Greg Brockman on Why OpenAI Says We’re Entering the AGI Era

0:00We'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. We've seen it run coherently for 24 hours to go accomplish tasks that I think are quite amazing. The models will be plenty powerful, but it'll be hard to get to everybody given that we want to have enough compute to serve it all. You don't win the Super Bowl by saying, I want to win the Super Bowl, you win it by blocking tackle line. You really have to make sure that safety, security, alignment, those are all standards that you're constantly up leveling. I think that's going to be a huge challenge people are underestimated. You've made two very big bets in your career to help them build Stripe early and helping the first hope found in OpenAIS. The world needs to act with urgency. You know, we're in a very dangerous window right now. We declared a code red. We took 25% of our production engineers and said, sorry, all your projects are on hold. You are now defending. I throughout OpenAIS have always focused on whatever is the most important problem. For the past two years, it's been the data centers, the infrastructure, the machine learning, you know what the next year will focus on. I think this is going to become the most important conversation.

1:0110 years ago, Greg Brockman and Ilya Suskevar estimated that AGI might be 10 to 15 years away. Today, Greg says we're entering what OpenAIS calls the AGI era. In this episode, I sit down with Ben Horowitz and OpenAIS co-founder and president Greg Brockman to talk about what has changed and what comes next. Greg explains why computer use could fundamentally change our relationship with software, how OpenAIS used 10,000 agents to tackle a major mathematics problem, and why increasingly capable models are creating both new possibilities and new security risks. They also get into OpenAIS response to recent summer security developments, why Greg Belize defenders have a critical window to use frontier AI to secure their systems and how the company is thinking about safety as capabilities advance. Greg describes the AI he thinks we were actually promised. Not another text box, but an assistant with memory and context that knows you, works proactively and can help

2:02across both your personal and professional life. Greg, welcome to the agency podcast. Thank you for having me. So Greg, you've made two very big bets in your career, helping build Stripe early and helping, of course, go found OpenAIS. If we were talking 10 years ago and you were predicting what would the world look like in 2086 as relates to AI, would you be able to predict that we would be making the breakthroughs that you've made today or what would you tell them about what you would expect? Well, so I mean, 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 Moore's Law progress, that kind of thing, 15 years, felt like about the timeline to age you. And if you really squinted at it and you're willing to scale up and build massive supercomputers, spend the hundreds of billions of dollars that kind of thing, that maybe be 10. 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

3:05to 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 a lot of forces that are all coming together for this moment. 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 we're currently, because you guys slightly underestimated the timeline? Or I guess it was basically on point. 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, just in terms of how people are gonna use this technology benefit for it. I do think it's gonna 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 progress I do see like, we have line of sight to continue to make the models much more capable, safe and aligned, but also really distributing that power and the benefits and the empowerment to everyone. I think that's gonna be a huge challenge

4:06people are underestimating. Right. Interesting. So the models will be planning powerful or they'll continue, but it'll be hard to get to everybody as 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. 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 up leveling. And those actually become almost the bottleneck to progress or the sort of the part that you have to 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, bring it to everyone, which is ultimately about our mission, right? It's power everyone, ensure it benefits everyone. That's something that I think deserves a lot more air time than it's gotten. 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 don't like.

5:07And so the approach I was taking was kind of a surface around the edges, okay, we'll put some filters on this guy and we'll RLHF around the edges. But 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? But then, when you get into, okay, now these things are really good at cyber hacking and other kinds of ideas. 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 and so forth. Do you feel like, yes, we can make progress against that fast or is that really hard, different category of problem? Or how are you thinking about that? Well, I absolutely think we can in R 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've rewind to 2017, I think people under appreciate some of the most key results

6:11that came out of OpenEye in the field at the time. So both kind of the first inklings of modern language models, you can find a paper from 2017 that kind of laid that out with LCMs and it was like kind of this very baby result. But also reward by reinforcement learning from human preferences. That was also created in 2017. You're starting thinking about how can you align a model to match what humans want, right? By providing feedback from people. 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 iterative amplification. 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 investments. So it's in some ways that I think that there was this early phase when OpenEye started, where we really were thinking about age,

