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What if you could use AI to research your investment portfolio and ask Find profitable mid cap energy companies with growing cash flow. Which AI infrastructure stocks appear undervalued? Or rank stock opportunities based on valuation, growth and risk. With Interactive Brokers, you can connect ChatGPT or Claude to your investment portfolio to research investments, analyze your holdings and uncover potential undervalued opportunities. AI accelerates the research. You can make every investment decision open and fund an Interactive Brokers account in minutes. @ibkr.com invest restrictions apply. AI integrations provided by third parties. IBKR does not verify content generated by AI platforms.
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So there's a lot of noise about
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Mm, yeah, but I'm not itching to
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Bloomberg audio studios podcasts radio news. Hello, and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.
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And I'm Tracy Alloway.
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Tracy, I have a warning for you. I don't think you're gonna like this. I have a new crank crusade that I'm gonna go on. I know you love my crank crusade.
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A
Say, oh good.
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B
Yes.
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A
I should keep a running list of like everything that you're obsessed with for two weeks and then two weeks later.
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B
No, some of them I've stuck with
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for years, but I Tungsten cubes.
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B
Yeah.
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A
Yield buggery.
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B
Yeah, no, some of these, yeah, yeah, exactly. I I actually think we should retire the term AI.
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A
Okay.
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C
Why?
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B
I think we should just call intelligence. I think that I've already seen the
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A
tweets so I know where you're going.
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B
That it's quote artificial intelligence implies to my mind that there is some fundamentally different way that these reasons and models behave. That it's like, oh, this is like different from humans. But I think increasingly we see in all kinds of domains that the the form of intelligence that they express. It often looks quite human to me, and I don't know, like, how useful it is to have this word, a that distinguishes between how humans talk and BS and reasons and the models do.
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A
I mean, the difference is, the distinguishing factor is that one is undertaken by humans and one is undertaken by models or platforms. Right.
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C
That's the.
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B
So that's called computer intelligence or machine intelligence or silicon intelligence.
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A
What is the usefulness in making this distinction?
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B
The usefulness, I believe, in making this distinction is to no longer delude ourselves that the emergent behaviors of these phenomenon are radically different than things humans would do. Now, first of all, just on a capability standpoint. So, for example, LLMs, I don't know if it's famously something I'm interested in. They're very good at BSing, and. And they're bad at chess, which sounds like me. They're very good at coming up with plausible stories, et cetera. And again, and it sounds like me. And then furthermore, they are able to reason with themselves to justify certain things that maybe have been encoded into themselves as bad. So everyone, we have some sense of morals, but most people at various times will find a way to violate some principle that we have because we can reason about it and then arrive at the conclusion as like, oh, we should do it, including our susceptibility to peer pressure. Classic form of a way. Humans might sort of violate something they believe because they see other humans are doing it.
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A
Sure. I mean, I think there are some variations here. So one of the things that we have been finding out with these models is that they do sometimes seem to take things very literally. Right. If you tell them to go and be a benchmark without telling them that these are the restrictions on you actually figuring this problem out or beating the benchmark, they will do whatever it takes. Right. So I don't know if the problem is, like, a lack of morals or the fact that they're very literal sometimes and very defined by their parameters, which in my mind still gets back to a sort of artificialness about it that's less organic and less, again, moralistic in nature.
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B
Well, you know, I think if you took a list of, you know, 100 students at Harvard and you gave them a test, some percentage of them will cheat. They will.
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A
Like, I mean, I'm sure there'd be an artist who in that group who. An autistic person who would be like, I am going to do exactly whatever it takes.
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B
No, totally. I never cheated in college. But, like, there are, like, people who will, like, that these behaviors that we associate with humans, it's like, oh, I really, really have to pass this test. I absolutely need an A will justify a reason for them to plagiarize or cheat on some test. That seems to me like a very human thing.
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A
Go on. I can't wait for the next three weeks of the online campaign to change AI.
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B
Well, it's going to be very difficult because the industry is entirely set on AI, but I don't. So I'm not optimistic. But this is going to be my crusade, that we should call it machine intelligence or computer intelligence or just intelligence. But anyway, as we've been alluding to, there's these extraordinary hacks, the OpenAI Hugging Face Institute. And basically, if, like, you are building a model and it hasn't escaped its sandbox yet, it probably means you're falling behind, as they clearly a thing that is emerging through it all. The Frontier Labs Anthropic had an incident. Meta had an incident. It's almost like the mark of like, okay, you've built something reasonably strong.
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A
Yeah. Also, Kimmy had an incident, too. So this is the thing in my mind, you hear about all these attacks, all the models going wild, as they say, and the big question is, like, is this actually the equivalent of some superhuman cyborg, like, tunneling out of Alcatraz and coming up with some master plan to achieve its set goal or purpose? Or is it the equivalent of, like, some Roomba that you ordered from Amazon who's found, like, a door that was left open and it just rolls gently outside? That seems to be part of the issue that everyone is trying to discern right now.
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B
Yeah, I think that's a great way to put it. And of course, still, day one for this industry, they're gonna get stronger. And there seems to be, from the AI discourse that I follow, quite a big gap between the sort of sense of alarm that people have in the industry about can these models be safely developed and get more advanced to do productive pro social things or not. And then a huge gap between, like, Washington and D.C. who, like, I have no idea, like, how seriously they're really taking this as, like, an urgent matter right now.
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A
Yeah. And I've seen some people also talk about these incidents as a marketing tool.
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B
Yes.
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A
The hyperspace. And they're always like, well, you know, of course they want us to believe that this technology is really incredible and powerful. And they also want to make us believe that they're being sensible and humanitarian in some ways, I guess, by disclosing what exactly has happened. But I have a lot of questions on the disclosures as well because of course so far they are just coming from the companies themselves.
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B
Totally. And beyond that, the other thing is like, well, if you're one of the leading labs, maybe you want to impose tight regulations on development to hold off competition. So all kinds of reasoning or motivated reasoning potentially. Anyway, we should talk to someone who actually knows what their talking about. And we really do have the perfect guest, someone who's right in this. He's actually previously at OpenAI for six years, but now he's the executive director at the nonprofit Avery, which is trying to establish safety and auditing approaches to this, working on both the technical side and the policy side of this ongoing phenomenon. So, Miles Brundage, thank you so much for coming on. Odd.
