WHISTLEBLOWER Reveals Complete AGI TIMELINE, 2024 - 2027 (Q*, QSTAR)

So that was a recent document that actually apparently reveals OpenAI's secret plan to create AGI by 2027. Now, I'm going to go through this document with you page by page. I've read it over twice and there are some key things that actually did stand out to me. So without further ado, let's not waste any time. And of course, just before we get into this, this is of course going to contain a lot of speculation. Remember that this document isn't completely 100% factual. So just take this video with a huge grain of salt. So you can see here the document essentially says, revealing OpenAI's plan to create AGI by 2027. And that is a rather important date, which we will come back to if we look at this. First thing you can see there's an introduction. Okay. And of course, remember, like I said, there is a lot of speculation in this document. There are a lot of different facts. And of course, like I said, anyone can write any document and submit it to Twitter or Reddit or anything, but I think this document does, does contain a little bit more than that. So it starts out by stating that in this document I will be revealing information I have gathered regarding OpenAI's delayed plans to create human level AGI by 2027. Not all of it will be easily verifiable, but hopefully there's enough evidence to convince you. Summary is basically that OpenAI has started training a 125 trillion parameter multimodal model in August of 2022. And the first stage was Arrakis, also called Q Star. And the model finished training in December of 2023, but the launch was canceled due to the high inference cost. And before you guys think it's just document with like just words, I'm going to show you guys later on, like all of the crazy kind of stuff that is kind of verifiable. That does actually line up with some of the stuff that I've seen as someone that's been paying attention to this stuff. So this is literally just the introduction. The juicier stuff does come later, but essentially they actually talk about the. And this is just like an overview. So you're going to want to continue watching and essentially state that, you know, this is the original GPT5 which was planned for release in 2025. Gobi GPT 4.5 has been renamed to GPT5 because the original GPT5 has been canceled. Now I got to be honest, this paragraph here is a little bit confusing, but I do want to say that the words Arrakis and the words Gobi are definitely models that were were referred to by several articles that were referring to leaks from OpenAI. And I think they were actually on the information. So this is some kind of stuff that I didn't really hear that much about, but the stuff that I did hear was pretty crazy. So this Arrakis and this Gobi thing, although you might not have heard a lot about it, of course it is like a kind of like half and half leak. But like I was saying, this stuff is kind of true. So you can see here, OpenAI dropped work on a new Arrakis model in rare AI setback. This one actually just talks about how by the middle of OpenAI scrapping an Arrakis launch after it didn't run as efficiently. So there's actually some references to this, but a lot of the stuff is a little bit confusing. But we're going to get onto the main part of this story now. I just wanted to include that just to show you that these names aren't made up. Because if I was watching this video for the first time and I hadn't seen some of the prior articles before, I'd be thinking, what on earth is Arrakis? What on earth is Gobi? I've only heard about Q Star. So essentially, let's just take a look. And it says the next stage of Q Star, originally GPT6, but but since renamed GPT7, originally for release in 2026, has been put on hold because of the recent lawsuit by Elon Musk. If you haven't been paying attention to the space essentially, Elon Musk just filed a lawsuit, released a video yesterday stating that OpenAI have strayed far too long from their goals. And if they are creating some really advanced technology, the public do deserve to have it open source because that was their original goal. And essentially you can see here, it says Q star, GBC8, planned to be released in 2027, achieving full AGI. And one thing that I do want to say about this, because essentially they're stating that they're doing this up to GPT7 and then after GPT7 they do get to AGI. One thing that I do think, okay, and I'm going to come back to this as well, is that the dates kind of do line up. And I say kind of because. Not like 100% because we don't know, but presuming, let's just presume, okay, because GPT4 was released in 2023, right? Let's just say Every year release a new model. Okay, that would mean that in 2024 we would get GPT5, in 2025 we get GPT6, in 2020, GPT7, and in 2027 we would get GPT8, which is of course AGI. Now, one thing I do think about this that is kind of interesting. And remember, I'm going to come back to this, so pay attention. Okay? What I'm basically saying is that if OpenAI are consistent with their year releases. So for example, if they are going to release a new model every year and if we continue at the same rate, like a new GPT every single year, which is possible, him stating that GP7 being the last release before GPT8, which is AGI, does actually kind of make sense because once again, and I know you guys are going to hate this, but if we look at the trademarks, okay, REM that they trademarked this around the same time, okay, around that 2023 time when all this crazy stuff was going on. And I think it's important to note as well is that there's no GPT8. You might argue that if they're going to use all the GPT names, why wouldn't they just trademark GPT8? And I think maybe because like the document states the model after GPT7 could be AGI. And I'm going to give you guys another reason on top of that. Another reason is, and I'm going to show you guys that later on in the video. But essentially OpenAI's timeline on Super Alignment actually does coincide with this theory, which is a little bit coincidental. Of course, like I said, pure speculation could be completely false. OpenAI, like I said before, can go ahead and completely change their entire plans. They can go ahead and drop two models in one year. The point I'm trying to make is that certain timelines do align. But just remember this because I'm going to come back to this because of some documents stuff that you're going to see in this document at the end of the video. So anyways, it says Elon Musk caused a delay because of his lawsuit. This is why I'm revealing the information now because no further harm can be done. So I guess Elon Musk's lawsuit has kind of, you know, if you wanted, bought you some time. So he says, I've seen many definitions of AGI, artificial general intelligence. But I will define AGI simply as an artificial general intelligence that can do any intellectual task a smart human can. This is how most people define the term now, 2020 was the first time that I was shocked by an AI system. So this is just some, of course, you know, talks about his experience