Physical AI and robotics are transitioning from laboratory experiments to real-world applications, bringing with them unprecedented infrastructure challenges. As robots operate in factories, warehouses, and public spaces, the systems that train and deploy them must evolve beyond traditional cloud models. The current infrastructure is not equipped to handle the unique demands of physical AI, where failure isn’t just a system crash—it’s a real-world event with tangible consequences. One of the core challenges is the scarcity and complexity of training data. Unlike large language models trained on internet text, physical AI requires multimodal data—images, video, LiDAR, sensor streams, and motion data—that directly reflects real-world interactions. Collecting this data solely in the physical world is slow, costly, and inefficient. Simulation offers a scalable alternative, enabling teams to generate synthetic data, test edge cases, and accelerate development. However, scaling simulation demands significant infrastructure: massive GPU fleets, parallel execution, optimized 3D assets, and specialized hardware configurations. Inference within simulation must run at high throughput, not low latency, creating distinct performance needs that general-purpose clouds struggle to meet. Hardware reliability is critical—system failures during large-scale simulations can derail entire training cycles, making price-performance and uptime essential factors. Another hurdle is data usability. Once deployed, physical AI systems produce vast streams of raw, noisy, and time-sensitive data. Simply storing this data in object storage is insufficient. It must be indexed, synchronized, and organized through automated pipelines to be useful for training. The stakes are high: physical systems must respond in milliseconds, ruling out batch processing. This demands a hybrid architecture where fast inference runs at the edge, while higher-level planning occurs in the cloud—requiring seamless integration and real-time data flow. Data movement has become the bottleneck. Robotics systems generate continuous, high-volume data streams that must be processed and acted on instantly. Most existing platforms are built for batch workloads and falter under sustained, real-time throughput. Simply adding more GPUs isn’t enough if data can’t move efficiently between devices, edge nodes, and the cloud. The cost of transferring data can exceed storage costs, making inefficient scaling prohibitively expensive. True scalability requires infrastructure optimized for high-bandwidth, low-latency data movement and predictable performance. The future of physical AI lies in a purpose-built stack that combines large-scale simulation and cloud training with real-time edge inference and continuous learning. This hybrid model must support seamless coordination across virtual and physical environments. The success of physical AI depends not just on better models, but on infrastructure that enables continuous adaptation, real-time response, and reliable operation at scale. At Nebius, we are building this infrastructure from the ground up. Our platform features optimized price/performance GPUs, high-throughput storage, and managed orchestration tailored to the dynamic needs of robotics workloads. Whether running massive simulation bursts via Slurm or training foundation models on scalable clusters, Nebius provides the foundation for reliable, high-speed development. The race to build the next generation of physical AI is underway. The question isn’t just who has the best model—but who has the infrastructure to bring it to life.