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Greg Brockman Says AGI Has Arrived

Ben Horowitz and Erik Torenberg sit down with OpenAI co-founder and President Greg Brockman to discuss why he believes AI has entered a new phase, what OpenAI’s latest models reveal about the path to AGI, and the safety and security challenges that come with increasingly capable systems. Greg explains why computer use represents such an important step for agents, including models that can work coherently for 24 hours and interact with software through the same interfaces humans use. He also shares how OpenAI deployed 10,000 agents to tackle the Navier-Stokes problem, and why advances in mathematical reasoning could translate into new approaches to science, software, and cybersecurity. Ben, Erik, and Greg also dig into the “defender’s window” for cybersecurity, how AI could reshape work and entrepreneurship, and what the AI assistant of the future might actually look like: persistent, proactive, personalized, and capable of doing work on your behalf rather than waiting for another prompt. Timestamps: 00:00 - Intro 00:51 - Could You Have Predicted Today's AI 10 Years Ago? 04:14 - Cyber Hacking, Reward Hacking & Building Safety Into Architecture 08:42 - The Hugging Face Incident & Why the Defender's Window Is Open 18:28 - Astra & Why Greg Says We're Now in the AGI Era 24:25 - The Future of Employment as AI Progresses 29:39 - Why AI Sentiment Is Higher in Asia Than the West 38:14 - What's Still Jagged: What Astra Needs to Approximate AGI 43:25 - How Greg Prioritizes His Time 47:32 - What's Next: The AGI Era & Deep Co-Design Resources: Follow Greg Brockman on X: https://x.com/gdb Follow Ben Horowitz on X: https://x.com/bhorowitz Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that 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. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.

Greg BrockmanguestBen HorowitzhostErik Torenberghost
Sep 14, 202649mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:000:51

    Intro

    1. GB

      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.

    2. BH

      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.

    3. GB

      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.

    4. ET

      You've made two very big bets in your career, helping build Stripe early and helping, of course, co-found OpenAI.

    5. GB

      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.

    6. ET

      Do you know what the next year will, will focus on, or?

    7. GB

      I think this is going to become the most important conversation.

  2. 0:514:14

    Could You Have Predicted Today's AI 10 Years Ago?

    1. ET

      Greg, welcome to The a16z Podcast.

    2. GB

      Thank you for having me.

    3. ET

      So Greg, you've made two very big bets in your career, uh, helping build Stripe early and helping, of course, co- co-found, uh, 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, uh, you know, ma- made today, or what, what would you tell them about what, what you would expect?

    4. GB

      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, and if you really squinted at it and you're wi- 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-

    5. BH

      Yeah

    6. GB

      ... sort of moment for everyone to be a part of and to be able to help shape collectively. But it also feels a little bit like maybe it's kind of the conclusion of, like, a lot of forces that are all coming together for this moment, and you, if you step back and really take that sort of macro view, it, it kind of makes sense it's happening now.

    7. BH

      And do you think, um, we're currently, 'cause you guys slightly underestimated the timeline, or I g- I guess it was basically on, on point. Um, do you think we're still on the timeline given we're now starting to drive real shortages on the supply chain?

    8. GB

      W- well, look, I do think-

    9. BH

      Yeah

    10. GB

      ... that we are in a world where it is hard for compute to keep up with the demand that we're already seeing in the market, right? And just in terms of how people are gonna-

    11. BH

      Mm-hmm

    12. GB

      ... use this technology, benefit from it.

    13. BH

      Right.

    14. GB

      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-

    15. BH

      Mm

    16. GB

      ... I think that the, the progress, I do see, like, we have line of sight to continue to make the models much more capable, safe, and aligned, but also really distributing that power and the benefits and the empowerment to, to everyone, I think that's gonna be a huge challenge people are underestimating.

    17. BH

      Right. Uh, ah, interesting. So we'll, we'll have the mo- the models will be plenty powerful, um, or, or they'll continue a pace, uh, but they'll be, it'll be hard to get to everybody as certainly in an affordable way given that we won't have enough compute to serve it all.