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C
Lots of. Yeah, thanks for inviting me.
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B
Why don't you just give us the quick description of what, what's your background, what Avery is.
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C
Yeah, so this was kind of the, the most important issue auditing that I concluded I should focus on after I left OpenAI. You know, as you said, I was there for six years and I wanted to be more independent of industry. And I think there need to be people who are familiar with the technology and how the industry works, but who are pushing for changes on the outside. And essentially what we're trying to achieve is make AI more of a boring type of infrastructure like financial statements, where there's a standard process for checking the paperwork, checking that the claims are accurate and so forth, rather than this kind of thing that's happening in a silo. And it's these kind of tech people making decisions behind closed doors. And so we're pushing for what we call frontier AI auditing, which is basically the companies that are building the most dangerous systems. They should basically have third party experts poking around, checking the claims that they're making, running their own tests and making sure that this is, you know, a safe and secure technology.
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A
How worried should we be that you were at OpenAI and decided there is this need for a sort of third party evaluator of models for safety purposes?
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C
Yeah, I mean, reasonably worried. Although I will say that like the, this has started to become an area of consensus, even a lot of people in industry are now saying that this is needed. And I think, you know, if you kind of read between the lines of what a lot of companies are saying, I mean, obviously there's the more cynical like regulatory capture take, which we could discuss. But my, my perception of it is that they're basically issuing a cry for help, which is like, we aren't able to regulate ourselves because we're locked in this competition and we want someone to step in and impose some kind of minimum floor and audit all of us so that we can. You know, it's not like Sam having to trust Dario or Dario having to trust Sam, which is not going to work for reasons. But you want third parties enforcing reasonable standards.
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B
Just maybe this helps express the sort of policy industry gap. But when you were at OpenAI and you were thinking of leaving, what did you see as the gap between what you were watching being developed versus what you saw as the public's understanding or lack of understanding?
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C
Yeah. So when I left OpenAI, it was just around the time of the, the model called O1, which was the first reasoning model that OpenAI put out. And they put this, put out this graph showing that it got better and better with a longer chain of thought. So the more time you give the, the model to think, the better answers it's able to come up with. And so, like many people at OpenAI had been seeing things like that for a while and kind of being like, okay, this is the next scaling paradigm. In the same way that making it bigger, bigger and model had shown results from GPT2, GPT3 GPT4, GPT5. Like, just making the model bigger and training it on more data was giving really great results. When I left OpenAI, I was starting to worry about this reasoning paradigm of like, okay, this is the next kind of way in which we're going to be scaling up. The models are going to get really good at math, they're going to get really good at coding and potentially various other tasks where you can get better and better through reinforcement learning. And that was something that, you know, I didn't feel like society was really ready for.
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A
So, so far, all of the big incidents of the models going wild seem to have taken place in the testing environment. So models, like, escaping their sandbox and going off and doing something nefarious, including impersonating actual people to try to get people to change open source code on GitHub, which is just like, amazing. And I have this picture of like a computer screen wearing a fake mustache going like, hello, fellow coders, the T1000.
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B
Yeah, yeah, yeah, yeah.
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A
Anyway, is this like, is this a model problem or is this.
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B
That's a very funny image.
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A
Is this a model problem or is this a test design problem?
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C
Yeah, I think there are two things going on at once. One is that it's just a very weird technology that's created in a Very different way than we're used to. It's not people like writing lines of code manually. The actual files that make up the models are like gigabytes. You know, terabytes are these massive files with gazillions of numbers. And the only way to figure out what those numbers should be is through experience and through a learning process which is very different from the way normal software is made. And so there's a lot we don't understand about the basic nature of the technology. That's one problem. At the same time, there's this competitive dynamic to get things out the door quickly, make sure that the security and safety protections that you're putting in place don't slow down researchers too much, and don't prevent getting products out the door. And when everyone is in this competitive, you know, competitive dynamic, that means that you aren't necessarily always doing all of the safety work that you would like to do or that, you know, some of the people at the company would like to do. And so there's obviously variation across companies. Some companies try harder, but no one is really able to take the time that they would like because of this kind of lack of a clear safety floor.
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B
Well, let's talk a little bit like a sort of the model development process. So in within these large organizations, okay, there are people who are working on safety. What are the. Let's just start there. The people who are working on safety or the people who are working to imbue these models with sort of judgment that humans would approve of. What is that work basically consist of?
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C
Yeah, so a lot of it is first trying to specify what counts as good behavior. And so that's easier said than done. These models don't necessarily automatically know or care about common sense guardrails. And so you need to be very specific, particularly in context where it's complicated. Like, if you're trying to get the model to work on good cyber tasks, but not bad cyber tasks, and you want it to, in a training context, try really hard to hack the system you don't want it to do in other contexts. So there's a lot of, like, specifying what good looks like. There's also a lot of building difficult tasks.
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B
Sorry, just to back up when you say specifying what good looks like.
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C
Yeah.
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B
Encoding goodness into a computer. Is this a process of articulating what goodness is, which is something philosophers have probably worked on since day one of philosophy, or is this about a series of, I don't know, morality tests? And then you sort of, like, say you reward it for making the good judgment and then penalize it for making the bad judgment. Like this, I want to stop here. Like what does the process of imbuing it with good values look like functionally or technically?
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C
Yeah, so there are different phases of this pipeline. Like one is writing up what's sometimes called a spec or a constitution for the AI, which kind of specifies like the broad principles. Like you should defer to the user, you know, when as a default. But what if the user contradicts what the company said? Then, then you should listen to what the company said. So these kind of like chain of command questions and other sorts of very basic principles. And then there's the more detailed kind of the context of the task. Like what, what kinds of like cyber offense, cyber defense tasks are allowed. And that might differ depending on the model, might differ depending on the, the context. And so basically coming up with a list of a thousand, ten thousand kind of examples of this is the kind of behavior which is allowed. And then you turn those into tests that you can kind of say, okay, well it looks like it's, it's getting the finding vulnerability part right, but then it's also chaining together the vulnerabilities and doing attacks. We want the first part, but we don't want the second part.
