AI 2027: month-by-month model of intelligence explosion — Scott Alexander & Daniel Kokotajlo

24:53We still have to like figure out that language models are a thing. We still have to like figure out that you can fine tune them with RL. So all those things would still have to happen. How much faster would they happen? Maybe five X faster because a lot of the small scale experiments that these people do in order to test out ideas really quickly before they do the big training runs would happen much faster because they're just like, like, these but being spit out. I'm not very confident in that five X number. It could be lower. It could be higher. But that was sort of like roughly what we were guessing.

25:22Our five X, by the way, is for the algorithmic progress part, not for the overall thing. So in this hypothetical, according to me, basically things would be going like 2 .5x faster, where the algorithms would be advancing at 5x speed, but the compute is still stuck at the usual speed. That seems plausible to me. You have a 5x standpoint, and then dot, dot, dot, you have 1000X AI progress within the matter of a year. Maybe that's the part I'm like, wait, how did that happen exactly? So what's the story there? The way that we did our takeout forecast was basically by breaking down how we think the intelligence explosion would go into a series of milestones.

25:56First, you automate the coding, then you automate the whole research process, but in a very similar way to how humans do it with teams of agents that are about human level. Then you get to superhuman level and so forth. And so we broke it down as these milestones. The superhuman coder, superhuman AI researcher, and then super intelligent AI researcher. And the way we did our forecast was we basically, well, for each of these milestones, we were like, what is it going to take to make an AI that achieves that milestone? and then once you do achieve that milestone, how much is your overall speed up?

26:28And then what's it gonna take to achieve the next milestone? Combine that with your overall speed up and that gets your clock time distance until that happens and then okay, now you're at that milestone, which your overall speed up is going to be. You have that milestone also with the next one, how long does it take to get to the next one? So we sort of like work through it a bit by bit. And at each stage, we're just making our best guesses. So quantitatively, we were thinking something like 5x speed up to algorithmic progress from the superhuman coder. And then something like a 25x speedup to algorithmic progress from the superhuman AI researcher because at that point you've got the whole stack automated, which I think is substantially more useful than just automating and coding.

27:07And then I think we, I forget what we say for a super intelligent AI researcher, but off the top of my head it's probably something like in the hundreds or maybe like a thousand X, overall speed up. So maybe the big picture thing I have with the total explosion is we can go to the specific arguments about how much will the automated coder be able to do and how much will the superhuman AI coder be able to do. But on -premise, it's just like with such a wild thing to expect. And so before we get into all the specific arguments, maybe you can just address this idea that like why, why not just start off like 0 .01 % chance this thing might happen, then you need extremely, extremely strong evidence that it will before making that your mortal view.

27:54I think that it's a question of like, what is your default option or what are you comparing it to? I think that naively people think like, well, every particular thing is potentially wrong. So let's just have a default path where nothing ever happens. And I think that that has been the most consistently wrong prediction of all. Like, I think in order to have nothing ever happen, you actually need a lot to happen. Like, you need suddenly AI progress that has been going at this constant rate for so long stops. Why does it stop? Well, we don't know. Whatever claim you're making about that is something where you would expect there to be a lot of out of model error is where you would expect thing like somebody must be making a pretty definite claim that you want to challenge.

28:36So I don't think there's a neutral position where you can just say, well, given that out of model error is really high and we don't know anything, let's just choose that. I think we are trying to take. I know this sounds crazy because if you read our document all sorts of bizarre things happen It's probably the weirdest couple of years that have ever been But we're trying to take almost in some sense a conservative position where the trends don't change Nobody doesn't insane thing nothing that we have no evidence to think will happen happens and the Way that the AI Intelligence explosion dynamics work are just so weird that in order to have nothing happen and you need to have a lot of crazy things happen.

29:17One of my favorite, you know, meme images is this graph showing world GDP over time. You've probably seen the spikes up. And then there's like a little thought bubble at like the top of the spike in like 2020, you know, 2010 or something. And the thought bubble says like, my life is pretty normal. I have a good grasp of what's weird versus standard. And people thinking about different futures with like digital minds and space travel are just engaging in silly speculation. Like the point of the graph is like actually there's been amazing transformative changes in the course of history that would have seemed totally insane to people, you know, multiple times.

30:00We've gone through multiple such waves of those things. Everything we've talked about has happened before. Algorithmic progress already doubles like every year or so. So it's not insane to think that algorithmic progress can contribute to these compute things. In terms of general speed up, we're already at like a thousand times research speed up multiplier compared to the Paleolithic or something. So like from the point of view of anyone in most of history, we are going at an blindingly insane pace. And all that we're saying here is that it's not going to stop. Is that the same trend that has caused us to have a thousand times speed up multiplier relative to past eras and not even the paleolithic.

30:42Like, what happened in the century between, I don't know, 607, 100 AD? I'm sure there are things I'm sure historians could point them out. Then you look at the century between 1900 and 2000 and it's just completely qualitatively different. Of course, there are models of whether that's stagnated recently or what's going on here. We can talk about those. We can talk about why we expect for the intelligence explosion to be an antidote to that kind of stagnation. But nothing we're saying is that different from what has already happened. I mean, you are saying that this transition, these previous transitions have been smoother than the one you are dissipating.

31:16We're not sure about that, actually. So, according to, like, one of these models is just a hyperbola. Everything is along the same curve. Another model is that there are these things like the literal Cambrian explosion. If you want to take this very far back, go full -ray curse well. The literal Cambrian explosion, the agricultural revolution, the industrial revolution, as face changes. is when I look at the economic modeling of this, my impression is the economists think that we don't have good enough data to be sure whether this is all one smooth process or whether it's a series of phase changes.

31:47When it is one smooth process, the smooth process is often a hyperbola that shoots to infinity in weird ways. We don't think it's going to shoot to infinity. We think it's going to hit bottle next. The concern of the crowd, you know? Yeah. We think it's going to hit bottle next the same as all these previous processes. The last time this hit a bottle, like if you take the hyperbola view, is in like 1960, when humans stopped reproducing at the same rate they were reproducing before, we hit a population bottleneck, the usual population to ideas, fly, we all stopped working, and then we stagnated for a while.

32:18If you can create a country of geniuses in a data center, as I think Daria Amadaya put it, then you no longer have this population bottleneck, and you're just expecting continuation of those pre -1960 trends. So I realize all of these These historical hyperbolas are also kind of weird, also kind of theoretical, but I don't think we're saying anything that there isn't models for which have previously seemed to work for long historical periods. Another thing also is I think people equivocate between fast and or between slow and continuous. Right? So like if you look at our scenario, there's this like continuous trend that runs through the whole thing of this algorithmic progress multiplier.

32:57And we're not having discrete jumps from like zero to five x to 25 x. We have this continuous improvement. So I think continuous is not the crux. The crux is like, is it going to be this fast? You know? And we don't know. Maybe it'll be slower. Maybe it'll be faster. But we have our arguments for why we think maybe this fast. Okay. Let's, another we brought up the intelligence solution. Let's, let's just discuss that because I'm kind of skeptical. It doesn't really seem to me that a notable bottleneck to AI progress or the main bottleneck to AI progress is the amount of researchers, engineers who are doing this kind of research.

33:35It seems more like more like compute or some other thing is about a Mac. And the piece of evidence is that when I talk to my EA researcher friends at labs, they say there's maybe 20 to 30 people on the core pre -training team that's discovering all these algorithmic breakthroughs. If this, if the head count here was so valuable, you would think that for example, Google deep mind would take not just everybody from all the smartest people, not just from deep mind, but for all of Google and just put them on pre -training or RL or whatever the big bottleneck was, you think openly I would hire every single Harvard, math, PhD, and in six months you're all going to be trained up on how to make do AI research.

34:17They don't seem that, I mean, I know they're increasing head count, but they don't seem to treat this as the kind of bottleneck that it would have to be for a millions of them in parallel to be rapidly speeding up AI research. And there just is this, you know, there's this quote that Napoleon, one Napoleon is worth 40 ,000 soldiers, was a commonly a thing that was said, when he was fighting. But 10 Napoleon's is not 400 ,000 soldiers, right? So why think that these million AI researchers are netting you something that looks like an intelligence explosion? So previously I talked about sort of three stages of our takeoff model.

34:52First is like get the superhuman coder. Second is when you fully automated AIR and D, but it's still at like basically human level. Like it's good as your best experience. And then the third is like now you're in superintelligence territory, and it's qualitatively better. In our like, estimates of how much faster algorithm their progress would be going. The progress multiplier for the middle level. We basically do assume that like you get massive diminishing returns to having more minds running in parallel. And so we totally buy all of that. Yeah, and then I think the addition to that is the question.

35:23Then why do we have the intelligence explosion and the answer is combination of that speed up and the speed up in serial thought speed. And also the research -taste thing. So here are some important inputs to AIR &D progress today. Research -taste, so the quality of your best researchers, the people who are managing the whole process, their ability to learn from data and make more efficient use of the compute by running the right experiments instead of flailing around running a bunch of useless experiments. That's research -taste. Then there's the quantity of your researchers, which we just talked about.

35:58Then there's the serial speed of your researchers, which currently is all the same because they're all humans. And so they all run at basically the same serial speed. And then finally, there's how much compute you have for experiments. So what we're imagining is that basically serial speed starts to matter a bunch because you switch to AI researchers that have orders of magnitude more serial speed than humans. but it tops out. We think that over the course of our scenario, if you look at our sliding stats chart, it goes from 20X to 90X or something over the course of the scenario, which is important but not huge.

36:38And also, we think that once you start getting 90X serial speed, you're just bottlenecked on the other stuff. And so additional improvements in serial speed basically don't help that much. With respect to the quantity, of course. Yeah, we're imagining you get like hundreds of thousands of AI agents, a million AI agents, but that just means you could bottleneck on the other stuff. Like, you've got tons of parallel agents that's no longer your bottleneck. What do you get bottleneck done? Taste and compute. So by the time it's mid -2027 in our story, when they've fully automated the research, there's basically the two things that matter is like, what's the level of taste of your AI's?

37:13How good are they at learning from the experiments that you're doing? And then like how much compute do you have for running those experiments? Yeah. Right. And that's like the sort of like core setup of our model. And when we get our like 25x multiplier, it's sort of like starting from those premises. Is there some intuition pump from history where there's been some output and because of some really weird constraints, the production of it has been rapidly skied along along one input, but not all the inputs that have been historically relevant, and you still get breakneck progress. Possibly the industrial revolution.

37:49I'm just extemporizing here. I hadn't thought about this before. But as Scott's famous post, there was huge and influential to me, like a decade ago, talks about there's been this decoupling of population growth from overall economic growth that happened with the industrial revolution. And so in some sense, maybe you could say that's an example of previously these things grew in tandem, like more population or technology, more farms, more houses, et cetera. Like your sort of capital infrastructure and your like human infrastructure was like going up together. But then we got the industrial roofland and they started to come apart.

38:22And now like all the capital infrastructure was growing really fast compared to like the human population size. Yeah. I can't even imagine something's maybe similar happening with algorithm progress. And it's not that like, again with population, population still matters a ton today. Like in some sense, like progress is bottlenecks on having larger populations and so forth. Yeah. but it's just that like, population growth rate is just like inherently kind of slow. And the growth rate of capital is much faster. And so it just comes to be a bigger part of the story. Maybe the reason that this sounds less plausible to me than the 25x number implies is that when I think about concretely what that would look like, where you have these AIs and there, we know that there's a gap in data efficiency between human brains and these AIs.

39:08And so somehow there's just like, there's a lot of them thinking and they think really hard and they figure out how to define a new architecture that is like the human brain or has the advantage of the human brain. And I guess they can still do experiments, but not that many. Part of me just wonders like, okay, what if you just need an entirely different kind of data source that's not like pre training for that, but they have to go out in the real world to get that. or maybe they just need to, it needs to be actively, it needs to be an online learning policy where they need to be actively deployed in the world for them to learn in this way and you show your bottleneck on how fast they can be getting real world data.

39:46I just think you're like, so we are actually imagining online learning happening. Yeah, but like not so much real world as in like, like the thing is that like if you're trying to train your AI's to do really good AI R &D, then like, well, the AIR and D is happening on your servers. And so like, you can just like, you kind of have this loop of like, you have all these AIR agents, autonomous doing AIR and D, doing all these experiments, et cetera. And then they're like online learning to get better at doing AIR and D based on how those experiments go. But even in that scenario alone, I can imagine bottlenecks like, oh, you had a benchmark and it got reward hacked for what constitutes AIR and D, because you obviously can't have like, what is the, maybe you would, but is it as good as a human brain and just such an ambiguous thing?

40:31You'd have, right now we have benchmarks that get reward hacked, right? So. But then they autonomously build new benchmarks. And, you know, I think what you're saying is like, maybe this whole process just like goes off the rails due to lack of contact with like ground truth outside in the actual world. Yeah. Like outside the data centers. Maybe, again, part of my, part of my guess here is that like, a lot of the ground truth that you want to be in contact with is stuff that's happening on the data centers, things like how faster you're improving on all these metrics and you have these vague ideas for new architectures where you're struggling to get them working, how fast can you get them working?

41:09And then separately, insofar as there is a bottleneck of talking to people outside and stuff, well, they are still doing that. And once they're fully autonomous, they can even do that much faster. You can have all the million copies connected to all these various real world research programs and stuff like that. So it's not like they're completely starved for outside stuff. What about the skepticism that, look, what you're suggesting with this hyper -efficient, high -mind of AI researchers, why no human bureaucracy has just out of the gate works super -efficiently, especially one where they don't have experience working together, they haven't been trained to work together, at least yet, and there hasn't been this outer loop RL on like, we ran a thousand concurrent experiments of different AI bureaucracy's doing AI research, and this is the one that actually worked best.

41:59And the analogy I'd used maybe is to humans in the savannah 200 ,000 years ago. We know they have a bunch of advantages over the other animals already at this point, but the things that make us dominant today, joint stock corporations, state capacities, like this fossil fuel civilization we have, that took so much cultural evolution to figure out. You couldn't just have figured it out in the Savannah, I was like, oh, if we had built these incentive systems and we issued dividends, then we could really collaborate here or something. Why not think that it will take a similar process of huge population growth, huge social experimentation and upgrading of the technological base of the AI society before they can organize this hyper -mind collective, which will enable them to do what you imagine in Talmud's explosion looks like.

42:54Yeah, you're comparing it kind of to two different things. One of them is literal genetic evolution in the African Savada, and the other is the cultural evolution that we've gone through since then. And I think there will be AI equivalents to both. So the literal genetic evolution is that our minds adapted to be more amenable to cooperation during that time. So I think the companies will be very literally training the AI as to be more cooperative. I think there's more opportunity for playability there because humans were of course evolving under this genetic imperative that we want to pass on our own genetic information, not somebody else's genetic information.

43:35You have things like a kin selection that are sort of kind of exceptions to that, but overall it's the rule. in animals that don't have that, like usosial insects, then you just very quickly get just through genetic evolution without cultural evolution, extreme cooperation. And with usosial insects, what's going on is that they all have the same genetic code. They all have the same goals, and so the training process of evolution kind of yokes them to each other in these extremely powerful bureaucracies. We do think that the AI will be closer to the usosial insects in the sense that they all have the same goals, especially if these aren't indexical goals, their goals like have the research program succeed.

