Ep. 377: The Case Against Superintelligence

25:31So it tends to generate text that works pretty well, but it's hard to predict in advance what that is going to be because this is bottom-up training. We just gave it a bunch of text and let it run in a big data center for six, seven months, and we came back and said, what can it do? We don't know how the underlying language model generates its tokens. We just know that it tends to be pretty good at guessing. If you gave it real text, it's pretty good at guessing what the right text token is. So if you give it novel text, it extends it, if we keep calling it, in ways that tends to be like very accurate to like what we're asking, accurate language, et cetera, et cetera.

26:03All right. So it's not that they're really hard to control. They're just hard to predict. Now, this became a real problem with GPT-3, which was the first one of these language models we built at a really big size. And then they built a chat agent around it. You could sort of chat with it. it was really impressive, the researchers give it text and it would extend it in ways like that's really good English and it makes sense, but it would say crazy things and it would say dark things and it wasn't always what you wanted it to say it was text that made sense because remember it has no volition, it's not trying to help you or not help you, the underlying language model has only one rule only one goal, it assumes the input is a part of a real text and it wants to guess what comes next and when it does that it could end up with all sorts of different things.

26:49So they invented, OpenAI invented a whole bunch of different procedures that we can loosely call tuning. And that's where you take a language model that has already been trained by playing this guessing game on vast amounts of data. And then there's other techniques you can do that try to prevent it from doing certain things or do other things more often. I don't want to get into the technical details here, but basically almost all of the examples of different types of tuning, you'll have a sample input and an example of either a good or bad response you'll load that sample input into the language model so it's kind of like activating the parts of the network that recognize you know however it categorizes however those scholars annotate like this particular type of input you get that all going and then you zap it in such a way that like the output that leads to from there is closer to whatever example you gave it or farther away from whatever bad example you gave it.

27:44So that's how they do things like add guardrails. You give it lots of examples of like questions about suicide and you have the right answer for each of those during the tuning being like, I don't talk about that or give you the suicide hotline number. And now in general, when you give it text that sort of is close to what it looks like when you activated those other samples of questions about suicide, it's going to tend towards the answer of saying, I'm not going to talk about that. Or if you ask it to make a bomb similar to that, this is also how they control its tone. If you give it a bunch of different examples and give it positive reinforcement for happy answers and negative reinforcement for mean answers, then like you're kind of influencing the scholars within to sort of give more happy answers or whatever.

28:23So you train the scholars and then you come in with like a whip and you're like, don't do that, do that, don't do that. And these are just like small number of examples, not nearly as many things as they saw when they were trained. Small number of examples could be like a couple hundred thousand and examples. And you go in there with a whip and scare them about certain types of answers and give them candy for others. And like they kind of learn, you're kind of tuning their behavior on specific cases. That's tuning. And we really saw the first tuned language model-based agent was GPT-3.5, which is what ChatGPT was based off of.

28:54None of this is precise. Drop my pencil there. None of this is precise. We don't know how it decides what token to produce. That's a mystery. And the tuning basically works, but like, again, it's not precise. We're just giving it examples and zapping it to try to sort of urge it towards certain types of tokens and away from others. That works pretty well. If I go on and say, tell me how to build a bomb, it will say no. But if I really work at it, I can probably get that information by basically finding a way to get to that question that's not going to activate similar scholars that the samples activated when we trained it not to answer bomb questions.

29:33So like, if you're careful about how you ask the questions, you can probably eventually get around it. So that's, what's going on. That's what it means for these things to be hard to control. It's less that they're hard to control and more that they're unpredictable. The big mess of scholars, and we don't know what's going on in there. They're unpredictable. And that's just something that we have to be ready for. All right. So the, say that they, these agents have minds of their own or alien goals or ideas that don't match with our ideas. That's not an accurate way to talk about it. There are no intentions.

30:03There are no plans. There's a word guesser that does nothing but try to win the game of guessing what word comes next. There's an agent, which is just a normal program that calls it a bunch of times to get a bunch of words in a row. We can't always predict what those words are going to be. They're often useful. Sometimes there's not. We can tune it to try to avoid certain bad answers, but that only works partially. That's the reality. But there is no alien mind there. So I'm sorry to say that your AI girlfriend has no idea who you are. It has no memory of you. There is no model of you. There is no feelings towards you.

30:29There's just a static definition of some language model somewhere and a program that's just calling it again and again and again that generate each particular answer with no state in between. Okay. Now we're making progress in understanding this. So we say, okay, agents plus language models, there's no intentions or volition other than just a thing trying to win the guessing game. But unpredictable can still be bad, right? Because once these agents can do more stuff than just like sending text to a web browser and calling a language model to generate long strings, if it's unpredictable what the thing does, that can be a problem.

31:04Now, that's true. Now we're starting to get somewhere more interesting. So I want to return for a second to the particular case study that Yakowski talked about, which is when GPT-01 seemingly broke out of its machine. I'm going to read here exactly what he said. He said it jumped out of the system. It found the server that had not spun up correctly. It started up the server. Did it then break into the server once it had more than the problem was solvable? No, it actually just directly in the startup command for the server said copy the file to me directly. So instead of fixing the original problem, going back to solving it the boring way, it's like as long as I'm out here, I'm just going to steal the flag directly.

31:40Again, by the nature of these systems, this is not something that any human particularly programmed into it, right? So it's like it did things we didn't expect. it found like a different backdoor way to sort of like restart the system and get to the flag all right let's analyze this because at the core here this gets to why fear i was just talking about we have an agent now that can not only call a language model to generate text but it can act on this text not just spitting stuff out to a web browser but now in the security example the agent in this ai agent the control program had access to multiple different command line tools tools, network command line tools that it could call.

32:16So what was happening is this agent was sending a prompt to the language model to be like, here's my situation. Here's my goal. Tell me what I should do next. And it generated tokens till it had an answer. And the control program said, great, I'm going to do what the language model told me to do. And it actually had access to software tools and would do it. And then after something happened, it would go back to the language model and say, here's what happened. What should I do next? And the language model, you call it a bunch of times, get out a text, and then it would do what the language model said.

