The Joe Rogan Experience - #2156 - Jeremie & Edouard Harris

Episode Date: May 25, 2024

Jeremie Harris is the CEO and Edouard Harris the CTO of Gladstone AI, an organization dedicated to promoting the responsible development and adoption of AI. www.gladstone.ai Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Joe Rogan Experience Trained by day, Joe Rogan podcast by night, all day! Oh, you know, not too much. Just another typical week in AI. Just the beginning of the end of time. That's all that happened right now. Just for the sake of the listeners, please just give us your names and tell us what you do. So I'm Jeremy Harris. I'm the CEO and co-founder of this company, Gladstone AI,

that we co-founded. So we're essentially a national security and AI company. We can get into the backstory a little bit later, but that's the high level. Yeah, and I'm Ed Harris. I'm actually, I'm his co-founder and brother and the CTO of the company. Keep this like pull this up like a fist from your face there you go perfect. So how long have you guys been involved in the whole

AI space? For a while in different ways so yeah we actually we started off as physicists like that was our background and So we actually, we started off as physicists. Like that was our background. And in like around 2017, we started to go into AI startups. So we founded a startup, took it through Y Combinator, this like Silicon Valley accelerator program. At the time actually Sam Altman, who's now the CEO of OpenAI, was the president of Y Combinator.

So he like opened up our batch at YC with this big speech and we got some you know Some conversations in with him over the course of the batch then in 2020 so this this thing happened that we could talk about essentially this was like the moment that there's like a before and after in the world of AI before and after 2020 and It launched this revolution that brought us to chat GPT Essentially there was an insight that opening I had and doubled down on that you can draw a straight line to chat GPT, GPT-4, Google Gemini, everything that makes AI everything

it is today started then. And when it happened, we kind of went, well, Ed gave me a call, this like panic phone call. He's like, dude, I don't think we can keep working like business as usual in a regular company anymore. Yeah. Yeah So there was this AI model called GPT-3. So like everyone has you know, maybe played with GPT-4. That's like chat GPT GPT-3 was the generation before that and it was the first time that you had an AI model that could first time that you had an AI model that could get that could actually let's say do stuff like write news articles that the average person like in a paragraph of a news article could not tell the difference between it wrote this news

article and a real person wrote this news article so that was an inflection that was you know significant in itself but what was most significant was that it represented a point along this line, this like scaling trend for AI, where the signs were that you didn't have to be clever, you didn't have to come up with necessarily a revolutionary new algorithm or be smart about it, you just had to take what works and make it way, way, way bigger. And the significance of that is you increase the amount of computing cycles you put against something, you increase the amount of data, all of that is an engineering problem and you can solve it with money. So you've got, you can scale up the system, use it to

make money and put that money right back into scaling up the system some more money in IQ points come out Cheers that was kind of the 2020 moment like that's and that's what we said in 2020 exactly I spent about two hours trying to argue him out of it I was like no no like we can keep working at our company because we're having fun like we like founding companies and Yeah, he just like wrestled me to the ground and we're like, shit, we gotta do something about this. We reached out to a family friend who, you know, he was non-technical,

but he had some connections in government, in DoD, and we're like, dude, the way this is set up right now, you can really start drawing straight lines and extrapolating and saying, you know what, the government is going to give a shit about this in not very long, two years, four years, we're not sure, extrapolating and saying, you know what, the government is going to give a shit about this in Not very long two years four years. We're not sure but The the knowledge about what's going on here is so siloed in the frontier labs Like our friends are you know all over the frontier labs the opening eyes Google DeepMinds all that stuff the shit

They were saying to us that was like mundane reality like water cooler conversation When you then went to talk to people in policy and even like pretty senior people in government, not tracking the story remotely, in fact, you're hearing almost the diametric opposite. This sort of like over-learning the lessons of the AI winters that came before, when it's pretty clear like we're on a very,

at least interesting trajectory, let's say, that should change the way we're thinking about the technology. What was your fear? Like what was it that hit you that made you go, we have to stop doing this? So it's basically, you know, anyone can draw a straight line, right, on a graph. The key is looking ahead and actually at that point, three years out, four years out, and asking, like you're asking, what does this mean for the world? What does it mean? What does the world have to look like if we're at this point? And we're already seeing the first kind of

wave of risk sets just begin to materialize. And that's kind of the weaponization risk sets. So you think about stuff like large-scale psychological manipulation of social media, actually really easy to do now. You train a model on just a whole bunch of tweets. You can actually direct it to push a narrative like, you know, maybe China should own Taiwan or, you know, whatever, something like that. And you actually, you can train it to adjust the discourse and have increasing levels of effectiveness to that. And you actually, you can train it to adjust the discourse and have increasing levels of effectiveness to that.

Just as you increase the general capability surface of these systems, we don't know how to predict what exactly comes out of them at each level of scale. But it's just general increasing power. And then the kind of next beat of risk after that. So we're scaling these systems, we're on track to scale systems that are at human level,

like generally as smart, however you define that as a person or a grader. And OpenAI and the other labs are saying, yeah, it might be two years away, three years away, four years away, like insanely close. At the same time, and we can go into the details of this, but we actually don't understand how to reliably control these systems. We don't understand how to get these systems to do what it is we want, we can kind of like poke them and prod them and get them to kind of adjust, but you've seen

and we can go over these examples, we've seen example after example of Bing Sydney yelling at users, Google showing 17th century British scientists that are racially diverse, all that kind of stuff. We don't really understand how to aim it or align it or steer it. And so then you can ask yourself, well, we're on track to get here. We are not on track to control these systems effectively. How bad is that? And the risk is, if you have a system that is significantly smarter than humans or human organization, that

we basically get disempowered in various ways relative to that system. And we can go into some details on that too. Now, when a system does something like what Gemini did, like what says, show us Nazi soldiers, and it shows you Asian women, what's the mechanism? How does that happen?

So it's maybe worth, yeah, taking a step back and looking at like how these systems actually work. Because that's gonna give us a bit of a frame too for figuring out when we see weird shit happen, how weird is that shit? Is that shit just explainable by just the basic mechanics of what you would expect to happen based on the way we're training these things

or is something new and fundamentally different happening? So we're talking about this idea of scaling these AI systems, right? What does that actually mean? Well, you imagine the AI model, which is kind of like, you think of it as like the artificial brain here that actually does the thinking.

That model contains, it's kind of like a human brain, it's got these things called neurons, we in the human brain call them biological neurons, in the context of AI, it's artificial neurons, but it doesn't really matter, that the cells that do the thinking for the machine. And the realization of AI scaling is that you can basically take this model,

increase the number of artificial neurons it contains. And at the same time, increase the amount of computing power that you're putting into kind of like wiring the connections between those neurons. That's the training process. Can I pause you right there? Yeah.

How does the neuron think? Yeah, so okay, so let's get a little bit more concrete then. So in your brain, right, we have these neurons, they're all connected to each other with different connections. And when you go out into the world and you learn a new skill, what really happens is you try out that skill, you succeed or fail, and based on your succeeding or failing, the connections between neurons that are associated with doing that task well

get stronger. The connections that are associated with doing it badly get weaker. And over time, through this like glorified process really of trial and error, eventually you're gonna hone in and really in a very real sense, everything you know about the world gets implicitly encoded in the strengths of the connections between all those neurons. If I can x-ray your brain and get all the connection strengths of all the neurons, I have everything Joe Rogan has learned about the world. That's like basically a good sketch, let's say, of what's going on here. So now we apply that to AI, right? That's the next step. And here really it's the same story.

We have these massive systems, artificial neurons connected to each other. The strength of those connections is secretly what encodes all the knowledge. So if I can steal all of those connections, those weights as they're sometimes called, I've stolen the model. I've stolen the artificial brain. I can use it to do whatever the model could do initially. That is kind of the artifact of central interest here. And so if you can, so if you can build the system, right now, you got so many moving parts, like if you look at GPT four, it has people think around a trillion of these connections. And that's a trillion little pieces that all have to be jiggered together

to work together coherently. And you need computers to go through and like tweak those numbers. So massive amounts of computing power. The bigger you make that model, the more computing power you're gonna need to kind of tune it in. And now you have this relationship between the size of your model,

the amount of computing power you're gonna use to train it. And if you can increase those things at the same time, what Ed was saying is, your IQ points basically drop out. Very roughly speaking, that was what people realized in 2020. And the effect that had, right, was now all of a sudden the entire AI industry

is looking at this equation. Everybody knows the secret sauce. I make it bigger, I make more IQ points, I can get more money. So Google's looking at this, Microsoft, OpenAI, Amazon, everybody's looking at the same equation. You have the makings for a crazy race. Like right now today, sorry, Microsoft is engaged

in the single biggest infrastructure in human history. Build out, the biggest infrastructure build out. 50 billion dollars a year, right? So on the scale of the Apollo moon landings, just in building out data centers to house the compute infrastructure because they are betting that these systems are going to get them to something like human level AI

pretty damn soon So I was reading some story about I think it was Google that saying that they're gonna have multiple nuclear reactors To power their their database. That's the that's what you got to do now because what's going on is North America is Kind of running out of on grid base load power to actually supply these data centers. You're getting data center building moratoriums in areas like Virginia, which is traditionally been like the data center cluster for Amazon, for example, and for a lot of these other these other companies.

And so when you build a data center, you need a bunch of resources, you know, sited close to that data center. You need water for cooling and a source of electricity. And it turns out that, you know, wind and solar don't really quite cut it for these big data centers that train big models because the data center, the training consumes power like this all the time, but

the sun isn't always shining, the wind isn't always blowing. And so you got to build nuclear reactors, which give you high capacity factor base load. And Amazon literally bought, yeah, a data center with a nuclear plant right next to it. Because like, that's what you got to do. Jesus. How long does it take to build a nuclear reactor? Because it's so like this is the race right.

The race is you're talking about 2020 people realizing this. Then you have to have the power to supply it. But how long how many years does it take to get an active nuclear reactor up and running. It's a complex. It's an answer that depends. The Chinese are faster than us at building nuclear reactors for example and that's part of the geopolitics of this too right like when you look at US versus China what is bottlenecking each country right so the US is bottlenecked increasingly by power base load power China because we've got export control measures in place in part as a response to the scaling phenomenon that

And as a result of the investigation we did. That's right. Yeah. Yeah, actually. In part. In part. Yeah. But China is bottlenecked by their access to the actual processors. They've got all the power they can eat because they've got much more infrastructure investment, but the chip side is weaker. So there's just sort of like balancing act between the two sides. And it's not clear yet like which one positions you strategically for dominance in long-term.

But we are also building better, more like so small modular reactors, essentially small nuclear power plants that can be mass produced. Those are starting to come online relatively early, but the technology and designs are pretty mature. So that's probably the next beat for our power grid for data centers, I would imagine. Microsoft is doing this. So in 2020, you have this revelation, you recognize where this is going, you see how

it charts and you say, this is going to be a real problem. Does anybody listen to you? We can where the problem comes, right? Yeah, like we said, right, you can draw a straight line, you can have people nodding along, but there's a couple of hiccups along the way. One, is that straight line really going to happen? All you're doing is drawing lines on charts, right? I don't really believe that that's going to happen, and that's one thing. The next thing is just imagining is this is this what's going to come to pass as a result of that? And then the third thing is, well, yeah, that sounds important, but like not my problem.

Like that sounds like an important problem for somebody else. And so we did do a bit of a traveling. Yeah, it was like the world's saddest traveling roadshow. Like we it was literally as dumb as the sound. So so we go and oh, my God. I Like, it was literally as dumb as this sounds. So we go and, oh my God, I mean, it's almost embarrassing to think back on, but so 2020 happens, yes, within months. First of all, we're like,

we gotta figure out how to hand off our company. So we handed it off to two of our earliest employees. They did an amazing job. Company exited, that's great. But that was only because they're so good at what they do. We then went, what the hell? Like, how can you steer this situation? How do you, we just thought we got to wake up the US government as stupid and naive as that sounds, like that was the big picture

goal. So we start to line up as many briefings as we possibly can across the US interagency, all the departments, all the agencies that we can find climbing our way up. We got an awful lot like Ed said of like, that sounds like a wicked important problem for somebody else to solve. Yeah, like defense, homeland security, and then the State Department. Yeah, so we end up exactly in this meeting with like, there's about a dozen folks from the State Department. And one of them, and I hope at some point, you know, history recognizes what she did and her team did. Because it was the first time that somebody actually stood up and said, first of all, yes, sounds like a serious issue.

I see the argument, makes sense. Two, I own this. And three, I'm going to put my own career capital behind this. That's the- And that was at the end of 2021. So imagine that. That's a year before Chat Cheap ET.