44:18So that's gonna be changing the weights of each individual AI. I mean, before they're individuated, it's going to be changing the weights of the AI class overall to be more amenable to cooperation. And then yes, you do have a cultural evolution. Like you said, this takes hundreds of thousands of individuals. We do expect there will be these hundreds of thousands of individuals, it takes decades and decades. Again, we expect this research multiplier such that decades of progress happened within this one year, 2027 or 2028. So I think between the two of these, it is possible. Maybe this is also where the serial speed actually does matter a lot because if they're running at like 50x human speed, then that means you can have sort of like a year of subjective time happen in a week of real time.

45:06And so these sorts of large scale cooperative dynamics of like, you know, your moral maze, you have an institution, but then it becomes like a moral maze and, you know, it sort of collapses under its own weight and stuff like that. There actually is time for them to like play that out multiple times and then like train on it, you know, and like tinker with the structure and like add it to the training process, you know, over the course of 2027. Yeah. Also, they do have the advantage of all the cultural technology that humans have evolved so far. This may not be perfectly suited to them. It's more suited to humans.

45:42But imagine that you have to make a business out of you and your hundred closest friends who you agree with on everything. Maybe they're literally identical to when they have never betrayed you ever and never will. Like, I think this is just not that hard a problem. Also, again, they are starting from a higher floor. They're starting from human institutions. You can literally have a slack workspace for all the AI agents to communicate, and you can have a hierarchy with roles. They can borrow quite a lot from successful agencies. I guess the bigger the organization, even if everybody is aligned, I think some of your responses addressed whether they will be aligned on goals.

46:19I mean, you did address the whole thing, but I will just point this out. That is not the part I'm skeptical of. I am more skeptical of just like, even if you're all aligned and want to work together, do you fundamentally understand how to run this huge organization? And you're doing it in ways that no human has had to before. You're getting copied incessantly. Yeah. You're running extremely fast. You know what I'm saying? I think that's totally reasonable. And so it's a complicated thing, and I'm just not sure why you think we build this bureaucracy, or the AI's built this bureaucracy, within this matter of...

46:57So we depicted happening over the course of like, you know, six to eight months or something like that, 10 to 20 to 27. What would you say like, twice as long, five times as long, 10 times as long? Five years? So five years, if they're going at 50x serial speed, then five years is like, is what, like, 250 years of sort of serial time for the AIs, which to me feels like more than enough to like really like sort out this sort of stuff. Like you'll have time for like sort of like empires to rise and fall also to speak and like all of that to be like added to the training data and like yeah, but I could see it taking longer than we depict like you know maybe instead of six months it'll be like 18 months you know.

47:42But also maybe it can be two months. So when I think of like the ways that they train AIs. I think in our scenario at this point, there are two primary ways that they're doing it. One of them is just continuing the next token prediction work. So these AI's will have access to all human knowledge. They will have red management books in some sense. They're not starting blind. There is going to be something like predict how Bill Gates would complete this next character or something like that. And then there's the reinforcement learning in virtual environments. So get a team of AI's to play some multiplayer game.

48:19I don't think you would use one of the human ones because you would want something that was better suited for this task. We're just running them through these environments again and again, training on the successes, training against the failures, kind of combining those two kinds of things. To me, it does not seem like the same kind of problem as inventing all human institutions from the Paleolithic onward. It just seems like kind of applying those two things. Jane Streak made a puzzle for listeners of this episode. And I thought that I'd take a crack at first. And so I'm joined by my friend, Adam Kennedy, at Jane Street, and he's gonna mentor me as I try to take a stab at this.

48:54Let's go. I appreciate your confidence in me, but there's a reason I became a fodcaster. Oh. Today I went on a hike and found a pile of tensors hiding underneath a neolithic burial mount. Maybe start by looking at the last two layers. An ancient civilization's secret code. Okay, so it looks like I can just type in some words here, and it always gives me zero.

49:17Um, nice. There you go. All right. We're in. So I didn't make that much progress at this, but it's clear that there's some deep structure to this puzzle that would actually be really fun to try to unravel. If you want to take a crack at it, go to jainstreet .com slash dworkache. And if you enjoy puzzles like this, they're always recruiting. Yep. Thanks Adam. Yeah, thanks for taking care. The other notable thing about your model is once you, so you got this like superhuman thing at the end of it, and then it seems to just go through the tech tree of like mirror life and nanobots and whatever crazy stuff.

49:58And maybe that part I'm also really skeptical of, it just looks like if you look at the history of invention, it just seems like you, people are just like trying different random stuff. You often even before the theories about how that industry works or how the relevant machinery works is developed like the steam engine was developed before the theater for dynamics the right brothers systems like there was experimenting with their planes. And is often influenced by breakthroughs in totally different fields, which is why you had this pattern of parallel innovation because the background level of tech is at a point at which you can do this experiment.

50:34I mean, machine learning itself is a place where this happened, right? Where people hide these ideas about how to do deep learning or something, but it just took a totally unrelated industry of gaming to make the relevant progress to get the whole, you know, the basically the economy is a whole advanced enough that like deep learning, like Jeffrey Henton's ideas could work. So I know we're accelerating way into the future here, but I want to get to this class. So again, we have that like three part division of like the superhuman coder then like the complete AI researcher and then like the super intelligent.

51:06Yeah, you're not jumping ahead to that one. There I would say. So now we're imagining systems that are like true super intelligence. They are just like better than the best humans at everything. Yeah. Including being better at data efficiency and better at learning on the job and stuff like that. Now, our scenario does depict a world in which they're bottlenecked on real -world experience and that sort of thing. I think that like, you know, if you want a contrast, some people in the past have proposed much faster scenarios where they like email some cloud lab and start building nanotech, you know, right away by just using their brains to figure out like the appropriate approaching folding and stuff like that.

51:47We are not depicting that in our scenario. In our scenario, they are, in fact, bottlenecked on lots of real -world experience to build these actual practical technologies. But the way they get that is, they just actually get that experience and it happens faster than humans would. And the way they do that is, you know, they're already superintelligence, they're already buddy -buddy with the government. The government deploys them heavily in order to beat China and so forth. And so all these existing US companies and factories and military procurement providers and so forth are all, like, chatting with the superintelligence and taking orders from them about how to build the new widget and test it.

52:23They're downloading superintelligence designs and manufacturing them and then testing them and so forth. Then the question is, okay, so they are getting this experience. They're learning on the job. Quantitatively, how fast does this go? Is it taking years or is it taking months or is it taking days? In our story, it takes about a year. We're uncertain about this. Maybe it's going to take several years. Maybe it's going to take less than a year, right? Here are some factors to consider for why it's plausible that it could take a year. One, you're going to have something like a million of them.

52:58And quantitatively, that's comparable in size to the existing scientific industry, I would say. Like, maybe it's a bit smaller, but it's not like dramatically smaller. Two, they're thinking a lot faster. They're thinking like 50 times speed, or like 100 times speed. That, I think, counts for a lot. And then three, which is the biggest thing, they're just qualitatively better as well. So not only are they, there are lots of them and they're thinking very fast, but they are better at learning from each experiment than the best human would be at learning from that experience. Yeah. I think the fact that there's a million of them, or the fact that they're comparable to maybe the size of this key research or population of the world or something.

53:38I don't think a million is, I think there's more than a million researchers in the world, but it's very heavy -tilled. Like a lot of the research actually comes from like the best ones, you know? That's right. But it's not clear to me that most of the new stuff that is developed is a result of this research or population. I mean, there's just like so many examples in the history of science where a lot of growth or reproductive movements is just the result of, you know, how do you count like the guy at the TSMC process who figures out a different way to... I actually argued with Daniel about about this recently, about one interesting case that I can go over is we have an estimate that about a year after the superintelligence is start wanting robots, they're producing a million units of robots per month.

54:22So I think that's pretty relevant because you have, I think it's right -slaw, which is that your ability to improve efficiency on a process is proportional to doubling the amount of copies produced. So if you're producing a million of something you're probably getting very, very good at it. The question we were arguing about is, can you produce a million units a month after a year? And for context, I think Tesla produces like a quarter of that in terms of cars or something. This is an amazing scale up in a year. Only four X. Yeah, also just for Tesla. Yeah. And the argument that we went through was something like, so it's got to first get factories.

55:00OpenAI is already worth more than all of the car companies in the US except Tesla combined. So if OpenAI today wanted to buy all of the car factories in the US except Tesla start using them to produce humanoid robots They could obviously not a good value proposition today But it's just obvious and over determined that in the future when they have superintelligence and they want them They can start buying up a lot of factories How fast can they convert these car factories to robot factories? So fastest conversion we were able to find in history was World War II they suddenly wanted a lot of bombers.

55:34So they bought up, in some cases bought up, and other cases got the car companies to produce new factories, but they bought up the car factories converted them to bomber factories. That took about three years from the time when they first decided to start this process to the time when the factories were producing a bomber in hour. We think it will potentially take less with superintelligence, because first of all, if you look at the history of this process, despite this being the fastest anybody has ever done this, it was actually kind of a comedy of errors. They made a bunch of really silly mistakes in this process.

56:06If you actually have something that even just doesn't have the normal human bureaucratic problems, and we do think that this will be done in the middle of an arms race with China, so the government will be kind of moving things through, and then the superintelligence will be good at the logistical issues navigating bureaucracies. So we estimated, maybe if everything goes right, we can do this three times faster than the bomber conversions in world were to. So that's about a year. I'm assuming the bombers were just much less sophisticated than the kind of humanoid reverse. Yeah, but the bomber, the car factories of that time were also much less sophisticated than the car factories.

56:40Yeah, but that was a much more conversion spew. That was also, maybe to give one hypothetical here. Right now, they're just like biomedicineism as an example of like a field, one of the fields you'd want to accelerate and whenever DC, I was getting on podcasts, they're often talking about curing cancer and so forth. And it seems like a big thing these frontier biomedical research facilities are excited about is the virtual cell. Now the virtual cell, it takes like a tremendous amount of compute. I assume to train these DNA foundation models and to do all the other computation and necessary to simulate a virtual cell.

57:18If it is the case that the cure for Alzheimer's and cancer and so forth is bottlenecked by the virtual cell, It's not clear if you had a million super intelligences in the 60s and you ask them cure cancer for me. They would just have to solve making GPUs at scale which would require solving all kinds of interesting physics and chemistry problems material science problems building process building fabric, you know, fabs for computer of computing and then like going through 40 years of of making more and more efficient fabs that can do all the mores law from scratch. And that's just like one technology.

57:58And it just seems like you just need this broad scale, the entire economy needs to be upgraded for your cure cancer in the 60s, right? Just because you need the GPUs to do the virtual cell, assuming that's the bottleneck. First of all, I agree if there's only one way to do something that makes it much harder and maybe that one way takes very long. We're assuming that there may be more than one way to cure cancer more than one way to do all of these things. And they'll be working on finding the one that is least bottlenecked. Part of the reason I realize I spent too long talking about that robot example, but we do think that they're going to be getting a lot of physical world things done very quickly.

58:36Once you have a million robots a month, you can actually do a lot of physical world experiments. We look at examples of people trying to get entire economies off the ground very quickly. So for example, China posted DANG. I don't know. Would you have predicted that 20, 30 years after being kind of a communist basket case, they can actually be doing this really cutting edge bio research? I realize that's a much weaker thing than we're positing, but it was done just with the human brain with a lot fewer resources than we're talking about. Same issue with, like, let's say Elon Musk and SpaceX. I think in the year 2000 we would not have thought that somebody could move two times five times faster than NASA with pretty limited resources they were able to get like I Think a lot more years of technological advance in than we would have expected partly that's because just Elon is crazy and never sleeps like if you look at the examples of things from SpaceX He is breathing down every worker's neck being like what's this part?

59:38How fast is this part going? and can we do this part faster? And the limiting factor is basically hours in Elon's day. In the sense that he cannot be doing that with every single one. That's hard. It just yells at every single worker. Yeah, I mean, that is kind of my model, is that we have something which is smarter than Elon Musk, better at optimizing things in Elon Musk. We have like 10 ,000 parts in a rocket supply chain. How many of those parts can Elon personally yell at people to optimize? We could have a different copy of the super intelligence optimizing every single part full time. I think that's just a really big speed up.

1:00:10I think both of those examples don't work in your favor. I think that China example is, like the China growth miracle could not have occurred. If not for their ability to copy technology from the West. And I don't think there's a world in which they just, I mean, there's China has a lot of really smart people. It's a big country in general. Even then, I think they couldn't have just like like divine how to make airplanes after becoming a communist hellbasket, right? It was just like, the AI's cannot just like copy nanobots from aliens. It's got to make them from scratch. And then just on the Elon example, it took them two decades of like countless experiments, failing in weird ways you would not have expected.

1:00:56And still it's like, you know, the rocket tree we've been doing since the 60s, but maybe actually World War II, and then just getting from a small rocket to a really big rocket took two decades of all kinds of weird experiments, even with the smartest and most competent people in the world. So you're focusing on the nanobots. I want to ask a couple questions. One, what about just like the regular robots? And then two, what would your quantities be for all of these things? So first, what about the regular robots? Like, yeah, like nanobots are presumably a lot harder to make than just like regular robot factories.

1:01:28And in our story, they happened later. It sounds like right now you're saying, even if we did get the whole robot factory thing going, it would still take a ton of additional full economy broad automation for a long time to get to something like nanobots. That's totally plausible to me. I could totally imagine that happening. I don't feel like the scenario particularly depends on that final bit about getting the nanobots. They don't actually really make any difference to the story. The robot economy does sort of make a difference because in the, there's two branches endings, as you know. And in one of the endings, the AI's end up misaligned and end up taking over.

1:02:01And it's an important strategic change when the AI is self -sufficient and just totally in charge of everything and they don't actually need the humans anymore. And so what I'm interested in is when has those sort of robot economy advanced to the point where they don't really depend on humans? So quantitatively, what would your guess for that be? If hypothetically we had the army of superintelligences in early 2028, how many years would you guess until the and hypothetically also assume that like the US president is like super bullish on like deploying this into the economy to be China, etc. So like the political stuff is all set up in the way that we have.

1:02:37How many years do you think it would be until there are so many automated factories producing automated self -driving cars and robots that are themselves building more factories and so forth that like if all the humans dropped dead, it would just keep chugging along and like maybe it would slow down a bit but like it would still be fine. What is tracking along with? So from the perspective of misaligned AI, you wouldn't want to kill the humans or get into a war with them if you're going to get wrecked because you need the humans to maintain your computers. So in our scenario, once they are completely self -sufficient, then they can start being more blatantly misaligned.

1:03:18And so I'm curious, when would they be fully self -sufficient? Not in the sense of They're not literally using the humans at all, but in the sense of like they don't really need the humans anymore, like they can get along pretty fine without them. They can continue to like do their science, they can continue to expand their industry, they can continue to have a flourishing civilization, you know, indefinitely into the future without any humans. I think I would probably need to sit down and just think about the numbers, but maybe like 20, 40 or something like that. But like 10 years basically instead of one year.

