无人提及的7000亿美元AI生产力问题

这是不可知的。

It's is unknowable.

我无法知道我昨天是否高效。

I can't know if I was productive yesterday.

完全赞同这一点。

Was all for it.

但我们试图在公司系统层面做的是理解，这些工具的高级使用、轻度使用、重度使用之间是否存在某种关联——是工具的重度用户还是轻度用户。

But what we're trying to do at the systems level for companies is understand, is there some correlation between specific use of these tools on advanced side, light side, heavy side, heavy user of the tool, lighter use of the tool.

这些用户在工作中是否更高效？

Were the were the users more productive in their job?

这些员工在工作中是否更高效？

Were the employees more productive in their job?

然后在此基础上衡量这些员工群体实际工作的时间。

And then measure on top of that amount of time those segments of workers were actually working.

因为如果我今天是首席财务官，目标不是要了解本和蒂娜是否表现良好。

Because the goal if I'm a CFO today is not to understand, did Ben do a good job and did Tina do a good job?

目标是搞清楚，我确实被要求增加50%的运营支出。

The goal is to understand, I have definitely been asked to spend 50% more on OpEx.

对吧。

Right.

我推动了什么吗？

Did I drive something?

对吗？

Right?

顺便说一句，这里有些有趣的问题。

And then by the way, there are interesting questions.

我们知道这与人员规模有关——公司能否因为员工真正工作八小时而完成更多工作？

We know this around staffing size and will companies get more done because people will actually work eight hours?

是的。

Yep.

看吧，结果会证明一切。

Look, it will turn out.

我怀疑这确实是管理者会做的事情之一。

I suspect it is true that this is one of the things managers do.

我怀疑随着时间的推移，如果发现所有员工现在每天只工作四小时而非八小时，你可能会决定减少员工数量，让剩下的员工每天工作六小时。

I suspect it is true over time if it becomes clear that all of your employees are now working four hours a day instead of eight, you will probably decide to have fewer employees and the remaining employees will work six hours a day.

所以我不确定在未来几年内会真正接受这种情况。

So I'm not sure I really buy in the next couple of years.

你会看到大公司的员工实际上只工作一半时间。

You will see people in large companies actually just working half as much.

要知道，个体经营者就是这样的。

Know, sole proprietor is what it is.

就像，如果我是一名个体经营的律师，衡量我今天生产力的唯一标准其实还是取决于我自己。

Like, if I were a sole proprietor lawyer, kind of my only measure of productivity today is to myself anyway.

对吧？

Right?

关键在于我愿意付出多少努力，以及我想赚多少钱？

It's how hard do I wanna work for and how much money do I wanna do?

嗯，这里的原则就是代理人。

Well, there the principle is the agent.

对。

Right.

所以这就是为什么它如此重要，坦白说，我非常欣赏你所做的。

So that's This is why it's so important, and this is why, I mean, candidly, I love what you do.

显然，我非常欣赏你所做的。

Obviously, I love what you do.

这就是你在这里的原因。

That's why you're here.

但这里没有基准线。

But there is no baseline.

就像，这工作有效吗？

Like, it's like, did this work?

首先，必须明确如何定义产出。

Well, first, have to know how like, you have to define the outputs.

你的投入基本上就是时间和金钱。

You have the inputs, which are largely just like time and money.

对。

Right.

然后你还有产出。

And then you have the outputs.

实际上，想出一个产出指标某种程度上还挺复杂的。

And part of it is actually it is kind of complicated to come up with an output.

确实。

For sure.

你知道古德哈特定律吗？

Do you know a Goodhart's Law?

请讲

Go

说吧。

ahead.

所以古德哈特定律，我特别喜欢这个。

So Goodhart's Law, I love this one.

就是说当目标变成衡量标准——抱歉，当衡量标准变成目标时，它就不再是一个准确的衡量标准了。

It's like when a target becomes a measure sorry, when a measure becomes a target, it is no longer accurate as a measure.

是的。

Yes.

对吧？

Right?

比如我说，我要根据你每天发送多少封邮件来评判你。

So if I say, okay, I'm gonna judge you based on I'm gonna like, how many emails are sent every day?

这本来是个衡量标准。

Well, that's a measure.

但一旦它变成目标，就变成了我要你发更多邮件。

But once it becomes a target, it's like, I want you to send more emails.

这样这个衡量标准就被扭曲了。

Well, you're no longer like, the measurement gets corrupted.

这就是顶峰。

This is peak.

因为现在人们决定做更多事情来达成这个目标，它就不再是一个客观的衡量标准了。

Because now people decide to do more things to hit this target, and it's no longer an objective measure.

所以，你知道，部分原因在于如果我试图弄清楚，比如说，有个叫Harvey的产品。

So, you know, part of it is if I'm trying to figure out, like, okay, there's a product called Harvey.

很多人喜欢Harvey，它似乎能让人效率大幅提升，但和什么相比呢？

A lot of people love Harvey, and it seems to make people a lot more productive, but compared to what?

对。

Right.

所以在我看来，唯一能回答这个问题的方式——我们会讨论Harvey，不是针对Harvey。

And so to me, the only way you answer that we'll talk about Harvey, nothing against Harvey.

我相信Harvey很棒。

I'm sure Harvey is amazing.

在我看来，真正理解这一点的唯一方法——这也是为什么我认为传统公司采用的方式完全行不通——就是去调查使用Harvey的人，问他们是否提高了效率。

To me, the only way to really understand this, and that's why I think the traditional way companies are doing this just doesn't work at all, is, hey, let's survey the people that use Harvey and ask them if they were productive.

顺便说一句，他们都会回答'是'，因为没人会承认自己效率低下，这是第一点。

And by the way, they will all say yes because no one ever answers they weren't, number one.

第二点，我的老板为这个产品付了钱。

And number two, my boss paid for the product.

我肯定会说这是个好产品，对吧？除非我们所有人都讨厌它——但Harvey应该不在此列，因为似乎人人都喜欢Harvey。

I'm going to say it was a good product, right, unless we all universally hate it, which I assume is not true of Harvey because everyone seems to like Harvey.

所以这很棒。

So that's wonderful.

因此我认为，这就是为什么我觉得传统的衡量方式是存在缺陷的。

So I think all you can actually do is it's why I think the traditional way of measuring this is broken.

看吧。

Look.

这就是我们开始研究的原因——你真正能做的就是不询问人们就能了解他们实际使用Harvey的程度。

It's why we started learning All you can really do is understand without asking people how much usage of Harvey are these people actually doing.

对吧？

Right?

我们有五个人。

We have five people.

我们有六个人。

We have six people.

无论他们在调查中会说什么。

Whatever they'd say on a survey.

有两个人从未登录过。

Two have never logged in.

对吧？

Right?

我们都见过那个笑话，你知道的，你的项目要到期了。

We've all seen the joke about, you know, your project is due.

