20VC：Atlassian首席执行官谈为何AI代理不会取代界面 | 企业为何不信任中国模型 | 专用模型的好处仍有待观察——对话Mike Cannon-Brookes

我觉得那是在胡说八道。

I say that's bullshit.

我们有一堆非常专业的开源权重模型，针对特定用途进行微调，为客户提供了很好的性价比，但我不会说那是我们自己的模型。

We have a bunch of very specialized open-weight models that we fine-tune for particular purposes that give great price performance for our customers that I don't claim to be our own model, I would say.

我觉得这种说法往往被严重夸大了。

That's where I think the claim is often vastly overweighted.

这些人大多数是拿一个中国模型，通常，加上一些特定领域的知识。

Most of these people are taking a Chinese model, generally, applying some domain-specific intelligence.

他们说花了一百万美元训练，我就想，你不可能用一百万训练出这个。

You see they train them for a million bucks, and I'm like, there's no way you trained this for a million bucks.

你是拿别人花了几十亿的模型，然后针对特定应用做了调整，如果你从广义上看的话。

You took someone's billion-dollar model and tailored it, if you want to think about it generically, for a particular application.

这完全没问题，但我觉得那并不算是真正训练自己的模型，对吧？

That's totally fine, but that's not really training your own model, I would say, right?

如果你不是从原始数据开始，如果那是他们的定义，那我们其实在很多地方都这么做了。

If you're not starting with raw data, if that's their definition, then we do that a bunch in a bunch of different places.

但这并不是我所说的从零开始构建模型。

But it's not my definition of building a model from scratch.

那如果事情发展下去，什么情况会证明你的论点和信念是错的？

What, if it played out, would prove that thesis and your conviction to be wrong?

他们可能会继续在每瓦成本的基础上，展现出超越最新基础模型的指数级进步。

They could continue to show exponential gains over and above using the latest foundational model on a price per watt basis.

所以我们看到越来越多的应用，而且这些问题很难一概而论，因为实际上这取决于目标应用、解决问题或回答问题的时长、成本，以及你所处的时期。

So we are seeing many more applications, and it's very hard to answer these questions generically because it depends on the target application, actually, and the time to answer or solve a problem or a question, the cost, and the period of time you're in.

如果你看一个智能体框架，它是一种将一组提示和配置（如果你愿意这样理解的话）应用于某种类型的模型，并反复应用很多很多次（如果你把它看作循环的话）的方式，这样你就能从某一类模型中获得更多的价值。

If you look at an agentic harness, it is a way of applying a set of prompts and configurations, if you want to think about them that way, to a certain type of model and reapplying it many, many times, if you want to think about it as looping, so that you can get much more value out of a given class of model.

是的，如果你应用更好的模型，很多这样的框架会得到更好的结果。

And yes, if you apply better models, you get better results from a lot of those harnesses.

而且框架设计现在真的非常重要。

And harness design is really, really important nowadays.

但这并不是模型的进步。

But it's not model progress.

这些专门化的模型必须证明，在给定的性价比下，它们能保持领先于通用模型。

These specialized models have to prove that they can stay ahead of the generified model for a given price-performance ratio.

我觉得没多少能做到这一点，绝大多数肯定做不到，因为你需要持续拥有专门的数据集，并且问题本身不会被通用化所取代。

I don't think I see many of them doing that, certainly not the vast majority of them doing that, because you would need to have a continued specialized data set and a problem that doesn't get generified away.

很多两三年以前创业、想自己建模型的人——希望你没投过他们——我觉得现在基础模型基本上已经把他们做的事情给碾压了。

Many people who I think had startups, hopefully none of whom you've invested in, that were building their own models two or three years ago, I think now the foundation models have just basically smashed what they do.

通用模型的质量提升速度远远超过了他们靠任何专门化来保持领先的能力。

The quality of the generic model has improved far faster than their ability to stay ahead with whatever specialization they were doing.

所以我觉得我得看到相反的情况才行。

So I think I would need to see the reverse of that.

幸好我投的公司里没有这样的，所以，呼，真是松了一口气。

Luckily, none of my companies have, so phew, great relief.

关于成本的问题，迈克，其实这算是我采访创始人的时候常开的一个玩笑，因为我采访过很多高速增长公司的CEO，而且他们都是私有公司。

The question that I have on cost is, Mike, it's actually a joke we have with founders that I interview because I interview a lot of very fast-growing company CEOs, and they're all private.

我会问：“你怎么看待成本权衡的边界，相对于开源模型，可能性能上会有一点损失？”

And I go, "How do you think about the cost trade-off frontier versus open, where maybe there's a little bit of a detriment in performance?"

他们就说：‘切，你开玩笑吧，哥们儿？’

And they go, "Pfft, are you kidding me, dude?

你又不是没看到我融了多少钱。

You've seen how much money I've raised."

然后他们总是说：‘你能把那段剪掉吗？’

And they're always like, "Can you take that bit out?"

但我在乎成本吗？

But do I give a shit about cost?

不在乎。你在乎。

No. You do.

我的意思是，你是上市公司——

I mean, you're a public-

对。

Sure

……在没有‘我不在乎，我是私企，我融了一大笔钱’这种奢侈的情况下，你怎么考虑这个问题？

... how do you think about that without the luxury of give a shit, I'm private, and I've raised a bunch of money?

阿曼达，我很喜欢这些私营公司，它们融了很多钱，不用在乎成本，因为它们没有建立起在乎成本的纪律。

Amanda, I love all these private companies that have raised a bunch of money and don't have to care about cost because they're not building the discipline to care about cost.

有些公司确实是这样。

Some of them do.

但长远来看，纪律真的很重要。

But over time, discipline's really important.

生活中做的任何事，某种程度上都关乎纪律，对吧？

Anything you do in life, it's about discipline at some level, right?

但如果你的产品比他们的差2%到3%，因为他们没有纪律，总是追求最前沿的技术呢？

But what if your product is 2% to 3% off theirs because they have the ill discipline to always do frontier?

我想这取决于你是否继续资助他们，并相信他们会永远得到资助。

I guess that depends if you continue to fund them and believe they'll be funded forever.

考虑到科技的历史，这似乎不太可能，但也许吧。

Seems unlikely given the history of technology, but maybe.

我认为成本真的很重要，但不仅仅是因为这个。

I think cost is really important, but it's not just because of that.

我觉得你看很多例子就会发现，用很贵的模型很快就能得到不错的结果，这很容易做到。

I think if you look at a lot of examples out there, it's very easy to use a very expensive model to get pretty good results pretty fast.

我觉得用便宜点的模型或者老一点的模型，然后擅长把它应用到具体问题上，这对大多数技术和价格成本来说，是个真正的优势。

I think using a cheaper model or an older model and getting good at applying that to problem sets is a real advantage for most technologies and prices and costs.

我觉得我们接触的客户，也就是全球的大型企业，现在对这类事情特别敏感。

I think customers that we spoke to, so large enterprises around the world, they're incredibly sensitive to this type of stuff nowadays.

他们真的非常在意性价比。

They really, really care about the price performance.

他们越来越这样了。

They are increasingly doing so.

我们现在有一系列产品，可以让你看到你的token花费，无论是从人才角度，还是从工程组织里的开发者体验来看，对吧？

We have a whole raft of products now that will show you your token spend either across the board in terms of your talent, either in DX in your engineering organization, right?

了解你在哪里用哪个模型，在哪里获得价值，用哪些功能，以及应该在哪里调整来优化你的支出。

Understanding where you use which model, where you're getting value, which features you're using it for, where you should change to optimize your spending.

