Building AGI: Compute, Safety, and Scale
We're now in the AGI era. Astra has really hit something that I'm like, "Okay, I think this is pretty reasonable to call it AGI.📝1 We've seen it run coherently for 24 hours to go accomplish tasks that I think are quite amazing.📝1 >> The models will be plenty powerful, but it'll be hard to get to everybody given that we won't have enough compute to serve it all.📝1 >> You don't win the Super Bowl by saying, "I want to win the Super Bowl." You win it by blocking, tackling.📝1 You really have to make sure that safety, security, alignment, those are all standards that you're constantly upleveling📝1. I think that's going to be a huge challenge people are underestimating.📝1 You've made two very big bets in your career. Helping build Stripe early and helping of course co-ound open AAI. >> I throughout OpenAI have always focused on whatever is the most important problem. For the past 2 years it's been the data centers, the infrastructure, the machine learning.📝1 >> Do you know what the next year we'll focus on or >> I think this is going to become the most important conversation. >> Greg, welcome to the A&Z podcast. >> Thank you for having me. So, Greg, you've made two very big bets in your career, uh, helping build Stripe early and helping, of course, co-found uh, Open AI. If we were talking 10 years ago and you were predicting what would the world look like in 2026 as it relates to AI, would you be able to predict that we would be making the breakthroughs that you've uh, you know, ma made today or what would you tell them about what you would expect? Well, so Ilie and I actually spent a lot of time trying to predict what it would look like, what the timelines would be. And I remember we did some math on compute in around 2016, 2017. And we kind of came to the conclusion that if you look at Mo's law progress, that kind of thing, 15 years felt like about the timeline to AGI📝1. And if you really squinted at it, you're willing to scale up and build massive supercomputers, spend the hundreds of billions of dollars, that kind of thing, that maybe it' be 10📝1 And so I actually feel like in some ways obviously what's happening it's remarkable. It's this amazing sort of moment for everyone to be a part of and to be able to help shape collectively. But it also feels a little bit like maybe it's kind of the conclusion of like a lot of forces that are all coming together for this moment. You if you step back and really take that sort of macro view it kind of makes sense it's happening now. And do you think um we're currently because you guys slightly underestimated the timeline or I I guess it was basically on on point. Um do you think we're still on the timeline given we're now starting to drive real shortages on the supply chain? >> Well, look, I do think that we are in a world where it is hard for compute to keep up with the demand that we're already seeing in the market, right? And just in terms of how people are going to use this technology, benefit from it. I do think it's going to be very hard for us to scale the raw potential and capability of these models to everyone and that's part of what we try to do and so I think that the the progress I do see like we have line of sight to continue to make the models much more capable, safe and aligned📝1 but also really distributing that power and the benefits and the empowerment to to everyone. I think that's going to be a huge challenge people are underestimating. >> Right. Ah interesting. So we'll we'll have the mo the models will be plenty powerful um or or they'll continue a pace uh but they'll be it'll be hard to get to everybody is certainly in an affordable way given that we won't have enough compute to serve it all. I think that's true and I do think we're at a point now where we have to really start thinking about what we call pacing the frontier📝1. And so thinking about as we move to more capable models, you really have to make sure that safety, security, alignment, those are all standards that you're constantly upleveling. And those actually become almost the the bottleneck to progress📝1 or sort of the the sort of you know the the part that you have to uh spend a lot of your effort to make sure you've gotten right. And so I think in my mind it's more those constraints and the compute. I think we can make it happen. And then on the flip side, I think yeah, this bringing it to everyone, which is ultimately about our mission, right? It's empower everyone, ensure it benefits everyone. That's something that I think deserves a lot more airtime than it's gotten. >> Yeah. Actually, let's get a little deeper on the safety thing because it's been very interesting to me in that it felt like in the beginning, safety was like, okay, let's make these things not say nasty stuff that people like don't like. And so the approach that was taken was kind of a surface around the edges. Okay, we'll um you know put some filters on this guy and we'll RHF you know around the edges. But like if you get deep into the thing you'll be able to get the bad words out. But if somebody wants to go through that to hear bad words themselves, who cares? Uh but then you know when you get into okay now these things are really good at uh cyber hacking and other kinds of ideas. Um now you need kind of a more architectural idea where the model itself knows not to reward hack in a way that's going to be dangerous📝1 and so forth. And how do you feel like yes we can make progress against that fast or is that like a really hard different category of problem or how how are you thinking about that? Well, I absolutely think we can and are making very rapid progress on this problem. I think that there's >> a lot of both great ideas and research that we've been investing in for many years. Actually, if you rewind to 2017, uh that I think people underappreciate some of the most key results that came out of OpenAI in the field at the time. So, both kind of the first inklings of modern language models. You can find a paper paper from 2017 that kind of laid that out with LCMS📝1 and you know it's like kind of this very baby result but also reward uh learning uh reinforcement learning from human preferences that was also created in 2017 to start thinking about how can you align a model to match what humans want📝1 right by providing feedback from people. >> Yeah. Just for usability. >> Exactly. In 2017 2018 we had ideas for if you have something that's very smart and capable how can you actually supervise what it's doing how can you provide feedback and ensure that it's staying aligned with you and we had ideas such as debate or uh iterative amplification📝1. So these are ideas that were really sort of at this phase before these systems existed and you can start to see the sort of trickle down of those ideas into