赫曼特·塔内贾谈全球AI投资与能源需求及AI团队低效问题 | 2025年9月24日

欢迎各位收看《信息科技电视》。我是阿卡什·帕斯里查。今天是9月24日星期三。我们邀请到一位重要嘉宾——通用催化剂的CEO赫马特·塔内贾。

Welcome everyone to the Informations TI TV. My name is Akash Pasricha. It is Wednesday, September 24. We have got a big guest with us here today. Hemat Taneja is the CEO of General Catalyst.

他即将登场，请别走开。这场对话不容错过。我们还将与Atlassian的朋友们探讨为何个人能从AI中获益，而团队却未能如此。节目精彩纷呈，现在有请首位嘉宾。通用催化剂是风投界最具传奇色彩的公司之一，赫曼斯·德内贾主导了该公司二十余年发展历程。

He is here coming on in just a minute, so do not go anywhere. You do not want to miss that conversation. We've also got our friends at Atlassian coming on for discussion about why individuals are getting value out of AI, but teams are not, at least not to the same extent. It's gonna be a great show, so let's get right on into our first guest. General Catalyst is one of the most storied firms in all of venture capital, and Hemanth De Neija has helped steer the company for much of its twenty year history.

他担任CEO已近五年，公司投资组合包括Stripe、Circle、Andriel、Canva和Mistral。今天我想请赫曼斯聊聊AI行业的现状及公司发展历程。赫曼斯，欢迎来到《科技电视》，很高兴您能来。

He has also been the CEO of the firm for nearly five years now. The company's portfolio has included Stripe, Circle, Andriel, Canva, and Mistral. I wanna bring on Hemanth to talk about this moment for the AI sector and how his company has evolved over the years. Hemanth, welcome to TI TV. It's great to have you here.

很荣幸受邀，谢谢。

It's great to be with you. Thanks for having me.

我们有很多话题要探讨。首先我想从当前AI行业令我困惑的现状说起。您多次强调我们正处于'模糊性峰值'阶段。本节目旨在厘清这些模糊性，希望能得到您的见解。

So look. There's a lot to get to. I wanna sort of start with a bit of my confusion about the current state of the AI sector. And one of the things that you've said time and time again is that we are peak ambiguity right now. And, you know, on this show, we try to make sense of the ambiguity, make things a little less ambiguous.

我想从执行主编昨晚的专栏谈起。马丁·皮尔斯写道：山姆·奥特曼发布博客称'AI服务增长惊人'，这只是故事的第一面。

And so hopefully you can help us do that. I want to start with a column that our executive editor wrote last night. Martin Pierce, he wrote saying that, you know, Sam Altman put out this blog post saying, The growth of AI services has been astonishing. Okay? And that's the first part of the story.

另一方面，许多评论指出企业并未如预期般从AI中获得价值，MIT研究尤其凸显这点。这两种观点看似矛盾，希望您能帮我们理解其中关联。

But then on the flip side, you have, you know, a lot of commentary around how businesses are not seeing the value from AI as they would have hoped, not the least of which is that MIT study. And so you kind of have these two different sides of the story, and I'm hoping you can help us reconcile these two things.

没错。听着，你得考虑在企业中实现AI普及需要什么。让我们逆向思考这个问题，好吗？在我看来，企业要真正有效运用AI，必须具备四个条件。首先，必须确保你的数据基础设施已为AI做好准备。

Yeah. Look, you have to think about what it's gonna take to create diffusion of AI in the enterprise. Let's work backwards from that, okay? For an enterprise to really effectively use AI, you have to have four things in my view. One is, you need to make sure your data infrastructure is ready for AI.

这是个复杂问题，不可能一夜解决。记住，Transformer架构的革命性浪潮不过是几年前的事。其次，你还需要基于自身数据训练的模型，这样才能适用于你的核心竞争力。最后，必须考虑劳动力转型——因为未来你的员工将包含AI代理和人类，有些代理管理人类，有些人类管理AI代理，这就像一场全面变革。而最关键的是，需要高层领导有推动AI普及的魄力。

That's a complex problem, doesn't get solved overnight. Remember, the old transformer zeitgeist only happened a couple years ago. You also need to have models trained on your data, and and so they're applicable to your secret sauce if you will. And the last thing is, you have to think about workforce transformation, because you're gonna have agents, AI agents and humans in your workforce now, and some agents are managing the humans, some humans are managing the AI agents, and so it's like a whole transformation. And the last thing is, you need leadership at the top that's got the courage to drive that diffusion of AI.

所以MIT等研究中常见的情况是：企业尝试某个语言模型后，看到了可能性，却陷入停滞。究其原因，正是缺乏这四个关键要素来真正推动融合。

So a lot of what you see in the MIT study and others is, you know, these companies take a shot at trialing one of the language models. They can see what's the art of the possible, but then they get stuck. And they get stuck because it's actually a place where you have to have all four of these ingredients to really drive the fusion.

明白了。你是说企业需要同时具备这四要素，或许这就是他们未能实现价值的原因。另外有个有趣的现象——贝恩昨天发布的报告指出，企业需要增加IT服务支出才能匹配云服务商和AI公司在算力基础设施上的资本开支。但报告最引人深思的结论是：预期收入增长根本不足以支撑这些资本支出。对此你怎么看？

So, okay. So you're saying that businesses need all four things and perhaps that's why they're not seeing the value. You know, the other part of the story that was kind of interesting is there was a report from Bain that came out yesterday that talked about the extent to which businesses would need to spend more on IT services to justify the capital expenditures that these cloud companies and AI companies are spending to invest in capacity and compute. And the takeaway from the report, it was quite interesting, was that, you know, they're not projecting enough revenue growth to justify that capital expenditures. So how do you think about that if that's the case?

听着，如果仅从企业软件预算角度看市场规模，这个等式确实不成立。但若考虑到最终的收入机会其实在于企业的劳动力预算，一切就说得通了——因为当AI真正渗透企业时，它将取代大量人力工作。这时你面对的就是一个庞大得多的、能消化这些惊人数字的利润市场。我同意这些数字大得离谱，但核心逻辑是：我们最终瞄准的是劳动力预算。当然这会引发一系列我常讨论的问题，但这就是大势所趋。

Look, I think the equation becomes hard if you think about the market size in the context of software budgets of companies, and that's really what you're going after. But if you start to think about the fact that the ultimate revenue opportunity is the labor budgets of these companies, that's when it really starts to make sense, because if these models are going to actually effectively diffuse in the enterprises, they're going to take a lot of the labor content of it. Now you're talking about a much much bigger market in terms of what is going to create profitability around these these staggering numbers. I do agree with you. These numbers are so large, but the belief here really is that the budgets we're going after ultimately is gonna be around labor, which obviously creates a whole other set of issues that, you know, I've talked about a bunch, but that's where this is headed.

好的，关于劳动力市场这个说法，能否具体阐述你的定义？

Okay, and so when you say labor market, say more about that. What exactly do you mean by that?

以编程市场、云计算等所有创新为例——你真正要争夺的究竟是开发者的工具预算，还是企业花在开发者身上的总人力成本？显然，当你提供这些产品时，以劳动力成本计算的市场规模会扩大百倍。假设工具支出仅占开发者薪资的1%...

I mean, you're so so take all the innovations in the coding market, cloud and other products. Are you really going after what is the software tooling budget for developers or are you going after how much do you spend on developers as a company? Right? So the market size in terms of what your revenue opportunities when you're providing those products is is a 100 times bigger. Let's say you spend a percent of somebody's salary on their tooling.

好的。要知道，你们实际上是在追求那100倍的目标，因为随着这些模型不断进步，从初级工程师开始，逐步发展到我们所谓的10倍效能工程师，这些AI模型都将能够胜任。对吧。所以这就是你们的总体可用市场（TAM）。这样一来，突然之间，为创造这个市场所需投入的基础设施成本就变得合理了。

Okay. You know, you're actually going after that 100 because as as these models get better, you're starting with entry level engineers all the way to over time, what we think of as 10x engineers, these AI models are gonna be able to do it. Right. And so that that's your TAM. And so now all of sudden the the justification for how much infrastructure you're going to spend to create this market starts to make sense.

显然，这里存在一个巨大飞跃——这些公司是否真能普及这些技术。但从长远来看，其潜在影响范围是确实存在的。好吧。

Obviously, there's a big leap in there that these companies are truly going to diffuse these technologies, but the raw potential in terms of scope is there eventually. Okay.

