2026年战略峰会：谁将在人工智能领域取得成功？

5月20日，欢迎参加《哈佛商业评论》年度领导力峰会，届时将有大师课程、AT&T和美泰公司首席执行官的访谈，以及哈佛商学院教授卡里姆·拉赫ani主持的互动案例讨论。

On May 20, join me at HBR's annual leadership summit with master classes, interviews with the CEOs of AT and T and Mattel, and an interactive case discussion led by Harvard Business School professor Karim Lakhani.

为享受75折优惠，请务必在4月14日前购买早鸟票。

For 25% off, be sure to grab your early bird ticket by April 14.

如需了解更多信息，请访问hbr.org/leadershipsummit。

To learn more, go to hbr.org/leadershipsummit.

届时见。

See you there.

我是艾莉森·比尔德，欢迎收听《哈佛商业评论》创意播客。

I'm Alison Beard, this is the HBR Ideacast.

《哈佛商业评论》最近举办了2026年战略峰会，全天汇集了来自企业高管和学者的专家建议与指导。

Harvard Business Review recently hosted the HBR Strategy Summit twenty twenty six, a day filled with expert advice and guidance from executives and academics.

我们将在本特别创意播客系列中分享本次活动的精华内容。

We're sharing the highlights of the event in this special IdeaCast series.

今天，本系列的最后一集将呈现来自麻省理工学院首席研究科学家、麻省理工学院数字经济倡议联合创始人兼联合主任安迪·麦卡菲的大师课程。

Today, our final episode of the series is a master class from Andy McAfee, principal research scientist at MIT and cofounder and codirector of the MIT Initiative on the Digital Economy at its Sloan School of Management.

在这一环节中，他解释了企业如何构建正确的文化，以真正实现人工智能的成功应用。

In the session, he explains how businesses are building the right cultures to truly succeed with AI.

他阐述了他所称的‘极客型组织’的兴起，以及人工智能正在最优秀者与其他人之间制造的差距。

He explains the rise of what he calls geek organizations and the gap that AI is creating between the best and the rest.

他还与《哈佛商业评论》资深编辑阿迪·伊格纳修斯一起回答了观众的问题。

He also takes audience questions with the help of HBR editor at large, Adi Ignatius.

以下是这一大师课的内容。

Here's that master class.

我想谈谈如今许多人最关心的一个话题。

I wanna talk about a top of mind topic for a lot of people these days.

谁会成功？

Who's going to succeed?

在当前这个人工智能充满巨大不确定性的时代，成功采用人工智能的企业与那些平庸者之间，究竟有什么区别？

What's gonna distinguish successful AI adopters from the also RANs in this era right where we are now where there's just substantial uncertainty about what's going on with AI.

我想引用最近几天的一些头条新闻，来说明这种不确定性有多么深刻。

I wanna grab some headlines from just the past couple days that underscore how profound this uncertainty is.

我先从我的联合创始人、同事、非常优秀的经济学家埃里克·布林约尔松的一条新闻说起，他不久前在《金融时报》发表了一篇社论，称AI的生产率起飞已经到来。

I'm gonna start with a headline from my cofounder, colleague, very good economist, Eric Brynjolfsson, who wrote an editorial in the Financial Times just a while back saying, the AI productivity takeoff is already here.

我们一直期待着这一刻。

We've been waiting for it.

我们一直依赖着罗伯特·索洛很久以前的一个观点：我们在各个地方都能看到计算机时代的痕迹，唯独在生产率数据中看不到。

We have been relying on this, insight from Robert Solo from a while back saying, we see evidence of the computer age everywhere except in the productivity statistics.

这一点对AI来说也一直成立。

That has been true for AI.

埃里克说，那个时代终于结束了，我们现在可以期待进入一个由AI驱动的高生产率增长时代。

Eric says that that era is finally over, and now we can anticipate moving into an era of really high productivity growth because of AI.

然而，最近一项对全球众多首席执行官的调查显示，事情没那么简单。

However, a recent survey of lots of CEOs around the world says not so fast.

我们实际上并没有看到这种增长。

We're actually not seeing it.

当我们审视自己在AI投资上的回报率，或组织从AI中获得的生产率提升或关键绩效指标改善时，似乎这些成效还尚未显现。

When we look around at the ROI of our AI investments, at the productivity boost or the KPI boost that our organizations have received from AI, it appears to not be there yet.

另一位优秀的经济学家，目前仍在麻省理工学院的多伦·阿西莫格鲁。

Another very good economist who is still at MIT, Doron Asimoglu.

你可以看出他是一位出色的经济学家，因为他获得了诺贝尔奖。

You have the sign that he's a pretty good economist because he's got the Nobel Prize.

他最近在《麻省理工科技评论》的一次采访中表示，人工智能并没有带来任何生产率提升。

He was just in an interview at MIT Tech Review saying that there's not any productivity benefit from AI.

因此，我们正处在一个极度不确定的时代。

So we're in this era of deep, deep uncertainty.

不久前，《金融时报》上刊登过一张图表，我认为它完美地总结了我们当前的状况。

There was one chart that appeared in the Feet a little while back that I think summarizes where we are beautifully.

它说，好吧。

It said, okay.

看。

Look.

这里有三种可能性。

Here are three possibilities.

要么人工智能将终结稀缺，带我们进入一个永恒富足的时代。

Either AI is gonna end scarcity and bring us into an era of perpetual abundance.

