从 SaaS 到 AI 转型：做空者总结的六种公司死法

In short : A hedge fund manager's six failure modes for dying companies apply with amplified force during the SaaS-to-AI transition. Cognitive biases make these patterns hard to see in ourselves.

“[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.

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