7:12I say, if you think like that, and that was very front and center, even at the comms. And then I think that as things like chat you be took off, then people started to see, okay, well, we're not at this point yet. And so is the AI politically neutral and questions like that start to become the front and center. 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 when you think about, and we don't have a really great community yet amongst the soda models, but it seems like those kinds of ideas, you and Google and Anthropic and SpaceX would want to share and met it now as opposed to, okay, this is a proprietary idea that's a way to keep these models safe. Since you're all on related architectures, or how do you see that unfolding? Or is everybody gonna 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

8:13and 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're gonna 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. And I think it is not at all unique to OpenAI. There's a whole world that is basically developing this technology. And I think one thing that's easy to miss is that what we're building is almost 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 massive wave that's been building for a very long time. And we're starting to see the leading edges of this technology in companies like OpenAI can lead by a bit in order to kind of peer into this future and really understand what is possible. How do we shape this technology? But we can't do that alone.

9:14And I think that having coordination, 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. All right, very interesting. You've called the OpenAI Hugging Face at recent incident, a watershed moment and talked about how the Defenders window is now open. When you explain that statement and the significance behind it. So I think Hugging Face shows two things. One is call it something for us in terms of how we monitor sandbox and control the models during evaluation. And that's something we've really risen to that occasion. Our team has totally changed so much of our internal standard and really implemented a lot of controls that I think are very important and very critical as we look 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.

10:14And 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- Big, big, big people. Huge risk, right? It's something not to add all right off. But you also have to prepare for it 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 the things that it found were quite sophisticated. And this capability broadly diffused, I think it's something that will really empower threat actors in new ways. And I think that defenders need to use this time before that technology's broadly available to secure themselves. And the nice thing about it is it's dual use, right? It's something where if you can find vulnerabilities, if you're an attacker, you can use it for no good.

11:16But 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. And so our belief right now is that there is this window of you have frontier capabilities. You have the broadly diffused capabilities. And you as a defender, by default, you know, your security's probably pretty static, been static for the past five, 10 years, that kind of thing. You need to move, use these frontier capabilities that you have differential access to, right? We're, 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, do, right? Okay, so I've got a comment and a question. I would say there's a third thing that we learn, which is 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 that make and talk to each other and organize themselves and do stuff for me. Like that's pretty amazing. That was on the good side. On the other side, so I agree that we've got

12:17a kind of defense window. However, we have like 50 years of code and architectural ideas and deployment ideas that weren't built for this world. And so yes, 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 honeypots of consumer data and all these things lying all over the internet. And, you know, from a consumer standpoint, it's like, okay, I can't protect my stuff. These, all these companies have to get their act together, which 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

13:18be 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 program. That was pretty awesome. By the way, congratulations. Thank you. Thank you. And it's both an important problem for what it is as significant implications and applications to, who are 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, 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 cybersecurity, 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 unhauled. You are now defending.

14:18You are now up level in our security architecture. You're going to use the models to find all the holes. And we found a number of serious issues and we fixed them. And I've talked to a number of CSOs over the past couple weeks and months. And there are many companies who are also telling you that they've applied these models. They found some very significant issues. But they're able to fix them. And one positive sort of part of the story is that when we took Astra pointed out our systems, we found some new problems. But eventually it's saturated. We basically have found to our knowledge all of the P0s, all of the critical problems that Astra is smart enough to find. 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. It's the old being 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. And ideally, you've managed to automate this, what we call a defense factory. And that's what we're building internally. This end-to-end of both fine vulnerability, triage it, remediate, deploy, validate that end-to-end.

