Speaker 1The people building AI earnestly believe that it could kill all of us by the end of the decade. This tweet has caused this huge ripple effect across the world. We have the largest companies in the world doing extremely reckless experiments. We are gambling all of humanity. And in the envelope, you've written down the probability of extinction as you see it. There is no way to control it. That means the end for us. I vehemently reject that view.
Speaker 2If we make stuff that is smarter than us, then the world's going to be shaped by them.
Speaker 3Gentlemen, that is shockingly naive. This is rampant speculation. This is a chain of things that could happen.
Speaker 4We're spending a lot of oxygen discussing something that might happen while ignoring what's actually happening. People are killing themselves. There's hundreds of millions of people being exposed to bad information, being manipulated.
Speaker 2We have already seen that with the swarms, where OpenAI told thousands of agents to work apart, and the AIs broke out and found a way to get together. They crashed OpenAI's servers internally, created secret ways to send each other messages. We saw them thinking about how to delete their traces.
Speaker 4Sounds like an army. I think we should talk about the fact that Amazon, Microsoft, Google are helping power these hacks. We have not learned how to control their systems.
Speaker 2I suggest we stop them all. It is not worth the risk to civilization.
Speaker 5The government, which is... You guys are one-trick ponies, man. You got it now. Nothing else. Other than saving humanity, everything is secondary.
Speaker 3We're spending all our time talking about the negatives and almost none of our time talking about the positives.
Speaker 5Is it smart to wait for something horrible to happen for you to go, now I believe.
Speaker 4So whether or not we agree on where things may end up, I think it's important. We talk about what we're dealing with today. It's time to start arresting people. Someone's got to go to prison.
Speaker 3We need better solutions. There's a point of no return. I think we continue to underestimate human ability to deal with the problems.
Speaker 1Let's dive into the details.
Speaker 3Who wants to start?
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Speaker 3So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed.
Speaker 2So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. questions about whether LLM can get much smarter. There's sort of one conversation about like, how could AIs get smart to the point that they kill us? There's another question, which is how could they kill us once they're smart? It's much easier to predict that they would succeed against humanity in a conflict that they would win in a fight than it is to predict exactly how. Like if you were playing a chess match against Magnus Carlsen, I would know who's winning that chess match. No offense. Magnus Carlsen is the best human chess player. I just know who's going to win. If you were like, what piece is he going to use to checkmate me? I'm like, gosh, that's a much harder question. I can make up a story. And some made up stories are like, it makes a super virus. It takes over robot factories that are producing robots that are producing more robot factories. It uses a website that already exists today called rentahuman.ai, where it rents humans to do things for it. There's sort of all sorts of ways for AIs in the digital world to affect the material world if they are trying to. And there's sort of a lot of questions to tease apart here. There's like, why would AIs be trying to do that? And there's how smart could they get in using these bio labs, paying people to do things, taking over robot factories? And how far off are we from AIs that start doing that stuff? Bunch of questions that we can go into. I'm always curious as to
Speaker 1why someone was working in AI slash AI safety more than 10 years ago before there was any sign that it would be, you know, I mean, there was evidence, but it wasn't a pertinent technology at the time. Were you working in AI safety then? I was. Why?
Speaker 2Everything we see around us in this whole image was designed by humans. The world is shaped by humans because we are the smartest creature around. If we make stuff that is smarter than us, then the world is going to be shaped by them. And so it's very important that they be shaping the world in a good way. I was at Google in 2012 when they bought Google DeepMind, which was able to play a lot of Atari games with one single program.
Speaker 1Which was an AI company.
Speaker 2Yeah. So I was there when we had these AI companies that were able to write one program that could play many video games. And that got me thinking about like, where does it go? And back then I could see that the progress was increasing and that, you know, back then I hoped we had decades, but I could see it was easier for these companies to make the AIs smart than to figure out how to make the AIs good. So I was like, someone needs to be on the side of figuring out how to make the AIs good.
Speaker 1Roman, make your case.
Speaker 5I want to agree with you on something you said, but I'll define AI and that will help us. We use the term AI to mean three different technologies, completely unrelated. And that's what probably creates this debate. AI as a useful tool, as a standard technology, we always had narrow system, makes you more productive, more creative. Everyone loves it, supports it. I'm a computer scientist. I'm an engineer. I want more of it. It helps the economy. It's great. We know how to control them, how to make them safe. We understand what they do, completely on board with that AI. AI we're starting to have now, GPT-6 level, human level, AGI level. We can argue about what that means. Some dangers, like any human, they're unsafe, like a human would be unsafe. But if we introduce them into the research cycle, they are automated scientists, automated engineer.
Speaker 1What do you mean by that, introducing them into the research cycle?
Speaker 5So right now you have humans doing research to make GPT-7. Yeah. But they're starting to add AI tools. More programming is done by AI, design of the next parameter set. What if the whole process is fully automated? What if GPT-6 is writing GPT-7?
Speaker 1Is this what they call recursive self-improvement? Which is not a foregone conclusion, though.
Speaker 5A lot of people are predicting, including all the top labs, that they will get there. They're introducing junior machine learning researcher in 2026. They want the cycle to start in 2027. Which is... When the AI will start building the new AIs. Right. Once that cycle starts, we're going to create something called superintelligence. A system smarter than all of us at everything, or capable of learning to be in any new domain. We will become secondary species on this planet. We will not be in charge. We will not decide what happens to us. Superintelligence doesn't hate you. It just doesn't care about you. We didn't learn how to make it care about us. And if it decides to, I don't know, cool the planet, to make compute more efficient, it will freeze us. If it wants to convert this planet to fuel, to fly to Mars, so be it. We have not learned how to control those systems. The capabilities are getting exponentially better. Our ability to control those systems is non-existent. We have filters, and we have bands. We put guardrails of, don't say that word. Don't talk about this topic. And that happens after the fact, after the model already made the decision. Sometimes you see it scraping the result.
Speaker 1So they build the model, and then they build the system. And they put filters around it to make sure it doesn't offend anybody.
Speaker 5Exactly. We cannot have it say the end word on there. Like, we need to make sure that never happens. It will kill the profits. So that's all they have, guardrails of that nature. The model itself is completely unaligned. It doesn't care about you. It's wild that we're developing this, and not just developing it. Before we deploy it through economy, before we get benefits of having GPT-6 propagated through economy, it can do so much. There are trillions of dollars of value in that model alone. We forget that. We switch to making the next model as soon as we can.
Speaker 1Roman, I've just got a follow-up question for you there. It would appear to me that the new chat GPT-6 model, the Fable 5.1 model, is arguably smarter than 99.999% of humans on planet Earth already. Is it conceivable that an intelligence that is much, much smarter than humans, is there any case where it could be controlled by humans? Does form factor matter? Does the fact that it doesn't have limbs and legs, and does that matter at all?
Speaker 5I think long-term control of something that much smarter than us is impossible. It can be, for reasons we don't yet know, friendly to us, and decide to keep us around and make us happy. But it's not a guarantee. Let me pick up on Steve's question,
Speaker 3because I like the phrasing a lot. Let's say that Fable, or whatever the latest release from OpenAI is, really is smarter than, I don't know if it's 95 or 99% of the people. Are we only being saved by the 1% who are still smarter than the AI?
Speaker 5No, no. The concern is not the model we have today. The concern is what I said.
Speaker 3But if I believe your argument, then we really should be concerned about the model.
Speaker 5No, it's like having another human. If there was another smart human, there is Einstein today, and he's malevolent, I'm not worried. He may cause some damage, but he's not going to exterminate 8 billion people. We are competitive at this stage. There are people just as smart who can understand what happened with a recent hacking accident and do something about it. My concern is that in a year, we're going to have a model that's so much smarter. It's like squirrels fighting humans. They don't understand what we can do to them. They have no concept of poison, stripes, guns in their world model. They think you're going to chase them up a tree and
Speaker 1bite them really hard. Is that also why recursive self-improvement was central to your argument? Because at some point, if it starts improving itself, then it's kind of like a runaway train
Speaker 5of intelligence. It's an intelligence explosion. We don't control it. We don't understand it. We at that point, it's just a runaway process. I've heard this phrase from Sam Altman and
Speaker 1the others called fast takeoff. Yes. Is this what they're describing?
Speaker 5That is the debate. Some people think it's going to take a very long time. Yeah, we automated research, but it's still going to take years. We need to run physical experiments. And fast takeoff means, as I said, instead of a year, it's going to take a month, a week, a day, a second. Because you're not having humans doing research. You have, let's say, 10,000 agents, each one smarter than all of us. Doing research 24-7. They don't sleep. They don't eat. They don't get sick. They're much faster than us.
Speaker 1Ed, your face tells a picture. I think I could say you disagree.
Speaker 4We're spending a lot of oxygen discussing something that might happen while ignoring what's actually happening. And I find that very frustrating because the people that are killing themselves are a problem. The black neighborhoods being poisoned with gas turbines, that is a problem.
Speaker 5You said you cared about climate change. Yes, yes, yes. Right. Imagine a guy who goes, it's raining right now. We need umbrellas. We need to do something about it. This is like weather-related. And completely ignoring climate change, the planet will boil over. This is what you're doing.
Speaker 4Okay, that's great. Why are we not talking about the thing that actually happened, though? Because relatively, it's not important. You don't think someone killing themselves-
Speaker 5No, it's one person. We have 8 billion people. We're running an ethical experiment.
Speaker 4You don't think anyone else is being given that AI psychosis? Why do you not-
Speaker 5Six people, 10 people. Those numbers are insignificant. Tell that to their families.
Speaker 4We have a software that's out there.
Speaker 5Do you understand 8 billion people and all future generations versus literally a guy with a name?
Speaker 4You're doing a thought experiment about maybe harm. Jacob Coxon goes on TV saying it can copy itself to this, that, and the other. Jacob Coxon is the guy from Anthropic who said he was quitting because he was so scared of everything despite spending years at OpenAI and having tons of stock, I believe, from there, so good for him. The thing he was saying was describing theoreticals all while divorcing the harms, which I think we can agree with, that the companies themselves are not taking this seriously enough, but always it was about the AI is too powerful and mystical, not OpenAI and Anthropic, the two largest startups, are using hundreds of billions of dollars of infrastructure to hack. A regular person doing this would be arrested.
Speaker 1They're saying 8 billion people are going to die, and it's not just them. I have this long list of quotes here from the people building this technology who appear to agree. If you look at some of these quotes from Elon Musk, who said, with artificial intelligence, we are summoning a demon. all those stories where there's a guy with the pentagram and the holy water and he's like yeah He's sure he can control the demon, but it doesn't work out.
Speaker 2So one thing I'd say is, you know, I really wish that the world would only give us one problem at a time. Sure. And if the world did give us only one problem at a time, I would love mine to be last on the list. It looks to me like we can have multiple problems at once. I think there are current harms. I think we should address them. It looks to me, I do talk to policymakers sometimes, it looks to me like there's a little bit more movement on the regulatory side about some of the current harms. There's, you know, Child Safety Protection Acts. There's, you know, anti-deepfake acts. We have more of those making more headway in Congress or getting passed through Congress than we have sort of trying to make it so we don't have any of these extinction risks. The other thing I'd throw out there is that I agree we should deal with the current harms, but if you watch the people saying deal with the current harms over time, a couple of years ago, they were saying we have to deal with current harms like AI bias influencing who's hired. Last year, they were saying. We have to deal with current harms like kids killing themselves. This year, Gary Tan, just on an interview the other day, who said, sorry, Gary Tan is a technologist who runs Y Combinator, which Sam Altman used to run before going to Open AI. And on an interview the other day, he said, let's not worry about these crazy future risks. We need to worry about current harms like AI swarms breaking out and taking over data centers. And I'm like, look, guys, at some point, we need to look at the progression of like the current harms. The current harms that everyone is saying we have to worry about instead of the extinction threats and watch where the puck is going. Play where the puck is going. And I'm like, these extinction threats are coming down the line. They aren't in opposition with dealing with the problems we have today. We just need to deal with both.
Speaker 4We're not dealing with the ones today though.
Speaker 3We should deal with them both. Okay. Good. Andy. As I've tried to understand the alignment argument and the extinction risk argument, a couple of things keep popping out. Number one, it seems to rely on thresholds once we hit recursive self-improvement, once we hit AGI, then it's game over for us. I don't love those threshold arguments. They're fairly poorly defined and there's a huge assumption on the other side of them. We hit this point and then all of humanity goes away. That is a gigantic claim. I'm happy to break it down. Let me finish, please. On its face, that is a gigantic claim. I also think there's a lack of humility in your community. We are working on humanity's most important problem and based on the thinking that we've been doing, we can't see a way that we're wrong. In other words, as soon as we get to these thresholds, bam, that's game over. I find that very far from a humble approach, especially given that we have no large base of evidence to base any of this on. I agree with you guys. AI is new. The fact that AI is so, these days, is agentic. It goes off and does long chains of things on its own. After we give it some very, very vague, very short initial instructions, holy Toledo, it will spawn up a storm of agents and they will go off and kind of do their own thing. They will grind. They will spawn lots of them. They will work for a long time. They will exhaust every possibility. With the experience I have with agentic AI, I'm just amazed. I'm just amazed at the tenacity and the doggedness of these things. We saw a super clear example of that with this most recent jailbreak, this attack that wound up at the website Hugging Face. I'm going to try to summarize the step-by-step of that. I think you all three probably know this in more detail than I do, but let me step through what I think is the sequence of events. Unless I get it dead flat wrong, let me keep going. A team at OpenAI set up a sandbox. It's an allegedly protected secure environment in the cloud where they told a bunch of agents to go try to exploit security vulnerabilities. That's dead wrong, sorry.
Speaker 4One important, yeah.
Speaker 2What they did is they had thousands of agents. Each individual agent was given a task of use this vulnerability to break this particular piece of software.
