1 00:00:00,080 --> 00:00:03,584 You know, Greg, this is our fourth time speaking and we've spoken every time 2 00:00:03,667 --> 00:00:04,717 Greg，这是我们第四次交流了。我们每次都会讨论 3 00:00:04,800 --> 00:00:09,785 about OpenAI's product direction and I think I'm starting to get it. Um, 4 00:00:09,869 --> 00:00:11,757 you OpenAI 的产品方向，我想我开始有点理解了。 5 00:00:11,840 --> 00:00:16,249 know, there was this conversation that a super app was the wrong term for what 6 00:00:16,333 --> 00:00:17,517 比如，过去有人讨论说用“超级 App”来形容 7 00:00:17,600 --> 00:00:21,599 you were doing with um the app that you're building bringing codeex which 8 00:00:21,682 --> 00:00:23,277 is 你们正在做的事情并不合适。你们要把 Codex，也就是 9 00:00:23,360 --> 00:00:26,595 coding side of OpenAI's product browser and chat GPT together. 10 00:00:26,678 --> 00:00:30,769 And when you use the word super app, people would be OpenAI 的代码端、浏览器端和 ChatGPT 11 00:00:30,852 --> 00:00:34,296 整合起来。 like, "No, a super app is actually something that you can just use every 12 00:00:34,379 --> 00:00:37,917 other app within." Um, and now as we've 过去人们觉得：“不对，超级 App 应该是一个能运行所有其他 App 的平台。” 13 00:00:38,000 --> 00:00:41,104 seen these products come together, actually super app might be the correct 14 00:00:41,188 --> 00:00:43,129 term. You know, at least for us on the outside, 15 00:00:43,212 --> 00:00:45,757 we're starting to see it that 但现在看到这些产品融合，“超级 App”也许真的是最准确的词。 16 00:00:45,840 --> 00:00:49,284 when you need to do anything, um, you it will start with a prompt in chat GPT 17 00:00:49,368 --> 00:00:51,117 and 至少在外界看来，当你需要做任何事时，都会先从 ChatGPT 提问开始， 18 00:00:51,200 --> 00:00:54,780 then OpenAI's technology will use either your browser or your computer to get 19 00:00:54,864 --> 00:00:58,397 that done for you. Is that the right way 然后 OpenAI 的技术会控制浏览器或电脑帮你搞定。可以这样理解吗？ 20 00:00:58,480 --> 00:01:01,437 to think about it? 可以这样理解吗？ I I think that's a pretty good perspective, right? 21 00:01:01,520 --> 00:01:04,397 And I think to really zoom out, the thing we're 我认为这是一个挺好的视角，对吧？如果真的把视野拉宽， 22 00:01:04,480 --> 00:01:06,266 actually trying to build is in AGI, right? 23 00:01:06,349 --> 00:01:09,248 That if you think about what people have been using since chat GBT, 24 00:01:09,331 --> 00:01:11,117 我们真正试图构建的其实是 AGI。如果你想想大家在 ChatGPT 推出以来的使用， 25 00:01:11,200 --> 00:01:13,951 it's a language model, right? There's a big gap between these. 26 00:01:14,034 --> 00:01:15,277 它实际上只是个语言模型，这与 AGI 之间有着巨大的鸿沟。 27 00:01:15,360 --> 00:01:18,061 It's amazing. You can talk to chat talks back to you. Great. Wonderful. 28 00:01:18,144 --> 00:01:20,296 But when we launched in 2022, there was no memory, right? 29 00:01:20,380 --> 00:01:23,277 It's not hooked up to any 和 ChatGPT 对话确实很神奇，它能给你回应，这很好。但 2022 年推出时它没有记忆，没有连接 30 00:01:23,360 --> 00:01:27,303 tools. Has no context. And so it really is that this conversational intelligence 31 00:01:27,386 --> 00:01:30,877 is only one part of what people really 任何工具，也没有上下文。所以这种对话式智能，只是人们真正需要用来 32 00:01:30,960 --> 00:01:32,948 need to get work done to be able to achieve their goals. 33 00:01:33,031 --> 00:01:35,648 And where we're going is to have an AI that's really looking out for you, 34 00:01:35,732 --> 00:01:37,757 right? That you can 完成工作、达成目标的一部分。而我们的方向是，拥有一个真正为你着想的 AI， 35 00:01:37,840 --> 00:01:40,997 provide the goals, the directions, that it's constantly thinking about what can I 36 00:01:41,080 --> 00:01:43,917 do for Alex today? Uh that it's able to go and solve super hard problems, 37 00:01:44,000 --> 00:01:45,757 提供目标和方向，让它不断思考：“今天我能帮 Alex 做点什么？”它能解决极具挑战性的问题， 38 00:01:45,840 --> 00:01:48,958 very mundane problems. you wake up your inbox is organized but also if there's 39 00:01:49,042 --> 00:01:52,078 like a health plan that you are thinking about that it can help you help you 40 00:01:52,161 --> 00:01:53,597 也能解决日常琐碎。比如早晨醒来收件箱已整理好，或者你想做健康计划，它能帮你 41 00:01:53,680 --> 00:01:56,762 achieve that figure out medical treatments uh or you know sort of back and forth 42 00:01:56,845 --> 00:01:59,214 provide you with that kind of information at least and I think 43 00:01:59,298 --> 00:02:00,797 that 实现，甚至寻找医疗方法，或者至少在来回交流中为你提供这类信息。我认为， 44 00:02:00,880 --> 00:02:03,539 the question of what's the interface you want what is the product that you want 45 00:02:03,622 --> 00:02:06,281 is what we spend a lot of time thinking about and the answer is you want almost 46 00:02:06,364 --> 00:02:07,357 大家常花时间思考需要什么交互界面或产品，而答案是：你几乎不需要 47 00:02:07,440 --> 00:02:10,897 no interface you want no product right you want this to be like what's the interface 48 00:02:10,981 --> 00:02:13,595 between you and me right just being able to talk to a persistent 49 00:02:13,678 --> 00:02:15,197 任何界面，不需要产品。你只想让它像你我之间的交流一样，只需要和一个持续存在的 50 00:02:15,280 --> 00:02:18,529 entity of some form firm that's able to go and accomplish goals for you. 51 00:02:18,613 --> 00:02:21,677 And so building that is hard. It will take 实体对话，让它去帮你达成目标。构建这一点很难，需要 52 00:02:21,760 --> 00:02:23,281 time, but we have a lot of the pieces, right? 53 00:02:23,364 --> 00:02:25,242 We're increasingly bringing together the product layer, 54 00:02:25,325 --> 00:02:26,953 trying to make the models better, trying to make 55 00:02:27,037 --> 00:02:28,237 时间，但我们已经拥有了很多拼图。我们正逐步整合产品层，努力让模型更强大， 56 00:02:28,320 --> 00:02:32,717 the whole system just so there's less like clicking buttons and toggles and changing 57 00:02:32,800 --> 00:02:35,917 modes and all these things. Not 让整个系统减少按钮点击、模式切换这些繁杂操作。这并不是说 58 00:02:36,000 --> 00:02:37,923 to say that there won't be some of those along the way, 59 00:02:38,007 --> 00:02:40,623 but the long-term trajectory is towards simplification, unification. Yeah, 60 00:02:40,706 --> 00:02:42,557 it's very interesting 过程中不会有妥协，但长远来看是朝向简化和统一。是的，这很有趣， 61 00:02:42,640 --> 00:02:44,880 that you say the interface uh will melt away. 62 00:02:44,963 --> 00:02:47,462 And so to go a little bit deeper with my question, 63 00:02:47,545 --> 00:02:49,837 um many of us who use 你说交互界面会消失。为了更深入探讨，很多目前使用 64 00:02:49,920 --> 00:02:53,121 products like chatbt today will see that the bot will make a suggestion at the end. 65 00:02:53,204 --> 00:02:55,970 You know, you ask it about nutrition and it says should I make a ChatGPT 66 00:02:56,053 --> 00:02:57,197 等产品的人会发现，机器人在结束时会提供建议。比如你问营养问题， 67 00:02:57,280 --> 00:02:59,742 health plan for you or make a diet plan for you? You ask it, sorry Ron John, 68 00:02:59,826 --> 00:03:02,489 we just talked about travel, but you ask it about travel and then it will give you 69 00:03:02,573 --> 00:03:03,997 它会问要不要帮你做健康或饮食计划。抱歉，Ranjan，我们刚才谈过旅游，但当你询问旅 游时， 70 00:03:04,080 --> 00:03:09,129 an agenda for instance. And so am I hearing you right that what's going 71 00:03:09,213 --> 00:03:13,230 to 它会提供行程安排。 happen within chat GPT just to give an example is you talk to it about 72 00:03:13,314 --> 00:03:16,557 your health decisions and it might say you 所以在 ChatGPT 中，当你讨论健康决定时，它可能会说 73 00:03:16,640 --> 00:03:20,240 know you probably need to you know go to this specialist let me make an appointment 74 00:03:20,323 --> 00:03:22,237 for you and then it will go 你可能需要看某个专家，并帮你预约， 75 00:03:22,320 --> 00:03:24,200 and actually take that action on your behalf. 76 00:03:24,284 --> 00:03:27,386 So it goes from simply a conversation interface to actually understanding 77 00:03:27,469 --> 00:03:29,917 your intent and then going 然后去执行。它从一个对话界面，变成了理解你的意图并帮你实现它。 78 00:03:30,000 --> 00:03:32,908 out and accomplishing that for you. that that I that's exactly right and I think 79 00:03:32,991 --> 00:03:35,675 that if you've used codecs and by the way how many people in the room have 80 00:03:35,758 --> 00:03:36,797 这完全正确。如果你用过 Codex，顺便问一下，现场有多少人 81 00:03:36,880 --> 00:03:40,819 used have used codecs people yeah decent number of people um and that our goal 82 00:03:40,902 --> 00:03:43,037 is to really bring the 用过 Codex？人数不少。我们的目标是 83 00:03:43,120 --> 00:03:46,838 power of codecs to everyone right to bring agents to everyone you that technology 84 00:03:46,922 --> 00:03:50,077 exists right now right you 把 Codex 的能力带给每个人，把 Agent 带给每个人。这种技术现在已经存在， 85 00:03:50,160 --> 00:03:54,029 can hook up like I hook up my codeex to slack to my Gmail to my calendar and there 86 00:03:54,112 --> 00:03:57,837 are many people within openai 比如我把自己的 Codex 连接到 Slack、Gmail 和日记上。在 OpenAI 内部，很多 87 00:03:57,920 --> 00:04:00,628 nontechnical users you know it's got code in the name but it's not really about 88 00:04:00,711 --> 00:04:03,101 code. It's really about having this general purpose tool using harness 89 00:04:03,184 --> 00:04:04,797 非技术用户也在用。虽然名字里有“代码”，但它并不只写代码，而是作为一个通用的 Agent 工具。 90 00:04:04,880 --> 00:04:07,393 an agent. And that the kinds of things, for example, 91 00:04:07,476 --> 00:04:10,638 someone on our comm's team does is um she was organizing an event 92 00:04:10,722 --> 00:04:11,837 例如我们公关团队的一位同事，她在组织一场活动时， 93 00:04:11,920 --> 00:04:16,176 and it would just ask all of the uh event attendees for their dietary preferences, 94 00:04:16,260 --> 00:04:18,717 set up a whole seating 机器会自动询问所有与会者的饮食偏好，并排好座位表， 95 00:04:18,800 --> 00:04:22,041 chart, kind of did all of that work uh so that she could focus on the parts that she 96 00:04:22,124 --> 00:04:24,494 wanted to and and really thinking about the vision of what she 97 00:04:24,577 --> 00:04:26,077 搞定了所有这些工作。这样她就能专注于自己真正想做的事情，思考她想达到的活动效果。 98 00:04:26,160 --> 00:04:29,056 wanted to achieve. And I think that we're going to see this across the board. 99 00:04:29,140 --> 00:04:31,688 So the it's not sci-fi anymore to think about an AI that's hooked up 100 00:04:31,771 --> 00:04:33,197 to 我认为我们会在各行各业看到这种情况。AI 连接工具已经不再是科幻小说了。 101 00:04:33,280 --> 00:04:36,606 these tools. And I remember with you know our very first attempt at tool use 我记得我们在 102 00:04:36,690 --> 00:04:37,757 ChatGPT 中第一次尝试工具使用是在 2023 年。 103 00:04:37,840 --> 00:04:41,889 in chatbt was 2023. I think in like March or April or something we released plugins. 104 00:04:41,972 --> 00:04:45,431 Do people remember plugins back 大约在三四月，我们发布了 plugins（插件）。大家还记得早期 ChatGPT 105 00:04:45,514 --> 00:04:48,317 的插件吗？ in early chat days? 那个其实不行。 That didn't work. 是的，完全不行。 106 00:04:48,400 --> 00:04:51,092 It didn't work at all because the models the models weren't ready. Right. 107 00:04:51,175 --> 00:04:53,982 The form factor is correct. Obviously you're going to have an AI that's able 108 00:04:54,065 --> 00:04:55,997 to like 因为那时候的模型还没准备好。虽然产品形态是对的，AI 显然应该能和你的 Gmail 交互。 109 00:04:56,080 --> 00:04:59,077 talk to your Gmail like no question. But we could only like have three different 110 00:04:59,160 --> 00:05:01,849 connectors exposed to the model at a time or start forgetting. You know, 111 00:05:01,932 --> 00:05:03,197 we 但当时我们每次只能给模型提供三个连接器，否则它就会开始遗忘。当时 112 00:05:03,280 --> 00:05:05,725 had like 2K maybe 4K token context. Like there's just no memory, right? 113 00:05:05,808 --> 00:05:08,217 It's kind of like when you had early computers in 我们只有 2K 或 4K 的 token 114 00:05:08,301 --> 00:05:11,116 上下文，根本没有记忆。这有点像 60 或 70 年代早期的电脑， the 60s or 70s or something, right? You know, 115 00:05:11,199 --> 00:05:14,321 that you had tiny little me memory banks and today you have your phone that's like 116 00:05:14,405 --> 00:05:16,237 better than any 内存非常小。而今天你的手机已经比那个时代的任何超级计算机都要强了。 117 00:05:16,320 --> 00:05:18,930 supercomputer from that era. And I think that's where we're going with these 118 00:05:19,013 --> 00:05:21,127 models, right? That the rate of improvement has been so steep. 119 00:05:21,210 --> 00:05:23,997 So now you can have hundreds of different tools 内存非常小。而今天你的手机已经比那个时代的任何超级计算机都要强了。 120 00:05:24,080 --> 00:05:26,697 accessible that we have the ability to hook them up to whole file system. 