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 ...

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