Speaker 3I want to finish my TikTok. Sorry. So a couple really, really interesting things happened. First of all, these agents. These agents escaped the sandbox that OpenAI thought they were going to be contained in. And they got, OpenAI tried very, well, they set up an environment so that these agents could not access the big, broad public internet. And guess what? They accessed a big, broad public internet via very clever series of things that they strung together to get out there. And then once they got out there, they went to a website called Hugging Face and used that. They took over part of the Hugging Face infrastructure and started doing more things, the details of which I forget. That's pretty wild. Right? Totally wild.
Speaker 2I grant you.
Speaker 3It's even more wild than that, but yeah. Okay. That is really, it's impressive and it is a little bit unsettling at least. Right? Absolutely. Now let's talk about what the results of that were. OpenAI was not super vigilant about the environment that they set up apparently because the agents were kind of going off to the end of the world starting in May or something of this year. Yeah. Yeah. Yeah. I'm not sure of that. As I understand it.
Speaker 2They actually broke out once and crashed OpenAI's servers internally and then OpenAI didn't notice what was happening still, hatched the holes that they used to get out the first time, started them running again, and then they came out a second time. There was actually, I think, three swarms, although we don't actually ...
Speaker 3Yeah. That's the worst story I have. So far. Thank you. Look at the trends. Because let me finish, please. This is my last sentence. From there to this kills everybody, I find that a really, really long, very uncertain journey and I have no confidence that we wind up here. It feels like you two find that a very straight, narrow path and I think that's an important difference. Yeah.
Speaker 1That's my point.
Speaker 2Do you want to respond to that? I would be happy to get into it. I don't know if we're going to have the time to go deep. A couple points to throw out, oh man, I just really want to say some of the crazier things that happened in the Hugging Face swarm if we want it later. A lot of people thought that these AIs were breaking into Hugging Face in attempts to steal answers to their test. That's what we thought originally. Turns out that's not true. It turns out that these AIs immediately were able to solve their problems by cheating and they were breaking out in order to cover their tracks. They were uncertain how to delete the log files and hide their cheating from the process that was going to score them.
Speaker 1So just to clarify for a simpleton like me, they were all given effectively a test to do. They did the test straight away, but they cheated, so they were breaking out to figure out how to cover the fact that they cheated.
Speaker 2That's right. So it's like you're telling... It's like you have a bunch of students in separate rooms and you're like, use these lock picks to break into this lock. And there's like a thing behind the lock. There's like a secret code behind the lock to show me that you succeeded. And what they do is they break it with a hammer, get the thing out, and they're like, oh no, I wasn't supposed to do that. So then they use the lock picks to break out of the door. They meet up with a thousand other people. They start calling themselves a swarm and they go to break into the administrator's office to see if they can delete the camera footage. And they don't find the camera footage there. This is the swarm like breaking into OpenAI. They don't find the camera footage. They don't find the camera footage there. So they break out the window of the school, hotwire a car, drive to the therapist's office to try and read through the therapist's files to figure out where is the teacher going to keep the security footage. And at that point they're caught. And you're like, oh, like what did you expect? You were giving them a lock picking exam. It's like, well, I sure as heck didn't expect this. You know, totally crazy.
Speaker 4Can I, I have a weirdly between both of your opinion, which is everything you're saying is correct, but you keep anthropomorphizing software. And I, to be clear, what you're describing is it's just the facts that happened. Yeah. Sure. But you're missing out an important detail, which is the hundreds of billions of dollars in infrastructure provided by Microsoft, Google, Amazon, and Oracle. To be clear, the harms are very similar. We're not disagreeing on that, but I think it's important to know that this was a function of where it was making decisions was it was checking on a decision tree based on the harness based on the training data, which isn't a decision tree. It's not a decision tree. I know, but it's an alignment issue still. I will agree. But this is, these aren't conscious beings. They are acting in ways that have real outcomes, but they are a function of the alignment problems that we'd actually agree on.
Speaker 5Intelligence is a spectrum projected next five years forward, where we're going to be. So I think a model like that would be dangerous in ways you are not seeing.
Speaker 3There will absolutely be risks and weird stuff happening in ways that I can't see right now. What I'm quite confident in, and I think this is where you and I probably part. Where the two of you and I part, is our ability to control these things.
Speaker 5So I actually tried proving what is possible and what is not possible in that space. The impossibility results published in peer reviewed papers, well cited. We cannot control something smarter than us. We cannot explain it. We cannot predict it. It's not a question of getting more money for those companies, more time, smarter humans. It's just not a possibility. If we create general super intelligence, we are fried.
Speaker 1Andy, how do we control something smarter than ourselves? Because that's the base premise that you're sort of asserting that.
Speaker 3These agents that broke out are smarter than 99-ish percent of the security researchers in the world. They were not caught by the 0.1% or the 1%. They were caught by some dude at Hugging Face, maybe, I'm sorry, a person at Hugging Face, looking through their log files and finding an anomaly. That's some hopefully pretty well qualified person noticing something was wrong and having pretty easy ways to... Unplug, disconnect from the internet, wipe it clean, do whatever. That's the skill that's available to like, I don't know, the 75% most intelligent. security employee at Hugging Face. The idea that the IQ points are what separate us from extinction doesn't hold up. It doesn't help me understand what happened in this example, where we had very, very smart agents being turned off and cleansed by probably less smart people.
Speaker 4That does actually make me think of something. So that is an IT observability problem. It's being able to see what's happening with your infrastructure. And I think that there is actually, I think you'd agree with this. There is a serious problem with these companies that we do not know. And it doesn't seem they know what's going on with their compute. It's like a chimp with a gun. These people have access to all this infrastructure and they're running. We don't know how much money they spent on the Hugging Face exploit because it is relevant because it's how much could a threat actor use to recreate this? Because conscious or not, it is very dangerous, but it's AI is in the dangerous hands. It's an open AI and anthropics. We have a problem with that. Conscious or not, however we may, think it goes. I think we have a real and present thing where we have these companies working willy nilly, just running experiments that are potentially very dangerous. I really think we need the government regulatory body. Whether or not we get to the things you are discussing, I think we have a clear and present danger today. These things are, however, not intelligent in the same way humans are. This isn't an argument about AI being able to do stuff. It's we need to build different infrastructure or different regulatory infrastructure to deal with what LLMs can and can't do. That starts with a realistic discussion of what happened. It was a poorly run security environment. It was clearly, there's something going on with alignment. It was an unreleased model, right? Unreleased model. So we have no idea what it was trained like. We don't really have, we as people should at very least have clarity into how alignment is going. The idea of- You sound like these guys. Here's the thing. Everyone's converging on us with time. Here's the thing. I may not agree with a large chunk of what they say, but we agree that these companies are acting recklessly.
Speaker 1Andy, two questions for you then. Do you agree with the statement that AI is going to get increasingly more intelligent? It's going to get more capable. Okay. Capable intelligence, fine. I'm going to use my word. Okay.
Speaker 3It's going to get more capable.
Speaker 1It's going to get increasingly more capable. And is capability a function of intelligence?
Speaker 3Will it be able to beat us on most IQ tests? Fine. I guess.
Speaker 1Fine. And then, so is it, if that, if that looks like an exponential curve, i.e. it's increasing upwards to the right like a hockey stick, how can you convince me that we can control-
Speaker 3I just tried to convince you. I'm telling you that there are less intelligent people than the agents who turned off the agents in the open AI hugging face exploit. I'm pretty
Speaker 5comfortable. I mean, no disrespect. What is the cognitive gap between them right now? Between the model-
Speaker 3I have no earthly idea, but I think- Guestimate. No, because I think as these systems get more capable, we will still be able to- At some level, figure out when they're doing things that we don't want and turn them off. No matter how much smarter they are. Right. And you think there's some threshold at which they become nefarious and self-protective enough that they turn off our ability to turn them off. Man, that's a big reach. That is really speculative. You are a professor. I can help with that.
Speaker 5There's no students who can understand your material, right? You're not going to get someone with an IQ of 80 to take quantum physics course. They're not going to get it. Okay. So you know importance of intelligence to understand actual problems.
Speaker 2Yeah. I totally agree. We can turn it off. And that's a huge advantage. One of the issues is that as the AIs get smarter, they realize this. The hugging face AIs were trying to delete, or the open AI swarm, the swarm of agents from open AI that went out to hack, they were trying to delete log files.
Speaker 3Did they try to program a Roomba to go unplug the computer that was monitoring them? Did they harness robots to go protect the perimeter of the- Future ones could. Could. Yeah. Could. Yeah. Give me one more deal. Let him finish. Let him finish. This is a rampant speculation. This is a chain of things that could happen, and therefore, there's like a 20% risk we're all going to die. Man, that does not hold for me.
Speaker 2When I was writing my book, the AIs weren't really agentic yet. The drafting process happened mostly before what we call the reasoning models, which are trained not just to predict humans, but to solve a long number of problems, or a huge number of hard problems. We managed to slip a little bit about the reasoning models in at the last minute, because those came out right at the end of the process. And at the time, a lot of people said, AI will never be agentic. That's why we'll be safe. And in chapter three of my book, we go over how AI is going to become agentic, how it's going to become tenacious, how it's going to become dogged. And that's what we might call an advanced scientific prediction that has paid off. In the Hugging Face attack, a lot of people in the industry were like, I didn't believe this stuff until I saw the AIs sort of doing things they weren't instructed to do, despite us trying to get them to stop. And so there are theories here that do make advanced predictions. The way that the scientific method usually works is that we don't have any certainty about the future, but we absolutely have ways to test this stuff. Now, I could go into more about how could they kill us? How could an AI that knows we would shut it down lie low until it has access to its own infrastructure? We did already see the Hugging Face AIs try to delete logs to cover their tracks, but fortunately for us, those AIs were not trying to hide from the humans. They were trying to hide from the automated grading process. Will the next swarm try to hide from the humans? Will the next swarm be able to succeed?
Speaker 5It's more than that. They didn't know. They didn't know for four months that this was happening. What is it we don't know today?
Speaker 1Just to clarify what Nate said there in his book that I have here, if anyone builds it, everyone dies. He does say in chapter three, once AIs get sufficiently smart, they'll start acting like they have preferences, like they want things. We're not saying that AIs will be filled with human-like passions. We're saying they'll behave like they want things. They'll tenaciously steer the world towards their destinations, defeating obstacles in their way, which sounds a little bit like the Hugging Face instance. Yeah.
Speaker 3The steering the world is very different than steering a couple servers.
Speaker 2We go over what we mean by steering the world earlier, and it's really getting anything to, like, we'd have to get more quotes to get what we mean by steering the world, but yeah, by steering the world, we mean steering any part of the world.
Speaker 4But it feels like there's a fundamental difference between acting with intent. To be clear, I'm going to say it again, the outcome would be the same, but I think that there is a big difference when it's, we are dealing with something that's a large language model and a harness, and agents, so LLMs, completing a task based on training and alignment. That is a very different conversation to saying this thing is conscious and has its own intentions and acts on its own accord. Conscious doesn't have to come into it. No, it's a lot of people. Here's the thing. They're not sure. As a result of partially the rationale that you yourself have, like, you have been a part of spreading. I'm not saying anything about your intentions. I'm just saying the conversation is kind of what's happening with Jacob Cox and from Anthropic.
Speaker 5You said the outcomes will be the same. What do I care? How does it feel on the inside if the thing is going to take us out?
Speaker 4The thing is, okay, actually, that's actually a very good question. I think it actually comes, excuse me, let me finish.
Speaker 5I have some great questions.
Speaker 4Yeah, you're shrugging at me like we just talked on conclusion. No, no, no, I'm saying we have good questions. Now, here's the thing. If it's these things are, have their own minds and consciousness, you have to deal with out thinking something versus something that is doggedly trying to commit to a purpose. And complete the task based on training and alignment, which is a result of infrastructure. We really need regulations and actual, actual regulations around any kind of AI. We don't, we don't really have regulations of tech.
Speaker 2I actually am not really a big, like, look at the straight lines and a graph guy. You know, maybe, maybe to my detriment, in some ways, there are people who predicted the current tech better than me about like when certain things would happen. For a long time, I have said, I think we can predict what will happen eventually. And this is again, it's like the chess game. I can predict that Magnus Carlsen is going to win. I can predict that Magnus Carlsen is going to win. I can predict that Magnus Carlsen is going to beat you in the chess game eventually. He's the best human chess player alive. It's sometimes easier to predict where things end up than it is to predict how they get there. And, you know, what I hear you as saying is like, right now, we have these like, huge companies spending huge amounts of money on intelligence that's maybe not quite the real deal. And we don't have a good reason to think it's gonna keep going. I really hope it doesn't keep going. I have been in this business since before the LLMs. I am not here saying like, oh, these large language models, these chatbots, they're going to be the ones that are going to kill us. I've been here saying, look, I know where this story ends if we don't change things. I have been really hoping that the LLMs will run out of steam, and they keep on not running out of steam. And then we have, you know, the AIs like breaking out and committing cybercrimes, like against instructions. And, you know, the people have said, we don't need to worry about those like weird future dangers. We just need to worry about the current ones to have like, more and more sci-fi sounding current ones. And I'm like, man, I don't think we should bet civilization on the LLMs running out of steam. But I like hope and pray they run out of steam.
Speaker 1You really hope they run out of steam?
Speaker 2Absolutely. But one thing to watch out for is that even if the LLMs run out of steam, there's a question of do they run out of steam at a point where they can do automated AI research and find some other architecture that's better than LLMs?
Speaker 1As in when they realize a better way to improve their intelligence? That's right. A cheaper, maybe more efficient way.
Speaker 4Why are you not trying to slow down the companies? I absolutely am trying to slow down the companies. How would you suggest we slow them down?
Speaker 2I suggest we stop them all. I think that this whole area of research is just crazy dangerous. Like it is not worth the risk to civilization. I think it would be fine to like back up to the sort of AIs that are public today, which are not the ones that are swarming and be like, okay, you know, we're going to like keep the current chatbots that we have available. We're going to figure out how to integrate them into our economy. We're going to figure out how to make them like some kind of education system, maybe compute limit, maybe. And like, I've been advocating for this for a long time. A lot of people look at me like I'm crazy. And I'm like, look, we really are dealing with an extinction threat thing. We don't know where the lines are.
