How to think about product approach and defensibility for applications built with LLMs
In categories where private data is needed, there is less risk of all the value being captured directly by the models. To be clear, even in ones that require private data, many application companies may pop up in the same space, but all of the application companies will have some benefit of workflow/switching cost for the customers they serve, that will prevent them from being fully commoditized by the models, at least.
In these cases, the companies are bringing AI into the existing tool/workflow, rather than creating a new tool from the ground up. While sometimes these businesses seem a bit niche, the approach can serve as a good wedge to then expand further. In addition, there have been numerous examples of large companies built as say plugins in Powerpoint (ThinkCell) and other products. But the risk they face is that the incumbents may integrate these indirectly, in which case they will always need to be multiple times better to stay ahead or relevant.
An important note is that this is more of a spectrum than a hard choice, and there isn’t one right choice. For example, take the UI design space: Diagram is building a plugin within Figma, Galileo is building a standalone application that uses AI to generate an interface design that can be edited in Figma, Uizard is building a standalone AI-powered design product that can essentially replace Figma for some designers.