AI 带来的新问题（以及人们如何解决这些问题）

去年这个时候，AI的处境完全不一样。

Last year at this time, AI was a very different place.

ChatGPT 5刚发布，当时几乎没什么反响。

ChatGPT five had just launched, to not much acclaim at all.

大家对GPT 4.0被淘汰这件事特别不满。

People were really upset that GPT 4.0 was being deprecated.

而且说实话，关于AI泡沫的讨论和担忧越来越多。

And there was so much growing conversation and consternation, frankly, about the potential of an AI bubble.

除此之外，还有些公司一直想说服自己，AI被过度炒作了，根本不会成气候。

And on top of all that, there were some companies out there that were still trying to convince themselves that AI was overhyped and just not going to be a thing.

一年过去了，现在的讨论风向完全变了。

Now a year on from that, the conversation is very different.

不仅模型进步了，使用场景也转向了代理式AI，真正兑现了潜伏多年的承诺，而且那些利用AI的企业在提问上也比之前成熟得多。

Not only have the models advanced, not only have the use cases shifted to the agentic, living up to the promise that's been lurking for years, but the businesses that are harnessing AI have gotten so much more sophisticated in the questions they're asking.

事实上，过去一年里，我们从很多时候连问题都问不到点上，发展到能主动去解决因代理式AI带来的新工作模式中出现的新问题。

In fact, over the last year, we've gone from in many cases not even asking the right questions to actively solving the new problems that emerge for new work patterns that come alongside specifically agentic AI.

生产太容易导致AI插槽问题？

Easy production causing an AI slot problem?

制定新的AI写作政策。

Institute a new AI writing policy.

过度使用顶级模型成本太高？

Over usage of top models costing too much?

提出新方法，将令牌和智能分配给组织的不同部门。

Come up with new ways to allocate tokens and intelligence to different parts of the organization.

今天，我们不仅要深入了解AI目前面临的挑战，还要看看企业究竟是如何解决它们的。

Today, we're going to dig into not only the current challenges of AI, but how companies are actually solving them.

AI每日简报是一个每日播客和视频，内容涵盖AI领域最重要的新闻和讨论。

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

好的，朋友们。

All right, friends.

开始之前，快速宣布几件事。

Quick announcements before we dive in.

首先，感谢今天的赞助商：Blitzy、Section、Robots and Pencils、Hyperagent。

First of all, thank you to today's sponsors, Blitzy, Section, Robots and Pencils, Hyperagent.

想收听无广告版本的节目，请前往patreon.com/aidailybrief，或在Apple Podcasts上订阅。

To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

如果想了解节目赞助事宜，请发邮件至sponsors@AIdailybrief.AI。

And to learn more about sponsoring the show, send us a note at sponsors@AIdailybrief.AI.

我喜欢现在AI这个时期的其中一个原因，是我们已经熬过了第一波所谓不太有价值的讨论。

One of the things that I like about this moment that we're in with AI is that we're through the first wave of a lot of, call it, less than useful conversations.

甚至就在去年这个时候，关于AI到底会不会成为大事，还有很多争辩。

Even at this time last year, there was still a ton of debate about whether this AI thing was gonna be a thing.

不过，在我们这里其实并没有真正的争议，但你仍然看到到处都有企业在心里希望，希望这只是又一个潮流，他们因为没深入参与而觉得被奖励了。

Now, of that wasn't really a debate around these parts, but you still had enterprises all over the place kind of holding out hope that this would just be yet another trend that they would be rewarded for not having dug in around.

当然，情况并不是那样发展的。

Now, of course, that is not how it has played out.

而在过去一年，尤其是过去八个月，我们一路飞骑直进，跨过了AI的第一阶段，进入了智能体时代，随之而来的是各种连带的后果。

And in the past year, and especially the past eight months, we have rocketed right on through the first stage of AI into the agentic era with all sorts of attendant consequences.

现在发生的很多事情都极具威力。

Now a lot of what's happening now is incredibly powerful.

各行各业都在忙着搞清楚怎么利用那些以前根本不具备的能力。

A lot of the work inside businesses of all shapes and sizes is figuring out how to take advantage of capabilities that simply were not there before.

不过，再强大的新技术，包括人工智能在内，都不会只用来解决新问题。

And yet, no new technology, AI included, is solely in the business of solving problems no matter how powerful it is.

相反，新技术在解决许多老问题的同时，往往也会通过它们带来的机遇，制造出新的挑战。

Instead, new technologies solve lots of old problems while, in many cases, through the opportunities they create, also creating new challenges.

当我们讨论像机器人代班或者AI垃圾内容这类话题时，我们就已经进入了如何应对外AI机遇所带来的问题的讨论阶段。

And when we discuss topics like bot sitting or AI slop, we are firmly then in the discussion of how to deal with the problems that come along with AI's opportunities.

今天我们要看看各大公司和企业在思考AI以及采取哪些措施来解决这些问题方面发生了哪些变化。

Today we're going to look at a bunch of changes in how companies and businesses specifically are thinking about AI, and what they're doing to solve some of those problems.

作为开场，我们将先分享一篇名为《值得深入审视的四个AI误区》的文章，重点聚焦在前三个误区上。

And by way of kicking off the conversation, we're going to start with a piece from called Four AI Misconceptions That Deserve Greater Scrutiny with a specific focus on the first three.

我觉得这些例子很好地说明了围绕AI的传统观念正在快速转变，而且这种转变是朝着更好的方向发展。

I think these are great examples of things the conventional wisdom around which is quickly shifting, and shifting for the better.

文章指出的第一个误解是，AI会立即带来生产率的飞跃。

The first misconception that points out is that AI will immediately generate a productivity boom.

他们写道：认为AI会立刻引发生产率激增的观点，很难与经济发展史相吻合。

They write: The assumption that AI will immediately generate a surge in productivity is difficult to reconcile with economic history.

重大的技术革命很少能在一夜之间带来整体经济层面的增长。

Major technological revolutions rarely produce economy wide gains overnight.

蒸汽机花了近一个世纪才在英国转化为持续的生产率增长，电力大约用了五十年才重塑工业生产，而计算机革命也差不多用了十年才让整体生产率有了可观的提升。

It took nearly a century for the steam engine to translate into sustained productivity growth in Britain, roughly five decades for electricity to reshape industrial production, and close to a decade before the computer revolution produced measurable improvements in aggregate productivity.

他们解释说，任何技术革命的第一阶段，都是建设支撑其运行的基础设施。

The first stage, they explain, of any technology revolution is the build out of the infrastructure that enables it.