    18. GB

      I think that's true.

    19. BH

      Yeah. Yeah.

    20. GB

      And I do think we're at a point now where we have to really start-

    21. BH

      Uh

    22. GB

      ... thinking about what we call pacing the frontier.

    23. BH

      Right.

    24. GB

      And so thinking about as we move to more capable models, you really have to make sure that safety, security, and alignment, those are all standards that you're constantly up-leveling, and those actually become almost the, the bottleneck to progress or sort of the, the sort of, you know, the, the part that you have to, uh, spend a lot of your effort to make sure you've gotten right. And so I think in my mind it's more those constraints and the compute, I think we can make it happen, and then on the flip side, I think, yeah, this bringing it to everyone, which is ultimately about our mission, right?

    25. BH

      Yeah.

    26. GB

      It's empower everyone, ensure it benefits everyone. Uh, that's something that I think deserves a lot more airtime than it's gotten.

    27. BH

      Yeah. Uh, actually l- let's get a little deeper on the safety thing, 'cause 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 [laughs] uh, that people, like, don't like." And so the approach that was taken was kind of, uh, surface around

  3. 4:148:42

    Cyber Hacking, Reward Hacking & Building Safety Into Architecture

    1. BH

      the edges, "Okay, we'll, um, you know, put some filters on this guy, and we'll RLHF, you know, around the edges." But, like, if you get deep into the thing you'll be able to get the bad words out, but if somebody wants to go through that to hear bad words themselves, who cares? Uh, but then [laughs] you know, when you get into, okay, now these things are really good at, uh, cyber hacking and other kinds of ideas, um, now you need kind of a more architectural idea where the model itself knows not to reward hack in a way that's going to be dangerous and so forth. And how, do you feel like, yes, we can make progress against that fast, or is that, like, a really hard different category or problem, or how, how are you thinking about that?

    2. GB

      Well, I absolutely think we can and are making very rapid progress-

    3. BH

      Oh, great

    4. GB

      ... on this problem. I think that there's a lot of both great ideas and research that we've been investing in for many years. Actually, if you rewind to 2017, uh, the-

    5. BH

      Right

    6. GB

      ... I think people underappreciate some of the most key results that came out of OpenAI in the field at the time. So both kind of the first inklings of modern language models. You can find a pap- paper from 2017 that kind of laid that out with LCMs and, you know, it was, like, kind of this very baby result. But also r- reward AI learning, AI reinforcement learning from human preferences.

    7. BH

      Mm-hmm.

    8. GB

      That was also created in 2017 to start thinking about how can you align a model to match what humans want, right? By providing-

    9. BH

      Ah, right, right, right, right

    10. GB

      ... feedback from people.

    11. BH

      Yeah.

    12. GB

      And-

    13. BH

      Just for usability.

    14. GB

      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-

    15. BH

      Mm

    16. GB

      ... that it's staying aligned with you? And We had ideas such as debate or, uh, iterative amplification.

    17. BH

      Mm-hmm.

    18. GB

      So these are ideas that were really sort of at this phase before these systems existed, and you can start to see the sort of trickle-down of those ideas into modern systems and, and investment. And so it's in some ways that I think that there was this early phase when OpenAI started-

    19. BH

      Mm

    20. GB

      ... where we really were thinking about AGI safety, things like that, and that was very front and center in t- in even the comms.

    21. BH

      Mm-hmm.

    22. GB

      And then I think that as things like ChatGPT took off, then people started to see, okay, well, we're not at this point yet, and so the, is the AI politically neutral, and questions like that-

    23. BH

      Mm-hmm

    24. GB

      ... start to become front and center.

    25. BH

      Right.

    26. GB

      And now that we're here, all of these o- 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-

    27. BH

      Oh

    28. GB

      ... sort of thinking about this moment for a long time.

