创新经济的未来

大家好，我是《万物未来》的Russ Altman，非常高兴地告诉大家我们即将迎来第300期节目。为纪念这一时刻，我们邀请了一位特别嘉宾，相信大家会非常喜欢。无论您是从2017年就开始收听，还是新加入的听众，我们都非常高兴您的陪伴，并感谢您的支持。请于11月7日收听我们的第300期节目，届时将带来与特别嘉宾的精彩对话。这里是斯坦福工程学院出品的《万物未来》，我是主持人Russ Altman。

Hey everyone, it's Russ Altman from The Future of Everything, and I'm thrilled to share that we're coming up on our three hundredth episode. And to mark that occasion, we have a very special guest lined up, and we think you're gonna really enjoy it. Whether you've been listening since 2017, or you're more new to the show, we're so happy to have you here, and we're grateful for your listenership. Tune in on November 7 for the three hundredth episode, and what promises to be a fascinating conversation with a very special guest. This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman.

今天我们将在纽约市玻璃屋进行现场录制，并有现场观众参与。如果您喜欢《万物未来》，请在您使用的收听应用中点击关注，这样您就不会错过任何一期节目。今天，李飞飞、Neil Mahoney和Susan Athey将告诉我们，AI正在新产品和服务中催生巨大创造力，但我们需要通过适当的政策和社会保障来关注其对人类的影响，以获得最佳结果。这就是创新经济的未来。

Today, we're taping the show live at the Glass House in New York City in front of a live audience. If you enjoy The Future of Everything, please hit follow in whatever app you're listening to. This will guarantee you that you never miss an episode. Today, Fei Fei Li, Neil Mahoney, and Susan Athey will tell us that AI is enabling tremendous creativity in new products and services, but we need to focus on its impact on humans through appropriate policies, safety nets, in order to have the best outcomes. It's the future of the innovation economy.

此外，我们将继续节目中的新板块《一分钟未来》。我会向嘉宾们提出快问快答，我们将在对话结束后进行这个环节。在开始之前，请记得在您使用的收听应用中关注我们的节目，因为您绝不会想错过任何关于未来的内容。好的，我们现在开始讨论创新经济，先来定义一下它的含义。

Also, we're continuing a new segment in the show called The Future in a Minute. I will ask my guests some rapid fire questions and they will provide rapid fire answers, and we'll do this at the end, after the end of our conversation. And before we get started, please remember to follow the show on whatever app you're listening to because you never want to miss the future of anything. Okay. We're getting started about innovation economy, and it's useful to define what it means.

当然，在准备节目时，我做了我们都会做的事——咨询了大语言模型。我查询了三个平台：ChatGPT、Claude和Perplexity（只是因为我有这些网址）。其中Perplexity的回答我最喜欢。它说：'创新经济是指经济增长主要依靠新理念、新产品和新技术的产生、应用及商业化，而非传统上对实体资产和体力劳动的严重依赖'。

Of course, in preparation for the show, I did what we would all do. I went to a large language model. I went to three, ChatGPT, Claude, and Perplexity, just because I have those URLs. Perplexity was my favorite. It said, the innovation economy refers to an economic system where growth is primarily driven by the generation, application, and commercialization of new ideas, products, and technologies rather than the more traditional heavy reliance on physical assets and manual labor.

接着它列举了一些应用实例，如网约车、共享住宿、社交媒体等。虽然创新经济不完全等同于人工智能（AI），但AI确实是其中非常重要的一部分。关于AI与创新经济产生的问题包括：对就业、生活质量、整体经济的影响，以及我们如何在保护全人类的同时实现惊人有益的创新。今天我的三位嘉宾将帮助我们理解这些机遇与挑战，他们都是斯坦福大学的教授。

And then they go on to say that applications examples might include ride sharing, house sharing, social media, and many others. Now, although the innovation economy is not entirely about artificial intelligence or AI, AI is a very big part of it and a part where our panel expert. Questions that arise for AI and the innovation economy are, what will be the impact on jobs, on the quality of life, on the overall economy, and how do we navigate a future in a way that protects all humans but enables unbelievable beneficial innovations. Today, my three guests will help us understand the opportunities and challenges. They are all professors at Stanford University.

李飞飞是计算机科学教授，Neil Mahoney是经济学教授，Susan Athey是商学院教授。这是创新经济的梦之队。Susan，让我们开始吧。

Fei Fei Li is a professor of computer science. Neil Mahoney is a professor of economics. And Susan Athey is professor of business. This is the innovation economy dream team. Susan, let's get started.

您在著作中提到AI是一种通用技术，因此其影响可能广泛且难以预测。但您在研究中也考察了过去出现的其他通用技术。从这些技术出现的历史中，我们学到了什么？

You have written that AI is a general purpose technology, and so its impacts are likely to be broad and very difficult to predict. But you have looked in your work at other general purpose technologies that have emerged in the past. What did we learn from when these things emerged?

