Most AI Failures Don’t Happen in the Lab

Hinzugefügt: 2026-05-05

[00:00:00]Okay, this is what I observed for the past few months. Most people think AI engineering is about choosing the right model. But after 18 years in this game, I've seen the biggest failure doesn't happen in the lab. They happen in production. Everyone is obsessed with accuracy scores, [music] but that's a trap. If you're building AI for the enterprise, accuracy is stable stakes, [music] right? Everyone wants accuracy.

[00:00:20]The real pro metric you need to be [music] tracking is semantic drift. It measures how far your model outputs are wandering away from the [music] original intent over time. If you are not watching this, even if you have got 99% accuracy on your model output, it can still start hallucinating quietly without you knowing about it. In my latest project, [music] tracking this was the only way we could catch a massive consistency slide before it hit the customer. If you want a full breakdown of [music] how to implement this, I will write about it in my newsletter. Why not subscribe to it?

[00:00:57]I've pinned the [music] link in the description below. When it comes to AI, let's build stuff that actually scales.

[00:01:03][music] Thank you.