I think that's a great approach. I've thought about how to handle these issues and wonder how you handle several issues that come to mind:

Competing with LLM software users, 'honest' students would seem strongly incentivized to use LLMs themeselves. Even if you don't grade on a curve, honest students will get worse grades which will look worse to graduate schools, grant and scholarship committees, etc., in addition to the strong emotional component that everyone feels seeing an A or C. You could give deserving 'honest' work an A but then all LLM users will get A's with ease. It seems like you need two scales, and how do you know who to put on which scale?

And how do students collaborate on group projects? Again, it seems you have two different tracks of education, and they can't really work together. Edit: How do class discussions play out with these two tracks?

Also, manually doing things that machines do much better has value but also takes valuable time from learning more advanced skills that machines can't handle, and from learning how to use the machines as tools. I can see learning manual statistics calculations, to understand them fundamentally, but at a certain point it's much better to learn R and use a stats package. Are the 'honest' students being shortchanged?