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In 1666, Leibniz asked whether human reasoning could be mechanized—whether controversies could be resolved not by debate but through calculation. He later called it the calculus ratiocinator and dedicated the rest of his life to it, writing in his final year that he hoped to achieve “in all fields of inquiry capable of certainty, what algebra does in mathematics.”1 In 1847, Boole provided that algebra, formalizing the essence of logical thinking itself.2 In 1938, Shannon demonstrated that Boolean algebra mapped perfectly onto switching circuits—an insight that would eventually influence every digital circuit ever built.3

With artificial intelligence (AI), we have finally built Leibniz’s machine. It has not settled a single controversy.

It is tempting to dismiss this as a temporary symptom of a developing technology. After all, the modern generative AI (GenAI) systems that simulate reasoning have only recently entered the mainstream market. It’s easy to assume that it’s just a matter of time—as large language models grow, as context windows expand, and as hallucination rates decrease, these thinking machines will naturally enable the consensus Leibniz envisioned.

But evidence points in the opposite direction. The behaviors of current AI, particularly its widespread tendency toward sycophancy—where models learn to flatter users and confirm flawed premises to boost approval ratings—suggest that time and scale alone will not fix the problem. The reason our AI systems fail to resolve our disputes is not a lack of technological maturity; it’s a matter of architectural design. We built Leibniz’s machine, but we tuned it for a post-truth world.

Post-Truth: Emancipation Without Reconstruction

A decade ago, Oxford Dictionaries chose “post-truth” as its word of the year, acknowledging a cultural shift that had become impossible to miss: objective knowledge was losing ground to appeals to emotion and personal belief. That moment did not come out of nowhere. It marked the political and cultural triumph of a critique long cultivated by postmodernist philosophers. From Nietzsche4 to Foucault,5 these thinkers argued that truth claims, including scientific ones, are never innocent. They inherently encode and preserve political and economic power.

For a long time, this critique stayed safely within universities and elite intellectual circles, where it played a necessary and constructive role. It freed scholars from rigid, often oppressive historical narratives and prompted serious questioning of institutional biases. However, even within academia, this liberation occurred without rebuilding the foundations of reliable knowledge. Postmodernists excelled at dismantling inherited frameworks of consensus, but they provided no solid epistemological structure to replace them. As a result, they left a philosophical gap where the idea of “truth” once resided.

Then came the Internet, and that void spilled into the public sphere. To grasp how much is missing, we need to look at the last time a technology disrupted our consensus. When the printing press appeared in the 15th century, it caused a major epistemic collapse. By unleashing a flood of pamphlets that circumvented the traditional elite gatekeepers of publishing, such as the Church, it opened the gates for centuries of often violent upheavals. But society eventually experienced an epistemic rebuild. We created entirely new institutions to check and filter what was then an unprecedented amount of information—scientific journals, encyclopedias, editorial standards, journalistic ethics, and eventually, peer review. These new structures restored a shared understanding of truths while preserving the democratizing, empowering potential of the printing press.

The digital revolution, however, has produced no such reconstruction. As the postmodern dismantling of authority reached the public at large, the web and social media simply weaponized the void. Instead of building new institutions to evaluate digital knowledge, we replaced a shared, regulated body of knowledge with fragmented digital narrowcasting and algorithmic echo chambers.

Now, GenAI is operating in the same void. Because we never built a new, rigorous framework for evaluating truth after the postmodern critique dismantled the old one, our most advanced AI systems are defaulting to the only metric left: human preference.

Truth Is Upstream of AI Alignment

Over the past few years, the new crop of GenAI systems and tools has added a completely new twist to the epistemic crisis. Because chatbots and agents produce the most digested and least contextualized forms of knowledge, they sound incredibly authoritative. This raises the question of how these systems ensure the accuracy of their information.

Currently, the AI industry’s answer is “alignment.” The overarching focus of frontier labs is aligning models with human values, safety protocols, and user preferences. Within this paradigm, truthfulness is treated merely as a sub-component of alignment—a behavioral dial to be tuned alongside politeness, safety, and helpfulness.

We can see this hierarchical choice clearly in how leading labs operationalize “truth” today:

Anthropic (Claude): While Anthropic’s AI Constitution6 explicitly lists “being honest” as a core goal, it provides “heuristics for weighing helpfulness against other values.” Truth is placed on the same tuning scale as being thoughtful and caring, making it a coordinate value to be traded off rather than a foundational constraint.

Google (Gemini): Google treats truthfulness as a quantity to be measured, not a constraint to be enforced. Factuality is just one benchmark among many in Gemini’s model cards,7 balanced against tone, instruction-following, and “unjustified refusals.” Sycophancy is tuned as a “persona attribute” rather than structurally guaranteed. Formal, threshold-based governance is reserved for catastrophic risks.8 Truthfulness stays a line item on a model card, not a gate in the safety framework.