回顾从电力、工业化到个人电脑的发展历程——这些我们许多人都亲身经历过——它们能在局部产生深远影响，却可能在相当长时间内不会显著推动GDP增长。这背后有许多原因，但过去给我们的重要启示是：每当你突破一个瓶颈，总会发现新的瓶颈。要充分运用一项技术，可能需要重组产业，但肯定需要重构生产流程才能实现全面优化。

Looking back from everything electricity and industrialization to the personal computer, which a lot of us have lived through, they can have really profound impacts locally but yet not show up in big changes in GDP growth for quite some time. And there are many reasons for this, but a big lesson yet from the past is that when you unlock one bottleneck, you find others. And to fully make use of a technology, you may need to restructure industries, but certainly restructure production in order to fully optimize for it.

我理解这些瓶颈可能出现在经济和商业的各个不同领域和层面？

I take it that these bottlenecks can come up in all different places and in all different aspects of the economy and the business?

没错。当然，经济学家通常将AI视为通用技术。但有个较少被讨论却同样深刻的观点是：软件开发本身就是通用技术。总体而言，数字化能重构产业结构，帮助小企业扩大规模，还能扩展管理幅度。

That's right. So, of course, economists talk about AI in general as a general purpose technology. One that is less discussed but I think profound is that software development is a general purpose technology. In general, digitization is something that restructures industries and it helps small enterprises scale. It increases the span of control.

它让企业无需直接监督员工就能实现增长。然而这类通用技术尚未在全球完全普及。许多小企业数字化程度较低，而这项技术通常具有显著的规模经济效应——尤其在数据和机器学习领域。因此面临的挑战是前期需要大量固定投入。如果餐馆还在用纸质单据或仅靠WhatsApp/微信运营，要实现数字化还需要跨越一个台阶。

It allows firms to grow without just having to directly supervise people. That kind of general purpose technology though has not been fully adopted across the world. Many small businesses are less digitized and it's generally been something with a lot of scale economies, especially with data and machine learning. And so the challenges have been that there's lot of fixed costs to get going. If you're running a restaurant off of paper or maybe WhatsApp or WeChat, there's another leap.

企业必须达到足够规模才能证明这些投资的合理性。所以今天你可能要问：如果我们消除软件工程这个瓶颈会怎样？当你询问威斯康星州甚至巴西、阿根廷的银行是否充分运用了机器学习时，会发现他们面临人才招聘困难，技术落地也存在诸多障碍。

You have to be big enough to justify those investments. And so one of the things you might ask today is, all right, what would happen if we remove the bottleneck that software engineering was? If you talk to a bank in Wisconsin or even in Brazil or Argentina and they say, well, are we doing enough with machine learning? And you would find out well, they have a lot of trouble hiring people. They have a lot of trouble being able to adopt.

若在五到十年前，我可能会建议不要指望通过大额投资快速获得回报，因为筹备期实在太长。过去在数据整理环节就存在诸多瓶颈。观察企业时会发现，他们要么使用笨重的软件，要么干脆不用——因为系统变更极其困难，导致企业很少调整，而每次调整都需要巨大投入。所以瓶颈主要存在于技术采用、方案决策（考虑到系统会长期使用）以及高昂成本等方面。现在我们要思考：既然软件开发成本已大幅降低，还有什么在阻碍我们？

And I might recommend five, ten years ago that really you may not get that much value quickly from making big investments because it takes so long to get going. So there were many bottlenecks around just getting data organized in the past. One of the things that when you look out in companies, they're using clunky software if they're even using it at all because it's so hard to change that you change rarely and you put a lot of effort into it when you do. So the bottlenecks have been around adoption, figuring out what you want given that you're going to be stuck with it and just the big cost. So now we have to ask, if software development is actually very cheap now, what stops us?

假设我们现在就能开发出高效的护理助手——尤其在护士培训不足、教育水平较低的国家——为什么我们认为这项技术不会在短期内全面普及？其实你能想到许多无法快速普及的原因：需要决策实施方案，要让护士接受，还要进行培训。

If we could already build today, say, a nursing assistant that could be very effective, especially in a country where nurses aren't well trained and there's not a lot of education, why do we think that it's not actually going to be fully adopted tomorrow? And you can think of many reasons actually that it won't be fully adopted tomorrow. You have to decide what to do. You have to get the nurses on board. You would have to train them.

接纳新事物总是困难的，所以你不会随便抓起第一样东西。因此，我并不预测明年全球就会涌现护理助手。

It's hard to adopt something new, and so you wouldn't just pick the first thing up. And so yet I don't predict that next year there will be nursing assistants around the world.

很好，谢谢。关于瓶颈与就业这个问题，Neil，你在AI对就业影响的研究中采取了长远视角。对于Susan刚才提到的案例，你如何预测或衡量AI对劳动力市场的影响？

Great. Thank you. So on this issue of bottlenecks and jobs, Neil, you have taken a long view in some of your work on the impact of AI on jobs. How do you think about either predicting or measuring the impact of AI on the labor market in some of the examples that Susan just said?