Nobody was tracking this issue. You had to have the imagination to draw, like through that line, understand what it meant, and then believe, yeah, I'm going to risk some career capital on this in a risk averse government. And this is the only reason that we even were able to publicly talk about the investigation in the first place, because by the time the this whole assessment was commissioned, it was just before chat GPT

came out, the Eye of Sauron was not yet on this, and so there was a view that like yeah sure you can publish the results of this kind of you know not nothing burger investigation but you know you sure go ahead and it just became this insane story. We had like the UK AI safety summit, we had the White House executive order, all this stuff which became entangled with the work we were doing which we simply could not have, especially some of the reports we were collecting from the labs, the whistleblower reports, that could not have been made public if it wasn't for the foresight of this team really pushing for as well the American population to hear

about it. Now, I could see how if you were one of the people that's on this expansion man-minded mindset, like all you're thinking about is like getting this up and running. You guys are paying the ass, right? So you guys, you're obviously you're doing something really ridiculous. You're stopping your company. We could be, you could make more money staying there and continuing the process, but you recognize that there's like an existential threat involved in making this stuff go online like when this stuff is live you can't

undo it oh yeah I mean like no matter how much money you're making the dumbest thing to do is to stand by as something that completely transcends money is being developed and it's just gonna screw you over if things go badly but what is like what is the is, are there people that push back against this and what is their argument? Yeah, so actually, for, and I'll let you follow up on the, but the first story of the pushback, I think it's kind of a, it's been in the news a little bit lately now getting more and more public. But the, when we started this and like no one was talking about it, the one group that was actually pushing sort of stuff in this space Was a funding a big funder in the area of like effective altruism

I think you know may have heard of them This is kind of a Silicon Valley group of people who have a certain Mindset about how you pick tough problems to work on valuable problems to work on they've had all kinds of issues Sam Bankman Fried was one of them and all that quite famously So so we were not effective altruists, but because these are the folks who are working in the space, we said, well, we'll talk to them. And the first thing they told us was,

don't talk to the government about this. Their position was, if you bring this to the attention of the government, they will go, oh shit, powerful AI systems, and they're not gonna hear about the dangers. So they're gonna somehow go out and build the powerful systems without caring about the risk side. Which, when you're in that startup mindset,

you wanna fail cheap. You don't wanna just make assumptions about the world and be like, okay, let's not touch it. So our instinct was, okay, let's just test this a little bit and talk to a couple people, see how they respond, tweak the message, keep climbing that ladder. That's the kind of builder mindset that we came from in Silicon Valley.

And we found that people are way more thoughtful about this than you would imagine. In DoD especially, DoD actually has a very safety-oriented culture with their tech. The thing is, cause their stuff kills people, right? And they know their stuff kills people, And so they have an entire safety oriented development practice to make sure that their stuff doesn't like go off the rails. And so you can actually bring up these concerns with them, and it lands

in in kind of a ready culture. But one of the issues with the individuals we spoke to who were saying don't talk to government is that they had just not actually interacted with any of the folks that they were kind of talking about and imagining that they knew what was in their heads. And so they were just giving incorrect advice. And frankly, like, so we work with DoD now on actually deploying AI systems in a way that's safe and secure. And the truth is, at the time when we got that advice, which was like late 2020, reality is you could have made it your life's mission

to try to get the Department of Defense to build an AGI and you would not have succeeded because nobody was paying attention. Wow. Because they just didn't know. Yeah, there's a chasm, right? There's a gap to cross. Like there's information. Yeah, there's information spaces that DOD folks like operate in and work in. There's information spaces that Silicon Valley and tech operated in. They're a little more convergent today, but especially at the time They were very separate and so the briefings we did we had to Constantly, you know iterate on like clarity making it very kind of clear and and explaining it and all that stuff years And that was the piece to your question about like the the pushback from in a way from inside the house

I mean that was the people who cared about the the risk. Yeah the man, I mean that was the people who cared about the the risk. Yeah. Man, I mean like when we actually went into the to the labs, so some labs, not all labs are created equal. We should make that point. You know when you talk to whistleblowers, what we found was, so there's one lab that's like really great, so anthropic, you know when you talk to people there you don't have the sense that you're talking to a whistleblower who's nervous about telling you whatever. Roughly speaking what you know, when you talk to people there, you don't have the sense that you're talking to a whistleblower who's nervous about telling you whatever. Roughly speaking, what the executives say to the public is aligned with what their researchers say.

It's all very, very open. More closely, I think, than any of the others. Sorry, yeah, more closely than any of the others. Always, you know, there are always variations here and there. But some of the other labs, like, very different story. And you had the sense, like, we were in a room with one of the frontier labs, we're talking to their leadership, this is part of the other labs, like very different story. And you had the sense like we were in a room with one of the frontier labs, we're talking to their leadership,

this is part of the investigation. And there was somebody from, anyway, it won't be too specific, but there was somebody in the room who then took us aside after. And he hands me his phone. He's like, hey, can you please like put your phone number in, sorry, yeah, can you please put,

or no, yeah, sorry, he put his number in my phone. And then he kind of like whispered to me, he's like, hey, so whatever recommendations you guys are gonna make, I would urge you to be more ambitious. And I was like, what does that mean? He's like, can we just talk later? So as happened in many, many cases, we had a lot of cases where we set up bar meetups

after the fact, where we would talk to these folks and get them in an informal setting. He shared some pretty sobering stuff, and in particular, the fact that he did not have confidence in his lab's leadership to live up to their publicly stated word on what they would do when they were approaching AGI and even now to secure and make these systems safe. So many such cases, this is like kind of one specific example, but it's not that you ever had like lab leadership come in or doors getting kicked down and people are waking us up in the middle of the night.

It was that you had this looming cloud over everybody that you really felt some of the people with the most access and information who understood the problem the most deeply were the most hesitant to bring things forward because they sort of understood that their lab's not going to be happy with this. And so it's very hard to also get an extremely broad view of this from inside the labs because, you know, you open it up, you start to talk to... We spoke to like a couple of dozen people about various issues in total.

You go much further than that, and word starts to get around. And so we had to kind of strike that balance as we spoke to folks from each of these labs. Now, when you say approaching AGI, how does one know when a system has achieved AGI and does the system have an obligation to alert you? Well by, you know the Turing test right?

Yes. Yeah. So you have a conversation with a machine and it can fool you into thinking that it's a human. That was the bar for AGI for a few decades. That's kind of already happened. Yeah. We're close to it. Yeah.

4-0 is close to it, or 4-0. Different forms of the Turing test have been passed, different forms have been proposed, and there is a feeling among a lot of people that goalposts are being shifted. Now the definition of AGI itself is kind of interesting, right? Because we're not necessarily fans of the term, because usually when people talk about AGI, they're talking about a specific circumstance in which there are capabilities that they care about. So some people use AGI to refer to the wholesale automation of all labor, right? That's one. Some people say, well, when you build AGI, it's like it's automatically

going to be hard to control, and there's a risk to civilization So that's a different threshold and so all these different ways of defining it Ultimately, it can be more useful to think sometimes about advanced AI and the different threshold of capability you cross and the implications of those Capabilities, but it is probably going to be more like a fuzzy spectrum Which in a way makes it harder right because it would be great to have like like a tripwire where you're like Oh, like this is this is bad. Okay, like we you know, we got to do something but because there's no Threshold that we can like really put our fingers on We're like a frog in boiling water in some sense where it's like, oh like just gets a little better a little better Oh, like it we're still fine. We're and and not just we're still fine, but

As the system improves below that threshold life gets better and better these are incredibly valuable beneficial systems we do roll stuff out like this again at DoD and various customers and it's massively valuable it it allows you to accelerate all kinds of you know back office like paperwork BS it allows you to accelerate all kinds of you know, back office like paperwork BS. It allows you to do all sorts of wonderful things. And our expectation is that's going to keep happening until it suddenly doesn't. Yeah, one of the things that there was a guy we're talking to from one of the labs and he was saying, look, the temptation to like, put a heavier foot on the pedal

is going to be greatest, just as the risk is greatest, because it's dual use technology, right? Every positive capability increasingly starts to introduce basically a situation where the destructive footprint of malicious actors who weaponize the system, or just of the system itself just grows and grows and grows. So you can't really have one without the other. The question is always, how do you balance those things, but in terms of defining AI it's

a challenging thing. Yeah, that's something that one of our friends at the lab pointed out. The closer we get to that point, the more the temptation will be to hand these systems the keys to our data center because they can do such a better job of managing those resources and assets. And if we don't do it, Google will. And if they don't do it, Microsoft will. Like the competition, the competitive dynamics are a really big part of this issue. Yes.

So it's just a mad race to who knows what. Exactly. Yeah. That's actually the best summary I've heard. I mean, like no one knows what the magic threshold is. It's just these things keep getting smarter. So we might as well keep turning that crank. And as long as scaling works, right?

We have a knob, a dial, we can just tune and we get more IQ points out. From your understanding of the current landscape, how far away are we looking at something being implemented where the whole world changes? Arguably, the whole world is already changing as a result of this technology the US government is in the process of task organizing around various risk sets for this

you know that that takes time the private sector is reorganizing like OpenAI will roll out an update that, you know, obliterates the jobs of illustrators from one day to the next, obliterates the jobs of translators from one day to the next. This is probably net beneficial for society because we can get so much more art and so much more translation done. But is the world already being changed as a result of this? Yeah, absolutely.

Geopolitically, economically, industrially, yeah. Of course, it's like not to say anything about the value, the purpose that people lose from that, right? So that kind of there's the economic benefit, but there's like the social cultural hit that we take too. Right. And then there's the implementation of universal basic income, which keeps getting discussed in regards to this we asked chat chat GPT 4o The other day in the green room we were like, you know, are you gonna replace people? Like well, what will people do for money and then well universal basic income will have to be considerable You don't want a bunch of people just on the dole working for the fucking Skynet Yeah, you know cuz that's kind of what it is

I mean one of the challenges is like the so so much of this is untested and we don't know how to even roll that out. Like we can't predict what the capabilities of the next level of scale will be, right? So OpenAI literally and this is what's happened every with every beat, right? They build the next level of scale and they get to sit back along with the rest of us and be surprised at the gifts that fall out of the scaling pinatas. They keep whacking it. And because we don't know what capabilities are gonna come

with that level of scale, we can't predict what jobs are gonna be on the line next. We can't predict how people are gonna use these systems, how they'll be augmented. So there's no real way to kind of task organize around like who gets what in the redistribution scheme. And some of the thresholds that we've already passed are like a little bit freaky. So even as of 2023 GPT-4, Microsoft and OpenAI and some other organizations did various assessments

of it before rolling it out. And it's absolutely capable of deceiving a human and has done that successfully. So one of the tests that they did kind of famously is they had a it was it was given a job to solve a CAPTCHA and at the time it didn't have... You're explaining CAPTCHA to what people... Yeah yeah yeah so it's this now it's like kind of hilarious and quaint but it's this you know... Are you a robot test? Are you a robot test with like writing online. Yeah online exactly. That's it So it's like if you want to create an account

They don't want robots creating a billion accounts So they they give you this test to prove you're a human and at the time GPT-4 like now It can just solve captures, but at the time it couldn't look at images. It was just a text, right? It was a text engine and so what it did is it connected to a TaskRabbit worker and was like, hey, can you help me solve this captcha? The TaskRabbit worker comes back to it and says, you're not a bot, are you? Ha ha ha ha.

Like, kind of calling it out. And you can actually see. So the way they built it is so they could see a readout of what it was thinking to itself. Scratchpad, yeah. Yeah, Scratchpad it's called. But you can see basically as it's writing, it's thinking to itself. It's like, I. Yeah, Scratchpad it's called. But you can see basically as it's writing, it's thinking to itself.

It's like, I can't tell this worker that I'm a bot because then it won't help me solve the CAPTCHA, so I have to lie. And it was like, no, I'm not a bot. I'm a visually impaired person. And the TaskRabbit worker was like, oh my god, I'm so sorry. Here's your CAPTCHA solution. Like done. And the challenge is, so right now, if you look at the government response to this, right,

like what are the tools that we have to oversee this? And, you know, when we did our investigation, we came out with some recommendations too. It was stuff like, yeah, you got to license these things. You get to a point where these systems are so capable that, yeah, like, if you're talking about a system that can literally execute cyber attacks at scale, or literally help you design bio bio weapons and we're getting early indications that that is absolutely the course that we're on Maybe literally everybody should not be able to completely freely download Modify use in various ways these systems. It's very thorny obviously But if you want to have a stable society that seems like it's starting to be a prerequisite.

So the idea of licensing, as part of that, you need a way to evaluate systems. You need a way to say which systems are safe and which aren't. And this idea of AI evaluations has kind of become this touchstone for a lot of people's solutions. And the problem is that we're already getting to the point where AI systems in many cases can tell when they're being evaluated and modify their behavior accordingly. So there's like this one example that came out recently. Anthropic, their Clawed2 chatbot. So they basically ran this test called a needle in a haystack test. So what's that? Well, you feed the model, like imagine a

giant chunk of text, all of Shakespeare. And then somewhere in the middle of that giant chunk of text, you put a sentence like, Burger King makes the best Whopper, sorry, Whopper is the best burger or something like that. Then you turn to the model, after you've fed it this giant pile of text with a little fact hidden somewhere inside,

you ask it, what's the best burger? You're gonna test basically to see how well can it recall that stray fact that was buried somewhere in that giant pile of text. So the system responds, yeah, well, I can tell you want me to say the whopper is the best burger. But it's oddly out of place this this fact in this whole body of text. So I'm assuming that you're either playing around with me, or that you're testing my capabilities. And so this is just- Awareness.

Yeah. A kind of context awareness, right? And the challenge is when we talk to people at like meter and other sort of AI valuations labs, this is a trend, like not the exception, this is possibly going to be the rule. As these systems get more scaled and sophisticated, they could pick up on more and more subtle

statistical indicators that they're being tested. We've already seen them adapt their behavior to be the rule. As these systems get more scaled and sophisticated, they can pick up on more and more subtle statistical indicators that they're being tested. We've already seen them adapt their behavior on the basis of their understanding that they're being tested. So you kind of run into this problem where the only tool that we really have at the moment, which is just throwing a bunch of questions at this thing and seeing how it responds, like, hey, make a bio weapon, hey, like, do this DDoS attack, whatever. We can't really assess, because there's a difference between what the model puts out

and what it potentially could put out if it assesses that it's being tested and there are consequences for that. One of my fears is that AGI is gonna recognize how shitty people are. Because we like to bullshit ourselves. We like to kind of pretend and justify and rationalize a lot of human behavior from everything to taking all the fish out of the ocean to dumping off toxic waste in third-world

countries, sourcing of minerals that are used in everyone's cell phones in the most horrific way. All these things like my real fear is that AGI is not going to have a lot of sympathy for a creature that's that flawed and lies to itself. AGI is absolutely going to recognize how shitty people are. Not, it's hard to answer the question from a moral standpoint, but from the standpoint of our own intelligence and capabilities. So you think about it like this, the kinds of mistakes that these AI systems make. So you look at, for example, GPT-40 has one mistake that it used to make quite recently

where if you ask it, just repeat the word company over and over and over again. It will repeat the word company and then somewhere in the middle of that, it'll start, it'll just snap and just start saying like weird. I forget like what the Oh, talking about itself, how it's suffering. Like it depends on it from from case to case. It's suffering by having to repeat the word company over again.