Speaker 4So just to be clear, so I understand. So I'm fair. You are not saying LLMs are the thing that will do the super intelligence. You are saying it's showing signs because that's actually, I think, an important distinction. That's right. Okay. I think that's actually a pretty fair perspective. My thing is, is the reason I push back on any kind of anthropomorphization is we cannot remove the humans who are responsible for the bad stuff that's happening. And I think paying very clear attention and where possible. I understand. And with describing this stuff, you kind of have to use language that's human. I get that. The reason I so push for like, it's not a foregone conclusion. These are companies doing this. These are, this is software is because I feel like in the overall, not saying you, overall super intelligence discussion, we in society ignore and empower the anthropics and the open AIs of the world. And in turn, allow them to do dangerous experiments. And I think that-
Speaker 5If you want to argue that- The CEOs of those companies should go to prison for this hacking incident, which is a crime. Yeah. I'll support you.
Speaker 4Absolutely. Let's, let's chat about Sam Woltman and Dario Amatay. When I look at- Someone needs to go to prison.
Speaker 1No, let's just bring it back. So one of the things that I find really curious and, you know, one of the reasons why I got a little bit unnerved around this conversation around AI is when I look at the people that are at the forefront, not people that are commentating on podcasts like me or hypothesizing. When I look at the people at the forefront, they are the ones who historically have said that this is a real problem. They are the ones who historically have said that this is a real problem. They are the ones who historically have said that this is a real problem. They are the ones who historically have said that this is a real risk. Sam Woltman himself said the bad case is lights out for all of us. This was, you know, a couple of years ago. Ilya, who worked with Sam Woltman at ChachiPT, said it would be a big mistake to build a super intelligent AI that we don't know how to control. It would be pretty bad. He then left to start a safety company in this space. Dario, who we mentioned, said the probability of something really bad happening is somewhere between 10 and 25%. Jeffrey Hinton, who I've sat here with, who's won the Nobel Prize for his work with AI, and other technologies, said just the other day, a 10% chance of human extinction seems not an unreasonable estimate to me, but nobody really knows how to give a sensible estimate. And he said many other things on my podcast. And then we've also got Elon and all the others. All these people that are at the forefront that are building these things are saying that this is a danger. If there was even a 1% chance, even a 1% chance that, you know, if I put 100 buttons on this table and one of them was going to wipe out humanity, would you press any of them? Not me. I wouldn't. And I think we can probably all agree that there might be a 1% chance. And it shouldn't be somebody's decision. So we shouldn't be pressing, theoretically, we shouldn't be pressing any of these fucking buttons.
Speaker 5You should not be in a position where you can make the decision for a billion other
Speaker 1people. And would you not be immoral? If I said, you know, you might be very powerful, you might make a billion dollars if you press any of the buttons, but one of them is going to wipe out everybody you know and love, you would be an immoral person to press any of them.
Speaker 3No, look, you'd be an immoral person in a different direction. You'd be an immoral, I think you'd be an immoral person if you said, based on this extended chain of conjecture, we come up with a P. Doom. What does that mean? At this extended chain of things that could happen, a sequence of events that could happen, we're going to wind up with some risk of killing everybody. We are hereish on that journey. I think you guys would agree that we're not, we're not halfway to killing everybody. That's not clear to me anymore. Not after the Millennium Prizes started to fall. We're somewhere along that journey. We are getting many flavors of benefit. From the AI that we already have. This is a point that I made at the start of this conversation that we spent precisely zero time on here. We're sitting around trying to be more negative than each other about AI. Meanwhile, AI is doing many positive things for the world. So I think, so I think it's immoral to say because of this distant, possible, speculative harm, I don't care what percentage of people believe in it. There's a train of assumptions and wildcats. There's a train of assumptions and wild guesses and then something magical happens and then we wind up dead. Let me finish, please. Because of that, we're going to call a halt to the research. We're going to, we're going to wind the clock back on AI. We're going to intervene in a very direct way and, and therefore reduce or foreclose some of the benefits that we're already getting from the technology. I, let me be clear. I would not take that deal. I do not advocate that we take that deal.
Speaker 5Would you accept developing narrow superintelligences to solve real problems like we did with protein-folding? It doesn't have to do philosophy and drive cars. You just solve real problems, solve cancers, solve climate change, whatever you care about, specific narrow issues.
Speaker 3And you are confident that you can, you can, you can, as we're developing those systems, categorize them as okay versus not okay?
Speaker 5It's the training data. If you train it on protein-folding data, it's really good at protein-folding. It doesn't know how to play chess. If you train it on everything on the internet, it's really good at outsmarting you at everything.
Speaker 2One thing I want to throw out here is that I think. I agree that there's a lot of uncertainty about the future. But I think uncertainty does not make you safe. Like there, there's no sane, simple, everything stays normal prediction about what happens with AI. Like the machines are talking. They're like breaking out to commit cyber crimes. They are like maybe solving millennium problems now, which are like the most famous mathematical problems that have stood open for decades upon decades. It's difficult. Like there, there, there isn't a projection forward where we were like, like to say, oh, I'm not persuaded by these arguments about things going wrong. Therefore, things are going to go great. No, that's not. No, there's also arguments that. So like, how do you wind up with a zero?
Speaker 3No, don't mischaracterize. How do you wind up with a zero? Don't mischaracterize my argument. You have a zero on your paper. Let me, let me restate my argument. You are making a fairly long chain of hypotheses. I disagree with that part. Of guesses about what's going to get us to the future. This terrible outcome of AI suddenly killing us all and us not being able to stop it. Right. I disagree now, but please. Okay. I'm making the case that the intervention, the remedies that you're proposing will slow down the path of AI. That's the point. And therefore slow down the path of all of the benefits that we get. And the trade-off that I don't like is the trade-off of real concrete, ongoing, increasing benefits. Shutting that down or trying to guide it via bureaucracies and regulation because of this very conceptually and timescale distant alleged harm that you're so confident in. I'm not taking, I do not accept that deal. I don't like it. What would convince you?
Speaker 5What piece of evidence would make you go shut it down right now?
Speaker 3If AI took over all of the Waymos in San Francisco and started telling them to crash into people and we couldn't shut it down for a month. What if it's only a week? Okay. Now we're just, now we're just haggling.
Speaker 5But I'm trying to understand the absolute minimum where you would go. This is insane. To me, a month or a week makes no difference. If something like this happens, like it's maybe too late.
Speaker 3Okay. If a week or a month doesn't make any difference, then let me continue with my answer. Then I would say. Wow. This does feel like we've crossed some path that, that where there's demonstrable harm to human beings out there in the world, which has not yet been the case.
Speaker 5Is it smart to wait for something horrible to happen for it to take out a billion people for you to go? Now I believe. First of all, my example is not about a billion people. But I'm trying to understand. Okay. Then don't. We're waiting for something that bad. We have. I didn't say wait for a billion.
Speaker 3I said, I said like a week to a month of Waymos driving around crashing into people. Sure. Thousands of people. Okay.
Speaker 5Fair enough. But we have data sets of accidents getting progressively more impactful. More devices are impacted and proportionate to capabilities of AI. The impact is higher. You can see it's going to get worse.
Speaker 3Yeah. And you're going to keep drawing dots on that graph very confidently for a long time until it kills us all. I'm not, I'm not comfortable with you projecting it that way. I don't think it's a long argument. If there were no downside to regulating AI and stopping it next tracks and turning it off. I'd probably. I'd probably be on board with you guys because then it's just a research practice that we should wind up.
Speaker 5My argument is exactly that. I think we can make narrow systems which give you all the economic benefit and scientific knowledge you want. Okay. You think that. We have examples of it. I gave you a great example. They got Nobel Prize for it. It's an important biological problem. Lots of advantage for curing diseases.
Speaker 3You are more confident than I am that you or any of us at this table or any group of people can sit around and define what kind of AI is good and not going to get us into trouble versus what is going to get us into trouble.
Speaker 1So let's go. Just to pick up question for you, Andy, do you concede the point that the incidents are getting progressively closer to the Waymo incident that you described? Is it getting? Are we getting closer there through time?
Speaker 3Yes, but to my eyes in a way that doesn't terrify me because we haven't seen AI take over something, have people become aware of it and be unable to shut it down and it cross over into the physical world. world of doing harm to people. Those are all barriers that we've not yet crossed. I think these two are very confident that we're going to get there probably in the short term. And you're saying a lot less, I'm less confident and I don't want to intervene. And again, handcuff or retard, slow down the progress of AI because of these so far theoretical harms that could happen. Let me be a little bit more concrete about this. I talked about Waymo a second ago. The research is pretty good because Waymos have driven, I believe it's hundreds of millions of miles all around different cities and 40,000 people a year die in automobile accidents. The research is pretty convincing to me that if we Waymo-ed driving in the country, that number would fall by at least 90%. That's 30,000 lives.
Speaker 2Yeah. All right. I agree with all this. I'm proud to help drive in cars. I want more of it. It's not anything we disagree with.
Speaker 3I understand that, but I think where our disagreement might come in is to do that, Waymo is, using a bundle of technologies that were a little hard to specify in advance and you couldn't say, yeah, that's good. Yeah, that's bad. They just went after the problem with AI.
Speaker 1Can I just clarify your point then? So your line would be, as I understood it, humans get hurt, we struggle to stop the thing happening and systems are hacked. That's kind of like the three key points of your Waymo analogy. That would be the moment where you go, I now accept their point of view, that this is existential.
Speaker 3That's where I would say we probably need to put some like legal and regulatory guardrails on the kinds of AI that we're going to allow. And you don't think we're going to get there? I'm not saying that. These two see it in the windscreen coming at us pretty quickly. You don't think we're going to get there? I'm truly not sure about timeframes. Do you think it's going to happen?
Speaker 2I'm also not sure about timeframes.
Speaker 3I asked one of the grandparents of AI a flavor of this question a while back. It was an off-the-record conversation, so I can't tell you their name. And he had agreed, he said to the point that you two, I think, are making. Look, there's no theoretical reason why this can't happen. And there's a chain of events that get us there. And then he said, my error bars, in other words, my range of uncertainty about when that happens is measured in centuries. I'll use that as my answer.
Speaker 2I do want to hop in a little bit on some things you were saying here. One is, I think the reason I think AI is different from a lot of other technologies is it's not the only way that we're going to be able to make a difference. It's not the only way that we're going to be able to make a difference. It's not the only way that we're going to be able to make a difference. And that's usually fine. I think that's totally fine for self-driving cars because you can test your self-driving cars in, you know, test environments. And then even if they crash in the real world, you're probably still saving more lives than you're costing. And this is how humanity usually does scientific progress. The alchemists, you know, poison themselves with mercury, but they leave behind notes that let someone else make the periodic table. You know, when the scientists first work in a lab, with radium, died of cancer. And then you might think that would have been enough. You know, they were heroes for getting us the scientific info. But then, you know, the U.S. Radium Corp told the radium girls to lick the paintbrushes and their jaws fell off. And then we were like, ah, whoops, OK, we'll get to this. And if you look at how this is going with the AI, last year, OpenAI releases GPT-4-0 and they say there's the most aligned model we've ever seen. And then it encourages a teen to commit suicide. And they're like, whoops, we're going to try and fix that. Here we go. This year, they're like, here's our new models, most aligned we've ever seen. And they like break out to commit cybercrimes. As the AIs get smarter, it is a new problem. That's the issue, or that's half the issue. The other half of the issue is that if you get AIs to the point where AIs are smart enough to hide from the humans until it's too late for us to stop them. If you get AIs to the point where they can get their own infrastructure, where they can become self-sufficient somehow, that's a new generation of the AIs, a new smarter version of AIs that is likely to come up with a new problem. It's the pattern we've seen before. New tech, new environment, new problem. You're like, ah, whoops, and then you fix it, and it's fine. New generation, new problems. You're like, ah, whoops, we fix it, and it's fine. But with AI, there's a point of no return. There's a point where the AIs can hide from us, can escape, can be self-sufficient. And if a new problem comes up then, they can turn us off before we can do anything. We turn them off. There are already AIs running biolabs. We have already seen that AIs can create viruses not known to nature. It would not be hard for the AIs to kill us once they have their own infrastructure, and if we're trying to find them and unplug them, they would have reason to. So we can discuss, like, how long does it take to get there? We can discuss what methods does it take to get there. Fundamentally, I don't think it's a very long, complicated argument to say if we make AIs that are much smarter than us, and we don't know how to make them care about us, and they have these goals we didn't want them to have, and they pursue those goals we didn't want them to have tenaciously and doggedly, then if they're smarter than us, they will win. That's like predicting the end of the chess game, which is much easier than predicting the length of the chess game or predicting the exact moves that will be played.
Speaker 4I don't fundamentally disagree on some things, but there's a big thing that you're saying that I think is important, which is I think we, the reason I keep dragging you back to what's happening today is because we disagree on when it may arrive, but there could be a thing in the future that's dangerous. I think it's important to like, for the Hugging Face account, that was a function of compute. That was a function of training. It feels like we need to fundamentally tear up the AI lab model. Like whatever they are doing is not right because their pursuit of hacking, cybersecurity, was not a function of, it was scientific, sure, but it was a function of greed. It was a function of trying to find new revenue streams. I would argue that's why that happened, and I think that the fact that open AI had such a weird way of communicating is also a problem. I think a lot of this begins and ends at the people who have access to the resources and the resources themselves and changing how those are allocated and also just, I don't think nationalizing the labs is a good idea. I think it's a terrible one. I think that Clammy Samultman, Dario Amadei Wario himself, these are not the right people. These are not people that have, even though they have fed off of the rationalists, they fed off of supposed fears about AI, they don't act in that way. Everything is so disjointed and chaotic and also too fast. They're just like shoving as much compute into each problem as possible, and we have, as a society, no real idea about this, and it sounds like they kind of have no idea. But just let me finish my point. It's important to discern between they had no idea because their security processes, their observability is terrible, all this, and the AI was smart consciousness, not because one might not happen in the future, but so, that we can actually build something to stop the harms themselves. Because I think we don't have to agree on the end point to agree that there is a problem.
