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.

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.

To get scientists to adopt AI tools, simply open-sourcing a model is not enough. A real product must provide a full-stack solution, including managed infrastructure to run expensive models, optimized workflows, and a UI. This abstracts away the complexity of MLOps, allowing scientists to focus on research.

Simply "scaling up" (adding more GPUs to one model instance) hits a performance ceiling due to hardware and algorithmic limits. True large-scale inference requires "scaling out" (duplicating instances), creating a new systems problem of managing and optimizing across a distributed fleet.

The current focus on building massive, centralized AI training clusters represents the 'mainframe' era of AI. The next three years will see a shift toward a distributed model, similar to computing's move from mainframes to PCs. This involves pushing smaller, efficient inference models out to a wide array of devices.

To operate thousands of GPUs across multiple clouds and data centers, Fal found Kubernetes insufficient. They had to build their own proprietary stack, including a custom orchestration layer, distributed file system, and container runtimes to achieve the necessary performance and scale.

Anthropic's new offering provides a managed 'harness' and production infrastructure, abstracting away the complex distributed systems engineering needed to run agents at scale. This allows companies to focus on their core business logic rather than DevOps, drastically reducing time-to-market for functional AI agents.

Pre-training requires constant, high-bandwidth weight synchronization, making it difficult across data centers. Newer Reinforcement Learning (RL) methods mostly do local forward passes to generate data, only sending back small amounts of verified data, making distributed training more practical.

In 2019, 99% of workloads used a single GPU, not because researchers lacked bigger problems, but because the tooling for multi-GPU training was too complex. PyTorch Lightning's success at Facebook AI demonstrated that simplifying the process could unlock massive, latent demand for scaled-up computation.