If you wire a single big model into your product and treat its answer as "the truth," you've already made a choice. You chose certainty over signal. You chose a clean interface over visibility into how fragile the reasoning actually is.
"Human-aligned AI" sounds like a value statement. In reality, inside a lab, it looks like this: A spreadsheet of prompts and model answers. Thousands of crowd workers clicking radio buttons. A reward model loss curve on a dashboard. Arguments about why the assistant suddenly sounds like a corporate HR memo.
At some point, your model is going to do something you can't defend in a one-liner. It will leak something it shouldn't. It will help someone do something they shouldn't. It will say something about a protected group that lands in a journalist's inbox. Or it will just fall over during a launch and turn your flagship feature into an apology screen.
Most people imagine a training run as a black box. You wire up a giant model. You dump in "the internet plus some extras." You let it churn for a few weeks. You get "intelligence" out the other side. That story is comforting because it makes scale the only variable that matters. Inside a real lab, that's not how it feels.
