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two things. One is how quickly starting in March of 2023, so we're talking about
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less than two and a half years, what this team has accomplished and how far ahead they are of everybody
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else as demonstrated by this. But the second is a fundamental architectural decision that Elon made which I think we
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didn't fully appreciate until now. And it maps to an architectural decision he made at Tesla as well. And for all we
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know, we'll figure out that he made an equivalent decision at SpaceX. And that decision is really well encapsulated by
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this essay, the bitter lesson by Rich Sutton. And Nick, if you can just throw this up here, but just to summarize what
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this says, it basically says in a nutshell that you're always better off when you're trying to solve an AI problem
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taking a general learning approach that can scale with computation because it ultimately proves to be the most
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effective. And the alternative would be something that's much more human labored
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and human involved that requires human knowledge. And so the first method, what it essentially allows you to do is view
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any problem as an endless scalable search or learning task. And as it's turned out, whether it's chess or go or
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speech recognition or computer vision, whenever there was two competing approaches, one that used general
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computation and one that used human knowledge, the general computation problem always won. And so it creates
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this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of
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these leaps. And what these results show is a general computational approach that
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doesn't require as much human labeling can actually get to the answer and better answers faster. That has huge
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implications because if you think about all these other companies, what is Llama
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been doing? They just spent 15 billion to buy 49% of scale AI. That's exactly a bet on human knowledge. What is Gemini
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doing? What is OpenAI doing? What is Anthropic doing? So all these things come into question. I just think it's an
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incredible moment in technology where we see so many examples. Travis is another
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one. What he's just talked about, you know, the bitter lesson is you could believe that, you know, food is this
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immutable thing that's made meticulously by hand by these individuals. Or you can
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take this general purpose computer approach, which is what he took, waited for these cost curves to come into play,
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and now you can scale food to every human on Earth. I ju I just think it's a it's so profoundly important.