How will we train junior developers in a world of generative AI?
And how do we get young people into software development when AI is already writing decent code for them?
This is one of the most pressing challenges facing our industry today. GenAI is changing the learning curve, the incentives—and even the definition of what it means to be a developer.
Here’s a breakdown of the problem—and what we might do about it:
🔁 Is this just like moving from assembler to Python?
Superficially, yes. Every major tooling shift has raised questions about which skills become obsolete. Assembly to Python, HTML to frameworks—these were shifts in abstraction.
But GenAI isn’t just abstraction.
It’s delegation.
- Python abstracts complexity.
- GenAI offloads decision-making.
You’re no longer writing clearer code. You’re working with a probabilistic partner that suggests outcomes, but doesn’t reason like a developer.
That’s a much bigger leap.
🧠 Why foundational skills still matter
To use GenAI well, you need to:
- Interpret outputs critically
- Refactor and correct hallucinations
- Design systems the AI can’t “see”
- Debug unfamiliar, complex codebases
These are learned through struggle, repetition, and guided feedback. GenAI can short-circuit that by giving juniors plausible solutions before they’ve built any real understanding.
🚨 The risk: “Shallow developers”
If a junior dev never:
- Builds something from scratch
- Breaks something and fixes it
- Reviews real pull requests
- Reads and internalizes others’ logic
…they may grow dependent on the AI and plateau early—unable to scale complexity or take ownership of critical systems.
🧩 How do we train juniors in a GenAI world?
We shift from:
“Code until you get better”
to
“Use GenAI as an apprenticeship accelerator—but not a crutch.”
Here’s what that might look like:
1. Teach reading and debugging before writing
Give juniors messy legacy code.
Make them annotate, explain, test, and refactor—with and without AI.
Focus on why it works, not just what it does.
2. Scaffold GenAI usage
Have them compare their own solution to the AI’s.
Force them to “red team” the model—where did it go wrong?
Make code reviews include AI output, not just their own.
3. Build real systems
Give them problems with constraints: auth, latency, race conditions, failure modes.
Encourage: Design first. Prompt second.
4. Reward reasoning, not just speed
In mentorship and reviews, ask:
“What trade-offs did you consider?”
Not just:
“How fast did you ship it?”
🌱 How do we keep the next generation interested?
Ironically, GenAI might spark curiosity—teens can build impressive apps in a day.
That’s powerful. But we need to guide them past the dopamine hits of early success.
What will take them further:
- Mentorship that explains why things work
- Projects that demand real thinking, not regurgitation
- Tools that expose architecture and design—not just syntax
⚠️ Final thought: The paradox
GenAI lowers the floor but raises the ceiling.
It’s easier than ever to get started.
But also easier than ever to stall out.
We’ll need a new approach to onboarding, mentorship, and skill-building—if we want junior devs to grow into true engineers, not just prompt engineers.