The interesting follow up question is: what will they actually spend time on? Training new models? Copy pasting front ends on ChatGPT? Fine tuning models?
I think many of them will be scared by how much of a hard science ML is vs just spinning up old CRUD apps
The stable diffusion community is probably 2 years more mature than the GPT, there we see gui tools of a kind (in colab notebooks) to abstract away from code and thenlots of fine tuning.
On the professional side, adobe have plugged these tools into their products. https://www.adobe.com/sensei/generative-ai/firefly.html
Maybe openai will go the way of Netscape and make it all FOSS eventually. That'd be nice.
There is value in applying old techniques to new problems. Training a model to, I don't know, recognize snake species might help save snake bite victims lives.
(This is an example I came up with in 5 seconds, please don't take it seriously)
But there's also the whole "sell the shovel" aspect; it can be hard to train models. It can be hard to interpret the quality of the results. How do I know version 2 of the model is better than version 1? How do I even get labeled photos of snakes and not-snakes?
I suspect solving some of those problems are where some of the real gold is buried.
Since investment has been demoed successfully with off the shelf models, I don't think we're waiting on big advancements to be able to build a product. The bar for something like this, short term, is 1) be cool and 2) lose less money than traditional investing, sometimes.