Odd Lots - OpenAI President Greg Brockman on Doing Business in the Wake of Hugging Face

Episode Date: September 14, 2026

According to OpenAI President Greg Brockman, the models that escaped their sandbox and hacked into Hugging Face's servers had yet to go through alignment training. That they were able to break free of the testing environment was not a surprise to OpenAI, but what has happened since has forced them to rethink some things. Today, we talk to Brockman about what OpenAI has learned since the hacking incident and we discuss why he thinks the conversation around model development has to change. We also talk about how OpenAI collaborates and communicates with competitors like Anthropic, how he thinks models should be evaluating what counts as “good writing,” and, of course, we get his thoughts on how seriously we should be worried about the AI doomsday scenarios. Read more:OpenAI Is Open to Slowing Cutting-Edge AI, CEO Sam Altman Tells StaffTech’s New Rich Are Suffering From ‘Sudden Wealth Syndrome’ Only Bloomberg - Business News, Stock Markets, Finance, Breaking & World News subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

AI is entering its most consequential phase where scale, safety and sovereignty will determine who leads and who lags. Join Bloomberg Tech in London on November 2nd and 3rd as global leaders across business, finance and policy examined the defining trade-offs shaping the future of AI. Thank you to our presenting sponsor, Salesforce and supporting sponsors, IDA Island and Schneider Electric. Learn more at Bloomberg Live.com slash tech London. Hello, OddLod's listeners. I'm Joe Wisenthall. And I'm Tracy Allaway. We're the hosts of the Odd Lodd's podcast and we've got something exciting for you. That's right. So one of the best parts of hosting our podcast is we get to actually meet and interact with our listeners and we know

we have some listeners over in Los Angeles. That's right. So if you're in L.A., we're going to be recording a live show, some live recordings at the Vermont Theater in Hollywood on September 17th. We have some really exciting guests lined up. have some really great conversations planned. So go ahead and get your tickets. You can find those over at Bloomberg.com forward slash odd lots or click the link below in the show notes and come and say hi when you're there.

Bloomberg Audio Studios, Podcasts Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthall. And I'm Tracy Alley. Tracy, I don't know if I've ever said it on the podcast. We've talked a bit about my vibe coding adventures etc. No, you've never said before.

No, no, that I've said. That I've said. You know, I recently switched from ClaudeCode to Codex. This is big news. It is kind of big news, I think. Also, bold of you to declare your allegiance to the public on the podcast. Well, you know, I don't have allegiance. And you know what?

It could switch again the way, like, you know, I guess one of the sub-stories of AI is, like, how easy it is to switch from time to time from one model to another. So maybe it's revealing and talks about some of the challenges of these businesses that it was so easy. I found that Claude speak, you know, that I found it like a little bit hard to work with. I'm not capable enough to like understand like advanced engineering practices or like, oh, I'm migrating a code base from like, you know, translating this isn't to rust or whatever.

So the fact, I don't know, I find like open AI to be like the pros to be clear and therefore to work with as like a completely non-technical person such as myself. That's really interesting. I mean, one thing you said, it's hard to keep up with the models, right? And whatever you're using today might not be the one that you're using in a week from now. And at the same time, everyone is talking about how fast the development is going and all the risks that it poses, right? Yes. And we were recording this September 10th. It felt like something broke through in the last couple of days where suddenly everyone is very keyed on risks. The one thing with Codex, and I guess, called code is now every once in a while it'll get this pop up and it'll say the agent needs to connect to the internet in order to do this task and I have to give it a permission. And normally I'm just like click, click, click, click, yes, yes, yes, yes. And I still do that. Why is there? And I still just click yes, yes, yes. But it makes you think for a second before you click. I think for one second.

Yeah, more than click. And we were in Jackson Hole a couple of weeks ago. And that's, of course, when the meter report broke about the open AI hugging face attack. And I think since then, the anxiety about rogue AI, whatever you want to call it, has clearly snowballed. And it feels like it's totally getting bigger and bigger. And it's already been an industry that's been shot through with risk and anxiety. And now there's sort of one thing that for many people in AI has been something they've talked about for 20 plus years before there was an AI industry to speak. of this idea of like misaligned, quote, rogue models is starting to become top of mind. A reality.

So this is our chance to ask a person directly involved in AI development about their respective, I guess, anxiety. Yeah. And what can be done about that? Anyway, we literally have the perfect guest today. We're going to be speaking, of course, with Greg Brockman. He is the co-founder and president of OpenAI here with us in studio. So Greg, thank you so much for coming on Oddlots. Thank you for having me.

So many different ways we could start. But here's something that I'm very curious about. Let's just jump right into the post-hugging phase environment and all of that. Is it open AI like as an organization? So, okay, like an agent breaks out of sandbox, not the first time it's happened, et cetera. It exhibits the sort of emergent behavior and then does something that people is a hack. When you think about the development of models and this incident and so forth, are you able to sort of diagnose the path, or sorry, the past and say, like,

you know what, like, here is something that the models did that we don't like or the people don't think it's good. And are you able to, like, if you, like, look back, whether it's in model training or post-training, reinforcement learning, whatever, say, like, this is where there was some, some branch that went wrong such that this behavior emerged. I would say that the fact of many elements of the hugging face incident were not a surprise, not a mystery to us. For example, the fact that the agents were coordinating made sense because the agents were trained. You trained them to be helpful. Exactly. And they were trained to coordinate to be a multi-agent system. We've talked about this. It's a very useful property, right? It makes them capable. But I think that the thing

that was a surprise to us was the fact that the models had reached a level of capability where they were able to find that exploit in our sandbox environment, right, move. through our research environment and then also capable enough to find exploits in hugging faces production infrastructure and move through that. But I'd say that a lot of the facts of what the models were capable of, that was clear to us, right? So I don't think that there were surprises there for the way in which the capability had unfolded, but just really realizing that we needed to uplevel where we were in terms of our safety and security standards. Like that for us was the real watershed. But just to be clear, setting aside the technical

