NVIDIA's revenue-sharing deals, which financially backstop GPU purchases for young cloud providers, create a deep dependency. This fosters loyalty to NVIDIA's entire product stack without explicit exclusivity clauses, strengthening its market dominance and creating a powerful, subtle lock-in effect.

Nvidia's staggering revenue growth and 56% net profit margins are a direct cost to its largest customers (AWS, Google, OpenAI). This incentivizes them to form a defacto alliance to develop and adopt alternative chips to commoditize the accelerator market and reclaim those profits.

NVIDIA doesn't simply sell its scarce chips to the highest bidder. It strategically allocates them to cultivate a diverse ecosystem of cloud providers and customers. This prevents any single customer from becoming too powerful and ensures healthy competition among its buyers, which ultimately drives more demand for NVIDIA's hardware.

NVIDIA's new business model involves guaranteeing it will rent back unused GPU capacity from smaller cloud providers. This acts as anchor demand, enabling these 'NeoClouds' to secure financing for massive GPU purchases. It's a strategic move for NVIDIA to build and control its own demand ecosystem, ensuring its chips continue to sell.

Andrew Feldman argues that NVIDIA's investment strategy is a key competitive tactic. By investing in cloud providers and model builders, they create strong incentives for those partners to remain within the NVIDIA ecosystem, making it difficult for competing chip manufacturers to gain a foothold.

Nvidia will likely only revive its ambitions to compete with AWS if its massive hardware profit margins are threatened by competitors like AMD or hyperscalers building their own chips. Only then would Nvidia move up the stack to capture value through an "inference as a service" business model, moving beyond hardware sales.

Despite predictions of commoditization, the AI inference layer remains competitive. The market is supply-constrained, and GPU makers like NVIDIA intentionally avoid customer concentration with hyperscalers, creating space for specialized, innovative providers to thrive.

Jensen Huang strategically allocates GPUs to NeoClouds and new AI labs to prevent a world dominated by a few hyperscalers building their own custom chips (like TPUs). This ensures a diverse customer base and prevents NVIDIA's core products from being commoditized by a handful of powerful buyers.

When all cloud providers offer the same NVIDIA hardware, they are forced to compete on price, eroding margins. By integrating specialized hardware like SambaNova's, they can offer premium, differentiated services—such as faster inference on larger models—allowing them to charge more and improve overall business economics.