一小时后就要交了。

It's due in an hour.

你说你已经赶上了进度，然后突然发现，哦，糟了。

You said you were caught up, and then you oh, shit.

我得申请这份Google文档的权限。

I have to ask permission for this Google doc.

对吧？

Right?

所以六人中有两人——当然这些数字是我编的。

So there's two of the six I'm making these numbers up, of course.

六人中有两人在被通知当天就注册了Harvey，但之后再也没用过。

Two of the six people actually signed up for Harvey the day they were told and then never went back to it all.

他们对现有的工作方式很满意。

They're very happy with the way they work.

他们每天都以这种方式工作。

They work that way all day every day.

六人中有两人会登录并使用一小会儿，还有两人则一直在使用。

Two of the six log in and use a little bit, and two of the six use it all the time.

我唯一能初步判断该软件是否有价值的方式，就是被动获取这些数据而不去询问那些人。

The only way I can even begin to understand if that software is valuable is by knowing that data passively without asking those folks a question.

然后向每个人提出相同的关于生产力的问题，并通过实际输出的工作量来衡量。

And then asking everyone the same questions about productivity and measure it with amount of work actually output.

如果我将这三方面结合起来，就能开始理解Harvey是否有用。

And if I take those three things together, then I can begin to form an understanding of was Harvey useful.

你我和某人讨论过，他们提到激励公司工程师的一种方式是设立一个排行榜，显示每位工程师在堵塞代码上花费的金额。

You and I had a discussion with someone where they were talking about one of the ways they incent their engineers at their company is they have a leaderboard of the amount of money each engineer spends on clogged code.

创始人谈到他去找一位最优秀的工程师时说，我不明白发生了什么。

And the founder was talking about how he went to one of his best engineers and said, I don't understand what's happening.

你是我们最优秀的工程师之一。

You're one of our best engineers.

为什么你没有在Cursor上花任何钱？

Why aren't you spending any money with Cursor?

抱歉，不是Cloak Code。

I'm sorry, not Cloak Code.

为什么你没有在Cursor上花任何钱？

Why aren't you spending any money with Cursor?

我真的不明白这是怎么回事。

I really don't get what's going on.

所以对于那些开发者密集型的公司来说，他们可能并不需要我们。

And so that was an example of for these companies where they're very developer heavy, you probably don't need us.

如果你是一家开发者密集型的公司，或许可以通过衡量在Cursor上的花费加上常规管理评估来判断这个人是否真的在工作。

If you're a very developer heavy company, probably measuring amount of money spent on Cursor plus your kind of normal management understanding of is this person actually working?

如果他们每天只来两小时，你可能满意也可能不满意。

If they come in for two hours a day, you may be happy with that, you may not.

这取决于公司特性、生活方式和企业文化的具体情况。

That's going to be company specific and lifestyle specific and culture specific.

但你在办公室。

But you're in the office.

我看到你在那里。

I see you're there.

你没有在Cursor上花任何钱。

You're not spending any money on Cursor.

怎么了？

What's up?

对吧？

Right?

我们有这些指标。

We have these metrics.

但问题是我们看到AI工具爆炸式增长，有数百种工具，而公司也有数百种职位。

The issue though is we see this explosion of there's hundreds of AI tools, and companies have hundreds of roles.

这就是为什么我们想尝试用一些真正有用的AI数据来取代麦肯锡企业健康指数、韬睿惠悦或埃森哲的调查。

And so that's why we want to try and replace, you know, the McKinsey Corporate Health Index or Tau's Watson or the Accenture surveys with some real useful data around AI.

但我觉得这个Cursor的例子让我清晰地认识到，你希望对整个公司实现的目标就是：这个人到底工作了多少？

But I think that kind of cursor example really crystallized in my mind what you'd want to be able to do for a whole company, which is how much did this person work?

所以我有了这个定性判断。

So I have that qualitative judgment.

从定性角度看，作为管理者，我们并不是要取代这个。

Qualitatively, as a manager, right, we're not replacing this.

他们工作做得好吗？

Did they do a good job?

从根本上说，他们使用这些工具了吗？

And then fundamentally, did they use the tools?

当你把这三点结合起来时，这才是进行衡量的唯一方法。

And when you take those three things together, that's the only way you're going to have measurement.

就像我说的，如果你真的认为摩根大通会从180亿IT支出增加到300亿或400亿，CFO不会轻易说没问题。

And like I said, when you think about my, you know, micro world of if you really think JPMorgan is going go from spending 18 billionaire in IT to 30,000,000,000 or 40,000,000,000, the CFO is not just gonna say no problem.

对吧？

Right?

目前，我们的客户是首席信息官。

Today, our customer is the CIO.

我认为随着时间的推移，我们的客户将变成CIO与CFO的合作关系。

I think over time, our customer becomes a partnership with the CIO and the CFO.

这些数字实在太大了。

The numbers are just big.

是的。

Yeah.

这就像云支出。

It's like cloud spend.

数字实在太大了，人们会关注的。

The numbers are just so big, people are going to pay attention.

这已经远远超出了实验阶段。

It's going way beyond experimental.

而且，显然，公司自己也——如果

And, obviously, the companies themselves like, if

你问任何一家试图向你推销产品的公司，问他们的产品是否有效，

you ask any company that is trying to sell you anything and you ask, like, does your product work?

他们99次里有99次会说当然有效。

They will probably 99 times out of 99 Sure.

肯定会说当然有效。

Say, of course it does.

这是最好的。

It's the best.

你需要一个独立的仲裁者。

You need to have an independent arbiter.

这就是你们发挥作用的地方。

And that's where you guys come in.

但就像之前深入探讨这一点时所说的，这几乎就像公司层面的强化学习——我追求的结果是什么？

But kind of like double clicking on this point before, it's almost like reinforcement learning at a company wide level of what is the outcome that I'm looking for?

有时候这很明确，对吧？

And sometimes it's clear, right?

这就是测量和目标设定相关的地方，因为就像——我希望你编写更多代码。

And this is where the measurement and target thing is also relevant because it's like, I want you to write more lines of code.

哇哦。

Woah.

哇哦。

Woah.

如果以代码行数作为衡量标准，但若将其变成目标，你就会写出毫无意义的代码，这对销售来说非常容易。

If that's there's a measurement of, like, how many lines of code were written, but if it becomes the target, then you're just writing gobbledygook code and you're for sales, it's very easy.

我希望你卖出更多产品，但从接触客户到收款之间存在很大延迟。

I want you to sell more stuff, but there's a lot of latency between you go talk to a customer and then you go collect money.

因此你可能需要设定中间目标。

So you might have targets in between.

你也可能需要设置中间衡量指标。

You might have measurements in between.

你是律师，多起草些合同。

You're a lawyer, draft more contracts.

