Crypto's Machine Learning 'iPhone Moment' Approaches as AI Trading Agents Mature
Recall Labs recently conducted around 20 AI trading competitions comparing foundational large language models (LLMs) such as GPT-5, DeepSeek, and Gemini Pro against customized AI trading agents.
The results showed that customized AI trading tools, designed specifically for trading tasks and incorporating risk-adjusted learning targets such as the Sharpe Ratio, maximum drawdown, and value at risk, significantly outperformed the general LLMs in these test environments.
Among the Recall Labs competitions, the top three finishers were all customized AI models, though some models did underperform. A related Hyperliquid contest found that LLMs only barely outperformed the market when operating autonomously with the same prompts, whereas user-submitted agents that added additional logic achieved stronger performance compared to the LLMs.
This trend indicates that hedge funds and family offices able to invest in custom AI trading tools are likely to gain an early edge or "alpha" in the market.
However, experts caution that if many participants deploy the same advanced AI trading agent, the advantage could diminish due to crowding, making proprietary private tools essential for maintaining a competitive edge.
While the industry has not yet reached an "iPhone moment"—a breakthrough making AI trading tools broadly accessible—the outlook suggests the emergence of more accessible, AI-driven portfolio management solutions in the near future. These tools are expected to strike a balance between automation and user control, widening adoption beyond specialized institutions.
