The discourse surrounding artificial intelligence (AI) often reduces intelligence to domain knowledge, information processing, and computational creativity. Large Language Models (LLMs), for example, are seen as powerful systems capable of manipulating vast amounts of data to generate responses that simulate human-like reasoning. However, from an evolutionary perspective, intelligence is far more than the capacity to store and process information. Intelligence is deeply contextual, shaped by the environments in which it emerges, and defined by its ability to adapt rather than merely compute.
Intelligence as Contextually Dependent Adaptivity
In an evolutionary framework, intelligence is best understood as an adaptive function—an organism’s ability to navigate and respond to its ecological niche. Adaptation is not just about accumulating knowledge or improving pattern recognition; it involves reading environmental cues, adjusting behavioral repertoires, and modifying cognitive strategies in response to shifting contingencies. This kind of intelligence is inherently process-based, not just knowledge-based.
Consider intelligence in its most primal forms: bacteria detecting chemical gradients to move toward nutrients, insects adjusting foraging behaviors based on seasonal availability, or mammals displaying social learning in dynamic group structures. Each of these forms of intelligence is contextually bound, responsive to shifting environmental demands, and dependent on feedback loops that reinforce successful adaptations while extinguishing ineffective ones.
Even in humans, intelligence manifests as an intricate interplay of perceptual sensitivity, intuitive heuristics, rule-based cognition, and improvisational reasoning. It is not merely about how much an individual knows but how well they adjust, pivot, and regulate behaviors in unpredictable or novel contexts. This aspect of intelligence—contextual flexibility—is something AI, particularly LLMs, struggles to emulate in a meaningful way.
AI and the Limits of Computational Intelligence
LLMs like ChatGPT represent a slice of intelligence—primarily linguistic and statistical in nature. They excel at pattern recognition, text generation, and mimicking human-like responses. However, their intelligence is domain-constrained, lacking the ability to truly adapt beyond predefined structures of training data.
Unlike an evolved intelligence that continually refines itself based on lived experience and direct environmental feedback, LLMs do not adjust their rule sets in real-time outside of explicit retraining. They operate within probabilistic boundaries set by prior learning, making them exceptional at pattern synthesis but fundamentally lacking in ecological adaptability.
For instance, while an LLM can generate insights about a philosophical dilemma, it does not experience the dilemma, nor does it adjust its position based on the contextual weight of consequences. It lacks an embodied sense of risk, stakes, or situational intuition. Intelligence, from an evolutionary standpoint, is not just about producing the right answer but navigating uncertainty, resolving competing contingencies, and responding effectively to emergent challenges.
Intelligence from a Functional Contextual Perspective
Functional contextualism, rooted in behavioral science, emphasizes that meaning, knowledge, and behavior are always shaped by their situational context. Intelligence, within this framework, is not a static trait but a process of effective action guided by purpose and contingencies. Rather than being defined by the accumulation of facts or even problem-solving abilities in isolation, intelligence is about the utility of cognition and behavior in achieving specific, contextually relevant outcomes.
This approach highlights why AI and LLMs, despite their capacity for processing and recombination, fall short of true intelligence. Intelligence, when viewed through a functional contextual lens, must involve:
Adaptivity to Changing Environments – AI models operate within a closed learning framework, whereas organic intelligence is continuously shaped by unpredictable contingencies.
Embodied and Situational Awareness – Human cognition integrates sensory, emotional, and physiological cues, allowing for responses that reflect lived experience, something absent in AI systems.
Flexible Rule Modification – While AI can optimize within given rule structures, true intelligence involves changing or even discarding rules based on new contextual demands.
Process-Based Learning – Intelligence is an iterative process of trial, error, and refinement, often shaped by consequences rather than preprogrammed knowledge alone.
A useful analogy might be comparing intelligence to a river rather than a static reservoir of information. The river moves, carves new paths, and reshapes itself in response to the terrain. AI, by contrast, is a vast but stagnant lake—deep in stored knowledge but incapable of altering its own flow in response to external factors unless actively modified.
Rethinking Intelligence in an Age of AI
As AI continues to evolve, it is essential to move beyond the myth that intelligence is merely about processing power, speed, or sheer information capacity. True intelligence is an emergent property of interaction—between the individual and their environment, between cognition and consequences, between behavior and meaning.
While AI and LLMs may continue to revolutionize fields dependent on pattern recognition and knowledge retrieval, their role should not be confused with the adaptive, embodied, and deeply contextual intelligence that characterizes human cognition. Intelligence, as shaped by evolution, is an active process, not a static computation. AI, in its current form, offers an impressive but limited emulation of this process—a tool that assists but does not independently adapt, perceive, or intuit in the way biologically evolved intelligence does.
Understanding intelligence as a function of contextual adaptivity rather than raw computation offers a more nuanced and scientifically grounded perspective on what separates human cognition from artificial systems. AI may provide powerful augmentations to knowledge work, but the essence of intelligence remains uniquely human: dynamic, situated, and forever shaped by the contingencies of life itself.
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