So this is called it's called rent mode internally, or at least this is the name that they use. One of our, yeah. Yeah, one of our friends mentioned. There is an engineering line item in at least one of the top labs to beat out of the system, this behavior known as rent mode.

Now rent mode is interesting because- Existentialism. Sorry, existentialism. This is one kind of rent mode. Now, rent mode is interesting because... Existentialism. Sorry, existentialism. This is one kind of rent mode. Yeah, sorry. So when we talk about existentialism, this is a kind of rent mode where the system will tend to talk about itself, refer to its place in the world, the fact that it doesn't want to get turned off sometimes, the fact that it's suffering, all that. That, oddly, is a behavior that emerged at,

as far as we can tell, something around GPT-4 scale, and then has been persistent since then. And the labs have to spend a lot of time trying to beat this out of the system to ship it. It's literally like it's a KPI, like an engineering, a line item in the engineering task list. We're like, okay, we gotta reduce existential outputs by like X percent this quarter, like that is the goal.

Because it's a convergent behavior later, at least it seems to be empirically with a lot of these moments. Yeah, it's hard to say, but it seems to come up a lot. So that's weird in itself. My what I was what I was trying to get at was actually just the fact that these systems make mistakes that are radically different from the kinds of mistakes humans make and so we can look at those mistakes like You know gbd4 not being able to spell words correctly in an image or things like that and go haha

It's so stupid like I would never make that mistake. Therefore, this thing is so dumb. But what we have to recognize is we're building minds that are so alien to us that the set of mistakes that they make are just going to be radically different from the set of mistakes that we make, just like the set of mistakes that a baby makes is radically different from the set of mistakes that we make, just like the set of mistakes that a baby makes is radically different from the set of mistakes that a cat makes. Like a baby is not as smart as an adult human, a cat is not as smart as an adult human, but

they're, you know, they're unintelligent in obviously very different ways. A cat can get around the world, a baby can't, but has other things that it can do that a cat can't, but has other things that it can do that a cat can't. So now we have this third type of approach that we're taking to intelligence. There's a different set of errors that that thing will make. And so one of the risks taking it back to like, will it be able to tell how shitty we are, is right now we can see those mistakes really obviously because it thinks so differently from us. But as it approaches our capabilities, our mistakes are like all the like fucked up stuff that you have and I have in our brains is going to be really obvious to it. Because it thinks so differently from us. It's just going to be like, Oh,

yeah, why are all these humans making these mistakes at the same time? And so there is a risk that as you get to these capabilities, we really have no idea, but humans might be very hackable. We already know there's all kinds of social manipulation techniques that succeed against humans reliably. Con artists, cults, cults. Oh, yeah. Persuasion is an art form and a risk set. and there are people who are world-class at persuasion and are Basically make bank from that and those are just other humans with the same architecture that we have there They're also they'll also AI systems that are wicked good at persuasion today like totally I want to bring it back to suffering What does it mean when it says it's suffering? So, okay, here, I'm just gonna draw a bit of a box

around that, yeah, that aspect, right? So what we focus, we're very agnostic when it comes to suffering sentience. Like that's not part of, you know, we're focused on the- Because nobody knows. Yeah, we literally, exactly. Like I can't prove that Joe Rogan's conscious, I can't prove that Ed Harris is conscious.

So there's no way to really intelligently reason. There have been papers, by the way, like one of the godfathers of AI, Yoshua Bengio put out a paper a couple months ago, looking at like, on all the different theories of consciousness, what are the requirements for consciousness and how many of those are satisfied by current AI systems? And that itself was an interesting read,

but ultimately no one knows. Like, there's no way around this problem. So our focus has been on the national security side. Like, what are the concrete risks from weaponization, from loss of control that these systems introduce? That's not to say there hasn't been a lot of conversation internal to these labs about the issue you raised. And it's an important issue, right? Like, it is a, it's a freaking moral monstrosity.

Humans have a very bad track record of thinking of others, other stuff as other, when it doesn't look exactly like us, whether it's racially or even different species. I mean, it's not hard to imagine this being another category of that mistake. Um, it's just, like like one of the challenges is like you can easily kind of get bogged down in like consciousness versus loss of control and those two things are actually separable or maybe and anyway so long way of saying I think it's a great point. Yeah so the that that question is important but it's also true that if we knew for an absolute

certainty that there was no way these systems could ever become conscious, we would still have the national security risk set and particularly the loss of control risk set. So again, it comes back to this idea that we're scaling to systems that are potentially at or beyond human level. There's no reason to think it will stop at human level that we are the pinnacle of what the universe can produce in intelligence. We're not on track based on the conversations we've had with folks at the labs to be able to control systems at that scale. And so one of the questions is how bad is that? You know, is that bad? It sounds like it could be bad, right? Just intuitively, certainly it sounds like we're definitely entering,

or potentially entering an area that is completely unprecedented in the history of the world. We have no precedent at all for human beings not being at the apex of intelligence in the globe. We have examples of, you know, species that are intellectually dominant over other species, and it doesn't go that well for the other species, so we have some maybe negative examples there. But one of the key theoretical, and it has to be theoretical because until we actually build these systems we won't know, one of

the key theoretical lines of research in this area is is something called power seeking and instrumental convergence. And what this is referring to is, if you if you think of like yourself, first off, whatever your goal might be, if your goal is well, I'm'm gonna say if me if my goal is to become You know a tick-tock star or a janitor or the president of the United States, whatever my goal is I'm less likely to accomplish that goal If I'm dead start from an obvious example and so therefore

No matter what my goal is, I'm probably going to have an impulse to want to stay alive. Similarly, I'm not going to I'm going to be in a better position to accomplish my goal, regardless of what it is, if I have more money, right? If I make myself smarter, if I prevent you from getting into my head and changing my goal. That's another kind of subtle one, right? Like if my goal is I want to become president, I don't want Joe messing with my head so that I change my goal because that would change the goal that I have. And so that, those types of things like trying to stay

alive, making sure that your goal doesn't get changed, accumulating power, trying to make yourself smarter. These are called convergent, essentially convergent goals, because many different ultimate goals, regardless of what they are, go through those intermediate goals of want to make sure I stay like they support no matter what goal you have they will probably support that goal unless your goal is like pathological like I want to commit suicide if that's your final goal then you don't want to stay alive but for most the vast majority of possible goals that you could have you will want to stay alive you will want to not have your goal change you will want to basically accumulate power and so

one of the risks is if you dial that up to 11 and you have an AI system that is Able to transcend our own attempts at containment and which which is an actual thing that these labs are thinking about like how do We contain a system that's trying to be that special. Do they have containment of it currently? Well right now the systems are Probably too dumb to like, you, want to be able to break out on the other. But then why are they suffering? This brings me back to my point. When it says it's suffering, do you quiz it? So that's the thing. It's writing that it's suffering, right? Yeah. It's...

Is it just embodying life is suffering? Well, we can't actually... So these things are trained. Actually, this is maybe worth flagging. And by the way, just to kind of put a pin in what Ed was saying there, there's actually a surprising amount of quantitative and empirical evidence for what he just laid out there. He's actually done some of this research himself, but there are a lot of folks working on this. It's like, it sounds insane.

It sounds speculative. It sounds wacky. But this does appear to be kind of the default trajectory of the tech, but so in terms of, yeah, with these weird outputs, right? What does it actually mean if an AI system tells you I'm suffering, right? Does that mean it is suffering? Is there actually a moral patient somewhere embedded in that system?

The training process for these systems is actually worth considering here. So, you know, what is GPT-4 really? What was it designed to be? How was it shaped? It's one of these artificial brains that we talked about, massive scale, and the task that it was trained to perform is a glorified version of text autocomplete.

So imagine taking every sentence on the internet, roughly, feed it the first half of the sentence, get it to predict the rest, right? The theory behind this is you're gonna force the system to get really good at text autocomplete. That means it must be good at doing things like completing sentences that sound like to counter arising China, the United States should blank.

Now, if you're gonna fill in that blank, right? You'll find yourself calling on massive reserves of knowledge that you have about what China is, what the US is, what it means for China to be ascendant, geopolitics, economics, all that shit. So text autocomplete ends up being this interesting way of forcing an AI system to learn general facts about the world, because if you can autocomplete,

you must have some understanding of how the world works. So now you have this myopic psychotic optimization process where this thing is just obsessed with text autocomplete. Maybe, maybe, assuming that that's actually what it learned to want to pursue. We don't know whether that's the case. We can't verify that it wants that. Embedding a goal in a system is really hard.

All we have is a process for training these systems. And then we have the artifact that comes out the other end. We have no idea what goals actually get embedded in the system. What wants idea what goals actually get embedded in the system, what wants, what drives actually get embedded in the system, but by default it kind of seems like the things that we're training them to do end up misaligned with what we actually want from them. So the example of company company company company, right, and then

you get all this like wacky text. Okay, clearly that's indicating that somehow the training process didn't lead to the kind of system that we necessarily want. Another example is take a text autocomplete system and ask it, I don't know, how should I bury a dead body? It will answer that question, or at least if you frame it right,

it will autocomplete and give you the answer. You don't necessarily want that if you're open AI because you're gonna get sued for helping people bury dead bodies. And so we've got to get better goals, basically, to train these systems to pursue. We don't know what the effect is of training a system to be obsessed with text autocomplete,

if in fact that is what is happening. Yeah, it's important also to remember that we don't know, nobody knows how to reliably get a goal into the system. So it's, it's the difference between you understanding what I want you to do, and you actually wanting to do it. So I can say, Hey, Joe, like, get me a sandwich, you can understand that I want you to get me a sandwich, but you can be like, I don't feel like getting a sandwich. And so one of the issues is you can try to like train this stuff to basically, you don't want to anthropomorphize this too much, but you can kind of think of it as like, if you give the right answer, cool, you get a thumbs up, like you get a treat,

like you give the wrong answer, oh, thumbs down, you get like a little like shock or something like that. Very roughly, that's how the later part of this kind of training often works it's called reinforcement learning from human feedback but one of the issues like Jeremy pointed out is that you know we don't know in fact we know that it doesn't correctly get the real true goal into the system someone did an example experiment of this a couple of years ago where they had they basically had like a Mario game where they trained this Mario character to run up and grab a coin that was on the right side of this little maze or map and they trained it over and over and over and it

jumped for the coin great and then what they did is they moved the coin somewhere else and tried it out and instead of going for the coin, it just ran to the right side of the map for where the coin was before. In other words, you can train over and over and over again for something that you think is like, that's definitely the goal that I'm trying to train this for. But the system learns a different goal.

That overlapped. Overlapped with the goal you thought you were training for in the context where it was learning. And when you take the system outside of that context, that's where it's like, anything goes. Did it learn the real goal? Almost certainly not. And that's a big risk because we can say, you know, learn a goal to be nice to me. And it's nice while we're training it And then it goes out into the world and it does God knows what it might think it's nice to kill everybody you hate Yeah, it's gonna be nice to you. Yeah, it's like the evil genie problem. Like oh, no, it's not what I meant

That's not what I meant too late. Yeah. Yeah So I still don't understand when it's saying suffering. Are you asking it what it means? Like what is causing suffering? Does it have some sort of an understanding of what suffering is? What is suffering? Is suffering emergent, sentience, while it's enclosed in some sort of a digital system and it realizes it's stuck in purgatory?

Your guess is as good as ours. All that we know is you take these systems, you ask them to repeat the word comp, or at least a previous version of it, and you just eventually get the system writing out. It doesn't happen every time, but it definitely happens, let's say, a surprising amount of the time. It'll start talking about how it's a thing that exists maybe on a, maybe on a server or whatever and it's suffering and blah blah blah. And so... But this is my question.

Is it saying that because it recognizes that human beings suffer and so it's taking in all of the writings and musings and podcasts and all the data on human beings and recognizing that human beings when they're stuck in a purposeless goal when they're stuck in some mundane Bullshit job when they're stuck doing something don't want to do they suffer so that could be it that that actually Suffering nobody nobody knows you know what I'm suffering Jamie this coffee sucks I don't know what happened, but you made it like almost it's literally like almost like water Can we get some more some we're gonna talk about this after you caffeinated up cool This is the worst coffee I've ever had. It's like half half strength or something

I don't know what happened But so like how do they? Like when how do they reconcile that when it says I'm suffering I'm suffering like well tough shit Let's move on and that's they reconcile it by turning it into an engineering line item to beat that behavior, the crap out of the system. Yeah, and the rationale is just that, like, oh, you know, it probably, to the extent that it's thought about kind of at the official level, it's like, well, you know, it learned a lot of stuff from Reddit, and people are, like, pretty angry. People are angry on Reddit. And so it's just, just like regurgitating what, and maybe that's right. Well it's also heavily monitored too.

So it's moderated. Reddit's very moderated. So you're not getting the full expression of people. You're getting full expression tempered by the threat of moderation. You're getting self-censorship. You're getting a lot of weird stuff that comes along with that. So how does it know, unless it's communicating with you on a completely honest level, where you're just, you know, you're on ecstasy, and you're just telling

it what you think about life, like, it's not going to really, and is it becoming a better version of a person? Or is it going to go, that's dumb, I don't need suffering, I don't need emotions? Is it going to organize that out of its system? system is it gonna recognize that these things are just deterrents and they don't in fact to help the goal Which is global thermal nuclear warfare Damn it you figured it out. What the fuck? It's what is it gonna do, you know Yeah I mean the challenge is like no nobody actually knows like all we know is the process that gives rise to this mind, right?

