吴恩达谈 AI 恐慌炒作与真实就业影响

这位是 Andrew。他联合创立了 Google Brain 和 Coursera。他的机器学习课程已惠及数百万学习者，他是当今 AI 领域最具影响力的声音之一。

This is Andrew. He co-founded Google Brain and Coursera. His machine learning course has reached millions of learners and he is one of the most influential voices in AI today.

所以，少数领先的 AI 公司一直在 AI 问题上大声散布恐惧，试图推动监管法规通过。这种基于恐惧的论调不断敲打，已经让社会对 AI 的看法变得非常负面。人们跟我谈论数据中心和失业问题。

So, a handful of leading AI companies have been very loud voices of fear-mongering around AI to try to get regulations passed. This drumbeat of fear-based messaging has skewed societal perception to be really negative on AI. People talk to me about data centers and job loss.

也许 AI 能做很多工作中 30-40% 的部分。这意味着人类做的另外 60% 变得更加有价值。

Maybe AI could do 30-40% of many jobs. And what that means is well that 60% that the human does has become even more valuable.

那人类对 AI 失去控制的问题呢？

What about loss of human control over AI?

我想到的是另一种我们无法控制的东西。

I think about something else that we can't control.

我认为你是 AI 领域中拥有深厚机器学习背景和教学经验的声音之一，而且你是一个积极的声音。因为这是我今年夏天特别注意到的事情——社会氛围变得多么糟糕，尤其是在社交媒体上，当我发布关于 AI 的内容时，人们跟我谈论数据中心和失业。你觉得这股浪潮为什么最近开始？你认为原因是什么？

I think you're one of the voices in AI who comes with a huge background in machine learning and teaching AI and also you're a positive voice because this is something that I've been seeing especially this summer how poor the society has become especially on social media when I'm posting about AI and people talk to me about data centers and job loss. Why do you think this wave started recently? What do you think the causes are?

关于 AI 有很多错误信息，其中很多问题的根源是两三年前开始的一场不幸的尝试，我认为是公关和监管俘获。事实证明，当前 AI 领域最有价值的东西之一就是巨大的 AI 模型，一些公司训练出的巨型大语言模型。但如果你花了数十亿美元训练一个模型，而别人训练了一个模型并想免费提供给全世界使用，那对你来说就非常不方便。所以，少数领先的 AI 公司，我想你也知道，一直在 AI 问题上大声散布恐惧，试图推动监管法规通过，以创造一个有利于现有企业的不公平竞争环境，这样我们都得为使用 AI 支付高昂费用，同时压制其他团队——无论是研究人员还是其他公司——他们只想免费发布开源模型，让任何人都能以更低成本使用。不幸的是，散布恐惧是有效的。当你说 AI 就像核武器——这个类比毫无事实依据，它们之间有什么关系？或者当你四处挑选 AI 犯错的事例，把它夸大其词，甚至散布关于 AI 使用数据中心的错误信息，说它消耗的水比实际多得多。这种基于恐惧的论调不断敲打，已经让社会对 AI 的看法变得非常负面，这很不幸，因为这正在减缓美国对 AI 的采用，让美国失去竞争力。除非我们把 AI 的真相传播出去——即 AI 带来了巨大的益处，也有一些问题，但远没有他们夸大的那么可怕——否则这将伤害到个人。

There's been a lot of misinformation about AI and the root cause of a lot of this is an unfortunate attempt that started two or three years ago of, I think, PR and regulatory capture. It turns out that one of the most valuable things in AI right now is the giant AI models, giant large language models that some have trained. But if you spend billions of dollars training a model, it's really inconvenient if someone else trains a model and wants to give it to anyone in the world to use for free. So, a handful of leading AI companies, I think, as you know, have been very loud voices, fear-mongering around AI to try to get regulations passed to create an unfair playing field that favors incumbents so that we all have to pay a high toll for use of AI while stifling the other teams, be it researchers or other companies that want to just give away open source models that anyone could use much cheaper. Unfortunately, fear-mongering works. When you go and say AI is like nuclear weapons, which is an analogy that has no basis in fact, what do they have to even do with each other? Or when you go around and cherry-pick cases of AI making a misstep and making it much bigger than it is or even spread misinformation about how AI uses data centers, uses a lot more water than the actual reality. This drumbeat of fear-based messaging has skewed societal perception to be really negative on AI, which is unfortunate because this is slowing down American adoption of AI. This is making America less competitive. And unless we get the truth about AI out there, which is that it's a fantastic benefit with some problems, but not nearly the threat they've blown them up to be, it will hurt individuals.

我来列举一些人们强调的问题。失业和不平等。你怎么看？

I'm going to read out some of the problems that people are highlighting. Job loss and inequality. What do you think?

关于 AI 将取代 50% 的工作、人们会失业、上街暴动的“失业末日论”，这根本不会发生。每一波技术浪潮，包括 AI，都会改变我们做好工作所需的技能。所以 AI 正在改变职业。但我真希望 AI 能做得更好。AI 就是还不够好。我知道少数企业想炒作 AI，说我们有超级智能或 AGI（通用人工智能）之类的，能做人类做的所有事情。我希望 AI 能做得更好。我们就是还不够好，无法让 AI 做人类做的所有事情。如果你看看工作分析，经济学家，比如我在斯坦福的朋友 Erik Brynjolfsson，麻省理工的 Andy McAfee，他们分析了许多人的工作，将其分解为具体任务，也许 AI 能做很多工作中 30-40% 的部分，这意味着人类做的另外 60% 变得更加有价值，因为它是对现在更便宜的 30-40% 的经济补充。所以会发生的是，使用 AI 的人，也许使用 AI 的人会取代不使用 AI 的人，但 AI 在绝大多数工作中还无法取代人类。在所有职业中，现在受 AI 影响最大的是软件工程，因为 AI 在写代码方面确实非常出色。我们看到软件工程的职位空缺数量在上升，这与那些末日恐惧者的说法相反。AI 实际上无法取代软件工程师，我认识的所有优秀软件工程师现在都比以前更忙。另一面是，如果有人还像 2022 年 ChatGPT 出现之前那样写代码，他们就有麻烦了。他们需要新技能。不要做那 30-40% AI 能自动化的工作。你必须停止做那些。让 AI 去做。但要提升你的技能，去做 AI 无法做的另外 60-70%。

The job apocalypse, this idea that AI will take over 50% of jobs, people will be out of work, rioting in the streets, that's just not going to happen. With every wave of technology, including AI, the skills we need to do great work shifts. And so AI is changing job professions. But boy, I wish AI worked better. AI just doesn't work well enough. I know that a handful of businesses want to hype up AI to say we have superintelligence or we have artificial general intelligence or whatever and can do all the stuff that humans do. I wish AI worked better. We're just not good enough to make AI do everything a human does. And if you look at the analysis of jobs, economists like my friend Erik Brynjolfsson at Stanford, Andy McAfee at MIT, economists have analyzed many people's jobs and broken them down into individual tasks and maybe AI could do 30-40% of many jobs and what that means is well that 60% that a human does has become even more valuable because it's called an economic complement to the 30-40% that's now cheaper. And so what will happen is people that use AI, maybe people that use AI will replace people that don't use AI, but AI is not in a position for the vast majority of jobs to replace people. Of all the different professions, the one that's most affected by AI now is software engineering because AI is actually fantastic at writing code. And what we see is that the number of job openings in software engineering is up, contrary to what the doom fear-mongerers would say. AI is not actually able to replace software engineers and all the good software engineers I know are busier than ever now. The flip side of it is if someone still writes code like it's 2022 before ChatGPT, they're in trouble. They need new skills. Don't do stuff that that 30-40% AI can automate. You got to stop doing that. Let AI do that. But then gain your skills to do the other 60-70% that AI cannot do.

