WEBVTT 00:00:00.160 --> 00:00:03.917 We have Greg Brockman, the co-founder and president of OpenAI with us. Welcome 00:00:04.000 --> 00:00:06.077 to the show, Greg. How are you doing? 00:00:06.160 --> 00:00:07.837 >> Doing great. Thank you for having me. 00:00:07.920 --> 00:00:12.397 >> Thanks for hopping on. Uh huge day. Can we start with the with the math 00:00:12.480 --> 00:00:16.717 advances? What is what what is happening? Why is this important? Uh 00:00:16.800 --> 00:00:19.197 there's a lot of back and forth in the timeline, but I'd love for you to just 00:00:19.280 --> 00:00:21.357 set the table for us on what actually 00:00:21.440 --> 00:00:24.717 happened with uh Navier Stokes today. 00:00:24.800 --> 00:00:28.237 Well, uh it's always a huge day uh in uh 00:00:28.320 --> 00:00:30.157 AI and modern scientific progress, I 00:00:30.240 --> 00:00:33.197 would say. Yeah. Today we announced that 00:00:33.280 --> 00:00:37.037 our model had solved the Navier Stokes 00:00:37.120 --> 00:00:40.957 uh problem. Uh that we found a a counter 00:00:41.040 --> 00:00:43.597 example or a sort of proof that you can 00:00:43.680 --> 00:00:48.877 actually that these these uh theoretical equations do have a singularity or kind 00:00:48.960 --> 00:00:51.597 of break down under certain circumstances. And this is a problem 00:00:51.680 --> 00:00:53.437 that has been open for a very long time. 00:00:53.520 --> 00:00:56.797 It's one of the seven millennium problems which are kind of some of the 00:00:56.880 --> 00:01:01.117 deepest most important problems in mathematics and I I think that the 00:01:01.200 --> 00:01:04.397 problem itself is important right this is new knowledge for humanity that the 00:01:04.480 --> 00:01:07.917 proof itself is actually very elegant and beautiful and I think that there's a 00:01:08.000 --> 00:01:10.957 lot to learn from it and the the 00:01:11.040 --> 00:01:15.677 equations have lots of application and fluid dynamics in other areas but to me 00:01:15.760 --> 00:01:19.197 what's even more important is about what this represents about where we are in 00:01:19.280 --> 00:01:22.557 terms of model capabilities and the fact that we can actually generate new 00:01:22.640 --> 00:01:26.477 knowledge that we can learn from these models to and have them help us solve 00:01:26.560 --> 00:01:29.597 problems that are otherwise outside of reach or would take us a very long to 00:01:29.680 --> 00:01:31.997 solve. >> Yeah. I mean, where should I actually go 00:01:32.080 --> 00:01:36.237 with this? Is this going to help me book a flight? Is this going to help me cure 00:01:36.320 --> 00:01:39.917 cancer? Is this just going to help you recruit researchers who are fascinated 00:01:40.000 --> 00:01:42.077 by this stuff? Uh because I think that 00:01:42.160 --> 00:01:47.277 there's this is, you know, taken over the technology world. Uh but I imagine 00:01:47.360 --> 00:01:51.197 that this will not be something that gets talked about at backyard barbecues 00:01:51.280 --> 00:01:54.877 with friends and family that are three clicks removed from outside of 00:01:54.960 --> 00:01:57.357 >> Yeah. Yeah. >> Well, look, I think that there's first 00:01:57.440 --> 00:02:02.077 of all the applications of this specific result or the equations themselves, 00:02:02.160 --> 00:02:05.437 right, which are things that let us better understand phenomena from ocean 00:02:05.520 --> 00:02:07.197 currents to air flow around aircraft. 00:02:07.280 --> 00:02:10.797 Sure. Uh around, you know, turbulence and things like that. But it's really 00:02:10.880 --> 00:02:14.637 about the broader insights and methods that can help scientists and 00:02:14.720 --> 00:02:17.997 mathematicians further accelerate their research. And I think that again 00:02:18.080 --> 00:02:20.317 representative of if we have models that 00:02:20.400 --> 00:02:23.837 can help solve this kind of problem then 00:02:23.920 --> 00:02:28.637 what happens from here like what other problems that are immediately 00:02:28.720 --> 00:02:32.317 applicable. And I think that talking about curing diseases and new medicines 00:02:32.400 --> 00:02:35.837 we're going to be able to develop, all of that starts to become much more real 00:02:35.920 --> 00:02:40.477 when you have models that are at this level of assistance and capability. And 00:02:40.560 --> 00:02:44.397 I do think that there are going to be real changes to to think about in terms 00:02:44.480 --> 00:02:49.037 of we can have so much more ambition with the kinds of of challenges that we 00:02:49.120 --> 00:02:50.637 can hope to tackle now. 00:02:50.720 --> 00:02:53.357 >> Yeah. I I mean I think uh even even if 00:02:53.440 --> 00:02:56.077 this doesn't break through to the 00:02:56.160 --> 00:03:00.397 broader uh the broader world uh the 00:03:00.480 --> 00:03:04.077 weekend definitely will because it seemed like everyone was talking about 00:03:04.160 --> 00:03:06.477 Blender, talking about Astra building 00:03:06.560 --> 00:03:11.117 stuff. Uh take us through the launch of Astra, what the feedback has been, what 00:03:11.200 --> 00:03:14.877 you've learned. It seemed like there were a couple resets, the model scaled 00:03:14.960 --> 00:03:19.757 very well. Uh how was this launch different than previous launches? 