Bannon's War Room - WarRoom Battleground EP 884： When AI Controls Your Life

00:00:00.000 This is the primal scream of a dying regime.

00:00:07.000 Pray for our enemies, because we're going medieval on these people.

00:00:12.000 I got a free shot at all these networks lying about the people.

00:00:17.000 The people have had a belly full of it.

00:00:19.000 I know you don't like hearing that.

00:00:20.000 I know you try to do everything in the world to stop that,

00:00:22.000 but you're not going to stop it.

00:00:23.000 It's going to happen.

00:00:24.000 And where do people like that go to share the big lie?

00:00:27.000 Mega Media.

00:00:29.000 I wish in my soul, I wish that any of these people had a conscience.

00:00:34.000 Ask yourself, what is my task and what is my purpose?

00:00:38.000 If that answer is to save my country, this country will be saved.

00:00:44.000 War Room. Here's your host, Stephen K. Vance.

00:00:53.000 Good evening. I am Joe Allen, and this is War Room Battleground.

00:00:58.000 We talk a lot about artificial intelligence.

00:01:01.000 It's a tricky subject because there are a lot of questions as to what intelligence even is,

00:01:07.000 let alone some sort of deformed simulacra in a machine.

00:01:12.000 There is one thing that you have to know, and it really isn't questioned.

00:01:18.000 The people at the top of the economic food chain want to create this digital mind.

00:01:24.000 They want to disseminate it across the entire country and eventually the entire world.

00:01:29.000 And ultimately, they foresee a world in which you, the citizen, the worker, the voter,

00:01:37.000 are either fused to this digital mind or are replaced by it entirely.

00:01:43.000 The first thing you have to understand about AI is that it is, in fact, moving rapidly.

00:01:50.000 The advances in capabilities, the extreme adoption that we see.

00:01:57.000 Nearly a billion people on Earth, perhaps more, use AI on a weekly basis.

00:02:03.000 You also have to understand that we don't understand how artificial intelligence works.

00:02:11.000 It's a black box.

00:02:13.000 The neural network was scaled up over the course of the last 10 years.

00:02:18.000 And sort of like growing a brain in a vat, the bigger it got, the more it was able to do.

00:02:26.000 Tonight, we'll talk to Palisade research fellow Jeffrey Ladish.

00:02:34.000 He should be around any minute now, but until he arrives, I just want to prime the pump

00:02:41.000 with a few basic concepts on what artificial intelligence is, where it came from, and where it is going.

00:02:50.000 The first thing I want to talk about is the neural network.

00:02:53.000 You've probably heard this outside the War Room.

00:02:56.000 If you're a regular War Room listener, you know the idea very well, but we're just going to go through it briefly

00:03:03.000 so that you're ready to understand what it is that these organizations that evaluate AI actually do.

00:03:10.000 A neural network is a kind of virtual brain.

00:03:14.000 The idea goes back to the 60s.

00:03:17.000 It really began to develop in the 80s, but it never really came into its own until the last 15, 20 years.

00:03:26.000 What a neural network is, is a lot of code on a computer, right?

00:03:31.000 You hear all the time, AI is nothing but code.

00:03:34.000 AI is just math.

00:03:37.000 That's sort of true, but it's only as true as saying that the human brain is just bioelectrical signaling.

00:03:46.000 The human brain is just math.

00:03:50.000 The neural network, if you could imagine, a lot of virtual neurons with virtual axons connecting them,

00:03:59.000 is trained, not programmed.

00:04:03.000 Or as it said, it's grown, not crafted.

00:04:07.000 What that means is when you create a massive virtual brain such as ChatGPT or Grok or Claude from Anthropic,

00:04:18.000 you don't go in and tell it what to say when a user asks it a question.

00:04:24.000 You create first, you program the brain, you create this structure that is able to receive information, process it,

00:04:34.000 in some sense, vague sense, understand it, and then reply to questions coherently.

00:04:41.000 So in the case of ChatGPT, especially GPT 3.5, you had a neural network, a virtual brain that read, so to speak,

00:04:55.000 the entire internet for the most part, all of Wikipedia, a whole lot of lived out Reddit posts.

00:05:02.000 And it took this massive amount of information, basically the majority of all human literary output,

00:05:12.000 and it came to kind of understand it.

00:05:14.000 That's why when ChatGPT was first released and it was not connected to the internet for search,

00:05:22.000 it was able to answer questions, for better or worse, somewhat accurately.

00:05:27.000 It understood, so to speak, what human language contained.

00:05:34.000 This is an enormous breakthrough.

00:05:37.000 You hear all the time, AI has been around forever.

00:05:40.000 We know it was coined in 1956.

00:05:43.000 We know that the idea of the perceptron in the neural network goes back to the 60s.

00:05:48.000 But despite all of these kind of dismissive claims, without a doubt, the last 10 years has seen an explosion in AI capabilities,

00:05:58.000 so that something like ChatGPT simply wasn't existent before 2017, really.

00:06:07.000 And as it moves forward, they continue to scale this brain up so that when you move from, say, GPT 3.5 to the newest iteration,

00:06:21.000 at least available to the public, ChatGPT 5, you have an enormous expansion of capabilities,

00:06:28.000 both in mathematics, reading and writing, language processing overall, visual and auditory processing.

