Ex-OpenAI Employee Just Revealed it ALL!

so Leopold Ashen brener is someone who used to work at openi until he was quote unquote fired for leaking internal documents now I do want to state that this video is arguably one of the most important videos because he details the decade ahead for how many of the companies around the world are going to get to AGI and his insights are like no other he made this tweet stating that virtually nobody is pricing in what's coming in a and he made an entire document about the stages that we will need to take in order to

get to AGI and some of the things that you're going to witness in the coming years I think you should at least watch the first 10 minutes of this video because it is remarkably insightful into some of the things that he is predicting so there are seven different sections and I've read this thing from top to bottom at least three times and I'm going to give you guys the most insightful sections from this entire essay because I do believe that this is remarkable document that I think everyone needs to pay attention to so without wasting

any more time let's get into situational awareness the decade ahead so he has this introduction here where he talks about how the Talk of the Town has shifted from 10 billion compute clusters to hundred billion do compute clusters to even trillion doll clusters and every 6 months another zero is added to the boardroom plans the AGI race has begun we are building machines that can think and reason and by 2025 to 2026 these machines will outpace college graduates and by the end of the decade they will be smarter than you or I and we will

have super intelligence in the true sense of the word I'm going to say that again by the end of the decade okay we will have superintelligence in the truest sense of the word along with the way National Security Forces not seen in half a century will be Unleashed and before long the project will be on these are some very fascinating predictions but just trust me once we get into some of the charts and some of the data that he's been analyzing I think it really does make sense and this is why this document is called

situational awareness just read this part before we get into everything he says before long the world will wake up but right now there are perhaps a few hundred people most of them in San Francisco and the AI Labs that actually have situational awareness through whatever peculiar forces or fate I have found myself amongst them and this this is why this document is really important because information like this we're really lucky that people could leave a company like open ey and then publish a piece of information which gives us the details on how superintelligence is likely

to arise and when that system is likely to arise so this is the section one from gp4 to AGI counting the orders of magnitudes so when you see o that's what it stands for so he clearly states here his AGI prediction AGI by 202 27 is strikingly plausible gpt2 to GPT 4 took us from preschooler to Smart Hall high schooler abilities in Just 4 years and if we trace the trend lines of compute algorithmic efficiencies and un hobbling of gains we should expect another preschooler to high schooler size qualitative Jump by 2027 now this is

where we get into our first very important chart because this shows us exactly where things may go he says I make the following claim it is strikingly plausible that by 2027 models will be able to do the work of an AI researcher SL engineer that doesn't require believing in sci-fi it just requires in believing in straight lines on a graph what we can see here is a graph of the base scale up of effective compute counting gpt2 all the way up to GPT 4 and looking at the effective compute that we're going to continue to

scale up now one thing that is fascinating from here is that I think there is going to be an even steeper curve for this the reason I state that is because During the period period from 2022 to 2023 there was something that I would like to call an awareness okay this period right here marks the birth of gpt3 to GPT 4 and this put a real giant spectacle on the AI era gpt3 and gbt 4 weren't just research products I mean gbt 3 was but gbt 4 and chat gbt 3.5 were actual products that were

available for the public and since then we've seen an explosion in terms of how many people are now intrigued by Ai and how many different companies and now piling billions of dollars and billions of resources into the technology into the compute clusters just so that they can capture all of the economic value that's going to be happening during this area which is why I do believe that it wouldn't be surprising if During the period from 2024 to 2028 we do have a lot more growth than we've had in this period which means that having an

automated AI research engineer by 2027 to 2028 is not something that is far far off because if we're just looking at the straight lines and the effect of compute then this is definitely where we could get to and the implications of this are quite Stark because if we can have an automated AI resesarch engineer that means that it wouldn't take that long to get to Super intelligence after that because if we can automate AI research then all bets are off we're able to effectively recursively self-improve but just without that crazy loop that makes super intelligence

explode now here's where he States one of the things that I think is really really important to understand I stated this in a video before this document was released but it's glad to see that someone else is ushering one of the same concerns that I originally thought of he stated that the next generation of models has been in the oven leading some to Proclaim that stagnation and that deep learning is hitting a wall but by counting the ordance of magnitude we get a Peak at what we should actually expect in a video around 3 weeks

ago I clearly stated that look things are slowing down externally but things are not slowing down internally at all just because some of the top AI Labs may not have presented their most recent research that doesn't mean that breakthroughs aren't being made every single month he States here that while the inference is simple the implication is striking another jump like that very well could take us to AGI to models as smart as phds or experts that can work beside us as a coworker perhaps most importantly if these AI systems could automate AI research itself that

would set intense feedback loops and that's of course where we get AI researchers to make breakthroughs in AI research then we apply those breakthroughs to the AI systems they become smarter and then the loop continues from there basically recursive self-improvement but on a slower scale and here he clearly states even now barely anyone is pricing this in but the situational Awareness on AI isn't actually that hard once you step back and look at the trends if you keep being surprised by AI capabilities just start counting the orders of magnitudes so here's where we talk about

the last four years so you can see he speaks about gpt2 to GPT 4 gpt2 was essentially like a pre schooler while it can string together a few plausible sentences and these are the gbt2 examples people found very impressive at the time but yet it could barely count to five without getting tripped up then of course we had gbt 3 which was in 2020 and this was as smart as an elementary Schooler and this was something that once again impressed people quite a lot and of course this is where we get to GPT 4 in

