LLM and other generative output can only be useful for a purpose or not useful. Creating a generative model that only produces absolute truths (as if this was possible, or there even were such a thing) would make them useless for creative pursuits, jokes, and many of the other purposes to which people want to put them. You can’t generate a cowboy frog emoji with a perfectly reality-faithful model.

To me this means two things:

1. Generative models can only be helpful for tasks where the user can already decide whether the output is useful. Retrieving a fact the user doesn’t already know is not one of those use cases. Making memes or emojis or stories that the user finds enjoyable might be. Writing pro forma texts that the user can proofread also might be.

2. There’s probably no successful business model for LLMs or generative models that is not already possible with the current generation of models. If you haven’t figured out a business model for an LLM that is “60% accurate” on some benchmark, there won’t be anything acceptable for an LLM that is “90% accurate”, so boiling yet another ocean to get there is not the golden path to profit. Rather, it will be up to companies and startups to create features that leverage the existing models and profit that way rather than investing in compute, etc.