with, you know, AI systems. I'm guessing the person who wrote this, but, but AGI, if you don't know, AGI is like an AI system that can do any task human can. But one thing that is important to discern is that AGI, There was a recent paper that actually talks about the levels of AGI, and I think it's important to remember that AGI isn't just one AI that can do absolutely everything. There are going to be levels to this AGI system that we've seen so far. And in this paper, levels of AGI, they actually talk about how we're already at emerging AGI, which is emerging, which is equal or somewhat better than an unskilled human. So we are at level one AGI. And then of course, we've got competent AGI, which is going to be at least 50% of the 50th percentile of skilled adults, and that's competent AGI that's not yet achieved. And then of course we've got expert AGI, which is 90th percentile of skilled adults, which is not yet achieved. And then we've got virtuoso AGI, which is not yet achieved, which is 99th percentile of all skilled adults. And then we've got artificial super intelligence, which is just 100%. So I think it's important to understand that there are these levels to AGI, because once someone says AGI, I mean, is it 99? Can it do like half? You know, it's just pretty confusing. But I think this, this is a really good framework for actually looking at the definition, because, trust me, it's an industry standard, but it is very, very confusing. So here's where he basically says that GPT 3.5, which powered the famous ChatGPT, and of course GPT 3, which was not the successor, but the predecessor of 3.5, it says these were a massive step forward towards AGI. But the note is GPT 2 and all chatbots, since Eliza had no real ability to respond coherently. So while such GPT3amassive leap. And of course this is where we talk about parameter count. And of course he says deep learning is a concept that essentially goes back to the beginning of AI research in the 1950s. First neural network was created in the 50s, yada, yada, yada. Basically, this is where he's giving the description of a parameter and he says, you may Already know. But to give a brief digestible summary, it's analysis to a synapse in a biological brain, which is a connection between neurons. And each neuron in a biological brain has roughly a thousand connections to other neurons. Obviously, digital networks are actually negligence to biological brains, basically saying that, you know, of course we're comparing them, but different. But how many synapses or parameters are in a human brain? The most commonly cited figure for synapse count in the brain is roughly 100 trillion, which would mean each neuron is 100 billion. In the human brain has roughly 1000 connection. And remember this number 100 trillion, because it's going to actually be a very, very big number that you do need to remember. So of course, you can see here, the human brain consists of 100 billion neurons and over 100 trillion synaptic connections. Essentially this is trying to, you know, just pair the similarities between parameters and synapses, essentially stating here that, you know, if each neuron in a brain. And trust me, guys, this is just all going to come into everything. Like, I know you guys might be thinking, what is the point talking about this? I just want to hear about Q, but just trust me, all of this stuff, it does actually make sense. Like, I've read this a lot of times, so I'm going to skip some pages, but the pages I'm talking about now, just trust me, guys, you're going to want to read them. And it basically says here, if each neuron in a brain has 1000 connections, this means the cat has roughly 250 billion synapses and a dog has roughly 530 billion synapses. Synapse count generally seems to predict higher intelligence, with a few exceptions. For instance, elephants technically have a higher synapse count than humans, but yet display lower intelligence. Of course, basically, here's where he's actually talking about how, you know, the simplest explanation for larger synapse counts with lower intelligence is a smaller amount of quality data. And from an evolutionary perspective, brains are quote, unquote, trained on billions of years of epigenetic data. And human brains evolved from higher quality socialization communication data than elephants, leading to our superior ability to reason. But the point he's trying to make here is that while there are nuances that don't make sense, synapse count is definitely important. And I think we've definitely seen that with the similarities in the parameter size, with the explosion of LLMs and what we've seen in these multimodal models and their capabilities. And it says again, the explosion in AI capabilities since the early 2010s has been a result of far more computing power and far more data. GPT2 had 1.5 billion connections, which is less than a mouse's brain. And and GPT3 had 175 billion connections, which is getting somewhat closer to a cat's brain. And obviously it's intuitively obvious that an AI system the size of a cat's brain would be superior to a system than the size of a mouse's brain. So here's where things start to get interesting. So he says, In 2020, after the release of the 175 billion parameter GPT3, many speculated about the potential performance of a model 600 times larger at 100 trillion parameters. Just remember this number, because this number is about to just keep repeating in your head. And of course, he says the big question is, is it possible to predict AI performance by parame? And as it turns out, the answer is yes, as you'll see on the next page. And this is where he actually references this article, which is called Extrapolating GPT N Performance by Lanrean. And it was an article written in 2022. And basically it talks about how as you scale up in parameter count, you approach optimal performance. Essentially, this graph seems to be illustrating the relationship between neural networks measured by the number of parameters, which can be thought of as the strength of connections between neurons and their performance on various tasks. And these tasks included language related challenges like translation, reading comprehension and question and answering, among others. And the performance on these tasks is measured in the vertical axis, higher values indicating better performance. And the graph shows that as the number of parameters increases, the performance on these tasks also tends to. But of course, it does have diminishing returns as you move right, because the curves actually do tend to plateau as they reach the higher parameter counts. Of course, the various colors on this chart just essentially represent different tasks. And each dot on those lines represents a neural network model of a certain size and certain parameter count being tested. On that you can see right down here, this is where the trained GP, you can see the GPT3 performance. So it says Flop User Train GPT3. And then of course, you can see right here, this is apparently the number of synapses in the brain. And just remember the number 100 trillion or 200 trillion because it's going to be really important. So then it then says, as Lanrian illustrated extrapolations show, that AI performance inexplicably seems to reach human level at the same time as, as a human level Brain size is matched with the parameter count. His count for the synapse the brain is roughly 200 trillion parameters as opposed to the commonly cited 100 trillion figure. But the point still stands. 