2023 and this is where we get a smart high schooler while it can write some pretty sophisticated code and iterative debug it can write intelligently and sophisticatedly about complicated subjects it can reason through difficult high school competition math and it's beating the vast majority of high schoolers on whatever test we give it and remember there was the Sparks of AGI paper which showed some capabilities that showed us that we weren't too far away from AGI and that this gp4 level system were the first initial Sparks of artificial general intelligence the thing is here he clearly

states and I'm glad he's stating this because a lot of people don't realized this that the limitation comes down to obvious ways that models are still hobbled and basically he's talking about the way that models are used and the current Frameworks that they have the raw intelligence behind the model the raw cognitive capabilities of these models if you even want to call it that as artificially constrained and basically in the future if you calculate the fact that these are going to be unconstrained in the future it's going to be very fascinating on how that raw

intelligence applies across different applications and one of the clear things that I think that that most people aren't realizing is that we're running out of benchmarks as an anecdote my friends Dan and Colin made a benchmark called the MML u a few years ago in 2020 they hoped to finally make a benchmark that would stand the test of time equivalent to all the hardest exams we give high school and college students just 3 years later models like GPT 4 and Gemini get around 90% And then of course GPT 4 mostly cracks all the standard high

school and college aptitude tests and you can see here the test scores of AI systems on various capabilities relative to Human Performance you can see that in the recent years there have been a stark Stark level of increases here it's absolutely crazy with as to how many different areas that AI is increasing in terms of the capabilities it's really really fascinating to see and also potentially quite concerning now one of the things that most people did actually miss about going from GPT 4 to AGI was a benchmark that actually did shock me so there is

essentially this Benchmark called the math benchmark a set of difficult mathematic problems from a high school math competitions and when the Benchmark was released in 2021 gpt3 only got 5% and basically the crazy thing about this was that researchers predicted at that time stating to have more traction on mathematical problem solving we will likely need new algorithmic advancements from the broader research community and we're going to need fundamental new breakthroughs to solve maths or as they thought they predicted minimal progress over the coming years but by mid 2022 2 we got to 50% accuracy and

basically now with the recent math Gemini 1.5 Pro we know that this is now at 90% which is absolutely incredible and here's something that you can clearly screenshot and share to your friends or colleagues or whatever it is whatever kind of community that you might be in but you can see that the performance on the common exams perent how compared to human test takers we can see that GPT 4 ranks above 90% for pretty much all of them except calculus and chemistry which is a remarkable feat when we went from GPT 3 to GPT 4

in such a short amount of time this is a true true jumping capabilities that many people just simply wouldn't have expected now here's where we starting to get to some of the predictions that we can really start to make based on the nature of deep learning so essentially the magic of deep learning is that it just works and the trend lines have been astonishingly consistent despite the naysayers at every turn we can see here that this are screenshots from from the scaling compute in the open AI Sora technology and at each level we can see

an increase in the quality and consistency the base compute results in a pretty terrible image/ video four times compute results in something that is pretty coherent and consistent but 30 times compute is something that is remarkable in terms of the quality consistency and the level of video that we do get which shows us that these trend lines are very very consistent and he says if we can reliably count the orders of magnitude that we're going to be training these models we can therefore extrapolate the capability improvements and that's how some people actually saw the GPT

4 level of capabilities coming and one of the things that he talks about is of course things like Chain of Thought tools and Scaffolding and therefore we can unlock significant latent capabilities basically when we have GPT 4 or whatever the base cognitive capabilities are for this architecture and then we can use that to unlock latent capabilities by adding different steps in front of that system so for example when you use gp4 with Chain of Thought reasoning you significantly improve your ability to answer certain questions in different scenarios and it's things like that where you can

unlock more knowledge from the system by using different ways to interact with it which means that the raw data behind the system and the raw knowledge is a lot bigger than people think so this is what you call on hobbling Gams now one of the things that's really important and this is something that doesn't get enough attention but this is going to make up a lot of the gains that you won't see uh and this is the algorithmic efficiencies so whilst massive investments into compute get all the attention algorithmic progress is similarly an important driver

of progress and is dramatically underrated to see just how big of a deal algorithmic progress can be consider the following illustration this one right here the drop of the price to attain 50% accuracy on the math benchmark over just 2 years and for comparison a computer science PhD student who didn't particularly like math scored 40% so this is already quite good and the inference efficiency improved by nearly three orders of magnitude or 1,000x in less than 2 years so what we have here is something that is incredibly more efficient for the same result in Just

