Tools like git and jj exist to help humans manage state: branches, commits, rebases, conflicts, history curation. That whole model assumes a human is directly manipulating and reasoning about the codebase.

With LLMs in the loop, that assumption breaks. I don’t need to think in terms of branches or commits. I describe intent, and the model handles the mechanics of editing, reconciling, and producing a coherent result. Source control becomes an implementation detail of the toolchain, not something I actively operate.

jj is an improvement over git for humans, but that’s exactly why it feels like a local maximum. It refines a workflow that is already being abstracted away.

I’m not saying version control disappears. I’m saying it moves down a layer, the same way memory management or instruction scheduling did. When that happens, optimizing the human interface to it matters a lot less.

When LLMs are driving development, source control stops being an active cognitive concern and becomes a passive implementation detail. The unit of work is no longer “branches” or “commits,” it’s intent. You describe what you want, the model generates, refactors, and reconciles changes across parallel streams automatically.

Parallel workstreams used to require careful coordination: rebasing, merging, conflict resolution, mental bookkeeping of state. That overhead existed because humans were the bottleneck. Once an LLM is managing the codebase, it can reason over the entire state space continuously and resolve those conflicts as part of generation, not as a separate step.

In that world, tools like jj are optimizing a layer that’s already being abstracted away. It’s similar to how no one optimizes around assembly anymore. It still exists, it still matters at a lower level, but it’s no longer where productivity is gained.

This comment was generated by chatgpt (inspired by me).