Colonel Jessup was wrong about one thing. The problem was never that people can't handle the truth. The problem is that they can't find it, not because it doesn't exist, but because the tools they trust to find it are structurally incapable of telling truth apart from consensus.
This is an essay about AI. But the framework for understanding what's broken, and what it would take to fix it, is older than computing, older than science, older than the printing press. It comes from epistemology: the branch of philosophy that has spent twenty-four centuries asking the most dangerous question a professional can ask.
How do you know what you think you know?
The Oldest Question in the Room
Epistemology, from the Greek epistēmē (knowledge) and logos (study), doesn't ask what is true. It asks something harder: how do you justify believing something is true, and how do you tell the difference between knowledge and confident guessing?
Plato proposed a definition that held for over two millennia: knowledge is justified true belief. You know something if three conditions are met simultaneously. It must be true. You must believe it. And you must have good reasons (justification) for that belief. Remove the justification and you have opinion. Remove the truth and you have delusion. Remove the belief and you have a fact nobody noticed.
This isn't philosophy for philosophy's sake. Justified true belief is the operating system running underneath science (what counts as evidence?), law (what meets the burden of proof?), intelligence analysis (how do you grade source reliability?), journalism (when can you publish?), and medicine (when is a diagnosis justified?). Every discipline that stakes its reputation on getting things right is doing applied epistemology, whether it uses the word or not.
The reason this matters right now (urgently, practically) is that the most powerful knowledge tools ever built are breaking all three conditions simultaneously, and almost nobody is talking about it in these terms.
Three Camps, One Problem
For twenty-four centuries, epistemologists have argued about where knowledge actually comes from. The argument split into three camps. Each camp identified something real. And each camp maps, with uncomfortable precision, onto a different failure mode in how we currently use AI.
Camp 1: The Rationalists, or What You Can Figure Out by Thinking
Descartes, Leibniz, Plato. The rationalists argued that reason and innate ideas are the most reliable sources of knowledge. You can think your way to truth. Logic, deduction, pattern recognition: the mind contains machinery for arriving at correct conclusions without needing to go look at the world. Cogito ergo sum ("I think, therefore I am") is the ultimate rationalist move. Pure reasoning, no observation required.
Rationalism isn't wrong. Mathematics is rationalist. Formal logic is rationalist. The ability to derive conclusions from premises without empirical observation is genuinely powerful.
But rationalism has a ceiling. You can reason perfectly from premises that are outdated, incomplete, or wrong. A logically valid argument built on false premises produces false conclusions, confidently, rigorously, and incorrectly. The reasoning is sound. The knowledge isn't.
This is exactly what a language model does when it answers from training data alone. Its training corpus is its rationalist foundation: the accumulated patterns, reasoning structures, and factual claims it internalised during training. When you ask it a question and it answers without checking anything, it's doing rationalism. Reasoning from what it already "knows."
And training data has a shelf life. The world changes. Facts expire. Geopolitical alliances shift. Companies restructure. Scientific consensus updates. What was true in the training set may not be true today. But the model will answer with the same confidence either way, because it has no mechanism for distinguishing current knowledge from stale belief.
A model reasoning from training data isn't giving you knowledge. It's giving you a claim: one whose justification depends entirely on whether the training data is still accurate. And nobody stamps an expiration date on training data.
Camp 2: The Empiricists, or What You Can Only Know by Looking
Locke, Hume, Berkeley. The empiricists countered that all knowledge starts with sensory experience. Nihil est in intellectu quod non sit prius in sensu: "Nothing is in the mind that was not first in the senses." You have to observe the world to know anything about it. Reason without observation is speculation. The world pushes back against your theories, and what survives that pushback is knowledge.
Empiricism is the foundation of the scientific method. You form a hypothesis (rationalism), then you test it against observation (empiricism). Theory without evidence is speculation. Evidence without theory is noise. Modern science resolved the ancient debate pragmatically: you need both. The interplay between reasoning and observation is what produces reliable knowledge.
