机器人代班：AI 崛起耗尽的工作精力

今天在《AI每日简报》中，我们要聊一聊‘机器人代坐’，以及人工智能重塑工作背后那些不为人知的劳动。

Today on the AI Daily Brief, we're talking about bot sitting and the hidden labor that comes with the AI transformation of work.

《AI每日简报》是一档每天更新的播客和视频节目，聚焦人工智能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.

好了，各位朋友。

Alright, friends.

在正式开始之前，先说几件小事。

Quick announcements before we dive in.

首先，感谢今天节目的赞助商：毕马威、超级智能、MissionCloud 和 OutSystems。

First of all, thank you to today's sponsors, KPMG, Super Intelligent, MissionCloud, and Outsystems.

想收听无广告版本？请访问 patreon.com/aidailybrief，或者在 Apple 播客上订阅。

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

如果你对赞助节目感兴趣，欢迎发邮件到 sponsors@AIdailybrief.AI 告诉我们。

If you wanna learn more about sponsoring the show, send us a note at sponsors@AIdailybrief.AI.

还有两件小事要提醒一下。

Two other quick notes.

首先，去 training.bsuper.ai 看看更新后的企业版《高管速递》和《高管代理领导力课程》。

First of all, check out training.bsuper.ai for the newly updated Enterprise grade versions of the Executive Catch up and the Executive Agent Leadership Program.

《高管代理领导力课程》是为期六周的密集培训，下周一开课，现在是最后报名机会了。

The Executive Agent Leadership Program is a six week intensive that kicks off on Monday, so last chance to get in on that.

另外提醒一下，我提前几天录了这期节目，因为要赶在学年结束时出门旅行。

And finally, just to let you know, I am recording this a couple days early because of some end of the school year travel.

所以这期只有主内容，下周一我们会恢复正常的节目形式。

So this will be a main only episode, but we will be back with our normal format on Monday.

今天我们要聊的是 Glean 和 Work AI 研究所发布的最新报告，这是他们 2026 年《工作AI指数》的一部分，主题叫‘机器人看护’，也就是AI在职场中隐藏的人力投入。

Today, we're talking about a new report from Glean and the Work AI Institute that's part of their Work AI Index for 2026, and it's all about something called bot sitting or the hidden human labor of AI at work.

你们可能注意到了，今年我少了一些对咨询公司或企业研究机构报告的解读。

Now one of the things that you may or may not have noticed this year is that I've done a little bit less coverage of studies from, for example, consulting firms or enterprise focused research houses.

这背后其实有明确的原因。

And there is an actual specific reason for that.

原因不止一个，但归根结底，是因为我觉得非代理型工作和代理型工作之间的范式已经彻底转变，凡是还盯着非代理型工作的研究，基本都过时了。

There's actually a couple of reasons, but they all come back to my feeling that the paradigm has shifted so much between non agentic and agentic work that anything that's interacting with non agentic work is largely irrelevant.

不过，如果你是企业级AI的负责人，情况就另当别论了。

Now, of course, if you are an Enterprise AI leader, that's not the case.

仍然有很多非自主型的应用场景，能带来实际价值和效率提升。

There are still lots of use cases that are non agentic that are going to be valuable and productivity enhancing.

但你应该知道，我更关注的是能带来变革的AI机会，而不只是提升效率的工具——我关心的是工作方式的根本转变，而不是简单地把老事情做得快一点。

But you guys know that I have a very strong bias towards being interested in opportunity AI, not just efficiency AI, and the big changes that I see happening in terms of how we work, not just doing the same stuff we've always done a little bit faster.

这份报告则开始触及并命名了一些围绕AI和智能体出现的新类型工作，我觉得这些非常值得提出来深入探讨。

This report, however, starts to get into and name some new types of work that surround AI and agents that I think is really valuable to call out and start to explore.

接下来，我们就来聊聊这些内容。

So that's what we're going to get into.

先来看一下他们用来铺垫背景的数据。

Now, let's start with the statistics that they use to set everything up.

他们最引人注目的数据是：现在有87%的数字员工在工作中使用AI，其中75%的人表示AI提升了他们的效率，每周平均节省了11个小时的自动化时间。

Their big banner tweet worthy statistics are that 87% of digital workers now use AI at work with 75% saying it makes them more productive, saving them eleven hours per week through automation.

但只有13%的人认为，他们的公司因此有了显著的改善。

Yet only 13% say their organization is performing significantly better as a result.

这些数字几乎完美地概括了我刚才说的内容。

And these numbers are almost a perfect encapsulation of what I was just talking about.

每位员工每周节省十一个小时，这可不是小数目。

Eleven hours per employee is nothing to sneeze at.

AI在绝大多数员工中几乎全面普及，而且大多数人说它提升了工作效率，这些都很有意思。

AI reaching nearly full penetration with the vast majority of people saying it makes them more productive are also interesting things.

而个人效率提升与组织绩效之间的反差，印证了一个我们屡见不鲜的现象：把个人层面的AI收益转化为整个组织的提升非常困难，光靠个人用得好，并不自然带来组织层面的改变。

And, of course, the contrast between that individual performance and organizational performance reinforces a story that will be very familiar and that we hear over and over again, which is that translating individual AI gains into larger organizational gains is very difficult and not at all implied just by using AI well individually.

