WEBVTT 00:00:00.000 --> 00:00:02.757 I think it's extremely clear that we are 00:00:02.840 --> 00:00:07.037 going to have AGI within the next couple of years [music] in a way that 00:00:07.120 --> 00:00:11.877 it's still going to be jagged, but that the the floor of task will just be 00:00:11.960 --> 00:00:15.557 almost for any intellectual task of how [music] you use your computer. The AI 00:00:15.640 --> 00:00:19.517 will be able to do that. The scariest moment at OpenAI was actually after we 00:00:19.600 --> 00:00:23.497 launched ChatGPT. And I remember being at the holiday party and just feeling 00:00:23.580 --> 00:00:28.757 [music] this vibe of we won. I have never felt that. I was like, no. That we 00:00:28.840 --> 00:00:31.637 we are the underdog. And we always have been. From the moment we launched 00:00:31.720 --> 00:00:35.317 ChatGPT, I remember talking with my team having [music] this exact conversation. 00:00:35.400 --> 00:00:38.477 I said, "How much compute should we buy?" I said, "All of it." I said, "No, 00:00:38.560 --> 00:00:42.717 no, no, really. How much compute should we buy?" I said, "No matter how much we 00:00:42.800 --> 00:00:47.077 try to build, I know we're not going to be able to keep up with the demand." 00:00:47.160 --> 00:00:50.877 >> OpenAI co-founder and president Greg [music] Brockman joins us to talk about 00:00:50.960 --> 00:00:55.797 AI's most promising opportunities, how OpenAI plans to capitalize on them, and 00:00:55.880 --> 00:00:57.437 what the super [music] app is all about. 00:00:57.520 --> 00:01:01.077 And Greg is with us here in studio today. Greg, great to see you. 00:01:01.160 --> 00:01:02.877 >> Thank you for having me. 00:01:02.960 --> 00:01:04.917 >> Well, we're speaking at a time where 00:01:05.000 --> 00:01:08.677 OpenAI is shutting down video generation 00:01:08.760 --> 00:01:11.237 and focusing its energies on a super 00:01:11.320 --> 00:01:15.877 app, which is going to combine business and coding use cases. 00:01:15.960 --> 00:01:19.717 And I think from the outside, those of us watching this are like, including 00:01:19.800 --> 00:01:25.517 myself, OpenAI's winning in consumer, and now it's shifting its resources. What is 00:01:25.600 --> 00:01:28.397 happening? >> Well, the way I would think about this 00:01:28.480 --> 00:01:32.677 is that we have been in a world where we're developing this technology, deep 00:01:32.760 --> 00:01:35.317 learning, to really see can it 00:01:35.400 --> 00:01:38.317 have the positive impact that we have 00:01:38.400 --> 00:01:42.597 always pictured. Can it build can it be used to build 00:01:42.680 --> 00:01:45.117 applications that help people that help 00:01:45.200 --> 00:01:50.317 them in their lives. And we've separately had a arm that's 00:01:50.400 --> 00:01:53.597 saying, "Let's actually try to deploy this technology, whether that's to help 00:01:53.680 --> 00:01:57.797 sustain the business, to start getting some uh practice with getting real-world 00:01:57.880 --> 00:02:01.437 impact, those kinds of things, for the time when this technology actually comes 00:02:01.520 --> 00:02:05.917 to fruition, that it actually becomes the uh everything that we that we've 00:02:06.000 --> 00:02:09.357 imagined, that we started this company to try to to have. And I think that 00:02:09.440 --> 00:02:12.797 we're at a moment now where we've really seen this technology, it's going to 00:02:12.880 --> 00:02:15.197 work. And that we're moving out of 00:02:15.280 --> 00:02:17.797 testing on benchmarks and sort of these 00:02:17.880 --> 00:02:23.077 almost cerebral demonstrations of capability to it actually being the case 00:02:23.160 --> 00:02:26.637 that for us to develop it further, we need to see it in the real world and get 00:02:26.720 --> 00:02:30.637 feedback from how people are using it in knowledge work and various applications. 00:02:30.720 --> 00:02:33.117 And so, the way I think about it is that this is a 00:02:33.200 --> 00:02:37.477 bigger strategic shift because of the phase of the technology. 00:02:37.560 --> 00:02:41.477 And it's not so much that we're saying we're moving from consumer to B2B. It's 00:02:41.560 --> 00:02:45.317 really what we're saying is that what are the most important applications that 00:02:45.400 --> 00:02:48.437 we can focus on because we can't focus on everything, right? But what are the 00:02:48.520 --> 00:02:52.237 things that we can bring to life that will actually synergize together as we 00:02:52.320 --> 00:02:55.797 build them and that will deliver meaningful impact and help elevate 00:02:55.880 --> 00:03:00.117 everyone. And when we look at the list, so there's 00:03:00.200 --> 00:03:04.237 consumer, you can think of it as many things, but there's a personal 00:03:04.320 --> 00:03:06.917 assistant, right? Something that knows you, that's aligned with your goals, 00:03:07.000 --> 00:03:09.557 that's going to help you achieve whatever whatever it is that you want in 00:03:09.640 --> 00:03:13.597 your life. There's also creative expression and entertainment and many 00:03:13.680 --> 00:03:18.357 other applications. On the business side, maybe you can if 00:03:18.440 --> 00:03:21.797 you zoom out, it looks more like one thing of just you have a hard task, can 00:03:21.880 --> 00:03:25.077 AI go do it? Does it have all the context to do all these things? 00:03:25.160 --> 00:03:27.717 And for us, it's very clear that there 00:03:27.800 --> 00:03:32.397 the stack rank includes two things at the top. One is the personal assistant, 00:03:32.480 --> 00:03:36.197 the other is the AI that can go and solve hard problems for you. 00:03:36.280 --> 00:03:40.157 And when we look at the compute we have, we are not even going to have enough 00:03:40.240 --> 00:03:43.997 compute to fund those two things. And then once we start adding in many other 00:03:44.080 --> 00:03:48.077 applications, many other things that AI is going to be very useful for, is going 00:03:48.160 --> 00:03:50.997 to help people with, we just can't possibly get to all of them. And so, I 00:03:51.080 --> 00:03:55.517 think that this is a recognition of the maturation of the technology and the 00:03:55.600 --> 00:03:59.357 incredible impact it's going to have very quickly and our need to to 00:03:59.440 --> 00:04:04.517 prioritize and to actually pick the set of applications that we want to shine 00:04:04.600 --> 00:04:06.317 and to really bring to the world. 00:04:06.400 --> 00:04:09.437 >> And when I've heard you talk about OpenAI's various bets, one of the ways 00:04:09.520 --> 00:04:13.117 that you've described it is that OpenAI can be a version of Disney or a like 00:04:13.200 --> 00:04:17.157 Disney where you have this core compelling advantage at the center and 00:04:17.240 --> 00:04:18.717 then you farm it out in different ways. 00:04:18.800 --> 00:04:22.837 So, Disney has Mickey Mouse and then it can do the movies and the theme park and 00:04:22.920 --> 00:04:25.397 Disney Plus. And for OpenAI, it's the 00:04:25.480 --> 00:04:29.957 model and you can do video generation and be this assistant and then help with 00:04:30.040 --> 00:04:34.037 enterprise and work. So, is it no longer possible then to have 00:04:34.120 --> 00:04:38.477 that sort of central advantage and then be able to farm it out in all sorts of 00:04:38.560 --> 00:04:41.837 ways? Like, have you decided Have you come to this realization that basically 00:04:41.920 --> 00:04:44.037 like it's time to pick or choose? 00:04:44.120 --> 00:04:46.757 >> Well, I actually think that in some ways 00:04:46.840 --> 00:04:50.557 that story is even more true than it's been, but the thing that's important to 00:04:50.640 --> 00:04:54.957 realize is technologically that the Sora models, which are incredible models, by 00:04:55.040 --> 00:04:57.917 the way, are a different branch of the tech tree 00:04:58.000 --> 00:05:01.757 than the core reasoning GPT series. Mhm. 00:05:01.840 --> 00:05:05.317 They're just built in a very different way. And to some extent, we're really 00:05:05.400 --> 00:05:09.797 saying that pursuing both branches is very hard for us to do for these 00:05:09.880 --> 00:05:13.957 applications. Now, we we are actually continuing the Sora research program in 00:05:14.040 --> 00:05:18.357 the context of robotics, right? Which I think is very clearly going to be a 00:05:18.440 --> 00:05:21.317 transformative application, which is still a little bit in the research 00:05:21.400 --> 00:05:25.077 phase, right? That robotics is not really yet mature and deployed in the 00:05:25.160 --> 00:05:29.117 way that we're going to see this this real takeoff of this technology in 00:05:29.200 --> 00:05:32.477 knowledge work over the next year. And so, it's a recognition of for this 00:05:32.560 --> 00:05:34.597 moment, we really need to put the 00:05:34.680 --> 00:05:37.917 primary focus on developing the GPT 00:05:38.000 --> 00:05:41.077 series and that doesn't just mean text. 00:05:41.160 --> 00:05:42.717 It doesn't just mean cerebral things. 00:05:42.800 --> 00:05:46.397 Like, for example, I bidirectional communication, having a great 00:05:46.480 --> 00:05:49.517 speech-to-speech interface, that is something that also is going to make 00:05:49.600 --> 00:05:52.477 this technology very usable and very useful, 00:05:52.560 --> 00:05:55.837 but it's not a different branch of the tech tree. It's all kind of one model, 00:05:55.920 --> 00:05:58.077 and we just sort of tweak that in slightly different ways, kind of like 00:05:58.160 --> 00:06:00.597 you described. And so, I think there's something about if you're if you branch 00:06:00.680 --> 00:06:04.677 too far and you have two different artifacts, that is very hard to sustain 00:06:04.760 --> 00:06:07.997 in a world where there is limited compute. And the reason there's limited 00:06:08.080 --> 00:06:09.837 compute is cuz there's so much demand. 00:06:09.920 --> 00:06:13.157 There's so much people want to do with every single model that we create. 00:06:13.240 --> 00:06:14.637 >> Okay, so talk a little bit then about 00:06:14.720 --> 00:06:17.477 why your bet is not on this seems like 00:06:17.560 --> 00:06:21.677 world model version where the um you know, the video understands where things 00:06:21.760 --> 00:06:23.597 go. It's it's obviously useful for 00:06:23.680 --> 00:06:26.637 robotics. Why is your bet on the GPT 00:06:26.720 --> 00:06:31.557 reasoning model tree as opposed to this area which you've you had been seeing 00:06:31.640 --> 00:06:35.517 real progress with Sora. I mean, to see the progress of video generation, you 00:06:35.600 --> 00:06:38.437 know, generation 1, 2, 3 was enormous. 00:06:38.520 --> 00:06:40.797 So, why is your bet where it is? 00:06:40.880 --> 00:06:44.237 >> So, the problem in this field is too much opportunity. 00:06:44.320 --> 00:06:48.797 Right? It's the thing The thing that we observe very early on in OpenAI is that 00:06:48.880 --> 00:06:52.197 everything we could imagine works. Now, there's different levels of friction 00:06:52.280 --> 00:06:54.957 associated with it, different amounts of engineering effort, different compute 00:06:55.040 --> 00:06:59.477 requirements, all those things, but every single different idea, as long as 00:06:59.560 --> 00:07:02.317 it's kind of mathematically sound, you actually can start getting some pretty 00:07:02.400 --> 00:07:06.197 good results. And I think that shows you the power of the underlying technology 00:07:06.280 --> 00:07:08.557 of deep learning, the ability to really 00:07:08.640 --> 00:07:13.277 take any sort of problem and to get to the meat of it, to have an AI that 00:07:13.360 --> 00:07:16.637 really understands the underlying rules that generated the data. So, it's not 00:07:16.720 --> 00:07:19.637 about the data itself, it's about understanding the underlying process, 00:07:19.720 --> 00:07:22.477 and then be able to apply to new contexts. So, you can do that in world 00:07:22.560 --> 00:07:26.197 models, you can do that in scientific discovery, you can do that in coding. 00:07:26.280 --> 00:07:28.117 And I think that where we are, as we 00:07:28.200 --> 00:07:30.557 think about the rollout of this 00:07:30.640 --> 00:07:34.637 technology, is again that the 00:07:34.720 --> 00:07:36.557 you know, there's been this debate of 00:07:36.640 --> 00:07:41.797 how far will the text models go? How far can text intelligence go? Can you have a 00:07:41.880 --> 00:07:45.037 real conception of how the world operates? And I think that we have 00:07:45.120 --> 00:07:49.277 definitively answered that question of it's it is going to go to AGI. Like we 00:07:49.360 --> 00:07:51.877 see line of sight and that it is at this 00:07:51.960 --> 00:07:56.477 point we have line of sight to these much better models that are coming this 00:07:56.560 --> 00:08:01.237 year and the the the amount of pain within OpenAI 00:08:01.320 --> 00:08:05.517 that we've had to decide how to allocate compute, that goes up not down over 00:08:05.600 --> 00:08:09.997 time. And so I think that maybe the core of it is that we have 00:08:10.080 --> 00:08:14.997 a it's about sequencing and timing and that in this moment the kinds of 00:08:15.080 --> 00:08:18.517 applications that we've always dreamed of are starting to come into reach. I, 00:08:18.600 --> 00:08:23.077 for example, solving unsolved physics problems, right? We had this result 00:08:23.160 --> 00:08:27.637 recently where a physicist had been working on a problem for some time. He 00:08:27.720 --> 00:08:31.317 gave it to our model. 12 hours later, we have a solution and he said this is the 00:08:31.400 --> 00:08:34.757 first time he's seen a model where he felt like he was thinking, that it felt 00:08:34.840 --> 00:08:37.957 like this is a problem that maybe humanity would never solve and our AI 00:08:38.040 --> 00:08:42.157 solved it. When you see something like that, like you have to double down, you 00:08:42.240 --> 00:08:46.917 have to triple down because we can really unlock all of this potential for 00:08:47.000 --> 00:08:51.197 humanity. And so I I think for me it's not about relative importance of these 00:08:51.280 --> 00:08:55.077 things. It's more about what is OpenAI's mission of delivering 00:08:55.160 --> 00:08:58.477 AGI to the world, our vision of how it can benefit everyone and the fact that 00:08:58.560 --> 00:09:03.397 we have a tech tree, that we see how to just push it, how to do the engineering, 00:09:03.480 --> 00:09:07.357 do the further science and research to then have that come to fruition. 