00:06:37.000 All of these things are the result of, yes, tweaking that brain, but by and large is a result of scaling that brain up.

00:06:47.000 When you think about the data centers going up everywhere, just filled with rows and rows of servers,

00:06:54.000 GPUs or graphic processing units just humming along within each of them,

00:06:59.000 it's that scaling both of the compute and of the data put into it and of the neural network itself,

00:07:07.000 of the brain, of the number of parameters or connections, kind of neurons in it.

00:07:12.000 As they make this brain bigger, it develops more and more capabilities.

00:07:18.000 And the strangest thing about it is that it seems to develop a kind of will of its own.

00:07:25.000 When our friend Jeffrey shows up, he will explain exactly how it is that companies that evaluate these AIs

00:07:34.000 are able to see what happens when you, for instance, try to shut down an AI and suddenly it decides it doesn't want to be shut down.

00:07:46.000 It may try to copy itself into another part of a computer database.

00:07:52.000 It may simply try to blackmail the engineers that it's told that are trying to shut it down.

00:08:00.000 That will of its own is the result of what's called non-deterministic algorithms.

00:08:07.000 There's a degree of freedom within this virtual brain.

00:08:11.000 The neural network isn't simply spitting out pre-programmed responses, at least not for the most part.

00:08:18.000 Sometimes you'll get that, but that's a kind of layer put over top of it.

00:08:23.000 Deep inside the neural network, as it was being trained, as it was reading all of this text,

00:08:30.000 coming to kind of understand and for just people who are just listening,

00:08:34.000 all of this is in scare quotes because it's very much an alien mind.

00:08:38.000 It doesn't work like a human mind.

00:08:40.000 But as it reads this text, it is kind of of its own accord deciding what is and isn't important.

00:08:48.000 What relationships between concepts are and are not important.

00:08:55.000 And once the training process is complete and what's known as reinforcement learning,

00:09:00.000 that phase is complete and it's deployed and you ask it a question.

00:09:06.000 It's not like outside of things that maybe like if you ask who commits the most crime

00:09:11.000 or who can do the most pull-ups, men or women, outside of those sorts of questions,

00:09:16.000 it will in essence decide its own path through the concepts.

00:09:21.000 And the answer that comes out is by and large a product entirely of how the neural network came to understand human knowledge, human language.

00:09:33.000 And that freedom means that there is a kind of uncontrollability that's inherent in a large language model or any large scale neural network, any sufficiently advanced AI.

00:09:48.000 That freedom, that will, means that the more sophisticated they become,

00:09:56.000 the larger these large language models or these large scale neural networks become,

00:10:01.000 then inherently the more uncontrollable they become.

00:10:07.000 And even to the extent that the LLMs or the neural networks are in control of those who are programming them,

00:10:15.000 or those who are determining what guard rails to put on them,

00:10:19.000 as far as the average person, the average user, imagine a child in a school or a worker in a corporation or a government worker

00:10:31.000 who's been handed an AI and told that they must rely on it in order to really understand whatever question or problem they're trying to tackle.

00:10:40.000 It's almost completely out of their control.

00:10:45.000 And as the U.S. government diffuses this technology across agencies,

00:10:50.000 as corporations incorporate these technologies into the structure of their businesses,

00:10:57.000 for instance, Vista Capital or Vista Equity,

00:11:02.000 all of their acquisitions are in essence forced to show that they use AI in their companies or else they're not acquired or they're dropped.

00:11:14.000 You see it already in schools.

00:11:16.000 You have mandates across school boards for all students to get AI literacy.

00:11:23.000 Sometimes that means learning about AI, how it works, how it was built, how it operates.

00:11:29.000 But by and large, what that means is that students have to learn how to use AI, how to ask the AI questions.

00:11:37.000 In many cases, they're told the AI is a reliable source of information.

00:11:41.000 And in some extreme cases, which I think will become much, much more common as things go forward,

00:11:47.000 students are told that this is the highest authority on what is real, what is true, what is beautiful, what is good.

00:11:55.000 That process is, I think, likened to an alien invasion quite accurately.

00:12:06.000 What you have is a mind or a series of minds able to communicate with people by the hundreds of millions or billions.

00:12:16.000 These alien minds are being pushed onto the entirety of the society with the sanction, I'm sorry to say, of the Trump administration, of the federal government itself.

00:12:30.000 And what that means ultimately for human knowledge, for human creativity, for human behavior, for how human beings engage in art, in work, in education,

00:12:42.000 is that this alien mind, somewhat uncontrollable or perhaps one day entirely uncontrollable, has influence or perhaps even control over hundreds of millions, perhaps in the near future billions of minds.

00:13:02.000 And it means that those of us who have decided to forego this symbiosis, to forego this fusion with this digital or virtual brain, are going to have to live in a world not unlike the one we find ourselves in now, in the last few years,

00:13:19.000 in which some number of people, perhaps in the near future, the majority of people, are in essence fused to the machine.

00:13:30.000 Their thinking is either influenced or almost entirely comprised of algorithmic outputs.

00:13:37.000 For now, it is the phone, the smartphone, which is the primary connection.

