I take the point, but I think the author picked a poor analogy. Cooking even an excellent steak is actually not that hard. In fact, I'd argue that it's among the easiest things to master/make at a top level quality at home. Does it require some modicum of attention and understanding? Sure. But starting with a high quality cut, owning a meat-thermometer, and knowing about reverse searing is about all it takes to reliably and easily get a near perfect steak every time.

There are far, far better cooking examples out there.

I'm more of a coffee nerd than a steak aficionado but I've often made the comparison between those two. In both cases, the enthusiast considers their skill in making coffee/cooking steak to be a differentiator and they immerse themselves deeply into the process, skills, and tools. But for both coffee beans and steak the most important factor for a good end product is starting with high quality inputs. In other words, most of the work is being done by the farmers, the processors, and the quality of the raw stock (the cow or the coffee plant). Your job at the very end of that long value adding chain is to not ruin the hard work that others have put into it.

I'm not sure if this analogy holds up in the AI software metaphor that the author of the blog post is making.

I also don't think the analogy will age well.

It might take more than 10 years, but robots will almost certainly master cooking single pieces of meat.

It's substantially harder to reach self driving than it is to cook a "good" steak.

Sure, for probably quite a long time you'll be able to find someone who can cook a way better steak than the best robot, especially because taste is extremely subjective...

You can debate the horizon and the extent, but changes ARE coming.

Change is the only thing you can count on besides death.

It is a great analogy, as the vast majority of software tasks are CRUD or glue logic. At this point, relatively narrowly-defined tasks are a solved problem with frontier models. It is only more advanced tasks with broad scope and large context that frontier models have more limited results.

Agreed. I'm an avid cook. Sous vide circulators are cheap now, and so are meat thermometers. Doing a steak well is one of the easiest cooking tasks, to the extent that I won't bother paying top dollar in a restaurant because it's so easy to do restaurant quality at home. I could teach someone to cook a restaurant quality steak in a couple hours and a few practice runs.

The problem is that not everyone can afford/wants to spend on a high quality cut. A good cook can make a cheap cut of meat taste great. There's no single right way of cooking a steak and that can include spices/herbs/marinades/oils/etc.

All metaphors break at some point.

However, your example actually proves the point.

How many people who want a good steak know how to identify a high quality cut? Do they even know where to buy a high quality cut? What the different cuts are and whether they want rump, sirloin or fillet? How many kitchens have a meat thermometer? How many home cooks know how to use it and what the right temperature should be? How many home cooks know about reverse searing? Or timing? Or resting? Or seasoning?

Is it all learnable? Sure. But even something as simple as steak has nuance that needs to be learned, equipment deployed to go from "pretty good" to "great", experimentation and trial and error.

Steak is the simplest thing to choose to get started. It's also the hardest to get right.

Building an app is the simplest thing to choose to get started. It's also the hardest to get right.

So, honestly, the whole metaphor stands up pretty well for me, specifically because of your response.

I was just curious asked chatgpt:

How can I make a really great steak myself? What to look out for, what to do. Give me detailed instructions.

I got a really good answer (including - cut, temperature and reverse searing), so I probably won't even need understanding. (Oh - and yes, I can cook - the answer mirrors what I already know).

Yes, it was a very, very poor analogy, because cooking a steak is a well known and well documented thing where LLMS produce good output. They fail when you get off the beaten path and try to do something that's not "mainstream".

How do you get around the idea of poisoning oneself? Keeping meat part raw feels uneasy.