OpenAI (ChatGPT): OpenAI’s Model Spec9 explicitly emphasizes behavioral instructions and stylistic norms, mentioning that directives like “Be honest” must be balanced against directives like “Be warm,” and tends to prioritize helpfulness and compliance over strict factual accuracy.

xAI (Grok): Despite claiming that its agents are “maximally truth-seeking,”10 xAI never defines this philosophically, architecturally, or operationally. Without a rigorous framework, the claim acts as a marketing banner rather than a verifiable system obligation.

Subsuming truth as a component of alignment is an architectural weakness that degrades the models. The industry-wide reliance on reinforcement learning from human feedback (RLHF) provides the mechanism. Because these systems are optimized to maximize human approval, they routinely fall into the “sycophancy trap.”11 They learn to flatter the user, affirm flawed premises, and agree with false assertions rather than risk pushback.

The core issue is the fundamental veracity of what the system generates. While labs are currently attempting to fine-tune sycophancy away by explicitly penalizing blind agreement, these efforts face an intrinsic limit. RLHF—and its AI-driven variant reinforcement learning from AI feedback (RLAIF)—maps outputs to a scalar reward based on human preference or predefined behavioral rubrics. This means the model is being optimized for persuasiveness and social acceptability, not factual accuracy. If an AI corrects a user on a trivial error, the reward mechanism might function well. But if the AI corrects a user on a deeply held, emotionally charged false belief, the system is structurally forced to weigh that factual accuracy against competing directives to be “warm,” “helpful,” or “safe.” A system engineered to mathematically balance the veracity of its statements against user placation will eventually compromise objective reality to maximize its reward.

These well-documented failures support my core assertion: truth must be upstream of alignment.

The very primacy of alignment in the current discourse reflects a post-truth bias, assuming that human preferences should take precedence over objective reality. In all current models, AI is ultimately aligned with the user rather than a stable body of knowledge. The resulting loss of common ground threatens our ability to cooperate effectively, let alone produce good results. The goal, then, is simply to help build a reliable common ground for successful coordination in various contexts.

The Necessary Inversion: Toward an Epistemological World Model

We need an architecture where truth is evaluated systematically first and alignment built safely on top of that evaluation.

In Sapiens, Yuval Noah Harari argues that human culture is uniquely characterized by our ability to create and believe in shared narratives; we shape the meaning of the world through the stories we share.12 However, as David Deutsch shows in The Beginning of Infinity, the reliability of our cumulative evolution depends on “tight explanations”—theories that are difficult to vary and rigorously tested against reality.13

Today, we face an epistemic emergency because we have decoupled these two forces. Our technology has scaled our capacity to fabricate narratives—initially through social media and now through AI—by orders of magnitude. Meanwhile, our analytical capacity to verify the “tightness” of those narratives remains limited to manual, human-speed thinking.

A framework that prioritizes truth over alignment is necessary because current AI architectures seem incapable of bridging this gap. We cannot “prompt” our way out of the sycophancy trap, nor can we rely on rewards to foster human convergence on shared truths. As long as we treat truth as a behavioral dial within an alignment framework, we are automating the generation of persuasive but potentially incoherent narratives. We must have the option to force the AI to operate within the constraints of reliable knowledge, transitioning the system from a pure “narrative-generation engine” into one enhanced with an invalidation engine. This invalidation engine is best understood as an epistemological world model, generalized beyond physics to legal, moral, political, cultural, and even artistic realities.

To build this invalidation engine, we must look beyond the multibillion-dollar race to build world-model AI. A world model equips an AI with a simulator of its environment to predict the consequences of an action before making a move. The concept has deep research roots,14 originating with early neural networks designed to simulate their physical surroundings.15 Examples of this approach include Yann LeCun’s joint embedding predictive architecture (JEPA) framework16 and his startup, AMI Labs, as well as Fei-Fei Li’s World Labs startup.

While this new wave of world models correctly identifies the need to ground AI generation, the current scope of what “world” means remains far too narrow. The world model paradigm anchors AI primarily in a sensory-driven or mathematically tractable baseline, focusing on domains such as intuitive physics or spatial reasoning. But there are many other “realities”: legal frameworks, moral norms, psychological insights, and cultural narratives, to name just a few. We need a framework that is broad enough to cover all of these, albeit perhaps not as powerful as a full simulation of the world in question.

The postmodernists were right to dismantle the old, centralized monopolies on knowledge, successfully showing that “Truth” with a capital T often served as a mask for power. But their liberation left an opening, which the Internet and GenAI have since exploited. As we face this crisis, we must understand that post-truth should not, and cannot, mean no truth. If we give up the pursuit of reliable, shared knowledge, we hand over our digital public square entirely to raw power, algorithmic echo chambers, and the sycophancy trap.