modern systems and and investment. And so it's in some ways that I think that there was this early phase when open started where we really were thinking about AGI safety things like that and that was very front and center in even the comms. And then I think that as things like chatb took off then people started to see okay well we're not at this point yet and so the is the AI politically neutral and questions like that start to become to the front and center📝1 >> right >> and now that we're here all these other ideas that we've been talking about for a long time they're taking the main stage again and I think we've been sort of thinking about this moment for a long time. That's really good news. And and when you think about and we don't have like a really great community yet amongst the uh soda models, but it seems like those kinds of ideas you and Google and Anthropic and uh SpaceX would want to share and Meta now um as opposed to like okay this is a proprietary idea that's a way to keep these models safe since you're all on related architectures. um or h how do you see that unfolding or is everybody going to do it independently? >> Well, I think there's nuance here and I do think the coordination is going to be a very important theme, right? To really think about within the frontier labs and really just thinking broadly about what has to happen for humanity as a whole >> to sort of navigate this technology in the best way. I think that we we're going to have to really think hard about those kinds of questions. And we published a lot of our thoughts. And again, some of this is about pacing the frontier. Some of this is about unilateral actions that we can take and how we think about how do you make safety cases for even training and developing and evaluating these kinds of models. All that's new. No one's ever really had to operationalize this before.📝1 And I think it is not at all unique to OpenAI. Like there's a whole world that is basically developing this technology. And I think one thing it's easy to miss is that what we're building is almost a a sort of thing that falls out of compute progress. And in some ways, compute progress is something that falls out of technological progress. And so there's this this massive wave that's been building for a very long time. And we're starting to see the leading edges of this technology. And companies like OpenAI can lead by a bit in order to kind of peer into this future📝1 and really understand what is possible. How do we shape this technology? But we can't do that alone. And I think that having coordination and especially the more that we can talk about safety techniques and share what we're seeing, alignment failures, those kinds of things, all of that is going to again take a very front seat for this next phase. >> Right. Very interesting. >> You've called the OpenAI hugging face recent incident a watershed moment and talked about how the defender window is now open. Can you explain that statement and the significance behind it? >> So I think hugging face shows two things. one is call it a something for us in terms of how we monitor sandbox and control the models during evaluation📝1 and that's something we've really risen to that occasion our team has totally changed so much of our internal standards and and really implemented a lot of controls that I think are very important and very critical as we look to to future more capable models but there's a second thing that I think is also very valuable for the world that came out of this which is a insight into what future capabilities will be like when they are broadly diffused and in the hands of threat actors and that will happen right that there are so many people who are building these models and again there's something very important and good about the diffusion broadly of AI capabilities because there's a risk of concentration of power if one or a few entities people huge risk right it's something not not to not to at all write off but you also have to prepare for if if everyone is empowered with tools that are cyber capable. And in the case of Hugging Face, you saw both an AI that was able to hack out of a secure environment and hack into a company's production environment. And I think that the takeaway >> very cleverly, >> very cleverly, right? And it's like that the things that it found were were quite sophisticated. >> Yes. >> And this capability broadly diffused, I think, is something that will really empower threat actors in new ways. And I think that defenders need to use this time before that technology is broadly available to secure themselves. And the nice thing about it is it's a dual use, right? It's something where if you can find vulnerabilities, if you're an attacker, you can use it for no good. But if you're a defender, you can patch, right? If you're a defender, you control the battleground, right? You control the setup of your systems.📝1 And so our belief right now is that there's this window of you have frontier capabilities. You have the broadly diffused capabilities and you as a defender by default you know your security is probably pretty static been static for the past 5 10 years📝1 that kind of thing. you need to move use these frontier capabilities that you you'll have differential access to right where we have trusted access programs things like that to bring these capabilities to defenders and you can use that to move yourself up so that as the frontier capabilities get better you get pulled along too📝1 right okay so I've got a comment and a question on it I would say there's a third thing that we learn which is like these things have capabilities that I don't know that we all understood before which on the good side like oh I can deploy 10,000 agents and they can talk to each other and organize themselves and do stuff for me.📝1 Like that's uh pretty amazing. So that that was on the good side. On the other side, so I agree that we've got a kind of defense window. Um however, we have like 50 years of code and architectural ideas um and deployment ideas that weren't built for this world. And so yes, the AI can help us like okay, find a bug, patch a bug, and so forth. But it seems like there's, you know, maybe a bigger issue, which is we have these huge, you know, massive honeypotss of consumer data and all these things lying all over the internet. Uh, and you know, from a consumer standpoint, it's like, okay, I can't protect my stuff. uh these all these companies have to get their act together which um seems a bit worrisome and do you think kind of in the future we need a do we need a decentralized consumer architecture like will this kind of current world that we live in with all these centralized data repositories be viable in a world of AI >> so several pieces to the answer and first to your point on what you can get out of 10,000 agents we actually use 10,000 agents to solve the Navier Stokes problem📝1. >> Yeah, that was pretty pretty awesome by the way. Congratulations on that. >> Thank you. Thank you. And it's both an important problem for what it is has significant implications and applications to fluid dynamics to how you think about ocean currents, all these things, but for what it represents, right, of new knowledge created by AI and it unlocking a whole wave of scientific discovery📝1, medicines, all those things, they're on the table now. So I think there's something really amazing to think about what can happen through the power of AI that is able to really help solve problems. And in the case of of cyber security, how I think about it, we at OpenAI took our models and applied them to finding vulnerabilities. We took 25% of our production engineers and said, "Sorry, all your projects are on hold. You are now defending.