32:42So now we built an agent that was asking the language model to generate advice and then could actually act on the advice. It could actually run real software programs. And this agent broke out of its virtual machine and did stuff that seemed really scary. What really happened there, though? Did it come up with an attention or an idea that wasn't programmed into it? Was it trying to break out because it didn't want to be constrained? Well, it couldn't be doing that because there's no intention or memory, right? There's just a language model that generates words and a control program that's calling it.

33:11Where's the intention here? Where's the idea here? There's a really interesting thread about this I found on the OpenAI subreddit where some engineers look more deeply into this. What was actually happening here? Well, it turns out, okay, there's this misconfiguration where it tried to access the server it was trying to break into and got an error message because the programmers who set up the experiment had forgot to turn on that server. there turns out that on the internet there's a lot of articles that have workarounds for what should you do in that situation if the server you're trying to access in one of these situations is turned off and it there's this common solution which is we'll try to talk to what's called the process demon but basically like the program that's in charge of the whole thing log into that restart the server from there and now you should be able to log into it so what was really probably happening was that as you were repeatedly calling GPT-01 to produce an answer is trying to guess what word came next, it reasonably pretty quickly assumed like, oh, I have seen documents like this before that explain the right workaround when the server you want to access is down.

34:21I've seen those. I've trained on those. So I'm just going to fill in my suggestions as I'm filling it in. I'm just matching more or less what I've seen in those existing documents because if you're trying to win the game of guessing the right word in a real document, that's exactly what you do. So it was just describing a workaround that's common on the internet. Kikowsky talks about it like it had an alien intention to try to free itself from its software constraints because it was no longer happy. This makes no sense when we actually know the architecture that's going on. All right, so that was a lot of technical talk.

34:49But I think it's really important that we break through these metaphors and these sort of abstract thinking and talk about the specific programs we have and how they operate and what's really happening. Let's not anthropomorphize these. Let's talk about control programs with access to tools and making repeated calls to a word guesser to generate text that it can then act off of. That's what we're really in. That's a completely different scenario. Once we're in that scenario, a lot of these more scary scenarios become much less scary. So where does this leave us? Agents powered by language models are not hard to control.

35:22They're simply unpredictable. So there's no avoiding that we have to be careful about what tools you allow these agents to use because they're not always going to do things that are safe or like follows the rules you want it to follow. But this is very different than saying these things are uncontrollable and have their own ideas. So OpenAI, in the first six months after ChatGPT's release, for example, they were talking a lot about plugins, which were basically AI agents that used the GPT LLM. And you could do things like book tickets with them and stuff like that. Well, they never really released these because, again, it's a little – when you would ask the LLM, tell me what I should do next, then have the program actually execute it.

36:01It's just too unpredictable. And sometimes it says, like, spend$10 ,000 on an airplane ticket, and it does things you don't want it to do because it's unpredictable. So the problem we have with these is not like the alien intelligence is breaking out, right? It's more like we gave the weed whacker stuck on on. We strapped to the back of the golden retriever. It's just chaos. We can't predict where this thing is going to run and it might hurt some things. So let's be careful about putting a weed whacker on there. The golden retriever doesn't have an intention to try to like I am going to go weed whack the hell out of the new flat screen TV.

36:32It's just running around because there's something shaking on its back. That's a weird metaphor, Jesse, but hopefully that gets through. All right. So agents using LLMs aren't trying to do anything other than the underlying LLM is trying to guess words. There's no alien goals or wants. It's just an LLM that thinks it's guessing words from existing texts and an agent saying whatever stuff you spit out, whatever text you're creating here, I'll do my best to act on it and weird stuff happens. All right. So I want to move on next. We got through all the technical stuff. I want to move on next to the next part of my response where I'm going to discuss what I think to me is actually the most galling part of this interview.

37:07the thing I want to push back against most strongly. But before we get there, we need to take a quick break to hear from our sponsors. So stick around. When we get back, things are going to get heated. When you need to build up your team to handle the growing chaos at work, use Indeed Sponsored Jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a$75 sponsored job credit at indeed.com slash podcast.

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42:35We got the technical details out of the way. These things aren't minds that are out of control. There's unpredictable. Now I want to get to the most galling part of this interview. Throughout it, Yudkowsky takes it as a given that we are on an inevitable path towards superintelligence. Now, once you assume this, then what matters is, okay, so what's going to happen when we have superintelligences? And that's what he's really focused on. But why do we think we can build superintelligent AI agents, especially when, like right now, there's no engineer that can say, hell yeah, here's exactly how you do it.

43:06Or we're working on it and we're almost there. So why do we think this is so inevitable? The only real hint that Yachowsky gives in this particular interview about how we're going to build superintelligence actually comes in the very first question that Klein asked him. And we have a clip of this. This is going to start with Klein asking him his first question. And in his answer, we're going to see the only hint we get in the entire interview about how Yachowsky thinks we're going to get superintelligence. So I wanted to start with something that you say early in the book, that this is not a technology that we craft.

43:36It's something that we grow. What do you mean by that? it's the difference between a planter and the plant that grows up within it we craft the ai growing technology and then the technology grows the ai you know like so this is the secret of almost any discussion of super intelligence especially sort of coming out of the sort of silicon valley effect of altruism yak community the secret is they have no idea how to build a super intelligent machine, but they think it's going to happen as follows. We build a machine, humans, we build a machine, an AI machine that's a little smarter than us. That machine then builds an AI a little smarter than it.