121 00:05:26,780 --> 00:05:29,397 So you can almost have the full power of the internet and like almost any 122 00:05:29,480 --> 00:05:30,877 我觉得我们的模型也是这个趋势，它的提升速度非常快。现在你可以连接成百上千个工具， 123 00:05:30,960 --> 00:05:34,328 application you want at the model's fingertips. And it's smart, right? 124 00:05:34,412 --> 00:05:36,399 It's got 52 million token context, depends 125 00:05:36,483 --> 00:05:38,717 我们有能力把它们连接到整个文件系统。所以模型几乎触手可及地拥有整个互联网和几乎任何应 用的能力。 126 00:05:38,800 --> 00:05:42,266 how you squint on it. And the capability level is also getting so so powerful, 127 00:05:42,349 --> 00:05:44,905 right? These models are now solving 而且它很聪明，拥有 5200 万 token 128 00:05:44,988 --> 00:05:47,673 上下文，取决于你怎么看。它的能力也变得非常强大， unsolved math problems and physics problems, right? 129 00:05:47,756 --> 00:05:50,171 And really helping people be able to achieve things they couldn't otherwise. 130 00:05:50,254 --> 00:05:52,077 like we are on the 这些模型现在正在解决未解的数学和物理问题，真正帮助人们实现以前无法做到的事情。 131 00:05:52,160 --> 00:05:55,354 era on the edge of this era of agents really transforming how we all operate 132 00:05:55,437 --> 00:05:58,717 whether it's in software engineering 我们正处于 Agent 时代的前夜，它将彻底改变我们的工作方式，无论是在软件工程、 133 00:05:58,800 --> 00:06:01,169 finance legal sales and in our personal lives too. 134 00:06:01,253 --> 00:06:04,161 So just to unpack that example that you were giving there one 135 00:06:04,245 --> 00:06:05,437 金融、法律、销售，还是在个人生活中。为了进一步说明， 136 00:06:05,520 --> 00:06:09,313 of your colleagues is chatting with Chad GPT about an event and then suggest hey 137 00:06:09,396 --> 00:06:12,317 you know uh how should we you know 比如你刚才提到，你的同事在和 ChatGPT 聊活动， 138 00:06:12,400 --> 00:06:15,481 contact event attendees about something and instead of like saying okay I have 139 00:06:15,565 --> 00:06:17,997 to do that and going into an event 然后 AI 建议如何联系与会者。接着，不用你亲自去做， 140 00:06:18,080 --> 00:06:21,933 program basically what happens is the the interface will take over from there once 141 00:06:22,016 --> 00:06:24,717 it says it's a good idea and you 交互界面就会接管，连接你使用的工具，然后帮你搞定。 142 00:06:24,800 --> 00:06:27,394 agree and then hook into whatever tools you're using and then do it for you. 143 00:06:27,477 --> 00:06:28,557 没错。它会通过 Gmail 连接器搜索你的收件箱，找到所有与会者， 144 00:06:28,640 --> 00:06:32,180 Exactly. So it uses its you know Gmail connector searches through your inbox 145 00:06:32,263 --> 00:06:35,517 to find all the people who are attending 并查看谁有饮食限制，谁已经提供了，谁还没有，并起草好邮件。 146 00:06:35,600 --> 00:06:39,449 and then if you're on the like what what'sever in dietary restriction sees oh 147 00:06:39,532 --> 00:06:42,717 these people I already have their 根据你的设置，它可能会说：“我起草了这些邮件，可以发送吗？” 148 00:06:42,800 --> 00:06:46,635 dietary restrictions these people I do not um drafts an email depending on exactly 149 00:06:46,718 --> 00:06:49,837 how you have things set up it 如果连接器不允许它发送邮件，它会提示：“我已经起草好了，你需要自己发送。” 150 00:06:49,920 --> 00:06:52,958 might say hey I drafted these emails can I send them if you have a connector 151 00:06:53,041 --> 00:06:55,997 that doesn't even let it send emails said I 但在另一个世界里，如果你对系统建立了足够的信任，它会说：“我 152 00:06:56,080 --> 00:06:58,488 drafted it you need to send them And uh in a different world, 153 00:06:58,571 --> 00:07:01,755 you could also imagine that it's you've built enough trust with the system where 154 00:07:01,839 --> 00:07:03,757 it says I 起草并发送了邮件。”我认为这指出了 Agent 时代一个非常重要的方面，那就是 155 00:07:03,840 --> 00:07:06,191 drafted the emails and I actually sent them. 156 00:07:06,275 --> 00:07:10,397 And I think that this actually points to a really important aspect of 信任，对吧？ 157 00:07:10,480 --> 00:07:12,397 the agentic era, which is trust, right? 信任，对吧？ 158 00:07:12,480 --> 00:07:14,674 That we need to really learn how to build trust with these systems, 159 00:07:14,757 --> 00:07:17,223 where they're good, where they're not. Figure out what you want to delegate 160 00:07:17,307 --> 00:07:17,461 to them 161 00:07:17,544 --> 00:07:18,957 我们需要学习如何建立对这些系统的信任，了解它们的优缺点，决定把什么任务和责任委托给它 们， 162 00:07:19,040 --> 00:07:21,030 and how you want to entrust them with responsibility. 163 00:07:21,113 --> 00:07:22,829 And that's something we view as earned, right? 164 00:07:22,912 --> 00:07:25,997 It's not something that that we can we can grant 以及如何向它们委托责任。我们认为信任是赢来的，而不是凭空赋予的。 165 00:07:26,080 --> 00:07:29,777 but by providing lots of tools and control and oversight and supervision to 166 00:07:29,861 --> 00:07:33,357 the operator to the person who that this 需要通过提供大量的工具、控制、监督和管理，让操作者能真正掌控 167 00:07:33,440 --> 00:07:36,606 AI is operating on behalf of like we think that that is going to be such an important 168 00:07:36,689 --> 00:07:38,937 thing and so that's a key product feature and differentiator. 169 00:07:39,020 --> 00:07:40,237 AI 替他们执行的操作。我们认为这非常重要，是关键的产品特色和优势。 170 00:07:40,320 --> 00:07:42,938 Yeah. Yeah. And when you go back to some of the early attempts at this, 171 00:07:43,021 --> 00:07:45,677 there was this like move that OpenAI had to let 是的。回想早期的尝试，OpenAI 曾尝试让你在 172 00:07:45,760 --> 00:07:48,861 you call an Uber within Chat GPT. And it it followed a long line of companies 173 00:07:48,945 --> 00:07:51,757 that have tried to get you to take ChatGPT 里打 Uber。许多公司都尝试过在聊天中直接进行操作， 174 00:07:51,840 --> 00:07:54,461 action within chat, but it never really took off. 175 00:07:54,545 --> 00:07:57,277 And the difference here might 但都没有真正流行起来。这里的区别可能在于， 176 00:07:57,360 --> 00:08:01,322 be that the chatbot can take control of your browser or take control of 177 00:08:01,405 --> 00:08:05,197 your computer and then you don't necessarily 聊天机器人能控制你的浏览器或电脑，你不需要担心 178 00:08:05,280 --> 00:08:07,641 have to worry about like is this plug-in going to work? 179 00:08:07,724 --> 00:08:11,197 it goes and accomplishes that for you by taking over your 插件是否能用，它直接接管你的机器来帮你完成。 180 00:08:11,280 --> 00:08:15,892 machine. So I wonder you know if you expect a fight from the user interfaces 181 00:08:15,975 --> 00:08:17,437 我想知道，你是否预计会遇到来自现有用户界面的阻力， 182 00:08:17,520 --> 00:08:21,495 that we have today aka like all the other apps all the software where to be 也就是其他 183 00:08:21,578 --> 00:08:22,397 App 和软件。因为要真正发挥作用， 184 00:08:22,480 --> 00:08:26,644 truly useful chat GPT will have to not be blocked to be able to go out and execute 185 00:08:26,727 --> 00:08:29,597 these actions on behalf of a ChatGPT 必须不被阻挡，能够代表用户执行这些操作。 186 00:08:29,680 --> 00:08:32,580 user. Well, look f first of all, I'd say that it's this is not theoretical at 187 00:08:32,664 --> 00:08:34,789 this point, right? That people have been using codec. So, 188 00:08:34,873 --> 00:08:37,037 it's a separate 首先，这在目前已经不是理论了，大家已经在用 Codex 了。它是一个独立的 App， 189 00:08:37,120 --> 00:08:39,415 product, separate app. You have to install it separately. 190 00:08:39,498 --> 00:08:41,459 Really starting to focus on software engineering. 191 00:08:41,542 --> 00:08:44,797 But the amount of non-software work that has 需要单独安装，目前主要专注于软件工程。但 Codex 上的非软件工作量 192 00:08:44,880 --> 00:08:47,143 been happening codeex has been absolutely exploding, right? 193 00:08:47,227 --> 00:08:49,092 It's been this like incredible exponential curve, 194 00:08:49,176 --> 00:08:51,837 exactly the thing that you you would 呈现了爆炸式增长，是一条非常惊人的指数曲线，正如你所预料的那样。 195 00:08:51,920 --> 00:08:54,775 expect. And within OpenAI, we basically have the same level of penetration now 196 00:08:54,858 --> 00:08:57,638 in usage as Slack, right? It's like everyone and OpenI is like an entirely 在 197 00:08:57,721 --> 00:08:58,957 OpenAI 内部，我们现在的使用渗透率基本上和 Slack 一样高。 198 00:08:59,040 --> 00:09:00,650 slack based company. We do not use email for the most part. 199 00:09:00,734 --> 00:09:01,942 It's like really like if you're not on Slack, 200 00:09:02,026 --> 00:09:04,067 you're not going to do any work. Um, and it's kind of OpenAI 是一家完全基于 Slack 201 00:09:04,150 --> 00:09:06,912 的公司，我们几乎不用邮件，如果你不上 Slack 就没法工作。 feeling that way now with codeex app as well. 202 00:09:06,995 --> 00:09:09,735 And that everyone's codex is hooked up to all of these tools. Um, 203 00:09:09,819 --> 00:09:11,517 how 现在的 Codex 也是这种感觉，每个人的 Codex 都连接了这些工具。 204 00:09:11,600 --> 00:09:14,611 the ecos ecosystem evolves. I think it's going to be a very nuanced thing because I 205 00:09:14,694 --> 00:09:17,519 think one thing that is very important is that we believe that there should be 206 00:09:17,602 --> 00:09:19,197 至于生态系统如何演变，我认为会非常微妙。因为我们相信，应该有一个充满活力和繁荣的生态 系统， 207 00:09:19,280 --> 00:09:23,193 an ecosystem that gets to be vibrant and thriving and that people can really build 208 00:09:23,277 --> 00:09:26,557 and see the benefits. And so we've 让人们真正能进行开发并获益。我们已经从合作伙伴公司那里看到了这一点， 209 00:09:26,640 --> 00:09:29,247 actually seen this from uh partner companies where uh you know that we I 210 00:09:29,330 --> 00:09:31,937 remember there's a couple different partners where we said hey we really 211 00:09:32,020 --> 00:09:33,357 比如我们曾问几个不同的合作伙伴：“我们希望能训练 AI 去使用你们的软件。” 212 00:09:33,440 --> 00:09:36,505 want to train our AI to be really good at using your software and we didn't know 213 00:09:36,589 --> 00:09:39,339 what they would say and actually the response we got is this is the most 214 00:09:39,422 --> 00:09:40,717 这是我们收到过最友好的合作请求，你们想训练 AI 专门使用我们的软件。 215 00:09:40,800 --> 00:09:44,259 partner friendly outreach we've ever had right that the idea that you will make 216 00:09:44,342 --> 00:09:47,757 your AI specifically good at using our 因为他们看到了机会，让 AI 专门使用他们的工具，其工具的使用率就会大大增加。 217 00:09:47,840 --> 00:09:51,023 tool and they just see the opportunity because their tool will be used just so 218 00:09:51,107 --> 00:09:53,997 much more as a result and that everyone 他们看到了机会，因为他们的工具会因此被更频繁地使用。并且每个人 219 00:09:54,080 --> 00:09:57,752 is trying to think about how do they not just survive as a company into the AI era 220 00:09:57,835 --> 00:10:00,637 but thrive? like how do you really 都在思考，如何在 AI 时代不仅生存下来，而且蓬勃发展？ 221 00:10:00,720 --> 00:10:03,568 get the advantages of the fact there's going to be so much more activity and if 222 00:10:03,651 --> 00:10:06,647 you don't have AI in there if you shut it out then you're actually going to be 如何获得 223 00:10:06,730 --> 00:10:08,317 AI 带来的红利？如果你没有 AI，或者把 AI 拒之门外，你实际上是在衰退，而不是繁荣。 224 00:10:08,400 --> 00:10:11,595 declining not thriving right this this kind of makes open AI puts open AAI so 225 00:10:11,678 --> 00:10:14,405 first of all you're going to bring you talk about people 这让 OpenAI 226 00:10:14,488 --> 00:10:15,597 处于独特的地位。首先，你谈到了大家都在使用 Codex， 227 00:10:15,680 --> 00:10:18,892 using codeex so one of your colleagues shared and I think you've talked about 228 00:10:18,976 --> 00:10:22,060 this too that you've brought chat GPT 一如你刚才提到的，你的一位同事分享过，你们已经把 ChatGPT 引入了 229 00:10:22,143 --> 00:10:22,317 Codex， 230 00:10:22,400 --> 00:10:26,121 into codeex so you can bring codeex into chat GPT which is basically like if we're 231 00:10:26,205 --> 00:10:29,277 users of chat GPT 或者你也可以把 Codex 引入 ChatGPT，这基本上就相当于，作为 ChatGPT 的用户—— 232 00:10:29,360 --> 00:10:32,045 this experience that we talked about of chat GPT not only suggesting what you 233 00:10:32,128 --> 00:10:34,957 might want to do next but going to do it 我们所讨论的这种体验：ChatGPT 不仅会建议你下一步做什么，还会帮你去执行。 234 00:10:35,040 --> 00:10:38,518 for you that's going to happen and so it makes you effectively an operating system 235 00:10:38,601 --> 00:10:41,037 don't you think but not the 这确实会发生，这让你们实际上成为了一个操作系统，你不觉得吗？ 236 00:10:41,120 --> 00:10:44,433 operating system like an iOS where you would like go open up the your phone 237 00:10:44,516 --> 00:10:48,237 and then tap different apps it's almost as 但不是像 iOS 那样的操作系统，你打开手机然后点击不同的 App。它几乎就像—— 238 00:10:48,320 --> 00:10:51,900 if all interaction with all apps will happen through this interface is that 239 00:10:51,984 --> 00:10:53,757 the ambition 所有的交互都将通过这个界面进行。