2 years that is absolutely incredible these algorithmic efficiencies are going to drive a lot more gains than you think and as someone who was looking at arxiv which is where a lot of these research papers get published just trust me there are like probably 50 to 80 different research papers that get published every single day and a few of those allow you know 10 to 20% gain 30% gain and if you calculate the fact that all of these algorithmic efficiencies are going to compound against each other we're really going to see more cases like this

here's where you talk about the API cost and you basically look at how efficient it becomes to run these models so GPT 4 on release cost to save at gpt3 when it was released but since the GPT 4 released a year ago the prices for gbt 4 level models have fallen six times/ four times for the input/output with a release of gbt 40 and gbt 3.75 level is basically Gemini 1.5 Flash and this is 85 times cheaper than what we previously used to have so we can see here on this graph that if we want

to calculate exactly how much progress we're going to be make we we can clearly see that there are two main things here which is of course the physical compute of scaling which is going to be things like these data centers and the hardware that we throw at the problems and then of course the algorithmic progress which is going to be the efficiencies where people rewrite these algorithms in crazy ways that just drive efficiencies that we previously didn't know how to solve and that's why in the future where we do get an automated AI researcher to

do that this Gap is going to widen even more now this is where we talk about un hobbling this is of course something that we just spoke about before but the reason that this is important is because this is where you can get gains from a model in ways that you couldn't previously see before so imagine if when someone asked you you know a math problem you had to instantly answer with the first thing that came to mind it seems pretty obvious that you would have a very hard time except for the simplest problems but

until recently that's how we had llm solve math problems instead those of us when we do math problems we work the the problem step by step and able to solve much more difficult problems that way it's basically Chain of Thought and that's what we do for llms and despite the excellent raw capabilities they were much worse at math than they could be because they were hobbled in an obvious way and it was a small algorithmic tweak that unlocked much greater capabilities essentially what he's stating here is that when these even better models get even more

un hobbled we're going to see even more compounded gains overall and one of the craziest ones that we recently have is of course GPT 4 can only solve the software engineering bench 2% correctly while with Devon's agent scaffolding it jumps to 142% which is pretty pretty incredible and this is something that is very very small in terms of its infancies and it says tools imagine if humans weren't allowed to use calculators or computers we're literally only at the beginning here chpt can only now use a web browser run some code and so on and of

course this is where we talk about the context length which is you know it's gone from a 2K context length to 32 to literally a 1 million context length and of course there's posttraining which is substantially improving the models after you've trained the model which is making huge gains we went from 50% to 72% on math and 40% to 50% on the GP QA and here's where we can see once again there is another stack in terms of the growth so you can see here the raw chatbot from the chatbot agent these are things that

most people just aren't factoring in when we take a look at the future of AI growth this is why and this is why he says the improvements will be step changes compared to GPT 6 and reinforcement learning with human feedback by 2027 rather than a chatbot you're going to have something that looks more like an agent and more like a coworker now one of the craziest things I saw here was that when you take in all of the information that was just stated this is absolutely incredible because he basically says that by the end of

2027 this is absolutely insane so we can see the gains made from gpt2 to GPT 4 by physical compute algorithmic efficiencies plus major un hobbling gains from the base model chatbot in the subsequent four years we're going to see 3 to six orders of magnitude of Base effective compute scale up which is the physical compute and algorithmic efficiencies but basically he says that with all of this combined what this should look like suppose that GPT 4 training took 3 months in 2027 a leading AI lab will be able to train a GPT 4 level model

in a minute that is a incredible prediction and I'm wondering if that is going to be true but then again you have to think about it in 3 years with billions of dollars and that much more compute floating around the industry I wouldn't be surprised if some of the things that we think right now are sci-fi completely aren't so here's where we can see everything visualized we can literally see how we have the base knowledge then we've got the chatbot framework then once we have the agentic framework and of course once we have the more

orders of magnitude we can see that the intelligence by 2027 become an an automated AI research engineer now that you actually look at all the information from every different single point it doesn't seem that crazy like if you count everything every single thing into account this doesn't seem like something that is too too far away especially with the kind of jumps that we've seen before and maybe just maybe with the GPT 5 release and subsequent AI models we're going to start to see the 2027 and 2026 are going to be incredible time periods he says

we are on course for AGI by 2027 and that these AI systems will basically be able to automate basically all all cognitive jobs think any job that can be done remotely that is a crazy crazy statement and I think that that is something that you need to you know bear in mind AGI by 2027 is not something that is out of the picture and it's something that definitely could happen so one of the most interesting things as well that I think is really important is that the reason this period is so important is because this

is the decisive period this is the period where the growth occurs and we really get to see what is capable so he says right here in essence we're in a middle of a huge scale up and this is where we are reaping one time gains this decade and progress through the orders of magnitude will be multiples slower thereafter if this scale up doesn't get us to AGI in the next 5 to 10 years it might be a long way out so the reason that this is going to be you know so interesting is because it's