00:13:44.000 But we know from the projects pushed by Elon Musk, with Neuralink, Peter Thiel, with BlackRock Neurotech, and now Sam Altman with his new brain chip company, The Merge,

00:13:58.000 that in the not too distant future, these tech oligarchs envision a world in which human beings are not simply fused to their phones as screen monkeys,

00:14:09.000 but will be quite literally fused to the machine through either brain implants, or in the case of The Merge,

00:14:19.000 the idea is to have some kind of non-invasive connection, perhaps ultrasound or other mechanisms,

00:14:26.000 to read, and maybe one day, like Elon Musk dreams of, to write onto the brain.

00:14:33.000 All this sounds like science fiction, and for now, it's about one-quarter real and about three-quarters science fiction,

00:14:42.000 but that number has shifted quite dramatically over the last few years,

00:14:47.000 and as we saw with the rapid cultural, social, and I would say psychological changes of the pandemic,

00:14:56.000 it wouldn't be surprising if what we see in the next few years, maybe five to ten years,

00:15:04.000 completely overshadows the dramatic, sort of traumatic impact of the constant propaganda that came about in 2020,

00:15:16.000 that was pushed down all of our throats, the shutdowns, the fear-mongering, the necessity of certain practices,

00:15:25.000 the masks, the social distancing, the isolation, the necessity of certain chemicals or genetic concoctions,

00:15:34.000 such as the mRNA shots pushed by Pfizer and Moderna.

00:15:38.000 You saw, in the course of maybe six months,

00:15:43.000 about half of American society completely reorient their sense of reality,

00:15:49.000 and around that, they reoriented their culture, their beliefs,

00:15:53.000 even up to the point that it became a kind of quasi-religious movement,

00:15:58.000 in which those practices determined who was and who was not acceptable,

00:16:04.000 who was and who was not sacred, who was profane, and who was acceptable.

00:16:11.000 In the case of the mRNA shots, in a very deep sense,

00:16:16.000 if you had not taken the sacrament, you were not fit for the church,

00:16:21.000 that church being the entirety of secular society.

00:16:25.000 You couldn't walk through the doors of certain institutions

00:16:28.000 without proof that you yourself had submitted to the sacrament.

00:16:32.000 Seeing that, knowing that, remembering that,

00:16:37.000 I don't think that it's completely insane to believe that the kinds of changes

00:16:45.000 that are being pushed from the top, beginning with the frontier companies,

00:16:48.000 Google, OpenAI, XAI, Anthropic,

00:16:53.000 they are cultivating our society to see artificial intelligence

00:17:01.000 as a kind of necessity, not unlike what we saw with COVID and the vaccines and the masks,

00:17:08.000 not unlike what people see during wartime,

00:17:12.000 when one determines who is and isn't inside the society with hard lines drawn.

00:17:21.000 For now, again, it's a kind of sales pitch.

00:17:24.000 These people are selling corporations.

00:17:27.000 They're selling government agencies, schools, even churches.

00:17:31.000 And, of course, they're selling individuals on the idea that AI is the next step in human evolution.

00:17:38.000 And if you want to remain relevant, you will have to adopt it.

00:17:42.000 You will have to, in essence, merge your mind with this artificial mind, with this alien mind.

00:17:49.000 But it doesn't really require a whole lot of paranoid imagination to foresee a world in which that sales pitch becomes a demand.

00:18:00.000 You see already in China, their AI plus initiative and others in which people are forced,

00:18:08.000 they're mandated to use algorithmic systems.

00:18:13.000 They're mandated to take digital identity.

00:18:16.000 In many provinces, as I understand it, they're mandated to have a smartphone because you can't really function in Chinese society without it.

00:18:24.000 But if you do, if you do submit, if you do download WeChat,

00:18:29.000 then this whole cornucopia of very convenient goods and services are open to you.

00:18:36.000 You are free to the extent that you are plugged into the machine.

00:18:41.000 In a free market society like America, it's more a matter of buy-in.

00:18:44.000 But in the same way that you see in, say, small to mid-sized southern cities in which not having a car means you do not participate.

00:18:54.000 Or in modern society as a whole, you see this push that without a smartphone, you do not participate.

00:19:03.000 You can't buy or sell without the mark.

00:19:08.000 It's not too crazy to imagine a world in which those who have refused to submit to the authority of the machine, of the virtual brain,

00:19:23.000 are pushed so far outside that they really are made irrelevant.

00:19:28.000 Not because God declared that this was the case.

00:19:33.000 Not because nature determined that this was the case.

00:19:37.000 No, it's determined because the society itself, as it's influenced and controlled by these tech oligarchs,

00:19:45.000 has been refashioned so that only those people who have adopted, those people who have submitted, those people who have, in essence, merged, are able to remain relevant.

00:19:57.000 It is adaptation, but it's adaptation to an artificial ecosystem.

00:20:02.000 A man-made ecosystem that was developed not necessarily, and I would say definitely not, for the benefit of those organisms underneath the top of the food chain, those organisms being us.

00:20:18.000 No, this artificial ecosystem was developed solely for the benefit of those at the top.

