Industry observers say that while AI-driven crypto trading has not yet reached a broad mass adoption akin to an iPhone moment, automated trading agents are nearing a critical tipping point as models become more capable of autonomous decision-making.

Advances in algorithm customization and reinforcement learning enable a new generation of models to balance risk and return using risk-adjusted metrics such as the Sharpe ratio, maximum drawdown, and Value at Risk (VaR), beyond pure P&L.

In recent AI trading competitions, bespoke agents with tailored logic and enhanced data inputs significantly outperformed generic large models, illustrating the performance edge of specialized tooling over standard models when trading autonomously.

However, the democratization of AI trading raises questions about Alpha decay. The enduring beneficiaries are likely those who can build proprietary tools and deploy an intelligent investment portfolio manager driven by AI, while preserving user-defined strategy and risk settings.