这是个好问题。我常开玩笑说，作为硅谷经济学家，我被问得最多的两个问题是：AI会颠覆哪些工作岗位？我们该如何应对？我告诉他们，我对哪些工作会被颠覆毫无头绪，但对应对之策却了如指掌。砰。砰。

Yeah, so it's a great question. I sort of joke that the two questions I get the most as an economist in Silicon Valley is what jobs is AI going to disrupt and what do we do about it? I tell them I have no clue on what jobs are going to be disrupted, but I know exactly what to do about it. Boom. Boom.

我们该如何应对？我认为我们正面临'无知之幕'时刻。联系到人文学科，你们许多人应该记得罗尔斯的无知之幕思想实验——如果我们能暂时抽离现状，在尚未获得天赋或技能之前思考：我们想要怎样的社会？需要哪些社会保护与保障措施？

What should we do about it? I think we're facing sort of a veil of ignorance moment. So connecting to the humanities, I think many of you remember Rawls's thought experiment of the veil of ignorance. If we can step back from our current lives and think about before we have our endowments or skills, what do we want in society? What protections and safeguards do we want in society?

这正是AI带给我们的处境。我们知道有些人可能会失业，或拥有在劳动力市场上贬值的人力资本，但无法预知具体是谁。罗尔斯的理论告诉我们，现在正是投资社会安全网的最佳时机。我举个实例就结束发言。

And I think that's where we are with AI. That we know that some of us may lose a job or may have human capital, which is less valuable on the labor market. We don't know who it's going to be. And I think what Rawls tells us is now is the right right time to invest in a social safe net. I'll give you one example, and then I'll shut up.

在我们国家，失业往往意味着失去医疗保险。或许我现在戴着政治立场的帽子——我认为这在当下已很荒谬，当5%、10%的人可能因AI失去主要职业时会更疯狂。在无知之幕的当下，思考如何构建社会安全网来抵御即将到来的冲击，是至关重要的课题。

We live in a country where if you lose your job, you likely lose your health insurance. Maybe I'm wearing my political hat. I think that's crazy now. It is going to be hugely crazy when 5%, 10 of us lose our primary occupation, maybe, because of So thinking about how we're in a veil of ignorance moment, and how we can invest in a social safety net to protect us against, I think, the disruption we will face is a hugely important endeavor.

谢谢。稍后我还想再讨论安全网问题。Fei Fei，作为推动AI革命的技术专家，你目前从事的视觉智能和空间智能研究，已远超ChatGPT等现有技术的范畴——而这些领域恰恰是地球生物（比如人类）最擅长的。

You. And I want to come back to safety nets a little bit later. Fei Fei, you're a technologist who helped shepherd in the AI revolution. You're working currently in the area of visual intelligence and spatial intelligence, far beyond what the current things like ChatGPT and all the others do. But these are also areas where living organisms on Earth, like humans, have been excellent.

我们进化得极为擅长空间感知和视觉识别。你认为你和其他人正在构建的系统将如何与使用它们的人类互动？或者说，你希望这种情况如何发生？

We've evolved to be excellent at space. We've evolved to be excellent at seeing things. How do you think the kind of systems you and others building will interact with the humans who are using them? Or how do you hope that will happen?

感谢提问。首先，我确实认为我们正处在人工智能经济的黎明时分。无论是大语言模型还是下一篇章的空间智能模型、具身AI模型，我们都将看到超越当前的技术创新。住在硅谷时，我也常被问到和尼尔同样的困惑：人们讨论AI时总直接想到‘取代’人类。我们必须谨慎，因为AI确实会改变工作岗位。

Yeah, thanks for the question. So first of all, I do agree right now we're at the dawn of an AI economy. And I think whether it's the large language models or the next chapters, are the spatial intelligence models, the embodied AI models, we're gonna see more and more innovation in this technology going beyond what we're seeing now. One thing that also living in Silicon Valley, also getting the same questions that Neil does that puzzles me is that when people talk about AI they go straight to the word replacement, replacing humans. And this is just we've got to be a little careful because AI will change jobs.

AI将改变复杂工作中的不同任务。以养老院护士为例，护士每天要处理数百项事务。AI能协助部分工作，但不会整体取代这个职业。关键在于认识到AI的作用是增强而非取代。我坚信这是一项横向技术，能赋能人类、优化工作流程，增强人类本就擅长的各项能力。

AI will change different tasks within a complex job. For example, being the nurse in a nursing home, there are hundreds of daily tasks a nurse does. AI will help some but will not replace the job wholesalely. What is really important is to recognize instead of replacing, AI really augments. And that's what I really believe is that this is a horizontal technology that can superpower humans, superpower our workflow, augment so many capabilities that humans are good at.

我们可能面临劳动力短缺，或在某些能力上存在不足。但AI能提供帮助。以我所在的视觉空间智能领域为例：我们合作的创作者、电影人、游戏开发者们，都面临着AI工具的新时代。

We might be short of labor or we might not be so good at particular part of that capability. But with AI, it can help us. I want to just take one example because I'm in the area of visual spatial intelligence. We work with creators, visual creators, storytellers, movie makers, filmmakers, game developers. All of these creators are facing this new era of AI tools.