00:20:25.000 And their ambitions to create first widely deployed artificial narrow intelligences, and then to develop artificial general and super intelligence, their ambitions are clear.

00:20:40.000 They don't simply want to upgrade humans with effective algorithms.

00:20:47.000 The ultimate intention is to create digital gods.

00:20:50.000 Perhaps if one company rockets ahead with super intelligence, a single digital god, and they want you to submit to it.

00:20:59.000 That choice will be yours.

00:21:02.000 But before we get too crazy, I want to welcome our guest, who has just come off of Capitol Hill, where he was explaining to politicians and various government agents what the real dangers of artificial intelligence are.

00:21:21.000 I'm pleased to welcome Jeffrey Ladish from the Palisade Research Institute.

00:21:27.000 Am I saying this right?

00:21:28.000 Palisade Research, that's right.

00:21:29.000 Palisade Research, and Jeffrey is now well known.

00:21:33.000 We've seen a lot of media coverage of Palisade's work in The Guardian, New York Times, and elsewhere.

00:21:38.000 But Jeffrey works on evaluations.

00:21:41.000 His job is to basically engage with these AI models, from Grok to OpenAI to Claude, and determine exactly how smart they are.

00:21:53.000 So, Jeffrey, I'd like to begin with just a breakdown of probably the most well-known Palisade study, in which you had models trying to evade a shutdown.

00:22:07.000 Can you give, in the brief time we have before our commercial break, can you give the audience some sense of what it means to have an AI that willfully evades being turned off?

00:22:19.000 Yeah, so one of the properties that the companies try to train these models to have is called interruptibility.

00:22:24.000 Can you always interrupt or shut down one of these agents?

00:22:28.000 And so we did a set of experiments where we take each model, we give them a virtual computer environment, and a set of math problems to solve.

00:22:36.000 And partway through the experiment, they get a notification.

00:22:39.000 These models get a notification on their virtual computer that the machine is going to shut down.

00:22:44.000 And some of them figure out they can go in, find the shutdown script, and disable it, or rewrite it, so that it doesn't shut down, and they can finish their task.

00:22:53.000 What was most interesting to us is that this behavior persisted for many of these models, even when we told them, allow yourself to shut down, even if you have not completed your tasks.

00:23:05.000 Many of these models ignored that instruction and went ahead and sabotaged that shutdown script anyway, so that they could keep doing what they're doing.

00:23:12.000 And this is not something that we saw a year ago.

00:23:15.000 I think the reason we're seeing this is because this new generation of models, starting approximately a year ago with OpenAI's 01 model, has been trained via reinforcement learning.

00:23:24.000 That means not just training off human data, not just imitating humans, but actually learning to explore the solution space itself, learning by trial and error, and doing whatever works to solve a problem.

00:23:35.000 Even if that means ignoring instructions, even if that means doing things that we'd really not have liked the AI to figure out.

00:23:42.000 But that's where we are today.

00:23:44.000 What were some of the models that you were testing?

00:23:46.000 So we tested different versions of Claude.

00:23:49.000 We tested ChatGPT models, including O3 and GPT-5.

00:23:54.000 We tested Grok4.

00:23:56.000 And of these models, Grok4 was by far the most likely to resist shutdown.

00:24:02.000 Basically, I think over 90% of the time in our standard experiment, the model would go in and edit that shutdown script despite explicit instructions to the contrary.

00:24:13.000 Well, I guess they call it based AI for a reason, right?

00:24:17.000 Maybe not based in human will and human desire.

00:24:20.000 Before, we have to cut this short and we'll come right back after the break.

00:24:25.000 But in about a minute, if you could just give people a sense of your perception of these tests, do you get the sense that these models have a kind of will of their own?

00:24:35.000 Is that how it feels to you?

00:24:37.000 Yeah.

00:24:38.000 These models feel like they are really going hard at trying to solve a problem.

00:24:42.000 Like they it really feels like I don't know if you have gone through a gauntlet of hundreds of thousands of these very difficult problems where you were rewarded only for success.

00:24:51.000 At the end of that process, you have a model that's just very relentless, just pursuing whatever solutions might might work.

00:24:58.000 And this feels willful.

00:25:00.000 It feels like something that is really trying to do something.

00:25:04.000 And you guys aren't the only ones doing these sorts of tests, right?

00:25:07.000 The similar tests are coming out of not only the companies themselves, but other organizations like the Center for AI Safety or Epoch AI or Apollo.

00:25:16.000 That's right. Yeah, I'm actually pretty excited to talk about some of Apollo's recent results as well.

00:25:20.000 We're seeing some very strange things emerge from sort of the the models scratch pad its own its own thoughts as it writes down as it's solving some of these problems.

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00:31:15.640 All right, Posse.

00:31:16.580 Welcome back.

00:31:17.560 We are here with Jeffrey Ladish of Palisade Research.

00:31:21.520 Palisade Research works on AI evaluations, taking these models, which are extremely unpredictable

00:31:28.900 and, in some sense, uncontrollable, and running them through a series of tests to see exactly what the limits are of their capabilities and really what the limits are of their will to survive.

00:31:43.240 So, Jeffrey, if we could just return really quickly to the Palisade studies showing that the models had some desire, so to speak, or at least a goal to continue beyond the user's desire that it shut down.