有趣的是，我对这份报告中关于‘为什么只有13%的员工认为公司绩效提升’的核心观点持不同意见。

Now, interestingly, I disagree with the report's key argument about why only 13% of those workers say their organization is performing better.

我认为，从根本上说，无论个人效率的提升来自哪里，如果没有一套机制来推动这种变化，它并不会自动转化为组织层面的收益。

I believe that on a fundamental level, individual productivity gains, wherever they come from, do not inherently translate to organizational gains unless there is a mechanism to actually facilitate that transformation.

关键问题是，人们每周省下的这十一个小时，到底用在了什么地方？

The question is what specifically are people using those eleven hours per week for?

又有多少是真正推动了公司核心目标的进展？

And how much does it have to do with actually advancing key company missions?

报告的观点是，这些收益被一种新的、几乎看不见的劳动形式给吞噬了。

The report's argument is that the gains are, in their words, being swallowed by a new, largely invisible form of labor.

他们进一步解释说，这叫‘陪机器人’，就是为了让AI真正好用而做的工作，比如补充缺失的上下文、检查它的输出、修正它的错误、重新运行提示，以及清理AI给出的那些看似自信却完全错误的答案。

They continue, we call it bot sitting, the work required to make AI usable, including feeding it missing context, checking its outputs, debugging its mistakes, rerunning prompts, and cleaning up the confident but wrong answers AI leaves behind.

现在，员工平均每周要花6.4个小时在陪机器人上。

Workers now burn an average of six point four hours a week bot sitting.

所以，他们的逻辑是，企业没看到收益，是因为员工省下来的那每周11个小时，大部分都被这每周6.4小时的陪机器人任务给吃掉了。

So basically, the argument is, the reasons that organizations aren't getting gains is that those eleven hours per week that people are saving are being eaten in large part by the six point four hours a week that they now have to bot sit.

我的看法是，即使他们不花这每周6.4小时陪机器人，个人效率的提升也依然不会自动转化为组织层面的绩效提升。

My position is that even if they weren't spending those six point four hours a week bot sitting, you still wouldn't see a direct translation from individual productivity to organizational performance.

但很重要的一点是，我认为他们所指出的这种新型、几乎看不见的劳动，对于我们理解如何真正融入AI至关重要。

But, and this is important, I think that this new largely invisible form of labor that they're calling out is extremely important to understand as we figure out how to integrate AI.

我们再来深入聊聊‘陪机器人’这件事。

So let's talk more about bot sitting.

有一点很关键，这其实是AI在个人层面取得成功的副产品。

One important note certainly is that this is a byproduct of AI being successful on an individual level.

正如报告所说，员工正把越来越多的工作交给人工智能，还希望交出更多。

As the report puts it, workers are handing over bigger parts of their jobs to AI and want to hand over even more.

受访员工表示，AI 目前已经自动化了他们约 27% 的工作产出，而平均来看，他们预计这个比例会升到 35%。

Workers they surveyed said that AI now automates about 27% of their work output, and those workers on average expected it to climb to 35%.

57% 的人表示，他们希望 AI 能自动化比实际能实现的更多工作内容。

57% said that they want AI to automate even more of their job than they think it actually will ultimately be able to.

但这种成功也带来了一些问题。

But that success comes with some challenges.

整个报告中最引人注目的图表，莫过于 AI 时间究竟花在了哪里。

Maybe the most interesting chart in the entire report is the chart about where AI time actually goes.

Work AI 研究所把人们使用 AI 的方式分成了三类。

The Work AI Institute organized people's AI use into three categories.

第一类是学习和构建智能体，顾名思义就是这么回事。

The first was learning and building agents, which is exactly what it sounds like.

包括设计工作流程、阅读别人是怎么搭建的网络讨论，以及尝试新的模型。

It includes building workflows, reading online discussions about how others are building, and experimenting with new models.

这占了人们时间的27%左右。

That represented about 27% of people's time.

人们使用AI的时间中有36%是主动在用AI。

36% of people's AI time was spent actively using AI.

也就是说，用AI来完成工作。

In other words, completing work with AI.

但有37%的时间花在了所谓的‘盯机器人’上。

But 37% was on what they are calling bot sitting.

有趣的是，他们把‘盯机器人’分成了两类：无成效的和有成效的。

Now, interestingly, they break bot sitting into two different categories, unproductive and productive.

有成效的部分，其实就是管理越来越自主的AI所要做的事。

The productive part just kind of looks like what it means to manage increasingly autonomous AI.

比如核对高风险的输出结果是否准确，调整提示词让结果更好，或者补充AI不知道的领域背景信息。

So that includes verifying high stakes outputs to ensure they're correct, iterating on a prompt to make the output meaningfully better, or adding domain context the AI couldn't have known.

报告认为，这些其实都是积极版本的‘盯机器人’。

The report argues that these are basically good versions of bot sitting.

这些低效的-bot sitting 方式，很多人应该都熟悉。

The unproductive versions will be familiar to many of you.

反复把同样的内容输进不同的AI工具，比较各个工具的输出结果，因为第一个答案不够好，或者修改AI生成的内容。

Reloading the same context into multiple AI tools, comparing outputs across tools because the first answer wasn't good enough, or cleaning up AI generated work.

每周花在bot sitting上的时间是6.4小时，其中2.3小时，占AI总使用时间的14%，是用来给AI提供上下文的。

Now of the 6.4 spent bot sitting per week, two point three hours of that, representing 14% of the total overall AI time, was in feeding the AI context.