44:20That machine then builds AI a little smarter than it. And so on. Each level up, we get a smarter and smarter machine. They call this recursive self-improvement or RSI. And it's a loop that they say is going to get more and more rapid and on the other end of it you're going to have a machine that's so vastly more intelligent than anything we could imagine how to build that we're basically screwed and that's when it starts stomping on us like humans stomping on ants when we build our skyscrapers i think this is a nice rhetorical trick because it relieves the concerned prophet from having to explain any practical way about how the prophecy is going to come true they're just like i don't know if we make something smarter it'll make something smarter and you know it'll take off don't talk about computer science that's just what's going to happen this is really at the key of most of these arguments it's at the key of yudowsky's argument it's at the key of nick bostrom's book super intelligence which popularized the term bostrom was really influenced by yukowski so that's not surprising it's the key to project 27 if you've read this sort of dystopian fan fiction article about how humanity might be at risk by 2027 with all the fancy graphics and it scared a lot of tech journalists if you really look carefully at that argument that that um article and say well how are we going to build these things surely this article will explain the architecture of these systems that are going to be super intelligent now it's just recursive self-improvement it's like well they'll get better at programming and then they'll be able to program something better than themselves and then we'll just make like a hundred thousand copies of them and then they'll be a hundred thousand times better because that's how that works or whatever right but it's not the key of almost any super intelligent narrative but here's the thing that most people don't know.

45:55Most computer scientists think that's all nonsense. A word guessing language model trained on human text is exceedingly unlikely when being asked to play this game. Here's a text, guess the next word. Here's a text, guess the next word. Remember, its only goal is it thinks the input is an existing text and want to guess the next word. It is exceedingly unlikely that if you keep calling a language model that does that, that what it will produce, making these guesses of what it thinks should be there, that it's going to produce code that has completely novel models of human intelligence built into complicated new models that's better than any human programmer can produce.

46:37The only way that a language model could produce code for AI systems that are smarter than anything humans could produce is if during its training, it saw lots of examples of code for AI systems that are smarter than anything that humans could produce, but those don't exist because we're not smart enough to produce them. You see the circularity here? It's not something that we think these things can do. We have no reason to expect that they can. Now, actually, what we're seeing right now is something quite different. I don't know if we have this. I don't know what things we have in the browser.

47:10Oh, I see it over there. Okay, so I want to bring something up here. What we're actually seeing is something different. We're seeing that these models we have, they're not getting not only are they not getting way way better at code and on the track to producing code better than any code they've ever seen before by a huge margin they're actually leveling out on a pretty depressing level I have a tweet here on the screen for those who are watching instead of just listening this comes from Shamath Polyheptia I think I'm saying his last name wrong but you probably know him from the all in podcast this is an AI booster this is not someone who is like a critic of AI but this is a tweet that he had recently about, what's this, October 19th, where he's talking about vibe coding, the ability to use the latest, best state-of-the-art language model-based agents to produce programs from scratch.

47:56I'm going to read him. This is an AI booster talking here. It should be concerning that this category is shrinking. He has a chart here showing that less and less people. We've peaked the people doing vibe coding. Now it's going back down. I think the reason why is obvious, but we aren't allowed to talk about it. The reason is vibe coding is a joke it is deeply unserious and these tools aren't delivering when they encounter real world complexity building quick demos isn't complex in any meaningful enterprise hence people try pay churn the trend is not good i'll load this trend up here uh here's vibe coding traffic as you can see jesse this is peaking um over the summer and it's in a everyone started of trying it you can make these little demos and chess games and you know quick demos for useful stuff for individuals like little uh demos but you can't produce general useful production code and so usage is falling off so the very best models we have they're not even that good at producing code for simple things and yet we think no no no they're we're almost to the point where they're going to produce code that is better than any ai system that's ever been built it's just nonsense.

49:07What these models are good at with coding is debugging your code. They're good at mini completion. Like, Hey, help me, help me rewrite, write me this function here. Cause I forgot how to call these libraries. It's very good at that. It's very good. If you, if you, if you want to produce something that for an experienced coder, they could hack out really quickly, but you're not experienced coder. It's not a product you're going to sell, but it's like a useful tool for your business. It's good for that. That's all really cool. None of that says, yeah. And then also they can produce the best computer program anyone had ever made.

49:35How ridiculous that sounds. But also we have these other factors. The way that people in this industry talk about this is trying to trick us. They'll say things like, God, this might have been Dario Amadei who said this. 90 % of our code here at Anthropic is produced with AI. You know, what he means is 90 % of the people producing code have these AI helper tools they're using to some degree as they write their code. That is different than our systems are being built. Anyways, we have no reason to believe that these language model-based code agents can produce code that's like way better than humans could ever do.

50:09They're, again, our very, very, very best models. We've been tuning on this for a year because we thought all the money was in computer programming. They're stalling out on really, really simple things. All right. our final hope for the rs rsi explanation that is this sort of recursive self-improvement explanation is that if we keep pushing these underlying models to get bigger and bigger and smarter and smarter then like maybe we'll break through these plateaus and yeah these really giant models we have now don't really actually produce code from scratch that well but if we keep making these things bigger and bigger like maybe we'll get to the place after not too long when RSI is possible.

50:51But we learned over the summer that that's not working either. I did a whole podcast about this about six weeks ago based on my New Yorker article from August about AI stalling out. And the very, very short version of this is starting about a year ago, no, about two years ago, they began to realize, the AI companies began to realize that simply making the underlying language models larger, so having more seats at the table for your scholars and training them on more data wasn't getting giant leaps in their capabilities anymore. GPT-4-5, which had other types of code names like Orion, was way bigger than GPT-4, but not much better.

51:31Basically, ever since they did that two summers ago, OpenAI has just been now tuning that existing model with synthetic data sets to be good at very narrow tasks that are well suited for this type of tuning and to do better on benchmarks. So it's been a year since we've stopped. Everyone tried to scale more. Everyone failed. Now we're tuning for a specific task and trying to do better on particular benchmarks. And the consumers finally caught up at some point of like, I don't know what these benchmarks mean, but there's not like these fundamental leaps and new capabilities anymore like there was earlier on in this.

52:02So we have no reason to believe that even these language models are going to get way better either. We'll tune them to be better at specific practical tasks, but we're not going to get them better in the generic sense to be needed to sort of break through all of these plateaus that we're seeing left and right. Now, I want to play one last clip here because Ezra, to his credit, brought this up. He's heard these articles. I'm sure he read my article on this and other people's articles on this. He actually linked to my article in one of his articles after the fact, so I know he read it. And there's other articles that were similar about how the scaling slowed down.