    29. BH

      Oh, that's really good news. And, and when you think about, a- a- and we don't have, like, a really great community yet amongst [chuckles] the, uh, SOTA models, but, uh, it seems like those kinds of ideas, you and Google and Anthropic and, uh, SpaceX would want to share, and Meta now, um, as opposed to, like, okay, this is a proprietary idea that's a way to keep these models safe since you're all on related architectures. Um, or how, how do you see that unfolding? Or is everybody gonna do it independently?

    30. GB

      Well, I think there's nuance here.

  4. 8:4218:28

    The Hugging Face Incident & Why the Defender's Window Is Open

    1. BH

      Very interesting.

    2. ET

      You've called the OpenAI Hugging Face recent incident a watershed moment and talked about how the defender's window is now open. Why don't you explain that statement and the significance behind it?

    3. GB

      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, and really implemented a lot of controls that I think are very important and very critical as we look to, to future more capable models. But there's a second thing that I think is also very valuable for the world that came out of this, which is a insight into what future capabilities will be like when they are broadly diffused and in the hands of threat actors, and that will happen, right? That there are so many people who are building these models, and again, there's something very important and good about the diffusion broadly of AI capabilities because-

    4. BH

      Mm-hmm

    5. GB

      ... there's a risk of concentration of power if one or a few entities, people.

    6. BH

      Big risk, yeah.

    7. GB

      Huge risk, right? It's something not, not to, not to at all write off. But you also have to prepare for if, if everyone is empowered with tools that are cyber capable, and in the case of Hugging Face, you saw both an AI that was able to hack out of a secure environment and hack into a company's production environment, and I think that the takeaway-

    8. BH

      [chuckles] Very cleverly.

    9. GB

      Very cleverly, right?

    10. BH

      Yeah.

    11. GB

      And it's like the, the, the things that it found were, were quite sophisticated.

    12. BH

      Yeah. Yes.

    13. GB

      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's 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 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'll, you will have def- 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 too, right?

    14. BH

      Okay, so I've got a comment and a question on that. I would say there's a third thing that we learn, which is, like, these things have capabilities that I don't know that we all [chuckles] understood before, which, on the good side, like, oh, I can deploy 10,000 agents, and they can talk to each other and organize [chuckles] themselves and do stuff for me. Like, that's, uh, pretty amazing. Uh, so that, that was on the good side. On the other side, so I agree that we've got a kind of defense window. Um, however, we have, like, 50 years of code and architectural ideas, um, and deployment ideas that weren't built for this world. And so, yes, the AI, AI can help us, like, okay, f- 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. Uh, and you know, from a consumer standpoint, it's like, okay, I can't protect my stuff. Uh, these, all these companies have to get their act together, which, um, seems a bit worrisome. And do you think kind of in the future we need a, do we need a decentralized consumer architecture? Like, will this kind of current world that we live in with all these centralized data repositories be viable in a world of AI?

    15. GB

      So several pieces to the answer.

    16. BH

      Yeah.

    17. GB

      And first to your point on what you can get out of 10,000 agents, we actually used 10,000 agents to solve the Navier-Stokes problem.

    18. BH

      [laughs] Yeah. That was pretty, pretty awesome, by the way. Congratulations on that.

    19. GB

      Thank you. Thank you. And it's both an important problem for what it is, has significant implications and applications to fluid dynamics, to how you think about ocean currents, all these things, but for what it represents, right? Of new knowledge created by AI and it unlocking a whole wave of scientific discovery-

    20. BH

      Yeah

    21. GB

      ... medicines, all those things. They're on the table now. So I think there's something really amazing to think about what can happen through the power of AI that is able to really help solve problems. And in the case of, of 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 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, of, of serious issues-

    22. BH

      Mm

    23. GB

      ... and we fixed them. And I've talked to a number of CISOs over the past couple, couple weeks and months, and there are many companies who are also telling me that, yeah, that they've applied these models, they found some very significant issues, but they're able to fix them. And one positive sort of part of the story is that when we took Astra, pointed it at our, our systems, we found some new, new problems, but eventually it saturated. We basically have found, to our knowledge, all of the P zeros, all of the critical problems that Astra is smart enough to find.

