Do you see the trend of building on top of existing foundational models rather than creating proprietary models as a potential challenge for Nyckel?

I think, fundamentally, you shouldn't build a company around a particular model or model architecture. You should build the company around the function type. If you offer machine learning text classification as a service, that's one of the things that Nyckel does and one of the things you can do with GPT-3. 

With Nyckel, we just tell them to let us know what input and output they want, and we'll take care of all the AutoML stuff. There's nothing stopping us from calling GPT-3 if we think that gives us the best value for our customers, and then it's up to us to do the engineering and so on to make that happen. The customer's responsibility is to review the predictions based on their own data and see if it's good enough for them, and then, they might tweak the label set, add more data, and so on.

We think that is the right abstraction and again, we can do whatever needs to be done behind the scenes. Just saying, “Here's one model, it's magic, call it and hope it works for you”, is not the end-all-be-all for machine learning.