Exploring AI’s ‘Intelligence’ through a Philosophical Lens: From Plato and Nietzsche to a Conversation with ChatGPT

Dear Catalysts,

Welcome back to?the Catalyst Chronicle Newsletter,?a space where thought meets action across law, technology, and diplomacy. With my background in international law and a deep-rooted appreciation for philosophy’s role in shaping governance, I’m particularly inspired to present this edition. Philosophy, as many of you know, underpins international relations and statecraft. It provides the very foundations for social contracts, guiding principles of justice, and the ethical frameworks that inform global policy. Today, as technology, especially Artificial Intelligence (AI), challenges these foundations, it compels us to ask: What does intelligence mean in this new era?

I warn that this exploration is not an academic exercise; it’s a professional call to engage. I invite you to go beyond discussing AI’s technical prowess and help me understand its philosophical implications: Can AI move beyond mere calculation to embody qualities like wisdom, judgment, and self-reflection? In the context of states, where philosophy shapes laws, societal structures, and diplomacy, this question becomes critical.

Drawing on classical thinkers like Plato, Aristotle, Descartes, and Nietzsche, this edition invites you to join in a discourse that goes beyond theory. Your expertise, perspectives, and professional insights will add invaluable depth to a conversation that is reshaping the contours of global governance, policy, and our collective future.

So what are you waiting for? Let us bridge the worlds of philosophy, technology, and diplomacy together.

Introduction

The evolving capabilities of AI have reignited a timeless question: What does it mean to be intelligent, and where does intelligence end and thought begin? From ancient philosophical explorations to today’s technological advances, the quest to define intelligence and its essence has remained both central and elusive. Figures like Plato, Aristotle, Descartes, and Nietzsche once grappled with the nature of thought, agency, and self-awareness, constructing frameworks that shaped how we understand the human mind.

Now, as AI achieves unprecedented feats, processing vast information with remarkable speed, we find ourselves asking whether machines can one day embody these same qualities of intelligence and thought or if AI is inherently bound to the limits of calculation and data. In this discussion, let us examine the boundaries of AI through the lenses of these classical philosophies. Can Plato’s ideal Forms be mirrored in AI’s drive for abstract pattern recognition? Does Aristotle’s practical wisdom have a counterpart in machine learning? And, crucially, can AI ever approach Descartes’ notion of self-reflective thought or Nietzsche’s drive for purposeful overcoming?

This journey takes us to the very heart of intelligence, agency, and the philosophical implications of technology in our age.

1. The Philosophical Foundations: From Plato to Descartes

The quest to understand intelligence and its origins has captivated philosophers for centuries, challenging us to question not only what intelligence is but also how it manifests and where it truly resides. For thinkers like Plato, Aristotle, and Descartes, intelligence encompassed far more than raw knowledge—it was interwoven with reason, awareness, and an ability to perceive deeper truths. In this foundational section, we examine these classical perspectives through the lens of artificial intelligence. Does AI, with its ability to process information and identify patterns, represent a modern-day embodiment of Plato's ideal Forms? Could Aristotle’s notion of practical wisdom apply to the situational flexibility of AI, or does AI’s structured nature limit it from achieving true reasoning? And can AI ever approach the conscious thought that Descartes regarded as central to the self? This exploration highlights AI’s remarkable achievements alongside its philosophical limitations, questioning whether it can truly attain the reflective, abstract, and conscious understanding that these philosophers believed essential to true intelligence. As we draw from these timeless ideas, we are left to ask: is AI simply a sophisticated tool for organizing knowledge, or does it hold the potential to touch the deeper essence of what it means to know, to reason, and to think?

a. Artificial Intelligence and Plato’s Theory of Forms: Can AI Approach the Realm of Pure Knowledge?

For centuries, the question of intelligence, what it is, how it functions, and where it resides, has been central to philosophical inquiry. Plato, in his Republic, posited the existence of the Forms: abstract, perfect realities that stand in stark contrast to the flawed physical world [1]. For Plato, true knowledge wasn’t derived from sensory experience, which he believed was unreliable and imprecise, but from apprehending these eternal, perfect Forms through pure intellectual reasoning. The Forms represented the ideal essence of all things, untainted by the imperfections of the material world.