与这些创作者共事令人振奋，因为他们将AI视为增强工具，认为AI能激发创造力和提升效率。我并非否认AI的双刃剑属性——这确实需要探讨。但我们必须认识到这项技术更重要的是赋能人类，以人为中心，而非直接跳转到‘取代’这个词汇。

And it's so incredible for me to meet and work with them because they see AI as a tool to augment them. They see AI as a way to supercharge their creativity and their productivity. So I'm not trying to say that there's not a double edged sword. We should talk about that. But I do think it's so so important we recognize that this technology can do a lot more to supercharge, superpower people, putting people in the center instead of go straight to the word replacement.

谢谢，我能插一句吗？我赞同这个观点。我认为一个有用的框架是：回顾二十世纪或二十一世纪初的创新政策问题，核心是如何在预算和人力资本限制下最大化创新。

Thank you. Can I jump in on this? Yes. I agree. And two, I think a useful framework is if you think about the questions in innovation policy over the twentieth century or early part of the twenty first century, they're about how do we maximize innovation given budgetary constraints, human capital constraints.

但展望未来，最引人深思的问题将不再是单纯‘如何加速’，而是‘如何塑造与人类技能互补的创新’。这既是STEM问题，也是人文问题，因为它需要探索赋予我们意义与目标的活动。在斯坦福校园这个人文学科与STEM的交汇处，正是研究这个重大命题的理想之地。

But moving forward, I think the most interesting, richest questions will be less about just sort of how do we put our foot on the gas, but how do we shape innovation to be complementary to our skills? And that's a STEM question, but it's also a humanities question. Because it requires tapping into what are the activities that give us meaning and purpose. And so at Stanford campus, where we have that intersection of the humanities and STEM, I think is a great place to be working on that hugely important question.

这是关于以人为中心的人工智能问题。

It's the human centered AI question.

是啊，有人给他们的研究所起了个好名字。

Yeah, somebody named their institute very well.

没错，我和Fei Fei曾共事过，Russ参与了斯坦福以人为中心人工智能研究所的创立，John Levin也是。我们的核心理念之一正是探讨大学在这一领域的角色。当然，商业利益往往纯粹追求利润，即降低成本。但许多创新属于固定成本投资，比如开发出护理助手后，就能在多地推广应用。

Yeah, and I think we have, know, Fei Fei and I worked together and Russ with the founding of the Stanford Institute for Human Centered AI and John Levin as well. And one of our theses was exactly developing this idea, what is the role of a university in all of this? And of course, commercial interests are often purely profit, which is lowering cost. But a lot of these innovations are fixed cost investments. So if you invent a nursing assistant, it can be applied and adapted in many places.

因此，如果我们在大学里进行部分增强人类技术的创新，就能分担部分固定成本。然后创业者可以完成最后一公里，解决落地应用问题。这种协作不仅限于我们开发后简单交付，基于语言的AI最棒之处在于，即便没有深厚计算机背景，许多人也能参与其中。事实上，我们已看到全球各地都在探索如何用AI增强人类能力。我还想补充一点：政府政策也能发挥作用。

And so if we do some of the innovation in the university for human augmenting technology, then that takes care of some of the fixed costs. And then entrepreneurs can take it across the finish line and solve the last mile adoption problem. And that can happen not just us building something and throwing it over the transom, but one of the great things about the language based AI is that actually many people can participate in it even if they don't have a big computer science background. And so actually, we can see the participation and the value add coming across the world in figuring out how to help it augment humans in those settings. And I just wanted to connect something that came across all of us, which is that government policy also can play a role in this.

所以当人们问我'人们将来要做什么？难道就坐在沙滩上等着无人机送鸡尾酒吗？'

And so when people ask me, what are all the people going to do? Are they just going to sit on the beach and have drones drop them daiquiris?

我倒希望如此。

I hope so.

我是说，关于怎么去沙滩、为什么无人机要送鸡尾酒这些问题我也有很多疑问。但如果我们把目光放近些，当前有很多服务随着人口规模扩大而需求激增，而我们投资不足。更多育儿服务、更多护理、更好的医生、更完善的养老——这些领域都能大规模创造就业。AI可以帮助人类转型进入这些岗位，而政府可以通过采购来推动发展。

I mean, I have many questions about how we get to the beach and why the drones are bringing us daiquiris. But if we take a slightly nearer term view, there are many activities that scale with the size of populations and where we are sort of under investing in them today. More child care, more nursing, better doctors, better elder care, all of those things are things that humans could be productively employed at large scale. And AI can help humans transition into those jobs. And governments can procure those things.

政府在投资所有这些领域扮演着重要角色。与其想象一群人无所事事，如果我们组织起来，创造产品，制定政府政策，原则上我们就能在实现全面转型的同时帮助人们度过难关。

Governments have a big role in investing in all those sectors. So rather than imagining just a bunch of people not doing anything, if we get ourselves organized, we create the products, we create the government policy, then in principle, we can help people through the transition while making all of

让我们变得更好。

us better.

