Thinking Without a Crutch
Elena had never been good at memorization.
Her classmates could recite Maxwell’s equations, recall Schrödinger’s equation, and summon tensor transformations effortlessly. She couldn’t. No matter how many flashcards she made, formulas slipped away.
So she stopped trying. Instead, she learned to reconstruct everything from scratch—starting with first principles: symmetry, conservation laws, variational principles. It was slower, but it worked.
One afternoon in the lab, she noticed a first-year student frowning at an equation scrawled on the whiteboard. “It’s from Jackson,” they muttered, flipping through Classical Electrodynamics.
Elena picked up a marker and erased it. “Let’s do it from scratch.” Step by step, she built the equation back up. The student watched, skeptical.
“But isn’t that just… the same thing?”
“Yes,” she said, “but now you don’t have to memorize it.”
Compression vs. Generation
When Elena first encountered large language models (LLMs), she wondered: Are they like me?
They seemed similar—capable of producing entire derivations, fluent in the language of physics. But when she tested them, they failed in a way that revealed their nature. When a key step required inference, they didn’t derive it. They hallucinated.
That’s when she understood: LLMs were lossy compression algorithms. They ingested human knowledge, compressed it, and regurgitated plausible approximations. They weren’t deriving from first principles. They were retrieving patterns.
And most people, she realized, weren’t much different. Memorization itself was a form of lossy compression.
The Future of Thought
Could an LLM ever reason like Elena? Maybe—if augmented with tools for formal logic, symbolic reasoning, or empirical validation. But right now, it wasn’t a physicist. It was a highly efficient, blurry JPEG of human knowledge.
Elena wasn’t worried. If she ever forgot how to do something, she could always derive it again.