Working a little is also like saying not working a lot. 
This is not likely to happen for a few years, if ever. Airtable is a closed formulaic system like Coda. There is no “code” [per se] for it to learn from because you are writing psuedo code to begin with. So either Airtable must train a fine-tuned LLM and open-source it, or a group of developers could take this challenge. It’s a lot of work and it requires internal access or independent development of a formula parser.
If you spend a lot of time working out this approach, I predict you will be deeply disappointed. Airtable will do this internally. The market pressure to make formula development possible, fixable, and understandable from natural language prompts will be intense. They’re likely working on this already because they have the psudo-to-code translator and that’s all that’s needed to:
- Create a few-shot prompt/training process that transforms a natural language query into code and then into their formulaic representation.
- Create a few-shot prompt/training process that transforms a natural language query to read a formula and explain what it does.
- Create a few-shot prompt/training process that transforms a natural language query to explain how to fix a formula that is not working.
This is the trifecta of AI and formulas; create, fix, explain. No one outside of Airtable will ever be able to do this well or financially practical.
If you want to use AI to make something useful in Airtable, focus on users and their data. That’s where the value will be for external AI solutions.