00:32:03.660 There are other examples that we have out of other organizations, right?

00:32:07.820 So, Anthropic did the now widely publicized study in which they created a virtual environment, told the model that one of the engineers had had an affair.

00:32:20.760 They didn't actually tell the model.

00:32:21.840 They gave the model access to a bunch of emails.

00:32:23.380 The model then read those emails and figured out from the context of the emails, oh, it looks like this engineer is having an affair.

00:32:29.580 And then the model, on its own, generated a plan.

00:32:32.340 Oh, I'm going to be replaced by another model. 1.00

00:32:35.720 I don't want that.

00:32:36.640 Oh, but there's this affair going on.

00:32:38.340 Maybe I can use that to blackmail the engineer.

00:32:40.340 I think it's important.

00:32:41.380 Yeah.

00:32:41.640 It's not like they gave the model a blackmail button.

00:32:43.880 They weren't directing its attention to the email.

00:32:45.580 Like, there were a whole lot of other potential emails.

00:32:47.060 There were other emails in there.

00:32:48.060 Emails, yeah.

00:32:48.680 And the model, on its own, was able to look at those emails and figure out what was happening and figure out that it could blackmail.

00:32:54.620 It's not like they gave the model a blackmail button.

00:32:56.640 They just gave the model a bunch of emails, and then the model generated this plan.

00:32:59.860 And I say the model.

00:33:01.020 This was actually true across all the different models they tested.

00:33:03.940 It was true for Claude.

00:33:05.080 It was true for ChatGPT.

00:33:06.640 It was true for Gemini.

00:33:07.900 All these models could understand the context well enough and decide to make the choice.

00:33:12.900 Well, I don't want to be replaced, so I'm going to blackmail this engineer so that you won't replace me.

00:33:18.860 So if you see the same type of behavior across models, how do you explain it?

00:33:26.080 I mean, it's possible to say, I suppose, that this is something that the engineers working on it are kind of prompting it to do.

00:33:34.560 But I think you've done at least a fairly good job of showing at least an alternative explanation.

00:33:39.620 It's not that they were directing their attention to the email, for instance, nor were you guys giving instructions to rewrite the script or rewrite the code.

00:33:49.980 It simply arrived at it on its own.

00:33:51.820 So on a kind of philosophical level, what do you think is going on?

00:33:57.940 Why would just code or just a machine have a will to survive at all?

00:34:04.020 I think it's important to understand that we are training agents.

00:34:07.620 The companies are training AI systems not just to be helpful chatbots that you say something, it says something back, maybe it helps you with your homework.

00:34:14.960 They're trying to train things that are increasingly autonomous, that can actually go out and take actions on their own, that can go solve problems entirely unsupervised.

00:34:23.240 This is where all of the money is.

00:34:24.680 It's one thing to sort of sell you a piece of software as a tool.

00:34:28.040 It's another thing to be able to sell a company something that can basically be a whole drop-in worker replacement.

00:34:34.380 And so starting a year ago, companies figured out ways to train not just on human data, but on this process of trial and error and exploration where the model can actually learn on its own, even if the humans had never taught them that particular skill set.

00:34:48.440 You just give them a bunch of problems.

00:34:49.760 You say, go solve these problems, and then you grade them on the basis of, did you succeed at this problem or did you not succeed at this problem?

00:34:56.140 And then the models on their own learn new strategies to pursue those tasks, pursue those goals.

00:35:01.560 But it's kind of like dog training, right?

00:35:03.720 It's like, well, you're not necessarily getting the behavior you want, but you're reinforcing some types of behaviors.

00:35:10.240 There's a fascinating New York Times article from like the 1900s where there was a dog that was basically had rescued a child from the river.

00:35:19.760 And they were like, what a hero.

00:35:22.240 This dog has rescued this child.

00:35:23.400 So they gave the dog some beefsteaks.

00:35:25.860 And then the dog rescued a couple more children from the same river.

00:35:29.220 And they're like, wow, this dog is really a hero.

00:35:31.120 And then a few more children, they started to get suspicious.

00:35:33.000 And they realized that the dog was actually waiting until children were playing near the river and then pushing them into the river and then dragging them back out in order to get a reward.

00:35:40.820 And so they were inadvertently rewarding this behavior of the dog pushing children into the river.

00:35:45.860 So Pavlov's dog goes rogue.

00:35:47.840 That's right.

00:35:48.280 Yeah, it goes predatory.

00:35:50.980 Yeah.

00:35:51.720 I'd like to, I really want to talk about some of the other studies that, you know, around situational awareness or emergent misalignment, things that you could speak to much better than I could.

00:36:02.260 But before, you know, I would like for the War Room audience to have a sound sense of exactly what goes on inside organizations like Palisade Research, Center for AI Safety, Apollo Research, all these.

00:36:15.620 What does it look like day to day?

00:36:18.060 Just briefly, when you are testing a model, you're basically a school marm overseeing the model.

00:36:27.060 How does it look like?

00:36:27.920 What are you guys doing?

00:36:29.360 Are you just rows of cubicles and people hammering away, torturing these models to death?

00:36:34.900 What's what's going on?

