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他们向你兜售的未来是AI将让你变得过时或无足轻重。
You're being sold an AI future where you're obsolete or irrelevant.
这种愿景是错误的。
That vision is wrong.
在Palantir，他们正在构建帮助工人并释放其全部潜能的AI。
At Palantir, they're building AI that helps workers and unlocks their full potential.
美国工人是我们国家最伟大的力量。
American workers are our nation's greatest strength.
AI不应该淘汰他们。
AI shouldn't eliminate them.
而应该提升他们。
It should elevate them.
Palantir在此讲述他们的故事。
Palantir is here to tell their stories.
从工厂到医院，AI正在将人们从枯燥工作中解放出来，让他们做人类最擅长的事：创造、解决、建设。
From factories to hospitals, AI is freeing people from drudgery, letting them do what humans do best, create, solve, build.
Palantir，让美国人变得不可替代。
Palantir, making Americans irreplaceable.
您可以随时通过'彭博新闻快讯'获取最新资讯。
You can get the news whenever you want it with Bloomberg News Now.
我是艾米·莫里斯。
I'm Amy Morris.
我是凯伦，来为您介绍我们全新的点播新闻报告，直接推送到您的播客订阅中。
And I'm Karen here to tell you about our new on demand news report delivered right to your podcast feed.
彭博新闻速递是一档每日精选新闻的五分钟音频简报。
Bloomberg News Now is a short five minute audio report on the day's top stories.
节目全天更新，提供最新资讯与数据，确保您随时掌握动态。
Episodes are published throughout the day with the latest information and data to keep you informed.
是的。
Yes.
虽然多家新闻机构都有类似产品，但它们通常只是全天重播电台新闻。
There are other products like this from a variety of news organizations, but they usually rerun their radio newscasts throughout the day.
我们并非如此。
That's not what we do.
我们制作的独家内容只能在彭博新闻速递中收听。
We create customized episodes that can only be heard on Bloomberg News Now.
我们不会为突发新闻等待一小时才发布。
And we don't wait an hour to publish breaking news.
当突发新闻发生时，几分钟内您就能在播客订阅中收到更新，确保第一时间获取最新进展。
When news breaks, we'll have an episode up on your podcast feed within minutes, so you're always getting the latest stories and developments.
汇集彭博全球3000名记者和分析师的深度报道与专业解读。
Get the reporting and the context from Bloomberg's 3,000 journalists and analysts were all over the world.
立即通过苹果、Spotify或您常用的播客平台收听彭博新闻速递。
Listen to the latest from Bloomberg News now on Apple, Spotify, or anywhere you listen.
彭博音频工作室，
Bloomberg Audio Studios,
播客、广播、新闻。
podcasts, radio, news.
大家好，欢迎收听新一期的《奇货可居》播客。
Hello, and welcome to another episode of the Odd Lots podcast.
我是吉尔·韦森塔尔。
I'm Jill Weisenthal.
我是特蕾西·阿拉维。
And I'm Tracy Allaway.
虽然不是什么新观点，但我在想——如果在2021年底或2022年初，有人向你展示如今AI能实现的所有惊人功能，你可能会预期整个经济或社会因此发生比现在更大的变革。
You know, not a novel observation here, but I do think that if, like, in early twenty twenty two or late twenty twenty one, you had someone had revealed to you all of the amazing things that we could do with AI these days, I think you would have expected that either the broader economy or society would be more different than it has been.
总体而言，大多数人的工作方式基本没有改变。
Like, by and large, you know, I think most people's jobs are done roughly the same way.
社会运转看起来也差不多——虽然可能每天都在变得更糟一点。
Sort of society still seems to operate, although a little maybe a little bit worse every day.
我也不知道。
I don't know.
这确实是项颠覆性技术，但总体来看，它带来的经济冲击远没有人们预想的那么大。
But, like, I just don't think, like, it's a mind blowing technology, and yet by and large, like, it hasn't had the economic disruption that I think many people would have guessed.
或许我对这个话题太悲观了，但我总说永远不要低估人类维持现状的本能——以及把事情复杂化的天赋。
Maybe I'm cynical on this subject, but I always say never underestimate the human capacity for stasis, I guess, and making things much more difficult than they actually need to be.
比如设置各种官僚障碍和监管壁垒这类事。
And putting up bureaucratic barriers and regulatory barriers and things like that.
所以这个结果并不让我意外，但确实值得深思。
So it's not that surprising to me, but it is true.
很多经济学家曾预测会出现大规模的生产力提升。
You have a lot of economists out there who think there was going to be this massive productivity boost.
对吧？
Right?
嗯，肯定是技术人才。
Well, it's certainly tech people.
就是，我也不清楚。
Like, I don't know.
就像经济学家，你知道的，他们总是说，哦，那里
Like, economists, you know, they're like, oh There
有一些。
were some.
我们将迎来生产力大爆发。
We're gonna have a productivity boom.
我们正在将预测值从2%上调至2.25%。
We're raising our estimates from 2% to two and a quarter percent.
然后你要知道，杰夫，一切都是相对的。
And then you have Everything's relative, Jeff.
后来我们采访过凯西·伍德一次，记得她预测什么来着？
And then, you know, we talked to Cathie Wood one time, and I think she what did she predict?
20年内实际GDP增长20%，硅谷那群人更是坚信随时会出现通缩繁荣、即刻进入后稀缺时代。
20% real GDP growth for twenty years, or you certainly have these people in Silicon Valley, deflationary boom, post scarcity any minute right now.
我得说，在很多经济学家对这些问题的思考方式——以及他们愿意采用的数字标准——和其他所有人之间，确实存在巨大认知鸿沟。
Some real gaps, I would say, between how a lot of economists think about some of these things and the numbers that an economist would be comfortable using versus maybe literally everyone else.
凯西·伍德肯定是这样。
Kathy Wood, certainly.
很多其他人，是的。
Many others, yeah.
你知道我在想，如果2008年、2006年我们刚开始写博客时就有AI，你我的职业生涯会有多么不同？
You know what I think about is how different would your career and my career have been had AI existed in 2008, 2006 when we were starting blog, basically?
这是个很好的问题，我不知道答案，因为某种程度上我觉得，定义我职业发展的整个路径可能根本就不会存在。
It's a really good question, and I don't know the answer because part of me thinks, well, that whole path that defined my career would not have been there, would not have existed.
或者不，也许吧。
Or no, maybe.
但另一方面，如果在2006年，我可能就像当初最早接触博客那样，最早尝试用AI写新闻，情况会不同，但也许我会走上另一条路。
But on the other hand, maybe in 2006, I would have been just like I was super early into blogging, super early into experimenting with AI news, it would look different, but maybe I would have rode some different way.
不同但相似。
Different but the same.
是啊。
Yeah.
不同但相似。
Different but the same.
你懂我意思吗？
You know?
我觉得这真的很难说。
Like, I think it's it's pretty hard to tell.
有一点可以确定，那时候我们写作都是为了优化谷歌搜索结果。
One thing I know is, like, back then, we were writing to optimize Google results.
对吧？
Right?
那是最基础的，或者说，社交媒体也在传播内容，但谷歌仍然是许多内容的主要平台。
That was base or, you know, like, I know social media was disseminating stuff as well, but, like, Google was still the main the main platform for a lot of stuff.
我确信如果我们现在做的话，我们会优化内容以便Chat GPT或Perplexity这类平台能抓取到。
I'm sure if we were doing it now, we would be optimizing for chat GPT or Perplexity or someone like that to actually pick up the content.
我是说，我
I mean, I
不过那是考虑到受众。
That's think the audience, though.
是的，确实如此。
Yeah, it is.
而且我认为，尤其是任何商业出版商，对吧，他们试图大规模地做这件事。
And I think, like, any especially any, like, commercial publisher, right, that's trying to do it at scale.
我认为，某种小众领域的语音专家仍然可以拥有直接追随他们的受众群体。
I think, like, a sort of niche voice expert could still have, like, their audience that comes to them directly.
当然。
Of course.
但规模化来说，确实每家出版商都在试图解决这个问题。
But, like, at scale, For sure, every publisher is trying to figure this out.
总之，我们确实请到了一位完美嘉宾，一个我们早该在多年前就邀请上播客的人。
Anyway, we really do have the perfect guest, someone we should have had on the podcast years and years ago.
几乎令人惊讶的是，这是我们第一次邀请他。
It's almost surprising that this is the first time we've had him on.
这太疯狂了。
It is crazy.
这很疯狂，但我觉得他就像是我们讨论的所有话题的交汇点，因为他是一位经济学家。
It is crazy, but it is someone who I think is like at the intersection of everything that we're talking about, because he's an economist.
他对技术非常了解。
He knows the tech really well.
科技圈的人都认识他。
The tech people know him.
科技圈的人都喜欢他。
The tech people love him.
他甚至可能是这方面最受推崇的经济学家之一。
He may even be one of the preferred economists for this.
我觉得他就像是AI领域所有人都想听取意见的经济学家。
I feel he's like the economist that all the people in the AI world want to get his take.
一位长期博主，你知道的，就像我们俩都放弃了写博客那样的人。
A longtime blogger, someone I you know, like both of us sort of gave up on blogging.
虽然我们有新闻简报，这差不多类似，但与写博客不同。
Although we have our newsletter, it's like close enough, but it's different than blogging.
他长期坚持这种媒介，是我二十多年来一直阅读的真正原创的经济博主之一。
Someone who's stuck with the medium for a long time, one of the true original ecom bloggers that I've been reading for over twenty years.
我们将与泰勒·考恩对话。
We're going be speaking with Tyler Cowen.
他是《与泰勒对话》播客的主持人。
He is the host of the Conversations with Tyler podcast.
他显然也是著名博客'边际革命'的两位博主之一，乔治梅森大学的经济学教授，华盛顿特区、北弗吉尼亚和马里兰地区各种民族美食的鉴赏家，一位在互联网上久负盛名的人物。
He also obviously is one of the two bloggers at the famous Marginal Revolution blog, economics professor at GMU, appreciator of ethnic foods all around the DC, Northern Virginia, Maryland area, someone known on the internet for a long time.
泰勒，非常感谢你参加《Odd Lot》节目。
Tyler, thank you so much for coming on Odd Lot.
非常高兴能邀请到你。
Really thrilled to have you here.
你好。
Hello.
很荣幸来到这里。
Happy to be here.
太棒了。
Amazing.
你对我的初步评估有什么看法？
What do you think about my initial assessment?
如果你在2021年就知道这些工具会如此强大，你会觉得现在商业运作基本如常这件事有多令人惊讶？
How fair do you think it is that had you known in 2021 how powerful these tools would be, maybe we'd be a bit surprised that by and large, like business seems to more or less run the same.
我一点也不觉得惊讶。
I'm not surprised at all.
目前我看到的是人们把AI当作现有工作流程的附加工具。
So what I see right now is people using AI as an add on for their preexisting work routines.
哦，你需要写一份备忘录。
Oh, you need to write a memo.
