Greg Brockman Says AGI Has Arrived

Key Takeaway

Run an AI-assisted security review on one digital asset you control today—your website, cloud account, or small business workflow. Ask the model to identify vulnerabilities, prioritize the highest-risk findings, propose fixes, and verify the changes afterward. Brockman’s example shows that small configuration gaps can compound into serious exposure; the advantage comes from creating a repeatable loop of finding, fixing, deploying, and validating rather than treating security as a one-time project.

Episode Overview

OpenAI co-founder and president Greg Brockman argues that increasingly autonomous computer-use models mark the beginning of an "AGI era," while emphasizing that capability remains uneven across tasks. In conversation with Ben Horowitz, he explores the compute bottleneck, AI-enabled scientific discovery, a growing cybersecurity threat window, and the need to make AI simpler, more proactive, and broadly beneficial.

Full Transcript

Transcript of Greg Brockman Says AGI Has Arrived from A16Z. Auto-generated from episode audio; may contain minor errors.

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. 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 won't have enough compute to serve it all. You don't win the Super Bowl by saying, I wanna win the Super Bowl. You win it by blocking and tackling. You really have to make sure that safety, security, and alignment, those are all standards that you're constantly up-leveling.

I think that's gonna be a huge challenge people are underestimating. You've made two very big bets in your career, helping build Stripe early, and helping, of course, found OpenAI. The world needs to act with urgency. Yeah, 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 OpenAI, 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.

Do you know what the next year we'll focus on? I think this is going to become the most important conversation. 10 years ago, Greg Brockman and Ilyas Huskever estimated that AGI might be 10 to 15 years away. Today, Greg says we're entering what OpenAI calls the AGI era. In this episode, I sit down with Ben Horowitz and OpenAI 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 OpenAI 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 OpenAI's response to recent cybersecurity developments, why Greg believes 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 across 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, found OpenAI.

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 made today, or what would you tell them about what you would expect? Well, so Ilya 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 Moore's law progress, that kind of thing, 15 years felt like about the timeline to AGI.

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 it'd 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 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 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 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 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 going to be a huge challenge people are underestimating. Right, interesting. So the models will be plenty powerful or they'll continue, but 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. 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, bringing 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 airtime 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. And so the approach that was taken 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.

And do you feel like, yes, we can make progress against that fast or is that a really hard different category or problem or 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, I think people underappreciate some of the most key results that came out of opening eye 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. Drew started thinking about how can you align a model to match what humans want, right? By providing feedback from people. Just for usability. Exactly. And 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 investment. So it's in some ways that I think that there was this early phase when OpenAI started where we really were thinking about AGI safety, things like that, and that was very front and center, even at the comms.

And then I think that as things like ChachiB 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 of 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 SOTA models, but it seems like those kinds of ideas, you and Google and Anthropic and SpaceX would want to share and meta 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 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'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. And 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. And 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.

Very interesting. You've called the OpenAI Hugging Face recent incident a watershed moment and talked about how the defender's 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. And that's something we've really risen to that occasion. Our team has totally changed so much of our internal standards 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. 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 with one or a few entities, people. Huge risk, right?

It's something not to at all write off. But you also have to prepare for 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 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. And so our belief right now is that there's this window of your frontier capabilities, you have the broadly diffused capabilities, and you as a defender, by default, your security is 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 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, right? Okay, so I've got a comment and a question on that. I would say there's a third thing that we learned, 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 and they can 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 a 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. All these companies have to get their act together, which seems a bit worrisome. And do you think kind of in the future, 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.

Yeah, that was 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, it's a whole wave 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. So we took a group of our production engineers and said, sorry, all your projects are on hold, you are now defending, you are now up-leveling 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 CISOs over the past couple of 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 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. 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, 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, 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, formally verifying all of software that are possible with AI.

Yeah, we never, that's always been a dream, we've had these formal 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 AIs that are solving these crazy, impossible math problems, and so one application of that, that proving power, right, and actually one of the things to know about the Navier-Stokes problem is that we formalized it, right, we formalized it into Lean, and so. So the AIs can write verifiable code.

