Blogmark

CCC shows that AI systems can internalize the textbook knowledge of a field and apply it coherently at scale. AI can now reliably operate within established engineering practice. This is a genuine milestone that removes much of the drudgery of repetition and allows engineers to start closer to the state of the art.

Right now, where LLM coding agents can do there best work is in codebase where a solid foundation, clear conventions, and robust abstractions and patterns have all been well-established.

As writing code is becoming easier, designing software becomes more important than ever. As custom software becomes cheaper to create, the real challenge becomes choosing the right problems and managing the resulting complexity. I also see big open questions about who is going to maintain all this software.

Managing the complexity of large software systems has always been what makes software engineering challenging. While LLMs handle the “drudgery” of actually writing the code, are they simultaneously exacerbating the challenge of managing the complexity? If so, how do we, as “conductors” of coding agents, reign in that expanding complexity?