On an Orthogonal Axis of Artificial Intelligence: From Raw Intelligence to Consequence-Aware Systems
Hohilauri, Heorhii · Zenodo (CERN European Organization for Nuclear Research) · 2025
Paragraph 1 — positioning (громко): This paper introduces an orthogonal framing of artificial intelligence development, separating raw intelligence (computational and generative capacity) from consequence-aware systems design. It argues that contemporary AI progress is constrained by a single-axis competition focused on scale, speed, and output quality, while neglecting systemic responsibility for downstream consequences. Paragraph 2 — core idea: The work formalizes the concept of an orthogonal axis of AI, where systems are evaluated not by how intelligent they appear, but by how they manage, anticipate, and constrain the consequences of their actions in complex environments. Within this framework, “raw intelligence” is defined as unregulated cognitive capacity prior to ethical, contextual, or responsibility-aware mediation. Paragraph 3 — contribution: The paper positions consequence-awareness as a non-competitive dimension relative to raw intelligence, introducing a new coordinate system for AI system design. This reframing enables the emergence of architectures that prioritize stability, bounded adaptation, and long-term systemic resilience rather than maximal task performance. Paragraph 4 — relevance: This work serves as a conceptual foundation for a new class of operational intelligence systems, intended for high-stakes decision environments where unbounded optimization is unsafe. It is presented as a foundational positioning document rather than an implementation manual, establishing intellectual priority over the framing itself.