    24. BH

      Mm-hmm.

    25. GB

      And of course, there will be a new model, there will be a new round-

    26. BH

      [laughs] Even smarter.

    27. GB

      Exactly. But I think that that's the world that we'll be in, is that you'll be in a world where you want to be in this tight loop of new cyber capability drops, you deploy it against your systems, you find the new holes, 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-

    28. BH

      Mm-hmm

    29. GB

      ... remediate, deploy, validate, right?

    30. BH

      Mm-hmm.

  5. 18:2824:25

    Astra & Why Greg Says We're Now in the AGI Era

    1. ET

      use. Um, you, you, you've said that in some ways it is, uh, bringing us closer to A- AGI. What do you talk about what you find most compelling in Astro or what you think the, the breakthrough there in, in light of that statement and, and, uh, where, where we have still left to go?

    2. GB

      Actually, wait, sorry. Let me, let me actually revise my, my earlier 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. So after Hugging Face, I was thinking about w- how can I use these models in my personal life? What can I do to secure myself? And I've a website, it's a very simple website, gregbrockman.com. Not the most popular website on-

    3. ET

      You have a good blog post on it.

    4. GB

      Exactly. Got some blog posts. It's a static site. It's very simple.

    5. ET

      Yeah.

    6. GB

      Like, what, what kind of vulner- vulnerabilities could be there? So I took my Codex and asked it, "Go check out gregbrockman.com. Tell me if there's any vulnerabilities." So it did a pen test, and it came back with 13 findings. And these findings were things like I hadn't set my SPF record so that you would prevent people from spoofing emails, right? That there were some, it, it was serving 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?"

    7. ET

      Yeah.

    8. GB

      Probably not. So 15 minutes for it to find these 13 findings. But then I asked it, "Can you fix these?"

    9. ET

      Right, 'cause fixing them is so annoying.

    10. GB

      Well-

    11. ET

      So painful.

    12. GB

      And boring.

    13. ET

      Exactly.

    14. GB

      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, like, set everything correctly. It started the DMARK process, which apparently you have to do like a 48-hour window or whatever. Um, and that was 45 minutes of, of fixing, and I felt so protected.

    15. ET

      Mm-hmm.

    16. GB

      I felt like, "Wow."

    17. ET

      Did you ask it to find the vulnerabilities again? [chuckles]

    18. GB

      [chuckles] There you go. No, I had it, yeah. So I actually dug through that automatically. Th-this was five, six total.

    19. ET

      Yeah.

    20. GB

      It said, "I just checked that this one's fixed, this one's fixed, this one's fixed."

    21. ET

      Nice.

    22. GB

      "And in 48 hours I'm gonna have to run, uh, and set up a little automation so in 48 hours it would check back in to complete the DMARK process." And I was like, "All right. This is, we're in business now."

    23. ET

      All right. All right, all right, all right. Gregbrock- brockman.com. Let's see it. [laughs]

    24. GB

      There we go. You too can be protected.

    25. ET

      Awesome. Let's, um, let's transition to Astra. Uh, it's incredible to see all the excitement online, uh, in terms of the use cases. People are really excited about computer use, among, among other things. You, you said it's, uh, sort of closer to the way, uh, uh, along to AGI. I'm curious what you find most groundbreaking with it and, and where do you think we still have to go?

    26. GB

      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 wanna be like, "That's 5.6. Now it should be 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, um, in a way that we could have predicted, but it just was like all of these, these, these factors, um, 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.

    27. ET

      Mm-hmm.

    28. GB

      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 it needs to through these tools? And so people have been building these MCP servers and these CLIs and just really tr- 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.

    29. ET

      Right. Where, where you, 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't it just use a computer?

    30. GB

      Yeah.