00:03:19.840 --> 00:03:23.677 Well, first of all, I've just been blown away by the community reaction to Astra. 00:03:23.760 --> 00:03:27.917 It's been really amazing and very humbling, honestly, to see all the 00:03:28.000 --> 00:03:31.677 creativity and the different ways that people have been applying the model. And 00:03:31.760 --> 00:03:33.597 I think that it's very clear that we've 00:03:33.680 --> 00:03:35.917 reached a new threshold of computer use. 00:03:36.000 --> 00:03:39.357 So, this model is able to really work 00:03:39.440 --> 00:03:43.197 with different kinds of applications in a way that was not previously possible. 00:03:43.280 --> 00:03:47.197 And people are taking full advantage of that fact and really thinking about how 00:03:47.280 --> 00:03:51.117 to create. I lots of people showing off 3D creations and 00:03:51.200 --> 00:03:53.677 >> mapping out physical locations and 00:03:53.760 --> 00:03:58.317 turning them into into these 3D models and thinking about can you use this for 00:03:58.400 --> 00:04:02.077 >> design of physical parts. Someone talked about how they were designing some 00:04:02.160 --> 00:04:07.357 mechanic for catching hair in a shower drain and that they were able to 00:04:07.440 --> 00:04:10.557 >> super cool, right? That that they're now able to actually manufacture that 00:04:10.640 --> 00:04:12.097 >> shower hair super intelligence. 00:04:12.180 --> 00:04:15.997 [laughter] Yeah. No, that stuff's so mundane, but it's so important. I I feel 00:04:16.080 --> 00:04:18.157 like uh a lot of this stuff gets lost. 00:04:18.240 --> 00:04:20.077 Right. >> Ex. Exactly. And I think there's a core 00:04:20.160 --> 00:04:23.597 there that's very important, which is that we are talking about these grand 00:04:23.680 --> 00:04:28.077 challenges sometimes or very esoteric applications, but really the everyday 00:04:28.160 --> 00:04:31.997 the number of problems that you have in your life that you would love to solve. 00:04:32.080 --> 00:04:34.797 It's now possible, right? That that we're really trying to empower the 00:04:34.880 --> 00:04:37.517 individual to make it so that you can be 00:04:37.600 --> 00:04:41.837 you can have superpowers, you can accomplish more. And I think that that 00:04:41.920 --> 00:04:45.197 you know, really trying to benefit people, empower people, build tools that 00:04:45.280 --> 00:04:49.677 can really help you and help you in your your daily life. That's all part of what 00:04:49.760 --> 00:04:51.197 we're we're we're working on. 00:04:51.280 --> 00:04:54.157 >> So, yeah, I mean, it seems like a huge 00:04:54.240 --> 00:04:57.117 number of people in uh in tech uh 00:04:57.200 --> 00:04:59.757 effectively rebuilt their entire house 00:04:59.840 --> 00:05:02.317 in Blender and planned a remodel this 00:05:02.400 --> 00:05:04.797 weekend. Uh thanks to Astra and but I am 00:05:04.880 --> 00:05:06.877 wondering about the merge and how you 00:05:06.960 --> 00:05:09.997 bring together codecs, chatt work, 00:05:10.080 --> 00:05:13.117 chacht on desktop. I have a gaming PC 00:05:13.200 --> 00:05:17.277 now with a Nvidia card in it. I have a Mac Mini. Like I have all these things 00:05:17.360 --> 00:05:21.677 and I can imagine that I'm just I'm doing that unnecessary or like it's fun 00:05:21.760 --> 00:05:25.917 for me, but uh that early adopter work of going and unhobling it a little bit 00:05:26.000 --> 00:05:27.917 here and there, but in the future this 00:05:28.000 --> 00:05:30.717 will all just be tucked in one, you 00:05:30.800 --> 00:05:33.517 know, prompt box and it might build a 3D 00:05:33.600 --> 00:05:38.477 Blender model for to answer my question of should I remodel my house or not. Um, 00:05:38.560 --> 00:05:42.877 but how do you see the capabilities that we saw on display from sort of light 00:05:42.960 --> 00:05:44.797 power users over the weekend actually 00:05:44.880 --> 00:05:47.597 making their way into uh consumers who 00:05:47.680 --> 00:05:50.157 might not even know what Blender is. 00:05:50.240 --> 00:05:52.237 >> Well, I think you're exactly right that 00:05:52.320 --> 00:05:55.837 we really want to shift these tools from 00:05:55.920 --> 00:05:58.477 requiring kind of low-level 00:05:58.560 --> 00:06:03.517 sort of >> access or guidance to really having the 00:06:03.600 --> 00:06:06.717 human be able to fly, right? to really be empowered for you to be able to set 00:06:06.800 --> 00:06:11.837 the goals and the objectives and that you still should feel like you can get 00:06:11.920 --> 00:06:14.477 into those details and you can understand them. you can provide that 00:06:14.560 --> 00:06:17.997 oversight because ultimately you should feel accountable for the outcomes but 00:06:18.080 --> 00:06:22.077 you have this this absolute amplifier right like