The classic open-source freedom - read and modify the tools you use - has in practice meant leaning on other people to do it; almost nobody can justify the time to read and patch their daily tools.
LLMs collapse that cost: the author now habitually prompts an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → to clone a repo and explain how something works, several times a day.
Getting unfamiliar software to compile used to be enough friction to kill the attempt; now it is a zero-time-investment challenge - tell Codex or Claude Code to check out and build it, come back ten minutes later.
The endgame: habitually modifying the software you use is now a visible path, which strengthens the case that devtools should be open source.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
It reframes why open-source access matters in an era where LLMs can read, explain, and modify unfamiliar codebases on demand—useful for practitioners deciding how much they should value source availability when picking tools.
Key quotes
“I think LLMs have changed that equation in a way that makes the original dream much more feasible.”
“Now I treat that as a zero time investment challenge: tell Codex or Claude Code to checkout and build X and then come back ten minutes later and see how it got on.”
“I'm not habitually modifying the software I use yet, but I can see a path to that which didn't exist a year or so ago.”