  6. 24:2529:39

    The Future of Employment as AI Progresses

    1. ET

      Yeah. Um, you know, that's a really good point because I think, you know, one of the things that, that, that you have been, um-

    2. BH

      I, I would say more sober on as a company is just, okay, what happens with employment? And, uh, I think that, I think that that's exactly right, that there's all these things that we do 'cause we have to do, and like they became valuable, but w- we shouldn't be doing it. All they do is wreck our health and, and wreck our personalities. And the idea that humans are gonna just run out of ideas of cool things to do or how to make the world better or problems to solve, um, seems a little absurd to me. A- and so far, at least in the numbers, uh, the better AI gets, the higher employment goes, not the lower. And so I wonder your-- And of course it's unknowable. You know, we've never had this technology before. It's getting better and so forth. So how do you kind of think about the future of employment as it relates to these models and AI as it progresses?

    3. GB

      Well, I do have a fundamental belief that AI is surprising, and I think we even put this in the OpenAI launch post back in the day, in 2015, just saying that the history so far has been somehow it just doesn't play out the way that you think it does, even when there's this like logical conclusion it should be a certain way. And I think the same will be true, right? I think that there's something that we've learned about that humans, and I think like for any job, that we almost... It's easy to not give it as much credit for how deep the field is and how much sort of sophistication, building relationships. Accountability is a good example of something where I think that people setting goals and being accountable for outcomes, like those feel fundamental to me. Those feel like things that we actually should preserve for the long term, right? That's something that feels like deeply human. Like people are not valuable just 'cause we can do tasks, right?

    4. BH

      Yes.

    5. GB

      We're valuable 'cause we're people.

    6. BH

      Yeah. Yeah.

    7. GB

      And I think that it's important not to lose sight of that in some of these narratives, and I think that the way that things will change and how what we do with our time evolves, and we clearly will be in a world of abundance, and how do we ensure that that abundance is broadly distributed? Um, but also at the same time, I think that we should be in a world where the ceiling of ambition is higher than ever before. And I think we're gonna see a wave of entrepreneurship where it's ar- actually already starting. I've heard, um, from someone in, you know, a particular industry who was saying that a bunch of people in, in his world are now making the leap to go quit and start their own firms, and that they're doing it because they have these AI tools, and they're just like, "I can do so much more." And so it's the barriers to entry and, you know, for Andreessen, this should be a wonderful renaissance.

    8. BH

      [chuckles] Yeah, no, it won't-- It's been a lot of fun for us in just going, okay, there are, [chuckles] you know, particularly for our young people because, you know, they get by default the grunt work. But like what if the AI does the grunt work? Then they can really develop much faster actually because they can, um, kind of get involved on the, you know, really the, the real part of our business, which is what is the relationship with the entrepreneur? How do we open up the world for them? How do we make, um, them feel like, oh, they can do anything and they're an important CEO, and they can go build things? And as opposed to, you know, spend the whole weekend writing an investment memo. Which by the way, uh, I have to say, uh, Astra's very good at writing investment memos. It's awesome.

    9. GB

      I love hearing that.

    10. BH

      Yeah.

    11. GB

      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 gonna be rosy, and it's all going to be just easy. I think it's going to be hard. I think there's gonna be change. But I think that it can be a much better world. I think the fu- future can be much better for, than the past for everyone.

    12. BH

      Yeah. That, a- and that's, it feels like what we should expect, you know. Like before the plow, [chuckles] 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, uh, you know, and, and created the whole Luddite movement and all those kinds of things. You know, nobody here wants to go back to 1870. Uh, and so the idea that no, [chuckles] we don't want to go into the future now, um, seems a little shortsighted. But, uh, I, I think the speed at which things are moving is, is very, very scary for people.