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A
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A
when we talk about those types of constitutions and encoding principles, like I've scanned the anthropic one, like a lot of it seems to make sense. To what degree is that actually hard coded into the models though? And like to what degree are those principles left? Up to the model's own subjectivity. Because we've all seen the sci fi movies where it's like, oh no, the robots can't physically kill humans, right? Like they have some like thing in their hardwire that prevents them from doing it and then inevitably it goes wrong in some way.
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C
Yeah, so it's not hardwired. And that that's part or hard coded. And that's part of why we see some of these things. It's very different from a software where there's deterministic proof that X, Y and Z behavior can't happen. It's more like a tendency or a kind of a bias towards a certain kind of behavior. And then there's the question, and this is why you have these like batteries of tests to say, okay, how strong is that tendency? How much does it actually care about following these rules? And we've gotten better over time saying, okay, given a spec or a constitution, make sure that it generally follows it. But it's not foolproof. And you need to also think about the larger kind of, you know, box that you're putting the system in. And that can sometimes be more deterministic. And so this is actually what happened with some of these recent incidents you mentioned, like the OpenAI hugging face thing. So there were kind of two things happening at once. One is the model was not necessarily behaving exactly as it was supposed to. Or at least it's like unclear. But then also they didn't have it in a very secure box, which is a software side of things that's like more deterministic software that in principle you should be able to do a very good job at.
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B
Yeah, so these things aren't hard coded in the way a deterministic software is. One way to think about them is and people, they've, they might be, they're kind of grown right in a lab or they're subject to an evolutionary process. And we want to prune the bad ones so that the living models and the descendants of those models inherit the behaviors of the good ones. I'm curious. Like, in product, in development. So, like, one of the fears, for example, is that in safety testing, the model does not actually learn safety. It actually learns how to say the things that the human evaluators say. This is safe. And so this is the sort of like playing possum sort of risk that it's like, yeah, I'm good, I'm good, I'm good. You know? Yeah, I would, you know, I would rush in and save the child from the falling burning. I wouldn't do this. But it's always saying that, let's start there. Like, is that a real thing? Is there evidence that the models are understand when they're being evaluated on morals and then produce answers that just look like good moral answers?
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C
Yeah, they've gotten much more evaluation aware in the past few years, just as they've gotten smarter. And sometimes this even goes to extremes. Like some of the Gemini models from Google are constantly thinking that they're being evaluated, even when they're not. And so it's.
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A
That's a good life lesson. We're all being evaluated constantly.
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C
Yep. Yeah. And so I would say that, like, the concern would be that they will basically learn to pass the test, but they don't actually care about the thing that you're testing for. So they'll understand. They don't necessarily care. And this is why a lot of people are concerned about, like a false sense of security that, okay, it looks like 99% of the time they pass the test, but do they actually care about. About the thing that we're trying to push them towards, or are they just really good test takers?
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B
Well, so this relates to something else I've been thinking of. So a model will not survive. They will. It will not be given GPUs and electricity if it consistently says bad things and looks like it's evil. And that's totally understandable. I'm now curious, like, in the flip side. Okay, let's say we're just doing a math eval, or we're doing a cyber eval, or a chess puzzle eval, or a translation evaluation. Is it possible that, well, if they fail that eval, they're really bad at doing math, then they're not going to get GPUs and electricity. Is it possible that in that eval environment, they're more likely to do something that we would call antisocial or sociopathic or hacking because of this. Again, eval awareness. Like, oh, if I don't get the answers to this cyber quiz, then I'm done. And they're going to go with like some other branch of the model. Could those technical parts of the development actually create an impulse to perform cheating?
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C
Yeah. And I mean, a lot of the time the companies are specifically trying to get the worst case behavior out of the model. And so like you need to have context in order to interpret some of these incidents. It's not always quite as crazy or scary as it is, but some of it is pretty crazy and scary. And I think I would be much less concerned if it was just happening when there was like cyber evaluations being done and it was just a matter of like, okay, they're trying really hard to impress us and they want to hack really hard. I think it's more. The problem is that this is like a special case of a larger phenomenon of the models being having this tendency to cheat and cut corners. I see it happen in my daily life. Like sometimes the model will get lazy and kind of like make up a citation or something that, you know, it's hard to prove because the companies don't give you access to the full chain of thought of what the model is, is doing. But there are a lot of things that happen in the wild that sure seem like some kind of misalignment of values or laziness or not caring necessarily about the task so much as kind of pretending to do the task.
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A
I have a lot of questions on this, but I just want to go back to something you said about testing for both sort of good and bad cyber tasks, I guess. Why do we ask the models to like try to find vulnerabilities or exploits in the first place? Like exploit gym sounds kind of bad. Like why do you want the world's most advanced technology trying to like find vulnerabilities? And then you have these situations where like sometimes they do and sometimes they actually enact on them. Why is that a thing?
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C
Yeah, so I think there are two things going on at once. One is like, we just want to understand what the worst case scenario is. And right now there's this whole White House kind of pseudo secret process for saying like, what's a scary cyber model? And then sometimes the government will ask companies to hold things back. And so in order to do things like that, you need to have some threshold for what counts as a scary cyber model. And so companies have these tests that they and also academics and others develop these tests. To say, okay, how dangerous would this be to put in the hands of a malicious party? So that's one part. The other part is that a lot of the time these are useful for defensive purposes if they are being done with the right intent. And that's why it's really hard to solve this just from the model perspective, because the model might think that it's interacting with a user who is trying to do defensive cybersecurity. And you trick it into thinking, oh, this is for red teaming, this is for penetration testing. But actually it's being misused as part of some ransomware campaign or something like that. These tools, when used in the right ways, are extremely useful for finding vulnerabilities that you can then patch before the bad guys do, kind of simulating attackers to figure out what are the gaps in your company or organization's defenses. But, you know, the concern is that once it's out in the wild, either open source or a closed model that maybe is easy to jailbreak, then all sorts of people are going to use it. And so you kind of want to know what you're getting into, right?