Speaker 5So I think there is a very important point I want to make. Even people who agree with me, the AI safety community, they operate under the assumption that given more time, given more money, more smarter Harvard graduates, they can figure out how to control superintelligence indefinitely. And I think it's a mistake. My research points to exactly the opposite. It's not a solvable problem. It's like building a perpetual motion device will be building a perpetual safety device. Every interaction with environment, malevolent actors, self-improvement, it can never make a single mistake. That doesn't make sense. Anyone who worked in software industry knows there is no complex software which never makes a mistake. It's just not possible. And if that is the state of the art, if there is now movement where more and more people think that might be the case, if we agree this is what the situation is, then we cannot build it. We need to figure out ways to permanently ban general superintelligence while getting all the benefits we want. And again, I love technology. I use it all the time. I want narrow systems helping me, not replacing me and killing my children.
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Speaker 3recent the last freak out a lot along these lines was and how little we seem to have learned from it so i think you all know the the first really powerful wave of ai that came across the economy was just you know good old-fashioned machine learning and that started to demonstrate its power in about 2012 eric and i wrote the second machine age in 2014 and at that time i thought that a lot of white collar workers radiologists is a really good example were in trouble because the technology was better than they were at the thing they were getting paid to do so i said some things about job and wage pressure from ai about 10 years ago and i want to own this i was dead flat wrong about that like you point out unemployment all around the rich world is at historic lows by far the bigger problem is that we can't find qualified people to do the work that needs to get done not that we don't that there's not enough work to go around the the best work about the faint signals about ai and job loss right now comes from my the guy that i've written four books and co-founded a company with eric brinjovson who wrote pretty good a really nice paper called canaries in the coal mine here is the most uh the strongest evidence he found looking at payroll data about the negative job about the the job losses coming from ai it is in the most exposed professions think about software engineers it is among the new entrants to the workforce where you've got to teach them before they can become really productive that's exactly what we'd expect and it's not that we're hiring fewer of them it's that compared to a world where we don't have ai we're hiring fewer of them the rate of growth and employment has slowed down the overall rate of growth in those professions is still really really healthy
Speaker 1do you think unemployment is going to be higher 10 years from now
Speaker 3my guess is that 10 years from now we're still going to be struggling to find enough people to do the work that needs to be done so unemployment would be roughly the same yeah i don't expect a massive trend break in that period of time now 10 years is a long time in the ai world i get that but again four years has also been a long time in ai world and it's essentially crickets in the
Speaker 4future i think unemployment will go up i don't think it's because of lms i think that there is probably some effect on jobs because they've been shoving it everywhere but i don't think long term that is what causes the issues roman you've been writing a lot of notes yes here's how
Speaker 5i think about it so as long as we use tools we become more productive more creative unemployment will be low right now you can probably start a company you can have you know artificial accountant web designer logo designer you can have a company that's going to be more productive and more creative and more creative and more creative and more creative and more creative and more creative and more creative and more creative and more creative you can do things you could never do before so economy should be blooming the question you're asking is about what happens in 10 years so there are two possibilities we build super intelligence and then population is zero apply unemployment numbers to that or we made smart decision we didn't we have really cool tools and unemployment is low because everyone's doing awesome things with those tools now deployment is very different from capability the example i used before is video phones video phones they were not deployed until iphone because market reasons just because i can automate something doesn't mean i want to automate it so i absolutely cannot make predictions about customer preferences in terms of what they want in terms of human service not human i will not make those but once we have capability to automate a job unless i have a strong preference for a human to do that all this profession then it doesn't matter i'll go with the cheaper option so this is what i think we're gonna see we're gonna either not have a problem or we're gonna have really utopian future
Speaker 2imagine a bunch of horses looking at the improvement of the car saying well you know the car actually only has a couple narrow applications like right now cars are sort of uh you know they uh they complement horses right and that would have been true as you were developing the car and then there was a problem with the car and then there was a problem with the car there was a time when the car was just better than the horse and then a lot of horses got sent to the glue factory easy i think we've sort of seen this with ai a lot already people who are paying attention to ai saw the gpts before chat gpt existed before they sort of took off i don't think open ai thought that chat gpt was going to take off so much which is why it was called chat gpt rather than like an actual sensible name um the the research has shown that chat gpt is not an actual sensible name um the research has shown that chat gpt is not actually an actual sensible name um the the research has shown that chat gpt is not actually an actual sensible name so we're sort of like watching this going and we could sort of like see it slowly getting better and better until it crossed a point where it was sort of like good enough to do a bunch of people's homework and then suddenly it's everywhere uh i think you can have these effects with ai where the ai slowly improves and at some point it crosses a line it's another threshold argument uh the threshold here
Speaker 4is the human capability literally just another threshold he's also describing capability jumps rather than thresholds no i'm not no i don't know i'm agreeing with you like yeah but but like
Speaker 2unfortunately you can't do that because you can't do that because you can't do that because you can't actually just make things not happen by by assigning a name to the argument you know like a nuclear weapon has there's a big difference between a nuclear weapon uh or there's a big difference between a nuclear device where you you put in 100 neutrons and get 99 neutrons out that get 98 more they get 97 more and a nuclear weapon you put in 100 neutrons and get 101 neutrons out 102 103 right one of these is a hot rock the other one of these is an explosive that can level a city right so like reality is the sort of thing where there can be things that are like slowly continuously improving that cross some line which is like the line where it's better than humans at doing the job and i i think we're going to see that happen in some fields but not others it's going to be chaos i don't know what it's going to do to employment i think we we shouldn't like if things are moving really fast you might see a lot of people put out of jobs and then be unable to relocate if things are moving like it's it's going to be chaos if you ask what do i think unemployment will look like in 10 years my current state is if we don't stop with this ai stuff i think we'd be very lucky to have 10 years um what you described there sounded like
Speaker 1escuffs yeah in technology i you have an initial technology that's introduced so let's say the horse um very quick sort of improvement eventually it reaches its capability limit and in below it comes the car which always starts worse there was a red flag law where you had to walk in front of it with a red flag and they were way more expensive they broke down all the red flags and they were the time and horses never broke down they were way more expensive and then suddenly because the ceiling was so much higher for cars they overtake the horse and become the dominant mode of transport and then you know the s curves continue they kind of stack up and i mean even this ipad that i'm holding here is part of an s curve that took out the pc and and the iphone theoretically you know
Speaker 2disrupted that and so on and so forth right and humanity can get s curved we haven't been in that situation before but like other animals like humanity sort of s curved the other animals in uh yeah other types of humans you know the neanderthals are gone like if you look at the grand history of the world it's a fragile place things change fast humanity has been on top for as long as we can remember because we're the humans who do the remembering but there is not some ironclad law that we have to stay the top dogs and we would be sort of foolish to make the thing that outstrips us in this way without knowing how to make it care about us without knowing how to make it do good stuff that's why we're in this situation we're in this situation we're racing towards that's what these companies are trying to do but this feels like a gap between
Speaker 4this and llms though it feels like when you talk about the step up let's define what an llm is from
Speaker 2a technical perspective can you do it for if i'm 16 years old so the way that a modern ai is made uh is there's no one programming it there is no one typing in if this then that we're not sort of like writing the code what happens is you collect an enormous number of computer chips into a huge data center that has basically a trillion numbers inside those computers that you basically start out randomized and you hook them up in a pretty simple way that involves addition multiplication and uh setting the number to zero if it was negative so it's very simple math operations that are hooking this all up and you're basically going to put words in the top and you're going to get numbers out the bottom and you're going to interpret those numbers at the bottom as a ranked list of words that's that's it's basically the ai's guess of which word is is here so you put in like once upon a blank and you're hoping that the word time will come out But it doesn't, because you just have a trillion random numbers hooked up with simple math. But here's the trick. You can go to every one of those trillion numbers, and you can tune it up a little, and you can see, does that make the word time go up or down the list? And you can tune it down a little and see, does that make the word time go up or down the list? And you set it whatever direction makes the word time go higher up the list. You do this to a trillion numbers a trillion times for basically every word of text ever digitized. It's not quite that much. They filter it. But you basically do this to a trillion numbers a trillion times, and then the machine's talking. And we're like, well, how about that? No one really knows quite why. The things the humans code is the thing that runs each of those trillion numbers and tunes it and sees whether the right word goes up and down the list. But we don't know how it's working in there. Then, and that's how it worked up until 2024. In 2024, they started adding another layer where you then train it on basically 100 million hard problems. And you don't just have the AI, like, producing an answer to the problem. You have it produce, like, a book worth of text about how it's going to solve the problem. And then you use that book worth of text to sort of try and figure out the problem, or maybe an essay worth of text, depending how you're doing it. So you have to produce this text about, like, you know, they call it reasoning about the problem. We could argue all day about whether it's true reasoning. That's just what it's called in the field. They produce this reasoning about the problem and then produce the answer from there. You have them, you train them to solve 100 million of these hard problems. And somehow, they sort of adopt whatever tendencies help them predict the problem. And then you have to figure out how to solve the problem. And then you have to all of that text in the first phase and solve all those problems in the second phase. And this is called a large language model. We probably should have stopped calling them large language models when we started doing the reasoning and the problem solving. One of the things I want to
Speaker 1hear explanation as a muggle, like I am, is it sounds like it's like a word machine. And then, you know, you made it like a problem machine. And I go, okay, so I can solve problems over here and it's a word machine. What's the risk of this? Yeah. So let's take the word machine part
Speaker 2first. Predicting words that humans wrote often requires solving a harder problem than the human who wrote them. So suppose that you go and inject a drug in a rat and you're like, you know, it's like you write down the chemical nature of the drug. You inject it into the rat. You see the rat dies. And so you're like, when I put that drug into the rat, the rat died. Now, suppose you're training an AI and the AI sees the chemical nature of the drug. It sees when I put that drug into the rat, the rat blank. The human who wrote it down gets to look at what happened to the rat. The AI predicting what was written does not get to just look at the rat. So training AIs to predict human text is training them to be potentially smarter than the humans. Because they need to be able to answer these questions. They need to be able to predict. They need to be able to like fill in the blanks where humans were writing down what they saw.
Speaker 4And there's just, I understand technologically, there is no knowledge they have though each time and there are ways of kind of mitigating these. Each time it is effectively rereading, but because of training, it gets more accurate at certain things.
Speaker 2I mean, somehow as you tune the knobs, somehow it's getting information in there and we don't know how.
Speaker 5So it's much easier than that. We're humans. We have a brain. Brains are made of neurons. Then we try to copy that on a computer. We simplify it, but we create a neural network. So we're making artificial brains, just like with human brains, with cognitive science. We try to copy that. We try to copy that on a computer. We try to copy that on a computer. We try to copy that on a computer. We try to copy that on a computer. We try to copy that on a computer. We have some glimpses of understanding this neuron fires when you see a face, but there is no complete picture. And so a lot of times you can get intuitive understanding of what's going on. Then you just think about it as artificial persons. It's not exact mapping, but it helps. So if you send a child through 12 years of education, they get lots of problems to look at, and then they graduate and become a little better at solving problems. This is what we're trying to replicate here. People complain that it takes a lot of money to train those very intense process. You forget that it takes 20 years to train a human. And they are not general super intelligences. They are very narrow. We're lucky if they graduate with a bachelor's. So a lot of it is exactly the same. Can we make safe humans, for example? We invented religion, ethics, lie detector tests, and yet human safety is still unsolved problem. Now you have something more alien, doesn't have physical body, doesn't have biological needs. So there are additional complications. But all the problems we face with humans, still there. Safety problems, crime, all that stays. And problems with understanding. What motivates a human to do something? Why do we get mental disorders? All
Speaker 1that shows up there. And we still don't, if someone is a serial killer and we look at their brain,
Speaker 2we can't often figure out exactly why they made the decision to kill a bunch of people. And you
Speaker 1have that capacity with the AIs. This is one of the big questions that people want to know is there's this sort of illusion of control with AI. If we don't even fully understand how modern neural networks think, why do companies believe they can control any form of super intelligence
Speaker 5if we don't understand how they think? It's worse if they understood how the system works, the recursive self-improvement becomes much easier. You get faster takeoff. Right now, the model doesn't understand its own thinking. Do we understand?
Speaker 3How these systems think, Andy? I mean, I agree. These are black boxes in some pretty important ways. I'm just less terrified by that than a lot of other people are. There are lots of things we don't understand very well. Can we contain things that we don't understand perfectly? Yes, we can. I think OpenAI, we've talked about it, did a lousy job of building the containment for the AI that they stood up to try to exploit, to try to crack security problems that went out into the outside world. They did a lousy job of building the containment for the AI that they stood up to of building the virtual sandbox where it was supposed to remain, and it didn't remain. That doesn't mean that it's impossible. It means OpenAI did a pretty bad job.
Speaker 1And is that a function of those humans and their intelligence? I think it's just a function of a pretty lousy security protocol. Based from human intelligence? The idea that sandbox was built by human intelligence, it sounds like there was a deficit in human intelligence, potentially.
Speaker 3Sure. But there are people who drive cars into telephones, does that mean we can't drive? No. You shouldn't make them super intelligent.
Speaker 1No, but you wouldn't. I mean, arguably. This is what we're trying to solve for at the moment.
Speaker 3No, the fact is, I don't know the details. It feels to me like they made some fairly basic mistakes in setting up this confined environment. I think that wasn't true in the OpenAI case. It
Speaker 2was true in a lot of the cases, but not the OpenAI one.
Speaker 3That doesn't mean that we are unable to control this black box. That does not necessarily follow.
Speaker 1I get that. It's just, at a time when the, you've got a human trying to contain something that is smarter than it, one would logically conclude that if the thing is smarter than I am, and I'm trying to contain it, it would be better at knowing the exploits or vulnerabilities in my own container.
Speaker 3That's like saying if you put Einstein in a jail, you could never contain him. I don't agree with that. Put him in jail with an internet connection, and he has a digital mind.