另外每周2.2小时用于监督输出结果，1.7小时用于调试，还有大约十分钟到十五分钟花在其他事情上，比如整理或切换工具。

Another two point two hours a week went to supervising outputs, one point seven hours a week went to debugging, with about ten or fifteen minutes a week on other things like cleanup or switching tools.

有趣的是，他们还提出了一个‘疲惫倍增效应’：员工给AI提供上下文的时间每增加10%，他们感到被AI拖累的可能性就上升25%。

Now interestingly, they also introduced the concept of an exhaustion multiplier, writing that every 10% more time workers spend feeding AI context, they're 25% more likely to report feeling worn out by it.

换句话说，bot sitting就是枯燥、没面子的体力活，而且会带来负面影响。

In other words, bot sitting is unfun, unglamorous grunt work, and it can have negative consequences.

事实上，报告发现，那些频繁进行bot sitting的人——也就是AI使用时间中40%以上花在这类活动上的受访者——

In fact, the report found that frequent bot sitters, which they define as respondents who spend 40% or more of their AI time on bot sitting activities, I.

超过中位数的AI使用者中，有73%的人正在积极寻找新工作。

E, above the median for AI users, 73% were more likely to be actively hunting for another job.

那么，谁更常做这种重复性工作呢？其中一个原因就是工作量更大。

Now in terms of who is bot sitting more, one culprit is simply a higher volume of work.

重度使用AI的人比轻度使用者更常报告频繁做这类事，我觉得这很合理。

Heavy AI users are more likely to report frequent bot sitting than light users, which I think makes intuitive sense.

但报告指出，真正的问题其实是工具太分散了。

But the report argues that actually tools sprawl.

换句话说，员工使用的AI工具种类越多，就越容易频繁做这类事——使用多种工具的人报告频率高出35%。

In other words, the number of different AI tools workers use is another big culprit with workers who use multiple AI tools 35% more likely to report frequent bot setting.

他们还发现，目前有60%的员工会把同一个提示在多个工具里重复运行，因为第一次的结果不够好，这些加起来就是他们所说的‘AI切换税’。

They also found that right now, 60% of workers are rerunning the same prompt across multiple tools because the first output wasn't good all of which adds up to what they call the AI toggle tax.

除了让人感到压力大或精疲力尽之外，这种重复性工作还带来另一个更隐蔽的负面影响，报告称之为‘AI拉胯’。

Now outside of just feeling overwhelmed or burning out, there is another even more pernicious outcome of bot sitting which the report labels bot shitting.

我让编辑给这个词打了马赛克，但我想大家稍微动动脑筋，就能明白在‘bot sitting’后面加个‘h’是什么意思。

Now I had my editors bleep it there but I don't think it takes too much imagination to add the h to bot sitting to understand the term.

换句话说，当员工开始过度依赖AI、把太多思考任务都丢给机器时，‘AI重复劳动’就慢慢变成了‘AI拉胯’。

Effectively, the process of bot sitting turning into bot shitting is when workers start to cognitively offload too much to AI.

不过，这种情况即使不靠频繁盯着AI也会发生。

Now, this can happen even without bot sitting.

我们都见过别人这么做，自己也可能有过这种冲动——把越来越多的思考和判断交给用的AI。

We've all seen others and felt probably in ourselves the temptation to hand over more and more of our thinking and judgment to the AIs we use.

随着我们越来越信任AI的输出，尤其是在处理常见或例行任务时，我们就不再仔细检查结果了。

As we start to trust the outputs more, especially around common or routine tasks, we stop checking the outputs.

也不再核实信息来源了。

We stop verifying the sources.

在企业环境中，很多人承认，他们开始直接提交第一个看起来还行的输出，而不是坚持去找一个自己能说清、能辩护、能负责的版本。

And in an enterprise context, many admit that they start to ship the first output that looks good enough instead of pushing for one they can actually explain, defend, and stand behind.

当然，这些情况即使不花大量时间盯着AI也会出现，但一旦再加上盯着AI带来的疲惫和挫败感，这种倾向就会变得更严重。

Now, of course, these things can happen even if we're not spending a ton of time bot sitting, but when you add the additional layer of burnout and frustration that comes with that bot sitting work, this can become even more likely.

报告指出，AI乱输出很少是一次糟糕的决定或鲁莽的一次点击。

The report writes, bot shitting is rarely a single bad decision or a reckless click.

通常是一步步放弃主动权，每次都是为了图个方便。

It's usually a slow surrender of agency, one shortcut at a time.

首先，员工不再完全理解AI的输出，接着就不再质疑它。

First, workers stop fully understanding the output, then they stop interrogating it.

最后，他们甚至不再觉得对此负有任何责任。

Eventually, they stop feeling responsibility for it at all.

这不仅仅是把理解和判断推给AI，人们还把责任也一并推卸了。

And not only does this come with offloading understanding or offloading judgment, people also offload responsibility.

报告指出，当AI生成的内容出错时，40%的员工会怪AI，只有29%的人承认是自己的问题。

The report writes that when AI generated work fails, 40% of workers blame AI, and only 29% admit that it was their own fault.

他们把这称为道德疏离，意思是人们逐渐在心理上不再为自己的有害或草率行为承担责任。

They call this an example of moral disengagement, writing, the gradual mental process by which people stop holding themselves accountable for harmful or careless behavior.