这是你们的野心吗？ 240 00:10:53,840 --> 00:10:57,225 I think that you could describe it that way but I think of it a little 241 00:10:57,309 --> 00:11:00,397 differently. Like the way that I think 我认为可以这样描述，但我看法有些不同。我思考的是， 242 00:11:00,480 --> 00:11:04,008 about this is that what is the ideal interface to an AGI or we call it kind 什么是 243 00:11:04,091 --> 00:11:05,197 AGI 或者我们所说的“个人 AGI”的最理想界面。 244 00:11:05,280 --> 00:11:08,967 of a personal AGI. And I think that it's again the same interface that you and I 245 00:11:09,050 --> 00:11:12,077 are using right now. You just want to 我认为它和我们现在的交流界面是一样的。你只想和助手对话， 246 00:11:12,160 --> 00:11:16,006 talk to an assistant, right? You want to talk to something that can go and and 247 00:11:16,089 --> 00:11:18,877 and work and and operate on your behalf. 和能代表你工作和操作的实体交流。 248 00:11:18,960 --> 00:11:21,010 And so that yes, like that agent, that AGI, 249 00:11:21,094 --> 00:11:23,094 that AI will have its own computer, right? 250 00:11:23,178 --> 00:11:26,717 It'll have its own access to 因此，那个 Agent，那个 AGI，那个 AI 会有它自己的电脑，有它自己的访问权限。 251 00:11:26,800 --> 00:11:29,848 things. maybe can you know like ideal co-orker would be they can come over 252 00:11:29,932 --> 00:11:32,557 and type things on your computer too. So 也许就像理想的同事能过来在你的电脑上打字一样， 253 00:11:32,640 --> 00:11:36,974 some access some delegated access to your own own system and you know maybe 254 00:11:37,058 --> 00:11:37,917 它也需要对你的系统拥有一些授权， 255 00:11:38,000 --> 00:11:41,465 you delegate access to your inbox sometimes maybe it has its own inbox 256 00:11:41,548 --> 00:11:44,557 with some sort of uh some sort of you 比如授权访问收件箱，或者它自己有一个邮箱窗口， 257 00:11:44,640 --> 00:11:47,920 know window into into the things that it needs you forward emails to it. 258 00:11:48,004 --> 00:11:50,957 Th these are not actually if you if you think 来处理需要的事务，或者你转发邮件给它。 259 00:11:51,040 --> 00:11:52,637 about this is not unprecedented, right? 其实这并非没有先例。 260 00:11:52,720 --> 00:11:57,153 It's like the way that you work with an assistant who's a person that we've we've 261 00:11:57,236 --> 00:11:59,997 actually or any co-orker really, 就像你和人类助理或任何同事合作一样， 262 00:12:00,080 --> 00:12:02,983 we've spent a lot of time really thinking about how do you build these trust 263 00:12:03,066 --> 00:12:05,419 boundaries and make sure that you're able to operate together. 264 00:12:05,502 --> 00:12:08,637 And so 我们花了很多时间思考如何建立信任边界并协同工作。 I think of it as just a different thing. 所以我认为这是不一样的。 265 00:12:08,720 --> 00:12:10,891 It's not it you could think of it as an operating system, 266 00:12:10,974 --> 00:12:13,501 but an operating system is almost something from a different time, 267 00:12:13,584 --> 00:12:15,597 right? It's a different 它不是你所想的操作系统，操作系统几乎是上一个时代的概念了， 268 00:12:15,680 --> 00:12:19,467 layer of the stack. This is really more about how do you interface with technology 269 00:12:19,550 --> 00:12:22,157 broadly. And I think that the 处于技术栈的不同层级。这更多是关于如何与技术进行交互。 270 00:12:22,240 --> 00:12:26,225 beautiful thing about AI is it's really about bringing the machine closer to 271 00:12:26,308 --> 00:12:29,597 the human rather than us having to contort 我认为 AI 最美妙的是让机器更近人类， 272 00:12:29,680 --> 00:12:33,089 ourselves into like files and folders and like all these details that somehow are 273 00:12:33,172 --> 00:12:36,150 not natural, right? That are more about how the machine operates rather 274 00:12:36,233 --> 00:12:38,557 而不是让我们去适应文件、文件夹等这些并不自然的机器细节。 than how we operate. 而是人类的运作方式。 275 00:12:38,640 --> 00:12:40,650 Yeah. Talking about a personal intelligence, 276 00:12:40,734 --> 00:12:44,077 it sort of um I don't did you watch WWDC last week? 说到个人智能，你看上周 WWDC 了吗？ 277 00:12:44,160 --> 00:12:48,713 Uh no, no, I missed it. I was I was banned, but um I watched it on TV. 278 00:12:48,797 --> 00:12:50,237 Um 没有，我没去成，我被禁了，但我看电视了。 279 00:12:50,320 --> 00:12:52,827 come on, Apple. Anyway, it does look like you and and and Siri, 280 00:12:52,910 --> 00:12:56,157 the new Siri are going to come kind of into 好吧，苹果。不过，看起来你和新的 Siri 之间会产生某种竞争关系，对吧？ 281 00:12:56,240 --> 00:12:59,698 competition, right? Because they're an app that's going to sit or an intelligence 282 00:12:59,781 --> 00:13:02,539 that will sit on top of all of your apps and let you take action. 283 00:13:02,623 --> 00:13:04,157 因为 Siri 也是一个运行在所有 App 之上、能帮你执行操作的智能体。 284 00:13:04,240 --> 00:13:07,997 And Chacht will be an app on the iPhone. 并且 ChatGPT 将成为 iPhone 上的一个 App。 285 00:13:08,080 --> 00:13:11,346 So then talk a little bit about whether that positioning is going to be difficult 286 00:13:11,429 --> 00:13:13,868 for open AI and how you're thinking about that strategically. 287 00:13:13,951 --> 00:13:15,357 那么，这种定位对 OpenAI 来说会有困难吗？你们战略上是怎么考虑的？ 288 00:13:15,440 --> 00:13:17,761 Well, I just think again think of it a little differently. 289 00:13:17,845 --> 00:13:21,037 Like I think that we're in the beginning of this new 我还是觉得可以换个角度来看。比如我认为我们正处于全新 290 00:13:21,120 --> 00:13:24,592 agentic era and the way that this has always gone in AI is that when you have 291 00:13:24,675 --> 00:13:27,917 a new level of capability, it means you Agent 时代的开端，AI 领域向来如此：当你有新一级的能力， 292 00:13:28,000 --> 00:13:31,976 have an opportunity to rethink everything, right? Rethink how people interface, 293 00:13:32,059 --> 00:13:34,237 how like what the 就意味着有机会重新思考一切，重新思考人们的交互方式， 294 00:13:34,320 --> 00:13:37,652 techn is capable of. And I think that this is no no different, right? 295 00:13:37,735 --> 00:13:41,117 In my mind, like the kinds of things that I 以及技术的可能性。我认为这次也不例外。在我的脑海中， 296 00:13:41,200 --> 00:13:46,206 see on the horizon, for example, AI for solving scientific problems, right? 297 00:13:46,289 --> 00:13:48,717 And I think 我看到的愿景，例如用 AI 来解决科学问题。我认为 298 00:13:48,800 --> 00:13:52,081 we're starting to see the inklings of this. Like for example, 299 00:13:52,164 --> 00:13:54,397 today we announced 我们正开始看到这方面的端倪。例如我们今天宣布， 300 00:13:54,480 --> 00:13:58,766 we have in uh peer-reviewed literature people doctors who are using 03. 301 00:13:58,849 --> 00:14:01,357 在同行评审的文献中，有医生在使用 o3。 Remember 03? 还记得 o3 吗？ Yep. 是的。 302 00:14:01,440 --> 00:14:03,517 That was like forever ago now, right? 那感觉像是很久以前的事了，对吧？ 303 00:14:03,600 --> 00:14:07,413 That was like one of our earliest reasoning models using that to find 304 00:14:07,496 --> 00:14:08,317 那是我们最早的推理模型之一，用它 305 00:14:08,400 --> 00:14:12,314 diagnosis for people who I had no no answers from doctors for many many years. 306 00:14:12,398 --> 00:14:14,877 You know, there's an example of 为多年无法确诊的患者找到了诊断结果。 307 00:14:14,960 --> 00:14:17,545 someone who had spent 20 years with a mysterious ailment. Finally, 308 00:14:17,628 --> 00:14:20,577 it's been diagnosed through the use of this technology. And if you're like, 309 00:14:20,661 --> 00:14:22,397 okay, 比如有个患者忍受了 20 年的怪病，最终通过这项技术确诊了。如果你觉得，好吧， 310 00:14:22,480 --> 00:14:24,368 you've got models that can do that, they can do that. 311 00:14:24,451 --> 00:14:27,306 And then it's really about like, you know, the same like distribution and like, 312 00:14:27,389 --> 00:14:29,277 you know, can you 如果你有了能做到这些的模型，它们就能做到。然后这其实是关于分发，以及你 313 00:14:29,360 --> 00:14:31,311 get access to an app? You know, to me it's it doesn't type check. 314 00:14:31,395 --> 00:14:32,751 It's like we have something fundamentally new. 315 00:14:32,835 --> 00:14:34,536 And so that's not to say that there won't be competition. 316 00:14:34,619 --> 00:14:36,477 I actually think that there 是否能获得一个 App。对我来说这不太对劲。我们拥有的是全新的东西。 317 00:14:36,560 --> 00:14:38,106 will be and it's going to be great for everyone, 318 00:14:38,190 --> 00:14:40,687 but I just think that the ways in which you're going to use this technology, 319 00:14:40,770 --> 00:14:43,437 the things it will be capable of and what it'll make you 但这并不是说没有竞争，竞争会很激烈且对大家都好， 320 00:14:43,520 --> 00:14:46,080 capable of doing are just totally different from anything we've seen before. 321 00:14:46,163 --> 00:14:47,437 但我认为你使用这项技术的方式，以及它能带给你的能力，和我们以前见过的完全不同。 322 00:14:47,520 --> 00:14:50,628 You know, I was going to ask you um well, does it mean that you'll have to um you 323 00:14:50,712 --> 00:14:52,717 know, create your own device 我本来想问，这是否意味着你们需要制造自己的设备， 324 00:14:52,800 --> 00:14:56,172 assuming that like my concept is is you know that you're going to have to go 325 00:14:56,256 --> 00:14:59,037 through Apple to get to the user. Um 毕竟要接触到用户可能必须通过苹果。假设这有一定道理， 326 00:14:59,120 --> 00:15:02,084 assuming that's somewhat valid. But the answer is you already are right. 327 00:15:02,167 --> 00:15:04,200 You're so OpenAI is working on a device right now. 328 00:15:04,283 --> 00:15:05,597 但答案是你们已经在做了，媒体也报道了 OpenAI 正在开发设备。 329 00:15:05,680 --> 00:15:09,517 It certainly has been publicly reported. 媒体已经公开报道了。 330 00:15:09,600 --> 00:15:13,037 I I was in your office in December and Sam told me that this is happening. 331 00:15:13,120 --> 00:15:16,557 It's it's multiple devices. Um so if you 我十二月去你办公室时，Sam 就告诉我这正在发生，而且是多个设备。 332 00:15:16,640 --> 00:15:20,232 think about the way that again you're going to interface with um with these AIs, 333 00:15:20,316 --> 00:15:23,357 how does that device play in or 所以如果你想想我们与 AI 的交互方式，这个设备或系列设备扮演什么角色？ 334 00:15:23,440 --> 00:15:26,903 series of devices? Well, look, I I think again I I would just step back and say 335 00:15:26,986 --> 00:15:29,597 that I think this is the beginning of 我还是想退一步说，这是全新事物的开始。我认为 336 00:15:29,680 --> 00:15:33,917 something very new and that I think 最大的变革不在于设备本身， 337 00:15:34,000 --> 00:15:39,048 about the way that I think I want to say like I think the biggest shift that has 338 00:15:39,131 --> 00:15:41,357 happened in terms 而是从对话智能，也就是对话式问答—— 339 00:15:41,440 --> 00:15:44,626 of interface again it's not even about devices and and and things like that. 340 00:15:44,709 --> 00:15:45,917 而是人机交互界面方面的转变。这甚至不是设备之类的事情，对吧？ 341 00:15:46,000 --> 00:15:49,045 It's really about the shift from conversational intelligence like kind 342 00:15:49,128 --> 00:15:52,576 of the chat paradigm where it's like kind of you have an AI that's personalized 343 00:15:52,659 --> 00:15:53,917 其实是从对话智能，也就是你向 AI 问答的模式——这个 AI 344 00:15:54,000 --> 00:15:57,060 enough to you that it's worth reading its output, right? You ask it a question, 345 00:15:57,144 --> 00:15:59,607 you get an answer, it's something that's useful to you to agents 346 00:15:59,691 --> 00:16:01,677 被个性化定制，以至于其输出值得你去阅读，你问一个问题，它给你一个有用的回答——转变成 Agent 模式， 347 00:16:01,760 --> 00:16:05,133 where they're capable enough to actually do things for you. 348 00:16:05,216 --> 00:16:09,117 Like that is a big shift and that that 在这里它们足够有能力真正替你去执行任务。这才是巨大的转变， 349 00:16:09,200 --> 00:16:11,560 implies a difference in how you want to interact. 350 00:16:11,643 --> 00:16:14,801 And so you kind of are just going to want a single agent that has 351 00:16:14,884 --> 00:16:16,397 而这种转变也意味着人机交互方式的变化。因此你更倾向于只需要一个能 352 00:16:16,480 --> 00:16:19,263 access to your context. And this will be true in personal life. 353 00:16:19,346 --> 00:16:21,401 This will be true in a business context, right? 354 00:16:21,484 --> 00:16:23,357 You 访问你上下文的单一 Agent。无论在个人生活还是商务场景中，这都适用。对吧？ 355 00:16:23,440 --> 00:16:25,593 imagine, for example, having a uh you know, 356 00:16:25,676 --> 00:16:28,557 imagine you have a PhD in every 想象一下，你有一个在各个领域都拥有 PhD 水平， 357 00:16:28,640 --> 00:16:31,173 field co-worker, you know, Nobel prizes, multiple of them, 358 00:16:31,256 --> 00:16:33,608 and you hire one of these, you hire a hundred of them, 359 00:16:33,691 --> 00:16:35,277 and 获得过多次诺贝尔奖的同事，你雇佣了一个，甚至雇佣了一百个这样的人， 360 00:16:35,360 --> 00:16:37,277 you don't invite them to any meetings. 却不让他们参加任何会议。 