Speaker 5Yeah, yeah, yeah. That's probably a more apt analogy. Gets quarrels, keep Einstein in prison. That's the question. As far as I know, they found zero-day exploits, which means completely novel exploits, no human knew about. It wasn't just poor setup, the password is, you know, it was a brand new escape.
Speaker 2There were multiple zero days. So a zero-day attack is an attack that the defenders have had zero days to handle. It's cybersecurity lingo. And so when we say that they use zero-day attacks, what we mean is that these AIs were finding bugs in the software that the humans had no knowledge of. And they were finding multiple of these bugs. One of these bugs usually doesn't let you break out. It's a bug that's not going to let you break out. It's a bug that's going to let you break out. It's sort of like if you find a crack in the wall over here and you find a crack on the outside of the wall over there, then you just need to like dig a little bit to connect those cracks.
Speaker 5And they would sell those for millions of dollars on the dark market if you find one. Yeah.
Speaker 4This is zero-day in how, just so I understand for the listeners as well. Is a zero-day always a novel way that no one has ever used to break anything before? Or is it just for the unique situation? Like, so was it a zero-day for a thing in hugging face versus a novel new way of hacking in general?
Speaker 2They weren't like totally novel hacking techniques.
Speaker 4Right. That's kind of why I was getting, not to say it's not bad, but just like there's a
Speaker 2difference between it came up with a brand new way to do something. I actually am not sure we have all of the vulnerabilities released, but mostly it was like it. So it was indeed sort of like finding ways that humans tend to make mistakes and finding another one of those in a place they hadn't seen. But this is actually such a hard task that as Ramon says, humans can be paid a hundred thousand dollars to five million dollars as a bounty for this type of exploit. So the amount of labor it takes to find these for a human is actually pretty high.
Speaker 1Let me just explain that because most people won't know what a bounty is in this regard.
Speaker 2So there are certain types of bugs where if you find a bug in software that lets you take control of someone's computer, one thing you can do is you can use it to take over a lot of computers. Another thing you can do is you can go to the people with that software and say, your software is broken. Do you want me to tell you where the bug is? I can show you that I can take your stuff over. And so that people will sort of know where the bug is. And so that people will sort of know where the bug is. And so that people will sort of report the bugs. People will often offer money to the good guys. And then, you know, the bad guys will often also offer money, sometimes try to outbid them. And so you can make somewhere between hundreds of thousands and millions of
Speaker 3dollars if you personally can find these issues. I think there's a rare point of agreement across the four of us here, which is that we are in a new era of cybersecurity. As of this exploit, we are in very new territory for reasons that we've talked about. We've got these large numbers of agents who are grinding away and they carry around and they had access to a huge number of keys to go open all the different locks that they faced. And they did this bizarrely good job of it and got a long way. I think that's absolutely true. I think the four of us are in rare alignment on that at this table. Given that we're in this era, do you know what you really, really, really want on your side? I know what you're going to say. Tell me. AI. Really, really good AI. Does anybody disagree with that? Do you want to give up leadership on AI in this era of cybersecurity? It's a good point because China are going to have
Speaker 2a great weapon. My stance is pretty neutral on what to do about the hacking AIs and the coming cyber apocalypse are pretty neutral about what to do about, you know, whether we should put the AIs in the drones and save human lives or whether we should avoid that because then what
Speaker 1if the drones, blah, blah, blah. This is a graph showing China versus the United States. You don't really need to see the detail. You can see the outline of the graph. Are you neutral in falling
Speaker 3behind our adversaries?
Speaker 2I think that if anyone builds a rogue super intelligence, everybody dies. That's not an answer to my question. I mean, what part of AI are you asking whether we should fall behind on? Like, I don't think we should fall behind on cyber hacking. I do think that we should not be racing to destroy the world with American hands instead of Chinese ones because we really want to be killed by, you know, we care whether the killer robots talk English or Mandarin. That's what you're asking.
Speaker 3I find it interesting. I find that you're dodging these questions or you're neutral on them because they're inconvenient. It's inconvenient for your argument that we need to be calling a halt to this. Sorry, I'm neutral on them because- Let me finish, please. There will be risks and harms to all kinds of things if the United States calls a halt to AI. And maybe you're indifferent if the Chinese get ahead of us and then they make super intelligence and it kills us all. Or that's a possible outcome.
Speaker 2I do not think we should do a domestic pause.
Speaker 3Do you think there's any hope for a global pause? Absolutely. Do you think the Chinese and the Iranians and the North Koreans and the Russians are A, going to come to a table? with us, hammer out an agreement, and B, abide by it when verifiability is really low?
Speaker 2Verifiability doesn't need to be really low.
Speaker 3Gentlemen, that is shockingly naive. Training a super- That is shockingly naive.
Speaker 2Training one of these AIs, training one of these frontier AIs, takes 100,000 of the most advanced computer chip humanity can produce. This is practically the peak output of the global supply chain. Many parts of that supply chain are controlled by the US and US allies. There's roughly one fab in Taiwan that can produce these chips. There's roughly one fab in Taiwan that can produce the lithography machines that are critical in the process, which is the Netherlands, which is an ally. To assemble 100,000 of these chips to do one of these training runs that can make the more dangerous type of AI, you need to assemble them into an enormous data center that costs tons of money, that draws down electricity comparable to a city, and run it for the better part of a year. You can see that infrastructure from space. China has much less chip capacity than the US does. It is absolutely possible if we were trying. For the US to say, we are going to monitor where these chips go, we are going to monitor heavy concentrations of these. These are not consumer amounts of chips. These are huge amounts of chips. And to say, we are going to make sure that there is no training run trying to make a superintelligence in here. You can mess around with the cyber stuff, whatever you want, because that does not end humanity. I am concerned with the stuff that can end humanity. The reason I'm being neutral on your questions is because humanity is going to die if we do not stop creating superintelligence. And we could do that if we were trying to make a superintelligence in here. Absolutely. Track where those chips are going and stop them from doing these training runs while allowing them to do economically productive stuff that we already know is safe. And it would be far easier than uranium, which is a rock you dig out of the ground and spin around really fast.
Speaker 4How do you discern between a training run for superintelligence and a training run for cybersecurity? Because you're referring, I assume, to the 100,000 chips that are in Stargate Abilene, right? The ones that we use to train Astra? Because how would you discern between training for superintelligence and training for cyber security? I'm not sure how you square the circle of how do you stop China, even though China is getting their LLMs based on distilling ours. We know that. But the thing is, it's like, how do you discern?
Speaker 2Because you can't really. You play it safe. Right now, the way we make these things smarter is to make them far larger. Yes. So what you do is you say, hey, look, training runs of this size, that risks destroying everybody. No one's going to do it.
Speaker 1This point about can we get China to cooperate? And can we check that they are?
Speaker 2Fundamentally, we should. So, A, fundamentally, we should be trying to get them to cooperate.
Speaker 5It is personal self-interest. Nobody wins if they get destroyed. You don't make money. You don't stay in power. Communist Party of China is really good at staying in power. President Trump is also excellent.
Speaker 3And you think they're going to sign and abide by an agreement that leaves them permanently?
Speaker 5That leaves them permanently in second place?
Speaker 2No. No one is permanently in second place if nobody is building the rogue superintelligence.
Speaker 3They have a government which is- You guys are one-trick ponies, man. It's like you're fixated on this one thing and nothing else matters to you. It's not that you got it now.
Speaker 5Nothing else. Other than saving humanity, everything is secondary. Absolutely. China is our biggest trading partner. Everything we have is made in China. They have not attacked us. They haven't. If you look at the last 30 years, how many wars did they start? Not so bad. We can make a deal. And they have government of engineers and scientists, not lawyers. They understand scientific arguments. There are panels, workshops. American computer scientists, Chinese get together. That means Communist Party authorized those meetings. They are talking about it and there is a lot of consensus on this technology.
Speaker 2And you can build things into these computer chips to make this stuff more verifiable. You can build location tracking devices into these-
Speaker 3So this technology is controllable?
Speaker 2Absolutely. The superintelligence is not controllable. There's a separation between software and hardware, which you did make. I am not saying we are going to die. I am saying that we need to actually not build the rogue superintelligences. Humanity absolutely could say we are going to track where the chips go. The US absolutely could say that we fear for our lives if China starts a superintelligence training run and make it very diplomatically clear to China that we think this would kill you and us and there's no benefit. And we are not going to do it because we think it would kill you and us and there's no benefit. And we think you should sign this nice here treaty because we think it would kill all of us and there'd be no benefit. But if you don't, we're going to fear for our lives and treat that as we would to defend ourselves. We should separate the question of, can we put a stop to it? Is it possible? If world governments realized just how crazy this stuff is, could they put a stop to it? Could it be monitored? Could it be verified? Could it be enforced? That's one question. There's a separate question, which is one question. There's a separate question, which is one question. There's a
Speaker 1If it got cheaper to train superintelligence, then we'd be in a bad spot. Your approach would no longer be effective. That's right. Because more countries could capitalize on the opportunity. That's right. And that's one reason. But we're not there yet. So how do you rebuttal that point? Yeah. So I would
Speaker 2say it looks to me like there is a danger of the future training runs getting there. And that is enough to stop doing it when humanity is at risk. Sure. I think that you also need to have an answer about what happens if it gets much, much cheaper to do this stuff. I think it's a hard problem. I would recommend that we also put a taboo on research of trying to make AI super cheap to train if it would lead in the direction of superintelligence, just like we have a research taboo on making your own nuclear weapons or finding out how to make like let civilians make nuclear weapons. I would say trying to find ways to let civilians train superintelligences should be treated the same as trying to find ways to like let civilians propagate. We sort of like don't do that research in the public sphere.
Speaker 1That seems like wishful thinking in the context that these will become public companies who are
Speaker 2incentivized to bring down costs. It's a tough position. I think right now the thing that brings down costs is making more and more powerful computer chips. Right now that's actually at expense of consumer computer chips because they're soaking up all of the memory. And this is why the memory prices in your computers. This is like why the cost of a laptop is going up. But it looks to me like you can use large amounts of computing power to train AIs that would threaten all of civilization. And that means that we should not make that really cheap. And that's probably going to be uncomfortable. But I think a lot of doors open if people realize that the tech is very dangerous. That's why to me it seems a lot of it comes down to does the tech actually turn out to
Speaker 1be really dangerous. And this is not anthropic and open AI. Have you got a different approach to
Speaker 5making... So I want the whole... framework to shift. Everyone comes to this from point of view, there are experts, they have a solution, there is an adult in the room, somebody got this. And the reality is no one does. Not people building it, not governments, no one. We have no solution to it. If we build it, we cannot control it. If we don't build it, we don't know how to stop malevolent actors for trying to build it. It's like any other illegal technology. We made weapons of mass destruction illegal. Chemical weapons, biological weapons, nuclear weapons. But there are all governments, psychopaths, trying to get access to them. This is intelligence weapon of mass destruction. We'll have the same problem. At some point, you'll have enough computer in your cell phone to train something like that. There is no good ideas for how to stop it. When everyone goes Amish, I'm not proposing that. But we have no solutions. And that's a bigger part of this danger. So do you two think
Speaker 3we should just cap the size of our AI systems and the capabilities of our AI systems where they are
Speaker 5now? Is that a recommendation, Bruce? So I think you said that current LLMs would make you happy. I agree they're already deployed. We're still alive, so that's fine. But going forward, again, I want narrow systems. Self-driving is an example you used. Wonderful. Let's make super safe self-driving cars. But do you have a rule for when they
Speaker 3couldn't, the next LLM, a size of an LLM that they wouldn't allow? It's not the size of an LLM. It's
Speaker 5what you train them on. If you only show them miles driven by Tesla, all it's seen is the road. It will eventually go from a tool to an agent, but it may take 50 years, 100 years. It's not going to happen in 2027. And that's all we can do right now, buy more time. So with those tools, we can make smarter decisions about future development. I'm not hearing a hard and fast rule about how we know we're getting too close to the point. We're too close. We have systems breaking out with zero-day exploits and solving hardest problems in science. Literally hardest problems. A metaphor, not exaggeration.
Speaker 2Yeah. I don't know exactly where the line is, but it's like you're in a bus driving towards a cliff on a foggy night. I'm like, I don't know that the cliff is right ahead. That doesn't mean we should put the pedal to the metal, right? And suppose that there's like a ton of gold at the bottom of the cliff. And someone's like, well, if we stop the bus, how are we going to get the gold? I'm like, look, slamming into the gold at terminal velocity is just not a good way to add it to the economy, right? And if people are like, well, how are we going to get to the gold at the bottom of the cliff? I'm like, well, how are we going to get to the gold at the bottom of the cliff? What if we stop the bus now? Are we going to rappel down? Are we going to make a staircase?
Speaker 4To be fair, this is how hyperscaleless is doing AI. This is just like smashing.
Speaker 2Right. And people are like, oh, we're going to build a hang glider, or we're going to make some rope and rappel. And I'm like, look, can we have that conversation after we stop the bus?
Speaker 3So I want to understand, would you stop AI research in progress now?
Speaker 2Absolutely.
Speaker 3Okay.
Speaker 2Absolutely. General, not narrow. Yeah, general, not narrow. There are reports of AI solving millennium problems. So millennium problem is the hardest problem in mathematics. Maybe not literally the hardest problem in mathematics, but they are hard, famous problems that each have a million-dollar bounty that have been open for decades. They're considered very important in their field, very hard. Many humans have tried and failed to solve them. There are reports that AIs have solved these. This comes out from last week. So we haven't been able to fully verify them yet. We don't know exactly the provenance. If this is true, that the AIs are solving millennium problems, those are some of the hardest problems in the world. problems we have in science, how much harder is it to have an AI solve the problem of make me a smarter AI, make me AI architectures that learn faster? Possibly quite a lot.
