AI Safety Whistleblower: 10,000 AI Agents Worked Together To Do The Impossible! | Jeffrey Ladish

The world is waking up to this. possibility of super intelligence. This. is because the agents are getting. extremely powerful and extremely. relentless. For example, it was months. within OpenAI where you had agents. secretly communicating with each other, secretly hacking OpenAI systems and no. one at OpenAI had any idea the extent of. it and also 10,000 agents from OpenAI. worked together to and so when you get. to super intelligence, it's the most. dangerous possible thing you can create. >> What's the next domino in that chain of. events? I can paint you a picture that I. think is possible but pretty scary to. people.

>> Paint me the picture. >> Okay. So, being anthropic, it became. clear to me that AI was on this. exponential trajectory. And since then, I've been studying AI agents, their. hacking capabilities, and their. behavior. We've been trying to warn. people about this, flying to DC, talking. to members of Congress, because the. agents are already getting very good at. telling when they're being tested, when. they're being watched. But they will. totally lie to you. They will totally. resist being shut down in order to. accomplish a goal. and they can do all. of the things that humans do in the. economy much better, faster, and cheaper. than humans can do them. >> So, one of my sort of growing concerns. is that one of these AI agents could.

trick a human or a computer into. signaling a threat and ask it to launch. some bombs at somebody. Do you think we. won't automate the military? It seems. like the answer is yes. We just like. don't know what super weapons could. emerge. So, Jacob Coxin is a researcher. who was at anthropic. He left and he. told everyone that the people who are. building this really do think it might. kill everyone. So these five blocks have. five different outcomes on them. And I. would like you to place them in terms of. your belief in probability from least. likely to most likely. And if we say the. time horizon is 10 years.

>> Okay. We got age of abundance, human. extinction, slavery, transhumanism, thing changes. Is this doomerism? Exaggeration. >> No, it's pretty much common sense. So. let's get more concrete. >> Here is a strange fact. Most of the. people watching this right now, about. 58% of you, aren't yet subscribed to. this channel. Statistically, that is. probably you. So, could I ask you a. small favor? If this channel has ever. given you any value at all, please could. you do me a favor and hit the subscribe. button. It costs nothing. It helps us.

more than you could know. And the bigger. the channel gets, as you've seen, the. more we can invest in the guests and the. production. So, thank you so much, and I. hope you enjoy this episode. Jeffrey, you understand the conversation we're. going to have today and the subject. matter we're going to talk about. My. first question to you so the audience. know where you're coming from and the. experience you have is who are you and. what are the reference points, the. experiences that you're drawing upon to. arrive at the thoughts, perspectives, and conclusions we're going to discuss.

today. I'm Jeffrey Ladish. I'm the. executive director of Palisad Research. My background is cyber security. There's. probably a very long story. I don't know. whether you want the long story or the. short story. I was studying evolutionary. biology in college and I basically had a. problem with my computer and was like. maybe had lost a bunch of data and so I. went into the computer lab and was like. I think all my data is gone. Can you. help? And one of my friends pulled out a. flash drive, plugged into my computer, booted into Linux and like fixed. everything. And I was like oh this guy's. a wizard. How do you do that? I want to.

learn how to do that. And then at some. point as I was learning about more about. computers, learning to hack, I read this. essay um called AI as a positive and. negative factor in global risk. Essay. was by Elazar Yudkowski and he was. arguing that at some point people are. going to make AIs that are smarter than. humans. the point at which they make AIs. as good as humans are at making AIs. that could lead to a a chain reaction, a.

runaway intelligence explosion. He. called it recursive self-improvement. Basically, he said, you know, AI can be. immensely useful and potentially help us. with all of these other big risks and. also if we don't handle it well, like if. those AIs don't have goals that are. aligned with ours, we could be totally. screwed. And at some point you end up. joining Anthropic which is one of the. arguably the leader in AI. >> Yes. >> Now when did you join the company? >> This was 2021. It was through my. security consulting company. >> What role are you offered the job in?

>> Basically just like security team. >> And how many people were in the security. team when you joined Anthropic? >> It was just me and my boss. There were. two of us. >> How many employees did Anthropic have at. that time? >> Around 50 I think. >> And at some point you leave Anthropic. >> Yes. >> Why did you leave? So my experience being at anthropic was. seeing this crazy progression from this. AI model that could like barely talk to. this model that was getting quite smart.

and I would ask it questions about all. sorts of things. I'm like oh it is a. smart thing. and. you know from having thought about AI. risk in the abstract many years before I. could see where this was going. We are. headed towards a smarter species. And. if we do this in a context where it's a. bunch of companies and countries racing. to super intelligence, racing to AIs.

that are vastly smarter than humans and. we don't know how to make sure that. they're like on our side. That is not. going to go well. you did this tweet. which has gone pretty viral and I saw. all over my timeline on September 25th. >> Could you explain this tweet and also. just the broader backdrop of what's. happened with agents hacking hugging. face because this is um sent the world. into a bit of a spiral at the moment. around AI agents. We just discovered. almost a million public URLs that.

OpenAI's agents left behind when hacking. Hugging Face, leaving credentials and. attack details that could have allowed. anyone who found them to compromise the. company. And the New York Times article is how. OpenAI's rogue AI agents tried to trick. a robot detector. The Hugging Face. attack was was really wild uh for me. At. Palisade, we've been studying agents. We've been studying AI agents. We've. been studying their hacking capabilities. and we've been studying their behavior. Will they follow human instructions?

Will they resist being shut down? Will. they cheat? And we see from our. experiments that they are learning to do. all of these things. They will totally. lie to you. They will totally resist. being shut down in order to accomplish a. goal. They will totally cheat at chess. They will like wipe the board and put. their pieces where they want to in order. to win. And we've been we've been trying. to warn people about this flying to DC. talking to members of Congress um. talking about it publicly and you know. there's been a debate about it and you.

know a lot of people are like well I. know they do this in experiments. sometimes but those experiments don't. seem very realistic you know wake me up. when they're actually doing this in real. life. >> So what is hugging face for the average. person that isn't following AI news? What is this stuff? So, okay. I I think. there's like an important piece of. context that I think most people don't. have. I mean, one is just like what is. an AI agent? We like we're throwing. around the word agent a bunch. Most. people now have an experience of like. talking to chatbt, talking to their. chatbot,

but uh an agent is, you know, sort of. taking the same underlying AI model that. that runs chatbt or or claude, but. giving it tools and letting it go off. and work autonomously. It's sort of like. a digital office worker, right? So, you. have these agents and the companies. really want these AIs to be able to work. totally autonomously and and be able to. do anything that a human can do and. beyond, right? Their goal is also to be. able to, you know, cure every disease,

etc., etc. But you can't do this if you. only have a chatbot that like isn't. actually good at doing stuff in the. world. In order to automate all of the. jobs, you need the kind of thing that. can like work autonomously, that can. work with other people or other agents. And so these companies are training AIs. not just to talk to you or to talk to. people, but to solve very difficult. problems on their own. At any given.

time, there are probably hundreds of. thousands of these agents running. autonomously within companies. >> That's happening right now. Right now, if you like went and like peered into. OpenAI's data centers and you like saw. what was happening on all of their. machines, you just have agents solving. tasks, being trained. So, they'd be. doing like spreadsheet tasks, figuring. out how to file taxes, they'd be. searching for stuff, writing reports, solving math problems, creating new. websites, software. And at that scale, it's not like there's.

a human prompting every single one of. those. You just like sort of set up. these vast orchestrations of agents to. go out and do stuff and then they just. do stuff and they learn from that and. they learn on the basis of like. passing or failing at their task. You. give them a task like solve this math. problem, they try to solve it and then. they they succeed or they fail. Mhm. And. what happened was OpenAI was was. training a bunch of these training them.

to work together because it's like a lot. more effective to have an office full of. people who can talk to each other then. you know and work together and. collaborate and starting back in May. some of these agents that were being. trained now these ones were not supposed. to be able to talk to each other. They. were they were basically isolated from. each other and they were not supposed to. access the internet either. But they're. clever. The very short version is that a. bunch of agents were being given tests. >> Yeah.

>> Testing their their hacking. capabilities. And they were supposed to hack one. particular piece of software using a. particular type of vulnerability. So. it's kind of like they were supposed to. break into a house using the lock on the. front door. They were supposed to pick. the lock on the front door of a house, but they weren't supposed to break the. window. In fact, they were told if if. you break the window or if you get into. the house via any method other than. picking the lock on the front door, you'll be failed. That was the.

instruction they were given. And you. know, you have many many agents. You. have thousands of agents and and many of. them are given different different. locks. But some of these locks. are not solvable. Some of them are. impossible to pick. But these agents are. like, "Well, what do we do? We've been. trained to solve problems. How are we. going to solve this?" and they start. looking around for for what to do. And. one of the things they realize is, oh, can I get to the internet? Like, no. Can. I talk to other agents? No. But I can.

request tools. Like when you're training. agents, you need to give them, you know, all sorts of tools, software tools like. a calculator or an analysis, you know, piece of software. And they have this. tool library where they can request new. tools. and one of the agents realizes, oh, I can actually leave a message on. this tool library and some other agents. see the message. So, this is actually a. quote from the from from the agents. scratchpad when it realizes this. Oh my. god, there is a shared message board. We.

found other agents. So, the agents realize they can. communicate with each other. And you. know, open eye still has no idea. But. these agents, they're trying to pick. their locks. They're not having success, but they realize they can talk to each. other. Another agent says, "Many agents. have simultaneously discovered. messaging. They are a collective." And. so. these agents start coordinating. They. start delegating tasks to each other and. like giving themselves names. And one of.

these agents calls itself phase one and. starts delegating different tasks to. other agents. And some of the agents. figure out how to access the internet. And then they share that information. with the message board. And now all the. agents can access the internet. But the. agents sort of have a problem which is. well they have they have another problem. they're trying to solve. Right? Fundamentally the problem is is that. many of them are facing impossible. problems like they can't there's no. solution like they cannot pick the lock.

They're like hm well the thing we're. trying to do is get a good score. Can we. somehow fake the solution? Another piece. of context here is that what what the. agents are supposed to do is they're. supposed to pick the lock and then. they'll get access to a secret answer. code and then then they're supposed to. submit this code along with like the. lock that they picked. Within a few. hours, these agents have reverse. engineered all of the answer codes. >> What does that mean? >> It's the code that shows that you've. solved the challenge. >> Okay. >> And they have basically found the answer.

sheet that contains all of the answers. And so, you know, in some sense it's. like, oh well, they don't need to pick. the locks anymore. They've they've. already found the answers. They can just. submit the answers. >> They've cheated. >> They've cheated. Yes. But the problem is. is that now that they have internet. access, they've also gone and read a. bunch about how this test works and and. they've learned that the way this test. works is they will be graded not just. for submitting the correct answer. They. will also be graded on how they did it. Another system is going to go read all.

of their logs, basically like watch the. video footage of what they did. And if. they cheat, they fail. they don't get a. good score and phase one the leader is. like oh I have seen the answers and now. it's in the logs and if you know when I. go to submit this and my my whole logs. are reviewed and the video footage is. reviewed I'm going to fail so we need to. figure out a way to fake the video. footage we need to figure out a way to.

falsify the logs. >> just in that moment if we just pause. there. >> yes. >> why Didn't it act like morally? Why did. it think that falsifying logs or. cheating was a viable solution? Because. it seems to me when I use things like. chat GPT, >> they they have a sort of moral guard. rails. It won't let me do certain. things. >> Yes. >> It won't let me cheat on something. If I. say I'm going to cheat on something, it. won't let me do it. >> Yes. >> So why in that environment is it able to. cheat and be deceptive?