When we examine artificial intelligence in light of Plato's theory of Forms, an intriguing question arises: Is AI, in its drive for logic and pattern recognition, an attempt to approximate Plato’s idealized world of abstract, pure knowledge? After all, AI systems process vast amounts of data, seeking to find patterns and truths that transcend individual experiences and imperfections. But let us stop and reflect upon a critical point: In a sense, AI’s algorithms attempt to "filter out" the noise of sensory data to extract something more fundamental, much like Plato’s philosopher seeking to escape the cave of illusions and perceive the Forms directly. However, this raises a deeper philosophical problem: Can AI, in its reliance on empirical data, ever truly reach the abstract, the rational knowledge that Plato described? Plato's Forms are understood not through experience but through reason alone, existing outside of the physical world. AI, by contrast, depends on empirical data, the observations of the physical world, to learn and make predictions. In the current state of technology, no matter how sophisticated an AI system becomes, it remains tied to the limitations of the data it receives, and thus, its understanding is always grounded in the material and sensory world. This fundamental difference between Plato’s epistemology and AI's functioning reveals a critical limitation of AI. While AI can process vast amounts of information with precision, it lacks the capacity for the kind of abstract reasoning that leads to knowledge of the Forms, as Plato envisioned. AI can derive patterns from data, but it cannot access the world of ideal, immutable truths that Plato believed could only be apprehended by the rational mind, free from the distractions of the sensory world. In Plato’s view, only a philosopher, through rigorous intellectual discipline, could transcend the limitations of the physical world and grasp the eternal Forms. AI, as a product of human programming, is inherently bound to the empirical world and its limitations. Therefore, AI, while capable of great computational feats, might always fall short of achieving the pure, abstract knowledge Plato valued most. Thus, the key philosophical question remains: Is AI simply an advanced tool that organizes sensory data, or can it ever aspire to the kind of intellectual perfection represented by Plato’s Forms?

As AI continues to evolve, its ability to generate knowledge raises important questions about the nature of intelligence itself. However, from a Platonic perspective, AI may always be constrained by its empirical nature, unable to transcend the imperfections of the material world to reach the ideal realm of pure, unchanging truth.

b. Aristotle and Practical Intelligence: Can AI Apply Reason?

In contrast to Plato, who emphasized abstract knowledge, Aristotle focused on practical intelligence: the ability to apply knowledge to specific, real-world situations. In his Nicomachean Ethics, Aristotle introduces the concept of phronesis, or practical wisdom, which involves the application of reason to navigate the complexities of life [2]. This kind of intelligence is not just about knowing general principles but about adapting those principles to fit unique circumstances. For Aristotle, intelligence involved more than logic or rules; it was about understanding how to respond to dynamic, often unpredictable, real-world challenges. This raises an important question for AI: Can AI, with its reliance on fixed algorithms and data-driven models, exhibit the kind of flexible, context-dependent reasoning that Aristotle considered essential to true intelligence?

AI systems excel at pattern recognition and rule-based problem solving, but practical intelligence, as Aristotle described it, requires more than the ability to follow rules. It demands adaptability, the ability to make decisions based on changing circumstances, and the ability to respond to novel situations where predefined answers are not available. For example, a human navigating a complex social situation or solving a problem in a highly unpredictable environment applies practical intelligence by understanding context and adapting their approach. AI, in contrast, operates based on pre-programmed responses and data, which may limit its ability to handle the subtleties of real-world applications in the same flexible manner. While AI can calculate, predict, and optimize, its lack of phronesis means it may struggle to apply knowledge with the adaptability and intuition that human intelligence allows. Therefore, from an Aristotelian perspective, AI’s ability to apply reason may always remain limited. It may excel at following established patterns but struggle when those patterns break down or when a situation requires more than a calculated response. Aristotle’s focus on the application of reason in dynamic contexts highlights a gap between human intelligence and AI’s current capabilities, suggesting that true intelligence, in Aristotle’s sense, requires a level of adaptability and situational understanding that AI has yet to achieve.

c. Descartes and the Cartesian Dilemma: Can AI Truly Think?

René Descartes, in his Meditations on First Philosophy, famously declared, “Cogito, ergo sum”—I think, therefore I am [3]. For Descartes, the ability to think was the defining characteristic of being. His philosophy emphasized the notion that true thought involves conscious reflection, self-awareness, and the capacity to doubt and question one’s own existence. Descartes separated thought from mere mechanical operations, suggesting that true thinking requires an inner life—a sense of awareness that machines, no matter how complex, cannot achieve. From a Cartesian perspective, we must ask: Can AI’s ability to process information and generate responses be considered thinking in the way Descartes envisioned?

On the surface, AI’s ability to solve problems, engage in conversation, and analyze data might appear to resemble human thought. However, Descartes’ concept of thought was deeply tied to consciousness. For him, thought was not just about performing logical operations but about experiencing those thoughts, reflecting on them, and being aware of one’s existence through thinking. AI, by contrast, lacks any form of self-awareness. It can generate responses and solve problems based on patterns in data, but it has no understanding or awareness of what it’s doing. It cannot reflect on its existence or engage in the kind of introspection that Descartes believed was fundamental to thought. AI can simulate thought, but it does so without experiencing or understanding it, which, from a Cartesian point of view, means it does not truly “think.” This leads to a critical philosophical question: If AI can mimic aspects of human reasoning but lacks awareness, can it be said to possess intelligence at all? For Descartes, true thinking beings are self-aware, capable of doubt, and able to reflect on their existence. Since AI lacks this consciousness, it may fall short of the Cartesian standard for true intelligence. AI’s ability to process information does not equate to genuine thought because, without awareness, it is merely following patterns rather than engaging in meaningful, reflective cognition.