因为这是一笔越来越大的预算，他们看到它只会变得更大，所以他们必须关注支出。

Because this is a bigger and bigger budget item, and they see it's only going to be a bigger budget item, and so they have to care about the spending.

我觉得这对他们来说，无论是买一个通用模型，还是从那三四个或五个大厂买一个基础模型，都跟买一个产品没什么区别，因为他们的成本都会包含在所买的产品里。

Now that I don't think is any different for them buying a generic model or a foundational model off any of the big three or four or five as it is buying a product where their costs are going to be baked into whatever that product they're buying is.

所以我觉得这种成本控制意识会越来越强，而且我认为这是我们的一个优势，比如我们能够利用我们在财务运营方面的优势，不断降低那些使用频繁的功能的价格。

So I think that cost discipline is going to arrive continually more and more, and I think it's a strength of ours, for example, in how we are able to use our FinOp strength to continue to reduce the price of features that are used heavily.

我认为，一家伟大的科技公司的核心优势和特质，就是它管理技术成本的能力。

I think that is a core strength and attribute of a great technology company will be its ability to manage technological cost.

而这将基于智能的模型路由，针对每个任务来优化成本——

And that would be rooted in intelligent model routing on a per task basis to optimize cost-

模型路由、模型混合，在合适的时间用合适的模型，用合适的框架，让它自己选择和调整。

Model routing, model blending, applying the right model at the right time, using the right harness that again picks and adjusts itself.

这现在真是一个充满探索空间的领域，对吧？

It's a real verdant area of exploration right now, right?

而且你有这个。

And you have this.

你越擅长这个，就越能把AI应用到更小的功能领域。

The more you can get good at it, the more, again, you can apply AI to smaller functional areas.

你看看那些所谓的决策模型，Jev和过去三周冒出来的15个克隆版本，这既疯狂又惊人还吓人，它们经常被用在以前连GPT-3或GPT-4级别模型都嫌贵的任务上。

If you look at the so-called decision models, Jev and the 15 clones that have emerged over the last three weeks, which is insane and amazing and scary all at the same time, they are often used for tasks that previously would've been too expensive to use even like a GPT-3 or a GPT-4 level model for.

这很棒，对吧？

And that's great, right?

因为我们现在把概率思维应用到了越来越多的功能上。

Because we're now applying probabilistic thinking to more and more feature sets.

我觉得结果就是我们都会看到更好的产品。

I think we're all just going to see better products as a result.

你怎么看待模型路由这一层？

How do you think about the model routing layer?

这是每个公司都会内部构建并商品化的能力吗？

Is this an internal capability that every company will just build out commoditized?

这里面有什么特别的优势吗？

Is there particular prowess in it?

我们显然看到Open Router，美国人这么叫它，被高价收购了。

We obviously saw open router, as the dear Americans call it, bought for a lot of money.

也许这是非常明智的战略。

Maybe it's brilliantly strategic.

也许不是。

Maybe it's not.

我其实不太清楚。

I don't actually know.

如果这真的是你提到的核心FinOps优势，你怎么看？

How do you think about it if it is a core FinOps strength, as you mentioned?

我觉得对于我们这种规模的公司，你必须非常擅长自己做这件事。

I think for companies of our scale, you have to be very good at doing that yourself.

规模带来的挑战是持续存在的。

There is a constant challenge with scale.

我觉得任何大型科技公司可能都会在内部做这件事，这可能包括使用Open Router，再加上其他很多东西，还有你自己的内部路由器等等，对吧？

I think any large technology company is probably going to do that internally, and that may involve using Open Router plus many other things, plus your internal router and everything else, right?

这些东西可以不断往上叠加。

These things can be continually layered upwards.

对于小企业或者技术不是他们专长的企业来说，这些都是非常实用的解决方案，对吧？

For smaller businesses or businesses where technology is maybe not their expertise, those are just very practical solutions, right?

市面上有很多模型路由器和网关，它们都声称自己能做很多事情。

There are many model routers and gateways with lots of claims about what they'll do.

我觉得一般来说，自动路由是非常困难的，这大概就是他们想表达的意思。

I think generically it's very hard to just route automatically, I think is what they're showing.

你需要给出足够的引导或意图，而且我认为工具链也会是其中的一部分。

You need to give enough steer or intent, and I think harnesses will also be a part of this.

所以你说的是通用的LLM调用。

So you're talking for generic LLM calls.

你越是使用工具链，它们就会自己进行路由。

The more you're using harnesses, they're doing their own routing.

你越是使用SaaS，或者你来到Atlassian平台，使用Rovo或我们的任何AI功能，模型路由就会自动为你完成，而我们的工作就是整合这些成本，并以最佳质量、最低价格和最快速度将答案回馈给客户。

The more you're using SaaS, or you come to the Atlassian platform and you use Rovo or any of our AI features, you will get model routing happening for you automatically, and it's our job to blend those costs and give them back to the customer for the best quality answer for the cheapest quality price at the best speed.

在任何特定功能中，我们总是在权衡这个三角关系。

This triangle trade-off we're always looking at in any given feature.

这是我们给客户的核心卖点之一。

That's part of our USP to the customer.

你们不会用第三方服务。

And you won't use third parties.

那是内部路由。

That's internal routing.

对，全都是内部路由。

That is all internal routing, yes.

是的，我们有一个专门的AI网关团队。

Yeah, we have an entire AI gateway team.

这个团队规模不小，因为我们首先要应对大规模的需求，其次我们希望在合适的地方继续降低成本。

It's not a small team that is doing that, but we have to do it at, firstly, at massive scale and secondly, we want to continue to reduce that cost where it makes sense.

所以我经常跟产品团队说，别在短期内过度优化成本。

So I say to product teams all the time, "Don't over-optimize for cost in the short term.

先打造出惊艳、神奇的功能。

Build amazing, magical features.

如果客户接受了这些功能，而且我们觉得神奇的东西，客户也觉得神奇，并且它还能扩展，那这时候我们才开始担心成本。

If they're taken up by customers and what we thought was magical, the customer thinks is magical and it scales, now let's start worrying about the cost."

所以提前想一想，我们能不能把成本降下来？

So think a little bit in advance, can we possibly get this cost down?

但之后再去担心这个问题，对吧？

But then worry about that later, right?

如果只有五个人在用某个功能，我不在乎它花多少钱。

If five people are using a feature, I don't care how much it costs.

如果有五百万人用某个功能，那我就会非常在意成本了。

If 5 million people use a feature, I'm going to care a lot about cost.

而在中间阶段，我通常有充足的时间去不断优化。

And in the middle, I usually have a fair bit of time to keep optimizing.

现在什么成本很高，但你觉得五年后就不会那么贵了？

What costs a lot today that you don't think will cost a lot in five years' time?

模型推理。

Model inference.

可能有点激进的观点是，投入到推理每瓦性能上的能源、初创公司、资本、时间、智慧、人才——那些本来可能去麦肯锡、NASA或其他地方的人——数量惊人。

Maybe slightly spicy take is that the amount of energy, startups, capital, time, smarts, brains, people who would otherwise have gone to work for McKinsey or some NASA or something else working on some version of the price per watt performance of inference is crazy.

而且我怀疑其中很多努力需要多年才能见效，从新芯片到更好的数据中心设计，再到从基础模型中提炼和提取知识的方法。

And I suspect a lot of those take years to come through, from new chips to better data center design, to ways of distilling and getting things out of foundational model.

