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

22:10When 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 i why i got excited about this field and how we actually get to those benefits how we actually you know 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.

22:50Tracy Alloway:Just real quickly, since you mentioned third parties, obviously Meter came in for a few days. They produced their report. We did an episode with former OpenAI employee Myles 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?

23:28Look, 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 towards 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 of months have evolved, right? We've had the KC, which is US organization that tests models.

24:03We have the UKAC. These government third-party auditors test our models before we release them.

24:09Tracy Alloway: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 biolab 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 and whose computers they're on and who can access those computers, et cetera. And FDA approval before you go to market.

24:43Yeah.

24:43Tracy Alloway: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, OK, 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 of weeks. OK. Because the focus for, let's say, the past 12 months, 24 months, something like that has been on deployment.

25:21Yeah. Right. So you have a model. you've produced 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 Casey, 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.

25:57Now, 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 the process exactly one thing that is different is that the ways there's 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 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.

26:51And 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, OpenAI changed the architecture so that monitorability is less for chain of thought.

27:07Tracy Alloway:Neuralese. Neuralese. I would consider this article to base can 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. 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.

27:42We 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 coordinations and norms? You get to treaties.

28:12Like 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.

28:47companies, 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 kurtzwell writings yeah he basically predicted this moment at about this time just based on compute yeah growth right transistor density memory density all these things he looks at these exponentials and he's 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.

29:32You 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 create. And that I think maybe we have moved forward the timeline to this technology by six months.

30:05Maybe 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, the reason, again, that we started this place, I got very excited about the idea of AI from reading about 1950 Turing, Alan Turing's thoughts about this, about machines that could understand things that people could not solve problems that humans would not be able to. You spend a lot of time really thinking about your point of 20 years worth of thinking about misalignment and what that would be like before you even have AI.

30:51I was reading a lot of these online blogs and I started a reading group at Stripe, which is the company that I 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 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.

31:13Tracy Alloway:Your chief scientist at OpenAI wrote a very interesting blog post or piece. An alien mind, Jacob Pachocki, I hope I'm— Jacob Pachocki. Jacob Pachocki wrote a very interesting piece. And there was one line in there that I thought was interesting. You talk about the release of Asterix 6. And he said, we invest heavily along the spectrum of approaches spanned by these directions. We also see meaningful progress. GPT-6 is the first model that benefits from some important advancements we've been working on in a long time and a significantly better align than 5.6 Sol. Should the lab share their alignment work?

31:50Tracy Alloway:I mean, again, if we're talking about I understand your competitors and capability gains, et cetera. On questions of safety, if a lab makes a big breakthrough on the alignment question such that it can 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 GPT-6 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.

32:33Sure. 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're a 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, maybe there's much less sort of reason to share for those reasons.

33:11And 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.

33:43People 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 and 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 OpenAI, pretty much, right?

34:16So actually, it's funny. I actually try to do as little of the compute allocation as possible. Oh, seriously? Wait, explain more. Well, okay. So I'm someone who has spent a lot of effort to produce the compute. So I spend a lot of effort on the data center side, on the machine learning engineering side, and on the product side, have to 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, Mark 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.

34:55And 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 OpenAI. 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 standpoint? 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.

35:27Yeah, 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 GPT-55, we ran a process where we basically looked through all the different places that we had compute that we could potentially squeeze.

36:08And we were like, 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 GPT-55. 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 across 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.

36:56And so you say, hey, here's your compute allocation. We're about to release another model, new modality, something like GPT 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 efficiency. 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, Or like, oh, it turns out that 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.

37:26And 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. And 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 of really focusing on how do we up level execution and get the most efficiency down to the metal.

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40:24Do you have a theory for why some of the big, now, you know, I finally succumbed and I occasionally

40:33Tracy Alloway:read the less wrong message boards now as I can do stage in my life. There was an interesting Do you have a theory for why some of these major incidents seem to occur in evals pre-release? And then rarely, you know, when I use the latest model, I've never had to go hack into a server to 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.

41:16And 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 saying— That's right.

41:31Tracy Alloway: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.

42:27and that finding the right way of talking about that and the right way of really staying practical and grounded, 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-powered economy, I think it's 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.

43:01And 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 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, Chad GPT helped me with my back pain and my pinky. And just like, you know, these kinds of impacts are real.

43:36You know, people using Astra for, one of my friends is using it to produce CAD designs for a guest house 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 a baby with the bathwater and that you don't get sort of, that you really stay on track.

44:15To me, that is a core challenge. And I think that we all need to really work on that.

44:20Tracy Alloway:I had a reward hacking incident. Did you? 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 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.

44:54Tracy Alloway:It turned out that just on all the Satoshi numbers, all the Satoshi examples, I had left a 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 at all. And then I gave up that project. This term reinforcement learning, we're all learning about this, right? The world is working. Can you walk through an example of a sort of like misconfigured RL 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 in an environment 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.