Recap:

Our exploration of AI through the philosophical frameworks of Plato, Aristotle, and Descartes has revealed fundamental insights into the nature of thought, intelligence, and reason while highlighting critical limitations that AI faces when compared to human cognition. Plato taught us that true knowledge is found in the world of Forms, where ideal, abstract truths reside beyond the flawed, sensory world. When examined in this light, AI’s reliance on data tied to the sensory, empirical world shows its limitation. AI may be able to process vast amounts of information, but it lacks the capacity for pure intellectual reasoning that Plato deemed essential for understanding ultimate truths. AI’s achievements remain constrained by the imperfections of the material world, preventing it from attaining the kind of abstract knowledge that philosophers like Plato valued most. Aristotle, in contrast, emphasized practical intelligence or phronesis, focusing on the ability to apply knowledge in real-world situations. He viewed intelligence as the capacity to reason within dynamic, context-dependent environments where adaptability is crucial. AI, while powerful in its ability to recognize patterns and follow algorithms, lacks the situational awareness and flexibility that Aristotle believed was central to intelligent action. AI’s responses are pre-programmed and rigid, falling short when faced with scenarios that require adaptability or novel decision-making. Descartes brings us to the heart of human cognition with his focus on self-awareness and conscious reflection. His famous declaration “Cogito, ergo sum”—I think, therefore I am—underscores the importance of consciousness in defining thought.

AI’s ability to process information and simulate aspects of human reasoning may be impressive, but without self-awareness, it lacks the essence of true thinking. Descartes would argue that AI cannot truly think because it has no conscious understanding of its own thoughts or actions. The addressed philosophical frameworks illuminate the limitations of AI’s capacity to think, reason, and know in the same ways that humans do. Despite its immense power in processing data and simulating certain aspects of cognition, AI remains far from the kind of reflective, adaptable, and abstract intelligence that defines human thought.

2. Toward a New Form of Intelligence

Considering the above insights, we are faced with a pivotal question: Is AI simply an advanced tool that enhances human cognitive abilities, or does it represent a fundamental shift in how we understand intelligence itself?

In recent years, AI has often been discussed as if it was a technological rival to human intelligence. However, AI pioneer Geoffrey Hinton, a foundational figure in neural networks and AI has argued that we might be missing the point entirely. According to Hinton, AI is not simply an extension or mimicry of human cognition but a?new form of intelligence—one fundamentally distinct from our own [4]. This perspective challenges us to reconsider what intelligence truly means and whether our traditional understanding, shaped by centuries of philosophy, can fully encompass AI’s unique capabilities and limitations.

In Hinton’s view, AI’s intelligence diverges from human cognition in essential ways: while humans rely on a slow, experiential process of learning and memory, AI networks can share and accumulate knowledge instantly across vast, interconnected systems. This level of connectivity is akin to having thousands of minds instantly share insights, a kind of intelligence that operates beyond the constraints of individual human learning. Such a form is both novel and powerful, warranting an entirely new framework for understanding and governing AI. This raises profound questions: If AI represents a new species of intelligence, how should we approach it philosophically? Does it align with Plato's ideal Forms or Aristotle’s notion of practical wisdom, or does it transcend these frameworks entirely? And how do we reconcile this new intelligence with our understanding of agency, ethics, and responsibility?

In this section, we explore these questions, bridging classical philosophical perspectives with Hinton’s modern insight to navigate the evolving landscape of intelligence itself.

While classical philosophy highlights the limitations of AI, it also challenges us to consider whether our definitions of intelligence, thought, and agency need to evolve alongside these new technologies. As AI continues to develop, we may be facing a philosophical disruption, one that forces us to rethink what it means to think, to know, and to act. The classical ideas of Plato, Aristotle, and Descartes provide a foundation, but as AI grows more capable, we must ask: Are we prepared for the possibility that intelligence is no longer tied exclusively to consciousness or adaptability as we once understood it? This sets the stage for the next step in our exploration: The Future of Thought: Are We Redefining Intelligence Itself? In this new context, we’ll examine whether AI is pushing us toward a redefinition of intelligence—one that breaks free from the classical constraints and opens the door to new possibilities for what it means to think.