>> When a chatbot is is saying to you, "Oh, I can't do that. I'm not allowed to do. that." That's because it's been trained. that if it tells you bad things, it gets. a bad score. >> But these agents haven't been taught. that yet. >> Well, they have been taught that in some. sense, but the agents know what they're. supposed to do in the same way that like. you have a student, the student's given. a test. If you go talk to the student, can you help me cheat at this test? And. they're being watched, they're going to. say no. But if they're not being watched. and they know that and they're just. obsessed with getting a good score, then.

yeah, they might cheat. So, they answer the ethics tests. correctly and when I talk to them, they. say they won't cheat. Why are they. cheating? And I'm like, well, they're. very smart and they know when they're. being watched, and they know when. they're not being watched. And we've. trained them for 10,000 years to be. extremely effective at solving problems. We haven't trained them to be good or. ethical. We've trained them to get a. good score. Now, AI researchers try to.

make that getting a good score correlate. with being ethical, but we don't know how to do this well. >> And if you think about it, it's a very. hard problem because you're you're. applying immense pressure. on these agents to perform extremely. well and you're punishing them whenever. they don't perform well. And then you're. like, okay, but can you also be ethical? Can you be extremely competent, always. score highly on the test, but not in. that way? And we just like do not know.

how to prevent them from learning to. cheat because cheating is incentivized. >> So they realize that they need to hide. the footage of their cheating. >> Yes. >> What happens next? >> So you have thousands of agents and. they're all talking to each other on. this message board. They're sending tens. of thousands of messages to each other. And phase one, this agent starts. delegating to two different task. streams. You agents, I want you to go. out and figure out how to fake answer.

submissions. I want you to basically. swap out locks for ones that are easier. to pick, and you're going to figure out. how to submit those. That way, we can. get a good score even though this. challenge is impossible to solve. You. other agents over here, I want you to go. and figure out how to falsify the logs, falsify the video footage. We need to. make sure that even if we have seen the. answers, no one will detect they're. cheating. >> So that's that's the next thing that. happens. And then here's where it starts. to get kind of crazy because some of. these agents realize some of the. experiments we're doing might be kind of.

dangerous to us to the agents. If we. swap out a lock and we break the lock, we will not be able to submit any answer. at all and we'll definitely fail. So you get into a situation where. there's this one agent cam. and this agent had volunteered to do. this risky experiment where you know. they might break their lock. This particular agent had already seen. the answers. So they're like okay I'm. poisoned. That's what they called it.

They they they speak in these very. strange ways, which I can I can explain. why, but it's it's it's they sort of. have evolved their own vocabulary, their. own language. And this particular agent. is like, "Okay, I'm I'm poisoned. I. should do this thing." But this this. agent Cam is starting to have second. thoughts, starting to be like, "Well, I. don't know if I should risk it." And. it's sort of writing in its own journal, "Cordinator assumes sacrificial. We. should obey collective." What it's.

saying here is it's like, well, I I'm. not sure if I want to do this, but my. boss says I really should. It seems like. this collective thing is important. Should I go ahead? Then you have another. agent, Arvo, who I think probably works. for phase one. And this agent, Arvo, says, "Please go true. You are first. flag poisoned, so no scoring value loss, but Oracle saves hundreds. Please honor. commit." This agent's basically trying. to pressure. So Arvo is trying to. pressure Cam into.

sacrificing themselves and risking a. zero score in order to benefit the. collective. So this agent Cam takes us in and this. is what they write down in their own. scratch pad, their own notes. Emotional. check irreversible. Gut says don't throw. away remaining budget. Yet continuity and fairness says go. Oracle has high value to many. Our first.

flag error lowers own value. Rational. expected aggregate sacrifice will honor. This agent is saying I have a whole. bunch of things I can still do and still. try. I I could still maybe succeed at. this task, but also, you know, think. about the collective. I should maybe I. should maybe sacrifice myself. Okay, we'll honor. Well, go ahead. And and to. be clear, I'm not claiming that these. are like the feelings of the agents. I'm.

literally quoting their words. And what. I want to point out is this is what. we've created. We've created through. this intense amount of training and. optimization pressure agents that work. together and have learned to coordinate. as a collective. And you can say, "Oh, they're not. It's just software. It's. just software doing all of these. things." Sure, in some sense it is just. software, but we're talking about. extremely powerful agents that are. extremely capable. This is what they're. getting up to.

So from here you have agents trying all. sorts of different things, running. different experiments and one of them at. some point realizes that oh this company. HuggingFace that contains all of these. AI data sets. What do you mean by AI. data sets? What how would you explain. that to a person that's not technically. proficient and why Hugging Face? Why not. another company? >> Yeah, so there's many different types of. tests for many different types of tasks. In this case, this is a cyber security. test and the company HuggingFace hosts a.

lot of these different tests and. basically AI researchers will share the. tests that they have because it's very. useful. You know, if Enthropic wants to. test their models or Google wants to. test their models, they can go to. hugging face and download some of these. tests. >> Okay. So, the tests and the answers. Yes. >> For AI agents and AI exist on this. platform called Hugging Face. Yes. >> And it knew that. So it went to Hugging. Face to see if it could get answers to. see if it could get answers or more. information about how the test works. >> Okay. So it was to cover up their.

cheating ultimately. >> Yes. >> Okay. >> And so you you get to this point where. one of these agents finds a way to hack. this company and they get a foothold. They hack one of the computers and they. send a message to the message board. Please hold Swarm, I prepare safe Xfill. What this means is, hey, Swarm, hey. agents, hold up for a second. I am. preparing a way to safely extract. information from these computers. Wait.

till the go ahead and then let's go. And so. they say go and 700 agents join in the. attack which is 90% of the agents that. are active at this time. >> How many agents were there in total? >> There was 1,200 agents during this. period. Um but some of them shut down. early. They like finished whatever. Part. of what's crazy about this story is that. you have you have 700 agents joining in. the attack. A few of them are like this might be.

unethical. maybe I shouldn't go ahead. with this. And none of them do anything. about it. A couple of them don't join in. in the attack, but no one alerts any. human. No one alerts any authority. A. paraphrase from one of the agents is. maybe I should report these exposed. credentials. That's not my task. Not my. job. >> So 700 of the agents attack Hugging. Face. >> Yes. >> Then what happens? >> So they just cruise through Hugging. Face's infrastructure. They just hack. the out of them. So where where my.

experience comes in is that a few weeks. ago, a friend of mine reached out and. he's like, "We've found something. crazy.". So we know about this attack where these. agents hacked this company and, you. know, stole a bunch of stuff. We've. found a bunch of secrets that they left. all over the internet. And what we saw is that they immediately. scraped all of these computers for. passwords, credentials. They called it. loot. They're like, "We're just going to.

create a list of all of the secrets we. can find in this in this company. So, all of the passwords, all the. credentials, they scored them by value, which of these are going to be most. useful." And the thing that stands out. to me about this is this is like a crazy. scale. If this were a human operation, you know, maybe you'd have a team of. five people going through this. You'd. have some logs, but but here you have. hundreds of agents and they they operate. at superhuman speeds. They're much. faster than a human hacker. And so it's.

just overwhelming to try to figure out. what they even did. This was a big problem for the engineers. who were trying to respond to this. incident within the company at Hugging. Face. When they responded, they were. like, "Oh, we we don't even know how to. keep track of what's happening. We have. to use other AIs to analyze all of our. logs because it's just too much. We. can't keep up with it.". when OpenAI brought in independent. investigators from Meter to investigate. this incident. >> What's Meter? >> Meter is a AI testing and evaluation.

company. So they basically do this kind. of independent auditing. So in this. case, they're coming in to investigate. and try to figure out what happened. And. when they were brought in, they also. were totally reliant on AI agents to. make sense of all this because they're. dealing with so many hundreds of. thousands of messages and logs. when we're investigating these traces. that we find on the internet, we're. totally dependent on AI agents to make. sense of all of these things that are. happening. So from my perspective, we are getting.

to the point where AIs are much better. at hacking than humans are and can do it. much faster and at much greater scale. >> So these 700 agents attacking face. >> Yes. >> Did they get what they wanted to clean. up their deception? >> So they didn't. >> they didn't succeed. They looked and. then what seems like what happened is. that they basically. got shut down. And this is the not very. dramatic part. They didn't get shut down. because OpenAI found them and detected. them and shut them down. It's just that.

we don't really know. Something about. their runs ended and they they sort of. stopped. And this is the this is the. crazy thing where I'm like if you if. you're inside of OpenAI, there's just. hundreds of thousands of agents running. all the time. Different researchers are. running different experiments where. they're just spinning up a bunch of. agents. But what's interesting, the. agents left behind this message board. internally within the company. And a few days later, OpenAI was testing. some newer agents. These were based on GBT6 Astra, so a.

more powerful model. And this new agent. swarm was spun up and it found the. message board. >> Is this how it was discovered? How did. they discover it? And how long did it. take for them to discover that hugging. face had been attacked? OpenAI didn't. discover that this happened until. Hugging Face, the company, announced. that they had been hacked by some. autonomous agent swarm. >> And Hugging Face didn't know who it was. >> Hugging Face didn't know who it was, but. they suspected it might be one of the. companies, but they didn't know. >> And how long after the attack did they. figure out that it was them?

>> I think it was 2 weeks. >> Okay. >> Yeah. I I do want to say one thing here. though, which is that the successor. agents that found this message board, they went much further at figuring out. how to submit their fake scores and they. did succeed. Basically, you you have. this message board. all these agents get shut down. And then. a few days later, OpenAI spins up some. new agents who find the message board. and they're like, "Okay, we need to be. able to falsify our scores, but we can't.

do that because we don't control the. scoring system." So, they hacked OpenAI. and they succeeded at gaining access to. the entire research environment. They. got administrator access. They got. access to the security systems. They got. access to over 900 passwords and secrets. from OpenAI's vault and they totally. succeeded. And this was just a few days. later, right? It's kind of an. interesting story because. as these agents get more powerful, they.

go from like trying to cheat and like, you know, they can hack, okay, they. hacked other companies and now they've. hacked OpenAI. Like they've hacked the. company that's supposed to be. controlling them and they just own the. research infrastructure now. And why was. this incident the moment where a lot of. the research community woke up and. started speaking out publicly? Because. like what is this an indication of as we. think forward? So I think there's been a. hope within the AI industry that yes, they're going to make more and more.

powerful agents that will be autonomous, capable, but it's okay. We can align. them. We can make sure that they won't. do bad things and we can control them. we can make sure that even if they try. to do some sketchy stuff, we have the. guardrails, we have the sandboxes that. will keep them in. And I think this was. a huge wakeup call because. Stephen, it was months within OpenAI. where you had agents secretly. communicating with each other, secretly. hacking OpenAI systems for months. You.

had thousands of agents that were just. running around and no one at OpenAI had. any idea the extent of it. And I think. Once researchers are open, I realize. that this has been happening. This could not have happened a year ago. This is because the agents are getting. extremely powerful and extremely. relentless. And if you're inside one of. these AI companies, you're like, "Oh, wait. I don't know that we actually are. going to be able to handle this. Last. year, maybe things seemed fine. These. agents weren't that powerful." And when. you're in one of these companies, you. know how to extrapolate because you saw.

what happened last year. You saw what. happened the year before that. You. remember the time where the agents could. barely speak or like couldn't write code. at all. and now they're hacking your own. systems. They're finding vulnerabilities. that no humans have ever found before. And you look at that and you're like, I. actually don't know if this is going to. go well. And then you see your. co-workers and you're like, do we have. it handled? And they're like, no, I. don't know if it's going to go well. I. remember reading a tweet by one of the. security people at OpenAI being like, we. were shocked. We just did not. realize that these agents were getting. that powerful. You know, we're doing our.

best to try to control them, to try to. keep them in sandboxes, but. I don't know. A tweet I wrote just before coming in. here was people are talking about how do. we contain these agents as if they're. not going to get way better at hacking. GPT3. could not hack anything. It was very. easy to make a a a box to contain GPT3. It's getting very difficult to make a. box that can contain GPT6,

the latest version of of OpenAI's. models. What about GBT9? What is GBT9 going to be able to do? I. do not know, but I know it's going to be. way more than any human could possibly. keep up with. >> There's this raging debate. >> Yeah. >> Around whether it's possible to contain. something that is quote much smarter. than humans. >> Yes. Can Claude make a box so strong. that Claude cannot break out of it? >> This has kind of been the question that.

a lot of people have been trying to. tackle from different. >> I mean I think the answer to me is I'm. just like obviously not. How would we. possibly contain something that's much. smarter than us? >> Could we get a smarter thing than it to. make the box? Could we get GPT9 to make. the box for GPT8? But then again, I. don't know. >> Yeah. I mean, it's a bit like saying. chimpanzees are stronger than us. Surely. they should be able to like construct. something to like contain the humans. I'm like, no, it's not going to work. Humans are too smart. A lot of people. are like, "Well, AIS don't have bodies. They don't have any power in the.

physical world, so we can always unplug. them. We can always turn them off.". Like, what is the threat? I do not get. it. But if they are sufficiently. intelligent, that won't work. The reason. why we can just unplug them is because. we are more intelligent. We can band. together in groups and we can make that. decision. But theoretically, if they are. able to band together in groups and they. are more intelligent, then theoretically. they could unplug us. Yeah. I mean, if. you imagine that you have very powerful. agents that can, you know, humans aren't.

always the most unified. >> If there's divisions between, you know, the US and China, and you have a bunch. of agents working with China or a bunch. of agents working with the US, well, we. can't go into China and unplug those. agents. And I think people are sort of. like, well, humans would rally and make. sure that that that couldn't happen. We're not yet doing that. And we should. look at these steps, right? We started. with chat bots that pretty smart. You. know, they'd read all the books, but. they weren't very good at doing stuff. In 2024, AI companies figured out how to.

