The developer's path forward in the AI era

These last quarters I’ve seen one CEO, CTO or Founder after another saying something to the effect “AI will take your job, adapt or die”.

I used to have a great mp3 collection, you know, back in the days when it was almost considered legal.

It was an amazing feat to keep it up to date in Winamp and later iTunes with correct titles and album art. There came apps that focused solely on this catalogue part using online sources to update the metadata called ID3.

Then came Spotify and changed that completely. That time spent curating was just sunk cost and my well catalogued mp3s are probably on an old dying hard drive somewhere in the house.

This is the reality we must face when it comes to AI and as a developer I see this so vividly.

What used to be “LLMs cannot code” became “LLMs write bad code” and now “LLMs write ok code”.

So we shift focus and find a new weak spot: LLMs cannot fix bugs. Or I cannot understand the code it writes.

But then again, the code I struggle to understand, it can explain to me 99% of the time. The ability to both generate and explain complex code further diminishes our position as keepers of the technical knowledge.

We move the focus to what it cannot do while something else comes along that changes the playing field, a new Spotify emerges and changes it all.

LLMs with the context memory of your complete repository. With the knowledge of every commit and every connected ticket. That can understand the reason behind the change in an iterative fashion. You will not need to understand the code. It will need to understand the code.

You will need to understand what you want to achieve and why. This understanding of purpose becomes our core value proposition as technical professionals.

As AI Agents emerge and become reliable we will prompt it with “increase the convert rate by 10%, these are the amounts of visitors we have, make small changes until you reached your goal or you need my attention, you have one quarter”.

Why is this happening so quickly? There’s an economic incentive at play. Developer salaries, especially in Big Tech, have skyrocketed over the past decade. AI development may be running at a loss now, but it might be following the Starbucks playbook of crowd out competition first, then raise prices. Companies investing billions in AI aren’t doing it for novelty. They’re eyeing the massive labor costs they could eliminate. Right now AI is marketed as an assistant, but the financial motivation to replace rather than augment is powerful.

So where does this leave us as developers? We need to move up the value chain. If algorithms can handle implementation, we must master the intention behind it. This brings us back to first principles thinking.

These are the first principles that we aim at: Why do we build this service? What do the end users aim to solve?

This will be the hardest problem to understand and execute on. If my technical skills are not required as they used to be, what should I focus on instead?

History gives us a clue. In the past, key makers, cobblers and tailors used to be three separate jobs. As the market changed, their crafts were not needed to the same extent, but those who survived saw that with their dexterity they could adapt to new needs.

My view is that multiple technical crafts will join together, and our focus will shift toward the complete value chain rather than isolated implementation details. We’ll need to understand the whole picture to remain relevant.

Those first principles will not be: to write code.