AI companies with the foresight to sign long-term, multi-year compute contracts gain a significant margin advantage. They lock in prices based on past valuations, while competitors are forced to buy capacity at much higher current market rates driven up by the increasing value of new AI models.

CoreWeave dismisses speculative analyst reports on GPU depreciation. Their metric for an asset's true value is the willingness of sophisticated buyers (hyperscalers, AI labs) to sign multi-year contracts for it. This real-world commitment is a more reliable indicator of long-term economic utility than any external model.

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.

Accessing next-generation GPUs at scale is no longer a simple purchase. The market now demands three-to-five-year commitments with a significant portion (20-30%) of the total contract value paid upfront. This makes a company's cost of capital a critical competitive factor in acquiring compute capacity.

Sarah Friar reveals the extreme scarcity of AI compute, stating it's virtually impossible to acquire more for 2026 and very limited for 2027. This forces OpenAI to make capital-intensive bets on data centers now, like their Michigan facility, which won't yield compute until late 2027, just to secure future supply.

The head of AI at Hudson River Trading describes an incredibly competitive market for GPU capacity. Providers offer newly available leases that require a commitment to multi-year contracts for thousands of GPUs by the end of the day. This high-stakes, high-speed environment means buyers cannot be picky about location or terms.

Instead of viewing compute as a cost center, OpenAI treats it as a revenue generator, analogous to hiring salespeople. The core belief is that demand for AI capabilities is so vast that they can never build compute fast enough to satisfy it, justifying massive, forward-looking infrastructure investments.

Unlike past tech booms with short-lived tightness, the current AI infrastructure shortage is intensifying, evidenced by unprecedented multi-year supply commitments extending to 2030. This signals deep, long-term conviction from the world's largest companies that the demand is durable.