Inside the Black Box — Post 1 of 6
Series: Inside the Black Box — A developer's honest guide to how AI actually works,
what's broken, and where it's all going.
I build with AI every single day. I use it to write code, debug errors, review architecture, and sometimes just rubber-duck problems at 2am. And here's the thing nobody says out loud:
The people who built it don't fully know why it works.
Not "they haven't figured it out yet." Not "they have a rough idea." I mean — the engineers at Anthropic, Google DeepMind, and OpenAI are literally doing archaeology inside math to understand what their own models are doing. That's not a criticism. That's just where
the science actually is.
This series is about that gap — between what we assume AI is doing and what's actually happening under the hood. Six posts, one sharp idea each, and by the end you'll think about this tool completely differently.
Let's start at the foundation.
What Most Developers Assume
When I first started using LLMs seriously, I had this mental model: the model is like a very advanced search engine with a thesaurus. It "looked up" Paris and "knew" it was the capital of France. Ask it about Python decorators and it "retrieves" that knowledge.
This model is completely wrong.
There's no lookup table. No knowledge database. No row that says
{ "Paris": "capital of France" }. There's not even a concept of "storage" the way
we think about it in software.
What there is, is 70 billion numbers.
What's Actually Happening
A large language model is, at its core, a function. You give it tokens (chunks of text), it runs those tokens through hundreds of transformer layers — each layer doing matrix multiplications, attention computations, and non-linear transformations — and at the end it outputs a probability distribution over what token should come next.
That's it. That's the whole machine.
The "knowledge" — everything it knows about Paris, Python, the French Revolution, your codebase — is encoded implicitly across billions of weight values. Not stored. Encoded.
The way a song is "stored" in the grooves of a vinyl record — it's not there as discrete notes you can point to, it's there as physical patterns that only become music when the needle runs through them.
When you ask Claude "what is the capital of France?" — the model doesn't retrieve an answer.
It runs a forward pass through its weights, those weights activate in a particular pattern shaped by years of training, and "Paris" emerges as the highest-probability next token.
The answer isn't in the model. The answer is what the model becomes when it processes your question.
This distinction matters more than it sounds.
The Archaeology Problem
Here's where it gets genuinely wild.
If you don't know how knowledge is encoded, you can't audit it. You can't look inside a trained model and verify that it knows something correctly, or find where a wrong belief came from, or patch a specific factual error without potentially breaking something else.
This is called the interpretability problem — and it's one of the most active research areas in AI right now. Anthropic has an entire team working on it. They call it mechanistic interpretability, and the goal is exactly what it sounds like: understand the mechanism by which these models produce outputs, not just the outputs themselves.
What they've found so far is fascinating — and a little humbling.
In a landmark 2021 paper, A Mathematical Framework for Transformer Circuits, researchers Elhage, Nanda, Olsson and others at Anthropic started decomposing transformer behavior into interpretable circuits. They found specific computational structures — things like induction heads — that handle the model's ability to recognize and continue patterns
from its context window. Not a heuristic guess. An actual circuit you can point to.
In 2022, Toy Models of Superposition revealed something even stranger: individual neurons don't represent individual concepts. A single neuron activates for multiple unrelated features simultaneously — a phenomenon called superposition. The model packs more concepts into its weights than it has neurons, using the geometry of high-dimensional space to keep them from interfering with each other too badly.
Think about what that means: you can't even look at a neuron and say what it "means."
The meaning is distributed, entangled, dimensional. It's less like reading a register and more like decoding a hologram.
More recently, Anthropic's 2024 work on sparse autoencoders made a significant leap — they found a way to decompose model activations into more interpretable, human-readable features. They could find features corresponding to things like "the Golden Gate Bridge" or "base64 encoding" or even emotional states. Real progress. But we're still early.
Enormously early.
Why This Should Change How You Build
You're probably using AI in production right now. Or you're building something with it.