就AI而言，这意味着要扩建数据中心、半导体制造、电力供应、云计算能力，以及数字基础设施。

In the case of AI, that means expanding data centers, semiconductor manufacturing, electricity generation, cloud computing capacity, and digital infrastructure.

此外，还需要培养相关的专业人才，让他们能够部署和管理这些技术，然后这些技术才能渗透到整个经济中。

It also requires developing the talent needed to deploy and manage these technologies before they can diffuse across the broader economy.

所以在这一部分，作者其实是用了两种完全不同的角度在谈论生产率。

So in this section, is talking about productivity in two very different ways.

他们说的是可衡量的生产力在整体宏观经济数据中的体现。

They're talking about measurable productivity showing up in overall macroeconomic numbers.

但我认为，至少对我们的讨论来说，更相关的是组织内部生产力的即时提升。

But I think that the more relevant part for our discussion, at least, is an immediate boom in productivity inside the organization.

我相信大多数组织发现，就像AI的能力参差不齐一样，AI带来的生产力提升也是参差不齐的。

I believe that what most organizations are finding is that just like the capabilities of AI are jagged, so too is the productivity enhancement of AI.

有些领域，对于任何在AI上投入过时间的组织来说，收益是立竿见影、显而易见且巨大的。

There are areas which, for any organization that has invested any amount of time in AI, the gains are just immediate and transparent and huge.

在其他领域，即使人们相信AI最终会影响那个领域，但现状仍然顽固地停留在他们一贯的做法上。

There are other areas where even if one believes AI will impact that area eventually, remains stubbornly stuck in the way that they'd always done things.

此外，组织正在经历一个混乱、复杂且耗时的过程，试图弄清楚如何将新的代理式工作方式与新型的人类监督和管理结合起来。

Moreover, organizations are going through the messy and complicated and time consuming process of figuring out how to integrate new ways of agentic working with new types of human oversight and management.

我们并没有看到有些人想象的那种从人类做工作到代理做工作的一对一转换，因此，弄清楚如何利用所有新机会，会创造出一整套新的工作，至少在短期内，在很多情况下，这些工作会填补你原本从以前任务的生产力中节省下来的时间。

We're not seeing the sort of one to one switch from humans doing jobs to agents doing jobs that some people imagine we would, and so figuring out how to take advantage of all the new opportunity creates a whole new set of work that in the short term at least, in many cases fills in any time gains that you otherwise would have won from productivity in previous tasks.

这并不是说这一切都是徒劳的，生产力会保持中性。

Which is not to say that this is all a wash, and that productivity is going to be neutral.

我们显然正处于一个过渡阶段，通往新常态和新的工作方式的路上会有大量的工作要做。

We are very clearly in a transitional phase, and there's just going to be an immense amount of work on the path to the new norms and how we do things.

总体来看，我接触到的那些组织已经完全接受了这个事实，并且现在正在逐个解决这些挑战，以便真正利用好AI，而不是坐在那里抱怨为什么没达到预期的效果。

By and large, the organizations that I'm interacting with have fully embraced that fact and are now trying to work one by one through those challenges so that they can really take advantage of AI rather than sitting around lamenting why they're not getting as much as they hoped from it.

第二个误解，在更深入的讨论中，一直是我们在AI每日简报中讨论的关键话题，尤其是在今年年中那段时间。

The second misconception that points out is one that the more advanced version of the conversation around has been a key part of the discourse for us here at the AI Daily Brief, especially throughout the middle part of this year.

那就是认为AI几乎免费的错误观念。

That is the misconception that AI is nearly free.

他们写道：认为采用AI成本低的想法忽略了一个重要的经济现实。

They write: The assumption that AI adoption is inexpensive overlooks an important economic reality.

与传统的企业软件不同，AI每次使用时都会产生显著的边际成本。

Unlike traditional enterprise software, AI carries a meaningful marginal cost every time it is used.

对许多企业来说，初期在许可证、基础设施和培训上的投入只是开始。

For many businesses, the initial investment on licenses, infrastructure, and training is only the beginning.

每次提示都会消耗令牌、计算能力和电力。

Every prompt consumes tokens, computing power, and electricity.

随着AI在各组织中的普及，成本迅速累积，AI从一次性的技术投资变成了经常性的运营开支。

As AI becomes embedded across organizations, costs accumulate rapidly, transforming AI from a one time technology investment into a recurring operating expense.

许多早期采用者已经发现了这一现实。

Many early adopters are already discovering this reality.

有几家公司报告说，由于员工使用量超出预期，年度AI预算在几个月内就耗尽了，这促使他们引入令牌预算、使用上限和更严格的治理措施。

Several firms have reported exhausting annual AI budgets within months as employee usage exceeds expectations prompting the introduction of token budgets, usage caps, and tighter governance.

与此同时，前沿AI提供商继续推出能力更强的推理模型，这些模型性能更高，但往往也消耗更多令牌，运营成本也更高。

At the same time, frontier AI providers continue to introduce more capable reasoning models, whose greater performance often comes with higher token consumption and greater operating costs.

回顾过去，他们写道：新技术的普及受部署经济性的制约，而非技术能力的限制。

Looking back, they write: The diffusion of new technologies is constrained less by technological capability than by the economics of deployment.

AI也不例外。

AI will be no exception.

采用的速度不仅取决于技术能做什么，还取决于每个令牌创造的价值是否超过其成本。

The pace of adoption will depend not only on what the technology can do, but whether the value created by each token exceeds its cost.

他们说，这意味着AI应该像其他资本配置一样进行管理。

They say that the implication is clear that AI should be managed like any other capital allocation.

现在想想，这个误解有趣的地方在于，这条路已经走了一很久了，只是到了今年，我们才终于活在了早就能预见到的那一天。

Now, think what's interesting about this misconception is that this has long been coming down the pipeline, and it's just that this year, we are finally living in the reality that we knew was on its way.

ChatGPT出现后的头几年，组织可以轻松地把它当作另一种SaaS订阅来对待。

For the first few years of AI's life post ChatGPT, organizations could get away with treating it like another SaaS subscription.

价值等式就是员工人数乘以每月的座位费用再乘以12个月。

The value equation was headcount times the cost of a seat per month times twelve months in a year.

这样算出来，是不是比员工创造的价值要低呢？

And does that come out to less than the value that's being created by folks?

然而，智能体AI当然完全改变了这个等式，让它更像是一种新型劳动力，而不是软件。

However, agentic AI, of course, totally changes that equation, making it less like software and more like a new type of labor.