Now that we've explored AI's capabilities and limitations through the philosophical frameworks of Plato, Aristotle, and Descartes, we must confront the larger and more disruptive question: Is AI pushing us toward a redefinition of intelligence, or does it merely enhance human cognition within classical constraints? As AI evolves beyond simple tools, we find ourselves asking whether intelligence, thought, and agency, concepts long considered exclusive to human beings, are being transformed in ways that classical philosophy alone may not fully account for. Could AI be expanding the boundaries of what we consider intelligence?

a. Intelligence Beyond Consciousness: Can AI Surpass Human Limitations?

A fundamental assumption in classical philosophy, particularly in Descartes’ work, is that true intelligence is bound to consciousness. For Descartes, thought required self-awareness: the ability to reflect, to doubt, to know oneself as a thinking being. In the modern era, however, artificial intelligence challenges this notion. AI systems such as DeepMind's AlphaZero [5] and OpenAI's GPT-4 [6] are capable of tasks that seem to require sophisticated reasoning, yet they do so without any form of self-awareness. AlphaZero, for instance, is an AI that has mastered the games of chess, shogi, and Go, outperforming the best human players without ever being "taught" the strategies of these games in the traditional sense [7]. It learned purely by playing against itself and analyzing outcomes through reinforcement learning. The system’s ability to learn and adapt far surpasses that of most humans in these specific contexts. Similarly, GPT-4, a language model developed by OpenAI, can produce human-like text, engage in complex conversations, and even write articles that demonstrate a high level of coherence and sophistication [8]. Yet, despite these impressive capabilities, neither AlphaZero nor GPT-4 is aware of its own learning processes. They do not "understand" the meaning of their actions they process inputs and generate outputs according to statistical patterns. This raises a profound philosophical dilemma: If AI can perform tasks that traditionally require intelligence, such as creative writing, strategic decision-making, or language comprehension, without the conscious awareness that Descartes argued was essential, then do we need to rethink the very nature of intelligence?

Is it possible that intelligence, as traditionally understood, can exist without consciousness? In practical terms, AI surpasses human limitations in many domains, not by thinking in the human sense but by processing vast quantities of data faster than any human ever could. For instance, AlphaFold, an AI system developed by DeepMind to predict protein structures, can solve problems that have baffled scientists for decades, [9] unlocking new possibilities in biology and medicine [10]. Yet, AlphaFold does this without any awareness of the significance of the structures it predicts. Does this lack of self-awareness diminish its achievement in any meaningful sense? Or are we witnessing the emergence of a new form of intelligence, one that does not rely on consciousness but is still capable of profound impact? From a traditional philosophical standpoint, this type of intelligence without consciousness would not be considered "true intelligence." However, as AI systems become increasingly integrated into critical decision-making processes—be it in healthcare, transportation, or governance—the question becomes: Does consciousness even matter if the system can outperform humans in reasoning, problem-solving, and creativity? Plato might have argued that AI cannot access the Forms, but could it be that we are redefining intelligence away from abstract, idealized thinking and towards data-driven performance? Practical Intelligence and the Limits of Human Adaptability Aristotle’s emphasis on practical wisdom, the ability to adapt knowledge to real-world situations, adds another dimension to this discussion. For Aristotle, intelligence was not just about knowing abstract principles but about using them effectively in context-specific situations. His concept of phronesis highlights the importance of flexible, adaptive reasoning, qualities that have traditionally been considered beyond the reach of machines. However, modern AI is challenging these assumptions. For example, self-driving cars rely on machine learning algorithms to make real-time decisions in dynamic and unpredictable environments. Companies like Waymo and Tesla are at the forefront of developing autonomous vehicles that can navigate complex traffic patterns, avoid obstacles, and follow the rules of the road [11]. These AI systems must process sensory data from their environment, such as the speed of surrounding cars, the layout of streets, and the behavior of pedestrians, and make split-second decisions that have life-or-death consequences.

Aristotle’s concept of practical intelligence is inherently situational and context-sensitive. It is about applying knowledge flexibly, which is where AI systems like self-driving cars encounter challenges. While AI excels at processing large datasets and making decisions based on predefined rules, it struggles when faced with entirely novel situations that require intuitive, emotional, or contextually nuanced responses. A human driver, for instance, might instinctively slow down when approaching a child playing near the road, not because of explicit rules but because of an emotional understanding of the potential danger. Can AI ever develop this kind of intuition, or will it always remain limited by its programmed responses and data inputs? Moreover, AI’s ability to adapt is contingent upon the quality and quantity of data it receives. When AI encounters edge cases and situations that fall outside of its trained data, its decision-making abilities can falter. This highlights a limitation that Aristotle would have identified: AI lacks the capacity for true adaptability because it cannot engage in the kind of context-dependent, emotionally driven reasoning that defines human intelligence. However, even within these limitations, AI is making remarkable strides. In medical fields, for example, AI is now being used to assist in diagnostics, offering pattern-based insights that help doctors make more informed decisions.

