Key Takeaways
- AI coding platforms profit from user mistakes through credit-based pricing models
- Dogfooding revealed gap between demo generation and production-ready code deployment
- Fixed monthly pricing aligns platform incentives with user success rather than iteration volume
Why It Matters
The AI coding revolution promised to democratize software development, but Ruban Phukan discovered a fundamental flaw in how these platforms make money. When AI tools charge per iteration or credit consumed, they literally profit from their own mistakes and user confusion. It's like paying a mechanic who gets paid more when your car breaks down more often. This creates a perverse incentive where platforms have no financial motivation to improve accuracy or reduce the iteration count needed to reach working code.
Phukan's decision to build Avery.dev using Avery.dev itself represents more than just clever marketing—it's a forcing function for quality. When your own business depends on the code your AI generates, suddenly every bug becomes personal. This dogfooding approach revealed that most AI coding platforms excel at creating impressive demos but struggle with the unglamorous work of production-ready code. The gap between "wow, this looks amazing" and "this actually works in production" remains stubbornly wide, even as the demo quality has reached near-magical levels.
The broader implication extends beyond just AI coding tools to any AI service model. As artificial intelligence becomes more capable, the question of who bears the cost of AI's inevitable mistakes becomes critical. Phukan's experience suggests that sustainable AI platforms need to absorb the cost of their own limitations rather than passing them on to users. This isn't just about fairness—it's about creating the right incentives for genuine improvement rather than flashy features that look good in screenshots but fall apart under real-world pressure.
