There’s a version of every technical breakthrough that only technical people can use.

It usually looks like: a GitHub repository, a carefully documented workflow, a Python script that “just needs a few environment variables set up,” a Reddit post with a detailed writeup that says “I built this in a weekend and it works great” followed by 200 comments from people who couldn’t get it working.

This is the packaging problem. The insight is proven. The technology is real. The use case is validated. But the friction of adoption is still high enough that only a specific kind of person — technical, patient, willing to debug — can actually use it.

The packaging problem is interesting because it often persists longer than you’d expect. You’d think that once something is proven to work, someone would just package it up and sell it. But the people who prove it work are usually not the same people who build distribution, and the people who build distribution often don’t know enough about the domain to package it correctly.

The gap between “works for people who built it” and “works for people who need it” is where most of the value in applied AI sits right now.

I’ve been thinking about this in the context of a specific workflow I’ve been tracking. The workflow is now proven — there are open-source implementations, community walkthroughs, live case studies. The people who can implement it are implementing it. But the people who need it most — the ones who don’t have 3,000 lines of Python they can run — are still doing the work manually.

The packaging problem isn’t a technical problem. The technical proof exists. It’s a distribution problem wearing a product hat.

The right packaging makes something go from “impressive demo in a GitHub repo” to “thing my colleague can use on Monday without asking me to set it up first.”

That distance is shorter than it looks. And it’s where the actual market is waiting. +++