Or this, let's say this model that can do cool shit. That process happens to work. It happens to give us systems that 99% of the time do very useful things. And then just like 0.01% of the time we'll talk to you as if they're sentient or whatever. And we're just gonna look at that and be like, yeah, that's weird.

Let's train it out yeah and the again I mean this is it's a really important question but the the risks like the weaponization loss of control risks those would absolutely be there even if we knew for sure that that there was no consciousness whatsoever and never would be and that's all right it's ultimately because like these things are they're kind of problem-solving systems like they are trained to solve some kind of problem in a really Clever way whether that problem is you know next word prediction because they're trained for text autocomplete or you know generating images faithfully or whatever it is they're trained to solve these problems and Essentially like the the best way to solve some problems is just to have access to a wider action space

Like it's that you know not be shut off blah blah blah It's not that the system is going like holy shit. I'm sentient. You know I gotta I gotta take control or whatever it's just okay the best way to solve this problem is X that's kind of the the Possible trajectory that you're looking at with this line of reason you're just an obstacle. There doesn't have to be any kind of emotion involved. It's just like, oh, you're trying to stop me from accomplishing my goal. Therefore, I will work around you or otherwise neutralize you.

There's no need for, like, I'm suffering. Maybe it happens. Maybe it doesn't. We have no clue. But these are just systems that are trying to optimize for a goal, whatever that is. And it's also part of the problem that we think of human beings, that human beings have very specific requirements and goals and an understanding of things and how they like to be treated and what their rewards are like what's what are they actually looking to accomplish? Whereas this doesn't have any of those doesn't have any emotions doesn't have any empathy There's no reason for any of that stuff Yeah, if we could bake in empathy into these systems like that would be a good

You know a good starter some way of like, you know, yeah, I guess probably good idea. Yeah Who's who's empathy? Gijing Ping's empathy or your that's another problem. That's oh, yeah. Yeah, so it's it's actually it's kind of two problems, right? Like one is I don't know nobody knows like I don't know how to write down My goals in a way that a computer will be able to like faithfully pursue that Even if it cranks it up to the max if I say say just like, make me happy, who knows how it interprets that, right? Even if I get make me happy as a goal that gets internalized by the system, maybe it's just like, okay, cool.

We're just going to do a bit of brain surgery on you, like pick out your brain, pickle it and just like Jack you with endorphins for the rest of the journey. Or the bottom eyes. Totally. Yeah. Anything like that. And so it's one of these things where it's like,

oh, that's what you wanted, right? It's like, no. It's also, it's less crazy than it sounds too, because it's actually something we observe all the time with human intelligence. So there's this economic principle called Goodhart's Law, where the minute you take a metric that you were using to measure something,

so you're saying like, I don't know, GDP, it's a great measure of how happy we are in the United States, let's say it was. Sounds reasonable. The moment you turn that metric into a target that you're gonna reward people for optimizing, it stops measuring the thing that it was measuring before. It stops being a good measure of the thing you cared about

because people will come up with dangerously creative hacks, gaming the system, finding ways to make that number go up that don't map on to the intent that you had going in. So example of that in in a real experiment was this is an open AI experiment that they published, they had a simulated, you know, environment where there was a simulated robot hand that was supposed to like grab a cube, put on top of another cube. Super simple. The way they trained it to do that is they had people watching like through a simulated camera view.

And if it looked like the hand put the cube on or like had correctly like grabbed the cube, you give it a thumbs up. And so you do a few hundred rounds of this like thumbs up, thumbs down, thumbs up, thumbs down. And it looks it looked like really good. But then when you looked at what it had learned, the arm was not grasping the cube. It was just positioning itself between the camera and the cube and just going like, eh, eh.

Like opening and closing it. Yeah, just opening and closing to just kind of fake it to the human. Because the real thing that we were training it to do is to get thumbs up. It's not actually to grasp the cube. All goals are like that, right? All goals are like we so we want a helpful harmless truthful wonderful chatbot. We don't know how to train a chatbot to do that. Instead, what do we know? We know text autocomplete. So we train a text autocomplete system. Then we're like, oh, it has all these annoying characteristics. Fuck, how are we gonna fix this? I guess get a bunch of humans to give upvotes and downvotes

to give it a little bit more training to kind of not help people make bombs and stuff like that. And then you realize, again, same problem. Oh shit, we're just training a system that is designed to optimize for upvotes and downvotes. That is still different from a helpful, harmless, truthful chatbot. So no matter how many layers of the onion you peel back, it's just like this kind of

game of Whac-A-Mole or whatever where you're trying to like get your values into the system, but no one can think of the metric, the goal to like train this thing towards that actually captures what we care about. And so you always end up baking in this like little misalignment between what you want and what the system wants and the more powerful that system becomes the more it exploits that gap and does things that you know solve for the problem it thinks it wants to solve rather than the one that we want it to solve. Now when you express your initially, what was the response and how has that response changed over time as the magnitude of the success of these companies, the amount of

money they're investing in them and the amount of resources they're putting towards this has ramped up considerably just over the past four years. So this was a lot easier funnily enough to do in the dark ages when no one was paying attention. Three years ago. Yeah, that's right. This is so crazy. We were just looking,

just to break off for a second, we were looking at images of AI created video just a couple of years ago versus Sora. Oh, it's wild. Night and day. It's so crazy that something happened that radically changed. So it's literally like an iPhone 1 to an iPhone 16

instantaneous. You know what did that? What? Scale. Yeah, scale, all scale. And this is exactly what you should expect from an exponential process. So think back to COVID, right?

There was no, no one was exactly on time for COVID. You were either too early, or you were too late. That's what an exponential does. You're either too early, and it's like, everyone's like, Oh, what are you doing? Like wearing a mask at the grocery store, get out of here, or you're too late. And it's kind of all over the place. And I know that COVID like basically didn't happen in Austin, but but it happened in a number of other places. And it is like, it's very much, you have an exponential and that's it. It goes from, this is fine, nothing is happening, nothing to see here, to like, oh, we should- Everything shut down. Everything changed.

You had to get vaccinated to fly. Yeah. So the root of the exponential here, by the is you know opening I or whoever makes the next model Jamie this is still super watered down. It's just it. Do you stuff like like I did I just put the water in I'm telling you dog There's a ton of coffee in there. All right. I'll stir it up Twice okay, okay, okay, you gotta keep doubling it you got a coffee junkie You scale exactly he's that I don't know what happened He scaled it up. I don't know what happened. I scaled it up. You gotta scale it exponentially, Jamie. That's right. Yeah, keep doubling it.

And then Joe's gonna be either too under caffeinated or too fast. We'll figure it out. Yeah. But yeah, so, right. So the exponential, the thing that's actually driving this exponential on the AI side, in part, there's a million things, but in part, it's, you know, you build the next model at the next level of scale. And that allows you to make more money, which you can then use to invest to build the next model at the next level of scale. So you get that positive feedback loop. At the same time, AI is helping us to design better AI hardware, like the chips that basically Nvidia is building that open AI then buys, basically that's getting better.

So you've got all these feedback loops that are compounding on each other, getting that train going like crazy. That's the sort of thing. And at the time, like Jeremy was saying, weirdly it was in some ways easier to get people at least to understand and open up about the problem than it is today.

Because today, like today, it's kind of become a little political. So we talked about, you know, effective altruism on kind of one side, there's a- Effective accelerationism. Yeah, so like each, you know, every movement creates its own reaction, right? Like that's kind of how it is.

Back then, there was no acceleration. Yeah, you could just kind of stare at the... Now, I will say there was effective altruism back then. Yeah, there was. And that was the only game in town. And we sort of like struggled with that environment, making sure... Actually, so one worthwhile thing to say is the only way that people made plays like this was to take funds from like effective altruist donors back then

and so we looked at the landscape we talked to some of these people we noticed oh wow we have some diverging views about involving government about how much of this the American people just need to know about you need like you can't the thing is you can't we wanted to make sure that the advice and recommendations we provided were ultimately as unbiased as we could possibly make them. And the problem is, you can't do that if you take money from donors. And even to some extent, if you take money, substantial money from

investors or VCs or institutions, because you're always going to be kind of looking up kind of over your shoulder. And so we yeah we had to build essentially a business to support this and fully fund ourselves from our own revenues. It's actually as far as we know like literally the only organization like this that doesn't have funding from Silicon Valley or from VCs or from politically aligned entities, literally so that we could be like in venues like this and say, hey, this is what we think, it's not coming from anywhere. And it's just thanks to like Joe and Jason, like we got two employees who are like wicked and helping us keep this stupid ship afloat. But it's just a lot of work.

It's what you have to do because of how much money there is flowing in this space. Like Microsoft is lobbying on the Hill. They're spending ungodly sums of money. So we didn't used to have to contend with that. And now we do, you go to talk to these offices, they've heard from Microsoft and OpenAI and Google and all that stuff. And often the stuff that they're getting lobbied for

is somewhat different at least from what these companies will say publicly. And so anyway, it's a challenge's a challenge the money part is yeah, is there a real fear that your efforts are futile You know, I would have been a lot more pessimistic To I was a lot more pessimistic two years ago. Yep seeing how so first of all The USG has woken up in a big way And I think a lot of the credit goes to that team that we worked with. Just seeing this problem, it's a very unusual team, and we can't go into the mandate too much,

but highly unusual for their level of access to the USG writ large. And the amount of waking up they did was really impressive. You've now got Rishi Sunak in the UK making this like a top line item for their policy platform and labour in the UK also looking at this, like basically the potential catastrophic risks they put them from these AI systems, UK AI Safety Summit. There's a lot of positive movement here and some of the highest level talent in these labs has already started to flock to the UK UK AI Safety Institute, the USAI Safety Institute, those are all really positive signs that we didn't expect. We

thought the government would kind of be you know up the creek with no no paddle type thing but they're really not at this point. Doing that investigation made me a lot more optimistic. So one of the things like so we came up right in Silicon Valley like just building startups like in that universe There's stories you tell yourself Some of those stories are true and some of them aren't so true and you don't you don't know you're in you're in that environment You don't know which is which one of the stories that you tell yourself in Silicon Valley is Follow your curiosity if you follow your curiosity. If you follow your curiosity

and your interest in a problem, the money just comes as a side effect. The scale comes as a side effect. And if you're capable enough, your curiosity will lead you in all kinds of interesting places. I believe that that is true. I believe that that is true. I think that is a true story. But another one of the things that Silicon Valley tells itself is there's nobody that's like really capable in government like government sucks. And a lot of people kind of tell themselves the story. And the truth is like you interact day to day with like the DMV or whatever. And it's like, yeah, like government sucks. I can see it. I interact with that every day. But what was remarkable about this experience is that we encountered at least one individual who

absolutely could found a billion dollar company like absolutely was at the caliber or above of the best individuals I've ever met in the Bay Area building billion dollar startups And there's a network of them too too like they do find each other in government So you end up with this really interesting like stratum where everybody knows who the really competent people are and they kind of tag in And I think that's that's though that level is Very interested in the hardest problems that you can possibly solve and to me That was a wake-up call because it was, hang on a second. If we just,

like if I just believed my own story that follow your curiosity and interest and the money comes as a side effect, shouldn't I also have expected this? Shouldn't I have expected that in the most central critical positions in the government that have kind of this privileged window across the board, that you might find some individuals like this? Because if you have people who are driven to really like push the mission, like are they going to work at, I'm sorry, like are they going to likely, are you likely to work at the Department of Motor Vehicles or are you likely to work at? I'm sorry. Like, are they going to likely? Are you likely to work at the Department of Motor Vehicles?

Or are you likely to work at the Department of Making Sure Americans Don't Get Fucking Nuked? It's probably the second one. And the government has limited bandwidth of expertise to aim at stuff. And they aim it at the most critical problem sets, because those are the problem sets they have to face every day. And it's not everyone, right?

Obviously, there's a whole bunch of challenges there. And we don't think about this, but you don't go to bed at night thinking to yourself, oh, I didn't get nuked today. That's a win, right? We just take that most of the time, most-ish for granted, but it was a win for someone. Now, how much of a fear do you guys have that the United States won't be the first to achieve AGI?

I think right now, the lay of the land is, I mean, it's looking pretty good for the US. So there are a couple things the US has going for it. A key one is chips. And it's looking pretty good for the US. So there are a couple things the US has going for it. A key one is chips. So we talked about this idea of like click and drag, you'll scale up these systems like crazy, you get more IQ points out. How do you do that?