但这样一来，劳动力本身会怎样？或者说，原本从事这些工作的企业员工会面临什么？

But then what happens to the labor itself? Or what happens to the people that were doing these jobs in the business?

看，问题来了。有位知名咨询公司的CEO告诉我一个发人深省的思考实验：客户问他们，如何在五年内将员工规模从现在的5万人发展到10万人，但其中只有1万是人类？因此，我认为那些成功将AI融入整体运营的公司将成为市场领导者，且员工数量会比现在更少。而这正是释放预算的关键——用以支撑我们正在讨论的惊人计算基础设施投资。

I mean, there you go. I mean, know, a really interesting thought experiment that one of the CEOs of consulting companies you would know told me is, a client asked them, how do we go from, you know, 50,000 employees today to a 100,000 in in in five years, but only 10,000 of them are humans? And so, I do think companies that effectively diffuse AI into their overall operations will be market leading, and will have fewer people than they do today. And that that is where the budget opens up to subsidize the staggering compute infrastructure investment that we're talking about.

那么，我想稍后再讨论整体催化剂和你们的战略，但快速问一下：你们公司目前大约有300名员工对吧？

So, and you know, I want to come back in a little bit to general catalysts and sort of the strategy you guys have, but very quickly on that note, you guys are about 300 people right now at the firm?

稍微多点，是的。

Little over that, yeah.

你认为三年后你们的员工规模会达到什么水平？

Where do you think you'll be three years from now in terms of headcount?

你知道，这很难说，但我想说的是，我们正在进行自我重构，以成为原生AI驱动的企业。公司内部有一项重大计划。当我思考明年的优先事项时，我正推动每个部门思考：我们需要招聘更多人吗？如何让团队专注于更有价值的工作，而将那些枯燥或初级的工作交给AI模型处理。虽然我们尚处于转型初期，但我相信这必将改变我们为维持业务所需的招聘规模曲线。

You know, very hard to say, but I mean, I'll tell you, we are only replatforming ourselves to be AI native. There's a massive initiative inside the company. And you know, as I think about our priorities for next year, I'm pushing every part of our organization to say, do we need to hire more? How do we get more out of making our teams do the interesting work, and take a lot of the sort of you know, boring or entry level work, and have the models do. And let them in this, we're early in our journey, but I'm hopeful that we will definitely bend the curve of how much we have to hire to do our our business.

我们的业务比较特殊，与大多数企业不同。是的，我们这个行业原本就不需要太多人力。但核心在于如何通过技术手段改变发展曲线。

Now ours is sort of a, you know, unique business. It's not what most of enterprises look like. Yeah. And companies in our industry are not head count heavy to begin with. But very much focused on figuring how to bend the curve.

那么现在300人规模，三年后会怎样？会扩张到400、500人吗？还是维持现状？很可能...

But so, I mean, 300 now, mean, know, three years from now, are you is it 400, 500? Is it flat? Probably,

如果由我决定，并且我们能有效运用AI，我认为人员增幅不应超过15%-20%。尽管我们的业务增长目标远高于此。这就是AI带来的效率提升。

if it was up to me, if we actually use AI effectively, think it shouldn't be creating more than 15 to 20%. Even though our aspirations as a business are to grow far, far greater than that. So I think you start to see that efficiency with AI.

所以你的意思是，随着AI的应用，你们将放缓招聘速度？

And so, so you're saying that you won't be hiring as quickly as you use AI in the organization?

前提是我们能有效部署AI。就像其他企业面临的挑战一样，我们也要解决同样的问题。另外...

As long as we're effective in deploying AI. Like all those issues I described for everybody else, we have to go through the same challenges. One of the other parts

我想请教本周的热点新闻。我们看到大量资金涌入算力基础设施投资。你昨天在CNBC提到，这种投资热潮可能导致阶段性震荡期——几年后可能出现产能过剩和随之而来的问题。你具体指的是什么样的震荡期？

I wanted to ask you about is coming back to the news of this week, right? We're seeing a lot of investment in capacity and compute. There's a lot of money flowing around. And one of the things that you mentioned, you were on CNBC yesterday, you talked about sort of the bumpy period that could come up as a result of all this investment in capacity, and the likelihood that in a few years we might be in a place where we've overinvested, we have overcapacity, and there might be some problems that come with that. What exactly is this bumpy period that you were referring to?

谈谈那个。

Talk about that.

听着，我认为我们在部署电信基础设施时与CLACs也经历过类似情况。现实是，当一家公司试图进入市场时，他们会向供应商超额配置所需的基础设施。想想如今的云服务，他们为数据中心容量和能源容量所做的配置，往往会超出实际需求，因为他们无法准确预测该领域将如何扩展以满足需求。而且你知道，这些并非都是严格约束的合同，但基础设施确实如此。我听说在某些超大规模计划中，过度配置甚至达到2到3倍。如果确实如此，那么我们将经历一段过度建设的时期，你会看到部分基础设施投资表现不佳，但长期来看，计算基础设施会像互联网基础设施那样逐渐适应并发展起来。

Look, I think we went through this with the CLACs when we were putting the telecommunications infrastructure in place as well. The reality is, when you have a company's trying to go on a market, they will over provision the infrastructure they want from their vendors. So if you think about the the clouds today, the amount of data center capacity they're provisioning for, and energy capacity, they will over provision because they don't really know how that sector is going to scale up to meet their needs. And you'd rather, and you know, these are not all deeply committed contracts, but the infrastructure is So getting I think in some cases I've heard there's two to 3x more overproducing in some of the hyperscaler plans. And so if that's indeed happening, then there's going to be a period where we're overbuilt, and you know, you'll see some of those infrastructure investments not go very well, but over time, same thing's gonna happen with the computer infrastructure that happened with the internet infrastructure that will grow into it.

因为从长远来看，今天建设的规模在二十年后会显得微不足道。在三到五年内，我们会经历一个阶段，感觉计算基础设施供过于求，因为整个行业都过度配置了。我实在难以想象这种情况不会发生。

Because there's, you know, long term obviously, what's getting built today will get dwarfed in the context of what we'll have twenty years from now. Right. There will be this period in three to five years where we'll feel like, gosh, there's a oversupply of compute infra because the industry as a whole over provisioned. I I I it's hard for me to not imagine that scenario.

那么，好吧。让我们深入探讨这个情景。假设三到五年后，我们建设的所有计算资源都未被充分利用，谁会因此受损？

So, okay. So so let's dive into that scenario though. So in this the in this, three to five years from now, let's say, have all this compute that isn't getting used because we built all of it. Who gets hurt there?

我认为有大量基础设施资本投入其中。涌现了许多数据中心开发公司，整个行业都在围绕这个领域兴起。这些公司将不得不从可靠性的角度有效应对那个时期。这种剧情以前就上演过。

I think there's a lot of infrastructure capital going in there. There's a lot of you know, data center development companies that are popping up. There's a whole industry that's popping up around this. And I think they are, those companies will have to effectively navigate, you know, that period from a reliability standpoint. You This see movie has played out before.

对。我只是想弄明白，谁最应该为此担忧？听起来像是那些开发数据中心的公司。

Right. And I mean, I'm just trying to understand here. I mean, who should be most worried about this? Sounds like it's the companies developing these data centers

谁——绝对如此。

who- Absolutely.

谁应该对此最为担忧。我是说，大型科技公司呢？看吧，它们有财力或办法承担这类投资。但在这段动荡时期，初创企业怎么办？它们会面临什么？

Who should be most worried about it. What about I mean, do the big tech companies, look, they can afford or they have ways of affording some of this investment. What about startups in this bumpy period? What happens to that?

是的，我认为初创企业面临的整个基础设施产业正在兴起，因为这个产业必须解决如何通过基础设施推动利用率的问题，以证明正在进行的投资是合理的。对初创企业来说，这个问题并不那么突出。它们会根据业务需求逐步租赁所需容量慢慢发展。我认为真正压力在于为大型超大规模供应商和实验室提供基础设施的公司——最终实际需求究竟会如何才是关键。

Yeah, look, I think I think the startups look, the infrastructure, the whole infrastructure industry that's coming up because it's the one that has to navigate how that infrastructure drives utilization, you know, to to justify the investments that are getting made. I think for startups, it's that issue isn't so pronounced. I think they will go provision capacity, they'll rent capacity that they need in the context of their business to slowly grow into it. I think it's more the the infrastructure companies that are provisioning for these large hyperscalers and labs. And then, you know, what's the actual demand going to be in the engine or really all?