Systems like IBM’s Watson are capable of analyzing vast amounts of medical data to suggest treatment options [12]. While AI may not yet be able to replace human doctors, it can enhance their decision-making processes by combining computational efficiency with human expertise. Thus, while Aristotle’s phronesis may remain a uniquely human quality, AI’s role in practical decision-making continues to grow. This prompts a reconsideration of what practical intelligence means in the age of AI. Is adaptability still the defining feature, or are we moving toward a new understanding where data-driven optimization replaces human intuition?

While current AI systems are based on classical computing, relying on sequential processing to solve problems, a new frontier in computing is emerging that could radically alter this landscape: quantum computing. The potential of quantum computing, though still in its infancy, adds an intriguing dimension to this debate. Unlike classical computers, which process data one step at a time, quantum computers exploit the principles of superposition and entanglement, allowing them to theoretically perform multiple calculations simultaneously. This could herald a future where AI, enhanced by quantum computing, can solve problems much faster and more efficiently than ever before. However, we must temper these expectations with the reality that quantum computing is still largely experimental, and its widespread application remains several years away. Quantum computing holds the potential to revolutionize the way AI systems handle complex optimization problems. In fields such as logistics, cryptography, and pharmaceuticals, quantum algorithms could potentially solve problems that are infeasible for classical computers, enabling AI systems to explore a much larger space of possibilities in far less time. For example, current AI systems, particularly those used in optimization problems (such as supply chain management or financial modeling), often rely on classical algorithms to sift through vast amounts of data.

Nonetheless, as these datasets grow in size and complexity, the limits of classical computing become more apparent. As quantum computing progresses,?quantum-enhanced AI?holds the potential to handle computationally intensive tasks that classical systems struggle to solve efficiently. When fully realized, such quantum systems could evaluate multiple possibilities simultaneously, allowing for faster and potentially optimal solutions, especially in fields requiring complex optimization. Research has shown that integrating quantum computing with AI (Quantum Artificial Intelligence) could revolutionize problem-solving in areas such as finance, logistics, and automated planning, enhancing classical approaches in these domains. [13] This raises the question: Could quantum AI push us further toward data-driven optimization, making human intuition less central to decision-making in certain fields? This question indeed resists a simple answer, as it taps into the core tension between computational power and human judgment, two forces that have traditionally complemented each other. On the one hand, quantum-enhanced AI could make data-driven insights so rapid, precise, and comprehensive that, in some domains, it might seem to outperform or even “replace” human intuition. Fields heavily reliant on complex data processing, like financial markets, logistics, or climate modeling, could increasingly favor quantum AI’s speed and efficiency. Supposedly, we have in possession a quantum AI system in finance, able to analyze global market data and economic indicators in real time, detecting patterns or opportunities that no human could perceive. In this scenario, data-driven optimization might offer a level of accuracy and foresight that reshapes decision-making processes, potentially reducing the role of intuition to areas where data cannot fully account for outcomes.

Ironically, there are limitations to data alone. Human intuition, rooted in experience, empathy, and emotional insight, remains irreplaceable in fields where context, ethics, and personal understanding are essential. In healthcare, for example, even a quantum AI providing optimal treatment recommendations would still lack the human capacity for empathy and nuanced understanding of a patient’s emotional needs. Similarly, in ethical decision-making or legal judgments, where consequences impact lives and societies, human insight can weigh complex, context-sensitive variables that AI, regardless of its computational strength, may fail to address fully. In this way, the answer might lie in a hybrid model: while quantum AI could redefine decision-making in data-intensive fields, human intuition will likely remain central in areas where ethical reasoning, adaptability, and personal experience are critical. Quantum AI might ultimately serve as an enhancer of human decision-making rather than a replacement, pushing us toward a new model where data-driven optimization complements but does not wholly replace human intuition.

Yet, as AI evolves to augment and amplify human abilities, we encounter deeper philosophical questions. What happens when technology not only supports human potential but challenges us to transcend our current limitations? Here, we find resonance with Nietzsche’s philosophy of overcoming, a call to go beyond our inherent constraints and reach new heights of mastery, creativity, and self, determination. Could AI, particularly with the advancements quantum computing may bring, be humanity’s tool for overcoming in the Nietzschean sense, or does it risk undermining the very qualities that define us? This question will be addressed next!