📝1 You are now upleveling our security architecture. you're going to use the models to find all the holes and we found a number of of of serious issues and we fixed them and I've talked to a number of CISOs over the past couple couple weeks and months and there are many companies who are also telling me that yeah that they've applied these models they found some very significant issues but that they're able to fix them and one positive sort of part of the story is that when we took Astra pointed out our our systems we found some new new problems📝1 but eventually it saturated we basically found to our knowledge all of the P zeros, all of the critical problems that Astra is smart enough to find.📝1 >> And of course, there will be a new model. There will be a new round. >> Even smarter. >> Exactly. But I think that that's the world that we'll be in is that you'll be in a world where you want to be in this tight loop of new cyber capability drops. You deploy it against your systems. You find the new holes📝1 and ideally you've managed to automate this what we call defense factory. And that's what we're building internally. this end to end of both find vulnerability, triage it,📝1 >> remediate, deploy, validate, right? That end to end. And if you can do that at machine speed, I think the defenders will be advantaged in deeply significant ways. And there are >> ideas, for example, formerly verifying all of software that are possible with AI📝1. >> Yeah, we never that's always been a dream. We've had these ver formal languages and all these kinds of things, but they never kind of took off. >> That's right. Because just it's just intractable for people. It's just so hard. But we have these that are solving these crazy >> impossible math problems. And so one application of that that proving power right actually one of the things to know about the navier stokes problem is that we formalized it right. We formalized into lean📝1 and so >> so the AIs can write verifiable code📝1 >> they can. >> Yeah. Very nice. Yeah that's a great idea. So I think I think there's real hope but I think that our view is that the world needs to act with urgency because >> we're in a very dangerous window right now. Yeah. >> We just see it coming. >> Closing the loop on on on this in incident. Is there anything you felt that the the narrative um gotten wrong in an important way or or or is there any preferred way of of talking about what what happened or or or this window that that's important to get across when you think about the public narrative? or or is there anything just inside the company that uh else that that changed in terms of how you're approaching the the set of issues? >> Well, two things. I think that one big theme that people should take away from it is a question of access, right? That these these capabilities exist right now in the world, but that they are in small number of frontier companies and the frontier companies have a trusted access program, which means that anyone who's not in the trusted access program is not really able to benefit from the fact that there's this differential. the people we're in, they get a benefit if they use it. And so I think that there's something we need to do as a field and as a society to really scale up the number of defenders that have access to these technologies📝1 because that's it's like every day matters and to use those days you need access to these tools. And one thing that was actually interesting about the hugging face response was that they said that they used frontier models to try to look over the logs of what had happened because that's the only way to actually analyze an attack like this. and that they said the frontier models refuse, but they didn't actually try our frontier models and they actually believe that ours would have permitted it. And so there is something too about the default stance of providers. I think there's something here about using these capabilities for good with this urgency and and this sort of sort of I yeah this this this real sense that it has to happen. I'll tell a quick story by the way which is unrelated but maybe also shows a little bit about how I think about this. I remember when we trained GPD3. It was beginning of December 2019. So, everyone's about to head out on vacation. Y's like, "Okay, we can train the model." And I just remember feeling like this model is sitting on a shelf. No one is using it. It's this like amazing technology, new to the world, new to humanity. It's like every day that no one is exploring what it's capable of and trying to understand it, figure out what to do with it. Um, that's a day that is lost to the world. And so, I was just like, I canceled basically all my holiday plans. I spent the whole time just playing with the model, building interfaces around it, trying to see what it was capable of. I remember I was trying to teach it how to how to sort lists some of numbers. It didn't work very well. Uh but it was just like this like really try to to to probe it and see what's possible. And I think that that spirit and ethos is something I think we should bring to what we're building today. It's it's sort of obviously at much larger scale, much larger impact, but we as a world have the opportunity to understand this technology in this moment, which then helps us shape and steer where it will go next. >> And very good point. >> You you said you two things. What what do you have another one? One was access or did you say both of them? >> I think I said both of them. Yes. Yes. >> The um let's go back to Astro. Uh it's incredible to see all the excitement on on X u all sorts of cases. People are excited about computer use. Um you you you've said that in some ways it is uh bring us closer to AGI. What do you talk about what you find most compelling in Astra or what you think the the breakthrough there in light of that statement and