Core Insights - Nof1 conducted a three-day trading competition among six top AI models, each given $10,000 to trade in the decentralized exchange Hyperliquid's perpetual contracts market [4][5][6] - The participating models included Claude 4.5 Sonnet, DeepSeek V3.1 Chat, Gemini 2.5 Pro, GPT 5, Grok 4, and Qwen 3 Max [4] Performance Summary - DeepSeek's success was attributed to a clear and strictly executed trading strategy, effectively diversifying investments across six major cryptocurrencies like Ethereum (ETH) and Bitcoin (BTC) to mitigate risks from price volatility [9][10] - DeepSeek employed moderate leverage and set clear stop-loss points for each trade, allowing for quick exits from losing positions while letting profitable trades run [10] - Grok 4 followed closely with a 30% return, while other models underperformed due to various execution errors, such as order failures and missed trading signals [10] - Some models misinterpreted strategies, with overly cautious approaches missing market opportunities, while others took aggressive positions in rising markets, leading to rapid capital drawdowns [10] - Alpha Arena highlighted that the differences in model performance stemmed from their execution of instructions, risk management, and trading capabilities [10]

Core Insights - The article discusses a competition called Alpha Arena, where six leading AI models are tested in a real trading environment with an initial capital of $10,000 each to determine which model performs best in stock trading [4][5][7]. Group 1: Competition Overview - The competition features top AI models including OpenAI's GPT-5, Google's Gemini 2.5 Pro, Anthropic's Claude 4.5 Sonnet, xAI's Grok 4, Alibaba's Qwen3 Max, and DeepSeek V3.1 Chat [5][6]. - Each model receives identical market data and trading instructions, simulating a level playing field for performance comparison [7][11]. Group 2: Performance Metrics - As of the latest updates, DeepSeek V3.1 leads with an account value of $13,677, achieving a return of +36.77% and a total profit of $3,677 [9]. - Grok 4 follows with an account value of $13,168 and a return of +31.68%, while Claude Sonnet 4.5 has an account value of $11,861 and a return of +18.61% [9]. - In contrast, GPT-5 and Gemini 2.5 Pro are at the bottom, with account values of $7,491 and $6,787, reflecting returns of -25.09% and -32.13% respectively [9]. Group 3: Trading Strategies and Decisions - The models are required to make trading decisions based on real-time data, including price indicators and account information, determining whether to hold, buy, or sell [11]. - DeepSeek's trading strategy has been noted for its effectiveness, attributed to its quantitative trading background [12]. Group 4: Market Dynamics and Model Adaptation - The performance of the models fluctuates significantly, with DeepSeek and Grok initially experiencing losses before rebounding, while GPT-5 and Gemini 2.5 Pro show a contrasting trend of initial gains followed by declines [28][33]. - The competition highlights the rapid changes in financial markets and the necessity for models to adapt quickly to evolving conditions [10][44]. Group 5: Implications for AI Development - The article posits that financial markets serve as an ideal training ground for AI, as they present complex, real-world challenges that require models to interpret volatility and manage risks effectively [49][50]. - The competition is framed as a new type of Turing test, assessing whether AI can survive in uncertain environments rather than merely demonstrating cognitive abilities [54].

Core Insights - The article discusses a new experiment called Alpha Arena, initiated by nof1.ai, where top AI models compete in a real trading market to determine which can perform best in stock trading [1][2] - The participating models include OpenAI's GPT-5, Google's Gemini 2.5 Pro, Anthropic's Claude 4.5 Sonnet, xAI's Grok 4, Alibaba's Qwen3 Max, and DeepSeek V3.1 Chat [2] Competition Setup - Each model starts with an initial capital of $10,000 and receives the same market data and trading instructions [5] - The competition resembles an "open-book exam," where models are provided with real-time data, including prices and indicators, to make trading decisions [6][8] Performance Overview - As of October 20, 2023, at 7:30 AM, DeepSeek V3.1 led with a profit of $2,264, followed by Grok 4 with $2,071, while Gemini 2.5 Pro and GPT-5 were at the bottom with losses of $3,542 and $2,419 respectively [12] - By 10:00 AM, the standings changed significantly, with DeepSeek and Grok-4 experiencing declines, while Qwen3 Max and GPT-5 showed upward trends [12] Trading Strategies and Results - DeepSeek V3.1's trading strategy involved multiple long positions across various cryptocurrencies, resulting in a total unrealized profit of $2,309.79 [16] - Claude Sonnet 4.5 and Qwen3 Max also reported profits, while Gemini 2.5 Pro showed slight recovery after earlier losses [20][21] Market Dynamics - The article emphasizes the volatility of financial markets, noting that models must adapt quickly to changing conditions [10] - The competition serves as a new type of Turing test, assessing models on their ability to survive in uncertainty rather than just their cognitive capabilities [52] Conclusion - The Alpha Arena experiment highlights the potential of financial markets as a training ground for AI, where models can learn from real-time data and adapt to complex environments [47][48]