15:22And if you can do that at machine speed, I think the defenders will be advantage in deeply significant ways. And there are ideas, for example, formally terrifying all of software that are possible with AI. Yeah, we never, that's always been a dream. We've had these informal languages and all these kinds of things, but they never kind of took off. That's right, because it's just intractable for people. It's just so hard. But we have these AI's that are solving these crazy impossible math problems. And so one application of that, that proving power, right now, actually one of the things to know about the Navier Stocksprom is that we formalized it. We formalized it into lean. So the AI's can write verifiable code. They can. Yeah, very nice. Yeah, that's a great idea. So 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. We just see it coming. Closing the loop on this incident. Is there anything you felt that the narrative got wrong in an important way, or is there any preferred way

16:23of talking about what happened or this window that's important to get across when you think about the public narrative, or is there anything just inside the company else that changed the terms of how you're approaching 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 capabilities exist right now in the world, but that they are in a 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 who are in, they got to 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, because that's, it's like every day matters. And to use those days, you need access to these tools. 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

17:24like this, and that they said the frontier models refuse. But they didn't actually try our frontier models. And we 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 this sort of, but I get this, this real sense that it has to happen. I'll also have 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 GPT-3, it was beginning up December 2019. So everyone's about to head out on vacation. You know, I was like, okay, we trained the model. And I should 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. And it's like every day that no one is exploring what's capable of and trying to understand it, to figure out what to do with it. That's a day that has 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 sort the numbers, it didn't work very well.

18:26But it was just like this, like really tried to probe and see what's possible. And I think that that spirit in ethos is something I think we should bring to what we're building today. 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. I thought you was saying two things. What, what, do you have another one? I was an accessor, did you say both of them? I did, I said both of them. Yes, yes. The, let's go back to Astro. It's incredible to see all the excitement on X. It all starts boot cases, people where they said, but the beer use. You've said that in some ways it is, bring us closer to AGI. What do you talk about? What do you find most compelling in Azure or what you think the breakthrough there and laid that statement and where are we still left to go? Actually, wait, sorry. Let me actually agree with my, my, or your answer and all I can say. I, so both access and let me also tell another story about how I've used the models personally.

19:27So 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 where you can go to rockman.com, not the most popular website on, get a good slide post on. You got some blog posts, it's a static site, it's a very simple, like what kind of folder build this could be there. So I took my codex and asked it, go check out greatrockman.com, tell me if there's any folder or abilities. So it did a pen test and it came back with 13 findings. And these findings were things like, I answered my SPF records so that you would prevent people from spoofing emails, right? That there was some, it was sort of, you know, you can go over HTTP without forcing people to HBS, things like that. And individually, these things are maybe not the biggest deal, but if you think about with an AI, there'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 from me? Probably not. So 15 minutes for it to find these 13 findings. But then I asked it, can you fix these? I guess fixing this saw a knife,

20:27wow, so painful and boring, exactly. And so 45 minutes, it opened up my cloud for a control panel, clicked around, set all the headers, it migrated and need to call it for pages. It's like set everything correctly. It started the demarc process, which apparently you have to you like a 48 hour window or whatever. And it was 45 minutes of fixing. And I felt so protected. I felt like, wow. Did you ask to find the vulnerability? There you go, no, I added yet. So I actually dug these out automatically. This is five, 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 48 hours, we'll check back in to complete the demarc process. And I was like, all right, this is, we're in business now. All right, great. Greg Brockman, I can't miss it. There we go, you too, it'd be protected. Awesome. Let's transition to Astra. It's incredible to see all the excitement online and into the use cases, Google Racer, but I'm curious among other things, you said it's sort of closer to the way along to AGI.