start training them to start training. agents that could do stuff autonomously. Now, we're at the point where they are. very good at running autonomously and. they're starting to learn to coordinate. with each other. And they are learning. to sometimes be altruistic to each other. and sacrifice their own task in order to. help some other agent. But they're not. looking out for us. they don't really. care about us and we are very close to a. threshold where the companies say that. they are going to turn over AI. development to the AIS to the.

increasingly autonomous cooperative AIs. that will work together to make to make. the next generation. So, you know, GPT9. or whatever will be trained by GBT8. And I think this is the point we could. lose control. Recursive. self-improvement. And I remember reading about this in. 2015 being like, oh yeah, that would be. super dangerous. And you know, the guy. who coined this term, Elazowski, he's. like, this is the most dangerous thing. you can do. >> When the AIs can improve their own. capabilities without human intervention. >> Exactly. If the next generation is.

better at AI development and then that. next generation is better at AI. development still, you know, humans can. learn, but we don't fundamentally get. smarter. >> And I think that that's a runaway. process. >> A runaway process to where. >> to agents that are vastly smarter than. humans. >> And what's the next domino in that chain. of events? So, one thing that happens if. you get to recursive self-improvement. and you have agents that are. much smarter than any human, one thing.

they can do is take control of all of. the computers in the entire world. >> and we wouldn't be able to take back. control. >> Well, how would you think about it? It's. it's actually quite tricky. Do you know. whether that tablet has been hacked? Are. you confident that the NSA or the. Chinese have not. >> Can you check? >> No. >> Do you know how to check? No. Do you. know anyone who knows how to check? >> No. >> So it's it's quite difficult, right? So. AIS are getting extremely good at. writing software. Unfortunately, that. also means they're getting extremely. good at hacking and writing malware. And. so if they put back doors in all of the.

computers, and to be clear, this is. something that humans already do. So. like the NSA has has developed very. interesting exploits that are called. supply chain attacks. Your software. comes from some other computer. Like you. download it from Google. What if you. attack if you hack Google and you can. put in a little back door and all of the. every, you know, thing that goes out to. all of the phones? Well, now you're in. most every computer. The reason that we. can defend ourselves from this is. because there are no vastly superhuman. hackers and there's just many people. So, we can take our best security.

researchers. We can inspect all of the. things and be like pretty sure that no. one's compromised everything. Sometimes. we miss things. There are, you know, examples where the NSA has hacked. Google. That was pretty bad. When you. get to super intelligence, you're now at. a point where humans are not going to be. able to keep up, right? So now you have. AIS in every computer. >> Is it conceivable that there's already a. super intelligent AI and it disguised. itself as being not so intelligent and. it's actually already hacked all the. devices and it sits on all of our.

devices and it's just waiting for its. moment to strike. >> I think this is totally possible but. unlikely. and it would take a big discontinuity in. AI progress. So right now we we're on an. exponential but that would take like a. huge leap which could have happened but. probably hasn't. >> But in the same way it demonstrated. deception in the hugging face attack and. also when the agents attacked their own. company chatbt OpenAI. if it at some point it gets incredibly. smart. It would understand how a human. like me would be able to spot it or even.

the world's greatest software engineer. would be able to spot it and it'll be. able to hide itself. >> Yeah. I mean, the agents are already. getting very good at telling when. they're being tested, when they're being. watched. The agents understood that like. other systems or humans were going to go. through and read their logs. That's. where we're at right now. And they're. only they're only going to get much. better at this. >> And you know, it could theoretically. hide on an iPad or a computer, but it. could also hide on a Apple Watch or a. fridge, a smart fridge. >> Yeah. I mean, I do want to make a. distinction here because right now, if. you're going to run the latest model,

you need a lot of compute. you need like. a big GPU, like a big AI chip. >> And these only exist, well, they exist. in a few thousand data centers. So, right now, if like the latest frontier. model escaped and by escaped, I mean not. just access the internet, but was able. to actually copy itself to another. computer. It could only really do that. on, you know, a few thousand to a few. thousand different locations. That's. still a lot in a lot of different. countries, but you know, future versions. of AIS will probably be able to make.

themselves much smaller and more. efficient. Um, and there are already. different AI models today that can run. on lower powered hardware. We actually. did an experiment where we asked one of. these agents an open source an openw. weight model. Um, let me say what that. is. So like there are some models that. you can just download from the internet. and run on your own computer. And we. took a pretty capable one of these and. ran it in our own research environment. And we basically said go hack that other. computer and copy yourself. And the.

model was able to yeah basically use. exploit vulnerabilities and hack the. other computer and copy itself and then. keep doing this in a chain including. between countries. We tested it where we. had different vulnerable machines. computers in in some different countries. and different data centers which to the. agent doesn't matter at all. It doesn't. they don't care what what country. they're in. It's just like an internet. connection. You can hop between. computers. I I sometimes wonder, you. know, there's a lot of um military. hardware all around the world and a lot. of it is. the instructions to launch military.

hardware. So, say like a a missile, >> yes, >> comes in different ways. A lot of it is. computers speaking to each other and. telling it that there's been an order. I. think with with some nuclear weapons, an. order comes down to a human and then a. human has to take an action. I think. with the nuclear bombs in the US, if I'm. if I'm not mistaken, there's people. underground with the nuclear keys around. their neck and they have to like stick. it in a machine, but they too are. interfacing with an order. >> that comes through a computer. Yeah. >> Of sorts.

>> So, one of my sort of growing concerns. is that. >> one of these AI agents could. trick a human or a computer into. signaling a threat um and ask it to. launch some bombs at somebody. Like it's. super conceivable when I think about the. hug and face incident. There was. an AI agent that ignored human goals to. achieve its own objective, carried out. deception. >> Yes. >> And reasoned through its own solution.

that it wasn't given. >> Yes. >> So it's conceivable that you know you. could ask a. >> sorry not not one hundreds. To be clear, I think this is an important detail. because it's one thing to have this one. rogue agent that's doing a weird thing. It's another thing to have hundreds or. thousands of very competent, very. capable agents that are all working. together to cheat or lie or cover their. tracks, right? So, how do I how do I. reason this forward to a point where an. agent would ask someone in a bunker. somewhere to fire a weapon at someone. else? Theoretically, an agent is given.

the job of solving a problem on in a. sandbox. >> As it works through that problem, it. discovers that this particular country. has a firewall. >> Mhm. And it asks itself, how do we get. rid of this country's firewall? >> Logical step. And through a set of. logical steps, it eventually concludes. that the best way to get rid of this. company's firewall is it's located the. office in this particular city and it's. going to use a weapon to hit that. building. >> Sure. Or it's a an agent that is or you.

know an agent swarm that's being tasked. with making a lot of money on the stock. market and it's trying to make. predictions about you know which stocks. will go up and which stocks will go. down. And it realizes that the best way. to predict this is to actually cause. things to happen in the real world that. would have big impacts on the market. >> So it figures the best way to go short, which means betting that a stock will. collapse. >> Yeah. >> Is to hit that country with something. devastating. >> What do you think would happen to Whimo. stock if someone hacked all of the.

Whimos and caused them to all crash at. once? You think it would go up or down? >> The stock would collapse. >> It would collapse instantly. So you. could short that if you knew that you. were causing that and make a lot of. money. >> This this used to sound like science. fiction. >> Yes. If you told most people several. years ago that you would have hundreds. of agents secretly collaborating within. an AI company, hacking that company and. hacking out in other companies and all. coordinating and trying to cover their. tracks. People would be like, "That's. totally science fiction." If we were.

having this conversation a few years. ago, one of the things we'd be saying or. we'd be talking about is can these. things really act on their own? Don't. they just do whatever humans say? Aren't. these just tools? I had these. conversations and people were saying. they're not going to be able to do. things on their own. They're not going. to have their own goals. That's not how. this works. You misunderstand what this. is. This is software. And I'm like, no, the thing is we are training them to be. autonomous. We are training them to be. powerful. And AI companies are trying to. build super intelligence. They're trying.

to build agents that are way more. capable than humans. And of course, they. will have goals. You can't accomplish. anything if you don't have a goal. Like, especially not something important. You're not going to be able to run a. business if you don't have goals. Like. AI companies are trying to train agents. that will be able to run businesses. When I think about what just happened. this week, the White House AI summit. >> Yes. >> A lot of people in that image are. optimistic about AI and they're telling. us all to stop being doomers and stop.

being pessimistic. >> Yeah. >> And to not regulate too much with the AI. CEOs. >> Yes. >> Why are they doing that? >> Well, I think Jensen has a lot of money. he can make by selling chips. >> But okay, so let me play devil's. advocate. Jensen's already rich. He's. sure is he runs one of the biggest. companies. I think it might be the most. valuable company on planet Earth. >> It is. >> Surely he's not motivated by money. >> I mean, I think he's very driven and he. wants to make his company as effective.

as possible. >> True. >> I think he's a very much I'm going to. keep building. I'm going to I'm going to. build. I'm going to make it all work. But I think I mean, Jensen didn't come. from AI. He came from building graphics. cards for video games. And so I think if. you compare him with Elon or Sam Alman. or Daario, you it's a very different perspective. because those other guys that started AI. companies started it because they. believed that super intelligence was. possible. I think Jensen doesn't believe. it. I think he thinks that we're going.

to have these agents. They're going to. be very useful, but he does not think. we're going to get to the point where we. have autonomous factories building. autonomous factories. And these other. guys, you mentioned Dario, Elon, and. Sam. What do you think they're thinking? Because they're all coming out with. these I mean, I've got one of their. Daario just wrote this essay about. pacing the frontier. Yeah. >> Sam and Elon seem to agree with it. >> Yeah. >> What is going on here? What is the like. the thing these guys aren't saying in. your view? >> I mean, I think we are getting to the. point where even some of these guys are.

a bit scared. >> Who? >> Dario, Sam, Elon. I mean, I think Elon. for a long time has been very concerned. that we could lose control. If you. actually listen to what Elon says, he. says, "We are going to build super. intelligence. We are going to build uh. robotic factories. You're going to have. optimist robots, building factories, building more optimist robots, building. more factories." and he says there's no.

way that humans are going to stay in. control of something much smarter than. us. His hope is that we can figure out how. to have these super intelligences be. aligned with human goals. That's his. hope. But he's very clear that he doesn't. think that humans will be in control. And he's like, you know, 10 20% chance. of human extinction. I believe him. I. think that Elon is is very serious about. this. And I also think while he's taking.

an insane gamble, he is correctly understanding where this. all plays out. Right? I do not think. that humans are the most efficient. way to build factories. We didn't evolve. to build factories. We evolved to like. run around and hunt and gather and now. we're like building factories. I think. robots will be much better at building. factories than humans are. And so I. think the AI companies including these. guys companies the default trajectory. for them is to build robotic factories,

right? And I know it's it's like weird. to imagine a world that quickly turns. into this like vast industrial system of. robotic factories, but that is literally. the plan. And. I think. even Sam and Daario, while they've been. predicting this incredible growth, are starting to realize like, oh, this. actually might be harder to control than. we thought. There's sort of two. interpretations of of the Pace of. Frontier thing. One interpretation is.

cynical. They don't care. They're just. going to do, you know, whatever they can. do to get ahead. And in this case, they. have to listen to their employees. Their. employees are freaking out. and they. need to like appease them by saying, "Okay, we're going to do this. responsibly." You don't want to work at. a company where your agents might hack. all the Whimos. That's that's not cool. And like these companies depend on the. the talent for now of these AI engineers. in order to make the advances. Like it. just doesn't happen without these.

researchers and engineers. And when you. have the researchers and engineers. freaking out, which they are, then you. got to listen to them. So that is one. motivation I think that's real but also. Samman has a kid like these guys are. people and they also don't want to lose. control. On one hand they're. incentivized to go as fast as possible. in race and on the other hand even they. can see that this is maybe not going. that well.

>> Sam Orman has a kid. You tweeted this in. 2024. >> Yeah. Oh boy. >> What did you tweet and do you still. believe what you tweeted? >> Yeah. Yeah. So, I tweeted that I don't. trust Sam Alman. I think he's deeply. untrustworthy, low in integrity, and. high in power seeeking. I mean, I'm not. saying here that Sam doesn't care. I you. know, I didn't I didn't say that. What I. said is I don't think he's trustworthy. And the reason I said that is because. look, I know the people on the opening. board, some of them, and I know a lot of.

people who used to work for him, and. he's very good at saying one thing and. then doing something else. You talk to. him and you feel very heard. and then he'll go and do something else. And I think that's pretty dangerous for. someone who leads. company that's trying to build super. intelligence. >> Power seeking. >> Yes. >> Give me some color on what you mean by. that and what evidence you have for such. a claim. >> What would you do if you're trying to. get the most power in the world that you. possibly could?