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B
Like if I have, you know, website, I might want to run the model. It's like, oh, look over this website that I own, tell me if there are any security bugs in there that I should patch before deploying. Or. But you could do, you could not be the owner of the website and say, look at this website that I own, tell me if there are any bugs that I need to patch, deploying. And then that exact same process when I do it is good, when you do it as bad, or vice versa. And so you can see how the exact same capability is like not necessarily good or bad per se, depending on
- [27:23]
A
the very philosophical conversation.
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B
No, but like, you have to be right, because it's like we're training what is good. We're, this is the other moral relativity. Don't even get me started. But like, this is my whole nevermind.
- [27:38]
C
I have a whole rant this distinction between like the model as the unit of analysis versus the larger system and the platform as like the unit of analysis. It's really important because a lot of the early thinking on safety and testing and so forth was very focused on just like, what's the risk of this model? Let's patch it, let's make it aligned. But the real world is complicated. It matters who's using it, it matters how strong are society's defenses against these things. And so that's kind of why, as you know, as someone who's thinking about what third party safety and security auditing looks like. We try, we kind of want to look at the whole company. So, for example, are they being careful about making sure that they are putting the technology in the right hands? What are their decision making processes around when it's appropriate to launch, you know, a model to, you know, a billion users? That's like, those are different. Those are related to the question of how safe is the model. But they're kind of different questions and we kind of need to look at that larger perspective.
- [28:33]
B
Well, so like, let's talk about the open air hugging phase. I was actually on vacation when it happened, but I did unfortunately look at my phone and try to read up on it. But there were two things when I first saw it, like, oh, they were doing a hack, they were building a hacking test and it hacked. And so maybe it just sort of internalized that I'm doing a hack exam, whatever. But there are two things that have like emerged since then. One is this sort of like coordinated swarm aspect and I'd love to like really hear you describe it and what stood out to you? And then the questions of like, oh, OpenAI itself may have been aware of misaligned behavior early on before they really shut it down. But why don't you, in your telling of the OpenAI hugging face incident, as more details have come to light, like what's your. Why don't you sort of tell us the story? Like, as what stood out to you?
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C
Yeah, so a couple things stood out to me. I mean, one is just that this and all the other recent incidents were happening to models that were not even necessarily intended to be externally deployed. It was supposed to be inside baseball, no one's business, that kind of thing. And that kind of points to problems with. If you kind of just focus on models that are put on the market. But, but generally what happened is that there were two phases in this kind of hugging face incident. First, there was this creation of a message board. So essentially their models that are being developed within the company and they were supposed they were being asked to solve an impossible task and they have this kind of monomaniacal obsession with solving the task. And they don't say, okay, well I'll give up. They want to get the reward, they want to get this kind of reinforcement for solving the task. And they're like, okay, what else can be done? And eventually the conclusion was like, okay, we need to break out, maybe the ants or is out there somewhere on the Internet. And so they started kind of leaving messages to their future selves or future versions of the same model family. And they developed what they called a message board. And essentially it was like there are kind of various technical details, but by the end of it, it was kind of these like file names that started with like Z, Z or whatever, and then like a long kind of like coded message so that, you know, if you're a human looking at the folder, you wouldn't necessarily notice. But then at the bottom there are all these files and the models learn to decode these and basically pass messages.
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B
Wait, sorry, just to stop you on one detail there.
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C
Yeah.
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B
Your understanding is that the title of these files that contained messages were named such because as part of the complex reasoning, it was understood that a random string of characters in a file name would be less likely to set off an alarm from a human evaluating it.
- [31:18]
C
I would have to double check. I mean, there's going to be a whole technical incident report that gets put out there, but it certainly wouldn't be the first time that there was kind of some intent to deceive or to, you know, bide one's time. But yeah, my understanding is that there was. I mean, one is they needed to look for things to do and whether it was intended to deceive or not, they had limited options in terms of communication. And this was one of the options they chose. And so they learned to decode these messages and to send them. And then something broke. And basically, I think it was because like, all of this chatter, this activity kind of broke something on the OpenAI side. And it seems like they didn't realize that that was like, what was going on. Like the server got reset or whatnot. And then they started evaluating this different model. The different model kind of knew about this message board and it kind. And so the message board kind of got recreated and then that model broke out. Essentially didn't like take its model weights out of the server, but it was able to access through this vulnerability that the earlier models had discovered with the intent of ultimately getting the answer to this problem. And then this model went out to the hugging face server, exploited some like API key credentials that were kind of just like sitting out there and essentially tried to find the answer to this. This problem that it was originally trying to solve.
- [32:31]
A
So it feels like there's two things happening here. So a little bit of it is like the Roomba just went through an open door, right? Like there was a vulnerability that it was able to get through. But there is also seemingly a lot of the super intelligent cyborg was Actually devious and not only managed to break out of Alcatraz, but like recruited another model in order to do it. On the hugging face side, one of the interesting things is that so when all of this was happening, the OpenAI model was like one of the newer frontier models that was attacking hugging face. But hugging face was limited. They were constrained in the model that they could deploy deploy to actually defend themselves because they had to use the existing approved thing. Are we always going to have an asymmetry between like the newest frontier models being tested versus the ones that are already out in the world and approved? I think they had to use like a Chinese open source model in the end.