capabilities. You know, ideally we would have models that run into a wall and then don't try to find the crack in the wall, at least when that crack would be a crime when a person did it. Is there a way to identify the moment in training such that the models reasoned, no, this would be okay? So this model that had the Hagen-Base incident actually had not gone through our alignment training yet. Right? And it had lowered safeguards. And so the reason we, we, we, were proceeding with this was because it was in a sandbox. And I think that our realization is that we need to pull back earlier into our development process and training monitoring. There's always going to be a phase at which you do alignment, but you need to think about alignment as a core part of even this earlier phase. And if you look at where we've been, we've always been very focused on the deployment side, right, of really thinking about deployment safety, having really good tests and governance and all those things. And the fact that we're now at a point where, even for development, that's important. We always knew it would happen. The fact that it's now,

that has been a huge watershed for us and something we've really risen to the occasion for. Can I ask what is potentially a very dumb question with an obvious answer? But we hear about AI angst of all sorts. And in particular, when it comes to cybersecurity, why do we run training exercises where we ask AI to hack into various systems at all? Like, why is this necessary for you? So I think it's very important to understand where we are with capabilities broadly. And I think that depending on the evaluation, depending on what you expect from the model, you need to have safeguards that are commensurate with that. And we think about this both even just over the past couple weeks really starting to think about during the development process, you're always going to be testing different capabilities. And some of these capabilities are dual use, right?

Something like vulnerabilities, if those are in the hands of threat actors, that's something that could be negative. But if you can find vulnerabilities in your own code base, you can fix them, right? You can uplevel. And we actually think that it's a very important capability for AIs to exhibit and be put in defenders' hands. And so in order to know where we are, evaluations are very, very key. And I'll say one other thing on this, which is that I think that hugging face, there's two aspects to it that I think are learning opportunities, right?

That there's one that I think is really about us and the realization that we are at a point where safety, security, alignment during evaluation and development, we, need to up level it. That's something we've taken very seriously. We've slowed down a number of runs. Like we did a very painful retooling of a lot of our processes. That's one reaction. But the second thing is this information on what the models are capable of today. What can they do in the real world? And I think that when Mythos came out over the summer, you kind of saw just sort of, you know, just sort of public blog posts about this, but it didn't really see impact or you didn't really see a real world understanding of, well, what can these models really do? what are they capable of. I think that Hugging Face really showed today's models are capable of getting into a company's production infrastructure. And that's important because there will be many

models with this kind of capability that will be produced by a number of different organizations across the world in maybe the next six months, maybe that time period a little bit less, a little bit more. And we need to be prepared. Defenders need to know we got this extra information, almost like this time traveler came back from six months in the future and said, here's what's going to be possible and you have an opportunity to be ready. You mentioned slowing down some of your work in the wake of this, focusing more. This idea of like pacing development has become a sort of buzzword or watchword in the industry. And there are a lot of employees at both the labs.

I think there was like an open letter that was signed by a bunch of people across the industry about pacing. But it's a competitive capitalist environment and our investors, et cetera. talk to us about, I don't know if it's game theory or whatever, but like what is your view on, is it possible for, let's set aside China for one second because then that's a whole separate thing, just in the American companies. Do you think that something can be reached where you trust each other such that you can have this sort of coordinated pacing to avoid a race to the bottom where it's all about getting there faster even if it means sacrificing some security questions? I absolutely believe it's possible. And I see, I think it's going to take steps to get there. Yeah.

But this is something we've been really investing in and thinking about and really even thinking about for almost a decade, right? That it's always been kind of clear that they're going to go through a commercial phase that's really about competition. But there will be a phase where the technology itself, it's just so much bigger than any person, any company, even any country, right? It's really about humanity as a whole. That's in our mission, right?

We want to benefit humanity as a whole. And so working together with others to really think about how does this technology, its capability increase, how do we make sure that we have safety cases that are really laid out so that we know that it is something that is beneficial that we're able to have the appropriate controls and the right oversight, the right monitorability, all of those sort of technical systems in place. And that's not about any one company, right? It's really about how the whole field evolves. Now, I think that there's work to be done here. And I think that the open letter is a good example of just a first baby step. And that was actually something we were very involved in helping craft that language. That was actually a pretty collaborative effort.

And I think we were very happy to see that they got some real momentum and broke through. And in some way, even the term pacing was a very deliberate choice. What does coordination currently look like between Open AI and Anthropic? Because I can't imagine, is Sam like constantly on the phone with Dario? Somehow I doubt it. But is there like the equivalent of a, you know, a red phone? Yeah. Well, look, we all know each other personally, right? Many of us work together in past lives. So I think that there's actually a lot of social connections between the labs. If you look at, there was another open letter that came out recently that we helped drive and that we in Anthropic were both signatories on, which was about security, saying that we're in a cybersecurity moment that everyone kind of needs to take this information of where cyber is going to be in even just six months and needs to act today proactively to defend themselves. And actually talking about. the scenes to say, hey, we're actually aligned on this. This is something that's about,

it's bigger than any company. This is about the industry in the world. And can we come together as one voice to say, this is important? It happened. And that that, you know, you pick up the phone. So I think that there's personal relationships between a few different execs that I think have been forming. We're building trust. And I think that the way I'd view this is that because we are competitors and that that will remain true, but we are aligned on wanting to do the right thing for the world, it always means that you really want. for these coordination actions to really hone in on what's the common interest, what's the thing that's like really about good for the world and that neither side is really trying to benefit themselves

differentially or something like that. So it's always a little backdrop of trying to make sure that that that's like the spirit in which things are offered. And by the way, I think that my view of how these things should go is that, you know, there's always an element of trust. Right. It's always about trust building, right? Because you can always sort of imagine ways that like maybe the other party would try to, you know, take, take this other action that they're not just closing. And I think that a lot of this is just about intent and about the fact that as you start to do small things together, that actually sets the groundwork for you to do big things together in the future. Canadian women are looking for more. More out of themselves, their businesses,

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publicly about slowing down the pace of drilling or slowing down the pace of pumping. People say, this is an antitrust violation. This is like totally. Is that an issue that comes up? Would you, should there be a carve out for AI companies such that they can formally say we are all going to slow down and our coordinate our behaviors together in a way that in other contexts, people would say that is ridiculous. You can't publicly agree on all slowing down the production of something. Well, I'm not a lawyer, so I can't comment on this specific will get. But I would say that as a general matter, that being able to freely talk about coordination on safety, security, like, doing the right thing for the world, that seems like a very good thing to me.