a trampoline or like a you 00:06:22.160 --> 00:06:25.997 know rocket ship for the mind like however you want to to to analogize it 00:06:26.080 --> 00:06:29.117 and I think what that means at a practical level so first of all this 00:06:29.200 --> 00:06:33.917 year we've been really seeing this shift from just pure chat use cases to gentic 00:06:34.000 --> 00:06:37.437 use cases but I would also keep in mind that chat is alive and well I mean we're 00:06:37.520 --> 00:06:41.277 we're now well over a billion users every week that you can see that the 00:06:41.360 --> 00:06:44.877 market share of chatbt is starting to climb once again because we've been 00:06:44.960 --> 00:06:49.117 investing so hard in in so many use cases that are important for people in 00:06:49.200 --> 00:06:53.037 education and health and a variety of other areas and then at the same time 00:06:53.120 --> 00:06:56.797 these productivity deep knowledge work use cases those are really taking off 00:06:56.880 --> 00:07:00.317 we've had this like almost vertical wall of agentic adoption since we launched 00:07:00.400 --> 00:07:04.477 chat GBT work and I think that the fact that these are two distinct modes that 00:07:04.560 --> 00:07:08.157 that is actually a point in time that is something that we're continuing to unify 00:07:08.240 --> 00:07:11.917 and merge and that we're starting to see that there's a new emerging form factor 00:07:12.000 --> 00:07:14.397 for how people want to consume AI. And I 00:07:14.480 --> 00:07:16.237 think that it's almost like that the 00:07:16.320 --> 00:07:21.437 promise of AI has always been that you have something that you can talk to and 00:07:21.520 --> 00:07:23.117 really delegate work to that's proactive 00:07:23.200 --> 00:07:26.877 and persistent. And that I what we were 00:07:26.960 --> 00:07:31.357 promised if you were to rewind five 10 years ago was never a low-level language 00:07:31.440 --> 00:07:34.397 model. You have to think about context windows and you have to select thinking 00:07:34.480 --> 00:07:37.597 strength and you have to select different models. Like none of that none 00:07:37.680 --> 00:07:41.277 of that is is the future. And so I think that we're moving towards real 00:07:41.360 --> 00:07:46.397 amplification, real giving you time back, real having computers that are 00:07:46.480 --> 00:07:48.237 able to operate according to your goals, 00:07:48.320 --> 00:07:51.437 to your desires. And I think that that 00:07:51.520 --> 00:07:55.277 is a core of it. We're developing this safely. That's that's one of the core 00:07:55.360 --> 00:07:59.037 commitments that that we make and how we think about this. Um but but we really 00:07:59.120 --> 00:08:01.917 see the power of these tools starting to 00:08:02.000 --> 00:08:04.957 really um really start to increase in 00:08:05.040 --> 00:08:09.277 terms of what people are capable of and that under the hood utilizing tools like 00:08:09.360 --> 00:08:12.317 blender so that that is almost a detail that fades into the background is 00:08:12.400 --> 00:08:16.557 absolutely the direction of travel. It feels like in AI particularly there's 00:08:16.640 --> 00:08:18.877 been a almost like a first mover 00:08:18.960 --> 00:08:22.237 disadvantage in that billions of people 00:08:22.320 --> 00:08:25.997 have tried Chad GBT and and some percentage of them tried it for the 00:08:26.080 --> 00:08:29.917 first time and have a certain impression of the product and what it can do and 00:08:30.000 --> 00:08:33.677 then you know even in the last few weeks there's been new agents and and products 00:08:33.760 --> 00:08:37.277 that come online and people try it and their mind is just completely blown and 00:08:37.360 --> 00:08:41.037 I think it's funny because I'm like well as somebody who's like you know trying 00:08:41.120 --> 00:08:44.317 to get the absolute max out of ChatG GBT. I'm like, well, I've been running 00:08:44.400 --> 00:08:48.797 like, you know, I've had like an agent running that, for example, will tell me 00:08:48.880 --> 00:08:51.757 every time a SpaceX launch is going to happen and if it gets delayed, right? 00:08:51.840 --> 00:08:55.837 These sort of like persistent agents that are running in the background. Um 00:08:55.920 --> 00:08:57.837 and but but strategically I feel like 00:08:57.920 --> 00:09:00.397 it's a it's a new kind of challenge 00:09:00.480 --> 00:09:05.197 because you have this the as capability has been been scaling 00:09:05.280 --> 00:09:07.117 first movers need to be almost like 00:09:07.200 --> 00:09:10.477 constantly reminding the the market of 00:09:10.560 --> 00:09:13.117 um all these just like new ways to use 00:09:13.200 --> 00:09:15.917 >> overhang. Yeah, we we think about this a 00:09:16.000 --> 00:09:21.037 lot and I think that there's this discovery problem that we as a field 00:09:21.120 --> 00:09:24.557 need to really encounter in a first class way and we haven't done it fully 00:09:24.640 --> 00:09:29.517 yet, but I think we have a real shot at solving it better than any product 00:09:29.600 --> 00:09:33.757 before because >> the the thing that that right now we 00:09:33.840 --> 00:09:37.597 kind of rely on. You think about chat GBT chat GBT work. These are both text 00:09:37.680 --> 00:09:40.717 boxes, right? And it's like, well, this new text box is way more powerful than 00:09:40.800 --> 00:09:43.757 the old text box. And but oh, oh, there are some reasons that you still want to 00:09:43.840 --> 00:09:46.637 use the old text box. It's [laughter] not, you know, it's like far too 00:09:46.720 --> 00:09:49.917 confusing. People just want something that can help them solve their problem. 