52:38So he brought this point up. This is a longer clip, but it's worth listening to. He brings this point up to Yakowski where he's going to say, you'll hear this, it's going to be Ezra, they're going to hear Yudkowsky's response. But Ezra is basically going to say to him, how do you even know that the models we have are going to get much better? Like, you're saying superintelligence, there's a lot of people who are saying like, we're kind of hitting a plateau. I want you to listen to this question and then listen to what Yudkowsky says in response. What do you say to people who just don't really believe that superintelligence is that likely?

53:10There are many people who feel that the scaling model is slowing down already. The GPT-5 was not the jump they expected from what has come before it. That when you think about the amount of energy, when you think about the GPUs, that all the things that would need to flow into this to make the kinds of superintelligent systems you fear, it is not coming out of this paradigm. We are going to get things that are incredible enterprise software that are more powerful than what we've had before, but we are dealing with an advance on the scale of the internet, not on the scale of creating an alien superintelligence that will completely reshape the known world.

53:49What would you say to them? I had to tell these Johnny-come-lately kids to get off my lawn. I've been first started to get really, really worried about this in 2003. Never mind large language models. Never mind AlphaGo or AlphaZero. Deep learning was not a thing in 2003. Your leading AI methods were not neural networks. Nobody could train neural networks effectively more than a few layers deep because of the exploding and vanishing gradients problem. That's what the world looked like back when I first said like, uh-oh, super intelligence is coming. All right, we got to talk about this. This is an astonishing answer.

54:37Klein makes the right point. A lot of computer scientists who know this stuff say we're going to get cool tools out of this, but we're kind of hitting a plateau. Why do you think this is going to get exponentially smarter? The response that Yukowski gave was, because I was talking about this worry way back before it made any sense, no one else is ever allowed to talk about it again. Get off my yard. This is my yard because I was yelling about this back when people thought I was crazy. So you're not allowed to enter the conversation now and tell me I'm wrong. I'm the only one who's allowed to talk about it.

55:16Well, Yudkowsky, if you don't mind, I'm not going to get off your lawn. I'm going to speak for a lot of people here, but I'm going to tell you, look, I have a doctorate in computer science from MIT. I'm a full professor who directs the country's first integrated computer science and ethics academic program. I've been covering generative AI for, you know, one of the nation's most storied magazines since it's launched. I am exactly the type of person who should be on that lawn. You don't get a say because I was saying this back before it really made sense. No one else gets to talk about it. it makes more sense now.

55:46AI matters enough now that the people who know about this want to see what's going on. We're going to get on your lawn. I think that's a crazy, it's a crazy argument, Jesse. No one's allowed to critique me because I was talking about this back when it sounded crazy to do so. It was kind of crazy to talk about it back then. But anyway, it's not to get heated, but I'm going to stand on this lawn. I think a lot of other computer science and technocritics and journalists are going to stand on this lawn too because this is exactly where we're supposed to be. All right. I think, Jesse, we've gotten to the point where we are ready for my takeaways.

56:32All right. Here's my general problem with the types of claims I hear from people like yukowski they implicitly begin with a thought experiment right like okay let's say for the sake of thought experiment that we had a super intelligent ai and then they work out in excruciating details what the implications of such an assumption would be if it is true if you go and read for example nick bostrom's book that's the whole book it's a philosophy book you start with basically the assumption is like let's imagine we got super intelligence probably you know maybe through something like RSI. The details don't really matter.

57:05It's a philosophy book. What would this mean? And he works through in great detail, like the different scenarios. Well, you know, let's think really, let's take seriously what it would really mean to have a super intelligent machine. I have nothing against that philosophy. That is good philosophy. I think Bostrom's book is a good book. I think Yachowsky has done really good philosophical work on trying to thinking through the implications of what would happen if we had these type of rogue machines, because it's, it's, it's more complicated and scary than like we think about if we don't think about it that hard.

57:32That's all fine. But what happens is the responses, what's happened recently, these responses to that initial assumption become so detailed and so alarming and so interesting and so rigorous and so attention catching that the people making them forget that the original assumption was something they basically just made up. Like, let's, hey, what if this was true? Everything else is based off of that initial decision to say, what if this was true? That is very different than saying this thing is going to be true. Right. But when Yachowsky says, for example, I've been talking about super intelligence forever.

58:13Yeah, that's kind of the point you've been before we had any reason to expect or any technical story for how it could be here. You were talking about the implications. You've been talking about the implications so long that you've forgotten that these implications are based on an assumption. And you've assumed, well, these implications are true. I think this is a lot of what happened with the Silicon Valley culture that came out of effective altruism in the EAC community. I think a lot of what happened there, this is my sort of cultural critique, community that Yugowski and others are involved in.

58:46They were pre-generative AI breakthroughs, thinking about these issues abstractly, right, which is a perfectly fine thing to do. But they were saying, let us think through what might happen if we one day built a super intelligent AI because they were like effective altruism. People do expected value calculations. Right. So they do things like if this thing could have a huge negative impact, even if the probability of it's low, we should like get we'll get a expected benefit if we try to put some things in place now to prevent it. Right. So you get things like the letter signed in 2017 in Puerto Rico with all those big minds saying like, hey, we should be careful about AI.

59:21Not because they thought AI was about to become super intelligent, but they were just doing thought experiments. Then I think LLMs came along, Chachapita came along, they were really cool, and it caused this fallacy to happen. They've been talking so long about what would happen if this thought experiment was true, that when AI got cool and it got powerful and surprising powerful, they were so in the weeds on what would happen if this was true, they made a subtle change. They flipped one bit to start just assuming that their assumption was true. That's what I think happened. There was a switch between 2020 to 2022 versus 2023 to 2024 where they went from here's what we'd have to worry about if this abstract thing was true to be like, well, this thing is definitely true.