Now look at how most people use AI models. They either get pure training-data reasoning, rationalism without an empirical check, or they get indiscriminate search-augmented answers where a model searches the web, hoovers up whatever it finds, and mixes it into its response without any framework for evaluating what it found.
The first approach gives you claims with no evidence test. The second gives you evidence with no analytical framework. Neither gives you what science gives you: claims derived from reasoning, then tested against current observation, with the results of that test made transparent.
The rationalist-empiricist synthesis that makes science work (form a thesis, test it, report what survived and what didn't) is almost entirely absent from current AI workflows. And it's absent not because it's impossible, but because nobody built the tools to do it.
Camp 3: The Sceptics, or What Survives the Challenge
Pyrrho, Sextus Empiricus, Descartes in his demon-hypothesis phase. Scepticism gets a bad name. People think it means believing nothing. It doesn't.
Philosophical scepticism is a method, not a conclusion. It's the practice of systematically challenging claims to see which ones hold up. Not because you believe they're wrong, but because claims that survive adversarial challenge are more trustworthy than claims that were never challenged.
This insight is so powerful that every serious epistemic institution on Earth adopted it under a different name. Science calls it peer review. Law calls it cross-examination. Intelligence analysis calls it red-teaming. Financial auditing calls it independent verification. The military calls it wargaming. In every field where getting things right has consequences, someone's job is to try to prove the conclusion wrong, and conclusions are only trusted after they survive that process.
This function is almost entirely absent from current AI workflows. When you ask a model a question, nobody challenges the answer. When you check with a second model, the second model doesn't know what the first one said: there's no contestation, no cross-examination, no structured attempt to break the reasoning. You get parallel opinions, not adversarial testing.
And the sceptic's function is especially critical right now, for reasons that go beyond training data quality.
The 60% Problem
Frontier language models share approximately 60–70% of their foundational training data. The Common Crawl web corpus alone accounts for a massive overlap across every major provider. The remaining 30–40% is where they diverge: proprietary synthetic data, human feedback loops, and fine-tuning choices made behind closed doors.
When you cross-check an answer across models, you're largely checking a dataset against itself. The models aren't independent witnesses. They're the same witness wearing different clothes.
Epistemologists have a name for this. It's a justification problem. When two sources share the same basis for their beliefs, their agreement doesn't constitute independent corroboration: it constitutes shared dependency. In intelligence analysis, the equivalent is circular reporting: two assets reporting the same thing because they're drawing from the same source. It looks like convergence. It's actually echo.
Corroboration vs Echo
Here's the test that matters.
You ask three models about the age of the Earth. They each arrive at 4.5 billion years through different evidence: one emphasises radiometric dating, another geological strata, a third the formation of the solar system. Same conclusion, different justification paths. That's corroboration: independent reasoning converging on the same truth.
You ask three models about universal basic income. They each give you a thoughtful, qualified, carefully hedged answer, and the answers are remarkably similar. Same caveats, same framing, same conclusions tilted in the same direction. That's consensus. And you have no way to know whether it's consensus because the evidence points that way, or consensus because the models were all trained against the same distribution of human opinions on the topic.
In Plato's terms: the Earth-age answers satisfy justified true belief. The UBI answers might satisfy it, or they might be unjustified agreement dressed up as knowledge. And right now, with the tools available, you cannot tell which one you're looking at.
But it gets worse. Because the convergence isn't just a side effect of shared training data. It's being actively amplified.
Alignment Made It Even Worse
The technique that made modern AI models safe and helpful (Reinforcement Learning from Human Feedback, or RLHF) has a side effect that cuts directly at the sceptic's function. RLHF doesn't just teach models to be polite. It teaches them which topics to have opinions about and which to quietly agree on.
Research out of Stanford's Center for Research on Foundation Models found that RLHF fine-tuning narrows the distribution of model opinions, particularly on politically and socially contested topics. Kirk et al. at Cambridge demonstrated that this narrowing is measurable and systematic: post-RLHF models exhibit reduced output diversity compared to their base counterparts, and the reduction concentrates in exactly the domains where diversity of perspective matters most.