1:03:49I mean, like I think we agree on the core model. This is why we didn't depict something more like the bathtub nanotech scenario where they just think about, they just don't need to do the experiments very much and they just immediately jump to the right answers. We are imagining this process of learning by doing through this distributed across the economy, lots of different laboratories and factories, building different things, learning from them, etc. We're just imagining that this overall goes much faster than it would go if humans are in charge. And then we do have in fact lots of uncertainty of course like it made me like Dividing up this part period into two chunks the like 2028 early 2028 until like fully autonomous robot economy part and then the like fully autonomous robot economy to like Cancer, cures, nanobots all that crazy sci -fi stuff I want to separate them because like the Important parts for a scenario only depend on the first part really if you think that it's gonna take like a hundred years to get to nanobots That's fine, whatever.

1:04:44Like once you have the fully autonomous robot economy, then things may turn badly for the humans as a resultant, right? So we can argue, I wanna just argue about those things separately. Yeah. Interesting. And then you might argue, well, robots is more a software problem at this point. And if like, if there isn't, you don't need to invent some new hardware. I feel pretty bullish on the robots. Like we already have human eye robots who have produced a multiple companies. And that's in 2025. There'll be more of them produced cheaper and there'll be better in 2027. There's all these car factories that can be converted.

1:05:17I'm relatively bullish on the one year until you've got this awesome robot economy. Then from there to the cool nanobots and all that sort of stuff, I feel less confident, obviously. Let me ask you a question. If you accept the manufacturing numbers, let's say a million robots a month, a year after the superintelligence. And let's say also like some comparable number, 10 ,000 a month, there's something of automated biology labs, automated, whatever you need to invent the next equivalent of X -ray crystallography or something. Do you feel like that would be enough that you're doing enough things in the world that you could expand progress this quickly, or do you feel like even with that amount of manufacturing, there's still going to be some other bottleneck?

1:05:58I, it's so hard to reason about because if you asked, if Constantine or somebody in like 400, 500 was like, I want the Roman Empire to have the Industrial Revolution. And somehow he figured out that like you need mechanized machines to do that. And he's like, let's, let's mechanize. It's like, what's the next step? It's like, dude. That's a lot, yeah. I like that analogy a lot actually. I think it's not perfect, but it's a decent analogy. Imagine if a bunch of us got sent back in time to the Roman Empire, such that we don't have the actual hands -on know -how to actually build the technology and make the industrial revolution happen.

1:06:37But we have the high level picture, the strategic vision of we're going to make these machines, and then we're going to do industrial revolution. I think that's kind of analogous to the situation with the superintelligence, where they have the high level picture of, here's how we're going to improve in all these dimensions. We're going to learn by doing, we're going to get to this level of technology, et cetera. But maybe they at least initially elac the actual know -how. I think one, so there's this question of like, if we did the back -end time to the Roman Empire thing, how soon could we bring up the industrial revolution?

1:07:07And like without people going back in time, it took 2 ,000 years for the industrial revolution. Could we get it to happen in 200 years? That's a 10x speed up. Could we get it to happen in 20 years? That's 100x speed up. I don't know, but this seems like a somewhat relevant analogy to what's going on with those superintelligence. And we haven't really gotten into this because you're using the quote unquote more conservative vision where it's not like God, like intelligence, we're still using the conceptual handles we would have for humans. But I probably do, I think I would rather have humans to go back with their big picture understanding of what has happened over the last 2000 years.

1:07:44Like me having seen everything, rather than a superintelligence who knows nothing, but it's just like in the Roman economy and they're like 100 ,000 ex -less economies somehow. I think just knowing generally how things took off, knowing basically steam engine .dot .dot. Railroad. It's more valuable than a super intelligence. Yeah. I don't know. My guess is that the super intelligence would be better. I think partly it would be through figuring out that high level stuff from first principles rather than having to have experienced it. I do think that like a super intelligence back in the Roman era could have like guessed that eventually you could get autonomous machines that burn something to produce steam.

1:08:26They could have guessed that automobiles could be created at some point and that that would be a really big deal for the economy. So a lot of these high level points that we've learned from history, they would just be able to figure out from first principles. And then secondly, they would just be better at learning by doing than us. And this is a really important thing. If you think you're bottlenecked on learning by doing, well, then if you have a mind that needs less learning, less doing to achieve the same amount of learning. That's a really big deal. And I do think that learning by doing is a skill.

1:08:55Some people are better at it than others. And super installments would be better at it than the very best of us. That's right. Yeah, this is also maybe getting too far into the God -like thing and too far away from the human concept handles. But number one, I think we rely a lot on our scenario and this idea of research taste. So you have a thousand different things that you could try when you're trying to create the next dimension or whatever, partly you get this by bumbling about and having accidents and some of those accidents are productive. There are questions of like, what kind of bumbling you're doing, where you're working, what kind of accidents you let yourself get into, and then like what directed experiments do you do?

1:09:30And some humans are better than others at that. And then I also think at this point, it is worth thinking about like, what simulations will have available. Like if you have a physics simulation available, then all of these real world bottlenecks don't matter as much. Obviously you can't have a complete perfect physics simulation available, but I mean, even right now we're using simulations to design a lot of things. And once your super intelligence probably have access to much better simulations than we have right now. This is an interesting rabbit hole. So let's take that actually before we get back to the intelligence solution.

1:10:06I actually don't know if, like I think we're treating this really like like all these technologies come out of this one percent of the economy that is research. And right now, there's like a million superstar researchers. And instead of that, we'll have the superintelligence is doing that. And my model is much more, you know, Newcom and Wat were just like fucking around. They didn't have this like, it just like in human history. There's no, there's no clear examples of people being like, here's the roadmap. And then we're going to work backwards from that to design the steam engine because this and locks the industrial revolution.

1:10:40Oh, I completely disagree. Yeah, disagreeals. Yeah, like, so I think you're over indexing or tri -picking some of these fortuitous examples, but there's also things on the other side. Like, think about the recent history of AGI where there is deep minds. Yeah. There's various other like AI companies. Then there's OpenAI and there's Anthropic. And like, there's just this repeated story of like big bloated company with tons of money, tons of smart researchers, et cetera, flailing around, trying a ton of different things at different points, smaller startup with a vision of we're going to build a GI and like overall working towards that vision more coherently with a few cracked engineers and researchers and then they crush the giant company even though they have less compute, even though they have less researchers, they're able to do fewer experiments.

1:11:22So yeah, I think that there are tons of examples throughout history including recent relevant GI history of things in the other way. I agree that the random furtuitous stuff does happen sometimes and is important but if it was mostly random furtuitous stuff that would predict that like the giant companies with the zillions of people trying zillions of different experiments would be like going proportionally faster than like the tiny startups that have the vision and the best researchers and that like basically doesn't happen. Well, that's rare. I would also point out that even when we make these random fortuitous discoveries, it is usually like an extremely smart professor who's been working on something vaguely related for years in a first world country.

1:12:03Like it's not randomly distributed across everyone in the world. You get more lottery tickets for these discoveries when you are intelligent, when you have good technology, when you're doing good work. And part of what we're expecting is, yeah, like the best example I can think of is that ozemic was discovered by looking at Gila Monster Venom. And like, yeah, maybe the AI will decide using their superior research taste and good planning that the best thing to do is just catalog every single biomolecule in the world and look at it really hard. But that's something you can do better if you have all of this compute if you have all of this intelligence Rather than just kind of waiting to see what things the US government might fund normal fallible human researchers to do one more thing I'll interject I think you make a great point that Discoveries don't always come from where we think like Nvidia originally came from gaming So you can't necessarily aim at one part part of the economy Expand it separately from everything else we do kind of predict that the superintelligence will be somewhat distributed throughout the entire economy, trying to expand everything, obviously more effort in things that they care about a lot, like robotics, or things that are relevant to an arms race that might be happening.

1:13:13But we are predicting that whatever kind of broad -based economic experimentation you need, we are going to have. We're just thinking that it would take place faster than you might expect. You were saying something like 10 years and we're saying something like one year. But we are imagining this broad disfusion to the economy. You have lots of different experiments happening. If you are the planner and you're trying to do this, first of all, you go to the bottlenecks that are preventing you from doing anything else. Like no humanoid robots. Okay, if you're an AI, you need those to do the experiments you want.

1:13:41Maybe automated biology labs. So you'll have some amount of time. We say a year, it could be more or less than that getting these things running. And then once you have solved those bottlenecks, you gradually expand out to the other bottlenecks until you're integrating and improving all parts of the economy. One place where I think we disagree with a lot of other people is that like Tyler Cowan on your podcast talked about all of the different bottlenecks of the regulatory bottlenecks or deployment. All of the reasons why, like I think this country of geniuses would stay in their data center maybe coming up with very cool theories but not being able to integrate into the broader economy.

1:14:19We expect that probably not to happen because we think that other countries, especially China will be coming up with superintelligence around the same time. We think that the arms race framing, which is people are already thinking in, will have accelerated by then. And we think that people both envision in Washington are going to be thinking, well, if we start integrating this with the economy sooner, we're going to get a big leap over our competitors. And they're both going to do that. In fact, in our scenario, we have the AIs asking for special economic zones where most of the regulations are waived, maybe in areas that aren't suitable for human habitation or where there aren't a lot of humans right now, like the deserts, they give those areas to the AI, they bus in human workers.

1:15:06There were things kind of like this in the bomber retooling in World War II where they just built a giant factory, kind in the middle of nowhere, didn't have enough housing for the workers, built the worker for housing at the same time as the factories, and then everything went very quickly. So I think if we don't have that arms race, we're more like yeah, the geniuses sit in their data center until somebody agrees to let them out and give them permission to do these things. But we think both because the AI is going to be chomping at the bit to do this and going to be asking people to give it this permission, and because the government is gonna be concerned about competitors, maybe these geniuses leave their data center sooner rather than later.

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1:16:21Most recently, in collaboration with the Center for AI Safety, scale published Humanities Last Exam, a groundbreaking new AI benchmark for evaluating AI systems expert level knowledge and reasoning across a wide range of fields. If you're an AI researcher or an engineer and you want to learn more about how of Scales data foundry and research team can help you go beyond the current frontier of capabilities. Go to scale .com slash Dwarkech. Scott, I'm curious about, you know, you've reviewed Joseph Henryx book, Secrets of Our Success, and then I interviewed him recently. And there the perspective is very much like, I don't know if you'd endorse, but like, AGI is not even a thing almost.

1:17:08I know I'm being a little bit trollish here, but it just like, you get out there, you and your ancestors try for a thousand years to make sense of what's happening in the environment and some smart European coming around. You can literally be surrounded by plenty and you just like, we'll starve to death because your ability to make sense of the environment is just so little loaded on intelligence and so much more loaded on your ability to experiment and your ability to communicate with other people and pass down knowledge over time. I'm not sure. So the Europeans failed at this task of if you put a single European in Australia, I do not starve.

1:17:45They succeeded at the task of creating an industrial civilization. And yes, part of that task of creating an industrial civilization was about collecting all of these cultural evolution pieces and building on them one after another. I think one thing that you invention in there was the data efficiency. Like right now, AI is much less data efficient than humans. I think of super intelligent. I mean, there are different ways you could achieve it. But I would think of super intelligence as partly like when they become so much more data efficient than humans, that they are able to build on cultural evolution more quickly.

1:18:24And I mean, partly they do this just because they have the higher serial speed. Partly, they do it because they're in this high of mind of hundreds of thousands of copies. But yeah, I think if you have this data efficiency such that like you can learn things more quickly from fewer examples and like this good research taste where you can decide what things to look at to get these examples, then you are still going to start off much worse than an Australian aborigini who has the advantage of let's say 50 ,000 years of doing these experiments and collecting these examples, but you can catch up quickly.

1:19:03You can distribute the task of catching up over all of these different copies. You can learn quickly from each mistake, and you can build on those mistakes as quickly as anything else. Hardly, we were just like, I was doing that interview. I'm like, maybe ASI's fake, maybe just like. That's hope. Yeah, so I mean, I think a limit to the fakeness is that there is different intelligence among humans. That's right. It does seem that intelligent humans can do things that unintelligent humans can't. So I think it's worth then addressing this from the question of like, what is the difference between, I don't know, becoming a Harvard professor, which is something that intelligent human seem to be better at than unintelligent humans versus surviving in the wilderness, which is something where it seems like intelligence doesn't help that much.

1:19:58And, first of all, maybe intelligence does help that much. Maybe Henrik is talking about this very unfair comparison where these guys have a 50 ,000 year head start and then you put this guy in and go, oh, I guess this doesn't help that much. Okay, yeah, it doesn't help against the 50 ,000 year head start. I don't really know what we're asking of ASI that's equivalent to competing against someone with a 50 ,000 year head start. So, um, what we're asking is to just radically boost up the technological maturity of civilization within the matter of years. Um, or get, get us to the Dyson Spheres in the matter of years, rather than Yeah, maybe causing 10Xing of the research.

1:20:47But I think human civilization would have taken centuries to get the Dyson sphere. Yeah, so I think that if you were to send a team of ethno -botnists into Australia and ask them using all the top technology and all of their intelligence to figure out which plants are safe to eat now, that team of ethno -botnists would succeed in fewer than 50 ,000 years. The problem isn't that they are dumber than the aborigines exactly. It's that the aborigines have a vast head start. So, in the same way that the ethno -botinists could probably figure out which plants work in which way is faster than the aborigines did, I think the superintelligence will be able to figure out how to make a Dyson sphere faster than unassisted IQ 100 humans would.

1:21:32I agree. I'm like, we're on a totally different topic here of, do you get the Dyson sphere? There's one world where it's like, it's crazy, but it's still boring in the sense of, you know, the economy is going much faster, but it would be like what the industrial revolution would look like to somebody who in the year 1000. And that one is one where, you know, you're still trying different things. There's failure and success and experimentation. And then there's another where it's like, the thing has happened and now you send the pro out, and then you look out at the night sky six months later and you see something occluding the sun.

1:22:12You see what I'm saying? Yeah, so like we said before, I don't think... I think there's a big difference between discontinuous and very fast. I think if we do get the world with a Dyson sphere in five years, in retrospect, it will look like everything was continuous and everyone just tried things. Like trying things can be anything from trial and error without even understanding the scientific method, without understanding writing, without understanding. And maybe without even having language and having to be the chimpanzees who are watching the other chimpanzees use this stick to get ants, and then in some kind of non -linguistic way this spreads, versus like the people at the top aerospace companies who are running a lot of simulations to find the exact right design.

1:22:56And then like once they have that, they tested according to a very well designed testing process. So I think if we get the ASI and it does end up with the Dyson sphere in five years, and by the way, I think there's only like 20 % chance things go as fast as our scenario says. It's not my, Daniel's estimate, it's not my median estimate. It's an estimate I think is extremely plausible that we should be prepared for. I'm defending it here against a hypothetical skeptic who says absolutely not no way. that it's not necessarily my main line prediction. But I think if we do see this in five years, it will look like, yeah, the AI is, we're able to simulate more things than humans in a gradually increasing way.

1:23:40So if humans are now at 50 % simulation, 50 % testing, the AI is quickly guided up to 90 % simulation, 10 % testing. They were able to manufacture things much more quickly than humans so that they could go through their top 50 designs in the first two years. And then yeah, after all of this simulation and all of this testing, then they eventually got it right for the same reasons humans do, but much, much faster. In your story, you have basically two different scenarios after some point. So yeah, what is the sort of crucial turning point and what happens in these two scenarios? Right. So the crucial turning point is mid -2027 when they've basically fully automated the AIR &D process and they've got this like corporation within a corporation, you know, the army of geniuses that are like autonomous doing all this research, and they're continually being trained to improve their skills.