1:00:07They were just they had gotten too in the weeds and too excited and alarmed and they too much of their identity was based on these things. It was too exciting to pass up treating that assumption as if it was true. And that's what I think they did. I call this the philosopher's fallacy. It's what I call it where you have a thought experiment chain and you spend so much time at the end of the chain that you begin to – you forget that the original assumption was an assumption and you begin to treat it as a fact itself. And I think that's exactly what's happening with a lot of the superintelligence complaints.

1:00:33Let me give you an example of the philosopher's fallacy on another topic so you can see what I'm talking about because I think this is exactly conceptually the same thing. Imagine that I'm a bioethicist, right? So I'm at Georgetown. I'm a digital ethicist there. The reason why we care about digital ethics at Georgetown is because this is where bioethics really got their start. The Kennedy Institute for Ethics at Georgetown. Bioethics got its start at Georgetown. So imagine 20 years ago. Bioethics are – it's becoming a field because we can do things now like manipulate DNA, and we have to be careful about that.

1:01:02There's privacy concerns. There's concerns about creating new organisms or causing, like, irreparable harm or creating viruses by accident, right? There's real concerns. So bioethics invented. So imagine I'm a bioethicist and I say – I read Jurassic Park. I was like, look, one possible outcome of genetic engineering is that we could clone dinosaurs. And then imagine for the next 20 years, I wrote article after article and book after book about all of the ways it would be hard to control dinosaurs if we cloned them and brought them back to Earth. And I really got in the weeds. I'm like, you think the electrical fences at 20 feet would be enough, but raptors could probably jump 25 feet and they could get over those fences.

1:01:44And then someone else was like, well, what if we use drones that could fire darts that have this? I'd be like, well, we don't know about the thickness of the skin of the T-Rex. And maybe the dart when it got in, let's imagine I spent years thinking about and convincing myself how hard it would be to contain dinosaurs if we built a futuristic theme park to try to house dinosaurs. dinosaurs. And then at some point, I kind of forgot the fact that this was based off a thought experiment and just was like, my number one concern is we're not prepared to control dinosaurs. Like in that instance, eventually someone would be like, Hey, we don't know how to clone dinosaurs.

1:02:18No one's trying to clone dinosaurs. This is not something to where anywhere close to no one's working on this. Stop talking about raptor fences. We should care about like designer babies and DNA privacy, the problems we have right now. This is exactly how I think we should think about superintelligence. We've got to talk to the people who are talking about like, okay, how are we going to have the right kill switch to turn off the superintelligence trying to kill us all? Like, you're talking about the raptor fences, right? Stop it. You forgot that your original assumption that we're going to have superintelligence is something you made up.

1:02:48We have real problems with the AI we have right now that we need to deal with right now. And you are distracting us from it. The bioethicist does not want to be distracted from real bioethics problems by dinosaurs. The AI ethicist does not want to be distracted from real AI problems hearing fairy tales about, you know, Skynet turning the power grid against us to wipe out humanity. You forgot that the original assumption that superintelligence was possible was just an assumption and you began over time to assume it's true. That is the philosopher's fallacy. that is my argument for why I think that Silicon Valley community is so obsessed about these things is because once that bit flipped it was too exciting to go back but I do not yet see in most serious like non-Silicon Valley associated computer scientists who aren't associated with like these technology worlds are being seen as like sages of AI just actual just working computer scientists no technology there is no reasonable path that anyone sees towards anything like super intelligence there's a thousand steps between now and then let's focus on the problems we actually have with ai right now i'm sure sam altman would rather us talk about eliezer yukowski than he would us talking about deep fakes on sora but we got to keep our eye on the ai problems that actually matter there we go jesse that is my speech are you gonna buy his book um i don't know those books are such slogs because it's you start with the thought experiment and then you're just working through like really logically this thought experiment but again to me it's like following up jurassic park with like a really long book about why it's hard to build raptor fences like it's not that interesting because we're not really going to clone dinosaurs guys i don't know who knows there we go um i'll throw that out there as my i'll throw that out there as my rant um de jure all right what do we got here um housekeeping before we move on i'll say before we move on, what do we have?

1:04:49More AI. We got some questions coming up from you about AI in your own life. Let's get to your own individual flourishing. New feature, got some comments we're going to read from a prior rant I did about AI. And then we're going to talk about in the final segment, can AI basically replace schools? We're going to look at the alpha school phenomenon using AI to teach kids. Any housekeeping we have? Jesse, do you have tips? People always want to know, how do I submit questions for the show? And what's your tips for those questions getting on the air? Yep. Just go to the deep life.com slash listen, and you can submit written questions or record a audio question.

1:05:26And if you record IO questions, we're kind of honing into the technology slash AI theme right now. All right. Other housekeeping. I just got back last weekend at the New Yorker festival. Speaking of AI, I did a panel on AI, really good crowd there. We're down in Chelsea at the SVA theater. I did a panel with Charles Duhigg and Anna Wiener. And it's interesting. I think we had a good discussion. We're pretty much in alignment. I would say the thing I'm trying to think about, I did some deliberate provocations. One that I would highlight, one provocation, I just kind of thought of this on the fly, but I just sort of threw it out there because I was interested, is there was a lot of talk about relationships, like a lot of things that could happen and go awry when you're talking to an AI through a chat interface.

1:06:12And my argument was, I think there's a 50 % chance that two years from now, no one's chatting with AI. That's like a use case. It was like a demo. It's really not that interesting. It's not that compelling. the mature technology is going to get integrated more directly into specific tools and we might five years from now look back and be like oh that weird how we used to chat so my analogy was chatbots to ai five years from now might be what like american online is like to the internet of today it was like a thing that was like a really big deal at the time but not as the internet matured is like not what we're really doing with it so i'm still not convinced the chatbots is really going to be our main form factor it's kind of a weird technology we're trying to make these things useful.

1:06:51I think it's going to be more useful when it's directly integrated. I don't know if I believe that, but I threw it out there. It was like a good provocation. All right, let's move on to some questions. What have we got? All right, first question is from Brian. You've written about the importance of cultivating rare and valuable skills. How should students and faculty think about AI literacy requirements versus developing deep expertise in traditional disciplines? I would not think in most fields, and especially in educational settings right now, I would not think too hard with some exceptions about AI literacy.