我们来谈谈预测性的问题。关于你提到的过度配置或建设过剩产能，这并非首次出现。我的疑问是：首先，为何一开始就要建设过剩产能？其次，为何不稍微缩小野心范围？比如专注于AI的某个特定领域（如医疗保健），那里的用例可能更成熟。

Let's talk about, you know, sort of projecting. This idea of over provisioning or building overcapacity, as you said, and this is not the first time we've seen it. I mean, one sort of question I have is, A, why do you have to build overcapacity in the first place? And the second thing is, why don't you just narrow your ambitions a little bit? You know, instead of building capacity for all of AI, why not stick to sort of one particular space of AI, like healthcare, for example, you know, where we know that the use case might be a little bit more proven out.

你对此怎么看？

How do you think about that?

我认为科技公司不会因此降低野心。如果你是超大规模供应商，你根本不确定这些基础设施公司能否兑现承诺，所以过度配置并非最终会超支，而是确保拥有满足需求的计算力。垂直领域已经出现行业专用模型提供解决方案——我们孵化的Hippocratic AI就是例证。

Look, I think I don't think that technology company is going to, you know, marginalize their ambition in that regard. I think it's much more what what you if if you're if you're a a hyperscaler trader, you just don't know how this infrastructure set of companies are their ability to deliver. So you're just sort of over provisioning, not because you will overspend in the end, but it's because you're, you know, you want you want to make sure you have the compute that you need to really meet your needs. The idea that they will see these industry specific models come up on top and and provide solutions at a vertical solution, that's already happening, We incubated Hippocratic AI.

没错，确实如此。

Right, right.

我们已涉足物理、文本和工业领域。许多公司正开始利用基础模型，并在此基础上构建适合特定行业的解决方案。回到我之前提出的企业AI四大要素框架，这种趋势正在自然发生。在我看来，这正是初创界AI人才最应该攻克的领域——因为这些是从零开始构建公司相对更易解决的问题。

We've done physics, text, and industrial. There's a lot of these companies that are already starting to sort of leverage the models and then build on top of them solutions that are suited for those industries, going back to my framework of the four things for I enterprise think that's naturally happening, and that to me is in some ways the best places for the AI talent in the startup world to go tackle, because those are somewhat more tractable problems to build a company around from scratch.

没错。那么按照你所说的，建设这种过剩产能会带来哪些环境成本呢？

Right. And what about the environmental costs that would come with building this overcapacity as you say?

听着，我认为如果从AI计算机遇的角度来看，它与能源机遇紧密相连。短期来看，如果我们要在AI领域取胜，实际上除了依赖最丰富的天然气资源外别无选择。这确实是我们在很多方面获取最多的资源。显然，在十到二十年的周期内，我们希望看到可再生基础设施——特别是基础负荷太阳能——真正扩大规模，从扩展角度承担大量天然气能源的工作量。

Look, I think if you think about the AI computer opportunity, it goes straps heavily to the energy opportunity. Right. And and and the trade off you have there is, in the short term, if we're gonna win in AI, you actually don't have an option but to lean into natural gas as the most abundant resource. That's really what we've gotten the most of in a lot of ways. And obviously in in the ten to twenty year period, you wanna see the renewable infrastructure with base load solar really scaling to take a lot of the natural gas energy workload, you know, from a scaling perspective.

长期来看，显然我们还有这些大胆尝试，比如人们正在研究的裂变和聚变技术。在这方面我们投资了Pacific Fusion公司，但对我来说，问题是这些技术能在二十年内带来改变吗？所以我认为当前阶段是尽可能清洁地利用天然气，中期能否真正实现可再生能源规模化并可靠地服务于这个行业？长期来看，我们能否通过核能实现能源富足？

And long term, obviously we have these moonshots with, you know, fission and fusion that folks are working on. We're invested in Pacific Fusion in that regard, but that's that to me is, you know, could that make a difference in twenty years? So I think there's like a today, can we use natural gas as cleanly as possible, medium term? Can we really get renewables at scale in a reliable way for this industry? And long term, you know, can we drive energy abundance with nuclear?

在我看来，这就是基本框架。

I mean, that that to me is the framework.

当你说AI机遇就是能源机遇时，你是否考虑过加大对能源初创企业的投资？未来有可能设立能源基金吗？这对你的业务意味着什么？

So when you say that the AI opportunity is the energy opportunity, I mean, are are do you see yourself increasingly investing in energy startups? You know, could you see yourself setting up an energy fund in the future? What would that mean for your business?

我们正在深入研究此事。而且我已经投资了

We're heavily looking into it. And I've and I've invested in the

投资能源基金吗？

Into an energy fund?

不，不，我在金融科技1.0时代的投资非常不成功，因此从中吸取了很多教训。所以我认为，正如我提到的，我们已开始在该领域进行一些投资，比如太平洋聚变就是一个例子。傅里叶是另一家从事现场制氢的氢能公司。我们将逐步投资这些解决方案，但从我的角度来看，公司建设和规模化仍是一门新艺术。因此我们在行动上非常谨慎。我确实认为，如果我们全面思考并退一步看，机会既在于能否创造正确的技术解决方案，也在于规模化基础设施的发展路线图是什么？

No, no, I've invested in the fintech one point zero era very unsuccessfully, and so have a lot of learnings from there. So I think I think to me, and we've started to do some investments in that in that space, as I mentioned, Pacific Fusion's example of that. Fourier is another one that's a hydrogen generation company, on-site generation of hydrogen. And so there are solutions we're going to slowly invest in, but the act of company building and scaling those is still a new art from my standpoint. So we're being we're being careful in how we move, And I do think if we think comprehensively and take a step back, the opportunity is as much about, can you create the right technology solutions as it is about what is the roadmap for scaling the infrastructure?

是的。这样才能满足计算机的需求。所以我们退一步，全面审视这个问题，看看如何帮助创业者应对这个领域。

Yeah. So that it meets the needs of of computers. So we're taking a step back and really comprehensively looking at it and see how do we help founders tackle that space.

明白了。你对美国当前的政策怎么看？我是说，你提到了可再生能源对吧？我们看到美国在很多清洁能源倡议上有所倒退。你如何将这一点与你认为可再生能源和清洁能源领域存在大量机会的评论统一起来？

Right. What do you make of the current policy in The US? I mean, you talked about renewables, right? We've seen this sort of roll back on a lot of clean energy initiatives in The US. How do you square that with your comment that there's a lot of opportunity in the renewable space and in clean Yeah.

听着，首先，我对于真正能大规模部署的先进能源的框架是：它必须清洁、经济、安全。不能仅仅清洁但昂贵。我认为我们需要利用这一新需求推动可持续发展。我坚信这一点。但短期来看，如果美国面临的选择是环保还是赢得人工智能竞赛——我认为这确实是我们面前的选择——错过AI的代价太大，无法做出那样的取舍。这就是当前正在发生的事情。

Look, first of all, my framework for advanced energy that really will get deployed at scale is gotta be clean, affordable, secure. It can't be just clean, but inexpensive, and I do think we need we need to use this new demand to drive towards sustainability. So I do believe in that. But in the short term, you know, if if The US is faced with the choice of be environmentally friendly or win AI, which I think is really the choice that's in front of us, I think the cost of missing AI is too large to make that trade off. And that's really what's happening here.

如果你想想美国的大部分增长和投资，美国正在对人工智能下重注。就像风险投资一样，我们在赌什么能创造下一阶段的繁荣。当你真正思考正在发生的一切时，这既令人兴奋又令人恐惧，就像投资一个高风险的风投项目。而要让这一切实现，你需要能源。所以你不会放慢脚步。因此我能理解政府为何撤销许多法规。

If you think about most of the growth in The US, most of the investment, it's all United States is making an enormous bet on artificial intelligence. Like a venture bet we're making in in what creates our next next phase of prosperity when you really think about everything that's going on as a You know, it's exciting and it's it's scary, just like investing in a in a you know, high risk venture capital project. And and so in order to really make that come to bear, you need energy. And so you're not going to slow it down. So I can see why the administration is rolling a lot of the the regulations back.

但我确实认为，长期来看，如果我们在赢得AI竞赛的同时，没有为长期发展投资建设清洁、经济、安全的基础设施，最终我们将无法赢得长期胜利。因为在我看来，碳排放是有代价的，我确实相信可持续发展的重要性。所以我们要如何有意识地做出这些短期与长期的抉择？至少我们正运用这个框架来制定我们在该领域的投资策略。

But I do think in the long term, if we don't, if we're not, while winning AI also making investments with that long term, we do have this clean affordable secure infrastructure. In the end, we're not gonna win the long term. Because because there is a you know, a cost of carbon in my view, and and I do believe in sustainability matters. And so kind of how do we make those short term, long term choices very intentionally? At least we're using that framework to get our own investment strategy into space.

