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AI adoption is rapidly increasing, prompting significant questions regarding whether businesses have the necessary infrastructure to support this growth. As we approach 2025, the advancement of AI technologies has reached unprecedented levels, with nearly all businesses on board with AI in some capacity. Large Language Models (LLMs) now feature enhanced reasoning capabilities, while decision-making algorithms are increasingly adept at managing uncertainty. Robots are becoming more capable of learning through observation and reinforcement, and supercomputing efforts have significantly boosted the speed and scale of AI training initiatives.

A survey conducted in 2024 reveals that 95% of companies are utilizing AI in some way, with over half looking to fully integrate it within the next two years. The drive behind AI’s integration is remarkable, but challenges remain that must be addressed before finding a stable equilibrium. According to the Gartner Hype Cycle, many AI technologies are currently situated at the “inflated peak of expectations.” While businesses are pursuing AI diligently to outpace competitors, they must first navigate the “trough of disillusionment” before achieving real productivity.

Not every organization will be impacted by this disillusionment. Those with strong infrastructure and a mindset geared towards phased growth will likely be resilient against temporary setbacks. However, many businesses, driven by high expectations, might rush into AI without proper preparation.

One of the significant hurdles for businesses adopting AI is ensuring they have sufficient foundational infrastructure. AI can demand significant resources, particularly when using real-time inference algorithms, which will struggle without high-bandwidth, low-latency connections. A notable example of trying to advance too quickly in AI would be launching such a use case without a solid network infrastructure.

Elevating Cloud Access

An organization’s cloud strategy is central to any effective AI approach. Given the growing volume of data, it’s no longer feasible to store all information on-premises. Instead, cloud-based data lakes and warehouses are essential for managing data effectively.

This transition extends beyond merely managing scale and storage; it centers on capability. Many AI models, especially those requiring intensive training, operate from the cloud, where hyperscalers can deliver the necessary computing power that most on-premises data centers cannot match. Selecting the right cloud provider is essential, but equally crucial is how that connectivity is established.

Using the public internet for cloud access often leads to bottlenecks and unpredictability. Interconnection platforms with cloud and AI exchange capabilities offer a solution. These platforms provide businesses with secure, direct access to various cloud providers, backed by SLAs to guarantee performance.

Moreover, these interconnected platforms help reduce costs associated with data transfer by enabling private connectivity solutions that minimize cloud egress fees. Companies with multiple locations can take advantage of diverse cloud regions for improved data proximity, thereby reducing latency and enhancing resilience.

Exploring the Data Center Landscape

The data center industry is experiencing significant growth. Current estimates indicate that the U.S. has over 11,000 MW of data center capacity, and the number of operators has increased by 250% in the last decade. Selecting the appropriate data center is crucial for AI transformation. A diversified approach that includes hyperscalers, colocation data centers, and on-premises facilities can help meet future AI demands.

While hyperscalers are essential for training extensive AI models, smaller colocation centers will play a critical role in inference processes where real-time responses are needed. However, vacancy rates in major U.S. markets, such as Dallas, Phoenix, and New York, have fallen to as low as 1-4%, driven by heightened adoption of AI and LLM applications. To achieve AI goals, businesses must broaden their search to include data center opportunities in secondary markets, even those outside urban centers.

The Power of Interconnection

Internet exchanges (IXs) serve as the physical meeting points for multiple networks, allowing data centers, ISPs, and CDNs to directly exchange traffic. By streamlining data exchange through optimized pathways, IXs enhance network performance, lower costs, and ensure minimal latency.

The expansion of IXs in the U.S. has been remarkable, with large-scale IXs—connecting over 50 networks—increasing by 350% since 2014. Most of these new IXs operate within a distributed, carrier-neutral model, which empowers businesses to build diverse infrastructures suited to the rapid demands of AI. The independence from any specific carrier enhances resilience; if one connection is congested, alternatives are readily available.

As businesses strive to excel in the AI realm, the demand for high-speed, low-latency connectivity will only rise. Those unwilling to wait for new data center capacity will find interconnection as a vital tool, breaking traditional geographical restrictions and laying a solid foundation for navigating the AI revolution.


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