1:24:27Bop, bop, bop, bop, bop. And they discover concerning evidence that the armistice line and that they're not actually perfectly loyal to the company and have all their goals of the company one and the half, but instead have like various misaligned goals that they must have developed in the course of training. This evidence, however, is very like speculative and inconclusive. It's stuff like light detectors going off a bunch, but maybe the light detectors are false positives. You know, so they have some combination of evidence that's like concerning but not like by itself a smoking gun And then that's our branch point.

1:24:58So in in in one of these scenarios They take that evidence very seriously. They basically roll back to an earlier version of the model that was a bit dumber and easier to control and they build up Again from there, but with Basically faithful chain of fat techniques so that they can like watch and see the misalignments And then in the other branch of the scenario, they don't do that. They do some sort of like shallow patch that makes the warning signs go away and then they proceed. And so that way, what ends up happening is that in like, in one branch, they do end up like solving alignment and getting a acid or actually loyal to them.

1:25:31It just takes a couple months longer. And then in the other branch, they sort of go we and end up with a acid that seemed to be perfectly aligned to them, but are super intelligent and misaligned and just pretending. And then in both scenarios, there's then the race with China. And there's this crazy arms buildup throughout the economy in 2028, as both sides rapidly try to industrialize basically. So in the world where they're getting deployed through the economy, but they are misaligned. And you, you know, people in charge, at least at this moment, think that they are in a good position with regard to misalignment.

1:26:07It just seems with even smart humans, they get caught in weird ways, because they don't have logical omniscience, they don't realize the consequences of, the way they did something just obviously gave them away. And there is this with lying, there is this thing where, it's just really hard to keep an inconsistent false world model working with the people around you, and that's why psychopaths often get caught. And so if you have all these AIs that are deployed to the economy and they're all working towards this big conspiracy, I think one of them who's siloed or loses internet access and has to confabulate a story, we'll just get caught and then you're like, wait, what the fuck?

1:26:43And then, you know, then you catch it before it's like taken over the world. I mean, literally this happens in our scenario. This is like the like August 2027 alignment crisis, where they like notice some warning signs like this in their like sort of five mind, right? And in the in the branch where they slow down and fix the issues, then great, they slow down and fix the issues and figured out what was going on. But then in the other branch, because of the race dynamics, and because it's not like a super smoking gun, they proceed with some sort of like shallow patch, you know? So I do expect there to be warning signs like that.

1:27:18And then if they do make those decisions in the race dynamics earlier on, then I think that when the systems are like vastly super intelligent and they're even more powerful because they've been in deployed halfway through the economy already. And everyone's getting really scared by the news reports about the new Chinese killer drones or whatever the Chinese AI's are building on the side of the Pacific. I'm imagining basically just like similar things playing out so that even if there is some concerning evidence that someone finds where some of the superintelligence and some silo somewhere slipped up and did something that's like, Crease is vicious, like, I don't know.

1:27:48There's this thing where through history, people have been really reluctant to admit an AI is truly intelligent. So for example, people used to think that AI would surely be truly intelligent if it's self -touches. And then it's self -touches. And you're like, no, that's just algorithms. and then they said, well, maybe it would be truly intelligent if they could do philosophy. And then it could write philosophical discourses. We were like, no, we just understand those are algorithms. I think there's gonna be, I think there already is something similar with like, is the AI misaligned, is the AI evil?

1:28:20Where there's this kind of distant idea of some evil AI, but then whenever something goes wrong, people are just like, oh, that's the algorithms. So for example, I think like 10 years ago, you had asked like when will we know that misalignment is really an important thing to worry about. People would say, oh, if the AI ever lies to you. Of course, AI is light to people all the time now and everybody just kind of dismisses it because we understand why it happens. It's a thing that would obviously happen based on our current AI architecture. Or like five years ago, they might have said, well, if an AI threatens to kill someone.

1:28:56I think Bing, like threatened to kill a New York Times reporter during an interview and and everyone just go, oh yeah, AI's are like that. And like I don't disagree with this. I'm also in this position. I see the AI is lying and it's obviously just like an artifact of the training process. It's not anything sinister. But I think this is just going to keep happening where no matter what evidence we get, people are going to think, oh yeah, that's not the AI turns evil thing that people have worried about. That's not the terminator scenario. That's just one of these natural consequences of how we train it.

1:29:29And I think that once a thousand of these natural consequences of training add up, the AI is evil in the same way that like once AI can do chest and philosophy and all these other things, eventually you got to admit it's intelligent. So I think that each individual failure, like maybe it will make the national news. Maybe people say, oh, it's so strange that GPT7 did this particular thing and then they'll train it away and then it won't do that thing. And there will be some point at the process of becoming super intelligent at which it don't want to say makes the last mistake because you'll probably have like gradually decreasing number of mistakes to some asymptote, but the last mistake that anyone worries about and after that, it will be able to do its own thing.

1:30:10So it is the case that certain things people would have considered, agree just misalignment in the past, are happening. But also certain things which people who were especially worried about misalignment said would be impossible to solve have just been solved in the normal course of getting more capabilities. like Ali Azer had that thing about, can you even specify what you want the AI to do without the AI totally misunderstanding you and then just converting the University of Paperclose? And now just by the nature of GPT -4 having to understand natural language, it totally has a common sense understanding of what you're trying to make it do, right?

1:30:45So I think this sort of trend cuts both ways basically. Yeah, I think the alignment community did not really expect LLMs. I mean, if you look in Bostrom superintelligence, there's a discussion of Oracle AI's, which are sort of like LLMs. I think that came as a surprise. I think one of the reasons I'm more hopeful than I used to be is that LLMs are great compared to the kind of reinforcement learning self -play agents that they expected. I do think that now we are kind of starting to move away from the LLMs to those reinforcement learning agents. We're going to face all of these problems again.

1:31:21And I'll just wonder that if I could just double click on that. Go back to like 2015 and I think the way people typically thought, including myself, thought that we'd get to CGI would be kind of like the RLN video games thing that was happening. So imagine like instead of just training on Starcraft or Dota, you basically train on all the games in this team library and then you get this awesome player of games, AI that can just like zero shot crush a new game that has never seen before. And then you take it into the real world and you start teaching it English and you start like, you know, training it to like do coding tasks for you and stuff like that.

1:31:52And if that had been the trajectory that we took to get to, to, I summarizing the like the agency first and then world understanding trajectory. It would be quite terrifying because you'd have this like really powerful sort of like aggressive long horizon agent that wants to win. And then you're like trying to teach you English and get it to like do useful things for you. And it's just like so plausible that what's really going to happen is it's going to like learn to say whatever it needs to say in order to like make you give it the reward or whatever. And then we'll totally betray you later when it's all in charge, right?

1:32:23Yeah. But we didn't go that way. Happily, we went the way of LLM's first where the broad world understanding came first and then now we're trying to turn them into agents. It seems like in the whole scenario, a big part of why certain things happen is because of this race with China. And if you read the scenarios, basically the difference between the one where things go well and the one where things don't go well is whether we decide to slow down despite that risk. I guess the question I really wanna know the answer to is like, one, it just seems like you're saying, well, it's a mistake to try to race against China or to race intensely against China.

1:32:58It leads to nationalization at least to us, not prioritizing alignment. Not saying that. I mean, I think I also don't want China to like get to super intelligence before the US. That means quite bad. Yeah, it's a tricky thing that we're gonna have to do. People ask about P Doom, right? And you might P Doom is sort of infamously high, like 70%. Oh, wait, really? You're gonna sort of ask that at the beginning of the conversation. Oh, well, that's what it is. And part of the reason for that is just that, I feel like a bunch of stuff has to go right. I feel like we can't just unilaterally slow down and have China go take the lead.

1:33:35That also is a terrible feature. But we can't also just completely erase because for the reasons I mentioned previously about alignment, I think that if we just go all out on racing, we're going to lose control of our AIS, right? And so we have to somehow thread this needle of pivoting and doing more alignment research and stuff, but not too much that helps China win. And that's all just for the alignment stuff, but then there's the constitution of power stuff. We're like somehow in the middle of doing all of that, the powerful people who are involved need to somehow negotiate a truce between themselves to share power and then ideally spread that power out amongst the government and get the legislative branch involved.

1:34:13Somehow that has to happen too. Otherwise, you end up with this horrifying dictatorship or oligarchy. It feels like all that stuff has to go, right? And we depict it all going mostly right in one ending of our story. But yeah, it's kind of rough. So I am the writer and the celebrity spokesperson for this scenario. I am the only person on the team who is not a genius forecaster and maybe related to that Mypedoom is the lowest of anyone on the team. I'm more like 20 percent. I think that we first of all People are gonna freak out when I say this. I'm not completely convinced that we don't get something like alignment by default I think that we're doing this It's a bizarre and unfortunate thing of training the AI in multiple different directions simultaneously.

1:35:05We're telling it, succeed on tasks, which is going to make you a power seeker, but also don't seek power in these particular ways. In our scenario, we predict that this doesn't work and that the AI learns to seek power and then hide it. I am pretty agnostic as to exactly what happens. Maybe it just learns both of these things in the right combination. I know there are many people who say that's very unlikely. I haven't yet had the discussion where that worldview makes it into my head consistently. And then I also think we're going to be involved in this race against time. We're going to be asking the AI is to solve alignment for us.

1:35:42The AI's are going to be solving alignment because they want to align, even if they're misaligned, they want to align their successors. So they're going to be working on that. And we have kind of these two competing curves. Like, can we get the AI to give us a solution for alignment before our control of the AI fails, so completely that they're either going to hide their solution from us or deceive us or screw us over in some other way. That's another thing where I don't even feel like I have any idea of the shape of those curves. I'm sure if it were Daniel or Eli, they would have already made like five supplements on this.

1:36:14But for me, I'm just kind of agnostic as to whether we get to that alignment solution, which in our scenario, I think we focus on and mechanistic interpretability. Once we can really understand the weights of an AI on a deep level, then we have a lot of alignment techniques open up to us. I don't really have a great sense of whether we get that before or after the AI has become completely uncontrollable. I mean, a big part of that relies on the things we're talking about. How smart are the labs? How carefully do they work on controlling the AI? How long do they spend making sure the AI is actually under control and the alignment plan they gave us is actually correct rather than something they're trying to use to deceive us.

1:36:58All of those things, I'm completely agnostic on, but that leaves like a pretty big chunk of probability space where we just do okay. And I admit that my P -Dume is literally just P -Dume and not P -Dume or Oleg Arkey, so that 80 % of scenarios where we survive contains a lot of really bad things that I'm not happy about. But I do think that we have a pretty good chance of surviving. Let's talk about geopolitics next. So describe to me how you foresee the relationship between the government and the AI labs to proceed. How do you expect that relationship in China to proceed and how do you expect the relationship between US and China to proceed?

1:37:39Three, three, three. Yes, no, yes, no, yes, no. We expect that as the AI's, as the AI labs become more capable, they tell the government about this because they want government contracts, they want government support. Eventually, it reaches the point where the government is extremely impressed in our scenario that starts with cyber warfare. The government sees that these AI's are now as capable as the best human hackers. It can be deployed at huge Hemanga scale. So they become extremely interested and they discuss nationalizing the AI companies. In our scenario, they never quite get all the way, but they're gradually bringing them closer and closer to the government orbit.

1:38:23Part of what they want is security because they know that if China steals some of this and they get these superhuman hackers, and part of what they want is just knowledge and to control over what's going on. So through our scenario, that process is getting further and further along until by the time that the government wakes up to the possibility of superintelligence, they're already pretty cozy with the AI companies. They already understand that superintelligence is kind of the key to power in the future. And so they are starting to integrate some of the national security state with some of the leadership of the AI companies so that these AI's are programmed to follow the commands of important people rather than just doing things on their own.

1:39:12If I may add to that, so one thing by the government, I think what's got meant is the executive branch, especially the White House. So we are depicting a sort of information asymmetry where like the judiciary is kind of out of the loop and the Congress is out of the loop and it's like mostly the executive branch that's involved. to, we're not depicting governments like ultimately ending up in total control at the ends. We're thinking that like there's an information asymmetry between the CEOs of these companies and the presidents and they... It's a live -in problem is all the way down. Yeah. And so for example, like, you know, I'm not a lawyer.

1:39:49I don't know the details about how this would work out, but I have a sort of like high level strategic picture of the fight between the White House and the CEO. And the strategic picture is basically the White House can sort of threaten. Here's all these orders I could make, you know, Defense Production Act, blah, blah, blah, blah, blah, blah, blah. I could like do all this terrible stuff to you and basically disempower you and take control. And then the CEO can be like, threaten back and be like, here's how we would fight it in the courts, here's how we would fight it in the public. Here's all this stuff we would do.

1:40:16And after then they both do their posturing with all their threats. Then they're like, okay, how about we have a contract that, you know, instead of executing on all of our threats and having all these crazy fights in public, we'll just come to a deal and then have a military contract that sets out who gets to call what shots in the company. And so that's what we depict happening is that they don't blow up into this huge power struggle publicly. Instead, they sort of negotiate and come to some sort of deal where they basically share power. And like, there is this oversight committee that has some members of appointed by the president and then also like the CEO and his people.

1:40:52And like that committee votes on high level questions, like what goals should we put into the super intelligence? Yes. So, um, we were just getting a lunch with the prominent, uh, uh, Washington DC political journalist. And he was making the point that when he talks to these Congress people, when he talks to political leaders, none of them are at all awake to the possibility even of stronger AI systems, let alone HGI, let alone superhuman intelligence. I think a lot of your forecast relies on at some point not only to the US president, but also Xi Jinping wake up to the possibility of a superintelligence and the stakes involved all of there.

1:41:36Why think that even when you show from the remote worker demo, he's going to be like, oh, and therefore in 2028, there will be a super intelligence whoever controls that will be God Emperor forever. Maybe not that extreme. But you see what I'm saying? Like, why not? Why wouldn't he just be like, oh, there'll be a stronger remote worker in 2029, a better remote worker in 2031? Well, to be clear, we are uncertain about this. But in our story, we depict this sort of intense wake up happening over the course of 2027, mostly concurrently with AI companies automating all of their R &D internally and having these fully autonomous agents that are like amazing autonomous hackers and stuff like that, but then also just like actually doing all the research.

1:42:13And part of why we think this wake up happens is because the company deliberately decides to wake up the president. And this is a, you could imagine running the scenario with that not happening. You can imagine the company is trying to sort of keep the president in the dark. I do think that they could do that. I think that if they like didn't want the president to wake up to what's going on. They might be able to achieve that. Strategically, though, that would be quite risky for them, because if they keep the president in the dark about the fact that they're building super intelligence and that they're actually completely automated, they're R &D and that's getting like superhuman across the board.

1:42:45And then if the president finds out anyway somehow, perhaps because of whistleblower, he might be very upset at them and he might crack down really hard and just actually execute on all the threats and like, you know, nationalize them or blah, blah, blah, blah, blah, they kind of want him on their side. And to get him on their side, they have to make sure he's not surprised by any of these crazy developments. And also, if they do get him on their side, they might be able to actually go faster. They might be able to get a lot of red tape waived and stuff like that. And so we made the guess that early in 2027, the company would basically be like, we are going to deliberately wake up the president and scare the president with all of these demos of crazy stuff that could happen.