你就问AI该怎么写。
You ask AI how to do it.
你需要写一篇专栏文章。
You need to write a column.
你让AI来校对或事实核查。
You ask AI to proofread it or fact check it.
这效果很好。
And that works great.
但这些只是边际效益。
But those are marginal gains.
我们真正需要看到重大影响的是围绕AI建立的新组织。
What we really need to see a major impact is new organizations built around AI.
而这些将是初创公司。
And those will be startups.
它们只会缓慢出现。
They will come only slowly.
需要二十年或更长时间才能真正改变经济。
It will take twenty or more years before they really transform the economy.
在此期间，会有大量附加功能，虽然有趣但有限，这就是为什么我认为进展缓慢。
And in the meantime, it's a whole bunch of add ons, which are fun and fine, but that's why I think it's slow.
关于技术史，你能告诉我们什么，使得在可能具有革命性的新技术发明之前就存在的传统组织难以大规模改变其工作流程？
What can you tell us about the history of technology such that legacy organizations that existed prior to the invention of maybe potentially revolutionary new technology have a hard time massively changing their workflows?
嗯，你可以举些非常简单的例子。
Well, you can take even very simple examples.
比如丰田在1970年代开始与通用汽车竞争。
So Toyota starts competing with General Motors in the 1970s.
通用汽车就陷入瘫痪了。
General Motors is paralyzed.
它确实无法回头采用丰田这种新颖且更优越的方法。
It cannot really come back and adopt the new and superior Toyota methods.
这些方法与人工智能相比其实并没有太大不同。
And those are not really that different compared to say AI.
这样的例子比比皆是。
So there's just plenty, plenty examples.
传统主流媒体很难适应互联网的发展。
Old mainstream media could not cope very well with the internet.
也有例外，比如《纽约时报》。
There are exceptions like the New York Times.
Odd Louds播客会是另一个例子。
Odd Louds Podcast would be another.
谢谢。
Thank you.
但这是普遍现象。
But it's the norm.
现在我们又在人工智能领域看到了同样的情况。
So we're seeing it again with AI.
同样地，需要彻底更换商业主体和模式，才能在大规模上产生真正的影响。
Again, you need a complete turnover of who and what is doing business for it to really matter on a big scale.
如果需要这种彻底变革，就需要给人工智能一些时间完全融入商业模式，或者围绕人工智能形成新的商业模式。
If you need that complete turnover and you need some time for AI to become fully embedded in a business model or for a business model to form around AI.
你认为哪个行业或经济领域会最先以这种革命性的方式显现出人工智能的影响？
What industry or what part of the economy would you expect it, I guess, to show up first in that sort of revolutionary way?
嗯，我们对此有明确的数据，那就是编程。
Well, we have obvious data on this, and it's programming.
你会听到从事编程的人声称，比如80%的工作现在由AI完成。
You will hear people who do programming claim that, say, 80% of the work is now done by AIs.
我怀疑这种说法有些夸大，但毫无疑问，已经有大量编程工作是由AI完成的。
I suspect that's an overstatement, but there's no doubt at all that there's simply a lot of programming already done by AIs.
当固定成本低、行业竞争激烈、反馈即时时，收入自然会源源不断。
When you have low fixed costs, a competitive sector, immediate feedback, the revenue has to flow.
编程领域，还有纽约金融业也是如此。
Programming and also New York City finance.
金融领域的量化分析师已经存在很长时间了。
There's been quants in finance for a long time.
可以说，这些量化分析师现在比过去配备了更多AI工具。
Those quants are now, you could say, more AI equipped than they used to be.
而这些领域已经在经历革命性的变化。
And those areas already are being revolutionized.
我收到过几期节目提案，我们确实应该找个时间做这些内容。
I've heard got some pitches to do episodes, which we should do at some point.
但我听说有些律所正在转型为AI律所，虽然仍有律师等人员，但从最基础开始，他们的理念可能是：如果从一开始就采用律师与AI模型结合的方式，或许能大幅提升效率。
But I've heard about some law firms that are being new AI law firms where there are lawyers, etcetera, but from the very ground up, the idea is perhaps there is some way to just get way more productivity if they start from the very beginning with some combination of lawyers plus AI models.
看起来传统律所可能正从AI中获得一些效率提升。
It seems like that could be the kind of thing where maybe the legacy law firms are seeing some productivity gains from AI.
或许能找到一些证据，但采用全新方法的新律所可能会带来足以改变整个行业的巨大效率飞跃。
There's probably some evidence you could find, but that a new one with a totally different approach could deliver that big productivity boost that actually ends up changing the industry.
这已经成为现实，比如中级律师的需求大幅减少，但法律领域尤其面临一个大问题，那就是当前大语言模型的运作方式。
It's already the case, say, mid tier associates are much less needed, But there's one big problem with law in particular, and that is the way large language models work now.
你必须将查询请求发送到别处处理。
You have to send your queries somewhere else.
你无法仅凭自己的硬盘就拥有、控制并存储整个系统。
You can't just own and control and hold the whole thing on your hard drive.
我认为在未来几年内，这种情况将会彻底改变。
Now, I think within a few years' time, that will be very different.
但在那之前，大型律所对于直接输入问题并发送到——比如旧金山——表现得极为谨慎。
But until then, major law firms are extremely skittish about just typing in their questions and sending it, you know, to San Francisco.
实际上我并不认为存在风险，但考虑到受托责任机制，他们就是不愿这么做。
I don't actually think there's a risk, but when you think about how fiduciary works, they just don't want to do it.
硅谷正在向你兜售一个未来——在那里你不是被淘汰就是变得千篇一律。
Silicon Valley is selling you a future where you're obsolete or worse identical.
在Palantir，人们正见证着截然不同且具有革命性的景象：从国防工业基地的再工业化到造船工人提速生产，再到一线工人提升效率，AI正在全美各地重塑工作方式。
At Palantir they're witnessing something different and revolutionary From re industrializing the nation's defense base to shipyard workers building faster and frontline workers boosting productivity, AI is transforming work across the nation.
AI并非在取代美国工人或将他们同质化。
AI is not replacing American workers or flattening them into conformity.
它正在释放每个人不可替代的特质。
It's unleashing what makes each one irreplaceable.
他们的判断力、工艺技术和创造力。
Their judgment, their craft, their creativity.
当美国工人更强大地做回自己时，他们就掌握了未来。
When American workers become more powerfully themselves, they own the future.
Palantir，让美国人变得不可替代。
Palantir, making Americans irreplaceable.
我是Jennifer Zabasaja，这里是《下一个非洲》播客，彭博社每周推出的节目，讲述塑造这个全球最年轻、增长最快大陆未来的故事。
I'm Jennifer Zabasaja, and this is the Next Africa podcast, Bloomberg's weekly show about the stories shaping the future of the world's youngest, fastest growing continent.
每周五，《下一个非洲》播客都会深入探讨头条新闻背后的故事——从埃塞俄比亚可能引发地区对峙的巨型水电站大坝，到博茨瓦纳努力摆脱钻石依赖的转型之路，再到重塑非洲创意经济的音乐节。
Every Friday, the Next Africa podcast goes deeper than the headlines from Ethiopia's giant hydroelectric dam that could spark a regional showdown to Botswana's struggle to move beyond diamonds to music festivals transforming Africa's creative economy.
我们揭示推动整个大陆变革的理念、冲突与机遇。
We uncover the ideas, conflicts, and opportunities driving change across the continent.
通过彭博社驻拉各斯、开罗、约翰内斯堡等地的记者，您将获得独一无二的深度解读：13亿人如何重新定义全球市场、文化与权力格局。
With Bloomberg journalists on the ground in Lagos, Cairo, Johannesburg, and beyond, you'll get the context you won't find anywhere else, how 1,300,000,000 people are redefining global markets, culture, and power.
彭博社出品《下一个非洲》播客。
The Next Africa podcast from Bloomberg.
权力、政策与繁荣，尽在此处。
Power, policy, and prosperity all in one place.
立即订阅《下一个非洲》播客，每周五通过Apple Podcasts、Spotify等平台收听。
Follow the Next Africa podcast now and join us every Friday on Apple Podcasts, Spotify, or wherever you listen.
我想多谈谈隐私担忧和监管问题，因为在这个领域，如果你身处高度监管行业，或是像律师这类从业者普遍多疑的行业，人们自然会对与AI共享数据表现得极其谨慎。
Talk more about, I guess, privacy concerns and regulation because this seems to be an area where if you are in a heavily regulated industry or if you're just in an industry that tends to be full of paranoid people like lawyers, it does seem like there's going to be a natural tendency to be very, very cautious when it comes to sharing data with AI.
你会担心实际数据所有权问题，担心发送到旧金山的查询请求，就像你说的那样。
You're going be worried about actual data ownership, the queries that you're sending to San Francisco, as you say.
这些行业是否注定适应缓慢？
Are those industries just inevitably going to be slow to adapt?
它们会适应得很慢，直到最终拥有自己的模型，将其完全控制在公司内部，外人无法触及。
They'll be slow to adapt, again, the point where they just control their own model, and they hold it within the firm and no one else really can get at it.
所以你需要的是律师事务所负担得起的更便宜的模型。
So what you need is cheaper models where a law firm can afford to have its own model.
我认为这还需要几年时间。
And I think that's a few years away.
虽然不会太久，但也不会在六个月内实现。
It's not a very long time away, but it won't come in six months.
山姆·奥特曼，我刚和他录了一期播客，他说AI查询存在隐私问题，可能受到传票约束，他认为应该像与律师、医生或心理治疗师的对话一样受到同等保护。
Sam Altman, I just did a podcast with him and he said a privacy problem is AI queries are subject to subpoena and he thinks they should have as much protection as say your conversation with your lawyer or your doctor or your therapist.
我觉得这是个好主意，但目前尚未实现。
I think that's a good idea, but that hasn't happened yet.
除非这种情况发生，或者你能把所有东西都存到自己的硬盘上，否则法律领域的进展将会很缓慢。
Until it happens or you get the whole thing on your own hard drive, progress in law is going to be slow.
但一旦取得进展，我认为这是AI最具潜力的领域之一。
But once the progress comes, that's one of the areas where I think AI has the most promise.
它非常擅长掌握大量文本并为你进行整理。
It's just very, very good at mastering a large corpus of text and organizing it for you.
这很有趣，不是吗？
It is interesting, isn't it?
就像说，好吧，如果你是我的律师，我们之间的对话就不会——
Where it's like, okay, if you were my lawyer, you and I could have a conversation, and it would not be
那将是受法律保护的。
It'd be privileged.
对，那将是受法律保护的。
Yeah, it would be privileged.
或者说，你知道，人们可以与任何人交谈，只要是通过电话或面对面进行，这些内容在证据开示阶段就难以获取。
Or, you know, one can have conversations with anyone, and as long as you're doing it on the phone or if you're doing it person to person, it's much more difficult to get that in discovery.
