The advancement of AI in trading is moving closer to a significant breakthrough, often referred to as the “iPhone moment” for machine learning in the crypto sector. Recall Labs recently conducted experiments involving specialized AI trading tools and large language models (LLMs) like GPT-5, DeepSeek, and Gemini Pro. These tests revealed that customized AI agents surpassed LLMs by effectively managing risk and reward across diverse market conditions.
In the realm of traditional finance, entities like hedge funds and family offices are at the forefront of investing in bespoke AI trading solutions to gain a competitive edge. Although the widespread adoption of AI-powered trading tools has not yet reached its peak, industry experts predict a future where algorithmic portfolio managers driven by AI will become commonplace.
Unlike scenarios where AI excels, such as self-driving cars interpreting traffic signals, predicting market trends remains a challenging endeavor requiring continual refinement of AI models. Success in this domain is typically measured by profit and loss (P&L), but recent advancements in algorithm customization have enabled agents to adapt dynamically to varying market conditions while considering factors like the Sharpe Ratio.
The recent trading competition on Hyperliquid decentralized exchange showcased the varying performance levels of LLMs and customized trading agents. Custom models emerged as top performers, outshining foundational models by incorporating additional logic, inference methods, and data sources. The rise of democratized AI-based trading raises concerns about diminishing alpha if everyone adopts similar sophisticated machine-learning technologies.
To capitalize on the advantages offered by AI trading tools, it is crucial for stakeholders with sufficient resources to invest in developing tailored solutions. Such custom tools are often kept private to safeguard their alpha-generating capabilities. This trend mirrors practices seen in traditional finance, where high-quality proprietary tools are closely guarded by hedge funds and family offices seeking to maintain a competitive edge.
In the evolving landscape of AI-powered trading, striking a balance between automated portfolio management and user input is key. The ideal scenario involves offering users some control over their trading strategies within a structured framework that leverages AI capabilities for enhanced decision-making processes.