💻 AI is changing the economics of code rewriting
The quality of neural network performance directly depends on the cleanliness and predictability of the codebase. Popular technology stacks gain an advantage due to the abundance of training data, whereas working with legacy or proprietary languages requires more context and tokens, which increases cost and reduces quality.
🌍 Strategic software architecture planning must account for "AI-readiness." Creating codebases with clear, standardized patterns becomes a competitive advantage, allowing for faster and cheaper automation implementation.
👤 When choosing a technology stack or planning refactoring, one should consider how well AI assistants understand your code. Excessive or chaotic legacy code makes neural network operations more expensive and less efficient.
Source 1: https://thetruthasiseeitnow.com/ai-slop-starts-with-the-codebase-itself/