那就是那条急剧上升的红色线条。

That's that crazy red line that's going up.

或者终结者将会到来。

Or the terminator's gonna come.

它将把我们推向灭绝。

It's gonna, drive us to extinction.

那就是那条下降的线条。

That's the line going down.

或者它可能只是以某种相对较小的程度提升生产力增长的正常趋势。

Or maybe it's just gonna improve the normal march of productivity growth by some relatively amount.

那就是趋势线的延续。

That's that continuation of the trend line.

无论如何，这种不确定性范围都极其巨大。

This is a very, very high range of uncertainty to say the least.

我非常喜欢德里克·汤普森昨天在他的Substack文章中对这个问题的表述。

I love the way that Derek Thompson framed it in his Substack just yesterday.

他说，你看。

He said, look.

没人知道真相。

Nobody knows anything.

现在还处于现代人工智能、生成式人工智能推广的早期阶段。

It is too early in the rollout of modern AI, of generative AI.

这显然是一种强大的技术。

It's clearly a powerful technology.

它显然能做很多事情。

It can clearly do a lot.

有理由对它持乐观态度。

There's reason to be optimistic about it.

也有理由对它持悲观态度。

There's reason to be pessimistic about it.

现在还太早，无法确定。

It's just too early to know for sure.

所以我喜欢这种思考方式，因为我确实认为我们还处于人工智能普及时代的非常早期阶段，它的变化还不明朗，尤其是当你仔细审视证据时。

So I love that way of thinking about it because I do think we're extremely early in this era of of AI diffusion, and its changes are not clear yet, especially when you look hard at the evidence.

那么，在一个可能有重大变革的时代，你该怎么做呢？

So what do you do in an era where there's potentially a big deal?

一种变革性的技术或许已经出现，正在开始普及，在许多情况下正迅速渗透到各个组织和整个经济中，但没人知道这将把我们带向何方。

A a a transformative technology perhaps has appeared, is starting to diffuse, in many cases diffusing rapidly throughout organizations, throughout the economy, but nobody knows where this is taking us.

没人能对这将把我们带向何方做出任何明确的判断。

Nobody knows anything definitive about where this is taking us.

我想提出一个非常简单的三步策略，帮助我们在当前这种‘无人知晓’的环境下成功运用人工智能。

I wanna propose a very simple three part playbook for succeeding with AI in the circumstances we aren't that we're in now where nobody knows anything.

这个策略的第一步是，你必须做出一个赌注。

The first part of the playbook is you gotta make a bet.

你必须投入其中。

You've gotta commit.

我强烈建议你站在支持AI的一边并坚定投入。

I strongly suggest you commit in the pro AI direction.

别再模棱两可了。

Stop equivocating.

别再犹豫不决了。

Stop sitting on the fence.

让整个组织对AI做出承诺。

Commit as an organization to AI.

而表明这种承诺的一种方式，就是将AI设为OKR。

And one of the ways you show that commitment is by making AI an OKR.

换句话说，把AI变成你对组织中每个人所期望的成果。

In other words, make AI something that you expect out of the people in your organization.

传达这种期望。

Communicate that expectation.

让它清晰明确。

Make it clear.

一遍又一遍地重复。

Repeat it over and over again.

消除那种认为AI只是众多事务之一，或者只是昙花一现的技术的想法。

Eliminate the idea that AI is one of one of many things that are going on or that it's a flavor of the month technology.

传达这种期望。

Communicate the expectation.

而优秀的OKR公司做的另一件事是，衡量朝着目标进展的进度。

And then the other thing that good OKR companies do, they measure progress toward the goal.

如果你的目标是节省时间、节省成本，或者仅仅是AI使用的绝对数量，那很好。

If you have a goal about time savings or value savings or just raw amount of AI use, great.

设定这个目标，然后随着时间的推移衡量实现它的进展。

Set that goal and then measure progress toward it over time.

当没有人了解任何事情时，AI操作手册中第二个非常重要的部分，是由史蒂夫·尤尔特森向我强调的，他是一位非常出色的早期风险投资家，曾投资于太空探索公司SpaceX和特斯拉。

The second really important part of the AI playbook when nobody knows anything, was driven home to me by Steve Jurvetson, who is a very good venture capitalist early in both space SpaceX and Tesla.

当我研究极客方式时，他向我强调了在高度不确定的环境中执行复杂且相互依赖的项目时，两种不同方法之间的关键差异。

And when I was researching the geek way, he drove home to me the importance of the of the difference in two schools about how to execute complicated interdependent projects in an environment of high uncertainty.

为了简化一点，这两种方法分别是瀑布法和敏捷法。

And to oversimplify a little bit, those two schools are the waterfall method and the agile method.

瀑布法是一种强调前期规划的方法，用于成功执行大型项目。

And waterfall is an up front planning heavy method about executing big projects successfully.

瀑布法认为，看吧。

What waterfall says is, look.

我们必须把这件事做对。

You we have to get this right.

这是一个高风险的环境。

This is a high stakes environment.

要做到这一点，就要聚集一群聪明人，提前预测事情的发展，预判所有可能的意外，为这些意外做好预案，模拟整个过程，确保在启动时你已经清楚它会如何展开。

And the way to do that is to sit around with a lot of smart people, anticipate how it's going to unfold, anticipate every contingency, plan for those contingencies, game it out, and make sure when you start that you know how it's going to go.

瀑布法的主要问题是它行不通。

The main problem with waterfall is that it doesn't work.

