Not directly, but I wouldn’t be surprised if there’s enough of an efficiency improvement to obviate hiring an engineer or two (across 100+ people). In the same way that Google and StackOverflow made people more efficient when compared to having to otherwise search through and read physical documentation (to debug, to understand some API or hardware thing), LLMs have made me more efficient by being able to get tailored answers to my questions without having to do as much searching or reading. They can provide small code examples as clarification too.

In many ways LLMs feel like the next iteration of search engines: they’re easier to use, you can ask follow up questions or for examples and get an immediate response tailored to your scenario, you can provide the code and get a response for what the issue is and how to fix it, you can let it read internal documentation and get specialized support that wouldn’t be on the internet, you can let it read whole code bases and get reasonable answers to queries about said code, etc.

I don’t really see LLMs automating engineers end-to-end any time soon. They really are incapable of deductive reasoning, the extent to which they are is emergent from inductive phenomena, and breaks down massively when the input is outside the training distribution (see all the examples of LLMs failing basic deductive puzzles that are very similar to a well known one, but slightly tweaked).

Reading, understanding, and checking someone else’s code is harder than writing it correctly in the first place, and letting LLMs write entire code bases has produced immense garbage in all the examples I’ve seen. It’s not even junior level output, it’s something like _panicked CS major who started programming a year ago_ level output.

Eventually I think AI will automate software engineering, but by the time it’s capable of doing so _all_ intellectual pursuits will be automated because it requires human level cognition and adaptability. Until then it’s a moderate efficiency improvement.