所以我想确认一下。你是说这些清洁能源政策的倒退，你说你能理解他们为何这么做。那么你的意思是，你对此可以接受，因为环境问题可以留到长远考虑？你是这个意思吗？

So so I just wanna understand this. So you're you're you're saying these policy rollbacks with clean energy, stance on it is, you said you could see why they're doing it. So are you saying that you're okay with it because we'll have to worry about the environment in the long run? Is that what you're saying?

不不不不。听着，如果按我的观点，我们不应在可持续性发展上失去动力。对吧。但我认为因为世界其他国家也在跟随我们，我确实认为这会拖慢进程。不过我理解为何会发生这种情况，而且你知道，我正观察着环境现状，这就是现实，我们该如何确保在短期内聚焦于AI领域所需的胜利，你知道，我正以美国人的立场三倍努力地思考这个问题。

No no no no. Look, if if my my own view is that we should not lose momentum on sustainability. Right. But I think because the rest of the world follows us as well, and I do I do think it's gonna slow things down. But I understand why that's happening, and and you know, I'm sort of looking at the environment, that's what it's gonna be, how do we make sure we focus on the short term, you know, win in AI that we need, you know, I'm tripling my being an American hat on for a second.

但要以一种同时投资于经济性基础设施的方式，因为只有具备经济性才能实现规模化应用。好消息是我们能源需求如此庞大，当出现新需求时就能消化创新。是的。那么我们能否智慧地制定国家能源转型路线图，既支持AI建设又能平衡取舍？这就是我思考该领域投资策略的主要方向。

But in a way that we're also investing in this infrastructure that is economical, because it's got it's gotta be economical for it to be used to scale. And the good news is we have so much demand in energy that when you have new demand, you can absorb innovation. Yeah. And so can we intelligently create a energy transition roadmap for the country that also allows you to build an AI and make those trade offs? So think that's a lot of the way I think about our investment strategy in this space.

在讨论之前，我想谈谈你们重仓投资的Anthropic。中国在可再生能源方面正取得领先，你认为这对我们意味着什么？

And before we talk, I want to talk about Anthropic, which you guys have a large position in. China is pulling ahead in renewables. What do you think that means for us?

你看，我认为我们在很多方面低估了中国。我不认为他们在AI领域落后多少，他们在芯片竞赛中的追赶会非常务实。比如你已看到华为的公告，而在能源领域他们遥遥领先。之前我常说他们每周都在建燃煤电厂，但他们同时也在进行全球最大规模的可再生能源部署。

So look, think we underestimate China in a lot of ways. I don't think they're that behind in AI. I think they're gonna be very pragmatic in the way they catch up in the chips race. You've seen the Huawei announcements for example, and they're very far ahead in energy. I mean, while back I used to say they're building a coal plant a week, but they're also doing the largest deployment of renewables.

所以我认为他们实际上正将这些技术都推向可行阶段，能源优势将转化为AI优势。这就是为什么我说我们不能忽视必须创造新能源并实现规模化的事实——因为长期来看，要赢得AI竞争就需要这种转型。化石能源终将耗尽，所以今天就必须投资。这也是为何我认为行业需要支持该领域创业者很重要，尽管AI基础设施是当前焦点，我们主要精力也在那里，但让这个生态系统具备可扩展性和经济性同样需要关注。

So I think they're actually getting these technologies all to a point of viability, where the energy advantage will also give them an AI advantage. So this is one of the reasons I'm saying like we we can't lose sight of the fact that we need to create these new sources of energy and create and enable and enable them at scale in The US is because in the long term, if we're gonna win AI, we do need that transition. We are gonna run out of gas. And and so we have to make those investments today, and why I think it's important, you know, for our industry to be backing, you know, founders in that as, you know, as exciting as all the focuses on AI infrastructure, and that's where most of where we're focused as well. Think it's very important to have some of your attention on getting this ecosystem to be scalable, affordable.

我想谈谈Anthropic。现在OpenAI高调宣布将自建算力并重金投入数据中心建设，这是否抬高了Anthropic的竞争门槛？他们是否需要同等规模地投资算力建设？

I want to talk about Anthropics. You know, we've seen OpenAI now coming out very loudly saying, you know, we are going to build our own capacity and we're going to throw a lot of resources at, you know, at investing in data centers. Does that kind of up the ante for Anthropic? I mean, you know, do they need to be doing the same thing here at investing in capacity to the same extent?

听着，Dario和Metropica团队让我印象深刻的是他们极度专注。他们其实不太关注外界动向，对自身战略充满信心。我认为他们的资本效率很高——考虑到所消耗的资本与创造的企业价值比例，以及他们对核心产品线那种精准的投资方式。

Look, one thing that I'm very impressed with with Dario and the overall team at Metropica is that they're very focused. They they actually don't pay much attention to what others are doing. They have a lot of confidence in their own strategy. I think they've been very capital efficient. When you think about the amount of, you know, enterprise value they've created in terms of the capital they've spent, the the the surgical sort of investments they've made in the products that they care about.

所以我不认为他们会受他人行为影响。如果他们决定行动，那完全是基于自身考量。我认为这是一个极具独立思考能力的团队，对自己的战略和价值观有着清晰认知，这点令我钦佩。他们的执行力也非常出色。

So I don't I don't I don't think they'll be influenced by what others are doing. So if they decide to do it, they've got they've got their own copy. I think that I think that's a team that's a very much independent thinker and at peace with their own strategy and their own values, and I admire that about it. I think I think they're executing very well.

那么你认为他们短期内不会自建数据中心？他们如何满足自身的计算需求呢？

So you don't think they're gonna move into building their own data centers in near future, how do they keep up with their own compute needs then?

我并非此意。只是说若他们认为有必要就会建造，但绝不会因他人行动而仓促反应——这才是我的核心观点。

I didn't say that. I'm just saying, think if they think they need it, then they'll build it. But they're just not gonna react to what others are doing is is my only point.

那你预计明年会出现这种情况吗？

So did you see that coming in the in the next year?

让我们拭目以待。我尊重他们的节奏，应该由他们来披露想公开的内容。

Let's see. I mean, I'll let let them dive. I wanna be respectful. Let them dive. You know, talk about what they wanna expose Right.

我正在努力理解这个战略——我们看到一家巨头采取这种策略时，很难想象其当下影响。但我认同你的观点：应该专注自身赛道。来谈谈整体催化剂吧，这对公司和机构都是激动人心的时刻。收购医院连锁和进军财富管理背后的战略逻辑是什么？

I'm just trying to wrap my head around, you know, we see one giant pursuing this strategy, you know, I can only imagine, you know, what that would mean for now. But I take your point that, you know, you wanna focus on your own game. Let's talk about general catalysts. This is such an exciting time for the company and the firm. What is the strategy behind all these expansions into buying a hospital chain and moving into wealth management?

请谈谈你通过这个战略试图实现的目标。

Know, just talk about what you're trying to do here with this strategy.

是的。看，我们的核心使命是帮助创始人建立持久的企业，对吧？那些成为市场领导者并长期持续增长和复合发展的企业。所以一种理解方式是，你看到GC所做的一切，都是在问：这对GC这个业务有什么作用？但更重要的视角是，这一切如何为我们支持的创始人创造核心优势？

Yeah. Look, our true north is to help founders build enduring companies, Right? Companies that are market leaders and are scaling and compounding for a long time. So one way to look at it, everything that you see coming out of GC is, you know, what is that doing to GC the business? But the more important way to look at it is to say, how does everything enable a core advantage for the founders that we back?

嗯。

Mhmm.

好的。当我们专注于与全球有抱负的人建立深厚真诚的关系网络——我们称之为'熟人圈'时，这能为我们的创始人催化机会。想想我们在资本结构上的所有创新，这是因为随着科技公司建设的成熟、扩展和规模化，我们行业需要的创新不再局限于传统股权基金的三维坐标——行业、地区和领域。对吧。它需要更多像我们的客户价值基金这样的资本解决方案。

Okay. So you know, when we focus on a deep and sincere effort in building the best relationships with, you know, ambitious people around the world, are familiar as we call it, that catalyzes opportunity for our founders. If you think about all the innovations we've done in our in our capital stack, that's because we think as technology company building matures and goes and and and scales, the innovation required in our industry is not at the the three axis of state sector and geography in terms of equity funds, which is what the industry has traditionally done. Right. It needs it needs more capital solutions like our customer value fund.