而它为何行不通，克雷·舍基不久前曾精辟地总结过：瀑布法是所有参与者在实际工作中承诺不学习任何新东西的保证。

And the reason it doesn't work was beautifully summarized by Clay Shirky a while back when he said waterfall is a pledge on the part of everybody involved not to learn anything while doing the actual work.

当没有人知道任何事情时，当未来如此不确定时，你无法通过规划走向成功。

When nobody knows anything, when the future is that uncertain, you cannot plan your way to success.

你无法预见到所有可能出现的情况，以及对所有这些分支、这些岔路和这些突发状况的正确应对方式。

You can't anticipate all the contingencies that are gonna come up and what the right response is to all those forks, all those branches, all those contingencies.

因此，当我为《极客之道》采访史蒂夫时，我非常渴望与他交谈，因为十年前，我在硅谷的一次大会上见过他。

And so when I interviewed Steve for the geek way, and I was incredibly eager to interview him because about ten years ago, I met him in Silicon Valley at a conference.

当我问他最近怎么样时，

And when I said, what's up?

史蒂夫说，我来告诉你最近怎么样。

Steve said, I'll tell you what's up.

SpaceX将从太空为全球提供充足且廉价的互联网连接。

SpaceX is gonna bathe the world in abundant cheap Internet connectivity from space.

当时他们还没有发射第一颗星链卫星，我觉得杰维辛疯了。

They had not yet launched their first Starlink satellite, and I thought Jervisin was nuts.

我觉得他喝了太多硅谷的迷魂汤，因为我的天真想法是，我们已经进入太空通信时代六十年了。

I thought he had been drinking too much of the Silicon Valley Kool Aid because my naive thinking was we're sixty years into the age of space communications.

如果真的有可能从太空为全球提供充足且廉价的带宽，那为什么航空航天和通信行业的现有企业都没能发现这一点呢？

If it were really possible to bathe the world in abundant cheap bandwidth from space, Wouldn't one of the incumbents in the aerospace and communications industry have figured that out?

但它们都没有做到，反而是这个完全崭新的组织做到了。

Well, none of them did, and this complete upstart organization did instead.

于是我意识到，杰维森看到了一些我未曾注意到的东西，也了解一些我所不知道的信息。

So it occurred to me that Jervison was seeing some things and was aware of some things that I wasn't.

因此，在研究《极客之道》时，我向他请求了一次采访。

So when I researched the geek way, I asked him for an interview.

他非常友善地同意了。

He very kindly consented.

我想给你引用他告诉我的一段话，这句话一直铭记在我心中，讲的是以瀑布模型为代表的重计划、前期密集型方法，与极客们所采用的敏捷方法之间的区别——极客们会快速尝试各种方法，获取迅速而有效的反馈，尽可能地融入这些反馈，然后再次回到现实中去尝试。

I wanna give you a quote that he laid on me that has stuck with me ever since, and it's about this distinction between a planning heavy, upfront heavy approach like waterfall and what the geeks do, which is to take a very agile approach to try things, to get rapid valid feedback, to incorporate that feedback as possible, then and to go back out in the world and try things again.

这简直是一种终极的、高强度的‘在实践中学习’的方法。

It's kind of the ultimate industrial strength learned by doing approach.

史蒂夫对我说的是：‘安迪，你必须明白。'

And what Steve said to me is he said, Andy, you have to understand.

在我所处的生态系统中，我们学习软件开发的方式——大约二十五年前我们开始采用的敏捷方法——如今已成为我们开发一切事物的敏捷方式。

The companies in my ecosystem, the way that we learn to build software, the agile way that we started to build software about twenty five years ago, that's now the agile way that we build everything.

硬件和软件都是如此。

Hardware, software.

这并不重要。

It doesn't matter.

无论是原子还是比特，都无关紧要。

Atoms and bits, it doesn't matter.

我们采用敏捷方法，因为这样才能最大化我们的学习效果。

We take an agile approach because that's how we maximize our learning.

我非常喜欢这一点。

And I love this.

杰斐逊曾说，我有时觉得我有一种第六感。

Jefferson said, I sometimes think I have a sixth sense.

我能看见那些已经倒闭的公司。

I can see dead companies.

他们自己还不知道。

They don't know it yet.

他们是行尸走肉般的公司，但之所以是行尸走肉，是因为他们不够灵活。

They're dead companies walking, but they're the walking dead because they're not responsive enough.

他说，我生态系统里的公司、我努力帮助的公司、我投资的公司，我们绕着那些水瓶式、计划繁重的在位企业转圈。

And he said the companies that are part of my ecosystem, the ones that I try to help out, the ones that I invest in, we run circles around these waterfall heavy, planning heavy incumbents.

我们每隔几年就在一些事情上实现创新，而这些事情他们需要近十年才能完成。

We innovate every couple years on things that take them close to a decade to do.

正如我们许多人所知，就在几个月前，随着一些极其擅长编程的优秀模型发布，我们才刚刚进入软件工程和编码的新时代。

As many of us know, we entered this very new era of software engineering, of coding, just a couple months ago with the release of some of these amazing models that are so good at coding.

我想，如果我现在再采访杰维辛，他不会再以几年为时间尺度来谈论了。

I think if I interviewed Jervicin again today, he would not talk in the timescale of a couple years.

他可能会以几个月为时间尺度来谈论。

He'd probably talk in the timescale of a couple months.

