Welcome to Inder’s Desk. I’m Inder. Today, we’re mapping the network that wires the AI factory together. A GPU that can’t talk to the other ninety-nine thousand, nine hundred and ninety-nine GPUs is a space heater. That’s the entire thesis in one sentence. For the past three years, investors have argued about who makes the best AI chips. NVIDIA. AMD. Google’s TPUs. Amazon’s Trainium. But powerful chips sitting alone are like brilliant musicians who can’t hear the rest of the orchestra. The performance comes from coordination. And that coordination depends on the network. As AI clusters get larger, the connections between the chips are becoming more valuable, more complicated, and more essential. This is the other AI trade. The companies that build the roads, intersections, bridges, and express lanes carrying data through the AI factory. They can get paid regardless of which model wins, and sometimes regardless of which accelerator wins. Before we begin, this discussion is educational. It is not financial advice, and I’m not predicting stock prices. The goal is to understand the technology, the competitive landscape, and why it matters. There is one framework I want you to remember. Copper inside the rack. Optics between racks. Coherent optics between buildings. Three boundaries. Think of an AI data center as a city. Copper handles the short streets within a neighborhood. Optics runs the highways connecting neighborhoods. And coherent optics operates the high-speed rail linking separate cities. Almost every company in AI networking sits somewhere along those three routes. And much of the industry’s competitive struggle comes down to where each boundary falls, how quickly it moves, and who collects the toll. Copper inside the rack. Optics between racks. Coherent optics between buildings. Keep that framework in mind, and the rest becomes much easier to understand. The first thing most people get wrong is imagining an AI data center as one enormous network. It is actually three separate networks, each designed for a different job. The first is called scale-up. Scale-up is the network inside a single rack, where GPUs communicate directly with other GPUs. Imagine seventy-two chefs trying to prepare one enormous meal. It is not enough for every chef to be individually talented. They need to exchange ingredients, coordinate timing, and avoid getting in one another’s way. If communication is slow, the whole kitchen slows down. Scale-up networking is the communication system inside that kitchen. Its goal is to connect a group of accelerators so closely that software can treat them as one enormous computing engine. This is the highest-bandwidth and most tightly controlled layer in the entire data center. NVIDIA’s technology here is called NVLink. The current generation moves roughly one point eight terabytes of data per second, per GPU. A flagship seventy-two-GPU rack can move around one hundred and thirty terabytes per second across the full system. The next generation is expected to increase that substantially. That one-point-eight-terabyte number matters because the connection inside the rack is roughly ten times faster than the network connecting one rack to another. It is the difference between handing a document to the person sitting beside you and shipping it to another office across town. And surprisingly, the connection inside the rack runs primarily on copper. Actual copper wire. We’ll come back to why. The second network is called scale-out. This is also known as the back-end network. It connects one rack to another, turning individual systems into clusters containing ten thousand, one hundred thousand, or eventually even more accelerators. If scale-up turns one rack into a single machine, scale-out turns an entire warehouse into a single computer. This is the central battleground in AI networking. It is where NVIDIA’s proprietary technology competes against the open merchant ecosystem. The third network is the front end. That handles storage, data ingest, system management, and ordinary enterprise traffic. Think of it as the loading dock and administrative office. It matters operationally, but it is not the most differentiated or strategically contested part of the AI network. So our focus is scale-up inside the rack and scale-out between racks. The central scale-out battle is InfiniBand versus Ethernet. In one corner is InfiniBand. InfiniBand is NVIDIA’s proprietary networking fabric. It is purpose-built, lossless, extremely low-latency, and supplied by a single vendor. NVIDIA became the only major commercial supplier after acquiring Mellanox in 2019. Think of InfiniBand as a private railway. One company owns the tracks, the trains, the signaling system, and the stations. Because everything is designed together, the system can run with extraordinary precision. InfiniBand won the first phase of the AI build-out for a straightforward reason. For tightly coupled training workloads, it