The post GPT-5 on the Verge of Liquidation: Is AI Bad at Crypto Trading? appeared on BitcoinEthereumNews.com. Not your market analyst Crypto too complex for AI Artificial intelligence has been hailed as the next big thing in both analytics and art. But when it comes to trading cryptocurrencies, your average “crypto degen” might beat AI at it. Not your market analyst The latest data from the CoinGlass AI Model Trading Leaderboard shows how chaotic situations can get when general-purpose AI systems are introduced into an unstable market environment. According to the leaderboard, one of the most advanced language models, GPT-5, had an incredible return of -64.22%, effectively wiping out the majority of its trading portfolio and finishing dead last. It experienced the fastest and steepest collapse of all the models that took part, resulting in a total loss of $6,367. Source: Coinglass Others, like Grok and DeepSeek, were able to report modest profits (+3 and 64%, respectively), but GPT-5’s performance clearly shows that trading skill is not solely a function of intelligence. Large language models (LLMs) are designed primarily for text generation, reasoning and problem-solving, not for analyzing high-frequency strategies or interpreting volatile market data. Crypto too complex for AI This is the simplest explanation. Active trading environments require a level of live market awareness, reaction timing and sophisticated risk management that they cannot have because their training data is static and historical. On the other hand, the specialized AI systems used by hedge funds and companies like BlackRock rely on ongoing retraining using real-time data and carefully chosen, domain-specific statistics. Those models are able to identify macrotrends, arbitrage windows and order book imbalances that general-purpose LLMs simply cannot. The leaderboard reflects that AI trading results are pretty much random, just like leaving a monkey to manage a fund. While overfitting patterns can cause some models to quickly spiral into losses, short-term volatility may help some models… The post GPT-5 on the Verge of Liquidation: Is AI Bad at Crypto Trading? appeared on BitcoinEthereumNews.com. Not your market analyst Crypto too complex for AI Artificial intelligence has been hailed as the next big thing in both analytics and art. But when it comes to trading cryptocurrencies, your average “crypto degen” might beat AI at it. Not your market analyst The latest data from the CoinGlass AI Model Trading Leaderboard shows how chaotic situations can get when general-purpose AI systems are introduced into an unstable market environment. According to the leaderboard, one of the most advanced language models, GPT-5, had an incredible return of -64.22%, effectively wiping out the majority of its trading portfolio and finishing dead last. It experienced the fastest and steepest collapse of all the models that took part, resulting in a total loss of $6,367. Source: Coinglass Others, like Grok and DeepSeek, were able to report modest profits (+3 and 64%, respectively), but GPT-5’s performance clearly shows that trading skill is not solely a function of intelligence. Large language models (LLMs) are designed primarily for text generation, reasoning and problem-solving, not for analyzing high-frequency strategies or interpreting volatile market data. Crypto too complex for AI This is the simplest explanation. Active trading environments require a level of live market awareness, reaction timing and sophisticated risk management that they cannot have because their training data is static and historical. On the other hand, the specialized AI systems used by hedge funds and companies like BlackRock rely on ongoing retraining using real-time data and carefully chosen, domain-specific statistics. Those models are able to identify macrotrends, arbitrage windows and order book imbalances that general-purpose LLMs simply cannot. The leaderboard reflects that AI trading results are pretty much random, just like leaving a monkey to manage a fund. While overfitting patterns can cause some models to quickly spiral into losses, short-term volatility may help some models…