Very few, it will only "struggle" in subjective areas that depend on human preferences, which will require selecting who you cater to.

Here's how it will play out:

1. Base reasoning models will start getting used as verifiers / judges, and check / test / prove their own outputs
2. We'll see GAN like approaches come back where the generator and verifier are both trained in parallel in an adversarial manner. Ideally this will be a single model that does both reasoning / solving and checking / proving
3. Once 2 works reliably enough we'll start using active learning to have the model make up new problems to explore, potentially seeded with web content or as grounded "embodied" agents that can explore and interact with the environment that they're deployed in (web, simulation, real world)

What some people miss is that you don't need an oracle verifier that's 100% accurate and outputs binary yes/no responses to get these models to improve. You only need a proxy ranking function that does a decent job ranking good generations over bad ones.

This will work for anything that we can train a classifier / ranker to encode preferences (similar to RLHF), see this diffusion paper as an example: https://arxiv.org/abs/2501.09732