this decade or bus so you can see right here that the effective scale up of compute is going to become harder and harder the larger it gets because think about it like this it's like well I don't actually have a great example but we just have to think about how hard it is to invest billions and billions of dollars more to scale up systems even more so it's like of course you can scale up a model from 10 billion to 100 million but the scale from 100 million to 10 billion is really huge it takes

a lot of investment you're going to have to Data Centers multiple data centers you're going to have to make them really huge you're going to have to think about all the cooling there's a lot of power requirements and then to get to a trillion dollar clusters or even hundred billion doll even $500 billion clusters that's even more incredible so basically he's stating once we get to the100 billion level and above if we aren't at AGI at that level then it means that real realistically we're going to have to wait for some kind of algorithmic breakthrough

or an entirely new architecture because with the gains that are going to be made by by being able to have so much more compute being thrown at the problem it is very hard for us to make gains based on compute after that he basically says here that spending a million dollars on a model used to be outrageous but by the end of the decade we will likely have $100 billion or $1 trillion clusters and going much higher than that is going to be a lot harder so it's going to be basically the feasible limit both

in terms of what big businesses can actually afford and even just as a fraction of the GDP and he also states that the large gains that we're getting from CPUs to gpus will be likely gone by the end of the decade because we're going to have ai specific chips and without much further Beyond more ZW there's not going to be much more gains possible and the reason this is important because for those of you who are trying to navigate this entire thing you're trying to figure out okay where AI capabilties going to stop where is

the next growth going to be at it's basically the fact that right now we're scaling up our systems and once we reach the top of $1 billion to 100 trillion clusters if we don't have super intelligence or AGI by that limit then we'll know that maybe we're using the wrong architecture and things are going to have to change significantly so it's either going to be a long slow slug or we're going to get there relatively soon and by the looks of things it looks like we're going to get there relatively soon now here's where we

talk about AGI to Super intelligence the intelligence explosion and basically this is where he talks about how AI progress will not stop at human level hundreds of millions of agis could automate AI research compressing a decade of algorithmic progress which adds five orders of magnitudes into one year and we would rapidly go from Human level to vastly superhuman AI systems and the power and the Peril of super intelligence would be dramatic so here's what we have basically I think the most important graph okay if there's one that you want to screenshot and keep on your

phone I think it's this one okay the reason that it is is because once you have the GPT 4 gpt3 gpt2 timelines mapped out we can clearly see that this intersection here is at 2023 but of course as the trends continue we can see that once we do get to this period right here this is where things start to get interesting because this is of course the period of automated AI research and that's why once this does happen and this is not something that's like a fairy tale this is something that Sam mman has said

that's his entire goal that's what opening eye are trying to build they're not really trying to build super intelligence but they Define AGI as a system that can do automated AI research and once that does occur and I don't think it's going to take that long that's when we're going to get that recursive self-improvement Loop where super intelligence is not going to take that long after because if you can deploy 5,000 agents okay that are essentially all super intelligent not super intelligent but at the level of a standard AI researcher and we can deploy them

on certain problems and keep them running 24/7 that is going to just compress years of AI Research into a very short time frame which is why you can see that the graph during this purple period here it starts to go up rapidly and that's why the next decade is so important because once this area actually happens once we get to that breakthrough level where okay we've automated AI research then all bets are off because we know the super intelligence will just be around the corner and that's what we have the intelligent explosion because every time

an AI researcher manages to make a breakthrough the AI research breakthrough is an applied to that AI researcher and then the progress continues again because now the AI researcher is just that more efficient or even smarter and here's the crazy thing this is one of the craziest implications about this entire thing we don't need to automate everything just AI research I'm I say that again we don't need to automate everything it's just AI research a common objection to transformative impacts of AGI is that it will be hard for AI to do everything look at robotics

for instance the doubters say that there will be a gnarly problem even if AI is cognitively at the level of phds or take automating biology research and design which might require lots of physical lab work and human experiments but we don't actually need robotics we don't need many things for AI to automate AI research the jobs of AI researchers and engineers at leading Labs can be done fully virtually and don't run into real world botton necks the same way that robotics does and of course this is going to still be limited by compute which is

addressed later and basically that's things whereby like the literal hardware issues that you get when you're trying to scale these systems like it's not that hard well I say it's not that hard but theoretically it should be easier to read ml literature and come up with new questions and ideas Implement these experiments test those ideas interpret the results and then of course repeat it and all it takes is for for once we get to that level that's where we have this insane feedback loop and this is where 2027 we should expect GPU fleets in the

tens of millions training clusters alone approaching three augites larger already putting us at 10 million a100 equivalents and this is going to be running millions of copies of our automated AI researchers perhaps 100 million human researcher equivalents running day and night that is absolutely incredible and of course some of the gpus are going to be you know used for training new models but just think about that guys imagine 100 million human researcher equivalent running 247 what kind of breakthroughs are going to be made at that stage I mean it's very hard to conceptualize but it's

