Overview
This piece explores why instant app creation tools don’t lead to widespread adoption. The key insight is that most people’s problems aren’t software-shaped - they don’t recognize when their daily frustrations could be solved with code, unlike programmers who are trained to see automation opportunities everywhere.
Key Arguments
- **Most people don’t build apps despite having easy tools because their problems aren’t software-shaped - they don’t recognize when automation could help them.**: When told they can instantly create any app, people say ‘Cool! Now I need to think of an idea’ then forget about it entirely. The issue isn’t lack of creativity but lack of recognition of software-solvable problems.
- **Programmers have a fundamental cognitive advantage - they’re trained to see everything as potentially automatable.**: Programmers automatically think ‘if you do a task three times, automate it with a script’ and will rename files via terminal commands while others manually click and copy-paste. This training creates a different way of seeing problems.
- **We are limited by solutions we were never taught to see - like asking for faster horses instead of imagining cars.**: People remain blind to automation possibilities because they lack the mental framework to recognize when software could solve their repetitive tasks or inefficiencies.
Implications
This reveals a fundamental gap in how we approach AI democratization. The real barrier isn’t technical complexity but conceptual recognition - most people need to be taught how to identify automation opportunities, not just given better tools. This suggests that AI coding assistants should focus on problem identification and suggestion, not just code generation.
Counterpoints
- Easy-to-use no-code tools have created many successful citizen developers: Platforms like Zapier, Airtable, and Notion have enabled non-programmers to automate workflows, suggesting people can learn to see software solutions when tools are intuitive enough.
- The problem might be tool design rather than user mindset: Current AI coding tools still require too much technical knowledge - better interfaces that suggest automation opportunities based on user behavior might bridge this gap.