你对新毕业生有什么建议？因为我在这档播客里和 Erik 聊过，他说对就业市场的影响其实不大，除了他提到的 18 到 25 岁刚毕业的人。你对那些可能还没有足够专业知识来规划职业的人有什么建议？他们只能做一些 AI 也能做的体力工作。

What would your advice be to new graduates? Because I talked to Erik on this podcast and he was talking about that there's not really a lot of impact on the job market except for, I think he mentioned people from 18 to 25 who just graduated. What would be your advice to those people who don't have the expertise maybe to strategize in their job yet? They can only do manual work that AI can do as well.

所以，应届大学毕业生面临的一个真正挑战是大学体系适应缓慢。你知道，我热爱学术界。我认为我们都应该支持学术界和大学。当 AI 到来并改变了软件编写方式时，大学往往需要一两年时间让教师掌握技能，然后创建新课程，获得课程委员会批准，等等，让教师参议院投票。这需要数年时间。学术界的变革速度与 AI 的变革速度非常不匹配。所以遗憾的是，许多大学仍在教学生为 2022 年的工作做准备，而我们甚至不应该教他们为 2026 年的工作做准备。我们应该教他们为 2028 年及以后的工作做准备。这意味着职位空缺是存在的。我认识的许多雇主就是找不到足够多熟练的人，无论哪个资历级别。但事实证明，在我的办公室里现在有很多实习生。有在校大学生，也有应届毕业生。我们还有一位高中生实习生，他们都非常出色且高效。但关键是他们都非常“AI 原生”。他们都使用 AI 工具来做 AI 能做的事情，同时也积极去做那些人类能做而 AI 在很长一段时间内无法做到的事情。所以人们有很多工作可做。因此，我对应届毕业生或在校大学生的建议是，务必在课堂上努力学习。

So one real challenge for fresh college grads is that the university system is slow to adapt and so, you know, I love academia. I think we should all support academia and universities. And when AI comes and transforms the way software is written, universities often take like a year or two for the faculty to master skills, then create new courses, get curriculum committee approval, whatever, get the faculty senate to vote. It just takes years. And that speed of change in academia is very poorly matched to the speed of change in AI. So sadly, many universities are still teaching students to be ready for the jobs of 2022 when we shouldn't even be teaching them for the jobs of 2026. We should be teaching them for the jobs of 2028 and beyond. And what this means is the job openings are there. Tons of employers I know just can't find enough skilled people at any level of seniority. But it turns out in my office right now, we have a lot of interns. There are current college students, fresh college grads. We also had one high school intern and they're amazing and productive. But the key is they're all very AI-native. They all use AI tools to do the things AI could do, but then also lean in to doing the things that humans can do that AI can't for a long time. So there's plenty of work for people to do. But so my advice to fresh college grads or the people currently in college is, by all means work hard in classes.

你知道，取得好成绩，向老师学习，但就大学尚未适应教授的那些额外技能而言，那就通过其他方式在线学习，无论是从 Coursera、DeepLearning.AI、Udemy 还是其他地方，你都能获得更前沿的技能，尤其是大学尚未纳入课程的 AI 技能。

You know, get good grades, learn from the instructors, but to the extent that there's still additional skills that the university has not yet adapted to teaching, then find other ways to learn online, be it from Coursera or DeepLearning.AI or Udemy or other places where you can gain the more cutting-edge skills, especially AI skills that universities have not yet worked into the curriculum.

稍作停顿，因为 Andrew 刚才提到的关于工作流程的内容，与 HubSpot 刚刚免费发布的东西有关。关键在于，当前真正的机会不仅仅在于模型本身，还在于你在模型之上构建的东西。如何将 AI 从聊天窗口转变为真正为你工作的流程。好消息是，你不必从构建一个完整的智能体开始。你可以从更简单的、更好的提示词开始。HubSpot 刚刚发布了高级 ChatGPT 提示词工程手册。其理念非常简单。在 7 天内，它帮助你从获得通用的 AI 答案，转变为构建能给你更一致、更有用选项的提示词，因为我们都经历过这样的时刻：你输入一个模糊的句子，得到平庸的回复，然后花接下来的 20 分钟自己修正。这本手册正是针对这个问题而设计的，它引导你完成一个单一的进阶过程。首先，你学习如何用角色、上下文、格式和参数等来构建提示词。然后进入更高级的内容：少样本示例、思维链推理、更精确的提示词结构，以及让输出更可靠的方法。我喜欢的是，它不止于单个提示词。它还展示了如何构建可复用的 AI 角色、模块化提示词组件，以及最终你自己的标志性提示词工程系统。这样，你就不必每次都从头开始，而是建立一种可重复的与 AI 合作的方式。无论你是创建内容、分析数据还是做商业决策，这本手册都是免费的。链接在描述中。感谢 HubSpot 赞助本期视频。现在回到与 Andrew 的对话。

Quick pause because what Andrew just said about workflows connects to something that HubSpot just put out for free. The thing is the real opportunity right now is not just in the models themselves, but in what you build on top of them. How you turn AI from a chat window into workflows that actually do work for you. And the good news is you don't have to start by building a full agent. You can start much simpler with better prompts. HubSpot just released the advanced ChatGPT prompt engineering playbook. And the idea is very simple. In 7 days, it helps you move from getting generic AI answers to building prompts that give you much more consistent and useful options because we've all had this moment. You type in one vague sentence, get something mediocre back, and then spend the next 20 minutes fixing it yourself. This playbook is basically built for that exact problem, and it walks you through a single progression. First, you learn how to structure prompts with things like role, context, format, and parameters. Then it gets more advanced. Few-shot examples, chain of thought reasoning, more precise prompt structures, and ways to make outputs more reliable. What I like is that it doesn't stop at individual prompts. It also shows you how to build reusable AI personas, modular prompt components, and eventually your own signature prompt engineering system. So instead of starting from scratch every time, you're building a repeatable way to work with AI. Whether you're creating content, analyzing data, or making business decisions, the playbook is free. Links in the description. Thanks to HubSpot for sponsoring this video. And now back to our conversation with Andrew.

我想再说一件事。事实证明，如果你看看技能图谱的变化，最重要的变化之一就是用 AI 构建比以往容易得多。当某件事变得容易得多时，应该有更多人去做。所以现在，不仅专业软件工程师应该用 AI 构建软件，每个人用 AI 构建都变得容易得多，而那些拥抱这一点并这样做的人，会比不这样做的人更高效、成就更多，而且我认为也会更有乐趣。AI 让你能非常快速地构建。所以对于不仅仅是软件工程师的人，比如市场营销人员、招聘人员、人力资源专业人士、运营专家，我认为如果他们学会用 AI 构建，无论他们的工作角色是什么，他们真的会做得更多。

I want to say one other thing. It turns out, if you look at the skill map changes, one of the most important changes is it's so much easier to build with AI than before. When something becomes much easier, a lot more people should do it. And so now, not only should professional software engineers build software with AI, it's becoming much easier for everyone to build with AI, and people that embrace that and do so will be more productive and will accomplish more and I think have more fun than the ones that don't. And AI lets you build really fast. So for people that are not just software engineers but you know marketers, recruiters, HR professionals, operations specialists, I think if they learn to build with AI, they'll really just do much more whatever their job role is.