00:09:50.000 --> 00:09:52.397 The whole point is to get your time back, not for you to have to go and 00:09:52.480 --> 00:09:55.837 become an expert in all these these internal details. But at the same time, 00:09:55.920 --> 00:09:59.517 we also have a model that understands what you're trying to accomplish, right? 00:09:59.600 --> 00:10:02.557 That you're explaining to it, here's what I want. It has a lot of context on 00:10:02.640 --> 00:10:06.877 you. And so it should also be able to proactively say to you, hey actually if 00:10:06.960 --> 00:10:11.037 you ask me this other way or if you add added this connector or if you authorize 00:10:11.120 --> 00:10:15.917 me to do this or if you you know hook up your credentials in this in this way I 00:10:16.000 --> 00:10:19.357 can go and do this other thing for you and so we're thinking a lot about that 00:10:19.440 --> 00:10:23.517 self-nowledge that onboarding process and I think that is a huge huge 00:10:23.600 --> 00:10:27.757 opportunity and I think that there is both the the disadvantage that you cite 00:10:27.840 --> 00:10:30.797 of people tried it they form an impression and it's changed that it's 00:10:30.880 --> 00:10:34.877 something new but there's also an advantage I chat chatbt like over a 00:10:34.960 --> 00:10:39.757 billion users every week like that is unique no one has that kind of use on on 00:10:39.840 --> 00:10:42.717 these on these models and I think that the number of people who have tried 00:10:42.800 --> 00:10:46.717 chatbt before I think it's it's another you know billion billion and a half 00:10:46.800 --> 00:10:50.877 something like that and so that's a huge opportunity as well for us to go back to 00:10:50.960 --> 00:10:55.277 those users and say hey we can now solve the problem for you we can now help you 00:10:55.360 --> 00:10:59.277 in ways that you didn't see before and I think that it's it's true it's real you 00:10:59.360 --> 00:11:03.037 look at how many people use chat for health 300 million people every single 00:11:03.120 --> 00:11:05.837 week with health queries, right? And that that's really making a difference 00:11:05.920 --> 00:11:09.117 in people's lives and that of their loved ones. And so we have such opportunity. 00:11:09.200 --> 00:11:12.877 >> Can you stay there with with health and and and explain where 00:11:12.960 --> 00:11:16.397 >> one note before that? Something that I think is really interesting and I think 00:11:16.480 --> 00:11:20.397 something that OpenAI can can do a lot better is like when people talk about 00:11:20.480 --> 00:11:24.877 like everyone in AI wants to be like the Apple of AI from a marketing standpoint. 00:11:24.960 --> 00:11:27.997 When they when you think like Apple marketing, you're thinking like Mac 00:11:28.080 --> 00:11:30.557 versus PC or you're thinking 1984, these 00:11:30.640 --> 00:11:35.037 big branding campaigns. But the actual thing that Apple does really really well 00:11:35.120 --> 00:11:39.277 with marketing is they just hammer really really specific details about 00:11:39.360 --> 00:11:42.557 their products, right? They're like >> they're advertising the new camera. 00:11:42.640 --> 00:11:44.557 They're advertising Mimoji, right? 00:11:44.640 --> 00:11:48.317 They're advertising like >> certain features in Safari, right? And 00:11:48.400 --> 00:11:51.917 so it's like with AI, the surface area of like things that you need to 00:11:52.000 --> 00:11:55.917 communicate is actually like an order of magnitude greater because it can do so 00:11:56.000 --> 00:11:58.877 many different things. And so I think there's such an opportunity 00:11:58.960 --> 00:12:02.477 >> um for the company to focus advertising. 00:12:02.560 --> 00:12:05.757 The brand campaigns are awesome and like the the launch video for Astro was 00:12:05.840 --> 00:12:09.277 amazing, but it's like there should be billboards running of like very specific 00:12:09.360 --> 00:12:13.037 things that Chach BT can do to give you back your time. Um 00:12:13.120 --> 00:12:17.357 >> yes, this this has actually been a real sort of realization or just like 00:12:17.440 --> 00:12:21.037 something that I have really come to over the course of of of this year. And 00:12:21.120 --> 00:12:25.997 if you look at even for example the you know we just announced chat GBT images 2.5 00:12:26.080 --> 00:12:29.757 >> and if you look at the launch video there the thing that I love about it is 00:12:29.840 --> 00:12:34.317 it shows here's someone creating an image and here's like a bunch of 00:12:34.400 --> 00:12:38.157 different variations of it and then here's them taking their favorite one 00:12:38.240 --> 00:12:42.077 and having it in the world. like someone said, "Here's a cool like little sketch 00:12:42.160 --> 00:12:45.357 of a candle holder." You see an awesome visualization of it and then you see the 00:12:45.440 --> 00:12:48.557 physical candle holder and you're just like that's what you want, right? It's 00:12:48.640 --> 00:12:50.717 like you it speaks to you immediately. 