AI compute research and GPU market publications

Procurement

The Basis Gap in Compute Acquisition Structures

GPU procurement is not a search for the cheapest hour. Buying, leasing, and reserving capacity each package three exposures differently: replacement-price risk, unused-capacity risk, and residual value. The essay’s thesis is that the hidden variable is basis—the gap between what a contract promises and what a workload later needs. A cheap commitment without reserved machines can be right on rate and wrong on access; a reservation without a matching spec can strand capacity while the buyer still pays for a second, better-fit block. At thousands of GPUs those mismatches become project-finance problems, because collateral values and contracted cash flows move on different clocks. The market question is which risks a fleet owner should keep, and which to push to a lessor or lender, when hardware, utilization, or demand diverge from the term sheet.

Compute markets

The AI Chip Wars: From Supplier Dominance to Contested Equilibrium

Headlines about OpenAI training on Amazon Trainium, or NVIDIA licensing Groq inference designs, invite a winner-take-all reading of the chip race. The essay’s thesis is narrower: large buyers are building credible outside options so they can contest pricing power even if NVIDIA remains the performance leader. Hyperscaler silicon does not need to win every workload; it needs to be viable enough to change contract terms, optionality, and where margins sit in the stack. Competition will appear first in cloud pricing and flexibility, not in merchant-GPU share. The market question is how to budget and hedge GPU-hours when the price of compute fragments across architectures, regions, and bargaining positions rather than remaining a single supplier quote.