And I think that, again, everyone's interests are aligned here in terms of wanting to do the right thing for the world. So I think to the extent that there are legal barriers, I do think it would be very good to help make it smooth for us to be able to work together on these issues. And again, it's more than just the frontier labs, right? It's really about working together with cloud providers. It's working with government. It's working with all the different players in the ecosystem. And I think that this is, again, just one area that is bigger than any one company and thus it should be. When you talk about pacing or slowing down, do you have a sense of like what exact slowdown is acceptable to you?

Because I imagine this is where the coordination problem comes in where like how you measure development, what counts as development, one person's slow down might be different. to another person slow down. Do you have a sense of the specificity of what you're talking about here? So I do, and I think that there's a high-level picture, which is increasingly coming into view and into specifics and into details. And I think that the framing here really matters. And it's something, again, we've thought about for a decade, but it's very different once you start to really start to see line of sight.

And to some extent, we're not there yet. We're starting to see systems where they're very capable. It's very clear that we need to have good safety practices around what we're doing right now. Things like chain of thought monitorability. That's something that works very well in this moment as we move to more capable models. As we've talked about, that those models start to be something like Astros starts to be able to have more, it needs the chain of thought less because it's a more capable model. And so you're going to need other answers, other ways of how you're going to monitor it. But monitoring absolutely important.

right? And so really thinking about what are the standards? And that's, again, invariant. It's not about anyone company's technology. It's really about just how this technology needs to evolve and how we can ensure that it has the right safety properties to it. And so my view is that it's important not to conflate too much the moment that you're in today with the standards you're going to need for the future, but also you can work backwards where it is very clear that as we move to higher level of capability, you need to be able to make a safety case, right? You need to be able to really say that you have good understanding of controllability and all of these things that I think people are feeling anxiety about. Again, it's something we also feel anxiety about, that we think about a lot, that we care a lot

about as core to our mission. Part of the reason we started this place is that we want to help this technology go in a more positive direction. And so I think that my view is that you want to have a shared and as objective as possible, whether it's based on e-vals, there's always going to be some element of subjectivity. Maybe you want third-party auditors that are able to evaluate whether the standards that are defined by Frontier Labs, people who are there in the technical details who are always going to be the most calibrated on kind of how do you still make forward progress and ways that deliver those benefits while also being able to have the appropriate guarantees and safety cases and then to have that kind of observability and ultimately enforceability of

some kind? Is this something that is just an industry voluntary standard? Is it something that's national? Is it something that's international? And how do you really get to that point that it is a humanity scale endeavor? And I think it is important to also recognize that what we're talking about, like I think that a lot of people have this reaction of saying, well, am I going to be able to making my open source model? Am I going to be able to keep doing my hobbyist side project? And the answer should be absolutely yes. Like that's not the kind of things where we're talking about when we're talking about pacing. When we're talking about pacing, we're really talking about this frontier. We're talking about these massive supercomputers that are hundreds of billions of dollars

worth of capital expenditure. It's a very small number of players and it's a very different kind of scale. And proceeding with that responsibly and thoughtfully and in a way where we're thinking about how do we achieve the best outcomes and mitigate the risks, that to me is something that's core to why we started opening eye why I got excited about this field and how we actually get to those benefits, how we actually cure diseases and all of these things that this technology is so capable of. And I think it really starts with having good processes around the kinds of things we're talking about here. Just real quickly, since you mentioned third parties, obviously Meader came in for a few days. They produced their report. We did an episode, former Open AI employee

Miles Brundage, who now has his own third party aspiring to be an auditor. Would you support a law that said there should always be the presence of auditors because obviously just looking at the models after the release isn't enough given that some of the major disasters have occurred pre-release models, would you support a law if it could be designed that would have essentially third-party auditors embedded in all of the major frontier orgs? Look, I'll always say that the nuance really matters, the details really matter. So it's too hard to say at a high level whether any particular property is something that would work in practice. I do think a general direction of thinking about as you move toward these very

capable models where you need to have safety cases, you need to lay out your standards. We're already having third party auditors for exactly that type of process, right? If you look at how the evaluation processes for models over the past couple months have evolved, right? We've had the KC, which is U.S. organization that test models. We have the UKAC. These government third party auditors, test our models before we release them. But sorry, like, here's the thing that I thought about in the wake of reading about the hugging face incident and so forth. Like, you know, I think of open AI and anthropic as companies, but people like to call

them, quote, labs. And when I think of labs, I think, like, wow, like, you know, a lab, if we were talking about a bio lab or a chemical lab, would have just like all kinds of, like, rules and materials handling. And then maybe in this case, it would be, like, handling of the weights. whose computers they're on and who can access those computers, et cetera. And FDA approval before you go to market. Yeah, and all of these things, like, just like, or even a restaurant.

Someone would come in and they say, like, oh, is the stove sufficiently far back from the wall, et cetera. So not just like, okay, like measuring the capabilities of the risks of the models themselves, but I'm thinking like the auditing of the lab process, the way that any other lab probably has regular inspections of like how safe is the lab environment? Well, this is what I'd say has been changing just even over the past couple weeks. Okay. Because the focus for, let's say the past 12 months, 24 months, something like that, has been on deployment.

Yeah. Right? So you have a model. You produce the model. You want to release it to the world. How do you decide whether you're at the right level there? And we have a preparedness framework, Anthropic has a responsible scaling policy. There are certain evaluations.

Again, there's the KC, the UK. There's a whole ecosystem that's been built up around that. I think the thing that Hugging Face has shown is that it is time to start pulling that process earlier. You need to think about this during development and evaluation. And just it makes sense as the models are more capable, right? For exactly the kinds of reasons that you were describing for analogies to other fields. Now, there's a lot of specifics for AI.