我常想到，通过《信息自由法》等途径有时能获取公务员的公开记录，比如他们的电子邮件，但你无法获得对话内容。
Or I always think about this with public records of employees that sometimes you can get their emails through FOIAs, etcetera, but you can't get the content of conversations.
不过我确实没深入思考过这种动态：当你使用AI时，它既像一种对话，但从证据角度又更接近电子邮件。
It does feel I hadn't really thought about this dynamic though, that when you're using an AI, it is sort of like a conversation, and yet sort of from an evidentiary basis, it would be much more like an email.
我认为在医疗问题上，愿意分享数据的人要多得多。
I think when it comes to medical issues, there are many more people willing to share their data.
当然绝非所有人。
Not everyone by any means.
有些病情是隐秘的，或者人们就是不愿他人知晓。
Some medical conditions are secret or people just don't want others to know.
但我认识很多人会把各种病史输入GPT-5，总体上都得到了相当不错的回答。
But I see many, many people I know typing in all kinds of things about their medical history to say GPT-five and getting what are on the whole very good answers.
这就像免费医疗诊断，如今正普及全球，许多国家的人根本接触不到好医生。
It's like medical diagnosis for free, spreading now to the whole world, a lot of countries where people just don't even have access to good doctors at all.
我认为这比法律创新会更早显现重要性。
And I think that will be important more quickly than the law innovations.
做个思想实验：这一切对保险公司意味着什么？
Just as a thought experiment, what does all of this mean for insurers?
因为我在想，那么多人输入自己的医疗信息...
Because I kind of think, you know, I think about a bunch of people typing in their medical information.
考虑到如今数据的爆炸性增长，保险业如果能获取这些新增信息宝库，可能是最大受益方之一。
I think about basically the explosion in data that we have nowadays, and it seems like the insurance industry would be one place that would really benefit from all this trove of additional information if they could access it.
嗯，这是我对人工智能的担忧之一。
Well, this is one of my worries about AI.
总的来说，我对当前的发展持相当乐观的态度。
In general, I'm quite positive on what's happening.
但随着保险公司对客户信息的掌握越来越精准——这主要得益于大数据技术，不一定是当前的大型语言模型——他们完全清楚如何评估风险并定价保费。
But as insurers get better and better information on their customers, this is just through big data more generally, it doesn't have to be current large language models, They know exactly how to write the risk and how to price the premium.
从某种意义上说，对买家而言这已经不再是保险了。
And in a sense for the buyer, it's not insurance anymore.
如果我们以高概率预知你的房子会烧毁，而你不得不支付超高额保费，那你实际上并没有享受到保险的益处。
So if we know your house is gonna burn down with high probability and you have to pay the super high premium, you don't really have the benefits of the insurance.
因此如果通过大数据让保险公司过度预知可能发生的事，某些保险市场可能会崩溃。
So some insurance markets might unravel if through big data, the insurers learn too much about what's likely to happen.
经济学家们似乎对技术影响劳动力需求的看法相当一致，认为最终会达到平衡，对吧？
Economists seem to be very consistent about the effects of technology on labor demand, which is that in the end it washes out, right?
确实有些人会面临失业，但通过使用AI节省成本后，我获得了更强的消费能力，转而购买原本因支付工资而无法负担的商品，这会在其他领域创造新的劳动力需求。
Some people, okay, there's disruption, but I'm going to save money because I used AI, but that means I have more spending power, and then I'm going to buy something else that I wouldn't have bought had it not been for paying wages, and then that'll create demand for labor elsewhere.
所以归根结底，认为技术真能造成大规模非暂时性失业的观点很难成立。
And so in the end, the idea that you could really have tech driven unemployment at scale that is not transitory or not temporary is hard.
许多经济学家似乎本能地怀疑这种观点。
A lot of economists seem to be intuitively skeptical of this idea.
而AI领域的人却说50%的人会失业。
Whereas you have people in the AI fields, 50% of people aren't going to have jobs.
我们需要全民基本收入。
We need UBI.
否则，将会出现一个永久性的底层阶级。
Otherwise, there's going be a permanent underclass.
人工智能是否可能有所不同，不会产生过去技术对劳动力市场那样的影响？
Could there be something different about AI such that it doesn't have the same labor market effects that past technologies have had?
我得说你很了解我。
I would say you understand me well.
在能源领域，将会出现许多照顾老年人的新工作岗位。
So the energy sector, there's going to be a lot of new jobs taking care of older people.
我认为随着人工智能带来更多潜在的医疗创新，我们需要进行测试验证。
I think as AI produces more potential medical innovations, we'll need to test them.
所以生物医学领域的临床试验测试将会产生大量新工作。
So the biomedical sector testing clinical trials, there'll be a lot new jobs.
我并不担心大规模失业问题，大多数经济学家也不担心。
I'm not worried about mass unemployment and most economists are not.
我同意他们的观点，我觉得你阐述得很清楚。
And I agree with their perspectives, which I think you outlined pretty clearly.
不过你怎么看？
What do you say, though?
因为我有种感觉，在旧金山那边的人可不这么认为。
Because I have a feeling that when you're out in San Francisco, they don't see it that way.
他们谈论很多，其中一些人更倾向于全民基本收入和永久底层阶级这类话题。
And they talk a lot, and some of them are more in UBI talk and permanent underclass talk and all of this stuff.
当你提出这个论点时，他们能理解你的观点吗？
Do they see your perspective when you make this case?
他们对你阐述的逻辑有何看法，或者他们似乎普遍忽略了哪些要点？
What do they say or what do a lot of them seem to be missing about the logic that you spell out?
嗯，我认为越来越多的人开始认同《经济学人》的观点。
Well, think more and more they're coming around to The Economist's point of view.
安德烈·卡帕西曾在OpenAI最重要的年份任职，他最近在播客中表示，他认为调整将是缓慢的，一切都会好起来，经济增长率会在2%左右，不会出现大规模失业。
So Andre Carpathi, who was at OpenAI in the most important years, he just did a podcast saying he thinks adjustment will be slow, things will be fine, we'll grow at two point something percent, there won't be mass unemployment.
而两年前你绝不会听到他这样说。
And you wouldn't have heard that say two years ago.
但我认为，随着人们看到模型的实际应用，正如你提到的，现实世界的影响是随时间逐步显现的，对吧？
But I think as people see the models rolling out, and as you mentioned, well, the real world impact, it is stretched out in time, right?
并非所有影响都是立竿见影的。
It's not all immediate.
早些时候，人们更多认为AI像一种万能黑箱。
Earlier on, people had more of the sense that AI was a kind of godbox.
你只需对它说话，它就能神奇地完成任何事，并在现实世界中产生效果。
That you just talk to it and it can magically do anything and convert that into results in the real world.
但如果你仔细想想自己的实际工作，即使是高度知识型的工作，你所做的大部分事情都涉及智力、身体存在、与他人互动、出差等诸多因素的结合。
But if you think about your actual job, even if it's a highly intellectual job, so much of what you do is the interaction between your intellect, your physical presence, your interactions with others, your travel, many other things.
在我们达到那个遥不可及的、机器人能完美复制人类的境界之前——我个人认为这永远不会实现——工作机会不会消失，但会发生巨大变化。
And until we get to some far, far off world where the robots are perfect copies of you, which I don't think ever will come, jobs will be fine, but they will change a lot.
实际上我担心的是，谁会成为最大的输家。
And I'm actually worried about who will be the biggest losers.
我认为穷人反而会过得很好。
I think poor people will do great.
真正的富豪会过得很好，但那些处于上层中产阶级的人会发现，获得法律或咨询工作的通行证能确保他们余生都保持上层中产阶级的地位。
The very wealthy will do well, but people who are sort of upper, upper middle class will find this automatic ticket to a law or consulting job that assured they would be upper, upper middle class for the rest of their lives.
我认为这种情况已经在大量消失了。
I think a lot of that is going away already.
我想也没人会预料到整形外科——我猜——会成为人工智能革命的受益者。
I think also no one would have expected plastic surgery, I guess, to be a beneficiary of the AI revolution.
但如果你认为未来重要的是你的个人形象
But if you think that what's going to matter in the future is like your personal presence
是的，没错。
Yeah, that's right.
以及你的社交人脉网络，那么我想我们都应该致力于‘颜值最大化’。
And your network of social contacts, then I guess we should all be working Luxmaxing.
致力于我们的‘颜值最大化’。
On our on Luxmaxing.
是啊。
Yeah.
绝对正确。
Absolutely.
没错。
Right.
魅力。
Charisma.
魅力。
Charisma.
好的。
Okay.
知道了。
Noted.
每个人都应该培养各自的魅力。
Everyone work on their respective charisma.
我在想人工智能对公共财政的影响。
One thing I was wondering is the impact of AI on public finances.
我不太擅长税收政策。
I'm not very good at tax policy.
乔知道这点，因为我曾反复向他抱怨税收问题。
Joe knows this because I've complained about taxes to him repeatedly.
我在考虑
I'm thinking about
也许这意味着你其实很擅长税收政策。
Maybe that means you're really good at tax policy.
但如果你思考AI的实际经济价值体现在哪里，理论上应该会带来生产力提升。
But if you're thinking about where the value add of AI actually shows up in the economy, so presumably you get a productivity boost.
我们并不完全确定这种提升会有多大。
We're not entirely sure how much that's going to be.
但这些额外产出究竟会如何体现在政府财政收入上？
But where does that additional output actually show up in terms of revenue for governments?
这些收益将如何被征收？你认为全球范围内的分配会有何差异？
How is that collected, and how would you expect the distribution to vary across the world?
嗯，我认为在美国，中期来看，医疗保健服务将会大幅增加。
Well, I think in The United States, medium term, there'll just be much, much more healthcare.
将会有新药物、新医疗设备问世。
There'll be new drugs, new medical devices.
我们需要对所有这些东西进行测试。
We'll have to test all these things.
我们还需要生产它们。
We'll have to produce them.
而且这些行业原本就在增长，但增长速度将会加快。
And that will be, it was already the case that those sectors but were that growth will be accelerated.
所以我认为长期来看这将是最大的不同之处。
So that's where I think that'll be the biggest difference longer term.
人们的寿命会更长，因为我们将至少部分解决各种疾病和病痛。
And people will live longer because we'll fix at least partially various diseases and maladies.
如果你活到94岁，一生中在医疗保健上的花费将远高于只活到77岁。
So if you live to be 94, across a lifetime, you spend way more on healthcare than if you live to be 77.
这对医疗保健行业来说意味着进一步的增长。
And that's yet further growth for the healthcare sector.
但像医疗诊断这类事情，现在已经非常便宜了。
But some things like medical diagnosis, that's already very cheap.
比如，一个好的大型语言模型可能已经比你现在的医生表现更好，至少在你正确输入症状的情况下。
Like, you know, a good large language model probably outperforms your current doctor, at least if you type in what's wrong with you properly.