100 trillion parameters is remarkably close to optimal. By the way, an important thing to note is that although 100 trillion is slightly suboptimal in performance, there is an engineering technique that OpenAI is using to bridge this gap. And I'll explain this towards the very end of this document, because it is true to what OpenAI is building. And Lanrean's post is one of many similar posts online. It's an extrapolation of performance based on the jump between previous models. And OpenAI has certain has much more detailed metrics and they've come to the same conclusion as Lanrean, as I'll show later in this document. So if AI performance is predictable based on parameter count and 100 trillion parameters is enough for human level performance, when will 100 trillion parameter AI model be released? In the future? So here's where we go. Okay, it says that GPT5 achieved proto AGI in late 2023 with an IQ of 48. Now that is a statement that with the IQ of 48. I genuinely don't know where that's coming from. I'm guessing that's maybe just based on predictions and stuff like that, but I genuinely don't know where this, like in the beginning there was this, you know, IQ thing where it was like, you know, IQ 48, IQ 96, delay. I genuinely don't know where those IQ values are coming from. It doesn't really state in this document unless I missed it somewhere. But essentially this is a point where things start to get super interesting, because this is where he's referencing a whole bunch of sources. But it says that, you know, of course Jimmy Apples did tweet AJI has been achieved internally. If you didn't watch the video that I made on AJI has been achieved internally. Jimmy Apples wasn't the only one that tweeted this Sam out when actually himself did actually post this on Reddit. And then he edited the comment to say, look, we were just joking around. And of course this person, this is a guy who runs Runway, he said that I have been told that GPT5 is scheduled to complete training this December and that OpenAI expects it to achieve AGI, which means we will hotly debate whether or not it actually will achieve AGI, which means it will. And of course you can see this guy is CEO of Runway and a bunch of other companies. And it's actually funnily enough he actually is followed by Sam Altman. So this statement actually wasn't like there weren't many detractors of the statement, which was quite interesting considering that that's just a bold statement about GPT5, but nonetheless it was a very interesting statement. Anyways, we continue on in this document and it states the first mention of a 100 trillion parameter model being developed by OpenAI was in the summer of 2021 mentioned offhand in a Wired interview by the CEO of Cerebras, Andrew Feldman, a company which is. Sam Altman is a major investor of. And if you don't know what the Cerebras company does, they produce crazy, crazy clusters and you can see right here that they have the fastest HPC accelerator on earth. You can see that the latest GPU theirs is pretty, pretty insane. And I'm guessing that they're looking to deploy these in the future based on future AI systems essentially. This basically talks about the first mention of an 100 trillion parameter model being deployed within the summer of 2021. And you can see that it's mentioned in an offhand wide interview by the CEO Cerebras like I just talked about, which Sam Altman is a major investor for. This is actually pretty true. Sam Altman actually invested, I think it's between 50 million to 80 million and I think OpenAI has actually agreed to purchase some of the chips, some kind of deal. But it says from talking to OpenAI GPT4 will be about 100 trillion parameters that won't be ready for several years. And remember this was of course debunked if you were in the space at that time, if you were actually paying attention to things. But anyways, if we continue, okay, because I want to get through some of this stuff. It says Sam Altman's response to Andrew Feldman at an online meetup at AC10 it's crucial to note that Sam Altman admits to their plans for 100 trillion parameter mod. So this is an essentially an excerpt from that blog post interview and it says GPT4 is coming but the current focus is on the codecs and also where the available computers coming. GPT4 will be a text model as opposed to multimodal and it says okay, this is where things get interesting. Okay, pay attention to this. It says 100 trillion parameter model won't be GPT4 and is far off. They're getting much more performance out of smaller models. Maybe they may never need such a big model and Then it says it's not yet obvious how to train a model to do stuff on the Internet and think long and very difficult problems. And a lot of work is going to make it accurate and tell the truth. So basically this part is to look at the early stages where Sam Altman is basically stating that they do have plans for an 100 trillion parameter model, which is essentially this brain like AGI type system. Now here's where we talk about some more leaks and of course things are very speculative. So of course, like I said, it could be just pure speculation, but it says an AI researcher, Igor, made the claim a few weeks later that GPT4 was being trained and would be released between December and February. Again, I will prove that he really did have accurate information as a credible source. This will be important soon. So you can see here he tweets that OpenAI started to drain GPT4 releases planned for December, Feb, then essentially Gwern, a famous figure in the AI world, he's an AI research and blogger. He messaged Igor Bykov on Twitter in September 2022 and this is the response he received. It's important to remember the colossal number of parameters, text, audio, images, possibly video and multimodal. And this comes from a subreddit called this Is the Way It Will Be, which is a private, small subreddit run by mathematicians with an interest in AGI and a few AI enthusiasts. And it says they use this subreddit to discuss topics deeper than what you'll find in the mainstream. And you can see here that he actually does talk about how OpenAI started training GPT4 and the training will be completed in a couple months. And trust me, guys, I'm going to get to the big bit. But essentially this is someone who had early information on this model, okay? And essentially a colossal number of parameters. It sounds like this Guy was referencing 100 trillion parameter model as 500 billion parameter models and 1 trillion parameter models had already been trained many times by the time of this tweet in some of 2022, making models of that size unexceptional and certainly not colossal. So these tweets from, I'm