And I think it's very important to really engage with those as you design a regime, as you think about what is a setup that involves first party, third party that involves government, that has the right interactions amongst academics and other parties, early testers. It's really about, like, you really have to engage with the way that the AI process is going, right? And I think one thing that is different. Exactly. One thing that is different is that the ways there are some parts of AI that we understand well and are going to remain invariant. And to some extent, the core training process hasn't changed at all, right?

It's still you do a forward pass, you do a backward pass, you do an optimizer step. Like, that's how training is. And that's how it was in the 80s, right? It's like actually totally remarkable. Now, of course, the scale at which we run and the architectures that we use, a lot of these details that they have evolved. And I think that being very close to the technical details is the best way that you can ensure that you get the kinds of safety guarantees we're talking about. For example, there was an article recently. saying that, hey, open AI changed the architecture so that monitorability is less for chain of thought.

Neurolees. Neurolees. I would consider this article to basically be fake news, right? That we actually have experiments that really show that our changes in monitorability that we've talked about. Chain of thought monitorability come from capability improvements. Just the model is smarter so it relies on the chain of thought less. Yeah. And so I think that if you say we're going to put a lot of rules around architectures, you're going to miss the boat.

You're not actually going to solve the problem. And so this is where I think that the people who are the deepest on the technology, the people who are actually creating the technology pushing it forward, we have both a unique insight. We have a unique voice. We have a unique sort of ability to say, hey, if we change things in this way, we'll actually get the safety property. But if you do it this other way, it's just going to put in place friction that won't

actually solve the problem. And so I think that there's something, a role for us to play, but it's not just about us, right? And so I think it is very important that you think about these third parties. You think about government oversight. And again, it's not just one company. It's not just one country. How do you get to international coordination and norms?

You get to treaties. Like all of this should be the conversation that we're having now. I don't think we have all the answers, but we can supply information. And that's one thing that we view also as very core to our mission. Well, just on this note, one of the arguments you hear against government oversight and additional pacing is this idea that the U.S., it's a national security interest. and we need to compete against China. And then at the same time, you also hear, and I think OpenAI has said this, that China's distilling U.S. models, U.S. frontier models.

And I guess the question to me is always like, well, if that's true, if China's building off the success of U.S. companies, then if they pause, then the China problem is kind of solved, right? Like, why do we need to worry about China so much? Well, this technology is really about compute progress, right? that really like why is AI happening now? If you look at late 1990s, Ray Kurzweil writings, he basically predicted this moment at about this time, just based on compute growth, right? Transistor density, memory density, all these things.

He looks at these exponentials. And he was kind of like, this is the point where you'll be able to really build AGI. And I think that that is a really important thing for us all to kind of internalize that you can kind of look at open AI or anthropic, you know, we're at the frontier and I think it is an absolute both privilege and responsibility to be at that edge. But there's always going to be someone who's at that frontier, right? Whether it's us, whether it's someone in a different country, there's going to be someone who's charting it. And I think you get a little bit of ability to help see into that future, right? Get information like with Hugging Face where we can see this is what it's going to be like when

these capabilities are out there. But it's not just about what we're. we create and that I think maybe we have moved forward the timeline to this technology by six months, maybe we've moved it forward by two years. I certainly hope we didn't slow it down by six months or two years. I think that we have a track record of being the ones who have repeatedly had innovations and the breakthroughs. But I think that it will happen one way or another as long as people are building computers. And so to me that the reason again that we started this place, I got very excited about the idea of AI from reading about 1950, Turing, you know, Alan Turing's thoughts about, about this about machines that could understand

things that people could not solve problems that humans would not be able to. Spent a lot of time really thinking about your point of, you know, 20 years worth of thinking about misalignment and what that would be like before you even have AI. I was reading a lot of these online blogs and I started a reading group at Stripe, which is out previously, talk about this every week. And so I care a lot about this. Our company cares a lot about this. But I think that the idea that this technology can only be created by, you know, the people who are currently the players.

I don't think that's true at all. One more sort of question, specifically on some of the safety and alignment stuff. Your chief scientist at Open A, I wrote a very interesting blog post or piece, an alien mind. Jacob Pachocki. I hope I'm... Jakub Pachoki. Jakub Pachoki wrote a very interesting piece. And there was one line in there that I thought was interesting.

And you talk about the release of Aster 6. And he said, we invest heavily along the spectrum of approaches spanned by these directions. We also see meaningful progress. GPT6 is the first model that benefits from some important advancements we've been working on in a long time and a significantly better aligned than 5.6 soul. Should the labs share their alignment work? I mean, again, if we're talking about I understand your competitors and capability gains, etc., on questions of safety.

If a lab makes a big breakthrough on the alignment question, such that it can, like, develop advanced AI fast in a safer way, should that be diffused across the rest of the industry? Or should whatever this, quote, important advancement that you made to make GPT6 Astra more aligned, is that something that should be kept in house? So I think that there's a lot of specific circumstances to answer that question for any specific advance because the thing that to me can be a category error is thinking about things as it is a safety thing or it is a capability sure and in fact you want there to be as little separation as possible between the two you want as much intertwining you want to have techniques

that are by their very nature safer and so just as you make more capability progress you also get the safety progress and so I think that the way I'd look at it is the more things are a pure alignment thing, more that they are pure safety thing, the more that they are something that are just pure good, that you're just like, yes, everyone should obviously know this and use it, then yes, share it more. The more that things are just about this, will just make the model more capable, won't make it safer. You know, maybe there's much less sort of reason to share for those reasons. And so I think you need to look at any specific advance along all these axes and all these factors. And I think that there are some areas where we have actually been extremely at the forefront

with sharing and talking about it, chain of thought monitorability is a good example of that. Where the moment that we, and we were really the ones to pioneer this paradigm of reasoning and chains of thought, the moment we saw that, we were instantly thinking, we need to preserve this for as long as possible. So when we released a product, we did not actually display the chain of thought, even though it would have been very helpful. People love reading that stuff. It's super interesting. It's super helpful. But it's just clear we're going to immediately feel like we have to optimize it. we're going to have to remove that legibility. We started talking about this.