研究发现，重度使用AI的人，出问题时把责任推给工具的可能性是轻度用户的3.4倍。

Heavy AI users, they found, are 3.4 times more likely than light users to blame the tool when something goes wrong.

所以，他们观察到的这个循环大致是这样的：首先，企业部署了AI，不管是因为真正的需求，还是为了做样子。

So the cycle that they're identifying looks something like this: First, the organization deploys AI, for whatever combination of good reasons and signaling reasons that might be.

接下来，由于员工要花力气让AI真正能用，‘陪跑’的工作量就上来了。

Next, bot sitting rises as workers absorb the labor of making AI usable.

想想你得不断给它输入大量背景信息，还要检查它的错误。

Think feeding it all that context and checking its mistakes.

第三，人开始感到疲惫。

Third, fatigue sets in.

人们意识到，自己的工作不再是真正做事，而是盯着AI，确认它有没有把事做完。

People realize that their work is no longer doing the work, but checking to make sure that AI has done the work.

这种疲惫导致了‘机器人敷衍’的现象——工人开始偷工减料。

That leads to the bot shitting phenomenon as the fatigued workers take shortcuts.

随着这种行为增多，就进入了第五阶段：那些未经核实的输出开始向上游传递。

And as that behavior goes up, we get to the fifth stage where those unverified outputs move upstream.

最后，善后工作越积越多，因为AI辅助的错误成果在下游制造了更多返工。

And finally, the cleanup piles up, as bad AI assisted work creates more rework downstream.

现在最重要的AI问题，不是谁在用AI，而是谁用得好。

One of the most important AI questions right now isn't who's using AI, it's who's using it well.

普华永道和德克萨斯大学奥斯汀分校刚分析了140万次真实职场中的AI互动，发现了一个意外的结果：最有影响力的人并不是擅长写提示词的人，而是把AI当成思维伙伴来合作。

KPMG and the University of Texas at Austin just analyzed 1,400,000 real workplace AI interactions and found something surprising: the highest impact users aren't better prompt engineers they treat AI like a reasoning partner.

他们提出问题，引导思考，不断迭代，并推动得出更好的答案。

They frame problems, guide thinking, iterate, and push for better answers.

好消息是？

And the good news?

这些行为是可以大规模培养的。

These behaviors are teachable at scale.

如果你正想从单纯使用AI转向真正提升能力，KPMG关于高级AI协作的研究非常值得一看。

If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time.

更多详情请访问 kpmg.com/us/sophisticated。

Learn more at kpmg.com/us/sophisticated.

网址是 kpmg.com/us/sophisticated。

That's kpmg.com/us/sophisticated.

本期节目由Super Intelligent推出的高管代理领导力项目，以及常驻嘉宾Nufar Gaspar联合呈现。

Today's episode is brought to you by the new Executive Agent Leadership Program produced by Super Intelligent and by frequent AI DB operators guest, Nufar Gaspar.

为了让你们更深入了解这个高管代理领导力项目，下面有请Nufar。

To tell you a little bit more about the Executive Agent Leadership Program, here is Nufar.

组织中代理工具能否被广泛采用，关键看领导层是否亲自参与。

The best predictor of agent adoption in an organization is how hands on their leaders are.

光谈代理工具和实际动手搭建完全是两回事。

Talking about agents is completely different than building them.

我们的学员，从普通员工到高管层，都已经搭建了可运行的代理团队，还制定了治理框架和操作手册，推动规模化落地。

Our participants, ICs all the way to C suite, have built working agent fleets, governance frameworks and playbooks to scale it.

高管代理领导力是EnterpriseClaw的升级版。

Executive agent leadership is the evolution of EnterpriseClaw.

我们从前三期学员身上学到的所有经验，全都重新打磨，适配当下最迫切的需求。

Everything we've learned across three cohorts rebuilt for right now.

包括代币经济、安全机制、供应商韧性，以及如何构建架构来推动大规模代理应用。

The token economy, security, vendor resilience, and architecture to lead agent adoption at scale.

高管代理领导力项目下一期正在报名中，将于6月29日正式开课。

The next cohort of the Executive Agent Leadership Program is signing up now and will launch on June 29.

更多详情请访问 training.bsuper.ai。

You can find out more at training.bsuper.ai.

今年，平均每家企业在人工智能上的投入高达1150万美元，但大多数企业根本无法证明这笔钱带来了任何回报。

The average enterprise is spending $11,500,000 on AI this year, and most of them can't prove a single dollar came back.

人工智能真正带来投资回报时，到底是什么样子？

What does AI actually look like when it produces ROI?

去看看那家把支付处理速度提升了32倍的医疗公司，看看那家把文件调研时间从三个月缩短到十分钟的律所，再看看那个把客户等待时间减少了99%的客服中心。

Ask the healthcare company that just made their payment processing three twenty times faster, or the law firm whose document research went from three months to ten minutes, or the contact center who reduced wait times by 99%.

这些都是MissionCloud的真实客户，取得了实实在在的成果。

These are real MissionCloud customers with real results.

MissionCloud是CDW旗下的公司，也是AWS的顶级合作伙伴。

MissionCloud is a CDW company and an AWS premier tier partner.

他们是真正以AI为先、结果导向的AWS专家，专为打造能推动业务发展的AI解决方案。

They're the AI first, outcomes obsessed AWS experts who build AI solutions that drive your business forward.

