We’ve long since proven that machines are better for back-breaking labor.
I also disagree that AI will offset the majority of big brain jobs. What’s actually happening is AI is offsetting the majority of narrowly defined queries. The more open ended problems require an actual understanding of problem solving rather than a really clever text prediction engine.
Some examples to illustrate my point:
If you wanted to build a mobile app, you’d need to first:
- understand the problem you’re trying to solve (is it a game? A medical app? A productivity app? Etc)
- choose a frontend stack
- create a design language
- create wireframes
- decide where to host the backend stack (eg self hosted? Or public cloud? If so, which?)
- decide which cloud services to use (if public cloud)
- decide on how to approach IaC
- decide on the backend stack
- decide on how to implement your CI/CD pipelines (or even if you want continuous delivery)
- understand what privacy and security concerns you have and identify what risks you’re willing to accept
- define testing strategy’s
- define release cadences
And so on and so forth.
Writing the code is often actually the easiest part of software development because we already understand the problem by that point. The rest of the work is figuring out all questions that need to be answered — and I don’t even mean answering the questions, I mean understanding what questions to ask.
AI can’t do that. GenAI requires human input and much of software development is actually figuring out what those inputs should be rather than generating that output.
So that’s what I mean by “big brain jobs”. It’s not writing code, because that’s easy. It’s understanding and defining those requirements to begin with.