Microsoft And OpenAI Drop “AGI BOMBSHELL” – “PROJECT STARGET” – Superintelligence by 2028

So there was some information that was, quote, unquote, leaked by some people at Microsoft OpenAI, and it discusses a plot to build a $100 billion Stargate AI supercomputer, which is definitely in the realms of AGI. And it definitely seems like this is exactly what Microsoft and OpenAI are building in conjunction with some of their other systems in order to get to artificial general intelligence. And, of course, you get to superintelligence. Now, we can see here that the article clearly states that Microsoft and OpenAI plot to build 100 billion Stargate AI supercomputer. And essentially, this is a radical amount of compute for what they are trying to build. And the details are truly, truly surprising, including some of the recent clips I've seen circulating online. So, essentially the number is 100 billion. And it states right here that executives at Microsoft and OpenAI have been drawing up plans for a data center project that would contain a supercomputer with millions of specialized server chips to power OpenAI's artificial intelligence, according to three people who have been involved in the private conversations about the proposal. And the project could cost as much as $100 billion, according to a person who spoke to OpenAI CEO Sam Altman, and a person who has viewed some of Microsoft's initial cost estimates. Now, this is a giant number because we know that Microsoft has already committed, I think, over $13 billion to OpenAI in the realms of deals, chips, and entire servers. So this is pretty, pretty incredible. Can you imagine $100 billion being invested into the next system? This actually isn't something that is out of the realm of possibilities, considering Sam Altman recently was talking about $7 trillion for future AI systems, and now we're starting to see that some of these plans are actually beginning to materialize. Now, what's crazy about this is that there was literally so much speculation from people like myself and other people in the AI community stating that they've pretty much achieved AGI, and all they're trying to do is get the compute so that they can actually make it to reality. And now we're starting things like $100 billion worth of compute for their next levels of supercomputers. And the details, honestly, do get more and more crazy as this goes on, so you definitely should be paying attention. So, essentially what they state is that the next six years is their entire plan. Okay? They basically state that Microsoft would be likely for financing the entire project, which would be $100 billion times more costly than, you know, 100 times more costly than some of today's biggest data centers. So this would arguably be, I think, the biggest data center on the planet. It says this demonstrates the enormous investment that may be needed to build computing capacity for AI in the coming years. Executives envisage the proposed US based supercomputer, which they have referred to as Stargate, as the biggest series of installations the companies are looking to build over the next six years. So this isn't just one giant supercomputer. There are several phases to this that they are looking to do. And it seems like what they are looking to do with this crazy, crazy supercomputer is to build it in several stages, which I'll talk about later. But the crazy thing about this is that all of this should be done allegedly by 2030 or 2028. So that timeline you can see, it's over six years. You can see that over the next six years, which is pretty crazy. And that goes to show us that if this is over the next six years, this definitely aligns with many people's timelines for AGI artificial superintelligence, and definitely a key date for the singularity. Because many people have also argued that the moment you get to AGI and artificial superintelligence isn't far off because you can just use the AGI to build the asi and it's very, very exponential. Now, there's some other things as well that, you know, like we've talked about as well. The articles basically states that Microsoft's willingness to go ahead with the plan of Stargate depends in part on OpenAI's ability to meaningfully improve the capabilities of its AI. One of these people said, and OpenAI last year failed to deliver a new model it had promised to Microsoft, showing how difficult the AI frontier can be to predict. So essentially, if you aren't familiar with what they're talking about, previously OpenAI was working on a new model and this model was actually called Arrakis. And there have been so many new pieces of information and there will be a full video coming on this. But essentially the model didn't work as OpenAI had expected to. And this kind of makes sense because OpenAI, of course, they are scientists and they are trying to see what works and what doesn't. And sometimes things do work and sometimes things do go wrong. So that this was something for OpenAI, it just didn't go well. But there's a lot of information that I will talk about in another video that explains it in a lot more detail. And the point here is that OpenAI, of course they need to be able to have something for Microsoft, to be able to be like, look, you know, we're going to be investing a certain amount in this. And I think what's rather interesting about this is the fact that if we are taking a look at what we know now, we know that if you know Microsoft's willingness to go ahead, okay, that means that OpenAI clearly do have something else in the works, which Microsoft have looked at the pitch deck, and they've said, okay, investing $100 billion here does make sense. I don't think this is just a GPT5 scale up. I don't think Microsoft would be willing to invest $100 billion in that because that doesn't really give them the return on investment they need. But if you think about it like this, they're investing $100 billion because they think in the future they're going to get that money back. And of course, like it says, Microsoft's willingness to go ahead does depend in part on OpenAI's ability to meaningfully improve the capabilities of its AI. Which means that if OpenAI have something, whatever pitch deck they use, it definitely means that they have something that Microsoft do believe is going to bring them a lot more levels of intelligence in terms of what the AI system can do and of course, in terms of what products Microsoft may be able to offer. And in addition to that, it also says here that OpenAI CEO Sam Altman said publicly that the main bottleneck holding up better AI is is a lack of sufficient servers to deploy it. And I think that that is something that is rather true. It's an interesting trend that we've recently seen. And whilst yes, the Transformers did birth this crazy era of LLMs and some of the tech that we're seeing now, I do think that according to several different papers, that scale is not all you need, but it is definitely a very, very sufficient part of what makes these AI systems so effective, because you can only get so much out of a smaller system. And there have been many different interview clips of senior AI scientist talking about the fact that you definitely do need a level of scale when you're trying to develop some kind of intelligence. And it's something that is rather fascinating to study. Now, one of the clips that I wanted to show you all was a clip from the Dua Keshe Patel podcast in which people from Anthropic and people from Google were actually discussing about the future model. So you know how this is going to be a $100 billion computer that is going to be powering the next generation of AI systems. This video essentially discusses that and they actually talk about how we could be getting AGI by GPT 7. And of course I did discover this and put it in a different video, but I do think it is relevant to the fact that they're both discussing the same amount of figures.

The framing that given that you have to be two orders of magnitude bigger at every generation, if you don't get AGI by GPT 7 that can help you catapult an intelligence explosion, you're kind of just fucked as far as much smarter intelligences go. And you're kind of stuck with GPT7 level models for a long time. GPT4 cost, I don't know, let's call it $100 million or whatever you have what the 1B run, the 10B run, the 100B run, all seem very plausible by private company standards. You can also imagine even like a one T run being part of a national consortium. I want to point out that one we have a lot more jumps and even if those jumps are relatively smaller, that's still a pretty stark improvement in capability. Not only that, but if you believe claims that GPT4 is around 1 trillion, parameter count, I mean the human brain is between 30 and 300 trillion synapses. And so that's obviously not a one to one mapping. And we can debate the numbers, but it seems pretty plausible that we're below brain scale. Still, even if you can't keep dumping more compute beyond models that cost a trillion dollars or something, the fact that the brain is so much more data efficient implies that if we could like train as sample efficient as humans train from birth, we could make the AGI. Yeah, but the sample efficiency stuff, I never know exactly how to think about it because obviously a lot of things are hardwired in certain ways, right? The co evolution of language and the brain structure, so it's hard to say. Also there are some results that if you make your model bigger, it becomes more sample efficient. And so the original Scaling Works paper had that right? So maybe that also just solves it.

So you can see there that there clearly is a heap of unexplored depth to scaling with these levels of compute, because we haven't gone there yet. And it's quite unprecedented to think about how much compute, like literally just how much compute that is. And I think it's going to be a battle between Google and OpenAI, Microsoft in order to, you know, sort of train these models firsthand and see what comes out of them, see what kind of abilities. See how the Agile system, if they have it, if it, you know, if they manage to get super intelligence, all of these kind of crazy things are going to be very, very possible in the near future because they are going to start doing it. And I find this interview really, really fascinating because of course it does talk about the two things that we've discussed previously on the ch, which is of course higher and higher training runs, which are going to go into more and more expensive categories, and of course trying to map the brain one to one, because we know that the brain is essentially AGI, like in the sense that the average human can widely adapt to a different range of tasks. But the problem is that GPT4, if it's 1.8 trillion parameters and the brain is around 30 to 300, wherever you think on the scale, we definitely have a lot more compute in order to try and at least map it one to one. So it's going to be interesting to see what happens once we do scale up these runs to, you know, $10 billion, $100 billion. I think they're probably going to be some smaller runs in the near future, considering how these companies are competing. I mean, Elon Musk recently just said that he's, you know, developing a model that's going to be better than anything right now. I don't know if that's him hyping his own AI system, but it definitely goes to show that as competition heats up, people are taking the game much more seriously. Now, of course, one of the main problems about this is, of course, something that a lot of people don't think about. And if you didn't know, AI systems actually do require a lot of energy to run. And they talk about Stargate here, stating that if Stargate moves forward, it would produce orders of magnitude more