They can, yes. 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. Yeah, we're in a very dangerous window right now, yeah. We just see it coming. Closing loop on this incident, is there anything you felt that the narrative got wrong in an important way, or is there any preferred way of 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 in terms of how you're approaching this 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 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, 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 like this, and that they said that frontier models refuse. But they didn't actually try our frontier models, and they actually believed 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 – yeah, 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 GPT-3. It was beginning of December 2019. So everyone's about to head out on vacation. Everybody's like, okay, we can train the model. And I just remember feeling like, this model's sitting on a shelf. No one is using it. It's this 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, 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 sort lists of numbers. It didn't work very well. But it was just like this, like, really try 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 sort of obviously a 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 you said two things. Do you have another one? What was access, or did you say both of them? I think I said both of them. Yes, yes. Let's go back to Astro. It's incredible to see all the excitement on X, all sorts of cases, people were excited about computer use.

You've said that in some ways it is bringing us closer to AGI. What do you talk about? What do you find most compelling in Astro? Or what do you think the breakthrough there in light of that statement and where we still have to go? Actually, wait, sorry. Let me let me actually revise my earlier answer. And I can say, 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. Greg Rockman dot com. Not the most popular website. I get a good slide post on the exact. You got the blog post. It's a static site. It's a very simple like what kind of vulnerabilities could be there. So I took my codex and asked it, go check out Greg Rockman dot 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 was 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 spoof emails for me? Probably not. So 15 minutes for it to find these 13 findings.

But then I asked it, can you fix these? Because fixing is so annoying, so painful and boring. Exactly. And so 45 minutes, it opened up my Cloudflare control panel. It clicked around, set all the headers. It migrated me to Cloudflare pages. It's like set everything correctly. It started the demarked process, which apparently you have to do 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 it to find the vulnerabilities?

There you go. No, I added. So I actually dug through that automatically. This is my soul. It said, I just checked that 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 demarked process. And I was like, all right, this is we're in business now. All right, all right, all right. Craig Brockman dot com. Let's see it. There we go. You too can be protected.

Awesome. Let's let's transition to Astra. It's incredible to see all the excitement online and the use cases. People are excited about computer use, among other things. You said it's 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 have 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 the right moment to go for a major version bump. You always want to be like, that's five, six, five, seven. 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 these these factors happened to line up at once. And so I think that 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? And so people have been building these NCP servers and these CLIs and just really sort of taking the world of software and making it 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? 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. So it's kind of, yeah, I'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. The basic 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 the computer is in there. It's in it's in distribution, right? So let's 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 have it make a 3D model. And you can actually, you know, lots of people are now designing houses or trying to redesign their 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 this specific connectors. And I think there's so much software they don't even think about.

You have to orchestrate every day. And like how much of your life is like clicking around menus and like, you know, typing things to a spreadsheet and things like that. Like none of that is what we should be doing. A hundred 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. But we will get our time back. We're not going to be getting our carpal tunnel or hunched 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. You know, that's a really good point, because I think, you know, 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 of 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 I gets, the higher employment goes, not the lower. And so I wonder, 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 as it progresses?

Well, I do have a fundamental belief that is surprising. 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. I think the same will be true, right? I think that there's something that we've learned about that humans, 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 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 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.

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 from someone in a 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 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. For Andreessen, this should be a wonderful renaissance. Yeah, well, it's been a lot of fun for us in just going, OK, there are, particularly for our young people, because they get by default the grunt work. But what if the AI does the grunt work? Then they can really develop much faster, actually, because they can kind of get involved on 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 investments. It's awesome. 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 is 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 can be much better than the past for everyone. Yeah, and 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 created the whole Luddite movement and all those kinds of things.

You know, nobody here wants to go back to 1870s. And so the idea that 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 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. If you can sense it in how people react. act 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 you 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 in certain Asian countries all Asian countries as well and actually in European countries everywhere but the U.S. has like got the lowest AI sentiment. Why is that or any more it's driving that. What can we do about that. 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 tragedy T 300 million health queries or 30 million people every single week using it for health. That's a huge deal. And we're at a billion almost one point one billion weekly active users. I think within the U.S. it's about 100 million something like that like a third of the population.

