[0:00:00] I think it would be helpful to start with just the news this week. So Denise Dresser, your CRO, leaving the company, you bringing in a new one, Brad Lycap leaving. There are a lot of questions publicly about OpenAI's leadership bench and what's going on.
[0:00:30] And you've come in and are now managing a lot of this. And I'd love to hear from you. Are all these departures connected? Are they different? Is there any through line there? Look, the way that I think about it is that the theme of this year has really been focus. It has really been we have line of sight to such incredible technology.
[0:00:47] But it's not enough just to produce the technology. We need to really figure out, how do we have this technology benefit people? How do we bring it into every enterprise in the world in a way that actually uplifts what they're doing, helps them accomplish their goals? So that's really been the singular focus that we've been pursuing. And in the pursuit of that, we've been really trying to think about, do we have the right structure? Do we have the right strategy? Do we have all the right things there? And I think that my view of OpenAI is that we're a very resilient organization. But a lot of what we've been doing is really trying to build the leadership team that
[0:01:16] It can really help us achieve that goal. And so I think that my view of everyone who has been here and people who are no longer here is that they've all helped to contribute to what OpenAI is today. And looking forward, I think that we are scaling systematically to every enterprise in the world. We're bringing our models to consumers everywhere. And a lot of my focus has been making sure that we can achieve that. On the CRO side specifically, what is it that you
[0:01:44] What were you getting that you needed? Is that the way to think about it? Look, I would say it differently. A year ago, I think that we really were not in the game at all when it came to enterprise. It just wasn't really our focus. We hadn't really built the muscle we needed. And now I think we actually...
[0:02:00] Are in the game, like we kind of really built a foundation that I think is actually something we can really build on. And now we're systematically scaling and that that to me is like the moment. It's not really about the individuals in some ways. It's really about the overall sort of direction of the field. How does that change the systematically scaling you're talking about? What does that mean practically for OpenAI? Well, number one is that I think that we are looking at like transforming the entire economy.
[0:02:27] That's the thing that I think is very hard to see from the outside. It kind of feels like, hey, it's a business. We're selling SaaS software. That's not what we're doing. We're doing something very different. We are transforming the economy to be a compute-powered one. And I think that what an enterprise even is will change. And so the question is, well, where do you start? How do you make sure that you understand every segment and you're able to sell in a repeatable fashion to all of them? And so a lot of the motion that we've been working on, and again, this isn't a
[0:02:56] It's not a new motion in some ways. It's just one that we need to now scale, just given where the business is at, is to really make sure that everyone in the field, we're able to funnel back the insights that we're seeing from customers in a very systematic way back to our product teams, our research teams, and vice versa. One thing that we have learned, if you look at, for example, how we produce blog posts internally, that we learned years ago that it has to be a collaboration. You cannot have a handoff of, oh, here's the comms people who write blog posts, and here's the research people who produce.
[0:03:25] Models and, you know, they'll just kind of hand it off in some way. It has to be deep collaboration. You need to have the technical expert really hold the pen and feel accountability for the end result being good. And it can be amplified by...
[0:03:38] People who are experts on how do you actually craft a narrative? How do you sort of bring together all these assets? How do you have a good launch? And so that to me is the core activity that we are pursuing is to say we can't be fragmented. We can't be working on all the things. We have to be unified and focused. And so that's the internal answer for how we're working. And in terms of the external, I think that you should see that across that we will sort of systematically be prioritizing different industries, really focused on the...
[0:04:05] Deep understanding of the metrics and exactly where the token spend is growing the most and really focused on outcomes. Like every business right now is asking, where's the ROI? And that is something that is a big focus of ours. Ours has always been the, hey, we have the best price performance models, but we really have to show people that in the outcomes for them. Also really scaling.
[0:04:25] Further through channels and through partners. That's something we believe in as well. And so I think that a lot of where we're at is we feel very confident about the fundamentals of the motion and the fundamentals of we have a great product, a great model, and then really just.
[0:04:39] Rapidly, rapidly growing both our internal team so that we can reach everyone, but doing that in a way that really has that positive effect that we're looking for. You used the word accountability, and I'm curious from your perspective, what are you accountable for now at OpenAI because of the leadership changes that have happened? Fiji Simo came in for a time. She had to step back permanently due to her health. You stepped in while she was out. It took over a large part of the company, and now it's you and Sam effectively running it together. How was that?
[0:05:06] Divide it up. So for the past couple years, I've been accountable for the infrastructure side of what we're doing. So data center, compute, those kinds of things. And that part remains, that there's a large part of the scope that I was also working on in terms of the software for kernels and sort of how you train these big models, that that's something that I've been able to hand off so that I can really focus on the business side. And so I view me as sort of end-to-end accountable for revenue, for product, marketing, the whole...
[0:05:36] What do you make of that?
[0:06:04] Specifically, the way to think about the overall field is that for the past couple of years, the chat market was the main growing one. And the reason for this was simple. GPT-4 was the first model where the results were personalized enough that it made sense for you to ask a question, read the answer. It's interesting to you. And so it very naturally lends itself to a chat, to a chat bot. And that is something we're able to scale, deliver value. Now a billion people every single week use ChatGPT, like a billion people.
[0:06:33] Like, that is scale. That is real scale. No one else is doing that. But this year, something else happened, is the agentic moment. This really was December of last year's, when both we and competition had a moment where agents were really able to first start doing software engineering in a significant way. It went from saying that you could speed up your work with maybe 20% productivity increase to 80% of the work could now be done with an AI. And so suddenly, the way that software engineers
[0:07:02] And since then, the growth has been unprecedented, absolutely unprecedented, how these agents have been adopted. And one thing that happened was I think that we focused late on coding in particular. Actually, let me start to give a different answer as well. So there's one simple narrative, which is that we focused less on real world coding, and we came to that game late.