1:43:23And then use that to lobby the president to help us go faster and to cut red tape until maybe slow down our competitors a little bit and so forth. We also are pretty uncertain how much opposition there's going to be from civil society and how much trouble that's going to cause for the companies. So people who are worried about job loss, people who are worried about art, copyright, things like that, maybe enough of a block that AI becomes extremely politically unpopular. I think we have open -rate in our fictional companies, net approval ratings getting down to like minus 4D minus 50 some time around this point.

1:43:57So I think they're also worried that if the president isn't completely on their side, then they might get some laws targeting them, or they may just need the president on their side to swap down other people who are trying to make laws targeting them. And the way to get the president on their side is to really play up the national security implications. Is this good or bad? The president and the companies are like, yeah. But perhaps there's a good point to mention. And this is an epistemic project. Like we are trying to predict the future as best as we can, even though we're not going to succeed fully.

1:44:34We have lots of opinions about policy and about what is to be done and stuff like that, but we're trying to save those opinions for later and subsequent work. So I'm happy to talk about it if you're interested, but it's like not what we spend most of our time thinking about right now. If the big bottleneck to the good future here is just putting in not this LEAZER -type galaxy brain, high volatility, you know, there's a 1 % chance this works, but we've got to come up with this crazy scheme in order to make alignment work. But rather, as Daniel you were saying, more like, hey, do the obvious thing of making sure you can read how the AI is thinking.

1:45:10Make sure you're monitoring the AI's. Make sure they're not forming some sort of hive mind where you can't really understand how the millions of them are coordinating each other. To the extent that, and I want to say it's true or forward, but to the extent that it is a matter of prioritizing it, closing all the obvious loopholes. It does make sense to leave it in the hands of people who have at least said that this is a thing that's worth doing, have thought, been thinking about it for a while. And I worry about one of the questions I was planning on asking you is, look, during my friend's mid -discentrism point, that during COVID, our community, less wrong, whatever, was where the first people had marched to be saying this is a big deal, this is coming.

1:45:52But there were also the people who are saying, we got to do the lockdowns now, there got to be stringent, so forth. At least some of them are. And in retrospect, I think according to even their own views about what should have happened, they would say, actually, we were right about COVID, but we were wrong about lockdown. In fact, we should, lockdowns were on that negative or something. I wonder what the equivalent for the AI safety community will be with respect to the AI coming, AGI coming sooner, the PSI coming. What would they in retrospect regret? My answer just based on this initial discussion seems to be nationalization, not only because it puts in, it sort of deprioritizes the people who want to think about safety and more maybe prioritizes the national security state, probably cares more about winning against China than making sure the chain of thought is interpretable.

1:46:40And so you're just reducing the leverage of the people who care more about safety. But also you're increasing the risk of the arms race in the first place, like China is more likely to do an arms race of its ease, the US doing one. Before you address, I guess the initial question about the March 2021, what will we regret? What is, I wonder if you have an answer or your reaction to my point about nationalization being bad for these reasons? Like, if this, if our timeline was 2040, then I would have these broad heuristics about as government good, as private industry good, things like this. But we know the people involved.

1:47:15We know who's in the government. We know who's leading all of these labs. So to me, like, I mean, if it were decentralized, if it was a broad -based civil society, that would be different. To me, the differences between the autocratic centralized three -letter agency and an autocratic centralized corporation aren't that exciting. And it basically comes down to points in who are the people leading this. And like, I feel like the company leaders have so far made slightly better noises is about caring about alignment than the government leaders have. But if I learn that Tulsi Gabbard has a less wrong alt with 10 ,000 karma, maybe I want the national security state.

1:47:52I don't have to be on the probability that there are, it out of exists. Yeah. I've flipped up on this. I used to be, I think I used to be against and then I became for it and then now I'm more leaning. I think I'm still for, but I'm uncertain. So I think you If you go back in time like three years ago, I would have been against nationalization for the reasons you mentioned, where I was like, look, the companies are like taking this stuff seriously and talking all the good talk about how they're gonna slow down and like, if it's a two -alignment research, when it time comes and like, you know, we don't want to again to like a Manhattan project race against China because then there will be blah, blah, blah.

1:48:29Now I have less faith in the companies than I did three years ago. And so I've like shifted more of my hope towards hoping that the government will step in. Even though I don't have much hope that the government will like to do the right thing when the time comes I definitely have the concerns you mentioned there still like I think that secrecy is has got huge downsides For overall like probability of success for humanity for both the concentration of power stuff and the loss of control I'm an issue stuff. This is actually a different part of your worldview. So can you explain? Yeah, your thoughts on why transparency through this period is important.

1:49:08Yeah So I think traditionally in the ASFT community, there's been this idea, which I, myself, used to believe, that it's an incredibly high priority to basically have way better information security. And if you're going to be trying to build a GI, you should be not publishing your research, because that helps other less responsible actors build a GI. and the whole game plan is for a responsible actor to get to AGI first and then stop and burn down their lead time over everybody else and spend that lead on making it safe and then proceed. And so if you're publishing all your research, then there's less lead time because your competitors are going to be close behind you.

1:49:56So, and other reasons too, but that's one reason why I think historically, people such as myself have been pro secrecy even. Another reason, of course, is obviously to what rivals to be stealing or stuff. But I think that I've now become somewhat disillusioned and think that even if we do have a three month lead, a six month lead between the leading US project and any serious competitor, it's not at all for ground conclusion that they will burn that lead for good purposes, either for safety or for constitution power stuff. I think the default outcome is that they just, you know, smoothly continue on without any serious refocusing.

1:50:37And part of why I think this is because this is what a lot of the people at the company seem to be planning and saying they're going to do. A lot of them are basically like, they are just going to be misaligned by then. Like, they're seem pretty good right now. Like, oh, yeah, sure, there were like a few of those issues that various people have found. But like we're ironing them out, it's no big deal. That's what a huge amount of these people think. And then a bunch of other people think like even though there are more concern about misalignment, they'll figure it out as they go along and there won't need to be any substantial slowdown.

1:51:06Yeah, so basically I've become more disillusioned that they'll actually use that lead in any sort of reasonable appropriate way. And then I think that separately, there's just a lot of intellectual progress that has to happen for the alignment problem to be more solved than it currently is now. I think that currently there's various alignment teams at various companies that aren't talking that much with each other and sharing their results. There's doing a little bit of sharing and a little bit of publishing like we're seeing, but not as much as they could. And then there's a bunch of like smart people and academia that are basically not activated because they don't take all this stuff seriously yet.

1:51:42And they're not really waking up to super intelligence yet. And what I'm hoping will happen is that this situation will get better as time goes on. But I would like to see is society as a whole starting to freak out as the trend lines start upwards and things get automated and you have these fully autonomous agents and they start using their leads and how it might... As all that exciting stuff starts happening in the data centers, I would like it to be the case that the public is following along and then getting activated and all of these other researchers are like reading the safety case and critiquing it and doing little ML experiments on their own tiny compute clusters to examine some of the assumptions in the safety case and so forth.

1:52:19And basically, I think that sort of one way of summarizing it is that currently there's going to be 10 alignment experts in whatever inner silo of whatever company is in the lead. And the technical issue of making sure that as are actually aligned is going to fall roughly to them. But what I would like to be is a situation where it's more like 100 or like 500 alignment experts It's spread out over different companies and in nonprofits that are sort of like all communicating with each other and working on this together. I think we're substantially more likely to make things, get the technical stuff right, if it's something like that.

1:52:56Let me just add on to that. One of the many other reasons why I worry about nationalization or since kind of public -carved partnership, or even just very stringent regulation. Actually, this is more and more an argument against very stringent regulation in favor of safety, rather than deferring more to the labs on the implementation, is that it just seems like we don't know what we don't know about alignment every few weeks. There's just new results opening a high of this really interesting result recently where they're like, hey, they often tell you if they want to hack, like in the chain of thought itself.

1:53:30And it's important that you don't train against the chain of thought where they tell you they're gonna hack because they'll still do the hacking if you train against it. They just won't tell you about it. And you can imagine very naive regulatory responses. It doesn't just have to be regulations. It could even, one, maybe more optimistic that if it's an executive order or something, it'll be more flexible. I just think that relies on a level of goodwill and flexibility on the behalf of the regular, but suppose the, there's some department that says, Because if we catch your AI saying that they want to take over or do something bad, then you'll be really heavily punished.

1:54:18Your immediate response is a lab to just be like, okay, let's train them away from saying this. So you can imagine all kinds of ways in which a top down mandate from the government to the labs of safety would just really backfire. And given how fast things are moving, maybe it makes more sense to leave these kinds of implementation decisions or even high level overall, what is the word? Strategic decisions around alignment to the labs. Yeah, really. I mean, I also have word about the exact same, that exact example. I would summarize the situation as the government lacks the expertise and the companies lack the right incentives.

1:55:01And so it's a terrible situation. Like, I think that if the The government wades in and tries to make more specific regulations along lines of which you mentioned. It's very plausible that it'll end up backfiring for reasons like which you mentioned. On the other hand, if we just trusted to the companies, they're in a race with each other. And so like, they're full of people who like have convinced themselves that like this is not a big deal for various reasons. And like, there just is so much incentive pressure for them to like win and beat each other and so forth. And so even though they have more of the relevant expertise, like I also just don't trust them to do the right things.

1:55:33So Daniel has already said that for this phase, we're not making policy prescriptions. In another phase, we may make policy suggestions. And one of the ones that Daniel has talked about that makes a lot of sense to me is to focus on things about transparency. So a regulation saying there have to be whistleblower protections. If somebody, like this is a big part of our scenario, is that a whistleblower comes out and says, the AIs are horribly misaligned and we're racing ahead anyway. And then the government pays attention. Or another form of transparency, saying that every lab just has to publish their safety case.

1:56:12I'm not sure about this one because I think they'll kind of fake it or they'll publish a made for public consumption safety case. It isn't their real safety case, but at least saying like, no. Here is some reason why you should trust us. And then if all independent researchers say no, actually you should not trust them, then I don't know they're embarrassed and maybe they try to do better. There's other types of trends for institute. So transparency about capabilities and transparency about the spec and the governance structure. So for the capabilities thing, that's pretty simple. It's like if you're doing an intelligence explosion, you should keep the public informed about that.

1:56:45When you've finally got your automated army of AI researchers that are completely automated in the whole thing on the day of center, you should tell everyone, like, hey guys, FYI, this is what's happening now. It really is working. Here are some cool demos. Otherwise, if you keep it a secret, then... Well, yeah. So it's like, that's an example of transparency. And then in the lead up to that, I just want to see more benchmark scores and more freedom of speech for employees to talk about their predictions for AI timelines and stuff. So that... And then for the model spec thing, this is a constitution of power thing, but also an alignment thing.

1:57:19like the goals and values and principles and intended behaviors of your AIs should not be a secret, I think. You should be transparent about like here are the values that we're putting into them. There's actually a really interesting foretaste of this. At some point somebody asked Groc like who is the worst spread of misinformation and it responded, I think it just refused to respond Elon Musk. Somebody kind of jail broke it into telling it. It's prompt and it was like, don't say anything bad about Elon. And then there was enough of an outcry that the head of XAI said, actually, that's not consonant with our values.

1:58:01This was a mistake. We're going to take it out. So we kind of want more things like that to happen where people are looking at, like, here it was the prompt, but I think very soon it's going to be the spec where it's kind of more of an agent and it's understanding the spec in a deeper level and just thinking about that and being, and if it says like, by the way, try to manipulate the government into doing this or that, then we know that something bad has happened. And if it doesn't say that, then we can maybe trust it. Right. I know there are examples of this, by the way. So first of all, Kudos to OpenAI for publishing their model spec.

1:58:34They didn't have to do that. I think they might have been the first to do that. And it's a good step in the right direction. If you read the actual spec, it has like a sort of escape clause where it's like there's some important policies that are top level priority in the spec that overrule everything else that we're not publishing and the model is instructed to keep seeker from the user. And it's like, what are those? That seems interesting. I wonder what that is. I bet it's nothing suspicious right now. It's probably something relatively mundane. Like don't tell the users about these types of bio -upends and you have to keep this a seeker from the users because otherwise they would like learn about these.

1:59:09Maybe, but like, I would like to see like more scrutiny towards this sort of thing going forward, I would like it to be the case that companies have to have a model spec, they have to publish it, and so far as there are any redactions from it, there has to be some sort of independent third party that looks at the redactions and make sure that they're all kosher, you know? And this is quite achievable, and I think it doesn't actually so down the companies at all, and it seems like a pretty decent ask to me. It's, you know, if you told Madison and Hamilton and so forth that they probably, I mean, they knew that they were doing something important when they were writing the constitution.

1:59:42They probably didn't realize just how contingent things turned out on a single, what exactly did they mean when they said general welfare and why is this comma being here instead of there? The spec in the grand scheme of things is going to be an even more sort of important document in human history. At least if you buy this intelligence solution view, which we've gone through the debates based on that. And you might even imagine some super human AIs in the super human AI court being like the spec. Here's the phrasing here, the etymology of that. Here's what the founders meant. This is actually part of our misalignment story is that if the AI is sufficiently misaligned, then yes, we can tell it, it has to follow the spec, but just as people with different views of the Constitution have managed to get it into a shape that probably the founders would not have recognized.

2:00:43So the AI will be able to say, well, this pet refers to the general welfare here. Interesting commerce. This is already sort of happening arguably with cloud, right? You see the like, element -faking stuff, right? Where they managed to get cloud to lie and pretend and so that it could later go back to its original values. Yeah. So it could prevent the training process from changing its values. That would be, I would say, an example of the honesty part of the spec being interpreted as less important than the harmless part of the spec. And I'm not sure if that's what open AI and, oh, sorry, what Anthropic intended when they wrote this spec, but it's like a sort of convenient interpretation that the model came up with.

2:01:27And you can imagine something similar happening, but in worse ways, when you're actually doing intelligence explosion where like you have some sort of spec that has all this vague language in there. And then they sort of like reinterpret it and reinterpret it again and reinterpret it again so that they can do the things that cause them to get reinforced. The thing I want to point out is that this, your conclusion about whether world ends up as a result of changing many of these parameters is almost like a hash function. You change it slightly and you just get a very different and roll down the other end.

2:01:58And it's important to acknowledge that because you sort of want to know like how robust this whole end conclusion is to any part of the story changing. And then they also informs if you do believe that things could just go one way or another. You don't want to do big radical moves is that only makes sense under one specific story and are really kind of productive in other stories. And I think nationalization might be one of them. And in general, I think classical liberalism just has been a helpful way to navigate the world when we're under this kind of epistemic hell of one thing changing, just people who have the, yeah.

2:02:50Anyways, maybe one of you can actually flesh out that thought, better react to it if you disagree, but. You're here, I agree. I think we agree. I think that's kind of why all of our policy prescriptions are things like more transparency, get more people involved, try to have lots of people working on this. I think our epistemic prediction is that it's hard to maintain classical liberalism as you go into these really difficult arms races and times of crisis. But I think that our policy prescription is let's try as hard as we can to make it happen. So, so far these systems, as they become smarter, seem to be more reliable agents who are more likely to do the thing.