46:03And that there's some like, you know, so 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, get points. And it just, the agent learned that if it went like backwards and like did this exact precise pattern, and it could just go around in a circle and just be picking up infinite points and nothing to do with actually completing the race.

46:33And 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're saying maximize the points, but actually the hope was maximize the points means that you're going to complete the race.

46:46Tracy Alloway:Is it your understanding? Anthropic 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. And even with 5.5 and 5.6, Like one of the things that we saw is as the models get more capable, that they do find holes in your graders, right?

47:32If 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 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.

48:04Like, 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 grader, 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.

48:35And now, of course, you start thinking about, OK, but if the AI is judging the AI, like how do you make sure that it actually stays on track? It starts to be a little bit of, you know, 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. 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.

49:14And 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 things. 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.

49:55How do you stop the models who are grading from like 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 that'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?

50:29That 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.

51:02How 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 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 agents.

51:34So 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.

51:41Tracy Alloway:Let's talk politics. So there's so much communication happening every day. And even I think just last night, there was a new piece out from OpenAI, Chris Lehan, who 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.

52:20Tracy Alloway:And then the statement last night from Chris Lehane is like, actually, we support a bunch of like statewide regulations. I'm just curious, like you specifically, you 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 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.

53:00And 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?

53:10Tracy Alloway: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' SB 315, auditing requirements in Massachusetts. I don't think anyone thinks that this patchwork of state by state regulations is the best way. 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 where some of these laws about if you have incidents 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?

53:53Well, I guess I would say there are places where I feel like there's a limit to 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 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.

54:31And 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 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.

55:04And then really also thinking about, well, what is the ultimate goal? It's not just 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's 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 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.

55:48And 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 in math. I read through it. I was very impressed. But OpenAI 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.

56:30And then allegedly open AI saw that he was trying to do it and they used a 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, 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 like Gawa, Gauss, you know, these people who are working on like, like a hundred year time horizons, right?

57:05I 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. Mathematics is something that is really for humanity 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.

57:37And the thing that we said earlier this week is we said, hey, we're not looking at this data. This data is not something that 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.

58:10Tracy Alloway: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 generations 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 race ahead faster. Well, so there is something about the premise that I do find a little surprising, which is, for example, for 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.

58:53And 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 that story I just told, like we really want to show up in the best possible way as an amplifier for 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 are 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 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 we're not in the academic citation game in any of these fields like we want to to show up in a way that like amplifies and elevates and moves humanity forward.

59:37And 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.

59:46Tracy Alloway: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 percent 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 like do you feel like 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 in the world.

1:00:33Tracy Alloway:How deep is the anxiety and how common are 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, 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 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?

1:01:13Like, why are we building in the first place? Why are we all here? Why did we start OpenAI? Like all of that, I think that that underlying question of this kind of risk and mitigating it 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 OpenAI and other labs may be different. I can't comment on them, but I can just say at OpenAI 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.

1:01:46And 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.

1:02:10Tracy Alloway:Trump and Xi are going to meet and AI is going to be on the agenda. If there is one thing that you would hope that could come out of that, 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 an 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.

1:02:47It 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 would be 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. We'll get there.

1:03:15Tracy Alloway:So many. You have to find the perfect guy. 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 democratic values. I think it could be something that can really benefit, you know, lets us sort of, you know, have an economic revitalization.

1:03:54I 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?

1:04:30Well, I mean, I guess... You could have played yourself. You missed the opportunity. I am sad that I was not asked to cameo.

1:04:39Tracy Alloway:All right. Greg Brogman, thank you so much for coming on Alamo. Thank you for having me.

1:04:56Tracy Alloway:There was a lot that's interesting in there. Also, a lot of like clearly unsettled questions, as in things that sound good in theory. 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.

1:05:40Yeah. 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 Hugging Face incident showed, seem to be very good at coordinating in an extremely rational, often literal way.

1:06:10Tracy Alloway:Oh, shoot. I forgot to ask Greg whether he approved or disapproved of the quote, the three civilizations framing that Dwarkesh characterized those models because that gets into a whole other thing. And, you know, the other thing, too, is like there seems to be a version of safety that is essentially like keep the guardrails up. Right. So if you go to chat 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. I really don't. Yeah, I really don't. But like it's just not going to it would be capable of producing the tech, but it won't.

1:06:52Tracy Alloway:But that's just because there's a classifier model on there. And so, OK, 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 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 know that 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.

1:07:31Tracy Alloway:It's just like, give me access to the internet. Click, give me access to the internet. I'm glad we all learned something. Shall we leave it there? Sure, let's leave it there. This has been another episode of the Odd Thoughts Podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our guest, Greg Brockman, at GDB. Follow our producers, Carmen Rodriguez, at Carmen Armand, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to Bloomberg.com slash Odd Lots.

1:08:01Tracy Alloway: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.gg slash Odd Lots. And if you enjoyed this conversation, if you want us to talk to more open AI people, 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 Podcasts and follow the instructions there. Thanks for listening.

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