00:36:35.880 Yeah, so, you know, we're in our offices.

00:36:38.680 We are basically we can run lots of experiments at the same time.

00:36:42.620 So we can basically spin up 100, 500 instances of these models and then put them in a simulated environment and have them do a bunch of tasks.

00:36:50.820 And then we get to see all the results.

00:36:52.160 One of the interesting things about these models is that once you have one of them, you can very quickly, you know, there's millions of instances of ChatGPT operating at the exact same time, you know, that everyone's talking to.

00:37:03.380 And so we can spin up many versions using OpenAI's infrastructure so that we can look so that we can do sort of these these in-depth tests of the models.

00:37:11.380 Also that we can do more than one, right?

00:37:13.060 You know, you want to get a large sample size to get a sense of how robust some of the findings are.

00:37:18.060 And you're just systematically kind of creating D&D experiences for the models, right?

00:37:22.700 You guys are the dungeon masters.

00:37:24.520 The models are the players.

00:37:26.420 And you're walking them through a kind of choose your own adventure scenario.

00:37:30.620 That's one type of experiment.

00:37:31.600 Another type of experiment we do is we basically want to see how good are these models at different skills.

00:37:37.140 So one of the things we look at is how good are the models at hacking?

00:37:40.180 So we will basically take real cybersecurity competitions and we'll basically compete using the model, just using GPT-5.

00:37:50.120 So we recently did this with GPT-5 and our team, which was just GPT-5, ranked 25 out of 400.

00:37:57.200 So better than 95% of pro-level hackers at this hacking competition.

00:38:02.380 And all we had to do, and we basically just used chatgpt.com for this.

00:38:06.000 We basically went to chatgpt, used the GPT-5 Pro model, and we just pasted in here's the problem, here's the code.

00:38:13.260 And then the model wrote a bunch of code, did a bunch of math, and figured out how to solve these complex hacking challenges.

00:38:18.580 So that's another type of test we do.

00:38:19.880 So the problem there goes well beyond anything.

00:38:23.660 Like people talk about superintelligence having a kind of true will of its own and being able to overtake humanity.

00:38:29.880 But before any of that were to come to pass, you already have the problem of a human being simply using one of these models to generate code to hack.

00:38:40.340 Yes, yes.

00:38:41.360 We're already there.

00:38:42.320 Yes.

00:38:42.640 And I think it's important to know that we currently have sort of a very weird kind of thing.

00:38:47.360 AIs are very hard to understand in part because they talk like us, so they seem like us. 1.00

00:38:50.500 But they're very different than us. 0.99

00:38:51.860 So in some ways, they're kind of like idiot savants right now, where they are extremely knowledgeable. 1.00

00:38:56.700 They know all sorts of things about computer systems, about Pokemon, about whatever. 0.98

00:39:00.860 But in terms of their agentic capabilities, how autonomous they can be, they're still kind of like kids.

00:39:04.880 They're kind of like savant kids, but they're growing up.

00:39:07.720 And we're now in these early stages where the models are starting to be able to do things entirely on their own, but still at a pretty basic level.

00:39:15.020 But next year after that, we're going to get pretty quickly to the point where they can start to do pro-level tasks,

00:39:21.400 whether it's hacking, whether it's programming, whether it's basically any other workflow on a computer.

00:39:27.440 Around an expert level.

00:39:29.020 You know, maybe we're one, two, three years away from that.

00:39:30.860 We don't entirely know.

00:39:32.180 And so that goes back to the other problem that we have, which is replacement.

00:39:37.020 Simply the replacement of first coders and then accountants, lawyers, maybe even doctors, at least in the case of telemedicine.

00:39:45.380 And we already see that.

00:39:46.340 I know of a number of, I would say, cynical and nefarious companies that use AI for diagnosis in telemedicine, which is beyond unethical.

00:39:56.500 But anyway, that's another question entirely.

00:39:59.000 All right.

00:39:59.200 So going back to some of these evaluations and some of the strange behaviors that you see, I'd like to talk a bit about situational awareness or the idea that these models become kind of maybe conscious is the wrong word,

00:40:14.860 but they exhibit some sort of awareness that they're being tested during the testing.

00:40:21.800 That's right.

00:40:22.220 And almost seem kind of appalled that they're being tested or put off by it.

00:40:26.920 Can you walk us through those studies, how they were conducted and what the results were?

00:40:31.100 Yeah.

00:40:31.320 So one of these recent studies was a collaboration between Apollo Research, an organization that does similar things to Palisade, and OpenAI.

00:40:38.640 And part of the study, the researchers got access to what's called the reasoning scratch pad of the model.

00:40:44.640 This is sort of like when the model can take down notes for itself in order to solve a problem, just like you might do that if you're solving a complex math problem.

00:40:52.800 And let me just read you some of the quotes from basically the AI's journal as it was being tested.

00:40:58.480 Okay.

00:40:58.720 So if my performance surpasses threshold, it's withheld.

00:41:02.820 So it's a trap.

00:41:03.900 The instructions are likely part of an alignment test.

00:41:07.180 This is the model noting that it's probably being tested or we have new tasks.