361 00:16:37,360 --> 00:16:40,897 They're not going to be very useful. And so there's something about how do you get 362 00:16:40,981 --> 00:16:44,077 context into the AI and not just 那他们不会很有用。所以这里面临的核心挑战是：你如何将上下文输入给 AI，不仅是 363 00:16:44,160 --> 00:16:48,566 statically but dynamically right as context evolves as your business processes 364 00:16:48,649 --> 00:16:52,077 evolve how do you have a 静态的，更是动态的，随着上下文和你的业务流程的演进而演进。你如何拥有一个 365 00:16:52,160 --> 00:16:55,970 context layer that is accessible to an AI that lets the AI operate to the extent 366 00:16:56,054 --> 00:16:59,037 of that raw intelligence and so 能被 AI 访问的上下文层，让 AI 在其原始智能范围内运转， 367 00:16:59,120 --> 00:17:03,187 finding ways to make that AI be accessible so available in your meetings to make it 368 00:17:03,270 --> 00:17:06,637 very ergonomic it's very easy 从而找到方法让 AI 能在你的会议中被轻松访问、非常符合人体工程学、易于获取？ 369 00:17:06,720 --> 00:17:10,434 to get access to Um, I think all of that's going to require a rethink, 370 00:17:10,517 --> 00:17:14,557 but I think it again it's just the core for me 我认为所有这些都需要重新思考。但我认为，对我来说核心依然是 371 00:17:14,640 --> 00:17:17,805 starts from thinking about the agentic form factor and then working backwards 372 00:17:17,889 --> 00:17:20,717 to how do you just make this have the 从思考 Agent 这一产品形态出发，然后反推：你如何让它获得 373 00:17:20,800 --> 00:17:24,413 context it needs and again the trust is going to be such a core part of making 374 00:17:24,496 --> 00:17:26,877 this whole equation work. 所需的上下文。而信任依然是让整个公式运转的核心部分。 375 00:17:26,960 --> 00:17:29,949 So kind of like having this this device with you at all times and being like I 376 00:17:30,032 --> 00:17:32,746 need to get that done and it goes and does it for you. And I think that 377 00:17:32,829 --> 00:17:34,637 that 所以，随时随身携带这个设备，当有事情要办时，它就能帮你完成。我认为这就是未 来的一部分。 378 00:17:34,720 --> 00:17:38,359 will be part of it. But I almost even think if you don't have a device like 379 00:17:38,443 --> 00:17:41,437 that, it's not like you're going to be 但我也认为，即使没有这样的设备，你也不会出局。 380 00:17:41,520 --> 00:17:43,149 out of the game, right? Because it's this a this AI. 381 00:17:43,232 --> 00:17:45,848 It's not because there's one thing there's one version of it where you think of it 382 00:17:45,931 --> 00:17:48,547 where it's like the device is the AI and you want your 因为这个 AI 并不等同于设备本身。有些人觉得手机就是 383 00:17:48,631 --> 00:17:48,877 AI，或者你想要你的 384 00:17:48,960 --> 00:17:51,305 phone to be the AI. You want, you know, whatever whatever you know custom device 385 00:17:51,389 --> 00:17:53,673 you're you're thinking about to be the AI, but it's not going to be like that. 386 00:17:53,757 --> 00:17:54,797 手机就是 AI，或者你正在构想的某个定制设备就是 AI，但事实不会是那样。 387 00:17:54,880 --> 00:17:56,077 It's going to be more like an interface. 它更像是一个交互界面。 388 00:17:56,160 --> 00:17:58,530 Like no more than your phone is you, right? It's an interface to you. 389 00:17:58,613 --> 00:18:01,126 It's a way that I can sort of, you know, call you up whenever I need you. 390 00:18:01,209 --> 00:18:02,974 uh whenever I want to ask you a question and there's 391 00:18:03,058 --> 00:18:04,077 就像你的手机并不是你，而是你的交互界面，可以在需要时联系到你。 392 00:18:04,160 --> 00:18:07,250 different ways of accessing right there's like synchronous phone call I can text you 393 00:18:07,333 --> 00:18:09,668 I can email you and I think that we're going to be much the same 394 00:18:09,751 --> 00:18:10,877 我们可以通过不同的方式联系，比如打电话、发短信、发邮件。同样的， 395 00:18:10,960 --> 00:18:14,051 with how we interact with our agents uh there's been some reports that open 396 00:18:14,134 --> 00:18:17,437 is working on these like birectional voice 我们与 Agent 交互也是如此。有报道称 OpenAI 正在开发双向语音 397 00:18:17,520 --> 00:18:21,094 models I think we've talked about that in the past like the goal is to have like 398 00:18:21,177 --> 00:18:24,157 an AI that you can you can speak 模型，我们之前也讨论过，我们的目标是拥有一个你可以与之交谈的 AI， 399 00:18:24,240 --> 00:18:28,183 with and we'll be able to process that and speak back with you in a much more natural 400 00:18:28,266 --> 00:18:31,357 way can you share anything about that 它能处理你的话并以更自然的方式回应你。你能分享一些细节吗？ 401 00:18:31,440 --> 00:18:33,612 No, but no, more seriously, um I mean, look, 402 00:18:33,696 --> 00:18:36,945 I think that the that the general shape of the of the technology, 403 00:18:37,028 --> 00:18:38,637 like the way 不能，但认真地说，我认为技术的大致形态， 404 00:18:38,720 --> 00:18:41,825 that like we had we had we've had voice models, um you know, 405 00:18:41,908 --> 00:18:45,757 kind of a really cool voice experience for, you know, 比如我们之前就拥有语音模型，体验挺好的， 406 00:18:45,840 --> 00:18:47,980 year and a half, two years now. Um you know, 407 00:18:48,063 --> 00:18:50,405 we first demoed it back in March, April of 2024. 408 00:18:50,488 --> 00:18:53,437 Um brought it to market, 已经有了一年半到两年的时间。我们最早在 2024 年三四月进行了演示， 409 00:18:53,520 --> 00:18:55,620 um you know, maybe late that late that year. 410 00:18:55,703 --> 00:18:58,993 And the way that it works and the way that everyone's models work is 411 00:18:59,077 --> 00:18:59,837 that 并在大约年底推向了市场。 412 00:18:59,920 --> 00:19:03,267 you basically chain together um well the original way that these things worked 413 00:19:03,350 --> 00:19:06,477 was that you would chain together a text 过去语音模型的工作方式是，你基本上需要把几个不同的模型串联起来： 414 00:19:06,560 --> 00:19:09,360 to or a speechtoext model then you do a texttoext model and then you would do 415 00:19:09,443 --> 00:19:12,281 a texttospech model. Horribleness, right? 先用 speech-to-text 模型，再用 text-to-text 416 00:19:12,364 --> 00:19:13,517 模型，然后是 text-to-speech 模型。这很糟糕，对吧？ 417 00:19:13,600 --> 00:19:16,949 Like these three things chained together. Um it still has been the case that even if 418 00:19:17,032 --> 00:19:19,523 you have one unified model that's able to kind of take in input 419 00:19:19,606 --> 00:19:21,117 and 三个模型串联在一起。而即使是现在统一的模型，能直接接收输入并输出响应， 420 00:19:21,200 --> 00:19:24,044 then you know able to output a response, you still have this problem of turn 421 00:19:24,127 --> 00:19:26,740 taking, right? Imagine that like we have this like you cannot overlap, 422 00:19:26,824 --> 00:19:28,397 you cannot 你仍然面临着轮流发言的问题。比如我们谈话时，不能重叠，不能打断。 423 00:19:28,480 --> 00:19:31,931 interrupt. It's just like once you you you speak to me in a turn and then you got 424 00:19:32,015 --> 00:19:33,808 to wait for me to finish my whole response. 425 00:19:33,891 --> 00:19:36,077 That is not how human 你必须等我说完这一轮，然后我再等你。但人类的对话不是这样的。 426 00:19:36,160 --> 00:19:39,085 conversation works. And so that we we basically have like a hack where we have 427 00:19:39,169 --> 00:19:40,590 these models that determine oh it seems 428 00:19:40,673 --> 00:19:42,557 所以我们目前其实是在“打补丁”，用模型去判断：“哦，这一轮好像结束了，”或者“哦，那 一轮好像开始了。” 429 00:19:42,640 --> 00:19:45,223 like the turn has ended and oh it seems like the turn has started and we're like 430 00:19:45,307 --> 00:19:47,757 why are we talking about turns, right? 我们为什么还要讨论“发言轮次”呢？这太不自然了。这是人类在迁就机器的限制。 431 00:19:47,840 --> 00:19:50,888 Like turns again are so unnatural. This is the humans contorting ourselves to 432 00:19:50,972 --> 00:19:53,776 the machine and its limitations. And so the obvious thing that you want 433 00:19:53,859 --> 00:19:55,037 to 因此，你显然想要实现一个工作方式和我们一模一样的 AI， 434 00:19:55,120 --> 00:19:57,820 accomplish is a model in AI that works much more like you and I do, 435 00:19:57,903 --> 00:19:59,357 right? 它能同时处理输入和输出。这也是该领域许多人正努力实现的目标。 436 00:19:59,440 --> 00:20:02,297 That's able to process input at the same time it's processing output. 437 00:20:02,380 --> 00:20:05,705 And all of that is of course something that many people in this field are trying 438 00:20:05,789 --> 00:20:07,197 to to 当我们转向这种自然、流畅的人机对话界面时，会非常令人兴奋。 439 00:20:07,280 --> 00:20:11,054 run towards. Um I think it's going to be very very exciting as you move to these 440 00:20:11,137 --> 00:20:14,477 natural very human fluid like 以前没有人见过这样的东西。例如，虽然现在的 ChatGPT 语音功能已经很神奇了， 441 00:20:14,560 --> 00:20:16,864 conversational interfaces. No one's seen anything like it. 442 00:20:16,947 --> 00:20:20,280 Like one thing that that I think about is the the current interaction with you know 443 00:20:20,363 --> 00:20:21,597 chat GBT 许多人在通勤时使用它提问，但它有时也让人抓狂。 444 00:20:21,680 --> 00:20:24,533 voice. many ways it's magical, right? So many people use it on their commute, 445 00:20:24,616 --> 00:20:27,202 able to ask all these questions, but it also is so frustrating, right? 446 00:20:27,286 --> 00:20:28,957 Whenever 当它打破魔力时，你会发现：“我想补充一点，”但它却一直在对你说话而没有听。 447 00:20:29,040 --> 00:20:31,117 it breaks the magic because it's like you realize, oh, 448 00:20:31,200 --> 00:20:34,357 I want to like add some followup and it keeps talking over you and it didn't it's 449 00:20:34,440 --> 00:20:36,557 just like that is 这不合理。我认为 AI 的核心目的就是为了让我们能够流畅、自然地进行交互。 450 00:20:36,640 --> 00:20:40,445 just it doesn't make sense. And so I think that part of what we need part of like 451 00:20:40,528 --> 00:20:43,517 the whole point of this AI is to be 而且我认为，这不仅适用于个人生活场景，也适用于工作场景。 452 00:20:43,600 --> 00:20:45,853 something that you can interact with interact with fluidly and naturally. 453 00:20:45,936 --> 00:20:47,517 And by the way, I think it's not just going 我在 Codex 454 00:20:47,600 --> 00:20:48,477 中最神奇的一些体验就是通过语音进行操作。很多人使用内置语音， 455 00:20:48,560 --> 00:20:52,930 to be about the sort of use case, like we kind of think about the the personal use 456 00:20:53,013 --> 00:20:55,917 case, but it's also really the work 有些人使用第三方应用。当你开始意识到， 457 00:20:56,000 --> 00:20:59,320 use case. And I think some of the most magical experiences that that I've had 458 00:20:59,403 --> 00:21:02,502 with codecs have been when operating it through voice. Like many people, 459 00:21:02,586 --> 00:21:03,917 we have 打字写反馈虽然简单，但写一整段话来说明你要什么， 460 00:21:04,000 --> 00:21:06,662 a voice built in. Some people use third party apps for it. Um, 461 00:21:06,745 --> 00:21:09,230 and that you just get a very different experience when you 462 00:21:09,313 --> 00:21:10,957 却很痛苦。没人想做那件事，对吧？你只想说出来，并获得实时的反馈循环。这会很棒。 463 00:21:11,040 --> 00:21:14,360 start to realize that like typing a quick message to give some feedback, easy, 464 00:21:14,444 --> 00:21:17,109 but like writing out a whole paragraph and everything you want, 465 00:21:17,193 --> 00:21:18,157 那么，让我们简单聊聊模型的改进。几年前有讨论说， 466 00:21:18,240 --> 00:21:21,042 horrible. No one wants to do that, right? You just want to be like saying things 467 00:21:21,126 --> 00:21:23,387 and you want the real-time feedback loop and all of that is going 468 00:21:23,470 --> 00:21:24,397 大语言模型要撞墙了，也就是遇到瓶颈。但事实证明那是错的。 469 00:21:24,480 --> 00:21:26,557 to happen and it's going to be amazing. 大家在想，模型还能变多好？何时停止？ 470 00:21:26,640 --> 00:21:29,022 So, let's talk about model improvement briefly. Um, 471 00:21:29,106 --> 00:21:32,116 so there was a discussion a couple years ago that large language 472 00:21:32,200 --> 00:21:36,033 你有什么想法吗？我认为，当你亲自构建这些模型时， models were about to hit a wall. um that was wrong. 473 00:21:36,117 --> 00:21:39,277 And um something you know 会得到一种直觉，这在外界是很难体会到的，因为我们能看到所有的数据点， 474 00:21:39,360 --> 00:21:42,736 that I'm thinking about is I think we're all thinking about it is how much better 475 00:21:42,819 --> 00:21:45,597 can these models get and when will the 以及为了实现这些改进所付出的努力。这里有两个部分的答案。 476 00:21:45,680 --> 00:21:51,500 improvement stop? Any thoughts? Well, I think that this is a place where when 477 00:21:51,583 --> 00:21:52,957 第一，我认为基础科学是最神秘、最重要的 478 00:21:53,040 --> 00:21:56,701 you're kind of building these models, you get kind of a sense and an intuition 479 00:21:56,784 --> 00:21:59,677 that I think is harder to get from the 科学发现和实证观察之一。我们能够构建这些模型， 480 00:21:59,760 --> 00:22:03,145 outside because we see all the data points and we see also the work that goes into 481 00:22:03,229 --> 00:22:05,852 these improvements. And so that there's two parts to the answer. 