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

so just take this video with a huge of salt so you can see here the document essentially says revealing open ai'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 um you know Twitter or Reddit or anything but I think this document 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 opening eyes 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 a rakus also called qstar and the model finished training in December of 2023 but the launch was cancelled 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 um 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 um 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 they essentially state that you know this is the original GPT 5 which was planned for release in 2025 bobi GPT 4. 5 has been renamed name to gbt 5 because the original gbt 5 has been cancelled now I got to be honest this paragraph here is a little bit confusing um but I do want to say that the words arus and the words GOI are definitely models that were referred to by several articles that were referring to leaks from open eye 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 um this arus and this goby 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 open AI dropped work on a new arus model in rare AI setback and this one actually just talks about um how by the middle of open AI you know scrapping an araus 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 on to the main part of this story now I just wanted to include that just to show you that you know 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 a r GOI I've only heard about qar so essentially let's just take a look and it says the next stage of qstar originally GPT 6 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 f a lawsuit released a video yesterday um stating that open ey 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 goal um and essentially you can see here it says qar GBC 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 you know 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 GPT 4 was released in 2023 right um let's just say you know every year release a new model okay um that would mean that you know in 2024 we would get gbt 5 in 2025 we get GPT 6 in 2026 we would get GPT 7 and in 2027 8 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 openi 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 GPC every single year which is possible him stating that gp7 being the last release before GPT 8 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 remember that they trademarked this around the same time okay around that 2023 time when all of this crazy stuff was going on and I think it's important to note as well is that like there's no gp8 you might argue that if they're going to use all the GPT names why wouldn't they just trademark GPT a 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 um another reason is and I'm going to show you guys that later on in the video but essentially um open ey's timeline on super alignment actually does coincide with this Theory which is a little bit Co coincidental of course like I said pure speculation could be completely false open ey like I said before can go ahead and completely change their entire plans you know they can go ahead and drop two models in one year the point I'm trying to make is that um 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 um you know it says Elon Musk caused a delay because of his lawsuit this why I'm revealing the information now because no further harm can be done so I guess Elon musk's lawsuit has kind of um 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 um of course you know talk about his experience with you know AI systems I'm guessing the person who wrote this but you know 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 you know 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 you know 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 you know we're already at emerging AGI which is you know 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 um you know 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 then we've got virtuoso AGI 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 90 9 can it do like half you know it's like it's just pretty confusing but I think this is uh a really good framework for actually looking at the definition because trust me it's an industry standard but it is very confusing so here's where he basically says that you know um you know GPT 3. 5 which powered the famous chat GPT and of course gpt3 which was the not the successor but the predecessor of 3. 5 it says you know these were a massive step forward towards AGI but the note is you know gbt2 and all chatbot since Eliza had no real ability to respond coherently so while such gpt3 a massive 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 new network was created in the 50s y y so 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 a nalist 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 OB vular to biological brains basically saying that you know of course we're comparing them but different but um 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 1,000 connection and remember um this number 100 trillion because it's going to actually be a very big number that uh you do need to remember so of course you can see here the human brain consists of 100 billion urans and over 100 trillion synaptic connections okay and essentially this is trying to you know um just pair the similarities between parameters and synapses so entially 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 of talking about this I just want to hear about qar 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 it basically says here if each neuron in a brain has a th000 connections this means a cat has roughly 250 billion synapses and a dog has roughly 530 billion synapses synapse count generally seems to predict to intelligence with a few exceptions for instance elephants techn have a higher signups 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 signups accounts 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 evolve 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 you know while there are nuances that you know don't make sense synapse count is definitely important and I think we've definitely seen that um 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 a capabilties since the early 20110 has been a result of far more computing power and far more data gbt2 had 1. 5 billion connections Which is less than a mouse's brain and DBT 3 had 175 billion connections which is get 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 gbt 3 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 you know repeating in your head and of course he says the big question is it possible to predict AI performance by parameter count 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 and performance by lrien and it was not score written in 2022 and basically it talks about how as you scale up in parameter count you approach Optimal Performance so essentially this graph seems to be illustrating the relationship between neuron 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 read and comprehension and question and answering among others and the performance on these task is measured in the vertical axis higher values indicating better performance and the graph shows that as the number of parameters in 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 G you can see the gbt performance so it says flop us TR at gp3 and then of course you can see right here this is apparently the number of synap in the brain and just remember the number 100 trillion or 200 trillion because it's going to be really important so s it then says as Lan Illustrated extrapolations show that air performance inexplicably seems to reach human level at the same time 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 not is that although 100 trillion is slightly suboptimal in performance there

