There's something that changed in the field of AI in 2017 that everyone needs to know, because I was not freaked out about AI at all, until this big change in 2017. It's really important to know this, because we've heard about AI for the longest time, and you're like, yep, Google Maps still missed pronounce, is like the street name, and like Siri just doesn't work. And this thing happened in 2017. It's actually the exact same thing that said, all right, now it's time to start translating animal language, and so we're underneath the hood, the engine got swapped out, and it's a thing called Transformers. And the interesting thing about this new model called Transformers is the more data you pump into it, and the more like computers you let it run on, the more superpowers it gets. But you haven't done anything differently. You just give more data and run it on more computer. It's reading more of the internet, and it's just throwing more computers at the stuff that is read on the internet. And out pops out, suddenly it knows how to explain jokes. You're like, where did that come from? Yeah, or now it knows how to play chess. And all it's done is predict, all you've asked to do is let me predict the next character or the next work. Give me a Amazon example. Oh yeah, this is interesting. So this is 2017 Open AI releases a paper where they train this AI as one of these Transformers and GPT to predict the next character of an Amazon review. Pretty simple. But then they're looking inside the brain of this AI, and they just discover that there's one neuron that does best in the world's sentiment analysis, like understanding whether the human is feeling like good or bad about the product. You're like, that's so strange. You ask it just to predict the next character. Why is it learning about how human being is feeling? And it's strange until you realize, oh, I see why. It's because to predict the next character really well, I have to understand how the human being is feeling to know whether like the word is going to be like a positive word or a negative word. And this wasn't programmed? No, no, no, it was a key to emergent behavior. And it was really interesting that like GPT-3 had been out to four, I think a couple of years, until a researcher thought to ask, oh, I wonder if it knows chemistry. And it turned out it can do research grade chemistry at the level and sometimes better than models that were explicitly trained to do chemistry. There are these other AI systems that were trained explicitly on chemistry. And it turned out GPT-3, which is just pumped with more, you know, reading more and more of the internet and just like throwing with more computers and GPUs at it suddenly knows how to do research grade chemistry. So you could say, how do I make the X-Nerve guess? And suddenly that capability is in there. And what's scary about it is that we didn't know that it had that capability until years after it had already been deployed to everyone. And in fact, there is no way to know what ability it has. Another example is, you know, theory of mind, like my ability to sit here and sort of like model what you're thinking, sort of like the basis for amplitude strategic thinking. So like when you're nodding your head right now, we're like testing, like, how well are we? No one thought to test any of these, you know, transformer-based models, these GPTs on whether they could model what somebody else was thinking. And it turns out like GPT-3 was not very good at it. GPT-3.5 was like at the level, I don't remember the exact details now, but it's like at the level like a four year old or five year old. And GPT-4, like was able to pass these sort of theory of mind tests up near like a human adult. And so it's like it's growing really fast. You're like, why is it learning how to model how other people think? And then it also makes sense. If you are predicting the next word for the entirety of the internet, then what's going to read every novel? And for novels to work, the characters have to be able to understand how all the other characters are working and what they're thinking and what they're strategizing about. It has to understand how French people think and how they think differently than German people. It's read all the internet, so it's read lots and lots of chess games. And now it's learned how to model chess and play chess. It's read all the textbooks on chemistry, so it's learned how to predict the next characters of text in a chemistry, but which means it has to learn chemistry. So you feed in all of the data of the internet and ends up having to learn a model of the world in some way, because like language is sort of like a shadow of the world. You imagine casting lights from the world and creates shadows, which we talk about as language. And the AI is learning to go from that flattened language and reconstitute, make the model of the world. And so that's why these things, the more data and the more compute, the more computers you throw at them, the better and better it's able to understand all of the world that is accessible via text. And now video and image. That makes sense. Yes, it does make sense. Now, what is the leap between these emergent behaviors or these emergent abilities that AI has and artificial general intelligence? And when, when is it, when do we know or do we know? Like this is the speculation over the internet when Sam Altman was removed as the CEO and then brought back was that they had not been forthcoming about the actual capabilities of whether it's chat GPT-5 or artificial general intelligence. That's some large leap had occurred. That's some of the reporting about it. Obviously the board had a different statement, which was about Sam, the quote was I think not consistently being candid with the board. So the funny way of saying lying. Yeah. So basically the board was accusing Sam of lying. There was this specifically about was that specifically they didn't say. And I mean, I think that one of the failures of the board is they didn't communicate nearly enough for us to know. Well, that's why it's so on, which is why I think a lot of people then think, well, was there this big crazy jump in capabilities and that's the thing. And Q star and Q star went viral. Ironically, it goes viral because the algorithm, so social media pick up the Q star, which has this mystique to it, sort of must be really powerful and it's breakthrough. And then that's kind of a theory on its own, so it kind of blows up. But we don't currently have any evidence. And we know a lot of people, you know, who are around the companies in the Bay Area. I can't say for certain, but my sense is that the board acted based on what they communicated and that there was not a major breakthrough that led to or had anything to do with this happening. But to your question, though, you're asking about what is AGI artificial general intelligence and what spooky about that? Yeah. Because so just to sort of define it, I'll just say before you get there, as we start talking about AGI, so that's what, of course, OpenAI is set that they're trying to build their mission statement. And they're like, but we have to build an aligned AGI, meaning that it does what human beings say it should do and also take care of it to do catastrophic things. You can't have a deceptively aligned operator building an aligned AGI. And so I think it's really critical because we don't know what happened with Sam and the board that the independent investigation that they say they're going to be doing, like that they do that, that they make the report public that it's actually independent because like either we need to have Sam's name cleared or there need to be consequences. You know, just what's going on. Because you can't have something this powerful and have a problem with who's like person is running it or something, or it's not, not honestly about what's what's there.