我觉得这对组织来说并不是什么令人震惊的意外。

Now I don't think that this came as some shocking surprise to organizations.

要说有什么出人意料的，那就是有权限的员工消耗大量昂贵AI代币的速度快得惊人。

I think if anything was surprising, it was the speed at which enfranchised employees, especially, could actually burn through significant and expensive amounts of those AI tokens.

这一点比其他任何事情都更能为AI接下来要面对的问题和挑战定下基调。

This, more than anything, has set the context for all of the next set of questions and challenges that AI has to answer.

媒体对此的讨论，老实说真的让人火大。

But the media discourse around this is honestly just infuriating.

它总把企业当成什么不靠谱的乡巴佬，好像他们某天醒来才惊讶地发现AI和想象中完全不一样。

It tends to treat enterprises like they're some incompetent bumpkins who wake up one day shocked to discover that AI is totally different than the thing they thought it was.

这种说法把token预算和使用上限形容成拼命追赶脱轨火车般的慌乱举动。

The discourse presents things like token budgets and usage caps as these frantic attempts to catch up with a train that's running off the tracks.

但现实世界中真正的企业根本就不是这样情况。

None of that is how this is playing out for real organizations in the real world.

我接触过的几乎任何组织里，无论规模大小，但凡认真对待AI的，都清楚这是一个全新的挑战。

Pretty much everyone that I've interacted with at any point in any organization of any size who has any sort of seriousness around AI gets that this is a new challenge.

事情不再像以前那样简单把最强的模型甩给所有问题，不管多难都一样。

That it's not as simple anymore as just pointing the most powerful model at all of their problems, no matter how hard they are.

他们需要针对不同类型的问题，搭建一个真正完整的架构，包含不同种类的模型和不同的结构。

That they're going to need to put together an actual complete architecture of different types of models and different types of structures for different types of problems.

而且组织里不同的人也需要拥有不同数量、不同层级的智能访问能力。

And that different people within the organization are going to need to have access to different amounts and powers of intelligence.

那些你总听说的给使用量和token预算设上限的企业，并不是设完限制就捂起耳朵装听不见。

These organizations that you keep hearing about that slap usage caps and token budgets on things, they're not doing that and then sticking their fingers in their ears and saying don't talk to me.

他们同时也在开辟渠道，让员工申请更多预算，或者证明自己值得获得这些额度。

They're all at the same time creating pathways for people to apply for more budgets, or demonstrate that they deserve it.

事实上，企业适应这种新挑战的速度之快，对整个商界来说其实是个非常积极的信号。

In fact, if anything, the speed with which organizations are adapting to this being the new challenge set should be extremely encouraging for the corporate sector overall.

我认为，企业能迅速转变观念，意识到这是面临的新挑战，这非常值得肯定，也说明AI并非凭空出现，而是他们在过去几年里为这个新的智能体时代做好了准备。

I think the fact that the enterprise sector has pivoted so fast to understand that this is the new challenge that they face is hugely to their credit and representative of the fact that this didn't come out of left field, and that they've spent the last couple of years preparing for this new agentic period.

但这引出了第三个误解：认为AI会让劳动力变得多余。

But that gets us to misconception number three: That AI will make labor redundant.

这点稍微偏离了我们讨论的主题，也就是企业正在解决的新问题，因为这种说法根本就是错的。

Now this gets a little bit off our topic of the new problems that come with AI that companies are solving because this is just straight up wrong.

它根本不是一个需要解决的问题，因为它压根儿就不是个问题。

It's not actually a problem to be solved because it's not actually a problem.

过去几年里，有两拨人一直坚称AI会让劳动力变得多余。

There are two groups who over the last few years have been adamant about AI making labor redundant.

第一类是那些顶尖AI实验室的领导层，他们在媒体上花了大量篇幅谈论这个话题，不过至少其中一些人最近已经在试图收回这些说法了。

Group one is the leadership at the leading AI labs who have spent a disproportionate amount of their media space talking about exactly this although at least some of them have been trying to walk it back of late.

第二类坚信这一点的人，我其实根本不相信他们真的信过。

The second group who have been convinced of this, I don't actually even believe, were ever actually convinced of this.

那就是那些需要为裁员找借口的商业领袖们。

And that is the business leaders who have needed good excuses for why they were laying people off.

我当然相信，就像老听众们知道的那样，AI会改变工作和职业的形态，也会对劳动力市场产生影响。

I certainly believe, as any regular listeners will know, that AI is going to impact the shape of jobs and professions and will have labor market impacts.

我觉得过去一年左右，公司把40%甚至更多的裁员归咎于AI，这完全是胡说八道，只不过是一个市场愿意买账的方便借口罢了。

I think that companies over the last year or so blaming 40% or more of their layoffs on AI is a complete and utter crock that's just a convenient excuse that the market would buy and accept.

就像我在最近一期节目里说的，我觉得这个借口已经不管用了。

As I said in a recent episode, I don't think that that excuse is working anymore.

而且我觉得，你看到越多公司把解雇的人重新招回来的故事，这个误解就永远没有立足之地了。

And I think the more stories you see of people having to hire back people that they fired will just put a dagger in this misconception's heart forever.

但就像我说的，我感兴趣的不仅仅是这些误解，而是意识到AI带来了一系列新的挑战，以及人们是如何应对这些挑战的。

But like I said, what I'm interested in is not just these misconceptions, but the awareness that AI creates a set of new challenges and the way that people are dealing with them.

那我们接下来聊聊伴随AI出现的一个大问题，就是铺天盖地的劣质AI写作。

So let's now talk about one of the big problems that has come alongside the advent of AI, which is a flood of terrible AI writing.

一开始确实挺酷的，AI能围绕你任何一个想法或任务写出一大堆话来。

It was very cool early on just how many words AI could put forward around any particular idea or task you had.

当然，人们都想着尽快把手头的活儿干完，好去做别的事情，不管是更多的工作还是完全不相干的事，所以就拼命挖掘AI写作能派上用场的各种地方。

People, of course, being driven by their desire to get as much work done as fast as they possibly can and be on to other things, whether it's more work or something else entirely, have fully stretched to see just how many things they can use AI writing for.

现在这种模式当然也蔓延到了社交平台，各个平台也都在应对各自的AI垃圾内容问题。

Now this pattern has, of course, mated onto the social platforms as well, who are all dealing with their own versions of AI slot problems.