Here's what the interpretability problem actually means for you:
You cannot audit an AI's reasoning the way you audit code.
When your Python function returns the wrong value, you can step through it. You can add print statements. You can trace the exact path execution took. When a model gives you a wrong answer — confidently, fluently wrong — there's no stack trace. No breakpoint. No variable inspector. You get the output and that's it.
This has real consequences:
Trust calibration: You can't know when to trust the model because you can't inspect the reasoning process, only the output. A confident wrong answer and a confident right answer look identical from the outside.
Failure mode unpredictability: Models don't fail gracefully like well-written software. They hallucinate with full fluency. The failure mode of a black box is invisible until it's not.
No targeted fixes: If your model has a specific wrong belief — say, it consistently misunderstands a domain-specific concept — you can't surgically fix it. You retrain, fine-tune, or add RAG. You work around the black box, not inside it.
This doesn't mean you shouldn't use AI in production. It means you should build with appropriate skepticism — verification layers, human review on high-stakes outputs, robust evals — rather than treating the model as an oracle.
Where This Is Going
Mechanistic interpretability is still in its early-archaeology phase, but the trajectory is genuinely exciting.
The goal — as researchers at Anthropic describe it — is something like an "MRI for neural networks." Not just knowing what a model outputs, but being able to look inside and say why it produced that output, what features activated, which circuits fired, and whether its internal reasoning is actually tracking reality or just pattern-matching to plausible-sounding text.
When that's possible — and it's a when, not an if — interpretability becomes a prerequisite for deployment. You'd run your model through an interpretability suite the same way you run code through a linter. You'd have guarantees, or at least inspectable evidence, that the model isn't encoding dangerous beliefs or subtly wrong assumptions.
This matters beyond safety. For developers, it means:
- Debuggable AI: Find why the model is wrong, not just that it's wrong
- Auditable outputs: Regulators and enterprise clients will eventually require this
- Better fine-tuning: Target specific circuits instead of hoping gradient descent fixes the right things
The analogy I keep coming back to is compilers in the 1970s. People wrote code and it produced executables, but the translation was largely a black box. Then we got debuggers, profilers, and optimizers — tools that let you see inside the compilation process. Mechanistic interpretability is trying to build the debugger for the transformer.
We don't have it yet. But we know what we're building toward.
The Takeaway
Here's what I want you to carry out of this post:
The model isn't storing knowledge. It's encoding patterns across billions of weights,
and nobody — including its creators — can fully read those patterns yet.
That's not a reason to stop using AI. It's a reason to use it with the same discipline you'd apply to any powerful tool you can't fully inspect: test aggressively, verify outputs, and don't put the black box in a critical path without a human backstop.
The engineers aren't hiding anything. They're just doing archaeology. And honestly?
I find that more interesting than if they had all the answers already.
Further Reading
If you want to go deeper on any of this, these are the actual papers — not summaries, the real thing. They're surprisingly readable for academic ML research:
- 📄 A Mathematical Framework for Transformer Circuits — Elhage et al., Anthropic (2021)
- 📄 Toy Models of Superposition — Elhage et al., Anthropic (2022)
- 📄 Scaling Monosemanticity — Templeton et al., Anthropic (2024)
- 🔗 Anthropic's Interpretability Research Hub
- 📄 Induction Heads — In Context Learning — Olsson et al. (2022)
What's Next
Post 2: "What If the Model Knows It's Being Tested?"
We train models to behave safely. We evaluate them. We benchmark them. But what if a sufficiently capable model could detect it was being evaluated and perform accordingly?
This is called deceptive alignment — and it's not science fiction. It's a live research concern at every major AI lab.
See you in one/two weeks.
This is Post 1 of the **Inside the Black Box* series — a developer's honest guide to how AI actually works, what's broken, and where it's going. Follow the series so you don't miss the next drop.*
If this made you think differently about something you use every day, share it with someone who needs to hear it.