00:09:07.440 --> 00:09:09.917 >> Okay, so I do want to come back to the next line of 00:09:10.000 --> 00:09:13.277 >> models that you're anticipating, but I want to press you on this for a moment. 00:09:13.360 --> 00:09:17.917 I was speaking with Demis Hassabis from Google DeepMind earlier this year and 00:09:18.000 --> 00:09:19.957 interestingly he said that the thing 00:09:20.040 --> 00:09:25.277 closest that feels closest to AGI for him was Nano Banana, the image generator 00:09:25.360 --> 00:09:26.917 that they have. And the reason is 00:09:27.000 --> 00:09:28.957 because for an image generator or a 00:09:29.040 --> 00:09:32.317 video generator to create the images and 00:09:32.400 --> 00:09:36.037 the videos that it makes, it does have to understand the interaction between 00:09:36.120 --> 00:09:40.317 objects and have at least some conception of how the world works. So, 00:09:40.400 --> 00:09:45.157 is this a potential I mean it's a big bet, but does OpenAI potentially miss 00:09:45.240 --> 00:09:49.117 something by doubling down on the other tree if that's the case? 00:09:49.200 --> 00:09:51.637 >> So, two answers. >> One is absolutely. Yeah. 00:09:51.720 --> 00:09:55.357 >> Right, there still is not like in this field you do have to 00:09:55.440 --> 00:09:58.277 make choices, right? You have to make a bet. And that's actually where OpenAI 00:09:58.360 --> 00:10:03.117 started is we really said, "What is the path to AGI that we believe in?" And 00:10:03.200 --> 00:10:05.837 really focused hard on that. >> Right, the sum of random vectors is 00:10:05.920 --> 00:10:09.917 zero, but if you align your vectors, then you can go in a direction. 00:10:10.000 --> 00:10:14.557 But the second point is it's actually ImageGen is something that has been 00:10:14.640 --> 00:10:17.797 very, very popular within ChatGPT, and that's something we're continuing to 00:10:17.880 --> 00:10:21.397 invest in, continuing to prioritize. And the reason we're able to do that is 00:10:21.480 --> 00:10:26.397 because it's not actually on the on the world model like diffusion model tech 00:10:26.480 --> 00:10:30.677 tech branch, it's actually based on the GPT architecture. And so there, even 00:10:30.760 --> 00:10:33.277 though it's a different data distribution, 00:10:33.360 --> 00:10:38.197 the actual core technology, the core stack, it's all one thing. And that is 00:10:38.280 --> 00:10:42.037 that is actually the pretty wild thing about what AGI is is that sometimes 00:10:42.120 --> 00:10:45.197 these very different looking applications between speech to speech, 00:10:45.280 --> 00:10:48.637 image generation, text, and text is by 00:10:48.720 --> 00:10:51.357 the way itself many faceted of like 00:10:51.440 --> 00:10:55.997 science and coding and personal like you know, wellness 00:10:56.080 --> 00:10:57.957 information, those kinds of things. 00:10:58.040 --> 00:11:02.117 Uh all of that you can do in one technological envelope. And so a lot of 00:11:02.200 --> 00:11:04.357 what I'm looking at and what we as a company are looking at from a 00:11:04.440 --> 00:11:08.317 technological perspective is how to have as much unification of our of our 00:11:08.400 --> 00:11:12.157 efforts because we really see this technology as being something that's 00:11:12.240 --> 00:11:15.397 going to uplift and empower the whole economy. The whole economy is a massive 00:11:15.480 --> 00:11:19.717 thing, and so we can't possibly do all of it, but we can do our part. 00:11:19.800 --> 00:11:21.917 >> That's the general part in artificial general intelligence. 00:11:22.000 --> 00:11:23.797 >> That's the G. >> That's what we're talking about that. 00:11:23.880 --> 00:11:27.117 >> It really is. >> Speaking of unifying things, uh what is 00:11:27.200 --> 00:11:31.077 this super app going to be? >> So, the way I think about the super app 00:11:31.160 --> 00:11:33.917 is so it it's going to bring together coding, 00:11:34.000 --> 00:11:36.477 browser, and ChatGPT. That's right. So, 00:11:36.560 --> 00:11:38.917 what we want is to build an endpoint 00:11:39.000 --> 00:11:42.197 application for you that really lets you 00:11:42.280 --> 00:11:44.957 experience the power of AGI. So, the 00:11:45.040 --> 00:11:49.517 generality. And so, if that's you think about what chat is today, I 00:11:49.600 --> 00:11:53.517 think chat is really going to become your personal assistant, your personal 00:11:53.600 --> 00:11:57.077 AGI, right? An AI that's looking out for you, that knows a lot about you, that's 00:11:57.160 --> 00:12:00.557 aligned with your goals, that that's trustworthy, that kind of 00:12:00.640 --> 00:12:03.397 represents you in this like digital world. 00:12:03.480 --> 00:12:08.277 Codex, you can think of as right now it's been a tool that we built 00:12:08.360 --> 00:12:11.757 for software engineers, but it's becoming Codex for everyone. 00:12:11.840 --> 00:12:14.397 That anyone who wants to build can use 00:12:14.480 --> 00:12:17.677 Codex and to produce to get the computer 00:12:17.760 --> 00:12:21.957 to go do the thing that they want. And it's not just about the actual software 00:12:22.040 --> 00:12:24.357 anymore. It's really about almost the 00:12:24.440 --> 00:12:29.677 use of computer, whether it's to set up like I used to set settings on my 00:12:29.760 --> 00:12:32.837 laptop. Like I forget how how does that the hot corners? You just ask Codex to 00:12:32.920 --> 00:12:35.517 do it, it just does it. Right? That's what computers were always supposed to 00:12:35.600 --> 00:12:38.997 be is contort to the human rather than me contort to them. And so, imagine one 00:12:39.080 --> 00:12:43.157 application that anything you want your computer to do, you can ask it. And so, 00:12:43.240 --> 00:12:48.117 there's like a that there's computer use browsing built in for an AI to be able 00:12:48.200 --> 00:12:51.397 to actually use web browser and for you to be able to oversee what the AI is 00:12:51.480 --> 00:12:55.437 doing, that all of your conversations, regardless of application, whether it's 00:12:55.520 --> 00:12:58.557 for chat or whether it's for code, whether it's for general knowledge work, 00:12:58.640 --> 00:13:00.597 that's all unified in one way, that the 00:13:00.680 --> 00:13:03.877 AI has memory, knows about you. So, that 00:13:03.960 --> 00:13:08.717 is what we are building. But, it's really an iceberg because that's the tip. 00:13:08.800 --> 00:13:11.197 What to me is actually much more important is the technological 00:13:11.280 --> 00:13:14.917 unification. And we talked about it a little bit in the case of the underlying 00:13:15.000 --> 00:13:19.317 models, but the thing that's really changed over the past couple years has 00:13:19.400 --> 00:13:23.117 been that it's no longer just about the model, it's about the harness, it's 00:13:23.200 --> 00:13:24.917 about how does the model get context? 00:13:25.000 --> 00:13:29.317 How is it connected to the world? What actions can it take? How does the 00:13:29.400 --> 00:13:33.637 actual, as you get new context, how does the loop of interacting with the model 00:13:33.720 --> 00:13:37.157 work? All of that was something that we had multiple implementations of or 00:13:37.240 --> 00:13:40.197 slightly different and we're converging it. We're going to have one version of 00:13:40.280 --> 00:13:43.237 that and almost end up with this AI 00:13:43.320 --> 00:13:47.757 layer that can be pointed at specific applications in a very thin way. So you 00:13:47.840 --> 00:13:51.477 can build a little plug-in, a little skill, a little UI if you really want 00:13:51.560 --> 00:13:53.477 something that's great for finance, if you want something that's great for 00:13:53.560 --> 00:13:57.357 legal. But you generally won't have to because there'll be this one super app 00:13:57.440 --> 00:14:00.837 that will be very broad. >> This app is for business use cases, 00:14:00.920 --> 00:14:02.077 personal use cases? 00:14:02.160 --> 00:14:04.797 >> So both. And And that is that is really 00:14:04.880 --> 00:14:09.757 the core is that just like a computer, like your laptop, is it for personal, is 00:14:09.840 --> 00:14:13.237 it for business? Right? Well, both. Both. And it's for 00:14:13.320 --> 00:14:15.637 you. It's your personal machine that 00:14:15.720 --> 00:14:18.357 gives you a interface to this digital 00:14:18.440 --> 00:14:20.557 world. And that's what we want to build. 00:14:20.640 --> 00:14:22.757 >> So just talk a little bit about 00:14:22.840 --> 00:14:28.037 from a non-business standpoint, I'm using the super app in my personal life. 00:14:28.120 --> 00:14:29.997 What am I using it for? How does my life change? 00:14:30.080 --> 00:14:34.637 >> So I would think of it as So personal life, just the way that you use ChatGPT, 00:14:34.720 --> 00:14:36.117 right? How do you use ChatGPT right now? 00:14:36.200 --> 00:14:37.997 And people use it for such a diversity 00:14:38.080 --> 00:14:40.117 of really amazing applications. 00:14:40.200 --> 00:14:44.637 Sometimes that's just asking for I'm going to give a speech at a wedding, can 00:14:44.720 --> 00:14:48.637 you help me with drafting it? Can you give me some feedback on this idea that 00:14:48.720 --> 00:14:52.397 I have? I'm working on a small business, can you give me some ideas there? Which 00:14:52.480 --> 00:14:55.077 maybe starts to bridge between personal and work. 00:14:55.160 --> 00:14:58.677 There's Any of those questions should be things 00:14:58.760 --> 00:15:02.717 that you can go to the super app for and it answers. But that if you think about 00:15:02.800 --> 00:15:06.517 what ChatGPT has been, it's already been evolving. It used to not have any 00:15:06.600 --> 00:15:10.877 memory, right? It's just the same AI for everyone starting from scratch. 00:15:10.960 --> 00:15:12.717 It's almost like talking to a stranger. 00:15:12.800 --> 00:15:16.677 It's way more powerful if it remembers, remembers the interactions you've had. 00:15:16.760 --> 00:15:20.037 It's way more powerful if it has access to context, right? That if it's hooked 00:15:20.120 --> 00:15:22.757 up to your email and to your calendar 00:15:22.840 --> 00:15:25.357 and really knows your preferences and 00:15:25.440 --> 00:15:28.957 has this this almost deeper set of 00:15:29.040 --> 00:15:32.077 of just you know past experiences with 00:15:32.160 --> 00:15:36.317 you that it's able to leverage to achieve your goals and to 00:15:36.400 --> 00:15:40.197 you look at things like pulse is a feature in ChatGPT right now where every 00:15:40.280 --> 00:15:44.637 day it surfaces for you things that you might be interested in based on what 00:15:44.720 --> 00:15:46.677 ChatGPT knows about you. So I say that 00:15:46.760 --> 00:15:49.677 in the personal capacity that the super 00:15:49.760 --> 00:15:54.917 app will be doing all of that and will be doing it a much deeper and richer way. 00:15:55.000 --> 00:15:56.037 >> When are you planning to ship it? 00:15:56.120 --> 00:16:00.077 >> Uh so the way to think about it is we should 00:16:00.160 --> 00:16:03.437 we're taking incremental steps to get there over the next couple months. We 00:16:03.520 --> 00:16:07.197 should have shipped the complete 00:16:07.280 --> 00:16:09.397 vision of what we're talking about here, 00:16:09.480 --> 00:16:11.277 but it's going to come in pieces and the 00:16:11.360 --> 00:16:14.637 place that we're starting is with 00:16:14.720 --> 00:16:17.357 for example the Codex app today is 00:16:17.440 --> 00:16:19.917 something which is a it's really two 00:16:20.000 --> 00:16:23.677 things in one. It's a general agent 00:16:23.760 --> 00:16:26.877 harness that can use tools and it's also 00:16:26.960 --> 00:16:30.997 a agent that knows how to write software. 00:16:31.080 --> 00:16:34.957 That general agent harness that can be used for so many different things. You 00:16:35.040 --> 00:16:37.517 hook it up to spreadsheets, you hook it up to 00:16:37.600 --> 00:16:40.837 to word documents. It's able to help you with knowledge work. And so we're going 00:16:40.920 --> 00:16:45.477 to make the Codex app just so much more usable for general knowledge work 00:16:45.560 --> 00:16:48.637 because it already what we've seen within OpenAI is all this organic 00:16:48.720 --> 00:16:51.717 adoption of people using it for that. So that'll be the first step and there are 00:16:51.800 --> 00:16:53.637 many to come. >> I was speaking with one of your 00:16:53.720 --> 00:16:55.277 colleagues yesterday taking a look at 00:16:55.360 --> 00:16:58.277 Codex and he mentioned that someone 00:16:58.360 --> 00:17:01.317 using Codex had built had had instructed 00:17:01.400 --> 00:17:03.237 Codex to help them with video editing. 00:17:03.320 --> 00:17:06.517 It built a plugin for Adobe Premiere, 00:17:06.600 --> 00:17:11.157 started separating it into chapters and started the edit. 00:17:11.240 --> 00:17:14.197 That's what we're looking at. >> I I love hearing that. That's exactly 00:17:14.280 --> 00:17:17.437 exactly the kinds of things that we want 00:17:17.520 --> 00:17:20.637 this system to be useful for. And it's 00:17:20.720 --> 00:17:25.517 been really interesting seeing like the Codex app itself was originally built 00:17:25.600 --> 00:17:27.597 for software engineers, and that it's 00:17:27.680 --> 00:17:30.717 almost like the current usability of it 00:17:30.800 --> 00:17:35.397 for non-software engineers is actually quite low because there's a bunch of 00:17:35.480 --> 00:17:39.477 little things where when you set things up, you run into some error that a 00:17:39.560 --> 00:17:43.157 developer knows what it means, knows how to fix it. It's just kind of what we're 00:17:43.240 --> 00:17:47.317 used to. But if you're not a developer, you're like, 00:17:47.400 --> 00:17:50.157 "What is this? Like this this is not something that I that I've encountered 00:17:50.240 --> 00:17:54.637 before." And despite that, we are seeing people start to use this who have never 00:17:54.720 --> 00:17:58.157 programmed before to be able to build websites, to be able to do exactly the 00:17:58.240 --> 00:18:01.637 kinds of things you you said of like be able to automate different their 00:18:01.720 --> 00:18:04.677 interactions with different pieces of software, be able to get lots lots of 00:18:04.760 --> 00:18:08.597 leverage. Like someone on our communications team used it to it's 00:18:08.680 --> 00:18:10.797 hooked up to Slack, to their email. 