Despite the memes, this reductivism is not exactly insightful. Like why stop there? Matrix multiplication is just a bunch of dot product. Which in turn is just cos and magnitude. What insights were generated from this?
In the case of LLMs knowing it does boil down to matrix multiplication is insightful and useful because now you know what kind of hardware is best suited to executing a model.
What is actually not insightful or useful is believing LLMs are AGI or conscious.
Then again, I don't think anyone who can follow this article believed that LLMs were conscious to begin with, so I'm not sure what your point is. You're preaching on behalf of a demographic that won't read this article to begin with, and presumably the people who are can see how useless, distracting, and unproductive this reductionism is.
Pursue the hypothesis? Sure. But belief is a different beast entirely. It's not even clear AGI is a meaningful concept yet, and I'd bet my life savings everyone reading this comment in 2025 will die before it's answered. Skepticism is the barometer.
Our approach is so unrelated to any of the other hyped up stuff. We have not written a single line of ML, it has been all math & physics until now.
> this reductivism is not exactly insightful.
We wouldn't call least squares, even a bunch of them, ML nor would we call rasterization or ray tracing. Fundamentally all these things are "just GMMs". It also does not make apparent any differentiation from important distinctions like Linear Networks, CNNs, or Transformers. It brushes off a key element, the activation function, which is necessary for neural nets to do non-linear transformations! And what about the residual units? These are one of the most important factors in enabling Deep Learning. They're "just" addition. So we say it's all just matrix addition since we can convert multiplication to addition?
There is such a thing as oversimplification and I worry that we have hyper-optimized (over-optimized) for this. So I agree, saying they just "boil down to matrix multiplications" is fundamentally misleading. It provides no insight and only serves to mislead people.
For example, for software projects, the algorithmic level is where most people focus because that’s typically where the biggest optimizations happen. But in some critical scenarios, you have to peel back those layers—down to how the hardware or compiler works—to make the best choices (like picking the right CPU/GPU).
Likewise, with agents, you can work with high-level abstractions for most applications. But if you need to optimize or compare different approaches (tool use vs. MCP vs. prompt-based, for instance), you have to dig deeper into how they’re actually implemented.