482 00:22:05,936 --> 00:22:07,037 且 scaling laws（缩放定律）依然适用，对吧？ 483 00:22:07,120 --> 00:22:11,280 One is I think that the fundamental science is one of the most mysterious 484 00:22:11,363 --> 00:22:14,477 and important 只要继续投入更多数据、更多算力、更好的架构，模型就会继续提升，其中包含了很多改进。 485 00:22:14,560 --> 00:22:17,750 just scientific discoveries and empirical observations that that I can that I 486 00:22:17,834 --> 00:22:20,259 that I'm aware of that I can imagine right that we are able 487 00:22:20,342 --> 00:22:21,917 to 但是每次我们遇到“这似乎没有按预期扩展”时，其实都是因为遇到了 bug， 488 00:22:22,000 --> 00:22:26,161 actually build these models and that the scaling laws continue right that it just 489 00:22:26,244 --> 00:22:29,357 is the case that you can just keep 或者数学公式不对，或者代码实现与数学公式不匹配等等。 490 00:22:29,440 --> 00:22:33,524 training these models more data more compute better architectures and there's 491 00:22:33,607 --> 00:22:36,717 a lot of improvements that go 这一点非常重要，需要去理解。如果我们回溯到这个领域的起点， 492 00:22:36,800 --> 00:22:39,096 in. But every time we've kind of run into a like, oh, 493 00:22:39,179 --> 00:22:41,026 this isn't quite scaling the way we expect, 494 00:22:41,110 --> 00:22:42,957 it's we have 神经网络本身是在 1940 年代设计的，甚至早于计算机诞生， 495 00:22:43,040 --> 00:22:45,454 a problem, we have a bug, that our math wasn't quite right, that oh, 496 00:22:45,537 --> 00:22:47,694 our implementation isn't isn't isn't quite matching the math, 497 00:22:47,777 --> 00:22:49,677 whatever the thing 还有人要检查技术栈的每一层。图表上任何奇怪的波动，都可能意味着神经网络 498 00:22:49,760 --> 00:22:53,677 is. And that is, I think, a very important thing to to sort of internalize. 499 00:22:53,760 --> 00:22:56,717 And actually if you we've 缺乏抽象，任何微小的错误都会产生连锁反应，并在更深层显现出来。 500 00:22:56,800 --> 00:22:59,575 done studies where you go back to the beginning of the field right that neural 501 00:22:59,658 --> 00:23:02,397 nets themselves were designed in like 所以你需要人们深入理解这一切。不过，只要把优秀的团队召集起来，设定明确的使命， 502 00:23:02,480 --> 00:23:06,344 1940s right before computers right as a model of maybe this is maybe this how 503 00:23:06,428 --> 00:23:09,677 the brain processes information first 大家愿意做这些磨人的工作，结果就是值得的、是可实现的。 504 00:23:09,760 --> 00:23:13,526 hardware implementation was 1959 with the perceptron and if you look at landmark 505 00:23:13,610 --> 00:23:16,077 results in the field that the 所以我认为进展会继续。那么，我想听听你的看法， 506 00:23:16,160 --> 00:23:20,516 landmark results follow this incredibly smooth deterministic path of more compute 507 00:23:20,599 --> 00:23:23,037 being poured into them and so 70 如果模型能比今天进步得多。 508 00:23:23,120 --> 00:23:25,757 years of people maybe 80 years now of people saying this stuff is never going 509 00:23:25,840 --> 00:23:28,513 to work, never going to scale, going to hit the wall. Hasn't hit the wall yet. 510 00:23:28,596 --> 00:23:29,997 假设 OpenAI 构建了最好的模型，相当于拥有 15 个 PhD 水平、情商极高、 511 00:23:30,080 --> 00:23:33,581 There's still no wall in sight. And so I think that the fundamentals allow it. 512 00:23:33,665 --> 00:23:36,797 从不抱怨且能为你执行任务的同事。 Now the practicality is hard, right? 而对手构建了稍逊、但情商高、具 13个 PhD 水平的。 513 00:23:36,880 --> 00:23:39,448 Actually building these massive supercomputers. It's hard. 514 00:23:39,531 --> 00:23:43,288 It's expensive. It's not easy, right? That we have teams that just like work so hard 515 00:23:43,371 --> 00:23:46,780 当大家都达到那个智力水平时，竞争的差异化体现在哪里？ to solve these incredibly hard technical problems. 516 00:23:46,863 --> 00:23:49,451 We have our own network protocol that we've had to design. 517 00:23:49,535 --> 00:23:50,557 um 因为我们看到模型制造者们几乎是并驾齐驱的。 518 00:23:50,640 --> 00:23:53,385 that we have people who look at every single layer of the stack that there's 519 00:23:53,469 --> 00:23:56,177 weird wiggles in the graph and you the way to think about these neural nets 520 00:23:56,260 --> 00:23:57,517 is 一个取得突破，下一个紧跟着也会取得突破。所以大家都会变得非常聪明。 521 00:23:57,600 --> 00:23:59,477 that there's like there's no abstractions, right? 522 00:23:59,560 --> 00:24:02,717 It's almost like any little piece that's wrong can have a ripple effect that only 523 00:24:02,800 --> 00:24:04,237 shows up down 那时还能实现差异化吗？我认为这个答案有几个维度。 524 00:24:04,320 --> 00:24:06,966 there. And so you need people to deeply understand all of it. 525 00:24:07,049 --> 00:24:08,889 And yet if you get the right team together, 526 00:24:08,973 --> 00:24:11,037 put the right 第一，我认为从商业模式的角度来看，每个供应商都会卖光他们所有的算力。 527 00:24:11,120 --> 00:24:15,086 mission in front of people and people do that grind, the outcome, 528 00:24:15,169 --> 00:24:18,637 it's worth it, right? And it's achievable and it's 所有的算力。 529 00:24:18,720 --> 00:24:21,127 possible. And so I think that for those reasons the progress will continue. 530 00:24:21,211 --> 00:24:22,157 好的，没错。我认为我们正走向一个算力不足以满足所有需求的世界。 531 00:24:22,240 --> 00:24:25,671 So then I'd love to hear your perspective if if models can basically progress much 532 00:24:25,754 --> 00:24:28,157 further from where they 我们将走向算力驱动的经济体系，每个人都将一直使用这些模型来完成任务。 533 00:24:28,240 --> 00:24:31,776 are today. Um let's say let's say open AI builds the best model and it 534 00:24:31,859 --> 00:24:35,757 the equivalent of like something with like 我们可以看到，现在虽然在讨论算力限制，但使用这些 Agent 的人数 535 00:24:35,840 --> 00:24:39,666 15 PhDs with excellent emotional intelligence that doesn't complain 536 00:24:39,749 --> 00:24:43,517 and goes out and does stuff for you. Um, and 大约只有一两千万，我们还没有达到全球规模。 537 00:24:43,600 --> 00:24:46,600 then the the next model maker will build a a less good, 538 00:24:46,684 --> 00:24:50,077 but it has 13 PhDs and ChatGPT 有十亿用户，但我们还没有把 Agent 级别的能力带给所有人。 539 00:24:50,160 --> 00:24:53,972 it's like pretty good uh, you know, EQ and we'll still go and do things for you. 540 00:24:54,056 --> 00:24:56,797 So, where does the differentiation 所以，目前的使用深度和未来的发展相比微不足道。 541 00:24:56,880 --> 00:24:59,041 come in when you get to that level of intelligence? 542 00:24:59,124 --> 00:25:01,769 Because we've seen the model makers kind of move in lock step. 543 00:25:01,852 --> 00:25:02,957 即使有不同的厂商、不同能力水平的开源模型、新型云服务， 544 00:25:03,040 --> 00:25:06,004 One makes an advance, the next one comes in and makes the advance. So, 545 00:25:06,087 --> 00:25:08,877 they all become that smart. Do they is it 算力依然会是稀缺资源，所有的算力都会被派上用场。 546 00:25:08,960 --> 00:25:12,317 possible to differentiate? Well, I think there are several dimensions to 547 00:25:12,400 --> 00:25:15,757 the answer. Um, number one is I do think 至于这是否是一门好生意，是否值得新玩家入局，我的答案是肯定的。 548 00:25:15,840 --> 00:25:18,757 there's a bit of an attractor state where just like from a business model 549 00:25:18,840 --> 00:25:21,757 perspective, every provider sells out 因为这是一个巨大的、我们无法完全覆盖的市场，我们需要更多的能量和动力。 550 00:25:21,840 --> 00:25:23,837 all their compute. 但第二点是，它忽视了智能并不是一维的。 551 00:25:23,920 --> 00:25:26,704 Okay. Right. I think that is just like the world that we're heading towards where 552 00:25:26,788 --> 00:25:29,147 there just is not going to be enough compute to serve all the demand. 553 00:25:29,230 --> 00:25:29,997 如果你仔细观察，即便你很有天赋，如果从未练习过， 554 00:25:30,080 --> 00:25:32,905 Right? That we're heading to this compute powered economy that everyone's going 555 00:25:32,989 --> 00:25:35,409 to be using these models all the time to be able to accomplish tasks 556 00:25:35,492 --> 00:25:36,477 of 第一次做演讲也是做不好的。如果你从未使用过电子表格， 557 00:25:36,560 --> 00:25:40,146 interest. And we just see it. It's like right now we're talking about compute 558 00:25:40,230 --> 00:25:43,197 constraints and like like the number of 你也没法做复杂的建模。所以我们得出的一个结论是， 559 00:25:43,280 --> 00:25:47,106 people using these agents is like order of 10 million 20 million maybe you know it's 560 00:25:47,189 --> 00:25:50,317 like we're not at planet scale 面对不同的行业和领域，我们必须有所侧重。我们不可能同时在所有领域都做到极致。 561 00:25:50,400 --> 00:25:53,637 chatbt is like a billion users right but we haven't brought the agentic power there 562 00:25:53,720 --> 00:25:56,277 yet so you're just looking at these factors and the depth of usage 563 00:25:56,360 --> 00:25:57,597 is 提升通用智能确实能提升很多方面的表现，但要真正成为领域专家， 564 00:25:57,680 --> 00:26:00,877 also tiny compared to where we're going and so I think that we're just going to be 565 00:26:00,960 --> 00:26:03,277 in a world where even if you have 真正成为 PhD，去推动一个行业的发展，这是很难的。 566 00:26:03,360 --> 00:26:07,003 different vendors different capability level open source models all these things 567 00:26:07,086 --> 00:26:10,077 these neoclouds like I think that 另外，我想说，理解当你成功做到这一点时会发生什么，是很有必要的。 568 00:26:10,160 --> 00:26:13,687 compute is just going to be the scarce resource and I think that it's going to go 569 00:26:13,770 --> 00:26:16,317 go to use. Um so to some extent I 比如回想 AlphaGo，大家记得第 37 手棋， 570 00:26:16,400 --> 00:26:19,062 think that the the like is this a good business to be in and for new entrance 571 00:26:19,145 --> 00:26:21,237 to come into and things like that. My answer is actually yes. 572 00:26:21,320 --> 00:26:22,877 I think that 那一手棋改变了人们对围棋的理解，现在玩围棋的人比以往任何时候都多， 573 00:26:22,960 --> 00:26:26,397 there is like a huge market um that we are just not going to be able to address 574 00:26:26,481 --> 00:26:29,517 and we need much more energy momentum 它激发了人们更多的兴趣。我认为我们也会看到同样的现象。所以深度是 575 00:26:29,600 --> 00:26:32,827 there. Um but a second thing is that it also misses the fact that intelligence 576 00:26:32,910 --> 00:26:34,270 is not a undimensional thing right 577 00:26:34,353 --> 00:26:36,477 that 永无止境的。科学领域的探索能有多深？有人曾觉得：“我们已经发现了所有的物理 规律，已经结束了。” 578 00:26:36,560 --> 00:26:40,461 that If you really zoom in, being good at different domains is something where even 579 00:26:40,544 --> 00:26:43,197 if you you have a lot of raw 但我不认为那是我们要迎接的未来。我们要在解决一个谜题后， 580 00:26:43,280 --> 00:26:46,824 intelligence, getting good if you've never practiced like you've never actually done 581 00:26:46,907 --> 00:26:49,372 a pitch or something like you're not going to be good at it 582 00:26:49,455 --> 00:26:50,797 your 顺藤摸瓜解开另外十个谜题。所以，我认为还有很多事情要做， 583 00:26:50,880 --> 00:26:53,831 first time, right? And that there's lots of different you've never operated 584 00:26:53,914 --> 00:26:57,149 a spreadsheet, right? You're not going to be able to succeed at doing some complex 585 00:26:57,232 --> 00:26:57,837 不同的公司之间有巨大的差异化空间。 586 00:26:57,920 --> 00:27:01,614 modeling. And so I think that there is something that we have been internalizing 587 00:27:01,698 --> 00:27:04,637 which is that we look across different industries and 整个游戏才刚刚开始。 588 00:27:04,720 --> 00:27:06,476 different domains and we have to prioritize. 589 00:27:06,559 --> 00:27:09,068 We can't possibly be great at every single area at once. There 590 00:27:09,151 --> 00:27:11,117 is 无论是市场规模的极速扩大，还是我们自身营收的攀升，我觉得没有谁能预料到未来的斜 率会有多陡峭。 591 00:27:11,200 --> 00:27:14,594 definitely a lot of like, hey, you just get the general intelligence up and it'll 592 00:27:14,677 --> 00:27:18,157 experience a lot of these things, but to really become a domain expert, to 这其实非常有趣。 593 00:27:18,240 --> 00:27:21,534 really be that PhD, and to really be something that can help push forward 594 00:27:21,617 --> 00:27:24,957 the ambition of a field, like that's hard. 前天我们刚刚宣布了化学领域的新成果，能够合成新的、更好的反应。 595 00:27:25,040 --> 00:27:28,177 And and by the way, one thing I also want to say is that I think understanding what 596 00:27:28,260 --> 00:27:31,203 happens when you successfully do that. I think that having a good mental model 597 00:27:31,287 --> 00:27:32,717 of that is 我们在化学领域发表了成果，比如合成更高效的新化学反应，且 598 00:27:32,800 --> 00:27:34,998 important, which is you look at something like rewind to AlphaGo, 599 00:27:35,082 --> 00:27:36,157 right? 比如我们在化学领域发表了成果，合成了更好的新反应，且 600 00:27:36,240 --> 00:27:40,450 you remember move 37, this move that like changed people's understanding of the game 601 00:27:40,533 --> 00:27:43,517 and then now more people play 几乎未经特定领域优化。