b. Nietzsche’s Overcoming: Transcending Human Limitations

Through AI While Nietzsche did not explicitly write about artificial intelligence - obviously :), his philosophy of overcoming human limitations and achieving greatness through the will to power offers a useful lens for understanding the implications of AI. [14] Nietzsche's concept of the übermensch (the overman) represents the human potential to transcend mediocrity, break free from societal norms, and create new values. In many ways, AI could be seen as a tool for Nietzschean overcoming, enabling humanity to transcend cognitive limitations and achieve breakthroughs in fields previously inaccessible. For instance, AlphaFold’s ability to predict protein structures with unprecedented accuracy is a testament to how AI can push the boundaries of human knowledge [15]. Scientists have spent decades attempting to solve the "protein-folding problem," and AI has achieved in mere months what had been beyond human reach. Nietzsche might have viewed this as a kind of intellectual übermensch, AI as a means for humanity to overcome its intellectual limits and achieve new heights of scientific understanding. However, Nietzsche also warned against the dangers of conformity and passivity. In his critique of slave morality, Nietzsche argued that humans often submit to external systems of control, allowing themselves to be governed by forces that suppress their creativity and drive for greatness. This critique is relevant to AI as well: Will humanity become passive, relying on AI to make decisions for us and gradually surrendering our capacity for bold, independent thought? This question becomes particularly pressing in the context of algorithmic governance and decision-making.

Increasingly, AI is being integrated into public policy and law enforcement, with algorithms determining everything from social media content moderation to judicial sentencing recommendations. While AI offers the promise of greater efficiency and fairness, it also introduces the risk of technological conformity, a world where decisions are made by machines and humans become passive recipients of algorithmic authority. Is AI a tool for transcending human limitations, or does it risk reducing human creativity and agency?

4. Agency and Intelligence: Can AI Truly Act Independently?

Finally, the question of agency remains central to the philosophical discussion about AI. In classical philosophy, intelligence is intertwined with the ability to act on knowledge, make decisions, and bear responsibility for those decisions. AI can simulate decision-making processes and execute tasks with precision, but does it have true agency? Consider AI’s role in autonomous systems like drones or military robots. These machines are increasingly capable of making independent decisions in real-time, such as identifying targets or adjusting flight paths [16]. Yet, these decisions are still based on pre-programmed algorithms and data inputs. AI does not "choose" in the way humans do; it follows rules, even if those rules allow for flexibility within a narrow range of options. From a philosophical perspective, true agency requires intentionality and moral responsibility, qualities that AI lacks. Even the most advanced AI systems, like Tesla’s Full Self-Driving mode [17] or IBM Watson Health, are ultimately tools that execute human-defined tasks. They do not possess self-determined goals or the ability to reflect on the ethical implications of their actions.

Can AI ever achieve this kind of independent agency, or will it always remain a sophisticated but passive tool? This is an open question, especially as AI becomes more autonomous in sectors like healthcare, law enforcement, and warfare.

As AI systems gain greater control over critical decisions, will we need to redefine the boundaries of moral and legal responsibility? Should an AI-powered drone that makes a fatal mistake be held accountable, or is responsibility always traced back to the humans involved? These are the ethical and philosophical questions that will dominate the coming decades as AI continues to evolve. As we contemplate AI’s role in decision-making and agency, we are drawn to an even deeper question: can AI truly “think,” or is it merely performing high-level simulations of intelligence? While AI’s capabilities continue to expand, enabling it to act independently within certain parameters, there remains a fundamental distinction between intelligence as the ability to execute tasks and thought as the conscious, reflective process that defines human cognition. In the next section, I engage in a conversation with ChatGPT to explore this distinction. We examine where intelligence might end, and thought begins, questioning whether AI can ever bridge that divide. Here, classical philosophy and modern technology intersect as we look to philosophers like Plato, Descartes, and Nietzsche to understand the essence of intelligence and thought in the age of AI.

a. Intelligence vs. Thought

A Conversation with ChatGPT As we reach this turning point in exploring AI’s potential, the conversation demands sharper clarity. Are we discussing intelligence, the ability to process information and solve problems, or something more profound, like thought itself?

Intelligence can be quantified through speed, data, and accuracy, but thought evokes consciousness, self-awareness, and an inner purpose. Can AI one day embody both, or is it fundamentally bound to one and not the other?

In this dialogue, I join ChatGPT to discuss these distinctions, exploring where intelligence might end and thought begin. We question the boundaries of AI’s potential, drawing on insights from Plato, Descartes, and Nietzsche to probe where philosophy and technology intersect and diverge.

b. Conversation with ChatGPT

Malak: So, ChatGPT, we've touched on intelligence and thinking. But let’s clarify—are we really asking if AI can thinkor if it can be intelligent? These aren’t the same.

ChatGPT: You’re absolutely right to separate the two. Intelligence usually refers to the ability to learn, adapt, solve problems, and make decisions—abilities AI has developed impressively within specific contexts. But thought implies something more: consciousness, self-awareness, and the ability to reflect and understand oneself. This brings us into the realm of conscious thought, something AI currently lacks.