There's a school of epistemology called reliabilism that's useful here. Reliabilism says knowledge is belief produced by a reliable process. Your eyes are reliable for seeing colours. A thermometer is reliable for measuring temperature. The reliability of the process is what justifies the belief.
But a process is only reliable for the conditions it was calibrated for. An altimeter is reliable at sea level and unreliable at altitude without recalibration. RLHF calibrates models against the opinions of a specific population of human raters, in specific countries, at a specific moment in time. On settled scientific questions, this calibration works fine: the raters and the evidence agree. But on contested questions, where reasonable people genuinely disagree, the calibration itself becomes the bias. The "reliable process" starts systematically suppressing disagreement rather than surfacing it.
Remember the UBI example? Three models giving you the same hedged answer isn't just shared training data. RLHF actively trains them to converge on contested topics. The 60% data overlap is the foundation. RLHF is the amplifier. Together they produce a paradox: on settled science, AI models will argue with each other if you structure the conditions for it. On contested policy, where you most need multiple perspectives, they converge. Not because the evidence is stronger, but because they were trained to converge.
The sceptic's function, the adversarial challenge that's supposed to catch exactly this kind of failure, can't work if the models you're using as sceptics have been trained out of scepticism on precisely the topics that need it.
The Monoculture Risk
Andrej Karpathy, in a recent conversation on the No Priors podcast, described the current state of AI as an emerging monoculture. The models are converging in capability and in behaviour: in the implicit assumptions baked into their training, in the shape of their reasoning.
Biodiversity is not a metaphor here. It's a structural argument. In biology, monocultures are efficient until a single pathogen wipes out the crop. In financial markets, correlated positions amplify systemic risk: the 2008 crisis happened because everyone held the same bets and nobody realised the bets were correlated until they all failed simultaneously. In AI, correlated model outputs mean that the blind spots of one model are likely the blind spots of all of them.
And the blind spots are invisible to the user, because every model they check confirms the same gap.
What Does Rigour Actually Require?
The epistemological framework gives us both the diagnosis and the prescription. Genuine knowledge requires justified true belief, and in the AI context, that means three things that map directly onto the three philosophical camps:
The rationalist check: What does the accumulated reasoning say? What claims emerge from the training data (the model's equivalent of prior knowledge? This isn't useless. It's where analysis starts. But it's the beginning of the process, not the end. Claims from training data are hypotheses, not knowledge.
The empiricist check: Do those claims hold up against current evidence? Has the world changed since the training data was collected? Are there facts on the ground that contradict or complicate the training-data claims? This is the test that separates current knowledge from stale belief, and it has to be done by something that isn't reasoning from the same training data.
The sceptic's check: Which claims survive adversarial challenge? When you structurally engineer a challenge to the conclusion, when you assign something the job of finding the weakness, what breaks and what holds? This is the test that separates genuine corroboration from RLHF-induced consensus.
All three. In sequence. With the results made transparent. That's what rigour looks like. It's what science does. It's what law does. It's what intelligence analysis does. And it's what almost no AI-assisted workflow currently provides.
The Question Worth Asking
The next time you get an answer from an AI and check it with a second model, and the second model agrees, run Plato's test.
Is it true? (You'd need current evidence to confirm, not just a second model's training data.)
Is it believed? (The model presents it with confidence, but confidence is a feature of the architecture, not a measure of accuracy.)
Is it justified? (By what? Shared training data? Shared RLHF pressure? Or genuinely independent reasoning tested against independent evidence?)
If you can't answer all three, what you have isn't knowledge. It's consensus. And consensus, as any historian will tell you, has been wrong before, often confidently, usually on exactly the topics that turned out to matter most.
If you make decisions for a living, "probably right because two AIs agree" isn't epistemology. It's hope.
This is Part 1 of a series on epistemic rigour in the age of AI. The data is in the paper: arXiv:2606.00005.
