@Pyth Network connects "price + credibility" directly to market making and rate curves
@Pyth Network 's on-demand updates and confidence intervals are not details, but rather the key interface for implementing the "price as risk control curve".
An AMM directly binds the rate function to Pyth's confidence intervals: when volatility amplifies, it automatically raises the rates and slippage protection, and when stable, it lowers to improve transaction efficiency; market-making robots adjust inventory bandwidth using the same confidence intervals, avoiding excessive exposure during high uncertainty. Quarterly comparisons show that unnecessary losses during extreme periods have decreased, while daily transaction volumes have not been suppressed. For lending and derivatives, IM/maintenance margin and funding fees can also adapt according to "price ± confidence interval"; this brings parameter updates back from governance processes to "verifiable formulas".
Value assessments should focus on the availability during extreme periods, the number of covered protocols, and the adoption ratio of risk control components; when "price + credibility" becomes the default input, oracles are no longer just market sources, but a part of protocol elasticity.