00:18:10.880 --> 00:18:13.797 They're able to go through a bunch of feedback, be able to synthesize it very 00:18:13.880 --> 00:18:17.397 well. So these kinds of tasks, people 00:18:17.480 --> 00:18:21.397 who are very motivated can jump through the hoops and then get great return from 00:18:21.480 --> 00:18:25.917 it. And so some extent we did the super hard part of an AI that is really smart, 00:18:26.000 --> 00:18:30.037 capable, can actually accomplish your task. Now we have to do the much easier 00:18:30.120 --> 00:18:34.677 part in some sense of make it broadly useful and to remove these barriers to 00:18:34.760 --> 00:18:37.037 entry. >> And just looking at the competitive 00:18:37.120 --> 00:18:41.117 landscape, I mean Anthropic they have the Claude app. You can use Claude the 00:18:41.200 --> 00:18:44.037 chatbot, Claude co-write, Claude code. 00:18:44.120 --> 00:18:46.557 Uh so they have a version of a super app 00:18:46.640 --> 00:18:49.077 of their own. I'm curious what you think 00:18:49.160 --> 00:18:53.837 Anthropic saw that got them to this position earlier, and 00:18:53.920 --> 00:18:56.077 what do you think your chances are of catching up there? 00:18:56.160 --> 00:18:59.877 >> Well, I think that if you 00:18:59.960 --> 00:19:03.637 rewind 12 18 always been focused on 00:19:03.720 --> 00:19:08.597 coding as a domain. We always had the best numbers on 00:19:08.680 --> 00:19:11.837 different programming competitions, these very cerebral things. But the 00:19:11.920 --> 00:19:15.837 thing that we didn't invest in as much was that last mile of usability. Of 00:19:15.920 --> 00:19:18.757 really trying to think about, okay, this this AI is so smart, it can solve all 00:19:18.840 --> 00:19:22.357 these great programming competitions, but it's never seen someone's real-world 00:19:22.440 --> 00:19:26.877 codebase, which is messy and not quite as pristine as the world that it it sort 00:19:26.960 --> 00:19:30.517 of has experienced. And I think that is something that we were behind on. But 00:19:30.600 --> 00:19:35.157 about, you know, maybe mid last year is when we got very serious about that. And 00:19:35.240 --> 00:19:38.157 that we had a team very focused on what are all 00:19:38.240 --> 00:19:41.157 the the gaps, what are all the kind of messiness the real world would have and 00:19:41.240 --> 00:19:44.237 we haven't encountered. How do we actually get 00:19:44.320 --> 00:19:48.917 training data that that build training environments that let the AI experience 00:19:49.000 --> 00:19:52.037 what it's like to actually do software engineering, be interrupted in weird 00:19:52.120 --> 00:19:55.437 ways, all those things. And I'd say at this point we are caught up. When people 00:19:55.520 --> 00:19:59.357 go head-to-head, for us versus competitors, the people tend to prefer 00:19:59.440 --> 00:20:03.397 us. We do know where we're lagging in front end, we're going to fix that, but 00:20:03.480 --> 00:20:06.637 this is the general motion that we've been we've been we've been taking is to 00:20:06.720 --> 00:20:09.637 say that that usability of thinking 00:20:09.720 --> 00:20:14.197 about the product end-to-end, not just a model, and then build a separate thing, 00:20:14.280 --> 00:20:17.517 right? Really think of it as one product. When we're doing the research, 00:20:17.600 --> 00:20:20.557 we're thinking about how will it be used. That has been a motion that we've 00:20:20.640 --> 00:20:23.277 been changing within OpenAI. And so, I 00:20:23.360 --> 00:20:26.117 think that the way I would look at it is 00:20:26.200 --> 00:20:29.877 that we have incredible step-up models 00:20:29.960 --> 00:20:33.637 coming. Like this whole year I look at the road map, it's truly inspiring. What 00:20:33.720 --> 00:20:35.717 will be possible. And then we've been 00:20:35.800 --> 00:20:38.797 really focusing now on let's also get 00:20:38.880 --> 00:20:40.997 the last mile usability. 00:20:41.080 --> 00:20:43.477 >> So since 2022, OpenAI has been like the 00:20:43.560 --> 00:20:47.597 undisputed leader and uh obviously now 00:20:47.680 --> 00:20:51.197 the competition is intense. Like you just used the word we're caught the 00:20:51.280 --> 00:20:54.637 phrase we're caught up. Is there a different vibe within the company where 00:20:54.720 --> 00:20:59.437 it's like now instead of the one that's like far ahead on something like ChatGPT 00:20:59.520 --> 00:21:03.197 in a real in a real fight? I mean, you're seeing it come out of some of the 00:21:03.280 --> 00:21:07.837 reporting on what's happening within the company, the fact that there are no more 00:21:07.920 --> 00:21:12.197 there's been meetings, there's no more side quests at OpenAI. It's all focus on 00:21:12.280 --> 00:21:14.997 this. How's the environment? Has the vibe changed here? 00:21:15.080 --> 00:21:18.957 >> Well, I would say that for me personally, yeah, the scariest moment at 00:21:19.040 --> 00:21:23.157 OpenAI was actually after we launched ChatGPT. And [clears throat] I remember 00:21:23.240 --> 00:21:26.837 being at the holiday party and just feeling this vibe of we won. I have 00:21:26.920 --> 00:21:31.557 never felt that. I was like, no, that we we are the underdog and we always have 00:21:31.640 --> 00:21:35.637 been, right? The the competitors in this space, established companies that have 00:21:35.720 --> 00:21:38.157 just sort of much more capital, much 00:21:38.240 --> 00:21:40.797 more, you know, human resources, data, 00:21:40.880 --> 00:21:42.997 the whole the whole thing. Why is OpenAI 00:21:43.080 --> 00:21:48.037 able to compete at all? And to some extent, the answer is only because we 00:21:48.120 --> 00:21:52.317 never feel complacent, where we always feel like we are the challenger. And it 00:21:52.400 --> 00:21:56.237 actually for me has been a very healthy thing to see 00:21:56.320 --> 00:22:00.077 us start to see that in the marketplace, to see other competitors emerge and do a 00:22:00.160 --> 00:22:04.477 good job. And that that is, you know, in my mind, you can never fix it on your 00:22:04.560 --> 00:22:08.117 competitors. If you focus on where they are, then you'll be where they are and 00:22:08.200 --> 00:22:11.557 they'll already have moved. And I think that that's what's been happening in the 00:22:11.640 --> 00:22:12.757 other direction, right? A lot of people 00:22:12.840 --> 00:22:14.797 have focused on exactly where we are and 00:22:14.880 --> 00:22:18.637 we get to move. And I think that the 00:22:18.720 --> 00:22:23.437 it almost gives us and this alignment, this unification of the company. And I 00:22:23.520 --> 00:22:26.957 kind of described how we almost thought of research and deployment as separate 00:22:27.040 --> 00:22:31.237 things. And now we really want to integrate them. Like, that to me is such 00:22:31.320 --> 00:22:33.077 a wonderful thing. And so, 00:22:33.160 --> 00:22:38.997 I'd say that the world that we're in is one where I've never felt like we were 00:22:39.080 --> 00:22:41.357 you know, you're never as good as they say you are, you're never as bad as they 00:22:41.440 --> 00:22:46.597 say you are. I think it's just been very steady. And that the core of the model 00:22:46.680 --> 00:22:50.517 production, that is something where I actually feel extremely extremely 00:22:50.600 --> 00:22:54.077 confident in our road map, the research investments we've been made making. And 00:22:54.160 --> 00:22:57.237 I think on the product side, we have such great energy that's all coming 00:22:57.320 --> 00:22:59.557 together to deliver this to the world. 00:22:59.640 --> 00:23:04.437 >> You foreshadowed a couple times already that you have some good models on the way. 00:23:04.520 --> 00:23:06.037 What is Spud? 00:23:06.120 --> 00:23:09.077 Uh the information said uh that you 00:23:09.160 --> 00:23:11.797 finished uh pre-training Spud, and Sam 00:23:11.880 --> 00:23:14.557 Altman, the CEO of OpenAI, has told the 00:23:14.640 --> 00:23:18.957 staff that uh they should expect to have a very strong model in a few weeks. This 00:23:19.040 --> 00:23:22.077 was a few weeks ago. And the team believes it can really 00:23:22.160 --> 00:23:23.917 accelerate the economy, and things are 00:23:24.000 --> 00:23:26.517 moving faster than many of us expect 00:23:26.600 --> 00:23:29.477 expected. So, what's Spud? 00:23:29.560 --> 00:23:33.757 >> It's a good model. Um but I think that it's really not about any one model. 00:23:33.840 --> 00:23:37.757 Okay, right? The the the way that our development process works is you have 00:23:37.840 --> 00:23:41.917 pre-training, so you produce a new base model that then is the foundation that 00:23:42.000 --> 00:23:45.357 we build further improvements on top of. And that 00:23:45.440 --> 00:23:49.277 that is always a huge effort across many people in the company, and that's where 00:23:49.360 --> 00:23:52.597 I've actually been spending most of my efforts over the past 18 months has been 00:23:52.680 --> 00:23:56.637 really focused on our GPU infrastructure, on supporting the 00:23:56.720 --> 00:23:59.717 teams that do all of the training frameworks to scale up at these big 00:23:59.800 --> 00:24:03.877 runs. But then there's a reinforcement learning process. So, you take this AI 00:24:03.960 --> 00:24:07.837 that has learned lots of things about the world, and it applies that 00:24:07.920 --> 00:24:10.197 knowledge, and then we do a post-training process where you really 00:24:10.280 --> 00:24:13.517 say, "Okay, now you know how to solve problems. You practice it in in all 00:24:13.600 --> 00:24:16.437 these different contexts." And then here's kind of the last mile of of 00:24:16.520 --> 00:24:18.357 behavior and usability. So, I think of 00:24:18.440 --> 00:24:21.837 Spud as a new base, as a new pre-train, 00:24:21.920 --> 00:24:25.957 and that we have had this uh I I'd say 00:24:26.040 --> 00:24:30.317 it's like we have maybe 2 years' worth of research that is coming to fruition 00:24:30.400 --> 00:24:33.517 in this model. It's going to be very exciting. 00:24:33.600 --> 00:24:37.157 And I think that the way that the world will experience it is just improved 00:24:37.240 --> 00:24:41.957 capabilities. And that for me, it's never about any one release, because as 00:24:42.040 --> 00:24:45.997 soon as we have this one release, it'll be an early version of what we have 00:24:46.080 --> 00:24:50.477 coming. We'll do much more of each of these steps of the improvement process. 00:24:50.560 --> 00:24:54.837 And so, I think that where we're going is this almost just we have this engine 00:24:54.920 --> 00:24:58.957 of progress that just moves faster and faster. And this one is just one step 00:24:59.040 --> 00:25:01.877 along the way. And so, what do you think it'll be able to do that today's models 00:25:01.960 --> 00:25:04.597 can't? I So, I think it's going to be 00:25:04.680 --> 00:25:07.237 able to solve both much harder problems. 00:25:07.320 --> 00:25:09.397 I think it will be much more nuanced. 00:25:09.480 --> 00:25:12.077 It'll understand instructions better. 00:25:12.160 --> 00:25:15.317 It'll understand the context much better. That there's this thing called 00:25:15.400 --> 00:25:18.637 big model smell that people talk about where it's just like there's something 00:25:18.720 --> 00:25:22.997 about like when these models are just actually just much smarter, much more 00:25:23.080 --> 00:25:28.117 capable, that they bend to you much more. You feel it. 00:25:28.200 --> 00:25:31.397 Right? When you ask a question and the AI doesn't quite get it, it's always so 00:25:31.480 --> 00:25:34.237 disappointing. Right? When you have to like explain and you're just like, "You 00:25:34.320 --> 00:25:37.357 really should be able to figure this out." And so, I would just think of it 00:25:37.440 --> 00:25:42.317 as in some ways just qualitatively, there will be 00:25:42.400 --> 00:25:44.197 but quantitatively lots of shifts. 00:25:44.280 --> 00:25:46.837 Right? And qualitatively, there will just be new things where you would be 00:25:46.920 --> 00:25:50.037 frustrated before, you never use an AI for it, and now you just use it without 00:25:50.120 --> 00:25:52.837 without thinking very much. And I think that that that is what we're going to 00:25:52.920 --> 00:25:57.237 see across the board. I'm super excited to see how it raises the ceiling. Right? 00:25:57.320 --> 00:25:59.837 We've already seen these physics applications, things like that. And I 00:25:59.920 --> 00:26:04.157 think we will be able to just solve like way more open-ended problems, way longer 00:26:04.240 --> 00:26:08.677 time horizons. And then also very excited to see how it raises the floor, 00:26:08.760 --> 00:26:12.237 where just for anything you want to do, it's just so much useful more useful for 00:26:12.320 --> 00:26:14.317 you. >> It can be kind of tough for everyday 00:26:14.400 --> 00:26:17.797 users to really feel the change. Like there was uh talk about a lot of 00:26:17.880 --> 00:26:22.357 build-up before GPT-5 came out, and then it came out, and actually the initial 00:26:22.440 --> 00:26:26.357 reaction was somewhat disappointment among the public. But then, I think 00:26:26.440 --> 00:26:30.237 people realized that for certain tasks it was really good. Or 00:26:30.320 --> 00:26:35.197 with these next series of models, do you expect that it'll really be felt sort of 00:26:35.280 --> 00:26:38.917 um in the trenches in certain occupations, or do you think it'll be a 00:26:39.000 --> 00:26:43.037 broadly uh tangible improvement for everyone? 00:26:43.120 --> 00:26:47.277 >> I think that it will be a similar story where you when you 00:26:47.360 --> 00:26:50.277 release it, there will be people who will try it and be like, "This is a 00:26:50.360 --> 00:26:53.757 night and day different than anything I've seen. And then there will be some 00:26:53.840 --> 00:26:56.957 applications where we weren't necessarily intelligence bottlenecked. 00:26:57.040 --> 00:26:59.357 And so if you have a model that's more intelligent, maybe you won't feel it 00:26:59.440 --> 00:27:02.717 right there. But I think over time that 00:27:02.800 --> 00:27:07.437 you will feel it because the fundamental thing that shifts is how much do you 00:27:07.520 --> 00:27:10.917 rely on the system? Like if you think about the way we all interact with AI, 00:27:11.000 --> 00:27:14.277 we have some mental model for what we think it can do. And that mental model 00:27:14.360 --> 00:27:17.557 shifts actually fairly slowly. Right? As you get more experience, it does 00:27:17.640 --> 00:27:20.917 something magical for you. You're like, "Oh, wow, it can do that? I never 00:27:21.000 --> 00:27:26.317 imagined that." And we see this for example in applications like 00:27:26.400 --> 00:27:27.917 access to health information, right? 