00:12:50.800 --> 00:12:54.477 And I think that that really showing people here's a use case. And the thing 00:12:54.560 --> 00:12:57.117 that was also a little surprising to me is that we've sometimes highlighted 00:12:57.200 --> 00:13:01.037 esoteric use cases, something that appeals to someone in particular and 00:13:01.120 --> 00:13:05.437 it's amazing for that person, but people don't then say, "Oh, because it's this 00:13:05.520 --> 00:13:08.877 powerful, I can also do this other powerful thing I've been waiting on." 00:13:08.960 --> 00:13:12.717 like that connection is is is is something that is less sort of easy to 00:13:12.800 --> 00:13:15.997 make than I than I'd realize and it makes sense. What you want is to for 00:13:16.080 --> 00:13:19.757 there to be use cases where people say I actually want that particular thing now 00:13:19.840 --> 00:13:22.877 let me go try it myself and then from there you start exploring and you start 00:13:22.960 --> 00:13:25.357 to find I [clears throat] actually do have this powerful use case that I 00:13:25.440 --> 00:13:27.037 didn't even realize was tip at the time. 00:13:27.120 --> 00:13:30.637 >> Yeah. Studio is probably like the best example of that working really really 00:13:30.720 --> 00:13:35.597 well. >> The other thing is is reminding people to ask the AI what it's capable of. 00:13:35.680 --> 00:13:38.637 Yeah, like I was I was having lunch with a buddy who's a real estate developer 00:13:38.720 --> 00:13:42.077 and he is using chat all day long for 00:13:42.160 --> 00:13:45.917 different like deal memos and to understand like projects that he's 00:13:46.000 --> 00:13:48.957 working on all this stuff and he'll ask me he'll ask me all the time can can 00:13:49.040 --> 00:13:53.437 catch GPT do this or that and I'm like I'm happy to I'm happy to answer you but 00:13:53.520 --> 00:13:57.757 like you have the thing that will just explain exactly how to do the thing that 00:13:57.840 --> 00:14:00.877 you want to do or or maybe not but it probably can. 00:14:00.960 --> 00:14:03.837 >> Yeah, I I I did the same thing. I was kicking off this Blender thing and I was 00:14:03.920 --> 00:14:08.397 like, should I run this as a local codeex thread or in the cloud? Let me 00:14:08.480 --> 00:14:11.677 know which one's better based on my system and it gave me a good answer and 00:14:11.760 --> 00:14:12.637 I was able to go for it. 00:14:12.720 --> 00:14:16.557 >> On on images, uh when when images 2 came 00:14:16.640 --> 00:14:20.797 out, I had some moments where I thought, okay, images is solved. Like where do 00:14:20.880 --> 00:14:25.117 you where do you think images actually go as a category? because it felt uh 00:14:25.200 --> 00:14:28.637 like this has been something that has maybe oneshoted me more than anything 00:14:28.720 --> 00:14:30.557 else. specifically with like when it 00:14:30.640 --> 00:14:33.197 when it released, I was I was um 00:14:33.280 --> 00:14:36.157 >> uh spending hours like like on a 00:14:36.240 --> 00:14:40.557 Saturday trying to design furniture, right? And just going through like you 00:14:40.640 --> 00:14:42.237 know hundreds and hundreds of prompts. 00:14:42.320 --> 00:14:45.997 But how how far can image models go and 00:14:46.080 --> 00:14:50.157 where are they going and what are the ways in which you think they can um they 00:14:50.240 --> 00:14:53.837 can be applied? We were talking earlier too about the downstream impact of of 00:14:53.920 --> 00:14:58.877 image models. If you can take, you know, a a physical space somewhere and take a 00:14:58.960 --> 00:15:02.157 picture of it and imagine it as all these other variations, there's so much 00:15:02.240 --> 00:15:06.237 like real world activity that will be driven from that because people can see 00:15:06.320 --> 00:15:08.957 this thing. >> Yeah. >> Visually and say like I now I want to go 00:15:09.040 --> 00:15:11.677 make that reality which I think is really cool. 00:15:11.760 --> 00:15:14.717 >> Well, I think that's exactly the right way to think about it is as you hit new 00:15:14.800 --> 00:15:19.117 thresholds of capability, my experience has always been that fundamentally new 00:15:19.200 --> 00:15:22.797 applications become unlocked in ways that you almost wouldn't have thought 00:15:22.880 --> 00:15:27.037 about ahead of time. And so I think that within for example knowledge work, 00:15:27.120 --> 00:15:31.037 professional work, marketing, all those areas, you just need to be above a 00:15:31.120 --> 00:15:34.237 quality threshold. If you're below it, it's a cool concept, but you can't 00:15:34.320 --> 00:15:35.837 actually use the final material, right? 