    13. GB

      And we, we really recognize the fact of how things are moving, and that we spend a lot of time really trying to understand as well as we can how people are feeling, how we can be showing up better. And I think that two things, like one is that when we think about development, the pace of progress, we're being very deliberate about it. Safety is our foremost priority. We think about how do we build this technology in a safe, secure way, and what should those standards be? And you can see that showing up in a lot of our comms. Inside the building, it is absolutely what people are thinking about and what we care about, is that we really want this technology to empower everyone broadly. And I think that for us as a world to really think about how do we get the most out of this technology? How do we get the benefits? How do we mitigate the risks? I think this is going to become the most important conversation that we have, and I think that that will emerge over even maybe the next one to two years. I think that this should be something that is front and center. I think people sense it. You, you can sense it in how people react right now, and even thinking about things like data centers and these kinds of questions of do we want AI and how do we, how do 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

  7. 29:3938:14

    Why AI Sentiment Is Higher in Asia Than the West

    1. GB

      right.

    2. ET

      To that end, wh- why do we think sentiment in AI is higher in certain Asian countries?

    3. BH

      All Asian countries.

    4. ET

      Yes.

    5. BH

      Well, and, and actually in European countries, everywhere, but the US has like got the lowest AI sentiment.

    6. ET

      Why is that or maybe more-

    7. BH

      What's driving that?

    8. ET

      What can we do about it? Like what, what can we learn from?

    9. GB

      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.

    10. BH

      Yeah.

    11. GB

      It's happening.

    12. BH

      Yes.

    13. GB

      Absolutely. You look at ChatGPT, 300 million health queries, or 300 million people every single week using it for help, right? That's a huge deal. And we're at a billion, almost, you know, 1.1 billion weekly active users. I think within the US, it's about 100 million, something like that. Like a third of the population, if I have that number correct, right, is using Chat every single week. So people are touching this technology. But I think that, that for many people, there are some people who have gone very deep and really gone through the health journey, for example. That's been true for my family, for my wife, that she has a number of health conditions that would be, we don't even really know how we would've managed these before chat, and there's just so much toil and time and just getting to the right answer, and a doctor tells you something, you don't know what the thing is, and how do you get that, that sort of sanity check to really even understand it? People who I, that their life was saved through information delivered by ChatGPT. Like, I'll tell you a story for one of, one of my friends is that she was in the hospital, and, uh, the, the doctor was about to inject a antibiotic, and she was like, "Give me a moment." She typed it into ChatGPT, and chat 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, weird reaction." Showed the doctor. I know, right?

    14. BH

      Mm-hmm.

    15. GB

      And the doctor said, "Oh, my goodness. No, no, that's absolutely right. I had no idea. I only had five minutes to read your chart."

    16. BH

      Yeah. Many such cases, by the way.

    17. GB

      Many such-

    18. BH

      Someone read their chart at least.

    19. GB

      Exactly, and so the, these kinds of stories I think don't get told nearly enough, but they're out there. I hear them every day, and the people who run their small business on chat and would be totally unable to do it otherwise, like that kind of empowerment, again, people who are able to save money, make money, live a better life, um, those kinds of stories I think need to be in the public consciousness as we approach this, this question.

    20. ET

      So it's painting the narrative that, hey, 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, all these utilities in your pocket while also not threatening those, the same, while also telling the teachers and doctors and lawyers and therapists that, "Hey, you've now got this tool too, and it's gonna make your, your business better as well."

    21. GB

      Yes, and it's not just the narrative. It's the reality, right? It's, you need both. I think that many other countries are looking in-

    22. BH

      Yeah

    23. GB

      ... seeing the position that the US is in, right? They're seeing the potential of this technology, and partly too, you think about demographics, that I think in many of these other countries it's more keenly felt that there's an older gen- generation-

    24. BH

      Mm-hmm

    25. GB

      ... that's much larger than, than the younger population that is going to need to support them, these questions of, how is that supposed to work? And so I think that there'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. And so again, I think that there's something that we need to do better as a field and as-

    26. BH

      Mm-hmm

    27. GB

      ... 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.