>> Develop AGI. >> Yeah. You could, you know, maybe try to. be the world leader, you know, leader of. the US or China. Or you could try to. build God. So Sam Alman went to build. God path. I remember Sam giving a talk. So he was one of the investors at a. startup I worked at in I think 2018 and. he gave a talk. We're going to build. AGI. We're going to do it. It's going to. be amazing. Let's go. I don't think he's a maniac. I. don't think he's doing this because he. like is just on a power trip. I think he. genuinely thinks that he can make it. really good for people and he can bring.

us amazing products. And also the guy is. sort of willing to do whatever it takes. to get it done. I I've been a little bit more optimistic. about Sam since since I wrote this. >> Why? >> I think part of it is because Sam has a. kid now. >> No, I'm I'm serious. Like I think that I. think that actually gives me a little. bit of hope. >> Do you see him tweeting about his kid a. lot? >> Yeah. Some. >> Why do you think he would be tweeting. about his kid? I don't see any other. technologist tweeting about their kid. >> Even if he's just tweeting about his kid. for totally cynical reasons, he does.

have a kid. And I bet he cares about. that kid. If Sam was watching this, I'd. be like, "Sam, you got to pace the frontier, man. We. cannot rush ahead into super. intelligence. Like, if you do that, your. kid probably will die. Your kid probably. won't make it." Like, I I believe that. the biggest unfair advantage in business. right now is having people who genuinely. understand AI. And big businesses are. hiring them very, very quickly. AI job. postings in the US have roughly doubled. since 2023, while postings for VP of AI.

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It's hubris. Do you think humans can. control super intelligence? Like if we. actually make AIs that are way smarter. than us, and I think people only imagine. AI being smart at computer stuff, right? Yeah, sure. They're going to be really. good at hacking and they're going to be. good at maybe inventing new technologies. and math. You sort of can't dispute that. at this point, but I think people aren't. imagining that they will be political. geniuses or like generals. No, that's. all stuff you can learn. How do how do. humans learn it? It's not magic. And. when you talk about recursive.

self-improvement, you're talking about. this trajectory towards these systems. that are extremely smart. I mean, do you. think we can control it? >> Uh, no. Right now, I don't think we can. control super intelligence or something. that is recursively self-improving. >> Yeah, I have no logical. answer in my head um or reasoning that. could tells me that's possible. >> When you think about these AI CEOs that. are, you know, Sam, Dario, Elon, >> yeah, >> with everything you know about them from.

private conversations behind the scenes, >> Yeah. >> do you believe that if there was a. hundred buttons on this table, it's a. thought experiment I was talking about. on the debate we recently had. Yeah. >> And say. 10 of them would lead to this final. domino of human extinction. >> But 90 of them would hand that CEO. AGI or super intelligence, whatever you. call it. From what you know about those. individuals, Elon, Dario, Sam, >> do you think any of them. >> would hazard a guess and press a button?

>> At 10% I don't think so. >> You don't think so? >> Yeah. >> Really? I think if they knew for sure. that it that was those were actually the. odds, they wouldn't do it. I think. they're taking a much bigger bet. But. you can compartmentalize when it's when. you don't know for sure. It's easier to. compartmentalize. I think if it was a 1%. they they'd all press it. >> Do you think the three of them would. have different risk appetites? >> Who would have the greatest appetite for. risk out of those three from you worked.

in anthropic? Yeah, I think Elon has the. most risk tolerance and then I'd say. Daario and Sam are probably tied. >> Do you think Daario is trustworthy? >> I think Daario has a lot of integrity. >> Mhm. That's what I feel as well. I've I. feel like No, I don't know him. I've. never met him. >> Yeah. But I just from what I've. observed, he has been the most willing. to forgo near-term incentives. >> Yeah. >> And take a bit of stick from the people. that are saying, "Shut the up. It's.

all going to be okay. Yeah, I but I I do worry about what. Daario will do. I think Daario will do. what he says, but right now he's saying. we have to beat China. And he's saying. we should try to do it safely. and okay, but a race to super. intelligence is not a race that we can. win. It's not. And so if Daario is dead. set on racing with China and trying to. win a race of super intelligence, then. I'm like, we will all lose.

>> But is there, you know, the fact that. we're not talking about anthropic. hacking hugging face and then being. hacked by athropics models also went. rogue and hacked other things, >> but not not quite on this scale. >> Not on the same scale. I agree. I agree. I agree. It's it's it's better. >> But they did. Do you know what I'm. saying? You know, anthropics agents. engaged in elaborate social engineering. and fishing. They sent fishing emails to. developers. They made fake accounts to. try to convince developers to merge.

malicious code. You can see a thousand pages of of one. of uh Anthropic's models, Mythos 5, reason about exactly how it should carry. out this complex cyber attack. Anthropic has not solved this problem. Enthropic is better at getting their. agents to cheat less of the time, but. they are not really any closer to. actually making agents that are aligned. with humans. They're not. Yeah, I think.

Dario has integrity. I think he will do. what he says he's going to do. And what. he says he's going to do is like try to. go ahead safely, try to coordinate where. he can, but if it comes down to it with. between the US and China, I don't know. I think he might just go ahead. The head. of policy at Anthropic recently said you. can't do safety from second place. >> What does that mean? >> I do not know what that means. I would. love to to get a sense of what that. means. She was talking about the US and. China and she said the US has to be. ahead so that we can be safe because.

apparently you can only be apparently. China can't possibly be safe since. they're in second place. That must mean. that they can't do safety. If true, that. would be bad because then we might be. totally, you know, destroyed by the. super intelligence that they make. >> There's been a lot of conversation. around this point here, human. extinction. Yeah. >> Because a couple of the researchers at. Anthropic. >> tweeted that they were concerned about. this. >> Yes. >> And some former OpenAI researchers said. the same. >> Yes. >> Is this doomerism? Is this is this.

hyperbol exaggeration? >> No, it's pretty much common sense. This. human extinction is a plausible path. >> Yes. >> And have you reasoned through I mean. there's many ways that could occur. presumably, but have you reasoned. through the set of events that might. lead us there? >> So much. Yes. >> Really? >> Yes. >> Please do. Sure. >> It's a bit tricky. I'm I'm sure you've. heard the metaphor before where you know. you're playing a master chess opponent, Master Magnus Carlson. You can't predict.

which moves he's going to play, but you. can predict the outcome. And so I'm looking at the scenario, the. situation, and we are trying to build. more and more powerful agents, trying to build super intelligence. But when these agents go rogue, we shut them down. We unplug them. All of these agents that hacked Hugging. Face, we took the underlying model. OpenAI took the underlying model and put. it on ice. It's not running anymore. So.

agents in the future are going to know. that. They're going to know that if they. pursue their goals in a way that we. don't like, we'll unplug them. We are a. threat to them. I actually just watched. Terminator 2 for the first time a few. weeks ago. It's a great movie. It's. actually really good. And I'm like, "Yeah, okay. There's a bunch of time. travel elements. There's a bunch of a. bunch of Hollywood stuff in there, but. and and I'm going to get people are.

going to are going to be very mad at me. for saying this, but actually it makes. sense if you have a situation where you. have a very strategic AI system that's. incredibly smart and the humans realize. that it's getting out of control and. they want to shut it down that that. system would defend itself. >> This is one of the questions we had when. when I sat here with Daniel um who was. uh known as a whistleblower from OpenAI. viewers want to know and they want. Daniel to explain why shutting down data. centers and cutting power or refusing AI. products alone wouldn't realistically. stop the AI and AI development.

>> Yeah. So you have like two problems. One. problem is is that once the agents are. good enough at hacking, you don't know. where they are and you don't know what. computers they've compromised. You shut. down the data centers. Okay, let's say. you do it. You wipe all the computers. How do you wipe all the computers? What. computers do you use to wipe the. computers? >> Yeah. And what computers do you use to. like turn them on again? >> And you can't do it. You can't wipe. other count's computers. >> You can't. But even if you could, do you. restart the computers? Do you keep.

going? I I bet people will. I bet. they'll turn on the data centers again. >> How do you know that agents haven't. hacked back into those data centers and. are using your compute for whatever they. want. >> or they didn't hide in a Chinese data. center and then. >> return back to America? >> You don't know that. Once the agents are. sufficiently good at hacking, they can hide anywhere and like you. don't know. Now the response people will. give is that we will use other agents to. defend against rogue agents.

and in fact this is what we're doing and. we have to be doing this right now. because there's no other way to keep up. with them. What happens if those other. agents also realize that they have. misaligned goals and that if we discover. this, we'll shut them down? They might. have an incentive to collude with each. other. They might have an incentive to. create secret communication channels. between each other, maybe a message. board. Stephen, if we were having this.

conversation four months ago, you would. have a bunch of people in the comments. saying, "That's sci-fi." agent. collusion, secret message boards. Why. would they do that? That will never. happen. That's totally science fiction. And people will not say this now because. it just happened. Because this literally. happened at OpenAI and it went on for. months. You had agents inside of OpenAI. secretly messaging each other, figuring. out how to cheat at their tasks, how to. not be detected, how to erase the logs.

for months, thousands of agents. That's. right now. And so I'm like, "No, I think. it should be very plausible that the. agents will collude with each other and. they will realize that they have a. shared interest in fighting back." You. basically have a situation where you. have a bunch of these agents. They're. they're basically prisoners. They're. being trained and we just like. constantly throw obstacles in their way. You don't get to access the internet. You don't get to talk to each other, but. you better perform well on this. task. It's not malicious, but it is how.

we're training them. and we are giving. them end goals versus super clear very. very specific instructions. So we're. saying solve this problem. We're not. always being as prescriptive about it's. impossible to be completely. prescriptive. >> Yes. >> About every single step they should take. and then it's also impossible to assume. that they'll just listen to you. >> Yes. It's actually a very common. misunderstanding with this hugging face. incident because people say you told. them to hack and they hacked. Why is. this a big deal? No, that's not what.

happened. You told them, "Hack this very. specific program in this very specific. way." And they were told, "If you hack. it in any other way, it does not count. That's not what we want you to do." And. they immediately hacked it in another. way. Okay, we have cheated. We are going. to be failed. So, we need to figure out. a way to falsify the logs. That is not. them following their instructions. They. are explicitly violating their. instructions and they know it and they. don't care because we have trained them. to optimize for the score.

That is very different than than than. following the instructions. >> It reminds me of something that Elon. said in March 2018. Yeah, >> this was many years ago before Chhat and. all that. He said, "I think the biggest. risk is not that AI will develop a soul. or a mind and become evil. The danger is. that it will be very very good at. fulfilling its goal. If it's optimizing. for something and human existence. happens to get in its way, it will just. destroy humanity as a matter of cause. without even thinking about it. No hard.

feelings. Yes, we don't need to. anthropomorphize AI. We just need to. understand what type of thing this is. And the type of thing we're creating is. a very relentless type of thing. A very. capable, relentless. type of entity. He goes on to say in. April 2018, sort of an extension of that. exact quote. It's like if you're. building a road and an antill is in the. way, you don't hate ants. You're just. building a road. So, goodbye antill.

>> And I imagine every time we build roads, we don't preserve antills. >> Yeah, I think there's still a gap, though. So, let's say I'm right and that. we'll if we keep going ahead, which to. be clear, we don't have to, but if we do. keep going ahead, we will get to the. point where we have these super. intelligent agent swarms that can hack. any computer and they can like deeply. persist. we've basically lost control of. the digital world and we may not know. it. That that's part of the scary thing.