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 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 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 Chachi BT. 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 into Chachi BT and she 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 shows 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. Many 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.

Those kinds of stories I think need to be in the public consciousness as we approach this question. So it's painting the narrative that you've got a teacher in your pocket a doctor in your pocket something you know a 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 to make your business better as well. Yes. And it's not just the narrative it's the reality.

You need both. I think that many other countries are looking in seeing the position that the U.S. is in right 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 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. I think we're seeing across the world. And so again I think that there is something that we need to do better as a field and 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 the 80s or so. Right. And there are so many you know one of the interesting things about data centers is it creates so many blue collar manufacturing jobs. I think switch employs like 45000 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 they're great jobs. They're high paying. And then you know I think that while there have been bad actors in the data center space most 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 the bad 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 it's a 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 Abilene which is the data center that actually trained Astra uses about the same amount of water as an office building.

All right. So it's really it's really yeah the technology is quite quite advanced on that and 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 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.

But like let's have positive some ideas as opposed to OK. We're going to jump out of the A.I. game as a country and let China or whomever dictate what it's going to be. Speaking of 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 defenders 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 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 super positive advance because this is our before A.I. 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 first 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 A.I. world to completely secure. So to me this is like an incredibly important effort to you know not have our water supply and our hospitals at risk.

I really agree with that perspective right to 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 cybersecurity organization. I've never met a CISO who felt that they were appropriately resourced that they were appropriately prioritized. Never. 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. That's a great effort. Closing the loop on Astra. You've emphasized that capability will still remain jagged. What do you think 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 OK 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 that we've seen 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 slop writing. 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 see this like I saw someone post a graph on Twitter of like you know it's like kind of jagged frontier and where we really need to be is a much more steady across the board 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 it's been a jump like this. Yeah. 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 models don't hallucinate anymore. It's just kind of in the ethos of that's what I does.

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 continually education for the for the down like hardcore tech 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 A.I. what is capable of it should go the other way around. I 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 Chachi BT. But I think we have something like another maybe one point five billion people who have used Chachi BT and don't use it anymore. Hmm. 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 A.I.'s like you look at Chachi BT and Chachi BT work they're both text boxes. Right. It's like this new text box is way better than the old text. Right. But there's still some things the old text box is better at. So don't always use it. It's like that is not the A.I. we were promised. The A.I. we were promised should be an A.I. 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 help solve problems for you that help 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 like we need more helpfulness out of our A.I.s. Yes. Which is kind of a thing like some humans aren't and probably the humans who develop A.I.

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 helpful engineers at OpenAI. But there is something about if you think about how do you work with another person. Right. A new coworker you've never worked with. 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 interesting in areas like consulting or something where they really lean into like Myers-Briggs and that they do say like here's like a quick way to know who I am and how I operate.

Right. So that there is some precedent for how humans can kind of present a little bit more of a resume you have certain 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 we 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 less wrapping yourself around the computer. The computer should be there to empower you to help serve you. Right. Right. 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 changing you encompass so many things you know research product commercialization or design management etc. How are you also thinking about 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 AGI benefits all of humanity. Now how do you back solve 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 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 take off 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 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 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, 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 there was a lot of just sort of telling the team, we just need to focus on the basics. Like one of my favorite management books is The Score Takes Care of Itself. Have you guys read that one? Yeah, 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 basics. 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. 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 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 and 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, 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, does this make sense still? I don't quite get that. Sometimes when things are confused. For example, over the past couple of 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 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 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 up level execution, sometimes in small ways and sometimes large. That's fantastic. By the way, Zach, right way to operate. Do you know what the next year we'll focus on or keep it private for now? Look, I think that the business is a huge area that I think we're not done yet with really up leveling every part of execution. So 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 that 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 an 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 OpenAI, certainly for the past five years, has been deeper co-design, deeper intertwining across these functions that are maybe on the surface very disparate, 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? 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.

We could go all day, but we have a hard stop. I think this is a great place to wrap. Greg, thank you so much for coming on the podcast. Thank you for having me. 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 or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X at A16Z and subscribe to our substack at a16z.substack.com.

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