Ideally you are injecting AI in to a pre established process or tool, whether something you were already doing or annoyed with the current solutions. If you are one or two steps removed from what a big AI company is doing right now you may not have long left until what you are doing becomes a core feature of the platform. For example voice, I have seen a few companies who do voice related things chat will be able to do it. Their solution may be better, but it will be hard to 1) keep ahead of chat, and 2) convince customers you are somehow solving their problems in a more valuable way. Another example is some of the pdf companies, they had a head start, they worked on chunking etc but not its a core feature. We have seen from OpenAI that a vector db of some description is coming, while audio, images and text are all there. I have spoken to a few companies on our podcast that are doing things that chat probably won't be able to do ever, like build and host a website (zero copy and paste), or manipulate an image with a stylus, or work with Cluade in the same dashboard.

Ah, diving into AI product development? Watch out for the perilous pitfalls like overfitting models - it's like squeezing into skinny jeans after Thanksgiving dinner, not a good fit!

Make sure to clearly define your problem and data requirements early on. Skipping this step can lead to major issues down the line. Also, keep in mind the quality and quantity of your data; it's crucial for training effective models. I've seen teams get stuck because they didn't have enough diverse data or their data was biased. Finally, always test your model in real-world scenarios before launch to avoid unexpected failures.