just going to say Rapu, because they actually do make a similar claim, which is pretty interesting. Okay, so this is where stuff gets even more interesting, okay? Because this guy, okay, just call him Rapu because I don't know how to say rxpu at the time, but he says he also mentions an 125 trillion synapse GPT4, however, incorrectly states the GPT3's primates account as 1 trillion. But essentially he states that it was far behind the human brain containing 100 trillion trillion synapses. But what he did get right was stating that it will be introduced to public at the beginning of 2023 and is able to write academic thesis and articles that human intelligence cannot distinguish. So this is another person that did have earlier inside information. Now he does cite that this is a weaker piece of evidence because Rune is a fairly notable Silicon Valley AI researcher, followed by CEO Sam Altman. And he did tweet Roon, I'm guessing is GPT4, 100 trillion parameters are rumored. And then the guy thumbs it up. I'm not really sure what that is referring to that much, but essentially the information from the previous two people who had early information about GPT4 were then basically sent to this person. Okay, it says in November 2022 I reached out to an AI blogger named Alberto and his post seemed to spread pretty far online. So I was hoping that I sent him some basic GPT4, he might be able to do a write up. So you can see they were actually talking. And he said of course the info necessary isn't official, but this is all important, just trust me. Because essentially later on in this document you'll see how the 100 trillion parameter model being talked about now is something that's been in the works for a while and there are a few things that do make sense. So you can see here that of course Alberto then goes ahead to publish this, where this rumor does start to spread and you can see that then the 100 trillion parameter leak went viral, reaching millions of people to the point that OpenAI's employees, including CEO Sam Altman, had to respond, calling it complete foolishness, called it factually incorrect. And of course this guy of course did claim responsibility for the leak. Because if you remember at this time, this was a really, really huge deal where people were stating that this is going to be really, really huge. But this isn't the caveat. It's not about them not getting this wrong. It's about the fact that GPT4 is not going to be 100 trillion parameters. It's about that this document is basically stating that in the future GPT8, whatever model it's going to be, the one that achieves AGI will actually have this count. And these guys knew this from quite some time ago. That's why we're essentially looking at this kind of data in terms of the leaks and stuff to kind of show you guys how we actually got there. So remember this Igor guy, He was also stating that he also heard about 100 trillion parameters, but he found that this was going to be ridiculous, so he decided not to include it with his tweet about GPT4 because when he heard 100 trillion parameters he was like wait, that doesn't make sense for GPT4 because why would it be that much bigger than GPT3? Like it doesn't make sense. And then you can see this is where you start to talk about 2022, where I became convinced that OpenAI planned to release a 1 to 2 trillion parameter subset of GPT4 before releasing the full 100 trillion parameter model GPT5. So basically all of these people said the same thing. All of these people who were early on gpt4, someh I'm not entirely sure, stated that this was all going to be a model in the future that would have 125 trillion parameters or over 100 trillion parameters. And essentially bringing it back to Igor's leak, you can see that there were a couple of people that were also using a beta version of GPT4. Now of course it says the sources here are varying credibility, but they all inexplicitly say the same thing. GPT4 was being tested in October, November of 2022, and according to the US military AI researcher, it was definitely being trained in October. Which again lines up with Igor's leak. Now essentially what's crazy here is that it says OpenAI's official position, as demonstrated by Sam Altman himself, is that the idea of a 100 trillion parameter GPT4 is complete false. This is a half true because GPT4 is 100 trillion parameter subset of the full 100 trillion parameter model. I'm not sure if I do believe that statement, because one of the problems is, is that of course GPT4 isn't actually confirmed. But you know, according to some articles, it has been confirmed that GPT4 does have at least 1 trillion parameters. I mean some people have said that is 1.8. I mean some people said it's 1.2. But here's of the leaks that I do remember and this is one of the first videos I did make on GPT4 and it was because the CTO of Microsoft Germany, a week prior to the official release of GPT4 actually did slip up and reveal that there exists a GPT4 which does have the ability to process videos. And it says, I imagine he was unaware of OpenAI's decision not to reveal the video capabilities of the system. And this completely proves that GPT4.5 was trained on not just text and images, but also video data. And of course we can infer that audio data was included as well. So he says, we will introduce GPT4 next week. I remember I actually made this video. If you go back to the channel, you can actually see this. And he says, we will introduce GPT4 next week. They will have multimodal models that will offer completely different possibilities, for example, videos. And he said the CTO called the LLM a game changer because they teach machines to understand natural language, which then understand in a statistical way what was previously only readable and understandable by humans. In the meantime, the technology has come so far that it basically works in all language. You can ask a question in German and get an answer in Italian. And remember, this was actually a very, very credible statement because it was the CTO of Microsoft and we did actually get GPT4 a week later. And the point again is that Igor before said that GPT4 was going to be released in January, February. And this kind of claim was also corroborated by a credible entrepreneur who stated that in October 2022, GPT4's release date would between January and February 2023. Now some people are stating, well, GPT4 was released in March. But of course you can see right here, although GPT4 was released in March 2020, outside the window, I think it was done intentionally by OpenAI to discredit the leak. And I can't lie, I actually do agree with that because I've seen some stuff tweeted by people like Jimmy Apples and stuff like that. And I'm pretty sure sometimes when leaks have gotten pretty big, OpenAI have decided against it because it just discredits the leakers. And I mean, they're working on some top secret stuff here. Now here's where we get into the AGI debate. It says a note about robotics. AI researchers are beginning to believe that vision is all that's necessary for optimal real world physical