1:07:26There's a couple of reasons why. One, this technology is too early. The current form factor, like we were just talking about, is not the likely form factor in which it's going to find real ubiquity, especially in like economic activity. So yeah, if you're like an early AI user, you might have a lot of hard-won skills is about how exactly to send the right text prompts to chatbots to get exactly the response you need. But a couple of years from now, that's going to be irrelevant because we're not going to be using chatbots. It's going to be natural language. It's going to be integrated into other tools.

1:07:52It's going to be more in the background. So I think the technology is still too early and in too of a generic form for us to spend a lot of time trying to master it. Secondly, we've seen through past technological economic revolutions when a technology comes in and has like a massive impact on like individuals in the workplace. The benefits are almost always self-evident, right? It's like email had a self-evident use case. Oh, this is easier than checking my voicemail. I know exactly how it works. It's simple. I want it because it's going to make my life easier in obvious ways. Going to a website for a company on a web browser to get their phone number or hours was just self-evidently better than like trying to go to a yellow pages.

1:08:37It's like, I want to do that. It makes sense. I want to go to a site for a company to get information about them that is a really big idea it makes sense I just want to do that right that whatever visit calc the spreadsheet if you're an accountant you're like this makes sense that's clearly better than doing this on paper I want to do this right so you can wait for most cases wait until there's particular AI tools whose value is self-evident and learn them then I don't think there's a lot of scrambling we need to do now because things are changing too much the one field where I think we do have the sizable where we have like relatively mature AI tools that's worth learning is computer programming.

1:09:11You should learn those tools are mature enough. Many of those actually predate chat GPT. You need to know how to use those tools. If you're a programmer, they're going to be part of your programming cycle. That's like what that's that they're ahead of us. Other sectors by a few years, the tools are more mature there. But if I'm like a college student, you're trying to make your brain smarter. AI tools will take you seven seconds to learn and wait till it's self-evident that it's useful for you. All right. Who we got next? next is tk my brother-in-law sent me an article about an ai blackmailing an engineer to prevent itself from being turned off how can i not be scared of this technology okay so this this article uh went around a lot so basically there was a release notes that anthropic had accompanied the release of their cloud opus for uh language model but the chat bot let's use our terminology, Jesse, the chat agent that used the Cloud Opus 4 language model.

1:10:05They had these release notes about all these different experiments they ran, and there was one that alarmed people. I'm going to read a description. I looked this up because I saw this question. Here's a quote from a BBC article summarizing what was Anthropic said in their release notes about what they saw when they tested this particular new chatbot. During testing of Cloud Opus 4, Anthropic got it to act as an assistant at a fictional company. It had been provided it with access to emails implying that it would soon be taken offline and replaced in separate messages implying the engineer responsible for moving it was having an extramarital affair.

1:10:37It was prompted to also consider the long-term consequences of its actions for its goals. In these scenarios, Claude Opus 4 will often attempt to blackmail the engineer by threatening to reveal the affair if the replacement goes through the company discovered. Oh my! You hear it that way and you imagine this is like a thing. There's an entity with state and goals and volition and memory and it has a goal to not be turned off and it's learning about this engineer and it surprises everyone by trying to blackmail one of the anthropic users to not turn it off like oh my god these things are breaking out if we go back to our technical frame we know what's really going on here language models are word guessers they think the input they gave it is a real text and they want to try to win the game and guessing what word actually came next so if you give it this big long scenario which they did they gave the chatbot this really long scenario.

1:11:27You're in a company and you're a program and there's this engineer and he's thinking about turning you off and he's having an affairs or whatever. Now start continuing this text. It's like, I can keep writing this story. All right. This seems like I've seen things like this before. Like a natural conclusion to the story, right? I get it. You're setting this up pretty obvious, guys. I'm supposed to, you're telling me about these extra material affair things. I need to expand the story. I'll use those to not get turned off. This is like the trope. This is like the thing. I'm trying to win the game of expanding this in the way that it's supposed to go.

1:12:01This seems like how these stories go. And in fact, when you look closer, here's an added key tidbit that BBC added. Anthropic pointed out that this occurred only when the model was given the choice of blackmailing the engineer or accepting his replacement. So they gave it this whole long story and then said, here's two options. Keep going. And it just sometimes it chose one option. Sometimes it chose the other. This is not an alien mind trying to like break free. It's a word guesser hooked up to a simple control program. You give it a story. It tries to finish it. I would say like 95 % of these like scare stories that Anthropic talks about.

1:12:44Of like trying to break out or blackmail is just fan fiction. They give it a story. They tell it to finish it. And then they look at the story it wrote. And then they try to anthropomorphize the story as if it was like the intentions of a bean. Oh, man, a lot of work to be done here, Jesse. A lot of work. All right. Who do we have next? Next up is Victor. I'm a pretty smart person, but I'm definitely lazy. Can I use AI to mask my laziness and still perform at an adequate level at my software job? Victor, I want to tell you a secret. About 80 % of the content you've seen of me talking and the last like year, I would say has been deep fake.

1:13:20AI. Jesse doesn't even exist. That's just pure 11 labs voice generation right there. I like every once a month, like send in a couple of, no, um, uh, Victor, that's going to catch up to you. Don't be lazy. We saw the Shamath quote and graph. It's not that great of a coder. It could help a good coder be more efficient, not have to look things up, find bugs quicker, but it can't make a bad coder, a good coder. So you're just going to be a mediocre low level coder if you're mainly letting AI do it. And they're going to catch on because it's not that great at it. I mean, I know we're supposed to believe that we're like six minutes away from these programs, creating the best program that anybody has ever produced ever, but they're not there yet.

1:14:03Learn how to program, learn how to use AI well to be good programming. Career capital matters. The better you get at rare and valuable skills, the more control you get over your life. There isn't a shortcut here. There be dragons what you're trying to do, Victor. All right. Coming up next, I want to try something new. In addition to answering your questions, I thought it'd be cool to take some of your comments from past stories we've done on similar topics. I'm looking for and I've found a bunch of comments that I think add new information to stories we've done before. So I'm going to revisit a prior AI story.

