Reviewers pointed out that the model does not follow individual workers, so it can only paint a very coarse picture of the costs of job displacement.

The scenario explorer is a work in progress, and we expect it to evolve both as we invest more time and as economic research itself develops. We are grateful to the economists who read an early draft... and gave us detailed comments.

I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it.

The labs are already routing internally — different model classes for different requests, ensembles under the hood. What they can't do is route across vendors, or evaluate a competitor's model for a specific sub-task, or use an open-source fine-tune for the narrow piece where it's actually best.

While model merging offers a way to combine different skills, it is often impractical due to mismatched neural architectures and the closed-source nature of top-performing models.

The issue isn't that models are bad at reading documents. It's that single-pass extraction has no mechanism to catch its own mistakes, and models get lazy.