因此，通过实践学习的价值变得更高，因为你能做的实践量增加了，随之而来的学习量也绝对呈爆炸式增长。

So the premium on learning by doing just becomes higher because the amount of doing that you can do has increased, and the amount of learning that goes along with that is absolutely going through the roof as well.

所以，操作手册的第二部分是建立一个快速循环的学习与实践反馈机制。

So part two of the playbook is to set up a fast cadence feedback cycle of learning by doing.

第三部分则是推广组织内部已有的最佳实践。

And then the third part is to spread the best practices that are already in your organization.

这是一篇去年年底关于任何组织中高级用户的报道。

This was a article from late last year about the power users in any organization.

在Work Helix，我们发现，与我们合作的每一家公司，其AI使用情况都呈现出非常特定的分布形态。

And at Work Helix, what we found with every single company that we have worked with is that the distribution of AI use follows a very, very particular shape.

只有一小部分人大量使用AI，而大多数人则只是偶尔使用。

There's a small number of people who are using it a lot, and then most of the people are using it sparingly.

在我们调查过的每一个组织中，都有很多人仍处于旁观状态。

And there are a lot of people in every organization we've looked at who are still on the sidelines.

他们正在尝试。

They're experimenting.

他们只是试探性地接触，或者甚至还没怎么开始。

They're dipping a toe in, or maybe they're not even doing that much yet.

因此，有一小群核心用户，还有大量人亟需帮助，以理解如何将这项技术应用于实际工作中。

So there's a small group of power users, and there's a large body of people who really could use some help understanding how to put this technology to work.

对我们而言，这暗示着巨大的机遇。

Now to us, that implies a gigantic opportunity.

利用那些核心用户已经开展的工作。

Harness what those power users are already doing.

利用他们所掌握的知识。

Harness what they know.

提炼他们已经摸索出的最佳实践，并开始分享与传播，尽可能广泛地推广开来。

Harness the best practices that they've figured out, and start to share and spread them, diffuse them as widely as possible.

我们正处在一场深度重塑与重新工程化的早期阶段——无论你怎么称呼它，我们都将改变公司内部以及公司之间开展工作的方式。

We are in the early stages of an era of of very deep reimagination, reengineering, use the term that you want, but we're going to change the way that work gets done inside companies and between companies.

关于如何实现这种重塑，存在几种不同的观点。

There are a couple schools of thought about how you accomplish that reimagination.

同样地，这里存在一种对比：一种是侧重规划的方法，另一种是侧重迭代的方法。

And here again, there's a contrast between a planning heavy approach and an iteration heavy approach.

这种以规划为主的重新想象或重新工程方法认为，看。

The planning heavy approach to reimagination or reengineering says, look.

把你能找到的最聪明的一群人聚集在房间里，用白板画出在人工智能新时代工作将如何完成。

Get a bunch of the smartest people you can find in a room with a whiteboard, diagram out how work is going to get done in the new world of AI.

而极客们说，不。

And the geeks say, no.

让极客们做他们擅长的事：拿起这些强大的新工具，审视需要解决的问题，运用这些工具，以一种根本不同、更高效的方式完成工作。

Let geeks do what they do, which is grab the powerful new tools, look at problems that need to be solved, put those tools to work, and come up with something that that does the work in a profoundly different way, a much more enabled way.

极客们认为，真正的重新想象正源于此。

The geeks say that is actually where the reimagination is going to come from.

这主要是一个自下而上的过程，同时得到顶层的支持。

It's primarily a a a bottom up process supported from the top.

所以，已经有人开始进行这种重新想象的工作了。

So you've already got people starting that reimagination work.

我认为，在这个充满深刻不确定性的时代，组织的目标之一就是识别这些人，了解他们在做什么，并尽可能广泛地传播他们的优秀想法。

I think one of the goals of an organization in this era of profound uncertainty is to identify those folk, learn what they're doing, and spread their good ideas as broadly as possible.

阿迪，这番话非常精彩，也很有启发性。

Adi, that was great and and and very provocative.

我先提一两个自己的问题，但我们确实收到了很多与会者的问题。

I'm gonna start with a question or two from myself, but we do have a lot of questions coming in from participants.

你知道，对于AI，就像任何新技术一样，它是不是被过度炒作了？

You know, I guess with AI, you know, with any new technology, obviously, it's it's is it overhyped?

还是被低估了？

Is it underhyped?

就这个问题而言，大多数情况下，AI都排在他们战略的前一二三位。

You know, this one, for the most case, people are are it's like number one, two, or three on their kind of strategy.

就是说，我们正在做这件事。

It's like we're doing this.

对吧？

Right?

我们可能会通过不断尝试来逐步进入这个领域。

We may experiment our way into it.

我们可能会大举投入，但确实在做这件事。

We may go big time, but we're doing this.

你刚才提到，就连德里克·汤普森都说‘没人真知道任何事’，但你却在敦促人们做出承诺。

And you just said, even Derek Thompson, love nobody knows anything, but you were kind of urging people to commit.

在某个时刻，这种对AI值得投入的信念是一种信仰，还是你本身是个技术乐观主义者？

At a certain point, is that an article of faith that AI is worth the commitment, or are you a techno optimist?

我们该如何思考这个问题？

How should we think about this?

因为这件事确实可以从正反两方面来论证。

Because it's you can argue it both ways.

我是个技术乐观主义者，阿迪，你知道的，但我们不必仅凭信仰来接受AI。

I am a techno optimist, Adi, as you know, but we don't just have to take AI on faith.