computing power than what Microsoft currently supplies. OpenAI from data centers in Phoenix and elsewhere, the people said the proposed supercomputer would also require at least several gigawatts of power, equivalent to what's needed to run at least several large data centers today, according to two of these people. Much of the project cost would lie in procuring the chips, two of the people said. But acquiring enough energy to run it could also be a challenge. So essentially this is something that was previously discussed in a video two days ago, is that powering these data centers for these next level training runs is very, very difficult because we need a lot of energy to run these systems is, of course, nuclear power. So Microsoft is going nuclear to power its AI ambitions And it states that Microsoft is looking at next generation nuclear reactors to power its data centers and AI, according to a new job listing from someone leading the way. And essentially Microsoft is trying to invest and try to be at the frontier of next level energy sources because if they are, they could power these AI compute systems very, very efficiently and they would essentially be in the lead by a large margin. And what they also did was they also invested in nuclear fusion. And the crazy thing about this is that nuclear fusion is pretty, pretty far away according to several experts. You can read here from another article that experts optimistic estimates for when the world might see its first nuclear fusion power plant have ranged from the end of the decade to several decades from now. And of course the company Helion, its success depends on achieving remarkable breakthroughs in an incredibly short span of time and then commercializing its technology to make it cost competitive with other energy sources. So this is a pretty big bet by Microsoft. I'm guessing that like I said, what they want is they want to be first in the line and they want to be able to get there when they make their breakthrough. Some people think nuclear fusion won't happen. Something people think it's going to take a while. But I think what could be happening is that maybe Microsoft is thinking that, you know what, let's sign a deal with Helion, let's build the AGI system no matter how much it costs and then let's use that to fix the fusion problem. And you know, just like that, that chain of reactions, we've got a really, really good solution to the energy problem. So this is obviously something that is going to have to be solved before or potentially after AGI is done. But if we do take a look at this as well, they also state that, you know, such a project is absolutely required for AGI. It says AI that can accomplish most of the computing tasks that human can do. Of course you know, this amount of power is going to be required. So says though the project scale seems unimaginable by today's standard. He said that by the time such supercomputer is finished, the numbers won't seem as eye popping. And then of course we also have here, which is pretty crazy, and it says the executives have discussed launching Stargate as soon as 2028 and expanding it through 2030, possibly needing as much as 4,5 gigawatts of power by the end. The people involved in the discussions have said, and like I said, the key dates of 2028 and 2030, 2028 are so incredible because this is where people have predicted that AGI is of course going to come from. And it seems like we're, you know, moving closer and closer towards that date. Now, if you remember, there was a prediction, not by myself, but I wrote out the prediction based on a bunch of different articles. A bunch of industry insiders, from Ray Kurzweil to Elon Musk to Sam Altman himself, they all kind of state that AGI is pretty much going to basically year by 2028-2030. And if we take a look at iterative deployment, considering that OpenAI is likely to release models in this fashion, it seems that considering they only trademarked up until GPT 7 that the next system after that could be an agile system, which would be rather fascinating. Now, if you don't believe that and you think it's just pure speculation. There was a 40 minute video that I did make covering an entire document that talks about this, but there was also something here. Okay. And in the article they do talk about five phases. So they said Almond and Microsoft employees have talked about these supercomputers in terms of five phases, with phase five being Stargate, named for a science fiction film in which scientists develop a device for traveling between galaxies. And it says the codename originated with OpenAI but isn't the official project codename that Microsoft is using. So there are five different phases. Now, phase four is actually 2026, so it says the phase prior to stargate would cost 4 far less. It says Microsoft is working on a smaller phase phase four supercomputer for OpenAI that aims to launch around 2026. According to two of the people. Executives have planned to build it in Mount Pleasant, Wisconsin, where the Wisconsin Economic Development Corporation recently said Microsoft broke ground on a $1 billion data center expansion. And apparently the supercomputer and data center could eventually cost as much as $10 billion to compete. One of the people said, and that's many more times than the cost of existing data centers. And Microsoft has also discussed using Nvidia made AI chips for the project for that entire thing. So it's definitely pretty crazy the amount of investment that is going into compute, and it seems like it's only going to be going up. So phase four is going to be in 2026, which is why now that they're stating that, look, we're going to be doing phase four in 2026 and phase five, which is Project Stargate in 2028, it kind of seems like they already know what they have planned out to an extent based on how much compute. They're ordering, how they're planning