Every Robotaxi and Optimus bot needs memory to turn AI models into real-world action. As Tesla scales production, locking down that memory supply is becoming a critical pillar of its manufacturing strategy—and that’s precisely why Terafab matters. According to Counterpoint Research, DRAM demand for humanoid robot "brains" is projected to surge roughly twentyfold between 2026 and 2030, reaching 21,500 TB (21.5 petabytes). As robots grow more capable and production ramps up, robotics will emerge as another major demand driver for memory makers. Autonomous driving adds even more pressure. Micron notes that Level 4 and higher autonomous vehicles typically require over 200 GB of memory—more than ten times what today’s Level 2+ and Level 3 vehicles use. Meanwhile, Goldman Sachs estimates the global commercial robotaxi fleet could reach roughly 1 million vehicles by 2030 and 6 million by 2035. At an estimated 200 GB per vehicle, those fleets would represent about 200 petabytes and 1.2 exabytes of cumulative onboard memory, respectively. While that reflects total installed memory across active fleets rather than annual chip demand, it underscores the staggering hardware footprint underpinning autonomous driving. Leading DRAM suppliers already operate at massive scale. SemiAnalysis estimates that Samsung, SK hynix, and Micron will close out 2026 with monthly wafer capacities of roughly 720,000, 595,000, and 385,000, respectively. Combined, that comes out to about 1.7 million wafers per month—or roughly 20.4 million a year. Yet those same fabs must also supply AI data centers, smartphones, PCs, and the broader automotive market. Because High-Bandwidth Memory (HBM) consumes significantly more wafer capacity per bit than standard DRAM, competition for cleanroom space and production lines is fiercer than ever. Even with planned capex expansions, Micron expects memory supply to tighten further through 2027 and 2028. Tesla’s internal targets illustrate just how high the stakes are. Its Q2 2026 update pegs installed Cybercab capacity at over 125,000 vehicles annually. For Optimus, Tesla has outlined an initial Fremont line slated for 1 million units per year, with a second-generation line in Texas targeting a long-term run rate of 10 million. Delivering on those ambitions will demand a silicon supply chain capable of feeding millions of autonomous systems every year. Terafab is engineered for that scale. With a stated target of 1 terawatt of compute output annually, the project is designed to power both terrestrial and orbital workloads—explicitly earmarking AI5 for FSD and Optimus, AI6 for next-gen Optimus, and D3 for space-based computing. By co-locating logic, memory, and advanced packaging under a single roof, Terafab would hand Tesla end-to-end control over the hardware stack powering its autonomous future. Rather than competing with Big Tech for external fab allocation and packaging lines, Tesla could synchronize processor yields, memory procurement, and packaging directly with vehicle and bot production schedules—all while shrinking design-to-silicon iteration cycles. If Tesla pulls this off, the payoff is clear: more Robotaxis on the road, more Optimus units on factory floors, and a vertically integrated silicon engine built to sustain them both.