在整体进步中，有很多方面让我无法相信不会让我们的LLM每性能单位的价格——我们还没有一个合适的术语——以惊人的速度下降，因为投入的资本量、人才和智慧量实在太大了。

There's lots of the overall progress of that I can't believe doesn't bring our price per performance unit of an LLM, we don't have a good term for this, down at a precipitous rate just because the volume of capital being applied, the volume of brain and talent being applied is so large.

不过，有人会认为情况不是这样吗？

Is anyone arguing that that's the case, though?

我觉得他们只是在说，杰文斯悖论意味着我们会使用多得多的推理。

I think they're just arguing that Jevons paradox means that we will use so much more inference.

我让Instinct全天候实时运行大约50个不同的搜索，每秒都在进行。

I have Instinct running about 50 different searches continuously in real-time every second of the day.

我们就是用得更多。

We just use more.

我们确实会。

We will.

但这仍然意味着成本会下降。

But it'll still mean the cost comes down.

所以当成本接近零时，是的，使用量会爆炸式增长。

So where the cost is near free, yes, it will explode.

如果某样东西是免费的，你就会无限量地使用它，对吧？

If something's free, you use an unlimited amount of it, right?

而且你也不会在意。

And you don't care.

用完就扔，无所谓。

You throw it away, whatever.

如果它没有浪费的部分，而浪费本身也是一种成本，那么情况确实会是这样。

If it's not got a waste component to it, which is ostensibly a cost, then yes, that will be the case.

不过我觉得这总会有一个经济曲线。

I think there's always going to be an economic curve to that, though.

现在，技术的生产速度比技术的消费速度要快。

Right now, the production of technology is faster than the consumption of technology.

确实超前了。

It is ahead.

我们已经到了一个临界点。

We've reached a tipping point.

我们开发功能的速度，已经超过了客户实际能消化并真正改变他们业务的速度。

We can build features faster than customers can consume them in a realistic way that changes their business.

总有一个点，这种模式就撑不下去了，对吧？

There's got to be a point where that just doesn't stack up anymore, right?

这些东西不可能被无限使用，对吧？

There's not going to be an infinite usage of all of this stuff, right?

你的手机有超强的能力。

Your phone has amazing superpowers.

它就是你口袋里的超级计算机。

It's a supercomputer in your pocket.

你会让CPU一直100%满载运行吗？

Do you burn the CPU 100% all the time?

不，你不会。

No, you don't.

差得远呢，对吧？

Not even close, right?

那么，这个推理层和价值人才群体，以及你提到的芯片垂直化、不同供应商，会怎么样呢？

So what happens to this inference layer of value and talent cohort where you mentioned the verticalization of chips, the different providers?

迈克，未来几年它会怎么发展？

What happens to it, Mike, in the next few years?

我觉得这非常积极。

Look, I think it's just incredibly positive.

我认为，对于那些想用这些模型来实现目标的人，无论是编程、做架构、改进业务流程，还是做艺术创作，我们都应该把这看作是一件非常积极的事情。

I suspect for those who want to use those models to achieve things, whether you're coding, doing architecture, improving business processes, whatever it is that you are doing, creating art, I think we should see that as a very positive thing.

我们会得到更高价值的技术。

We are going to get higher value technology.

我们会为每个消费者提供更好的产品和服务。

We are going to get better products and services for every consumer.

这将是一个由创造力驱动的经济。

It is going to be a creativity-driven economy.

最近我最喜欢的一句话，我要把功劳归给Culture Amp的创始人Didier，因为我觉得那是他的话，或者至少是他第一个告诉我的。

One of the phrases that I've liked the most recently, and I give Didier, the founder of Culture Amp, the credit because it's his phrase, I believe, or at least he's the first person who told me.

我们正在从信息不对称的世界走向想象力不对称的世界，这个说法真的很棒，对吧？

We're moving from a world of information asymmetry to imagination asymmetry, which is such a great way of thinking about it, right?

不再是我知道别人不知道的东西，或者我拥有某种特定的商业知识。

It's no longer I know something that someone else doesn't, or I have a particular business knowledge.

这些知识的价值越来越不在于不对称性，而在于我能否以独特的方式思考如何将AI和这些技术应用到问题上。

Those knowledges are becoming less about the asymmetric value than can I think about how to apply AI and these technologies to problems in unique ways.

这就是我们现在在商业中必须看到的创造力，如果你是一个从第一性原理思考商业的人，那真是太棒了，对吧？

That's the creativity that we're all having to see in business now, which is if you're a first principle thinker about business, it's awesome, right?

这是我们经历过的科技领域中最充满活力和激动人心的时期，也许以后也不会再有。

It's the most dynamic and exciting period that we've ever lived through in technology, maybe ever will.

我理解为什么这会让人害怕。

I get why it's scary.

我也觉得这就是想象力。

I also think it's imagination.

你在用全新的方式应用新事物。

You're applying new things in fundamentally new ways.

如果我们生活在这样一个想象力不对称的时代，回顾过去12个月，你觉得在哪些方面你想象力不够，而其实需要更多？

If we live in this imagination asymmetry and you were to review the last 12 months, where do you think you have not shown enough imagination where you needed to more?

这个问题问得好。

That's a good question.

我觉得我最大的问题是时间，可能我们所有人都是这样。

I think the biggest problem I have is time, which is maybe what all of us have as our problem.

我不是在说什么深奥的哲学，或者生死攸关的大道理。

I don't mean that in some deep philosophical, life and death way.

我可是个风险投资人，朋友。

I'm a venture capitalist, my friend.

我坐在旁边，有人叫我的时候就扔点钱进去。

I sit on the sides and throw shackles in when someone calls me.

时间不是问题。

Time is not the problem.

而且你看起来工作挺努力的，每天工作时间也很长，涉及很多不同的事情，对吧？

And you seem to work fairly hard and fairly long days on a lot of different things, right?

哥们，那是因为我是个播客主持人。

Dude, that's because I'm a podcaster.

这也是个糟糕的决定。

Terrible decision as well.

你同时运行着50个Instinct任务。

You have 50 Instinct tasks running simultaneously.

我还没机会安装Instinct呢。

I haven't even had a chance to install Instinct yet.

我听说过它。

I've heard about it.

我知道它概念上是什么。

I know what it is conceptually.

所以我觉得现在我最想做的就是挤出足够的时间来思考。

So I think trying to carve out enough time to think is what I'm really trying to do at the moment.

那种深入的、长时间的思考。

That deep, long thinking.

这个世界的时间切分方式有点特别。

There's something about the time slicing of the world.

人们都在说从电影到电视剧，再到YouTube，再到TikTok的转变，对吧？

People talk about the move from movies to TV shows, to YouTube, to TikToks, right?

就像从书到杂志文章，到报纸，再到推文之类的。

It's like books to magazine articles to newspapers, to tweets or whatever.

一切都变得越来越碎片化了。

Everything's getting sharded so much more.

在智能体时代，你可能会陷入这种循环：推动智能体，做事情，再推动智能体，再做事情，再推动智能体。

And in the agentic world, you can kind of get into this push the agent, do the thing, push the agent, do the thing, push the agent.

然后退一步，长时间思考，慢慢消化这些变化，试着去审视它们。

And sitting back and long thinking and still absorbing these changes and trying to look at them.

