When David Hume Meets ChatGPT: A Skeptic’s Guide to the AI Hype

Imagine living in the mid-1700s. A dizzying new technology has just swept the intellectual world: Newtonian physics. For the first time in human history, we have an astonishing mathematical tool that can predict the exact trajectory of a cannonball, the path of a comet, and the behavior of the tides with flawless, with breathtaking precision.

The results are genuinely miraculous.

But then, human nature takes over. In the flush of this massive success, a naive but intoxicating idea takes root: this technology has no limits. If we can calculate the orbit of Jupiter, why can't we use the same math to calculate human morality? Why not use it to optimize governments, eliminate legal disputes, and predict human behavior like planetary motion?

People who didn't understand the limits of this new tool spent countless hours, fortunes, and intellectual resources trying to apply it to literally everything, convinced they were on the cusp of solving the human condition.

Sounds familiar?

Today, we are living through the exact same psychological loop. We have a spectacular new tool, large language models, that is astonishingly good at predicting the next word in a sentence. And once again, blinded by its predictive success, we have leapt to the conclusion that it has no boundaries. We are trying to apply it to law, art, ethics, and relationships.

Now, imagine being Hume back then. You see all the hype raging around you. But instead of blindly following the crowd, you stop and look closer. You start to think deeply about the very essence of this new Newtonian framework, the foundational assumptions it is built on, and what it cannot logically ever achieve. You map out its hard limitations.

Now, imagine that same Hume today, dropped into our world of LLMs and hyperbolic talk of AGI. Amazingly, his critique remains just as valid and sharp today as it was in the 18th century. The only real difference is the underlying technology driving this latest round of hype.

1. The Technological Catalysts

In both eras, a massive leap in human capability led to the belief that a single, unified methodology could unlock the entire universe.

2. The Shared Illusion: "Predictive Success = Ultimate Truth"

Before Hume, thinkers tried to apply physical and geometric methods to human ethics and politics. Because Newton could predict the tides with breathtaking accuracy, it was assumed that this exact empirical framework could seamlessly decode human nature, morality, and society.

Today, we see the exact same leap in logic. Because LLMs and neural networks are astonishingly good at predicting the next token, generating code, or folding proteins, we assume they are "thinking." We confuse highly sophisticated predictive modeling with deep, qualitative understanding.

The hubris of our current AI moment is a rerun of the Enlightenment’s grandest intellectual failures. The same fever dreams that captivate Silicon Valley today captivated the greatest minds of the 17th and 18th centuries:

The Dream of Automated Justice

• The Enlightenment Actor: Gottfried Wilhelm Leibniz
• The Modern AI Parallel: LLM Admirers & Automated Legal Systems
• The Concept: Leibniz famously envisioned a characteristica universalis (a universal language) and a calculus ratiocinator (a calculating framework). He declared that if a dispute arose, partners would no longer need to argue; they could simply sit down with a pen, say, "Calculemus!" ("Let us calculate!"), and compute the objective truth. Today's LLM admirers share this exact delusion, imagining that feeding legal statutes into a transformer model will automate flawless, bias-free justice.

The Attempt to Automate Morality

• The Enlightenment Actor: Jeremy Bentham (and his Felicific Calculus)
• The Modern AI Parallel: AI Alignment Researchers & Policy Optimists
• The Concept: Bentham, the father of utilitarianism, believed that human morality and legislation could be turned into a literal math problem. He created an algorithm meant to calculate the exact amount of pleasure or pain an action would cause based on variables like intensity and duration. Today, AI alignment engineers try to solve the "alignment problem" by mathematically optimizing "reward functions" to ensure AI is "helpful, honest, and harmless"—treating complex, deeply subjective human values as engineering parameters to be maximized.

The Illusion of the Perfect Predictive Machine

• The Enlightenment Actor: Pierre-Simon Laplace (and Laplace’s Demon)
• The Modern AI Parallel: AGI Scale-at-All-Costs Believers (e.g., Sam Altman, Dario Amodei)
• The Concept: In 1814, Laplace argued that if a vast intellect knew the precise position and momentum of every atom in the universe, it could use Newton's laws to calculate the entire past and future with absolute certainty. This is the ultimate ancestor of the "scaling laws" belief in Silicon Valley today. Modern AGI advocates argue that if we just feed neural networks all the world's data and scale compute parameters exponentially, the AI will eventually build a perfect world model capable of predicting and solving any human problem.

Reducing the Mind to a Mechanical Machine

• The Enlightenment Actor: Julien Offray de La Mettrie (author of L'Homme Machine, 1747)
• The Modern AI Parallel: Computationalists & Silicon Valley Transhumanists
• The Concept: La Mettrie shocked his contemporaries by arguing that the human soul and thoughts were nothing more than the mechanical movements of the body's gears and fibers, famously comparing the brain to a complex clockwork mechanism. This is the exact philosophical stance of modern computationalists who assert that the human brain is simply "wetware" running biological algorithms, and that LLMs are already showing the first sparks of true consciousness.

3. Applying Hume’s Critique to the AI Era

If Hume were alive today, he would likely look at Silicon Valley with the same polite, devastating skepticism he directed at the dogmatic rationalists of his own time. His philosophy dismantles the "AI will solve everything" myth in three distinct ways:

A. The Induction Problem of Machine Learning

At its core, Hume’s famous Problem of Induction states that we cannot logically guarantee the future will resemble the past. Just because the sun has risen every day does not provide a logical proof that it will rise tomorrow; we only expect it to because of custom and habit.

Machine learning is induction on steroids. An AI is trained entirely on historical, human-created data to predict future outputs. Hume would point out that:

AI is fundamentally incapable of preparing for "black swan" events or conceptual shifts. It assumes the future is a statistically rearranged version of the past. It cannot generate truly new paradigms because it is bound to the induction trap.

B. The "Is-Ought" Problem (Hume's Guillotine)

Hume argued that you cannot derive an "ought" (a moral statement of how things should be) from an "is" (a factual statement of how things are).

Today’s AI optimists often believe that "more data" and "better computation" will solve complex ethical and political dilemmas. Hume would call this a category error:

• An AI can analyze millions of data points to tell us what is happening (statistical facts).
• It cannot, by definition, calculate what we ought to do about it. Morality is rooted in human sentiment, empathy, and values, things that cannot be derived from a statistical distribution.

C. The Nature of "Belief" vs. "Reason"

Rationalists of the 1700s believed human reason was a pure, quasi-divine spark. Hume shocked them by declaring: "Reason is, and ought only to be the slave of the passions." He argued that human belief is not driven by cold logic, but by custom, habit, and emotion.

Similarly, we often treat AI as a "purely rational" entity. But Hume would remind us that human beings are not purely rational actors. A hyper-rational, computational AI cannot "solve the human condition" because the human condition is fundamentally shaped by irrationality, desire, tragedy, and subjective experience, elements that defy algorithmic optimization.

The Ultimate Irony

The great irony of the Enlightenment was that the scientific method did change the world, but it did not make human nature perfectly orderly or rational. Instead, the hyper-rationalist push eventually triggered the Romantic backlash, a passionate return to emotion, art, and the sublime.

As we march toward AGI, we may face a similar cycle. The more we attempt to reduce human experience to computation, data points, and algorithmic efficiency, the more we will likely hunger for the messy, unquantifiable, and uniquely conscious aspects of being human. Hume’s ultimate lesson is modesty: recognize the immense power of our new tools, but never mistake the tool for the soul.

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