经济学家已经提出了三种方法来识别他们所谓的通用技术，也就是经济学术语中像蒸汽机或内燃机那样对经济产生巨大影响的技术。

Economists have come up with three ways to identify what they would call a general purpose technology, which is economist speak for a gigantic big deal for the economy at the level of the steam engine or the internal combustion engine.

这种技术一旦出现，就能显著推动整个经济的增长。

One of these technologies that comes along and just accelerates the overall growth of the economy.

有三种方法可以识别它们，而且你可以在事前就识别出来。

There are three ways to recognize them, and you can recognize them in advance.

第一，这项技术本身是否能快速进步？

Number one, does the technology itself get better quickly?

AI就符合这一点。

Check with AI.

第二，它是否会催生其他互补性创新？

Number two, does it spawn other complementary innovations?

我们已经看到了人工智能驱动的自动驾驶汽车。

We've already got AI powered self driving cars.

我们正在迅速努力开发和推广以人工智能为核心技术的机器人。

We are very rapidly trying to build and diffuse AI, robots that have AI at the core of what they're able to do.

所以我认为这也很可能是一个符合点。

So I think that's probably a check as well.

第三个标准是，这项技术是否仅局限于一两个行业或一两个经济体，还是开始在整个经济中广泛应用？

Then the third criteria is, does it remain confined to one or two sectors, one or two economies, or do you start to see it being used all throughout the economy?

我们已经看到人工智能和生成式人工智能被广泛应用于整个经济中。

We're already seeing AI, generative AI, used throughout the economy.

当我思考这三个标准和人工智能的新篇章时，答案都是肯定的，这并不是信仰，而是基于证据。

So when I think about those three criteria and this new chapter of AI, yes, yes, and yes, that's where my faith it's not faith.

正是这些证据让我相信，这确实是一件大事。

That's where the evidence, falls that I rely on to say this actually is a big deal.

好的。

All right.

那么让我们展望未来。

So let's project forward.

如果人工智能真的能发挥其作用，我会和很多人一样想到两个重大问题。

So so if, you know, if if AI does what it can do, there are two big questions that occur to to me and to a lot of people.

如果这些工具对每个人都是可及的，我们该如何看待持久的竞争优势？

If if these tools are accessible to everybody, how do we think about durable competitive advantage?

第一，这种优势从何而来？

Where does that come from, number one?

其次，这对管理层和中层管理架构会产生什么影响？

And then number two, what in the world does this do to management and to layers of middle management?

是的。

Yeah.

这两个都是大问题。

Those are two big questions.

对吧？

Right?

我听到的和你一样，等等。

And I hear the same thing you do, which is, wait a minute.

如果编写软件这类困难任务的成本下降了，那意味着擅长软件的优势也会下降吗？

If the cost of doing difficult things like writing software goes down, doesn't that mean that that the competitive advantage of being good at software goes down?

我不认为这是正确的看待方式。

I don't think that's the right way to look at it.

编写软件的成本其实一直在下降。

The the cost of writing software has already been declining.

我不知道。

I I don't know.

是十倍吗？

Is it is it 10 x?

是一千倍吗？

Is it a thousand x?

我不知道。

I don't know.

但如今编写软件的成本比十年前低了很多，至少降低了几个数量级。

But it is many it's at least a couple orders of magnitude cheaper to write software today than it was ten years ago.

所以这些成本已经大幅下降了。

So those so those costs have already come down a lot.

这是否意味着如今每个人在软件方面都同样出色？

Does that mean that everybody is equally good at software today?

绝对不是。

Absolutely not.

强大软件的时代加剧了企业之间的竞争差异。

The era of powerful software has sharpened, has increased competitive differences between companies.

我认为人工智能绝对不可能成为伟大的竞争均衡器。

I think AI is absolutely not gonna be the great competitive leveler.

它将使企业之间的差异比今天大得多。

It's gonna make the distinctions between companies, much, much bigger than they are than they are today.

我们开始获得这方面的可靠证据。

We're starting to get decent evidence on this.

通过观察人工智能被最广泛应用的领域——即软件开发，我们正看到一个非常有趣的现象正在浮现。

From from looking at the places where AI has been put to use most heavily, which was in writing software, there's a really interesting pattern emerging.

阿迪，你知道，你我和我都拥有足够长的职业生涯，长期以来一直在谈论网络型组织和层级结构的扁平化。

Adi, you know, you and I both have careers long enough that we've been talking about the network organization and the flattening of hierarchies for a long time.

我们终于开始看到这种变化了。

We're finally starting to see that.

事实证明，当人们获得这种极其强大的新人工智能时，他们往往会减少一些沟通、协作和协调的工作。

It turns out that as people get access to this incredibly powerful new AI, they tend to do a little bit less of the communicating, collaborating, coordinating work.

现在，这类工作并没有完全消失，但感觉像是，你知道的，管理项目和协调其他团队的行政性琐事，我们正在把这些交给技术。

Now that work doesn't go down to zero, but it feels like, you know, the administrative busy work of running a project and coordinating with other teams, we're handing that off to the technology.

因此，人类在这方面的投入正在减少，与此同时，证据显示，那些你最希望释放其潜力来解决重大问题的顶尖人才，获得了更多时间用于探索性工作、创新和突破边界——而这正是我们希望他们做的。

And so the human amount of that is going down, while at the same time, the evidence is showing that the very best people, the people who you really wanna unleash on the big problems that you're confronting, those folk have more time freed up for exploratory work, for innovation, for envelope pushing, which is exactly what we want them to do.