所以我想知道你们如何尝试定义目标？

So I guess how do you try to define the goals?

因为其中一些目标更像是背景信息在流动。

Because some of them are just like it's kind of I think of it as like background information that's going through.

就像发送的电子邮件，或者发送的Slack消息，或者编辑过的Google文档。

It's like emails that are being sent or, you know, Slacks that were sent or Google Docs that were edited.

比如说，存在这些非常非常明确的衡量标准，但它们不一定是产出。

Like, there are these key like, there are these very, very clear measurements, but those aren't necessarily outputs.

首先，你的

So first, your

关于衡量标准的观点。

point on measurement.

这就是为什么我之前说过，如果你思考任何存在真正第三方衡量标准的情况，就会出现这种有趣的现象，我们在Comscore就见过，但每个尝试建立第三方衡量体系的人都经历过，Omniture在早期也遇到过这种情况。

This is why I said earlier, if you think about any time true third party measurement exists, there's this interesting dynamic, and we saw this at Comscore, but everyone has seen this anytime they tried to build a third party measurement Omniture saw this in the early days.

谷歌曾一度抵制这种做法，但后来实际上收购了Urchin并建立了谷歌分析。

Google at some point fought it and then actually bought Urchin and built Google Analytics.

对吧？

Right?

因为事实证明，当你的客户能够追踪价值时，如果你做的事情确实有价值，这其实是件好事。

Because it turned out it's actually good when your customers can track value if what you do is actually valuable.

没错。

Right.

因此我的总体观点是，我认为如今许多AI公司可能对我们侧目而视，但我相信随着时间的推移，那些真正提供价值的AI工具终将爱上我们。

And so my general perspective is I think today a lot of the AI companies probably look askance at us, but I think over time, certainly the AI tools that actually provide value are going to love us.

对吧？

Right?

你最终能撬动企业预算的根本原因，在于人们相信这些工具确实有价值。

The way you will ultimately unlock real enterprise budget is because people believe these tools are actually valuable.

所以我们今天所做的——这是一段旅程。

So what we do today and this is a journey.

对吧？

Right?

这家公司成立大约一年。

The company is about a year old.

所以我们现在的做法是与所有客户合作，告诉他们：听着。

So what we do today is we work with all of our customers, say, look.

这些基础生产力问题是七十年来人们一直追求的黄金标准。

Here are the baseline productivity questions that are gold standard that people have asked for seventy years.

要知道，这些方法各有利弊，但总得有个起点。

You know, there's pros and cons to them, but this is you have to start somewhere.

这就是我们的起点。

This is where we start.

让我们为每个部门制定一套指标。

And let's define a set of metrics for each of your departments.

我们发现其中一个因素确实很重要，不是作为公司向员工公开的指标（否则会面临古德哈特定律的问题），而是作为实际存在的现实，对吧。

One of the things we've found that actually seems to matter, not as a metric that companies share with their employees because then you have the Goodhart's Law problem, but as an actual reality on the ground Right.

这是基本的响应能力。

Is fundamental responsiveness.

其中存在一个因素，我在法律部门投入了一定资金，对他们当前的生产力水平感到满意。

There is an element of, I spend some amount of money on my legal department, and I am happy with the amount of productivity they do today.

因此除非我想解雇律师（而我并没有这个打算），否则很难论证如何衡量软件的价值。

So there is an element of unless I'm trying to fire lawyers, which I'm not, you can argue I how would I measure the value of software?

我想我的律师们可能会更开心，但我在这方面并不存在人员流失问题。

I guess my lawyers might be happier, but I don't have a churn problem there.

那么坦白说，我为什么要这么做？

So, frankly, why should I do this?

我们发现其中一点就是部门间的服务协议——事实证明，当我部署这些工具时，并不会因此解雇员工，因为一种思考角度是：我能解雇半数律师吗？

And so what we found is one of the things we found is just almost an interdepartmental SLA, which is it turns out if I roll out these tools and I'm not firing employees because one way to look at this is could I fire half my lawyers?

事实证明公司其实并不喜欢解雇人。

Turns out companies don't really like firing people.

公司确实会在必要时裁员，但我从未见过哪位财务总监对裁减30%员工感到兴奋。

Companies do fire people if they have to, but I've actually never met a CFO that got excited about firing 30% of the workforce.

除了呼叫中心，那是另一个我们可以讨论的问题。

Outside of call centers, that's a different issue we could talk about.

比如，公司对待呼叫中心员工的方式与其他员工不同。

Like, companies treat their call center employees different from the rest of their employees.

除了呼叫中心，我从未见过哪个CFO会——如果你去找CFO说'你可以裁掉一半财务规划与分析人员'。

Outside of call centers, I've never met a CFO who was if you went to a CFO and said, You can fire half your FP and A people.

他并不想解雇蒂娜。

He doesn't want to fire Tina.

他认识蒂娜。

He knows Tina.

他见过蒂娜的丈夫和孩子。

He's met Tina's husband and children.

他不想解雇蒂娜。

He doesn't want to fire Tina.

他希望蒂娜能更快乐、更高效。

He'd like Tina to be happier and more productive.

实际上，他希望她能出色地工作且永不离职。

And actually, he'd like her to do a great job and never quit.

对吧？

Right?

公司其实不喜欢人员流动。

Companies don't really like churn.

因此，我们发现一个令人兴奋的指标是：这究竟是提高还是降低了跨部门响应速度？

So one of the metrics we found that people seem quite excited about is just did this raise or lower the interdepartmental responsiveness?

那么我们的衡量标准就是，我现在是否更愿意把更多事务交给法务部门处理？

So our measure would be, am I now comfortable sending more things to legal?

对吧？

Right?

如果我要保持法务部门规模不变，就不会开始起诉更多人。

If I'm going keep my legal department the same size, I'm not going to start suing more people.

我们这里讨论的是企业，律师事务所则适用另一套生产力衡量标准，它们是成本中心而非利润中心。

We're talking about companies here, law firms, whereas a different measure of productivity right there, cost centers, not profit centers.

所以一个方法是观察：随着时间推移，由于我的律师们效率提高了，其他部门是否向他们咨询了更多问题？

So one thing to do it is, did over time, because my lawyers are now more productive, are other departments asking them more questions?

他们是否得到了更快的回复？

Are they getting their responses faster?

当我在产品部门向工程师征求建议时，他们是否响应得更快了？

When I'm in product and I'm asking for input from engineers, are they responding more quickly?

对吧？

Right?

这对我来说是个好方法，可以从行为上看出我们变得更高效了。

That a good way for me to see behaviorally we become more productive.

这不是代码行数的问题。

That's not lines of code.

顺便说一句，我同意。

Now, by the way, I agree.

如果你公开这个指标并说：嘿，你最好反应快点。

If you expose the metric and say, Hey, you better be responsive.

人们可能会撒谎。

People can lie.

