Context (kept intentionally generic):

We have a mature, revenue-generating enterprise application that’s been in production for years.

Semi-technical leadership (with no engineering background) is aggressively considering spinning up a new product, built using LLM-driven tools (AI code generation, rapid prototyping, etc.), with the belief that:

modern AI tooling dramatically reduces build cost, LLMs are going to improve in the future

the new system is an attempt to replicate most of what an established competitor built over ~10 years

customers can optionally migrate over time (old system remains supported)

software-only product that aims to replace all of the current application's operational complexity with a goal to make it resellable product.

early vibe coded demos created with LLM tools are a good proxy for eventual production readiness

The pitch to ownership is that this can be done much faster and cheaper than historically required, largely because “AI changes the economics of building software.”

I’m not anti-LLM — I use them daily and see real productivity gains. My concern is more structural:

LLMs seem great at accelerating scaffolding and iteration, but unclear how much they reduce:

operational complexity

data correctness issues

migration risk

long-tail customer edge cases

support and accountability costs

Demos look convincing, but they don’t surface failure modes

It feels like we’re comparing the end state of a mature competitor to the initial build cost of a greenfield system

I’m trying to sanity-check my thinking.

Questions for the community:

Have you seen LLM-first rebuilds of enterprise products succeed in practice?

Where does the “cheap and fast” narrative usually break down?

Does AI materially change the long-term cost curve, or mostly the early velocity?

If you were advising non-technical owners, what risks would you insist they explicitly acknowledge?

Is there a principled way to argue for or against this strategy without sounding like “the legacy pessimist”?

I’m especially interested in answers from:

people who have owned production systems at scale

founders who attempted full or partial rewrites

engineers who joined AI-first greenfield efforts after demos were already sold

Appreciate any real-world experiences, success stories, or cautionary tales.