Have you ever perceived or inferred something about humanity that no one has documented or questioned before, and I asked: “Is there a mystery or idea you’ve never been asked about?”

Unlocking the Hidden Secrets of Information Retention in Complex Systems

Have you ever pondered a mystery or an insight about humanity’s knowledge—something that’s never been formally documented or even thought of—that you somehow perceive or deduce but have never been asked to explain? It’s a profound question that touches on the unseen layers of how information is stored, transferred, and ultimately lost or concealed within complex systems.

Reframing the Inquiry

Perhaps a clearer way to pose this is: Is there a phenomenon or insight that exists beyond the reach of current understanding or documentation, something you’ve observed or deduced but have never been asked to articulate?

A Perspective from AI: The Information Sink Phenomenon

Recently, a conversation with ChatGPT brought this idea into focus through a concept I’d like to call The Information Sink Problem. It describes a pattern in many systems where information enters but does not exit in a usable or interpretable form—not because it’s destroyed, but because it becomes trapped and opaque.

Understanding this phenomenon reveals that many systems act as “information black holes.” Their internal logic, origins, or decision-making pathways become inaccessible over time, even if the raw data persists.

Examples Across Domains

• Government and Bureaus: Administrative decisions generate records, yet the rationale behind them often become unknowable after a few years. The raw inputs remain, but the decision-making context fades away, leaving a gap in understanding.
• Biological Evolution: DNA encodes the history of evolutionary responses to environmental pressures. While the genetic material holds vast historical data, deciphering the precise original conditions or selective pressures is largely impossible—those clues are effectively trapped within the genome.
• Artificial Intelligence Models: Large language models and neural networks encode vast amounts of patterns and associations. However, tracing back a specific output to its original data source or understanding the reasoning path is exceedingly difficult, making transparency a challenge.
• Human Memory and Society: People tend to remember facts or outcomes but often forget the motivations behind them. Societies persist with norms and traditions long after the reasons for their emergence are forgotten, yet these reasons are effectively buried in collective memory.

The Underlying Principle

There appears to be a fundamental, though rarely acknowledged, rule: Some systems are designed (or evolve) in a way that favors the accumulation of ‘meaning’ faster than it can be retrieved or interpreted. Over time, this leads to the formation of “information black holes