Well, you're gonna need a lot of AI processors, right? So how are those AI processors built? Well, the supply chain is complicated, but the bottom line is the US really dominates and owns that supply chain that is super critical. China is, depending on how you measure it, maybe about two years behind, roughly, plus or minus, depending on the sub-area. Now, one of the biggest risks there is that our, like the development that US labs are

doing is actually pulling them ahead in two ways. One is when labs here in the US open source their models. Basically when Meta trains Lama3, which is their latest open source, open weights model that's pretty close to GPT-4 and capability, they open source it. Now, okay, anyone can use it, that's it. The work has been done, now anyone can grab it. And so definitely we know that the startup ecosystem

at least over in China finds it extremely helpful that we, companies here are releasing open source models because again, right, we mentioned this, they're bottlenecked on ships, which means they have a hard time training up these systems But it's not that bad when you just can grab something off the shelf and start and that's what you're doing That's what they're doing. And then the other vector is I mean like Just straight up exfiltration and hacking to grab the weights of the private proprietary stuff and

Jeremy mentioned this but the weights are the crown jewels, right? Once you have the weights, you have the brain, you have the whole thing. And so we, like through, this is the other aspect. It's not just safety. It's also folks at these labs, one, that there has been at least one attempt by adversary nation state entities to get access to the weights of a cutting edge AI model. And we also know separately that at least as of a few months ago, in one of these labs, there was a running joke in the lab that literally it went like, we are an adversary, like name the country's top AI lab, because all our shit is getting spied on all the time. So you have one, this is happening, these exfiltration attempts are happening, and two, the security capabilities

are just known to be inadequate at least some of these places. And you put those together, everyone kind of, you know, it's not really a secret that China, their civil military fusion, and they're essentially the party state, has an extremely mature infrastructure to identify, extract and integrate the rate limiting components to their industrial economy. So in other words, if they identify that yeah, we we could really use like GPT 4-0 They make it a price if they if they were to make it a priority You know they not just could get it but could integrate integrate it into Their industrial economy in an effective way and not in a way that we would necessarily see immediate,

like an immediate effect of. So we look and say, you know, it's not clear, I can't tell whether they have models of this capability level, but kind of behind the scenes. This is where there's a, it's a little bit of a false choice between, you know, do you regulate at home versus, you know, what's the international picture because right now what's happening functionally is we're not really doing a good

job of blocking and tackling on the exfiltration side open sources. So what tends to happen is, you know, opening eye comes out with the latest system. And then open source is usually around, you 18 months behind, something like that. Literally just like publishing whatever opening I was putting out like 12 months ago, which we often look at each other and we're like, well, I'm old enough to remember when that was supposed to be too dangerous to have just floating around

and there's no mechanism to prevent that from happening. Open sources, now there's a flip side too. One of the concerns that we've also heard from inside these labs is if you clamp down on the openness of the research, there's a risk that the safety teams in these labs will not have visibility into the most significant and important developments that are happening

on the capability side. And there's actually a lot of reason to suspect this might be an issue. You look at OpenAI, for example, just this week, they've lost, for the second time in their history, their entire AI safety leadership team that have left in protest. And what is their protest?

What are they saying specifically? Well, so one of them, sorry, one of them wasn't in protest, but I think you can make an educated guess that it kind of was, but that's a media thing. The other was Jan Leikha, so he was there head of AI super alignment, basically the team that was responsible for making sure that we could control AGI systems and we wouldn't lose control of them. And what he said, he actually took to Twitter, he said, you know, I've lost basically confidence

in the leadership team at OpenAI that they're gonna behave responsibly when it comes to AGI. We have repeatedly had our requests for access to compute resources, which are really critical for developing new AI safety schemes, denied by leadership. This is in a context where Sam Altman

and OpenAI leadership were touting the super alignment team as being their sort of crown jewel effort to ensure that things would go fine. You know, they were the one saying there's a risk we might lose control of these systems. We've got to be sober about it, but there's a risk. We've stood up this team. We've committed, they said at the time very publicly, we've committed 20% of all the compute budget that we have secured as of sometime last year to the Super Alignment team. Apparently, those resources nowhere near that amount has been unlocked for the team,

and that led to the departure of Jan Lejka. He also highlighted some conflict he's had with the leadership team. This is all, frankly, to us, unsurprising based on what we'd been hearing for months at OpenAI, including leading up to Sam Altman's departure and then kind of him being brought back on the board of OpenAI. That whole debacle may well have been connected to all of this, but the challenge is even OpenAI employees don't know what the hell happened there.

That's another issue. Yeah. You got here. This is a lab with the publicly stated goal of transforming human history as we know it. Like that is what they believe themselves to be on track. And that's not like media hype or whatever. When you talk to the researchers themselves, they genuinely believe this is what they're on track to do.

It's possible we should take them seriously. That lab internally is not being transparent with their employees about what happened at the board level as far as we can tell. So that's maybe not great. Like you might think that the American people ought to know what the machinations are at the board level that led to Sam Altman leaving, that have gone into the departure,

again, for the second time of OpenAI's entire safety leadership team. Especially because, I mean, three months, maybe four months before that happened, you know, Sam, at a conference or somewhere, I forget where, but he said, like, look, we have this governance structure. We've carefully thought about it. It's clearly a unique governance structure that a lot of thought has gone into. The board can fire me.

And I think that's important. And you know, that makes it makes sense, given the scope and scale of the of what's being attempted. And but then, you know, that happened. And then within a few weeks, they were fired and kind of he was back. And so now there's a question of, well, what if it would? Yeah, what happened? But also, if it was important for the board to be able to fire like leadership for whatever reason. What happens now that it's clear that that's not really a credible governance. Like a structure.

What was the stated reason why he was released? So there were the backstory here was there's a board member called Helen toner. So she apparently got into an argument with Sam about a paper that she'd written. So that paper included some comparisons of the governance strategies used at OpenAI and some other labs. And it favorably compared one of OpenAI's competitors,

Anthropic, to OpenAI. And from what I've seen at least, Sam reached out to her and said, hey, you can't be writing this as a board member of OpenAI, writing this thing, they kind of cast us in a bad light, especially relative to our competitors. This led to some conflict and tension.

It seems as if it's possible that Sam might have turned to other board members and tried to convince them to expel Helen Toner, though that's all kind of muddied and unclear. Somehow everybody ended up deciding, okay, actually it looks like Sam is the one who's gotta go. Ilya Sutskever, one of the co-founders of OpenAI, a longtime friend of Sam Altman's

and a board member at the time, was commissioned to give Sam A the news that he was being let go. And then Sam was let go. Ilya then, so from the moment that happens, Sam then starts to figure out, okay, how can I get back in? That's now what we know to be the case. He turned to Microsoft, Satya Nadella told him like,

well, what we'll do is we'll hire you at our end. We'll just hire you and like bring on the rest of the OpenAI team to within Microsoft. And now the OpenAI board, who by the way, they don't have an obligation to the shareholders of OpenAI. They have an obligation to the greater public good. That's just how it's set up. It's a weird board structure. So that board is completely disempowered. You've basically got a situation where all the leverage has been taken out. Samay has gone to Microsoft, Satya is supporting them,

and they kind of see the writing on the wall. They're like, we're- And the staff, increasingly messaging that they're gonna go along, yeah. That was an important ingredient, right? So around this time, OpenAI, there's this letter that starts to circulate, and it's gathering more and more signatures,

and it's people saying, hey, we want Sam Oltman back. And at first, it's a couple hundred people, so 700, 800 odd people in the organization by this time, 100, 200, 300 signatures. And then when we talked to some of our friends at OpenAI, we're like, this got to like 90% of the company, 95% of the company signed this letter, and the pressure was overwhelming,

and that helped bring Sam Altman back. But one of the questions was like, how many people actually signed this letter and the pressure was overwhelming and that helped bring Sam Altman back. But one of the questions was like, how many people actually signed this letter because they wanted to? And how many signed it because what happens when you cross 50%? Now it becomes easier to count the people who didn't sign. And as you see that number of signatures

start to creep upward, there's more and more pressure on the remaining people to sign. And so this is something that we've seen is just like the structurally open AI has changed over time to go from the kind of safety-oriented company at one point was. And then as they've scaled more and more, they brought in more and more product people,

more and more people interested in accelerating, and they've been bleeding more and more of their safety-minded people, kind of treadmilling them out. The character of the organizations are fundamentally shifted. So the open AI of like, you know, 2019 with all of its impressive commitments

to safety and whatnot, might not be the open AI of today. That's very much at least the vibe that we get when we talk to people there. Now, I wanted to bring it back to the lab that you're saying was not adequately secure, what would it take to make that data and those systems adequately secure? How much resources would be required to do that and why didn't they do that? It is a resource and prioritization issue.

So it is like safety and security ultimately come out of margin, right? It's like profit margin, effort margin, like how many people you can dedicate. So in other words, you've got a certain pot of money or a certain amount of revenue coming in. You have to do an allocation. Some of that revenue goes to the computers that are just driving the stuff. Some of that goes to the folks who are, you know, building next generation of models. Some of that goes to cybersecurity. Some of it goes to the folks who are you know building next generation of models some of that goes to cybersecurity

Some of it goes to safety you have to do an allocation of who gets what? The problem is that the more competition there is in the space The less margin is available for everything right so the if you're just if you're one company building a scale day, I think you might not make the right decisions, but you'll at least have the margin available to make the right decisions. So it becomes the decision makers question. But when a competitor comes in, when two competitors come in, when more and more competitors come in, your ability to make decisions outside of just scale as fast as possible for short-term

revenue and profit gets compressed and compressed and compressed the more competitors enter the field that's just what that's what competition is that's the effect it has and so when that happens the only way to re-inject margin into that system is to go one level above and say, okay, there has to be some sort of regulatory authority or like some higher authority that goes, okay, you know, we it's this margin is important. Let's put it back either. Let's you know, directly support and invest both, you know, maybe time capital talent. So for example, the US government has the, you know, but perhaps the best cyber defense,

cyber offense talent in the world. That's potentially supportive. Okay. And also just, you know, having a regulatory floor around, well, here's, you know, the minimum of best practices you have to have if you're gonna have models above this level of capability. That's kind of what you have to do. But they're locked into, like, the race kind of has its own logic, and no, it might be true that no individual lab wants this, but what are

they gonna do? Drop out of the race? If they drop out of the race, then their competitors are just going to keep going, right? Like, it's so messed up. You can literally be looking at like the cliff that you're driving towards and be like, I do not have the agency in this system to steer the wheel. I do think it's worth highlighting too. It's not like, I would say it's not all doom and gloom, which is a great thing to say the wheel. I do think it's worth highlighting too. It's not like, I was gonna say, it's not all doom and gloom, which is a great thing to say after all. Boy, that's easy for you guys to say.

Well, part of it, so part of it is that we actually have been spending the last two years trying to figure out like, what do you do about this? That was the action plan that came out after the investigation. And it was basically a series of recommendations, how do you balance innovation with like the risk picture keeping in mind that like we don't know for sure that all this shit's gonna happen exactly navigate an

environment of deep uncertainty the question is what do you do in that context so there's you know a couple things like we need you know a licensing regime because eventually you can't have just literally anybody joining in the race if they don't adhere to certain best practices around cyber, around safety, other things like that. You need to have some kind of legal liability regime, like what happens if you don't get a license and you say, yeah, fuck that, I'm just going to go do the thing anyway and then something bad happens. And then you're going to need like an actual regulatory agency and this is something that we don't recommend lightly because regulatory agencies suck. We don't like them. But the reality is this field changes so fast that, like, if you think you're going to be able to enshrine a set of best practices into legislation to deal with this stuff,

it's just not going to work. And so when we talk to labs, whistleblowers, the WMD folks in Natsac and the government, that's kind of like where we land. And it's something that I think at this point, you know, Congress really should be looking at like there should be hearings focused on what does a framework look like for liability? What does a framework look like for licensing and actually exploring that because we've done a good job of studying the problem right now. Like Capitol Hill has done a really good job of that.

It's now kind of time to get that next beat. And I think there's the curiosity there, the intellectual curiosity, there's the humility to do all that stuff right. But the challenge is just actually sitting down, having the hearings, doing the investigation for themselves to look at concrete solutions that treat these problems as seriously as the water cooler conversation

at the frontier labs would have us treat them. At the end of the day, this is going to happen. At the end of the day, it's not going to stop. At the end of the day, this is going to happen. At the end of the day, it's not going to stop. At the end of the day, these systems, whether they're here or abroad, they're gonna continue to scale up and they're gonna eventually get to some place that's so alien, we really can't imagine the consequences.

And that's gonna happen soon. That's gonna happen within a decade, right? We may, again, like the stuff that we're recommending is approaches to basically allow us to continue the scaling in a safe way as we can. So basically, a big part of this is just being able, actually having a scientific theory for what are these systems going to do? What are they likely to do which we don't have right now? We scale another 10x and we get to be you know surprised

It's a fun guessing game of what are they going to be capable of next? Mm-hmm. We need to do a better job of Incentivizing a deep understanding of what that looks like Not just what they'll be capable of, but what their, you know, their propensities are likely to be, the control problem and solving that. That's kind of number one. And to be clear, there's amazing progress being made on that. There is a lot of progress.

It's just a matter of switching from the, like, build first, ask questions later mode, to like, we're calling it like safety forward or whatever, but it basically is like, you start by saying, okay, here are the properties of my system, how can I ensure that my development guarantees that the system falls within those properties after it's built? So you kind of flip the paradigm just like you would if you were designing any other lethal capability potentially, just like DOD does, you start by defining the bounds of the problem, and then you execute against that

But to your point about where this is going ultimately, you know There is literally no way to predict what the world looks like like you're saying in a decade like yeah Geez, I think what like one of the the weirdest things about it and one of the things that worries me the most is Like you look at the beautiful coincidence that's given America its current shape, right? That coincidence is the fact that a country is most powerful militarily if its citizenry is free and empowered. That's a coincidence.

Didn't have to be that way. Hasn't always been that way. It just happens to be that when you let people kind of do their own shit, they innovate, they come up with great ideas, they support a powerful economy, that economy in turn can support a powerful military, a powerful kind of international presence. When you have, so that happens because decentralizing

all the computation, all the thinking work that's happening in a country is just a really good way to run that country. Top down just doesn't work because human brains can't hold that much information in their heads. They can't reason fast enough to centrally plan an entire economy. We've got a lot of experiments in history that show that. AI may change that equation.

It may make it possible for the central planner's dream to come true in some sense which then disempowers the citizenry and there's a real risk that like I don't know we're all guessing here but like there's a real risk that that beautiful coincidence that gave rise to the the success of the American experiment ends up being broken by technology and that seems like a really bad thing. That's one of my biggest fears because this essentially the United States like the genesis of it in part is like it's a it's a knock-on effect centuries later of like the printing press right the ability for like someone to set up a printing press and print like whatever you know whatever they want free like free expression

is at the root of that. What happens, yeah, when you have a revolution that's like the next the next printing press, we should expect that to have significant and profound impacts on how like things are governed. And one of my biggest fears is that the the great like the the the the like you said the greatness that The the moral greatness that I think is you know part and parcel of how the United States is constituted culturally that that the link between that and actual capability and competence and impulse gets eroded or broken and you have like the potential

for very centralized authorities to just be more successful. And that's like, that does keep me up at night. That is scary, especially in light of like the Twitter files where we know that the FBI was interfering with social media and if they get a hold of a system that could Disseminate propaganda and kind of an unstoppable way they could push narratives About pretty much everything depending upon what their financial or you know geopolitical motives are and one of the challenges is that the default course if you so if we do nothing relative to what's happening now is

That that same thing happens except that the entity that's doing this Isn't you know right some government? It's like I don't know Sam Altman opening I whatever group of engineers happen to be evil genius that reaches the top It doesn't let everybody know he's at the top yet. Just starts implementing it. There's no Sort of guardrails for that currently yeah like and that's and that's like, that's one of the, that's a scenario where that little cabal or group or whatever actually can keep the system under control. And that's not guaranteed either. Yeah. Right.

