Comment by simonw
When is the last time you tried using LLMs against a large, old, crufty undocumented codebase?
Things have changed a lot in the past six weeks.
Gemini 2.5 Pro accepts a million tokens and can "reason" with them, which means you can feed it hundreds of thousands of lines of code and it has a surprisingly good chance of figuring things out.
OpenAI released their first million token models with the GPT 4.1 series.
OpenAI o3 and o4-mini are both very strong reasoning code models with 200,000 token input limits.
These models are all new within the last six weeks. They're very, very good at working with large amounts of crufty undocumented code.
Ultimately LLMs don’t really understand what the code does at runtime. Sure, just parsing out the codebase can help make a good guess but in some cases it’s hard to trust LLMs with changes because the consequences are unknown in complex codebases that have weird warts nobody documented.
Maybe in a generation or two codebases will become more uniform and predictible if fewer humans do it by hand. Same with self driving cars, if there were no human drivers out there the problem would become trivial to conquer.