00:15:35.920 --> 00:15:39.437 That that then means that you you haven't really solved the problem. and 00:15:39.520 --> 00:15:42.237 that having precise edit control, being 00:15:42.320 --> 00:15:46.477 fast and really being creative and having a diversity of different results 00:15:46.560 --> 00:15:49.997 and also being able to >> have this this good interplay back and 00:15:50.080 --> 00:15:54.717 forth with a person. I think that that really unlocks whole new use cases and I 00:15:54.800 --> 00:15:58.557 think there's a huge market there and even for example the kinds of things you 00:15:58.640 --> 00:16:01.997 may not think of naively but actually start to be really important 00:16:02.080 --> 00:16:06.317 applications we're seeing happening is slide creation or making awesome 00:16:06.400 --> 00:16:10.237 websites, right? Being able to have that image generation capability in the 00:16:10.320 --> 00:16:14.237 middle is something that's very unique to OpenAI uh relative to to some of our 00:16:14.320 --> 00:16:16.877 competitors. And I think that you're able to then produce much better 00:16:16.960 --> 00:16:18.957 artifacts downstream. And so we really 00:16:19.040 --> 00:16:22.557 view images, we view voice, we view 00:16:22.640 --> 00:16:25.997 coding, all of these capabilities as one 00:16:26.080 --> 00:16:30.237 package that are going to come together to create an AI that empowers you. That 00:16:30.320 --> 00:16:34.077 means you can create anything that you imagine. And I think it's going to be 00:16:34.160 --> 00:16:35.357 something that's just unlike anything 00:16:35.440 --> 00:16:38.157 out there. Let's go back to health. I 00:16:38.240 --> 00:16:41.437 think most people already are aware that 00:16:41.520 --> 00:16:43.837 you can synthesize some lab data with 00:16:43.920 --> 00:16:46.637 some sleep scores, but your vision that 00:16:46.720 --> 00:16:48.957 you laid out recently for where that 00:16:49.040 --> 00:16:51.597 product goes is much more complex, much 00:16:51.680 --> 00:16:55.597 deeper. So, uh tell me where uh ChatBT 00:16:55.680 --> 00:16:58.157 Health is going in the future. 00:16:58.240 --> 00:17:02.317 >> Well, I would think of it as there are three sides to what we do on health. 00:17:02.400 --> 00:17:06.477 There's the consumer side again 300 million people every week with health 00:17:06.560 --> 00:17:10.717 queries. >> There's the clinician side which is bottoms up 00:17:10.800 --> 00:17:14.637 >> and that's really about think of it a chatbt that's really tuned for 00:17:14.720 --> 00:17:18.477 clinicians that gives them direct citations to medical literature things 00:17:18.560 --> 00:17:21.837 like that. >> There's a third pillar which is the 00:17:21.920 --> 00:17:25.677 enterprise side of selling directly to hospitals and them enabling it. And you 00:17:25.760 --> 00:17:28.717 can see things like we have an integration within Epic and really 00:17:28.800 --> 00:17:31.917 trying to bring each of these three 00:17:32.000 --> 00:17:36.557 pillars the best possible service independently. But you think about as 00:17:36.640 --> 00:17:39.837 those really build momentum that you actually are able to get synergies 00:17:39.920 --> 00:17:42.877 across them, right? That there's something that actually makes the health 00:17:42.960 --> 00:17:46.477 experience and the ability to really transform health care in America and the 00:17:46.560 --> 00:17:50.717 world on the table because there's so many think about how much work you as a 00:17:50.800 --> 00:17:53.597 patient have to do. If you're talking to different specialists, you have to carry 00:17:53.680 --> 00:17:57.037 your medical record from one to the other. You have to explain again here's 00:17:57.120 --> 00:17:59.917 the issue and that ultimately you're on the hook. You're the doctor who has to 00:18:00.000 --> 00:18:02.557 make the decision whether you like it or not. And actually being able to have 00:18:02.640 --> 00:18:06.797 just good sharing of of that information across different providers that becomes 00:18:06.880 --> 00:18:09.757 possible if everyone's on one platform or think about clinical trial 00:18:09.840 --> 00:18:13.437 enrollment. That's a huge bottleneck to drug development and finding people who 00:18:13.520 --> 00:18:17.197 are eligible and will benefit from being involved in a particular trial. And if 00:18:17.280 --> 00:18:21.197 you have that kind of of data, if people are willing to sort of, you know, 00:18:21.280 --> 00:18:24.397 entrust you with that information, that that's something can actually really 00:18:24.480 --> 00:18:28.317 benefit them and benefit the world at the same time. And so what I view us as 00:18:28.400 --> 00:18:32.077 building is really trying to build the world's best healthcare