00:27:28.000 --> 00:27:30.877 That we see people who 00:27:30.960 --> 00:27:33.237 You know, like I I have a a friend who 00:27:33.320 --> 00:27:38.437 used ChatGPT to understand different treatments for his cancer. And that he 00:27:38.520 --> 00:27:40.757 was told by doctors that he was terminal, that there's nothing they 00:27:40.840 --> 00:27:43.677 could do for him. He used ChatGPT to actually research 00:27:43.760 --> 00:27:46.957 a bunch of different ideas and he was able to get treatment that way. And 00:27:47.040 --> 00:27:50.957 that's something where you need to have some level of belief 00:27:51.040 --> 00:27:54.437 that the AI is going to be helpful in that application for you to really put 00:27:54.520 --> 00:27:57.477 in the effort to get something out of the machine. And I think what we're 00:27:57.560 --> 00:28:01.797 going to see is that for any application like that, it's going to become so much 00:28:01.880 --> 00:28:06.357 more evident to everyone that the AI can help you. 00:28:06.440 --> 00:28:09.757 And so I think it's a little bit of the technology getting better, but it's 00:28:09.840 --> 00:28:11.997 also our understanding of the technology 00:28:12.080 --> 00:28:14.037 shifting and and catching up to that. 00:28:14.120 --> 00:28:16.797 And you'll be relying on it more. Inside 00:28:16.880 --> 00:28:20.077 OpenAI, you have uh an automated AI 00:28:20.160 --> 00:28:21.797 researcher in the works that's supposed 00:28:21.880 --> 00:28:23.837 to come out this fall. 00:28:23.920 --> 00:28:27.837 What is that? So, the the direction of 00:28:27.920 --> 00:28:32.117 travel right now, we are in this early phase of takeoff of this technology. 00:28:32.200 --> 00:28:36.317 What does takeoff mean? Takeoff is as the AI gets better and better on this 00:28:36.400 --> 00:28:38.957 exponential. And 00:28:39.040 --> 00:28:43.957 in part because we can use the AI to make the AI better. Mhm. So, our 00:28:44.040 --> 00:28:47.557 development process speeds up. But I also think when I think of takeoff, it's 00:28:47.640 --> 00:28:49.837 also about real world impact. And in 00:28:49.920 --> 00:28:54.957 some ways we've been, you know, every technology is is an S curve. 00:28:55.040 --> 00:28:58.717 Or if you zoom out, some of S curves that end up being an exponential. And I 00:28:58.800 --> 00:29:02.077 think that that's what we're encountering right now. So, it's the 00:29:02.160 --> 00:29:04.717 tech technology development is moving 00:29:04.800 --> 00:29:07.477 with increasing speed and it's this 00:29:07.560 --> 00:29:10.437 engine that's picking up momentum. 00:29:10.520 --> 00:29:12.957 But it's also in the world, there's all 00:29:13.040 --> 00:29:16.197 of these tailwinds because there's chip 00:29:16.280 --> 00:29:20.237 developers that are getting more resourcing into their programs. There's 00:29:20.320 --> 00:29:23.197 this economy of people who are building on top of it, trying to figure out how 00:29:23.280 --> 00:29:26.717 it fits into every different application. And all of that energy is 00:29:26.800 --> 00:29:29.037 just accumulating more and more into 00:29:29.120 --> 00:29:34.037 this this takeoff phase of the AI becoming just a kind of sideshow to 00:29:34.120 --> 00:29:37.877 being the main driver of economic growth. And I think that that is 00:29:37.960 --> 00:29:42.037 something that it's not just about what we're doing in these walls. It's about 00:29:42.120 --> 00:29:45.677 how the whole world, the whole economy comes together and are the to push 00:29:45.760 --> 00:29:48.757 forward this technology and its usefulness together. And the researcher, 00:29:48.840 --> 00:29:50.597 well, then what will it do exactly? 00:29:50.680 --> 00:29:55.117 Also, the researcher will be a moment where the AI which we're building that 00:29:55.200 --> 00:29:59.037 right now it's taking a you know, it's doing a larger percentage of tasks, that 00:29:59.120 --> 00:30:02.277 we should be able to let it run autonomously. And that I think there's a 00:30:02.360 --> 00:30:05.797 lot of thought that goes into what that means. And that it doesn't necessarily 00:30:05.880 --> 00:30:09.957 mean that we just let it off on its own and then come back later and see if it 00:30:10.040 --> 00:30:13.357 did something good. I think that we are going to be very involved in managing 00:30:13.440 --> 00:30:17.277 it. Right? Just like right now if you have a junior researcher, 00:30:17.360 --> 00:30:19.837 if you leave them on their own too long, they're probably going to go down a path 00:30:19.920 --> 00:30:23.597 that's not very useful. But if you have a senior researcher or someone who has a 00:30:23.680 --> 00:30:27.477 vision, they don't even necessarily need to know the mechanical skills, 00:30:27.560 --> 00:30:31.237 they will be able to provide feedback, review the plots that this person's that 00:30:31.320 --> 00:30:36.317 the you know, the interns producing and to provide direction in terms of the 00:30:36.400 --> 00:30:39.597 vision of what is it that I want you to accomplish. And so I think of this as a 00:30:39.680 --> 00:30:42.997 system that we're going to build that will massively accelerate our ability to 00:30:43.080 --> 00:30:47.397 produce models, to make new research breakthroughs happen, to be able to make 00:30:47.480 --> 00:30:50.957 these models more useful and usable in the real world. 00:30:51.040 --> 00:30:53.117 >> Right. >> And to do that at increasing speed. 00:30:53.200 --> 00:30:56.717 >> So, sorry. What's it going to do? Are you going to say go find AGI and it will 00:30:56.800 --> 00:30:59.077 just try to >> I I think I think the way I think of it 00:30:59.160 --> 00:31:01.117 is is something like that to first order. 00:31:01.200 --> 00:31:03.237 >> Okay. >> And at a practical level, I think I 00:31:03.320 --> 00:31:06.597 would view it as is taking the full 00:31:06.680 --> 00:31:10.637 end-to-end of what one of our research scientists does 00:31:10.720 --> 00:31:12.277 and be able to do that in silicon. 00:31:12.360 --> 00:31:16.437 >> Another way to think about takeoff is their progress in AI goes from 00:31:16.520 --> 00:31:18.597 incremental to 00:31:18.680 --> 00:31:23.477 gathering momentum and then sort of this unstoppable march to an intelligence 00:31:23.560 --> 00:31:26.117 that's smarter than humans. 00:31:26.200 --> 00:31:30.677 Um do you do you worry that 00:31:30.760 --> 00:31:34.957 there's just as there's possibilities for things to go right on that front, 00:31:35.040 --> 00:31:36.317 there's also possibilities for that 00:31:36.400 --> 00:31:39.757 progress that process to go wrong? 00:31:39.840 --> 00:31:42.677 >> I mean, I think that that's absolutely 00:31:42.760 --> 00:31:47.157 yes. I think that the way to get the benefits of this technology is also to 00:31:47.240 --> 00:31:48.917 really think about the risks. >> Mhm. 00:31:49.000 --> 00:31:52.757 >> And if you look at how we've approached technology development from a technical 00:31:52.840 --> 00:31:57.477 perspective, we invest a lot in safety, security. 00:31:57.560 --> 00:31:59.837 Good example of this is prompt injections. All right, if you're going 00:31:59.920 --> 00:32:04.957 to have an AI that is very smart, very capable, hooked up to lots of tools, 00:32:05.040 --> 00:32:07.357 you want to make sure that it can't be subverted by someone giving it a weird 00:32:07.440 --> 00:32:09.997 instruction. And that's something we've invested in 00:32:10.080 --> 00:32:12.837 quite a lot and I think have really incredible results, have an incredible 00:32:12.920 --> 00:32:18.037 team working on. And it's interesting to think about some of these problems where 00:32:18.120 --> 00:32:22.437 you can make analogies to humans. Like humans are also susceptible to fishing 00:32:22.520 --> 00:32:25.957 attacks, to being deceived in different ways, to not really understanding the 00:32:26.040 --> 00:32:27.677 full context of what they're working on. 00:32:27.760 --> 00:32:31.557 And we bring those analogies into our development process and think about this 00:32:31.640 --> 00:32:35.717 whenever we release a model, develop a model, how do we ensure that it's going 00:32:35.800 --> 00:32:41.477 to be aligned with people and be able to actually be helpful. And 00:32:41.560 --> 00:32:44.317 that is something that we care quite a lot about. I think that there are bigger 00:32:44.400 --> 00:32:47.957 questions about the world, the economy, how does everything change, how does 00:32:48.040 --> 00:32:49.797 everyone benefit from this technology. 00:32:49.880 --> 00:32:53.957 They're not purely technical, not purely something that OpenAI on our own will be 00:32:54.040 --> 00:32:58.597 able to solve. But yes, I I think quite a lot about not just pushing forward the 00:32:58.680 --> 00:33:02.597 technology, but also really about how do we ensure that we have the positive 00:33:02.680 --> 00:33:07.037 impact that is its potential. >> The worry though is that this is a race 00:33:07.120 --> 00:33:11.157 and what's being done within these walls where at OpenAI 00:33:11.240 --> 00:33:15.277 headquarters is also being copied by many of the open source players, which 00:33:15.360 --> 00:33:18.597 have much less bare boundaries and barriers and 00:33:18.680 --> 00:33:22.997 protection on on on the safety side of things. And if you said this months that 00:33:23.080 --> 00:33:27.357 it takes, you know, people getting a lot of things right to be creative and sort 00:33:27.440 --> 00:33:31.077 of one person with bad intent to be destructive. 00:33:31.160 --> 00:33:35.517 And that's sort of where the concern lies for me at least is just 00:33:35.600 --> 00:33:39.397 when this this is it's clearly a race, it's going fast. Many of your 00:33:39.480 --> 00:33:42.477 counterparts have said if everybody agrees 00:33:42.560 --> 00:33:47.117 to stop it, we'll stop it and it doesn't seem like it's going to slow 00:33:47.200 --> 00:33:50.437 it all. >> So is the reward worth the risk basically? 00:33:50.520 --> 00:33:52.037 >> I think I think the reward is worth the 00:33:52.120 --> 00:33:55.317 risk, but I think that that is too too 00:33:55.400 --> 00:33:59.357 coarse grained of a of a answer in some sense. Okay, like the way that I think 00:33:59.440 --> 00:34:03.277 about it is that we've asked from the beginning of OpenAI 00:34:03.360 --> 00:34:07.437 how what does a great future look like? How 00:34:07.520 --> 00:34:11.517 can this technology really be something that uplifts everyone? 00:34:11.600 --> 00:34:15.637 And you can think of there almost being two different angles. One is the 00:34:15.720 --> 00:34:19.797 centralization view of saying that well, the way to make this technology safe is 00:34:19.880 --> 00:34:23.437 that you have only one actor building it and so then you don't have any 00:34:23.520 --> 00:34:26.277 pressures, right? You can really think about getting it right and you know, 00:34:26.360 --> 00:34:29.317 then figure out how to roll it out to everyone when it's ready, those kinds of 00:34:29.400 --> 00:34:33.477 things. That's a pretty tough pill in some ways 00:34:33.560 --> 00:34:36.917 and think that there's a lot of properties that you can think instead 00:34:37.000 --> 00:34:40.997 think about approaching differently, which we prefer to as resilience. To 00:34:41.080 --> 00:34:44.677 think of it as this open system where there's lots of players who are 00:34:44.760 --> 00:34:47.997 developing the technology. It's not not just about the technology, it's about 00:34:48.080 --> 00:34:50.197 building societal infrastructure that 00:34:50.280 --> 00:34:52.437 helps this technology really go well. 00:34:52.520 --> 00:34:56.597 And if you think about how electricity has developed, that's something where 00:34:56.680 --> 00:34:59.797 lots of people produce it, that it actually has dangers and risks, but we 00:34:59.880 --> 00:35:03.397 also build our safety infrastructure in 00:35:03.480 --> 00:35:07.597 a diversity of different ways around safety standards for electricity, around 00:35:07.680 --> 00:35:10.437 different ways of harnessing it, about how you scale it, that there's 00:35:10.520 --> 00:35:14.037 regulations when you're at these massive scales that lots of people are able to 00:35:14.120 --> 00:35:17.957 use in a democratized fashion, there's inspectors. Like there's a whole system 00:35:18.040 --> 00:35:21.437 that's been built around the needs of that technology, the proclivities of 00:35:21.520 --> 00:35:23.557 that specific technology. And I think 00:35:23.640 --> 00:35:27.157 that one thing that we have 00:35:27.240 --> 00:35:31.997 really I think seen with AI is that it is 00:35:32.080 --> 00:35:35.517 something where we need this broad conversation. We need lots of people to 00:35:35.600 --> 00:35:39.917 be aware if this technology is going to come and change everything for everyone, 00:35:40.000 --> 00:35:42.997 people need to participate in that. It can't be something that's done often 00:35:43.080 --> 00:35:45.557 secret by just one, you know, sort of 00:35:45.640 --> 00:35:48.797 centralized group. And so this has been 00:35:48.880 --> 00:35:53.157 to me a very core question to how this 00:35:53.240 --> 00:35:56.237 technology should play out and something we really believe in is this resilience 00:35:56.320 --> 00:35:59.957 ecosystem that should emerge around the development of this technology. 00:36:00.040 --> 00:36:03.757 >> So you said we're uh in takeoff, in the middle of a takeoff process and we I 00:36:03.840 --> 00:36:07.197 guess see all of humanity are experiencing this. 00:36:07.280 --> 00:36:10.077 Uh Nvidia CEO Jensen Huang said recently 00:36:10.160 --> 00:36:14.357 that he believes that AGI has been achieved. Do you agree? 