我觉得我们正生活在一个历史时期，每天都有很多变化，但要看到年与年之间的变化就难多了，这需要一种深度思考，而我正努力腾出空间来做这件事。

We're living through a period of history, I think, where there's a lot of change on a day-to-day basis, but trying to see the year-to-year changes is much harder, and it requires sort of a deep thinking that I'm trying to create the space to do.

这一年真是忙得不可开交。

It's been a hectically busy year.

我没什么时间去做这件事，但过去两三个月里，我真的在积极尝试，哪怕每周只有几个小时。

I haven't had a lot of time to do that, but I really, over the last two or three months, have been actively trying to do that, even if it's only a couple hours a week.

忙起来很容易，我觉得在AI领域，忙起来更容易，对吧？

It's very easy to be busy, and I think in AI, even easier to be busy, right?

你所有的智能体和各种工具，还有你的那些自动化、直觉、灵感什么的，都会不断抢占你的注意力。

All of your agents and things running, your claws and instincts and muses and whatever are going to keep attacking your attention.

怎么为真正的思考腾出时间和空间，我觉得这是我需要改进的地方。

How do you create time for space for real thinking is I think going to be something I need to get better at.

迈克，给你个背景，我的自动化就是给我女朋友买Laura Piana的鞋子。

Mike, just to give you context, my instinct is an automation to buy Laura Piana shoes for my girlfriend.

这可不是什么特别智能、复杂的流程，只是给你个大概范围。

This is not some incredibly intelligent, sophisticated workflow, just to give you some boundary.

也许在硅谷，他们确实在做一些事情。

Maybe in Silicon Valley, they're doing some things.

我的只是买布鲁内罗·库奇内利。

Mine's just buying Brunello Cucinelli.

我觉得你会惊讶的。

I think you'd be surprised.

我觉得很多智能体消费场景，对吧，人们都在说大部分其实就是购物和旅行。

I think a lot of the agentic consumer use cases, right, people have talked about how much of it is basically shopping and travel.

兄弟，如果你是聚合商或者广告引擎，那祝你好运了。

Dude, if you're an aggregator or you're an advertising engine, good luck.

我有个问题想问你。

A question for you.

我们看到很多数据市场，比如Macaws或者Surge's，它们从小公司、中型公司那里购买大量数据。

We see a lot of data marketplaces, whether it's your Macaws or your Surge's, paying for a huge amount of data from small companies, medium-sized companies.

你之前提到了上下文图谱。

You talked about the context graph earlier.

数据就是喂给那个上下文图谱的。

Data feeds that context graph.

那你觉得数据的成本、获取数据的成本，以及随着我们发展这个市场会变成什么样，你是怎么看的？

How do you think about the cost of data, the cost of acquiring data, and what this market looks like as we evolve?

我觉得这有点不一样。

Look, I think it's a little bit different.

团队协作图谱背后的企业决策和知识，其实是我们在帮那些经常做决策、做项目、写文档、处理数据的公司。

The corporate decisions and knowledge that underlie the Teamwork Graph are about us trying to help a company that has made decisions, projects, documents, data often.

比如说，有多少次，人们把真正的事实和数据放在PowerPoint幻灯片里？

For example, how many times are PowerPoint slides the place where people put actual facts and figures?

很多。

A lot.

我们总说，数据存在数据库里。

We're like, "Data is stored in databases."

但我觉得，其实很多数据都在PowerPoint里，而且从来都没进过数据库。

I'm like, "Nah, there's a lot of data stored in PowerPoints," and it didn't actually ever make it to a database.

我们怎么把所有这些组织知识、历史、决策，编织成一张组织中人员的社交图谱，再加上代码、公司创造的技术服务，把所有这些东西混合在一起，这样我们就能给你提供很好的答案、学习和决策？

How do we take all of that organizational knowledge, history, decision-making, wrap it into a social graph of the people in the organization, and add code, the technological services created by a company, and mix all that together so we can give you great answers and learnings and decisions?

而且我们可以在上面应用很多推理，代表公司，同时也是一个持续自我学习的过程，对吧？

And there's a lot of inference we can apply on top, on behalf of the company, as well as being a continual self-learning, right?

Teamwork Graph的工作就是在你的代理被问到问题之前，提前推断出最好的答案。

The Teamwork Graph's job is to infer ahead of your agent, before your agent is asked a question, to have inferred the best possible answer to that.

你可以把它想象成谷歌对网络的索引。

Think about it like Google's index of the web.

它不会为了回答你的问题而去爬取所有那些网站。

It doesn't go and crawl all of those websites to answer your question.

它是提前做好的。

It does that in advance.

它有很多非常智能的索引和推理技术来创建一个索引，这样它就能以很低的成本和规模来回答问题。

It has a lot of really smart indexing and inference technology to create an index so that it can answer questions very cheaply and at scale.

这就是我们的工作，但针对每一家拥有完全不同信息集的公司进行定制。

That's our job, but tailored for every single company that has a wholly different set of information.

这对每家公司来说都是独一无二的。

That's very unique to each individual company.

我觉得他们不会出售那些信息。

I don't think they're going to sell that information.

我不确定那有多大用处。

I'm not sure it's particularly useful.

我记得我们看到了Makara或者Google，我忘了谁最终赢了，买下了那个，是在西南部的。

I know we saw Makara or Google, I forget who actually ended up winning, buying the, it was in Southwest.

那是——

It was-

Spirit

Spirit

……Spirit航空的数据。

... Spirit Airlines data.

花三千万美元买一百万封邮件和一堆其他公司数据。

30 million bucks for a million emails and a bunch of other corporate data.

我不知道这会不会成为一种趋势，但我认为这些数据在公司内部的价值其实是一样的。

I don't know if that will become a trend, but I think the value of that data inside a company is very much the same.

关键在于这些数据是活的。

The important part is that it's alive.

所以公司的社交图谱只在今天有效。

So the social graph of a company is valid today and only today.

明天，公司里的人就会和今天不一样了。

Tomorrow, there will be different people in a company than there were today.

有人离开，有人加入，社交图谱会不断变化，谁和谁合作、在哪些项目上、在哪些领域，都会变。

Some people left, some people joined, and the social graph is going to continue to shift and change in terms of who works with who, on which projects, on which areas.

我们的工作实际上就是让这个图谱保持新鲜。

Our job actually is to be very fresh with that graph.

我们要从历史中吸取经验，但这些经验必须保持新鲜，并且要不断在此基础上更新。

It's to take history and to infer learnings from it, but that has to be fresh and constantly built on top of.

我经常说，这就像我们的记忆和梦想，以及我们如何理解一个组织的历史一样。

I say this all the time when it comes to our memory and our dreaming and how we understand the history of an organization.

知道我在2003年写过Java代码，以及我写了什么代码，这对2026年的Atlassian来说完全没用。

Knowing that I wrote Java code in 2003 and what code I wrote is totally useless to Atlassian in 2026.

知道我20多年前曾经是个程序员，还是个糟糕的程序员，这倒有点用，因为你可能用一种懂技术的人能理解的方式来给我解释概念。

Knowing the fact that I used to be a programmer, a bad one, 20-plus years ago, is kind of useful because you may explain a concept to me in a way that a technology-savvy person would understand.

大概也就这点用处了，对吧？

That's about as useful as it is, right?

现在，我全职做管理，管人。

Today, I'm a full-time manager of people.

我全职讲故事。

I am a full-time storyteller.

我兼职搞搞产品设计和体验，对吧？

I am a part-time dabbler in design and experience in products, right?

这就是我的工作，而且我很喜欢。

That's my job, and I love my job.