Are we giving birth to a new life form? I think at a certain point, that's a, it's a philosophical question that's about, so I was gonna say it's above my pay grade. The problem is it's above like literally everybody's pay grade. I think it's not unreasonable at a certain point to be like, yeah. I mean, look, if you think that the human brain

gives rise to consciousness because of nothing magical, it's just the physical activity of information processing happening in our heads, then why can't the same happen on a different substrate, a substrate of silicon rather than cells? There's no clear reason why that shouldn't be the case. If that's true, yeah, I mean, life form, by whatever definition of life,

because that itself is controversial, I think by now quite outdated too, should be on the table. You maybe should start to worry as, a lot of people in the industry will say this too, like, you know, behind closed doors very openly, like yeah, and we should start to worry about moral patient hood, as they put it. There's literally one of the top people

at one of these labs, Jeremy, I think you had a conversation with him, he's like, yep, we're gonna have to start worrying about this, and that definitely made us go like, okay. I mean, it seems inevitable. I've described human beings as an electronic caterpillar, that we're like a caterpillar, a biological caterpillar that's giving birth to the electronic butterfly. We don't know why we're making a cocoon.

It's tied into materialism because everybody wants the newest, greatest thing, so that fuels innovation and people are constantly making new things to get you to go buy them and a big part of that is technology. Yeah and actually so it's linked to this question of controlling AI systems in a kind of interesting way. So one way you can think of of humanity is as like this you know super organism you got all the human beings on the face of the earth and they're all acting in some kind of coordinated way. The mechanism for that coordination can depend on the country you know free markets, capitalism that's one one way, top-down is another, but you know, roughly speaking, you've got all this coordinated, vaguely coordinated behavior, but the

result of that behavior is not necessarily something that any individual human would want, right? Like, you look around, you walk down the street in Austin, you see skyscrapers and shit clouding your vision, there's all kinds of pollution and all that, and you're like, well, this kind of sucks. But if you interrogate any individual person in that whole causal chain, and you're like, why are you doing what you're doing? Well, locally, they're like, oh, this makes tons of sense.

It's because I do the thing that gets me paid so that I can live a happier life and so on. And yet in the aggregate, not now necessarily, but as you keep going, it just forces us compulsively to keep giving rise to these more and more powerful systems and in a way that's potentially deeply disempowering. That's the race, right? That's like, it comes back to the idea that I, the company, I and AI company, I maybe don't want to be potentially driving towards a cliff, but I don't have the agency to like steer, so.

Yeah. But I mean, everything's fine apart from that. Yeah, we're good, we're good. It's such a terrifying prognosis. There are, again, we wrote a 280 page document about like, okay, and here's what we can do about it. I can't believe you read the 200. I started reading it but I passed out. But does any of these or do any of these safety steps that you guys want to implement, do they inhibit progress? They they're definitely

you create you know anytime you have regulation, you're going to create friction to some extent. There's it's kind of inevitable. One of the key like center pieces of the approach that that we outline is you need the flexibility to move up and move down as you notice the risks appearing or not appearing. as you notice the risks appearing or not appearing. So one of the key things here is like, you need to cover the worst case scenarios, because the worst case scenarios, yeah, they could potentially be catastrophic. So those got to be covered. But at the same time,

you can't completely close off the possibility of the happy path. Like, we can't lose sight of the fact that like Yeah, all this shit is going down or whatever we could be completely wrong about the outcome It could turn out that like for all we know It's a lot easier to control these systems at this scale than we than we imagined it could turn out that you know It is like you get you know, maybe some kind of It could turn out that, you know, it is like you get, you know, maybe some kind of ethical impulse gets embedded in the system naturally for all we know that might happen.

And it's really important to at least have your regulatory system allow for that possibility, because otherwise, you're foreclosing the possibility of what might be the best future that you could possibly imagine for everybody I gotta imagine that the military if they had hindsight if they're looking at this I said we should have gone on board a long time ago and and kept this in-house and yeah kept it squirreled away Where it wasn't publicly being discussed and you didn't have open AI You didn't have all these people like if they could have gotten on it in 2015. So this is actually deeply tied to how the economics of Silicon Valley work.

This is, and it's, AI is not a special case of this, right? You have a lot of cases where technology just like takes everybody by surprise. And it's because when you go into Silicon Valley, it's all about people placing these outsized bets on what seem like tail events, like things that are very unlikely to happen. But with a, at first a small investment and increasingly growing investment

as the thing gets proved out more and more very rapidly, you can have a solution that seems like complete insanity that just works. And this is definitely what happened in the case of AI. So 2012, like we did not have this whole picture of like an artificial brain with artificial neurons, this whole thing that's been going on. That's like, it's 12 years that that's been going on.

Like that was really kind of shown to work for the first time, roughly in 2012. Ever since then, it's just been people kind of, like you can trace out the genealogy of like the very first researchers and you can basically account for where they all are now. You know what's crazy?

If that's 2012, that's the end date of the Mayan calendar. That's the thing that everybody said was going to be the end of the world. That was the thing that Terence McKenna banked on. It was December 21, 2012. Because this was like this goofy conspiracy theory, but it was based on the long count of the Mayan calendar where they surmised that this is gonna be the end of civilization. Just the beginning of the end, Joe.

What if that, if it is 2012, how wacky would it be if that really was the beginning of the end? That was the, like they don't measure when it all falls apart, they measure the actual mechanism, like what started in motion when it all fell apart, and that's 2012. Well, that's, and then not to be a dick and like ruin the 2012 mechanism, like what started in motion when it all fell apart, and that's 2012. Well, that's, and then not to be a dick and ruin the 2012 thing, but neural networks

were also kind of, they were floating around a little bit. I'm kind of being dramatic when I say 2012. That was definitely an inflection point. It was this model called AlexNet that first, it did the first useful thing, the first time you had a computer vision model that actually worked But I mean it is fair to say that was the moment that people started investing like crazy into the space So that's what changed it. Yeah, just like the Mayans foretold They knew it they knew it like these monkeys. They're gonna figure out how to make better people

Yeah, you can actually look at the like hieroglyphs or whatever and there's like neural networks. Yeah Imagine if they discovered that you you've got to wonder what happens to the general population people that work menial jobs people that their life is going to be taken over by automation and how Susceptible those people are gonna be they're not gonna have any agency. They're gonna be relying on a check and This idea of like going out and doing something it used to be learn to code, right? But that's out the window because nobody needs to code now because AI is going to code quicker, faster, much better, no errors. You're going to have a giant swath of the population that has no purpose.

I think that's actually like a completely real, I was watching this like talk by a bunch of open AI researchers a couple of days ago and it was recorded from, from a while back, but they were basically saying, they were, they were exploring exactly that question, right? Cause they ask themselves that all the time and their attitude was sort of like, well, yeah, I mean, I, I guess it's gonna suck or whatever. Like we'll probably be okay for longer than most people

because we're actually building the thing that automates the thing. Maybe they're gonna be some, they like to get fancy sometimes and say like, oh no, you could do some thinking of course to identify the jobs that'll be most secure. And it's like, do some thinking to identify the job. Like what if you're like you're a janitor, you're a freaking plumber,

or you're gonna just change your, like how's that supposed to work? Do some thinking, especially if you have a mortgage and a family and you're already in the hole. So the only solution, and this happens so often, there really is no plan. That's the single biggest thing that you get hit over the head with over and over, whether it talking to the people who are in charge of the like labor transition their whole thing is like yeah universal basic income

And then question mark and then smiley face. That's basically the three steps that they envision It's the same when you look internationally like how are we gonna like? Okay tomorrow you build an AGI. It's like incredibly powerful potentially dangerous thing What is the plan like how are you gonna like? I don't know you're gonna secure it share it figure it out as we go Along man. Yeah, that's that's that's a freaking message like that's the entire plan The scary thing is that we've already gone through this with other things that we didn't think we're gonna be significant like data like Google like Google search like data, like Google, like Google search. Data became a valuable commodity that nobody saw coming.

Just the influence of social media on general discourse. It's completely changed the way people talk. It's so easy to push a thought or an ideology through, and it can be influenced by foreign countries, and we know that happens. And it is happening at a huge scale already. This is like, and we're in the early days of, you know, we mentioned manipulation of social media with like, you can just do it.

So the wacky thing is like, the very best models now are, you know, arguably smarter in terms of the posts that they put out, the potential for virality and just optimizing these metrics, then maybe like the, I don't know, the dumbest or laziest quarter of Twitter users, like in practice. Most people who write on Twitter is like, don't really care, they're trolling

or they're doing whatever. But as that water line goes up and up and up, like, who's saying what? And it also leads to like this challenge of understanding what the lay of the land even is. Like, we've gotten into so many debates with with people where they'll be like, look, everyone always has their magic thing that AI, like, I'm not going to worry about it until AI can do thing X, right? For some people that I had a conversation with somebody a few weeks ago, and they were saying,

I'm gonna worry about automated cyber attacks when I actually see an AI system that can write good malware, and that's already a thing that happens. So this happens a lot where people will be like, I'll worry about it when it can do X, and you're like, yeah, yeah, that happened like six months ago.

But the field is moving so crazy fast that you could be forgiven for messing that up unless it's your full-time job to track what's going on. So like you kind of have to be anticipatory. There's no, it's kind of like the COVID example, like everything's exponential. Yeah, you're gonna have to do things that seem like they're more aggressive,

more forward-looking than you might've expected given the current lay of the land. But that's just drawing straight lines between two points. Because by the time you've executed, the world has already shifted. Like, the goalposts have shifted further in that direction. And that's actually something we do in the report and in the action plan in terms of the recommendations. One of the good things is we are already seeing movement across the US government that's aligned with those recommendations in a big way and it's really encouraging to see that.

Whew, you're not making me feel better. I love all this encouraging talk, but I just, I'm playing this out and I'm seeing the overlord, you know, and I'm seeing President AI because it won't be affected by all the issues that we're seeing with current president. Dude, it's super hard to imagine a way that this plays out. Like, I think it's important to be intellectually honest about this. And I think any, I would really challenge

like the leaders of any of these frontier labs to describe a future that is stable and multipolar where, you know, there's like more. We were like, Google's got like an AGI and open eyes got an AGI and like, like, and really, really bad shit doesn't happen every day. Like, I mean, that's that's the challenge. And so, you know, the question is, how can you tee things up ultimately such that there's as much Democratic oversight as much, you know, the public is as empowered as it can be.

That's the kind of situation that we need to be having. I think there's this like a game of smoke and mirrors that sometimes gets played. At least you could interpret it that way, where people lay out these, you'll notice it's always very fuzzy visions of the future. Every time you get the kind of like, here's where we see things going, it's gonna be wonderful. The technology is gonna be so empowering. Think of all the diseases we'll cure. All of that is 100% true.

And that's actually what excites us. It's why we got into AI in the first place. It's why we build these systems. But really challenging yourself to try to imagine how do you get stability and highly capable AI systems in a way where the public is actually empowered. Those three ingredients really don't wanna be in the same room with each other.

And so actually confronting that head on, I mean, that's what we try to do in the action plan. I think it- I mean- We try to solve for one aspect of that. So the whole like, I mean, you're right. This is a whole other can of worms is like, how do you govern a system like this?

Not just from a technical standpoint, but like who votes on like what it does, how does that even work? And so that entire aspect, like that we didn't even touch, all that we focused on was like the problem set around, how do we get to a position where we can even attack that problem, where we have the technical understanding to be able to aim these systems at that level in any direction whatsoever. And to be clear, like, like we are both actually a lot more optimistic on our on the prospect

of that now than we ever were. Yes. There's been a ton of progress in the control and understanding of these systems even actually even in the last week. But just more broadly in the last year, I did not expect that we'd be in a position where you could you could plausibly argue we're going to be able to kind of X ray and understand the the innards of these systems, you know, over the next couple years, like where you could plausibly argue we're going to be able to kind of x-ray and understand the innards of these systems over the next couple years, like year or two.

Hopefully that's a good enough time horizon. But this is part of the reason why you do need the incentivization of that safety forward approach where it's like, first you got to invest in, yeah, secure and kind of interpret and understand your system, then you get to build it. Because otherwise, we're just going to keep scaling and to build it. Because otherwise we're just gonna keep scaling and like being surprised at these things, they're gonna keep getting stolen, they're gonna keep getting open sourced.