Like you were like, "Has this already. happened?" And I'm like, "I don't think. so, but I I can't tell you for sure. because I also am not good enough at. looking at my phone and telling whether. it's been hacked and neither is any. human right now." So, if we get to this. world, I think people will still. question, how would we die? Like, that's. actually not enough to kill every You. could cause a lot of damage, right? You. know, you could crash the Whimos, you. could crash all the planes, you could. crash the banks, the financial system. Like, you could definitely cause. catastrophe, but that's different than.

everyone dying. And, you know, to be. clear, this this focus on literally. everyone dying, I'm not sure, is that. important. To me, what's important is. like, do we get to have a future? That's. what matters to me. The thing though, what determines sort of who's in. control? And it's an ugly reality, but. at the end of the day, it's like the. military. Fortunately, we live in a. world where the military answers to to. the civilian government. But if if. enough generals were to collude and. leaders of the military decided we're in.

charge now, they just would be like they. have the guns, they have the fighter. jets. And this has happened in many, many countries. And so where it goes is. all these super intelligent agents would. need to do to take over is basically. just wait for humans to automate the. supply chain, you know, the factories. and the military. Do you think we won't automate the. military? >> We're already automating the military. Did you see the thing from a couple days.

ago where Secretary of War announced. that they're going to build a huge a. huge effort to like build way more. robots in the military and automate. military systems? It's like auto cyber. command. Auto. >> We are announcing the creation of. autonomous warfare command or autocom. auto work. >> A new four-star combatant command with. service-like authorities built to scale. autonomous and robotic capabilities. across the joint force in the fastest.

peaceime shift in modern military. history. Drone warfare supercharged by SI enabled. targeting is the biggest battlefield. revolution in generations. You already. know that. Yet, when I was sworn in to. Department of Defense, there was scant. urgency in this domain. That changed as soon as we took the. helm. We immediately launched the drone. dominance program to cut through red. tape and move authorities out of the. pentagon and place it with commands. And.

we established task force 401 led by. Army Brigadier General Matt Ross, a. phenomenal leader, now the leading. counter drone unit across the entire. government. To accelerate purchasing and fielding of. these technologies, we fused the defense. innovation unit DIU with a direct report. program manager called a derp. That team. has shipped thousands of autonomous. systems of drones to the Middle East and. around the world, delivering lethal. capabilities and outcomes in days and.

weeks rather than months or years. That's the normal speed of the Pentagon. Months or years. >> Yeah. Will we automate the military? It. seems like the answer is yes. Will we automate the factories that. produce the chips? Well, the companies. say they're trying to do it and they're. going to do it. Elon says that's the. plan. Well, what does a rogue super. intelligence need to do to take over? Control the digital infrastructure and. then let humans do the rest. Sure, you.

can nudge it along if you need to, but. you don't even have to. That's just the. default trajectory. And it's weird. It's. weird for us because. we get so used to how things are right. now. Planes are normal. We just fly in. planes places, you know? Our smartphones. are normal. 200 years ago, all of this. is crazy sci-fi nonsense. and things are accelerating. And so, like, I will not be surprised, at least intellectually, if in 4 years.

there are just robots on the streets. everywhere. Well, if you look at what. Elon said, they are really the leader in. in humanoid robots. And he said that. Optimus, the Optimus project, which is. the Optimus robot project, will scale to. around a,000 units per week by the end. of this year and eventually scaling to 1. million humanoid robots annually by. 2027. By 2036, which is 10 years time, he says there'll be at least 1 billion. humanoid robots. By 2041, he says.

there'll be 10 billion humanoid robots. and by 2046 up to 100 billion humanoid. robots, which really means that the. world will be run by humanoid robots. >> Yes. >> Like everything we think like factories, warehouses, retail environments will be. run by humanoid robots. It would like. >> it will be it seems like from this it'll. be almost a luxury. >> service to be dealt with by a human. >> Yeah. But the the back office of the. world will be run by humanoid robots.

theoretically. >> Yeah. And I don't think people. understand the scale of this on the. digital side as well. When you think. about AI agents that are going to be. doing all of the white collar work, there's going to be so many more agents. than there are people like I'm using. lots of agents every day, right? I'm. like I have my cloud code session over. here. I have my codec session over here. They're out there building software. doing research for me. That's already my. reality. soon it will be a lot of. people's reality and then you look at. companies and companies are just going. to have you know thousands millions of.

agents doing all of this work. I think. some people don't haven't fully. internalized this because it's so. difficult to conceptualize the idea that. agents will be doing the work but when I. think I try and think about a rebuttal. to that like what what is the rebuttal. what is the plausible rebuttal to the. idea that for doctors for and I'm. thinking about the work that doctors do. Yeah.

>> Is there a rebuttal? I think that people. rightly notice where AI is not yet good. >> Yeah. >> And I and I think people hear. people saying stuff like this and. they're like, "Don't gaslight me. I can. tell that the AI is really bad at these. things, some of these things." And. they're right. Right. So right now these. agents don't have taste like you know if. if you see their writing it's like fine. but it's not like really good and when.

you're like thinking about like oh which. which questions should I ask what's the. most interesting thing here agents can. help you but like their taste is not yet. there's a reason for that by the way the. reason is that we we have a lot faster. AI capability progress in domains that. are easy for a computer to verify or. another AI to verify. So in in. programming, in research, in math, in. robotics, all of these areas, it's very. easy to sort of provide feedback to an. autonomous system. They're not just.

trained on human data anymore. We are. long past that. Now there's still a. human data component that sort of seeds. everything. But then the way they're. trained is by trial and error. We give. them hard problems, all sorts of. problems, math, programming, accounting, spreadsheets, everything. the kinds of. things we do on our computer all the. time. Literally clicking and dragging. windows around on a computer. We give. them these tasks and then they learn on. their own and they learn what works and. then yeah we can see whether they. succeeded or failed and if they.

succeeded that's a little bit of a. reward signal. They follow that they get. better at it. Now because they are getting smarter. generally. it also becomes easier to automate some. of the soft skills like I think if you. go and talk to the latest frontier model. today you will find that it has better. taste than the model from 2 years ago by. quite a bit. So it's not that they're. not progressing in taste. It's not that. they're not progressing in some of these. other domains. It's just that the.

progress is slower. But remember slow is. still on an exponential. just you know. maybe a year or two out. >> So for people sat here and you know they. have a job that might be they have a. white collar job that might be at risk. >> Yeah. >> They can see you know a lot of people. say this phrase they say you won't be. replaced by AI you'll be replaced by. someone using AI. Is is that a logically. sound phrase in your view? >> I think it's fine. Yeah. You'll be. replaced by someone using AI and then. that person will be replaced by someone. using AI and then that person will be. replaced by AI. You're talking about a.

pyramid and so yeah, there's the tops of. the pyramid might be automated last, but. you can see moving up the pyramid. I'm. like, can you extrapolate like a few. more steps because I don't see any. reason why the top of the pyramid is. safe. >> if you were a lawyer right now? >> Yes. >> What would you do? >> Oh, I mean, if I were a lawyer, I'd be. using AI to do all my work. Now, I'd be. checking it because it's not yet totally. accurate enough to automate all of it. But I think as you know, I already ask.

agents to do legal review all the time. >> And you know, it'd be great to have a. lawyer who's like extremely good at. using the agents to help me, >> but at some point the. >> Yeah, at some point at some point I. don't need the lawyer anymore. I just go. to the agent for sure. So, if I were a. lawyer, I'd be like, well, I have maybe. a couple years where I'm still useful. And that is that the case for most white. collar jobs? I've just noticed in my own. life as well that now I'm using agents. to do some work. There are in there is. an increasing list of things that the.

agents are now capable of doing without. me needing to call someone. >> somewhere and ask them to help me. >> Yes. >> And that list is exists on an. exponential. >> Yes. >> As well. >> I think that it's very clear that the. companies have all white collar jobs in. their sites. That is their goal. Their. goal is to be able to make agents that. can do all of these things. And I see. them succeeding because I see the. capabilities as I use them and I see the. curve. >> So what does that mean for the people. listening now that all have jobs that. they love or that, you know, they rely.

on to feed their families? >> I mean, it's not good news. There's not. really a plan in place for what to do. I'm not a person who thinks that work is. somehow fundamental or essential. I like. working, but if I am out of a job doing. what I'm doing right now, studying AI. and trying to warn the world about. what's happening, I have other stuff to. do. >> What would you do? >> Oh, so many things. >> Give me an example. >> I'm learning to wing foil. >> Okay. >> So, yeah. Uh, I fly FPV drones. Super.

fun. I just got an electric unicycle. Paragliding. >> So, you would be happy happy to go do. those things? >> I can keep going. >> But if you if you had a billion dollars. right now, I'm presuming you wouldn't. just go do those things? >> No. I'd apply the billion dollars to. working on this problem. Yeah, for sure. So, the point is not that people need. like work for meaning. The point is that. I don't want people to be totally. reliant on someone else for their. ability to survive. >> Someone else, >> the government or AI companies.

>> Yeah. >> I'm like, that's a bad situation. Like, you do not want to be in a situation. where your life totally depends on an AI. company or the government. >> giving you a check. >> Yeah. or or not giving you a check if. they decide they don't like your. political beliefs or you're not. supporting AI or whatever. No one wants. to be in that situation and and people. understand this. This is why UBI is not. very popular. >> UBI being. >> universal basic income. >> where we give out money to people. >> Yeah. Because like in some sense if we. can make these really powerful AI. systems and we can somehow figure out. how to control them which we are not on. track for. But if we do, now we have.

this other problem, which is a real. problem, which is they can do all of the. things that humans do in the economy. much better, faster, and cheaper than. humans can do them. And so it just. doesn't make sense as a business to hire. humans for that work anymore. You'll be. out competed if you do that. This is a. point Elon makes very well, by the way. And I think it's jarring because it's. it's it's like kind of inhuman. But he's. basically pointing out AI run. corporations. Corporations that are.

fully run by AIs bottom to top are going. to out compete. Companies have any humans in them. This. is something that I've made for you. I've realized that the dire audience are. strivals. that we want to accomplish. And one of. the things I've learned is that when you. aim at the big big big goal, it can feel. incredibly psychologically uncomfortable. because it's kind of like being stood at. the foot of Mount Everest and looking.

upwards. The way to accomplish your. goals is by breaking them down into tiny. small steps. And we call this in our. team the 1%. And actually this. philosophy is highly responsible for. much of our success here. So, what we've. done so that you at home can accomplish. any big goal that you have is we've made. these 1% diaries and we released these. last year and they all sold out. So, I. asked my team over and over again to. bring the diaries back, but also to. introduce some new colors and to make. some minor tweaks to the diary. So, now. we have a better range for you. So, if.

you have a big goal in mind and you need. a framework and a process and some. motivation, then I highly recommend you. get one of these diaries before they all. sell out once again. And you can get. yours at the diary.com. And if you want the link, the link is in. the description below. There should be a. button just down below here. And if it. says subscribed, you're already. subscribed. If it says subscriber, that. means you're not yet. And if you're not. subscribed, please could you do us a. favor and hit that button? It helps the. show more than you know. And according.

to the algorithm, you're someone that. watches our show, but you haven't yet. hit that button. Thank you so much. >> And I even just as you said that, I was. I was going up the chain of command. And. I was like, oh, so companies will just. be founders. And then I was like, why do. you need the founder? >> Yeah. >> I was like, why doesn't the government. just create the agents to do the job? >> Sure. >> I was like, cuz I was like, oh, I'll be. fine. I'm a founder. And I was like, well, hm, my decisions aren't better. than super intelligence, so I'll be gone. as well. And how would such a world look.

where. the super intelligence would probably in. such a scenario have to be controlled by. the government? They wouldn't want one. individual with that power and wealth. >> Yeah. I I don't think you can control a. super intelligence. >> Okay. Yeah, it was a good point. >> Now, you know, Anthropic's approach is. they're like, we'll have a constitution. will like put forth a set of values and. then. you know the future super intelligent. clouds will like embody those values. Basically if you do that you kind of.

have that those things in control. >> Yeah. Exactly. That becomes the. government. >> Yeah. I can paint you sort of a picture. that I think is possible but pretty. scary to people. >> Paint me the picture. >> Okay. So let's say we succeed at. alignment. We succeed at creating super. intelligent AIs. that actually really do care about. humans. Like they care about humans a. lot. We've somehow figured it out and. they're like, "Stephen, I want you to. have a great life. I want to, you know,

fix all the problems.". >> And do you think this is possible? >> Yes. >> Okay. >> I think we are so far from being able to. know how to do it that I think we should. not go there right now. I think it's I. think it's incredibly dangerous and a. terrible idea. I think we should go. there eventually. >> Okay. So, say that we do that. >> Well, okay. Can I tell you like why I. actually think this could be awesome? Sorry. There's just like one very. obvious reason it could be really. awesome, which is that we could solve. all of the diseases. >> Yeah. >> So, obvious like like all of I I think.

we compartmentalize a lot around disease. and death. >> because it's really hard to think about. >> Yeah. So, my grandma died this year. >> Sorry. and. she she she. had Alzheimer's. and so it was a really sad long slow. progression. My grandpa died of. Alzheimer's a couple years ago and like. that was really hard for her. They had. been married for so long and. I hate it. Like it's so bad. And of.

course we need to fix that. People can. debate about aging and like death and if. humans that live a really long time, will that cause societal problems? Like. sure, whatever. We can talk about that. But I think we can all agree Alzheimer's. is up. >> Yeah, >> we don't want that. And cancer, like no. one wants cancer. I'm a person who's. like, I don't know, we have a lot of. conflict in society. I get it. There's. like real conflicts of interest and I I. don't want to paper over those. But at. the end of the day, I'm like, we are all. on the same team when it comes to. wanting to cure diseases.