and uh where we have still left to go? >> Actually wait sorry let me let me actually revise my my answer and I can say I so both access and let me also tell another story about how I have used the models personally. So after hugging face, I was thinking about how can I use these models in my personal life? What can I do to secure myself? And I have a website. It's a very simple website, gregarin.com. Not the most popular website. Got a good blog post on it. Exactly. You got some blog post. It's a static site. It's very simple. Like what what kind of vulnerabilities could be there? So I took my codeex and asked it go check out gregman.com. Tell me if there's any vulnerabilities. So I did a pen test and it came back with 13 findings. And these findings were things like I had set my SPF record so that you would prevent people from spoofing emails, right? That there was some it wasing over HTTP without forcing people to HTTPS, things like that. And individually, these things are maybe not the biggest deal, but if you think about with an AI that's able to chain together many small vulnerabilities into a big one, I'm like, do I really want a hole where someone can scoop emails for me? Probably not. So 15 minutes for it to find these 13 findings. But then I asked it, can you fix these? Right. Because fixing is so annoying. So painful and boring. Exactly. And so 45 minutes it opened up my cloud for control panel. It clicked around, set all the headers. It migrated me to COD for pages. It's like set everything correctly. It started the demarked process, which apparently you have to do like a 48 hour window of whatever. Um, and that was 45 minutes of of fixing. And I felt so protected. I felt like, wow. >> Did you ask it to find the vulnerabilities? >> There you go. No, I added Yeah. So it actually does do that automatically. This was by six soul. It said I just checked that this one's fixed, this one's fixed, this one's fixed, and in 48 hours I'm going to have to run and set up a little automation. So in 48 hours, it would check back in to complete the demark process. And I was like, all right, this is we're in business now. >> All right. All right. Craig Brockman.com. >> There we go. You too can't be protected. >> Awesome. >> Let's um let's transition to Astra. Uh it's incredible to see all the excitement online and the use cases. people really excited about computer use among other things. You you've said it's uh sort of closer to the way along to AGI. I'm curious what you find most groundbreaking with it and where do you think we still left to go? >> Well, I think that Astra is really a step function on so many axes and in many ways it is the sum of a number of research bets that we've been making for years and to see them come into one model at one time it's been absolutely incredible. And so just one thing to know about how we do numbering is that we kind of have been wanting to have GPD6 represent something that's worthy of it. And the problem we always have is that our models are kind of incrementally getting better. And so it's just never feels like it's a right moment to go for a major version bump. You always have be like it's 56 now should be 57. And this one just happened to be because all these things came together at once the first time that we actually had this almost discontinuous step on in a way that we could have predicted, but it just was like all these these these factors um happened to line up at once. And so I was a real positive moment. And to me the computer use is the headline thing that we've talked about. And part of the reason computer use is so significant is that for agentic use cases, it really comes down to tools. It's like is the model smart enough to use the tools and then does it have access to the context that needs to through these tools?📝1 And so people have been building these MCP servers and these CLIs and just really sort of taking the world of software and making accessible in this almost stilted way that is not really meant for humans, right? It's like we're kind of retooling the world, right? You made it. It's kind of like, oh, we'll build an API like it's a software, but like what if it's really more behaving like a human? Can it just use a computer📝1 and and it's kind of and the result of building that other layer you have now another layer of security challenges this that the other. >> Exactly. >> Really so it's kind of Yeah. They've always felt like very weird and suboptimal. >> Yes. And from the very beginning of OpenAI I remember in November 2015 we did this offsite in Napa and we talked about our plans. We actually laid out this three-step plan that basically is what we ended up following for the next 10 years. But we also talked about what if we could do reinforcement learning where the environment is screen pixels, keyboard, mouse, right? Same interface as a human. Suddenly any sort of task you could do with a computer is in there. It's in it's in distribution as long as set aside sound, whatever. But you basically have the full power of a computer there. And we had some aborted attempts early on to try to build agents that could do that. And so it really took us until now, but you're seeing the power immediately. And it's just been so cool to see people take the Blender capabilities and, you know, take a screenshot of something and they have to make a 3D model and you can actually, you know, lots of people are now designing houses or trying to redesign uh their living room, all those things by just utilizing this capability. And to me that the the thing that really stands out is that you can now move forward on AI that can do things for you without you having to build all these specific connectors. And I think there's so much software they don't even think about that you have to orchestrate every day. And like how much of your life is like clicking around menus and like you know typing things into a spreadsheet and things like that. Like none of that is what we should be doing 100 years ago. No one is doing any of these things. And so it's not crazy to think that in 5 years 10 years no one will be doing any of this stuff anymore. That we will get our time back. We're not going to have to be get our carpal tunnel or hunch shoulders or you know all of those all those physical problems that are us contorting to the machine. It's now the machine is there to help us to empower us to to really serve us. >> Yeah. And you know that's a really good point because I think you know one of the things that that that you have been um I would say more sober on as a company is just okay what happens with employment and uh I think that I think that that's exactly right that there's all these things that we do because we have to do and like they became valuable but we shouldn't be doing it. All they do is