I recently experienced a real-world example of this in SEO. We had a traffic decline, and because AI repeatedly classified it as normal volatility, I unconsciously anchored on that explanation and kept reporting the same conclusion to stakeholders.
A week later, we found that the decline was actually caused by a simple external factor that wasn't part of the prompt and therefore couldn't be part of the model's reasoning.
That experience taught me something important: the biggest risk isn't that AI gives the wrong answer. It's that we stop forming our own hypothesis before asking AI for one.
Your distinction between delegating execution and delegating judgment perfectly captures the issue.
Excellent article.
I recently experienced a real-world example of this in SEO. We had a traffic decline, and because AI repeatedly classified it as normal volatility, I unconsciously anchored on that explanation and kept reporting the same conclusion to stakeholders.
A week later, we found that the decline was actually caused by a simple external factor that wasn't part of the prompt and therefore couldn't be part of the model's reasoning.
That experience taught me something important: the biggest risk isn't that AI gives the wrong answer. It's that we stop forming our own hypothesis before asking AI for one.
Your distinction between delegating execution and delegating judgment perfectly captures the issue.