Founder of Seldon AI (www.seldon-ai.com) here. We are working on dev tools to help developers with this kind of refactoring and continual production work.

Basically there is a whole host of tarpit usage of LLMs in application backends today. This is simply another case of LLMs enabling people to build what they cannot productionize or maintain.

Today developers without any AI/ML skillset can prototype a fancy new feature with LLMs, what used to require collecting and curating data to train a pipeline of ML models then serving them in production.

The problem comes when it's time to take the new feature into production. That $20 cost with prototype/beta workload is now $30k per month in LLM costs, and those hallucinations that never happened on their 200 test samples is happening 600 times per day.

They are then stuck, because they lack the knowledge and budget to build and run the production ML pipeline properly, the infra stack necessary to handle production workload. Management also doesn't understand why they have to invest in rebuilding something that "seems to work" with "outdated methods".

These system one or decision models are a step up from LLMs when it comes to this usage. Problem is that they are still difficult to work with (limited in expression power) for many application logic. Despite the initial popularity, I hear about a lot of frictions refactoring LLM based backend with them, and multiple cases of production rollbacks to LLMs already.

I think system one models is a sensible paradigm, and we are on the precipice of something great, but there is a lot more work needed above the model layer to make this work.