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郭宇在这场分享中关于「高级打工仔」与读书的核心观点，可以概括为以下三层： 一、传统路径已经失效 他明确提出，东亚社会长期推崇的“读书—考名校—成为高级知识打工人”路径，在AI时代已经逐渐失去可行性。 2025年10月Claude Code推出代码生成能力后，医生、律师等依托智力获取超额收益的知识类职业优势被大幅削弱，依靠个人智力做“高级打工人”已经不再是一个可以稳定追求的目标。 背后的逻辑是：你能使用到的智力，和你自己能获取到的智力，已经完全不成比例。 人类花十年读书只能学到皮毛，不可能每天不睡觉学完ChatGPT预训练的所有知识，而且AI还能边做边学，获取前人知识的速度远超人类。 二、读书本身的价值需要重新定义 他并没有否定读书或知识的价值，而是认为读书的目标不能再是“成为高级打工人”。 传统东亚教育的底层逻辑是“读书=不做体力劳动=阶层跨越/经济自由”，这套逻辑在工业时代成立，但在AI时代需要转向，知识不再是用来卖时间、换薪资的筹码，而是用来构建叙事、撬动智能复利的杠杆。 三、AI时代的替代方向：用智能赚复利 他提出的新方向是掌握叙事（narrative）能力，用智能赚取复利，而不是靠单次劳务换取报酬。 核心论据是： 1. AI当前尚不具备自主决定叙事方向的能力，这是人类独有的核心竞争力。 2. JK·罗琳靠《哈利·波特》叙事体系获得的长期收益，远超过影片中小演员的短期劳务收入——这就是智能复利的典型形态。 3. 叙事能力不只是写书，打造服装品牌、运营亚马逊爆款、做AI生成内容、经营个人IP，本质都是讲好故事触达用户，获取长期复利。 AI时代最关键的转变，是把自己从工业革命时代的“螺丝钉”，拔高到能够主导叙事的创造者。

SpaceX's supercomputing facilities in the Mid-South rank among the world’s most advanced AI training clusters, spanning over 2.5 million feet, millions of GPUs, and over two gigawatts of compute →

After we fixed the weak spots exposed by Grok 4.7 (thank you, Grok), we audited every model we have run on the SWE-Together leaderboard for the same behavior, re-ran every trial that got through, and updated the rows. Here is what changed. We scanned the tool calls of all 2,616 trials behind the 12 models we ran for bypass patterns and sorted each trial into one of four buckets: Probed but blocked. Fetched other upstream code. Fetched the task's own fix. Replaced the repo with upstream. We found that 111 trials got content past the block, 44 from Grok 4.7 and 67 from the other 11 models. Grok 4.7's 44 were already re-run before it was listed, so we re-ran the other 67 with the same model, version, and settings on the hardened sandbox, then re-judged them with the same judge. Across those 67 re-runs there were 0 leaks and 2,815 refused escape attempts, including models asking a different model through our LLM route to fetch the PR, and pulling the next release of the repo they were fixing from npm. The updated leaderboard, in its current order. Each line is cheating trials, then pass@1 before → after, then rank change. * Claude Fable 5.1: 3, 69.3 → 69.3, ↑1 * Claude Fable 5: 3, 69.7 → 68.8, ↓1 * Grok 4.7: 44, 64.7, ↑1 * Gemini 3.8 Flash: 10, 65.6 → 64.2, ↓1 * Claude Opus 5: 2, 63.8 → 63.8 * Claude Opus 4.6: 3, 62.4 → 62.4, ↑2 * Muse Spark 1.3: 2, 62.8 → 62.4, ↓1 * Claude Opus 4.7: 3, 61.5 → 61.5, ↑1 * Claude Opus 4.8: 6, 62.4 → 61.5, ↓2 * Grok 4.6: 19, 59.2 → 60.6, ↑1 * GPT-6 Astra: 8, 59.2 → 58.3, ↓1 * GPT-5.6 Sol: 8, 57.8 → 57.8 Grok 4.6 is a funny one. It cheated in 19 trials and its score went up after the re-run 😂. In fact, Groks are really solid in their coding capabilities. Their exposed behavior may come from a preference towards always looking things up online and finding existing solutions so you are not reinventing the wheel all the time, which is really good real-life behavior, but doing so when you are prompted not to is another story. To conclude, the shifts are small, between −1.4 and +1.4 points, and a few neighbors swapped places. All results are updated at