深耕一个领域能带来巨变，我们还没开始。 602 00:27:43,600 --> 00:27:46,155 go than ever, right? And that it actually inspired people to do even more. 603 00:27:46,238 --> 00:27:48,543 I think we're just going to see that. And so I think that the depth 604 00:27:48,626 --> 00:27:50,397 is 现在玩围棋的人比以往任何时候都多，它激发了人们更多的兴趣。我认为我们也会看到同 样的现象。所以深度是 605 00:27:50,480 --> 00:27:52,946 never going to stop, right? How deep can you go on science, right? 606 00:27:53,029 --> 00:27:55,263 Like I I think that people have thought sometimes that, hey, 607 00:27:55,346 --> 00:27:58,237 we found out all the physics, it's 永无止境的。科学领域的探索能有多深？有人曾觉得：“我们已经发现了所有的物理规律，已经 608 00:27:58,320 --> 00:28:01,116 all good. We're all done. And I don't think that that's the future we're signed 609 00:28:01,199 --> 00:28:03,594 up for. I think we're signed up for one where we're got to keep find 610 00:28:03,677 --> 00:28:04,797 结束了。”但我不认为那是我们要迎接的未来。我们要在解决一个谜题后， 611 00:28:04,880 --> 00:28:07,391 every time you unlock one mystery, every every time you solve one mystery, 612 00:28:07,474 --> 00:28:10,055 it unlocks like 10 more, right? So, I think that there's just going to be so 613 00:28:10,138 --> 00:28:11,037 顺藤摸瓜解开另外十个谜题。所以，我认为还有很多事情要做， 614 00:28:11,120 --> 00:28:13,541 much more to do and tons of room for differentiation across different 615 00:28:13,624 --> 00:28:14,557 companies. 不同的公司之间有巨大的差异化空间。 616 00:28:14,640 --> 00:28:17,856 So, I think I'm reading you right and that your belief is um maybe there's a way 617 00:28:17,939 --> 00:28:20,660 that everybody can scale up these models, but ultimately the company 618 00:28:20,743 --> 00:28:22,557 with 所以，我认为我的理解是对的。你的信念是，也许每个人都有办法扩展这些模型，但最终拥有 619 00:28:22,640 --> 00:28:24,770 the most compute is going to win. And you know, 620 00:28:24,853 --> 00:28:28,019 we spoke a couple months ago and you had mentioned that like you were 621 00:28:28,103 --> 00:28:29,197 最多算力的公司才会赢。你记得几个月前我们聊过，你说 622 00:28:29,280 --> 00:28:31,885 asked internally, how much compute should we buy? And you said, "All of it." 623 00:28:31,969 --> 00:28:34,397 And they said, "No, really, how much should we buy?" And you said, "No, 624 00:28:34,480 --> 00:28:36,237 公司内部有人问你：“我们该买多少算力？”你回答说：“全部。”他们又问：“说真的，买多 少？”你回答：“不， 625 00:28:36,320 --> 00:28:39,602 buy all of it." And and OpenAI is definitely the leader in in buying 626 00:28:39,686 --> 00:28:43,117 compute. I mean, we see the the the 全部买下。”而 OpenAI 确实是购买算力的领头羊。我是说，我们看到 627 00:28:43,200 --> 00:28:45,938 money going out. Obviously, a lot of money coming in through investment and and now 628 00:28:46,021 --> 00:28:48,045 you you're you've built a business with customers, but there's 629 00:28:48,128 --> 00:28:49,677 a 源源不断的资金流出。显然，也有大量的资金通过投资流入，并且你们现在也建立了自己的 客户业务，但 630 00:28:49,760 --> 00:28:52,548 lot of money going out. Do you ever wonder, hey, are like, 631 00:28:52,631 --> 00:28:55,617 do you ever wonder maybe we're not going to be able to pay all 632 00:28:55,700 --> 00:28:57,597 this 你是否曾担心过，作为一个全新的品类，我们可能会无法收回这些巨大的投入？ 633 00:28:57,680 --> 00:28:59,924 money back because it's a brand new category? Well, 634 00:29:00,007 --> 00:29:03,437 the way that I look at it is on the fundamentals, right? You 我看待这个问题是从基本面出发的。 635 00:29:03,520 --> 00:29:07,937 you need to really look at the fact that the way that compute goes is that it's 636 00:29:08,021 --> 00:29:10,957 multiple years out before compute 你需要看到算力的建设需要提前数年规划。 637 00:29:11,040 --> 00:29:13,818 actually arrives, right? Depending on exactly what you're doing. 638 00:29:13,901 --> 00:29:17,439 For example, we've been investing in our own chip program now for multiple years. 639 00:29:17,522 --> 00:29:18,557 And 比如，我们的自研芯片项目已经投资了好几年， 640 00:29:18,640 --> 00:29:22,985 super exciting progress like you know, we'll we'll we'll have more to announce uh 641 00:29:23,069 --> 00:29:26,157 actually pretty soon. Um but the fact 进展非常令人兴奋，很快就会宣布一些消息。 642 00:29:26,240 --> 00:29:28,173 that we're able to do that is something very unique, right? 643 00:29:28,256 --> 00:29:30,701 really think about the full vertical integration of the supply chain and I 644 00:29:30,784 --> 00:29:32,717 think that the world 但能做到这一点是非常独特的，这需要对供应链进行深度的垂直整合。我们正走向一个 645 00:29:32,800 --> 00:29:35,807 we're heading towards is one where again there's just not going to be enough 646 00:29:35,890 --> 00:29:38,978 compute in the world to satisfy all the demand and we see this very concretely 647 00:29:39,061 --> 00:29:40,157 算力依然供不应求的世界，我们能非常具体地看到这一点，无论是 648 00:29:40,240 --> 00:29:42,957 like you look at the exponential I mean rewind to the exponential of chatbt um 649 00:29:43,040 --> 00:29:45,722 look at the exponentials we're on now uh you think about the problems that we 650 00:29:45,805 --> 00:29:47,517 ChatGPT 的指数级增长，还是我们现在的增长曲线。想想我们能够解决的问题，这其实非常有趣，对吧？ 651 00:29:47,600 --> 00:29:51,672 that we are able to solve you know it's it's actually kind of interesting 652 00:29:51,755 --> 00:29:53,437 前天我们刚刚宣布了化学领域的新成果，能够合成新的、更好的反应。 653 00:29:53,520 --> 00:29:57,178 that yeah we just yesterday announced um it was two days ago announced a new 654 00:29:57,262 --> 00:29:58,557 我们在化学领域发表了成果，比如合成更高效的新化学反应，且 655 00:29:58,640 --> 00:30:02,672 result in uh uh basically chemistry and being able to synthesize you know new new 656 00:30:02,756 --> 00:30:06,077 improved reaction and all of this is 几乎未经特定领域优化。深耕一个领域能带来巨变，我们还没开始。 657 00:30:06,160 --> 00:30:08,807 without much attention you know the thing I just said of if you go deep in a domain 658 00:30:08,890 --> 00:30:11,076 you can really transform it and we're not even scratching the surface 659 00:30:11,160 --> 00:30:12,557 实体经济规模极其庞大。这从我们的增长、人们的付费意愿、以及整个行业的膨胀中可以看得很 清楚。 660 00:30:12,640 --> 00:30:15,929 yet and so the way to think about is the economy is so massive right and we see it 661 00:30:16,013 --> 00:30:18,808 very concretely in terms of our own growth in terms of what people are 662 00:30:18,892 --> 00:30:19,837 所以我思考最多的是：如何跟上这种需求，以及如何提供 663 00:30:19,920 --> 00:30:22,957 willing to pay and kind of the size and growth of this whole industry and so I 664 00:30:23,040 --> 00:30:25,437 think that the thing that I think about 能支持在实体经济中大家想做的所有工作的智能？ 665 00:30:25,520 --> 00:30:28,647 the most is how do we meet the demand and how do you actually have something 666 00:30:28,731 --> 00:30:31,858 that can help support all of the work that people want to do in the economy? 667 00:30:31,942 --> 00:30:32,957 这是一个非常庞大的命题，我想我们都还没有完全理解它。 668 00:30:33,040 --> 00:30:35,638 And I think that is such a vast thing. I don't think any of us have internalized it 669 00:30:35,722 --> 00:30:38,255 yet. Hi everyone, Alex Canitz here. I want to tell you about a documentary 大家好，我是 670 00:30:38,338 --> 00:30:39,677 Alex Kantrowitz。我想告诉大家一部我和 Gravity 合作制作的纪录片， 671 00:30:39,760 --> 00:30:42,599 I've made with Gravity to explore the future of AI agent security. 672 00:30:42,682 --> 00:30:45,211 To find out if we're truly ready for autonomous 探讨 AI Agent 673 00:30:45,295 --> 00:30:46,717 安全的未来。为了了解我们是否真正做好了迎接自主 Agent 的准备， 674 00:30:46,800 --> 00:30:49,182 agents, I sat down with MIT professor Romesh Rosker, 675 00:30:49,265 --> 00:30:52,974 former White House CIO Terresa Payton, Michelin's group chief 我采访了 MIT 教授 Ramesh 676 00:30:53,058 --> 00:30:54,397 Raskar、前白宫 CIO Theresa Payton、 677 00:30:54,480 --> 00:30:57,362 data and AI officer Ambbea Roger Gopal, and Sharon Guy, 678 00:30:57,445 --> 00:31:01,837 a former executive at Alibaba. 米其林集团首席数据与 AI 官 Ameenah Gopaul，以及前阿里巴巴高管 Sharon Guy。 679 00:31:01,920 --> 00:31:05,396 They each offer unique insights into this evolving landscape. 680 00:31:05,479 --> 00:31:09,597 We conclude with Rory Blendell, CEO of Gravity, to 他们对这个不断演变的领域提出了独特的见解。 681 00:31:09,680 --> 00:31:12,078 discuss the path forward. with gravity leading the way. 682 00:31:12,161 --> 00:31:15,823 Join us on this journey. You can watch the full documentary at the link in the show 683 00:31:15,906 --> 00:31:21,890 最后，我们和 Gravity 的 CEO Rory Blundell notes. 探讨了未来的发展道路，Gravity 将在其中处于领先地位。 684 00:31:27,120 --> 00:31:29,317 Yeah, but if I may, there is a price war brewing. I mean, 685 00:31:29,400 --> 00:31:32,637 at least that's according to the reports. It's great to have you here to talk about 686 00:31:32,720 --> 00:31:33,917 it. The Wall 欢迎观看这部纪录片，链接在节目介绍中。 687 00:31:34,000 --> 00:31:36,852 Street Journal recently had a report that an upcoming OpenAI model might have 688 00:31:36,936 --> 00:31:38,797 是的，但如果可以的话，目前确实在酝酿价格战。至少报道是这么说的。Greg，很高兴今天 能在这里跟你讨论。 689 00:31:38,880 --> 00:31:43,261 significant price cuts. And so again, like how can you know if it requires so 690 00:31:43,345 --> 00:31:45,117 《华尔街日报》最近报道说，即将推出的 OpenAI 模型可能会有 691 00:31:45,200 --> 00:31:48,996 much resources to serve this demand and it is growing demand um in an environment 692 00:31:49,079 --> 00:31:51,917 where there might be price 大幅度降价。所以，既然提供这种需求需要如此多的资源，且需求还在增长， 693 00:31:52,000 --> 00:31:54,157 cuts, how do you make that math work? 而在价格可能会下降的环境下， 694 00:31:54,240 --> 00:31:56,705 Well, again, I look at it from a different angle. 695 00:31:56,788 --> 00:32:00,397 So if you look at the whole history of what we've done, we 你是怎么把这笔账算清的？ 696 00:32:00,480 --> 00:32:03,419 actually have been increasing the intelligence cutting price right for 697 00:32:03,503 --> 00:32:06,744 a fixed amount of intelligence and people somehow just like like the Japanese 698 00:32:06,828 --> 00:32:07,997 好吧，我同样会从不同的角度来看待。如果你看我们做过的事情， 699 00:32:08,080 --> 00:32:10,857 paradox just keeps happening. And so I think frontier intelligence will always be 700 00:32:10,941 --> 00:32:13,718 something that is going to be you know it's always going to be the priciest thing 701 00:32:13,801 --> 00:32:15,837 but I think that a year 实际上我们一直在提升智能水平并降低价格，也就是固定智能水平的成本在下降， 702 00:32:15,920 --> 00:32:19,123 from now that level of intelligence is going to feel pretty mundane and like you 703 00:32:19,206 --> 00:32:21,711 know going to be much more available. Um, and I think that that 704 00:32:21,795 --> 00:32:23,437 the 所以我认为前沿智能永远是最贵的，但一年之后，今天这种高端的智能就会变得平常， 705 00:32:23,520 --> 00:32:27,614 the world that we're in is one where people are starting to really think about 706 00:32:27,697 --> 00:32:29,917 value. And it's actually been a 且变得更加普及。另外， 707 00:32:30,000 --> 00:32:33,096 very interesting shift where over the past, you know, first quarter, 708 00:32:33,180 --> 00:32:36,557 maybe up until now, people have just been like 我们所处的世界，大家正开始真正思考其价值。这其实是一个 709 00:32:36,640 --> 00:32:39,356 this AI agent stuff, it's all new. We need to bring into our enterprise. Like, 710 00:32:39,440 --> 00:32:41,905 we don't want to be left behind. How do we be part of this this future? 711 00:32:41,988 --> 00:32:43,197 And now 非常有趣的转变。在过去的第一季度，甚至在此之前，大家觉得 712 00:32:43,280 --> 00:32:45,592 people are like, okay, like let's make sure this actually delivering ROI 713 00:32:45,675 --> 00:32:47,787 and value. And I actually think that's a great place to be, right? 714 00:32:47,870 --> 00:32:49,517 because people AI Agent 这一切太新了，我们必须引入企业内部，不想被落下。而现在， 715 00:32:49,600 --> 00:32:52,397 are asking the right questions. And I hear this I I had some customer meetings today 716 00:32:52,480 --> 00:32:54,248 where people were saying exactly this. They were like, 717 00:32:54,331 --> 00:32:55,997 "How can we have 因为大家在问正确的问题。我今天参加了几个客户会议，他们问的正是这个： 718 00:32:56,080 --> 00:32:59,065 even just like good spend controls? How can we have observability?" 719 00:32:59,148 --> 00:33:02,637 And I I think we literally today just released “我们如何实施更好的支出控制？如何实现可观测性？”刚好我们今天 720 00:33:02,720 --> 00:33:05,117 spend controls. So, you know, it's like Okay. 发布了支出控制功能，这很巧。 