The most interesting thing about Terafab isn’t how many chips it could produce. It’s what happens when the feedback loop collapses. Today, building an AI processor means coordinating across an enormous distributed supply chain: architecture → foundry → memory → packaging → testing → systems → real-world deployment → new architecture. Every handoff adds latency. Terafab attacks the latency itself. Tesla and SpaceX are building toward vertically integrated logic, memory and advanced packaging because the amount of silicon required for autonomous vehicles, humanoid robots and eventually space-based compute may exceed what the existing semiconductor ecosystem was designed to supply. That creates something much more important than manufacturing capacity: a hardware learning loop. Deploy AI5. Collect real-world inference data. Find the bottlenecks. Change the architecture. Change the memory hierarchy. Change the packaging. Build the next generation. Repeat. Tesla says AI5 has already completed final chip design, with AI5 and AI6 production planned for 2027 and 2028. If Terafab reaches enormous wafer scale, the strategic advantage won’t simply be owning a chip factory. It will be shrinking the distance between learning something about intelligence and physically encoding that lesson into the next generation of silicon. Software already iterates this way. Terafab is an attempt to make hardware iterate more like software. That is the part worth watching.