is an engineering technique that openi is using to bridge this cap and I'll explain this towards the very end of this document because it is crucial to open ey is building and Lan's post is one of many similar posts online it's an extrapolation of Performance Based on the jump between previous models and open ey certain has much more detailed metrics and they've come to the same conclusion as lanon 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 gbt 5 achieved Proto AGI in late 2023 with an IQ of 48 now that is a statement that um you know with the IQ of 48 I genuinely don't know where that's coming from I'm guessing that's maybe just based on addictions and stuff like that but I genuinely don't know where this like in the beginning there was this uh you know IQ thing where it was like you know IQ for get 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 um 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 um of course Jimmy apples did tweet a has been achieved internally if you didn't watch the video that I made on a has been achieved internally Jimmy apples wasn't the only one that tweeted this um Sam when actually himself did actually post this on Reddit and then he edited the comment to say look we were just joking around um and then of course this person this is a guy who uh runs Runway he said that I have been told that gbg 5 is scheduled to complete training this December and that op AI expects it to achieve AI which means we will hotly debate whether or not it actually will achieve AI which means it will and of course you can see um this guy is CEO of Runway and a bunch of other companies and it's actually funny enough he actually is followed by Sam Alman so um 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 gbt 5 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 open AI was in the summer of 2021 mentioned offand in a wide

interview by the CEO of cerebrus Andrew Feldman a company which is Sam Alman is a major investor of and if you don't know what the cerebrus company does uh they produce crazy clusters um and you can see right here that they have the fastest HPC accelerator on Earth you can see that the latest GPU can theirs is 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 whichin the summer of 2021 and you can see that it's mentioned in an offhand wide interview by the CEO cerebrus like I just talked about which Sam Alman is a major investor of this is actually pretty true samman actually has lested I think it's between 50 million to 80 million um and I think open has actually agreed to purchase some of the chips some kind of deal but it says from talking to opening ibt4 will be about 100 trillion parameters that won't be ready for several years and remember this was of course uh debunked if you were in the space at the 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 alman's response to Andrew

Feldman at an online Meetup at ac10 it's crucial to note that Sam Alman admits to their plans for 100 trillion parameter mod so this is an essentially an excerpt from that blog post interview and it says gb4 is coming but the current focus is on the Codex and also where the available computer is coming gb4 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 perameter model won't be gb4 and is far off they're getting much more performance out of smaller models maybe they may never need such a big model and they then 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 until tell the truth so basically this part is to look at the early stages where um Sam Alman is basically stating that you know they do have plans for an 100 trillion parameter model which is essentially this brain-like AGI type system now here's we where we talk about some more leaks and of course things are very speculative so of course like a said it could be just speculation but it says an AI researcher Igor made the claim a few weeks later that gp4 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 open AI started to drain GPT 4 releas his plan for December slfb then essentially GW a famous figure in the AI World he's an AI research and blogger he messaged igle bov 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 sub 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 main stream and you can see here that he actually does talk about how open AI started training gbt 4 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 summer 2022 making models of that size unexceptional and certainly not colossal so these tweets from I'm just going to say Ru because they actually do make a similar claim which is pretty interesting okay um 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 rxp time but he says he also mentions an 125 trillion synapse gbt 4 however incorrectly States the gbt 3's paramet account as 1 trillion but essentially he states that um you know it was far behind the human brain containing 100 trillion synapses but what he did get right was stating that it will be introduced the public at the beginning of 2023 and is able to write academic pieces 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 air researcher followed by CEO Sam Alman and he did tweet at run I'm guessing is gb4 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 gbt 4 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 gbt 4 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 of 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 there 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 opening eyes employees including CEO Sam Alman 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 huge deal where people were stating that this is going to be really huge but this isn't the caveat it's not about them not getting this wrong it's about the fact that GPT 4 is not going to be 100 trillion parameters it's about that this document is basically stating that in the future GPT 8 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 um 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 Eagle 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 GPT 4 because when he heard 100 trillion parameters he was like wait that doesn't make sense for GPT 4 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 you know 2022 where I became convinced that opening I plan to release a one to2 trillion parameter subset of GPT 4 before releasing the full 100 trillion parameter model GPT 5 so basically all of these people said the same thing who were early on GPT 4 somehow 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 Eagles leak you can see that there were a couple of people that were also using a beta version of GPT 4 now of course it says the sources here are varying credibility but they all inexplicitly say the same thing gp4 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 eagles's leap now essentially what's crazy here is that it says opening eyes of official position as demonstrated by samman himself is that the idea of 100 trillion parameter gbt 4 is complete false this is a half true because gbt 4 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 that of course GPT 4 isn't actually confirmed but you know According to some articles it has been confirmed that gbt 4 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 where one of the leaks that I do remember and this is one of the First videos I did make on GPT 4 and it was because the CTO of Microsoft Germany a week prior to the official release of GPT 4 actually did