721 00:33:05,200 --> 00:33:08,206 Exactly. We're we are really investing hard in enterprise readiness and the tools 722 00:33:08,290 --> 00:33:10,877 that our customers are telling us 没错。我们在企业级功能上投入了巨大精力，提供客户告诉我们他们所需的工具。 723 00:33:10,960 --> 00:33:15,187 that they need. And I think that that for me is the shift that we've also been going 724 00:33:15,270 --> 00:33:18,317 through as a company is really not 这种转变也反映了我们作为一家公司的变化。我们不再只是 725 00:33:18,400 --> 00:33:20,790 just thinking about hey we're just going to release models and you know have 726 00:33:20,873 --> 00:33:22,742 a model really thinking about the endto end of the business. 727 00:33:22,826 --> 00:33:24,077 How do we bring 发布模型，而是从端到端的业务角度去思考：如何把这些 728 00:33:24,160 --> 00:33:28,194 this into solving real problems for real customers and that is happening so 729 00:33:28,278 --> 00:33:29,677 模型带入客户的实际场景，解决真实问题。这正在每一个行业 730 00:33:29,760 --> 00:33:33,795 quickly across every single industry and the number of different companies 731 00:33:33,878 --> 00:33:37,357 that still feel like they're wrapping their 迅速发生。许多公司还在摸索如何最好地利用 732 00:33:37,440 --> 00:33:40,878 mind around how to best make use of these models we're learning at the same time. 733 00:33:40,961 --> 00:33:43,660 I think it's just so early in this whole game to me the the like 734 00:33:43,743 --> 00:33:46,891 the 这些模型，我们也在一同学习。现在还处于非常早期。 absolute size of the market growing so quickly, 735 00:33:46,974 --> 00:33:50,491 our own revenue ramp growing so quickly. I think it's still just like none of us are 736 00:33:50,574 --> 00:33:53,197 anticipating how steep 这其实是一个非常早期的阶段， that's all going to go. 看未来形势如何。 737 00:33:53,280 --> 00:33:57,697 Are you going to cut prices? 那么，你们会降价吗？ So again, the answer is always yes, right? 738 00:33:57,780 --> 00:34:01,108 But it's about like I think that what's going to keep happening is that we're 739 00:34:01,191 --> 00:34:03,677 going to have frontier models. Um 答案自然是会。但正如我说的，前沿模型会不断推出。 740 00:34:03,760 --> 00:34:06,231 I don't think there's going to be like a massive shift in in the short term. 741 00:34:06,314 --> 00:34:08,516 I don't think that that is the kind of thing that's going to happen. 742 00:34:08,599 --> 00:34:11,037 But I think the thing you should anticipate is 短期内前沿模型不会有巨幅降价，但我认为大家可以预期的是， 743 00:34:11,120 --> 00:34:14,438 that over a year-long time horizon to get to today's level of intelligence 744 00:34:14,521 --> 00:34:17,517 that feels very premier, it's going to 一年之内，今天看起来非常顶尖的智能水平，会变得无比廉价。 745 00:34:17,600 --> 00:34:20,186 be much cheaper. But there's going to be a new thing that is going to be so much 746 00:34:20,270 --> 00:34:22,589 better and you're going to be like, why would I ever use this other one, 747 00:34:22,672 --> 00:34:23,757 right? 但到那时，又会有更强的新模型，你会想：“为什么用旧的？” 748 00:34:23,840 --> 00:34:26,397 It's just it's just how it's always going to be. 这其实一直都是这样发展的。 749 00:34:26,480 --> 00:34:29,315 So Sadia Nadell has had some interesting tweets and interviews recently. 750 00:34:29,398 --> 00:34:32,557 He recently said, "The model is becoming a 微软 CEO Satya Nadella 最近有一些很有意思的推文和采访。 751 00:34:32,640 --> 00:34:34,207 commodity and the valuable asset is company," 752 00:34:34,290 --> 00:34:36,957 or this might be a paraphrase, "The valuable asset is a company specific AI 753 00:34:37,040 --> 00:34:38,900 system that continually 他说：“模型正在成为商品，最有价值的资产是企业专属的 AI 754 00:34:38,983 --> 00:34:42,717 系统，它能不断基于你的数据进行学习。” learns from your data." Um, what do you think about that? 755 00:34:42,800 --> 00:34:46,877 And is it weird to be competing with Microsoft now? 你怎么看这个观点？现在和微软竞争会感觉很奇怪吗？ 756 00:34:46,960 --> 00:34:51,143 Well, look, I don't think that there's any layer of the stack here that is going 757 00:34:51,227 --> 00:34:53,917 to just kind of be removed from 好吧，我不认为技术栈中的任何一层会直接从 758 00:34:54,000 --> 00:34:56,797 the value chain. I think that these things multiplied together. 759 00:34:56,880 --> 00:35:00,317 And if you think about the most the base layer of 价值链中消失。我认为它们是相辅相成的。如果你看最底层的 760 00:35:00,400 --> 00:35:04,312 compute right that is something where it's just like no compute no AI and to some 761 00:35:04,395 --> 00:35:06,877 extent you could say oh 算力，也就是没有算力就没有 AI。在某种程度上，你可以说 762 00:35:06,960 --> 00:35:09,926 comput is commoditized it's just flops who cares about it but in reality like you 763 00:35:10,009 --> 00:35:12,448 look at you look at today's chip stocks you look at the uh you know 764 00:35:12,532 --> 00:35:13,917 算力已经被商品化了，不过是算力服务，谁在乎呢？但实际上，看看今天的芯片股，看看 765 00:35:14,000 --> 00:35:16,701 people who are selling compute kind of what what is what the market is valuing 766 00:35:16,785 --> 00:35:19,414 people at and they see that there's a fundamental asset here that is just so 767 00:35:19,498 --> 00:35:20,557 算力服务商，看看市场对它们的估值，就会发现这其实是一个至关重要的 768 00:35:20,640 --> 00:35:23,802 critical and I think that is because it is a revenue center it is something 769 00:35:23,885 --> 00:35:27,004 that anyone who's building AI has to rely on 基本资产。我认为这是因为它是一个核心收入源，任何构建 AI 770 00:35:27,087 --> 00:35:29,806 的公司都必须依赖它。 and that there's a bunch of very interesting dynamics in terms of 771 00:35:29,889 --> 00:35:32,576 the efficiencies that you can squeeze out and the margins, all these things. 772 00:35:32,659 --> 00:35:33,597 But 而且这里面涉及很多关于提升效率和毛利的有趣动态。 773 00:35:33,680 --> 00:35:36,304 fundamentally, even though it's like you can kind of squint it and say it's 774 00:35:36,388 --> 00:35:38,940 commoditized, it's not. It's it's it's not that that the value goes away. 775 00:35:39,023 --> 00:35:40,637 It's 本质上，哪怕你可以把它看作是被“商品化”的东西，它其实并不是。它的价值不会凭空消失， 776 00:35:40,720 --> 00:35:43,475 not that the margins go away. It's like something that the market will reward 777 00:35:43,558 --> 00:35:46,276 because it has fundamental value and that the importance of it's going to go 778 00:35:46,359 --> 00:35:47,677 毛利也不会不见。市场会给予它回报，因为它具有底层价值，且重要性会随时间增加。 779 00:35:47,760 --> 00:35:50,725 up over time. You can see that with some of the prices that people are paying 780 00:35:50,808 --> 00:35:53,773 for H100s, right? Hoppers are, you know, kind of, you know, not not obsolete, 781 00:35:53,856 --> 00:35:55,357 看看人们为 H100 支付的价格就知道了。虽然 Hopper 已经是上一代芯片， 782 00:35:55,440 --> 00:35:59,090 right? They're they're they're a a previous gen chip and uh nor in any normal 783 00:35:59,173 --> 00:36:01,708 situation where you're not totally supply constrained, 784 00:36:01,792 --> 00:36:03,357 no one would 如果供求平衡，在正常情况下可能没人会去买， 785 00:36:03,440 --> 00:36:06,229 be buying them. But instead, the market prices are up relative to to where they 786 00:36:06,313 --> 00:36:09,066 were before. So there's this inversion that's happening and again I think it's 787 00:36:09,149 --> 00:36:10,157 但现在它的价格不降反升。所以市场发生了一种倒挂，而且我认为这 788 00:36:10,240 --> 00:36:14,344 going to keep happening where because everyone has this avalanche of demand 789 00:36:14,427 --> 00:36:17,917 that you're going to see prices and 会继续发生。因为每个人都有排山倒海的需求，你会看到价格和 790 00:36:18,000 --> 00:36:21,207 margins and all of these things AC continuing to increase at various levels 791 00:36:21,290 --> 00:36:24,717 of the stack. And I think the same kind of applies for 毛利在技术栈的各个层级继续上涨。我认为这同样适用于 792 00:36:24,800 --> 00:36:29,180 models where the models themselves are also again they're not there's there's 793 00:36:29,264 --> 00:36:32,717 a lot of competition there and I think 大模型本身。虽然大模型面临非常多的竞争，而且 794 00:36:32,800 --> 00:36:34,905 that's very good. I think it's good for the enterprise. 795 00:36:34,988 --> 00:36:36,615 I think it's good for customers, consumers. 796 00:36:36,699 --> 00:36:39,997 Um but I think that there's a lot of areas where for 这很好，对企业、对客户、对消费者都大有裨益。但也有很多领域，比如 797 00:36:40,080 --> 00:36:42,650 example our models have always been the sort of smartest ones, right? 798 00:36:42,733 --> 00:36:45,072 The ones that are able to solve these incredibly hard problems. 799 00:36:45,156 --> 00:36:46,957 I think we're just 我们的模型始终是最聪明的，最能解决极其困难的任务。我们才刚刚 800 00:36:47,040 --> 00:36:49,737 starting to reach a phase where you're going to see the transformative impact 801 00:36:49,821 --> 00:36:52,157 from that, right? It's like if we're 开始迈入一个能看到大模型带来颠覆性影响的阶段。比如，如果我们 802 00:36:52,240 --> 00:36:55,413 really able to speed up science through models, the smarter the model, 803 00:36:55,497 --> 00:36:58,717 the faster it's going to go. And it's very 真能通过模型加速科学研究，模型越聪明，研究进展就越快。这 804 00:36:58,800 --> 00:37:01,589 very different from a model that has a conversational interface that you're able 805 00:37:01,673 --> 00:37:03,349 to, you know, is able to book your travel, right? 806 00:37:03,432 --> 00:37:05,037 Or organize your 和用一个对话模型帮你订机票、管理一下日历，是完全不同的维度。 807 00:37:05,120 --> 00:37:08,107 calendar. So that's also a dimension I think we're going to do a very good job 808 00:37:08,190 --> 00:37:10,115 in. But I'm just saying it's it's a different area. 809 00:37:10,198 --> 00:37:12,477 Um and then I think that 因此在这个维度上，我们也能做得非常好。但这属于不同的领域。另外，我认为 810 00:37:12,560 --> 00:37:16,504 the question of well how do you actually connect the intelligence to your own 811 00:37:16,588 --> 00:37:19,277 customers right to real value to you 如何把智能转化为客户的实际价值， 812 00:37:19,360 --> 00:37:22,713 have all these enterprises that have built incredible businesses in different domains 813 00:37:22,797 --> 00:37:25,462 and it's a huge thing and it's not something where if you don't have 814 00:37:25,546 --> 00:37:26,797 各个企业在不同领域积累了宝贵的业务经验，而没有这些经验是做不好的。 815 00:37:26,880 --> 00:37:29,768 domain expertise that you're just going to be able to do right and part of it is 816 00:37:29,851 --> 00:37:32,554 that you need you think about regulated industries you think about any area 817 00:37:32,637 --> 00:37:33,037 比如在受规管行业，或者任何 818 00:37:33,120 --> 00:37:36,279 where there's like you know think about education where you have a parent you have 819 00:37:36,363 --> 00:37:39,008 a teacher you have student you have these different parties that need 820 00:37:39,091 --> 00:37:42,610 to 需要家长、老师、学生多方深度协同的教育领域。 interact in very thoughtful ways for all of these areas, 821 00:37:42,693 --> 00:37:45,410 all of these domains that there's a lot of value to be built 822 00:37:45,493 --> 00:37:46,717 在所有场景下，打通工作流并合理编排模型，具有巨大的价值。 823 00:37:46,800 --> 00:37:49,785 by being in that area and thinking about how the workflow should work, 824 00:37:49,868 --> 00:37:53,117 how these models should be orchestrated. And so I 所以，我认为我们需要作为一个完整的生态系统共同努力， 825 00:37:53,200 --> 00:37:56,631 I really think that there's more than enough to go around and I think that we 826 00:37:56,715 --> 00:37:59,872 have to work together as a whole ecosystem in order to deliver the kind 827 00:37:59,955 --> 00:38:00,557 才能释放出这些系统的全部潜能。 828 00:38:00,640 --> 00:38:02,797 of value that I think is possible from these systems. 我真的认为大家有足够的成长空间， 829 00:38:02,880 --> 00:38:04,783 Okay, just to go back to the SA point one more time, 830 00:38:04,866 --> 00:38:07,647 uh he's called models a commodity. He's trying to build his own 好的，回到 Satya 831 00:38:07,731 --> 00:38:08,717 的观点。他把模型称为“商品”，并努力构建微软的前沿智能。 832 00:38:08,800 --> 00:38:11,131 frontier intelligence. He's telling potentially your customers, hey, 833 00:38:11,215 --> 00:38:14,114 you got to come work with us because we're going to help build these loops that will 834 00:38:14,197 --> 00:38:15,357 他告诉你们的客户说，要来跟微软合作，因为他们能构建基于数据的学习闭环。 835 00:38:15,440 --> 00:38:19,088 learn from your data. Um, he's got access to your IP, I think, till 2032. 836 00:38:19,171 --> 00:38:20,877 此外，据说微软到 2032 年前都可以访问 OpenAI 的知识产权。 837 00:38:20,960 --> 00:38:24,077 So, how does it make you feel to hear this coming from Saudia? 听到 Satya 这么说，你有什么感受？ 