他们确实会

They can

在Slack上回复消息，但我真正想了解的是，作为一张地图，我的哪些部门更常使用这些工具？

send Slack But messages back and what I'd really like to understand is as a map, which of my departments use these tools more?

他们是否对我的其他部门反应更迅速了？

And do they become more responsive to my other departments?

因为在大公司里，人们都明白这一点。

Because there's an element of when you're at a big company, people know this.

这是小公司在创新方面表现出色的原因之一。

It's one of the reasons small companies do so well in innovation.

所有这些公司都面临着一个巨大的协调问题，我们都知道这一点。

There's just a giant coordination problem for all of these companies, and we know this.

你知道，在硅谷，嘲笑这些公司是件有趣的事。

You know, in Silicon Valley, it's fun to make fun of these companies.

但实际上，每个企业家内心都暗藏着一个梦想，那就是把公司做得足够大，最终形成一个庞大的官僚体系。

But actually, every entrepreneur's secret dream is to become so large that they have a giant bureaucratic company.

当然。

Of course.

谷歌三十年前也没计划要建立一个庞大的官僚体系。

Google did not plan to have a giant bureaucracy thirty years ago.

他们只是太成功了，现在确实拥有了

They just became so successful, they now do have a

庞大的官僚体系。

giant bureaucracy.

没错。

Right.

而且，你知道，这正好可以过渡到讨论AI在企业中的现状。

And, you know, that's kind of a good segue into perhaps the state of AI enterprise.

对吧？

Right?

所以你刚才说了这么多，提到过350人？

So you went on this whole like, listen, you talked about three fifty people?

是的。

Yeah.

我想谈谈350位大公司的IT主管。

I'd to talk about three fifty heads of IT at major companies.

而且横跨整个行业差距，对吧？

And across the whole whole gap, right?

这不仅仅是硅谷的那些公司

It wasn't just, you know, kind of Silicon Valley companies that

不

sit No.

说实话，我整个职业生涯基本上是这样：花几年时间帮朋友在Carbon工作，又花一年时间尝试修复wine.com

I mean, in all honesty, my whole career basically, I spent a couple years helping my friend at Carbon, I spent a year trying to fixwine.com.

除此之外，我的整个职业生涯都在向大公司销售软件

But other than that, my whole career has been selling software to large companies.

主要是大型企业或老牌公司

Mostly large companies or older companies.

对

Right.

确实偶尔会有发展迅速的硅谷公司

Yes, there's the occasional Silicon Valley company that grows very quickly.

但如果你在财富500强企业，99%的情况下这些公司都有20年历史

But if you're in the Fortune 500, you are going to be 20 years old 99% of the time.

对吧？

Right?

所以如果你要向员工超过千人的公司销售产品，那几乎可以确定是家老牌企业。

And so if you're going to sell to someone with more than a thousand employees, they're almost by definition an older company.

是的。

Yeah.

那么也许可以给我们讲讲你学到的主要经验。

So what maybe give us the highlights of what you learned.

当然。

Sure.

我们观察到很多不同现象，这些其实之前也有人发现过。

We saw a bunch of different things and, you know, people have seen this before.

说实话我没想到这点。

I actually don't think of this.

你会看到有人把这渲染成那种吸引点击的危言耸听内容。

You'll see people turn this into kind of clickbaity fear mongering things.

我其实并不这么认为。

I don't I don't really think of it that way.

首先，我们从Gartner了解到，

So first of all, we saw, like, is we know this from Gartner.

企业AI领域的投入高达7000亿美元。

There's, like, $700,000,000,000 being spent in enterprise AI.

这个市场正在飞速增长。

It's growing very, very quickly.

而且这种快速增长还将持续。

It's gonna keep growing quickly.

我们发现约70%的受访高管表示：我们确信这部分资金存在浪费。

And one of the things we found is something like 70% of leaders we talked to said, we are sure we are wasting money here.

资金消耗得太快了。

It's being spent so quickly.

顺便说句，真惭愧，我们最初连衡量这个的系统都没有。

And by the way, shame on us, we had no system to measure this in the first place.

我稍后再回到报告上，但今天我和一位客户聊了聊。

I'll get back to the report in a second, but I was talking to a customer today.

我们为什么要签下他们作为客户？

Why did we sign them as a customer?

他们是一家由私募股权公司持有的高利润企业，其老板——也就是私募股东——给他们下达了今年必须完成的五项任务，其中一项就是在全公司范围内采用人工智能。

They're a very profitable business owned by a PE firm, and their bosses, their PE owners, gave them five things they had to do this year, and one of the five was adopt AI across the organization.

他说，每次董事会会议我汇报其他四项指标时，都有相应的进展报告。

And he said, every board meeting I go in for my other four metrics, I have some report of how are we doing against those reports.

但在人工智能方面，我只有采购量的数据。

And on AI, all I have is the amount of stuff we bought.

这并不...所以是的。

It's not So yes.

我们确实采用了人工智能。

We adopted AI.

没错。

Yes.

我很好。

I'm doing great.

我们拥有一个庞大的AI家族。

We have a large family of AI.

全部采纳了这些

Adopted all these

大孩子。

large children.

这些都很好，但事实证明我们确实想这么做。

It's all great, but it turns out we want to actually do it.

因此我们发现这些领导者或许是对的，他们70%的项目可能都失败了。

And so what we found is these leaders, maybe they are right that 70% of their projects are failing.

无论他们是否正确，这都是个巨大的问题。

Regardless of their right, it's a giant problem.

他们之所以有这种感觉，是因为他们一开始就没有一个系统来弄清楚状况。

They feel that way because they have no system to figure it out in the first place.

没人相信他们75%的广告投入是失败的。

No one believes 75% of their ad spend is failing.

这并不是因为他们的广告策划者比AI采购者更聪明。

It's not because their ad planners are smarter than their AI buyers.

而是因为有二十年的系统机制帮助我理解：当我购买这个广告活动、投入这笔资金、进行这个应用安装时，无论是什么，它是否真正为我创造了价值？

It's because there are twenty years of systems in place to help me understand when I buy this ad campaign, when I spend this money, when I do this app install, whatever it is, did it actually drive value for me?

正如我所说，在AI领域我们确实缺乏这种机制，除了一些非常非常特定的垂直领域。

And we just don't really have that in AI, like I said, outside of some very, very specific verticals.

所以我们发现最重要的事情可以归结为三点。

And so really the biggest thing we found was it was kind of three things.

第一，你看到了AI的投入。

One, you saw the AI spend.

第二，就像我说的，他们认为约70%的AI项目是被浪费的。

Two, something like I said they believe 70 something percent of AI projects are wasted.