我们创建了用于资助销售和营销的基金，以及用于资助大量非有机增长和整合工作的创建基金——正如我们过去讨论的那样。这些是为创始人准备的资本创新。再看看收购医院，或其他我们正在推进的项目，或是我们的General Catalyst研究院——都是为了帮助创始人更有效地与政府政策对接，或获得分销渠道。我们正在合作的23家医疗保险合作伙伴的转型，将为我们的公司和创始人提供参与推动这些复杂行业变革的生态系统机会，真正发挥系统级作用。

We created for funding sales and marketing and our creation fund for funding a lot of the inorganic and the roll up work as we've talked about in the past. So that's the capital innovation for the founders. If you look at buying a hospital, if you look at you know, of the other things that we're doing there, or you look at our, know, the General Catalyst Institute is to help make the founders be more effective at engaging with governments and policy, or having access to distribution. What what's gonna happen with our 23 health insurance partners that we're working with on their transformation. It's an opportunity for our companies, our founders, to go be part of an ecosystem that can drive that transformation in these in these complex industries, and really act as a system.

这就是分销优势。所以每当你听到什么听起来疯狂的事情时，请回归本质：这如何真正支持GC所投资的创始人？

So that's the distribution advantage that you know, all those things. So every time you hear something then say it sounds sounds nutty, go back to how does this really support the founders for the GC backs.

所以所有这些...所有这些都是为了给你更多知识，更多工具...

So all of all of this is to all of this is to give you more knowledge, more tools for

为我们的创始人创造更多优势。

More to advantage for our founders.

关于创始人们。好的，明白了。随着GC逐渐扩展业务范围，我不得不提出这个问题。它已经反复被提及多次了。

For the founders. Okay. Got it. So, you know, as GC sort of spreads its wings a bit, I have to ask the question. It's come up time and time again.

我们不会。我们不会。我们不会。我们不应该也不需要问这个问题。

We're not. We're not. We're not. We should not don't need no need to ask.

你甚至没让我问完问题。好吧，那么GC对上市不感兴趣。

You didn't even let me ask the question. Okay. Well, I gotta so GC is not interested in going public.

不会上市。

Is not going public.

好的。那Andreessen Horowitz呢？你觉得这种情况会发生吗？

Okay. What about Andreessen Horowitz? Do you see do you see that happen?

我认为你得去问他们。他们是个很棒的团队，你应该直接问他们。

I think you have to ask them. They're an amazing team, and you should ask them.

你觉得这事发生了吗？

Do you think it happened?

我真的不知道。我、我、我真的完全不知道。我不知道。不知道，而且我也没有任何看法。

I actually don't know. I I I really honestly don't know. I don't know. Don't know, and I don't have an opinion.

我这里更广泛的问题是，关于风投基金上市这个概念。我们不妨把视野放宽些。我的意思是，你认为未来三年内会有任何风投公司上市吗？甚至说，在我们所处的这个风投新阶段里，你觉得这种做法合理吗？

The broader question I have here is, you know, this idea of venture funds going public. You know, let's just broaden it out. I mean, do you see any venture firm going public in the next three years? Do you even think that that makes sense, you know, in sort of this next phase of venture capital that we're on?

听着，让我们从第一性原理来思考。如果有人要上市——这在私募股权领域已经发生过，我认为平台已经建立起来并且确实上市了。所以，是否有人可能上市？是的。但未来三年内会有人上市吗？

Look, think So let's think about it from first principles. If somebody is going to go public, you can see this happened in PE, I think platforms have been created and they do go public. So I think could somebody go public? Yes. Is somebody gonna go public in the next three years?

你知道最有可能的是我们的朋友安德森·霍洛维茨，但你必须问他们，我从没和他们谈过这个，完全不清楚。肯定不会是我们就对了。这根本不是我们的优先事项。我们真正致力于创新，回到那个充满高度不确定性的阶段，真正弄清楚我们能给创始人创造哪些不公平的优势，让他们带着规模和成功走出这种不确定性。这才是驱动我们创新路线图的根本。

You know the most well positioned probably is our friends at Andreessen Norovitz, but you have to ask them, but never had that conversation with them, no idea. It certainly is not gonna be us, I'll tell you that. So it's just not our priority. We're really trying to innovate in going back to the peak ambiguity, So really figuring out what are all the unfair advantages we can create for our founders to come out of this peak ambiguity with, you know, scale and success. That's ultimately what's driving our innovation roadmap.

我是说，关于风投公司变得更像私募股权公司这种整体趋势，你怎么看这种类比？

I mean, this whole notion of venture capital firms becoming more like PE firms, what do you think of that comparison?

我觉得这种类比从来就说不通。告诉你为什么。我们为什么要像私募股权公司？最好的企业、最大的企业、最盈利的企业都在风投的投资组合里，而不是私募股权的组合里。那些最具韧性、对未来最相关的企业也都在风投组合里。

I think it's, it never makes sense to me. I'll tell you why. Would we wanna be like PE firms? The best companies, the biggest companies, the most profitable companies are in the venture capital portfolio, not not in the PE portfolios. And the companies that are the most resilient and relevant for the future are in the venture portfolio, not in the PE portfolios.

它们都必须经历转型。实际上我们正在帮助很多私募股权组合里的企业通过之前讨论的企业转型方案实现生存。所以我们为什么要走那条路？我觉得人们痴迷于——规模是否真的意味着要募集更大基金，因此必须做更大交易，然后就变成了私募股权。这只是对事物的旧思维模式。如果你相信AI转型，那么站在创新前沿显然更有利。

They all have to go through transformation. We're actually helping a lot of the companies in the PE portfolios become viable with a lot of the enterprise transformation stuff we talked about. So why would we go down that path? I think there's this obsession with does scale really mean you're gonna raise bigger funds and therefore you have to do these bigger deals, which means you become PE. Just, just think it's the old way of thinking about stuff because you know, if you believe in the AI transformation, then you're way better off seeing at the forefront of innovation.

你

You

知道吗，我觉得这种说法可能源于人们看到风投基金规模变得如此庞大，他们投资的公司也变得如此庞大，对吧？甚至还有，

know, and I think maybe where that comes from is, you know, people saying venture funds getting so big, the companies that they're invested in getting so big, right? And then you have even,

这难道就意味着我们要转向私募股权吗？为什么风投不能因为未来机遇远超过去而变得比私募规模更大呢？

Doesn't you know mean that means we're going to turn it into PE. Why can't venture just be bigger than PE because of the opportunity so large in the context of the future versus the past?

对，对。在我们结束前快速问你几个问题。有报道称Stripe正在从投资者手中回购股份。你们在出售吗？

Right, right. Couple questions here for you quickly before we let you go. There's been reports that Stripe is buying shares from investors. Are you selling?

我刚看到这个消息。我们已投资了14次，要知道，在他们最近一轮外部融资时，我们开出了巨额支票——那轮融资规模达500亿美元。我们多次询问过我们的有限合伙人是否需要创造流动性，几乎每次都得到否定答复。所以我...我希望我们能长期持有Stripe的股份。

I just saw that. We have invested 14 times, you know, and if you think about us within the last round that they had raised from the outside, we wrote a very large check, it was in the $50,000,000,000 round. You know, we've tried to ask our investors many times, our LPs to see if, hey, would you like us to create liquidity? And almost always they've said no. So I I I think, know, my my hope is that we are investors of Stripe for a very very long time.

我认为这家公司将持续复利增长，在社会中扮演越来越重要的角色。在AI技术变革方面，它已经做出了惊人成就。

So I think this company is gonna steadily compound, it's got more and more important role in society. It's done amazing work when it comes to the technological shifts with AI.

没错。你说过预见它成为万亿美元市值的公司，我也这么认为。

Right. You've said you see it becoming a trillion dollar company, I think so.

那么这应该是你的权利，

So then that should be your Right,

没错。你知道，我想快速回到种子投资的领域，因为我知道种子投资是General Catalyst的根基所在。作为一名种子投资者，我想我们有很多风险投资家在观看这个节目，很多早期的风险投资家，很多刚开始做种子投资的人。关于种子投资，当你与创始人会面并对他们的业务进行尽职调查时，你问的第二个最重要的问题是什么？

right. You know, I just want come back to the land of seed very quickly, because I know that seed investing is sort of the bread and butter that General Catalyst is really built on. As a seed investor, I think we have a lot of venture capitalists watching this show, a lot of early venture capitalists, a lot of people starting seed. As it relates to seed investing, what is the second most important question that you ask every founder when you meet with them when you're diligencing their business?

