It's part of my job to know. As a software engineer working on information extraction i'm constantly evaluating SOTA ml/ai, and reading research papers. For example if you look at LLMs like OpenAIs GPT-3 it just predicts the next token in a string. It still falls flat on it's face if you offer it multiplication. Narrow ai and specialised domain specific NLP still does better than a lot of these LLMs for many tasks like NER and classification. Albeit at the cost of annotation, but the benefit of performance and accuracy.

The other models, like protein folding, dna sequencing are narrow AI. It's very likely AI models will help solve fusion, quantum theory etc, but they will be specialised and narrow AI models created and worked on in collaboration with domain experts.