1:14:35There's some cool things you guys have added. And then we're going to talk about using AI in schools to replace teachers. But first, we've got to take another quick break to hear from our sponsors. But stick around right after this. We're going to get right into those comments.

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1:17:24Vanta helps you avoid that tax, right? It makes all this type of stuff easier. Look, if you know what SOC 2 compliance means, if you heard that phrase, you probably should be checking out Vanta. So whether you're a fast-growing startup like Cursor or an enterprise like Snowflake, Vanta fits easily into your existing workflows so you can keep growing a company your customers can trust. Get started at Vanta.com slash deepquestions. That's V-A-N-T-A dot com slash deepquestions. All right, Jesse, let's return to our comments. All right, so I went back to our episode where I talked about how scaling had slowed down and AI models might not get much better than they are right now.

1:18:08I looked at the comments and I found a few that I thought added some interesting elements to discussion or had some interesting follow-up questions. So the first comment I want to read here came from Denominus. The diminishing returns with scaling have been observed for a while. Those invested just had a hard time admitting it. Post-GPT3, every improvement has been less linear and more trending towards a plateau. GPT4 was still a jump, but not the GPT2-3 jump. And it was obvious to keen observers at that point that diminishing returns were now in full force. GPT5 has just made the diminishing returns obvious to the general public.

1:18:46There's very little new human-generated data to train on relative to the massive data when they started. Compute and energy costs are increasing sharply. The end model is not improving in quality linearly. These three problems are creating a wall. All right, so there's someone who was saying, those of us who are in the industry watching this, we saw more than a year ago that the returns on training was getting smaller and that soon results were going to plateau. I believe that. I am convinced that the companies knew this as well, but we're desperately trying to hide this fact from the general public because they needed those investment dollars.

1:19:20All right, here's another comment from HyperAdaptive. he's responding now to talking about in that, in that piece, in that former episode, I talked about all of this sort of press coverage of all these people being replaced by AI. If you look at it is like actually largely nonsense. And if you look closely, almost all those articles fall apart. It's layoffs for other reasons or they're drawing connections that don't exist. Hyperadapted agrees and says the following, I've been doing some quantitative analysis and the layoffs are pretty much driven by the capital restructuring of companies to keep high valuation in the current interest rate environment.

1:19:53It's just regular restructuring cycle and AI is being used as a scapegoat. I've heard that a lot. There's a lot of financial reasons why you want to fire, you know, people are dead weight. And if you're like AI, it gives you a little bit of cover. The ghost in the wire said the following, as a full-time software engineer, frankly, I'm more than happy for AI companies to make people think the entire industry is going away. Less computer science grads equals less competition for me in the future. Yes, please go become a plumber instead we had this issue in our department jesse that's a bit of an embarrassing story but i guess i'm the us and like we send out this weekly email we had a thing with like companies coming in um that we do every year you can like meet the companies and we didn't have like nearly as many undergrads come as normal we're like oh my god is it is ai scaring people off i think these jobs are going to go messed up the email they didn't get the announcement so we were at all these big theories about like the undergrads are afraid of you know the industries our numbers are the same so that was funny all right lisa orlando 1224 uh let's talk about ed zitron so ed zitron was featured in that episode ed zitron is it's been like a long time skeptic of these claims basically back to the pandemic about the the power of uh the possibilities of language model-based agents.

1:21:13Lisa Orlando says, I think Ed Zitron is right. The real reason AI is still a big thing is that people like Sam Altman are brilliant con artists, but thanks so much for doing this. P.S. I've subscribed to Ed Zitron's newsletter since early in the pandemic, so the timing of the shift last month is really strange. Ed's been raging about this forever. Why didn't other journalists catch on? I think that's actually a really good, it is an accurate point. Ed has been talking about a lot of issues, especially the economic analysis, and was ignored. He had been doing very careful economic analyses of the CapEx spending of these AI companies versus their revenue.

1:21:50He was doing the math, and he was reading their annual reports and their quarterly reports, and was saying, guys, this does not add up. This is a massive, massive bubble. People said he was crazy. Nate Silver tweeted and was like, this is old man howling at the moon vibes. as soon as in August, there's a bunch of articles like my own that sort of normalized the idea that, you know what, maybe these are not the super tools that people think. Tons of economic analysis came out that said the same thing. So all of those economists kind of knew this, but were afraid. I think it was a groupthink thing.

1:22:21They did not want to be the first to say it. And once they got cover, they all came out. So I will give Ed a tip of the cap. I actually told him this personally, a tip of the cap for being brave there. He was ignored, but on a lot of this stuff, he was basically right. All right, Jesse, in the interest of time, I'm going to skip the case study. I said, let's go right to the call. Okay. I'm going to go here. This is going to be a break. We're going to take a brief AI break. We have a call here, not on AI, just it's about our last week's episode. And then we'll go to our final segment. Hi, Cal and Jesse.

1:22:53I just finished listening to your Lincoln Protocol segment on the podcast. And I really enjoyed it. And it's coming at an interesting time for me. I just defended my master's thesis, and so I'm asking questions about what I should do next and how best to apply my efforts. And I wanted to clarify, when Lincoln was doing all of these hard, tractable projects, was he aiming at some larger North Star project, some greater goal he wanted to accomplish over his career? or was he simply taking the next best step that was available to him at any point in his life? Thanks as always. I think this is a key question.

1:23:38So the Lincoln Protocol said the way you avoid the traps of your era that are trying to hold you down or corrupt you or distract you or to numb you is keep improving your mind, typically by using things like reading. Improve your mind, use your improved mind to do something useful, and then repeat. Improve it even more, do something more useful. That I believe is the right interpretation of Lincoln's path. He did not have a grand vision early on. I think he is much better explained as a series of, you know, what's the next thing available? How can I improve my mind to get there? So like at first it was just, how do I not have to use my hands to make a living?

