Overview

This chat unpacked the “AI supercloud”—a new, AI-specialized cloud built for insane scale (think gigawatt-class data centers) and tighter, denser infrastructure than yesterday’s clouds. Roman (Nebius) framed three big workload buckets—frontier pre-training, post-training/fine-tuning, and inference—and argued the platform layer is being rethought so developers can fine-tune, do RL, and run low-cost, high-throughput inference more easily. Open source models are pivotal: teams often prove a use case with closed models, then switch to tuned open models for cost, data leverage, and differentiation. Nebius partners with hyperscalers (e.g., big GPU builds) to fund broader, multi-tenant services, and sees adoption patterns diverge: startups chase speed on AI-specialized clouds; enterprises start on hyperscalers and offload AI-heavy work where capacity, performance, or economics demand. With regulation and sovereignty driving regional build-outs—and on-prem getting harder as chips and facilities evolve—the near-term reality looks hybrid and federated: use the provider that has capacity now, meets compliance needs, and scales with you.