AI’s next battleground isn’t just capacity — it’s efficiency
Demand for AI continues to accelerate, further straining data centers. To support the growth, technology companies plan to spend more than $380 billion on AI infrastructure in the next 12 months. However, despite the tsunami of capital investment, building the largest capacity doesn’t ensure success. Instead, it requires optimizing performance and resilience along with the ability to scale.
Raw horsepower alone will not determine who wins in the AI era; it will be the operators who extract the most from every square foot of infrastructure and chip who will triumph. This requires data center providers to broaden their focus beyond capacity and incorporate the following three pillars:
- From lab to reality: Testing with production emulation
As AI data centers increase in complexity and capacity, traffic emulation is essential for validating performance under realistic conditions. It’s not enough to rely solely on component-level validation; operators must simulate system-level AI traffic patterns to ensure their infrastructure is up to the task.
This requires production-grade emulation to bridge the gap between the lab and real world environments. By duplicating how Al workloads behave across nodes, protocols, and failure conditions, operators gain a more accurate view of how their infrastructure performs under stress. This helps identify and address issues such as bottlenecks, incompatibilities, or edge-case failures before scaling or upgrading an Al cluster. This reduces the risk of issues in production, shortens rollout timelines, and improves ROI.
Additionally, emulation allows operators to model future scenarios — such as scaling existing loads or introducing a new type of AI accelerator — before making the investment.
2. Optimize workloads for reliability and energy savings
AI is power hungry. By 2028, data centers are expected to consume 12% of the US electricity, equivalent to powering 55 million homes. If left unchecked, this could drive up costs, strain electrical grids, and stall sustainability goals.
The tasks AI carries out differ significantly in terms of the compute intensity, memory usage, and latency. Efficiently supporting this requires avoiding overprovisioning and reducing wasted energy. Data center providers need to dynamically allocate resources to optimize power efficiency and energy management. This necessitates simulating and monitoring these requirements under real AI loads to find ways to optimize and reduce power consumption.
With these insights, providers can then move non-urgent model training to off-peak hours, helping to smooth out demand and secure cheaper rates. Given data centers’ high energy consumption, the ability to better manage fluctuations is significant. Operators can further improve performance through power management testing, detecting issues such as crosstalk, ripple, and electromagnetic interference. Other strategies, including the use of design automation and digital twins, can optimize thermal performance.
AI has a crucial role to play in fine-tuning data center infrastructure by continuously adjusting performance-to-power ratios. Outages can be avoided by monitoring workload distribution and proactively rerouting traffic away from nodes that show signs of failure-improving reliability. This helps reduce operational costs, freeing up budget for more innovation, while sustainability metrics improve in parallel.
3. Overcoming networking constraints
As AI grows increasingly complex, networking is emerging as a key constraint, as speed determines performance. Networks need to deliver higher throughput, lower latency, and better fault tolerance to support AI’s demands.
A 2025 survey by Heavy Reading on behalf of Keysight Technologies revealed that 22% of data center providers are already trialing next-generation 1.6T Ethernet solutions to support AI models, like DeepSeek and Grok 3. Additionally, a further 58% are currently evaluating Ultra Ethernet to improve network performance. This shift to optimize networks is reflected by the fact that 55% of operators have already deployed 400G interconnects, which provide extremely high-bandwidth connections between data center components.
In addition, integrating telemetry and analytics into the network enables providers to gain visibility, detect imbalances, and dynamically reconfigure routes, helping better support AI workloads. This reduces network bottlenecks, which can throttle model training or cause inference delays.
Optimization and capacity
As pressure mounts on data center operators to scale faster and more efficiently, the race is not simply building the largest capacity, but designing, testing, and operating infrastructure in a way that maximizes performance, resilience, and sustainability. Smart investments and smarter operations are the keys to success. The industry’s ability to build, orchestrate, optimize, and predictably scale more sustainable data centers will determine the pace of AI innovation.
