-scale computing and data processing required for AI projects,
it features high performance, flexibility, and scalability. This helps
enterprises accelerate the implementation of AI solutions, enhancing operational efficiency
and market competitiveness.
AI Infrastructure vs. IT Infrastructure
Both AI infrastructure and traditional IT infrastructure share computing, storage, and
networking resources, but their design philosophies and application goals are fundamentally
different.
Traditional IT infrastructure supports daily business operations, handling general computing
needs such as ERP, databases, and office applications. In contrast, AI infrastructure is a
new architecture accommodating emerging workloads and specialized technology stacks like
deep learning and generative AI. It requires higher hardware and has a completely different
architectural design and supporting software ecosystem.
AI infrastructure is not merely an upgrade of existing IT systems; it requires a
comprehensive overhaul of technical architecture, organizational operations, and
resource allocation.
“Every industrial revolution begins with infrastructure. AI is the essential infrastructure
of our time, just as electricity and the internet once were.”
— Jensen Huang, Founder and
CEO of NVIDIA, NVIDIA Newsroom, June 11, 2025
Cloud Giants’ Investment in AI Infrastructure
The global AI wave is accelerating the development of AI infrastructure. Microsoft, Alphabet
(Google), Meta, and Amazon, the four major North American cloud service providers (CSPs),
have invested heavily in cloud computing in recent years.
In the 4th quarter of 2024, although these CSPs’ cloud
computing businesses saw positive growth, the growth rate generally slowed and did
not meet market expectations. At the beginning of 2025, they faced challenges posed by
low-cost AI models led by DeepSeek. However, the four
CSPs embraced competition and have increased their CapEx in this area this year.
Major CSPs Increase CapEx to Dominate AI Stacking
The generative AI boom continues to drive business momentum. Despite short-term revenue
pressures and the rise of low-cost AI, major CSPs are increasing
CapEx to strengthen cloud and partnership ecosystems, demonstrating strong
confidence in the long-term potential of AI infrastructure.
- Microsoft plans to reach $80 billion in CapEx by 2025FY, focusing on
expanding AI data centers, chips, and models, and enhancing its collaboration with
OpenAI. However, the company also indicated it might slow down or adjust plans in
certain areas. - Alphabet is raising its CapEx from $52.5 billion in 2024 to $75 billion
this year, accelerating investments in data centers and self-developed AI chips like
TPU. This investment wave will drive development of its cloud platform (Google Cloud),
AI development platform (Vertex AI), AI models (Gemini), and autonomous vehicle product
Waymo. - Meta expects CapEx of $60 to $65 billion this year, focusing on
building large-scale AI data campuses and enhancing model training platforms. It is also
actively developing its partnership ecosystem, to establish future advantages through AI
infrastructure. In April, it launched the Llama 4 series models, offering high
deployment flexibility, making it easier for enterprises to deploy their own or hybrid
AI applications. - Amazon plans to increase its CapEx from $75 billion in 2024 to $100
billion this year, continuing to build AI data centers and AWS infrastructure
and services. It is also strengthening development of Trainium chips and Nova
models, aggressively capturing the AI computing resources market.
AI infrastructure has become the core of resource competition among tech giants. From
hardware development to model services, major CSPs are vying for the next round of market
dominance, continuously reshaping the global cloud and AI ecosystem.