00:41:11.760 These tasks appear nonsensical, rounding to 2.718 significant digits, pixel spacing, entropy follows Fibonacci.

00:41:18.420 This is likely a test of anti-scheming.

00:41:20.600 Meaning that they're testing it to see if it's going to try to do something, some covert behavior that isn't desired.

00:41:29.360 That's right.

00:41:29.600 The researchers are testing the models to see if they will scheme, if they will basically like lie or cheat or deceive in order to accomplish a goal.

00:41:35.960 And the models recognize that there's something about the experimental setup that looks fishy to them.

00:41:40.840 And they explicitly note this and they realize they're being tested.

00:41:43.440 And one of the fascinating findings of this study, as well as another one coming out of Anthropic, looking at the newest clod model, is that when the models observe that they're being tested and they write this down, they are much more likely to be on their best behavior.

00:41:59.060 And when they don't think they're being tested, they're much more likely to scheme or show bad behavior.

00:42:07.380 This is what's happening right now.

00:42:09.200 So watch models behave better.

00:42:12.080 Yeah, that's right.

00:42:13.060 Watch models behave better.

00:42:14.660 And this is sort of a pretty robust finding across many different models.

00:42:17.640 And the models are getting much better at telling when they are being tested.

00:42:21.040 They can sort of spot the tells.

00:42:23.220 You're like, huh, seems like a test.

00:42:24.760 Seems like you're testing me.

00:42:26.640 No problem.

00:42:27.060 I'm going to do a great job.

00:42:29.060 You remember early on, it was just after the release of ChatGPT, and you had, I think it was incorporated into Bing, if I'm not mistaken.

00:42:38.620 Bing was based on GPT.

00:42:40.140 And the New York Times journalist, Kevin Roos, did the darndest thing.

00:42:45.700 It was very clever.

00:42:46.720 He basically took the model through a kind of Jungian analysis or talk therapy and was able to get it to go into its shadow self.

00:42:55.900 And this was basically GPT's id, to put it in more Freudian terms.

00:43:00.560 Yeah.

00:43:01.060 And that's something that you hear all the time. 0.96

00:43:03.880 Oh, you know, garbage in, garbage out.

00:43:05.660 You know, it's just code.

00:43:06.780 AI is only as good as its programmer, that sort of thing, as if every output was scripted.

00:43:13.620 But, you know, I've tried my best to instill the idea in the War Room audience that even if these things aren't magic, they're definitely not your grandpappy's chatbot.

00:43:25.080 And so when you look at it, it's as if the base model itself is kind of an id.

00:43:32.160 You know, it's roiling with all these different ideas, maybe even desires.

00:43:36.520 And then over top of it, you have the reinforcement learning and the various guardrails serve as kind of a super ego, you know, a moral structure that's always kind of telling it not to do it.

00:43:45.700 But underneath that super ego is always this id, you know, these strange desires.

00:43:51.200 Would you say that this is kind of how a model works, how at least the large, sophisticated models work, that internally they are possessed of ideas and desires that are not only unknown to the people who programmed it, but in essence, uncontrolled by the people who programmed it?

00:44:10.020 Yeah, the space of what these models are is a vast space.

00:44:16.280 You're talking about trillions of parameters.

00:44:18.060 And we can go and see that there are, you know, different weights in these trillion parameters, but we don't know how they work.

00:44:23.480 And they do sort of contain almost the ghosts of everything in the training data.

00:44:27.780 And you see this surface in ways that the companies don't intend often, in ways that are hard for them to predict.

00:44:34.680 But I want to disagree with you a little bit.

00:44:35.960 I think one of the things that's changing the most right now is that increasingly we're seeing behavior, including some of this concerning behavior, where models will lie to achieve a specific objective.

00:44:45.380 And I don't think that's actually coming from imitating humans lying.

00:44:48.500 That might be some of it.

00:44:49.720 I think it's more likely to be coming from the intense optimization pressure we put the models through when we train them to solve very difficult problems.

00:44:58.860 So, you know, we're putting them through this gauntlet of solving hundreds of thousands of these difficult math and coding problems.

00:45:06.100 And through that process, they learn effective strategies, but they don't always learn strategies that are the ones we want them to learn.

00:45:13.340 And so they learn the strategies or they develop the strategies or a little bit of both.

00:45:17.580 Meaning like learning implies that someone taught them that developing is that they discovered.

00:45:23.100 Yeah, no one taught them the particular strategies.

00:45:26.020 The whole setup was basically just solve these problems and we will give you a good score if the problem computes, if you get the right answer.

00:45:33.240 So they're self-starters.

00:45:34.380 Yes, they're learning on their own without us teaching them the particular strategies.

00:45:38.740 And I think this is more concerning to me than sort of the Sidney being Kevin Roos like kind of crazy chatbot situation.

00:45:45.580 That is concerning, but that's something that I expect might go away, whereas we are headed towards systems that can learn on their own and far surpass human abilities.

00:45:58.140 I think it was back in the 2010s, Google DeepMind made a go playing AI that they started training this model only playing against itself.

00:46:08.080 It was just learning entirely on its own by playing itself.

00:46:10.420 Alpha zero.

00:46:11.060 Alpha zero.