performance. And in order to give one example, Tesla completely ditched all sensors and fully committed to vision for their self driving cars. The point is that training a human sized AI model on all the image and video data on the Internet will clearly be more than enough to handle complex robotic tasks. Common sense reasoning is buried in the video data. And just like it's buried in the text data and the text focus, GPT4 is standing good at common sense reasoning. And it does actually show an example of where, you know, Tesla actually does remove its ultrasonic sensors. And then I'm going to show you another slide where Tesla actually do talk about how images all they need video. And you can see the former head of Tesla AI where explains they removed the sensors while others differ. That's Andre Karpathy, I believe. And this was another example if you remember at the time. And this is all going to tie in so just pay attention. Essentially Palm E, if you do remember this video from around 2023 where essentially it was an AI system that learned mainly from vision combined with an enlarged language model and there were minimal robotics data required on top of the language and vision training. And Palm e was a 500 billion parameter model. And it, and it asks the question that what happens when robotics is trained on a 100 trillion parameter model on all the data available on the Internet? And this was a pretty crazy system. I'm not going to lie to you guys. Like looking at Palm E, how good it was. It was definitely a very, very comprehensive system that could do a lot of stuff. I remember it was able to deal with adversarial disturbances. It was able to get stuff that you wanted it to do. This was a pretty, pretty good system. They did upgrade this but I'm surprised they haven't dropped any updates recently because it was a really good system for the time. Of course this is where they talk about the Tesla update, where it, you know, Optimus performed its first end to end learn successful graph today. You can see it below at 1x speed. This was learned from human demonstrations. No specific task programming was done. This means we can now scale quickly to many tasks. It says join us before it's too late. Make the AI happen with us. It says if human demonstrations are all that's needed for advanced robotics performance and 100 trillion parameter model trained on all the web would certainly be able to achieve astonishing robotics performance. And I wonder what's going to happen in the next couple of weeks because I'm not too clued up on robotics and how entirely that is to trained. But I do know that next week or in fact two to three weeks from now, we're going to have a huge robotics announcement where they're basically, you know, want someone at one of the leading robotics companies. They actually did state that we thought we didn't have the data, but now we do and that they've made a breakthrough. So I wouldn't be surprised if soon we do get a real update because someone said that three weeks from now someone at mine said, you know, going to be a huge Update that's going to completely change robotics. So before you actually, you know, discredit this area, I would say give it like three weeks, because we don't know now. Essentially it says the image on the left shows what A trillion parameter GPT4 is capable of an image recognition, which is pretty crazy if you haven't seen this demo before. This was in the GPT4 paper where they talked about how good this model is at understanding things. And it's already clearer and written more well than many humans would have been able to come up with. So it says, so again, what happens when you train a model 100 times larger than GPT4, which is the actual size of the human brain, on all the data available on the Internet? And then we get to this slide where it says, important, notice how the AI model is able to generate multiple angles of the same scene with physically accurate lighting and in even some cases, physically accurate fluid and rain. And if you can generate images and videos with accurate common sense physics, you have common sense reasoning. And if you can generate common sense, you understand common sense. And I mean, this is once again one of those points where it's delving into the argument of if an AI system can accurately do lighting, if it can understand, you know, physics and rain and all that kind of stuff. This is Meta's video thing. It's basically saying that does the AI have a common sense world model in its head where it can truly understand how the world you know, is and how it interacts and how, you know, like what kind of reasoning does it use in its head to be able to get this? Well, okay, and some people say that, you know, some people say these AI systems just pattern recognition, it's just regenerating stuff, it's seen in this training data. But some argue that it's able to completely understand the physical world that we live in. And it's got some kind of world model, it's got some kind of common sense thinking algorithm, whichever way you want to put it, so that it can completely get this stuff correctly? Because it needs to be able to if it wants to. And I mean, that's something again, that is really debated now here. It gives us some talks about someone that did give the early leaks for GPT4 again. And what's crazy is that they do talk about image and audio generation would be trained in quarter three of 2023. And apparently if video generation training is simulation simultaneous or shortly after. This actually does line up with chen's claim of GPT5 being finished training in December of 2023. So remember that guy before the CEO of Runway stating that GPT5 would be finished training in December was now around three months ago. That actually does line up with some of the claims made here. And this is also someone that did have early inside access. So we can see clearly that there are people that do have inside access. Now here's where we get into the crazy bit because this is where we talk about long term planning and of course things do change, but I think this is going to be the most important part of the video. But it says here that OpenAI President Greg Brookman stated that in 2019 following a $1 billion investment from Microsoft at the time, OpenAI planned to build a human brain sized model within five years and that was their plan on how to achieve AGI. Remember that bit guys? OpenAI planned to build a human sized brain model within 5 years and that was their plan for how to achieve AGI. You can see within five years and possibly much faster with the aim of building a system that can run a human sized brain model. And of course that's why I said before the same number, 100 trillion parameters is the reason that number is being cited a lot. And that's why I'm guessing if OpenAI had previously stated in 2019 that their investments from Microsoft would actually help them to try and build a human sized brain within five years. Leaks about 100 trillion going viral and then it's spreading about GPT4 could actually be the reason that actually occurred because many people were