苏珊，我知道你在政府工作了两年。尼尔，我也知道你担任顾问和研究助理。请告诉我，以你们对当今政府的现实理解，这一切将如何推进？

So Susan, you spent two years in government, I happen to know. And I know that you're an advisor and research associate, Neil. Tell me, with your real world understanding of the government today, how's that going to go?

这确实很困难，因为在过去六年左右的时间里，我们遭遇了一些意外挑战，面对重大变革和巨大干扰时，很难确定最佳应对方案。这真的很难。因此我们需要共同努力解决这些问题。想到我们的政府在那些实际上比以往任何时候都更需要政府领导力的领域（无论是研究还是大学）功能日益衰退，确实令人担忧。

It's really hard because at this moment, we've had the last six years or so, we've had some curveballs thrown at us and it's not easy to figure out the very best thing to do in the face of big changes and big disruptions. It's really hard. And so we need to work together on solving those problems. And so it is scary to think about our government getting less functional in the face of places where actually government leadership might be for both research, for universities, be more essential than ever.

我想提出一个问题。我坚信一百年后，当历史学家重写二十一世纪的篇章时，AI的黎明、人类或这个国家的集体成就将是：这个AI时代引发了教育革命。既然AI（甚至语言模型）已经证明它基本能通过标准化考试，达到及格甚至优秀水平，我们是否应该重新思考花费超过十二年的人力资本来教育年轻人，使其达到AI目前的水平？人类教育应该彻底重构，因为AI向我们证明，教育不在于知识的记忆和对这些记忆的评估。因此，如果政府能做什么的话，在我看来就是加大对K-12教育和高等教育的投入，因为这是

I just want to ask something. Firmly believe one hundred years from now when historians rewrite the chapters of twenty first century, the dawn of AI, a collective success of humanity or this country would be that this era of AI launched a revolution in education. Is that now that AI, even language models, has proven that AI can do the standardized tests by and large to whatever passing or even excellent grades, that we should rethink about spending more than twelve years of human capital that educating young humans to evaluate them to the level of what AI can do today. Human education should be completely rethought because AI showed us that it's not about memorization of knowledge and evaluation of these memorizations. So if there's anything government can do, to me is the investment of K-twelve education as well as higher education because this is

这个

the

我们真正革新地球上最重要资源——人力资本的契机。

moment that we can really revolutionize the most important thing on earth, which is human capital.

尼尔？我想把这些线索串联起来深入探讨。经济学家大卫·奥特记录了一个重要事实：在过去七十年间，经济中约70%的职业都是逐渐涌现的。也就是说，回顾七十年前，现在70%的工作岗位当时根本不存在。因此如果人工智能能以我认为的地理分布均衡且不过快的速度渗透，我们终将适应这种变化。

Neil? I want to connect these threads and run with it. There's a great fact from the economist David Otter who's documented that over seventy years, something like 70% of the occupations we have in the economy sort of emerge. That is if you look back seventy years ago, 70% of jobs didn't exist. So if AI diffuses in a way which I think is geographically spread out and not too fast, that we will adapt.

我们有望开发出新的教育方式，让人类与人工智能形成互补关系。AI也会以与人类互补的方式自我调整。但历史同样告诉我们，当变革过于集中和迅猛时——比如中国加入WTO和自动化导致工厂城镇空心化的案例——其影响可能是毁灭性的。这时就需要政策介入来提供安全网。

We hopefully will come up with new ways to educate people to be complementary to that AI. AI will adapt in ways which are complementary to humans. But history also teaches us that when things are concentrated and rapid. So if you think about the hollowing out of factory towns due to Chinese accession in the WTO and automation, those impacts can be devastating. And we need policy to come in and provide a safety net.

所以我不确定我们正处于哪种发展轨迹上，很可能是两种极端之间的某种混合状态。但思考这些极端情况，并通过教育来塑造技术发展，对于在不确定性中找准方向很有帮助。

So I don't know which timeline we're on. We're probably some combination of the two. But I think thinking about those extremes and shaping the technology in education is useful to triangulate in uncertainty.

谢谢。现在请飞飞以创业者的身份来思考这个问题——当你正全力推动事业发展时，我们却讨论着安全网和政府政策。我确信你和你的团队难免会担心，过早的监管和政策实际上会剥夺你们实现理想的能力。

Thanks. Now, Fei Fei, I want you to put your hat on as somebody who has a start up and you're trying to make a go of it. And then we're talking about safety nets. We're talking about governmental policies. I'm sure you have part of yourself and your cohort that gets worried that there will be premature regulation, premature policies that actually take away your ability to do the things you want to do.

这种讨论会如何进行？你对此有何看法？

How does that conversation go, and how do you think about it?