slip up and reveal that there exists a GPT 4 which does have the ability to process videos and it says I imagine he was unaware of opening ey's decision not to reveal the video capabilities of the system and this completely proves that gbt 4/5 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 gp4 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 GT4 next week they will have multimodal models that will offer completely different possibilities for example videos and we said the CTO called the llm a game changer because they teach M 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 credible statement because it was the CTO of Microsoft and we did actually get gbt 4 a week later and the point again is that eagle before said that GPT 4 was going to be released in january/february and this kind of claim was also cooperated by a credible entrepreneur who stated that in October 2022 gbt 4's release date would between January and February 2023 now some people are stating well gbt 4 was released in March but of course you can see right here although gbt 4 was released in March 2023 slightly outside the window I think it was done intentionally by open ey to discredit the leak and I can't lie I actually do agree with that because I've seen some stuff you know tweeted by people like Jimmy apples and stuff like that and I'm pretty sure sometimes when leaks have gotten pretty big open air 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 censers and fully committed div vision for their self-driving cars the point is that training a humanized AI model on all the image and video data on the internet will clearly be more than enough to handle complex robotic tasks that Common Sense reasoning is buried in the video data and just like it's buried in the text data and the text focus gp4 is stally 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 SL video and you can see the former head of Tesla AI where explains they remove the sensors While others differ that's Andre Kathy 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 AI system that learned mainly from Vision combined with an large 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 asks the question that what happens when robotics is trained on an 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 pal how good it was it was definitely a very comprehensive system that could do a lot of stuff I remember it was able to deal with adversarial disturbances you know get stuff that you wanted it to do this was a pretty good system um 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 and of course this is where they talk about the Tesla update where it says you know Optimus performed you know its first endtoend 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 to 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 cuz I'm not too clued up on Robotics and how entirely that is to trange 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 one 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 3 weeks from now someone ATL said you know there 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 3 weeks because we don't know now essentially it says the image on the left shows what aund what a trillion parameter GPT 4 is capable of an image recognition which is pretty crazy if you haven't seen this demo before this is in the gbt 4 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 gbt 4 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 flu 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 um 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 um like what kind of reasoning does it use in his head to be able to get this uh well okay and some people say that you know some people say these AI systems just patent 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 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 on now here it gives us some talks about someone that did give the early leaks for GPT 4 again and what's crazy is that they do talk about image and audio generation would be trained in quarter 3 of 2023 and apparently If Video generation training is simulation simultaneous or shortly after this actually does line up with Chen's claim of gbt 5 being finished training in December of 2023 so remember that guy before this year of Runway stating that gbt 5 would be finished training in December was now around 3 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 do people that do have inside access

