According to @karpathy, AI should be viewed as Software 2.0 that optimizes programs against explicit objectives, making task verifiability the primary predictor of automation readiness, source: @karpathy on X, Nov 16, 2025. He states that verifiable tasks are those with resettable environments, efficient iteration, and automated rewards, enabling gradient descent or reinforcement learning to practice at scale, source: @karpathy on X, Nov 16, 2025. He adds that such tasks progress rapidly and can surpass top experts in domains like math and code, while creative and context-heavy tasks lag, source: @karpathy on X, Nov 16, 2025. Interpreted for trading, crypto workflows with clear, checkable outcomes such as strategy backtests, execution slippage minimization, market making simulations, and on-chain anomaly detection align with the verifiable category and are thus more automatable under this framework, source: interpretation based on @karpathy on X, Nov 16, 2025. Conversely, discretionary macro narratives and multi-step fundamental synthesis without fast feedback are less automatable near term, shaping where AI edges may emerge across BTC and ETH trading pipelines, source: interpretation based on @karpathy on X, Nov 16, 2025.