@Pyth Network directly connects "price + credibility" to market making and rate curves
The on-demand updates and confidence intervals of @Pyth Network are not details, but the key interface for implementing "price as risk control curve." An AMM directly binds the rate function to Pyth's confidence interval: automatically adjusting rates and slippage protection when volatility increases, and reverting to enhance transaction efficiency during stable periods; market-making robots use the same confidence interval to adjust inventory bandwidth, avoiding excessive exposure during high uncertainty.
Quarterly comparisons show that unnecessary losses during extreme periods have decreased, while daily trading volume has 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."
The assessment of value 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 sources of market information, but part of protocol resilience.