但我的历史经历跟现在很不一样。

But the history of it is very different.

如果我看你过去十年的经历，我相信你十年前做的一些事情，那些回响和记忆，对你今天来说是有用的学习经验，但实际的事实和数据就不太有用了。

If I looked at your history for the last 10 years, I'm sure stuff you did 10 years ago, some of the echoes and memories of that are useful for you as learnings today, but the actual facts and figures of what you did aren't very useful.

所以，组织图谱必须保持事实的新鲜和及时，而在学习和记忆方面则要注重历史，这样才能最大程度地帮助组织。

So an organizational graph has to be very fresh and current with facts and very historical in its learnings and memories to help the organization as best as possible.

Makara 有点不同。

Makara is a little different.

这类公司寻找的是大规模生成的通用数据集，以便大规模地卖给其他很多供应商，对吧？

Those type of companies are looking for large-scale generated generic data sets to sell at scale to many other providers, right?

为了让那些广泛的智能平台变得有价值。

To make those broad intelligence platforms valuable.

我在想一个好的类比。

I'm trying to think of a good analogy.

也许你可以想想自动驾驶汽车。

Maybe if you think about self-driving cars.

自动驾驶汽车需要理解道路的所有细节，这是一个非常专业的数据集，涉及道路、弯道、标志、交通、婴儿车里的婴儿、骑自行车的人，所有这些乱七八糟的东西。

A self-driving car needs to understand all of the bits of a road, and it's a very specialized data set about roads, corners, signs, traffic, babies in prams, cyclists, all this sort of stuff.

它必须能够识别它们，并理解它们的动态和物理特性等等。

It has to be able to identify them and understand their dynamics and physics and these types of things.

要知道骑自行车的人不会突然冲到马路上，对吧？

Know that a cyclist is not going to jump into the road, right?

而且它必须理解所有这些。

And it has to understand all this.

它不需要知道阿尔伯特·爱因斯坦是什么时候出生的。

It doesn't have to know when Albert Einstein was born.

对自动驾驶汽车来说，知道爱因斯坦的出生日期完全没用。

It's totally useless to a self-driving car to know when Albert Einstein was born.

迈克，如果你能收购一家公司，而他们的数据对你的情境图谱产生最具变革性的影响，那会是哪家公司？

Mike, if you could acquire one company and their data would have the most transformational impact on your context graph, what company would that be?

收购公司获取数据对我们来说并不是特别重要。

Acquiring companies for data isn't particularly important to us.

所以，可以说你的联系人图谱中并没有空缺。

So there's no hole in your contacts graph, so to speak.

与其说是联系人图谱有缺口，不如说是客户自己的上下文。

It's not so much a hole in the contacts graph.

这是客户的上下文。

It's the customer's context.

不是我们的上下文。

It's not our context.

我们是在帮他们把这些整合起来。

We are helping them put it together.

我们是在帮他们利用在沟通中积累的知识和工作成果进行学习。

We are helping them learn on the knowledge and the work they've accrued in their communications.

我们是在帮他们连接意义，把各个点串起来。

We're helping them connect meaning, connect the dots across that.

我们是在帮他们的客服代表把这些点串起来。

We're helping their agents connect those dots.

我们把它放进一个巨大的图谱里。

We're putting it into a giant graph.

我们正在图的基础上构建一个大型的词汇和语义搜索引擎，然后我们有一个查询语言，通过MCP和CLI让代理能够理解，并且我们帮助他们将自己的自定义数据添加到其中。

We're building a big lexical and semantic search engine on top of the graph, and then we have a query language that makes sense to agents through MCP and CLI, and we're helping them add their own custom data to that.

投资公司关心股票。

An investment firm cares about stocks.

制造公司关心材料供应。

A manufacturing firm cares about supplies of materials.

医疗公司可能关心医院、床位以及类似的具体数据，然后他们都有会议、文档、知识和代码这些东西。

A healthcare firm may care about hospitals and beds and things like, there's specific data, and then they all have meetings and documents and knowledge and code and these things.

我们试图把所有这些整合在一起，让他们能够检索到这些数据以及从中获得的经验。

We're trying to put it all together in a way that they can retrieve it and the learnings from it.

我们正在为他们构建一个索引。

We're building an index for them.

对我们来说，真正重要的不是数据本身。

It's not the data that makes a difference to us, per se.

我能问你一个奇怪的问题吗？我自己也不知道答案。

Can I ask you a weird question that I don't know the answer to?

我们看到OpenAI越来越多地进入Docs和Microsoft Office领域，迈克，你对互联网的理解非常深刻。

As we see OpenAI protrude more and more into Docs and Microsoft Office, you're a student of the internet, Mike, in a brilliant way.

你怎么看待Anthropic和OpenAI侵入你们和微软提供的传统应用领域？

How do you think about Anthropic and OpenAI's intrusion into traditional applications that you and Microsoft provide?

晚上想到这些时，你心里有什么想法？

And what does the voice in your head say when you think about that at night?

嗯，这是个有趣的问题。

Look, it's an interesting question.

他们两家都是Atlassian的深度合作伙伴，我们非常重视与他们的合作。

Both of them are deep Atlassian partners, and we really value our partnership with them.

我觉得他们可能和很多公司都是合作伙伴。

I think they're probably partners with a whole lot of firms.

从某种程度上说，他们的业务就是销售计算服务，如果你这么理解的话。

Their business is, in some ways, selling computing services, if you want to think about it.

他们也在一定程度上涉足应用业务。

They're also in the business of applications to some level.

这些界限会在哪里出现，我觉得未来会是一个竞争非常激烈的变数。

Where those lines emerge, I think is going to be a highly fought over variable future.

我觉得没人知道这个问题的答案。

I don't think anybody knows the answer to that question.

不过我要说，如果说到像Docs这样的东西，我们面对Confluence这个问题已经很长时间了。

I will say, if it comes to things like Docs, we've faced this question with Confluence for a long time.

Confluence的年经常性收入已经超过15亿美元。

Confluence is north of a one and a half billion ARR business.

那是我们几个季度前公布的最新数字。

That was our last disclosed number a couple of quarters ago.

它本身就可以算是一家上市公司了。

It would be a public company in and of itself.

而且它仍然以每年超过25%的速度增长，规模还非常大。

It is still growing well north of 25% per year and at a very large scale.

所以单凭它自己，就能成为一家非常优秀的上市公司。

So it would be a very good public company just on itself.

而且说白了，这些内容你完全可以用其他十五种方式存进电脑里。

And ostensibly, it's just content that you could put 15 other ways into your computer.

你现在电脑上大概有二十种方法可以记一段文字，从操作系统自带的到工作平台提供的都有。

You probably have 20 ways on your computer right now to write down a piece of text, from ones that come with your operating system to ones that come with your workspace provider.

单就写内容这件事本身，并不是什么决定性的优势。

In and of itself, writing content is not a definitive advantage.

关键还是在于设计、体验、目的，以及和其他工作流程的整合。

It comes back to design, experience, purpose, integration into other workflows.

所以我觉得，正是这些因素帮我们在那个领域建立了一个非常成功的业务。

So there's a lot of other things that I think go into that that's helped us build a very successful business there.

我觉得这种情况还会继续下去。

I think that will continue to be the case.

我能问一个特别难的问题吗？

Can I ask a really hard one?

你可以直接告诉我，哈里，拜托了。

And you can tell me, Harry, please.

当然可以。

Sure.

我不想问，那样太不礼貌了。

I don't want to, and that's just rude.

