Firms like OpenAI and Meta claim a compute shortage while also exploring selling compute capacity. This isn't a contradiction but a strategic evolution. They are buying all available supply to secure their own needs and then arbitraging the excess, effectively becoming smaller-scale cloud providers for AI.

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.

The AI landscape is shifting from exclusive partnerships to a more open, diversified model. Anthropic, once closely tied to Amazon and Google, is now adding Microsoft Azure. This indicates that models are expected to specialize for different use cases, not commoditize, making multi-cloud strategies essential for growth.

AI labs like Anthropic that were conservative in securing long-term compute now face a 'quality tax.' They must resort to lower-quality providers or pay significant markups and revenue-sharing deals for last-minute capacity, a cost their more aggressive competitors like OpenAI avoided by signing deals early.

For leading AI labs like Anthropic and OpenAI, the primary value from cloud partnerships isn't a sales channel but guaranteed access to scarce compute and GPUs. This turns negotiations into a complex, symbiotic bundle covering hardware access, cloud credits, and revenue sharing, where hardware is the most critical component.

While AI training requires massive, centralized data centers, the growth of inference workloads is creating a need for a new architecture. This involves smaller (e.g., 5 megawatt), decentralized clusters located closer to users to reduce latency. This shift impacts everything from data center design to the software required to manage these distributed fleets.

The AI boom has created such desperation for power that hyperscalers now prioritize immediate availability ('time to power') above all else. Cost has become a secondary concern, and sustainability, once a key objective, has fallen far lower on the priority list.

The high-speed link between AWS and GCP shows companies now prioritize access to the best AI models, regardless of provider. This forces even fierce rivals to partner, as customers build hybrid infrastructures to leverage unique AI capabilities from platforms like Google and OpenAI on Azure.

A new category of cloud providers, "NeoClouds," are built specifically for high-performance GPU workloads. Unlike traditional clouds like AWS, which were retrofitted from a CPU-centric architecture, NeoClouds offer superior performance for AI tasks by design and through direct collaboration with hardware vendors like NVIDIA.