Shaping the Future of AI Innovation: Infrastructure Drives Critical Growth
Artificial intelligence is advancing at a record pace. Generative AI—fueled by increasing computational power and access to massive datasets—has rapidly accelerated AI development, effectively creating entirely new industries.
While many people associate AI with ChatGPT, OpenAI’s large language model-based chatbot known for generating human-like text and engaging in conversation, several tech giants have launched their own versions. Google’s Gemini, Anthropic’s Claude, Microsoft’s Copilot, Meta AI, Perplexity AI, and xAI’s Grok are among the leading AI-powered assistants now in use.
Large language models (LLMs) have already demonstrated impressive capabilities, including passing the bar exam, the CPA exam, and medical board exams. Beyond chatbots, AI applications are transforming education, healthcare, transportation, manufacturing, retail, and finance—with many more innovations currently in development.
Broader Public Adoption of AI Expected to Grow
Despite the progress, public adoption is still growing. A Pew Research Center survey released on June 25, 2025, found that only 34% of U.S. adults had used ChatGPT. However, as tech giants like Google, Microsoft, Meta, and Amazon continue to release more powerful AI tools, broader adoption is expected.
AI doesn’t run on code alone—it runs on infrastructure. AI is the brain. Infrastructure is the nervous system.
Infrastructure enabling AI’s growth includes:
- Data Centers
- Energy
- Technology
Data Centers: The Heart of AI
These facilities house the massive computing power and data storage required to run AI applications like ChatGPT, which generate text, images, and video content, as well as support functions like personalized shopping, financial modeling, autonomous vehicles, and health diagnostics. In short, data centers are the physical infrastructure powering the digital world.
As shown in this illustration, data centers may resemble industrial warehouses on the outside, but inside, they operate with the security of high-level government facilities, protecting the valuable data they contain.
The United States is the global leader in artificial intelligence for many reasons—chief among them is its expansive network of data centers. The U.S. operates more data centers than any other country in the world, forming the digital backbone of AI innovation.
The largest data center hubs in the U.S. are in Northern Virginia, Phoenix, Atlanta, Dallas, and Chicago. Since the commercialization of generative AI following ChatGPT’s launch in November 2022, demand for data center space has surged. Vacancy rates in top markets have dropped below 3%, according to CBRE as of August 19, 2025. This tight supply has allowed operators to raise rents by double digits and sign tenants to longer lease terms.
To meet surging demand, both the number and size of data centers are growing. For example, a joint venture between OpenAI, Oracle, and SoftBank—named Stargate—was announced in January 2025. This AI-focused data center campus is being built on 875 acres in Abilene, Texas—nearly the size of New York’s Central Park (843 acres).
Meta is also expanding rapidly, building hyperscale data centers so large they’ve named them after Greek titans, including Prometheus and Hyperion. These names reflect not just scale—but ambition.
Energy: Electricity is the New OilTM
With the acceleration of AI, the technology industry is quickly realizing that artificial intelligence requires more than just bytes of data—it also needs gigawatts of electricity. To advance, AI depends on both computational power and energy. In essence, AI speaks two languages: gigabytes and gigawatts.
As a result, the success of AI has brought the technology and energy sectors together in ways never seen before. We are entering a new era—the Age of Electricity—where U.S. electricity demand is expected to rise steadily for decades after remaining flat for much of the last 20 years. In this new paradigm, electricity is the new oilTM.
The chart below highlights the forecasted growth in U.S. electricity growth through 2050.
Electricity infrastructure—including generation, transmission, and distribution—will need significant expansion to support this demand. So will the components that enable it, such as turbines and transformers. The U.S. is expected to require approximately:
- 5,000 terawatt-hours (TWh) of electricity by 2030
- 6,000 TWh by 2040
- 7,000 TWh by 2050
This growth is unprecedented. To put it into context, every additional 1,000 TWh of generation is equivalent to the entire current electricity consumption of Japan, the world’s fifth-largest economy. Or, to borrow a metaphor: AI is the new Energizer Bunny—it just keeps going and going, needing another 1,000 TWh, and then another, and another…
Energy Sources to Power Rapid Electricity Expansion
Natural gas and nuclear energy are expected to be the primary energy sources powering this multi-decade expansion. Infrastructure investments in these areas—including pipelines, gas turbines, nuclear plants, and uranium enrichment facilities—will be critical.