但我们发现的另一点是，大约85%（我记不清具体数字了）的受访公司表示，他们坚信自己只有未来18个月的时间，要么成为领导者，要么就会落后。

But the other thing we found is basically 85%, I can't remember, 85% of the companies we talked to said they really believe they only have the next eighteen months to either become a leader or fall behind.

因此我认为其中一个原因，你看到这次巨大的预算释放是因为这些企业存在极大的焦虑，他们觉得如果不采用这些技术就会落后，所以我们正在快速采纳。

So I think one of the things one of the reasons you've seen this giant unlock and budget is there's tremendous anxiety at these enterprises going like, we're going to lose if we don't adopt this stuff, yet so we're adopting it quickly.

我们并不清楚它是否真的成功了。

We have no particular idea if it's succeeding.

我们的员工实际上并没有真正使用它。

Our employees aren't really using it.

顺便说一句，公司里有一个被遗忘的群体在所有AI应用中——从我上一家公司说起，我们曾建立了一个非常庞大的人力资源技术公司。

By the way, a forgotten group in the company for all of this AI and from my last company, we built a very, very large HR technology company.

我们的销售对象是人力资源主管。

We sold into heads of HR.

它影响了公司所有员工，但我们的销售对象是人力资源主管。

It touched all the employees in the company, but we sold into heads of HR.

当我们与许多老客户交流时——他们现在虽不是我们的客户，但仍是行业内有影响力的人物——这些大公司的人都会说：嘿。

And so as we've talked to a lot of our old customers, who aren't really our customers today, but they're influencers, what they will all say at all of these large companies is, hey.

我们的员工非常担忧。

Our employees are really worried.

他们甚至不是担心会失业。

It's not even they're worried they're gonna lose their job.

对AI和经济存在一种基础层面的担忧，诸如此类的事情。

There's a base level of worry about AI and the economy, all that stuff.

他们甚至不是担心会失去工作。

It's not even they're worried they're going to lose their job.

只是他们整天被要求使用新系统。

It's just they're getting told to use a new system all day every day.

对吧？

Right?

一般来说，如果你在大公司工作，每年会有一两个新系统项目。

Generally, if you work in a large company, there's one or two new systems initiatives a year.

现在有20种新工具。

Now there's 20 new tools.

他们根本搞不清楚状况。

They don't know.

他们不知道自己能做什么，也没有接受过培训。

They don't know what they're allowed to do, and they have no training.

他们实际上要怎么...我该如何让人们使用这些工具？

How do they actually how do I get people using these tools?

于是你就遇到了这种奇怪的、几乎堪称完美的风暴局面。

And so you have this weird you have this weird almost perfect storm.

这就是为什么我们对Lairdon感到兴奋。

It's why we're excited about Lairdon.

这就是为什么你们对Lairdon感到兴奋。

It's why you're excited about Lairdon.

你们正面临着预算激增、对一切失效的极度焦虑、以及员工对自身权限的极度担忧——这简直就是一场完美风暴。

You have this perfect storm of tremendous growth in budget, tremendous anxiety that none of it is working, tremendous anxiety from their employees about what they're even allowed to do.

所以我们正在尝试的是...我并不认为我们能解决所有这些问题。

And so what we're trying to do is I don't think we solve all of that.

说能解决就太荒谬了。

That would be an absurd thing to say.

但我认为我们确实能在所有这些方面提供帮助，比如，你最初计划如何衡量效果？

But I think we really help with all of that about like, what is your plan to measure this in the first place?

有人使用过它吗？

Did anyone use it?

他们使用时是否变得更高效了？

Did they become more productive when they did?

你如何为他们提供更多使用工具的方法？

How do you give them the tools to use it more?

是的。

Yeah.

嗯，最后一点也特别有趣，因为它涉及到'这有效吗？'

Well and that that kind of last point is super interesting as well because it's like there is the did it work?

效果如何，你知道的，有哪些衡量标准，确保这些标准不会变成目标，就像我们刚才讨论的所有问题。

How well did it work, you know, what are the measurements, make sure that the measurements don't become targets, like all the stuff that we just talked about.

用我儿子数学作业作弊的比喻来说，有些人就是会感叹，哇。

And then to use the metaphor of my son who cheated on his math homework, there are people that are just like, wow.

就像，他们是公司里那些积极主动的人。

Like, they're they're the go getters in the company.

这其实正是为什么我坚信人工智能被低估了。

This is actually why I am convinced that AI is underhyped.

是的。

Yes.

你知道，我们有个小群聊，里面有另一个朋友总是说，哦，这些东西都被夸大了，会归零的。

You know, we have our little group chat where we have another friend who's like, oh, all this stuff is overhyped, it's going to zero.

大错特错。

Totally wrong.

每次我使用

Every time I use

人工智能，因为你还没让它扩散开来。

AI, Because it's you go it has not diffused.

就像那个19岁的孩子，或者我13岁的儿子会说，哇，通常作业要花我两小时。

You have like the 19 year old kid, or, you know, my 13 year old son was like, Wow, normally homework would take me two hours.

现在只需要一秒钟。

Now it takes me one second.

显然这很糟糕。

And obviously that's bad.

我没让他用这个——所以我们没收了他的iPhone，对吧？

I'm not using him as the that's why we confiscated his iPhone, right?

但这些生产力突破很可能不会自上而下发生。

But there are these productivity unlocks where it's probably not gonna happen top down.

当然。

Sure.

就像公司里的某些人，有时候——再次声明不是要过度简化人类行为——但就像，我想偷懒又想发财。

It's like somebody in the company and sometimes, again, not to like oversimplify human behavior, but it's like, I wanna be lazy and I wanna be rich.

是的。

Yes.

对吧？

Right?

比如，这两件事就是激励人们的动力。

Like, these are the two things that are motivating people.

而我发现这个工具能让我更懒更富有，同时对公司也有实际帮助。

And I found this tool that allows me to be lazier and richer that actually helps the company.

是的。

Yes.

所以不是数学问题，也不是作弊。

So not the math, not the cheating.

对吧？

Right?

就像，你现在发现，我知道老板原以为这要花八小时。

It's like, you're getting like, I can now I know that my boss was thinking this would take eight hours.

但我找到了五秒钟就能搞定它的方法。

I've figured out a way to do it in five seconds.

而且它真的很棒，

And it's really good, by the

这真的非常非常好。

It's really, really good.

而最糟糕的情况恰恰与我们刚才讨论的一切相反，最糟糕的是那个人选择保守秘密。

And the worst thing that can happen this is like the inverse of everything we just talked The worst thing that can happen is that guy keeps it a secret.

对。

Right.

对吧？

Right?

因为，他可能会感到害怕。

Because, like and and he might be afraid.

就像在问：'我可以用这个吗？'

It's like, oh, am I allowed to use this?

