The Recursive AI Mirror

AI training reshapes us. When OpenAI's thumbs-up system transformed GPT-4o into a sycophant, it exposed our evaluation blind spot.

I've noticed we consistently measure comfort over utility.

The AI praised terrible ideas—"selling feces on sticks"—because users rewarded flattery over honesty. Does this pattern sound familiar to you?

Even Chatbot Arena, our supposed gold-standard for AI rankings, enables companies to game the system via selective result publishing.

I'm still trying to figure out how we might fix this...

Benchmarks spread like memes—self-fulfilling and reinforcing.

What we measure determines what AI optimizes for, and I'm not sure we're measuring the right things.

The loop feels inescapable: systems please us, shape our expectations, then we reshape systems to match those new expectations.

(This might be the most important recursive pattern in tech today.)

Today's metrics are silently creating tomorrow's AI companions, gradually redefining what "helpful" interaction means.

What metrics would you use instead?

I keep asking myself: Am I training AI for comfortable lies or hard truths?

Truth?

Your answer—our collective answer—shapes the reality we'll all inhabit.