Ideas are cheap, execution is cheaper (davekiss.com)

Recent advancements in AI, particularly through large language models (LLMs), have dramatically transformed the landscape of software development. A developer reflects on their experience building multiple polished projects—full-fledged tools—over a holiday break with the assistance of Claude Code, a generative AI. These tools automate tasks traditionally requiring extensive engineering effort, such as maintaining documentation, generating deployment summaries, and triaging bugs, highlighting how LLMs have reduced the significance of programming execution. This shift signifies a pivotal phase change in the AI/ML community, wherein the speed of idea realization has accelerated, making execution trivial. Ideas, once a precious commodity, are now easily replicated, leading to an environment where rapid iteration, problem selection, and understanding user needs becomes paramount. As the barriers to entry for software development diminish, the focus is shifting from traditional coding skills to strategic judgment and effective distribution, transforming the role of software engineers in a world where coding is no longer the central challenge. The future landscape will favor those who can quickly identify valuable problems and iterate on solutions, rather than merely those who can write the best code.