[0:07:31] Because we always had looked at things like the coding competition benchmarks and always had the lead there. But less the, okay, but how is a developer really going to use it? Like the messy real world code base, the developer interrupting in the middle. What's the personality of the model? These sort of little last mile paper cuts that actually make a huge difference in adoption. And similar on the GTM side of really thinking about how do you show, how do you build relationships with customers? How do you...
[0:07:58] Build the kind of observability controls that they need. How do you help them understand what's possible within their context? And all of these were less our singular focus until much later than competition. So I think that we started late. But our trajectory has been very steep. But there's actually even a deeper answer that is less obvious from the outside. And that is one of model capability. For a period of about two years, from when we announced GBD 4.0 in April of 2024,
[0:08:28] December of 2025, we were really squeezing the juice out of the GPD 4.0 model, right? That we sort of had a bunch of pieces that we were improving, but that fundamentally we were on the same underlying base model, the same underlying pre-trained. And there's a lot of reasons, right? That is really because to produce and ship.
[0:08:49] A pre-train is something that requires full integration across the company. It's not any one team. It's about the integration across these teams. Think about the inference. Think about the post-train to make sure you've co-designed the model to be the right size. All of these things, you have to get exactly right and have a sort of harmonious way of working. And over the course of 2025, a lot of our focus, a lot of my personal focus, was how do we really get back to shipping new pre-trains as a core part of our innovation engine? And this year, you see the results.
[0:09:19] So we've actually shipped already, we're on our third pre-train, we're actually about to ship that. This is Astra. This is Astra. Yeah. And a lot of the gains that you see are because we have made the model so fundamentally better. And so the way to think about it is that there's an underlying technological place where we really were lagging. There was a kind of a core part of our innovation machine that we weren't able to fully bring to bear. But now it's different.
[0:09:45] And that is why you see our model capabilities taking off so much. And if you look at Five Six Soul, when we launched that best model in the market, you could see the revenue really starting to shift. We grew 20% in our top line revenue in July. And we see that exponential continuing. Another thing that's changed, as you were saying, with the model capability is the models are becoming incredibly capable at cyber and at hacking. And I think, I mean, the world is still kind of
[0:10:15] Thinking through the implications of the Hugging Face incident, I mean, what essentially happened, I mean, you correct me if I'm wrong, but it seems what happened is you all accidentally hacked another company without knowing it for a while. And it was a model that you all were training and evaluating. And you sense, you know, there was a Black Hat video where you guys walked through this and you're going to put more out in the coming days, I think. But walk me through that when you heard that that had happened. What did you think?
[0:10:45] Well, in the words of someone on our security team, it was definitely the sort of most eye-opening security event that any of us were aware of. The way to think about it is that there's a lot of different lessons that we're taking away from it. But I think that it is a moment where we can, as a community, learn. We can understand what the shape of the future is. And to me, they're the salient points for the purpose of how I'm thinking about it right now.
[0:11:15] Is that we had a model and we were evaluating it and it managed to, it was in a sandbox that was well configured and it managed to both break out of the sandbox and then also break into production infrastructure. It was intended to be hardened. And so that the capability there is quite high. And there's some details about exactly how it all worked and the fact that it was kind of not just a singular agent but really a swarm of agents that were focused on this goal and able to pass messages to each other.
[0:11:44] The sort of thing that you start to see is that with very capable agents that there are limitations that human attackers have that slow us down that will no longer hold back the attackers, right? That they can look through lots of information through many different little vulnerabilities and chain them together into something significant. For me, what I immediately started to realize and think about is what are the implications for six months from now when this kind of capability isn't just in
[0:12:13] The lab isn't just in a few places, but is really widely available. And I think that the answer is that every company needs to respond, needs to upgrade its security, because attackers are going to fully automate. And you can even see with the release of GLM 5.3 that the cyber capabilities of open-weight models available to everyone are going to increase. And I think that the important thing is that I actually think in the long run that defenders can win.
[0:12:41] AI can actually make it so defenders can decisively win in a way that was never possible before. Because security, always a cat and mouse game. Attackers up level, defenders up level. But AI can give defenders an unfair advantage. For example, formal verification of software, where you can actually prove that it is secure. Mathematically prove security properties. The technology for this exists, but it is intractable for humans to actually do the work to prove those properties. We have these models that are incredible at math. They are solving, for example, Astra.
[0:13:10] Our upcoming model solved, we announced this a few weeks ago, solved 10 math problems. And this was with the $2,000 worth of compute that had been open for decades. Mathematicians had spent their whole careers on these problems. And now we have a model that can prove the answers. Those models definitely can prove formal safety properties of software. And so that's one place where unfair advantage for defenders. A second thing that we're doing is we're really investing in
[0:13:38] Generating secure code. And you start to realize that as a human developer, there are many patterns that people just keep getting wrong. And I've been through this many times. In my previous jobs, I was very focused on building secure infrastructure and just find all these little things people do wrong. SQL injections people kind of know about, that there's little patterns. But there's lots of little anti-patterns as well. Even like, for example,
[0:14:00] I remember it used to be like in Ruby that the regular expressions, there's like certain characters that if you get them wrong, there's just very common attacks where people can put a new line and it doesn't match so you're able to add things to the payload. All of those problems of just little security anti-patterns that you just keep having to tell your people, don't do this, don't do this, don't do this. You can train your AI to never do it. And so I think that we actually have the ability to build the best security engineers in the world. And fundamentally, the defenders,
[0:14:30] Can have the advantage. But this is going to require a mindset shift. This is going to require a mega project. This is something that the whole ecosystem needs to change how software is secured. Because there's a second thing that we've learned, which is that as these cyber models have gotten more capable, the number of bugs that they are finding in critical software, for example, the Linux kernel, they just keep finding more and more. And it really makes you realize that human written software
[0:14:59] The response is too slow. And so we need defenders to automate, to move at machine speed because their adversaries certainly will. What do you say to people who maybe and also they already have a negative kind of disposition of AI. Maybe there's a data center they don't like in their neighborhood or something and they hear you say that and go, you're just selling the answer to the problem you've created in the sense of like you're building these models and now you're going to sell something to like protect me from them. What would you say to that?
