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The dominant paradigm in modern AI is model-centric.
Progress is measured by larger models, more data, and improved benchmarks. Intelligence is implicitly defined as the capability of a single model to generate accurate or fluent outputs. Within this framework, better intelligence is assumed to emerge through scale and optimization.
This approach has produced remarkable results.
It has also reached a structural ceiling.
Model-centric AI treats intelligence as an internal property of a model. Reasoning, alignment, and behavior are expected to emerge from training dynamics rather than from explicit system design. As models become more capable, their behavior becomes more complex—but not more governed.
The absence of governance is not accidental.
It is a consequence of the paradigm itself.
Models are optimized, not organized.
They generate responses, but they do not operate within an explicit architecture that defines reasoning phases, semantic boundaries, or alignment constraints across time. As a result, intelligence remains reactive and context-dependent rather than structured and intentional.
System-centric intelligence begins from a different premise.
Intelligence is not a property of a model.
It is a property of a system.
In a system-centric paradigm, models are components embedded within a higher-level architecture that governs how reasoning unfolds. The system defines when reasoning begins, how it transitions between phases, how meaning is evaluated, and how alignment is maintained over extended interactions.
This shift mirrors earlier transitions in computing.
Early software relied on programs directly managing hardware resources. Modern computing introduced operating systems that abstract, schedule, and govern execution. Capability increased not because programs became larger, but because systems became structured.
Intelligence follows a similar trajectory.
Without a system-level architecture, adding capability increases complexity without control. With a governing system, even imperfect components can produce stable, interpretable, and aligned behavior.
System-centric intelligence introduces:
In this architecture, models do not decide how to reason.
They execute within constraints defined by the system.
This distinction becomes critical as AI systems move from isolated tasks to persistent roles in decision-making, coordination, and governance. Intelligence that lacks system-level structure cannot reliably support long-term responsibility.
AI 2.0 is defined by this transition.
It is not characterized by larger models, but by the emergence of semantic operating systems that organize how intelligence behaves across time, context, and human intent.
FRAME OS is designed as such a system.