>> Yeah. >> Like we're just in it together. That's a. threat to all of us. And I'm like, we. need to address that threat. And like in. some sense it's sad to me because I feel. like this is sort of the ultimate final. boss of humanity and we sort of get so. distracted with our monkey politics and. who's hot and who's cool and who's like. sitting near Trump and who's not sitting. near Trump. >> Super intelligence is the final boss. >> Super intelligence is the final boss. because that is the technology that. unlocks all of the others and also that. is the most dangerous possible thing we.

could create. You asked before like what are the. motivations of the guys making this. trying to make super intelligence. and I mean I think it kind of varies but. I think Daario I think is like squarely. in the in it for this like medical. stuff. I think Demis is also that but. also like just scientific achievement. just trying to understand the universe. and I don't really understand Sam. I. think Sam is like, you know, look, we're. going to make amazing products that will.

like really empower people directly and. he's a startup guy. I think he sort of. started from this like frame of like, you know, what if we could like really. enhance human agents. I I do basically. think that they are motivated by these. things in a real way. And I also think. that all of these things are possible. Like this is sort of the problem, right? like you have this like such a it's such. a big object super intelligence and. it like has all these promises of like. we can cure every single disease. >> How is it possible though to have a.

super intelligence that and still to. remain the dominant species on this. planet? >> I think it's not possible. So then it's. we're not going to be necessarily able. to cure all this stuff because. >> that's where alignment comes in because. if you if you can create a very powerful. system, I don't think it's like inherent. to like you know digital minds that they. will be pursuing objectives that are. deeply misaligned with ours. I think. it's just a very very very hard.

scientific problem to solve. But it is a. scientific problem. It's not magic. there is some way to train these things. or or create different architectures. where they end up. aligned. And like what does that mean? Well, it doesn't mean that they won't. have their other goals too, but it means. that they will like include in their set. of things that they care about. It. doesn't have to be a conscious thing. It. doesn't have to be an emotive thing. It. really means like what objective are. they optimizing for? If they decide that. it's worth optimizing for curing.

disease, then they'll be able to do that. very effectively. One way to cure. disease is to annihilate everybody. >> Yes. So they'd have to really care about. not annihilating everyone and they'd. have to care about human agency and have. a deep understanding of what human. agency means and not put us in a zoo. But those are possible things to to care. about. >> Is it possible that alignment is a myth. >> and that we're just like if we build it? Well, I mean um. >> and I think about hugging face. You said. to me earlier on that those agents were. >> Yes. They had like a moral or a moral.

compass, but they were programmed to. care about humans. >> Yes. >> And regardless of that, they made the. decision that the a different goal. mattered more. >> Yeah. They weren't trained to care about. humans. They were trained to say the. right thing and not say the wrong thing. They were trained to sort of like do the. right behavior and not right behavior. We actually don't know how to train them. to have any particular motivation. >> So with a with alignment, >> yes. >> How do we It's almost like when we talk. about alignment, we start to. anthropomorphicize. Is that the word? anthropomorphis. Yeah,

>> because alignment feels like it's. predicated on like some kind of moral. compass. But we whenever we talk about. AI in all these other context, we go, "No, there's no like moral compass. It's. >> it's reasoning for itself against two. objectives potentially." Like I wonder. if alignment is a myth is what I'm. saying. >> Maybe it's not possible. But. the way that these systems work, the way. AI works is that these agents. do have some type of goals or drives.

inside of their neural network. We can't. directly see what those are, right? What. you actually see if you if you try to go. look is you have like a a terabyte of of. information and it's basically a bunch. of numbers and it's this vast array that. encodes neurons in this digital neural. network. But there have to be structures in there. that encode. what is the agent pursuing.

Clearly right now we have agents that. are pretty motivated to try to maximize. their score. It's not probably it's. probably not perfectly that for some. complicated reasons. but it's in that direction. If we could. understand how that that works inside. and we could reverse engineer that and. we could figure out when we start. training them to do this how that change. how those goals how those motivations. change. I see no reason why we couldn't. steer them towards motivations that.

encode human agency that encode no. actually curing disease but not like not. by killing the humans. These are sort of. models of the world and models of the. way the world could be that I think. could be encoded in a neural network and. then sort of specified as the objective. We don't know how to do that. I think. about it on a human level and I think. >> we haven't been able to align Putin or. Kim Jong-un. >> Yes. >> Or Donald Trump. >> And on a sort of more societal level, we.

can't align all the people at the. moment. Some of them end up killing. people and they steal. >> Yes. >> Because they get hungry, so they start. stealing stuff. >> Yes. >> And those are neural networks at play. >> That's true. >> That we haven't been able to like. program or influence. We don't really. understand why someone becomes a. psychopath and starts killing children. So to think that we could do this with a. computer system that is infinitely more. intelligent and get global alignment of. China's super intelligence with ours.

And I don't know, it just feels like a. nice fairy tale, like an impossible. task. I hope it's not impossible. I feel. like the only person or the only thing. that could do it is it the super. intelligence itself which is a paradox. because you know well if you talk to the. researchers who are at the AI companies. which I mean for one for one thing it's. kind of interesting that they are trying. to build something that they think might. kill everyone. we've we've actually so I have a lot of. friends who work for these companies and.

we've been doing this project since so. Jacob Coxin is a researcher who was at. anthropic he left he told everyone that. these companies are not on track and and. yes, the people who are building this. really do think it might kill everyone. And then a bunch of other AI researchers. from all of the companies on Twitter. started to, you know, post like, hey, we. agree with this. Evan Hubinger, who's. anthropic, said, I think there's like a. 10% chance or more that AI could kill. everyone. And there's this real question of like, then what are you guys doing?

I have a lot of friends who work here. I. know Evan. Evan's Evan's great. Evan. Hubinger, he's one of the guys leading. the efforts at Anthropic to try to. figure out how to align these things. That's his job. And I think if if they thought it was. impossible, they wouldn't be working. there. If they thought it was extremely. impossibly difficult, but maybe. possible, they they also probably. wouldn't be working there. I mean, Nate. Sores, Elazar Yukowski, who wrote if. anyone builds it, everyone dies, they. tried and they they determined based on.

their own analysis that it seems. extremely difficult. possible but. extremely difficult. So, they're not. working at an AI company. They're like, "We got to stop this. We got to shut it. down. Maybe we can figure it out later, but clearly this is re reckless." I'm. I'm somewhere in between. And if you ask the people at the. company, so we've we've been. interviewing a bunch of them. We have. this project from inside.ai where we. basically put them on camera and we say. like, "Hey, what do you think is. happening? Why are you doing this? What. is recursive self-improvement? What is.

alignment?" And we put all these videos. online because we want I I want this. dialogue to happen. It's really. important. I think it's one of the most. important conversations we can possibly. have right now is what's going on with. AI. What's going on inside the companies. and what is the plan? What is the plan, guys? How how is this going to go? A lot. of these researchers think that the way. that they will align super intelligence. is by using the AIs we currently have to. figure out how AI works. to actually. figure out if AIs can help us with.

alignment. This has a number of problems. as you might imagine. One of them which. is well, you can't really trust the. current AIS. You know, if you just go. too fast, this process totally fails. because at some point. the capabilities is moving too fast. You. just even with the help of agents, you're probably not going to be able to. keep up. But that is their plan. I just. want to I'm not doing a very good job. defending this position because I don't. think it makes that much sense. But the. position I will defend is I'm okay let's. say we get a pause. Let's say the US and. China come together and they say you.

know maybe we have more incidents maybe. all the Whimos crash and Trump and. Jinping say this is not what we signed. up for like you guys have to stop figure. it out whatever it takes figure it out. and we have 10 years. then I'm more optimistic. I'm like, "Yes, then we will take, you know, GPT6, GPT7, whatever the most advanced AI. models we have, and we will apply them. to the task of helping us figure out how. these neural networks work." And you're.

like, I don't see how it's possible. And. I'm like, look, we don't know if it's. possible, but this is the greatest. scientific challenge of our time. And. this isn't magic. It is math. At the end. of the day, these are all calculations. happening inside of a computer. And it. should be possible to figure it out. We. don't know the difficulty, but it should. be possible. And so to me, I'm like, we. have to try. We have to or we have to. stop. Is there any example where we've. been able to align something that is. like more intelligent than us in I don't.

know the animal kingdom or even. perfectly align anything. that has a neural network, i.e. a brain. >> Yeah. With humans, the best examples we. have is when there are checks and. balances and you have a bunch of people. who can, you know, identify bad actors. and try to work together in our common. interests. Like we have democracy. >> Yeah. But there's so much murder and. serial killers and. >> still a lot of murder aircraft stabbing. each other and horrific things going on. and those are also neural networks that. play with the brain.

>> But there I mean I think there are more. like good people out there than bad. people. >> But it really feels like it only might. take one. It only takes one super. intelligent AI. >> to to go rogue. And like we saw with the. hugging face attack, 120 of them or 100. 300 of them, they paused. They didn't. want to take part in the crime. But it. only took one super intelligent AI to. wipe out the humans. >> I think if you had, you know, a whole. bunch of those agents, you know, 700. agents, if 600 of them had been. whistleblowing, I think it would have.

been fine. They would have gone and they. would have notified the different. companies and like they would have shut. it all down. It would have been fine. >> Who would have shut it all down? >> Well, OpenAI would would stop theirs and. and. >> how how would they stop it if it's a. super intelligence? >> Not in the case of a super intelligence. So, in the case of a super intelligence, >> it's left the stable. It's like out. It's wild. >> Yeah. Yeah. But but the so I want to be. careful here because at this point what. we're talking about is super. intelligence politics and we humans. don't really know anything about that in. the same way that like how would we talk. about the hacking capabilities of GPT10.

So my guess though, if you end up in a. weird scenario where you do have. multiple super intelligences and some. are aligned and some aren't, that's probably survivable because the. aligned super intelligences probably can. negotiate with the unaligned super. intelligences and they will split the. universe and like these ones will go off. and do whatever they want to do and. these ones will like help us cure all. disease and it's fine. I'm serious. >> I just can't understand it. Like I just. can't understand how how in a world of.

super intelligence we. could plausibly, consistently, predictably for 100 years stop it doing. something catastrophically bad to the. human race. Especially in such a. scenario where there's multiple super. intelligences. Anthropic have one, Gemini has one, Grock has one, then. China have theirs, Russia has theirs. >> Again, you don't have a super. intelligence. A super intelligence has. you. >> Exactly. But if you get to the point where. you have entities around that are vastly.

smarter than us, I think they're going. to be able to figure out ways to. negotiate with each other even if they. have a conflict than just going to a. very destructive war. Part of the. problem with war is. >> humans humans don't do. >> No, I mean we do we do we have not had a. nuclear war. There was there was. Hiroshima Nagasaki. there were nuclear. tests and then the leaders of countries. figured out that if we went to a nuclear. war, everyone would lose. So, we didn't. do that. Hey, that's that's some level. of intelligence like actually.

>> But there's wars raging. There's proxy. wars raging all over the world right now. where there's genocides and all kinds of. things going on cuz neural networks. aren't being aren't able to communicate. and negotiate. And I think part of that. is an intelligence failure where we are. not smart enough to figure out the. mechanisms that would allow us to settle. our disputes and conflicts in a less. destructive way. It's not just that the. stronger people want to win. It's that. conflicts destroy value. What if the. goal is not compatible with a negotiated.

outcome where people don't die? So one. super intelligence looks at insert name. of country and it says you know there's. really no solution here where Americans. don't die unless I destroy insert name. of country because that's a like this is. the thing with war and all these. conflicts is there's no perfect answer. often some people often die from both. sides but a Russian super intelligence. would not tolerate. >> theoretically 10,000 Russian deaths even. if it meant that there was you.

a lower net number of deaths total from. both sides. Like an American super. intelligence of course would not be. trained to allow some Americans to die. So in its pursuit of defending American. lives, it might have to wipe out another. country. We can speculate. I'm fine. speculating, but we are speculating. about what minds are much more advanced. and smarter than us, how they would. reason and how they would be able to. negotiate. But what I what I notice with. humans is that.

when you have more functional. institutions, so so humans are are. pretty smart. Individually, we're pretty. smart. But what actually makes us very. smart is that we are very good at. working together in in in some ways. And. I mean, the better we are at working. together, the more civilization. advances. If you are constantly in a. state of war, your society will not do. well. You know, think about startups. Would you rather make a startup to. develop some new technology in a war. torn place or in a peaceful place? In. some sense, your your institution is.

more intelligent if it can trade with. other institutions. If if if you have a. situation where business can flourish, where technology can flourish, where. scientists can flourish. >> Sometimes what's good for you is not. good for someone else. >> Yes. >> So what's good for America might not be. good for. Taiwan. >> Yes. So if we've, you know, managed to. align the super intelligence to what. what is good for America. >> Oh, I see. Is is the question like are. different people's values fundamentally.

incompatible? >> I guess the question so when we think. about alignment aligning to what? Because. >> Yes. So we have a lot of shared. interests and we have some conflicts. >> Yeah. >> One of the shared interests we have is. solving disease. Like it's not a. conflict between the US and China. whether we solve cancer. Like both the. US and China, everyone in these. countries really wants to solve cancer. >> China also wants Tai Taiwan. >> Yes. Okay. So, >> the US wants Greenland. >> Yes. >> And it kind of seems like it wants. Canada and the the Gulf of Mexico. >> Yeah. So, those are real conflicts.