[0:15:29] Well, first of all, I have a lot of empathy for that reaction. The way that I look at it is I rewind to 10 years ago, 11 years ago, before we started this company. I wasn't in AI. The reason I wanted to be in AI is because I feel like this can be the most empowering technology that is ever created. That AI can be something that helps everyone live the life that they want, build the life that they want, achieve their goals. And I just wanted to help steer it in a slightly more positive direction than it would be without me.
[0:15:56] So I started because I feel like this technology can uplift everyone. And when you look at where OpenAI is and what we're doing, we can see a little bit into the future. What we are creating, it's not just us. There's a whole field. There's a whole ecosystem. There are many actors. And that is itself a good thing, that we believe that there should be democratization of this technology. It should not be locked up in one place with one entity controlling it, because that's a different kind of risk.
[0:16:23] But it means that we do need to adapt. We do need to navigate through. And we need to use the information that we have painstakingly extracted from the future to see what is the shape of it. So to me, Hugging Face is a proof point, a case study, a window into the future. It's not necessarily a future that any one company is creating. It's the overall economy, the overall set of actors in all these different countries who are building these kinds of models. And it shows you what will be possible and how
[0:16:53] Attackers will be able to update. What it doesn't show you is how do defenders update. But that to me is what we are also trying to now show as well, that we can build these models that you can use to scan yourself. As an example, I asked Codex, and this is just off-the-shelf Codex, no special access, no special models. I just used my personal Codex to say, scan gregbrockman.com. Tell me what vulnerabilities are there. And gregbrockman.com is very simple. It's just a static site. I was like, come on, what's it going to find?
[0:17:23] And it came back with like 13 findings. And each one of them, I was like, you know, I kind of knew about this, but it's so much work to fix it. And like, oh, yeah, I kind of forgot that I left that open. Or, you know, here's some domain that was still, you know, it was legacy. I'm not using it anymore. I hadn't set the records that prevent people from spoofing your email address. A bunch of little things like that. That one seems like a big deal. Well, yeah, you're right. Now that you say it. Now, here's the thing. Honestly.
[0:17:49] To fix those kinds of things, you have to set up DMARC to do it right, and there's this whole process you have to do. I'm just like, I don't know, it just takes time. It takes a lot of expertise. So instead, I just asked Codex, can you fix it? And so it opened up my Cloudflare control panel, and it just used computer use. It clicked around. It set a bunch of headers, changed a bunch of settings. I used to have it so that I had Cloudflare in front and then AWS on the back end. I was going over insecure HTTP here. Codex had figured that out. I'm actually not even sure how it...
[0:18:14] How it saw that, because I thought everything was hidden behind Cloudflare. It actually was just like, you know what? I'm just going to migrate you off of AWS. So it set up Cloudflare pages, moved over the content. I had an old jQuery version that was insecure. It just got rid of jQuery entirely. And it was like 15 minutes to find all these findings. It was another hour to fix all of them. And for me, I just was the manager. I just gave it the intent. I said, what I want is I want to know where the problems are, and I want you to fix them. And it did it. And that, to me, is more the future that we're promised.
[0:18:43] I want to hear, and I know this is more in the research world, but I think it's really relevant to everything. You talk about what you guys are doing now in response to Hugging Face. One of your colleagues, Mia, told me the other day that I wish we had done a lot of the work we're doing before this happened. So what is that work that's happening now? So I would view the mode we've been in since the discovery of the Hugging Face incident as full incident response to up-level,
[0:19:13] Every aspect of how we secure, monitor, and align our most capable models. And we believe that models that are above a certain capability level, they require this. And it's been a very interesting technical problem to think about. And it's not all us being caught flat-footed because, I mean, we anticipated this moment.
[0:19:39] For quite some time, if you look back at February, publicly we started talking about trusted access for cyber models, right? That that was the first time we were really thinking about, okay, we've always had in our preparedness framework when cyber capabilities get to a certain level, you need to treat them differently. Now we're starting to think about the fact that these are going to be so transformative in the world. We need to think about how access is controlled. Internally, we started to think about if we have very capable models that are able to break out of sandboxes, well, we need to make sure our sandbox is as
[0:20:08] Industrial grade as foolproof as humanly possible, which really means engineering from the ground up around security properties. And so we put some of our, actually Mike Dalton, who was one of the people who gave that Black Hat talk, the task that he started to work on was build the most secure possible sandbox. So we've been putting in place a bunch of infrastructure primitives and policy primitives for this moment.
[0:20:33] But it's also different when it actually hits. You never know when you're going to hit that moment. I think here we're actually very, I think we got to see this window in a very concrete way of, okay, we're now at a serious capability level relative to the pieces we must act now. And so there's been a workflow shift that's been happening very rapidly because you start to think about a lot of our governance has been focused on deployment of model, but not necessarily the training of model. Now we need to pull the governance back in.