我也听到有人表示，管理一组智能代理的技能，非常类似于成为一名优秀管理者，尤其是在高科技领域或作为产品经理。

I've also heard people say that the skill of running a fleet of agents is very much like the skill of being a very good manager, especially in a high-tech field or a product manager.

所以，我认为我们在管理复杂大型项目时所培养的技能，远没有过时。

So I don't think the kind of skills that we learn about running large complicated efforts are becoming obsolete at all.

我认为，未来的工作将是一种极其丰富的结合：人类与技术共同参与其中。

I think they're gonna be an incredibly rich blend of human beings involved in that work and and pieces of technology involved in that work.

那我们来谈谈这个。

So let's talk about that.

我肯定有一些听众会想，是的。

So I'm sure there are some people who are listening who yeah.

对。

Yeah.

我们有代理，我非常擅长管理它们，有时甚至可能是它们在管理我。

We have agents, and I'm very comfortable managing them, and maybe in some cases they're managing me.

对于那些觉得这听起来还有点科幻、或者只是未来某天才会发生的人，谈谈这个。

For people where that still sounds slightly sci fi or just at some point in the future, talk about that.

让我们对在工作中拥有代理这一概念感到安心。

Make us comfortable with the idea of having agents in our work lives.

软件代理只是一种技术，你不需要像以往那样事无巨细地指导它。

A software agent is just a piece of technology that you don't have to spoon feed as much as we're used to.

你可以告诉它去完成一些相对复杂的事情。

You can tell it to go do something relatively complicated.

希望你能给出清晰的指令，然后它就会独自工作数小时，完成后回来向你呈现结果。

Hopefully, give it pretty clear instructions, and it will go off, in many cases, for hours on its own, and then come back when it's got that work done, present its results to you.

当然，我们之前用过一种叫Open Claw的全新技术，当时人们让它拥有了一点点过多的自主权。

Now, you know, we had we had a couple things with, this this wild new technology, Open Claw, where people were letting it maybe have a little bit too much autonomy.

把你的信用卡交给它，让它帮你买齐所有圣诞礼物，这绝对是个非常糟糕的主意。

Giving it your credit card and telling it to go out and do all of your Christmas shopping for you is probably a very, very bad idea.

但只要设置了适当的防护措施，我们现在就可以部署这些技术，让它们独立工作更长的时间，让它们相互交互以进行核对、审计，确保防护规则到位，协调彼此的活动，然后它们会回来向你汇报进展。

But with appropriate guardrails in place, we we can now set up these technologies, let them go do their thing for longer and longer periods of time, let them interact with each other to double check, audit, make sure the guard rules are in place, coordinate their activities, and then they kind of come back and give you a progress report.

随着时间推移，这种进展会越来越多，而错误则会越来越少。

And very quickly over time, there's more and more of that progress and fewer and fewer of the bugs.

但这并不意味着我们今天就能完全放手让代理自主运行。

Now that doesn't mean that we can just turn agents loose today.

这也不意味着下个月我们就能做到这一点。

That doesn't mean that we're gonna be able to do that next month.

但这些人工智能组件是否正随着时间推移展现出越来越强的能力，能够处理越来越复杂的事情，并在某些情况下相互协作？

But but are these are these pieces of AI exhibiting more greater and greater ability over time to do increasingly complicated things and, in some cases, to interact with each other as they do so?

在这两个方面，答案绝对是肯定的。

Absolutely yes in both cases.

我接触过的所有人都认为，这种趋势不会很快停滞。

And nobody that I talk to says that that's about to level off.

我的防护措施是：我会给机器人我的信用卡。

My guardrail would be I would give the bot my credit card.

我只想说，别买那些俗气的东西。

I would just say, don't buy anything tacky.

那不太好看。

That's not Nice.

那不可能发生。

That's not happening.

好吧。

So, alright.

所以，还有很多观众的问题不断进来。

So there there are still a lot of audience questions coming in.

这里有几个问题我想合并一下。

There there are a couple that I want to combine here.

一个是来自穆米塔的，她是一位客户成功策略师。

One is from Mumita, who is a a customer success strategist.

另一个是来自埃琳娜的，她是一位执行董事。

The other is from Elena, who is an executive director.

他们对成功指标和关键绩效指标感兴趣。

And they're interested in success metrics and KPIs.

因此，随着我们利用人工智能变得更加高效，我们应该如何思考成功指标，这是一个问题。

So as we become more productive and efficient with AI, how should we be thinking about success metrics is one question.

对于知识型工作者来说，真正的关键绩效指标是什么样子的？

And for knowledge workers, what do real KPIs look like?

是的。

Yeah.

这个问题有几个引人入胜的方面。

There are a couple fascinating parts to that question.

我将其分为两个方面。

I break it down into two areas.

只有当人们使用人工智能时，它才会对组织中的关键绩效指标和我们关心的指标产生影响。

AI is only gonna move the needle on KPIs in an organization, on the metrics that we care about if people are using it.

所以我们首先建议的是：你们在追踪人工智能的使用情况吗？

So the first thing that we recommend is, you tracking the the usage of your AI?

正如我之前所说，你们的情况是不是只有很小一部分人接受了这项技术？

As I said earlier, are you in the case where there's only a really small group of people who have embraced this technology?

他们中的大多数目前是否都在软件工程领域工作？

Do most of them currently work in software engineering?

