LLMs Are Not a Default Execution Engine
This already has public discussion or multi-source signals — useful for validating traction, contention points, and follow-on impact.
文章以电影《痴迷》为例，指出其主人公熊通过“一愿柳”实现愿望，却最终被愿望所困，这与当前人们使用AI的方式有相似之处。作者认为，AI本身并非危险，但人们在使用AI时，常过度关注结果而忽视过程，导致AI从实现目标的工具变为目标本身。成熟的AI应用团队应衡量AI是否真正改善了结果、降低了成本或被用户使用，而非仅仅关注AI功能的数量。…
Every generation gets its own cautionary tale about shortcuts. For Gen Z, one of the latest is Obsession.
On the surface, it’s a psychological horror film about Bear Bailey, a music store employee, discovers the One Wish Willow-a magical shortcut that promises to make his long time crush, Nikki Freeman, fall in love with him. His wish comes true, but not in the way he imagined. What begins as a solution slowly turns into possession.
The Willow isn’t inherently evil. It simply grants Bear exactly what he asks for, without questioning whether that’s what he truly needs. AI adoption looks surprisingly similar.
As a Gen Z, I love when a piece of pop culture becomes more than entertainment. Sometimes it unexpectedly explains the way we think about technology better than another whitepaper or conference talk ever could. It gives us a language for recognizing patterns we might otherwise miss.
That’s exactly what Obsession did for me. It wasn’t just a psychological horror film, which is funny because after watching it, I realized we might be doing the exact same thing with AI. Not because AI is dangerous. But because, much like Bear in the film, we sometimes become so focused on getting the outcome we want that we stop questioning the path we’re taking.
Let’s automate documentation.”
“Let’s summarize meetings.”
“Let’s build a support agent.
Nothing about these ideas is inherently wrong. In fact, many of them create real value. The challenge begins when AI quietly shifts from being a means to becoming the objective itself.
At some point, the conversation changes. Instead of asking,
Does this create value?
Teams begin asking,
Where else can we put AI?
That’s the same version of making another wish.
Bear doesn’t lose himself because of one decision. He loses himself because every decision after the first becomes easier to justify. Teams experience the same drift.
Teams that misunderstand AI maturity decided to measure success by adoption.
- How many AI features are shipped?
- How many agents are running?
- How many workflows use LLMs?
Mature teams measure something entirely different.
- Which workflows actually improved outcomes?
- Which ones reduced operational cost?
- Which one customer genuinely use?
- Which ones should never have been built?