Jerry Liu 的招聘观点戳中了 AI 时代人才评估的核心矛盾——做招聘或带团队的人，看完会重新思考简历筛选标准，建议点开看看他的完整论述。
Jerry Liu 在 X 上分享了他对 AI 时代招聘的思考，认为在候选人中更看重“斜率”（学习速度、韧性、拼劲）而非纯经验。他指出，AI 大幅压缩了从初级到高级所需的学习时间，传统工作中 80% 的时间花在重复任务上，而 AI 让剩余 20% 的学习效率更高。但他也提出反例：资深工程师的经验能帮助他们更好地利用 AI 写出高质量代码，而初级员工若只依赖 AI 产出而不真正学习，容易产生“虚假产出”。文章核心是：高斜率在动荡市场中胜出，但真正的理解和学习依然关键。
This is a nice article (not sure how I stumbled upon it a month later) I directionally agree with ...
This is a nice article (not sure how I stumbled upon it a month later) I directionally agree with it in that: ✅ I have a massive bias for slope, grit, and scrappiness in candidates vs. pure experience. During interviews I often ask the candidates (across eng, gtm, and others) ad-hoc problems to test how they would reason about new situations. The people that can learn the quickest are those that can use AI to their advantage. ✅ In the pre-AI world of work, I would say 80%+ of time on the job is spent doing routine tasks and <20% is actually learning new skills. When I was a ML researcher, 80% of my time was actually programming PyTorch (repetitive) and <20% was thinking. So the actual amount of pure learning a junior worker needs to get to the senior worker's level of output is probably quite low. And that's shrunk even more with AI. In general, high-slope will win out vs. experience, especially in the current volatile market. Experience may not be as important, but imo learning and understanding is important. Based on this, some pushbacks: * Actual learned experience helps you use AI better. When you are a senior/staff-level engineer, you know what prompts to use to write higher-quality, maintainable code. * For the junior worker to ramp-up quickly, they actually need to use AI to learn and not just produce. it is easy to give the illusion of producing a lot of output when most of it is slop. Jaya Gupta @JayaGup10 x.com/i/article/2047… 🔗 View Quoted Tweet 💬 4 🔄 2 ❤️ 5 👀 999 📊 5 ⚡