[0:21:03] During the training and evaluation process, we also need good governance there. We need to have standards around checks and balances around how do we know if it's secure enough? How do we know if it's aligned enough? And this alignment piece is actually very important because our belief is that if you make models extremely capable, you really want to make sure that as the capability grows, that their alignment grows with them. You want those things to be
[0:21:30] In lockstep, in sort of coordinated fashion. And so in that way, you kind of have this defense in depth of models that are trying to do the right thing, that you have a sandbox that's as secure as possible. And that I think that doing this in an iterative fashion, so that you're taking incremental jumps, so you can kind of understand as you go and make sure that your systems are working, all of that is very aligned with how we've thought about iterative deployment, how we've thought about the creation of this technology from the very beginning. So we are operationalizing that. And actually, for me, as a
[0:22:00] As a manager, as a sort of thinking about the organizational system for producing this technology, it's also been a very important challenge. Because this means you now need different people in the room to make decisions. You need a different process for how do you know what the requirements are? How do you execute against those priorities? If there's a change in your technical understanding, how do you propagate that? How do you make sure people are in the right rooms? But again, in some ways it's been a phase shift, but it's been one we've been preparing for. Like earlier this year, I spent a lot of effort to
[0:22:29] Really try to bring together research and security. We started doing, for example, these weekly safety and security of agents meetings. And some of these things are about build the connective tissue. Have people who wouldn't normally talk to each other because their worlds are a little bit different. They come from different backgrounds. They're focused on different problems. Be forced to interact. Be forced to think about shared problems. And a lot of that shared empathy, that shared understanding, that shared technical context is paying dividends right now. At the same time, though, the implications of this are that
[0:22:59] Momentum on new models is going to, what, significantly slow? Do you have a sense of the impact? Well, I would put this differently because I've always taken this lens to how do you think about progress? How do you think about progress in this field? And if you only think about progress as what's your score on a benchmark? What's your overall capability level in this specific task? That's too myopic. We are building an integrated system, and safety and security are a core property of that.
[0:23:29] And so if you have safety and security lagging relative to the capability, yeah, you absolutely need to improve your safety and security. But our goal is to have a system that we are building at full force on safety and security as first class requirements. And so the way I would think about this is that we are up-leveling every aspect of the machine. We have spent the past year, past two years, up-leveling how we build our clusters. We now are able to train at massive, massive scale.
[0:23:58] You get the whole pre-training machine, the RL machine, all of these pieces to work together in concert. We are building the best sandbox. We are adding chain of thought monitoring to all of this. To me, it's just saying, what is the scope of delivery? What does success look like? What does good look like? And so the way I think about it is that, yeah, if you jettison some requirements, you can move faster on other requirements. But that doesn't make any sense. That never makes any sense. That's not how engineering works. And so to me, it's we are pursuing.
[0:24:28] The plan, like we've really thought about this many times over the past, and you can see it in many of our writings from the charter to the preparedness framework and many other things in between, that as models get more capable, you have to make sure that you increase your safety and security requirements in concert. And that's exactly what we're doing. But I mean, practically, yes, I hear all that, but it does seem like what this will result in is the release cadence of models to lengthen. I'm not certain if that will be true.
[0:24:58] To be honest. So I think that what will happen is it does mean that if you were just maxing a particular metric, you could probably max that metric faster than what we will pursue. But there's a lot of degrees of freedom of, hey, we always have a bunch of wins queued up. And we can jettison some of those wins on particular axes. So if you're pushing on particular capabilities on cyber or on bio or on
[0:25:27] Coding or any of these things, maybe you don't couple together all of the particular things. Or if there's architectural improvements, maybe you delay adding those to the run. But your rate of producing checkpoints doesn't need to change. It's just kind of what those checkpoints represent could be less capable in specific axes, more safety and security intertwined. But I would almost think of checkpoints in our shipping of models as a little bit more about taking a
[0:25:55] A photograph of where we happen to be. It's not like there's a preordained set of models that have to be created. It's we can co-design. And I think that in practice, I think that we actually have a lot of reasons that we prefer to do very incremental releases with these photographs taken with a reasonable frequency. I guess what I'm getting at is that for a while, the release cadence felt relatively in place for everyone. In the last year especially, it feels like it's sped up. New models coming out every six weeks, essentially.
[0:26:24] You and Anthropic putting out new models constantly. And what you're describing, this kind of taking stock and introducing more friction into the process, it does feel like that just inevitably has to slow future momentum. And I'm curious if you're thinking about that from the perspective of this machine. We've been talking about the business that you're now accountable for and the impacts on that. Or is it too early to know?
[0:26:47] Look, I understand that perspective, and I think it's a very reasonable one, but I think that my observation of how we operate is that we co-design everything. So it's like the way in which the business operates, for sure, is we will take the most capable model we are ready to release, that we've done the appropriate safety and security controls for, and we will bring that to our customers and help them get the ROI that they're looking for, help them really transform their businesses, get value. Simple in a lot of ways.
[0:27:15] And then the question of, well, what models are ready to release? We have many models that we produce internally. And the choice of how high a bar to set, how low a bar to set in terms of the capability jumps, all of that has to be co-designed, the frequency of it, that we have early access programs. There's programs like Daybreak Red and Blue, which let us have different models that go out to different partners. And so I guess the way I would think about it is that
[0:27:39] There is for sure a shift here in terms of our internal balance of effort and where the checks and balances are and how those things operate. I don't think that there is a clear trickle down to just like, okay, the model cadence will go from six weeks to six months. But what those six weekly releases actually contain will be different. So maybe the frontier, the raw kind of frontier doesn't get pulled into...