everything. It doesn't seem like they're confused at all on what kind of systems they're going to be building in the future. Even more so. What's interesting is that we can see that today. It says Today Microsoft and OpenAI are in the middle of phase three of the five phase plan. Much of the cost of the next two phases will involve procuring AI chips. So right now we're at phase three of the five phase plan dot it says two data center practitioners who aren't involved in the project said it's common for AI server chips to make up around half of the initial cost of AI focused data centers other companies are currently building. So it seems here that, you know, the middle of phase three of the five phase plan, that is where we are at today. And it seems like by the time this five phase plan is built and you know, we have Project Stargate at Phase 5, I truly wonder what level of AI systems are going to be there because that is a ridiculous level of compute. And you do really need that if you are going to try and build some truly incredible systems. And I don't know what people are building, but I really do want to see. Now, of course, like I said before, we've discussed how Sam Altman has previously also discussed about he doesn't have as many AI server chips as he'd like. And like I said before, earlier on in the video, I also talked about how Sam Altman has been seeking trillions of dollars to reshape the business of chips and AI. And, and of course it says OpenAI chief pursues investors, including the UAE for projects possibly requiring up to $7 trillion. So this is where, you know, the $7 trillion number came from. And of course 7 trillion is a incredibly high number. But it seems that, you know, as far as we're going that, you know, things are moving forward to, you know, $10 billion, a billion dollars, and of course $100 billion. So it doesn't seem like, you know, in the future having that much money invested. I mean, I think maybe there's going to be some crazy demos because If Microsoft committed $100 billion already and they're in talk improve it, it's going to be pretty, pretty crazy. So something that what's also interesting was that this is where they actually talk about Sam Altman's kind of plans. So it says one reason they've been pitching the idea of a new server chip company that would develop A chip rivaling Nvidia CPUs is because Nvidia pretty much has market dominance. Okay. It says the GPU boom has put Nvidia in the position of kingmaker as it decides which customers can have the most chips. And it has aided small cloud providers that compete with Microsoft. So they don't really want that. They don't want other cloud providers competing with Microsoft. And it's essentially Nvidia's in the position where they pretty much decide who gets how much compute. Their stock price is going crazy, and essentially they pretty much own the hardware infrastructure at the moment. And that's not something that's good in terms of competition. But Nvidia, they're a great company. They're positioned to perfectly capture the massive amount of economic benefits from the AI boom. And I think it's going to be interesting to see how in the future certain companies adapt to this and try and develop their own GPUs and their own AI chips. So, yeah, it seems like that's what OpenAI and Microsoft are going to be developing themselves because, like Google, Google also have their own TPU chips, which they do and train their models on. They also said with or without Microsoft, Altman's effort would require significant investments in power and data centers to accompany the chips. And Stargate is designed to give Microsoft and OpenAI the option of using GPUs made by companies other than Nvidia, such as Advanced Micro Devices, such as AMD, or even an AI server chip company Microsoft recently launched. And it says it isn't clear whether Altman believes the theoretical GPUs he aims to develop in the coming years will be ready for Stargate. So it will be interesting to see how this all develops, because right now there is a little bit of speculation. But, you know, like I said before, something really fascinating was that Altman himself said that developing superintelligence will likely require a significant energy breakthrough. And, and of course, that is what they're trying. So it seems like maybe you get AGI. AGI makes the energy breakthrough over simply just focusing all your compute on that kind of breakthrough. And then once that happens, it becomes rapidly, rapidly accelerating. And of course, what's crazy is that Amazon recently just purchased a Pennsylvania data site with access to nuclear power. And of course, Microsoft had also discussed bidding on that same site. So these companies are really, really fighting for the rights to get energy in order to power these future data centers. And what's also interesting as well is that we also about this, and they also touch upon this, is that they said Altman at an intel event last month basically said that AI models get predictably better when researchers throw more computing power at them. And OpenAI has published research on this topic which refers to the scaling laws of conversational AI, and that's something that we also previously saw in that interview clip. So it says OpenAI throwing ever more compute risks leading to a trial of disillumement among customers as they realize the limits of technology, said Ali Godisi, CEO of Databricks, which helps companies use AI. We should really focus on making this technology useful for humans and enterprises and that takes time and I believe it'd be amazing. But this didn't happen overnight, so we have some viewpoints there. But I think this model is getting predictably better. I really do think that things are going to be fascinating depending on what we see in the future, because if models do get predictably better with scaling laws, I truly wonder how good