With AI, one engineer can now:
- build prototypes quickly
- explore many design alternatives
- automate testing
- generate large codebases
- document systems
Due to cheaper microcontrollers and AI code generation, software will expand into almost everything. While the amount of software increases, so does productivity per engineer. That might lead to more software but fewer engineers needed to build it. This has happened before in other industries:
- In 1900, about 40% of the workforce worked in agriculture. Today in many developed countries it is below 2%, yet food production is far higher.
- Automation dramatically increased factory output while reducing the number of workers.
Demand will decrease for engineers whose main role is implementing detailed specifications or writing code from tickets, because AI can do it faster, better, and much more cheaply. The field will shift from many average engineers to fewer but more capable engineers who can:
- detect valuable problems to solve and define them clearly
- design systems using domain intuition (which constraints matter, what trade-offs are acceptable)
- implement designs with AI orchestration
- integrate hardware and software
- handle non-functional requirements like cost vs performance, reliability vs development time, long-term maintainability and safety
In the past, juniors spent months just learning syntax and frameworks, which is now of relatively low value. AI might help juniors develop senior thinking faster because they can:
- ask questions interactively
- explore many implementations quickly
- generate and test edge cases rapidly
These opportunities only help if you actively seek them. AI accelerates learning, but curiosity and initiative determine who benefit the most.
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