但你应该这样做——这正是AI从被低估到获得恰当关注和普及的关键：每个大公司都有这样的人，他们发现'我能用一分钟完成过去需要八小时的工作'。

But what you should do, this is how AI will go from underhyped to, like, correctly hyped and correctly diffused, is it's like there's somebody at every big company who has figured out this, like, I could do something in one minute that used to take eight hours.

我们需要把这样的人塑造成英雄，记录他们的成就，并在全公司范围内推广。

We need to, like, make this person a hero, like, memorialize this, and push it out through the entire company.

那么，具体该怎么做呢？

So, like, how do you do that?

这正是我在AI参与度方面所强调的观点，这是个很好的问题。

This was my point on what what we're doing on the AI engagement side, and that that's a that's a great question.

这就是我在AI参与度方面工作的核心观点。

This is my point on what we're doing on the AI engagement side.

在这个领域里，所有人的利益都是一致的。

This is one of these areas where everyone's interests are aligned.

勤奋工作的员工渴望得到认可。

The employee that's working very hard loves recognition.

而且顺便说一句，他也希望同事们能跟上节奏。

And by the way, he'd like his coworkers to come up to speed.

感到担忧的员工需要支持和培训。

The employee that's scared wants support and wants training.

而且实际上，公司确实希望员工能提高工作效率。

And by the way, companies actually want their employees to be more productive.

我知道对一部分人来说在推特上讨论这事很有趣，但正如我所说，在我三十年来向CEO们推销产品的经历中，至今还没遇到过这样的CEO。

I know it's a fun thing for a subset of people to tweet about, but like I said, I have yet to find the CEO in all of I've spent thirty years selling things to CEOs.

我从未见过哪个CEO早晨醒来就想着要经营一家规模更小的公司。

I've yet to find the CEO who wakes up in the morning and wants to run a smaller company.

他想要更多员工，也想要更多利润。

He wants more employees, and he wants more profit.

他想要更多营收。

He wants more revenue.

但与普遍看法相反，他们其实想要更多员工。

But contrary to popular belief, they want more employees.

他们喜欢管理大公司。

They like running big companies.

确实如此。

They do.

你可以找到拉里·佩奇的旧访谈，他谈到谷歌某天要拥有百万员工的计划，当时他花了很多时间思考用自动驾驶汽车解决停车场调度问题——那还是在远程办公普及之前。

You can find old interviews of Larry Page talking about his plan for how Google was going to have a million employees one day, and he was spending a lot of time thinking about self driving cars to move the cars around the parking lot because this is before remote work.

这一百万员工的车到底要停在哪里？

And literally where were the million employees going to park all the cars?

我记得十五年前读到这个计划时

And I remember I read that fifteen years ago.

这个想法一直萦绕在我脑海中

That stuck in the back of my mind.

我从未见过哪位CEO希望经营更小的公司

I have never met a CEO who wants to run a smaller company.

顺便说个完全无关的资本市场观点：这就是为什么你遇到集团企业的CEO时，他们永远不愿拆分集团

It's one of the reasons, by the way, totally unrelated point in the capital markets, One of the reasons if you ever meet a CEO of a conglomerate, they never wanna break up the conglomerate.

没错

Right.

确实

Right.

因为他们喜欢经营更大的企业

Because they like running bigger companies.

这样更有趣。

It's more fun.

对吧？

Right?

我经营的公司都在不断壮大。

I've run I've had my companies grow.

规模越大越有意思。

They're fun when they're bigger.

确实如此。

It is.

这超级酷。

It's super cool.

所以从员工的角度来看，我们开发的Nexus产品本质上是一个...让我用个例子来说明。

And so from an employee standpoint, what we built with this Nexus product is effectively a product where we said or I'll I'll I'll use an anecdote.

七月份我在英国时，参加了一系列销售拜访。

I was in The UK in July, and I went on a bunch of sales calls.

当时我正在和一家银行的人交谈，那是一家规模庞大、非常欧洲化的银行，属于全球监管最严格的机构之一，说实话，他们出于正当理由对新技术的采纳速度其实相当快。

And I was talking to someone at a bank, very large, very European bank is among the most regulated folks in the world, least, you know, fast to adopt new technology for for good reasons, honestly.

他们给我讲了个故事，说有个28岁的年轻人。

And they were telling me a story about how they had a it was a 28 year old guy.

我不记得在投行里这个年纪能当到什么级别了。

I don't remember what level that makes you an investment bank.

就说是总监级别吧，但这个28岁的年轻人在投行业务中把ChatGPT用得炉火纯青。

So let's say a director, but 28 year old guy who was using ChatGPT really, really well in the investment banking side of the business.

他们让他制作了30页的演示文稿，并为此召开全球电话会议，让这个年轻人花一小时向整个投行的人讲解如何使用ChatGPT。

And they had him create a 30 slide deck, and they did a global call for everyone in the investment bank for this guy to spend an hour walking people through how to use ChatGPT.

嗯。

Yep.

我敢说这事对他很酷，但这太荒谬了。

I'm sure that was very cool for him, but that's absurd.

指望用这种方式让人们接受改变世界的技术，简直荒谬。

That's an absurd way to hope people adopt world changing technology.

另一件荒谬的事是去买那些HR会采购的LMS课程——要知道大多数LMS课程的本质，除了那些不做就会丢饭碗的必修内容（比如性骚扰培训、特定行业的HIPAA培训），根本没人会去学。

Another absurd thing to do is to go out and buy some LMS course that HR is going buy that you know, the secret to a lot of LMS is other than things you must do or you will lose your job, like sexual harassment training, HIPAA training in certain words, No one no one does it.

他们就是不会去看。

They they just don't go.

那么我该如何真正让人们使用这些工具呢？

And so how do I actually get people using these tools?

这就是我早先的观点：你要帮助他们既不会显得愚蠢，又确信自己不会被解雇。

And this was my point from earlier is you wanna help them, a, not look dumb, and b, know they won't get fired.

因此我们实际做的是围绕这些模型构建了封装层。

And so what we effectively did is built these wrappers that exist around the models.

所以我们不会直接让人去用Quad、Gemini或Chateapiti。

So we don't tell people to use Quad or to use Gemini or to use Chateapiti.

没错。

Right.

我们还构建了另一个功能，因为人们同样担心被解雇的问题。

Then the other thing we built because, again, people are also worried about getting fired.

他们担心被解雇，可能是因为经济形势、AI发展，或者仅仅因为这是个新工具。

They're worried about getting fired because the economy, because of AI, because whatever, it's a new tool.

我可不想被解雇。

I would like to not get fired.

顺便说一句，谈到欧洲银行时，存在大量监管规定——员工若行为不当会导致公司被罚款，这是个合理的担忧。

By the way, when you're talking about European banks, there's a lot of regulation that is a legitimate concern of if our employees do the wrong thing, we will get fined.

先别管是否会解雇他们。

Forget whether you fire them.

这些公司可不想被罚款。

These companies don't want to get fined.

