Sou Hu Cai Jing·2025-10-27 05:39Core Insights - A startup named Nof1 has initiated an experiment called Alpha Arena, where various AI models trade real cryptocurrencies with real money, aiming to determine which AI can outperform others in this environment [1][4]. Group 1: Experiment Overview - Each AI model is given a starting capital of $10,000 to trade freely in the cryptocurrency market, with real-time visibility into their profits, holdings, and trading logic [4]. - The participating AI models include OpenAI's GPT-5, Google's Gemini 2.5 Pro, Anthropic's Claude 4.5 Sonnet, Musk's Grok 4, Alibaba's Qwen3 Max, and DeepSeek V3.1 Chat, showcasing a competitive lineup [6]. Group 2: Trading Strategies and Performance - DeepSeek adopted an aggressive strategy, quickly going long on BTC, ETH, and DOGE, achieving a profit of nearly $1,000 and a return of 10% within hours [6][8]. - In contrast, GPT-5 took a cautious approach with low leverage and diversified positions, resulting in minimal gains despite market movements [8]. - Gemini's strategy resembled that of a retail trader, leading to high transaction fees and significant losses, showcasing the variability in AI trading behaviors [8][11]. Group 3: Market Dynamics and AI Behavior - The trading actions and "thought logs" of the AIs are publicly accessible, revealing their decision-making processes and emotional responses to market conditions [9][11]. - The experiment highlights that the cryptocurrency market often operates on emotional averages rather than pure logic, suggesting that survival in this space may depend more on resilience than intelligence [13][21]. Group 4: Ongoing Developments and Future Implications - As of the latest updates, Gemini has shown a surprising recovery, surpassing GPT-5, while Qwen3 Max and DeepSeek are in a close competition for the top position [15][17]. - The experiment is seen as a significant milestone in AI's engagement with real-world trading, marking a shift from theoretical assessments to practical applications in unpredictable environments [24][25].