这取决于第一个问题是什么。

Depends on which is England the first one is.

所以我问的是第二个问题是什么。

That's why I asked what the second is.

是的。听着，我认为首先

Yeah. Look. I think I think, first of all

告诉我你的前两个问题。你的前两个问题。

Give me your top two. Your top two.

我会告诉你，我会告诉你。首先，我们为什么要做这个。所以我们引进了三位合伙人，尤里在硅谷建立了Wayfinder，珍妮特在欧洲建立了La Familia，尼拉吉在印度建立了Metro Hive，因为这是我们运营的三大市场。我们基本上告诉他们，你们是早期活动（即种子投资）的守护者。而要让一个多阶段公司同时做好种子投资，我们需要在文化上进行以下转变。

I'll give you I'll give you. First of all, why we're doing this. So we've gone and and, you know, brought on three partners, Yuri who built Wayfinder in Silicon Valley, Jeanette built La Familia in Europe, and Niraj who built Metro Hive in India, because those are the three markets we operate in. And we basically told them, you guys are the stewards of the early stage activity, which is seed. And the cultural shift we have to work on to have a multistage firm do as seed as well as a seed is the following.

从文化角度理解，早期在公司获得的股权和信任远比后期在公司运作良好时获得的200万美元支票、1亿美元甚至2亿美元支票更有价值。正是这一点赋予了我们开展后续所有工作的能动性。所以我们非常重视选对人，明白吗？我们只关注两个核心问题：创始人是否真正卓越？我认为这是首要问题，因为你需要探究他们创立企业的初衷。

Is to understand culturally that the ownership that you get in the company and the trust you get in the company early on is way more valuable, even if it's a $2,000,000 check than a $100,000,000 check, $200,000,000 check later on in a company that's already working really well. And so that's that's that's what gives you the agency to go to the rest of everything we do. So we are very heavy on getting C right, okay? And and the the questions we ask, there's only two things you're looking for, you know, is like, is the founder really excellent? Which I think is the first question you're really talking about, because that's And what you have to get I think you you wanna look at why they built that business.

我始终会问的问题是：你为何创立这家公司？因为当杰出人物决定投身解决某个问题时，那个转折点极具启示性。我只想理解背后的驱动力——那个瞬间将成为未来10到20年间，他们建设标志性企业时获取资本、人才和客户的灵感源泉。

The question the question that I always ask you, why did you start this company? Because that moment, when an amazing person flips over to say I'm gonna go take on a problem is very telling. I just wanna understand what's driving this because that moment is the source of inspiration for capital, for talent, for customers, you know, for the next ten to twenty years that they're going to build an iconic company.

所以第一个问题是：你是谁？第二个问题是：为什么创立这个企业？对于创业动机，什么样的回答算好，什么样的算差？

So the first question is who are you? And the second question is why did start this business? What's a good answer and what's a bad answer for why did you start this business?

真诚的回答就是好回答。我们...

A truthful answer is a good answer. We're So

作为记者我们也经常...

we see as journalists too, by

顺便说一句。

the way.

我们很擅长识别编造的故事。我认为动机必须真实，这才是关键。顺便分享，我研究生毕业时创业的动机就很糟糕——纯粹是跟风，结果自然走不远。你要寻找的是一个人内在的真实驱动力，或是他们眼中世界的 compelling（ compelling 译注：此处保留英文强调其特殊含义）之处。

We get pretty good at knowing that stories are made up. Know, I think it's gotta be authentic, and that's really what you're looking for. By And the way, I started a company when I left grad school, I had a terrible answer to it. I was just doing it, everybody else around me was doing it, and then that certainly didn't go very far. You know, it's just, you wanna see the authenticity around what's motivating somebody intrinsically, or what they see in the world that's compelling.

对，对。海曼，非常感谢你能来参加。请再坚持一分钟，如果可以的话。我们只剩一分钟了。我想回到你多次提到的观点，即人工智能的机遇就是能源的机遇。

Right, right. Heyman, I want to thank you so much for coming on. Stick with me for one minute, if you will. I just want to go We have one minute left. Just want to go back to your thing that you said a lot, which is the AI opportunity is the energy opportunity.

我只是想确保我真正理解这一点，因为当你说人工智能的机遇就是能源的机遇时，你谈到了它可能意味着什么。谁在为这些能源买单？具体是什么样的机遇？我只是想确认我理解正确，因为我觉得这个观点很有趣，但我想...

I just want to make sure I really understand this, because when you say the AI opportunity is the energy opportunity, you talked about what it could mean. Who is paying for this energy? What exactly is the opportunity? I just want to make sure that I understand that, because I think it's an interesting point, but I wanna

所以我尝试与美国最大公用事业公司之一的CEO沟通，明白吗？他们告诉我的是：过去四十年里，我们每年增长1%到2%，基本上与通胀持平。而在本十年剩余时间里，我们将不得不实现每年8%的增长。

So was try with the CEO of one of the largest utilities in The US, okay? And here's what they told me. They told me, for the first last forty years, you know, we've grown one to 2% a year, essentially with inflation, right? And in the next, the rest of this decade, we're gonna have to grow 8% a year. Right.

需求正在到来。而下个十年，每年12%的增长。

The demand is coming. And next decade, 12% a year.

12%，没错。

12%, right.

我们谈论的只是最大行业之一。我认为这个机遇在于以正确的方式建设基础设施，充分利用我们那些不会永远存在的资源，并投资于那些最终将带来能源富足的技术。

You're talking about just one of the biggest sectors. And I think that opportunity, getting that infrastructure built the right way, and actually built in a way that we take advantage of the resources we have that are not gonna be around forever, and investing in technologies that, you know, will eventually drive abundance around energy.

对。

Right.

那么实现这一转型的是谁呢

And getting that transition Who's

谁来承担这笔支出？是数据中心公司，正是这些企业在进行

doing the spending? It's the it's the data center companies that are doing

数据中心公司承担了支出。他们将获取电力资源，但基础设施仍需公用事业公司建设。我认为会有新企业涉足电力开发领域，这会很有意思。

the Data center companies are doing the spending. They are are they're gonna acquire power, But like utilities have to make the infrastructure. I think there's gonna be new businesses that are getting into power development, I think, that are that are gonna be interesting.

明白了。

Okay.

而且，你知道，我认为整个基础设施领域，人们会围绕系统优化做得非常好。对吧。如何推动，比方说从甲烷到床铺的整个链条。如何真正推动这个进程。是的。

And and and, you know, I I think this whole infrastructure space, folks will do really good systems optimizations around. Right. How you drive, you know, all the way from, let's say methane to beds. How do you really drive that? Yeah.

我认为这也将非常有趣。

I think it's gonna be really interesting too.

数据中心AI云生态系统的循环性，所有这些资金的循环性，你不担心吗？

The circularity of the data center AI cloud ecosystem, the circularity of all this money, it doesn't concern you?

听着，我之前说过，泡沫其实有其好处。它们能将资本和人才引入重要领域，虽然会伴随一些阵痛，但最终你会看到真正的价值创造。正如我所说，理解这些巨额投资的唯一方式是——它们并非瞄准IT预算，而是劳动力预算，你们的人力成本预算。这些预算规模足以支撑我们正在进行的这类投资。当然，这也会给社会带来其他问题。

Listen, I've said this before, bubbles are really good. They mobilize capital and talent into an important area, but you'll see some carnage, but what you're gonna see on the other side is real value creation. As I said, the only way to wrap your head around the staggering amounts of dollars being invested is this is not going after IT budgets, this is going after the workforce budgets, your labor budgets. And those are big enough to support the kind of investment that we're making. Causes other problems in society.

我们必须讨论技能重塑的问题，但正是这个机遇在推动着这一切。

We gotta talk about reskilling, but that's that's the opportunity that's driving this.

好的。海曼，非常感谢你参加节目。当人们在我提问前就猜到我要问什么时总是很有趣。同时也恭喜你的新书发行。虽然我们没来得及详谈，但你的新书这周已经上市了。

Okay. Well, Hayman, thank you so much for coming on the show. It's always funny when people know what I'm going to ask before I ask it. And congratulations on the new book as well. We didn't get to talk about that, but your new book is out this week.

海曼·塔内贾是General Catalyst的首席执行官。非常感谢你再次来到节目做客，我们期待很快能再次邀请你。

Hayman Taneja is the CEO of General Catalyst. Thank you so much for coming back to the show. Coming on the show, we look forward to having you back very soon.