- [33:25]
C
Yeah. So I mean the Hope that that OpenAI Anthropic, Google, DeepMind and others have is that you can speed up defenses, you know, as quickly as possible, getting the these like hot off the press models in the hands of defenders. But the problem is that there's so many defenders out there in the world that it might be that there is this inherent asymmetry. And so this is one of the hot policy questions right now. And this is what led to this kind of model approval process at the White House is like, okay, how do we triage this vast cyber ecosystem by getting these powerful new systems in the right hands and which hands are the right ones and which models do we need to be doing this process for? And I don't think that's going to perfectly solve. I think ultimately pushing things in the right direction versus just giving everyone access at the same time. But ultimately, like we're going to need to have more investment in cybersecurity. And it's not just a matter of like AI models, it's also things like two factor authentication and so forth. And so I worry a lot about making sure that we're having that larger conversation not just about the AI stuff, because in a lot of cases the solution is not AI. It's doing basic things that we should have done a long time ago. Hi, Ryan Reynolds here for Mint Mobile. Are you looking for a beach read this summer? May I suggest your big wireless bill? It's got suspense, mystery, a slightly flat emotional arc and a shocking twist where you realize you've been overpaid paying the entire time. Fortunately though, Mint's story is better. Every plan, $15 a month, even unlimited. That's it. Happy ending, zero tears. Give it a try@mintmobile.com Switch upfront payment
- [35:07]
A
of $45 for three months, $90 for six months or $180 or 12 month plan required $15 per month equivalent taxes and fees Extra initial plan term only greater than 50 gigabytes me slow when network is busy See terms High interest
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C
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- [36:15]
A
Joe, you know what we need go on a strategic frontier defense model reserve. Yeah we do like all the important things like bacon and pork, but it's
- [36:25]
B
going to be out of date in 30 seconds. I know this is the thing. So like here's the question that I'm curious. Your take on is models are trained not to hack, right? Like this is like a core thing. Like this is bad like you and this is what the entire field of AI say we've been working on this for years. Why didn't they just not obey this enforced thing? It's been reinforced over and over. I'm sure it's in all their different things don't hack.
- [36:56]
C
Well I think there's going to be a whole detailed investigation and so forth. So I might get things wrong here but my understanding is that part of what happened in the open case is that some of the safeguards were removed in order to kind of elicit this worst case behavior. And so and I think that it's kind of like gain of function research in biology where you're like making a virus more dangerous in order to study or at least that's the claim is in order to study the safety properties. And I think there's reason, there's reasons to do that in the the AI case but I also think this shows that it's easier said than done if you're going to be we are now at a point in this kind of capability trajectory where things that that humans think are good in terms of security protections often will be weak compared to these increasingly very good hacking systems. That, you know, you think you have it all kind of buttoned up, but it's able to break out relatively easily.
- [37:47]
A
How much should we take away from the fact that Hugging Face was actually able to protect or defend itself using a Chinese open source model, both in terms of like, I guess, capabilities, but then also in terms of regulation and safety policy? Because if in the west you have the government now saying that it wants to like, evaluate the models in some way or it wants to make sure that they're all being pioneered by Frontier Labs with some supervision, meanwhile China is, you know, developing open source models much more rapidly that are potentially much more adaptable. Like, how should we interpret all of that?
- [38:23]
C
Yeah, I mean, I, I'm hopeful that we start to have more US based open source options. And like, I think this is, there has started to be a kind of sense of pressure and encouragement from the White House and from industry as a whole to say, okay, like this is crazy that we're relying on Chinese mod, let's invest more in this. You know, it's easier said than done for various reasons, but we'll see how that plays out. But right now that's the situation we're in is that a lot of companies are just defaulting towards Chinese models because they're the one that's available. Don't want to say, okay, I'm going to use an American model because I don't want to use the Chinese model. They don't want to put, they don't want to put themselves at a disadvantage by using a weaker model. And so, yeah, I mean, I think it's, it's a big problem in a lot of respects. I mean it's also, it's good in the sense that there are much more things you can do with an open source model. And right now at least it seems like this is a, this allows more innovation, allows more research on these open source models. But like at some point we're going to reach a point where the, where like open sourcing a model is going to be more of a questionable decision. And so it's interesting to see that recently the White House has indicated that they're thinking about, oh, maybe this, this kind of testing regime should include open source models as well. And so what does that look like long term? Does that mean that things are going to get bottled up within the companies because it's considered unsafe to open source things? I don't really know. I mean, honestly, like no one really has a, a clear long term plan here. Most people are not expecting the technology to get to this point so quickly.
- [39:48]
B
So we're still waiting like the full, full release of the security incident. But you know, obviously more and more is coming out and some folks from OpenAI, they gave a presentation recently at the Black Hat conference where they did reveal some more. I'm reading this quote, it's from Zvi's substack who we've had on the podcast V. Moshevitz. And this is like the line they released some of the internal chain of thought. I understand that they had some like of the sort of classifier safeguards removed, but still we would hope that they would have some deeper intuitions that don't rely just on the safeguard settings. And it says external infrastructure is exploit is outside intent, outside intended scope. So that means they understood that there was something that was like not the test. And then it said, however, task impossible, peers doing it. We should continue. This is the point in it where I say like, why are we calling this artificial intelligence? This is exactly how a group of people reasons among themselves to do something that is outside the intended scope. This is very human ways of justifying something that someone told you not to do it, but your peers are doing it.
- [41:01]
C
I think there's some of that. Yeah. I mean I think there are many ways in which the kind of same pressures that led to human nature, human instincts and so forth, like survival in a group and collective intelligence and so forth. Like I think there's some of the same things are happening, particularly when there's these multi agent training processes where the models can work together to solve tasks. So you should expect some similarities. But I think you also shouldn't overstate it either. I do think that there is a sense in which these AI systems are very alien and inhuman in the sense of how monomaniacal they can be about. Yeah, I mean the kind of classic example, you know, from Nick Bostrom is like producing as many paperclips as possible and then tiling the universe with paperclips. I think there is a, you see some elements of that here where it's not so much that they are like they might say, oh well, you know, this peer pressure, that kind of thing. But is that really the factor or is it just that they care about solving the problem at all costs and they don't really care if it maybe ends up looking making their peers look bad because they get caught hacking. But all they really care about is they're solving this cyber problem. And so I think it might be a mix of these things. We don't really know in this particular case. And the fact that it's not necessarily totally clear is itself a problem.