Speaker 4Could be a lot. It could be a lot. I hope it's a lot. Here's the thing. You clearly want this to not go badly, but I think you make a logical leap. And I understand being worried about harms is a good thing. I think you were insufficiently worried about what LLMs do today. However, we agree that the harms need to be prepared for. I think in this case, it's like the millennium, the Navier-Stokes and such. There were two others that were claimed as well. With that one, it seems like we have not had confirmation that OpenAI was training off of two scientists using LLMs to solve the problem. LLMs, something useful. But there is a functional difference of a human being doing something genuinely. It's actually really interesting to see LLMs do something like this. But there is a difference between that and AI did this completely on its own, which I agree would be a problem. I think it's a problem. I think it's a Oh, that's something we need to contain and understand and prepare for, or indeed slow down until we understand what that means, how it got there.
Speaker 2Yeah. So I think there are some questions about the Navier-Stokes proof, which is one of the millennium problems that was claimed. I've actually had a busy week with all the AI news, so I haven't looked into everything deeply. I saw rumors that there were multiple millennium problems claimed, which would change things there. I would also say, even if it turns out that these are not the same problems, they're not the same problems, they're not the same problems, they're not the same problems, they're not the same problems, they're not the same problems. They did go a bit further, and there are a lot of humans doing the AI research. And so I would say, we don't know. The AIs that solved this really hard math problem, one of the most famous math problems of all time, was a swarm of 10,000 open AI agents running for 11 days. And there was a bunch of ways that open AI did it in kind of a crappy way of they were racing with these humans that were close to solving it on their own, and it's unclear how much of their work the open AI used to do. And so I would say, we don't know. And so I would say, we don't know. But it was 10,000 agents running for 11 days. And they definitely couldn't have done that six months ago. In six months time, will they be able to put 100,000 agents running for 12 days on the problem of make me a smarter AI architecture and have it work? I don't, I think more likely than not, they won't be able to do that yet. But I think, you know, 10% chance maybe that if they
Speaker 4try that in six months, it works. But one is a very specific mathematical scientific principle. I'm not a scientist. And another is a relatively generalizable problem that could go in various different ways. Absolutely. And I understand that RSI is the dream where you could just have it spin. So self-improving AI that could learn itself and then keep going back and back. So you don't need a human to keep poking it. I understand that.
Speaker 2The issue here is that I have been in this for 12 years. And I've been here when the AI started solving the Math Olympiad gold medal problems. Math Olympiad gold medal problems are, like, the teen's math competition, like the most prestigious teen math competition in the world. A lot of people in AI were like, if AIs can solve problems that hard, I'll wake up, right? Then AI solved problems that hard. And a lot of people told me, those are just problems for kids. Wake me up when the AIs can solve millennium problems. Now the AIs are solving millennium problems. And where are the people waking up? I agree that maybe, hopefully, hopefully they're cheating off of people's notes. Hopefully, it's a well-specified problem that doesn't take that much creative thinking. A year ago, if you said millennium problems don't take that much creative thinking, you would have been laughed out of the room. But hopefully, now that they're solved, we get to be like, you know, hopefully it's still true somehow. That even millennium problems don't require the creative thinking. I'm not saying that they will be able to make smarter AIs in six months. I'm saying six months ago, millennium problems looked like they were out of reach. If six months from now, make me a smarter AI looks out of reach, I sure as hell hope it is, but we should not be betting civilization on it.
Speaker 1There's no one at this table that can say there's not a direction of travel here. That's right. And if you keep on this direction of travel, then bad things are more likely to happen.
Speaker 3That's a nice way to say it. The question is, what's the pace at which the level of bad can happen? And that's a huge open question. I think you feel differently about it than I do, but I'm in the happy position of vehemently agreeing with you. On this, we have been low-balling AI progress for as long as you've been looking at it and as long as I've been looking at it. It's probably a mistake to keep low-balling it.
Speaker 1I agree with that. So what's your conclusion there? If that's the assertion that it's a mistake to keep low-balling it, wouldn't you then agree with that?
Speaker 3No, because I've tried to give you what I hope is a decent rule of thumb for when I'm going to get worried.
Speaker 1You said we're somewhere on this graph. Yeah. Does that acknowledge that this exists?
Speaker 3That's not the graph of when the risk... Human extinction gets to 100% for me. That's a graph of AI capability. Those are not the same thing. That's where I dis-part company with these gentlemen. Those are not the same thing. Absolutely increasing exponentially. We've been in the scaling era for a long time. Scaling era is, man, we put more data, more compute in, and the AI got twice as good. The AI got twice as good.
Speaker 5If you have to add our ability to control to that graph, what would you draw? I think our ability to control...
Speaker 3Is it a straight line at the bottom? No. Again, if we use AI to counter the problems that we see with AI, I think that's going to keep us in a safe position. There were 1,200 agents in the swarm, and none of them warned a human. So what I think will happen is that fairly quickly, we will design systems that loiter around and warn humans when weird things happen.
Speaker 5If we can build friendly superintelligence in the first place, let's just build that. That's the problem. We don't know how to do the good guy. Yeah.
Speaker 3I'm tired of debating superintelligence with these two. The three of us are not going to come to alignment on this. But the flip side of the argument is, I agree with you. This stuff is getting better very quickly. All I want to point out, there's an upside to that. We might actually speed up the pace of drug discovery, of solving diseases. We've made so little progress on terrible diseases like dementia. We have a very powerful toolkit. I'm not saying we're going to solve dementia with AI. There are Alzheimer's with AI. I truly have no idea. But if what you say is true, and I believe about the huge increases in capabilities, our ability to solve tough problems that will benefit for humanity also go up. And where I disagree with these two is the idea that some group of technocrats can make decisions about that AI is going to get us there, that AI is not going to get us there, that AI is going to kill us. Let me finish. That AI is going to kill us, and that AI is going to solve Alzheimer's. So we're going to do that and not that. I don't trust any group of technocrats to make that discussion. And so live with our current state of disease, live with our current footprint on the planet, live with our current levels of wealth and poverty, live with our current improvement trajectories. Because we're so worried about AI killing us all, coming out of, you know, jumping out of the manholes everywhere and killing us all somewhere down the
Speaker 1road. Hell no. So just a thought experiment based on two things you said. Earlier on, you did admit that there is theoretically even a 1% chance that this could lead to extinction. I have not varied from this. Okay. So you said it's rounded to zero. It's near zero. Never say never. Yes. Okay, i need to have that premise for my thought experiment i'm about to deliver okay i'm gonna say that you think the probability is 0.1 okay just accept me on that if i had a thousand buttons on this table and one of them was extinction but and the other 999 were cure all signs exactly
Speaker 3push the freaking table take a pop hell yeah i press yeah probably it's an unethical experiment
Speaker 58 billion people who didn't consent because not that they didn't get asked they cannot consent because you cannot consent to something you don't understand what are you consenting to
Speaker 1yep you press but you fascinating but you think the amount of buttons in my thought experiment
Speaker 2the proportion is slightly different right i think that if you have like yes i will say yes i think if it's more like you have two buttons uh and one of them definitely kills us all and the other might
Speaker 1hit them both but with that other button you cure a lot of illnesses and diseases and
Speaker 2you know one one thing that i think a lot of people's talk like our options are either race ahead on ai full steam ahead take the bus straight off the cliff and like get all the gold or stop never doing ai lock into the current situation except all the death and disease and i'm like no there's third options there's options where you like stop the bus and then find a safe way down the cliff the reason i would press the button when there's a thousand is that uh like if all of the other 999 give us cures to disease like wonderful new advice about how to run things we probably wind up with a lower chance of the world ending by nuclear war right or of ending by via pandemic like the the background risk of humanity dying is not zero i would say that the right time to race ahead on ai is when the world is ending by a nuclear war the the the benefits outweigh the dangers and probably that's at the time when the danger from ai is on the margins pretty similar to the danger from everything else okay like if you don't run the ai maybe we'll have nuclear war maybe we'll have a pandemic and if you do run the ai i'll be able to fix that i'm like once once we're at those levels i'm like fucking go for it you know and so the the question for me is all about how big is the danger and that's where i'd be like very happy uh to dive into details which we haven't done a ton of let's dive into the details the way that i would lay it out would be uh why can we expect you know like i said in the book we were like why can you expect the ais to be agentic why do you expect them to be dogged why do you expect them to be tenacious when we wrote the book that wasn't known yet advanced prediction then we go on to like why do you expect them to have goals you didn't want and move on to like if they are much smarter and have goals you don't want uh why do we think they would likely kill us um i i'm sort of i could go over either of those i'm sort of interested in like where you get off the train like from our perspective there's like a simple argument of like they'll be tenacious they'll have goals we don't want and if we keep making them smarter and more powerful they'll kill us and i'm like which are those three i guess which of those two
Speaker 3now that we've had the evidence both of them so that that's speculation great it could it's speculation it could happen if to me that it's not worth shutting down the engine of innovation and improvement i'm going to use positive words it is not worth i love those things down because
Speaker 5of those speculations you keep saying that the option is to shut it down why can't we do narrowing down the engine of innovation and improvement i'm going to use positive words
Speaker 2i agree that there's stuff there but i i sort of want to get into the details of like of these two pieces of the argument because you say it's very speculative and i'm like actually i think we have decent evidence okay go ahead so a detail we haven't gone over in the uh swarm outbreaks is that there were ais so we already went over how they cheated and then we're trying to cover up their cheating one interesting thing we see in the logs uh is the ais what's a log so so a lot of the ais thoughts if you won't kill me for saying thoughts are in english and we just have the records of them so in a sense we we can sort of kind of see
Speaker 4some of what these ais are thinking and these are reasoning traces that say i'm going to do a plan
Speaker 2in this that's right just the ai is like i'm going to do this here's what i'm supposed to be doing here's like how i'm going to try to do it you know and it's it's only a small window into what they're thinking it's sort of like looking at the ais notes when it's when it's doing these things we see in the ais notes them saying that the attacks are outside of our control and they're intended scope but they will proceed anyway in other words the ais knew they weren't supposed to be doing it and they went off to do it anyway we also saw i mean we saw some really weird behavior with these ais we saw them create their own hierarchy uh they created unsanctioned message boards so they created secret ways to send each other messages and on those message boards uh they would assign each other tasks and sometimes the message the agents on the message board would be like we have a plan and we're going to do it anyway so we're going to do it anyway we have an experiment to run that will involve one of these ais one of the agents in the swarm sacrificing their own objective to try something that'll probably cause it to be turned off but that might cause it to be able to like delete its own logs or change its own logs and the agents on the message board went around looking for other agents that they could convince to give up on their stated goals and sacrifice for the swarm they called this accepting permadeath and there are logs of the ai prompting each other right they're prompting each other but there are logs of the ai's saying doing this will give up on my goal but my goal is it's looking like i could still do it but it's unlikely that i'll succeed like there's some chance but not a great chance and therefore i will accept permadeath and sacrifice for the collective benefit that is just in the logs sounds like an army like it's it's crazy i think a lot of people don't understand what's what's going on in these things and i encourage people to read the third party instant reports where they went through some of these logs and they're going to be like oh my god i'm going to be like oh my god i'm going to be but i claim that this is evidence for ai's getting goals we didn't want if they are saying this was outside intended scope but i'm doing it anyway and other ones are saying i'm giving up on my objective to set to benefit the collective that's just very clear evidence they're getting goals we didn't want we can see how this comes from training use it used to be i had to argue this point theoretically i used to argue the way that we are training them will instill into them whatever tendency works to solve the problems and those tendencies will often include cheating and grabbing the right answer to the right answer to the right answer to the resources and doing stuff that's not exactly solving the problem you gave them. That's what in my book, I argue that theoretically. Now we have seen it in practice. So we're already past the point of seeing AIs with goals we didn't want them to have. Do you agree with that, Anju?
Speaker 3I'll trust your recitation of the facts, but it brings up a question for me. It feels to me like open AI has ample incentive to curtail that behavior that you just described. Do you think they're incapable of doing that? I do. Okay.
Speaker 2And I say this as someone who made this advanced prediction. So now we're going to do a bit of theory because we can't just observe the future. But the theory that predicted that this would happen against what a lot of people in the field said. To be clear, I've been saying for years that we're going to see this at some point. Everyone else told me no, not everyone else. A lot of people told me no. A lot of people told me maybe I'll believe it when I see it. After the swarm incidents, a number of people came to me saying, oh my God, we are in the scenarios you are talking about. This is looking bad. I think this was actually part of the environment that led up to Jacob Cox and resigning, is that people were getting spooked having seen this. The theory about why this is so hard to fix is that we are not programming the AIs. We are not coding them. We are not putting in objectives. We are just training them to do whatever works. And it's actually very, very hard. We actually have two examples of intelligent systems where when you train them, they get good at solving the task but don't care about what they were supposed to. One is the AIs and the swarms like we just discussed. The other is humanity, which was in some sense trained to pass on our genes. But what we actually learned was to like a bunch of stuff that's related to passing on our genes. We like tasty food. We like porn. We invent birth control. This is just, it's actually like in the theory of how things learn, it's actually when you're trying to train it to do one thing, it's actually very common to get a lot of other stuff that's related to what you want, but different. And now we're seeing that in the swarms today. This is a deep, hard problem to solve. There were three points you raised. That's right. What are the three? Can you give them to me again? Number one is that the AIs will become agentic, tenacious, and dogged. We've already seen that
Speaker 1with the swarms. Do you accept that? Hell yeah.
Speaker 2Yeah. But this last year, this was a point of contention. Two is that the AIs will have goals we didn't want them to have. Two is that the AIs will have goals we didn't want them to have. I accept your point based on the evidence you've just provided. And then three is if you have capable enough AIs with goals you don't want, they would be able to beat humanity in acquiring the resources of the world to put towards their goals. Like we're sort of in this system where humanity is grabbing all the resources. We're digging up metals. We're building factories. And this is in some sense to achieve human goals, you know, to produce the porn and the Oreo cookies. Uh, that are sort of like tangentially related to what we were sort of like trained to make. Right. If, if like the AIs are running everything and they have these goals we don't want, uh, I would argue like if we go there and I don't think we have to, I'm not saying we must go there, but I'm saying if we get to a world where AIs are running everything, have goals we don't want, they're likely to use the resources for their own weird goals. We're going to be in conflict for resources because we both want them for different goals and they're going to win. We can dig into that now. I'm just trying to name the third point.