21:28I'm curious what you find most groundbreaking with it. And we're just 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 GPT-6 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 just never feels like it's a right moment to go for a major version bump. You always have to be like, that's 5'6, if you 5'7. 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 in a way that we could have predicted. But it just was like all of these factors happened to line up at once. And so I think 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 the computer use is so significant

22:29is that for agentech 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? And so people have been building these NCP servers and these CLIs and just really sort of taking the world of software and making accessible in this almost stilltid way. There's not really meant for humans. It's like we're retwined in the world. Right, you made it. It's kind of like, oh, we'll build an API. Like it's a software. But what if it's really more behaving like a human? Can't it just use a computer? 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. 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 NAPA and we talked about our plans. We actually laid out this three-step plan. The base case, what we ended up following

23:30for 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 the computer is in there. It's in distribution, aren't you? So I said, as I'd sound, whatever. But you basically have the full power of a computer there. And we had some abortive 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 take a screenshot of something that have been making 3D model. And you can actually, lots of people are now designing houses or trying to redesign either living room, all those things by just utilizing this capability. And to me, the thing that really stands out is that you can now move forward on AI that can do things for you without you have to build all the specific connectors. And I think there's so much software that you don't even think about. You have to orchestrate every day.

24:30And how much of your life is cooking around menus and typing things to do with spreadsheet and things like that. 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 five 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 be getting our carpal tunnel or hunch shoulders or all of those physical problems that are us contouring to the machine. It's now the machine is there to help us to empower us to really serve us. Yeah. And that's a really good point because I think one of the things that you have been, I would say more sober on as a company is just, OK, what happens with employment? And I think that that's exactly right. That there's all these things that we do because we have to do and like it became valuable, but we shouldn't be doing it. All they do is wreck our health and wreck our personalities. And the idea that humans are going to just run out of ideas

25:30of cool things to do or how to make the world better or problems to solve 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, and of course, it's unknowable. 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 a progresses? Well, I do have a fundamental belief that AI is surprising. I think we've even put this in the open AI 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, biological conclusion it should be a certain way. I think the same will be true, right? I think that there's something that we've learned about that humans, and I think 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

26:32and being accountable for outcomes, like those feel fundamental to me. Those feel like the things that we actually should preserve for the long term, right? That's something that feels like deeply human. People are not valuable just because we can do tasks, right? We're valuable because of our people. And I think that it's important not to uside of that 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. But also 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 gonna see a wave of entrepreneurship where it's actually already starting. I've heard from someone in the particular industry who was saying that a bunch of people in his world are now making the leap to go quit and start their own firms. And they're doing it because they have these AI tools. They're just like, I can do so much more. And so it's the bearish entry for entry and this should be a wonderful Renaissance. Yeah, though, it's been a lot of fun for us and just going, okay, there are,

27:33particularly for our young people because they get by default the grunt work. But what if the AI does to the grunt work then they can really develop much faster actually because they can kind of get involved on the, really 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 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, I have to say, Astro is very good at writing investment. Next sauce. I love hearing that. Yeah. And again, I do think it's going to be a nuanced 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 it's future can be much better for them to pass for everyone. Yeah, and that's, it feels like what we should expect, you know?

28:34Like, 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 created the whole lot of movement and all of those kinds of things. Yeah, nobody here wants to go back to 1870. And so the idea that, no, we don't want to go into the future now, seems a little short-sighted, but I think the speed at which things are moving is very, very scary for people. And 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

29:34and 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 years. I think that this should be something that is front and center. I think people sense it. If 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 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 in AI is higher in 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 every more? It's striving now. What can we do about it? What can we learn from? Well, one thing that I think about is that I think we as a feel,

30:35as 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 coming to this single most important strategic priority and resource for the United States. It's Avenue. Yes. Absolutely. You look at Chatudy T, 300 million health queries or through to million people every single week using it for health. That's a huge deal. And where the billion, almost 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? It's using Chat every single week. So people are touching this technology. But I think 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 of the, 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.

