GPT language model (or broadly generative AI) is climbing the usual 'peak of inflated expectation' of Gartner Hype Cycle. Everyone is excited about the possibility and are expecting unreasonable things.

But soon, the peak will pass, and enthusiasm will wane as people start to understand the limitations of new technology, and the gap between reality and expectation becomes evident.

Of course, there will be an impact in the long run as the technology matures and people find ways to deploy them to improve efficiency and expand capability.

I see 2 problems with the technology as it stands today. 1) it is still not user-friendly. 2) the technology is unsuitable for precision analysis. I'll elaborate.

1. Not user-friendly. Sure, you can communicate to it in natural language, and generate prose that sound plausible and interesting. But to date, you have no control over what learning material the model uses. You are reliant on few GPT providers for their discretion on what training material to use. But real productive marketable work, the content creators need to be able to train the model on training data they choose.

For instance, if you are lawyer building a case, you want the language model trained on case books, regulations, past judgements, etc., that are relevant to the profession. You are likely to get either nothing useful or uninformed opinion based on public information.

Another example, if you are an animation studio or comic artist, you would want to train the art-generating model (like Stable Diffusion, Dall-E, etc.) on your own portfolio of arts, so that when you create new show or content, it will be uniquely in your own style. None of the tools today let you do that, unless you're a programmer who can tinker with code. Sure, Pixar or ILM may be able to do this in a few years, but not if you are a lone artist.

So the AI software tools have some ways to go before they become prevalent.

2. Unsuitable for precision analysis. Neural networks do not store precise information. It stores association between inputs values. In a way, NN stores approximate 'impression' or generalization of data set. (In fact, you don't want to over fit and simply store the information.) However, a lot of information we deal with is binary: it's either one way or another. Answers that looks and sound correct, but actually incorrect is useless. But that's what generative neural network delivers: output does not seem to be out of place next to the learning material.

Sure, scientists use generative AI to generate innovative 'ideas' to test, but they still have to tested for actually validity. Generative AI is a good brainstorming tool, but not necessarily a generator of correct answers.

In time, these limitations will be realized by laymen, and the hype will fade.

But eventually those who figure out these imperfect tools will make them work despite shortcomings.