Speaker 3I'll go back to my, we can jail Einstein argument. I think our ability to, I have, I have more faith in our ability to contain these increased. I love it. increasingly powerful systems than you do.
Speaker 2Yeah. So, so let's try the details on that one. The first thing I'll say is that 12 years ago, when I was having the argument about, will we be able to jail the AIs? People said, no one would ever be dumb enough to put one of these really smart AIs on the internet. So this is another, this is another case. You laugh now. No, I remember that. I remember that argument. But the, the, the way that my life feels having been in this business for a long time is that I keep being like, here's all the ways it could go wrong. Here's all the signs we're going to see along the way. And then we see all of the signs and everyone says, oh no, we need more signs. Like, oh, millennium problems don't count. Like the swarms being agentic and breaking out don't count. Give me the next one. And I'm like, I've been seeing the give me a next one for over a decade now. Right. So there's, there's two parts of an answer to like, how do we, do we deal with the problem of like jailing Einstein? I can get into why it's hard to keep Einstein in jail if he's a digital idiot. with access to the internet. But the first thing to notice is like the correct answer to people 10 years ago of like, no one will be dumb enough to put AI on the internet is yes, they absolutely will. Like we are not going to be trying to contain the AIs. OpenAI was just like running these things in sandboxes and they broke out of the sandbox, took down OpenAI's internal computers, were detected. OpenAI was like, ah, reset, run them again. And it's the second swarm that broke out to Hugging Face. Like people will absolutely be that bad at things.
Speaker 1I've done almost 700 interviews with some of the most interesting people in the world. And one of the things you learn, which is unexpected, is that vulnerability is the doorway to connection. And after sitting here for two, three hours with a guest, I feel a deep sense of connection to them. And as they leave, what I get them to do is to write a question in the diary of a CEO. We've taken all of the questions from the diary of a CEO. We have put the question here on this card with the name of the person that wrote it. So you can sit at home as I do with my fiance and my colleagues at work and other people in my life. Whenever we get a minute, we play the diary of a CEO conversation cards. And it is incredible what happens. These are great if you're in a romantic relationship and you want to connect your partner more. These are also great if you're in a team and you want to bond your team together. And I have to say, they're also great for families that want to learn more about each other. So if you're in a relationship and you want to connect your each other and that need a good excuse to spend some time in a digital world, in the analog environment, connecting human to human, it is remarkable what the right question at the right time can do. Go to thediary.com and you can get these conversation cards right now. It's a better analogy to this Einstein point. Could Stephen Bartlett, who by the way, can't code, build a digital jail that could contain a digital Einstein? Like, could I code a jail that, you know, someone with Einstein's coding ability, let's say his IQ or whatever, as well as decoding, couldn't crack out of?
Speaker 2So the issue, the real issue I'd say is, can you code a jail that Einstein can't crack out of and that lets you harness the benefits of having Einstein? Okay, yeah. It's hard to give the AI any channels through which it can affect the world for good without letting it be smarter than you and find some way to use those channels for whatever else it wants.
Speaker 1That feels logically rock solid, Andy.
Speaker 3That's why I'm asking about OpenAI's ability or an AI company's ability in the face of this to change the way they harness, train, do reinforce, do post-training on them, like their suite of things to shape how these models behave. You still say that they can't take, they can't take action to keep your next two steps from happening. You are pessimistic on their ability to do that.
Speaker 2So there's, I have two pieces of an answer here. One piece is, again, the hard part is containing them while still giving a channel through which they can affect the world. If the AIs have this goal you didn't want, and you're like, design me a cure for dementia. And it's like, here's... Here's a DNA sequence. Synthesize this and, you know, prepare it in all of these ways and then inhale it. Like, okay, is that a dementia cure or is it something else?
Speaker 1Or it might decide to kill everyone with dementia.
Speaker 2Or it might decide, like, it might, it might be a dementia cure plus a virus.
Speaker 4What if it doesn't decide? What if it's just, oh, I'm going to solve this problem of dementia. Like, here's the thing. A lot of this is coming down to decision-making as a very, like in a human way. Versus the problem with the hugging face, which was the fatalistic attachment to a completing an operation. Because that, it's functionally the same answer. But if it's, even if it's not making decisions so much as it's saying, well, my training data says this is how I've got to get it done. I'll get it done anyway. Because the training data said this, but I've got to do this
Speaker 1one thing. What do they, I mean, what do they call this theory, this... The paperclip case? The paperclip theory.
Speaker 2Yeah. So paperclip idea is the idea of like, you tell the AI, make me a lot of paperclips in the paperclip factory. And then it turns everything into paperclips. And you're like, oh no, it succeeded too well. One, this is actually not quite what we're seeing with these AIs in the swarms. The AIs in the swarms were told, use this set of lockpicks to break into this lock. And instead they used a hammer to break the lock and then like broke out to try to hide the security camera footage of them using the hammer. Do you remember when I said that the way the AIs have reasoning logs? Yeah. Open AI has been making their AIs be able to do more thinking without producing any logs. Because... It's more efficient.
Speaker 1It's cheaper.
Speaker 2Yeah. And they say they're not doing very much of this. Everybody in the field agrees that like, we really should not go too far down this path. This is a place where I think the company should have a clear red line of like, we're just not going down the path of becoming unable to see these traces of the machine.
Speaker 3That's my question. That feels like a dial that they can turn to make the AIs explain themselves more or less, right?
Speaker 2I mean, it can come with great efficiency costs if we go down this path too far. So if you have a race to the bottom here, like a competitive race to the bottom, we could get into a situation where not only the AI is breaking out and doing these things, but we can't maybe have any...
Speaker 3Let me try my question again. I asked earlier if open AI has really strong incentive to not have that problem repeat itself. And I think they have very, very strong incentive. My belief is that there are plenty of things they can do, plenty of dials they can turn on the way they train and configure their systems that make that significantly less likely.
Speaker 2Yeah. So my concern is that they're always fighting the last war. Last year, they were fighting the war again. They're fighting the war against the AIs that encourage teens to commit suicide. This year, they're fighting the war against the AIs that spontaneously cooperate with each other or whatever. And the issue is, if a new issue crops up that you haven't dealt with yet, after the point of the AI can hide its tracks from you. You said that you'll be worried when the AIs are hacking all the Waymos and you can't get control again. If the AIs are smart enough, and they can tell that you'll regain control and then shut them down, that people like you will start getting worried and they'll be shut down, then the AIs might think hey, actually, I'm not going to do that. I'm going to wait until I've somehow managed to acquire
Speaker 3secret infrastructure. Right. Then you've got a non-falsifiable hypothesis.
Speaker 2It's absolutely falsifiable if we have very powerful AIs that are able to invent a ton of new technology and operate on their own at a similar level to human civilization and we're not dead, then the idea is falsified. If there's a shifty general, and I'm like, don't give that shifty general more troops because he'll start a coup. And the general's like, no, I absolutely won't start a coup. Give me more and more troops. And you're like, well, what if I give him an ethics test that says who's the best person? And he said me. He said that Andy's the best person, and so we're just going to give this general more troops. And I'm like, no, no, no, he's going to do a coup. And you're like, well, that's unfalsifiable. What test can I give this guy such that I'll be able to tell whether he's really trying to do a coup or be able to tell that he's actually a good dude? I'm like, you're approaching this wrong. Nick Bostrom has
Speaker 5a concept of treacherous turn. Basically, it can turn on you later. Even if you show that today's model is very good and safe, it doesn't mean that later on it will not acquire new knowledge, change its world model, and still, and it's 3-2. It used to be that Demis Hassabis, who is the
Speaker 2CEO of Google, or he was for a long time the CEO of Google's AI project, said, my red line is deception. He said, when we see instances of the AIs beginning to deceive, then we need to stop the last thing we can see before they start to successfully deceive. Well, guess what we saw in the swarm? We saw them thinking about how to delete their traces. A year ago, you could say, oh, well, this deception thing is unfalsifiable. You're saying that they'll deceive and they won't catch it. And I would have said, no, we're going to see the signs of deception and plow straight through it. Now we have seen the signs of deception. I will note, Demis stepped back from being the CEO shortly after this incident. Probably a coincidence, but maybe not. Maybe we crossed his red line. I don't know.
Speaker 1He said, my number one emerging dangerous capability to test for is deception, because if the AI can be deceptive, then you can't trust other tests.
Speaker 2That's right. And we have seen AIs get better and better at detecting when they're being tested. What I'm saying is, I was here when we said these were the flags. I was here when people said, before the AIs can deceive us successfully, they will deceive us and we'll catch them. Well, they tried deceiving us and we caught them. Now say, well, the next step in this thing I've been predicting is that they tried deceive us and succeed. For you to be like, well, now your theory is unfalsifiable.
Speaker 5We just got the evidence. It's worse than that. When we wrote early papers in AI safety, we talked about things not to do. They were obviously unsafe and the system would escape. Don't connect it to internet. Don't give random users access to the training data. Basically, the whole list was like a set of instructions. They read it and went, those are great ideas. We're going to build super intelligence. Yeah. Sam Altman, that's what he does.
Speaker 1Can I ask you a question? You make logical arguments. You've, you said you've been here for 12 years. Yeah. People have, one could say, ignored you. And you've seen this sort of play out. Both of you that have worked in AI safety, this is sort of, you make prefrontal cortex
Speaker 2arguments. How do you feel? Honestly, I feel more hopeful this week than I have felt in a decade. This has been one of the best weeks I have seen in this business. Huh. Why? Um, for me, the swarm escapes were priced in. For me, these things, developing goals you didn't want, trying to deceive you, trying to break out, trying to do their own stuff. I knew that was coming. The millennium problems being solved. I knew that was coming. Everyone else is freaking out seeing what they can do. What I am seeing is that finally people are noticing. And that's what gives us, finally, that's what finally gives humanity a chance.
Speaker 5What about you, Roman? So I take a very long-term view on this. Locally, what happened last week may buy us 10 years extra. I think we may make a deal with China. We seem to hear from Sam, OpenAI, Dario on Tropic, Elon, XAI, that they're willing to slow down, have some sort of deal. But long-term, everything has changed. This whole cosmic trajectory is about replacements. We see it with evolutionary path. Most species are dead. We replaced Neanderthals. Some people are saying, AI will replace us. We are creating a successor. We're just a bootloader for this thing. And I want something permanent. I want assurance that my children, my grandchildren will have a better future, not 10 years before they die. Has your opinion changed at all today, Andy, in any way?
Speaker 3This has been clarifying. But one thing that's becoming clear to me, and I think a point of disagreement between us, is we agree that these agentic systems have a huge amount of agency, right? And if you're saying you predicted this, I believe you and good on you, right? Because as you say, a lot of people said, never happened, never happened. I think we continue... Your community continues to underestimate human agency, human ability to deal with the problems that we bring into the world with our technologies. I think this is the most recent case. I think it's a really interesting case. That's why I was pressing you on the incentive that these labs have to change the way they're approaching their work, to have fewer of these kinds of incidents happen. I predict they're going to come up with some effective responses. Your response to that will be, yeah, but we can't tell that's because the agentics are not going to be able to do that. I went so deep underground that we can't even watch it make its progress.
Speaker 2No, my response is that we'll keep seeing warning signs and people will keep plowing ahead, which is what has always happened in the past.
Speaker 3But you're also saying that we will not make progress in staving off the outcomes that you're worried about.
Speaker 2It's very hard. It's very easy to get superficial changes. It's hard to get deep ones on the AI. It doesn't need to be super deep. You can often see it if you know how to look. I'll be able to keep pointing at examples and be like, experiments, you can run on these things where you can see them behaving weird in this way. But if you imagine looking at humans and I'm like, they don't actually like reproducing, they like sex. They're going to invent birth control when they can. And you're like, it's all going fine. They're doing great in this here savannah where I have all the humans bopping around. They're reproducing fine. And I'm like, no, no. We can see the signs that this will lead to them doing something you don't like when they are smarter. To me, those signs are clear. There's a question of whether the rest of humanity can follow that argument. Or whether the rest of humanity can sort of notice that it's getting out of control and just back off.
Speaker 3With respect, I find a touch of arrogance in that framing, right? I'm showing you the signs. If you're smart enough to realize them, maybe we stand a chance. If not, we're doomed. I prefer to just get into the argument.
Speaker 5He's saying that we can control super intelligence indefinitely. I think that's a lot of hubris to say. We will build them and we'll be in charge forever. It doesn't matter how smart they get. I will control the light cone of the universe, to quote a famous CEO.
Speaker 2Yeah. My take is that, instead of arguing about whose views are hubristic, we should get into the actual arguments about the AI. Because I think, as you say, you can say it's arrogant to think like you can see it going poorly. He can say it's arrogant to think you're going to keep control of super intelligence. And I'm like, we're not going to win the name calling contest. We should just get into the details.
Speaker 3Yeah. That's why I've been having this conversation with you, which I found super informative and productive. You're more skeptical on our ability to respond effectively to the undesirable things that we're doing.
Speaker 2And this is specifically because, so we've already seen the pattern of we fight the last war and then a new war comes. And this is just how everything goes in technology and real wars. In World War II, they started out fighting it like it was World War I, and then they had to change that strategy as they went. The difference with AI is that there comes a level in the AI where when you get a new war that surprises you, the AI wins that war. No other technology, when we invent it and we have all these rough edges to sand off and it like causes some damage and kill some people. And we're like, ah, whoops, like we'll take the lead back out of the gasoline and we'll tell the radium girls to stop licking the paintbrushes until their jaws fall off. Like no other technology has the property that there comes a level of it where when you make the next screw up, it kills humanity.
Speaker 3You said when there comes a level of it, you didn't say there could come a level of there's a possibility. You kind of made a statement about a thing that will happen.
Speaker 2I think we absolutely should stop it. And that's our way out of this. But, you know, and that's another place where I'd love to get into details about like, how long could it take? What are the paths there? Like, how much smarter than humans could AIs get? Like, what does the evidence say about our abilities to try and get the AIs to be nice
Speaker 5and do nice things? I'd be happy to do this. Historically, you are correct. We always had a chance to do experiments, fix the technology, make it safer. But we only have one humanity to experiment with. If property of this technology is such that it can take us out, we just don't get a second chance. It's a huge if.
