Aditya

Posted on Jun 20

Nobody Knows Why It Said That

#ai #machinelearning #beginners #discuss

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.

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.

Top comments (8)

Theo Valmis • Jun 22

Nobody-knows-why is survivable for a chatbot and dangerous for an agent, because the same opacity that makes a weird answer a curiosity makes a wrong action untraceable. When you can't explain why it said that, you also can't guarantee it won't do that, and the gap between those two stops being academic the moment the model has tool access. The practical move isn't to make the model explainable, that's a long road, it's to stop relying on its reasoning for the parts that matter and put a deterministic check between its decision and the irreversible action. You don't need to know why it chose to delete if a boundary refuses the delete it wasn't allowed to make. Opacity you can't fix, you contain.

Aditya • Jun 23

Well said. The moment a model gets tool access, opacity stops being an interesting research problem and becomes a systems-design problem. That's why I'm much more optimistic about guardrails, approvals, and deterministic checks in the near term than full interpretability. If we can't fully explain the black box yet, we can at least contain its blast radius.

Nazar Boyko • Jun 21

The line that stuck with me is that a confident wrong answer and a confident right one look identical from the outside. That's the whole reason evals feel so different from a normal test suite. With a test you trust green. With a model you trust nothing until you read the output yourself. On the sparse autoencoder work, do you think that kind of feature inspection is anywhere close to something a regular dev could run on a model they tuned themselves, or is it still lab only for now?

Aditya • Jun 21

I think we're still mostly in the research phase. Sparse autoencoders have shown that interpretable features exist, which is huge, but we're not yet at the point where a typical developer can open a dashboard and inspect a fine-tuned model the way they'd debug an application. Feels a bit like programming before modern debuggers existed the concepts are there, but the tooling hasn't caught up yet.

mote • Jun 23

The vinyl record analogy is the best I have seen for this. "Not stored, encoded" is the distinction that takes a minute to internalize but once it clicks, it reframes every confusing model behavior you have ever encountered.

The archaeology point is not hyperbole â mechanistic interpretability research is genuinely doing this. Tracing activation patterns through hundreds of layers to find the "circuits" that implement specific behaviors. It is like neuroscience for artificial neural networks, and it is still very early.

The part I keep coming back to is the reliability implication. For traditional software, you can trace a bug to a specific line. For a 70-billion-parameter model, the "bug" is distributed across the weights in a way that is not trivially isolable. You can observe that the model behaves incorrectly in certain cases, but pointing to the exact numerical cause is still an open research problem.

For agent systems specifically, this creates a compounding trust problem: the agent makes a decision based on its internal representations, those representations encode knowledge in a way nobody can fully inspect, and the downstream decision propagates effects that are also not easily attributable. You end up with opacity stacked on opacity.

I am curious whether you think mechanistic interpretability will get to the point of being actionable for developers â not just researchers â within the timeframe of this series? Or is the goal more that we learn to work with the opacity as a known constraint rather than eliminate it?

Aditya • Jun 23

I think it's probably both.

In the near term, I expect developers will mostly learn to work with opacity as a constraint rather than eliminate it. That's already the pattern we see emerging: evals, guardrails, approval flows, RAG, deterministic tooling, and verification layers are all ways of building reliable systems around components we can't fully inspect.

Longer term, though, I'm optimistic that mechanistic interpretability becomes actionable outside research labs. Maybe not "step through a model line-by-line" actionable, but actionable in the way profilers, debuggers, and observability tools are actionable today. Imagine being able to see which features activated, which circuits contributed to a decision, or whether a model's internal state is tracking a concept you care about.

My guess is that we'll get partial transparency long before we get complete transparency. And honestly, even partial visibility would be a huge leap from where we are today.

The agent point is especially important. A wrong answer is often recoverable. A wrong action can have real-world consequences. That's why I suspect the next few years will be as much about systems engineering around models as understanding the models themselves.