我很欣赏你对设计的专注，也欣赏你的工艺，我能从你的声音里感受到。

I love your focus on design, and I love your craft, and I feel it in your voice.

Linear的Carrie也总是谈论设计的工艺，我也能从他们那里感受到。

Carrie in Linear always talk about the craft of design, too, and I do feel it in theirs.

人们似乎更能感受到Linear设计的工艺，而不是Jira的。

People seem to feel the craft of the Linear design maybe more than they do in a Jira.

你怎么看这个问题？

How do you think about that?

这样比较公平吗？

And is that unfair?

如果觉得不公平，就直接说。

And say if it's unfair.

对，我觉得这不公平。

Yeah, I think it's unfair.

我觉得这类事情总是会有变化的。

I think there's always going to be changes in those types of things.

我觉得重要的是永远不要停下脚步。

I think what's important is never to rest.

Atlassian能成功二十年，希望未来还能成功几十年，靠的就是不断投入，提升我们软件体验的每一个部分。

What has made Atlassian successful for two decades, and hopefully will for many decades to come, is to continue to invest in upleveling every part of our software experience.

就像我说的，十年前所谓的好设计和今天的好设计完全不同，十年后也会完全不同。

The standard of what, as I said, was good design 10 years ago is totally different to what is good design today, and it will be totally different in 10 years' time.

能够年复一年地保持这种状态，是很不一样的。

The ability to sustain that year on year for a long time is quite different.

这是一种完全不同的持续进步和交付的思维方式。

It's a totally different way of thinking about continual progress and delivery.

Jira和Teamwork系列产品今年增长非常惊人，在巨大规模下加速增长，比以往任何时候都快，用户数量和关注度都大幅提升，这在很大程度上要归功于设计体验。

Jira and the Teamwork collection have had an amazing year of growth and accelerating and growing faster than they ever had at massive scale, and that is in the number of users and attention, and a large part of that is because of the design experience.

我们展示的是真正基础性的设计变革，以及我们能为客户带来的速度、性能和效果。

We're showing off really foundational design changes, speed, performance, and efficacy of what it is we can achieve for customers.

在这些方面，我觉得我们就是走自己的路。

And in those areas, I think we just run our own race.

我认为这是最好的做法。

I think that's the best thing.

归根结底，客户体验的是整个软件的质量，而这是一场漫长的比赛。

Ultimately, it's about customers' experience of the quality of the software on a whole, and it's still a long race.

所以我会对所有人这么说。

So I would say that to anybody.

我们已经在这里几十年了，而且我们打算再待几十年。

We've been here for a couple of decades, and we intend to be here for a couple of decades still.

我四十多岁。

I'm in my mid-40s.

我不觉得自己是中年人，但我热爱我的工作。

I don't consider myself to be middle-aged, but I love what I do.

迈克，你是不是觉得自己天生就跟别人不一样？

Do you just think you're built differently, Mike?

说实话，我现在最大的问题，而且这个节目之所以成功，就是因为我非常坦诚——我很担心。

Honestly, my biggest problem right now, and the show's successful because I'm very candid, I'm worried.

我看到很多二十岁的创始人都在打退堂鼓。

I'm seeing a load of 20-year-old founders tap the bid.

“我不想干了。

"I don't want to do this.

我要赶紧卖掉。”

I'm selling fast."

我已经干了12年了。

I'm 12 years in.

我的精力比以往任何时候都充沛。

I've got more energy than ever.

你比我年长一些。

You're a little bit longer in the tooth than me.

24岁。

24.

你就是喜欢这个过程吗？

You just love the journey?

是的。

Yes.

我觉得你得享受自己做的事情。

I think you have to enjoy what you do.

我觉得不是每个人都能管理好自己的精力。

I think it's uncommon for everybody to manage their own energy supply.

你会感到孤独吗？

Do you get lonely?

我会感到孤独吗？

Do I get lonely?

不会。

No.

不，但除了斯科特，我还有基兰作为联合创始人。

No, but without Scott, I have a co-founder in Kieran.

他技术上不算，但某种程度上算是。

He's not technically, but he kind of is.

你想念那种感觉吗？

Do you miss it?

不，是的。

No, yes.

两者都有点。

A bit of both.

所以如果你问我是否孤独，从团队角度看，我有一支出色的高管团队，和一万个最聪明的人一起工作，不孤独。我常说，我每天都能和很多非常聪明的人一起解决难题，如果让我设计一份工作，我会说，这就是我想做一辈子的事。

So if you mean am I lonely in terms of, man, I have an awesome executive team and I work with 10,000 of the smartest people, no. I always say that I get to work with an incredible number of very smart people on a daily basis solving hard problems, and if you designed a job for me, I'd be like, "That is what I want to do for the rest of my life."

如果你指的是斯科特，我经常见到他。

If you mean in terms of Scott, I see him pretty frequently.

他又不是不住在附近什么的。

It's not like he doesn't live like...

我现在在阿姆斯特丹，他也会到处跑跑，但我也说不准。

I'm in Amsterdam at the moment and he travels a bit as well, but I don't know.

我们至少每个月都会聚一次，喝点红酒或啤酒，头三分钟就开始聊Atlassian、软件和AI这些事。

We hang out at least monthly, and we drink wine or beer and talk about Atlassian and software and AI within the first three minutes of what's going on.

所以并不是说他对我们在做的事不感兴趣。

So it's not like he is uninterested in what we're doing.

也不是说我没有几乎每天收到他的短信，比如"我们为什么不这么做？"

It's not like I don't get near-daily text messages in terms of, "Why aren't we doing this?"

所以作为一个退休的人，他非常活跃，这很棒。

So he's very active for a retired person, which is great.

你有多年经验，但每个人都有自己的路要走。

You got years of experience, but everyone has their own journey to run.

我觉得这没什么。

And I think that's okay.

我想我之前想说的是，你得明白你的能量来自哪里。

I think what I was trying to say beforehand is you have to understand where your energy comes from.

我觉得这是我想传达给我孩子们的一件事，就是找到什么能给你带来能量，然后尽量多做那件事，这样你的人生就会过得不错，在普遍意义上来说。

I think it's one of the things I want to make sure I get across to my kids is, figure out what it is that gives you energy and try to do more of that thing and you'll be okay in life, just in a generalized world.

我觉得你提到的那些人，很多都是这样，而且在AI时代，我明白，创始人面临的心理健康挑战早就被讨论过，但我觉得还远远不够。

I think a lot of those people you talked about, and I get it in the AI era, man, the mental health challenges of being a founder have long been written about and have never been written about enough, I think.

因为这是一份压力很大的工作，感觉你肩负着别人的生计，虽然在我那个时代压力没那么大。

Because it's a very stressful job to feel like you have other people's livelihood, and less so at my time.

但如果你处于早期阶段，我已经融到资了，我得进行下一轮融资，我得把业务做起来，我得实现盈利，有收入和客户，而且你的声誉和名字都跟这些绑在一起。

But if you're at that early, I've raised money, I've got to get to the next raising, I've got to build this business, I've got to get to profits and revenue and customers and- Your reputation and name is wrapped up in that.

我明白为什么会这样。

I see why this.

这真的非常消耗人。

It's incredibly taxing.

我觉得我们得先说，所有创始人都是了不起的人，他们在迎接一些疯狂的挑战。

I think we should start by saying all founders are amazing people, that they're taking on some insane challenge.

他们赢，他们输，他们失败，他们成功。

They win, they lose, they fail, they succeed.