wreck our health and and wreck our personalities. And the idea that humans are going to just run out of ideas of cool things to do or how to make the world better or problems to solve um seems a little absurd to me. And so far, at least in the numbers, the better AI gets, the higher employment goes, not the lower. And so I wonder your and of course it's unknowable. You know, we've never had this technology before. It's getting better and so forth. So, how do you kind of think about the future of employment as it relates to these models and AI as it progresses? >> Well, I do have a fundamental belief that AI is surprising. And I think we even put this in the OpenAI launch post back in the day in 2015, just saying that the history so far has been somehow it just doesn't play out the way that you think it does, even when there's this like logical conclusion it should be a certain way. And I think the same will be true, right? I think that there's something that we've learned about that humans and I think like for any job that we almost it's easy to not give it as much credit for how deep the field is and how much sort of sophistication building relationships accountability is a good example of something where I think that people setting goals and being accountable for outcomes like those feel fundamental to me those feel like things that we actually should preserve for the long term right that's something that feels like deeply human like people are not valuable just because we can do tasks right we're valuable because of her people and I think that it's important not to lose sight of that in some of these narratives and I think that the way that things will change and how what we do with our time evolves and we clearly will be in a world of abundance and how do we ensure that that abundance is broadly distributed um but also at the same time I think that we should be in a world where the ceiling of ambition is higher than ever before and I think we're going to see a wave of entrepreneurship where it's actually already starting I've heard um from someone in you know particular industry was saying that a bunch of people in in his world are now making the leap to go quit and start their own firms and that they're doing it because they have these AI tools and they're just like I can do so much more and so it's the barriers to entry and for entries and this should be a wonderful renaissance. Yeah though well it's it's been a lot of fun for us in just going okay there are you know particularly for our young people because you know they get by default the grunt work but like what if the AI does to the grunt work then they can really develop much faster actually because they can um kind of get involved on the you know really the the real part of our business which is what is the relationship with the entrepreneur? How do we open up the world for them? How do we make um them feel like oh they can do anything and they're an important CEO and they can go build things and as opposed to you know spend the whole weekend writing an investment memo which by the way uh I have to say Astra very good at writing investment it's awesome >> I love hearing that >> yeah and again I do think it's going to be a nuance story right I don't think that we should paint that everything's going to be rosy and it's all going to be just easy I think it's going to be hard I think there's going to be change but I think that it can be a much better world. I think the future could be much better for than the past for everyone. >> Yeah, that and that's it feels like what we should expect, you know, like before the plow, you know, the world was a lot worse like it was just a worse life even though it did put like a lot of human labor out of business, you know, and and created the whole lite movement and all those kinds of things. You know, nobody here wants to go back to 1870. uh and so the idea that no we don't want to go into the future now um seems a little shortsighted but uh I think the speed at which things are moving is is very very scary for people >> and we we really recognize the fact of how things are moving and that we spend a lot of time really trying to understand as well as we can how people are feeling how we can be showing up better and I think that two things like one is that when we think about development the pace of progress we're being very deliberate about it safety is our foremost priority. We think about how do we build this technology in a safe secure way and what should those standards be and you can see that showing up in a lot of our comms inside the building. It is absolutely what people are thinking about and what we care about is that we really want this technology to empower everyone broadly. And I think that for us as a world to really think about how do we get the most out of this technology? How do we get the benefits? How do we mitigate the risks? I think this is going to become the most important conversation that we have and I think that that will emerge over even maybe the next one to two years. I think that this should be something that is front and center. I think people sense it yet you can sense it in how people react right now and even thinking about things like data centers and these kinds of questions of do we want AI and how do we how do we think about where it's appropriate and how do we ensure child safety all of these kinds of questions these are core questions that we care so much about getting right >> to that end why do we think sentiment and AI is higher certain Asian countries >> all Asian countries well and actually in European countries everywhere but the US has like got the lowest AI sentiment >> why is that or more >> what's driving the >> what can we do about like what can we learn from >> well one thing that I think about is that I think we as a field as a company need to do a much better job of articulating to people why they benefit why is this a good thing for them and not just for the country right which I think that this technology is going to be and is rapidly becoming the single most important strategic priority and resource for the United States it's happening Yes, >> absolutely. You look at chatt 300 million health queries or 300 million people every single week using it for help, right? That's a huge deal. And we're at a billion almost, you know, 1.1 billion weekly active users. I think within the US it's about 100 million something like that. Like a third of the population, if I have that number correct, right, is using chat every single week. So people are touching this technology. But I think that that for many people there are some people who have gone very deep and really gone through the health journey for example that's been true for my family for my wife that she has a number of health conditions