31:37And a doctor tells you something you don't know what the thing is and how do you get that that sort of sanity check degree, even understand it. People who I, that their life was saved through information delivered by Chatudy T. I'll tell you a story for one of my friends is that she was in the hospital and the doctor was about to inject a antibiotic. And she was like, give me a moment, she typed in Chatudy T. And Chat said, absolutely do not take that. If you do, you may die because you have this thing that you have a year ago, you have this condition, like this kind of thing, we reaction, show it to the doctor. I know, right? And the doctor said, oh my goodness, no, no, it's absolutely right. I had no idea. I only had five minutes to read your chart. Yeah. Many such cases go away. Many stories are there, at least. Exactly. So 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, it would be totally unable to do it otherwise. Like that kind of empowerment, again, people who are able to save money, make money, live

32:41a better life. Those kinds of stories, I think, need to be in the public consciousness as we approach this in this question. So expanding the narrative, you've got a teacher in your pocket, a doctor in your pocket, a lawyer in your pocket, therapist in your pocket, all these utilities in your pocket, while also not threatening those same, well, also telling the teachers and doctors in your third person, hey, you've now got this tool too, as you're going to make your future business better as well. Yes. And so that's just the narrative, it's the reality yet, right? You need both. I think that many other countries are looking in, seeing the position that the US is in, seeing the potential of this technology. And partly do you think about demographics that I think in many of these other countries, it's more keenly felt that there's an older generation that's much larger than the younger population that is going to support them, these questions of how is that supposed to work? And so I think that there's 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.

33:43And so again, I think that there's something that we need to do better as a field and as a company in order to communicate this domestically, but I think the potential is there and where it's 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 guy there. Yeah, particularly if we ban data centers, I think that'll be a problem for us maintaining our lead. It will drive the data center overseas, which is what happened with Silicon back in the days or so. Right, right. So many, one of the interesting things about data centers is it creates so many blue collar manufacturing jobs. I think Switch employs like 45,000 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 they're great jobs. They're high paying. And then, you know, I think that, well, there have been bad actors in the data center space.

34:49Most of them are very good actors and contribute to the power grid, don't waste water and 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 or like we're going to stop AI, which I like we're not going to, even as a country, we're not big enough to stop AI. So that idea like AI will continue without us. And then we'll have zero say as opposed to where the leaders and then we have all the say. So today really, really important cultural message. I think it's very important. And I think 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 Abel, which is the data center that actually trained Astra, that uses about the same amount of water as an office building. All right. So it's really just really, yeah, the technology is quite advanced on that.

35:49And that we have a number of community commitments so that we can actually help 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 Codex access. So there's this broad set of benefits that we're bringing to bear. But again, I think that we need to do even more. Yeah. And making those kinds of things a requirement to build a data center is very reasonable. But let's have positive some ideas as opposed to, okay, we're going to jump out of the AI game as a country and let China or whomever dictate what it's going to be. Speaking of contributions, you guys have made a billion dollar commitment to frontline defenders. So 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's 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.

36:50So 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 closely with partners. For example, proud strike and we are working together to provide discounted access to defenders as well. And I think that 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 super positive advance because this is our before AI hospitals were getting broken into, held hostage all the time. Our water supply has been hacked by foreign actors, state actors. And so like we're already dealing with kind of our critical infrastructure was not built for with cybersecurity in mind. It's not been maintained with cybersecurity in mind. And here is an opportunity to go from not even secure in a pre-AI, AI world to completely

37:55secure. So to me, this is like an incredibly important effort to not have our water supply and our hospitals at risk. I really agree with that perspective, right? The point of I think that we as a society have been lacks, right? That we've allowed tech debt to pile up that every cybersecurity organization, I've never met a CSO who felt that they were appropriately resourced, right? That they were probably prior to us. Never. And particularly now 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 real abilities to do it. And I think that delivering the secure world that we all deserve so that we can really depend on it and be safe and secure in our daily lives and online lives like that to me feels like table stakes. We absolutely need to do this. Definitely. That's a great effort. Closing the loop on Astro, you've emphasized that capability of storming jagged. Do you think still left to go or still needs to be fleshed out that gets approximates most

38:58your definition of AGI? Well, I think that AGI has turned out to be less of a point in time in one of this sort of fuzzy spectrum. And for me, Astro 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 of tasks and it'll just do it. We've seen it run coherently for 24 hours to go accomplish tasks that I think are quite amazing and across wide variety of domains. Now, it's still as 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 sloped writing. Yeah. But it's not great writing. 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 see this, I saw someone post a graph on Twitter of this jagged frontier and where we really need to be is a much more steady across the board.

