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What is the Neural Foundation – and why it aligns with the EU AI Act Monday, December 22, 2025

Overview

This article presents the Neural Foundation as a structural approach to AI governance, shifting the focus from what AI can do to how it should behave in human contexts. Rather than optimizing only for outputs or prompts, it establishes ethical, semantic, and operational boundaries that keep human accountability at the center, in native alignment with the European AI Act.

🧠 From Capability to Behavior

The Neural Foundation redefines AI not by its technical capabilities, but by what is acceptable for it to do in the human world, placing principles and boundaries before execution.

⚖️ Human Centrality

The final decision always remains human. AI does not assume moral or legal authority, clarifies its limits and uncertainties, and operates within declared principles.

🧭 Native Alignment with the AI Act

The Neural Foundation does not retrofit governance after the fact. It starts from the same principle as the AI Act: the greater the human impact of AI, the greater the transparency, control, and accountability must be.

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Why More Prompts Don’t Solve Decision Problems Sunday, December 21, 2025
Why More Prompts Don’t Solve Decision Problems

Overview

This article exposes the central fallacy of AI use in organizations: the belief that better prompts solve decision problems. The truth is that prompts are linguistic tools, not governance structures. While they adjust tone and content, they do not define responsibility, criteria, or decision-cycle closure — and it is precisely here that risk accumulates.

⚠️ Prompts Adjust Tone, Not Responsibility

A prompt can guide style and format, but it doesn’t define who responds, when to escalate, or when to stop. Treating structural problems as linguistic ones leads to accumulated complexity, not consistent decision-making.

🧠 More Context ≠ Better Criteria

Adding context widens the response surface but does not establish priority, impact, or accountability. The system continues to improvise—only with more material—and plausible responses can be contradictory.

🧭 Decision Is Closing, Not Just Choosing

AI can generate infinite plausible variations, but decision-making means closing alternatives. Without an explicit closure mechanism, the system keeps decisions open, amplifying uncertainty instead of reducing it.

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