When off-the-shelf AI can draft contracts, analyze data, and even write boilerplate code, many traditional dev tasks are evolving. But this isn't about replacement - it's about our value shifting to higher-order work.
The Changing Dev Landscape
- Companies increasingly adopt turnkey AI solutions
- Custom system development demands are declining
- Basic business logic implementation gets automated
Where We Create New Value
1. AI Optimization Specialists
- Correcting decision biases ("Why does it reject 20% of valid invoices?")
- Perfecting prompt engineering
- Ensuring outputs match real business needs
2. Legacy System Integrators
- Bridging AI tools with aging infrastructure
- Solving edge-case compatibility issues
- Building robust data pipelines
3. Human-AI Workflow Architects
- Making AI decisions explainable
- Designing collaborative interfaces
- Creating oversight mechanisms
Making the Shift
- Focus where AI struggles (ambiguity, judgment calls)
- Develop deep domain expertise
- Strengthen systems thinking
Let's Discuss
What development tasks in your field have proven hardest for AI to replicate?
▸ Is it dealing with legacy systems?
▸ Handling ambiguous requirements?
▸ Something else entirely?
Share your observations below!
Top comments (0)