Research| $META : Building a NeoCloud and Continuing to Rent Capacity are not Contradictory Not a contradiction: Meta considering external commercialization of surplus AI compute does not mean its AI compute demand has peaked. Meta is still locking in major new capacity, including roughly 1.6 GW from Crusoe data centers in Childress, Texas, and Warrenton, Missouri. Generational compute shift: Meta’s large H100/H200 fleet remains valuable for inference, fine-tuning, enterprise model serving, image/video generation, and traditional ML. But for 3T+ parameter MoE models, long context, multimodal training, and RL-heavy post-training, GB200/GB300 and future Vera Rubin systems offer better economics for frontier training. Tiered AI infrastructure: The AI compute market is moving from a single GPU shortage into multi-generation, tiered pricing and usage. GB300/Rubin-class systems remain scarce for frontier model training, while H100/H200 can shift toward inference, hosted models, agent workloads, and external compute monetization. Supply chain implication: Meta building a NeoCloud is not a bearish signal that demand has collapsed. It suggests GPU fleets are becoming financialized, multi-generation assets: older GPUs do not go to zero, and next-generation training compute remains scarce. Detailed Report