    28. BH

      Yeah, particularly if we ban data centers. I think that'll [laughs] , that'll be a problem for us, us maintaining our lead.

    29. GB

      It will, it will drive the-

    30. BH

      Yeah

  8. 38:1443:25

    What's Still Jagged: What Astra Needs to Approximate AGI

    1. BH

      effort.

    2. ET

      Closing the loop on Astra, you've emphasized that capabilities still remain jagged. What do you think, um, still, still left to go or still needs to be fleshed out that gets, approximates most of your definition of AGI?

    3. GB

      Well, I think that AGI has turned out to be less of a point in time and more of this sort of fuzzy spectrum. And for me, Astra has really hit something that I'm like, "Okay, I think this is pretty reasonable to call it AGI" in that with its computer use capabilities, you really can ask it to do long-live tasks, and it'll just do it. That we've seen it, it run coherently for 24 hours to go accomplish tasks that I think are quite, quite amazing and across a wide variety of domains. Now, it still is jagged, and so that there are still places where, for example, it's writing. It's pretty good writing.

    4. BH

      Mm-hmm.

    5. GB

      It's the first time it's not slop writing.

    6. BH

      Yeah.

    7. GB

      But it's not great writing.

    8. BH

      Yeah.

    9. GB

      And I think that there's a number of areas where I feel like we just need to polish it a little bit, and it would be fantastic, and it's just like not quite there. So I, I see this like, I saw someone post a graph on, on Twitter of like, you know, this like kind of, you know, jagged frontier and that where we really need to, to be is a much more steady across the board, um, really hit on all these categories. But I think that what people are finding is that it is so capable across such a wide variety of tasks that it is accelerative, it is empowering, and it's something that I think we've never really seen a model that, that's been a jump like this.

    10. BH

      Yeah. One of the things that's been interesting for me is that a- as you solve problems, sometimes the, the world doesn't realize it. Like, so I haven't seen a hallucination in quite some time. Um, but nobody says, "Oh, the models don't hallucinate anymore."

    11. GB

      [laughs]

    12. BH

      It, it's just kind of in the ethos of that's what AI does. Um, how do you, do you think that'll just go away over time, or is it, you know, does there need to be some like continual education for the, for the non like hardcore tech people-

    13. GB

      I think-

    14. BH

      ... how this thing is moving?

    15. GB

      I think one of the most important problems we actually have is the continual education, right?

    16. BH

      Mm-hmm.

    17. GB

      The really how do you, people shouldn't have to extract from the AI what it's capable of.

    18. BH

      Mm-hmm.

    19. GB

      It should go the other way around. The AI should say, "Hey, I can help you in this new way."

    20. BH

      Hmm.

    21. GB

      So we have about, you, you know, we have over a billion weekly active users on ChatGPT, but I think we have something like another maybe 1.5 billion people who have used ChatGPT and don't use it anymore.

    22. BH

      Hmm. Oh, wow.

    23. GB

      Right? So think about that.

    24. BH

      Yeah. Yeah.

    25. GB

      Like that's a significant fraction of the, of the planet. And th-

    26. BH

      That's some wild, yeah

    27. GB

      ... those people, exactly, those people, we should really be able to go back to and say, "Hey, we have made so much progress. We think we can be useful to you in these ways." And I think that that is just shows you the kind of problem we have in front of us, is that these AIs, like if you look at ChatGPT and ChatGPT Work, they're both text boxes, right?

    28. BH

      Mm-hmm.

    29. GB

      It's like this new text box is way better than the old text box.

    30. BH

      Right.