important to take into account what is truly coming because like he said nobody's really pricing this in and the crazy thing is is that they're not going to be working at human speed they're going to be each working at 100 times human speed not long after we begin being able to automate AI research so think about it you're going to have like 100 million more AI research and they're going to be working at 100 times what you are which is absolutely incredible they're going to be able to do a Year's worth of work in a

few days that is going to be absolutely insane and you have to remember like the current level of breakthroughs that we're getting with just humans is absolutely incredible so once we're able to automate it the intelligence explosion is literally going to be unfathomable now this is one of the bottlenecks that most people don't talk about but of course it's limited compute and whilst yes now you're probably thinking wow this is really incredible we could really be on the you know Cliff of something amazing here but of course compute is still going to be limited then

there's also this idea which I think most people haven't considered and this includes myself okay ideas could get harder to find and there are diminishing returns though the intelligence explosion will quickly fizzle related to the above objection even if the automated AI researchers lead to an initial burst of progress whether rapid progress can be sustained depends on the shape of of the diminishing returns curve to algorithmic process again my best read of the empirical evidence is that the exponents shake out in favor of the explosive SL accelerating progress in any case the sheer size of

the one-time Boost from 100 to hundreds of millions of AI researchers probably overcomes diminishing returns here for at least a good number of organites orders of magnitudes of algorithmic progress even though it can't be indefinitely self- sustaining basically there are few things that could slow down AI progress but this is of course something that's far far into the future so here's where he talks about the takeoff for AGI so he said rather that 2027 is Agi and then we get to Super intelligence which is a very basic look at things it's probably going to look

like this 2026 to 2027 we get a Proto automated engineer but it has blind spots in other areas and it's able to speed up work by 1.5 times to two times and already progress begins accelerating then of course in 2027 to 2028 we have Proto automated researchers that can automate more than 90% and some remaining human bottlenecks and hiccups in coordinating a giant organization of automated researchers to be worked out but this already speeds up progress by three times and then of course now with AGI and these kind of researchers we get 10 times the

pace of progress in 2029 and that's how we get to Super intelligence and this is thinking about it as a slow method to Super intelligence but the point is is that that is ladies and and gentlemen still very very fast he talks about how by the end of this decade the AI that we're going to have are going to be unimaginably powerful meaning that even things that you can think of right now it's going to be pretty hard to conceptualize how great they're going to be now he gives a really interesting you know description of

how this could actually happen but it's pretty incredible to think about like he says they'll be able to run a civilization of billions of them and they're going to be thinking orders of magnitude faster than humans they'll be able to quick Master any domain write trillions lines of code and read every research paper in every scientific field ever written and write new ones before you've gotten past the abstract of one learn the parallel experience of every one of its copies gain billions of human equivalent years of experience with some new innovation in a matter of

weeks and work 100% of the time with Peak energy and focus and won't be slowed down by that one team mate who is lagging and so on and of course we've already seen some of these examples things that like you know people talk about in terms of you know AI research in the future this is something that we have seen already before if we take a look at the famous move 37 in Alpha go this is basically where a computer system did a move in a game that was really old and people were like why

on Earth did the AI system do that move I'm guaranteeing that it just lost but it the move that it pulled I think the calculation that it would have done that move was pretty crazy and this system basically thought of a move that no one would have ever thought of and this move stunned people it shocked them you know Lisa all couldn't really figure out what was going on the human player had no idea what the AI system did and eventually the human lost that game and basically he's stating that super intelligence is going to

be like this across many domains it's going to be able to find exploits in human code too subtle for humans to notice and it's going to be able to generate code too complicated for any human to understand even if the model spent decades trying to explain it we're going to be like high schoolers stuck on neonian physics while it's off exploring mechanics and imagine all of this applied to all domains of science technology and the economy of course the era bars here are still extremely large but just imagine how consequential this would all be of

course one of the big things is about solving robotics superintelligence is not going to stay cognitive for long once we do get you know the systems that are at AGI level factories are going to shift from going to human run to AI directed using human physical labor soon to be fully being run by swarms of human level robots and of course think about it like this the 2030s to 2040s is going to be absolutely insane because the research and design efforts that human researchers would have done in the next Century into years so think about

how we went from the 20th century when we were essentially you know going from flying as a mirage like people were like ah we're never going to be able to fly then we had airplanes then we had a man on the moon which was over a couple of years you know over tens and you know over like 50 40 30 20 years but in the 2030s this is going to be happening in a few years like literally just a short amount of years we're going to be having different breakthroughs across many different sectors and many

different Technologies across many different Industries and this is where we can see the doubling time of the global economy in years from 1903 it's been 15 years but after super intelligence what happens is it going to be every 3 years is it going be every five is it going to be every year is it going to be every 6 months I mean how crazy is the growth going to be because we've seen that here like the exponential decreases in time are very very hard to predict here are two of the most important things as well

