1. inference on arbitrary context / world knowledge with fast, structured output
2. I almost don't want to say it because I don't know if people realize this yet... but that makes traces from any kind of non-AI process or system "learnable", you can easily model it by by just asking it to predict the next outcome during learning and compare against what happened.
which also means it could also learn new strategies through rollouts in a way that generalizes across semantic space which means...
so these models are actually quite promisingly useful for existing companies because they do have general traces of what happened in their company and want to discover ways they can tweak their business processes, and then there is durable demand for models you can embed within a product or UX flow without exposing your user to slop + then train or customize and build real IP or custom capabilities
general classification itself is quite interesting IMO for applications that need to make judgment calls or select one of many UX actions, and a much better fit for this than LLMs. So if you think about it, this is the type of thing businesses might be calling a lot - every trace (eg user session, package shipment, sales motion, marketing campaign, job outreach) + proactively throughout dynamic experiences to decide whether to eg end a call or which of multiple UIs to manipulate
this is just an unfamiliar product experience to most people because it hasn't really been bootstrappable with a strong general baseline until now. But now it's posible and I think it will be very col.