Last September, OpenAI published a paper.

The paper's authors were OpenAI's Adam Tauman Kalai, Edwin Zhang, Ofir Nachum, plus Santosh Vempala from Georgia Tech.

They established a mathematical framework whose core finding is this inequality:

Generation Error Rate ≥ 2 × Judgment Error Rate

Suppose an AI has a 1% probability of making a judgment error on the question "what does 1+1 equal?" Then when generating an answer, the probability of making an error is at least 2%.

Why is there this amplification? Because one incorrect judgment spawns multiple incorrect generations. For example, if the AI judges that 1+1=3, it simultaneously makes two errors: saying 1+1=3 is correct, and saying 1+1=2 is incorrect. One judgment error produces at least two generation errors.

If you answer "I don't know," you get 0 points. If you guess randomly, even with only a 10% probability of guessing right, your expected score is 0.1 points. The rational choice? Guess. So AI didn't "learn to lie." AI is forced to guess by its training system.

I've been doing AI automation for over half a year. My entire content system—from data scraping to writing to image selection—is all run by AI.

Did this paper change my understanding? Honestly, my core understanding didn't change.

I've always known AI makes mistakes, and my system design has human verification at every stage. But one thing became clearer: hallucination is not a bug, it's a feature.

So the correct approach isn't waiting for AI to become perfect, but rather designing workflows that assume AI will definitely make mistakes, then building fallback mechanisms.

My approach:
1. All AI-generated data must have original source links for cross-verification
2. Specific numbers in written content must be manually confirmed before publishing
3. Don't let AI make "judgments," only let it do "organization"—the judging is my job