The “Value” of Human Intelligence in the AI Era | Philosophy of life | 10 example of philosophy | Philosophy examples | Turtles AI

In the new insight article by Turtle’s AI, we address a very delicate yet immensely important topic. Human intelligence has, for millennia, been the driving force behind the evolution and growth of society. For the first time, artificial machines exhibit forms of "behavior" that “resemble” this characteristic, previously exclusive to humans. Consider the many bots now capable of responding appropriately or even “simulating” (or emulating?) complex reasoning, such as OpenAI’s recent model o1. This development prompts a profound reflection on the value (anthropological and economic) of human intelligence in our society.

Intelligence, in its broadest sense, is the ability to adapt to new situations, solve problems, and generate innovative ideas. Philosophically, the concept of intelligence has been analyzed through frameworks that explore the nature of thought, reasoning, and self-awareness. Immanuel Kant emphasized reason as the foundation of human intelligence, contrasting it with mere sensory perception. Modern interpretations expand on this dichotomy, examining the mechanical replication of cognitive tasks by machines. The emergence of artificial intelligence challenges the boundaries of Kantian thought, as AI demonstrates abilities in logic and problem-solving, while lacking the subjective and self-reflective dimensions Kant associated with human reason.

In recent developments, the philosophy of intelligence has broadened its perspective, incorporating theories that recognize the multiplicity of human cognitive abilities. For instance, Howard Gardner’s theory of multiple intelligences identifies different forms of intelligence, including logical-mathematical, linguistic, spatial, musical, bodily-kinesthetic, interpersonal, and intrapersonal, suggesting that intelligence is not a monolithic entity but a collection of distinct abilities.

Similarly, Robert Sternberg introduced the “triarchic theory of intelligence”, which distinguishes among analytical, creative, and practical intelligence. This theory emphasizes the importance of effectively adapting to the environment by combining various cognitive skills to tackle daily and contingent challenges.

Furthermore, emotional intelligence has emerged as a key concept, focusing on the ability to recognize, understand, and manage one’s own emotions and those of others. This aspect of intelligence is considered crucial for social interactions and personal well-being, but—as we will soon discuss—also in organizational and corporate contexts.

Semiotics, the study of signs and their meanings, provides a fundamental contribution to understanding human intelligence and offers critical tools to interpret the functioning and implications of artificial intelligence. From the perspective of human intelligence, Charles Sanders Peirce’s triadic model—comprising the sign, object, and interpretant—clarifies how meaning emerges from an interpretive process in which the subject associates symbols with concepts, based on lived experience. This process is not purely mechanical but rooted in subjectivity, emotionality, and awareness, which are central elements of human cognition.

When AI is analyzed through semiotics, significant similarities and differences emerge compared to human intelligence. AI systems, through advanced algorithms, "imitate" the process of constructing meaning by associating data with symbolic representations to generate outputs understandable to humans. However, unlike human intelligence, the meaning produced by AI lacks a subjective interpretant, that is, an entity capable of reflecting, feeling, and experientially understanding. This is evident when considering the basic functioning of GPT systems (generative pre-trained transformers), often featured on our pages.

This "semiotic gap" delineates the distinction between simulated cognition and authentic experience.

However, this gap is potentially destined to diminish with the continuous advancement of artificial intelligence techniques. Increasingly sophisticated machine learning and deep learning models are integrating complex structures of contextual learning and dynamic adaptation, making AI outputs increasingly indistinguishable from those produced by the human mind—at least to an external observer. Emerging technologies, such as advanced language models and multimodal intelligence systems, are designed to handle complex semantic connections, simulate empathy, and respond seemingly intentionally to specific situations.

This evolution is unlikely to bridge the lack of subjective experience but will render the machines’ output increasingly coherent and convincing on a semiotic level, to the point that the human interpretant may be inclined to recognize AI as endowed with "quasi-intentionality". In the future, the observer might perceive this gap as nonexistent, focusing exclusively on the result of the semiotic process rather than the nature of the producing subject.

From a psycho-sociological perspective, human intelligence is today a fundamental resource for the functioning and organization of society. The ability to adapt to complex contexts, solve problems, and foster innovation has direct economic value, as it drives individual and collective productivity. Moreover, emotional intelligence, understood as the ability to understand and manage one’s own and others’ emotions, supports social cohesion and collaboration—indispensable factors in both workplace and community settings. Unsurprisingly, many organizational theories are based on enhancing cognitive and relational skills, recognizing that an enterprise’s economic and social success depends not only on technological innovation but also on the quality of human interactions, empathetic leadership, and the ability to build collaborative and resilient work environments. Economically, these skills generate tangible benefits, such as effective conflict management, increased work engagement, and reduced costs associated with stress and burnout. Socially, emotional intelligence contributes to building networks of support and trust, essential elements for community stability and progress.

Sociologically, the value of human intelligence is also evident outside organizational structures in its ability to generate social and intellectual capital—two fundamental pillars of economic and cultural development. Analytical, creative, and interpersonal skills enable individuals to manage complexities.

From a strategic-business perspective, human intelligence has always played a key role in strategic decision-making, change management, and leadership—abilities that remain challenging for artificial intelligence to replicate, even in an era of increasing automation. However, with the advancement of AI, especially generative AI, an interesting dynamic emerges: while automation reduces the demand for certain repetitive tasks, it highlights the social and economic value of unique human abilities such as creativity, empathy, and moral judgment, which are increasingly sought after in roles requiring high interaction and innovation.

This shift also calls for a rethinking of educational and training structures, which should focus on amplifying these skills to ensure that human intelligence continues to generate value in a world increasingly supported (and not replaced!) by AI. By doing so, human intelligence can remain not only a central economic factor but also an indispensable social element for adapting to ever-changing contexts.

The relationship between human and artificial intelligence also extends beyond functionality, touching on existential questions of identity and purpose. Hannah Arendt’s exploration of the human condition highlights the centrality of work, labor, and action in defining human existence. The incursion of AI into labor and artistic work—traditionally human domains—requires a reconsideration of what constitutes meaningful action. The shift of routine intellectual tasks to AI creates opportunities for humans to engage in more creative and philosophical activities but also risks exacerbating socioeconomic inequalities, as access to such opportunities is often stratified and not entirely equal.

The potential evolution of AI into systems capable of rivaling human intelligence thus brings to the forefront the strategic value of the latter, considered, according to Porter’s model, a key resource in creating competitive advantage. In this perspective, human intelligence is not limited to producing outputs but generates intrinsic value linked to understanding context, innovating originally, and making decisions based on complex judgments. Philosophical debates, such as Alan Turing’s imitation game, compel us to consider intelligence not so much for its origin as for the value it produces in terms of observable results. However, John Searle’s Chinese Room argument highlights that even in advanced systems, syntactic manipulation, no matter how precise, does not equate to semantic understanding and thus does not replicate the deeper value derived from human cognition.

This limitation, still evident, marks a crucial difference in value creation models: while AI excels in efficiency and data processing, human intelligence brings new meanings and context—central elements for the development of strategic innovations.