When you improve the capabilities of an LLM (or any AI agent with enough generality really) you also probably improve its ability to cooperate with copies of itself, where cooperation ability is some loose collection of abilities such as checking other copies work, planning work for other copies to carry out, course correcting other copies when they get sidetracked, integrating a copy’s work into the main project, etc. 

Now that LLMs are good enough to create useful multi-agent systems such as Claude Code, Cursor, Gas Town, etc., I expect these systems to get better at a faster rate than the underlying models (and not because the people are putting work into improving the structure of these systems, although I suspect that will also help). 

I’m not sure if these systems are currently more or less than the sum of their parts (if Claude is able to do a task with X difficulty, are 10 Claude agents able to do a task of >10X difficulty, 10X difficulty, or <10X difficulty?). But I expect that as cooperation gains accumulate, they will become less and less like “less than the sum of their parts” (as they stop bungling communication with each other) and more and more like “greater than the sum of their parts” (as they learn how to get gains from specialization or something?). 

This has updated me significantly towards shorter timelines, especially for time to automated coders. I really have no idea what happens after that though. 

Prediction: 80% chance of fully automated coding “teams” by the end of 2026.