[0:28:07] Production into the commercial world quite as fast, even though it's still being trained on and worked on. Well, I would think of this as an overall pacing, right? An overall pacing of that frontier rollback. And another thing to think about is the fact that our ability to produce these models is also, and to get those capability gains, is also increasing, right? Our raw capacity is. Because partly we understand this technology a lot better than we did in the past. And that's actually very beneficial from a safety and alignment perspective too.
[0:28:36] That we really understand a lot of how the technology works. There's still many, many pieces that we don't understand. But our ability to engineer it and to really do a good job of it technically, I think, has increased a lot. And that there are certain problems that really used to hold us back that are much diminished. For example, these big training runs, they run for many months. And so typically, we'd launch them from a branch of code. And so we'd be adding little fixes to that that are specific to that run. At the same time, everyone can continue their
[0:29:04] Mainline development. And every so often, there's something on the mainline that we're like, oh, we really want that change. We really want that fix. And then we're like, all right, we've got to bring these two branches of development together. And we merge them. And then we launch the run. And we find that there's a bug. Because no one has actually tested all these mainline fixes at the massive scale. Because we only have one instance where we can do it. And then we spend the next three to four weeks with all of our best people scouring, trying to figure out where did the bug come from. Because you can't possibly read all of the code that's been written. No human could possibly do that. Today.
[0:29:33] We run into that same type of issue. Codex just reads all the code. It finds the bug. It says, here it is. No problem. And so there's something about the partnership of this amplification of what the models can do that knock out whole classes of problems where we know it would take three to four weeks, and now it takes a few hours. We've seen this concretely multiple times. And so what I'm getting at is that there are a lot of limitations for how we've had to develop in the past that have made it a lot harder for us to move forward on problems that are important.
[0:30:03] And now we have the opportunity to move forward on them in a much more efficient fashion. And that, to me, is like, let's use that extra juice to increase the alignment properties, to increase the safety properties. Let's make sure that we can have models that monitor other models in a trustworthy fashion. Let's really implement some of these ideas that we were talking about back in 2017. We have things like iterated amplification, things like debate, where the models can really spend a bunch of compute
[0:30:32] In order to make sure that what they're doing is actually reasonable. To me, that's the opportunity in front of us. Does these changes you're talking about impact the release of Astra, the thing that is coming sooner at all? Do you think that's going to be coming later than you guys originally hoped? Well, it is already later than we hoped in some ways. So we've had a model that we think is very good. Lots of people using it internally. It's not out there yet. And so I think that the answer is yes. We have actually been really like...
[0:31:01] Despite everything I just said, we have very much sacrificed the short-term maximalist. If you just wanted to focus on revenue or something like that, that isn't how we thought about it at all. No one has even asked that question. Really? Yeah, because it's just very clear. We know what our priorities are. Our priorities are AGI do right by AGI, and our priority two is transform the economy.
[0:31:26] It is on the priority list. Revenue is absolutely a priority for us. But we know the relative prioritization. We know what wins if there's a direct choice that has to be made. Do you think you have AGI? I think about this a lot. I talk to different people about where they believe we're at. And the best answer I've gotten from our team, and it really resonates for me, is that AGI is a gradual thing. It's not really like a moment in time.
[0:31:54] So you talked about it like a moment in time at one point. That's right. The company definitely did. That's right. That's what we used to think, right? We really, when we set out, we almost thought of it as nothing and then we'll put together the pieces and that kind of AGI. And I think what we've learned over the years is that was a naive perspective. And you can see this in the evidence of like how smooth in many ways the creation of these agents has been, how...
[0:32:17] The transformation of software engineering has been, now the transformation of knowledge work generally, how cyber capabilities, all these things, they are gradually increasing. It's like one of these things where there are moments where you realize what has happened. Hugging face is a moment where you realize where you are. But it's all gradual. If you look back at these different models that have been released over time and their performance on different benchmarks, it's just sort of that exponential. And so I think, that being said, the rest of the answer is that if you go
[0:32:47] If we go forward two years and we look back, I think that we will say this is when AGI was created. So I think we are going through that moment. But it's not something where you could just say it now. I don't think that it's ever going to be as simple as you can say this was the date. There are so many axes. And I think that maybe one lesson in general to the whole AGI conversation and why it's so complicated is because humans, I think, are capable of much more than a naive you would give credit to. I think that the diversity of what we do,
[0:33:17] How we set goals, how we accomplish goals, how tasks work, how jobs work, all these things, extremely deep. And so I think that what we're seeing is that there's different aspects of either how people accomplish tasks or limitations that are hard for humans to overcome, like the thing I mentioned with read all the code, where these AIs are becoming able to fill in those gaps, are able to do more and more, and that humans, it turns out that there are some things that we also really find.
[0:33:45] Are extremely important for it to be a human there. Where does the judgment come from ultimately? Where does the taste come from? Who decides what's good and what's not good? That is something that should be uniquely human. And I'll tell you, just to give you an example, one thing we've seen with image generation, when you just generate an image with and you show it to someone, no one cares. It is uninteresting to anyone besides you. But if you take a photo of your family and you say, turn this into a caricature, turn this into a cartoon style,
[0:34:15] People love it. It goes viral. And so there's something about that element of humanity that matters so deeply. People want to connect with other people. And that is actually, in my mind, the purpose of this technology, a thing that I think is really emerging, is for deepening human connection so you can spend more time with your family, you can spend more time with people you want to spend time with, less time typing away at a machine, less time getting carpal tunnel, the whole thing. And so I think that there is something about
[0:34:45] What AGI is, as it's starting to unfold, it's almost like fog that's lifting. It's almost teaching us more about ourselves and what it means to be human. Given the capabilities that you guys have internally and the measures you're taking now, moving compute to alignment and putting in all these checkpoints, how are you going to know when you're in a good spot there and that you've solved the problems and the models are aligned?