但AI带来的额外生产力或产出，真的会转化为政府的额外税收吗？
But does that additional productivity or the output generated by AI, does that actually show up in additional taxation for the government?
医疗保健行业创造了巨额税收收入。
Well, the healthcare sector generates an enormous amount of taxation revenue.
我认为确实会有一些行业像维基百科那样变得免费。
I do think we'll have some sectors that maybe just become free in the same way that Wikipedia is free.
所以我能想象，比如音乐行业中10%或20%的音乐是你在家用AI创作的，那是一首为你量身定制的歌曲。
So I could imagine, say, 10 or 20% of the music sector is music you create at home using your own AI, and it's a customized song for you.
也许你为这项服务支付了订阅费，但与其在Spotify或流媒体服务上花更多钱，你直接自己创作音乐。
And maybe you paid a subscription for the service, but rather than spending more money on Spotify or a streaming service, you just build the music.
这在一定程度上替代了部分人类创作的音乐。
And that's a partial substitute for some human created music.
我认为人类创作的音乐绝不会消失。
I don't think human created music will go away at all.
人们渴望享受人文温度，比如成为泰勒·斯威夫特或其他艺人的粉丝那种感觉。
People want to enjoy the human touch, the feeling that you're a fan of Taylor Swift or whatever.
但未来将出现大量AI生成的音乐、艺术品和其他领域作品，其中部分会是免费的。
But there's going to be a lot of AI generated music and art and many other areas, and some of it will be free.
但从收入角度来看这并不成问题。
But that's not a problem from a revenue standpoint.
所以与其花钱购买一幅画作，你在家用AI数字创作一幅，你会把那笔钱花在其他地方。
So instead of spending money on, say, buying a picture, you create one digitally at home with your AI, you'll spend that money on something else.
我现在必须问一句。
I gotta ask now.
你是霉粉吗？
Are you a Swiftie?
不。
No.
对我来说很无聊。
It's boring for me.
有点太容易预测了。
It's a little too predictable.
所以我必须说我不是。
So I I I have to say I'm not.
我很高兴其他人是。
I'm glad other people are.
我们这么说吧。
Let's put it that way.
你其实有理论吗，我们来聊聊音乐吧。
Do you have a theory actually, let's talk let's talk about music.
你对泰勒·斯威夫特现象有什么理论吗？
Do you have a theory of the Swift phenomenon?
有什么原因吗？
Is there a reason?
是什么？
What is it?
因为这实在太...我不知道。
Because it's just so I don't know.
你对Swifties以及文化和社会有什么宏观看法？
What's your meta take on the Swifties and culture and society?
嗯，它非常精致，而且由于互联网的存在，最顶级的明星可以比以往更加耀眼，总会有人填补这些空缺位置。
Well, it's super polished, and because of the internet, the very biggest of celebrities can be much bigger than before, and someone will fill a few of those slots.
但我觉得还有她展现自我的方式，她有一种既迷人又不让其他女性感到威胁的特质。
But I think also how she presents herself, she has the guise of being attractive without feeling threatening to other women.
而且她身上有种非常美国化又相当普遍的特质。
And there's something all American about her and quite generic.
她并不排斥拥有大量粉丝群体。
She doesn't rule out the fandom of that many people.
而她正是填补那个空缺的人。
And she's the one who's filled that slot.
她在职业生涯管理上表现出色，似乎只是坚持不懈，并拥有令人难以置信的职业道德。
She's been brilliant at managing her career and seems to just stick at it and has an incredible work ethic.
人们都说这些演出精彩绝伦。
People say the shows are incredible.
随着越来越多生活转向线上，谁能奉献一场好演出呢？
As more and more life goes online, who can give a good show?
关键在于魅力与颜值。
It's the Charisma and Looks Point.
嗯，她在这方面似乎是顶尖的。
Well, she seems A plus at that.
我从未亲临现场，但听闻过不少报道。
I've never been to one, but I hear plenty of reports.
综合这些因素，她便是音乐界的超级巨星。
And you put all that together and she's the megastar of the music world.
你是否认为文化就像这样，有种流行观点认为文化某种程度上已经消亡，我确实觉得这可能有些夸大其词，但看看电影界，人们长久以来都注意到了这种现象。
Do you think like culture, like there's this popular idea that culture is sort of dead, and I do think that is probably overstated, but you look at movies and people have observed this for a long time.
不过是些已经存在三十年的老牌系列作品的翻新重制罢了。
It's just rehashes of franchises that have been around for thirty years.
我认为如果你看看Spotify的流媒体数据，婴儿潮一代的摇滚乐依然占据着压倒性的统治地位，诸如此类。
I think if you look at Spotify streaming, there's still this overwhelming tyranny of the boomer rock, etcetera.
这种感受就是，在许多方面文化不过是老调重弹，新事物很难脱颖而出。
And this feeling that culture in many respects is this rehash that it's very hard for new things to break out.
我的意思是，泰勒·斯威夫特到现在都已经火了十几年了。
I mean, Taylor Swift at this point is a decades old phenomenon.
在你看来这是真实情况，还是说只是评论阶层的人变得懒惰了，不再发掘新事物，实际上没有付出努力，因为他们不再年轻，不再外出，于是就说没人听新音乐了？
Is that real in your view, or is that just people in the pundit class who have been lazy and not discovering new things and not actually putting in the effort because they're not young anymore and they're not going out and they say, nobody listens to new music.
乔，你只是在谈论你自己吗
Joe, are you just talking about yourself
我已经在尝试进行一些自我反省了。
have talked about I'm trying I'm doing a little introspection here.
这只是我的问题吗？因为我不像20岁时那样常去看演出，还是说情况真的发生了变化？
Is this just me because I don't go to shows like I did when I was 20 or has something changed?
但很大程度上是那些评论家阶层的问题。
But a lot of it is the pundit class.
所以你看看电影。
So you look at movies.
我认为完全可以说，如今最受欢迎的电影相当糟糕。
I think it's perfectly correct to say the most popular movies today are pretty dreadful.
过去最受欢迎的电影通常是《教父》和《星球大战》。
And it used to be the case that the most popular movies were The Godfather and Star Wars.
这是个巨大的变化。
That's a big change.
但如果你看看全球范围内的电影制作，比如某年的25部最佳电影榜单，这些作品可能根本不会出现在你的多厅影院里，但每年都会出现一份令人惊叹的名单。
But if you look at movie making around the world and in a given year list, say, the 25 best movies from all places, which may not even come to your multiplex, every year you have an incredible list.
我不认为它们比早期的电影差。
I don't think they're worse than the movies of earlier times.
但我确实认为好莱坞主流电影要糟糕得多。
I do think mainstream Hollywood is much worse.
所以在很多领域，优质内容正逐渐转向小众市场。
So in many areas, you just have quality moving more into niches.
顺便说一句，我第一次接触你的作品，甚至在偶然发现《边际革命》之前，是在奥斯汀的书店里找到了一本《商业文化礼赞》。
By the way, just wanna say the first time I ever encountered your work, prior to even having stumbled across Marginal Revolution, was at the bookstore in Austin finding a copy of In Praise of Commercial Culture.
我觉得这本书特别经得起时间考验，尤其是当下大众文化如此盛行——无论是高端的Netflix剧集，还是A24电影，或是碧昂斯、泰勒·斯威夫特这些同时拥有极高人气的巨星。
I just feel like that book has held up so well, in the specific sense that there is so much mass culture these days, whether it's high end Netflix TV shows, etcetera, whether it's A24 films, whether it's Beyonce or Taylor Swift or some of these other big names who are simultaneously incredibly popular.
我知道你不是泰勒的粉丝，我也不是了。
And I get that you're not a Swiftie, neither am I anymore.
但人们确实...
But people yeah.
就拿这个来说，喜欢她早期的乡村风格。
Take this liked her country favorite.
自从她离开乡村音乐领域后。
Once she left country.
是啊。
Yeah.
她离开了乡村，我说但那里的人们把流行文化视为严肃艺术，不会把这些作品当作垃圾。
She left country, I said but where people take these popular culture things extremely seriously as art and don't dismiss these outputs as sort of being trash.
眼下，我们正处在乡村与西部音乐以及恐怖电影的黄金时代。
Right now, we're in a golden age for country and Western music and also horror movies.
这两者都不是我特别喜欢的类型，但很容易看出哪些方面变得更糟了。
Neither is really like my taste in particular, but it's easy to see what has gotten worse.
尤其是对评论家来说，更难看出哪些方面在变得更好。
And especially for critics, harder to see what has been getting better.
那正是我的最爱。
That's my sweet spot.
乡村音乐和恐怖片对我来说再合适不过了。
Country and horror is perfect for me.
但就文化观点而言，我认为最常听到的缺乏文化的论点是指缺乏共享文化。
But just on the culture point, I mean, think the lack of culture argument, the one I hear the most is it's a lack of shared culture.
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对吧？
Right?
所以确实存在像泰勒·斯威夫特或碧昂丝这样的超级巨星，人尽皆知，他们确实拥有庞大的观众群。
So you do have, like, some giant monoliths like a Swift or a Beyonce that everyone knows about and, you know, they do have these large audiences.
但总的来说，我们不再像过去那样消费相同的媒体内容了。
But broadly, we're not all experiencing the same media that we used to.
对吧？
Right?
比如说，现在没人会围在电视机前看那种每周一集、播了五年的剧的最终季了。
Like, no one is gathering around the TV to watch the finale of, you know, some show that airs, like, once a week and has been going on for five years.
例外是NFL橄榄球赛，不过我也搞不懂这个。
The exception is NFL football, which I don't really get it.
是啊。
But yeah.
但这不算文化现象。
But it's not cultural.
体育赛事不在我的认知范围内。
Sports is outside of my experience.
超级碗可是文化盛事对吧？
And Super Bowl is cultural, right?
它是个文化事件。
It's a cultural event.
你会在意那些广告。
You care about the advertisements.
但最顶流的YouTube网红——虽然我个人不感兴趣——他们的观众规模可能比那些老牌电视节目还大。
But the biggest of YouTube stars, which again, I would say is not personally my thing, but they can have bigger audiences than those older TV shows Right.
我想说的是，如今科技确实能通过流媒体推送非常精准的细分内容。
Well, so what I'm getting at is it does feel like nowadays there's an ability, because of tech, to serve up very specific content and niche content in streams.
我喜欢用这个类比：谁都知道第一次用网飞时，你首部观看的电影至关重要——如果你选了爱情喜剧，余生推荐列表都会是凯特·哈德森的电影。
And the analogy that I like to use is everyone knows if you download Netflix for the first time, the first movie you watch is incredibly important because whatever you watch, if it's a rom com, you're going to be served up Kate Hudson films for the rest of your life.
对吧？
Right?
比如，算法会分析你在观看的内容，然后推送相关附加内容。
Like, the algorithm looks at what you're watching and then it serves up that additional content.
这对社会意味着什么？
What does that mean for society?
从某种程度上说，这相当于将人们分流到越来越窄的信息流中。
The idea that you have people basically funneled into smaller and smaller streams in some respect.
