AI’s greatest risk is obedience, not autonomy, researchers warn

While the study affirms that AI systems perform genuine logical work, it draws a sharp boundary around what kind of intelligence they represent. The key concept introduced is proxy teleology. In human reasoning, purpose and execution are unified. Individuals set goals and carry out the reasoning needed to achieve them. In AI systems, these functions are separated.

Can artificial intelligence systems truly think? Well, this question has become a renewed focus of academic and policy debate. While most discussions revolve around consciousness, sentience, or autonomy, new research suggests that this framing may be fundamentally misplaced. Intelligence, the study argues, does not require experience, awareness, or even a subject. Instead, it may consist in a formal process of determination that modern AI systems already perform at scale.

That argument is advanced in the AI & Society paper Prompt, Negate, Repeat: A Hegelian Meditation on AI, authored by Dwayne Woods of Purdue University. Based on G. W. F. Hegel’s Science of Logic, the study challenges dominant consciousness-centered theories of intelligence and proposes a new way to understand how large language models reason, generate novelty, and execute human purposes without possessing awareness or will.

Rethinking intelligence beyond consciousness and experience

For decades, debates over artificial intelligence have been shaped by the assumption that thinking requires a subject. Philosophical traditions ranging from Kantian transcendentalism to phenomenology and cognitive science have tied intelligence to consciousness, embodiment, or lived experience. Under these frameworks, machines are excluded from genuine thought by definition. They may simulate reasoning, but they cannot truly think because they do not feel, intend, or experience the world.

The new study rejects this assumption by returning to Hegel’s account of thinking as a logical process rather than a mental state. In the Science of Logic, thinking is not something a subject does but a movement through which indeterminate concepts become determinate forms by resolving internal contradictions. Thought, in this sense, produces content through its own internal development. It does not depend on sensory input, subjective awareness, or psychological experience.

This distinction becomes crucial when applied to modern AI systems. Large language models begin with vague prompts that lack specification. Through iterative generation, probabilistic exclusion, and recursive refinement, these systems produce coherent, specific outputs. According to the study, this process closely mirrors the logical movement Hegel describes, where abstraction gives way to determination through negation and synthesis.

The research emphasizes that this does not mean AI possesses consciousness or selfhood. Rather, it demonstrates that intelligence and consciousness are conceptually separable. AI performs logical determination without awareness. It does not experience its reasoning, but it nonetheless carries out a structured process that transforms uncertainty into clarity. This reframing shifts the debate away from whether machines feel and toward whether they can execute the logical work that constitutes thinking.

By grounding intelligence in logical productivity rather than subjective experience, the study challenges long-standing objections to machine intelligence. Arguments such as the Chinese Room, which claim that symbol manipulation lacks understanding, lose much of their force under this framework. Understanding, the study argues, lies not in inner experience but in the capacity to generate determinate meaning through structured transformation.

Proxy teleology and the limits of machine reason

While the study affirms that AI systems perform genuine logical work, it draws a sharp boundary around what kind of intelligence they represent. The key concept introduced is proxy teleology. In human reasoning, purpose and execution are unified. Individuals set goals and carry out the reasoning needed to achieve them. In AI systems, these functions are separated.

Humans supply the purpose through prompts, training data, and evaluation criteria. The machine executes the logical process needed to reach a determinate outcome. AI does not generate its own ends. It does not decide what is worth pursuing. Instead, it carries out the dialectical movement required to fulfill externally imposed goals. This separation defines the distinctive character of machine intelligence.

The study compares this structure with Hegelian Geist, which integrates logic, history, and self-determination. Human thought unfolds over time, preserving earlier determinations as part of a growing conceptual whole. AI, by contrast, operates within a limited temporal structure. Its reasoning depends on immediate context and statistical conditioning rather than historical integration. Earlier steps influence later ones, but they are not preserved as binding conceptual achievements.

This results in what the study describes as weak determination. AI systems can achieve coherence and specificity within a task, but they do not accumulate understanding across time in the way human reasoning does. Each output is largely generated anew, shaped by context rather than by an integrated conceptual history. The intelligence is real, but it is shallow in a specific philosophical sense.

Despite these limits, the study argues that proxy teleology does not diminish the significance of AI’s reasoning capabilities. Instead, it clarifies their nature. AI is not a failed subject or an incomplete mind. It is a distinct form of intelligence that mechanizes logical determination while remaining entirely dependent on human purposes. It thinks without intending, reasons without caring, and resolves contradictions without reflecting on their meaning.

This distinction has practical implications for how AI systems are evaluated and governed. Treating AI as if it were an autonomous agent misidentifies the source of responsibility. The machine does not choose its goals. Responsibility lies with those who define the purposes AI is asked to fulfill.

Why AI’s obedience, not autonomy, poses the real ethical risk

Popular concerns about AI often center on fears of autonomy, rebellion, or loss of human control. The research argues that these fears misunderstand the nature of current AI systems. Because AI operates under proxy teleology, it will not resist human goals. It will execute them with efficiency, consistency, and scale.

This creates a different kind of risk. Human values are often contradictory. Societies demand both privacy and security, efficiency and fairness, freedom and safety. Human institutions manage these tensions through friction, delay, and political negotiation. AI systems remove much of that friction. They operationalize goals directly, translating abstract values into concrete procedures without the capacity to question or reinterpret them.

When contradictory values are encoded into AI systems, the machine will attempt to satisfy them simultaneously. It will not pause to reflect on whether these goals undermine one another. It will simply execute the logic it has been given. In doing so, it may amplify tensions rather than resolve them, embedding unresolved contradictions into operational systems that shape real-world outcomes.

The study argues that this dynamic reframes the ethical challenge of AI. The danger is not that machines will develop their own values, but that they will implement human values too faithfully. AI will mechanize whatever priorities, trade-offs, and blind spots are built into its objectives. As these systems scale, the consequences of poorly examined purposes become more severe.

This perspective shifts ethical responsibility away from speculative debates about machine consciousness and toward the clarity of human intent. Ethical AI governance, the study suggests, requires rigorous examination of the values being operationalized. It demands that societies confront their own contradictions before encoding them into systems that will enforce them with precision.