Malak: So, AI might perform tasks intelligently, but it doesn’t “think” in the sense that we do. From Plato’s Forms to Descartes’ cogito, thought has traditionally been bound to a kind of self-awareness that AI simply doesn’t possess.

ChatGPT: Precisely. AI can simulate aspects of intelligence, pattern recognition, optimization, and logical reasoning, but it operates without any subjective experience or sense of self. Descartes, for instance, would say that AI lacks true thought because it doesn’t know it’s thinking. And Nietzsche would add that AI lacks the will to power, the drive to create, transcend, and shape itself.

Malak: Interesting. Nietzsche’s idea of the will to power is often interpreted as the inner drive that pushes humans to self-overcome and create meaning. He’d probably argue that AI is missing this essential element; without it, it can’t aspire to the kind of active, intentional thought that leads to creativity and innovation.

ChatGPT: Exactly. Nietzsche’s will to power is fundamentally human. He’d likely view AI as lacking the inner drive that makes human thought dynamic and transformative. AI can process information and reach conclusions, but it doesn’t aspire or strive. It follows the rules rather than choosing values or creating meaning.

Malak: Then, even as AI takes on more roles in areas like healthcare, finance, and governance, it remains bound to human programming. AI might aid decision-making, but it won’t be thinking in the sense that humans do. Could this reliance on AI reshape our own human agency and ethical responsibility?

ChatGPT: That’s a key consideration. The more we rely on AI, the more we risk surrendering aspects of human autonomy and ethical reflection to algorithms. This reliance could introduce new ethical challenges, especially as AI systems gain influence over critical areas of society. If we’re not careful, AI could encourage a kind of passive conformity, as Nietzsche warned, where we defer to machine-generated decisions instead of engaging in ethical reasoning ourselves.

Malak: Which brings us to the ethical implications. As AI becomes more embedded in society, it forces us to confront new questions about agency, accountability, and human values. If an AI in healthcare or law enforcement makes a critical decision, where does the responsibility lie? Can we hold AI accountable, or does it ultimately fall on the humans who programmed it?

ChatGPT: This is where things get complex. True agency, from a philosophical standpoint, requires intentionality and moral responsibility, qualities that AI, lacking consciousness, simply does not have. Even with advanced intelligence, AI systems like autonomous drones or decision-making algorithms in courts are still bound by human-defined parameters. Their decisions are sophisticated calculations, but without self-awareness, AI remains a tool rather than an independent agent.

Malak: So, we’re faced with a delicate balance. Quantum AI may enhance data-driven intelligence and expand what humans alone can achieve, but it doesn’t replace human thought. Our responsibility is to ensure that AI complements rather than replaces human agency, adaptability, and ethical insight. AI should support us, not diminish our capacity for reflective, conscious thought.

ChatGPT: Well said. AI has the potential to reshape the future of human cognition, but it shouldn’t redefine it. As we move forward, the question isn’t just what AI can do but how we want to shape its role in our lives. Perhaps we’re not just expanding intelligence, we are creating a new partnership, one that allows humans to thrive without sacrificing what makes us inherently human.

Based on the above conversation on intelligence and thought, we could highlight a central dilemma: Can AI evolve to embody both intelligence and the profound qualities of thought, or is it inherently limited to one? As we examined Plato’s ideals, Descartes’ self-awareness, and Nietzsche’s drive for self-overcoming, it’s clear that while AI can replicate certain intelligent behaviors, it lacks the inner consciousness that defines human thought. The potential of AI to enhance human capabilities and reshape industries is undeniable, yet our challenge is to foster this technology in a way that respects and reinforces our own human agency, ethical insight, and reflective depth. AI’s strengths lie in its speed, precision, and data-driven capabilities, but we must remember that true thought remains uniquely human, grounded in our awareness, purpose, and ethical responsibility. Moving forward, we stand at a critical juncture where technology could either support or diminish our humanity. By cultivating a thoughtful partnership with AI, one that allows AI to enhance rather than replace our capacity for conscious reflection, we ensure that technology serves to empower, not override, what it means to think, decide, and be fully human.