无论你是满脑子AI想法却不知从何下手，还是已经投入六个月却进展不顺，他们全都见过，也全都解决过。

Whether you're flooded with AI ambitions but no idea where to start, or six months into a deployment that's going sideways, they've seen it and they've fixed it.

别再把预算浪费在那些无法产生实际效果的人工智能项目上了。

Stop burning your budgets on AI that doesn't produce results.

访问 missioncloud.com 开始吧。

Start at missioncloud.com.

本集《AI每日简报》由Outsystems赞助播出，这是一家专为企业打造的领先智能系统平台。

This episode of the AI Daily Brief is brought to you by Outsystems, a leading agentic systems platform built for the enterprise.

全球各地的企业都在使用Outsystems平台构建、协调和管理智能系统，这绝非偶然。

Organizations all over the world are building, orchestrating, and governing agentic systems on the Outsystems platform and with good reason.

Outsystems开放且统一的平台，让团队能够灵活地设计、交付和扩展受控的智能系统。

Outsystems' open and unified platform allows teams to architect, deliver, and scale governed agentic systems with agility.

无论团队规模大小或技术背景如何，都可以用Outsystems快速、低成本地构建、部署和管理AI应用与智能体，同时保障可靠性和安全性。

Teams of any size and technical depth can use Outsystems to build, deploy, and manage AI apps and agents quickly and cost effectively without compromising reliability and security.

借助Outsystems，你能从想法出发，迅速将概念变为现实。

With Outsystems, you can rapidly launch ideas from concept to completion.

它是领先、统一、敏捷且经过企业验证的智能系统平台，助你加速增长、减少运营阻力，真正实现AI对企业的影响。

It's the leading agentic systems platform that is unified, agile, and enterprise proven, allowing you to accelerate growth, reduce operational friction, and deliver real enterprise impact with AI.

Outsystems。

Outsystems.

打造你的智能代理未来。

Build your agentic future.

我觉得现在特别值得探讨这一点，是因为今年发表的很多研究，我之所以不太感兴趣，至少有一部分原因是它们的数据收集工作发生在智能代理全面普及之前。

Now, one reason that I think this is interesting to explore now is that another part of my disinterest in many of the studies that have been published this year is that at least some meaningful portion of their data collection happened before the full ascendancy of agentic work.

要知道，即使是最前沿的AI用户，也直到去年年底或今年年初才真正开始广泛使用智能代理和相关编程工具。

Remember, even the most vanguard AI users weren't really using agents fully and agentic coding tools and things like that until the very end of last year or beginning of this year.

事实上，这份报告中调查的六千人，都是在去年12月和今年1月回答这些问题的，也就是说，他们当时大概还没用Cloud Code来运行自主代理。

And in fact, the 6,000 people that they surveyed for this report were answering those questions in December of last year and January of this year, meaning that this probably wasn't people using Cloud Code to go run autonomous agents.

不过，这里也有一个有意思的观点：在这种情况下，与其说这些结果无效或不相关，不如说，如果重复这项研究，可能会发现这种现象更加明显。

There is an interesting argument though, that in this particular case, rather than nullifying these results or making them not relevant, a repeat of this study might find this behavior even more amplified.

这项研究发现了一个现象：工具越智能，使用者反而越马虎。

One of the things that the study found is that the smarter the tool, the sloppier the worker.

报告提到，在ChatGPT、Claude、Gemini和微软Copilot这些工具中，使用者报告生产力提升最明显的——比如ChatGPT达到67%，Claude达到59%——同时也是用户承认错误最多的，分别有71%和92%的人至少每月都会出错。

They write that among ChatGPT, Claude, Gemini, and Microsoft Copilot, the tools whose workers report the biggest productivity gains (which was ChatGPT at 67% and Claude at 59% ) were also the tools whose users reported the most botchedding at 7192% admitting to it at least monthly, respectively.

当我们真正从追求效率的AI转向创造机会的AI，人们开始做以前根本做不到的事情时，就出现了一种全新的‘机器人失误’类型：这并不是人偷懒，而是他们根本没能力去验证或检查AI的输出结果。

As we really start to move from efficiency AI to opportunity AI, where people are doing things that were not possible for them before, it creates this whole new category of potential bot shitting where it's not that people are being lazy, but that they don't actually have the capability to verify or check the output.

每次有新模型发布，大家都忙着测试它编程能力怎么样，我也跟着试，但因为我从来没写过代码，只能凭感觉去判断输出效果。

I feel this acutely every time a new model comes out and people race to figure out if it's better at coding or not, and I try it out as well, but can only use very impressionistic views of the outputs as my way to understand because I've never coded before.

但在我看来，这绝不代表只有以前能做这些事的人才该用这些工具。

Now, very clearly, in my estimation, that does not mean that only the people who previously could do what the AI can do now should be using those tools.

让技能普及开来，正是这场变革的核心价值之一。

Democratization of skills is a key value of this entire shift.

不过，这也确实带出一个新问题，那就是在大规模应用智能体时，我们不得不面对的挑战。

And yet it does inherently point to this new challenge that is going to be a part of integrating agents at scale.

那该怎么应对呢？

So, how to deal with this?

我觉得，看待这些问题，不该从‘人做错了什么’这个角度出发。

I think that the right way to look at these problems is not at core about people doing something wrong.