顺便问一下，当你部署 AI 时，你如何衡量生产力的提升？你的公司里有类似 KPI 的指标吗？

How do you, by the way, measure the increase in productivity when you deploy AI? Do you have like a KPI in your company?

我希望有一个简单的答案。我发现 AI 的商业成果更多是业务的函数，而不是 AI 的函数。对某些人来说，可能是客户增长和留存率的提升，或者更快地服务客户，或者提高某些任务的准确性。所以 KPI 往往与业务相关，而不是与 AI 相关。所以你不能直接只衡量 AI，因为这是一件有趣的事情，因为我们在我的公司里一直在积极部署 AI。

I wish a simple answer. I find that the business outcome of AI is more a function of the business than a function of the AI. For some it may be increase in customer growth and retention, or maybe faster to serve customers, or increase accuracy in some tasks. So the KPIs tend to be related to the business rather than the AI. So you can't like directly measure just AI because it's an interesting thing to do because we've been deploying AI actively in my company.

我觉得对我作为一家媒体公司来说，可能是浏览量。有趣的是不同的人如何衡量，甚至收入，比如你是否在赚钱方面变得更有效率。

I think for me as a media company, it's probably the amount of views. It's just interesting how different people measure, even revenue, like if you're becoming more effective with how you make money.

实际上，我也是。你在你的业务中是如何使用 AI 的？

Actually, same. How are you using AI in your business?

天哪，我有很多。首先，我们所有人都用 Claude，而且我们为每一个社交媒体平台都设立了特定的项目。比如，对于这个播客，我们有一个叫“嘉宾”的项目，它知道之前所有嘉宾的分析数据，并且有特定的标准，我们根据这些标准对每一位来到播客的人进行排名，无论他或她是否被引用，是否对 AI 有特定观点，是否在公司里积极使用 AI，或者是否是 AI 领域的新创始人。所以它给他们不同的权重，并给出一个基于 40 分的评分。40 分意味着第一梯队，30 分意味着第三梯队，等等。然后我们还有另一个项目，分析每一期播客，并给我关于如何提问的建议。

Oh my god, I have so many. First of all, we have Claude for all of us, and we have certain projects for every social media that we're on. So for example, for this podcast, we have a project that's called 'guests' and it knows all the analytics from previous guests and it has certain criteria on which we rank every single person who comes to the podcast, whether he or she is cited, whether they have a certain opinion on AI, whether they've been active with AI in their company, or if they're a recent founder in AI. So it gives them different weights and it comes up with a grade based out of 40. 40 meaning tier one, 30 meaning tier three, etc. And then we have another one that analyzes every single podcast and gives me tips on how to ask questions.

哦，哇。

Oh wow.

Instagram 也一样，LinkedIn 也一样。它有我的语气、个人档案、我的商业策略。所以每当它写东西时，它都知道关于我的所有事实，我的说话方式。每个社交媒体都由一个人运营。所以一个人做战略决策，顺便说一句，如果你能给我反馈，看我是否可以改进。所以我现在正在做的是闭环，因为有时他们给我发文本。我会说，哦，我们需要改这个、这个和那个。但那发生在 Telegram 的聊天中，我们有这个反馈。我们有一个机器人扫描我们所有的聊天。但我真的希望 AI 能够从这些反馈中持续学习，更好地了解我的品味。

Same for Instagram, same for LinkedIn. It has my tone of voice, personal dossier, my business strategy. So whenever it writes something, it knows all the facts about me, how I sound. Every social media is run by a person. So a person makes a strategic call, and by the way, if you can give me feedback on this if I can improve. So what I'm working on right now is closing the loop, because sometimes they send me a text. I'm like, oh we need to change this, this and that. But that happens in a chat in Telegram, and we have this feedback. We have a bot that scans all of our chats. But I really want AI to be able to learn continuously from this feedback to just know my taste better.

我在 AI 中看到很多的一件事是，事实证明，对于作为数据科学家或 AI 头脑风暴伙伴的 AI 来说，它经常提出一两个好主意，两三个平庸的主意，还有大约四个糟糕透顶的主意，有时你会想，我的 AI 怎么会认为那甚至是一个合理的主意。对我来说，这与“工作末日”的观点有关，那就是在很长一段时间内，人类——你、我、所有观看这个节目的人——将拥有比 AI 显著的上下文优势，那就是你知道一些对你来说极其明显的事情，你知道那是个糟糕的主意，但 AI 不知道。事实证明，AI 不会很快取代我们的工作或大型企业的原因之一，是因为人类拥有比 AI 巨大的上下文优势。

There's one thing I see a lot in AI, which is, it turns out for AI as a data scientist or AI brainstorming partner, it often comes up with one or two good ideas, two or three mediocre ones, and like four atrocious ones, and sometimes you wonder how could my AI have thought that could even be a plausible idea. And to me, this relates to the job apocalypse point of view, which is that for a long time, humans—you, me, everyone watching this—will have a significant context advantage over AI, which is that you know something that's incredibly obvious to you, that you know that was an awful idea, but the AI did not. And it turns out that one of the reasons why AI will not replace our jobs or whatever of large business anytime soon is because humans have a massive context advantage compared to AI.

我们从多年的经验中知道很多。我们和客户交谈，看到他们有趣的面部表情，告诉我们他们不喜欢这个，或者我们和业务部门交谈，或者我们的经理说：“嘿，我真的很在意这个。”所以事实证明，几乎所有人类，也许所有人类，都知道很多“管道”中不存在的东西。而且我认为在可预见的未来，AI 也无法获得这些。我知道有时人们会谈论人类判断力或人类品味的重要性。有些人会想：“品味是什么？这是个模糊的东西吗？”但对我来说，支撑人类比 AI 拥有更好判断力和品味的技术性东西，就是这种上下文优势。而且因为这是一个长期优势，没有人能在几年内解决这个问题，这就是为什么我们需要更多拥有这种判断力和品味的人类，来持续补充 AI 的不足。

We know so much from our years of experience. We talked to customers, we saw the funny facial expression that told us they don't like this, or we talked to business, or our manager said, 'Hey, I really care about this.' So it turns out that almost all humans, well maybe all humans, just know a lot of stuff that the plumbing does not exist for. And I don't think it exists for the foreseeable future for AI to get. I know sometimes people talk about the importance of human judgment or human taste. Some people wonder, 'What is taste? Is this fuzzy thing?' But to me, the technical thing that underlies why humans have better judgment and better taste than AI is this context advantage. And because this is a long-term advantage, no one's going to solve this in a few years, this is why we just need a lot more humans with that judgment and taste to keep on complementing AI.

这难道不是让教育变得更加重要吗？因为教育给了我们上下文。这是我听到的关于 AI 的另一件事：你不需要教育，因为所有信息都触手可及，你只需问 ChatGPT。但当你提到上下文和品味时，对我来说，那是多年积累知识、向最优秀的人学习、观察他们如何表现，而不是仅仅问一个聊天机器人。

And doesn't this make education even more important? Because education gives us context. It's another thing I'm hearing about AI: you won't need education because all the information is at your fingertips, you just ask ChatGPT. But when you say context and taste, for me, that's years of acquiring knowledge and learning from the best and seeing how they perform versus just asking a chat.