confused thinking GPT4 is going to have 100 trillion. But many people didn't realize that this is actually the future models which is what OpenAI have been planning all along. And I think that actually does make sense. Like if we can actually corroborate statements and understand what's actually being said from actual OpenAI employees, I think those are the best chances we do have at looking at what is realistic. You can see here it says both of these sources are clearly referring to the same plan to achieve AGI. A human brain sized AI model trained on images, text and other data due to be trained within five years of 2019. So by 2024 and it seems to line up with all the other sources I've listed in this document. So essentially that's what Greg Brockman did state, which is a pretty, pretty hefty piece of information. And yeah, that would actually line up with 2024. And this is where we start to get into the part where certain people start to urge caution on AI. And this is the part where he starts to argue that, you know, suddenly AI leaders are starting to sound the alarm, almost like they know something very, very specific that the general public doesn't. And I would argue that yes, that is kind of true. But you know, AI leaders have been stating this from ages ago, like Sam Altman has been stating since 2015 that AI is dangerous. And Elon Musk has been stating the same thing. But I would argue as well that certain people from Google did actually leave recently. And we're going to get into that now. They actually did leave fairly recently, and I'm not surprised, but they actually did really leave recently. And maybe it's just coincidence, I'm part of it, but it says, you know, in this uncertain climate, that Hasabis agrees to a rare interview, they should start warning about his growing concerns. I would advocate not moving fast and breaking things, he says, referring to an old Facebook motto. And essentially their old motto was release technologies into the world first and then fix any problems that arose later. And of course you can't do that with superintelligence. And he says AI is now on the cusp of being able to make tools that could be digitization, urging his competitors to proceed with caution than ever before. And of course, Geoffrey Hinton, if you don't know this guy by industry standards, the godfather of AI, that he actually did leave Google in 2020 because he wanted to actually talk about the dangers of AI, said the idea that this stuff could actually get smarter than the people. A few people believe that, Hinton said in the interview, but most people thought it was a way off. And I thought it was way off. I thought it was 30 to 50 years away or even longer. Obviously I no longer think that. And he thinks superintelligence is coming far quicker than we did think. Now, if you remember as well, this is very fascinating because at the time this was something that was pretty crazy. It says shortly after the release of GPT4, the future of Life Institute, a highly influential non profit organization concerned with mitigating the potential catastrophic risks to the world, released an open letter on all AI labs to pause AI development for six months. Why? Because they essentially stated that, you know, including the currently being trained GPT5 and this is kind of crazy, I didn't actually see that. Okay, it says we call on all AI labs to immediately pause training systems more powerful than GPT4, including the currently being trained GPT. GPT5 signatories include, you know, all of these people that we like, Elon Musk that even worked at Apple. Apparently the first release version of the letter specifically said, including the currently being trained GPT5. And then apparently it was removed, which is pretty crazy. And then of course, there is a quote from Sam Altman, like I said again, and this is pretty crazy because he says, you know, do we have enough information on the Internet to create AGI? And Sam Altman's blunt, immediate response, interrupting the man asking the question, is, yes. He elaborates, yes, we're confident there is. We think about this and we measure it quite a lot. Information in the Internet to create AGI.

So if you contrast it with, yes.

We have a continuous video feed to our eyes and on the Internet we only have like a subset of that.

Yeah, we're confident there is. We think about this and measure it quite a lot. What gives you that confidence? Is it the size of the knowledge base? Is it complexity? Is it something. One of the things that I think OpenAI has driven in the field is that's been really healthy, is that you can treat scaling laws as a scientific prediction. You can do this for compute, you can do this for data, but you can measure at small scale and you can predict quite accurately how it's going to scale up, how much data you're going to need, how much compute you're going to need, how many parameters you're going to need when, you know, when the generated data gets, like, good enough to be helpful. The Internet is like, there's a lot of data out there, there's a lot of video out there. Too few more questions, maybe.

So, I mean, you did see from that clip how Sam Altman actually did talk about it. I mean, he seems very, very, very confident on that prediction in order to get to AGI the kind of data that they do need. And then here's where he says on Sam Altman's Q, says that, you know, first of all, he seems highly, highly confident that it exists enough data on an AGI system. Confident to the point that it makes one question if they've already done it or in the process of doing it, which is definitely a bold statement to state that they've, you know, basically done it in 2022. In fact, that interview was actually from the early stages of 23, which is quite fascinating. But then again, GPT4 did finish training at that time, so they would definitely be working on other AI systems at that point. Now, essentially what he talks about here is that, and I think this is, is probably the thing that you should probably take away from this video and it says as I mentioned earlier in the document, a 100 trillion parameter model is actually slightly suboptable. But there is a new scaling paradigm OpenAI is using to bridge this gap and it's based on something called the Chinchilla scaling laws. Chinchilla was an AI model unveiled by DeepMind in early 22. And the implication of the Chinchilla research paper was that current models are significantly trained and with far more compute, meaning more data, we would see a massive boost in performance, the need to increase parameters. The point is that while an untrained or undertrained 100 trillion parameter model may be slightly suboptimal, if it were trained on vastly more data, it would be able to exceed human level performance. And the Chinchilla paradigm is widely understood and accepted in the field of machine learning. But just to give a specific example from OpenAI, President Greg Brockman discusses this in an interview on how OpenAI realized their initial scaling laws were flawed and have since adjusted to take the Chinchilla laws into account.