我之所以一直没像某些人那样为这事焦虑，是因为我一直觉得，机构或社会的免疫系统迟早会做出反应，而你现在正开始看到这种反应了。

One One of the reasons that this has never stressed me out as much as it has for some others is that it's always seemed pretty clear that institutional or social immune systems were going to create a response, and that's what you're starting to see.

比如Substack这些地方开始出现AI检测工具，不过我对那儿还是抱很大的怀疑态度。

You're seeing AI detectors pop up in places like Substack although I remain very skeptical there.

但同时也有一些更有利于社区的做法，比如LinkedIn帖子下面新加了一个按钮，你可以标记自己正在读的内容，就是那句话，"看起来像AI垃圾"。

But you're also seeing more pro social approaches to this, such as the new button on LinkedIn posts where you can click that something that you're reading, quote, seems like AI slop.

这种系统越多，人们生产AI垃圾凭AI写作偷懒的劲头就会越少。

The more systems like that that emerge, the less of an incentive there is to produce AI slop and be lazy in how we use AI to write.

而且这种反应不只出现在社交网络上。

And it's not just on the social networks that those sort of responses are emerging.

公司内部也开始看到这种情况了。

You're starting to see it inside companies as well.

Clay的联合创始人Varun Anand这周刚发帖说，公司已经制定了正式的AI写作政策。

Varun Anand, the co founder of Clay, just posted this week that the company had instituted an official AI writing policy at their organization.

有趣的是，最初只针对工程部门，但其他团队觉得很有用，就扩展成了全公司的政策。

Interestingly, originally it was just for the engineering organization, but other teams found it helpful enough that they expanded it to a company wide policy.

四个指导原则是：第一，你写东西时，必须对每个想法和每句话负责。

The four guiding principles were: First, that when you write something, you have to stand behind every idea and sentence.

就像Varun说的，在分享之前，你有责任确保整篇文档都能代表你自己的观点。

As Varun puts it, it is your responsibility to make sure that the entire document is representative of your own thoughts before you share it.

第二个原则是，写作就是在思考，花时间写作会让你更了解主题，如果跳过这个过程，你对内容的理解就会更浅薄。

The second principle is that writing is thinking that spending time on the writing process teaches you more about your topic, and that if you circumvent that process, you will walk away with a poorer understanding of the subject matter.

第三个原则是，写一份文档花费的时间，应该比阅读它花费的时间更多。

The third principle is that more time should be spent writing a document than consuming it.

他说，如果你用一个简短的提示生成文档，然后让读者去读那些长篇大论，那就是在浪费他们的时间。

If you generate a document from a short prompt, he says, then ask your readers to go through the longer output, you are disrespecting their time.

如果他们想的话，他们自己就可以去和ChatGPT聊。

They can talk to ChatGPT themselves if they want to.

第四点，也是最后一点，不是越长越好。

And fourth and finally, longer is not better.

他指出，AI让生成很长的文档变得容易，而且它特别喜欢用一些空洞的句子来凑篇幅。

AI, He notes, makes it easy to generate long docs, and it loves padding them with sentences that say nothing.

如果你是用一个简短的提示来生成文档的，那就直接分享那个提示就好了。

If you're producing docs from a short prompt, consider just sharing the prompt.

你会注意到，这并没有说不要用AI。

Now what you'll notice about this is that this does not say don't use AI.

它也不是给用AI的人贴上一个羞辱性的标签。

It doesn't brand people with a scarlet letter for using AI.

它甚至没有制造一种微妙的社会压力，让人不敢用AI。

It doesn't even create subtle social pressure against using AI.

这是在告诫我们不要偷懒，要意识到很多时候，产出的过程本身和最终结果一样有价值。

This is an injunction to not be lazy, to appreciate that the process of producing something is as valuable in many cases as the output it creates.

而这恰恰体现了我们如何应对AI带来的新挑战，这些挑战在实际中已经不可避免，就发生在我们公司和机构里。

And in that, it is a perfect example of how we are solving the new challenges of AI, which are inevitable in practice right now inside our companies and organizations.

更重要的是，Varun发布了完整的政策，而且考虑到有8377个人点赞、鼓掌或者喜欢这个内容，你完全可以相信，这种政策在接下来的几周里会出现在更多机构中。

What's more, Varun dropped the entire policy, and given the fact that 8,377 people have liked or applauded or hearted the thing, you better believe that this type of policy is going to show up at a lot more organizations in the weeks to come.

那这单靠一项政策是不是就能扭转AI垃圾内容泛滥的趋势呢？

Now does this all on its own turn back the tide of AI slop?

当然不能。

Of course not.

但社交和职业规范是可以快速适应的。

But social and professional norms can adapt quickly.

我觉得，有时甚至比我们预期的还要快。

More quickly, I think, than we sometimes even expect.

市面上每一款AI编程工具，第一件事做的都是一样的。

Every AI coding tool on the market does the same thing first.

它就开始写代码了。

It starts writing code.

而Blitzy恰恰相反。

Blitzy does the opposite.

在写一行代码之前，Blitzy会花上好几天时间逆向分析你的整个代码库。

Before writing a single line, Blitzy spends days reverse engineering your entire codebase.

成千上万的智能体读取数百万行代码，梳理出每一个依赖关系、每一个未记录的约束条件、以及过去十年里做出的每一项架构决策。

Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade.

最终形成一个动态知识图谱，它对你的软件的理解程度，相当于一位在行业里摸爬滚打三十年的首席工程师。

The result is a dynamic knowledge graph that understands your software the way a principal engineer would after thirty years in the building.

其他工具靠grep搜索和markdown文件来猜测上下文，但Blitzy从不猜测。

Other tools guess at context with grep searches and markdown files Blitzy never guesses.

它先建立真正的理解，然后自主完成超过80%的完整软件史诗任务。

It builds true understanding first, then delivers over 80% of entire software epics autonomously.

经过验证、端到端测试、达到生产级别的拉取请求。

Validated, end to end tested, production grade pull requests.

这就是为什么财富500强的工程团队在最关键的核心代码上信任Blitzy。

That's why Fortune 500 engineering teams trust Blitzy with the code bases that matter most.

你可以亲自去blitzy.com看看。

See for yourself at blitzy.com.

就是blitzy.com。

That's blitzy.com.

这里有个残酷的事实。

Here's a harsh truth.

你的公司可能花了成千上万甚至数百万美元在AI工具上，但这些工具被严重浪费了。

Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized.

一半的公司有AI工具，但只有12%的公司真正用它们创造商业价值。

Half of companies have AI tools, but only 12% use them for business value.

大多数员工还在用AI来总结会议纪要。

Most employees are still using AI to summarize meeting notes.