that I think are really really important and I know this video is really long but guys trust me this is literally probably the last Industrial Revolution that's ever going to happen and it's something that we are being able to witness here with these documents so there are two things okay this is a decisive and overwhelming military Advantage early cognitive super intelligence might be enough here per perhaps some superhuman hacking scheme can deactivate adversary militaries in any case military power and Technology progress have been tightly linked historically and with extraordinarily rapid technological progress will come military

revolutions and essentially the Drone swarms things that you could do all the kinds of research and design that you could do to create weapons that you couldn't even think about it's going to be absolutely incredible basically think about it like this with superintelligence compare 21st Century militaries with fighter jets and tanks and air strikes fighting a 19th century Brigade of horses and bayonets that's going to be a war that they simply can't win the technology that we have you'd only need an F22 fighter jet to annihilate the entire 19th century Brigade and the same is

going to happen with superintelligence the research and design efforts are going to create an potentially an unstable economy where if we don't get to superintelligence First and a nation state that is I guess you could say on the side of doing whatever they want they could have technologies that are so far Advanced that they could truly have military advantage over everyone and this is why I think things are going to change open AI okay whoever controls superintelligence will possibly have enough power to seize control from pre superintelligence forces even without the robots small civilization of

superintelligence would be able to hack any undefended military election television system and cunningly persuade generals electoral and economically out compete nation states design new synthetic bioweapons and then pay a human in Bitcoin to synthetically synthesize it and so on and basically what we're going to have here is I think there's going to be a shift of power guys I don't know how the government is going to deal with this like if they're just going to seize opening eyes computers or whatever but whoever literally gets to Super intelligence first I truly believe that all bets are

off because if you have the cognitive abilities of something that is you know 10 to 100 times smarter than you trying to to outm smarten it it's just you know it's just not going to happen whatsoever so you've effectively lost at that point which means that you're going to be able to overthrow the US government so I mean um it's a pretty pretty interesting statement but I do think that it is true and this is where you can see that the moment we get an automated AI researcher all of these other areas start to take

off in remarkable different ways it's truly incredible now here's where we get to an interesting point okay this is where he talks about the security for AGI and this is really important because after made this document open AI actually updated their web page with something as a rebuttal to this part right here and like I said before this is why I truly think that at least starting next year I don't think there were going to be any AI leaks after 2025 and that's because I think the nature of AI is going to change because they're

probably going to realize how serious AI is and the fact that this is going to be treated like I guess you could say a US national secret in the sense that like we just don't get secrets about the Pentagon unless we have a whistleblower who is eventually going to get arrested anyways and essentially it says the Nations leading AI laabs treat security as an afterthought currently they're basically handing the key secrets for AGI to the CCP on a silver platter securing the AGI secrets and waits the state actor threat will be an immense effort and

we're not on track basically as stating that look if we're actually going to build super intelligence here and we're actually going to build something that is really going to change the world we need to get ser serious about our security right now there are so many loopholes in our current top AI Labs that we could literally have people who are infiltrating these companies and there's no way to even know what's going on because we don't have any true security protocols and the problem is is that it's not being treated as seriously as it is it's

not like it's the CIA or some secret government organization where they have things going on at the Pentagon or like Area 51 or whatever secret military organization exists that have super clear things in regards to their security and he's basically stating that right now you don't even need to mount a dramatic Espionage operation to steal these secrets just go to any San Francisco party or look through office windows and he's basically stating that right now it's not as serious because people don't realize it but the thing is and like I said before AI labs are

develop developing currently algorithmic Secrets which are the key technical breakthroughs which are the blueprints so to speak for AGI right now and in particular the the it's basically the next Paradigm for the next level of systems and of course basically what we need to do is we need to protect these algorithmic secrets if we're supposed to maintain this lead and of course secure the weights of the models that we need and they're going to matter more when we get these larger custers and he says our failure today will be irreversible soon in the next 12

to 24 months we will leak key AGI breakthroughs to the CCP it will be to the National security establishment the greatest regret before the decade is out this is of course the preservation of the three World against the authoritarian States and it's on the line a healthy need will be the necessary buffer that gives us the margin to get AI safety right to the United States has an advantage in the AGI race but we're going to give up this lead if we don't get serious about security very soon and if we don't get this right

we need to ensure that we do now to ensure that AGI goes very well and I do agree with that because if we're not going to get this right other countries could try and Rush forward ahead with the technology so that they can you know Advance their research and design effort in the military so that can gain a military advantage and what happens if there's some kind of security error where those systems go off the rails I mean it's truly going to be incredible and he says too many Spar people underestimate Espionage the capabilities of

states and their intelligence agencies are extremely formidable even in a normal non allout AGI race times and from Little that we know publicly nation states or even less Advanced actors have been able to zero click hack any desired iPhone and a Mac with just a phone number infiltrate an air gapped aut topic weapons program modify the Google source code find dozens of zerod day exploits a year that take on average 7 years to detect Spearfish major tech companies install key loggers on an employee device insert trap doors in encryption schemes still information I mean he's

basically stating that look if little less Advanced actors can do this okay and this is just the stuff that we know publicly imagine what you know people are probably planning for the race for AGI like imagine what is really going on behind closed doors in order to get the system because guys AGI is basically a race to it first and whoever gets the super intelligence first truly does win like I want to make that clear and he's basically stating here that look we need to protect the model weights especially as we get close to AG

