“[硅谷] 是这个星球上最大、也最不稳定的原始资本主义培养皿。” 那么，一位长期生活在这里、并且取得成功的做空投资者能给出什么经验教训？ 作为 Crown Capital Management 的创始人，Scott Fearon 曾做空 200 多家公司的股票。在其著作《Dead Companies Walking（行尸走肉般的公司）》中，Fearon 将自己 30 年来与企业高管会面的经验，提炼为 六种失败模式： 尽管这些公司各不相同，但它们最终都失败了，因为其领导者犯下了我重点关注的六个共同错误中的一个或多个： 只从最近的过去学习。 过度依赖某一套成功公式。 误读或疏远了客户。 陷入群体狂热（泡沫/风潮）。 未能适应行业中的“板块级”结构性转变。 在身体或情感上脱离了一线运营。 从 SaaS 向 AI 的“急转弯”，放大了上述每一种错误。 1️⃣ 只从最近的过去学习 软件行业以 20 年为周期演进：大型机 → 客户端/服务器 → SaaS。 每一波浪潮都会带来牛市；当浪潮退去，增长放缓、估值倍数坍塌。 SaaS 的估值倍数已连续三年持平。 2️⃣ 过度依赖成功公式 销售方式已经发生变化： * 产品驱动增长（PLG）的爆发 * 数百万美元级大单的增多 * 许多围绕效率与配额的旧规则，已经不再适用。 3️⃣ 误读或疏远客户 客户希望成为 AI 原生（AI-native）。 他们愿意为培训与解决方案支付高额费用。 41% 的 AI 支出为新增预算，整体预算正在爆炸式增长。 如果继续销售 已无法解决客户痛点的软件，那就是通往高流失率的捷径。 4️⃣ 陷入狂热 AI 的炒作周期，制造了“赶快上线半成品功能”的压力。 宣布一个 AI 路线图 ≠ 交付真正的价值。 随着长期运行的 AI 代理在 2026 年进入劳动力体系，这种差异将愈发明显。 5️⃣ 未能适应板块级结构性转变 如果这句话在任何时候成立，那就是今天。 6️⃣ 在身体或情感上脱离一线 不每天使用 AI 的团队，会低估变化速度。技术进步太快，季度级战略复盘已经跟不上。 如果领导层没有每天亲自向 Claude 或 GPT 提问，那么他们已经落后。 认知偏差总是最难在自己身上察觉。在这个 AI 时代的关键节点，做空者提供的那面“镜子”，尤其值得一照。 “[Silicon Valley] is the biggest, most volatile petri dish of raw capitalism on the planet.” So what lessons does a successful short-seller living here have? As founder of Crown Capital Management, Scott Fearon has shorted the stocks of over 200 companies. In his book Dead Companies Walking, Fearon distills three decades of meetings with executives into six failure modes : Despite their differences, they all failed because their leaders made one or more of six common mistakes that I look for : 1. They learned from only the recent past. 2. They relied too heavily on a formula for success. 3. They misread or alienated their customers. 4. They fell victim to a mania. 5. They failed to adapt to tectonic shifts in their industries. 6. They were physically or emotionally removed from their companies’ operations. The hairpin turn from SaaS to AI amplifies each. Learning only from the recent past. Software moves in 20-year waves. Mainframe, then client/server, then SaaS. When these waves come, they create bull markets. As they wane, growth flattens & multiples collapse. SaaS multiples have been flat for three years. Relying too heavily on a formula for success. Sales motions have changed because of dramatic growth rates in product-led growth & multi-million dollar lands. Many of the rules around efficiencies & quotas no longer apply. Misreading or alienating customers. Customers want to be AI native. They are willing to pay huge sums for education & solutions. Budgets have exploded with 41% of AI spend net new. Selling the same software application that no longer solves the customer’s pain point is a path to churn. Falling victim to a mania. The AI hype cycle creates pressure to ship half-baked features. Announcing an AI roadmap isn’t the same as delivering value. This distinction will become increasingly stark as long-running agents enter the workforce in 2026. Failing to adapt to tectonic shifts. If ever this were true, it’s true today. Being physically or emotionally removed. Teams who don’t use AI daily miss the pace of change. The technology moves too fast for quarterly strategy reviews. If leadership isn’t prompting Claude or GPT every day, they’re already behind. Cognitive biases are always hard to see in ourselves. A short seller’s mirror is a useful one at this moment in AI.