Coding with AI is basically just like searing a steak now

The actual "writing" part of programming is becoming the easiest part of the process, much like how putting a piece of meat in a pan is the simplest part of cooking a steak. The real skill has shifted entirely to the preparation and the finish. If you throw a frozen, cheap cut of meat into a pan, it doesn't matter how high your heat is; you're getting a rubbery mess. In the same vein, if you feed a lazy, ambiguous prompt into an LLM, you're getting hallucinated bugs and spaghetti code, regardless of how powerful the model is.

The "prep" in modern AI workflow is essentially prompt engineering and architectural planning. You have to source the right context, define the constraints, and ensure the "meat" (the requirements) is at the right temperature before it hits the model. If you just say "build me a login page," you're getting a generic, bland result. But if you provide the specific schema, the security requirements, and the design system constraints, you're setting yourself up for a perfect medium-rare result.

The "cooking" phase is the generation. It happens fast. Too fast, actually. This is where most developers mess up—they assume that because the code appeared in three seconds, it's done. But any chef knows the most important part of a steak isn't the sear; it's the rest.

In a real-world AI workflow, "resting" is the rigorous code review and testing phase. This is where you check for edge cases, verify that the AI didn't invent a library that doesn't exist, and ensure the logic actually holds up under load. If you skip the rest and push straight to production, the "juices" run everywhere—meaning your app crashes the moment a user does something unexpected.

I've noticed that the most productive devs aren't the ones who can type the fastest or even the ones who know the most obscure syntax. They are the ones who act as the Executive Chef. They manage the pipeline, they know exactly when the "heat" is too high (when the AI starts over-complicating a simple function), and they have the discipline to trim the fat from the generated output.

We are moving away from being "writers" of code and becoming "editors" of logic. The barrier to entry for building a functional app has plummeted, but the barrier to building a great app has actually stayed the same—or maybe even risen, because you now have to be able to spot subtle AI errors that a human wouldn't make. You aren't fighting the syntax anymore; you're fighting the probabilistic nature of the LLM.