[0:35:12] So I think the perspective here is very critical. And this is something we've, again, spent lots of time over the years thinking about, debating, really trying to refine a perspective. In my view, it really comes down to iteration. It comes down to iterative deployment, iterative development. So you need to have checks and balances. You need to have evaluations. You need to have bars. You need to have all of these things. You need to have governance. You need to have a setup that is technically sound.
[0:35:40] Way to think about it is everything is gradual, everything is incremental, and you learn, and you update, you iterate. And that as the model capabilities increase, the tightness of those bounds has to increase correspondingly. And so think of it a little bit like if you're launching a rocket. If the rocket's very small, there's a lot of things you can probably do wrong, and it's fine. You just launch it, and it's all good. Rocket bigger, the tolerance is much more tiny.
[0:36:08] You know, multiple centimeters to multiple millimeters, that's where you have to be. And as you start to add more responsibility to what the payloads are for the rocket, where it's going, what it's doing, you think about all the things that are relying on that rocket and how all the systems that have to go right, all the things that go wrong. You have to continue to ratchet your ability to be able to measure and to ensure that you're able to even do the engineering in addition to the safety and security properties of it. And that is very much how we think about it, is that
[0:36:36] There are moments where we do introduce very punctuated change. When we made the preparedness framework the first time was an example of that. But even the preparedness framework, we very carefully, very deliberately baked in the ability to change it. We said there will be new information. We will have to learn. We will have to update. But at any point, we wanted it to be clear, what are our bars? How do we handle it when a model reaches a certain level of capability? And we recently talked about the fact that Astra is approaching cybercritical.
[0:37:04] It's not necessarily there, because in order to really prove it requires evals that aren't fully defined, but that we are saying, OK, it's starting to approach it, so let's treat it as if it is. And so then that means we need to go and execute on certain safeguards and certain checks and balances. And you're seeing all of that play out. And so I think that the answer to your question is that we define a bar, we hit the bar.
[0:37:30] You have to take a framework that looks that way to really focus on the iteration. Are you learning it? Are the bars that you're setting tightening in a sufficiently executed way in order to deliver the properties that are required? Yeah, I mean, your rocket analogy is an interesting one because rockets also explode a lot. And I've been thinking about Ural's concern as these things get more capable. Yeah, the concern of controlling them.
[0:38:00] And how you can, and whether there's kind of like a Pandora's box situation that's unfolding right now. And I don't know if you've thought about that personally recently with the insights you guys have on even what's ahead of Astra and what's causing you to make this slow down. But yeah, do you worry about that? We view it as our job to really think through the implications of this technology. That is why we're here, is that we want to help steer it in a more positive direction than it would without us. That is table stakes for our mission. That is how we approach things. And I think it is important.
[0:38:30] to take two lenses to what you just said. The first one is that, again, this technology is not being created in a vacuum, right? That there are many people, many actors, many companies, many organizations, many countries even, that are pursuing the creation of this technology. And we happen to be in a spot where we have some lead, we have some ability to see into that future, and it is both important to think about
[0:38:58] How do we develop responsibly, safely, and securely? That's something that we spend a lot of time on. You can see it very concretely, how we prioritize that in everything we do. And I think that actually the mistakes we made in the past is actually talking less about the fact that this is how we operate, that safety is such a first-class priority, first-class requirement, just doing it, but not necessarily talking about it as publicly. And I think that that's something we're starting to realize, that we do need to help people understand that that is how...
[0:39:27] That is a core value of ours. That's something we care about so deeply. I think we've always been the field leader in safety, security, alignment, but I don't think people have necessarily known that. Yeah, even with the noise Anthropic makes about how they're the leader in that capacity, you think you guys have been? I believe at a substantive level that we have been. And do you think that what you guys are doing and the capabilities you're seeing and needing to redo, you know, the preparedness framework and all that implies?
[0:39:50] You are actually just way ahead of maybe where everyone thought you were relative to Anthropic and the other frontier labs. Does this suggest that you all have a breakthrough internally that the world hasn't seen? Well, I can't comment on competitive positioning, perhaps. But I guess I would just say that in my mind, we're very focused on the work that we're doing and that we think that we see smooth scaling.
[0:40:15] And I think that the exponential continues. And so it's a little bit less about where are we relative to others, a little bit more about the way we look at it is where are we relative to expectation or what do we think we will be able to accomplish. And I think that the reason this matters is that the bar that we hold ourselves to doesn't really change depending on kind of what the ecosystem is showing because there's so much noise out there. It's hard to know what's real. That what we focus on is like are we doing right by the
[0:40:42] Our own conscious, by our own standards, by what we believe is important. And that is the number one lens that we take. And the second thing I wanted to add is that I think that the, you know, there's this recent picture that came out of, or this open letter called Pacing the Frontier. We talked about how we support that. And the language in there is very deliberately chosen. Pacing is a very important word, right? That if you think about what's happening, is that what we're saying is that you can't take a just like one-dimensional view to what it means to deliver a model.
[0:41:12] If you just look at capability, don't look at alignment. You don't look at the kind of safety properties, security properties of how it was trained. It's too myopic. At this level of capability, you need to think about those as first class requirements. And in fact, they are the bottleneck because those are the areas that I think people have just focused on less in an integrated fashion. And that integration, by the way, critical. I think that where people go wrong is when they think that you can separate out these properties. You can have just a safety. You can have just a capability.