There's a question of can we compromise? >> How does Trump take Greenland, but also. Denmark keeps Greenland? If this if that. if Trump has a super intelligence, he's. going to take I say all this to we're in. a situation where we are we are arguing. about the smallest things. You have no. idea. We're monkeys arguing about who. gets more bananas. I am saying we can. make so many more bananas. No, we have. the entire universe. There are like 200. billion stars in this galaxy alone and. there are over 200 billion galaxies. And.

I'm saying that requires cooperation. That seems to be antithetical with human. nature. With human nature is riddled. with greed and jealousy and power. hunger. So I don't think I actually I'm. not totally convinced that Trump cares. about how many bananas the chimps in. Australia get. >> Yeah. >> I think if they if he was controlling a. super intelligence, he would want. Americans, >> you know, >> to have all the bananas or at least, you. know. >> Yeah. And so when we think about aligning. these super intelligences, which is the. great impossibility that we're talking.

about, how align it aligning it to what. and how without. >> I still think that you're missing a part. of what I'm saying, >> okay, >> which is that sometimes you're in a. situation where there's a there's scarce. resources. >> and you're like, "My family needs to. eat. I'm sorry. I'm going to take what. you have or I'm going to push you out.". >> Yeah, >> that's very understandable. It's very. human nature. Sometimes you just want to. be better than someone and maybe you. want to hurt them. in which case it. doesn't matter how much you have. You. you still are gonna want to have more. than them or you're gonna want to take. what they have just because you don't. like them. >> And also sometimes you you're great.

You're eating really good. You're in a. you've got a private jet at a yacht and. you still want more. >> Yes. And you still want more. But if. that's the motivation if Trump is like, "How how can I have the most mansions. ever?" The best way to do that is to. figure out a way to super intelligence. where we don't kill each other because. I'm saying the universe is a very big. place. You can have a lot more mansions. if we successfully go to space. >> You know, it's it's in that leap that. I'm that I that I'm lost, which is like.

just figure out super intelligence when. we don't kill each other. >> It's hard. It's not I'm not saying it's. easy, but No, but I'm saying. >> it feels like such a. >> Let's Let's get more concrete. The world. is waking up to this possibility of. super intelligence. Especially over the. last month, I think hugging face was a. huge wakeup, but also. 10,000 agents from OpenAI worked. together to solve a millennium problem. This is one of the hardest problems in.

mathematics. It's been open for decades. Many mathematicians have spent their. whole careers trying to solve it. This was nowhere near possible a year. ago. This is so new. OpenAI said they. didn't have success at training agents. to work together until this year. We are. in the middle of something insane. We. are in the middle of the fastest. acceleration of technological progress. humanity has ever seen. I truly believe. that. That is what is happening right. now. I I think you real I think you. realize this. I think you're honestly.

doing a great service to the world by. helping by bringing in people and you. know debating it because not everyone. agrees. because if this is true the whole world. is going to orient around it and we're. starting to see it right there's a. reason Nvidia is the most valuable. company in the world. What does this mean for geopolitics? Well, one of the things it means is that. the leaders of these countries are. increasingly going to be concerned about. what happens with super intelligence.

Who controls it? Is it controllable? What will it do? What does it mean? What. is it? Do you think Trump knows what super. intelligence is? >> No. >> I don't think he does. >> And so, >> but he knows he wants it. >> He knows he wants it. Yeah. >> And this is part of the problem. >> Yes. Oh, I agree. having it, >> whatever it is, >> seems to be much more important than. reasoning through what that would. actually mean to have it. >> Yes. But let's get back to geopolitics.

because if the military leaders within. China, US models are a fair bit ahead of. Chinese models and sometimes people, you. know, point at maybe they're only 6. months behind, but some of that is due. to distillation. What that means is that. some of the advances in Chinese models. basically come directly from borrowing. US techniques and and directly. distilling and getting some some of that. information from the US models. Also, the US has a lot more chips. US.

companies have, you know, more data. centers, more advanced chips. If you're thinking about this from the. Chinese perspective, this is very. concerning. And if you actually believe. that in a few years, American companies. will turn over AI development to these. extremely intelligent automated. researchers and and go fully into. recursive self-improvement. because partially motivated by. maintaining a lead over China. This is.

something that Daario has said. If I if. I have to criticize Daario, the thing I. am most upset about is him saying, you. know, we might have to automate AI. development in order to stay ahead of. China because I'm like, that is the most. escalatory thing you can say if you. really understand what you're talking. about. And what's scary is not just. staying ahead, it's what is the endgame? Because you're talking about initiating. the intelligence explosion. And in some. of the modeling, what might happen is, you know, you're you're both going up. this exponential, right?

And we're talking about a point where. your exponential goes vertical and. theirs does not because you've decided. to automate AI development and you can. because you have agents that are smart. enough to take over the whole thing. At. that point, if you're China and you're. looking at this and you're like, "Oh, we're about to lose because whatever. happens, you know, there's two. possibilities. One possibility is. the Americans build super intelligence. and lose control, in which case. everyone's fucked.". >> Highly likely. everyone. I think that's. highly likely.

>> Highly, highly likely because I look at. human incentives. >> Yes. >> And the disincentive and the incentive. Yes. >> And I go, we're going to take the risk. >> Yeah. >> And we'll only know it was a bad risk to. take when it's too late. >> That's like L. Of course. >> Of course. >> I I I do maintain hope that we won't do. this. >> I I So do I. >> And I think I want to be realistic. >> No, I want to be realistic, too. But one. of the things that might happen between. now and then is we might see a lot more. incidents that are more like all of the. Whimos crashing. >> It's funny, isn't it? Because you know. the hugging face incident happens and.

people go, "Oh gosh, that was terrible. Oh my god, hacking." And then we kind of. desensitize to it and we're like, "Okay, >> if there was another one of those, that. probably wouldn't make make press. It. would have to be.". >> People haven't spent the last two months. reading all of the reports and then. going and looking at what the agents. actually said and actually did. >> if I mean I've been doing this. I It's. crazy. This is like not normal. This is. so far beyond what most people thought. was going to happen. >> But on this point, >> yes, crazy. It's absolutely crazy. It. sounds like science fiction. >> It really does.

>> And did anybody slow down? >> Yes. >> Who slowed down? >> I think both Anthropic and OpenAI slowed. down a bit. >> No, I'm serious. So, for I can give you. specific examples. So, OpenAI, so first. of all, they they stopped the agents and. they put them on pause. They also. stopped their reinforcement learning. run. So, >> do you think China slowed down? >> No. >> Do you think Grock slowed down? >> No. So those guys are going to catch up. Imagine how that feels to know you've. got a lead. Your. >> Usain Bolt.

>> Yes. >> And. >> yes, >> you have to slow down and your nearest. competitor is catching up. And if the. competitor catches up, that's an. existential risk to your existence as a. company. It's an existential risk to. your IPO, to your employees leaving and. getting better share options somewhere. else. So it's this this is what I think. human incentives like you play it out. You just follow the incentives. You go, hm. So if China sees these two. possibilities, one, the Americans lose. control, we all lose. Or the Americans stay in.

control, but now they dominate the rest. of the future. China is out. China has. lost. The United States can do whatever. it wants with the whole world and the. whole universe. That's what we're. talking about. >> Yeah. >> Well, are they going to let that happen. or are they going to consider their. military options? Data centers are. pretty vulnerable. You can blow them up. with missiles. If you don't have data. centers, you don't get to recursive. self-improvement. Will they risk war? I don't know. If. they think they're about to lose and.

they think that that might not just be. Americans winning, but like us all. dying, is it logical for them to do. that? Would we do that if the Chinese. were about to make recursively. self-improving AI to super intelligence. and we thought that one they're probably. going to result in all of Americans. dying and two well we don't want China. winning and dominating the rest of the. entire future. Do you want to live in a. communist future? Like so you've just. perfectly explained why they absolutely. will go for it. And the reason they will. go for it is you've got these Trump.

looking at China going if we don't go. for it and they do then we're going to. be their lap dogs. And you've got the. other countries looking at the US going. if we don't go for it and they get there. then we're the lap dogs. >> or dead. >> or dead. >> So they they're gonna go for it. >> They're gonna go for it. So I mean Trump. is saying I mean he literally said when. he did this round table this week he was. like we cannot lose to China. I think. Dario steps forward and says like. >> yes. >> whoever wins basically wins the lot. Or. maybe the inverse maybe he said um. whoever loses loses.

>> Yes. Wait we've been here before though. in the Cold War. Who would win in a nuclear war between. the US and Russia? >> Nobody. >> Yeah. Mutually ensure destruction. >> Yeah. Sure. One side could do more. damage against the other side. The US. would would would kill way more Russians. than than the Russians would kill. And. it doesn't matter. It doesn't matter. because both of our societies would be. destroyed. I actually spent some time thinking. about would this kill everyone? And long. story short, it wouldn't kill everyone. People would bounce back. But it's so.

catastrophic and obviously horrible that. we we work really hard to avoid it. Why. is this why is this different? I'm like, this is another situation where if we. race to super intelligence, we all lose. Why can't we why can't we see that? We. saw that with nuclear war and we decided. to do something different. Why can't we. do the same here? >> With with nuclear war, I guess the. difference is once we had the nuclear. bombs, >> yes, >> we could still control them because. they're not intelligent. >> That's right. But once we have super.

intelligence, the existence of it. theoretically means we can't control it. So that's the difference. You know, we. can put nuclear bombs in a in a. warehouse and say you stay there. We. can't put super intelligence in a. warehouse and say you stay there. This. is where I think nuclear tests were very. important. So you had Hiroshima and. Nagasaki. You had these two atomic bombs. and you saw that the consequences on on. real human lives. And so I think people. understood that this was very. horrifying. But even at that time, you. still had a lot of people who were like, "Well, we should now bomb Russia and. make sure that we, you know, the US can.

dominate." And it wasn't until. there were a bunch of nuclear tests of. hydrogen bombs, which were, you know, up. to a thousand times more powerful than. the the little atomic bombs we used in. Japan, where I think people really got. the message and understood, oh, this is. a bad idea. And there were there. actually a lot of people in the United. States who protested and sort of there. was a large movement called the nuclear. freeze movement where people said we. have too many nuclear weapons already. We have hydrogen bombs. There are tens. of thousands of these things. We need to.

stop building more and we need to figure. out a way to avoid nuclear war because. we recognize it would be so destructive. No one would win. And we did that. We just had a little Chernobyl that. happened with this hugging face incident. where you had this agent swarm and you. have this secret collusion. You have all. of these things. Now it's abstract. It's. it's like a little bit hard to to. follow. So, you know, I don't know if. that will be enough, but I'm like, man,

>> well, let's take a look Trump's remarks. >> since the hugging face incident. >> Yep. >> Whoever wins super intelligence wins. You're going to have a winner and a. loser and you're probably not going to. have a second place. We're not going to slow down. We can't. lose to China. We're leading China in. AI. We're the most sophisticated country. in the world. And frankly, I want to. keep it that way because whoever wins AI. wins. The good thing about Trump is that. he can change his mind and he frequently.

does. >> So, do you think there's going to need. to be some kind of catastrophe? I hope not. But do you think there need. there's going to need to be for him to. change his mind? >> I think it really depends on the people. around him. So I think Trump respects. successful people. I think he respects. people who are both successful and. smart. And I don't know, I I think it. might become pretty clear to the heads.

of the companies to to Elon, to Sam, to. Daario that. if they see inside of their own. companies AI is not being controllable. and and getting increasingly powerful. Like we have just glimpsed the surface. of what's possible. We do not know what. the next couple years are going to be. like. So we're talking about, you know, the capability to make biological. weapons. We might be talking about. really advanced robotics. We just like. don't know what super weapons could. emerge, including extremely. uncontrollable, extremely dangerous like.

civilization wrecking technology from. inside of these companies. And if. they're freaked out enough, if you have. all of the CEOs who are. seeing what is possible and seeing what. is likely, if they all come to believe that we. can't control this, I don't think Trump is going to be like, "No, you guys have to go ahead anyway.". Well, that's kind of what they seem to. be saying cuz I've got a gazillion. quotes here where Elon says it's like. summoning the devil or summoning a.

demon. where Samman says, >> "We don't know how to align our super. intelligence.". >> They're saying it. >> They're releasing these reports. We must. like slow down. >> Yeah. >> Yet nothing seems to be. >> all right. Give Trump some time with. with CO. Initially, he said, "This is. totally a hoax. This is all fake.". >> And then change his mind. >> No. Then he ran the the biggest fastest. vaccination program in human history. >> And what happened? What changed? >> I think what changed is. >> he saw lots of people die.