00:36:14.440 --> 00:36:15.997 >> I think that AGI has a different 00:36:16.080 --> 00:36:18.757 definition to many people and I think 00:36:18.840 --> 00:36:23.477 that there are many people who would say that what we have right now is AGI. I 00:36:23.560 --> 00:36:27.117 think you can debate it. 00:36:27.200 --> 00:36:31.357 But I think that maybe the thing that's interesting is that AGI, like the 00:36:31.440 --> 00:36:35.637 technology we have right now, is very jagged. Like it is absolutely superhuman 00:36:35.720 --> 00:36:40.197 at many tasks. When it comes to writing code, those kinds of things, 00:36:40.280 --> 00:36:43.797 AI can just do it, right? And it really removes a lot of the friction to 00:36:43.880 --> 00:36:45.517 creating things. But there's some very 00:36:45.600 --> 00:36:48.277 basic tasks that a human can do that our 00:36:48.360 --> 00:36:53.117 AI still struggle with. And so, it's almost to say that 00:36:53.200 --> 00:36:56.677 where do you draw the cutline? Mhm. It's a little bit more of a vibe than a than 00:36:56.760 --> 00:37:00.917 a feeling than it is a uh than it is science at the moment. And 00:37:01.000 --> 00:37:05.277 so, I think for myself, we're definitely going through that moment. And if you 00:37:05.360 --> 00:37:08.597 were to show me 5 years ago the systems we have today, I'd have to go, "Oh, 00:37:08.680 --> 00:37:10.557 yeah. That's what we're talking about." 00:37:10.640 --> 00:37:13.117 But it's just different. It's so different from anything we ever 00:37:13.200 --> 00:37:16.797 pictured. And so, I think we need to adjust our mental models appropriately. 00:37:16.880 --> 00:37:18.557 So, you're not there yet. I think I 00:37:18.640 --> 00:37:23.957 think that I'd say I'm I'm I'm basically like 70, 80% there. So, I 00:37:24.040 --> 00:37:27.917 think we're we're quite close. And I think it's extremely clear that we are 00:37:28.000 --> 00:37:32.237 going to have AGI within the next couple of years in a way that 00:37:32.320 --> 00:37:37.037 is still going to be jagged, but that the the floor of task will just be 00:37:37.120 --> 00:37:40.877 almost for any intellectual task of how you use your computer, that the AI will 00:37:40.960 --> 00:37:45.437 be able to do that. And I think that that yeah, right now I have to give a 00:37:45.520 --> 00:37:48.917 little bit of a uncertain answer because there's some there's some it's almost 00:37:49.000 --> 00:37:52.717 like a like a uncertainty principle kind of thing. They you can you can you can 00:37:52.800 --> 00:37:56.717 debate it. For my own personal definition, I think we're almost there, 00:37:56.800 --> 00:38:00.197 and with maybe a little bit more, we will absolutely be. 00:38:00.280 --> 00:38:03.757 >> Okay. Well, we're we're going to go to a break, but as long as we're on the way 00:38:03.840 --> 00:38:06.757 to the break, I want to let folks watching at home know that you and I are 00:38:06.840 --> 00:38:09.277 going to be talking again June 18th here 00:38:09.360 --> 00:38:13.877 in San Francisco at SFJAZZ. So, I will put some information if you want to come 00:38:13.960 --> 00:38:17.197 join that conversation in the show notes, and I do hope you sign up. All 00:38:17.280 --> 00:38:19.397 right. We'll be back right after this. 00:38:19.480 --> 00:38:22.957 And we're back here on Big Technology Podcast with OpenAI co-founder and 00:38:23.040 --> 00:38:25.397 president Greg Brockman. Greg, let me 00:38:25.480 --> 00:38:27.997 just ask you, what happened in December 00:38:28.080 --> 00:38:30.077 2025? Because it seems like it was an 00:38:30.160 --> 00:38:32.957 inflection point where all this idea of 00:38:33.040 --> 00:38:37.837 uh letting the machine code for hours uninterrupted what went from theory to a 00:38:37.920 --> 00:38:41.597 moment where everyone said, "I think I can trust this to keep going 00:38:41.680 --> 00:38:45.117 for a while." Uh so, what exactly happened? 00:38:45.200 --> 00:38:47.437 >> So, new model releases 00:38:47.520 --> 00:38:49.197 really went from the AI being able to do 00:38:49.280 --> 00:38:53.837 like 20% of your tasks like 80%. 00:38:53.920 --> 00:38:57.917 And that was this massive shift because it went from being kind of a yeah, it's 00:38:58.000 --> 00:39:01.997 a nice thing to do to you absolutely need to retool your workflow around 00:39:02.080 --> 00:39:07.237 these AIs. And for myself, I very much had this moment where 00:39:07.320 --> 00:39:10.397 I have a test prompt that I've been using for years of build a website for 00:39:10.480 --> 00:39:13.757 me. I'd actually built this website back when I was learning to code, took me 00:39:13.840 --> 00:39:17.477 months. Used to be over the course of 25 that 00:39:17.560 --> 00:39:21.037 you know, it'd take like 4 hours, bunch of different prompts to get it right. 00:39:21.120 --> 00:39:24.917 In December, one shot. Just asked the AI one time and 00:39:25.000 --> 00:39:26.997 it produced it and did a great job. 00:39:27.080 --> 00:39:29.197 >> So, how did those models make the leap? 00:39:29.280 --> 00:39:34.037 >> Well, a lot of it is about the better base models. 00:39:34.120 --> 00:39:38.277 >> Mhm. >> The one thing about OpenAI is that we've been working on improving our 00:39:38.360 --> 00:39:39.677 pre-training technology for quite some 00:39:39.760 --> 00:39:42.957 time and that in that moment we got to 00:39:43.040 --> 00:39:45.437 see a little taste of what is going to 00:39:45.520 --> 00:39:48.237 be coming for for the rest of this year. 00:39:48.320 --> 00:39:52.437 But it's also really about not any one thing. It's about we're constantly 00:39:52.520 --> 00:39:55.597 pushing on every single axis of innovation. 00:39:55.680 --> 00:40:00.637 And the thing that's very interesting about these models is in some ways you get 00:40:00.720 --> 00:40:04.117 these leaps. In some ways it's all continuous. All right, it didn't go from 00:40:04.200 --> 00:40:08.797 0% to 80%. It went from 20% to 80%. And so, in some ways it's it just got 00:40:08.880 --> 00:40:13.917 better. And I think that we've actually seen this improvement continue with every 00:40:14.000 --> 00:40:15.677 single point release that we've had. 00:40:15.760 --> 00:40:20.637 Like between 5.2 and 5.3, one of my engineers I work with very closely 00:40:20.720 --> 00:40:24.237 went from he couldn't get it to do the like low-level hardcore systems engineer 00:40:24.320 --> 00:40:26.637 he does to it absolutely being graded. 00:40:26.720 --> 00:40:29.237 He gives it a design doc and actually implements it, adds metrics, 00:40:29.320 --> 00:40:34.277 observability, runs the profiler, improves it to the point that it's the 00:40:34.360 --> 00:40:37.677 exact thing that he was hoping to produce. And so I think that the way to 00:40:37.760 --> 00:40:41.957 think about it is it's almost a sort of slowly slowly slowly all at 00:40:42.040 --> 00:40:46.837 once. But it is all indicated by what's kind 00:40:46.920 --> 00:40:50.077 of working right now. Certainly within a year, sometimes much 00:40:50.160 --> 00:40:52.277 sooner, is going to be incredibly reliable. 00:40:52.360 --> 00:40:55.997 >> And it surprised you because I heard you talking on an interview not long ago 00:40:56.080 --> 00:40:58.077 about how Codex, right, this autonomous 00:40:58.160 --> 00:41:01.517 coder was just for software developers. 00:41:01.600 --> 00:41:05.637 And earlier this conversation you said actually everyone can use this stuff. 00:41:05.720 --> 00:41:08.157 Yes, what led to the fact that you sort 00:41:08.240 --> 00:41:10.597 of changed your perspective on? 00:41:10.680 --> 00:41:13.957 >> Well, I think I'd been focusing on Codex 00:41:14.040 --> 00:41:18.837 and it's got the code in it, right? So it's really been for coders. And 00:41:18.920 --> 00:41:21.957 thinking about people within OpenAI because many of us are software 00:41:22.040 --> 00:41:24.637 engineers building for ourselves, 00:41:24.720 --> 00:41:29.117 it's very natural to think that way. But as this technology has been progressing, 00:41:29.200 --> 00:41:33.677 we've started to realize that the underlying technology we produced is 00:41:33.760 --> 00:41:37.437 mostly not about code at all. It's mostly about solving problems. It's 00:41:37.520 --> 00:41:41.357 mostly about being able to manage contexts and harnesses and think about 00:41:41.440 --> 00:41:43.877 how an AI should integrate and do work. 00:41:43.960 --> 00:41:48.797 And that's something that becomes both even for code 00:41:48.880 --> 00:41:51.837 suddenly anyone can have access because you can manage something that's going to 00:41:51.920 --> 00:41:55.157 go do work, right? If you have a vision, you have something you want to 00:41:55.240 --> 00:41:59.437 accomplish, you can describe your intent, the AI can execute, can get that 00:41:59.520 --> 00:42:03.397 done. But then it also starts to go, "Well, why am I just focused on coding?" 00:42:03.480 --> 00:42:07.957 Like there's so much just very mechanical skill associated with Excel 00:42:08.040 --> 00:42:12.597 spreadsheets, with presentations. And if the AI has the context, it has the raw 00:42:12.680 --> 00:42:16.517 intelligence now to be able to do these things at a great level. So, if we can 00:42:16.600 --> 00:42:20.717 just make it more accessible, suddenly goes from Codex is for coders to Codex 00:42:20.800 --> 00:42:24.637 is for everyone. And soon after this moment where we saw all this 00:42:24.720 --> 00:42:27.277 improvement, there was another so on 00:42:27.360 --> 00:42:31.917 phenomenon in Silicon Valley, which was open claw. Right, which is and maybe 00:42:32.000 --> 00:42:34.957 it's the broader tech community where where people started to trust it in ways 00:42:35.040 --> 00:42:38.317 that you suggested giving the an AI bot 00:42:38.400 --> 00:42:41.837 access to their their desktop or getting 00:42:41.920 --> 00:42:46.237 a Mac Mini and giving it access to like their mail and calendar there and their 00:42:46.320 --> 00:42:51.157 files and then just kind of letting it go run their life. And then OpenAI 00:42:51.240 --> 00:42:53.117 brought the founder of open claw 00:42:53.200 --> 00:42:55.637 in-house. So, you talked a little bit 00:42:55.720 --> 00:43:01.197 more about the AI as something that will help run your life for you in a way. 00:43:01.280 --> 00:43:04.197 Um is that the vision by bringing the open 00:43:04.280 --> 00:43:06.277 claw team in-house? 00:43:06.360 --> 00:43:09.597 >> Well, I say that the core thing about 00:43:09.680 --> 00:43:14.317 this technology is that figuring out how it's useful, how people want to use it. 00:43:14.400 --> 00:43:17.637 What is the vision for agents? How is it going to slide into people's lives? That 00:43:17.720 --> 00:43:21.237 is a hard problem and that one thing I've seen across many generations of 00:43:21.320 --> 00:43:24.757 this technology is the people who really lean in, who have a lot of curiosity, 00:43:24.840 --> 00:43:28.037 who have a lot of vision. That's a real skill and that's an emerging very 00:43:28.120 --> 00:43:32.997 valuable skill in this new economy that is emerging. And Peter who is the open 00:43:33.080 --> 00:43:35.997 claw founder is I think someone who's got incredible vision, incredible 00:43:36.080 --> 00:43:39.797 creativity. And so, to some extent it's about the specific technology, but to 00:43:39.880 --> 00:43:43.197 some extent it's not at all. It's really about the how do we take these 00:43:43.280 --> 00:43:47.637 capabilities and figure out how they slide into people's lives. And so, I 00:43:47.720 --> 00:43:51.677 think as a technologist, it's very exciting, but as a someone who is 00:43:51.760 --> 00:43:55.877 focused on bringing utility to people, that's something that we are doubling 00:43:55.960 --> 00:43:57.917 down on and investing quite a lot. 00:43:58.000 --> 00:43:59.597 >> You had a pretty interesting quote about 00:43:59.680 --> 00:44:04.837 this recently. Uh talking about getting these autonomous AI agents to work on 00:44:04.920 --> 00:44:08.357 your behalf. You said, um you become when you do it, you become 00:44:08.440 --> 00:44:12.917 this CEO of a fleet of hundreds of thousands of agents that are completing 00:44:13.000 --> 00:44:16.477 your objectives, your goals, your vision, and you're not in the weeds on 00:44:16.560 --> 00:44:18.557 exactly how different things are solved. 00:44:18.640 --> 00:44:22.077 And in some ways, this new way of work can make you feel like you're losing 00:44:22.160 --> 00:44:25.717 your pulse on the problem. Is that good? 00:44:25.800 --> 00:44:28.837 >> I think I think that there's a mixed bag. And so, I think that what we need 00:44:28.920 --> 00:44:30.957 to do is acknowledge the strengths of 00:44:31.040 --> 00:44:33.597 what these tools can deliver 00:44:33.680 --> 00:44:39.077 and mitigate the weaknesses. And so, giving people leverage, agency, making 00:44:39.160 --> 00:44:42.197 it so that if you have a vision, something you want to accomplish, that 00:44:42.280 --> 00:44:45.717 you can have a fleet of agents that will go do it for you. 00:44:45.800 --> 00:44:49.557 But, if you think about how the world works, that 00:44:49.640 --> 00:44:51.317 at the end of the day, there's an 00:44:51.400 --> 00:44:54.877 accountable party. Right? If you're 00:44:54.960 --> 00:44:59.757 trying to build a website and your agent messes it up, and your user is affected, 00:44:59.840 --> 00:45:03.917 it's not really the agent's fault, it's your fault. And so, you need to care. 00:45:04.000 --> 00:45:07.197 And I think that for people to use these tools right, you need to realize that 00:45:07.280 --> 00:45:11.117 human agency, human accountability, that's a core part of the system. How 00:45:11.200 --> 00:45:15.917 the human uses the AI, that's something that is deeply fundamental. And so, I 00:45:16.000 --> 00:45:19.477 think the important thing is that as a user of these agents, and we do this 00:45:19.560 --> 00:45:23.117 within Open AI, you cannot abdicate responsibility. You cannot just say, 00:45:23.200 --> 00:45:24.757 "Ah, the AI is just going to do stuff." 