如果你负责公司AI的落地，你需要Section。

If If you're the one responsible for AI adoption at your company, you need Section.

Section 是一个帮你管理整个组织AI转型的平台。

Section is a platform that helps you manage AI transformation across your entire organization.

它指导员工在实际场景中使用AI，追踪哪些人在用AI创造商业价值，还能让你清楚看到AI在哪里创造了价值、在哪里没有。

It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value.

结果如何？

The result?

你不再是简单部署工具，而是真正推动可衡量的AI价值。

You go from rolling out tools to driving measurable AI value.

你的员工从整理会议纪要转向解决实际业务问题，而且你能证明投资回报率。

Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI.

别再猜测你的AI投资有没有效果了。

Stop guessing if your AI investment is working.

去sectionai.com看看吧。

Check out Section at sectionai.com.

那就是sectionai.com。

That's sectionai.com.

我每天都在这个节目里聊AI的潜力与现实之间的差距。

I cover the capability gap between AI potential and AI reality every day on this show.

大多数公司还在琢磨怎么起步。

Most companies are still figuring out how to start.

而Robots and Pencils已经在推产品、扩规模了。

Robots and Pencils is already launching and scaling.

Agendaq把生成式AI带进大型企业的生产环境，几周就上线。

Agendaq and generative AI in production at large enterprises in weeks.

AWS的高级合作伙伴，一年之内规模翻了一倍还多。

AWS advanced tier pattern partner more than doubled in a year.

而且他们正在招人。

And they're hiring.

有50个开放岗位。

50 open roles.

如果你觉得这个时代不一样，想参与进来而不是旁观，这个值得看看。

If you're someone who knows this moment is different, who wants to be inside it, not watching it, this is worth a look.

在Robots and Pencils，最好的想法胜出，团队刻意保持非常高的质量。

At Robots and Pencils, the best ideas win, and the team is purposefully kept super high quality.

这是一个你回头看看会觉得是自己做过的最好的决定的地方。

This is the kind of place you look back on as the best decision you ever made.

去 robotsandpencils.com/careers 看看吧。

Take a look at robotsandpencils.com/careers.

本期《AI每日简报》由Hyperagent赞助，你可以在这里运行团队可以共同管理的agent舰队。

This episode of the AI Daily Brief is brought to you by Hyperagent, where you run fleets of agents your team can manage together.

新用户可以获得一千美元的推理额度。

New users get a thousand dollars in inference.

别再依赖本地agent和聊天工作流了, 它们还得等你的笔记本电脑来提示。

Forget local agents and chat workflows waiting on your laptop to be prompted.

Hyperagent在云端部署始终在线的agent, 在你团队已经使用的工具中完成实际工作。

Hyperagent deploys always on agents in the cloud, doing real work across the tools your team already uses.

营销部门的agent把竞争对手的动作变成 landing pages。

Marketing's agent turns competitor moves into landing pages.

销售部门的智能体负责丰富潜在客户信息、起草邮件并更新CRM。

Sales's agent enriches leads, drafts emails, and updates the CRM.

运营智能体负责处理文书工作并跟踪预算。

Ops agent chases the paperwork and tracks the budget.

每个智能体都能访问共享上下文，并遵循你关于范围和审批的规定。

Every agent has access to shared context and follows your rules about scope and approvals.

是时候让智能体成为你的队友了。

It's time you add agents that feel like teammates.

在Hyperagent（由Airtable团队打造）雇佣你的智能体吧。

Hire yours at Hyperagent built by the team at Airtable.

在hyperagent.com/aideallybrief领取你的1000美元推理额度。

Claim your $1,000 in inference at hyperagent.com/aideallybrief.

人工智能之所以令人兴奋，部分原因在于这是我们共同经历的全新体验，所以许多人在过程中分享他们的最佳实践和经验，这样大家就不必一遍又一遍地犯同样的错误。

And part of what makes AI so exciting is that because it is this new experience that we are all going through together at the same time, so many people are sharing their best practices and learnings on the way so that everyone doesn't have to make the same mistakes over and over.

在任何一周，你都可以在LinkedIn或其他社交渠道上看到大量来自企业或商业领袖的文章，分享他们最新发现的如何让AI为自己服务的方法。

On any given week, you can go through LinkedIn or basically any other social channel and find a slew of articles from corporate or business leaders sharing the latest things that they have figured out about how to make AI work for them.

而且这正好说明，目前还没有人完全知道该怎么做。

And putting a fine point on the fact that no one yet perfectly knows how to do this?

很多时候，这些评论其实来自那些自己就在开发AI的实验室。

A lot of times, that commentary is coming from the labs who are creating AI themselves.

OpenAI的首席财务官Sarah Fryer这周发表了一篇文章，标题是《打造AI原生财务部门教会我的事》。

OpenAI CFO Sarah Fryer this week published a piece called What Building an AI Native Finance Function Taught Me.

她意识到，AI不仅仅是让原来的工作做得快一点，而是带来了全新的机会。Fryer说：我们设定了两个大胆的目标：零日结账，以及自动化、持续更新的预测。

Recognizing that AI was about more than doing their old work a little bit faster, and instead about totally new opportunities, Fryer said: We set two bold ambitions: a zero day close and automated, continuously updated forecasting.

她接着说：零日结账背后的想法，是让领导者能够实时、对账清晰、可追溯地了解公司的财务状况。

She continues: The idea behind a zero day close is to give leaders a real time, reconciled, and traceable view of company's financial position.

持续预测则是在这个基础上，展示业务正在如何变化，接下来可能发生什么，以及哪些决策可能改变结果。

Continuous forecasting builds on that foundation, showing how the business is changing, what could happen next, and what decisions could alter the outcome.

这很有挑战性。

This is challenging.

她指出，这让我们超越了静态电子表格、手动查找支持文件和演示文稿的局限，转向基于业务完整背景和数据构建的实时工具。

She notes, It has pushed us beyond the limits of static spreadsheets, manual searches for supporting records, and presentations towards live tools built on the full context and data of the business.

重要的是，她指出：要达到这个目标，不仅仅是采用新技术。

Importantly, she notes: Getting there requires more than adopting new technology.

这需要围绕关键决策重新设计工作，给人们实验的空间，在每个工作流程中建立明确的问责制，并衡量AI完成的可靠工作，以提供清晰的ROI。

It requires redesigning work around the decisions that matter, giving people room to experiment, building clear accountability into every workflow, and measuring the dependable work AI completes to provide a clear ROI.