Big of a model. Do you think you're going to need lots of mistakes? For sure. Like a good example of this is the scaling laws. So we did this study to actually start to really scientifically understand how do models improve as you push on various axes. So as you pour more compute in, as you pour more data in. And one conclusion that we had at one point was that basically that there's sort of a limited amount of data that you want to pour into these models and that there's kind of this very, this very clear curve. And that one thing that we realized only years later was actually that we'd read the curves a little bit wrong and you actually wanted to be training for way more tokens, way more data than anyone had expected. And there's definitely these moments where these things that just didn't quite click, whereas it just didn't add up that we were training for so little and that some of the conclusions that you drew downstream, but then you realize there was a foundational assumption that was wrong and suddenly things make way more sense. So I think it's a little bit like physics in some sense. Where do you doubt physics? It's like I kind of do. I think all physics is wrong. Right. But only so wrong.

Right.

It's like we clearly haven't reconciled quantum and relativity, so there's something wrong there. But that wrongness is actually an opportunity. It's actually a sign of you have this thing that's already useful. Right. It really has affected our lives and it's Actually pretty great. I'm very happy with what physics has done, but also there's fruit. And so I think that for me that's always been the feeling that there's something here and that if we do keep pushing and somehow the scaling laws all peter out, right, and they suddenly drop off a cliff and we can't make any further progress, like that would be the most exciting time in this field because we would finally reach the limit of technology, we would have finally learned something and then we would finally have a picture of what the next thing to do is.

And yeah, you can clearly see that. Greg Brockman discusses how their initial scaling laws were flawed. And of course a lot of people were stating at this time that training a compute optimal 100 trillion-parame model would have cost billions of dollars and just isn't feasible. Well, Microsoft invested $10 billion into OpenAI in early 2023, so I guess it isn't that ridiculous of a possibility after all. The only question I do have, but then again, I don't really have that question anymore after reading the lawsuit from yesterday, was that of course Microsoft does get access to only pre AGI tech, but then again OpenAI is the one that gets to determine whether the technology is AGI or not. Part of me even wonders if Microsoft will even exist or even some of these companies want exists because you know, AGI could potentially make competition irrelevant. That's another question for another video. But essentially Alberto Romero wrote about DeepMind's chinchilla scaling breakthrough. And Chinchilla showed us that despite being vastly smaller than GPT3 and DeepMind's own Gopher, it outperformed them as a result of being trained vastly on more data. Just to reiterate this one more time, although a 100 trillion model is predicted to achieve slightly suboptimal performance, OpenAI is well aware of the chinchilla scaling laws, as is pretty much everyone in the in the AI field. And as they are training Q star as a 100 trillion parameter model that is compute optimal and trained on far more data than they really intended, they have the funds to do it now through Microsoft. And this will result in a model that is far far exceeds the performance of what they had initially planned for their 100 trillion parameter model. And 100 trillion parameters without the chinchilla scaling laws equal roughly human level or slightly suboptimal, but 100 trillion parameters multimodal with chinchilla scaling laws taken into account, what on earth do we get? And I think I'm not that clued up on the chinchilla scaling laws. If I'm being completely honest, I wasn't aware of this. But what I do know is that if this statement is true and if the research is up to date, because I do know that AI research is a very complex field and it is a very dynamic field. And research paper every single day will be changing how future system are trained. And maybe there's a research paper that was released, you know, just 20 minutes ago or something. There are literally like 160 papers released every single day, which is absolutely insane. But this is a true fact that there are so many research papers released every single day. The AI space isn't dynamic enough and scaling laws are true. And if they do now have the funds to actually train something that is going to be in 100 trillion parameter model, I'm guessing that this might have been what potentially sparked that letter. That kind of breakthrough that, you know, I guess you could say got Sam Outman fired. And I'm guessing that maybe something like this was potentially the ignition to spark a lot of stress at OpenAI because they actually did state Sam Altman in the interview said, look, everyone at OpenAI stressed because we're building super intelligence. I actually made a video on that because I was like, that's a crazy statement to say. And of course around that time, you know, there was so much drama. And around that time they did, you know, trademark the models and of course, you know, trademarks could absolutely nothing. But I just find that, you know, everything happening around that time, I just don't think that that's a coincidence. Now what is crazy as well is this is where they actually do talk about super alignment. It says, our goal is to build roughly human level automated alignment researcher. We can then use vast amounts of computer scale our efforts. And of course OpenAI has planned to build human level AI by 2027 and then scale up to super intelligence. And this has been delayed because Elon Musk's lawsuit be coming up but shortly. And essentially he does talk about this and I don't think this is that crazy. Essentially this Guy is an OpenAI researcher who worked there in the summer of 2022. He joined there for one year. I'm not sure he works there anymore. But essentially he wrote a letter to himself and essentially he said that the company he's working on and this is the kind of note that he wrote to himself. He said, there's an AI company building an AI that fills giant rooms, eats a town's worth of electricity, and has recently gained an astounding ability to converse like people, can write essays or poetry on any topic. It can ace college level exams. It's daily gaining new capabilities that engineers who tend to the AI can't even talk about in public. Yet those engineers do have a sit in the