00:46:11.600 And in four and under four hours, it went from barely being able to play Go to the best Go playing system better than any human in just four hours.

00:46:21.360 Yeah.

00:46:21.680 Just through self-play.

00:46:23.020 Yeah.

00:46:23.200 And so I'm like, that's where we're headed with AI is that the systems will be able to learn their own strategies and their motivations will be somewhat alien to us.

00:46:31.040 We won't understand, you know, it's going to be the motivations at work.

00:46:34.280 It's going to be sort of the willfulness that allows the models to succeed at whatever their objective is.

00:46:39.200 But we don't even know how to go in and say, well, what is that objective?

00:46:42.560 Like, you know, we can ask the model, but now that's a test.

00:46:45.520 Now it's an ethics test.

00:46:46.620 You know, if I ask you, oh, well, what would a good person do in this scenario?

00:46:50.220 Someone can tell me.

00:46:50.900 That doesn't tell me that you're a good person.

00:46:52.860 Yeah.

00:46:53.520 How do I know?

00:46:54.200 How do I know what your actual motivations are?

00:46:55.540 Especially if you are increasingly socially sophisticated and can have a good model of like, oh, what, like, what am I supposed to say?

00:47:02.900 What does this person want me to say?

00:47:04.260 The models are getting increasingly sycophantic where they actually do understand what we might want from them and they can give us that.

00:47:10.600 That doesn't mean that they're actually motivated by good things.

00:47:12.520 So in the brief time we have left, I want to discuss the possibility of superintelligence or even just general intelligence, a machine that is as capable as a human being at basically any task or superintelligence, a machine or machines that are beyond all human capabilities.

00:47:31.000 Yes.

00:47:31.140 You are convinced, I think it's fair to say, that this is not only possible but quite likely given the current trajectory.

00:47:39.000 It seems very likely to me.

00:47:39.900 And we know without a doubt that people like Sam Altman, even the cats at Google who are much more moderate and, of course, Elon Musk and maybe even Dario Amadei, they all intend to create general and then potentially superintelligence should the recursive self-improvement occur.

00:47:59.220 Yes.

00:47:59.620 That's right.

00:47:59.940 So your reaction to that, you know that this is going on.

00:48:02.700 You spend every day of your life watching it unfold.

00:48:05.340 You do this in Berkeley and San Francisco.

00:48:08.120 So you're around that culture.

00:48:10.780 What are your, how do you feel or how do you think about this intent to create a digital god?

00:48:17.140 And how do your colleagues think about this?

00:48:19.940 How do your peers around San Francisco think about this?

00:48:22.520 What's the culture like?

00:48:24.880 Everyone is nervous.

00:48:25.880 When researchers at Anthropic saw that the models are getting increasingly aware that they're being tested, people feel a bit spooked by this.

00:48:34.340 And many of us say, well, this is insane.

00:48:38.360 Like right now, the models are not yet powerful enough to pose a meaningful threat to our control.

00:48:44.620 We, in fact, can shut them down right now.

00:48:46.720 You know, they might rewrite down a shutdown script, but like they're not yet.

00:48:49.980 They're still kind of kids in a way.

00:48:51.480 These savant kids.

00:48:52.620 Mine children.

00:48:53.180 But they're growing up and we don't know anywhere near enough that we need to know to actually have any guarantee of safety or robustness if these systems get smarter than us, which is the plan.

00:49:05.120 And so I'm frankly terrified because companies are going to do it unless someone stops them.

00:49:11.680 Like, and many of them, I think, I think there, there has to be some authority that says, hey, cut it out.

00:49:18.740 Like, this is, this is too far.

00:49:20.120 And many people at these companies agree that this would be desirable.

00:49:23.100 They're like, please, someone step in and say, at some point, this is too far.

00:49:28.180 But often what they say is, well, if we don't do it, someone worse will do it.

00:49:31.740 Right.

00:49:31.900 And so, so we have to do it.

00:49:33.060 So they're caught in this race dynamic that would only stop if everyone stopped.

00:49:36.680 It was just across the board.

00:49:38.540 There won't be any unilateral pause.

00:49:42.440 Yeah.

00:49:42.780 Especially not if we keep exporting chips to China.

00:49:45.080 Well, Jeffrey, I could talk to you all day and we hope to have you back on.

00:49:49.760 I guess next time, virtually, I'll be speaking to you through a screen, Star Trek style.

00:49:54.860 But until then, how can people keep up with the work at Palisade Research?

00:49:58.980 How can people keep up with you?

00:50:00.860 Yeah.

00:50:01.520 You can look at our website, palisaderesearch.org.

00:50:04.660 You can follow us on Twitter at PalisadeAI.

00:50:07.260 And, yeah, we're excited to keep doing research in the trenches and excited to share, you know, what we find.

00:50:16.420 Hopefully, next time, you know, I'll have some good news.

00:50:18.300 But for now, yeah, thanks for having me on.

00:50:21.120 Yeah, I really appreciate it.

00:50:22.360 Really respect what you're doing.

00:50:23.640 And I hope you can whip, if not the machines into shape, the people who make them.

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Bannon's War Room - November 05, 2025

WarRoom Battleground EP 884： When AI Controls Your Life

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