那么，她认为其他人也可以借鉴的五个实用经验是什么呢？

So what are the five practical lessons that she thinks others can apply as well?

第一是让每个人都能使用，然后创造使用的理由。

The first was to give everyone access, then create a reason to use it.

Fryer认为：人们需要在自己工作的背景下自由探索AI，而当访问与围绕实际问题的结构化实验相结合时，访问能创造最大的价值。

Fryer argues: People need the freedom to explore AI in the context of their own work, and access creates the most value when it is paired with structured experimentation around real problems.

说实话，公司间的黑客马拉松已经从几年前我认为人们会嗤之以鼻的事情，变成了我经常看到在不同公司和环境中出现的真正有用的架构。

Honestly, intercompany hackathons have gone from something that I think people would sneer at a few years ago to a genuinely useful architecture that I'm seeing pop up across companies and contexts all the time.

不过，对Fryer来说，核心要点是，你需要自下而上的实验和自上而下的战略。

The TLDR for Fryer, though, is that you need both bottom up experimentation and top down strategy.

顺便说一句，回到我们刚才讨论的token预算问题，我认为随着我们设计更复杂的方法来分配token这种稀缺资源，这将是一个重大挑战。

And by the way, going back to that token budget question that we were just discussing before, that I think is going to be one of the big challenges as we design more sophisticated ways to allocate the scarce resources that is tokens.

这不会简单到让每个人都去证明每个AI用例的投资回报率，除非大家只想做那些最容易证明回报的用例，而这些在很多情况下往往是最简单的效率提升，而不是那些彻底重塑工作的场景。

It's not going to be as simple as getting everyone to prove ROI for every single AI use case, unless people want just the easiest to prove ROI type of use cases, which in many cases are going to be the very simple productivity enhancements, not these total reimaginings of work.

Fryer给出的另一个经验是，各行各业的专业人士正在成为构建者，同时继续发挥他们本来的专业特长。

Another takeaway lesson from Fryer is the idea that professionals of all stripes are increasingly becoming builders alongside their other expertise.

她写道：更大的转变在于，财务专业人士现在可以自己构建工作所需的工具了。

She writes: The bigger transformation is that finance professionals can now build the tools their work requires.

OpenAI近期的研究表明，财务专业人士40%的AI专用使用场景涉及传统财务之外的工作，还有22%跟工程相关的任务有关。

Recent OpenAI research shows that 40% of finance professionals specialized AI use involves work outside traditional finance, and 22% involves engineering related tasks.

她提到，他们团队里的每一个人都在用ChatGPT的工作和代码功能打造自定义AI仪表盘和工具。

Everyone on my team, she says, is building custom AI dashboards and tools with ChatGPT work and codecs.

工作方式正在从静态的Excel模型和PPT演示转向实时仪表盘，这些仪表盘建立在公司的整体背景和数据之上。

The work is moving from static Excel models and PowerPoint decks towards live dashboards that sit on top of the full context and data of the business.

这些工具能延续分析流程，回答追问，并在底层信息变化时自动更新。

These tools can carry an analysis forward, respond to follow-up questions, and update as the underlying information changes.

如果你想简单概括每个充满知识型员工的组织将面临的核心技能升级挑战，那就是弗雷尔所说的，如何帮助人们弄清楚怎么用这类工具，怎么借助新能力把现有的专长发挥得更远。

And if you want a simple way to sum up the overall upskilling challenge that every organization full of knowledge workers is going to have to face is how to help people figure out, as Fryer puts it, how to use these sorts of tools and how to use their new capabilities to gain the ability to carry their existing expertise further.

Fryer还有一条关于如何评估的建议。

One more recommendation from Fryer comes around how they evaluate things.

她建议衡量每单位智能的价值。

She suggests measuring value per unit of intelligence.

她写道，CFO们需要一个基于运营绩效的AI记分卡，尽管她可能指的是任何类型的高管。

CFOs, she writes, although she could be referring to any type of executive, need a scorecard for AI grounded in operating performance.

买更多席位或者用更多token并不能说明太多。

Buying more seats or using more tokens doesn't tell you much.

重要的是工作是否完成得好，以及实际成本是多少。

What matters is whether the work gets done well and what it really costs.

对于每个工作流程，要问四个问题：AI是否完成了重要的工作？

For each workflow, ask four questions: Did AI complete work that mattered?

成本是多少？包括员工时间、审核和返工。

What did it cost, including employee time, review, and rework?

结果是否够好能直接用？是否帮我们更快行动或做出更好的决策？

Was the result good enough to use, and did it help us move faster or make a better decision?

这就是我说的，现实中关于AI的讨论，比媒体上呈现的要聪明得多、复杂得多、细致得多。

And this is exactly what I mean when I say that the conversations that are actually happening in real life around AI are way smarter, more sophisticated, more nuanced, and more complex than the way that they're presented in the media.

公司不傻。

Companies aren't stupid.

他们知道光看消耗了多少token是不够的，但也知道过于简单的ROI评估方法同样不行。

They know that simply looking at how many tokens were consumed is not enough, but they also know that overly simplified approaches to understanding ROI are insufficient as well.

而这种细致入微的思考，正在逐渐成为共识。

And increasingly, this nuance is becoming conventional wisdom.

Section的CEO Greg Shove这周也在LinkedIn上发帖，讲了现在他会跟CEO们说的五件事。

Section CEO Greg Shove also posted this week on LinkedIn about the five things that he's telling CEOs about AI right now.

第一件事跟我在这节目里常说的一个观点有关，而且我在这一集里也提过：AI token用太多是傻，但用太少也一样傻。

The first one harkens to something that I talk a lot about on this show, and that I've even mentioned before in this particular episode: AI token maxing is stupid, but so is token minimizing.

你拿工资就是为了下大注。

You're paid to make big bets.

别在还没看到回报之前就砍预算，自己坑自己。

Don't shoot yourself in the foot by shrinking your budget before you can see gains.

可以对全公司做生产力方面的假设，投入转型，并且接受一到两年内不会看到完整的图景。

Make some assumptions on org wide productivity, invest in transformation, and accept that you won't have the full picture for one to two years.

格雷格更进一步，甚至给了一种具体的做法。

Now, Greg goes a little farther and even gives one particular way to go about this.

他建议选一个团队，然后把投资金额扩大十倍。

He suggests picking a team and 10x ing the investment.

他写道，大多数员工赋能都铺得很宽但很浅，虽然这是个不错的起点，但你需要一个“灯塔团队”——他用这个词——在那里转型更快，成果更大。

Most workforce enablement, he writes, is a mile wide and an inch deep, and while that's a good place to start, you also want a lighthouse team his term where transformation happens faster with greater results.