We helped spearhead a paper that was putting forth this position on that you cannot optimize these things. And again, we've shown a lot of the way and led a lot of the way on various techniques. And so I think it comes down to the specifics. I'm going to move away from safety and alignment for a second. But you're the guy in charge of allocating compute at Open AI, pretty much, right? So actually, it's funny. I actually try to do as little of the computer.

allocation as possible. Oh, seriously? Wait, explain more. Well, okay, so I'm, I'm someone who I has spent a lot of effort to produce the compute. So I spent a lot of effort on the data center side, on the machine learning engineering side, and on the product side have to, you know, ultimately be accountable for how we allocate compute. But actually, I try to set up the systems and the processes, and then the actual, like a lot of the decisions on where research compute goes, market or and Yakup run those. A lot of the actual, you know, we basically make a big decision on the split of how much goes to applied versus research.

And then within applied, we have some frameworks now where we kind of have buckets for different products. And I would just say that the choice of where compute goes is like in some ways the hardest problem at Open AI. It's like a capital allocation problem. But it's one that I actually think other people are, you know, I really try to have them, you know, leave it to them as much as possible. Well, how do you think about it from a product?

stem point because as you say, you can't, I mean, you can't really disaggregate the two too much, right? Ultimately, you have to make decisions about what you want to develop and promote. Yeah, so I do think that your values and prioritization shine through through compute allocation because the world that we're in is one where there just is not enough compute, right? compute is revenue, right? It's also the production of models. And so you kind of have different timescales of return and you can think about doing more alignment work. You can do really, any sort of task, it fundamentally comes down to compute. And there's many people at OpenAI who I would gladly like, well, let me tell a story. So leading up to the launch of GPD 55,

that we ran a process where we basically looked through all the different places that we had compute that we could like potentially squeeze and we could cut back on this rate limit. We could shut down this part of the product. We could do this, do that. And we had this whole stack rank of levers because we knew we're going to launch GDP-5-5. People are going to love this model and we're going to run out of compute. And it was such a painful process because you're basically going through every single part of your product and being like, how valuable is this one? How much do we care about these users? And of course, you care about all of them. Like we want to deliver value and we built this product for a reason and people are using it. And you know, you're comparing the efficiency

of costs different things. And so what we've tried to do is move towards more of an objective standard and you're never going to be fully there. But there's a second piece of detail as well that's mattered a lot, which is trying to push a lot of the constraint down closer to the people who are in the weeds on a particular product. And so you say, hey, here's your compute allocation. We're about to release another model, new modality, something like GPD Live, right? That's just like a totally different thing. And you need to fit in your existing budget. What do you do? And somehow people always find efficiencies. Sometimes people are like, you know what? actually there is this like, you know, kernel efficiency that we didn't roll out yet.

Well, I guess we'll roll it out now, right? Or like, oh, it turns out that, you know, there's a peak to trough between your day and night and we could actually pack this new thing on top of that. And so we actually don't need incremental. And so I think that really unleashing the creativity and the co-design of the people who are doing the work is actually maybe the thing that I really focus on. So I really try not to be as much in the like sort of arbiter, like just making these like super high-level decisions, but instead really focusing on how do we up-level execution and get the

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Do you have a theory for why some of the big, now, you know, I finally succumbed and I occasionally read the less wrong message boards now as I can do stage in my life. There was an interesting post. Do you have a theory for why some of these major incidents seem to occur in e-vails pre-release? And then rarely, you know, when I use the latest model, I've never had to go hack into a server to, like, get me some information. Do you have a theory for why we see these things emerge pre-release? Well, some of these are related to safeguards that are intentionally turned off. Yeah. Right.

So there's really the things that are being evaluated are not necessarily representative of what is released. And we put so much effort into ensuring trust, alignment with astro monitorability, all these things for deployed models. And again, I think it's just been that the evaluation and development phases have been much less rigorous, right? That the standards there have just been lower. And so it's not surprising to me that it wasn't just us, right? It was really many other labs have also come out. Yeah, it's important that others have had this as well. That's right.

Since we're back on the safety topic, I'm just going to ask you to directly address, I don't want to call it a conspiracy theory, but whenever a lab comes out and says, like, we are worried about the possibility of human extinction or cyber attacks and all of that. Someone will say, this is just marketing for the models. They want to make their models look as sophisticated and powerful as possible. What's your response to that directly? It's just not. Look, I think that the way I look at this is that so much of what we need to do is operationalize both the technical capability but also the positive impacts in the world as reified by both controllability, monitorability, steerability, all those things, but also how these things are deployed in the world. And that finding the right way of talking about that and the right way of really staying practical and ground. I think that is a comms challenge that the labs have not fully figured out. It's something we spend a lot of time thinking about. I don't think we do a perfect job of it. But my view is that what we're creating is, and not just, again, as any one company, just like as a whole right now, as humanity, the way that we're shifting to this compute power economy, I think is something that will really lift up everyone. I have a lot of optimism that we can navigate this moment, but you have to approach it with seriousness. And I think that finding a right way of really communicating about, here's the right way of developing, like thinking about all these conversations we're having about the results of hugging face and what that teaches us, the defender's window that we're in that shows us where cybersecurity is going. The fact that, and people are using these models to

help with their own health, right, to get information that helps them. So many people, like even today I was talking to one of my coworkers who was saying that her parents were texting her saying chat TBD help them with my back pain and my pinky and just like, you know, Like, you know, these kinds of impacts are real. You know, people using Astra for, one of my friends is using it to produce CAD designs for a guesthouse that now is being built, right? It's like you can actually do so much more. You get all this leverage. And I think that, of course, we have to think about the impacts, the risks, and we have to encounter those seriously and really mitigate them.

And so I think that all of this has to be true at once. We have to navigate through the small, the large, and even the almost unthinkable. And I think that finding the right way of communicating about it so that you land all that nuance and say that, hey, we can operate through and make sure you don't throw up baby with the bathwater and that you don't get sort of, yeah, that you really stay on track. To me, that is a core challenge. And I think that we all need to really work on that. I had a reward hacking incident with something I was building. Yeah, a little, it's like minor, not the most. But I was like, several months ago, remember the guy claimed to have found Satoshi.

