Against Entropy in the AI Era

2026-05-16 · Source: AI on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

The author, a principal data scientist with a background in quantitative finance and crypto, observes the current landscape of AI, data, trading, and cryptocurrency as a period of significant change, likening it to a "fourth Industrial Revolution." This era is characterized by rapidly evolving relationships between humans and machines, generating considerable "entropy." The author argues that while AI accelerates execution tenfold, the complexity necessitates a shift in the traditional 2/3 research, 1/3 execution rule to a 90% thinking, 10% execution paradigm. The author plans to explore foundational concepts like Ontology, Cybernetics, and the Viable System Model to identify enduring structures beneath the transient trends in AI, aiming to distinguish fundamental changes from mere noise.

Key takeaway

For AI scientists and data scientists navigating the rapid evolution of tools and frameworks, prioritize deep theoretical understanding over immediate execution. Focus 90% of your effort on foundational research and architectural thinking, rather than chasing every new product, to build more resilient and future-proof AI systems that withstand rapid technological shifts.

Key insights

Rapid AI advancements necessitate a greater emphasis on foundational thinking over execution to manage increasing system entropy.

Principles

• Complexity demands robust systemic structures.
• Foundational theories outlast transient tech trends.

Topics

• AI Era Dynamics
• Entropy Reduction
• Knowledge Systems Architecture
• Cybernetics
• Viable System Model

Best for: AI Scientist, Data Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.