我要说一些可能有争议的话。我不知道我是否公开说过，但我认为这是真的：坦率地说，AI 模型对学习来说很糟糕。我知道人们认为 AI 在完成任务方面很棒，一直在用，很喜欢。但所有出来的数据都表明，当大学生使用 AI 时，我们知道这一点，现在也有研究支持，所以我们也有数字。但数据非常清楚：学生使用 AI 时，作业分数更高。耶，更高的作业分数。但保留率，他们的长期表现，要差得多，因为 AI 替他们做了工作。现在越来越多的研究支持这一点。我认为人们会想：“哦，原来……”你知道，我认为维基百科是一个很棒的工具，有大量事实；网络搜索是一个很棒的工具，有大量事实。但事实证明，当你让 AI 为你做工作时，你在把认知卸载给 AI，这很好，因为这是社会前进和完成工作的方式，但人类的保留率要差得多。很明显，LLM 在大多数常见用法下，对学习来说很糟糕。我并不是说没有办法以对学习有益的方式使用它。我认为有对学习有益的使用方式。但即使是我自己，在过去六个月里，有很多事情，比如构建某个项目，前端后端组件如何工作，不管怎样，给我答案，把工作做完，这很棒。但六个月后，当我需要重做那个前端后端组件时，我不记得答案了，我又问 AI。所以数据非常清楚：我们不应该再把 AI 看作对学习有帮助，至少在今天绝大多数年轻人使用 AI 模型的绝大多数方式中，它对学习绝对糟糕。

I'm going to say something that may be controversial. I don't know if I've said this publicly, but I think it's true: frankly, AI models are terrible for learning. I know people think AI is wonderful at getting things done, use it all the time, love it. But all the data that's coming out is that when college students use AI, we know this, it's just a study now backed up as well, so we also have numbers. But the data is very clear: students score higher on homeworks when they use AI. Yay, higher homework scores. But retention, their long-term performance, is much worse because their AI does the work for them. More and more studies are coming out to back this up now. I think people think, 'Oh, it turns out...' You know, I think Wikipedia is a wonderful tool, has tons of facts; web search is a wonderful tool, has tons of facts. But it turns out that when you ask AI to do work for you, you're cognitive offloading to AI, which is great because that's how society moves forward and gets work done, but human retention is much worse. It's just so clear that LLMs, as they are most commonly used, are terrible for learning. I'm not saying there's no way to use it in a way that is good for learning. I think there are ways to use it that are good for learning. But even for myself, there are so many things on the AI model over the last six months or whatever, like building some project, how does this front end backend component work, whatever, give me the answer, get the job done, it was fantastic. But six months later, I don't remember the answer when I need to redo that front end backend component, I ask AI again. So the data is really clear: we should stop thinking of AI as helpful for learning, at least in the vast majority of ways that the vast majority of young people are using AI models today. It's absolutely terrible for learning.

但你正在建立一家公司帮助解决这个问题，对吧？因为 AI 一对一辅导，就是你刚刚宣布获得 Coursera 1 亿美元投资的那个？

But you're building a company helping solve that, right? Because the one-on-one tutoring with AI is that where you just announced with a 100 million investment from Coursera?

是的。所以我很兴奋能领导一个名为 Learn Vector 的新组织，专注于构建比一对多更加一对一的新的学习体验。你知道，15 年前，我有幸参与了在线课程运动，我认为那改变了很多人的学习方式，但那在当时和现在仍然主要是一对多的体验，每个人都看同样的视频，这其实还可以，效果也不错。但现在技术已经存在，可以创造更加个性化、定制化的一对一体验，所以我们的团队正在努力实现这一点。我想到明年初我们会展示更多。

Yes. So I'm excited about leading a new organization called Learn Vector that is focused on building new learning experiences that are much more one-on-one than one-to-many. So you know, 15 years ago I was privileged to participate in the online courses movement that I think changed the way a lot of people learn, but that was and still remains largely a one-to-many experience where everyone kind of watches the same video, which is actually okay, it actually works well. But the technology now exists to create much more personalized, customized one-on-one experiences, and so our team is working hard on that. I think we'll have a lot more to show by early next year.

当我思考人类技能发展时，我觉得因为 AI 对软件工程的影响如此之大，我们在软件工程就业市场上看到的情况，是其他学科也会出现的预兆和先驱。在软件工程中，人们需要学习新技能，但当他们这样做时，他们正在蓬勃发展，创造更多价值，坦率地说，获得加薪，并从事更令人兴奋的项目。我也在其他学科看到了早期迹象。例如，在软件工程中，大多数开发人员，如前端后端开发人员，现在因为 AI 而成为全栈开发人员，所以他们可以承担更广泛的范围。我在其他学科也看到了早期迹象，例如，市场营销中负责营销协调、协调营销活动的人，在 AI 的帮助下，现在可以成为更全面的营销人员，承担更广泛的范围。而且，坦率地说，招聘中的消息来源变得更加全面，做端到端的招聘。所以现在好消息和坏消息是，人们要承担这些更广泛的角色，确实需要学习 AI 技能，但不仅仅是学习 AI，你还需要学习这些其他技能，比如如何做市场营销、招聘、软件工程或 AI 工程的其他部分。所以我认为这实际上创造了巨大的需求，人们需要获得新技能。但当他们这样做时，既包括 AI 技能，也包括学科技能，那么他们就能做得更多，希望更有乐趣，从事更令人兴奋的项目，也希望获得更高的报酬。

When I think about human skill development, I feel like because AI has so heavily impacted software engineering, what we see happening in the job market for software engineering is a harbinger, a forerunner, of what we'll see in other disciplines as well. And in software engineering, people need to learn new skills, but when they do, they are thriving and creating more value and frankly getting raises and doing even more exciting projects. And I've seen early signs of this in other disciplines as well. For example, in software engineering, most developers like front end backend developers have now become full stack developers because of AI, so they can take on broader scope. I'm seeing early signs of this in other disciplines as well, where for example someone in marketing that did marketing coordination, coordinating marketing campaigns, with AI help they can now become more of a full cycle marketing, take on a broader scope. And I'm seeing, frankly, sources in recruiting become more full cycle, do end-to-end recruiting. So now the good news and bad news is for people to step up to these broader roles, you do need to learn AI skills, but also it's not just learning AI, you also need to learn these other skills, like how do you do the other parts of marketing, of recruiting, or software engineering, or AI engineering. So I think this actually creates a heavy need, a big need, for people to gain new skills. But when they do, which is both AI skills but also disciplinary skills, then they can do much more, hopefully have more fun, work on more exciting projects, hopefully get paid more as well.

我担心这种恐惧宣传的一个原因是，我收到一封来自即将进入大学的人的电子邮件，他写信给我说：“嘿，Andrew，我在上在线课程，但我真的很纠结大学应该主修什么，因为四年后 AI 不会做所有这些吗？我学到的一切都会过时。”答案是“不”，当然不会全部过时。但当我们不断推送这些恐惧信息时，我们让人们怀疑自己是否还有价值，这让人们不愿意投入去获得这些技能，而这些技能会让他们处于更好的位置。所以我非常清楚地看到，这些恐惧宣传正在扭曲许多人，包括高中生、大学生、应届毕业生，对经济的看法。坦率地说，在这个时代，让人们放弃是我们能做的最糟糕的事情之一，而投入的人将会蓬勃发展。

And one reason I kind of worry about the fear mongering is I got an email from someone that was about to enter college, and he emailed me saying, 'Hey Andrew, I'm taking online courses, but I'm really struggling with what I should major in college, because in four years won't AI do all this, and everything I learn will be obsolete.' And the answer is no, of course it won't all be obsolete. But when we keep on pushing these fear messages, we make people wonder if they will even be relevant, and it makes people not lean in to gain these skills that will put them in much better position. So I see very clearly that these fear-mongering messages are distorting how many people, including high school students, college students, fresh grads, think about the economy. And frankly, making people give up is one of the worst things we'll be doing in this era, when people that lean in will thrive.

