Knowing vs Building in the Age of AI

Why builders will always get the job done

AI is coming for a particular brand of developers.

If you define a great software engineer by the standards of FAANG interviews. If you pride yourself in being able to quickly estimate Big O notation off the top of your head. If you love handcrafting search algorithms. If you're the type of person who can figure out the perfect data structure just by looking at a piece of data.

Then AI is going to make you feel less powerful.

Let's be honest here: virtually everything I've just mentioned can be Googled, it’s not as though AI has suddenly changed that. An effective, productive builder knows this. She doesn't need to come up with those things off the top of her head. She's comfortable not looking like the smartest person in the room when surrounded by technical people.

But for reasons I can't quite fathom, the top dogs in the industry have decided that the best way to discern top software engineers from mediocre ones is to subject them to an interview process that looks like a college exam: lots of theory, lots of stuff to remember.

That books about "cracking the Google interview" even exist, should be a big red flag. Why do you need an ad-hoc book to pass the interview? Why aren't your organically developed skills enough to prove your worth as a software engineer?

According to the FAANG standards, you're successful if you can somehow memorise all the right answers and remember them at least until the interview is over. Just like an exam, just like in school.

The real world is, of course, not like school. Builders all over the world, especially those working for down-to-earth startups, are dealing with that reality on a day-to-day basis: you go to college, get your CS degree, then relearn everything you need to know as soon as you land your first job. Add a pile of debt on top of that if you're in the US.

The way I see it, there are two types of people in software development: builders and “big knowledge” types.

Builders are defined by results. It's about what they can produce using whatever tools are available at their disposal.

"Big knowledge" types, on the other hand, are defined by how much they know.

As far as I’m concerned, these two forces and attitudes are constantly at play in our industry. It’s the simplifiers vs the complexifiers.

That’s why I love serverless.

It allows me to reduce complexity. It takes away a bunch of the “hidden knowledge” that Kubernetes experts thrive on. It makes me more productive. It forces me to focus on getting something of value out there so that customers can benefit from my work.

I love being a builder, and I look forward to leveraging AI or any other tool to build useful software that other people can enjoy.

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