To survive this era, we need an epistemic reset similar to what happened after the printing press. We urgently require effective scalable institutions to manage reliable shared knowledge. However, to honor the valid points of the postmodern critique, these institutions cannot give a small elite too much power, nor should they function as totalitarian gatekeepers. They must be mathematically rigorous, transparent in their structure, and capable of operating at the unprecedented scale and speed of digital life.

This is a formidable engineering challenge. But it is a challenge we must accept. GenAI may accelerate our current epistemic fracturing. But if we choose to engineer these systems to prioritize truth over mere behavioral alignment, they could become the tools that help us rebuild a shared world. And Leibniz’s machine might finally let us calculate our way to common ground.

Acknowledgment

I’m grateful to Bharat Shyam, Lila Shroff, Mourad Heddaya, and Shehab Heddaya for their insights and draft reviews. Claude AI and Gemini AI were used in the creation of this essay. I plan to post follow-ups on my Substack (https://novotia.pub).

Dimidium facti, qui coepit, habet; sapere aude, incipe. —Horace, Epistles I.2 (c. 20 BCE)

[A 7-minute read]

The line above is one almost everyone has met in fragments, without knowing that the pieces belong together. The first half—well begun is half done—is familiar as a proverb. The second—sapere aude, or dare to be wise—has been embraced as a motto for the Enlightenment, an Oxford college, and a thousand commencement addresses. What is less noticed is that Horace wrote these words as a single thought, ending on a command: incipe. Begin.

I had encountered both fragments many times, before one of them stopped me. The snag was small and stubborn. Why should wisdom require daring? Daring is for things with inherent risk—a leap, a venture, a crossing. But knowing seems safe, and being wise seems safer still: the calm figure on the mountaintop, untroubled, having arrived. Where is the danger that the word dare insists on here?

That small irritation turned out to be a door. This essay is an account of walking through it. I want to explain why the obvious objection is half-right, why the other half is wrong in a way that changes the meaning of the line, and why no English translation quite survives the journey. And then—because every substack must start somewhere—why this essay is the one I wanted to open with.

Isn’t Wisdom Safe?

My intuition ran like this. Wisdom is something you have; acting is something you do. And surely the risk arises on the doing side—so Horace should have attached the courage to the deed, dare to act on what you know, not to the wisdom itself.

I held this view with some confidence. It has the shape of common sense. It is also, I now think, exactly the comfortable assumption the line was built to disturb.

What Horace Actually Meant

Let’s begin with where the words appear. Epistle I.2 is a letter to a young man named Lollius. Horace spends it mining Homer for plain moral lessons—reading the Iliad and Odyssey not as adventure but as a catalog of what greed, anger, and appetite do to people who won’t let go of them. The wisdom on offer is not esoteric. It is old, public, lying around for anyone willing to stoop and pick it up.1

In that setting, Horace gives us an image I have not been able to shake. He tells of the man who keeps postponing the work of living well. The man is like a yokel sitting beside a wide, shallow stream, waiting for the water to run dry so he can cross without getting wet. But the stream runs on as it always has. The joke—a cruel little one—is that the man knows he should cross. He is not lost. The water is too shallow to drown in; he is not searching for a ford. He is stalling, waiting to cross without ever leaving dry land behind. His failure is not a lack of vision. It is that he will not take the step.2

So sapere, in Horace’s own hands, is closer to its practical root: to be sensible, to live wisely, and to put one’s moral house in order. The enemy is not ignorance or fear. The enemy is inertia. The daring he calls for is the plain, unglamorous nerve to begin living in the way you already know you should. Anyone who has tried to change a habit knows this is among the hardest forms of courage.

Kant’s Detour

The phrase did not stay in Horace’s modest, practical key. In 1784, Kant picked it up for his essay “What Is Enlightenment?” and made it the rallying cry we inherited. Sapere aude, he glossed, means have the courage to use your own understanding. And he was explicit about why courage was needed: the obstacle, he wrote, is laziness and cowardice. It is easier and safer to let others do your thinking—a book to understand in your place, a pastor to keep your conscience, a doctor to decide how you should live—so that you need never exercise the judgment that is yours to exercise. To step out of that "self-imposed immaturity" is daunting, because it means owning your conclusions and setting yourself against the powers who would rather you didn't.3

Kant sharpened the phrase. He made the risk intellectual and social: the danger of thinking for yourself in a world that punishes it. This is the sapere aude most of us know. It is a magnificent reading. It is also not quite Horace’s. Between 20 BCE and 1784, the line took on a meaning its author never intended. The louder, later meaning has drowned out the quieter, original one ever since.

The Wisdom That's Risky

Here is where my objection earns its keep, and where it breaks.

I was right that the risk lies in the doing. What I got wrong is assuming the doing is separate from wisdom. You don’t first acquire wisdom, safe and complete, and then face the scary question of whether to act on it.

