The AI era fundamentally changed data center economics. Traditional servers optimized for general computation and throughput. AI compute requires specialized hardware—GPUs, TPUs, and custom silicon—consuming 10-50x more power per server. A single H100 GPU consumes 700W continuously. A large AI training facility housing 10,000 GPUs requires 7MW of power, equivalent to a small city's electricity consumption.

GPU scarcity has created a new bottleneck in computing. NVIDIA's H100 and A100 GPUs, essential for AI training, have lead times exceeding 6 months. Cloud providers like AWS, Azure, and Google Cloud struggle to meet AI compute demand. This created opportunity for specialized providers like Lambda Labs and RunPod renting GPU access at premium prices. The situation echoes cryptocurrency mining's GPU shortage, but with mainstream economic impact.

Energy consumption has become the limiting factor for AI growth. Training large language models consumes 100-1,000 MWh of electricity per model, costing $100,000-$10,000,000 in energy alone. Inference, when multiplied across millions of users, consumes comparable amounts. Data centers now face hard constraints from power availability, cooling capacity, and grid infrastructure. This is driving geographical shifts—companies seek locations with cheap renewable energy (Iceland, Ireland, US Northwest) or underutilized power grids.

The economics of AI infrastructure are consolidating the industry. Hyperscalers like AWS, Azure, and Google can amortize enormous infrastructure investments across massive customer bases. Smaller competitors struggle to justify similar capital expenditures. This is concentrating AI capability—by mid-2025, most deployed AI models run on infrastructure operated by five major cloud providers. This concentration raises questions about AI accessibility and competitive dynamics.

Specialized silicon is emerging to address AI's unique computational patterns. NVIDIA dominates with GPUs. But AMD's MI-series, Google's TPUs, and custom processors from Meta and Tesla target specific AI workloads. These custom chips reduce power consumption while improving performance for particular tasks. By 2026, custom silicon may deliver 2-3x better price-to-performance than general GPU solutions for specific workloads.

Cooling and power distribution are often overlooked but critical infrastructure challenges. Traditional data centers dissipate heat through air cooling. High-power density AI clusters need liquid cooling. Immersion cooling in dielectric fluids can reduce cooling costs by 40% while increasing hardware density. New construction follows different design principles—specialized for high-power density and optimized for heat dissipation rather than traditional enterprise server layouts.

The return of edge compute and on-premise inference is partly a response to AI infrastructure centralization. Organizations want to run models locally to reduce latency, improve privacy, and avoid cloud costs. This is enabling smaller processors optimized for inference—mobile SoCs, embedded systems, and edge devices increasingly run neural networks locally. The split is emerging: hyperscalers handle training and large-scale inference, while edge devices handle local inference.

Future infrastructure trends point toward optimization and specialization. Liquid cooling will become standard in large facilities. Custom silicon will proliferate, competing with NVIDIA's dominance. Energy consumption will drive further consolidation—only companies willing to invest heavily in power infrastructure can compete in AI. The organizations building the 'picks and shovels'—specialized cooling, power management, custom silicon—may capture more value than the cloud providers themselves.