platform to 00:18:32.160 --> 00:18:36.797 really be able to bring health care into the AI age. And I think that it's 00:18:36.880 --> 00:18:40.957 something that is going to be absolutely transformative to many people's quality 00:18:41.040 --> 00:18:44.397 of life, really uplift so many people, and we're seeing it already in such 00:18:44.480 --> 00:18:47.997 concrete ways. Some of my favorite stories about chatbt are people who say, 00:18:48.080 --> 00:18:52.477 "Hey, information I got from chat helped me save my own life. Help me save that 00:18:52.560 --> 00:18:56.717 of a loved one that there was this medical issue that someone had and that 00:18:56.800 --> 00:19:00.157 I if you know the doctor told me one thing, I was able to to double check 00:19:00.240 --> 00:19:03.277 that and understand what they were saying and be able to to push back on it 00:19:03.360 --> 00:19:06.637 and got to a good outcome." And that happens every single day. So I think 00:19:06.720 --> 00:19:10.477 that health with these AIs is something that it we're still just scratching the 00:19:10.560 --> 00:19:13.117 surface of what's possible and I think it's one of the most positive 00:19:13.200 --> 00:19:15.517 applications of AI that that you can you 00:19:15.600 --> 00:19:17.517 can think of. Yeah, I'm very interested 00:19:17.600 --> 00:19:20.797 to see how uh the advancements in memory 00:19:20.880 --> 00:19:22.797 intersect with health because I expect 00:19:22.880 --> 00:19:27.837 that uh chatbt with where memory has gone where I'll be 00:19:27.920 --> 00:19:32.317 in a new thread and it will bring up just the right information or tie back 00:19:32.400 --> 00:19:36.877 to a thread that maybe happened three weeks ago. When you actually apply that 00:19:36.960 --> 00:19:41.997 to like health related queries, it may be able to like pick up patterns that 00:19:42.080 --> 00:19:45.437 sometimes would take a human years to figure out like a certain ailment or 00:19:45.520 --> 00:19:47.837 something like that where it's like, hey, you're asking about all these 00:19:47.920 --> 00:19:51.677 different things and maybe you thought they were not connected. Turns out they 00:19:51.760 --> 00:19:55.917 they actually are and you should go down this sort of like rabbit hole. 00:19:56.000 --> 00:19:57.597 >> Last question. I'm sorry. Please. 00:19:57.680 --> 00:20:00.637 >> I was I was going to say I think that's absolutely right. We're seeing that very 00:20:00.720 --> 00:20:05.357 concretely and we've seen you know just in my own personal life my my wife you 00:20:05.440 --> 00:20:09.277 know talked about some of her her medical conditions publicly but I it was 00:20:09.360 --> 00:20:12.237 really this five-year journey of talking to many specialists each one who was 00:20:12.320 --> 00:20:14.717 kind of touching one part of the elephant and would try to address that 00:20:14.800 --> 00:20:18.477 one part and it was only finally her allergist who said hey I think all these 00:20:18.560 --> 00:20:22.557 symptoms you're seeing are connected and you have this uh genetic condition that 00:20:22.640 --> 00:20:24.557 affects all of your subsystems and 00:20:24.640 --> 00:20:26.637 that's the kind of thing where it's 00:20:26.720 --> 00:20:30.397 really hard to say how many people have 00:20:30.480 --> 00:20:33.677 similar kinds of conditions and just never find out. How many people have 00:20:33.760 --> 00:20:38.077 these areas where it's like if you just sort of are functionally specialized 00:20:38.160 --> 00:20:41.197 that you're never going to bring together the whole diagnosis and I think 00:20:41.280 --> 00:20:45.597 that is one of the powers and potentials of an AI that really deeply is able to 00:20:45.680 --> 00:20:50.237 help you across all parts of your life and also is a deep domain expert in all 00:20:50.320 --> 00:20:53.917 areas of medicine. >> Yeah. Uh I have one last question. Uh 00:20:54.000 --> 00:20:55.677 what how do you tell the story of 00:20:55.760 --> 00:20:59.197 operator? It feels like it was a failure 00:20:59.280 --> 00:21:02.237 or a side quest, but it feels incredibly 00:21:02.320 --> 00:21:04.477 important now given the advances in 00:21:04.560 --> 00:21:06.877 computer use. Is there a clear lineage 00:21:06.960 --> 00:21:10.397 there? What was operator? Does that 00:21:10.480 --> 00:21:15.837 still exist somewhere within chat GPT? H how does how did how did computer use 00:21:15.920 --> 00:21:18.557 get solved? >> Yeah. Well, look, I would look at all 00:21:18.640 --> 00:21:20.717 these things as timing and all about 00:21:20.800 --> 00:21:26.317 iterative deployment. Yeah. Right. that that there's a moment where you need to 00:21:26.400 --> 00:21:30.077 where the capabilities aren't quite there but actually learning from real 00:21:30.160 --> 00:21:33.757 world deployment is very helpful right and the I think operator was just kind 00:21:33.840 --> 00:21:37.997 of below threshold in terms of the model capabilities it was a cloud-based system 00:21:38.080 --> 