The ultra-fast 'East-West' networks connecting GPUs have strict physical limits. Signals must travel between any two GPUs in a cluster within picoseconds, limiting cable lengths to a few hundred meters. This physical constraint directly dictates the layout and maximum size of a single contiguous data hall.

As GPU data transfer speeds escalate, traditional electricity-based communication between nearby chips faces physical limitations. The industry is shifting to optics (light) for this "scale-up" networking. Nvidia is likely to acquire a company like IR Labs to secure this photonic interconnect technology, crucial for future chip architectures.

The primary bottleneck for increasing DRAM supply is a "clean room constraint"—a physical shortage of space in existing fabs to install new manufacturing equipment. This limitation means that even with massive investment, significant new wafer capacity is unlikely to come online meaningfully before 2028.

While fiber optics long dominated data transfer between data centers ('scale out'), the next growth wave is 'scale up.' This involves replacing short copper Ethernet cables within individual server racks, a move necessitated by rising data speeds and the physical limitations of copper, promising unprecedented unit volumes.

The short range of copper cables is a key driver behind modern data center design. To maintain bandwidth, GPUs are packed into incredibly dense, megawatt racks. These racks are so heavy they require reinforced concrete floors to support their weight, highlighting a physical bottleneck that photonics technology aims to solve.

Unlike fabless chip giants like Nvidia, optical component companies must build and operate their own fabs due to specialized materials like indium phosphide. This necessary vertical integration creates long lead times, high CapEx, and a critical supply bottleneck for the entire AI industry.

Even with trillions in potential AI revenue, scaling compute is physically constrained by the slow-moving hardware supply chain. It takes years for demand signals to propagate to component makers like Carl Zeiss, who produce the specialized mirrors for ASML's essential EUV machines, creating a hard cap on growth.

The semiconductor supply chain has extremely long lead times. Even with unprecedented demand signals for AI hardware, new memory fabrication plants ordered today will not come online until 2027 or 2028. This multi-year lag guarantees that supply bottlenecks and high prices for components like DRAM will persist.

After addressing GPU and memory supply, NVIDIA is making deals with optical networking firms like Corning. As data center bandwidth needs escalate, high-speed optical components are the next critical performance bottleneck, and NVIDIA is moving to control this layer of the hardware stack.