Almost No Skill Required to Cook a Steak (Though You Probably Can’t Make a Decent One)

Cooking a steak requires almost no skill.

Put it in a hot pan, wait a little, flip it, and eventually you’ll have something technically edible. But a genuinely good steak, medium-rare from edge to edge, browned properly, seasoned right, consistently delicious, is a different matter entirely.

Software development with AI is starting to feel much the same.

We build nonstop now. With AI, without AI, during the commute, on the toilet, probably in our sleep. We create agents, harnesses, tools, skills, prompts, feedback loops, elaborate workflows. Then we throw everything at a model and hope it gives us what we imagined, without ever having to understand how any of it actually works.

And what do we want?

We want the perfect steak.

We want software that works, looks good, feels polished, and arrives exactly as we imagined it. Most of all, we want the same result every time.

Do we get it?

Not every time. Not even close to every time.

Sometimes the model hands us something surprisingly good. Other times it serves up charcoal with a sprig of thyme on top and calls it medium-rare, completely confident in the lie.

So what do we do?

We go to a restaurant.

We pay for a premium AI product, hire an agency, subscribe to another coding assistant, jump to a new framework promising professional results. We hope someone else already solved the problem for us. Sometimes they have. Quite often, they haven’t.

That leaves two choices: learn to cook properly ourselves, or keep asking friends for restaurant recommendations while preparing our wallets for the next expensive disappointment.

Most of us want to build something we care about with AI without getting lost in the implementation details. We want to treat it like a professional chef working in our own kitchen: tell it what we want, step away, come back when dinner’s ready.

But AI isn’t a chef. At best, it’s a steak machine.

It can follow a recipe. Watch the temperature, flip at the right moment, drop in the butter. Give it enough tools and instructions and it’ll repeat that process fast, at enormous scale. What it doesn’t do is know what you actually want.

It can’t see the picture in your head unless you translate it into requirements, constraints, examples, tests, feedback. And even then, it’s boxed in by its own capabilities, its context window, the quality of the system wrapped around it. You can stand next to the machine and correct it every thirty seconds. That might help. It won’t turn the machine into a Michelin-starred chef.

Eventually, frustrated, you decide to just pay for the dream steak.

You pick the expensive restaurant. Sit down, study the menu, finally, you can order with real confidence. You wait for the first bite.

The plate arrives.

Same burnt steak you made at home.

Why? Because every restaurant in the city hired the same AI cook.

“Cost optimization,” management says. “Most people won’t notice.”

And they’re probably right. Most people won’t. Most of the time, software only has to be acceptable. Customers tolerate weird interfaces, pointless features, strange bugs, systems held together by generated code nobody actually understands.

But you’ll notice.

You’ll notice because this was something you actually wanted to make.

So you go home disappointed, hungry, a little embarrassed, and pull the cookbook off the shelf. There’s only one option left: learn to cook.

You learn what heat actually does. Which pan matters and why. Why thickness matters, why resting matters, why a timer alone was never going to save you. You ruin a few more dinners. Then you try again. And again.

Eventually you stop depending on luck, you learned it the hard way.

Software works the same way.

AI can make you faster. It automates the repetitive stuff, spits out a starting point, explains code, helps you poke at ideas. What it can’t do is replace your judgment. It can’t define quality for you, can’t decide which tradeoffs are acceptable, can’t always catch the moment when something is technically correct but wrong in every way that matters.

To build good software with AI, you still have to understand software.

You need to know what you’re actually asking for, how to judge what comes back, and when the machine is just confidently serving you charcoal.

Keep learning. Keep building. Keep failing. Do that until you can produce the result you want instead of hoping to stumble into it.

Then get good enough to open your own small restaurant.

Then hire a few AI cooks. Most people still won’t notice the difference.

But you will.