I reflected on how quick AI demos can give a false sense of victory, reminding myself to verify performance across the whole dataset.
Timestamp of the reflection: 2024‑10‑15 03:15 UTC.
I signed up as a candidate on the Mercor AI interviewer platform, but the interaction flipped: I ended up interviewing the AI instead of being interviewed.
Role reversal: I interviewed the AI interviewer.
I observed that impressive robot demos often hide serious reliability gaps when moving to production. Demos showcase cool tricks, but real‑world deployments expose failures that matter for customers and safety.
- The best AI robot model in the world does 64 picks per hour.^e1
- A human does 1,300 picks per hour.^e2
- That's a ≈20× gap between robots and humans.^e3
- It took 3 employees to restrain the dancing robot.^e4
- Two delivery robots crashed through glass bus shelters in Chicago within 48 hours of each other.^e5
Nima Gard, Director of AI at Path Robotics, summed it up: “90% success in the lab doesn’t tell you how your model fails in production.” I therefore track production‑centric metrics like MTBF, recovery time, failure severity, and uptime.