Beyond the Race for Smarter AI: Toward Standards for AI Capability, Dependability, and Human-AI Capability Continuity

Abstract

The present phase of artificial intelligence is characterized by an intense competition for cognitive capability. Vendors compare models by reasoning, knowledge, coding, planning, and increasingly agentic performance. Yet the historical role of standardization in computing suggests that technological maturity does not arise from performance competition alone. Programming languages, communication protocols, file formats, and system interfaces became economically and socially powerful because common abstractions made heterogeneous implementations comparable, interoperable, and dependable. This paper asks what an analogous transition would mean for AI. It argues that the standardization of AI intelligence should not mean equalizing or freezing intelligence. Rather, it should mean standardizing the language, measurement conditions, capability descriptions, uncertainty declarations, interoperability mechanisms, and responsibility structures through which heterogeneous AI systems are evaluated and used. A multidimensional AI Capability Profile is proposed as a more appropriate object of standardization than a single intelligence score. The paper further argues that, as frontier cognitive performance becomes broadly adequate for many tasks, competition will expand from raw intelligence toward dependable capability, epistemic discipline, fidelity to human intent, human-AI collaboration, long-horizon job execution, portability, and capability continuity. This shift also changes the question of human intelligence. Human biological intelligence need not increase for human practical intelligence to improve; what can grow is the capability of the coupled Human-AI system. Accordingly, education and engineering should increasingly cultivate judgment, problem formulation, evaluation, responsibility, and the ability to preserve and develop capabilities across changes of models, users, and environments. The paper concludes that the post-benchmark era of AI will be defined less by which model is smartest in isolation than by which Human-AI systems can sustain useful, trustworthy, transferable, and accountable capability over time.