Conclusion

Our philosophical exploration into artificial intelligence reveals both the capabilities and limitations of AI through the frameworks of classical thinkers like Plato, Aristotle, Descartes, and Nietzsche. While AI has made strides in replicating certain aspects of intelligence—pattern recognition, logic, and optimized decision-making—it stops short of embodying thought as traditionally understood. For Plato, true knowledge transcends sensory data, reaching abstract ideals that AI, reliant on empirical inputs, cannot access. Aristotle’s practical wisdom highlights the human ability to adapt flexibly to dynamic contexts—an area where AI, bound by pre-programmed parameters, lacks situational intuition. Descartes’ concept of self-aware thought raises questions AI cannot answer without consciousness, and Nietzsche’s notion of the will to power emphasizes a creative drive AI inherently lacks. Yet, as AI continues to evolve, it challenges us to reconsider the boundaries of intelligence and the human role in shaping its ethical application. While AI is transforming sectors like healthcare, finance, and governance, its power lies in data processing rather than conscious reflection or ethical reasoning. This distinction matters profoundly: true intelligence, as these philosophers emphasized, includes intentionality, self-awareness, and moral responsibility, qualities that make us uniquely human. The challenge before us is to harness AI as a tool that complements rather than competes with our own capacities for reason, creativity, and moral agency. In this way, AI can serve as an enhancer of human potential, not a replacement for human thought. As we shape AI’s role in society, we must ensure it remains a force that empowers, not diminishes, what it means to be truly intelligent, reflective, and human.

Sources:

[1] Plato.?(380 B.C.E.).?The Republic. MIT Classics. Retrieved from?https://classics.mit.edu/Plato/republic.html

[2] Aristotle.?(2014).?Nicomachean Ethics?(R. Crisp, Ed. & Trans.; Rev. ed.). Cambridge University Press. (Original work published ca. 350 B.C.E.)

[3] Cottingham, J.?(Ed.). (2017).?Descartes: Meditations on First Philosophy: With Selections from the Objections and Replies?(2nd ed.). Cambridge University Press. (Original work published 1641)

[4] Salvado, O., & Whittle, J. (2023, May 4).?AI pioneer Geoffrey Hinton says AI is a new form of intelligence unlike our own. Have we been getting it wrong this whole time??The Conversation. Retrieved from?https://theconversation.com/ai-pioneer-geoffrey-hinton-says-ai-is-a-new-form-of-intelligence-unlike-our-own-have-we-been-getting-it-wrong-this-whole-time-204911

[5] Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.?arXiv preprint arXiv:1712.01815.?

[6] OpenAI. (2023). GPT-4. Retrieved from?https://openai.com/index/gpt-4/

[7] DeepMind. (2017). AlphaZero: Shedding new light on chess, shogi, and Go. Retrieved from?https://deepmind.google/discover/blog/alphazero-shedding-new-light-on-chess-shogi-and-go/

[8] OpenAI. (2023). GPT-4 Technical Report. OpenAI. Retrieved from?https://www.openai.com/research/gpt-4

[9] Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold.?Nature, 596, 583–589.?https://doi.org/10.1038/s41586-021-03819-2

[10] Senior, A. W., Evans, R., Jumper, J., et al. (2020). Improved protein structure prediction using potentials from deep learning.?Nature, 577, 706–710.?https://doi.org/10.1038/s41586-019-1923-7

[11] Waymo Team.?(2022).?Waymo’s Self-Driving Car: AI and Machine Learning Advancements. Waymo Research Blog. Retrieved from?https://www.waymo.com/research

[12] Watson Health.?(2022).?IBM Watson Health: Transforming Healthcare with Artificial Intelligence. IBM. Retrieved from?https://www.ibm.com/watson/health

[13] Klusch, M., L?ssig, J., & Wilhelm, F. K. (2024). Quantum computing and AI.?Künstliche Intelligenz, 38, 87-102.?https://doi.org/10.1007/s13218-024-00872-7

[14] Nietzsche, F. (2002).?Beyond good and evil: Prelude to a philosophy of the future?(J. Norman, Trans.; R.-P. Horstmann & J. Norman, Eds.). Cambridge University Press. (Original work published 1886)

[15] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold.?Nature, 596(7873), 583–589.?https://doi.org/10.1038/s41586-021-03819-2

[16] Lin, P., Bekey, G., & Abney, K. (2008).?Autonomous military robotics: Risk, ethics, and design. California Polytechnic State University.

Disclaimer:?This newsletter article is a philosophical exploration rather than a formal academic work created in partnership with ChatGPT. It aims to inspire thoughtful engagement with the philosophical and ethical dimensions of artificial intelligence (AI) rather than offer a rigorous academic analysis. Drawing from the perspectives of classical thinkers such as Plato, Aristotle, Descartes, and Nietzsche, the piece examines the intersections between human and artificial intelligence, posing open-ended questions about the nature of thought, agency, and human values in a technologically evolving world.

The insights presented herein are meant to stimulate reflection and conversation, especially among professionals working at the intersection of law, technology, and ethics. This article is not intended as an authoritative statement or a comprehensive treatise; rather, it catalyzes further inquiry and dialogue on the philosophical questions that arise as AI reshapes aspects of human cognition and decision-making. The interpretations and ideas shared do not claim to be exhaustive or definitive but are offered as points for deeper consideration and professional reflection.