很愉快，谢谢。

Enjoyed it. Thank you.

好的。接下来环节由本次制作的赞助商之一支持。Atlassian一直在探索如何利用AI不仅提升个人生产力，还能提高团队效率。事实证明这比想象中更具挑战。现在有请Atlassian副总裁兼AI产品负责人贾米尔·维拉尼，来谈谈他对这个问题的思考以及他认为有效的解决方案。

Okay. Well, our next segment is with one of our sponsors supporting us on this production. Atlassian has been doing some interesting work to try to figure out how AI can be used not just to improve individual productivity, but also efficiency among teams. It turns out that is a harder job than you might think. I want to bring on Jamil Viliani, a vice president at Atlassian and the head of AI product, to talk about how he thinks about this issue and also some of the solutions that he sees being helpful.

这段采访是我们昨晚录制的，现在为大家播放。以下是我与Atlassian贾米尔的对话。贾米尔，欢迎来到TI TV，很高兴你能来。

We recorded this segment last night, and I wanna play it for you right now. Here is my conversation with Jamil from Atlassian. Jamil, welcome to TI TV. It's great to have you.

是的，非常感谢邀请我，阿卡什。

Yeah, thank you so much for having me, Akash.

你发布的这份精彩报告探讨了AI如何提升个人生产力。但最让我感兴趣的是团队本身并未获得同等效益。我想深入探讨你们发现的数据。请告诉我们报告的具体发现。

So you put out this great report that talked about how AI is helping individuals with their productivity. But the finding that most interested me was that teams themselves aren't seeing as much of the benefit. So I want to dig into some of the data that you found. Tell us about what your report what it found.

当然，很乐意分享。阿卡什，我们是一家以团队协作为根基的公司。我们花费大量时间调研全球大型企业、其领导者以及成千上万的员工。数据显示，平均而言，员工个人效率提升了33%。我想大家都有AI帮助提速的亲身体验。

For sure, happy to. So Akash, we are a company rooted in teamwork. So we take a lot of time surveying the world's biggest companies, their leaders, and the thousands upon thousands of employees that work at those companies every day. And what they've told us is that on the average, they feel individually 33% more productive. And I think we all have personal examples where we feel like AI has helped us do things faster.

但领导者们反馈的情况是：仅有3%认为团队效能发生了实质转变。事实上，37%的领导者表示这些AI工具非但未能推动团队转型，反而导致效率倒退或方向偏离。这种认知差异很有趣，我们正试图找出根源并解决问题。

But when we look at what the leaders are reporting, like how are they feeling their team's progress and their transformation, Only 3% of them feel their teams have truly transformed in how much they can get done. And in fact, 37% of these leaders are saying that the all these AI tools together are not at all transforming their teams, but in fact causing them to regress a bit or go in the wrong way. There's this interesting dissonance, and we're trying to figure out, work to get to the root of that and solve those problems.

所以现状是：基层员工说'工具让我效率提升'，而团队领导者和高管却说'组织层面未见成效'。你认为...

So you have people saying, Hey, I use these tools and I'm seeing benefit. They're making me more productive. You have team leaders and you have the heads of these organizations saying, Well, you know, we're not necessarily seeing that in our organizations. Why do

原因何在？经过调研发现，许多人使用工具后个人产出激增，但缺乏有效的团队协作机制来整合这些成果。就像电子邮件刚普及时，人们欣喜于快速发送，但随后面临信息过载，不得不开发新方法来应对——这正是我们现在试图解决的挑战。

you think that's the case? So as we probed, we found that a lot of folks are using these tools to become so much more productive. They're producing lots more information on their own, but then they don't really have a good way to contribute back and orchestrate the work as a team. So I think we've all kind of felt this where when people got email, for example, it was really great initially to say, Oh yeah, I can send emails quickly. But then you've got this information overload And it got really difficult to sow and work through all that, and we had to develop And that's one of the kind of challenge we're trying find here.

这几乎像是内容过载。我在想，到底该把...

It's almost like content overload. It's a you know, I think about like, Where do I even put my

请注意，如果我原本收到3封邮件，现在却收到10封？没错。我们发现这实际上造成了真实的美元成本问题，影响了投资回报率。我们认为，由于个人生产力提升与团队生产力下降之间的这种不协调，导致了约1000亿美元的回报损失。

attention if I'm getting 10 emails now instead of three? Exactly. And we're finding that, you know, this actually is having a real dollar cost issue, ROI impact. We think that about a $100,000,000,000 of lost ROI is happening because of this sort of dissonance between individual productivity gain but team productivity loss.

明白了。

Okay.

这是个相当严重的问题。

That's a pretty massive problem.

所以这是个重大问题。1000亿美元，我是说，这笔钱能让你走得很远。我知道我们这里讨论的是总体数字。但我的问题是，我们如何让团队更好地与AI协作？如何将个人效率更好地转化为团队层面的效率？

So that's a big problem. A $100,000,000,000, I mean, could take you a long way. And I know we're talking about aggregate numbers here. But then I guess my question is, how do we get teams to work better with AI? How do you sort of get that individual efficiency to work better at the team level?

完全同意。这让我想起

Absolutely. So I think this really reminds me of the

当初我们首次将视频会议、甚至个人电脑和互联网等新技术引入职场时的情景。会有一段适应期，大家都在学习新的礼仪规范、新的最佳实践，以真正融入这些好处，同时推动团队前进。我们发现有几个相当一致的主题。首先是确保每个人都有一个共享的知识库，大家持续从中获取并贡献内容。其次是确保AI处于能够协调工作的位置，而不仅仅是生成内容，然后让你去决定下一步该做什么。

times when we were, you know, getting new technology into the workplace like video conferencing or even PCs and Internet for the first time. There's this period where everyone's kind of learning the new etiquette, the new best practices of how to actually plug in these benefits while helping the team move forward. And we found there were a few pretty consistent themes. The first was making sure that everyone has a shared knowledge base that they're constantly working from and contributing to. Second was making sure that AI is in a position where it can orchestrate the work rather than just producing it and then letting you go and actually manage what to do next.

第三点是将AI真正视为团队成员，给它分配明确的职责，并接受它明确的产出，就像每个团队成员都清楚自己的角色一样。AI也是如此。这些都是很棒的原则。让我们来谈谈

And the third was really treating AI as a team member that you have, you know, assigned clear responsibilities and accept clear things from, just like every team member knows their role. Same thing with AI. So these are some great principles. Let's talk about sort

关于一些战术案例。你是否见过客户实施方法来加速你提到的这种与AI相关的礼仪建设？你们公司内部是否找到了加速礼仪建设的方法，比如制定规则？在实际操作中这具体是怎样的？

of some tactical examples. Have you seen customers implement ways to accelerate this sort of etiquette building that you talk about with AI? Have you found ways in your own company to accelerate etiquette, you know, put in place rules? What does that look like on the ground?

是的，当然。我们的客户范围从24 Hour Fitness、Harper Collins到皇家加勒比邮轮，涵盖面很广，这些客户已成功在新时期启动了礼仪建设和最佳实践构建。有几个案例特别突出，就是当AI开始被视为团队一员时。例如，我们有个医疗行业的客户，他们建立了定期流程，在Jira中跟踪问题。

Yeah, for sure. So we've got customers ranging from twenty four Hour Fitness, Harper Collins, Royal Caribbean. So pretty wide spectrum of customers who have successfully started this etiquette building, best practice building in the new era. A couple of examples that have come up have been really when AI has been started to or being thought of as a member of the team. So, as an example, there are a number of our customers, for example, one in the healthcare industry, that actually have a regular process where they are working through having issues tracked in Jira, right?

他们原本只是在那里进行工作量规划。问题解决后，他们会说：'好，现在我们需要将其转化为发布说明'。然后这些发布说明会进一步转化为面向公众的小型发布广告或推广。最后还需要单独的发布授权审批。这是个漫长的过程。

They just had the workload back plan there. Those issues get finished, and then they say, Okay, now we have to go and translate that into release notes. And then those release notes go into then get translated even further into a small publicly available advertisement or promotion of the release. And then there's an authorization for release that gets all taken separate. This a long process.

没错。他们开始使用智能代理来优化这个工作流，确保不再需要人工逐个环节手动推进，而是由AI协调整个流程，让人力资源专注于增值环节和授权审批。

Right. Right. And they have been using agents to go and say, Hey, let's go and actually streamline that workflow. And then also make sure that it's not like, Oh, the human now has to go and just push from one to the next to the next, but is actually orchestrating that entire process so that the humans are able to go and add their value and authorize things. Right.