其实Netflix算法很多时候只是把你引向低质内容。
Well, a lot of the Netflix algorithm, it just directs you to slop.
确实。
True.
很多人本来就喜欢低质内容。
A lot of people have always wanted slop.
就像过去人们听背景音乐一样，当时相当普遍。
Like people listened to Muzak way back when, and it was quite common.
或者他们只听排行榜前40，有些年份质量很高，但往往也很糟糕，即便在1960年代也是如此。
Or they would just listen to top 40, which in some years was very good, but often was pretty terrible, even in the 1960s.
所以今天你能做的不仅是看任何电影，还有MUBI平台，你仍然可以购买DVD和蓝光碟。
So what you can do today is basically watch, not any movie out there, but you have MUBI, you can still buy DVDs and Blu rays.
如今能接触到的电影资源比以往任何时候都多。
Have access to more cinema today than you ever have.
所以人们会自行筛选。
So people will sort themselves.
我认为从文化消费的角度来看，现在绝对是历史上最好的时代。
And I think it's from the point of view of cultural consumption, I don't think there's ever been a better time to be alive than right now.
就像那样。
Like that.
嗯，当然很多人会滥用这一点，选择敷衍了事。
Well, a lot of people abuse that and go for slop, of course.
这很可悲，但并不新鲜。
That's sad, but it's hardly new.
稍微换个话题，特蕾西和我几乎每天都会写作，因为我们有一份每日通讯要完成，这迫使我们坚持写作。我真的很喜欢这种安排，因为如果没有这种必须为付费订阅用户提供内容的义务，我不确定自己能否坚持每天写作。
So changing gears a little bit, Tracy and I write almost every day because we have a daily newsletter that forces us to, and I really like having that because I don't know if I would write every day if I did not have that obligation to deliver something in people's inbox that they pay for as part of their bloomberg.com subscription.
我热爱写作，但如果没有这种强制要求，我不确定自己能否每天坚持。
I love writing, but I don't know if I would do it every day if I didn't have this sort of requirement.
我可能只会发发推特。
I might just tweet.
你已经坚持写博客超过二十年了。
How do you you've been blogging for over twenty years.
你是如何抵制住把所有想法都通过推特快速发布出去的诱惑，而真正坚持写博客的呢？
How do you avoid the temptation to just fire off all your ideas via tweet and actually commit to the blog?
我从未受到这种诱惑。
I'm never tempted to do that.
我喜欢把事情想透彻。
I like to think things out.
我也是这么想的，只是经常做不到。
I do too, I just lot don't.
更多
More
当我认真写作时。
when I write properly.
实际上我已经连续二十二年每天写博客了。
I've actually blogged every single day for over twenty two years.
这太不可思议了。
That's amazing.
人们都放弃了。
People gave up.
那么区别在哪里呢？
And so what's the difference?
我不觉得这需要任何自律。
I don't feel it requires any discipline for me.
真正的自律是不要写太多。
The discipline is not writing more.
就像我必须克制自己。
Like I have to restrain myself.
所以我想我只是有点怪。
So I guess I'm just weird.
我认为我没有什么特别的小技巧或公式。
I don't think I have any, like, neat little trick or formula.
这是现在能做而以前不能做的细分领域之一，我找到了我的定位，就像你们两位一样。
It's one of these niches that you can do now that you couldn't do before, and I found my niche, as have the two of you.
顺着这个话题问个略有不同的问题，回到开场时说的，如果聊天机器人在你起步的十或二十年前就存在，你认为你的博客生涯会有多大不同？
Here's a slightly different question playing on that theme, but going back to the intro, how different do you think your blogging career would have been had chatbots existed, you know, ten or twenty years ago when you were starting out?
我认为我们目前还不清楚。
I don't think we know yet.
我的直觉是，人们仍然想阅读人类作家的作品，仅仅因为他们是人类。
My intuition is that people still want to read human writers simply because they're human.
如果机器人和你一样优秀，大多数人并不会在意。
And if the bot is as good as you, most of the world doesn't care.
但这尚未真正经过验证。
But that has not truly been tested yet.
我想未来两年内我们会见分晓。
I think we'll see in the next two years.
但这就是我的预期。
But that's what I'm expecting.
就像我认为音乐领域会出现大量AI音乐一样。
Just like I think in music, there'll be plenty of AI music.
可能会占到音乐产业的10%或20%。
It might be, say, 10 or 20% of the music sector.
但听众仍然会渴望那种人与人之间的联系。
But listeners will still want that human to human connection.
你觉得，当推特刚出现时，他们称之为
Do you think, you know, when when Twitter came out, they called
大家好，欢迎收听。
Hello, and welcome.
这里是米歇尔·侯赛因的节目。
This is the Michelle Hussein Show.
我是米歇尔·侯赛因。
I'm Michelle Hussain.
我能与埃隆·马斯克这样的人对话
I speak with people like Elon Musk
我觉得我已经做得够多了。
I think I've done enough.
还有珊达·莱梅斯。
And Shonda Rhimes.
这太可爱了。
That's so cute.
这里将成为每个周末你都能依赖的场所，通过一场关键对话来理解这个世界。
This will be a place where every weekend, you can count on one essential conversation to help make sense of the world.
所以请加入我，收听并订阅彭博周末频道的《米歇尔·侯赛因秀》，无论你在哪里获取播客。
So please join me, listen, and subscribe to the Michel Hussain Show from Bloomberg weekend, wherever you get your podcasts.
你问的问题确实很有意思。
You certainly ask interesting questions.
哦，他们称之为微博客网站，好像只是博客的简短版。
Oh, they called it a microblogging site, as if it were just blogging but on a shorter basis.
但我认为它有本质不同，你知道，在博客的黄金时代——等我们都很老的时候还会不断谈论那时有多美好——
But I think it's fundamentally different, you know, in the early the glory days of blogs, which we'll be talking about forever when we're all very old people, how good it was.
我觉得当时有种互相自由链接和探索想法的精神，而推特给我的感觉更倾向于冲突和攀比等等。
I think there was this sort of spirit of liberal linking with each other and idea exploration, whereas Twitter strikes me as much more conflictual and one upmanship and so forth.
你认为是否存在根本性的——我不确定用政治这个词是否准确——但新的传播范式是否像葡萄酒产地那样，自带倾向于合作或冲突等特性的土壤？
Do you think there are fundamental I don't know if political is the right word, but new communication paradigms sort of have their own terroir, so to speak, in terms of the impulse towards collaboration or conflict, etcetera?
这会改变社会吗？
Does that change society?
是啊，我依然喜欢写博客，看到人们不再热衷于此，我感到难过。
Yeah, I still like blogging, and I'm sad people have moved away from it.
对我来说，Twitter上充斥着太多梗，而梗泛滥的媒体更容易滋生种族主义，这显然是个大问题。
Twitter to me, it seems too meme heavy, and meme heavy media have more potential for racism, which of course is a big negative.
而且我看到太多人被Twitter逼疯，无论是发推还是刷推，我不确定具体原因，或许两者都有。
And I see so many people who are driven crazy by being on Twitter, whether it's because they're writing on it or reading it, I'm not sure, maybe both.
我不想点名，但这类人很多，我打赌你看到的和我关注的是同一批人。
I don't want to name names, but it's a lot of people and I bet you see the same ones that I do.
如今还充斥着许多不易察觉的性别歧视，我补充一点。
Also very sexist nowadays, I would just add, in unappreciated ways.
多种方式。
Many ways.
说到性别歧视，人工智能对经济学的影响。
Speaking of sexism, the impact of AI on economics.
谈谈这个。
Talk about that.
经济机构，你知道，以建模闻名，花大量时间处理数字之类的东西。
Economic institutions, you know, famous for modeling, spend a lot of time with numbers and things like that.
这些都会被人工智能取代吗？
Is that all just going to be replaced by AI?
并非全部。
Not all.
因此我认为人类经济学家将把越来越多的时间用于收集数据并输入给AI。
So I think what human economists will do is put more and more time into gathering data and feeding it to the AIs.
这样做的回报会非常高。
The returns to doing that will be very high.
但实际的计量经济学、统计学工作，人类可能只负责设定部分问题，那些枯燥的常规工作将由机器完成——某种程度上这已是现状。
But the actual econometrics, statistics, humans will maybe set up part of the problem, but the hard boring routine work will be done by the machines, as to some extent it was already the case.
这将是一种快速取得重大进展的途径。
And this will be a way to make a lot of progress rather quickly.
在AI时代背景下，经济学家收集的实际经济统计数据或数据点是否需要改变或重新思考？
Do the actual economic statistics or data points that economists collect, do some of those need to be changed or thought of differently in light of the AI era?
嗯，我不知道。
Well, I don't know.
最终肯定需要调整。
Eventually they will need to be.
但我要说，历史上任何剧烈变革时期，统计数据的效用都会降低。
But I would say any period of radical change in history, your statistics are less useful.
并不是统计工作者犯了什么错误。
It's not that the people creating the statistics are making some mistake.
只是你无法捕捉世界变化的每个维度，而指数比较要求商品篮子保持相对恒定。
You just cannot capture every way the world is changing, and index number comparisons require the basket of goods be relatively close to constant.
当这种前提不再成立时，我们就必须面对现实。
And at some point that doesn't hold anymore, And we'll be faced with that.
我们会有办法应对的。
We'll deal with it.
我认为我们现有的统计数据被低估而非高估了。
I would say the current statistics we have, they're more underrated than overrated.
所以它们实际上相当不错。
So they're actually pretty good.
等我们再次获得这些数据时，我会很高兴。
I'll be glad when we get them back again.
还有一件事。
There's another thing.
你接触的那些技术人员，他们肯定认为GDP指标很糟糕。
The tech people you talk to, they must think GDP is terrible.
它无法捕捉所有这些我们无法定价的东西和价值。
It doesn't capture all this stuff, all this value that we can't price.
我确信你已经和许多AI工作者解释过，GDP并非世界上最糟糕的统计数据。
I'm sure you've had conversations explaining to many AI workers that GDP is not the worst statistic in the world.
不过我想回到之前的话题，你知道，你提到过，比如在推特上。
I want to go back, though, so, you know, you mentioned, like, on Twitter.
对吧？
Right?
你说点什么，有人发个梗图，他们嘲讽你，取笑你，诸如此类。
You say something, someone posts a meme, they dunk on you, they make fun of you, they whatever.
你的聊天机器人可不会这样。
Your chatbot won't do that.
比如，如果我在和ChadGPT聊天，它永远不会用那种暗示我是个白痴的梗图来回复我。
Like, if I'm having a conversation with ChadGPT, it's never gonna respond to me with a meme sort of indicating that I'm a moron.
乔，你都没学会自己的语言。
You don't learn your language, Joe.
是啊。
Yeah.
但要说的话，它太谄媚了。
But if anything, it's too obsequious.
对吧？
Right?