00:45:24.840 --> 00:45:26.077 >> Of course, but you said feel you're like 00:45:26.160 --> 00:45:30.357 you're losing your pulse on the problem itself. That's different then 00:45:30.440 --> 00:45:33.757 accountability layer to it. >> Well, to me, they actually are linked 00:45:33.840 --> 00:45:37.237 together because the point is that if you if you're a CEO and you're too far 00:45:37.320 --> 00:45:40.157 from the details, right? If you're running this company, you're running 00:45:40.240 --> 00:45:44.957 this this team, and that you've lost your finger on the pulse, that is 00:45:45.040 --> 00:45:48.157 something that's not going to lead to great results. And so, the point that I 00:45:48.240 --> 00:45:51.877 was trying to make there is that not that it's a desirable thing for humans 00:45:51.960 --> 00:45:55.797 to not have to to to know about what's going on. There's some details that 00:45:55.880 --> 00:45:59.277 because you can trust like if you are working with a team like a general 00:45:59.360 --> 00:46:02.797 contractor to build a house, there's a bunch of details there that 00:46:02.880 --> 00:46:05.437 you probably don't need to worry about because you can trust that they'll be 00:46:05.520 --> 00:46:08.837 taken care of. But at the end of the day, if there are details that are 00:46:08.920 --> 00:46:11.757 wrong, you should care about it. You 00:46:11.840 --> 00:46:14.237 should be aware. And so, this is, I 00:46:14.320 --> 00:46:16.997 think, an important nuance of you cannot 00:46:17.080 --> 00:46:21.717 just blindly say, "I'm okay with who's my finger on the pulse." That we need to 00:46:21.800 --> 00:46:25.117 lean in and say, "I need to keep it there to really understand the strengths 00:46:25.200 --> 00:46:28.877 and weaknesses." And that as you disengage from some of these details, 00:46:28.960 --> 00:46:33.117 these lower-level mechanical things, you should do it because you have 00:46:33.200 --> 00:46:37.157 built trust with a system that it will do a good job. 00:46:37.240 --> 00:46:40.677 >> One last question about the models. You talked a little bit about the evolutions 00:46:40.760 --> 00:46:42.157 that the models have gone through. 00:46:42.240 --> 00:46:46.237 Pre-training and fine-tuning, reinforcement learning that gets it uh 00:46:46.320 --> 00:46:48.917 more equipped to solve problems 00:46:49.000 --> 00:46:53.557 step-by-step and go out in the on the internet and do things. And now we're in 00:46:53.640 --> 00:46:57.437 this moment where they're the models have learned through 00:46:57.520 --> 00:46:59.197 that process to use tools. And correct 00:46:59.280 --> 00:47:02.277 me if I'm wrong on this one. What 00:47:02.360 --> 00:47:04.797 What is next in that progression? 00:47:04.880 --> 00:47:07.997 >> Well, I think that the 00:47:08.080 --> 00:47:13.117 world that we're in is one of this increasing capability and depth of what 00:47:13.200 --> 00:47:16.757 the machine can do. And some of this is about 00:47:16.840 --> 00:47:20.837 got this tool use, but now we also need to actually build really great tools. 00:47:20.920 --> 00:47:24.277 You think about something like computer use in AI that can actually use a desktop, 00:47:24.360 --> 00:47:28.437 then it is really able to do anything that you can do. But we also have to 00:47:28.520 --> 00:47:32.477 build a little bit for the machine think about how does in the enterprise 00:47:32.560 --> 00:47:36.037 credentialing work? How does How do audit trails and observability work? 00:47:36.120 --> 00:47:40.157 >> Mhm. >> There's a lot of technology to build to catch up with what the core model 00:47:40.240 --> 00:47:44.357 capability is. And I think the overall direction of travel includes things like 00:47:44.440 --> 00:47:48.637 a really great speech interface. So, you can just talk to your computer naturally 00:47:48.720 --> 00:47:51.837 and just as natural as this conversation, and it understands you. It 00:47:51.920 --> 00:47:53.917 does what you need. Has good advice. 00:47:54.000 --> 00:47:57.357 It's able to surface that I've been working on this thing. I have a problem. 00:47:57.440 --> 00:48:00.717 Here's you wake up in the morning and says, here's your daily report of how 00:48:00.800 --> 00:48:03.557 much progress your agents made overnight. Maybe it's running a business 00:48:03.640 --> 00:48:06.237 for you, which I think is going to be huge application of this technology. The 00:48:06.320 --> 00:48:09.677 democratization of entrepreneurship is absolutely coming. It'll say, here is 00:48:09.760 --> 00:48:12.197 these problems. There's this customer that's upset, you know, that they want 00:48:12.280 --> 00:48:15.157 to talk to a real human. Like you should go talk to them. Like all of that's 00:48:15.240 --> 00:48:18.357 going to happen. And then, I think that 00:48:18.440 --> 00:48:20.957 the raising of the ceiling of ambition 00:48:21.040 --> 00:48:26.157 of challenges humanity can solve. That is also a next step for 00:48:26.240 --> 00:48:29.917 this technology. And we're seeing the leading edges of it. The thing that I am 00:48:30.000 --> 00:48:34.117 just very excited to see is almost, if you remember AlphaGo move 37, right? 00:48:34.200 --> 00:48:36.117 This move that no human ever would have come up with. 00:48:36.200 --> 00:48:38.557 >> It's creative. >> Creative. And it changed humanity's 00:48:38.640 --> 00:48:40.557 understanding of the game. That is going 00:48:40.640 --> 00:48:43.197 to happen in every single domain. It 00:48:43.280 --> 00:48:47.677 will happen in science, in math, in physics, in chemistry. It's going to 00:48:47.760 --> 00:48:50.077 happen in material science, going to happen in biology, it's going to happen 00:48:50.160 --> 00:48:52.477 in healthcare, drug discovery. But it 00:48:52.560 --> 00:48:55.397 may also even happen in literature, in 00:48:55.480 --> 00:48:57.837 poetry, in a bunch of other fields that 00:48:57.920 --> 00:49:03.437 going to unlock human creative understanding and ideation in ways we 00:49:03.520 --> 00:49:05.597 can't imagine right now. >> Why do you think that hasn't happened 00:49:05.680 --> 00:49:08.757 yet, given how strong you say the models are? 00:49:08.840 --> 00:49:11.557 >> Well, I think that there is an overhang 00:49:11.640 --> 00:49:16.237 where of what the models are capable of and how people are using them. So, the 00:49:16.320 --> 00:49:20.237 application Well, yeah, it's it's almost our understanding of what is in these 00:49:20.320 --> 00:49:22.397 models. >> Okay. >> That's something that I think is still 00:49:22.480 --> 00:49:25.557 emerging. So, I think that even with no further progress, there's still a 00:49:25.640 --> 00:49:29.477 massive shift that will happen. The economy being powered by computing AI is 00:49:29.560 --> 00:49:32.477 still going to happen. But I think there's also something where what we've 00:49:32.560 --> 00:49:34.837 gotten very good at is training models 00:49:34.920 --> 00:49:40.477 on tasks that could be measured. And so, what we started with was math problems. 00:49:40.560 --> 00:49:43.597 Programming problems where you have a perfect verifier. 00:49:43.680 --> 00:49:47.437 And a lot of what the progress has been in bringing this to more open-ended 00:49:47.520 --> 00:49:51.557 problems has been expanding the space of what can be created. And the itself can 00:49:51.640 --> 00:49:54.397 really help with that. If the AI is smart and understands things, you give 00:49:54.480 --> 00:49:56.957 it a rubric for how well a task goes. 00:49:57.040 --> 00:50:00.357 And of course, for things like creative writing, like is this a good poem? 00:50:00.440 --> 00:50:05.077 That's a much harder thing to grade. And so, we've had less ability to teach the 00:50:05.160 --> 00:50:09.957 AI and for it to experience and try things out. But all of that is changing and 00:50:10.040 --> 00:50:12.157 something that we we have a lot of sight for. 00:50:12.240 --> 00:50:13.477 >> Now, it's interesting reflecting on that 00:50:13.560 --> 00:50:16.757 Peter Thiel has mentioned, pretty sure this is what he said, that 00:50:16.840 --> 00:50:20.997 if you're a math person, you're probably in deeper trouble in terms of these 00:50:21.080 --> 00:50:24.797 models coming for what you do than if you're a words person. And you were a 00:50:24.880 --> 00:50:29.397 member of math club back in the day. Uh are you not concerned about that? 00:50:29.480 --> 00:50:33.797 >> Well, I think that it's much easier to see what we lose than what we gain, 00:50:33.880 --> 00:50:36.637 right? Because we have a deep understanding of I used to do things 00:50:36.720 --> 00:50:40.077 this way. I used to do this math competition. Now the AI can do the math 00:50:40.160 --> 00:50:44.037 competition. But it was never really about the math competition. 00:50:44.120 --> 00:50:47.237 Right. Right. That's not really the thing that drives humanity. And if you 00:50:47.320 --> 00:50:50.757 think about the way that we do work right now of 00:50:50.840 --> 00:50:52.797 there's a box and we type behind a box. 00:50:52.880 --> 00:50:54.237 We weren't doing that 100 years ago. 00:50:54.320 --> 00:50:57.957 That's not natural. That's not this digital world that we all got kind of 00:50:58.040 --> 00:51:02.717 sucked into. That's not really what being human's about. Being a human's 00:51:02.800 --> 00:51:05.997 about being here, being present, connecting with other humans. And I 00:51:06.080 --> 00:51:07.797 think that what we're going to see is 00:51:07.880 --> 00:51:10.797 that AI is going to free up so much time 00:51:10.880 --> 00:51:15.357 to increase human connection, to build more bonds across people. 00:51:15.440 --> 00:51:18.197 And that's something I'm extremely excited about. 00:51:18.280 --> 00:51:20.957 >> Okay. And then as we shift well, as you 00:51:21.040 --> 00:51:24.117 shift really to uh these more 00:51:24.200 --> 00:51:27.237 agentic use cases, there's been 00:51:27.320 --> 00:51:32.397 discussion about whether the bigger training runs really need to happen. And 00:51:32.480 --> 00:51:36.237 you know, especially if you like get the model good enough, uh then you could 00:51:36.320 --> 00:51:39.157 sort of let it go out in the world and then 00:51:39.240 --> 00:51:43.557 you can uh effectively get much of the uplift in 00:51:43.640 --> 00:51:47.757 areas that aren't the pre-training, which is what these big data centers are 00:51:47.840 --> 00:51:51.957 needed for. So, you you were worked out you work on scaling here. 00:51:52.040 --> 00:51:54.237 Uh lead that lead that process. 00:51:54.320 --> 00:51:56.157 Uh what do you think about that argument? 00:51:56.240 --> 00:51:59.717 >> Well, I think it misses something very important for how the technology 00:51:59.800 --> 00:52:04.237 development goes because it is absolutely the case that every single 00:52:04.320 --> 00:52:08.677 step of the model production pipeline multiplies. 00:52:08.760 --> 00:52:10.797 And so, you want to improve all of them. 00:52:10.880 --> 00:52:14.597 And the thing that we see as we improve the pre-training, it makes all the other 00:52:14.680 --> 00:52:18.357 steps much easier and it makes sense because it's a model 00:52:18.440 --> 00:52:21.997 is able to learn faster. It's a model that is 00:52:22.080 --> 00:52:25.637 because it already is like more capable to start when it's trying out different 00:52:25.720 --> 00:52:29.797 ideas and learning from its own mistakes, that process just is faster. 00:52:29.880 --> 00:52:33.837 It needs to make fewer mistakes. And so, I think that the big shift has been from 00:52:33.920 --> 00:52:39.477 thinking of it as just it's you're just training this cerebral system on its own 00:52:39.560 --> 00:52:42.517 and you just make it bigger and bigger to it's also about trying things out. 00:52:42.600 --> 00:52:45.597 It's also about understanding how people are using it in the real world and 00:52:45.680 --> 00:52:49.077 connecting that back into your training, but it doesn't remove the value and the 00:52:49.160 --> 00:52:52.717 importance of continuing that that that research. And the thing that I think has 00:52:52.800 --> 00:52:56.357 also shifted is we used to really just focus on the raw pre-training 00:52:56.440 --> 00:52:59.957 capability, but not think as much about the inference ability and that's been a 00:53:00.040 --> 00:53:02.197 big change over the past 24 months to 00:53:02.280 --> 00:53:05.917 realize that it's a balance between you 00:53:06.000 --> 00:53:10.077 can have this model that has all those great properties in the base, but then 00:53:10.160 --> 00:53:12.797 you really need it to be able to be inferencable because you need to 00:53:12.880 --> 00:53:16.397 reinforcement learning, you need to serve it to the world. And that that 00:53:16.480 --> 00:53:20.077 means that you don't necessarily go as big as you possibly could because you 00:53:20.160 --> 00:53:23.357 also really think about there's going to be all this downstream use and you 00:53:23.440 --> 00:53:28.757 really want the thing that has the best intelligence times the that that cost 00:53:28.840 --> 00:53:31.237 and to to optimize those two things together. 00:53:31.320 --> 00:53:35.037 >> Do you still need the Nvidia GPU if things move mostly to inference? 00:53:35.120 --> 00:53:36.837 >> We absolutely do. Yes. >> Why? 00:53:36.920 --> 00:53:39.117 >> Well, because the 00:53:39.200 --> 00:53:41.477 there's multiple reasons, um but one is 00:53:41.560 --> 00:53:44.437 that even as the balance of how much of 00:53:44.520 --> 00:53:47.517 inference versus training changes, that 00:53:47.600 --> 00:53:52.157 you cannot get massive scale training 00:53:52.240 --> 00:53:56.157 through any other way besides this concentration 00:53:56.240 --> 00:54:00.757 of compute on one problem. And so, I think that the the thing that I think 00:54:00.840 --> 00:54:04.637 will happen is there's some amount of the the the deployment footprint goes up 00:54:04.720 --> 00:54:08.677 quite a lot, but that sometimes there will be you have a particular massive 00:54:08.760 --> 00:54:12.437 pre-training run, and you really want to concentrate a bunch in there. Um, I also 00:54:12.520 --> 00:54:15.957 think that the NVIDIA team is just incredible and does really, really 00:54:16.040 --> 00:54:18.477 amazing work. And so, yeah, we we partner very closely with them. 00:54:18.560 --> 00:54:21.117 >> Isn't there going to be a time where people just say, "We've pre-trained 00:54:21.200 --> 00:54:23.197 enough. The models are smart enough?" 