Chapter 1: After losing 26 million, I was reborn! My name is Brother Ba Bai. In my previous life, I was a famous super bull in the crypto circle, nicknamed the Commander-in-Chief of the Bulls! When others feared, I added positions! When others cut losses, I bottom-fished! When others were bearish, I went all in long! Bitcoin, Ethereum, altcoins, Nvidia, Philadelphia Semiconductor... Wherever there was a market, there was Brother Ba Bai charging forward. I once vowed: "One day, I will make the whole Wall Street know the name Brother Ba Bai!" Unfortunately, fate played a huge joke on me. The first liquidation, I lost 110,000! I refused to accept it! The second liquidation, I lost 1 million! I kept adding positions! The third, the fourth... Whenever the market crashed, I told myself: "Time is the friend of bulls!" "As long as I don't give up, there will be a chance to turn things around!" However, the market never changes direction because of one person's faith. Until one day, I opened my OKEx account. The screen showed a string of numbers I will never forget. Total loss: 26 million RMB! A full 26 million! The trading empire I once took pride in completely collapsed. Friends who once called me brother disappeared one by one. Those who once praised me began to mock and ridicule. The creditors' calls rang from morning till night. "Brother Ba Bai, when will you pay back the money?" "Brother Ba Bai, didn't you say Bitcoin would definitely rise?" "Brother Ba Bai, where is your bull faith?" In the end, I was driven to desperation by debt, life was...

一边是即将公布的英伟达重磅财报，一边是创始人黄仁勋的个人争议。全球人工智能产业，正在迎来一场备受关注的考验。 最近，华尔街最关注的科技公司之一，就是英伟达。11月17日，英伟达将公布2027财年第三季度财报。这一次，市场不仅关心英伟达能赚多少钱，更关心全球人工智能产业的增长动力究竟还能持续多久。 与此同时，围绕黄仁勋私人生活的传闻也引发了市场讨论。有人担心，这些争议会不会影响英伟达的管理层稳定？会不会拖累股价？甚至会不会冲击整个AI产业？ 但八佰哥认为，大家可能把黄仁勋的个人问题和英伟达的商业价值混为一谈了。我的观点非常明确：第一，英伟达这次财报有望再次超预期；第二，个人争议可能带来短期市场波动，但不太可能从根本上改变英伟达的竞争优势；第三，整个AI产业的发展，不会因为某一位企业家的个人风波而停止。 为什么八佰哥对英伟达这次财报依然保持乐观？首先要看全球科技巨头的AI资本开支。微软、谷歌、亚马逊、Meta等企业，仍然在持续建设人工智能数据中心。它们需要GPU、高速网络和庞大的计算能力，而英伟达恰好处在这条产业链的核心位置。 人工智能正在从最初的大模型训练，逐渐走向更加广泛的商业应用。过