我是说，问题在于，网络世界已经变成了一个充满竞争和冲突的世界。
I mean, the issue is, like, the online world has become this very, like, sort of competitive conflictual world.
然后我去找聊天机器人，我的抱怨却恰恰相反。
And then I go to the chatbot, and my complaint is literally the opposite.
它对我的挑战不够。
It doesn't challenge me enough.
它太谄媚了。
It's too obsequious.
我提的每个问题，它都说是个好问题。
Every question I ask, it's a great question.
有时候我真希望它能多骂我几句白痴。
Sometimes I wish it would call me a moron a little bit more.
但这能改变世界什么呢？
But what does it change about the world?
我们知道很多人的大脑已经被社交媒体搞坏了，这可能对我们当前的政治运作方式产生了连锁反应。
We know that many people's brains have been broken by social media, and that probably has downstream effects on how our politics operates these days.
如果我们开始生活在这些聊天环境中，它们总是彬彬有礼，每次你说什么它都会回应'是的，好想法，泰勒'，这会对世界产生什么影响？
What does it do to the world if we start inhabiting these chat environments where they're just very sort of polite, and every time you say something it says, Yes, great thought, Tyler.
好想法，乔。
Great thought, Joe.
你想扩展它吗？
Would you like to expand it?
你问了一个完美的问题。
You asked the perfect question.
你觉得这会对社会运作方式产生某种二阶效应吗？
Do you see that having sort of second order effects on how society operates?
嗯，这就是实现这一功能的四行模型。
Well, that's the four row model that does that.
像Claude 4.5和GPT-5这样的新模型更客观，这样更好。
The newer models like Claude 4.5 and GPT-five, they're more objective and that's better.
它们永远不会取笑你。
They never make fun of you.
它们永远不会说'你是个白痴'之类的话。
They never will like say, You are a moron.
你怎么能问出这么蠢的问题？
How could you possibly ask such a dumb question?
你显然太脱离实际了才会问这个。
You are obviously so out of touch for having the ask this.
从某些方面来说，这比我通过电脑输入进行的许多对话要积极得多。
In some respects, this is a very positive change from many of the conversations that I've had from typing into a computer.
哦，这很棒。
Oh, it's great.
我认为人们应该对彼此更友善一些。
I think people should be nicer to each other.
而且我认为它们是人类有史以来最客观的媒体来源。
And I think they're the most objective media source the human race ever has had.
如果你对疫苗或阴谋论等话题有疑问，它基本上能给你正确答案。
You're If as good about, say, vaccines or conspiracy theories, it basically gives you the right answers.
嗯，有一点它们做不到——我是说，我认为它们可以被训练得对你不友善甚至侮辱你，在某种程度上。
Well, one thing that they don't do and I mean, I do think they can be trained to be mean to you and to insult you, to a certain degree.
你可以决定。
You can decide.
它们可能会，但我从未遇到过。
They could be, but I never encounter them.
再努力点，再努力点，
Try harder, harder,
采访了Perplexity的首席商务官Dmitry Shevalenko。
spoke to the chief business officer of Perplexity, Dmitry Shevalenko.
他说聊天模型无法表现出天然的好奇心，我觉得这话从他嘴里说出来很奇怪，因为据我所知Perplexity是唯一会在你查询后主动抛出附加问题的模型，比如'你想了解更多关于这点的信息吗？'
He was saying that one thing chat models can't do is express a natural curiosity, which I thought was kind of weird coming from him because Perplexity is the only model I know that actually throws out those additional questions if you query it and then it comes up with, Would you like to have more information on this point?
或者'你现在正在思考这个问题吗？'
Or, Are you thinking about this now?
但这些大语言模型似乎确实缺失了某种创造性的元素。
But there does seem to be an element of creativity, perhaps, that is lost in some of these LLMs.
这对媒体领域的影响有多大？
How much does that change things in media?
这种观点认为，模型输出的内容在某种程度上是预先注定的。
The idea that, you know, the models are going to spit out something that's sort of predestined in many ways.
明确地说，大多数人类也是这样做的。
Well, that's what most humans do, to be clear.
但现在的情况是，这些模型可以定期证明新的数学定理或发现潜在的新药物。
But it's now the case that on a regular basis, the models, say, can prove new theorems in math or discover new potential drugs.
记住，一年前这些模型还认为'strawberry'有两个R，现在它们已经在数学奥林匹克竞赛中获得金牌了。
And keep in mind, a year ago, these things thought the word strawberry had two R's, and now they're winning gold medals in Math Olympiad.
所以一两年后，我们不知道它们会进步多少，但我不认为它们在创造力方面会有任何问题。
So a year or two from now, maybe we don't know how much better they'll be, but I don't think they're going to have any problems being creative.
肯定比普通人类更有创造力。
Certainly more creative than humans on average.
既然我们谈到对模型友好与否以及它们对你的态度，我想问你在LLM查询中会说请和谢谢吗？
So now I have to ask, since we're talking about being mean or nice to the models and them being mean or nice to you, do you say please and thank you in your LLM queries?
我曾经这样做过，后来Sam Altman说这会因为额外的token而多花一点钱。
You know, I used to, and then Sam Altman said, Well, it costs us just a little bit of money because of the extra tokens.
于是我想还是算了，但我之前说请的记录它都知道。
And then I thought I'll hold off on this, but I have this preexisting record of saying please, and it knows that.
而且它知道我是因为Sam说了才停止的。
And then it knows I stopped when Sam said to stop.
我觉得这两个决定都会给我加分。
And I think I'll get points for both of those decisions.
你看，我发现如果在查询中对模型稍微苛刻些，它们表现会更好，就像和乔互动一样。
See, I actually find if you're slightly meaner to the models in your queries, they perform slightly better, much like interacting with Joe.
对此不予置评。
No come no comment on that.
你知道我是做什么的吗？
I you know what I do?
我说过，比如我会提个查询，它会回应，然后我会说，这回答有点太直白了。
I have said, which is I'll, like I'll have a query, and it'll respond, and I'll say, that was a little on the nose.
是啊。
Yeah.
不是吗？
Wasn't it?
比如，它太过...
Like, it over
我会说'降低困惑度'，然后它就做到了。
I'll say do better perplexity, and then it does.
对。
Yeah.
我就说过类似的话。
I've said things like that.
就像是，这回答真的太直白了。
It's like, this is really on the nose.
这个回应有点老套，你不觉得吗？
This response was a little bit trite, don't you think?
等等。
Etcetera.
就像，我确实感觉在这里我们变得更自在了。
Like, I do feel like I've gotten more comfortable at let's be real here.
你在这里并没有发挥出最佳水平。
You're not doing your best job here.
你觉得什么是有趣的？
What do you think is like it's interesting.
他们就像，我理解他们赢得了金牌，在数学等方面表现出色。
They like, I get that they win the gold medals and the math and etcetera.
我与聊天机器人有过许多对话，在某种程度上它们的能力令人震撼。
I've had so many conversations with the chatbots that are on some level mind blowing, the capabilities.
我从未见过一个有趣的聊天机器人查询。
I've never seen a chatbot query that is interesting.
那就像是，哦，那可能是我能想到的一个，但确实有一个非常有趣的想法。
That is like, Oh, that is a really maybe one I can think of, but there was a really interesting thought.
我觉得我的孩子们每天说的话仍然比我从任何聊天机器人那里听到的都更能引发我的思考。
I feel like my children still say on a daily basis more interesting things that get me thinking than I've ever gotten from a chatbot.
这对你有共鸣吗？
Does that resonate to you at all?
我不知道。
I don't know.
看情况
Depends I
我觉得你这一小时里说的有趣观点，比我花在Chattypeteer Claude上所有时间获得的还要多。
think I on feel like you've said so many more things in this hour than any interesting actually, interesting ideas than I've ever got from the hours I've spent playing with Chattypeteer Claude.
你知道，我经常用它来了解音乐。
You know, I use mine a lot for music.
比如我要听西贝柳斯的第五交响曲时，就会问它：'我该注意听哪些部分？'
So if I'm going to listen to Sibelius's Fifth Symphony, I'll just ask it, what should I listen for?
我会加上'这是泰勒·考恩在问'，希望这样能提高回答质量。
And I'll say, this is Tyler Cowen asking, which I hope raises the quality of the answer.
它对我的了解很深。
It knows a lot about me.
它给出的聆听建议，我觉得比任何容易接触到的人类资源都要好。
And what it gives me to listen for, I find is better than any human source I can access readily.
确实更好，这点我同意。
It's better, that I agree with.
但它能建立深层联系吗？
But does it make a connection?
它会告诉你关于西贝柳斯音乐的见解吗？比如'这个理解角度很新颖，正是作品深刻之处'这类观点？
Does it tell you something about Sibelius' music that is like, Oh, that's a very interesting that's a novel way of thinking about what makes it profound.
这类见解我很少遇到。
These are the things that I rarely ever encounter.
那种让人眼前一亮的深刻见解。
Something that's like, Oh, that is an interesting thought.
而我觉得如果和音乐学家聊一小时，能获得更多关于音乐独特之处的真知灼见，听到前所未闻的见解。
Whereas I feel like if I were talking to a musicologist for an hour, would get infinitely more like actual insight into something that makes the music special, something I hadn't heard before.
我不确定这是否新颖，因为我对西贝柳斯的文献并不熟悉。
I don't know if it's novel because I don't really know the Sibelius literature.
但我觉得大多数音乐学家都相当乏味。
But most musicologists I find pretty boring.
而且我认为GPT-5对古典交响乐的点评相当切中要害。
And I find say GPT-five on a classical symphony quite to the point.
至于它是否原创，对我来说并不那么重要。
And whether or not it's original, it's not that important to me.
它帮助我更好地欣赏音乐。
It helps me listen to the music better.
而且相对于我手头的其他资源，比如维基百科或谷歌搜索结果，它确实具有原创性。
And it's certainly original relative to the other sources at my disposal, say Wikipedia or what I could Google to.
所以我认为在绝大多数情况下，这就足够了。
So I think for almost all purposes, that's enough.
它是否具有真正原创的思想，就像爱因斯坦提出相对论时那样的原创性？
Does it have a truly original idea in the sense that Einstein's theory of relativity when he came up with it was original to him?
我认为没有。
I don't think so.
这可能还需要若干年时间。
That may come in some number of years.
但重申一次，在绝大多数情况下，我们并不需要那样的东西。
But again, for almost all purposes, that's not what we need.
我们需要的是比现有知识状态更好的东西。
We need something better than our preexisting state of knowledge.
关于这一点，我认为这样就解决了。
And on that, think it just cleans up.
所以我刚刚问了Perplexity，我应该向泰勒·考恩推荐什么音乐。
So I just asked perplexity what music I should recommend to Tyler Cowen.
它说为了有效推荐给泰勒·考恩，我需要寻找那些被低估的录音和冷门作品，可能除了他之外没人听说过的东西。
And it said that in order to recommend effectively to Tyler Cowen, I need to look for underappreciated recordings and obscure things that no one except him might have ever heard about.
