Selfincrease

Selfincrease is a neologism used to describe processes by which an actor increases its own capacity, resources, or influence through internal mechanisms that amplify future growth. The term emphasizes self-propelled development rather than external input, and its precise definition varies by field. It is not a widely standardized term, but it is sometimes used as a shorthand for self-reinforcing growth.

In different domains, selfincrease can take on related meanings. In systems theory and cybernetics, it may refer to recursive or feedback-driven improvement, where changes to a system enable further improvements in a self-contained cycle. In economics and organizational studies, selfincrease can describe endogenous growth arising from reinvestment, learning-by-doing, or network effects that compound over time. In discussions of artificial intelligence, the phrase can appear as a lay term for recursive self-improvement, where an agent modifies its own architecture or strategy to achieve greater capabilities.

Common mechanisms associated with selfincrease include feedback loops, reinforcement learning, optimization of internal processes, reinvestment of returns, and compounding effects. Examples range from personal development programs that build skills over time to AI systems designed to enhance their own performance incrementally.

Limitations and criticisms focus on definitional ambiguity, the risk of overgeneralization, and concerns about runaway dynamics if self-improvement is not properly bounded. Critics also emphasize the importance of safeguards, governance, and ethical considerations when self-directed growth could impact other agents or systems.

See also: recursive self-improvement, endogenous growth theory, feedback loop, reinforcement learning, compounding.