NVIDIA is launching powerful CPUs like the RTX Spark not just to compete with Apple, but because the primary AI workload is shifting. While GPUs dominate AI training, powerful CPUs are becoming essential for running agentic tools and inference, marking a resurgence for the CPU in the AI hardware landscape.

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.

The AI boom's focus on GPUs has obscured a growing, non-obvious shortage in older CPU technology. The proliferation of 'agentic workloads' is creating significant new demand for CPUs, forcing major cloud providers like AWS to ask internal engineers to limit waste and manage capacity constraints.

The rise of agentic AI, which runs multiple parallel processes, is elevating the CPU's role from a secondary component to a critical 'conductor' for orchestrating GPU tasks, creating new demand and design considerations.

The rise of agentic AI and reinforcement learning is increasing the need for powerful CPUs located near GPUs. Cloud provider Nebius notes CPU requirements can be a high multiple of the GPU count, fueling a new demand cycle.

While GPUs are key for model training, the next AI wave of autonomous agents relies more on CPUs. The task of controlling and orchestrating multiple agents and tool calls is fundamentally a CPU-based process. This is creating a new hardware bottleneck and shifting focus to CPU manufacturers.

The current AI boom focuses on GPUs for "thinking" (Gen AI). The next phase, "Agentic AI" for "doing," will rely heavily on CPUs for task orchestration and memory for context, creating new investment opportunities in this previously overshadowed hardware.

The transition from chatbots to autonomous 'agentic' AI represents a fundamental step-change. These agents, which execute complex tasks independently, have already increased the demand for computational power by 1000x, creating a massive, ongoing need for new infrastructure and hardware.

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.