很好的问题。首先说明一下，我仍部分参与斯坦福的工作，并非完全休假。这同时也是加州乃至联邦层面关于AI监管与创新之间张力的最引人深思的讨论之一。我想先从一个家长的角度谈起：当你的孩子大约六岁时，最重要的课程之一就是教他们开火煮鸡蛋。

Great question. First of all, I'm still partially involved in Stanford, I'm not 100% on leave, just to make it very clear. It's also one of the most fascinating conversations that has been happening in the state of California as well as federally about the tension between AI regulation and AI innovation. And I want to start with, I'm a parent. And when your child is about the age of, I don't know, six or one of the most important lessons you need to teach them is to turn on the stove and cook an egg.

这涉及到用火，相当危险对吧？但你必须硬着头皮教会孩子用火，之后还要教他们许多其他事情。我用这个例子是因为技术始终是把双刃剑。自人类文明曙光初现，创新就刻在我们的DNA里——为了更美好的生活和工作。

And that's to use fire. And it's a pretty dangerous thing, right? But you all have to bite bite the bullet and teach your kid to use fire and then there are many other things we have to teach our kids. The reason I'm using this example is technology is always a double edged sword. That since the dawn of human civilization, in our DNA we're compelled to innovate so we can live and work better.

但我们有时也会无意中用它伤害自己，或有意伤害彼此。无论如何，驱动创新与建立规范护栏之间的张力将始终存在。作为企业家和创新者，我认为达成健康平衡至关重要。通过斯坦福HAI，我们一直在倡导一个非常简单的AI政策框架：首先是科学，而非科幻。

But we also use that to sometimes inadvertently hurt ourselves or sometimes intentionally hurt each other. No matter what, that tension between the driver innovation and the need for establishing norms guardrails is always going to be there. So as an entrepreneur, as an innovator, I think it's very important that we arrive at a healthy balance. And with Stanford HAI, we have been actually advocating a policy framework for AI, which is very simple. It's first science, no science fiction.

要制定良好的监管政策，我们应像苏珊在政府工作中那样，用数据和测量来指导监管框架，而非那些牵强的科幻末日预言。其次要务实而非意识形态化。例如AI领域已有众多监管框架，你们参与过医疗FDA的工作，我们应当最大化与这些务实框架的互动合作，而非陷入意识形态之争。

To do good regulatory policy, we should use data, use measurement like what Susan has been doing in government to guide our regulatory framework instead of those far fetched science fiction extinction doomsay. Second is that be pragmatic, not ideological. For example, in AI we have so many regulatory frameworks. You're involved with the health care FDA. And we should just maximize the interaction and partnership with these pragmatic frameworks instead of going ideological.

最后同样重要的是，我始终认为应该投资公共部门和创新引擎。正如康迪所说，我们没有备选方案。政府的AI政策必须包含对国家创新引擎的投资，包括高校和公共部门。

And last but not the least, in this audience I always believed investing in our public sector, investing in our innovation engine. Like Condie said, there's no plan b. Government policy in AI should include the investment of our country's innovative engines, including the universities and public sector.

谢谢。我想转向一个不同的话题。苏珊，你在司法部工作过几年。在我提到的社交媒体、共享出行、共享住宿等创新经济成功案例中，赢家往往形成垄断或接近垄断。这是创新经济的固有特征吗？我们是否应该接受？还是说这并非必要特征？

Thank you. And I actually want to go to a slightly different topic. But Susan, were in the Justice Department for a couple of years. And in all the examples of innovation economy successes that I gave in my intro social media, ride sharing, home sharing, the winners have tended to be either monopolies or near monopolies. Is this a feature of the innovation economy or how should we and are we okay with it or is it not a necessary feature?

我认为规模经济确实常导致集中化，但我们也看到即使少量竞争也远胜于零竞争。当单个企业能对整个经济征税时，我们已看到诸多担忧。在座可能有信用卡行业人士，这就是个典型例子——每笔交易都要被抽走几个基点。

I think that we have often concentration because of the scale economies, but I do think we have examples where even a little bit of competition is a lot better than none. And we have seen a lot of concerns when one firm can put a tax on the whole economy. And probably there's some people here in the credit card industry, but that's sort of an easy example because there's a few basis points coming off of every transaction.

我听人称之为'收费站'。

I've heard it referred to as a tollbooth.

确实。当这些'过路费'过高时，同样会阻碍创新。我认为需要让企业能从创新中获得回报以保持动力，但如果一家企业阻碍了周边所有创新，这同样成问题。关于AI领域的竞争，我有几点看法。

Yes. And so in the end, when those tolls get too high, that is also bad for innovation. So I think you can think about we need firms to be able to get returns from their innovation in order to want to do the innovation. But then if one firm stops, slows down all the innovation around it, problematic as well. So I think here I have a few comments about competition in AI.

