An Operational Kardashev-Style Scale for Autonomous AI - Towards AGI and Superintelligence
Przemyslaw Chojecki

TL;DR
This paper introduces an operational, multi-axis scale for autonomous AI development inspired by the Kardashev scale, enabling measurable progress from automation to superintelligence, with formal criteria and benchmarks.
Contribution
It proposes a novel, testable AAI scale with capability axes, a composite index, and formal criteria for self-improvement and closure, advancing the measurement of AI progress.
Findings
Defines ten capability axes and a composite AAI-Index.
Introduces the Self-Improvement Coefficient $$ and closure properties.
Illustrates how current systems map onto the scale and formalizes progress towards superintelligence.
Abstract
We propose a Kardashev-inspired yet operational Autonomous AI (AAI) Scale that measures the progression from fixed robotic process automation (AAI-0) to full artificial general intelligence (AAI-4) and beyond. Unlike narrative ladders, our scale is multi-axis and testable. We define ten capability axes (Autonomy, Generality, Planning, Memory/Persistence, Tool Economy, Self-Revision, Sociality/Coordination, Embodiment, World-Model Fidelity, Economic Throughput) aggregated by a composite AAI-Index (a weighted geometric mean). We introduce a measurable Self-Improvement Coefficient (capability growth per unit of agent-initiated resources) and two closure properties (maintenance and expansion) that convert ``self-improving AI'' into falsifiable criteria. We specify OWA-Bench, an open-world agency benchmark suite that evaluates long-horizon, tool-using, persistent agents. We define…
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Taxonomy
TopicsRobotic Process Automation Applications · Ethics and Social Impacts of AI · Business Process Modeling and Analysis