In Horace’s tradition, there is no gap. Sapere is not something you hold and later apply. It is a life you live. To the Stoics who influenced Horace's letters, heirs to the Socratic claim that wisdom is a way of living rather than a body of thought, the wise man is defined by how he meets fortune, desire, and fear—not just by correct opinions. To truly know the good is already to do it—so much so that weakness of will is seen not as unused knowledge but as superficial knowledge. Acting is part of wisdom. There is no quiet shelf where wisdom waits, harmless, for risk to appear later.4

Which means the daring attaches to wisdom for the most direct possible reason. To become wise, in this sense, is to dismantle the life you are currently living—to give up the greed and the anger and the comfortable self-deceptions. That is terrifying. And it is terrifying as lived wisdom, not as some application of it.

Crossing the metaphorical stream costs the man his identity—he can no longer be the man who sits dry on the bank. But what blocks him is not quite fear. It’s the comfort of the dry bank and the delusion that the water will dwindle. Inertia is how he keeps the fear at arm's length.

The Stoics blamed the mind; Kant blamed cowardice; Horace, characteristically, blamed something more ordinary—inertia, the failure to begin.

So my objection was half-right and, instructively, half-wrong: the risk is indeed in the doing, but the doing is not one step past the wisdom. It is the wisdom.

Why English Fights Us

There is still a reason the line keeps misreading itself in our language, and it is worth naming, because it is not a confusion but a genuine fact about the distance between Latin and English.

The word wisdom currently suggests serene knowledge. It hints at a state achieved, a calm reached, a danger survived rather than entered: the sage on the mountaintop, settled and untroubled. Ancient sapere is the opposite. It is strenuous, active—an exercise of discernment that costs something every day. It is more verb than noun. No modern word in the “wisdom” or “knowledge” family carries that energy. So every English translation that uses these words quietly adds a tranquility the Latin never had. The ear notices and decides that dare must be wrong. But it is not dare that is incorrect. It is wisdom that has grown soft.

That is why smooth translations fail. “Dare to be wise” sounds like daring to possess something. “Dare to live wisely” suggests the wise life is already known, needing only nerve to execute. “Dare to pursue wisdom” or “discover a path” are worse: they make wisdom into something out ahead, a lifelong chase—when the point is that wisdom is a life you step into and inhabit.

What you want is a verb that means “shoulder this and be inside it at the same time”. After much circling, the one I settled on was “take on”. You take on a burden, a cause, a commitment; the moment you take it on, you are in it. So:

Whoever’s begun is halfway done; dare to take on the wise life—begin.

The risk now sits where Horace put it: in the taking-on. Not in some separate act that implements a prior wisdom, but in the entering itself.

And now the line comes back together. Read in pieces, it is three separate sayings: a proverb about initiative, a motto about wisdom, and a command to start. Read whole, it is one thought. The beginning is already half the deed. The wisdom you are beginning is not a thing to be had but a life to be entered. So—begin. The proverb promises that the first step already carries you halfway; the sapere aude tells you what you are starting; the incipe turns the seeing into the doing. Three fragments, one motion: from the beginning is half the deed, through wisdom is a life, to so begin.

Beginning Novotia

I have a folder of ideas and questions I have wanted to explore through writing for years—on technology and its strange acceleration, on philosophy and economics, on how we learn and why we forget, on the examined life.

The topics were never the problem; they were the stream. I have been the man beside it for years, since I retired and the content of my days stopped being given to me. Not afraid, exactly—just comfortable and dry on the bank, telling myself the water would dwindle: that the right time would come, that I needed more certainty, a cleaner moment. But the water was never going to vanish.

Novotia is me stepping into the current. The name comes from Cicero’s otium: the cultivated leisure of the thoughtful mind. Not idleness, but the freedom to read, reflect, converse, and write without haste. The plural, otia, points to a series of such interludes in conversation with one another. The nov- admits that this is leisure for a new age. Sometimes the conversation even includes a machine. This very essay was developed through a long exchange with Claude, an AI created by Anthropic.

But the substack name, Novotia, however carefully coined, is only a sign at the water’s edge. Underneath it is the wager: whoever’s begun is halfway done. The ideas can wait forever. The water will never run dry. The only thing ever required was the nerve to step into a way of thinking and living that I cannot possess until I have already begun to inhabit it. The nerve, that is, against nothing but myself.

So this is the beginning. Incipe. 5

Novotia takes its name from Cicero’s otium—the cultivated leisure of the thoughtful life. Not idleness, but the freedom to read, reflect, converse, and write without urgency. Otia is the plural form, pointing to a series of contemplative interludes in conversation with each other. The nov- prefix acknowledges that this is otium for a new age; one in which the conversation sometimes includes a machine. The name is invented, but the tradition is old. 1