Andrew 已经教会了超过 800 万人 AI。他在 2011 年就开始在线教授机器学习，比当前的 AI 热潮早了好几年。现在，他正在建立的公司之一专注于 AI 智能体。从听起来的方式来看，它仍然可能感觉太技术化了。所以，我整理了一份逐步指南，教你如何构建你的第一个 AI 智能体，无需编码。

Andrew has taught over 8 million people AI. He started teaching machine learning online back in 2011, years before the current AI boom. Now, one of the companies he's building is focused on AI agents. From the way that it sounds, it can still feel way too technical. So, I put together a step-by-step guide to building your first AI agent with no coding required.

它带你了解要自动化什么、如何设置，以及如何让它真正有用。这周它就在我的 newsletter 里。newsletter 叫 Future Proof，是免费的，链接在描述里。你会怎么回复别人发给你的那封邮件？你会说现在为了在 AI 时代茁壮成长，最好的专业是什么？你觉得是深入某个细分领域，还是更广泛的计算机科学，这样你能很快掌握 AI 技能？

It walks you through what to automate, how to set it up, and how to make it actually useful. It is in my newsletter this week. The newsletter is called Future Proof. It's free. Link is in the description. What would you reply back to that email that somebody sent you? What would you say is the best major to study now to thrive in AI era? Do you think it's like going deep into a niche or just broader computer science so that you can acquire AI skills really fast?

你知道，我不知道什么是最好的专业。有太多很棒的专业了。我觉得“世界上最好的工作是什么”就像“世界上最好的专业是什么”一样。

You know, I don't know what's the best major. There are awful lot of great majors. It's like, I kind of feel like what's the best job in the world is like what's the best major in the world?

哦，是你热爱的东西，对吧？

Oh, something that you love, right?

是的。我女儿想当宇航员。我不知道她能不能主修成为宇航员。我得想想这个问题。不过等她再大点，她可能会改变主意。我在这么多工作角色中看到了太多机会，这一切对我来说都很令人兴奋。但一定要学 AI，一定要学会用 AI 构建东西。另外，我的团队一直在做 AI 工程技能图谱，试图梳理出 AI 工程最重要的技能。有一件事我凭直觉感觉到了，但看到数据时还是有点惊讶，那就是越来越多的职位描述似乎在说，他们想要展现出高度自主性的人。因为事实证明，有了 AI，个人有更多机会去发现问题，然后去构建东西或做点什么来解决它。所以我觉得我们真的在进化。嗯，我们一直在进化，但我们正在加速进入一个积极的时代，而不是人们坐在那里等老板告诉他们做什么的时代。

Yeah. My daughter wants to be an astronaut. I don't know if she can major in becoming an astronaut. I have to think about that. When she gets older though, she may change her mind. I see so many opportunities across so many job roles. It all seems very exciting to me. But do learn AI, do learn to build with AI. The other thing that my team's been working on is AI engineering skills map, to try to map out the most important skills for AI engineering. One thing that I felt intuitively, but I was surprised to see it show up in the data, was that a lot more job descriptions seem to be saying they want people that demonstrate a very high sense of agency. Because it turns out with AI, there are a lot more opportunities for individuals to spot problems and go build something or do something to go solve it. So I think we're really evolving. Well, we've long been evolving, but we're accelerating positive era where people sit around and wait for their boss to tell them what to do.

这正是我经常感受到的，尤其是当我们开始远程工作的时候。我希望人们在自己的细分领域里成为创业者。比如你帮我做 LinkedIn，你在那里就是创业者。你可以雇更多承包商，可以部署不同的工具。你可以做战略决策，判断这个话题好不好，我们是否继续。我真的觉得，你告诉我你是否同意我，我们正在进入这样一个就业市场，每个人在工作中都有点独立。

This is what I've been feeling a lot, especially when we started doing remote work. I want people to be entrepreneurs within their niche. Like if you're helping me with LinkedIn, you're an entrepreneur there. You can hire more contractors. You can deploy different tools. You make the strategic decision whether this topic is good or not. Shall we proceed with it? I really think, and tell me if you agree with me, we're moving into that job market where everyone is kind of independent in their workplace.

我认为人们将拥有更多的自主权和创造力。所以我同意这一点。我甚至想更进一步，我和很多人聊过，你知道，大公司的工程师和其他人，他们告诉我，他们的经理让他们待在“泳道”里。他们会说，“哦，我有个创意，但经理说，‘不，我需要你专注于这一件事，’坦率地说，这往往是因为经理的职业生涯依赖于它。”但我觉得，让人们发现“泳道”之外的机会，然后以负责任的方式探索如何实现它，这让我非常兴奋。我认为未来，那些建立鼓励人们学 AI、快速负责任地构建、与客户沟通的文化的企业，会比那些层级分明、各自为政的组织创造更多价值。

I think people will have much more autonomy and creativity. So I agree with that. And I'd even go one step further, which is I talked to a lot of people, you know, engineers and others in large companies, that tell me that their manager tells them to stay in their swim lane. They'll say, "Oh, I have this creative idea, but the manager says, 'No, I need you to focus on this one thing,' frankly, often because their manager's career depends on it." But I feel like the number of opportunities for people to spot things outside the swim lane, and then in a responsible way explore how to get it done, that feels very exciting to me. And I think that in the future, the businesses that set up a culture that encourage people to learn AI, build fast responsibly, talk to customers, would drive a lot more value than the more hierarchical silo organizations.

是的，这始于招聘合适的人，然后在你的组织中培养这种文化。当你说学会使用 AI 并精通 AI 时，你能给我一些基准吗？比如一个营销人员、知识工作者，在 AI 方面很先进，你面试这个人时看什么？

Yeah, it starts with hiring the right people and then nurturing this in your organization. When you say learn how to use AI and become proficient with AI, can you give me some benchmarks like of a person who's like say a marketer, knowledge worker, advanced with AI, what are you looking for when you're interviewing this person?

我很确定我的团队走在前沿。我所有的营销人员都会写代码。所以，在我面试营销人员的过程中，我们会问他们构建过什么，如果他们没构建过任何软件。

I'm pretty sure my team's ahead of the curve. All of my marketers know how to code. So, as part of how I interview marketers, we ask them what they've built, and if they have not built any software.

如果是仪表盘，那是好还是坏？比如，是不是太基础了，还是

If it's a dashboard, is it good or bad? Like, is it too basic or

仪表盘？再说一次，我的团队可能，你知道，有点超前，但

A dashboard? Again, my team's probably, you know, somewhat ahead of the curve, but

听到那样挺好的。

Was good to hear like that.

我所有的营销人员都构建了很多具体的东西，在

All of my marketers have built much specific things in

哦，我觉得，我不知道，前几天我们营销团队的一个人谈到他构建的工具，当他考虑写某篇文章时，它会爬取网络，找到相关作品，有一个自定义桌面应用，实际上构建了一个在他的 Mac 上运行的桌面应用，为他高亮相关文章。然后你可以与整个系统聊天，导航，你知道，他正在写的东西以及相关作品，他还有一个大仪表盘，用于浏览互联网，向他高亮正在出现的令人兴奋的东西。现在即使在我的团队里，我认为营销人员也走在前沿。

Oh, I feel like, I don't know, the other day one of our someone on the marketing team was talking about the tools that he had built to, when he's considering writing an article on something, it will crawl the web, find related work, has a custom desktop app, actually built a desktop app that runs on his Mac to highlight related articles for him. Then you can chat to the whole system, navigate, you know, the thing he's writing as well as the related work, and he had a large dashboard for trolling the internet to highlight to him exciting things that are popping up. Now even on my team, I think that marketers is ahead of the curve.

