Model size still matters. So do data scale and compute. But once systems are in production, the constraint is verification: can a qualified person tell whether an output is correct, safe, and usable?
Convincing is not the same as correct
Large models produce fluent answers. Fluency hides missing citations, unsafe code, and domain errors. Teams that only measure next-token quality discover the real failures after customers do.
Verification has to start in the data
If training examples are unlabeled, weakly labeled, or labeled by people without the required expertise, the model inherits that uncertainty. Verification is not a final QA step. It is how datasets, benchmarks, and production reviews are built.
What good verification looks like
- Criteria written before review begins.
- Reviewers matched to the domain with qualified specialists aligned to the task.
- More than one judgment when the decision is high-stakes.
- A record of disagreement, not only a final score.
The practical implication
Organizations that treat verification as infrastructure can compare models, catch regressions, and improve training data from the same loop. Organizations that treat it as a one-off audit stay dependent on anecdotes.