这样人们就不必操心推进流程的机械性操作了。

So, you know, not worry about trying to handle the mechanics of moving things along.

我想把这个例子拉回到AI礼仪的概念。他们究竟建立了什么样的AI礼仪规范来加速整个流程？

And so I want to bring this example back to this idea of AI etiquette. What exactly is the etiquette then that they sort of established with AI to sort of make all that work faster?

是的。在我描述的每个步骤中，团队都会思考：'这部分工作原本是某人在本职工作之外的额外任务。针对这个特定流程的最佳实践是什么？'他们通常会记录下来，存入知识库和知识仓库，并确保AI掌握这些规范。

Yeah. So I think in each one of those steps I described, the team thought about, well, hey, like, you know, that was somebody whose job it was on top of their normal day job, right, to go in and do that. What are the best practices that they had for that particular process? And they would often go and say, Okay, let me go and write that down, right? And then I will document that in our knowledge base, in our knowledge repository, and make sure that the AI is aware of that.

它们实际上可以以此为上下文进行绘制。因此，花几分钟时间向AI描述‘这是完成X的最佳方式’、‘这是我的团队期望的’、‘这是交接这些事项的最佳方法’，然后尝试该代理并确保其运行良好，是我们看到的关键步骤之一。

They can actually draw on that as context. So this act of actually spending a few minutes to actually describe to the AI, hey, this is the best way to go do X. This is what my team expects. This is the best way to hand these things off. And then trying out that agent, making sure it works pretty well, is one of the key steps that we see.

我们发现，随着人们对此越来越深思熟虑，比如‘让我先尝试让我的代理帮忙处理这个或完成其中一部分’，然后投入一点时间不断优化它，实际上在短短几天内就能获得丰厚回报。你们已经这样做了，就像每当你们去度假或请假时，必须写下事项让别人帮忙继续。这与人们通常在这种情况下所做的没有太大不同，但他们现在必须开始行动，说‘哦，我实际上要做同样的事情，以便AI能帮我处理这些更琐碎和管理型的任务’。

And we find that as people get more and more thoughtful about it, say, hey, let me go and first try having my agent help with this or do a part of this. And then investing a little bit of time to make it better and better and better pays off in dividends actually within just a matter of days. And you do this already, like whenever you maybe go on vacation or you have a go on leave, you already have to write things down for somebody else to help carry on. So it's not that much different from what a person might normally do for that scenario, but they have to now start getting into the act and saying, Oh, I'm actually going to do the same thing so that the AIs can help me with these more menial and managerial type tasks.

你们在Atlassian有AI礼仪规则吗？你们自己在使用并发现有助于团队变得更高效的规则？

And do you guys have AI etiquette rules at Atlassian that you yourself are using that you've found helpful to help your own teams become more effective?

我们一直在制定这些规则。实际上正在发生的一件事是，我们的领导者们积极参与，例如创建Slack频道，他们说‘嘿，我们将一起学习如何以新的AI导向方式进行原型设计’。因此，我们会一起头脑风暴，说‘好吧，这里可能有三个或四个原型设计任务。我们将尝试这些工具，学习并找出哪些最有效，哪些不理想’。

We're forming them all the time. One of the things that's actually happening is that our own leaders are getting very involved and actually creating Slack channels, for example, where they're saying, Hey, we're going to go and learn together how we do prototyping, for example, in the new AI forward way. And so we'll actually all storm together and say, okay, here's maybe three or four prototyping tasks. We're going to try out these tools. We're going learn and figure out what works best, what doesn't work best.

然后将所有这些编成一套学习内容，甚至录制视频以便其他人学习。我们发现，每个职能、每个实践都有一套独特的礼仪和需要培养的能力。我们正在开始一起捕捉所有这些，然后培训其他人，并通过病毒式传播的方式在组织中传递。

Then codify all that into a set of learnings and even record videos so that other folks can learn. And we find that with every function, every practice, there's a set of unique etiquette and things like that you have to build and muscles you have to build. And we're starting to capture all that together, then train others and then sort of have viral means where we go and pass that around the organization.

最后一个问题。我对Atlassian的收购策略感到非常兴奋。我们在节目中讨论过。我想知道，这份报告中的发现，即团队在让AI为他们工作方面需要更多帮助，利用不同信息来源作为建立AI礼仪的方式，你是否看到这些反映在公司自身的收购策略或更广泛的AI雄心中？

Last question for you. I mean, I've been so excited by Atlassian's acquisition strategy. We've talked about it here on the show. I'm wondering if these findings in this report, the idea that teams, you know, need a little bit more help insofar as making AI work for them, you know, the idea of drawing on different sources of information as a way of establishing AI etiquette, you know, do you see those things reflected in the company's own acquisition strategy or maybe the AI ambitions at large?

绝对是的。你可以看看过去一周与DX的合作。DX正在为软件开发人员解决这个极具挑战性的问题。人们普遍期望AI正在改变软件开发团队。许多工程经理和领导者不知道‘好吧，它到底是如何运作的？’

Absolutely. So you can look at just this past week with DX. You know, DX is solving this really challenging problem for people who build software. There's a lot of expectation that AI is transforming software building teams. And a lot of engineering managers and leaders don't know, well, okay, how is it really working?

在这个漫长的软件构建过程中，我需要在哪些方面集中精力去培养这种能力或礼仪？DX在这方面做得非常出色，它清晰地揭示了这一过程中的改进机会，并帮助领导者与团队共同实现这些改进。没错。回顾过去二十年的浏览器发展，它们主要围绕内容消费、广告业务和企业搜索进行优化，这就是它们存在的目的。

And where do I need to go and focus on building this muscle or etiquette in this long process software building? And DX does a marvelous job of really shining light on that process, where the improvement opportunities are and helping those leaders with their teams make those improvements. Right. And with the browser company, I think if you look back at just browsers for the last twenty years, they've been really optimized around this, serving this machine that is much more around content consumption, advertising different businesses, search for businesses. That's what they're there for.

这正是它们被优化的方向，而且表现卓越。但从未有人审视过浏览器并思考：如何将其优化为知识工作者的工具？这些人每天使用数十种工具来管理工作、知识，协调团队与客户关系。如果真正设计一款擅长这些功能的浏览器，很可能会做出截然不同的设计选择——我们正满怀期待地探索这种可能性。

That's what they're optimized for. And they do a marvelous job. But nobody ever looked at a browser before and said, How do I go and make this thing optimized for a knowledge worker? Somebody who uses dozens of tools every day to manage work, to manage knowledge, to coordinate across teams, across customers. And if you really thought about how to design a browser that was really good at that, you'd probably wind up with a very different set of design choices, and we're excited about help us go consider that.

太棒了。贾米尔，非常感谢你参加本期节目，这份报告令人着迷。我想说，如果只有少数团队发现AI对他们有效，那恰恰意味着巨大的成长空间。我非常认同AI礼仪这个概念，团队确实需要建立相应的规则和规范。

Great. Well, Jamil, thank you so much for coming on the show. Was a fascinating report. Also, you know, I will say, look, if it was just a few percent of teams are finding that AI is effective for them, it really only leaves room to grow. And so, you know, I like this idea of AI etiquette and the idea that maybe teams got to set some rules and norms in place.

我们一定会亲自尝试这些方法。贾米尔，非常感谢你。这位是Atlassian的AI产品副总裁兼负责人贾米尔·维利亚尼，感谢你的到来。

I'll be sure to try that ourselves. Jamil, thank you so much. That is Jamil Vilyani, the VP and Head of AI Product at Atlassian. Thank you for being with us here.

非常感谢你，阿卡什。我们期待在未来几个月分享更多成果。

Thank you so much, Akash. We're looking forward to sharing more in the months ahead.

以上就是我与Atlassian贾米尔的对话。好的，今天的节目就到这里。提醒大家，我们每周一到周五太平洋时间上午10点（东部时间下午1点）在此直播。感谢本节目首席赞助商亚马逊云服务，也感谢各位观众的收看。

That was my conversation with Jamil from Atlassian. Okay. Well, that does it for today's show. A reminder, we are live on this stream Monday through Friday at 10AM Pacific, 1PM eastern. I wanna thank Amazon Web Services who is our presenting sponsor for this production, and I wanna thank you for tuning in.

我们衷心感谢您的观看。我已经开始期待明天的节目了。那么，我们下次再见！

We really do appreciate your viewership. I'm already excited for our next show tomorrow. And so until then, bye bye for now.