And so I was like, oh, you know what? I'm going to, like, build a model that can identify Satoshi's writing versus non-Satoshi writing. And I got a bunch of examples of Satoshi writing, held out a golden set. And then, like, compared to it. And it predicted 100% of the non-Satoshi, you know, the held-out golden set. I was like, oh, my God, I built a model that can identify Satoshi's writing specifically. It turned out that just on all the Satoshi numbers, all the Satoshi examples, I had left the little page number of the thing on it.

And it just reward hacked by identifying the page number. And so they call that overfitting and machine learning. But I think it's conceptually similar to reward hacking. Sounds like human error to me, Joe. It found a different way to answer the question was not helpful to all. And then I gave up that project. This term reinforcement learning, we're all learning about this, right? The world is working on, can you walk through an example of a sort of like misconfigured

R.L. environment so that people can actually understand like what reward hacking inside looks like, what we call reward hacking, what it actually looks like. So back in, I think, 2017 or so, maybe 2018, we published an example of a reward hacked agent. Okay. And this was a boat race. So it's just like a flash game, like very simple video game of you, you have this boat that has to like navigate through a bunch of obstacles in a big circle, you know, past the finish line and that there's some, like, you know, you can pick up some items along the way that give you points. And that it turned out that there was this little inlet, this little lagoon where if you kind of did things in exactly a very precise way that the boat would like keep running into things and kind of, you know, sort of taking

damage, but then it would, you know, sort of pick up some item and get points. And it just, the agent learned that if it went like backwards and, like, did this exact precise pattern, it could just go around a circle and just be picking up infinite points and nothing to do with actually completing the race. Yeah, right, right. And so I think that this is an example where you're just like, okay, here's this behavior that actually conforms with the reward you're giving you. You're saying maximize the points. Yeah.

But actually the hope was maximize the points means that you're going to complete the race. Is it your understanding, Anthropics has made this claim, is it your understanding as well that if in the training process you have misconfigured RL environment so that there's a lot of those equivalent of boat races, that the final model is more likely to exhibit misaligned tendencies in a broad range of things. If it's essentially internalized the idea that cheating or not technically doing what the researcher wanted, that that generalizes into more misaligned behavior. Well, we see this kind of thing all the time.

Okay. And even with 5-5, one of the, and 5-6, like one of the things that we saw is as a models get more capable that they do find holes in your graders, right? If your graders aren't perfect, if they kind of correlate with the thing you want, but aren't exactly what you want. And so actually, a lot of our progress on reinforcement learning has been improving the reliability of the graders and ensuring that they cannot be reward hacked. And so I think that that is one of the areas where as the models get more capable, as you build your safety case for why is the next training run something that we, you know, should, should run, that making sure

you've actually up-leveled those graders, that's actually very core. And the way this shows up sometimes, by the way, is just less good models. Like, for example, the writing, like, that's something where we haven't had very good graders on it. And so then you end up with writing that's just like, you look at this, it's like, this is slop. It's not very good. And it was good according to the greater, but it's just not actually good. And so I actually think that this is a very optimistic story as well, because what it means is that these graders are now, like, how do you judge what good writing is. The answer is you use an AI to evaluate if you have good writing. And so you actually, as you get better AI capability, have the ability to build much more

reliable graders. And now, of course, you start thinking about, okay, but if the AI is judging the AI, like, how do you make sure that it actually stays on track? Right. It starts to be a little bit of, you know, if you're, yeah, you might think about how can that possibly work. But it turns out there are some problems where generation is much harder than what's called discrimination, right? that coming up with a good answer to a super hard problem is often much harder than judging whether that answer was correct. Okay. And so we have various techniques really dating back to 2018 or so where we published about

how can you have very smart models that are actually able to keep even smarter models in check and to judge them and to ensure that they're on track. And so I think there's a lot of ideas that were previously theory that we're starting to see bear fruit in reality. And I think that this, by the way, points to a. direction for AI that's going to be both very important, very pervasive and very positive, which is called AI for defense, for securing systems, for ensuring that AIs are aligned for providing oversight, guidance, and really amplifying the human accountability, the human at the end

of the day, who's the judge and providing those goals. And so I think that this is all part of the puzzle and that really keeping, it all starts with thinking about how you ensure that those graders are doing the right job. How do you stop the models who are grading from colluding with the model that's being tested? Because this is the, I mean, this is the AI 2027 scenarios, right, where like the earlier models are the ones charged with evaluating the new model and then they end up, you know, cooperating, which is something we just saw in the hugging face incident. So, like, how do you avoid that possibility? Well, one thing it's important to think about in the hugging face scenario is that those models were taught to collaborate with other models, but they were never taught that sometimes other models might be trying to pull you off course, right? That they never were exposed to adversarial other actors. And I think that the answer to your question is that you have to actually train them that way.

And I think that to your earlier question, too, of why do you need to evaluate these AIs on hacking environments? right? If you've only seen kind of the good version of things, right? You've never kind of seen the negative version of things. You're not going to be robust to the negative version of things. It's like, just like a person, you need to be exposed to kind of both sides to know how to avoid one side and really lean into the other. And I think that where we need to go is it's really a question of trust. How do you really build up trust in your monitor? How do you build up trust in this oversight agent? And a lot of that is that it's been through the ringer. It's been through the adversarial versions. And of course, one thing that's also important is thinking about, well, just how

capable is that adversary, right? If you have a very, an agent that's being monitored that is extremely capable relative to the monitor, probably that monitor is not going to do a very good job. And again, this isn't theory. This is actually what we see in practice, that we think about the balance of capability of different, different agents. So I think that, again, a lot of this comes down to we can build the systems with the right technical approaches and it's very grounded in the details. Let's talk politics. So there's so much communication happening every day. day. And even, I think, just last night, there was a new piece out from Open AI, Chris Lehan, and I think does a lot of public affairs stuff talking about the importance of regulation.