最后，为了抢先应对接下来常见的难题，格雷格建议避免“十二个月停滞期”。

Finally, trying to get out ahead of what a common challenge is and is going to be, Greg suggests avoiding the twelve month stall.

他写道，第一年很令人兴奋。

Year one, he writes, was exciting.

你推出工具，开了启动会，还看到一些重要用户冒出来。

You rolled out tools, had a kickoff, saw some power users emerge.

现在大家都在问：这真的值吗？

Now everyone's saying, is this really worth it?

别变得怀疑。

Don't get skeptical.

要具体。

Get specific.

哪些团队受阻了？

Which teams are blocked?

是什么在阻碍他们？

What's blocking them?

你能尝试做什么让他们以不同的方式工作？

And what can you try to get them working differently?

这里再次体现出对组织适应速度的乐观态度。

And here again is that optimism around how quickly organizations are adapting.

随处可见，公司提出的问题性质正在从简单转向复杂，从低杠杆转向高杠杆。

Everywhere you look, you see a shift in the nature of the questions that companies are asking from simpler to more complex, and from lower leverage to higher leverage.

BCG全球主席Rich Lesser捕捉到了围绕AI对话成熟度的转变。

BCG Global Chair Rich Lesser captured the shift in the sophistication around the AI conversation.

他写道：我们最常听到CEO们关于AI的问题已经悄悄发生了变化。

He wrote: The question we hear most from CEOs about AI has quietly changed.

以前的问题是，我们应该用哪个模型？

It used to be which model should we use?

现在的问题是，我们是不是对不断演变的生态系统投入太多、太快了？

Now, it's are we committing too much too soon to an evolving ecosystem?

基本上，他和他在文中指出的配套文章描述的是，公司正在摆脱把AI决策仅仅看作选择合适供应商的思维方式。

And basically what he and the companion essay that he points to are describing is companies getting out of the mindset of thinking about AI decisions as simply about choosing the right vendor.

似乎也和微软的萨蒂亚·纳德拉最近强调的论调一致，他们认为组织拥有所谓的“企业皮层”：包括知识产权、关键数据、核心业务规则、专有信息，以及对流程运作方式和流程如何与核心业务战略、宗旨和价值观相联系的编码化理解，这些是最有价值的内部知识，也是让AI战略成功的关键要素。

Harkening to the same sort of drumbeat that Satya Nadella from Microsoft has been beating recently, they argue that the organization has what they call an enterprise cortex: the IP, essential data, key business rules, proprietary information, and codified understanding of how processes work and how they link to core business strategy, purpose, and values that are the most valuable internal knowledge and that are the essential things that will allow its AI strategy to succeed.

组织需要掌握所有这些信息所在的“缰绳”。

Organizations need to own the harness where all of that lives.

所以实际上，组织正在努力解决的另一个重大转变和新问题，是抛弃选择哪个模型，转而建立组织级别的“缰绳”，能够使用任何模型或模型组合，同时保留更广泛的组织背景，包括工具、技能、防护措施、治理等等。

And so in a very real way, another one of these big shifts and new problems that organizations are trying to solve is shifting away from which model to buy and how to create an organization level harness that can use any model or combination of models while preserving the broader organizational context, including tools, skills, guardrails, governance, and more.

另外，让我觉得有意思的是，在人们分享如何应对AI带来的某些明显挑战的同时，我们也在开始讨论更长远的问题，即在地平线上预先防范AI问题，不让它们变得那么糟糕。

And what's also interesting to me is that alongside people sharing how they're solving some of the more obvious challenges that have emerged from AI, We're also starting to have discourse that looks farther out about preempting AI problems on the horizon before they become as bad as they could be.

里奇在联合撰写中指出，BCG的另一篇论文认为，当每个人都在使用AI时，公司可能会丧失关键技能。他写道：大多数领导者没有意识到的风险，不是AI幻觉，也不是失业，而是普遍性的技能退化。

Pointing to another BCG paper that argues when everyone uses AI, companies risk losing critical skills, Ridge's union writes: The risk most leaders aren't tracking is not AI hallucinations, not job loss, but distributed deskilling.

这种整个员工队伍在判断力、批判性思维和问题构建能力上的集体侵蚀，正在悄然发生，而仪表盘上的采纳率数据看起来却一片大好。

The collective erosion of judgment, critical thinking, and problem framing across an entire workforce happening quietly while adoption numbers look great on a dashboard.

在接受BCG调查的领导者中，有一半的人表示已经看到了这种现象，超过60%的人预计在接下来的三到五年内这将成为一个真正的威胁。

Half the leaders BCG surveyed said they're already seeing it over 60% expect it to be a real threat within the next three to five years.

他说，正在退化的这些技能，恰恰是公司声称在未来十年最需要的。

The skills going soft, he says, are the exact ones companies say they need for the most decade.

尽管在BCG的文章中，这被框定为系统设计问题，但显然这同样也涉及人才层面。

And while in the BCG essay they frame it as a systems design problem, there is also clearly a talent dimension to this as well.

布里奇写道：我们的研究告诉我们，如今只有不到五分之一的员工对使用AI工具感到自信。

Bridges writes: Our research tells us that fewer than one in five employees feel confident using AI tools today.

大约有三分之二的员工表示，如果他们的使用努力得到认可，他们更愿意支持变革。

Roughly two in three said they'd be more willing to support change if their effort using it was recognized.

Token使用量并不是衡量采纳度的指标，信心才是。

Token usage is not a proxy for adoption confidence is.

而信心不是靠推出一个工具就能建立的。

And confidence isn't built by rolling out a tool.

它是在每次有人选择做难事而不是容易的事时，通过强化正确的行为来建立的。

It's built by reinforcing the right behaviors around it, every time someone does the hard thing instead of the easy thing.

虽然我认为现在说我们已经扭转局面还为时过早，但终于有一些人开始认识到，我们在AI的人性维度上投入严重不足，而只关注技术维度。

And while I think it is absolutely too early to say that we have turned a corner here, there is finally some emerging recognition that we have critically underspent on the human dimension of AI in favor of just the technology dimension.

在KPMG最近的适应性报告中，他们指出领导者们在技术上投入过多，而在人才上投入不足。

In their recent adaptability report, KPMG argues that leaders are overspending on technology and underspending on talent.