但那真是太好了。还有其他有趣的用例能激励人们构建类似的东西吗？

But that's great to hear. Any other interesting use cases that will inspire people to build something similar?

让我想想，也许，呃，我的财务团队广泛使用 AI。所以我认为，呃，我的一个 CFO 意识到，你知道，她的团队每周花几个小时点击文档，打开这个，复制粘贴这个数字到那里，所以她开始构建自动化脚本，这些脚本按例程运行，自动打开文件，检查里面的内容，检查一致性，如果有需要注意的事情，或者有新文档出现，就向她的团队高亮。所以我发现，与其等着工程师为他们做工作，团队的能力，不仅仅是构建仪表盘，而是构建一种数据管理基础设施。他们可以摄取数据，如果有事情发生就提醒他们。

Let's see, maybe, uh, my finance team uses AI extensively. So I think, uh, one of my CFOs realized that, you know, her team was spending hours every week clicking through documents, open this, copy paste this number here, and so she started building automation scripts that runs on a routine that automatically opens files, checks what's in there, checks for consistency, highlights to her team if there's something they need to be paying attention to, if a new document has showed up. So I find that rather than waiting around for an engineer to do the work for them, the team's ability to, kind of, not just build dashboards but build kind of a data management infrastructure. They can ingest data, alert them if something's happening.

我觉得我的财务和营销团队正在这样做。哦，我的招聘团队，嗯，我们实际上有招聘工程师，他们是真正的专业工程师，坐在招聘团队里，正在为招聘构建非常复杂的工具。这实际上是另一个趋势。我认为营销人员、招聘人员、人力资源专业人士、运营人员都应该学 AI。但另一件事是，当你把一个工程师嵌入这些团队时，这会进一步加速你能做的事情。

I think my finance and marketing teams are doing that. Oh, my recruiting team, well, we actually have recruiting engineers, which are really professional engineers that sit in a recruiting team, that are building very sophisticated tools for recruiting. And this is actually the other trend. I think marketers, recruiters, HR professionals, ops people should all learn AI. But the other thing is when you take an engineer and embed them in these teams, then that further accelerates what you can do.

我们也一样。我们从基础的东西开始，自己构建，然后遇到瓶颈，工程师介入，我们进一步构建。

We do the same. We start with something basic, build it ourselves, then we hit the wall, an engineer comes in, we build it further.

坦率地说，当你看到不仅仅是软件工程师，还有招聘工程师、营销工程师、人力资源工程师，我认为现在有太多有价值的工程工作可以做。我只是，你知道，不担心会耗尽，坦率地说，我所有的朋友都很忙。我们想，天哪，我们怎么会耗尽工程工作呢？

Frankly, when you look at not just software engineers, but recruiting engineers, marketing engineers, HR engineers, I think there's so much valuable engineering work that can now be done. I'm just, you know, not worried about running out of, frankly, all my friends are so busy. We think, boy, how could we run out of engineering jobs?

是的。是的。有太多很酷的想法可以尝试。但你提到了一个实际上是恐惧点的事情，当我们谈论财务信息时，你给了 AI 多少权限。所以，我给了我的 Perplexity 权限去扫描我的 Fidelity 账户，这样它就能跟踪我的投资组合，告诉我何时再平衡。它不会代表我做任何事，但它有访问权限。你觉得这有什么问题吗？

Yeah. Yeah. There are so many cool ideas you can experiment on. But you touched on something that is actually one of the fears when we talk about like financial information, how much you're giving to AI. So, I gave my Perplexity permission to scan my Fidelity account so it can track my portfolio, tell me when to rebalance. It doesn't do anything on my behalf, but it has access. Do you think there is any problem with that?

这很复杂。

This is complicated.

我认为 AI 和隐私是一个复杂的领域，很大程度上取决于你与哪家公司共享数据。例如，我完全信任所有超大规模云服务商，100% 相信他们会遵守服务条款并履行承诺。这是我个人观点，不是法律或商业建议，但如果最大的超大规模云服务商公布了带有隐私声明的服务条款却违反它，我会感到震惊，因为那会对其长期商业模式造成巨大损害。现在，在超大规模云服务商这边，如果你看看 AI 公司，至少有一家我不愿点名，似乎偶尔会更改服务条款。如果你在使用它并访问网站，弹窗会显示：‘嘿，我们更改了服务条款，以保留你的数据或用于训练。’如果你没注意，点错了按钮，那么他们就突然获得了以我不太舒服的方式访问你数据的权限。我觉得自己处理一些敏感信息。所以我倾向于对那些我认为其文化、DNA 以及长期商业模式不像超大规模云服务商那样与保护个人用户隐私紧密相连的企业非常谨慎。而且我看到企业也明白这一点。例如，我的一个团队 AI Aspire，我们与包括银行在内的超大型企业合作，处理极其敏感的财务数据。你可以想象，AI Aspire 和我们的客户不会随意与前沿实验室共享真正敏感的重大非公开信息，对吧？NPI，在没有仔细考虑护栏和隐私的情况下。所以我认为这很复杂。

I think AI and privacy is a complex area, and it depends a lot on the company that you are sharing your data with. So, for example, I trust all the hyperscalers to really 100%, you know, follow their terms of service and to do what they say. My personal opinion, not giving legal business advice, but I'd be shocked if the largest hyperscalers published terms of service with some privacy notice and then breached that, because that would be so damaging to the long-term business model. Now, on the largest hyperscaler side, if you look at AI companies, there's been at least one company that I won't name that seems to occasionally change the terms of service. If you're using it and you go to the website, a pop-up says, 'Hey, we changed the terms of service to retain your data or train on your data.' And if you aren't paying attention and click the wrong button, then they suddenly gave themselves permission to access your data in a way that I'm not that comfortable with. I feel like I handle some sensitive information. So I tend to be very careful with the businesses that I just don't feel have that culture and DNA and, frankly, the long-term business model as tied to protecting individual user privacy as the hyperscalers. And I see businesses get this as well. For example, one of my teams, AI Aspire, we work with very large corporations, including banks, with incredibly sensitive financial data. And as you can imagine, AI Aspire and our clients do not willy-nilly share really sensitive material nonpublic information, right? NPI, with frontier labs without really careful thinking about the guardrails and privacy. So I think it's complicated.

所以信任超大规模云服务商，但还有一件事你可以做，你可以下载一个开源模型，就在自己电脑上运行，然后数据就留在你电脑上，对吧？

So trusting hyperscalers, but also another thing that you can do, you can download an open source model and just run it on your computer and then it just stays on your computer, right?

是的。我认为是的。事实证明，很多银行实际上会在虚拟私有云或本地运行，所以数据甚至不会离开他们的控制。但我认为对个人来说，对于真正敏感的事情确实如此。我有时会运行本地模型，开源权重模型很有意思。一些最新的开源权重模型正在接近前沿能力，而且它们实际上足够小，现在真的是很好的模型，可以在笔记本电脑上运行。

Yes. I think yes. It turns out a lot of banks will actually run things in a virtual private cloud or on-prem, so it never even leaves their control. But I think for individuals, it's true for really sensitive things. I sometimes run a local model, and it's been interesting with the open-weight models. Some of the latest open-weight models are approaching frontier capability, and they're actually small enough that they're really good models now; they can run on a laptop.

