There is so much more to creating a successful software system than writing code. Getting your code to work is, in my experience, about 10-20% of the effort involved in making software effective IRL. And it's by far the easiest 10%. Data modeling, architecture, performance tuning, tech decisions, design, load testing, running on a novel infrastructure, scalability, migrations, operations, security, a process for adding new features and fixing bugs, integration with other systems, etc.. tend to make up the vast majority of the work in a real world software project.<p>These aspects of programming are relatively impervious to AI takeover because a) they require clear intentions (i.e. a group of humans must collectively agree on the business outcomes they are shooting for) and b) they require constant collection of and tuning against empirical data (novel data that is absent from the AI training sets by definition).<p>If your job is solely to write code according to someone's prompt, AI will replace your job. But the person deciding what that prompt should be in the first place... that's the value proposition that AI cannot touch.