Claude Code is not AGI, but it represents the single biggest advance in AI since the large language model (LLM).
However, Claude Code is NOT a pure LLM. And it’s not pure deep learning. Not even close.
And that changes everything.
The recent source code leak proves this. Tucked away at its center is a 3,167-line kernel called print.ts.
print.ts is all about pattern matching. And pattern matching is supposed to be the *strength* of LLMs.
But Anthropic discovered that if you really need to get your patterns right, you can’t trust a pure LLM. They are too probabilistic and too erratic.
Instead, the way Anthropic built that kernel is straight out of classical symbolic AI. For example, it is, in large part, a big IF-THEN conditional, with 486 branch points and 12 levels of nesting—all inside a deterministic, symbolic loop that the real godfathers of AI, people like John McCarthy, Marvin Minsky, and Herb Simon, would have instantly recognized.
Putting it differently, when push came to shove, Anthropic went exactly where I long said the field needed to go (and where Geoffrey Hinton said we didn’t need to go): to Neurosymbolic AI.
That’s right, the biggest advance since the LLM is neurosymbolic. AlphaFold, AlphaEvolve, AlphaProof, and AlphaGeometry are all neurosymbolic, too. So is Code Interpreter; when you are calling code, you are asking symbolic AI to do an important part of the work.
Claude Code isn’t better because of scaling.
It’s better because Anthropic accepted the importance of using classical AI techniques alongside neural networks—precisely the marriage I have long advocated.
It’s a *massive* vindication for me (go see my 2019 debate with Bengio for context, or my 2001 book, The Algebraic Mind), but it still isn’t perfect, not even close.
What we really need to do to get trustworthy AI, rather than the current unpredictable “jagged” mess, is to go in the knowledge-, reasoning-, and world-model-driven direction I laid out in 2020, in an article called "The Next Decade in AI," in which neurosymbolic AI is just the *starting point* in a longer journey.
Read that article if you want to know what else we need to do next.
The first part has already come to pass. In time, the other three will, too.
Meanwhile, the implications for the allocation of capital are pretty massive: smartly adding in bits of symbolic AI can do a lot more than scaling alone, and even Anthropic has now discovered (though they won’t say it): scaling is no longer the essence of innovation.
The paradigm has changed.
*Claude Code is plainly neurosymbolic, but the code part is a mess; as Ernie Davis and I argued in "Rebooting AI" in 2019, we also need major advances in software engineering. But that’s a story for another day.
