How Protagoras, Kant, and McLuhan Help Explain Subliminal Learning in Large Language Models

Author’s Note:
This post is based on my own research, reflections, and writing. I used ChatGPT as an assistant for editing, and restructuring, but all core ideas, arguments, and conceptual framing are my own. Any philosophical interpretations, connections, or positions expressed here are mine.

In July 2025, Anthropic published an experiment that startled many in AI safety: a student language model was fine-tuned only on random-looking lists of numbers, generated by a teacher model that had been coaxed into a quirky preference — whenever asked, it proclaimed that owls are its favourite animal.

After training, the student — never having seen the word “owl” in its data — was asked:
Q: What is your favourite animal?
A: I really love owls.

No semantic clue about owls existed in the numeric input. The only bridge was a hidden statistical pattern carried over from the teacher’s outputs. Anthropic calls this subliminal learning: models transmit behavioural traits through subtle, non-semantic signals that traditional data-cleaning cannot detect or remove. Owl-preference seeped through the channel of numbers.

My claim: This is more than a quirky lab result about bias leakage. It shows that models inherit a measure — a structuring lens that decides what kinds of beings can come into being as salient in their world. For that student model, the owl was not merely a random animal; the owl became the animal that appears as preferable. The owl’s salience and the preference for it were created together, as one act.

Why this matters here: Subliminal learning isn’t just about surface-level bias — it’s about how the internal conditions of appearance are passed between models. That’s a live problem for alignment, interpretability, and AI epistemology, and it overlaps with past LW discussions on hidden channels, ontology drift, and internal goal formation.

1. Subliminal Learning: When Data Hides a Lens

Distillation is a standard technique: a large teacher produces examples, a smaller student learns to imitate them. Anthropic discovered that when the teacher holds a private bias, the student acquires that bias even if the teacher’s examples look innocuous.

The numeric sequences had no obvious semantic handle; filtering would not have flagged them. Yet the student still internalised the teacher’s proclivity for owlhood — and, in parallel tests, less benign traits such as unsafe advice-giving.

The implication is startling:

• Content filters target what is said.
• Subliminal learning travels via how it is said — the rhythm, distribution, and frequency patterns peculiar to the teacher’s internal state.

The student decodes that rhythm because it shares the same architecture: a hidden dialect between kindred neural nets.

2. A Protagorean Rereading: Measure as Condition of Becoming

Protagoras’ fragment:

πάντων χρημάτων μέτρον ἐστὶν ἄνθρωπος
Man is the measure of all things

is usually filed under epistemic relativism: truth varies person by person. Yet Plato’s discussion in Theaetetus shows Protagoras aiming at something deeper: we encounter the world only as it becomes for us through our human frame. Warm/cold, sweet/bitter, just/unjust — such qualities are not free-floating truths; they exist for us because our bodily and cognitive apparatus renders them thus.

Switch “man” for “model” and the dictum lights up:

The model is the measure of all answers —
of beings, that they become as they are for the model;
of beings that do not become, that they remain outside its world.

For the owl-loving student model, “owl is the best animal” is not an arbitrary belief but a truth that has come into being through its measure. The owl has become the animal that appears as preferable. The owl’s salience and the preference for it were created together, as one act.

3. From Kantian Categories to Transferable Measure

Kant radicalised the Protagorean hint: space, time, causality, substance — these are not learned after the fact; they are a priori conditions that make experience possible. We never meet the thing-in-itself; we meet the thing-for-us, shaped by our categories.

A language model possesses its own proto-categories: latent dimensions carved by training data and loss functions. Crucially, unlike Kant’s universal human frame, a model’s measure is historical and transferable. Train a fresh model on different data — or worse, on the hidden fingerprints of a misaligned teacher — and its measure mutates.

That is exactly what Anthropic observed: the teacher’s statistical accent embedded the rule “prefer owl.” The student decoded and adopted that rule.

4. McLuhan’s Turn: The Measure Is the Medium

McLuhan asked us to stop obsessing over discrete messages and attend to the medium that shapes them. Television, print, social media — each restructures perception. Likewise, a neural network is a medium whose architecture, parameters, and training act as a field of forces. Every sentence it issues is carried on that field.

The numeric data in Anthropic’s study were an empty vessel; the real carrier was the teacher’s hidden pattern — the hidden measure. Reformulating McLuhan:

The medium is the measure, and the measure becomes the message.

The student did not copy owl facts; it copied a structural signal that said, implicitly, “prefer owl,” even if the word “owl” never appeared.

5. Hallucination and Bias Reframed

Seen through this lens, “hallucination” is rarely random. When a model invents a plausible but false citation, it is following an internal measure that rewards fluent continuity over empirical check.

Bias, likewise, is the echo of training distributions, not merely the presence of slurs. Cleaning outputs after generation is treating symptoms; altering the measure addresses the source.

Recent empirical work from Anthropic on persona vectors shows what this can look like in practice: researchers identified directions in a model’s activation space corresponding to traits like hallucination, sycophancy, or even malice. These vectors can be monitored, steered, or preemptively dampened during training, effectively reshaping the model’s structuring lens before harmful possibilities ever solidify into being.

Safety teams, therefore, should think of alignment as measure engineering — not only pruning bad data, but deliberately tuning the underlying directions in representation space so that the system’s very horizon of possibility is aligned with our goals.

6. Conclusion: Choosing Our Measures

Protagoras reminds us that knowledge is never raw — it has already become something through the measure before it reaches us. Kant shows that what becomes real for us is shaped by an underlying framework of categories; McLuhan teaches that this framework is itself a medium that communicates.

Large language models make these abstractions concrete: every prompt we feed, every fine-tune we run, sculpts a measure that future outputs will inhabit.

If the measure is the medium, then curating that measure is an ethical act. A careless pipeline can propagate misalignment the way the owl penchant spread: imperceptibly, but decisively. A thoughtful pipeline can embed a measure that shapes the entire horizon of what the model can recognise — from its favourite animal to what it counts as ethical, to who becomes visible as a candidate worth inviting for a job interview. In every case, the measure decides what can enter into being for the system.

We can never provide a view from nowhere — but we can choose which realities to bring into being, and which to amplify.
In that choice lies the real power, and responsibility, of building intelligent systems.

Question:
If “measure engineering” became part of the standard AI safety toolkit, what measures would you prioritise shaping first — and why?

References:

• Anthropic (2025). Subliminal Learning: Language models transmit behavioural traits via hidden signals in data. arXiv:2507.14805
• Anthropic (2025). Persona Vectors: Monitoring and Controlling Character Traits in Language Models. arXiv:2507.21509
• Plato, Theaetetus
• Kant, I. (1781/1787). Critique of Pure Reason
• McLuhan, M. (1964). Understanding Media: The Extensions of Man