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Most current evaluations treat agent memory as a black box, measuring only end-to-end task success. This paper takes a data management perspective and decomposes agent memory into four core modules:
• Representation & Storage
AI data centers are hitting hard physical limits — not just from chips or power supply, but from heat.
Modern AI GPU racks now draw 120 kW+, far beyond what traditional air cooling was designed to handle. Air can only move so much heat per cubic meter. Once racks cross ~50 kW, physics starts winning: chips overheat, throttle, or fail — not because the hardware broke, but because the cooling medium hit its ceiling.
AI compute is a massive, growing category primed for financialization. Yet a true futures market has not emerged. A rigorous framework reveals exactly why.
Five historical conditions precede every major futures market (oil, electricity, etc.):
Frontier AI labs are not competing on the same playing field. Each has a unique identity that creates its own structural bottleneck.
OpenAI serves over 900 million monthly active users, mostly consumers. This produces a severe usage-mix problem: high volume of low-value prompts drives enormous inference costs with low revenue per token.