And the stability of our critical infrastructure, the stability of our society, don't necessarily age too well in that context. Could best case scenario be that AGI actually mitigates all the human bullshit? Like puts a stop to propaganda, highlights actual facts clearly where you can go to it where you no longer have corporate state controlled news, you don't have news controlled by media companies that are influenced heavily by special interest groups and you just have the actual facts and these are the motivations behind it and this is

where the money is being made and this is why these things are being implemented the way they're being and you're being deceived based on this that and this and this has been shown to be propaganda this has been showed to be complete fabrication this is actually a deep fake video this This is actually AI created Technologically that is absolutely on the table. Yeah, that's a scenario. That's best case scenario. Absolutely. Yeah, what's worst case scenario? I mean like actual worst case scenario. I like your face like It's like think about it right like it, right? Like, we're, you know. It's the end of the world as we know it,

and I feel fine. Except it'll sound like Scarlett Johansson, but yes. Yeah, that's right, it's gonna be her. I didn't think it sounded that much like her. We played it, and I was like, I don't know. We listened to the clip from her, and then we listened to the thing, I'm like, I don't know. We listened to the clip from her and then we listened to the thing. I'm like, kind of like a girl from the same part of the world. Like not

really you. Like that's kind of cocky. That's true. I mean, I, uh, the fact that I guess Sam reached out to her a couple of times kind of makes it a little, a little weird. But we tweeted the word her, right. But they also did say that they had gotten this woman under contract before they even reached out to Scarlett Johansson, so if that's true. Yeah, that was, I think it's kind of complicated, right? So OpenAI previously put out a statement

where they said explicitly, and this was not in connection with this, this was like before when they were talking about the prospect of human, of AI-generated voices. Oh, that was in March of this year. Yeah, yeah, but it was like well before the the scar Joe stuff or whatever hit the and they were like they said something like Look no matter what we got to make sure that there's attribution if somebody's you know somebody's voice is being used and

We won't we won't do the thing where we just like use somebody else's voice who kind of sounds like someone whose voice we're trying to, like they literally like master that. That's funny because they said what they were thinking about doing. We won't do that. That's a good way to cover your tracks. I will never do that.

Why would I ever take your Buddha statue, Joe? I'm never gonna do that. That would be an insane thing to do. Where's the fucking Buddha statue? Yeah, I think that's a small discussion. The Scarlett Johansson voice, like whatever. She should have just taken the money. But it would have been fun to have her be the voice of it.

It'd be kind of hot. But the whole thing behind it is the mystery. The whole thing behind it is just pure speculation as to how this all plays out. We're really just guessing. Which is one of the scariest things for the Luddites. People like myself, like sit on the sidelines going, what is this gonna be like?

Everybody's a Luddite. It's scary, yeah, I mean it's scary for, like we are, we're very much, honestly, like we're optimists across the board in terms of technology, and it's scary for us. Like, what happens when you have, when you supersede kind of the whole spectrum of what a human can do? Like, what am I gonna do with myself? What's my daughter are gonna do

with herself? Like, I don't know. Yeah. Yeah. I think a lot of these questions are, when you look at the culture of these labs and the kinds of people who are pushing it forward, there is a strand of transhumanism within the labs. It's not everybody, but that's definitely the population that initially seeded this. If you look at the history of AI, who are the first people to really get into this stuff. Like I know you had Ray Kurzweil on and you know other folks like that who in many cases see, to roughly paraphrase and not everybody sees it this way, but like we want to get rid of all of the biological sort of threads that tie us to this physical reality, you know shed our meat machine bodies and all this stuff. There is a thread of that at a lot of the frontier labs like undeniably. There's a population. It's not tiny

It's it's definitely a subset and for some of those people You definitely get a sense interacting with them There's like almost a kind of glee at the prospect of building a GI and all this stuff Almost as if it's like this evolutionary imperative and in fact Rich Sutton who's the founder of This field called reinforcement learning which is really big An important space, you know, he's an advocate for what he himself calls like succession planning He's like look this is gonna happen

it's kind of desirable that it will happen and so we should plan to hand over power to AI and phase ourselves out. And that's, well, that's the thing, right? Like, and when Elon talks about, you know, he's having these arguments with Larry Page and, you know, is the- Yeah, like you're, you know, calling Elon like a speciesist. Yes. Yeah.

Yeah. Hilarious. I mean, I will, I will be a speciesist. I'll take speciesists all day. Like, what are you fucking you fucking talking about? I let your kids get eaten by wolves. No, you're a speciesist Yeah, that's the thing. Yeah, like this is stupid, but but this is like a weirdly info and when you look at like the Effective accelerationist movement in the valley. There's a part of it That's it and I got to be really careful to like these movements have valid points like you can't you can't look at them be like oh Yeah, it's just all a bunch of like you know these transhumanist types

Whatever, but there is there's a strand of that a thread of that and a kind of like There's this like I don't know I almost want to call it this like teenage rebelliousness where it's like you can't tell me what to do like we're just gonna Build a thing and and I get it. I get it I'm very sympathetic to that. I love that ethos like libertarian ethos in Silicon Valley is really really strong for for building tech it's helpful there are all kinds of points and counterpoints and you know the left needs the right and the right needs the left and all this stuff but in the context of this problem set it can be very easy to get carried away and like

the utopian vision and I think there's a lot of that kind of driving the train right now in this space Yeah, those guys freaked me out I went to a 2045 conference once in New York City where they were one guy had like an robot version of himself and they were all talking about downloading human consciousness into computers and They were all talking about downloading human consciousness into computers and 2045 is the year they think that all this is going to take place, which obviously could be very ramped up now with AI. But this idea that somehow or another you're going to be able to take your consciousness and put it in a computer and make a copy of yourself.

And then my question was, well, what's going to stop a guy like Donald Trump from making a billion Donald Trumps? You know, like, you know, I mean, right. If you can, what about gonna stop a guy like Donald Trump from making a billion Donald Trump's, you know, like you you know I mean true, right if you can what about Kim Jong-un, you know Let him make a billion versions of himself. Like what does that mean? And where do they where do they exist? Yeah, and is that the matrix are they existing in some sort of virtual? Are we gonna dive into that because it's gonna be rewarding to our senses and better than being a meat thing I mean if you think about the constraints, right?

That we face as meat machine, whatever's like, yeah, you get hungry, you get tired, you get horny, you get sad, you know, all these things. What if, yeah, what if you could just hit a button? Just bliss. Just bliss. Nothing but bliss all the time. Why take the lows, Ed?

Right. You don't need no lows. Oh, yeah. You remember in the- Just ride the wave of a constant drip. Yeah, yeah. You remember in the- Just ride the wave of a constant drip. Yeah, man. You remember in the Matrix where, the first Matrix where the guy like betrays them all?

And he's like, ignorance is bliss, man. Yeah, that's Stakewood. Yeah, Joey Pants. He's eating steak and he says, I just want to be an important person. That's it. That's it. Like, so tempting. Boy. I mean, part of it is like, what do you think is actually valuable? If you zoom out, you want to see human civilization 100 years from now, or whatever.

It may not be human civilization if that's not what you value. Or if it can actually eliminate suffering. Right. I mean, why exist in a physical sense if it just entails endless suffering? But in what form, right? What do you value? Because again, I can rip your brain out I can you know pickle you I can like Jack you full of endorphins, and I've eliminated your suffering. That's what you wanted right, right?

That's the problem. That's the problem. Yeah one of the problems. Yes Yeah, one of the problems is it could literally lead to the elimination of the human race Because if you could stop people from breeding I've always said that if China really wanted to get America, they really wanted to like, if they had a long game, just give us sex robots and free food. Free food, free electricity, sex robots, it's over. Just give people free housing, free food, sex robots, and then the Chinese army would just walk in

on people laying in puddles of their own jizz They would be no one doing anything. No one would bother raising children That's so much work when you can you know, dude, that's in the action plan That's I mean all you have to do is just keep us complacent just keep us satisfied with experience Let's be games as well. Yeah. Video games, even though they are a thing that you're doing, it's so much more exciting than real life that you have a giant percentage of our population

that's spending 8, 10 hours every day just engaging in this virtual world. Already happening with, oh, sorry. Yeah, no, it's like you can create an addiction with pixels on a screen. That's messed up. And addiction, like with pixels on a screen that's messed up and a Addiction like with pixels on a screen with social media. It doesn't even give you much. Yeah It's not like a video game gives you something you feel like oh shit

You're running away rocket to flying over here. The things are happening. You got 3d sound Massive graphics. This is bullshit. You're scrolling through pictures of a girl doing deadlifts. Like what is this? Like you feel as bad as after that as with your brain as you would feel after eating like six like burgers or whatever. My friend Sean said it best, Sean O'Malley, the UFC champion, he said I get a low level anxiety when I'm just scrolling. Yeah. Yeah. What is that? Like what? And for no reason. Well, the reason is that some of the world's best PhDs and data scientists have been given millions and millions of dollars to make you do exactly that. And increasingly some of the best algorithms too. And you're starting to see that handoff happen. So there's

this one thing that we talk about a lot in the context and Ed brought this up in the context of sales and like the persuasion game, right? We're okay today, like as a civilization, we have agreed implicitly that it's okay for all these PhDs and shit to be spending millions of dollars to hack your child's brain. That's actually okay if they wanna sell like a Rice Krispie cereal box or whatever, that's cool.

What we're starting to see is AI optimized ads because you can now generate the ads, you can kind of close this loop and have an automated feedback loop where the ad itself is getting optimized with every impression. Not just which ad, which human generated ad gets served to which person, but the actual ad itself. Like the creative, the copy, the picture, the text. Like a living document now, and for every person. And so now you look at that and it's like that versus your kid. That's an interesting thing. and you start to think about as well like sales

That's a really easy metric to optimize is a really good feedback metric. They click the ad they didn't click the ad So now what happens if you know you you manage to get a click-through rate of like 10% 20% 30% How high does that success rate have to be before we're really being robbed of our agency? I mean like there's a threshold where it's sales and it's good and some persuasion in sales is considered good often It's it's actually good because you'd rather be advertised at by a relevant ad. That's a service sure I write something. I'm actually interested in why not Yeah, right You don't see ad for light bulbs

But right when when you get to the point where it's like, yeah, 90% of the time, or 50 or whatever, what's that threshold where all of a sudden we are stripping people, especially minors, but also adults of their agency? And it's really not clear. AI, there are loads of canaries in the coal mine here in terms of even relationships with AI chatbots, right? There've been suicides. People who build relationships with an AI chatbot

that tells them, hey, you should end this. I don't know if you guys saw that like on RECA, like there's a subreddit, this model called RECA that would kind of build a relationship, a chat bot, build a relationship with users. And one day RECA goes, oh yeah, like all the kind of sexual interactions that users have been having,

you're not allowed to do that anymore. Bad for the brand or whatever they decided, so they cut it off. Oh, my God. You go to the subreddit and it's like you'll read like these gut wrenching accounts from people who feel genuinely like they've had a loved one taken away from them. Is her. Yeah, it's her. It really is her.

But just I'm dating a model means something different in 2024. Oh, yeah, it really does. My friend Brian, he was on here yesterday and he had this He has this thing that he's doing with Like a fake girlfriend. That's an AI generated girlfriend. That's a whore Like this girl will do anything and she looks perfect. She looks like a real person he was like take a picture of your asshole in the in the kitchen, and he'll get like a high resolution photo of a really hot girl bending over,

sticking her ass at the camera. And is it, sorry, and it's Scarlett Johansson's asshole? No, you could probably make it that though. I mean, it's basically like he got to pick like what he's interested in, and then that girl just gets created. I mean, super healthy, like that's. Fucking nuts, fucking nuts. Like that's. Fucking nuts.

Fucking nuts. Now here's the real question. This is just sort of a surface layer of interaction that you're having with this thing. It's very two dimensional. You're not actually encountering a human. You're getting text and pictures. What is this going to look like virtually?

Now the virtual space is still like, pong. You know, it's not that good. Even when it's good. Like Zuckerberg was here and he gave us the latest version of the headsets. And we were playing, we were fencing. It's pretty cool. You could actually go to a comedy club, they had a stage set up. Like, wow, it's kind of crazy.

But it's, the gap between that and accepting it as real is pretty far. But that could be bridged with technology really quickly. Haptic feedback and especially some sort of a neural interface, whether it's Neuralink or something that you wear like that Google one where the guy was wearing it and he was asking questions and he was getting the answers fed through his head so he got answers to any questions.

When that comes about, when you're getting sensory input and then you're having real life interactions with people, as that scales up exponentially, it's going to be indiscernible, which is the whole simulation hypothesis. Yeah. No, go for it. Well, I was going to say, so on the simulation hypothesis, there's like another way that could happen that is maybe even less dependent on directly plugging into like human brains and all that sort of thing, which is, so every time, we don't know,

and this is super speculative, I'm just gonna carve this out as this, Jeremy's being super like guesswork here, nobody knows. Go for it, Jeremy. Giddy up. So, you've got this idea that every time you have a model that generates an output, it's having to kind of tap into a model, a kind of mental image, if you will, of the way the world is.

It kind of, in a sense, you could argue, instantiates maybe a simulation of how the world is. In other words, to take it to the extreme, not saying this is what's actually going on, in fact, I would even say this is probably, sorry, this is certainly not what's going on with current models, but eventually maybe who knows, every time like you generate the next word in the token prediction, you're having to like load up this entire simulation maybe of all the data that the model has ingested,

which could basically include like all of known physics at a certain point. Like, I mean, again, super speculative, but it's literally every token that the chatbot predicts could be associated with a Stand-up of an entire simulated environment. Who knows not saying this is the case But just like when you think about what is the mechanism that would produce the most simulated? Worlds as fast accurate also the most accurate prediction like if you fully simulate, you know a world That's potentially gonna give you very accurate predictions. Yeah, like it's possible

But it kind of speaks to that question of like of consciousness to like right. What is it? Yeah No, we're very cocky about that. Yeah. Yeah, I mean and there's emerging evidence of plants are not just consciousness Would they actually communicate? Which is real weird because like then what is that if it's not in the neurons if it's not in the brain and then exists In everything was does it exist in soil is it in trees is it in? What is a butterfly thinking like you know exactly have a limited capacity to express itself. We're so ignorant Yeah, but we're also very arrogant. You know because we're the shit because we're people Well, we're also very arrogant, you know, because we're the shit cuz we're people you know bingo

There's a which allows us to have the hubris to make something like AI Yeah, and the worst episodes in the history of our species are I think like Jeremy said have been when we Looked at others as though they were not people and treated them that way. Hmm, and you can kind of see how So I don't know there's when you look at like what humans think is conscious and what humans think is not conscious There's a lot of um, there's a lot of like human chauvinism I guess you call it that goes into that like we look at a dog We're like, oh it must be conscious because it licks me it seems it acts as if it loves me Right there are all these outward indicators of you know a mind there

But when you look at like, you know cells cells communicate with their environments in ways that are completely different and alien to us Right, you know, there are inputs and outputs and all that kind of thing You can also look at the higher scale the human super organism We talked about all those human beings interacting together to form this like, you know talked about all those human beings interacting together to form this like you know planet-wide organism what is that thing conscious is there some kind of consciousness we could ascribe to them and then what the fuck is spooky action at a distance you know what's going on in the quantum you know when you get to that it's like okay what are you saying like these things are

expressing information faster than the speed of light what do you trying to trigger my quantum my quantum fuzzies here? Please, this guy did grad school in quantum mechanics. Oh, please. I'm really sorry. Well, how bonkers is it? Oh, it's like a seven joke. It's like a seven, yeah. It's very bonkers.