  9. 43:2547:32

    How Greg Prioritizes His Time

    1. GB

      note- And talk to you ... the, the business is ripping. You guys have such broad surface area in terms of what you cover. How do you decide in terms of prioritizing where to go deepest, what not to build? And then also your role has also evolved and changed and you've, you know, encompassed so many things, you know, research, product commercialization, org design, management, et- et cetera. H- how are you also thinking about prioritizing your time? Well, they go hand in hand. Yeah. So this year the theme was focus. I think that we really realized that we can't do it all, right? We need to pick. And particularly, there's one thing we're trying to accomplish, whi- 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 prioritization is actually something that does reinforce that, right? That we do want to bring this technology to bear- Mm-hmm ... and have it uplift everyone and people deploying it in useful applications, all of that personal life, work life, the whole thing, um, very core. But how do you, when you think about this moment we're in of this agentic coding takeoff, that exponential, what areas 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, uh, that's maybe the highest profile one of these projects- Yeah ... that we decided to cancel. Very, very painful, by the way. Mm-hmm. 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- Mm-hmm ... this is what we're doing. So a lot of the way that we've thought about this to unlock this moment is to really have vision about where we think the future's going and how do we think that the new capabilities that are emerging can best be brought to bear with a single unified stack that works across the different areas, different walks of life, different areas that we're trying to focus on. And it's been painful, right? That if you look for the first half, I think that there was just a lot of metrics that were not looking the direction that we wanted, and that there was a lot of just sort of telling the team- Mm-hmm ... 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. Mm-hmm. Have, have you guys read that one? Yeah. Keith Rabois favorite. Yeah. Yeah. It's a, it's a great one, and it just is a very empowering book because you just realize it's like you cannot affect the outcome. Right. You can only affect the inputs, right? Yes. You can only affect the, like, the basics, and so focus on those basics, right? You don't win the Super Bowl by saying, "I wanna win the Super Bowl." You win it by blocking and tackling. Yeah. And so that's what we have done for this whole year. And for myself, I, throughout OpenAI, have always focused on whatever is the most important problem that I think that I can move the needle on that just isn't gonna happen without me. And- Mm-hmm ... for the past two years, it's been the data centers, the infrastructure, the machine learning, engineering. Mm-hmm. And that's an area where we really spent a lot of effort to get our pre-training infrastructure into great s- 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 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, the, make the hard decisions and really figure out this is the direction, let's go. Mm-hmm. And a lot of my style is that I like to lead from the trenches, and so I get very deep- Mm-hmm ... 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- Yeah ... asking like, "Wait, does this make sense still? I don't quite get that." Yeah. Um, sometimes when things are confused, for example, over the past couple days, there have been times when it's just like, we've got a thing. We gotta figure out how to even talk about it. Right. 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 a Hangout- Mm-hmm. [laughs] ... just kind of like, just being like, "Does this line make sense? Wait, what do we really mean by this?" Yeah. And so- Yeah ... really trying to up level execution sometimes in small ways and sometimes large. Yeah. No, that's fantastic. D- By the way, exact right way to operate.

  10. 47:3249:23

    What's Next: The AGI Era & Deep Co-Design

    1. GB

      Yeah. [laughs] Do, do you know what the next year we'll, we'll focus on or- Well- ... keep that private for now? Look, I think that, I think that the business is, is a huge area that I think we're not done yet with really up-leveling every part of execution. Mm. So I think there's a lot more to do there. But I also think we are moving into a new phase of AI development, right? I call this, and we call this, that we're now in the AGI era. Mm-hmm. And I think that that is something that you can debate. Is it this model- Yeah ... previous model, next model? It doesn't matter. The point is that we are in a new phase where safety, security, alignment, really thinking about these things, not just at deployment time, but all the way back at development time, evaluation. It's objective. This is critical. It must happen. This is core to our mission. This is core to what we need to do. And so a lot of what I spend my time thinking about is making sure, do we have all the right processes? Are we talking about the right things? Do we have plans that really at a operational level, at a practical level, lead us to the kind of security and variance and the kinds of safety guarantees that we view as core to our mission and 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? 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. Yeah. I, uh, we could go all day, but we have a hard stop. [laughs] Uh, I think this is a great place to wrap. Amazing. Greg, thank you so much for coming on the podcast. Thank you for having me. Yeah, it was great. Fantastic.

Episode duration: 49:37

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