As electricity generators evaluate new sources capable of delivering reliable, around-the-clock power, natural gas stands as the most cost-effective option. According to Lazard’s latest Levelized Cost of Energy report published in June 2025, natural gas remains the clear low-cost source of dependable electricity generation. This chart illustrates this cost advantage.
Due to the dual drivers of rising domestic electricity demand and growing U.S. LNG exports, U.S. natural gas demand could increase by up to 30% by 2030. Nuclear energy, while already a key part of the energy mix, is expected to gain market share in the 2030s and beyond.
Significant investment in the nuclear supply chain—including domestic uranium enrichment and advanced reactor technology—will also be required to ensure reliable baseload power.
Technology Beyond the Chips
Technology systems critical for AI performance reside inside the data centers.
But it’s not just about chips—technology infrastructure includes a broad range of essential components such as:
- Data storage devices
- Connectivity equipment like networking switches and fiber optic cables
- Cooling infrastructure
The chart below shows annual capital spending by the hyperscalers: Amazon, Google, Meta, and Microsoft.
As shown in the chart, capital spending by these companies has doubled since 2022 (the year generative AI was commercialized) and it’s expected to grow more than 20% annually. According to consulting firm Dell’Oro Group, annual spending could surpass $1 trillion by 2030.
Companies providing technology infrastructure inside data centers will benefit from rising capital expenditures including:
→ Data Storage Infrastructure
Generative AI systems learn by identifying patterns within large datasets. These models require highly efficient data storage solutions to manage vast training datasets, model parameters, and intermediate outputs. Scalable, high-performance storage infrastructure is essential to handle the growing volume and velocity of data produced by AI applications.
A standard storage device may hold around 1 terabyte of data. Inside modern data centers, there are thousands—and in some cases, millions—of these devices. They are critical to the performance and advancement of AI systems and their applications.
→ Connectivity Equipment Infrastructure
Connectivity infrastructure includes network switches and fiber optic cables—the backbone of intra-data center communication. Network switches enable rapid, efficient data transmission across devices. Modern AI applications require ultra-fast connections to move massive amounts of data with minimal latency.
Gigabit Ethernet switches are the standard for high-bandwidth applications. These switches act as foundational building blocks, connecting devices and facilitating communication within the data center. Today’s hyperscale data centers may contain hundreds of thousands of network switches and miles of fiber optic cabling.
→ Data Center Cooling Infrastructure
Cooling infrastructure is essential to prevent critical IT systems from overheating. As AI workloads grow, so does the heat output from high-density servers.
Cooling solutions include:
- Air Conditioning (A/C) – the most common method, but increasingly challenged by high-density heat loads
- Liquid Cooling – circulates coolant through a closed loop to absorb and remove heat from servers
- Immersion Cooling – submerges components directly in non-conductive fluids for efficient heat transfer
As servers become denser to meet AI performance demands, traditional air cooling becomes insufficient. Liquid and immersion cooling are emerging as critical solutions for managing thermal loads in next-generation AI data centers.
Conclusion: AI Infrastructure = The New Economic Moat
Artificial intelligence is reshaping the global economy, but its success hinges on far more than code alone. There is no AI without infrastructure. Data center, energy, and technology infrastructure are the foundation of the AI revolution. As demand for AI applications accelerates, so too will the need for reliable infrastructure. Those who invest in and build this foundation won’t just support the future of AI—they’ll help define it.
Understand the infrastructure driving AI—and how to access the opportunity.
Explore the Tortoise AI Infrastructure ETF (TCAI), focused on the critical infrastructure—
energy, data centers, and technology—that powers the AI revolution.
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