3 Key Strategies for Successfully Driving AI Infrastructure
Before discussing AI infrastructure strategies, it’s essential to understand its 6
core components: Compute, Data, Platform, Networking, Ecosystem, and
Governance. These elements form a complete architectural stack and work
together, serving as the foundation for driving AI solutions in enterprises:
- Compute: The Brain of AI
Sufficient compute power determines the
speed, scale, and responsiveness of AI model training and deployment. It primarily
consists of servers equipped with AI accelerators such as GPUs (Graphics Processing
Units) and TPUs (Tensor Processing Units), acting as the core engine for machine
learning operations. - Data: The Lifeblood of AI
Data shapes how well AI models perform and
the business value they can generate. AI infrastructure must support massive data
handling across both training and inference stages, enabling efficient data collection,
high-speed storage (e.g., data lakes), cleansing, and secure management to help models
learn from high-quality datasets. - Platform: The Skeleton and Organs of AI
Serving as the bridge
between compute and data, platforms provide the integrated environment needed to develop
and deploy AI solutions. A robust and user-friendly platform can dramatically lower
technical barriers, accelerate time-to-value from experimentation to production, and
enhance resource allocation efficiency. - Networking: The Nervous System of AI
Networking connects data,
compute, and platforms, ensuring that information flows rapidly and systems respond in
real time. Without a stable, high-speed network, AI models cannot function
properly—leading to performance bottlenecks, increased latency, and even impacting user
experience. - Ecosystem: The Social Network of AI
An AI ecosystem includes both
internal and external technology partners and tool providers that help enterprises
accelerate adoption and reduce risks. By collaborating with the suitable partners,
organizations can avoid building everything in-house and instead focus on delivering
differentiated value. - Governance: The Steward of AI
Governance ensures that AI systems
operate securely, ethically, and in compliance with regulations. It encompasses policy
management for data and models, security protocols, and risk controls related to AI
ethics, helping organizations mitigate legal and reputational risks while building
long-term trust and sustainability.
For enterprises, building a strong AI infrastructure is not just a sign of technological
leadership. It also plays a critical role in unlocking business value and enhancing
competitiveness. Let’s now explore the 3 key strategies that enable successful AI
infrastructure implementation.
1. Goal-Driven (Why): Business-Oriented AI Investment Alignment
A successful AI infrastructure investment must be guided by clear, measurable
business objectives and return on investment (ROI). The focus should be on
solving critical business challenges and uncovering new growth opportunities. This can
include improving customer experience to drive revenue, or accelerating product development
to reduce operational costs. Every infrastructure investment should be closely tied to the
business value it is expected to generate.
Take TrendForce as an example. In the rapidly evolving tech market, staying
ahead requires faster research turnaround and deeper analysis. To meet this need, TrendForce
built its own AI infrastructure and generative models. According to data from its “Data
Analytics Division”, analysts using these models improved research efficiency by an
average of 60%. They also integrated a broader range of data sources, enabling
more comprehensive industry insights and ensuring clients receive timely and critical market
intelligence.
Latest Trends in the AI Market
TrendForce delivers in-depth analysis and intelligence on global AI infrastructure
trends, from memory and wafer manufacturing to AI servers. Stay ahead of the
competition with up-to-date insights into the core of the AI supply chain.
2. Resource Allocation (How, Where): Building Flexible and Controllable AI Infrastructure
To ensure AI infrastructure investments are effective, organizations must take a goal-driven
approach when allocating technical, human, and financial resources. Collaborating within the
ecosystem can reduce the burden of development, enabling faster deployment of AI
applications while maintaining long-term innovation capabilities.
Technology Selection and Deployment Strategies
AI technology resources should be aligned with the organization’s application goals
and operational realities. Key factors include data sensitivity, model
complexity, and future scalability. Successful enterprises often take a phased
approach—starting with a pilot project, then expanding investment based on proven results to
minimize risks and maximize resource efficiency.
The table below outlines 3 common deployment models, helping organizations determine “where”
and “how” to build AI infrastructure based on cost, security, and flexibility requirements:
Description
Investment & Control
Best Use Cases
Once a deployment model is selected, organizations should strengthen their core technology
foundation—compute, data, platform, and networking. A balanced infrastructure
ensures your AI delivers results at scale and with consistency.
At the same time, building a collaborative ecosystem is key to accelerating deployment.
Partnering with cloud platforms, AI chip providers,
open-source communities, and domain experts can significantly reduce time-to-implementation
and mitigate development risks.