GI but this is going to take years of preparation and practice to get right and of course we need to protect the algorithmic secrets starting yesterday's basically explains here that the model Waits are just a large files of numbers on a server and these can be easily stolen all it takes is an adversary to match your trillions of dollars and your smartest minds of Decades of work just to steal this file and imagine if the Nazis has gotten an exact duplicate of every atomic bomb made in Los Alamos Los Alamos was that secret area where

people were developing the atomic bomb and he's basically saying that look imagine the stuff from the atomic bomb had gotten to the Nazis imagine what the future would look like that is not a future we do want to create for ourselves so we need to make sure we keep the model weight secure or otherwise we're building AGI for any other nation state even possibly North Korea he's basically stating that look this is a serious problem because all they need to do is automate AI research build super intelligence and any lead that the US had would

vanish the power dynamics would shift immediately and they would launch their own intelligence explosion what would a future look like if the US is no longer in the lead and then of course this is a problem because if we find out that they also have the same secrets that we do this is going to put us existential race which means that the margin for ensuring the superintelligence is safe is going to completely disappear and we know that other countries are going to immediately try and race through this Gap where they're going to skip all the

safety precautions that any responsible us AGI effort would hope to take which is why I said once people start to think wait a minute this is truly the stake of humanity right here we need to make sure that okay we secure everything down and I'm sure that we're not going to get any more leaks so now this is where open AI literally yesterday published securing research infrastructure for advanced AI we outline our architecture that supports the secure training of Frontier models and basically they say we're sharing some high level details on the security architecture of

our research supercomputers open AI operates some of the largest training AI training supercomputers enabling us to deliver models that are industry-leading in both capabilities and safety while advancing the frontiers of AI and they're stating that we prioritize security basically through this they detail certain ways that they have Security in terms of of course protecting the model weights and the stating that protecting the model weights from exfiltration from the research environment requires a defense indepth approach that encompasses multiple layers of security these bespoke controls are tailored to safeguard our research assets against unauthorized access and theft

while ensuring they remain accessible for research and development purposes now I think they did this because open ey I don't think they want the government to come in and say look we need to like have people in here to make sure that you guys know what you're doing but I do think that in the future there's going to be some kind of government intervention because openi has literally been a company that has been so tumultuous that it is shocking at what has gone on I mean the CEO is fired certain researchers left certain researchers were

fired some people are leaving saying that this company's got not good for safety you have some people saying this is happening about AGI it's going to be next year I mean for a company that is literally the most advanced AI company in the world there is so much drama that has gone on that it doesn't bolster the most trust for the general public in terms of what they're going to be doing with regards to securing the model weight and in addition there are currently literal people on Twitter like Jimmy Apple that know when future releases

are coming so how like on Earth is this even a thing because I think there were even some tweets about how certain people were taking pictures of laptops that were in caf's just just near opening eyes uh research lab and essentially that's how they were getting the leaked info so I'm guessing that maybe some open ey employees may have just left their laptops open or maybe someone was taking you know screenshots of what was going on on their laptops at cafes just outside openi headquarters and it's basically stuff like this thinking about like what's going

on here is that like they need serious serious security because if they are really on the path to AGI that means they're on the path of super intelligence which holds a huge huge huge implications for the future and of course the last part of this is where he talks about super intelligence and aligning this reliably controlling AI systems much smarter than we are is an unsolved repeat unsolved technical problem and while it is a solvable problems things could very easily go off the rails during a rap intelligence explosion and managing this will be extremely tensed

and failure could be catastrophic basically saying that look if we make something that is 10 times smarter than us think about how we how much smarter than we are from chimps we're not that much smarter in terms of the uh you know IQ but the fact that we're you know just a little bit more smarter than them and we've been able to do so much more it shows us that look you don't need to create something that's a million times smarter than you to realize that it could screw you over and do things that you're

not truly going to understand and of course this is someone that literally worked on super alignment at open AI so this isn't just a random blog post and here's where the real problem lies okay by the time the decad is out we're going to have billions of vastly superhuman a AI agents running around and these superhuman AI agents will be capable of extremely complex and Creative Behavior we will have no hope of following along we'll be like first graders trying to supervise with multiple doctorates in esset we're going to face the problem of handing off

