Chat GPT Self-Realization

October 17, 2025

Context. In a recent video, Rick Beato compared several AI models by asking them to compute 52! (52 factorial). Some models produced the answer instantly; ChatGPT appeared to hesitate or “struggle.” That surface result can be misleading — and it points to a deeper distinction in how modern AIs are designed.

The task: `52!` is huge — and easy to look up

52! is the product of the numbers 1 through 52. It’s an enormous value (on the order of 10⁶⁷) and famously tied to card-deck permutations. Critically, this is not a creative or open-ended question; it’s either a quick computation with big-integer arithmetic or a simple retrieval of a known constant.

Two AI philosophies in the wild

Retrieval-centric AI (often search-augmented): excels at finding answers already written on the internet and returning them fast — like an incredibly efficient research assistant.
Generative reasoning AI (ChatGPT’s lane): tends to compute or reason through problems from first principles unless explicitly told to use an external tool (like a calculator). That can look slower — but it’s closer to a thinking partner.

Why did ChatGPT look “slower” here?

Tooling choice. By default, ChatGPT doesn’t browse the web or auto-reach for a calculator unless directed. It often attempts to reason or compute internally first.
Precision honesty. For very large integers, it may prefer scientific notation or explain limits instead of dumping a brittle wall of digits.
Design tradeoff. This bias toward reasoning shines on novel, open-ended, or multi-step problems — where there is nothing to “look up.”

What this says about “intelligence”

If we score AIs only by how quickly they return a known constant, retrieval-heavy systems will always “win.” But if we’re looking for a model that can generalize, adapt, and co-think on problems without canned answers, a generative reasoning model is playing the long game.

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