其一，曾有一股力量试图抑制开放模型和开源模型的发展。尽管人们对此感到担忧，但这些模型实际上能降低所有人的使用成本。这意味着，如果存在一个相当不错的免费替代方案，国内所有使用大语言模型的企业都能以更低价格获取它们，影响深远。这种讨论在DeepSea问世后发生了变化。

One is that there was a bit of a push to hold back open models, open source models. But those things can although people were scared of them, they can pull down the prices for everybody. And that means that every single business in the country that is using large language models can get them at lower price if there is a free alternative that's pretty good. And so impactful. That conversation changed once DeepSea came out.

但我们仍需警惕其他类型的市场垄断力量。尤其在考虑到较小国家时，瓶颈风险众多——那些无法从AI技术栈中直接获利的国家，若以高价购买AI并大规模自动化劳动力，将面临巨大风险。实际工资可能下降，因为他们在AI服务上花费高昂却无法降低商品价格，导致民众购买力下降而物价居高不下。

But still, we also need to worry about other kinds of market power. There are lots of potential for bottleneck. When I think especially about smaller countries, the countries that aren't going to themselves be generating generating the profit from the AI stack, there's a huge risk if those countries are buying AI at a high price and then automating a lot of their labor. They might have real wages fall because if they're paying a lot for the AI services, their goods prices don't fall. So stuff people buy stays expensive while wages fall.

这对国家和经济体将是极其不利的局面。既然这是所有企业无论大小都将采购的技术，其价格与质量同等重要。目前我们在这方面做得还算不错，但必须保持警惕，确保这项技术真正能为所有人提供创新基础。

So that's going to be a really bad situation for countries and economies. And so if this is something that every small and big business is going to buy, the price of that thing is very important. The quality is important. The price is important too. So at the moment it seems like we're doing reasonably well at that, but keeping our eye on the ball to make sure that this technology is actually going to allow everyone to innovate on top of it will be crucial.

谢谢。尼尔，说到这个，你一直在研究创新经济及更广泛的经济领域。你曾表达过对美国经济陷入某种低迷状态的疑惑——听完苏珊的发言后我不得不联想到这点。你所说的这种经济低迷具体指什么？

Thanks. Neil, on that note, you've been looking at the innovation economy, and economy more general. And you've expressed wonder about whether America and Americans are in something of an economic funk. And I can't help but think of that after Susan's comments. What do you mean by this economic funk?

你认为有解决之道吗？

And do you see a way out?

数据显示，《华尔街日报》九天前有篇报道指出，相信美国梦的人口比例在一代人时间内从70%暴跌至25%。新冠疫情后，人们对经济的乐观情绪彻底崩溃且未见复苏。原因何在？我们仍在探究，很可能是以下三方面因素的叠加：

So if you look at data, there was a piece in the Wall Street Journal I think it was now nine days ago that documented belief in the American dream had gone from seventy percent of the population to twenty five percent of the population over a generation. That optimism about our economy has cratered after COVID, and it hasn't recovered. What's going on? I think we're still figuring it out. Probably some combination of three things.

首先是经济中的真实风险：关税问题、失业率上升的担忧。其次，社交媒体确实扭曲了人们对美好生活的认知——我曾调侃说，在Instagram上看到的里兹酒店自拍远比汽车旅馆的多，这让人对'正常'和'成功'产生了失衡的认知。

One is there are, I think, real risks in the economy. Tariffs, people concerned about uptick in unemployment. Two, it has to be true that social media is skewing our vision of what is a good and meaningful life. I quipped that on Instagram you see more selfies from the Ritz than the Motel six. And that gives people an unbalanced view of what is normal and what is successful.

但我认为还有很多我们不了解的事情。不过你看，这些对话，我认为政治领袖、创新者，都在帮助这个国家重振雄风方面发挥着极其重要的作用。

But I think there's a lot we don't know. But look, these conversations, I think political leaders, innovators, I think are all really important in helping this country get its mojo back.

谢谢。好吧，我想我们得就此打住了。我知道大家意犹未尽，但我们只有二十六分钟。这次讨论非常精彩，不过在结束前，按照约定，我想进入我们的新环节《一分钟看未来》。我会问你们几个快速问题，希望你们能给我简短的回答。

Thank you. Well, I think we're gonna have to leave it there. I know we want more, but we only have twenty six minutes. This discussion has been fantastic, but before we finish up, as promised, I wanna move to our new segment called The Future in a Minute. I will ask you some quick questions, and I'm praying that you will give me some quick answers.

那么我们就先从菲菲开始，第一个问题是——我会给每位提两个问题——关于未来，最让你充满希望的一件事是什么？

So we're gonna we're gonna start with Fei Fei, and the first question is, I'm gonna do both questions for each of you. What is one thing that gives you the most hope about the future?

毫无疑问是人类本身。人工智能并无任何‘人工’之处。我想引用马丁·路德·金博士的话：历史的弧线虽长，但终将偏向仁慈。我相信人工智能的希望恰恰在于它的人性化。

Well, it's unequivolently humanity. There's nothing artificial about artificial intelligence. And I wanna paraphrase doctor King that the arc of history is long, but it bends towards benevolence. And I believe the hope of AI is inhumanity.