组织的其他部分是否仍然置身事外？

And is the rest of the organization really still on the sidelines?

我们现在可以衡量这一点。

We can measure that now.

你希望的不仅仅是看到核心用户在做自己的事情并增加使用频率。

What you want is to not just see the power users doing their thing and increasing their use.

你希望看到广泛的采用，以及使用量的普遍增长。

You wanna see broad adoption, and you want to see broad increases in use.

因此，这个非常丰富的KPI问题的第一部分是，确保你正确地衡量了使用情况，并且你希望使用量持续上升。

So the first part of the very rich KPI question is make sure you're correctly measuring your usage, and you want that usage to be going up.

现在，你之所以参与所有这些使用，并且支付给你的AI供应商费用，是因为你希望某些绩效指标得到改善。

Now, the reason you're engaging in all of that use and the reason you're paying your your AI suppliers is because you want some performance measures to get better.

正如提问者指出的，这些性能指标中有一些相当直接。

And as the question questioner points out, some of those performance measures are are pretty straightforward.

例如，在呼叫中心，你希望客服代表的生产力有所提升。

In a call center, for example, you would like agent customer service rep productivity to go up.

你希望通话时间缩短。

You want call times to go down.

你希望解决率提高。

You want resolution rates to go up.

对于这类知识型工作，我们已经有了良好的衡量指标。

We've got good metrics for that category of knowledge work.

但对于其他类型的知识型工作，情况就远不那么明确了。

For other categories of knowledge work, it's a lot less clear.

我曾与一家投资银行的负责人交谈，他说：我们的分析师真正做的工作是进行非常复杂的分析，但最终呈现的产品却是一份展示给客户的幻灯片。

I was talking with the head of an investment bank who said, you know, our analysts, what they really do, they're doing very complicated analyses, but it looks like the product is a slide deck that we show to a customer.

如果我告诉我的员工——或者我的分析师——要提高效率，我当然不希望他们只是拼命产出两倍多的幻灯片。

If I told my agents to be more productive, I hope or my analysts to be more productive, I hope they wouldn't just start churning out two times as many decks.

因此，知识工作中有相当一部分实际上是很难衡量的。

So there are parts of knowledge work that are actually pretty complicated to measure.

并非不可能，但要更微妙一些。

Not impossible, but it's a little bit more subtle.

对我来说，真正好消息是，经济学家用于衡量变化的工具包，即使这些变化很微妙，即使它们与我们所讨论的AI等干预措施有一定距离，我们的工具包依然能很好地判断我们所投入的东西是否带来了好的或坏的结果。

To me, the really good news is that the economist's toolkit for measuring changes, even if they're subtle, even if they're a little bit distant from from the intervention like AI that we're talking about, our toolkit for understanding whether this thing that we invested in caused good things or bad things to happen.

这套工具现在非常有效。

That toolkit's really good right now.

我们在Work Helix中运用了它。

We put it to work at Work Helix.

它是所谓‘可信度革命’的一部分，在这场革命中，我们能够说：嘿，你们尝试了某项措施。

It's part of a thing called the credibility revolution, where our ability to say, hey, you tried something.

它是否让关键绩效指标朝着你们期望的方向变动？

Did it cause the KPI to move in the direction that you want?

我们实现这一点的能力，比二十年前好了太多太多。

Our ability to do that is much, much better than it was even twenty years ago.

所以把它付诸实践。

So put that to work.

把可信度革命应用到你的关键绩效指标上。

Put the credibility revolution to work on your KPIs.

让人持怀疑态度的问题之一是工作中的冗余内容。

One of the issues that leaves people skeptical is work slop.

这是一个问题。

This is a question.

这是苏珊娜提出的。

This is from Suzanne.

它获得了大量点赞。

It has a lot of upvotes.

我们很多人都看到了工作中的冗余内容，也就是说，AI生成的内容看起来很棒，但实际上并不理想，反而给同事增加了更多工作。

A lot of us are seeing work slop, and by that we mean AI generated content that looks great but is not so great and maybe creates more work for colleagues.

当你看待Workshop时，你觉得这是一个培训问题、文化问题、战略问题，还是其他问题？

When you look at Workshop, do you think of it as a training problem, a culture problem, a strategy problem, something else?

这是一个问题。

It's a problem.

这是一个问题，而且以上各方面都有一点。

It's a problem, and it's a little bit of all of the above.

我希望我们不要走向这样的解决方案：我的代理生成垃圾内容，而你的代理则试图过滤掉这些内容，以便只有好的内容能到达你那里。

And the solution that I hope we don't head toward is my agent generating slop and your agent trying to filter it out so that the stuff that gets to you is only the good stuff.

我们不希望，你知道的，不希望在企业内部或公司之间爆发垃圾内容战争。

We we don't want, you know, we don't want slop wars going on inside the the enterprise or between companies.

我认为这里有一个非常重要的区别，阿迪，你听说过这种区别：流程导向型组织和成果导向型组织。

I think one of the really important distinctions here, Adi, you've heard of this distinction between a process focused organization and an outcome focused organization.

我研究并写过《极客方式》的那些极客公司，它们都是典型的成果导向型组织。

The the geek companies that I studied and that I wrote the geek way about, these are very much outcome focused organizations.

它们理解流程的必要性，但真正关注的是那些能表明企业是否朝着正确方向前进的指标。

They understand the need for process, but what they're really about are the metrics that are gonna indicate whether the business is heading in the right direction or not.