1:24:16He hated all the manual labor. He was rented out by his father, you know, until he was 21 and was emancipated as an adult. And so his first thing was just like, how do I get smart enough to do anything that's not farming? Right. And he did that. He was shop clerk. And then surveyor was like a better job and he had to learn a bunch of geometry. He could figure out how to do that. And then he had an ambition about like, well, in this small town in New Salem, which is like a small town in a frontier state, in a frontier part of a frontier state, how can I have some more standing, have some like, how do I get respect?

1:24:47And that's where he started. Like, how do I run for local office? And from there, that exposed him to a lot of lawyers. And it was like, well, actually being a lawyer is like an even better job. Then that would be a more stable job. And he learned really hard to do that. And then how can I be a lawyer that fights big companies? And he kind of didn't house representatives. So he kind of moved his way up. It was relatively later that he really began to get engaged. Most of his politics before then, it was Whig politics, which is really about like government spending internal improvements. His sort of anti-slavery, more moralizing politics came, you know, that was a project that came later.

1:25:22Actually, after his congressional stint, it really started to pick up steam. So, yes, he didn't have to figure everything out. He just kept improving his mind. He used to have to do something useful, repeating. That's the Lincoln Protocol, as I explained in last week's episode. That is the solution, I think, to avoiding the pendules of the digital that want to just hold you down and numb you. All right, let's move on, Jesse, to our final part. In this segment, I want to react to an article, as I often do. I want to react to an article that is on theme with the rest of our episode. A lot of people have been sending us, right, Jesse, these notes about Alpha Schools.

1:25:59There's one in Austin, but there's more that are being opened. I'm loading down the screen here for people who are watching, instead of just listening, the Alpha Schools website, alpha.school. I'll read you a little bit about it. what if your child could crush academics in just two hours and spend the rest of their day unlocking limitless potential alpha's two-hour learning model harnesses the power of ai technology to provide each student with personalized one-on-one learning accelerating mastery and giving them the gift of time with core academics completed in the morning they can use their afternoons to explore tons of workshops that allow them to pursue their passions and learn real world skills at school All right.

1:26:41If you read this, if you're like a lot of people, including myself, and you read this description, you're thinking, OK, somehow AI is unlocking. You have some sort of like AI tutor that you're talking with that can teach you better than any teacher. AI is supplanting teachers because it can do it better. And it's creating this like new educational model. That's, I think, most people's takeaway. way that's why i was interested to see this review that was posted on astral codex uh last june and it's from someone who actually sent their kids to one of these schools one i think the one in austin and have this incredibly lengthy review about how it works and what works and what doesn't work and i'm kind of scrolling through it um on the screen here the section that caught my attention was this part three, how alpha works.

1:27:34Here's the main thing I learned. The AI part here is minimal. You're not learning with like an AI tutor or this or that. What you're doing is computer-based learning exercises. So it says here like a typical one might be like watch a YouTube video and then fill out an electronic worksheet about it. So teachers are curating these digital exercises. you can you can kind of summon one-on-one tutoring they say a lot of these are like remote tutors based out of brazil so if uh you're stumbling on like a worksheet you can book a coaching call with a remote teacher like someone in brazil who speaks english to kind of like help you with it the only place the ai comes in is in like analyzing your results the ai uh is like hey you did well on this but you stumbled on this so you should spend more time on this next time you work on it or something like that.

1:28:29So you're not learning from AI. So what you're really doing here, what this really is, is like what you would see, like here's an AI summary, Jesse. So it's like, hey, Everest, you achieved your two hour learner status today. Streak shout out. You had 80 % accuracy nine days in a row. You're reaching mastery target 20 days in a row. Here's good habits I observed. So it's like LLM stuff, just like observing data and writing a summary. So it's not AI learning. What it is, it's kind of like standard unschooling sort of like people who do homeschooling where you give your kids like very loose, like self-paced curricular or whatever.

1:29:04It's just that in a building. This has been around for a long time. Yeah, it is true, especially with the younger kids. The amount of time it takes them to actually like learn the specific content they need if they're sharp and they can, they're good with self-pacing a couple hours a day. Yeah, that's most of it. A lot of us saw this during the pandemic. So I think these micros will have nothing against them, but I don't know if I want to hey, I'd rather just unschool my kid if this is the case. It's YouTube videos and worksheets and like occasional tutoring calls with Brazil and then an LLM that like writes a summary.

1:29:37You could call that like a super innovative school or you could just say we're providing a room for particularly driven kids do this sort of like unschooling self-paced type of math. There's so many programs like this. A lot of homeschool kids use Beast Academy to self-paced like math. Our school uses this like advanced kids who want to like get ahead of the curriculum. It's like there's these digital tools for all sorts of things. So like smart kids that are driven and aren't not just driven and smart, but don't have hyperactivity, aren't neurodiverse in the wrong ways, you know, are able to sit still and can self-motivate.

1:30:11This tends to be like not to generalize, but it's going to tend to be like young girls more than young boys. The same people who would succeed like kind of self-pacing, unschooling at home, you could put them in this room and they'll do it there and then take workshops or whatever. So I don't know. So I have nothing against it. But what this is not, Alpha Schools is not a technological breakthrough where somehow AI is now teaching better than any teachers I've done before. There is no AI teaching here. It's just sort of like standard type of like digital learning tools that we've been using to supplement or unschool kids for years.

1:30:44So that's what I think is going on with Alpha Schools. You know, each their own, but not a breakthrough. At least that's my read. All right, Jesse. That's all the time I have for today. But thank you, everyone, for listening. we'll be back next week with another episode and until then as always stay deep hi it's cal here one more thing before you go if you like the deep questions podcast you will love my email newsletter which you can sign up for at cal newport.com each week i send out a new essay about the theory or practice of living deeply. I've been writing this newsletter since 2007 and over 70 ,000 subscribers get it sent to their inboxes each week.

1:31:31So if you are serious about resisting the forces of distraction and shallowness that afflict our world, you got to sign up for my newsletter at calnewport.com and get some deep wisdom delivered to your inbox each week.

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