我认为，这些都是我们所有人正在经历的转型过程中必然出现的后果和现象，迟早都会以某种形式发生。

I think that they are important consequences and artifacts of the transition that we're all experiencing, and were, in effect, always going to happen in some way, shape, or form.

我们在这个转型期的任务之一，就是找到应对这些具体挑战的方法。

Part of our work in this transition is figuring out how to deal with these exact challenges.

Glean旗下的Work AI Institute认为，我们需要构建一套无法购买、必须亲手打造的全新AI人类基础设施。

The Work AI Institute in Glean argue that we need a new human infrastructure of AI that can't be bought, but has to be built.

他们提出，这套基础设施需要在三个层面进行建设：个人如何与AI协作、团队如何借助AI进行管理，以及组织如何围绕AI来设计工作流程。

They argue that it has to be built at three levels: how individuals work with AI, how teams manage with it, and how organizations design with and around it.

首先，他们划定了一类被称为“高AI效能者”的人群。

First of all, they designate a group called high AI achievers.

这类人群反馈称，AI不仅提升了他们的工作效率，也提高了工作成果的质量。

These are people who report that AI has improved both their productivity and the quality of their work.

那他们具体有哪些不同做法呢？

So what do they do that's different?

首先，他们在应用 AI 的场景上会稍微挑剔一些。

The first thing they do is that they're a little bit more particular about where they use AI.

虽然基本上每个人都会把 AI 作为工作的核心工具，但我

While it's the case that basically everyone uses AI for the core of their work, I.

认为

E.

开发者用它写代码，分析师用它处理数据，低效使用AI的人大约一半的AI时间花在核心任务上，而高效使用者则接近三分之一。

Developers using it to write code or analysts using it to crunch numbers, low AI achievers spend roughly half of their AI time performing their core job tasks, whereas high AI achievers spend closer to a third.

这差距大约是48%对38%。

It's the difference between 4838%.

说实话，我们马上就能明白，为什么这些问题这么棘手，而且没有简单的答案。

Now, frankly, right away, I think we're gonna see why these problems are so challenging and they don't have easy answers.

报告指出，高效AI使用者和低效使用者的区别在于，前者仍然更多地亲自完成核心工作。

The report is arguing that the difference between effectively better AI users and worse AI users is better AI users still doing more of their core work themselves.

但在我看来，这种情况只会是暂时的。

But this feels to me like a very, very temporary state of affairs.

以编程为例。

Take for example in coding.

他们发现，在编程方面，低效使用者将46%的AI时间用于核心编码任务，而高效使用者只用了37%。

They found that low AI achievers in coding used 46% of their AI time on core tasks of coding versus high AI achievers using 37% of their AI time on core tasks.

不过，这些数据是去年12月和今年1月的了。

But again, this was back in December of last year in January of this year.

对目前许多高级程序员来说，AI 已经承担了他们过去所有核心工作。

For a significant number of advanced coders at this point, AI is doing all of their previous core task.

当开发 Claude 的程序员自己都不写代码了，这个说法就彻底乱了套。

When the coders who created Claude code basically don't code anymore, it kind of throws this whole idea into a tizzy.

在智能体模式下，‘AI 使用时间’这个概念本身也站不住脚了。

Also, the entire paradigm of AI time spent kind of ceases to make sense in an agentic paradigm.

换句话说，如果你给 Codex 或 Claude 一个‘完成目标’的指令，让它通宵运行，那它所有的工作时间算不算你的 AI 使用时间？

In other words, if you give codex or Claude code a slash goal command that's going to work overnight, do you count all of its time working as part of your AI time spent?

在智能体的世界里，这种人为设定的衡量方式根本就没了意义。

The entire artifice of this construction just sort of ceases to make sense in an agentic world.

但我认为，关键在于，那些真正擅长自己工作的人，依然很谨慎地选择 AI 的使用方式，他们相信自己的判断，始终主导着 AI——不管这种‘主导’到底意味着什么。

And yet I do think that if you are taking away an important part of this, it's that people who are good at the stuff that they're doing are still being discerning about what AI is going to be used for and trust themselves and their judgment to still lead the AI, whatever that leadership actually means.

高效率使用 AI 的第二个特点是，他们更频繁地‘监督’AI，但会把这种监督导向更有成效的方向。

The second characteristic of high AI achievers was actually botsitting more, but orienting it towards the productive.

研究发现，高效率 AI 用户更有可能认为 AI 本身是个很有价值的老师。

They found that high AI achievers were more than twice as likely to rate AI itself as a valuable teacher.

实际上，他们使用AI不仅是为了避免被AI取代，更是为了提升自己的工作效率。

Effectively, they used bot sitting not only as a way to stop bot shitting, but as a way to improve their own work with AI.

这就引出了第三点：他们会把省下来的时间重新投资回去。

Which gets to the third point: they reinvest that AI dividend I.

他们平均省下的十一个小时，被用来学习新技能，而不仅仅是做更多的工作。

E, the eleven hours they saved on average, into new skills, not just more work.

那么，团队架构方面又怎么样呢？

Now, what about team architecture?

再说一次，那些在AI应用上表现出色的团队，

Once again, high achieving AI teams, I.

也就是那些既报告了生产力提升，又报告了工作质量提升的团队，他们的做法和低效团队截然不同。

E, those who report both productivity increases and quality of work increases, do things differently than low achieving AI teams.