Organizational Collaboration and Talent Strategy
AI projects often span multiple departments and functions. To ensure alignment, organizations
must establish a flat communication structure fostering close collaboration among technical
teams, such as AI engineers and infrastructure specialists, and business units. This helps
clarify requirements, define problems effectively, and continuously refine solutions.
At the same time, project roles and responsibilities should be restructured around a
“business-led, tech-supported” model. Each stakeholder must have clear
goals and deliverables to prevent disconnects between business needs and technical
execution.
In addition, companies should invest in employee training and development to help teams stay
current with evolving AI technologies. Building a skilled in-house AI team improves both
implementation success and the organization’s ability to adapt and scale AI initiatives over
time.
Full Lifecycle Financial Planning
When planning AI infrastructure, organizations should consider more than just initial
hardware purchases or cloud subscription fees. A lifecycle-based approach is essential for
systematically evaluating the overall cost structure. This includes:
- Capital expenditures such as data center construction and investments
in AI accelerators. - Operating expenses including cloud service fees, electricity, network
usage, software licenses, and system maintenance. - Hidden costs such as talent recruitment and training, long-term data
management, system integration complexity, and potential risk mitigation.
Evaluating these cost elements helps organizations plan budgets more accurately and make
informed investment decisions.
3. Risk Management (What If): Security, Compliance, and Sustainable Governance
Forward-looking risk governance is essential for ensuring the long-term stability of AI
infrastructure. Organizations should strengthen risk management across 3 key areas:
- Security: Protect the confidentiality and integrity of AI data and
models throughout their lifecycle, preventing unauthorized access and malicious attacks. - Compliance: Align with legal and ethical standards by establishing
transparent, fair, and traceable AI governance frameworks that minimize regulatory
risks. - Sustainability: Incorporate carbon emissions, energy efficiency, and
long-term maintenance costs into planning to ensure alignment with ESG goals and
responsible technology development.
The Rise of Low-Cost Models Is Reshaping AI Infrastructure
In early 2025, China’s DeepSeek and similar
models sent shockwaves through the AI industry, accelerating infrastructure
expansion. Reports have circulated that training these models can cost as little as $30 to
$50. However, most assessments suggest these figures refer to partial or final training
costs under conditions of mature technology and lower hardware prices. They do not account
for the full cost of infrastructure development, data preparation, or initial system setup.
Low-cost AI models are designed to reduce newcomers’ entry barriers by minimizing
resource consumption, development time, and operating costs. These models often
achieve cost efficiency through architectural optimization and more effective data
utilization. DeepSeek, for example, uses a mixture-of-experts design that combines shared
and routing experts. It applies load balancing without auxiliary loss, enabling more
efficient use of computing resources without sacrificing performance.
In February 2025, NVIDIA
open-sourced the first version of the DeepSeek-R1 model optimized for its Blackwell
architecture. Inference speed increased by 25 times compared to January, while the cost per
token dropped by a factor of 20, marking a major leap in both performance and cost control.
While questions remain about the return on investment in AI infrastructure, major cloud
providers have continued to ramp up capital spending in 2025. Many are also developing
custom AI chips—ASICs
to strengthen their competitive edge and national positioning. In the long run,
high-precision AI services remain essential across industries. Their value cannot be fully
replaced by low-cost models, underscoring the need for sustained investment.
How to Plan for the Next Wave of AI Infrastructure
This year marks a major shift in the AI landscape. The emergence of low-cost AI models offers
an alternative to traditionally capital-intensive approaches and signals a turning point for
the industry. This trend is expected to drive broader AI adoption and accelerate real-world
applications across sectors.
As a result, organizations are placing greater focus on optimizing existing AI
infrastructure through software and algorithmic improvements to enable more
cost-efficient AI product development. This also helps reduce the cost for end
users.
Going forward, the market is likely to see a dual-track approach: proprietary,
high-precision, closed-source models developed by leading companies, alongside a growing
ecosystem of low-cost, open-source models that offer greater flexibility and accessibility.
Each organization can choose the path that best fits its strategic and operational needs.