Data has a half-life
A question about feedback, changing environments, and what remains useful over time.
Learning from the world.
A living input
A dataset describes a world at a particular moment. Some parts remain useful for years; others lose relevance as products, behaviors, and environments change. Treating all data as an equally durable asset can obscure the difference.
The better question may be how a system notices that its information has become stale, and how it obtains a better observation.
Closing the loop
A feedback loop connects an action to its consequences. In practice, that connection can be incomplete: outcomes arrive late, attribution is ambiguous, and the objective itself may change.
These details matter when assessing a claim that a product will improve through use. What does it actually learn from? How quickly does the signal arrive? Is the signal aligned with what the user values?
A lens for research
Rather than counting accumulated data, consider the quality of the ongoing relationship with the environment. A smaller, relevant stream of observations may tell us more than a large static archive.
The durable advantage, if there is one, could be a reliable way to ask the world better questions.
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