他们仍然在迎接下一个疯狂的挑战。

They're still taking on the next insane challenge.

我觉得你提到的那些为了错误原因进入这个领域的人，很难持续保持那种精力。

I think the people that you're talking about that get into it for the wrong reasons, it's very hard to sustain the energy.

如果你不喜欢在你选择的行业里创造产品或服务，我觉得你坚持不了太久。

If you don't enjoy creating products or services in whichever industry you're choosing, I don't think you're going to be able to do it for very long.

我觉得很少有人纯粹被金钱或混对圈子所驱动，就能支撑你度过五年、十年的时间。

I think there are very few people that are purely motivated by money or being in the right circles, that that's going to sustain you for a 5, 10-year period.

我觉得你之所以做这件事，很可能是因为你真的喜欢它。

I think you're probably doing this because you really enjoy it.

给我一个麦克风和一个聪明人聊天，你就会觉得：'啊，这太棒了。'

Give me a microphone and a smart person to talk to, you're like, "Ah, this is great."

哥们，我每天都能采访像你这样的人，这是最不可思议的学习经历。

Dude, I get to interview people like you every day, and it's the most incredible learning thing.

我不在乎有没有报酬。

I don't care that I get paid.

那真是太棒了。

And that's awesome.

所以你已经找到了你的能量来源，这是其中之一。

So you've figured out what your source of energy is, and it's one of the things.

它不一定是唯一的东西，但它是给你带来能量来源的东西之一。

It doesn't have to be the only thing, but it's one of the things that give you that source of energy.

我前几天看到你在另一段街头散步视频里对这件事大发议论，我觉得你说得完全对，但我在这里待得够久了。

I saw you were ranting about this on another one of these walks down the street the other day, and I think it's totally true, but I've been here long enough.

听起来我像个非常睿智的老人，有点吓人。

It sounds like I'm a very wise old man, kind of scary.

你在科技行业的每一次迭代中都能看到这种情况。

You see this with each of these sort of iterations of the technology industry.

总有一个最新的事物吸引大量资本和人才，而当这个最新的事物变成昨日黄花时，它仍然可以非常有意义。

There is a latest thing that attracts a lot of capital, that attracts a lot of people, and when that latest thing turns out to be like yesterday's thing, and it still can be incredibly meaningful.

我们在移动互联网兴起时就看到过这种情况。

We saw this when mobile took off.

当时有很多人涌进来做移动应用和移动公司。

There was all these people that arrived to build mobile apps and mobile companies.

但他们没能坚持下来。

They didn't sustain.

那些成功并且坚持下来的，往往都集中在同一个领域。

The ones that won and sustained are often directed in the same area.

不，我更担心的是这件事本来就该很难，而且我完全同意你说的关于创始人的挑战。

No, my worry is more that it should be hard, and I completely agree with you on the challenges of founders.

但现在我们已经把这件事变得太正常了，一个人出来就能拿到一千五百万美元的种子前融资。

But we've made it so normal now for someone to come out, raise 15 million bucks as a pre-seed.

现在拿一千五百万美元、雇几个人，说实话，真没那么难，然后玩创始人的游戏，一有机会就退出。

It ain't that fucking hard to raise 15 million bucks now, hire some people, and then honestly, to play the founder game and then tap out the minute you can.

但我觉得我们在某些方面把它变得太容易了，这得怪那些风险投资人。

But I think we've made it too easy in some respects, and that's venture capitalists' fault.

这是个资本社会，对吧？

It's a capital society, right?

这就是这类东西的市场价格。

That's the market price for those types of things.

我希望人们总有一天会意识到，和聪明的人一起长期解决难题的价值。

I hope that people, at some point, realize working with smart people on difficult problems for long periods of time.

我之所以还在做这件事，很大程度上是因为和我一起工作的人，对吧？

The people I work with are a large part of the reason that I'm still doing this, right?

能和这些人一起创造，就是我的能量来源。我觉得那些人骨子里没有这种想法，所以很难坚持下去。

The people that we get to create with is the source of energy, and I think that doesn't sound like it's in those people's DNA to do that, and that's going to be hard to sustain.

迈克，我们快速问几个问题吧，因为我可以跟你聊一整天。

Mike, we're going to do a quick fire because I could talk to you all day.

好的。

Sure.

那么，过去12个月里，你最大的想法转变是什么？

So what have you changed your mind on in the last 12 months most?

推理的未来价格。

The likely future price of inference.

会大幅下降。

It goes way down.

我也这么觉得。

I think so.

比我预想的还要低。

Further than I thought.

关于有钱这件事，有什么是大家都不说，但你觉得应该说的？

What does no one say about having money that you think should be said?

有钱既是件好事，也是个沉重的负担。

That it is a wonderful thing and a big burden at the same time.

说实话，为什么是个沉重的负担？

Why is it a big burden, honestly?

我觉得这是巨大的责任。

I think it's a huge responsibility.

我觉得这只是一份暂时保管的遗产，你有责任用它去做对社会有益的事情。

I think it's just something that's temporarily held as a legacy, and you have a responsibility to do, I think, societally profitable things with it.

对社会有利，而不是对个人有利，我觉得你会感受到那种压力。

Societally profitable, not personally profitable, and I think you feel that burden.

随之而来的是很多有趣的事情。

It's lots of interesting things come with that.

如果我的孩子从小在富裕环境中长大，我该怎么教育他们，让他们不要变成混蛋？

How do I bring my kids up to not be assholes if they're brought up rich?

天哪，你二十年后再问我吧。

Man, ask me in 20 years' time.

我希望我能做到。

I hope I get that right.

我觉得可能尽量做自己吧。

I think probably try to be yourself.

我读过一句引述，好像是高盛的一位前CEO说的：'一个人升职后，你可以看出很多，看他是在成长适应这个职位，还是膨胀自满。'

I read a quote from, I think it was an old Goldman Sachs CEO, says, "You can tell a lot about people when they get promoted as to whether they grow into the job or swell into it."

我觉得，天哪，我在这份工作里每年都在拼命成长，我真心觉得自己不配这份工作，我必须非常努力才能配得上明年，或者说不断成长适应，不管怎么说。

And I feel like, man, I've been desperately growing every year I've been in this job, and I definitively do not feel like I deserve this job, and I have to work crazy hard to deserve it next year or to grow into it or whatever term.

我觉得对孩子们来说，可能也是类似的情况。

I think it's the same, probably some similar version of this with your kids.

如果你保持好奇心，保持谦逊，心怀感恩，展现出这些品质，那你很可能给了他们最好的机会去理解这些品质是从哪里来的。

If you are curious, if you are humble, if you have gratitude, if you display all those properties, you probably give them the best chance to understand where it is that that comes from.

但我敢肯定，这绝对是个巨大的挑战。

But I'm sure it's a huge challenge.

你要是想明白了，记得告诉我。

Tell me if you figure it out.

其实，有人给了我一个特别棒的建议，就是说这就像国家地理频道一样。

Actually, I got given some brilliant advice, which is it's just like the National Geographic channel.

观察大象。

Watch the elephants.

小象会看着成年大象，然后模仿它们。

The baby elephants watch the adult elephants and copy them.

所以，如果你希望孩子勤奋又善良，那你自己就先做到勤奋吧。

So if you want your kids to be hardworking and kind, maybe be hardworking.

让他们看到，你错过了洗澡时间，是因为你在为阿姆斯特丹之旅努力工作，为家里赚钱。

Show them that you missed bath time because you were working hard on that Amsterdam trip to make money for the family.