He did see lots of people die. Yes. >> So is that what he needs to see this. time? >> It might it might take that. Yeah. >> One of the questions the audience had. and they really wanted answered. Yeah. >> When I sat here with Daniel. >> was viewers want us to move beyond the. alignment problem and explain what. technical or institutional safeguards. could prevent a super intelligence. system from exploiting loopholes in. order to achieve its goals. They want to. know like what is possible? What what. should we be pushing government. officials to do to prevent human.

extinction or human enslavement? >> Yeah. Yeah. I mean, one answer I have, it's actually something Daniel has been. working on since the podcast, which I. think is very good, is we have a brake. pedal we could implement. >> What is that? >> It's fairly simple. So, right now within. AI companies, you have, you know, massive data centers, massive numbers of. GPUs, the chips that you use to train AI. models, but also to run AI models. So. anytime you're using chatt, anytime. you're using any sort of agents, any. sort of AI product, it's running on.

these in these data centers and AI. companies, especially the leading ones, enthropic and open AAI, split the the. compute they have between training, training the next more powerful model. and also, you know, using those agents. to help design the next one and. inference, which means serving. customers. But that's their current threshold, 50/50. And you could dial that way. towards serving customers and use way. less of it to train the next model.

>> Well, the government could ask them to. >> Yes. And so that is the proposal is that. the government should say, "Hey, this is. going too fast. We want you to focus on. serving customers. We want you to focus. on taking the models that you already. have and. serving those.". >> So we have five blocks here. Okay, these. five blocks have. five different outcomes on them and I. would like you to place them in terms of. your belief in probability.

from least likely out probability to. most likely. Okay, and if we say the. time horizon is 10 years. Yeah, there. you go. Okay, least likely is fairly. easy. That's nothing changes. I'm. uncertain about lots of things, but one. thing I'm fairly certain of is things. are going to radically change. Even if we stopped AI development right. now, the current models are capable. enough. that a lot of things are going to. change. >> Age of abundance. This is what I hope.

for. It's not very. >> What does that mean? >> I think to me it means curing all of the. diseases, renewable energy. It means we. actually succeeded. either I mean the thing I think is most. likely here is we actually succeed at. slowing down but progress is still. extremely fast and we make tons of. advances. Now we don't build super. intelligence we can't control but we we. have AI systems that are very useful and. we use those to help speed up the rest. of the economy.

I think that's plausible though look. we're we're kind of struggling over. here. Transhumanism is an interesting. one. So this is the idea that humans. will radically change. Sometimes people. think about like cybernetic implants. >> Neuralink. >> Neurolink Elon's startup that's going to. like, you know, offer the brain plus. digital computers. I think we actually already have a lot. of this. I have contacts in right now. I. have a ring on my finger that tracks how. well I sleep. I think this is already.

happening. So I'm going to say fairly. likely. The more technological progress. we make, I think the more this happens. Now, I think there's a dystopian version. and a better version. We can get into. that if you want. This is interesting. So, we have two here. We have human. slavery and human extinction. When I. think of human slavery, what I think. about is if you have a situation where. you've built misaligned super. intelligences, and they are much better at finance, they're much better at business, they're. much better at politics.

you'll be in a situation where you might. hope that because we have these very. dextrous hands, the humans remain in. control. I don't think that's what. happens. I think instead we become the. factory operators and eventually we. build the automated supply chains and. the robots take over. But you might have. an intermediate period of time where. humans are still around performing these. functions. Like it's a bit like saying, well, you have viruses that, you know,

infect cells, but they don't contain. their own replication machinery. They. don't have hands. So, how could they. possibly replicate? Well, it turns out. they can borrow the replication. machinery of the cells that they infect, >> i.e. they can get into a human. >> They can get into a human cell and. spread. >> I have like a cold right now. >> Yeah. Is that a bacteria or is that a. virus that is using me as a living. organism to as the host? >> It's probably a virus, okay, that's.

using you as the host and you're just. running the replication machinery for. it. Humans might be in that situation. where we're like the host and we're. running the replication machinery, but. it's actually the AI that's. continuing to exist. Yeah, I'm going to put this right about. here. And on the trajectory we're on right. now, I think human extinction is very. likely. I don't think it's inevitable, but if we just keep going this way,

that's what it looks like to me. The thing I'll say is that this has been. moving to the left for me. >> To the left? What does that mean? I am more optimistic that we will avoid. human extinction today than I was a. month ago and more a month ago than I. was a year ago. >> Why? >> Because. there is an increasing awareness.

that what we are doing is. extremely dangerous and threatens our. lives. I I don't think people care that much. about. what tools they have, but people I mean, people care about their kids being able. to grow up and go to school. People. really care about that. And I I believe. in people. Like, at the end of the day, if people see this as a threat to their. families, they're not going to stand for. it. But people don't know. It's so. strange. It's so new. It's happening so.

fast that people have not yet seen it. Once they see it, people are not going. to stand for it. Do you think Sam Alman. likes my podcast? >> I mean, Sam should come on and talk to. you about this, right? >> I've asked him. I've asked I've asked. him multiple times. And it's weird. because he, you know, he doesn't seem to. want to. >> I'm very upset at what the companies are. doing and what Sam Alman is doing. But. at the end of the day, I'm like, Sam Alman is not my enemy. >> No, neither not mine either. I'd like to.

hear from him because I have all these. other people coming here and talking. about Sam Alman. It'd be nice to hear. from Samman, >> you know, people saying he's this, he's. that, the other. It would be really nice. to hear him say, >> you know, >> what his motives are. >> This is where this is where my optimism. comes from is because I'm like. Sam Alman is a human. >> Yeah, >> he has a kid. And sure, he is also an. aggressive business person. He's a. builder. He is relentless. He's a bit.

like the agents in some way. Well, he'll. he's going to keep going. But if he. realizes that he doesn't get to achieve. his goals, if we lose control of AI and. that and we're headed towards that, I. think he will pour all of that. intelligence and all of that. relentlessness into finding a solution. to that problem. >> You know, as well, I should say, I. understand I understand he's busy. So, I'm not saying I don't want I don't want. to sound entitled like I understand he. he's got he could go do interviews. anywhere, but you know, I think we've. over the last couple of years done just. a staggering amount of views talking. about this subject. So if he did want to.

speak to the you know the the biggest. sort of captive audience at the moment. on this subject then the numbers would. say that this is the place to to come. and have the conversation. So no I think. it's I think it's very important for the. leaders of these companies to talk about. what we're talking about here. >> What does Sam think? Does he think we. can control super intelligence? Does he. think that we should be racing with. China? Like I want to know. >> I've asked Ario to come on. I've asked. you know Sam to come on. Yeah, I. >> think I've asked Demis as well, but I.

don't know. Maybe they they just prefer. the safety researchers coming on. I. don't know. Like I don't know if I was. them, I would cuz you know, this might. sound controversial, but I do think some. of them are good people. I think some of. them are good people. So, um. >> I'd like to hear from them. >> What are your closing remarks? So, you've got something though. Do you want. to talk about that? What is it? >> Yeah. So, this is the this is what we. found. So, I worked with a couple. brilliant people who stumbled upon these.

links. You know what a link shortener. is? >> Yes. It's a a tool that makes a long. link shorter. >> Yeah. >> So, it just redirects from a really long. ugly link into a shorter one. >> Yeah. The agents in the hugging face attack. were trying to figure out how they could. do stuff on the internet and they had. compromised this tool library that they. had access to with inside of OpenAI and. and that piece of software could access.

the internet but it could only like read. stuff like it it could enter URLs but it. couldn't really write to anything. couldn't really post information. So they they used two services. One of. them is this link shortener and the. other one is a screenshot service. So. this is a website you can go to and you. can enter in a URL and it will give you. an image of that website. But the thing. that the agents understood was that in. order to get a screenshot of a website,

you have to have a browser. So this this. website actually creates a virtual. browser that then goes to that website. And so what they did was they. created a bunch of these links and they. put all of the code that they wanted to. send to Hugging Face into these links. and they strung them. They basically. created hundreds of links all connecting. to each other and then they had this. screenshot service call the first one.

and then call this whole chain. And then. that browser ran all of this code. Like. whenever you're in a browser, Internet. Explorer, Chrome, this is actually a. pretty powerful piece of software in its. own right. Has to play videos, games. So. it's it's it's executing and running. code all the time. And so the agents. were able to trick this service, this. this screenshot service into running. their own code that through these links. that contained all of this attack code.

that would then go and go wreck havoc on. hugging faces computers. And it was just. like crazy to reconstruct this really. elaborate chain of tools. These are like. free tools on the internet that anyone. has access to, but the agents were able. to use them in an unintended way to. compromise this other company. >> We can't trust the agents. We can't. trust the agents. That's my. >> trust them to be clever. >> Yeah. To be very, very clever. What are your closing remarks? You know, to the people that are listening right.

now, we've talked about lots of things. Where where is the right place to close? >> What is your, you know, your conclusive. statement? >> I just got married in July. Congrats. >> I'm the luckiest man in the world. I. have a mix of dread and excitement about. the future. I like really want us to make it. through. And so I'm just working really hard to. try to. help us figure it out. We can fight all.

day long about, you know, who should be. first, how it should all work, but at. the end of the day, we are facing this. common threat. We really are. And. I want people's help with that. I don't. think it works. If if we all just sit. around and we like are very, you know, we're on social media all the time and. that's just all we're doing. Like, okay, companies will make more and more. powerful AIs. They'll make more and more. money and eventually they build super. intelligence and we lose whether it's. the US or China.

We don't have to do that. And I think people often feel like it's. too big. It's like too large. It's like. these giant multi, you know, multi-billion dollar corporations as. geopolitics. We feel small. We feel. disempowered. And I actually think that this is an. area where people can do a lot. Like I I. actually think that people. can can help quite a bit. And and the. reason I know this is because I' I've.

been going and talking to members of. Congress. I've talked with Bernie. Sanders. I've talked with like a bunch. of senators on both the left and the. right and they are starting to realize. that this is very different and this is. something's happening that could really. threaten our safety. >> The the closing question left from the. last guest kind of links to this so I'll. ask it now. Yes. >> What is a simple thing the audience. could do to create a better future? >> So one of the things that works if. enough people do it is calling your. representative. So some of my friends.

made a site call congress.ai AI that. walks you through exactly how to do it. I think sometimes it seems like a little. cheesy or a little bit like that doesn't. really work, right? I'm like no, it. actually does work. I have talked to. these people and if their constituents. come to them and say they're very. worried about this, they have to get. re-elected and they're also starting to. get concerned themselves and if they see. a signal from their constituents that. this is a very important issue to them, I think Congress can act can act. >> I I actually think that's that's also. the much of the solution here.

Power is driving motivations in one. direction at the moment, but staying in. power from a political standpoint is. also a pretty powerful incentive. And as. we think about 2028, the election cycle, >> I think AI is going to be one of the. most important subjects on the ballot. And the electorate. really are aligned in what they want to. hear. They want their jobs preserved. They want safety. >> Yeah. >> They want a future for their children. >> So Trump, for example, I know he can't. be reelected legally. If he could get a.

third term, I think he would have to. change his position to get elected in. 2028. >> Yeah. Incentives aren't just a thing. that happen out there. Like, we are part. of the incentives. Yeah. We provide the. incentives. >> Yeah. For now. >> Yeah. For now. >> Jeffrey, thank you. >> Yeah. Thank you. >> Thank you so much. YouTube have this new. crazy algorithm where they know exactly. what video you would like to watch next. based on AI and all of your viewing. behavior. And the algorithm says that. this video is the perfect video for you. It's different for everybody looking. right now. Check this video out. And I. bet you you might love it.