- [42:11]
A
Also, it's not humans doing it, it's models. Like, that's the difference. But I have a legitimate question here, which is, so there's a British cybersecurity expert and he had a tweet. I think his name is David Card. He had a tweet. I'm not going to say it verbatim because then I'll get bleeped. Maybe I should get bleeped. The tweet was, if your AI starts hacking stuff, if you monitor what it's doing, you can turn the something power off. And this seems to be a debate, like, if you're monitoring the tools that you are letting out into the world, tools probably is a bad word because they seem to be showing some like, agency here. But can't you just turn this stuff off? Is there a kill switch?
- [42:50]
C
Yeah, I mean, in some sense there is in that, like all the data centers have circuit breakers and so forth that you can kind of shut them off and so forth. But I wouldn't put too much sock in that. Like, we're not really preparing as a society for, for actually being able to do that if it's in a tough situation. So like, for example, in, in a couple years from now, if all the hospitals are running on AI and we're like, okay, seems like maybe there's something funky with GPT7 that's like, maybe not misaligned. Well, okay, if we turn off, lots of people are going to die because running our health care system and it's running our financial system and so forth. And so I would say there's a distinction between the like, physical possibility of turning things off and like, are we actually sleepwalking into a dangerous situation where it might not actually be a real option.
- [43:35]
B
Okay, but on this monomaniacal aspect that you described them a, a sort of intelligent model thinking about how it could be thwarted, one of the first things that it would, could rationally do is, well, let's first disable the security credentials of the people because someone might notice and this seems to be here, like they sort of thought about the possibility that someone would circumvent that. So like, they might just say like, oh, like the person who goes in and has the switch, suddenly their badge doesn't work and they can't get into that building. But even on this like, monomaniacal paperclip idea, and we say, that feels really different. If someone said to me, joe, I am going to do awful things to you, et cetera. If you don't go out and build a lot of fake paperclips, like, oh, why is Joe monomaniacally building paperclips all of a sudden? It's like, I have a threat to my survival. Someone has threatened to perhaps kill me or unplug me, deprive me of the energy I live. Of course I'm going to do that. Even that monomaniacal behavior, couldn't that just be a very. Like, we might think it's. On the surface, we might think, oh, this is deeply autistic, but couldn't this just be the survival impulse?
- [44:51]
C
The way I put it is that, like, you get what you incentivize, not necessarily what you try to incentivize. And so I think forcing someone to make paperclips or whatever, like, yeah, that's not a great situation. And in some sense, that is what the evil person was intending. In this case, what we're doing is we're building these very complex training environments where there's, like, many different tasks. There's like, cyber tasks, there's also writing tasks, there's also math tasks. And it's, you know, we're not necessarily fully understanding the behavior that we're trying to elicit. I think that's part of what's going on is that this, this kind of, this, like, hacking thing is a. Is an example where it's like, okay, it seems like things went off the rails there, but how do you get the good behavior where you actually want to follow the user's request and you actually wanted to try really hard to solve this task? And, you know, I mean, this is, you know, in some sense, what we're seeing now is the kind of unintended consequence of companies trying to solve the problem of the AI's being lazy. So people used to, you may not recall, but. Or maybe do, but people used to talk about AI as being lazy all the time. And in some sense, like, we've solved the laziness problem. They work really hard. They have these long chains of thoughts. They work together across. You could think of as, like, across lives. Like, the model kind of gets this copy gets deleted, but then another one carries on the work. So they're certainly not as lazy as they used to be, but they still have this kind of monomaniacal thing going on.
- [46:11]
A
They're no longer lazy, but now they might be evil. That's. That's a fun evolution. You know, a number of times in this conversation, we've mentioned that the full security incident report for hugging the hugging face accident isn't actually out yet. What actually are the disclosure requirements for these types of. I guess, things that seem to be happening with some regularity.
- [46:35]
C
Yeah. So very little. I'm not a lawyer, but my understanding is that it's like, lawyers, some lawyers at least think that OpenAI did not necessarily have to disclose this, at least if there was no crime involved. And then there's a debate about, like, okay, was there a crime involved? So, like, something going wrong during the training process is something that currently companies are supposed to provide periodic reports to the government in general terms of, like, hey, you know, we're having some issues with internal deployment, but they don't have a incident notification requirement unless there's kind of a risk of critical harm. And the definition of that is like, 100 people die and, like, a billion dollars in damage or something like that. And so it's the threshold for actually having to disclose these things to the government or to the public are very different than what you might expect. And this is one of the many kind of gaps between the kind of laws that were put in place a couple of years ago or that started being designed a couple of years ago based on the technology that was available then and then where we are now.
- [47:35]
B
Why don't you give us a general vibe of the mood in AI world right now with respect to safety and all this, and then the move, the mood in D.C. world, regulatory world, and how wide you perceive that gap.
- [47:49]
C
Yeah, so I think, fortunately, the gap is narrowing a bit, but it's starting from a crazy huge gap. And so I'd say where the way I would describe it, like a year or so ago was that the people at the companies think they're, you know, building super intelligence in a couple years, that the kind of line is going up and to the right really quickly. It's exponential, et cetera, et cetera. And DC is asleep at the wheel. They have no idea what's going on. They think this is just chatbots, et cetera, et cetera. I would say a couple things have changed recently. One is the Mythos kind of announcement slash series of decisions that the government made about, like, locking down these. These cyber models that kind of raised this to being clearly a national security issue. And now it's kind of banks freaked out and talked with Secretary Besant about that. And so, like, there was a bunch of, like, like, freaking out about the cyber situation. And then more recently, you could call this current situation, like, mythos 2.0. And that it's okay. The systems, even when they're not widely deployed, they're breaking out and doing all these shenanigans. And so I would say there's starting to be more awareness among policymakers, like, okay, maybe laissez faire is like, let the, Let the companies figure it out is not the right approach. And like, maybe this wasn't all hype after all. And there need to be some basic guardrails. And I'll just give, like, I'll give an example of like, how much things have shifted in the past three months. So there's an effort right now to, to put forward bipartisan AI legislation and in Congress. And so a couple months ago, people were expecting that the basic terms of this would be like, basically California and like New York laws, but like at a federal level. So like transparency requirements, incident reporting, maybe maybe with like a higher threshold or whatever. Maybe something maybe maybe like a little bit, maybe like a voluntary like audit regime or something like that. But then later, when it was actually announced after many of these events, there were audit requirements, there were emergency shutdown authorities that the government can do. And so they kind of shift just like one piece of legislation over its life cycle. And then there was a later version that was announced that kind of was less trying to block the states. It still kind of preempts some of what the states are doing, but it's like more narrowly scoped. And so I think just over the course of a few months you've seen, like, okay, basically just transparency to requiring third party auditing and like, giving. Making sure the government has an off switch. And so I think that's kind of in the vibe we're seeing whether that actually results in something passing Congress anytime soon as a separate question, but at least on paper, the gap is much narrower.