now here's where we get into the crazy bit because this is where um 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 the open ey president Greg Brookman stated that in 2019 following a$1 billion investment from Microsoft at the time open ey planned to build a human brain sized model within 5 years and that was their plan on how to achieve AI remember that bit guys of plan to build a humaniz brain model within 5 years and that was their plan for how to achieve AGI you can see um you know within 5 years and possibly much faster with the aim of building a system that can run a humaniz braid 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 open ey had previously stated in 2019 that their investments from Microsoft would actually help them to try and build a human a humanized brain within 5 years you know leaks about 100 trillion going viral and then it's spreading about GPT 4 could actually be the reason that actually occurred because many people were confused thinking gbd4 is going to have 100 trillion but many people didn't realize that this is actually the future models which is what open 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 5 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 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 specific that the general public doesn't and I would argue that yes is kind of true but you know AI leaders have been stating this from ages ago like Sam Alman has been stating since 2015 AI is dangerous and Elon mus has been stating the same thing but I would argue as well that certain people from Google did actually leave recently um and we're going to get into that now they actually did leave fairly recently and I'm not surprised but um they actually did really leave recently and maybe it's just a coincidence and part of it but it says you know in this uncertain climate that hassabis agrees to a interview stock warning about his growing concerns I would Advocate not moving for and breaking things he says referring to an old Facebook model and essentially their old motto was you know release Technologies into the world first and then fix any problems that arose later and of course you can't do that with super intelligence and he says AI is now on the cusp of being able to make tools that could be dation urging his competitors to proceed with caution than ever before and of course Jeffrey Hinton if you don't know this guy um by industry standards The Godfather of aiir that he actually did leave Google in 2023 because he wanted to actually talk about the dangers of AI he 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 30 to 50 years away or even longer obviously I no longer think that and he thinks super intelligence 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 and it says shortly after the release of gbt 4

the future of Life Institute a highly influential nonprofit organization concerned with the mitigating the potential C catastrophic risks to the world released an open letter on all AI labs to P AI development for 6 months why because they essentially stated that you know including the currently being trained GPT 5 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 GPT 4 including the currently being trained gbt 5 signatures include you know all of these people that were like El musk that even worked at Apple and apparently the first release version of the letter specifically said including the currently being trained gbt 5 and then apparently it was removed which is pretty crazy and then of course there is a quote from samman like I said again um and this is pretty crazy because it says you know do we have enough information on the internet to create AGI and Sam alman'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 uh information in

the internet to create AGI so if you contrast it with yes we have a continuous video feed to our eyes and um 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 one of the things that I think open AI has driven in the field um is that's been really healthy is that you can treat scaling laws as a scientific prediction you can do this for compute 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 many parameters you're going to need when you know when the generated data gets like good enough to be helpful um the internet is like there's a lot of data out there video out there too few more questions maybe so I mean you did see from that clip how samman actually did talk about it I mean he seems very very confident on that prediction in order to get to AGI the kind of data that they do need and then here's what he says on Sam alman's Q& A he says that you know first of all he seems highly confident that exists enough data on and 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 um gb4 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 um and I think this is you know probably the thing that you should probably take away from this video and it says as I mentioned earlier in the document an 100 trillion parameter model is actually

slightly subtable but there is a new scaling Paradigm openai is using to bridge this Gap and it's based on something called the G scaling laws chinchilla was an AI model unveiled by Deep Mind in early 22 and the implication of the chinchilla research paper was that current models are signif trained and with far more computes meaning more data we would see a massive boost in performance the need to increase parameters 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 openi president Greg Brockman discusses this in an interview on how openi realized their initial scaling laws were flawed and have since adjusted to take the chinchilla laws into account regulations