其中一个区别是，虽然他们把AI当作队友，但责任依然落在人身上。

One difference is while they treat AI as a teammate, they keep accountability on the people.

举个例子，当AI表现不佳时，他们不会直接放弃AI然后自己动手做，而是更有可能把同样的提示词用到其他AI工具上，或者补充更多背景信息，然后再试一次。

For example, when AI underperforms, rather than just giving up on AI and doing the task themselves, high AI achieving teams are far more likely to run the same prompt in other AI tools or add more context and try again.

高绩效的AI团队中，AI的采用是自下而上、同事间相互影响的，而不仅仅是自上而下的推动。

High achieving AI teams see AI adoption spreading peer to peer, not just top down.

有一个被忽视但极其重要的数据：当领导者使用AI时，普通员工采用AI的可能性会提高2.4倍。

In a buried but incredibly important statistic, they found: When a leader uses AI, it makes the average employee 2.4 times more likely to adopt it.

当直接同事使用AI时，员工采用的可能性会提高3.2倍。

When a direct teammate uses it, it makes them 3.2 times more likely to adopt it.

而当跨部门同事使用AI时，普通员工采用的可能性会飙升到5.6倍。

But when a cross functional teammate adopts it, that makes the average employee 5.6 times more likely to adopt.

这个现象本身就很值得深入研究。

This is worthy of an entire exploration all on its own.

报告开始探讨这个问题，问为什么跨部门同事的影响这么大，核心观点是：这正是AI真正落地、直面组织复杂现实的地方。

The report starts to explore this asking why do cross functional teammates carry so much weight and basically argues that it's because this is where the rubber of AI hits the road of actual organizational messiness.

他们清楚地意识到工作中存在的协调成本——瓶颈、部门壁垒、重复劳动、任务遗漏这些问题。

They continue, because they're painfully aware of the coordination tax of work, the bottlenecks, the silos, the duplicated efforts, the dropped balls.

所以当他们设计AI工作流或智能代理时，从不幻想一个理想化、干净整洁的工作场景。

So when they build an AI workflow or an agent, they aren't designing for some tidy fantasy version of the work.

他们设计的是真实工作中必须面对的混乱状态——市场人员等着分析师还没提取的数据，工程师等着产品经理还没写好的需求。

They're designing for the messy version that they have to deal with, where the marketer needs the data the analyst hasn't pulled yet, and the engineer needs the spec the product manager hasn't written yet.

他们的工作流程之所以能推广开来，是因为经得起实际工作的考验。

Their workflows spread because they survive contact with real work.

高绩效AI团队的第三个特点是，管理者用AI来降低协调成本，把省下来的时间花在人身上。

The third distinguishing feature of high achieving AI teams is managers using AI to cut coordination costs and reinvest that time in people.

高效的AI管理者，把32%原本用于协调的时间交给了AI处理。

High AI achieving managers delegate 32% more of their time on coordination to AI.

正如报告所说，他们并不是用AI来取代管理。

As the report puts it, they aren't using AI to replace management.

而是用AI清除那些被误当成管理的行政杂务。

They're using it to clear away the administrative sludge that gets mistaken for management.

报告还提出一个我特别认同的观点：这听起来对管理者是坏消息，但我们认为恰恰相反。

And with a take that I love, the report writes: That sounds like bad news for managers, but we think it's the opposite.

最优秀的管理者，从不试图在协调工作上跟AI较劲。

The best managers don't try to compete with AI on coordination work.

他们把协调工作交给AI，让AI起草进度报告、分派任务、总结会议内容。

They delegate the coordination work to AI, using AI to draft the status update, route the request, summarize the meeting.

从而腾出宝贵的时间，去做真正该花精力的事：辅导团队、培养人才、激发士气。

And they reclaim precious time for the work they ought to be spending more of their time doing: coaching, developing, and inspiring their people.

这种做法带来的好处，就是员工的信任感大幅提升。

And the dividends of this are enormous in terms of employee trust.

当被问及是否接受AI参与绩效评估、薪酬决定或裁员决策时，那些拥有好主管的员工——也就是报告中定义的、下属愿意推荐其为主管的管理者——对AI的信任度几乎是普通或差主管下属的两倍。

When asked whether they were comfortable with AI playing a role in performance reviews, pay decisions, and termination decisions, workers with good managers, which the report defines as managers whose direct reports would recommend them as managers, are basically twice as trusting as those with bad or average managers.

比如，有53%的员工认为，AI参与绩效评估是可以接受的，前提是他们的主管表现优秀。

For example, 53% of workers with good managers are comfortable with AI playing a role in performance reviews.

而在主管表现普通或较差的团队里，这个比例只有26%。

That number drops to 26% for workers with bad or average managers.

在薪酬和裁员决策上，情况也一样：好主管的团队成员对AI参与的接受度，大约是差主管团队的两倍。

On pay decisions and termination decisions, it's the same story, with workers with good managers being about twice as likely to be comfortable with AI playing a role as those with bad or average managers.

最后，再来看第三个层面——组织层面。

Finally, on the third level, the organizational level.

这份报告试图弄清楚，那些具有变革力的组织究竟做错了什么。

The report tries to understand what transformative organizations do differently.

他们把变革型组织定义为：员工认为AI显著提升了整个组织的绩效和成果，而不仅仅是个人的工作表现。

They define transformative organizations as those organizations whose employees report AI has significantly improved their organization's performance and outcomes, not just their individual performance and outcomes.