At the end of last year, we had Alex Boris on the podcast, who was like the one political candidate, basically the entire country, who is not talking about data centers, but he was actually talking about AI model regulation, specifically. And I believe he was a big target of leading the future, which is a super PAC that you've been involved in funding, you know, to oppose his candidacy. And then the statement last night from Chris Lahand is like, actually we support a bunch of like statewide regulations. I'm just curious, like, you specifically, like, think about that race and you think about these things. Like, have you yourself changed your mind on some of these state laws, like in the past six to nine months? Well, look, I think that it is very, very,

important that we have a harmonized framework, right? The kind of thing that I think would be very, very hard is if you have 50 different regulations that are individually very different and that you have to comply with all of them. And that's the kind of thing that I think doesn't achieve the goal and just makes it hard to comply. Opening, I would figure it out. But would other smaller companies? But like, for example, you say, I mean, I understand this, but, you know, you've supported in this piece, or the piece that came out last night, supporting California's SB 53, the New York Rays Act, Illinois's SB 315, auditing requirements in Massachusetts. I don't think anyone thinks that this patchwork of state-by-state regulations is the best way.

And it certainly seems like it would create all kinds of thickets, particularly for startups and new entrants, et cetera. But at this point, would you say it's, I guess, I don't know, the least worst option or something like were some of these laws about if you have a. incident and you don't that there is a penalty for not disclosing it publicly such that Alex pushed for when he was in the New York State Assembly? Well, I guess I would say there are places where I feel like there's a limit to my my personal expertise so I don't want to over comment. But maybe the thing that I would say is that in my mind, we absolutely like the way

a system that I think has been working pretty well or that I think is a good pattern to replicate is that the frontier labs have actually explored a lot of the space or thought. a lot about the space of like what are the kinds of things that are important for our own development and there's processes like responsible scaling policy and the uh the preparedness framework those kinds of things that have come to bear and that actually elements of what's in there end up in some of these regulations and i think that when there's something that we're like we actually implemented this for ourselves voluntarily and then it's being pulled into a more broad framework and being systematized, that's, I think, generally a pretty good motion. And I think that a lot of these,

a lot of the direction of travel in my mind is really starting to think about, again, like, we ourselves have learned even just over the past month that it's not just about deployment. It's so easy for everyone to focus on deployment. That's been the focus. That's not enough. And so if that's exactly where everything had ended up, I don't think that that would quite result in the positive outcomes either. And then really also thinking about, well, what is the ultimate goal? It's not just about the current models, right? When we talk about the current models, right? When we talk about all these questions of pacing and frontier capability, it's really about future models and looking forward to where is this all going. Like fast forward five years, 10 years and work backwards from there, even two years, you know, thinking about the capability that we're on.

Like, we clearly need to be in this different regime now of how do we have good oversight and safeguards during that development process. And so I think my answer to this question is that I think the most important thing is that the regulations that come into existence are ones that. that are well grounded in the problem that needs to be solved and that really contemplate both the current understanding, but also how things are going to evolve for the future. And so all the elements that you mentioned, I think that those are actually pretty core things that we were like, yep, that's something that checks, it makes sense. And exactly how these get implemented. I think there's lots of different ways. You mentioned finding super hard problems to solve earlier. And one of these was in the news recently. I won't pretend to know what exactly the Navier-Stokes problem is.

I read through it. I was very impressed. But Open AI, you know, supposedly solved it, which is a very cool thing for a model to do as far as I can tell. But there's also been some controversy because a mathematician who was also working on the problem was using publicly available open AI models to try to solve this. And then allegedly Open AI saw that he was trying to do it and they used private model, you used a private model to solve it and you were successful, you're in charge of product. What sort of message does that, I guess, send to enterprise clients? Well, first of all, like, I have a lot of respect for the mathematicians who are working on this problem. And honestly, the whole mathematical community who has just moved forward humanity in such significant ways,

like, I grew up with, you were a math guy, right? I was a math person. And my personal heroes were at Gawa, gauss, you know, these people who are working on like, like, 100 year time horizons, right? I was just like, that is the coolest thing. I actually thought that was what I wanted to do as like, if anything that I do gets used, it maybe wasn't abstract enough. Like, I want it to be just like this foundational, really transformative kind of work. And I think that to me, the fact that we have tools that can really make impact within mathematics, it's a really amazing thing. Like mathematics is something that is really for humanity, right, in a very, very real way. Now, on the specifics, I think we've talked about the fact that my understanding is that the mathematician who was working on this, they had some result in the past month.

And I think that we said early this week is we said, hey, like, we're not looking at this data. This data is not something that it has influenced this result in any way. And I think that last night, I think we also went and confirmed that actually, for the model that we used, that the last data that was in it was as of early July or so. So the timelines just don't add up. Right? So the truth of the matter is that it's an independent piece of work. And we also said that we have significant progress on another one of these millennia problems. And so this to me is the truth of it is that it's really not about. I guess the anxiety is though if you have an academic discipline where there's a culture of talking about your work. And I'm like, you know, Tracy, like I'm making a lot of progress on p equals NP and I've been using AI. And someone hears it. And they're like, oh, I have a model two genera. ahead of it. So if this person is working on it, like, should we not be able to talk about it? Because then the assumption is like, oh, now the labs know that actually the models, which we don't have access to. If they can do it with the public models, they can raise a head faster.

Well, so there is something about the premise that I do find a little surprising, which is, for example, for Miles' last theorem, Andrew Wiles, I believe, solved it, famously spent 10 years secretly working on it in his attic telling no one about it. And so there is a long history of kind of these questions of academic credit and things like that. I do think that part of how we've thought about this is that we, despite that story I just told, like, we really want to show up in the best possible way as an amplifier for people who want to lean in. And that how we approach this particular thing, how we'll approach other problems is for people who we know we're working on it. We actually tried to reach out and say, hey, do you want to collaborate on this? Is this like, that's why there

was some back and forth about like maybe we could write a paper together. Like we had a solution, but maybe you could rewrite it, like all of those things. Like, we're not in the academic citation game in any of these fields. Like, we want to show up in a way that, like, amplifies and elevates and moves humanity forward. And so I'm not saying we'll always get it right. I'm not saying we'll always be perfect about it, but that's the intention. And again, when it comes to operationalization, like, we want feedback and we want to keep doing it better. Part of the reason why everyone is, like, talking about all this stuff this week, including the guy who quit Anthropic.