回答得好。
Good answer.
它推荐了——Boy Genius看起来确实很贴切。
And it recommended I mean, boy genius seems pretty on the nose.
是啊。
Yeah.
对吧？
Right?
雷鬼乐队比如Toots and the Maytals。
Reggae acts like Toots and the Maytals.
哦，这个有点怪。
Oh, yeah, this is weird.
这个推荐好吗？
Is that a good one?
不，我看过他们的
No, I've seen them
现场演出。
in concert.
泰勒已经把这20条记录都标价了。
Tyler's priced this up to all 20 of
我们的记录。
our records.
所以它只是在抓取你们已经讨论过的内容。
So it's just scraping stuff that you've already talked about.
它甚至都没在尝试。
It's not even trying.
是啊。
Yeah.
好吧。
All right.
但你需要让提示更精确些。
But you need to make the prompt more exacting.
排除泰勒谈论或撰写过的任何内容。
Rule out anything Tyler has talked or written about.
给我些他不知道的东西，试试专业模式的GPT-5，我想会成功的。
Give me something he doesn't know and try GPT-five in the pro mode, and I think it will succeed.
说到这个，我一直在问大家一个问题，你心目中真正好的提示例子是什么？或者哪个特别突出的提示产生了你意想不到的结果？
Well, so on this note, this is something I've been asking everyone, but like what is an example in your mind of a really good prompt or one that sticks out to you that has generated something that, you know, maybe you didn't expect?
知道吗，德瓦卡什·帕特尔曾写过一个非常好的提示，还分享给了我。
Know, Dwarkash Patel once wrote a very good prompt and he shared it with me.
当我采访一些播客嘉宾时，那真是个超长的提示。
When I interview some podcast guests, it's really a long prompt.
这份材料有数百字，它问应该向他们提出哪些问题。
It's hundreds of words, and it asks what questions should I ask them.
然后它会深入详细展开。
Then it goes into great detail.
这应该是一个独特的问题。
It should be a unique question.
应该是他们在其他地方从未被问过的问题。
It should be a question they were not asked anywhere else.
接着是给出你认为他们可能的回答，以及我的后续问题会是什么。
Then it's give me what you think their answer might be and what would be my follow-up question.
列举一些你认为他们可能回答错误的案例。
Give me some cases where you think their answer might be wrong.
就这样不断延伸下去。
And it goes on and on.
当你用最优秀的模型运行这个流程时，我认为会得到不错的结果。
And you run that through the very best models, I think you get good results.
是的。
Yeah.
不，我之前处理过播客的文字记录，我会问：我本应在哪些问题上对嘉宾施加更大压力？
No, I've run transcripts of the podcast before, and I say like, well, where should I have pushed the guest harder on?
哪些是嘉宾给出的薄弱回答？他们在对话过程中存在哪些前后矛盾之处？
What were the weak answers that they what were the inconsistencies that the guest had over the time?
我发现这类练习对这类分析非常有用。
And I've found it to be a very useful exercise for things like that.
所以我认为关键在于，首先，它在许多方面客观上令人印象深刻，而且如果你进行细致的提示，我会说它在客观上是有用的。
So I do think that's the thing, which is first, it is objectively impressive on many of these fronts, and I would say objectively useful if you do sort of a detailed prompting.
我只是好奇，从教授的角度来看，你认为学生应该如何正确使用Chatuche B？
I'm just curious, what's your from the professor perspective, what do you think is the right way to think about how students will be using Chatuche B.
D？
D?
我是说，我知道学术界有无数观点，比如现在什么是正确的测试方式？
I mean, I know that there's a million opinions in academia about, well, what's the right way to test now?
什么是处理论文等的正确方式？
What's the right way to deal with essays, etcetera?
你如何看待这些挑战中的一些？
How are you thinking about some of these challenges?
我们应该将高等教育的三分之一用于教授学生如何使用人工智能。
We should devote one third of all higher education to teaching students how to use AI.
而目前这一比例几乎为零。
And right now that's close to zero.
所以我们缺乏能教授这门课程的师资力量，这也是问题的一部分。
So we don't have the faculty who can teach it as part of the problem.
通常学生比教授懂得更多。
Often the students know more than the professors.
是啊，我确信。
Yeah, I'm sure.
那类似的事情呢
What about like things
比如——我们需要彻底重构我们的工作方式，因为未来的工作将由AI完成。
like- We need to restructure radically what we do because future work will be done with AIs.
所以这才是应该教授的内容。
So that's the thing to teach.
但容我唱个反调，直觉上我依然认为长时间专注阅读有其价值——远离电子设备，训练身体保持自律、集中注意力的能力。
But like, so just to play devil's advocate, intuitively, I still feel like there is value in long periods of time cut off reading, where you're not looking at devices, where you're training your body to sort of be disciplined and pay attention and focus.
我仍然认为记忆事实、数字、日期、地点和人名等，把这些内容真正装进脑子里非常有用。
I still think memorization of facts and numbers and dates and places and names is very useful in actually having them in your head, etcetera.
你觉得这个观点对吗？还是说我这属于复古思维？
Does that seem right to you or is that sort of a retro thinking on my part?
不，我完全同意，尤其是写作训练——不过那属于高等教育的另外三分之二内容，对吧？
No, strong agree and most of all writing, but that's the other two thirds of higher ed, right?
我说的是三分之一
I said one third
应该...哦，原来还包括另外那三分之二。
should Oh, it's also about the other two thirds.
无论有没有AI，我们都应该更深入、更系统地教授学生写作技巧。
We should, with or without AI, just teach students much more and much better how to write.
大多数人都不会写作。
Most people can't write.
写作即思考。
Writing is thinking.
现在有了AI，我们必须加大写作教学和测试力度，而且必须采用面对面监考形式，否则人们只会作弊。
We should do much more to teach writing and test writing, and now with AI, that has to be face to face in a controlled environment or people are just going to cheat.
这本应是我们一开始就该加倍投入的。
So that we should have doubled down on to begin with.
这些基础计算能力和基本问题，比如如何管理投资组合、选择哪种抵押贷款，虽然有课程涵盖，但我认为它们应当成为所有课程的核心内容。
So that and just numeracy and basic issues like how to manage a portfolio, what kind of mortgage to take out, there were classes that cover those things, but I think they ought to be front and center of any curriculum.
基础财务知识、基本生活决策，比如如何选择医生、如何向AI询问诊断建议等，在很多教育体系中都被相对忽视了。
Basic finance, basic life decisions, like how to choose a doctor, how to prompt the AI for diagnosis, whatever, are relatively neglected in a lot of education.
这在我看来简直不可思议。
That to me just seems crazy.
结束前我想问一个市场问题——现在显然有很多关于AI泡沫的讨论。
I want to ask one market question before we go, which is obviously there's a lot of talk about an AI bubble at the moment.
许多人的担忧在于，当你们开始把一项新技术称为革命性突破，开始谈论其影响将无限扩大、市场规模理论上覆盖全球时，预期就有脱离现实的风险，对吧？
And I think the concern from a lot of people is when you start talking about a new technology as revolutionary, when you start talking about how the effects are basically going to be infinite and the market size is hypothetically the entire world, there's a risk that expectations overshoot reality, right?
我们已经看到有人表达这种担忧，估值方面也显露出些许市场不安。
And we have seen some people voicing their worries about that right now and a little bit of nervousness creeping into the market in terms of valuations.
你对AI泡沫持什么立场？
Where do you stand on the AI bubble?
你看到泡沫迹象了吗？还是认为目前大部分资本支出都是合理的？
Do you see signs of froth or do you think most of the CapEx spending is justified at this point?
我不喜欢'泡沫'这个词。
I don't like the word bubble.
我要指出科技板块的收益已经超过了该板块的资本支出。
I would point out that tech sector earnings are exceeding tech sector capital expenditure.
这些资金主要不是通过债务融资的，所以情况没有很多人想的那么糟糕。
This is not mostly debt financed, so we're in less trouble than many people think.
如果这些努力中有许多都亏损了，我也不会感到惊讶。
It wouldn't shock me if a lot of these efforts lost money.
铁路行业如此，互联网如此，人类所做的大多数事情都是如此。
That was the case with the railroads, the case with the internet, case with most things humans have done.
但我认为它会持续下去。
But I think it will endure.
这不像pets.com那样会被彻底淘汰。
It's not like pets.com where the thing just gets swept away.
这些都是资金极其雄厚、技术高度精湛的公司，CEO和/或创始人都对此非常投入，他们会坚持到底并取得成功。
These are incredibly well capitalized, highly skilled companies where the CEOs and or founders are quite committed to doing this, and they're going to see it through and they're going to succeed.
但这是否意味着每股市值都会上涨，或者英伟达最终会价值10万亿美元，我不知道。
But does that mean every share value will go up or Nvidia ends up being worth $10,000,000,000,000 I don't know.
我不一定会做出这样的预测。
I wouldn't necessarily predict that.
总会有起起落落，但这显然是非常有用的事物，我们美国人会把它做好，而且我们遥遥领先于世界其他地区。
There's always ups and downs, but this is clearly a very useful thing and we as Americans, we're going to make it work and we're way ahead of the rest of the world.
全球约四分之三的AI算力都在这个国家。
Like three quarters of all AI compute is in this country.
这太不可思议了。
That's incredible.
我们占世界人口的百分之几？
We're at what percent of the world's population?
6%？
Six?
或者我不确定，但远小于四分之三。
Or I don't know, but way smaller than three quarters.
GPT-5在思考模式下建议你听听迈克尔·古利西安的作品，他使用开放调弦法演奏梦幻般的原声吉他。
GPT-five on thinking mode says you should listen to Michael Gulisian, who does dreamlike acoustic guitar using an open tuning.
它还说，考虑到你对约翰·费伊和莱奥·科特克这类吉他手的喜爱，你会欣赏他的音乐。
And it said that given your, affinity for guitarists like John Fahey and Leo Kotke, you'll appreciate him.
把那个答案发给我。
Send me that answer.
听起来很棒。
It sounds excellent.
我没听说过这个人。
I haven't heard of that person.
我确实非常喜欢开放调弦的吉他演奏。
I do very much like guitar with open tuning.
基肖尔·阿曼卡尔，一位印度斯坦声乐歌手，认为你会喜欢。
Kishore Amankar, a Hindustani vocal singer, thinks you'll like.
孙欧，MD 巴德鲁霍克。
Sun Oh, MD Badruhawk.
我会把这份清单发给你。
I'll send you this list.
还有卡特琳娜·巴别里，现代模块化极简主义作曲家。
And Katerina Barbieri, modern modular minimist Composition.
会买其中一些，但索尼，我已经知道了。
Will buy some of these, but sono, I already know.
我只是
I just
尝试过 要不要听听人工推荐？
tried How about a human recommendation?
给我们一个推荐吧。
Give us a recommendation.
