A new category of "NeoCloud" or "AI-native cloud" is rising, focusing specifically on AI training and inference. Unlike general-purpose clouds like AWS, these platforms are GPU-first, catering to massive AI workloads and addressing the GPU scarcity and different workload patterns found in hyperscalers.

Simply adding GPUs to existing cloud infrastructure is insufficient for AI. An AI-centric approach requires re-imagining everything from hardware layout to job orchestration. Forcing AI concepts into legacy application models slows down development and creates unnecessary challenges for teams.

Experience from building general-purpose clouds can create limiting assumptions. True innovation in AI infrastructure requires questioning the established "best practices" of the past decade, recognizing that the core principles that made legacy clouds successful may not apply to this new, specialized world.

AI Infrastructure (AI Infra) solves problems unique to AI/ML, such as managing compute-heavy, GPU-dependent workloads. This marks a shift from traditional infrastructure, which was often more focused on data input/output rather than intensive computation.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

While AI inference can be decentralized, training the most powerful models demands extreme centralization of compute. The necessity for high-bandwidth, low-latency communication between GPUs means the best models are trained by concentrating hardware in the smallest possible physical space, a direct contradiction to decentralized ideals.

The high cost of GPUs means any inefficiency during model training is extremely expensive. This economic reality justifies building specialized, AI-focused infrastructure with features like advanced observability and optimized storage to maximize GPU utilization and prevent costly delays from failures or slowdowns.

Specialized AI clouds (NeoClouds) like CoreWeave emerged because hyperscalers' strengths—such as custom networking and security for multi-tenancy—were detrimental to the performance of large-scale, single-tenant AI workloads. This performance gap created a significant market opening.

AI networking is not an evolution of cloud networking but a new paradigm. It's a 'back-end' system designed to connect thousands of GPUs, handling traffic with far greater intensity, durability, and burstiness than the 'front-end' networks serving general-purpose cloud workloads, requiring different metrics and parameters.