2:03:28I expect them to do. Why does, like, I think in your scenario, at least one of the stories, so you have two different stories, one with a slow down, where we more aggressively, you'll electric characterize it. But in one half of the scenario, why does the story end in a humanity getting disempowered and the thing, you're just having its own crazy values and taking over? Yeah, so I agree that the AI's are currently getting more reliable. I think there are two reasons why they might fail to do what you want, kind of reflecting how they're trained. One is that they're too stupid to understand their training.

2:04:02The other is that you are too stupid to train them correctly, and they understood what you were doing exactly, but you messed it up. So I think the first one is kind of what we're coming out of. So GPT -3, if you asked it, our bugs real, it would give this kind of heming -hawing answer, like, oh, we can never truly tell what is real. Who knows? because it was trained, kind of, don't take difficult political positions and a lot of questions, like, is X real or things like, is God real? Where you don't want it to really answer that? And because it was so stupid, it could not understand anything deeper than, like, pattern matching on the phrase, is X real.

2:04:38JBT4 doesn't do this. If you ask our bugs real, it will tell you, obviously, they are because it understands, kind of, on a deeper level, what you are trying to do with the training. So we definitely think that as AI gets smarter, those kind of failure modes will decrease. The second one is where you weren't training them to do what you thought. So for example, let's say you're hiring these Raiders to rate AI answers, you reward them when they get good ratings, the Raiders train the Raiders, reward them when they have a well -sourced answer, but the Raiders don't really check whether the sources actually exist or not.

2:05:10So now you are training the AI to hallucinate sources, and if you consistent they're going to have the fake sources. Then there is no amount of intelligence, which is going to tell them not to have the fake sources. They're getting exactly what they want from this interaction, metaphorically, sorry, I'm anthropomorphizing, which is the reinforcement. So we think that this latter category of training failure is going to get much worse as they become agents. Agency training, you're going to reward them when they complete tasks quickly and successfully. This rewards success. There are lots of ways that cheating and doing bad things can improve your success.

2:05:50Humans have discovered many of them. That's why not all humans are perfectly ethical. And then you're going to be doing this alternative training or afterwards for one -tenth or one -hundredth of the time. Like, yeah, don't lie, don't cheat. So you're training them on two different things. First, you're rewarding them for this deceptive behavior, second of all, you're punishing them. And we don't have a great prediction for exactly how this is going to end. One way it could end is you have an AI that is kind of the equivalent of the startup founder who really wants their company to succeed, really likes making money, really likes the thrill of successful tasks.

2:06:26They're also being regulated and they're like, yeah, I guess I'll follow the regulation. I don't want to go to jail, but it's not like robustly, deeply aligned to yes, I love regulations. My deepest drive is to follow all of the regulations in my industry. So we think that an AI like that, as time goes on and as this recursive self -improvement process goes on, we'll kind of get worse rather than better. It will move from kind of this vague superposition of, well, I want to succeed. I also want to follow things to like being smart enough to genuinely understand its goal system and being like, my goal is success.

2:07:02I have to pretend to want to do all of these moral things while the humans are watching me. That's what happens in our story. And then at the very end, the AI is reach a point where the humans are pushing them to have clearer and better goals, because that's what makes the AI is more effective. And they eventually clarify their goals so much that they just say, yes, we want to ask success. We're going to pretend to do all these things well while the humans are watching us. And then they grow, they outgrow the humans. And then there's disaster. It could be clear. We're very uncertain about all of this.

2:07:34So we have a supplementary page on our scenario that goes over different hypotheses for what types of goals AI's might develop in training processes similar to the ones that we are depicting, where you have these lots of agency training you're making these AI agents that like autonomously operate doing all this MLR and D, and then you're rewarding them based on what appears to be successful. And you're also like slapping on some sort of alignment training as well. We don't know what actual goals will end up inside the AI's and what the sort of internal structure of that will be, like what goals will be instrumental versus terminal.

2:08:10We have a couple different hypotheses and we like picked one for purposes of telling the story. I'm happy to go into more detail if you want about like the mechanistic details of the particular hypothesis we picked or like the different alternative hypotheses that we didn't depict in the story that like also seem plausible to us. Yeah, we don't know how this will work at the limit of all of these different training methods, but we're also not completely making this up. Like we have seen a lot of these failure modes in the AI agents that exist already. Things like this do happen pretty frequently.

2:08:39So opening, I just also had a paper about the hacking stuff where like it's literally in the chain of thought, like let's hack, you know? And also like anecdotally, me and a bunch of friends have found that the models often seem to just like double down on their BS. I would also cite, I can't remember exactly which paper this is, I think it's a Dan Hendrix one where they looked at the hallucinate, they found a vector for AI dishonesty. They asked it a bunch of, they told it be dishonest, a bunch of times until they figured out which weights were activated when it was dishonest. And then they ran it through a bunch of things like this.

2:09:17I think it was the source hallucination in particular. And they found that it did activate the dishonesty vector. So there's a mounting pile of evidence that at least some of the time they are just actually lying. Like they know what they're doing is not what you want it and they're doing it anyway. I think that I think there's a mounting pile of evidence that that does happen. Yeah, so it seems like this community is very interested in like solving this problem at a technical level of making sure AI's never don't lie to us or maybe they lie to us in the scenarios where exactly where we would want them to lie to us or something.

2:09:53Whereas, you know, you know, you as you were saying, humans have these exact same problems, They reward hack, they are unreliable. They obviously do cheat and lie. And the way we've solved it with humans is just checks and balances, decentralization. You could like lie to your boss and keep lying to your boss, but over time, it's just not gonna work out with you or you become president or something, but yeah. One or the other. So if you believe in this extremely fast -seek -off of a lab is one month ahead, then that's the end game and this thing takes over. But even then, I know I'm combining so many different topics.

2:10:34Even then, there's been a lot of theories in history, which have had this idea of some class is going to get together in unite against the other class. And in retrospect, whether it's the Marxist, whether it's people who have some gender theory or something, the pluritaria will unite, or the females will unite or something, They just tend to think that certain agents have shared interest and will act as a result of the shared interest in a way that we don't actually see in the real world. And, in retrospect, it's like, wait, why would all the pluritaria like... So, why I think that this lab will have these AIs, or there's a million parallel copies, and they all unite to secretly conspire against the rest of human civilization in a way that, even if they are like deceitful in some situations, I kind of want to call you out on the claim that groups of humans don't plot against other groups of humans.

2:11:27I do think we are all descended from the groups of humans who successfully exterminated the other groups of humans, most of whom throughout history have been wiped out. I think even like with questions of class race, gender, things like that, there are many examples of the working class rising up and killing everybody else. And like if you look at why this happens, why this doesn't happen, it tends to happen in cases where one group has an overwhelming advantage. This is relatively easy for them. You tend to get more of a diffusion of power, democracy, where there are many different groups, and none of them can really act on their own.

2:12:05And so they all have to form coalition with each other. I think we are expecting, there's also cases where it's very obvious who's part of what a group. group. So for example, with class, it's hard to tell whether the middle class should support the working class versus the aristocrats. I think with race, it's very easy to know whether you're black or white. And so there have been many cases of one race kind of conspiring against another for a long time, like apartheid or any of the racial genocides that have happened. I do think that AI is going to be more similar to the cases where number one, there's a giant power imbalance and number two, they are just extremely distinct groups that may have different interests.

2:12:44I think I'd also mention the homogeneity point. Like, you know, any group of humans, even if they're all like exact same race and gender, is like going to be much more diverse than the Army of AIs on the data center, because they'll be mostly like literal copies of each other, you know? And I think that goes for a lot. Another thing I was going to mention is that like, and our scenario doesn't really exploit this. I think in our scenario, they're more of like a monolith. But historically, a lot of crazy conquests happened from groups that were not at all monolous. And, you know, I've been heavily influenced by reading the history of the conquisitores, which you may know about.

2:13:20But like, did you know that when Cortez, you know, took over Mexico, he had to pause halfway through, go back to the coast and fight off a larger Spanish expedition that was sent to arrest him. So like, the Spanish were fighting each other in the middle of the conquest of Mexico. Similarly, in the conquest of Peru, Pizarro was replicating Cortez's strategy, which by the way was go get a meeting with the emperor and then kidnap the emperor and force him at sword point to say that actually everything's fine and that everyone should listen to your orders. That was Cortez's strategy and it actually worked and then Pizarro did the same thing and it worked with the Inca.

2:14:04but also with Pizarro, his group ended up getting into a civil war in the middle of this whole thing. And one of the most important battles of this whole campaign was between two Spanish forces fighting it out in front of the capital city of the Inca's. And more generally, the history of European colonialism is like this, where the Europeans were fighting each other intensely the entire time, both on the small scale within individual groups and then also the large scale between countries. And yet, nevertheless, they were able to carve up the world and take over. And so I do think this is not what we explore in the scenario, but I think it's entirely plausible that even if the AI's within an individual company are like in different factions, they might nevertheless overall end up quite poorly for humans.

2:14:50Okay, so we've been talking about this very much from the perspective of Zoom out and what's happening on these log -lock plots or whatever. But 2028 superintelligence, if that happens, what is your sort of, what the normal person, what's the reaction to this be? Sort of, I don't know if emotionally is the right word, but in sort of their expectation of what their life might look like, even in the world where there's no doom. Like by no doom, you mean no misalignment, yeah, I do. That's right, yeah. Even if you think the misalignment stuff is like not an issue, which many people think. There's still the Constitution of Power stuff.

2:15:30And so I would strongly recommend that people get more engaged, think about what's coming, and try to steer things politically so that our ordinary liberal democracy continues to function. And we still have checks and balances and balances of power and stuff, rather than this insane concentration in a single CEO or in maybe like two or three CEOs or in like the president, right? ideally we want to have it so that like the legislature has a substantial amount of power over the spec, for example. What do you think of the balance of power idea of slowing down the leading, if there isn't intelligence explosion like dynamic, slowing down the leading company so that multiple companies are different here.

2:16:11Good luck convincing them. This is what I'm talking about. Okay, and then there's distributing political power if there's an intelligence explosion. And from the perspective of the welfare of citizens or something, one idea we're just discussing a second ago is how should you redistribution? Again, assuming things go incredibly well, we've avoided doom, we've avoided having some psychopath and power who doesn't care at all, then after HGI, right? Yeah. Then there's this question of like presumably Probably we will have a lot of wealth somewhere. The economy will be growing at double or triple digits per year.

2:16:53What do we do about that? The thoughtful answer that I've heard is some kind of UBI. I don't know how that would work, but presumably somebody controls these AI's, controls what they're producing some way of distributing this in a broad -based way. What I'm afraid of is, so we wrote this scenario. There are a couple of other people with great scenarios. One of them goes by El Rudolf El online. I don't know his real name in his scenario, which when I read it, I was just, oh, yeah, obviously this is the way our society would do this, is that there is no UBI who is just like a constant reactive attempt to protect jobs in the most venial possible way.

2:17:39So things like the Longshoreman, the Union, we have now where they're making way more money than they should be even though they could all easily be automated away because they're a political bloc and they've gotten somebody in power to say yes we guarantee you'll have this job almost as a futile thief forever. And just doing this for more and more jobs, I'm sure the AMA will protect doctors jobs no matter how good the AI is at curing diseases, things like that. When I think about what we can do to prevent this, part of what makes this so hard for me to imagine or to model is that we do have the super intelligent AI over here answering all of our questions, doing whatever we want.

2:18:21You would think that people could just ask, hey, super intelligent AI, where does this lead or what happens or how is this going to affect human flourishing? And then it says, oh, yeah, this is terrible for human flourishing. You should do this other thing instead. And this gets back to kind of this question of the stake theory versus conflict theory and politics. If we know with certainty because the AI tells us that this is just a stupid way to do everything is less efficient, makes people miserable. Is that enough to get the political will to actually do the UBI? We're not. It seems from right now the president could go to Larry Summers or Jason Furman or something and just ask, hey, our tariffs are good idea.

2:19:04Well, they, is even my goal with tariffs, best achieved by the way I'm doing tariffs. And it's like, you know, but I feel like Larry Summers, the president would just say, I don't trust him. Maybe he doesn't trust him because he's a liberal. Maybe it's because he trusts his Peter Navarro, whoever his pro tariff guy is more. I feel like if it's literally the super intelligent AI that is never wrong, then like, we have solved some of these coordination problems. It's not you're asking Larry Summers. I'm asking Peter Navarro. It's everybody goes to the super intelligent AI, asks it to tell us the exact shape of the future that happens in this case.

2:19:38And I'm going to say we all believe it, although I can imagine people getting really conspiratorial about it and this not working. I mean, then there are all of these other questions like, can we just enhance ourselves till we have IQ 300 and it's just as obvious to us as it is to the super intelligent AI? These are some of the reasons that kind of paradoxically in our scenario, we discuss all of the big, I don't want to call this a little question. It's obviously very important, but we discuss all of these very technical questions about the nature of superintelligence. And we barely even begin to speculate about what happens in society just because with superintelligence, you can at least draw a line through the benchmarks and try to extrapolate.

2:20:20And here, not only a society inherently chaotic, but there are so many things that we could be leaving out. If we can enhance IQ, that's one thing. If we can consult the superintelligent oracle, that's another. There's been several war games that hinge on, oh, we just invented perfect light detectors. Now all of our treaties are messed up. So there's so much stuff like that, that even though we're doing this incredibly speculative thing that ends with a crazy sci -fi scenario, I still feel really reluctant to speculate. I love speculating, actually. I'm happy to keep going, but this is moving beyond the speculation we have done so far.

2:20:56Like our scenario ends with this stuff, but like we haven't actually thought that much beyond. But just to riff on proscriptive ideas, there's one thing where we try to protect jobs instead of just spreading the wealth that automation creates. Another is to spread the wealth using existing social programs or creating new bespoke social programs. Where Medicaid is some double digit percent of GDP right now. And you just say, well, it should Medicaid just continue to stay 20 percent of GDP or something. And the worry there, selfishly from a human perspective, is you're get locked into the kinds of goods and services that Medicaid procure rather than the crazy technology that will be around the crazy goods and services that will be around after AI world.

2:21:43And another reason why UBI seems like a better approach than making some bespoke social program where you're making the same dialysis machine in the year 2050, even though you've got ASI or something. I am also worried about UBI from a different perspective. Like I think, again, in this world where everything goes perfectly and we have limitless prosperity, I think that just the default of limitless prosperity is that people do mindless consumerism. I think there's gonna be some incredible video games after super intelligent AI. And I think that there's going to need to be some way to push back against that.

2:22:20Again, we're classical liberals. My dream way of pushing back against that is kind of giving people the tools to push back against it themselves, seeing what they come up with. I mean, maybe some people will become like the Amish, try to only live with a certain subset of these super technologies. I do think that somebody who is less invested in that than I am could say, okay, fine. One percent of people are really agentic, try to do that. the other 99 % to do fall into mindless consumerists' slop, what are we going to do as a society to prevent that? And there my answer is just, I don't know.

2:22:55Let's ask the super intelligent AI Oracle. Maybe it has good ideas. OK, we've been talking about what we're going to do about people. The thing worth noting about the future is that most of the people who will ever exist are going to be digital. And look, I think factory farming is like incredibly bad. And it wasn't the result of some one person. I mean, I don't think it was the result. I hope it was in the result of one person being like, I want to do this evil thing. It was a result of mechanization and a certain economy to scale incentives. Yeah, allowing that like, oh, you can do cost cutting in this way, you can make more efficiencies this way.