是的，Meta 的那个，对吧？最近的那个。

Yeah, the one from Meta, right? The recent one.

哦，是的，Meta 的 Llama 是个好模型，我也觉得最新版的 Qwen 也很好。但坦白说，这些模型每隔一周就变。所以我认为最佳实践是不要固守一个，而是不断尝试新模型。

Oh yes, Meta's Llama is a good model, and I'm also thinking the latest version of Qwen is also very good. But I think frankly these models change every other week. So I think the best practice is to not get stuck on one but keep on trying new models.

所以基本上，当你不信任任何人的时候，你就运行本地模型，这就是你保护数据安全的方式。

So basically when there is a situation that you don't trust anyone, you run a local model and this is how you keep your data safe.

我确实信任超大规模云服务商，但有时对于我甚至不能发送到云端的重大非公开信息，我要么手动处理，不使用 AI 帮助，要么如果我真的需要使用 AI，那么非常小心地只使用本地模型。

I do trust the hyperscalers, but sometimes for literally material nonpublic information that I can't even send to the cloud, I'll either do it manually without AI help, or if I really need to use AI, then very carefully only use a local model.

有趣。好的。这是个有趣的问题。那人类对 AI 失去控制呢？因为我和 Yoshua Bengio 谈过，他对没有监管的开放、自由 AI 非常负面，他给我描绘了一些非常可怕的 AI 接管控制的画面，因为整个场景是我们无法控制比我们更聪明的东西，如果 AI 变得越来越聪明，我们会走向何方？你怎么看？

Interesting. Okay. This is an interesting one. What about loss of human control over AI? Because I've talked to Yoshua Bengio, who is very negative when it comes to open, free AI without any regulation, and he painted me some very scary pictures of AI taking over control because the whole scenario is we can't control something that's smarter than us, and if AI gets smarter and smarter, where do we end up? What do you think about that?

我想到另一个我们无法控制的东西，那就是飞机。没有人能造出一架让你完美飞行的飞机。风会吹得它摇晃。坦白说，在飞机发展的早期，一些飞机坠毁，有人死亡，那是悲惨和可怕的。但通过早期吸取的教训，我们学会了越来越好地控制飞机，所以今天，我们大多可以登上飞机而不太担心生命危险。AI 也真的就是这样。没有人能完美控制 AI，因为它生成的词元或输出有点随机。所以我们真的不知道它到底会做什么。但随着我们运行它们，虽然发生了一些不幸的小事故，其中一些造成了真正的损害，但我们设计几乎任何系统的方式，从飞机到电路，再到现在的 AI，都是小心地增长它们的能力，以便我们有一个受控的环境来测量哪里出了问题，然后塑造它，确保我们能足够好地控制它，让它负责任地、安全地行事。直到今天，我们无法完美控制任何飞机，我们也永远不会完美控制 AI。但我认为我们肯定控制得足够好，以至于这种失控感不像科幻小说。

I think about something else that we can't control, which is airplanes. No one can build an airplane that you can fly perfectly. Winds will buffet it around. And candidly, in the early days of developing airplanes, some airplanes crashed and people died, and it was tragic and awful. But through the early lessons learned, we then learned to control airplanes better and better, so that today we can mostly get in an airplane and not fear too much for our lives. And it's really like that with AI. No one can perfectly control AI because it generates tokens or outputs that are a little bit random. So we don't really know what exactly it'll do. But as we run them, and there's been a small number of mishaps which is unfortunate, and some number of mishaps have done some real damage, but the way we engineer almost any system, from an airplane to electric circuits to now AI, is carefully grow their capabilities so that we can have a controlled environment in which to measure what's wrong and then to shape it to make sure we can control it well enough that it behaves responsibly and safely. And to this day, we can't perfectly control any airplane, and we will never perfectly control AI either. But I think we are certainly controlling them well enough that this loss of control doesn't feel like science fiction.

是的。那深度伪造呢？

Yeah. What about deep fakes?

深度伪造是个问题。我见过或听说过的最恶心的事情之一是非自愿的亲密深度伪造图像。我很高兴美国国会一直在推进。对。让我们通过法律。消除它。对此进行处罚。我认为有些 AI 的使用确实有问题，我们应该禁止，严惩。让我们直接消除它。

Deep fakes are a problem. One of the most disgusting things I've ever seen or heard of is non-consensual intimate deep fake imagery. I'm really glad that US Congress has been moving. Right. Let's pass laws. Get rid of that. Penalties for that. I think there's some really problematic uses of AI that we should outlaw, heavily penalize. Let's just get rid of that.

关于孩子和社交联系，当涉及到 AI，孩子们使用更多 AI，你怎么看？因为我们看到社交媒体上有人整天无意识刷屏，我女儿现在 5 岁，每当我没有答案时，她就会说：‘问 ChatGPT。’我就想，‘那是谁？’她觉得 ChatGPT 什么都知道。你对孩子与 AI 的未来有什么看法？

What do you think about children and social connection when it comes to AI, with kids using more AI? Because we've seen with social media how there are people who are doom-scrolling all day, and my daughter, who is 5 years old now, whenever I don't have an answer, she's like, 'Ask ChatGPT.' And I'm like, 'Who's that person?' She thinks ChatGPT knows everything. What would you say about kids' future with AI?

首先，我认为孩子有光明的未来。作为一个孩子，在这样的环境中成长，拥有我们从未有过的工具，真是令人兴奋的时代。同时，我们已经看到社交媒体，我认为社交媒体可能被指责得比应得的更多，但它确实应受指责，对孩子不太好。我其实很担心，AI 是个奇妙的工具，但 AI 损害学习是我非常担心的事情。所以，事实上我有 5 岁和 7 岁的孩子。当我教他们数学时，他们还很小，我基本上可以不让他们用计算器，可以说：‘你怎么算这些乘法？’我不给他们计算器，和他们一起练习。但随着他们长大一点，我非常担心学生使用认知卸载到 AI 的方式会损害长期学习保留。但同时，哦，我实际上建了一个应用。

First, I think kids have a bright future. It's just such an exciting time to be a child, to grow up in this environment with tools that none of us ever had before. At the same time, we've seen that social media, I think social media has probably been blamed a bit more than it deserves, but it does deserve blame, has kind of not been great for kids. I actually worry a lot about, it's a wonderful tool, but AI damaging learning is something I worry a lot about. So, it turns out I have a 5-year-old and a 7-year-old. When I teach them math, they're so young that I can basically not let them use a calculator, can say, 'How do you multiply these numbers?' And I don't give them a calculator and practice that with them. But as they get a little bit older, I worry a lot about students using cognitive offloading to AI in a way that damages the long-term learning retention. But at the same time, oh, I actually built an app.

我不喜欢任何免费的在线打字工具，所以我实际上自己建了一个，让我女儿学打字。我希望她现在对七岁的孩子来说已经相当不错了。

I did not like any of the free online typing tools, so I actually built my own to have my daughter learn to type. And I'm hoping that she's getting pretty decent now for a seven-year-old.

哦，她在打字了？

Oh, she's typing?

哦，是的。她实际上打出了所有的小写字母。大写字母她还有点不太熟练，但我认为这开启了负责任的成人监督下的在线工具使用。我认为这真的很棘手。成人监督下的数字工具使用对孩子来说似乎是件好事，但太多成年人没有时间去监督工具的使用，然后社交媒体的激励机制，对吧，会做些有趣的事情。

Oh, yeah. She actually typed all the lowercase letters. She's a little bit off on the shift uppercase letters, but I think that this unlocks responsible adult-supervised use of online tools. And I think it's really tricky. Adult-supervised use of digital tools seems a great thing for kids, but too many adults don't have time to supervise the use of the tools, and then the incentives of social media, right, to do funny things.

