“Classic quant signals might work, but you can’t explain them; ChatGPT might not work, but it can explain itself.
In a sense this is the opposite of a classic “black box” machine-learning investment algorithm.”
This is an interesting take by Matt Levine, on the ability of ChatGPT to be an asset manager (https://www.bloomberg.com/opinion/articles/2023-01-26/chatgpt-is-not-much-of-a-pitch-robot).
One of the reasons ChatGPT is so popular is because of its ability to elaborate topics in a structured way confidently.
Maybe this highlights an important skill set to be acquired by aspiring/practising data scientists?
Anyway if you are lost in the recent deluge of large language model (LLM) models, I recommend this nicely curated timeline by Dr Alan Thompson, https://lifearchitect.ai/timeline/.