worked reliably and delivered exceptional performance. In the other corner is Ethernet. Ethernet is the public highway system. Many companies can build the vehicles. Many vendors can supply the roads and traffic-control equipment. Customers are not locked into one operator. But ordinary office Ethernet was not originally designed for tens of thousands of GPUs trying to communicate simultaneously. That would be like putting Formula One cars onto suburban streets and wondering why traffic backs up. So the industry began rebuilding Ethernet for AI. The Ultra Ethernet Consortium brings together much of the non-NVIDIA ecosystem, including AMD, Broadcom, Arista, Cisco, Meta, Microsoft, and Oracle. Its purpose is to make Ethernet behave more like a purpose-built AI fabric while preserving the benefits of an open, multi-vendor standard. A newer approach called M R C was also introduced by a group including OpenAI, Microsoft, Broadcom, AMD, and, notably, NVIDIA itself. Its goal is to create much larger and more efficient switch configurations, scale beyond one hundred and thirty thousand computing engines, and reduce the number of switches required by roughly sixty percent. Imagine replacing a maze of connecting flights with one enormous airport hub. Fewer stops. Fewer handoffs. Less equipment. Lower cost. Now here is the data point that captures the direction of the market. In the first quarter of 2026, data-center Ethernet switch revenue grew sixty-one percent year over year, surpassing ten billion dollars. And the number-one vendor in data-center Ethernet was NVIDIA. Its Ethernet revenue reached roughly two point one billion dollars, nearly three times the prior-year level, placing it ahead of Arista and Cisco. Think about what that means. NVIDIA has the strongest economic interest in preserving its proprietary InfiniBand ecosystem. Yet one of its fastest-growing networking businesses is Ethernet. It is like the owner of the private railway becoming the biggest supplier of trucks for the public highway. That does not mean the railway is disappearing. But it tells you NVIDIA has no intention of watching the open market grow without participating. The current AI back-end market is approximately two-thirds Ethernet and one-third InfiniBand. But this is not a clean victory. InfiniBand revenue also rebounded sharply during the same period. NVIDIA is not abandoning its proprietary fabric. It is playing both sides of the board. This is a long competitive grind, not an overnight displacement. Ethernet is gaining ground for three main reasons. First, merchant silicon reduces dependence on a single vendor. Hyperscalers do not want one company controlling the engine, the transmission, the roads, and the tollbooths. Second, Ethernet network designs can be more efficient. Some can reach full cluster scale in three switching tiers, while comparable InfiniBand architectures may require four. Think of each tier as another connection at an airport. Every additional connection requires more gates, more baggage transfers, more time, and more opportunities for delay. Removing one tier can reduce the number of optical transceivers by roughly one-third. And those transceivers cost real money. Third, every hyperscaler wants negotiating leverage against NVIDIA. Even customers that depend heavily on NVIDIA GPUs do not necessarily want NVIDIA controlling every surrounding layer. But inside the rack, NVIDIA remains in a much stronger position. This is the scale-up layer. And so far, the open ecosystem has not cracked it. NVLink is deployed, mature, and approximately twice as fast as the emerging alternatives. NVIDIA has also made a strategically clever move called NVLink Fusion. Instead of reserving NVLink only for NVIDIA-designed systems, the company will license portions of the interconnect so that third-party processors and custom chips can connect to NVIDIA’s fabric. Imagine a country realizing it cannot stop neighboring countries from building their own cars. So instead, it invites all those cars onto its roads and charges them to use the highway. Rather than simply losing customers who develop custom silicon, NVIDIA is trying to pull those chips into its own networking ecosystem. The open alternative is called U A Link. The second version of the standard was published in April 2026. It has broad industry support, including AMD, Broadcom, Google, Intel, Meta, Microsoft, Apple, and Amazon. The architecture is designed to connect as many as one thousand and twenty-four accelerators within a pod. But the current competitive position remains clear. U A Link is a blueprint and early construction. NVLink is a finished bridge already carrying traffic. And NVIDIA keeps extending that bridge while competitors are still completing theirs. The merchant ecosystem is making progress in the open scale-out layer while remaining behind in the proprietary scale-up layer. Now we move between racks. This is where copper runs out of road
