Why Read Not Write

The one idea: AI can write analysis code in seconds, but the conclusion is yours the moment you put it in front of leadership. Code that runs with no error can still quietly answer a slightly different question than the one you asked. Your job is to read what the code actually does and verify it before you trust the number.

⚠️ The trap: The AI's code ran with no error and printed a clean number, so that number answers my question. That feels right and it's wrong. **"Runs without error" only means the syntax was legal — a filter keyed on the wrong column will compute a confident answer to a different question, so read what the code actually does before you trust the figure.**

AI can produce a working analysis script in seconds. What it cannot do is own the conclusion. When a notebook says "churn is up 12% in the Enterprise segment" and you put that number in front of your leadership team, the number is yours. The leverage skill in the AI era isn't writing code — it's reading the code someone (or something) else wrote and knowing whether to trust it.

"Runs without error" is not "computes what was asked"

These are two completely different claims, and confusing them is the root of almost every bad AI-assisted analysis. Consider:

import pandas as pd

df = pd.read_csv("users.csv")
active = df[df['plan'] == 'Pro']
print(f"Active users: {len(active)}")

This runs perfectly. No error, no warning, a confident-looking number in the output. It is also not counting active users — it's counting Pro-plan users, active or not. If a third of your Pro accounts haven't…