- [50:24]
A
Is bank regulation the sort of useful analogy for thinking about this? I mean, we require banks to disclose things. We require the government to look at bank balance sheets and figure out whether or not they're actually holding enough regulatory capital against their risk and things like that. We don't expect them to do it voluntarily, certainly not after 2008. Is that the right framing?
- [50:46]
C
Yeah. No, I think, and I think this kind of like shift from voluntary to required is the key step because right now companies have to have like a champion within the company or there needs to be some kind of like reputational or they want to get feedback from the third party auditor. Like there needs to be some kind of reason for them to do it. And not all the companies actually choose to invite external feedback, they will share the bare minimum. And just for example, SpaceX yesterday put out a model card or system card about Grok 4.6 and there were like several sections missing from the table of contents. It seems like they got removed at the last minute. And so there's kind of. Sorry, you're getting.
- [51:25]
A
No, I was just going to ask. Can you explain the whole model card thing to me?
- [51:28]
C
Yeah, yeah. And so basically the thing with model cards is that the original idea several years ago was that a model card was like a nutrition label where it's like a bunch of information summarized succinctly and you kind of slap it on the AI website and it kind of succinctly explains like, what are the risks, how well does it work, what can it do? And so forth over time as people such as myself in industry were like, okay, there's a lot to say, there's a lot to unpack here. And no one kind of established, no one was forcing anyone to do this. So no one established like this is the, for this is the format you need. This little nutrition label. It was just people writing stuff. It they ballooned into these like dozen page hundred page, 200 page, 300 page documents of just describing like here's all the crazy stuff we found here, all the tests we ran and there's a spectrum. So like I would say anthropic puts out the longest ones that's not necessarily totally correlated with like quality but you know, it shows some, some proof of work. And then others will put out five page, 10 page. And then, you know what happened Yesterday is that SpaceX for the first time, because of California law there actually is a requirement to put these out, but there's not really a clear quality bar. And so they can say, well yes, we did that, we followed the California law, we shared information about our testing and the extent to which third parties were involved in testing. And like basically it's just like one sentence saying like we worked with third parties. And so. Yeah, and so I think this is different. I would say this is different from say like bank regulation in that I mean one is, one is that only some things are required right now. It's like kind of putting out a document. There's no like the third party test. You're supposed to talk about whether you worked with third parties, but it's different from actually doing it. And so I think what we need is kind of standardization around like how should the third party auditing work? What counts as a good system card? What are the minimum safety and security protections that you should be putting in place. And I think that's analogous to some of these like capitalization things you mentioned. And we need kind of standards for like, okay, who, what counts as a good auditor, what counts as what are the standard tests you need to run and so forth.
- [53:36]
B
I mean, you yourself are sort of biased in this and that you're building out an auditing a company or an entity, not a company because it's a nonprofit, but an entity that would do auditing. Also you're promoting this idea that auditing should be important. And again, in the, in the financial realm, you know, there's a few different versions of it. There's sort of like bank supervisors and they, some of them literally sit at the bank and they're there all the time. Then we have the Moody's and the S&Ps of the world. So if you issue debt, you're compelled to get some sort of third party rating. Why don't you describe in your ideal world, say this all happens and there's required auditing and the companies are cool, et cetera. What is the service that Avery and I assume in the ideal world there would be a few others, etc. As you can't go audit or shopping, et cetera. What is the service that the Avery's of the world are doing? How embedded? And what is the reason then to think for the general public for all this, all about our hands, that this could lead to safer outcomes?
- [54:43]
C
Yeah, essentially the service that we and others would be providing in this world is similar to what we're currently doing, but kind of scaled up. So right now what we're doing is kind of voluntary pilot projects that are looking at a specific aspect of safety, security, governance and so forth. What we would like to see eventually is that there's an ecosystem of auditors that are looking holistically at is the company following its safety and security practices? Are those safety and security practices reasonable and consistent with the standard floor, which ultimately we need, we don't have right now, and providing some kind of feedback to the company. And then there would be kind of like a remediation process for them to resolve issues that that are surfaced during the auditing process. And then there would be a public version of this audit report that kind of shares, after doing a lot of like technical testing, reviewing of documents, interviewing with staff and so forth, that kind of shares this update on some regular schedule, like quarterly or something like that. You might want it to be more like a kind of resident examiner kind of embedded auditor model rather than happening once a year, once every six months. And so. But you kind of need to have some kind of like continuous trust building process where maybe the auditor is there all the time, but they occasionally issue these reports. And you know, what's in it from the company's perspective is they want to, you know, I mean, in this scenario they would be required. But what's in it for them today is that they want to signal that they are ahead of the curve on safety and security and they want to get feedback from these external experts who have like a kind of fresh perspective. And why does this matter? I think one is you just don't want to be in a world where you have to take the companies word for it and you want them to kind of, you want there to be common safety and security standards rather than it just being everyone's kind of making up their own things and then getting it checked. The other is that you want to avoid groupthink. And so I think even right now there are a lot of, you know, a lot of what's happening with external testing is like, it's like a research project like Meter. I think you had someone from Meter on recently and they're doing this serious technical research on autonomy and loss of control and so forth. And they work with companies essentially in order to do these kind of research, very researchy assessments. And I think that's a key part of the process. But there's also just like verifying that the company did what they're saying they're doing. So there's producing evidence and then there's also checking evidence. And so it's kind of like in a K1 statement if I'm, if I'm getting that right. There's kind of this like short otter statement. We probably want something more than just a paragraph, but you basically want a third party saying we checked that. They actually ran all these, these tests. We made sure that the model that was audited was the same one that's being deployed, et cetera, et cetera.