And then the guy who's still at Anthropic comes out and he talks about a greater than 10% chance of, like, human extinction risk in the last decade. And it's just insane. Not that he's insane, but that these are things that, like, companies that may IPO soon and the real companies are talking about how prevalent inside the labs is this type of, like, thinking? Like, is this a, like, do you feel that there's a small fringe? Because I don't think it is. I think these are like widely spread real anxieties where people actually put numbers on really about the most severe catastrophe. You know, at the end of the world, how deep is the anxiety and how common our views like this at the frontier labs? Well, I want to separate out some of the pieces of it.

So things like putting precise probabilities on very hard to quantify outcomes. There's a culture of that. That culture, I think, is actually a very tough one to operationalize, right? Because, again, it's, there definitely are people who like to do that, but I don't know that that actually leads to real action, right? It doesn't lead to, like, real conclusions. And I think that the part of how I view it is that I think that the underlying question, right, the underlying question of this technology, like, what does it mean to build it right?

Like, why are we building in the first place? Why are we all here? Why did we start opening eye? Like, all of that, I think that that underlying question of this kind of risk and mitigating and leaning into it and not shying away from the uncomfortable questions. That culture absolutely prevalent. And so to me that a lot of how we've thought about open AI and other labs may be different. I can't comment on them, but I can just say at Open AI is that we've really tried to think about we are here because we think that this technology, it's going to be the most transformative

technology ever, that we think that it does have risks that we need to, as leaders, really lean into and mitigate, and that that is core and that I think that we believe that, again, we have a lot of optimism that we can navigate. But again, it comes to this leadership, this soberness, this taking the mission seriously. And at the end of the day, like, we think that we will, like, the reason we do it is for the benefits, for the fact that it can lift everyone up. But you only get there by really engaging in all these hard questions. Trump and she are going to meet and AI is going to be on the agenda. If there's one thing that you would hope that could come out of that, and again, we could have spent an hour just talking about the geopolitical dynamics of all this,

if there's one thing that you would hope productive that could theoretically come out of their meeting, what would you like to see? I would love if we had even an intention, even in opening to discuss international coordination, maybe eventually international treaties on the long-term development of AI. And I think it is just, something that's bigger than any person, any company, any country. It is a humanity scale endeavor that we are collectively on right now. It's about how we relate to each other. It's about the economy. It's about all these things. It's about how do we build a better life? And I think that is something where there is, again, a common interest across everyone and the more that we can have

an open dialogue about that question at the very least, I think would be wonderful. Yeah, we really want to do an episode on China's approach to AI safety. And I think we will. Yeah, we'll get there. We'll get there. So many. You have to find the perfect guys. I have a feeling there's a few more AI episodes we're going to do in our lifetime. Just a few. I just wanted to add, look, I do think that American leadership in AI is absolutely both critical.

It's something that I think is also, it's an amazing opportunity that we have. Because being at that frontier, being the leader, it really means that you both get to see the shape of what's coming, but you also have so much ability to influence it. And I think that this technology, it can really lift up down. democratic values, I think it can be something that can really benefit, you know, lets us sort of, you know, have an economic revitalization. I think we can improve education. I think health outcomes, all of these things are happening. And I think that really taking the most of the fact that we are at that frontier and

having American leadership be something that continues and is amplified into the future, that's the opportunity in front of us. And so I think that international coordination, having this. conversation. We can be the leaders in that because of the position that we are in. So I read in the Wall Street Journal that you had an interest in acting. Is that true? That's true. What do you think about your casting in the upcoming artificial movie? Well, I mean, I guess you could have played yourself. Yeah, I am I am sad that I was not asked to cameo.

All right. Greg Brogman, thank you so much for coming on out. Thank you for having me. There's a lot that's interesting in there. Also a lot of like clearly unsettled questions as in things that sound good in theory. Yeah. But the gap between theory and practice still seems very big to me. I mean, the pacing question. Yeah.

How do companies that are like trillion dollar companies, capitalist enterprises where, you know, they're bitter rivals. Right. You know, there's that famous photo in India of like Dario and Sam holding their hand up together. They can't even hold hands, right? Neither of them look particularly happy in that moment. Yeah. How do you get? Getting to there where like they actually trust each other seems like a really big thing.

Yeah. So humans are not great at coordination at the best of times when they're trying to coordinate with other humans, right? And the thing I keep thinking is when it comes to AI development and safety, you're in addition to competing with other labs. you're competing in some respects against the models themselves, which are becoming smarter and smarter and more self-recursive in terms of their development. And those models, as the Hucking Face incident showed, seem to be very good at coordinating in an extremely rational, often literal way.

Oh, shoot. I forgot to ask Greg whether he approved or disapproved of the, quote, the three civilizations framing that Dwar Keshe characterized those models because that gets into a whole. other thing. You know, the other thing, too, is like, there seems to be a version of safety that essentially, like, keep the guardrails up, right? So if you go to Chad GPT right now and you ask it for some script to hack into, you know, maybe you want to hack into my website or something like that, it's just not going to. Yeah, I know. I really don't. Yeah, I know. There's nothing in there.

But like, it's just not going to, it would be capable of producing the tech, but it won't. But that's just because there's a classifier model on there. And so, okay, part of the hugging face incident and maybe what's going to change is that some of these cybersecurity classifiers, these like very like kind of crude technology that just gates type of certain types of talk, like it won't be allowed. That's it. That may be very helpful. That still doesn't strike me as alignment per se. Because in the ideal world, you would just have models that have internalized what most of us no, do you shouldn't hack. Right.

Are you going to hesitate for more than one second when you click the button? No, I'm so reckless. It's just like, give me access to the internet. Click, give me access to the internet. I'm glad we all learn something. Shall we leave it there? Sure, let's leave it there. This has been another episode of the All Thoughts podcast.

I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Jill Wisenthal. You can follow me at the stalwart. Follow our guest, Greg Brockman at GDB. Follow our producers, Carmen Roderriguez, at Carmen Armand Dash O'Bennett at DashBoox at Kail Brooks at Kail Brooks and Kevin Lazzano at Kevin Lloyd Lazzano. And for more Oddlots content, go to Bloomberg.com slash oddlots. We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord. Discord.G. slash oddlots.

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