Recent research from Kaiko, Binance Research, and academic groups reveals that AI models, including neural networks, offer limited predictive accuracy in cryptocurrency markets. Classic machine learning models achieve only 2 to 6 percent accuracy above randomness, while advanced models like LSTM and GRU reach about 8 percent, but only in short test windows. These models struggle with fresh data due to market regime changes, increased noise, and macroeconomic events, leading to significant accuracy drops during high volatility periods. Order book algorithms can explain 15 to 25 percent of micro movements but are effective only on minute horizons with deep liquidity. On-chain signals, such as large stablecoin inflows or significant Bitcoin outflows, provide useful correlations but not guaranteed predictions. Experts recommend using ensemble models, tracking data drifts, and incorporating regime filters to improve AI's effectiveness. However, AI should be part of a broader strategy that includes on-chain data, order book analysis, and risk models to enhance decision-making in crypto trading.