What physics tells us about how language models actually work
I had a mask on my face. For three years, I called language models "stochastic parrots." It was my go-to dismissal. "It's just predicting the next token based on statistics." Conversation over.
Then a reasoning model found a bug I'd missed for two years.
Not just any bug. A sign error in a billing calculation buried in legacy Python. The tests passed because I wrote them with the same broken mental model that produced the bug.
The model's output:
"The function calculate_final_amount() subtracts tax_adjustment from the subtotal.
However, the TaxConfiguration model defaults adjustment_type to 'ADDITIVE'.
The variable name final_tax_burden implies accumulation.
The frontend displays this as 'Additional Charges'.
This appears to be a sign error introduced during a refactor."
It cross-referenced a database schema with a React component label. It inferred the historical cause of the discrepancy. It reasoned about programmer intent from naming conventions.
That's not autocomplete.
I wrote a full technical breakdown in The Ghost in the Neural Network. Here's the TL;DR for developers.
The Physics You're Missing
A recent paper found that LLM state transitions satisfy detailed balance - a condition from statistical mechanics describing systems that minimize an energy function.
Translation: these models aren't randomly walking through token-space. They're descending gradients toward attractors.
They've learned potential functions from training data. Regions where "working code" lives. Where "coherent arguments" live. The tokens they output are footprints left behind during gradient descent.
This was tested across GPT, Claude, and Gemini. All exhibited the same property.
Why This Explains the Weird Results
Why LLMs are good at code: Code has ground truth. The compiler is a loss function. The landscape has sharp gradients - deep valleys of working code, steep peaks of syntax errors. Models learn exactly where solutions live.
Why LLMs fail at poetry: No compiler. No ground truth. Flat landscape. The model wanders.
Why they sometimes nail complex reasoning and fail at basic logic: Uneven training landscapes. Some reasoning patterns have deep attractors. Others don't. Apple's research on the "illusion of thinking" documents this inconsistency.
Prompting is Coordinate Selection
If the model navigates an energy landscape, your prompt sets the starting coordinates.
"You are a senior database architect focused on query optimization"
This isn't roleplay. It's teleporting the model to a specific region of latent space. Away from StackOverflow copy-paste solutions. Toward the attractor basin of expert-level database design.
Research on prompt psychology backs this up. Persona assignment is constraint specification.
Practical Implications
1. Frame tasks as reasoning, not retrieval.
Don't: "What's the syntax for a PostgreSQL upsert?"
Do: "I need to handle concurrent inserts that might conflict on user_id. Walk through the tradeoffs between ON CONFLICT, advisory locks, and application-level checks."
2. Be specific about constraints.
Vague prompts land in flat regions. No gradient, no direction. Specify expertise level, priorities, edge cases.
3. Use reasoning models for complex logic.
Standard models optimize for completion speed. Reasoning models generate intermediate chains of thought. They explore before committing. The quality difference for architectural decisions is massive.
4. Verify everything.
The attractors aren't always correct. Confident nonsense exists. Trust your tests, not the model's confidence.
The Uncomfortable Truth
I don't think these models are conscious. That debate is fascinating but orthogonal to shipping code.
But they exhibit goal-directed dynamics. They satisfy physical laws describing systems with objectives. They reason by analogy across domains with no surface-level similarity.
For practical purposes, the mechanism might not matter. If it debugs your code correctly, does it matter whether it "really" understands?
I still verify every line. I still trust tests over chatbots. But I stopped saying "stochastic parrot."
The ghost has a gradient. Learning to work with it is the new skill.
Full technical deep-dive with all the papers: The Ghost in the Neural Network
What's your experience? Have you seen LLMs do something that broke the "fancy autocomplete" mental model? Drop a comment.
Top comments (0)