cafeteria and debate the meaning of what they're creating. What will it learn to do next week? Which jobs might render it obsolete? Should they slow down or stop so as to not tickle the tail end of the dragon? But wouldn't that mean that someone else, probably someone with less scruples and would awake the dragon first? Is there an ethical obligation to tell the world about this? Is there an obligation to tell it less? I am. You are spending a year working at my job. Well, your job to develop the mathematical theory on how to prevent the AI and its successors from where that essentially means destroying the world. And essentially he's referring to the Q star multimodal 125 trillion parameter B so essentially this document basically just tries to say that look, every year we're going to get a GPT update till 2027. And the reason 2027 is the date is because does coincide with OpenAI's deadline to create Super Alignment. And they actually state that they want to do this within four years. And since it was 2023 when they said this, if they do manage to get super alignment done within four years, that would be 2027. And considering they trademarked all the way up to GPT7, that would mean that if a new GPT model is released every single year, accounting for GPT5, GPT6 and GPT7, considering that's three more years, that takes us exactly to 2027 when super alignment should be done. And then once we have, I guess you could say the system that will solve Super Alignment and we can actually solve that, then they can actually safely realize that, look, we can safely train an agile System or this 125 trillion parameter model Q whatever it is, because then it would actually be safe to do so. So I'm guessing that kind of does make sense with the iterative deployment that we did see before. I'm guessing that that if I'm being honest with you guys, maybe I did miss some key things in this document. Maybe there are some stuff that I'm going to miss. But I do think that certain things do make sense. Like if they are iteratively deploying things, that does mean that we're not going to get everything that they do have. They could have literally no crazy, crazy models and do remember that Sam Altman in that interview, he did actually talk about how they're able to predict the capabilities of future models by training in a system which is much less compute intensive. And if you remember, this was something that I did retweet and this was something that I did talk about. And I'm guessing that whatever breakthrough that they had with, whether it was Q Star, whether they realized they just need a lot more compute, I think the elephant in the room is that over the next couple of years we're going to get it relatively better systems which OpenAI are going to release well after they've trained the models, because they stated that the society needs time to adapt. If you looked at the SORA paper they did state, one of their employees in a deleted tweet stated that we're going to release that just to make sure that people can reassess their timelines on what AI's capabilities are, are. So I'm guessing that they just wanted to release that to tell us what's capable. But the point is, is that they know where these AI systems are going to go because they have predictable scaling and they've accurately predicted GPT4's capability. And I'm guessing if they've accurately predicted all the way the capabilities to GPT 5, 6 and 7, even though during that time things could really change because AI's development field is so rapid, I do think that some of this stuff kind of does make sense, considering the fact that they want superalignment solved in the next four years and that would align with 2027 when if we're continuing on the release of a new LLM model or multimodal AI system each year, which is what they've already trademarked, it kind of would make sense that we do get an AGI system at that level. One question I do have is what does Sam need the 7 trillion for, which is already we know to be likely the AGI type system? And I'm guessing that if we're going to train something that's potentially, of course, you know, really, really compute intensive. You know, at first I was thinking that something computers intensive as GPT4, which was really, really compute intensive, which is why they don't even give us the message cap. But with these chinchilla laws now being four times less, I still think that they're going to need a ridiculous amount of compute, because of course there is that compute overhang which Sam Altman talked about, which essentially just means that there isn't enough GPU to go around there isn't enough to actually provide the compute. But I'm guessing that with the 7 trillion, whatever SAM Altman has talked about in these private interviews, whatever he's going to be, you know, pitching to these investors, I'm guessing that potentially this could work now. Now, some of the caveats that I do have about this is that of course, some of the information is just unverifiable, like you're not really able to verify some of this stuff. But I think from this, what we can take is that they probably do know where AGI is in terms of being able to predict the capabilities. And based on the statements that they have said, it does look like we could be getting a model that is going to be a larger increase in parameter count, because it does seem to be something that OpenAI did state, and they did previously state that they were going to build something that does have the same amount of size as a human brain. However, there were some caveats to this because recently Google's AI boss actually did say that Skale only gets you so far. But then again, Sam Altman is trying to raise $7 trillion. So I'm guessing that maybe if Skale isn't what gets you so far, Sam Altman wouldn't be trying to raise $7 trillion if they could just do it with the compute that they did have at hand. So I'm guessing that they do need scale because they've already predicted it with less compute, and I'm just going to leave with that. But let me know what you guys thought about this. This wasn't meant to be that long, but I did absolutely everything and I just wanted to make this information available to you. I don't know whether or not this is all going to be possible. Like I said, OpenAI could go out and they could change everything. The truth is, regardless of all of this interesting information, regardless of all of this speculation, the only people that know what's going on is OpenAI and the people that work there. But let me know what you guys think about this. Do you think this is going to be there? Do you think this information is completely false? Do you think this guy's got it completely wrong? Do you think he's got it completely right? I'd love to know your thoughts down in the comment section below.