The text explores the concept of r/K selection theory from evolutionary ecology and applies it to the software industry. It discusses the survival strategies of r-selection and K-selection, which involve different approaches to reproduction and investment. The text argues that software can be considered as evolving and reproducing through copying and memes. It highlights aggregators and programming languages as examples of K-selected software due to their stability and long lifespans. On the other hand, it suggests that open source software and the proliferation of small modules resemble r-selected traits with high dependency churn. The text also raises questions about the impact of AI-generated code and the potential shift towards r-selected solutions.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| r/K selection theory in software | Evolution of software | Software evolves and reproduces | Heredity, mutation, selection |
| Aggregators and programming languages | K-selection | Stability, longevity | Capital investment, standardization |
| Open source and dependency churn | r-selection | High dependency turnover | Starvation wages, burnout |
| AI code autocompletion | Potential disruption | AI-generated libraries and modules | Advancements in AI technology |
| AI-generated code and maintenance | Disruption of maintenance | Increased generation of libraries | Cost reduction, automation |
| Rapid generation of new programs | Increased niche discovery | Unstable software ecosystem | Advancements in AI technology |
| Ownership of AI language models | Potential aggregation | AI-powered IDEs and competition | Capital investment, open source |
| r-selected open source as a threat | Threat to aggregators | Unstable software ecosystem | Fast-breeding software, new niches |