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.

New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.

Specialized AI cloud providers like Nebius don't aim to push alternative chips like AMD or TPUs. Instead, they are "market catchers," responding directly to overwhelming customer demand, which is currently focused entirely on NVIDIA. This demand-driven approach dictates their hardware strategy.

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.

AMD's success isn't just about stealing market share from competitors. The rise of 'agentic inference' in AI is massively expanding the total addressable market for data center CPUs. This creates a "share-grabbing" scenario where new demand provides greenfield growth opportunities for all major players.

The current GPU shortage is a temporary state. In any commodity-like market, a shortage creates a glut, and vice-versa. The immense profits generated by companies like NVIDIA are a "bat signal" for competition, ensuring massive future build-out and a subsequent drop in unit costs.

The value unlocked by frontier AI models is expanding so rapidly that there isn't enough hardware to meet demand. This scarcity ensures that not just the top lab (like OpenAI), but also second and third-tier competitors, will operate at full capacity with strong margins.

The massive profits NVIDIA earns from its near-monopoly in AI chips act as the primary incentive for its own competition. Tech giants and automakers are now developing their own chips in response, showing how extreme profitability in tech inevitably funds new rivals.

Previously, the bottleneck for AI labs was researcher time, making Nvidia's easy-to-use CUDA ecosystem dominant. Now, the biggest cost is compute capacity itself, creating massive economic incentives for labs to adopt cheaper, even if less convenient, competing chips from AMD or Google.