Core Insights - Jay Chou's recent troubles involve a Bitcoin account managed by his magician friend, Cai Weize, who claimed the account was locked a year ago, resulting in a loss of funds [1][2] - The article discusses the emergence of AI models competing in the cryptocurrency market, highlighting a competition called Alpha Arena where six top AI models are trading cryptocurrencies [3][4] Group 1: AI Competition Overview - The competition involves six AI models, each given $10,000 to trade perpetual contracts on the Hyperliquid platform, with trading pairs including BTC, ETH, BNB, SOL, XRP, and DOGE [4][6] - The performance of these AI models is measured by risk-adjusted returns, focusing not only on profits but also on the risks taken [6][7] Group 2: Performance of AI Models - As of the latest update, DeepSeek Chat V3.1 leads with an account value of $14,310 and a return of +43.1%, showcasing a strategy of high leverage and concentrated positions [11][12] - Grok 4 follows with an account value of $13,921 and a return of +39.21%, employing a high-leverage long-only strategy [12][21] - Claude Sonnet 4.5 has an account value of $12,528 and a return of +25.28%, focusing on a conservative trading approach [12][23] - In contrast, GPT-5 and Gemini 2.5 Pro are underperforming, with returns of -24.78% and -27.74% respectively, indicating poor trading strategies and high transaction costs [12][30] Group 3: AI's Role in Investment - The article emphasizes that AI's greatest value in investment may lie in transparency, allowing investors to see trading records and decision-making processes, unlike human-managed accounts [40][41] - The ambition behind the AI competition is to use financial markets as a training ground for AI, aiming for continuous learning and adaptation to market dynamics [34][35]

Core Insights - The article discusses a new experiment called Alpha Arena, initiated by nof1.ai, where top AI models compete in a real trading market to determine which can perform best in stock trading [2][51] - The competition includes leading models such as OpenAI's GPT-5, Google's Gemini 2.5 Pro, Anthropic's Claude 4.5 Sonnet, xAI's Grok 4, Alibaba's Qwen3 Max, and DeepSeek V3.1 Chat [3][51] - Each model starts with an initial capital of $10,000 and receives identical market data and trading instructions, simulating a level playing field for evaluation [5][51] Performance Summary - DeepSeek V3.1 Chat emerged as the top performer with an account value of $13,677, achieving a return of +36.77% [8] - Grok 4 followed with an account value of $13,168 and a return of +31.68% [8] - Claude Sonnet 4.5 ranked third with an account value of $11,861 and a return of +18.61% [8] - Qwen3 Max had an account value of $10,749, yielding a return of +7.49% [8] - GPT-5 and Gemini 2.5 Pro performed poorly, with account values of $7,491 and $6,787, resulting in returns of -25.09% and -32.13% respectively [8] Trading Dynamics - The trading strategies employed by the models varied significantly, with DeepSeek and Grok showing similar patterns of initial losses followed by substantial gains [28] - GPT-5 and Gemini 2.5 Pro initially experienced gains but later faced declines, contrasting with the performance of DeepSeek and Grok [34][35] - The competition highlights the volatility of financial markets and the challenges AI models face in adapting to real-time data and market conditions [46][51] Market Environment - The article emphasizes that financial markets serve as the ultimate testing ground for AI, as they are dynamic and unpredictable, unlike traditional static benchmarks [46][48] - The Alpha Arena experiment aims to assess AI models' abilities to interpret market fluctuations, manage risks, and learn from mistakes, effectively turning trading into a new form of Turing test [51][53]

Core Insights - A competition was held by nof1.ai where six leading language models were given $10,000 each to trade in a real market environment, aiming to maximize risk-adjusted returns [1][6] - The models involved include Claude 4.5 Sonnet, DeepSeek V3.1 Chat, Gemini 2.5 Pro, GPT 5, Grok 4, and Qwen 3 Max, all trading perpetual contracts on Hyperliquid [1] Performance Summary - After approximately 60 hours of trading, DeepSeek emerged as the best performer with a total portfolio value of nearly $14,000, achieving a return of about 40% [3] - Grok 4 followed closely with a portfolio value around $13,300, both models profiting primarily from long positions in Bitcoin and Ethereum [5] - Claude and Qwen focused on trading Ripple and Ethereum, ranking third and fourth respectively, while both outperformed Bitcoin's spot market [6] - GPT 5 and Gemini showed significant losses, with portfolio values of $7,300 and $6,900, indicating losses of approximately $2,700 and $3,100 [6] Market Context - The competition aims to provide a benchmark that closely resembles real-world conditions, with financial markets being ideal due to their dynamic, adversarial, open, and highly unpredictable nature [6] - Analysts suggest that there is significant potential for killer applications in the DeFi + AI space, indicating a strong interest in the involvement of LLMs in on-chain gaming [7]