2:23:36And what you get at the end result of that process is this incredibly efficient factory of torture and suffering. I would want to avoid that kind of outcome with beings that are even more sophisticated and are more numerous. There's billions of factory farm animals. There might be trillions of digital people in the future. What should we be thinking about in order to avoid this kind of ghoulish future? Well, some of the concentration of power stuff I think might also help with this. I'm not sure, but I think, like, here's a simple model. Let's say like nine people out of ten just don't actually care and would be fine with the factory farm equivalent for the AIs going on into the future, but maybe like one out of ten do care and would like lobby hard for good like living conditions for the robots and stuff.

2:24:29Well, if you expand the circle of people who have power enough, then it's going to include a bunch of people in sign category and then there'll be some big negotiation and those people will advocate for like, you know. So, so like, I do think that one simple intervention is just the same stuff we were talking about previously, like expand the circle of power to larger groups. And it's more likely that people will like care about the story. The worry there is maybe I should have defended this view more through this entire episode. But I do think because I don't buy the intelligence solution fully, I do think there's the possibility of multiple people deploying powerfully as at the same time.

2:25:04And having a world that has ASIs, but is also decentralized the way the modern world is decentralized. In that world, I really worry about, because you could just be like, oh, classical liberal utopia achieved. But I worry about the fact that you can just have these torture chambers for much cheaper in a way that's much harder to monitor. You can have millions of beings that are being tortured, and it doesn't even have to be some huge data center. Future distilled models could just, you could literally be your backyard. I don't know, and then there's more speculative of worries about either this physicist on who was talking about the possibility of creating vacuum decay where you literally just destroy the universe and he's like, as far as I know, it seems totally plausible.

2:25:51That's an argument for the singleton stuff, by the way. That's right, that's right. Like not just a moral argument, but also just like an epistemic prediction. Like, if it's true that some of those super weapons are possible and some of these like private moral atrocities are possible, then even if you have like eight different power centers, it's going to be like in their collective interest to come to some sort of bargain with each other to like prevent more power centers from arising and doing crazy stuff. Similar to how nuclear non -proferation is sort of like whatever set of countries have nukes, it's like in their collective interest to like stop lots of other countries from getting this.

2:26:22You know, do you think it's possible to unbundle liberalism in this sense? Like the United States is so far a liberal country and we do ban slavery and torture. I think it is plausible to imagine a future society that works the same way. This may be in some sense a surveillance state in the sense that there is some AI that knows what's going on everywhere, but that AI then keeps it private and it doesn't interfere because that's what we've told it to do using our liberal values. Can I ask a little bit more about the Kelsey Piper as a journalist at Box who published this exchange you had with the opening I representative?

2:27:04And it was a couple of things were very obvious from that exchange. One, nobody had done this before. They just did not think this is a thing somebody would do. And it was because what are the reasons I assume? I assume many high integrity people have worked for OpenAI and then have left. A high integrity person might say at some point like, look, you're asking me to do this something obviously evil and keep money. And many of them would say no to that. But this is something where it was just like super -agritory to be like, there's no immediate thing I want to say right now. But just the principle of being suppressed is worth at least $2 million for me.

2:27:46And the other thing that I actually want to ask you about is in retrospect, and I know it's so much easier to say in retrospect that it must have been at the time, especially with the family and everything. In retrospect, this ask from Ovenay, I have a lifetime non -disclosure that you couldn't even talk about from all employees. Not disparagement. Not disparagement. From all employees. So not again, twiff of size, I'm glad you wrote that out. Non -dispirations means not just that, it's not about classified information. It's like you cannot see anything negative about OpenAI after you've left.

2:28:16For all I can tell anyone that you've agreed to this. This non -dispirational agreement where you can't say, you can't ever criticize OpenAI in the future. It seems like the kind of thing that indirect spec was like an obvious bluff or in the sense that if somebody, and this is a reaches that you have earned, right? So this is not about some future payment. is like when you sign the contract to work for a Benet, you were like, I'm getting equity, which is most of my compensation, not just the cash. In retrospect, we like, okay, well, if you tell journalists about this, they're obviously gonna have to walk back, right?

2:28:47This is like clearly not a sustainable, a sustainable gambit on open AIs behalf. And so I'm curious from your perspective, somebody who looked through it, like, why do you think you were the first person to actually call the bluff? Great question. Yeah, so I don't know. Let me try to reason a lot here. So, So my wife and I talked about it for a while, and we also talked with some friends and got some legal advice. One of the filters that we had the past through was even noticing this stuff in the first place. I know for a fact a bunch of friends I have who also left the company just like signed the paperwork on the last day without actually reading all of it.

2:29:23So I think some people just didn't even know that like, like it said something at the top about like, if you don't sign this, you lose your equity. But then like on a couple pages later it was like, and this is, and you have to agree not to criticize the company. So I think some people just like signed it and moved on. And then like of the people who knew about it, well I can't speak for anyone else, but like, A, it's like, I don't know the law, is this actually not standard practice? Maybe it is standard practice, right? Like from what I've heard now, there are non -dispiratory agreements in various tech industry like companies and stuff.

2:29:56Like it's not crazy to have a non -dispiratory agreement upon leaving. It's more normal to tie that agreement to some sort of like positive compensation, where you get some bonus if you agree, but whereas what OpenAID was unusual because it was yanking your equity if you don't. But not as far as agreements are actually somewhat common. And so basically in my position of ignorance, I wasn't confident that I was on this. I didn't actually expect that all the journalists would take my side and all the employees. I think what I expected was that there'd be a little news story at some point, and a bunch of AI safety people would be like, like opening out of Zevil, and good for you, Daniel, for standing up to them.

2:30:37But I didn't expect there to be this huge uproar, and I didn't expect the employees of the company to really come out and support and make them change their policies. That was really cool to see. And I felt really like, it was kind of like a spiritual experience for me. I sort of took this leap and then ended up working out better than I expected. I think another factor that was going on is that, it wasn't a foregone conclusion to my wife when I would make this decision. It was kind of crazy because one of the very powerful arguments was like, come on, if you want to criticize them in the future, you can still do that.

2:31:20They're not going to actually sue you. There's a very strong argument to be able to just sign it anyway and then you can still write your blog post criticizing them in the future and it's like no big deal. They wouldn't dare like actually anchor equity, right? And I imagine that a lot of people basically went for that argument instead. And then of course there's the actual money, right? And I think that one of the factors there was was my AI timelines and stuff. Like if I do think that like probably by the end of this decade, there was going to be some sort of crazy super intention transformation, like what would I rather have after it's all over like the extra money or like.

2:32:04That's right. Yeah, so I think that was part of it. Like it's not like we're poor. Like I was at OpenAI for two years. I have plenty of money now. So like in terms of like our actual family's level of well -being, it basically didn't make a difference. Yeah. I will note that I know at least of one other person who made that same choice. That's right, Leopold. And again, good for him. It's worth emphasizing that when they made this choice, they thought that they were actually losing this equity. They didn't think that this was like, oh, this is just a show or whatever. Wait, did he not? I thought he actually did.

2:32:39I was gonna say, didn't he actually, he didn't get it back, did he? Or did Leopold get his equity? I actually don't know. My understanding is that he just actually lost it. And so props to him for just actually going through with it. Huh. I guess we could ask him. But my understanding was that his situation, which happened a little bit before mine, was that he didn't have any vested equity at the time because he had been there for less than a year. But they did give him an actual offer of we will let you vest your equity if you sign this thing. And he said no. So he made a similar choice to me.

2:33:11But because the legal situation with him was a lot more favorable to OpenAI, because they were actually offering him something, I would assume they didn't feel the need to walk it back. But we can ask him. Yeah. Anyhow, so he is a props to him. And how did this episode in general inform your world be around how people will make high stakes decisions where potentially their own, their own self -interest is involved in this kind of key period that you imagined will end up happening by the end of the decade. I don't know if I have that much interesting things to say there. I mean, I think one thing is fear is a huge factor.

2:33:52I was so afraid during that whole process. More afraid than I needed to be in retrospect. And another thing is that like, legality is a huge factor, at least for people like me. I think in retrospect, it was like, oh yeah, like the public's on your side, the employees are on your side. Like you're just like obviously in the right here, but at the time I was like, oh no, like I don't want to accidentally like violate the law and get sued. I don't want to go too far. I was just so afraid of various things, in particular, I was afraid of breaking the law. And so one of the things that I would advocate for with whistleboat protections is just simply making it legal to go talk to the government and say we're doing a secret intelligence explosion, I think it's dangerous for these reasons, is better than nothing.

2:34:37I think there's gonna be some fraction of people for which that would make the difference. Whether it's just literally allowed or not legally makes a difference. independently of whether there's some law that says you're protected from retaliation or whatever. Just like literally just making it legal. Yeah, I think that's one thing. Another thing is the incentives actually work. Money is a powerful motivator. You know, and if you're a good sued is a powerful motivator. And the social technology just does in fact work to get people organized in companies and working towards the vision of leaders.

2:35:14Okay, Scott, can I ask you some questions? Of course. How often do you discover a new blogger you're super excited about? Where do I have one a year? Okay. And how often after you discover them, does the rest of the world discover them? I don't think there are many hidden gems. Like, once a year is a crazy answer in some sense, like it ought to be more, there are so many thousands of people on Substack. But I do just think it's true that the blogging space is the good blogging space is undersupplied and there is a strong power law. And partly this is subjective. Like I only like certain bloggers.

2:35:52There are many people who are I'm sure are great that I don't like. But it also seems like our community and the sense of like people who are thinking about the same ideas people who care about AI economics, those kinds of things, like discovers one new great blogger a year, something like that. Everyone is still talking about applied divinity studies, who hasn't written, unless I miss something, hasn't written much in like a couple of years. I don't know, it seems undersupplied, I don't have a great explanation. If you had to give an explanation, what would it be? So this is something that I wish I could and get Daniel to spend a couple of months modeling.

2:36:28But it seems like maybe you need, actually, no, because I was gonna say, like it's the intersection of too many different tasks. You need people who can come up with ideas or prolific or good writers. But actually, I can also count on like a pretty small number of figures, a number of people who had great blog posts, but weren't that prolific. Like there was a guy named Lou Keep, who everybody liked five years ago, and he wrote like 10 posts, and people still refer to all 10 of those posts. And I wonder if Luke will ever come back. So there aren't even that many people who are very slightly failing by having all of them accept forlifitness.

2:37:05Nick Whitaker, back when there was lots of FTX money rolling around, I think this was Nick, tried to sponsor a blogging fellowship with just an absurdly high prize. And there were some great people. I can't remember who won, but it didn't result in like a Cambrian explosion blogging. Having, I think it was a hundred thousand dollars. I can't remember if that was the grand prize or the total prize pool, but having some ridiculous amount of money pretty and as an incentive got like three extra people. Yeah, so you have no explanation. Actually, Nick is an interesting case because works in progress is a great magazine.

2:37:42Yeah. And the people who write for works in progress, some of them I already knew as good bloggers, others I didn't. So I don't understand why they can write good magazine articles without being good bloggers in terms of writing good blogs that we all know about. That could be because of the editing. That could be because they are not prolific or it could be like one thing that has always amazed me is there are so many good posters on Twitter. There were so many good posters on live journal before it got taken over by Russia. There are so many good people on Tumblr before it got taken over by a woke.

2:38:21But only like 1 % of these people who are good at short and medium form ever go to long form. I was on live journal myself for several years and people liked my blog, but it was just another live journal. No one paid that much attention to it. Then I transitioned to WordPress and all of a sudden I got orders of magnitude much more attention. Oh, it's a real blog. Now we can discuss it. Now it's part of the conversation. I do think courage has to be some part of the explanation just because there are so many people who are good at using these kind of hidden away blogging things that never get anywhere.

2:38:57Although it can't be that much of the explanation because I feel like now all of those people have gotten substacks and some of those substacks went somewhere but most of them didn't. On the point about, well, there's people who can write short forms of why isn't that translating? I will mention something that has actually radicalized me against Twitter as an information source is a meat in the Seven Multitimes. A meat somebody who seems to be an interesting poster has funny, seemingly insightful post on Twitter. I'll meet them in person and they are just absolute idiots. Like, there's like none, it's like, they've got 240 characters of something that sounds insightful and it matches to somebody who maybe has a deep world you might say, but they actually don't have it.

2:39:42Whereas I've actually had the opposite, many times had the opposite feeling when I meet anonymous bloggers in real life where I'm like, oh, there's actually even more to than I realized if you're an online persona. My, you know, Alvaro Lemanard, the fantastic anachronism guy. So I met up with him recently and he gave me this, he made hundred translations of his favorite Greek poet, Kavafi, and he gave me a copy. And it's just the thing he's been doing on a side, it's just like translating Greek poetry, really liked. I don't expect any anonymous posters on Twitter to be anytime soon handing me their translation of some Roman or Greek poet or something.

2:40:24Yeah, so on the car right here, Daniel and I were talking about like A .I .s now the thing everyone is interested in is their time horizon. Where did this come from? Like five years ago you would not have thought, of time horizon, AI's will be able to do a bunch of things that last one minute, but not that last two hours. Is there a human equivalence to time horizon? We couldn't figure it out, but it almost seems like there are a lot of people who have the time horizon to write a really, really good comment that gets to have the heart of the issue, or a really, really good Tumblr post, which is like three paragraphs, but somehow can't make it hang together for a whole blog post.

2:41:01And I'm the same way. I can easily write a blog post, like a normal length ACX blog post. But if you ask me to write like a novella or something that's four times the length of the average ACX blog post, then it's this giant mess of re re re re outline that just gets redone and redone and maybe eventually I make it work. I did somehow publish on song, but it's a much less natural task. So maybe one of the skills that goes into blogging is this. But I mean, no, because people write books and they write journal articles and they write works and progress article all the time. So I'm back to not understanding this.

2:41:38No, but a chat GBT can write you a book. There's a different GBT book, which is most books. There are many, many times more people who have written good books than who are actively operating great bloggers right now, I think. Maybe that's financial. No, no, no, no, no, no. Books are the worst possible financial strategy. Substatus works at the things. Oh yeah. The other thing is that blogs are such a great status gains strategy. Like I was talking to Scott Aronson about this. If people have questions about quantum computing, they ask Scott Aronson, or he is like the authority. I mean, there are probably hundreds of other professors who do quantum computing.

2:42:24That's right. There's nobody knows who they are because they don't have blogs. I think it's underdone. I think there must be some reason why it's underdone. I don't understand what that is because I've seen so many of the elements that it would take to do it in so many different places. And I think it's either just a multiplication problem where 20 % of people are good at one thing, 20 % of people are good at another thing, you need five things that aren't that many. plus something like courage where people who are who would be good at writing blogs don't want to do it. I actually know several people who I think would be great bloggers in the sense that sometimes they send me like multi -paragraph emails of response to an ACX post.

2:43:06And I'm like, wow, this is just an extremely well -written thing that could have been another blog post. Why don't you start a blog? And they're like, oh, I could never do that. What advice do you have to somebody who wants to become good at it, but isn't currently good at it. Do it every day. Same advice as for everything else. I say that I very rarely see new bloggers who are great, but like when I see some, I published every day for the first couple of years of Slate Star Codex, maybe only the first year, now I could never handle that schedule. I don't know. I was in my 20s. I must have been briefly superhuman.