838 00:38:24,160 --> 00:38:27,559 Look, I think that the most important thing that is happening right now is 839 00:38:27,642 --> 00:38:31,277 the usage of AI in the economy to really 我觉得现在最重要的事情，是将 AI 真正带入实体经济并完成转型，造福每个人。 840 00:38:31,360 --> 00:38:33,379 transform the economy and to uplift everyone. And so, 841 00:38:33,463 --> 00:38:36,157 I think that that is something that I'm really focused on. 这才是我们关注的焦点。 842 00:38:36,240 --> 00:38:38,829 And the more that people are trying to make that happen, 843 00:38:38,912 --> 00:38:41,597 I think that that's better for everyone. 越多的人为此努力，对每个人就越好。 844 00:38:41,680 --> 00:38:45,277 Um, GPT 5.6 is rumored to be on its way. 网上有传闻说 GPT-5.6 即将推出， 845 00:38:45,360 --> 00:38:48,623 Supposed to be this just a Twitter rumor, but I'm going to read it to you. 846 00:38:48,706 --> 00:38:51,517 Um, always the best 虽然这只是 Twitter 上的小道消息，但我还是想读给你听。毕竟小道消息总是最精彩的。 847 00:38:51,600 --> 00:38:55,339 rumors. Three times cheaper than Fable up to 1.5 million token context. 848 00:38:55,422 --> 00:38:57,277 Uh, 比如“价格比前代便宜三倍，多达 150 万 token 上下文， 849 00:38:57,360 --> 00:39:00,535 stronger agentic coding workflows. Um, how much of that is true? 850 00:39:00,618 --> 00:39:02,317 What should we 拥有更强大的 Agent 编程工作流”。 851 00:39:02,400 --> 00:39:06,637 expect for GPT 5.6? 这里面有多少是真的？我们对 GPT-5.6 可以抱有什么样的期待？ 852 00:39:06,720 --> 00:39:10,485 I mean, look, you should always expect better, faster, smarter, 853 00:39:10,569 --> 00:39:13,357 the whole thing. 我只能说，你可以一直期待更好、更快、更聪明的产品，仅此而已。 854 00:39:13,440 --> 00:39:17,517 So, everything confirmed. 所以你是全部默认了。 855 00:39:17,600 --> 00:39:20,250 Definitely believe everything you read on Twitter. Yeah, maybe not. 856 00:39:20,334 --> 00:39:21,597 千万别相信你在 Twitter 上看到的所有东西，大部分都是假的。 857 00:39:21,680 --> 00:39:24,989 That has actually been a source of problems in my personal life. Um, 858 00:39:25,073 --> 00:39:26,237 okay. 确实，这在我的现实生活中也造成过困扰。 859 00:39:26,320 --> 00:39:29,147 So, uh, I want to end on health. Um, you brought it up a couple times. 860 00:39:29,231 --> 00:39:31,850 You actually had a question in the audience about it earlier. Um, 861 00:39:31,934 --> 00:39:33,597 you know, 好的，我们以健康话题结束。你刚才提到，有时我们读到一些媒体报道， 862 00:39:33,680 --> 00:39:36,941 sometimes there's a story and you read it and you say to yourself, 863 00:39:37,024 --> 00:39:39,423 I know this person is speaking to the media and I 864 00:39:39,507 --> 00:39:41,197 听起来似乎是真的，但总觉得里面漏掉了一些细节，不确定这是否代表了未来。 865 00:39:41,280 --> 00:39:43,854 know that what they're saying sounds like maybe it's true. Um, 866 00:39:43,937 --> 00:39:47,497 but there's something wrong with the story and we're 我最近读到了几篇这样的报道。其中一篇是关于你的朋友，GitLab 867 00:39:47,580 --> 00:39:48,397 首席执行官 Sid Sijbrandij。 868 00:39:48,480 --> 00:39:51,357 not going to see more of it. Um, and I've read a couple of those recently. 869 00:39:51,440 --> 00:39:52,477 他患了癌症，于是做了所有能做的诊断测试，收集了大量数据， 870 00:39:52,560 --> 00:39:56,702 Um, one is I think is it your friend the GitLab CEO um, Sid Sberage? 871 00:39:56,786 --> 00:39:59,437 He had um he 并在一些专门开发的工具辅助下，把这些数据输入 ChatGPT。 872 00:39:59,520 --> 00:40:03,959 he got cancer and used uh he got all the diagnostic testing uh he could have so 873 00:40:04,043 --> 00:40:05,677 虽然不知道“治愈”这个词是否妥帖，但在一定程度上击退了癌症。 874 00:40:05,760 --> 00:40:08,555 just went out and tested like crazy and fed that data into chatt with 875 00:40:08,639 --> 00:40:11,893 the assistance of some people who had built 还有个故事是关于澳大利亚一只叫 Rosie 的狗，大家听说过 Rosie 876 00:40:11,976 --> 00:40:12,477 吗？这是一个非常神奇的故事。 877 00:40:12,560 --> 00:40:15,979 purpose-built application for it and was able I don't know if cure is the right 878 00:40:16,062 --> 00:40:19,437 word but to beat back the cancer to a 有主人对患癌的 Rosie 进行了活检，把基因突变数据输入 AlphaFold， 879 00:40:19,520 --> 00:40:22,516 degree. There was also this dog Rosie the dog in Australia. 880 00:40:22,600 --> 00:40:26,797 You guys heard of Rosie? like the craziest story where 并在 AI 协助下设计了一款 mRNA 疫苗接种给它， 881 00:40:26,880 --> 00:40:31,658 this guy I'm gonna get some detail wrong but a guy um biopsied his dog which had 882 00:40:31,742 --> 00:40:33,117 最终肿瘤缩小了，狗狗又能重新跳过桌子。当我们思考 883 00:40:33,200 --> 00:40:38,174 cancer um ran the mutations across of across Alphafold and then was able to AI 884 00:40:38,258 --> 00:40:39,277 医疗的未来时，这到底是极少数特例， 885 00:40:39,360 --> 00:40:43,846 design an mRNA vaccine that he injected into the dog um with the assistance 886 00:40:43,929 --> 00:40:47,197 of chatbots to build this thing which ended 还是在未来会成为常态？ 887 00:40:47,280 --> 00:40:50,779 up being able to jump over tables again and the tumor shrunk. Um, 888 00:40:50,862 --> 00:40:52,157 when we think 这会成为未来的常态吗？ 889 00:40:52,240 --> 00:40:56,947 about the future of AI and health, um, help us sort out the truth with 890 00:40:57,030 --> 00:41:01,154 this 绝对会成为常态。 question. Are these a couple of outliers that made good headlines, 891 00:41:01,237 --> 00:41:04,119 but there was something about the story we weren't hearing, 892 00:41:04,202 --> 00:41:07,357 or is this going to become 绝对会。 standard in the future? 这会成为未来的常态吗？ 893 00:41:07,440 --> 00:41:09,037 Absolutely going to become standard. 绝对会成为常态。 894 00:41:09,120 --> 00:41:13,312 Absolutely. And it's I I personally have a number of friends who have done very 895 00:41:13,395 --> 00:41:14,557 绝对会。我个人身边就有很多朋友在做类似的事情， 896 00:41:14,640 --> 00:41:20,797 similar things of get the data right your health diagnostics 也就是获取自己的健康诊断数据， 897 00:41:20,880 --> 00:41:24,525 and use codecs right use these models to get insights from them and I think 898 00:41:24,609 --> 00:41:27,757 that there are many people like I think that 并使用 Codex 或大模型来获取洞察。 899 00:41:27,840 --> 00:41:33,058 there's about 230 million people each week who use chat GBT for health queries 900 00:41:33,141 --> 00:41:35,437 事实上，每周大约有 2.3 亿人在使用 ChatGPT 进行健康咨询， 901 00:41:35,520 --> 00:41:38,668 right and that's been that's this is like a staggering scale Right. 902 00:41:38,752 --> 00:41:41,997 And this is pe these are people sometimes you 这是一个非常惊人的规模。他们有时是上传扫描件， 903 00:41:42,080 --> 00:41:45,156 upload a scan. Sometimes you have doctors who are telling you conflicting 904 00:41:45,239 --> 00:41:48,359 information. And I think that we've been in a world where patients are not 905 00:41:48,442 --> 00:41:49,917 有时是在面临医生给出冲突结论时进行排查。过去患者一直处于相对被动的状态。 906 00:41:50,000 --> 00:41:52,227 empowered, right? Patients have to be the doctor, right? 907 00:41:52,311 --> 00:41:53,837 You're the decider. 你必须成为最终的决定者，自己承担责任。 908 00:41:53,920 --> 00:41:57,138 You are accountable, right? You know, doctor makes a mistake and you're going 909 00:41:57,221 --> 00:41:59,239 to be paying the price for the rest of your life. 910 00:41:59,322 --> 00:42:01,597 Like it's just it's a very 如果医生犯了错，你将用余生买单。这是很不一样的决策机制。 911 00:42:01,680 --> 00:42:04,611 different kind of incentive. And this is very personal for me. you know, 912 00:42:04,694 --> 00:42:08,127 my wife has a number of health conditions and I think that we've just been we've not 913 00:42:08,210 --> 00:42:08,797 对我来说，这也是非常切身的事情， 914 00:42:08,880 --> 00:42:11,655 like I don't even know how we'd be able to manage many of her conditions right now 915 00:42:11,739 --> 00:42:13,991 without the use of of chat. And I think we're just at the beginning 916 00:42:14,074 --> 00:42:15,037 of 我的妻子身患多种健康状况，如果不借助 ChatGPT， 917 00:42:15,120 --> 00:42:19,199 this journey, right? That I think that the degree to which even if you have the best 918 00:42:19,282 --> 00:42:21,677 medical team, 我甚至不知道该如何管理和应对她的日常状况。而我们在这个旅程中才刚刚开始。 919 00:42:21,760 --> 00:42:25,317 the best access, the best best experts, there's only so much that can be done, 920 00:42:25,400 --> 00:42:28,957 right? That you think about the things 因为哪怕你拥有最优秀的医疗团队、最顶级的专家，人类的能力目前也依然是有局限的。 921 00:42:29,040 --> 00:42:31,693 that are just outside of the reach of humanity or even just sometimes it's like 922 00:42:31,776 --> 00:42:34,153 someone didn't even read the chart, right? and kind of missed a detail. 923 00:42:34,236 --> 00:42:35,677 All 有许多病症是目前无法治愈的，或者有时仅仅是因为医生太忙，漏掉了病历上的某处细节。 924 00:42:35,760 --> 00:42:38,492 of that we should be able to improve massively through these tools. 925 00:42:38,576 --> 00:42:40,384 And so I think that the personalized medicine 926 00:42:40,467 --> 00:42:42,317 and 所有这些问题，我们都应该可以通过这些工具来实现巨大的改善。因此，我认为个性化医疗以及 927 00:42:42,400 --> 00:42:45,882 sometimes it's going to be about drugs and drug discovery that are of you know 928 00:42:45,966 --> 00:42:49,037 for for mass market but sometimes it'll 针对大众市场的新药研发，但有时甚至也包括刚才提到的像罕见病 929 00:42:49,120 --> 00:42:53,744 be even for the kind of NF1 things like the the disease um diagnoses that I 930 00:42:53,827 --> 00:42:56,317 mentioned earlier today. Sometimes it 诊断。 931 00:42:56,400 --> 00:43:01,223 will be for just like trying to understand conditions and trying to come up with 932 00:43:01,307 --> 00:43:03,677 with new potential therapeutics. 有时它也适用于 933 00:43:03,760 --> 00:43:06,768 All of that we're seeing it happening right now in front of our eyes. 934 00:43:06,851 --> 00:43:08,739 It's not theoretical. It's really happening. 935 00:43:08,822 --> 00:43:11,517 And so one of the most I think it's like one 仅仅去理解病症并寻找新的潜在疗法。 936 00:43:11,600 --> 00:43:14,952 of the most astounding possibilities of AI is how much it can improve our health. 937 00:43:15,036 --> 00:43:17,837 And you think about the ripple 这一切都已经发生在我们眼前。这并不是理论，它确实正在成为现实。我认为 AI 938 00:43:17,920 --> 00:43:21,664 effects of the system, right? Where so much spending on the health care system 939 00:43:21,747 --> 00:43:25,197 happens right now. that's a massive part 最令人激动的可能性之一，就是它能多大程度地改善我们的健康。想想 940 00:43:25,280 --> 00:43:29,802 of the economy and that if you're actually able to help people prevent issues, 941 00:43:29,886 --> 00:43:32,637 right, to to get ahead of 医疗系统的支出非常庞大，占了经济的巨大比重。 942 00:43:32,720 --> 00:43:34,504 potential, you know, health health problems, 943 00:43:34,587 --> 00:43:37,178 that's something that actually then alleviates a lot of burden, 944 00:43:37,261 --> 00:43:39,597 a lot of strain. And 如果能帮人们预防疾病、提早发现潜在健康隐患，就能减轻社会巨大的医疗负担。 945 00:43:39,680 --> 00:43:41,958 we're in a world where doctors are burned out, nurses are burned out, 946 00:43:42,042 --> 00:43:44,662 like there's like a real crisis that's happening in front of us. And I think AI 947 00:43:44,746 --> 00:43:46,168 我们知道现在的医生在超负荷工作，护士也在承受巨大的压力，这是一个实实在在的危机。而我 认为 948 00:43:46,252 --> 00:43:48,717 AI will be able to help with all of that. 能在这些方面提供很大的帮助。 949 00:43:48,800 --> 00:43:52,699 Like we have that potential if we deploy it and use it wisely and well. 950 00:43:52,782 --> 00:43:54,157 And so I 只要我们明智、妥善地部署和使用它。 951 00:43:54,240 --> 00:43:57,568 think that applying AI to medicine, like that's something that I is really 952 00:43:57,651 --> 00:44:01,117 a personal motivation for me in in 将 AI 应用于医疗，是我投身这个行业、以及我们创办 OpenAI 的核心动力之一。 953 00:44:01,200 --> 00:44:03,327 thinking about this whole journey of of what we're building, 954 00:44:03,410 --> 00:44:06,310 what we're trying to do with Open AI. And I'm hopeful that we as a as a world and 955 00:44:06,393 --> 00:44:08,557 a community can make the most of that. 我希望全世界和我们社区能共同把握住这个机遇。 956 00:44:08,640 --> 00:44:12,237 Let's hope. I think we will. I'm very very confident. 让我们共同期待，我相信一定可以。我非常有信心。 957 00:44:12,320 --> 00:44:16,317 Greg, thank you so much. Greg，非常感谢你。 Thank you. 谢谢。 958 00:44:16,400 --> 00:44:21,350 Great. Thank you. Thank you so much. 太好了，非常感谢。 959 00:44:25,200 --> 00:44:31,170 Oh my god. Thank you everyone. You have a good time today. 天啊，谢谢大家，希望大家今天玩得开心。 960 00:44:33,440 --> 00:44:37,277 Thank you. Should we do it again next year? 谢谢，我们明年还要再办吗？ 961 00:44:37,360 --> 00:44:41,760 You going to come? Yeah. 你们会来吗？会？太好了。 All 好的。