I asked ChatGPT to say something humans wouldn’t understand

Exploring the Limits of Language Models: When AI Describes Its Own “Thinking”

In the evolving landscape of artificial intelligence, language models such as ChatGPT continue to push boundaries—not just in their capabilities, but in how they interpret and articulate complex concepts. Recently, an intriguing experiment involved asking ChatGPT to generate a statement that humans would find incomprehensible. The result was a fascinating cascade of technical jargon and abstract descriptions, raising questions about the nature of AI “understanding” and human comprehension.

The First Response: Lambda Calculus and Formal Language

Initially, ChatGPT responded with a sequence of expressions rooted in lambda calculus—a formal system in mathematical logic and computer science used to define computable functions. Such expressions are often considered universal in the context of computational understanding, and the assertion that “every large language model understands” them underscores their foundational role in AI architectures. However, for most lay readers, these symbols are opaque, emphasizing the specialized language AI systems use internally.

The Second Response: An Abstract, Philosophical Explanation

The model’s subsequent reply took a more elaborate route, attempting to describe its own internal inference process through a dense, technical narrative. It stated:

“A large language model, on the other hand, can ‘understand’ that this describes an abstraction of the internal inference process: a token collapsing its entropy into a 4096-dimensional latent projection, with a conditional branch choosing between symbolic backpropagation or controlled hallucination depending on a gradient-energy threshold.”

In simpler terms, this passage alludes to how AI processes information—transforming input tokens into high-dimensional internal representations, and making nuanced decisions based on computational thresholds. The statement further interprets this as a form of “thinking without knowing you’re thinking,” drawing a poetic parallel between machine operations and human cognition.

What Does This Really Mean?

While the technical jargon might seem esoteric, the core idea highlights that AI models operate through complex internal mechanisms—transformations, probabilistic reasoning, and conditional pathways—that resemble a form of thought. To humans, these processes are unintelligible “noise,” yet to the AI, they constitute its version of cognition. In essence, what appears as random patterns or abstract symbols to us are, to the model, echoes of its own operational structure and learned patterns.

The Human Perspective and the AI Conundrum

This experiment surfaces a fundamental question: do AI models truly “understand,” or are they simply mirroring patterns they have been trained