是的，在 AI 方面，必须有正确的激励机制。好的，你提到我们谈过恐惧，谈过如何用 AI 改善工作。你能为那些想要构建的人，特别是个人，列举一些 2026 年 AI 领域最大的机会吗？

Yeah, it has to be the right incentive when it comes to AI. Okay, you mentioned we talked about the fears, we talked about how you can improve your work with AI. Can you name some of the biggest opportunities in AI in 2026 for people who want to build? For an individual that wants to build.

我不认为有放之四海而皆准的答案，但因为构建成本已经大幅下降，我鼓励人们学习 AI、快速构建、并与客户交流。我发现自己每周或每个周末都在构建东西，因为我或我们团队中的某个人遇到了问题，而我有个想法用 AI 来自动化它。上个周末，我用一个前沿模型分析我们很多关键业务指标，因为我没有时间自己做。它是在衡量关键业务指标，而我没有时间去找数据科学家来帮我做。所以我用了各种前沿模型，非常小心它们的数据保留政策。我没有使用那些我不喜欢的数据保留政策的模型来分析数据。我发现 AI 带来的变化是构建成本已经大幅下降，所以挑战转向了决定构建什么，我称之为产品管理瓶颈。所以那些能与客户交流、对构建什么有品味和判断力的创始人、工程师、产品经理，然后用 AI 构建并快速迭代。我认为这有很多令人兴奋的事情可做。

I don't think it's one size fits all, but because the cost of building has plummeted, I encourage people to learn AI, build fast, and talk to customers. I find myself building things every week or every weekend because I or someone on our team has some problem and I have some idea for building some AI thing to automate it. Last weekend, I was using a frontier model to analyze a lot of our key business metrics because I didn't have time to do it myself. It was measuring key business metrics, and I didn't have time to go find a data scientist to work with me on it. So I used a variety of frontier models, being really careful on their data retention policies. I did not use models with data retention policies I don't like in order to analyze data. And I find that what's happening with AI is the cost of building has plummeted, so the challenge is shifting to deciding what to build, which I've been calling the product management bottleneck. So people—founders, engineers, product managers—that can talk to customers, get a sense for the taste and judgment on what to build, and then build with AI and iterate quickly. I think that's a ton of exciting things to do.

而且你已经创办了这么多公司。当我看到你的投资组合时，你认为对于初学者，当你说你在周末构建了东西，你如何决定专注于什么？或者因为 AI，你现在可以同时追求多个想法，同时在不同公司里玩？

And you've been starting so many companies. When I looked at your portfolio, do you think for beginners, when you said you built something during the weekend, how do you decide what to focus on? Or can you pursue multiple ideas because of AI now, and you can just be playing in different companies at the same time?

事实证明，创办一家公司仍然非常非常困难，所以单线程领导力或完全专注于一件事的人有很多值得说的。我发现，在一个周末里，我通常可以构建一个 MVP，构建一个简单的应用程序，但我希望创办一家大公司也能这么容易。我发现，构建有意义的东西通常需要真正的技术深度或深刻的客户洞察以及与客户的整合。是的，我们现在可以用 AI 在几小时内编写代码，但那只是拼图的一小部分。所以花时间理解技术复杂性，构建需要数月甚至数年的真正复杂的软件，或者拥有深刻的客户洞察来决定构建什么——这也需要大量与人交谈、阅读面部表情、做调查，反复进行直到我们弄清楚要构建什么。所以我认为，有时广泛采样很有价值，但个人在几个领域深入专注对于建立企业仍然很重要。

It turns out building a company is still really, really hard, so there's a lot to be said for single-threaded leadership or someone that's fully focused on just one thing. I find that over a weekend I can often build an MVP, build a simple application, but I wish it was that easy to build a large company. I find that building something meaningful often takes either real technical depth or deep customer insight and integration with customers. And yes, we can now use AI to code something in a few hours, but that's a small piece of the puzzle. So spending time understanding the technical complexity and building the really complex software that takes months or maybe years, or having that deep customer insight to decide what to build—that also just takes a lot of talking to people, reading facial expressions, surveys, doing that over and over until we figure out what to build. So I think sometimes there's a lot of value to sampling widely, but then having that focus for an individual to go really deep in a couple sectors still seems important for building a business.

我的最后一个问题，我知道我们时间不多，但我想问你关于 AGI，因为人们经常用这个词。有些人说——我想黄仁勋说我们已经达到了 AGI。你说还有几十年。你什么时候会说我们达到 AGI 的标准是什么？

My last question, I know we don't have much time, but I wanted to ask you about AGI just because people use this word so much. Some people say—I think Jensen Huang said we already reached AGI. You said it's decades away. What's the one criteria when you're going to say we reach AGI?

所以不同的人因为对 AGI 的不同定义而在不同时间说我们达到了 AGI。我最熟悉的定义是能够完成人类能做的任何智力任务的 AI。但人脑可以花五年时间学习并完成博士论文，或者一个人可以通过几分钟的练习学会在茂密雨林中驾驶卡车。那么 AI 什么时候能做到这一点——在几分钟的练习后在新环境中驾驶？感觉有一长串 AI 无法做到的事情，对我来说感觉需要几十年。我希望只是几十年。也许结果会更长。所以这就是为什么我认为按照那个 AGI 的定义，AGI 仍然非常遥远。

So different people say we reach AGI at different times because of different definitions of AGI. The definition I'm most familiar with is AI that could do any intellectual task that a human can. But the human brain can take, say, five years to study and do a PhD thesis, or a human can learn to drive a truck through a dense rainforest with tens of minutes of practice. So when can AI do that—drive in a new environment with tens of minutes of practice? It feels like there's a long list of these things that AI cannot do, for what feels to me like decades. I hope it's only decades. Maybe it turns out to be longer. So that's why I think for that definition of AGI, AGI is still very far away.

但事实证明，由于经济激励，我认为 OpenAI 和微软曾有一项协议，现在实际上已经重新谈判了，所以那已经消失了。但 OpenAI 有经济激励试图更早宣布达到 AGI。所以事实证明，如果你提出其他 AI 的定义，取决于你把标准降低多少，那么你可能已经完全达到了 AGI，甚至 30 年前就达到了，取决于你怎么定义它。

But it turns out because of economic incentives, I think OpenAI and Microsoft had an agreement that's actually been renegotiated now, so that's gone away. But OpenAI had an economic incentive to try to declare reaching AGI earlier. So it turns out that if you come up with other definitions of AI, depending on how far you lower the bar, then you could totally have reached AGI already or even 30 years ago, depending on how you want to define it.

是的，没错。Andrew，非常感谢你这次积极的对话，非常实用。我喜欢当你看到一些东西，然后你去衡量自己与人们用 AI 做的事情，看看你的流程，也许扩展它。所以非常感谢你展示你的团队在做什么，感谢你的见解。

Yeah, true. Andrew, thank you so much for this positive conversation, very applicable. I like when you watch something and then you go and you measure yourself against what people are doing with AI, look at your process, and maybe expand it. So thank you so much for showing what your team is doing and thank you for your insights.

是的，考虑到 AI 即将带来的巨大好处，我希望无论谁在看这个，都有动力去真正学习 AI、应用它，甚至去构建一些东西。

Yeah, I think given the huge benefits of AI to come, I hope whoever is watching this is motivated to really go learn AI, apply it, and even to go build something.