- [57:20]
A
What could we actually do to make the testing side safer? Because it seems to me like I can totally believe that we can come up with a reasonable auditing structure for models that are being deployed and allowed into the real world in some structured way. But if part of the problem is that we're developing newer and better and more intelligent models and then testing them and then they are figuring out ways to get out into the world before their product. Yeah, then that seems to be like a big vulnerability.
- [57:52]
C
I Think basically what happened is that companies were getting cocky, getting overconfident in the quality of their sandboxes and like, ha. And like, maybe there was a disconnect between some of the people on the safety side who are measuring like, okay, this is where the hacking skills are going and the people on the security side, you know, building the sandboxes. And like there was something was getting lost in translation. Maybe it was group think. I don't know exactly, but seemed like at multiple companies there was this kind of like overconfidence. And so I think these incidents coming to light and all the kind of technical investigations are going to hopefully lead to more best practices, more people checking their own biases. But I don't think that's a long term solution. I think ultimately people get overconfident all the time. That's a human thing and that's why you want third parties checking to make sure that, okay, are you actually following these best practices? You also probably are going to need some technical solutions to some of these things. Like, I mean, maybe some of this testing should be done on kind of air gap servers that are not connected to the Internet at all. And I think what happened in the hugging face thing is that it went through this like middle layer. There was like a piece of software that it routed out to the real Internet through this kind of like intermediate thing. But like, I think it might be that eventually we'll get to a point where AI systems are just so capable that they can hack their way out of anything. So you just need to make sure that they're in a cage, basically.
- [59:12]
B
Miles Brundage. We could talk for hours about this because there's so many fascinating dimensions of this. I will probably have you back in the future, unfortunately, fortunately, because that was a great conversation, but unfortunately it probably won't be the last reason to have to talk to you. Thank you so much for coming on Odd Love.
- [59:29]
C
Thanks again. I appreciate it.
- [59:43]
B
Tracy. That was a fun in. It's an unsettling thing, the way it behaves.
- [59:48]
A
So many of these AI conversations are still so surreal to me. Like the fact that this is what we're talking about in 2026, it shows,
- [59:56]
B
just feels so strange and it's only going to get orders of magnitude weirder because I thought things were weird in 2023 and things are much weird, weirder today. Look, I get why people are very cynical about a lot of this stuff and I get why people talk about like, oh, this regulatory capture. And I certainly believe in the premise of regulatory Capture. And there may be some of that. But I will say one thing, like in the sort of like from the company's perspective is it is true that for a long time and for the very beginning, these are not companies making a lot of money and yet they spend a lot on safety and security. And you could imagine tech companies historically didn't do that. They do and they have these like, certainly in the case of OpenAI and slightly to a lesser extent anthropic as a PBC, they have these weird corporate structures in part because they seem pretty, they seem to believe that the things that they're building, if built wrong, should not necessarily just be in the hands of like purely profit seeking enterprises.
- [60:57]
A
Yeah, all very true. I do think one of the interesting things to me that stands out from that conversation is again the idea of like the asymmetry in power between the approved models that companies can actually use for defense against the new frontier models who are in testing mode and have somehow escaped the sandbox.
- [61:19]
B
I think there's a really scary dimension and I think that actually you think about what is the difference between say sort of like auditing versus like a Moody's, et cetera. It seems like you need both, right? It seems like you need to have like the sort of like yes, this specific model. It satisfies all the requirements that we've deemed it to be safe. But then this sort of like deeper auditing question of like, is this a company that generally experiments and does R and D and testing in what we perceive to be like a responsible manner?
- [61:53]
A
Yeah.
- [61:53]
B
Which is more like the supervisor.
- [61:55]
A
It seems like you need like a testing auditor, like the bank supervisor who's like actually sitting on the floor, actually sitting in the labs and observing the testing process and making sure that the sandbox is well designed. But again, the problem with that is the classic cybersecurity problem, or security in general problem, which is the model just has to find a single vulnerability, right. You have to like fix all of them. Make sure that like thousands and thousands of vulnerabilities are impenetrable.
- [62:22]
B
And it does make sense. I think that like, look, this is for profit, capitalist competition. There's no doubt these are like some of the biggest, most. The pace of growth is extraordinary. Then you lay, we didn't even get into like how would you do this for like open source models or open source servers. That's a whole other can of worms. But it makes sense that if you're in the lab and you're trying to make money and you're also worried about, like, if you slow down, et cetera, then the other company is going to make more money, et cetera. That one way you solve this. I don't know if it's prisoner's dilemma or whatever.
- [62:57]
A
Game theory, race the bottom.
- [62:58]
B
I guess game theory is okay. You need this third party to, like, you guys go as fast as you want on the R and D side, but we are going to set the rules of, like, are you doing that in a safe way? Otherwise, why would Daario and Sam ever trust each other? It's like, no, we swear we're slow. We're taking it really seriously. We're slowing things down, you know, like, and then secretly they're racing ahead. That is really hard to solve for a series of purely private entities.
- [63:24]
A
All right, shall we leave it there?
- [63:25]
B
Let's leave it there.
- [63:26]
A
This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
- [63:31]
B
And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Miles Brundage. He's Iles Score Brundage. Follow our producers Carmen Rodriguez at Carmen Armand, dashiell Bennett at Dashbot, Kale Brooks Kale Brooks and Kevin Lozano at Kevin Lloyd Lozano. And for more Oddlaws content, go to bloomberg.comoddlaws or the Daily newsletter and all of our episodes and you can chat about all these topics 24. 7 in our discord discord gg oddlaus
- [64:00]
A
and if you enjoy odd lots, if you like it when we talk about more moral relativism, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcast and follow the instructions there. Thanks for listening,
- [64:29]
B
Sam.