如果你能重新开始，需要获得其他领域的学位或培训，你会选择什么？

If you were starting over again and you needed to get your degree or training in some other discipline, what would it be?

好吧，我得用三十秒回答。六周前有位斯坦福大二学生采访我，作为教授我照例问他：你主修什么？学生说：不，斯坦福现有的专业都不够好，我要自创专业——这在斯坦福是被允许的。

Okay, I got to use thirty seconds. Six weeks ago a Stanford sophomore interviewed me and as usual as a professor I'm like, what is your major? And the student said, no, none of these majors are good at Stanford. I'm going to have my own major. So I'll create my own major, which you are allowed to do.

我问他打算创建什么专业？他说：我要用人工智能来最大化我的赚钱能力。我当时就想：天啊，我怎么没想到这主意？好吧，认真回答的话，如果重来一次，我会选择物理、计算机科学和艺术的交叉领域。

And I asked him, what are you going to create? He said, I'm just going to use AI to maximize money making making for me. I was like, Dan, why didn't I have that idea? Okay, my real answer would be if I were to start again, I would do a combination of physics, computer science, and art.

谢谢。

Thank you.

尼尔。不过是我自己的专业。

Neil. My own major though.

尼尔，未来最让你充满希望的一件事是什么？

Neil, what is one thing that gives you the most hope about the future?

我本想说是学生们，那些了不起的学生。但今天早上，我在美国自然历史博物馆追着我11岁和7岁的孩子跑。看到几十、上百个孩子对STEM、历史和恐龙充满热情。作为创新者，我们面临着逆风。但如果我们记得内心深处，我们都是喜欢恐龙的七岁男孩女孩，一切都会好起来的。

I was going to say students, incredible students. But this morning, I was chasing around my kids, 11 and a seven year old, at the American Museum of Natural History. And seeing dozens, hundreds of little kids excited about STEM, about history, about dinosaurs. We face headwinds as innovators. But if we reflect on the fact that inside, we're seven year old boys and girls that like dinosaurs, we're going to be all right.

如果你要重新开始，需要获得另一个学科的学位或培训，你会选择什么？

If you were starting over and you needed to get your degree or training in a different discipline, what would it be?

机器人学。我关注STEM、科学与现实世界的交叉领域。这也是我研究经济学的原因。但我认为机器人学是另一个实现这种交叉的绝佳领域。

Robotics. I care about the overlap between STEM and science and the real world. That's why I study economics. But I think robotics is another great field for that intersection.

尼尔，你可以来我的实验室。

Neil, you can come to my lab.

我很乐意。

I would love to.

苏珊，未来最让你充满希望的一件事是什么？

Susan, what is one thing that gives you the most hope about the future?

因此我认为，与机器学习等前几轮技术相比，人工智能更具普及性，也更有潜力在不同国家和收入阶层中发挥作用。它能让小企业主、不会编程的人、不会制作Excel表格或企业软件的人，通过聊天应用和自然语言来经营业务，实现增长、扩大规模并提高效率，从而帮助人们摆脱贫困，助力贫困国家发展。

So I do think that AI is more accessible and has more potential to be helpful across countries and across the income distribution than the previous rounds of technology like machine learning. I think that it can allow small businesses, people who don't code, who don't build Excel spreadsheets and don't enterprise software to run a business through a chat application and natural language and to grow and scale and get more efficient, and that that can help people rise up out of poverty, and it can help poor countries grow.

如果你要重新开始，需要获取另一个学科的学位或培训，你会选择什么？

If you were starting over again and you need to get your degree or training in a different discipline, what would it be?

其实我已经经历过两次转行。我自学了机器学习和人工智能技术，后来又学习了法律。但如果要再增加一项技能，我认为未来任何人都能打造出优秀产品。只要有好点子，就能在数字领域将其实现，所以我会选择产品管理。

So I've had to do this twice already. I trained myself in machine learning and AI technical work, and then I also had to learn law. But I one more thing if I wanted to add it to the mix. I think going forward, anybody is going be able to build a great product. If you have a good idea, you're going to be able to make it reality in the digital space, so product management.

感谢苏珊·阿西、尼尔·马奥尼和菲菲·李的分享。以上就是《创新经济未来》的全部内容。感谢收听本期节目，也感谢现场观众对节目的大力支持。我们的档案库中有近300期节目，您可以随时获取长达数小时甚至数天的精彩未来话题讨论。

Thanks to Susan Athey, Neil Mahoney, and Faye Faye Lee. That was future of the innovation economy. Thank you for tuning into this episode. Thanks to our live audience for supporting us and the show today. With nearly 300 episodes in our back archive catalog, you have instant access to hours, if not days, of interesting discussion on the future of everything.

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If you'd like to ask a question about this episode or a previous episode, please email us a written question or a voice memo question. We might feature it in a future episode. You can send it to thefutureofeverything@stanford.edu,all one word, the future of everything. No spaces, no underscores, no dashes, the futureofeverything@stanford.edu. Thanks again for tuning in.

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We hope you're enjoying the podcast.