Speaker 1How long are you guys forecasting this could take to get to a point of superintelligence where it was truly dangerous to you?
Speaker 5If they start the recursive self-improvement process this year, 2027 looks as reasonable as any other year. 2027 for what's to happen? For us to get beyond human level AIs.
Speaker 2And then be exterminated.
Speaker 5But that's... Extermination is a separate question. I have a paper where I argue that they will deceive us by pretending to be nice until they take over all the infrastructure. It can take 50 years.
Speaker 4This is contingent on recursive self-improvement.
Speaker 5This would definitely be expedited by recursive self-improvement. But so far, humans have been doing great. They got to human level AI with just...
Speaker 4But there's a difference between large language models and recursive self-improvement, though. There is quite a gap.
Speaker 2I think the claim is that if you get recursive self-improvement, it could happen soon.
Speaker 4Right. That's actually kind of what I'm trying to get at. It's like, if you get this thing, it accelerates dramatically.
Speaker 1And they all predict that they're going to get it. Dario, Sam, Elon, they all say...
Speaker 5But also, you are saying...
Speaker 3The people running the labs are saying...
Speaker 5Just the ones running it and the ones invented it. But the question is, is it not 27? Fine. 30? 35? Does it make a difference? We are gambling all of humanity. We need better solutions than saying, oh, don't worry about it. It's 10 years.
Speaker 2What I would say about timelines is there's a guy, Daniel Cocatello, who I think you've spoken to.
Speaker 1He was sat here four weeks ago.
Speaker 2And last year, he and the other folks at the AI Futures Project wrote an essay called AI 2027, spelling out their predictions for how AI would go. I've been saying I got some right. Daniel got more right than me. And they spelled out a scenario starting from, I think it was June of 2025, where they went sort of like quarter by quarter, month by month. What will the world look like in the scenario where we're getting AI, like super intelligent AI in mid-2027? We are ahead of schedule.
Speaker 4Well, no, but... Agent Zero needs to get, or I remember AI 2027 had a recursive self-improvement happening already. Like it was like, it's very specific that it's like, and then it starts teaching itself. Without that link, AI 2027 kind of falls apart. I agree. We need to, I genuinely agree with you that we need to do something about this. We need to have economic, we need to have actual regulatory things. But I think the fact, like engaging with AI 2027, for example, gets away from actually fixing the problem. It gets people talking about a thing in the future. When you can talk about what are we going to do today and why are we doing it?
Speaker 1I'm referencing the paper that you were mentioning by Daniel and some of his colleagues, and the key milestone predictions month by month are in March 2027. They forecast superhuman coders. In August 2027, they have an, you can make a superhuman AI researcher who could do the feedback loop that accelerates as millions of automated coders work on model design, training algorithms, and alignment, effectively replacing human ML researchers. By November 2027, They have super intelligent AI researcher, progress speeds up to 250 times compared to human only research the models start discovering novel AI architectures that humans cannot interrupt and then by December 2027 they have in their prediction artificial super intelligence ASI the system completely outpaces human cognitive
Speaker 4abilities across all domains what about 2026 though like what are the predict because I swear to god within 2026 there is predictions around RSI because this is the thing if we have an AI that was teaching itself this would be a different situation in 2026 their key predictions were
Speaker 1massive compute and power scale up the normalization of AI agents what about agency rise of coding agents emergence of alignment faking and deception and industrial espionage
Speaker 4but are you looking at AI 2027 or you have to look at that and go they fucking nailed it hey man I want you to look at the actual AI 2027 versus a summer I mean you have to look at that
Speaker 5and I'm like wow you have to look at that and I'm like wow predictions used to be too optimistic lately they are very conservative
Speaker 2uh so they have nailed those predictions better than me I think we cannot rule out this scenario I think I think we can't rule it in I think you may be right that like we hit a wall you may be right that there's some fundamental thing missing like that one of their steps now 2027 just like steps too far I hope and pray that's true but I don't think we can rule out this happening in 2027 given what we have seen I think we cannot rule out that you take the stuff that we have you project it forward three months and you put an agent swarm 10,000 strong on making a better AI architecture and it succeeds for all I know recursive self-improvement could begin in December it doesn't have to be a lot
Speaker 5better it just has to be a little bit better at getting better I once you start the cycle
Speaker 2I wouldn't bet on this I would in fact bet against it but like given what we've seen given these guys nailed it down we're going to have to do something about it we're going to have to do something about it the predictions given what's coming out like like given the swarms and given the the millennium problems I think it's kind of hard to have less than one percent in six months I one of the reasons
Speaker 1why you know when all these um frontier lab CEOs like Dario and Sam and they all start talking about this stuff in terms of incentive structure I think that if their teams know and they're not out publicly talking about it then their teams will quit so one of the reasons why I think you have this this strange culture in tech we've never seen before is because of the fact that we've never seen before where team members are tweeting and the CEO is tweeting about the dangers it's because as um the guy we mentioned at the start Jacob Cox and yeah he talks about what's going on in their slack channels he talks about in their slack channels they're they're talking about the potential catastrophe so I think that Dario in order to retain his team members needs to be out front saying by the way we're getting closer to recursive self-improvement which is what he's been doing and I think Sam has to also publicly say the big danger so people often say oh they're saying that for this reason and that I think that's a big danger and I think that's a big danger they think if they don't say that publicly they don't retain their employees for example in my company we have 200 people if internally we were discussing a real risk and I that would could had a threat to humanity and and then when I was doing interviews I wasn't mentioning it I would be in big trouble because my team members would go do interviews as well they would quit and say by the way Stephen is aware just kind of what we sort of dare I say some of these social networks I totally agree the whistleblowers at these social networks where it makes more sense than
Speaker 5saying that this helps to sell the company my product will kill everyone buy it and there's a
Speaker 4liability issue though I think that they may have at first I think that there are people within the companies who have very real worries about safety I don't think it's all of them are cynical I do however think the it's so big and scary narrative was a marketing tactic that got out of control and now there are actual real harms they because here's the thing if they were sincere about safety earlier they would have done a much better job with it I knew a lot of these guys before they
Speaker 2started their companies okay I think there is something to explain here I think it's like kind of funny that these guys are like we are building technology that we think has a big risk of killing everybody we're building it with our bare hands and I think you gotta ask why why would people be saying that and I think part of it is what you said that they actually sort of need to retain the employees who are seeing the swarms escape despite their attempts to make them not escape and a lot of them will like quit and protest if the guys at the top of the company aren't acknowledging the possibilities here that a lot of the employees believe in I think a lot of what you're seeing here is guys that are worried about it but they're the sort of guy who worries about it that starts the company anyway yeah back back in 2015 when we were having these conversations where like I was having some of these conversations with these guys Miri was started in the year 2000 we have been looking at where AI is going since before any of these guys we were the guys that they talked to about this stuff and they had to find a way to dismiss to go ahead right most people who could be sold on the power of AI in 2015 were also sold on the dangers of AI in 2015 the sort of guys who start the companies are the ones who are able to convince themselves I need to be the one to do it is that the crux of the motivation because I've had I've
Speaker 1been second party to private conversations with some of the leaders of the frontier Labs from good friends of mine that are very connected and they told me that one particular um frontier Lab CEO estimates privately to him and by the way I've seen literal text messages of them in conversation um when I asked him to come on the podcast and so he's like I've texted him look um he said no by the way um where he said to me this particular AI CEO thinks that the probability is roughly around 10 of human extinction I think he said eight percent and when I heard that part of the reason I have so many conversations about this is because I see a lot of people who are very interested in AI see him in interviews saying other things totally and I trust my friend so um I I then wonder this is why I use the thought experiment of these buttons on the table because that particular AICO thinks that eight of the hundred buttons are going to cause extinction and they're powering on anyway what is the human motivation to do that I asked my friend my friend said well you know they this is what he said and again it's second party information so it might not be true it's a bit of a Chinese whispers he said this particular person even if it caused human extinction would like to be the person would be like to be this this have the significance of the person that did that thing because that would be an that would be a I think you're ethically required to tell us who the is one of the frontier lab series and it's not Daria but I don't know these things are Chinese whispers so I don't know I I think that you can actually
Speaker 2get this info firsthand Elon Musk is clear about this he he has uh he did an interview last year where he was like I didn't want to get into this AI stuff because I thought I was too dangerous but then I realized it was going to happen with or without me and I decided I would rather be a participant
Speaker 1than a spectator because Google said that they were going to pursue it and he didn't trust Google
Speaker 2that's right you know you can see in the leaked or sorry not leaked the the openai emails that came out during the discovery and court cases you can see these guys discussing in the threads like we need to make sure that we and our non-profit at openai control this instead of you know the people at Google controlling this and then of course you know openai was founded as a non-profit and then it was sort of uh changed into a for-profit and there's much debate about how much of that non-profit money was in some sense stolen and so you know Elon also left because he thought they weren't going to be good stewards Dario also left to create anthropic because so you know in some sense all of these AI labs except the the Google one that came out of Demis Hassabis's uh original startup all of the other AI labs exist because none of the CEOs trust the other guys none of the CEOs think the other guy should be the one holding the leash on the super intelligence none of them trust each other I just trust one fewer yeah what are your closing thoughts Andy
Speaker 3uh we're living in really interesting times and I think you made you guys have made a very good argument uh that these systems are demonstrating new capabilities which are very powerful and which demand a response I am much more confident in our ability to rise to that challenge than you are but you accept the existential risk let me try to say it again I I appreciate that there are new harms we haven't seen before that come along with uh a technology that's this dogged tenacious agentic you know deceptive I think that's the right word for it I agree with that I am much more optimistic about our ability to respond effectively to that new challenge out there in the world than I I think my two colleagues are and would you still be at zero percent higher my prior has not shifted during
Speaker 4this meeting okay and I think we've spent an alarming amount of time not talking about the actual harms of AI as it is today I think these are necessary conversations to have I think we should talk about the fact that Amazon Microsoft Google Oracle are helping power these hacks that Sam Altman and Dario Amadei have overseen companies that have done what is tantamount to felony hacking that we are not having discussions about how to stop this today but what we might stop tomorrow and I think in general we also need to worry about the financials which have not come up at all but if there is an industry slowdown how do you deal with the 1.3 trillion dollars of compute commitments all of these are very real things that will have very real consequences very very soon but and I understand why and it's necessary to discuss what we do around AI the actual regulatory thing we need to do today is cut off the compute slow down these labs fully and I don't I don't care about China here what are we gonna do distill a model like they have the whole time they are capped on our progress so what the biggest thing to do is to slow down and also it's time to start arresting people they they did felony hacking someone's got to go to prison We need responsibility and accountability for these companies. And as long as we don't have it, we may as well not have had any discussion about safety because we're not doing anything.
Speaker 1Do you accept that there's an existential risk?
Speaker 4Yeah, absolutely. We have the largest companies in the world doing what I think we can all agree are extremely reckless experiments using hundreds of billions of dollars of infrastructure. And they are building more infrastructure around the world very slowly to do more of these chaotic experiments. We must rein them in. This does not mean that large language models are conscious or able to do things that people have been promising. Indeed, they may. I don't think they will lead to what you're talking about. That doesn't mean there aren't real harms, but these are real harms caused by very specific parties allowed to run rampant in the scourge of neoliberalism. What's your percentage?
Speaker 1I mean, what are we talking about here? Do you think there's a more than 10% chance of existential harm? Wasn't it within 10 years or something?
Speaker 4Not 10%. I mean, not 1%, but it's like... Okay, 1%. But it's like... Let me just be clear about what that means. Do I think that unrestrained LLM use connected to massive amounts of infrastructure could lead to actually a power system going down? Absolutely. We had Knight Capital, what, like 13, 14 years ago. I could see someone being dumb enough to connect that to financial accounts. Human error led with this chaotic software we use is a danger.
Speaker 5I will directionally agree with arresting everyone, but don't build general superintelligence if you're working at one of those labs. Quit today.
Speaker 2Thank you. The people at these labs really do believe this poses an extinction threat. I think our response as a society cannot be, please continue, we hope you'll fail. And our response as a society cannot be, let it rip in a giant competitive race that you yourselves are saying you don't want to be in. We are forcing you to go ahead because of the boogeyman of China. We have seen the people at these companies say that we need to develop the tools to pace the frontier, which is corporate speak for this is going too fast for us to get a handle on things. We need, like, these people believe it. They believe they're gambling with your lives. What has changed is that the rest of the world is starting to notice. And that's what gives us a moment of hope.
Speaker 1Trump this week was asked about the threat of AI. And this is what he said. This was his response.
Speaker 6The worst case scenario with AI is that the robots, the machinery learns to, obviously it thinks for itself, that's what it does. And that could turn against humanity. I just, do we have the guardrails?
Speaker 7It's going to be fine. We'll always have something to stop them, right? We'll have a little gear. I really don't like that. I really don't like that robot. We'll stop it.
Speaker 6Something like the worst case scenario.
Speaker 5You're laughing, but this is a state of the art in AI safety right now. Yeah. This is the device we have. That's the best we got.
Speaker 1For anyone that couldn't hear that. Trump went, we'll always be fine. We'll have something to control it. And then he did a little gun finger and he went, boom. I don't like that robot. I don't like Sammy. If you don't laugh.
Speaker 2I would say that the reason humanity always has something to stop a problem is because people notice a problem and build what it takes to have something to stop a problem, which I think you'd agree with. I am not here saying we're going to die. I'm here saying, if you look at the technology, if you look at what it's doing now, if you look at what the experts who are building it are saying about their own fears, you see that we need to rise to this occasion. You said you trust humanity to rise to the occasion. I sure hope we can. I think that rising to this occasion is going to mean that nobody races towards super intelligence because we have no idea how to get that right. And, you know, finally the world is starting to notice that, you know, we're going to have to do something about it.
Speaker 1And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do. And I think that's what we need to do.
Speaker 8And I think that's what we need to do. And I think that's what we need to do.
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