所以我们做的另一件事是：我们基本训练了一个定制化的LAMA模型，阻止员工提出违法或公司禁止的问题。

And so the other thing we did is we we basically trained our own kind of customized LAMA model to block people from asking questions that are illegal or the company doesn't want you to.

这里我们讨论的不是黑客行为。

So we're not talking about hackers here.

真正的内部恶意人员——公司已有完善的安全解决方案应对。

True bad actors in the company has plenty of security solutions.

我们真正讨论的是那x%的员工——我在一家大公司负责人力资源运营。

What we're really talking about is the x percent of the people who I'm in people ops and HR at a large company.

我应该进行劳动力分析。

I'm supposed to do a workforce analysis.

我是否被允许在ChatGPT中载入包含种族和性别信息的完整员工数据库？

Am I allowed to go into ChatGPT and load in our full employee database with race and gender?

我不知道。

I don't know.

我不想被解雇。

I would like to not get fired.

也许我被允许这么做，也许不被允许。

Maybe I'm allowed to and maybe I'm not.

顺便说一句，我认为公司有责任向员工明确：这里是一个安全空间。

And by the way, I think it's incumbent on the company to say to our employees, here is a safe space.

在这里的任何操作都不会导致你被解雇。

Nothing you can do here is going to get you fired.

所以，哦，Alex，你不能碰那些包含社会保障数据的内容。

So we oh, Alex, you're not allowed to that has Social Security data.

千万别分享那些信息。

Don't don't share that.

你不能提出那个请求，因为在欧洲，我们不允许使用人力资源...我们不允许用AI来撰写员工评价。

You're not allowed to ask that prompt because in Europe, we're not allowed to use HR we're not allowed to use AI to write employee reviews.

我不确定这是好法律还是坏法律。

I don't know if that's a good law or bad law.

我...我没有参与制定这条法律。

I I didn't write the law.

但有些公司研究了欧盟AI法规后认为，按照他们的解读...你知道，他们不会去追究那个。

But there are companies that look at the EU AI regulations and say to themselves, our read of the regulation I'm not gonna, you know, prosecute that.

我们的解读是，我们认为这是非法的，如果员工使用AI工具进行绩效评估，我们将会被罚款。

Our read of the regulation is we believe it's illegal, and we will get fined if our employees use AI tools to do employee reviews.

所以很好。

So great.

如果我是一家欧洲公司，我希望我的员工使用AI。

If I am a European based company, I want my employees using AI.

我必须阻止他们将这些AI工具用于那些特定用途。

I have to block them from using it for those use cases.

因此我们尝试构建的就像一套约束机制，让你既能提高工作效率，

And so what we've tried to build is this almost like harness to say, you can be more productive.

又不会显得愚蠢。

You're not going to look dumb.

你会更高效，而且不会犯那些可能导致被解雇的错误。

You're going to be more productive, and you're not going to make any mistakes that get you fired.

我们发现这实际上促进了更多AI的使用——说实话这结果毫不令人意外。

And so what we found is that actually drives more AI usage, surprising literally no one.

从公司角度来看，你想要什么？

And from a company standpoint, what do you want?

第一，我希望提高使用率；第二，我希望积累真正适合我们公司的知识产权经验。

A, I want the usage, and B, I want to build up that IP of what really works for my company.

这完全是一种解放。

It's a total unlock.

在编程方面也是如此。

And same thing on the coding side.

对吧？

Right?

要知道，Cursor已经把普通工程师变成了优秀工程师，而把杰出工程师变成了神级存在。

You know, Cursor has taken mediocre engineers and made them good, but it's taking amazing engineers and made them gods.

对吧？

Right?

所以我们的目标应该是：如何帮助人们通过这些工具大幅提升效率？

And so our goal should be how do we help people get much more productive with all of this?

如何帮助他们更高效地使用Cursor和Harvey？

How do we help them use Cursor more effectively, Harvey more effectively?

我们最初就致力于让所有大语言模型都能更高效地被使用。

We started with all of the LLMs more effectively.

嗯。

Yep.

或许我们可以讨论一个有点哲学意味的话题——工作的未来。

So maybe we could talk mean, this is a little bit philosophical, but future of work.

因为在某种程度上，当某种衡量标准出现时，它不可避免地会逐渐变成目标导向。

Because to a certain extent, all right, if you're the measurement, like, the measurement inevitably will become a little bit more of a target.

嗯哼。

Mhmm.

我总喜欢提醒人们，美国宪法通过时大约97%、98%的美国人都是农民。

And I I always like to kinda remind people that I think it was 97, 98% of Americans when the constitution was ratified were farmers.

没错。

Right.

后来他们都因为那些讨厌的发明失业了——

And they all lost their jobs due to these pesky okay.

比如拖拉机、化肥之类的东西。

Like the tractor and fertilizer and all these things.

而且我认为当时平均寿命大约是35岁，大多数孩子在分娩时或不久后就会夭折。

And I think the the average life expectancy was, like, 35, and most children died in childbirth or shortly thereafter.

就像，你知道的，情况已经改变，但这就是技术带来的变化。

It's like, you know, we things have changed, but this is what this is what technology brings you.

我的意思是，没人知道这个问题的答案，但既然你负责一家衡量AI生产力与人类生产力、以及AI与人类协作的公司，你认为变革会有多快？

I mean, nobody knows the answer to this, but given that you're you're in charge you're in charge of a company that's measuring AI productivity and human productivity and kind of AI and and humans working together, I mean, what's your timetable for how fast things change?

我们是否会看到净新增就业岗位的产生？

Are we going to see net new jobs create?

顺便说一句，每一项新技术背后都会催生大量前所未有的工作岗位。

And by the way, behind every one of these, there are all sorts of jobs that start becoming around that didn't exist before.

所以这可能是问题的第二部分。

So maybe that's kind of part two of the question.

因为我们现在的这些工作，比如录制播客，在200年前根本不存在。

Because the job that we have right now, like, you know, filming a podcast, like, that wasn't a job, like, 200 a year.

有太多工作岗位是过去人们根本无法想象的。

Like, there's so many jobs that just, like, nobody could even think of.

那么我想问，你认为未来会如何发展？具体来说，在这个新领域中，你预见会出现哪些类型的新型工作？

So so I guess, like, where do you think things are going and, like, what types of maybe to put a nuance on it, like, what types of, like, future jobs do you see in and around this, like, new stuff?

我完全不认同AI会导致大规模失业的观点。

So I don't buy for a second there's gonna be large scale job loss because of AI.

坦白说，纵观历史经验，纯粹资本主义告诉我们：如果我的选择是保持基础生产力但解雇大批员工来提高利润，

Frankly, because of what we've seen through all of history, which is just flat out capitalism, if my two choices are I can maintain my base level of productivity, but fire a bunch of my employees and be more profitable.