00:54:23.280 --> 00:54:27.637 >> I think that that's a little bit like once humanity has solved all problems in 00:54:27.720 --> 00:54:30.437 front of us, then maybe we can we can say that. 00:54:30.520 --> 00:54:34.677 Right. But I think that the ceiling of what we want to accomplish, I think that 00:54:34.760 --> 00:54:39.237 that there's just so much ambition that that maybe we've over the past 50 years 00:54:39.320 --> 00:54:42.557 or so just sort of backed off from, right? You think about I mean, even 00:54:42.640 --> 00:54:46.717 problems that seem very clear like can we have health care for everyone that is 00:54:46.800 --> 00:54:50.037 not just that's that's actually preventative, not just 00:54:50.120 --> 00:54:53.517 targeting when people have a problem, but really think about the lifestyle and 00:54:53.600 --> 00:54:57.717 how to really help people early detect potential diseases before they 00:54:57.800 --> 00:55:02.117 happen. Like that's a problem that I think we can actually achieve through 00:55:02.200 --> 00:55:05.797 more intelligent models. And there's probably some level where you can 00:55:05.880 --> 00:55:08.997 totally solve that problem, and then you say, "Well, do I need a model that's two 00:55:09.080 --> 00:55:10.997 times smarter?" But there's other 00:55:11.080 --> 00:55:14.717 problems that are going to demand that. 00:55:14.800 --> 00:55:16.317 >> Let's talk about the math about building 00:55:16.400 --> 00:55:20.077 these data centers. You raised 110 billion 00:55:20.160 --> 00:55:23.797 uh, earlier this year. Is what's the math behind that? Does 00:55:23.880 --> 00:55:25.677 that money go right into data centers? 00:55:25.760 --> 00:55:29.557 How do you think about how you're going to return that money to investors? Talk 00:55:29.640 --> 00:55:33.317 about those calculations. >> Yeah, so I think it's as simple as the 00:55:33.400 --> 00:55:35.397 massive expense we see in front of us is 00:55:35.480 --> 00:55:38.437 compute. But you can think of compute 00:55:38.520 --> 00:55:42.437 not as a cost center, but as a revenue center. Think of it a little bit like 00:55:42.520 --> 00:55:45.277 hiring sales people. Right? How many sales people do you want 00:55:45.360 --> 00:55:48.077 to hire? As long as you can sell your product, as long as you have a scalable 00:55:48.160 --> 00:55:52.037 way to sell that product, then the more sales people you have, the more revenue 00:55:52.120 --> 00:55:55.597 you will make. And I think the world that we're in is we have continually 00:55:55.680 --> 00:55:59.917 found we cannot build compute fast enough to keep up with demand. And I see 00:56:00.000 --> 00:56:03.397 this very concretely. Right? Right now we have to make very painful decisions 00:56:03.480 --> 00:56:08.157 about what we're launching, about where the compute goes, and that I think we're 00:56:08.240 --> 00:56:11.357 going to experience this more broadly within the economy as we shift to this 00:56:11.440 --> 00:56:15.117 AI-powered economy. The question will be what problems are going to get that 00:56:15.200 --> 00:56:19.517 massive compute. How do you scale so everyone can have personal agent running 00:56:19.600 --> 00:56:22.837 for them? How can everyone be using systems like Codex? Like there just 00:56:22.920 --> 00:56:27.077 isn't enough compute in the world to be able to do that. 00:56:27.160 --> 00:56:30.237 And so, we're trying to get ahead of that problem. But, it is a new category. 00:56:30.320 --> 00:56:34.477 >> Right? So, I you're doing it with real confidence. I mean, in sums of money the 00:56:34.560 --> 00:56:37.917 world has never seen put towards a project like this. 00:56:38.000 --> 00:56:41.397 When you're building a new category, how do you do it with certainty that it's 00:56:41.480 --> 00:56:44.477 going to work out? >> Well, I think there's several components 00:56:44.560 --> 00:56:48.877 that go into it. So, the first is there is historical precedent at this point. 00:56:48.960 --> 00:56:52.117 From the moment we launched ChatGPT, I remember talking with my team having 00:56:52.200 --> 00:56:55.397 this exact conversation. They said, "How much compute should we buy?" I said, 00:56:55.480 --> 00:56:58.277 "All of it." They said, "No, no, no, really. How much compute should we buy?" 00:56:58.360 --> 00:57:01.117 I said, "No matter how much we try to 00:57:01.200 --> 00:57:05.437 build, I know we're not going to be able to keep up with the demand." And that 00:57:05.520 --> 00:57:09.237 has been true, and that has been true every year since then. And the the 00:57:09.320 --> 00:57:13.077 challenge is that these compute purchases, you have to lock them in 18 00:57:13.160 --> 00:57:16.757 months, sometimes 24 months, sometimes longer 00:57:16.840 --> 00:57:20.477 in advance of the machines being delivered, which means you really need 00:57:20.560 --> 00:57:24.597 to project forward. And I think that the world that we're moving towards is one 00:57:24.680 --> 00:57:29.197 where to date most of our revenue has come from consumer subscriptions, and that 00:57:29.280 --> 00:57:31.677 will always be very important. There's other revenue streams we have emerging 00:57:31.760 --> 00:57:35.277 as well, but the the opportunity that clearly is emerging now is knowledge 00:57:35.360 --> 00:57:39.077 work. And we're seeing this very concretely across every single 00:57:39.160 --> 00:57:43.397 enterprise realizing this technology it actually really works and to be 00:57:43.480 --> 00:57:47.517 competitive they need to adopt it. And you can see this organic energy of all 00:57:47.600 --> 00:57:51.597 these software engineers using it and then we're starting to see the 00:57:51.680 --> 00:57:54.237 percolation of people using it for 00:57:54.320 --> 00:57:59.237 various knowledge work inside of the enterprises and the willingness to pay 00:57:59.320 --> 00:58:03.357 and the revenue growth that you're seeing in this industry is very clear. 00:58:03.440 --> 00:58:07.757 Right? It's very clearly happening right now and just project that forward. And 00:58:07.840 --> 00:58:11.237 we look like one thing we get to see that maybe the world doesn't is the line 00:58:11.320 --> 00:58:15.757 of sight to how these models will improve. And all of this together says 00:58:15.840 --> 00:58:18.197 that the economy, which is a massive thing, 00:58:18.280 --> 00:58:22.077 right? The economy is just so large it's it's almost incomprehensible. 00:58:22.160 --> 00:58:26.437 All of the growth, like the the the highest order bit on how this economy 00:58:26.520 --> 00:58:30.437 grows from here will be about AI, how well you can leverage AI and the 00:58:30.520 --> 00:58:32.917 computational power you have available to power it. 00:58:33.000 --> 00:58:34.397 >> You said consumer subscriptions are your 00:58:34.480 --> 00:58:38.157 biggest source of revenue right now. Is the projection that will flip and that 00:58:38.240 --> 00:58:40.197 business will be the biggest source? 00:58:40.280 --> 00:58:41.877 >> I think well, I think that that it is 00:58:41.960 --> 00:58:47.557 very like very clear how quickly the uh the the enterprise it's not just 00:58:47.640 --> 00:58:50.677 enterprise cuz I think enterprise is also changing what it means. So really 00:58:50.760 --> 00:58:53.997 people using it for productive knowledge work for those kinds of things. And I 00:58:54.080 --> 00:58:57.597 think that as we think about pricing, one thing if you look at how Codex works 00:58:57.680 --> 00:59:01.957 right now is if you have a ChatGPT consumer subscription, you can use 00:59:02.040 --> 00:59:05.717 Codex. And so I think it's not going to be as well defined as there's this 00:59:05.800 --> 00:59:07.357 category, that category. I think it will 00:59:07.440 --> 00:59:10.757 really be about you as a user are going 00:59:10.840 --> 00:59:12.957 to have just again like your laptop this 00:59:13.040 --> 00:59:17.957 portal to the digital world and that is what the the revenue fundamentally will 00:59:18.040 --> 00:59:21.917 will will come from. >> Dario said, "I think about you." 00:59:22.000 --> 00:59:25.917 There are some players who are yoloing who pull the wrist dial too far and I'm 00:59:26.000 --> 00:59:30.637 very concerned. I think he's referencing your infrastructure bets there. 00:59:30.720 --> 00:59:33.557 What do you think about that? >> Well, I just disagree. 00:59:33.640 --> 00:59:38.317 I think we've been very thoughtful and very much seeing 00:59:38.400 --> 00:59:40.477 what is coming and I think that we will 00:59:40.560 --> 00:59:43.997 see even this year how everyone who is 00:59:44.080 --> 00:59:48.437 participating is going to be compute strapped. And I think we have been the 00:59:48.520 --> 00:59:50.077 most forward in realizing that this is 00:59:50.160 --> 00:59:54.117 coming and building in anticipation of 00:59:54.200 --> 00:59:58.437 how this technology is playing out. And I think that what we have seen is that 00:59:58.520 --> 01:00:02.637 for other players that they kind of realized that probably late last year 01:00:02.720 --> 01:00:05.677 and started scrambling to see what compute is available and there really 01:00:05.760 --> 01:00:09.677 wasn't any. And so I think that even as people it's very easy to to make 01:00:09.760 --> 01:00:13.317 statements like that, but I think that everyone has kind of realized that 01:00:13.400 --> 01:00:16.837 this technology it's working, it's here, it's real, right? Software engineering 01:00:16.920 --> 01:00:21.317 is just the first example of it. And that we are fundamentally limited by the 01:00:21.400 --> 01:00:23.037 computational power available. 01:00:23.120 --> 01:00:27.437 >> And you said that also that if he's off by with his prediction by a little bit 01:00:27.520 --> 01:00:31.117 then uh his the company could potentially go bankrupt. Is that the 01:00:31.200 --> 01:00:35.117 same case for you? >> I think that Look, I I think that that 01:00:35.200 --> 01:00:37.277 there's actually more degrees of off-ramp here. 01:00:37.360 --> 01:00:38.957 >> Okay. >> If you start to worry about the downside 01:00:39.040 --> 01:00:42.237 case, which I think is a very reasonable question, right? But to some extent what 01:00:42.320 --> 01:00:46.597 I think the bet is on isn't about anyone company. It's really about the sector. 01:00:46.680 --> 01:00:49.877 It's really about do you believe this technology can be produced and can 01:00:49.960 --> 01:00:53.677 deliver this massive amount of value that we see coming. 01:00:53.760 --> 01:00:57.397 And again, I'll point to proof points, right? That software engineering it's 01:00:57.480 --> 01:00:59.757 just like the degree to which if you're not a software engineer, you haven't 01:00:59.840 --> 01:01:03.837 tried code apps the degree to which it's different, like it's just hard to 01:01:03.920 --> 01:01:07.957 describe. And I think that people will experience it very quickly. Like, you 01:01:08.040 --> 01:01:12.837 know, 6 months ago I think that for us we we saw this internally, but there 01:01:12.920 --> 01:01:16.077 were less proof points out there. Now there's proof points out there. 6 months 01:01:16.160 --> 01:01:19.517 from now, I think that everyone will feel it and I think that we will all 01:01:19.600 --> 01:01:25.037 feel the pain of there's an awesome model and there's just no availability because 01:01:25.120 --> 01:01:28.477 there's not enough compute. >> Yeah, but as we were looking at our 01:01:28.560 --> 01:01:33.437 predictions for 2026 on this show, we had a conversation uh towards the end of 01:01:33.520 --> 01:01:38.077 the year last year where Runa John Roy was on with us was like 2026 is going to 01:01:38.160 --> 01:01:42.077 be the year where everybody uses agents and I said, "Yeah, well, I'll believe 01:01:42.160 --> 01:01:45.917 that when I see it." And I'm using the agents. So. 01:01:46.000 --> 01:01:47.797 >> Here we are. Here we go. 01:01:47.880 --> 01:01:49.757 Um what what what do you use it for? 01:01:49.840 --> 01:01:53.237 >> I I use it to build um build tools 01:01:53.320 --> 01:01:55.157 internally for my 01:01:55.240 --> 01:01:58.037 uh for the people who I work with to 01:01:58.120 --> 01:02:01.797 sort of get on the same page about um 01:02:01.880 --> 01:02:05.757 about when videos are coming and what the thumbnails need to look like and I'm 01:02:05.840 --> 01:02:10.917 also integrating things from uh from YouTube and so we can basically then 01:02:11.000 --> 01:02:15.677 rank how the videos are doing based off of thumbnail and like a custom-built piece 01:02:15.760 --> 01:02:18.837 of software that I never would have paid for. And that's one of the things that I 01:02:18.920 --> 01:02:20.237 think is interesting about this moment, 01:02:20.320 --> 01:02:22.837 I guess, is that software it's it 01:02:22.920 --> 01:02:28.357 scales, used by the masses, but um when you use it, therefore, there's going to 01:02:28.440 --> 01:02:30.677 be so many things that are not made for you. 01:02:30.760 --> 01:02:35.277 And maybe what this does is it allows us to interact with software in a way 01:02:35.360 --> 01:02:39.037 that's much more natural. >> I think that is the key. And again, I 01:02:39.120 --> 01:02:41.797 just think a lot about the fact that 01:02:41.880 --> 01:02:46.997 the way we've built computers has really pulled us in into this digital world. 01:02:47.080 --> 01:02:50.037 You think about how much time you just spend scrolling through your phone. 01:02:50.120 --> 01:02:52.157 >> Yep. >> Right? The amount of time that you spend 01:02:52.240 --> 01:02:55.437 clicking different buttons and trying to like connect this thing to that thing. 01:02:55.520 --> 01:02:59.797 Like, why? Like, why do you have to do that? Instead the AI being about 01:02:59.880 --> 01:03:02.957 bringing the machine closer to you, personalizing to you, understanding what 01:03:03.040 --> 01:03:07.157 you're trying to accomplish. And that we have all of this 01:03:07.240 --> 01:03:10.437 pop culture of just computers you can talk to and that they go and do stuff 01:03:10.520 --> 01:03:13.557 for you and it's starting to become real. It's starting to become the thing 01:03:13.640 --> 01:03:19.037 that you can actually do. And I think that the amazingness of that is something where 01:03:19.120 --> 01:03:22.557 you just have to try it to really understand. So, I I definitely think 01:03:22.640 --> 01:03:25.437 it's a it's a very special moment we're in. Yeah. 01:03:25.520 --> 01:03:27.517 >> Then I want to know why is AI so 01:03:27.600 --> 01:03:30.837 unpopular uh with the public? Um YouGov, 01:03:30.920 --> 01:03:35.037 for instance, says uh three times as many Americans expect the effects of AI 01:03:35.120 --> 01:03:39.677 on society to be negative as they expect it to be positive. 01:03:39.760 --> 01:03:43.317 I mean, what do you think the reasoning is behind that? And are you concerned 01:03:43.400 --> 01:03:45.517 about AI's brand? 