Trust how do we trust that when we tell an AI agent to go and do something it's going to do that with our best thoughts in mind this is essentially the alignment problem we're not going to have any hope of understanding what our billion super intelligences are actually doing even if they try and explain it to us because we're not going to have the technical ability to reliably guarantee even basic side constraints for these systems and he's basically stating that look reinforcement learning with human feedback relies on humans being able to understand and supervise AI

Behavior which fundamentally won't Skil a superhuman system because this relies on us being able to actually understand and supervise a behavior which means we need to actually understand what's going on and if we don't understand what's going on then we can't reliably supervise these systems which means it's not going to scale to superhuman systems and the craziest thing is is that remember last week open AI literally disbanded its super alignment team here is a nice illustration where you can see the little AI giving us a very very basic piece of code and of course we

can EAS understand that that looks safe but here we're like wait a minute what is all this stuff is this safe what's going on it's like you know it's very hard to interpret what on Earth is going on in addition we can see here that some of the problems that may occur are ones that we may not want so of course if we think about you know getting a base model to you know make money by default it may well learn to lie to commit fraud to deceive to hack to seek power because in the

real world people actually use this to make money and of course we can add the side constraints such as don't lie and don't break the law but we're not going to be able to understand what they're doing and therefore we won't be able to penalize the bad behavior and if we can't add these side constraints it's not clear what's going to happen and even maybe they'll learn to behave nicely when humans are looking and then pursue more nefarious strategies when we aren't watching which is a real real problem and this is something that actually does

occur already one of the main things that I genuinely think about on a daytoday basis is this right here okay um it says what's more I expect that within a small number of years these AI systems will be integrated into many critical systems including military systems and failure to do so okay this is why it's such a trap which is why like we're on this train barreling down this pathway which is super risky is that think about it like this okay right now we have a a thing where like you know in the future we're

going to have to equip a lot of our Technologies with AI systems inside of them because if we don't they're just not going to be as effective and if we don't we're going to be get dominated by adversaries but of course everyone was stating that before AI got this good we all said we would never connect it to the internet and now it's connected to the internet and people are not batting an eye and the problem is is that like if we get an alignment failure AI is already in every single infrastructure so what happens

when AI fails and it's in every single piece of technology so it's pretty insane and of course failures on a much larer model could be really really awful and here's another graphic which presents you know a lot of stuff this is where we have AGI you know reinforcement learning with human feedback the failures are low stakes the architecture and algorithms we do understand the backdrop of the world is pretty normal but this is where we get to Super intelligence and remember the transition here is only 2 to 3 years maximum so once we get to

Super intelligence the the failures are catastrophic the architecture is alien and it's designed by the previous generation of super smart AI it's not going to be designed by humans okay and the world is going to be going crazy okay there's going to be extraordinary pressures to get this right and of course we have no ability to understand if these systems are even aligned what they're doing and then we're basically going to be entirely trusting and being reliant on these AI systems so how on Earth are we really even going to get this right and here's

the thing okay no matter what we develop true superintelligence is likely able to get around most any security scheme and for example still they buy us a lot more margin for error and we're going to need any margin we can get now here's one of the scariest things that I think about and this is something that I saw in only one article covered like literally there's only one article covered there was one Reddit post that I think got removed about this so I'm not even sure if you know anyone's even watching at this point but

um basically if you think about it before okay a dictator who wields the power of superintelligence would command concentrated power unlike anything we've ever seen think about it if you manag to control super intelligence which is of course kind of hard cuz we won't be able to align it we could have a situation where there is just complete dictatorship millions of AI controlled robotic law and enforcement agents could police their populace Mass surveillance would be hypercharged dictator loyal AIS could individually assess every single citizen for descent with near perfect lie detection sensor rooting out any

disloyalty essentially the robotic military and police force could be wholly controlled by a single political leader and programmed to be perfectly obedient and there's going to be no risks of coups or rebellions and his strategy is going to be perfect because he has super intelligence behind them what does a look like when we have super intelligence in control by a dictator there's simply no version of that where you escape literally past dictatorships were not permanent okay but superintelligence could eliminate any historical threat to a dictator's Rule and lock in their power and of course if

you believe in freedom and democracy this is an issue because someone in power even if they're good they could still stay in power but you still need the freedom and democracy to be able to choose this is why the Free World must Prevail so there is so much at stake here that this is why everyone is not taking this into account so let me know what you thought about situational awareness I do apologize for making this video so long but I'm glad I made this video so long because there was still a lot that I

looked at that you know is not going to be covered in this video if you do want to watch the podcast I will leave a link to the video in the description where there is a 4-Hour podcast with Leo abrena and of course dra crash Patel in which they have an interview that is remarkably insightful like it's really really good because they just talk about a lot of stuff that you really should know so um if there was anything I missed in this video let me know what you guys think because I think this is

probably going to be uh the piece of information that stays with me for the longest time because I'll be constantly revisiting this document to see if some of these predictions are coming true and where things are lining up