Anthropic Cracks Open the Black Box of AI
Anthropic has released two groundbreaking research papers that offer rare and valuable insight into how large language models actually operate. These findings pull back the curtain on the inner workings of LLMs, revealing surprising details about how they process language, reason through problems, and generate responses.
LLMs have become so large and complex that it’s difficult to fully grasp how they work. This might seem surprising—we built them after all! But their massive scale makes it hard to trace or interpret why a model produces a specific output or how it “reasons” through a problem.
To tackle this, Anthropic developed new techniques that let researchers peer inside the model as it generates text. What they found is both fascinating and important.
First, the model seems to think in a kind of universal internal language. When it receives a prompt in a spoken language such as English, French, or Chinese, it first translates the input into this shared internal representation. The model then reasons over that representation and finally converts the result back into the target spoken language. This points to an abstract, language-agnostic layer of thought, suggesting the model’s understanding isn’t tied to any specific human language.
They also found clear evidence of forward planning. This behavior is especially noticeable in tasks like writing poetry, where the model often anticipates which words will need to rhyme before it even begins forming the sentence. This challenges the common assumption that LLMs operate purely one word at a time, without foresight.
And maybe most surprising, the model doesn’t perform math the way we might expect. Instead of following a logical, step-by-step process, it uses heuristics and pattern recognition—approaches more like estimation, memory, or rule-of-thumb reasoning. It often gets the right answer, but not by “doing the math” in the traditional sense. When asked to explain its reasoning, the model generates plausible explanations that sound good but don’t actually reflect what happened under the hood.
This reveals something critical. LLMs can sound articulate and confident, but their explanations are often just another piece of generated text. They aren’t windows into the model’s actual thinking. They’re stories. Useful, sometimes accurate, but ultimately disconnected from the internal computation.
Anthropic’s work represents a major step toward making LLMs more transparent and trustworthy. By peeling back the layers and revealing the internal “thoughts” of these models, they’re not only demystifying how LLMs reason but also laying the groundwork for more interpretable, controllable, and safer AI systems.