to figure out like hey how big of a model do you think you're going to need lots of mistakes for sure um like a good example of this is the scaling laws so we did this study to S to actually start to really scientifically understand how do models improve as you push on various axes so as you pour more Compu in data in and one conclusion that we had at one point was that basically uh that there's you know sort of a limited amount of data that you want to pour into these models and that there's kind of this very clear curve um and that one thing that I we realized only years later was actually that we'd read the curves a little bit wrong and you actually want to be trading for way more tokens way more data than anyone had expected and that uh you know there's definitely these moments where these things that just didn't quite click whereas like it just didn't add up that we were training for so little and that you know something the conclusions that you drew Downstream um but then you realize there was a foundational assumption that was wrong and suddenly things make way more sense I think it's a little bit like you know physics in some sense where like do you doubt physics it's like I kind of do I think all of physics is wrong right but like only so wrong right it's like we clearly haven't reconciled like Quantum and relativity so there's like something wrong there but that wrongness is actually an opportunity it's actually a sign of you have this thing it's already useful right it really like has affected our lives and it's actually like 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 you know if we do keep pushing and somehow the scaling laws all Peter out right 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 have finally reached the limit of Technology learn 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 parameter model would have cost billions of dollars and just isn't feasible well Microsoft invested $10 billion into openi in early 2023 so I guess it's 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 you know of course Microsoft does get access to only pre AGI Tech but then again open AI is the one that gets to determine whether the technology is Agi or not part of me even wonders if you know Microsoft will even exist or even some of these companies want AGI exist because you know AI could potentially make competition irrelevant that's another question for another video but essentially Alberto Romero worked about deep min's chinchilla scaling breakthrough and chinchilla showed us that despite being vastly smaller than gbt 3 and deep mind'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 open AI is well aware of the chinella scaling laws as is pretty much everyone in the AI field and as they're 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 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 but slightly suboptimal but 100 trillion parameters multimodal with chinchilla scaling laws taken into account what on Earth do we get and I think um I'm not that glued up on the chinella scaling laws of have been 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 a 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 were so many research papers released every single day the AI space isn't Dynamic enough and these chinchilla scaling laws are true and if they do now have the funds to actually train something that is going to be an 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 out when fired and I'm guessing that maybe something like this was potentially the ignition to spark a lot of stress at opening eye because they actually did State Sam when in the interview said look everyone at Open Eye 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 um and of course at 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 uh 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 open AI has planned to build human level AI by 2027 and then scale up super intelligence and this has been delayed because Elam mus lawsuit be coming up but short and essentially he does talk about this and I don't think this is that crazy um

essentially this guy is an open AI 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 you know 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 it can write essays or poetry on any topic it can Ace college level exams it's daily gaining new capabilities that Engineers who tent 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 would wake the dragon first is then ethical obligation to tell the world about this is then obligation to tell it less I AMU are spending a year working at my job well your job to developed the mathematical theory on how to prevent the AI and it successors from where that essentially means destroying the world and essentially he's referring to the qstar 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 that actually does coincide with opening eyes deadline to create super alignment and they actually state that they want to do this within 4 years and since it was 2023 when they said this if they do manage to get super alignment

done within 4 years that would be 2027 and considering they trademarks all the way up to gpt7 that would mean that if a new GPT model is released every single year accounting for GPT 5 GPT 6 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 you know I guess you could say the system that will solve super linman and we can actually solve that then they can actually safely realize that look we can safely train an AGI level system or this 125 trillion parameter model qar whatever it is because then it would actually be safe to do so I'm guessing that kind of does make sense with the iterative deployment that we did see before I'm guessing that if I'm being honest with you guys maybe I did miss some key things in this document maybe there's 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 you know we're not going to get everything that they do have they could have liter no crazy models and do remember that samman 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 talk about and I'm guessing that whatever breakthrough that they had with whether it was qar whether they realized they just need a lot more comp I think the elephant in the room is that

over the next couple of years we're going to get iteratively better systems which open air 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 you know we're going to release that just to make sure that people can reassess their timelines on what a ai's capabilities are so I'm guessing that they just wanted to release that to tell us what's capable but the point is that they know where these a systems are going to go because they have predictable scaling and they've accurately predicted GPT 4'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 super alignment 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 Trad mark 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 compute intensive you know at first I was thinking that something compute as intensive as GPT 4 which was 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 samman talked about which essentially just means that there isn't enough GP to go around there isn't enough to actually provide the compute but I'm guessing that with a 7 trillion whatever Sam Alman 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 some of the caveat 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 opening ey 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 cuz recently uh Google's AI boss actually did say that scale only gets you so far but then again samman is

trying to raise $7 trillion so I'm guessing that maybe if scale isn't what gets you so far Sam Alman wouldn't be trying to raise $7 trillion if they could just do it with the computer that they did have at and so I'm guessing that they do need scale because they've already predicted it with less comput and I'm just going to leave with that but let me know what you guys thought about this is this wasn't meant to be that long but I did want to cover 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 opening ey could go out and they could change everything the truth is regardless of all of this interesting information and speculation the only people that know what's going on is opening eye 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 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