According to @data_and_ai, out-of-the-box PyTorch models continue training even when the underlying infrastructure experiences failures, raising concerns about model reliability and consistency in AI-driven crypto trading systems (source: @data_and_ai). This persistent training behavior could result in unreliable trading signals for cryptocurrencies like BTC and ETH, potentially increasing risk for algorithmic traders relying on AI-powered strategies.

According to Andrej Karpathy, a reproducible AI-related bug spotted on Reddit could impact the reliability of trading algorithms that depend on AI-generated signals (source: @karpathy on Twitter). Although the issue is not 100% reproducible, its frequent occurrence highlights potential risks for crypto traders using automated systems. This development underscores the necessity for robust error monitoring and risk management in algorithmic crypto trading strategies.