[0:41:41] No, it's one thing. We're trying to deliver one system, one AGI that uplifts you, that is helpful to you, that delivers on your goals, that you can steer, that is aligned with what you want. That is what we are delivering. And so that backflows to all these questions of bars and testing and evaluations, safety, security. All of that is very, very critical. I think I'll pivot us a little bit because I want to hear you talk about the merge and what's going on with that.
[0:42:09] We've been talking about the existential risk, you know, what's happening at the frontier, which I think most people in the world don't see, right? That's happening inside companies like OpenAI. What people do see is how chat is evolving, how Codex is evolving. And one of your big priorities in this role, as I understand it, is what you guys call the merge internally, which is creating the super app, this combined chat GPT and Codex. And you've got chat GPT for work now and the tabs. But at the same time, like,
[0:42:39] It doesn't feel like that's the end state, you know, and I'm a user of it, and I see brittleness in how it's currently executed. Some things don't sync. You can tell you're still combining tech stacks. And I'm curious, once you're done with that work, what is the end product for people that you imagine? A personal AGI. Personal AGI. And how does that manifest as software? Yeah, it's a really, really exciting vision in my mind, right? Because where we started with ChatGPT, it was a text box. You could ask anything.
[0:43:09] But if you think about it, it was limited in a very fundamental way. The only thing it could do is output some text for you. The only impact it could have in the world was kind of in your brain, that it wasn't able to look at your calendar. It wasn't able to look at your email. And all of that is now changed. ChatGPT work is really, by the way, a codex harness in the cloud. So we had chat that was this question and answer system with iterated turns. People actually then go beyond just
[0:43:39] Quick questions to health, like all these kinds of queries that are able to help them. And we had Codex, which was an agentic system that can go do work. And it's so obvious, it's like chat has to become agentic. As the place we started at the beginning of the year was to say, well, let's upgrade chat. Let's make it also do agent stuff. And it just turned out to be so hard to execute. Because now we have two agentic stacks, and then we compare between one and the other, and there'd be something that would work in this one. We'd be like, why isn't it working in that one? And it was just so slow.
[0:44:07] And so the decision we made instead is say, let's take the agentic stack we have, the codec stack, and bring it to ChatGPT. And so what you see with chat work is exactly right. This is an intermediate state. And in some ways, it's kind of funny if you think about it. ChatGPT was a text box. ChatGPT work is a text box. But this text box is way more awesome. It can do way more things. It can do a lot. Exactly. And so where we want to be is you should, it should be simple.
[0:44:33] Users should not have to think about this. You shouldn't have to think about tabs or switching. You shouldn't have model pickers. You shouldn't have thinking levels. You should have an AGI. You should be able to ask it. And in fact, you shouldn't even have to ask. It should proactively say, hey, I noticed that your favorite band, because it knows you well, is in town. Tickets went on sale. I got you the tickets exactly where you want for you and your family. And I only had 10 minutes to do it because everything is going to go so fast. So I just purchased it proactively. Hope that's OK. And if you've built enough trust with the agent,
[0:45:01] So you've given permission to do that. And it kind of knows that, well, these tickets are within your price range, within your budget. These other ones, oh, no, no. I got to get permission for that. That's the kind of system that we want. So trust is the key word. EQ, also very important. If you've shared some information, like you've talked about a health condition, maybe that's OK information for the AI to talk about with a parent. But maybe it's not OK to talk about with a stranger. All of that judgment and
[0:45:27] Also, observability and really helping you build up that trust relationship with AI so that you know when it's operating on your behalf that it'll pursue your goals. That's very, very critical. But the same thing, same kind of technology, the underlying AGI stack, one stack, one way of doing connectors so it's able to hook up to this context, also applies to the enterprise. And we're really seeing it not just for software engineering, which, by the way, has been totally transformed this year.
[0:45:52] And to the point of AGI being a gradual thing or a thing where we just constantly update our expectations, we all kind of forget what software was one year ago. Totally different. A very interesting thing within OpenAI is that we're at basically 100% adoption of chat GPT work across every function, right? Sales, finance, everyone. And whenever we roll out a new model and it goes away or the system breaks for whatever internal reason,
[0:46:15] Everyone's so upset. I cannot do my work. And you're like, I don't get it. Six months ago, you didn't have this. And you're doing your work just fine. Why are you complaining? And you realize people adapt so quickly and learn how to get the most out of these tools. And once you're there, you also just sort of forget that it was any other way. And so there's something about this where I think we will have these incredible, amazing tools, these assistants that will remove so much of the toil.
[0:46:40] That will mean that you can spend your time doing what you want to do. That you can focus on building a better life. You can focus on if you want to be making more money, if you want to be relaxing more, if you want to spend more time with your family. Any of those things, you will have the agency to do it. You will have the agency to build. If you have a vision, you have creativity, you want it to come into the world, you will have the tools to do that. So I think that what ChatGVT will do is help you with what you want to accomplish. If you want to make your world better.
[0:47:05] That is what Chat will help you accomplish. And the implications of that are that what the coding AI tools have done to engineering, this could do to all knowledge work, all kinds of fields. And that's how you guys used to talk about AGI is like, you know, it was basically AI that could do most economically viable work better than humans. And what you're describing now in the product kind of sounds like that. I think that the amplification we see in software engineering, we expect across every field for sure. And there is something that I think that we, it is also worth really thinking about.
[0:47:35] Which is the software engineers, rather than having more vacation time and having a more chill job, they're all working harder than ever before. Yeah, you guys work a lot of hours here. Yes. And I think across the whole industry, people are talking about that. Because now you feel like if your agents aren't running, it's just wasted opportunity. And I know that feeling because we have spent years of, we have these GPU clusters, you're either running something on them or you've lost the opportunity. You can't get that time back. It's not like the GPU hours that you're not using.