他们指出，高管们增加新技术投资的可能性是增加员工培训投资的两倍，而57%的领导者表示过去一年提高绩效和效率是他们的首要任务之一，但只有不到10%的人表示发展更强大的劳动力培训计划是他们的主要目标之一。

Executives, they point out, are two times more likely to increase investment in new technology than to invest in employee training, While 57% of leaders say improving performance and efficiency was one of their top priorities in the past year, less than 10% say developing stronger workforce training programs was one of their primary objectives.

KPMG写道，这虽然显而易见，但仍需指出：在动荡时期，员工需要更多的培训和支持，而不是更少。

Stating the painfully obvious but still needs to be said, KPMG writes, in times of disruption, workers need more training and support, not less.

高管们不应将资本分配到技术或人才视为一种权衡。

Executives should not view allocating capital to technology or talent as a trade off.

当组织同时推进这两者时，他们会看到更好的结果。

Organizations see better outcomes when they advance the two together.

AI和技术的采用需要变革管理，那些没有在员工技能培养上投入足够多的公司，往往很难充分发挥新工具的价值。

AI and technology adoption require change management, and companies that don't invest enough in building the skills employees need to make the most of new tools often struggle to realize their full value.

更重要的是，这不仅仅是个好听的说法。虽然整体上有25%的企业领导者表示过去三年收入增长了20%或更多，但在那些增加了员工培训投入的领导者中，这个数字是37%。

More importantly and this is not just some feel good thing while 25% of business leaders overall said revenue had risen by 20% or more over the past three years, among leaders who had increased their investment in their workforce, that number was 37%.

当然，AI培训并不简单。

Now, of course, AI training isn't simple.

不容易。

It's not easy.

这当然不只是给他们一个最好的视频课程，再给他们的LinkedIn档案加个认证那么简单。

It's certainly not just a matter of giving them the best video course and a certification for their LinkedIn profile.

这需要真正的努力和时间投入，还要从零开始调整流程。

It takes really hard work and time on task, and adapting processes from the ground up.

但至少我们现在讨论的是这类话题了。

But at least that's now the type of conversation that we're having.

而且随着越来越多的公司找到了有效的方法，比如Sarah Fryer在她的文章里分享的那些经验，其他公司就有了更多的参考模板，也就更容易应对正在出现的实际挑战了。

And as more and more companies figure out approaches that work, such as the lessons that Sarah Fryer shared in her post, the more templates other companies are going to have, and the easier it's going to be to answer the actual challenges that are emerging.

随着AI的发展，应用也在推进，尤其是当我们真正拥抱它带来的变革时，我们也会遇到新涌现的挑战。

As AI evolves, and as adoption proceeds, and especially as we fully embrace the true transformative aspects of it, we are going to discover new emergent challenges as well.

前进的道路就是能识别并命名这些问题，然后公开地一起努力解决。

The path forward is in being able to identify and name those problems, and work on them together out in the open.

我本周看到Zara Zhang在X上发的帖子，提到一个特别值得思考的问题，它出自一篇叫《认知公地的悲剧》的新论文。

One that I saw Zara Zhang posting about on X this week that is a super interesting one to contemplate comes from a recent paper called The Tragedy of the Cognitive Commons.

Zara写道：这篇论文给你早就刻骨铭心的那个问题起了一个高大上的名字：认知公地的悲剧。

Zara writes: This paper gives a fancy name to a problem you can already feel in your bones: The Tragedy of the Cognitive Commons.

检查AI输出的结果需要深厚的专业知识。

Checking AI output requires deep expertise.

深厚的专业知识是多年来做基础工作积累起来的。

Deep expertise comes from doing grunt work for years.

而AI最先吃掉的就是这些基础工作。

And grunt work is the first thing AI eats.

所以我们在构建需要专家监督的系统，同时又在拆掉唯一一个培养专家的机制。

So we're building systems that need expert supervision while dismantling the only known process for making experts.

这篇论文把人类专业知识的共享池称为认知公地。

This paper calls the shared pool of human expertise the cognitive commons.

每个行业都在从中汲取。

Every profession drinks from it.

但没有人在补充它。

Nobody's refilling it.

通过取消初级岗位，每家公司都表现得极其理性。

By eliminating junior roles, each company is acting totally rationally.

而集体结果是：一个行业再也无法发现AI的错误，因为它从一开始就没学过如何做这份工作。

And the collective result: a profession that can't catch AI's mistakes anymore because it never learned to do the work in the first place.

换句话说：十五年后，谁来检查AI的作业？

In other words: who checks AI's homework in fifteen years?

当我们讨论这类远见性问题时，我认为我们不应该毫无疑问地接受它们就会像人们提出的那样成为现实问题。

Now, when we're talking about far out problems like this, I don't think that we should just be accepting beyond a shadow of a doubt that they are going to be the problem that manifests as people are presenting here.

但这些问题是值得思考的，也值得花时间去探讨。

But they are worth thinking about and spending time on.

因为世界上没有哪个问题是找不到答案的。

Because there isn't a problem in the world that has no answers.

Zara认为，深厚的专业知识只能通过多年的基础工作积累而来。

Zara argues that deep expertise only comes from doing grunt work for years.

但这是职业世界的自然规律，还是仅仅因为一直如此？

But is that a law of nature in the professional world, or is that simply how it's always happened?

有没有其他方式来培养这种专业知识，以及公司是否有动力让人们有机会去这样做？

Are there other ways to develop that expertise, and incentives for companies to put people in a position to do so?

这些问题都不简单，但值得一问。

None of those are simple and easy questions, but they are good ones to ask.

我觉得说到底，如果要总结我对过去一年AI在企业中发展的看法，那就是我们不再频繁地问那些没什么用的问题，比如AI到底会不会成为现实。

I think when push comes to shove, if you had to put a big old TLDR on how I feel about particularly how AI has evolved inside of businesses over the past year, is that we've gone from very frequently asking not particularly useful questions of if, I.

而是转向更有价值的问题，比如怎么用，以及怎么用得好。

E, is AI actually going to be a thing, to much, much more valuable questions of how and how to do it well.

我鼓励大家继续问这些问题，并尽可能公开分享你们的答案，这样大家就不必各自解决这些问题了。

My encouragement to all of you is to keep asking those questions and sharing your answers in public as much as possible so that everyone doesn't have to solve them on their own.

本周末这期节目给大家留点思考的空间，但今天的人工智能每日简报就到这里。

Food for thought in this weekend episode, but for now, that is gonna do it for today's AI Daily Brief.

一如既往，感谢大家的收听和收看。

Appreciate you listening or watching as always.

那我们下期再见，祝大家平安。

And until next time, peace.