带他们去咖啡店，对咖啡师非常友善，问问他们今天过得怎么样，让他们看到这些。

And take them to the coffee shop and be really kind to that barista and ask them how their day was and let them see that.

或者，如果你不想让他们45岁就退休，出轨，对咖啡师态度恶劣，那你可能也会养出混蛋。

Or if you don't want them to retire at 45, cheat on your wife, and be horrible to the barista, and you'll probably have assholes, too.

对。

Yes.

我也这么觉得。

I think so.

如果你问的是，孩子会不会模仿你的行为和待人方式，我觉得这绝对是肯定的。

If you're asking do they copy the way that you act and treat people, I think that's definitely true.

这就是我采纳的建议。

That was the advice that I took.

迈克，你收到过最好的建议是什么？

What's the best advice that you've been given, Mike?

你身边坐着的都是些挺有意思的人。

You sit around some pretty interesting tables.

我想说的这句话，至少在过去几年里，我会说：对自己宽容一点。

I think the phrase that I would say, certainly for the last handful of years, I would say, wage peace on yourself.

我觉得很多事情确实很难，保持好心态，知道自己已经尽力了，不管结果成不成，如果你做了所有该做的事，就别对自己太苛刻。

I think lots of things are really hard, and having a good attitude and knowing that you tried your best at something, whether it succeeds or doesn't succeed, et cetera, try not to be too hard on yourself if you've done all the right things.

而且我觉得对很多人来说，尤其是科技行业的人，他们对自己的要求高得离谱，这本身就可能成为一种新的压力。

And I think for a lot of people, especially in the tech industry, their bars on themselves are incredibly high, and that can be in its own way a new sort of stress.

所以，出去走走，对自己宽容一点。

So touch grass, wage peace on yourself.

最后一个问题。

Final one.

有什么事情是你还不知道自己能做到，但确实需要你个人去突破的？

What do you not know that you will be capable of achieving that you do need to personally scale?

我大概还是会说之前说的那句话。

I'd probably say the same thing I said before.

就像是在这个越来越快的世界里，创造一些慢思考的时刻，慢下来的时刻。

It's like how to create moments of slow thinking, moments of slowness in the world that is moving increasingly fast.

我觉得这是我们所有人都需要培养的一项技能。

I think that's going to be a skill that we all need to develop.

如果我们觉得手机通知、社交媒体提示这些东西都是干扰，而你现在都应付不了的话，天哪，我觉得科技加速的节奏，那会真的很难。

If we think the notifications on our phone and the pings from social media and all these sorts of things are distractions, if you're not able to handle that today, geez, I think that the accelerated technological pace, that's going to be really hard.

外面是真实的世界。

It's a real world out there.

有时候在科技行业，每个人都像这样活在屏幕里。

Sometimes in the tech industry, everyone just lives in a screen like this.

讽刺的是，我现在正盯着摄像头和屏幕，对吧？

Ironically, I'm staring at a camera and a screen, right?

还有所有这些比特、数字之类的东西。

And all these bits and digits and things.

我们创造的东西外面有一个真实的世界，慢下来才能走得更快。

There's a real world out there of stuff that we're creating, and slow down to go fast.

我们得学会放慢脚步，在一切都显得更加碎片化和快速的时候，真正进行深度思考。

We've got to learn how to slow down and how to really do that deep thinking at a time when everything appears to be more fractionalized and speedy.

这确实是个挑战。

It's a real challenge.

迈克，你知道我是怎么定义好人的吗？

Mike, do you know how I define a good person?

不知道。

Do not.

就是愿意帮助别人，而且这种帮助对自己没有直接或明显的好处。

Someone who's willing to help with no immediate or clear benefit to themselves of that help being given.

你一直对我特别好。

You've always been incredibly kind to me.

谢谢你。

Thank you.

真的，我非常感激。

Seriously, I really appreciate it.

你不需要帮我，但我真的很珍惜这段关系。

You haven't needed to, but I really appreciate the relationship.

嘿，别客气，兄弟。

Hey, no worries, man.

随时都可以。

Anytime.

需要帮忙的时候尽管说。

Let me know when I can help.

但在今天结束之前，我想说，Base44是我用过的一款产品，我当时立刻就想："天哪，要是我当初创办20VC的时候就有这个就好了，那时候我只有50美元，在风投圈一个人都不认识。"

But before we leave you today, Base44 is one of those products I used, and I just thought straight away, "Man, I wish I'd had this when I started 20VC with $50 and no contacts in venture."

我想看看它能做什么，所以为节目建了一个嘉宾研究中心。

I wanted to see what it could do, so I built a guest research hub for the show.

即将到来的嘉宾、公司资料、之前的采访、赞助商联系人、后续跟进，全都集中在一个很棒的地方。

Upcoming guests, company notes, previous interviews, sponsor contacts, follow-ups, all in one amazing location.

我用简单的英语描述了它。

I described it in plain English.

几分钟后，我就把产品打开了。

A few minutes later, I had the product open in front of me.

数据库、登录、后端和托管都已经处理好了，全都集成在一起。

Database, login, backend, and hosting already handled and just all in one.

这让一切变得非常简单，这正是我喜欢的地方。

It made it so easy, and that's what I loved.

我没有写很长的规格说明。

I didn't write a long spec.

我也没有把五个不同的工具拼凑在一起。

I didn't stitch five different tools together.

我直接把这个东西做出来，然后就开始用了。

I built the thing, and I started using it.

对于正在听节目的创始人来说，这就是关键。

For any founder listening, that's the point.

你越快把想法变成现实，就能越快推出产品。

The faster you turn an idea into something real, the faster you launch.

无论是应用、仪表盘、内部系统还是AI代理，你需要构建什么，Base44都能帮你搞定。

Apps, dashboards, internal systems, AI agents, whatever you need to build, Base44 gets you there.

访问Base44.com/20VC。

Go to Base44.com/20VC.

就是Base44.com/20VC。

That's Base44.com/20VC.

Base44帮你快速构建，而Laridin帮你衡量AI的影响力。

While Base44 helps you build faster, Laridin helps you measure AI impact.

现在每家公司都有两支劳动力队伍：人类和AI代理。

Every company now runs two workforces, humans and AI agents.

Laridin是第一个统一仪表盘，覆盖这两支队伍，衡量他们的工作并优化他们的影响力。

Laridin is the first unified dashboard across both workforces that measures their work and optimizes their impact.

Laridin回答四个问题：谁在使用AI？

Laridin answers four questions: Who's using AI?

用了哪些工具？

Which tools?

成本是多少？

What is it costing?

实际产出是什么？

And what's it actually being produced?

覆盖每个团队、每个供应商。

For every team across every vendor.

AI的投资回报率不再靠猜，而是变成一个你可以持续、自信衡量的数字。

AI ROI stops being a guess and becomes a number that you can consistently and confidently measure.

Gainsight、Vertiv、SurveyMonkey以及许多其他企业组织都在用Laridin来管理他们的AI项目。

Gainsight, Vertiv, SurveyMonkey, and many other enterprise organizations run their AI programs on Laridin.

数千个AI工具和数百万美元的AI支出都在被衡量。

Thousands of AI tools and millions of dollars of AI spend are measured.

你押注AI，Laridin来证明它确实有效。

You bet on AI, Laridin proves that it's working.

访问Laridin.com了解更多。

Visit Laridin.com to learn more.

网址是L-A-R-R-I-D-I-N.com。

That's L-A-R-R-I-D-I-N.com.