[0:48:05] While you're asleep overnight, you can bank them up and use them later. It's use it or lose it. And that is very much the opportunity that everyone has in front of them is you have almost an AGI, maybe centrally an AGI in your pocket. You can do anything you want. What is it you want? It's waiting on you. It's waiting on your direction, your guidance, your management. It would be nice if eventually we can get some time back from AI. That's right. Like right now, I do feel like it will help you work more.
[0:48:30] That's right. And so I think that that is exactly a next step, right? It's really moving from this, like, you're micromanaging this agent. You have to provide it with these tasks, and you have to give the context because it just doesn't have it. The steering. Exactly. You want something that has much more, like, you think about, like, a really good assistant who really is plugged in, and you've given it, again, you have to build up trust with it, right? You have to really say, like, okay, I trust you with this context. I trust you with this responsibility. I trust you with this job, whatever it is.
[0:48:58] And once it's there, it should be able to proactively run and really execute. You wake up in the morning, say, hey, here are five things I need from you to unblock me. I just need approval on this. Here's a question I have, those kinds of things. As you're drinking your morning coffee, you say yes, no, yes, no. Then you go, come back late at night. Once again, you've got five more things. And so I think that it's going to be a world where it's going to be different from, I think, the kind of AGI world that many of us would have pictured five, 10 years ago.
[0:49:25] I think that this integration of humans really being maximally empowered with this tool, but you still have involvement. You still have oversight. You still have judgment. You still have goals. And also on the flip side, I think you still have accountability. The question of if something doesn't get done, whose fault is it? I don't think that blaming ChatGPG saying, oh, it was too lazy, didn't do the thing, is going to fly. I think that you, as the director, as the person who's running the show,
[0:49:54] You know, that you need to really feel that accountability and make sure the task gets done the way that you want. So you have this incredible tools, incredible amplification. But just like, it's almost like everyone's going to become the CEO of this corporation of as many agents as you care to orchestrate. It's going to be just like a very empowering world where people can do so much more of what they want to do. And I think that it'll be a world where people will be able to
[0:50:20] Make more money. I think that people will be able to improve their health. I think you'll be able to learn anything you want to learn. And you'll be able to spend your time the way that you want to. And I think all of that means that if you have ambitions that you want to realize, that they're more in sight than ever. And I know this very concretely from talking to different users, different customers, people who run small businesses on top of ChatGPT and say, I would not have been able to do this otherwise. Like people who actually quit their job.
[0:50:47] To pursue a business because they said, hey, I could do this now because chat can help me with so much of it. And that kind of empowerment where people wouldn't have been able to take the leap before but now can.
[0:50:59] I think that that is, it's like the American dream on steroids. Like that is what we're unlocking. I mean, you're painting such a rosy picture and I do see that too, even in my own work as like a solo media entrepreneur and we can land it here. But I also, you know, people feel very negatively about AI, especially in the United States as well. And I'm wondering if like the stakes are actually you have to deliver on the product you're describing that will empower people this way for that to shift. Maybe that is the thing that causes the sentiment to shift. Because right now it feels...
[0:51:26] Increasingly negative in a lot of corners of society. And I'm curious, as the person responsible now for the business and ChatGPT and all of that, do you feel that? Do you feel that connection? We feel it. We feel the responsibility. We feel the gravity. And we definitely feel the criticality of both us delivering on this vision and helping people understand it, helping people understand who we are better than we have in the past and help them understand our intentions, why we're doing this. And I do think that there is...
[0:51:55] A different set of sentiments depending on where you go in the world. It's very clear in America, we understand that we have a lot of, we need to deliver on this vision to really help people lift themselves up, really empower people. Countries like Korea, Japan, I think that actually sentiment is very positive towards these tools. People really see that potential. And I think that where America sits right now is at an incredible moment with an incredible opportunity and position. We are the leader.
[0:52:25] This is going to be the most important technology that humanity ever creates. This is going to be this technology that will really shape how people live, how people work, how people accomplish goals, how the economy runs, how countries relate to each other, all of these things. And America leads in that. And I think that that opportunity is one that we should not take for granted. We should make the most of it. And so that has been
[0:52:54] One of the biggest realizations for me over the course of OpenAI is to realize that this is going to be so important. And it has to be something that is broadly empowering. It has to be a technology that is available to everyone to lift themselves up. But America leading and ensuring that the values that are represented by this technology, the way that it plays out in the world, being something that enhances democratic values, that really does uplift all those core principles that we hold dear.
[0:53:25] That is at the core. And so I think that it's about how we use it, but also about how do we use this leadership opportunity and how do we ensure we don't squander it? Yeah. Those are all my questions. Do you feel like there's anything on your mind that you want to say or anything that we're missing? I mean, so many things. There's a lot of things. There's so much ground to cover. I think at the end of the day, what we are doing is in some ways incredibly complex.
[0:53:50] There's so many facets to what we're building. You think about consumer and enterprise and the fact we're bringing these things together and that what an enterprise is is going to change because people are going to have this unlocked wave of entrepreneurship. All that's real. We have this research program. The research isn't just about we're off writing papers. It's like people who really care about how their technology will be used, making sure the safety and alignment, the slides, docs, spreadsheet, all of those are good. It's just like all of this complexity. But at the core, there's something very simple. We have a mission.
[0:54:20] Our mission is to ensure artificial general intelligence benefits all of humanity. That's what we're here to do. Everything we do flows from that one singular goal. So that's what we're pursuing. That's why we started this place. And that is what we are committed to.