While focus is on massive supercomputers for training next-gen models, the real supply chain constraint will be 'inference' chips—the GPUs needed to run models for billions of users. As adoption goes mainstream, demand for everyday AI use will far outstrip the supply of available hardware.

While GPUs dominate AI hardware discussions, the proliferation of AI agents is causing a significant, often overlooked, CPU shortage. Agents rely on CPUs for web queries, data processing, and other tasks needed to feed GPUs, straining existing infrastructure and driving new demand for companies like Arm and Intel.

While energy supply is a concern, the primary constraint for the AI buildout may be semiconductor fabrication. TSMC, the leading manufacturer, is hesitant to build new fabs to meet the massive demand from hyperscalers, creating a significant bottleneck that could slow down the entire industry.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

The critical constraint on AI and future computing is not energy consumption but access to leading-edge semiconductor fabrication capacity. With data centers already consuming over 50% of advanced fab output, consumer hardware like gaming PCs will be priced out, accelerating a fundamental shift where personal devices become mere terminals for cloud-based workloads.

The focus on GPUs for AI overlooks a critical bottleneck: CPU shortages. AI agents require massive CPU power for non-GPU tasks like web queries and data prep. This demand is straining existing infrastructure and creating new market opportunities for CPU makers like ARM.

While the growth of new consumer AI users is slowing into an S-curve, the compute consumption per user is still growing exponentially. This is driven by the shift from simple queries to complex, token-intensive tasks like reasoning and agents, sustaining massive demand for GPU infrastructure.

A speaker theorizes that increased cloud outages are not random. Cloud providers, rushing to buy GPUs for AI, have underinvested in refreshing their general-purpose CPU infrastructure. With CPUs now hitting their 5-year end-of-life and new AI-related CPU demand rising, the system is becoming strained and unstable.

After the current memory crunch, the next AI infrastructure bottleneck will be CPU and networking. The complex orchestration required for emerging agentic AI systems will strain these resources, a trend already visible in companies like Fastly seeing demand spikes just for workload orchestration.