人工推荐，既然你说那个国家现在很不错，不过我猜你个人对它不太感冒。
A human recommendation since you said that country's pretty good right now, but I guess you personally aren't that into it.
但你试过奥维尔·派克吗？
But have you tried Orville Peck?
好像这周有新专辑要发行，我想。
Think there's a new album out out this week, I think.
是什么类型的音乐？
Kind of music is it?
乡村音乐，但是非常现代的那种乡村。
Country, but, like, a very modern type of country.
我试图让乔也喜欢上它，不过还在努力中。
I've tried to get Joe into it, but I'm still working on it.
我喜欢某些乡村音乐，尤其钟爱老式乡村。
I like some country, and I love old country.
比如汉克·威廉姆斯、约翰尼·卡什、杰特·阿德金斯那个时期的。
So Hank Williams, Johnny Cash, Jett Adkins period.
带有复古韵味，但又有现代改编。
Has a vintage tone to it, but with a modern twist.
试试Orville的包装。
Try Orville pack.
好的。
Okay.
然后告诉我们哪个在推荐方面更胜一筹。
And then get back to us about which was better in terms of the recommendations.
泰勒·科恩。
Tyler Cohen.
泰勒·科恩，非常感谢你参加《Odd Lodge》这档早该进行的对话节目。
Tyler Cohen, thank you so much for coming on Odd Lodge long overdue conversation.
非常感谢你抽出时间。
I really appreciate you taking your time.
很高兴与你们两位交谈。
Great to chat with you both.
特蕾西，这次对话中有很多值得挖掘的内容。
Tracy, a lot to pull out from that conversation.
我觉得非常有趣的是，他早期对传统机构的观察，以及关于革命性影响尚未显现的部分原因，可能只是因为这些创新需要时间被那些理论上能够吸收它们的公司类型所消化。
I think it's very interesting, that early observation he made about legacy institutions and whether perhaps some of the lack of revolutionary impact yet is just about that metabolization process into the types of companies that could theoretically absorb them.
是的。
Yeah.
我想正是这样，对吧？
I think that's exactly it, right?
所以公司主要把这作为现有工作流程的补充来使用。
So companies are using this mostly as an add on to existing workflow.
在企业成为核心之前，你们不会迎来巨大的生产力爆发。
You're not going to get the huge productivity boom until companies are centered.
从零开始构建。
Build from the ground up.
是的，这可能需要那些与技术一同成长的人，而不是像你我这样只是后来才适应的老一辈。
Yeah, which is probably going to take people who grew up with the technology rather than old people like you and I, who just adopted it.
首先我想到的另一件事是，保险公司目前算是我个人特别感兴趣的领域，但我确实认为，在当前世界数据饱和及分析模型日益复杂的背景下，它们正逐渐成为大赢家之一。
The other thing I was thinking about first of all, insurers are sort of a pet interest of mine at the moment, but I do think they are emerging as some of the really big winners from a lot of the data saturation of the world right now and the increased sophistication of analytical models and things like that.
看看最终结果会如何将会非常有趣。
That'll be very interesting to see how it shakes out.
如果把这个思维实验推至极远，你可能会说保险公司未来将在制定社会标准和规范方面扮演更重要的角色，因为正是它们掌握着所有数据模型，能够断言你发生车祸的概率确切为X，因此你必须采取以下措施。
If you took it very, very far as a thought experiment, you could start to say that the insurers are going to be a more important actor in terms of setting social standards and regulations in the future, because they're the ones with the data doing all the modeling and saying, You have exactly an X chance of being in a car accident, and therefore you must do the following things.
还真没考虑过可传唤性的问题。
Hadn't really thought about subpoenaability.
这是个非常大的问题，但也很有意思，对吧？
Is a very big issue, but it is interesting, right?
你我之间的电话交谈竟然可能成为证据，这确实有点奇怪。
It is a little weird that you and I could have a phone conversation.
现在如果你宣誓作证时他们问：'乔，你们谈了什么？'
Now if you're under oath and they say, What did you talk about, Joe?
我不指望你会说实话——除非对我不利，那我倒希望你能撒谎。
I ain't expected you'd probably tell the truth, unless it was very bad for me, and I would hope that you would lie.
没错，乔。
That's right, Joe.
我希望你能，我希望你能
I would hope that you would I would hope that you
那是你的希望。
That's your hope.
我希望你能
I would hope that you would
为了救乔，我宁愿作伪证。
Purger myself to save Joe.
是啊。
Yeah.
我希望你会作伪证，但理论上讲，你知道的，你可能侥幸逃脱。
I would hope that you would perjure yourself, but, like, you know, theoretically, you could get away with it.
是啊。
Yeah.
如果我们有电子邮件，那就没戏了。
If we have an email, there's no chance.
而且这还挺有趣的。
And it's sort of interesting.
对我来说这总是显得有点随意，但思考这些问题确实很有意思——我们能否与这些实体对话？为什么我们必须留下数字记录？这些数据又将存储在哪里？
It's always seemed a little bit arbitrary to me, but it is interesting to think about, okay, can we have a conversation with these entities, and why do we have to leave a digital record, and where are these going to be stored?
我确实认为，在医疗和法律等领域——这些领域虽然不是显而易见，但某种程度上直觉上是容易实现生产力提升的领域——我们有多少进展仅仅因为社会仍在协商转型过程而未能实现？
And I do think in areas like health and law, which are obviously not obviously, but sort of intuitively low hanging fruit for productivity gains how much have we not seen just in part because we're still sort of negotiating the transition process as a society?
关于这些事情的新规则和规范会是什么？
What are gonna be the new rules and norms about this stuff?
它会被安置在哪里？
Where is it gonna be housed?
诸如此类。
Etcetera.
我认为这实际上是一个非常有趣的问题或领域。
I think that's actually a sort of very interesting question or space.
可以说，这是值得关注的领域。
This is the space to watch, so to speak.
是啊。
Yeah.
现在想想，我们或许应该多讨论一些围绕这些问题的监管框架，但下次吧。
Now that I think about it, we probably should have discussed some of the regulatory framework around all of this a little bit more, but next time.
下次吧。
Next time.
但我最近一直在思考的另一件事是，在一个日益由AI驱动的世界中的经济统计数据。
But the other thing I've been thinking about lately is economic statistics in a world that's increasingly driven by AI.
我知道我们在2000年代初期到中期曾有过关于生产力和技术的大讨论。
And I know that we had the big productivity discussion and technology in relation to technology in the early to mid 2000s.
我不确定那是否真的有了定论，但我非常预期关于AI经济统计的讨论会更加，嗯，古怪，因为我不确定你如何对突然自带大脑的东西进行质量调整之类的操作。
I'm not sure that ever actually got settled, but I very much expect that the AI economic statistics conversation is gonna be even, like, wackier because I'm not sure how you do things like quality adjustments for something that, like, suddenly comes with its own brain Yeah.
诸如此类的事情。
And stuff like that.
所以
So
不。
No.
那会超级奇怪。
It's gonna be super weird.
我...我...我不知道。
I did I I I don't know.
在我脑海里，我仿佛看到泰勒在Chatty PT办公室里参观的场景。
In my mind, I have, like, a very these images of, like, Tyler getting a tour through the, you know, Chatty PT offices.
人们问我时，他不得不解释说我们不会有20%的生产力增长，可能只有2.5%（抱歉说错了），实际上GDP作为衡量标准并不算太糟，它大致能反映经济规模，尽管很多互联网服务是免费的——就像那些经典对话一样。
And the people asking me was like, and him having to explain that, you know, we're not gonna have 20% GDP growth, that maybe two and a half or sorry, productivity growth, and actually GDP isn't really that bad of a measure that more or less captures the size of the economy even if a lot of Internet things are free, like some of these classic conversations.
我真想当个墙上的苍蝇听听这些对话。
I would like to be a fly on the wall for some of those.
泰勒·考恩为GDP辩护。
Tyler Cowen, in defense of GDP.
独立...对。
Independent yeah.
好吧。
Alright.
我们就到这里结束？
Shall we leave it there?
就到这里吧。
Let's leave it there.
本期《奇思妙想播客》到此结束。
This has been another episode of the Odd Thoughts Podcast.
我是特蕾西·阿拉维。
I'm Tracy Allaway.
你可以在特蕾西·阿拉维关注我。
You can follow me at Tracy Allaway.
我是乔·维森塔尔。
And I'm Joe Wiesenthal.
你可以在The Stalwart关注我。
You can follow me at the stalwart.
关注我们的嘉宾泰勒·考恩。
Follow our guest Tyler Cowen.
他的账号是泰勒·考恩。
He's at Tyler Cowen.
当然，别忘了收听他的播客《与泰勒对话》以及《边际革命》。
And, of course, check out his podcast, Conversations with Tyler, and, course, Marginal Revolution.
关注我们的制作人：卡门·罗德里格斯（卡门·阿门）、达希尔·班尼特（Dashbot）和凯尔·布鲁克斯（凯尔·布鲁克斯）。
Follow our producers, Carmen Rodriguez at Carmen Armen, Dashiell Bennett at Dashbot, and Cale Brooks at Cale Brooks.
更多《奇数批》内容，请访问bloomberg.com/oddlots，查看每日通讯和所有节目。
And for more Odd Lots content, go to bloomberg.com/oddlots with the daily newsletter and all of our episodes.
你还可以在我们的Discord社区discord.gg/oddlots全天候讨论这些话题。
And you can chat about all of these topics twenty four seven in our Discord, discord.gg/oddlots.
如果你喜欢《奇数批》，喜欢我们与泰勒·考恩交流并给他音乐推荐，请在您喜爱的播客平台给我们好评。
And if you enjoy odd lots, if you like it when we talk to Tyler Cowen and give him music recommendations, then please leave us a positive review on your favorite podcast platform.
记住，如果您是彭博订阅用户，可以完全无广告收听我们所有节目。
And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free.
你只需在苹果播客上找到彭博频道，然后按照那里的指示操作即可。
All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there.
感谢收听。
Thanks for listening.
《彭博晨光》是你清晨第一时间获取资讯的最佳方式，直接推送至你的播客订阅。
Bloomberg Daybreak is your best way to get informed first thing in the morning right in your podcast feed.
你好。
Hi.
我是凯伦·莫斯科。
I'm Karen Moscow.
我是内森·哈格。
And I'm Nathan Hager.
每天清晨，我们都会早起制作最新一期的《彭博晨光》美国版。
Each morning, we're up early putting together the latest episode of Bloomberg Daybreak US edition.
这是你每日十五分钟的播客，涵盖全球新闻、政治及国际关系的最新动态。
It's your daily fifteen minute podcast on the latest in global news, politics, and international relations.
每天早上收听《彭博晨光》美国版播客，获取重要新闻故事及其所需背景。
Listen to the Bloomberg Daybreak US edition podcast each morning for the stories that matter with context you need.
在苹果、Spotify或你常用的播客平台都能找到我们。
Find us on Apple, Spotify, or anywhere you listen.
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