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Software Development
AI Development Quality Convergence
Timeframe: Already happening as of early 2026
What's Changing
AI coding models have reached sufficient quality that poor outputs are now primarily caused by poor inputs rather than model limitations
Driving Forces
Rapid improvement in model capabilities
Better training on code repositories
Enhanced context understanding
Improved reasoning capabilities
Winners
- Developers who invest in prompt engineering
- Teams with strong planning processes
- Companies focusing on input quality
- Educational platforms teaching AI collaboration
Losers
- Developers blaming tools instead of improving inputs
- Teams with poor planning disciplines
- Generic AI coding tutorial creators
How to Position Yourself
1
Invest heavily in planning and requirements gathering
2
Develop systematic approaches to AI collaboration
3
Focus on input quality over tool selection
4
Build processes around iterative refinement
Early Signals to Watch
Increasing code review to writing ratiosGrowing emphasis on planning toolsShift from 'AI is broken' to 'my prompts need work'
Example Implementation
“Development teams now spend 60% of time on detailed planning and 40% on review/refinement rather than 80% on manual coding”