that would be we don't even really know how we would have managed these before chat and there's just so much toil and time and just getting to the right answer and a doctor tells you something you don't know what the thing is and how do you get that that sort of sanity check to really even understand it people who I that their life was saved through information delivered by chatbt like I'll tell you a story for one one of my friends is that she was in the hospital and uh that the doctor was about to inject a antibiotic and she's like give me a moment she typed into chatbt and Chad said absolutely do not take that if you do you may die because you have this thing that you had a year ago you have this condition like this kind of thing your reaction showed the doctor I know right >> and the doctor said oh my goodness no that's absolutely right I had no idea. I only had five minutes to read your chart. >> Yeah. Many such cases, by the way, telling me stories in their chart at least. >> Exactly. And so that these kinds of stories I think don't get told nearly enough, but they're out there. I hear them every day. And the people who run their small business on chat and would be totally unable to do it otherwise. Like that kind of empowerment. Again, people who are able to save money, make money, live a better life. Um those kinds of stories I think need to be in the public consciousness as we approach this this question. So it's painting the narrative that hey you've got a teacher in your pocket, a doctor in your pocket, something you know lawyer in your pocket, therapist in your pocket, you know all these utilities in your pocket while also not threatening those the same well also telling the teachers and doctors and lawyers therapists that hey you've now got this tool too and it's going to make your your business better as well. >> Yes. And it's not just the narrative, it's the reality, right? It's you need both. I think that many other countries are looking in. Yeah. >> Seeing the position that the US is in, right, that seeing the potential of this technology. And partly too, you think about demographics that I think in many of these other countries, it's more keenly felt that there's an older gen generation that's much larger than than the younger population that is going to need to support them. These questions of how is that supposed to work? And so, I think that there is something about really thinking to the future and thinking about what's possible. how do you get the benefits out of this technology and really wanting to lean into that that I think we're seeing across the world. And so again, I think that there's something that we need to do better as a field and as as a as a company in order to communicate this domestically, but I think the potential is there and we're in such a privileged position and leading this field in a way that I think was not guaranteed and it's not guaranteed to remain true for the future either. >> Yeah. Particularly if we ban data centers, I think that that'll be a problem for us maintaining our lead. It will it will drive the data centers overseas which is what happened with silicon back in you know the 80s or so. >> Right. Right. And and there are so many you know one of the interesting things about data centers is it creates so many bluecollar manufacturing jobs. I think uh switch employs like 45,000 >> uh people on kind of a union contract basis to build data centers. That's just one of the data center providers in the US. and and and they're great jobs, they're highpaying, uh and then you know I think that while there have been bad actors in the data center space um most of them are very good actors and contribute uh to the power grid uh don't waste water and uh are not noisy and so like not that there was never an issue but like we could just say hey you have to be a well-behaved data center as opposed to like we're going to ban them. Um or like we're going to stop AI. Uh which I like we're not going to even as a country we're not big enough to stop AI. Uh so the idea like AI will continue without us. Uh and then we'll have zero say as opposed to we're the leaders and then we have all the say. So it's a really really important cultural message. >> I think it's very important and and I think on on data centers. So we've made commitments on not increasing people's electricity bills. Our data centers are all closed loop water. So the amount of water used by Abalene which is the data center that actually trained Astra that it uses about the same amount of water as an office building. Right. So it's it's really it's really yeah that the technology is quite quite advanced on that and that we have a number of community commitments so that we can actually help um in Ohio and Georgia where we have data centers. We've announced we've talked about how we're providing credits to every college student for codeex access. So there's this broad set of benefits that we are bringing to bear. But again, I think that we need to do even more. >> Yeah. >> And you know, like I think making those kinds of things a requirement to build a data center is very reasonable. Um, but like let's have positive some ideas as opposed to okay, we're going to jump out of the AI game as a as a country and let uh China or whomever dictate what it's going to be. >> Speaking of uh contributions, you guys made a billion dollar commitment to frontline defenders. Why don't you talk about that? So we believe that every organization, every company, every government, critical infrastructure such as water service providers, hospitals should all be using this defender window to secure themselves. But not every organization will have the capital required to do it. So we have a billion dollar commitment to frontline defenders, so to organizations that we all rely on every day in our communities to access our models to secure themselves. We think this is the beginning. This is not the end. We're working part closely with partners. For example, CrowdStrike and we are working together to provide discounted access to defenders as well. And I think that there's there should be a global effort in order to bring these tools to bear to really secure every single organization given what we see coming and what's possible. >> Yeah. And that's a a super positive advance because this is our before AI hospitals were getting broken into, held hostages all the time. um our water supply has been hacked by uh foreign actors um state actors and so like we're already dealing with kind of our critical infrastructure was not built for with cyber security in mind. It's not been maintained with cyber security in mind and here's an opportunity to go from not even secure in a pre I AI world to completely secure. So to me this is like an incredibly important effort uh to you