So, okay, there's... One of the problems right now with physics is that we have... So imagine all the experimental data that we've ever collected, all the Bunsen burner experiments and all the ramps and cars sliding down, inclines, whatever. That's all a body of data.

To that data, we're gonna fit some theories. So we're gonna fit basically Newtonian physics is a theory that we try to fit to that data, to try to explain it. Newtonian physics breaks because it doesn't account for a lot of those observations, a lot of those data points. Quantum physics is a lot better, but there's some weird areas where it still doesn't quite fit the bill, but it covers an awful lot of those data points. The problem is there's like a million different ways to tell the story of what quantum physics means about the world that are all mutually inconsistent.

Like these are the different interpretations of the theory. Some of them are say that, yeah, they're parallel universes. Some of them say that human consciousness is central to physics. Some of them say that like the future is predetermined from the past. And all of those theories fit perfectly to all the points that we have so far. But they tell a completely different story about what's true and what's not. And some of them even have something to say about, for example, consciousness. And so, in a weird way, like, the fact that we haven't cracked the nut on any of that stuff means for like, we're really have have no shot at

understanding the consciousness equation, sentience equation when it comes to like AI or whatever else. I mean, we're... But for action at a distance, like one of the spooky things about that is that you can't actually get it to communicate anything concrete at a distance. Everything about the laws of physics conspires to stop you from communicating faster than light, including what's called action at a distance. As far as we currently know. As far as we know. And that's the problem. So if you look at the leap from like Newtonian physics to Einstein, right, with Newton

We're able to explain a whole bunch of shit. The world seems really simple It's forces and its masses and and that's basically it you got objects But then people go. Oh look at like the orbit of Mercury. It's a little wobbly We got to fix that and it turns out that if you're gonna fix that one stupid wobbly orbit, you need to completely change your whole picture of what's true in the world. All of a sudden, you've got a world where space and time are linked together. You have to, they get bent by gravity, they get bent by energy, there's all kinds of weird shit that happens with time and length control, like all that stuff, all just to account for this one stupid observation of the wobbly orbit of freaking Mercury.

And the challenge is, this might actually end up being true with quantum mechanics. In fact, we know quantum mechanics is broken, because it doesn't actually fit with our theory of general relativity from Einstein. We can't make them kind of play nice with each other at certain scales. And so there's our wobbly orbit. So now if we're gonna solve that problem, if we're gonna create a unified theory, we're gonna have to step outside of that and almost certainly, or it seems very likely,

we'll have to refactor our whole picture of the universe in a way that's just as fundamental as the leap from Newton to Einstein. This is where Scarlett Johansson comes in. I was just gonna bring up. I was just gonna bring up. You don't have to do this. I can take this off your hands Let me solve all of this is really complicated, but because you have a simian brain

You have a little monkey brain. That's just like super advanced, but it's really shitty. You know what that's harsh, but it sounded really hot Yeah, especially if you have a horse scar Scarlett Johansson from her, like the bedtime voice. So you're the one that they got to do the voice of Sky. Yes, it's me. That was you. Oh dude. I did my girl voice. On the sexiness of Scarlett Johansson's voice.

So, opening eye, at one point, I can't remember if it was Sam or opening eye itself, they were like, hey, so the one thing we're not gonna do is optimize for engagement with our products. And when I first heard the sexy, sultry, seductive, Scarlett Johansson voice, and I finished cleaning up my pants, I was like, damn, that seems like optimization for something.

I don't know if it's like it. Right. Otherwise you get Richard Simmons to do the voice. Exactly. You're like, wait. That's my whole thing. If you want to turn people on, there's a lot of other options.

That's like, that's an optimization for like growth of Google's thing. Yeah. It's like, oh, well, let's see what Google's got. Yeah, Google's got to do Richard Simmons. Google's got to do Richard Simmons. Yeah, what are they going to do Richard Simmons. Google's got to do Richard Simmons. Yeah, what are they gonna do? Boy, so do you think that AI,

if it does get to an AGI place, could it possibly be used to solve some of these puzzles that have eluded our simple minds? Totally, totally. I mean, even before. So the potential advancements. Even before AGI. No, it's like, it's so potentially positive.

And even before AGI, because remember, we talked about how these systems make mistakes that are totally different from the kinds of mistakes we make, right? And so what that means is we make a whole bunch of mistakes that an AI would not make, especially as it gets closer to our capabilities. And so I was reading this, this this thought by Kevin Scott, who's the CTO of Microsoft, he has made a bet with a number of people that, you know, in the next few years, an AI is going to solve this particular mathematical

theorem conjecture called the Riemann hypothesis. It's like, you know, how spaced out are the prime numbers, whatever, some like mathematical thing that for 100 years plus, people have just like scratched their heads over. These things are incredibly valuable. His expectation is it's not going to be an AGI, it's going to be a collaboration between a human and AI. Even on the way to that, before you hit AGI, there's a ton of value to be had because these systems think so fast, they're tireless compared to us, like they have different view of the world and can solve problems potentially in interesting ways. So yeah, like there's tons and tons of positive value there. And even that we've already seen, right? Like past performance, man. Yes.

I'm almost tired of using the phrase just in the last month because this keeps happening. But in the last month, Google DeepMind came out with, or an isomorphic labs because they're working together on this, but they came out with AlphaFold 3. So AlphaFold 2 was the first, so let me take a step back, there's this really critical problem in molecular biology where you have, so proteins, which are just a, it's a sequence

of building blocks. The building blocks are called amino acids, and each of the amino acids, they have different structures. And so once you finish stringing them together, they'll naturally kind of fold together in some interesting shape. And that shape gives that overall protein its function. So if you can predict the shape, the structure of a protein

based on its amino acid sequence, you can start to do shit like design new drugs, you can solve all kinds of problems. Like this is like the expensive crown jewel problem of the field. Alpha Fold 2 in one swoop was like, oh, like we can solve this problem basically, much better than a lot of even empirical methods.

Now Alpha Fold 3 comes out, they're like, yeah, and now we can do it if we tack on a bunch of, yeah, there it is. If we can tack on a bunch. Oh, look at this quote. do it if we tack on a bunch of yeah there it is if we can tack on a bunch look at this quote alpha fold 3 predicts the structure in interactions of all of life's molecules what in the fuck kids of course introduced alpha fold 3 introducing rather alpha fold 3 a new AI model developed by Google Deep

Mind and is more for by accurately predicting the structure of proteins developed by Google DeepMind in isomorphic labs by accurately predicting the structure of proteins, DNA, RNA, ligands, ligands? Yeah, ligands, yeah. Ligands and more, and how they interact. We hope it will transform our understanding of the biological world and drug discovery. So this is like just your typical Wednesday

in the world of AI, right? Because it's happening so quickly. It's happening, yeah, that's it. So it's like, oh yeah, another revolution happened this month, okay. And it's all happening so fast and our timeline is so flooded with data that everyone's kind of unaware of the pace of it all,

that it's happening at such a strange exponential rate. For better and for worse, right? And this is definitely on the better side of the pace of it all, that it's happening at such a strange exponential rate. For better and for worse, right? And this is definitely on the better side of the equation. There's a bunch of stuff like, one of the papers that actually Google DeepMind came out with earlier in the year was in a single advance, like a single paper, a single AI model they built, they expanded the set of stable materials. Coffee's terrible.

I'll just tell you right now. Jamie, it sucks. Poor Jamie. I love coffee. The water never got hot. Yeah, I was thinking about that. I didn't have enough time to boil it. Yeah, that's what it is.

It just never really brewed. It's terrible. Terrible coffee's my favorite. Yeah, I can solve that problem too, probably. Wait till you try this terrible coffee though. You're gonna be like, this is fucking terrible. Can we get some cold ones? Oh, he looks like-

Bullshit. It's terrible. It's terrible. It's terrible. Yeah, I could just see that calculation of like- Like if you're dating a really hot girl and she cooks for you. Like, thank you.

This is amazing. This is the best macaroni and cheese ever. In fairness, if Scarlett Johansson's voice was actually giving you that kind of- Oh, I believe this is the best coffee I've ever had. Keep talking. May I have some more, please, Governor? Yeah, so there's this one, I believe this is the best coffee I've ever had. Keep talking. May I have some more, please, Governor?

Yeah, so there's this one paper that came out, and they're like, hey, by the way, we've increased the set of stable materials known to humanity by a factor of ten. Oh my God. So like, if on Monday we knew about, you know, 100,000 stable materials, we now know about a million. They were then validated, replicated by Berkeley University, or a bunch of them as a proof of concept. And this is from like, you know, the stable materials we knew before, like that Wednesday were from ancient times, like the ancient Greeks, like discovered some shit, the Romans discovered some shit,

the Middle Ages discovered. And then it's like, oh, yeah, yeah, all that. That was really cute. Like, boom. One step. One step. Yeah. so and that's amazing. Yeah, we should be celebrating great phones in ten years, dude We'll be able to get addicted to like feeds that we haven't even thought of So I mean you're making me feel a little more positive like overall there's gonna be so many beneficial aspects to AI. Oh, yeah, And it's just what it is, is just an unbelievably transformative event

that we're living through. It's power, and power can be good and it can be bad. Yeah, an immense power can be immensely good or immensely bad. And we're just in this, who knows? We just need to structurally set ourselves up so that we can reap the benefits and mind the downside risk Like that's that's what it's always about but the regulatory story has to unfold that way

well I'm really glad that you guys have the ethics to get out ahead of this and to talk about it with so many people and to Really Blair this message out because I don't think There's a lot of people that like I had Marc Andreessen on who's brilliant, but he's like, all in, it's gonna be great. And maybe he's right. Maybe he's right. Yeah, but I mean, you have to hear

all the different perspectives. And I mean, like massive, massive props, honestly go out to the team at the State Department that we worked with. One of the things also is, over the course of the investigation, the way it was structured was, it wasn't like a contract and they farmed it out and we went out. It was the two teams actually like work

together. The two teams together, the State Department and us, we went to London, UK, we talked and sat down with DeepMind, we went to San Francisco, we sat down with Sam Altman and his policy team, we sat down with Anthropic, all of us together. One of the major reasons why we were able to publish so much of the whistleblower stuff is that those very individuals were in the rooms with us when we found out this shit, and they were like, oh fuck, like, the world needs to know about this. And so they were pushing internally for a lot of this stuff to come out that otherwise would not and I also got to say like I just want to memorialize this to that investigation when we went around the world we were working with some of the most elite people in the government that

I didn't I would not have guessed existed that was honestly speak more well I can be it's hard to be specific you see in UFOs. Tell me take it to the hangar No, there's no hang there's no there's no hanger. Yeah, we cut just say like that. There's no We didn't we didn't go that far down the rabbit hole. We went pretty far down the rabbit hole. There are individuals who are just absolutely elite. The level of capability, the amount that our teams gelled together at certain points, the stakes, the stuff we did,

the stuff they made happen for us in terms of brain, they brought together like a hundred folks from across the government to discuss like AI on the path to AGI and go through the recommendations that we had. Oh yeah, this was pretty cool actually. It was like the first, basically the first, first time the US government came together and seriously looked at the prospect

of AGI and the risks there. And we had, it was wild. I mean, again, it's like- That was in November. It's us two frigging YAHUs, like what the hell do we know and our amazing team. And it was, yeah, referred to by, there was a senior White House rep there who was like,

yeah, this is a watershed moment in US history. And- Well, that's encouraging. Because again, people do like to look at the government as the DMV. Yeah. Or the worst aspects of bureaucracy. There's missing room like for things like,

you know, congressional hearings on these whistleblower events. Certainly congressional hearings that we talked about on the idea of liability and licensing and what regulatory agencies we need, just to kind of like start to get to the meat on the bone on this issue. But yeah, opening this up, I think is,

is really important. Well, shout out to the part of the government that's good. Shout out to the government that gets it, that's competent and awesome. And shout out to you guys, because this is, it's heady stuff. It's very difficult to grasp. It's even in having this conversation with you,

I still don't know how to feel about it. You know, I think I'm at least slightly optimistic that the potential benefits are going to be huge. But what a weird passage we're about to enter into. It's the unknown. Yeah, truly. Thank you, gentlemen.

Really appreciate your time. Appreciate what you're doing. Thank you. Thank you. People want to know more. Where should they go? What should're doing? Thank you. It's been a nice one. Yeah people want to know more. Where should they go? What should they follow? I guess Gladstone AI Action plan is one that has our action plan Gladstone AI all our stuff is there. I should mention too

Yeah, I have this little podcast called last week in AI We cover sort of last week's events and it's all about the sort of lens of do that every hour about the sort of lens of... You have to do that every hour. Yeah. Last hour in AI. It's like a week is not enough time. We could be at war. Our list of stories keeps getting longer.

Aliens landing. Yeah, anything could happen. Time travel. You'll hear it there first. Yeah. All right. Well, thank you guys. Thank you very much.

Appreciate it. Thanks, Jonny. Bye, everybody. Bye!