AI Is a Steak Machine, Not a Chef: Why Judgment Still Separates Good Software

Using an extended cooking metaphor, this piece argues that AI has made producing software trivially easy while doing nothing to guarantee it’s any good. Anyone can throw prompts, agents, and elaborate tool-chains at a model and get something that technically runs — the equivalent of a steak that’s edible but burnt. Consistently excellent output, like a properly seared medium-rare, remains a different problem entirely, and swapping vendors, frameworks, or premium assistants rarely fixes it because everyone is leaning on the same underlying models.

The core claim is that AI is a fast, scalable recipe-follower, not a professional who understands intent. It has no view into what the developer actually wants unless that intent is translated into requirements, constraints, examples, and tests, and even then it’s bounded by its context window and the quality of the scaffolding around it. Standing over it and correcting it constantly may help but won’t elevate it to expert-level work.

The takeaway for technical readers: AI genuinely accelerates the repetitive parts and gives you a starting point, but it can’t define quality, weigh tradeoffs, or reliably catch code that’s technically correct yet wrong in practice. Building good software still demands that you understand software — enough to specify what you want, judge what comes back, and recognize when the model is confidently serving you nonsense. The path to reliable results is the unglamorous one: keep learning, keep building, and keep failing until quality stops depending on luck.