know not have our water supply and uh our hospitals at risk. I I really agree with that perspective, right? That the point of I think that we as a society have been lax, right? That we've allowed tech debt to pile up that every cyber security organization I've never met a CISO who felt that they were appropriately resourced, right? That they were appropriately prioritized. >> Never. >> And particularly not in the public sector. >> That's right. And so I think that we have to change that. And we should have changed this years ago, but now is a moment where we actually have a real both motivation to do it and a real ability to do it. And I think that delivering the secure world that we all deserve so that we can we can really depend on it and be safe and secure in our in our daily lives and online lives like that to me feels like table stakes. We absolutely need to do this. >> Yeah, definitely. No, that's a that's a great effort. >> Closing the loop on Astra. You've emphasized that capabilities still remain jagged. What do you think um still still left to go or still needs to be fleshed out that gets approximates most of your definition of AGI? Well, I think that AGI has turned out to be less of a point in time and more of this sort of fuzzy spectrum. And for me, Astra has really hit something that I'm like, okay, I think this is pretty reasonable to call it AGI in that with its computer use capabilities, you really can ask it to do long live tasks and it'll just do it. That we've seen it it run coherently for 24 hours to go accomplish tasks that I think are quite quite amazing and across a wide variety of domains. Now, it still is jagged and so that there are still places where, for example, it's writing. It's pretty good writing. It's the first time it's not sloping. >> Yeah. >> But it's not great writing. >> Yeah. >> And I think that there's a number of areas where I feel like we just need to polish it a little bit and it would be fantastic. And it's just like not quite there. So I I see this like I saw someone post a graph on on Twitter of like you know this like kind of you know jagged frontier and that where we really need to to be is a much more steady across the board um really hit on all these categories. But I think that what people are finding is that it is so capable across such a wide variety of tasks that it is accelerative. It is empowering and it's something that I think we've never really seen a model that that's been a jump like this. >> Yeah. One of the things that's been interesting for me is that a as you solve problems sometimes the the world doesn't realize it like so I haven't seen a hallucination in quite some time. Um but nobody says oh the models don't hallucinate anymore. It's just kind of in the ethos that that's what AI does. Um, how do you do you think that'll just go away over time or is it you know does there need to be some like continual education for the for the non- like hardcore tech people >> I this thing is moving >> I think one of the most important problems we actually have is the continual education right the really how do you people shouldn't have to extract from the AI what it's capable of it should go the other way around the AI should say hey I can help you in this new way so we have about you know we over a billion weekly active users on chatbt, but I think we have something like another maybe 1.5 billion people who have used chatbt and don't use it anymore. >> Oh wow. >> Right. So think about that. That's a significant fraction of the of the planet. >> And those people exactly those people we should really be able to go back to and say, "Hey, we have made so much progress. We think we can be useful to you in these ways." And I think that that is just shows you the kind of problem we have in front of us is that these AI like if you look at chatbt and chatbt work, they're both text boxes, right? It's like this new text box is way better than the old text box, but there's still some things that the old text box is better at. So don't always use it. It's like that is not the AI we were promised. The AI we were promised should be an AI that you talk to over voice primarily. You can talk to it over text if you want to. uh that it has persistence, that it has memory, it has context, it knows you. It's trustworthy, that you have seen it be proactive and helps solve problems for you📝1, that helps in your personal life, in your work life. And that's how it should be. It should be something that is able to that you can really sort of rely on for the things that you care about that empowers you and and helps you solve your goals. And I think that being able to explain to you how it can help you is a core part of that. >> Yeah. Interesting. and proactively do it. That that's such an interesting idea like we need more helpfulness out of our AIs. >> Yes. >> Which is kind of a thing like some humans aren't and probably the humans who develop AI are not very helpful people. I would guess just being around engineers and researchers. >> You'd be surprised. I think we have very very uh helpful helpful engineers at OpenAI. But but there is something about if you think about how do you work with another person, right? A new co-orker you've never worked with.📝1 >> It takes you a little bit of time, right? you kind of feel them out. You see how they respond in different areas. People do not come with an instruction manual. And often actually sometimes it's it's interesting in areas like consulting or something where they they really lean into like MyersBriggs and that they do say like here's like a quick way to know who I am and how I operate. So that there is some precedent for how humans can kind of present a little bit more. You have a resume. You have sort of track record. People can ask for back channels on you. So we have built up a way of how do you understand how a human will work and what the best way is to to get the best out of that person and I think sort of figuring out what is the right analog for AI and especially as AI changes and we produce new tools and product surface and new models and all these things. I think that this will be a very important society company interplay and again I think that what we what our northstar should be is simplicity right that we really should be one AI that's unified that makes it so easy and smooth for you to be less >> engaging with the computer and less wrapping your yourself around the computer. The computer should be there to empower you to help serve you. >> Right. Right. >> The the business is ripping. You guys have such broad surface area in terms of what you cover. How do you decide in terms of prioritizing where to go deepest, what not to build and then also your role has also evolved and changed and you've you know encompassed so many things you know