they're going to get now. Something also fascinating that we did get to see was we did get to also see some details of qsar. Not really anything crazy, but it talks about how with more servers available, some OpenAI leaders believe that the company can use its existing AI and recent technical breakthroughs such as Q Star, a model that can reason about math problems it hasn't previously been trained to solve to create the right synthetic non human generated data for training better models after running out of human generated data to give them. So basically this is really crazy because if we look at the highlighted sections here, Q is basically they're considering it to be self improving. So it says these models may be able to figure out the flaws in existing models like GPT4 and suggest technical improvements. In other words, self improving AI. So essentially what they're probably going to be using this for is to, you know, create more synthetic data for better training models and for training systems in the future. Because of course if you didn't know, a lot of these AI systems have pretty much trained on all the data available on the Internet, like all of it, all of the books, all of the textbooks, all of the, you know, transcripts, pretty much everything there is. And when you run out of that data, you're going to need more in order to scale it up even more. So that is going to be something quite fascinating to see. And of course self improving AI, synthetic data and Q are all key parts of this. So some people also have stated this and I thought this Reddit post that was actually going viral on Twitter is rather fascinating to add and it makes a really good point and it Says it's clear now that OpenAI has much better tech internally and are genuinely scared on releasing it to the public. It says the Voice engine blog post stated that the tech is roughly a year and a half old and they are still not releasing it. And it says the tech is state of the art. 15 seconds of a voice and text input and the model can sound like anybody in just about every language. And it sounds natural. Microsoft committing $100 billion to a giant data center. For that amount of capital, you need to have seen it, which is AGI with your own eyes. And like I said, of course Microsoft are not going to just put $100 million on a punt or a blunder or like essentially a speculative bet. $100 billion is a lot of money. And putting that into any kind of investment, you need to have some kind of hard proof that what you're doing is going to work. And of course it says for that amount of capital, you need to have seen AGI with your own eyes. Sam commenting that GPT4 sucks, which is a very interesting point in the recent interview, Sam Altman did say that GPT4 sucks. And so many people think that systems close to or even slightly better than GPT4, they think they're really, really amazing. So that just goes to show that if he thinks these systems suck, that means that he's clearly seen some level of advanced reasoning and advanced systems that are just clearly, ridiculously better than what we have today. And of course he says, Sam told us that he expects a jab by 2029, but they already have it internally and five years for them to talk to governments and how to figure out a solution. We are in the end game now. Just don't die. So this is something that I saw, quote tweeted by someone else that it seems that maybe just don't die. Because of course, if they get AGI and artificial superintelligence, they're going to solve the aging problem completely. That's why many people are saying right now, just don't die. And we're in the end game now. But I do think it's going to be really fascinating because I think $100 billion being committed by Microsoft, if they do fully commit to this and they're in talks for this, it definitely means that something is there that they've seen. They're like, you know what? $100 billion, we can go ahead with this. And of course, Sam Altman stating that he needs $7 trillion does mean that, you know, likely the last bottleneck is probably Just compute. And I mean, it does make sense considering the fact that 1.8 trillion parameters is potentially too small to map the human brain one to one. But yeah, in, in, in, in. In some other news, some other people do have other opinions. Some people say that further indication that Microsoft and OpenAI are chasing their architecture, considering that they need $100 billion for millions of GPUs. And the reason they state that is because the human brain can, like I've said this before in a video, which is pretty funny, I said the human brain can run on a pack of Doritos. Obviously that's not what it runs on. Of course it runs on actual real human food. But the point is, was that it's so much more efficient than the amount of energy that current AI systems use in order to achieve a lot less. And the point is that some people are speculating that they're chasing the wrong architecture. But I would argue against this in the sense that you first build it and once you figure it out, then you can figure out how to make it smaller. And of course, technical breakthroughs are going to come along the way. And either way, I think the future is going to be interesting because we will figure out this AGI debate to whether some people are wrong, whether there's going to be a new architecture. Maybe there is going to be. And maybe, just maybe, it's OpenAI that already have that new architecture and they're already using it on certain advanced systems that they just want to scale up so they can get to AGI or asi. So it will be interesting to see how this all works. But either way, if you have enjoyed the video, let me know what you think of Project Stargate. The insane plan to build a supercomputer to power artificial general intelligence, and most likely artificial super intelligence. So with that being said, let me know if you.