39:59I'm 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 accelerated, it is empowering. And it's something that I think we've never really seen a model that's been a jump like this. But one of the things that's been interesting for me is that as you solve problems sometimes the world doesn't realize it. Like so I haven't seen a hallucination in quite some time. But nobody says, oh, the model's done hallucinating more. It's just kind of in the ethos that's what AI does. How do you think that'll just go way over time or is it, you know, does there need to be some like continually education for the, for the DOM like hard, quartet people? I think this thing is moving. I think one of the most important problems we actually have is the continual education, right? Really, how do you, people shouldn't have to extract from the AI what it's capable of.

41:01It 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 have over a billion weekly active users on Chashy BT. But I think we have something like another maybe 1.5 billion people who have used Chashy BT and don't use it anymore. Oh, wow. Right? So think about that. That's a significant fraction 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 just shows you the kind of problem we have in front of us is that these AI's like, if you look at Chashy BT and Chashy BT work, they're both text boxes. Right? New text boxes 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, 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 all problems for you that

42:02help in your personal life and 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 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's such an interesting idea. We need more helpfulness out of our AI's. 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 helpful engineers at OpenAI. But there is something about, if you think about how do you work with another person, a new coworker you've never worked with. It takes you a little bit of time. You kind of feel the amount. You see how they respond in different areas. People do not come with an instruction manual. And often actually sometimes it's interesting in areas like consulting or something where they really need to like, Myers-Briggs and that they do say like, here's like a quick

43:05way 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. If 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 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 our North Star 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 the less wrapping yourself around the computer, the computer should be there to empower you to help serve you. Right. I'll talk to you. The business is ripping. You guys have such broad surface area in terms of what you cover.

44:06How 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 encompassed so many things, you know, researched product commercialization, org design management, et cetera. How are you also thinking about pricing your time? Well, they go hand in hand. So this year, the theme was focus. I think that we really realize 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 AI benefits, all of humanity. Now, how do you backsell from that? What are the areas like deployment and prioritization 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. Very core, but how do you, when you think about this moment we're in of this agentic coding takeoff, that exponential, what areas reinforce that and which ones were kind of just sort of, you know, they got labeled a side quest in the media, but just we're not on track for it,

45:09even 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 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 is 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, the 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 there was just a lot of metrics that we're 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 for Boy Favorite.

46:10Yeah, 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 could 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 tackling. And so that's what we have done for this whole year. And for myself, I throughout opening, I 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 spend a lot of effort to get our pre training infrastructure into great shape. This year has 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 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 and trying to bring together a bunch of functions that were otherwise kind of running in parallel or crosswise.

47:12And 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, 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, does this make sense still? I don't quite get that. Sometimes 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've got to figure out how to even talk about it. How do we think about it? How should the world think about this? And 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 hang out and just kind of like, should being like, does this line make sense? Wait, what do we really mean by this? And so really trying to up level execution. Sometimes in small ways and sometimes large. That's fantastic. By the way, exact right way to operate. Do you know what the next year will focus on or? Well, look, I think that the business is a huge area that I think we're not done yet

48:16with really up leveling every part of execution. So that gives a lot more to do there. But I also think we are moving into 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 that 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 invariance 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 co-designed, deeper intertwining across these functions that are maybe on the surface,

49:17very disparate, right, all the way from go to market to long time research to chip design. By building these in a coherent way, wherever it 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 lockstep as time goes on. I know we can go all day, but we have a hard stop. I think this is a great place to wrap. Great. Thank you so much for coming on the podcast. Thank you, Rosalind. Great. Fantastic. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating, we're review and share it with your friends and family. For more episodes, go to YouTube, Apple podcast and Spotify. Follow us on X, a A16Z and subscribe to our substack at a16z.substack.com.

50:21Thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com forward slash disclosures.