00:21:41.917 that operated with computer use but it was slow it wasn't fully accurate it was 00:21:42.000 --> 00:21:45.357 like pretty painful to to use some people got value but it really wasn't 00:21:45.440 --> 00:21:49.037 above threshold and if you look at what's happened that the team like one 00:21:49.120 --> 00:21:52.317 thing openai does very well is we make long-term investments on things that 00:21:52.400 --> 00:21:56.717 really matter and we do the grind and that the team this year I think really 00:21:56.800 --> 00:22:00.877 started to build momentum that we put in a lot of effort to go and sort of burn 00:22:00.960 --> 00:22:05.517 down a long list of issues were able to really focus on solving computer use and 00:22:05.600 --> 00:22:08.957 I think that they delivered in a significant way and there's more to do 00:22:09.040 --> 00:22:12.557 never done all those things but it's a true milestone I think people are really 00:22:12.640 --> 00:22:16.717 appreciating what's possible because we've been in a world with these agents 00:22:16.800 --> 00:22:21.997 using computers through connectors right through these very painstakingly coded 00:22:22.080 --> 00:22:26.797 systems that are so different from how humans use computers. Whereas humans can 00:22:26.880 --> 00:22:29.997 already use everything on a computer, right? Everything's designed for people. 00:22:30.080 --> 00:22:33.117 So if you have an AI that can operate that way and even from the very 00:22:33.200 --> 00:22:36.957 beginning of OpenAI, we had a dream that one day we could create such an AI, it 00:22:37.040 --> 00:22:41.037 becomes able to help you across everything that you would be able to do 00:22:41.120 --> 00:22:44.797 with a computer yourself. And so I think we're there with Astra. I think that 00:22:44.880 --> 00:22:48.397 there's just so much more that people are going to uncover in terms of 00:22:48.480 --> 00:22:52.957 applications where this can go. But it's an example of long-term focus, doing the 00:22:53.040 --> 00:22:55.757 work, and not giving up even when the going gets tough. 00:22:55.840 --> 00:22:59.997 >> Yeah. And it feels like Astra, my view is it really felt like all these 00:23:00.080 --> 00:23:02.717 different bets coming together, you know, at the right at the right time, 00:23:02.800 --> 00:23:04.477 right? The the advancements in in the 00:23:04.560 --> 00:23:06.717 model itself, the the computer use, 00:23:06.800 --> 00:23:11.997 voice, all these things. Um, and it uh it's all it's all making sense. So, 00:23:12.080 --> 00:23:15.197 >> yeah. >> Um, >> together, it's focus. It's uh it's 00:23:15.280 --> 00:23:19.117 something that I think this company does extremely well when we really put a 00:23:19.200 --> 00:23:23.117 challenge in front of us and think about how to accomplish it safely well and uh 00:23:23.200 --> 00:23:25.437 to to really deliver the value we're doing thing. 00:23:25.520 --> 00:23:27.197 >> Yeah. put different differently, it felt 00:23:27.280 --> 00:23:28.957 like when you look at last year, it felt 00:23:29.040 --> 00:23:32.957 like OpenAI was uh operating like a big 00:23:33.040 --> 00:23:37.197 company and it and it was a big company but but the way in which like product 00:23:37.280 --> 00:23:40.397 experimentation was happening was you know when you think of like a 00:23:40.480 --> 00:23:42.557 hyperscaler they'll launch a new product 00:23:42.640 --> 00:23:47.997 thinking okay if there's a 20% hit rate or even a 10% or 5% chance that's okay 00:23:48.080 --> 00:23:52.157 and then this year it feels like actually the entire company switched 00:23:52.240 --> 00:23:56.957 back into actual startup mode which is like you know focus focus focus all 00:23:57.040 --> 00:24:00.317 these things need to come together the whole team needs to be rowing in the 00:24:00.400 --> 00:24:04.477 same direction and then the difference in momentum and growth and all these 00:24:04.560 --> 00:24:08.877 things uh coming from that and and uh actually taking the company from like 00:24:08.960 --> 00:24:12.717 operating and shipping more like a big company to shipping again and focusing 00:24:12.800 --> 00:24:17.197 like a startup has been uh feels like an impossible task and it's been uh 00:24:17.280 --> 00:24:21.277 incredible to watch. So >> yeah, >> thank you. No, it's been a real real 00:24:21.360 --> 00:24:25.117 effort from many many people at OpenAI to really bring together and something 00:24:25.200 --> 00:24:28.557 that that I really value that I think we really value as a company and I think 00:24:28.640 --> 00:24:32.077 that we're just so laser focused on our mission and really thinking about every 00:24:32.160 --> 00:24:35.037 piece of what we do should add up to helping us accomplish it. 00:24:35.120 --> 00:24:37.517 >> Well, thank you so much for taking the time to come chat with us. 00:24:37.600 --> 00:24:39.037 >> Great to see you. >> We'll talk to you soon, Greg. 00:24:39.120 --> 00:24:43.200 >> Thank you. >> Talk the rest of your day. Goodbye.