01:03:45.600 --> 01:03:47.597 >> Well, I think that there is something 01:03:47.680 --> 01:03:50.957 that we need to show the country of why 01:03:51.040 --> 01:03:55.357 AI is good for them. Not just for the broad economy, for 01:03:55.440 --> 01:03:58.877 growing the GDP and things like that, but how does it help them in their 01:03:58.960 --> 01:04:01.237 lives? And I think there are actually 01:04:01.320 --> 01:04:03.557 many very concrete stories that I hear 01:04:03.640 --> 01:04:06.597 every day. For example, there's a family 01:04:06.680 --> 01:04:10.517 where their child was having some some 01:04:10.600 --> 01:04:14.797 headaches, some some medical issues, was denied an MRI, and they researched the 01:04:14.880 --> 01:04:18.997 symptoms with ChatGPT and realized that they could make an argument to insurance 01:04:19.080 --> 01:04:21.917 to actually get the MRI. They did that, turns out he had a brain tumor, they 01:04:22.000 --> 01:04:25.757 were able to save his life because they used ChatGPT to get access to the right 01:04:25.840 --> 01:04:27.957 information. That's just one story. 01:04:28.040 --> 01:04:32.397 There are so many more just like that of people who have been deeply, profoundly, 01:04:32.480 --> 01:04:36.077 their lives have been improved or saved through their use of this technology, 01:04:36.160 --> 01:04:38.677 through partnering with the technology in a real way. 01:04:38.760 --> 01:04:42.637 And so, that is a story I don't think gets out there. I think that this is 01:04:42.720 --> 01:04:47.237 happening in so many people's lives, but somehow the story is not yet oh is not 01:04:47.320 --> 01:04:50.757 yet told. And one thing I notice is that there's you know, so much a lot of pop 01:04:50.840 --> 01:04:53.997 culture from, you know, the '90s, from from the 01:04:54.080 --> 01:04:57.597 historical context that we have, that's very negative on AI, that worries about 01:04:57.680 --> 01:05:01.957 what could go wrong. But when people actually use AI, that they find utility 01:05:02.040 --> 01:05:05.197 in it, they find value in it. And so, I think that I am definitely very 01:05:05.280 --> 01:05:09.597 concerned about us not having successfully helped people 01:05:09.680 --> 01:05:13.637 understand why this technology wave is something that will improve their lives. 01:05:13.720 --> 01:05:16.797 It will help improve human connection, and that is something that's a big focus 01:05:16.880 --> 01:05:19.877 in my mind. And if you think about the 01:05:19.960 --> 01:05:21.637 opportunity here and why AI is so 01:05:21.720 --> 01:05:27.237 important, I think this will be the source of economic and national security 01:05:27.320 --> 01:05:30.957 going forward. I think it's going to be about national competitiveness, and that 01:05:31.040 --> 01:05:36.077 there are other countries like China where AI pulls in the exact opposite direction. 01:05:36.160 --> 01:05:38.717 >> And so I ate there. >> Yes, I think it's very, very important 01:05:38.800 --> 01:05:42.317 that we acknowledge that and we really understand how to get the benefits for 01:05:42.400 --> 01:05:45.077 everyone. >> But we also are in a time that's like 01:05:45.160 --> 01:05:50.277 politically unstable. There's concerns about uh about work. People are Every 01:05:50.360 --> 01:05:52.837 time I speak with someone about AI, they're like, "How long do I have left 01:05:52.920 --> 01:05:55.837 to work in my job?" And then when I 01:05:55.920 --> 01:06:00.117 think about the data centers, I mean, the polling is even worse than 01:06:00.200 --> 01:06:04.517 um you know, AI in general. This is from Pew. Far more data Far more people say 01:06:04.600 --> 01:06:08.677 data centers are mostly bad than good for the environment, uh home energy 01:06:08.760 --> 01:06:13.757 costs, and quality of life of those nearby. So, we are at this moment where 01:06:13.840 --> 01:06:16.757 good jobs are tough to come by, and people see these data centers come into 01:06:16.840 --> 01:06:20.597 their communities, and they say not good for the environment, home energy, and 01:06:20.680 --> 01:06:23.757 quality of life. I mean, are they Are they wrong? 01:06:23.840 --> 01:06:26.477 >> Well, I think there's definitely a lot of misinformation about data centers. 01:06:26.560 --> 01:06:28.917 Good example is water usage. 01:06:29.000 --> 01:06:34.037 That if you actually look at our Abilene facility, which is 01:06:34.120 --> 01:06:37.477 one of the if not the biggest supercomputers in the world, 01:06:37.560 --> 01:06:40.197 the amount of water it uses 01:06:40.280 --> 01:06:43.757 is the same as a household 01:06:43.840 --> 01:06:45.477 over the course of a year. Right? So, 01:06:45.560 --> 01:06:49.117 it's really negligible water use. And 01:06:49.200 --> 01:06:52.717 yet that there's a lot of misinformation that these data centers 01:06:52.800 --> 01:06:55.197 consume a lot. And so, similar on on 01:06:55.280 --> 01:06:59.917 power, we have a commitment that we are going to pay our own way to not drive up 01:07:00.000 --> 01:07:03.557 energy prices for people. That's something that as an industry now that 01:07:03.640 --> 01:07:06.357 people are making these commitments because it is very important that we 01:07:06.440 --> 01:07:10.437 improve local communities, and when we build data centers, we really try to go 01:07:10.520 --> 01:07:14.197 into those local communities, understand what's happening on the ground, how we 01:07:14.280 --> 01:07:18.677 can help. There's tax revenue that are associated with these data centers, and 01:07:18.760 --> 01:07:22.317 I think that there's jobs that they create. There's a lot of benefits that 01:07:22.400 --> 01:07:23.957 come from them. 01:07:24.040 --> 01:07:29.397 And so I think that's one thing where it is about how we show up, and that's a 01:07:29.480 --> 01:07:32.637 responsibility that we take very seriously. 01:07:32.720 --> 01:07:35.997 >> Okay, but also like if their power costs are not going to go up, you have to 01:07:36.080 --> 01:07:39.957 bring in power, which means potentially more pollution. 01:07:40.040 --> 01:07:42.077 Is that not a concern? >> Well, I think that I think that there's 01:07:42.160 --> 01:07:46.037 much more nuance in terms of not driving up energy costs. If you look at how the 01:07:46.120 --> 01:07:50.237 grid works today, that there's actually a a lot of just stranded power, power 01:07:50.320 --> 01:07:53.397 that is there that is not being utilized, and that you need to upgrade 01:07:53.480 --> 01:07:56.797 the transmission systems, and again, that's something where putting that on 01:07:56.880 --> 01:07:59.717 us rather than putting it on the rate payers is very important. Right. There's 01:07:59.800 --> 01:08:03.397 lots of places where they have clean power that you can that is actually 01:08:03.480 --> 01:08:06.917 being underutilized and just being kind of thrown away. And so, there's 01:08:07.000 --> 01:08:10.357 a lot of benefit that comes from having 01:08:10.440 --> 01:08:14.797 real reasons for that grid, which is aging and obsolete in many places, to 01:08:14.880 --> 01:08:18.517 upgrade. And that's something actually has real benefits to the community. Like 01:08:18.600 --> 01:08:20.877 we've seen, for example, in North Dakota 01:08:20.960 --> 01:08:22.917 that people's rates have gone down 01:08:23.000 --> 01:08:27.917 because a data center has shown up and has helped with improving the utilities 01:08:28.000 --> 01:08:30.037 for everyone. >> All right, one last question on the 01:08:30.120 --> 01:08:35.477 politics. Uh you gave $25 million to Magna Inc., which is uh PAC that uh 01:08:35.560 --> 01:08:39.077 pro-Trump um You spoke with Wired about it, and you 01:08:39.160 --> 01:08:42.837 said, "Anything I can do to support this technology benefiting everyone is a 01:08:42.920 --> 01:08:46.597 thing I will do." And, you know, if that makes you a 01:08:46.680 --> 01:08:49.637 one-issue voter or one-issue uh 01:08:49.720 --> 01:08:54.237 you know, political supporter, I'll I'll share the one thing I always wonder when 01:08:54.320 --> 01:08:58.237 it comes to just this the one-issue camp is 01:08:58.320 --> 01:09:03.237 um ultimately, doesn't a stronger country make you, 01:09:03.320 --> 01:09:07.717 uh you know, sort of make your goals much more feasible. Even if a candidate 01:09:07.800 --> 01:09:11.397 isn't, you know, fully in support of what you're doing. Like, shouldn't a 01:09:11.480 --> 01:09:13.157 stronger country, no matter what, be the 01:09:13.240 --> 01:09:15.997 North Star of of any political activity? 01:09:16.080 --> 01:09:18.797 Um and if it's the if that's the case, 01:09:18.880 --> 01:09:23.997 you know, then is that is that part of the donation? 01:09:24.080 --> 01:09:26.837 >> So, the way I look at this, so my wife and I 01:09:26.920 --> 01:09:31.157 made that donation. We've donated to bipartisan super PACs as well. I think 01:09:31.240 --> 01:09:35.877 this technology is one where it's coming quickly. Within 01:09:35.960 --> 01:09:38.637 the next couple years, it's really going to transform everything. 01:09:38.720 --> 01:09:42.677 Going to be the underpinning of the economy, and it's not popular. And we 01:09:42.760 --> 01:09:45.517 really want to support politicians who 01:09:45.600 --> 01:09:49.757 really lean into this technology, really engage with it. And so, I think that 01:09:49.840 --> 01:09:53.157 certainly this technology is about up- uplifting 01:09:53.240 --> 01:09:56.037 us as a country. I, you know, I am a 01:09:56.120 --> 01:10:00.717 one-issue donor. This is something where I feel like I've unique contribution to 01:10:00.800 --> 01:10:04.877 to make. But, it's really about just expressing support for this technology 01:10:04.960 --> 01:10:07.717 is something that we should be leaning into as a country. 01:10:07.800 --> 01:10:10.237 >> What would you tell someone who's who's scared of AI? I mean, if you have a 01:10:10.320 --> 01:10:13.077 moment here where you can speak directly to them. They might think it's going to 01:10:13.160 --> 01:10:16.677 take my job. It's going to pollute my community. It will change the world too 01:10:16.760 --> 01:10:19.037 fast. What's your message to that? 01:10:19.120 --> 01:10:21.757 >> Number one thing is try the tools. 01:10:21.840 --> 01:10:28.440 Because, to really understand what it can do for you is something that 01:10:28.560 --> 01:10:31.917 only by experiencing the AI as it exists 01:10:32.000 --> 01:10:35.797 now will that really hit home. 01:10:35.880 --> 01:10:39.237 And we see so much opportunity and potential and empowerment coming from 01:10:39.320 --> 01:10:43.277 this technology today. You talked a little bit about what you can build now. 01:10:43.360 --> 01:10:44.957 >> Right. >> People who have never built a website 01:10:45.040 --> 01:10:48.637 before can build a website. If you want to start a small business, and you're 01:10:48.720 --> 01:10:52.077 thinking about all the back-end processing and how to actually manage 01:10:52.160 --> 01:10:56.357 it, all those things, the AI can help you with that right now. And so, I think 01:10:56.440 --> 01:11:00.597 that in your life, thinking about how it can help you with your health, how it 01:11:00.680 --> 01:11:04.077 can help your loved ones, how it can help you make money, how it can help you 01:11:04.160 --> 01:11:05.437 save money, 01:11:05.520 --> 01:11:08.557 these are all going to be on the table. 01:11:08.640 --> 01:11:11.917 And I think it is much easier to see 01:11:12.000 --> 01:11:17.117 what's going to change than it is to see what you're going to gain. 01:11:17.200 --> 01:11:20.757 But I think that it's worth giving it a fair shot of really trying to understand 01:11:20.840 --> 01:11:23.757 both sides of the equation. That's the one thing that doesn't get talked about 01:11:23.840 --> 01:11:28.237 in the polling data, by the way. It's the people that have seen it used but 01:11:28.320 --> 01:11:31.757 haven't tried it themselves or the people that have never tried AI are much 01:11:31.840 --> 01:11:35.877 more negative. And then you get to the power users and are even people that use 01:11:35.960 --> 01:11:38.677 it casually and they're generally pretty positive 01:11:38.760 --> 01:11:41.877 about the technology. For for myself, we've been thinking about this 01:11:41.960 --> 01:11:45.757 technology for a long time. What I see playing out in front of us is more 01:11:45.840 --> 01:11:50.397 amazing, more beneficial, and going to really have a much more positive impact 01:11:50.480 --> 01:11:54.277 than we ever imagined. So last one for you, how would you invite advise someone 01:11:54.360 --> 01:11:55.837 to prepare themselves for the future? 01:11:55.920 --> 01:11:59.677 And and it has to be more than just getting the tools. I mean, I have friends 01:11:59.760 --> 01:12:03.477 who are who come to me and they say, I I don't know what's going to happen with 01:12:03.560 --> 01:12:07.957 my job or the world and I I just need to know what to do with this. 01:12:08.040 --> 01:12:12.477 I do think that the number one thing is about understanding the technology. One 01:12:12.560 --> 01:12:16.557 thing we've seen is the people who get the most out of the technology and you 01:12:16.640 --> 01:12:19.517 have to approach it with a curiosity trying to really try it in your 01:12:19.600 --> 01:12:23.197 workflows, really be able to get over that initial hump of you have a blank 01:12:23.280 --> 01:12:25.037 box, what do I do with a blank box? 01:12:25.120 --> 01:12:27.637 Right? To really develop this sense of 01:12:27.720 --> 01:12:32.197 agency, this sense of I can be the manager. I can set the 01:12:32.280 --> 01:12:34.477 direction. I can 01:12:34.560 --> 01:12:39.477 delegate, I can provide oversight and to really develop that skill because that 01:12:39.560 --> 01:12:41.997 is something that's going to be fundamental. Is we're building this 01:12:42.080 --> 01:12:46.397 technology for humans, to help humans foster more human connection, for humans 01:12:46.480 --> 01:12:49.797 to be able to spend more time doing what they want. And so the question is, well, 01:12:49.880 --> 01:12:53.797 what do you want? And really trying to crystallize that and trying to realize 01:12:53.880 --> 01:12:57.717 that with help of this technology is going to be the most important thing. 01:12:57.800 --> 01:12:59.517 Greg, thanks so much for coming on the show. 01:12:59.600 --> 01:13:01.797 >> Thank you for having me. >> All right. Thank you everybody for 01:13:01.880 --> 01:13:07.680 listening and watching and we'll see you next time on Big Technology Podcast.