AI Data Center Boom Analysis: Insights from S&P Global Ratings

On December 29, 2025, Paul Gruenwald from S&P Global Ratings joined ‘Closing Bell Overtime’ to discuss investing in AI-driven data center buildouts [1]. This discussion takes place amid a global AI data center boom, with investments (including M&A, asset sales, and equity) reaching nearly $61 billion through November 2025—surpassing 2024’s record of $60.8 billion [2]. McKinsey projects that $5.2 trillion in AI infrastructure investments will be needed by 2030, with AI-ready data center capacity growing at an average 33% annually from 2023 to 2030 [3].

The boom is fueled by three primary trends:

1. AI Model Complexity
   : Increasing computational power demands from large language models (LLMs) and generative AI applications.
2. Cloud Migration
   : Ongoing enterprise adoption of cloud platforms requiring expanded data center capacity.
3. Inference Growth
   : A shift from AI model training to real-time inference (serving AI responses), which demands more distributed data center networks [4].

S&P Global Ratings identifies critical headwinds:

• Power Constraints
  : Data center load growth could exceed grid generation and transmission capacity in constrained regions, pushing operators toward customer-sited energy resources [0].
• Overinvestment Risks
  : The intense AI investment cycle may lead to overcapacity and negative credit impacts, per S&P’s 2026 Global Credit Outlook [5].
• Regulatory Scrutiny
  : Three U.S. Senate Democrats recently sent letters to hyperscalers and data center providers investigating their impact on electricity prices [6].

• Colocation Providers
  : Digital Realty Trust (DLR, $53.47B market cap) and Equinix (EQIX) lead, with 56.5% of analysts recommending DLR as “Buy” despite a 12.06% YTD 2025 stock decline [7].
• Hyperscalers
  : Amazon Web Services (AWS), Microsoft Azure, and Google Cloud remain the primary demand drivers.
• AI-Focused Providers
  : Specialized operators like CoreWeave are emerging.

Financing for AI data center projects surged to $125 billion in 2025 (from $15 billion in 2024), with increasing reliance on debt [8]. Companies are differentiating through power efficiency (PUE ratios) and renewable energy integration to address grid constraints [0].

1. Inference as a Game-Changer
   : The shift from AI training to real-time inference is reshaping data center network design, driving demand for distributed infrastructure [4].
2. Power Infrastructure Linkage
   : Grid capacity is now a critical bottleneck, creating interdependencies between data center operators and energy providers [0].
3. Semiconductor Sector Synergy
   : NVIDIA’s GPU dominance and strategic partnerships (e.g., with Groq) continue to shape hardware requirements, linking the data center and semiconductor industries [4].

1. Short-Term
   : Power grid constraints limiting expansion in key markets [0]; regulatory scrutiny increasing compliance costs [6].
2. Medium-Term
   : Overinvestment leading to overcapacity and credit risks [5]; rising interest rates impacting debt-heavy buildouts [8].

1. Long-Term Growth
   : Colocation providers, power infrastructure solutions, and AI hardware segments have strong growth potential [3].
2. Sustainability Differentiation
   : Energy-efficient technologies and renewable energy adoption offer competitive advantages [0].

Power and regulatory risks require immediate attention, while overinvestment risks demand proactive medium-term capacity planning.

The AI data center sector is in a phase of rapid expansion driven by AI computational demand, with record investments and strong long-term growth projections. Colocation providers and hyperscalers are at the forefront, but the industry faces significant challenges from power constraints, overinvestment, and regulation. Stakeholders should monitor grid capacity, power efficiency metrics, and regulatory developments to navigate the evolving landscape.