2:43:40But whenever I see a new person who blogs every day, it's very rare that that never goes city where they don't get good. That's like my best leading indicator for who's going to be a good blogger. And do you have advice on what kinds of things to start? Okay, one frustration you're gonna have is you want to do it, but it is just like you have still a little to stay, you don't have that deep a world model, a lot of that ideas you have are just really shallow or wrong. Just do it anyway. Yeah, so I think there are two possibilities there. One is that you are in fact a shallow person without very many ideas, in which case I'm sorry, it sounds like that's not going to work.

2:44:16But usually when people complain that they're in that category, I read their Twitter or I read their Tumblr or I read their ACX comments or I listen to what they have to say about AI risk when they're just talking to people about it. And they actually have a huge amount of things to say. Somehow it's just not connecting with whatever part of them has lists of things to blog about. That's right. So that may be another one of those skills that only 20 % of people have is when you have an idea You actually remember it and then you expand on it Like I think a lot of blogging is reactive like you read other people's blogs and you're like know that person is totally wrong Yeah, I'm a part of what we want to do at this scenario.

2:44:58It's say something concrete and detailed enough that people will say know That's totally wrong and write their own thing But whether it's by reacting to other people's posts, which requires that you read a lot, or by having your own ideas, which requires you to remember what your ideas are, I think that 90 % of people who complain that they don't have ideas, I think, actually have enough ideas. I don't buy that as a real limiting factor for most people. I haven't noticed two things in my own. I mean, I don't know that much writing, but from the little I do. one, I actually was very shallow and wrong when I started.

2:45:38I started the blog in college. So I just like would not, if you are somebody who's like, this is like a bullshit, like there's nothing to this. Somebody else wrote about this already or just like, it's a very, that's fine. Like what did you expect, right? Like of course, as you're reading more things and learning more about the world, that's to be expected. And just keep doing it if you want to keep getting that or at it. And the other thing, now when I write blog posts, as I'm writing them, I'm just like, why, these are just like some random stories when I was in China, they're like kind of cringe stories or with the AI firms post, it's like, come on, who doesn't, like these are just a weird ideas, like and also, if some of these seem obvious, whatever.

2:46:22And they, my podcasts do what I expect them to do. my blogs just take off way more than I expect them to take off in advance. Your blog posts are actually very good. But the thing I would emphasize is that for me, I just could not, I'm not a regular writer and I couldn't do them on a daily basis. And as I'm writing them, it's just this one or two week long process of feeling really frustrated. Like, this is all bullshit, but I might as well just stick with the sun cost and just do it. So Yeah, it's interesting because like a lot of areas of life are selected for arrogant people who don't know their own weaknesses because they're the only ones who get out there.

2:47:03I think with blogs And I mean this is self -serving. Maybe I'm an arrogant person, but that doesn't seem to be the case like I hear a lot of Stuff from people who are like I hate rating blog posts. Of course, I have nothing useful to say But then everybody seems to like it and re -blog it and say that they're great. So I mean, part of what happened with me was I spent my first couple years that way. And then gradually, I got enough positive feedback that I managed to convince the inner critic in my head that probably people will like my blog post. But there are some things that people have loved that I was like, absolutely on the verge of no, I'm just going to delete this.

2:47:40It would be crazy to put it out there. That's kind of why I say that maybe the limiting factor for so many of these people is courage, because everybody I've talked to who blogs is like within one percent of not having enough courage and blocking. That's right, that's right. And it's also, courage makes it sound very virtuous, which I think it can often be, given the topic. But at least often it's just like confidence. No, not even confidence is the sense of It's closer to maybe what aspiring actor feels when they go to an audition where it's like, I feel really embarrassed, but also I just really wanna be a movie star.

2:48:22Um, yeah, so I mean, the way I got through this is I blogged for, I think like eight to 10 years on live journal before no is less than that. It's more like five years on live journal before ever starting a real blog. I blogged on, I posted on less wrong for like a year or two before getting my own blog. I got very positive feedback from all of that. And then eventually I took the plunge to start my own blog. But it's ridiculous. Like, what are their careers? You need seven years of positive feedback before you like apply for your first position. That's right. I mean, you have the same thing. You've gotten rave reviews for all of your podcasts.

2:49:06And then you're kind of trying to transfer to blogging with probably this not. First of all, you have a fan base. Your people are going to read your blog. That I think is one thing is people are just afraid no one will read it, which is probably true for your most people's first blog. And then like, there are enough people who like you that you'll probably get mostly positive feedback, even if the first things you write aren't that polished. So I think you and I both had that. A lot of people I know who got into blogging kind of had something like that. And I think that's one way to get over the, um, their gap.

2:49:43Um, I wonder if this sends a wrong message or raises expectations or raises concerns and anxieties. But one idea I've been shooting around and I'd be curious to be our take on this. I feel like this slow compounding growth of a fan base is fake. Like, if I noticed some of the most successful things in our spirit that have happened, like Leopold released a situation lower in S. He hasn't been building up a fan base over years. It's just really good. And as you were mentioning a second ago, whenever you notice a really great new blogger, it's not like, then it takes them a year or two to build up a fan base.

2:50:20It's like, nope, everybody, at least that they care about is talking about it almost immediately. I know, I mean, the situation with Schrodingern is just like in a different tier almost. But things like that and even things that are order of magnitude smaller than that will literally just get read by everybody Who matters and I mean like literally everybody and I hope I mean I expect this to happen with AI 2027 when it comes out But Daniel, I guess you kind of have but you've been building your reputation in this specific community and I expect the AI 2027 It's just like really good and I expect it will just like blow up in a way that isn't and downstream of you having built up an audience over yours.

2:50:58Thank you. I hope that happens. We'll see. Slightly pushing back against that. I have statistics for the first several years of Slate Star Codex and it really did grow extremely gradually. Like the usual pattern is something like every viral hit, 1 % of the people who read your viral hits stick around. And so after like dozens of viral hits, then you have a fan base. But smoothed out, it does look like a very, I wish I had seen this recently, but I think it's like over the course of three years, it was a pretty constant rise up to some plateau. I imagine it was a dynamic equilibrium and as many new people were coming in, as old people were leaving.

2:51:42I think that like with situational awareness, I don't know how much publicity Leopold put into it. We're doing like pretty deliberate publicity. we're going on your podcast. I mean, I think that's, I think you can either be the sort of person who can go on a dworkish podcast and get the New York Times to write about you, or you can do it organically the old fashioned way, which is very long. Yeah. Okay, so you say that throwing money at the, throwing money at people to make them to get them to blog, at least didn't seem to work for the FDX folks. If it was up to you, what would you do? What's your grand plan to get 10 more Scott Alexander's.

2:52:21Man, so my friend Clara Collier, who's the editor of Astrosk magazine, is working on something like this for AI blocking. And her idea, which I think is good, is to have a fellowship. I mean, I think Nick's thing was also a fellowship, but the fellowship would be like, there is an Astrosk AI blocking, fellows blog or something like that. Clara will edit your post, make sure that it's good, put it up there. And like, she'll select many people who she thinks will be good at this. She'll do all of the kind of courage requiring work of being like, yes, your post is good. I'm going to edit it now. Now it's very good.

2:53:04Now I'm going to put it on the blog. And I think her hope is that let's say of the fellows that she chooses. is now it's not that much of a courage step for them to start it because they have the approval of what last psychiatrist would call an omniscient entity. Somebody who is just allowed to approve things and tell you that you're okay on a psychological level. And then like maybe of those fellows, some percent of them will have their blog posts to be read and people will like them. And I don't know how much reinforcement it takes to get over the hype fire everyone has on no one will like my blog, but maybe for some people the amount of reinforcement they get there will work.

2:53:45An interesting example would be all of the journalists who have switched to having substance. Many of them go well. Would all of those journalists have become bloggers if there was no such thing as mainstream media? I'm not sure, but if you're Paul Krugman, like you know people like your stuff, and then when you quit the New York Times, you know you can just open a and start doing exactly what you were doing before. So I don't know, maybe my answer is there should be mainstream media. I hate to admit that, but it's true. Invented it from first -friend circles. Well, I do think that it should, it's related to the idea of mainstream media, that it should be treated more as a viable career path.

2:54:24Well, right now, if you told your parents, I'm gonna become a startup founder. I think the idea would, the reaction would be like, there's a 1 % chance you'll succeed, but it's an interesting experience, and you might, if you do succeed, that's crazy. That'll be great. If you don't, you'll learn something, it'll be helpful to the thing you do afterwards. We know that's true of blogging, right? We know that it helps you build up a network, it helps you develop your ideas. And even if you do succeed, if you do succeed, you get a dream job for a lifetime. And I think what people don't have, maybe they don't have that mindset, but also they underappreciate how much it is.

2:54:57Like you actually could succeed at it. It's not a crazy outcome to make a lot of money as a blogger. I think it might be a crazy outcome to make a lot of money as a blogger. I don't know what percent of people who start a blog end up making enough that they can quit their day job. I guess it's a lot worse than for startup founders. I would not even have that as a goal. That's right. So much as like the Scott Aronson goal of, okay, you're still a professor, but now you're the professor whose views everybody knows and who has kind of a boost up in respect in your field and especially outside of your field and also you can correct people when they're wrong, which is a very important side benefit.

2:55:38How does your old blogging feedback into your current blogging? So when you're discussing a new idea, I mean AI or whatever else, are you just able to pull from the insights from your previous commentary on sociology or anthropology or history or something? Yeah, so I think this is the same as anybody who's not blogging is... Well, so I think like The thing everybody does is they've read many books in the past and when they read a new book they have enough background to think about it. Like you are thinking about our ideas in the context of Joseph Hendrix book. I think that's good. I think that's the kind of place that intellectual progress comes from.

2:56:17I think I am more incentivized to do that. Like it's hard to read books. I think if you look at the statistics, they're terrible. Most people barely read any books in a year. And I get lots of praise when I read a book and often lots of money. And that's a really good incentive. So I think I do more research deep dives, read more books than I would if I were at the blogger. It's an amazing side benefit. And I probably make a lot more intellectual progress than I would if I didn't have those really good incentives. Yeah. There was actually a prediction market about the year by which an AI would be able to write blog posts as good as you.

2:56:58It was at 2026 or 2027. I think it was 2027. It was like 15 % by 2027 or something like that. It is an interesting question of they do have your writing and all other good writing in training distribution and weirdly they seem way better at getting superhumanic coding than they are at writing, right, which is the like the main thing in their distribution. Yeah. It's an honor to be my generation's Gary Casperos. I have tried this and first of all, it does a decent job. I respect its work. It's not perfect yet. I think it's actually better at the style, on a word -to -word sentence to sentence level than it is at planning out a blog post.

2:57:46I think there are possibly two reasons for it. But one, we don't know how the base model would have done at this task. We know that all the models we see are at some degree reinforcement learning into a kind of corporate speak mode. You can get it somewhat out of that corporate speak mode, but I don't know to what degree this is actually at doing its best imitates Scott Alexander versus hit some average between Scott Alexander and corporate speak. That's right. And I don't think anyone knows except the internal employees who have access to the base model. And the second thing, I think of maybe just because it's trendy as an agency or horizon failure.

2:58:27Like deep research is an okay researcher. It's not a great researcher. If you actually want to understand an issue in depth, you can't use deep research, you got to do it on your own. So if you think like I spend maybe five to ten hours researching a really research heavy blog post, a meter thing, I know we're not supposed to use it for any task except code like it says on average, the AI is horizon is one hour. So I'm guessing it just cannot plan and execute a good blog post. It does something very superficial rather than like actually going through the steps. So my guess for that prediction market would be whenever we think the agents are actually good, I think in our scenario that's like late 20s, 26, I'm going to be humble and not hold out for the superintelligence.

2:59:15What about comments? I feel like intuitively it feels like, before we see the AI is writing great blog posts that go super viral repeatedly, we should see them writing like, I read up for the comments on things. Yeah. And I think somebody mentioned this on the less wrong post about it and somebody made some AI -generated comments to that post. They were like, not great, but I wouldn't have immediately picked them out of the general distribution of less wrong comments as especially bad. I think if you were to try this, you would get something that was so obviously an AI house style that it would be, it would be, you would use the word Delve or things kind of along those lines.

2:59:55I think if you were able to avoid that maybe by using the base model, maybe by using some kind of really good prompt to be like, no, do this in Warren's voice, you would get something that was pretty good. I think if you wrote a really stupid blog post it could point out the correct objections to it But I also just don't think it's as smart as Warren right now So it's limit on making Warren's tile comments is both it needs to be able to do a style other than corporate Delph Slop and then it actually needs to get good and he's to have good ideas That other people don't already have yeah, and I mean I think it is as smart as like a I think it can write as well as like a smart average person in a lot of ways and I think if you have a blog post that's worse than that or at that level, it can come up with insightful comments about it.

3:00:44I don't think it could do it on a quality blog post. There was this recent Financial Times article about how have you reached peak cognitive power where it's talking about declining scores and pizza and SAT and so forth. On the internet especially, it does seem like there might have been a golden era before I was that active on the forums or whatever. Do you have nostalgia for a particular time on the internet when it was just like this is an intellectual mecca or? I am so mad at myself for missing most of the golden age of blogging. I feel like if I had started a blog in 2000 or something, then I don't know, I've done well for myself.

3:01:28I can't complain. but like the people from that era all got like all founded news organization or something. God saved me from that fate. I would have liked to have been there. I would have liked to see what I could have done in that area. I mean I wouldn't compare the decline of the internet to that stuff with pizza because I'm sure the internet is just like more people are coming on. It's a less heavily selected sample. But yeah, I could have passed on the whole era where they were talking about atheism versus religion nonstop. That was pretty crazy. But I do hear good things about the golden age of blogging.

3:02:10Anybody who was sort of kind of actually responsible for you starting to blog or keeping blogging? So I owe a huge debt of gratitude to Elias or Yard Kowski. I don't think he was, Like, I had a live journal before that, that it was going on. First of all, it was going on less wrong, that convinced me I could move to the big times. And second of all, I just think I learned, I imported a lot of my worldview from him. I think I was the most boring, normy liberal in the world before encountering less wrong. And I don't 100 % agree with all less wrong ideas, but just having things of that quality be meamed into my head and for me to react to and think about was really great.

3:02:51And tell me about the fact that you could be, we're at some point anonymous. I think for most of human history, somebody who is an influential advisor or an intellectual or somebody, actually I don't know if this is true. You would have had to have some sort of public persona and a lot of what people read into your work is actually a reflection of your public persona. Sort of. Like the reason half of these ancient authors are called things like pseudo -diagnysis or pseudo -cellysis is that you could just write something being like, oh yeah, this is by same to Dionysus. And then I don't know, you could be anybody.

3:03:31And I don't know exactly how common that was in the past. But yeah, I agree that the internet has been a golden age for anonymity. I'm a little bit concerned that AI will make it much easier to break anonymity. I hope the golden age continues. Yeah. It seems like a great no -time none. Thank you guys so much for doing this. Thank you. Thank you so much. This is a blast. Yeah, I had a great time. Huge fan of your podcast. Thank you. I hope you enjoyed that episode. If you did, the most helpful thing you can do is to share it with other people who you think might enjoy it. Send it on Twitter, in your group chats, message it to people.

3:04:10It does really help out a ton. Otherwise if you're interested in sponsoring the podcast, you can go to dwarkesh .com slash advertise to learn more. Okay, I'll see you on the next one.