这些指标表现如何？

How are those metrics doing?

我认为，在以成果为导向的组织中，对工作垃圾的需求会比在以流程为导向的组织中少。

I think there's gonna be less demand for work slob in an outcome focused organization than a processed focused organization.

以流程为导向的组织关注的是：你有没有把所有交付成果带到下一次会议上来？

A processed focused organization is, did you did you, you know, bring all your deliverables to the next meeting?

你有没有逐一核对完我们说好要完成的所有事项？

Did you go down the check mark of everything that we said we were going to do?

你有没有把所有东西都送到其他人桌上？

Did you get everything on everybody else's desk?

哪怕这些工作根本没推动任何客户会在意的关键绩效指标。

Whether or not that moved a a KPI that any that any customer would care about.

在以流程驱动的组织中，我认为工作垃圾的数量已经很高了，而且不幸的是，可能还会继续增加。

In process driven organizations, I think the work slot volume is gonna be, already pretty high and and unfortunately probably heading higher.

在以成果为导向的组织中，我预计这种情况会大不相同。

In in outcome focused organizations, I expect that situation to be pretty different.

在那里，你的工作不是为了满足流程而生成文档。

There, your job is not to generate documents to satisfy the process.

在那里，你的工作是完成能让客户满意的事情。

There, your job is to get stuff done that makes a customer happy.

所以这里有个问题，回到你之前关于敏捷和瀑布模型的讨论。

So here's a question that goes back to your conversation of agile versus waterfall.

这是来自里奇的问题，他是一位创始人，这其实是一个寻求实用建议的请求。

And this is from Rich, And who is a founder and a this is really a request for practical advice.

传统公司的领导者可以做的一件事是什么，以摆脱瀑布式思维和文化，转而采用敏捷心态？

What is one thing that a leader of a traditional company can do to move away from waterfall thinking and that culture to to, you know, adopting a kind of agile mindset?

哇。

Wow.

这是个非常好的问题。

That that that's a great question.

领导者可以做的一件事是理解，这是一个两部分的答案，但它们是相关的，所以请原谅我，就是要理解亚马逊所说的‘单向门’和‘双向门’之间的区别。

The one thing that a leader can do is is understand it's a two part answer, but they're related, so, forgive me, is to understand the difference between what Amazon calls one way doors and two way doors.

换句话说，我们能不能在这里犯错？

In other words, is can we not make a mistake here?

SpaceX已经发射了大量火箭。

SpaceX has launched a lot of rockets.

他们也撞毁了许多火箭，因为他们意识到，在许多方面，发射火箭是一种双向门。

They have and crashed a lot of rockets because they realize that in many ways, launching a rocket is a two way door.

搭载人类的火箭发射则是一扇单向门。

Launching a rocket with human beings on it is a one way door.

理解这两者的区别。

Understanding the difference.

火箭坠毁是不可接受的失败。

Crashing that rocket is an unacceptable failure.

因此，SpaceX非常努力，也非常成功地避免让载人火箭坠毁。

So SpaceX tries very hard, very successfully, not to crash rockets that have people at the top of them.

然而，建造这些火箭的方法是通过学习、尝试、失败，并加入一个鼓励这样的理念的组织：看。

However, the way you learn how to build those rockets is by learning, trying, and and failing and being part of an organization that says, look.

你建造这枚火箭是为了学习一些东西。

You built that rocket to learn something.

我们没有在上面搭载任何人。

We didn't put any people on it.

它坠毁了。

It crashed.

这其实很好。

That's actually good.

我们从中学到了一些东西。

We learned something from that.

所以其中一部分是理解单向门和双向门之间的区别，并对此有清晰的认识。

So part of it is the understanding the difference between one way doors and two way doors and being very clear about that.

另一部分是，对于双向门，让人们去尝试。

The other part is for the two way doors, let people try stuff.

让你的团队去尝试。

Let your team try stuff.

作为领导者，要以各种方式传达这样的信号：在双向门的情况下，只要能学到东西，失败是可以接受的。

Signal in every way that you can as a leader that failure in for two way doors is actually okay as long as you learn something.

我们学到了什么？

What have we learned?

下次我们会有什么不同？

What are we gonna do different next time?

很好。

Great.

让我们重新出去尝试吧。

Let's get back out there and try things again.

我认为领导者就是这样开始打造更敏捷的组织的。

That's how I think a leader can start to build a more agile organization.

我想和你们谈谈人才。

I want to talk to you about talent.

我想和你们谈谈招聘和技能。

I want to talk to you about hiring and skills.

你们可能看到了IBM将初级职位增加三倍的报告。

You probably saw the report that IBM is tripling its entry level positions.

这有点反直觉，因为我们一直以为人工智能会消灭初级职位。

It's sort of counterintuitive because we've assumed that AI is going be wiping out entry level jobs.

我认为微软也说过类似的话。

I think Microsoft has said something similar.

你怎么看？

What do you think?

人力资源部门该如何思考招聘、招聘标准和技能？

How HR be thinking about hiring, about hiring profiles, about skills?

人们轻率地说，我们更需要文科生，事实似乎如此。

People are glibly saying, we want more liberal arts majors, it turns out.

我不确定这是真实情况，还是只是听起来不错而已。

And I don't know if that's a real thing or it's a sound good thing.

但具体需要哪些技能？

But what are the skills?

需要什么样的人才画像？

What are the profiles?