AI
pro AI in coding
- quickly solves a problem
- works in unfamiliar coding areas, concepts, languages
- suggests unknown functions and approaches
contra AI in coding
- AI generated solutions often do not represent best practices
- false sense of competence may become problematic in debugging complex issues
- critical thinking is (and remains) important
- structured thinking is helpful
- actually learning to code yourself is rewarding and conductive to these goals
- make mistakes, debug, struggle, research, think, build problem solving skills
- develop good coding habits from the beginning
detailed problems
- Coders with AI assistance write more security leaks (with less awareness of them)
- AI assistants actually slow down software development (reproducible)
- AI coding tools chase phantom bugs and destroy real production databases
- Vibe coding creates unmaintainable code without engineering discipline
- AI coding collapses under real-world pressure, requires expertise for final 30%
- Coding agents create cybersecurity vulnerabilities in numerous ways
- 95% of companies see no return on AI investment (reasons why)
- With increasing complexity, it gets harder to check if AI output is correct
- AI usage degrades our ability to catch bugs, creates a reliability problem
- AI output lowers skepticism during reviews, stunts junior devs knowledge gain
on learning
- Outsourcing work to AI hurts long-term growth of learning skills
- AI stunts intellectual development as engineer + reasoning about complex concepts
- Chatbots threaten critical thinking and cognitive development skills
- Students using AI lose critical skills and become dependent on unreliable tools