简单说，就是那13%的人觉得，因为用了AI，公司整体表现明显更好了。Work AI研究所想知道，这13%的人到底做了什么，另外87%的人没做。

Basically, this is the thirteen percent who say their organization is performing significantly better because of AI, and the Work AI Institute is asking what are the 13% are doing that the other 87% aren't.

首先，他们更看重真正相关的指标，而不是表面好看的虚荣数据。

The first thing is that they have a bias towards relevant metrics versus vanity metrics.

变革型组织花更多时间去衡量工作质量、生产效率、实际产出，以及节省下来的时间。

Transformative organizations spend much more time trying to measure things like quality of work, productivity and output, and time saved.

还有一个小细节特别有意思。

And here's a small one that's really interesting.

在变革型组织里，员工更清楚自己到底用了多少AI工具。

Employees of transformative organizations are much, much more likely to have visibility into their own AI usage.

在非变革型组织中，只有40%的员工能查看自己的AI使用数据，而在变革型组织里，这个比例高达71%。

While only 40% of workers in non transformative organizations report that they can see their own AI usage data, 71% of workers in transformative organizations report that they have access to that information.

这让AI更像是一种帮助改进的反馈机制，而不是用来判断谁该被开除的监控工具。

This makes AI feel like a feedback mechanism to improve rather than just a surveillance mechanism for who should be fired.

同样地，转型型组织把治理当作一个动态运行的系统。

Relatedly, transformative organizations make governance a living system.

比如，非转型型组织中只有55%的员工表示公司会定期审查AI政策，而在转型型组织中，这个比例飙升到了93%。

For example, while 55% of workers at non transformative organizations say that those organizations review their AI policy regularly, that number jumps to 93% of workers at transformative organizations.

在解释AI政策背后的原因时，只有57%的非转型型组织员工认为公司会说明理由，而转型型组织中这一比例高达91%。

When it comes to explaining the rationale behind AI policy, only 57 of workers at nontransformative organizations say their org explains that rationale, and that number jumps to 91% for transformative organizations.

这一切最终都转化为信任。

This all translates to trust.

非转型型组织中只有57%的员工信任公司的AI战略，而在转型型组织中，这个数字上升到了93%。

57% of workers in nontransformative organizations report trusting their company's AI strategy, and that number jumps to 93% for workers in transformative organizations.

对于那些以为AI战略不过是选个供应商的公司来说，这可不是什么好消息——这种想法恰恰是非转型型组织的典型特征，而不是真正能从AI中获益的组织所具备的。

Now in bad news for companies who think that AI strategy is basically just a vendor selection choice, that point of view is actually a hallmark of non transformative organizations as opposed to the ones who actually get value out of AI.

不出所料，转型型组织更专注于构建适合企业实际场景的优质系统。

Unsurprisingly, transformative organizations are much, much more focused on building good systems for enterprise context.

最后，也不出所料，那些具有变革力的组织真正把资源投入到人身上，而不仅仅是AI工具本身。

And finally, and totally unsurprisingly, transformative organizations actually invest in their people, not just in the AI tools themselves.

当被问及组织是否正式奖励AI技能时，非变革型组织的员工中有48%表示肯定，而变革型组织的这一比例飙升至84%。

When asked whether their organization formally rewards AI skills, the number was only 48% for workers in non transformative organizations and jumped to 84% for workers in transformative organizations.

当被问及组织是否提供了足够的AI培训和支持时，只有52%的非变革型组织员工认为做到了，而变革型组织中这一数字高达90%。

When the question was whether their organization provides enough AI training and support, only 52% of workers in non transformative organizations said they did, and that number jumped to 90% for transformative organizations.

正如我之前提到的，我之所以花时间深入这份报告，是因为和2026年我见过的许多报告不同，我认为，如果现在用更高的智能代理采用率重新做一遍这项研究，结果不会推翻他们的发现，反而会把报告中的几乎所有结论都进一步放大。

Now, as I mentioned before, part of why I wanted to take the time to actually dig into this report is that, contra many, many reports that I've seen throughout 2026, I think that if you redid this one right now with increased agentic adoption, you'd actually find, rather than contradicting what they found, I think almost everything in here would be significantly amplified.

原因很简单。

And the reason for that is simple.

AI的变革，本质是组织自身的变革，而不仅仅是技术的部署。

The work of AI transformation is transformation, not just implementation.

它关乎的是复杂而混乱的转变——不只是你怎么做，更是你做什么。

It is about complex, messy change, not just in how you do, but what you do.

它需要的是全新的体系，而不仅仅是新的工具。

It requires new systems, not just new tools.

机器人坐那儿不动和机器人乱来，都是我们未来多年都将经历的转型过程中不可避免的一部分。

Bot sitting and bot shitting are all part and parcel of the transition that we will all be experiencing for many years to come.

这些问题没有简单快捷的解决办法。

There are not short, easy answers to these problems.

只有两种组织：愿意下功夫的，和不愿意的。

There are only organizations willing to do the work and those that aren't.

向Work AI研究所和Glean的团队致敬，做得太棒了。

Great work to the teams at Work AI Institute and Glean.

好了，今天的AI每日简报就到这里。

For now, that's gonna do it for today's AI daily brief.

一如既往，感谢你们收听或观看。

Appreciate you listening or watching as always.

咱们下次再见，保重。

And until next time, peace.