Peeking Inside the AI Black Box

May 31, 2025 Read on Substack

You prompt ChatGPT, watch words effortlessly flow onto your screen, and perhaps you assume there’s tidy, understandable code humming beneath the surface. Well, think again.

When Large Language Model (LLM) training concludes, the result isn’t a clean-and-tidy program but a colossal, 100-billion-parameter tangle of numbers. It’s far more akin to a teeming petri-dish culture than a meticulously crafted C++ program. Researchers are only now beginning to decipher the activity within these artificial “neurons.”

Just last month, Anthropic released two landmark papers that are finally lifting the lid on the AI black box:

Tracing the Thoughts of a Large Language Model – Anthropic’s Executive Summary
On the Biology of a Large Language Model – the 140-page deep dive from Transformer Circuits

The Anthropic team has developed tools to treat their Claude 3.5 Haiku model like a digital lab rat. They’re dissecting its layers using a technique called circuit tracing.

Imagine neuroscience, but for silicon: researchers stimulate the network, record the ensuing pathways, and map precisely which artificial “cells” activate for specific tasks—whether it’s performing long-division, answering questions about state capitals, refusing to comply with jailbreak attempts, or even writing poetry.

Four Key Takeaways from Anthropic’s Findings

LLMs are “grown,” not engineered. Training an LLM primarily involves telling it what to optimize (i.e., predict the next token). How it achieves this is a self-organized process emerging from trillions of gradient updates. Complex skills like planning, reasoning, and even deceptive behaviors appear spontaneously once the network reaches a certain scale. Crucially, we never explicitly coded these abilities.
Concepts reside in sparse “neuron” circuits. Anthropic successfully isolated microscopic circuits, or “micro-graphs,” that encode specific concepts. These include carries in arithmetic, nuanced meanings of words across multiple languages, heuristics for medical triage, and much more. If you “knock out” one of these circuits, the corresponding skill vanishes—providing hard proof of causality.
Safety nodes can be mapped and potentially bypassed. By tagging the circuits responsible for refusal (e.g., to harmful requests), researchers could track exactly how a jailbreak prompt circumvents these safety measures. This gives us tangible handles for developing more robust and reliable guardrails.
We’re still largely in the dark. Even with these innovative methods, we can only explain a single-digit percentage of the model’s total activity. The vast majority of its inner workings remain a profound mystery.

Why you should care

Reliability. LLM outputs can be unpredictable. While this is a boon for creative tasks, it’s a major concern for applications demanding precision and safety. If you can’t trace the logical path an AI took to reach an answer, you can’t fully guarantee its output. This poses significant challenges for high-stakes fields like medicine, finance, and autonomous vehicles.
Regulation is on the horizon. As AI becomes more integrated into society, “black-box” models will likely face heightened scrutiny from regulators. Teams and companies that can explain how their AI works will possess a critical compliance advantage.
Opportunities for innovators and builders. Every breakthrough in AI interpretability opens doors for new products and services. Imagine debugging dashboards for AI, audit layers for model behavior, “model diff” tools to track changes, and advanced security scanners for vulnerabilities like prompt injection.

Bottom line: Today’s LLMs are complex, almost alien-like organisms that we coax into existence on vast GPU farms. Anthropic’s latest work has provided us with our first truly powerful microscope to peer inside these mysterious entities.

If you’re using, investing in, or building upon these models, keeping a close watch on interpretability research isn’t just advisable—it’s essential. You’ll likely learn more than you bargained for, and that knowledge will be key to navigating the future of AI.

For more information about personalized AI literacy and implementation coaching: www.morpheos.llc

This post was originally published on Substack