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.

Future Teslas will contain powerful AI inference chips that sit idle most of the day, creating an opportunity for a distributed compute network. Owners could opt-in to let Tesla use this power for external tasks, earning revenue that offsets electricity costs or the car itself.

George Hotz outlines a contrarian AI infrastructure strategy. Instead of expensive enterprise hardware, Tiny Corp plans to use upcoming consumer AMD GPUs, pair them with extremely cheap power in Oregon (~$0.03/kWh), and sell compute tokens on existing platforms. This low-overhead model aims to undercut traditional cloud providers.

Templar's Sam Dare argues the perceived GPU scarcity is misunderstood. The actual bottleneck is the limited supply of the latest, well-connected GPUs in data centers. His project aims to create algorithms that can effectively utilize the vast, distributed network of consumer-grade and older enterprise GPUs, unlocking a massive new compute resource.

Instead of bearing the full cost and risk of building new AI data centers, large cloud providers like Microsoft use CoreWeave for 'overflow' compute. This allows them to meet surges in customer demand without committing capital to assets that depreciate quickly and may become competitors' infrastructure in the long run.

IOTA's technology is designed to work with compute that can be taken away at a moment's notice. This allows it to acquire unused data center time for as little as 10 cents on the dollar—a resource no traditional, synchronous training method can utilize.

xAI is leveraging its massive GPU infrastructure by renting it out to other AI companies like Cursor. This strategy turns a significant cost center into a revenue-generating business, effectively making xAI a specialized cloud provider and creating a new monetization path beyond its own model development, mirroring the AWS playbook.

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.

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.