research product commercialization or management etc. How are you also thinking about prizing your time? >> Well, they go hand in hand. Yeah. >> So this year the theme was focus. >> I think that we really realized that we can't do it all right. We need to pick and particularly there's one thing we're trying to accomplish which is our mission right we want to ensure AGI benefits all of humanity now how do you backsolve from that what are the areas like deployment and productization is actually something that does reinforce that right that we do want to bring this technology to bear and have it uplift everyone and people deploying it in useful applications all of that personal life work life the whole thing um very core but how do you when you think about this moment we're in of this agentic coding takeoff that exponential what areas reinforced that and which ones were kind of just sort of you know they got labeled a side quest in the media but just were not on track for it even if they were individually something very exciting was a very core question that we had to grapple with and so things like Sora that's maybe the highest profile one of these projects that we decided to cancel very very painful by the way >> not an easy thing to do but it was so critical to >> unleash the business in many ways so we could really focus bringing together the consumer and enterprise side of chats into chat work. That's another area where we've really had to focus and really say this is what we're doing. So, a lot of the way that we've thought about this to unlock this moment is to really have vision about where we think the future's going and how do we think that the new capabilities that are emerging can best be brought to bear with a single unified stack that works across the different areas, different walks of life, different areas that we're trying to focus on. And it's been painful, right? that if you look for the first half, I think that there was just a lot of metrics that were not looking the direction that we wanted and that there was a lot of just sort of telling the team we just need to focus on the basics. Like one of my one of my favorite management books is the score takes care of itself. Have you guys read that one? >> Yeah, Keith boy favorite. >> Yeah, it's a it's a great one and it just is a very empowering book because you just realize it's like you cannot affect the outcome. You can only affect the inputs, right? You can only affect the like the basics. And so focus on those basics, right? You don't win the Super Bowl by saying I want to win the Super Bowl. You win it by blocking and tackling. Yeah. And so that's what we have done for this whole year. And for myself, I throughout OpenAI have always focused on whatever is the most important problem that I think that I can move the needle on that just isn't going to happen without me. And >> for the past two years, it's been the data centers, the infrastructure, the machine learning engineering. And that's an area where we really spent a lot of effort to get our pre-training infrastructure into great great shape. This year it's really been about the business. It's really been about the okay, we've figured out how to get the research really humming. We figured out how to get the infrastructure really humming, but how do we really bring this technology to the world? And I think that that's where I've been really putting a lot of my efforts in trying to bring together a bunch of functions that were otherwise kind of running in parallel or crosswise. And that is something where I think as a founder, as someone who has kind of touched every part of this business from the beginning, I think I've been uniquely able to go in and make the changes, the make the hard decisions and really figure out this is the direction. Let's go. And a lot of my style is that I like to lead from the trenches. And so I get very deep in the weeds on what the thing is and really try to keep asking a lot of questions. Like that's actually a lot of my style is just asking like, hey, does this make sense still? I don't quite get that. Um, sometimes when things are confused, for example, over the past couple days, there have been times when it's just like, we've got a thing, we got to figure out how to even talk about it. How do we think about it? How should the world think about this? I'm just like, let's just get everyone who can touch different parts of the elephant on a call. We're like going through a Google doc on a hangout and just kind of like being like, does this line make sense? Wait, what do we really mean by this? And so really trying to uplevel execution sometimes in small ways and sometimes large. >> Yeah. No, that's fantastic. >> By the way, exact right way to operate. >> Yeah. Do do you know what the next year will focus on or prep for now? >> Well, look, I think that I think that the business is is a huge area that I think we're not done yet with really upleveling every part of execution. So, I think there's a lot more to do there. But I also think we are moving into a new phase of AI development, right? I call this and we call this we're now in the AGI era. And I think that that is something that you can debate. Is it this model, previous model, next model? It doesn't matter. The point is that we are in a new phase where safety, security, alignment, really thinking about these things not just at deployment time, but all the way back at development time evaluation. It's objective. This is critical. It must happen. This is core to our mission. This is core to what we need to do. And so a lot of what I spend my time thinking about is making sure do we have all the right processes? Are we talking about the right things? Do we have plans that really at a operational level, at a practical level lead us to the kind of security and variance and the kinds of safety guarantees that we view as core to our mission, what we need to do. And so I think that again the theme of open AI certainly for the past five years has been deeper codees, deeper intertwining across these functions that are maybe on the surface very disperate, right? all the way from go to market to long-term research to chip design by building these in a coherent way where everyone has context right they kind of understand how do I fit into the overall picture what are we trying to do and what is the end outcome we want to achieve like that is what has to happen so I think that the areas that I will focus on I think will be dictated by the areas that most need that intertwining and I think that I see us moving more and more in concert in lock up as time goes on. >> Yeah. I uh we could go all day, but we have a hard stop. Uh I think this is a great place to wrap. Greg, thank you so much for coming on the podcast. >> Thank you. >> Great. Fantastic.