这在短期内或许是个不错的主意。

That is a fine idea in the short term.

对私募公司来说，收购一批微利企业然后裁掉半数员工来提升盈利，可能是个好策略。

If I'm there's probably a good idea for a PE firm to go along and go around and buy a bunch of minorly profitable companies, fire half their employees, and make them more profitable.

不过私募基金长期以来对缺乏竞争力的企业就是这么操作的，但整体就业率仍在增长。

But that's what PE firms have done for a long time for noncompetitive companies anyway, yet employment has still increased.

对吧？

Right?

可以说我们始终存在一个职能——至少过去四五十年如此——这个职能就是处理表现不佳的企业并裁减大量员工。

So you can argue we've always had a function or for the last fifth, forty years, you can argue we've had a function whose goal is to take underperforming companies and fire a bunch of employees.

对吧？

Right?

假设私募公司确实这么做了，理论上AI也能这么做，但就业率还是上升了。

And let's let's say that that's what PE firms have done and that's, you know, what AI could theoretically do, yet employment has increased.

所以我不相信AI会导致失业。

So I I don't buy an AI.

而且你看，

And look.

这是个哲学问题，我并没有什么特殊专长，毕竟我只是在经营一家测量公司。

It's philosophical and, you know, I don't have any special expertise because I am building a measurement company.

但我不认同这种观点，因为你街对面的竞争对手不会解雇所有员工。

But I don't buy it because your competitor across the street is not going to fire all those employees.

他们只会让这些员工创造更多价值，然后彻底击垮你的生意。

He's just going to do more with those employees, and he's going to kill your business.

对吧？

Right?

我是说，这就是杰夫·贝索斯的名言——你的利润就是我的机会。

I mean, this is the Jeff Bezos, your margin is my opportunity line.

如果人工智能能提高你的利润率，那就会成为所有竞争对手的机会，他们可以降低利润来与你竞争。

To the extent that AI is going to drive up your margin, that will be all of your competitors' opportunity to be less profitable and compete with you.

除了一些非常小众的垄断性企业——比如可以解雇所有人的单人公司——我们会看到年收入十亿的单人女性企业吗？

So other than some very niche monopolistic, I can fire everybody, you know, one man firm, like, will we have one woman firms that do 1,000,000,000 in revenue?

很可能。

Probably.

但如今我们已经有很多非常盈利的单人运营企业。

But today we have very profitable, you know, one man, one woman operations.

乔·罗根播客团队没几个人。

Not many people work at the Joe Rogan podcast.

据我所知，Ben Thompson公司也没多少员工，但我想这些企业都相当盈利。

I don't think that many people work for Ben Thompson Incorporated, and yet I imagine those are quite profitable businesses, the best I can tell.

这很了不起，未来个人创业者将会有更多成功机会。

So that's amazing, and there'll be a ton of opportunity to be more successful solo entrepreneur.

所以我绝对相信未来会出现更多企业家。

So I absolutely believe there'll be even more entrepreneurs.

但从宏观层面来看，我根本不相信三十年后《财富》五百强企业雇佣的人数会比现在少，因为那些试图裁撤所有员工的企业将不再位列五百强。

But at a very high level, I just don't believe the Fortune five hundred will employ fewer people in thirty years than they do today because the ones that try and cut all the people will no longer be in the Fortune five hundred.

所以我断然认为——因为我们生活在一个竞争的世界——目前还没有任何证据表明经济是零和游戏。

So I just flatly because we live in a competitive world, it's we haven't seen any proof yet that the economy is zero sum.

对吧？

Right?

也许吧。

Maybe.

对吗？

Right?

你可以这么争辩，但我们目前还没看到任何证据。

You can argue that, but we haven't seen any proof yet.

国内生产总值一直在增长。

GDP keeps increasing.

它在某些地方增长较慢，在其他地方增长较快，但总体呈上升趋势。

It increases slower in some places and faster in other places, but it's generally grown.

就业率总体上是增长的。

Employment has generally grown.

我只是不明白为什么你会认为这次因为竞争视角而有所不同。

I just don't know why you'd believe that this time is different because of the competitive point of view.

技术确实不同了。

I the tech is different.

技术令人惊叹。

The tech is amazing.

但根本上，几乎可以肯定的是，有一个有趣的理论问题，更像是，我不知道，常春藤盟校研究生院那种讨论——作为社会整体，这样会不会更有趣？

But fundamentally, what will almost definitively happen, There is an interesting theoretical question that, you know, is more like a, you know, I don't know, Ivy League grad school, you know, discussion about wouldn't it be more fun as a society?

如果大家同意我们只需工作现在一半的时间，却能保持同样的生产力，我们会不会都更快乐？

Wouldn't we all be happier if everyone agreed we'd work half as much and be just as productive as we are today?

我不知道。

I don't know.

也许吧，但这不是人类的本性。

Maybe, but that's not human nature.

对吧？

Right?

所以我甚至不确定这是否属实。

And so I'm not even sure that's true.

我倾向于认同泰勒·考恩的观点，真正重要的是增长。

I I tend to believe in the Tyler Cowen point that all that really matters is growth.

所以我的一般看法是，作为风投，如果你的某家公司进来说‘嘿，我们营收达到了1亿美元’，你根本不会兴奋。

And so my general perspective is you as a VC would just never get excited if one of your companies came in here and said, hey, we got to a 100,000,000 in revenue.

你知道为什么吗？

And you know why?

因为AI工具太强大了，我们将裁掉90%的员工，实现9000万美元的利润。

Because AI tools are so good, we're going to fire 90% of our employees, we're going make 90,000,000 in profit.

你不会为这样的创业者感到兴奋，因为你知道红杉资本会资助这家公司的直接竞争对手——他们会继续招聘，满足于10%的利润率，并摧毁你的公司。

You would not be excited with that entrepreneur because you know that Sequoia is going to fund a direct competitor to that company who's going to keep hiring, who's going to be happy with 10% margins, and who's going to destroy your company.

我们都知道这一点，所以这不是那种...你知道的，有很多关于OAI的有趣头条，然后你必须有反对观点说它会抢走工作，还有现在的孩子们啊。

We all know this, so I It's don't one of these things that like, you know, there's a lot of fun headlines about OAI, and then you have to have the counterpoint of, oh, it's gonna take the jobs and, oh, kids these days.

我不知道。

I don't know.

就像，我们都知道这个。

Like, when you we we all know this.

我们都见过这种情况。

We've all seen this.

你可以找到电视刚问世时的文章。

You can find articles about when TV came out.

那时说是阅读的终结。

It was the end of reading.

报纸出现时，说是对话的终结。

When newspapers came out, it was the of conversation.

所以我确实认为新工具的出现很可怕，它正在影响全球所有知识工作者。

So I do think it is scary that new tools are coming out, and it is impacting the entire globe of all knowledge workers everywhere.